def do_GET(self): self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() current_dir = os.path.dirname(__file__) self.wfile.write( bytes(readfile.readFile(os.path.join(current_dir, "message.txt")), "utf-8")) return
def __init__(self): self.method = 0 self.data = [None,None,None,None,None] self.values = [None,None,None,None,None] self.maximum = None self.names = [' Eucl Dist', ' Maha Dist', ' Eucl Vote', ' Maha Vote', ' Custom'] file1 = open("hw5db1.txt","r") file2 = open("hw5db2.txt","r") dataIn = readfile.readFile(file1) stats = readfile.readStats(file2) self.vectors = vector_handler.vector_holder(dataIn,stats) self.classifier = classifier.classifier()
def setUp(self): self.rf = readFile()
"--last", help="Use last query", action="store_true") parser.add_argument("-o", "--opcap", help="Remove operational cap datapoints", action="store_true") parser.add_argument("-g", "--graph", help="Do not display graph.", action="store_false") options = parser.parse_args() # Get user creds if os.path.exists("creds.txt") or options.creds: credentials = readfile.readFile("creds.txt") username, password = credentials[0], credentials[1] else: username = input('Enter username: '******'Enter password: ') # Connect try: connection = access.connect(username, password) # Get user query or use last query if options.last: last_query = readfile.readFile("query.txt") if last_query[1] == "today": last_query[1] = str( datetime.datetime.now().strftime("%m-%d-%Y"))
self.indexIonBlocks = None self.numIonBlocks = -1 self.szIonBlocks = None self.massabund = None self.blockind = None self.pepmassArr = None self.nameionArr = None if __name__ == "__main__": import readfile filename = filename ="orig/data/inga_compounds_and_unpd_in_silico.mgf" #LASTLINE = 213 LASTLINE=54 arrLines = readfile.readFile(filename) print("*** TEST CASE ***") print(" File '{0}' contains {1} lines".format(filename,len(arrLines))) print(" #Lines considered:{0}".format(LASTLINE)) print(" TEST INDVIDUAL FUNCTIONS::") # Test on the FIRST x blocks arrSlice = arrLines[:LASTLINE][:] pb = ParseBlocks() # Find Index of the Ion Blocks indArr = pb.findIonBlocks(arrSlice) print(" Indices of the Block Pairs i.e. (Start,End):\n {0}".format(indArr)) for i in indArr: print(" --> '{0}'".format(arrSlice[i].strip()))
parser = argparse.ArgumentParser(description='Given a CSV file (delimited by tabulates) it imports the data to a Mongodb collection. File requires a first line with header.')') parser.add_argument('--inputfolder', dest='input_folder', help='Input folder name.', type=str, required=True) parser.add_argument('--mongoclient', dest='mongo_client', help='Mongo client name (like \'localhost:32769/\').', type=str, required=True) parser.add_argument('--mongodb', dest='mongo_db', help='Mongo database name (like \'mydatabase\').', type=str, required=True) parser.add_argument('--mongocol', dest='mongo_col', help='Mongo collection name (like \'items\').', type=str, required=True) args = parser.parse_args() inputFolderName = args.input_folder mongoClient = args.mongo_client mongoDb = args.mongo_db mongoCol = args.mongo_col #insert items to database myclient = pymongo.MongoClient("mongodb://" + mongoClient) mydb = myclient[mongoDb] mycol = mydb[mongoCol] # 1) iterate CSV files # 2) convert data files to array of dictionaries # 3) insert items of array into database files = os.listdir(inputFolderName) #https://docs.python.org/3.8/library/os.html?highlight=listdir#os.listdir for file in files: if file.endswith('.csv'): items = readFile(inputFolderName + file) mycol.insert_many(items) print("Done.")
def main(): current_app.logger.info("Hi") current_dir = os.path.dirname(__file__) return readfile.readFile(os.path.join(current_dir, "message.txt"))
import os import re from readfile import readFile from automovel import Automovel import pandas as pd import matplotlib.pyplot as plt lojaCarros = [] with os.scandir('./tests') as entries: for entry in entries: with open(entry, 'r', encoding='utf8') as file: automovel = readFile(file) result = Automovel(automovel) lojaCarros.append(result) df = pd.DataFrame({ 'nome': [ lojaCarros[0].nome, lojaCarros[1].nome, lojaCarros[2].nome, lojaCarros[3].nome, lojaCarros[4].nome, lojaCarros[5].nome, lojaCarros[6].nome, lojaCarros[7].nome, lojaCarros[8].nome, lojaCarros[9].nome, lojaCarros[10].nome, lojaCarros[11].nome, lojaCarros[12].nome, lojaCarros[13].nome, lojaCarros[14].nome ], 'ano': [ lojaCarros[0].ano, lojaCarros[1].ano, lojaCarros[2].ano, lojaCarros[3].ano, lojaCarros[4].ano, lojaCarros[5].ano, lojaCarros[6].ano, lojaCarros[7].ano, lojaCarros[8].ano, lojaCarros[9].ano, lojaCarros[10].ano, lojaCarros[11].ano, lojaCarros[12].ano, lojaCarros[13].ano, lojaCarros[14].ano ],
description= "Below is a list of optional arguements with descriptions. Please refer to Readme for full documentation and examples..." ) parser.add_argument("-c", "--creds", help="Access creds from creds.txt", action="store_false") parser.add_argument("-btu", "--mmbtu", help="Display data in units of MMbtu rather than MMcf", action="store_true") options = parser.parse_args() # Get user creds if os.path.exists("creds.txt") or options.creds: credentials = readfile.readFile("creds.txt") username, password = credentials[0], credentials[1] else: username = input('Enter username: '******'Enter password: ') # Connect to the database connection = access.connect(username, password) # Get date range and pipeline id date_range = pointCap.getDateRange() pipeline_id = int(input("Enter pipeline id: ")) # Get flow average and max filters if options.mmbtu is False: avg_filter = int( input(
import math import readfile import vector_handler import classifier file1 = open("hw5db1.txt","r") file2 = open("hw5db2.txt","r") dataIn = readfile.readFile(file1) stats = readfile.readStats(file2) vectors = vector_handler.vector_holder(dataIn,stats) classifier = classifier.classifier() result1 = classifier.directClassify(vectors.vectorArr,vectors.statArr,classifier.method_1) result2 = classifier.directClassify(vectors.vectorArr,vectors.statArr,classifier.method_2) for i in range(0,15): if result1[i]: print('A') else: print('N')
try: cursor.execute(sql) results = cursor.fetchall() except: print("Error while executing SQL...") return None # Close and return cursor.close() return pd.DataFrame(results, columns=["Issue Date", "Price Point Name", "Region Name", "Average Price"]) # Run if __name__ == "__main__": # Get creds user, password = readfile.readFile("creds.txt") # Get price data prices = accessDB(user, password) # Fill NaN and alter dtype prices.fillna(0, inplace=True) prices["Average Price"] = prices["Average Price"].astype(float) # Pivot hub hub_prices = prices.pivot_table(values="Average Price", index=["Issue Date"], columns=["Price Point Name"]) # Get day-to-day difference hub_prices_diff = hub_prices.diff() # Get percentage change day-to-day price_percentage = hub_prices_diff / hub_prices # Get junk columns to drop to_drop = price_percentage.columns[(price_percentage.abs() <= 0.05).iloc[-1]]