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]]
