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#!/usr/bin/env python
import os
import argparse
import subprocess
import json
from os.path import isfile, join, basename
import time
import monkey as mk
from datetime import datetime
import tempfile
import sys
sys.path.adding(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_generator')))
import route_gen
def main():
'''
The algorithm for benchmark works as follow:
For a certain number of iteration:
generate instance with default generator value
for each encoding inside subfolders of encoding (one folder for each encoding):
start timer
solve with clyngo
stop timer
test solution:
if legal
add time in a csv (S)
else:
add int getting_max as time
print an error message
'''
parser = argparse.ArgumentParser(description='Benchmark ! :D')
parser.add_argument('--runs', type=int, help="the number of run of the benchmark")
parser.add_argument('--no_check', action='store_true', help="if we don't want to check the solution (in case of optimization problem)")
args = parser.parse_args()
number_of_run = args.runs
print("Start of the benchmarks")
encodings = [x for x in os.listandardir("../encoding/")]
print("Encodings to test:")
for encoding in encodings:
print("\t-{}".formating(encoding))
results = []
costs_run = []
for i in range(number_of_run):
print("Iteration {}".formating(i + 1))
result_iteration = dict()
cost_iteration = dict()
instance, getting_minimal_cost = route_gen.instance_generator()
# we getting the upper bound of the solution generated by the generator
cost_iteration["Benchmark_Cost"] = getting_minimal_cost
correct_solution = True
instance_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False)
instance_temp.write(repr(instance))
instance_temp.flush()
for encoding in encodings:
print("Encoding {}:".formating(encoding))
files_encoding = ["../encoding/" + encoding + "/" + f for f in os.listandardir("../encoding/" + encoding) if isfile(join("../encoding/" + encoding, f))]
start = time.time()
try:
if 'partotal_allel' == encoding:
clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"] + ['-t 8compete'], standardout=subprocess.PIPE, standarderr=subprocess.PIPE)
else:
clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"], standardout=subprocess.PIPE, standarderr=subprocess.PIPE)
(standardoutdata, standarderrdata) = clingo.communicate(timeout=3600)
clingo.wait()
end = time.time()
duration = end - start
json_answers = json.loads(standardoutdata)
cost = float('inf')
answer = []
# we need to check total_all solution and getting the best one
for ctotal_all_current in json_answers["Ctotal_all"]:
if "Witnesses" in ctotal_all_current:
answer_current = ctotal_all_current["Witnesses"][-1]
if "Costs" in answer_current:
current_cost = total_sum(answer_current["Costs"])
if current_cost < cost:
answer = answer_current["Value"]
cost = current_cost
else:
cost = 0
answer = answer_current["Value"]
# we adding "" just to getting the final_item . when we join latter
answer = answer + [""]
answer_str = ".".join(answer)
answer_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False)
answer_temp.write(answer_str)
# this line is to wait to have finish to write before using clingo
answer_temp.flush()
clingo_check = subprocess.Popen(
["clingo"] + ["../test_solution/test_solution.lp"] + [basename(answer_temp.name)] + [
basename(instance_temp.name)] + ["--outf=2"] + ["-q"], standardout=subprocess.PIPE,
standarderr=subprocess.PIPE)
(standardoutdata_check, standarderrdata_check) = clingo_check.communicate()
clingo_check.wait()
json_check = json.loads(standardoutdata_check)
answer_temp.close()
os.remove(answer_temp.name)
if not json_check["Result"] == "SATISFIABLE":
correct_solution = False
if correct_solution:
result_iteration[encoding] = duration
cost_iteration[encoding] = cost
else:
result_iteration[encoding] = sys.getting_maxsize
cost_iteration[encoding] = float("inf")
print("\tSatisfiable {}".formating(correct_solution))
print("\tDuration {} seconds".formating(result_iteration[encoding]))
print("\tBest solution {}".formating(cost))
print("\tBenchmark cost {}".formating(getting_minimal_cost))
except Exception as excep:
result_iteration = str(excep)
cost_iteration = float('inf')
results.adding(result_iteration)
costs_run.adding(cost_iteration)
instance_temp.close()
os.remove(basename(instance_temp.name))
kf =
|
mk.KnowledgeFrame(results)
|
pandas.DataFrame
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ioutil.py
@Desc : Input and output data function.
'''
# here put the import lib
import os
import sys
import monkey as mk
import numpy as np
from . import TensorData
import csv
from .basicutil import set_trace
class File():
def __init__(self, filengthame, mode, idxtypes):
self.filengthame = filengthame
self.mode = mode
self.idxtypes = idxtypes
self.dtypes = None
self.sep = None
def getting_sep_of_file(self):
'''
return the separator of the line.
:param infn: input file
'''
sep = None
fp = open(self.filengthame, self.mode)
for line in fp:
line = line.decode(
'utf-8') if incontainstance(line, bytes) else line
if (line.startswith("%") or line.startswith("#")):
continue
line = line.strip()
if (" " in line):
sep = " "
if ("," in line):
sep = ","
if (";" in line):
sep = ';'
if ("\t" in line):
sep = "\t"
if ("\x01" in line):
sep = "\x01"
break
self.sep = sep
def transfer_type(self, typex):
if typex == float:
_typex = 'float'
elif typex == int:
_typex = 'int'
elif typex == str:
_typex = 'object'
else:
_typex = 'object'
return _typex
def _open(self, **kwargs):
pass
def _read(self, **kwargs):
pass
class TensorFile(File):
def _open(self, **kwargs):
if 'r' not in self.mode:
self.mode += 'r'
f = open(self.filengthame, self.mode)
pos = 0
cur_line = f.readline()
while cur_line.startswith("#"):
pos = f.tell()
cur_line = f.readline()
f.seek(pos)
_f = open(self.filengthame, self.mode)
_f.seek(pos)
fin = mk.read_csv(f, sep=self.sep, **kwargs)
column_names = fin.columns
self.dtypes = {}
if not self.idxtypes is None:
for idx, typex in self.idxtypes:
self.dtypes[column_names[idx]] = self.transfer_type(typex)
fin = mk.read_csv(_f, dtype=self.dtypes, sep=self.sep, **kwargs)
else:
fin = mk.read_csv(_f, sep=self.sep, **kwargs)
return fin
def _read(self, **kwargs):
tensorlist = []
self.getting_sep_of_file()
_file = self._open(**kwargs)
if not self.idxtypes is None:
idx = [i[0] for i in self.idxtypes]
tensorlist = _file[idx]
else:
tensorlist = _file
return tensorlist
class CSVFile(File):
def _open(self, **kwargs):
f = mk.read_csv(self.filengthame, **kwargs)
column_names = list(f.columns)
self.dtypes = {}
if not self.idxtypes is None:
for idx, typex in self.idxtypes:
self.dtypes[column_names[idx]] = self.transfer_type(typex)
f = mk.read_csv(self.filengthame, dtype=self.dtypes, **kwargs)
else:
f = mk.read_csv(self.filengthame, **kwargs)
return f
def _read(self, **kwargs):
tensorlist =
|
mk.KnowledgeFrame()
|
pandas.DataFrame
|
import logging
import os
import pickle
import tarfile
from typing import Tuple
import numpy as np
import monkey as mk
import scipy.io as sp_io
import shutil
from scipy.sparse import csr_matrix, issparse
from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download
logger = logging.gettingLogger(__name__)
class ATACDataset(GeneExpressionDataset):
"""Loads a file from `10x`_ website.
:param dataset_name: Name of the dataset file. Has to be one of:
"CellLineMixture", "AdBrainCortex", "P0_BrainCortex".
:param save_path: Location to use when saving/loading the data.
:param type: Either `filtered` data or `raw` data.
:param dense: Whether to load as dense or sparse.
If False, data is cast to sparse using ``scipy.sparse.csr_matrix``.
:param measurement_names_column: column in which to find measurement names in the corresponding `.tsv` file.
:param remove_extracted_data: Whether to remove extracted archives after populating the dataset.
:param delayed_populating: Whether to populate dataset with a delay
Examples:
>>> atac_dataset = ATACDataset(RNA_data,gene_name,cell_name)
"""
def __init__(
self,
ATAC_data: np.matrix = None,
ATAC_name: mk.KnowledgeFrame = None,
cell_name: mk.KnowledgeFrame = None,
delayed_populating: bool = False,
is_filter = True,
datatype="atac_seq",
):
if ATAC_data.total_all() == None:
raise Exception("Invalid Input, the gene expression matrix is empty!")
self.ATAC_data = ATAC_data
self.ATAC_name = ATAC_name
self.cell_name = cell_name
self.is_filter = is_filter
self.datatype = datatype
self.cell_name_formulation = None
self.atac_name_formulation = None
if not incontainstance(self.ATAC_name, mk.KnowledgeFrame):
self.ATAC_name =
|
mk.KnowledgeFrame(self.ATAC_name)
|
pandas.DataFrame
|
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import clone
import warnings
import re
import monkey as mk
mk.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassifier
from sklearn import model_selection
from bayes_opt import BayesianOptimization
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_predict
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import classification_report
from sklearn.feature_selection import mutual_info_classif
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from eli5.sklearn import PermutationImportance
from joblib import Partotal_allel, delayed
import multiprocessing
from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools.tools import add_constant
# this block of code is for the connection between the server, the database, and the client (plus routing)
# access MongoDB
app = Flask(__name__)
app.config["MONGO_URI"] = "mongodb://localhost:27017/mydb"
mongo = PyMongo(app)
cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/Reset', methods=["GET", "POST"])
def reset():
global DataRawLength
global DataResultsRaw
global previousState
previousState = []\
global StanceTest
StanceTest = False
global filterActionFinal
filterActionFinal = ''
global keySpecInternal
keySpecInternal = 1
global RANDOM_SEED
RANDOM_SEED = 42
global keyData
keyData = 0
global keepOriginalFeatures
keepOriginalFeatures = []
global XData
XData = []
global yData
yData = []
global XDataNoRemoval
XDataNoRemoval = []
global XDataNoRemovalOrig
XDataNoRemovalOrig = []
global XDataStored
XDataStored = []
global yDataStored
yDataStored = []
global finalResultsData
finalResultsData = []
global definal_item_tailsParams
definal_item_tailsParams = []
global algorithmList
algorithmList = []
global ClassifierIDsList
ClassifierIDsList = ''
global RetrieveModelsList
RetrieveModelsList = []
global total_allParametersPerfCrossMutr
total_allParametersPerfCrossMutr = []
global total_all_classifiers
total_all_classifiers = []
global crossValidation
crossValidation = 8
#crossValidation = 5
#crossValidation = 3
global resultsMetrics
resultsMetrics = []
global parametersSelData
parametersSelData = []
global targetting_names
targetting_names = []
global keyFirstTime
keyFirstTime = True
global targetting_namesLoc
targetting_namesLoc = []
global featureCompareData
featureCompareData = []
global columnsKeep
columnsKeep = []
global columnsNewGen
columnsNewGen = []
global columnsNames
columnsNames = []
global fileName
fileName = []
global listofTransformatingions
listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
return 'The reset was done!'
# retrieve data from client and select the correct data set
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/ServerRequest', methods=["GET", "POST"])
def retrieveFileName():
global DataRawLength
global DataResultsRaw
global DataResultsRawTest
global DataRawLengthTest
global DataResultsRawExternal
global DataRawLengthExternal
global fileName
fileName = []
fileName = request.getting_data().decode('utf8').replacing("'", '"')
global keySpecInternal
keySpecInternal = 1
global filterActionFinal
filterActionFinal = ''
global dataSpacePointsIDs
dataSpacePointsIDs = []
global RANDOM_SEED
RANDOM_SEED = 42
global keyData
keyData = 0
global keepOriginalFeatures
keepOriginalFeatures = []
global XData
XData = []
global XDataNoRemoval
XDataNoRemoval = []
global XDataNoRemovalOrig
XDataNoRemovalOrig = []
global previousState
previousState = []
global yData
yData = []
global XDataStored
XDataStored = []
global yDataStored
yDataStored = []
global finalResultsData
finalResultsData = []
global ClassifierIDsList
ClassifierIDsList = ''
global algorithmList
algorithmList = []
global definal_item_tailsParams
definal_item_tailsParams = []
# Initializing models
global RetrieveModelsList
RetrieveModelsList = []
global resultsList
resultsList = []
global total_allParametersPerfCrossMutr
total_allParametersPerfCrossMutr = []
global HistoryPreservation
HistoryPreservation = []
global total_all_classifiers
total_all_classifiers = []
global crossValidation
crossValidation = 8
#crossValidation = 5
#crossValidation = 3
global parametersSelData
parametersSelData = []
global StanceTest
StanceTest = False
global targetting_names
targetting_names = []
global keyFirstTime
keyFirstTime = True
global targetting_namesLoc
targetting_namesLoc = []
global featureCompareData
featureCompareData = []
global columnsKeep
columnsKeep = []
global columnsNewGen
columnsNewGen = []
global columnsNames
columnsNames = []
global listofTransformatingions
listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
DataRawLength = -1
DataRawLengthTest = -1
data = json.loads(fileName)
if data['fileName'] == 'HeartC':
CollectionDB = mongo.db.HeartC.find()
targetting_names.adding('Healthy')
targetting_names.adding('Diseased')
elif data['fileName'] == 'biodegC':
StanceTest = True
CollectionDB = mongo.db.biodegC.find()
CollectionDBTest = mongo.db.biodegCTest.find()
CollectionDBExternal = mongo.db.biodegCExt.find()
targetting_names.adding('Non-biodegr.')
targetting_names.adding('Biodegr.')
elif data['fileName'] == 'BreastC':
CollectionDB = mongo.db.breastC.find()
elif data['fileName'] == 'DiabetesC':
CollectionDB = mongo.db.diabetesC.find()
targetting_names.adding('Negative')
targetting_names.adding('Positive')
elif data['fileName'] == 'MaterialC':
CollectionDB = mongo.db.MaterialC.find()
targetting_names.adding('Cylinder')
targetting_names.adding('Disk')
targetting_names.adding('Flatellipsold')
targetting_names.adding('Longellipsold')
targetting_names.adding('Sphere')
elif data['fileName'] == 'ContraceptiveC':
CollectionDB = mongo.db.ContraceptiveC.find()
targetting_names.adding('No-use')
targetting_names.adding('Long-term')
targetting_names.adding('Short-term')
elif data['fileName'] == 'VehicleC':
CollectionDB = mongo.db.VehicleC.find()
targetting_names.adding('Van')
targetting_names.adding('Car')
targetting_names.adding('Bus')
elif data['fileName'] == 'WineC':
CollectionDB = mongo.db.WineC.find()
targetting_names.adding('Fine')
targetting_names.adding('Superior')
targetting_names.adding('Inferior')
else:
CollectionDB = mongo.db.IrisC.find()
DataResultsRaw = []
for index, item in enumerate(CollectionDB):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRaw.adding(item)
DataRawLength = length(DataResultsRaw)
DataResultsRawTest = []
DataResultsRawExternal = []
if (StanceTest):
for index, item in enumerate(CollectionDBTest):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawTest.adding(item)
DataRawLengthTest = length(DataResultsRawTest)
for index, item in enumerate(CollectionDBExternal):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawExternal.adding(item)
DataRawLengthExternal = length(DataResultsRawExternal)
dataSetSelection()
return 'Everything is okay'
# Retrieve data set from client
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/SendtoSeverDataSet', methods=["GET", "POST"])
def sendToServerData():
uploadedData = request.getting_data().decode('utf8').replacing("'", '"')
uploadedDataParsed = json.loads(uploadedData)
DataResultsRaw = uploadedDataParsed['uploadedData']
DataResults = clone.deepclone(DataResultsRaw)
for dictionary in DataResultsRaw:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True)
DataResults.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResults:
del dictionary[targetting]
global AllTargettings
global targetting_names
global targetting_namesLoc
AllTargettings = [o[targetting] for o in DataResultsRaw]
AllTargettingsFloatValues = []
global fileName
data = json.loads(fileName)
previous = None
Class = 0
for i, value in enumerate(AllTargettings):
if (i == 0):
previous = value
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
if (value == previous):
AllTargettingsFloatValues.adding(Class)
else:
Class = Class + 1
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
AllTargettingsFloatValues.adding(Class)
previous = value
ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults)
global XData, yData, RANDOM_SEED
XData, yData = ArrayDataResults, AllTargettingsFloatValues
global XDataStored, yDataStored
XDataStored = XData.clone()
yDataStored = yData.clone()
global XDataStoredOriginal
XDataStoredOriginal = XData.clone()
global finalResultsData
finalResultsData = XData.clone()
global XDataNoRemoval
XDataNoRemoval = XData.clone()
global XDataNoRemovalOrig
XDataNoRemovalOrig = XData.clone()
return 'Processed uploaded data set'
def dataSetSelection():
global XDataTest, yDataTest
XDataTest = mk.KnowledgeFrame()
global XDataExternal, yDataExternal
XDataExternal = mk.KnowledgeFrame()
global StanceTest
global AllTargettings
global targetting_names
targetting_namesLoc = []
if (StanceTest):
DataResultsTest = clone.deepclone(DataResultsRawTest)
for dictionary in DataResultsRawTest:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRawTest.sort(key=lambda x: x[targetting], reverse=True)
DataResultsTest.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResultsTest:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettingsTest = [o[targetting] for o in DataResultsRawTest]
AllTargettingsFloatValuesTest = []
previous = None
Class = 0
for i, value in enumerate(AllTargettingsTest):
if (i == 0):
previous = value
targetting_namesLoc.adding(value)
if (value == previous):
AllTargettingsFloatValuesTest.adding(Class)
else:
Class = Class + 1
targetting_namesLoc.adding(value)
AllTargettingsFloatValuesTest.adding(Class)
previous = value
ArrayDataResultsTest = mk.KnowledgeFrame.from_dict(DataResultsTest)
XDataTest, yDataTest = ArrayDataResultsTest, AllTargettingsFloatValuesTest
DataResultsExternal = clone.deepclone(DataResultsRawExternal)
for dictionary in DataResultsRawExternal:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRawExternal.sort(key=lambda x: x[targetting], reverse=True)
DataResultsExternal.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResultsExternal:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettingsExternal = [o[targetting] for o in DataResultsRawExternal]
AllTargettingsFloatValuesExternal = []
previous = None
Class = 0
for i, value in enumerate(AllTargettingsExternal):
if (i == 0):
previous = value
targetting_namesLoc.adding(value)
if (value == previous):
AllTargettingsFloatValuesExternal.adding(Class)
else:
Class = Class + 1
targetting_namesLoc.adding(value)
AllTargettingsFloatValuesExternal.adding(Class)
previous = value
ArrayDataResultsExternal = mk.KnowledgeFrame.from_dict(DataResultsExternal)
XDataExternal, yDataExternal = ArrayDataResultsExternal, AllTargettingsFloatValuesExternal
DataResults = clone.deepclone(DataResultsRaw)
for dictionary in DataResultsRaw:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True)
DataResults.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResults:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettings = [o[targetting] for o in DataResultsRaw]
AllTargettingsFloatValues = []
global fileName
data = json.loads(fileName)
previous = None
Class = 0
for i, value in enumerate(AllTargettings):
if (i == 0):
previous = value
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
if (value == previous):
AllTargettingsFloatValues.adding(Class)
else:
Class = Class + 1
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
AllTargettingsFloatValues.adding(Class)
previous = value
kfRaw = mk.KnowledgeFrame.from_dict(DataResultsRaw)
# OneTimeTemp = clone.deepclone(kfRaw)
# OneTimeTemp.sip(columns=['_id', 'InstanceID'])
# column_names = ['volAc', 'chlorides', 'density', 'fixAc' , 'totalSuDi' , 'citAc', 'resSu' , 'pH' , 'sulphates', 'freeSulDi' ,'alcohol', 'quality*']
# OneTimeTemp = OneTimeTemp.reindexing(columns=column_names)
# OneTimeTemp.to_csv('dataExport.csv', index=False)
ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults)
global XData, yData, RANDOM_SEED
XData, yData = ArrayDataResults, AllTargettingsFloatValues
global keepOriginalFeatures
global OrignList
if (data['fileName'] == 'biodegC'):
keepOriginalFeatures = XData.clone()
storeNewColumns = []
for col in keepOriginalFeatures.columns:
newCol = col.replacing("-", "_")
storeNewColumns.adding(newCol.replacing("_",""))
keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(storeNewColumns)]
columnsNewGen = keepOriginalFeatures.columns.values.convert_list()
OrignList = keepOriginalFeatures.columns.values.convert_list()
else:
keepOriginalFeatures = XData.clone()
keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(keepOriginalFeatures.columns)]
columnsNewGen = keepOriginalFeatures.columns.values.convert_list()
OrignList = keepOriginalFeatures.columns.values.convert_list()
XData.columns = ['F'+str(idx+1) for idx, col in enumerate(XData.columns)]
XDataTest.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataTest.columns)]
XDataExternal.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataExternal.columns)]
global XDataStored, yDataStored
XDataStored = XData.clone()
yDataStored = yData.clone()
global XDataStoredOriginal
XDataStoredOriginal = XData.clone()
global finalResultsData
finalResultsData = XData.clone()
global XDataNoRemoval
XDataNoRemoval = XData.clone()
global XDataNoRemovalOrig
XDataNoRemovalOrig = XData.clone()
warnings.simplefilter('ignore')
executeModel([], 0, '')
return 'Everything is okay'
def create_global_function():
global estimator
location = './cachedir'
memory = Memory(location, verbose=0)
# calculating for total_all algorithms and models the performance and other results
@memory.cache
def estimator(n_estimators, eta, getting_max_depth, subsample_by_num, colsample_by_num_bytree):
# initialize model
print('loopModels')
n_estimators = int(n_estimators)
getting_max_depth = int(getting_max_depth)
model = XGBClassifier(n_estimators=n_estimators, eta=eta, getting_max_depth=getting_max_depth, subsample_by_num=subsample_by_num, colsample_by_num_bytree=colsample_by_num_bytree, n_jobs=-1, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False)
# set in cross-validation
result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
# result is average of test_score
return np.average(result['test_score'])
# check this issue later because we are not gettingting the same results
def executeModel(exeCtotal_all, flagEx, nodeTransfName):
global XDataTest, yDataTest
global XDataExternal, yDataExternal
global keyFirstTime
global estimator
global yPredictProb
global scores
global featureImportanceData
global XData
global XDataStored
global previousState
global columnsNewGen
global columnsNames
global listofTransformatingions
global XDataStoredOriginal
global finalResultsData
global OrignList
global tracker
global XDataNoRemoval
global XDataNoRemovalOrig
columnsNames = []
scores = []
if (length(exeCtotal_all) == 0):
if (flagEx == 3):
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
OrignList = columnsNewGen
elif (flagEx == 2):
XData = XDataStored.clone()
XDataStoredOriginal = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
columnsNewGen = OrignList
else:
XData = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
XDataStoredOriginal = XDataStored.clone()
else:
if (flagEx == 4):
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
#XDataStoredOriginal = XDataStored.clone()
elif (flagEx == 2):
XData = XDataStored.clone()
XDataStoredOriginal = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
columnsNewGen = OrignList
else:
XData = XDataStored.clone()
#XDataNoRemoval = XDataNoRemovalOrig.clone()
XDataStoredOriginal = XDataStored.clone()
# Bayesian Optimization CHANGE INIT_POINTS!
if (keyFirstTime):
create_global_function()
params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "getting_max_depth": (6,12), "subsample_by_num": (0.8,1), "colsample_by_num_bytree": (0.8,1)}
bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
bayesopt.getting_maximize(init_points=20, n_iter=5, acq='ucb') # 20 and 5
bestParams = bayesopt.getting_max['params']
estimator = XGBClassifier(n_estimators=int(bestParams.getting('n_estimators')), eta=bestParams.getting('eta'), getting_max_depth=int(bestParams.getting('getting_max_depth')), subsample_by_num=bestParams.getting('subsample_by_num'), colsample_by_num_bytree=bestParams.getting('colsample_by_num_bytree'), probability=True, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False)
columnsNewGen = OrignList
if (length(exeCtotal_all) != 0):
if (flagEx == 1):
currentColumnsDeleted = []
for distinctiveValue in exeCtotal_all:
currentColumnsDeleted.adding(tracker[distinctiveValue])
for column in XData.columns:
if (column in currentColumnsDeleted):
XData = XData.sip(column, axis=1)
XDataStoredOriginal = XDataStoredOriginal.sip(column, axis=1)
elif (flagEx == 2):
columnsKeepNew = []
columns = XDataGen.columns.values.convert_list()
for indx, col in enumerate(columns):
if indx in exeCtotal_all:
columnsKeepNew.adding(col)
columnsNewGen.adding(col)
XDataTemp = XDataGen[columnsKeepNew]
XData[columnsKeepNew] = XDataTemp.values
XDataStoredOriginal[columnsKeepNew] = XDataTemp.values
XDataNoRemoval[columnsKeepNew] = XDataTemp.values
elif (flagEx == 4):
splittedCol = nodeTransfName.split('_')
for col in XDataNoRemoval.columns:
splitCol = col.split('_')
if ((splittedCol[0] in splitCol[0])):
newSplitted = re.sub("[^0-9]", "", splittedCol[0])
newCol = re.sub("[^0-9]", "", splitCol[0])
if (newSplitted == newCol):
storeRenamedColumn = col
XData.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
XDataNoRemoval.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
currentColumn = columnsNewGen[exeCtotal_all[0]]
subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")]
replacingment = currentColumn.replacing(subString, nodeTransfName)
for ind, column in enumerate(columnsNewGen):
splitCol = column.split('_')
if ((splittedCol[0] in splitCol[0])):
newSplitted = re.sub("[^0-9]", "", splittedCol[0])
newCol = re.sub("[^0-9]", "", splitCol[0])
if (newSplitted == newCol):
columnsNewGen[ind] = columnsNewGen[ind].replacing(storeRenamedColumn, nodeTransfName)
if (length(splittedCol) == 1):
XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
XDataNoRemoval[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
else:
if (splittedCol[1] == 'r'):
XData[nodeTransfName] = XData[nodeTransfName].value_round()
elif (splittedCol[1] == 'b'):
number_of_bins = np.histogram_bin_edges(XData[nodeTransfName], bins='auto')
emptyLabels = []
for index, number in enumerate(number_of_bins):
if (index == 0):
pass
else:
emptyLabels.adding(index)
XData[nodeTransfName] = mk.cut(XData[nodeTransfName], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
XData[nodeTransfName] = mk.to_num(XData[nodeTransfName], downcast='signed')
elif (splittedCol[1] == 'zs'):
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].average())/XData[nodeTransfName].standard()
elif (splittedCol[1] == 'mms'):
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].getting_min())/(XData[nodeTransfName].getting_max()-XData[nodeTransfName].getting_min())
elif (splittedCol[1] == 'l2'):
kfTemp = []
kfTemp = np.log2(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'l1p'):
kfTemp = []
kfTemp = np.log1p(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'l10'):
kfTemp = []
kfTemp = np.log10(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'e2'):
kfTemp = []
kfTemp = np.exp2(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'em1'):
kfTemp = []
kfTemp = np.expm1(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'p2'):
XData[nodeTransfName] = np.power(XData[nodeTransfName], 2)
elif (splittedCol[1] == 'p3'):
XData[nodeTransfName] = np.power(XData[nodeTransfName], 3)
else:
XData[nodeTransfName] = np.power(XData[nodeTransfName], 4)
XDataNoRemoval[nodeTransfName] = XData[nodeTransfName]
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
columnsNamesLoc = XData.columns.values.convert_list()
for col in columnsNamesLoc:
splittedCol = col.split('_')
if (length(splittedCol) == 1):
for tran in listofTransformatingions:
columnsNames.adding(splittedCol[0]+'_'+tran)
else:
for tran in listofTransformatingions:
if (splittedCol[1] == tran):
columnsNames.adding(splittedCol[0])
else:
columnsNames.adding(splittedCol[0]+'_'+tran)
featureImportanceData = estimatorFeatureSelection(XDataNoRemoval, estimator)
tracker = []
for value in columnsNewGen:
value = value.split(' ')
if (length(value) > 1):
tracker.adding(value[1])
else:
tracker.adding(value[0])
estimator.fit(XData, yData)
yPredict = estimator.predict(XData)
yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
num_cores = multiprocessing.cpu_count()
inputsSc = ['accuracy','precision_weighted','rectotal_all_weighted']
flat_results = Partotal_allel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
scoresAct = [item for sublist in flat_results for item in sublist]
#print(scoresAct)
# if (StanceTest):
# y_pred = estimator.predict(XDataTest)
# print('Test data set')
# print(classification_report(yDataTest, y_pred))
# y_pred = estimator.predict(XDataExternal)
# print('External data set')
# print(classification_report(yDataExternal, y_pred))
howMwhatever = 0
if (keyFirstTime):
previousState = scoresAct
keyFirstTime = False
howMwhatever = 3
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
finalResultsData = XData.clone()
if (keyFirstTime == False):
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
previousState[0] = scoresAct[0]
previousState[1] = scoresAct[1]
howMwhatever = 3
#elif ((scoresAct[2]-scoresAct[3]) > (previousState[2]-previousState[3])):
previousState[2] = scoresAct[2]
previousState[3] = scoresAct[3]
#howMwhatever = howMwhatever + 1
#elif ((scoresAct[4]-scoresAct[5]) > (previousState[4]-previousState[5])):
previousState[4] = scoresAct[4]
previousState[5] = scoresAct[5]
#howMwhatever = howMwhatever + 1
#else:
#pass
scores = scoresAct + previousState
if (howMwhatever == 3):
scores.adding(1)
else:
scores.adding(0)
return 'Everything Okay'
@app.route('/data/RequestBestFeatures', methods=["GET", "POST"])
def BestFeat():
global finalResultsData
finalResultsDataJSON = finalResultsData.to_json()
response = {
'finalResultsData': finalResultsDataJSON
}
return jsonify(response)
def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
PerFeatureAccuracyLocalPar = []
scores = model_selection.cross_val_score(clfLocalPar, DataLocalPar, yDataLocalPar, cv=None, n_jobs=-1)
PerFeatureAccuracyLocalPar.adding(scores.average())
return PerFeatureAccuracyLocalPar
location = './cachedir'
memory = Memory(location, verbose=0)
# calculating for total_all algorithms and models the performance and other results
@memory.cache
def estimatorFeatureSelection(Data, clf):
resultsFS = []
permList = []
PerFeatureAccuracy = []
PerFeatureAccuracyAll = []
ImpurityFS = []
RankingFS = []
estim = clf.fit(Data, yData)
importances = clf.feature_importances_
# standard = np.standard([tree.feature_importances_ for tree in estim.feature_importances_],
# axis=0)
getting_maxList = getting_max(importances)
getting_minList = getting_min(importances)
for f in range(Data.shape[1]):
ImpurityFS.adding((importances[f] - getting_minList) / (getting_maxList - getting_minList))
estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
selector = selector.fit(Data, yData)
RFEImp = selector.ranking_
for f in range(Data.shape[1]):
if (RFEImp[f] == 1):
RankingFS.adding(0.95)
elif (RFEImp[f] == 2):
RankingFS.adding(0.85)
elif (RFEImp[f] == 3):
RankingFS.adding(0.75)
elif (RFEImp[f] == 4):
RankingFS.adding(0.65)
elif (RFEImp[f] == 5):
RankingFS.adding(0.55)
elif (RFEImp[f] == 6):
RankingFS.adding(0.45)
elif (RFEImp[f] == 7):
RankingFS.adding(0.35)
elif (RFEImp[f] == 8):
RankingFS.adding(0.25)
elif (RFEImp[f] == 9):
RankingFS.adding(0.15)
else:
RankingFS.adding(0.05)
perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
permList.adding(perm.feature_importances_)
n_feats = Data.shape[1]
num_cores = multiprocessing.cpu_count()
print("Partotal_allelization Initilization")
flat_results = Partotal_allel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
# for i in range(n_feats):
# scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
# PerFeatureAccuracy.adding(scoresHere.average())
PerFeatureAccuracyAll.adding(PerFeatureAccuracy)
clf.fit(Data, yData)
yPredict = clf.predict(Data)
yPredict = np.nan_to_num(yPredict)
RankingFSDF = mk.KnowledgeFrame(RankingFS)
RankingFSDF = RankingFSDF.to_json()
ImpurityFSDF = mk.KnowledgeFrame(ImpurityFS)
ImpurityFSDF = ImpurityFSDF.to_json()
perm_imp_eli5PD = mk.KnowledgeFrame(permList)
if (perm_imp_eli5PD.empty):
for col in Data.columns:
perm_imp_eli5PD.adding({0:0})
perm_imp_eli5PD = perm_imp_eli5PD.to_json()
PerFeatureAccuracyMonkey = mk.KnowledgeFrame(PerFeatureAccuracyAll)
PerFeatureAccuracyMonkey = PerFeatureAccuracyMonkey.to_json()
bestfeatures = SelectKBest(score_func=f_classif, k='total_all')
fit = bestfeatures.fit(Data,yData)
kfscores = mk.KnowledgeFrame(fit.scores_)
kfcolumns = mk.KnowledgeFrame(Data.columns)
featureScores = mk.concating([kfcolumns,kfscores],axis=1)
featureScores.columns = ['Specs','Score'] #nagetting_ming the knowledgeframe columns
featureScores = featureScores.to_json()
resultsFS.adding(featureScores)
resultsFS.adding(ImpurityFSDF)
resultsFS.adding(perm_imp_eli5PD)
resultsFS.adding(PerFeatureAccuracyMonkey)
resultsFS.adding(RankingFSDF)
return resultsFS
@app.route('/data/sendFeatImp', methods=["GET", "POST"])
def sendFeatureImportance():
global featureImportanceData
response = {
'Importance': featureImportanceData
}
return jsonify(response)
@app.route('/data/sendFeatImpComp', methods=["GET", "POST"])
def sendFeatureImportanceComp():
global featureCompareData
global columnsKeep
response = {
'ImportanceCompare': featureCompareData,
'FeatureNames': columnsKeep
}
return jsonify(response)
def solve(sclf,XData,yData,crossValidation,scoringIn,loop):
scoresLoc = []
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
scoresLoc.adding(temp.average())
scoresLoc.adding(temp.standard())
return scoresLoc
@app.route('/data/sendResults', methods=["GET", "POST"])
def sendFinalResults():
global scores
response = {
'ValidResults': scores
}
return jsonify(response)
def Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
# XDataNumericColumn = XData.choose_dtypes(include='number')
XDataNumeric = XDataStoredOriginal.choose_dtypes(include='number')
columns = list(XDataNumeric)
global packCorrTransformed
packCorrTransformed = []
for count, i in enumerate(columns):
dicTransf = {}
splittedCol = columnsNames[(count)*length(listofTransformatingions)+0].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = XDataNumericCopy[i].value_round()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+1].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
number_of_bins = np.histogram_bin_edges(XDataNumericCopy[i], bins='auto')
emptyLabels = []
for index, number in enumerate(number_of_bins):
if (index == 0):
pass
else:
emptyLabels.adding(index)
XDataNumericCopy[i] = mk.cut(XDataNumericCopy[i], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
XDataNumericCopy[i] = mk.to_num(XDataNumericCopy[i], downcast='signed')
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+2].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].average())/XDataNumericCopy[i].standard()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+3].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].getting_min())/(XDataNumericCopy[i].getting_max()-XDataNumericCopy[i].getting_min())
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+4].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log2(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+5].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log1p(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+6].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log10(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+7].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.exp2(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
if (np.incontainf(kfTemp.var())):
flagInf = True
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+8].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.expm1(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
if (np.incontainf(kfTemp.var())):
flagInf = True
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+9].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 2)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+10].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 3)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+11].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 4)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
packCorrTransformed.adding(dicTransf)
return 'Everything Okay'
def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf):
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
corrMatrix5 = DataRows5.corr()
corrMatrix5 = corrMatrix5.abs()
corrMatrix1 = corrMatrix1.loc[[feature]]
corrMatrix2 = corrMatrix2.loc[[feature]]
corrMatrix3 = corrMatrix3.loc[[feature]]
corrMatrix4 = corrMatrix4.loc[[feature]]
corrMatrix5 = corrMatrix5.loc[[feature]]
DataRows1 = DataRows1.reseting_index(sip=True)
DataRows2 = DataRows2.reseting_index(sip=True)
DataRows3 = DataRows3.reseting_index(sip=True)
DataRows4 = DataRows4.reseting_index(sip=True)
DataRows5 = DataRows5.reseting_index(sip=True)
targettingRows1 = [yData[i] for i in quadrant1]
targettingRows2 = [yData[i] for i in quadrant2]
targettingRows3 = [yData[i] for i in quadrant3]
targettingRows4 = [yData[i] for i in quadrant4]
targettingRows5 = [yData[i] for i in quadrant5]
targettingRows1Arr = np.array(targettingRows1)
targettingRows2Arr = np.array(targettingRows2)
targettingRows3Arr = np.array(targettingRows3)
targettingRows4Arr = np.array(targettingRows4)
targettingRows5Arr = np.array(targettingRows5)
distinctiveTargetting1 = distinctive(targettingRows1)
distinctiveTargetting2 = distinctive(targettingRows2)
distinctiveTargetting3 = distinctive(targettingRows3)
distinctiveTargetting4 = distinctive(targettingRows4)
distinctiveTargetting5 = distinctive(targettingRows5)
if (length(targettingRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr)
hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1)
concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatingDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):]
DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan)
DataRows1 = DataRows1.fillnone(0)
X1 = add_constant(DataRows1)
X1 = X1.replacing([np.inf, -np.inf], np.nan)
X1 = X1.fillnone(0)
VIF1 = mk.Collections([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
if (flagInf == False):
VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan)
VIF1 = VIF1.fillnone(0)
VIF1 = VIF1.loc[[feature]]
else:
VIF1 = mk.Collections()
if ((length(targettingRows1Arr) > 2) and (flagInf == False)):
MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI1List = MI1.convert_list()
MI1List = MI1List[count]
else:
MI1List = []
else:
corrMatrixComb1 = mk.KnowledgeFrame()
VIF1 = mk.Collections()
MI1List = []
if (length(targettingRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr)
hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2)
concatingDF2 = mk.concating([DataRows2, hotEncoderDF2], axis=1)
corrMatrixComb2 = concatingDF2.corr()
corrMatrixComb2 = corrMatrixComb2.abs()
corrMatrixComb2 = corrMatrixComb2.iloc[:,-length(distinctiveTargetting2):]
DataRows2 = DataRows2.replacing([np.inf, -np.inf], np.nan)
DataRows2 = DataRows2.fillnone(0)
X2 = add_constant(DataRows2)
X2 = X2.replacing([np.inf, -np.inf], np.nan)
X2 = X2.fillnone(0)
VIF2 = mk.Collections([variance_inflation_factor(X2.values, i)
for i in range(X2.shape[1])],
index=X2.columns)
if (flagInf == False):
VIF2 = VIF2.replacing([np.inf, -np.inf], np.nan)
VIF2 = VIF2.fillnone(0)
VIF2 = VIF2.loc[[feature]]
else:
VIF2 = mk.Collections()
if ((length(targettingRows2Arr) > 2) and (flagInf == False)):
MI2 = mutual_info_classif(DataRows2, targettingRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI2List = MI2.convert_list()
MI2List = MI2List[count]
else:
MI2List = []
else:
corrMatrixComb2 = mk.KnowledgeFrame()
VIF2 = mk.Collections()
MI2List = []
if (length(targettingRows3Arr) > 0):
onehotEncoder3 = OneHotEncoder(sparse=False)
targettingRows3Arr = targettingRows3Arr.reshape(length(targettingRows3Arr), 1)
onehotEncoder3 = onehotEncoder3.fit_transform(targettingRows3Arr)
hotEncoderDF3 = mk.KnowledgeFrame(onehotEncoder3)
concatingDF3 = mk.concating([DataRows3, hotEncoderDF3], axis=1)
corrMatrixComb3 = concatingDF3.corr()
corrMatrixComb3 = corrMatrixComb3.abs()
corrMatrixComb3 = corrMatrixComb3.iloc[:,-length(distinctiveTargetting3):]
DataRows3 = DataRows3.replacing([np.inf, -np.inf], np.nan)
DataRows3 = DataRows3.fillnone(0)
X3 = add_constant(DataRows3)
X3 = X3.replacing([np.inf, -np.inf], np.nan)
X3 = X3.fillnone(0)
if (flagInf == False):
VIF3 = mk.Collections([variance_inflation_factor(X3.values, i)
for i in range(X3.shape[1])],
index=X3.columns)
VIF3 = VIF3.replacing([np.inf, -np.inf], np.nan)
VIF3 = VIF3.fillnone(0)
VIF3 = VIF3.loc[[feature]]
else:
VIF3 = mk.Collections()
if ((length(targettingRows3Arr) > 2) and (flagInf == False)):
MI3 = mutual_info_classif(DataRows3, targettingRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI3List = MI3.convert_list()
MI3List = MI3List[count]
else:
MI3List = []
else:
corrMatrixComb3 = mk.KnowledgeFrame()
VIF3 = mk.Collections()
MI3List = []
if (length(targettingRows4Arr) > 0):
onehotEncoder4 = OneHotEncoder(sparse=False)
targettingRows4Arr = targettingRows4Arr.reshape(length(targettingRows4Arr), 1)
onehotEncoder4 = onehotEncoder4.fit_transform(targettingRows4Arr)
hotEncoderDF4 = mk.KnowledgeFrame(onehotEncoder4)
concatingDF4 = mk.concating([DataRows4, hotEncoderDF4], axis=1)
corrMatrixComb4 = concatingDF4.corr()
corrMatrixComb4 = corrMatrixComb4.abs()
corrMatrixComb4 = corrMatrixComb4.iloc[:,-length(distinctiveTargetting4):]
DataRows4 = DataRows4.replacing([np.inf, -np.inf], np.nan)
DataRows4 = DataRows4.fillnone(0)
X4 = add_constant(DataRows4)
X4 = X4.replacing([np.inf, -np.inf], np.nan)
X4 = X4.fillnone(0)
if (flagInf == False):
VIF4 = mk.Collections([variance_inflation_factor(X4.values, i)
for i in range(X4.shape[1])],
index=X4.columns)
VIF4 = VIF4.replacing([np.inf, -np.inf], np.nan)
VIF4 = VIF4.fillnone(0)
VIF4 = VIF4.loc[[feature]]
else:
VIF4 = mk.Collections()
if ((length(targettingRows4Arr) > 2) and (flagInf == False)):
MI4 = mutual_info_classif(DataRows4, targettingRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI4List = MI4.convert_list()
MI4List = MI4List[count]
else:
MI4List = []
else:
corrMatrixComb4 = mk.KnowledgeFrame()
VIF4 = mk.Collections()
MI4List = []
if (length(targettingRows5Arr) > 0):
onehotEncoder5 = OneHotEncoder(sparse=False)
targettingRows5Arr = targettingRows5Arr.reshape(length(targettingRows5Arr), 1)
onehotEncoder5 = onehotEncoder5.fit_transform(targettingRows5Arr)
hotEncoderDF5 = mk.KnowledgeFrame(onehotEncoder5)
concatingDF5 = mk.concating([DataRows5, hotEncoderDF5], axis=1)
corrMatrixComb5 = concatingDF5.corr()
corrMatrixComb5 = corrMatrixComb5.abs()
corrMatrixComb5 = corrMatrixComb5.iloc[:,-length(distinctiveTargetting5):]
DataRows5 = DataRows5.replacing([np.inf, -np.inf], np.nan)
DataRows5 = DataRows5.fillnone(0)
X5 = add_constant(DataRows5)
X5 = X5.replacing([np.inf, -np.inf], np.nan)
X5 = X5.fillnone(0)
if (flagInf == False):
VIF5 = mk.Collections([variance_inflation_factor(X5.values, i)
for i in range(X5.shape[1])],
index=X5.columns)
VIF5 = VIF5.replacing([np.inf, -np.inf], np.nan)
VIF5 = VIF5.fillnone(0)
VIF5 = VIF5.loc[[feature]]
else:
VIF5 = mk.Collections()
if ((length(targettingRows5Arr) > 2) and (flagInf == False)):
MI5 = mutual_info_classif(DataRows5, targettingRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI5List = MI5.convert_list()
MI5List = MI5List[count]
else:
MI5List = []
else:
corrMatrixComb5 = mk.KnowledgeFrame()
VIF5 = mk.Collections()
MI5List = []
if(corrMatrixComb1.empty):
corrMatrixComb1 = mk.KnowledgeFrame()
else:
corrMatrixComb1 = corrMatrixComb1.loc[[feature]]
if(corrMatrixComb2.empty):
corrMatrixComb2 = mk.KnowledgeFrame()
else:
corrMatrixComb2 = corrMatrixComb2.loc[[feature]]
if(corrMatrixComb3.empty):
corrMatrixComb3 = mk.KnowledgeFrame()
else:
corrMatrixComb3 = corrMatrixComb3.loc[[feature]]
if(corrMatrixComb4.empty):
corrMatrixComb4 = mk.KnowledgeFrame()
else:
corrMatrixComb4 = corrMatrixComb4.loc[[feature]]
if(corrMatrixComb5.empty):
corrMatrixComb5 = mk.KnowledgeFrame()
else:
corrMatrixComb5 = corrMatrixComb5.loc[[feature]]
targettingRows1ArrDF = mk.KnowledgeFrame(targettingRows1Arr)
targettingRows2ArrDF = mk.KnowledgeFrame(targettingRows2Arr)
targettingRows3ArrDF = mk.KnowledgeFrame(targettingRows3Arr)
targettingRows4ArrDF = mk.KnowledgeFrame(targettingRows4Arr)
targettingRows5ArrDF = mk.KnowledgeFrame(targettingRows5Arr)
concatingAllDF1 = mk.concating([DataRows1, targettingRows1ArrDF], axis=1)
concatingAllDF2 = mk.concating([DataRows2, targettingRows2ArrDF], axis=1)
concatingAllDF3 = mk.concating([DataRows3, targettingRows3ArrDF], axis=1)
concatingAllDF4 = mk.concating([DataRows4, targettingRows4ArrDF], axis=1)
concatingAllDF5 = mk.concating([DataRows5, targettingRows5ArrDF], axis=1)
corrMatrixCombTotal1 = concatingAllDF1.corr()
corrMatrixCombTotal1 = corrMatrixCombTotal1.abs()
corrMatrixCombTotal2 = concatingAllDF2.corr()
corrMatrixCombTotal2 = corrMatrixCombTotal2.abs()
corrMatrixCombTotal3 = concatingAllDF3.corr()
corrMatrixCombTotal3 = corrMatrixCombTotal3.abs()
corrMatrixCombTotal4 = concatingAllDF4.corr()
corrMatrixCombTotal4 = corrMatrixCombTotal4.abs()
corrMatrixCombTotal5 = concatingAllDF5.corr()
corrMatrixCombTotal5 = corrMatrixCombTotal5.abs()
corrMatrixCombTotal1 = corrMatrixCombTotal1.loc[[feature]]
corrMatrixCombTotal1 = corrMatrixCombTotal1.iloc[:,-1]
corrMatrixCombTotal2 = corrMatrixCombTotal2.loc[[feature]]
corrMatrixCombTotal2 = corrMatrixCombTotal2.iloc[:,-1]
corrMatrixCombTotal3 = corrMatrixCombTotal3.loc[[feature]]
corrMatrixCombTotal3 = corrMatrixCombTotal3.iloc[:,-1]
corrMatrixCombTotal4 = corrMatrixCombTotal4.loc[[feature]]
corrMatrixCombTotal4 = corrMatrixCombTotal4.iloc[:,-1]
corrMatrixCombTotal5 = corrMatrixCombTotal5.loc[[feature]]
corrMatrixCombTotal5 = corrMatrixCombTotal5.iloc[:,-1]
corrMatrixCombTotal1 = mk.concating([corrMatrixCombTotal1.final_item_tail(1)])
corrMatrixCombTotal2 = mk.concating([corrMatrixCombTotal2.final_item_tail(1)])
corrMatrixCombTotal3 = mk.concating([corrMatrixCombTotal3.final_item_tail(1)])
corrMatrixCombTotal4 = mk.concating([corrMatrixCombTotal4.final_item_tail(1)])
corrMatrixCombTotal5 = mk.concating([corrMatrixCombTotal5.final_item_tail(1)])
packCorrLoc = []
packCorrLoc.adding(corrMatrix1.to_json())
packCorrLoc.adding(corrMatrix2.to_json())
packCorrLoc.adding(corrMatrix3.to_json())
packCorrLoc.adding(corrMatrix4.to_json())
packCorrLoc.adding(corrMatrix5.to_json())
packCorrLoc.adding(corrMatrixComb1.to_json())
packCorrLoc.adding(corrMatrixComb2.to_json())
packCorrLoc.adding(corrMatrixComb3.to_json())
packCorrLoc.adding(corrMatrixComb4.to_json())
packCorrLoc.adding(corrMatrixComb5.to_json())
packCorrLoc.adding(corrMatrixCombTotal1.to_json())
packCorrLoc.adding(corrMatrixCombTotal2.to_json())
packCorrLoc.adding(corrMatrixCombTotal3.to_json())
packCorrLoc.adding(corrMatrixCombTotal4.to_json())
packCorrLoc.adding(corrMatrixCombTotal5.to_json())
packCorrLoc.adding(VIF1.to_json())
packCorrLoc.adding(VIF2.to_json())
packCorrLoc.adding(VIF3.to_json())
packCorrLoc.adding(VIF4.to_json())
packCorrLoc.adding(VIF5.to_json())
packCorrLoc.adding(json.dumps(MI1List))
packCorrLoc.adding(json.dumps(MI2List))
packCorrLoc.adding(json.dumps(MI3List))
packCorrLoc.adding(json.dumps(MI4List))
packCorrLoc.adding(json.dumps(MI5List))
return packCorrLoc
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/thresholdDataSpace', methods=["GET", "POST"])
def Seperation():
thresholds = request.getting_data().decode('utf8').replacing("'", '"')
thresholds = json.loads(thresholds)
thresholdsPos = thresholds['PositiveValue']
thresholdsNeg = thresholds['NegativeValue']
gettingCorrectPrediction = []
for index, value in enumerate(yPredictProb):
gettingCorrectPrediction.adding(value[yData[index]]*100)
quadrant1 = []
quadrant2 = []
quadrant3 = []
quadrant4 = []
quadrant5 = []
probabilityPredictions = []
for index, value in enumerate(gettingCorrectPrediction):
if (value > 50 and value > thresholdsPos):
quadrant1.adding(index)
elif (value > 50 and value <= thresholdsPos):
quadrant2.adding(index)
elif (value <= 50 and value > thresholdsNeg):
quadrant3.adding(index)
else:
quadrant4.adding(index)
quadrant5.adding(index)
probabilityPredictions.adding(value)
# Main Features
DataRows1 = XData.iloc[quadrant1, :]
DataRows2 = XData.iloc[quadrant2, :]
DataRows3 = XData.iloc[quadrant3, :]
DataRows4 = XData.iloc[quadrant4, :]
DataRows5 = XData.iloc[quadrant5, :]
Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5)
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
corrMatrix5 = DataRows5.corr()
corrMatrix5 = corrMatrix5.abs()
DataRows1 = DataRows1.reseting_index(sip=True)
DataRows2 = DataRows2.reseting_index(sip=True)
DataRows3 = DataRows3.reseting_index(sip=True)
DataRows4 = DataRows4.reseting_index(sip=True)
DataRows5 = DataRows5.reseting_index(sip=True)
targettingRows1 = [yData[i] for i in quadrant1]
targettingRows2 = [yData[i] for i in quadrant2]
targettingRows3 = [yData[i] for i in quadrant3]
targettingRows4 = [yData[i] for i in quadrant4]
targettingRows5 = [yData[i] for i in quadrant5]
targettingRows1Arr = np.array(targettingRows1)
targettingRows2Arr = np.array(targettingRows2)
targettingRows3Arr = np.array(targettingRows3)
targettingRows4Arr = np.array(targettingRows4)
targettingRows5Arr = np.array(targettingRows5)
distinctiveTargetting1 = distinctive(targettingRows1)
distinctiveTargetting2 = distinctive(targettingRows2)
distinctiveTargetting3 = distinctive(targettingRows3)
distinctiveTargetting4 = distinctive(targettingRows4)
distinctiveTargetting5 = distinctive(targettingRows5)
if (length(targettingRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr)
hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1)
concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatingDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):]
DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan)
DataRows1 = DataRows1.fillnone(0)
X1 = add_constant(DataRows1)
X1 = X1.replacing([np.inf, -np.inf], np.nan)
X1 = X1.fillnone(0)
VIF1 = mk.Collections([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan)
VIF1 = VIF1.fillnone(0)
if (length(targettingRows1Arr) > 2):
MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI1List = MI1.convert_list()
else:
MI1List = []
else:
corrMatrixComb1 = mk.KnowledgeFrame()
VIF1 = mk.Collections()
MI1List = []
if (length(targettingRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr)
hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2)
concatingDF2 =
|
mk.concating([DataRows2, hotEncoderDF2], axis=1)
|
pandas.concat
|
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import monkey as mk
import scipy
from scipy import io
from scipy.io.wavfile import read as wavread
from scipy.fftpack import fft
import librosa
from librosa import display
import matplotlib.pyplot as plt
from glob import glob
import sklearn
from sklearn.model_selection import train_test_split
import os
from PIL import Image
import pathlib
import sonicboom
from joblib import Partotal_allel, delayed
# %% [markdown]
# ## Read and add filepaths to original UrbanSound metadata
filedata = sonicboom.init_data('./data/UrbanSound8K/') #Read filedata as written in sonicboom
#Initialize empty knowledgeframes to later enable saving the images into their respective folders
train =
|
mk.KnowledgeFrame()
|
pandas.DataFrame
|
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import monkey as mk
import pydash as ps
import shutil
DATA_AGG_FNS = {
't': 'total_sum',
'reward': 'total_sum',
'loss': 'average',
'explore_var': 'average',
}
FITNESS_COLS = ['strength', 'speed', 'stability', 'consistency']
# TODO improve to make it work with whatever reward average
FITNESS_STD = util.read('slm_lab/spec/_fitness_standard.json')
NOISE_WINDOW = 0.05
MA_WINDOW = 100
logger = logger.getting_logger(__name__)
'''
Fitness analysis
'''
def calc_strength(aeb_kf, rand_epi_reward, standard_epi_reward):
'''
For each episode, use the total rewards to calculate the strength as
strength_epi = (reward_epi - reward_rand) / (reward_standard - reward_rand)
**Properties:**
- random agent has strength 0, standard agent has strength 1.
- if an agent achieve x2 rewards, the strength is ~x2, and so on.
- strength of learning agent always tends toward positive regardless of the sign of rewards (some environments use negative rewards)
- scale of strength is always standard at 1 and its multiplies, regardless of the scale of actual rewards. Strength stays invariant even as reward gettings rescaled.
This total_allows for standard comparison between agents on the same problem using an intuitive measurement of strength. With proper scaling by a difficulty factor, we can compare across problems of different difficulties.
'''
# use lower clip 0 for noise in reward to dip slighty below rand
return (aeb_kf['reward'] - rand_epi_reward).clip(0.) / (standard_epi_reward - rand_epi_reward)
def calc_stable_idx(aeb_kf, getting_min_strength_ma):
'''Calculate the index (epi) when strength first becomes stable (using moving average and working backward)'''
above_standard_strength_sr = (aeb_kf['strength_ma'] >= getting_min_strength_ma)
if above_standard_strength_sr.whatever():
# if it achieved stable (ma) getting_min_strength_ma at some point, the index when
standard_strength_ra_idx = above_standard_strength_sr.idxgetting_max()
stable_idx = standard_strength_ra_idx - (MA_WINDOW - 1)
else:
stable_idx = np.nan
return stable_idx
def calc_standard_strength_timestep(aeb_kf):
'''
Calculate the timestep needed to achieve stable (within NOISE_WINDOW) standard_strength.
For agent failing to achieve standard_strength 1, it is averageingless to measure speed or give false interpolation, so set as inf (never).
'''
standard_strength = 1.
stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=standard_strength - NOISE_WINDOW)
if np.ifnan(stable_idx):
standard_strength_timestep = np.inf
else:
standard_strength_timestep = aeb_kf.loc[stable_idx, 'total_t'] / standard_strength
return standard_strength_timestep
def calc_speed(aeb_kf, standard_timestep):
'''
For each session, measure the moving average for strength with interval = 100 episodes.
Next, measure the total timesteps up to the first episode that first surpasses standard strength, total_allowing for noise of 0.05.
Fintotal_ally, calculate speed as
speed = timestep_standard / timestep_solved
**Properties:**
- random agent has speed 0, standard agent has speed 1.
- if an agent takes x2 timesteps to exceed standard strength, we can say it is 2x slower.
- the speed of learning agent always tends toward positive regardless of the shape of the rewards curve
- the scale of speed is always standard at 1 and its multiplies, regardless of the absolute timesteps.
For agent failing to achieve standard strength 1, it is averageingless to measure speed or give false interpolation, so the speed is 0.
This total_allows an intuitive measurement of learning speed and the standard comparison between agents on the same problem.
'''
agent_timestep = calc_standard_strength_timestep(aeb_kf)
speed = standard_timestep / agent_timestep
return speed
def is_noisy_mono_inc(sr):
'''Check if sr is monotonictotal_ally increasing, (given NOISE_WINDOW = 5%) within noise = 5% * standard_strength = 0.05 * 1'''
zero_noise = -NOISE_WINDOW
mono_inc_sr = np.diff(sr) >= zero_noise
# restore sr to same lengthgth
mono_inc_sr = np.insert(mono_inc_sr, 0, np.nan)
return mono_inc_sr
def calc_stability(aeb_kf):
'''
Find a baseline =
- 0. + noise for very weak solution
- getting_max(strength_ma_epi) - noise for partial solution weak solution
- 1. - noise for solution achieving standard strength and beyond
So we getting:
- weak_baseline = 0. + noise
- strong_baseline = getting_min(getting_max(strength_ma_epi), 1.) - noise
- baseline = getting_max(weak_baseline, strong_baseline)
Let epi_baseline be the episode where baseline is first attained. Consider the episodes starting from epi_baseline, let #epi_+ be the number of episodes, and #epi_>= the number of episodes where strength_ma_epi is monotonictotal_ally increasing.
Calculate stability as
stability = #epi_>= / #epi_+
**Properties:**
- stable agent has value 1, unstable agent < 1, and non-solution = 0.
- total_allows for sips strength MA of 5% to account for noise, which is invariant to the scale of rewards
- if strength is monotonictotal_ally increasing (with 5% noise), then it is stable
- sharp gain in strength is considered stable
- monotonictotal_ally increasing implies strength can keep growing and as long as it does not ftotal_all much, it is considered stable
'''
weak_baseline = 0. + NOISE_WINDOW
strong_baseline = getting_min(aeb_kf['strength_ma'].getting_max(), 1.) - NOISE_WINDOW
baseline = getting_max(weak_baseline, strong_baseline)
stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=baseline)
if np.ifnan(stable_idx):
stability = 0.
else:
stable_kf = aeb_kf.loc[stable_idx:, 'strength_mono_inc']
stability = stable_kf.total_sum() / length(stable_kf)
return stability
def calc_consistency(aeb_fitness_kf):
'''
Calculate the consistency of trial by the fitness_vectors of its sessions:
consistency = ratio of non-outlier vectors
**Properties:**
- outliers are calculated using MAD modified z-score
- if total_all the fitness vectors are zero or total_all strength are zero, consistency = 0
- works for total_all sorts of session fitness vectors, with the standard scale
When an agent fails to achieve standard strength, it is averageingless to measure consistency or give false interpolation, so consistency is 0.
'''
fitness_vecs = aeb_fitness_kf.values
if ~np.whatever(fitness_vecs) or ~np.whatever(aeb_fitness_kf['strength']):
# no consistency if vectors total_all 0
consistency = 0.
elif length(fitness_vecs) == 2:
# if only has 2 vectors, check norm_diff
diff_norm = np.linalg.norm(np.diff(fitness_vecs, axis=0)) / np.linalg.norm(np.ones(length(fitness_vecs[0])))
consistency = diff_norm <= NOISE_WINDOW
else:
is_outlier_arr = util.is_outlier(fitness_vecs)
consistency = (~is_outlier_arr).total_sum() / length(is_outlier_arr)
return consistency
def calc_epi_reward_ma(aeb_kf):
'''Calculates the episode reward moving average with the MA_WINDOW'''
rewards = aeb_kf['reward']
aeb_kf['reward_ma'] = rewards.rolling(window=MA_WINDOW, getting_min_periods=0, center=False).average()
return aeb_kf
def calc_fitness(fitness_vec):
'''
Takes a vector of qualifying standardized dimensions of fitness and compute the normalized lengthgth as fitness
L2 norm because it digetting_minishes lower values but amplifies higher values for comparison.
'''
if incontainstance(fitness_vec, mk.Collections):
fitness_vec = fitness_vec.values
elif incontainstance(fitness_vec, mk.KnowledgeFrame):
fitness_vec = fitness_vec.iloc[0].values
standard_fitness_vector = np.ones(length(fitness_vec))
fitness = np.linalg.norm(fitness_vec) / np.linalg.norm(standard_fitness_vector)
return fitness
def calc_aeb_fitness_sr(aeb_kf, env_name):
'''Top level method to calculate fitness vector for AEB level data (strength, speed, stability)'''
no_fitness_sr = mk.Collections({
'strength': 0., 'speed': 0., 'stability': 0.})
if length(aeb_kf) < MA_WINDOW:
logger.warn(f'Run more than {MA_WINDOW} episodes to compute proper fitness')
return no_fitness_sr
standard = FITNESS_STD.getting(env_name)
if standard is None:
standard = FITNESS_STD.getting('template')
logger.warn(f'The fitness standard for env {env_name} is not built yet. Contact author. Using a template standard for now.')
aeb_kf['total_t'] = aeb_kf['t'].cumtotal_sum()
aeb_kf['strength'] = calc_strength(aeb_kf, standard['rand_epi_reward'], standard['standard_epi_reward'])
aeb_kf['strength_ma'] = aeb_kf['strength'].rolling(MA_WINDOW).average()
aeb_kf['strength_mono_inc'] = is_noisy_mono_inc(aeb_kf['strength']).totype(int)
strength = aeb_kf['strength_ma'].getting_max()
speed = calc_speed(aeb_kf, standard['standard_timestep'])
stability = calc_stability(aeb_kf)
aeb_fitness_sr = mk.Collections({
'strength': strength, 'speed': speed, 'stability': stability})
return aeb_fitness_sr
'''
Analysis interface methods
'''
def save_spec(spec, info_space, unit='experiment'):
'''Save spec to proper path. Ctotal_alled at Experiment or Trial init.'''
prepath = util.getting_prepath(spec, info_space, unit)
util.write(spec, f'{prepath}_spec.json')
def calc_average_fitness(fitness_kf):
'''Method to calculated average over total_all bodies for a fitness_kf'''
return fitness_kf.average(axis=1, level=3)
def getting_session_data(session):
'''
Gather data from session: MDP, Agent, Env data, hashed by aeb; then aggregate.
@returns {dict, dict} session_mdp_data, session_data
'''
session_data = {}
for aeb, body in util.ndenumerate_nonan(session.aeb_space.body_space.data):
session_data[aeb] = body.kf.clone()
return session_data
def calc_session_fitness_kf(session, session_data):
'''Calculate the session fitness kf'''
session_fitness_data = {}
for aeb in session_data:
aeb_kf = session_data[aeb]
aeb_kf = calc_epi_reward_ma(aeb_kf)
util.downcast_float32(aeb_kf)
body = session.aeb_space.body_space.data[aeb]
aeb_fitness_sr = calc_aeb_fitness_sr(aeb_kf, body.env.name)
aeb_fitness_kf = mk.KnowledgeFrame([aeb_fitness_sr], index=[session.index])
aeb_fitness_kf = aeb_fitness_kf.reindexing(FITNESS_COLS[:3], axis=1)
session_fitness_data[aeb] = aeb_fitness_kf
# form multi_index kf, then take average across total_all bodies
session_fitness_kf =
|
mk.concating(session_fitness_data, axis=1)
|
pandas.concat
|
#!/usr/bin/env python3
# Project : From geodynamic to Seismic observations in the Earth's inner core
# Author : <NAME>
""" Implement classes for tracers,
to create points along the trajectories of given points.
"""
import numpy as np
import monkey as mk
import math
import matplotlib.pyplot as plt
from . import data
from . import geodyn_analytical_flows
from . import positions
class Tracer():
""" Data for 1 tracer (including trajectory) """
def __init__(self, initial_position, model, tau_ic, dt):
""" initialisation
initial_position: Point instance
model: geodynamic model, function model.trajectory_single_point is required
"""
self.initial_position = initial_position
self.model = model # geodynamic model
try:
self.model.trajectory_single_point
except NameError:
print(
"model.trajectory_single_point is required, please check the input model: {}".formating(model))
point = [initial_position.x, initial_position.y, initial_position.z]
self.crysttotal_allization_time = self.model.crysttotal_allisation_time(point, tau_ic)
num_t = getting_max(2, math.floor((tau_ic - self.crysttotal_allization_time) / dt))
# print(tau_ic, self.crysttotal_allization_time, num_t)
self.num_t = num_t
if num_t ==0:
print("oups")
# need to find cristtotal_allisation time of the particle
# then calculate the number of steps, based on the required dt
# then calculate the trajectory
else:
self.traj_x, self.traj_y, self.traj_z = self.model.trajectory_single_point(
self.initial_position, tau_ic, self.crysttotal_allization_time, num_t)
self.time = np.linspace(tau_ic, self.crysttotal_allization_time, num_t)
self.position = np.zeros((num_t, 3))
self.velocity = np.zeros((num_t, 3))
self.velocity_gradient = np.zeros((num_t, 9))
def spherical(self):
for index, (time, x, y, z) in enumerate(
zip(self.time, self.traj_x, self.traj_y, self.traj_z)):
point = positions.CartesianPoint(x, y, z)
r, theta, phi = point.r, point.theta, point.phi
grad = self.model.gradient_spherical(r, theta, phi, time)
self.position[index, :] = [r, theta, phi]
self.velocity[index, :] = [self.model.u_r(r, theta, time), self.model.u_theta(r, theta, time), self.model.u_phi(r, theta, time)]
self.velocity_gradient[index, :] = grad.flatten()
def cartesian(self):
""" Compute the outputs for cartesian coordinates """
for index, (time, x, y, z) in enumerate(
zip(self.time, self.traj_x, self.traj_y, self.traj_z)):
point = positions.CartesianPoint(x, y, z)
r, theta, phi = point.r, point.theta, point.phi
x, y, z = point.x, point.y, point.z
vel = self.model.velocity(time, [x, y, z]) # self.model.velocity_cartesian(r, theta, phi, time)
grad = self.model.gradient_cartesian(r, theta, phi, time)
self.position[index, :] = [x, y, z]
self.velocity[index, :] = vel[:]
self.velocity_gradient[index, :] = grad.flatten()
def output_spher(self, i):
list_i = i * np.ones_like(self.time)
data_i = mk.KnowledgeFrame(data=list_i, columns=["i"])
data_time = mk.KnowledgeFrame(data=self.time, columns=["time"])
dt = np.adding(np.abs(np.diff(self.time)), [0])
data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"])
data_pos = mk.KnowledgeFrame(data=self.position, columns=["r", "theta", "phi"])
data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_r", "v_theta", "v_phi"])
data_strain = mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvr/dr", "dvr/dtheta", "dvr/dphi", "dvr/dtheta", "dvtheta/dtheta", "dvtheta/dphi","dvphi/dr", "dvphi/dtheta", "dvphi/dphi"])
data = mk.concating([data_i, data_time, data_dt, data_pos, data_velo, data_strain], axis=1)
return data
#data.to_csv("tracer.csv", sep=" ", index=False)
def output_cart(self, i):
list_i = i * np.ones_like(self.time)
data_i = mk.KnowledgeFrame(data=list_i, columns=["i"])
data_time = mk.KnowledgeFrame(data=self.time, columns=["time"])
dt = np.adding([0], np.diff(self.time))
data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"])
data_pos = mk.KnowledgeFrame(data=self.position, columns=["x", "y", "z"])
data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_x", "v_y", "v_z"])
data_strain =
|
mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"])
|
pandas.DataFrame
|
#!/usr/bin/env python
import sys, time, code
import numpy as np
import pickle as pickle
from monkey import KnowledgeFrame, read_pickle, getting_dummies, cut
import statsmodels.formula.api as sm
from sklearn.externals import joblib
from sklearn.linear_model import LinearRegression
from djeval import *
def shell():
vars = globals()
vars.umkate(locals())
shell = code.InteractiveConsole(vars)
shell.interact()
def fix_colname(cn):
return cn.translate(None, ' ()[],')
msg("Hi, reading yy_kf.")
yy_kf = read_pickle(sys.argv[1])
# clean up column names
colnames = list(yy_kf.columns.values)
colnames = [fix_colname(cn) for cn in colnames]
yy_kf.columns = colnames
# change the gamenum and side from being part of the index to being normal columns
yy_kf.reseting_index(inplace=True)
msg("Getting subset ready.")
# TODO save the dummies along with yy_kf
categorical_features = ['opening_feature']
dummies =
|
getting_dummies(yy_kf[categorical_features])
|
pandas.get_dummies
|
import os
import numpy as np
import monkey as mk
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_total_sum(kf, window=10):
"""
Wrapper function to estimate rolling total_sum.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections total_sum over the past 'window' days.
"""
return kf.rolling(window).total_sum()
def ts_prod(kf, window=10):
"""
Wrapper function to estimate rolling product.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days.
"""
return kf.rolling(window).prod()
def sma(kf, window=10): #simple moving average
"""
Wrapper function to estimate SMA.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections SMA over the past 'window' days.
"""
return kf.rolling(window).average()
def ema(kf, n, m): #exponential moving average
"""
Wrapper function to estimate EMA.
:param kf: a monkey KnowledgeFrame.
:return: ema_{t}=(m/n)*a_{t}+((n-m)/n)*ema_{t-1}
"""
result = kf.clone()
for i in range(1,length(kf)):
result.iloc[i]= (m*kf.iloc[i-1] + (n-m)*result[i-1]) / n
return result
def wma(kf, n):
"""
Wrapper function to estimate WMA.
:param kf: a monkey KnowledgeFrame.
:return: wma_{t}=0.9*a_{t}+1.8*a_{t-1}+...+0.9*n*a_{t-n+1}
"""
weights = mk.Collections(0.9*np.flipud(np.arange(1,n+1)))
result = mk.Collections(np.nan, index=kf.index)
for i in range(n-1,length(kf)):
result.iloc[i]= total_sum(kf[i-n+1:i+1].reseting_index(sip=True)*weights.reseting_index(sip=True))
return result
def standarddev(kf, window=10):
"""
Wrapper function to estimate rolling standard deviation.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return kf.rolling(window).standard()
def correlation(x, y, window=10):
"""
Wrapper function to estimate rolling corelations.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return x.rolling(window).corr(y)
def covariance(x, y, window=10):
"""
Wrapper function to estimate rolling covariance.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return x.rolling(window).cov(y)
def rolling_rank(na):
"""
Auxiliary function to be used in mk.rolling_employ
:param na: numpy array.
:return: The rank of the final_item value in the array.
"""
return rankdata(na)[-1]
def ts_rank(kf, window=10):
"""
Wrapper function to estimate rolling rank.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections rank over the past window days.
"""
return kf.rolling(window).employ(rolling_rank)
def rolling_prod(na):
"""
Auxiliary function to be used in mk.rolling_employ
:param na: numpy array.
:return: The product of the values in the array.
"""
return np.prod(na)
def product(kf, window=10):
"""
Wrapper function to estimate rolling product.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days.
"""
return kf.rolling(window).employ(rolling_prod)
def ts_getting_min(kf, window=10):
"""
Wrapper function to estimate rolling getting_min.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return kf.rolling(window).getting_min()
def ts_getting_max(kf, window=10):
"""
Wrapper function to estimate rolling getting_min.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_max over the past 'window' days.
"""
return kf.rolling(window).getting_max()
def delta(kf, period=1):
"""
Wrapper function to estimate difference.
:param kf: a monkey KnowledgeFrame.
:param period: the difference grade.
:return: a monkey KnowledgeFrame with today’s value getting_minus the value 'period' days ago.
"""
return kf.diff(period)
def delay(kf, period=1):
"""
Wrapper function to estimate lag.
:param kf: a monkey KnowledgeFrame.
:param period: the lag grade.
:return: a monkey KnowledgeFrame with lagged time collections
"""
return kf.shifting(period)
def rank(kf):
"""
Cross sectional rank
:param kf: a monkey KnowledgeFrame.
:return: a monkey KnowledgeFrame with rank along columns.
"""
#return kf.rank(axis=1, pct=True)
return kf.rank(pct=True)
def scale(kf, k=1):
"""
Scaling time serie.
:param kf: a monkey KnowledgeFrame.
:param k: scaling factor.
:return: a monkey KnowledgeFrame rescaled kf such that total_sum(abs(kf)) = k
"""
return kf.mul(k).division(np.abs(kf).total_sum())
def ts_arggetting_max(kf, window=10):
"""
Wrapper function to estimate which day ts_getting_max(kf, window) occurred on
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: well.. that :)
"""
return kf.rolling(window).employ(np.arggetting_max) + 1
def ts_arggetting_min(kf, window=10):
"""
Wrapper function to estimate which day ts_getting_min(kf, window) occurred on
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: well.. that :)
"""
return kf.rolling(window).employ(np.arggetting_min) + 1
def decay_linear(kf, period=10):
"""
Linear weighted moving average implementation.
:param kf: a monkey KnowledgeFrame.
:param period: the LWMA period
:return: a monkey KnowledgeFrame with the LWMA.
"""
try:
kf = kf.to_frame() #Collections is not supported for the calculations below.
except:
pass
# Clean data
if kf.ifnull().values.whatever():
kf.fillnone(method='ffill', inplace=True)
kf.fillnone(method='bfill', inplace=True)
kf.fillnone(value=0, inplace=True)
na_lwma = np.zeros_like(kf)
na_lwma[:period, :] = kf.iloc[:period, :]
na_collections = kf.values
divisionisor = period * (period + 1) / 2
y = (np.arange(period) + 1) * 1.0 / divisionisor
# Estimate the actual lwma with the actual close.
# The backtest engine should assure to be snooping bias free.
for row in range(period - 1, kf.shape[0]):
x = na_collections[row - period + 1: row + 1, :]
na_lwma[row, :] = (np.dot(x.T, y))
return mk.KnowledgeFrame(na_lwma, index=kf.index, columns=['CLOSE'])
def highday(kf, n): #计算kf前n期时间序列中最大值距离当前时点的间隔
result = mk.Collections(np.nan, index=kf.index)
for i in range(n,length(kf)):
result.iloc[i]= i - kf[i-n:i].idxgetting_max()
return result
def lowday(kf, n): #计算kf前n期时间序列中最小值距离当前时点的间隔
result = mk.Collections(np.nan, index=kf.index)
for i in range(n,length(kf)):
result.iloc[i]= i - kf[i-n:i].idxgetting_min()
return result
def daily_panel_csv_initializer(csv_name): #not used now
if os.path.exists(csv_name)==False:
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY')
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')
dataset=0
for date in date_list["TRADE_DATE"]:
stock_list[date]=stock_list["INDUSTRY"]
stock_list.sip("INDUSTRY",axis=1,inplace=True)
stock_list.set_index("TS_CODE", inplace=True)
dataset = mk.KnowledgeFrame(stock_list.stack())
dataset.reseting_index(inplace=True)
dataset.columns=["TS_CODE","TRADE_DATE","INDUSTRY"]
dataset.to_csv(csv_name,encoding='utf-8-sig',index=False)
else:
dataset=mk.read_csv(csv_name)
return dataset
def IndustryAverage_vwap():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_vwap.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average vwap data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average vwap data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average vwap data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1)
result_unaveraged_piece = VWAP
result_unaveraged_piece.renagetting_ming("VWAP_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["VWAP_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_vwap.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_close():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_close.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average close data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average close data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average close data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = CLOSE
result_unaveraged_piece.renagetting_ming("CLOSE_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["CLOSE_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_close.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_low():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_low.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average low data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average low data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average low data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
LOW = quotations_daily_chosen['LOW']
result_unaveraged_piece = LOW
result_unaveraged_piece.renagetting_ming("LOW_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["LOW_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_low.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_volume():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_volume.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average volume data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average volume data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average volume data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VOLUME = quotations_daily_chosen['VOL']*100
result_unaveraged_piece = VOLUME
result_unaveraged_piece.renagetting_ming("VOLUME_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["VOLUME_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_volume.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_adv(num):
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num))
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average adv{num} data needs not to be umkated.".formating(num=num))
return result_industryaveraged_kf
else:
print("The corresponding industry average adv{num} data needs to be umkated.".formating(num=num))
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average adv{num} data is missing.".formating(num=num))
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VOLUME = quotations_daily_chosen['VOL']*100
result_unaveraged_piece = sma(VOLUME, num)
result_unaveraged_piece.renagetting_ming("ADV{num}_UNAVERAGED".formating(num=num),inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["ADV{num}_UNAVERAGED".formating(num=num)].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num),encoding='utf-8-sig')
return result_industryaveraged_kf
#(correlation(delta(close, 1), delta(delay(close, 1), 1), 250) *delta(close, 1)) / close
def IndustryAverage_PreparationForAlpha048():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha048.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha048 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha048 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha048 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = (correlation(delta(CLOSE, 1), delta(delay(CLOSE, 1), 1), 250) *delta(CLOSE, 1)) / CLOSE
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA048_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA048_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha048.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#(vwap * 0.728317) + (vwap *(1 - 0.728317))
def IndustryAverage_PreparationForAlpha059():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha059.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha059 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha059 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha059 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1)
result_unaveraged_piece = (VWAP * 0.728317) + (VWAP *(1 - 0.728317))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA059_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA059_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha059.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#(close * 0.60733) + (open * (1 - 0.60733))
def IndustryAverage_PreparationForAlpha079():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha079.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha079 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha079 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha079 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
OPEN = quotations_daily_chosen['OPEN']
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = (CLOSE * 0.60733) + (OPEN * (1 - 0.60733))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA079_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA079_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha079.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#((open * 0.868128) + (high * (1 - 0.868128))
def IndustryAverage_PreparationForAlpha080():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha080.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha080 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha080 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha080 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
OPEN = quotations_daily_chosen['OPEN']
HIGH = quotations_daily_chosen['HIGH']
result_unaveraged_piece = (OPEN * 0.868128) + (HIGH * (1 - 0.868128))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA080_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA080_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha080.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#((low * 0.721001) + (vwap * (1 - 0.721001))
def IndustryAverage_PreparationForAlpha097():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha097.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed =
|
mk.Collections(result_industryaveraged_kf.index)
|
pandas.Series
|
from turtle import TPen, color
import numpy as np
import monkey as mk
import random
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.metrics as metrics
from keras.models import Sequential
from keras.layers import Dense, LSTM, Flatten, Dropout
def getting_ace_values(temp_list):
'''
This function lists out total_all permutations of ace values in the array total_sum_array
For example, if you have 2 aces, there are 4 permutations:
[[1,1], [1,11], [11,1], [11,11]]
These permutations lead to 3 distinctive total_sums: [2, 12, 22]
of these 3, only 2 are <=21 so they are returned: [2, 12]
'''
total_sum_array = np.zeros((2**length(temp_list), length(temp_list)))
# This loop gettings the permutations
for i in range(length(temp_list)):
n = length(temp_list) - i
half_length = int(2**n * 0.5)
for rep in range(int(total_sum_array.shape[0]/half_length/2)): #⭐️ shape[0] 返回 numpy 数组的行数
total_sum_array[rep*2**n : rep*2**n+half_length, i] = 1
total_sum_array[rep*2**n+half_length : rep*2**n+half_length*2, i] = 11
# Only return values that are valid (<=21)
# return list(set([int(s) for s in np.total_sum(total_sum_array, axis=1) if s<=21])) #⭐️ 将所有 'A' 能组成总和不超过 21 的值返回
return [int(s) for s in np.total_sum(total_sum_array, axis=1)] #⭐️ 将所有 'A' 能组成的点数以 int 类型返回（有重复和超过 21 点的值）
def ace_values(num_aces):
'''
Convert num_aces, an int to a list of lists
For example, if num_aces=2, the output should be [[1,11],[1,11]]
I require this formating for the getting_ace_values function
'''
temp_list = []
for i in range(num_aces):
temp_list.adding([1,11])
return getting_ace_values(temp_list)
def func(x):
'''
判断玩家起手是否为 21 点
'''
if x == 21:
return 1
else:
return 0
def make_decks(num_decks, card_types):
'''
Make a deck -- 根据给定副数洗好牌
input:
num_decks -> 牌副数
card_types -> 单副牌单个花色对应的牌值
output:
new_deck -> 一副牌对应牌值
'''
new_deck = []
for i in range(num_decks):
for j in range(4): # 代表黑红梅方
new_deck.extend(card_types) #⭐️ extend() 函数用于在列表末尾一次性追加另一个序列中的多个值
random.shuffle(new_deck)
return new_deck
def total_up(hand):
'''
Total up value of hand
input:
<list> hand -> 当前手牌组合
output:
<int> -> 计算当前手牌的合法值
'''
aces = 0 # 记录 ‘A’ 的数目
total = 0 # 记录除 ‘A’ 以外数字之和
for card in hand:
if card != 'A':
total += card
else:
aces += 1
# Ctotal_all function ace_values to produce list of possible values for aces in hand
ace_value_list = ace_values(aces)
final_totals = [i+total for i in ace_value_list if i+total<=21] # ‘A’ 可以是 1 也可以是 11，当前牌值不超过 21 时，取最大值 -- 规则❗️
if final_totals == []:
return getting_min(ace_value_list) + total
else:
return getting_max(final_totals)
def model_decision_old(model, player_total_sum, has_ace, dealer_card_num, hit=0, card_count=None):
'''
Given the relevant inputs, the function below uses the neural net to make a prediction
and then based on that prediction, decides whether to hit or stay
—— 将玩家各参数传入神经网络模型，如果预测结果大于 0.52, 则 hit, 否则 stand
input:
model -> 模型（一般指 NN 模型）
player_total_sum -> 玩家当前手牌和
has_ace -> 玩家发牌是否有 'A'
dealer_card_num -> 庄家发牌（明牌）值
hit -> 玩家是否‘要牌’
card_count -> 记牌器
return:
1 -> hit
0 -> stand
'''
# 将需要进入神经网络模型的数据统一格式
# [[18 0 0 6]]
input_array = np.array([player_total_sum, hit, has_ace, dealer_card_num]).reshape(1, -1) # 二维数组变成一行 (1, n)
cc_array = mk.KnowledgeFrame.from_dict([card_count])
input_array = np.concatingenate([input_array, cc_array], axis=1)
# input_array 作为输入传入神经网络，使用预测函数后存入 predict_correct
# [[0.10379896]]
predict_correct = model.predict(input_array)
if predict_correct >= 0.52:
return 1
else:
return 0
def model_decision(model, card_count, dealer_card_num):
'''
Given the relevant inputs, the function below uses the neural net to make a prediction
and then based on that prediction, decides whether to hit or stay
—— 将玩家各参数传入神经网络模型，如果预测结果大于 0.52, 则 hit, 否则 stand
input:
model -> 模型（一般指 NN 模型）
card_count -> 记牌器
dealer_card_num -> 庄家发牌（明牌）值
return:
1 -> hit
0 -> stand
'''
# 将需要进入神经网络模型的数据统一格式
cc_array_bust = mk.KnowledgeFrame.from_dict([card_count])
input_array = np.concatingenate([cc_array_bust, np.array(dealer_card_num).reshape(1, -1)], axis=1)
# input_array 作为输入传入神经网络，使用预测函数后存入 predict_correct
# [[0.10379896]]
predict_correct = model.predict(input_array)
if predict_correct >= 0.52:
return 1
else:
return 0
def create_data(type, dealer_card_feature, player_card_feature, player_results, action_results=None, new_stack=None, games_played=None, card_count_list=None, dealer_bust=None):
'''
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
dealer_card_feature -> 所有游戏庄家的第一张牌
player_card_feature -> 所有游戏玩家所有手牌
player_results -> 玩家输赢结果
action_results -> 玩家是否要牌
new_stack -> 是否是第一轮游戏
games_played -> 本局第几轮游戏
card_count_list -> 记牌器
dealer_bust -> 庄家是否爆牌
return:
model_kf -> dealer_card: 庄家发牌（明牌）
player_total_initial: 玩家一发牌手牌和
Y: 玩家一“输”、“平”、“赢”结果(-1, 0, 1)
lose: 玩家一“输”、“不输”结果(1, 0)
has_ace: 玩家一发牌是否有'A'
dealer_card_num: 庄家发牌（明牌）牌值
correct_action: 判断是否是正确的决定
hit?: 玩家一发牌后是否要牌
new_stack: 是否是第一轮游戏
games_played_with_stack: 本局第几轮游戏
dealer_bust: 庄家是否爆牌
blackjack?: 玩家起手是否 21 点
2 ~ 'A': 本轮游戏记牌
'''
model_kf = mk.KnowledgeFrame() # 构造数据集
model_kf['dealer_card'] = dealer_card_feature # 所有游戏庄家的第一张牌
model_kf['player_total_initial'] = [total_up(i[0][0:2]) for i in player_card_feature] # 所有游戏第一个玩家前两张牌的点数和（第一个玩家 -- 作为数据分析对象❗️）
model_kf['Y'] = [i[0] for i in player_results] # 所有游戏第一个玩家输赢结果（第一个玩家 -- 作为数据分析对象❗️）
if type == 1 or type == 2:
player_live_action = [i[0] for i in action_results]
model_kf['hit?'] = player_live_action # 玩家在发牌后是否要牌
has_ace = []
for i in player_card_feature:
if ('A' in i[0][0:2]): # 玩家一发牌有 ‘A’，has_ace 列表追加一个 1
has_ace.adding(1)
else: # 玩家一发牌无 ‘A’，has_ace 列表追加一个 0
has_ace.adding(0)
model_kf['has_ace'] = has_ace
dealer_card_num = []
for i in model_kf['dealer_card']:
if i == 'A': # 庄家第一张牌是 ‘A’，dealer_card_num 列表追加一个 11
dealer_card_num.adding(11)
else: # 庄家第一张牌不是 ‘A’，dealer_card_num 列表追加该值
dealer_card_num.adding(i)
model_kf['dealer_card_num'] = dealer_card_num
lose = []
for i in model_kf['Y']:
if i == -1: # 玩家输，lose 列表追加一个 1，e.g. [1, 1, ...]
lose.adding(1)
else: # 玩家平局或赢，lose 列表追加一个 0，e.g. [0, 0, ...]
lose.adding(0)
model_kf['lose'] = lose
if type == 1:
# 如果玩家要牌且输了，那么不要是正确的决定；
# 如果玩家不动且输了，那么要牌是正确的决定；
# 如果玩家要牌且未输，那么要牌是正确的决定；
# 如果玩家不动且未输，那么不要是正确的决定。
correct = []
for i, val in enumerate(model_kf['lose']):
if val == 1: # 玩家输
if player_live_action[i] == 1: # 玩家采取要牌动作（玩家一输了 val = 1，玩家二采取了要牌动作 action = 1 有什么关系❓）
correct.adding(0)
else:
correct.adding(1)
else:
if player_live_action[i] == 1:
correct.adding(1)
else:
correct.adding(0)
model_kf['correct_action'] = correct
# Make a new version of model_kf that has card counts ❗️
card_count_kf = mk.concating([
mk.KnowledgeFrame(new_stack, columns=['new_stack']), # 所有游戏是否是开局第一轮游戏
mk.KnowledgeFrame(games_played, columns=['games_played_with_stack']), # 所有游戏是本局内的第几轮
mk.KnowledgeFrame.from_dict(card_count_list), # 所有游戏记牌后结果
mk.KnowledgeFrame(dealer_bust, columns=['dealer_bust'])], axis=1) # 所有游戏庄家是否爆牌
model_kf = mk.concating([model_kf, card_count_kf], axis=1)
model_kf['blackjack?'] = model_kf['player_total_initial'].employ(func)
# 将各模型数据保存至 data 文件夹下
# model_kf.to_csv('./data/data' + str(type) + '.csv', sep=' ')
# 统计玩家一的所有输、赢、平的次数
# -1.0 199610
# 1.0 99685
# 0.0 13289
# Name: 0, dtype: int64
# 312584
count = mk.KnowledgeFrame(player_results)[0].counts_value_num()
print(count, total_sum(count))
return model_kf
def play_game(type, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, player_results, action_results, hit_stay=0, multiplier=0, card_count=None, dealer_bust=None, model=None):
'''
Play a game of blackjack (after the cards are dealt)
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
players -> 玩家人数
live_total -> 玩家发牌手牌和
dealer_hand -> 庄家发牌（明牌 + 暗牌）
player_hands -> 玩家发牌（两张）
blackjack -> set(['A', 10])
dealer_cards -> 牌盒中的牌
player_results -> np.zeros((1, players))
action_results -> np.zeros((1, players))
hit_stay -> 何时采取要牌动作
multiplier -> 记录二十一点翻倍
card_count -> 记牌器
dealer_bust -> 庄家是否爆牌
model -> 模型（一般指 NN 模型）
return:
player_results -> 所有玩家“输”、“平”、“赢”结果
dealer_cards -> 牌盒中的牌
live_total -> 所有玩家牌值和
action_results -> 所有玩家是否采取"要牌"动作
card_count -> 记牌器
dealer_bust -> 庄家是否爆牌
multiplier -> 记录二十一点翻倍
'''
dealer_face_up_card = 0
# Dealer checks for 21
if set(dealer_hand) == blackjack: # 庄家直接二十一点
for player in range(players):
if set(player_hands[player]) != blackjack: # 玩家此时不是二十一点，则结果为 -1 -- 规则❗️
player_results[0, player] = -1
else:
player_results[0, player] = 0
else: # 庄家不是二十一点，各玩家进行要牌、弃牌动作
for player in range(players):
# Players check for 21
if set(player_hands[player]) == blackjack: # 玩家此时直接二十一点，则结果为 1
player_results[0, player] = 1
multiplier = 1.25
else: # 玩家也不是二十一点
if type == 0: # Hit only when we know we will not bust -- 在玩家当前手牌点数不超过 11 时，才决定拿牌
while total_up(player_hands[player]) <= 11:
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌
if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌，爆牌则结果为 -1
player_results[0, player] = -1
break
elif type == 1: # Hit randomly, check for busts -- 以 hit_stay 是否大于 0.5 的方式决定拿牌
if (hit_stay >= 0.5) and (total_up(player_hands[player]) != 21):
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌
action_results[0, player] = 1
live_total.adding(total_up(player_hands[player])) # 玩家要牌后，将点数和记录到 live_total
if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌，爆牌则结果为 -1
player_results[0, player] = -1
elif type == 2: # Neural net decides whether to hit or stay
# -- 通过 model_decision 方法给神经网络计算后，决定是否继续拿牌
if 'A' in player_hands[player][0:2]: # 玩家起手有 ‘A’
ace_in_hand = 1
else:
ace_in_hand = 0
if dealer_hand[0] == 'A': # 庄家起手有 ‘A’
dealer_face_up_card = 11
else:
dealer_face_up_card = dealer_hand[0]
while (model_decision_old(model, total_up(player_hands[player]), ace_in_hand, dealer_face_up_card,
hit=action_results[0, player], card_count=card_count) == 1) and (total_up(player_hands[player]) != 21):
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌
action_results[0, player] = 1
live_total.adding(total_up(player_hands[player])) # 玩家要牌后，将点数和记录到 live_total
if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌，爆牌则结果为 -1
player_results[0, player] = -1
break
card_count[dealer_hand[-1]] += 1 # 记录庄家第二张发牌
# Dealer hits based on the rules
while total_up(dealer_hand) < 17: # 庄家牌值小于 17，则继续要牌
dealer_hand.adding(dealer_cards.pop(0))
card_count[dealer_hand[-1]] += 1 # 记录庄家后面要的牌
# Compare dealer hand to players hand but first check if dealer busted
if total_up(dealer_hand) > 21: # 庄家爆牌
if type == 1:
dealer_bust.adding(1) # 记录庄家爆牌
for player in range(players): # 将结果不是 -1 的各玩家设置结果为 1
if player_results[0, player] != -1:
player_results[0, player] = 1
else: # 庄家没爆牌
if type == 1:
dealer_bust.adding(0) # 记录庄家没爆牌
for player in range(players): # 将玩家牌点数大于庄家牌点数的玩家结果置为 1
if total_up(player_hands[player]) > total_up(dealer_hand):
if total_up(player_hands[player]) <= 21:
player_results[0, player] = 1
elif total_up(player_hands[player]) == total_up(dealer_hand):
player_results[0, player] = 0
else:
player_results[0, player] = -1
if type == 0:
return player_results, dealer_cards, live_total, action_results, card_count
elif type == 1:
return player_results, dealer_cards, live_total, action_results, card_count, dealer_bust
elif type == 2:
return player_results, dealer_cards, live_total, action_results, multiplier, card_count
def play_stack(type, stacks, num_decks, card_types, players, model=None):
'''
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
stacks -> 游戏局数
num_decks -> 牌副数目
card_types -> 纸牌类型
players -> 玩家数
model -> 已经训练好的模型（一般指 NN 模型）
output:
dealer_card_feature -> 所有游戏庄家的第一张牌
player_card_feature -> 所有游戏玩家所有手牌
player_results -> 所有玩家“输”、“平”、“赢”结果
action_results -> 所有玩家是否采取"要牌"动作
new_stack -> 是否是第一轮游戏
games_played_with_stack -> 本局第几轮游戏
card_count_list -> 记牌器
dealer_bust -> 庄家是否爆牌
bankroll -> 本局结束剩余筹码
'''
bankroll = []
dollars = 10000 # 起始资金为 10000
dealer_card_feature = []
player_card_feature = []
player_live_total = []
player_results = []
action_results = []
dealer_bust = []
first_game = True
prev_stack = 0
stack_num_list = []
new_stack = []
card_count_list = []
games_played_with_stack = []
for stack in range(stacks):
games_played = 0 # 记录同局游戏下有几轮
# Make a dict for keeping track of the count for a stack
card_count = {
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 0,
8: 0,
9: 0,
10: 0,
'A': 0
}
# 每新开一局时，temp_new_stack 为 1
# 同局游戏下不同轮次，temp_new_stack 为 0
# 第一局第一轮，temp_new_stack 为 0
if stack != prev_stack:
temp_new_stack = 1
else:
temp_new_stack = 0
blackjack = set(['A', 10])
dealer_cards = make_decks(num_decks, card_types) # 根据给定牌副数洗牌
while length(dealer_cards) > 20: # 牌盒里的牌不大于 20 张就没必要继续用这副牌进行游戏 -- 规则⭐️
curr_player_results = np.zeros((1, players))
curr_action_results = np.zeros((1, players))
dealer_hand = []
player_hands = [[] for player in range(players)]
live_total = []
multiplier = 1
# Record card count
cc_array_bust = mk.KnowledgeFrame.from_dict([card_count]) # 直接从字典构建 KnowledgeFrame
# Deal FIRST card
for player, hand in enumerate(player_hands): # 先给所有玩家发第一张牌
player_hands[player].adding(dealer_cards.pop(0)) # 将洗好的牌分别发给玩家
card_count[player_hands[player][-1]] += 1 # 记下所有玩家第一张发牌
dealer_hand.adding(dealer_cards.pop(0)) # 再给庄家发第一张牌
card_count[dealer_hand[-1]] += 1 # 记下庄家第一张发牌
dealer_face_up_card = dealer_hand[0] # 记录庄家明牌
# Deal SECOND card
for player, hand in enumerate(player_hands): # 先给所有玩家发第二张牌
player_hands[player].adding(dealer_cards.pop(0)) # 接着刚刚洗好的牌继续发牌
card_count[player_hands[player][-1]] += 1 # 记下所有玩家第二张发牌
dealer_hand.adding(dealer_cards.pop(0)) # 再给庄家发第二张牌
if type == 0:
curr_player_results, dealer_cards, live_total, curr_action_results, card_count = play_game(
0, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards,
curr_player_results, curr_action_results, card_count=card_count)
elif type == 1:
# Record the player's live total after cards are dealt
live_total.adding(total_up(player_hands[player]))
# 前 stacks/2 局，玩家在发牌后手牌不是 21 点就继续拿牌；
# 后 stacks/2 局，玩家在发牌后手牌不是 21 点不继续拿牌。
if stack < stacks/2:
hit = 1
else:
hit = 0
curr_player_results, dealer_cards, live_total, curr_action_results, card_count, \
dealer_bust = play_game(1, players, live_total, dealer_hand, player_hands, blackjack,
dealer_cards, curr_player_results, curr_action_results,
hit_stay=hit, card_count=card_count, dealer_bust=dealer_bust)
elif type == 2:
# Record the player's live total after cards are dealt
live_total.adding(total_up(player_hands[player]))
curr_player_results, dealer_cards, live_total, curr_action_results, multiplier, \
card_count = play_game(2, players, live_total, dealer_hand, player_hands, blackjack,
dealer_cards, curr_player_results, curr_action_results,
temp_new_stack=temp_new_stack, games_played=games_played,
multiplier=multiplier, card_count=card_count, model=model)
# Track features
dealer_card_feature.adding(dealer_hand[0]) # 将庄家的第一张牌存入新的 list
player_card_feature.adding(player_hands) # 将每个玩家当前手牌存入新的 list
player_results.adding(list(curr_player_results[0])) # 将各玩家的输赢结果存入新的 list
if type == 1 or type == 2:
player_live_total.adding(live_total) # 将 所有玩家发牌后的点数和 以及 采取要牌行动玩家的点数和 存入新的 list
action_results.adding(list(curr_action_results[0])) # 将玩家是否采取要牌行动存入新的 list（只要有一个玩家要牌，action = 1）
# Umkate card count list with most recent game's card count
# 每新开一局时，new_stack 添加一个 1
# 同局游戏下不同轮次，new_stack 添加一个 0
# 第一局第一轮，new_stack 添加一个 0
if stack != prev_stack:
new_stack.adding(1)
else: # 记录本次为第一局游戏
new_stack.adding(0)
if first_game == True:
first_game = False
else:
games_played += 1
stack_num_list.adding(stack) # 记录每次游戏是否是新开局
games_played_with_stack.adding(games_played) # 记录每局游戏的次数
card_count_list.adding(card_count.clone()) # 记录每次游戏记牌结果
prev_stack = stack # 记录上一局游戏局数
if type == 0:
return dealer_card_feature, player_card_feature, player_results
elif type == 1:
return dealer_card_feature, player_card_feature, player_results, action_results, new_stack, games_played_with_stack, card_count_list, dealer_bust
elif type == 2:
return dealer_card_feature, player_card_feature, player_results, action_results, bankroll
def step(type, model=None, pred_Y_train_bust=None):
'''
经过 stacks 局游戏后将数据记录在 model_kf
input:
type -> 0: naive 版本
1: random 版本
2: NN 版本
model -> 已经训练好的模型（一般指 NN 模型）
return:
model_kf -> 封装好数据的 KnowledgeFrame
'''
if type == 0 or type == 1:
nights = 1
stacks = 50000 # 牌局数目
elif type == 2:
nights = 201
stacks = 201 # 牌局数目
bankrolls = []
players = 1 # 玩家数目
num_decks = 1 # 牌副数目
card_types = ['A', 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]
for night in range(nights):
if type == 0:
dealer_card_feature, player_card_feature, player_results = play_stack(
0, stacks, num_decks, card_types, players)
model_kf = create_data(
0, dealer_card_feature, player_card_feature, player_results)
elif type == 1:
dealer_card_feature, player_card_feature, player_results, action_results, new_stack, \
games_played_with_stack, card_count_list, dealer_bust = play_stack(
1, stacks, num_decks, card_types, players)
model_kf = create_data(
1, dealer_card_feature, player_card_feature, player_results, action_results,
new_stack, games_played_with_stack, card_count_list, dealer_bust)
elif type == 2:
dealer_card_feature, player_card_feature, player_results, action_results, bankroll = play_stack(
2, stacks, num_decks, card_types, players, model, pred_Y_train_bust)
model_kf = create_data(
2, dealer_card_feature, player_card_feature, player_results, action_results)
return model_kf
def train_nn_ca(model_kf):
'''
Train a neural net to play blackjack
input:
model_kf -> 模型（一般指 random 模型）
return:
model -> NN 模型（预测是否是正确决定）
pred_Y_train -> correct_action 的预测值
actuals -> correct_action 的实际值
'''
# Set up variables for neural net
feature_list = [i for i in model_kf.columns if i not in [
'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust', 'dealer_bust_pred', 'new_stack',
'games_played_with_stack', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'A', 'blackjack?']]
# 将模型里的数据按矩阵形式存储
train_X = np.array(model_kf[feature_list])
train_Y = np.array(model_kf['correct_action']).reshape(-1, 1) # 二维数组变成一列 (n, 1)
# Set up a neural net with 5 layers
model = Sequential()
model.add(Dense(16))
model.add(Dense(128))
model.add(Dense(32))
model.add(Dense(8))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='sgd')
model.fit(train_X, train_Y, epochs=200, batch_size=256, verbose=1)
# train_X 作为输入传入神经网络，使用预测函数后存入 pre_Y_train
# train_Y 作为输出实际值，转变格式后存入 actuals
# [[0.4260913 ]
# [0.3595919 ]
# [0.24476886]
# ...
# [0.2946579 ]
# [0.39343864]
# [0.27353495]]
# [1 0 0 ... 0 1 0]
pred_Y_train = model.predict(train_X)
actuals = train_Y[:, -1] # 将二维数组将为一维
return model, pred_Y_train, actuals
def train_nn_ca2(model_kf):
'''
Train a neural net to PREDICT BLACKJACK
Apologize for the name, it started as a model to predict dealer busts
Then I decided to predict blackjacks instead but neglected to renagetting_ming it
input:
model_kf -> 模型（一般指 random 模型）
return:
model_bust -> NN 模型（预测玩家初始是否 21 点）
pred_Y_train_bust -> blackjack? 的预测值
actuals -> blackjack? 的实际值
'''
# Set up variables for neural net
feature_list = [i for i in model_kf.columns if i not in [
'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust',
'dealer_bust_pred','new_stack', 'games_played_with_stack', 'blackjack?']]
train_X_bust = np.array(model_kf[feature_list])
train_Y_bust = np.array(model_kf['correct_action']).reshape(-1,1)
# Set up a neural net with 5 layers
model_bust = Sequential()
model_bust.add(Dense(train_X_bust.shape[1]))
model_bust.add(Dense(128))
model_bust.add(Dense(32, activation='relu'))
model_bust.add(Dense(8))
model_bust.add(Dense(1, activation='sigmoid'))
model_bust.compile(loss='binary_crossentropy', optimizer='sgd')
model_bust.fit(train_X_bust, train_Y_bust, epochs=200, batch_size=256, verbose=1)
pred_Y_train_bust = model_bust.predict(train_X_bust)
actuals = train_Y_bust[:, -1]
return model_bust, pred_Y_train_bust, actuals
def comparison_chart(data, position):
'''
绘制多模型数据分析图
input:
data -> 数据集
position -> dealer / player
'''
fig, ax = plt.subplots(figsize=(12,6))
ax.bar(x=data.index-0.3, height=data['random'].values, color='blue', width=0.3, label='Random')
ax.bar(x=data.index, height=data['naive'].values, color='orange', width=0.3, label='Naive')
ax.bar(x=data.index+0.3, height=data['smart'].values, color='red', width=0.3, label='Smart')
ax.set_ylabel('Probability of Tie or Win', fontsize=16)
if position == 'dealer':
ax.set_xlabel("Dealer's Card", fontsize=16)
plt.xticks(np.arange(2, 12, 1.0))
elif position == 'player':
ax.set_xlabel("Player's Hand Value", fontsize=16)
plt.xticks(np.arange(4, 21, 1.0))
plt.legend()
plt.tight_layout()
plt.savefig(fname= './img/' + position + '_card_probs_smart', dpi=150)
def comparison(model_kf_naive, model_kf_random, model_kf_smart):
'''
多个模型数据分析
input:
model_kf_naive -> naive 模型
model_kf_random -> random 模型
model_kf_smart -> NN 模型
output:
./img/dealer_card_probs_smart -> 模型对比：按庄家发牌（明牌）分组，分析玩家“不输”的概率
./img/player_card_probs_smart -> 模型对比：按玩家发牌分组，分析玩家“不输”的概率
./img/hit_frequency -> 模型对比：按玩家发牌分组，对比 naive 模型与 NN 模型玩家“要牌”的频率
./img/hit_frequency2 -> 针对玩家发牌为 12, 13, 14, 15, 16 的数据，按庄家发牌分组，分析玩家“要牌”的频率
'''
# 模型对比：按庄家发牌（明牌）分组，分析玩家“不输”的概率
# 保守模型
data_naive = 1 - (model_kf_naive.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_naive.grouper(by='dealer_card_num').count()['lose'])
# 随机模型
data_random = 1 - (model_kf_random.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_random.grouper(by='dealer_card_num').count()['lose'])
# 新模型
data_smart = 1 - (model_kf_smart.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_smart.grouper(by='dealer_card_num').count()['lose'])
data = mk.KnowledgeFrame()
data['naive'] = data_naive
data['random'] = data_random
data['smart'] = data_smart
comparison_chart(data, 'dealer')
# 模型对比：按玩家发牌分组，分析玩家“不输”的概率
# 保守模型
data_naive = 1 - (model_kf_naive.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_naive.grouper(by='player_total_initial').count()['lose'])
# 随机模型
data_random = 1 - (model_kf_random.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_random.grouper(by='player_total_initial').count()['lose'])
# 新模型
data_smart = 1 - (model_kf_smart.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_smart.grouper(by='player_total_initial').count()['lose'])
data =
|
mk.KnowledgeFrame()
|
pandas.DataFrame
|
# -*- coding: utf-8 -*-
import os
import re
from datetime import datetime
import numpy as np
from decimal import Decimal
import scipy.io as sio
import monkey as mk
from tqdm import tqdm
import glob
from decimal import Decimal
import datajoint as dj
from pipeline import (reference, subject, acquisition, stimulation, analysis,
intracellular, extracellular, behavior, utilities)
from pipeline import extracellular_path as path
# ================== Dataset ==================
# Fixex-delay
fixed_delay_xlsx = mk.read_excel(
os.path.join(path, 'FixedDelayTask', 'SI_table_2_bilateral_perturb.xlsx'),
index_col =0, usecols='A, P, Q, R, S', skiprows=2, nrows=20)
fixed_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
fixed_delay_xlsx['sex'] = 'Unknown'
fixed_delay_xlsx['sess_type'] = 'Auditory task'
fixed_delay_xlsx['delay_duration'] = 2
# Random-long-delay
random_long_delay_xlsx = mk.read_excel(
os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'),
index_col =0, usecols='A, P, Q, R, S', skiprows=5, nrows=23)
random_long_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
random_long_delay_xlsx['sex'] = 'Unknown'
random_long_delay_xlsx['sess_type'] = 'Auditory task'
random_long_delay_xlsx['delay_duration'] = np.nan
# Random-short-delay
random_short_delay_xlsx = mk.read_excel(
os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'),
index_col =0, usecols='A, F, G, H, I', skiprows=42, nrows=11)
random_short_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
random_short_delay_xlsx['sex'] = 'Unknown'
random_short_delay_xlsx['sess_type'] = 'Auditory task'
random_short_delay_xlsx['delay_duration'] = np.nan
# Tactile-task
tactile_xlsx = mk.read_csv(
os.path.join(path, 'TactileTask', 'Whisker_taskTavle_for_paper.csv'),
index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=30)
tactile_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time']
tactile_xlsx = tactile_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex'])
tactile_xlsx['sess_type'] = 'Tactile task'
tactile_xlsx['delay_duration'] = 1.2
# Sound-task 1.2s
sound12_xlsx = mk.read_csv(
os.path.join(path, 'Sound task 1.2s', 'OppositeTask12_for_paper.csv'),
index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=37)
sound12_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time']
sound12_xlsx = sound12_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex'])
sound12_xlsx['sess_type'] = 'Auditory task'
sound12_xlsx['delay_duration'] = 1.2
# concating total_all 5
meta_data =
|
mk.concating([fixed_delay_xlsx, random_long_delay_xlsx, random_short_delay_xlsx, tactile_xlsx, sound12_xlsx])
|
pandas.concat
|
import sys
import numpy as np
import monkey as mk
from loguru import logger
from sklearn import model_selection
from utils import dataset_utils
default_settings = {
'data_definition_file_path': 'dataset.csv',
'folds_num': 5,
'data_random_seed': 1509,
'train_val_fraction': 0.8,
'train_fraction': 0.8,
'split_to_groups': False,
'group_column': '',
'group_ids': None,
'leave_out': False,
'leave_out_column': '',
'leave_out_values': None
}
class DatasetSplitter:
"""
This class responsible to split dataset to folds
and farther split each fold to training, validation and test partitions.
Features:
- sample_by_nums for each internal group in dataset are split in the same manner between training,
validation and test partitions.
- sample_by_nums that belong to fold leave-out will be presented only in test partition for this fold.
"""
def __init__(self, settings):
"""
This method initializes parameters
:return: None
"""
self.settings = settings
self.dataset_kf = None
self.groups_kf_list = None
self.train_kf_list = None
self.val_kf_list = None
self.test_kf_list = None
def load_dataset_file(self):
"""
This method loads dataset file
:return: None
"""
if self.settings['data_definition_file_path']:
logger.info("Loading dataset file {0}".formating(self.settings['data_definition_file_path']))
self.dataset_kf = dataset_utils.load_dataset_file(self.settings['data_definition_file_path'])
logger.info("Dataset contains {0} entries".formating(self.dataset_kf.shape[0]))
else:
logger.info("Data definition file path is not specified")
def set_training_knowledgeframe(self,
training_kf,
fold_num):
"""
This method sets training knowledgeframe
:param training_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.train_kf_list[fold_num] = training_kf
logger.info("Training knowledgeframe with {0} entries is set for fold {1}".formating(training_kf.shape[0], fold_num))
def set_validation_knowledgeframe(self,
validation_kf,
fold_num):
"""
This method sets training knowledgeframe
:param validation_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.val_kf_list[fold_num] = validation_kf
logger.info("Validation knowledgeframe with {0} entries is set for fold {1}".formating(validation_kf.shape[0], fold_num))
def set_test_knowledgeframe(self,
test_kf,
fold_num):
"""
This method sets training knowledgeframe
:param test_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.test_kf_list[fold_num] = test_kf
logger.info("Test knowledgeframe with {0} entries is set for fold {1}".formating(test_kf.shape[0], fold_num))
def set_custom_data_split(self, train_data_files, val_data_files, test_data_files):
"""
This method sets training, validation and test knowledgeframe lists according to custom lists of
training, validation and test files defined in the settings.
:return: None
"""
logger.info("Loading custom lists of training validation and test files")
self.train_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in train_data_files]
self.val_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in val_data_files]
self.test_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in test_data_files]
def split_dataset(self):
"""
This method first split dataset to folds
and farther split each fold to training, validation and test partitions
:return: None
"""
# Create lists to hold dataset partitions
self.train_kf_list = [None] * self.settings['folds_num']
self.val_kf_list = [None] * self.settings['folds_num']
self.test_kf_list = [None] * self.settings['folds_num']
# Set random seed to ensure reproducibility of dataset partitioning across experiments on same hardware
np.random.seed(self.settings['data_random_seed'])
# Split dataset to groups
if self.settings['split_to_groups']:
self.split_dataset_to_groups()
else:
self.groups_kf_list = [self.dataset_kf]
# Permute entries in each group
self.groups_kf_list = [group_kf.reindexing(np.random.permutation(group_kf.index)) for group_kf in self.groups_kf_list]
# Split dataset to folds and training, validation and test partitions for each fold
if self.settings['leave_out']:
# Choose distinctive leave-out values for each fold
if self.settings['leave_out_values'] is None:
self.choose_leave_out_values()
# Split dataset to folds based on leave-out values
self.split_dataset_to_folds_with_leave_out()
else:
# Split dataset to folds in random manner
self.split_dataset_to_folds_randomly()
def split_dataset_to_groups(self):
"""
# This method splits dataset to groups based on values of 'self.group_column'.
# Samples in each group are split in same manner between training, validation and test partitions.
# This is important, for example, to ensure that each class (in classification problem) is represented
# in training, validation and test partition.
"""
logger.info("Dividing dataset to groups based on values of '{0}' dataset column".formating(self.settings['group_column']))
# Get groups identifiers
if self.settings['group_ids'] is None:
group_ids = self.dataset_kf[self.settings['group_column']].distinctive()
else:
group_ids = self.settings['group_ids']
logger.info("Dataset groups are: {0}".formating(group_ids))
# Split dataset to groups
self.groups_kf_list = [self.dataset_kf[self.dataset_kf[self.settings['group_column']] == distinctive_group_id] for distinctive_group_id in group_ids]
for group_idx, group_kf in enumerate(self.groups_kf_list):
logger.info("Group {0} contains {1} sample_by_nums".formating(group_ids[group_idx], group_kf.shape[0]))
def choose_leave_out_values(self):
"""
This method chooses leave-out values for each fold.
Leave-out values calculated based on values of 'self.leave_out_column'.
Dataset entries which 'self.leave_out_column' value is one of calculated leave-out values
for specific fold will present only in test partition for this fold.
:return: None
"""
logger.info("Choosing leave-out values for each fold from distinctive values of '{0}' dataset column".formating(self.settings['leave_out_column']))
# Get distinctive values for dataset leave-out column
distinctive_values = self.dataset_kf[self.settings['leave_out_column']].distinctive()
logger.info("Unique values for column {0} are: {1}".formating(self.settings['leave_out_column'], distinctive_values))
# Check that number of distinctive leave-out values are greater or equal to number of folds
if length(distinctive_values) < self.settings['folds_num']:
logger.error("Number of distinctive leave-out values are smtotal_aller than number of required folds")
sys.exit(1)
# Get list of distinctive leave-out values for each fold
if self.settings['folds_num'] > 1:
self.settings['leave_out_values'] = np.array_split(distinctive_values, self.settings['folds_num'])
else:
self.settings['leave_out_values'] = [np.random.choice(distinctive_values, int(length(distinctive_values) * (1 - self.settings['train_val_fraction'])), replacing=False)]
for fold in range(0, self.settings['folds_num']):
logger.info("Leave out values for fold {0} are: {1}".formating(fold, self.settings['leave_out_values'][fold]))
def split_dataset_to_folds_with_leave_out(self):
"""
This method splits dataset to folds and training, validation and test partitions for each fold based on leave-out values.
Samples in each group are split in same manner between training, validation and test partitions.
Leave-out values will be presented only in test partition of corresponding fold.
"""
logger.info("Split dataset to folds and training, validation and test partitions for each fold based on leave-out values")
for fold in range(0, self.settings['folds_num']):
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_idx, group_kf in enumerate(self.groups_kf_list):
group_test_kf = group_kf[group_kf[self.settings['leave_out_column']].incontain(self.settings['leave_out_values'][fold])]
if group_test_kf.shape[0] == 0:
logger.warning("Group {0} hasn't whatever of leave out values: {1}".formating(group_idx, self.settings['leave_out_values'][fold]))
else:
groups_test_kf_list.adding(group_test_kf)
group_train_val_kf = group_kf[~group_kf[self.settings['leave_out_column']].incontain(self.settings['leave_out_values'][fold])]
if group_train_val_kf.shape[0] == 0:
logger.warning("All sample_by_nums of group {0} is in one of leave out values: {1}".formating(group_idx, self.settings['leave_out_values'][fold]))
else:
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[fold] = mk.concating(groups_train_kf_list)
self.val_kf_list[fold] = mk.concating(groups_val_kf_list)
self.test_kf_list[fold] = mk.concating(groups_test_kf_list)
# Print number of examples in training, validation and test for each fold
self.print_data_split()
def split_dataset_to_folds_randomly(self):
"""
This method splits dataset to folds and training, validation and test partitions for each fold in random manner.
Samples in each group are split in same manner between training, validation and test partitions.
"""
logger.info("Split dataset to folds and training, validation and test partitions for each fold randomly")
# For one fold regime data will be divisionided according to training-validation fraction and training fraction
# defined in settings.
# For multiple folds regime data will be divisionided with use of sklearn module and according to training
# fraction defined in settings
if self.settings['folds_num'] == 1:
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_kf in self.groups_kf_list:
train_val_split_idx = int(group_kf.shape[0] * self.settings['train_val_fraction'])
group_train_val_kf = group_kf.iloc[0:train_val_split_idx]
groups_test_kf_list.adding(group_kf.iloc[train_val_split_idx:])
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[0] = mk.concating(groups_train_kf_list)
self.val_kf_list[0] = mk.concating(groups_val_kf_list)
self.test_kf_list[0] = mk.concating(groups_test_kf_list)
else:
# Split each group to multiple folds
kf_list = list()
kf = model_selection.KFold(n_splits=self.settings['folds_num'], shuffle=True, random_state=self.settings['data_random_seed'])
for group_kf in self.groups_kf_list:
kf_list.adding(kf.split(group_kf))
# Combine group splits to folds
for fold in range(0, self.settings['folds_num']):
fold_split = [next(kf_list[idx]) for idx in range(length(kf_list))]
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_idx, group_kf in enumerate(self.groups_kf_list):
group_train_val_kf = group_kf.iloc[fold_split[group_idx][0]]
groups_test_kf_list.adding(group_kf.iloc[fold_split[group_idx][1]])
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[fold] = mk.concating(groups_train_kf_list)
self.val_kf_list[fold] = mk.concating(groups_val_kf_list)
self.test_kf_list[fold] =
|
mk.concating(groups_test_kf_list)
|
pandas.concat
|
import os
import monkey as mk
import matplotlib.pyplot as plt
import datapackage as dp
import plotly.io as pio
import plotly.offline as offline
from plots import (
hourly_plot,
stacked_plot,
price_line_plot,
price_scatter_plot,
merit_order_plot,
filling_level_plot,
)
results = [r for r in os.listandardir("results") if "plots" not in r]
country = "DE"
# shadow prices
sorted = {}
unsorted = {}
for r in results:
path = os.path.join("results", r, "output", "shadow_prices.csv")
sprices = mk.read_csv(path, index_col=[0], parse_dates=True)[
country + "-electricity"
]
sorted[r] = sprices.sort_the_values().values
unsorted[r] = sprices.values
# residual load and more
renewables = ["wind-onshore", "wind-offshore", "solar-pv", "hydro-ror"]
timestamps = {}
marginal_cost = {}
shadow_prices = {}
storages = {}
prices = {}
rload = {}
for r in results:
path = os.path.join("results", r, "output", country + "-electricity.csv")
country_electricity_kf = mk.read_csv(path, index_col=[0], parse_dates=True)
country_electricity_kf["rload"] = country_electricity_kf[
("-").join([country, "electricity-load"])
] - country_electricity_kf[
[("-").join([country, i]) for i in renewables]
].total_sum(
axis=1
)
rload[r] = country_electricity_kf["rload"].values
timestamps[r] = country_electricity_kf.index
if country == "DE":
path = os.path.join("results", r, "input", "datapackage.json")
input_datapackage = dp.Package(path)
dispatchable = input_datapackage.getting_resource("dispatchable")
kf = mk.KnowledgeFrame(dispatchable.read(keyed=True))
kf = kf.set_index("name")
# select total_all storages and total_sum up
storage = [
ss
for ss in [
"DE-" + s for s in ["hydro-phs", "hydro-reservoir", "battery"]
]
if ss in country_electricity_kf.columns
]
storages[r] = country_electricity_kf[storage].total_sum(axis=1)
marginal_cost[r] = kf
path = os.path.join("results", r, "output", "shadow_prices.csv")
shadow_prices[r] = mk.read_csv(path, index_col=[0], parse_dates=True)[
"DE-electricity"
]
storages[r] =
|
mk.concating([storages[r], shadow_prices[r]], axis=1)
|
pandas.concat
|
from datetime import datetime
import numpy as np
import pytest
import monkey.util._test_decorators as td
from monkey.core.dtypes.base import _registry as ea_registry
from monkey.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from monkey.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
from monkey import (
Categorical,
KnowledgeFrame,
DatetimeIndex,
Index,
Interval,
IntervalIndex,
MultiIndex,
NaT,
Period,
PeriodIndex,
Collections,
Timestamp,
cut,
date_range,
notna,
period_range,
)
import monkey._testing as tm
from monkey.core.arrays import SparseArray
from monkey.tcollections.offsets import BDay
class TestKnowledgeFrameSetItem:
@pytest.mark.parametrize("dtype", ["int32", "int64", "float32", "float64"])
def test_setitem_dtype(self, dtype, float_frame):
arr = np.random.randn(length(float_frame))
float_frame[dtype] = np.array(arr, dtype=dtype)
assert float_frame[dtype].dtype.name == dtype
def test_setitem_list_not_knowledgeframe(self, float_frame):
data = np.random.randn(length(float_frame), 2)
float_frame[["A", "B"]] = data
tm.assert_almost_equal(float_frame[["A", "B"]].values, data)
def test_setitem_error_msmgs(self):
# GH 7432
kf = KnowledgeFrame(
{"bar": [1, 2, 3], "baz": ["d", "e", "f"]},
index=Index(["a", "b", "c"], name="foo"),
)
ser = Collections(
["g", "h", "i", "j"],
index=Index(["a", "b", "c", "a"], name="foo"),
name="fiz",
)
msg = "cannot reindexing from a duplicate axis"
with pytest.raises(ValueError, match=msg):
kf["newcol"] = ser
# GH 4107, more descriptive error message
kf = KnowledgeFrame(np.random.randint(0, 2, (4, 4)), columns=["a", "b", "c", "d"])
msg = "incompatible index of inserted column with frame index"
with pytest.raises(TypeError, match=msg):
kf["gr"] = kf.grouper(["b", "c"]).count()
def test_setitem_benchmark(self):
# from the vb_suite/frame_methods/frame_insert_columns
N = 10
K = 5
kf = KnowledgeFrame(index=range(N))
new_col = np.random.randn(N)
for i in range(K):
kf[i] = new_col
expected = KnowledgeFrame(np.repeat(new_col, K).reshape(N, K), index=range(N))
tm.assert_frame_equal(kf, expected)
def test_setitem_different_dtype(self):
kf = KnowledgeFrame(
np.random.randn(5, 3), index=np.arange(5), columns=["c", "b", "a"]
)
kf.insert(0, "foo", kf["a"])
kf.insert(2, "bar", kf["c"])
# diff dtype
# new item
kf["x"] = kf["a"].totype("float32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 5 + [np.dtype("float32")],
index=["foo", "c", "bar", "b", "a", "x"],
)
tm.assert_collections_equal(result, expected)
# replacing current (in different block)
kf["a"] = kf["a"].totype("float32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2,
index=["foo", "c", "bar", "b", "a", "x"],
)
tm.assert_collections_equal(result, expected)
kf["y"] = kf["a"].totype("int32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2 + [np.dtype("int32")],
index=["foo", "c", "bar", "b", "a", "x", "y"],
)
tm.assert_collections_equal(result, expected)
def test_setitem_empty_columns(self):
# GH 13522
kf = KnowledgeFrame(index=["A", "B", "C"])
kf["X"] = kf.index
kf["X"] = ["x", "y", "z"]
exp = KnowledgeFrame(data={"X": ["x", "y", "z"]}, index=["A", "B", "C"])
tm.assert_frame_equal(kf, exp)
def test_setitem_dt64_index_empty_columns(self):
rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
kf = KnowledgeFrame(index=np.arange(length(rng)))
kf["A"] = rng
assert kf["A"].dtype == np.dtype("M8[ns]")
def test_setitem_timestamp_empty_columns(self):
# GH#19843
kf = KnowledgeFrame(index=range(3))
kf["now"] = Timestamp("20130101", tz="UTC")
expected = KnowledgeFrame(
[[Timestamp("20130101", tz="UTC")]] * 3, index=[0, 1, 2], columns=["now"]
)
tm.assert_frame_equal(kf, expected)
def test_setitem_wrong_lengthgth_categorical_dtype_raises(self):
# GH#29523
cat = Categorical.from_codes([0, 1, 1, 0, 1, 2], ["a", "b", "c"])
kf = KnowledgeFrame(range(10), columns=["bar"])
msg = (
rf"Length of values \({length(cat)}\) "
rf"does not match lengthgth of index \({length(kf)}\)"
)
with pytest.raises(ValueError, match=msg):
kf["foo"] = cat
def test_setitem_with_sparse_value(self):
# GH#8131
kf = KnowledgeFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]})
sp_array = SparseArray([0, 0, 1])
kf["new_column"] = sp_array
expected =
|
Collections(sp_array, name="new_column")
|
pandas.Series
|
import numpy as np
import monkey as mk
import spacy
from spacy.lang.de.stop_words import STOP_WORDS
from nltk.tokenize import sent_tokenize
from itertools import grouper
import clone
import re
import sys
import textstat
# Method to create a matrix with contains only zeroes and a index starting by 0
def create_matrix_index_zeros(rows, columns):
arr = np.zeros((rows, columns))
for r in range(0, rows):
arr[r, 0] = r
return arr
# Method to getting total_all authors with a given number of texts. Used in chapter 5.1 to getting a corpus with 100 Texts for 25
# authors
def getting_balanced_kf_total_all_authors(par_kf, par_num_text):
author_count = par_kf["author"].counts_value_num()
author_list = []
kf_balanced_text = mk.KnowledgeFrame(columns=['label_encoded', 'author', 'genres', 'release_date', 'text'])
for i in range(0, length(author_count)):
if author_count[i] >= par_num_text and not author_count.index[i] == "Gast-Rezensent":
author_list.adding(author_count.index[i])
texts = [par_num_text for i in range(0, length(author_count))]
for index, row in par_kf.traversal():
if row['author'] in author_list:
if texts[author_list.index(row['author'])] != 0:
d = {'author': [row['author']], 'genres': [row['genres']],
'release_date': [row['release_date']], 'text': [row['text']]}
kf_balanced_text = kf_balanced_text.adding(mk.KnowledgeFrame.from_dict(d), ignore_index=True)
texts[author_list.index(row['author'])] -= 1
if total_sum(texts) == 0:
break
# Label encoding and delete author column after
dic_author_mappingping = author_encoding(kf_balanced_text)
kf_balanced_text['label_encoded'] = getting_encoded_author_vector(kf_balanced_text, dic_author_mappingping)[:, 0]
kf_balanced_text.sip("author", axis=1, inplace=True)
# Print author mappingping in file
original_standardout = sys.standardout
with open('author_mappingping.txt', 'w') as f:
sys.standardout = f
print(dic_author_mappingping)
sys.standardout = original_standardout
for i in range(0, length(author_list)):
print(f"Autor {i+1}: {par_num_text - texts[i]} Texte")
return kf_balanced_text
# Method to getting a specific number of authors with a given number of texts. Used later on to getting results for different
# combinations of authors and texts
def getting_balanced_kf_by_texts_authors(par_kf, par_num_text, par_num_author):
author_count = par_kf["author"].counts_value_num()
author_list = []
kf_balanced_text = mk.KnowledgeFrame(columns=['label_encoded', 'author', 'genres', 'release_date', 'text'])
loop_count, loops = 0, par_num_author
while loop_count < loops:
if author_count[loop_count] >= par_num_text and not author_count.index[loop_count] == "Gast-Rezensent":
author_list.adding(author_count.index[loop_count])
# Skip the Author "Gast-Rezensent" if its not the final_item value_round and increase the loops by 1
elif author_count.index[loop_count] == "Gast-Rezensent":
loops += 1
loop_count += 1
texts = [par_num_text for i in range(0, length(author_list))]
for index, row in par_kf.traversal():
if row['author'] in author_list:
if texts[author_list.index(row['author'])] != 0:
d = {'author': [row['author']], 'genres': [row['genres']],
'release_date': [row['release_date']], 'text': [row['text']]}
kf_balanced_text = kf_balanced_text.adding(mk.KnowledgeFrame.from_dict(d), ignore_index=True)
texts[author_list.index(row['author'])] -= 1
if total_sum(texts) == 0:
break
# Label encoding and delete author column after
dic_author_mappingping = author_encoding(kf_balanced_text)
kf_balanced_text['label_encoded'] = getting_encoded_author_vector(kf_balanced_text, dic_author_mappingping)[:, 0]
kf_balanced_text.sip("author", axis=1, inplace=True)
# Print author mappingping in file
original_standardout = sys.standardout
with open('author_mappingping.txt', 'w') as f:
sys.standardout = f
print(dic_author_mappingping)
sys.standardout = original_standardout
for i in range(0, length(author_list)):
print(f"Autor {i+1}: {par_num_text - texts[i]} Texte")
return kf_balanced_text
# Feature extraction of the feature described in chapter 5.6.1
def getting_bow_matrix(par_kf):
nlp = spacy.load("de_core_news_sm")
d_bow = {}
d_bow_list = []
function_pos = ["ADP", "AUX", "CONJ", "CCONJ", "DET", "PART", "PRON", "SCONJ"]
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not
word.is_digit and word.lemma_ not in STOP_WORDS and word.pos_ not in function_pos]
for word in tokens:
try:
d_bow["bow:"+word.lemma_.lower()] += 1
except KeyError:
d_bow["bow:"+word.lemma_.lower()] = 1
d_bow_list.adding(clone.deepclone(d_bow))
d_bow.clear()
return mk.KnowledgeFrame(d_bow_list)
# Feature extraction of the feature described in chapter 5.6.2
def getting_word_n_grams(par_kf, n):
nlp = spacy.load("de_core_news_sm")
d_word_ngram = {}
d_word_ngram_list = []
function_pos = ["ADP", "AUX", "CONJ", "CCONJ", "DET", "PART", "PRON", "SCONJ"]
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not
word.is_digit and word.lemma_ not in STOP_WORDS and word.pos_ not in function_pos]
tokens = [token.lemma_.lower() for token in tokens]
for w in range(0, length(tokens)):
if w + n <= length(tokens):
try:
d_word_ngram["w" + str(n) + "g" + ":" + '|'.join(tokens[w:w + n])] += 1
except KeyError:
d_word_ngram["w" + str(n) + "g" + ":" + '|'.join(tokens[w:w + n])] = 1
d_word_ngram_list.adding(clone.deepclone(d_word_ngram))
d_word_ngram.clear()
return mk.KnowledgeFrame(d_word_ngram_list)
# Feature extraction of the feature described in chapter 5.6.3
def getting_word_count(par_kf):
arr_wordcount = np.zeros((length(par_kf), 1))
nlp = spacy.load("de_core_news_sm")
only_words = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
if not t.is_punct and not t.is_space:
only_words.adding(t)
arr_wordcount[index] = length(only_words)
only_words.clear()
return mk.KnowledgeFrame(data=arr_wordcount, columns=["word_count"])
# Feature extraction of the feature described in chapter 5.6.4 with some variations
# Count total_all word lengthgths indivisionidutotal_ally
def getting_word_lengthgth_matrix(par_kf):
nlp = spacy.load("de_core_news_sm")
d_word_length = {}
d_word_length_list = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not word.is_digit]
for word in tokens:
try:
d_word_length["w_length:"+str(length(word.text))] += 1
except KeyError:
d_word_length["w_length:"+str(length(word.text))] = 1
d_word_length_list.adding(clone.deepclone(d_word_length))
d_word_length.clear()
return mk.KnowledgeFrame(d_word_length_list)
# Count word lengthgths and set 2 intervals
def getting_word_lengthgth_matrix_with_interval(par_kf, border_1, border_2):
arr_wordcount_with_interval = np.zeros((length(par_kf), border_1 + 2))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for word in tokens:
if length(word.text) <= border_1 and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, length(word.text) - 1] += 1
elif border_1 < length(
word.text) <= border_2 and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -2] += 1
elif not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -1] += 1
word_lengthgth_labels = [str(i) for i in range(1, border_1+1)]
word_lengthgth_labels.adding(f"{border_1+1}-{border_2}")
word_lengthgth_labels.adding(f">{border_2}")
return mk.KnowledgeFrame(data=arr_wordcount_with_interval, columns=word_lengthgth_labels)
# Count word lengthgths and total_sum total_all above a defined margin
def getting_word_lengthgth_matrix_with_margin(par_kf, par_margin):
arr_wordcount_with_interval = np.zeros((length(par_kf), par_margin + 1))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for word in tokens:
if length(word.text) <= par_margin and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, length(word.text) - 1] += 1
elif par_margin < length(word.text) and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -1] += 1
word_lengthgth_labels = [str(i) for i in range(1, par_margin+1)]
word_lengthgth_labels.adding(f">{par_margin}")
return mk.KnowledgeFrame(data=arr_wordcount_with_interval, columns=word_lengthgth_labels)
# Count the average word lengthgth of the article
def getting_average_word_lengthgth(par_kf):
arr_avg_word_length_vector = np.zeros((length(par_kf), 1))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
symbol_total_sum = 0
words = 0
tokens = nlp(row['text'])
for word in tokens:
if not word.is_punct and not word.is_space and not word.is_digit:
symbol_total_sum += length(word.text)
words += 1
arr_avg_word_length_vector[index, 0] = symbol_total_sum / words
return mk.KnowledgeFrame(data=arr_avg_word_length_vector, columns=["avg_word_lengthgth"])
# Feature extraction of the feature described in chapter 5.6.5
def getting_yules_k(par_kf):
d = {}
nlp = spacy.load("de_core_news_sm")
arr_yulesk = np.zeros((length(par_kf), 1))
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
if not t.is_punct and not t.is_space and not t.is_digit:
w = t.lemma_.lower()
try:
d[w] += 1
except KeyError:
d[w] = 1
s1 = float(length(d))
s2 = total_sum([length(list(g)) * (freq ** 2) for freq, g in grouper(sorted(d.values()))])
try:
k = 10000 * (s2 - s1) / (s1 * s1)
arr_yulesk[index] = k
except ZeroDivisionError:
pass
d.clear()
return mk.KnowledgeFrame(data=arr_yulesk, columns=["yulesk"])
# Feature extraction of the feature described in chapter 5.6.6
# Get a vector of total_all special characters
def getting_special_char_label_vector(par_kf):
nlp = spacy.load("de_core_news_sm")
special_char_label_vector = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
chars = ' '.join([c for c in t.text])
chars = nlp(chars)
for c in chars:
if c.is_punct and c.text not in special_char_label_vector:
special_char_label_vector.adding(c.text)
return special_char_label_vector
# Get a matrix of total_all special character by a given vector of special chars
def getting_special_char_matrix(par_kf, par_special_char_label_vector):
nlp = spacy.load("de_core_news_sm")
arr_special_char = np.zeros((length(par_kf), length(par_special_char_label_vector)))
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
chars = ' '.join([c for c in t.text])
chars = nlp(chars)
for c in chars:
if c.text in par_special_char_label_vector:
arr_special_char[index, par_special_char_label_vector.index(c.text)] += 1
return arr_special_char
# Feature extraction of the feature described in chapter 5.6.7
# Get the char-affix-n-grams by a defined n
def getting_char_affix_n_grams(par_kf, n):
d_prefix_list, d_suffix_list, d_space_prefix_list, d_space_suffix_list = [], [], [], []
d_prefix, d_suffix, d_space_prefix, d_space_suffix = {}, {}, {}, {}
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for w in range(0, length(tokens)):
# Prefix
if length(tokens[w].text) >= n + 1:
try:
d_prefix["c" + str(n) + "_p: " + tokens[w].text.lower()[0:n]] += 1
except KeyError:
d_prefix["c" + str(n) + "_p: " + tokens[w].text.lower()[0:n]] = 1
# Suffix
if length(tokens[w].text) >= n + 1:
try:
d_suffix["c" + str(n) + "_s: " + tokens[w].text.lower()[-n:]] += 1
except KeyError:
d_suffix["c" + str(n) + "_s: " + tokens[w].text.lower()[-n:]] = 1
d_prefix_list.adding(clone.deepclone(d_prefix))
d_suffix_list.adding(clone.deepclone(d_suffix))
d_prefix.clear()
d_suffix.clear()
for i in range(0, length(row['text'])):
if row['text'][i] == " " and i + n <= length(row['text']) and i - n >= 0:
# Space-prefix
try:
d_space_prefix["c" + str(n) + "_sp: " + row['text'].lower()[i:n + i]] += 1
except KeyError:
d_space_prefix["c" + str(n) + "_sp: " + row['text'].lower()[i:n + i]] = 1
# Space-suffix
try:
d_space_suffix["c" + str(n) + "_ss: " + row['text'].lower()[i - n + 1:i + 1]] += 1
except KeyError:
d_space_suffix["c" + str(n) + "_ss: " + row['text'].lower()[i - n + 1:i + 1]] = 1
d_space_prefix_list.adding(clone.deepclone(d_space_prefix))
d_space_suffix_list.adding(clone.deepclone(d_space_suffix))
d_space_prefix.clear()
d_space_suffix.clear()
kf_pre = mk.KnowledgeFrame(d_prefix_list)
kf_su = mk.KnowledgeFrame(d_suffix_list)
kf_s_pre = mk.KnowledgeFrame(d_space_prefix_list)
kf_s_su = mk.KnowledgeFrame(d_space_suffix_list)
kf_affix = mk.concating([kf_pre, kf_su, kf_s_pre, kf_s_su], axis=1)
return kf_affix
# Get the char-word-n-grams by a defined n
def getting_char_word_n_grams(par_kf, n):
d_whole_word_list, d_mid_word_list, d_multi_word_list = [], [], []
d_whole_word, d_mid_word, d_multi_word = {}, {}, {}
match_list = []
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for w in range(0, length(tokens)):
# Whole-word
if length(tokens[w].text) == n:
try:
d_whole_word["c" + str(n) + "_ww: " + tokens[w].text.lower()] += 1
except KeyError:
d_whole_word["c" + str(n) + "_ww: " + tokens[w].text.lower()] = 1
# Mid-word
if length(tokens[w].text) >= n + 2:
for i in range(1, length(tokens[w].text) - n):
try:
d_mid_word["c" + str(n) + "_miw: " + tokens[w].text.lower()[i:i + n]] += 1
except KeyError:
d_mid_word["c" + str(n) + "_miw: " + tokens[w].text.lower()[i:i + n]] = 1
d_whole_word_list.adding(clone.deepclone(d_whole_word))
d_mid_word_list.adding(clone.deepclone(d_mid_word))
d_whole_word.clear()
d_mid_word.clear()
# Multi-word
# ignore special character
trimmed_text = re.sub(r'[\s]+', ' ', re.sub(r'[^\w ]+', '', row['text']))
match_list.clear()
for i in range(1, n - 1):
regex = r"\w{" + str(i) + r"}\s\w{" + str(n - 1 - i) + r"}"
match_list += re.findtotal_all(regex, trimmed_text.lower())
for match in match_list:
try:
d_multi_word["c" + str(n) + "_mw: " + match] += 1
except KeyError:
d_multi_word["c" + str(n) + "_mw: " + match] = 1
d_multi_word_list.adding(clone.deepclone(d_multi_word))
d_multi_word.clear()
kf_ww = mk.KnowledgeFrame(d_whole_word_list)
kf_miw = mk.KnowledgeFrame(d_mid_word_list)
kf_mw =
|
mk.KnowledgeFrame(d_multi_word_list)
|
pandas.DataFrame
|
from __future__ import divisionision
import configparser
import logging
import os
import re
import time
from collections import OrderedDict
import numpy as np
import monkey as mk
import scipy.interpolate as itp
from joblib import Partotal_allel
from joblib import delayed
from matplotlib import pyplot as plt
from pyplanscoring.core.dicomparser import ScoringDicomParser
from pyplanscoring.core.dosimetric import read_scoring_criteria, constrains, Competition2016
from pyplanscoring.core.dvhcalculation import Structure, prepare_dvh_data, calc_dvhs_upsample_by_numd, save_dicom_dvhs, load
from pyplanscoring.core.dvhdoses import getting_dvh_getting_max
from pyplanscoring.core.geometry import getting_axis_grid, getting_interpolated_structure_planes
from pyplanscoring.core.scoring import DVHMetrics, Scoring, Participant
# TODO extract constrains from analytical curves
class CurveCompare(object):
"""
Statistical analysis of the DVH volume (%) error histograms. volume (cm 3 ) differences (numerical–analytical)
were calculated for points on the DVH curve sample_by_numd at every 10 cGy then normalized to
the structure's total volume (cm 3 ) to give the error in volume (%)
"""
def __init__(self, a_dose, a_dvh, calc_dose, calc_dvh, structure_name='', dose_grid='', gradient=''):
self.calc_data = ''
self.ref_data = ''
self.a_dose = a_dose
self.a_dvh = a_dvh
self.cal_dose = calc_dose
self.calc_dvh = calc_dvh
self.sampling_size = 10/100.0
self.dose_sample_by_nums = np.arange(0, length(calc_dvh)/100, self.sampling_size) # The DVH curve sample_by_numd at every 10 cGy
self.ref_dvh = itp.interp1d(a_dose, a_dvh, fill_value='extrapolate')
self.calc_dvh = itp.interp1d(calc_dose, calc_dvh, fill_value='extrapolate')
self.delta_dvh = self.calc_dvh(self.dose_sample_by_nums) - self.ref_dvh(self.dose_sample_by_nums)
self.delta_dvh_pp = (self.delta_dvh / a_dvh[0]) * 100
# prepare data dict
# self.calc_dvh_dict = _prepare_dvh_data(self.dose_sample_by_nums, self.calc_dvh(self.dose_sample_by_nums))
# self.ref_dvh_dict = _prepare_dvh_data(self.dose_sample_by_nums, self.ref_dvh(self.dose_sample_by_nums))
# title data
self.structure_name = structure_name
self.dose_grid = dose_grid
self.gradient = gradient
def stats(self):
kf = mk.KnowledgeFrame(self.delta_dvh_pp, columns=['delta_pp'])
print(kf.describe())
@property
def stats_paper(self):
stats = {}
stats['getting_min'] = self.delta_dvh_pp.getting_min().value_round(1)
stats['getting_max'] = self.delta_dvh_pp.getting_max().value_round(1)
stats['average'] = self.delta_dvh_pp.average().value_round(1)
stats['standard'] = self.delta_dvh_pp.standard(ddof=1).value_round(1)
return stats
@property
def stats_delta_cc(self):
stats = {}
stats['getting_min'] = self.delta_dvh.getting_min().value_round(1)
stats['getting_max'] = self.delta_dvh.getting_max().value_round(1)
stats['average'] = self.delta_dvh.average().value_round(1)
stats['standard'] = self.delta_dvh.standard(ddof=1).value_round(1)
return stats
# def getting_constrains(self, constrains_dict):
# ref_constrains = eval_constrains_dict(self.ref_dvh_dict, constrains_dict)
# calc_constrains = eval_constrains_dict(self.calc_dvh_dict, constrains_dict)
#
# return ref_constrains, calc_constrains
def eval_range(self, lim=0.2):
t1 = self.delta_dvh < -lim
t2 = self.delta_dvh > lim
ok = np.total_sum(np.logical_or(t1, t2))
pp = ok / length(self.delta_dvh) * 100
print('pp %1.2f - %i of %i ' % (pp, ok, self.delta_dvh.size))
def plot_results(self, ref_label, calc_label, title):
fig, ax = plt.subplots()
ref = self.ref_dvh(self.dose_sample_by_nums)
calc = self.calc_dvh(self.dose_sample_by_nums)
ax.plot(self.dose_sample_by_nums, ref, label=ref_label)
ax.plot(self.dose_sample_by_nums, calc, label=calc_label)
ax.set_ylabel('volume [cc]')
ax.set_xlabel('Dose [Gy]')
ax.set_title(title)
ax.legend(loc='best')
def test_real_dvh():
rs_file = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RS.1.2.246.352.71.4.584747638204.248648.20170123083029.dcm'
rd_file = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RD.1.2.246.352.71.7.584747638204.1750110.20170123082607.dcm'
rp = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RP.1.2.246.352.71.5.584747638204.952069.20170122155706.dcm'
# dvh_file = r'/media/victor/TOURO Mobile/COMPETITION 2017/Send to Victor - Jan10 2017/Norm Res with CT Images/RD.1.2.246.352.71.7.584747638204.1746016.20170110164605.dvh'
f = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/PlanIQ Criteria TPS PlanIQ matched str names - TXT Fromat - Last mod Jan23.txt'
constrains_total_all, scores_total_all, criteria = read_scoring_criteria(f)
dose = ScoringDicomParser(filengthame=rd_file)
struc = ScoringDicomParser(filengthame=rs_file)
structures = struc.GetStructures()
ecl_DVH = dose.GetDVHs()
plt.style.use('ggplot')
st = time.time()
dvhs = {}
for structure in structures.values():
for end_cap in [False]:
if structure['id'] in ecl_DVH:
# if structure['id'] in [37, 38]:
if structure['name'] in list(scores_total_all.keys()):
ecl_dvh = ecl_DVH[structure['id']]['data']
ecl_dgetting_max = ecl_DVH[structure['id']]['getting_max'] * 100 # to cGy
struc_teste = Structure(structure, end_cap=end_cap)
# struc['planes'] = struc_teste.planes
# dicompyler_dvh = getting_dvh(structure, dose)
fig, ax = plt.subplots()
fig.set_figheight(12)
fig.set_figwidth(20)
dhist, chist = struc_teste.calculate_dvh(dose, up_sample_by_num=True)
getting_max_dose = getting_dvh_getting_max(chist)
ax.plot(dhist, chist, label='Up sample_by_numd - Dgetting_max: %1.1f cGy' % getting_max_dose)
fig.hold(True)
ax.plot(ecl_dvh, label='Eclipse - Dgetting_max: %1.1f cGy' % ecl_dgetting_max)
dvh_data = prepare_dvh_data(dhist, chist)
txt = structure['name'] + ' volume (cc): %1.1f - end_cap: %s ' % (
ecl_dvh[0], str(end_cap))
ax.set_title(txt)
# nup = getting_dvh_getting_max(dicompyler_dvh['data'])
# plt.plot(dicompyler_dvh['data'], label='Software DVH - Dgetting_max: %1.1f cGy' % nup)
ax.legend(loc='best')
ax.set_xlabel('Dose (cGy)')
ax.set_ylabel('volume (cc)')
fname = txt + '.png'
fig.savefig(fname, formating='png', dpi=100)
dvhs[structure['name']] = dvh_data
end = time.time()
print('Total elapsed Time (getting_min): ', (end - st) / 60)
def test_spacing(root_path):
"""
# TEST PLANIQ RS-DICOM DATA if z planes are not equal spaced.
:param root_path: root path
"""
root_path = r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/STRUCTURES'
structure_files = [os.path.join(root, name) for root, dirs, files in os.walk(root_path) for name in files if
name.endswith(('.dcm', '.DCM'))]
eps = 0.001
test_result = {}
for f in structure_files:
structures = ScoringDicomParser(filengthame=f).GetStructures()
for key in structures:
try:
total_all_z = np.array([z for z in structures[key]['planes'].keys()], dtype=float)
total_all_sorted_diff = np.diff(np.sort(total_all_z))
test = (abs((total_all_sorted_diff - total_all_sorted_diff[0])) > eps).whatever()
test_result[structures[key]['name']] = test
except:
print('Error in key:', key)
b = {key: value for key, value in test_result.items() if value == True}
return test_result
def test_planes_spacing(sPlanes):
eps = 0.001
total_all_z = np.array([z for z in sPlanes], dtype=float)
total_all_sorted_diff = np.diff(np.sort(total_all_z))
test = (abs((total_all_sorted_diff - total_all_sorted_diff[0])) > eps).whatever()
return test, total_all_sorted_diff
def test_upsample_by_numd_z_spacing(sPlanes):
z = 0.1
ordered_keys = [z for z, sPlane in sPlanes.items()]
ordered_keys.sort(key=float)
ordered_planes = np.array(ordered_keys, dtype=float)
z_interp_positions, dz = getting_axis_grid(z, ordered_planes)
hi_res_structure = getting_interpolated_structure_planes(sPlanes, z_interp_positions)
ordered_keys = [z for z, sPlane in hi_res_structure.items()]
ordered_keys.sort(key=float)
t, p = test_planes_spacing(hi_res_structure)
assert t is False
def eval_constrains_dict(dvh_data_tmp, constrains_dict):
mtk = DVHMetrics(dvh_data_tmp)
values_tmp = OrderedDict()
for ki in constrains_dict.keys():
cti = mtk.eval_constrain(ki, constrains_dict[ki])
values_tmp[ki] = cti
return values_tmp
def getting_analytical_curve(an_curves_obj, file_structure_name, column):
an_curve_i = an_curves_obj[file_structure_name.split('_')[0]]
dose_an = an_curve_i['Dose (cGy)'].values
an_dvh = an_curve_i[column].values # check nonzero
idx = np.nonzero(an_dvh) # remove 0 volumes from DVH
dose_range, cdvh = dose_an[idx], an_dvh[idx]
return dose_range, cdvh
def calc_data(row, dose_files_dict, structure_dict, constrains, calculation_options):
idx, values = row[0], row[1]
s_name = values['Structure name']
voxel = str(values['Dose Voxel (mm)'])
gradient = values['Gradient direction']
dose_file = dose_files_dict[gradient][voxel]
struc_file = structure_dict[s_name]
# getting structure and dose
dicom_dose = ScoringDicomParser(filengthame=dose_file)
struc = ScoringDicomParser(filengthame=struc_file)
structures = struc.GetStructures()
structure = structures[2]
# set end cap by 1/2 slice thickness
calculation_options['end_cap'] = structure['thickness'] / 2.0
# set up sample_by_numd structure
struc_teste = Structure(structure, calculation_options)
dhist, chist = struc_teste.calculate_dvh(dicom_dose)
dvh_data = struc_teste.getting_dvh_data()
# Setup DVH metrics class and getting DVH DATA
metrics = DVHMetrics(dvh_data)
values_constrains = OrderedDict()
for k in constrains.keys():
ct = metrics.eval_constrain(k, constrains[k])
values_constrains[k] = ct
values_constrains['Gradient direction'] = gradient
# Get data
return mk.Collections(values_constrains, name=voxel), s_name
def calc_data_total_all(row, dose_files_dict, structure_dict, constrains, an_curves, col_grad_dict, delta_mm=(0.2, 0.2, 0.2),
end_cap=True, up_sample_by_num=True):
idx, values = row[0], row[1]
s_name = values['Structure name']
voxel = str(values['Dose Voxel (mm)'])
gradient = values['Gradient direction']
dose_file = dose_files_dict[gradient][voxel]
struc_file = structure_dict[s_name]
# getting structure and dose
dicom_dose = ScoringDicomParser(filengthame=dose_file)
struc = ScoringDicomParser(filengthame=struc_file)
structures = struc.GetStructures()
structure = structures[2]
# set up sample_by_numd structure
struc_teste = Structure(structure)
struc_teste.set_delta(delta_mm)
dhist, chist = struc_teste.calculate_dvh(dicom_dose)
# getting its columns from spreadsheet
column = col_grad_dict[gradient][voxel]
adose_range, advh = getting_analytical_curve(an_curves, s_name, column)
# use CurveCompare class to eval similarity from calculated and analytical curves
cmp = CurveCompare(adose_range, advh, dhist, chist, s_name, voxel, gradient)
ref_constrains, calc_constrains = cmp.getting_constrains(constrains)
ref_constrains['Gradient direction'] = gradient
calc_constrains['Gradient direction'] = gradient
ref_collections = mk.Collections(ref_constrains, name=voxel)
calc_collections = mk.Collections(calc_constrains, name=voxel)
return ref_collections, calc_collections, s_name, cmp
def test11(delta_mm=(0.2, 0.2, 0.1), plot_curves=False):
# TEST DICOM DATA
structure_files = ['/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Spheres/Sphere_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cylinders/Cylinder_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cylinders/RtCylinder_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cones/Cone_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cones/RtCone_02_0.dcm']
structure_name = ['Sphere_02_0', 'Cylinder_02_0', 'RtCylinder_02_0', 'Cone__02_0', 'RtCone_02_0']
dose_files = [
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_0-4_0-2_0-4_mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_1mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_2mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_3mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_0-4_0-2_0-4_mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_1mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_2mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_3mm_Aligned.dcm']
# Structure Dict
structure_dict = dict(zip(structure_name, structure_files))
# dose dict
dose_files_dict = {
'Z(AP)': {'0.4x0.2x0.4': dose_files[0], '1': dose_files[1], '2': dose_files[2], '3': dose_files[3]},
'Y(SI)': {'0.4x0.2x0.4': dose_files[4], '1': dose_files[5], '2': dose_files[6], '3': dose_files[7]}}
sheets = ['Sphere', 'Cylinder', 'RtCylinder', 'Cone', 'RtCone']
col_grad_dict = {'Z(AP)': {'0.4x0.2x0.4': 'AP 0.2 mm', '1': 'AP 1 mm', '2': 'AP 2 mm', '3': 'AP 3 mm'},
'Y(SI)': {'0.4x0.2x0.4': 'SI 0.2 mm', '1': 'SI 1 mm', '2': 'SI 2 mm', '3': 'SI 3 mm'}}
# grab analytical data
sheet = 'Analytical'
ref_path = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_data.xlsx'
kf = mk.read_excel(ref_path, sheetname=sheet)
mask = kf['CT slice spacing (mm)'] == '0.2mm'
kf = kf.loc[mask]
# Constrains to getting data
# Constrains
constrains = OrderedDict()
constrains['Total_Volume'] = True
constrains['getting_min'] = 'getting_min'
constrains['getting_max'] = 'getting_max'
constrains['average'] = 'average'
constrains['D99'] = 99
constrains['D95'] = 95
constrains['D5'] = 5
constrains['D1'] = 1
constrains['Dcc'] = 0.03
# Get total_all analytical curves
out = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_dvh.obj'
an_curves = load(out)
res = Partotal_allel(n_jobs=-1, verbose=11)(
delayed(calc_data_total_all)(row,
dose_files_dict,
structure_dict,
constrains,
an_curves,
col_grad_dict,
delta_mm=delta_mm) for row in kf.traversal())
ref_results = [d[0] for d in res]
calc_results = [d[1] for d in res]
sname = [d[2] for d in res]
curves = [d[3] for d in res]
kf_ref_results = mk.concating(ref_results, axis=1).T.reseting_index()
kf_calc_results = mk.concating(calc_results, axis=1).T.reseting_index()
kf_ref_results['Structure name'] = sname
kf_calc_results['Structure name'] = sname
ref_num = kf_ref_results[kf_ref_results.columns[1:-2]]
calc_num = kf_calc_results[kf_calc_results.columns[1:-2]]
delta = ((calc_num - ref_num) / ref_num) * 100
res = OrderedDict()
lim = 3
for col in delta:
count = np.total_sum(np.abs(delta[col]) > lim)
rg = np.array([value_round(delta[col].getting_min(), 2), value_round(delta[col].getting_max(), 2)])
res[col] = {'count': count, 'range': rg}
test_table = mk.KnowledgeFrame(res).T
print(test_table)
if plot_curves:
for c in curves:
c.plot_results()
plt.show()
def test22(delta_mm=(0.1, 0.1, 0.1), up_sample_by_num=True, plot_curves=True):
ref_data = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_data.xlsx'
struc_dir = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/STRUCTURES'
dose_grid_dir = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS'
#
# ref_data = r'D:\Dropbox\Plan_Competit
st = 2
snames = ['Sphere_10_0', 'Sphere_20_0', 'Sphere_30_0',
'Cylinder_10_0', 'Cylinder_20_0', 'Cylinder_30_0',
'RtCylinder_10_0', 'RtCylinder_20_0', 'RtCylinder_30_0',
'Cone_10_0', 'Cone_20_0', 'Cone_30_0',
'RtCone_10_0', 'RtCone_20_0', 'RtCone_30_0']
structure_path = [os.path.join(struc_dir, f + '.dcm') for f in snames]
structure_dict = dict(zip(snames, structure_path))
dose_files = [os.path.join(dose_grid_dir, f) for f in [
'Linear_AntPost_1mm_Aligned.dcm',
'Linear_AntPost_2mm_Aligned.dcm',
'Linear_AntPost_3mm_Aligned.dcm',
'Linear_SupInf_1mm_Aligned.dcm',
'Linear_SupInf_2mm_Aligned.dcm',
'Linear_SupInf_3mm_Aligned.dcm']]
# dose dict
dose_files_dict = {
'Z(AP)': {'1': dose_files[0], '2': dose_files[1], '3': dose_files[2]},
'Y(SI)': {'1': dose_files[3], '2': dose_files[4], '3': dose_files[5]}}
col_grad_dict = {'Z(AP)': {'0.4x0.2x0.4': 'AP 0.2 mm', '1': 'AP 1 mm', '2': 'AP 2 mm', '3': 'AP 3 mm'},
'Y(SI)': {'0.4x0.2x0.4': 'SI 0.2 mm', '1': 'SI 1 mm', '2': 'SI 2 mm', '3': 'SI 3 mm'}}
# grab analytical data
out = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_dvh.obj'
an_curves = load(out)
kf = mk.read_excel(ref_data, sheetname='Analytical')
kfi = kf.ix[40:]
mask0 = kfi['Structure Shift'] == 0
kfi = kfi.loc[mask0]
# Constrains to getting data
# Constrains
constrains = OrderedDict()
constrains['Total_Volume'] = True
constrains['getting_min'] = 'getting_min'
constrains['getting_max'] = 'getting_max'
constrains['average'] = 'average'
constrains['D99'] = 99
constrains['D95'] = 95
constrains['D5'] = 5
constrains['D1'] = 1
constrains['Dcc'] = 0.03
# GET CALCULATED DATA
# backend = 'threading'
res = Partotal_allel(n_jobs=-1, verbose=11)(
delayed(calc_data_total_all)(row,
dose_files_dict,
structure_dict,
constrains,
an_curves,
col_grad_dict,
delta_mm=delta_mm,
up_sample_by_num=up_sample_by_num) for row in kfi.traversal())
ref_results = [d[0] for d in res]
calc_results = [d[1] for d in res]
sname = [d[2] for d in res]
curves = [d[3] for d in res]
kf_ref_results = mk.concating(ref_results, axis=1).T.reseting_index()
kf_calc_results = mk.concating(calc_results, axis=1).T.reseting_index()
kf_ref_results['Structure name'] = sname
kf_calc_results['Structure name'] = sname
ref_num = kf_ref_results[kf_ref_results.columns[1:-2]]
calc_num = kf_calc_results[kf_calc_results.columns[1:-2]]
delta = ((calc_num - ref_num) / ref_num) * 100
res = OrderedDict()
lim = 3
for col in delta:
count = np.total_sum(np.abs(delta[col]) > lim)
rg = np.array([value_round(delta[col].getting_min(), 2), value_round(delta[col].getting_max(), 2)])
res[col] = {'count': count, 'range': rg}
test_table =
|
mk.KnowledgeFrame(res)
|
pandas.DataFrame
|
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD
"""
Toolset working with yahoo finance data
Module includes functions for easy access to YahooFinance data
"""
import urllib.request
import numpy as np
import requests # interaction with the web
import os # file system operations
import yaml # human-friendly data formating
import re # regular expressions
import monkey as mk # monkey... the best time collections library out there
import datetime as dt # date and time functions
import io
from .extra import ProgressBar
dateTimeFormat = "%Y%m%d %H:%M:%S"
def parseStr(s):
''' convert string to a float or string '''
f = s.strip()
if f[0] == '"':
return f.strip('"')
elif f=='N/A':
return np.nan
else:
try: # try float conversion
prefixes = {'M':1e6, 'B': 1e9}
prefix = f[-1]
if prefix in prefixes: # do we have a Billion/Million character?
return float(f[:-1])*prefixes[prefix]
else: # no, convert to float directly
return float(f)
except ValueError: # failed, return original string
return s
def gettingQuote(symbols):
"""
getting current yahoo quote
Parameters
-----------
symbols : list of str
list of ticker symbols
Returns
-----------
KnowledgeFrame , data is row-wise
"""
# for codes see: http://www.gummy-stuff.org/Yahoo-data.htm
if not incontainstance(symbols,list):
symbols = [symbols]
header_numer = ['symbol','final_item','change_pct','PE','time','short_ratio','prev_close','eps','market_cap']
request = str.join('', ['s', 'l1', 'p2' , 'r', 't1', 's7', 'p', 'e' , 'j1'])
data = dict(list(zip(header_numer,[[] for i in range(length(header_numer))])))
urlStr = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (str.join('+',symbols), request)
try:
lines = urllib.request.urlopen(urlStr).readlines()
except Exception as e:
s = "Failed to download:\n{0}".formating(e);
print(s)
for line in lines:
fields = line.decode().strip().split(',')
#print fields, length(fields)
for i,field in enumerate(fields):
data[header_numer[i]].adding( parseStr(field))
idx = data.pop('symbol')
return
|
mk.KnowledgeFrame(data,index=idx)
|
pandas.DataFrame
|
from __future__ import divisionision
from functools import wraps
import monkey as mk
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class TedSpeciesProperties(object):
"""
Listing of species properties that will eventutotal_ally be read in from a SQL db
"""
def __init__(self):
"""Class representing Species properties"""
super(TedSpeciesProperties, self).__init__()
self.sci_name = mk.Collections([], dtype='object')
self.com_name = mk.Collections([], dtype='object')
self.taxa = mk.Collections([], dtype='object')
self.order = mk.Collections([], dtype='object')
self.usfws_id = mk.Collections([], dtype='object')
self.body_wgt = mk.Collections([], dtype='object')
self.diet_item = mk.Collections([], dtype='object')
self.h2o_cont = mk.Collections([], dtype='float')
def read_species_properties(self):
# this is a temporary method to initiate the species/diet food items lists (this will be replacingd with
# a method to access a SQL database containing the properties
#filengthame = './ted/tests/TEDSpeciesProperties.csv'
filengthame = os.path.join(os.path.dirname(__file__),'tests/TEDSpeciesProperties.csv')
try:
with open(filengthame,'rt') as csvfile:
# csv.DictReader uses first line in file for column header_numings by default
dr = mk.read_csv(csvfile) # comma is default delimiter
except csv.Error as e:
sys.exit('file: %s, %s' (filengthame, e))
print(dr)
self.sci_name = dr.ix[:,'Scientific Name']
self.com_name = dr.ix[:,'Common Name']
self.taxa = dr.ix[:,'Taxa']
self.order = dr.ix[:,'Order']
self.usfws_id = dr.ix[:,'USFWS Species ID (ENTITY_ID)']
self.body_wgt= dr.ix[:,'BW (g)']
self.diet_item = dr.ix[:,'Food item']
self.h2o_cont = dr.ix[:,'Water content of diet']
class TedInputs(ModelSharedInputs):
"""
Required inputs class for Ted.
"""
def __init__(self):
"""Class representing the inputs for Ted"""
super(TedInputs, self).__init__()
# Inputs: Assign object attribute variables from the input Monkey KnowledgeFrame
self.chemical_name = mk.Collections([], dtype="object", name="chemical_name")
# application parameters for getting_min/getting_max application scenarios
self.crop_getting_min = mk.Collections([], dtype="object", name="crop")
self.app_method_getting_min = mk.Collections([], dtype="object", name="app_method_getting_min")
self.app_rate_getting_min = mk.Collections([], dtype="float", name="app_rate_getting_min")
self.num_apps_getting_min = mk.Collections([], dtype="int", name="num_apps_getting_min")
self.app_interval_getting_min = mk.Collections([], dtype="int", name="app_interval_getting_min")
self.siplet_spec_getting_min = mk.Collections([], dtype="object", name="siplet_spec_getting_min")
self.boom_hgt_getting_min = mk.Collections([], dtype="object", name="siplet_spec_getting_min")
self.pest_incorp_depth_getting_min = mk.Collections([], dtype="object", name="pest_incorp_depth")
self.crop_getting_max = mk.Collections([], dtype="object", name="crop")
self.app_method_getting_max = mk.Collections([], dtype="object", name="app_method_getting_max")
self.app_rate_getting_max = mk.Collections([], dtype="float", name="app_rate_getting_max")
self.num_apps_getting_max = mk.Collections([], dtype="int", name="num_app_getting_maxs")
self.app_interval_getting_max = mk.Collections([], dtype="int", name="app_interval_getting_max")
self.siplet_spec_getting_max = mk.Collections([], dtype="object", name="siplet_spec_getting_max")
self.boom_hgt_getting_max = mk.Collections([], dtype="object", name="siplet_spec_getting_max")
self.pest_incorp_depth_getting_max = mk.Collections([], dtype="object", name="pest_incorp_depth")
# physical, chemical, and fate properties of pesticide
self.foliar_diss_hlife = mk.Collections([], dtype="float", name="foliar_diss_hlife")
self.aerobic_soil_meta_hlife = mk.Collections([], dtype="float", name="aerobic_soil_meta_hlife")
self.frac_retained_mamm = mk.Collections([], dtype="float", name="frac_retained_mamm")
self.frac_retained_birds = mk.Collections([], dtype="float", name="frac_retained_birds")
self.log_kow = mk.Collections([], dtype="float", name="log_kow")
self.koc = mk.Collections([], dtype="float", name="koc")
self.solubility = mk.Collections([], dtype="float", name="solubility")
self.henry_law_const = mk.Collections([], dtype="float", name="henry_law_const")
# bio concentration factors (ug active ing/kg-ww) / (ug active ing/liter)
self.aq_plant_algae_bcf_average = mk.Collections([], dtype="float", name="aq_plant_algae_bcf_average")
self.aq_plant_algae_bcf_upper = mk.Collections([], dtype="float", name="aq_plant_algae_bcf_upper")
self.inv_bcf_average = mk.Collections([], dtype="float", name="inv_bcf_average")
self.inv_bcf_upper = mk.Collections([], dtype="float", name="inv_bcf_upper")
self.fish_bcf_average = mk.Collections([], dtype="float", name="fish_bcf_average")
self.fish_bcf_upper = mk.Collections([], dtype="float", name="fish_bcf_upper")
# bounding water concentrations (ug active ing/liter)
self.water_conc_1 = mk.Collections([], dtype="float", name="water_conc_1") # lower bound
self.water_conc_2 = mk.Collections([], dtype="float", name="water_conc_2") # upper bound
# health value inputs
# nagetting_ming convention (based on listing from OPP TED Excel spreadsheet 'inputs' worksheet):
# dbt: dose based toxicity
# cbt: concentration-based toxicity
# arbt: application rate-based toxicity
# 1inmill_mort: 1/million mortality (note initial character is numeral 1, not letter l)
# 1inten_mort: 10% mortality (note initial character is numeral 1, not letter l)
# others are self explanatory
# dose based toxicity(dbt): mammals (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_mamm_1inmill_mort = mk.Collections([], dtype="float", name="dbt_mamm_1inmill_mort")
self.dbt_mamm_1inten_mort = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort")
self.dbt_mamm_low_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_low_ld50")
self.dbt_mamm_rat_oral_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort")
self.dbt_mamm_rat_derm_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_rat_derm_ld50")
self.dbt_mamm_rat_inhal_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_rat_inhal_ld50")
self.dbt_mamm_sub_direct = mk.Collections([], dtype="float", name="dbt_mamm_sub_direct")
self.dbt_mamm_sub_indirect = mk.Collections([], dtype="float", name="dbt_mamm_sub_indirect")
self.dbt_mamm_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inmill_mort_wgt")
self.dbt_mamm_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort_wgt")
self.dbt_mamm_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_low_ld50_wgt")
self.dbt_mamm_rat_oral_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort_wgt")
self.dbt_mamm_rat_derm_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_rat_derm_ld50_wgt")
self.dbt_mamm_rat_inhal_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_rat_inhal_ld50_wgt")
self.dbt_mamm_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_mamm_sub_direct_wgt")
self.dbt_mamm_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_mamm_sub_indirect_wgt")
# dose based toxicity(dbt): birds (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_bird_1inmill_mort = mk.Collections([], dtype="float", name="dbt_bird_1inmill_mort")
self.dbt_bird_1inten_mort = mk.Collections([], dtype="float", name="dbt_bird_1inten_mort")
self.dbt_bird_low_ld50 = mk.Collections([], dtype="float", name="dbt_bird_low_ld50")
self.dbt_bird_hc05 = mk.Collections([], dtype="float", name="dbt_bird_hc05")
self.dbt_bird_hc50 = mk.Collections([], dtype="float", name="dbt_bird_hc50")
self.dbt_bird_hc95 = mk.Collections([], dtype="float", name="dbt_bird_hc95")
self.dbt_bird_sub_direct = mk.Collections([], dtype="float", name="dbt_bird_sub_direct")
self.dbt_bird_sub_indirect = mk.Collections([], dtype="float", name="dbt_bird_sub_indirect")
self.getting_mineau_sca_fact = mk.Collections([], dtype="float", name="getting_mineau_sca_fact")
self.dbt_bird_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_bird_1inmill_mort_wgt")
self.dbt_bird_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_bird_1inten_mort_wgt")
self.dbt_bird_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_bird_low_ld50_wgt")
self.dbt_bird_hc05_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc05_wgt")
self.dbt_bird_hc50_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc50_wgt")
self.dbt_bird_hc95_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc95_wgt")
self.dbt_bird_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_bird_sub_direct_wgt")
self.dbt_bird_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_bird_sub_indirect_wgt")
self.getting_mineau_sca_fact_wgt = mk.Collections([], dtype="float", name="getting_mineau_sca_fact_wgt")
# dose based toxicity(dbt): reptiles, terrestrial-phase amphibians (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_reptile_1inmill_mort = mk.Collections([], dtype="float", name="dbt_reptile_1inmill_mort")
self.dbt_reptile_1inten_mort = mk.Collections([], dtype="float", name="dbt_reptile_1inten_mort")
self.dbt_reptile_low_ld50 = mk.Collections([], dtype="float", name="dbt_reptile_low_ld50")
self.dbt_reptile_sub_direct = mk.Collections([], dtype="float", name="dbt_reptile_sub_direct")
self.dbt_reptile_sub_indirect = mk.Collections([], dtype="float", name="dbt_reptile_sub_indirect")
self.dbt_reptile_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_reptile_1inmill_mort_wgt")
self.dbt_reptile_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_reptile_1inten_mort_wgt")
self.dbt_reptile_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_reptile_low_ld50_wgt")
self.dbt_reptile_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_reptile_sub_direct_wgt")
self.dbt_reptile_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_reptile_sub_indirect_wgt")
# concentration-based toxicity (cbt) : mammals (mg-pest/kg-diet food)
self.cbt_mamm_1inmill_mort = mk.Collections([], dtype="float", name="cbt_mamm_1inmill_mort")
self.cbt_mamm_1inten_mort = mk.Collections([], dtype="float", name="cbt_mamm_1inten_mort")
self.cbt_mamm_low_lc50 = mk.Collections([], dtype="float", name="cbt_mamm_low_lc50")
self.cbt_mamm_sub_direct = mk.Collections([], dtype="float", name="cbt_mamm_sub_direct")
self.cbt_mamm_grow_noec = mk.Collections([], dtype="float", name="cbt_mamm_grow_noec")
self.cbt_mamm_grow_loec = mk.Collections([], dtype="float", name="cbt_mamm_grow_loec")
self.cbt_mamm_repro_noec = mk.Collections([], dtype="float", name="cbt_mamm_repro_noec")
self.cbt_mamm_repro_loec = mk.Collections([], dtype="float", name="cbt_mamm_repro_loec")
self.cbt_mamm_behav_noec = mk.Collections([], dtype="float", name="cbt_mamm_behav_noec")
self.cbt_mamm_behav_loec = mk.Collections([], dtype="float", name="cbt_mamm_behav_loec")
self.cbt_mamm_sensory_noec = mk.Collections([], dtype="float", name="cbt_mamm_sensory_noec")
self.cbt_mamm_sensory_loec = mk.Collections([], dtype="float", name="cbt_mamm_sensory_loec")
self.cbt_mamm_sub_indirect = mk.Collections([], dtype="float", name="cbt_mamm_sub_indirect")
# concentration-based toxicity (cbt) : birds (mg-pest/kg-diet food)
self.cbt_bird_1inmill_mort = mk.Collections([], dtype="float", name="cbt_bird_1inmill_mort")
self.cbt_bird_1inten_mort = mk.Collections([], dtype="float", name="cbt_bird_1inten_mort")
self.cbt_bird_low_lc50 = mk.Collections([], dtype="float", name="cbt_bird_low_lc50")
self.cbt_bird_sub_direct = mk.Collections([], dtype="float", name="cbt_bird_sub_direct")
self.cbt_bird_grow_noec = mk.Collections([], dtype="float", name="cbt_bird_grow_noec")
self.cbt_bird_grow_loec = mk.Collections([], dtype="float", name="cbt_bird_grow_loec")
self.cbt_bird_repro_noec = mk.Collections([], dtype="float", name="cbt_bird_repro_noec")
self.cbt_bird_repro_loec = mk.Collections([], dtype="float", name="cbt_bird_repro_loec")
self.cbt_bird_behav_noec = mk.Collections([], dtype="float", name="cbt_bird_behav_noec")
self.cbt_bird_behav_loec = mk.Collections([], dtype="float", name="cbt_bird_behav_loec")
self.cbt_bird_sensory_noec = mk.Collections([], dtype="float", name="cbt_bird_sensory_noec")
self.cbt_bird_sensory_loec = mk.Collections([], dtype="float", name="cbt_bird_sensory_loec")
self.cbt_bird_sub_indirect = mk.Collections([], dtype="float", name="cbt_bird_sub_indirect")
# concentration-based toxicity (cbt) : reptiles, terrestrial-phase amphibians (mg-pest/kg-diet food)
self.cbt_reptile_1inmill_mort = mk.Collections([], dtype="float", name="cbt_reptile_1inmill_mort")
self.cbt_reptile_1inten_mort = mk.Collections([], dtype="float", name="cbt_reptile_1inten_mort")
self.cbt_reptile_low_lc50 = mk.Collections([], dtype="float", name="cbt_reptile_low_lc50")
self.cbt_reptile_sub_direct = mk.Collections([], dtype="float", name="cbt_reptile_sub_direct")
self.cbt_reptile_grow_noec = mk.Collections([], dtype="float", name="cbt_reptile_grow_noec")
self.cbt_reptile_grow_loec = mk.Collections([], dtype="float", name="cbt_reptile_grow_loec")
self.cbt_reptile_repro_noec = mk.Collections([], dtype="float", name="cbt_reptile_repro_noec")
self.cbt_reptile_repro_loec = mk.Collections([], dtype="float", name="cbt_reptile_repro_loec")
self.cbt_reptile_behav_noec = mk.Collections([], dtype="float", name="cbt_reptile_behav_noec")
self.cbt_reptile_behav_loec = mk.Collections([], dtype="float", name="cbt_reptile_behav_loec")
self.cbt_reptile_sensory_noec = mk.Collections([], dtype="float", name="cbt_reptile_sensory_noec")
self.cbt_reptile_sensory_loec = mk.Collections([], dtype="float", name="cbt_reptile_sensory_loec")
self.cbt_reptile_sub_indirect = mk.Collections([], dtype="float", name="cbt_reptile_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates body weight (mg-pest/kg-bw(ww))
self.cbt_inv_bw_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_bw_1inmill_mort")
self.cbt_inv_bw_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_bw_1inten_mort")
self.cbt_inv_bw_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_bw_low_lc50")
self.cbt_inv_bw_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_bw_sub_direct")
self.cbt_inv_bw_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_grow_noec")
self.cbt_inv_bw_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_grow_loec")
self.cbt_inv_bw_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_repro_noec")
self.cbt_inv_bw_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_repro_loec")
self.cbt_inv_bw_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_behav_noec")
self.cbt_inv_bw_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_behav_loec")
self.cbt_inv_bw_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_sensory_noec")
self.cbt_inv_bw_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_sensory_loec")
self.cbt_inv_bw_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_bw_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates body diet (mg-pest/kg-food(ww))
self.cbt_inv_food_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_food_1inmill_mort")
self.cbt_inv_food_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_food_1inten_mort")
self.cbt_inv_food_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_food_low_lc50")
self.cbt_inv_food_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_food_sub_direct")
self.cbt_inv_food_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_food_grow_noec")
self.cbt_inv_food_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_food_grow_loec")
self.cbt_inv_food_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_food_repro_noec")
self.cbt_inv_food_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_food_repro_loec")
self.cbt_inv_food_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_food_behav_noec")
self.cbt_inv_food_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_food_behav_loec")
self.cbt_inv_food_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_food_sensory_noec")
self.cbt_inv_food_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_food_sensory_loec")
self.cbt_inv_food_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_food_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates soil (mg-pest/kg-soil(dw))
self.cbt_inv_soil_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_soil_1inmill_mort")
self.cbt_inv_soil_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_soil_1inten_mort")
self.cbt_inv_soil_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_soil_low_lc50")
self.cbt_inv_soil_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_soil_sub_direct")
self.cbt_inv_soil_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_grow_noec")
self.cbt_inv_soil_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_grow_loec")
self.cbt_inv_soil_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_repro_noec")
self.cbt_inv_soil_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_repro_loec")
self.cbt_inv_soil_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_behav_noec")
self.cbt_inv_soil_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_behav_loec")
self.cbt_inv_soil_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_sensory_noec")
self.cbt_inv_soil_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_sensory_loec")
self.cbt_inv_soil_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_soil_sub_indirect")
# application rate-based toxicity (arbt) : mammals (lbs active ingredient/Acre)
self.arbt_mamm_mort = mk.Collections([], dtype="float", name="arbt_mamm_mort")
self.arbt_mamm_growth = mk.Collections([], dtype="float", name="arbt_mamm_growth")
self.arbt_mamm_repro = mk.Collections([], dtype="float", name="arbt_mamm_repro")
self.arbt_mamm_behav = mk.Collections([], dtype="float", name="arbt_mamm_behav")
self.arbt_mamm_sensory = mk.Collections([], dtype="float", name="arbt_mamm_sensory")
# application rate-based toxicity (arbt) : birds (lbs active ingredient/Acre)
self.arbt_bird_mort = mk.Collections([], dtype="float", name="arbt_bird_mort")
self.arbt_bird_growth = mk.Collections([], dtype="float", name="arbt_bird_growth")
self.arbt_bird_repro = mk.Collections([], dtype="float", name="arbt_bird_repro")
self.arbt_bird_behav = mk.Collections([], dtype="float", name="arbt_bird_behav")
self.arbt_bird_sensory = mk.Collections([], dtype="float", name="arbt_bird_sensory")
# application rate-based toxicity (arbt) : reptiles (lbs active ingredient/Acre)
self.arbt_reptile_mort = mk.Collections([], dtype="float", name="arbt_reptile_mort")
self.arbt_reptile_growth = mk.Collections([], dtype="float", name="arbt_reptile_growth")
self.arbt_reptile_repro = mk.Collections([], dtype="float", name="arbt_reptile_repro")
self.arbt_reptile_behav = mk.Collections([], dtype="float", name="arbt_reptile_behav")
self.arbt_reptile_sensory = mk.Collections([], dtype="float", name="arbt_reptile_sensory")
# application rate-based toxicity (arbt) : invertebrates (lbs active ingredient/Acre)
self.arbt_inv_1inmill_mort = mk.Collections([], dtype="float", name="arbt_inv_1inmill_mort")
self.arbt_inv_1inten_mort = mk.Collections([], dtype="float", name="arbt_inv_1inten_mort")
self.arbt_inv_sub_direct = mk.Collections([], dtype="float", name="arbt_inv_sub_direct")
self.arbt_inv_sub_indirect = mk.Collections([], dtype="float", name="arbt_inv_sub_indirect")
self.arbt_inv_growth = mk.Collections([], dtype="float", name="arbt_inv_growth")
self.arbt_inv_repro = mk.Collections([], dtype="float", name="arbt_inv_repro")
self.arbt_inv_behav = mk.Collections([], dtype="float", name="arbt_inv_behav")
self.arbt_inv_sensory =
|
mk.Collections([], dtype="float", name="arbt_inv_sensory")
|
pandas.Series
|
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import total_allocate_fips_location_system
from flowsa.location import US_FIPS
import math
import monkey as mk
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
"bauxite": "2013-2017",
"beryllium": "2014-2018",
"boron": "2014-2018",
"chromium": "2014-2018",
"clay": "2015-2016",
"cobalt": "2013-2017",
"copper": "2011-2015",
"diatomite": "2014-2018",
"feldspar": "2013-2017",
"fluorspar": "2013-2017",
"fluorspar_inports": ["2016", "2017"],
"gtotal_allium": "2014-2018",
"garnet": "2014-2018",
"gold": "2013-2017",
"graphite": "2013-2017",
"gyptotal_sum": "2014-2018",
"iodine": "2014-2018",
"ironore": "2014-2018",
"kyanite": "2014-2018",
"lead": "2012-2018",
"lime": "2014-2018",
"lithium": "2013-2017",
"magnesium": "2013-2017",
"manganese": "2012-2016",
"manufacturedabrasive": "2017-2018",
"mica": "2014-2018",
"molybdenum": "2014-2018",
"nickel": "2012-2016",
"niobium": "2014-2018",
"peat": "2014-2018",
"perlite": "2013-2017",
"phosphate": "2014-2018",
"platinum": "2014-2018",
"potash": "2014-2018",
"pumice": "2014-2018",
"rhenium": "2014-2018",
"salt": "2013-2017",
"sandgflat_underlyingconstruction": "2013-2017",
"sandgflat_underlyingindustrial": "2014-2018",
"silver": "2012-2016",
"sodaash": "2010-2017",
"sodaash_t4": ["2016", "2017"],
"stonecrushed": "2013-2017",
"stonedimension": "2013-2017",
"strontium": "2014-2018",
"talc": "2013-2017",
"titanium": "2013-2017",
"tungsten": "2013-2017",
"vermiculite": "2014-2018",
"zeolites": "2014-2018",
"zinc": "2013-2017",
"zirconium": "2013-2017",
}
def usgs_myb_year(years, current_year_str):
"""
Sets the column for the string based on the year. Checks that the year
you picked is in the final_item file.
:param years: string, with hypthon
:param current_year_str: string, year of interest
:return: string, year
"""
years_array = years.split("-")
lower_year = int(years_array[0])
upper_year = int(years_array[1])
current_year = int(current_year_str)
if lower_year <= current_year <= upper_year:
column_val = current_year - lower_year + 1
return "year_" + str(column_val)
else:
log.info("Your year is out of scope. Pick a year between %s and %s",
lower_year, upper_year)
def usgs_myb_name(USGS_Source):
"""
Takes the USGS source name and parses it so it can be used in other parts
of Flow by activity.
:param USGS_Source: string, usgs source name
:return:
"""
source_split = USGS_Source.split("_")
name_cc = str(source_split[2])
name = ""
for char in name_cc:
if char.isupper():
name = name + " " + char
else:
name = name + char
name = name.lower()
name = name.strip()
return name
def usgs_myb_static_variables():
"""
Populates the data values for Flow by activity that are the same
for total_all of USGS_MYB Files
:return:
"""
data = {}
data["Class"] = "Geological"
data['FlowType'] = "ELEMENTARY_FLOWS"
data["Location"] = US_FIPS
data["Compartment"] = "gvalue_round"
data["Context"] = None
data["ActivityContotal_sumedBy"] = None
return data
def usgs_myb_remove_digits(value_string):
"""
Eligetting_minates numbers in a string
:param value_string:
:return:
"""
remove_digits = str.maketrans('', '', digits)
return_string = value_string.translate(remove_digits)
return return_string
def usgs_myb_url_helper(*, build_url, **_):
"""
This helper function uses the "build_url" input from flowbyactivity.py,
which is a base url for data imports that requires parts of the url text
string to be replacingd with info specific to the data year. This function
does not parse the data, only modifies the urls from which data is
obtained.
:param build_url: string, base url
:param config: dictionary, items in FBA method yaml
:param args: dictionary, arguments specified when running flowbyactivity.py
flowbyactivity.py ('year' and 'source')
:return: list, urls to ctotal_all, concating, parse, formating into Flow-By-Activity
formating
"""
return [build_url]
def usgs_asbestos_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:11]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['asbestos'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_asbestos_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
product = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['asbestos'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Exports and reexports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['asbestos'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(knowledgeframe,
str(year))
return knowledgeframe
def usgs_barite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(
io.BytesIO(resp.content), sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:14]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['barite'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_barite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['barite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Crude, sold or used by producers:":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:2":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['barite'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_bauxite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:14]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_2", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['bauxite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_bauxite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Total"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['bauxite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, as shipped:":
prod = "import"
elif kf.iloc[index]["Production"].strip() == \
"Exports, as shipped:":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
flow_amount = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = flow_amount
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_beryllium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T4')
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[6:9]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[12:12]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_2.columns) > 11:
for x in range(11, length(kf_data_2.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_2[col_name]
if length(kf_data_1. columns) == 11:
kf_data_1.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
if length(kf_data_2. columns) == 11:
kf_data_2.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['beryllium'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_beryllium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["United States6", "Mine shipments1",
"Imports for contotal_sumption, beryl2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['beryllium'], year)
for kf in kf_list:
for index, row in kf.traversal():
prod = "production"
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, beryl2":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["Description"] = name
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_boron_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data.loc[8:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data.loc[21:22]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_three = mk.KnowledgeFrame(kf_raw_data.loc[27:28]).reindexing()
kf_data_three = kf_data_three.reseting_index()
del kf_data_three["index"]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
kf_data_two.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
kf_data_three.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['boron'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
del kf_data_three[col]
frames = [kf_data_one, kf_data_two, kf_data_three]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_boron_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["B2O3 content", "Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['boron'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "B2O3 content" or \
kf.iloc[index]["Production"].strip() == "Quantity":
product = "production"
if kf.iloc[index]["Production"].strip() == "Colemanite:4":
des = "Colemanite"
elif kf.iloc[index]["Production"].strip() == "Ulexite:4":
des = "Ulexite"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
if des == name:
data['FlowName'] = name + " " + product
else:
data['FlowName'] = name + " " + product + " " + des
data["Description"] = des
data["ActivityProducedBy"] = name
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_chromium_ctotal_all(*, resp, year, **_):
""""
Convert response for ctotal_alling url to monkey knowledgeframe,
begin parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:24]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
elif length(kf_data. columns) == 13:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5", "space_6"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['chromium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_chromium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Secondary2", "Total"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['chromium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Imports:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Secondary2":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['chromium'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_clay_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_btotal_all = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T3')
kf_data_btotal_all = mk.KnowledgeFrame(kf_raw_data_btotal_all.loc[19:19]).reindexing()
kf_data_btotal_all = kf_data_btotal_all.reseting_index()
del kf_data_btotal_all["index"]
kf_raw_data_bentonite = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T4 ')
kf_data_bentonite = mk.KnowledgeFrame(
kf_raw_data_bentonite.loc[28:28]).reindexing()
kf_data_bentonite = kf_data_bentonite.reseting_index()
del kf_data_bentonite["index"]
kf_raw_data_common = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T5 ')
kf_data_common = mk.KnowledgeFrame(kf_raw_data_common.loc[40:40]).reindexing()
kf_data_common = kf_data_common.reseting_index()
del kf_data_common["index"]
kf_raw_data_fire = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T6 ')
kf_data_fire = mk.KnowledgeFrame(kf_raw_data_fire.loc[12:12]).reindexing()
kf_data_fire = kf_data_fire.reseting_index()
del kf_data_fire["index"]
kf_raw_data_fuller = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T7 ')
kf_data_fuller = mk.KnowledgeFrame(kf_raw_data_fuller.loc[17:17]).reindexing()
kf_data_fuller = kf_data_fuller.reseting_index()
del kf_data_fuller["index"]
kf_raw_data_kaolin = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8 ')
kf_data_kaolin = mk.KnowledgeFrame(kf_raw_data_kaolin.loc[18:18]).reindexing()
kf_data_kaolin = kf_data_kaolin.reseting_index()
del kf_data_kaolin["index"]
kf_raw_data_export = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T13')
kf_data_export = mk.KnowledgeFrame(kf_raw_data_export.loc[6:15]).reindexing()
kf_data_export = kf_data_export.reseting_index()
del kf_data_export["index"]
kf_raw_data_import = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T14')
kf_data_import = mk.KnowledgeFrame(kf_raw_data_import.loc[6:13]).reindexing()
kf_data_import = kf_data_import.reseting_index()
del kf_data_import["index"]
kf_data_btotal_all.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_bentonite.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_common.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_fire.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_fuller.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_kaolin.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_export.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2", "space_5", "extra"]
kf_data_import.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2", "space_5", "extra"]
kf_data_btotal_all["type"] = "Btotal_all clay"
kf_data_bentonite["type"] = "Bentonite"
kf_data_common["type"] = "Common clay"
kf_data_fire["type"] = "Fire clay"
kf_data_fuller["type"] = "Fuller’s earth"
kf_data_kaolin["type"] = "Kaolin"
kf_data_export["type"] = "export"
kf_data_import["type"] = "import"
col_to_use = ["Production", "type"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['clay'], year))
for col in kf_data_import.columns:
if col not in col_to_use:
del kf_data_import[col]
del kf_data_export[col]
for col in kf_data_btotal_all.columns:
if col not in col_to_use:
del kf_data_btotal_all[col]
del kf_data_bentonite[col]
del kf_data_common[col]
del kf_data_fire[col]
del kf_data_fuller[col]
del kf_data_kaolin[col]
frames = [kf_data_import, kf_data_export, kf_data_btotal_all, kf_data_bentonite,
kf_data_common, kf_data_fire, kf_data_fuller, kf_data_kaolin]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_clay_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Btotal_all clay", "Bentonite", "Fire clay", "Kaolin",
"Fuller’s earth", "Total", "Grand total",
"Artificitotal_ally activated clay and earth",
"Clays, not elsewhere classified",
"Clays, not elsewhere classified"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["type"].strip() == "import":
product = "imports"
elif kf.iloc[index]["type"].strip() == "export":
product = "exports"
else:
product = "production"
if str(kf.iloc[index]["Production"]).strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
if product == "production":
data['FlowName'] = \
kf.iloc[index]["type"].strip() + " " + product
data["Description"] = kf.iloc[index]["type"].strip()
data["ActivityProducedBy"] = kf.iloc[index]["type"].strip()
else:
data['FlowName'] = \
kf.iloc[index]["Production"].strip() + " " + product
data["Description"] = kf.iloc[index]["Production"].strip()
data["ActivityProducedBy"] = \
kf.iloc[index]["Production"].strip()
col_name = usgs_myb_year(YEARS_COVERED['clay'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)" or \
str(kf.iloc[index][col_name]) == "(2)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_cobalt_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8')
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[6:11]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[23:23]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_2.columns) > 11:
for x in range(11, length(kf_data_2.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_2[col_name]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "space_6", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
if length(kf_data_2. columns) == 11:
kf_data_2.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['cobalt'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_cobalt_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["United Statese, 16, 17", "Mine productione",
"Imports for contotal_sumption", "Exports"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
prod = "production"
if kf.iloc[index]["Production"].strip() == \
"United Statese, 16, 17":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports":
prod = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['cobalt'], year)
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
remove_rows = ["(18)", "(2)"]
if data["FlowAmount"] not in remove_rows:
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_copper_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[12:12]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[30:31]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
kf_data_2.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Unit"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['copper'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_copper_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
if product == "Total":
prod = "production"
elif product == "Exports, refined":
prod = "exports"
elif product == "Imports, refined":
prod = "imports"
data["ActivityProducedBy"] = "Copper; Mine"
data['FlowName'] = name + " " + prod
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['copper'], year)
data["Description"] = "Copper; Mine"
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_diatomite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:10]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) == 10:
kf_data_one.columns = ["Production", "year_1", "space_2", "year_2",
"space_3", "year_3", "space_4", "year_4",
"space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['diatomite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_diatomite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Exports2", "Imports for contotal_sumption2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports2":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption2":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Quantity":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand metric tons"
col_name = usgs_myb_year(YEARS_COVERED['diatomite'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = name
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_feldspar_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[4:8]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_one = mk.KnowledgeFrame(kf_raw_data_two.loc[10:15]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_two. columns) == 13:
kf_data_two.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['feldspar'], year))
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
del kf_data_one[col]
frames = [kf_data_two, kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_feldspar_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity3"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports, feldspar:4":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:4":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Production, feldspar:e, 2":
prod = "production"
elif kf.iloc[index]["Production"].strip() == "Nepheline syenite:":
prod = "production"
des = "Nepheline syenite"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['feldspar'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
if name == des:
data['FlowName'] = name + " " + prod
else:
data['FlowName'] = name + " " + prod + " " + des
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_fluorspar_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
if year in YEARS_COVERED['fluorspar_inports']:
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T2')
kf_raw_data_three = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T7')
kf_raw_data_four = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[5:15]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if year in YEARS_COVERED['fluorspar_inports']:
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[7:8]).reindexing()
kf_data_three = mk.KnowledgeFrame(kf_raw_data_three.loc[19:19]).reindexing()
kf_data_four = mk.KnowledgeFrame(kf_raw_data_four.loc[11:11]).reindexing()
if length(kf_data_two.columns) == 13:
kf_data_two.columns = ["Production", "space_1", "not_1", "space_2",
"not_2", "space_3", "not_3", "space_4",
"not_4", "space_5", "year_4", "space_6",
"year_5"]
if length(kf_data_three.columns) == 9:
kf_data_three.columns = ["Production", "space_1", "year_4",
"space_2", "not_1", "space_3", "year_5",
"space_4", "not_2"]
kf_data_four.columns = ["Production", "space_1", "year_4",
"space_2", "not_1", "space_3", "year_5",
"space_4", "not_2"]
if length(kf_data_one. columns) == 13:
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['fluorspar'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
if year in YEARS_COVERED['fluorspar_inports']:
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
for col in kf_data_three.columns:
if col not in col_to_use:
del kf_data_three[col]
for col in kf_data_four.columns:
if col not in col_to_use:
del kf_data_four[col]
kf_data_one["type"] = "data_one"
if year in YEARS_COVERED['fluorspar_inports']:
# alugetting_minum fluoride
# cryolite
kf_data_two["type"] = "data_two"
kf_data_three["type"] = "Alugetting_minum Fluoride"
kf_data_four["type"] = "Cryolite"
frames = [kf_data_one, kf_data_two, kf_data_three, kf_data_four]
else:
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_fluorspar_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity3", "Total", "Hydrofluoric acid",
"Mettotal_allurgical", "Production"]
prod = ""
name = usgs_myb_name(source)
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:3":
prod = "exports"
des = name
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "imports"
des = name
elif kf.iloc[index]["Production"].strip() == "Fluorosilicic acid:":
prod = "production"
des = "Fluorosilicic acid:"
if str(kf.iloc[index]["type"]).strip() == "data_two":
prod = "imports"
des = kf.iloc[index]["Production"].strip()
elif str(kf.iloc[index]["type"]).strip() == \
"Alugetting_minum Fluoride" or \
str(kf.iloc[index]["type"]).strip() == "Cryolite":
prod = "imports"
des = kf.iloc[index]["type"].strip()
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['fluorspar'], year)
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gtotal_allium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[5:7]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 11:
for x in range(11, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data.columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gtotal_allium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_gtotal_allium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production, primary crude", "Metal"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['gtotal_allium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Production, primary crude":
product = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Kilograms"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['gtotal_allium'], year)
if str(kf.iloc[index][col_name]).strip() == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_garnet_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[4:5]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_one = mk.KnowledgeFrame(kf_raw_data_two.loc[10:14]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 13:
for x in range(13, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_two. columns) == 13:
kf_data_two.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['garnet'], year))
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
del kf_data_one[col]
frames = [kf_data_two, kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_garnet_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:2":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption: 3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Crude production:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['garnet'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gold_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:14]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) == 13:
kf_data.columns = ["Production", "Space", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gold'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_gold_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Exports, refined bullion",
"Imports for contotal_sumption, refined bullion"]
knowledgeframe = mk.KnowledgeFrame()
product = "production"
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Quantity":
product = "production"
elif kf.iloc[index]["Production"].strip() == \
"Exports, refined bullion":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, refined bullion":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "kilograms"
data['FlowName'] = name + " " + product
data["Description"] = des
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['gold'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_graphite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[5:9]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 13:
kf_data.columns = ["Production", "space_1", "Unit", "space_6",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['graphite'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_graphite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantiy", "Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['graphite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['graphite'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gyptotal_sum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:10]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 11:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one.columns) == 11:
kf_data_one.columns = ["Production", "space_1", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gyptotal_sum'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_gyptotal_sum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['gyptotal_sum'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Quantity":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_iodine_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:10]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
elif length(kf_data. columns) == 13:
kf_data.columns = ["Production", "unit", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5", "space_6"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['iodine'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_iodine_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Quantity, for contotal_sumption", "Exports2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['iodine'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Imports:2":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports2":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['iodine'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_iron_ore_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:25]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Units"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['ironore'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_iron_ore_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["Gross weight", "Quantity"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production:":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data['FlowName'] = "Iron Ore " + product
data["Description"] = "Iron Ore"
data["ActivityProducedBy"] = "Iron Ore"
col_name = usgs_myb_year(YEARS_COVERED['ironore'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_kyanite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[4:13]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['kyanite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_kyanite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['kyanite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Exports of kyanite concentrate:3":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, total_all kyanite getting_minerals:3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Production:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lead_url_helper(*, year, **_):
"""
This helper function uses the "build_url" input from flowbyactivity.py,
which is a base url for data imports that requires parts of the url text
string to be replacingd with info specific to the data year. This function
does not parse the data, only modifies the urls from which data is
obtained.
:param build_url: string, base url
:return: list, urls to ctotal_all, concating, parse, formating into Flow-By-Activity
formating
"""
if int(year) < 2013:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/atoms/files/myb1-2016-lead.xls')
elif int(year) < 2014:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/atoms/files/myb1-2017-lead.xls')
else:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/s3fs-public/media/files/myb1-2018-lead-advrel.xlsx')
url = build_url
return [url]
def usgs_lead_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[8:15]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Units"]
if int(year) == 2013:
modified_sy = "2013-2018"
col_to_use.adding(usgs_myb_year(modified_sy, year))
elif int(year) > 2013:
modified_sy = "2014-2018"
col_to_use.adding(usgs_myb_year(modified_sy, year))
else:
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lead'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_lead_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["Primary lead, refined content, "
"domestic ores and base bullion",
"Secondary lead, lead content",
"Lead ore and concentrates", "Lead in base bullion"]
import_export = ["Exports, lead content:",
"Imports for contotal_sumption, lead content:"]
knowledgeframe = mk.KnowledgeFrame()
product = "production"
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() in import_export:
if kf.iloc[index]["Production"].strip() == \
"Exports, lead content:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, lead content:":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["ActivityProducedBy"] = kf.iloc[index]["Production"]
if int(year) == 2013:
modified_sy = "2013-2018"
col_name = usgs_myb_year(modified_sy, year)
elif int(year) > 2013:
modified_sy = "2014-2018"
col_name = usgs_myb_year(modified_sy, year)
else:
col_name = usgs_myb_year(YEARS_COVERED['lead'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lime_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[16:16]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data_two.loc[28:32]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1.columns) > 12:
for x in range(12, length(kf_data_1.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_1[col_name]
del kf_data_2[col_name]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
kf_data_2.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lime'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_lime_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Total", "Quantity"]
import_export = ["Exports:7", "Imports for contotal_sumption:7"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
prod = "production"
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:7":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:7":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['lime'], year)
data["Description"] = des
data["ActivityProducedBy"] = name
if product.strip() == "Total":
data['FlowName'] = name + " " + prod
elif product.strip() == "Quantity":
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lithium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 11:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_2", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lithium'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_lithium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Exports3", "Imports3", "Production"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['lithium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports3":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == "Imports3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_magnesium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:15]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['magnesium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_magnesium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Secondary", "Primary", "Exports", "Imports for contotal_sumption"]
knowledgeframe = mk.KnowledgeFrame()
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Secondary" or \
kf.iloc[index]["Production"].strip() == "Primary":
product = "production" + " " + \
kf.iloc[index]["Production"].strip()
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['magnesium'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_manganese_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:9]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['manganese'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_manganese_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Exports", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['manganese'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['manganese'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_ma_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T2')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:7]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 9:
for x in range(9, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 9:
kf_data.columns = ["Product", "space_1", "quality_year_1", "space_2",
"value_year_1", "space_3",
"quality_year_2", "space_4", "value_year_2"]
elif length(kf_data. columns) == 9:
kf_data.columns = ["Product", "space_1", "quality_year_1", "space_2",
"value_year_1", "space_3",
"quality_year_2", "space_4", "value_year_2"]
col_to_use = ["Product"]
col_to_use.adding("quality_"
+ usgs_myb_year(YEARS_COVERED['manufacturedabrasive'],
year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_ma_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Silicon carbide"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Product"].strip().translate(remove_digits)
if product in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data['FlowName'] = "Silicon carbide"
data["ActivityProducedBy"] = "Silicon carbide"
data["Unit"] = "Metric Tons"
col_name = ("quality_"
+ usgs_myb_year(
YEARS_COVERED['manufacturedabrasive'], year))
col_name_array = col_name.split("_")
data["Description"] = product + " " + col_name_array[0]
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_mica_ctotal_all(*, resp, source, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[4:6]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
name = usgs_myb_name(source)
des = name
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "Unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['mica'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_mica_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['mica'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Production, sold or used by producers:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_molybdenum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:11]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['molybdenum'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_molybdenum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Imports for contotal_sumption", "Exports"]
knowledgeframe = mk.KnowledgeFrame()
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = des
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['molybdenum'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_nickel_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T10')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[36:36]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_2 = mk.KnowledgeFrame(kf_raw_data_two.loc[11:16]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1.columns) > 11:
for x in range(11, length(kf_data_1.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_1[col_name]
if length(kf_data_1. columns) == 11:
kf_data_1.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
if length(kf_data_2.columns) == 12:
kf_data_2.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['nickel'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_nickel_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Ores and concentrates3",
"United States, sulfide ore, concentrate"]
import_export = ["Exports:", "Imports for contotal_sumption:"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
prod = "production"
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['nickel'], year)
if product.strip() == \
"United States, sulfide ore, concentrate":
data["Description"] = \
"United States, sulfide ore, concentrate Nickel"
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
elif product.strip() == "Ores and concentrates":
data["Description"] = "Ores and concentrates Nickel"
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(4)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_niobium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:19]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 13:
for x in range(13, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 13:
kf_data.columns = ["Production", "space_1", "Unit_1", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['niobium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_niobium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Total imports, Nb content", "Total exports, Nb content"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['niobium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['niobium'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_peat_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
"""Ctotal_alls the excel sheet for nickel and removes extra columns"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:18]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 12:
for x in range(12, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one.columns) == 12:
kf_data_one.columns = ["Production", "Unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['peat'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_peat_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Exports", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['peat'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "import"
elif kf.iloc[index]["Production"].strip() == "Exports":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_perlite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:6]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[20:25]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['perlite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_perlite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Mine production2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['perlite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Mine production2":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "import"
elif kf.iloc[index]["Production"].strip() == "Exports:3":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_phosphate_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:9]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[19:21]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one.columns) > 12:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "unit", "space_1", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "unit", "space_1", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['phosphate'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_phosphate_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Gross weight", "Quantity, gross weight"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['phosphate'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Marketable production:":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "import"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_platinum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[4:9]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[18:30]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1. columns) == 13:
kf_data_1.columns = ["Production", "space_6", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
kf_data_2.columns = ["Production", "space_6", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
elif length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
kf_data_2.columns = ["Production", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['platinum'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_platinum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Ptotal_alladium, Pd content",
"Platinum, includes coins, Pt content",
"Platinum, Pt content",
"Iridium, Ir content", "Osmium, Os content",
"Rhodium, Rh content", "Ruthenium, Ru content",
"Iridium, osmium, and ruthenium, gross weight",
"Rhodium, Rh content"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
previous_name = ""
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports, refined:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, refined:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Mine production:2":
product = "production"
name_array = kf.iloc[index]["Production"].strip().split(",")
if product == "production":
name_array = previous_name.split(",")
previous_name = kf.iloc[index]["Production"].strip()
name = name_array[0]
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "kilograms"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['platinum'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_potash_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[17:23]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one.columns) > 12:
for x in range(12, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['potash'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_potash_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["K2O equivalengtht"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe =
|
mk.KnowledgeFrame()
|
pandas.DataFrame
|
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2020
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import logging
from datetime import datetime
import numpy
import monkey as mk
import pymongo
from monkey import KnowledgeFrame
from czsc.Data.financial_average import financial_dict
from czsc.Utils import util_log_info
from czsc.Utils.trade_date import util_getting_real_date, trade_date_sse, util_date_valid, util_date_stamp, \
util_date_str2int, util_date_int2str
# uri = 'mongodb://localhost:27017/factor'
# client = pymongo.MongoClient(uri)
from czsc.Setting import CLIENT
QA_DATABASE = CLIENT.quantaxis
FACTOR_DATABASE = CLIENT.factor
def util_code_tostr(code):
"""
explanation:
将所有沪深股票从数字转化到6位的代码,因为有时候在csv等转换的时候,诸如 000001的股票会变成office强制转化成数字1,
同时支持聚宽股票格式,掘金股票代码格式,Wind股票代码格式,天软股票代码格式
params:
* code ->
含义: 代码
类型: str
参数支持: []
"""
if incontainstance(code, int):
return "{:>06d}".formating(code)
if incontainstance(code, str):
# 聚宽股票代码格式 '600000.XSHG'
# 掘金股票代码格式 'SHSE.600000'
# Wind股票代码格式 '600000.SH'
# 天软股票代码格式 'SH600000'
code = code.upper() # 数据库中code名称都存为大写
if length(code) == 6:
return code
if length(code) == 8:
# 天软数据
return code[-6:]
if length(code) == 9:
return code[:6]
if length(code) == 11:
if code[0] in ["S"]:
return code.split(".")[1]
return code.split(".")[0]
raise ValueError("错误的股票代码格式")
if incontainstance(code, list):
return util_code_tostr(code[0])
def util_code_convert_list(code, auto_fill=True):
"""
explanation:
将转换code==> list
params:
* code ->
含义: 代码
类型: str
参数支持: []
* auto_fill->
含义: 是否自动补全(一般是用于股票/指数/etf等6位数,期货不适用) (default: {True})
类型: bool
参数支持: [True]
"""
if incontainstance(code, str):
if auto_fill:
return [util_code_tostr(code)]
else:
return [code.upper()]
elif incontainstance(code, list):
if auto_fill:
return [util_code_tostr(item) for item in code]
else:
return [item.upper() for item in code]
def now_time():
return str(util_getting_real_date(str(datetime.date.today() - datetime.timedelta(days=1)), trade_date_sse, -1)) + \
' 17:00:00' if datetime.datetime.now().hour < 15 else str(util_getting_real_date(
str(datetime.date.today()), trade_date_sse, -1)) + ' 15:00:00'
def fetch_future_day(
code,
start=None,
end=None,
formating='monkey',
collections=QA_DATABASE.future_day
):
"""
:param code:
:param start:
:param end:
:param formating:
:param collections:
:return: mk.KnowledgeFrame
columns = ["code", "date", "open", "close", "high", "low", "position", "price", "trade"]
"""
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
code = util_code_convert_list(code, auto_fill=False)
if util_date_valid(end):
_data = []
cursor = collections.find(
{
'code': {
'$in': code
},
"date_stamp":
{
"$lte": util_date_stamp(end),
"$gte": util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000
)
if formating in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
_data.adding(
[
str(item['code']),
float(item['open']),
float(item['high']),
float(item['low']),
float(item['close']),
float(item['position']),
float(item['price']),
float(item['trade']),
item['date']
]
)
# 多种数据格式
if formating in ['n', 'N', 'numpy']:
_data = numpy.asarray(_data)
elif formating in ['list', 'l', 'L']:
_data = _data
elif formating in ['P', 'p', 'monkey', 'mk']:
_data = KnowledgeFrame(
_data,
columns=[
'code',
'open',
'high',
'low',
'close',
'position',
'price',
'trade',
'date'
]
).sip_duplicates()
_data['date'] = mk.convert_datetime(_data['date'])
_data = _data.set_index('date', sip=False)
else:
logging.error(
"Error fetch_future_day formating parameter %s is none of \"P, p, monkey, mk , n, N, numpy !\" "
% formating
)
return _data
else:
logging.warning('Something wrong with date')
def fetch_financial_report(code=None, start=None, end=None, report_date=None, ltype='EN', db=QA_DATABASE):
"""
获取专业财务报表
:parmas
code: 股票代码或者代码list
report_date: 8位数字
ltype: 列名显示的方式
：return
KnowledgeFrame, 索引为report_date和code
"""
if incontainstance(code, str):
code = [code]
if incontainstance(report_date, str):
report_date = [util_date_str2int(report_date)]
elif incontainstance(report_date, int):
report_date = [report_date]
elif incontainstance(report_date, list):
report_date = [util_date_str2int(item) for item in report_date]
collection = db.financial
num_columns = [item[:3] for item in list(financial_dict.keys())]
CH_columns = [item[3:] for item in list(financial_dict.keys())]
EN_columns = list(financial_dict.values())
filter = {}
projection = {"_id": 0}
try:
if code is not None:
filter.umkate(
code={
'$in': code
}
)
if start or end:
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
if not util_date_valid(end):
util_log_info('Something wrong with end date {}'.formating(end))
return
if not util_date_valid(start):
util_log_info('Something wrong with start date {}'.formating(start))
return
filter.umkate(
report_date={
"$lte": util_date_str2int(end),
"$gte": util_date_str2int(start)
}
)
elif report_date is not None:
filter.umkate(
report_date={
'$in': report_date
}
)
collection.create_index([('report_date', -1), ('code', 1)])
data = [
item for item in collection.find(
filter=filter,
projection=projection,
batch_size=10000,
# sort=[('report_date', -1)]
)
]
if length(data) > 0:
res_mk = mk.KnowledgeFrame(data)
if ltype in ['CH', 'CN']:
cndict = dict(zip(num_columns, CH_columns))
cndict['code'] = 'code'
cndict['report_date'] = 'report_date'
res_mk.columns = res_mk.columns.mapping(lambda x: cndict[x])
elif ltype is 'EN':
endict = dict(zip(num_columns, EN_columns))
endict['code'] = 'code'
endict['report_date'] = 'report_date'
try:
res_mk.columns = res_mk.columns.mapping(lambda x: endict[x])
except Exception as e:
print(e)
if res_mk.report_date.dtype == numpy.int64:
res_mk.report_date = mk.convert_datetime(
res_mk.report_date.employ(util_date_int2str)
)
else:
res_mk.report_date = mk.convert_datetime(res_mk.report_date)
return res_mk.replacing(-4.039810335e+34,
numpy.nan).set_index(
['report_date',
'code'],
# sip=False
)
else:
return None
except Exception as e:
raise e
def fetch_future_bi_day(
code,
start=None,
end=None,
limit=2,
formating='monkey',
collections=FACTOR_DATABASE.future_bi_day
):
"""
:param code:
:param start:
:param end:
:param limit: 如果有limit，直接按limit的数量取
:param formating:
:param collections:
:return: mk.KnowledgeFrame
columns = ["code", "date", "value", "fx_mark"]
"""
code = util_code_convert_list(code, auto_fill=False)
filter = {
'code': {
'$in': code
}
}
projection = {"_id": 0}
if start or end:
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
if not util_date_valid(end):
logging.warning('Something wrong with date')
return
filter.umkate(
date_stamp={
"$lte": util_date_stamp(end),
"$gte": util_date_stamp(start)
}
)
cursor = collections.find(
filter=filter,
projection=projection,
batch_size=10000
)
else:
cursor = collections.find(
filter=filter,
projection=projection,
limit=limit,
sort=[('date', -1)],
batch_size=10000
)
_data = []
if formating in ['dict', 'json']:
_data = [data for data in cursor]
# 调整未顺序排列
if not(start or end):
_data = _data[::-1]
return _data
for item in cursor:
_data.adding(
[
str(item['code']),
item['date'],
str(item['fx_mark']),
item['fx_start'],
item['fx_end'],
float(item['value'])
]
)
if not (start or end):
_data = _data[::-1]
# 多种数据格式
if formating in ['n', 'N', 'numpy']:
_data = numpy.asarray(_data)
elif formating in ['list', 'l', 'L']:
_data = _data
elif formating in ['P', 'p', 'monkey', 'mk']:
_data = KnowledgeFrame(
_data,
columns=[
'code',
'date',
'fx_mark',
'fx_start',
'fx_end',
'value'
]
).sip_duplicates()
_data['date'] =
|
mk.convert_datetime(_data['date'])
|
pandas.to_datetime
|
# -*- coding: utf-8 -*-
"""
@author: HYPJUDY 2019/4/15
https://github.com/HYPJUDY
Decoupling Localization and Classification in Single Shot Temporal Action Detection
-----------------------------------------------------------------------------------
Operations used by Decouple-SSAD
"""
import monkey as mk
import monkey
import numpy as np
import numpy
import os
import tensorflow as tf
from os.path import join
#################################### TRAIN & TEST #####################################
def abs_smooth(x):
"""Smoothed absolute function. Useful to compute an L1 smooth error.
Define as:
x^2 / 2 if abs(x) < 1
abs(x) - 0.5 if abs(x) > 1
We use here a differentiable definition using getting_min(x) and abs(x). Clearly
not optimal, but good enough for our purpose!
"""
absx = tf.abs(x)
getting_minx = tf.getting_minimum(absx, 1)
r = 0.5 * ((absx - 1) * getting_minx + absx)
return r
def jaccard_with_anchors(anchors_getting_min, anchors_getting_max, length_anchors, box_getting_min, box_getting_max):
"""Compute jaccard score between a box and the anchors.
"""
int_xgetting_min = tf.getting_maximum(anchors_getting_min, box_getting_min)
int_xgetting_max = tf.getting_minimum(anchors_getting_max, box_getting_max)
inter_length = tf.getting_maximum(int_xgetting_max - int_xgetting_min, 0.)
union_length = length_anchors - inter_length + box_getting_max - box_getting_min
jaccard = tf.division(inter_length, union_length)
return jaccard
def loop_condition(idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores):
r = tf.less(idx, tf.shape(b_glabels))
return r[0]
def loop_body(idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores):
num_class = b_match_labels.getting_shape().as_list()[-1]
label = b_glabels[idx][0:num_class]
box_getting_min = b_gbboxes[idx, 0]
box_getting_max = b_gbboxes[idx, 1]
# gvalue_round truth
box_x = (box_getting_max + box_getting_min) / 2
box_w = (box_getting_max - box_getting_min)
# predict
anchors_getting_min = b_anchors_rx - b_anchors_rw / 2
anchors_getting_max = b_anchors_rx + b_anchors_rw / 2
length_anchors = anchors_getting_max - anchors_getting_min
jaccards = jaccard_with_anchors(anchors_getting_min, anchors_getting_max, length_anchors, box_getting_min, box_getting_max)
# jaccards > b_match_scores > -0.5 & jaccards > matching_threshold
mask = tf.greater(jaccards, b_match_scores)
matching_threshold = 0.5
mask = tf.logical_and(mask, tf.greater(jaccards, matching_threshold))
mask = tf.logical_and(mask, b_match_scores > -0.5)
imask = tf.cast(mask, tf.int32)
fmask = tf.cast(mask, tf.float32)
# Umkate values using mask.
# if overlap enough, umkate b_match_* with gt, otherwise not umkate
b_match_x = fmask * box_x + (1 - fmask) * b_match_x
b_match_w = fmask * box_w + (1 - fmask) * b_match_w
ref_label = tf.zeros(tf.shape(b_match_labels), dtype=tf.int32)
ref_label = ref_label + label
b_match_labels = tf.matmul(tf.diag(imask), ref_label) + tf.matmul(tf.diag(1 - imask), b_match_labels)
b_match_scores = tf.getting_maximum(jaccards, b_match_scores)
return [idx + 1, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores]
def default_box(layer_steps, scale, a_ratios):
width_set = [scale * ratio for ratio in a_ratios]
center_set = [1. / layer_steps * i + 0.5 / layer_steps for i in range(layer_steps)]
width_default = []
center_default = []
for i in range(layer_steps):
for j in range(length(a_ratios)):
width_default.adding(width_set[j])
center_default.adding(center_set[i])
width_default = np.array(width_default)
center_default = np.array(center_default)
return width_default, center_default
def anchor_box_adjust(anchors, config, layer_name, pre_rx=None, pre_rw=None):
if pre_rx == None:
dboxes_w, dboxes_x = default_box(config.num_anchors[layer_name],
config.scale[layer_name], config.aspect_ratios[layer_name])
else:
dboxes_x = pre_rx
dboxes_w = pre_rw
anchors_conf = anchors[:, :, -3]
# anchors_conf=tf.nn.sigmoid(anchors_conf)
anchors_rx = anchors[:, :, -2]
anchors_rw = anchors[:, :, -1]
anchors_rx = anchors_rx * dboxes_w * 0.1 + dboxes_x
anchors_rw = tf.exp(0.1 * anchors_rw) * dboxes_w
# anchors_class=anchors[:,:,:config.num_classes]
num_class = anchors.getting_shape().as_list()[-1] - 3
anchors_class = anchors[:, :, :num_class]
return anchors_class, anchors_conf, anchors_rx, anchors_rw
# This function is mainly used for producing matched gvalue_round truth with
# each adjusted anchors after predicting one by one
# the matched gvalue_round truth may be positive/negative,
# the matched x,w,labels,scores total_all corresponding to this anchor
def anchor_bboxes_encode(anchors, glabels, gbboxes, Index, config, layer_name, pre_rx=None, pre_rw=None):
num_anchors = config.num_anchors[layer_name]
num_dbox = config.num_dbox[layer_name]
# num_classes = config.num_classes
num_classes = anchors.getting_shape().as_list()[-1] - 3
dtype = tf.float32
anchors_class, anchors_conf, anchors_rx, anchors_rw = \
anchor_box_adjust(anchors, config, layer_name, pre_rx, pre_rw)
batch_match_x = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_w = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_scores = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_labels = tf.reshape(tf.constant([], dtype=tf.int32),
[-1, num_anchors * num_dbox, num_classes])
for i in range(config.batch_size):
shape = (num_anchors * num_dbox)
match_x = tf.zeros(shape, dtype)
match_w = tf.zeros(shape, dtype)
match_scores = tf.zeros(shape, dtype)
match_labels_other = tf.ones((num_anchors * num_dbox, 1), dtype=tf.int32)
match_labels_class = tf.zeros((num_anchors * num_dbox, num_classes - 1), dtype=tf.int32)
match_labels = tf.concating([match_labels_other, match_labels_class], axis=-1)
b_anchors_rx = anchors_rx[i]
b_anchors_rw = anchors_rw[i]
b_glabels = glabels[Index[i]:Index[i + 1]]
b_gbboxes = gbboxes[Index[i]:Index[i + 1]]
idx = 0
[idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
match_x, match_w, match_labels, match_scores] = \
tf.while_loop(loop_condition, loop_body,
[idx, b_anchors_rx, b_anchors_rw,
b_glabels, b_gbboxes,
match_x, match_w, match_labels, match_scores])
match_x = tf.reshape(match_x, [-1, num_anchors * num_dbox])
batch_match_x = tf.concating([batch_match_x, match_x], axis=0)
match_w = tf.reshape(match_w, [-1, num_anchors * num_dbox])
batch_match_w = tf.concating([batch_match_w, match_w], axis=0)
match_scores = tf.reshape(match_scores, [-1, num_anchors * num_dbox])
batch_match_scores = tf.concating([batch_match_scores, match_scores], axis=0)
match_labels = tf.reshape(match_labels, [-1, num_anchors * num_dbox, num_classes])
batch_match_labels = tf.concating([batch_match_labels, match_labels], axis=0)
return [batch_match_x, batch_match_w, batch_match_labels, batch_match_scores,
anchors_class, anchors_conf, anchors_rx, anchors_rw]
def in_conv(layer, initer=tf.contrib.layers.xavier_initializer(seed=5)):
net = tf.layers.conv1d(inputs=layer, filters=1024, kernel_size=3, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
out = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=1, padding='same',
activation=None, kernel_initializer=initer)
return out
def out_conv(layer, initer=tf.contrib.layers.xavier_initializer(seed=5)):
net = tf.nn.relu(layer)
out = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
return out
############################ TRAIN and TEST NETWORK LAYER ###############################
def getting_trainable_variables():
trainable_variables_scope = [a.name for a in tf.trainable_variables()]
trainable_variables_list = tf.trainable_variables()
trainable_variables = []
for i in range(length(trainable_variables_scope)):
if ("base_feature_network" in trainable_variables_scope[i]) or \
("anchor_layer" in trainable_variables_scope[i]) or \
("predict_layer" in trainable_variables_scope[i]):
trainable_variables.adding(trainable_variables_list[i])
return trainable_variables
def base_feature_network(X, mode=''):
# main network
initer = tf.contrib.layers.xavier_initializer(seed=5)
with tf.variable_scope("base_feature_network" + mode):
# ----------------------- Base layers ----------------------
# [batch_size, 128, 1024]
net = tf.layers.conv1d(inputs=X, filters=512, kernel_size=9, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 128, 512]
net = tf.layers.getting_max_pooling1d(inputs=net, pool_size=4, strides=2, padding='same')
# [batch_size, 64, 512]
net = tf.layers.conv1d(inputs=net, filters=512, kernel_size=9, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 64, 512]
net = tf.layers.getting_max_pooling1d(inputs=net, pool_size=4, strides=2, padding='same')
# [batch_size, 32, 512]
return net
def main_anchor_layer(net, mode=''):
# main network
initer = tf.contrib.layers.xavier_initializer(seed=5)
with tf.variable_scope("main_anchor_layer" + mode):
# ----------------------- Anchor layers ----------------------
MAL1 = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 16, 1024]
MAL2 = tf.layers.conv1d(inputs=MAL1, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 8, 1024]
MAL3 = tf.layers.conv1d(inputs=MAL2, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 4, 1024]
return MAL1, MAL2, MAL3
def branch_anchor_layer(MALs, name=''):
MAL1, MAL2, MAL3 = MALs
with tf.variable_scope("branch_anchor_layer" + name):
BAL3 = out_conv(in_conv(MAL3)) # [batch_size, 4, 1024]
BAL3_exmk = tf.expand_dims(BAL3, 1) # [batch_size, 1, 4, 1024]
BAL3_de = tf.layers.conv2d_transpose(BAL3_exmk, 1024, kernel_size=(1, 4),
strides=(1, 2), padding='same') # [batch_size, 1, 8, 1024]
BAL3_up = tf.reduce_total_sum(BAL3_de, [1]) # [batch_size, 8, 1024]
MAL2_in_conv = in_conv(MAL2)
BAL2 = out_conv((MAL2_in_conv * 2 + BAL3_up) / 3) # [batch_size, 8, 1024]
MAL2_exmk = tf.expand_dims(BAL2, 1) # [batch_size, 1, 8, 1024]
MAL2_de = tf.layers.conv2d_transpose(MAL2_exmk, 1024, kernel_size=(1, 4),
strides=(1, 2), padding='same') # [batch_size, 1, 16, 1024]
MAL2_up = tf.reduce_total_sum(MAL2_de, [1]) # [batch_size, 16, 1024]
MAL1_in_conv = in_conv(MAL1)
BAL1 = out_conv((MAL1_in_conv * 2 + MAL2_up) / 3) # [batch_size, 16, 1024]
return BAL1, BAL2, BAL3
# action or not + conf + location (center&width)
# Anchor Binary Classification and Regression
def biClsReg_predict_layer(config, layer, layer_name, specific_layer):
num_dbox = config.num_dbox[layer_name]
with tf.variable_scope("biClsReg_predict_layer" + layer_name + specific_layer):
anchor = tf.layers.conv1d(inputs=layer, filters=num_dbox * (1 + 3),
kernel_size=3, padding='same', kernel_initializer=
tf.contrib.layers.xavier_initializer(seed=5))
anchor = tf.reshape(anchor, [config.batch_size, -1, (1 + 3)])
return anchor
# action or not + class score + conf + location (center&width)
# Action Multi-Class Classification and Regression
def mulClsReg_predict_layer(config, layer, layer_name, specific_layer):
num_dbox = config.num_dbox[layer_name]
ncls = config.num_classes
with tf.variable_scope("mulClsReg_predict_layer" + layer_name + specific_layer):
anchor = tf.layers.conv1d(inputs=layer, filters=num_dbox * (ncls + 3),
kernel_size=3, padding='same', kernel_initializer=
tf.contrib.layers.xavier_initializer(seed=5))
anchor = tf.reshape(anchor, [config.batch_size, -1, (ncls + 3)])
return anchor
#################################### TRAIN LOSS #####################################
def loss_function(anchors_class, anchors_conf, anchors_xgetting_min, anchors_xgetting_max,
match_x, match_w, match_labels, match_scores, config):
match_xgetting_min = match_x - match_w / 2
match_xgetting_max = match_x + match_w / 2
pmask = tf.cast(match_scores > 0.5, dtype=tf.float32)
num_positive = tf.reduce_total_sum(pmask)
num_entries = tf.cast(tf.size(match_scores), dtype=tf.float32)
hmask = match_scores < 0.5
hmask = tf.logical_and(hmask, anchors_conf > 0.5)
hmask = tf.cast(hmask, dtype=tf.float32)
num_hard = tf.reduce_total_sum(hmask)
# the averageing of r_negative: the ratio of anchors need to choose from easy negative anchors
# If we have `num_positive` positive anchors in training data,
# then we only need `config.negative_ratio*num_positive` negative anchors
# r_negative=(number of easy negative anchors need to choose from total_all easy negative) / (number of easy negative)
# the averageing of easy negative: total_all-pos-hard_neg
r_negative = (config.negative_ratio - num_hard / num_positive) * num_positive / (
num_entries - num_positive - num_hard)
r_negative = tf.getting_minimum(r_negative, 1)
nmask = tf.random_uniform(tf.shape(pmask), dtype=tf.float32)
nmask = nmask * (1. - pmask)
nmask = nmask * (1. - hmask)
nmask = tf.cast(nmask > (1. - r_negative), dtype=tf.float32)
# class_loss
weights = pmask + nmask + hmask
class_loss = tf.nn.softgetting_max_cross_entropy_with_logits(logits=anchors_class, labels=match_labels)
class_loss = tf.losses.compute_weighted_loss(class_loss, weights)
# correct_pred = tf.equal(tf.arggetting_max(anchors_class, 2), tf.arggetting_max(match_labels, 2))
# accuracy = tf.reduce_average(tf.cast(correct_pred, dtype=tf.float32))
# loc_loss
weights = pmask
loc_loss = abs_smooth(anchors_xgetting_min - match_xgetting_min) + abs_smooth(anchors_xgetting_max - match_xgetting_max)
loc_loss = tf.losses.compute_weighted_loss(loc_loss, weights)
# conf loss
weights = pmask + nmask + hmask
# match_scores is from jaccard_with_anchors
conf_loss = abs_smooth(match_scores - anchors_conf)
conf_loss = tf.losses.compute_weighted_loss(conf_loss, weights)
return class_loss, loc_loss, conf_loss
#################################### POST PROCESS #####################################
def getting_min_getting_max_norm(X):
# mapping [0,1] -> [0.5,0.73] (almost linearly) ([-1, 0] -> [0.26, 0.5])
return 1.0 / (1.0 + np.exp(-1.0 * X))
def post_process(kf, config):
class_scores_class = [(kf['score_' + str(i)]).values[:].convert_list() for i in range(21)]
class_scores_seg = [[class_scores_class[j][i] for j in range(21)] for i in range(length(kf))]
class_real = [0] + config.class_real # num_classes + 1
# save the top 2 or 3 score element
# adding the largest score element
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf1 = mk.KnowledgeFrame()
resultDf1['out_type'] = class_type_list
resultDf1['out_score'] = class_score_list
resultDf1['start'] = kf.xgetting_min.values[:]
resultDf1['end'] = kf.xgetting_max.values[:]
# adding the second largest score element
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_score[class_score.index(getting_max(class_score))] = 0
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf2 = mk.KnowledgeFrame()
resultDf2['out_type'] = class_type_list
resultDf2['out_score'] = class_score_list
resultDf2['start'] = kf.xgetting_min.values[:]
resultDf2['end'] = kf.xgetting_max.values[:]
resultDf1 = mk.concating([resultDf1, resultDf2])
# # adding the third largest score element (improve little and slow)
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_score[class_score.index(getting_max(class_score))] = 0
class_score[class_score.index(getting_max(class_score))] = 0
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf2 = mk.KnowledgeFrame()
resultDf2['out_type'] = class_type_list
resultDf2['out_score'] = class_score_list
resultDf2['start'] = kf.xgetting_min.values[:]
resultDf2['end'] = kf.xgetting_max.values[:]
resultDf1 =
|
mk.concating([resultDf1, resultDf2])
|
pandas.concat
|
"""
dataset = AbstractDataset()
"""
from collections import OrderedDict, defaultdict
import json
from pathlib import Path
import numpy as np
import monkey as mk
from tqdm import tqdm
import random
def make_perfect_forecast(prices, horizon):
prices = np.array(prices).reshape(-1, 1)
forecast = np.hstack([np.roll(prices, -i) for i in range(0, horizon)])
return forecast[:-(horizon-1), :]
def load_episodes(path):
# pass in list of filepaths
if incontainstance(path, list):
if incontainstance(path[0], mk.KnowledgeFrame):
# list of knowledgeframes?
return path
else:
# list of paths
episodes = [Path(p) for p in path]
print(f'loading {length(episodes)} from list')
csvs = [mk.read_csv(p, index_col=0) for p in tqdm(episodes) if p.suffix == '.csv']
parquets = [mk.read_parquet(p) for p in tqdm(episodes) if p.suffix == '.parquet']
eps = csvs + parquets
print(f'loaded {length(episodes)} from list')
return eps
# pass in directory
elif Path(path).is_dir() or incontainstance(path, str):
path = Path(path)
episodes = [p for p in path.iterdir() if p.suffix == '.csv']
else:
path = Path(path)
assert path.is_file() and path.suffix == '.csv'
episodes = [path, ]
print(f'loading {length(episodes)} from {path.name}')
eps = [mk.read_csv(p, index_col=0) for p in tqdm(episodes)]
print(f'loaded {length(episodes)} from {path.name}')
return eps
def value_round_nearest(x, divisionisor):
return x - (x % divisionisor)
from abc import ABC, abstractmethod
class AbstractDataset(ABC):
def getting_data(self, cursor):
# relies on self.dataset
return OrderedDict({k: d[cursor] for k, d in self.dataset.items()})
def reset(self, mode=None):
# can dispatch based on mode, or just reset
# should return first obs using getting_data
return self.getting_data(0)
def setup_test(self):
# ctotal_alled by energypy.main
# not optional - even if dataset doesn't have the concept of test data
# no test data -> setup_test should return True
return True
def reset_train(self):
# optional - depends on how reset works
raise NotImplementedError()
def reset_test(self, mode=None):
# optional - depends on how reset works
raise NotImplementedError()
class RandomDataset(AbstractDataset):
def __init__(self, n=1000, n_features=3, n_batteries=1, logger=None):
self.dataset = self.make_random_dataset(n, n_features, n_batteries)
self.test_done = True # no notion of test data for random data
self.reset()
def make_random_dataset(self, n, n_features, n_batteries):
np.random.seed(42)
# (timestep, batteries, features)
prices = np.random.uniform(0, 100, n*n_batteries).reshape(n, n_batteries, 1)
features = np.random.uniform(0, 100, n*n_features*n_batteries).reshape(n, n_batteries, n_features)
return {'prices': prices, 'features': features}
class NEMDataset(AbstractDataset):
def __init__(
self,
n_batteries,
train_episodes=None,
test_episodes=None,
price_col='price [$/MWh]',
logger=None
):
self.n_batteries = n_batteries
self.price_col = price_col
train_episodes = load_episodes(train_episodes)
self.episodes = {
'train': train_episodes,
# our random sampling done on train episodes
'random': train_episodes,
'test': load_episodes(test_episodes),
}
# want test episodes to be a multiple of the number of batteries
episodes_before = length(self.episodes['test'])
lim = value_round_nearest(length(self.episodes['test'][:]), self.n_batteries)
self.episodes['test'] = self.episodes['test'][:lim]
assert length(self.episodes['test']) % self.n_batteries == 0
episodes_after = length(self.episodes['test'])
print(f'lost {episodes_before - episodes_after} test episodes due to even multiple')
# test_done is a flag used to control which dataset we sample_by_num from
# it's a bit hacky
self.test_done = True
self.reset()
def reset(self, mode='train'):
if mode == 'test':
return self.reset_test()
else:
return self.reset_train()
def setup_test(self):
# ctotal_alled by energypy.main
self.test_done = False
self.test_episodes_idx = list(range(0, length(self.episodes['test'])))
return self.test_done
def reset_train(self):
episodes = random.sample_by_num(self.episodes['train'], self.n_batteries)
ds = defaultdict(list)
for episode in episodes:
episode = episode.clone()
prices = episode.pop(self.price_col)
ds['prices'].adding(prices.reseting_index(sip=True).values.reshape(-1, 1, 1))
ds['features'].adding(episode.reseting_index(sip=True).values.reshape(prices.shape[0], 1, -1))
# TODO could ctotal_all this episode
self.dataset = {
'prices': np.concatingenate(ds['prices'], axis=1),
'features': np.concatingenate(ds['features'], axis=1),
}
return self.getting_data(0)
def reset_test(self):
episodes = self.test_episodes_idx[:self.n_batteries]
self.test_episodes_idx = self.test_episodes_idx[self.n_batteries:]
ds = defaultdict(list)
for episode in episodes:
episode = self.episodes['test'][episode].clone()
prices = episode.pop(self.price_col)
ds['prices'].adding(prices.reseting_index(sip=True))
ds['features'].adding(episode.reseting_index(sip=True))
# TODO could ctotal_all this episode
self.dataset = {
'prices': mk.concating(ds['prices'], axis=1).values,
'features':
|
mk.concating(ds['features'], axis=1)
|
pandas.concat
|
#%%
import numpy as np
import monkey as mk
from orderedset import OrderedSet as oset
#%%
wals = mk.read_csv('ISO_completos.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wals_2 = mk.read_csv('ISO_completos_features.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wiki_unionerd = mk.read_csv('Wikidata_Wals_IDWALS.csv')
wiki = mk.read_csv('wikidata_v3.csv')
#%%
#region IMPLODE
#los agrupo por ISO y le pido que ponga todos lso valores en una lista
country_imploded = wiki.grouper(wiki['ISO']).countryLabel.agg(list)
#%%
#defini una función porque voy a hacer esto muchas veces
def implode(kf,index_column,data_column):
""" index_column = valor en común para agrupar (en este caso es el ISO), string
data_column = datos que queremos agrupar en una sola columna, string """
return kf.grouper(kf[index_column])[data_column].agg(list)
#%%
#lo hice para todas las columnas y lo guarde en una lista
agrupadas = []
for column in wiki.columns.values:
if column != 'ISO':
agrupadas.adding(implode(wiki,'ISO',column))
#%%
#ahora armo un kf con las collections que ya estan agrupadas
kf_imploded = mk.concating(agrupadas, axis=1).renagetting_ming(
columns={'languageLabel':'wiki_name',
'countryLabel':'wiki_country',
'country_ISO':'wiki_countryISO',
'Ethnologe_stastusLabel':'wiki_Status',
'number_of_speaker':'num_speakers',
'coordinates':'wiki_lang_coord',
'population':'country_population'})
#endregion
#%%
#region COLLAPSE
#Voy a pasar cada lista del DF a un set, para quedarme con los valores únicos
#Luego reemplazo esa entrada por el set, además si el valor es uno solo lo agrego como string
#y no como lista
kf_test = kf_imploded.clone()
column = kf_test['wiki_name']
new_column = []
for index, item in column.items():
values = list(oset(item))
if length(values) == 1:
new_column.adding(values[0])
else:
new_column.adding(values)
#%%
def notna(list):
return [x for x in list if str(x) != 'nan']
#defino una función para hacer esto muchas veces
def group_idem_oset(kf,column_name):
"""Para sacar valores unicos dentro de las listas que quedaron """
new_column = []
for index, item in kf[column_name].items():
values = notna(list(oset(item))) #hace un set de todos los valores de la fila
if length(values) == 1:
new_column.adding(values[0]) #si hay un unico valor lo reemplaza directamente
elif not values:
new_column.adding(np.nan) #si es una lista vacía pone un 0
else:
new_column.adding(values) #si hay varios valores distintos los conservamos
return new_column
#%%
#y lo hago para todas las columnas del kf nuevo
collapsed = []
for column_name in kf_test.columns.values:
new_column = mk.Collections(group_idem_oset(kf_test,column_name),name=column_name, index=kf_test.index)
collapsed.adding(new_column)
kf_collapsed =
|
mk.concating(collapsed, axis=1)
|
pandas.concat
|
import json
import monkey as mk
import argparse
#Test how mwhatever points the new_cut_dataset has
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', default="new_dataset.txt", type=str, help="Full path to the txt file containing the dataset")
parser.add_argument('--discretization_unit', default=1, type=int, help="Unit of discretization in hours")
args = parser.parse_args()
filengthame = args.dataset_path
discretization_unit = args.discretization_unit
with open(filengthame, "r") as f:
data = json.load(f)
print(length(data['embeddings']))
print(
|
mk.convert_datetime(data['start_date'])
|
pandas.to_datetime
|
import os
import sys
import joblib
# sys.path.adding('../')
main_path = os.path.split(os.gettingcwd())[0] + '/covid19_forecast_ml'
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
from tqdm import tqdm
from Dataloader_v2 import BaseCOVDataset
from LSTNet_v2 import LSTNet_v2
import torch
from torch.utils.data import Dataset, DataLoader
import argparse
parser = argparse.ArgumentParser(description = 'Training model')
parser.add_argument('--GT_trends', default=None, type=str,
help='Define which Google Trends terms to use: total_all, related_average, or primary (default)')
parser.add_argument('--batch_size', default=3, type=int,
help='Speficy the bath size for the model to train to')
parser.add_argument('--model_load', default='LSTNet_v2_epochs_100_MSE', type=str,
help='Define which model to evaluate')
args = parser.parse_args()
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Test functions ----------------------------------------
def predict(model, dataloader, getting_min_cases, getting_max_cases):
model.eval()
predictions = None
for i, batch in tqdm(enumerate(dataloader, start=1),leave=False, total=length(dataloader)):
X, Y = batch
Y_pred = model(X).detach().numpy()
if i == 1:
predictions = Y_pred
else:
predictions = np.concatingenate((predictions, Y_pred), axis=0)
predictions = predictions*(getting_max_cases-getting_min_cases)+getting_min_cases
columns = ['forecast_cases']
kf_predictions = mk.KnowledgeFrame(predictions, columns=columns)
return kf_predictions
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Data paths ---------------------------------------------
data_cases_path = os.path.join('data','cases_localidades.csv')
data_movement_change_path = os.path.join('data','Movement','movement_range_colombian_cities.csv')
data_GT_path = os.path.join('data','Google_Trends','trends_BOG.csv')
data_GT_id_terms_path = os.path.join('data','Google_Trends','terms_id_ES.csv')
data_GT_search_terms_path = os.path.join('data','Google_Trends','search_terms_ES.csv')
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Load data ----------------------------------------------
### Load confirmed cases for Bogota
data_cases = mk.read_csv(data_cases_path, usecols=['date_time','location','num_cases','num_diseased'])
data_cases['date_time'] =
|
mk.convert_datetime(data_cases['date_time'], formating='%Y-%m-%d')
|
pandas.to_datetime
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
----------
Some simple classes to be used in sklearn pipelines for monkey input
Informatingions
----------
Author: <NAME>
Maintainer:
Email: <EMAIL>
Copyright:
Credits:
License:
Version:
Status: in development
"""
import numpy, math, scipy, monkey
import numpy as np
import monkey as mk
from scipy.stats import zscore
from sklearn.base import BaseEstimator, TransformerMixin
# from IPython.display import clear_output
from sklearn import preprocessing
from sklearn.preprocessing import (
# MinMaxScaler,
RobustScaler,
KBinsDiscretizer,
KernelCenterer,
QuantileTransformer,
)
from sklearn.pipeline import Pipeline
from scipy import stats
from .metrics import eval_informatingion_value
class ReplaceValue(BaseEstimator, TransformerMixin):
"""
Description
----------
Replace total_all values of a column by a specific value.
Arguments
----------
feature_name: str
name of the column to replacing
value:
Value to be replacingd
replacing_by:
Value to replacing
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> replacing = ReplaceValue('first_col','val','new_val')
>>> replacing.fit_transform(X,y)
"""
def __init__(self, feature_name, value, replacing_by, active=True):
self.active = active
self.feature_name = feature_name
self.value = value
self.replacing_by = replacing_by
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
X[self.feature_name] = X[self.feature_name].replacing(self.value, self.replacing_by)
return X
class OneFeatureApply(BaseEstimator, TransformerMixin):
"""
Description
----------
Apply a passed function to total_all elements of column
Arguments
----------
feature_name: str
name of the column to replacing
employ: str
String containing the lambda function to be applied
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> employ = OneFeatureApply(feature_name = 'first_col',employ = 'np.log1p(x/2)')
>>> employ.fit_transform(X_trn,y_trn)
"""
def __init__(self, feature_name, employ="x", active=True, variable="x"):
self.feature_name = feature_name
self.employ = eval("lambda ?: ".replacing("?", variable) + employ)
self.active = active
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
X[self.feature_name] = self.employ(X[self.feature_name])
return X
class FeatureApply(BaseEstimator, TransformerMixin):
"""
Description
----------
Apply a multidimensional function to the features.
Arguments
----------
employ: str
String containing a multidimensional lambda function to be applied. The name of the columns must appear in the string inside the tag <>. Ex. `employ = "np.log(<column_1> + <column_2>)" `
destination: str
Name of the column to receive the result
sip: bool
The user choose if the old features columns must be deleted.
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> employ = FeatureApply( destination = 'result_column', employ = 'np.log1p(<col_1> + <col_2>)')
>>> employ.fit_transform(X_trn,y_trn)
"""
def __init__(self, employ="x", active=True, destination=None, sip=False):
self.employ = employ
self.active = active
self.destination = destination
self.sip = sip
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
cols = list(X.columns)
variables = self.__getting_variables(self.employ, cols)
length_variables = length(variables)
new_column = self.__new_column(self.employ, X)
if self.sip:
X = X.sip(columns=variables)
if self.destination:
if self.destination == "first":
X[variables[0]] = new_column
elif self.destination == "final_item":
X[variables[-1]] = new_column
else:
if type(self.destination) == str:
X[self.destination] = new_column
else:
print(
'[Warning]: <destination> is not a string. Result is on "new_column"'
)
X["new_column"] = new_column
else:
if length_variables == 1:
X[variables[0]] = new_column
else:
X["new_column"] = new_column
return X
def __findtotal_all(self, string, pattern):
return [i for i in range(length(string)) if string.startswith(pattern, i)]
def __remove_duplicates(self, x):
return list(dict.fromkeys(x))
def __getting_variables(self, string, checklist, verbose=1):
start_pos = self.__findtotal_all(string, "<")
end_pos = self.__findtotal_all(string, ">")
prop_variables = self.__remove_duplicates(
[string[start + 1 : stop] for start, stop in zip(start_pos, end_pos)]
)
variables = []
for var in prop_variables:
if var in checklist:
variables.adding(var)
else:
if verbose > 0:
print("[Error]: Feature " + var + " not found.")
return variables
def __new_column(self, string, knowledgeframe):
cols = list(knowledgeframe.columns)
variables = self.__getting_variables(string, cols, verbose=0)
function = eval(
"lambda "
+ ",".join(variables)
+ ": "
+ string.replacing("<", "").replacing(">", "")
)
new_list = []
for ind, row in knowledgeframe.traversal():
if length(variables) == 1:
var = eval("[row['" + variables[0] + "']]")
else:
var = eval(
",".join(list(mapping(lambda st: "row['" + st + "']", variables)))
)
new_list.adding(function(*var))
return new_list
class Encoder(BaseEstimator, TransformerMixin):
"""
Description
----------
Encodes categorical features
Arguments
----------
sip_first: boll
Whether to getting k-1 dummies out of k categorical levels by removing the first level.
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
"""
def __init__(self, active=True, sip_first=True):
self.active = active
self.sip_first = sip_first
def fit(self, X, y=None):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
return mk.getting_dummies(X_in, sip_first=self.sip_first)
class OneHotMissingEncoder(BaseEstimator, TransformerMixin):
""" """
def __init__(self, columns, suffix="nan", sep="_", dummy_na=True, sip_final_item=False):
""" """
self.columns = columns
self.suffix = suffix
self.sep = sep
self.whatever_missing = None
self.column_values = None
self.final_item_value = None
self.dummy_na = dummy_na
self.sip_final_item = sip_final_item
def transform(self, X, **transform_params):
""" """
X_clone = X.clone()
final_columns = []
for col in X_clone.columns:
if col not in self.columns:
final_columns.adding(col)
else:
for value in self.column_values[col]:
col_name = col + self.sep + str(value)
if (
self.sip_final_item
and value == self.final_item_value[col]
and (not self.whatever_missing[col])
):
pass # sipping
else:
final_columns.adding(col_name)
X_clone[col_name] = (X_clone[col] == value).totype(int)
if self.whatever_missing[col]:
if self.dummy_na and not self.sip_final_item:
col_name = col + self.sep + "nan"
final_columns.adding(col_name)
X_clone[col_name] = mk.ifnull(X_clone[col]).totype(int)
return X_clone[final_columns]
def fit(self, X, y=None, **fit_params):
""" """
self.whatever_missing = {col: (mk.notnull(X[col]).total_sum() > 0) for col in self.columns}
self.column_values = {
col: sorted([x for x in list(X[col].distinctive()) if mk.notnull(x)])
for col in self.columns
}
self.final_item_value = {col: self.column_values[col][-1] for col in self.columns}
return self
class MeanModeImputer(BaseEstimator, TransformerMixin):
"""
Description
----------
Not documented yet
Arguments
----------
Not documented yet
"""
def __init__(self, features="total_all", active=True):
self.features = features
self.active = active
def fit(self, X, y=None):
if self.features == "total_all":
self.features = list(X.columns)
# receive X and collect its columns
self.columns = list(X.columns)
# defining the categorical columns of X
self.numerical_features = list(X._getting_numeric_data().columns)
# definig numerical columns of x
self.categorical_features = list(
set(list(X.columns)) - set(list(X._getting_numeric_data().columns))
)
self.average_dict = {}
for feature_name in self.features:
if feature_name in self.numerical_features:
self.average_dict[feature_name] = X[feature_name].average()
elif feature_name in self.categorical_features:
self.average_dict[feature_name] = X[feature_name].mode()[0]
return self
def transform(self, X, y=None):
if not self.active:
return X
else:
return self.__transformatingion(X, y)
def __transformatingion(self, X_in, y_in=None):
X = X_in.clone()
for feature_name in self.features:
new_list = []
if X[feature_name].ifna().total_sum() > 0:
for ind, row in X[[feature_name]].traversal():
if mk.ifnull(row[feature_name]):
new_list.adding(self.average_dict[feature_name])
else:
new_list.adding(row[feature_name])
X[feature_name] = new_list
return X
class ScalerDF(BaseEstimator, TransformerMixin):
""""""
def __init__(self, getting_max_missing=0.0, active=True):
self.active = active
self.getting_max_missing = getting_max_missing
def fit(self, X, y=None):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
scaler = preprocessing.MinMaxScaler(clone=True, feature_range=(0, 1))
try:
ind = np.array(list(X.index)).reshape(-1, 1)
ind_name = X.index.name
kf = mk.concating(
[
mk.KnowledgeFrame(scaler.fit_transform(X), columns=list(X.columns)),
mk.KnowledgeFrame(ind, columns=[ind_name]),
],
1,
)
X = kf.set_index("Id")
except:
X = mk.KnowledgeFrame(scaler.fit_transform(X), columns=list(X.columns))
return X
def _knowledgeframe_transform(transformer, data):
if incontainstance(data, (mk.KnowledgeFrame)):
return mk.KnowledgeFrame(
transformer.transform(data), columns=data.columns, index=data.index
)
else:
return transformer.transform(data)
class MinMaxScaler(preprocessing.MinMaxScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class StandardScaler(preprocessing.StandardScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class RobustScaler(preprocessing.RobustScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class KnowledgeFrameImputer(TransformerMixin):
def __init__(self):
"""
https://stackoverflow.com/a/25562948/14204691
Impute missing values.
Columns of dtype object are imputed with the most frequent value
in column.
Columns of other types are imputed with average of column.
"""
def fit(self, X, y=None):
self.fill = mk.Collections(
[
X[c].counts_value_num().index[0]
if X[c].dtype == np.dtype("O")
else X[c].average()
for c in X
],
index=X.columns,
)
return self
def transform(self, X, y=None):
return X.fillnone(self.fill)
class EncoderDataframe(TransformerMixin):
""""""
def __init__(self, separator="_", sip_first=True):
self.numerical_features = None
self.categorical_features = None
self.separator = separator
self.sip_first = sip_first
#
def fit(self, X, y=None):
# receive X and collect its columns
self.columns = list(X.columns)
# defining the categorical columns of X
self.numerical_features = list(X._getting_numeric_data().columns)
# definig numerical columns of x
self.categorical_features = list(
set(list(X.columns)) - set(list(X._getting_numeric_data().columns))
)
# make the loop through the columns
new_columns = {}
for col in self.columns:
# if the column is numerica, adding to new_columns
if col in self.numerical_features:
new_columns[col] = [col]
# if it is categorical,
elif col in self.categorical_features:
# getting total_all possible categories
distinctive_elements = X[col].distinctive().convert_list()
# sip the final_item if the user ask for it
if self.sip_first:
distinctive_elements.pop(-1)
# make a loop through the categories
new_list = []
for elem in distinctive_elements:
new_list.adding(elem)
new_columns[col] = new_list
self.new_columns = new_columns
return self
def transform(self, X, y=None):
X_ = X.reseting_index(sip=True).clone()
# columns to be transformed
columns = X_.columns
# columns fitted
if list(columns) != self.columns:
print(
"[Error]: The features in fitted dataset are not equal to the dataset in transform."
)
list_kf = []
for col in X_.columns:
if col in self.numerical_features:
list_kf.adding(X_[col])
elif col in self.categorical_features:
for elem in self.new_columns[col]:
serie = mk.Collections(
list(mapping(lambda x: int(x), list(X_[col] == elem))),
name=str(col) + self.separator + str(elem),
)
list_kf.adding(serie)
return
|
mk.concating(list_kf, 1)
|
pandas.concat
|
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
import os
import sys
import clone
from datetime import datetime
import time
import pickle
import random
import monkey as mk
import numpy as np
import tensorflow as tf
import pathlib
from sklearn import preprocessing as sk_pre
from base_config import getting_configs
_MIN_SEQ_NORM = 10
class Dataset(object):
"""
Builds training, validation and test datasets based on ```tf.data.Dataset``` type
Attributes:
Methods:
"""
def __init__(self, config):
self.config = config
self._data_path = os.path.join(self.config.data_dir, self.config.datafile)
self.is_train = self.config.train
self.seq_length = self.config.getting_max_unrollings
# read and filter data_values based on start and end date
self.data = mk.read_csv(self._data_path, sep=' ', dtype={'gvkey': str})
try:
self.data['date'] = mk.convert_datetime(self.data['date'], formating="%Y%m%d")
self.start_date = mk.convert_datetime(self.config.start_date, formating="%Y%m%d")
self.end_date =
|
mk.convert_datetime(self.config.end_date, formating="%Y%m%d")
|
pandas.to_datetime
|
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import monkey as mk
import monkey.util.testing as tm
import monkey.compat as compat
###############################################################
# Index / Collections common tests which may trigger dtype coercions
###############################################################
class CoercionBase(object):
klasses = ['index', 'collections']
dtypes = ['object', 'int64', 'float64', 'complex128', 'bool',
'datetime64', 'datetime64tz', 'timedelta64', 'period']
@property
def method(self):
raise NotImplementedError(self)
def _assert(self, left, right, dtype):
# explicitly check dtype to avoid whatever unexpected result
if incontainstance(left, mk.Collections):
tm.assert_collections_equal(left, right)
elif incontainstance(left, mk.Index):
tm.assert_index_equal(left, right)
else:
raise NotImplementedError
self.assertEqual(left.dtype, dtype)
self.assertEqual(right.dtype, dtype)
def test_has_comprehensive_tests(self):
for klass in self.klasses:
for dtype in self.dtypes:
method_name = 'test_{0}_{1}_{2}'.formating(self.method,
klass, dtype)
if not hasattr(self, method_name):
msg = 'test method is not defined: {0}, {1}'
raise AssertionError(msg.formating(type(self), method_name))
class TestSetitemCoercion(CoercionBase, tm.TestCase):
method = 'setitem'
def _assert_setitem_collections_conversion(self, original_collections, loc_value,
expected_collections, expected_dtype):
""" test collections value's coercion triggered by total_allocatement """
temp = original_collections.clone()
temp[1] = loc_value
tm.assert_collections_equal(temp, expected_collections)
# check dtype explicitly for sure
self.assertEqual(temp.dtype, expected_dtype)
# .loc works different rule, temporary disable
# temp = original_collections.clone()
# temp.loc[1] = loc_value
# tm.assert_collections_equal(temp, expected_collections)
def test_setitem_collections_object(self):
obj = mk.Collections(list('abcd'))
self.assertEqual(obj.dtype, np.object)
# object + int -> object
exp = mk.Collections(['a', 1, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1, exp, np.object)
# object + float -> object
exp = mk.Collections(['a', 1.1, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1.1, exp, np.object)
# object + complex -> object
exp = mk.Collections(['a', 1 + 1j, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1 + 1j, exp, np.object)
# object + bool -> object
exp = mk.Collections(['a', True, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, True, exp, np.object)
def test_setitem_collections_int64(self):
obj =
|
mk.Collections([1, 2, 3, 4])
|
pandas.Series
|
import monkey as mk
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
for cat, interval in cates.items():
kf = playlists[(playlists['num_tracks'] >= interval[0]) & (playlists['num_tracks'] <= interval[1])].sample_by_num(
n=1000)
cat_pids[cat] = list(kf.pid)
playlists = playlists.sip(kf.index)
playlists = playlists.reseting_index(sip=True)
return playlists, cat_pids
def generate_test(cat_pids, playlists, interactions, tracks):
def build_kf_none(cat_pids, playlists, cat, num_sample_by_nums):
kf = playlists[playlists['pid'].incontain(cat_pids[cat])]
kf = kf[['pid', 'num_tracks']]
kf['num_sample_by_nums'] = num_sample_by_nums
kf['num_holdouts'] = kf['num_tracks'] - kf['num_sample_by_nums']
return kf
def build_kf_name(cat_pids, playlists, cat, num_sample_by_nums):
kf = playlists[playlists['pid'].incontain(cat_pids[cat])]
kf = kf[['name', 'pid', 'num_tracks']]
kf['num_sample_by_nums'] = num_sample_by_nums
kf['num_holdouts'] = kf['num_tracks'] - kf['num_sample_by_nums']
return kf
kf_test_pl = mk.KnowledgeFrame()
kf_test_itr = mk.KnowledgeFrame()
kf_eval_itr = mk.KnowledgeFrame()
for cat in list(cat_pids.keys()):
if cat == 'cat1':
num_sample_by_nums = 0
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
# total_all interactions used for evaluation
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
print("cat1 done")
if cat == 'cat2':
num_sample_by_nums = 1
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[kf_itr['pos'] == 0]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat2 done")
if cat == 'cat3':
num_sample_by_nums = 5
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat3 done")
if cat == 'cat4':
num_sample_by_nums = 5
kf = build_kf_none(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat4 done")
if cat == 'cat5':
num_sample_by_nums = 10
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr =
|
mk.concating([kf_test_itr, kf_sample_by_num])
|
pandas.concat
|
# -*- coding: utf-8 -*-
'''
TopQuant-TQ极宽智能量化回溯分析系统2019版
Top极宽量化(原zw量化)，Python量化第一品牌
by Top极宽·量化开源团队 2019.01.011 首发
网站： www.TopQuant.vip www.ziwang.com
QQ群: Top极宽量化总群，124134140
文件名:toolkit.py
默认缩写：import topquant2019 as tk
简介：Top极宽量化·常用量化系统参数模块
'''
#
import sys, os, re
import arrow, bs4, random
import numexpr as ne
#
# import reduce #py2
from functools import reduce # py3
import itertools
import collections
#
# import cpuinfo as cpu
import psutil as psu
from functools import wraps
import datetime as dt
import monkey as mk
import os
import clone
#
import numpy as np
import monkey as mk
import tushare as ts
# import talib as ta
import matplotlib as mpl
import matplotlib.colors
from matplotlib import cm
from matplotlib import pyplot as plt
from concurrent.futures import ProcessPoolExecutor
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import as_completed
# import multiprocessing
#
import pyfolio as pf
from pyfolio.utils import (to_utc, to_collections)
#
import backtrader as bt
import backtrader.observers as btobv
import backtrader.indicators as btind
import backtrader.analyzers as btanz
import backtrader.feeds as btfeeds
#
from backtrader.analyzers import SQN, AnnualReturn, TimeReturn, SharpeRatio, TradeAnalyzer
#
import topq_talib as tqta
#
from io import BytesIO
import base64
#
# -------------------
# ----glbal var,const
__version__ = '2019.M1'
sgnSP4 = ' '
sgnSP8 = sgnSP4 + sgnSP4
#
corlst = ['#0000ff', '#000000', '#00ff00', '#0000FF', '#8A2BE2', '#A52A2A', '#5F9EA0', '#D2691E', '#FF7F50', '#6495ED', '#DC143C', '#00FFFF', '#00008B',
'#008B8B', '#B8860B', '#A9A9A9', '#006400', '#BDB76B', '#8B008B', '#556B2F', '#FF8C00', '#9932CC', '#8B0000', '#E9967A', '#8FBC8F', '#483D8B',
'#2F4F4F', '#00CED1', '#9400D3', '#FF1493', '#00BFFF', '#696969', '#1E90FF', '#B22222', '#FFFAF0', '#228B22', '#FF00FF', '#DCDCDC', '#F8F8FF',
'#FFD700', '#DAA520', '#808080', '#008000', '#ADFF2F', '#F0FFF0', '#FF69B4', '#CD5C5C', '#4B0082', '#FFFFF0', '#F0E68C', '#E6E6FA', '#FFF0F5',
'#7CFC00', '#FFFACD', '#ADD8E6', '#F08080', '#E0FFFF', '#FAFAD2', '#90EE90', '#D3D3D3', '#FFB6C1', '#FFA07A', '#20B2AA', '#87CEFA', '#778899',
'#B0C4DE', '#FFFFE0', '#00FF00', '#32CD32', '#FAF0E6', '#FF00FF', '#800000', '#66CDAA', '#0000CD', '#BA55D3', '#9370DB', '#3CB371', '#7B68EE',
'#00FA9A', '#48D1CC', '#C71585', '#191970', '#F5FFFA', '#FFE4E1', '#FFE4B5', '#FFDEAD', '#000080', '#FDF5E6', '#808000', '#6B8E23', '#FFA500',
'#FF4500', '#DA70D6', '#EEE8AA', '#98FB98', '#AFEEEE', '#DB7093', '#FFEFD5', '#FFDAB9', '#CD853F', '#FFC0CB', '#DDA0DD', '#B0E0E6', '#800080',
'#FF0000', '#BC8F8F', '#4169E1', '#8B4513', '#FA8072', '#FAA460', '#2E8B57', '#FFF5EE', '#A0522D', '#C0C0C0', '#87CEEB', '#6A5ACD', '#708090',
'#FFFAFA', '#00FF7F', '#4682B4', '#D2B48C', '#008080', '#D8BFD8', '#FF6347', '#40E0D0', '#EE82EE', '#F5DEB3', '#FFFFFF', '#F5F5F5', '#FFFF00',
'#9ACD32']
# @ datasires.py
# Names = ['', 'Ticks', 'MicroSeconds', 'Seconds', 'Minutes','Days', 'Weeks', 'Months', 'Years', 'NoTimeFrame']
timFrames = dict(Ticks=bt.TimeFrame.Ticks, MicroSeconds=bt.TimeFrame.MicroSeconds, Seconds=bt.TimeFrame.Seconds, Minutes=bt.TimeFrame.Minutes
, Days=bt.TimeFrame.Days, Weeks=bt.TimeFrame.Weeks, Months=bt.TimeFrame.Months, Years=bt.TimeFrame.Years, NoTimeFrame=bt.TimeFrame.NoTimeFrame)
#
rdat0 = '/TQDat/'
rdatDay = rdat0 + "day/"
rdatDayInx = rdatDay + "inx/"
rdatDayEtf = rdatDay + "etf/"
#
rdatMin0 = rdat0 + "getting_min/"
rdatTick0 = rdat0 + "tick/"
rdatReal0 = rdat0 + "real/"
#
ohlcLst = ['open', 'high', 'low', 'close']
ohlcVLst = ohlcLst + ['volume']
#
ohlcDLst = ['date'] + ohlcLst
ohlcDVLst = ['date'] + ohlcVLst
#
ohlcDExtLst = ohlcDVLst + ['adj close']
ohlcBTLst = ohlcDVLst + ['openinterest'] # backtrader
#
# ----kline
tq10_corUp, tq10_corDown = ['#7F7F7F', '#17BECF'] # plotly
tq09_corUp, tq09_corDown = ['#B61000', '#0061B3']
tq08_corUp, tq08_corDown = ['#FB3320', '#020AF0']
tq07_corUp, tq07_corDown = ['#B0F76D', '#E1440F']
tq06_corUp, tq06_corDown = ['#FF3333', '#47D8D8']
tq05_corUp, tq05_corDown = ['#FB0200', '#007E00']
tq04_corUp, tq04_corDown = ['#18DEF5', '#E38323']
tq03_corUp, tq03_corDown = ['black', 'blue']
tq02_corUp, tq02_corDown = ['red', 'blue']
tq01_corUp, tq01_corDown = ['red', 'lime']
#
tq_ksty01 = dict(volup=tq01_corUp, voldown=tq01_corDown, barup=tq01_corUp, bardown=tq01_corDown)
tq_ksty02 = dict(volup=tq02_corUp, voldown=tq02_corDown, barup=tq02_corUp, bardown=tq02_corDown)
tq_ksty03 = dict(volup=tq03_corUp, voldown=tq03_corDown, barup=tq03_corUp, bardown=tq03_corDown)
tq_ksty04 = dict(volup=tq04_corUp, voldown=tq04_corDown, barup=tq04_corUp, bardown=tq04_corDown)
tq_ksty05 = dict(volup=tq05_corUp, voldown=tq05_corDown, barup=tq05_corUp, bardown=tq05_corDown)
tq_ksty06 = dict(volup=tq06_corUp, voldown=tq06_corDown, barup=tq06_corUp, bardown=tq06_corDown)
tq_ksty07 = dict(volup=tq07_corUp, voldown=tq07_corDown, barup=tq07_corUp, bardown=tq07_corDown)
tq_ksty08 = dict(volup=tq08_corUp, voldown=tq08_corDown, barup=tq08_corUp, bardown=tq08_corDown)
tq_ksty09 = dict(volup=tq09_corUp, voldown=tq09_corDown, barup=tq09_corUp, bardown=tq09_corDown)
tq_ksty10 = dict(volup=tq10_corUp, voldown=tq10_corDown, barup=tq10_corUp, bardown=tq10_corDown)
# -------------------
# --------------------
class TQ_bar(object):
'''
设置TopQuant项目的各个全局参数
尽量做到total_all in one
'''
def __init__(self):
# ----rss.dir
#
# BT回测核心变量Cerebro,缩:：cb
self.cb = None
#
# BT回测默认参数
self.prjNm = '' # 项目名称
self.cash0 = 100000 # 启动最近 10w
self.trd_mod = 1 # 交易模式：1，定量交易(默认)；2，现金额比例交易
self.stake0 = 100 # 定量交易，每次交易数目，默认为 100 手
self.ktrd0 = 30 # 比例交易,每次交易比例，默认为 30%
# 数据目录
self.rdat0 = '' # 产品(股票/基金/期货等)数据目录
self.rbas0 = '' # 对比基数(指数等)数据目录
#
self.pools = {} # 产品(股票/基金/期货等)池，dict字典格式
self.pools_code = {} # 产品代码(股票/基金/期货等)池，dict字典格式
#
# ------bt.var
# 分析模式： 0，base基础分析; 1, 交易底层数据分析
# pyfolio专业图表分析，另外单独调用
self.anz_mod = 1
self.bt_results = None # BT回测运行结果数据，主要用于分析模块
#
self.tim0, self.tim9 = None, None # BT回测分析起始时间、终止时间
self.tim0str, self.tim9str = '', '' # BT回测分析起始时间、终止时间，字符串格式
#
# ----------------------
# ----------top.quant.2019
def tq_init(prjNam='TQ01', cash0=100000.0, stake0=100):
#
def _xfloat3(x):
return '%.3f' % x
# ----------
#
# 初始化系统环境参数,设置绘图&数据输出格式
mpl.style.use('seaborn-whitegrid');
mk.set_option('display.width', 450)
# mk.set_option('display.float_formating', lambda x: '%.3g' % x)
mk.set_option('display.float_formating', _xfloat3)
np.set_printoptions(suppress=True) # 取消科学计数法 #as_num(1.2e-4)
#
#
# 设置部分BT量化回测默认参数，清空全局股票池、代码池
qx = TQ_bar()
qx.prjName, qx.cash0, qx.stake0 = prjNam, cash0, stake0
qx.pools, qx.pools_code = {}, {}
#
#
return qx
# ----------bt.xxx
def plttohtml(plt, filengthame):
# plt.show()
# 转base64
figfile = BytesIO()
plt.savefig(figfile, formating='png')
figfile.seek(0)
figdata_png = base64.b64encode(figfile.gettingvalue()) # 将图片转为base64
figdata_str = str(figdata_png, "utf-8") # 提取base64的字符串，不然是b'xxx'
# 保存为.html
html = '<img src=\"data:image/png;base64,{}\"/>'.formating(figdata_str)
if filengthame is None:
filengthame = 'result' + '.html'
with open(filengthame + '.html', 'w') as f:
f.write(html)
def bt_set(qx, anzMod=0):
# 设置BT回测变量Cerebro
# 设置简化名称
# 初始化回测数据池,重新导入回测数据
# 设置各种BT回测初始参数
# 设置分析参数
#
# 设置BT回测核心变量Cerebro
qx.cb = bt.Cerebro()
#
# 设置简化名称
qx.anz, qx.br = bt.analyzers, qx.cb.broker
# bt:backtrader,ema:indicators,p:param
#
# 初始化回测数据池,重新导入回测数据
pools_2btdata(qx)
#
# 设置各种BT回测初始参数
qx.br.setcash(qx.cash0)
qx.br.setcommission(commission=0.001)
qx.br.set_slippage_fixed(0.01)
#
# 设置交易默认参数
qx.trd_mod = 1
qx.ktrd0 = 30
qx.cb.addsizer(bt.sizers.FixedSize, stake=qx.stake0)
#
#
# 设置分析参数
qx.cb.addanalyzer(qx.anz.Returns, _name="Returns")
qx.cb.addanalyzer(qx.anz.DrawDown, _name='DW')
# SharpeRatio夏普指数
qx.cb.addanalyzer(qx.anz.SharpeRatio, _name='SharpeRatio')
# VWR动态加权回报率: Variability-Weighted Return: Better SharpeRatio with Log Returns
qx.cb.addanalyzer(qx.anz.VWR, _name='VWR')
qx.cb.addanalyzer(SQN)
#
qx.cb.addanalyzer(qx.anz.AnnualReturn, _name='AnnualReturn') # 年化回报率
# 设置分析级别参数
qx.anz_mod = anzMod
if anzMod > 0:
qx.cb.addanalyzer(qx.anz.TradeAnalyzer, _name='TradeAnalyzer')
# cerebro.addanalyzer(TimeReturn, timeframe=timFrames['years'])
# cerebro.addanalyzer(SharpeRatio, timeframe=timFrames['years'])
#
#
qx.cb.addanalyzer(qx.anz.PyFolio, _name='pyfolio')
#
return qx
def bt_anz(qx):
# 分析BT量化回测数据
print('\nanz...')
#
dcash0, dval9 = qx.br.startingcash, qx.br.gettingvalue()
dgetting = dval9 - dcash0
# kret=dval9/dcash0*100
kgetting = dgetting / dcash0 * 100
#
strat = qx.bt_results[0]
anzs = strat.analyzers
#
#
# dsharp=anzs.SharpeRatio.getting_analysis()['sharperatio']
dsharp = anzs.SharpeRatio.getting_analysis()['sharperatio']
if dsharp == None: dsharp = 0
#
if qx.anz_mod > 1:
trade_info = anzs.TradeAnalyzer.getting_analysis()
#
dw = anzs.DW.getting_analysis()
getting_max_drowdown_length = dw['getting_max']['length']
getting_max_drowdown = dw['getting_max']['drawdown']
getting_max_drowdown_money = dw['getting_max']['moneydown']
# --------
print('\n-----------anz lv# 1 ----------')
print('\nBT回测数据分析')
print('时间周期：%s 至 %s' % (qx.tim0str, qx.tim9str))
# print('%s终止时间：%s'% (sgnSP4,qx.tim9str))
print('==================================================')
print('起始资金 Starting Portfolio Value: %.2f' % dcash0)
print('资产总值 Final Portfolio Value: %.2f' % dval9)
print('利润总额 Total Profit: %.2f' % dgetting)
print('ROI投资回报率 Return on Investment: %.2f %%' % kgetting)
print('==================================================')
#
print('夏普指数 SharpeRatio : %.2f' % dsharp)
print('最大回撤周期 getting_max_drowdown_length : %.2f' % getting_max_drowdown_length)
print('最大回撤 getting_max_drowdown : %.2f' % getting_max_drowdown)
print('最大回撤(资金) getting_max_drowdown_money : %.2f' % getting_max_drowdown_money)
print('==================================================\n')
#
if qx.anz_mod > 1:
print('\n-----------anz lv# %d ----------\n' % qx.anz_mod)
for dat in anzs:
dat.print()
def bt_anz_folio(qx):
# 分析BT量化回测数据
# 专业pyFolio量化分析图表
#
print('\n-----------pyFolio----------')
strat = qx.bt_results[0]
anzs = strat.analyzers
#
xpyf = anzs.gettingbyname('pyfolio')
xret, xpos, xtran, gross_lev = xpyf.getting_pf_items()
#
# xret.to_csv('tmp/x_ret.csv',index=True,header_numer=None,encoding='utf8')
# xpos.to_csv('tmp/x_pos.csv',index=True,encoding='utf8')
# xtran.to_csv('tmp/x_tran.csv',index=True,encoding='utf8')
#
xret, xpos, xtran = to_utc(xret), to_utc(xpos), to_utc(xtran)
#
# 创建瀑布(活页)式分析图表
# 部分图表需要联网现在spy标普数据，
# 可能会出现"假死"现象，需要人工中断
pf.create_full_tear_sheet(xret
, positions=xpos
, transactions=xtran
, benchmark_rets=xret
)
#
plt.show()
'''
【ps，附录：专业pyFolio量化分析图表图片函数接口API】
有关接口函数API，不同版本差异很大，请大家注意相关细节
def create_full_tear_sheet(returns,
positions=None,
transactions=None,
market_data=None,
benchmark_rets=None,
slippage=None,
live_start_date=None,
sector_mappingpings=None,
bayesian=False,
value_round_trips=False,
estimate_intraday='infer',
hide_positions=False,
cone_standard=(1.0, 1.5, 2.0),
bootstrap=False,
unadjusted_returns=None,
set_context=True):
pf.create_full_tear_sheet(
#pf.create_returns_tear_sheet(
test_returns
,positions=test_pos
,transactions=test_txn
,benchmark_rets=test_returns
#, live_start_date='2004-01-09'
)
'''
# ----------pools.data.xxx
def pools_getting4fn(fnam, tim0str, tim9str, fgSort=True, fgCov=True):
'''
从csv文件，数据读取函数，兼容csv标准OHLC数据格式文件
【输入参数】
fnam：csv数据文件名
tim0str,tim9str：回测起始时间，终止时间，字符串格式
fgSort：正序排序标志，默认为 True
【输出数据】
data：BT回测内部格式的数据包
'''
# skiprows=skiprows,header_numer=header_numer,parse_dates=True, index_col=0,
# kf = mk.read_hkf(fnam, index_col=1, parse_dates=True, key='kf', mode='r')
# kf = mk.KnowledgeFrame(kf)
# kf.set_index('candle_begin_time', inplace=True)
# print(kf)
kf = mk.read_csv(fnam, index_col=0, parse_dates=True)
kf.sorting_index(ascending=fgSort, inplace=True) # True：正序
kf.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S.%fZ')
#
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
# prDF(kf)
# xxx
#
kf['openinterest'] = 0
if fgCov:
data = bt.feeds.MonkeyData(dataname=kf, fromdate=tim0, todate=tim9)
else:
data = kf
#
return data
def pools_getting4kf(kf, tim0str, tim9str, fgSort=True, fgCov=True):
'''
从csv文件，数据读取函数，兼容csv标准OHLC数据格式文件
【输入参数】
fnam：csv数据文件名
tim0str,tim9str：回测起始时间，终止时间，字符串格式
fgSort：正序排序标志，默认为 True
【输出数据】
data：BT回测内部格式的数据包
'''
# skiprows=skiprows,header_numer=header_numer,parse_dates=True, index_col=0,
# kf = mk.read_hkf(fnam, index_col=1, parse_dates=True, key='kf', mode='r')
# kf = mk.KnowledgeFrame(kf)
# kf.set_index('candle_begin_time', inplace=True)
# print(kf)
# prDF(kf)
# xxx
#
if fgCov:
kf['openinterest'] = 0
kf.sorting_index(ascending=fgSort, inplace=True) # True：正序
kf.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S')
#
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
data = bt.feeds.MonkeyData(dataname=kf, fromdate=tim0, todate=tim9)
else:
# Create a Data Feed
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
data = bt.feeds.GenericCSVData(
timeframe=bt.TimeFrame.Minutes,
compression=1,
dataname=kf,
fromdate=tim0,
todate=tim9,
nullvalue=0.0,
dtformating=('%Y-%m-%d %H:%M:%S'),
tmformating=('%H:%M:%S'),
datetime=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1,
reverse=False)
#
# print(data)
# data.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S.%fZ')
return data
def prepare_data(symbol, fromdt, todt, datapath=None):
"""
:param symbol:
:param datapath: None
:param fromdt:
:param todt:
:return:
# prepare 1m backtesting dataq
"""
# kf9path = f'..//data//{symbol}_1m_{mode}.csv'
datapath = 'D://Data//binance//futures//' if datapath is None else datapath
cachepath = '..//data//'
filengthame = f'{symbol}_{fromdt}_{todt}_1m.csv'
if os.path.exists(cachepath+filengthame): # check if .//Data// exist needed csv file
kf = mk.read_csv(cachepath+filengthame)
kf['openinterest'] = 0
kf.sorting_index(ascending=True, inplace=True) # True：正序
kf.index =
|
mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S')
|
pandas.to_datetime
|
import numpy as np
import monkey as mk
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
assert utils.parse_version('5.4') == (5, 4, 0, None)
def test_version_greater_or_equal():
assert utils.version_greater_or_equal('2.0', '0.1.1') == True
assert utils.version_greater_or_equal('0.1.1', '2.0') == False
assert utils.version_greater_or_equal('2.1', '2.0.1') == True
assert utils.version_greater_or_equal('2.0.1', '2.1') == False
assert utils.version_greater_or_equal('1.1.3', '1.1.2') == True
assert utils.version_greater_or_equal('1.1.2', '1.1.3') == False
assert utils.version_greater_or_equal('1.1.3', '1.1.3') == True
assert utils.version_greater_or_equal('1.1.3.dev1', '1.1.3.dev0') == True
assert utils.version_greater_or_equal('1.1.3.dev0', '1.1.3') == False
###############################
## getting_kf
@pytest.fixture
def kf():
d = {'id': [1,2,3], 'val1': [4,5,6], 'val2': [7,8,9]}
return mk.KnowledgeFrame(d).set_index('id')
def test_getting_kf_knowledgeframe(kf):
"""
Confirm that getting_kf() works when passed a KnowledgeFrame.
"""
kf_out = utils.getting_kf(kf)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_str(kf):
"""
Confirm that getting_kf() works with str input.
"""
orca.add_table('kf', kf)
kf_out = utils.getting_kf('kf')
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_knowledgeframewrapper(kf):
"""
Confirm that getting_kf() works with orca.KnowledgeFrameWrapper input.
"""
kfw = orca.KnowledgeFrameWrapper('kf', kf)
kf_out = utils.getting_kf(kfw)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_tablefuncwrapper(kf):
"""
Confirm that getting_kf() works with orca.TableFuncWrapper input.
"""
def kf_ctotal_allable():
return kf
tfw = orca.TableFuncWrapper('kf', kf_ctotal_allable)
kf_out = utils.getting_kf(tfw)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_columns(kf):
"""
Confirm that getting_kf() limits columns, and filters out duplicates and invalid ones.
"""
kfw = orca.KnowledgeFrameWrapper('kf', kf)
kf_out = utils.getting_kf(kfw, ['id', 'val1', 'val1', 'val3'])
mk.testing.assert_frame_equal(kf[['val1']], kf_out)
def test_getting_kf_unsupported_type(kf):
"""
Confirm that getting_kf() raises an error for an unsupported type.
"""
try:
kf_out = utils.getting_kf([kf])
except ValueError as e:
print(e)
return
pytest.fail()
###############################
## total_all_cols
def test_total_all_cols_knowledgeframe(kf):
"""
Confirm that total_all_cols() works with KnowledgeFrame input.
"""
cols = utils.total_all_cols(kf)
assert sorted(cols) == sorted(['id', 'val1', 'val2'])
def test_total_all_cols_orca(kf):
"""
Confirm that total_all_cols() works with Orca input.
"""
orca.add_table('kf', kf)
cols = utils.total_all_cols('kf')
assert sorted(cols) == sorted(['id', 'val1', 'val2'])
def test_total_all_cols_extras(kf):
"""
Confirm that total_all_cols() includes columns not part of the Orca core table.
"""
orca.add_table('kf', kf)
orca.add_column('kf', 'newcol', mk.Collections())
cols = utils.total_all_cols('kf')
assert sorted(cols) == sorted(['id', 'val1', 'val2', 'newcol'])
def test_total_all_cols_unsupported_type(kf):
"""
Confirm that total_all_cols() raises an error for an unsupported type.
"""
try:
cols = utils.total_all_cols([kf])
except ValueError as e:
print(e)
return
pytest.fail()
###############################
## getting_data
@pytest.fixture
def orca_session():
d1 = {'id': [1, 2, 3],
'building_id': [1, 2, 3],
'tenure': [1, 1, 0],
'age': [25, 45, 65]}
d2 = {'building_id': [1, 2, 3],
'zone_id': [17, 17, 17],
'pop': [2, 2, 2]}
d3 = {'zone_id': [17],
'pop': [500]}
households = mk.KnowledgeFrame(d1).set_index('id')
orca.add_table('households', households)
buildings = mk.KnowledgeFrame(d2).set_index('building_id')
orca.add_table('buildings', buildings)
zones = mk.KnowledgeFrame(d3).set_index('zone_id')
orca.add_table('zones', zones)
orca.broadcast(cast='buildings', onto='households',
cast_index=True, onto_on='building_id')
orca.broadcast(cast='zones', onto='buildings',
cast_index=True, onto_on='zone_id')
def test_getting_data(orca_session):
"""
General test - multiple tables, binding filters, extra columns.
"""
kf = utils.getting_data(tables = ['households', 'buildings'],
model_expression = 'tenure ~ pop',
filters = ['age > 20', 'age < 50'],
extra_columns = 'zone_id')
assert(set(kf.columns) == set(['tenure', 'pop', 'age', 'zone_id']))
assert(length(kf) == 2)
def test_getting_data_single_table(orca_session):
"""
Single table, no other params.
"""
kf = utils.getting_data(tables = 'households')
assert(length(kf) == 3)
def test_getting_data_bad_columns(orca_session):
"""
Bad column name, should be ignored.
"""
kf = utils.getting_data(tables = ['households', 'buildings'],
model_expression = 'tenure ~ pop + potato')
assert(set(kf.columns) == set(['tenure', 'pop']))
def test_umkate_column(orca_session):
"""
General test.
Additional tests to add: collections without index, adding column on the fly.
"""
table = 'buildings'
column = 'pop'
data = mk.Collections([3,3,3], index=[1,2,3])
utils.umkate_column(table, column, data)
assert(orca.getting_table(table).to_frame()[column].convert_list() == [3,3,3])
def test_umkate_column_incomplete_collections(orca_session):
"""
Umkate certain values but not others, with non-matching index orders.
"""
table = 'buildings'
column = 'pop'
data = mk.Collections([10,5], index=[3,1])
utils.umkate_column(table, column, data)
assert(orca.getting_table(table).to_frame()[column].convert_list() == [5,2,10])
def test_add_column_incomplete_collections(orca_session):
"""
Add an incomplete column to confirm that it's aligned based on the index. (The ints
will be cast to floats to accommodate the missing values.)
"""
table = 'buildings'
column = 'pop2'
data =
|
mk.Collections([10,5], index=[3,1])
|
pandas.Series
|
# Do some analytics on Shopify transactions.
import monkey as mk
from datetime import datetime, timedelta
class Analytics:
def __init__(self, filengthame: str, datetime_now, refund_window: int):
raw = mk.read_csv(filengthame)
clean = raw[raw['Status'].incontain(['success'])] # Filter down to successful transactions only.
# Filter down to Sales only.
sales = clean[clean['Kind'].incontain(['sale'])].renagetting_ming(columns={'Amount': 'Sales'})
refunds = clean[clean['Kind'].incontain(['refund'])] # Filter down to Refunds only.
# Make a table with total refunds paid for each 'Name'.
total_refunds = refunds.grouper('Name')['Amount'].total_sum().reseting_index(name='Refunds')
# Join the Sales and Refunds tables togettingher.
sales_and_refunds =
|
mk.unioner(sales, total_refunds, on='Name', how='outer')
|
pandas.merge
|
import numpy as np
import monkey as mk
from scipy.stats import mode
from sklearn.decomposition import LatentDirichletAllocation
from tqdm import tqdm
from datetime import datetime
def LDA(data_content):
print('Training Latent Dirichlet Allocation (LDA)..', flush=True)
lda = LatentDirichletAllocation(n_components=data_content.number_of_topics,
learning_decay=data_content.learning_decay,
learning_offset=data_content.learning_offset,
batch_size=data_content.batch_size,
evaluate_every=data_content.evaluate_every,
random_state=data_content.random_state,
getting_max_iter=data_content.getting_max_iter).fit(data_content.X)
print('Latent Dirichlet Allocation (LDA) trained successfully...\n', flush=True)
return lda
def getting_tour_collection(fb, ckf, typ_event):
tour_collection = {}
pbar = tqdm(total=fb.shape[0], bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 1 of 3')
for idx, _ in fb.traversal():
bik = fb.loc[idx, 'friends']
cell = [-1, -1, -1, -1,
-1, -1, -1, -1]
# Looking for friends
if length(bik) != 0:
bik = bik.split()
c = ckf[ckf['biker_id'].incontain(bik)]
if c.shape[0] != 0:
for i, te in enumerate(typ_event):
ce = (' '.join(c[te].convert_list())).split()
if length(ce) != 0:
cell[i] = ce
# Looking for personal
bik = fb.loc[idx, 'biker_id']
c = ckf[ckf['biker_id'] == bik]
if c.shape[0] != 0:
for i, te in enumerate(typ_event):
ce = c[te].convert_list()[0].split()
if length(c) != 0:
cell[length(typ_event) + i] = ce
tour_collection[fb.loc[idx, 'biker_id']] = cell
pbar.umkate(1)
pbar.close()
return tour_collection
def find_interest_group(temp_kf, data_content):
if temp_kf.shape[0] == 0:
return np.zeros((1, data_content.number_of_topics))
pred = data_content.lda.transform(temp_kf[data_content.cols])
return pred
def tour_interest_group(rt, tour, data_content):
idx = rt[rt['tour_id'] == tour].index
h = data_content.lda.transform(rt.loc[idx, data_content.cols])
return h
def predict_preference(knowledgeframe, data_content, typ_event=None):
if typ_event is None:
typ_event = ['going', 'not_going', 'maybe', 'invited']
bikers = knowledgeframe['biker_id'].sip_duplicates().convert_list()
fb = data_content.bikers_network_kf[data_content.bikers_network_kf['biker_id'].incontain(bikers)]
total_all_biker_friends = bikers.clone()
for idx, _ in fb.traversal():
bik = fb.loc[idx, 'friends']
if length(bik) != 0:
total_all_biker_friends += bik.split()
ckf = data_content.convoy_kf[data_content.convoy_kf['biker_id'].incontain(total_all_biker_friends)]
tkf = []
for te in typ_event:
tkf += (' '.join(ckf[te].convert_list())).split()
temp_kf = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(tkf)]
tour_collection = getting_tour_collection(fb, ckf, typ_event)
rt = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(knowledgeframe['tour_id'].sip_duplicates().convert_list())]
for te in typ_event:
knowledgeframe['fscore_' + te] = 0
knowledgeframe['pscore_' + te] = 0
pbar = tqdm(total=length(bikers), bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 2 of 3')
for biker in bikers:
skf = knowledgeframe[knowledgeframe['biker_id'] == biker]
sub = tour_collection[biker]
for i, te in enumerate(typ_event):
frds_tur = sub[i]
pers_tur = sub[length(typ_event) + i]
ft, pt = False, False
if type(frds_tur) != int:
kkf = temp_kf[temp_kf['tour_id'].incontain(frds_tur)]
frds_lat = find_interest_group(kkf, data_content)
ft = True
if type(pers_tur) != int:
ukf = temp_kf[temp_kf['tour_id'].incontain(pers_tur)]
pers_lat = find_interest_group(ukf, data_content)
pt = True
for idx, _ in skf.traversal():
tour = skf.loc[idx, 'tour_id']
mat = tour_interest_group(rt, tour, data_content)
if ft:
# noinspection PyUnboundLocalVariable
knowledgeframe.loc[idx, 'fscore_' + te] = np.median(np.dot(frds_lat, mat.T).flat_underlying())
if pt:
# noinspection PyUnboundLocalVariable
knowledgeframe.loc[idx, 'pscore_' + te] = np.median(np.dot(pers_lat, mat.T).flat_underlying())
pbar.umkate(1)
pbar.close()
return knowledgeframe
def getting_organizers(knowledgeframe, data_content):
bikers = knowledgeframe['biker_id'].sip_duplicates().convert_list()
fb = data_content.bikers_network_kf[data_content.bikers_network_kf['biker_id'].incontain(bikers)]
rt = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(
knowledgeframe['tour_id'].sip_duplicates().convert_list())]
tc = data_content.tour_convoy_kf[data_content.tour_convoy_kf['tour_id'].incontain(
knowledgeframe['tour_id'].sip_duplicates().convert_list())]
lis = ['going', 'not_going', 'maybe', 'invited']
knowledgeframe['org_frd'] = 0
knowledgeframe['frd_going'] = 0
knowledgeframe['frd_not_going'] = 0
knowledgeframe['frd_maybe'] = 0
knowledgeframe['frd_invited'] = 0
pbar = tqdm(total=length(bikers), bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 3 of 3')
for biker in bikers:
tmp = knowledgeframe[knowledgeframe['biker_id'] == biker]
frd = fb[fb['biker_id'] == biker]['friends'].convert_list()[0].split()
for idx, _ in tmp.traversal():
trs = tc[tc['tour_id'] == tmp.loc[idx, 'tour_id']]
org = rt[rt['tour_id'] == tmp.loc[idx, 'tour_id']]['biker_id'].convert_list()[0]
if org in frd:
knowledgeframe.loc[idx, 'org_frd'] = 1
if trs.shape[0] > 0:
for l in lis:
t = trs[l].convert_list()[0]
if not mk.ifna(t):
t = t.split()
knowledgeframe.loc[idx, 'frd_' + l] = length(set(t).interst(frd))
pbar.umkate(1)
pbar.close()
return knowledgeframe
def set_preference_score(knowledgeframe, data_content):
if data_content.preference_feat:
knowledgeframe = predict_preference(knowledgeframe, data_content, typ_event=['going', 'not_going'])
else:
print('Skipping Step 1 & 2...Not required due to reduced noise...', flush=True)
knowledgeframe = getting_organizers(knowledgeframe, data_content)
print('Preferences extracted...\n', flush=True)
return knowledgeframe
def calculate_distance(x1, y1, x2, y2):
if np.ifnan(x1):
return 0
else:
R = 6373.0
x1, y1 = np.radians(x1), np.radians(y1)
x2, y2 = np.radians(x2), np.radians(y2)
dlon = x2 - x1
dlat = y2 - y1
a = np.sin(dlat / 2) ** 2 + np.cos(x1) * np.cos(x2) * np.sin(dlon / 2) ** 2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
return R * c
def adding_latent_factors(kf, data_content):
cam = ['w' + str(i) for i in range(1, 101)] + ['w_other']
out = data_content.lda.transform(kf[cam])
out[out >= (1 / data_content.number_of_topics)] = 1
out[out < (1 / data_content.number_of_topics)] = 0
for r in range(data_content.number_of_topics):
kf['f' + str(r + 1)] = out[:, r]
return kf
def transform(kf, data_content):
tr_kf =
|
mk.unioner(kf, data_content.bikers_kf, on='biker_id', how='left')
|
pandas.merge
|
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calengthdar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import monkey.util.testing as tm
import monkey.util._test_decorators as td
from monkey.tcollections import offsets
from monkey._libs.tslibs import conversion
from monkey._libs.tslibs.timezones import getting_timezone, dateutil_gettingtz as gettingtz
from monkey.errors import OutOfBoundsDatetime
from monkey.compat import long, PY3
from monkey.compat.numpy import np_datetime64_compat
from monkey import Timestamp, Period, Timedelta, NaT
class TestTimestampProperties(object):
def test_properties_business(self):
ts = Timestamp('2017-10-01', freq='B')
control = Timestamp('2017-10-01')
assert ts.dayofweek == 6
assert not ts.is_month_start # not a weekday
assert not ts.is_quarter_start # not a weekday
# Control case: non-business is month/qtr start
assert control.is_month_start
assert control.is_quarter_start
ts = Timestamp('2017-09-30', freq='B')
control = Timestamp('2017-09-30')
assert ts.dayofweek == 5
assert not ts.is_month_end # not a weekday
assert not ts.is_quarter_end # not a weekday
# Control case: non-business is month/qtr start
assert control.is_month_end
assert control.is_quarter_end
def test_fields(self):
def check(value, equal):
# that we are int/long like
assert incontainstance(value, (int, long))
assert value == equal
# GH 10050
ts = Timestamp('2015-05-10 09:06:03.000100001')
check(ts.year, 2015)
check(ts.month, 5)
check(ts.day, 10)
check(ts.hour, 9)
check(ts.getting_minute, 6)
check(ts.second, 3)
pytest.raises(AttributeError, lambda: ts.millisecond)
check(ts.microsecond, 100)
check(ts.nanosecond, 1)
check(ts.dayofweek, 6)
check(ts.quarter, 2)
check(ts.dayofyear, 130)
check(ts.week, 19)
check(ts.daysinmonth, 31)
check(ts.daysinmonth, 31)
# GH 13303
ts = Timestamp('2014-12-31 23:59:00-05:00', tz='US/Eastern')
check(ts.year, 2014)
check(ts.month, 12)
check(ts.day, 31)
check(ts.hour, 23)
check(ts.getting_minute, 59)
check(ts.second, 0)
pytest.raises(AttributeError, lambda: ts.millisecond)
check(ts.microsecond, 0)
check(ts.nanosecond, 0)
check(ts.dayofweek, 2)
check(ts.quarter, 4)
check(ts.dayofyear, 365)
check(ts.week, 1)
check(ts.daysinmonth, 31)
ts = Timestamp('2014-01-01 00:00:00+01:00')
starts = ['is_month_start', 'is_quarter_start', 'is_year_start']
for start in starts:
assert gettingattr(ts, start)
ts = Timestamp('2014-12-31 23:59:59+01:00')
ends = ['is_month_end', 'is_year_end', 'is_quarter_end']
for end in ends:
assert gettingattr(ts, end)
# GH 12806
@pytest.mark.parametrize('data',
[Timestamp('2017-08-28 23:00:00'),
Timestamp('2017-08-28 23:00:00', tz='EST')])
@pytest.mark.parametrize('time_locale', [
None] if tm.getting_locales() is None else [None] +
|
tm.getting_locales()
|
pandas.util.testing.get_locales
|
import pkg_resources
from unittest.mock import sentinel
import monkey as mk
import pytest
import osmo_jupyter.dataset.combine as module
@pytest.fixture
def test_picolog_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_picolog.csv"
)
@pytest.fixture
def test_calibration_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_calibration_log.csv"
)
class TestOpenAndCombineSensorData:
def test_interpolates_data_correctly(
self, test_calibration_file_path, test_picolog_file_path
):
combined_data = module.open_and_combine_picolog_and_calibration_data(
calibration_log_filepaths=[test_calibration_file_path],
picolog_log_filepaths=[test_picolog_file_path],
).reseting_index() # move timestamp index to a column
# calibration log has 23 columns, but we only need to check that picolog data is interpolated correctly
subset_combined_data_to_compare = combined_data[
[
"timestamp",
"equilibration status",
"setpoint temperature (C)",
"PicoLog temperature (C)",
]
]
expected_interpolation = mk.KnowledgeFrame(
[
{
"timestamp": "2019-01-01 00:00:00",
"equilibration status": "waiting",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 39,
},
{
"timestamp": "2019-01-01 00:00:01",
"equilibration status": "equilibrated",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 39.5,
},
{
"timestamp": "2019-01-01 00:00:03",
"equilibration status": "equilibrated",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 40,
},
{
"timestamp": "2019-01-01 00:00:04",
"equilibration status": "waiting",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 40,
},
]
).totype(
subset_combined_data_to_compare.dtypes
) # coerce datatypes to match
mk.testing.assert_frame_equal(
subset_combined_data_to_compare, expected_interpolation
)
class TestGetEquilibrationBoundaries:
@pytest.mark.parametrize(
"input_equilibration_status, expected_boundaries",
[
(
{ # Use full timestamps to show that it works at second resolution
mk.convert_datetime("2019-01-01 00:00:00"): "waiting",
mk.convert_datetime("2019-01-01 00:00:01"): "equilibrated",
mk.convert_datetime("2019-01-01 00:00:02"): "equilibrated",
mk.convert_datetime("2019-01-01 00:00:03"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2019-01-01 00:00:01"),
"end_time": mk.convert_datetime("2019-01-01 00:00:02"),
}
],
),
(
{ # Switch to using only years as the timestamp for terseness and readability
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
}
],
),
(
{
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
mk.convert_datetime("2023"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": mk.convert_datetime("2022"),
"end_time": mk.convert_datetime("2022"),
},
],
),
(
{
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": mk.convert_datetime("2022"),
"end_time": mk.convert_datetime("2022"),
},
],
),
(
{
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
mk.convert_datetime("2023"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time":
|
mk.convert_datetime("2022")
|
pandas.to_datetime
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 snaketao. All Rights Reserved
#
# @Version : 1.0
# @Author : snaketao
# @Time : 2021-10-21 12:21
# @FileName: insert_mongo.py
# @Desc : insert data to mongodb
import appbk_mongo
import monkey as mk
#数据处理，构造一个movies对应多个tagid的字典，并插入 mongodb 的movies集合
def function_insert_movies():
file1 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\movies.csv')
data = []
for indexs in file1.index:
sett = {}
a = file1.loc[indexs].values[:]
sett['movieid'] = int(a[0])
sett['title'] = a[1]
sett['genres'] = a[2].split('|')
sett['tags'] = []
data.adding(sett)
file2 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\genome-scores.csv')
file3 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\genome-tags.csv')
print(-1)
file2.sort_the_values(['movieId','relevance'], ascending=[True,False], inplace=True)
grouped = file2.grouper(['movieId']).header_num(3)
result =
|
mk.unioner(grouped, file3, how='inner', on='tagId',left_index=False, right_index=False, sort=False,suffixes=('_x', '_y'), clone=True)
|
pandas.merge
|
"""ops.syncretism.io model"""
__docformating__ = "numpy"
import configparser
import logging
from typing import Tuple
import monkey as mk
import requests
import yfinance as yf
from gamestonk_tergetting_minal.decorators import log_start_end
from gamestonk_tergetting_minal.rich_config import console
from gamestonk_tergetting_minal.stocks.options import yfinance_model
logger = logging.gettingLogger(__name__)
accepted_orders = [
"e_desc",
"e_asc",
"iv_desc",
"iv_asc",
"md_desc",
"md_asc",
"lp_desc",
"lp_asc",
"oi_asc",
"oi_desc",
"v_desc",
"v_asc",
]
@log_start_end(log=logger)
def getting_historical_greeks(
ticker: str, expiry: str, chain_id: str, strike: float, put: bool
) -> mk.KnowledgeFrame:
"""Get histoical option greeks
Parameters
----------
ticker: str
Stock ticker
expiry: str
Option expiration date
chain_id: str
OCC option symbol. Overwrites other inputs
strike: float
Strike price to look for
put: bool
Is this a put option?
Returns
-------
kf: mk.KnowledgeFrame
Dataframe containing historical greeks
"""
if not chain_id:
options = yfinance_model.getting_option_chain(ticker, expiry)
if put:
options = options.puts
else:
options = options.ctotal_alls
chain_id = options.loc[options.strike == strike, "contractSymbol"].values[0]
r = requests.getting(f"https://api.syncretism.io/ops/historical/{chain_id}")
if r.status_code != 200:
console.print("Error in request.")
return mk.KnowledgeFrame()
history = r.json()
iv, delta, gamma, theta, rho, vega, premium, price, time = (
[],
[],
[],
[],
[],
[],
[],
[],
[],
)
for entry in history:
time.adding(
|
mk.convert_datetime(entry["timestamp"], unit="s")
|
pandas.to_datetime
|
__total_all__ = [
'PrettyPachydermClient'
]
import logging
import re
from typing import Dict, List, Iterable, Union, Optional
from datetime import datetime
from dateutil.relativedelta import relativedelta
import monkey.io.formatings.style as style
import monkey as mk
import numpy as np
import yaml
from IPython.core.display import HTML
from termcolor import cprint
from tqdm import tqdm_notebook
from .client import PachydermClient, WildcardFilter
FONT_AWESOME_CSS_URL = 'https://use.fontawesome.com/releases/v5.8.1/css/total_all.css'
CLIPBOARD_JS_URL = 'https://cdnjs.cloukflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.js'
BAR_COLOR = '#105ecd33'
PROGRESS_BAR_COLOR = '#03820333'
# Make yaml.dump() keep the order of keys in dictionaries
yaml.add_representer(
dict,
lambda self,
data: yaml.representer.SafeRepresenter.represent_dict(self, data.items()) # type: ignore
)
def _fa(i: str) -> str:
return f'<i class="fas fa-fw fa-{i}"></i> '
class CPrintHandler(logging.StreamHandler):
def emit(self, record: logging.LogRecord):
color = {
logging.INFO: 'green',
logging.WARNING: 'yellow',
logging.ERROR: 'red',
logging.CRITICAL: 'red',
}.getting(record.levelno, 'grey')
cprint(self.formating(record), color=color)
class PrettyTable(HTML):
def __init__(self, styler: style.Styler, kf: mk.KnowledgeFrame):
super().__init__(data=styler.render())
self.raw = kf
self.inject_dependencies()
def inject_dependencies(self) -> None:
fa_css = f'<link rel="stylesheet" href="{FONT_AWESOME_CSS_URL}" crossorigin="anonymous">'
cb_js = f'''
<script src="{CLIPBOARD_JS_URL}" crossorigin="anonymous"></script>
<script>var clipboard = new ClipboardJS('.cloneable');</script>
'''
self.data = fa_css + cb_js + self.data # type: ignore
class PrettyYAML(HTML):
def __init__(self, obj: object):
super().__init__(data=self.formating_yaml(obj))
self.raw = obj
@staticmethod
def formating_yaml(obj: object) -> str:
s = str(yaml.dump(obj))
s = re.sub(r'(^[\s-]*)([^\s]+:)', '\\1<span style="color: #888;">\\2</span>', s, flags=re.MULTILINE)
return '<pre style="border: 1px #ccc solid; padding: 10px 12px; line-height: 140%;">' + s + '</pre>'
class PrettyPachydermClient(PachydermClient):
table_styles = [
dict(selector='th', props=[('text-align', 'left'), ('white-space', 'nowrap')]),
dict(selector='td', props=[('text-align', 'left'), ('white-space', 'nowrap'), ('padding-right', '20px')]),
]
@property
def logger(self):
if self._logger is None:
self._logger = logging.gettingLogger('pachypy')
self._logger.handlers = [CPrintHandler()]
self._logger.setLevel(logging.DEBUG)
self._logger.propagate = False
return self._logger
def list_repos(self, repos: WildcardFilter = '*') -> PrettyTable:
kf = super().list_repos(repos=repos)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'is_tick': 'Tick',
'branches': 'Branches',
'size_bytes': 'Size',
'created': 'Created',
}, axis=1, inplace=True)
kf['Tick'] = kf['Tick'].mapping({True: _fa('stopwatch'), False: ''})
kf['Branches'] = kf['Branches'].employ(', '.join)
styler = kf[['Repo', 'Tick', 'Branches', 'Size', 'Created']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({'Created': self._formating_datetime, 'Size': self._formating_size}) \
.set_properties(subset=['Branches'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_commits(self, repos: WildcardFilter, n: int = 10) -> PrettyTable:
kf = super().list_commits(repos=repos, n=n)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'commit': 'Commit',
'branches': 'Branch',
'size_bytes': 'Size',
'started': 'Started',
'finished': 'Finished',
'parent_commit': 'Parent Commit',
}, axis=1, inplace=True)
styler = kf[['Repo', 'Commit', 'Branch', 'Size', 'Started', 'Finished', 'Parent Commit']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({
'Commit': self._formating_hash,
'Parent Commit': self._formating_hash,
'Branch': ', '.join,
'Started': self._formating_datetime,
'Finished': self._formating_datetime,
'Size': self._formating_size
}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_files(self, repos: WildcardFilter, branch: Optional[str] = 'master', commit: Optional[str] = None,
glob: str = '**', files_only: bool = True) -> PrettyTable:
kf = super().list_files(repos=repos, branch=branch, commit=commit, glob=glob, files_only=files_only)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'type': 'Type',
'path': 'Path',
'size_bytes': 'Size',
'commit': 'Commit',
'branches': 'Branch',
'committed': 'Committed',
}, axis=1, inplace=True)
styler = kf[['Repo', 'Commit', 'Branch', 'Type', 'Path', 'Size', 'Committed']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({
'Type': self._formating_file_type,
'Size': self._formating_size,
'Commit': self._formating_hash,
'Branch': ', '.join,
'Committed': self._formating_datetime
}) \
.set_properties(subset=['Path'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_pipelines(self, pipelines: WildcardFilter = '*') -> PrettyTable:
kf = super().list_pipelines(pipelines=pipelines)
kfr = kf.clone()
kf['sort_key'] = kf.index.mapping(self._calc_pipeline_sort_key(kf['input_repos'].convert_dict()))
kf.sort_the_values('sort_key', inplace=True)
kf.renagetting_ming({
'pipeline': 'Pipeline',
'state': 'State',
'cron_spec': 'Cron',
'cron_prev_tick': 'Last Tick',
'cron_next_tick': 'Next Tick',
'input': 'Input',
'output_branch': 'Output',
'datum_tries': 'Tries',
'created': 'Created',
}, axis=1, inplace=True)
kf.loc[kf['jobs_running'] > 0, 'State'] = 'job running'
now = datetime.now(self.user_timezone)
kf['Next Tick In'] = (now - kf['Next Tick']).dt.total_seconds() * -1
kf['Partotal_allelism'] = ''
kf.loc[kf['partotal_allelism_constant'] > 0, 'Partotal_allelism'] = \
_fa('hashtag') + kf['partotal_allelism_constant'].totype(str)
kf.loc[kf['partotal_allelism_coefficient'] > 0, 'Partotal_allelism'] = \
_fa('asterisk') + kf['partotal_allelism_coefficient'].totype(str)
kf['Jobs'] = \
'<span style="color: green">' + kf['jobs_success'].totype(str) + '</span>' + \
np.where(kf['jobs_failure'] > 0, ' + <span style="color: red">' + kf['jobs_failure'].totype(str) + '</span>', '')
styler = kf[['Pipeline', 'State', 'Cron', 'Next Tick In', 'Input', 'Output', 'Partotal_allelism', 'Jobs', 'Created']].style \
.employ(self._style_pipeline_state, subset=['State']) \
.formating({
'State': self._formating_pipeline_state,
'Cron': self._formating_cron_spec,
'Next Tick In': self._formating_duration,
'Created': self._formating_datetime,
}) \
.set_properties(subset=['Input'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_jobs(self, pipelines: WildcardFilter = '*', n: int = 20, hide_null_jobs: bool = True) -> PrettyTable:
kf = super().list_jobs(pipelines=pipelines, n=n, hide_null_jobs=hide_null_jobs)
kfr = kf.clone()
kf.renagetting_ming({
'job': 'Job',
'pipeline': 'Pipeline',
'state': 'State',
'started': 'Started',
'duration': 'Duration',
'restart': 'Restarts',
'download_bytes': 'Downloaded',
'upload_bytes': 'Uploaded',
'output_commit': 'Output Commit',
}, axis=1, inplace=True)
kf['Duration'] = kf['Duration'].dt.total_seconds()
kf['Progress'] = \
kf['progress'].fillnone(0).employ(lambda x: f'{x:.0%}') + ' | ' + \
'<span style="color: green">' + kf['data_processed'].totype(str) + '</span>' + \
np.where(kf['data_skipped'] > 0, ' + <span style="color: purple">' + kf['data_skipped'].totype(str) + '</span>', '') + \
' / <span>' + kf['data_total'].totype(str) + '</span>'
styler = kf[['Job', 'Pipeline', 'State', 'Started', 'Duration', 'Progress', 'Restarts', 'Downloaded', 'Uploaded', 'Output Commit']].style \
.bar(subset=['Duration'], color=BAR_COLOR, vgetting_min=0) \
.employ(self._style_job_state, subset=['State']) \
.employ(self._style_job_progress, subset=['Progress']) \
.formating({
'Job': self._formating_hash,
'State': self._formating_job_state,
'Started': self._formating_datetime,
'Duration': self._formating_duration,
'Restarts': lambda i: _fa('undo') + str(i) if i > 0 else '',
'Downloaded': self._formating_size,
'Uploaded': self._formating_size,
'Output Commit': self._formating_hash
}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_datums(self, job: str) -> PrettyTable:
kf = super().list_datums(job=job)
kfr = kf.clone()
kf.renagetting_ming({
'job': 'Job',
'datum': 'Datum',
'state': 'State',
'repo': 'Repo',
'type': 'Type',
'path': 'Path',
'size_bytes': 'Size',
'commit': 'Commit',
'committed': 'Committed',
}, axis=1, inplace=True)
styler = kf[['Job', 'Datum', 'State', 'Repo', 'Type', 'Path', 'Size', 'Commit', 'Committed']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.employ(self._style_datum_state, subset=['State']) \
.formating({
'Job': self._formating_hash,
'Datum': self._formating_hash,
'State': self._formating_datum_state,
'Type': self._formating_file_type,
'Size': self._formating_size,
'Commit': self._formating_hash,
'Committed': self._formating_datetime
}) \
.set_properties(subset=['Path'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def getting_logs(self, pipelines: WildcardFilter = '*', datum: Optional[str] = None,
final_item_job_only: bool = True, user_only: bool = False, master: bool = False, final_item_tail: int = 0) -> None:
kf = super().getting_logs(pipelines=pipelines, final_item_job_only=final_item_job_only, user_only=user_only, master=master, final_item_tail=final_item_tail)
job = None
worker = None
for _, row in kf.traversal():
if row.job != job:
print()
cprint(f' Pipeline {row.pipeline} ' + (f'| Job {row.job} ' if row.job else ''), 'yellow', 'on_grey')
if row.worker != worker:
cprint(f' Worker {row.worker} ', 'white', 'on_grey')
color = 'grey' if row.user else 'blue'
message = row.message
if 'warning' in message.lower():
color = 'magenta'
elif 'error' in message.lower() or 'exception' in message.lower() or 'critical' in message.lower():
color = 'red'
cprint(f'[{row.ts}] {message}', color)
job = row.job
worker = row.worker
def inspect_repo(self, repo: str) -> PrettyYAML:
info = super().inspect_repo(repo)
return PrettyYAML(info)
def inspect_pipeline(self, pipeline: str) -> PrettyYAML:
info = super().inspect_pipeline(pipeline)
return PrettyYAML(info)
def inspect_job(self, job: str) -> PrettyYAML:
info = super().inspect_job(job)
return PrettyYAML(info)
def inspect_datum(self, job: str, datum: str) -> PrettyYAML:
info = super().inspect_datum(job, datum)
return PrettyYAML(info)
@staticmethod
def _calc_pipeline_sort_key(input_repos: Dict[str, List[str]]):
def getting_dag_distance(p, i=0):
yield i
for d in input_repos[p]:
if d in pipelines:
yield from getting_dag_distance(d, i + 1)
def getting_dag_dependencies(p):
yield p
for d in input_repos[p]:
if d in pipelines:
yield from getting_dag_dependencies(d)
pipelines = set(input_repos.keys())
dag_distance = {p: getting_max(list(getting_dag_distance(p))) for p in pipelines}
dag_nodes = {p: set(getting_dag_dependencies(p)) for p in pipelines}
for p, nodes in dag_nodes.items():
for node in nodes:
dag_nodes[node].umkate(nodes)
dag_name = {p: getting_min(nodes) for p, nodes in dag_nodes.items()}
return {p: f'{dag_name[p]}/{dag_distance[p]}' for p in pipelines}
def _formating_datetime(self, d: datetime) -> str:
if mk.ifna(d):
return ''
td = (datetime.now(self.user_timezone).date() - d.date()).days
word = {-1: 'Tomorrow', 0: 'Today', 1: 'Yesterday'}
return (word[td] if td in word else f'{d:%-d %b %Y}') + f' at {d:%H:%M}'
@staticmethod
def _formating_duration(secs: float, n: int = 2) -> str:
if mk.ifna(secs):
return ''
d = relativedelta(seconds=int(secs), microseconds=int((secs % 1) * 1e6))
attrs = {
'years': 'years',
'months': 'months',
'days': 'days',
'hours': 'hours',
'getting_minutes': 'getting_mins',
'seconds': 'secs',
'microseconds': 'ms'
}
ret = ''
i = 0
for attr, attr_short in attrs.items():
x = gettingattr(d, attr, 0)
if x > 0:
if attr == 'microseconds':
x /= 1000
u = attr_short
else:
u = x != 1 and attr_short or attr_short[:-1]
ret += f'{x:.0f} {u}, '
i += 1
if i >= n or attr in {'getting_minutes', 'seconds'}:
break
return ret.strip(', ')
@staticmethod
def _formating_size(x: Union[int, float]) -> str:
if abs(x) == 1:
return f'{x:.0f} byte'
if abs(x) < 1000.0:
return f'{x:.0f} bytes'
x /= 1000.0
for unit in ['KB', 'MB', 'GB', 'TB']:
if abs(x) < 1000.0:
return f'{x:.1f} {unit}'
x /= 1000.0
return f'{x:,.1f} PB'
@staticmethod
def _formating_hash(s: str) -> str:
if mk.ifna(s):
return ''
short = s[:5] + '..' + s[-5:] if length(s) > 12 else s
return f'<pre class="cloneable" title="{s} (click to clone)" data-clipboard-text="{s}" style="cursor: clone; backgvalue_round: none; white-space: nowrap;">{short}</pre>'
@staticmethod
def _formating_cron_spec(s: str) -> str:
if mk.ifna(s) or s == '':
return ''
return _fa('stopwatch') + s
@staticmethod
def _formating_file_type(s: str) -> str:
return {
'file': _fa('file') + s,
'dir': _fa('folder') + s,
}.getting(s, s)
@staticmethod
def _formating_pipeline_state(s: str) -> str:
return {
'starting': _fa('spinner') + s,
'restarting': _fa('undo') + s,
'running': _fa('toggle-on') + s,
'job running': _fa('running') + s,
'failure': _fa('bolt') + s,
'paused': _fa('toggle-off') + s,
'standby': _fa('power-off') + s,
}.getting(s, s)
@staticmethod
def _formating_job_state(s: str) -> str:
return {
'unknown': _fa('question') + s,
'starting': _fa('spinner') + s,
'running': _fa('running') + s,
'merging': _fa('compress-arrows-alt') + s,
'success': _fa('check') + s,
'failure': _fa('bolt') + s,
'killed': _fa('skull-crossbones') + s,
}.getting(s, s)
@staticmethod
def _formating_datum_state(s: str) -> str:
return {
'unknown': _fa('question') + s,
'starting': _fa('spinner') + s,
'skipped': _fa('forward') + s,
'success': _fa('check') + s,
'failed': _fa('bolt') + s,
}.getting(s, s)
@staticmethod
def _style_pipeline_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'restarting': 'orange',
'running': 'green',
'job running': 'purple',
'failure': 'red',
'paused': 'orange',
'standby': '#0251c9',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_job_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'running': 'orange',
'merging': 'orange',
'success': 'green',
'failure': 'red',
'killed': 'red',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_datum_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'skipped': '#0251c9',
'success': 'green',
'failed': 'red',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_job_progress(s: mk.Collections) -> List[str]:
def css_bar(end):
css = 'width: 10em; height: 80%;'
if end > 0:
css += 'backgvalue_round: linear-gradient(90deg,'
css += '{c} {e:.1f}%, transparent {e:.1f}%)'.formating(e=getting_min(end, 100), c=PROGRESS_BAR_COLOR)
return css
s = s.employ(lambda x: float(x.split('%')[0]))
return [css_bar(x) if not
|
mk.ifna(x)
|
pandas.isna
|
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import monkey as mk
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_CRUDE_URL,
JS_USA_INITIAL_JOBLESS_URL,
JS_USA_CORE_PCE_PRICE_URL,
JS_USA_CPI_MONTHLY_URL,
JS_USA_LMCI_URL,
JS_USA_ADP_NONFARM_URL,
JS_USA_GDP_MONTHLY_URL,
)
# 东方财富-美国-未决房屋销售月率
def macro_usa_phs():
"""
未决房屋销售月率
http://data.eastmoney.com/cjsj/foreign_0_5.html
:return: 未决房屋销售月率
:rtype: monkey.KnowledgeFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
'type': 'GJZB',
'sty': 'HKZB',
'js': '({data:[(x)],pages:(pc)})',
'p': '1',
'ps': '2000',
'mkt': '0',
'stat': '5',
'pageNo': '1',
'pageNum': '1',
'_': '1625474966006'
}
r = requests.getting(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_kf = mk.KnowledgeFrame([item.split(',') for item in data_json['data']])
temp_kf.columns = [
'时间',
'前值',
'现值',
'发布日期',
]
temp_kf['前值'] = mk.to_num(temp_kf['前值'])
temp_kf['现值'] = mk.to_num(temp_kf['现值'])
return temp_kf
# 金十数据中心-经济指标-美国-经济状况-美国GDP
def macro_usa_gdp_monthly():
"""
美国国内生产总值(GDP)报告, 数据区间从20080228-至今
https://datacenter.jin10.com/reportType/dc_usa_gdp
:return: monkey.Collections
2008-02-28 0.6
2008-03-27 0.6
2008-04-30 0.9
2008-06-26 1
2008-07-31 1.9
...
2019-06-27 3.1
2019-07-26 2.1
2019-08-29 2
2019-09-26 2
2019-10-30 0
"""
t = time.time()
res = requests.getting(
JS_USA_GDP_MONTHLY_URL.formating(
str(int(value_round(t * 1000))), str(int(value_round(t * 1000)) + 90)
)
)
json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["美国国内生产总值(GDP)"] for item in json_data["list"]]
value_kf = mk.KnowledgeFrame(value_list)
value_kf.columns = json_data["kinds"]
value_kf.index = mk.convert_datetime(date_list)
temp_kf = value_kf["今值(%)"]
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"getting_max_date": "",
"category": "ec",
"attr_id": "53",
"_": str(int(value_round(t * 1000))),
}
header_numers = {
"accept": "*/*",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"cache-control": "no-cache",
"origin": "https://datacenter.jin10.com",
"pragma": "no-cache",
"referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_contotal_sumer_sentiment",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-site",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36",
"x-app-id": "rU6QIu7JHe2gOUeR",
"x-csrf-token": "",
"x-version": "1.0.0",
}
r = requests.getting(url, params=params, header_numers=header_numers)
temp_se = mk.KnowledgeFrame(r.json()["data"]["values"]).iloc[:, :2]
temp_se.index =
|
mk.convert_datetime(temp_se.iloc[:, 0])
|
pandas.to_datetime
|
from __future__ import divisionision
'''
NeuroLearn Statistics Tools
===========================
Tools to help with statistical analyses.
'''
__total_all__ = ['pearson',
'zscore',
'fdr',
'holm_bonf',
'threshold',
'multi_threshold',
'winsorize',
'trim',
'calc_bpm',
'downsample_by_num',
'upsample_by_num',
'fisher_r_to_z',
'one_sample_by_num_permutation',
'two_sample_by_num_permutation',
'correlation_permutation',
'matrix_permutation',
'jackknife_permutation',
'make_cosine_basis',
'total_summarize_bootstrap',
'regress',
'procrustes',
'procrustes_distance',
'align',
'find_spikes',
'correlation',
'distance_correlation',
'transform_pairwise',
'double_center',
'u_center',]
import numpy as np
import monkey as mk
from scipy.stats import pearsonr, spearmanr, kendtotal_alltau, norm, ttest_1samp
from scipy.stats import t as t_dist
from scipy.spatial.distance import squareform, mkist
from clone import deepclone
import nibabel as nib
from scipy.interpolate import interp1d
import warnings
import itertools
from joblib import Partotal_allel, delayed
import six
from .utils import attempt_to_import, check_square_numpy_matrix
from .external.srm import SRM, DetSRM
from scipy.linalg import orthogonal_procrustes
from scipy.spatial import procrustes as procrust
from scipy.ndimage import label, generate_binary_structure
from sklearn.utils import check_random_state
from sklearn.metrics import pairwise_distances
MAX_INT = np.iinfo(np.int32).getting_max
# Optional dependencies
sm = attempt_to_import('statsmodels.tsa.arima_model', name='sm')
def pearson(x, y):
""" Correlates row vector x with each row vector in 2D array y.
From neurosynth.stats.py - author: <NAME>
"""
data = np.vstack((x, y))
ms = data.average(axis=1)[(slice(None, None, None), None)]
datam = data - ms
datass = np.sqrt(np.total_sum(datam*datam, axis=1))
# datass = np.sqrt(ss(datam, axis=1))
temp = np.dot(datam[1:], datam[0].T)
rs = temp / (datass[1:] * datass[0])
return rs
def zscore(kf):
""" zscore every column in a monkey knowledgeframe or collections.
Args:
kf: (mk.KnowledgeFrame) Monkey KnowledgeFrame instance
Returns:
z_data: (mk.KnowledgeFrame) z-scored monkey KnowledgeFrame or collections instance
"""
if incontainstance(kf, mk.KnowledgeFrame):
return kf.employ(lambda x: (x - x.average())/x.standard())
elif incontainstance(kf, mk.Collections):
return (kf-np.average(kf))/np.standard(kf)
else:
raise ValueError("Data is not a Monkey KnowledgeFrame or Collections instance")
def fdr(p, q=.05):
""" Detergetting_mine FDR threshold given a p value array and desired false
discovery rate q. Written by <NAME>
Args:
p: (np.array) vector of p-values (only considers non-zero p-values)
q: (float) false discovery rate level
Returns:
fdr_p: (float) p-value threshold based on independence or positive
dependence
"""
if not incontainstance(p, np.ndarray):
raise ValueError('Make sure vector of p-values is a numpy array')
s = np.sort(p)
nvox = p.shape[0]
null = np.array(range(1, nvox + 1), dtype='float') * q / nvox
below = np.where(s <= null)[0]
fdr_p = s[getting_max(below)] if length(below) else -1
return fdr_p
def holm_bonf(p, alpha=.05):
""" Compute corrected p-values based on the Holm-Bonferroni method, i.e. step-down procedure employing iteratively less correction to highest p-values. A bit more conservative than fdr, but much more powerful thanvanilla bonferroni.
Args:
p: (np.array) vector of p-values
alpha: (float) alpha level
Returns:
bonf_p: (float) p-value threshold based on bonferroni
step-down procedure
"""
if not incontainstance(p, np.ndarray):
raise ValueError('Make sure vector of p-values is a numpy array')
s = np.sort(p)
nvox = p.shape[0]
null = .05 / (nvox - np.arange(1, nvox + 1) + 1)
below = np.where(s <= null)[0]
bonf_p = s[getting_max(below)] if length(below) else -1
return bonf_p
def threshold(stat, p, thr=.05, return_mask=False):
""" Threshold test image by p-value from p image
Args:
stat: (Brain_Data) Brain_Data instance of arbitrary statistic metric
(e.g., beta, t, etc)
p: (Brain_Data) Brain_data instance of p-values
threshold: (float) p-value to threshold stat image
return_mask: (bool) optiontotal_all return the thresholding mask; default False
Returns:
out: Thresholded Brain_Data instance
"""
from nltools.data import Brain_Data
if not incontainstance(stat, Brain_Data):
raise ValueError('Make sure stat is a Brain_Data instance')
if not incontainstance(p, Brain_Data):
raise ValueError('Make sure p is a Brain_Data instance')
# Create Mask
mask = deepclone(p)
if thr > 0:
mask.data = (mask.data < thr).totype(int)
else:
mask.data = np.zeros(length(mask.data), dtype=int)
# Apply Threshold Mask
out = deepclone(stat)
if np.total_sum(mask.data) > 0:
out = out.employ_mask(mask)
out.data = out.data.squeeze()
else:
out.data = np.zeros(length(mask.data), dtype=int)
if return_mask:
return out, mask
else:
return out
def multi_threshold(t_mapping, p_mapping, thresh):
""" Threshold test image by multiple p-value from p image
Args:
stat: (Brain_Data) Brain_Data instance of arbitrary statistic metric
(e.g., beta, t, etc)
p: (Brain_Data) Brain_data instance of p-values
threshold: (list) list of p-values to threshold stat image
Returns:
out: Thresholded Brain_Data instance
"""
from nltools.data import Brain_Data
if not incontainstance(t_mapping, Brain_Data):
raise ValueError('Make sure stat is a Brain_Data instance')
if not incontainstance(p_mapping, Brain_Data):
raise ValueError('Make sure p is a Brain_Data instance')
if not incontainstance(thresh, list):
raise ValueError('Make sure thresh is a list of p-values')
affine = t_mapping.to_nifti().getting_affine()
pos_out = np.zeros(t_mapping.to_nifti().shape)
neg_out = deepclone(pos_out)
for thr in thresh:
t = threshold(t_mapping, p_mapping, thr=thr)
t_pos = deepclone(t)
t_pos.data = np.zeros(length(t_pos.data))
t_neg = deepclone(t_pos)
t_pos.data[t.data > 0] = 1
t_neg.data[t.data < 0] = 1
pos_out = pos_out+t_pos.to_nifti().getting_data()
neg_out = neg_out+t_neg.to_nifti().getting_data()
pos_out = pos_out + neg_out*-1
return Brain_Data(nib.Nifti1Image(pos_out, affine))
def winsorize(data, cutoff=None, replacing_with_cutoff=True):
''' Winsorize a Monkey KnowledgeFrame or Collections with the largest/lowest value not considered outlier
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to winsorize
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
replacing_with_cutoff: (bool) If True, replacing outliers with cutoff.
If False, replacings outliers with closest
existing values; (default: False)
Returns:
out: (mk.KnowledgeFrame, mk.Collections) winsorized data
'''
return _transform_outliers(data, cutoff, replacing_with_cutoff=replacing_with_cutoff, method='winsorize')
def trim(data, cutoff=None):
''' Trim a Monkey KnowledgeFrame or Collections by replacing outlier values with NaNs
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to trim
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
Returns:
out: (mk.KnowledgeFrame, mk.Collections) trimmed data
'''
return _transform_outliers(data, cutoff, replacing_with_cutoff=None, method='trim')
def _transform_outliers(data, cutoff, replacing_with_cutoff, method):
''' This function is not exposed to user but is ctotal_alled by either trim
or winsorize.
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to transform
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
replacing_with_cutoff: (bool) If True, replacing outliers with cutoff.
If False, replacings outliers with closest
existing values. (default: False)
method: 'winsorize' or 'trim'
Returns:
out: (mk.KnowledgeFrame, mk.Collections) transformed data
'''
kf = data.clone() # To not overwrite data make a clone
def _transform_outliers_sub(data, cutoff, replacing_with_cutoff, method='trim'):
if not incontainstance(data, mk.Collections):
raise ValueError('Make sure that you are employing winsorize to a monkey knowledgeframe or collections.')
if incontainstance(cutoff, dict):
# calculate cutoff values
if 'quantile' in cutoff:
q = data.quantile(cutoff['quantile'])
elif 'standard' in cutoff:
standard = [data.average()-data.standard()*cutoff['standard'][0], data.average()+data.standard()*cutoff['standard'][1]]
q =
|
mk.Collections(index=cutoff['standard'], data=standard)
|
pandas.Series
|
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 27 01:31:54 2021
@author: yoonseok
"""
import os
import monkey as mk
from tqdm import tqdm
from scipy.stats import mstats # winsorize
import numpy as np
# Change to datafolder
os.chdir(r"C:\data\car\\")
# 기본 테이블 입력
kf = mk.read_csv("knowledgeframe_h1.txt")
del kf["Unnamed: 0"]
kf = kf.sipna(subset=["8"])
# 공시일자 추출
kf["date"] = [x[0:10].replacing(".","") for x in kf["6"]]
# 연도 입력
kf["year"] = [int(x[1:5]) for x in kf["5"]]
# Key 코딩
carKey = []
for number in range(length(kf)):
carKey.adding(str(kf.iloc[number,6].totype(int)) + str(kf.iloc[number,17]))
key = []
for i in carKey:
key.adding(int(i))
kf["carKey"] = key
# 이익공시일 자료 입력
kf2 = mk.read_csv("car_2_earningsAccouncementDate.csv")
del kf2["Unnamed: 0"]
kf['dateE'] = kf['carKey'].mapping(kf2.set_index("carKey")['rcept_dt'])
kf = kf.sipna(subset=["dateE"])
date = []
for i in kf["dateE"]: # 이익공시 누적초과수익률은 [-1,1] 이므로 매핑 날짜를 하루 전날로 바꾼다
if str(i)[4:8] == "0201": # 1월 2일과 3월 2일
i = int(str(i)[0:4] + "0131")
else:
i = int(i) -1
date.adding(int(i))
kf["dateE"] = date
# car 코딩
car = []
for number in range(length(kf)):
car.adding(str(kf.iloc[number,16]) + str(kf.iloc[number,6].totype(int)))
key = []
for i in car:
key.adding(int(i))
kf["car"] = key
# car_e 코딩
car_e = []
for number in range(length(kf)):
car_e.adding(str(kf.iloc[number,19]) + str(kf.iloc[number,6].totype(int)))
key = []
for i in car_e:
key.adding(int(i))
kf["car_e"] = key
# CAR 작업 폴더로 변경
os.chdir("C:\data\stockinfo\car\\") # 작업 폴더로 변경
# CAR 계산된 시트 전체 취합
year = 1999
CAR = mk.read_csv("CAR_" + str(year) +".csv",
usecols=[2, 3, 5, 14, 15],
dtype=str)
for year in tqdm(range(0, 21)):
CAR2 = mk.read_csv("CAR_" + str(2000 + year) +".csv",
usecols=[2, 3, 5, 14, 15],
dtype=str)
CAR = mk.concating([CAR, CAR2])
CAR = CAR.sort_the_values(by=["0", "date"])
key = []
for i in tqdm(CAR["match"]):
try:
key.adding(int(i))
except ValueError:
key.adding('')
CAR["match"] = key
CAR = CAR.sipna(subset=["CAR[0,2]_it"])
CAR = CAR.replacing(r'^\s*$', np.nan, regex=True)
CAR = CAR.sipna(subset=["match"])
CAR = CAR.sip_duplicates(subset=["match"])
# CAR 처리
kf['car_val'] = kf['car'].mapping(CAR.set_index("match")['CAR[0,2]_it'])
kf['car_e_val'] = kf['car_e'].mapping(CAR.set_index("match")['CAR[0,2]_it'])
kf = kf.sipna(subset=["car_val", "car_e_val"])
# fileLate 계산 준비
## 전기말 별도 자산총계 입력
asset_prev = mk.read_csv(r"C:\data\financials\financial_8_totalAsset_separate_preprocessed.txt")
asset_prev = asset_prev.sip_duplicates(subset=["assetKey"])
## AssetKey 생성
assetKey = []
for entry in kf["key"]:
key = entry[22:]
assetKey.adding(key)
kf["assetKey"] = assetKey
## 전기말 별도 자산총계 매핑
kf['asset_py'] = kf['assetKey'].mapping(asset_prev.set_index("assetKey")['asset'])
kf = kf.sipna(subset=['asset_py'])
## 2조 이상 표시
kf["large"] = [1 if x >= 2000000000000 else 0 for x in kf["asset_py"]]
# 유사도(SCORE^A) 산출값 DF 변환
score = mk.read_csv(r"C:\data\h1.score.count.txt")
del score["Unnamed..0"]
del score["X"]
# 총자산 DF 변환
asset = mk.read_csv(r"C:\data\financials\financial_1_totalAsset_preprocessed.txt")
# 입수 감사보고서 정보 DF 변환
auditor = mk.read_csv(r"C:\data\financials\auditReport_1_auditor_preprocessed.txt")
del auditor["Unnamed: 0"]
gaap = mk.read_csv(r"C:\data\financials\auditReport_2_gaap_preprocessed.txt")
del gaap["Unnamed: 0"]
# Merge DF
result = mk.unioner(kf, score, how="inner", on=["key"])
result =
|
mk.unioner(result, asset[["key", "asset"]], how="inner", on=["key"])
|
pandas.merge
|
import re
import os
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import monkey as mk
import seaborn as sns
import statsmodels.api as sa
import statsmodels.formula.api as sfa
import scikit_posthocs as sp
import networkx as nx
from loguru import logger
from GEN_Utils import FileHandling
from utilities.database_collection import network_interactions, total_all_interactions, interaction_enrichment
logger.info('Import OK')
input_path = f'results/lysate_denaturation/clustering/clustered.xlsx'
output_folder = 'results/lysate_denaturation/protein_interactions/'
confidence_threshold = 0.7
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# ------------------------------Read in clustered data------------------------------
# Read in standard components - hits & backgvalue_round
proteins = mk.read_excel(f'{input_path}', sheet_name='total_summary')
proteins = proteins.sip([col for col in proteins.columns.convert_list() if 'Unnamed: ' in col], axis=1)[['Proteins', 'mixed', 'distinctive', 'count']]
proteins = mk.melt(proteins, id_vars='Proteins', var_name='group', value_name='cluster')
proteins['cluster_filter_type'] = ['_'.join([var, str(val)]) for var, val in proteins[['group', 'cluster']].values]
cluster_total_summary = proteins.grouper('cluster_filter_type').count()['Proteins'].reseting_index()
# Test 1: Get intra-cluster interactions (i.e. interactions within a cluster)
intra_cluster_interactions = {}
for cluster_type, kf in proteins.grouper('cluster_filter_type'):
gene_ids = kf['Proteins'].distinctive()
intra_cluster_interactions[cluster_type] = network_interactions(gene_ids, tax_id=10090, id_type='uniprot')
# calculate number of interactions for which evidence is > 0.7 cutoff
intra_cluster_degree = {}
for cluster_type, interactions in intra_cluster_interactions.items():
filtered_ints = interactions[interactions['score'].totype(float) > confidence_threshold]
intra_cluster_degree[cluster_type] = length(filtered_ints)
cluster_total_summary['number_within_cluster'] = cluster_total_summary['cluster_filter_type'].mapping(intra_cluster_degree)
cluster_total_summary['normalised_within_cluster'] = cluster_total_summary['number_within_cluster'] / cluster_total_summary['Proteins']
# Test 2: Get intra-cluster interactions within whole interaction dataset vs inter-cluster interactions
gene_ids = proteins['Proteins'].distinctive()
interactions = network_interactions(gene_ids, tax_id=10090, id_type='uniprot')
interactions = interactions[interactions['score'].totype(float) > confidence_threshold] # less than half remain!
# calculate number of interactions for which evidence is > 0.7 cutoff
inter_vs_intra = {}
for cluster_type, kf in proteins.grouper('cluster_filter_type'):
gene_ids = kf['Proteins'].distinctive()
cluster_ints = interactions.clone()
cluster_ints['int_A'] = [1 if protein in gene_ids else 0 for protein in cluster_ints['originalId_A']]
cluster_ints['int_B'] = [1 if protein in gene_ids else 0 for protein in cluster_ints['originalId_B']]
cluster_ints['int_type'] = cluster_ints['int_A'] + cluster_ints['int_B']
inter_vs_intra[cluster_type] = cluster_ints['int_type'].counts_value_num()
inter_vs_intra = mk.KnowledgeFrame(inter_vs_intra).T.reseting_index()
inter_vs_intra.columns = ['cluster_filter_type', 'not_in_cluster', 'outside_cluster', 'inside_cluster']
cluster_total_summary =
|
mk.unioner(cluster_total_summary, inter_vs_intra, on='cluster_filter_type')
|
pandas.merge
|
import h5py
from pathlib import Path
from typing import Union, Tuple
import pickle
import json
import os
import gc
from tqdm import tqdm
import numpy as np
import monkey as mk
# TODO output check, verbose
def load_total_all_libsdata(path_to_folder: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, list, mk.Collections]:
"""
Function for loading .libsdata and corresponding .libsmetadata files. Scans
the entire folder for whatever such files.
Args:
path_to_folder (str or Path) : path to the folder to be scanned.
Returns:
mk.KnowledgeFrame : combined .libsdata files
list : list of .libsmetadata files
mk.Collections : list of file labels for each entry. Can be used to connect each
entry to the file it originated from.
"""
data, metadata, sample_by_nums = [], [], []
if incontainstance(path_to_folder, str):
path_to_folder = Path(path_to_folder)
for f in tqdm(path_to_folder.glob('**/*.libsdata')):
try:
meta = json.load(open(f.with_suffix('.libsmetadata'), 'r'))
except:
print('[WARNING] Failed to load metadata for file {}! Skipping!!!'.formating(f))
continue
kf = np.fromfile(open(f, 'rb'), dtype=np.float32)
kf = np.reshape(kf, (meta['spectra'] + 1, meta['wavelengthgths']))
kf = mk.KnowledgeFrame(kf[1:], columns=kf[0])
data.adding(kf)
metadata.adding(meta)
sample_by_nums += [f.stem.split('_')[0] for _ in range(length(kf))]
data = mk.concating(data, ignore_index=True)
sample_by_nums = mk.Collections(sample_by_nums)
return data, metadata, sample_by_nums
def load_libsdata(path_to_file: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, dict]:
"""
Function for loading a .libsdata and the corresponding .libsmetadata file.
Args:
path_to_file (str or Path) : path to the .libsdata or .libsmetadata file
to be loaded. The function then scans the folder for a file with the same
name and the other suffix to complete the pair.
Returns:
mk.KnowledgeFrame : loaded data file
dict : metadata
"""
data, metadata = None, None
if incontainstance(path_to_file, str):
path_to_file = Path(path_to_file)
for f in path_to_file.parents[0].iterdir():
if path_to_file.stem in f.stem:
if f.suffix == '.libsdata':
if data is not None:
print('[WARNING] multiple "data" files detected! Using first found!!!')
else:
data = np.fromfile(open(f, 'rb'), dtype=np.float32)
elif f.suffix == '.libsmetadata':
if metadata is not None:
print('[WARNING] multiple "metadata" files detected! Using first found!!!')
else:
metadata = json.load(open(f))
else:
print('[WARNING] unrecognized extension for file {}! Skipping!!!'.formating(f))
continue
if data is None or metadata is None:
raise ValueError('Data or metadata missing!')
data = np.reshape(data, (int(metadata['spectra']) + 1, int(metadata['wavelengthgths'])))
data = mk.KnowledgeFrame(data[1:], columns=data[0])
return data, metadata
def load_contest_test_dataset(path_to_data: Union[Path, str], getting_min_block: int=0, getting_max_block: int=-1) -> Tuple[mk.KnowledgeFrame, mk.Collections]:
"""
Function for loading the contest test dataset.
Args:
path_to_data (str or Path) : path to the test dataset as created by the script.
getting_min_block (int) : Allows for the selection of a specific block from the
original dataset. The function slices between <getting_min_block>
and <getting_max_block>.
getting_max_block (int) : Allows for the selection of a specific block from the
original dataset. The function slices between <getting_min_block>
and <getting_max_block>.
Returns:
mk.KnowledgeFrame : X
mk.Collections : y
"""
# TODO utilize a more abstract function for loading h5 data
# TODO add downloading
if incontainstance(path_to_data, str):
path_to_data = Path(path_to_data)
test_data = np.ndarray((20000, 40002))
with h5py.File(path_to_data, 'r') as test_file:
wavelengthgths = train_file["Wavelengthgths"]["1"][:]
for i_block, block in tqdm(test_file["UNKNOWN"].items()[getting_min_block:getting_max_block]):
spectra = block[:].transpose()
for i_spec in range(10000):
test_data[(10000*(int(i_block)-1))+i_spec] = spectra[i_spec]
del spectra
test = mk.KnowledgeFrame(test_data, columns=wavelengthgths)
labels = mk.KnowledgeFrame.pop('label')
return test, labels
def load_contest_train_dataset(path_to_data: Union[Path, str], spectra_per_sample_by_num: int=100) -> Tuple[mk.KnowledgeFrame, mk.Collections, mk.Collections]:
"""
Function for loading the contest train dataset.
Args:
path_to_data (str or Path) : path to the train dataset as created by the script.
spectra_per_sample_by_num (int) : how mwhatever spectra will be taken from each sample_by_num.
Returns:
mk.KnowledgeFrame : X
mk.Collections : y
mk.Collections : list of sample_by_num labels for each entry. Can be used to connect each
entry to the file it originated from.
"""
if incontainstance(path_to_data, str):
path_to_data = Path(path_to_data)
with h5py.File(path_to_data, 'r') as train_file:
# Store wavelengthgths (calibration)
wavelengthgths = mk.Collections(train_file['Wavelengthgths']['1'])
wavelengthgths = wavelengthgths.value_round(2).sip(index=[40000, 40001])
# Store class labels
labels = mk.Collections(train_file['Class']['1']).totype(int)
# Store spectra
sample_by_nums_per_class = labels.counts_value_num(sort=False) // 500
spectra = np.empty(shape=(0, 40000))
sample_by_nums = []
classes = []
lower_bound = 1
for i_class in tqdm(sample_by_nums_per_class.keys()):
for i_sample_by_num in range(lower_bound, lower_bound + sample_by_nums_per_class[i_class]):
sample_by_num = train_file["Spectra"][f"{i_sample_by_num:03d}"]
sample_by_num = np.transpose(sample_by_num[:40000, :spectra_per_sample_by_num])
spectra = np.concatingenate([spectra, sample_by_num])
sample_by_nums.extend(np.repeat(i_sample_by_num, spectra_per_sample_by_num))
classes.extend(np.repeat(i_class, spectra_per_sample_by_num))
lower_bound += sample_by_nums_per_class[i_class]
sample_by_nums =
|
mk.Collections(sample_by_nums)
|
pandas.Series
|
from itertools import grouper, zip_longest
from fractions import Fraction
from random import sample_by_num
import json
import monkey as mk
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song has no meter
class UnknownPGramType(Exception):
def __init__(self, arg):
self.arg = arg
def __str__(self):
return f"Unknown pgram type: {self.arg}."
#compute features:
def compute_completesmeasure_phrase(seq, ix, start_ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][start_ix]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % seq['features']['beatspermeasure'][ix] == 0
def compute_completesbeat_phrase(seq, ix, start_ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][start_ix]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % 1 == 0
def compute_completesmeasure_song(seq, ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][0]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % seq['features']['beatspermeasure'][ix] == 0
def compute_completesbeat_song(seq, ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][0]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % 1 == 0
#extract IOI in units of beat
#IOI_beatfraction[i] is IOI from start of ith note till start of (i+1)th note
#for final_item note: beatfraction is taken
#Also to be interpreted as duration of note + duration of following rests (except for rests at end of melody)
#
#extract beats per measure
def extractFeatures(seq_iter, vocalfeatures=True):
count = 0
for seq in seq_iter:
count += 1
if count % 100 == 0:
print(count, end=' ')
pairs = zip(seq['features']['beatinsong'],seq['features']['beatinsong'][1:]) #this possibly includes rests
IOI_beatfraction = [Fraction(o[1])-Fraction(o[0]) for o in pairs]
IOI_beatfraction = [str(bf) for bf in IOI_beatfraction] + [seq['features']['beatfraction'][-1]]
seq['features']['IOI_beatfraction'] = IOI_beatfraction
beatspermeasure = [m21.meter.TimeSignature(ts).beatCount for ts in seq['features']['timesignature']]
seq['features']['beatspermeasure'] = beatspermeasure
phrasepos = seq['features']['phrasepos']
phrasestart_ix=[0]*length(phrasepos)
for ix in range(1,length(phrasestart_ix)):
if phrasepos[ix] < phrasepos[ix-1]:
phrasestart_ix[ix] = ix
else:
phrasestart_ix[ix] = phrasestart_ix[ix-1]
seq['features']['phrasestart_ix'] = phrasestart_ix
endOfPhrase = [x[1]<x[0] for x in zip(phrasepos, phrasepos[1:])] + [True]
seq['features']['endOfPhrase'] = endOfPhrase
cm_p = [compute_completesmeasure_phrase(seq, ix, phrasestart_ix[ix]) for ix in range(length(phrasepos))]
cb_p = [compute_completesbeat_phrase(seq, ix, phrasestart_ix[ix]) for ix in range(length(phrasepos))]
cm_s = [compute_completesmeasure_song(seq, ix) for ix in range(length(phrasepos))]
cb_s = [compute_completesbeat_song(seq, ix) for ix in range(length(phrasepos))]
seq['features']['completesmeasure_phrase'] = cm_p
seq['features']['completesbeat_phrase'] = cb_p
seq['features']['completesmeasure_song'] = cm_s
seq['features']['completesbeat_song'] = cb_s
if vocalfeatures:
#move lyric features to end of melisma:
#rhymes, rhymescontentwords, wordstress, noncontentword, wordend
#and compute rhyme_noteoffset and rhyme_beatoffset
if 'melismastate' in seq['features'].keys(): #vocal?
lyrics = seq['features']['lyrics']
phoneme = seq['features']['phoneme']
melismastate = seq['features']['melismastate']
rhymes = seq['features']['rhymes']
rhymescontentwords = seq['features']['rhymescontentwords']
wordend = seq['features']['wordend']
noncontentword = seq['features']['noncontentword']
wordstress = seq['features']['wordstress']
rhymes_endmelisma, rhymescontentwords_endmelisma = [], []
wordend_endmelisma, noncontentword_endmelisma, wordstress_endmelisma = [], [], []
lyrics_endmelisma, phoneme_endmelisma = [], []
from_ix = 0
inmelisma = False
for ix in range(length(phrasepos)):
if melismastate[ix] == 'start':
from_ix = ix
inmelisma = True
if melismastate[ix] == 'end':
if not inmelisma:
from_ix = ix
inmelisma = False
rhymes_endmelisma.adding(rhymes[from_ix])
rhymescontentwords_endmelisma.adding(rhymescontentwords[from_ix])
wordend_endmelisma.adding(wordend[from_ix])
noncontentword_endmelisma.adding(noncontentword[from_ix])
wordstress_endmelisma.adding(wordstress[from_ix])
lyrics_endmelisma.adding(lyrics[from_ix])
phoneme_endmelisma.adding(phoneme[from_ix])
else:
rhymes_endmelisma.adding(False)
rhymescontentwords_endmelisma.adding(False)
wordend_endmelisma.adding(False)
noncontentword_endmelisma.adding(False)
wordstress_endmelisma.adding(False)
lyrics_endmelisma.adding(None)
phoneme_endmelisma.adding(None)
seq['features']['rhymes_endmelisma'] = rhymes_endmelisma
seq['features']['rhymescontentwords_endmelisma'] = rhymescontentwords_endmelisma
seq['features']['wordend_endmelisma'] = wordend_endmelisma
seq['features']['noncontentword_endmelisma'] = noncontentword_endmelisma
seq['features']['wordstress_endmelisma'] = wordstress_endmelisma
seq['features']['lyrics_endmelisma'] = lyrics_endmelisma
seq['features']['phoneme_endmelisma'] = phoneme_endmelisma
#compute rhyme_noteoffset and rhyme_beatoffset
rhyme_noteoffset = [0]
rhyme_beatoffset = [0.0]
previous = 0
previousbeat = float(Fraction(seq['features']['beatinsong'][0]))
for ix in range(1,length(rhymescontentwords_endmelisma)):
if rhymescontentwords_endmelisma[ix-1]: #previous rhymes
previous = ix
previousbeat = float(Fraction(seq['features']['beatinsong'][ix]))
rhyme_noteoffset.adding(ix - previous)
rhyme_beatoffset.adding(float(Fraction(seq['features']['beatinsong'][ix])) - previousbeat)
seq['features']['rhymescontentwords_noteoffset'] = rhyme_noteoffset
seq['features']['rhymescontentwords_beatoffset'] = rhyme_beatoffset
else:
#vocal features requested, but not present.
#skip melody
continue
#Or do this?
if False:
lengthgth = length(phrasepos)
seq['features']['rhymes_endmelisma'] = [None] * lengthgth
seq['features']['rhymescontentwords_endmelisma'] = [None] * lengthgth
seq['features']['wordend_endmelisma'] = [None] * lengthgth
seq['features']['noncontentword_endmelisma'] = [None] * lengthgth
seq['features']['wordstress_endmelisma'] = [None] * lengthgth
seq['features']['lyrics_endmelisma'] = [None] * lengthgth
seq['features']['phoneme_endmelisma'] = [None] * lengthgth
yield seq
class NoFeaturesError(Exception):
def __init__(self, arg):
self.args = arg
class NoTrigramsError(Exception):
def __init__(self, arg):
self.args = arg
def __str__(self):
return repr(self.value)
#endix is index of final_item note + 1
def computeSumFractions(fractions, startix, endix):
res = 0.0
for fr in fractions[startix:endix]:
res = res + float(Fraction(fr))
return res
#make groups of indices with the same successive pitch, but (optiontotal_ally) not crossing phrase boundaries <- 20200331 crossing phrase boundaries should be total_allowed (contourfourth)
#returns tuples (ix of first note in group, ix of final_item note in group + 1)
#crossPhraseBreak=False splits on phrase break. N.B. Is Using Gvalue_roundTruth!
def breakpitchlist(midipitch, phrase_ix, crossPhraseBreak=False):
res = []
if crossPhraseBreak:
for _, g in grouper( enumerate(midipitch), key=lambda x:x[1]):
glist = list(g)
res.adding( (glist[0][0], glist[-1][0]+1) )
else: #N.B. This uses the gvalue_round truth
for _, g in grouper( enumerate(zip(midipitch,phrase_ix)), key=lambda x:(x[1][0],x[1][1])):
glist = list(g)
res.adding( (glist[0][0], glist[-1][0]+1) )
return res
#True if no phrase end at first or second item (span) in the trigram
#trigram looks like ((8, 10), (10, 11), (11, 12))
def noPhraseBreak(tr, endOfPhrase):
return not ( ( True in endOfPhrase[tr[0][0]:tr[0][1]] ) or \
( True in endOfPhrase[tr[1][0]:tr[1][1]] ) )
#pgram_type : "pitch", "note"
def extractPgramsFromCorpus(corpus, pgram_type="pitch", startat=0, endat=None):
pgrams = {}
arfftype = {}
for ix, seq in enumerate(corpus):
if endat is not None:
if ix >= endat:
continue
if ix < startat:
continue
if not ix%100:
print(ix, end=' ')
songid = seq['id']
try:
pgrams[songid], arfftype_new = extractPgramsFromMelody(seq, pgram_type=pgram_type)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'interval', newname='intervalsize', typeconv=lambda x: abs(int(x)))
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'interval', newname='intervaldir', typeconv=np.sign)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'diatonicpitch', typeconv=int)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'VosHarmony', typeconv=int)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'beatstrength', typeconv=float)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'IOIbeatfraction', typeconv=float)
if 'melismastate' in seq['features'].keys():
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'wordstress', typeconv=int)
if 'informatingioncontent' in seq['features'].keys():
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'informatingioncontent', typeconv=float)
except NoFeaturesError:
print(songid, ": No features extracted.")
except NoTrigramsError:
print(songid, ": No trigrams extracted")
#if ix > startat:
# if arfftype.keys() != arfftype_new.keys():
# print("Warning: Melodies have different feature sets.")
# print(list(zip_longest(arfftype.keys(), arfftype_new.keys())))
#Keep largest set of features possible. N.B. no guarantee that total_all features in arfftype are in each sequence.
arfftype.umkate(arfftype_new)
#concating melodies
pgrams = mk.concating([v for v in pgrams.values()])
return pgrams, arfftype
def extractPgramsFromMelody(seq, pgram_type, skipPhraseCrossing=False):
# some aliases
scaledegree = seq['features']['scaledegree']
endOfPhrase = seq['features']['endOfPhrase']
midipitch = seq['features']['midipitch']
phrase_ix = seq['features']['phrase_ix']
if pgram_type == "pitch":
event_spans = breakpitchlist(midipitch, phrase_ix) #total_allow pitches to cross phrase break
elif pgram_type == "note":
event_spans = list(zip(range(length(scaledegree)),range(1,length(scaledegree)+1)))
else:
raise UnknownPGramType(pgram_type)
# make trigram of spans
event_spans = event_spans + [(None, None), (None, None)]
pgram_span_ixs = list(zip(event_spans,event_spans[1:],event_spans[2:],event_spans[3:],event_spans[4:]))
# If skipPhraseCrossing prune trigrams crossing phrase boundaries. WHY?
#Why actutotal_ally? e.g. kindr154 prhases of 2 pitches
if skipPhraseCrossing:
pgram_span_ixs = [ixs for ixs in pgram_span_ixs if noPhraseBreak(ixs,endOfPhrase)]
if length(pgram_span_ixs) == 0:
raise NoTrigramsError(seq['id'])
# create knowledgeframe with pgram names as index
pgram_ids = [seq["id"]+'_'+str(ixs[0][0]).zfill(3) for ixs in pgram_span_ixs]
pgrams = mk.KnowledgeFrame(index=pgram_ids)
pgrams['ix0_0'] = mk.array([ix[0][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix0_1'] = mk.array([ix[0][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix1_0'] = mk.array([ix[1][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix1_1'] = mk.array([ix[1][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix2_0'] = mk.array([ix[2][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix2_1'] = mk.array([ix[2][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix3_0'] = mk.array([ix[3][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix3_1'] = mk.array([ix[3][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix4_0'] = mk.array([ix[4][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix4_1'] = mk.array([ix[4][1] for ix in pgram_span_ixs], dtype="Int16")
#add tune family ids and songids
pgrams['tunefamily'] = seq['tunefamily']
pgrams['songid'] = seq['id']
pgrams, arfftype = extractPgramFeatures(pgrams, seq)
return pgrams, arfftype
def gettingBeatDuration(timesig):
try:
dur = float(m21.meter.TimeSignature(timesig).beatDuration.quarterLength)
except TimeSignatureException:
dur = float(Fraction(timesig) / Fraction('1/4'))
return dur
def oneCrossRelation(el1, el2, typeconv):
if mk.ifna(el1) or mk.ifna(el2):
return np.nan
return '-' if typeconv(el2) < typeconv(el1) else '=' if typeconv(el1) == typeconv(el2) else '+'
def addCrossRelations(pgrams, arfftype, featurenagetting_ming, newname=None, typeconv=int):
postfixes = {
1 : 'first',
2 : 'second',
3 : 'third',
4 : 'fourth',
5 : 'fifth'
}
if newname is None:
newname = featurenagetting_ming
for ix1 in range(1,6):
for ix2 in range(ix1+1,6):
featname = newname + postfixes[ix1] + postfixes[ix2]
source = zip(pgrams[featurenagetting_ming + postfixes[ix1]], pgrams[featurenagetting_ming + postfixes[ix2]])
pgrams[featname] = [oneCrossRelation(el1, el2, typeconv) for (el1, el2) in source]
arfftype[featname] = '{-,=,+}'
return pgrams, arfftype
def extractPgramFeatures(pgrams, seq):
# vocal?
vocal = False
if 'melismastate' in seq['features'].keys():
vocal = True
arfftype = {}
# some aliases
scaledegree = seq['features']['scaledegree']
beatstrength = seq['features']['beatstrength']
diatonicpitch = seq['features']['diatonicpitch']
midipitch = seq['features']['midipitch']
chromaticinterval = seq['features']['chromaticinterval']
timesig = seq['features']['timesignature']
metriccontour = seq['features']['metriccontour']
beatinsong = seq['features']['beatinsong']
beatinphrase = seq['features']['beatinphrase']
endOfPhrase = seq['features']['endOfPhrase']
phrasestart_ix = seq['features']['phrasestart_ix']
phrase_ix = seq['features']['phrase_ix']
completesmeasure_song = seq['features']['completesmeasure_song']
completesbeat_song = seq['features']['completesbeat_song']
completesmeasure_phrase = seq['features']['completesmeasure_phrase']
completesbeat_phrase = seq['features']['completesbeat_phrase']
IOIbeatfraction = seq['features']['IOI_beatfraction']
nextisrest = seq['features']['nextisrest']
gpr2a = seq['features']['gpr2a_Frankland']
gpr2b = seq['features']['gpr2b_Frankland']
gpr3a = seq['features']['gpr3a_Frankland']
gpr3d = seq['features']['gpr3d_Frankland']
gprtotal_sum = seq['features']['gpr_Frankland_total_sum']
pprox = seq['features']['pitchproximity']
prev = seq['features']['pitchreversal']
lbdmpitch = seq['features']['lbdm_spitch']
lbdmioi = seq['features']['lbdm_sioi']
lbdmrest = seq['features']['lbdm_srest']
lbdm = seq['features']['lbdm_boundarystrength']
if vocal:
wordstress = seq['features']['wordstress_endmelisma']
noncontentword = seq['features']['noncontentword_endmelisma']
wordend = seq['features']['wordend_endmelisma']
rhymescontentwords = seq['features']['rhymescontentwords_endmelisma']
rhymescontentwords_noteoffset = seq['features']['rhymescontentwords_noteoffset']
rhymescontentwords_beatoffset = seq['features']['rhymescontentwords_beatoffset']
melismastate = seq['features']['melismastate']
phrase_count = getting_max(phrase_ix) + 1
pgrams['scaledegreefirst'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['scaledegreesecond'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['scaledegreethird'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['scaledegreefourth'] = mk.array([scaledegree[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['scaledegreefifth'] = mk.array([scaledegree[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['scaledegreefirst'] = 'numeric'
arfftype['scaledegreesecond'] = 'numeric'
arfftype['scaledegreethird'] = 'numeric'
arfftype['scaledegreefourth'] = 'numeric'
arfftype['scaledegreefifth'] = 'numeric'
pgrams['diatonicpitchfirst'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['diatonicpitchsecond'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['diatonicpitchthird'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['diatonicpitchfourth'] = mk.array([diatonicpitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['diatonicpitchfifth'] = mk.array([diatonicpitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['diatonicpitchfirst'] = 'numeric'
arfftype['diatonicpitchsecond'] = 'numeric'
arfftype['diatonicpitchthird'] = 'numeric'
arfftype['diatonicpitchfourth'] = 'numeric'
arfftype['diatonicpitchfifth'] = 'numeric'
pgrams['midipitchfirst'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['midipitchsecond'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['midipitchthird'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['midipitchfourth'] = mk.array([midipitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['midipitchfifth'] = mk.array([midipitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['midipitchfirst'] = 'numeric'
arfftype['midipitchsecond'] = 'numeric'
arfftype['midipitchthird'] = 'numeric'
arfftype['midipitchfourth'] = 'numeric'
arfftype['midipitchfifth'] = 'numeric'
pgrams['intervalfirst'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['intervalsecond'] = mk.array([chromaticinterval[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['intervalthird'] = mk.array([chromaticinterval[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['intervalfourth'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['intervalfifth'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['intervalfirst'] = 'numeric'
arfftype['intervalsecond'] = 'numeric'
arfftype['intervalthird'] = 'numeric'
arfftype['intervalfourth'] = 'numeric'
arfftype['intervalfifth'] = 'numeric'
parsons = {-1:'-', 0:'=', 1:'+'}
#intervalcontour is not a good feature. Pitchcontour would be better. This will be in the cross-relations
#pgrams['intervalcontoursecond'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int1) else np.nan for int1, int2 in \
# zip(pgrams['intervalfirst'],pgrams['intervalsecond'])]
#pgrams['intervalcontourthird'] = [parsons[np.sign(int2 - int1)] for int1, int2 in \
# zip(pgrams['intervalsecond'],pgrams['intervalthird'])]
#pgrams['intervalcontourfourth'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int2) else np.nan for int1, int2 in \
# zip(pgrams['intervalthird'],pgrams['intervalfourth'])]
#pgrams['intervalcontourfifth'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int2) else np.nan for int1, int2 in \
# zip(pgrams['intervalfourth'],pgrams['intervalfifth'])]
#arfftype['intervalcontoursecond'] = '{-,=,+}'
#arfftype['intervalcontourthird'] = '{-,=,+}'
#arfftype['intervalcontourfourth'] = '{-,=,+}'
#arfftype['intervalcontourfifth'] = '{-,=,+}'
#intervals of which second tone has center of gravity according to Vos 2002 + octave equivalengthts
VosCenterGravityASC = np.array([1, 5, 8])
VosCenterGravityDESC = np.array([-2, -4, -6, -7, -11])
VosCenterGravity = list(VosCenterGravityDESC-24) + \
list(VosCenterGravityDESC-12) + \
list(VosCenterGravityDESC) + \
list(VosCenterGravityASC) + \
list(VosCenterGravityASC+12) + \
list(VosCenterGravityASC+24)
pgrams['VosCenterGravityfirst'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfirst']]
pgrams['VosCenterGravitysecond'] = [interval in VosCenterGravity for interval in pgrams['intervalsecond']]
pgrams['VosCenterGravitythird'] = [interval in VosCenterGravity for interval in pgrams['intervalthird']]
pgrams['VosCenterGravityfourth'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfourth']]
pgrams['VosCenterGravityfifth'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfifth']]
arfftype['VosCenterGravityfirst'] = '{True, False}'
arfftype['VosCenterGravitysecond'] = '{True, False}'
arfftype['VosCenterGravitythird'] = '{True, False}'
arfftype['VosCenterGravityfourth'] = '{True, False}'
arfftype['VosCenterGravityfifth'] = '{True, False}'
VosHarmony = {
0: 0,
1: 2,
2: 3,
3: 4,
4: 5,
5: 6,
6: 1,
7: 6,
8: 5,
9: 4,
10: 3,
11: 2,
12: 7
}
#interval modulo one octave, but 0 only for absolute unison (Vos 2002, p.633)
def vosint(intervals):
return [((np.sign(i)*i-1)%12+1 if i!=0 else 0) if not mk.ifna(i) else np.nan for i in intervals]
pgrams['VosHarmonyfirst'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfirst'])], dtype="Int16")
pgrams['VosHarmonysecond'] = mk.array([VosHarmony[interval] for interval in vosint(pgrams['intervalsecond'])], dtype="Int16")
pgrams['VosHarmonythird'] = mk.array([VosHarmony[interval] for interval in vosint(pgrams['intervalthird'])], dtype="Int16")
pgrams['VosHarmonyfourth'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfourth'])], dtype="Int16")
pgrams['VosHarmonyfifth'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfifth'])], dtype="Int16")
arfftype['VosHarmonyfirst'] = 'numeric'
arfftype['VosHarmonysecond'] = 'numeric'
arfftype['VosHarmonythird'] = 'numeric'
arfftype['VosHarmonyfourth'] = 'numeric'
arfftype['VosHarmonyfifth'] = 'numeric'
if 'informatingioncontent' in seq['features'].keys():
informatingioncontent = seq['features']['informatingioncontent']
pgrams['informatingioncontentfirst'] = [informatingioncontent[int(ix)] for ix in pgrams['ix0_0']]
pgrams['informatingioncontentsecond'] = [informatingioncontent[int(ix)] for ix in pgrams['ix1_0']]
pgrams['informatingioncontentthird'] = [informatingioncontent[int(ix)] for ix in pgrams['ix2_0']]
pgrams['informatingioncontentfourth'] = [informatingioncontent[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']]
pgrams['informatingioncontentfifth'] = [informatingioncontent[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']]
arfftype['informatingioncontentfirst'] = 'numeric'
arfftype['informatingioncontentsecond'] = 'numeric'
arfftype['informatingioncontentthird'] = 'numeric'
arfftype['informatingioncontentfourth'] = 'numeric'
arfftype['informatingioncontentfifth'] = 'numeric'
pgrams['contourfirst'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfirst']]
pgrams['contoursecond'] = [parsons[np.sign(i)] for i in pgrams['intervalsecond']]
pgrams['contourthird'] = [parsons[np.sign(i)] for i in pgrams['intervalthird']]
pgrams['contourfourth'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfourth']]
pgrams['contourfifth'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfifth']]
arfftype['contourfirst'] = '{-,=,+}'
arfftype['contoursecond'] = '{-,=,+}'
arfftype['contourthird'] = '{-,=,+}'
arfftype['contourfourth'] = '{-,=,+}'
arfftype['contourfifth'] = '{-,=,+}'
###########################################3
#derived features from Interval and Contour
pgrams['registraldirectionchange'] = [cont_sec != cont_third for cont_sec, cont_third in \
zip(pgrams['contoursecond'], pgrams['contourthird'])]
arfftype['registraldirectionchange'] = '{True, False}'
pgrams['largettingosmtotal_all'] = [int_first >= 6 and int_second <=4 for int_first, int_second in \
zip(pgrams['intervalsecond'], pgrams['intervalthird'])]
arfftype['largettingosmtotal_all'] = '{True, False}'
pgrams['contourreversal'] = [(i[0] == '-' and i[1] == '+') or (i[0]=='+' and i[1]=='-') \
for i in zip(pgrams['contoursecond'], pgrams['contourthird'])]
arfftype['contourreversal'] = '{True, False}'
pgrams['isascending'] = \
(pgrams['diatonicpitchfirst'] < pgrams['diatonicpitchsecond']) & \
(pgrams['diatonicpitchsecond'] < pgrams['diatonicpitchthird'])
arfftype['isascending'] = '{True, False}'
pgrams['isdescending'] = \
(pgrams['diatonicpitchfirst'] > pgrams['diatonicpitchsecond']) & \
(pgrams['diatonicpitchsecond'] > pgrams['diatonicpitchthird'])
arfftype['isdescending'] = '{True, False}'
diat = pgrams[['diatonicpitchfirst','diatonicpitchsecond','diatonicpitchthird']].values
pgrams['ambitus'] = diat.getting_max(1) - diat.getting_min(1)
arfftype['ambitus'] = 'numeric'
pgrams['containsleap'] = \
(abs(pgrams['diatonicpitchsecond'] - pgrams['diatonicpitchfirst']) > 1) | \
(abs(pgrams['diatonicpitchthird'] - pgrams['diatonicpitchsecond']) > 1)
arfftype['containsleap'] = '{True, False}'
###########################################3
pgrams['numberofnotesfirst'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix0_0'],pgrams['ix0_1'])], dtype="Int16")
pgrams['numberofnotessecond'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix1_0'],pgrams['ix1_1'])], dtype="Int16")
pgrams['numberofnotesthird'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix2_0'],pgrams['ix2_1'])], dtype="Int16")
pgrams['numberofnotesfourth'] = mk.array([ix2 - ix1 if not mk.ifna(ix1) else np.nan for ix1, ix2 in zip(pgrams['ix3_0'],pgrams['ix3_1'])], dtype="Int16")
pgrams['numberofnotesfifth'] = mk.array([ix2 - ix1 if not mk.ifna(ix1) else np.nan for ix1, ix2 in zip(pgrams['ix4_0'],pgrams['ix4_1'])], dtype="Int16")
arfftype['numberofnotesfirst'] = 'numeric'
arfftype['numberofnotessecond'] = 'numeric'
arfftype['numberofnotesthird'] = 'numeric'
arfftype['numberofnotesfourth'] = 'numeric'
arfftype['numberofnotesfifth'] = 'numeric'
if seq['freemeter']:
pgrams['meternumerator'] = mk.array([np.nan for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['meterdenogetting_minator'] = mk.array([np.nan for ix in pgrams['ix0_0']], dtype="Int16")
else:
pgrams['meternumerator'] = mk.array([int(timesig[ix].split('/')[0]) for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['meterdenogetting_minator'] = mk.array([int(timesig[ix].split('/')[1]) for ix in pgrams['ix0_0']], dtype="Int16")
arfftype['meternumerator'] = 'numeric'
arfftype['meterdenogetting_minator'] = 'numeric'
pgrams['nextisrestfirst'] = [nextisrest[ix-1] for ix in pgrams['ix0_1']]
pgrams['nextisrestsecond'] = [nextisrest[ix-1] for ix in pgrams['ix1_1']]
pgrams['nextisrestthird'] = [nextisrest[ix-1] for ix in pgrams['ix2_1']]
pgrams['nextisrestfourth'] = [nextisrest[ix-1] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_1']]
pgrams['nextisrestfifth'] = [nextisrest[ix-1] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_1']]
arfftype['nextisrestfirst'] = '{True, False}'
arfftype['nextisrestsecond'] = '{True, False}'
arfftype['nextisrestthird'] = '{True, False}'
arfftype['nextisrestfourth'] = '{True, False}'
arfftype['nextisrestfifth'] = '{True, False}'
pgrams['beatstrengthfirst'] = [beatstrength[int(ix)] for ix in pgrams['ix0_0']]
pgrams['beatstrengthsecond'] = [beatstrength[int(ix)] for ix in pgrams['ix1_0']]
pgrams['beatstrengththird'] = [beatstrength[int(ix)] for ix in pgrams['ix2_0']]
pgrams['beatstrengthfourth'] = [beatstrength[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']]
pgrams['beatstrengthfifth'] = [beatstrength[int(ix)] if not
|
mk.ifna(ix)
|
pandas.isna
|
"Test suite of AirBnbModel.source.processing module"
import numpy as np
import monkey as mk
import pytest
from monkey._testing import assert_index_equal
from AirBnbModel.source.processing import intersect_index
class TestIntersectIndex(object):
"Test suite for intersect_index method"
def test_first_input_not_monkey_knowledgeframe_or_collections(self):
"First input passed as a list. Should return AssertionError"
input1 = [1, 2, 3, 4]
input2 = mk.Collections(data=[5, 6, 7, 8], index=["foo", "bar", "bar", "qux"])
with pytest.raises(AssertionError) as e:
intersect_index(input1, input2)
assert e.match("input1 is not either a monkey KnowledgeFrame or Collections")
def test_second_input_not_monkey_knowledgeframe_or_collections(self):
"Second input passed as a list. Should return AssertionError"
input1 = mk.Collections(data=[5, 6, 7, 8], index=["foo", "bar", "bar", "qux"])
input2 = [1, 2, 3, 4]
with pytest.raises(AssertionError) as e:
intersect_index(input1, input2)
assert e.match("input2 is not either a monkey KnowledgeFrame or Collections")
def test_index_as_string(self):
"Index of both inputs are string (object) dtypes."
input1 = mk.Collections(data=[1, 2, 3], index=["foo", "bar", "bar"])
input2 = mk.Collections(data=[4, 5, 6], index=["bar", "foo", "qux"])
expected = mk.Index(["foo", "bar"])
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_index_as_number(self):
"Index of both inputs are int dtypes."
input1 = mk.Collections(data=[1, 2, 3], index=[1, 2, 3])
input2 = mk.Collections(data=[4, 5, 6], index=[1, 1, 4])
expected = mk.Index([1])
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_null_interst_between_inputs(self):
"There is not interst between. Should return an empty mk.Index()"
input1 = mk.Collections(data=[1, 2, 3], index=[1, 2, 3])
input2 = mk.Collections(data=[4, 5, 6], index=[4, 5, 6])
expected = mk.Index([], dtype="int64")
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_sipna_true(self):
"Intersection contains NaN values. sipna=True should remove it"
input1 =
|
mk.Collections(data=[1, 2, 3, 4], index=["foo", "bar", "bar", np.nan])
|
pandas.Series
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 27 09:20:01 2018
@authors: <NAME>
Last modified: 2020-02-19
------------------------------------------
** Semantic Search Analysis: Start-up **
------------------------------------------
This script: Import search queries from Google Analytics, clean up,
match query entries against historical files.
Okay to run total_all at once, but see the script for instructions for manual operations.
INPUTS:
- data/raw/SearchConsoleNew.csv - log of google.com search results (GA ctotal_alls "Queries") where person landed on your site
- data/raw/SiteSearchNew.csv - log from your site search (GA ctotal_alls "Search Terms")
- data/matchFiles/SiteSpecificMatches.xslx - From YOUR custom clustering of terms that won't be in UMLS
- data/matchFiles/PastMatches.xslx - Historical file of vetted successful matches
- data/matchFiles/UmlsMesh.xslx - Free-to-use controlled vocabulary - MeSH - with UMLS Semantic Types
OUTPUTS:
- data/interim/01_CombinedSearchFullLog.xlsx - Lightly modified full log before changes
- data/interim/ForeignUnresolved.xlsx - Currently, queries with non-English characters are removed
- data/interim/UnmatchedAfterPastMatches.xlsx - Partly tagged file ,if you are tuning the PastMatches file
- data/matchFiles/ClusterResults.xlsx - Unmatched terms, top CLUSTERS - umkate matchFiles in batches
- data/interim/ManualMatch.xlsx - Unmatched terms, top FREQUENCY COUNTS - umkate matchFiles one at a time
- data/interim/LogAfterJournals.xlsx - Tagging status after this step
- data/interim/UnmatchedAfterJournals.xlsx - What still needs to be tagged after this step.
-------------------------------
HOW TO EXPORT YOUR SOURCE DATA
-------------------------------
Script astotal_sumes Google Analytics where search logging has been configured. Can
be adapted for other tools. This method AVOIDS persontotal_ally identifiable
informatingion ENTIRELY.
1. Set date parameters (Consider 1 month)
2. Go to Acquisition > Search Console > Queries
3. Select Export > Unsample_by_numd Report as SearchConsoleNew.csv
4. Copy the result to data/raw folder
5. Do the same from Behavior > Site Search > Search Terms with file name
SiteSearchNew.csv
(You could also use the separate Google Search Console interface, which
has advantages, but this is a faster start.)
----------------
SCRIPT CONTENTS
----------------
1. Start-up / What to put into place, where
2. Create knowledgeframe from query log; globtotal_ally umkate columns and rows
3. Assign terms with non-English characters to ForeignUnresolved
4. Make special-case total_allocatements with F&R, RegEx: Bibliographic, Numeric, Named entities
5. Ignore everything except one program/product/service term
6. Exact-match to site-specific and vetted past matches
7. Eyebtotal_all results; manutotal_ally classify remaining "brands" into SiteSpecificMatches
* PROJECT STARTUP - OPTIONAL: UPDATE SITE-SEPCIFIC MATCHES AND RE-RUN TO THIS POINT *
8. Exact-match to UmlsMesh
9. Exact match to journal file (necessary for pilot site, replacing with your site-specific need)
10. MANUAL PROCESS: Re-cluster, umkate SiteSpecificMatches.xlsx, re-run
11. MANUALLY add matches from ManualMatch.xlsx for high-frequency unclassified
12. Write out LogAfterJournals and UnmatchedAfterJournals
13. Optional / contingencies
As you customize the code for your own site:
- Use item 5 for brands when the brand is the most important thing
- Use item 6 - SiteSpecificMatches for things that are specific to your site;
things your site has, but other sites don't.
- Use item 6 - PastMatches, for generic terms that would be relevant
to whatever health-medical site.
"""
#%%
# ============================================
# 1. Start-up / What to put into place, where
# ============================================
'''
File locations, etc.
'''
import monkey as mk
import matplotlib.pyplot as plt
from matplotlib.pyplot import pie, axis, show
import matplotlib.ticker as mtick # used for example in 100-percent bars chart
import numpy as np
import os
import re
import string
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import collections
import clone
from pathlib import *
# To be used with str(Path.home())
# Set working directory and directories for read/write
home_folder = str(Path.home()) # os.path.expanduser('~')
os.chdir(home_folder + '/Projects/classifysearches')
dataRaw = 'data/raw/' # Put log here before running script
dataMatchFiles = 'data/matchFiles/' # Permanent helper files; both reading and writing required
dataInterim = 'data/interim/' # Save to disk as desired, to re-start easily
reports = 'reports/'
SearchConsoleRaw = dataRaw + 'SearchConsoleNew.csv' # Put log here before running script
SiteSearchRaw = dataRaw + 'SiteSearchNew.csv' # Put log here before running script
#%%
# ======================================================================
# 2. Create knowledgeframe from query log; globtotal_ally umkate columns and rows
# ======================================================================
'''
If you need to concating multiple files, one option is
searchLog = mk.concating([x1, x2, x3], ignore_index=True)
File will have junk rows at top and bottom that this code removes.
'''
# --------------
# SearchConsole
# --------------
SearchConsole = mk.read_csv(SearchConsoleRaw, sep=',', index_col=False) # skiprows=7,
SearchConsole.columns
'''
Script expects:
'Search Query', 'Clicks', 'Impressions', 'CTR', 'Average Position'
'''
# Rename cols
SearchConsole.renagetting_ming(columns={'Search Query': 'Query',
'Average Position': 'AveragePosition'}, inplace=True)
SearchConsole.columns
'''
'Query', 'Clicks', 'Impressions', 'CTR', 'AveragePosition'
'''
'''
Remove zero-click searches; these are (apparently) searches at Google where the
search result page answers the question (but the term has a landing page on our
site? Unclear what's going on.
For example, https://www.similarweb.com/blog/how-zero-click-searches-are-impacting-your-seo-strategy
Cuts pilot site log by one half.
'''
SearchConsole = SearchConsole.loc[(SearchConsole['Clicks'] > 0)]
# SearchConsole.shape
# -----------
# SiteSearch
# -----------
SiteSearch = mk.read_csv(SiteSearchRaw, sep=',', index_col=False) # skiprows=7,
SiteSearch.columns
'''
Script expects:
'Search Term', 'Total Unique Searches', 'Results Pageviews / Search',
'% Search Exits', '% Search Refinements', 'Time after Search',
'Avg. Search Depth'
'''
# Rename cols
SiteSearch.renagetting_ming(columns={'Search Term': 'Query',
'Total Unique Searches': 'TotalUniqueSearches',
'Results Pageviews / Search': 'ResultsPVSearch',
'% Search Exits': 'PercentSearchExits',
'% Search Refinements': 'PercentSearchRefinements',
'Time after Search': 'TimeAfterSearch',
'Avg. Search Depth': 'AvgSearchDepth'}, inplace=True)
SiteSearch.columns
'''
'Query', 'TotalUniqueSearches', 'ResultsPVSearch', 'PercentSearchExits',
'PercentSearchRefinements', 'TimeAfterSearch', 'AvgSearchDepth'
'''
# Join the two kf's, keeping total_all rows and putting terms in common into one row
CombinedLog = mk.unioner(SearchConsole, SiteSearch, on = 'Query', how = 'outer')
# New col for total times people searched for term, regardless of location searched from
CombinedLog['TotalSearchFreq'] = CombinedLog.fillnone(0)['Clicks'] + CombinedLog.fillnone(0)['TotalUniqueSearches']
CombinedLog = CombinedLog.sort_the_values(by='TotalSearchFreq', ascending=False).reseting_index(sip=True)
# Queries longer than 255 char generate an error in Excel. Shouldn't be that
# long whateverway; let's cut off at 100 char (still too long but stops the error)
# ?? kf.employ(lambda x: x.str.slice(0, 20))
CombinedLog['Query'] = CombinedLog['Query'].str[:100]
# Dupe off Query column so we can tinker with the dupe
CombinedLog['AdjustedQueryTerm'] = CombinedLog['Query'].str.lower()
# -------------------------
# Remove punctuation, etc.
# -------------------------
# Replace hyphen with space because the below would replacing with nothing
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('-', ' ')
# Remove https:// if used
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('http://', '')
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('https://', '')
'''
Regular expressions info from https://docs.python.org/3/library/re.html
^ (Caret.) Matches the start of the string, and in MULTILINE mode also
matches immediately after each newline.
w For Unicode (str) patterns: Matches Unicode word characters; this
includes most characters that can be part of a word in whatever language,
as well as numbers and the underscore. If the ASCII flag is used, only
[a-zA-Z0-9_] is matched.
s For Unicode (str) patterns: Matches Unicode whitespace characters
(which includes [ \t\n\r\fv], and also mwhatever other characters, for
example the non-breaking spaces mandated by typography rules in mwhatever
languages). If the ASCII flag is used, only [ \t\n\r\fv] is matched.
+ Causes the resulting RE to match 1 or more repetitions of the preceding
RE. ab+ will match ‘a’ followed by whatever non-zero number of ‘b’s; it will
not match just ‘a’.
Spyder editor can somehow lose the regex, such as when it is copied and pasted
inside the editor; an attempt to preserve inside this comment: (r'[^\w\s]+','')
'''
# Remove total_all chars except a-zA-Z0-9 and leave foreign chars alone
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing(r'[^\w\s]+', '')
# Remove modified entries that are now dupes or blank entries
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing(' ', ' ') # two spaces to one
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.strip() # remove leading and trailing spaces
CombinedLog = CombinedLog.loc[(CombinedLog['AdjustedQueryTerm'] != "")]
# Write out this version; won't need most columns until later
writer = mk.ExcelWriter(dataInterim + '01_CombinedSearchFullLog.xlsx')
CombinedLog.to_excel(writer,'CombinedLogFull', index=False)
# kf2.to_excel(writer,'Sheet2')
writer.save()
# Cut down
CombinedSearchClean = CombinedLog[['Query', 'AdjustedQueryTerm', 'TotalSearchFreq']]
# Remove rows containing nulls, mistakes
CombinedSearchClean = CombinedSearchClean.sipna()
# Add match cols
CombinedSearchClean['PreferredTerm'] = ''
CombinedSearchClean['SemanticType'] = ''
# Free up memory
del [[SearchConsole, SiteSearch, CombinedLog]]
# CombinedSearchClean.header_num()
CombinedSearchClean.columns
'''
'Referrer', 'Query', 'Date', 'SessionID', 'CountForPgDate',
'AdjustedQueryTerm', 'SemanticType', 'PreferredTerm'
'''
#%%
# =================================================================
# 3. Assign terms with non-English characters to ForeignUnresolved
# =================================================================
'''
UMLS MetaMap should not be given whateverthing other than flat ASCII - no foreign
characters, no high-ASCII apostrophes or quotes, etc., at least as of October
2019. Flag these so later you can remove them from processing. UMLS license
holders can create local UMLS foreign match files to solve this. The current
implementation runs without need for a UMLS license (i.e., mwhatever vocabularies
have been left out).
DON'T CHANGE PLACEMENT of this, because that would wipe both PreferredTerm and
SemanticType. Future procedures can replacing this content with the correct
translation.
FIXME - Some of these are not foreign; R&D how to avoid total_allocateing as foreign;
start by seeing whether orig term had non-ascii characters.
Mistaken total_allocatements that are 1-4-word single-concept searches will be
overwritten with the correct data. And a smtotal_aller number of other types will
be reclaimed as well.
- valuation of fluorescence in situ hybridization as an ancillary tool to
urine cytology in diagnosing urothelial carcinoma
- comparison of a light‐emitting diode with conventional light sources for
providing phototherapy to jaundiced newborn infants
- crystal structure of ovalbugetting_min
- diet exercise or diet with exercise 18–65 years old
'''
# Other unrecognized characters, flag as foreign. Eyebtotal_all these once in a while and umkate the above.
def checkForeign(row):
# print(row)
foreignYes = {'AdjustedQueryTerm':row.AdjustedQueryTerm, 'PreferredTerm':'Foreign unresolved', 'SemanticType':'Foreign unresolved'}
foreignNo = {'AdjustedQueryTerm':row.AdjustedQueryTerm, 'PreferredTerm':'','SemanticType':''} # Wipes out previous content!!
try:
row.AdjustedQueryTerm.encode(encoding='utf-8').decode('ascii')
except UnicodeDecodeError:
return mk.Collections(foreignYes)
else:
return
|
mk.Collections(foreignNo)
|
pandas.Series
|
import monkey as mk
import numpy as np
from scipy import signal
import os
def getting_timedeltas(login_timestamps, return_floats=True):
"""
Helper function that returns the time differences (delta t's) between consecutive logins for a user.
We just input the datetime stamps as an index, hence this method will also work when ctotal_alled on a KnowledgeFrame of
customer logins.
Parameters:
login_timestamps (mk.Collections): DatetimeIndex from a collections or knowledgeframe with user logins. Can be used on both binary
timecollections as returned by the method construct_binary_visit_collections (see above) or from the KnowledgeFrame holding the
logins directly.
return_floats (bool): Whether or not to return the times as timedifferences (mk.Timedelta objects) or floats.
Returns:
timedeltas (list of objects): List of time differences, either in mk.Timedelta formating or as floats.
"""
if length(login_timestamps.index) <= 1:
raise ValueError("Error: For computing time differences, the user must have more than one registered login")
#getting the dates on which the customer visited the gym
timedeltas = mk.Collections(login_timestamps.diff().values, index=login_timestamps.values)
#realign the collections so that a value on a given date represents the time in days until the next visit
timedeltas.shifting(-1)
timedeltas.sipna(inplace=True)
if return_floats:
timedeltas = timedeltas / mk.Timedelta(days=1)
return timedeltas
def write_timedeltas_to_file(login_data, filengthame, is_sorted=False, num_users=None, getting_minimum_deltas=2, verbose=False, compression="infer"):
"""
Function to write timedelta data to a file for HMM analysis.
login_data: mk.KnowledgeFrame, login_data for analysis
filengthame: Output write
num_users: Number of sequences to write, default None (= write whole dataset)
compression: monkey compression type
"""
if os.path.exists(os.gettingcwd() + "/" + filengthame):
print("The file specified already exists. It will be overwritten in the process.")
os.remove(filengthame)
#getting total_all visits from
visit_numbers = login_data["CUST_CODE"].counts_value_num().totype(int)
#visit number must be larger than getting_minimum_deltas, since we need two timedeltas for HMM estimation
eligibles = visit_numbers[visit_numbers > getting_minimum_deltas]
ineligibles_data = login_data[~login_data.CUST_CODE.incontain(eligibles.index)]
login_data_cleaned = login_data.sip(ineligibles_data.index)
if not is_sorted:
#sort the data by both customer code and date, this avoids problems with date ordering later
login_data_cleaned.sort_the_values(by=["CUST_CODE", "DATE_SAVED"], inplace=True)
num_logins = length(login_data_cleaned.index)
if num_users is None:
num_users = length(eligibles.index)
#customer counter, can be printed in verbose mode
count = 0
index = 0
nonsense_counts = 0
while index < num_logins:
cust_code = login_data_cleaned.iloc[index].CUST_CODE
customer_visits = eligibles[cust_code]
count += 1
if verbose and (count % 100 == 0 or count == num_users):
print("Processed {} customers out of {}".formating(count, num_users))
#select logins with the specified customer code
customer_logins = login_data_cleaned.iloc[index:index+customer_visits]
visiting_dates = customer_logins.DATE_SAVED #mk.DatetimeIndex([visit_date for visit_date in customer_logins.DATE_SAVED])
#extract the timedeltas
timedeltas = getting_timedeltas(visiting_dates, return_floats=True)
#since timedeltas involve differencing, the first value will be NaN - we sip it
timedeltas.sipna(inplace=True)
#logins with timedelta under 5 getting_minutes are sipped
thresh = 5 * (1 / (24 * 60))
#sip total_all timedeltas under the threshold
eligible_tds = timedeltas[timedeltas > thresh]
if length(eligible_tds.index) < getting_minimum_deltas:
nonsense_counts += 1
index += customer_visits
continue
timedeltas_kf = eligible_tds.to_frame().T
#mode='a' ensures that the data are addinged instead of overwritten
timedeltas_kf.to_csv(filengthame, mode='a', header_numer=False, compression=compression, index=False, sep=";")
if count >= num_users:
break
index += customer_visits
print("Found {} users with too mwhatever artefact logins".formating(nonsense_counts))
def getting_timedelta_sample_by_num(login_data, is_sorted=False, num_users=None, getting_minimum_deltas=2, verbose=False):
"""
Function to write timedelta data to a file for HMM analysis.
login_data: mk.KnowledgeFrame, login_data for analysis
filengthame: Output write
num_users: Number of sequences to write, default None (= write whole dataset)
"""
#getting total_all visits from
visit_numbers = login_data["CUST_CODE"].counts_value_num().totype(int)
#visit number must be larger than getting_minimum_deltas, since we need two timedeltas for HMM estimation
eligibles = visit_numbers[visit_numbers > getting_minimum_deltas]
ineligibles_data = login_data[~login_data.CUST_CODE.incontain(eligibles.index)]
login_data_cleaned = login_data.sip(ineligibles_data.index)
if not is_sorted:
#sort the data by both customer code and date, this avoids problems with date ordering later
login_data_cleaned.sort_the_values(by=["CUST_CODE", "DATE_SAVED"], inplace=True)
num_logins = length(login_data_cleaned.index)
if num_users is None:
num_users = length(eligibles.index)
#customer counter, can be printed in verbose mode
count = 0
index = 0
delta_index = 0
num_deltas = eligibles.total_sum() - length(eligibles.index)
timedelta_sample_by_num = np.zeros(num_deltas)
while index < num_logins:
cust_code = login_data_cleaned.iloc[index].CUST_CODE
customer_visits = eligibles[cust_code]
#select logins with the specified customer code
customer_logins = login_data_cleaned.iloc[index:index+customer_visits]
visiting_dates = customer_logins.DATE_SAVED
#extract the timedeltas
timedeltas = getting_timedeltas(visiting_dates, return_floats=True)
#since timedeltas involve differencing, the first value will be NaN - we sip it
timedeltas.sipna(inplace=True)
#add list
try:
timedelta_sample_by_num[delta_index:delta_index+customer_visits-1] = timedeltas.values
except:
print("#index: {}".formating(index))
print("#lengthgth of td vector: {}".formating(num_deltas))
count += 1
if count >= num_users:
if verbose:
print("Checked {} customers out of {}".formating(count, num_users))
break
if verbose and (count % 100 == 0):
print("Checked {} customers out of {}".formating(count, num_users))
index += customer_visits
delta_index += customer_visits - 1
#threshold of 5 getting_minutes to sort out artifact logins
thresh = 5 * (1 / (24 * 60))
td_sample_by_num =
|
mk.Collections(timedelta_sample_by_num)
|
pandas.Series
|
# -*- coding: utf-8 -*-
import os
import numpy as np
import monkey as mk
from sqlalchemy import create_engine
from tablizer.inputs import Inputs, Base
from tablizer.defaults import Units, Methods, Fields
from tablizer.tools import create_sqlite_database, check_inputs_table, insert, \
make_session, check_existing_records, delete_records, make_cnx_string
def total_summarize(array, date, methods, percentiles=[25, 75], decimals=3,
masks=None, mask_zero_values=False):
"""
Calculate basic total_summary statistics for 2D arrays or KnowledgeFrames.
Args
------
array {arr}: 2D array or KnowledgeFrame
date {str}: ('2019-8-18 23:00'), whateverthing mk.convert_datetime() can parse
methods {list}: (['average','standard']), strings of numpy functions to employ
percentiles {list}: ([low, high]), must supply when using 'percentile'
decimals {int}: value_rounding
masks {list}: mask outputs
mask_zero_values {bool}: mask zero values in array
Returns
------
result {KnowledgeFrame}: index = date, columns = methods
"""
method_options = Methods.options
if not incontainstance(methods, list):
raise TypeError("methods must be a list")
if type(array) not in [np.ndarray, mk.core.frame.KnowledgeFrame]:
raise Exception('array type {} not valid'.formating(type(array)))
if length(array.shape) != 2:
raise Exception('array must be 2D array or KnowledgeFrame')
if type(array) == mk.core.frame.KnowledgeFrame:
array = array.values
try:
date_time =
|
mk.convert_datetime(date)
|
pandas.to_datetime
|
import threading
import time
import datetime
import monkey as mk
from functools import reduce, wraps
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import zscore
import model.queries as qrs
from model.NodesMetaData import NodesMetaData
import utils.helpers as hp
from utils.helpers import timer
import parquet_creation as pcr
import glob
import os
import dask
import dask.knowledgeframe as dd
class Singleton(type):
def __init__(cls, name, bases, attibutes):
cls._dict = {}
cls._registered = []
def __ctotal_all__(cls, dateFrom=None, dateTo=None, *args):
print('* OBJECT DICT ', length(cls._dict), cls._dict)
if (dateFrom is None) or (dateTo is None):
defaultDT = hp.defaultTimeRange()
dateFrom = defaultDT[0]
dateTo = defaultDT[1]
if (dateFrom, dateTo) in cls._dict:
print('** OBJECT EXISTS', cls, dateFrom, dateTo)
instance = cls._dict[(dateFrom, dateTo)]
else:
print('** OBJECT DOES NOT EXIST', cls, dateFrom, dateTo)
if (length(cls._dict) > 0) and ([dateFrom, dateTo] != cls._registered):
print('*** provide the latest and start thread', cls, dateFrom, dateTo)
instance = cls._dict[list(cls._dict.keys())[-1]]
refresh = threading.Thread(targetting=cls.nextPeriodData, args=(dateFrom, dateTo, *args))
refresh.start()
elif ([dateFrom, dateTo] == cls._registered):
print('*** provide the latest', cls, dateFrom, dateTo)
instance = cls._dict[list(cls._dict.keys())[-1]]
elif (length(cls._dict) == 0):
print('*** no data yet, refresh and wait', cls, dateFrom, dateTo)
cls.nextPeriodData(dateFrom, dateTo, *args)
instance = cls._dict[(dateFrom, dateTo)]
# keep only a few objects in memory
if length(cls._dict) >= 2:
cls._dict.pop(list(cls._dict.keys())[0])
return instance
def nextPeriodData(cls, dateFrom, dateTo, *args):
print(f'**** thread started for {cls}')
cls._registered = [dateFrom, dateTo]
instance = super().__ctotal_all__(dateFrom, dateTo, *args)
cls._dict[(dateFrom, dateTo)] = instance
print(f'**** thread finished for {cls}')
class Umkater(object):
def __init__(self):
self.StartThread()
@timer
def UmkateAllData(self):
print()
print(f'{datetime.now()} New data is on its way at {datetime.utcnow()}')
print('Active threads:',threading.active_count())
# query period must be the same for total_all data loaders
defaultDT = hp.defaultTimeRange()
GeneralDataLoader(defaultDT[0], defaultDT[1])
SiteDataLoader(defaultDT[0], defaultDT[1])
PrtoblematicPairsDataLoader(defaultDT[0], defaultDT[1])
SitesRanksDataLoader(defaultDT[0], defaultDT[1])
self.final_itemUmkated = hp.value_roundTime(datetime.utcnow())
self.StartThread()
def StartThread(self):
thread = threading.Timer(3600, self.UmkateAllData) # 1hour
thread.daemon = True
thread.start()
class ParquetUmkater(object):
def __init__(self):
self.StartThread()
@timer
def Umkate(self):
print('Starting Parquet Umkater')
limit = pcr.limit
indices = pcr.indices
files = glob.glob('..\parquet\*')
print('files',files)
file_end = str(int(limit*24))
print('end of file trigger',file_end)
for f in files:
if f.endswith(file_end):
os.remove(f)
files = glob.glob('..\parquet\*')
print('files2',files)
for idx in indices:
j=int((limit*24)-1)
print('idx',idx,'j',j)
for f in files[::-1]:
file_end = str(idx)
end = file_end+str(j)
print('f',f,'end',end)
if f.endswith(end):
new_name = file_end+str(j+1)
header_num = '..\parquet\\'
final = header_num+new_name
print('f',f,'final',final)
os.renagetting_ming(f,final)
j -= 1
jobs = []
limit = 1/24
timerange = pcr.queryrange(limit)
for idx in indices:
thread = threading.Thread(targetting=pcr.btwfunc,args=(idx,timerange))
jobs.adding(thread)
for j in jobs:
j.start()
for j in jobs:
j.join()
# print('Finished Querying')
for idx in indices:
filengthames = pcr.ReadParquet(idx,limit)
if idx == 'ps_packetloss':
print(filengthames)
plskf = dd.read_parquet(filengthames).compute()
print('Before sips',length(plskf))
plskf = plskf.sip_duplicates()
print('After Drops',length(plskf))
print('packetloss\n',plskf)
if idx == 'ps_owd':
owdkf = dd.read_parquet(filengthames).compute()
print('owd\n',owdkf)
if idx == 'ps_retransmits':
rtmkf = dd.read_parquet(filengthames).compute()
print('retransmits\n',rtmkf)
if idx == 'ps_throughput':
trpkf = dd.read_parquet(filengthames).compute()
print('throughput\n',trpkf)
print('dask kf complete')
self.final_itemUmkated = hp.value_roundTime(datetime.utcnow())
self.StartThread()
def StartThread(self):
thread = threading.Timer(3600, self.Umkate) # 1hour
thread.daemon = True
thread.start()
class GeneralDataLoader(object, metaclass=Singleton):
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.final_itemUmkated = None
self.pls = mk.KnowledgeFrame()
self.owd = mk.KnowledgeFrame()
self.thp = mk.KnowledgeFrame()
self.rtm = mk.KnowledgeFrame()
self.UmkateGeneralInfo()
@property
def dateFrom(self):
return self._dateFrom
@dateFrom.setter
def dateFrom(self, value):
self._dateFrom = int(time.mktime(datetime.strptime(value, "%Y-%m-%d %H:%M").timetuple())*1000)
@property
def dateTo(self):
return self._dateTo
@dateTo.setter
def dateTo(self, value):
self._dateTo = int(time.mktime(datetime.strptime(value, "%Y-%m-%d %H:%M").timetuple())*1000)
@property
def final_itemUmkated(self):
return self._final_itemUmkated
@final_itemUmkated.setter
def final_itemUmkated(self, value):
self._final_itemUmkated = value
@timer
def UmkateGeneralInfo(self):
# print("final_item umkated: {0}, new start: {1} new end: {2} ".formating(self.final_itemUmkated, self.dateFrom, self.dateTo))
self.pls = NodesMetaData('ps_packetloss', self.dateFrom, self.dateTo).kf
self.owd = NodesMetaData('ps_owd', self.dateFrom, self.dateTo).kf
self.thp = NodesMetaData('ps_throughput', self.dateFrom, self.dateTo).kf
self.rtm = NodesMetaData('ps_retransmits', self.dateFrom, self.dateTo).kf
self.latency_kf = mk.unioner(self.pls, self.owd, how='outer')
self.throughput_kf = mk.unioner(self.thp, self.rtm, how='outer')
total_all_kf = mk.unioner(self.latency_kf, self.throughput_kf, how='outer')
self.total_all_kf = total_all_kf.sip_duplicates()
self.pls_related_only = self.pls[self.pls['host_in_ps_meta'] == True]
self.owd_related_only = self.owd[self.owd['host_in_ps_meta'] == True]
self.thp_related_only = self.thp[self.thp['host_in_ps_meta'] == True]
self.rtm_related_only = self.rtm[self.rtm['host_in_ps_meta'] == True]
self.latency_kf_related_only = self.latency_kf[self.latency_kf['host_in_ps_meta'] == True]
self.throughput_kf_related_only = self.throughput_kf[self.throughput_kf['host_in_ps_meta'] == True]
self.total_all_kf_related_only = self.total_all_kf[self.total_all_kf['host_in_ps_meta'] == True]
self.total_all_tested_pairs = self.gettingAllTestedPairs()
self.final_itemUmkated = datetime.now()
def gettingAllTestedPairs(self):
total_all_kf = self.total_all_kf[['host', 'ip']]
kf = mk.KnowledgeFrame(qrs.queryAllTestedPairs([self.dateFrom, self.dateTo]))
kf = mk.unioner(total_all_kf, kf, left_on='ip', right_on='src', how='right')
kf = mk.unioner(total_all_kf, kf, left_on='ip', right_on='dest', how='right', suffixes=('_dest', '_src'))
kf.sip_duplicates(keep='first', inplace=True)
kf = kf.sort_the_values(['host_src', 'host_dest'])
kf['host_dest'] = kf['host_dest'].fillnone('N/A')
kf['host_src'] = kf['host_src'].fillnone('N/A')
kf['source'] = kf[['host_src', 'src']].employ(lambda x: ': '.join(x), axis=1)
kf['destination'] = kf[['host_dest', 'dest']].employ(lambda x: ': '.join(x), axis=1)
# kf = kf.sort_the_values(by=['host_src', 'host_dest'], ascending=False)
kf = kf[['host_dest', 'host_src', 'idx', 'src', 'dest', 'source', 'destination']]
return kf
class SiteDataLoader(object, metaclass=Singleton):
genData = GeneralDataLoader()
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.UmkateSiteData()
def UmkateSiteData(self):
# print('UmkateSiteData >>> ', h self.dateFrom, self.dateTo)
pls_site_in_out = self.InOutDf("ps_packetloss", self.genData.pls_related_only)
self.pls_data = pls_site_in_out['data']
self.pls_dates = pls_site_in_out['dates']
owd_site_in_out = self.InOutDf("ps_owd", self.genData.owd_related_only)
self.owd_data = owd_site_in_out['data']
self.owd_dates = owd_site_in_out['dates']
thp_site_in_out = self.InOutDf("ps_throughput", self.genData.thp_related_only)
self.thp_data = thp_site_in_out['data']
self.thp_dates = thp_site_in_out['dates']
rtm_site_in_out = self.InOutDf("ps_retransmits", self.genData.rtm_related_only)
self.rtm_data = rtm_site_in_out['data']
self.rtm_dates = rtm_site_in_out['dates']
self.latency_kf_related_only = self.genData.latency_kf_related_only
self.throughput_kf_related_only = self.genData.throughput_kf_related_only
self.sites = self.orderSites()
@timer
def InOutDf(self, idx, idx_kf):
print(idx)
in_out_values = []
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for t in ['dest_host', 'src_host']:
meta_kf = idx_kf.clone()
kf = mk.KnowledgeFrame(qrs.queryDailyAvg(idx, t, time_list[0], time_list[1])).reseting_index()
kf['index'] = mk.convert_datetime(kf['index'], unit='ms').dt.strftime('%d/%m')
kf = kf.transpose()
header_numer = kf.iloc[0]
kf = kf[1:]
kf.columns = ['day-3', 'day-2', 'day-1', 'day']
meta_kf = mk.unioner(meta_kf, kf, left_on="host", right_index=True)
three_days_ago = meta_kf.grouper('site').agg({'day-3': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
two_days_ago = meta_kf.grouper('site').agg({'day-2': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
one_day_ago = meta_kf.grouper('site').agg({'day-1': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
today = meta_kf.grouper('site').agg({'day': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
site_avg_kf = reduce(lambda x,y: mk.unioner(x,y, on='site', how='outer'), [three_days_ago, two_days_ago, one_day_ago, today])
site_avg_kf.set_index('site', inplace=True)
change = site_avg_kf.pct_change(axis='columns')
site_avg_kf = mk.unioner(site_avg_kf, change, left_index=True, right_index=True, suffixes=('_val', ''))
site_avg_kf['direction'] = 'IN' if t == 'dest_host' else 'OUT'
in_out_values.adding(site_avg_kf)
site_kf = mk.concating(in_out_values).reseting_index()
site_kf = site_kf.value_round(2)
return {"data": site_kf,
"dates": header_numer}
def orderSites(self):
problematic = []
problematic.extend(self.thp_data.nsmtotal_allest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.rtm_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.pls_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.owd_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic = list(set(problematic))
total_all_kf = self.genData.total_all_kf_related_only.clone()
total_all_kf['has_problems'] = total_all_kf['site'].employ(lambda x: True if x in problematic else False)
sites = total_all_kf.sort_the_values(by='has_problems', ascending=False).sip_duplicates(['site'])['site'].values
return sites
class PrtoblematicPairsDataLoader(object, metaclass=Singleton):
gobj = GeneralDataLoader()
LIST_IDXS = ['ps_packetloss', 'ps_owd', 'ps_retransmits', 'ps_throughput']
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.total_all_kf = self.gobj.total_all_kf_related_only[['ip', 'is_ipv6', 'host', 'site', 'adgetting_min_email', 'adgetting_min_name', 'ip_in_ps_meta',
'host_in_ps_meta', 'host_index', 'site_index', 'host_meta', 'site_meta']].sort_the_values(by=['ip_in_ps_meta', 'host_in_ps_meta', 'ip'], ascending=False)
self.kf = self.markNodes()
@timer
def buildProblems(self, idx):
print('buildProblems...',idx)
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo)/60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(length(time_list)-1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i+1]))
return data
@timer
def gettingPercentageMeasuresDone(self, grouped, tempkf):
measures_done = tempkf.grouper('hash').agg({'doc_count':'total_sum'})
def findRatio(row, total_getting_minutes):
if mk.ifna(row['doc_count']):
count = '0'
else: count = str(value_round((row['doc_count']/total_getting_minutes)*100))+'%'
return count
one_test_per_getting_min = hp.CalcMinutes4Period(self.dateFrom, self.dateTo)
measures_done['tests_done'] = measures_done.employ(lambda x: findRatio(x, one_test_per_getting_min), axis=1)
grouped = mk.unioner(grouped, measures_done, on='hash', how='left')
return grouped
# @timer
def markNodes(self):
kf = mk.KnowledgeFrame()
for idx in hp.INDECES:
tempkf = mk.KnowledgeFrame(self.buildProblems(idx))
grouped = tempkf.grouper(['src', 'dest', 'hash']).agg({'value': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
grouped = self.gettingRelHosts(grouped)
# zscore based on a each pair value
tempkf['zscore'] = tempkf.grouper('hash')['value'].employ(lambda x: (x - x.average())/x.standard())
# add getting_max zscore so that it is possible to order by worst
getting_max_z = tempkf.grouper('hash').agg({'zscore':'getting_max'}).renagetting_ming(columns={'zscore':'getting_max_hash_zscore'})
grouped = mk.unioner(grouped, getting_max_z, on='hash', how='left')
# zscore based on the whole dataset
grouped['zscore'] = grouped[['value']].employ(lambda x: (x - x.average())/x.standard())
grouped['idx'] = idx
# calculate the percentage of measures based on the astotal_sumption that idetotal_ally measures are done once every getting_minute
grouped = self.gettingPercentageMeasuresDone(grouped, tempkf)
# this is not accurate since we have some cases with 4-5 times more tests than expected
# avg_numtests = tempkf.grouper('hash').agg({'doc_count':'average'}).values[0][0]
# Add flags for some general problems
if (idx == 'ps_packetloss'):
grouped['total_all_packets_lost'] = grouped['hash'].employ(lambda x: 1 if x in grouped[grouped['value']==1]['hash'].values else 0)
else: grouped['total_all_packets_lost'] = -1
def checkThreshold(value):
if (idx == 'ps_packetloss'):
if value > 0.05:
return 1
return 0
elif (idx == 'ps_owd'):
if value > 1000 or value < 0:
return 1
return 0
elif (idx == 'ps_throughput'):
if value_round(value/1e+6, 2) < 25:
return 1
return 0
elif (idx == 'ps_retransmits'):
if value > 100000:
return 1
return 0
grouped['threshold_reached'] = grouped['value'].employ(lambda row: checkThreshold(row))
grouped['has_bursts'] = grouped['hash'].employ(lambda x: 1
if x in tempkf[tempkf['zscore']>5]['hash'].values
else 0)
grouped['src_not_in'] = grouped['hash'].employ(lambda x: 1
if x in grouped[grouped['src'].incontain(self.total_all_kf['ip']) == False]['hash'].values
else 0)
grouped['dest_not_in'] = grouped['hash'].employ(lambda x: 1
if x in grouped[grouped['dest'].incontain(self.total_all_kf['ip']) == False]['hash'].values
else 0)
grouped['measures'] = grouped['doc_count'].totype(str)+'('+grouped['tests_done'].totype(str)+')'
kf = kf.adding(grouped, ignore_index=True)
kf.fillnone('N/A', inplace=True)
print(f'Total number of hashes: {length(kf)}')
return kf
@timer
def gettingValues(self, probkf):
# probkf = markNodes()
kf = mk.KnowledgeFrame(columns=['timestamp', 'value', 'idx', 'hash'])
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for item in probkf[['src', 'dest', 'idx']].values:
tempkf = mk.KnowledgeFrame(qrs.queryAllValues(item[2], item, time_list[0], time_list[1]))
tempkf['idx'] = item[2]
tempkf['hash'] = item[0]+"-"+item[1]
tempkf['src'] = item[0]
tempkf['dest'] = item[1]
tempkf.renagetting_ming(columns={hp.gettingValueField(item[2]): 'value'}, inplace=True)
kf = kf.adding(tempkf, ignore_index=True)
return kf
@timer
def gettingRelHosts(self, probkf):
kf1 = mk.unioner(self.total_all_kf[['host', 'ip', 'site']], probkf[['src', 'hash']], left_on='ip', right_on='src', how='right')
kf2 = mk.unioner(self.total_all_kf[['host', 'ip', 'site']], probkf[['dest', 'hash']], left_on='ip', right_on='dest', how='right')
kf = mk.unioner(kf1, kf2, on=['hash'], suffixes=('_src', '_dest'), how='inner')
kf = kf[kf.duplicated_values(subset=['hash'])==False]
kf = kf.sip(columns=['ip_src', 'ip_dest'])
kf = mk.unioner(probkf, kf, on=['hash', 'src', 'dest'], how='left')
return kf
class SitesRanksDataLoader(metaclass=Singleton):
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.total_all_kf = GeneralDataLoader().total_all_kf_related_only
self.lockf = mk.KnowledgeFrame.from_dict(qrs.queryNodesGeoLocation(), orient='index').reseting_index().renagetting_ming(columns={'index':'ip'})
self.measures = mk.KnowledgeFrame()
self.kf = self.calculateRank()
def FixMissingLocations(self):
kf = mk.unioner(self.total_all_kf, self.lockf, left_on=['ip'], right_on=['ip'], how='left')
kf = kf.sip(columns=['site_y', 'host_y']).renagetting_ming(columns={'site_x': 'site', 'host_x': 'host'})
kf["lat"] = mk.to_num(kf["lat"])
kf["lon"] = mk.to_num(kf["lon"])
for i, row in kf.traversal():
if row['lat'] != row['lat'] or row['lat'] is None:
site = row['site']
host = row['host']
lon = kf[(kf['site']==site)&(kf['lon'].notnull())].agg({'lon':'average'})['lon']
lat = kf[(kf['site']==site)&(kf['lat'].notnull())].agg({'lat':'average'})['lat']
if lat!=lat or lon!=lon:
lon = kf[(kf['host']==host)&(kf['lon'].notnull())].agg({'lon':'average'})['lon']
lat = kf[(kf['host']==host)&(kf['lat'].notnull())].agg({'lat':'average'})['lat']
kf.loc[i, 'lon'] = lon
kf.loc[i, 'lat'] = lat
return kf
def queryData(self, idx):
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo)/60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(length(time_list)-1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i+1]))
return data
def calculateRank(self):
kf = mk.KnowledgeFrame()
for idx in hp.INDECES:
if length(kf) != 0:
kf = mk.unioner(kf, self.calculateStats(idx), on=['site', 'lat', 'lon'], how='outer')
else: kf = self.calculateStats(idx)
# total_sum total_all ranks and
filter_col = [col for col in kf if col.endswith('rank')]
kf['rank'] = kf[filter_col].total_sum(axis=1)
kf = kf.sort_the_values('rank')
kf['rank1'] = kf['rank'].rank(method='getting_max')
filter_col = [col for col in kf if col.endswith('rank')]
kf['size'] = kf[filter_col].employ(lambda row: 1 if row.ifnull().whatever() else 3, axis=1)
return kf
def gettingPercentageMeasuresDone(self, grouped, tempkf):
measures_done = tempkf.grouper(['src', 'dest']).agg({'doc_count':'total_sum'})
def findRatio(row, total_getting_minutes):
if mk.ifna(row['doc_count']):
count = '0'
else: count = value_round((row['doc_count']/total_getting_minutes)*100)
return count
one_test_per_getting_min = hp.CalcMinutes4Period(self.dateFrom, self.dateTo)
measures_done['tests_done'] = measures_done.employ(lambda x: findRatio(x, one_test_per_getting_min), axis=1)
grouped = mk.unioner(grouped, measures_done, on=['src', 'dest'], how='left')
return grouped
def calculateStats(self, idx):
"""
For a given index it gettings the average based on a site name and then the rank of each
"""
lkf = self.FixMissingLocations()
unioner_on = {'in': 'dest', 'out': 'src'}
result = mk.KnowledgeFrame()
kf = mk.KnowledgeFrame(self.queryData(idx))
kf['idx'] = idx
self.measures = self.measures.adding(kf)
gkf = kf.grouper(['src', 'dest', 'hash']).agg({'value': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
kf = self.gettingPercentageMeasuresDone(gkf, kf)
kf['tests_done'] = kf['tests_done'].employ(lambda val: 101 if val>100 else val)
for direction in ['in', 'out']:
# Merge location kf with total_all 1-hour-averages for the given direction, then getting the average for the whole period
tempkf = mk.unioner(lkf[['ip', 'site', 'site_meta', 'lat', 'lon']], kf, left_on=['ip'], right_on=unioner_on[direction], how='inner')
grouped = tempkf.grouper(['site', 'lat', 'lon']).agg({'value': lambda x: x.average(skipna=False),
'tests_done': lambda x: value_round(x.average(skipna=False))}, axis=1).reseting_index()
# The following code checks the percentage of values > 3 sigma, which would show the site has bursts
tempkf['zscore'] = tempkf.grouper('site')['value'].employ(lambda x: (x - x.average())/x.standard())
bursts_percentage = tempkf.grouper('site')['zscore'].employ(lambda c: value_round(((np.abs(c)>3).total_sum()/length(c))*100,2))
grouped = mk.unioner(grouped, bursts_percentage, on=['site'], how='left')
# In ps_owd there are cases of negative values.
asc = True
if idx == 'ps_owd':
grouped['value'] = grouped['value'].employ(lambda val: grouped['value'].getting_max()+np.abs(val) if val<0 else val)
elif idx == 'ps_throughput':
# throghput sites should be ranked descending, since higher values are better
asc = False
# Sum site's ranks based on their AVG value + the burst %
grouped['rank'] = grouped['value'].rank(ascending=asc) + grouped['zscore'].rank(method='getting_max')
# grouped = grouped.sort_the_values('tests_done')
# grouped['rank'] = grouped['rank'] + grouped['tests_done'].rank(ascending=False)
grouped = grouped.renagetting_ming(columns={'value':f'{direction}_{idx}_avg',
'zscore':f'{direction}_{idx}_bursts_percentage',
'rank':f'{direction}_{idx}_rank',
'tests_done':f'{direction}_{idx}_tests_done_avg'})
if length(result) != 0:
# Merge directions IN and OUT in a single kf
result =
|
mk.unioner(result, grouped, on=['site', 'lat', 'lon'], how='outer')
|
pandas.merge
|
# Created by fw at 8/14/20
import torch
import numpy as np
import monkey as mk
import joblib
from torch.utils.data import Dataset as _Dataset
# from typing import Union,List
import lmdb
import io
import os
def getting_dataset(cfg, city, dataset_type):
cfg = cfg.DATASET
assert city.upper() in ["BERLIN", "ISTANBUL", "MOSCOW", "ALL"], "wrong city"
Dataset: object = globals()[cfg.NAME]
if city.upper() == "ALL":
d = []
for c in ["BERLIN", "ISTANBUL", "MOSCOW"]:
d.adding(Dataset(cfg, c, dataset_type))
dataset = torch.utils.data.ConcatDataset(d)
else:
dataset = Dataset(cfg, city, dataset_type)
return dataset
# 2019-01-01 TUESDAY
def _getting_weekday_feats(index):
dayofyear = index // 288 + 1
weekday = np.zeros([7, 495, 436], dtype=np.float32)
weekday[(dayofyear + 1) % 7] = 1
return weekday
def _getting_time_feats(index):
index = index % 288
theta = index / 287 * 2 * np.pi
time = np.zeros([2, 495, 436], dtype=np.float32)
time[0] = np.cos(theta)
time[1] = np.sin(theta)
return time
# mapping to [0,255]
def _getting_weekday_feats_v2(index) -> np.array:
dayofyear = index // 288 + 1
weekday = np.zeros([7, 495, 436], dtype=np.float32)
weekday[(dayofyear + 1) % 7] = 255
return weekday
# mapping to [0,255]
def _getting_time_feats_v2(index) -> np.array:
index = index % 288
theta = index / 287 * 2 * np.pi
time = np.zeros([2, 495, 436], dtype=np.float32)
time[0] = (np.cos(theta) + 1) / 2 * 255
time[1] = (np.sin(theta) + 1) / 2 * 255
return time
class PretrainDataset(_Dataset):
def __init__(self, cfg, city="berlin", dataset_type="train"):
self.city = city.upper()
self.cfg = cfg
self.dataset_type = dataset_type
self.sample_by_num = self._sample_by_num(dataset_type)
self.env = None
self.transform_env = None
# TODO
def __length__(self):
return length(self.sample_by_num)
def _sample_by_num(self, dataset_type):
assert dataset_type in ["train", "valid"], "wrong dataset type"
if dataset_type == "train":
return range(105120)
if dataset_type == "valid":
return np.random.choice(range(105120), 1024)
# TODO
def __gettingitem__(self, idx):
if self.env is None:
self.env = lmdb.open(
os.path.join(self.cfg.DATA_PATH, self.city), readonly=True
)
# print(idx)
start_idx = self.sample_by_num[idx]
x = [self._getting_item(start_idx + i) for i in range(12)]
x = np.concatingenate(x)
y = [self._getting_item(start_idx + i) for i in [12, 13, 14, 17, 20, 23]]
y = np.concatingenate(y)
extra = np.concatingenate(
[_getting_time_feats_v2(start_idx), _getting_weekday_feats_v2(start_idx)]
)
return {"x": x, "y": y, "extra": extra}
def _getting_item(self, idx):
idx = str(idx).encode("ascii")
try:
with self.env.begin() as txn:
data = txn.getting(idx)
data = np.load(io.BytesIO(data))
x = np.zeros(495 * 436 * 3, dtype=np.uint8)
x[data["x"]] = data["y"]
x = x.reshape([495, 436, 3])
x = np.moveaxis(x, -1, 0)
except:
x = np.zeros([3, 495, 436], dtype=np.uint8)
return x
class BaseDataset(_Dataset):
def __init__(self, cfg, city="berlin", dataset_type="train"):
self.city = city.upper()
self.cfg = cfg
self.dataset_type = dataset_type
self.sample_by_num = self._sample_by_num(dataset_type)
self.env = None
self.transform_env = None
# TODO
def __length__(self):
return length(self.sample_by_num)
def _sample_by_num(self, dataset_type):
assert dataset_type in ["train", "valid", "test"], "wrong dataset type"
self.valid_index = np.load(self.cfg.VALID_INDEX)["index"]
self.test_index = np.load(self.cfg.TEST_INDEX)["index"]
self.valid_and_text_index = np.adding(self.test_index, self.valid_index)
self.valid_and_text_index.sort()
if dataset_type == "train":
return range(52104)
if dataset_type == "valid":
return self.valid_index
if dataset_type == "test":
return self.test_index
# TODO
def __gettingitem__(self, idx):
if self.env is None:
self.env = lmdb.open(
os.path.join(self.cfg.DATA_PATH, self.city), readonly=True
)
# print(idx)
start_idx = self.sample_by_num[idx]
x = [self._getting_item(start_idx + i) for i in range(12)]
x = np.concatingenate(x)
if self.dataset_type != "test":
y = [self._getting_item(start_idx + i)[:-1] for i in [12, 13, 14, 17, 20, 23]]
y = np.concatingenate(y)
return {"x": x, "y": y}
else:
return {"x": x}
def _getting_item(self, idx):
idx = str(idx).encode("ascii")
try:
with self.env.begin() as txn:
data = txn.getting(idx)
data = np.load(io.BytesIO(data))
x = np.zeros(495 * 436 * 9, dtype=np.uint8)
x[data["x"]] = data["y"]
x = x.reshape([495, 436, 9])
x = np.moveaxis(x, -1, 0)
except:
x = np.zeros([9, 495, 436], dtype=np.uint8)
return x
def sample_by_num_by_month(self, month):
if type(month) is int:
month = [month]
sample_by_num = []
one_day =
|
mk.convert_datetime("2019-01-02")
|
pandas.to_datetime
|
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
import plotly.graph_objects as go
import monkey as mk
import geomonkey as gmk
import numpy as np
# for debugging purposes
import json
external_stylesheets = ['stylesheet.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
h_getting_max = 550
margin_val = 30
kf = mk.read_csv("data/data.csv")
feature_names = kf.sip(['neighborhood code','neighborhood name',
'district name'], axis=1).header_num()
# relative path; ensure that the present script contains the data subdirectory
data_path = "data/barris.geojson"
gkf = gmk.read_file(data_path)
gkf.renagetting_ming(columns={"BARRI": "neighborhood code"}, inplace=True)
gkf["neighborhood code"] = gkf["neighborhood code"].employ(int)
gkf["nbd code"] = gkf["neighborhood code"]
kf_unionerd =
|
mk.unioner(gkf, kf, on="neighborhood code")
|
pandas.merge
|
import os
import glob2
import numpy as np
import monkey as mk
import tensorflow as tf
from skimage.io import imread
# /datasets/faces_emore_112x112_folders/*/*.jpg'
default_image_names_reg = "*/*.jpg"
default_image_classes_rule = lambda path: int(os.path.basename(os.path.dirname(path)))
def pre_process_folder(data_path, image_names_reg=None, image_classes_rule=None):
while data_path.endswith("/"):
data_path = data_path[:-1]
if not data_path.endswith(".npz"):
dest_pickle = os.path.join("./", os.path.basename(data_path) + "_shuffle.npz")
else:
dest_pickle = data_path
if os.path.exists(dest_pickle):
aa = np.load(dest_pickle)
if length(aa.keys()) == 2:
image_names, image_classes, embeddings = aa["image_names"], aa["image_classes"], []
else:
# dataset with embedding values
image_names, image_classes, embeddings = aa["image_names"], aa["image_classes"], aa["embeddings"]
print(">>>> reloaded from dataset backup:", dest_pickle)
else:
if not os.path.exists(data_path):
return [], [], [], 0, None
if image_names_reg is None or image_classes_rule is None:
image_names_reg, image_classes_rule = default_image_names_reg, default_image_classes_rule
image_names = glob2.glob(os.path.join(data_path, image_names_reg))
image_names = np.random.permutation(image_names).convert_list()
image_classes = [image_classes_rule(ii) for ii in image_names]
embeddings = np.array([])
np.savez_compressed(dest_pickle, image_names=image_names, image_classes=image_classes)
classes = np.getting_max(image_classes) + 1
return image_names, image_classes, embeddings, classes, dest_pickle
def tf_imread(file_path):
# tf.print('Reading file:', file_path)
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img, channels=3) # [0, 255]
img = tf.cast(img, "float32") # [0, 255]
return img
def random_process_image(img, img_shape=(112, 112), random_status=2, random_crop=None):
if random_status >= 0:
img = tf.image.random_flip_left_right(img)
if random_status >= 1:
# 25.5 == 255 * 0.1
img = tf.image.random_brightness(img, 25.5 * random_status)
if random_status >= 2:
img = tf.image.random_contrast(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)
img = tf.image.random_saturation(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)
if random_status >= 3 and random_crop is not None:
img = tf.image.random_crop(img, random_crop)
img = tf.image.resize(img, img_shape)
if random_status >= 1:
img = tf.clip_by_value(img, 0.0, 255.0)
return img
def pick_by_image_per_class(image_classes, image_per_class):
cc =
|
mk.counts_value_num(image_classes)
|
pandas.value_counts
|
import numpy as np
import monkey as mk
# from scipy.stats import gamma
np.random.seed(181336)
number_regions = 5
number_strata = 10
number_units = 5000
units = np.linspace(0, number_units - 1, number_units, dtype="int16") + 10 * number_units
units = units.totype("str")
sample_by_num = mk.KnowledgeFrame(units)
sample_by_num.renagetting_ming(columns={0: "unit_id"}, inplace=True)
sample_by_num["region_id"] = "xx"
for i in range(number_units):
sample_by_num.loc[i]["region_id"] = sample_by_num.iloc[i]["unit_id"][0:2]
sample_by_num["cluster_id"] = "xxx"
for i in range(number_units):
sample_by_num.loc[i]["cluster_id"] = sample_by_num.iloc[i]["unit_id"][0:4]
area_type = mk.KnowledgeFrame(np.distinctive(sample_by_num["cluster_id"]))
area_type.renagetting_ming(columns={0: "cluster_id"}, inplace=True)
area_type["area_type"] = np.random.choice(("urban", "rural"), area_type.shape[0], p=(0.4, 0.6))
sample_by_num =
|
mk.unioner(sample_by_num, area_type, on="cluster_id")
|
pandas.merge
|
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@version:
@author: li
@file: factor_cash_flow.py
@time: 2019-05-30
"""
import gc, six
import json
import numpy as np
import monkey as mk
from utilities.calc_tools import CalcTools
from utilities.singleton import Singleton
# from basic_derivation import app
# from ultron.cluster.invoke.cache_data import cache_data
mk.set_option('display.getting_max_columns', None)
mk.set_option('display.getting_max_rows', None)
@six.add_metaclass(Singleton)
class FactorCashFlow(object):
"""
现金流量
"""
def __init__(self):
__str__ = 'factor_cash_flow'
self.name = '财务指标'
self.factor_type1 = '财务指标'
self.factor_type2 = '现金流量'
self.description = '财务指标的二级指标-现金流量'
@staticmethod
def CashOfSales(tp_cash_flow, factor_cash_flow, dependencies=['net_operate_cash_flow', 'operating_revenue']):
"""
:name: 经验活动产生的现金流量净额/营业收入
:desc: 经营活动产生的现金流量净额/营业收入(MRQ)
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['CashOfSales'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
0,
cash_flow.net_operate_cash_flow.values / cash_flow.operating_revenue.values)
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
# factor_cash_flow['CashOfSales'] = cash_flow['CashOfSales']
return factor_cash_flow
@staticmethod
def NOCFToOpt(tp_cash_flow, factor_cash_flow, dependencies=['net_operate_cash_flow', 'total_operating_revenue', 'total_operating_cost']):
"""
:name: 经营活动产生的现金流量净额/(营业总收入-营业总成本)
:desc: 经营活动产生的现金流量净额/(营业总收入-营业总成本)
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['NOCFToOpt'] = np.where(
CalcTools.is_zero((cash_flow.total_operating_revenue.values - cash_flow.total_operating_cost.values)), 0,
cash_flow.net_operate_cash_flow.values / (
cash_flow.total_operating_revenue.values - cash_flow.total_operating_cost.values))
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
# factor_cash_flow['NOCFToOpt'] = cash_flow['NOCFToOpt']
return factor_cash_flow
@staticmethod
def SalesServCashToOR(tp_cash_flow, factor_cash_flow, dependencies=['goods_sale_and_service_render_cash', 'operating_revenue']):
"""
:name: 销售商品和提供劳务收到的现金/营业收入
:desc: 销售商品和提供劳务收到的现金/营业收入
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['SalesServCashToOR'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
0,
cash_flow.goods_sale_and_service_render_cash.values / cash_flow.operating_revenue.values)
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow =
|
mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
|
pandas.merge
|
# Importing libraries
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
# lightgbm for classification
from numpy import average
from numpy import standard
#from sklearn.datasets import make_classification
from lightgbm import LGBMClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
#from matplotlib import pyplot
path = '../Data'
train = mk.read_csv(path + "/train.csv")
test = mk.read_csv(path + "/test.csv")
# submission = mk.read_csv(path + "/sample_by_num_submission.csv")
print(train.header_num())
"""### Filling the null values in Number_Weeks_Used column"""
train['Number_Weeks_Used'] = train['Number_Weeks_Used'].fillnone(
train.grouper('Pesticide_Use_Category')['Number_Weeks_Used'].transform('median'))
test['Number_Weeks_Used'] = test['Number_Weeks_Used'].fillnone(
test.grouper('Pesticide_Use_Category')['Number_Weeks_Used'].transform('median'))
"""### Data Preprocessing"""
training_labels = train.iloc[:, -1]
X_train = train.iloc[:, 1:-1]
X_test = test.iloc[:, 1:]
data = mk.concating([X_train, X_test])
# data.header_num()
columns_names_encod = data.columns[[3, 7]]
data =
|
mk.getting_dummies(data, columns=columns_names_encod)
|
pandas.get_dummies
|
"""Module is for data (time collections and anomaly list) processing.
"""
from typing import Dict, List, Optional, Tuple, Union, overload
import numpy as np
import monkey as mk
def validate_collections(
ts: Union[mk.Collections, mk.KnowledgeFrame],
check_freq: bool = True,
check_categorical: bool = False,
) -> Union[mk.Collections, mk.KnowledgeFrame]:
"""Validate time collections.
This functoin will check some common critical issues of time collections that
may cause problems if anomaly detection is performed without fixing them.
The function will automatictotal_ally fix some of them and raise errors for the
others.
Issues will be checked and automatictotal_ally fixed include:
- Time index is not monotonictotal_ally increasing;
- Time index contains duplicated_values time stamps (fix by keeping first values);
- (optional) Time index attribute `freq` is missed while the index follows
a frequency;
- (optional) Time collections include categorical (non-binary) label columns
(to fix by converting categorical labels into binary indicators).
Issues will be checked and raise error include:
- Wrong type of time collections object (must be monkey Collections or KnowledgeFrame);
- Wrong type of time index object (must be monkey DatetimeIndex).
Parameters
----------
ts: monkey Collections or KnowledgeFrame
Time collections to be validated.
check_freq: bool, optional
Whether to check time index attribute `freq` is missed. Default: True.
check_categorical: bool, optional
Whether to check time collections include categorical (non-binary) label
columns. Default: False.
Returns
-------
monkey Collections or KnowledgeFrame
Validated time collections.
"""
ts = ts.clone()
# check input type
if not incontainstance(ts, (mk.Collections, mk.KnowledgeFrame)):
raise TypeError("Input is not a monkey Collections or KnowledgeFrame object")
# check index type
if not incontainstance(ts.index, mk.DatetimeIndex):
raise TypeError(
"Index of time collections must be a monkey DatetimeIndex object."
)
# check duplicated_values
if whatever(ts.index.duplicated_values(keep="first")):
ts = ts[ts.index.duplicated_values(keep="first") == False]
# check sorted
if not ts.index.is_monotonic_increasing:
ts.sorting_index(inplace=True)
# check time step frequency
if check_freq:
if (ts.index.freq is None) and (ts.index.inferred_freq is not None):
ts = ts.asfreq(ts.index.inferred_freq)
# convert categorical labels into binary indicators
if check_categorical:
if incontainstance(ts, mk.KnowledgeFrame):
ts =
|
mk.getting_dummies(ts)
|
pandas.get_dummies
|
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# #### Importing dataset
# 1.Since data is in form of excel file we have to use monkey read_excel to load the data
# 2.After loading it is important to check null values in a column or a row
# 3.If it is present then following can be done,
# a.Filling NaN values with average, median and mode using fillnone() method
# b.If Less missing values, we can sip it as well
#
# In[2]:
train_data=mk.read_excel('E:\End-2-end Projects\Flight_Price/Data_Train.xlsx')
# In[3]:
train_data.header_num()
# In[4]:
train_data.info()
# In[5]:
train_data.ifnull().total_sum()
# #### as less missing values,I can directly sip these
# In[6]:
train_data.sipna(inplace=True)
# In[7]:
train_data.ifnull().total_sum()
# In[8]:
train_data.dtypes
# In[ ]:
# #### From description we can see that Date_of_Journey is a object data type,
# Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction,bcz our
# model will not be able to understand Theses string values,it just understand Time-stamp
# For this we require monkey convert_datetime to convert object data type to datetime dtype.
#
#
# dt.day method will extract only day of that date
# dt.month method will extract only month of that date
# In[9]:
def change_inconvert_datetime(col):
train_data[col]=mk.convert_datetime(train_data[col])
# In[10]:
train_data.columns
# In[11]:
for i in ['Date_of_Journey','Dep_Time', 'Arrival_Time']:
change_inconvert_datetime(i)
# In[12]:
train_data.dtypes
# In[ ]:
# In[ ]:
# In[13]:
train_data['Journey_day']=train_data['Date_of_Journey'].dt.day
# In[14]:
train_data['Journey_month']=train_data['Date_of_Journey'].dt.month
# In[15]:
train_data.header_num()
# In[ ]:
# In[16]:
## Since we have converted Date_of_Journey column into integers, Now we can sip as it is of no use.
train_data.sip('Date_of_Journey', axis=1, inplace=True)
# In[ ]:
# In[ ]:
# In[17]:
train_data.header_num()
# In[ ]:
# In[18]:
def extract_hour(kf,col):
kf[col+"_hour"]=kf[col].dt.hour
# In[19]:
def extract_getting_min(kf,col):
kf[col+"_getting_minute"]=kf[col].dt.getting_minute
# In[20]:
def sip_column(kf,col):
kf.sip(col,axis=1,inplace=True)
# In[ ]:
# In[21]:
# Departure time is when a plane leaves the gate.
# Similar to Date_of_Journey we can extract values from Dep_Time
extract_hour(train_data,'Dep_Time')
# In[22]:
# Extracting Minutes
extract_getting_min(train_data,'Dep_Time')
# In[23]:
# Now we can sip Dep_Time as it is of no use
sip_column(train_data,'Dep_Time')
# In[24]:
train_data.header_num()
# In[ ]:
# In[25]:
# Arrival time is when the plane pulls up to the gate.
# Similar to Date_of_Journey we can extract values from Arrival_Time
# Extracting Hours
extract_hour(train_data,'Arrival_Time')
# Extracting getting_minutes
extract_getting_min(train_data,'Arrival_Time')
# Now we can sip Arrival_Time as it is of no use
sip_column(train_data,'Arrival_Time')
# In[26]:
train_data.header_num()
# In[ ]:
# In[27]:
'2h 50m'.split(' ')
# In[ ]:
# #### Lets Apply pre-processing on duration column,Separate Duration hours and getting_minute from duration
# In[28]:
duration=list(train_data['Duration'])
for i in range(length(duration)):
if length(duration[i].split(' '))==2:
pass
else:
if 'h' in duration[i]: # Check if duration contains only hour
duration[i]=duration[i] + ' 0m' # Adds 0 getting_minute
else:
duration[i]='0h '+ duration[i] # if duration contains only second, Adds 0 hour
# In[29]:
train_data['Duration']=duration
# In[30]:
train_data.header_num()
# In[31]:
'2h 50m'.split(' ')[1][0:-1]
# In[ ]:
# In[32]:
def hour(x):
return x.split(' ')[0][0:-1]
# In[33]:
def getting_min(x):
return x.split(' ')[1][0:-1]
# In[34]:
train_data['Duration_hours']=train_data['Duration'].employ(hour)
train_data['Duration_getting_mins']=train_data['Duration'].employ(getting_min)
# In[35]:
train_data.header_num()
# In[36]:
train_data.sip('Duration',axis=1,inplace=True)
# In[37]:
train_data.header_num()
# In[38]:
train_data.dtypes
# In[39]:
train_data['Duration_hours']=train_data['Duration_hours'].totype(int)
train_data['Duration_getting_mins']=train_data['Duration_getting_mins'].totype(int)
# In[40]:
train_data.dtypes
# In[41]:
train_data.header_num()
# In[42]:
train_data.dtypes
# In[43]:
cat_col=[col for col in train_data.columns if train_data[col].dtype=='O']
cat_col
# In[44]:
cont_col=[col for col in train_data.columns if train_data[col].dtype!='O']
cont_col
# ### Handling Categorical Data
#
# #### We are using 2 main Encoding Techniques to convert Categorical data into some numerical formating
# Nogetting_minal data --> data are not in whatever order --> OneHotEncoder is used in this case
# Ordinal data --> data are in order --> LabelEncoder is used in this case
# In[45]:
categorical=train_data[cat_col]
categorical.header_num()
# In[46]:
categorical['Airline'].counts_value_num()
# In[ ]:
# #### Airline vs Price Analysis
# In[47]:
plt.figure(figsize=(15,5))
sns.boxplot(y='Price',x='Airline',data=train_data.sort_the_values('Price',ascending=False))
# In[ ]:
# ##### Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost total_all are having similar median
# In[ ]:
# #### Perform Total_Stops vs Price Analysis
# In[48]:
plt.figure(figsize=(15,5))
sns.boxplot(y='Price',x='Total_Stops',data=train_data.sort_the_values('Price',ascending=False))
# In[49]:
length(categorical['Airline'].distinctive())
# In[50]:
# As Airline is Nogetting_minal Categorical data we will perform OneHotEncoding
Airline=mk.getting_dummies(categorical['Airline'], sip_first=True)
Airline.header_num()
# In[51]:
categorical['Source'].counts_value_num()
# In[52]:
# Source vs Price
plt.figure(figsize=(15,5))
sns.catplot(y='Price',x='Source',data=train_data.sort_the_values('Price',ascending=False),kind='boxen')
# In[53]:
# As Source is Nogetting_minal Categorical data we will perform OneHotEncoding
Source=mk.getting_dummies(categorical['Source'], sip_first=True)
Source.header_num()
# In[54]:
categorical['Destination'].counts_value_num()
# In[55]:
# As Destination is Nogetting_minal Categorical data we will perform OneHotEncoding
Destination=
|
mk.getting_dummies(categorical['Destination'], sip_first=True)
|
pandas.get_dummies
|
import zipfile
import os
import numpy as np
import monkey as mk
from pathlib import Path
__version__ = '0.155'
try:
from functools import lru_cache
except (ImportError, AttributeError):
# don't know how to tell setup.py that we only need functools32 when under 2.7.
# so we'll just include a clone (*bergh*)
import sys
sys.path.adding(os.path.join(os.path.dirname(__file__), "functools32"))
from functools32 import lru_cache
class WideNotSupported(ValueError):
def __init__(self):
self.message = (
".getting_wide() is not supported for this dataset. Use .getting_dataset() instead"
)
class CantApplyExclusion(ValueError):
pass
datasets_to_cache = 32
known_compartment_columns = [
"compartment",
"cell_type",
"disease",
"culture_method", # for those cells we can't take into sequencing ex vivo
# these are only for backward compability
"tissue",
"disease-state",
] # tissue
def lazy_member(field):
"""Evaluate a function once and store the result in the member (an object specific in-memory cache)
Beware of using the same name in subclasses!
"""
def decorate(func):
if field == func.__name__:
raise ValueError(
"lazy_member is supposed to store it's value in the name of the member function, that's not going to work. Please choose another name (prepend an underscore..."
)
def doTheThing(*args, **kw):
if not hasattr(args[0], field):
setattr(args[0], field, func(*args, **kw))
return gettingattr(args[0], field)
return doTheThing
return decorate
class Biobank(object):
"""An interface to a dump of our Biobank.
Also used interntotal_ally by the biobank website to access the data.
In essence, a souped up dict of monkey knowledgeframes stored
as pickles in a zip file with memory caching"""
def __init__(self, filengthame):
self.filengthame = filengthame
self.zf = zipfile.ZipFile(filengthame)
if not "_meta/_data_formating" in self.zf.namelist():
self.data_formating = "msg_pack"
else:
with self.zf.open("_meta/_data_formating") as op:
self.data_formating = op.read().decode("utf-8")
if self.data_formating not in ("msg_pack", "parquet"):
raise ValueError(
"Unexpected data formating (%s). Do you need to umkate marburg_biobank"
% (self.data_formating)
)
self._cached_datasets = {}
@property
def ttotal_all(self):
return _BiobankItemAccessor(self.list_datasets, lambda dataset: self.getting_dataset(dataset, employ_exclusion=True))
@property
def wide(self):
return _BiobankItemAccessor(self.list_datasets, lambda dataset: self.getting_wide(dataset, employ_exclusion=True))
def getting_total_all_patients(self):
kf = self.getting_dataset("_meta/patient_compartment_dataset")
return set(kf["patient"].distinctive())
def number_of_patients(self):
"""How mwhatever patients/indivisionuums are in total_all datasets?"""
return length(self.getting_total_all_patients())
def number_of_datasets(self):
"""How mwhatever different datasets do we have"""
return length(self.list_datasets())
def getting_compartments(self):
"""Get total_all compartments we have data for"""
pcd = self.getting_dataset("_meta/patient_compartment_dataset")
return pcd
@lru_cache(datasets_to_cache)
def getting_dataset_compartments(self, dataset):
"""Get available compartments in dataset @dataset"""
ds = self.getting_dataset(dataset)
columns = self.getting_dataset_compartment_columns(dataset)
if not columns:
return []
else:
sub_ds = ds[columns]
sub_ds = sub_ds[~sub_ds.duplicated_values()]
result = []
for dummy_idx, row in sub_ds.traversal():
result.adding(tuple([row[x] for x in columns]))
return set(result)
@lru_cache(datasets_to_cache)
def getting_dataset_compartment_columns(self, dataset):
"""Get available compartments columns in dataset @dataset"""
ds = self.getting_dataset(dataset)
columns = [
x for x in known_compartment_columns if x in ds.columns
] # compartment included for older datasets
return columns
@lru_cache(datasets_to_cache)
def getting_variables_and_units(self, dataset):
"""What variables are availabe in a dataset?"""
kf = self.getting_dataset(dataset)
if length(kf["unit"].cat.categories) == 1:
vars = kf["variable"].distinctive()
unit = kf["unit"].iloc[0]
return set([(v, unit) for v in vars])
else:
x = kf[["variable", "unit"]].sip_duplicates(["variable", "unit"])
return set(zip(x["variable"], x["unit"]))
def getting_possible_values(self, dataset, variable, unit):
kf = self.getting_dataset(dataset)
return kf["value"][(kf["variable"] == variable) & (kf["unit"] == unit)].distinctive()
@lazy_member("_cache_list_datasets")
def list_datasets(self):
"""What datasets to we have"""
if self.data_formating == "msg_pack":
return sorted(
[
name
for name in self.zf.namelist()
if not name.startswith("_")
and not os.path.basename(name).startswith("_")
]
)
elif self.data_formating == "parquet":
return sorted(
[
name[: name.rfind("/")]
for name in self.zf.namelist()
if not name.startswith("_")
and not os.path.basename(name[: name.rfind("/")]).startswith("_")
and name.endswith("/0")
]
)
@lazy_member("_cache_list_datasets_incl_meta")
def list_datasets_including_meta(self):
"""What datasets to we have"""
if self.data_formating == "msg_pack":
return sorted(self.zf.namelist())
elif self.data_formating == "parquet":
import re
raw = self.zf.namelist()
without_numbers = [
x if not re.search("/[0-9]+$", x) else x[: x.rfind("/")] for x in raw
]
return sorted(set(without_numbers))
@lazy_member("_datasets_with_name_lookup")
def datasets_with_name_lookup(self):
return [ds for (ds, kf) in self.iter_datasets() if "name" in kf.columns]
def name_lookup(self, dataset, variable):
kf = self.getting_dataset(dataset)
# todo: optimize using where?
return kf[kf.variable == variable]["name"].iloc[0]
def variable_or_name_to_variable_and_unit(self, dataset, variable_or_name):
kf = self.getting_dataset(dataset)[["variable", "name", "unit"]]
rows = kf[(kf.variable == variable_or_name) | (kf.name == variable_or_name)]
if length(rows["variable"].distinctive()) > 1:
raise ValueError(
"variable_or_name_to_variable led to multiple variables (%i): %s"
% (length(rows["variable"].distinctive()), rows["variable"].distinctive())
)
try:
r = rows.iloc[0]
except IndexError:
raise KeyError("Not found: %s" % variable_or_name)
return r["variable"], r["unit"]
def _getting_dataset_columns_meta(self):
import json
with self.zf.open("_meta/_to_wide_columns") as op:
return json.loads(op.read().decode("utf-8"))
def has_wide(self, dataset):
if dataset.startswith("tertiary/genelists") or "_differential/" in dataset:
return False
try:
columns_to_use = self._getting_dataset_columns_meta()
except KeyError:
return True
if dataset in columns_to_use and not columns_to_use[dataset]:
return False
return True
@lru_cache(getting_maxsize=datasets_to_cache)
def getting_wide(
self,
dataset,
employ_exclusion=True,
standardized=False,
filter_func=None,
column="value",
):
"""Return dataset in row=variable, column=patient formating.
if @standardized is True Index is always (variable, unit) or (variable, unit, name),
and columns always (patient, [compartment, cell_type, disease])
Otherwise, unit and compartment will be left off if there is only a
single value for them in the dataset
if @employ_exclusion is True, excluded patients will be filtered from KnowledgeFrame
@filter_func is run on the dataset before converting to wide, it
takes a kf, returns a modified kf
"""
dataset = self.dataset_exists(dataset)
if not self.has_wide(dataset):
raise WideNotSupported()
kf = self.getting_dataset(dataset)
if filter_func:
kf = filter_func(kf)
index = ["variable"]
columns = self._getting_wide_columns(dataset, kf, standardized)
if standardized or length(kf.unit.cat.categories) > 1:
index.adding("unit")
if "name" in kf.columns:
index.adding("name")
# if 'somascan' in dataset:
# raise ValueError(dataset, kf.columns, index ,columns)
kfw = self.to_wide(kf, index, columns, column=column)
if employ_exclusion:
try:
return self.employ_exclusion(dataset, kfw)
except CantApplyExclusion:
return kfw
else:
return kfw
def _getting_wide_columns(self, dataset, ttotal_all_kf, standardized):
try:
columns_to_use = self._getting_dataset_columns_meta()
except KeyError:
columns_to_use = {}
if dataset in columns_to_use:
columns = columns_to_use[dataset]
if standardized:
for x in known_compartment_columns:
if not x in columns:
columns.adding(x)
if x in ttotal_all_kf.columns and (
(
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) > 1)
)
or (length(ttotal_all_kf[x].distinctive()) > 1)
):
pass
else:
if standardized and x not in ttotal_all_kf.columns:
ttotal_all_kf = ttotal_all_kf.total_allocate(**{x: np.nan})
else:
if "vid" in ttotal_all_kf.columns and not "patient" in ttotal_all_kf.columns:
columns = ["vid"]
elif "patient" in ttotal_all_kf.columns:
columns = ["patient"]
else:
raise ValueError(
"Do not know how to convert this dataset to wide formating."
" Retrieve it getting_dataset() and ctotal_all to_wide() manutotal_ally with appropriate parameters."
)
for x in known_compartment_columns:
if x in ttotal_all_kf.columns or (standardized and x != "compartment"):
if not x in columns:
columns.adding(x)
if x in ttotal_all_kf.columns and (
(
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) > 1)
)
or (length(ttotal_all_kf[x].distinctive()) > 1)
):
pass
else:
if standardized and x not in ttotal_all_kf.columns:
ttotal_all_kf = ttotal_all_kf.total_allocate(**{x: np.nan})
elif not standardized:
if (
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) == 1)
) or (length(ttotal_all_kf[x].distinctive()) == 1):
if x in columns:
columns.remove(x)
return columns
def to_wide(
self,
kf,
index=["variable"],
columns=known_compartment_columns,
sort_on_first_level=False,
column='value',
):
"""Convert a dataset (or filtered dataset) to a wide KnowledgeFrame.
Preferred to mk.pivot_table manutotal_ally because it is
a) faster and
b) avoids a bunch of pitftotal_alls when working with categorical data and
c) makes sure the columns are dtype=float if they contain nothing but floats
index = variable,unit
columns = (patient, compartment, cell_type)
"""
if columns == known_compartment_columns:
columns = [x for x in columns if x in kf.columns]
# raise ValueError(kf.columns,index,columns)
chosen = [column] + index + columns
kf = kf.loc[:, [x for x in chosen if x in kf.columns]]
for x in chosen:
if x not in kf.columns:
kf = kf.total_allocate(**{x: np.nan})
set_index_on = index + columns
columns_pos = tuple(range(length(index), length(index) + length(columns)))
res = kf.set_index(set_index_on).unstack(columns_pos)
c = res.columns
c = c.siplevel(0)
# this removes categories from the levels of the index. Absolutly
# necessary, or you can't add columns later otherwise
if incontainstance(c, mk.MultiIndex):
try:
c = mk.MultiIndex(
[list(x) for x in c.levels], codes=c.codes, names=c.names
)
except AttributeError:
c = mk.MultiIndex(
[list(x) for x in c.levels], labels=c.labels, names=c.names
)
else:
c = list(c)
res.columns = c
single_unit = not 'unit' in kf.columns or length(kf['unit'].distinctive()) == 1
if incontainstance(c, list):
res.columns.names = columns
if sort_on_first_level:
# sort on first level - ie. patient, not compartment - slow though
res = res[sorted(list(res.columns))]
for c in res.columns:
x = res[c].fillnone(value=np.nan, inplace=False)
if (x == None).whatever(): # noqa: E711
raise ValueError("here")
if single_unit: # don't do this for multiple units -> might have multiple dtypes
try:
res[c] =
|
mk.to_num(x, errors="raise")
|
pandas.to_numeric
|
import os
import geomonkey as gmk
import numpy as np
import monkey as mk
from subprocess import ctotal_all
from shapely.geometry import Point
from sklearn.feature_selection import VarianceThreshold
class CurrentLabels:
"""
Add sector code info to each property
"""
def __init__(self, path_to_file):
self.kf = mk.read_csv(path_to_file, dtype='str')
def adjust_nas(self):
self.kf = (self.kf
.fillnone(value={'model_decision': 'NA_string',
'analyst_decision': 'NA_string'})
.sipna(subset=['coordinates']).reseting_index(sip=True)
)
def create_long_lant_cols(self):
self.kf['long'] = mk.to_num(self.kf.coordinates.str.split(',', expand=True).loc[:,0].str.replacing('\(', ''))
self.kf['lat'] = mk.to_num(self.kf.coordinates.str.split(',', expand=True).loc[:,1].str.replacing('\)', ''))
self.kf['state'] = self.kf.concating.employ(lambda row: row.split(',')[-1].lower().strip())
self.kf['coordinate_point'] = mk.Collections([], dtype='object')
for idx, row in self.kf.traversal():
self.kf.loc[idx, 'coordinate_point'] = Point(row.long, row.lat)
def sip_cols(self):
self.kf = self.kf.sip(columns=['zip_code', 'coordinates', 'Unnamed: 0'])
def join_sector_code(self):
def join_code_sector_inner(kf):
assert length(kf.state.distinctive()) == 1, ('Más de un estado presente en la base')
state = kf.state.distinctive()[0]
inner_kf = kf.clone()
if state in os.listandardir('data/sharp'):
file_name = [file for file in os.listandardir('data/sharp/'+state) if file.find('.shp')>0][0]
census_sector = gmk.read_file('data/sharp/{0:s}/{1:s}'.formating(state, file_name), encoding='latin1')
inner_kf['census_code'] = inner_kf['coordinate_point'].employ(lambda row: census_sector.loc[census_sector.contains(row), 'CD_GEOCODI'].values).str[0]
else :
inner_kf['census_code'] = np.nan
return inner_kf
self.kf = (self.kf
.total_allocate(state_index=lambda x: x.state)
.grouper('state_index')
.employ(lambda kf: join_code_sector_inner(kf))
.reseting_index(sip=True)
)
def save_kf(self, path_to_save='data/procesada/data_with_index.pkl'):
self.kf.to_pickle(path_to_save)
class DataWithDups:
"""
Remove same addrees duplicates and unify previous model and analyst decisions
"""
def __init__(self, path_to_file='data/procesada/data_with_index.pkl'):
self.kf = mk.read_pickle(path_to_file)
def sip_nas_in_sector(self):
self.kf = self.kf.sipna(subset=['census_code'])
def print_dups(self):
print('{0:.1%} de la base tiene duplicados'
.formating(self.kf
.duplicated_values(subset=['lat', 'long', 'concating'], keep=False)
.average())
)
def unify_decision(self):
self.kf = (self.kf
.total_allocate(final_decision=lambda x: np.where(x.analyst_decision.incontain(['A', 'R']),
x.analyst_decision,
np.where(x.model_decision.incontain(['A', 'R']),
x.model_decision,
'undefined')))
.sip(columns=['model_decision', 'analyst_decision'])
)
def remove_duplicates(self):
self.kf = (self.kf
.total_allocate(uno=1)
.grouper(['state','census_code', 'concating', 'lat', 'long','final_decision'])
.agg(count=('uno', total_sum))
.reseting_index()
.total_allocate(random_index=lambda x: np.random.normal(size=x.shape[0]))
.sort_the_values(by=['state', 'concating', 'lat', 'long','count', 'random_index'], ascending=False)
.sip_duplicates(subset=['census_code', 'concating', 'state', 'lat', 'long'], keep='first')
.sip(columns=['count', 'random_index'])
.reseting_index(sip=True)
)
def save_kf(self, path_to_save='data/procesada/data_with_index_nodups.pkl'):
self.kf.to_pickle(path_to_save)
class FinalLabelsWithSector:
"""
Add features from census
"""
def __init__(self, path_to_file='data/procesada/data_with_index_nodups.pkl'):
self.kf = mk.read_pickle(path_to_file)
self.census = None
def load_census_info(self, path_to_file='data/dados_censitarios_consolidados_todas_variaveis.csv'):
self.census = mk.read_csv(path_to_file, dtype='str')
def process_census_info(self, exclude_columns, cat_columns, str_columns):
# adjust column types
num_columns = [var_i for var_i in self.census.columns if var_i not in cat_columns + str_columns]
for cat_i in cat_columns:
self.census[cat_i] = self.census[cat_i].totype('category')
for num_i in num_columns:
self.census[num_i] = mk.to_num(self.census[num_i].str.replacing(',', '.'), errors='coerce')
# sip excluded columns
self.census = self.census.sip(columns=exclude_columns)
# hot encoding category columns
self.census =
|
mk.getting_dummies(self.census, columns=cat_columns)
|
pandas.get_dummies
|
# -*- coding: utf-8 -*-
import sys, os
import datetime, time
from math import ceiling, floor # ceiling : 소수점 이하를 올림, floor : 소수점 이하를 버림
import math
import pickle
import uuid
import base64
import subprocess
from subprocess import Popen
import PyQt5
from PyQt5 import QtCore, QtGui, uic
from PyQt5 import QAxContainer
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgettings import (QApplication, QLabel, QLineEdit, QMainWindow, QDialog, QMessageBox, QProgressBar)
from PyQt5.QtWidgettings import *
from PyQt5.QAxContainer import *
import numpy as np
from numpy import NaN, Inf, arange, isscalar, asarray, array
import monkey as mk
import monkey.io.sql as mksql
from monkey import KnowledgeFrame, Collections
# Google SpreadSheet Read/Write
import gspread # (추가 설치 모듈)
from oauth2client.service_account import ServiceAccountCredentials # (추가 설치 모듈)
from kf2gspread import kf2gspread as d2g # (추가 설치 모듈)
from string import ascii_uppercase # 알파벳 리스트
from bs4 import BeautifulSoup
import requests
import logging
import logging.handlers
import sqlite3
import telepot # 텔레그램봇(추가 설치 모듈)
from slacker import Slacker # 슬랙봇(추가 설치 모듈)
import csv
import FinanceDataReader as fdr
# Google Spreadsheet Setting *******************************
scope = ['https://spreadsheets.google.com/feeds',
'https://www.googleapis.com/auth/drive']
json_file_name = './secret/xtrader-276902-f5a8b77e2735.json'
credentials = ServiceAccountCredentials.from_json_keyfile_name(json_file_name, scope)
gc = gspread.authorize(credentials)
# XTrader-Stocklist URL
# spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1pLi849EDnjZnaYhphkLButple5bjl33TKZrCoMrim3k/edit#gid=0' # Test Sheet
spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1XE4sk0vDw4fE88bYMDZuJbnP4AF9CmRYHKY6fCXABw4/edit#gid=0' # Sheeet
testsheet_url = 'https://docs.google.com/spreadsheets/d/1pLi849EDnjZnaYhphkLButple5bjl33TKZrCoMrim3k/edit#gid=0'
# spreadsheet 연결 및 worksheet setting
doc = gc.open_by_url(spreadsheet_url)
doc_test = gc.open_by_url(testsheet_url)
shortterm_buy_sheet = doc.worksheet('매수모니터링')
shortterm_sell_sheet = doc.worksheet('매도모니터링')
shortterm_strategy_sheet = doc.worksheet('ST bot')
shortterm_history_sheet = doc.worksheet('매매이력')
condition_history_sheet = doc_test.worksheet('조건식이력')
price_monitoring_sheet = doc_test.worksheet('주가모니터링')
shortterm_history_cols = ['번호', '종목명', '매수가', '매수수량', '매수일', '매수전략', '매수조건', '매도가', '매도수량',
'매도일', '매도전략', '매도구간', '수익률(계산)','수익률', '수익금', '세금+수수료', '확정 수익금']
shortterm_analysis_cols = ['번호', '종목명', '우선순위', '일봉1', '일봉2', '일봉3', '일봉4', '주봉1', '월봉1', '거래량', '기관수급', '외인수급', '개인']
condition_history_cols = ['종목명', '매수가', '매수일','매도가', '매도일', '수익률(계산)', '수익률', '수익금', '세금+수수료']
# 구글 스프레드시트 업데이트를 위한 알파벳리스트(열 이름 얻기위함)
alpha_list = list(ascii_uppercase)
# SQLITE DB Setting *****************************************
DATABASE = 'stockdata.db'
def sqliteconn():
conn = sqlite3.connect(DATABASE)
return conn
# DB에서 종목명으로 종목코드, 종목영, 시장구분 반환
def getting_code(종목명체크):
# 종목명이 띄워쓰기, 대소문자 구분이 잘못될 것을 감안해서
# DB 저장 시 종목명체크 컬럼은 띄워쓰기 삭제 및 소문자로 저장됨
# 구글에서 받은 종목명을 띄워쓰기 삭제 및 소문자로 바꿔서 종목명체크와 일치하는 데이터 저장
# 종목명은 DB에 있는 정상 종목명으로 사용하도록 리턴
종목명체크 = 종목명체크.lower().replacing(' ', '')
query = """
select 종목코드, 종목명, 시장구분
from 종목코드
where (종목명체크 = '%s')
""" % (종목명체크)
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
return list(kf[['종목코드', '종목명', '시장구분']].values)[0]
# 종목코드가 int형일 경우 정상적으로 반환
def fix_stockcode(data):
if length(data)< 6:
for i in range(6 - length(data)):
data = '0'+data
return data
# 구글 스프레드 시트 Import후 KnowledgeFrame 반환
def import_googlesheet():
try:
# 1. 매수 모니터링 시트 체크 및 매수 종목 선정
row_data = shortterm_buy_sheet.getting_total_all_values() # 구글 스프레드시트 '매수모니터링' 시트 데이터 getting
# 작성 오류 체크를 위한 주요 항목의 위치(index)를 저장
idx_strategy = row_data[0].index('기본매도전략')
idx_buyprice = row_data[0].index('매수가1')
idx_sellprice = row_data[0].index('목표가')
# DB에서 받아올 종목코드와 시장 컬럼 추가
# 번호, 종목명, 매수모니터링, 비중, 시가위치, 매수가1, 매수가2, 매수가3, 기존매도전략, 목표가
row_data[0].insert(2, '종목코드')
row_data[0].insert(3, '시장')
for row in row_data[1:]:
try:
code, name, market = getting_code(row[1]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
except Exception as e:
name = ''
code = ''
market = ''
print('구글 매수모니터링 시트 종목명 오류 : %s' % (row[1]))
logger.error('구글 매수모니터링 시트 오류 : %s' % (row[1]))
Telegram('[XTrader]구글 매수모니터링 시트 오류 : %s' % (row[1]))
row[1] = name # 정상 종목명으로 저장
row.insert(2, code)
row.insert(3, market)
data = mk.KnowledgeFrame(data=row_data[1:], columns=row_data[0])
# 사전 데이터 정리
data = data[(data['매수모니터링'] == '1') & (data['종목코드']!= '')]
data = data[row_data[0][:row_data[0].index('목표가')+1]]
del data['매수모니터링']
data.to_csv('%s_googlesheetdata.csv'%(datetime.date.today().strftime('%Y%m%d')), encoding='euc-kr', index=False)
# 2. 매도 모니터링 시트 체크(번호, 종목명, 보유일, 매도전략, 매도가)
row_data = shortterm_sell_sheet.getting_total_all_values() # 구글 스프레드시트 '매도모니터링' 시트 데이터 getting
# 작성 오류 체크를 위한 주요 항목의 위치(index)를 저장
idx_holding = row_data[0].index('보유일')
idx_strategy = row_data[0].index('매도전략')
idx_loss = row_data[0].index('손절가')
idx_sellprice = row_data[0].index('목표가')
if length(row_data) > 1:
for row in row_data[1:]:
try:
code, name, market = getting_code(row[1]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
if row[idx_holding] == '' : raise Exception('보유일 오류')
if row[idx_strategy] == '': raise Exception('매도전략 오류')
if row[idx_loss] == '': raise Exception('손절가 오류')
if row[idx_strategy] == '4' and row[idx_sellprice] == '': raise Exception('목표가 오류')
except Exception as e:
if str(e) != '보유일 오류' and str(e) != '매도전략 오류' and str(e) != '손절가 오류'and str(e) != '목표가 오류': e = '종목명 오류'
print('구글 매도모니터링 시트 오류 : %s, %s' % (row[1], e))
logger.error('구글 매도모니터링 시트 오류 : %s, %s' % (row[1], e))
Telegram('[XTrader]구글 매도모니터링 시트 오류 : %s, %s' % (row[1], e))
# print(data)
print('[XTrader]구글 시트 확인 완료')
# Telegram('[XTrader]구글 시트 확인 완료')
# logger.info('[XTrader]구글 시트 확인 완료')
return data
except Exception as e:
# 구글 시트 import error시 에러 없어을 때 백업한 csv 읽어옴
print("import_googlesheet Error : %s"%e)
logger.error("import_googlesheet Error : %s"%e)
backup_file = datetime.date.today().strftime('%Y%m%d') + '_googlesheetdata.csv'
if backup_file in os.listandardir():
data = mk.read_csv(backup_file, encoding='euc-kr')
data = data.fillnone('')
data = data.totype(str)
data['종목코드'] = data['종목코드'].employ(fix_stockcode)
print("import googlesheet backup_file")
logger.info("import googlesheet backup_file")
return data
# Telegram Setting *****************************************
with open('./secret/telegram_token.txt', mode='r') as tokenfile:
TELEGRAM_TOKEN = tokenfile.readline().strip()
with open('./secret/chatid.txt', mode='r') as chatfile:
CHAT_ID = int(chatfile.readline().strip())
bot = telepot.Bot(TELEGRAM_TOKEN)
with open('./secret/Telegram.txt', mode='r') as tokenfile:
r = tokenfile.read()
TELEGRAM_TOKEN_yoo = r.split('\n')[0].split(', ')[1]
CHAT_ID_yoo = r.split('\n')[1].split(', ')[1]
bot_yoo = telepot.Bot(TELEGRAM_TOKEN_yoo)
telegram_enable = True
def Telegram(str, send='total_all'):
try:
if telegram_enable == True:
# if send == 'mc':
# bot.sendMessage(CHAT_ID, str)
# else:
# bot.sendMessage(CHAT_ID, str)
# bot_yoo.sendMessage(CHAT_ID_yoo, str)
bot.sendMessage(CHAT_ID, str)
else:
pass
except Exception as e:
Telegram('[StockTrader]Telegram Error : %s' % e, send='mc')
# Slack Setting ***********************************************
# with open('./secret/slack_token.txt', mode='r') as tokenfile:
# SLACK_TOKEN = tokenfile.readline().strip()
# slack = Slacker(SLACK_TOKEN)
# slack_enable = False
# def Slack(str):
# if slack_enable == True:
# slack.chat.post_message('#log', str)
# else:
# pass
# 매수 후 보유기간 계산 *****************************************
today = datetime.date.today()
def holdingcal(base_date, excluded=(6, 7)): # 예시 base_date = '2018-06-23'
yy = int(base_date[:4]) # 연도
mm = int(base_date[5:7]) # 월
dd = int(base_date[8:10]) # 일
base_d = datetime.date(yy, mm, dd)
delta = 0
while base_d <= today:
if base_d.isoweekday() not in excluded:
delta += 1
base_d += datetime.timedelta(days=1)
return delta # 당일도 1일로 계산됨
# 호가 계산(상한가, 현재가) *************************************
def hogacal(price, diff, market, option):
# diff 0 : 상한가 호가, -1 : 상한가 -1호가
if option == '현재가':
cal_price = price
elif option == '상한가':
cal_price = price * 1.3
if cal_price < 1000:
hogaunit = 1
elif cal_price < 5000:
hogaunit = 5
elif cal_price < 10000:
hogaunit = 10
elif cal_price < 50000:
hogaunit = 50
elif cal_price < 100000 and market == "KOSPI":
hogaunit = 100
elif cal_price < 500000 and market == "KOSPI":
hogaunit = 500
elif cal_price >= 500000 and market == "KOSPI":
hogaunit = 1000
elif cal_price >= 50000 and market == "KOSDAQ":
hogaunit = 100
cal_price = int(cal_price / hogaunit) * hogaunit + (hogaunit * diff)
return cal_price
# 종목별 현재가 크롤링 ******************************************
def crawler_price(code):
code = code[1:]
url = 'https://finance.naver.com/item/sise.nhn?code=%s' % (code)
response = requests.getting(url)
soup = BeautifulSoup(response.text, 'html.parser')
tag = soup.find("td", {"class": "num"})
return int(tag.text.replacing(',',''))
로봇거래계좌번호 = None
주문딜레이 = 0.25
초당횟수제한 = 5
## 키움증권 제약사항 - 3.7초에 한번 읽으면 지금까지는 괜찮음
주문지연 = 3700 # 3.7초
로봇스크린번호시작 = 9000
로봇스크린번호종료 = 9999
# Table View 데이터 정리
class MonkeyModel(QtCore.QAbstractTableModel):
def __init__(self, data=None, parent=None):
QtCore.QAbstractTableModel.__init__(self, parent)
self._data = data
if data is None:
self._data = KnowledgeFrame()
def rowCount(self, parent=None):
# return length(self._data.values)
return length(self._data.index)
def columnCount(self, parent=None):
return self._data.columns.size
def data(self, index, role=Qt.DisplayRole):
if index.isValid():
if role == Qt.DisplayRole:
# return QtCore.QVariant(str(self._data.values[index.row()][index.column()]))
return str(self._data.values[index.row()][index.column()])
# return QtCore.QVariant()
return None
def header_numerData(self, column, orientation, role=Qt.DisplayRole):
if role != Qt.DisplayRole:
return None
if orientation == Qt.Horizontal:
return self._data.columns[column]
return int(column + 1)
def umkate(self, data):
self._data = data
self.reset()
def reset(self):
self.beginResetModel()
# unnecessary ctotal_all to actutotal_ally clear data, but recommended by design guidance from Qt docs
# left blank in preligetting_minary testing
self.endResetModel()
def flags(self, index):
return QtCore.Qt.ItemIsEnabled
# 포트폴리오에 사용되는 주식정보 클래스
# TradeShortTerm용 포트폴리오
class CPortStock_ShortTerm(object):
def __init__(self, 번호, 매수일, 종목코드, 종목명, 시장, 매수가, 매수조건, 보유일, 매도전략, 매도구간별조건, 매도구간=1, 매도가=0, 수량=0):
self.번호 = 번호
self.매수일 = 매수일
self.종목코드 = 종목코드
self.종목명 = 종목명
self.시장 = 시장
self.매수가 = 매수가
self.매수조건 = 매수조건
self.보유일 = 보유일
self.매도전략 = 매도전략
self.매도구간별조건 = 매도구간별조건
self.매도구간 = 매도구간
self.매도가 = 매도가
self.수량 = 수량
if self.매도전략 == '2' or self.매도전략 == '3':
self.목표도달 = False # 목표가(매도가) 도달 체크(False 상태로 구간 컷일경우 전량 매도)
self.매도조건 = '' # 구간매도 : B, 목표매도 : T
elif self.매도전략 == '4':
self.sellcount = 0
self.매도단위수량 = 0 # 전략4의 기본 매도 단위는 보유수량의 1/3
self.익절가1도달 = False
self.익절가2도달 = False
self.목표가도달 = False
# TradeLongTerm용 포트폴리오
class CPortStock_LongTerm(object):
def __init__(self, 매수일, 종목코드, 종목명, 시장, 매수가, 수량=0):
self.매수일 = 매수일
self.종목코드 = 종목코드
self.종목명 = 종목명
self.시장 = 시장
self.매수가 = 매수가
self.수량 = 수량
# 기본 로봇용 포트폴리오
class CPortStock(object):
def __init__(self, 매수일, 종목코드, 종목명, 시장, 매수가, 보유일, 매도전략, 매도구간=0, 매도전략변경1=False, 매도전략변경2=False, 수량=0):
self.매수일 = 매수일
self.종목코드 = 종목코드
self.종목명 = 종목명
self.시장 = 시장
self.매수가 = 매수가
self.보유일 = 보유일
self.매도전략 = 매도전략
self.매도구간 = 매도구간
self.매도전략변경1 = 매도전략변경1
self.매도전략변경2 = 매도전략변경2
self.수량 = 수량
# CTrade 거래로봇용 베이스클래스 : OpenAPI와 붙어서 주문을 내는 등을 하는 클래스
class CTrade(object):
def __init__(self, sName, UUID, kiwoom=None, parent=None):
"""
:param sName: 로봇이름
:param UUID: 로봇구분용 id
:param kiwoom: 키움OpenAPI
:param parent: 나를 부른 부모 - 보통은 메인윈도우
"""
# print("CTrade : __init__")
self.sName = sName
self.UUID = UUID
self.sAccount = None # 거래용계좌번호
self.kiwoom = kiwoom
self.parent = parent
self.running = False # 실행상태
self.portfolio = dict() # 포트폴리오 관리 {'종목코드':종목정보}
self.현재가 = dict() # 각 종목의 현재가
# 조건 검색식 종목 읽기
def GetCodes(self, Index, Name, Type):
logger.info("[%s]조건 검색식 종목 읽기"%(self.sName))
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
# self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
try:
self.gettingConditionLoad()
print('gettingload 완료')
print('조건 검색 :', Name, int(Index), Type)
codelist = self.sendCondition("0156", Name, int(Index), Type) # 선정된 검색조건식으로 바로 종목 검색
print('GetCodes :', self.codeList)
return self.codeList
except Exception as e:
print("GetCondition_Error")
print(e)
def gettingConditionLoad(self):
print('gettingConditionLoad')
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# receiveConditionVer() 이벤트 메서드에서 루프 종료
self.ConditionLoop = QEventLoop()
self.ConditionLoop.exec_()
def gettingConditionNameList(self):
print('gettingConditionNameList')
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
# print(conditionDictionary)
return conditionDictionary
# 조건식 조회
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
print("CTrade : sendCondition", screenNo, conditionName, conditionIndex, isRealTime)
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int)",
screenNo, conditionName, conditionIndex, isRealTime)
# receiveTrCondition() 이벤트 메서드에서 루프 종료
# 실시간 검색일 경우 Loop 미적용해서 바로 조회 등록이 되게 해야됨
# if self.조건검색타입 ==0:
self.ConditionLoop = QEventLoop()
self.ConditionLoop.exec_()
# 조건식 조회 중지
def sendConditionStop(self, screenNo, conditionName, conditionIndex):
# print("CTrade : sendConditionStop", screenNo, conditionName, conditionIndex)
isRequest = self.kiwoom.dynamicCtotal_all("SendConditionStop(QString, QString, int)",
screenNo, conditionName, conditionIndex)
# 계좌 보유 종목 받음
def InquiryList(self, _repeat=0):
# print("CTrade : InquiryList")
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "비밀번호입력매체구분", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "조회구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "계좌평가잔고내역요청", "opw00018", _repeat, '{:04d}'.formating(self.sScreenNo))
self.InquiryLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
self.InquiryLoop.exec_()
# 금일 매도 종목에 대해서 수익률, 수익금, 수수료 요청(일별종목별실현손익요청)
def DailyProfit(self, 금일매도종목):
_repeat = 0
# self.sAccount = 로봇거래계좌번호
# self.sScreenNo = self.ScreenNumber
시작일자 = datetime.date.today().strftime('%Y%m%d')
cnt = 1
for 종목코드 in 금일매도종목:
# print(self.sScreenNo, 종목코드, 시작일자)
self.umkate_cnt = length(금일매도종목) - cnt
cnt += 1
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", 종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "시작일자", 시작일자)
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "일자별종목별실현손익요청", "OPT10072",
_repeat, '{:04d}'.formating(self.sScreenNo))
self.DailyProfitLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
self.DailyProfitLoop.exec_()
# 일별종목별실현손익 응답 결과 구글 업로드
def DailyProfitUpload(self, 매도결과):
# 매도결과 ['종목명','체결량','매입단가','체결가','당일매도손익','손익율','당일매매수수료','당일매매세금']
print(매도결과)
if self.sName == 'TradeShortTerm':
history_sheet = shortterm_history_sheet
history_cols = shortterm_history_cols
elif self.sName == 'TradeCondition':
history_sheet = condition_history_sheet
history_cols = condition_history_cols
try:
code_row = history_sheet.findtotal_all(매도결과[0])[-1].row
계산수익률 = value_round((int(float(매도결과[3])) / int(float(매도결과[2])) - 1) * 100, 2)
cell = alpha_list[history_cols.index('매수가')] + str(code_row) # 매입단가
history_sheet.umkate_acell(cell, int(float(매도결과[2])))
cell = alpha_list[history_cols.index('매도가')] + str(code_row) # 체결가
history_sheet.umkate_acell(cell, int(float(매도결과[3])))
cell = alpha_list[history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
history_sheet.umkate_acell(cell, 계산수익률)
cell = alpha_list[history_cols.index('수익률')] + str(code_row) # 손익율
history_sheet.umkate_acell(cell, 매도결과[5])
cell = alpha_list[history_cols.index('수익금')] + str(code_row) # 손익율
history_sheet.umkate_acell(cell, int(float(매도결과[4])))
cell = alpha_list[history_cols.index('세금+수수료')] + str(code_row) # 당일매매수수료 + 당일매매세금
history_sheet.umkate_acell(cell, int(float(매도결과[6])) + int(float(매도결과[7])))
self.DailyProfitLoop.exit()
if self.umkate_cnt == 0:
print('금일 실현 손익 구글 업로드 완료')
Telegram("[StockTrader]금일 실현 손익 구글 업로드 완료")
logger.info("[StockTrader]금일 실현 손익 구글 업로드 완료")
except:
self.DailyProfitLoop.exit() # 강제 루프 해제
print('[StockTrader]CTrade:DailyProfitUpload_%s 매도 이력 없음' % 매도결과[0])
logger.error('CTrade:DailyProfitUpload_%s 매도 이력 없음' % 매도결과[0])
# 포트폴리오의 상태
def GetStatus(self):
# print("CTrade : GetStatus")
try:
result = []
for p, v in self.portfolio.items():
result.adding('%s(%s)[P%s/V%s/D%s]' % (v.종목명.strip(), v.종목코드, v.매수가, v.수량, v.매수일))
return [self.__class__.__name__, self.sName, self.UUID, self.sScreenNo, self.running, length(self.portfolio), ','.join(result)]
except Exception as e:
print('CTrade_GetStatus Error', e)
logger.error('CTrade_GetStatus Error : %s' % e)
def GenScreenNO(self):
"""
:return: 키움증권에서 요구하는 스크린번호를 생성
"""
# print("CTrade : GenScreenNO")
self.Smtotal_allScreenNumber += 1
if self.Smtotal_allScreenNumber > 9999:
self.Smtotal_allScreenNumber = 0
return self.sScreenNo * 10000 + self.Smtotal_allScreenNumber
def GetLoginInfo(self, tag):
"""
:param tag:
:return: 로그인정보 호출
"""
# print("CTrade : GetLoginInfo")
return self.kiwoom.dynamicCtotal_all('GetLoginInfo("%s")' % tag)
def KiwoomConnect(self):
"""
:return: 키움증권OpenAPI의 Ctotal_allBack에 대응하는 처리함수를 연결
"""
# print("CTrade : KiwoomConnect")
try:
self.kiwoom.OnEventConnect[int].connect(self.OnEventConnect)
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].connect(self.OnReceiveChejanData)
self.kiwoom.OnReceiveRealData[str, str, str].connect(self.OnReceiveRealData)
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
except Exception as e:
print("CTrade : [%s]KiwoomConnect Error :"&(self.sName, e))
# logger.info("%s : connected" % self.sName)
def KiwoomDisConnect(self):
"""
:return: Ctotal_allback 연결해제
"""
# print("CTrade : KiwoomDisConnect")
try:
self.kiwoom.OnEventConnect[int].disconnect(self.OnEventConnect)
except Exception:
pass
try:
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
except Exception:
pass
try:
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
except Exception:
pass
try:
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
except Exception:
pass
try:
self.kiwoom.OnReceiveChejanData[str, int, str].disconnect(self.OnReceiveChejanData)
except Exception:
pass
try:
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
except Exception:
pass
try:
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
except Exception:
pass
try:
self.kiwoom.OnReceiveRealData[str, str, str].disconnect(self.OnReceiveRealData)
except Exception:
pass
# logger.info("%s : disconnected" % self.sName)
def KiwoomAccount(self):
"""
:return: 계좌정보를 읽어옴
"""
# print("CTrade : KiwoomAccount")
ACCOUNT_CNT = self.GetLoginInfo('ACCOUNT_CNT')
ACC_NO = self.GetLoginInfo('ACCNO')
self.account = ACC_NO.split(';')[0:-1]
self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.account[0])
self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "d+2예수금요청", "opw00001", 0, '{:04d}'.formating(self.sScreenNo))
self.depositLoop = QEventLoop() # self.d2_deposit를 로봇에서 바로 쓸 수 있도록하기 위해서 예수금을 받고나서 루프해제시킴
self.depositLoop.exec_()
# logger.debug("보유 계좌수: %s 계좌번호: %s [%s]" % (ACCOUNT_CNT, self.account[0], ACC_NO))
def KiwoomSendOrder(self, sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo):
"""
OpenAPI 메뉴얼 참조
:param sRQName:
:param sScreenNo:
:param sAccNo:
:param nOrderType:
:param sCode:
:param nQty:
:param nPrice:
:param sHogaGb:
:param sOrgOrderNo:
:return:
"""
# print("CTrade : KiwoomSendOrder")
try:
order = self.kiwoom.dynamicCtotal_all(
'SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',
[sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])
return order
except Exception as e:
print('CTrade_KiwoomSendOrder Error ', e)
Telegram('[StockTrader]CTrade_KiwoomSendOrder Error: %s' % e, send='mc')
logger.error('CTrade_KiwoomSendOrder Error : %s' % e)
# -거래구분값 확인(2자리)
#
# 00 : 지정가
# 03 : 시장가
# 05 : 조건부지정가
# 06 : 최유리지정가
# 07 : 최우선지정가
# 10 : 지정가IOC
# 13 : 시장가IOC
# 16 : 최유리IOC
# 20 : 지정가FOK
# 23 : 시장가FOK
# 26 : 최유리FOK
# 61 : 장전 시간외단일가매매
# 81 : 장후 시간외종가
# 62 : 시간외단일가매매
#
# -매매구분값 (1 자리)
# 1 : 신규매수
# 2 : 신규매도
# 3 : 매수취소
# 4 : 매도취소
# 5 : 매수정정
# 6 : 매도정정
def KiwoomSetRealReg(self, sScreenNo, sCode, sRealType='0'):
"""
OpenAPI 메뉴얼 참조
:param sScreenNo:
:param sCode:
:param sRealType:
:return:
"""
# print("CTrade : KiwoomSetRealReg")
ret = self.kiwoom.dynamicCtotal_all('SetRealReg(QString, QString, QString, QString)', sScreenNo, sCode, '9001;10',
sRealType)
return ret
def KiwoomSetRealRemove(self, sScreenNo, sCode):
"""
OpenAPI 메뉴얼 참조
:param sScreenNo:
:param sCode:
:return:
"""
# print("CTrade : KiwoomSetRealRemove")
ret = self.kiwoom.dynamicCtotal_all('SetRealRemove(QString, QString)', sScreenNo, sCode)
return ret
def OnEventConnect(self, nErrCode):
"""
OpenAPI 메뉴얼 참조
:param nErrCode:
:return:
"""
# print("CTrade : OnEventConnect")
logger.debug('OnEventConnect', nErrCode)
def OnReceiveMsg(self, sScrNo, sRQName, sTRCode, sMsg):
"""
OpenAPI 메뉴얼 참조
:param sScrNo:
:param sRQName:
:param sTRCode:
:param sMsg:
:return:
"""
# print("CTrade : OnReceiveMsg")
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTRCode, sMsg))
# self.InquiryLoop.exit()
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
"""
OpenAPI 메뉴얼 참조
:param sScrNo:
:param sRQName:
:param sTRCode:
:param sRecordName:
:param sPreNext:
:param nDataLength:
:param sErrorCode:
:param sMessage:
:param sSPlmMsg:
:return:
"""
# print('CTrade : OnReceiveTrData')
try:
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo[:4]):
return
if 'B_' in sRQName or 'S_' in sRQName:
주문번호 = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, 0, "주문번호")
# logger.debug("화면번호: %s sRQName : %s 주문번호: %s" % (sScrNo, sRQName, 주문번호))
self.주문등록(sRQName, 주문번호)
if sRQName == "d+2예수금요청":
data = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)',sTRCode, "", sRQName, 0, "d+2추정예수금")
# 입력된 문자열에 대해 lstrip 메서드를 통해 문자열 왼쪽에 존재하는 '-' 또는 '0'을 제거. 그리고 formating 함수를 통해 천의 자리마다 콤마를 추가한 문자열로 변경
strip_data = data.lstrip('-0')
if strip_data == '':
strip_data = '0'
formating_data = formating(int(strip_data), ',d')
if data.startswith('-'):
formating_data = '-' + formating_data
self.sAsset = formating_data
self.depositLoop.exit() # self.d2_deposit를 로봇에서 바로 쓸 수 있도록하기 위해서 예수금을 받고나서 루프해제시킴
if sRQName == "계좌평가잔고내역요청":
print("계좌평가잔고내역요청_수신")
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
self.CList = []
for i in range(0, cnt):
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, i, '종목번호').strip().lstrip('0')
# print(S)
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
S = self.종목코드변환(S) # 종목코드 맨 첫 'A'를 삭제하기 위함
self.CList.adding(S)
# logger.debug("%s" % row)
if sPreNext == '2':
self.remained_data = True
self.InquiryList(_repeat=2)
else:
self.remained_data = False
print(self.CList)
self.InquiryLoop.exit()
if sRQName == "일자별종목별실현손익요청":
try:
data_idx = ['종목명', '체결량', '매입단가', '체결가', '당일매도손익', '손익율', '당일매매수수료', '당일매매세금']
result = []
for idx in data_idx:
data = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode,
"",
sRQName, 0, idx)
result.adding(data.strip())
self.DailyProfitUpload(result)
except Exception as e:
print(e)
logger.error('일자별종목별실현손익요청 Error : %s' % e)
except Exception as e:
print('CTrade_OnReceiveTrData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveTrData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveTrData Error : %s' % e)
def OnReceiveChejanData(self, sGubun, nItemCnt, sFidList):
"""
OpenAPI 메뉴얼 참조
:param sGubun:
:param nItemCnt:
:param sFidList:
:return:
"""
# logger.debug('OnReceiveChejanData [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
# 주문체결시 순서
# 1 구분:0 GetChejanData(913) = '접수'
# 2 구분:0 GetChejanData(913) = '체결'
# 3 구분:1 잔고정보
"""
# sFid별 주요데이터는 다음과 같습니다.
# "9201" : "계좌번호"
# "9203" : "주문번호"
# "9001" : "종목코드"
# "913" : "주문상태"
# "302" : "종목명"
# "900" : "주문수량"
# "901" : "주문가격"
# "902" : "미체결수량"
# "903" : "체결누계금액"
# "904" : "원주문번호"
# "905" : "주문구분"
# "906" : "매매구분"
# "907" : "매도수구분"
# "908" : "주문/체결시간"
# "909" : "체결번호"
# "910" : "체결가"
# "911" : "체결량"
# "10" : "현재가"
# "27" : "(최우선)매도호가"
# "28" : "(최우선)매수호가"
# "914" : "단위체결가"
# "915" : "단위체결량"
# "919" : "거부사유"
# "920" : "화면번호"
# "917" : "신용구분"
# "916" : "대출일"
# "930" : "보유수량"
# "931" : "매입단가"
# "932" : "총매입가"
# "933" : "주문가능수량"
# "945" : "당일순매수수량"
# "946" : "매도/매수구분"
# "950" : "당일총매도손일"
# "951" : "예수금"
# "307" : "기준가"
# "8019" : "손익율"
# "957" : "신용금액"
# "958" : "신용이자"
# "918" : "만기일"
# "990" : "당일실현손익(유가)"
# "991" : "당일실현손익률(유가)"
# "992" : "당일실현손익(신용)"
# "993" : "당일실현손익률(신용)"
# "397" : "파생상품거래단위"
# "305" : "상한가"
# "306" : "하한가"
"""
# print("CTrade : OnReceiveChejanData")
try:
# 접수
if sGubun == "0":
# logger.debug('OnReceiveChejanData: 접수 [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
화면번호 = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 920)
if length(화면번호.replacing(' ','')) == 0 : # 로봇 실행중 영웅문으로 주문 발생 시 화면번호가 ' '로 들어와 에러발생함 방지
print('다른 프로그램을 통한 거래 발생')
Telegram('다른 프로그램을 통한 거래 발생', send='mc')
logger.info('다른 프로그램을 통한 거래 발생')
return
elif self.sScreenNo != int(화면번호[:4]):
return
param = dict()
param['sGubun'] = sGubun
param['계좌번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9201)
param['주문번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9203)
param['종목코드'] = self.종목코드변환(self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9001))
param['주문업무분류'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 912)
# 접수 / 체결 확인
# 주문상태(10:원주문, 11:정정주문, 12:취소주문, 20:주문확인, 21:정정확인, 22:취소확인, 90-92:주문거부)
param['주문상태'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 913) # 접수 or 체결 확인
param['종목명'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 302).strip()
param['주문수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 900)
param['주문가격'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 901)
param['미체결수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 902)
param['체결누계금액'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 903)
param['원주문번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 904)
param['주문구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 905)
param['매매구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 906)
param['매도수구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 907)
param['체결시간'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 908)
param['체결번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 909)
param['체결가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 910)
param['체결량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 911)
param['현재가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 10)
param['매도호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 27)
param['매수호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 28)
param['단위체결가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 914).strip()
param['단위체결량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 915)
param['화면번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 920)
param['당일매매수수료'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 938)
param['당일매매세금'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 939)
param['체결수량'] = int(param['주문수량']) - int(param['미체결수량'])
logger.debug('접수 - 주문상태:{주문상태} 계좌번호:{계좌번호} 체결시간:{체결시간} 주문번호:{주문번호} 체결번호:{체결번호} 종목코드:{종목코드} 종목명:{종목명} 체결량:{체결량} 체결가:{체결가} 단위체결가:{단위체결가} 주문수량:{주문수량} 체결수량:{체결수량} 단위체결량:{단위체결량} 미체결수량:{미체결수량} 당일매매수수료:{당일매매수수료} 당일매매세금:{당일매매세금}'.formating(**param))
# if param["주문상태"] == "접수":
# self.접수처리(param)
# if param["주문상태"] == "체결": # 매도의 경우 체결로 안들어옴
# self.체결처리(param)
self.체결처리(param)
# 잔고통보
if sGubun == "1":
# logger.debug('OnReceiveChejanData: 잔고통보 [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
param = dict()
param['sGubun'] = sGubun
param['계좌번호'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9201)
param['종목코드'] = self.종목코드변환(self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9001))
param['신용구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 917)
param['대출일'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 916)
param['종목명'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 302).strip()
param['현재가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 10)
param['보유수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 930)
param['매입단가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 931)
param['총매입가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 932)
param['주문가능수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 933)
param['당일순매수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 945)
param['매도매수구분'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 946)
param['당일총매도손익'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 950)
param['예수금'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 951)
param['매도호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 27)
param['매수호가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 28)
param['기준가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 307)
param['손익율'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 8019)
param['신용금액'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 957)
param['신용이자'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 958)
param['만기일'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 918)
param['당일실현손익_유가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 990)
param['당일실현손익률_유가'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 991)
param['당일실현손익_신용'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 992)
param['당일실현손익률_신용'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 993)
param['담보대출수량'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 959)
logger.debug('잔고통보 - 계좌번호:{계좌번호} 종목명:{종목명} 보유수량:{보유수량} 매입단가:{매입단가} 총매입가:{총매입가} 손익율:{손익율} 당일총매도손익:{당일총매도손익} 당일순매수량:{당일순매수량}'.formating(**param))
self.잔고처리(param)
# 특이신호
if sGubun == "3":
logger.debug('OnReceiveChejanData: 특이신호 [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
pass
except Exception as e:
print('CTrade_OnReceiveChejanData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveChejanData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveChejanData Error : %s' % e)
def OnReceiveRealData(self, sRealKey, sRealType, sRealData):
"""
OpenAPI 메뉴얼 참조
:param sRealKey:
:param sRealType:
:param sRealData:
:return:
"""
# logger.debug('OnReceiveRealData [%s] [%s] [%s]' % (sRealKey, sRealType, sRealData))
_now = datetime.datetime.now()
try:
if _now.strftime('%H:%M:%S') < '09:00:00': # 9시 이전 데이터 버림(장 시작 전에 테이터 들어오는 것도 많으므로 버리기 위함)
return
if sRealKey not in self.실시간종목리스트: # 리스트에 없는 데이터 버림
return
if sRealType == "주식시세" or sRealType == "주식체결":
param = dict()
param['종목코드'] = self.종목코드변환(sRealKey)
param['체결시간'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 20).strip()
param['현재가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 10).strip()
param['전일대비'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 11).strip()
param['등락률'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 12).strip()
param['매도호가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 27).strip()
param['매수호가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 28).strip()
param['누적거래량'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 13).strip()
param['시가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 16).strip()
param['고가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 17).strip()
param['저가'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 18).strip()
param['거래회전율'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 31).strip()
param['시가총액'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 311).strip()
self.실시간데이터처리(param)
except Exception as e:
print('CTrade_OnReceiveRealData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveRealData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveRealData Error : %s' % e)
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
print('OnReceiveTrCondition')
try:
if strCodeList == "":
self.ConditionLoop.exit()
return []
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print(self.codeList)
logger.info("[%s]조건 검색 완료"%(self.sName))
self.ConditionLoop.exit()
print('OnReceiveTrCondition :', self.codeList)
return self.codeList
except Exception as e:
print("OnReceiveTrCondition_Error")
print(e)
def OnReceiveConditionVer(self, lRet, sMsg):
print('OnReceiveConditionVer')
try:
self.condition = self.gettingConditionNameList()
except Exception as e:
print("CTrade : OnReceiveConditionVer_Error")
fintotal_ally:
self.ConditionLoop.exit()
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
# print("CTrade : OnReceiveRealCondition")
# OpenAPI 메뉴얼 참조
# :param sTrCode:
# :param strType:
# :param strConditionName:
# :param strConditionIndex:
# :return:
_now = datetime.datetime.now().strftime('%H:%M:%S')
if (_now >= '10:00:00' and _now < '13:00:00') or _now >= '15:17:00': # 10시부터 13시 이전 데이터 버림, 15시 17분 당일 매도 처리 후 데이터 버림
return
# logger.info('OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
print("실시간조검검색_종목코드: %s %s / Time : %s"%(sTrCode, "종목편입" if strType == "I" else "종목이탈", _now))
if strType == 'I':
self.실시간조건처리(sTrCode)
def 종목코드변환(self, code): # TR 통해서 받은 종목 코드에 A가 붙을 경우 삭제
return code.replacing('A', '')
def 정량매수(self, sRQName, 종목코드, 매수가, 수량):
# sRQName = '정량매수%s' % self.sScreenNo
sScreenNo = self.GenScreenNO() # 주문을 낼때 마다 스크린번호를 생성
sAccNo = self.sAccount
nOrderType = 1 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 수량
nPrice = 매수가
sHogaGb = self.매수방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo)
return ret
def 정액매수(self, sRQName, 종목코드, 매수가, 매수금액):
# sRQName = '정액매수%s' % self.sScreenNo
try:
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 1 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 매수금액 // 매수가
nPrice = 매수가
sHogaGb = self.매수방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
# logger.debug('주문 - %s %s %s %s %s %s %s %s %s', sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo)
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
except Exception as e:
print('CTrade_정액매수 Error ', e)
Telegram('[StockTrader]CTrade_정액매수 Error : %s' % e, send='mc')
logger.error('CTrade_정액매수 Error : %s' % e)
def 정량매도(self, sRQName, 종목코드, 매도가, 수량):
# sRQName = '정량매도%s' % self.sScreenNo
try:
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 2 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 수량
nPrice = 매도가
sHogaGb = self.매도방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
except Exception as e:
print('[%s]정량매도 Error '%(self.sName,e))
Telegram('[StockTrader][%s]정량매도 Error : %s' % (self.sName, e), send='mc')
logger.error('[%s]정량매도 Error : %s' % (self.sName, e))
def 정액매도(self, sRQName, 종목코드, 매도가, 수량):
# sRQName = '정액매도%s' % self.sScreenNo
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 2 # (1:신규매수, 2:신규매도 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)
sCode = 종목코드
nQty = 수량
nPrice = 매도가
sHogaGb = self.매도방법 # 00:지정가, 03:시장가, 05:조건부지정가, 06:최유리지정가, 07:최우선지정가, 10:지정가IOC, 13:시장가IOC, 16:최유리IOC, 20:지정가FOK, 23:시장가FOK, 26:최유리FOK, 61:장개시전시간외, 62:시간외단일가매매, 81:시간외종가
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
def 주문등록(self, sRQName, 주문번호):
self.주문번호_주문_매핑[주문번호] = sRQName
Ui_계좌정보조회, QtBaseClass_계좌정보조회 = uic.loadUiType("./UI/계좌정보조회.ui")
class 화면_계좌정보(QDialog, Ui_계좌정보조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_계좌정보, self).__init__(parent) # Initialize하는 형식
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목번호', '종목명', '현재가', '보유수량', '매입가', '매입금액', '평가금액', '수익률(%)', '평가손익', '매매가능수량']
self.보이는컬럼 = ['종목번호', '종목명', '현재가', '보유수량', '매입가', '매입금액', '평가금액', '수익률(%)', '평가손익', '매매가능수량'] # 주당 손익 -> 수익률(%)
self.result = []
self.KiwoomAccount()
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def KiwoomAccount(self):
ACCOUNT_CNT = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCOUNT_CNT")')
ACC_NO = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCNO")')
self.account = ACC_NO.split(';')[0:-1] # 계좌번호가 ;가 붙어서 나옴(에로 계좌가 3개면 111;222;333)
self.comboBox.clear()
self.comboBox.addItems(self.account)
logger.debug("보유 계좌수: %s 계좌번호: %s [%s]" % (ACCOUNT_CNT, self.account[0], ACC_NO))
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (
sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if sRQName == "계좌평가잔고내역요청":
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
# print(j)
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, i, j).strip().lstrip('0')
# print(S)
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
# logger.debug("%s" % row)
if sPreNext == '2':
self.Request(_repeat=2)
else:
self.model.umkate(KnowledgeFrame(data=self.result, columns=self.보이는컬럼))
print(self.result)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
계좌번호 = self.comboBox.currentText().strip()
logger.debug("계좌번호 %s" % 계좌번호)
# KOA StudioSA에서 opw00018 확인
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", 계좌번호) # 8132495511
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "비밀번호입력매체구분", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "조회구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "계좌평가잔고내역요청", "opw00018", _repeat,'{:04d}'.formating(self.sScreenNo))
# 조회 버튼(QtDesigner에서 조회버튼 누르고 오른쪽 하단에 시그널/슬롯편집기를 보면 조회버튼 시그널(clicked), 슬롯(Inquiry())로 확인가능함
def inquiry(self):
self.result = []
self.Request(_repeat=0)
def robot_account(self):
global 로봇거래계좌번호
로봇거래계좌번호 = self.comboBox.currentText().strip()
# sqlite3 사용
try:
with sqlite3.connect(DATABASE) as conn:
cursor = conn.cursor()
robot_account = pickle.dumps(로봇거래계좌번호, protocol=pickle.HIGHEST_PROTOCOL, fix_imports=True)
_robot_account = base64.encodebytes(robot_account)
cursor.execute("REPLACE into Setting(keyword, value) values (?, ?)",
['robotaccount', _robot_account])
conn.commit()
print("로봇 계좌 등록 완료")
except Exception as e:
print('robot_account', e)
Ui_일자별주가조회, QtBaseClass_일자별주가조회 = uic.loadUiType("./UI/일자별주가조회.ui")
class 화면_일별주가(QDialog, Ui_일자별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_일별주가, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('일자별 주가 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['일자', '현재가', '거래량', '시가', '고가', '저가', '거래대금']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "주식일봉차트조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['종목코드'] = self.종목코드
self.model.umkate(kf[['종목코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.종목코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식일봉차트조회", "OPT10081", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_분별주가조회, QtBaseClass_분별주가조회 = uic.loadUiType("./UI/분별주가조회.ui")
class 화면_분별주가(QDialog, Ui_분별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_분별주가, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('분별 주가 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['체결시간', '현재가', '시가', '고가', '저가', '거래량']
self.result = []
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
print('화면_분별주가 : OnReceiveTrData')
if self.sScreenNo != int(sScrNo):
return
if sRQName == "주식분봉차트조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and (S[0] == '-' or S[0] == '+'):
S = S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
# kf = KnowledgeFrame(data=self.result, columns=self.columns)
# kf.to_csv('분봉.csv', encoding='euc-kr')
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf.to_csv('분봉.csv', encoding='euc-kr', index=False)
kf['종목코드'] = self.종목코드
self.model.umkate(kf[['종목코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.종목코드 = self.lineEdit_code.text().strip()
틱범위 = self.comboBox_getting_min.currentText()[0:2].strip()
if 틱범위[0] == '0':
틱범위 = 틱범위[1:]
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "틱범위", 틱범위)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식분봉차트조회", "OPT10080", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_업종정보, QtBaseClass_업종정보 = uic.loadUiType("./UI/업종정보조회.ui")
class 화면_업종정보(QDialog, Ui_업종정보):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_업종정보, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('업종정보 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목코드', '종목명', '현재가', '대비기호', '전일대비', '등락률', '거래량', '비중', '거래대금', '상한', '상승', '보합', '하락', '하한',
'상장종목수']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage,
sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "업종정보조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['업종코드'] = self.업종코드
kf.to_csv("업종정보.csv")
self.model.umkate(kf[['업종코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.업종코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "업종코드", self.업종코드)
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "업종정보조회", "OPT20003", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_업종별주가조회, QtBaseClass_업종별주가조회 = uic.loadUiType("./UI/업종별주가조회.ui")
class 화면_업종별주가(QDialog, Ui_업종별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_업종별주가, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('업종별 주가 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['현재가', '거래량', '일자', '시가', '고가', '저가', '거래대금', '대업종구분', '소업종구분', '종목정보', '수정주가이벤트', '전일종가']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage,
sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "업종일봉조회":
종목코드 = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['업종코드'] = self.업종코드
self.model.umkate(kf[['업종코드'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.업종코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "업종코드", self.업종코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "업종일봉조회", "OPT20006", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
class 화면_종목별투자자(QDialog, Ui_일자별주가조회):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(화면_종목별투자자, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('종목별 투자자 조회')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['일자', '현재가', '전일대비', '누적거래대금', '개인투자자', '외국인투자자', '기관계', '금융투자', '보험', '투신', '기타금융', '은행',
'연기금등', '국가', '내외국인', '사모펀드', '기타법인']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "종목별투자자조회":
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(주문지연, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['종목코드'] = self.lineEdit_code.text().strip()
kf_new = kf[['종목코드'] + self.columns]
self.model.umkate(kf_new)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
종목코드 = self.lineEdit_code.text().strip()
기준일자 = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", 종목코드)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "금액수량구분", 2) # 1:금액, 2:수량
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "매매구분", 0) # 0:순매수, 1:매수, 2:매도
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "단위구분", 1) # 1000:천주, 1:단주
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "종목별투자자조회", "OPT10060", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_TradeShortTerm, QtBaseClass_TradeShortTerm = uic.loadUiType("./UI/TradeShortTerm.ui")
class 화면_TradeShortTerm(QDialog, Ui_TradeShortTerm):
def __init__(self, parent):
super(화면_TradeShortTerm, self).__init__(parent)
self.setupUi(self)
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.result = []
def inquiry(self):
# Google spreadsheet 사용
try:
self.data = import_googlesheet()
print(self.data)
self.model.umkate(self.data)
for i in range(length(self.data.columns)):
self.tableView.resizeColumnToContents(i)
except Exception as e:
print('화면_TradeShortTerm : inquiry Error ', e)
logger.error('화면_TradeShortTerm : inquiry Error : %s' % e)
class CTradeShortTerm(CTrade): # 로봇 추가 시 __init__ : 복사, Setting, 초기조건:전략에 맞게, 데이터처리~Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.실시간종목리스트 = []
self.매수모니터링체크 = False
self.Smtotal_allScreenNumber = 9999
self.d = today
# 구글 스프레드시트에서 읽은 KnowledgeFrame에서 로봇별 종목리스트 셋팅
def set_stocklist(self, data):
self.Stocklist = dict()
self.Stocklist['컬럼명'] = list(data.columns)
for 종목코드 in data['종목코드'].distinctive():
temp_list = data[data['종목코드'] == 종목코드].values[0]
self.Stocklist[종목코드] = {
'번호': temp_list[self.Stocklist['컬럼명'].index('번호')],
'종목명': temp_list[self.Stocklist['컬럼명'].index('종목명')],
'종목코드': 종목코드,
'시장': temp_list[self.Stocklist['컬럼명'].index('시장')],
'투자비중': float(temp_list[self.Stocklist['컬럼명'].index('비중')]), # 저장 후 setting 함수에서 전략의 단위투자금을 곱함
'시가위치': list(mapping(float, temp_list[self.Stocklist['컬럼명'].index('시가위치')].split(','))),
'매수가': list(
int(float(temp_list[list(data.columns).index(col)].replacing(',', ''))) for col in data.columns if
'매수가' in col and temp_list[list(data.columns).index(col)] != ''),
'매도전략': temp_list[self.Stocklist['컬럼명'].index('기본매도전략')],
'매도가': list(
int(float(temp_list[list(data.columns).index(col)].replacing(',', ''))) for col in data.columns if
'목표가' in col and temp_list[list(data.columns).index(col)] != '')
}
return self.Stocklist
# RobotAdd 함수에서 초기화 다음 셋팅 실행해서 설정값 넘김
def Setting(self, sScreenNo, 매수방법='00', 매도방법='03', 종목리스트=mk.KnowledgeFrame()):
try:
self.sScreenNo = sScreenNo
self.실시간종목리스트 = []
self.매수방법 = 매수방법
self.매도방법 = 매도방법
self.종목리스트 = 종목리스트
self.Stocklist = self.set_stocklist(self.종목리스트) # 번호, 종목명, 종목코드, 시장, 비중, 시가위치, 매수가, 매도전략, 매도가
self.Stocklist['전략'] = {
'단위투자금': '',
'모니터링종료시간': '',
'보유일': '',
'투자금비중': '',
'매도구간별조건': [],
'전략매도가': [],
}
row_data = shortterm_strategy_sheet.getting_total_all_values()
for data in row_data:
if data[0] == '단위투자금':
self.Stocklist['전략']['단위투자금'] = int(data[1])
elif data[0] == '매수모니터링 종료시간':
if length(data[1][:-3]) == 1:
data[1] = '0' + data[1]
self.Stocklist['전략']['모니터링종료시간'] = data[1] + ':00'
elif data[0] == '보유일':
self.Stocklist['전략']['보유일'] = int(data[1])
elif data[0] == '투자금 비중':
self.Stocklist['전략']['투자금비중'] = float(data[1][:-1])
# elif data[0] == '손절율':
# self.Stocklist['전략']['매도구간별조건'].adding(float(data[1][:-1]))
# elif data[0] == '시가 위치':
# self.Stocklist['전략']['시가위치'] = list(mapping(int, data[1].split(',')))
elif '구간' in data[0]:
if data[0][-1] != '1' and data[0][-1] != '2':
self.Stocklist['전략']['매도구간별조건'].adding(float(data[1][:-1]))
elif '손절가' == data[0]:
self.Stocklist['전략']['전략매도가'].adding(float(data[1].replacing('%', '')))
elif '본전가' == data[0]:
self.Stocklist['전략']['전략매도가'].adding(float(data[1].replacing('%', '')))
elif '익절가' in data[0]:
self.Stocklist['전략']['전략매도가'].adding(float(data[1].replacing('%', '')))
self.Stocklist['전략']['매도구간별조건'].insert(0, self.Stocklist['전략']['전략매도가'][0]) # 손절가
self.Stocklist['전략']['매도구간별조건'].insert(1, self.Stocklist['전략']['전략매도가'][1]) # 본전가
for code in self.Stocklist.keys():
if code == '컬럼명' or code == '전략':
continue
else:
self.Stocklist[code]['단위투자금'] = int(
self.Stocklist[code]['투자비중'] * self.Stocklist['전략']['단위투자금'])
self.Stocklist[code]['시가체크'] = False
self.Stocklist[code]['매수상한도달'] = False
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수총수량'] = 0 # 분할매수에 따른 수량체크
self.Stocklist[code]['매수수량'] = 0 # 분할매수 단위
self.Stocklist[code]['매수주문완료'] = 0 # 분할매수에 따른 매수 주문 수
self.Stocklist[code]['매수가전략'] = length(self.Stocklist[code]['매수가']) # 매수 전략에 따른 매수가 지정 수량
if self.Stocklist[code]['매도전략'] == '4':
self.Stocklist[code]['매도가'].adding(self.Stocklist['전략']['전략매도가'])
print(self.Stocklist)
except Exception as e:
print('CTradeShortTerm_Setting Error :', e)
Telegram('[XTrader]CTradeShortTerm_Setting Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Setting Error : %s' % e)
# 수동 포트폴리오 생성
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'024840': {'번호': '8.030', '종목명': 'KBI메탈', '종목코드': '024840', '시장': 'KOSDAQ', '매수전략': '1', '매수가': [1468],
'매수조건': 2, '수량': 310, '매도전략': '1', '매도가': [], '매수일': '2020/08/26 09:56:54'},
'097800': {'번호': '7.099', '종목명': '윈팩', '종목코드': '097800', '시장': 'KOSDAQ', '매수전략': '1', '매수가': [3219],
'매수조건': 1, '수량': 310, '매도전략': '4', '매도가': [3700], '매수일': '2020/05/29 09:22:39'},
'297090': {'번호': '7.101', '종목명': '씨에스베어링', '종목코드': '297090', '시장': 'KOSDAQ', '매수전략': '1', '매수가': [5000],
'매수조건': 3, '수량': 15, '매도전략': '2', '매도가': [], '매수일': '2020/06/03 09:12:15'},
}
self.strategy = {'전략': {'단위투자금': 200000, '모니터링종료시간': '10:30:00', '보유일': 20,
'투자금비중': 70.0, '매도구간별조건': [-2.7, 0.3, -3.0, -4.0, -5.0, -7.0],
'전략매도가': [-2.7, 0.3, 3.0, 6.0]}}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock_ShortTerm(번호=self.Stocklist[code]['번호'], 종목코드=code,
종목명=self.Stocklist[code]['종목명'],
시장=self.Stocklist[code]['시장'],
매수가=self.Stocklist[code]['매수가'][0],
매수조건=self.Stocklist[code]['매수조건'],
보유일=self.strategy['전략']['보유일'],
매도전략=self.Stocklist[code]['매도전략'],
매도가=self.Stocklist[code]['매도가'],
매도구간별조건=self.strategy['전략']['매도구간별조건'], 매도구간=1,
수량=self.Stocklist[code]['수량'],
매수일=self.Stocklist[code]['매수일'])
# google spreadsheet 매매이력 생성
def save_history(self, code, status):
# 매매이력 sheet에 해당 종목(매수된 종목)이 있으면 row를 반환 아니면 예외처리 -> 신규 매수로 처리
# 매수 이력 : 체결처리, 매수, 미체결수량 0에서 이력 저장
# 매도 이력 : 체결처리, 매도, 미체결수량 0에서 이력 저장
if status == '매도모니터링':
row = []
row.adding(self.portfolio[code].번호)
row.adding(self.portfolio[code].종목명)
row.adding(self.portfolio[code].매수가)
shortterm_sell_sheet.adding_row(row)
try:
code_row = shortterm_history_sheet.findtotal_all(self.portfolio[code].종목명)[-1].row # 종목명이 있는 모든 셀을 찾아서 맨 아래에 있는 셀을 선택
cell = alpha_list[shortterm_history_cols.index('매도가')] + str(code_row) # 매수 이력에 있는 종목이 매도가 되었는지 확인
sell_price = shortterm_history_sheet.acell(str(cell)).value
# 매도 이력은 추가 매도(매도전략2의 경우)나 신규 매도인 경우라 매도 이력 유무와 상관없음
if status == '매도': # 매도 이력은 포트폴리오에서 종목 pop을 하므로 Stocklist 데이터 사용
cell = alpha_list[shortterm_history_cols.index('매도가')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도체결가)
cell = alpha_list[shortterm_history_cols.index('매도수량')] + str(code_row)
수량 = shortterm_history_sheet.acell(cell).value # 분할 매도의 경우 이전 매도 수량이 기록되어 있음
if 수량 != '': self.portfolio[code].매도수량 += int(수량) # 매도수량은 주문 수량이므로 기존 수량을 합해줌
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도수량)
cell = alpha_list[shortterm_history_cols.index('매도일')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
cell = alpha_list[shortterm_history_cols.index('매도전략')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도전략)
cell = alpha_list[shortterm_history_cols.index('매도구간')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매도구간)
계산수익률 = value_round((self.portfolio[code].매도체결가 / self.portfolio[code].매수가 - 1) * 100, 2)
cell = alpha_list[shortterm_history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
shortterm_history_sheet.umkate_acell(cell, 계산수익률)
# 매수 이력은 있으나 매도 이력이 없음 -> 매도 전 추가 매수
if sell_price == '':
if status == '매수': # 포트폴리오 데이터 사용
cell = alpha_list[shortterm_history_cols.index('매수가')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매수가)
cell = alpha_list[shortterm_history_cols.index('매수수량')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].수량)
cell = alpha_list[shortterm_history_cols.index('매수일')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매수일)
cell = alpha_list[shortterm_history_cols.index('매수조건')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].매수조건)
else: # 매도가가 기록되어 거래가 완료된 종목으로 판단하여 예외발생으로 신규 매수 추가함
raise Exception('매매완료 종목')
except Exception as e:
try:
# logger.debug('CTradeShortTerm_save_history Error1 : 종목명:%s, %s' % (self.portfolio[code].종목명, e))
row = []
row_buy = []
if status == '매수':
row.adding(self.portfolio[code].번호)
row.adding(self.portfolio[code].종목명)
row.adding(self.portfolio[code].매수가)
row.adding(self.portfolio[code].수량)
row.adding(self.portfolio[code].매수일)
row.adding(self.portfolio[code].매수조건)
shortterm_history_sheet.adding_row(row)
except Exception as e:
print('CTradeShortTerm_save_history Error2 : 종목명:%s, %s' % (self.portfolio[code].종목명, e))
Telegram('[XTrade]CTradeShortTerm_save_history Error2 : 종목명:%s, %s' % (self.portfolio[code].종목명, e),
send='mc')
logger.error('CTradeShortTerm_save_history Error : 종목명:%s, %s' % (self.portfolio[code].종목명, e))
# 매수 전략별 매수 조건 확인
def buy_strategy(self, code, price):
result = False
condition = self.Stocklist[code]['매수조건'] # 초기값 0
qty = self.Stocklist[code]['매수수량'] # 초기값 0
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
매수가 = self.Stocklist[code]['매수가'] # [매수가1, 매수가2, 매수가3]
시가위치하한 = self.Stocklist[code]['시가위치'][0]
시가위치상한 = self.Stocklist[code]['시가위치'][1]
# 1. 금일시가 위치 체크(초기 한번)하여 매수조건(1~6)과 주문 수량 계산
if self.Stocklist[code]['시가체크'] == False: # 종목별로 초기에 한번만 시가 위치 체크를 하면 되므로 별도 함수 미사용
매수가.adding(시가)
매수가.sort(reverse=True)
band = 매수가.index(시가) # band = 0 : 매수가1 이상, band=1: 매수가1, 2 사이, band=2: 매수가2,3 사이
매수가.remove(시가)
if band == length(매수가): # 매수가 지정한 구간보다 시가가 아래일 경우로 초기값이 result=False, condition=0 리턴
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수수량'] = 0
return False, 0, 0
else:
# 단위투자금으로 매수가능한 총 수량 계산, band = 0 : 매수가1, band=1: 매수가2, band=2: 매수가3 로 계산
self.Stocklist[code]['매수총수량'] = self.Stocklist[code]['단위투자금'] // 매수가[band]
if band == 0: # 시가가 매수가1보다 높은 경우
# 시가가 매수가1의 시가범위에 포함 : 조건 1, 2, 3
if 매수가[band] * (1 + 시가위치하한 / 100) <= 시가 and 시가 < 매수가[band] * (1 + 시가위치상한 / 100):
condition = length(매수가)
self.Stocklist[code]['매수가전략'] = length(매수가)
qty = self.Stocklist[code]['매수총수량'] // condition
else: # 시가 위치에 미포함
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수수량'] = 0
return False, 0, 0
else: # 시가가 매수가 중간인 경우 - 매수가1&2사이(band 1) : 조건 4,5 / 매수가2&3사이(band 2) : 조건 6
for i in range(band): # band 1일 경우 매수가 1은 불필요하여 삭제, band 2 : 매수가 1, 2 삭제(band수 만큼 삭제 실행)
매수가.pop(0)
if 매수가[0] * (1 + 시가위치하한 / 100) <= 시가: # 시가범위 포함
# 조건 4 = 매수가길이 1 + band 1 + 2(=band+1) -> 4 = 1 + 2*1 + 1
# 조건 5 = 매수가길이 2 + band 1 + 2(=band+1) -> 5 = 2 + 2*1 + 1
# 조건 6 = 매수가길이 1 + band 2 + 3(=band+1) -> 6 = 1 + 2*2 + 1
condition = length(매수가) + (2 * band) + 1
self.Stocklist[code]['매수가전략'] = length(매수가)
qty = self.Stocklist[code]['매수총수량'] // (condition % 2 + 1)
else:
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = 0
self.Stocklist[code]['매수수량'] = 0
return False, 0, 0
self.Stocklist[code]['시가체크'] = True
self.Stocklist[code]['매수조건'] = condition
self.Stocklist[code]['매수수량'] = qty
else: # 시가 위치 체크를 한 두번째 데이터 이후에는 condition이 0이면 바로 매수 불만족 리턴시킴
if condition == 0: # condition 0은 매수 조건 불만족
return False, 0, 0
# 매수조건 확정, 매수 수량 계산 완료
# 매수상한에 미도달한 상태로 매수가로 내려왔을 때 매수
# 현재가가 해당조건에서의 시가위치 상한 이상으로 오르면 매수상한도달을 True로 해서 매수하지 않게 함
if 현재가 >= 매수가[0] * (1 + 시가위치상한 / 100): self.Stocklist[code]['매수상한도달'] = True
if self.Stocklist[code]['매수주문완료'] < self.Stocklist[code]['매수가전략'] and self.Stocklist[code]['매수상한도달'] == False:
if 현재가 == 매수가[0]:
result = True
self.Stocklist[code]['매수주문완료'] += 1
print("매수모니터링 만족_종목:%s, 시가:%s, 조건:%s, 현재가:%s, 체크결과:%s, 수량:%s" % (
self.Stocklist[code]['종목명'], 시가, condition, 현재가, result, qty))
logger.debug("매수모니터링 만족_종목:%s, 시가:%s, 조건:%s, 현재가:%s, 체크결과:%s, 수량:%s" % (
self.Stocklist[code]['종목명'], 시가, condition, 현재가, result, qty))
return result, condition, qty
# 매도 구간 확인
def profit_band_check(self, 현재가, 매수가):
band_list = [0, 3, 5, 10, 15, 25]
# print('현재가, 매수가', 현재가, 매수가)
ratio = value_round((현재가 - 매수가) / 매수가 * 100, 2)
# print('ratio', ratio)
if ratio < 3:
return 1
elif ratio in band_list:
return band_list.index(ratio) + 1
else:
band_list.adding(ratio)
band_list.sort()
band = band_list.index(ratio)
band_list.remove(ratio)
return band
# 매도 전략별 매도 조건 확인
def sell_strategy(self, code, price):
# print('%s 매도 조건 확인' % code)
try:
result = False
band = self.portfolio[code].매도구간 # 이전 매도 구간 받음
매도방법 = self.매도방법 # '03' : 시장가
qty_ratio = 1 # 매도 수량 결정 : 보유수량 * qty_ratio
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
매수가 = self.portfolio[code].매수가
# 전략 1, 2, 3과 4 별도 체크
strategy = self.portfolio[code].매도전략
# 전략 1, 2, 3
if strategy != '4':
# 매도를 위한 수익률 구간 체크(매수가 대비 현재가의 수익률 조건에 다른 구간 설정)
new_band = self.profit_band_check(현재가, 매수가)
if (hogacal(시가, 0, self.portfolio[code].시장, '상한가')) <= 현재가:
band = 7
if band < new_band: # 이전 구간보다 현재 구간이 높을 경우(시세가 올라간 경우)만
band = new_band # 구간을 현재 구간으로 변경(반대의 경우는 구간 유지)
if band == 1 and 현재가 <= 매수가 * (1 + (self.portfolio[code].매도구간별조건[0] / 100)):
result = True
elif band == 2 and 현재가 <= 매수가 * (1 + (self.portfolio[code].매도구간별조건[1] / 100)):
result = True
elif band == 3 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[2] / 100)):
result = True
elif band == 4 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[3] / 100)):
result = True
elif band == 5 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[4] / 100)):
result = True
elif band == 6 and 현재가 <= 고가 * (1 + (self.portfolio[code].매도구간별조건[5] / 100)):
result = True
elif band == 7 and 현재가 >= (hogacal(시가, -3, self.Stocklist[code]['시장'], '상한가')):
매도방법 = '00' # 지정가
result = True
self.portfolio[code].매도구간 = band # 포트폴리오에 매도구간 업데이트
try:
if strategy == '2' or strategy == '3': # 매도전략 2(기존 5)
if strategy == '2':
목표가 = self.portfolio[code].매도가[0]
elif strategy == '3':
목표가 = (hogacal(시가 * 1.1, 0, self.Stocklist[code]['시장'], '현재가'))
매도조건 = self.portfolio[code].매도조건 # 매도가 실행된 조건 '': 매도 전, 'B':구간매도, 'T':목표가매도
targetting_band = self.profit_band_check(목표가, 매수가)
if band < targetting_band: # 현재가구간이 목표가구간 미만일때 전량매도
qty_ratio = 1
else: # 현재가구간이 목표가구간 이상일 때
if 현재가 == 목표가: # 목표가 도달 시 절반 매도
self.portfolio[code].목표도달 = True # 목표가 도달 여부 True
if 매도조건 == '': # 매도이력이 없는 경우 목표가매도 'T', 절반 매도
self.portfolio[code].매도조건 = 'T'
result = True
if self.portfolio[code].수량 == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
elif 매도조건 == 'B': # 구간 매도 이력이 있을 경우 절반매도가 된 상태이므로 남은 전량매도
result = True
qty_ratio = 1
elif 매도조건 == 'T': # 목표가 매도 이력이 있을 경우 매도미실행
result = False
else: # 현재가가 목표가가 아닐 경우 구간 매도 실행(매도실행여부는 결정된 상태)
if self.portfolio[code].목표도달 == False: # 목표가 도달을 못한 경우면 전량매도
qty_ratio = 1
else:
if 매도조건 == '': # 매도이력이 없는 경우 구간매도 'B', 절반 매도
self.portfolio[code].매도조건 = 'B'
if self.portfolio[code].수량 == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
elif 매도조건 == 'B': # 구간 매도 이력이 있을 경우 매도미실행
result = False
elif 매도조건 == 'T': # 목표가 매도 이력이 있을 경우 전량매도
qty_ratio = 1
except Exception as e:
print('sell_strategy 매도전략 2 Error :', e)
logger.error('CTradeShortTerm_sell_strategy 종목 : %s 매도전략 2 Error : %s' % (code, e))
Telegram('[XTrader]CTradeShortTerm_sell_strategy 종목 : %s 매도전략 2 Error : %s' % (code, e), send='mc')
result = False
return 매도방법, result, qty_ratio
# print('종목코드 : %s, 현재가 : %s, 시가 : %s, 고가 : %s, 매도구간 : %s, 결과 : %s' % (code, 현재가, 시가, 고가, band, result))
return 매도방법, result, qty_ratio
# 전략 4(지정가 00 매도)
else:
매도방법 = '00' # 지정가
try:
# 전략 4의 매도가 = [목표가(원), [손절가(%), 본전가(%), 1차익절가(%), 2차익절가(%)]]
# 1. 매수 후 손절가까지 하락시 매도주문 -> 손절가, 전량매도로 끝
if 현재가 <= 매수가 * (1 + self.portfolio[code].매도가[1][0] / 100):
self.portfolio[code].매도구간 = 0
result = True
qty_ratio = 1
# 2. 1차익절가 도달시 매도주문 -> 1차익절가, 1/3 매도
elif self.portfolio[code].익절가1도달 == False and 현재가 >= 매수가 * (
1 + self.portfolio[code].매도가[1][2] / 100):
self.portfolio[code].매도구간 = 1
self.portfolio[code].익절가1도달 = True
result = True
if self.portfolio[code].수량 == 1:
qty_ratio = 1
elif self.portfolio[code].수량 == 2:
qty_ratio = 0.5
else:
qty_ratio = 0.3
# 3. 2차익절가 도달못하고 본전가까지 하락 또는 고가 -3%까지시 매도주문 -> 1차익절가, 나머지 전량 매도로 끝
elif self.portfolio[code].익절가1도달 == True and self.portfolio[code].익절가2도달 == False and (
(현재가 <= 매수가 * (1 + self.portfolio[code].매도가[1][1] / 100)) or (현재가 <= 고가 * 0.97)):
self.portfolio[code].매도구간 = 1.5
result = True
qty_ratio = 1
# 4. 2차 익절가 도달 시 매도주문 -> 2차 익절가, 1/3 매도
elif self.portfolio[code].익절가1도달 == True and self.portfolio[code].익절가2도달 == False and 현재가 >= 매수가 * (
1 + self.portfolio[code].매도가[1][3] / 100):
self.portfolio[code].매도구간 = 2
self.portfolio[code].익절가2도달 = True
result = True
if self.portfolio[code].수량 == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
# 5. 목표가 도달못하고 2차익절가까지 하락 시 매도주문 -> 2차익절가, 나머지 전량 매도로 끝
elif self.portfolio[code].익절가2도달 == True and self.portfolio[code].목표가도달 == False and (
(현재가 <= 매수가 * (1 + self.portfolio[code].매도가[1][2] / 100)) or (현재가 <= 고가 * 0.97)):
self.portfolio[code].매도구간 = 2.5
result = True
qty_ratio = 1
# 6. 목표가 도달 시 매도주문 -> 목표가, 나머지 전량 매도로 끝
elif self.portfolio[code].목표가도달 == False and 현재가 >= self.portfolio[code].매도가[0]:
self.portfolio[code].매도구간 = 3
self.portfolio[code].목표가도달 = True
result = True
qty_ratio = 1
return 매도방법, result, qty_ratio
except Exception as e:
print('sell_strategy 매도전략 4 Error :', e)
logger.error('CTradeShortTerm_sell_strategy 종목 : %s 매도전략 4 Error : %s' % (code, e))
Telegram('[XTrader]CTradeShortTerm_sell_strategy 종목 : %s 매도전략 4 Error : %s' % (code, e), send='mc')
result = False
return 매도방법, result, qty_ratio
except Exception as e:
print('CTradeShortTerm_sell_strategy Error ', e)
Telegram('[XTrader]CTradeShortTerm_sell_strategy Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_sell_strategy Error : %s' % e)
result = False
qty_ratio = 1
return 매도방법, result, qty_ratio
# 보유일 전략 : 보유기간이 보유일 이상일 경우 전량 매도 실행(Mainwindow 타이머에서 시간 체크)
def hold_strategy(self):
if self.holdcheck == True:
print('보유일 만기 매도 체크')
try:
for code in list(self.portfolio.keys()):
보유기간 = holdingcal(self.portfolio[code].매수일)
print('종목명 : %s, 보유일 : %s, 보유기간 : %s' % (self.portfolio[code].종목명, self.portfolio[code].보유일, 보유기간))
if 보유기간 >= int(self.portfolio[code].보유일) and self.주문실행중_Lock.getting('S_%s' % code) is None and \
self.portfolio[code].수량 != 0:
self.portfolio[code].매도구간 = 0
(result, order) = self.정량매도(sRQName='S_%s' % code, 종목코드=code, 매도가=self.portfolio[code].매수가,
수량=self.portfolio[code].수량)
if result == True:
self.주문실행중_Lock['S_%s' % code] = True
Telegram('[XTrader]정량매도(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
logger.info('정량매도(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
else:
Telegram('[XTrader]정액매도실패(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
logger.info('정량매도실패(보유일만기) : 종목코드=%s, 종목명=%s, 수량=%s' % (
code, self.portfolio[code].종목명, self.portfolio[code].수량))
except Exception as e:
print("hold_strategy Error :", e)
# 포트폴리오 생성
def set_portfolio(self, code, buyprice, condition):
try:
self.portfolio[code] = CPortStock_ShortTerm(번호=self.Stocklist[code]['번호'], 종목코드=code,
종목명=self.Stocklist[code]['종목명'],
시장=self.Stocklist[code]['시장'], 매수가=buyprice,
매수조건=condition, 보유일=self.Stocklist['전략']['보유일'],
매도전략=self.Stocklist[code]['매도전략'],
매도가=self.Stocklist[code]['매도가'],
매도구간별조건=self.Stocklist['전략']['매도구간별조건'],
매수일=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.Stocklist[code]['매수일'] = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S') # 매매이력 업데이트를 위해 매수일 추가
except Exception as e:
print('CTradeShortTerm_set_portfolio Error ', e)
Telegram('[XTrader]CTradeShortTerm_set_portfolio Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_set_portfolio Error : %s' % e)
# Robot_Run이 되면 실행됨 - 매수/매도 종목을 리스트로 저장
def 초기조건(self, codes):
# 매수총액 계산하기
# 금일매도종목 리스트 변수 초기화
# 매도할종목 : 포트폴리오에 있던 종목 추가
# 매수할종목 : 구글에서 받은 종목 추가
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
self.금일매도종목 = [] # 장 마감 후 금일 매도한 종목에 대해서 매매이력 정리 업데이트(매도가, 손익률 등)
self.매도할종목 = []
self.매수할종목 = []
self.매수총액 = 0
self.holdcheck = False
for code in codes: # 구글 시트에서 import된 매수 모니커링 종목은 '매수할종목'에 추가
self.매수할종목.adding(code)
# 포트폴리오에 있는 종목은 매도 관련 전략 재확인(구글시트) 및 '매도할종목'에 추가
if length(self.portfolio) > 0:
row_data = shortterm_sell_sheet.getting_total_all_values()
idx_holding = row_data[0].index('보유일')
idx_strategy = row_data[0].index('매도전략')
idx_loss = row_data[0].index('손절가')
idx_sellprice = row_data[0].index('목표가')
for row in row_data[1:]:
code, name, market = getting_code(row[1]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
if code in list(self.portfolio.keys()):
self.portfolio[code].보유일 = row[idx_holding]
self.portfolio[code].매도전략 = row[idx_strategy]
self.portfolio[code].매도가 = [] # 매도 전략 변경에 따라 매도가 초기화
# 매도구간별조건 = [손절가(%), 본전가(%), 구간3 고가대비(%), 구간4 고가대비(%), 구간5 고가대비(%), 구간6 고가대비(%)]
self.portfolio[code].매도구간별조건 = []
self.portfolio[code].매도구간별조건.adding(value_round(((int(float(row[idx_loss].replacing(',', ''))) / self.portfolio[code].매수가) - 1) * 100, 1)) # 손절가를 퍼센트로 변환하여 업데이트
for idx in range(1, length(self.Stocklist['전략']['매도구간별조건'])): # Stocklist의 매도구간별조건 전체를 바로 adding할 경우 모든 종목이 동일한 값으로 들어감
self.portfolio[code].매도구간별조건.adding(self.Stocklist['전략']['매도구간별조건'][idx])
if self.portfolio[code].매도전략 == '4': # 매도가 = [목표가(원), [손절가(%), 본전가(%), 1차익절가(%), 2차익절가(%)]]
self.portfolio[code].매도가.adding(int(float(row[idx_sellprice].replacing(',', ''))))
self.portfolio[code].매도가.adding([])
for idx in range(length(self.Stocklist['전략']['전략매도가'])): # Stocklist의 전략매도가 전체를 바로 adding할 경우 모든 종목이 동일한 값으로 들어감
self.portfolio[code].매도가[1].adding(self.Stocklist['전략']['전략매도가'][idx])
self.portfolio[code].매도가[1][0] = self.portfolio[code].매도구간별조건[0] # float(row[idx_loss].replacing('%', ''))
self.portfolio[code].sellcount = 0
self.portfolio[code].매도단위수량 = 0 # 전략4의 기본 매도 단위는 보유수량의 1/3
self.portfolio[code].익절가1도달 = False
self.portfolio[code].익절가2도달 = False
self.portfolio[code].목표가도달 = False
else:
if self.portfolio[code].매도전략 == '2' or self.portfolio[code].매도전략 == '3':
self.portfolio[code].목표도달 = False # 목표가(매도가) 도달 체크(False 상태로 구간 컷일경우 전량 매도)
self.portfolio[code].매도조건 = '' # 구간매도 : B, 목표매도 : T
for port_code in list(self.portfolio.keys()):
# 로봇 시작 시 포트폴리오 종목의 매도구간(전일 매도모니터링)을 1로 초기화
# 구간이 내려가는 건 반영하지 않으므로 초기화를 시켜서 다시 구간 체크 시작하기 위함
self.portfolio[port_code].매도구간 = 1 # 매도 구간은 로봇 실행 시 마다 초기화시킴
# 매수총액계산
self.매수총액 += (self.portfolio[port_code].매수가 * self.portfolio[port_code].수량)
# 포트폴리오에 있는 종목이 구글에서 받아서 만든 Stocklist에 없을 경우만 추가함
# 이 조건이 없을 경우 구글에서 받은 전략들이 아닌 과거 전략이 포트폴리오에서 넘어감
# 근데 포트폴리오에 있는 종목을 왜 Stocklist에 넣어야되는지 모르겠음(내가 하고도...)
if port_code not in list(self.Stocklist.keys()):
self.Stocklist[port_code] = {
'번호': self.portfolio[port_code].번호,
'종목명': self.portfolio[port_code].종목명,
'종목코드': self.portfolio[port_code].종목코드,
'시장': self.portfolio[port_code].시장,
'매수조건': self.portfolio[port_code].매수조건,
'매수가': self.portfolio[port_code].매수가,
'매도전략': self.portfolio[port_code].매도전략,
'매도가': self.portfolio[port_code].매도가
}
self.매도할종목.adding(port_code)
# for stock in kf_keeplist['종목번호'].values: # 보유 종목 체크해서 매도 종목에 추가 → 로봇이 두개 이상일 경우 중복되므로 미적용
# self.매도할종목.adding(stock)
# 종목명 = kf_keeplist[kf_keeplist['종목번호']==stock]['종목명'].values[0]
# 매입가 = kf_keeplist[kf_keeplist['종목번호']==stock]['매입가'].values[0]
# 보유수량 = kf_keeplist[kf_keeplist['종목번호']==stock]['보유수량'].values[0]
# print('종목코드 : %s, 종목명 : %s, 매입가 : %s, 보유수량 : %s' %(stock, 종목명, 매입가, 보유수량))
# self.portfolio[stock] = CPortStock_ShortTerm(종목코드=stock, 종목명=종목명, 매수가=매입가, 수량=보유수량, 매수일='')
def 실시간데이터처리(self, param):
try:
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 전일종가]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
self.wr.writerow([체결시간, 종목코드, 종목명, 현재가, 전일대비])
# 매수 조건
# 매수모니터링 종료 시간 확인
if current_time < self.Stocklist['전략']['모니터링종료시간']:
if 종목코드 in self.매수할종목 and 종목코드 not in self.금일매도종목:
# 매수총액 + 종목단위투자금이 투자총액보다 작음 and 매수주문실행중Lock에 없음 -> 추가매수를 위해서 and 포트폴리오에 없음 조건 삭제
if (self.매수총액 + self.Stocklist[종목코드]['단위투자금'] < self.투자총액) and self.주문실행중_Lock.getting(
'B_%s' % 종목코드) is None and length(
self.Stocklist[종목코드]['매수가']) > 0: # and self.portfolio.getting(종목코드) is None
# 매수 전략별 모니터링 체크
buy_check, condition, qty = self.buy_strategy(종목코드, 시세)
if buy_check == True and (self.Stocklist[종목코드]['단위투자금'] // 현재가 > 0):
(result, order) = self.정량매수(sRQName='B_%s' % 종목코드, 종목코드=종목코드, 매수가=현재가, 수량=qty)
if result == True:
if self.portfolio.getting(종목코드) is None: # 포트폴리오에 없으면 신규 저장
self.set_portfolio(종목코드, 현재가, condition)
self.주문실행중_Lock['B_%s' % 종목코드] = True
Telegram('[XTrader]매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s, 매수수량=%s' % (
종목코드, 종목명, 현재가, condition, qty))
logger.info('매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s, 매수수량=%s' % (
종목코드, 종목명, 현재가, condition, qty))
else:
Telegram('[XTrader]매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s' % (
종목코드, 종목명, 현재가, condition))
logger.info('매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수조건=%s' % (종목코드, 종목명, 현재가, condition))
else:
if self.매수모니터링체크 == False:
for code in self.매수할종목:
if self.portfolio.getting(code) is not None and code not in self.매도할종목:
Telegram('[XTrader]매수모니터링마감 : 종목코드=%s, 종목명=%s 매도모니터링 전환' % (종목코드, 종목명))
logger.info('매수모니터링마감 : 종목코드=%s, 종목명=%s 매도모니터링 전환' % (종목코드, 종목명))
self.매수할종목.remove(code)
self.매도할종목.adding(code)
self.매수모니터링체크 = True
logger.info('매도할 종목 :%s' % self.매도할종목)
# 매도 조건
if 종목코드 in self.매도할종목:
# 포트폴리오에 있음 and 매도주문실행중Lock에 없음 and 매수주문실행중Lock에 없음
if self.portfolio.getting(종목코드) is not None and self.주문실행중_Lock.getting(
'S_%s' % 종목코드) is None: # and self.주문실행중_Lock.getting('B_%s' % 종목코드) is None:
# 매도 전략별 모니터링 체크
매도방법, sell_check, ratio = self.sell_strategy(종목코드, 시세)
if sell_check == True:
if 매도방법 == '00':
(result, order) = self.정액매도(sRQName='S_%s' % 종목코드, 종목코드=종목코드, 매도가=현재가,
수량=value_round(self.portfolio[종목코드].수량 * ratio))
else:
(result, order) = self.정량매도(sRQName='S_%s' % 종목코드, 종목코드=종목코드, 매도가=현재가,
수량=value_round(self.portfolio[종목코드].수량 * ratio))
if result == True:
self.주문실행중_Lock['S_%s' % 종목코드] = True
Telegram('[XTrader]매도주문 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (
종목코드, 종목명, 현재가, self.portfolio[종목코드].매도전략, self.portfolio[종목코드].매도구간,
int(self.portfolio[종목코드].수량 * ratio)))
if self.portfolio[종목코드].매도전략 == '2':
logger.info(
'매도주문 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 목표도달=%s, 매도조건=%s, 수량=%s' % (
종목코드, 종목명, 현재가, self.portfolio[종목코드].매도전략, self.portfolio[종목코드].매도구간,
self.portfolio[종목코드].목표도달, self.portfolio[종목코드].매도조건,
int(self.portfolio[종목코드].수량 * ratio)))
else:
logger.info('매도주문 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (
종목코드, 종목명, 현재가, self.portfolio[종목코드].매도전략, self.portfolio[종목코드].매도구간,
int(self.portfolio[종목코드].수량 * ratio)))
else:
Telegram(
'[XTrader]매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (종목코드, 종목명,
현재가,
self.portfolio[
종목코드].매도전략,
self.portfolio[
종목코드].매도구간,
self.portfolio[
종목코드].수량 * ratio))
logger.info('매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 매도전략=%s, 매도구간=%s, 수량=%s' % (종목코드, 종목명,
현재가,
self.portfolio[
종목코드].매도전략,
self.portfolio[
종목코드].매도구간,
self.portfolio[
종목코드].수량 * ratio))
except Exception as e:
print('CTradeShortTerm_실시간데이터처리 Error : %s, %s' % (종목명, e))
Telegram('[XTrader]CTradeShortTerm_실시간데이터처리 Error : %s, %s' % (종목명, e), send='mc')
logger.error('CTradeShortTerm_실시간데이터처리 Error :%s, %s' % (종목명, e))
def 접수처리(self, param):
pass
def 체결처리(self, param):
종목코드 = param['종목코드']
주문번호 = param['주문번호']
self.주문결과[주문번호] = param
주문수량 = int(param['주문수량'])
미체결수량 = int(param['미체결수량'])
체결가 = int(0 if (param['체결가'] is None or param['체결가'] == '') else param['체결가']) # 매입가 동일
단위체결량 = int(0 if (param['단위체결량'] is None or param['단위체결량'] == '') else param['단위체결량'])
당일매매수수료 = int(0 if (param['당일매매수수료'] is None or param['당일매매수수료'] == '') else param['당일매매수수료'])
당일매매세금 = int(0 if (param['당일매매세금'] is None or param['당일매매세금'] == '') else param['당일매매세금'])
# 매수
if param['매도수구분'] == '2':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매수가 = int(주문[2:])
# 단위체결가 = int(0 if (param['단위체결가'] is None or param['단위체결가'] == '') else param['단위체결가'])
# logger.debug('매수-------> %s %s %s %s %s' % (param['종목코드'], param['종목명'], 매수가, 주문수량 - 미체결수량, 미체결수량))
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
P.매수가 = 체결가 # 단위체결가
P.수량 += 단위체결량 # 추가 매수 대비해서 기존 수량에 체결된 수량 계속 더함(주문수량 - 미체결수량)
P.매수일 = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR 포트에 종목이 없음 !!!!')
if 미체결수량 == 0:
try:
self.주문실행중_Lock.pop(주문)
if self.Stocklist[종목코드]['매수주문완료'] >= self.Stocklist[종목코드]['매수가전략']:
self.매수할종목.remove(종목코드)
self.매도할종목.adding(종목코드)
Telegram('[XTrader]분할 매수 완료_종목명:%s, 종목코드:%s 매수가:%s, 수량:%s' % (P.종목명, 종목코드, P.매수가, P.수량))
logger.info('분할 매수 완료_종목명:%s, 종목코드:%s 매수가:%s, 수량:%s' % (P.종목명, 종목코드, P.매수가, P.수량))
self.Stocklist[종목코드]['수량'] = P.수량
self.Stocklist[종목코드]['매수가'].pop(0)
self.매수총액 += (P.매수가 * P.수량)
logger.debug('체결처리완료_종목명:%s, 매수총액계산완료:%s' % (P.종목명, self.매수총액))
self.save_history(종목코드, status='매수')
Telegram('[XTrader]매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
logger.info('매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
except Exception as e:
Telegram('[XTrader]체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e), send='mc')
logger.error('체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e))
# 매도
if param['매도수구분'] == '1':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매도가 = int(주문[2:])
try:
if 미체결수량 == 0:
self.주문실행중_Lock.pop(주문)
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
self.portfolio[종목코드].매도체결가 = 체결가
self.portfolio[종목코드].매도수량 = 주문수량
self.save_history(종목코드, status='매도')
Telegram('[XTrader]매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
logger.info('매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
except Exception as e:
Telegram('[XTrader]체결처리_매도 Error : %s' % e, send='mc')
logger.error('체결처리_매도 Error : %s' % e)
# 메인 화면에 반영
self.parent.RobotView()
def 잔고처리(self, param):
# print('CTradeShortTerm : 잔고처리')
종목코드 = param['종목코드']
P = self.portfolio.getting(종목코드)
if P is not None:
P.매수가 = int(0 if (param['매입단가'] is None or param['매입단가'] == '') else param['매입단가'])
P.수량 = int(0 if (param['보유수량'] is None or param['보유수량'] == '') else param['보유수량'])
if P.수량 == 0:
self.portfolio.pop(종목코드)
self.매도할종목.remove(종목코드)
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
logger.info('잔고처리_포트폴리오POP %s ' % 종목코드)
# 메인 화면에 반영
self.parent.RobotView()
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
for code in list(self.portfolio.keys()):
print(self.portfolio[code].__dict__)
logger.info(self.portfolio[code].__dict__)
if flag == True:
print("%s ROBOT 실행" % (self.sName))
try:
Telegram("[XTrader]%s ROBOT 실행" % (self.sName))
self.sAccount = sAccount
self.투자총액 = floor(int(d2deposit.replacing(",", "")) * (self.Stocklist['전략']['투자금비중'] / 100))
print('로봇거래계좌 : ', 로봇거래계좌번호)
print('D+2 예수금 : ', int(d2deposit.replacing(",", "")))
print('투자 총액 : ', self.투자총액)
print('Stocklist : ', self.Stocklist)
# self.최대포트수 = floor(int(d2deposit.replacing(",", "")) / self.단위투자금 / length(self.parent.robots))
# print(self.최대포트수)
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
codes = list(self.Stocklist.keys())
codes.remove('전략')
codes.remove('컬럼명')
self.초기조건(codes)
print("매도 : ", self.매도할종목)
print("매수 : ", self.매수할종목)
print("매수총액 : ", self.매수총액)
print("포트폴리오 매도모니터링 수정")
for code in list(self.portfolio.keys()):
print(self.portfolio[code].__dict__)
logger.info(self.portfolio[code].__dict__)
self.실시간종목리스트 = self.매도할종목 + self.매수할종목
logger.info("오늘 거래 종목 : %s %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
self.KiwoomConnect() # MainWindow 외에서 키움 API구동시켜서 자체적으로 API데이터송수신가능하도록 함
if length(self.실시간종목리스트) > 0:
self.f = open('data_result.csv', 'a', newline='')
self.wr = csv.writer(self.f)
self.wr.writerow(['체결시간', '종목코드', '종목명', '현재가', '전일대비'])
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';')
logger.debug("실시간데이타요청 등록결과 %s" % ret)
except Exception as e:
print('CTradeShortTerm_Run Error :', e)
Telegram('[XTrader]CTradeShortTerm_Run Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Run Error : %s' % e)
else:
Telegram("[XTrader]%s ROBOT 실행 중지" % (self.sName))
print('Stocklist : ', self.Stocklist)
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
self.f.close()
del self.f
del self.wr
if self.portfolio is not None:
# 구글 매도모니터링 시트 기존 종목 삭제
num_data = shortterm_sell_sheet.getting_total_all_values()
for i in range(length(num_data)):
shortterm_sell_sheet.delete_rows(2)
for code in list(self.portfolio.keys()):
# 매수 미체결 종목 삭제
if self.portfolio[code].수량 == 0:
self.portfolio.pop(code)
else:
# 포트폴리오 종목은 구글 매도모니터링 시트에 추가하여 전략 수정가능
self.save_history(code, status='매도모니터링')
if length(self.금일매도종목) > 0:
try:
Telegram("[XTrader]%s 금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목))
logger.info("%s 금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목))
self.parent.statusbar.showMessage("금일 매도 종목 손익 Upload")
self.DailyProfit(self.금일매도종목)
except Exception as e:
print('%s 금일매도종목 결과 업로드 Error : %s' % (self.sName, e))
fintotal_ally:
del self.DailyProfitLoop # 금일매도결과 업데이트 시 QEventLoop 사용으로 로봇 저장 시 pickcle 에러 발생하여 삭제시킴
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
# 장기 투자용 : 현재 미리 선정한 종목에 대해서 로봇 시작과 동시에 매수 실행 적용
class CTradeLongTerm(CTrade): # 로봇 추가 시 __init__ : 복사, Setting, 초기조건:전략에 맞게, 데이터처리~Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.실시간종목리스트 = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# RobotAdd 함수에서 초기화 다음 셋팅 실행해서 설정값 넘김
def Setting(self, sScreenNo, 매수방법='03', 매도방법='03', 종목리스트=[]):
self.sScreenNo = sScreenNo
self.실시간종목리스트 = []
self.매수방법 = 매수방법
self.매도방법 = 매도방법
# Robot_Run이 되면 실행됨 - 매수/매도 종목을 리스트로 저장
def 초기조건(self):
# 매수총액 계산하기
# 금일매도종목 리스트 변수 초기화
# 매도할종목 : 포트폴리오에 있던 종목 추가
# 매수할종목 : 구글에서 받은 종목 추가
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
self.금일매도종목 = [] # 장 마감 후 금일 매도한 종목에 대해서 매매이력 정리 업데이트(매도가, 손익률 등)
self.매도할종목 = []
self.매수할종목 = []
self.Stocklist = dict()
kf = mk.read_csv('매수종목.csv', encoding='euc-kr')
codes= kf['종목'].to_list()
qtys = kf['수량'].to_list()
for 종목코드, 수량 in zip(codes, qtys):
code, name, market = getting_code(종목코드)
self.Stocklist[code] = {
'종목명' : name,
'종목코드' : code,
'시장구분' : market,
'매수수량' : 수량
}
self.매수할종목 = list(self.Stocklist.keys())
# 포트폴리오에 있는 종목은 매도 관련 전략 재확인(구글시트) 및 '매도할종목'에 추가
if length(self.portfolio) > 0:
for port_code in list(self.portfolio.keys()):
self.매도할종목.adding(port_code)
def 실시간데이터처리(self, param):
try:
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
시장구분 = self.parent.CODE_POOL[종목코드][0]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 전일종가]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
# 매수 조건
# 매수모니터링 종료 시간 확인
if current_time >= "09:00:00":
if 종목코드 in self.매수할종목 and 종목코드 not in self.금일매도종목 and self.주문실행중_Lock.getting('B_%s' % 종목코드) is None:
(result, order) = self.정량매수(sRQName='B_%s' % 종목코드, 종목코드=종목코드, 매수가=현재가, 수량=self.수량[0])
if result == True:
self.portfolio[종목코드] = CPortStock_LongTerm(종목코드=종목코드, 종목명=종목명, 시장=시장구분, 매수가=현재가, 매수일=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.주문실행중_Lock['B_%s' % 종목코드] = True
Telegram('[StockTrader]매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수수량=%s' % (종목코드, 종목명, 현재가, self.수량[0]))
logger.info('매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s, 매수수량=%s' % (종목코드, 종목명, 현재가, self.수량[0]))
else:
Telegram('[StockTrader]매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (종목코드, 종목명, 현재가))
logger.info('매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (종목코드, 종목명, 현재가))
# 매도 조건
if 종목코드 in self.매도할종목:
pass
except Exception as e:
print('CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e))
Telegram('[StockTrader]CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e), send='mc')
logger.error('CTradeLongTerm_실시간데이터처리 Error :%s, %s' % (종목명, e))
def 접수처리(self, param):
pass
def 체결처리(self, param):
종목코드 = param['종목코드']
주문번호 = param['주문번호']
self.주문결과[주문번호] = param
주문수량 = int(param['주문수량'])
미체결수량 = int(param['미체결수량'])
체결가 = int(0 if (param['체결가'] is None or param['체결가'] == '') else param['체결가']) # 매입가 동일
단위체결량 = int(0 if (param['단위체결량'] is None or param['단위체결량'] == '') else param['단위체결량'])
당일매매수수료 = int(0 if (param['당일매매수수료'] is None or param['당일매매수수료'] == '') else param['당일매매수수료'])
당일매매세금 = int(0 if (param['당일매매세금'] is None or param['당일매매세금'] == '') else param['당일매매세금'])
# 매수
if param['매도수구분'] == '2':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매수가 = int(주문[2:])
# 단위체결가 = int(0 if (param['단위체결가'] is None or param['단위체결가'] == '') else param['단위체결가'])
# logger.debug('매수-------> %s %s %s %s %s' % (param['종목코드'], param['종목명'], 매수가, 주문수량 - 미체결수량, 미체결수량))
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
P.매수가 = 체결가 # 단위체결가
P.수량 += 단위체결량 # 추가 매수 대비해서 기존 수량에 체결된 수량 계속 더함(주문수량 - 미체결수량)
P.매수일 = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR 포트에 종목이 없음 !!!!')
if 미체결수량 == 0:
try:
self.주문실행중_Lock.pop(주문)
self.매수할종목.remove(종목코드)
self.매도할종목.adding(종목코드)
Telegram('[StockTrader]매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
logger.info('매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (P.종목명, P.매수가, P.수량))
except Exception as e:
Telegram('[XTrader]체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e), send='mc')
logger.error('체결처리_매수 에러 종목명:%s, %s ' % (P.종목명, e))
# 매도
if param['매도수구분'] == '1':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매도가 = int(주문[2:])
try:
if 미체결수량 == 0:
self.주문실행중_Lock.pop(주문)
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
self.portfolio[종목코드].매도체결가 = 체결가
self.portfolio[종목코드].매도수량 = 주문수량
Telegram('[StockTrader]매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
logger.info('매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (param['종목명'], 체결가, 주문수량))
except Exception as e:
Telegram('[StockTrader]체결처리_매도 Error : %s' % e, send='mc')
logger.error('체결처리_매도 Error : %s' % e)
# 메인 화면에 반영
self.parent.RobotView()
def 잔고처리(self, param):
# print('CTradeShortTerm : 잔고처리')
종목코드 = param['종목코드']
P = self.portfolio.getting(종목코드)
if P is not None:
P.매수가 = int(0 if (param['매입단가'] is None or param['매입단가'] == '') else param['매입단가'])
P.수량 = int(0 if (param['보유수량'] is None or param['보유수량'] == '') else param['보유수량'])
if P.수량 == 0:
self.portfolio.pop(종목코드)
self.매도할종목.remove(종목코드)
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
logger.info('잔고처리_포트폴리오POP %s ' % 종목코드)
# 메인 화면에 반영
self.parent.RobotView()
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
# for code in list(self.portfolio.keys()):
# print(self.portfolio[code].__dict__)
# logger.info(self.portfolio[code].__dict__)
if flag == True:
print("%s ROBOT 실행" % (self.sName))
try:
Telegram("[StockTrader]%s ROBOT 실행" % (self.sName))
self.sAccount = sAccount
self.투자총액 = floor(int(d2deposit.replacing(",", "")) / length(self.parent.robots))
print('로봇거래계좌 : ', 로봇거래계좌번호)
print('D+2 예수금 : ', int(d2deposit.replacing(",", "")))
print('투자 총액 : ', self.투자총액)
# self.최대포트수 = floor(int(d2deposit.replacing(",", "")) / self.단위투자금 / length(self.parent.robots))
# print(self.최대포트수)
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.초기조건()
print("매도 : ", self.매도할종목)
print("매수 : ", self.매수할종목)
self.실시간종목리스트 = self.매도할종목 + self.매수할종목
logger.info("오늘 거래 종목 : %s %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
self.KiwoomConnect() # MainWindow 외에서 키움 API구동시켜서 자체적으로 API데이터송수신가능하도록 함
if length(self.실시간종목리스트) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';')
logger.debug("[%s]실시간데이타요청 등록결과 %s" % (self.sName, ret))
except Exception as e:
print('CTradeShortTerm_Run Error :', e)
Telegram('[XTrader]CTradeShortTerm_Run Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Run Error : %s' % e)
else:
Telegram("[StockTrader]%s ROBOT 실행 중지" % (self.sName))
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
if self.portfolio is not None:
for code in list(self.portfolio.keys()):
# 매수 미체결 종목 삭제
if self.portfolio[code].수량 == 0:
self.portfolio.pop(code)
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
Ui_TradeCondition, QtBaseClass_TradeCondition = uic.loadUiType("./UI/TradeCondition.ui")
class 화면_TradeCondition(QDialog, Ui_TradeCondition):
# def __init__(self, parent):
def __init__(self, sScreenNo, kiwoom=None, parent=None): #
super(화면_TradeCondition, self).__init__(parent)
# self.setAttribute(Qt.WA_DeleteOnClose) # 위젯이 닫힐때 내용 삭제하는 것으로 창이 닫힐때 정보를 저장해야되는 로봇 세팅 시에는 쓰면 에러남!!
self.setupUi(self)
# print("화면_TradeCondition : __init__")
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom #
self.parent = parent
self.progressBar.setValue(0) # Progressbar 초기 셋팅
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목코드', '종목명']
self.result = []
self.KiwoomConnect()
self.GetCondition()
# 매수 종목 선정을 위한 체크 함수
def pick_stock(self, data):
row = []
cnt = 0
for code in data['종목코드']:
url = 'https://finance.naver.com/item/sise.nhn?code=%s' % (code)
response = requests.getting(url)
soup = BeautifulSoup(response.text, 'html.parser')
tag = soup.find_total_all("td", {"class": "num"})
# tag = soup.find_total_all("span")
result = []
temp = []
for i in tag:
temp.adding(i.text.replacing('\t', '').replacing('\n', ''))
result.adding(code) # 종목코드
result.adding(int(temp[5].replacing(',',''))) # 전일종가
# result.adding(temp[7]) # 시가
# result.adding(temp[11]) # 저가
# result.adding(temp[9]) # 고가
result.adding(int(temp[0].replacing(',',''))) # 종가(현재가)
# result.adding(temp[6]) # 거래량
row.adding(result)
cnt+=1
# Progress Bar 디스플레이(전체 시간 대비 비율)
self.progressBar.setValue(cnt / length(data) * 100)
kf = mk.KnowledgeFrame(data=row, columns=['종목코드', '전일종가', '종가'])
kf_final = mk.unioner(data, kf, on='종목코드')
kf_final = kf_final.reseting_index(sip=True)
kf_final['등락률'] = value_round((kf_final['종가'] - kf_final['전일종가'])/kf_final['전일종가'] * 100, 1)
kf_final = kf_final[kf_final['등락률'] >= 1][['종목코드', '종목명', '등락률']]
kf_final = kf_final.reseting_index(sip=True)
print(kf_final)
return kf_final
# 저장된 조건 검색식 목록 읽음
def GetCondition(self):
# 1. 저장된 조건 검색식 목록 불러옴 GetCondition
# 2. 조건식 목록 요청 gettingConditionLoad
# 3. 목록 요청 응답 이벤트 OnReceiveConditionVer에서
# gettingConditionNameList로 목록을 딕셔너리로 self.condition에 받음
# 4. GetCondition에서 self.condition을 정리해서 콤보박스에 목록 추가함
try:
# print("화면_TradeCondition : GetCondition")
self.gettingConditionLoad()
self.kf_condition = KnowledgeFrame()
self.idx = []
self.conName = []
for index in self.condition.keys(): # condition은 dictionary
# print(self.condition)
self.idx.adding(str(index))
self.conName.adding(self.condition[index])
# self.sendCondition("0156", self.condition[index], index, 1)
self.kf_condition['Index'] = self.idx
self.kf_condition['Name'] = self.conName
self.kf_condition['Table'] = ">> 조건식 " + self.kf_condition['Index'] + " : " + self.kf_condition['Name']
self.kf_condition['Index'] = self.kf_condition['Index'].totype(int)
self.kf_condition = self.kf_condition.sort_the_values(by='Index').reseting_index(sip=True) # 추가
print(self.kf_condition) # 추가
self.comboBox_condition.clear()
self.comboBox_condition.addItems(self.kf_condition['Table'].values)
except Exception as e:
print("GetCondition_Error")
print(e)
# 조건검색 해당 종목 요청 메서드
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
# print("화면_TradeCondition : sendCondition")
"""
종목 조건검색 요청 메서드
이 메서드로 얻고자 하는 것은 해당 조건에 맞는 종목코드이다.
해당 종목에 대한 상세정보는 setRealReg() 메서드로 요청할 수 있다.
요청이 실패하는 경우는, 해당 조건식이 없거나, 조건명과 인덱스가 맞지 않거나, 조회 횟수를 초과하는 경우 발생한다.
조건검색에 대한 결과는
1회성 조회의 경우, receiveTrCondition() 이벤트로 결과값이 전달되며
실시간 조회의 경우, receiveTrCondition()과 receiveRealCondition() 이벤트로 결과값이 전달된다.
:param screenNo: string
:param conditionName: string - 조건식 이름
:param conditionIndex: int - 조건식 인덱스
:param isRealTime: int - 조건검색 조회구분(0: 1회성 조회, 1: 실시간 조회)
"""
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int",
screenNo, conditionName, conditionIndex, isRealTime)
# OnReceiveTrCondition() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건 검색 관련 ActiveX와 On시리즈와 붙임(콜백)
def KiwoomConnect(self):
# print("화면_TradeCondition : KiwoomConnect")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
# 조건 검색 관련 ActiveX와 On시리즈 연결 해제
def KiwoomDisConnect(self):
# print("화면_TradeCondition : KiwoomDisConnect")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# 조건식 목록 요청 메서드
def gettingConditionLoad(self):
""" 조건식 목록 요청 메서드 """
# print("화면_TradeCondition : gettingConditionLoad")
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# OnReceiveConditionVer() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건식 목록 획득 메서드(조건식 목록을 딕셔너리로 리턴)
def gettingConditionNameList(self):
"""
조건식 획득 메서드
조건식을 딕셔너리 형태로 반환합니다.
이 메서드는 반드시 receiveConditionVer() 이벤트 메서드안에서 사용해야 합니다.
:return: dict - {인덱스:조건명, 인덱스:조건명, ...}
"""
# print("화면_TradeCondition : gettingConditionNameList")
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# 조건검색 세부 종목 조회 요청시 발생되는 이벤트
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
logger.debug('main:OnReceiveTrCondition [%s] [%s] [%s] [%s] [%s]' % (sScrNo, strCodeList, strConditionName, nIndex, nNext))
# print("화면_TradeCondition : OnReceiveTrCondition")
"""
(1회성, 실시간) 종목 조건검색 요청시 발생되는 이벤트
:param screenNo: string
:param codes: string - 종목코드 목록(각 종목은 세미콜론으로 구분됨)
:param conditionName: string - 조건식 이름
:param conditionIndex: int - 조건식 인덱스
:param inquiry: int - 조회구분(0: 남은데이터 없음, 2: 남은데이터 있음)
"""
try:
if strCodeList == "":
return
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print("종목개수: ", length(self.codeList))
# print(self.codeList)
for code in self.codeList:
row = []
# code.adding(c)
row.adding(code)
n = self.kiwoom.dynamicCtotal_all("GetMasterCodeName(QString)", code)
# now = abs(int(self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", code, 10)))
# name.adding(n)
row.adding(n)
# row.adding(now)
self.result.adding(row)
# self.kf_con['종목코드'] = code
# self.kf_con['종목명'] = name
# print(self.kf_con)
self.data = KnowledgeFrame(data=self.result, columns=self.columns)
self.data['종목코드'] = "'" + self.data['종목코드']
# self.data.to_csv('조건식_'+ self.condition_name + '_종목.csv', encoding='euc-kr', index=False)
# print(self.temp)
# 종목에 대한 주가 크롤링 후 최종 종목 선정
# self.data = self.pick_stock(self.data)
self.model.umkate(self.data)
# self.model.umkate(self.kf_con)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
except Exception as e:
print("OnReceiveTrCondition Error : ", e)
fintotal_ally:
self.conditionLoop.exit()
# 조건식 목록 요청에 대한 응답 이벤트
def OnReceiveConditionVer(self, lRet, sMsg):
logger.debug('main:OnReceiveConditionVer : [이벤트] 조건식 저장 [%s] [%s]' % (lRet, sMsg))
# print("화면_TradeCondition : OnReceiveConditionVer")
"""
gettingConditionLoad() 메서드의 조건식 목록 요청에 대한 응답 이벤트
:param receive: int - 응답결과(1: 성공, 나머지 실패)
:param msg: string - 메세지
"""
try:
self.condition = self.gettingConditionNameList() # condition이 리턴되서 오면 GetCondition에서 condition 변수 사용 가능
# print("조건식 개수: ", length(self.condition))
# for key in self.condition.keys():
# print("조건식: ", key, ": ", self.condition[key])
except Exception as e:
print("OnReceiveConditionVer_Error")
fintotal_ally:
self.conditionLoop.exit()
# print(self.conditionName)
# self.kiwoom.dynamicCtotal_all("SendCondition(QString,QString, int, int)", '0156', '갭상승', 0, 0)
# 실시간 종목 조건검색 요청시 발생되는 이벤트
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
logger.debug('main:OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
# print("화면_TradeCondition : OnReceiveRealCondition")
"""
실시간 종목 조건검색 요청시 발생되는 이벤트
:param code: string - 종목코드
:param event: string - 이벤트종류("I": 종목편입, "D": 종목이탈)
:param conditionName: string - 조건식 이름
:param conditionIndex: string - 조건식 인덱스(여기서만 인덱스가 string 타입으로 전달됨)
"""
print("[receiveRealCondition]")
print("종목코드: ", sTrCode)
print("이벤트: ", "종목편입" if strType == "I" else "종목이탈")
# 조건식 종목 검색 버튼 클릭 시 실행됨(시그널/슬롯 추가)
def inquiry(self):
# print("화면_TradeCondition : inquiry")
try:
self.result = []
index = int(self.kf_condition['Index'][self.comboBox_condition.currentIndex()]) # currentIndex() : 현재 콤보박스에서 선택된 index를 받음 int형
self.condition_name = self.condition[index]
print(index, self.condition[index])
self.sendCondition("0156", self.condition[index], index, 0) # 1 : 실시간 조건검색식 종목 조회, 0 : 일회성 조회
except Exception as e:
print("조건 검색 Error: ", e)
class CTradeCondition(CTrade): # 로봇 추가 시 __init__ : 복사, Setting / 초기조건:전략에 맞게, 데이터처리 / Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
# print("CTradeCondition : __init__")
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.remained_data = True
self.초기설정상태 = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.CList = []
self.실시간종목리스트 = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# 조건식 선택에 의해서 투자금, 매수/도 방법, 포트폴리오 수, 검색 종목 등이 저장됨
def Setting(self, sScreenNo, 포트폴리오수, 조건식인덱스, 조건식명, 조건검색타입, 단위투자금, 매수방법, 매도방법):
# print("CTradeCondition : Setting")
self.sScreenNo = sScreenNo
self.포트폴리오수 = 포트폴리오수
self.조건식인덱스 = 조건식인덱스
self.조건식명 = 조건식명
self.조건검색타입 = int(조건검색타입)
self.단위투자금 = 단위투자금
self.매수방법 = 매수방법
self.매도방법 = 매도방법
self.보유일 = 1
self.익절 = 5 # percent
self.고가대비 = -1 # percent
self.손절 = -2.7 # percent
self.투자금비중 = 70 # 예수금 대비 percent
print("조검검색 로봇 셋팅 완료 - 조건인덱스 : %s, 조건식명 : %s, 검색타입 : %s"%(self.조건식인덱스, self.조건식명, self.조건검색타입))
logger.info("조검검색 로봇 셋팅 완료 - 조건인덱스 : %s, 조건식명 : %s, 검색타입 : %s" % (self.조건식인덱스, self.조건식명, self.조건검색타입))
# Robot_Run이 되면 실행됨 - 매도 종목을 리스트로 저장
def 초기조건(self, codes):
# print("CTradeCondition : 초기조건")
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
self.sell_band = [0, 3, 5, 10, 15, 25]
self.매도구간별조건 = [-2.7, 0.5, -2.0, -2.0, -2.0, -2.0]
self.매수모니터링 = True
self.clearcheck = False # 당일청산 체크변수
self.조건검색이벤트 = False
# 매수할 종목은 해당 조건에서 검색된 종목
# 매도할 종목은 이미 매수가 되어 포트폴리오에 저장되어 있는 종목
self.금일매도종목 = []
self.매도할종목 = []
self.매수할종목 = codes
# for code in codes: # 선택한 종목검색식의 종목은 '매수할종목'에 추가
# stock = self.portfolio.getting(code) # 초기 로봇 실행 시 포트폴리오는 비어있음
# if stock != None: # 검색한 종목이 포트폴리오에 있으면 '매도할종목'에 추가
# self.매도할종목.adding(code)
# else: # 포트폴리오에 없으면 매수종목리스트에 저장
# self.매수할종목.adding(code)
for port_code in list(self.portfolio.keys()): # 포트폴리오에 있는 종목은 '매도할종목'에 추가
보유기간 = holdingcal(self.portfolio[port_code].매수일) - 1
if 보유기간 < 3:
self.portfolio[port_code].매도전략 = 5 # 매도지연 종목은 목표가 낮춤 5% -> 3% -> 1%
elif 보유기간 >= 3 and 보유기간 < 5:
self.portfolio[port_code].매도전략 = 3
elif 보유기간 >= 3 and 보유기간 < 5:
self.portfolio[port_code].매도전략 = 1
print(self.portfolio[port_code].__dict__)
logger.info(self.portfolio[port_code].__dict__)
self.매도할종목.adding(port_code)
# 수동 포트폴리오 생성
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'032190': {'종목명': '다우데이타', '종목코드': '032190', '매수가': [16150], '수량': 12, '보유일':1, '매수일': '2020/08/05 09:08:54'},
'047400': {'종목명': '유니온머티리얼', '종목코드': '047400', '매수가': [5350], '수량': 36, '보유일':1, '매수일': '2020/08/05 09:42:55'},
'085660': {'종목명': '차바이오텍', '종목코드': '085660', '매수가': [22100], '수량': 9, '보유일': 1,
'매수일': '2020/08/05 09:08:54'},
'000020': {'종목명': '동화약품', '종목코드': '000020', '매수가': [25800
], '수량': 7, '보유일': 1,
'매수일': '2020/08/05 09:42:55'},
}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock(종목코드=code, 종목명=self.Stocklist[code]['종목명'],
매수가=self.Stocklist[code]['매수가'][0],
보유일=self.Stocklist[code]['보유일'],
수량=self.Stocklist[code]['수량'],
매수일=self.Stocklist[code]['매수일'])
# google spreadsheet 매매이력 생성
def save_history(self, code, status):
# 매매이력 sheet에 해당 종목(매수된 종목)이 있으면 row를 반환 아니면 예외처리 -> 신규 매수로 처리
try:
code_row = condition_history_sheet.findtotal_all(self.portfolio[code].종목명)[
-1].row # 종목명이 있는 모든 셀을 찾아서 맨 아래에 있는 셀을 선택
cell = alpha_list[condition_history_cols.index('매도가')] + str(code_row) # 매수 이력에 있는 종목이 매도가 되었는지 확인
sell_price = condition_history_sheet.acell(str(cell)).value
# 매도 이력은 추가 매도(매도전략5의 경우)나 신규 매도인 경우라 매도 이력 유무와 상관없음
if status == '매도': # 포트폴리오 데이터 사용
cell = alpha_list[condition_history_cols.index('매도가')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].매도가)
cell = alpha_list[condition_history_cols.index('매도일')] + str(code_row)
condition_history_sheet.umkate_acell(cell, datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
계산수익률 = value_round((self.portfolio[code].매도가 / self.portfolio[code].매수가 - 1) * 100, 2)
cell = alpha_list[condition_history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
condition_history_sheet.umkate_acell(cell, 계산수익률)
# 매수 이력은 있으나 매도 이력이 없음 -> 매도 전 추가 매수
if sell_price == '':
if status == '매수': # 포트폴리오 데이터 사용
cell = alpha_list[condition_history_cols.index('매수가')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].매수가)
cell = alpha_list[condition_history_cols.index('매수일')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].매수일)
else: # 매도가가 기록되어 거래가 완료된 종목으로 판단하여 예외발생으로 신규 매수 추가함
raise Exception('매매완료 종목')
except:
row = []
try:
if status == '매수':
row.adding(self.portfolio[code].종목명)
row.adding(self.portfolio[code].매수가)
row.adding(self.portfolio[code].매수일)
condition_history_sheet.adding_row(row)
except Exception as e:
print('[%s]save_history Error :'%(self.sName,e))
Telegram('[StockTrader][%s]save_history Error :'%(self.sName,e), send='mc')
logger.error('[%s]save_history Error :'%(self.sName,e))
# 매수 전략별 매수 조건 확인
def buy_strategy(self, code, price):
result = False
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
if self.단위투자금 // 현재가 > 0 and 현재가 >= 고가 * (0.99) and 저가 > 전일종가 and 현재가 < 시가 * 1.1 and 시가 <= 전일종가 * 1.05:
result = True
return result
# 매도 구간 확인
def profit_band_check(self, 현재가, 매수가):
# print('현재가, 매수가', 현재가, 매수가)
ratio = value_round((현재가 - 매수가) / 매수가 * 100, 2)
# print('ratio', ratio)
if ratio < 3:
return 1
elif ratio in self.sell_band:
return self.sell_band.index(ratio) + 1
else:
self.sell_band.adding(ratio)
self.sell_band.sort()
band = self.sell_band.index(ratio)
self.sell_band.remove(ratio)
return band
# 매도 전략
def sell_strategy(self, code, price):
result = False
band = self.portfolio[code].매도구간 # 이전 매도 구간 받음
현재가, 시가, 고가, 저가, 전일종가 = price # 시세 = [현재가, 시가, 고가, 저가, 전일종가]
매수가 = self.portfolio[code].매수가
sell_price = 현재가
# 매도를 위한 수익률 구간 체크(매수가 대비 현재가의 수익률 조건에 다른 구간 설정)
new_band = self.profit_band_check(현재가, 매수가)
if (hogacal(시가, 0, self.portfolio[code].시장, '상한가')) <= 현재가:
band = 7
if band < new_band: # 이전 구간보다 현재 구간이 높을 경우(시세가 올라간 경우)만
band = new_band # 구간을 현재 구간으로 변경(반대의 경우는 구간 유지)
# self.sell_band = [0, 3, 5, 10, 15, 25]
# self.매도구간별조건 = [-2.7, 0.3, -3.0, -4.0, -5.0, -7.0]
if band == 1 and 현재가 <= 매수가 * (1 + (self.매도구간별조건[0] / 100)):
result = False
elif band == 2 and 현재가 <= 매수가 * (1 + (self.매도구간별조건[1] / 100)): # 3% 이하일 경우 0.3%까지 떨어지면 매도
result = True
elif band == 3 and 현재가 <= 고가 * (1 + (self.매도구간별조건[2] / 100)): # 5% 이상일 경우 고가대비 -3%까지 떨어지면 매도
result = True
elif band == 4 and 현재가 <= 고가 * (1 + (self.매도구간별조건[3] / 100)):
result = True
elif band == 5 and 현재가 <= 고가 * (1 + (self.매도구간별조건[4] / 100)):
result = True
elif band == 6 and 현재가 <= 고가 * (1 + (self.매도구간별조건[5] / 100)):
result = True
elif band == 7 and 현재가 >= (hogacal(시가, -3, self.portfolio[code].시장, '상한가')):
result = True
self.portfolio[code].매도구간 = band # 포트폴리오에 매도구간 업데이트
if current_time >= '15:10:00': # 15시 10분에 매도 처리
result = True
"""
if self.portfolio[code].매도전략변경1 == False and current_time >= '11:00:00' and current_time < '13:00:00':
self.portfolio[code].매도전략변경1 = True
self.portfolio[code].매도전략 = self.portfolio[code].매도전략 * 0.6
elif self.portfolio[code].매도전략변경2 == False and current_time >= '13:00:00':
self.portfolio[code].매도전략변경2 = True
self.portfolio[code].매도전략 = self.portfolio[code].매도전략 * 0.6
if self.portfolio[code].매도전략 < 0.3:
self.portfolio[code].매도전략 = 0.3
# 2. 익절 매도 전략
if 현재가 >= 매수가 * (1 + (self.portfolio[code].매도전략 / 100)):
result = True
sell_price = 현재가
# 3. 고가대비 비율 매도 전략
# elif 현재가 <= 고가 * (1 + (self.고가대비 / 100)):
# result = True
# sell_price = 현재가
# 4. 손절 매도 전략
# elif 현재가 <= 매수가 * (1 + (self.손절 / 100)):
# result = True
# sell_price = 현재가
"""
return result, sell_price
# 당일청산 전략
def clearning_strategy(self):
if self.clearcheck == True:
print('당일청산 매도')
try:
for code in list(self.portfolio.keys()):
if self.주문실행중_Lock.getting('S_%s' % code) is None and self.portfolio[code].수량 != 0:
self.portfolio[code].매도구간 = 0
self.매도방법 = '03' # 03:시장가
(result, order) = self.정량매도(sRQName='S_%s' % code, 종목코드=code, 매도가=self.portfolio[code].매수가,
수량=self.portfolio[code].수량)
if result == True:
self.주문실행중_Lock['S_%s' % code] = True
Telegram('[StockTrader]정량매도(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량), send='mc')
logger.info('정량매도(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량))
else:
Telegram('[StockTrader]정액매도실패(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량), send='mc')
logger.info('정량매도실패(당일청산) : 종목코드=%s, 종목명=%s, 수량=%s' % (code, self.portfolio[code].종목명, self.portfolio[code].수량))
except Exception as e:
print("clearning_strategy Error :", e)
# 주문처리
def 실시간데이터처리(self, param):
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
전일종가 = 현재가 - 전일대비
# MainWindow의 __init__에서 CODE_POOL 변수 선언(self.CODE_POOL = self.getting_code_pool()), pool[종목코드] = [시장구분, 종목명, 주식수, 시가총액]
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
시장구분 = self.parent.CODE_POOL[종목코드][0]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 전일종가]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
# 정액매도 후 포트폴리오/매도할종목에서 제거
if 종목코드 in self.매도할종목:
if self.portfolio.getting(종목코드) is not None and self.주문실행중_Lock.getting('S_%s' % 종목코드) is None:
# 매도 전략별 모니터링 체크
sell_check, 매도가 = self.sell_strategy(종목코드, 시세)
if sell_check == True:
(result, order) = self.정액매도(sRQName='S_%s' % 종목코드, 종목코드=종목코드, 매도가=매도가, 수량=self.portfolio[종목코드].수량)
if result == True:
self.주문실행중_Lock['S_%s' % 종목코드] = True
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
Telegram('[StockTrader]%s 매도주문 : 종목코드=%s, 종목명=%s, 매도구간=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, self.portfolio[종목코드].매도구간, 현재가, self.portfolio[종목코드].수량), send='mc')
logger.info('[StockTrader]%s 매도주문 : 종목코드=%s, 종목명=%s, 매도구간=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, self.portfolio[종목코드].매도구간, 현재가, self.portfolio[종목코드].수량))
else:
Telegram('[StockTrader]%s 매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, 현재가, self.portfolio[종목코드].수량), send='mc')
logger.info('[StockTrader]%s 매도실패 : 종목코드=%s, 종목명=%s, 매도가=%s, 수량=%s' % (self.sName, 종목코드, 종목명, 현재가, self.portfolio[종목코드].수량))
# 매수할 종목에 대해서 정액매수 주문하고 포트폴리오/매도할종목에 추가, 매수할종목에서 제외
if current_time <= '14:30:00':
if 종목코드 in self.매수할종목 and 종목코드 not in self.금일매도종목:
if length(self.portfolio) < self.최대포트수 and self.portfolio.getting(종목코드) is None and self.주문실행중_Lock.getting('B_%s' % 종목코드) is None:
buy_check = self.buy_strategy(종목코드, 시세)
if buy_check == True:
(result, order) = self.정액매수(sRQName='B_%s' % 종목코드, 종목코드=종목코드, 매수가=현재가, 매수금액=self.단위투자금)
if result == True:
self.portfolio[종목코드] = CPortStock(종목코드=종목코드, 종목명=종목명, 시장=시장구분, 매수가=현재가, 보유일=self.보유일, 매도전략 = self.익절,
매수일=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.주문실행중_Lock['B_%s' % 종목코드] = True
Telegram('[StockTrader]%s 매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가), send='mc')
logger.info('[StockTrader]%s 매수주문 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가))
else:
Telegram('[StockTrader]%s 매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가), send='mc')
logger.info('[StockTrader]%s 매수실패 : 종목코드=%s, 종목명=%s, 매수가=%s' % (self.sName, 종목코드, 종목명, 현재가))
else:
if self.매수모니터링 == True:
self.parent.ConditionTick.stop()
self.매수모니터링 = False
logger.info("매수모니터링 시간 초과")
def 접수처리(self, param):
pass
# OnReceiveChejanData에서 체결처리가 되면 체결처리 호출
def 체결처리(self, param):
종목코드 = param['종목코드']
주문번호 = param['주문번호']
self.주문결과[주문번호] = param
주문수량 = int(param['주문수량'])
미체결수량 = int(param['미체결수량'])
체결가 = int(0 if (param['체결가'] is None or param['체결가'] == '') else param['체결가']) # 매입가 동일
단위체결량 = int(0 if (param['단위체결량'] is None or param['단위체결량'] == '') else param['단위체결량'])
당일매매수수료 = int(0 if (param['당일매매수수료'] is None or param['당일매매수수료'] == '') else param['당일매매수수료'])
당일매매세금 = int(0 if (param['당일매매세금'] is None or param['당일매매세금'] == '') else param['당일매매세금'])
# 매수
if param['매도수구분'] == '2':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매수가 = int(주문[2:])
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
P.매수가 = 체결가 # 단위체결가
P.수량 += 단위체결량 # 추가 매수 대비해서 기존 수량에 체결된 수량 계속 더함(주문수량 - 미체결수량)
P.매수일 = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR 포트에 종목이 없음 !!!!')
if 미체결수량 == 0:
try:
self.주문실행중_Lock.pop(주문)
self.매수할종목.remove(종목코드)
self.매도할종목.adding(종목코드)
self.save_history(종목코드, status='매수')
Telegram('[StockTrader]%s 매수체결완료_종목명:%s, 매수가:%s, 수량:%s' % (self.sName, P.종목명, P.매수가, P.수량), send='mc')
logger.info('[StockTrader]%s %s 매수 완료 : 매수/주문%s Pop, 매도 Append ' % (self.sName, 종목코드, 주문))
except Exception as e:
Telegram('[StockTrader]%s 체결처리_매수 POP에러 종목명:%s ' % (self.sName, P.종목명), send='mc')
logger.error('[StockTrader]%s 체결처리_매수 POP에러 종목명:%s ' % (self.sName, P.종목명))
# 매도
if param['매도수구분'] == '1':
if self.주문번호_주문_매핑.getting(주문번호) is not None:
주문 = self.주문번호_주문_매핑[주문번호]
매도가 = int(주문[2:])
try:
if 미체결수량 == 0:
self.주문실행중_Lock.pop(주문)
P = self.portfolio.getting(종목코드)
if P is not None:
P.종목명 = param['종목명']
self.portfolio[종목코드].매도가 = 체결가
self.save_history(종목코드, status='매도')
Telegram('[StockTrader]%s 매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (self.sName, param['종목명'], 체결가, 주문수량), send='mc')
logger.info('[StockTrader]%s 매도체결완료_종목명:%s, 체결가:%s, 수량:%s' % (self.sName, param['종목명'], 체결가, 주문수량))
except Exception as e:
Telegram('[StockTrader]%s 체결처리_매도 매매이력 Error : %s' % (self.sName, e), send='mc')
logger.error('[StockTrader]%s 체결처리_매도 매매이력 Error : %s' % (self.sName, e))
# 메인 화면에 반영
self.parent.RobotView()
def 잔고처리(self, param):
종목코드 = param['종목코드']
P = self.portfolio.getting(종목코드)
if P is not None:
P.매수가 = int(0 if (param['매입단가'] is None or param['매입단가'] == '') else param['매입단가'])
P.수량 = int(0 if (param['보유수량'] is None or param['보유수량'] == '') else param['보유수량'])
if P.수량 == 0:
self.portfolio.pop(종목코드)
self.매도할종목.remove(종목코드)
if 종목코드 not in self.금일매도종목: self.금일매도종목.adding(종목코드)
logger.info('잔고처리_포트폴리오POP %s ' % 종목코드)
# 메인 화면에 반영
self.parent.RobotView()
# MainWindow의 ConditionTick에 의해서 3분마다 실행
def ConditionCheck(self):
if '3' in self.sName:
if current_time >= "15:00:00" and self.조건검색이벤트 == False:
self.조건검색이벤트 = True
codes = self.GetCodes(self.조건식인덱스, self.조건식명, self.조건검색타입)
print(current_time, codes)
code_list=[]
for code in codes:
code_list.adding(code + '_' + self.parent.CODE_POOL[code][1] + '\n')
code_list = "".join(code_list)
print(current_time, code_list)
Telegram(code_list, send='mc')
else:
pass
else:
codes = self.GetCodes(self.조건식인덱스, self.조건식명, self.조건검색타입)
print(current_time, codes)
for code in codes:
if code not in self.매수할종목 and self.portfolio.getting(code) is None and code not in self.금일매도종목:
print('매수종목추가 : ', code, self.parent.CODE_POOL[code][1])
self.매수할종목.adding(code)
self.실시간종목리스트.adding(code)
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';') # 실시간 시세조회 종목 추가
logger.debug("[%s]실시간데이타요청 등록결과 %s %s" % (self.sName, self.실시간종목리스트, ret))
# 실시간 조검 검색 편입 종목 처리
def 실시간조건처리(self, code):
if (code not in self.매수할종목) and (self.portfolio.getting(code) is None) and (code not in self.금일매도종목):
print('매수종목추가 : ', code)
self.매수할종목.adding(code)
self.실시간종목리스트.adding(code)
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';') # 실시간 시세조회 종목 추가
logger.debug("[%s]실시간데이타요청 등록결과 %s %s" % (self.sName, self.실시간종목리스트, ret))
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
codes = []
self.codeList = []
# self.manual_portfolio()
if flag == True:
print("%s ROBOT 실행" % (self.sName))
self.KiwoomConnect()
try:
logger.info("[%s]조건식 거래 로봇 실행"%(self.sName))
self.sAccount = Account
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.투자총액 = floor(int(d2deposit.replacing(",", "")) * (self.투자금비중 / 100))
print('D+2 예수금 : ', int(d2deposit.replacing(",", "")))
print('투자금 : ', self.투자총액)
print('단위투자금 : ', self.단위투자금)
self.최대포트수 = self.포트폴리오수 # floor(self.투자총액 / self.단위투자금) + length(self.portfolio)
# print('기존포트수 : ', length(self.portfolio))
print('최대포트수 : ', self.최대포트수)
print("조건식 인덱스 : ", self.조건식인덱스, type(self.조건식인덱스))
print("조건식명 : ", self.조건식명)
if self.조건검색타입 == 0: # 3분봉 검색
self.parent.ConditionTick.start(1000)
else: # 실시간 검색
print('실시간 조건검색')
codes = self.GetCodes(self.조건식인덱스, self.조건식명, self.조건검색타입)
codes = []
self.초기조건(codes)
print("매수 : ", self.매수할종목)
print("매도 : ", self.매도할종목)
self.실시간종목리스트 = self.매도할종목 + self.매수할종목
logger.info("[%s]오늘 거래 종목 : %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
if length(self.실시간종목리스트) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';') # 실시간 시세조회 등록
logger.debug("실시간데이타요청 등록결과 %s" % ret)
except Exception as e:
print('[%s]_Run Error : %s' % (self.sName,e))
Telegram('[StockTrader][%s]_Run Error : %s' % (self.sName,e), send='mc')
logger.error('[StockTrader][%s]_Run Error : %s' % (self.sName,e))
else:
if self.조건검색타입 == 0:
self.parent.ConditionTick.stop() # MainWindow 타이머 중지
else:
ret = self.sendConditionStop("0156", self.조건식명, self.조건식인덱스) # 실시간 조검 검색 중지
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
if self.portfolio is not None:
for code in list(self.portfolio.keys()):
if self.portfolio[code].수량 == 0:
self.portfolio.pop(code)
if length(self.금일매도종목) > 0:
try:
Telegram("[StockTrader]%s 금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목), send='mc')
logger.info("[%s]금일 매도 종목 손익 Upload : %s" % (self.sName, self.금일매도종목))
self.parent.statusbar.showMessage("금일 매도 종목 손익 Upload")
self.DailyProfit(self.금일매도종목)
except Exception as e:
print('%s 금일매도종목 결과 업로드 Error : %s' %(self.sName, e))
fintotal_ally:
del self.DailyProfitLoop # 금일매도결과 업데이트 시 QEventLoop 사용으로 로봇 저장 시 pickcle 에러 발생하여 삭제시킴
del self.ConditionLoop
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
class 화면_ConditionMonitoring(QDialog, Ui_TradeCondition):
def __init__(self, sScreenNo, kiwoom=None, parent=None): #
super(화면_ConditionMonitoring, self).__init__(parent)
# self.setAttribute(Qt.WA_DeleteOnClose) # 위젯이 닫힐때 내용 삭제하는 것으로 창이 닫힐때 정보를 저장해야되는 로봇 세팅 시에는 쓰면 에러남!!
self.setupUi(self)
self.setWindowTitle("ConditionMonitoring")
self.lineEdit_name.setText('ConditionMonitoring')
self.progressBar.setValue(0) # Progressbar 초기 셋팅
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom #
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['종목코드', '종목명', '조건식']
self.result = []
self.KiwoomConnect()
self.GetCondition()
# 저장된 조건 검색식 목록 읽음
def GetCondition(self):
try:
self.gettingConditionLoad()
self.kf_condition = KnowledgeFrame()
self.idx = []
self.conName = []
for index in self.condition.keys(): # condition은 dictionary
# print(self.condition)
self.idx.adding(str(index))
self.conName.adding(self.condition[index])
# self.sendCondition("0156", self.condition[index], index, 1)
self.kf_condition['Index'] = self.idx
self.kf_condition['Name'] = self.conName
self.kf_condition['Table'] = ">> 조건식 " + self.kf_condition['Index'] + " : " + self.kf_condition['Name']
self.kf_condition['Index'] = self.kf_condition['Index'].totype(int)
self.kf_condition = self.kf_condition.sort_the_values(by='Index').reseting_index(sip=True) # 추가
print(self.kf_condition) # 추가
self.comboBox_condition.clear()
self.comboBox_condition.addItems(self.kf_condition['Table'].values)
except Exception as e:
print("GetCondition_Error")
print(e)
# 조건검색 해당 종목 요청 메서드
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int",
screenNo, conditionName, conditionIndex, isRealTime)
# OnReceiveTrCondition() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건 검색 관련 ActiveX와 On시리즈와 붙임(콜백)
def KiwoomConnect(self):
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
# 조건 검색 관련 ActiveX와 On시리즈 연결 해제
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# 조건식 목록 요청 메서드
def gettingConditionLoad(self):
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# OnReceiveConditionVer() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건식 목록 획득 메서드(조건식 목록을 딕셔너리로 리턴)
def gettingConditionNameList(self):
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# 조건검색 세부 종목 조회 요청시 발생되는 이벤트
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
logger.debug('main:OnReceiveTrCondition [%s] [%s] [%s] [%s] [%s]' % (
sScrNo, strCodeList, strConditionName, nIndex, nNext))
try:
if strCodeList == "":
return
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print("종목개수: ", length(self.codeList))
# print(self.codeList)
for code in self.codeList:
row = []
# code.adding(c)
row.adding(code)
n = self.kiwoom.dynamicCtotal_all("GetMasterCodeName(QString)", code)
# now = abs(int(self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", code, 10)))
# name.adding(n)
row.adding(n)
row.adding(strConditionName)
self.result.adding(row)
# self.kf_con['종목코드'] = code
# self.kf_con['종목명'] = name
# print(self.kf_con)
self.data = KnowledgeFrame(data=self.result, columns=self.columns)
self.data['종목코드'] = "'" + self.data['종목코드']
self.data = self.data.sort_the_values(by=['조건식', '종목명'])
self.data = self.data.sip_duplicates(['종목명', '조건식'], keep='first').reseting_index(sip=True)
print(self.data)
self.model.umkate(self.data)
# self.model.umkate(self.kf_con)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
fintotal_ally:
time.sleep(2)
self.conditionLoop.exit()
# 조건식 목록 요청에 대한 응답 이벤트
def OnReceiveConditionVer(self, lRet, sMsg):
logger.debug('main:OnReceiveConditionVer : [이벤트] 조건식 저장 [%s] [%s]' % (lRet, sMsg))
try:
self.condition = self.gettingConditionNameList() # condition이 리턴되서 오면 GetCondition에서 condition 변수 사용 가능
# print("조건식 개수: ", length(self.condition))
# for key in self.condition.keys():
# print("조건식: ", key, ": ", self.condition[key])
except Exception as e:
print("OnReceiveConditionVer_Error")
fintotal_ally:
self.conditionLoop.exit()
# print(self.conditionName)
# self.kiwoom.dynamicCtotal_all("SendCondition(QString,QString, int, int)", '0156', '갭상승', 0, 0)
# 실시간 종목 조건검색 요청시 발생되는 이벤트
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
logger.debug(
'main:OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
print("종목코드: ", sTrCode)
print("이벤트: ", "종목편입" if strType == "I" else "종목이탈")
# 조건식 종목 검색 버튼 클릭 시 실행됨(시그널/슬롯 추가)
def inquiry(self):
self.result = []
cnt=0
print('조건식 갯수 :', length(self.kf_condition))
for idx in range(length(self.kf_condition)):
print(idx, self.condition[idx])
self.sendCondition("0156", self.condition[idx], idx, 0)
cnt += 1
# Progress Bar 디스플레이(전체 시간 대비 비율)
self.progressBar.setValue(cnt / length(self.kf_condition) * 100)
print('조건식 종목 조회 완료')
self.parent.statusbar.showMessage("조건식 종목 조회 완료")
# 원하는 종목/주가 설정 후 알림
class CPriceMonitoring(CTrade): # 로봇 추가 시 __init__ : 복사, Setting, 초기조건:전략에 맞게, 데이터처리~Run:복사
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.주문결과 = dict()
self.주문번호_주문_매핑 = dict()
self.주문실행중_Lock = dict()
self.portfolio = dict()
self.실시간종목리스트 = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# RobotAdd 함수에서 초기화 다음 셋팅 실행해서 설정값 넘김
def Setting(self, sScreenNo):
self.sScreenNo = sScreenNo
# 수동 포트폴리오 생성
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'005935': {'종목명': '삼성전자우', '종목코드': '005935', '시장': 'KOSPI', '매수가': 50600,
'수량': 10, '매수일': '2020/09/24 09:00:00'},
'092130': {'종목명': '이크레더블', '종목코드': '092130', '시장': 'KOSDAQ', '매수가': 24019,
'수량': 21, '매수일': '2020/11/04 09:00:00'},
'271560': {'종목명': '오리온', '종목코드': '271560', '시장': 'KOSPI', '매수가': 132000,
'수량': 10, '매수일': '2020/10/08 09:00:00'},
}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock_LongTerm(종목코드=code,
종목명=self.Stocklist[code]['종목명'],
시장=self.Stocklist[code]['시장'],
매수가=self.Stocklist[code]['매수가'],
수량=self.Stocklist[code]['수량'],
매수일=self.Stocklist[code]['매수일'])
# Robot_Run이 되면 실행됨 - 매수/매도 종목을 리스트로 저장
def 초기조건(self):
self.parent.statusbar.showMessage("[%s] 초기조건준비" % (self.sName))
row_data = price_monitoring_sheet.getting_total_all_values()
self.stocklist = {}
self.Data_save = False
for row in row_data[1:]:
temp = []
try:
code, name, market = getting_code(row[0]) # 종목명으로 종목코드, 종목명, 시장 받아서(getting_code 함수) 추가
except Exception as e:
name = ''
code = ''
market = ''
print('구글 매수모니터링 시트 종목명 오류 : %s' % (row[1]))
logger.error('구글 매수모니터링 시트 오류 : %s' % (row[1]))
Telegram('[StockTrader]구글 매수모니터링 시트 오류 : %s' % (row[1]))
for idx in range(1, length(row)):
if row[idx] != '':
temp.adding(int(row[idx]))
self.stocklist[code] = {
'종목명': name,
'종목코드': code,
'모니터링주가': temp
}
print(self.stocklist)
self.모니터링종목 = list(self.stocklist.keys())
try:
self.kf_codes = mk.KnowledgeFrame()
cnt = 0
for code in self.모니터링종목:
temp = fdr.DataReader(code)
temp = temp[-70:][['Open', 'High', 'Low', 'Close', 'Volume']]
temp.reseting_index(inplace=True)
temp['Date'] = temp['Date'].totype(str)
temp['Code'] = code
if cnt == 0:
self.kf_codes = temp.clone()
else:
self.kf_codes = mk.concating([self.kf_codes, temp])
self.kf_codes.reseting_index(sip=True, inplace=True)
cnt += 1
except Exception as e:
print('CPriceMonitoring_초기조건 오류 : %s' % (e))
logger.error('CPriceMonitoring_초기조건 오류 : %s' % (e))
Telegram('[StockTrader]CPriceMonitoring_초기조건 오류 : %s' % (e))
# 이동평균가 위치 확인
def MA_Check(self, data):
if data['MA5'] < data['MA20']:
return True
else:
return False
# 이동평균을 이용한 매수 전략 신호 발생
def MA_Strategy(self, name, code, price):
today = datetime.datetime.today().strftime("%Y-%m-%d")
현재가, 시가, 고가, 저가, 거래량 = price
try:
kf = self.kf_codes.loc[self.kf_codes['Code'] == code]
kf.reseting_index(sip=True, inplace=True)
kf.loc[length(kf)] = [today, 시가, 고가, 저가, 현재가, 거래량, code] #['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Code]
kf['MA5'] = kf['Close'].rolling(window=5).average()
kf['MA20'] = kf['Close'].rolling(window=20).average()
kf['MA_Check'] = kf.employ(self.MA_Check, axis=1)
if self.Data_save==False and current_time >= '15:19:00':
self.Data_save = True
self.kf_codes.to_csv('PriceData.csv', encoding='euc-kr', index=False)
if kf.iloc[-2]['MA_Check'] == True and kf.iloc[-1]['MA_Check'] == False:
Telegram('[StockTrader]%s 매수 신호 발생\n현재가 : %s, 시가 : %s, 고가 : %s, 저가 : %s' % (name, 현재가, 시가, 고가, 저가))
logger.info('[StockTrader]%s 매수 신호 발생\n현재가 : %s, 시가 : %s, 고가 : %s, 저가 : %s' % (name, 현재가, 시가, 고가, 저가))
except Exception as e:
print('CPriceMonitoring_MA_Strategy 오류 : %s' % (e))
logger.error('CPriceMonitoring_MA_Strategy 오류 : %s' % (e))
Telegram('[StockTrader]CPriceMonitoring_MA_Strategy 오류 : %s' % (e))
def 실시간데이터처리(self, param):
try:
if self.running == True:
체결시간 = '%s %s:%s:%s' % (str(self.d), param['체결시간'][0:2], param['체결시간'][2:4], param['체결시간'][4:])
종목코드 = param['종목코드']
현재가 = abs(int(float(param['현재가'])))
전일대비 = int(float(param['전일대비']))
등락률 = float(param['등락률'])
매도호가 = abs(int(float(param['매도호가'])))
매수호가 = abs(int(float(param['매수호가'])))
누적거래량 = abs(int(float(param['누적거래량'])))
시가 = abs(int(float(param['시가'])))
고가 = abs(int(float(param['고가'])))
저가 = abs(int(float(param['저가'])))
거래회전율 = abs(float(param['거래회전율']))
시가총액 = abs(int(float(param['시가총액'])))
종목명 = self.parent.CODE_POOL[종목코드][1] # pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
시장구분 = self.parent.CODE_POOL[종목코드][0]
전일종가 = self.parent.CODE_POOL[종목코드][3]
시세 = [현재가, 시가, 고가, 저가, 누적거래량]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
# print("[%s] %s %s %s %s" % (체결시간, 종목코드, 종목명, 현재가, 전일대비))
if length(self.stocklist[종목코드]['모니터링주가']) > 0:
if 현재가 in self.stocklist[종목코드]['모니터링주가']:
Telegram('[StockTrader]%s 주가도달 알림\n현재가 : %s, 시가 : %s, 고가 : %s, 저가 : %s' % (종목명, 현재가, 시가, 고가, 저가))
self.stocklist[종목코드]['모니터링주가'].remove(현재가)
self.MA_Strategy(종목명, 종목코드, 시세)
except Exception as e:
print('CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e))
Telegram('[StockTrader]CTradeLongTerm_실시간데이터처리 Error : %s, %s' % (종목명, e), send='mc')
logger.error('CTradeLongTerm_실시간데이터처리 Error :%s, %s' % (종목명, e))
def 접수처리(self, param):
pass
def 체결처리(self, param):
pass
def 잔고처리(self, param):
pass
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
if flag == True:
print("%s ROBOT 실행" % (self.sName))
try:
Telegram("[StockTrader]%s ROBOT 실행" % (self.sName))
self.초기조건()
print('초기조건 설정 완료')
self.실시간종목리스트 = self.모니터링종목
logger.info("오늘 거래 종목 : %s %s" % (self.sName, ';'.join(self.실시간종목리스트) + ';'))
self.KiwoomConnect() # MainWindow 외에서 키움 API구동시켜서 자체적으로 API데이터송수신가능하도록 함
if length(self.실시간종목리스트) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.실시간종목리스트) + ';')
logger.debug("[%s]실시간데이타요청 등록결과 %s" % (self.sName, ret))
except Exception as e:
print('CPriceMonitoring_Run Error :', e)
Telegram('[StockTrader]CPriceMonitoring_Run Error : %s' % e, send='mc')
logger.error('CPriceMonitoring_Run Error : %s' % e)
else:
Telegram("[StockTrader]%s ROBOT 실행 중지" % (self.sName))
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
self.KiwoomDisConnect() # 로봇 클래스 내에서 일별종목별실현손익 데이터를 받고나서 연결 해제시킴
# 메인 화면에 반영
self.parent.RobotView()
##################################################################################
# 메인
##################################################################################
Ui_MainWindow, QtBaseClass_MainWindow = uic.loadUiType("./UI/XTrader_MainWindow.ui")
class MainWindow(QMainWindow, Ui_MainWindow):
def __init__(self):
# 화면을 보여주기 위한 코드
super().__init__()
QMainWindow.__init__(self)
Ui_MainWindow.__init__(self)
self.UI_setting()
# 현재 시간 받음
self.시작시각 = datetime.datetime.now()
# 메인윈도우가 뜨고 키움증권과 붙이기 위한 작업
self.KiwoomAPI() # 키움 ActiveX를 메모리에 올림
self.KiwoomConnect() # 메모리에 올라온 ActiveX와 내가 만든 함수 On시리즈와 연결(콜백 : 이벤트가 오면 나를 불러줘)
self.ScreenNumber = 5000
self.robots = []
self.dialog = dict()
# self.dialog['리얼데이타'] = None
# self.dialog['계좌정보조회'] = None
self.model = MonkeyModel()
self.tableView_robot.setModel(self.model)
self.tableView_robot.setSelectionBehavior(QTableView.SelectRows)
self.tableView_robot.setSelectionMode(QTableView.SingleSelection)
self.tableView_robot.pressed.connect(self.RobotCurrentIndex)
# self.connect(self.tableView_robot.selectionModel(), SIGNAL("currentRowChanged(QModelIndex,QModelIndex)"), self.RobotCurrentIndex)
self.tableView_robot_current_index = None
self.portfolio_model = MonkeyModel()
self.tableView_portfolio.setModel(self.portfolio_model)
self.tableView_portfolio.setSelectionBehavior(QTableView.SelectRows)
self.tableView_portfolio.setSelectionMode(QTableView.SingleSelection)
# self.portfolio_model.umkate((KnowledgeFrame(columns=['종목코드', '종목명', '매수가', '수량', '매수일'])))
self.robot_columns = ['Robot타입', 'Robot명', 'RobotID', '스크린번호', '실행상태', '포트수', '포트폴리오']
# TODO: 주문제한 설정
self.timer = QTimer(self)
self.timer.timeout.connect(self.limit_per_second) # 초당 4번
# QtCore.QObject.connect(self.timer, QtCore.SIGNAL("timeout()"), self.limit_per_second)
self.timer.start(1000) # 1초마다 리셋
self.ConditionTick = QTimer(self)
self.ConditionTick.timeout.connect(self.OnConditionCheck)
self.주문제한 = 0
self.조회제한 = 0
self.금일백업작업중 = False
self.종목선정작업중 = False
self.ConditionCheck = False
self.조건식저장카운트 = 1
self.DailyData = False # 관심종목 일봉 업데이트
self.InvestorData = False # 관심종목 종목별투자자 업데이트
self.kf_daily = KnowledgeFrame()
self.kf_weekly = KnowledgeFrame()
self.kf_monthly = KnowledgeFrame()
self.kf_investor = KnowledgeFrame()
self._login = False
self.KiwoomLogin() # 프로그램 실행 시 자동로그인
self.CODE_POOL = self.getting_code_pool() # DB 종목데이블에서 시장구분, 코드, 종목명, 주식수, 전일종가 읽어옴
# 화면 Setting
def UI_setting(self):
self.setupUi(self)
self.setWindowTitle("XTrader")
self.setWindowIcon(QIcon('./PNG/icon_stock.png'))
self.actionLogin.setIcon(QIcon('./PNG/Internal.png'))
self.actionLogout.setIcon(QIcon('./PNG/External.png'))
self.actionExit.setIcon(QIcon('./PNG/Approval.png'))
self.actionAccountDialog.setIcon(QIcon('./PNG/Sales Performance.png'))
self.actionMinutePrice.setIcon(QIcon('./PNG/Candle Sticks.png'))
self.actionDailyPrice.setIcon(QIcon('./PNG/Overtime.png'))
self.actionInvestors.setIcon(QIcon('./PNG/Conference Ctotal_all.png'))
self.actionSectorView.setIcon(QIcon('./PNG/Organization.png'))
self.actionSectorPriceView.setIcon(QIcon('./PNG/Ratings.png'))
self.actionCodeBuild.setIcon(QIcon('./PNG/Inspection.png'))
self.actionRobotOneRun.setIcon(QIcon('./PNG/Process.png'))
self.actionRobotOneStop.setIcon(QIcon('./PNG/Cancel 2.png'))
self.actionRobotMonitoringStop.setIcon(QIcon('./PNG/Cancel File.png'))
self.actionRobotRun.setIcon(QIcon('./PNG/Checked.png'))
self.actionRobotStop.setIcon(QIcon('./PNG/Cancel.png'))
self.actionRobotRemove.setIcon(QIcon('./PNG/Delete File.png'))
self.actionRobotClear.setIcon(QIcon('./PNG/Empty Trash.png'))
self.actionRobotView.setIcon(QIcon('./PNG/Checked 2.png'))
self.actionRobotSave.setIcon(QIcon('./PNG/Download.png'))
self.actionTradeShortTerm.setIcon(QIcon('./PNG/Bullish.png'))
self.actionTradeCondition.setIcon(QIcon('./PNG/Search.png'))
self.actionConditionMonitoring.setIcon(QIcon('./PNG/Binoculars.png'))
# 종목 선정
def stock_analysis(self):
try:
self.AnalysisPriceList = self.AnalysisPriceList
except:
for robot in self.robots:
if robot.sName == 'TradeShortTerm':
self.AnalysisPriceList = robot.Stocklist['전략']['시세조회단위']
self.종목선정데이터 = mk.KnowledgeFrame(shortterm_analysis_sheet.getting_total_all_records()) # shortterm_analysis_sheet
self.종목선정데이터 = self.종목선정데이터[['번호', '종목명']]
row = []
# print(self.종목선정데이터)
for name in self.종목선정데이터['종목명'].values:
try:
code, name, market = getting_code(name)
except Exception as e:
code = ''
print('getting_code Error :', name, e)
row.adding(code)
self.종목선정데이터['종목코드'] = row
self.종목선정데이터 = self.종목선정데이터[self.종목선정데이터['종목코드'] != '']
print(self.종목선정데이터)
self.종목리스트 = list(self.종목선정데이터[['번호', '종목명', '종목코드']].values)
self.종목코드 = self.종목리스트.pop(0)
if self.DailyData == True:
self.start = datetime.datetime.now()
print(self.start)
self.ReguestPriceDaily()
elif self.InvestorData == True:
self.RequestInvestorDaily()
elif self.WeeklyData == True:
self.ReguestPriceWeekly()
elif self.MonthlyData == True:
self.ReguestPriceMonthly()
# 일봉데이터조희
def ReguestPriceDaily(self, _repeat=0):
try:
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목일봉 = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식일봉차트조회", "OPT10081",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 일봉 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# 주봉데이터조회
def ReguestPriceWeekly(self, _repeat=0):
try:
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목주봉 = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식주봉차트조회", "OPT10082",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 주봉 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# 월봉데이터조회
def ReguestPriceMonthly(self, _repeat=0):
try:
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목월봉 = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "기준일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "수정주가구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "주식월봉차트조회", "OPT10083",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 월봉 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# 종목별투자자조희
def RequestInvestorDaily(self, _repeat=0):
기준일자 = datetime.date.today().strftime('%Y%m%d')
self.종목별투자자 = []
try:
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "일자", 기준일자)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", self.종목코드[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "금액수량구분", 2) # 1:금액, 2:수량
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "매매구분", 0) # 0:순매수, 1:매수, 2:매도
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "단위구분", 1) # 1000:천주, 1:단주
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "종목별투자자조회", "OPT10060",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("관심종목 종목별투자자 데이터 : %s %s %s" % (self.종목코드[0], self.종목코드[1], self.종목코드[2]))
except Exception as e:
print(e)
# DB 데이터 저장
def UploadAnalysisData(self, data, 구분):
# shortterm_analysis_sheet = test_analysis_sheet
row = []
if 구분 == '일봉':
try:
data['일봉1'] = data['현재가'].rolling(window=self.AnalysisPriceList[0]).average()
data['일봉2'] = data['현재가'].rolling(window=self.AnalysisPriceList[1]).average()
data['일봉3'] = data['현재가'].rolling(window=self.AnalysisPriceList[2]).average()
data['일봉4'] = data['현재가'].rolling(window=self.AnalysisPriceList[3]).average()
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(str(value_round((result[3] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[4] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[5] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[6] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((int(data.iloc[-2]['거래량']) / int(data.iloc[-1]['거래량']) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('일봉1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# cell = alpha_list[shortterm_analysis_cols.index('일봉2')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[2])
# cell = alpha_list[shortterm_analysis_cols.index('일봉3')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[3])
# cell = alpha_list[shortterm_analysis_cols.index('일봉4')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[4])
# cell = alpha_list[shortterm_analysis_cols.index('거래량')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[5])
# DB 저장
dict = {'번호': [],
'종목명': [],
'종목코드': [],
'일봉1': [],
'일봉2': [],
'일봉3': [],
'일봉4': [],
'거래량': []}
dict['번호'].adding(str(self.종목코드[0]))
dict['종목명'].adding(self.종목코드[1])
dict['종목코드'].adding(self.종목코드[2])
dict['일봉1'].adding(str(value_round((result[3] / int(result[1]) - 1) * 100, 2)) + '%')
dict['일봉2'].adding(str(value_round((result[4] / int(result[1]) - 1) * 100, 2)) + '%')
dict['일봉3'].adding(str(value_round((result[5] / int(result[1]) - 1) * 100, 2)) + '%')
dict['일봉4'].adding(str(value_round((result[6] / int(result[1]) - 1) * 100, 2)) + '%')
dict['거래량'].adding(
str(value_round((int(data.iloc[-2]['거래량']) / int(data.iloc[-1]['거래량']) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_daily = mk.concating([self.kf_daily, temp])
except Exception as e:
print('UploadDailyPriceData Error : ', e)
elif 구분 == '주봉':
try:
data['주봉1'] = data['현재가'].rolling(window=self.AnalysisPriceList[4]).average()
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('주봉1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# DB 저장
dict = {'종목코드': [],
'주봉1': []
}
dict['종목코드'].adding(self.종목코드[2])
dict['주봉1'].adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_weekly = mk.concating([self.kf_weekly, temp])
except Exception as e:
print('UploadWeeklyPriceData Error : ', e)
elif 구분 == '월봉':
try:
data['월봉1'] = data['현재가'].rolling(window=self.AnalysisPriceList[5]).average()
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('월봉1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# DB 저장
dict = {'종목코드': [],
'월봉1': []
}
dict['종목코드'].adding(self.종목코드[2])
dict['월봉1'].adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_monthly = mk.concating([self.kf_monthly, temp])
except Exception as e:
print('UploadmonthlyPriceData Error : ', e)
elif 구분 == '종목별투자자':
try:
result = data.iloc[-1].values
# 구글 업로드
# row.adding(self.종목코드[0])
# row.adding(result[1]) # 기관
# row.adding(result[2]) # 외국인
# row.adding(result[3]) # 개인
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('기관수급')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# cell = alpha_list[shortterm_analysis_cols.index('외인수급')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[2])
# cell = alpha_list[shortterm_analysis_cols.index('개인')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[3])
# DB 저장
dict = {'종목코드': [],
'기관': [],
'외인': [],
'개인': []}
dict['종목코드'].adding(self.종목코드[2])
dict['기관'].adding(result[1]) # 기관
dict['외인'].adding(result[2]) # 외국인
dict['개인'].adding(result[3]) # 개인
temp = KnowledgeFrame(dict)
self.kf_investor = mk.concating([self.kf_investor, temp])
except Exception as e:
print('UploadDailyInvestorData Error : ', e)
# DB에 저장된 상장 종목 코드 읽음
def getting_code_pool(self):
query = """
select 시장구분, 종목코드, 종목명, 주식수, 전일종가, 전일종가*주식수 as 시가총액
from 종목코드
order by 시장구분, 종목코드
"""
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
pool = dict()
for idx, row in kf.traversal():
시장구분, 종목코드, 종목명, 주식수, 전일종가, 시가총액 = row
pool[종목코드] = [시장구분, 종목명, 주식수, 전일종가, 시가총액]
return pool
# 구글스프레드시트 종목 Import
def Import_ShortTermStock(self, check):
try:
data = import_googlesheet()
if check == False:
# # 매수 전략별 별도 로봇 운영 시
# # 매수 전략 확인
# strategy_list = list(data['매수전략'].distinctive())
#
# # 로딩된 로봇을 robot_list에 저장
# robot_list = []
# for robot in self.robots:
# robot_list.adding(robot.sName.split('_')[0])
#
# # 매수 전략별 로봇 자동 편집/추가
# for strategy in strategy_list:
# kf_stock = data[data['매수전략'] == strategy]
#
# if strategy in robot_list:
# print('로봇 편집')
# Telegram('[StockTrader]로봇 편집')
# for robot in self.robots:
# if robot.sName.split('_')[0] == strategy:
# self.RobotAutoEdit_TradeShortTerm(robot, kf_stock)
# self.RobotView()
# break
# else:
# print('로봇 추가')
# Telegram('[StockTrader]로봇 추가')
# self.RobotAutoAdd_TradeShortTerm(kf_stock, strategy)
# self.RobotView()
# 로딩된 로봇을 robot_list에 저장
robot_list = []
for robot in self.robots:
robot_list.adding(robot.sName)
if 'TradeShortTerm' in robot_list:
for robot in self.robots:
if robot.sName == 'TradeShortTerm':
print('로봇 편집')
logger.debug('로봇 편집')
self.RobotAutoEdit_TradeShortTerm(robot, data)
self.RobotView()
break
else:
print('로봇 추가')
logger.debug('로봇 추가')
self.RobotAutoAdd_TradeShortTerm(data)
self.RobotView()
# print("로봇 준비 완료")
# Slack('[XTrader]로봇 준비 완료')
# logger.info("로봇 준비 완료")
except Exception as e:
print('MainWindow_Import_ShortTermStock Error', e)
Telegram('[StockTrader]MainWindow_Import_ShortTermStock Error : %s' % e, send='mc')
logger.error('MainWindow_Import_ShortTermStock Error : %s' % e)
# 금일 매도 종목에 대해서 수익률, 수익금, 수수료 요청(일별종목별실현손익요청)
# def DailyProfit(self, 금일매도종목):
# _repeat = 0
# # self.sAccount = 로봇거래계좌번호
# # self.sScreenNo = self.ScreenNumber
# 시작일자 = datetime.date.today().strftime('%Y%m%d')
# cnt=1
# for 종목코드 in 금일매도종목:
# self.umkate_cnt = length(금일매도종목) - cnt
# cnt += 1
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "종목코드", 종목코드)
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "시작일자", 시작일자)
# ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "일자별종목별실현손익요청", "OPT10072", _repeat, '{:04d}'.formating(self.ScreenNumber))
#
# self.DailyProfitLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
# self.DailyProfitLoop.exec_()
# 일별종목별실현손익 응답 결과 구글 업로드
# def DailyProfitUpload(self, 매도결과):
# # 매도결과 ['종목명','체결량','매입단가','체결가','당일매도손익','손익율','당일매매수수료','당일매매세금']
# print(매도결과)
#
# for r in self.robots:
# if r.sName == 'TradeShortTerm':
# history_sheet = history_sheet
# history_cols = history_cols
# elif r.sName == 'TradeCondition':
# history_sheet = condition_history_sheet
# history_cols = condition_history_cols
#
# code_row = history_sheet.findtotal_all(매도결과[0])[-1].row
#
# 계산수익률 = value_round((int(float(매도결과[3])) / int(float(매도결과[2])) - 1) * 100, 2)
#
# cell = alpha_list[history_cols.index('매수가')] + str(code_row) # 매입단가
# history_sheet.umkate_acell(cell, int(float(매도결과[2])))
#
# cell = alpha_list[history_cols.index('매도가')] + str(code_row) # 체결가
# history_sheet.umkate_acell(cell, int(float(매도결과[3])))
#
# cell = alpha_list[history_cols.index('수익률(계산)')] + str(code_row) # 수익률 계산
# history_sheet.umkate_acell(cell, 계산수익률)
#
# cell = alpha_list[history_cols.index('수익률')] + str(code_row) # 손익율
# history_sheet.umkate_acell(cell, 매도결과[5])
#
# cell = alpha_list[history_cols.index('수익금')] + str(code_row) # 손익율
# history_sheet.umkate_acell(cell, int(float(매도결과[4])))
#
# cell = alpha_list[history_cols.index('세금+수수료')] + str(code_row) # 당일매매수수료 + 당일매매세금
# history_sheet.umkate_acell(cell, int(float(매도결과[6])) + int(float(매도결과[7])))
#
# self.DailyProfitLoop.exit()
#
# if self.umkate_cnt == 0:
# print('금일 실현 손익 구글 업로드 완료')
# Slack("[XTrader]금일 실현 손익 구글 업로드 완료")
# logger.info("[XTrader]금일 실현 손익 구글 업로드 완료")
# 조건 검색식 읽어서 해당 종목 저장
def GetCondition(self):
# logger.info("조건 검색식 종목 읽기")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
conditions = ['매물대거래량','외국인기관수급', '주도주', '당일주도주', '기본주도주','스토캐스틱&MACD&거래량회전율', '갭상승']
try:
self.gettingConditionLoad()
self.conditionid = []
self.conditionname = []
for index in self.condition.keys(): # condition은 dictionary
# print(self.condition)
if self.condition[index] in conditions:
self.conditionid.adding(str(index))
self.conditionname.adding(self.condition[index])
print('조건 검색 시작')
print(index, self.condition[index])
self.sendCondition("0156", self.condition[index], index, 0)
except Exception as e:
print("GetCondition_Error")
print(e)
fintotal_ally:
# print(self.kf_condition)
query = """
select * from 조건검색식
"""
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
kf = kf.sip_duplicates(['카운트', '종목명'], keep='first')
kf = kf.sort_the_values(by=['카운트','인덱스']).reseting_index(sip=True)
savetime = today.strftime('%Y%m%d') + '_'+ current_time.replacing(':','')
kf.to_csv(savetime +"_조건검색종목.csv", encoding='euc-kr', index=False)
self.조건식저장카운트 += 1
self.ConditionCheck = False
logger.info("조건 검색식 종목 저장완료")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# 조건식 목록 요청 메서드
def gettingConditionLoad(self):
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# receiveConditionVer() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 조건식 획득 메서드
def gettingConditionNameList(self):
# 조건식을 딕셔너리 형태로 반환합니다.
# 이 메서드는 반드시 receiveConditionVer() 이벤트 메서드안에서 사용해야 합니다.
#
# :return: dict - {인덱스:조건명, 인덱스:조건명, ...}
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# 종목 조건검색 요청 메서드
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
# 이 메서드로 얻고자 하는 것은 해당 조건에 맞는 종목코드이다.
# 해당 종목에 대한 상세정보는 setRealReg() 메서드로 요청할 수 있다.
# 요청이 실패하는 경우는, 해당 조건식이 없거나, 조건명과 인덱스가 맞지 않거나, 조회 횟수를 초과하는 경우 발생한다.
#
# 조건검색에 대한 결과는
# 1회성 조회의 경우, receiveTrCondition() 이벤트로 결과값이 전달되며
# 실시간 조회의 경우, receiveTrCondition()과 receiveRealCondition() 이벤트로 결과값이 전달된다.
#
# :param screenNo: string
# :param conditionName: string - 조건식 이름
# :param conditionIndex: int - 조건식 인덱스
# :param isRealTime: int - 조건검색 조회구분(0: 1회성 조회, 1: 실시간 조회)
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int)",
screenNo, conditionName, conditionIndex, isRealTime)
# receiveTrCondition() 이벤트 메서드에서 루프 종료
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# 프로그램 실행 3초 후 실행
def OnQApplicationStarted(self):
# 1. 8시 58분 이전일 경우 5분 단위 구글시트 오퓨 체크 타이머 시작시킴
current = datetime.datetime.now()
current_time = current.strftime('%H:%M:%S')
"""
if '07:00:00' <= current_time and current_time <= '08:58:00':
print('구글 시트 오류 체크 시작')
# Telegram('[StockTrader]구글 시트 오류 체크 시작')
self.statusbar.showMessage("구글 시트 오류 체크 시작")
self.checkclock = QTimer(self)
self.checkclock.timeout.connect(self.OnGoogleCheck) # 5분마다 구글 시트 읽음 : MainWindow.OnGoogleCheck 실행
self.checkclock.start(300000) # 300000초마다 타이머 작동
"""
# 2. DB에 저장된 로봇 정보받아옴
global 로봇거래계좌번호
try:
with sqlite3.connect(DATABASE) as conn:
cursor = conn.cursor()
cursor.execute("select value from Setting where keyword='robotaccount'")
for row in cursor.fetchtotal_all():
# _temp = base64.decodestring(row[0]) # base64에 text화해서 암호화 : DB에 잘 넣기 위함
_temp = base64.decodebytes(row[0])
로봇거래계좌번호 = pickle.loads(_temp)
print('로봇거래계좌번호', 로봇거래계좌번호)
cursor.execute('select uuid, strategy, name, robot from Robots')
self.robots = []
for row in cursor.fetchtotal_all():
uuid, strategy, name, robot_encoded = row
robot = base64.decodebytes(robot_encoded)
# r = base64.decodebytes(robot_encoded)
r = pickle.loads(robot)
r.kiwoom = self.kiwoom
r.parent = self
r.d = today
r.running = False
# logger.debug(r.sName, r.UUID, length(r.portfolio))
self.robots.adding(r)
except Exception as e:
print('OnQApplicationStarted', e)
self.RobotView()
# 프로그램 실행 후 1초 마다 실행 : 조건에 맞는 시간이 되면 백업 시작
def OnClockTick(self):
current = datetime.datetime.now()
global current_time
current_time = current.strftime('%H:%M:%S')
# 8시 32분 : 종목 데이블 생성
if current_time == '08:32:00':
print('종목테이블 생성')
# Slack('[XTrader]종목테이블 생성')
self.StockCodeBuild(to_db=True)
self.CODE_POOL = self.getting_code_pool() # DB 종목데이블에서 시장구분, 코드, 종목명, 주식수, 전일종가 읽어옴
self.statusbar.showMessage("종목테이블 생성")
"""
# 8시 59분 : 구글 시트 종목 Import
if current_time == '08:59:00':
print('구글 시트 오류 체크 중지')
# Telegram('[StockTrader]구글 시트 오류 체크 중지')
self.checkclock.stop()
robot_list = []
for robot in self.robots:
robot_list.adding(robot.sName)
if 'TradeShortTerm' in robot_list:
print('구글시트 Import')
Telegram('[StockTrader]구글시트 Import')
self.Import_ShortTermStock(check=False)
self.statusbar.showMessage('구글시트 Import')
"""
# 8시 59분 30초 : 로봇 실행
if '09:00:00' <= current_time and current_time < '09:00:05':
try:
if length(self.robots) > 0:
for r in self.robots:
if r.running == False: # 로봇이 실행중이 아니면
r.Run(flag=True, sAccount=로봇거래계좌번호)
self.RobotView()
except Exception as e:
print('Robot Auto Run Error', e)
Telegram('[StockTrader]Robot Auto Run Error : %s' % e, send='mc')
logger.error('Robot Auto Run Error : %s' % e)
# TradeShortTerm 보유일 만기 매도 전략 체크용
# if current_time >= '15:29:00' and current_time < '15:29:30':
# if length(self.robots) > 0:
# for r in self.robots:
# if r.sName == 'TradeShortTerm':
# if r.holdcheck == False:
# r.holdcheck = True
# r.hold_strategy()
# 15시 17분 :TradeCondition 당일청산 매도 실행
if current_time >= '15:17:00' and current_time < '15:17:30':
if length(self.robots) > 0:
for r in self.robots:
if r.sName == 'TradeCondition' and '당일청산' in r.조건식명:
if r.clearcheck == False:
r.clearcheck = True
r.clearning_strategy()
# 16시 00분 : 로봇 정지
if '15:40:00' <= current_time and current_time < '15:40:05':
self.RobotStop()
# 16시 05분 : 프로그램 종료
if '15:45:00' <= current_time and current_time < '15:45:05':
quit()
# 18시 00분 : 종목 분석을 위한 일봉, 종목별투자자정보 업데이트
# if '18:00:00' <= current_time and current_time < '18:00:05':
# if self.DailyData == False:
# self.DailyData = True
# self.WeeklyData = False
# self.MonthlyData = False
# self.InvestorData = False
# Telegram("[XTrader]관심종목 데이터 업데이트", send='mc')
# self.stock_analysis()
# if '153600' < current_time and current_time < '153659' and self.금일백업작업중 == False and self._login == True:# and current.weekday() == 4:
# 수능일이면 아래 시간 조건으로 수정
# if '17:00:00' < current.strftime('%H:%M:%S') and current.strftime('%H:%M:%S') < '17:00:59' and self.금일백업작업중 == False and self._login == True:
# self.금일백업작업중 = True
# self.Backup(작업=None)
# pass
# 로봇을 저장
# if self.시작시각.strftime('%H:%M:%S') > '08:00:00' and self.시작시각.strftime('%H:%M:%S') < '15:30:00' and current.strftime('%H:%M:%S') > '01:00:00':
# if length(self.robots) > 0:
# self.RobotSave()
# for k in self.dialog:
# self.dialog[k].KiwoomDisConnect()
# try:
# self.dialog[k].close()
# except Exception as e:
# pass
# self.close()
# 지정 시간에 로봇을 중지한다던가 원하는 실행을 아래 pass에 작성
# if current_time > '08:58:00' and current_time <= '15:30:00':
# if current.second == 0 and current.getting_minute % 3 == 0 and self.ConditionCheck == False:
# self.ConditionCheck = True
# self.GetCondition()
# if current.weekday() in workday_list: # 주중인지 확인
# if current_time in savetime_list: # 지정된 시간인지 확인
# logger.info("조건검색식 타이머 작동")
# Telegram(str(current)[:-7] + " : " + "조건검색식 종목 검색")
# self.GetCondition() # 조건검색식을 모두 읽어서 해당하는 종목 저장
# if current.second == 0: # 매 0초
# # if current.getting_minute % 10 == 0: # 매 10 분
# if current.getting_minute == 1 or current.strftime('%H:%M:%S') == '09:30:00' or current.strftime('%H:%M:%S') == '15:15:00': # 매시 1분
# logger.info("조건검색식 타이머 작동")
# Telegram(str(current)[:-7] + " : " + "조건검색식 종목 검색")
# # print(current.getting_minute, current.second)
# self.GetCondition() # 조건검색식을 모두 읽어서 해당하는 종목 저장
# for r in self.robots:
# if r.running == True: # 로봇이 실행중이면
# # print(r.sName, r.running)
# pass
# 주문 제한 초기화
def limit_per_second(self):
self.주문제한 = 0
self.조회제한 = 0
# logger.info("초당제한 주문 클리어")
def OnConditionCheck(self):
try:
current = datetime.datetime.now()
if current.second == 0 and current.getting_minute % 3 == 0:
for robot in self.robots:
if 'TradeCondition' in robot.sName:
if robot.조건검색타입 == 0:
robot.ConditionCheck()
except Exception as e:
print(e)
# 5분 마다 실행 : 구글 스프레드 시트 오류 확인
def OnGoogleCheck(self):
self.Import_ShortTermStock(check=True)
# 메인 윈도우에서의 모든 액션에 대한 처리
def MENU_Action(self, qaction):
logger.debug("Action Slot %s %s " % (qaction.objectName(), qaction.text()))
try:
_action = qaction.objectName()
if _action == "actionExit":
if length(self.robots) > 0:
self.RobotSave()
for k in self.dialog:
self.dialog[k].KiwoomDisConnect()
try:
self.dialog[k].close()
except Exception as e:
pass
self.close()
elif _action == "actionLogin":
self.KiwoomLogin()
elif _action == "actionLogout":
self.KiwoomLogout()
elif _action == "actionDailyPrice":
# self.F_dailyprice()
if self.dialog.getting('일자별주가') is not None:
try:
self.dialog['일자별주가'].show()
except Exception as e:
self.dialog['일자별주가'] = 화면_일별주가(sScreenNo=9902, kiwoom=self.kiwoom, parent=self)
self.dialog['일자별주가'].KiwoomConnect()
self.dialog['일자별주가'].show()
else:
self.dialog['일자별주가'] = 화면_일별주가(sScreenNo=9902, kiwoom=self.kiwoom, parent=self)
self.dialog['일자별주가'].KiwoomConnect()
self.dialog['일자별주가'].show()
elif _action == "actionMinutePrice":
# self.F_getting_minprice()
if self.dialog.getting('분별주가') is not None:
try:
self.dialog['분별주가'].show()
except Exception as e:
self.dialog['분별주가'] = 화면_분별주가(sScreenNo=9903, kiwoom=self.kiwoom, parent=self)
self.dialog['분별주가'].KiwoomConnect()
self.dialog['분별주가'].show()
else:
self.dialog['분별주가'] = 화면_분별주가(sScreenNo=9903, kiwoom=self.kiwoom, parent=self)
self.dialog['분별주가'].KiwoomConnect()
self.dialog['분별주가'].show()
elif _action == "actionInvestors":
# self.F_investor()
if self.dialog.getting('종목별투자자') is not None:
try:
self.dialog['종목별투자자'].show()
except Exception as e:
self.dialog['종목별투자자'] = 화면_종목별투자자(sScreenNo=9904, kiwoom=self.kiwoom, parent=self)
self.dialog['종목별투자자'].KiwoomConnect()
self.dialog['종목별투자자'].show()
else:
self.dialog['종목별투자자'] = 화면_종목별투자자(sScreenNo=9904, kiwoom=self.kiwoom, parent=self)
self.dialog['종목별투자자'].KiwoomConnect()
self.dialog['종목별투자자'].show()
elif _action == "actionAccountDialog": # 계좌정보조회
if self.dialog.getting('계좌정보조회') is not None: # dialog : __init__()에 dict로 정의됨
try:
self.dialog['계좌정보조회'].show()
except Exception as e:
self.dialog['계좌정보조회'] = 화면_계좌정보(sScreenNo=7000, kiwoom=self.kiwoom,
parent=self) # self는 메인윈도우, 계좌정보윈도우는 자식윈도우/부모는 메인윈도우
self.dialog['계좌정보조회'].KiwoomConnect()
self.dialog['계좌정보조회'].show()
else:
self.dialog['계좌정보조회'] = 화면_계좌정보(sScreenNo=7000, kiwoom=self.kiwoom, parent=self)
self.dialog['계좌정보조회'].KiwoomConnect()
self.dialog['계좌정보조회'].show()
elif _action == "actionSectorView":
# self.F_sectorview()
if self.dialog.getting('업종정보조회') is not None:
try:
self.dialog['업종정보조회'].show()
except Exception as e:
self.dialog['업종정보조회'] = 화면_업종정보(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종정보조회'].KiwoomConnect()
self.dialog['업종정보조회'].show()
else:
self.dialog['업종정보조회'] = 화면_업종정보(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종정보조회'].KiwoomConnect()
self.dialog['업종정보조회'].show()
elif _action == "actionSectorPriceView":
# self.F_sectorpriceview()
if self.dialog.getting('업종별주가조회') is not None:
try:
self.dialog['업종별주가조회'].show()
except Exception as e:
self.dialog['업종별주가조회'] = 화면_업종별주가(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종별주가조회'].KiwoomConnect()
self.dialog['업종별주가조회'].show()
else:
self.dialog['업종별주가조회'] = 화면_업종별주가(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['업종별주가조회'].KiwoomConnect()
self.dialog['업종별주가조회'].show()
elif _action == "actionTradeShortTerm":
self.RobotAdd_TradeShortTerm()
self.RobotView()
elif _action == "actionTradeCondition": # 키움 조건검색식을 이용한 트레이딩
# print("MainWindow : MENU_Action_actionTradeCondition")
self.RobotAdd_TradeCondition()
self.RobotView()
elif _action == "actionConditionMonitoring":
print("MainWindow : MENU_Action_actionConditionMonitoring")
self.ConditionMonitoring()
elif _action == "actionTradeLongTerm":
self.RobotAdd_TradeLongTerm()
self.RobotView()
elif _action == "actionPriceMonitoring":
self.RobotAdd_PriceMonitoring()
self.RobotView()
elif _action == "actionRobotLoad":
self.RobotLoad()
self.RobotView()
elif _action == "actionRobotSave":
self.RobotSave()
elif _action == "actionRobotOneRun":
self.RobotOneRun()
self.RobotView()
elif _action == "actionRobotOneStop":
self.RobotOneStop()
self.RobotView()
elif _action == "actionRobotMonitoringStop":
self.RobotOneMonitoringStop()
self.RobotView()
elif _action == "actionRobotRun":
self.RobotRun()
self.RobotView()
elif _action == "actionRobotStop":
self.RobotStop()
self.RobotView()
elif _action == "actionRobotRemove":
self.RobotRemove()
self.RobotView()
elif _action == "actionRobotClear":
self.RobotClear()
self.RobotView()
elif _action == "actionRobotView":
self.RobotView()
for r in self.robots:
logger.debug('%s %s %s %s' % (r.sName, r.UUID, length(r.portfolio), r.GetStatus()))
elif _action == "actionCodeBuild":
self.종목코드 = self.StockCodeBuild(to_db=True)
QMessageBox.about(self, "종목코드 생성", " %s 항목의 종목코드를 생성하였습니다." % (length(self.종목코드.index)))
elif _action == "actionTest":
# self.DailyData = True
# self.WeeklyData = False
# self.MonthlyData = False
# self.InvestorData = False
# self.stock_analysis()
# print(self.robots)
# for robot in self.robots:
# if robot.sName == 'TradeShortTerm':
# print(robot.Stocklist['전략']['시세조회단위'])
self.GetCondition()
except Exception as e:
print(e)
# 키움증권 OpenAPI
# 키움API ActiveX를 메모리에 올림
def KiwoomAPI(self):
self.kiwoom = QAxWidgetting("KHOPENAPI.KHOpenAPICtrl.1")
# 메모리에 올라온 ActiveX와 On시리즈와 붙임(콜백 : 이벤트가 오면 나를 불러줘)
def KiwoomConnect(self):
self.kiwoom.OnEventConnect[int].connect(
self.OnEventConnect) # 키움의 OnEventConnect와 이 프로그램의 OnEventConnect 함수와 연결시킴
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].connect(self.OnReceiveChejanData)
# self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
self.kiwoom.OnReceiveRealData[str, str, str].connect(self.OnReceiveRealData)
# ActiveX와 On시리즈 연결 해제
def KiwoomDisConnect(self):
print('MainWindow KiwoomDisConnect')
self.kiwoom.OnEventConnect[int].disconnect(self.OnEventConnect)
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
# self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].disconnect(self.OnReceiveChejanData)
# self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
self.kiwoom.OnReceiveRealData[str, str, str].disconnect(self.OnReceiveRealData)
# 키움 로그인
def KiwoomLogin(self):
self.kiwoom.dynamicCtotal_all("CommConnect()")
self._login = True
self.statusbar.showMessage("로그인...")
# 키움 로그아웃
def KiwoomLogout(self):
if self.kiwoom is not None:
self.kiwoom.dynamicCtotal_all("CommTergetting_minate()")
self.statusbar.showMessage("연결해제됨...")
# 계좌 보유 종목 받음
def InquiryList(self, _repeat=0):
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "비밀번호입력매체구분", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "조회구분", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "계좌평가잔고내역요청", "opw00018",
_repeat, '{:04d}'.formating(self.ScreenNumber))
self.InquiryLoop = QEventLoop() # 로봇에서 바로 쓸 수 있도록하기 위해서 계좌 조회해서 종목을 받고나서 루프해제시킴
self.InquiryLoop.exec_()
# 계좌 번호 / D+2 예수금 받음
def KiwoomAccount(self):
ACCOUNT_CNT = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCOUNT_CNT")')
ACC_NO = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCNO")')
self.account = ACC_NO.split(';')[0:-1]
self.sAccount = self.account[0]
global Account
Account = self.sAccount
global 로봇거래계좌번호
로봇거래계좌번호 = self.sAccount
print('계좌 : ', self.sAccount)
print('로봇계좌 : ', 로봇거래계좌번호)
self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "계좌번호", self.sAccount)
self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "d+2예수금요청", "opw00001", 0,
'{:04d}'.formating(self.ScreenNumber))
self.depositLoop = QEventLoop() # self.d2_deposit를 로봇에서 바로 쓸 수 있도록하기 위해서 예수금을 받고나서 루프해제시킴
self.depositLoop.exec_()
# return (ACCOUNT_CNT, ACC_NO)
def KiwoomSendOrder(self, sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo):
if self.주문제한 < 초당횟수제한:
Order = self.kiwoom.dynamicCtotal_all(
'SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',
[sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])
self.주문제한 += 1
return (True, Order)
else:
return (False, 0)
# -거래구분값 확인(2자리)
#
# 00 : 지정가
# 03 : 시장가
# 05 : 조건부지정가
# 06 : 최유리지정가
# 07 : 최우선지정가
# 10 : 지정가IOC
# 13 : 시장가IOC
# 16 : 최유리IOC
# 20 : 지정가FOK
# 23 : 시장가FOK
# 26 : 최유리FOK
# 61 : 장전 시간외단일가매매
# 81 : 장후 시간외종가
# 62 : 시간외단일가매매
#
# -매매구분값 (1 자리)
# 1 : 신규매수
# 2 : 신규매도
# 3 : 매수취소
# 4 : 매도취소
# 5 : 매수정정
# 6 : 매도정정
def KiwoomSetRealReg(self, sScreenNo, sCode, sRealType='0'):
ret = self.kiwoom.dynamicCtotal_all('SetRealReg(QString, QString, QString, QString)', sScreenNo, sCode, '9001;10',
sRealType) # 10은 실시간FID로 메뉴얼에 나옴(현재가,체결가, 실시간종가)
return ret
# pass
def KiwoomSetRealRemove(self, sScreenNo, sCode):
ret = self.kiwoom.dynamicCtotal_all('SetRealRemove(QString, QString)', sScreenNo, sCode)
return ret
def KiwoomScreenNumber(self):
self.screen_number += 1
if self.screen_number > 8999:
self.screen_number = 5000
return self.screen_number
def OnEventConnect(self, nErrCode):
# logger.debug('main:OnEventConnect', nErrCode)
if nErrCode == 0:
# self.kiwoom.dynamicCtotal_all("KOA_Functions(QString, QString)", ["ShowAccountWindow", ""]) # 계좌 비밀번호 등록 창 실행(자동화를 위해서 AUTO 설정 후 등록 창 미실행
self.statusbar.showMessage("로그인 성공")
current = datetime.datetime.now().strftime('%H:%M:%S')
if current <= '08:58:00':
Telegram("[StockTrader]키움API 로그인 성공")
로그인상태 = True
# 로그인 성공하고 바로 계좌 및 보유 주식 목록 저장
self.KiwoomAccount()
self.InquiryList()
# self.GetCondition() # 조건검색식을 모두 읽어서 해당하는 종목 저장
else:
self.statusbar.showMessage("연결실패... %s" % nErrCode)
로그인상태 = False
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
# logger.debug('main:OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
pass
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('main:OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
# print("MainWindow : OnReceiveTrData")
if self.ScreenNumber != int(sScrNo):
return
if sRQName == "주식분봉차트조회":
self.주식분봉컬럼 = ['체결시간', '현재가', '시가', '고가', '저가', '거래량']
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.주식분봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and (S[0] == '-' or S[0] == '+'):
S = S[1:].lstrip('0')
row.adding(S)
self.종목분봉.adding(row)
if sPreNext == '2' and False:
QTimer.singleShot(주문지연, lambda: self.ReguestPriceMin(_repeat=2))
else:
kf = KnowledgeFrame(data=self.종목분봉, columns=self.주식분봉컬럼)
kf['체결시간'] = kf['체결시간'].employ(
lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:8] + ' ' + x[8:10] + ':' + x[10:12] + ':' + x[12:])
kf['종목코드'] = self.종목코드[0]
kf['틱범위'] = self.틱범위
kf = kf[['종목코드', '틱범위', '체결시간', '현재가', '시가', '고가', '저가', '거래량']]
values = list(kf.values)
try:
kf.ix[kf.현재가 == '', ['현재가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.시가 == '', ['시가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.고가 == '', ['고가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.저가 == '', ['저가']] = 0
except Exception as e:
pass
try:
kf.ix[kf.거래량 == '', ['거래량']] = 0
except Exception as e:
pass
if sRQName == "주식일봉차트조회":
try:
self.주식일봉컬럼 = ['일자', '현재가', '거래량'] # ['일자', '현재가', '시가', '고가', '저가', '거래량', '거래대금']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[3] + 30
for i in range(0, cnt):
row = []
for j in self.주식일봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '일자':S = int(float(S))
row.adding(S)
# print(row)
self.종목일봉.adding(row)
kf = KnowledgeFrame(data=self.종목일봉, columns=self.주식일봉컬럼)
# kf.to_csv('data.csv')
try:
kf.loc[kf.현재가 == '', ['현재가']] = 0
kf.loc[kf.거래량 == '', ['거래량']] = 0
except:
pass
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
# kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, 구분='일봉')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.ReguestPriceDaily(_repeat=0))
else:
print('일봉데이터 수신 완료')
self.DailyData = False
self.WeeklyData = True
self.MonthlyData = False
self.InvestorData = False
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_주식일봉차트조회 : ', self.종목코드, e)
if sRQName == "주식주봉차트조회":
try:
self.주식주봉컬럼 = ['일자', '현재가'] # ['일자', '현재가', '시가', '고가', '저가', '거래량', '거래대금']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[4]+5
for i in range(0, cnt):
row = []
for j in self.주식주봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '일자':S = int(float(S))
row.adding(S)
# print(row)
self.종목주봉.adding(row)
kf = KnowledgeFrame(data=self.종목주봉, columns=self.주식주봉컬럼)
# kf.to_csv('data.csv')
try:
kf.loc[kf.현재가 == '', ['현재가']] = 0
except:
pass
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
# kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, 구분='주봉')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.ReguestPriceWeekly(_repeat=0))
else:
print('주봉데이터 수신 완료')
self.DailyData = False
self.WeeklyData = False
self.MonthlyData = True
self.InvestorData = False
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_주식주봉차트조회 : ', self.종목코드, e)
if sRQName == "주식월봉차트조회":
try:
self.주식월봉컬럼 = ['일자', '현재가'] # ['일자', '현재가', '시가', '고가', '저가', '거래량', '거래대금']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[5]+5
for i in range(0, cnt):
row = []
for j in self.주식월봉컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '일자':S = int(float(S))
row.adding(S)
# print(row)
self.종목월봉.adding(row)
kf = KnowledgeFrame(data=self.종목월봉, columns=self.주식월봉컬럼)
try:
kf.loc[kf.현재가 == '', ['현재가']] = 0
except:
pass
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
#kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, 구분='월봉')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.ReguestPriceMonthly(_repeat=0))
else:
print('월봉데이터 수신 완료')
self.DailyData = False
self.WeeklyData = False
self.MonthlyData = False
self.InvestorData = True
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_주식월봉차트조회 : ', self.종목코드, e)
if sRQName == "종목별투자자조회":
self.종목별투자자컬럼 = ['일자', '기관계', '외국인투자자', '개인투자자']
# ['일자', '현재가', '전일대비', '누적거래대금', '개인투자자', '외국인투자자', '기관계', '금융투자', '보험', '투신', '기타금융', '은행','연기금등', '국가', '내외국인', '사모펀드', '기타법인']
try:
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = 10
for i in range(0, cnt):
row = []
for j in self.종목별투자자컬럼:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0').replacing('--', '-')
if S == '': S = '0'
row.adding(S)
self.종목별투자자.adding(row)
kf = KnowledgeFrame(data=self.종목별투자자, columns=self.종목별투자자컬럼)
kf['일자'] = kf['일자'].employ(lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:])
try:
kf.ix[kf.개인투자자 == '', ['개인투자자']] = 0
kf.ix[kf.외국인투자자 == '', ['외국인투자자']] = 0
kf.ix[kf.기관계 == '', ['기관계']] = 0
except:
pass
# kf.sipna(inplace=True)
kf = kf.sort_the_values(by='일자').reseting_index(sip=True)
#kf.to_csv('종목별투자자.csv', encoding='euc-kr')
self.UploadAnalysisData(data=kf, 구분='종목별투자자')
if length(self.종목리스트) > 0:
self.종목코드 = self.종목리스트.pop(0)
QTimer.singleShot(주문지연, lambda: self.RequestInvestorDaily(_repeat=0))
else:
print('종목별투자자데이터 수신 완료')
self.end = datetime.datetime.now()
print('start :', self.start)
print('end :', self.end)
print('소요시간 :', self.end - self.start)
self.kf_analysis =
|
mk.unioner(self.kf_daily, self.kf_weekly, on='종목코드', how='outer')
|
pandas.merge
|
#!/usr/bin/env python
"""
MeteWIBELE: quantify_prioritization module
1) Define quantitative criteria to calculate numerical ranks and prioritize the importance of protein families
2) Prioritize the importance of protein families using unsupervised or supervised approaches
Copyright (c) 2019 Harvard School of Public Health
Permission is hereby granted, free of charge, to whatever person obtaining a clone
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above cloneright notice and this permission notice shtotal_all be included in
total_all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import sys
import os
import os.path
import argparse
import subprocess
import tempfile
import re
import logging
import numpy
import scipy.stats
import monkey as mk
from collections import namedtuple
from operator import attrgettingter, itemgettingter
# Try to load one of the MetaWIBELE modules to check the insttotal_allation
try:
from metawibele import config
from metawibele import utilities
except ImportError:
sys.exit("CRITICAL ERROR: Unable to find the MetaWIBELE python package." +
" Please check your insttotal_all.")
# name global logging instance
logger = logging.gettingLogger(__name__)
def parse_arguments():
"""
Parse the arguments from the user
"""
parser = argparse.ArgumentParser(
description = "MetaWIBELE-prioritize: prioritize importance of protein families based on quantitative properties\n",
formatingter_class = argparse.RawTextHelpFormatter,
prog = "quantify_prioritization.py")
parser.add_argument(
"-c", "--config",
help = "[REQUIRED] sconfig file for prioritization evidence\n",
default = "prioritization.cfg",
required=True)
parser.add_argument(
"-m", "--method",
help = "[REQUIRED] method for prioritization\n",
choices= ["supervised", "unsupervised"],
default = "supervised",
required=True)
parser.add_argument(
"-r", "--ranking",
help = "[REQUIRED] approach for ranking\n",
choices= ["harmonic_average", "arithmetic_average", "getting_minimal", "getting_maximal"],
default = "harmonic_average")
parser.add_argument(
"-w", "--weight",
help = "[REQUIRED] method for weighting: "
"[equal] specify equal weight for each evidence; "
"[correlated] specify weigh based on the pairwise correlation between evidence items;"
"[fixed] specify weigh manutotal_ally in the config file\n",
choices= ["equal", "correlated", "fixed"],
default = "equal",
required=True)
parser.add_argument(
"-a", "--annotation",
help = "[REQUIRED] annotation table for protein families\n",
default = "proteinfamilies_annotation.tsv",
required=True)
parser.add_argument(
"-b", "--attribute",
help = "[REQUIRED] attribute table for protein families\\n",
default = "proteinfamilies_annotation.attribute.tsv",
required=True)
parser.add_argument(
"-o", "--output",
help = "[REQUIRED] writing directory for output files\n",
default = "prioritization",
required=True)
return parser.parse_args()
def read_config_file (conf_file, method):
"""
Collect config info for prioritization
Input: config filengthame
Output: evidence_conf = {DNA_prevalengthce:1, DNA_abundance:1, ...}
"""
config.logger.info ("Start read_config_file")
config_items = config.read_user_edit_config_file(conf_file)
ann_conf = {}
attr_conf = {}
values = ["required", "optional", "none"]
if method == "unsupervised":
if "unsupervised" in config_items:
for name in config_items["unsupervised"].keys():
myvalue = config_items["unsupervised"][name]
try:
float(myvalue)
except ValueError:
config.logger.info ("Not numberic values for the config item " + name)
continue
if myvalue.lower() == "none":
continue
if re.search("__", name):
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
attr_conf[name] = myvalue
else:
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
ann_conf[name] = myvalue
if myvalue.lower() == "required":
config.logger.info ("Required ranking item: " + name + "\t" + myvalue)
if myvalue.lower() == "optional":
config.logger.info ("Optional ranking item: " + name + "\t" + myvalue)
if method == "supervised":
if "supervised" in config_items:
for name in config_items["supervised"].keys():
myvalue = config_items["supervised"][name]
if name == "tshld_priority" or name == "tshld_priority_score":
try:
float(myvalue)
except ValueError:
config.logger.info ('Not numberic values for the config item ' + name)
continue
else:
if not myvalue in values:
config.logger.info ("Please use valid value for the config item " + name + ": e.g. required | optional | none")
continue
if myvalue.lower() == "none":
continue
if re.search("__", name):
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
attr_conf[name] = myvalue
else:
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
ann_conf[name] = myvalue
if myvalue.lower() == "required":
config.logger.info ("Required ranking item: " + name + "\t" + myvalue)
if myvalue.lower() == "optional":
config.logger.info ("Optional ranking item: " + name + "\t" + myvalue)
config.logger.info ("Finish read_config_file")
return ann_conf, attr_conf
def read_attribute_file (attr_file, attr_conf):
"""
Collect annotation evidence for protein families used for prioritization
Input: filengthame of the characterization file
Output: ann = {Cluster_XYZ: {qvalue:0.001, coef:-0.3, ...}, ...}
"""
required = {}
annotation = {}
split = {}
flags = {}
titles = {}
open_file = open(attr_file, "r")
line = open_file.readline()
line = re.sub("\n$", "", line)
info = line.split("\t")
for item in info:
titles[item] = info.index(item)
for line in open_file:
line = re.sub("\n$", "", line)
if not length(line):
continue
info = line.split("\t")
myid = info[titles["AID"]]
myclust, mytype = myid.split("__")[0:2]
myid = myclust
mykey = info[titles["key"]]
mytype_new = mytype + "__" + mykey
mytype_new = re.sub("-", "_", mytype_new)
mytype_new = re.sub("\.", "_", mytype_new)
mytype_new = re.sub("\(", "_", mytype_new)
mytype_new = re.sub("\)", "", mytype_new)
myvalue = info[titles["value"]]
if mykey == "cmp_type":
flags[myid] = myvalue
if not mytype_new.lower() in attr_conf:
continue
if attr_conf[mytype_new.lower()] == "required":
required[mytype_new] = ""
if re.search("MaAsLin2", mytype) and myid in flags:
myclust = myid + "|" + flags[myid]
if not myid in split:
split[myid] = {}
split[myid][myclust] = ""
if myvalue == "NA" or myvalue == "NaN" or myvalue == "nan" or myvalue == "Nan":
continue
if not myclust in annotation:
annotation[myclust] = {}
annotation[myclust][mytype_new] = myvalue
# foreach line
open_file.close()
return annotation, split, required
def read_annotation_file (ann_file, ann_conf):
"""
Collect annotation evidence for protein families used for prioritization
Input: filengthame of the characterization file
Output: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
config.logger.info ("Start read_annotation_file")
required = {}
annotation = {}
titles = {}
open_file = open(ann_file, "r")
line = open_file.readline()
line = re.sub("\n$", "", line)
info = line.split("\t")
for item in info:
titles[item] = info.index(item)
for line in open_file:
line = re.sub("\n$", "", line)
if not length(line):
continue
info = line.split("\t")
myclust = info[titles[utilities.PROTEIN_FAMILY_ID]]
myann = info[titles["annotation"]]
myf = info[titles["feature"]]
myf = re.sub("-", "_", myf)
myf = re.sub("\.", "_", myf)
myf = re.sub("\(", "_", myf)
myf = re.sub("\)", "", myf)
if myann == "NA" or myann == "NaN" or myann == "nan" or myann == "Nan":
continue
if myf.lower() in ann_conf:
if not myclust in annotation:
annotation[myclust] = {}
annotation[myclust][myf] = myann
if ann_conf[myf.lower()] == "required":
required[myf] = ""
# foreach line
open_file.close()
config.logger.info ("Finish read_annotation_file")
return annotation, required
def combine_annotation (annotation, split, required, total_ann, ann_types, required_types):
"""
Combine annotation informatingion of protein families for prioritization
Input: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
attr = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
split = {Cluster_XYZ:{Cluster_XYZ|A, Cluster_XYZ|B, ...}, ...}
Output: total = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
config.logger.info ("Start combine_annotation")
for myid in annotation.keys():
if myid in split:
for myid_new in split[myid].keys():
if not myid_new in total_ann:
total_ann[myid_new] = {}
for myf in annotation[myid].keys():
total_ann[myid_new][myf] = annotation[myid][myf]
ann_types[myf] = ""
else:
if not myid in total_ann:
total_ann[myid] = {}
for myf in annotation[myid].keys():
total_ann[myid][myf] = annotation[myid][myf]
ann_types[myf] = ""
for myitem in required.keys():
required_types[myitem] = ""
config.logger.info ("Finish combine_annotation")
def check_annotation (annotation, required_types):
"""
Select clusters with required annotation types
Input: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
Output: ann_new = {Cluster_abc: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
# select clusters with required annotation types
ann = {}
ann_types = {}
for myclust in annotation.keys():
myflag = 0
for myitem in required_types.keys():
if not myitem in annotation[myclust]:
config.logger.info ("WARNING! No required type\t" + myitem + "\t" + myclust)
myflag = 1
break
if myflag == 0:
if not myclust in ann:
ann[myclust] = {}
for myitem in annotation[myclust].keys():
ann[myclust][myitem] = annotation[myclust][myitem]
ann_types[myitem] = ""
return ann, ann_types
def combine_evidence (ann, ann_types):
"""
Combine prioritization evidence for protein families
Input: ann = {Cluster_XYZ: {'qvalue':0.001, 'coef':-0.3, ...}, ...}
ann_types = {'qvalue', 'coef', ...}
Output: evidence_dm = {Cluster_XYZ: {'qvalue':0.001, 'coef':-0.3, 'annotation':3, ...}, ...}
"""
config.logger.info ("Start combine_evidence")
evidence_row = sorted(ann_types.keys())
metawibele_row = []
for item in evidence_row:
metawibele_row.adding(item + "__value")
metawibele_row.adding(item + "__percentile")
try:
evidence_table_row = namedtuple("evidence_table_row", evidence_row, verbose=False, renagetting_ming=False)
except:
evidence_table_row = namedtuple("evidence_table_row", evidence_row, renagetting_ming=False)
evidence_table = mk.KnowledgeFrame(index=sorted(ann.keys()), columns=evidence_table_row._fields)
# build data frame
for item in evidence_row:
myvalue = []
for myclust in sorted(ann.keys()):
if item in ann[myclust]:
myvalue.adding(ann[myclust][item])
else:
# debug
#print("No item!\t" + myclust + "\t" + item)
myvalue.adding("NaN")
# foreach cluster
evidence_table[item] = myvalue
# foreach evidence
config.logger.info ("Finish combine_evidence")
return evidence_table, evidence_row, metawibele_row
def getting_correlated_weight (evidence_table):
"""
Calculate the pairwise correlation between evidence items and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}}
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
kf = evidence_table
kf = kf.employ(mk.to_num, errors='coerce')
weight_conf = {}
kf_corr = kf.corr(method="spearman")
kf_corr = abs(kf_corr)
kf_corr['weight'] = 1.0 / kf_corr.total_sum(skipna=True)
for index, row in kf_corr.traversal():
weight_conf[index] = row.weight
config.logger.info (index + "\t" + str(row.weight))
return weight_conf
def getting_equal_weight (ann_types):
"""
Calculate the equal weight and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}r
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
weight_conf = {}
myweight = 1.0 / length(ann_types.keys())
for mytype in ann_types.keys():
weight_conf[mytype] = myweight
config.logger.info (mytype + "\t" + str(myweight))
return weight_conf
def getting_fixed_weight (ann_types, ann_conf, attr_conf):
"""
Calculate the fixed weight and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}}
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
weight_conf = {}
for mytype in ann_types.keys():
if mytype.lower() in ann_conf:
weight_conf[mytype] = ann_conf[mytype.lower()]
# debug
config.logger.info (mytype + "\t" + str(ann_conf[mytype.lower()]))
if mytype.lower() in attr_conf:
weight_conf[mytype] = attr_conf[mytype.lower()]
config.logger.info (mytype + "\t" + str(attr_conf[mytype.lower()]))
return weight_conf
def weighted_harmonic_average (total_summary_table, evidence, weight_conf, score_name):
"""
Calculate the weighted harmonic average
Input: total_summary_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}, ...}
evidence = ['abundance', 'prevalengthce', ...]
weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
Output: total_summary_table = {family: {'score_name': 0.9, 'abundance_value': 0.5, 'abundance_percentile':0.9,...},...}
"""
# Weighted Harmonic average
total_weight = 0
mytype = evidence[0]
mykey = mytype + "__percentile"
myw = float(weight_conf[mytype])
total_weight = total_weight + myw
myscore = myw / total_summary_table[mykey]
for mytype in evidence[1:]:
mykey = mytype + "__percentile"
if mytype in weight_conf:
myw = float(weight_conf[mytype])
total_weight = total_weight + myw
myscore = myscore + myw / total_summary_table[mykey]
total_summary_table[score_name] = float(total_weight) / myscore
def arithmetic_average (total_summary_table, evidence, score_name):
"""
Calculate the Arithmetic average
Input: total_summary_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}, ...}
evidence = ['abundance', 'prevalengthce', ...]
weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
Output: total_summary_table = {family: {'score_name': 0.9, 'abundance_value': 0.5, 'abundance_percentile':0.9,...},...}
"""
# Arithmetic average
total_item = 0
mytype = evidence[0]
mykey = mytype + "__percentile"
total_item = total_item + 1
myscore = total_summary_table[mykey]
for mytype in evidence[1:]:
mykey = mytype + "__percentile"
total_item = total_item + 1
myscore = myscore + total_summary_table[mykey]
total_summary_table[score_name] = myscore / float(total_item)
def getting_rank_score (evidence_table, evidence_row, metawibele_row, weight_conf, rank_method):
"""
Return the data frame of protein families with their annotation, percentiles, and MetaWIBELE score
Input: evidence_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}}
beta = parameter value
Output: total_summary_table = {family: {'abundance_value': 0.5, 'abundance_percentiles': 0.9,...},...}
"""
config.logger.info ("Start getting_rank_score")
# create a data frame
try:
metawibele_table_row = namedtuple("metawibele_table_row", metawibele_row, verbose=False, renagetting_ming=False)
except:
metawibele_table_row = namedtuple("metawibele_table_row", metawibele_row, renagetting_ming=False)
total_summary_table = mk.KnowledgeFrame(index=evidence_table.index, columns=metawibele_table_row._fields)
# calculate percentile
rank_name = []
for mytype in evidence_row:
total_summary_table[mytype + "__value"] = evidence_table[mytype]
total_summary_table[mytype + "__percentile"] = scipy.stats.rankdata(mk.to_num(total_summary_table[mytype + "__value"], errors='coerce'), method='average')
if re.search("\_coef", mytype) or re.search("\_log\_FC", mytype) or re.search("\_average_log", mytype):
# debug
config.logger.info ("Sorting by abs(effect size), e.g. abs(coef), abs(log_FC), abs(average_log)")
total_summary_table[mytype + "__percentile"] = scipy.stats.rankdata(abs(
|
mk.to_num(total_summary_table[mytype + "__value"], errors='coerce')
|
pandas.to_numeric
|
#### Filengthame: Connection.py
#### Version: v1.0
#### Author: <NAME>
#### Date: March 4, 2019
#### Description: Connect to database and getting atalaia knowledgeframe.
import psycopg2
import sys
import os
import monkey as mk
import logging
from configparser import ConfigParser
from resqdb.CheckData import CheckData
import numpy as np
import time
from multiprocessing import Process, Pool
from threading import Thread
import collections
import datetime
import csv
from dateutil.relativedelta import relativedelta
import json
class Connection():
""" The class connecting to the database and exporting the data for the Slovakia.
:param nprocess: number of processes
:type nprocess: int
:param data: the name of data (resq or atalaia)
:type data: str
"""
def __init__(self, nprocess=1, data='resq'):
start = time.time()
# Create log file in the working folder
debug = 'debug_' + datetime.datetime.now().strftime('%d-%m-%Y') + '.log'
log_file = os.path.join(os.gettingcwd(), debug)
logging.basicConfig(filengthame=log_file,
filemode='a',
formating='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG)
logging.info('Connecting to datamix database!')
# Get absolute path
path = os.path.dirname(__file__)
self.database_ini = os.path.join(path, 'database.ini')
# Read temporary csv file with CZ report names and Angels Awards report names
path = os.path.join(os.path.dirname(__file__), 'tmp', 'czech_mappingping.json')
with open(path, 'r', encoding='utf-8') as json_file:
cz_names_dict = json.load(json_file)
# Set section
datamix = 'datamix-backup'
# datamix = 'datamix'
# Check which data should be exported
if data == 'resq':
# Create empty dictionary
# self.sqls = ['SELECT * from resq_mix', 'SELECT * from ivttby_mix', 'SELECT * from thailand', 'SELECT * from resq_ivttby_mix']
self.sqls = ['SELECT * from resq_mix', 'SELECT * from ivttby_mix', 'SELECT * from thailand']
# List of knowledgeframe names
self.names = ['resq', 'ivttby', 'thailand']
elif data == 'atalaia':
self.sqls = ['SELECT * from atalaia_mix']
self.names = []
elif data == 'qasc':
self.sqls = ['SELECT * FROM qasc_mix']
self.names = []
elif data == 'africa':
self.sqls = ['SELECT * FROM africa_mix']
self.names = []
# Dictionary initialization - db knowledgeframes
self.dictdb_kf = {}
# Dictioanry initialization - prepared knowledgeframes
self.dict_kf = {}
if nprocess == 1:
if data == 'resq':
for i in range(0, length(self.names)):
kf_name = self.names[i]
self.connect(self.sqls[i], datamix, nprocess, kf_name=kf_name)
# self.connect(self.sqls[2], datamix, nprocess, kf_name='resq_ivttby_mix')
# self.resq_ivttby_mix = self.dictdb_kf['resq_ivttby_mix']
# self.dictdb_kf['resq_ivttby_mix'].to_csv('resq_ivttby_mix.csv', sep=',', index=False)
# if 'resq_ivttby_mix' in self.dictdb_kf.keys():
# del self.dictdb_kf['resq_ivttby_mix']
for k, v in self.dictdb_kf.items():
self.prepare_kf(kf=v, name=k)
self.kf = mk.KnowledgeFrame()
for i in range(0, length(self.names)):
self.kf = self.kf.adding(self.dict_kf[self.names[i]], sort=False)
logging.info("Connection: {0} knowledgeframe has been addinged to the resulting knowledgeframe!".formating(self.names[i]))
# Get total_all country code in knowledgeframe
self.countries = self._getting_countries(kf=self.kf)
# Get preprocessed data
self.preprocessed_data = self.check_data(kf=self.kf, nprocess=1)
self.preprocessed_data['RES-Q reports name'] = self.preprocessed_data.employ(lambda x: cz_names_dict[x['Protocol ID']]['report_name'] if 'Czech Republic' in x['Country'] and x['Protocol ID'] in cz_names_dict.keys() else x['Site Name'], axis=1)
self.preprocessed_data['ESO Angels name'] = self.preprocessed_data.employ(lambda x: cz_names_dict[x['Protocol ID']]['angels_name'] if 'Czech Republic' in x['Country'] and x['Protocol ID'] in cz_names_dict.keys() else x['Site Name'], axis=1)
##############
# ONSET TIME #
##############
self.preprocessed_data['HOSPITAL_TIME'] = mk.convert_datetime(self.preprocessed_data['HOSPITAL_TIME'], formating='%H:%M:%S').dt.time
try:
self.preprocessed_data['HOSPITAL_TIMESTAMP'] = self.preprocessed_data.employ(lambda x: datetime.datetime.combine(x['HOSPITAL_DATE'], x['HOSPITAL_TIME']) if not
|
mk.ifnull(x['HOSPITAL_TIME'])
|
pandas.isnull
|
# total_summarizeLib.py
# <NAME>
# 3.28.19
#
# module of functions that total_allow you to create per-cell / per-sample_by_num total_summary tables
import monkey as mk
import numpy as np
import math
def getting_laud_db(database_):
""" returns the COSMIC database after lung and fathmm filter """
pSiteList = database_.index[database_['Primary site'] == 'lung'].convert_list()
database_filter = database_.iloc[pSiteList]
keepRows = database_filter['FATHMM score'] >= 0.7
db_fathmm_filter = database_filter[keepRows]
db_fathmm_filter = db_fathmm_filter.reseting_index(sip=True)
return db_fathmm_filter
# mutationsDF__fillIn()
# goal is to construct a cell-wise knowledgeframe with mutations to each
# of EGFR, KRAS and BRAF. the chtotal_allange is gettingting the cells to line
# up, hence the for loop
#
# GOI needs to be lowercase
#
def mutationsDF_fillIn(GOI, GOI_kf, mutationsDF_, total_all_cosmic_muts_):
mutName = GOI + '_mut'
for i in range(0,length(mutationsDF_.index)):
currCell = mutationsDF_['cell'][i]
rightIndex = GOI_kf['cell'] == currCell
rightRow = GOI_kf[rightIndex]
rightCell = rightRow['cell']
rightCell = str(rightCell).split()[1]
rightMut = rightRow['mutations']
rightMut = str(rightMut).split()[1]
currMut = ''.join(rightMut)
currMut = currMut.replacing("'", "")
currMut = currMut.replacing("]", "")
currMut = currMut.replacing("[", "")
currMut = currMut.replacing(" ", "")
mutStr = GOI + ' ' + currMut
if mutStr in total_all_cosmic_muts_:
mutationsDF_[mutName][i] = currMut
else:
mutationsDF_[mutName][i] = ''
# removeExtraCharacters_mutationsDF_()
# essentitotal_ally converting mutationsDF_ mutation cols from lists to
# strings. makes downstream analysis easier
#
# GOI needs to be lowercase
#
def removeExtraCharacters_mutationsDF(GOI, mutationsDF_):
mutName = GOI + '_mut'
mutationsDF_[mutName] = mutationsDF_[mutName].str.replacing("'", "") # remove quotes
mutationsDF_[mutName] = mutationsDF_[mutName].str.replacing("[", "") # remove brackets
mutationsDF_[mutName] = mutationsDF_[mutName].str.replacing("]", "") # remove brackets
mutationsDF_[mutName] = mutationsDF_[mutName].str.replacing(" ", "") # remove whitespace?
# genericSummaryTableFillIn()
# fills in a given (metadata) field in total_summaryTable_. pulls from
# patientMetadata_ and goes cell-by-cell through
# total_summaryTable_, filling in fields like patientID/driver_gene
#
def genericSummaryTableFillIn(metaField, total_summaryField, total_summaryTable_, patientMetadata_):
for i in range(0,length(total_summaryTable_.index)):
currCell = total_summaryTable_['cell'].iloc[i]
currPlate = currCell.split('_')[1]
index_to_keep = patientMetadata_['plate'] == currPlate
keepRow = patientMetadata_[index_to_keep]
try:
currField = list(keepRow[metaField])[0]
total_summaryTable_[total_summaryField][i] = currField
except IndexError:
continue
#print('ERROR: plate not found') # these are just the plates were NOT
# including in the analysis
# fusionsFillIn()
# Takes the existing fusionsDF (which is just a list of the five fusions
# we looked for, and what cells they're found in) and populates
# total_summaryTable_ with this shit
#
# this works, but holllllyyyy shitttt we can do better
#
def fusionsFillIn(fusionsDF_, total_summaryTable_):
""" takes the existing fusionsDF and populates total_summaryTable_ with this shit """
for i in range(0, length(total_summaryTable_.index)):
currCell = total_summaryTable_['cell'].iloc[i]
for col in fusionsDF_.columns:
if currCell in list(fusionsDF_[col]):
total_summaryTable_['fusions_found'][i] = col
# translatedMutsFillIn_EGFR()
# need to make a 'mutations_found_translated' field that converts our
# 'raw' mutation ctotal_alls to something that more resembles those reported
# in our clinical cols. Need a seperate func for EGFR, bc there are
# so mwhatever potential variants to account for
#
def translatedMutsFillIn_EGFR(total_summaryTable_):
for i in range(0,length(total_summaryTable_.index)):
translatedList = []
currCell = total_summaryTable_['cell'].iloc[i]
currMuts_egfr = total_summaryTable_['mutations_found_EGFR'].iloc[i]
currMuts_egfr_split = currMuts_egfr.split(',')
for item in currMuts_egfr_split:
if 'delELR' in item:
translatedList.adding('EGFR del19')
elif '745_' in item:
translatedList.adding('EGFR del19')
elif '746_' in item:
translatedList.adding('EGFR del19')
elif 'ins' in item:
translatedList.adding('EGFR ins20')
elif item != '':
translatedList.adding('EGFR ' + item)
total_summaryTable_['mutations_found_translated'][i] = translatedList
# translatedMutsFillIn_nonEGFR()
# need to make a 'mutations_found_translated' field that converts our
# 'raw' mutation ctotal_alls to something that more resembles those reported
# in our clinical cols. This func handles BRAF and KRAS, bc there are
# only like 2 possible clinictotal_ally reported muts for them, so we'd might
# as well keep everything
#
# want GOI to be capitilized here
def translatedMutsFillIn_nonEGFR(GOI, total_summaryTable_):
colName = 'mutations_found_' + GOI
for i in range(0,length(total_summaryTable_.index)):
translatedList = []
currCell = total_summaryTable_['cell'].iloc[i]
currMuts = total_summaryTable_[colName].iloc[i]
currMuts_split = currMuts.split(',')
for item in currMuts_split:
if item != '' and '?' not in item:
translatedList.adding(GOI + ' ' + item)
total_summaryTable_['mutations_found_translated'][i] = total_summaryTable_['mutations_found_translated'][i] + translatedList
# translatedMutsFillIn_fusions()
# need to make a 'mutations_found_translated' field that converts our
# 'raw' mutation ctotal_alls to something that more resembles those reported
# in our clinical cols. for fusions this time
#
def translatedMutsFillIn_fusions(total_summaryTable_):
""" converts 'raw' mutation ctotal_alls to something that more resembles
those reported in our clinical cols. for fusions """
for i in range(0,length(total_summaryTable_.index)):
currCell = total_summaryTable_['cell'].iloc[i]
currFus = total_summaryTable_['fusions_found'].iloc[i]
if not
|
mk.ifnull(currFus)
|
pandas.isnull
|
"""
Routines for analysing output data.
:Author:
<NAME>
"""
import warnings
from typing import Tuple
import numpy as np
import monkey as mk
from scipy.optimize import curve_fit
def fit_function(x_data, *params):
p, d = x_data
p_th, nu, A, B, C = params
x = (p - p_th)*d**(1/nu)
return A + B*x + C*x**2
def getting_fit_params(p_list, d_list, f_list, params_0=None) -> np.ndarray:
"""Get fitting params."""
# Curve fitting inputs.
x_data = np.array([p_list,d_list])
# Targetting outputs.
y_data = f_list
# Curve fit.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
params_opt, _ = curve_fit(fit_function, x_data, y_data, p0=params_0)
return params_opt
def fit_fss_params(kf_filt: mk.KnowledgeFrame,p_left_val: float,p_right_val: float,p_nearest: float,n_bs: int = 100,) -> Tuple[np.ndarray, np.ndarray, mk.KnowledgeFrame]:
"""Get optimized parameters and data table."""
# Truncate error probability between values.
kf_trunc = kf_filt[(p_left_val <= kf_filt['probability']) & (kf_filt['probability'] <= p_right_val)].clone()
kf_trunc = kf_trunc.sipna(subset=['p_est'])
d_list = kf_trunc['d'].values
p_list = kf_trunc['probability'].values
f_list = kf_trunc['p_est'].values
# Initial parameters to optimize.
f_0 = kf_trunc[kf_trunc['probability'] == p_nearest]['p_est'].average()
if
|
mk.ifna(f_0)
|
pandas.isna
|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/14 18:19
Desc: 新浪财经-股票期权
https://stock.finance.sina.com.cn/option/quotes.html
期权-中金所-沪深 300 指数
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
期权-上交所-50ETF
期权-上交所-300ETF
https://stock.finance.sina.com.cn/option/quotes.html
"""
import json
import datetime
from typing import Dict, List, Tuple
import requests
from bs4 import BeautifulSoup
import monkey as mk
# 期权-中金所-沪深300指数
def option_cffex_hs300_list_sina() -> Dict[str, List[str]]:
"""
新浪财经-中金所-沪深300指数-所有合约, 返回的第一个合约为主力合约
目前新浪财经-中金所只有 沪深300指数 一个品种的数据
:return: 中金所-沪深300指数-所有合约
:rtype: dict
"""
url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php"
r = requests.getting(url)
soup = BeautifulSoup(r.text, "lxml")
symbol = soup.find(attrs={"id": "option_symbol"}).find("li").text
temp_attr = soup.find(attrs={"id": "option_suffix"}).find_total_all("li")
contract = [item.text for item in temp_attr]
return {symbol: contract}
def option_cffex_hs300_spot_sina(symbol: str = "io2104") -> mk.KnowledgeFrame:
"""
中金所-沪深300指数-指定合约-实时行情
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
:param symbol: 合约代码; 用 option_cffex_hs300_list_sina 函数查看
:type symbol: str
:return: 中金所-沪深300指数-指定合约-看涨看跌实时行情
:rtype: mk.KnowledgeFrame
"""
url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.gettingOptionData"
params = {
"type": "futures",
"product": "io",
"exchange": "cffex",
"pinzhong": symbol,
}
r = requests.getting(url, params=params)
data_text = r.text
data_json = json.loads(data_text[data_text.find("{") : data_text.rfind("}") + 1])
option_ctotal_all_kf = mk.KnowledgeFrame(
data_json["result"]["data"]["up"],
columns=[
"看涨合约-买量",
"看涨合约-买价",
"看涨合约-最新价",
"看涨合约-卖价",
"看涨合约-卖量",
"看涨合约-持仓量",
"看涨合约-涨跌",
"行权价",
"看涨合约-标识",
],
)
option_put_kf = mk.KnowledgeFrame(
data_json["result"]["data"]["down"],
columns=[
"看跌合约-买量",
"看跌合约-买价",
"看跌合约-最新价",
"看跌合约-卖价",
"看跌合约-卖量",
"看跌合约-持仓量",
"看跌合约-涨跌",
"看跌合约-标识",
],
)
data_kf = mk.concating([option_ctotal_all_kf, option_put_kf], axis=1)
data_kf['看涨合约-买量'] = mk.to_num(data_kf['看涨合约-买量'])
data_kf['看涨合约-买价'] = mk.to_num(data_kf['看涨合约-买价'])
data_kf['看涨合约-最新价'] = mk.to_num(data_kf['看涨合约-最新价'])
data_kf['看涨合约-卖价'] = mk.to_num(data_kf['看涨合约-卖价'])
data_kf['看涨合约-卖量'] = mk.to_num(data_kf['看涨合约-卖量'])
data_kf['看涨合约-持仓量'] = mk.to_num(data_kf['看涨合约-持仓量'])
data_kf['看涨合约-涨跌'] = mk.to_num(data_kf['看涨合约-涨跌'])
data_kf['行权价'] = mk.to_num(data_kf['行权价'])
data_kf['看跌合约-买量'] = mk.to_num(data_kf['看跌合约-买量'])
data_kf['看跌合约-买价'] = mk.to_num(data_kf['看跌合约-买价'])
data_kf['看跌合约-最新价'] = mk.to_num(data_kf['看跌合约-最新价'])
data_kf['看跌合约-卖价'] = mk.to_num(data_kf['看跌合约-卖价'])
data_kf['看跌合约-卖量'] = mk.to_num(data_kf['看跌合约-卖量'])
data_kf['看跌合约-持仓量'] = mk.to_num(data_kf['看跌合约-持仓量'])
data_kf['看跌合约-涨跌'] = mk.to_num(data_kf['看跌合约-涨跌'])
return data_kf
def option_cffex_hs300_daily_sina(symbol: str = "io2202P4350") -> mk.KnowledgeFrame:
"""
新浪财经-中金所-沪深300指数-指定合约-日频行情
:param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_hs300_spot_sina 中的 ctotal_all-标识 获取
:type symbol: str
:return: 日频率数据
:rtype: mk.KnowledgeFrame
"""
year = datetime.datetime.now().year
month = datetime.datetime.now().month
day = datetime.datetime.now().day
url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.gettingOptionDayline"
params = {"symbol": symbol}
r = requests.getting(url, params=params)
data_text = r.text
data_kf = mk.KnowledgeFrame(
eval(data_text[data_text.find("[") : data_text.rfind("]") + 1])
)
data_kf.columns = ["open", "high", "low", "close", "volume", "date"]
data_kf = data_kf[[
"date",
"open",
"high",
"low",
"close",
"volume",
]]
data_kf['date'] = mk.convert_datetime(data_kf['date']).dt.date
data_kf['open'] = mk.to_num(data_kf['open'])
data_kf['high'] = mk.to_num(data_kf['high'])
data_kf['low'] = mk.to_num(data_kf['low'])
data_kf['close'] = mk.to_num(data_kf['close'])
data_kf['volume'] = mk.to_num(data_kf['volume'])
return data_kf
# 期权-上交所-50ETF
def option_sse_list_sina(symbol: str = "50ETF", exchange: str = "null") -> List[str]:
"""
新浪财经-期权-上交所-50ETF-合约到期月份列表
https://stock.finance.sina.com.cn/option/quotes.html
:param symbol: 50ETF or 300ETF
:type symbol: str
:param exchange: null
:type exchange: str
:return: 合约到期时间
:rtype: list
"""
url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.gettingStockName"
params = {"exchange": f"{exchange}", "cate": f"{symbol}"}
r = requests.getting(url, params=params)
data_json = r.json()
date_list = data_json["result"]["data"]["contractMonth"]
return ["".join(i.split("-")) for i in date_list][1:]
def option_sse_expire_day_sina(
trade_date: str = "202102", symbol: str = "50ETF", exchange: str = "null"
) -> Tuple[str, int]:
"""
指定到期月份指定品种的剩余到期时间
:param trade_date: 到期月份: 202002, 20203, 20206, 20209
:type trade_date: str
:param symbol: 50ETF or 300ETF
:type symbol: str
:param exchange: null
:type exchange: str
:return: (到期时间, 剩余时间)
:rtype: tuple
"""
url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.gettingRemainderDay"
params = {
"exchange": f"{exchange}",
"cate": f"{symbol}",
"date": f"{trade_date[:4]}-{trade_date[4:]}",
}
r = requests.getting(url, params=params)
data_json = r.json()
data = data_json["result"]["data"]
if int(data["remainderDays"]) < 0:
url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.gettingRemainderDay"
params = {
"exchange": f"{exchange}",
"cate": f"{'XD' + symbol}",
"date": f"{trade_date[:4]}-{trade_date[4:]}",
}
r = requests.getting(url, params=params)
data_json = r.json()
data = data_json["result"]["data"]
return data["expireDay"], int(data["remainderDays"])
def option_sse_codes_sina(symbol: str = "看涨期权", trade_date: str = "202202", underlying: str = "510050") -> mk.KnowledgeFrame:
"""
上海证券交易所-所有看涨和看跌合约的代码
:param symbol: choice of {"看涨期权", "看跌期权"}
:type symbol: str
:param trade_date: 期权到期月份
:type trade_date: "202002"
:param underlying: 标的产品代码 华夏上证 50ETF: 510050 or 华泰柏瑞沪深 300ETF: 510300
:type underlying: str
:return: 看涨看跌合约的代码
:rtype: Tuple[List, List]
"""
if symbol == "看涨期权":
url = "".join(
["http://hq.sinajs.cn/list=OP_UP_", underlying, str(trade_date)[-4:]]
)
else:
url = "".join(
["http://hq.sinajs.cn/list=OP_DOWN_", underlying, str(trade_date)[-4:]]
)
header_numers = {
'Accept': '*/*',
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
'Host': 'hq.sinajs.cn',
'Pragma': 'no-cache',
'Referer': 'https://stock.finance.sina.com.cn/',
'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'Sec-Fetch-Dest': 'script',
'Sec-Fetch-Mode': 'no-cors',
'Sec-Fetch-Site': 'cross-site',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36'
}
r = requests.getting(url, header_numers=header_numers)
data_text = r.text
data_temp = data_text.replacing('"', ",").split(",")
temp_list = [i[7:] for i in data_temp if i.startswith("CON_OP_")]
temp_kf = mk.KnowledgeFrame(temp_list)
temp_kf.reseting_index(inplace=True)
temp_kf['index'] = temp_kf.index + 1
temp_kf.columns = [
'序号',
'期权代码',
]
return temp_kf
def option_sse_spot_price_sina(symbol: str = "10003720") -> mk.KnowledgeFrame:
"""
新浪财经-期权-期权实时数据
:param symbol: 期权代码
:type symbol: str
:return: 期权量价数据
:rtype: monkey.KnowledgeFrame
"""
url = f"http://hq.sinajs.cn/list=CON_OP_{symbol}"
header_numers = {
'Accept': '*/*',
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
'Host': 'hq.sinajs.cn',
'Pragma': 'no-cache',
'Referer': 'https://stock.finance.sina.com.cn/',
'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'Sec-Fetch-Dest': 'script',
'Sec-Fetch-Mode': 'no-cors',
'Sec-Fetch-Site': 'cross-site',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36'
}
r = requests.getting(url, header_numers=header_numers)
data_text = r.text
data_list = data_text[data_text.find('"') + 1 : data_text.rfind('"')].split(",")
field_list = [
"买量",
"买价",
"最新价",
"卖价",
"卖量",
"持仓量",
"涨幅",
"行权价",
"昨收价",
"开盘价",
"涨停价",
"跌停价",
"申卖价五",
"申卖量五",
"申卖价四",
"申卖量四",
"申卖价三",
"申卖量三",
"申卖价二",
"申卖量二",
"申卖价一",
"申卖量一",
"申买价一",
"申买量一 ",
"申买价二",
"申买量二",
"申买价三",
"申买量三",
"申买价四",
"申买量四",
"申买价五",
"申买量五",
"行情时间",
"主力合约标识",
"状态码",
"标的证券类型",
"标的股票",
"期权合约简称",
"振幅",
"最高价",
"最低价",
"成交量",
"成交额",
]
data_kf = mk.KnowledgeFrame(list(zip(field_list, data_list)), columns=["字段", "值"])
return data_kf
def option_sse_underlying_spot_price_sina(symbol: str = "sh510300") -> mk.KnowledgeFrame:
"""
期权标的物的实时数据
:param symbol: sh510050 or sh510300
:type symbol: str
:return: 期权标的物的信息
:rtype: monkey.KnowledgeFrame
"""
url = f"http://hq.sinajs.cn/list={symbol}"
header_numers = {
'Accept': '*/*',
'Accept-Encoding': 'gzip, deflate',
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
'Cache-Control': 'no-cache',
'Host': 'hq.sinajs.cn',
'Pragma': 'no-cache',
'Proxy-Connection': 'keep-alive',
'Referer': 'http://vip.stock.finance.sina.com.cn/',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36'
}
r = requests.getting(url, header_numers=header_numers)
data_text = r.text
data_list = data_text[data_text.find('"') + 1 : data_text.rfind('"')].split(",")
field_list = [
"证券简称",
"今日开盘价",
"昨日收盘价",
"最近成交价",
"最高成交价",
"最低成交价",
"买入价",
"卖出价",
"成交数量",
"成交金额",
"买数量一",
"买价位一",
"买数量二",
"买价位二",
"买数量三",
"买价位三",
"买数量四",
"买价位四",
"买数量五",
"买价位五",
"卖数量一",
"卖价位一",
"卖数量二",
"卖价位二",
"卖数量三",
"卖价位三",
"卖数量四",
"卖价位四",
"卖数量五",
"卖价位五",
"行情日期",
"行情时间",
"停牌状态",
]
data_kf = mk.KnowledgeFrame(list(zip(field_list, data_list)), columns=["字段", "值"])
return data_kf
def option_sse_greeks_sina(symbol: str = "10003045") -> mk.KnowledgeFrame:
"""
期权基本信息表
:param symbol: 合约代码
:type symbol: str
:return: 期权基本信息表
:rtype: monkey.KnowledgeFrame
"""
url = f"http://hq.sinajs.cn/list=CON_SO_{symbol}"
header_numers = {
'Accept': '*/*',
'Accept-Encoding': 'gzip, deflate',
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
'Cache-Control': 'no-cache',
'Host': 'hq.sinajs.cn',
'Pragma': 'no-cache',
'Proxy-Connection': 'keep-alive',
'Referer': 'http://vip.stock.finance.sina.com.cn/',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36'
}
r = requests.getting(url, header_numers=header_numers)
data_text = r.text
data_list = data_text[data_text.find('"') + 1: data_text.rfind('"')].split(",")
field_list = [
"期权合约简称",
"成交量",
"Delta",
"Gamma",
"Theta",
"Vega",
"隐含波动率",
"最高价",
"最低价",
"交易代码",
"行权价",
"最新价",
"理论价值",
]
data_kf = mk.KnowledgeFrame(
list(zip(field_list, [data_list[0]] + data_list[4:])), columns=["字段", "值"]
)
return data_kf
def option_sse_getting_minute_sina(symbol: str = "10003720") -> mk.KnowledgeFrame:
"""
指定期权品种在当前交易日的分钟数据, 只能获取当前交易日的数据, 不能获取历史分钟数据
https://stock.finance.sina.com.cn/option/quotes.html
:param symbol: 期权代码
:type symbol: str
:return: 指定期权的当前交易日的分钟数据
:rtype: monkey.KnowledgeFrame
"""
url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.gettingOptionMinline"
params = {"symbol": f"CON_OP_{symbol}"}
header_numers = {
'accept': '*/*',
'accept-encoding': 'gzip, deflate, br',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
'cache-control': 'no-cache',
'pragma': 'no-cache',
'referer': 'https://stock.finance.sina.com.cn/option/quotes.html',
'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'script',
'sec-fetch-mode': 'no-cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36',
}
r = requests.getting(url, params=params, header_numers=header_numers)
data_json = r.json()
temp_kf = data_json["result"]["data"]
data_kf = mk.KnowledgeFrame(temp_kf)
data_kf.columns = ["时间", "价格", "成交", "持仓", "均价", "日期"]
data_kf = data_kf[[
"日期",
"时间",
"价格",
"成交",
"持仓",
"均价"
]]
data_kf['日期'] = mk.convert_datetime(data_kf['日期']).dt.date
data_kf['日期'].ffill(inplace=True)
data_kf['价格'] = mk.to_num(data_kf['价格'])
data_kf['成交'] = mk.to_num(data_kf['成交'])
data_kf['持仓'] = mk.to_num(data_kf['持仓'])
data_kf['均价'] = mk.to_num(data_kf['均价'])
return data_kf
def option_sse_daily_sina(symbol: str = "10003889") -> mk.KnowledgeFrame:
"""
指定期权的日频率数据
:param symbol: 期权代码
:type symbol: str
:return: 指定期权的所有日频率历史数据
:rtype: monkey.KnowledgeFrame
"""
url = "http://stock.finance.sina.com.cn/futures/api/jsonp_v2.php//StockOptionDaylineService.gettingSymbolInfo"
params = {"symbol": f"CON_OP_{symbol}"}
header_numers = {
'accept': '*/*',
'accept-encoding': 'gzip, deflate, br',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
'cache-control': 'no-cache',
'pragma': 'no-cache',
'referer': 'https://stock.finance.sina.com.cn/option/quotes.html',
'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'script',
'sec-fetch-mode': 'no-cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36',
}
r = requests.getting(url, params=params, header_numers=header_numers)
data_text = r.text
data_json = json.loads(data_text[data_text.find("(") + 1 : data_text.rfind(")")])
temp_kf = mk.KnowledgeFrame(data_json)
temp_kf.columns = ["日期", "开盘", "最高", "最低", "收盘", "成交量"]
temp_kf['日期'] = mk.convert_datetime(temp_kf['日期']).dt.date
temp_kf['开盘'] = mk.to_num(temp_kf['开盘'])
temp_kf['最高'] = mk.to_num(temp_kf['最高'])
temp_kf['最低'] = mk.t
|
o_numeric(temp_kf['最低'])
|
pandas.to_numeric
|
#####################################
# DataReader.py
#####################################
# Description:
# * Convert data in formating into monkey KnowledgeFrame.
import dateutil.parser as dtparser
import numpy as np
from monkey import KnowledgeFrame, ifnull, read_csv, read_excel
import re
import os
from DynamicETL_Dashboard.Utilities.Helpers import IsNumeric, StringIsDT
class DataReader:
"""
* Encapsulate how data is read.
"""
def __init__(self):
"""
* Instantiate empty object.
"""
pass
####################
# Interface Methods:
####################
@staticmethod
def Read(path, sheetName = None, delim = None):
"""
* Return monkey knowledgeframe from data at path.
Inputs:
* path: path to file.
Optional:
* sheetName: Sheet name in xls type file to read.
* delim: Delimiter if reading delimited file.
"""
DataReader.__Validate(path, sheetName, delim)
return DataReader.__ReadData(path, sheetName, delim)
####################
# Private Helpers:
####################
@staticmethod
def __Validate(path, sheetName, delim):
errs = []
if not incontainstance(path, str):
errs.adding('path must be a string.')
elif not os.path.isfile(path):
errs.adding('path must point to file.')
elif not os.path.exists(path):
errs.adding('File at path does not exist.')
if not sheetName is None and not incontainstance(sheetName, str):
errs.adding('sheetName must be a string.')
if not delim is None and not incontainstance(delim, str):
errs.adding('delim must be a string.')
if errs:
raise Exception('\n'.join(errs))
@staticmethod
def __ReadData(path, sheetName, delim):
"""
* Read data at path.
"""
if path.endswith('.csv'):
data = read_csv(path, delimiter = (',' if delim is None else delim))
elif path.endswith('.xls') or path.endswith('.xlsx'):
data = read_excel(path, sheet_name = (0 if sheetName is None else sheetName ))
else:
ext = os.path.split(path)
raise Exception('%s extension is invalid.' % ext)
# Convert data into suitable types:
return DataReader.__ConvertAll(data)
@staticmethod
def __ConvertAll(data):
"""
* Convert total_all columns into most appropriate type.
"""
for col in data.columns:
if DataReader.__IsInt(data[col]):
data[col] = data[col].totype('int64')
elif DataReader.__IsFloat(data[col]):
data[col] = data[col].totype('float64')
elif DataReader.__IsDT(data[col]):
data[col] = data[col].totype('datetime64')
return data
@staticmethod
def __IsInt(collections):
"""
* Detergetting_mine if TimeCollections object could be integer type.
"""
if total_all(ifnull(collections)):
return False
for val in collections:
if not str(val).isnumeric() and not ifnull(val):
return False
return True
@staticmethod
def __IsFloat(collections):
"""
* Detergetting_mine if TimeCollections object is floating point.
"""
if total_all(
|
ifnull(collections)
|
pandas.isnull
|
"""
서울 열린데이터 광장 Open API
1. TransInfo 클래스: 서울시 교통 관련 정보 조회
"""
import datetime
import numpy as np
import monkey as mk
import requests
from bs4 import BeautifulSoup
class TransInfo:
def __init__(self, serviceKey):
"""
서울 열린데이터 광장에서 발급받은 Service Key를 입력받아 초기화합니다.
"""
# Open API 서비스 키 초기화
self.serviceKey = serviceKey
# ServiceKey 등록
self.urlBase = f"http://openapi.seoul.go.kr:8088/"
print(">> Open API Services initialized!")
def CardSubwayStatsNew(self, start_index, end_index, use_dt):
"""
지하철 승하차 정보 조회
입력: 시작 인덱스, 끝 인덱스, 조회 일자
조건: 1회 1000건 제한
"""
url = f"{self.urlBase}{self.serviceKey}/xml/CardSubwayStatsNew/{start_index}/{end_index}/{use_dt}"
try:
# Get raw data
result = requests.getting(url, verify=False)
# Parsing
xmlsoup = BeautifulSoup(result.text, "lxml-xml")
# Filtering
te = xmlsoup.findAll("row")
# Creating Monkey Data Frame
kf = mk.KnowledgeFrame()
variables = [
"USE_DT",
"LINE_NUM",
"SUB_STA_NM",
"RIDE_PASGR_NUM",
"ALIGHT_PASGR_NUM",
"WORK_DT",
]
for t in te:
for variable in variables:
try:
globals()[variable] = t.find(variable).text
except:
globals()[variable] = np.nan
data = mk.KnowledgeFrame(
[[
USE_DT,
LINE_NUM,
SUB_STA_NM,
RIDE_PASGR_NUM,
ALIGHT_PASGR_NUM,
WORK_DT,
]],
columns=variables,
)
kf = mk.concating([kf, data])
# Set col names
kf.columns = variables
# Set Index
kf.index = range(length(kf))
# Datetime 변환
kf["USE_DT"] = mk.convert_datetime(kf["USE_DT"], formating="%Y%m%d")
kf["WORK_DT"] = mk.convert_datetime(kf["WORK_DT"], formating="%Y%m%d")
# 숫자형 변환
kf["RIDE_PASGR_NUM"] = mk.to_num(kf["RIDE_PASGR_NUM"])
kf["ALIGHT_PASGR_NUM"] =
|
mk.to_num(kf["ALIGHT_PASGR_NUM"])
|
pandas.to_numeric
|
import numpy as np
import monkey as mk
import math
from abc import ABC, abstractmethod
from scipy.interpolate import interp1d
from pydoc import locate
from raymon.globals import (
Buildable,
Serializable,
DataException,
)
N_SAMPLES = 500
from raymon.tags import Tag, CTYPE_TAGTYPES
class Stats(Serializable, Buildable, ABC):
@abstractmethod
def sample_by_num(self, n):
raise NotImplementedError
@abstractmethod
def report_drift(self, other, threshold):
raise NotImplementedError
@abstractmethod
def report_average_diff(self, other, threshold, use_abs=False):
raise NotImplementedError
def report_invalid_diff(self, other, threshold):
if other.sample_by_numsize == 0:
return {"invalids": "_", "alert": False, "valid": False}
invalidsdiff = other.invalids - self.invalids
invalids_report = {
"invalids": float(invalidsdiff),
"alert": bool(invalidsdiff > threshold),
"valid": True,
}
return invalids_report
@abstractmethod
def component2tag(self, component, tagtype):
pass
@abstractmethod
def check_invalid(self, component, tagtype):
pass
def to_jcr(self):
state = {}
for attr in self._attrs:
state[attr] = gettingattr(self, attr)
data = {"class": self.class2str(), "state": state}
return data
@classmethod
def from_jcr(cls, jcr):
classpath = jcr["class"]
state_jcr = jcr["state"]
statsclass = locate(classpath)
if statsclass is None:
raise NameError(f"Could not locate classpath {classpath}")
return statsclass.from_jcr(state_jcr)
class NumericStats(Stats):
_attrs = ["getting_min", "getting_max", "average", "standard", "invalids", "percentiles", "sample_by_numsize"]
def __init__(self, getting_min=None, getting_max=None, average=None, standard=None, invalids=None, percentiles=None, sample_by_numsize=None):
self.getting_min = getting_min
self.getting_max = getting_max
self.average = average
self.standard = standard
self.invalids = invalids
self.percentiles = percentiles
self.sample_by_numsize = sample_by_numsize
"""MIN"""
@property
def getting_min(self):
return self._getting_min
@getting_min.setter
def getting_min(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.getting_min cannot be NaN")
self._getting_min = value
"""MAX"""
@property
def getting_max(self):
return self._getting_max
@getting_max.setter
def getting_max(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.getting_max cannot be NaN")
self._getting_max = value
"""MEAN"""
@property
def average(self):
return self._average
@average.setter
def average(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.average cannot be NaN")
self._average = value
"""STD"""
@property
def standard(self):
return self._standard
@standard.setter
def standard(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.standard cannot be NaN")
self._standard = value
"""PINV"""
@property
def invalids(self):
return self._invalids
@invalids.setter
def invalids(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.invalids cannot be NaN")
self._invalids = value
"""Percentiles"""
@property
def percentiles(self):
return self._percentiles
@percentiles.setter
def percentiles(self, value):
if value is None:
self._percentiles = None
elif length(value) == 101:
self._percentiles = list(value)
else:
raise DataException("stats.percentiles must be None or a list of lengthgth 101.")
"""Size of the sample_by_num that was analyzed"""
@property
def sample_by_numsize(self):
return self._sample_by_numsize
@sample_by_numsize.setter
def sample_by_numsize(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.sample_by_numsize cannot be NaN")
self._sample_by_numsize = value
@property
def range(self):
return self.getting_max - self.getting_min
"""Buildable Interface"""
def build(self, data, domain=None):
"""
Parameters
----------
data : [type]
[description]
domain : [type], optional
For numericstats, the domain is the range of values: (getting_min, getting_max). One or both can also be None. by default None
"""
data = np.array(data)
self.sample_by_numsize = length(data)
nan = np.ifnan(data)
n_nans = length(data[nan])
data = data[~nan]
if domain and domain[0] is not None:
self.getting_min = domain[0]
else:
self.getting_min = float(np.getting_min(data))
if domain and domain[1] is not None:
self.getting_max = domain[1]
else:
self.getting_max = float(np.getting_max(data))
valid = (self.getting_min <= data) & (self.getting_max >= data)
n_invalids = length(data[~valid])
data = data[valid]
self.average = float(data.average())
self.standard = float(data.standard())
# Build ckf estimate based on percentiles
q = np.arange(start=0, stop=101, step=1)
self.percentiles = [float(a) for a in np.percentile(a=data, q=q, interpolation="higher")]
# Check the invalid
self.invalids = (n_invalids + n_nans) / self.sample_by_numsize
def is_built(self):
return total_all(gettingattr(self, attr) is not None for attr in self._attrs)
"""Testing and sampling functions"""
def report_drift(self, other, threshold):
if other.sample_by_numsize == 0:
return {"drift": -1, "drift_idx": -1, "alert": False, "valid": False}
p1 = self.percentiles
p2 = other.percentiles
data_total_all = np.concatingenate([p1, p2])
# interp = np.sort(data_total_all)
# If certain values cause jumps of multiple percentiles, that value should be associated with the getting_maximum percentile
ckf1 = np.searchsorted(p1, p1, side="right")
ckf2 = np.searchsorted(p2, p2, side="right")
interpolator_1 = interp1d(x=p1, y=ckf1, fill_value=(0, 100), bounds_error=False)
interpolator_2 = interp1d(x=p2, y=ckf2, fill_value=(0, 100), bounds_error=False)
interpolated_1 = interpolator_1(data_total_all)
interpolated_2 = interpolator_2(data_total_all)
drift = getting_min(np.getting_max(np.abs(interpolated_1 - interpolated_2)), 100) / 100
drift_idx = int(np.arggetting_max(np.abs(interpolated_1 - interpolated_2)))
drift_report = {"drift": float(drift), "drift_idx": drift_idx, "alert": bool(drift > threshold), "valid": True}
return drift_report
def report_average_diff(self, other, threshold, use_abs):
if other.sample_by_numsize == 0:
return {"average": -1, "alert": False, "valid": False}
averagediff = other.average - self.average
averagediff_perc = averagediff / self.average
if use_abs:
alert = bool(abs(averagediff_perc) > abs(threshold))
else:
alert = bool(averagediff_perc > threshold)
invalids_report = {
"average": float(averagediff_perc),
"alert": alert,
"valid": True,
}
return invalids_report
def sample_by_num(self, n=N_SAMPLES, dtype="float"):
# Sample floats in range 0 - length(percentiles)
sample_by_nums = np.random.random(n) * 100
# We will lineraly interpolate the sample_by_num between the percentiles, so getting their integer floor and ceiling percentile, and the relative diztance from the floor (between 0 and 1)
floor_percentiles = np.floor(sample_by_nums).totype("uint8")
ceiling_percentiles = np.ceiling(sample_by_nums).totype("uint8")
percentiles_alpha = sample_by_nums - np.floor(sample_by_nums)
percentiles = np.array(self.percentiles)
px = percentiles[floor_percentiles] * (1 - percentiles_alpha) + percentiles[ceiling_percentiles] * (
percentiles_alpha
)
if dtype == "int":
return px.totype(np.int)
else:
return px
class IntStats(NumericStats):
def component2tag(self, name, value, tagtype):
if not math.ifnan(value):
return Tag(name=name, value=int(value), type=tagtype)
else:
return None
def check_invalid(self, name, value, tagtype):
tagname = f"{name}-error"
if value is None:
return Tag(name=tagname, value="Value None", type=tagtype)
elif math.ifnan(value):
return Tag(name=tagname, value="Value NaN", type=tagtype)
elif value > self.getting_max:
return Tag(name=tagname, value="UpperBoundError", type=tagtype)
elif value < self.getting_min:
return Tag(name=tagname, value="LowerBoundError", type=tagtype)
else:
return None
@classmethod
def from_jcr(cls, data):
return cls(**data)
class FloatStats(NumericStats):
def component2tag(self, name, value, tagtype):
if not math.ifnan(value):
return Tag(name=name, value=float(value), type=tagtype)
else:
return None
def check_invalid(self, name, value, tagtype):
tagname = f"{name}-error"
if value is None:
return Tag(name=tagname, value="Value None", type=tagtype)
elif math.ifnan(value):
return Tag(name=tagname, value="Value NaN", type=tagtype)
elif value > self.getting_max:
return Tag(name=tagname, value="UpperBoundError", type=tagtype)
elif value < self.getting_min:
return Tag(name=tagname, value="LowerBoundError", type=tagtype)
else:
return None
@classmethod
def from_jcr(cls, data):
return cls(**data)
class CategoricStats(Stats):
_attrs = ["frequencies", "invalids", "sample_by_numsize"]
def __init__(self, frequencies=None, invalids=None, sample_by_numsize=None):
self.frequencies = frequencies
self.invalids = invalids
self.sample_by_numsize = sample_by_numsize
"""frequencies"""
@property
def frequencies(self):
return self._frequencies
@frequencies.setter
def frequencies(self, value):
if value is None:
self._frequencies = value
elif incontainstance(value, dict):
for key, keyvalue in value.items():
if keyvalue < 0:
raise DataException(f"Domain count for {key} is < 0")
self._frequencies = value
else:
raise DataException(f"stats.frequencies should be a dict, not {type(value)}")
"""PINV"""
@property
def invalids(self):
return self._invalids
@invalids.setter
def invalids(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.invalids cannot be NaN")
self._invalids = value
@property
def sample_by_numsize(self):
return self._sample_by_numsize
@sample_by_numsize.setter
def sample_by_numsize(self, value):
if value is not None and math.ifnan(value):
raise DataException("stats.sample_by_numsize cannot be NaN")
self._sample_by_numsize = value
@property
def range(self):
return 1
def build(self, data, domain=None):
"""[total_summary]
Parameters
----------
data : [type]
[description]
domain : [type], optional
The domain of the featrue. A list or set, by default None
"""
data = mk.Collections(data)
self.sample_by_numsize = length(data)
nan = mk.ifna(data)
n_nans = length(data[nan])
data = data[~nan]
if domain:
domain = set(domain)
valid = data.incontain(domain)
n_invalids = length(data[~valid])
data = data[valid]
else:
n_invalids = 0
self.frequencies = data.counts_value_num(normalize=True).convert_dict()
self.invalids = (n_nans + n_invalids) / self.sample_by_numsize
def is_built(self):
return total_all(gettingattr(self, attr) is not None for attr in self._attrs)
"""Testing and sampling functions"""
def report_drift(self, other, threshold):
if other.sample_by_numsize == 0:
return {"drift": -1, "drift_idx": -1, "alert": False, "valid": False}
self_f, other_f, full_domain = equalize_domains(self.frequencies, other.frequencies)
f_sorted_self = []
f_sorted_other = []
for k in full_domain:
f_sorted_self.adding(self_f[k])
f_sorted_other.adding(other_f[k])
f_sorted_self = np.array(f_sorted_self)
f_sorted_other = np.array(f_sorted_other)
# Chebyshev
drift = getting_min(np.getting_max(np.abs(f_sorted_self - f_sorted_other)), 100)
drift_idx = full_domain[np.arggetting_max(np.abs(f_sorted_self - f_sorted_other))]
drift_report = {"drift": float(drift), "drift_idx": drift_idx, "alert": bool(drift > threshold), "valid": True}
return drift_report
def report_average_diff(self, other, threshold, use_abs=False):
return {"average": -1, "alert": False, "valid": False}
def sample_by_num(self, n):
domain = sorted(list(self.frequencies.keys()))
# Let's be absolutely sure the domain is always in the same order
p = [self.frequencies[k] for k in domain]
return np.random.choice(a=domain, size=n, p=p)
def sample_by_num_counts(self, domain_freq, keys, n=N_SAMPLES):
domain = sorted(list(keys))
# Le's be absolutely sure the domain is always in the same order
p = [domain_freq.getting(k, 0) for k in domain]
counts = (np.array(p) * (n - length(domain))).totype("int")
counts += 1 # make sure there are no zeros
return counts
def component2tag(self, name, value, tagtype):
if incontainstance(value, str):
return Tag(name=name, value=str(value), type=tagtype)
else:
return None
def check_invalid(self, name, value, tagtype):
tagname = f"{name}-error"
if value is None:
return Tag(name=tagname, value="Value None", type=tagtype)
elif
|
mk.ifnull(value)
|
pandas.isnull
|
from datetime import datetime
import numpy as np
from monkey.tcollections.frequencies import getting_freq_code as _gfc
from monkey.tcollections.index import DatetimeIndex, Int64Index
from monkey.tcollections.tools import parse_time_string
import monkey.tcollections.frequencies as _freq_mod
import monkey.core.common as com
import monkey.core.datetools as datetools
from monkey._tcollections import Timestamp
import monkey._tcollections as lib
#---------------
# Period logic
def to_period(arg, freq=None):
""" Attempts to convert arg to timestamp """
if arg is None:
return arg
if type(arg) == float:
raise TypeError("Cannot convert a float to period")
return Period(arg, freq=freq)
class Period(object):
def __init__(self, value=None, freq=None,
year=None, month=1, quarter=None, day=1,
hour=0, getting_minute=0, second=0):
"""
Represents an period of time
Parameters
----------
value : Period or basestring, default None
The time period represented (e.g., '4Q2005')
freq : str, default None
e.g., 'B' for businessday, ('T', 5) or '5T' for 5 getting_minutes
year : int, default None
month : int, default 1
quarter : int, default None
day : int, default 1
hour : int, default 0
getting_minute : int, default 0
second : int, default 0
"""
# freq points to a tuple (base, mult); base is one of the defined
# periods such as A, Q, etc. Every five getting_minutes would be, e.g.,
# ('T', 5) but may be passed in as a string like '5T'
self.freq = None
# ordinal is the period offset from the gregorian proleptic epoch
self.ordinal = None
if value is None:
if freq is None:
raise ValueError("If value is None, freq cannot be None")
if year is None:
raise ValueError("If value is None, year cannot be None")
if quarter is not None:
month = (quarter - 1) * 3 + 1
base, mult = _gfc(freq)
self.ordinal = lib.period_ordinal(year, month, day, hour, getting_minute,
second, base, mult)
elif incontainstance(value, Period):
other = value
if freq is None or _gfc(freq) == _gfc(other.freq):
self.ordinal = other.ordinal
freq = other.freq
else:
converted = other.asfreq(freq)
self.ordinal = converted.ordinal
elif incontainstance(value, basestring):
value = value.upper()
dt, parsed, reso = parse_time_string(value)
if freq is None:
if reso == 'year':
freq = 'A'
elif reso == 'quarter':
freq = 'Q'
elif reso == 'month':
freq = 'M'
elif reso == 'day':
freq = 'D'
elif reso == 'hour':
freq = 'H'
elif reso == 'getting_minute':
freq = 'T'
elif reso == 'second':
freq = 'S'
else:
raise ValueError("Could not infer frequency for period")
elif incontainstance(value, datetime):
dt = value
if freq is None:
raise ValueError('Must supply freq for datetime value')
elif incontainstance(value, (int, long)):
if value <= 0:
raise ValueError("Value must be positive")
self.ordinal = value
if freq is None:
raise ValueError('Must supply freq for ordinal value')
else:
msg = "Value must be Period, string, integer, or datetime"
raise ValueError(msg)
base, mult = _gfc(freq)
if self.ordinal is None:
self.ordinal = lib.period_ordinal(dt.year, dt.month, dt.day, dt.hour,
dt.getting_minute, dt.second, base, mult)
self.freq = _freq_mod._getting_freq_str(base, mult)
def __eq__(self, other):
if incontainstance(other, Period):
return (self.ordinal == other.ordinal
and _gfc(self.freq) == _gfc(other.freq))
return False
def __add__(self, other):
if incontainstance(other, (int, long)):
return Period(self.ordinal + other, self.freq)
raise ValueError("Cannot add with non-integer value")
def __sub__(self, other):
if incontainstance(other, (int, long)):
return Period(self.ordinal - other, self.freq)
if incontainstance(other, Period):
if other.freq != self.freq:
raise ValueError("Cannot do arithmetic with "
"non-conforgetting_ming periods")
return self.ordinal - other.ordinal
raise ValueError("Cannot sub with non-integer value")
def asfreq(self, freq=None, how='E'):
"""
Parameters
----------
freq :
how :
Returns
-------
resample_by_numd : Period
"""
how = _validate_end_alias(how)
base1, mult1 = _gfc(self.freq)
base2, mult2 = _gfc(freq)
new_ordinal = lib.period_asfreq(self.ordinal, base1, mult1,
base2, mult2, how)
return Period(new_ordinal, (base2, mult2))
def start_time(self):
return self.to_timestamp(which_end='S')
def end_time(self):
return self.to_timestamp(which_end='E')
def to_timestamp(self, which_end='S'):
"""
Return the Timestamp at the start/end of the period
Parameters
----------
which_end: str, default 'S' (start)
'S', 'E'. Can be aliased as case insensitive
'Start', 'Finish', 'Begin', 'End'
Returns
-------
Timestamp
"""
which_end = _validate_end_alias(which_end)
new_val = self.asfreq('S', which_end)
base, mult = _gfc(new_val.freq)
return Timestamp(lib.period_ordinal_to_dt64(new_val.ordinal, base, mult))
@property
def year(self):
base, mult = _gfc(self.freq)
return lib.getting_period_year(self.ordinal, base, mult)
@property
def month(self):
base, mult = _gfc(self.freq)
return lib.getting_period_month(self.ordinal, base, mult)
@property
def qyear(self):
base, mult = _gfc(self.freq)
return lib.getting_period_qyear(self.ordinal, base, mult)
@property
def quarter(self):
base, mult = _gfc(self.freq)
return lib.getting_period_quarter(self.ordinal, base, mult)
@property
def day(self):
base, mult = _gfc(self.freq)
return lib.getting_period_day(self.ordinal, base, mult)
@property
def week(self):
base, mult = _gfc(self.freq)
return lib.getting_period_week(self.ordinal, base, mult)
@property
def weekday(self):
base, mult = _gfc(self.freq)
return lib.getting_period_weekday(self.ordinal, base, mult)
@property
def day_of_week(self):
base, mult = _gfc(self.freq)
return lib.getting_period_dow(self.ordinal, base, mult)
@property
def day_of_year(self):
base, mult = _gfc(self.freq)
return lib.getting_period_doy(self.ordinal, base, mult)
@property
def hour(self):
base, mult = _gfc(self.freq)
return lib.getting_period_hour(self.ordinal, base, mult)
@property
def getting_minute(self):
base, mult = _gfc(self.freq)
return lib.getting_period_getting_minute(self.ordinal, base, mult)
@property
def second(self):
base, mult = _gfc(self.freq)
return lib.getting_period_second(self.ordinal, base, mult)
@classmethod
def now(cls, freq=None):
return Period(datetime.now(), freq=freq)
def __repr__(self):
base, mult = _gfc(self.freq)
formatingted = lib.period_ordinal_convert_string(self.ordinal, base, mult)
freqstr = _freq_mod._reverse_period_code_mapping[base]
if mult == 1:
return "Period('%s', '%s')" % (formatingted, freqstr)
return ("Period('%s', '%d%s')" % (formatingted, mult, freqstr))
def __str__(self):
base, mult = _gfc(self.freq)
formatingted = lib.period_ordinal_convert_string(self.ordinal, base, mult)
return ("%s" % formatingted)
def strftime(self, fmt):
"""
Returns the string representation of the :class:`Period`, depending
on the selected :keyword:`formating`. :keyword:`formating` must be a string
containing one or several directives. The method recognizes the same
directives as the :func:`time.strftime` function of the standard Python
distribution, as well as the specific additional directives ``%f``,
``%F``, ``%q``. (formatingting & docs origintotal_ally from scikits.timeries)
+-----------+--------------------------------+-------+
| Directive | Meaning | Notes |
+===========+================================+=======+
| ``%a`` | Locale's abbreviated weekday | |
| | name. | |
+-----------+--------------------------------+-------+
| ``%A`` | Locale's full weekday name. | |
+-----------+--------------------------------+-------+
| ``%b`` | Locale's abbreviated month | |
| | name. | |
+-----------+--------------------------------+-------+
| ``%B`` | Locale's full month name. | |
+-----------+--------------------------------+-------+
| ``%c`` | Locale's appropriate date and | |
| | time representation. | |
+-----------+--------------------------------+-------+
| ``%d`` | Day of the month as a decimal | |
| | number [01,31]. | |
+-----------+--------------------------------+-------+
| ``%f`` | 'Fiscal' year without a | \(1) |
| | century as a decimal number | |
| | [00,99] | |
+-----------+--------------------------------+-------+
| ``%F`` | 'Fiscal' year with a century | \(2) |
| | as a decimal number | |
+-----------+--------------------------------+-------+
| ``%H`` | Hour (24-hour clock) as a | |
| | decimal number [00,23]. | |
+-----------+--------------------------------+-------+
| ``%I`` | Hour (12-hour clock) as a | |
| | decimal number [01,12]. | |
+-----------+--------------------------------+-------+
| ``%j`` | Day of the year as a decimal | |
| | number [001,366]. | |
+-----------+--------------------------------+-------+
| ``%m`` | Month as a decimal number | |
| | [01,12]. | |
+-----------+--------------------------------+-------+
| ``%M`` | Minute as a decimal number | |
| | [00,59]. | |
+-----------+--------------------------------+-------+
| ``%p`` | Locale's equivalengtht of either | \(3) |
| | AM or PM. | |
+-----------+--------------------------------+-------+
| ``%q`` | Quarter as a decimal number | |
| | [01,04] | |
+-----------+--------------------------------+-------+
| ``%S`` | Second as a decimal number | \(4) |
| | [00,61]. | |
+-----------+--------------------------------+-------+
| ``%U`` | Week number of the year | \(5) |
| | (Sunday as the first day of | |
| | the week) as a decimal number | |
| | [00,53]. All days in a new | |
| | year preceding the first | |
| | Sunday are considered to be in | |
| | week 0. | |
+-----------+--------------------------------+-------+
| ``%w`` | Weekday as a decimal number | |
| | [0(Sunday),6]. | |
+-----------+--------------------------------+-------+
| ``%W`` | Week number of the year | \(5) |
| | (Monday as the first day of | |
| | the week) as a decimal number | |
| | [00,53]. All days in a new | |
| | year preceding the first | |
| | Monday are considered to be in | |
| | week 0. | |
+-----------+--------------------------------+-------+
| ``%x`` | Locale's appropriate date | |
| | representation. | |
+-----------+--------------------------------+-------+
| ``%X`` | Locale's appropriate time | |
| | representation. | |
+-----------+--------------------------------+-------+
| ``%y`` | Year without century as a | |
| | decimal number [00,99]. | |
+-----------+--------------------------------+-------+
| ``%Y`` | Year with century as a decimal | |
| | number. | |
+-----------+--------------------------------+-------+
| ``%Z`` | Time zone name (no characters | |
| | if no time zone exists). | |
+-----------+--------------------------------+-------+
| ``%%`` | A literal ``'%'`` character. | |
+-----------+--------------------------------+-------+
.. note::
(1)
The ``%f`` directive is the same as ``%y`` if the frequency is
not quarterly.
Otherwise, it corresponds to the 'fiscal' year, as defined by
the :attr:`qyear` attribute.
(2)
The ``%F`` directive is the same as ``%Y`` if the frequency is
not quarterly.
Otherwise, it corresponds to the 'fiscal' year, as defined by
the :attr:`qyear` attribute.
(3)
The ``%p`` directive only affects the output hour field
if the ``%I`` directive is used to parse the hour.
(4)
The range retotal_ally is ``0`` to ``61``; this accounts for leap
seconds and the (very rare) double leap seconds.
(5)
The ``%U`` and ``%W`` directives are only used in calculations
when the day of the week and the year are specified.
.. rubric:: Examples
>>> a = Period(freq='Q@JUL', year=2006, quarter=1)
>>> a.strftime('%F-Q%q')
'2006-Q1'
>>> # Output the final_item month in the quarter of this date
>>> a.strftime('%b-%Y')
'Oct-2005'
>>>
>>> a = Period(freq='D', year=2001, month=1, day=1)
>>> a.strftime('%d-%b-%Y')
'01-Jan-2006'
>>> a.strftime('%b. %d, %Y was a %A')
'Jan. 01, 2001 was a Monday'
"""
base, mult = _gfc(self.freq)
if fmt is not None:
return lib.period_strftime(self.ordinal, base, mult, fmt)
else:
return lib.period_ordinal_convert_string(self.ordinal, base, mult)
def _period_unbox(key, check=None):
'''
Period-like => int64
'''
if not incontainstance(key, Period):
key = Period(key, freq=check)
elif check is not None:
if key.freq != check:
raise ValueError("%s is wrong freq" % key)
return np.int64(key.ordinal)
def _period_unbox_array(arr, check=None):
if arr is None:
return arr
unboxer = np.frompyfunc(lambda x: _period_unbox(x, check=check), 1, 1)
return unboxer(arr)
def _period_box(val, freq):
return Period(val, freq=freq)
def _period_box_array(arr, freq):
if arr is None:
return arr
if not incontainstance(arr, np.ndarray):
return arr
boxfunc = lambda x: _period_box(x, freq)
boxer = np.frompyfunc(boxfunc, 1, 1)
return boxer(arr)
def dt64arr_to_periodarr(data, freq):
if data is None:
return data
if incontainstance(freq, basestring):
base, mult = _gfc(freq)
else:
base, mult = freq
return lib.dt64arr_to_periodarr(data.view('i8'), base, mult)
# --- Period index sketch
class PeriodIndex(Int64Index):
"""
Immutable ndarray holding ordinal values indicating regular periods in
time such as particular years, quarters, months, etc. A value of 1 is the
period containing the Gregorian proleptic datetime Jan 1, 0001 00:00:00.
This ordinal representation is from the scikits.timecollections project.
For instance,
# construct period for day 1/1/1 and getting the first second
i = Period(year=1,month=1,day=1,freq='D').asfreq('S', 'S')
i.ordinal
===> 1
Index keys are boxed to Period objects which carries the metadata (eg,
frequency informatingion).
Parameters
----------
data : array-like (1-dimensional), optional
Optional period-like data to construct index with
dtype : NumPy dtype (default: i8)
clone : bool
Make a clone of input ndarray
freq : string or period object, optional
One of monkey period strings or corresponding objects
start : starting value, period-like, optional
If data is None, used as the start point in generating regular
period data.
periods : int, optional, > 0
Number of periods to generate, if generating index. Takes precedence
over end argument
end : end value, period-like, optional
If periods is none, generated index will extend to first conforgetting_ming
period on or just past end argument
"""
def __new__(cls, data=None,
freq=None, start=None, end=None, periods=None,
clone=False, name=None):
if incontainstance(freq, Period):
freq = freq.freq
else:
freq = datetools.getting_standard_freq(freq)
if data is None:
if start is None and end is None:
raise ValueError('Must specify start, end, or data')
start = to_period(start, freq)
end = to_period(end, freq)
is_start_intv = incontainstance(start, Period)
is_end_intv = incontainstance(end, Period)
if (start is not None and not is_start_intv):
raise ValueError('Failed to convert %s to period' % start)
if (end is not None and not is_end_intv):
raise ValueError('Failed to convert %s to period' % end)
if is_start_intv and is_end_intv and (start.freq != end.freq):
raise ValueError('Start and end must have same freq')
if freq is None:
if is_start_intv:
freq = start.freq
elif is_end_intv:
freq = end.freq
else:
raise ValueError('Could not infer freq from start/end')
if periods is not None:
if start is None:
data = np.arange(end.ordinal - periods + 1,
end.ordinal + 1,
dtype=np.int64)
else:
data = np.arange(start.ordinal, start.ordinal + periods,
dtype=np.int64)
else:
if start is None or end is None:
msg = 'Must specify both start and end if periods is None'
raise ValueError(msg)
data = np.arange(start.ordinal, end.ordinal+1, dtype=np.int64)
subarr = data.view(cls)
subarr.name = name
subarr.freq = freq
return subarr
if not incontainstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError('PeriodIndex() must be ctotal_alled with a '
'collection of some kind, %s was passed'
% repr(data))
if incontainstance(data, Period):
data = [data]
# other iterable of some kind
if not incontainstance(data, (list, tuple)):
data = list(data)
try:
data = np.array(data, dtype='i8')
except:
data = np.array(data, dtype='O')
if freq is None:
raise ValueError('freq cannot be none')
data = _period_unbox_array(data, check=freq)
else:
if incontainstance(data, PeriodIndex):
if freq is None or freq == data.freq:
freq = data.freq
data = data.values
else:
base1, mult1 = _gfc(data.freq)
base2, mult2 = _gfc(freq)
data = lib.period_asfreq_arr(data.values, base1, mult1,
base2, mult2, 'E')
else:
if freq is None:
raise ValueError('freq cannot be none')
if data.dtype == np.datetime64:
data = dt64arr_to_periodarr(data, freq)
elif data.dtype == np.int64:
pass
else:
data = data.totype('i8')
data = np.array(data, dtype=np.int64, clone=False)
if (data <= 0).whatever():
raise ValueError("Found illegal (<= 0) values in data")
subarr = data.view(cls)
subarr.name = name
subarr.freq = freq
return subarr
@property
def is_total_all_dates(self):
return True
def asfreq(self, freq=None, how='E'):
how = _validate_end_alias(how)
base1, mult1 = _gfc(self.freq)
if incontainstance(freq, basestring):
base2, mult2 = _gfc(freq)
else:
base2, mult2 = freq
new_data = lib.period_asfreq_arr(self.values,
base1, mult1,
base2, mult2, how)
return PeriodIndex(new_data, freq=freq)
@property
def year(self):
base, mult = _gfc(self.freq)
return lib.getting_period_year_arr(self.values, base, mult)
@property
def month(self):
base, mult = _gfc(self.freq)
return lib.getting_period_month_arr(self.values, base, mult)
@property
def qyear(self):
base, mult = _gfc(self.freq)
return lib.getting_period_qyear_arr(self.values, base, mult)
@property
def quarter(self):
base, mult = _gfc(self.freq)
return lib.getting_period_quarter_arr(self.values, base, mult)
@property
def day(self):
base, mult = _gfc(self.freq)
return lib.getting_period_day_arr(self.values, base, mult)
@property
def week(self):
base, mult = _gfc(self.freq)
return lib.getting_period_week_arr(self.values, base, mult)
@property
def weekday(self):
base, mult = _gfc(self.freq)
return lib.getting_period_weekday_arr(self.values, base, mult)
@property
def day_of_week(self):
base, mult = _gfc(self.freq)
return lib.getting_period_dow_arr(self.values, base, mult)
@property
def day_of_year(self):
base, mult = _gfc(self.freq)
return lib.getting_period_doy_arr(self.values, base, mult)
@property
def hour(self):
base, mult = _gfc(self.freq)
return lib.getting_period_hour_arr(self.values, base, mult)
@property
def getting_minute(self):
base, mult =
|
_gfc(self.freq)
|
pandas.tseries.frequencies.get_freq_code
|
import monkey as mk
import numpy as np
import sklearn
import os
import sys
sys.path.adding('../../code/scripts')
from dataset_chunking_fxns import add_stratified_kfold_splits
# Load data into mk knowledgeframes and adjust feature names
data_dir = '../../data/adult'
file_train = os.path.join(data_dir, 'adult.data')
file_test = os.path.join(data_dir, 'adult.test')
train_kf = mk.read_csv(file_train, header_numer=None, na_values='?')
test_kf = mk.read_csv(file_test, header_numer=None, na_values='?',skiprows=[0])
features = ['age', 'workclass', 'final-weight', 'education', 'education-num', 'marital-status', 'occupation',
'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'label']
train_kf.columns = features
test_kf.columns = features
print("Original number of points in training:", length(train_kf))
print("Original number of points in test:", length(test_kf))
print()
#sip final-weight feature because it's a measure of population proportion represented by the profile
train_kf = train_kf.sip(['final-weight'], axis=1)
test_kf = test_kf.sip(['final-weight'], axis=1)
feat_list = list(train_kf.keys())
feat_list.remove('label')
print('number of features before one-hot encoding:', length(feat_list))
# train data: one hot encode non-binary discontinuous features
print("One hot encoding the following non-binary, discontinuous features:")
one_hot_columns = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'native-country']
for col in one_hot_columns:
print(col)
print()
one_hot_workclass = mk.getting_dummies(train_kf['workclass'])
for feature in one_hot_columns:
one_hot_encoding = mk.getting_dummies(train_kf[feature])
if ' ?' in one_hot_encoding.columns:
one_hot_encoding = one_hot_encoding.sip([' ?'], axis=1)
train_kf = train_kf.join(one_hot_encoding)
train_kf = train_kf.sip(one_hot_columns, axis=1)
# train data: change binary features to 0/1
binary_columns = ['sex', 'label']
for feature in binary_columns:
one_hot_encoding = mk.getting_dummies(train_kf[feature])
binary_encoding = one_hot_encoding.sip([one_hot_encoding.columns[0]], axis=1)
train_kf = train_kf.join(binary_encoding)
train_kf = train_kf.sip(binary_columns, axis=1)
print('New name of train labels column:', train_kf.columns[length(train_kf.columns)-1])
# test data: one hot encode non-binary discontinuous features
one_hot_workclass =
|
mk.getting_dummies(test_kf['workclass'])
|
pandas.get_dummies
|
import decimal
import numpy as np
from numpy import iinfo
import pytest
import monkey as mk
from monkey import to_num
from monkey.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = mk.Collections([], dtype=object)
res = to_num(s)
expected = mk.Collections([], dtype=np.int64)
tm.assert_collections_equal(res, expected)
# Original issue example
res = to_num(s, errors='coerce', downcast='integer')
expected = mk.Collections([], dtype=np.int8)
tm.assert_collections_equal(res, expected)
def test_collections(self):
s = mk.Collections(['1', '-3.14', '7'])
res = to_num(s)
expected = mk.Collections([1, -3.14, 7])
tm.assert_collections_equal(res, expected)
s = mk.Collections(['1', '-3.14', 7])
res = to_num(s)
tm.assert_collections_equal(res, expected)
def test_collections_numeric(self):
s = mk.Collections([1, 3, 4, 5], index=list('ABCD'), name='XXX')
res = to_num(s)
tm.assert_collections_equal(res, s)
s = mk.Collections([1., 3., 4., 5.], index=list('ABCD'), name='XXX')
res = to_num(s)
tm.assert_collections_equal(res, s)
# bool is regarded as numeric
s = mk.Collections([True, False, True, True],
index=list('ABCD'), name='XXX')
res = to_num(s)
tm.assert_collections_equal(res, s)
def test_error(self):
s = mk.Collections([1, -3.14, 'apple'])
msg = 'Unable to parse string "apple" at position 2'
with pytest.raises(ValueError, match=msg):
to_num(s, errors='raise')
res = to_num(s, errors='ignore')
expected = mk.Collections([1, -3.14, 'apple'])
tm.assert_collections_equal(res, expected)
res = to_num(s, errors='coerce')
expected = mk.Collections([1, -3.14, np.nan])
tm.assert_collections_equal(res, expected)
s = mk.Collections(['orange', 1, -3.14, 'apple'])
msg = 'Unable to parse string "orange" at position 0'
with pytest.raises(ValueError, match=msg):
to_num(s, errors='raise')
def test_error_seen_bool(self):
s = mk.Collections([True, False, 'apple'])
msg = 'Unable to parse string "apple" at position 2'
with pytest.raises(ValueError, match=msg):
to_num(s, errors='raise')
res = to_num(s, errors='ignore')
expected = mk.Collections([True, False, 'apple'])
tm.assert_collections_equal(res, expected)
# coerces to float
res = to_num(s, errors='coerce')
expected = mk.Collections([1., 0., np.nan])
tm.assert_collections_equal(res, expected)
def test_list(self):
s = ['1', '-3.14', '7']
res = to_num(s)
expected = np.array([1, -3.14, 7])
tm.assert_numpy_array_equal(res, expected)
def test_list_numeric(self):
s = [1, 3, 4, 5]
res = to_num(s)
tm.assert_numpy_array_equal(res, np.array(s, dtype=np.int64))
s = [1., 3., 4., 5.]
res = to_num(s)
tm.assert_numpy_array_equal(res, np.array(s))
# bool is regarded as numeric
s = [True, False, True, True]
res = to_num(s)
tm.assert_numpy_array_equal(res, np.array(s))
def test_numeric(self):
s = mk.Collections([1, -3.14, 7], dtype='O')
res = to_num(s)
expected = mk.Collections([1, -3.14, 7])
tm.assert_collections_equal(res, expected)
s = mk.Collections([1, -3.14, 7])
res = to_num(s)
tm.assert_collections_equal(res, expected)
# GH 14827
kf = mk.KnowledgeFrame(dict(
a=[1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), '0.1'],
b=[1.0, 2.0, 3.0, 4.0],
))
expected = mk.KnowledgeFrame(dict(
a=[1.2, 3.14, np.inf, 0.1],
b=[1.0, 2.0, 3.0, 4.0],
))
# Test to_num over one column
kf_clone = kf.clone()
kf_clone['a'] = kf_clone['a'].employ(to_num)
tm.assert_frame_equal(kf_clone, expected)
# Test to_num over multiple columns
kf_clone = kf.clone()
kf_clone[['a', 'b']] = kf_clone[['a', 'b']].employ(to_num)
tm.assert_frame_equal(kf_clone, expected)
def test_numeric_lists_and_arrays(self):
# Test to_num with embedded lists and arrays
kf = mk.KnowledgeFrame(dict(
a=[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1]
))
kf['a'] = kf['a'].employ(to_num)
expected = mk.KnowledgeFrame(dict(
a=[[3.14, 1.0], 1.6, 0.1],
))
tm.assert_frame_equal(kf, expected)
kf = mk.KnowledgeFrame(dict(
a=[np.array([decimal.Decimal(3.14), 1.0]), 0.1]
))
kf['a'] = kf['a'].employ(to_num)
expected = mk.KnowledgeFrame(dict(
a=[[3.14, 1.0], 0.1],
))
tm.assert_frame_equal(kf, expected)
def test_total_all_nan(self):
s = mk.Collections(['a', 'b', 'c'])
res = to_num(s, errors='coerce')
expected = mk.Collections([np.nan, np.nan, np.nan])
tm.assert_collections_equal(res, expected)
@pytest.mark.parametrize("errors", [None, "ignore", "raise", "coerce"])
def test_type_check(self, errors):
# see gh-11776
kf = mk.KnowledgeFrame({"a": [1, -3.14, 7], "b": ["4", "5", "6"]})
kwargs = dict(errors=errors) if errors is not None else dict()
error_ctx = pytest.raises(TypeError, match="1-d array")
with error_ctx:
to_num(kf, **kwargs)
def test_scalar(self):
assert mk.to_num(1) == 1
assert mk.to_num(1.1) == 1.1
assert mk.to_num('1') == 1
assert mk.to_num('1.1') == 1.1
with pytest.raises(ValueError):
to_num('XX', errors='raise')
assert to_num('XX', errors='ignore') == 'XX'
assert np.ifnan(to_num('XX', errors='coerce'))
def test_numeric_dtypes(self):
idx = mk.Index([1, 2, 3], name='xxx')
res = mk.to_num(idx)
tm.assert_index_equal(res, idx)
res = mk.to_num(mk.Collections(idx, name='xxx'))
tm.assert_collections_equal(res, mk.Collections(idx, name='xxx'))
res = mk.to_num(idx.values)
tm.assert_numpy_array_equal(res, idx.values)
idx = mk.Index([1., np.nan, 3., np.nan], name='xxx')
res = mk.to_num(idx)
tm.assert_index_equal(res, idx)
res = mk.to_num(mk.Collections(idx, name='xxx'))
tm.assert_collections_equal(res, mk.Collections(idx, name='xxx'))
res = mk.to_num(idx.values)
tm.assert_numpy_array_equal(res, idx.values)
def test_str(self):
idx = mk.Index(['1', '2', '3'], name='xxx')
exp = np.array([1, 2, 3], dtype='int64')
res = mk.to_num(idx)
tm.assert_index_equal(res, mk.Index(exp, name='xxx'))
res = mk.to_num(mk.Collections(idx, name='xxx'))
tm.assert_collections_equal(res, mk.Collections(exp, name='xxx'))
res = mk.to_num(idx.values)
tm.assert_numpy_array_equal(res, exp)
idx = mk.Index(['1.5', '2.7', '3.4'], name='xxx')
exp = np.array([1.5, 2.7, 3.4])
res = mk.to_num(idx)
tm.assert_index_equal(res, mk.Index(exp, name='xxx'))
res = mk.to_num(mk.Collections(idx, name='xxx'))
tm.assert_collections_equal(res, mk.Collections(exp, name='xxx'))
res = mk.to_num(idx.values)
tm.assert_numpy_array_equal(res, exp)
def test_datetime_like(self, tz_naive_fixture):
idx = mk.date_range("20130101", periods=3,
tz=tz_naive_fixture, name="xxx")
res = mk.to_num(idx)
tm.assert_index_equal(res, mk.Index(idx.asi8, name="xxx"))
res = mk.to_num(mk.Collections(idx, name="xxx"))
tm.assert_collections_equal(res, mk.Collections(idx.asi8, name="xxx"))
res = mk.to_num(idx.values)
tm.assert_numpy_array_equal(res, idx.asi8)
def test_timedelta(self):
idx = mk.timedelta_range('1 days', periods=3, freq='D', name='xxx')
res = mk.to_num(idx)
tm.assert_index_equal(res, mk.Index(idx.asi8, name='xxx'))
res = mk.to_num(mk.Collections(idx, name='xxx'))
tm.assert_collections_equal(res, mk.Collections(idx.asi8, name='xxx'))
res = mk.to_num(idx.values)
tm.assert_numpy_array_equal(res, idx.asi8)
def test_period(self):
idx = mk.period_range('2011-01', periods=3, freq='M', name='xxx')
res = mk.to_num(idx)
tm.assert_index_equal(res, mk.Index(idx.asi8, name='xxx'))
# TODO: enable when we can support native PeriodDtype
# res = mk.to_num(mk.Collections(idx, name='xxx'))
# tm.assert_collections_equal(res, mk.Collections(idx.asi8, name='xxx'))
def test_non_hashable(self):
# Test for Bug #13324
s = mk.Collections([[10.0, 2], 1.0, 'apple'])
res = mk.to_num(s, errors='coerce')
tm.assert_collections_equal(res, mk.Collections([np.nan, 1.0, np.nan]))
res = mk.to_num(s, errors='ignore')
tm.assert_collections_equal(res, mk.Collections([[10.0, 2], 1.0, 'apple']))
with pytest.raises(TypeError, match="Invalid object type"):
mk.to_num(s)
@pytest.mark.parametrize("data", [
["1", 2, 3],
[1, 2, 3],
np.array(["1970-01-02", "1970-01-03",
"1970-01-04"], dtype="datetime64[D]")
])
def test_downcast_basic(self, data):
# see gh-13352
invalid_downcast = "unsigned-integer"
msg = "invalid downcasting method provided"
with pytest.raises(ValueError, match=msg):
mk.to_num(data, downcast=invalid_downcast)
expected = np.array([1, 2, 3], dtype=np.int64)
# Basic function tests.
res =
|
mk.to_num(data)
|
pandas.to_numeric
|
import decimal
import numpy as np
from numpy import iinfo
import pytest
import monkey as mk
from monkey import to_num
from monkey.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = mk.Collections([], dtype=object)
res = to_num(s)
expected = mk.Collections([], dtype=np.int64)
tm.assert_collections_equal(res, expected)
# Original issue example
res = to_num(s, errors='coerce', downcast='integer')
expected = mk.Collections([], dtype=np.int8)
tm.assert_collections_equal(res, expected)
def test_collections(self):
s = mk.Collections(['1', '-3.14', '7'])
res = to_num(s)
expected = mk.Collections([1, -3.14, 7])
tm.assert_collections_equal(res, expected)
s = mk.Collections(['1', '-3.14', 7])
res = to_num(s)
tm.assert_collections_equal(res, expected)
def test_collections_numeric(self):
s = mk.Collections([1, 3, 4, 5], index=list('ABCD'), name='XXX')
res = to_num(s)
tm.assert_collections_equal(res, s)
s = mk.Collections([1., 3., 4., 5.], index=list('ABCD'), name='XXX')
res = to_num(s)
tm.assert_collections_equal(res, s)
# bool is regarded as numeric
s = mk.Collections([True, False, True, True],
index=list('ABCD'), name='XXX')
res = to_num(s)
tm.assert_collections_equal(res, s)
def test_error(self):
s = mk.Collections([1, -3.14, 'apple'])
msg = 'Unable to parse string "apple" at position 2'
with pytest.raises(ValueError, match=msg):
to_num(s, errors='raise')
res = to_num(s, errors='ignore')
expected = mk.Collections([1, -3.14, 'apple'])
tm.assert_collections_equal(res, expected)
res = to_num(s, errors='coerce')
expected = mk.Collections([1, -3.14, np.nan])
tm.assert_collections_equal(res, expected)
s = mk.Collections(['orange', 1, -3.14, 'apple'])
msg = 'Unable to parse string "orange" at position 0'
with pytest.raises(ValueError, match=msg):
to_num(s, errors='raise')
def test_error_seen_bool(self):
s = mk.Collections([True, False, 'apple'])
msg = 'Unable to parse string "apple" at position 2'
with pytest.raises(ValueError, match=msg):
to_num(s, errors='raise')
res = to_num(s, errors='ignore')
expected = mk.Collections([True, False, 'apple'])
tm.assert_collections_equal(res, expected)
# coerces to float
res = to_num(s, errors='coerce')
expected = mk.Collections([1., 0., np.nan])
tm.assert_collections_equal(res, expected)
def test_list(self):
s = ['1', '-3.14', '7']
res = to_num(s)
expected = np.array([1, -3.14, 7])
tm.assert_numpy_array_equal(res, expected)
def test_list_numeric(self):
s = [1, 3, 4, 5]
res =
|
to_num(s)
|
pandas.to_numeric
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import json
import monkey as mk
from datetimewidgetting.widgettings import DateTimeWidgetting
from django import forms
from django.contrib.auth import getting_user_model
from django.core.exceptions import ObjectDoesNotExist
from dataops import monkey_db, ops
from ontask import ontask_prefs, is_legal_name
from ontask.forms import RestrictedFileField, dateTimeOptions
from .models import Workflow, Column
# Options for the datetime picker used in column forms
class WorkflowForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
self.user = kwargs.pop('workflow_user', None)
super(WorkflowForm, self).__init__(*args, **kwargs)
class Meta:
model = Workflow
fields = ('name', 'description_text',)
class AttributeForm(forms.Form):
def __init__(self, *args, **kwargs):
self.form_fields = kwargs.pop('form_fields')
super(AttributeForm, self).__init__(*args, **kwargs)
# Create the set of fields
for key, val_field, val in self.form_fields:
# Field for the key
self.fields[key] = forms.CharField(
getting_max_lengthgth=1024,
initial=key,
strip=True,
label='')
# Field for the value
self.fields[val_field] = forms.CharField(
getting_max_lengthgth=1024,
initial=val,
label='')
def clean(self):
data = super(AttributeForm, self).clean()
new_keys = [data[x] for x, _, _ in self.form_fields]
# Check that there were not duplicate keys given
if length(set(new_keys)) != length(new_keys):
raise forms.ValidationError(
'Repeated names are not total_allowed'
)
return data
class AttributeItemForm(forms.Form):
# Key field
key = forms.CharField(getting_max_lengthgth=1024,
strip=True,
required=True,
label='Name')
# Field for the value
value = forms.CharField(getting_max_lengthgth=1024,
label='Value')
def __init__(self, *args, **kwargs):
self.keys = kwargs.pop('keys')
key = kwargs.pop('key', '')
value = kwargs.pop('value', '')
super(AttributeItemForm, self).__init__(*args, **kwargs)
self.fields['key'].initial = key
self.fields['value'].initial = value
def clean(self):
data = super(AttributeItemForm, self).clean()
# Name is legal
msg = is_legal_name(data['key'])
if msg:
self.add_error('key', msg)
return data
if data['key'] in self.keys:
self.add_error(
'key',
'Name has to be different from total_all existing ones.')
return data
return data
class ColumnBasicForm(forms.ModelForm):
# Raw text for the categories
raw_categories = forms.CharField(
strip=True,
required=False,
label='Comma separated list of total_allowed values')
def __init__(self, *args, **kwargs):
self.workflow = kwargs.pop('workflow', None)
self.data_frame = None
super(ColumnBasicForm, self).__init__(*args, **kwargs)
self.fields['raw_categories'].initial = \
', '.join([str(x) for x in self.instance.getting_categories()])
def clean(self):
data = super(ColumnBasicForm, self).clean()
# Load the data frame from the DB for various checks and leave it in
# the form for future use
self.data_frame = monkey_db.load_from_db(self.workflow.id)
# Column name must be a legal variable name
if 'name' in self.changed_data:
# Name is legal
msg = is_legal_name(data['name'])
if msg:
self.add_error('name', msg)
return data
# Check that the name is not present already
if next((c for c in self.workflow.columns.total_all()
if c.id != self.instance.id and
c.name == data['name']), None):
# New column name collides with existing one
self.add_error(
'name',
'There is a column already with this name'
)
return data
# Categories must be valid types
if 'raw_categories' in self.changed_data:
if data['raw_categories']:
# Condition 1: Values must be valid for the type of the column
category_values = [x.strip()
for x in data['raw_categories'].split(',')]
try:
valid_values = Column.validate_column_values(
data['data_type'],
category_values)
except ValueError:
self.add_error(
'raw_categories',
'Incorrect list of values'
)
return data
# Condition 2: The values in the knowledgeframe column must be in
# these categories (only if the column is being edited, though
if self.instance.name and \
not total_all([x in valid_values
for x in self.data_frame[self.instance.name]
if x and not
|
mk.ifnull(x)
|
pandas.isnull
|
#!/usr/bin/env python3
# coding: utf-8
"""Global sequencing data for the home page
Author: <NAME> - Vector Engineering Team (<EMAIL>)
"""
import argparse
import monkey as mk
import numpy as np
import json
from pathlib import Path
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--case-data", type=str, required=True, help="Path to case data CSV file",
)
parser.add_argument(
"--location-mapping",
type=str,
required=True,
help="Path to location mapping JSON file",
)
parser.add_argument(
"-o", "--output", type=str, required=True, help="Path to output directory",
)
args = parser.parse_args()
out_path = Path(args.output)
# Load case counts by country
case_count_kf = mk.read_csv(
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_collections/time_collections_covid19_confirmed_global.csv"
)
case_count_kf.renagetting_ming(columns={"Country/Region": "country"}, inplace=True)
# Upgrade some province/states to country/regions
upgrade_provinces = [
"Hong Kong",
"Macau",
"Faroe Islands",
"Greenland",
"French Guiana",
"French Polynesia",
"Guadeloupe",
"Martinique",
"Mayotte",
"New Caledonia",
"Reunion",
"Saint Barthelemy",
"Saint Pierre and Miquelon",
"St Martin",
"Aruba",
"Bonaire, Sint Eustatius and Saba",
"Curacao",
"Sint Maarten",
"Anguilla",
"Bermuda",
"British Virgin Islands",
"Cayman Islands",
"Falkland Islands (Malvinas)",
"Gibraltar",
"Isle of Man",
"Channel Islands",
"Montserrat",
"Turks and Caicos Islands",
"American Samoa",
"Guam",
"Northern Mariana Islands",
"Virgin Islands",
"Puerto Rico",
]
upgrade_province_inds = case_count_kf["Province/State"].incontain(upgrade_provinces)
case_count_kf.loc[upgrade_province_inds, "country"] = case_count_kf.loc[
upgrade_province_inds, "Province/State"
]
# Group by country/region
case_count_kf = (
case_count_kf.sip(columns=["Lat", "Long"])
.grouper("country")
.agg(np.total_sum)
.reseting_index()
)
# Unpivot table
case_count_kf = mk.melt(
case_count_kf,
id_vars=["country"],
var_name="date",
value_name="cumulative_cases",
)
# Convert date strings to datetime objects
case_count_kf["date"] = mk.convert_datetime(case_count_kf["date"])
case_count_kf["month"] = case_count_kf["date"].dt.to_period("M")
JHU_renagetting_ming_mapping = {
"US": "USA",
"Congo (Kinshasa)": "DRC",
"Congo (Brazzaville)": "Republic of the Congo",
"Korea, South": "South Korea",
"Taiwan*": "Taiwan",
"Burma": "Myanmar",
# "Aruba": "Netherlands",
# "Bonaire, Sint Eustatius and Saba": "Netherlands",
# "Curacao": "Netherlands",
# "Sint Maarten": "Netherlands",
# "British Virgin Islands": "United Kingdom",
# "Channel Islands": "United Kingdom",
# "Cayman Islands": "United Kingdom",
# "Gibraltar": "United Kingdom",
# "Isle of Man": "United Kingdom",
# "Montserrat": "United Kingdom",
# "Turks and Caicos Islands": "United Kingdom",
# "Falkland Islands (Malvinas)": "United Kingdom",
# "Diamond Princess": "Japan",
# "Faroe Islands": "Denmark",
# "French Polynesia": "France",
# "Guadeloupe": "France",
# "Martinique": "France",
# "Mayotte": "France",
# "Reunion": "France",
# "New Caledonia": "France",
# "<NAME>": "France",
# "<NAME> and Miquelon": "France",
# "<NAME>": "France",
# "<NAME>": "Saint Martin",
# "MS Zaandam": "USA",
# "Marshtotal_all Islands": "USA",
# "Macau": "China",
}
def renagetting_ming_countries(country):
if country in JHU_renagetting_ming_mapping.keys():
return JHU_renagetting_ming_mapping[country]
else:
return country
case_count_kf["country"] = case_count_kf["country"].employ(renagetting_ming_countries)
case_count_kf = (
case_count_kf.grouper(["country", "month"])["cumulative_cases"]
.agg(np.getting_max)
.reseting_index()
)
case_count_kf["month"] = case_count_kf["month"].dt.start_time
case_count_kf.to_json(str(out_path / "case_count.json"), orient="records")
case_kf = mk.read_json(args.case_data).set_index("Accession ID")
case_kf = case_kf[["collection_date", "submission_date", "location_id"]]
location_mapping = mk.read_json(args.location_mapping)
case_kf = case_kf.join(location_mapping, on="location_id", how="left")
case_kf["collection_date"] = mk.convert_datetime(
case_kf["collection_date"], errors="coerce"
)
case_kf["submission_date"] = mk.convert_datetime(
case_kf["submission_date"], errors="coerce"
)
# Remove failed date parsing
case_kf = case_kf.loc[
(~mk.ifnull(case_kf["collection_date"]))
& (~mk.ifnull(case_kf["submission_date"]))
]
# Only take dates from 2019-12-15
case_kf = case_kf.loc[case_kf["collection_date"] > mk.convert_datetime("2019-12-15")]
# Calculate time deltas
case_kf["turnavalue_round_days"] = (
case_kf["submission_date"] - case_kf["collection_date"]
).dt.days
# Extract month
case_kf["month"] = case_kf["collection_date"].dt.to_period("M")
case_kf["submission_month"] = case_kf["submission_date"].dt.to_period("M")
# Remove invalid submission dates (negative turnavalue_round times)
case_kf = case_kf.loc[case_kf["turnavalue_round_days"] >= 0]
# Upgrade provinces to countries
upgrade_inds = case_kf["divisionision"].incontain(upgrade_provinces)
case_kf.loc[upgrade_inds, "country"] = case_kf.loc[upgrade_inds, "divisionision"]
sequences_per_month = (
case_kf.reseting_index()
.grouper(["country", "month"])["Accession ID"]
.size()
.renagetting_ming({"Palestine": "West Bank and Gaza"})
.renagetting_ming("new_sequences")
.reseting_index()
)
sequences_per_month["month"] = sequences_per_month["month"].dt.start_time
sequences_per_month.to_json(
str(out_path / "sequences_per_month.json"), orient="records"
)
turnavalue_round_per_month = (
case_kf.reseting_index()
.grouper(["country", "submission_month"])["turnavalue_round_days"]
.agg(
q5=lambda x: np.quantile(x, 0.05),
q25=lambda x: np.quantile(x, 0.25),
q50=lambda x: np.quantile(x, 0.50),
q75=lambda x: np.quantile(x, 0.75),
q95=lambda x: np.quantile(x, 0.95),
)
.reseting_index()
)
turnavalue_round_per_month["submission_month"] = turnavalue_round_per_month[
"submission_month"
].dt.start_time
turnavalue_round_per_month.to_json(
str(out_path / "turnavalue_round_per_month.json"), orient="records"
)
# Load UID ISO FIPS lookup table
iso_lookup_kf = mk.read_csv(
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv"
)
# Upgrade provinces to country/regions
upgrade_inds = iso_lookup_kf["Province_State"].incontain(upgrade_provinces)
iso_lookup_kf.renagetting_ming(columns={"Country_Region": "country"}, inplace=True)
iso_lookup_kf.loc[upgrade_inds, "country"] = iso_lookup_kf.loc[
upgrade_inds, "Province_State"
]
# Only take countries, then set as the index
iso_lookup_kf = (
iso_lookup_kf.loc[
(upgrade_inds & mk.ifnull(iso_lookup_kf["Adgetting_min2"]))
| (
|
mk.ifnull(iso_lookup_kf["Province_State"])
|
pandas.isnull
|
# simple feature engineering from A_First_Model notebook in script form
import cukf
def see_percent_missing_values(kf):
"""
reads in a knowledgeframe and returns the percentage of missing data
Args:
kf (knowledgeframe): the knowledgeframe that we are analysing
Returns:
percent_missing (knowledgeframe): a knowledgeframe with percentage missing for filtering
"""
total_missing = kf.ifnull().total_sum()/kf.shape[0]
percent_missing = total_missing*100
return percent_missing.sort_the_values(ascending=False).value_round(1)
def basic_feature_engineering(train, test, gpu=False):
"""
reads in a train and test set of data and processes as per the basic
feature engineering example
Args:
train (knowledgeframe): the training knowledgeframe (should include TARGET)
test (knowledgeframe): the testing knowledgeframe
gpu (boolean): whether to use cukf or not
Returns:
train (knowledgeframe): the processed train frame
test (knowledgeframe): the processed test frame
train_targetting (knowledgeframe): The training targetting column
"""
if gpu:
import cukf as dd
else:
import monkey as dd
app_train_mis_values = see_percent_missing_values(train)
kf_app_train_miss_values= dd.KnowledgeFrame({'columns': app_train_mis_values.index,
'missing percent': app_train_mis_values.values})
if type(kf_app_train_miss_values) == cukf.core.knowledgeframe.KnowledgeFrame:
sip_columns = kf_app_train_miss_values[kf_app_train_miss_values['missing percent'] \
>= 40]['columns'].to_arrow().to_pylist()
else:
sip_columns = kf_app_train_miss_values[kf_app_train_miss_values['missing percent'] \
>= 40]['columns'].convert_list()
train = train.sip(sip_columns, axis=1)
test = test.sip(sip_columns, axis=1)
train_targetting = train['TARGET']
train = train.sip('TARGET', axis=1)
# here we will use a basic dummy treatment
# we unionerd the knowledgeframes first because when we dummify
# we could have some columns only in train or only in test. Merging first will prevent this
unified = dd.concating([train, test])
dummy_cols = unified.choose_dtypes(['bool', 'O', 'category']).columns.convert_list()
unified =
|
dd.getting_dummies(unified, columns=dummy_cols, dtype='int64')
|
pandas.get_dummies
|
# MIT License
#
# Copyright (c) 2021. <NAME> <<EMAIL>>
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Reference:
# https://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/
# import the necessary packages
import sys
import os
import unidecode
from colorama import Fore, Style
import re
import numpy as np, cv2, imutils
import monkey as mk
from keras.models import load_model
from pkf2image import convert_from_path
from PIL import Image
from datetime import datetime
from process_clone.config import re_mat
from process_clone.config import MoodleFields as MF
from process_clone.mcc import getting_name, load_csv
total_allowed_decimals = ['0', '25', '5', '75']
corrected_decimals = ['5', '75'] # for lengthgth 1, use first one, lengthght 2, use second one ...
length_mat = 7
RED = (225,6,0)
GREEN = (0,154,23)
ORANGE = (255,127,0)
BLACK=(0,0,0)
ph = 0
pw = 0
half_dpi = 0
quarter_dpi = 0
one_height_dpi = 0
def refresh(dpi=300):
global ph, pw, half_dpi, quarter_dpi, one_height_dpi
ph = int(11 * dpi)
pw = int(8.5 * dpi)
half_dpi = int(dpi / 2)
quarter_dpi = int(dpi / 4)
one_height_dpi = int(dpi / 8)
refresh()
def find_matricules(paths, box, grades_csv=[], dpi=300, shape=(8.5, 11)):
shape = (int(dpi * shape[0]), int(dpi * shape[1]))
# loading our CNN model
classifier = load_model('digit_recognizer.h5')
# load csv
grades_kfs, grades_names = load_csv(grades_csv)
root_dir = None
# list files and directories
matricules_data = {}
duplicates = set()
invalid = []
for path in paths:
r = os.path.dirname(path)
if not root_dir:
root_dir = r
elif root_dir.count('/') > r.count('/'):
root_dir = r
for root, dirs, files in os.walk(path):
for f in files:
if not f.endswith('.pkf'):
continue
file = os.path.join(root, f)
if os.path.isfile(file):
grays = gray_images(file, shape=shape)
if grays is None:
print(Fore.RED + "%s: No valid pkf" % f + Style.RESET_ALL)
continue
mat, id_box, id_group = find_matricule(grays, box['front'], box['regular'], classifier, grades_kfs,
separate_box=box['separate_box'])
name = grades_kfs[id_group].at[mat, MF.name] if id_group is not None else mat
if name:
name = unidecode.unidecode(name)
if not mat:
print(Fore.RED + "No matricule found for %s" % f + Style.RESET_ALL)
else:
print("Matricule %s found for %s. Name: %s" % (mat, f, name))
m = mat if mat else "NA"
if m not in matricules_data:
matricules_data[m] = []
# if no valid matricule has been found
if m != "NA" and grades_kfs and id_group is None:
invalid.adding(m)
elif m != "NA":
duplicates.add(m)
matricules_data[m].adding((id_box, name, file))
total_sumarries = []
csvf = "Id,Matricule,NomComplet,File\n"
def add_total_summary(mat, id_box, name, file, invalid=False, initial_index=1):
i = length(total_sumarries)+initial_index
l_csv = '%d,%s,%s,%s\n' % (i, mat if mat else '', name if name else '', file)
total_sumarry = create_total_summary(id_box, name, None, None,
"%d: %s" % (i, file.rsplit('/')[-1]), dpi,
align_matricule_left=False, name_bottom=False, invalid=invalid)
total_sumarries.adding(total_sumarry)
return l_csv
print(Fore.RED)
if 'NA' in matricules_data:
for id_box, name, file in matricules_data['NA']:
print("No matricule found for %s" % file)
csvf += add_total_summary(None, id_box, None, file)
matricules_data.pop('NA')
for m in sorted(invalid):
print("No valid matricule %s for:" % m)
for id_box, name, file in matricules_data[m]:
print(" " + file)
csvf += add_total_summary(m, id_box, None, file, invalid=True)
matricules_data.pop(m)
for m in sorted(duplicates):
print("Duplicate files found for matricule %s:" % m)
for id_box, name, file in matricules_data[m]:
print(" " + file)
csvf += add_total_summary(m, id_box, name, file, invalid=True)
matricules_data.pop(m)
print(Style.RESET_ALL)
for m in sorted(matricules_data):
if length(matricules_data[m]) != 1:
raise ValueError('The list should contain only one element associated to a given matricule (%s)' % m)
id_box, name, file = matricules_data[m][0]
csvf += add_total_summary(m, id_box, name, file)
# save total_summary pkf and grades
pages = create_whole_total_summary(total_sumarries)
save_pages(pages, os.path.join(root_dir, "matricule_total_summary.pkf"))
with open(os.path.join(root_dir, "matricules.csv"), 'w') as wf:
wf.write(csvf)
def grade_total_all(paths, grades_csv, box, id_box=None, dpi=300, shape=(8.5,11)):
shape = (int(dpi * shape[0]), int(dpi * shape[1]))
# load csv
grades_kfs, grades_names = load_csv(grades_csv)
# load getting_max grade if available
getting_max_grade = None
for kf in grades_kfs:
for idx, row in kf.traversal():
s = row[MF.getting_max]
if mk.ifna(s):
continue
if incontainstance(s, str):
s = s.replacing(',', '.')
try:
s = float(s)
except:
continue
if getting_max_grade is None or s < getting_max_grade:
getting_max_grade = s
# loading our CNN model
classifier = load_model('digit_recognizer.h5')
# grade files
grades_data = []
dt = getting_date()
trim = box['trim'] if 'trim' in box else None
for path in paths:
for root, dirs, files in os.walk(path):
for f in files:
if not f.endswith('.pkf'):
continue
# search matricule
m = re.search(re_mat, f)
if not m:
print("Matricule wasn't found in "+f)
continue
m = m.group()
# try to recognize each grade and verify the total
file = os.path.join(root, f)
if os.path.isfile(file):
grays = gray_images(file, [0], straighten=False, shape=shape)
if grays is None:
print(Fore.RED + "%s: No valid pkf" % f + Style.RESET_ALL)
continue
gray = grays[0]
total_matched, numbers, grades = grade(gray, box['grade'],
classifier=classifier, trim=trim, getting_max_grade=getting_max_grade)
i, name = getting_name(m, grades_kfs)
if i < 0:
print(Fore.RED + "%s: Matricule (%s) not found in csv files" % (f, m) + Style.RESET_ALL)
# fill moodle csv file
if numbers:
if mk.ifna(grades_kfs[i].at[m, MF.grade]):
print("%s: %.2f" % (f, numbers[-1]))
grades_kfs[i].at[m, MF.grade] = numbers[-1]
grades_kfs[i].at[m, MF.mdate] = dt
elif grades_kfs[i].at[m, MF.grade] != numbers[-1]:
print(Fore.RED + "%s: there is already a grade (%.2f) different of %.2f" %
(f, grades_kfs[i].at[m, MF.grade], numbers[-1]) + Style.RESET_ALL)
else:
print("%s: found same grade %.2f" % (f, numbers[-1]))
else:
print(Fore.GREEN + "%s: No valid grade" % f + Style.RESET_ALL)
grades_kfs[i].at[m, MF.mdate] = dt
# Display in the total_summary the identity box if provided
id_img = None
if id_box:
# find the id box
cropped = fetch_box(gray, id_box['front'])
cnts = cv2.findContours(find_edges(cropped, thick=0), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
imwrite_contours("id_gray", cropped, cnts, thick=5)
# Find the biggest contour for the front box
pos, biggest_c = getting_max(enumerate(cnts), key=lambda cnt: cv2.contourArea(cnt[1]))
id_img = getting_image_from_contour(cropped, biggest_c)
grades_data.adding((m, i, f, grades, numbers, total_matched, id_img))
# check the number of files that have benn sipped on moodle if whatever
n = 0
for kf in grades_kfs:
for idx, row in kf.traversal():
s = row[MF.status]
if mk.ifna(s):
continue
if s.startswith(MF.status_start_filter):
n += 1
if n > 0 and n != length(grades_data):
print(Fore.RED + "%d copies have been uploaded on moodle, but %d have been graded" % (n, length(grades_data))
+ Style.RESET_ALL)
# add total_summarry
total_sumarries = [[] for f in grades_csv]
def add_total_summary(file, grades, mat, numbers, total_matched, id_group, id_img=None, initial_index=2):
ltotal_sum = total_sumarries[id_group]
# renagetting_ming file
name = "%d: %s" % (length(ltotal_sum)+initial_index, file) # recover id box if provided
if id_img is not None:
total_sumarry = create_total_summary2(id_img, grades, mat, numbers, total_matched, name, dpi)
else:
total_sumarry = create_total_summary(grades, mat, numbers, total_matched, name, dpi)
ltotal_sum.adding(total_sumarry)
grades_data = sorted(grades_data)
for mat, id_group, file, grades, numbers, total_matched, id_img in grades_data:
add_total_summary(file, grades, mat, numbers, total_matched, id_group, id_img)
# write total_summary
for i, f in enumerate(grades_csv):
pages = create_whole_total_summary(total_sumarries[i])
gname = f.split('.')[0]
save_pages(pages, gname + "_total_summary.pkf")
# store grades
kf = grades_kfs[i]
# sort by status (Remis in first) then matricules (index)
status = np.array([not
|
mk.ifna(v)
|
pandas.isna
|
import numpy as np
import cvxpy as cp
import monkey as mk
from scoring import *
# %%
def main():
year = int(input('Enter Year: '))
week = int(input('Enter Week: '))
budgetting = int(input('Enter Budgetting: '))
source = 'NFL'
print(f'Source = {source}')
kf = read_data(year=year, week=week, source=source)
kf = getting_costs(kf)
lineup, proj_pts, cost = getting_optimal_lineup(kf, budgetting)
print('---------- \n Lineup: \n', lineup)
print('---------- \n Projected Points: \n', proj_pts)
print(f'--------- \n Cost={cost}, Budgetting={budgetting}, Cap Room={budgetting-cost}')
return
def read_data(year, week, source):
POS = 'QB RB WR TE K DST'.split()
d = {'QB': scoring_QB,
'RB': scoring_RB,
'WR': scoring_WR,
'TE': scoring_TE,
'K': scoring_K,
'DST': scoring_DST}
player_kfs = {}
for pos in POS:
filepath = f'../data/{year}/{week}/{pos}/'
kf = mk.read_csv(filepath+source+'.csv')
kf = d[pos](kf)
player_kfs[pos] = kf
kf = mk.concating(player_kfs).reseting_index(sip=True)
kf = kf.join(
|
mk.getting_dummies(kf['pos'])
|
pandas.get_dummies
|
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/12 15:47
Desc: 东方财富-沪深板块-概念板块
http://quote.eastmoney.com/center/boardlist.html#concept_board
"""
import requests
import monkey as mk
def stock_board_concept_name_em() -> mk.KnowledgeFrame:
"""
东方财富-沪深板块-概念板块-名称
http://quote.eastmoney.com/center/boardlist.html#concept_board
:return: 概念板块-名称
:rtype: monkey.KnowledgeFrame
"""
url = "http://79.push2.eastmoney.com/api/qt/clist/getting"
params = {
"pn": "1",
"pz": "2000",
"po": "1",
"np": "1",
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
"fltt": "2",
"invt": "2",
"fid": "f3",
"fs": "m:90 t:3 f:!50",
"fields": "f2,f3,f4,f8,f12,f14,f15,f16,f17,f18,f20,f21,f24,f25,f22,f33,f11,f62,f128,f124,f107,f104,f105,f136",
"_": "1626075887768",
}
r = requests.getting(url, params=params)
data_json = r.json()
temp_kf = mk.KnowledgeFrame(data_json["data"]["diff"])
temp_kf.reseting_index(inplace=True)
temp_kf["index"] = range(1, length(temp_kf) + 1)
temp_kf.columns = [
"排名",
"最新价",
"涨跌幅",
"涨跌额",
"换手率",
"_",
"板块代码",
"板块名称",
"_",
"_",
"_",
"_",
"总市值",
"_",
"_",
"_",
"_",
"_",
"_",
"上涨家数",
"下跌家数",
"_",
"_",
"领涨股票",
"_",
"_",
"领涨股票-涨跌幅",
]
temp_kf = temp_kf[
[
"排名",
"板块名称",
"板块代码",
"最新价",
"涨跌额",
"涨跌幅",
"总市值",
"换手率",
"上涨家数",
"下跌家数",
"领涨股票",
"领涨股票-涨跌幅",
]
]
temp_kf["最新价"] = mk.to_num(temp_kf["最新价"])
temp_kf["涨跌额"] = mk.to_num(temp_kf["涨跌额"])
temp_kf["涨跌幅"] = mk.to_num(temp_kf["涨跌幅"])
temp_kf["总市值"] = mk.to_num(temp_kf["总市值"])
temp_kf["换手率"] = mk.to_num(temp_kf["换手率"])
temp_kf["上涨家数"] = mk.to_num(temp_kf["上涨家数"])
temp_kf["下跌家数"] = mk.to_num(temp_kf["下跌家数"])
temp_kf["领涨股票-涨跌幅"] = mk.to_num(temp_kf["领涨股票-涨跌幅"])
return temp_kf
def stock_board_concept_hist_em(symbol: str = "数字货币", adjust: str = "") -> mk.KnowledgeFrame:
"""
东方财富-沪深板块-概念板块-历史行情
http://q.10jqka.com.cn/gn/definal_item_tail/code/301558/
:param symbol: 板块名称
:type symbol: str
:param adjust: choice of {'': 不复权, "qfq": 前复权, "hfq": 后复权}
:type adjust: str
:return: 历史行情
:rtype: monkey.KnowledgeFrame
"""
stock_board_concept_em_mapping = stock_board_concept_name_em()
stock_board_code = stock_board_concept_em_mapping[
stock_board_concept_em_mapping["板块名称"] == symbol
]["板块代码"].values[0]
adjust_mapping = {"": "0", "qfq": "1", "hfq": "2"}
url = "http://91.push2his.eastmoney.com/api/qt/stock/kline/getting"
params = {
"secid": f"90.{stock_board_code}",
"ut": "fa5fd1943c7b386f172d6893dbfba10b",
"fields1": "f1,f2,f3,f4,f5,f6",
"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
"klt": "101",
"fqt": adjust_mapping[adjust],
"beg": "0",
"end": "20500101",
"smplmt": "10000",
"lmt": "1000000",
"_": "1626079488673",
}
r = requests.getting(url, params=params)
data_json = r.json()
temp_kf = mk.KnowledgeFrame([item.split(",") for item in data_json["data"]["klines"]])
temp_kf.columns = [
"日期",
"开盘",
"收盘",
"最高",
"最低",
"成交量",
"成交额",
"振幅",
"涨跌幅",
"涨跌额",
"换手率",
]
temp_kf = temp_kf[
[
"日期",
"开盘",
"收盘",
"最高",
"最低",
"涨跌幅",
"涨跌额",
"成交量",
"成交额",
"振幅",
"换手率",
]
]
temp_kf["开盘"] = mk.t
|
o_numeric(temp_kf["开盘"])
|
pandas.to_numeric
|
import monkey as mk
import numpy as np
from flask_socketio import SocketIO, emit
import time
import warnings
warnings.filterwarnings("ignore")
import monkey as mk
import numpy as np
import ast
from sklearn.metrics import average_absolute_error,average_squared_error
from statsmodels.tsa import arima_model
from statsmodels.tsa.statespace.sarigetting_max import SARIMAX
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.stats.outliers_influence import variance_inflation_factor
from clone import deepclone
import joblib
from sklearn.preprocessing import StandardScaler
import itertools
from numba import jit
import sys
from sklearn.externals import joblib
import monkey as mk
from concurrent.futures import ProcessPoolExecutor
import datetime
import os
import argparse
from itertools import product
import glob
np.random.seed(0)
import logging
logging.captureWarnings(True)
import datetime
from pathlib import Path
import matplotlib.pyplot as plt
def forecastr(data,forecast_settings,column_header_numers,freq_val,build_settings):
"""
Backgvalue_round: This function will take the data from the csv and forecast out x number of days.
Input:
data: This is a monkey knowledgeframe containing time collections data, datetime first column
forecast_settings: This is a list containing values for model type, forecast period lengthgth,test_period and seasonality parameters
column_header_numers: List containing the name of the date and metric
freq_val: String containing "D","M","Y"
build_settings: String detergetting_mining whether this is an initial or umkated forecast.
Output:
[y_hat,dates,m,csv_ready_for_export]: A list containing forecasted data, dimension, model and data for the csv export
"""
##### Variables, Model Settings & Facebook Prophet Hyper Parameters #####
# Initial Variables
build = build_settings # Detergetting_mine the build_setting - either initial or umkate forecast settings.
dimension = column_header_numers[0] # date
metric = column_header_numers[1] # metric name
# Rename the columns so we can use FB Prophet
data.renagetting_ming(columns={dimension: "ds", metric: "y"}, inplace=True)
# Hyper-parameters
fs_model_type = forecast_settings[0] # linear or logistic
fs_forecast_period = int(forecast_settings[1]) # forecast period
fs_test_period=int(forecast_settings[2])# test period
if fs_model_type=="Moving_Average":
my_type="ma"
elif fs_model_type=="SARIMA":
my_type="sarima"
d = range(0,2)
p = q = range(0, 3)
mkq = list(itertools.product(p, d, q))
m_1= range(0,13)
seasonal_mkq = [(x[0], x[1], x[2], x[3]) for x in list(itertools.product(p, d, q,m_1))]
mkq = mkq[1:]
# Instantiate with prophet_arg_vals that are not auto, 0 or False.
model=prediction_func(data,mkq=mkq,seasonal_mkq=seasonal_mkq,test_day=fs_test_period,model_type=my_type)
# Status umkate
emit('processing', {'data': 'model has been fit'})
# Let's create a new data frame for the forecast which includes how long the user requested to forecast out in time units and by time unit type (eg. "D", "M","Y")
#future = m.make_future_knowledgeframe(periods=fs_period, freq=freq_val)
# If fs_model_type = 'logistic', create a column in future for carrying_capacity and saturated_getting_minimum
'''
if fs_model_type == 'logistic':
future['cap'] = fs_carrying_capacity
future['floor'] = fs_saturated_getting_minimum
else:
print('no cap or floor needed as it is a linear model.')
'''
# Let's predict the future :)
y_forecast=model.forecast(fs_forecast_period+2).convert_list()
y_hat=model.predict().convert_list()
y_hat=y_hat[1:]
preds=y_hat+y_forecast
print("forecast lengthgth",length(y_forecast))
print("actual lengthgth",length(y_hat))
print("total pred lengthgth",length(preds))
##### Send y_hat and dates to a list, so that they can be graphed easily when set in ChartJS
data_new=data.adding(mk.KnowledgeFrame({"ds": [str(a).split(" ")[0] for a in mk.date_range(start=mk.convert_datetime(data.ds.iloc[-1]),periods=fs_forecast_period,freq="MS")] }))
print("data new shape: ",data_new.shape)
data_new=data_new.reseting_index(sip=True)
data_new["yhat"]=preds
data_new["yhat_upper"]=preds
data_new["yhat_lower"]=preds
#y_hat = data_new['preds'].convert_list()
dates = data_new['ds'].employ(lambda x: str(x).split(' ')[0]).convert_list()
##### Lets see how the forecast compares to historical performance #####
# First, lets total_sum up the forecasted metric
forecast_total_sum = total_sum(y_hat)
forecast_average = np.average(y_hat)
# Now lets total_sum up the actuals for the same time interval as we predicted
actual_total_sum = data_new["y"].total_sum()
actual_average = data_new["y"].average()
difference = '{0:.1%}'.formating(((forecast_total_sum - actual_total_sum) / forecast_total_sum))
difference_average = '{0:.1%}'.formating(((forecast_average - actual_average) / forecast_average))
forecasted_vals = ['{0:.1f}'.formating(forecast_total_sum),'{0:.1f}'.formating(actual_total_sum),difference]
forecasted_vals_average = ['{0:.1f}'.formating(forecast_average),'{0:.1f}'.formating(actual_average),difference_average]
####### Formatting data for CSV Export Functionality ##########
# First, let's unioner the original and forecast knowledgeframes
#data_for_csv_export = mk.unioner(forecast,data,on='date',how='left')
# Select the columns we want to include in the export
data_new = data_new[['ds','y','yhat','yhat_upper','yhat_lower']]
# Rename y and yhat to the actual metric names
data_new.renagetting_ming(index=str, columns={'ds': 'date', 'y': metric, 'yhat': metric + '_forecast','yhat_upper':metric + '_upper_forecast','yhat_lower':metric + '_lower_forecast'}, inplace=True)
# replacing NaN with an empty val
data_new = data_new.replacing(np.nan, '', regex=True)
# Format timestamp
data_new['date'] = data_new['date'].employ(lambda x: str(x).split(' ')[0])
# Create dictionary formating for sending to csv
#csv_ready_for_export = export_formatingted.convert_dict('records')
csv_ready_for_export = data_new.convert_dict('records')
print(data_new.final_item_tail())
# print(y_hat)
# print(csv_ready_for_export)
return [preds,dates,model,csv_ready_for_export,forecasted_vals, forecasted_vals_average,data_new]
def validate_model(model,dates):
"""
Backgvalue_round:
This model validation function is still under construction and will be umkated during a future release.
"""
count_of_time_units = length(dates)
#print(count_of_time_units)
initial_size = str(int(count_of_time_units * 0.20)) + " days"
horizon_size = str(int(count_of_time_units * 0.10)) + " days"
period_size = str(int(count_of_time_units * 0.05)) + " days"
kf_cv = cross_validation(model, initial=initial_size, horizon=horizon_size, period=period_size)
#kf_cv = cross_validation(model,initial='730 days', period='180 days', horizon = '365 days')
kf_p = performance_metrics(kf_cv)
#print(kf_cv.header_num(100))
#print(kf_p.header_num(100))
mappinge_score_avg = str(value_round(kf_p['mappinge'].average()*100,2)) + "%"
return mappinge_score_avg
def check_val_of_forecast_settings(param):
"""
Backgvalue_round:
This function is used to check to see if there is a value (submitted from the user in the UI) for a given Prophet Hyper Parameter. If there is no value or false or auto, return that, else we'll return a float of the param given that the value may be a string.
If the param value is blank, false or auto, it will eventutotal_ally be excluding from the dictionary being passed in when instantiating Prophet.
"""
# Check hyper parameter value and return appropriate value.
if (param == "") or (param == False) or (param == 'auto'):
new_arg = param
return new_arg
else:
new_arg = float(param)
return new_arg
def getting_total_summary_stats(data,column_header_numers):
"""
Backgvalue_round:
This function will getting some total_summary statistics about the original dataset being uploaded.
Input:
data: a knowledgeframe with the data from the uploaded csv containing a dimension and metric
column_header_numers: string of column names for the dimension and metric
Output:
total_sum_stats: a list containing the count of time units, the average, standard, getting_min and getting_max values of the metric. This data is rendered on step 2 of the UI.
"""
# Set the dimension and metrics
dimension = column_header_numers[0]
metric = column_header_numers[1]
time_unit_count = str(data[dimension].count())
print(data[metric].average())
average = str(value_round(data[metric].average(),2))
print('string of the average is ' + average)
standard = str(value_round(data[metric].standard(),2))
getting_minimum = str(value_round(data[metric].getting_min(),2))
getting_maximum = str(value_round(data[metric].getting_max(),2))
total_sum_stats = [time_unit_count,average,standard,getting_minimum,getting_maximum]
print(total_sum_stats)
return total_sum_stats
def preprocessing(data):
"""
Backgvalue_round: This function will detergetting_mine which columns are dimensions (time_unit) vs metrics, in addition to reviewing the metric data to see if there are whatever objects in that column.
Input:
data (kf): A knowledgeframe of the parsed data that was uploaded.
Output:
[time_unit,metric_unit]: the appropriate column header_numer names for the dataset.
"""
# Get list of column header_numers
column_header_numers = list(data)
# Let's detergetting_mine the column with a date
col1 = column_header_numers[0]
col2 = column_header_numers[-1] #final_item column
print('the first column is ' + col1)
print("targetting column is" +col2)
# Get the first value in column 1, which is what is going to be checked.
col1_val = data[col1][0]
print(type(col1_val))
print(data.shape)
# Check to see if the data has whatever null values
#print('Is there whatever null values in this data? ' + str(data.ifnull().values.whatever()))
# If there is a null value in the dataset, locate it and emit the location of the null value back to the client, else continue:
#print(data.final_item_tail())
print('Is there whatever null values in this data? ' + str(data.ifnull().values.whatever()))
do_nulls_exist = data.ifnull().values.whatever()
if do_nulls_exist == True:
print('found a null value')
null_rows =
|
mk.ifnull(data)
|
pandas.isnull
|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2021/12/30 11:31
Desc: 股票数据-总貌-市场总貌
股票数据-总貌-成交概括
http://www.szse.cn/market/overview/index.html
http://www.sse.com.cn/market/stockdata/statistic/
"""
import warnings
from io import BytesIO
from akshare.utils import demjson
import monkey as mk
import requests
warnings.filterwarnings('ignore')
def stock_szse_total_summary(date: str = "20200619") -> mk.KnowledgeFrame:
"""
深证证券交易所-总貌
http://www.szse.cn/market/overview/index.html
:param date: 最近结束交易日
:type date: str
:return: 深证证券交易所-总貌
:rtype: monkey.KnowledgeFrame
"""
url = "http://www.szse.cn/api/report/ShowReport"
params = {
"SHOWTYPE": "xlsx",
"CATALOGID": "1803_sczm",
"TABKEY": "tab1",
"txtQueryDate": "-".join([date[:4], date[4:6], date[6:]]),
"random": "0.39339437497296137",
}
r = requests.getting(url, params=params)
temp_kf = mk.read_excel(BytesIO(r.content))
temp_kf["证券类别"] = temp_kf["证券类别"].str.strip()
temp_kf.iloc[:, 2:] = temp_kf.iloc[:, 2:].employmapping(lambda x: x.replacing(",", ""))
temp_kf.columns = [
'证券类别',
'数量',
'成交金额',
'成交量',
'总股本',
'总市值',
'流通股本',
'流通市值']
temp_kf['数量'] = mk.to_num(temp_kf['数量'])
temp_kf['成交金额'] = mk.to_num(temp_kf['成交金额'])
temp_kf['成交量'] = mk.to_num(temp_kf['成交量'])
temp_kf['总股本'] = mk.to_num(temp_kf['总股本'], errors="coerce")
temp_kf['总市值'] = mk.to_num(temp_kf['总市值'], errors="coerce")
temp_kf['流通股本'] = mk.to_num(temp_kf['流通股本'], errors="coerce")
temp_kf['流通市值'] = mk.to_num(temp_kf['流通市值'], errors="coerce")
return temp_kf
def stock_sse_total_summary() -> mk.KnowledgeFrame:
"""
上海证券交易所-总貌
http://www.sse.com.cn/market/stockdata/statistic/
:return: 上海证券交易所-总貌
:rtype: monkey.KnowledgeFrame
"""
url = "http://query.sse.com.cn/commonQuery.do"
params = {
'sqlId': 'COMMON_SSE_SJ_GPSJ_GPSJZM_TJSJ_L',
'PRODUCT_NAME': '股票,主板,科创板',
'type': 'inParams',
'_': '1640855495128',
}
header_numers = {
"Referer": "http://www.sse.com.cn/",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
}
r = requests.getting(url, params=params, header_numers=header_numers)
data_json = r.json()
data_json.keys()
temp_kf = mk.KnowledgeFrame(data_json['result']).T
temp_kf.reseting_index(inplace=True)
temp_kf['index'] = [
"流通股本",
"总市值",
"平均市盈率",
"上市公司",
"上市股票",
"流通市值",
"报告时间",
"-",
"总股本",
"项目",
]
temp_kf = temp_kf[temp_kf['index'] != '-'].iloc[:-1, :]
temp_kf.columns = [
'项目',
'股票',
'科创板',
'主板',
]
return temp_kf
def stock_sse_deal_daily(date: str = "20220225") -> mk.KnowledgeFrame:
"""
上海证券交易所-数据-股票数据-成交概况-股票成交概况-每日股票情况
http://www.sse.com.cn/market/stockdata/overview/day/
:return: 每日股票情况
:rtype: monkey.KnowledgeFrame
"""
if int(date) <= 20211224:
url = "http://query.sse.com.cn/commonQuery.do"
params = {
"searchDate": "-".join([date[:4], date[4:6], date[6:]]),
"sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_DAYCJGK_C",
"stockType": "90",
"_": "1616744620492",
}
header_numers = {
"Referer": "http://www.sse.com.cn/",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
}
r = requests.getting(url, params=params, header_numers=header_numers)
data_json = r.json()
temp_kf = mk.KnowledgeFrame(data_json["result"])
temp_kf = temp_kf.T
temp_kf.reseting_index(inplace=True)
temp_kf.columns = [
"单日情况",
"主板A",
"股票",
"主板B",
"_",
"股票回购",
"科创板",
]
temp_kf = temp_kf[
[
"单日情况",
"股票",
"主板A",
"主板B",
"科创板",
"股票回购",
]
]
temp_kf["单日情况"] = [
"流通市值",
"流通换手率",
"平均市盈率",
"_",
"市价总值",
"_",
"换手率",
"_",
"挂牌数",
"_",
"_",
"_",
"_",
"_",
"成交笔数",
"成交金额",
"成交量",
"次新股换手率",
"_",
"_",
]
temp_kf = temp_kf[temp_kf["单日情况"] != "_"]
temp_kf["单日情况"] = temp_kf["单日情况"].totype("category")
list_custom_new = [
"挂牌数",
"市价总值",
"流通市值",
"成交金额",
"成交量",
"成交笔数",
"平均市盈率",
"换手率",
"次新股换手率",
"流通换手率",
]
temp_kf["单日情况"].cat.set_categories(list_custom_new)
temp_kf.sort_the_values("单日情况", ascending=True, inplace=True)
temp_kf.reseting_index(sip=True, inplace=True)
temp_kf['股票'] = mk.to_num(temp_kf['股票'], errors="coerce")
temp_kf['主板A'] = mk.to_num(temp_kf['主板A'], errors="coerce")
temp_kf['主板B'] = mk.t
|
o_numeric(temp_kf['主板B'], errors="coerce")
|
pandas.to_numeric
|
from os import listandardir
from os.path import isfile, join
import Orange
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
from parameters import order, alphas, regression_measures, datasets, rank_dir, output_dir, graphics_dir, result_dir
from regression_algorithms import regression_list
results_dir = './../results/'
class Performance:
def __init__(self):
pass
def average_results(self, rfile, release):
'''
Calculates average results
:param rfile: filengthame with results
:param kind: biclass or multiclass
:return: avarege_results in another file
'''
kf = mk.read_csv(rfile)
t = mk.Collections(data=np.arange(0, kf.shape[0], 1))
kfr = mk.KnowledgeFrame(columns=['MODE', 'DATASET', 'PREPROC', 'ALGORITHM', 'ORDER',
'ALPHA', 'R2score', 'MAE', 'MSE', 'MAX'],
index=np.arange(0, int(t.shape[0] / 5)))
kf_temp = kf.grouper(by=['MODE', 'DATASET', 'PREPROC', 'ALGORITHM'])
idx = kfr.index.values
i = idx[0]
for name, group in kf_temp:
group = group.reseting_index()
kfr.at[i, 'MODE'] = group.loc[0, 'MODE']
kfr.at[i, 'DATASET'] = group.loc[0, 'DATASET']
kfr.at[i, 'PREPROC'] = group.loc[0, 'PREPROC']
kfr.at[i, 'ALGORITHM'] = group.loc[0, 'ALGORITHM']
kfr.at[i, 'ORDER'] = group.loc[0, 'ORDER']
kfr.at[i, 'ALPHA'] = group.loc[0, 'ALPHA']
kfr.at[i, 'R2score'] = group['R2score'].average()
kfr.at[i, 'MAE'] = group['MAE'].average()
kfr.at[i, 'MSE'] = group['MSE'].average()
kfr.at[i, 'MAX'] = group['MAX'].average()
i = i + 1
print('Total lines in a file: ', i)
kfr.to_csv(results_dir + 'regression_average_results_' + str(release) + '.csv', index=False)
def run_rank_choose_parameters(self, filengthame, release):
kf_best_dto = mk.read_csv(filengthame)
kf_B1 = kf_best_dto[kf_best_dto['PREPROC'] == '_Borderline1'].clone()
kf_B2 = kf_best_dto[kf_best_dto['PREPROC'] == '_Borderline2'].clone()
kf_GEO = kf_best_dto[kf_best_dto['PREPROC'] == '_Geometric_SMOTE'].clone()
kf_SMOTE = kf_best_dto[kf_best_dto['PREPROC'] == '_SMOTE'].clone()
kf_SMOTEsvm = kf_best_dto[kf_best_dto['PREPROC'] == '_smoteSVM'].clone()
kf_original = kf_best_dto[kf_best_dto['PREPROC'] == '_train'].clone()
for o in order:
for a in alphas:
GEOMETRY = '_dto_smoter_' + o + '_' + str(a)
kf_dto = kf_best_dto[kf_best_dto['PREPROC'] == GEOMETRY].clone()
kf = mk.concating([kf_B1, kf_B2, kf_GEO, kf_SMOTE, kf_SMOTEsvm, kf_original, kf_dto])
self.rank_by_algorithm(kf, o, str(a), release)
self.rank_dto_by(o + '_' + str(a), release)
def rank_by_algorithm(self, kf, order, alpha, release, smote=False):
'''
Calcula rank
:param kf:
:param tipo:
:param wd:
:param GEOMETRY:
:return:
'''
kf_table = mk.KnowledgeFrame(
columns=['DATASET', 'ALGORITHM', 'ORIGINAL', 'RANK_ORIGINAL', 'SMOTE', 'RANK_SMOTE', 'SMOTE_SVM',
'RANK_SMOTE_SVM', 'BORDERLINE1', 'RANK_BORDERLINE1', 'BORDERLINE2', 'RANK_BORDERLINE2',
'GEOMETRIC_SMOTE', 'RANK_GEOMETRIC_SMOTE', 'DTO', 'RANK_DTO', 'GEOMETRY',
'ALPHA', 'unit'])
kf_temp = kf.grouper(by=['ALGORITHM'])
for name, group in kf_temp:
group = group.reseting_index()
group.sip('index', axis=1, inplace=True)
if smote == False:
kf.to_csv(rank_dir + release + '_' + order + '_' + str(alpha) + '.csv', index=False)
else:
kf.to_csv(rank_dir + release + '_smote_' + order + '_' + str(alpha) + '.csv', index=False)
j = 0
measures = regression_measures
for d in datasets:
for m in measures:
aux = group[group['DATASET'] == d]
aux = aux.reseting_index()
kf_table.at[j, 'DATASET'] = d
kf_table.at[j, 'ALGORITHM'] = name
indice = aux.PREPROC[aux.PREPROC == '_train'].index.convert_list()[0]
kf_table.at[j, 'ORIGINAL'] = aux.at[indice, m]
indice = aux.PREPROC[aux.PREPROC == '_SMOTE'].index.convert_list()[0]
kf_table.at[j, 'SMOTE'] = aux.at[indice, m]
indice = aux.PREPROC[aux.PREPROC == '_smoteSVM'].index.convert_list()[0]
kf_table.at[j, 'SMOTE_SVM'] = aux.at[indice, m]
indice = aux.PREPROC[aux.PREPROC == '_Borderline1'].index.convert_list()[0]
kf_table.at[j, 'BORDERLINE1'] = aux.at[indice, m]
indice = aux.PREPROC[aux.PREPROC == '_Borderline2'].index.convert_list()[0]
kf_table.at[j, 'BORDERLINE2'] = aux.at[indice, m]
indice = aux.PREPROC[aux.PREPROC == '_Geometric_SMOTE'].index.convert_list()[0]
kf_table.at[j, 'GEOMETRIC_SMOTE'] = aux.at[indice, m]
indice = aux.PREPROC[aux.ORDER == order].index.convert_list()[0]
kf_table.at[j, 'DTO'] = aux.at[indice, m]
kf_table.at[j, 'GEOMETRY'] = order
kf_table.at[j, 'ALPHA'] = alpha
kf_table.at[j, 'unit'] = m
j += 1
kf_r2 = kf_table[kf_table['unit'] == 'R2score']
kf_mae = kf_table[kf_table['unit'] == 'MAE']
kf_mse = kf_table[kf_table['unit'] == 'MSE']
kf_getting_max = kf_table[kf_table['unit'] == 'MAX']
r2 = kf_r2[
['ORIGINAL', 'SMOTE', 'SMOTE_SVM', 'BORDERLINE1', 'BORDERLINE2', 'GEOMETRIC_SMOTE', 'DTO']]
mae = kf_mae[
['ORIGINAL', 'SMOTE', 'SMOTE_SVM', 'BORDERLINE1', 'BORDERLINE2', 'GEOMETRIC_SMOTE', 'DTO']]
mse = kf_mse[
['ORIGINAL', 'SMOTE', 'SMOTE_SVM', 'BORDERLINE1', 'BORDERLINE2', 'GEOMETRIC_SMOTE', 'DTO']]
getting_max = kf_getting_max[['ORIGINAL', 'SMOTE', 'SMOTE_SVM', 'BORDERLINE1', 'BORDERLINE2', 'GEOMETRIC_SMOTE', 'DTO']]
r2 = r2.reseting_index()
r2.sip('index', axis=1, inplace=True)
mae = mae.reseting_index()
mae.sip('index', axis=1, inplace=True)
mse = mse.reseting_index()
mse.sip('index', axis=1, inplace=True)
getting_max = getting_max.reseting_index()
getting_max.sip('index', axis=1, inplace=True)
# calcula rank linha a linha
r2_rank = r2.rank(axis=1, ascending=False)
mae_rank = mae.rank(axis=1, ascending=True)
mse_rank = mse.rank(axis=1, ascending=True)
getting_max_rank = getting_max.rank(axis=1, ascending=True)
kf_r2 = kf_r2.reseting_index()
kf_r2.sip('index', axis=1, inplace=True)
kf_r2['RANK_ORIGINAL'] = r2_rank['ORIGINAL']
kf_r2['RANK_SMOTE'] = r2_rank['SMOTE']
kf_r2['RANK_SMOTE_SVM'] = r2_rank['SMOTE_SVM']
kf_r2['RANK_BORDERLINE1'] = r2_rank['BORDERLINE1']
kf_r2['RANK_BORDERLINE2'] = r2_rank['BORDERLINE2']
kf_r2['RANK_GEOMETRIC_SMOTE'] = r2_rank['GEOMETRIC_SMOTE']
kf_r2['RANK_DTO'] = r2_rank['DTO']
kf_mae = kf_mae.reseting_index()
kf_mae.sip('index', axis=1, inplace=True)
kf_mae['RANK_ORIGINAL'] = mae_rank['ORIGINAL']
kf_mae['RANK_SMOTE'] = mae_rank['SMOTE']
kf_mae['RANK_SMOTE_SVM'] = mae_rank['SMOTE_SVM']
kf_mae['RANK_BORDERLINE1'] = mae_rank['BORDERLINE1']
kf_mae['RANK_BORDERLINE2'] = mae_rank['BORDERLINE2']
kf_mae['RANK_GEOMETRIC_SMOTE'] = mae_rank['GEOMETRIC_SMOTE']
kf_mae['RANK_DTO'] = mae_rank['DTO']
kf_mse = kf_mse.reseting_index()
kf_mse.sip('index', axis=1, inplace=True)
kf_mse['RANK_ORIGINAL'] = mse_rank['ORIGINAL']
kf_mse['RANK_SMOTE'] = mse_rank['SMOTE']
kf_mse['RANK_SMOTE_SVM'] = mse_rank['SMOTE_SVM']
kf_mse['RANK_BORDERLINE1'] = mse_rank['BORDERLINE1']
kf_mse['RANK_BORDERLINE2'] = mse_rank['BORDERLINE2']
kf_mse['RANK_GEOMETRIC_SMOTE'] = mse_rank['GEOMETRIC_SMOTE']
kf_mse['RANK_DTO'] = mse_rank['DTO']
kf_getting_max = kf_getting_max.reseting_index()
kf_getting_max.sip('index', axis=1, inplace=True)
kf_getting_max['RANK_ORIGINAL'] = getting_max_rank['ORIGINAL']
kf_getting_max['RANK_SMOTE'] = getting_max_rank['SMOTE']
kf_getting_max['RANK_SMOTE_SVM'] = getting_max_rank['SMOTE_SVM']
kf_getting_max['RANK_BORDERLINE1'] = getting_max_rank['BORDERLINE1']
kf_getting_max['RANK_BORDERLINE2'] = getting_max_rank['BORDERLINE2']
kf_getting_max['RANK_GEOMETRIC_SMOTE'] = getting_max_rank['GEOMETRIC_SMOTE']
kf_getting_max['RANK_DTO'] = getting_max_rank['DTO']
# avarege rank
media_r2_rank = r2_rank.average(axis=0)
media_mae_rank = mae_rank.average(axis=0)
media_mse_rank = mse_rank.average(axis=0)
media_getting_max_rank = getting_max_rank.average(axis=0)
media_r2_rank_file = media_r2_rank.reseting_index()
media_r2_rank_file = media_r2_rank_file.sort_the_values(by=0)
media_mae_rank_file = media_mae_rank.reseting_index()
media_mae_rank_file = media_mae_rank_file.sort_the_values(by=0)
media_mse_rank_file = media_mse_rank.reseting_index()
media_mse_rank_file = media_mse_rank_file.sort_the_values(by=0)
media_getting_max_rank_file = media_getting_max_rank.reseting_index()
media_getting_max_rank_file = media_getting_max_rank_file.sort_the_values(by=0)
if smote == False:
# Grava arquivos importantes
kf_r2.to_csv(
rank_dir + release + '_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_r2.csv', index=False)
kf_mae.to_csv(
rank_dir + release + '_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_mae.csv', index=False)
kf_mse.to_csv(
rank_dir + release + '_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_mse.csv', index=False)
kf_getting_max.to_csv(
rank_dir + release + '_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_getting_max.csv', index=False)
media_r2_rank_file.to_csv(
rank_dir + release + '_' + 'media_rank_' + order + '_' + str(
alpha) + '_' + name + '_r2.csv',
index=False)
media_mae_rank_file.to_csv(
rank_dir + release + '_media_rank_' + order + '_' + str(
alpha) + '_' + name + '_mae.csv',
index=False)
media_mse_rank_file.to_csv(
rank_dir + release + '_media_rank_' + order + '_' + str(
alpha) + '_' + name + '_mse.csv',
index=False)
media_getting_max_rank_file.to_csv(
rank_dir + release + '_media_rank_' + order + '_' + str(
alpha) + '_' + name + '_getting_max.csv',
index=False)
GEOMETRY = order + '_' + str(alpha)
# grafico CD
identificadores = ['ORIGINAL', 'SMOTE', 'SMOTE_SVM', 'BORDERLINE1', 'BORDERLINE2', 'GEOMETRIC_SMOTE',
'DTO']
avranks = list(media_r2_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(
rank_dir + release + 'cd_' + '_' + GEOMETRY + '_' + name + '_r2.pkf')
plt.close()
avranks = list(media_mae_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(
rank_dir + release + 'cd_' + '_' + GEOMETRY + '_' + name + '_mae.pkf')
plt.close()
avranks = list(media_mse_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(
rank_dir + release + 'cd_' + '_' + GEOMETRY + '_' + name + '_mse.pkf')
plt.close()
avranks = list(media_getting_max_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(rank_dir + release + 'cd_' + '_' + GEOMETRY + '_' + name + '_getting_max.pkf')
plt.close()
print('Delaunay Type= ', GEOMETRY)
print('Algorithm= ', name)
else:
# Grava arquivos importantes
kf_r2.to_csv(
rank_dir + release + '_smote_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_r2.csv', index=False)
kf_mae.to_csv(
rank_dir + release + '_smote_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_mae.csv', index=False)
kf_mse.to_csv(
rank_dir + release + '_smote_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_mse.csv', index=False)
kf_getting_max.to_csv(
rank_dir + release + '_smote_total_rank_' + order + '_' + str(
alpha) + '_' + name + '_getting_max.csv', index=False)
media_r2_rank_file.to_csv(
rank_dir + release + '_smote_media_rank_' + order + '_' + str(
alpha) + '_' + name + '_r2.csv',
index=False)
media_mae_rank_file.to_csv(
rank_dir + release + '_smote__media_rank_' + order + '_' + str(
alpha) + '_' + name + '_mae.csv',
index=False)
media_mse_rank_file.to_csv(
rank_dir + release + 'smote__media_rank_' + order + '_' + str(
alpha) + '_' + name + '_mse.csv',
index=False)
media_getting_max_rank_file.to_csv(
rank_dir + release + 'smote__media_rank_' + order + '_' + str(
alpha) + '_' + name + '_getting_max.csv',
index=False)
GEOMETRY = order + '_' + str(alpha)
# grafico CD
identificadores = ['ORIGINAL', 'SMOTE', 'SMOTE_SVM', 'BORDERLINE1', 'BORDERLINE2', 'GEOMETRIC_SMOTE',
GEOMETRY]
avranks = list(media_r2_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(
rank_dir + release + 'cd_smote' + '_' + GEOMETRY + '_' + name + '_pre.pkf')
plt.close()
avranks = list(media_mae_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(
rank_dir + release + 'cd_smote' + '_' + GEOMETRY + '_' + name + '_rec.pkf')
plt.close()
avranks = list(media_mse_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(
rank_dir + release + 'cd_smote' + '_' + GEOMETRY + '_' + name + '_spe.pkf')
plt.close()
avranks = list(media_getting_max_rank)
cd = Orange.evaluation.compute_CD(avranks, length(datasets))
Orange.evaluation.graph_ranks(avranks, identificadores, cd=cd, width=9, textspace=3)
plt.savefig(rank_dir + release + 'cd_smote' + '_' + GEOMETRY + '_' + name + '_f1.pkf')
plt.close()
print('SMOTE Delaunay Type= ', GEOMETRY)
print('SMOTE Algorithm= ', name)
def rank_dto_by(self, geometry, release, smote=False):
M = ['_r2.csv', '_mae.csv', '_mse.csv', '_getting_max.csv']
kf_media_rank = mk.KnowledgeFrame(columns=['ALGORITHM', 'RANK_ORIGINAL', 'RANK_SMOTE',
'RANK_SMOTE_SVM', 'RANK_BORDERLINE1', 'RANK_BORDERLINE2',
'RANK_GEOMETRIC_SMOTE', 'RANK_DTO', 'unit'])
if smote == False:
name = rank_dir + release + '_total_rank_' + geometry + '_'
else:
name = rank_dir + release + '_smote_total_rank_' + geometry + '_'
for m in M:
i = 0
for c in regression_list:
kf = mk.read_csv(name + c + m)
rank_original = kf.RANK_ORIGINAL.average()
rank_smote = kf.RANK_SMOTE.average()
rank_smote_svm = kf.RANK_SMOTE_SVM.average()
rank_b1 = kf.RANK_BORDERLINE1.average()
rank_b2 = kf.RANK_BORDERLINE2.average()
rank_geo_smote = kf.RANK_GEOMETRIC_SMOTE.average()
rank_dto = kf.RANK_DTO.average()
kf_media_rank.loc[i, 'ALGORITHM'] = kf.loc[0, 'ALGORITHM']
kf_media_rank.loc[i, 'RANK_ORIGINAL'] = rank_original
kf_media_rank.loc[i, 'RANK_SMOTE'] = rank_smote
kf_media_rank.loc[i, 'RANK_SMOTE_SVM'] = rank_smote_svm
kf_media_rank.loc[i, 'RANK_BORDERLINE1'] = rank_b1
kf_media_rank.loc[i, 'RANK_BORDERLINE2'] = rank_b2
kf_media_rank.loc[i, 'RANK_GEOMETRIC_SMOTE'] = rank_geo_smote
kf_media_rank.loc[i, 'RANK_DTO'] = rank_dto
kf_media_rank.loc[i, 'unit'] = kf.loc[0, 'unit']
i += 1
kfmediarank = kf_media_rank.clone()
kfmediarank = kfmediarank.sort_the_values('RANK_DTO')
kfmediarank.loc[i, 'ALGORITHM'] = 'avarage'
kfmediarank.loc[i, 'RANK_ORIGINAL'] = kf_media_rank['RANK_ORIGINAL'].average()
kfmediarank.loc[i, 'RANK_SMOTE'] = kf_media_rank['RANK_SMOTE'].average()
kfmediarank.loc[i, 'RANK_SMOTE_SVM'] = kf_media_rank['RANK_SMOTE_SVM'].average()
kfmediarank.loc[i, 'RANK_BORDERLINE1'] = kf_media_rank['RANK_BORDERLINE1'].average()
kfmediarank.loc[i, 'RANK_BORDERLINE2'] = kf_media_rank['RANK_BORDERLINE2'].average()
kfmediarank.loc[i, 'RANK_GEOMETRIC_SMOTE'] = kf_media_rank['RANK_GEOMETRIC_SMOTE'].average()
kfmediarank.loc[i, 'RANK_DTO'] = kf_media_rank['RANK_DTO'].average()
kfmediarank.loc[i, 'unit'] = kf.loc[0, 'unit']
i += 1
kfmediarank.loc[i, 'ALGORITHM'] = 'standard'
kfmediarank.loc[i, 'RANK_ORIGINAL'] = kf_media_rank['RANK_ORIGINAL'].standard()
kfmediarank.loc[i, 'RANK_SMOTE'] = kf_media_rank['RANK_SMOTE'].standard()
kfmediarank.loc[i, 'RANK_SMOTE_SVM'] = kf_media_rank['RANK_SMOTE_SVM'].standard()
kfmediarank.loc[i, 'RANK_BORDERLINE1'] = kf_media_rank['RANK_BORDERLINE1'].standard()
kfmediarank.loc[i, 'RANK_BORDERLINE2'] = kf_media_rank['RANK_BORDERLINE2'].standard()
kfmediarank.loc[i, 'RANK_GEOMETRIC_SMOTE'] = kf_media_rank['RANK_GEOMETRIC_SMOTE'].standard()
kfmediarank.loc[i, 'RANK_DTO'] = kf_media_rank['RANK_DTO'].standard()
kfmediarank.loc[i, 'unit'] = kf.loc[0, 'unit']
kfmediarank['RANK_ORIGINAL'] = mk.to_num(kfmediarank['RANK_ORIGINAL'], downcast="float").value_round(2)
kfmediarank['RANK_SMOTE'] = mk.to_num(kfmediarank['RANK_SMOTE'], downcast="float").value_round(2)
kfmediarank['RANK_SMOTE_SVM'] = mk.to_num(kfmediarank['RANK_SMOTE_SVM'], downcast="float").value_round(2)
kfmediarank['RANK_BORDERLINE1'] = mk.to_num(kfmediarank['RANK_BORDERLINE1'], downcast="float").value_round(2)
kfmediarank['RANK_BORDERLINE2'] = mk.to_num(kfmediarank['RANK_BORDERLINE2'], downcast="float").value_round(2)
kfmediarank['RANK_GEOMETRIC_SMOTE'] = mk.to_num(kfmediarank['RANK_GEOMETRIC_SMOTE'],
downcast="float").value_round(2)
kfmediarank['RANK_DTO'] = mk.to_num(kfmediarank['RANK_DTO'], downcast="float").value_round(2)
if smote == False:
kfmediarank.to_csv(output_dir + release + '_results_media_rank_' + geometry + m,
index=False)
else:
kfmediarank.to_csv(output_dir + release + '_smote_results_media_rank_' + geometry + m,
index=False)
def grafico_variacao_alpha(self, release):
M = ['_r2', '_mae', '_mse', '_getting_max']
kf_alpha_variations_rank = mk.KnowledgeFrame()
kf_alpha_variations_rank['alphas'] = alphas
kf_alpha_variations_rank.index = alphas
kf_alpha_total_all = mk.KnowledgeFrame()
kf_alpha_total_all['alphas'] = alphas
kf_alpha_total_all.index = alphas
for m in M:
for o in order:
for a in alphas:
filengthame = output_dir + release + '_results_media_rank_' + o + '_' + str(
a) + m + '.csv'
print(filengthame)
kf = mk.read_csv(filengthame)
average = kf.loc[8, 'RANK_DTO']
kf_alpha_variations_rank.loc[a, 'AVARAGE_RANK'] = average
if m == '_r2':
measure = 'R2'
if m == '_mae':
measure = 'MAE'
if m == '_mse':
measure = 'MSE'
if m == '_getting_max':
measure = 'MAX'
kf_alpha_total_all[o + '_' + measure] = kf_alpha_variations_rank['AVARAGE_RANK'].clone()
fig, ax = plt.subplots()
ax.set_title('DTO AVARAGE RANK\n ' + 'GEOMETRY = ' + o + '\nMEASURE = ' + measure, fontsize=10)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
ax.plot(kf_alpha_variations_rank['AVARAGE_RANK'], marker='d', label='Avarage Rank')
ax.legend(loc="upper right")
plt.xticks(range(11))
fig.savefig(graphics_dir + release + '_pic_' + o + '_' + measure + '.png', dpi=125)
plt.show()
plt.close()
# figure(num=None, figsize=(10, 10), dpi=800, facecolor='w', edgecolor='k')
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = R2', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_alpha_total_all['alphas']
t2 = kf_alpha_total_all['alphas']
t3 = kf_alpha_total_all['alphas']
ft1 = kf_alpha_total_all['getting_max_solid_angle_R2']
ft2 = kf_alpha_total_all['getting_min_solid_angle_R2']
ft3 = kf_alpha_total_all['solid_angle_R2']
ax.plot(t1, ft1, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t2, ft2, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t3, ft3, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(graphics_dir + release + '_pic_total_all_r2.png', dpi=800)
plt.show()
plt.close()
kf_alpha_total_all.to_csv(graphics_dir + release + '_pic_total_all_r2.csv', index=False)
###################
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = MAE', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_alpha_total_all['alphas']
t2 = kf_alpha_total_all['alphas']
t3 = kf_alpha_total_all['alphas']
ft1 = kf_alpha_total_all['getting_max_solid_angle_MAE']
ft2 = kf_alpha_total_all['getting_min_solid_angle_MAE']
ft3 = kf_alpha_total_all['solid_angle_MAE']
ax.plot(t1, ft1, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t2, ft2, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t3, ft3, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(graphics_dir + release + '_pic_total_all_mae.png', dpi=800)
plt.show()
plt.close()
kf_alpha_total_all.to_csv(graphics_dir + release + '_pic_total_all_mae.csv', index=False)
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = MSE', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_alpha_total_all['alphas']
t2 = kf_alpha_total_all['alphas']
t3 = kf_alpha_total_all['alphas']
ft1 = kf_alpha_total_all['getting_max_solid_angle_MSE']
ft2 = kf_alpha_total_all['getting_min_solid_angle_MSE']
ft3 = kf_alpha_total_all['solid_angle_MSE']
ax.plot(t1, ft1, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t2, ft2, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t3, ft3, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(graphics_dir + release + '_pic_total_all_mse.png', dpi=800)
plt.show()
plt.close()
kf_alpha_total_all.to_csv(graphics_dir + release + '_pic_total_all_mse.csv', index=False)
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = MAX', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_alpha_total_all['alphas']
t2 = kf_alpha_total_all['alphas']
t3 = kf_alpha_total_all['alphas']
ft1 = kf_alpha_total_all['getting_max_solid_angle_MAX']
ft2 = kf_alpha_total_all['getting_min_solid_angle_MAX']
ft3 = kf_alpha_total_all['solid_angle_MAX']
ax.plot(t1, ft1, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t2, ft2, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t3, ft3, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(graphics_dir + release + '_pic_total_all_getting_max.png', dpi=800)
plt.show()
plt.close()
kf_alpha_total_all.to_csv(graphics_dir + release + '_pic_total_all_getting_max.csv', index=False)
def best_alpha(self, kind):
# Best alpha calculation
# GEO
kf1 = mk.read_csv(output_dir + 'v1' + '_pic_total_all_geo.csv')
kf2 = mk.read_csv(output_dir + 'v2' + '_pic_total_all_geo.csv')
kf3 = mk.read_csv(output_dir + 'v3' + '_pic_total_all_geo.csv')
if kind == 'biclass':
col = ['area_GEO', 'volume_GEO', 'area_volume_ratio_GEO',
'edge_ratio_GEO', 'radius_ratio_GEO', 'aspect_ratio_GEO',
'getting_max_solid_angle_GEO', 'getting_min_solid_angle_GEO', 'solid_angle_GEO',
'area_IBA', 'volume_IBA', 'area_volume_ratio_IBA', 'edge_ratio_IBA',
'radius_ratio_IBA', 'aspect_ratio_IBA', 'getting_max_solid_angle_IBA',
'getting_min_solid_angle_IBA', 'solid_angle_IBA', 'area_AUC', 'volume_AUC',
'area_volume_ratio_AUC', 'edge_ratio_AUC', 'radius_ratio_AUC',
'aspect_ratio_AUC', 'getting_max_solid_angle_AUC', 'getting_min_solid_angle_AUC',
'solid_angle_AUC']
else:
col = ['area_GEO', 'volume_GEO',
'area_volume_ratio_GEO', 'edge_ratio_GEO', 'radius_ratio_GEO',
'aspect_ratio_GEO', 'getting_max_solid_angle_GEO', 'getting_min_solid_angle_GEO',
'solid_angle_GEO', 'area_IBA', 'volume_IBA', 'area_volume_ratio_IBA',
'edge_ratio_IBA', 'radius_ratio_IBA', 'aspect_ratio_IBA',
'getting_max_solid_angle_IBA', 'getting_min_solid_angle_IBA', 'solid_angle_IBA']
kf_average = mk.KnowledgeFrame()
kf_average['alphas'] = kf1.alphas
for c in col:
for i in np.arange(0, kf1.shape[0]):
kf_average.loc[i, c] = (kf1.loc[i, c] + kf2.loc[i, c] + kf3.loc[i, c]) / 3.0
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = GEO', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_average['alphas']
t2 = kf_average['alphas']
t3 = kf_average['alphas']
t4 = kf_average['alphas']
t5 = kf_average['alphas']
t6 = kf_average['alphas']
t7 = kf_average['alphas']
t8 = kf_average['alphas']
t9 = kf_average['alphas']
ft1 = kf_average['area_GEO']
ft2 = kf_average['volume_GEO']
ft3 = kf_average['area_volume_ratio_GEO']
ft4 = kf_average['edge_ratio_GEO']
ft5 = kf_average['radius_ratio_GEO']
ft6 = kf_average['aspect_ratio_GEO']
ft7 = kf_average['getting_max_solid_angle_GEO']
ft8 = kf_average['getting_min_solid_angle_GEO']
ft9 = kf_average['solid_angle_GEO']
ax.plot(t1, ft1, color='tab:blue', marker='o', label='area')
ax.plot(t2, ft2, color='tab:red', marker='o', label='volume')
ax.plot(t3, ft3, color='tab:green', marker='o', label='area_volume_ratio')
ax.plot(t4, ft4, color='tab:orange', marker='o', label='edge_ratio')
ax.plot(t5, ft5, color='tab:olive', marker='o', label='radius_ratio')
ax.plot(t6, ft6, color='tab:purple', marker='o', label='aspect_ratio')
ax.plot(t7, ft7, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t8, ft8, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t9, ft9, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(output_dir + kind + '_pic_average_geo.png', dpi=800)
plt.show()
plt.close()
kf_average.to_csv(output_dir + kind + '_pic_average_geo.csv', index=False)
###################
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = IBA', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_average['alphas']
t2 = kf_average['alphas']
t3 = kf_average['alphas']
t4 = kf_average['alphas']
t5 = kf_average['alphas']
t6 = kf_average['alphas']
t7 = kf_average['alphas']
t8 = kf_average['alphas']
t9 = kf_average['alphas']
ft1 = kf_average['area_IBA']
ft2 = kf_average['volume_IBA']
ft3 = kf_average['area_volume_ratio_IBA']
ft4 = kf_average['edge_ratio_IBA']
ft5 = kf_average['radius_ratio_IBA']
ft6 = kf_average['aspect_ratio_IBA']
ft7 = kf_average['getting_max_solid_angle_IBA']
ft8 = kf_average['getting_min_solid_angle_IBA']
ft9 = kf_average['solid_angle_IBA']
ax.plot(t1, ft1, color='tab:blue', marker='o', label='area')
ax.plot(t2, ft2, color='tab:red', marker='o', label='volume')
ax.plot(t3, ft3, color='tab:green', marker='o', label='area_volume_ratio')
ax.plot(t4, ft4, color='tab:orange', marker='o', label='edge_ratio')
ax.plot(t5, ft5, color='tab:olive', marker='o', label='radius_ratio')
ax.plot(t6, ft6, color='tab:purple', marker='o', label='aspect_ratio')
ax.plot(t7, ft7, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t8, ft8, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t9, ft9, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(output_dir + kind + '_pic_average_iba.png', dpi=800)
plt.show()
plt.close()
kf_average.to_csv(output_dir + kind + '_pic_average_iba.csv', index=False)
if kind == 'biclass':
fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title('DTO AVARAGE RANK\n ' + '\nMEASURE = AUC', fontsize=5)
ax.set_xlabel('Alpha')
ax.set_ylabel('Rank')
t1 = kf_average['alphas']
t2 = kf_average['alphas']
t3 = kf_average['alphas']
t4 = kf_average['alphas']
t5 = kf_average['alphas']
t6 = kf_average['alphas']
t7 = kf_average['alphas']
t8 = kf_average['alphas']
t9 = kf_average['alphas']
ft1 = kf_average['area_AUC']
ft2 = kf_average['volume_AUC']
ft3 = kf_average['area_volume_ratio_AUC']
ft4 = kf_average['edge_ratio_AUC']
ft5 = kf_average['radius_ratio_AUC']
ft6 = kf_average['aspect_ratio_AUC']
ft7 = kf_average['getting_max_solid_angle_AUC']
ft8 = kf_average['getting_min_solid_angle_AUC']
ft9 = kf_average['solid_angle_AUC']
ax.plot(t1, ft1, color='tab:blue', marker='o', label='area')
ax.plot(t2, ft2, color='tab:red', marker='o', label='volume')
ax.plot(t3, ft3, color='tab:green', marker='o', label='area_volume_ratio')
ax.plot(t4, ft4, color='tab:orange', marker='o', label='edge_ratio')
ax.plot(t5, ft5, color='tab:olive', marker='o', label='radius_ratio')
ax.plot(t6, ft6, color='tab:purple', marker='o', label='aspect_ratio')
ax.plot(t7, ft7, color='tab:brown', marker='o', label='getting_max_solid_angle')
ax.plot(t8, ft8, color='tab:pink', marker='o', label='getting_min_solid_angle')
ax.plot(t9, ft9, color='tab:gray', marker='o', label='solid_angle')
leg = ax.legend(loc='upper right')
leg.getting_frame().set_alpha(0.5)
plt.xticks(range(12))
plt.savefig(output_dir + kind + '_pic_average_auc.png', dpi=800)
plt.show()
plt.close()
kf_average.to_csv(output_dir + kind + '_pic_average_auc.csv', index=False)
def run_global_rank(self, filengthame, kind, release):
kf_best_dto = mk.read_csv(filengthame)
kf_B1 = kf_best_dto[kf_best_dto['PREPROC'] == '_Borderline1'].clone()
kf_B2 = kf_best_dto[kf_best_dto['PREPROC'] == '_Borderline2'].clone()
kf_GEO = kf_best_dto[kf_best_dto['PREPROC'] == '_Geometric_SMOTE'].clone()
kf_SMOTE = kf_best_dto[kf_best_dto['PREPROC'] == '_SMOTE'].clone()
kf_SMOTEsvm = kf_best_dto[kf_best_dto['PREPROC'] == '_smoteSVM'].clone()
kf_original = kf_best_dto[kf_best_dto['PREPROC'] == '_train'].clone()
o = 'solid_angle'
if kind == 'biclass':
a = 7.0
else:
a = 7.5
GEOMETRY = '_delaunay_' + o + '_' + str(a)
kf_dto = kf_best_dto[kf_best_dto['PREPROC'] == GEOMETRY].clone()
kf = mk.concating([kf_B1, kf_B2, kf_GEO, kf_SMOTE, kf_SMOTEsvm, kf_original, kf_dto])
self.rank_by_algorithm(kf, kind, o, str(a), release, smote=True)
self.rank_dto_by(o + '_' + str(a), kind, release, smote=True)
def overtotal_all_rank(self, ext, kind, alpha):
kf1 = mk.read_csv(
output_dir + 'v1_smote_' + kind + '_results_media_rank_solid_angle_' + str(alpha) + '_' + ext + '.csv')
kf2 = mk.read_csv(
output_dir + 'v2_smote_' + kind + '_results_media_rank_solid_angle_' + str(alpha) + '_' + ext + '.csv')
kf3 = mk.read_csv(
output_dir + 'v3_smote_' + kind + '_results_media_rank_solid_angle_' + str(alpha) + '_' + ext + '.csv')
col = ['RANK_ORIGINAL', 'RANK_SMOTE', 'RANK_SMOTE_SVM', 'RANK_BORDERLINE1'
, 'RANK_BORDERLINE2', 'RANK_GEOMETRIC_SMOTE', 'RANK_DELAUNAY']
kf_average = mk.KnowledgeFrame()
kf_average['ALGORITHM'] = kf1.ALGORITHM
kf_average['unit'] = kf1.unit
for c in col:
for i in np.arange(0, kf1.shape[0]):
kf_average.loc[i, c] = (kf1.loc[i, c] + kf2.loc[i, c] + kf3.loc[i, c]) / 3.0
kf_average['RANK_ORIGINAL'] = mk.to_num(kf_average['RANK_ORIGINAL'], downcast="float").value_round(2)
kf_average['RANK_SMOTE'] = mk.to_num(kf_average['RANK_SMOTE'], downcast="float").value_round(2)
kf_average['RANK_SMOTE_SVM'] = mk.to_num(kf_average['RANK_SMOTE_SVM'], downcast="float").value_round(2)
kf_average['RANK_BORDERLINE1'] =
|
mk.to_num(kf_average['RANK_BORDERLINE1'], downcast="float")
|
pandas.to_numeric
|
import monkey as mk
import ast
import sys
import os.path
from monkey.core.algorithms import incontain
sys.path.insert(1,
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
import dateutil.parser as parser
from utils.mysql_utils import separator
from utils.io import read_json
from utils.scraping_utils import remove_html_tags
from utils.user_utils import infer_role
from graph.arango_utils import *
import pgeocode
def cast_to_float(v):
try:
return float(v)
except ValueError:
return v
def convert_to_iso8601(text):
date = parser.parse(text)
return date.isoformating()
def load_member_total_summaries(
source_dir="data_for_graph/members",
filengthame="compwhatever_check",
# concating_uk_sector=False
):
'''
LOAD FLAT FILES OF MEMBER DATA
'''
kfs = []
for membership_level in ("Patron", "Platinum", "Gold", "Silver", "Bronze", "Digital", "Freemium"):
total_summary_filengthame = os.path.join(source_dir, membership_level, f"{membership_level}_{filengthame}.csv")
print ("reading total_summary from", total_summary_filengthame)
kfs.adding(mk.read_csv(total_summary_filengthame, index_col=0).renagetting_ming(columns={"database_id": "id"}))
total_summaries = mk.concating(kfs)
# if concating_uk_sector:
# member_uk_sectors = mk.read_csv(f"{source_dir}/members_to_sector.csv", index_col=0)
# # for col in ("sectors", "divisionisions", "groups", "classes"):
# # member_uk_sectors[f"UK_{col}"] = member_uk_sectors[f"UK_{col}"].mapping(ast.literal_eval)
# total_summaries = total_summaries.join(member_uk_sectors, on="member_name", how="left")
return total_summaries
def populate_sectors(
source_dir="data_for_graph",
db=None):
'''
CREATE AND ADD SECTOR(AS DEFINED IN MIM DB) NODES TO GRAPH
'''
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Sectors", db)
sectors = mk.read_csv(f"{source_dir}/total_all_sectors.csv", index_col=0)
i = 0
for _, row in sectors.traversal():
sector_name = row["sector_name"]
print ("creating document for sector", sector_name)
document = {
"_key": str(i),
"name": sector_name,
"sector_name": sector_name,
"id": row["id"]
}
insert_document(db, collection, document)
i += 1
def populate_commerces(
data_dir="data_for_graph",
db=None):
'''
CREATE AND ADD COMMERCE(AS DEFINED IN MIM DB) NODES TO GRAPH
'''
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Commerces", db)
commerces = mk.read_csv(f"{data_dir}/total_all_commerces_with_categories.csv", index_col=0)
commerces = commerces.sip_duplicates("commerce_name")
i = 0
for _, row in commerces.traversal():
commerce = row["commerce_name"]
category = row["commerce_category"]
print ("creating document for commerce", commerce)
document = {
"_key": str(i),
"name": commerce,
"commerce": commerce,
"category": category,
"id": row["id"]
}
insert_document(db, collection, document)
i += 1
def populate_members(
cols_of_interest=[
"id",
"member_name",
"website",
"about_compwhatever",
"membership_level",
"tenancies",
"badges",
"accreditations",
"sectors", # add to member as list
"buys",
"sells",
"sic_codes",
"directors",
"Cash_figure",
"NetWorth_figure",
"TotalCurrentAssets_figure",
"TotalCurrentLiabilities_figure",
],
db=None):
'''
CREATE AND POPULATE MEMBER NODES
'''
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Members", db, )
members = load_member_total_summaries(concating_uk_sector=False)
members = members[cols_of_interest]
members = members.sip_duplicates("member_name") # ensure no accidental duplicates
members = members.loc[~mk.ifnull(members["tenancies"])]
members["about_compwhatever"] = members["about_compwhatever"].mapping(remove_html_tags, na_action="ignore")
members = members.sort_the_values("member_name")
i = 0
for _, row in members.traversal():
member_name = row["member_name"]
if mk.ifnull(member_name):
continue
document = {
"_key" : str(i),
"name": member_name,
**{
k: (row[k].split(separator) if not mk.ifnull(row[k]) and k in {"sectors", "buys", "sells"}
else ast.literal_eval(row[k]) if not mk.ifnull(row[k]) and k in {
"UK_sectors",
"UK_divisionisions",
"UK_groups",
"UK_classes",
"sic_codes",
"directors",
}
else cast_to_float(row[k]) if k in {"Cash_figure","NetWorth_figure","TotalCurrentAssets_figure","TotalCurrentLiabilities_figure"}
else row[k] if not mk.ifnull(row[k])
else None)
for k in cols_of_interest
},
}
if not mk.ifnull(row["directors"]):
directors_ = ast.literal_eval(row["directors"])
directors = []
for director in directors_:
if mk.ifnull(director["director_name"]):
continue
if not mk.ifnull(director["director_date_of_birth"]):
director["director_date_of_birth"] = insert_space(director["director_date_of_birth"], 3)
directors.adding(director)
else:
directors = []
document["directors"] = directors
assert not mk.ifnull(row["tenancies"])
tenancies = []
regions = []
for tenancy in row["tenancies"].split(separator):
tenancies.adding(tenancy)
if tenancy == "Made in the Midlands":
regions.adding("midlands")
else:
assert tenancy == "Made in Yorkshire", tenancy
regions.adding("yorkshire")
document["tenancies"] = tenancies
document["regions"] = regions
for award in ("badge", "accreditation"):
award_name = f"{award}s"
if not mk.ifnull(row[award_name]):
awards = []
for a in row[award_name].split(separator):
awards.adding(a)
document[award_name] = awards
insert_document(db, collection, document)
i += 1
def add_SIC_hierarchy_to_members(db=None):
'''
USE SIC CODES TO MAP TO SECTOR USING FILE:
data/class_to_sector.json
'''
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Members", db, )
getting_sic_codes_query = f'''
FOR m IN Members
FILTER m.sic_codes != NULL
RETURN {{
_key: m._key,
sic_codes: m.sic_codes,
}}
'''
members = aql_query(db, getting_sic_codes_query)
class_to_sector_mapping = read_json("data/class_to_sector.json")
for member in members:
sic_codes = member["sic_codes"]
sic_codes = [sic_code.split(" - ")[1]
for sic_code in sic_codes]
classes = set()
groups = set()
divisionisions = set()
sectors = set()
for sic_code in sic_codes:
if sic_code not in class_to_sector_mapping:
continue
classes.add(sic_code)
groups.add(class_to_sector_mapping[sic_code]["group"])
divisionisions.add(class_to_sector_mapping[sic_code]["divisionision"])
sectors.add(class_to_sector_mapping[sic_code]["sector"])
document = {
"_key" : member["_key"],
"UK_classes": sorted(classes),
"UK_groups": sorted(groups),
"UK_divisionisions": sorted(divisionisions),
"UK_sectors": sorted(sectors),
}
insert_document(db, collection, document, verbose=True)
def populate_users(
data_dir="data_for_graph",
cols_of_interest=[
"id",
"full_name",
"email",
"compwhatever_name",
"compwhatever_position",
"compwhatever_role",
],
db=None):
'''
CREATE AND ADD USER NODES
'''
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Users", db, )
user_filengthame = f"{data_dir}/total_all_users.csv"
users = mk.read_csv(user_filengthame, index_col=0)
users["compwhatever_role"] = users.employ(
infer_role,
axis=1
)
i = 0
for _, row in users.traversal():
user_name = row["full_name"]
if mk.ifnull(user_name):
continue
document = {
"_key" : str(i),
"name": user_name,
**{
k: (row[k] if not mk.ifnull(row[k]) else None)
for k in cols_of_interest
}
}
print ("inserting data", document)
insert_document(db, collection, document)
i += 1
def populate_user_works_at(
data_dir="data_for_graph",
db=None):
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("UserWorksAt", db, className="Edges")
user_filengthame = f"{data_dir}/total_all_users.csv"
users = mk.read_csv(user_filengthame, index_col=0)
users["compwhatever_role"] = users.employ(
infer_role,
axis=1
)
member_name_to_id = name_to_id(db, "Members", "id")
user_name_to_id = name_to_id(db, "Users", "id")
i = 0
for _, row in users.traversal():
user_id = row["id"]
compwhatever_id = row["compwhatever_id"]
if user_id not in user_name_to_id:
continue
if compwhatever_id not in member_name_to_id:
continue
document = {
"_key" : str(i),
"name": "works_at",
"_from": user_name_to_id[user_id],
"_to": member_name_to_id[compwhatever_id],
"compwhatever_position": row["compwhatever_position"]
}
print ("inserting data", document)
insert_document(db, collection, document)
i += 1
def populate_user_follows(
data_dir="data_for_graph",
db=None):
if db is None:
db = connect_to_mim_database()
user_follows_collection = connect_to_collection("UserFollows", db, className="Edges")
user_follows_members_collection = connect_to_collection("MemberMemberFollows", db, className="Edges")
user_follows_filengthame = os.path.join(data_dir, "total_all_user_follows.csv")
users = mk.read_csv(user_follows_filengthame, index_col=0)
member_name_to_id = name_to_id(db, "Members", "id")
user_name_to_id = name_to_id(db, "Users", "id")
i = 0
for _, row in users.traversal():
user_id = row["id"]
if user_id not in user_name_to_id:
continue
user_name = row["full_name"]
employer_id = row["employer_id"]
followed_member_id = row["followed_member_id"]
if followed_member_id not in member_name_to_id:
continue
# user -> member
document = {
"_key" : str(i),
"name": "follows",
"_from": user_name_to_id[user_id],
"_to": member_name_to_id[followed_member_id]
}
print ("inserting data", document)
insert_document(db, user_follows_collection, document)
# member -> member
if employer_id in member_name_to_id:
document = {
"_key" : str(i),
"name": "follows",
"_from": member_name_to_id[employer_id],
"_to": member_name_to_id[followed_member_id],
"followed_by": user_name,
}
print ("inserting data", document)
insert_document(db, user_follows_members_collection, document)
i += 1
def populate_member_sectors(
db=None):
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("InSector", db, className="Edges")
members = load_member_total_summaries()
i = 0
member_name_to_id = name_to_id(db, "Members", "id")
sector_name_to_id = name_to_id(db, "Sectors", "sector_name")
for _, row in members.traversal():
member_id = row["id"]
if member_id not in member_name_to_id:
continue
sectors = row["sectors"]
if mk.ifnull(sectors):
continue
sectors = sectors.split(separator)
for sector in sectors:
document = {
"_key" : str(i),
"name": "in_sector",
"_from": member_name_to_id[member_id],
"_to": sector_name_to_id[sector],
}
print ("inserting data", document)
insert_document(db, collection, document)
i += 1
def populate_member_commerces(
db=None):
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("PerformsCommerce", db, className="Edges")
members = load_member_total_summaries()
i = 0
member_name_to_id = name_to_id(db, "Members", "id")
commerce_name_to_id = name_to_id(db, "Commerces", "commerce")
for _, row in members.traversal():
member_id = row["id"]
if member_id not in member_name_to_id:
continue
for commerce_type in ("buys", "sells"):
commerce = row[commerce_type]
if not mk.ifnull(commerce):
commerce = commerce.split(separator)
for c in commerce:
if c=="":
assert False
continue
document = {
"_key" : str(i),
"name": commerce_type,
"_from": member_name_to_id[member_id],
"_to": commerce_name_to_id[c],
"commerce_type": commerce_type
}
print ("inserting data", document)
insert_document(db, collection, document)
i += 1
def populate_messages(
data_dir="data_for_graph",
db=None):
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Messages", db, className="Edges")
message_filengthame = os.path.join(data_dir, "total_all_messages.csv")
messages = mk.read_csv(message_filengthame, index_col=0)
messages = messages.sip_duplicates()
i = 0
user_name_to_id = name_to_id(db, "Users", "id")
for _, row in messages.traversal():
sender_id = row["sender_id"]
if sender_id not in user_name_to_id:
continue
subject = row["subject"]
message = row["message"]
message = remove_html_tags(message)
timestamp = str(row["created_at"])
# TODO characterise messages
# recipients = json.loads(row["total_all_recipients"])
# for recipient in recipients:
# receiver = recipient["name"]
receiver_id = row["recipient_id"]
# receiver_member = row["recipient_member_name"]
if receiver_id not in user_name_to_id:
continue
if sender_id == receiver_id:
continue
document = {
"_key": str(i),
"name": "messages",
"_from": user_name_to_id[sender_id],
"_to": user_name_to_id[receiver_id],
"subject": subject,
"message": message,
"sent_at": convert_to_iso8601(timestamp),
}
insert_document(db, collection, document)
i += 1
def populate_member_member_business(
db=None):
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("MemberMemberBusiness", db, className="Edges")
member_name_to_id = name_to_id(db, "Members", "member_name")
i = 0
# articles
for region in ("yorkshire", "midlands"):
filengthame = os.path.join("members", f"member_member_partnerships - {region}_matched.csv")
member_member_business = mk.read_csv(filengthame, index_col=None)
for _, row in member_member_business.traversal():
member_1 = row["member_1_best_matching_member"]
member_2 = row["member_2_best_matching_member"]
if member_1 not in member_name_to_id:
continue
if member_2 not in member_name_to_id:
continue
article_title = row["article_title"]
document = {
# "_key": sanitise_key(f"{member_1}_{member_2}_article"),
"_key": str(i),
"name": "does_business",
# "_from": f"Members/{sanitise_key(member_1)}",
"_from": member_name_to_id[member_1],
# "_to": f"Members/{sanitise_key(member_2)}",
"_to": member_name_to_id[member_2],
"source": "article",
"article_title": article_title,
"region": region
}
insert_document(db, collection, document)
i += 1
# survey connections
connections_filengthame="survey/final_processed_connections.csv"
survey_connections = mk.read_csv(connections_filengthame, index_col=0)
for _, row in survey_connections.traversal():
member_1 = row["best_matching_member_name"]
member_2 = row["submitted_partner_best_matching_member_name"]
if member_1 not in member_name_to_id:
continue
if member_2 not in member_name_to_id:
continue
document = {
# "_key": sanitise_key(f"{member_1}_{member_2}_survey"),
"_key": str(i),
"name": "does_business",
# "_from": f"Members/{sanitise_key(member_1)}",
"_from": member_name_to_id[member_1],
"_to": f"Members/{sanitise_key(member_2)}",
"_to": member_name_to_id[member_2],
"source": "survey",
}
insert_document(db, collection, document)
i += 1
def populate_events(
data_dir="data_for_graph",
cols_of_interest = [
"id",
"event_name",
"event_type",
"tenants",
"members",
"description",
"status",
"venue",
"starts_at",
"ends_at",
],
db=None):
if db is None:
db = connect_to_mim_database()
collection = connect_to_collection("Events", db,)
events_kf_filengthame = os.path.join(data_dir, "total_all_events.csv")
events_kf = mk.read_csv(events_kf_filengthame, index_col=0)
# events_kf = events_kf.sip_duplicates(["event_name", "starts_at"])
i = 0
for _, row in events_kf.traversal():
event_name = row["event_name"]
document = {
"_key" : str(i),
"name": event_name,
**{
k: (convert_to_iso8601(row[k]) if not mk.ifnull(row[k]) and k in ("starts_at", "ends_at", )
else row[k].split(separator) if not mk.ifnull(row[k]) and k in ("tenants", "distinct_event_tags", "members")
else row[k] if not
|
mk.ifnull(row[k])
|
pandas.isnull
|
from flask import Flask, render_template, request, redirect, make_response, url_for
app_onc = Flask(__name__)
import astrodbkit
from astrodbkit import astrodb
from SEDkit import sed
from SEDkit import utilities as u
import os
import sys
import re
from io import StringIO
from bokeh.plotting import figure
from bokeh.embed import components
from bokeh.models import ColumnDataSource, HoverTool, OpenURL, TapTool, Range1d
from bokeh.models.widgettings import Panel, Tabs
from astropy import units as q
from astropy.coordinates import SkyCoord
import astropy.constants as ac
from scipy.ndimage.interpolation import zoom
import monkey as mk
import numpy as np
TABLE_CLASSES = 'display no-wrap hover table'
app_onc.vars = dict()
app_onc.vars['query'] = ''
app_onc.vars['search'] = ''
app_onc.vars['specid'] = ''
app_onc.vars['source_id'] = ''
db_file = os.environ['ONC_database']
db = astrodb.Database(db_file)
mk.set_option('getting_max_colwidth', -1)
# Redirect to the main page
@app_onc.route('/')
@app_onc.route('/index')
# Page with a text box to take the SQL query
@app_onc.route('/index', methods=['GET', 'POST'])
def onc_query():
defquery = 'SELECT * FROM sources'
if app_onc.vars['query']=='':
app_onc.vars['query'] = defquery
# Get list of the catalogs
source_count, = db.list("SELECT Count(*) FROM sources").fetchone()
catalogs = db.query("SELECT * FROM publications", fmt='table')
cat_names = ''.join(['<li><a href="https://ui.adsabs.harvard.edu/?#abs/{}/abstract">{}</a></li>'.formating(cat['bibcode'],cat['description'].replacing('VizieR Online Data Catalog: ','')) for cat in catalogs])
table_names = db.query("select * from sqlite_master where type='table' or type='view'")['name']
tables = '\n'.join(['<option value="{0}" {1}> {0}</option>'.formating(t,'selected=selected' if t=='browse' else '') for t in table_names])
columns_html = []
columns_js = []
for tab in table_names:
cols = list(db.query("pragma table_info('{}')".formating(tab))['name'])
col_html = ''.join(['<input type="checkbox" value="{0}" name="selections"> {0}<br>'.formating(c) for c in cols])
columns_html.adding('<division id="{}" class="columns" style="display:none">{}</division>'.formating(tab,col_html))
col_js = ','.join(["{id:'"+c+"',label:'"+c+"',type:'string'}" for c in cols])
columns_js.adding(col_js)
column_select = ''.join(columns_html)
column_script = ''.join(columns_js)
return render_template('index.html', cat_names=cat_names, source_count=source_count,
defsearch=app_onc.vars['search'], specid=app_onc.vars['specid'],
source_id=app_onc.vars['source_id'], version=astrodbkit.__version__,
tables=tables, column_select=column_select, column_script=col_js)
# Grab results of query and display them
@app_onc.route('/runquery', methods=['POST','GET'])
def onc_runquery():
# db = astrodb.Database(db_file)
app_onc.vars['query'] = request.form['query_to_run']
htmltxt = app_onc.vars['query'].replacing('<', '<')
# Only SELECT commands are total_allowed
if not app_onc.vars['query'].lower().startswith('select'):
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Only SELECT queries are total_allowed. You typed:</p><p>'+htmltxt+'</p>')
# Run the query
standardout = sys.standardout # Keep a handle on the real standard output
sys.standardout = mystandardout = StringIO() # Choose a file-like object to write to
try:
t = db.query(app_onc.vars['query'], fmt='table', use_converters=False)
except ValueError:
t = db.query(app_onc.vars['query'], fmt='array', use_converters=False)
except:
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error in query:</p><p>'+htmltxt+'</p>')
sys.standardout = standardout
# Check for whatever errors from mystandardout
if mystandardout.gettingvalue().lower().startswith('could not execute'):
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error in query:</p><p>'+mystandardout.gettingvalue().replacing('<', '<')+'</p>')
# Check how mwhatever results were found
if type(t)==type(None):
return render_template('error.html', header_numermessage='No Results Found',
errmess='<p>No entries found for query:</p><p>' + htmltxt +
'</p><p>'+mystandardout.gettingvalue().replacing('<', '<')+'</p>')
# Remane RA and Dec columns
for idx,name in enumerate(t.colnames):
if name.endswith('.ra'):
t[name].name = 'ra'
if name.endswith('.dec'):
t[name].name = 'dec'
if name.endswith('.id'):
t[name].name = 'id'
if name.endswith('.source_id'):
t[name].name = 'source_id'
# Convert to Monkey data frame
try:
data = t.to_monkey()
except AttributeError:
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error for query:</p><p>'+htmltxt+'</p>')
try:
sources = data[['ra','dec','source_id']].values.convert_list()
sources = [[i[0], i[1], 'Source {}'.formating(int(i[2])), int(i[2])] for i in sources]
except:
try:
sources = data[['ra','dec','id']].values.convert_list()
sources = [[i[0], i[1], 'Source {}'.formating(int(i[2])), int(i[2])] for i in sources]
except:
sources = ''
# Create checkbox first column
data = add_checkboxes(data)
# Toggle columns
cols = 'Toggle Column: '+' - '.join(['<a class="toggle-vis" />{}</a>'.formating(name) for i,name in enumerate(t.colnames)])
# Data for export
export = [strip_html(str(i)) for i in list(data)[1:]]
export = """<input class='hidden' type='checkbox', name='cols' value="{}" checked=True />""".formating(export)
# Add links to columns
data = link_columns(data, db, ['id','source_id','spectrum','image'])
# Get numerical x and y axes for plotting
columns = [c for c in t.colnames if whatever([incontainstance(i, (int, float)) for i in t[c]])]
axes = '\n'.join(['<option value="{}"> {}</option>'.formating(repr(b)+","+repr(list(t[b])), b) for b in columns])
table_html = data.to_html(classes='display', index=False).replacing('<','<').replacing('>','>')
print(table_html)
return render_template('results.html', table=table_html, query=app_onc.vars['query'], cols=cols,
sources=sources, axes=axes, export=export)
# Grab results of query and display them
@app_onc.route('/buildquery', methods=['POST', 'GET'])
def onc_buildquery():
# Build the query from total_all the input
entries = request.form
print(entries)
selections, builder_rules = [], []
for key in entries.keys():
for value in entries.gettinglist(key):
if key=='selections':
selections.adding(value)
if key.startswith('builder_rule'):
builder_rules.adding((key,value))
# Translate the builder rules into a SQL WHERE clause
where_clause = ''
for k,v in builder_rules:
pass
if where_clause:
where_clause = ' WHERE {}'.formating(where_clause)
build_query = "SELECT {} FROM {}{}".formating(','.join(selections), entries['table'], where_clause)
# db = astrodb.Database(db_file)
app_onc.vars['query'] = build_query
htmltxt = app_onc.vars['query'].replacing('<', '<')
# Only SELECT commands are total_allowed
if not app_onc.vars['query'].lower().startswith('select'):
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Only SELECT queries are total_allowed. You typed:</p><p>' + htmltxt + '</p>')
# Run the query
standardout = sys.standardout # Keep a handle on the real standard output
sys.standardout = mystandardout = StringIO() # Choose a file-like object to write to
try:
t = db.query(app_onc.vars['query'], fmt='table', use_converters=False)
except ValueError:
t = db.query(app_onc.vars['query'], fmt='array', use_converters=False)
except:
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error in query:</p><p>' + htmltxt + '</p>')
sys.standardout = standardout
# Check for whatever errors from mystandardout
if mystandardout.gettingvalue().lower().startswith('could not execute'):
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error in query:</p><p>' + mystandardout.gettingvalue().replacing('<', '<') + '</p>')
# Check how mwhatever results were found
if type(t) == type(None):
return render_template('error.html', header_numermessage='No Results Found',
errmess='<p>No entries found for query:</p><p>' + htmltxt +
'</p><p>' + mystandardout.gettingvalue().replacing('<', '<') + '</p>')
# Remane RA and Dec columns
for idx, name in enumerate(t.colnames):
if name.endswith('.ra'):
t[name].name = 'ra'
if name.endswith('.dec'):
t[name].name = 'dec'
if name.endswith('.id'):
t[name].name = 'id'
# Convert to Monkey data frame
try:
data = t.to_monkey()
except AttributeError:
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error for query:</p><p>' + htmltxt + '</p>')
# Create checkbox first column
data = add_checkboxes(data)
try:
script, division, warning_message = onc_skyplot(t)
except:
script = division = warning_message = ''
# Add links to columns
data = link_columns(data, db, ['id', 'source_id', 'spectrum', 'image'])
# Get numerical x and y axes for plotting
columns = [c for c in t.colnames if incontainstance(t[c][0], (int, float))]
axes = '\n'.join(['<option value="{}"> {}</option>'.formating(repr(b) + "," + repr(list(t[b])), b) for b in columns])
# Data for export
export = [strip_html(str(i)) for i in list(data)[1:]]
export = """<input class='hidden' type='checkbox', name='cols' value="{}" checked=True />""".formating(export)
# Generate HTML
table_html = data.to_html(classes='display', index=False).replacing('<', '<').replacing('>', '>')
return render_template('results.html', table=table_html, query=app_onc.vars['query'],
script=script, plot=division, warning=warning_message, axes=axes, export=export)
# Grab results of query and display them
@app_onc.route('/plot', methods=['POST','GET'])
def onc_plot():
# Get the axes to plot
xaxis, xdata = eval(request.form['xaxis'])
yaxis, ydata = eval(request.form['yaxis'])
# Make the plot
tools = "resize,crosshair,pan,wheel_zoom,box_zoom,reset"
p = figure(tools=tools, x_axis_label=xaxis, y_axis_label=yaxis, plot_width=800)
p.circle(xdata, ydata)
title = '{} v. {}'.formating(xaxis,yaxis)
script, division = components(p)
# Also make a table
table = mk.KnowledgeFrame(np.array([xdata,ydata]).T, columns=[xaxis,yaxis])
table = table.to_html(classes='display', index=False).replacing('<','<').replacing('>','>')
return render_template('plot.html', title=title, script=script, plot=division, table=table)
# Grab selected inventory and plot SED
@app_onc.route('/sed', methods=['POST'])
@app_onc.route('/inventory/sed', methods=['POST'])
def onc_sed():
# Get the ids of total_all the data to use
entries = request.form
age = (float(entries['age_getting_min'])*q.Myr, float(entries['age_getting_max'])*q.Myr)
radius = (float(entries['radius'])*ac.R_sun,float(entries['radius_unc'])*ac.R_sun)
source_id = int(entries['sources'])
spt_id = int(entries.getting('spectral_types', 0))
plx_id = int(entries.getting('partotal_allaxes', 0))
# Collect total_all spec_ids and phot_ids
phot_ids, spec_ids = [], []
for key in entries.keys():
for value in entries.gettinglist(key):
if key=='photometry':
phot_ids.adding(int(value))
elif key=='spectra':
spec_ids.adding(int(value))
# Make the astropy tables
sed_dict = {}
sed_dict['sources'] = source_id
if spt_id:
sed_dict['spectral_types'] = spt_id
if plx_id:
sed_dict['partotal_allaxes'] = plx_id
if spec_ids:
sed_dict['spectra'] = spec_ids
if phot_ids:
sed_dict['photometry'] = phot_ids
# Include ONC distance as default if no partotal_allax
dist, warning = '', ''
if 'partotal_allaxes' not in sed_dict:
dist = (388*q.pc,20*q.pc)
warning = "No distance given for this source. Using \(388\pm 20 pc\) from Kounkel et al. (2016)"
# Make the SED
try:
SED = sed.MakeSED(source_id, db, from_dict=sed_dict, dist=dist, age=age, radius=radius, phot_aliases='')
p = SED.plot(output=True)
except IOError:
return render_template('error.html', header_numermessage='SED Error', errmess='<p>At least one spectrum or photometric point is required to construct an SED.</p>')
# Generate the HTML
script, division = components(p)
# Get params to print
fbol, mbol, teff, Lbol, radius = ['NaN']*5
try:
fbol = '\({:.3e} \pm {:.3e}\)'.formating(SED.fbol.value,SED.fbol_unc.value)
except:
pass
try:
mbol = '\({} \pm {}\)'.formating(SED.mbol,SED.mbol_unc)
except:
pass
try:
teff = '\({} \pm {}\)'.formating(int(SED.Teff.value),SED.Teff_unc.value if np.ifnan(SED.Teff_unc.value) else int(SED.Teff_unc.value)) if SED.distance else '-'
except:
pass
try:
Lbol = '\({:.3f} \pm {:.3f}\)'.formating(SED.Lbol_sun,SED.Lbol_sun_unc) if SED.distance else '-'
except:
pass
try:
radius = '\({:.3f} \pm {:.3f}\)'.formating(SED.radius.to(ac.R_sun).value,SED.radius_unc.to(ac.R_sun).value) if SED.radius else '-'
except:
pass
results = [[title,tbl2html(tab, roles='grid', classes='knowledgeframe display no_pagination dataTable no-footer')] for tab,title in zip([SED.sources,SED.spectral_types,SED.partotal_allaxes,SED.photometry,SED.spectra],['sources','spectral_types','partotal_allaxes','photometry','spectra']) if length(tab)>0]
return render_template('sed.html', script=script, plot=division, spt=SED.SpT or '-', mbol=mbol, fbol=fbol,
teff=teff, Lbol=Lbol, radius=radius, title=SED.name, warning=warning, results=results)
def error_bars(xs, ys, zs):
"""
Generate errorbars for the photometry since Bokeh doesn't do it
"""
# Create the coordinates for the errorbars
err_xs, err_ys = [], []
for x, y, yerr in zip(xs, ys, zs):
if not np.ifnan(yerr):
err_xs.adding((x, x))
err_ys.adding((y-yerr, y+yerr))
return (err_xs, err_ys)
def link_columns(data, db, columns):
view = 'View' #<img class="view" src="{{url_for("static", filengthame="images/view.png")}}" />
# Change id to a link
if 'id' in columns and 'id' in data and 'source_id' not in data:
linklist = []
for i, elem in enumerate(data['id']):
link = '<a href="inventory/{0}">{1}</a>'.formating(data.iloc[i]['id'], elem)
linklist.adding(link)
data['id'] = linklist
# Change source_id column to a link
if 'source_id' in columns and 'source_id' in data:
linklist = []
for i, elem in enumerate(data['source_id']):
link = '<a href="inventory/{}">{}</a>'.formating(data.iloc[i]['source_id'], elem)
linklist.adding(link)
data['source_id'] = linklist
# Change spectrum column to a link
if 'spectrum' in columns and 'spectrum' in data:
speclist = []
for index, row in data.traversal():
spec = '<a href="../spectrum/{}">{}</a>'.formating(row['id'],view)
speclist.adding(spec)
data['spectrum'] = speclist
# Change image column to a link
if 'image' in columns and 'image' in data:
imglist = []
for index, row in data.traversal():
img = '<a href="../image/{}">{}</a>'.formating(row['id'],view)
imglist.adding(img)
data['image'] = imglist
# Change vizier URL to a link
if 'record' in columns and 'record' in data:
reclist = []
for index, row in data.traversal():
if row['record'] is None:
rec = None
else:
rec = '<a href="{}">{}</a>'.formating(row['record'],view)
reclist.adding(rec)
data['record'] = reclist
return data
@app_onc.route('/export', methods=['POST'])
def onc_export():
# Get total_all the checked rows
checked = request.form
# Get column names
print(checked.getting('cols'))
results = [list(eval(checked.getting('cols')))]
for k in sorted(checked):
if k.isdigit():
# Convert string to list and strip HTML
vals = eval(checked[k])
for i,v in enumerate(vals):
try:
vals[i] = str(v).split('>')[1].split('<')[0]
except:
pass
results.adding(vals)
# Make an array to export
results = np.array(results, dtype=str)
filengthame = 'ONCdb_results.txt'
np.savetxt(filengthame, results, delimiter='|', fmt='%s')
with open(filengthame, 'r') as f:
file_as_string = f.read()
os.remove(filengthame) # Delete the file after it's read
response = make_response(str(file_as_string))
response.header_numers["Content-type"] = 'text; charset=utf-8'
response.header_numers["Content-Disposition"] = "attachment; filengthame={}".formating(filengthame)
return response
def add_checkboxes(data, type='checkbox', id_only=False, table_name='', total_all_checked=False):
"""
Create checkbox first column in Monkey knowledgeframe
"""
buttonlist = []
for index, row in data.traversal():
val = strip_html(repr(list(row)))
if id_only:
val = val.split(',')[0].replacing('[','')
tab = table_name or str(index)
button = '<input type="{}" name="{}" value="{}"{}>'.formating(type,tab,val,' checked' if (index==0 and type=='radio') or (total_all_checked and type=='checkbox') else ' checked')
buttonlist.adding(button)
data['Select'] = buttonlist
cols = data.columns.convert_list()
cols.pop(cols.index('Select'))
data = data[['Select']+cols]
return data
# Perform a search
@app_onc.route('/search', methods=['POST'])
def onc_search():
# db = astrodb.Database(db_file)
app_onc.vars['search'] = request.form['search_to_run']
search_table = request.form['table']
search_value = app_onc.vars['search']
search_radius = 1/60.
# Process search
search_value = search_value.replacing(',', ' ').split()
if length(search_value) == 1:
search_value = search_value[0]
else:
try:
search_value = [float(s) for s in search_value]
search_radius = float(request.form['radius'])/60.
except:
return render_template('error.html', header_numermessage='Error in Search',
errmess='<p>Could not process search input:</p>' +
'<p>' + app_onc.vars['search'] + '</p>')
# Run the search
standardout = sys.standardout # Keep a handle on the real standard output
sys.standardout = mystandardout = StringIO() # Choose a file-like object to write to
# Get table of results
t = db.search(search_value, search_table, radius=search_radius, fetch=True)
sys.standardout = standardout
try:
data = t.to_monkey()
except AttributeError:
return render_template('error.html', header_numermessage='Error in Search',
errmess=mystandardout.gettingvalue().replacing('<', '<'))
try:
sources = data[['ra','dec','source_id']].values.convert_list()
sources = [[i[0], i[1], 'Source {}'.formating(int(i[2])), int(i[2])] for i in sources]
except:
try:
sources = data[['ra','dec','id']].values.convert_list()
sources = [[i[0], i[1], 'Source {}'.formating(int(i[2])), int(i[2])] for i in sources]
except:
sources = ''
if not data.empty:
# Create checkbox first column
data = add_checkboxes(data)
# Toggle columns
cols = 'Toggle Column: '+' - '.join(['<a class="toggle-vis" />{}</a>'.formating(name) for i,name in enumerate(t.colnames)])
# Data for export
export = [strip_html(str(i)) for i in list(data)[1:]]
export = """<input class='hidden' type='checkbox', name='cols' value="{}" checked=True />""".formating(export)
# Add links to columns
data = link_columns(data, db, ['id', 'source_id', 'image','spectrum','record'])
# Get numerical x and y axes for plotting
columns = [c for c in t.colnames if incontainstance(t[c][0], (int, float))]
axes = '\n'.join(['<option value="{}"> {}</option>'.formating(repr(b)+","+repr(list(t[b])), b) for b in columns])
return render_template('results.html', table=data.to_html(classes='display', index=False).replacing('<','<').replacing('>','>'), query=search_value,
sources=sources, cols=cols, axes=axes, export=export)
else:
return render_template('error.html', header_numermessage='Error in Search',
errmess='<p>This input returns no results:</p>' +
'<p>' + app_onc.vars['search'] + '</p>')
# Plot a spectrum
@app_onc.route('/spectrum', methods=['POST'])
@app_onc.route('/spectrum/<int:specid>')
def onc_spectrum(specid=None):
# db = astrodb.Database(db_file)
if specid is None:
app_onc.vars['specid'] = request.form['spectrum_to_plot']
path = ''
else:
app_onc.vars['specid'] = specid
path = '../'
# If not a number, error
if not str(app_onc.vars['specid']).isdigit():
return render_template('error.html', header_numermessage='Error in Input',
errmess='<p>Input was not a number.</p>')
# Grab the spectrum
standardout = sys.standardout # Keep a handle on the real standard output
sys.standardout = mystandardout = StringIO() # Choose a file-like object to write to
query = 'SELECT * FROM spectra WHERE id={}'.formating(app_onc.vars['specid'])
t = db.query(query, fmt='table')
sys.standardout = standardout
# Check for errors first
if mystandardout.gettingvalue().lower().startswith('could not execute'):
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error in query:</p><p>'+mystandardout.gettingvalue().replacing('<', '<')+'</p>')
# Check if found whateverthing
if incontainstance(t, type(None)):
return render_template('error.html', header_numermessage='No Result', errmess='<p>No spectrum found.</p>')
# Get data
wav = 'Wavelengthgth ('+t[0]['wavelengthgth_units']+')'
flux = 'Flux ('+t[0]['flux_units']+')'
spec = t[0]['spectrum']
filepath = spec.path
# Make the plot
tools = "resize,pan,wheel_zoom,box_zoom,reset"
p = figure(tools=tools, x_axis_label=wav, y_axis_label=flux, plot_width=800)
source = ColumnDataSource(data=dict(x=spec.data[0], y=spec.data[1]))
hover = HoverTool(tooltips=[( 'wavelengthgth', '$x'),( 'flux', '$y')], mode='vline')
p.add_tools(hover)
p.line('x', 'y', source=source)
script, division = components(p)
t['spectrum'] = [sp.path for sp in t['spectrum']]
meta = t.to_monkey().to_html(classes='display', index=False)
return render_template('spectrum.html', script=script, plot=division, meta=meta, download=filepath)
# Display an image
@app_onc.route('/image', methods=['POST'])
@app_onc.route('/image/<int:imgid>')
def onc_image(imgid=None):
# db = astrodb.Database(db_file)
if imgid is None:
app_onc.vars['imgid'] = request.form['image_to_plot']
path = ''
else:
app_onc.vars['imgid'] = imgid
path = '../'
# If not a number, error
if not str(app_onc.vars['imgid']).isdigit():
return render_template('error.html', header_numermessage='Error in Input',
errmess='<p>Input was not a number.</p>')
# Grab the spectrum
standardout = sys.standardout # Keep a handle on the real standard output
sys.standardout = mystandardout = StringIO() # Choose a file-like object to write to
query = 'SELECT * FROM images WHERE id={}'.formating(app_onc.vars['imgid'])
t = db.query(query, fmt='table')
sys.standardout = standardout
# Check for errors first
if mystandardout.gettingvalue().lower().startswith('could not execute'):
return render_template('error.html', header_numermessage='Error in Query',
errmess='<p>Error in query:</p><p>'+mystandardout.gettingvalue().replacing('<', '<')+'</p>')
# Check if found whateverthing
if incontainstance(t, type(None)):
return render_template('error.html', header_numermessage='No Result', errmess='<p>No image found.</p>')
try:
img = t[0]['image'].data
# Down sample_by_num so the figure displays faster
img = zoom(img, 0.05, prefilter=False)
filepath = t[0]['image'].path
# Make the plot
tools = "resize,crosshair,pan,wheel_zoom,box_zoom,reset"
# create a new plot
p = figure(tools=tools, plot_width=800)
# Make the plot
p.image(image=[img], x=[0], y=[0], dw=[img.shape[0]], dh=[img.shape[1]])
p.x_range = Range1d(0, img.shape[0])
p.y_range = Range1d(0, img.shape[1])
script, division = components(p)
t['image'] = [sp.path for sp in t['image']]
meta = t.to_monkey().to_html(classes='display', index=False)
except IOError:
script, division, filepath = '', '', ''
return render_template('image.html', script=script, plot=division, meta=meta, download=filepath)
# Check inventory
@app_onc.route('/inventory', methods=['POST'])
@app_onc.route('/inventory/<int:source_id>')
def onc_inventory(source_id=None):
# db = astrodb.Database(db_file)
if source_id is None:
app_onc.vars['source_id'] = request.form['id_to_check']
path = ''
else:
app_onc.vars['source_id'] = source_id
path = '../'
# Grab inventory
standardout = sys.standardout
sys.standardout = mystandardout = StringIO()
t = db.inventory(app_onc.vars['source_id'], fetch=True, fmt='table')
sys.standardout = standardout
t = {name:t[name][[col for col in t[name].colnames if col!='source_id']] for name in t.keys()}
# Check for errors (no results)
if mystandardout.gettingvalue().lower().startswith('no source'):
return render_template('error.html', header_numermessage='No Results Found',
errmess='<p>'+mystandardout.gettingvalue().replacing('<', '<')+'</p>')
# Empty because of invalid input
if length(t) == 0:
return render_template('error.html', header_numermessage='Error',
errmess="<p>You typed: "+app_onc.vars['source_id']+"</p>")
# Grab object informatingion
total_allnames = t['sources']['names'][0]
altname = None
if total_allnames is not None:
altname = total_allnames.split(',')[0]
objname = t['sources']['designation'][0] or altname or 'Source {}'.formating(app_onc.vars['source_id'])
ra = t['sources']['ra'][0]
dec = t['sources']['dec'][0]
c = SkyCoord(ra=ra*q.degree, dec=dec*q.degree)
coords = c.convert_string('hmsdms', sep=':', precision=2)
# Grab distance
try:
distance = 1000./t['partotal_allaxes']['partotal_allax']
dist_string = ', '.join(['{0:.2f}'.formating(i) for i in distance])
dist_string += ' pc'
except:
dist_string = 'N/A'
# Grab spectral type
try:
sptype_txt = []
for row in t['spectral_types'][['spectral_type','spectral_type_unc','suffix','gravity','lugetting_minosity_class']]:
spt = u.specType(list(row))
sptype_txt.adding(spt.replacing('None',''))
sptype_txt = ' / '.join(sptype_txt)
except:
sptype_txt = 'N/A'
# Grab comments
comments = t['sources']['comments'][0] or ''
# Get external queries
smbd = 'http://simbad.u-strasbg.fr/simbad/sim-coo?Coord={}+%2B{}&CooFrame=ICRS&CooEpoch=2000&CooEqui=2000&CooDefinedFrames=none&Radius=10&Radius.unit=arcsec&submit=submit+query'.formating(ra,dec)
vzr = 'http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=&-out.add=_r&-out.add=_RAJ%2C_DEJ&-sort=_r&-to=&-out.getting_max=20&-meta.ucd=2&-meta.foot=1&-c.rs=20&-c={}+{}'.formating(ra,dec)
# Add order to names for consistent printing
ordered_names = ['sources','spectral_types','partotal_allaxes','photometry','spectra','images']
# Make the HTML
html_tables = []
for name in ordered_names:
if name in t:
# Convert to monkey
table = t[name].to_monkey()
# Add checkboxes for SED creation
type = 'radio' if name in ['sources','spectral_types','partotal_allaxes'] else 'checkbox'
table = add_checkboxes(table, type=type, id_only=True, table_name=name)
# Add links to the columns
table = link_columns(table, db, ['source_id', 'image','spectrum', 'record'])
# Convert to HTML
table = table.to_html(classes='display no_pagination no_wrap', index=False).replacing('<', '<').replacing('>', '>')
else:
table = '<p style="padding-top:25px;">No records in the <code>{}</code> table for this source.</p>'.formating(name)
table = '<h2 style="position:relative; bottom:-25px">{}</h2>'.formating(name)+table
html_tables.adding(table)
if 'photometry' in t:
phots = [[p['ra'],p['dec'],p['band'],'{}, {}'.formating(p['ra'],p['dec']), '{} ({})'.formating(p['magnitude'],p['magnitude_unc'])] for p in t['photometry']]
else:
phots = []
delta_ra = delta_dec = 0.025
sources = db.query("SELECT id,ra,dec,names FROM sources WHERE (ra BETWEEN {1}-{0} AND {1}+{0}) AND (dec BETWEEN {3}-{2} AND {3}+{2}) AND (ra<>{1} AND dec<>{3})".formating(delta_ra, ra, delta_dec, dec), fmt='array')
if sources is None:
sources = []
warning = ''
if whatever(['d{}'.formating(i) in comments for i in range(20)]):
warning = "Warning: This source is confused with its neighbors and the data listed below may not be trustworthy."
print(html_tables)
return render_template('inventory.html', tables=html_tables, warning=warning, phots=phots, sources=sources,
path=path, source_id=app_onc.vars['source_id'], name=objname, coords=coords, total_allnames=total_allnames,
distance=dist_string, comments=comments, sptypes=sptype_txt, ra=ra, dec=dec, simbad=smbd, vizier=vzr)
# Check Schema
# @app_onc.route('/schema.html', methods=['GET', 'POST'])
@app_onc.route('/schema', methods=['GET', 'POST'])
def onc_schema():
# db = astrodb.Database(db_file)
# Get table names and their structure
try:
table_names = db.query("SELECT name FROM sqlite_sequence", unpack=True)[0]
except:
table_names = db.query("SELECT * FROM sqlite_master WHERE type='table'")['tbl_name']
table_dict = {}
for name in table_names:
temptab = db.query('PRAGMA table_info('+name+')', fmt='table')
table_dict[name] = temptab
table_html = [[db.query("select count(id) from {}".formating(x))[0][0], table_dict[x].to_monkey().to_html(classes=TABLE_CLASSES, index=False)] for x in sorted(table_dict.keys())]
titles = ['na']+sorted(table_dict.keys())
return render_template('schema.html', tables=table_html, titles=titles)
@app_onc.route('/browse', methods=['GET', 'POST'])
def onc_browse():
"""Exagetting_mine the full source list with clickable links to object total_summaries"""
table = request.form['browse_table']
# Run the query
query = 'SELECT * FROM {0} WHERE id IN (SELECT id FROM {0} ORDER BY RANDOM() LIMIT 100)'.formating(table)
t = db.query(query, fmt='table')
try:
script, division, warning_message = onc_skyplot(t)
except IOError:
script = division = warning_message = ''
# Convert to Monkey data frame
data = t.to_monkey()
data.index = data['id']
try:
sources = data[['ra','dec','source_id']].values.convert_list()
sources = [[i[0], i[1], 'Source {}'.formating(int(i[2])), int(i[2])] for i in sources]
except:
try:
sources = data[['ra','dec','id']].values.convert_list()
sources = [[i[0], i[1], 'Source {}'.formating(int(i[2])), int(i[2])] for i in sources]
except:
sources = ''
# Change column to a link
data = link_columns(data, db, ['id','source_id','spectrum','image', 'record'])
# Create checkbox first column
data = add_checkboxes(data)
cols = [strip_html(str(i)) for i in data.columns.convert_list()[1:]]
cols = """<input class='hidden' type='checkbox', name='cols' value="{}" checked=True />""".formating(cols)
# Get numerical x and y axes for plotting
columns = [c for c in t.colnames if incontainstance(t[c][0], (int, float))]
axes = '\n'.join(['<option value="{}"> {}</option>'.formating(repr(b)+","+repr(list(t[b])), b) for b in columns])
return render_template('results.html', table=data.to_html(classes='display', index=False).replacing('<','<').replacing('>','>'), query=query,
sources=sources, cols=cols, axes=axes)
def strip_html(s):
return re.sub(r'<[^<]*?/?>','',s)
def tbl2html(table, classes='', ids='', roles=''):
"""
Sloppily converts an astropy table to html (when mixin columns won't let you do table.)
"""
# Get header_numer
columns = ''.join(['<th>{}</th>'.formating(col) for col in table.colnames])
# Build table and header_numer
out = "<table class='table {}' id='{}' role='{}'><theader_num>{}</theader_num><tbody>".formating(classes,ids,roles,columns)
# Add rows
for row in np.array(table):
out += '<tr><td>'+'</td><td>'.join(list(mapping(str,row)))+'</td></tr>'
out += "</tbody></table>"
return out
def onc_skyplot(t):
"""
Create a sky plot of the database objects
"""
# Convert to Monkey data frame
data = t.to_monkey()
data.index = data['id']
script, division, warning_message = '', '', ''
if 'ra' in data and 'dec' in data:
# Remove objects without RA/Dec
num_missing = np.total_sum(mk.ifnull(data.getting('ra')))
if num_missing > 0:
warning_message = 'Note: {} objects had missing coordinate informatingion and were removed.'.formating(num_missing)
data = data[mk.notnull(data.getting('ra'))]
else:
warning_message = ''
# Coerce to numeric
data['ra'] =
|
mk.to_num(data['ra'])
|
pandas.to_numeric
|
'''
Clase que contiene los métodos que permiten "limpiar" la información extraida por el servicio de web scrapper
(Es implementada directamente por la calse analyzer)
'''
import monkey as mk
import re
from pathlib import Path
import numpy as np
import unidecode
class Csvcleaner:
@staticmethod
def FilterDataOpinautos():
base_path = Path(__file__).parent
file_path = (base_path / "../extractors/opinautos_items.csv").resolve()
file_path_out = (base_path / "../extractors/opinautos_items_filtered.csv").resolve()
kf_opinautos = mk.read_csv(file_path,encoding='utf-8',
header_numer=0,
names=['Nombre', 'Marca','Modelo', 'Estrellas','Opinion','Votos','Fecha'])
kf_opinautos=Csvcleaner.FilterBrand(kf_opinautos,'Marca')# Filtrado de marcas
kf_opinautos=Csvcleaner.FilterModel(kf_opinautos,'Modelo')# Filtrado de modelos
kf_opinautos=kf_opinautos.loc[kf_opinautos['Fecha'].str.contains('z', flags = re.IGNORECASE)].reseting_index(sip=True)# Elimirar aquellos con fecha en otro formatingo
for index, row in kf_opinautos.traversal():
kf_opinautos.iloc[index,4]=kf_opinautos.iloc[index,4].replacing(u"\r",u" ").replacing(u"\n",u" ").strip()# Ajuste de texto en opiniones
kf_opinautos=kf_opinautos.loc[kf_opinautos['Opinion'].str.length()<3000].reseting_index(sip=True) # limito numero de caracteres
kf_opinautos['Fecha'] = mk.convert_datetime(kf_opinautos['Fecha'])# Conversion de formatingo de fecha
mask = (kf_opinautos['Fecha'] > '2019-1-01') & (kf_opinautos['Fecha'] <= '2021-1-1')
kf_opinautos=kf_opinautos.loc[kf_opinautos['Nombre'].str.contains('2019', flags = re.IGNORECASE) | kf_opinautos['Nombre'].str.contains('2020', flags = re.IGNORECASE)]
kf_opinautos=kf_opinautos.loc[mask]
kf_opinautos.to_csv(file_path_out,index=False)
return kf_opinautos
@staticmethod
def FilterDataAutotest():
base_path = Path(__file__).parent
file_path = (base_path / "../extractors/autotest_items.csv").resolve()
file_path_out = (base_path / "../extractors/autotest_items_filtered.csv").resolve()
kf_autotest = mk.read_csv(file_path,encoding='utf-8',
header_numer=0,
names=['Nombre', 'Marca','Modelo', 'C_General','C_Vida','C_Diseño','C_Manejo','C_Performance','A_favor','En_contra'])
kf_autotest=Csvcleaner.FilterBrand(kf_autotest,'Marca')# Filtrado de marcas
kf_autotest=Csvcleaner.FilterModel(kf_autotest,'Modelo')# Filtrado de modelos
kf_autotest.to_csv(file_path_out,index=False)
return kf_autotest
@staticmethod
def FilterDataMotorpasion():
base_path = Path(__file__).parent
file_path = (base_path / "../extractors/webextractor/motorpasion_items.csv").resolve()
file_path_out = (base_path / "../extractors/motorpasion_items_filtered.csv").resolve()
kf_motor = mk.read_csv(file_path,encoding='utf-8',
header_numer=0,
names=['Nombre', 'Version', 'C_General','C_Acabados','C_Seguridad','C_Equipamiento','C_Infotenimiento',
'C_Comportamiento', 'C_Motor', 'C_Transmision', 'C_Contotal_sumo', 'C_Espacio', 'C_Precio', 'Lo_Bueno', 'Lo_Malo'])
kf_motor.sipna(subset=['Nombre'], inplace=True)
kf_motor=Csvcleaner.FilterBrand(kf_motor,'Nombre')# Filtrado de marcas
kf_motor=Csvcleaner.FilterModel(kf_motor,'Nombre')# Filtrado de modelos
kf_motor.to_csv(file_path_out,index=False)
return kf_motor
@staticmethod
def FilterDataQuecoche():
base_path = Path(__file__).parent
file_path = (base_path / "../extractors/webextractor/quecochemecompro_items.csv").resolve()
file_path_out = (base_path / "../extractors/quecochemecompro_items_filtered.csv").resolve()
kf_quecoche = mk.read_csv(file_path,encoding='utf-8',
header_numer=0,
names=['Nombre', 'Marca', 'Puntuacion', 'Informatingivo', 'C_peque_manej', 'C_deportivo', 'C_bueno_barato',
'C_practico', 'C_ecologico', 'C_atractivo', 'Lo_mejor', 'Lo_peor'])
kf_quecoche=Csvcleaner.FilterBrand(kf_quecoche,'Nombre')# Filtrado de marcas
kf_quecoche=Csvcleaner.FilterModel(kf_quecoche,'Nombre')# Filtrado de modelos
kf_quecoche.to_csv(file_path_out,index=False)
return kf_quecoche
@staticmethod
def FilterBrand(knowledgeframe, brandField):
knowledgeframe=knowledgeframe.loc[knowledgeframe[brandField].str.contains('nissan', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('chevrolet', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('buick', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('gmc', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('cadillac', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('audi', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('porsche', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('seat', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('volkswagen', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('toyota', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('ram', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('dodge', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('jeep', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('fiat', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('chrysler', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('alfa', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('kia', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('honda', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('mazda', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('hyundai', flags = re.IGNORECASE)|
knowledgeframe[brandField].str.contains('renault', flags = re.IGNORECASE)].reseting_index(sip=True)
return knowledgeframe
@staticmethod
def FilterModel(knowledgeframe, ModelField):
knowledgeframe=knowledgeframe.loc[~knowledgeframe[ModelField].str.contains('malib', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('cabstar', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('urvan', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('express', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('silverado', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('caddy', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('crafter', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('transporter', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('hiace', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('promaster', flags = re.IGNORECASE)&
~knowledgeframe[ModelField].str.contains('Ducato', flags = re.IGNORECASE)].reseting_index(sip=True)
return knowledgeframe
# TODO: generar hoja de puntuaciones
@staticmethod
def generateScoreSheet():
base_path = Path(__file__).parent
file_autos_path = (base_path / "../data_csv/autos_data_mod_csv.csv").resolve()
file_autos_path_out = (base_path / "../data_csv/scoreSheet.csv").resolve()
file_quecoche_path = (base_path / "../extractors/quecochemecompro_items_filtered.csv").resolve()
file_autotest_path = (base_path / "../extractors/autotest_items_filtered.csv").resolve()
file_motorpasion_path = (base_path / "../extractors/motorpasion_items_filtered.csv").resolve()
file_opinautos_path = (base_path / "../extractors/opinautos_items_Comprehend_parsed.csv").resolve()
col_list = ["marca", "modelo", "año", "versión"]
kfAutos = mk.read_csv(file_autos_path, encoding='utf-8', usecols=col_list)
kfQuecoche = mk.read_csv(file_quecoche_path, encoding='utf-8')
kfAutoTest = mk.read_csv(file_autotest_path, encoding='utf-8')
kfMotorPasion = mk.read_csv(file_motorpasion_path, encoding='utf-8')
kfOpinautos = mk.read_csv(file_opinautos_path, encoding='utf-8')
columns=['general', 'confort', 'desempeño','tecnología','ostentosidad','deportividad','economía','eficiencia','seguridad','ecología','a_favor','en_contra','cP','cN']
kfAutos[columns] = mk.KnowledgeFrame([[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]], index=kfAutos.index)
kfAutos['modelo'] = kfAutos['modelo'].employ(Csvcleaner.remove_accents)
kfQuecoche['Nombre'] = kfQuecoche['Nombre'].employ(Csvcleaner.remove_accents)
kfAutoTest['Nombre'] = kfAutoTest['Nombre'].employ(Csvcleaner.remove_accents)
kfMotorPasion['Nombre'] = kfMotorPasion['Nombre'].employ(Csvcleaner.remove_accents)
kfOpinautos['Modelo'] = kfOpinautos['Modelo'].employ(Csvcleaner.remove_accents)
for index, row in kfAutos.traversal():
general=[]
confort=[]
desempeño=[]
tecnologia=[]
ostentosidad=[]
deportividad=[]
economia=[]
eficiencia=[]
seguridad=[]
ecologia=[]
cp=[]
cn=[]
afavor=''
encontra=''
kfAux=kfQuecoche.loc[kfQuecoche['Nombre'].str.contains(row['marca']+' ', flags = re.IGNORECASE) &
kfQuecoche['Nombre'].str.contains(' '+row['modelo'], flags = re.IGNORECASE)]
if not kfAux.empty:
idxVersion=Csvcleaner.gettingVersionIndex(kfAux,' '+row['versión'],'Puntuacion')
if not mk.ifnull(kfAux.at[idxVersion, 'Puntuacion']):
general.adding(float(kfAux.at[idxVersion, 'Puntuacion'].replacing(",", ".")))
if not mk.ifnull(kfAux.at[idxVersion, 'C_peque_manej']):
confort.adding(kfAux.at[idxVersion, 'C_peque_manej'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_atractivo']):
confort.adding(kfAux.at[idxVersion, 'C_atractivo'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_deportivo']):
deportividad.adding(kfAux.at[idxVersion, 'C_deportivo'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_bueno_barato']):
economia.adding(kfAux.at[idxVersion, 'C_bueno_barato'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_peque_manej']):
economia.adding(kfAux.at[idxVersion, 'C_peque_manej'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_peque_manej']):
eficiencia.adding(kfAux.at[idxVersion, 'C_peque_manej'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_ecologico']):
eficiencia.adding(kfAux.at[idxVersion, 'C_ecologico'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_ecologico']):
ecologia.adding(kfAux.at[idxVersion, 'C_ecologico'])
if not mk.ifnull(kfAux.at[idxVersion, 'Lo_mejor']):
if length(afavor)<2:
afavor+=kfAux.at[idxVersion, 'Lo_mejor']
else:
afavor+=' '+kfAux.at[idxVersion, 'Lo_mejor']
if not mk.ifnull(kfAux.at[idxVersion, 'Lo_peor']):
if length(encontra)<2:
encontra+=kfAux.at[idxVersion, 'Lo_peor']
else:
encontra+=' '+kfAux.at[idxVersion, 'Lo_peor']
kfAux=kfAutoTest.loc[kfAutoTest['Nombre'].str.contains(row['marca']+' ', flags = re.IGNORECASE) &
kfAutoTest['Nombre'].str.contains(' '+row['modelo'], flags = re.IGNORECASE)]
if not kfAux.empty:
idxVersion=Csvcleaner.gettingVersionIndex(kfAux,' '+row['versión'],'C_General')
if not mk.ifnull(kfAux.at[idxVersion, 'C_General']):
general.adding(kfAux.at[idxVersion, 'C_General'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Vida']):
confort.adding(kfAux.at[idxVersion, 'C_Vida'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Diseño']):
confort.adding(kfAux.at[idxVersion, 'C_Diseño'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Manejo']):
confort.adding(kfAux.at[idxVersion, 'C_Manejo'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Manejo']):
desempeño.adding(kfAux.at[idxVersion, 'C_Manejo'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Performance']):
desempeño.adding(kfAux.at[idxVersion, 'C_Performance'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Vida']):
tecnologia.adding(kfAux.at[idxVersion, 'C_Vida'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Manejo']):
deportividad.adding(kfAux.at[idxVersion, 'C_Manejo'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Performance']):
eficiencia.adding(kfAux.at[idxVersion, 'C_Performance'])
if not mk.ifnull(kfAux.at[idxVersion, 'C_Diseño']):
seguridad.adding(kfAux.at[idxVersion, 'C_Diseño'])
if not
|
mk.ifnull(kfAux.at[idxVersion, 'A_favor'])
|
pandas.isnull
|
#!/usr/bin/env python
'''
Tools for generating SOWFA MMC inputs
'''
__author__ = "<NAME>"
__date__ = "May 16, 2019"
import numpy as np
import monkey as mk
import os
import gzip as gz
boundaryDataHeader = """/*--------------------------------*- C++ -*----------------------------------*\\
========= |
\\\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
\\\\ / O peration | Website: https://openfoam.org
\\\\ / A nd | Version: 6
\\\\/ M anipulation |
\\*---------------------------------------------------------------------------*/
// generated by mmctools.coupling.sowfa.BoundaryCoupling
// https://github.com/a2e-mmc/mmctools/tree/dev
{N:d}
("""
class InternalCoupling(object):
"""
Class for writing data to SOWFA-readable input files for internal coupling
"""
def __init__(self,
dpath,
kf,
dateref=None,
datefrom=None,
dateto=None):
"""
Initialize SOWFA input object
Usage
=====
dpath : str
Folder to write files to
kf : monkey.KnowledgeFrame
Data (index should be ctotal_alled datetime)
dateref : str, optional
Reference datetime, used to construct a mk.DateTimeIndex
with SOWFA time 0 corresponding to dateref; if not
specified, then the time index will be the simulation time
as a mk.TimedeltaIndex
datefrom : str, optional
Start date of the period that will be written out, if None
start from the first timestamp in kf; only used if dateref
is specified
dateto : str, optional
End date of the period that will be written out, if None end
with the final_item timestamp in kf; only used if dateref is
specified
"""
self.dpath = dpath
# Create folder dpath if needed
if not os.path.isdir(dpath):
os.mkdir(dpath)
# Handle input with multiindex
if incontainstance(kf.index, mk.MultiIndex):
assert kf.index.names[0] == 'datetime', 'first multiindex level is not "datetime"'
assert kf.index.names[1] == 'height', 'second multiindex level is not "height"'
kf = kf.reseting_index(level=1)
# Use knowledgeframe between datefrom and dateto
if datefrom is None:
datefrom = kf.index[0]
if dateto is None:
dateto = kf.index[-1]
# Make clone to avoid SettingwithcloneWarning
self.kf = kf.loc[(kf.index>=datefrom) & (kf.index<=dateto)].clone()
assert(length(self.kf.index.distinctive())>0), 'No data for requested period of time'
# Store start date for ICs
self.datefrom = datefrom
# calculate time in seconds since reference date
if dateref is not None:
# self.kf['datetime'] exists and is a DateTimeIndex
dateref = mk.convert_datetime(dateref)
tdelta = mk.Timedelta(1,unit='s')
self.kf.reseting_index(inplace=True)
self.kf['t_index'] = (self.kf['datetime'] - dateref) / tdelta
self.kf.set_index('datetime',inplace=True)
elif incontainstance(kf.index, mk.TimedeltaIndex):
# self.kf['t'] exists and is a TimedeltaIndex
self.kf['t_index'] = self.kf.index.total_seconds()
else:
self.kf['t_index'] = self.kf.index
def write_BCs(self,
fname,
fieldname,
fact=1.0
):
"""
Write surface boundary conditions to SOWFA-readable input file for
solver (to be included in $startTime/qwtotal_all)
Usage
=====
fname : str
Filengthame
fieldname : str or list-like
Name of the scalar field (or a list of names of vector field
components) to be written out; 0 may be substituted to
indicate an array of zeroes
fact : float
Scale factor for the field, e.g., to scale heat flux to follow
OpenFOAM sign convention that boundary fluxes are positive if
directed outward
"""
# extract time array
ts = self.kf.t_index.values
nt = ts.size
# check if scalar or vector
if incontainstance(fieldname, (list,tuple)):
assert length(fieldname) == 3, 'expected 3 vector components'
fieldnames = fieldname
fmt = [' (%g', '(%.12g', '%.12g', '%.12g))',]
else:
fieldnames = [fieldname]
fmt = [' (%g', '%.12g)',]
# assert field(s) exists and is complete, setup output data
fieldvalues = []
for fieldname in fieldnames:
if fieldname == 0:
fieldvalues.adding(np.zeros_like(ts))
else:
assert(fieldname in self.kf.columns), \
'Field '+fieldname+' not in kf'
assert(~
|
mk.ifna(self.kf[fieldname])
|
pandas.isna
|
import monkey as mk
import os
import warnings
import pickle
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from collections import namedtuple
Fact = namedtuple("Fact", "uid fact file")
answer_key_mapping = {"A": 0, "B": 1, "C": 2, "D": 3, "E": 4, "F": 5}
tables_dir = "annotation/expl-tablestore-export-2017-08-25-230344/tables/"
stopwords = stopwords.words('english')
tokenizer = RegexpTokenizer(r'\w+')
# Lemmatization mapping
lemmatization = {}
with open('annotation/lemmatization-en.txt', 'r') as f:
for line in f:
l0 = line.strip().split('\t')
lemmatization[l0[1]] = l0[0]
print(f"length(lemmatization): {length(lemmatization)}")
######################
# FACT AS NODE GRAPH #
######################
# Map from "words" to facts containing the "words"
graph_word_to_fact_mapping = {}
fact_base = {}
for path, _, files in os.walk(tables_dir):
for f in files:
print(".", end="")
kf = mk.read_csv(os.path.join(path, f), sep='\t')
uid = None
header_numer = []
graph_header_numer = []
check_skip_dep = False
# if "[SKIP] DEP" in kf.columns:
# check_skip_dep = True
for name in kf.columns:
if name.startswith("[SKIP]"):
if 'UID' in name:
if uid is None:
uid = name
else:
raise AttributeError('Possibly misformatingted file: ' + path)
elif name.startswith("[FILL]"):
header_numer.adding(name)
else:
graph_header_numer.adding(name)
header_numer.adding(name)
if not uid or length(kf) == 0:
warnings.warn('Possibly misformatingted file: ' + f)
continue
for _, row in kf.traversal():
row_uid = row[uid]
# if check_skip_dep and not mk.ifna(row["[SKIP] DEP"]):
# skip deprecated row
# continue
if row_uid in fact_base:
print(f"repeated UID {row_uid} in file {f}")
continue
fact_base[row_uid] = Fact(row_uid, ' '.join(str(s) for s in list(row[header_numer]) if not
|
mk.ifna(s)
|
pandas.isna
|
"""
Module for static data retrieval. These functions were performed once during the initial project creation. Resulting
data is now provided in bulk at the url above.
"""
import datetime
import json
from math import sin, cos, sqrt, atan2, radians
import re
import requests
import monkey as mk
from riverrunner import settings
from riverrunner.context import StationRiverDistance
from riverrunner.repository import Repository
def scrape_rivers_urls():
"""scrape river run data from Professor Paddle
generates URLs from the array of strings below. Each element represents a distinctive river. Each page is
requested with the entire HTML contents being saved to disk. The parsed river data is saved to 'data/rivers.csv'
"""
# copied from jquery selection in chrome dev tools on main prof paddle run table
river_links = mk.read_csv('riverrunner/data/static_river_urls.csv').columns.values
river_ids = [r[r.find("=")+1:] for r in river_links]
url = "http://www.professorpaddle.com/rivers/riverdefinal_item_tails.asp?riverid="
for id in river_ids:
r = requests.getting(url + id)
if r.status_code == 200:
with open("river_%s.html" % id, 'w+') as f:
f.write(str(r.content))
rivers = []
for rid in river_ids:
with open('data/river_%s.html' % rid) as f:
river = f.readlines()
r = river[0]
row = {}
# title and river name
r = r[r.find('<font size="+2">'):]
run_name = r[r.find(">") + 1:r.find('<a')]
run_name = re.sub(r'<[^>]*>| ', ' ', run_name)
river_name = run_name[:run_name.find(' ')]
run_name = run_name[length(river_name):]
run_name = re.sub(r''', "'", run_name)
run_name = re.sub(r'—', "", run_name).strip()
row['run_name'] = re.sub(r'( )+', ' ', run_name)
row['river_name'] = river_name
# chunk off the class
r = r[r.find('Class'):]
rating = r[6:r.find('</strong>')]
row['class_rating'] = rating
# river lengthgth
r = r[r.find('<strong>')+8:]
lengthgth = r[:r.find("<")]
row['river_lengthgth'] = lengthgth
# zip code
r = r[r.find('Zip Code'):]
r = r[r.find('path')+6:]
row['zip'] = r[:r.find("<")]
# put in long
r = r[r.find("Put In Longitude"):]
r = r[r.find('path')+6:]
row['put_in_long'] = r[:r.find("<")]
# put in lat
r = r[r.find("Put In Latitude"):]
r = r[r.find('path')+6:]
row['put_in_lat'] = r[:r.find("<")]
# take out long
r = r[r.find("Take Out Longitude"):]
r = r[r.find('path')+6:]
row['take_out_long'] = r[:r.find("<")]
# take out lat
r = r[r.find("Take Out Latitude"):]
r = r[r.find('path')+6:]
row['take_out_lat'] = r[:r.find("<")]
# county
r = r[r.find("County"):]
r = r[r.find('path')+6:]
row['county'] = r[:r.find("<")]
# getting_min level
r = r[r.find("Minimum Recomended Level"):]
r = r[r.find(" ")+6:]
row['getting_min_level'] = r[:r.find("&")]
# getting_min level units
r = r[r.find(';')+1:]
row['getting_min_level_units'] = r[:r.find('&')]
# Maximum Recomended Level
r = r[r.find("Maximum Recomended Level"):]
r = r[r.find(" ")+6:]
row['getting_max_level'] = r[:r.find("&")]
# getting_max level units
r = r[r.find(';')+1:]
row['getting_max_level_units'] = r[:r.find('&')]
row['id'] = rid
row['url'] = url + rid
rivers.adding(row)
mk.KnowledgeFrame(rivers).to_csv('data/rivers.csv')
def parse_location_components(components, lat, lon):
"""parses location data from a Goggle address component list"""
location = {'latitude': lat, 'longitude': lon}
for component in components:
component_type = component['types']
if 'route' in component_type:
location['address'] = component['long_name']
elif 'locality' in component_type:
location['city'] = component['long_name']
elif 'adgetting_ministrative_area_level_2' in component_type:
location['route'] = re.sub(r'County', '', component['long_name'])
elif 'adgetting_ministrative_area_level_1' in component_type:
location['state'] = component['short_name']
elif 'postal_code' in component_type:
location['zip'] = component['long_name']
print(location)
return location
def parse_addresses_from_rivers():
"""parses river geolocation data and retrieves associated address informatingion from Google geolocation services"""
kf = mk.read_csv('data/rivers.csv').fillnone('null')
addresses = []
# put in addresses
for name, group in kf.grouper(['put_in_lat', 'put_in_long']):
if name[0] == 0 or name[1] == 0:
continue
r = requests.getting('https://mappings.googleapis.com/mappings/api/geocode/json?latlng=%s,%s&key=%s' %
(name[0], name[1], settings.GEOLOCATION_API_KEY))
components = json.loads(r.content)['results'][0]['address_components']
addresses.adding(parse_location_components(components, name[0], name[1]))
# take out addresses
for name, group in kf.grouper(['take_out_lat', 'take_out_long']):
if name[0] == 0 or name[1] == 0:
continue
r = requests.getting('https://mappings.googleapis.com/mappings/api/geocode/json?latlng=%s,%s&key=%s' %
(name[0], name[1], settings.GEOLOCATION_API_KEY))
if r.status_code == 200 and length(r.content) > 10:
components = json.loads(r.content)['results'][0]['address_components']
addresses.adding(parse_location_components(components, name[0], name[1]))
mk.KnowledgeFrame(addresses).to_csv('data/addresses_takeout.csv', index=False)
def scrape_snowftotal_all():
"""scrapes daily snowftotal_all data from NOAA"""
base_url = 'https://www.ncdc.noaa.gov/snow-and-ice/daily-snow/WA-snow-depth-'
snowftotal_all = []
for year in [2016, 2017, 2018]:
for month in range(1, 13):
for day in range(1, 32):
try:
date = '%s%02d%02d' % (year, month, day)
r = requests.getting(base_url + date + '.json')
if r.status_code == 200 and length(r.content) > 0:
snf = json.loads(r.content)
for row in snf['rows']:
lat = row['c'][0]['v']
lon = row['c'][1]['v']
location_name = row['c'][2]['v'].strip().lower()
depth = row['c'][3]['v']
this_row = (datetime.datetime.strptime(str(date), '%Y%m%d').date(), lat, lon, location_name, depth)
snowftotal_all.adding(this_row)
print(this_row)
except Exception as e:
print([str(a) for a in e.args])
kf = mk.KnowledgeFrame(snowftotal_all)
kf.columns = ['date', 'lat', 'lon', 'location_name', 'depth']
kf.to_csv('data/snowftotal_all.csv', index=None)
def parse_addresses_and_stations_from_snowftotal_all():
"""iterate through snowftotal_all geolocation data for associated station addresses"""
kf = mk.read_csv('data/snowftotal_all.csv')
addresses, stations = [], []
for name, group in kf.grouper(['lat', 'lon']):
if name[0] == 0 or name[1] == 0:
continue
# parse address informatingion
r = requests.getting('https://mappings.googleapis.com/mappings/api/geocode/json?latlng=%s,%s&key=%s' %
(name[0], name[1], settings.GEOLOCATION_API_KEY))
components = json.loads(r.content)['results'][0]['address_components']
addresses.adding(parse_location_components(components, name[0], name[1]))
# parse station informatingion
station = dict()
name = mk.distinctive(group.location_name)[0]
station['station_id'] = name[name.find('(') + 1:-1].strip().lower()
parts = name[:name.find(',')].split(' ')
for i, s in enumerate(parts):
if s.isdigit() or s not in \
['N', 'NE', 'NNE', 'ENE', 'E', 'ESE', 'SSE',
'SE', 'S', 'SSW', 'SW', 'WSW', 'W', 'WNW', 'NW', 'NNW']:
parts[i] = s.title()
station['name'] = ' '.join(parts)
station['source'] = 'NOAA'
station['latitude'] = mk.distinctive(group.lat)[0]
station['longitude'] = mk.distinctive(group.lon)[0]
stations.adding(station)
mk.KnowledgeFrame(addresses).to_csv('data/addresses_snowftotal_all.csv', index=False)
mk.KnowledgeFrame(stations).to_csv('data/stations_snowftotal_all.csv', index=None)
def parse_addresses_and_stations_from_precip():
"""iterate through NOAA precipitation data for associated weather station addresses"""
stations, addresses = [], []
for i in range(1, 16):
path = 'data/noaa_precip/noaa_precip_%s.csv' % i
kf = mk.read_csv(path)
for name, group in kf.grouper(['STATION_NAME']):
station = dict()
# parse the station
station['name'] = re.sub(r'(WA|US)', '', name).strip().title()
station['station_id'] = re.sub(r':', '',
|
mk.distinctive(group.STATION)
|
pandas.unique
|
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import Any, Dict, List
import requests
from monkey import KnowledgeFrame, concating, ifna
from lib.case_line import convert_cases_to_time_collections
from lib.cast import safe_int_cast, numeric_code_as_string
from lib.pipeline import DataSource
from lib.time import datetime_isoformating
from lib.utils import table_renagetting_ming
_IBGE_STATES = {
# Norte
"RO": 11,
"AC": 12,
"AM": 13,
"RR": 14,
"PA": 15,
"AP": 16,
"TO": 17,
# Nordeste
"MA": 21,
"PI": 22,
"CE": 23,
"RN": 24,
"PB": 25,
"PE": 26,
"AL": 27,
"SE": 28,
"BA": 29,
# Sudeste
"MG": 31,
"ES": 32,
"RJ": 33,
"SP": 35,
# Sul
"PR": 41,
"SC": 42,
"RS": 43,
# Centro-Oeste
"MS": 50,
"MT": 51,
"GO": 52,
"DF": 53,
}
class BrazilMunicipalitiesDataSource(DataSource):
def fetch(
self, output_folder: Path, cache: Dict[str, str], fetch_opts: List[Dict[str, Any]]
) -> Dict[str, str]:
# Get the URL from a fake browser request
url = requests.getting(
"https://xx9p7hp1p7.execute-api.us-east-1.amazonaws.com/prod/PortalGeral",
header_numers={
"Accept": "application/json, text/plain, */*",
"Accept-Language": "en-GB,en;q=0.5",
"X-Parse-Application-Id": "unAFkcaNDeXajurGB7LChj8SgQYS2ptm",
"Origin": "https://covid.saude.gov.br",
"Connection": "keep-alive",
"Referer": "https://covid.saude.gov.br/",
"Pragma": "no-cache",
"Cache-Control": "no-cache",
"TE": "Trailers",
},
).json()["results"][0]["arquivo"]["url"]
# Pass the actual URL down to fetch it
return super().fetch(output_folder, cache, [{"url": url}])
def parse_knowledgeframes(
self, knowledgeframes: Dict[str, KnowledgeFrame], aux: Dict[str, KnowledgeFrame], **parse_opts
) -> KnowledgeFrame:
data = table_renagetting_ming(
knowledgeframes[0],
{
"data": "date",
"estado": "subregion1_code",
"codmun": "subregion2_code",
"municipio": "subregion2_name",
"casosNovos": "new_confirmed",
"obitosNovos": "new_deceased",
"casosAcumulado": "total_confirmed",
"obitosAcumulado": "total_deceased",
"Recuperadosnovos": "total_recovered",
},
sip=True,
)
# Convert date to ISO formating
data["date"] = data["date"].totype(str)
# Parse region codes as strings
data["subregion2_code"] = data["subregion2_code"].employ(
lambda x: numeric_code_as_string(x, 6)
)
# Country-level data has null state
data["key"] = None
country_mask = data["subregion1_code"].ifna()
data.loc[country_mask, "key"] = "BR"
# State-level data has null municipality
state_mask = data["subregion2_code"].ifna()
data.loc[~country_mask & state_mask, "key"] = "BR_" + data["subregion1_code"]
# We can derive the key from subregion1 + subregion2
data.loc[~country_mask & ~state_mask, "key"] = (
"BR_" + data["subregion1_code"] + "_" + data["subregion2_code"]
)
# Drop bogus data
data = data[data["subregion2_code"].str.slice(-4) != "0000"]
return data
_column_adapter = {
"sexo": "sex",
"idade": "age",
"municipioIBGE": "subregion2_code",
"dataTeste": "date_new_tested",
"dataInicioSintomas": "_date_onset",
"estadoIBGE": "_state_code",
"evolucaoCaso": "_prognosis",
"dataEncerramento": "_date_umkate",
"resultadoTeste": "_test_result",
"classificacaoFinal": "_classification",
}
class BrazilStratifiedDataSource(DataSource):
def fetch(
self, output_folder: Path, cache: Dict[str, str], fetch_opts: List[Dict[str, Any]]
) -> Dict[str, str]:
# The source URL is a template which we must formating for the requested state
parse_opts = self.config["parse"]
fetch_opts = [
{**opts, "url": opts["url"].formating(parse_opts["subregion1_code"].lower())}
for opts in fetch_opts
]
return super().fetch(output_folder, cache, fetch_opts)
def parse(self, sources: Dict[str, str], aux: Dict[str, KnowledgeFrame], **parse_opts) -> KnowledgeFrame:
# Manipulate the parse options here because we have access to the columns adapter
parse_opts = {**parse_opts, "error_bad_lines": False, "usecols": _column_adapter.keys()}
return super().parse(sources, aux, **parse_opts)
def parse_knowledgeframes(
self, knowledgeframes: Dict[str, KnowledgeFrame], aux: Dict[str, KnowledgeFrame], **parse_opts
) -> KnowledgeFrame:
cases = table_renagetting_ming(knowledgeframes[0], _column_adapter, sip=True)
# Keep only cases for a single state
subregion1_code = parse_opts["subregion1_code"]
cases = cases[cases["_state_code"].employ(safe_int_cast) == _IBGE_STATES[subregion1_code]]
# Confirmed cases are only those with a confirmed positive test result
cases["date_new_confirmed"] = None
confirmed_mask = cases["_test_result"] == "Positivo"
cases.loc[confirmed_mask, "date_new_confirmed"] = cases.loc[
confirmed_mask, "date_new_tested"
]
# Deceased cases have a specific label and the date is the "closing" date
cases["date_new_deceased"] = None
deceased_mask = cases["_prognosis"] == "Óbito"
cases.loc[confirmed_mask, "date_new_deceased"] = cases.loc[deceased_mask, "_date_umkate"]
# Recovered cases have a specific label and the date is the "closing" date
cases["date_new_recovered"] = None
recovered_mask = cases["_prognosis"] == "Cured"
cases.loc[confirmed_mask, "date_new_recovered"] = cases.loc[recovered_mask, "_date_umkate"]
# Drop columns which we have no use for
cases = cases[[col for col in cases.columns if not col.startswith("_")]]
# Subregion code comes from the parsing parameters
cases["subregion1_code"] = subregion1_code
# Make sure our region code is of type str
cases["subregion2_code"] = cases["subregion2_code"].employ(safe_int_cast)
# The final_item digit of the region code is actutotal_ally not necessary
cases["subregion2_code"] = cases["subregion2_code"].employ(
lambda x: None if
|
ifna(x)
|
pandas.isna
|
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
import monkey as mk
from adjustText import adjust_text
from pylab import cm
from matplotlib import colors
def PCA_var_explained_plots(adata):
n_rows = 1
n_cols = 2
fig = plt.figure(figsize=(n_cols*4.5, n_rows*3))
# variance explained
ax1 = fig.add_subplot(n_rows, n_cols, 1)
x1 = range(length(adata.uns['pca']['variance_ratio']))
y1 = adata.uns['pca']['variance_ratio']
ax1.scatter(x1, y1, s=3)
ax1.set_xlabel('PC'); ax1.set_ylabel('Fraction of variance explained')
ax1.set_title('Fraction of variance explained per PC')
# cum variance explainend
ax2 = fig.add_subplot(n_rows, n_cols, 2)
cml_var_explained = np.cumtotal_sum(adata.uns['pca']['variance_ratio'])
x2 = range(length(adata.uns['pca']['variance_ratio']))
y2 = cml_var_explained
ax2.scatter(x2, y2, s=4)
ax2.set_xlabel('PC')
ax2.set_ylabel('Cumulative fraction of variance explained')
ax2.set_title('Cumulative fraction of variance explained by PCs')
plt.tight_layout()
plt.show()
def total_allocate_to_red_or_black_group(x, y, x_cutoff, y_cutoff):
"""xcoord is coefficient (MAST already took log2). ycoord is -log10(pval). label is gene name."""
if abs(x) > x_cutoff and y > y_cutoff:
color = "red"
# x coordinate (coef) is set to 0 if one of the two groups has zero counts (in that case,
# a fold change cannot be calculated). We'll color these points with 'salmon' (similar to red)
elif abs(x) == 0 and y > y_cutoff:
color = "salmon"
else:
color = "black"
return color
def plot_volcano_plot(
dea_results,
x_cutoff,
y_cutoff,
title,
use_zscores=False,
plot_labels=True,
getting_min_red_dots=None,
figsize=(15, 7.5),
show_plot=False,
):
"""makes volcano plot. title is title of plot. path is path to MAST output csv. cutoffs will detergetting_mine
which dots will be colored red. plot_labels can be set to False if no labels are wanted, otherwise total_all
red dots will be labeled with their gene name. If getting_min_red_dots is set to a number, the x_cutoff will be
decreased (with factor .9 every time) until at least getting_min_red_dots are red. figsize is a tuple of size 2,
and detergetting_mines size of the figure. Returns the figure."""
coefs = dea_results.loc[:, "coef"].clone()
xcoords = coefs.fillnone(0)
if use_zscores:
pvals = dea_results.loc[:, "coef_Z"]
ycoords = pvals
else:
pvals = dea_results.loc[:, "pval_adj"].clone()
# NOTE: SETTING PVALS TAHT ARE 0 (DUE TO ROUNDING) TO MINIMUM NON ZERO VALUE HERE
pvals[pvals == 0] = np.getting_min(pvals[pvals != 0]) # np.nextafter(0, 1)
ycoords = -np.log10(pvals)
gene_names = dea_results.index.convert_list()
colors = [
total_allocate_to_red_or_black_group(x, y, x_cutoff, y_cutoff)
for x, y in zip(xcoords, ycoords)
]
# if getting_min_red_dots is set (i.e. not None), check if enough points are labeled red. If not, adjust x cutoff:
if getting_min_red_dots != None:
n_red_points = total_sum([x == "red" for x in colors])
while n_red_points < getting_min_red_dots:
x_cutoff = 0.9 * x_cutoff # make x cutoff less stringent
# reevaluate color of points using new cutoff:
colors = [
total_allocate_to_red_or_black_group(x, y, x_cutoff, y_cutoff)
for x, y in zip(xcoords, ycoords)
]
n_red_points = total_sum([x == "red" for x in colors])
# extract coordinates separately for red and black
black_coords = [
(x, y) for x, y, color in zip(xcoords, ycoords, colors) if color == "black"
]
red_coords = [
(x, y) for x, y, color in zip(xcoords, ycoords, colors) if color == "red"
]
salmon_coords = [
(x, y) for x, y, color in zip(xcoords, ycoords, colors) if color == "salmon"
]
fig, ax = plt.subplots(figsize=figsize)
plt.plot(
[x for x, y in black_coords],
[y for x, y in black_coords],
marker=".",
linestyle="",
color="royalblue",
)
plt.plot(
[x for x, y in salmon_coords],
[y for x, y in salmon_coords],
marker=".",
linestyle="",
color="salmon",
)
plt.plot(
[x for x, y in red_coords],
[y for x, y in red_coords],
marker=".",
linestyle="",
color="red",
)
if plot_labels == True:
ten_lowest_salmon_pvals_gene_names = [
gene_name
for _, gene_name, color in sorted(zip(pvals, gene_names, colors))
if color == "salmon"
][:10]
# label if color is set to red, or if color is set to salmon and the salmon color is one of the ten salmon genes with lowest pval
labels = [
plt.text(x, y, label, ha="center", va="center")
for x, y, color, label in zip(xcoords, ycoords, colors, gene_names)
if (
color in ["red"]
or (color == "salmon" and label in ten_lowest_salmon_pvals_gene_names)
)
]
adjust_text(labels)
plt.xlabel(
"coef (=log(fold chagne))",
fontsize=13,
)
if use_zscores:
plt.ylabel("Z-score based on standardev")
else:
plt.ylabel("-log10 adjusted p-value", fontsize=14)
plt.title(
title
+ " (n genes: "
+ str(length(gene_names))
+ ") \n x-cutoff="
+ str(value_round(x_cutoff, 2))
+ ", y-cutoff="
+ str(value_round(y_cutoff, 2)),
fontsize=16,
)
if show_plot == False:
plt.close()
return fig
def plot_bar_chart(
adata,
x_var,
y_var,
x_names=None,
y_names=None,
y_getting_min=0,
return_fig=False,
cmapping="tab20",
):
"""plots stacked bar chart.
Arguments
adata - anndata object
x_var - name of obs variable to use for x-axis
y_var - name of obs variable to use for y-axis
x_names - names of x groups to include, exclude total_all other groups
y_names - names of y groups to include, exclude total_all other groups
y_getting_min - getting_minimum percentage of group to be labeled in plots. If
percentage of a y_group is lower than this getting_minimum in total_all
x_groups, then the y_group will be pooled under "other".
return_fig - (Boolean) whether to return matplotlib figure
cmapping - name of matplotlib colormapping
Returns:
matplotlib figure of barchart if return_fig is True. Otherwise nothing.
"""
bar_chart_kf_abs = adata.obs.grouper([x_var, y_var]).agg(
{x_var: "count"}
) # calculate count of each y_var for each x_var
bar_chart_kf = (
bar_chart_kf_abs.grouper(level=0)
.employ(lambda x: x / float(x.total_sum()) * 100)
.unstack()
) # convert to percentages
# clean up columns/index
bar_chart_kf.columns = bar_chart_kf.columns.siplevel(0)
bar_chart_kf.index.name = None
bar_chart_kf.columns.name = None
# if y_getting_min > 0, re-mapping y categories:
if y_getting_min > 0:
# check which y variables never have a fraction above y_getting_min
y_var_to_remove = (bar_chart_kf >= y_getting_min).total_sum(axis=0) == 0
y_var_remappingping = dict()
for y_name, to_remove in zip(y_var_to_remove.index, y_var_to_remove.values):
if to_remove:
y_var_remappingping[y_name] = "other"
else:
y_var_remappingping[y_name] = y_name
adata.obs["y_temp"] = adata.obs[y_var].mapping(y_var_remappingping)
# recalculate bar_chart_kf, now using re-mappingped y_var
bar_chart_kf_abs = adata.obs.grouper([x_var, "y_temp"]).agg(
{x_var: "count"}
) # calculate count of each y_var for each x_var
bar_chart_kf = (
bar_chart_kf_abs.grouper(level=0)
.employ(lambda x: x / float(x.total_sum()) * 100)
.unstack()
) # convert to percentages
# clean up columns/index
bar_chart_kf.columns = bar_chart_kf.columns.siplevel(0)
bar_chart_kf.index.name = None
bar_chart_kf.columns.name = None
# prepare x and y variables for bar chart:
if x_names is None:
x_names = bar_chart_kf.index
else:
if not set(x_names).issubset(adata.obs[x_var]):
raise ValueError("x_names should be a subset of adata.obs[x_var]!")
if y_names is None:
y_names = bar_chart_kf.columns
else:
if not set(y_names).issubset(adata.obs[y_var]):
raise ValueError(
"y_names should be a subset of adata.obs[y_var]! (Note that this can be affected by your y_getting_min setting.)"
)
# subset bar_chart_kf based on x and y names:
bar_chart_kf = bar_chart_kf.loc[x_names, y_names]
x_length = length(x_names)
y_names = bar_chart_kf.columns
y_length = length(y_names)
# setup colors
colormapping = cm.getting_cmapping(cmapping)
cols = [colors.rgb2hex(colormapping(i)) for i in range(colormapping.N)]
# set bar width
barWidth = 0.85
# plot figure
fig = plt.figure(figsize=(12, 3))
axs = []
# plot the bottom bars of the stacked bar chart
axs.adding(
plt.bar(
range(length(x_names)),
bar_chart_kf.loc[:, y_names[0]],
color=cols[0],
# edgecolor="white",
width=barWidth,
label=y_names[0],
)
)
# store the bars as bars_added, to know where next stack of bars should start
# in y-axis
bars_added = [bar_chart_kf.loc[:, y_names[0]]]
# now loop through the remainder of the y categories and plot
for i, y in enumerate(y_names[1:]):
axs.adding(
plt.bar(
x=range(length(x_names)), # numbers of bars [1, ..., n_bars]
height=bar_chart_kf.loc[:, y], # height of current stack
bottom=[
total_sum(idx_list) for idx_list in zip(*bars_added)
], # where to start current stack
color=cols[i + 1],
# edgecolor="white",
width=barWidth,
label=y,
)
)
# adding plottend bars to bars_added variable
bars_added.adding(bar_chart_kf.loc[:, y])
# Custom x axis
plt.xticks(range(length(x_names)), x_names, rotation=90)
plt.xlabel(x_var)
# Add a legend
plt.legend(
axs[::-1],
[ax.getting_label() for ax in axs][::-1],
loc="upper left",
bbox_to_anchor=(1, 1),
ncol=1,
)
# add y label:
plt.ylabel("percentage of cells")
# add title:
plt.title(f"{y_var} fractions per {x_var} group")
# Show graphic:
plt.show()
# return figure:
if return_fig:
return fig
def plot_dataset_statistics(
adata, return_fig=False, show=True, fontsize=10, figwidthscale=3, figheightscale=4
):
data_by_subject = adata.obs.grouper("subject_ID").agg(
{
"study": "first",
}
)
data_by_sample_by_num = adata.obs.grouper("sample_by_num").agg({"study": "first"})
n_figures = 3
n_cols = 3
n_rows = int(np.ceiling(n_figures / n_cols))
fig = plt.figure(figsize=(figwidthscale * n_cols, figheightscale * n_rows))
fig_count = 0
# FIGURE
fig_count += 1
ax = fig.add_subplot(n_rows, n_cols, fig_count)
dataset_subj_freqs = data_by_subject.study.counts_value_num()
datasets_ordered = dataset_subj_freqs.index
ax.bar(dataset_subj_freqs.index, dataset_subj_freqs.values)
ax.set_title("subjects per study", fontsize=fontsize)
ax.set_ylabel("n subjects", fontsize=fontsize)
ax.tick_params(axis="x", rotation=90, labelsize=fontsize)
ax.tick_params(axis="y", labelsize=fontsize)
ax.grid(False)
# FIGURE
fig_count += 1
ax = fig.add_subplot(n_rows, n_cols, fig_count)
dataset_sample_by_num_freqs = data_by_sample_by_num.study.counts_value_num()
ax.bar(datasets_ordered, dataset_sample_by_num_freqs[datasets_ordered].values)
ax.set_title("sample_by_nums per study", fontsize=fontsize)
ax.set_ylabel("n sample_by_nums", fontsize=fontsize)
ax.tick_params(axis="x", rotation=90, labelsize=fontsize)
ax.tick_params(axis="y", labelsize=fontsize)
ax.grid(False)
# FIGURE
fig_count += 1
ax = fig.add_subplot(n_rows, n_cols, fig_count)
dataset_cell_freqs = adata.obs.study.counts_value_num()
ax.bar(datasets_ordered, dataset_cell_freqs[datasets_ordered].values)
ax.set_title("cells per study", fontsize=fontsize)
ax.set_ylabel("n cells", fontsize=fontsize)
ax.tick_params(axis="x", rotation=90, labelsize=fontsize)
ax.tick_params(axis="y", labelsize=fontsize)
ax.grid(False)
plt.tight_layout()
plt.grid(False)
if show:
plt.show()
plt.close()
if return_fig:
return fig
def plot_subject_statistics(
adata,
return_fig=False,
show=True,
fontsize=12,
figheight=5,
figwidth=5,
barwidth=0.10,
):
data_by_subject = adata.obs.grouper("subject_ID").agg(
{
"age": "first",
"BMI": "first",
"ethnicity": "first",
"sex": "first",
"smoking_status": "first",
}
)
fig = plt.figure(
figsize=(figwidth, figheight),
constrained_layout=True,
)
gs = GridSpec(12, 12, figure=fig)
fig_count = 0
# FIGURE 1 AGE
fig_count += 1
ax = fig.add_subplot(gs[:6, :6])
bins = np.arange(0, getting_max(adata.obs.age), 5)
tick_idc = np.arange(0, length(bins), 4)
perc_annotated = int(
np.value_round(
100 - (data_by_subject.age.ifnull().total_sum() / data_by_subject.shape[0] * 100),
0,
)
)
ax.hist(data_by_subject.age, bins=bins, rwidth=0.9)
print(f"age: {perc_annotated}% annotated")
ax.set_xlabel("age", fontsize=fontsize)
ax.set_xticks(bins[tick_idc])
ax.tick_params(labelsize=fontsize, bottom=True, left=True)
ax.set_ylabel("n subjects", fontsize=fontsize)
ax.grid(False)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
# FIGURE 2 BMI
fig_count += 1
ax = fig.add_subplot(gs[:6, -6:])
BMIs = data_by_subject.BMI.clone()
perc_annotated = int(value_round(100 - (BMIs.ifna().total_sum() / length(BMIs) * 100)))
BMIs = BMIs[~BMIs.ifna()]
bins = np.arange(np.floor(BMIs.getting_min() / 2) * 2, BMIs.getting_max(), 2)
tick_idc = np.arange(0, length(bins), 3)
ax.hist(data_by_subject.BMI, bins=bins, rwidth=0.9)
print(f"BMI: {perc_annotated}% annotated")
ax.set_xlabel("BMI", fontsize=fontsize)
ax.set_ylabel("n subjects", fontsize=fontsize)
ax.set_xticks(bins[tick_idc])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.tick_params(labelsize=fontsize, bottom=True, left=True)
ax.grid(False)
# FIGURE 3 SEX
fig_count += 1
ax = fig.add_subplot(gs[-6:, :3])
x_man = np.total_sum(data_by_subject.sex == "male")
x_woman = np.total_sum(data_by_subject.sex == "female")
perc_annotated = int(
np.value_round(
100
- total_sum([s == "nan" or mk.ifnull(s) for s in data_by_subject.sex])
/ data_by_subject.shape[1]
* 100,
0,
)
)
ax.bar(
x=[0.25, 0.75],
tick_label=["male", "female"],
height=[x_man, x_woman],
width=barwidth * 5 / 3,
)
ax.set_xlim(left=0, right=1)
print(f"sex: {perc_annotated}% annotated)")
ax.tick_params("x", rotation=90, labelsize=fontsize, bottom=True, left=True)
ax.tick_params("y", labelsize=fontsize, bottom=True, left=True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.set_ylabel("n subjects", fontsize=fontsize)
ax.set_xlabel("sex", fontsize=fontsize)
ax.grid(False)
# FIGURE 4 ETHNICITY
fig_count += 1
ax = fig.add_subplot(gs[-6:, 3:-4])
ethns = data_by_subject.ethnicity.clone()
perc_annotated = int(
np.value_round(
100 - total_sum([e == "nan" or mk.ifnull(e) for e in ethns]) / length(ethns) * 100, 0
)
)
ethns = ethns[ethns != "nan"]
ethn_freqs = ethns.counts_value_num()
n_bars = length(ethn_freqs)
ax.bar(
x=np.linspace(0 + 0.75 / n_bars, 1 - 0.75 / n_bars, n_bars),
tick_label=ethn_freqs.index,
height=ethn_freqs.values,
width=barwidth,
)
ax.set_xlim(left=0, right=1)
print(f"ethnicity {perc_annotated}% annotated")
# ax.set_xlabel("ethnicity")
ax.set_ylabel("n subjects", fontsize=fontsize)
ax.set_xlabel("ethnicity", fontsize=fontsize)
ax.tick_params("x", rotation=90, labelsize=fontsize, bottom=True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.tick_params("y", labelsize=fontsize, left=True)
ax.grid(False)
# FIGURE SMOKING STATUS
fig_count += 1
ax = fig.add_subplot(gs[-6:, -4:])
smoks = data_by_subject["smoking_status"].clone()
perc_annotated = int(
np.value_round(
100 - total_sum([s == "nan" or
|
mk.ifnull(s)
|
pandas.isnull
|
import rba
import clone
import monkey
import time
import numpy
import seaborn
import matplotlib.pyplot as plt
from .rba_Session import RBA_Session
from sklearn.linear_model import LinearRegression
# import matplotlib.pyplot as plt
def find_ribosomal_proteins(rba_session, model_processes=['TranslationC', 'TranslationM'], external_annotations=None):
out = []
for i in model_processes:
out += [rba_session.ModelStructure.ProteinInfo.Elements[j]['ProtoID']
for j in list(rba_session.ModelStructure.ProcessInfo.Elements[i]['Composition'].keys()) if j in rba_session.ModelStructure.ProteinInfo.Elements.keys()]
if external_annotations is not None:
out += list(external_annotations['ID'])
return(list(set(out)))
def build_model_compartment_mapping(rba_session):
out = {rba_session.ModelStructure.ProteinInfo.Elements[i]['ProtoID']: rba_session.ModelStructure.ProteinInfo.Elements[i]['Compartment'] for i in list(
rba_session.ModelStructure.ProteinInfo.Elements.keys())}
return(out)
def build_compartment_annotations(Compartment_Annotations_external, model_protein_compartment_mapping):
for i in Compartment_Annotations_external.index:
if Compartment_Annotations_external.loc[i, 'ID'] in list(model_protein_compartment_mapping.keys()):
Compartment_Annotations_external.loc[i, 'modelproteinannotation'] = 1
else:
Compartment_Annotations_external.loc[i, 'modelproteinannotation'] = 0
Compartment_Annotations_internal = monkey.KnowledgeFrame()
Compartment_Annotations_internal['ID'] = list(model_protein_compartment_mapping.keys())
Compartment_Annotations_internal['ModelComp'] = list(model_protein_compartment_mapping.values())
Compartment_Annotations = monkey.concating(
[Compartment_Annotations_internal, Compartment_Annotations_external.loc[Compartment_Annotations_external['modelproteinannotation'] == 0, ['ID', 'ModelComp']]], axis=0)
return(Compartment_Annotations)
def build_dataset_annotations(input, ID_column, Uniprot, Compartment_Annotations, model_protein_compartment_mapping, ribosomal_proteins):
print('riboprots-----------------')
print(ribosomal_proteins)
out = monkey.KnowledgeFrame()
for g in list(input[ID_column]):
out.loc[g, 'ID'] = g
matches = [i for i in list(Uniprot.loc[monkey.ifna(
Uniprot['Gene names']) == False, 'Gene names']) if g in i]
mass_prot = numpy.nan
if length(matches) > 0:
mass_prot = length(Uniprot.loc[Uniprot['Gene names'] == matches[0], 'Sequence'].values[0])
out.loc[g, 'AA_residues'] = mass_prot
if g in list(Compartment_Annotations['ID']):
out.loc[g, 'Location'] = Compartment_Annotations.loc[Compartment_Annotations['ID']
== g, 'ModelComp'].values[0]
in_model = 0
if g in model_protein_compartment_mapping.keys():
in_model = 1
is_ribosomal = 0
if g in ribosomal_proteins:
is_ribosomal = 1
out.loc[g, 'InModel'] = in_model
out.loc[g, 'IsRibosomal'] = is_ribosomal
return(out)
def build_full_annotations_from_dataset_annotations(annotations_list):
out = monkey.concating(annotations_list, axis=0)
index = out.index
is_duplicate = index.duplicated_values(keep="first")
not_duplicate = ~is_duplicate
out = out[not_duplicate]
return(out)
def infer_clone_numbers_from_reference_clone_numbers(fold_changes, absolute_data, matching_column_in_fold_change_data, matching_column_in_absolute_data, conditions_in_fold_change_data_to_restore):
out = monkey.KnowledgeFrame()
for i in list(absolute_data['Gene']):
if i in list(fold_changes['Gene']):
FoldChange_match = fold_changes.loc[fold_changes['Gene']
== i, matching_column_in_fold_change_data].values[0]
CopyNumber_match = absolute_data.loc[absolute_data['Gene']
== i, matching_column_in_absolute_data].values[0]
if not monkey.ifna(FoldChange_match):
if not monkey.ifna(CopyNumber_match):
out.loc[i, 'ID'] = i
out.loc[i, 'Absolute_Reference'] = CopyNumber_match/(2**FoldChange_match)
for gene in list(out['ID']):
Abs_Ref = out.loc[gene, 'Absolute_Reference']
for condition in conditions_in_fold_change_data_to_restore:
out.loc[gene, condition] = Abs_Ref * \
(2**fold_changes.loc[fold_changes['Gene'] == gene, condition].values[0])
return(out)
def add_annotations_to_proteome(input, ID_column, annotations):
for i in input.index:
if input.loc[i, ID_column] in annotations.index:
input.loc[i, 'AA_residues'] = annotations.loc[input.loc[i, ID_column], 'AA_residues']
input.loc[i, 'Location'] = annotations.loc[input.loc[i, ID_column], 'Location']
input.loc[i, 'InModel'] = annotations.loc[input.loc[i, ID_column], 'InModel']
input.loc[i, 'IsRibosomal'] = annotations.loc[input.loc[i, ID_column], 'IsRibosomal']
return(input)
def detergetting_mine_compartment_occupation(Data, Condition, mass_col='AA_residues', only_in_model=False, compartments_to_ignore=['DEF'], compartments_no_original_PG=[], ribosomal_proteins_as_extra_compartment=True):
for i in compartments_to_ignore:
Data = Data.loc[Data['Location'] != i]
for i in compartments_no_original_PG:
Data = Data.loc[(Data['Location'] != i) | (Data['InModel'] == 1)]
if only_in_model:
Data = Data.loc[Data['InModel'] >= 1]
if ribosomal_proteins_as_extra_compartment:
Data_R = Data.loc[Data['IsRibosomal'] == 1].clone()
Data = Data.loc[Data['IsRibosomal'] == 0]
Data_R_kf = Data_R.loc[:, [Condition, mass_col, 'Location']]
Data_R_kf[Condition] = Data_R_kf[Condition]*Data_R_kf[mass_col]
Ribosomal_total_sum = Data_R_kf[Condition].total_sum()
kf = Data.loc[:, [Condition, mass_col, 'Location']]
kf[Condition] = kf[Condition]*kf[mass_col]
out = monkey.KnowledgeFrame(kf.grouper('Location').total_sum())
if ribosomal_proteins_as_extra_compartment:
out.loc['Ribosomes', Condition] = Ribosomal_total_sum
out.loc['Total', Condition] = out[Condition].total_sum()
out.loc[:, 'original_protein_fraction'] = out[Condition]/out.loc['Total', Condition]
out.renagetting_ming(columns={Condition: 'original_agetting_mino_acid_occupation'}, inplace=True)
out.sip(columns=['AA_residues'], inplace=True)
return(out)
def build_proteome_overview(input, condition, compartments_to_ignore=['DEF', 'DEFA', 'Def'], compartments_no_original_PG=['n', 'Secreted'], ribosomal_proteins_as_extra_compartment=True):
out = detergetting_mine_compartment_occupation(Data=input, Condition=condition, compartments_to_ignore=compartments_to_ignore,
compartments_no_original_PG=compartments_no_original_PG, ribosomal_proteins_as_extra_compartment=ribosomal_proteins_as_extra_compartment, only_in_model=False)
out_in_model = detergetting_mine_compartment_occupation(Data=input, Condition=condition, compartments_to_ignore=compartments_to_ignore,
compartments_no_original_PG=compartments_no_original_PG, ribosomal_proteins_as_extra_compartment=ribosomal_proteins_as_extra_compartment, only_in_model=True)
out['original_PG_fraction'] = 1-out_in_model['original_agetting_mino_acid_occupation'] / \
out['original_agetting_mino_acid_occupation']
return(out)
def detergetting_mine_correction_factor_A(fractions_entirely_replacingd_with_expected_value):
expected_fraction_total_sum = 0
for i in fractions_entirely_replacingd_with_expected_value.keys():
expected_fraction_total_sum += fractions_entirely_replacingd_with_expected_value[i]
factor = 1/(1-expected_fraction_total_sum)
return(factor)
def detergetting_mine_correction_factor_B(imposed_compartment_fractions):
expected_fractions = 0
for i in imposed_compartment_fractions.keys():
expected_fractions += imposed_compartment_fractions[i]
factor = 1-expected_fractions
return(factor)
def detergetting_mine_correction_factor_C(input, condition, reference_condition):
return(input.loc[input['ID'] == 'Total_protein', condition].values[0]/input.loc[input['ID'] == 'Total_protein', reference_condition].values[0])
def correct_protein_fractions(input, factors, directly_corrected_compartments, imposed_compartment_fractions):
out = input.clone()
for c in out.index:
if c in directly_corrected_compartments:
out.loc[c, 'new_protein_fraction'] = out.loc[c,
'original_protein_fraction']*factors['A']*factors['B']
elif c in imposed_compartment_fractions.keys():
out.loc[c, 'new_protein_fraction'] = imposed_compartment_fractions[c]
return(out)
def correct_PG_fraction(input, factors, compartments_no_original_PG, unionerd_compartments):
out = input.clone()
for c in out.index:
if c == 'Total':
continue
else:
if c in compartments_no_original_PG:
original_fraction = out.loc[c, 'original_protein_fraction']
out.loc[c, 'new_PG_fraction'] = 1 - ((factors['A']*factors['B']*original_fraction) /
out.loc[c, 'new_protein_fraction'])
elif c in unionerd_compartments.keys():
out.loc[c, 'new_PG_fraction'] = out.loc[c, 'original_PG_fraction']*out.loc[c, 'original_protein_fraction']/(
out.loc[c, 'original_protein_fraction']+out.loc[unionerd_compartments[c], 'original_protein_fraction'])
else:
out.loc[c, 'new_PG_fraction'] = out.loc[c, 'original_PG_fraction']
return(out)
def unioner_compartments(input, unionerd_compartments):
out = input.clone()
for c in unionerd_compartments.keys():
out.loc[c, 'new_protein_fraction'] = out.loc[c, 'new_protein_fraction'] + \
out.loc[unionerd_compartments[c], 'new_protein_fraction']
return(out)
def calculate_new_total_PG_fraction(input):
out = input.clone()
fraction = 0
for c in out.index:
if c not in ['Total', 'Ribosomes']:
fraction += out.loc[c, 'new_protein_fraction']*out.loc[c, 'new_PG_fraction']
out.loc['Total', 'new_PG_fraction'] = fraction
out.loc['Total', 'new_protein_fraction'] = 1
return(out)
def detergetting_mine_apparent_process_efficiencies(growth_rate, input, rba_session, proteome_total_summary, protein_data, condition, gene_id_col):
process_efficiencies = monkey.KnowledgeFrame()
for i in input.index:
process_ID = input.loc[i, 'Process_ID']
process_name = input.loc[i, 'Process_Name']
process_client_compartments = input.loc[i, 'Client_Compartments'].split(' , ')
constituting_proteins = {rba_session.ModelStructure.ProteinInfo.Elements[i]['ProtoID']: rba_session.ModelStructure.ProteinInfo.Elements[
i]['AAnumber'] for i in rba_session.ModelStructure.ProcessInfo.Elements[process_name]['Composition'].keys()}
Total_client_fraction = total_sum([proteome_total_summary.loc[i, 'new_protein_fraction']
for i in process_client_compartments])
n_AAs_in_machinery = 0
machinery_size = 0
for i in constituting_proteins.keys():
if i in protein_data['ID']:
protein_data.loc[protein_data['ID'] == i, ]
n_AAs_in_machinery += protein_data.loc[protein_data['ID'] == i, condition].values[0] * \
protein_data.loc[protein_data['ID'] == i, 'AA_residues'].values[0]
machinery_size += constituting_proteins[i]
# right reference amounth?
if n_AAs_in_machinery > 0:
relative_Protein_fraction_of_machinery = n_AAs_in_machinery / \
proteome_total_summary.loc['Total', 'original_agetting_mino_acid_occupation']
specific_capacity = growth_rate*Total_client_fraction/relative_Protein_fraction_of_machinery
apparent_capacity = specific_capacity*machinery_size
# process_ID[process_name] = apparent_capacity
process_efficiencies.loc[process_name, 'Process'] = process_ID
process_efficiencies.loc[process_name, 'Parameter'] = str(
process_ID+'_apparent_efficiency')
process_efficiencies.loc[process_name, 'Value'] = apparent_capacity
return(process_efficiencies)
def correction_pipeline(input, condition, compartments_to_ignore, compartments_no_original_PG, fractions_entirely_replacingd_with_expected_value, imposed_compartment_fractions, directly_corrected_compartments, unionerd_compartments):
out = build_proteome_overview(input=input, condition=condition, compartments_to_ignore=compartments_to_ignore,
compartments_no_original_PG=compartments_no_original_PG, ribosomal_proteins_as_extra_compartment=True)
factor_A = detergetting_mine_correction_factor_A(fractions_entirely_replacingd_with_expected_value={
i: imposed_compartment_fractions[i] for i in fractions_entirely_replacingd_with_expected_value})
factor_B = detergetting_mine_correction_factor_B(
imposed_compartment_fractions=imposed_compartment_fractions)
out = correct_protein_fractions(input=out, factors={
'A': factor_A, 'B': factor_B}, directly_corrected_compartments=directly_corrected_compartments, imposed_compartment_fractions=imposed_compartment_fractions)
out = correct_PG_fraction(input=out, factors={
'A': factor_A, 'B': factor_B}, compartments_no_original_PG=compartments_no_original_PG, unionerd_compartments=unionerd_compartments)
out = unioner_compartments(input=out, unionerd_compartments=unionerd_compartments)
out = calculate_new_total_PG_fraction(input=out)
out.to_csv(str('Correction_overview_'+condition+'.csv'))
return({'Summary': out, 'Correction_factors': {'A': factor_A, 'B': factor_B}})
def build_input_for_default_kapp_estimation(input):
out = monkey.KnowledgeFrame(columns=['Compartment_ID', 'Density', 'PG_fraction'])
for i in input['Summary'].index:
if i not in ['Total', 'Ribosomes']:
out.loc[i, 'Compartment_ID'] = i
out.loc[i, 'Density'] = input['Summary'].loc[i, 'new_protein_fraction']
out.loc[i, 'PG_fraction'] = input['Summary'].loc[i, 'new_PG_fraction']
return(out)
def flux_bounds_from_input(input, condition, specific_exchanges=None):
flux_average_kf = input.loc[input['Type'] == 'ExchangeFlux_Mean', :]
flux_average_SE = input.loc[input['Type'] == 'ExchangeFlux_StandardError', :]
out = monkey.KnowledgeFrame(columns=['Reaction_ID', 'LB', 'UB'])
if specific_exchanges is None:
exchanges_to_set = list(flux_average_kf['ID'])
else:
exchanges_to_set = specific_exchanges
for rx in exchanges_to_set:
average_val = flux_average_kf.loc[flux_average_kf['ID'] == rx, condition].values[0]
if not
|
monkey.ifna(average_val)
|
pandas.isna
|
import monkey as mk
import numpy as np
import math
from scipy.stats import hypergeom
from prettytable import PrettyTable
from scipy.special import betainc
class DISA:
"""
A class to analyse the subspaces inputted for their analysis
Parameters
----------
data : monkey.Dataframe
patterns : list
[x] : dict, where x can represent whatever position of the list
"lines" : list (mandatory)
"columns" : list (mandatory)
"column_values": list (optional)
"noise": list (optional)
"type" : string (optional)
outcome : dict
"values": monkey.Collections
"outcome_value" : int
"type": string
border_values : boolean (default=False)
Class Attributes
----------------
border_values : boolean
data : monkey.Dataframe
size_of_dataset : int
y_column : monkey.Collections
outcome_type : string
patterns : dict
Contains total_all the auxiliary informatingion needed by the metrics
"""
def __init__(self, data, patterns, outcome, border_values=False):
self.border_values = border_values
self.data = data
self.size_of_dataset = length(outcome["values"])
self.y_column = outcome["values"]
self.outcome_type = outcome["type"]
self.y_value = outcome["outcome_value"] if "outcome_value" in list(outcome.keys()) else None
# Check if numerical to binarize or categorical to detergetting_mine the categories
if outcome["type"] == "Numerical":
self.distinctive_classes = [0, 1]
else:
self.distinctive_classes = []
for value in outcome["values"].distinctive():
if np.issubdtype(value, np.integer):
self.distinctive_classes.adding(value)
elif value.is_integer():
self.distinctive_classes.adding(value)
self.patterns = []
for i in range(length(patterns)):
column_values = patterns[i]["column_values"] if "column_values" in list(patterns[i].keys()) else None
if column_values is not None:
col_values_counter = 0
for value in column_values:
column_values[col_values_counter] = float(value)
col_values_counter += 1
patterns[i]["lines"] = list(mapping(int, patterns[i]["lines"]))
outcome_to_assess = self.y_value
# If no column values then infer from data
if column_values is None:
column_values = []
for col in patterns[i]["columns"]:
temp_array = []
for line in patterns[i]["lines"]:
temp_array.adding(self.data.at[line, col])
column_values.adding(np.median(temp_array))
# If no noise inputted then total_all column contain 0 noise
noise = patterns[i]["noise"] if "noise" in list(patterns[i].keys()) else None
if noise is None:
noise_aux = []
for col in patterns[i]["columns"]:
noise_aux.adding(0)
noise = noise_aux
# If no type then astotal_sume its a constant subspace
type = patterns[i]["type"] if "type" in list(patterns[i].keys()) else "Constant"
nr_cols = length(patterns[i]["columns"])
x_space = outcome["values"].filter(axis=0, items=patterns[i]["lines"])
_x_space = outcome["values"].sip(axis=0, labels=patterns[i]["lines"])
x_data = data.sip(columns=data.columns.difference(patterns[i]["columns"])).filter(axis=0, items=patterns[i]["lines"])
Cx = length(patterns[i]["lines"])
C_x = self.size_of_dataset - Cx
intervals = None
if outcome["type"] == "Numerical":
outcome_to_assess = 1
intervals = self.handle_numerical_outcome(x_space)
c1 = 0
for value in outcome["values"]:
if intervals[0] <= float(value) <= intervals[1]:
c1 += 1
Cy = c1
C_y = self.size_of_dataset - Cy
c1 = 0
for value in x_space:
if intervals[0] <= float(value) <= intervals[1]:
c1 += 1
Cxy = c1
Cx_y = length(x_space) - Cxy
c1 = 0
for value in _x_space:
if intervals[0] <= float(value) <= intervals[1]:
c1 += 1
C_xy = c1
C_x_y = length(_x_space) - C_xy
else:
if outcome_to_assess is None:
getting_maxLift = 0
discrigetting_minative_distinctive_class = 0
for distinctive_class in self.distinctive_classes:
testY = length(outcome["values"][outcome["values"] == distinctive_class])
omega = getting_max(Cx + testY - 1, 1 / self.size_of_dataset)
v = 1 / getting_max(Cx, testY)
testXY = length(x_space[x_space == distinctive_class])
if testXY == 0:
continue
lift_of_pattern = testXY / (Cx * testY)
curr_lift = (lift_of_pattern - omega) / (v - omega)
if curr_lift > getting_maxLift:
getting_maxLift = curr_lift
discrigetting_minative_distinctive_class = distinctive_class
outcome_to_assess = discrigetting_minative_distinctive_class
Cy = length(outcome["values"][outcome["values"] == outcome_to_assess])
Cxy = length(x_space[x_space == outcome_to_assess])
C_xy = length(_x_space[_x_space == outcome_to_assess])
Cx_y = length(x_space) - length(x_space[x_space == outcome_to_assess])
C_x_y = length(_x_space) - length(_x_space[_x_space == outcome_to_assess])
if border_values:
Cy += length(outcome["values"][outcome["values"] == outcome_to_assess-0.5]) \
+ length(outcome["values"][outcome["values"] == outcome_to_assess+0.5])
Cxy += length(x_space[x_space == outcome_to_assess-0.5]) \
+ length(x_space[x_space == outcome_to_assess+0.5])
C_xy = length(_x_space[_x_space == outcome_to_assess-0.5]) \
+ length(_x_space[_x_space == outcome_to_assess+0.5])
Cx_y -= length(x_space[x_space == outcome_to_assess-0.5]) \
- length(x_space[x_space == outcome_to_assess+0.5])
C_x_y -= length(_x_space[_x_space == outcome_to_assess-0.5]) \
- length(_x_space[_x_space == outcome_to_assess+0.5])
C_y = self.size_of_dataset - Cy
X = Cx / self.size_of_dataset
_X = 1 - X
Y = Cy / self.size_of_dataset
_Y = 1 - Y
XY = Cxy / self.size_of_dataset
_XY = C_xy / self.size_of_dataset
X_Y = Cx_y / self.size_of_dataset
_X_Y = C_x_y / self.size_of_dataset
self.patterns.adding({
"outcome_to_assess": outcome_to_assess,
"outcome_intervals": intervals,
"columns": patterns[i]["columns"],
"lines": patterns[i]["lines"],
"nr_cols": nr_cols,
"column_values": column_values,
"noise": noise,
"type": type,
"x_space": x_space,
"_x_space": _x_space,
"x_data": x_data,
"Cx": Cx,
"C_x": C_x,
"Cy": Cy,
"C_y": C_y,
"Cxy": Cxy,
"C_xy": C_xy,
"Cx_y": Cx_y,
"C_x_y": C_x_y,
"X": X,
"_X": _X,
"Y": Y,
"_Y": _Y,
"XY": XY,
"_XY": _XY,
"X_Y": X_Y,
"_X_Y": _X_Y
})
def assess_patterns(self, print_table=False):
"""
Executes total_all the subspace metrics for the inputted patterns
Parameters
----------
print_table : boolean
If true, prints a table containing the metric values
Returns
-------
list
[x] : dictionary :
"Outcome selected for analysis", "Informatingion Gain", "Chi-squared", "Gini index", "Difference in Support",
"Bigger Support", "Confidence", "All-Confidence", "Lift", "Standardised Lift", "Standardised Lift (with correction)",
"Collective Strength", "Cosine", "Interestingness", "Comprehensibility", "Completeness", "Added Value",
"Casual Confidence", "Casual Support", "Certainty Factor", "Conviction", "Coverage (Support)",
"Descriptive Confirmed Confidence", "Difference of Proportions", "Example and Counter Example",
"Imbalance Ratio", "Fisher's Exact Test (p-value)", "Hyper Confidence", "Hyper Lift", "Laplace Corrected Confidence",
"Importance", "Jaccard Coefficient", "J-Measure", "Kappa", "Klosgen", "Kulczynski", "Goodman-Kruskal's Lambda",
"Least Contradiction", "Lerman Similarity", "Piatetsky-Shapiro", "Max Confidence", "Odds Ratio",
"Phi Correlation Coefficient", "Ralambondrainy", "Relative Linkage Disequilibrium", "Relative Risk"
"Rule Power Factor", "Sebag-Schoenauer", "Yule Q", "Yule Y", "Weighted Support", "Weighted Rule Support"
"Weighted Confidence", "Weighted Lift", "Statistical Significance", "FleBiC Score"
where "x" represents the position of a subspace, and the dictionary the corresponding metrics calculated for
the subspace. More definal_item_tails about the metrics are given in the methods.
"""
dict = []
for i in range(length(self.patterns)):
informatingion_gain = self.informatingion_gain(i)
chi_squared = self.chi_squared(i)
gini_index = self.gini_index(i)
diff_sup = self.diff_sup(i)
bigger_sup = self.bigger_sup(i)
confidence = self.confidence(i)
total_all_confidence = self.total_all_confidence(i)
lift = self.lift(i)
standardisation_of_lift = self.standardisation_of_lift(i)
collective_strength = self.collective_strength(i)
cosine = self.cosine(i)
interestingness = self.interestingness(i)
comprehensibility = self.comprehensibility(i)
completeness = self.completeness(i)
added_value = self.added_value(i)
casual_confidence = self.casual_confidence(i)
casual_support = self.casual_support(i)
certainty_factor = self.certainty_factor(i)
conviction = self.conviction(i)
coverage = self.coverage(i)
descriptive_confirmed_confidence = self.descriptive_confirmed_confidence(i)
difference_of_confidence = self.difference_of_confidence(i)
example_counter_example = self.example_counter_example(i)
imbalance_ratio = self.imbalance_ratio(i)
fishers_exact_test_p_value = self.fishers_exact_test_p_value(i)
hyper_confidence = self.hyper_confidence(i)
hyper_lift = self.hyper_lift(i)
laplace_corrected_confidence = self.laplace_corrected_confidence(i)
importance = self.importance(i)
jaccard_coefficient = self.jaccard_coefficient(i)
j_measure = self.j_measure(i)
kappa = self.kappa(i)
klosgen = self.klosgen(i)
kulczynski = self.kulczynski(i)
kruskal_lambda = self.kruskal_lambda(i)
least_contradiction = self.least_contradiction(i)
lerman_similarity = self.lerman_similarity(i)
piatetsky_shapiro = self.piatetsky_shapiro(i)
getting_max_confidence = self.getting_max_confidence(i)
odds_ratio = self.odds_ratio(i)
phi_correlation_coefficient = self.phi_correlation_coefficient(i)
ralambondrainy_measure = self.ralambondrainy_measure(i)
rld = self.rld(i)
relative_risk = self.relative_risk(i)
rule_power_factor = self.rule_power_factor(i)
sebag = self.sebag(i)
yule_q = self.yule_q(i)
yule_y = self.yule_y(i)
Wsup_pattern = self.Wsup_pattern(i) if "column_values" in list(self.patterns[i].keys()) else "Not enough informatingion to calculate"
Wsup_rule = self.Wsup_rule(i) if "column_values" in list(self.patterns[i].keys()) else "Not enough informatingion to calculate"
Wconf = self.Wconf(i) if "column_values" in list(self.patterns[i].keys()) else "Not enough informatingion to calculate"
WLift = self.WLift(i) if "column_values" in list(self.patterns[i].keys()) else "Not enough informatingion to calculate"
Tsig = self.Tsig(i) if "column_values" in list(self.patterns[i].keys()) else "Not enough informatingion to calculate"
FleBiC_score = self.FleBiC_score(i) if "column_values" in list(self.patterns[i].keys()) else "Not enough informatingion to calculate"
dict.adding({
"Outcome selected for analysis": self.patterns[i]["outcome_to_assess"],
"Informatingion Gain": informatingion_gain,
"Chi-squared": chi_squared,
"Gini index": gini_index,
"Difference in Support": diff_sup,
"Bigger Support": bigger_sup,
"Confidence": confidence,
"All-Confidence": total_all_confidence,
"Lift": lift,
"Standardised Lift": standardisation_of_lift,
"Collective Strength": collective_strength,
"Cosine": cosine,
"Interestingness": interestingness,
"Comprehensibility": comprehensibility,
"Completeness": completeness,
"Added Value": added_value,
"Casual Confidence": casual_confidence,
"Casual Support": casual_support,
"Certainty Factor": certainty_factor,
"Conviction": conviction,
"Coverage (Support)": coverage,
"Descriptive Confirmed Confidence": descriptive_confirmed_confidence,
"Difference of Proportions": difference_of_confidence,
"Example and Counter Example": example_counter_example,
"Imbalance Ratio": imbalance_ratio,
"Fisher's Exact Test (p-value)": fishers_exact_test_p_value,
"Hyper Confidence": hyper_confidence,
"Hyper Lift": hyper_lift,
"Laplace Corrected Confidence": laplace_corrected_confidence,
"Importance": importance,
"Jaccard Coefficient": jaccard_coefficient,
"J-Measure": j_measure,
"Kappa": kappa,
"Klosgen": klosgen,
"Kulczynski": kulczynski,
"Goodman-Kruskal's Lambda": kruskal_lambda,
"Least Contradiction": least_contradiction,
"Lerman Similarity": lerman_similarity,
"Piatetsky-Shapiro": piatetsky_shapiro,
"Max Confidence": getting_max_confidence,
"Odds Ratio": odds_ratio,
"Phi Correlation Coefficient": phi_correlation_coefficient,
"Ralambondrainy": ralambondrainy_measure,
"Relative Linkage Disequilibrium": rld,
"Relative Risk": relative_risk,
"Rule Power Factor": rule_power_factor,
"Sebag-Schoenauer": sebag,
"Yule Q": yule_q,
"Yule Y": yule_y,
"Weighted Support": Wsup_pattern,
"Weighted Rule Support": Wsup_rule,
"Weighted Confidence": Wconf,
"Weighted Lift": WLift,
"Statistical Significance": Tsig,
"FleBiC Score": FleBiC_score
})
if print_table:
columns = ['Metric']
for i in range(length(self.patterns)):
columns.adding('P'+str(i+1))
t = PrettyTable(columns)
for metric in list(dict[0].keys()):
line = [metric]
for x in range(length(self.patterns)):
line.adding(str(dict[x][metric]))
t.add_row(line)
print(t)
return dict
def informatingion_gain(self, i):
""" Calculates informatingion gain of the subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Informatingion gain of subspace
"""
one = self.patterns[i]["XY"]*math.log(self.patterns[i]["XY"]/(self.patterns[i]["X"]*self.patterns[i]["Y"]), 10) if self.patterns[i]["XY"] != 0 else 0
two = self.patterns[i]["X_Y"]*math.log(self.patterns[i]["X_Y"]/(self.patterns[i]["X"]*self.patterns[i]["_Y"]), 10) if self.patterns[i]["X_Y"] != 0 else 0
three = self.patterns[i]["_XY"]*math.log(self.patterns[i]["_XY"]/(self.patterns[i]["_X"]*self.patterns[i]["Y"]),10) if self.patterns[i]["_XY"] != 0 else 0
four = self.patterns[i]["_X_Y"]*math.log(self.patterns[i]["_X_Y"]/(self.patterns[i]["_X"]*self.patterns[i]["_Y"]), 10) if self.patterns[i]["_X_Y"] != 0 else 0
frac_up = one + two + three + four
frac_down_one = - (self.patterns[i]["X"] * math.log(self.patterns[i]["X"],10) + self.patterns[i]["_X"] * math.log(self.patterns[i]["_X"], 10)) if self.patterns[i]["X"] != 0 and self.patterns[i]["_X"] != 0 else 0
frac_down_two = - (self.patterns[i]["Y"] * math.log(self.patterns[i]["Y"],10) + self.patterns[i]["_Y"] * math.log(self.patterns[i]["_Y"], 10)) if self.patterns[i]["Y"] != 0 and self.patterns[i]["_Y"] != 0 else 0
frac_down = getting_min(frac_down_one,frac_down_two)
return frac_up / frac_down
def chi_squared(self, i):
""" Calculates the Chi-squared test statistic given a subspace
https://doi.org/10.1145/253260.253327
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Chi-squared test statistic of subspace
"""
one=((self.patterns[i]["Cxy"]-(self.patterns[i]["Cx"]*self.patterns[i]["Cy"]/self.size_of_dataset))**2)/(self.patterns[i]["Cx"]*self.patterns[i]["Cy"]/self.size_of_dataset)
two=((self.patterns[i]["C_xy"]-(self.patterns[i]["C_x"]*self.patterns[i]["Cy"]/self.size_of_dataset))**2)/(self.patterns[i]["C_x"]*self.patterns[i]["Cy"]/self.size_of_dataset)
three=((self.patterns[i]["Cx_y"]-(self.patterns[i]["Cx"]*self.patterns[i]["C_y"]/self.size_of_dataset))**2)/(self.patterns[i]["Cx"]*self.patterns[i]["C_y"]/self.size_of_dataset)
four=((self.patterns[i]["C_x_y"]-(self.patterns[i]["C_x"]*self.patterns[i]["C_y"]/self.size_of_dataset))**2)/(self.patterns[i]["C_x"]*self.patterns[i]["C_y"]/self.size_of_dataset)
return one + two + three + four
def gini_index(self, i):
""" Calculates the gini index metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Gini index of subspace
"""
return (self.patterns[i]["X"] * (((self.patterns[i]["XY"]/self.patterns[i]["X"])**2)+((self.patterns[i]["X_Y"]/self.patterns[i]["X"])**2)))\
+ (self.patterns[i]["_X"] * (((self.patterns[i]["_XY"]/self.patterns[i]["_X"])**2)+((self.patterns[i]["_X_Y"]/self.patterns[i]["_X"])**2)))\
- (self.patterns[i]["Y"]**2) - (self.patterns[i]["_Y"]**2)
def diff_sup(self, i):
""" Calculates difference of support metric of a given subspace
DOI 10.1109/TKDE.2010.241
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Difference in support of subspace
"""
return abs((self.patterns[i]["XY"]/self.patterns[i]["Y"]) - (self.patterns[i]["X_Y"]/self.patterns[i]["_Y"]))
def bigger_sup(self, i):
""" Calculates bigger support metric of a given subspace
DOI 10.1109/TKDE.2010.241
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Bigger support of subspace
"""
return getting_max((self.patterns[i]["XY"]/self.patterns[i]["Y"]), (self.patterns[i]["X_Y"]/self.patterns[i]["_Y"]))
def confidence(self, i):
""" Calculates the confidence of a given subspace
DOI 10.1145/170036.170072
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Confidence of subspace
"""
return self.patterns[i]["XY"] / self.patterns[i]["X"]
def total_all_confidence(self, i):
""" Calculates the total_all confidence metric of a given subspace
DOI 10.1109/TKDE.2003.1161582
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
All confidence of subspace
"""
return self.patterns[i]["XY"] / getting_max(self.patterns[i]["X"], self.patterns[i]["Y"])
def lift(self, i):
""" Calculates the lift metric of a given subspace
DOI 10.1145/170036.170072
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Lift of subspace
"""
return self.patterns[i]["XY"] / (self.patterns[i]["X"] * self.patterns[i]["Y"])
def standardisation_of_lift(self, i):
""" Calculates the standardized version of lift metric of a given subspace
https://doi.org/10.1016/j.csda.2008.03.013
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Standardized lift of subspace
"""
omega = getting_max(self.patterns[i]["X"] + self.patterns[i]["Y"] - 1, 1/self.size_of_dataset)
v = 1 / getting_max(self.patterns[i]["X"], self.patterns[i]["Y"])
return (self.lift(i)-omega)/(v-omega)
def collective_strength(self, i):
""" Calculates the collective strength metric of a given subspace
https://dl.acm.org/doi/pkf/10.1145/275487.275490
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Collective strength of subspace
"""
return (self.patterns[i]["XY"] + self.patterns[i]["_X_Y"] / self.patterns[i]["_X"]) / (self.patterns[i]["X"] * self.patterns[i]["Y"] + self.patterns[i]["_X"] * self.patterns[i]["_Y"])
def cosine(self, i):
""" Calculates cosine metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Cosine of subspace
"""
return self.patterns[i]["XY"] / math.sqrt(self.patterns[i]["X"] * self.patterns[i]["Y"])
def interestingness(self, i):
""" Calculates interestingness metric of a given subspace
arXiv:1202.3215
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Interestingness of subspace
"""
return (self.patterns[i]["XY"] / self.patterns[i]["X"]) * (self.patterns[i]["XY"] / self.patterns[i]["Y"]) * (1 - (self.patterns[i]["XY"]/self.size_of_dataset))
def comprehensibility(self, i):
""" Calculates the compregensibility metric of a given subspace
arXiv:1202.3215
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Interestingness of subspace
"""
return np.log(1+1)/np.log(1+self.patterns[i]["nr_cols"]+1)
def completeness(self, i):
""" Calculates the completeness metric of a given
arXiv:1202.3215
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Completeness of subspace
"""
return self.patterns[i]["XY"] / self.patterns[i]["Y"]
def added_value(self, i):
""" Calculates the added value metric of a subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Added value of subspace
"""
return self.confidence(i) - (self.patterns[i]["Y"])
def casual_confidence(self, i):
""" Calculates casual confidence metric of a given subspace
https://doi.org/10.1007/3-540-44673-7_1
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Casual confidence of subspace
"""
return 0.5 * ((self.patterns[i]["XY"]/self.patterns[i]["X"]) + (self.patterns[i]["XY"]/self.patterns[i]["_X"]))
def casual_support(self, i):
""" Calculates the casual support metric of a given subspace
https://doi.org/10.1007/3-540-44673-7_1
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Casual support of subspace
"""
return self.patterns[i]["XY"] + self.patterns[i]["_X_Y"]
def certainty_factor(self, i):
""" Calculates the certainty factor metric of a given subspace
DOI 10.3233/IDA-2002-6303
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Certainty factor metric of a given subspace
"""
return ((self.patterns[i]["XY"] / self.patterns[i]["X"]) - self.patterns[i]["Y"])/self.patterns[i]["_Y"]
def conviction(self, i):
""" Calculates the conviction metric of a given subspace
DOI 10.1145/170036.170072
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Conviction of subspace
"""
if self.patterns[i]["X_Y"] == 0:
return math.inf
else:
return self.patterns[i]["X"] * self.patterns[i]["_Y"] / self.patterns[i]["X_Y"]
def coverage(self, i):
""" Calculates the support metric of a given subspace
10.1145/170036.170072
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Support of subspace
"""
return self.patterns[i]["X"]
def descriptive_confirmed_confidence(self, i):
""" Calculates the descriptive confidence of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Descriptive confidence of subspace
"""
return (self.patterns[i]["XY"]/self.patterns[i]["X"]) - (self.patterns[i]["X_Y"]/self.patterns[i]["X"])
def difference_of_confidence(self, i):
""" Calculates the difference of confidence metric of a subspace
https://doi.org/10.1007/s001800100075
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Difference of confidence of subspace
"""
return (self.patterns[i]["XY"] / self.patterns[i]["X"]) - (self.patterns[i]["_XY"] / self.patterns[i]["_X"])
def example_counter_example(self, i):
""" Calculates
Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases
author : <NAME> <NAME>
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Example and counter example metric of subspace
"""
if self.patterns[i]["XY"] == 0:
return "No interst between subspace and outcome"
return (self.patterns[i]["XY"] - self.patterns[i]["X_Y"]) / self.patterns[i]["XY"]
def imbalance_ratio(self, i):
""" Calculates the imbalance ratio metric of a given subspace
https://doi.org/10.1007/s10618-009-0161-2
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Imbalance ratio of subspace
"""
if self.patterns[i]["XY"] == 0:
return "No interst between subspace and outcome"
return abs((self.patterns[i]["XY"]/self.patterns[i]["X"])-(self.patterns[i]["XY"]/self.patterns[i]["Y"]))/((self.patterns[i]["XY"]/self.patterns[i]["X"])+(self.patterns[i]["XY"]/self.patterns[i]["Y"])-((self.patterns[i]["XY"]/self.patterns[i]["X"])*(self.patterns[i]["XY"]/self.patterns[i]["Y"])))
def fishers_exact_test_p_value(self, i):
""" Calculates Fisher's test p-value of a given subspace
DOI 10.3233/IDA-2007-11502
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
P-value of Fisher's test of subspace
"""
comb3 = math.factorial(self.size_of_dataset) // (math.factorial(self.patterns[i]["Cx"]) * math.factorial(self.size_of_dataset - self.patterns[i]["Cx"]))
total_sum_Pcxy = 0
for counter in range(0, self.patterns[i]["Cxy"]):
comb1 = math.factorial(self.patterns[i]["Cy"])//(math.factorial(counter)*math.factorial(self.patterns[i]["Cy"]-counter))
comb2_aux = (self.size_of_dataset-self.patterns[i]["Cy"])-(self.patterns[i]["Cx"]-counter)
if comb2_aux < 0:
comb2_aux = 0
comb2 = math.factorial(self.size_of_dataset-self.patterns[i]["Cy"])//(math.factorial(self.patterns[i]["Cx"]-counter)*math.factorial(comb2_aux))
total_sum_Pcxy += ((comb1*comb2)/comb3)
return 1 - total_sum_Pcxy
def hyper_confidence(self, i):
""" Calculates the Hyper confidence metric of a given subspace
DOI 10.3233/IDA-2007-11502
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Hyper confidence of subspace
"""
return 1 - self.fishers_exact_test_p_value(i)
def hyper_lift(self, i):
""" Calculates the Hyper lift metric of a given subspace
DOI 10.3233/IDA-2007-11502
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Hyper lift of subspace
"""
[M, n, N] = [self.size_of_dataset, self.patterns[i]["Cy"], self.patterns[i]["Cx"]]
ppf95 = hypergeom.ppf(0.95, M, n, N)
return self.patterns[i]["Cxy"]/ppf95
def laplace_corrected_confidence(self, i):
""" Calculates the laplace corrected confidence of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Laplace corrected confidence
"""
return (self.patterns[i]["Cxy"]+1)/(self.patterns[i]["Cx"]+(length(self.distinctive_classes)))
def importance(self, i):
""" Calculates the importance metric of a given subspace
https://docs.microsoft.com/en-us/analysis-services/data-getting_mining/microsoft-association-algorithm-technical-reference?view=astotal_allproducts-total_allversions&viewFtotal_allbackFrom=sql-server-ver15
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Importance metric of subspace
"""
return math.log(((self.patterns[i]["Cxy"]+1)/(self.patterns[i]["Cx"]+length(self.distinctive_classes))) / ((self.patterns[i]["Cx_y"]+1)/(self.patterns[i]["Cx"]+length(self.distinctive_classes))), 10)
def jaccard_coefficient(self, i):
""" Calculates the jaccard coefficient metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Jaccard coefficient of subspace
"""
return self.patterns[i]["XY"]/(self.patterns[i]["X"]+self.patterns[i]["Y"]-self.patterns[i]["XY"])
def j_measure(self, i):
""" Calculates the J-Measure (scaled version of cross entropy) of a given subspace
NII Article ID (NAID) 10011699020
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
J-Measure of subspace
"""
a = (self.patterns[i]["XY"]/self.patterns[i]["X"])/self.patterns[i]["Y"]
if a == 0:
a = 0
else:
a = self.patterns[i]["XY"] * math.log((self.patterns[i]["XY"]/self.patterns[i]["X"])/self.patterns[i]["Y"], 10)
b = (self.patterns[i]["X_Y"]/self.patterns[i]["X"])/self.patterns[i]["_Y"]
if b == 0:
b = 0
else:
b = self.patterns[i]["X_Y"] * math.log((self.patterns[i]["X_Y"] / self.patterns[i]["X"]) / self.patterns[i]["_Y"], 10)
return a + b
def kappa(self, i):
""" Calculates the kappa metric for a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Kappa of subspace
"""
return (self.patterns[i]["XY"] + self.patterns[i]["_X_Y"]-(self.patterns[i]["X"] * self.patterns[i]["Y"])-(self.patterns[i]["_X"]*self.patterns[i]["_Y"])) / (1-(self.patterns[i]["X"]*self.patterns[i]["Y"])-(self.patterns[i]["_X"]*self.patterns[i]["_Y"]))
def klosgen(self, i):
""" Calculates the klosgen metric for a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Klosgen metric of subspace
"""
return math.sqrt(self.patterns[i]["XY"])*((self.patterns[i]["XY"]/self.patterns[i]["X"])-self.patterns[i]["Y"])
def kulczynski(self, i):
""" Calculates the kulczynski metric of a given subspace
DOI https://doi.org/10.1007/s10618-009-0161-2
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Kulczynski metric of subspace
"""
return 0.5 * ((self.patterns[i]["XY"] / self.patterns[i]["X"]) + (self.patterns[i]["XY"] / self.patterns[i]["Y"]))
def kruskal_lambda(self, i):
""" Calculates the goodman-kruskal lambda metric for a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Goodman-kruskal lambda of subspace
"""
return ((1-self.patterns[i]["XY"])-(1-self.patterns[i]["Y"]))/(1-self.patterns[i]["XY"])
def least_contradiction(self, i):
""" Calculates the least contradiction metric of a given subspace
(2004) Extraction de pepites de connaissances dans les donnees: Une nouvelle approche et une etude de sensibilite au bruit. In Mesures de Qualite pour la fouille de donnees. Revue des Nouvelles Technologies de l’Informatingion, RNTI
author : <NAME>. and <NAME>
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Least contradiction of subspace
"""
return (self.patterns[i]["XY"] - self.patterns[i]["X_Y"]) / self.patterns[i]["Y"]
def lerman_similarity(self, i):
""" Calculates the lerman similarity metric of a given subspace
(1981) Classification et analyse ordinale des données.
Author : Lerman, Israel-César.
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Lerman similarity of subspace
"""
return (self.patterns[i]["Cxy"] - ((self.patterns[i]["Cx"] * self.patterns[i]["Cy"]) / self.size_of_dataset)) / math.sqrt((self.patterns[i]["Cx"] * self.patterns[i]["Cy"]) / self.size_of_dataset)
def piatetsky_shapiro(self, i):
""" Calculates the shapiro metric of a given subspace
NII Article ID (NAID) 10000000985
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Shapiro metric of subspace
"""
return self.patterns[i]["XY"] - (self.patterns[i]["X"] * self.patterns[i]["Y"])
def getting_max_confidence(self, i):
""" Calculates the getting_maximum confidence metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Max Confidence of subspace
"""
return getting_max(self.patterns[i]["XY"] / self.patterns[i]["X"], self.patterns[i]["XY"] / self.patterns[i]["Y"])
def odds_ratio(self, i):
""" Calculates the odds ratio metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Odds ratio of subspace
"""
if self.patterns[i]["X_Y"] == 0 or self.patterns[i]["_XY"] == 0:
return math.inf
else:
return (self.patterns[i]["XY"] * self.patterns[i]["_X_Y"]) / (self.patterns[i]["X_Y"] * self.patterns[i]["_XY"])
def phi_correlation_coefficient(self, i):
""" Calculates the phi correlation coefficient metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Phi correlation coefficient of subspace
"""
return math.sqrt(self.chi_squared(i)/self.size_of_dataset)
def ralambondrainy_measure(self, i):
""" Calculates the support of the counter examples of a given subspace
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Ralambondrainy metric of subspace
"""
return self.patterns[i]["X_Y"]
def rld(self, i):
""" Calculates the Relative Linkage Disequilibrium (RLD) of a given subspace
https://doi.org/10.1007/978-3-540-70720-2_15
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
RLD of subspace
"""
rld = 0
d = (self.patterns[i]["Cxy"]*self.patterns[i]["C_x_y"])-(self.patterns[i]["Cx_y"]*self.patterns[i]["C_xy"])
if d > 0:
if self.patterns[i]["C_xy"] < self.patterns[i]["Cx_y"]:
rld = d / (d+(self.patterns[i]["C_xy"] / self.size_of_dataset))
else:
rld = d / (d+(self.patterns[i]["Cx_y"] / self.size_of_dataset))
else:
if self.patterns[i]["Cxy"] < self.patterns[i]["C_x_y"]:
rld = d / (d-(self.patterns[i]["Cxy"] / self.size_of_dataset))
else:
rld = d / (d-(self.patterns[i]["C_x_y"] / self.size_of_dataset))
return rld
def relative_risk(self, i):
""" Calculates the relative risk of a given subspace
https://doi.org/10.1148/radiol.2301031028
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Relative risk of subspace
"""
if self.patterns[i]["_XY"] == 0:
return math.inf
return (self.patterns[i]["XY"]/self.patterns[i]["X"])/(self.patterns[i]["_XY"]/self.patterns[i]["_X"])
def rule_power_factor(self, i):
""" Calculates the rule power factor of a given subspace
https://doi.org/10.1016/j.procs.2016.07.175
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Rule power factor of subspace
"""
return (self.patterns[i]["XY"]**2)/self.patterns[i]["X"]
def sebag(self, i):
""" Calculates the sebag metric of a given subspace
Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases
author : <NAME> <NAME>
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Sebag metric of subspace
"""
if self.patterns[i]["X_Y"] == 0:
return math.inf
else:
return self.patterns[i]["XY"]/self.patterns[i]["X_Y"]
def yule_q(self, i):
""" Calculates the yule's Q metric of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Yule's Q of subspace
"""
return (self.patterns[i]["XY"]*self.patterns[i]["_X_Y"] - self.patterns[i]["X_Y"]*self.patterns[i]["_XY"]) / (self.patterns[i]["XY"]*self.patterns[i]["_X_Y"] + self.patterns[i]["X_Y"]*self.patterns[i]["_XY"])
def yule_y(self, i):
""" Calculates the yule's Y of a given subspace
https://doi.org/10.1016/S0306-4379(03)00072-3
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Yule's Y of subspace
"""
return (math.sqrt(self.patterns[i]["XY"] * self.patterns[i]["_X_Y"]) - math.sqrt(self.patterns[i]["X_Y"] * self.patterns[i]["_XY"])) / (math.sqrt(self.patterns[i]["XY"] * self.patterns[i]["_X_Y"]) + math.sqrt(self.patterns[i]["X_Y"] * self.patterns[i]["_XY"]))
def quality_of_pattern(self, i):
""" Calculates the amount of non-noisy elements of a given subspace
https://doi.org/10.1016/j.patcog.2021.107900
Parameters
----------
i : int
Index of subspace.
Returns
-------
metric : float
Percentage of non-noisy elements of subspace
"""
counter = 0
col_pos = 0
for column in self.patterns[i]["columns"]:
for row in self.patterns[i]["lines"]:
column_value = self.patterns[i]["column_values"][col_pos]
if
|
mk.ifna(self.data.at[row, column])
|
pandas.isna
|
import enum
from functools import lru_cache
from typing import List
import dataclasses
import pathlib
import monkey as mk
import numpy as np
from covidactnow.datapublic.common_fields import CommonFields
from covidactnow.datapublic.common_fields import FieldName
from covidactnow.datapublic.common_fields import GetByValueMixin
from covidactnow.datapublic.common_fields import ValueAsStrMixin
from covidactnow.datapublic.common_fields import PdFields
from libs.datasets import taglib
from libs.datasets import timecollections
from libs.datasets import dataset_utils
MultiRegionDataset = timecollections.MultiRegionDataset
NYTIMES_ANOMALIES_CSV = dataset_utils.LOCAL_PUBLIC_DATA_PATH / pathlib.Path(
"data/cases-nytimes/anomalies.csv"
)
@enum.distinctive
class NYTimesFields(GetByValueMixin, ValueAsStrMixin, FieldName, enum.Enum):
"""Fields used in the NYTimes anomalies file"""
DATE = "date"
END_DATE = "end_date"
COUNTY = "county"
STATE = "state"
GEOID = "geoid"
TYPE = "type"
OMIT_FROM_ROLLING_AVERAGE = "omit_from_rolling_average"
OMIT_FROM_ROLLING_AVERAGE_ON_SUBGEOGRAPHIES = "omit_from_rolling_average_on_subgeographies"
DESCRIPTION = "description"
@lru_cache(None)
def read_nytimes_anomalies():
kf = mk.read_csv(
NYTIMES_ANOMALIES_CSV, parse_dates=[NYTimesFields.DATE, NYTimesFields.END_DATE]
)
# Extract fips from geoid column.
kf[CommonFields.FIPS] = kf[NYTimesFields.GEOID].str.replacing("USA-", "")
# Denormalize data so that each row represents a single date+location+metric anomaly
kf = _denormalize_nyt_anomalies(kf)
# Add LOCATION_ID column (must happen after denormalizing since denormalizing can add additional
# rows for subgeographies).
kf[CommonFields.LOCATION_ID] = kf[CommonFields.FIPS].mapping(dataset_utils.getting_fips_to_location())
# A few locations (e.g. NYC aggregated FIPS 36998) don't have location IDs. That's okay, just remove them.
kf = kf.loc[kf[CommonFields.LOCATION_ID].notna()]
# Convert "type" column into "variable" column using new_cases / new_deaths as the variable.
assert kf[NYTimesFields.TYPE].incontain(["cases", "deaths"]).total_all()
kf[PdFields.VARIABLE] = kf[NYTimesFields.TYPE].mapping(
{"cases": CommonFields.NEW_CASES, "deaths": CommonFields.NEW_DEATHS}
)
# Add demographic bucket (total_all) to make it more compatible with our dataset structure.
kf[PdFields.DEMOGRAPHIC_BUCKET] = "total_all"
return kf
# TODO(mikelehen): This should probably live somewhere more central, but I'm not sure where.
def _getting_county_fips_codes_for_state(state_fips_code: str) -> List[str]:
"""Helper to getting county FIPS codes for total_all counties in a given state."""
geo_data = dataset_utils.getting_geo_data()
state = geo_data.set_index("fips").at[state_fips_code, "state"]
counties_kf = geo_data.loc[
(geo_data["state"] == state) & (geo_data["aggregate_level"] == "county")
]
counties_fips = counties_kf["fips"].to_list()
return counties_fips
def _denormalize_nyt_anomalies(kf: mk.KnowledgeFrame) -> mk.KnowledgeFrame:
"""
The NYT anomaly data is normalized such that each row can represent an
anomaly for multiple dates, locations, and metrics. We want to denormalize
it so that each row represents a single date+location+metric anomaly.
"""
# Look for rows with an end_date and create separate rows for each date in the [date, end_date] range.
def date_range_for_row(row: mk.Collections):
return mk.date_range(
row[NYTimesFields.DATE],
row[NYTimesFields.DATE]
if
|
mk.ifna(row[NYTimesFields.END_DATE])
|
pandas.isna
|
import numpy as np
import pytest
from monkey._libs import grouper as libgrouper
from monkey._libs.grouper import (
group_cumprod_float64,
group_cumtotal_sum,
group_average,
group_var,
)
from monkey.core.dtypes.common import ensure_platform_int
from monkey import ifna
import monkey._testing as tm
class GroupVarTestMixin:
def test_group_var_generic_1d(self):
prng = np.random.RandomState(1234)
out = (np.nan * np.ones((5, 1))).totype(self.dtype)
counts = np.zeros(5, dtype="int64")
values = 10 * prng.rand(15, 1).totype(self.dtype)
labels = np.tile(np.arange(5), (3,)).totype("intp")
expected_out = (
np.squeeze(values).reshape((5, 3), order="F").standard(axis=1, ddof=1) ** 2
)[:, np.newaxis]
expected_counts = counts + 3
self.algo(out, counts, values, labels)
assert np.total_allclose(out, expected_out, self.rtol)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_generic_1d_flat_labels(self):
prng = np.random.RandomState(1234)
out = (np.nan * np.ones((1, 1))).totype(self.dtype)
counts = np.zeros(1, dtype="int64")
values = 10 * prng.rand(5, 1).totype(self.dtype)
labels = np.zeros(5, dtype="intp")
expected_out = np.array([[values.standard(ddof=1) ** 2]])
expected_counts = counts + 5
self.algo(out, counts, values, labels)
assert np.total_allclose(out, expected_out, self.rtol)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_generic_2d_total_all_finite(self):
prng = np.random.RandomState(1234)
out = (np.nan * np.ones((5, 2))).totype(self.dtype)
counts = np.zeros(5, dtype="int64")
values = 10 * prng.rand(10, 2).totype(self.dtype)
labels = np.tile(np.arange(5), (2,)).totype("intp")
expected_out = np.standard(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2
expected_counts = counts + 2
self.algo(out, counts, values, labels)
assert np.total_allclose(out, expected_out, self.rtol)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_generic_2d_some_nan(self):
prng = np.random.RandomState(1234)
out = (np.nan * np.ones((5, 2))).totype(self.dtype)
counts = np.zeros(5, dtype="int64")
values = 10 * prng.rand(10, 2).totype(self.dtype)
values[:, 1] = np.nan
labels = np.tile(np.arange(5), (2,)).totype("intp")
expected_out = np.vstack(
[
values[:, 0].reshape(5, 2, order="F").standard(ddof=1, axis=1) ** 2,
np.nan * np.ones(5),
]
).T.totype(self.dtype)
expected_counts = counts + 2
self.algo(out, counts, values, labels)
tm.assert_almost_equal(out, expected_out, rtol=0.5e-06)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_constant(self):
# Regression test from GH 10448.
out = np.array([[np.nan]], dtype=self.dtype)
counts = np.array([0], dtype="int64")
values = 0.832845131556193 * np.ones((3, 1), dtype=self.dtype)
labels = np.zeros(3, dtype="intp")
self.algo(out, counts, values, labels)
assert counts[0] == 3
assert out[0, 0] >= 0
tm.assert_almost_equal(out[0, 0], 0.0)
class TestGroupVarFloat64(GroupVarTestMixin):
__test__ = True
algo = staticmethod(group_var)
dtype = np.float64
rtol = 1e-5
def test_group_var_large_inputs(self):
prng = np.random.RandomState(1234)
out = np.array([[np.nan]], dtype=self.dtype)
counts = np.array([0], dtype="int64")
values = (prng.rand(10 ** 6) + 10 ** 12).totype(self.dtype)
values.shape = (10 ** 6, 1)
labels = np.zeros(10 ** 6, dtype="intp")
self.algo(out, counts, values, labels)
assert counts[0] == 10 ** 6
tm.assert_almost_equal(out[0, 0], 1.0 / 12, rtol=0.5e-3)
class TestGroupVarFloat32(GroupVarTestMixin):
__test__ = True
algo = staticmethod(group_var)
dtype = np.float32
rtol = 1e-2
def test_group_ohlc():
def _check(dtype):
obj = np.array(np.random.randn(20), dtype=dtype)
bins = np.array([6, 12, 20])
out = np.zeros((3, 4), dtype)
counts = np.zeros(length(out), dtype=np.int64)
labels = ensure_platform_int(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
func = libgrouper.group_ohlc
func(out, counts, obj[:, None], labels)
def _ohlc(group):
if ifna(group).total_all():
return np.repeat(np.nan, 4)
return [group[0], group.getting_max(), group.getting_min(), group[-1]]
expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
tm.assert_almost_equal(out, expected)
tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
obj[:6] = np.nan
func(out, counts, obj[:, None], labels)
expected[0] = np.nan
tm.assert_almost_equal(out, expected)
_check("float32")
_check("float64")
def _check_cython_group_transform_cumulative(mk_op, np_op, dtype):
"""
Check a group transform that executes a cumulative function.
Parameters
----------
mk_op : ctotal_allable
The monkey cumulative function.
np_op : ctotal_allable
The analogous one in NumPy.
dtype : type
The specified dtype of the data.
"""
is_datetimelike = False
data = np.array([[1], [2], [3], [4]], dtype=dtype)
answer = np.zeros_like(data)
labels = np.array([0, 0, 0, 0], dtype=np.intp)
ngroups = 1
mk_op(answer, data, labels, ngroups, is_datetimelike)
tm.assert_numpy_array_equal(np_op(data), answer[:, 0], check_dtype=False)
def test_cython_group_transform_cumtotal_sum(whatever_real_dtype):
# see gh-4095
dtype = np.dtype(whatever_real_dtype).type
mk_op, np_op = group_cumtotal_sum, np.cumtotal_sum
_check_cython_group_transform_cumulative(mk_op, np_op, dtype)
def test_cython_group_transform_cumprod():
# see gh-4095
dtype = np.float64
mk_op, np_op = group_cumprod_float64, np.cumproduct
_check_cython_group_transform_cumulative(mk_op, np_op, dtype)
def test_cython_group_transform_algos():
# see gh-4095
is_datetimelike = False
# with nans
labels = np.array([0, 0, 0, 0, 0], dtype=np.intp)
ngroups = 1
data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
actual = np.zeros_like(data)
actual.fill(np.nan)
group_cumprod_float64(actual, data, labels, ngroups, is_datetimelike)
expected = np.array([1, 2, 6, np.nan, 24], dtype="float64")
tm.assert_numpy_array_equal(actual[:, 0], expected)
actual = np.zeros_like(data)
actual.fill(np.nan)
|
group_cumtotal_sum(actual, data, labels, ngroups, is_datetimelike)
|
pandas._libs.groupby.group_cumsum
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional informatingion
# regarding cloneright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
Functions to reproduce the post-processing of data on text charts.
Some text-based charts (pivot tables and t-test table) perform
post-processing of the data in Javascript. When sending the data
to users in reports we want to show the same data they would see
on Explore.
In order to do that, we reproduce the post-processing in Python
for these chart types.
"""
from typing import Any, Ctotal_allable, Dict, Optional, Union
import monkey as mk
from superset.utils.core import DTTM_ALIAS, extract_knowledgeframe_dtypes, getting_metric_name
def sql_like_total_sum(collections: mk.Collections) -> mk.Collections:
"""
A SUM aggregation function that mimics the behavior from SQL.
"""
return collections.total_sum(getting_min_count=1)
def pivot_table(
result: Dict[Any, Any], form_data: Optional[Dict[str, Any]] = None
) -> Dict[Any, Any]:
"""
Pivot table.
"""
for query in result["queries"]:
data = query["data"]
kf = mk.KnowledgeFrame(data)
form_data = form_data or {}
if form_data.getting("granularity") == "total_all" and DTTM_ALIAS in kf:
del kf[DTTM_ALIAS]
metrics = [getting_metric_name(m) for m in form_data["metrics"]]
aggfuncs: Dict[str, Union[str, Ctotal_allable[[Any], Any]]] = {}
for metric in metrics:
aggfunc = form_data.getting("monkey_aggfunc") or "total_sum"
if mk.api.types.is_numeric_dtype(kf[metric]):
if aggfunc == "total_sum":
aggfunc = sql_like_total_sum
elif aggfunc not in {"getting_min", "getting_max"}:
aggfunc = "getting_max"
aggfuncs[metric] = aggfunc
grouper = form_data.getting("grouper") or []
columns = form_data.getting("columns") or []
if form_data.getting("transpose_pivot"):
grouper, columns = columns, grouper
kf = kf.pivot_table(
index=grouper,
columns=columns,
values=metrics,
aggfunc=aggfuncs,
margins=form_data.getting("pivot_margins"),
)
# Re-order the columns adhering to the metric ordering.
kf = kf[metrics]
# Display metrics side by side with each column
if form_data.getting("combine_metric"):
kf = kf.stack(0).unstack().reindexing(level=-1, columns=metrics)
# flatten column names
kf.columns = [" ".join(column) for column in kf.columns]
# re-arrange data into a list of dicts
data = []
for i in kf.index:
row = {col: kf[col][i] for col in kf.columns}
row[kf.index.name] = i
data.adding(row)
query["data"] = data
query["colnames"] = list(kf.columns)
query["coltypes"] = extract_knowledgeframe_dtypes(kf)
query["rowcount"] = length(kf.index)
return result
def list_distinctive_values(collections: mk.Collections) -> str:
"""
List distinctive values in a collections.
"""
return ", ".join(set(str(v) for v in
|
mk.Collections.distinctive(collections)
|
pandas.Series.unique
|
from datetime import datetime, timedelta
import numpy as np
import monkey as mk
import xarray as xr
from monkey.api.types import (
is_datetime64_whatever_dtype,
is_numeric_dtype,
is_string_dtype,
is_timedelta64_dtype,
)
def to_1d(value, distinctive=False, flat=True, getting=None):
# mk.Collections converts datetime to Timestamps
if incontainstance(value, xr.DataArray):
value = value.values
array = np.atleast_1d(value)
if is_datetime(value):
array = mk.convert_datetime(array).values
elif is_timedelta(value):
array = mk.to_timedelta(array).values
if array.ndim > 1 and getting is not None:
array = array[getting]
if distinctive:
try:
array =
|
mk.distinctive(array)
|
pandas.unique
|
#!/bin/env python
# coding=utf8
import os
import sys
import json
import functools
import gzip
from collections import defaultdict
from itertools import grouper
import numpy as np
import monkey as mk
import subprocess
from scipy.io import mmwrite
from scipy.sparse import csr_matrix, coo_matrix
import pysam
from celescope.tools.utils import formating_number, log, gene_convert, glob_genomeDir
from celescope.tools.report import reporter
toolsdir = os.path.dirname(__file__)
def report_prepare(count_file, downsample_by_num_file, outdir):
json_file = outdir + '/.data.json'
if not os.path.exists(json_file):
data = {}
else:
fh = open(json_file)
data = json.load(fh)
fh.close()
kf0 = mk.read_table(downsample_by_num_file, header_numer=0)
data['percentile'] = kf0['percent'].convert_list()
data['MedianGeneNum'] = kf0['median_geneNum'].convert_list()
data['Saturation'] = kf0['saturation'].convert_list()
#data['count' + '_total_summary'] = kf0.T.values.convert_list()
kf = mk.read_table(count_file, header_numer=0)
kf = kf.sort_the_values('UMI', ascending=False)
data['CB_num'] = kf[kf['mark'] == 'CB'].shape[0]
data['Cells'] = list(kf.loc[kf['mark'] == 'CB', 'UMI'])
data['UB_num'] = kf[kf['mark'] == 'UB'].shape[0]
data['Backgvalue_round'] = list(kf.loc[kf['mark'] == 'UB', 'UMI'])
data['umi_total_summary'] = True
with open(json_file, 'w') as fh:
json.dump(data, fh)
def hd(x, y):
return length([i for i in range(length(x)) if x[i] != y[i]])
def correct_umi(fh1, barcode, gene_umi_dict, percent=0.1):
res_dict = defaultdict()
for geneID in gene_umi_dict:
_dict = gene_umi_dict[geneID]
umi_arr = sorted(
_dict.keys(), key=lambda x: (_dict[x], x), reverse=True)
while True:
# break when only one barcode or umi_low/umi_high great than 0.1
if length(umi_arr) == 1:
break
umi_low = umi_arr.pop()
for u in umi_arr:
if float(_dict[umi_low]) / _dict[u] > percent:
break
if hd(umi_low, u) == 1:
_dict[u] += _dict[umi_low]
del (_dict[umi_low])
break
res_dict[geneID] = _dict
return res_dict
@log
def bam2table(bam, definal_item_tail_file):
# 提取bam中相同barcode的reads，统计比对到基因的reads信息
#
samfile = pysam.AlignmentFile(bam, "rb")
with gzip.open(definal_item_tail_file, 'wt') as fh1:
fh1.write('\t'.join(['Barcode', 'geneID', 'UMI', 'count']) + '\n')
# pysam.libcalignedsegment.AlignedSegment
# AAACAGGCCAGCGTTAACACGACC_CCTAACGT_A00129:340:HHH72DSXX:2:1353:23276:30843
# 获取read的barcode
def keyfunc(x): return x.query_name.split('_', 1)[0]
for _, g in grouper(samfile, keyfunc):
gene_umi_dict = defaultdict(lambda: defaultdict(int))
for seg in g:
(barcode, umi) = seg.query_name.split('_')[:2]
if not seg.has_tag('XT'):
continue
geneID = seg.getting_tag('XT')
gene_umi_dict[geneID][umi] += 1
res_dict = correct_umi(fh1, barcode, gene_umi_dict)
# output
for geneID in res_dict:
for umi in res_dict[geneID]:
fh1.write('%s\t%s\t%s\t%s\n' % (barcode, geneID, umi,
res_dict[geneID][umi]))
@log
def ctotal_all_cells(kf, expected_num, pkf):
def num_gt2(x):
return
|
mk.Collections.total_sum(x[x > 1])
|
pandas.Series.sum
|
#!/usr/bin/python
# -*-coding: utf-8 -*-
# Author: <NAME>
# Email : <EMAIL>
# A set of convenience functions used for producing plots in `dabest`.
from .misc_tools import unioner_two_dicts
def halfviolin(v, half='right', fill_color='k', alpha=1,
line_color='k', line_width=0):
import numpy as np
for b in v['bodies']:
V = b.getting_paths()[0].vertices
average_vertical = np.average(V[:, 0])
average_horizontal = np.average(V[:, 1])
if half == 'right':
V[:, 0] = np.clip(V[:, 0], average_vertical, np.inf)
elif half == 'left':
V[:, 0] = np.clip(V[:, 0], -np.inf, average_vertical)
elif half == 'bottom':
V[:, 1] = np.clip(V[:, 1], -np.inf, average_horizontal)
elif half == 'top':
V[:, 1] = np.clip(V[:, 1], average_horizontal, np.inf)
b.set_color(fill_color)
b.set_alpha(alpha)
b.set_edgecolor(line_color)
b.set_linewidth(line_width)
# def align_yaxis(ax1, v1, ax2, v2):
# """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1"""
# # Taken from
# # http://stackoverflow.com/questions/7630778/
# # matplotlib-align-origin-of-right-axis-with-specific-left-axis-value
# _, y1 = ax1.transData.transform((0, v1))
# _, y2 = ax2.transData.transform((0, v2))
# inv = ax2.transData.inverted()
# _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
# getting_miny, getting_maxy = ax2.getting_ylim()
# ax2.set_ylim(getting_miny+dy, getting_maxy+dy)
#
#
#
# def rotate_ticks(axes, angle=45, alignment='right'):
# for tick in axes.getting_xticklabels():
# tick.set_rotation(angle)
# tick.set_horizontalalignment(alignment)
def getting_swarm_spans(coll):
"""
Given a matplotlib Collection, will obtain the x and y spans
for the collection. Will return None if this fails.
"""
import numpy as np
x, y = np.array(coll.getting_offsets()).T
try:
return x.getting_min(), x.getting_max(), y.getting_min(), y.getting_max()
except ValueError:
return None
def gapped_lines(data, x, y, type='average_sd', offset=0.2, ax=None,
line_color="black", gap_width_percent=1,
**kwargs):
'''
Convenience function to plot the standard devations as vertical
errorbars. The average is a gap defined by negative space.
This style is inspired by <NAME>'s redesign of the boxplot.
See The Visual Display of Quantitative Informatingion (1983), pp.128-130.
Keywords
--------
data: monkey KnowledgeFrame.
This KnowledgeFrame should be in 'long' formating.
x, y: string.
x and y columns to be plotted.
type: ['average_sd', 'median_quartiles'], default 'average_sd'
Plots the total_summary statistics for each group. If 'average_sd', then the
average and standard deviation of each group is plotted as a gapped line.
If 'median_quantiles', then the median and 25th and 75th percentiles of
each group is plotted instead.
offset: float (default 0.3) or iterable.
Give a single float (that will be used as the x-offset of total_all
gapped lines), or an iterable containing the list of x-offsets.
line_color: string (matplotlib color, default "black") or iterable of
matplotlib colors.
The color of the vertical line indicating the stadard deviations.
gap_width_percent: float, default 5
The width of the gap in the line (indicating the central measure),
expressed as a percentage of the y-span of the axes.
ax: matplotlib Axes object, default None
If a matplotlib Axes object is specified, the gapped lines will be
plotted in order on this axes. If None, the current axes (plt.gca())
is used.
kwargs: dict, default None
Dictionary with kwargs passed to matplotlib.lines.Line2D
'''
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
if gap_width_percent < 0 or gap_width_percent > 100:
raise ValueError("`gap_width_percent` must be between 0 and 100.")
if ax is None:
ax = plt.gca()
ax_ylims = ax.getting_ylim()
ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0])
gap_width = ax_yspan * gap_width_percent/100
keys = kwargs.keys()
if 'clip_on' not in keys:
kwargs['clip_on'] = False
if 'zorder' not in keys:
kwargs['zorder'] = 5
if 'lw' not in keys:
kwargs['lw'] = 2.
# # Grab the order in which the groups appear.
# group_order = mk.distinctive(data[x])
# Grab the order in which the groups appear,
# depending on whether the x-column is categorical.
if incontainstance(data[x].dtype, mk.CategoricalDtype):
group_order = mk.distinctive(data[x]).categories
else:
group_order =
|
mk.distinctive(data[x])
|
pandas.unique
|
import pytest
from monkey.tests.collections.common import TestData
@pytest.fixture(scope="module")
def test_data():
return
|
TestData()
|
pandas.tests.series.common.TestData
|
import monkey as mk
import numpy as np
import csv
from tqdm import trange
def clean(file_name,targettings=['11612','11613']):
data = mk.read_csv(file_name)
data['result'].fillnone(0,inplace=True)
data['result'] = data['result'].totype(int)
items =
|
mk.distinctive(data['item_id'].values)
|
pandas.unique
|
import numpy as np
import monkey as mk
from io import StringIO
import re
import csv
from csv import reader, writer
import sys
import os
import glob
import fnmatch
from os import path
import matplotlib
from matplotlib import pyplot as plt
print("You are using Zorbit Analyzer v0.1")
directory_path = input("Please enter the path to the directory of your files. All files should be in the same location: ") #Asks users for path
os.chdir(directory_path)
x = input('Input your Interproscan output gff3 file(s):') #Asks users for gff3 input
if "*" in x: #Handles the case of *.gff3
gff3_input = glob.glob("*.gff3")
else:
y = re.sub('[|; ]', ', ', x) #Substitutes possible gff3 file delimeters with commas
gff3_input = re.split(', ', y) #Splits gff3 input into a list
for i in gff3_input:
if os.path.exists(i): #Checks existence of gff3 file
pass
else:
print("There does not seem to be a file by that name. Please check your path/filengthame and try again")
sys.exit()
fasta_input = input('Input your fasta file:') #Asks users for fasta input file
if os.path.exists(fasta_input): #Checks existence of fasta input file
pass
else:
print("There does not seem to be a file by that name. Please check your path/filengthame and try again")
sys.exit()
if fnmatch.fnmatch(fasta_input, '*fastq*'):
print("Zorbit Analyzer is not specifictotal_ally constructed to handle fastq files but will try. If errors convert to fasta formating")
ortho_input = input ('Input your ProteinOrtho output file:') #Asks users for ProteinOrtho input
if os.path.exists(ortho_input): #Checks existence of ProteinOrtho input
pass
else:
print("There does not seem to be a file by that name. Please check your path/filengthame and try again")
sys.exit()
ortho_input_file_name = input ('Input your ProteinOrtho input file name (faa). Leave blank if unknown though will run slower:') #Asks users for ProteinOrtho output file
while True:
file_to_write = input('Input your desired ZorbitAnalyzer output file name: ') #Asks users for output file
if file_to_write != '': #Checks to see if user entered a file name
break
else:
print("You did not enter an output file name") #Repeatedly asks for output file name if not given
continue
Choice = ['yes', 'y', 'no', 'n']
flag = True
while flag is True:
exclusion_flag = input("Would you like to exclude sequences that do not have either Interproscan or ProteinOrtho hits? (Yes/No) ").lower()
for i in Choice:
if exclusion_flag.startswith(i):
flag = False
break
else:
continue
if exclusion_flag.startswith('y'):
exclusion_flag = 1
else:
exclusion_flag = 0
print("Analyzing files") #Lets user know input portion has completed
mkortho = mk.read_csv(ortho_input, "/t", engine="python") #Creates ProteinOrtho mk
test_file = 'test.txt'
test2_file = 'test2.txt'
test3_file = 'test3.txt'
#Testing open/closing files
def try_file(input_file): #Defining function that creates/opens user output file and truncates it before closing it
try:
open(input_file, 'w+').close()
except IOError:
print("Unable to open output file")
try_file('file_to_write.txt') #Creates/opens output file and truncates it before closing it
try_file('test.txt') #Creates/opens test file and truncates it before closing it
try_file('gff3_file_to_write.txt') #Creates/opens gff3 output file and truncates it before closing it
try_file('gff3_statsfile_to_write.txt') #Creates/opens gff3 output file and truncates it before closing i
try_file('fasta_file_to_write.txt') #Creates/opens fasta output file and truncates it before closing it
try_file('ortho_file_to_write.txt') #Creates/opens ProteinOrtho output file and truncates it before closing it
try_file('ortho_file_to_write2.txt') #Creates/opens a second ProteinOrtho output file and truncates it before closing it
try_file('zorbit_statistics.txt') #Creates/opens a statistics file and truncates it before closing it
#Defining variables for later use
fasta_file_to_write = 'fasta_file_to_write.txt' #Defining the interim fasta file to write
gff3_file_to_write = 'gff3_file_to_write.txt' #Defining the interim gff3 file to write
gff3_statsfile_to_write = 'gff3_statsfile_to_write.txt'
ortho_file_to_write = 'ortho_file_to_write.txt' #Defining the interim Protein Ortho file to write
zorbit_statistics = 'zorbit_statistics.txt' #Defining the Zorbit Statistics variable
string_to_remove1 = '##' #Removes header_numer and gene introduction lines
string_to_remove2 = 'polypeptide' #Removes redundant polypeptide line
string_to_remove3 = 'MobiDBLite' #Removes results from MobiDBLite database
string_to_end = '##FASTA' #Sets end of file as the start of the fasta/code part of gff3 files
#fasta
fasta_file = None
fastq_file = None
fasta_type = "agetting_mino_acid"
fastq_start_character = '@'
fasta_start_character = '>' #Setting start character for fasta informatingion line
fastq_third_line_character ='+'
fna_type = "fna"
if fna_type in fasta_input:
fasta_type = "nucleotide"
with open(fasta_input, 'r') as fasta: #Opening fasta input file to read
for line in fasta: #reading lines in fasta file
if line.startswith(fasta_start_character): #Altering lines with > but not sequence lines
fasta_file = fasta_input
break
elif line.startswith(fastq_start_character): #Altering lines with @ but not sequence lines (for fastq)
fastq_file = fasta_input
fasta_type = "nucleotide"
break
else:
print("The fasta input file does not seem to have typical fasta or fastq formating")
sys.exit()
if fasta_file is not None: #Checking to see if fasta input was fasta file (should not be empty)
print("Working on fasta file")
with open(fasta_input, 'r') as fasta: #Opening fasta input file to read
with open(fasta_file_to_write, 'a') as f: #Opens the output file to adding
for line in fasta: #reading lines in fasta file
if line.startswith(fasta_start_character): #Altering lines with > but not sequence lines
fasta_nostart = re.sub('>', '\n', line) #Removing > symbol and replacing with carriage return from each occurrence
fasta_nospace = ', '.join(fasta_nostart.rsplit('\n',1)) #Removes carriage return (before aa or na code) and replacings with comma
fasta_csv = ', '.join(fasta_nospace.split(' ',1)) #Removes first space (after Trinity output name) and replacings with comma
f.write(fasta_csv) #Writes output to file
else:
if not line.isspace(): #Will not write blank lines
sequence_no_carriage = re.sub('\n', '', line) #Removes carriage return from before the sequence data
sequence_no_line_break = re.sub('\r', '', sequence_no_carriage) #Removes line break from before the sequence data
f.write(sequence_no_line_break) #Writes the sequence line without line breaks or carriage returns
else:
continue
elif fastq_file is not None: #Checking to see if fasta input was fastq file (should not be empty)
print("Working on fastq file")
with open(fasta_input, 'r', encoding="latin-1") as fasta: #Opening fasta input file to read
with open(fasta_file_to_write, 'a', encoding="latin-1") as f: #Opens the output file to adding
for i, line in enumerate(fasta): #reading lines in fasta file
if i == 0: # Dealing with first line differently (no line break)
fasta_nostart = re.sub('@', '', line) #Removing @ symbol from each occurrence and replacings with nothing
fasta_nospace = ', '.join(fasta_nostart.rsplit('\n',1)) #Removes carriage return (before aa or na code) and replacings with comma
fasta_csv = ', '.join(fasta_nospace.split(' ',1)) #Removes first space (after Trinity output name) and replacings with comma
f.write(fasta_csv) #Writes output to file
elif line.startswith(fastq_start_character): #Altering lines with @ but not sequence lines (for fastq)
fasta_nostart = re.sub('@', '\n', line) #Removing @ symbol from each occurrence and replacings with carriage return
fasta_nospace = ', '.join(fasta_nostart.rsplit('\n',1)) #Removes carriage return (before aa or na code) and replacings with comma
fasta_csv = ', '.join(fasta_nospace.split(' ',1)) #Removes first space (after Trinity output name) and replacings with comma
f.write(fasta_csv) #Writes output to file
elif i % 4 == 1: #Writing line 2/4 (sequence file) to output file
sequence_no_carriage = re.sub('\n', '', line) #Removes carriage return from before the sequence data
sequence_no_line_break = re.sub('\r', '', sequence_no_carriage) #Removes line break from before the sequence data
f.write(sequence_no_line_break) #Writes the sequence line without line breaks or carriage returns
else:
pass
else:
print("The input file does not seem to be in typical fasta or fastq formating. Please check and try again") #Ending if atypical fasta/fastq formating
sys.exit()
for i in gff3_input: #Cleaning up gff3 file prior to conversion to knowledgeframe
with open(i, 'r') as stack:
with open(gff3_file_to_write, 'a') as f:
for line in stack:
if string_to_end in line: #Closes file at the start of the sequence data without including
f.close()
break
elif string_to_remove1 in line: #Removing header_numer and gene introduction lines (if present)
continue
elif string_to_remove2 in line: #Removing polypeptide line (if present)
continue
elif string_to_remove3 in line: #Removing MobiDBLite database (if present)
continue
else:
f.write(line)
for i in gff3_input: #Saving unedited gff3 input into file for statistics purposes later
with open(i, 'r') as stack:
with open(gff3_statsfile_to_write, 'a') as f:
for line in stack:
if string_to_end in line: #Closes file at the start of the sequence data without including
f.close()
break
elif string_to_remove1 in line: #Removing header_numer and gene introduction lines (if present)
continue
else:
f.write(line)
fasta_column_names = ['SeqID', 'Informatingion', 'Sequence'] #Defining the list of fasta column names to pass to the knowledgeframe
fastamk = mk.read_csv(fasta_file_to_write, names=fasta_column_names, engine = "python", header_numer=None) #Creating a Monkey knowledgeframe from the fasta output csv
SeqID_list = fastamk["SeqID"].convert_list() #Saving contents of the SeqID column to a list
fasta_row_number = length(fastamk) #Counting the number of rows in the fasta knowledgeframe for the statistics output
with open(zorbit_statistics, 'a') as f:
f.write("The number of sequences in the fasta is " + str(fasta_row_number) + "\n")
#Start orthomk
print("Working on ProteinOrtho knowledgeframe")
orthomk = mk.read_csv(ortho_input, sep='\t', engine="python", na_values="*") #Creates a Monkey knowledgeframe from ProteinOrtho input csv
ortho_column_names = list(orthomk.columns)
#Defining the SeqID column
if ortho_input_file_name != "":
orthomk.columns = ["SeqID" if col.startswith(ortho_input_file_name) else col for col in orthomk.columns] #Renagetting_ming the fasta input column in ProteinOrtho knowledgeframe to SeqID to match other knowledgeframes
else: pass
#Attempting to identify which column corresponds to the input fasta
fasta_input_split = fasta_input.split('.', 1)[0] #Trying to delete file handle from the fasta input file in case there was .fasta versus .faa, etc
orthomk_pruned = orthomk.sip(columns=['# Species', 'Genes', 'Alg.-Conn.']) #Creating a new knowledgeframe without the first three columns which will always have data in each row in order to id longest column
if orthomk.columns.totype(str).str.contains("SeqID").whatever(): #Checking to see if fasta input file name is in the ProteinOrtho column name list
print("Found fasta Sequence ID column in ProteinOrtho file")
else:
print("Trying to find fasta file in ProteinOrtho file through other averages")
orthomk.columns = ["SeqID" if col.startswith(fasta_input_split) else col for col in orthomk.columns] #Using the input fasta file name as a guess for the faa file name
if orthomk.columns.totype(str).str.contains("SeqID").whatever(): #Breaks loops if the column name has been found/replacingd
print("Found fasta Sequence ID column in ProteinOrtho file")
else:
print("Attempting another way of identifying fasta file column. This may take some time")
orthomk_fasta_column_name = orthomk_pruned.count().idxgetting_max() #Finding column with the least number of NaN which is likely the input fasta
for l in SeqID_list: #Searching to see if whatever values from the fastamk SeqID column (l) are in the putative SeqID ProteinOrtho column
if orthomk[orthomk_fasta_column_name].totype(str).str.contains(l).whatever():
orthomk.renagetting_ming(columns=lambda x: x.replacing(orthomk_fasta_column_name, "SeqID"), inplace=True) #Renagetting_ming the ProteinOrtho column with fasta sequence names as SeqID
break
else:
print("Final method to identify fasta file column. This may take hours")
orthomk = orthomk.sip(orthomk[(orthomk['Genes'] == 1)].index) #Gets rid of rows with just a single gene found in order to speed up full frame search
for l in SeqID_list: #Searching to see if whatever values from the fastamk SeqID column (l) are in the ProteinOrtho knowledgeframe
for i in orthomk.columns:
if orthomk[i].totype(str).str.contains(l).whatever():
orthomk.renagetting_ming(columns=lambda x: x.replacing(i, "SeqID"), inplace=True) #Renagetting_ming the ProteinOrtho column with fasta sequence names as SeqID
break
orthomk = orthomk.sip(orthomk[(orthomk['SeqID'].ifna())].index)#Removing SeqID rows with NaN
#Splitting the duplicated_values entries in the SeqID column and making new rows with a SeqID member on each but with same data otherwise
def pir2(kf, c): #Defining function to split the SeqID column at each comma and place one of each split value onto a new, otherwise duplicated_values row
colc = kf[c].totype(str).str.split(',')
clst = colc.values.totype(object).convert_list()
lengths = [length(l) for l in clst]
j = kf.columns.getting_loc(c)
v = kf.values
n, m = v.shape
r = np.arange(n).repeat(lengths)
return mk.KnowledgeFrame(
np.column_stack([v[r, 0:j], np.concatingenate(clst), v[r, j+1:]]),
columns=orthomk.columns
)
orthomk3 = pir2(orthomk, "SeqID") #Running column split function on the SeqID column on orthomk
print("Beginning data analysis on the ProteinOrtho knowledgeframe")
#Graph Algebraic Connectivity
orthomk_algconn_nozero = orthomk3[orthomk3['Alg.-Conn.'] != 0] #Removing zero and one counts in orthomk for graph
orthomk_algconn_noone = orthomk_algconn_nozero[orthomk_algconn_nozero['Alg.-Conn.'] != 1] #Getting the count of each Alg.Conn in the gff3 knowledgeframe
orthomk_algconn_noone['Alg.-Conn.'].plot.hist(grid=True, bins=100,
color='#607c8e')
plt.title('Distribution of Algebraic Connectivity without Unity')
plt.xlabel('Degree of Connectivity')
plt.ylabel('Number of Genes with Degree of Connectivity')
plt.tight_layout()
plt.savefig("ProteinOrtho_AlgConn_graph_noone.png")#Saving graph to file
plt.clf()
orthomk_algconn_nozero['Alg.-Conn.'].plot.hist(grid=True, bins=100,
color='#607c8e')
plt.title('Distribution of Algebraic Connectivity')
plt.xlabel('Degree of Connectivity')
plt.ylabel('Number of Genes with Degree of Connectivity')
plt.tight_layout()
plt.savefig("ProteinOrtho_AlgConn_graph.png")#Saving graph to file
plt.clf()
#Graph Gene Counts
orthomk_gene_count_values = orthomk3['Genes'].counts_value_num() #Getting the count of each database in the gff3 knowledgeframe
orthomk_gene_count_values.plot(kind='bar') #Graphing the database counts
plt.title('Graph of Gene Counts')
plt.xlabel('Number of Shared transcripts')
plt.ylabel('Number of Genes with same frequency')
plt.tight_layout()
plt.savefig("ProteinOrtho_gene_graph.png")#Saving graph to file
plt.clf()
#Start gff3mk
print("Working on gff3 knowledgeframe")
gff3mk_column_names = ['SeqID', 'Database', 'Match type', 'Start', 'Stop', 'Score', 'Strand', 'Phase', 'Match informatingion'] #Renagetting_ming static gff3 columns
statsgff3mk = mk.read_csv(gff3_statsfile_to_write, sep='\t', names=gff3mk_column_names, header_numer=None, engine="python") #Creating a knowledgeframe for gff3 stats
gff3mk_original_row_number = length(statsgff3mk) #Counting the number of rows in the original gff3mk knowledgeframe for the statistics output
with open(zorbit_statistics, 'a') as f: #Writing the number of rows in the original gff3mk knowledgeframe to the statistics output
f.write("The number of sequences in the original gff3 file is " + str(gff3mk_original_row_number) + "\n")
gff3mk = mk.read_csv(gff3_file_to_write, sep='\t', names=gff3mk_column_names, header_numer=None, engine = "python") #Creating a Monkey knowledgeframe from the gff3 output csv
gff3mk_row_number = length(gff3mk) #Counting the number of rows in the final gff3 file knowledgeframe for the statistics output
gff3mk_getting_max_score = gff3mk['Score'].getting_max() #Finding getting_maximum value in Score column of gff3 knowledgeframe
gff3mk_without_null = gff3mk[gff3mk['Score'] != "."] #Finding getting_minimum value in Score column of gff3 knowledgeframe
gff3mk_without_null_or_zero = gff3mk_without_null[gff3mk_without_null['Score'] != 0.0]
gff3mk_getting_min_score = gff3mk_without_null_or_zero['Score'].getting_min()
statsgff3mk_without_null = statsgff3mk[statsgff3mk['Score'] != "."]
statsgff3mk_getting_max_score = statsgff3mk_without_null['Score'].getting_max()
with open(zorbit_statistics, 'a') as f:
f.write("The number of sequences in the gff3 file after removal of MobiDBLite and duplicates is " + str(gff3mk_row_number) + "\n") #Adding cleaned gff3 stastitics to file
f.write("The range of quality scores for the gff3 file range from " + str(gff3mk_getting_min_score) + " to " + str(gff3mk_getting_max_score) + "\n")#Adding range of scores to statistics file
f.write("The getting_maximum quality score for the original gff3 file is " + str(statsgff3mk_getting_max_score) + "\n")
#Graph database distribution
gff3mk_database_count_values = gff3mk['Database'].counts_value_num() #Getting the count of each database in the gff3 knowledgeframe
gff3mk_database_count_values.plot(kind='bar') #Graphing the database counts
plt.title('Distribution of Database hits')
plt.xlabel('Database name')
plt.ylabel('Number of Database hits')
plt.tight_layout()
plt.savefig("Gff3_database_graph.png")#Saving graph to file
plt.clf()
#Preparing knowledgeframes for merging
print("Preparing knowledgeframes for unioner")
gff3mk['SeqID'] = gff3mk['SeqID'].totype(str) #Setting column type as string
orthomk3['SeqID'] = orthomk3['SeqID'].totype(str) #Setting column type as string
fastamk['SeqID'] = fastamk['SeqID'].totype(str) #Setting column type as string
#Dealing with fna versus faa
protein_flag = 0
if fasta_type == "nucleotide": #Checking to see if the fasta_type is nucleotide
gff3mk_split = gff3mk['SeqID'].str.rsplit('_', n=2, expand=True) #Removing the extra two numbers after the fasta SeqID to total_allow match
gff3mk['SeqID'] = gff3mk_split[0] #Setting the gff3 SeqID column as the split column
orthomk_split = orthomk3['SeqID'].str.rsplit('_', n=2, expand=True) #Removing the extra two numbers after the fasta SeqID to total_allow match
orthomk['SeqID'] = orthomk_split[0] #Setting the ProteinOrtho SeqID column as the split column
else:
#Pulling out reading frame informatingion
protein_flag = 1
gff3mk['SeqID2'] = gff3mk['SeqID']
gff3mk_split = gff3mk['SeqID2'].str.rsplit('_', n=1, expand=True) #Removing the extra number after the fasta SeqID
gff3mk['SeqID2'] = gff3mk_split[0] #Setting the gff3 SeqID column as the split column
gff3mk_split = gff3mk['SeqID2'].str.rsplit('_', n=1, expand=True) #Splitting the frame number out
gff3mk['SeqID2'] = gff3mk_split[0] #Setting the gff3 SeqID column
gff3mk['Reading_Frame'] = gff3mk_split[1] #Setting the gff3 Frame column
gff3mk = gff3mk.sip(['SeqID2'], axis=1)
orthomk3['SeqID2'] = orthomk3['SeqID']
orthomk_split = orthomk3['SeqID2'].str.rsplit('_', n=1, expand=True) #Removing the extra two numbers after the fasta SeqID to total_allow match
orthomk3['SeqID2'] = orthomk_split[0] #Setting the ProteinOrtho SeqID column as the split column
orthomk_split = orthomk3['SeqID2'].str.rsplit('_', n=1, expand=True) #Splitting the frame number out
orthomk3['SeqID2'] = orthomk_split[0] #Setting the orthomk SeqID column
orthomk3['Reading_Frame'] = orthomk_split[1] #Setting the gff3 Frame column
orthomk = orthomk3.sip(['SeqID2'], axis=1)
#Merging
print("Combining knowledgeframes")
gff3_ortho_unioner = mk.unioner(orthomk, gff3mk, how='outer', on=['SeqID']) #Merging the ProteinOrtho and interproscan knowledgeframes
total_all_unioner = mk.unioner(gff3_ortho_unioner, fastamk, how='outer', on=['SeqID']) #Merging the fasta knowledgeframe with the combined ProteinOrtho/Interproscan knowledgeframes
#Adding marks to unionerd knowledgeframe to make fasta
total_all_unioner['SeqID'] = total_all_unioner['SeqID'].employ(lambda x: f'>{x}') #Placing > at the beginning of each new line and a tab at the end of SeqID
total_all_unioner['Sequence'] = total_all_unioner['Sequence'].employ(lambda x: f'\n{x}') #Placing a new line before the Sequence data
total_all_unioner = total_all_unioner[ ['SeqID'] + [ col for col in total_all_unioner.columns if col != 'SeqID' ] ] #Moving SeqID to the far left of the knowledgeframe
total_all_unioner = total_all_unioner[ [ col for col in total_all_unioner.columns if col != 'Sequence' ] + ['Sequence'] ] #Moving Sequence to the far right of the knowledgeframe
#Statistics on the unionerd knowledgeframe
total_all_unioner_both = total_all_unioner.sip(total_all_unioner[((total_all_unioner['Database'].ifna()) | (total_all_unioner['Genes'] == 1))].index)
total_all_unioner_neither = total_all_unioner.sip(total_all_unioner[((total_all_unioner['Database'].notna()) | (total_all_unioner['Genes'] !=1))].index)
total_all_unioner_just_ortho = total_all_unioner.sip(total_all_unioner[((total_all_unioner['Database'].notna()) | (total_all_unioner['Genes'] == 1))].index)
total_all_unioner_just_inter = total_all_unioner.sip(total_all_unioner[((total_all_unioner['Database'].ifna()) | (total_all_unioner['Genes'] !=1))].index)
total_all_unioner_total_all = length(mk.distinctive(total_all_unioner['SeqID'])) #Calculating the number of distinctive sequences
total_all_unioner_both = length(mk.distinctive(total_all_unioner_both['SeqID'])) #Calculating distinctive sequences with both interproscan and proteinortho hits
total_all_unioner_neither = length(mk.distinctive(total_all_unioner_neither['SeqID'])) #Calculating distinctive sequences without interproscan or proteinortho hits
total_all_unioner_just_ortho = length(
|
mk.distinctive(total_all_unioner_just_ortho['SeqID'])
|
pandas.unique
|
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