def create_relationship(
old_dict_list): # recebe a lista de dicionarios de t_atividade
"""
Esta função tem o objetivo de montar um relacionamento entre 't_atividade' e 'custeio'.
Neste relacionamento deve haver o ID de ambas as partes assim como o valor referente ao relacionamento
:param old_dict_list:
:return:
"""
custeio_dict_list = readcsv(
"csv_test/custeio.csv") # lista de dicionario do custeio
relationship_dict_list = [
] # lista de dicionarios que formará o .csv do relacionamento
for atividade_dict in old_dict_list: # Itera sobre cada dicionario da lista de atividades
#DEBUG
#print(atividade_dict['nome'])
for custeio_dict in custeio_dict_list: # Itera sobre cada dicionario da lista de custeios
"""
Plano de implementação:
1. Estabelecer relacionamento entre todos os elementos de t_atividade e custeio ( X )
2. Eliminar os relacionamentos que não possuam valor ( X )
3. Tranformar os dados restantes em uma lista de dicionário para serem inseridos no .csv ( )
3.1. Nomear as celulas da primeira linha do .csv com os cabeçalhos ( )
"""
relationship_dict = {}
# custeio_dict['id_custeio'] + "-" + custeio_dict['nome'] =
# 'id_custeio-nome_custeio' que forma uma chave para o dicionario
# Assim podemos recuperar o valor do custeio relcaionado a atividade
# Se não houver um valor relacionado a uma relação entre t_atividade e custeio esta relação não deve aparecer na tabela
# lembrando que a chave referente a cada custeio na planilha t_atividade é formada pelo id_custeio + nome_custeio
if atividade_dict[custeio_dict['id_custeio'] + "-" +
custeio_dict['nome']] != "":
relationship_dict['id_atividade'] = atividade_dict[
'id_atividade']
relationship_dict['id_custeio'] = custeio_dict['id_custeio']
relationship_dict['valor'] = atividade_dict[
custeio_dict['id_custeio'] + "-" + custeio_dict['nome']]
relationship_dict_list.append(relationship_dict)
#print(relationship_dict)
#DEBUG
#print("ID Atividade: " + atividade_dict['id_atividade'] + " -> " +
# "ID Custeio: " + custeio_dict['id_custeio'] + " " +
# custeio_dict['nome'] + ": " +
# atividade_dict[ custeio_dict['id_custeio'] + "-" + custeio_dict['nome']])
#print("---------------------------------------------------------------------")
#for dicio in old_dict_list:
# print("A atividade ID " + dicio['id_atividade'] +
# " '" + dicio['nome'] + "' esta relacionada ao custeio DE VALOR:" + dicio['11-passagens'])
return relationship_dict_list
def test_readcsv():
from readcsv import readcsv
raw_data = readcsv('test_readcsv.csv')
assert raw_data == {'time': [1, 7, 1.2], 'voltage': [2, 9, 4.5]}
assert raw_data != {
'time': [1, '', 7, 'c', 1.2],
'voltage': [2, 6, 9, 10, 4.5]
}
def allcountries():
"""
Cette fonction permet de réduire la liste des pays en supprimant
les doublons avec set() et de les classer
par ordre croissant avec sorted().
On retrouve chaque pays du fichier dans une liste.
This function allows you to reduce the list of countries by deleting
the duplicates with set() and to sort them
in ascending order with sorted().
Each country in the file is found in a list.
"""
logging.debug("Utilisation de la fonction allcountries()")
df = readcsv('Region')
countries = list(sorted(set(df['Region'].to_list())))
logging.debug(countries)
return countries
def allyears():
"""
Cette fonction permet de réduire la liste des années en supprimant
les doublons avec set() et de les classer
par ordre croissant avec sorted().
On retrouve chaque année du fichier dans une liste.
This function allows you to reduce the list of years by deleting
the duplicates with set() and to sort them
in ascending order with sorted().
Each year of the file can be found in a list.
"""
logging.debug("Utilisation de la fonction allyears()")
df = readcsv('Year', 'Region')
years = list(sorted(set(df['Year'].to_list())))
logging.debug(years)
return years
def showphoto(path,
starttime,
phototimefile=r'D:\mitacs\image\photo\phototime.csv'):
photodata = readcsv(phototimefile)
phototime = []
for i in range(0, len(photodata)):
phototime.append(
datetime.datetime.strptime(photodata[i][1], '%Y-%m-%d %H:%M:%S'))
print(phototime[i])
if phototime[i] > starttime:
if (phototime[i] - starttime) > (starttime - phototime[i - 1]):
img = Image.open(photodata[i - 1][0])
print(photodata[i - 1][0])
else:
img = Image.open(photodata[i][0])
print(photodata[i][0])
img.show()
def readctg(ctgcsv, excludeitself = False):
## include itself
r = readcsv.readcsv(ctgcsv, 'int')
ctg = r.getData()
id = np.unique(ctg[:,0])
ctgdic = {}
for item in id:
ctgdic[item] = []
if excludeitself:
for item in ctg:
if item[0] <> item[1]:
ctgdic[item[0]].append(item[1])
else:
for item in ctg:
ctgdic[item[0]].append(item[1])
return ctgdic
def byyear(year):
"""
Cette fonction permet, en fonction de l'année choisi, de récupérer
la moyenne des émission totales de CO2 (en Milliers de tonnes)
au niveau mondial.
This function allows, depending on the chosen year, to retrieve
average total CO2 emissions (in Thousands of tons)
worldwide.
"""
logging.debug(f"Utilisation de la fonction byyear({year})")
globalemission = ["Emissions (thousand metric tons of carbon dioxide)"]
df = readcsv('Year', 'Value', 'Emission', 'Region')
df = df.loc[df['Year'].isin([str(year)])]
df = df.loc[df['Emission'].isin(globalemission)]
res = {}
res["Year"] = year
res["Total"] = round(df['Value'].mean(), 3)
logging.debug(res)
return res
def bycountry(country):
"""
Cette fonction permet, en fonction du pays choisi, de récupérer
l'entrée la plus récente concernant l'émission totale de CO2
en Milliers de tonnes.
This function allows, depending on the chosen country, to retrieve
the most recent entry for total CO2 emissions
in Thousands of tons.
"""
logging.debug(f"Utilisation de la fonction bycountry({country.title()})")
df = readcsv('Region', 'Year', 'Value')
df = df.loc[df['Region'].isin([country])]
df = df.sort_values(by='Year', ascending=False)
res = {}
res["Country"] = str(df.iloc[0][0]).title()
res["Year"] = int(df.iloc[0][1])
res["Emissions"] = float(df.iloc[0][2])
logging.debug(res)
return res
def bypercapita(country):
"""
Cette fonction permet, en fonction du pays choisi,
d'afficher les émissions de CO2 (en tonnes par habitant)
par rapport aux différentes années de relevés.
This function allows, depending on the chosen country,
display CO2 emissions (in tons per capita)
in relation to the different survey years.
"""
logging.debug(f"Utilisation de la fonction bypercapita({country.title()})")
capita = ["Emissions per capita (metric tons of carbon dioxide)"]
df = readcsv('Region', 'Year', 'Emission', 'Value')
df = df.loc[df['Region'].isin([country])]
df = df.loc[df['Emission'].isin(capita)]
res = {}
nbannee = len(allyears())
i = 0
while i < nbannee:
res[int(df.iloc[i][1])] = float(df.iloc[i][3])
i += 1
logging.debug(res)
return res
def test_readcsv(capsys):
dirpath = Path("C:/code/cohort4/python-IO")
filename = "Census_by_Community_2019.csv"
hdr = "************************************************************\n"
hdr += "************** Calgary Public Data Summary ***************\n"
hdr += "************************************************************\n"
content1 = "Residential and SOUTH: 230129"
content2 = "Industrial and NORTH: 0"
content3 = "Overall Total: 1283177"
content4 = "Total number of records: 307"
ftr = "************************************************************\n"
ftr += "*************** End of Report Summary ********************\n"
ftr += "************************************************************\n"
result = readcsv(dirpath, filename)
captured = capsys.readouterr()
reportdata = readfile(dirpath, "report.txt")
assert result == {
"keyvaluepair": {
"RES_CNT": 10,
"CLASS": 1,
"SECTOR": 5
},
"linenum": 308
}
assert hdr in captured.out
assert ftr in captured.out
assert content1 in captured.out
assert content2 in captured.out
assert content3 in captured.out
assert content4 in captured.out
assert os.path.isfile(os.path.join(dirpath, "report.txt")) == True
assert content1 in reportdata
assert content2 in reportdata
assert content3 in reportdata
assert content4 in reportdata
def drowFathers(slicelabel):
label_data = readcsv(r'D:\mitacs\audio classification\label.csv')
branch = []
for z in range(1, len(slicelabel), 2):
drowFather2child(label_data, slicelabel[z], branch)
print(branch)
x = 0
while x < len(branch):
y = x + 2
while y < len(branch):
if branch[x] == branch[y] and branch[x + 1] == branch[y + 1]:
branch.pop(y + 1)
branch.pop(y)
y = y + 2
x = x + 2
print(branch)
branch2 = list(map(lambda x: x.replace(' ', '\n'), branch))
print(branch2)
slicelabel = list(map(lambda x: x.replace(' ', '\n'), slicelabel))
g = Digraph('测试图片', format="png")
for x in range(1, len(slicelabel), 2):
print(slicelabel[x])
color = round(8 * float(slicelabel[x + 1]), 3)
print(color)
g.node(slicelabel[x],
color='0.000 %f %f' % (color, color),
shape='circle',
width='2',
height='2',
penwidth='%f' % color)
for i in range(0, len(branch) - 1, 2):
g.node(branch2[i], shape='circle', width='2', height='2')
g.node(branch2[i + 1], shape='circle', width='2', height='2')
g.edge(branch2[i + 1], branch2[i])
# i = i+2
#print(g.source)
g.view()
The calculated values are compiled into a dictionary called metrics and
written into a JSON file under the same name as the input in the folder
output_data. Once the file is properly written to the json file, a new
message is entered into the log.
"""
logging.basicConfig(filename="Main_Log.txt",
format='%(asctime)s %(message)s',
datefmt='%m/%d/%Y %I:%M:%S %p')
Successful_Read = False
while Successful_Read is False:
filename = input("Enter filename: ")
try:
raw_data = readcsv('test_data/' + filename + '.csv')
except FileNotFoundError:
print("File not found, enter new filename")
continue
try:
validate_data(raw_data)
except DiffListLengthError:
print("File contains lists of different lengths, "
"enter new filename")
continue
except NegTimeValueError:
print("File contains negative time values, " "enter new filename")
continue
logging.warning("Info: " + filename + ".csv was read and validated")
Successful_Read = True
print data
datadict = {}
for item in data:
datadict[item[0]] = int(item[1])
print datadict
for key in sorted(datadict):
#print "%s: %s" % (key, datadict[key])
print key, '\t', datadict[key]
"""
tractcsv = "C:/_DATA/CancerData/NewEnglandDiseaseData/tract/tractinfo_intonly.csv"
countycsv = "C:/_DATA/CancerData/NewEnglandDiseaseData/tract/countyinfo_intonly.csv"
r = readcsv.readcsv(tractcsv)
tractdata = r.getData()
r = readcsv.readcsv(countycsv)
countydata = r.getData()
countydict = {}
scale = []
for i in xrange(len(countydata)):
countydict[int(countydata[i, 4])] = i
scale.append(1.0 * countydata[i, 5] / countydata[i, 6])
print countydict
# tractpop = np.zeros(len(countydata))
for i in xrange(len(tractdata)):
# print tractdata
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
import numpy as np
import readcsv
from scipy import stats
#--------------------------------------------------------------------------
#MAIN
if __name__ == "__main__":
filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/'
sigmacsv = filePath + 'sigma_summary.csv'
r = readcsv.readcsv(sigmacsv)
data = r.getData()
diff = data[:,0] - data[:,1]
print 't-statistic = %6.3f, pvalue = %6.4f' % stats.ttest_1samp(diff,0.0)
import csv
from readcsv import readcsv
CSV_FILE_PATH = "csv_test/atividade.csv"
ATIVIDADE_DICT_LIST = readcsv(
CSV_FILE_PATH
) # Retorna a lista de dicionário gerado a partir de atividade.csv
def show_dicts(dict_list, showkeys=False):
"""
Função auxiliar que mostra o estado da lista de dicionários
passada como parâmentro. Esta função tem o objetivo apenas
de facilitar o debug
:param dict_list:
:return:
"""
for dicio in dict_list:
print(dicio)
if showkeys:
print("-------------------------------------------------")
print("KEYS LIST")
print("[", end='')
for key in dict_list[0].keys():
print(key, end=' ')
print("]")
print("-------------------------------------------------")
def create_related_csv(filedirectory, dict_list): #['id_custeio', 'nome']
# -*- coding: UTF-8 -*-
import urllib2
from datetime import datetime
import sys
from readcsv import readcsv
from bs4 import BeautifulSoup
reload(sys)
sys.setdefaultencoding('UTF-8')
stock_id_dict = readcsv()
def examine_ipodate(stocks_array):
# stocks_array = stocks.split("\n")
stocks_to_remove_array = []
print stocks_array
for x in stocks_array:
if x.startswith("00"): # delete ETF
print "ETF: "+ x
stocks_array.remove(x)
if int(x)>=10000: # delete 可轉換公司債
stocks_to_remove_array.append(str(x))
for x in stocks_to_remove_array:
stocks_array.remove(x)
stock_examined_id = []
stock_examined_shortname = []
stock_failed_id = []
stock_failed_shortname = []
#plotshapefile.plotshapefile('C:/_DATA/CancerData/SatScan/NorthEasternUS.shp', plotvalue, 'Set3', 'maxllr_data')
#debug---------------/
return maxllr
#--------------------------------------------------------------------------
#MAIN
if __name__ == "__main__":
start_time = datetime.now()
print "begin at " + str(start_time)
filePath = 'C:/_DATA/CancerData/NewEnglandDiseaseData/'
randomdatacsv = filePath + 'format/Random.csv'
r = readcsv.readcsv(randomdatacsv, 'int')
randomdata = r.getData()
pop = randomdata[:,1]
popsum = sum(pop)
repeatTime = len(randomdata[0,:])-2
repeatTime = 1
numUnit = len(pop)
maxLLRlist = []
llr = likelihoodratio.likelihoodratio(sum(randomdata[:,2]), popsum)
printstep = 100
for repeat in xrange(repeatTime):
if repeat%printstep == 0:
print repeat, str(datetime.now())
repeat = 2
tap_nb = deepcopy(traintable)
for x in tap_nb:
#if hamkhoangcach(x,z,w,colnum-1) < d: #truyen vao colnum da~ xac dinh
d = hamkhoangcach(x,z,w,colnum-1)
x.append(d)
print("phan tu x trong nb",x)
print("khoang cach: ",d)
#tap_nb.append(y)
tap_nb.sort(key=lambda y: y[-1]) #y[-1] lay phan tu cuoi cung
tap_nbk = tap_nb[0:k] #lay k phan tu dau tien
return tap_nbk
if __name__ == '__main__':
w = [1,1,1,1,1,1,1,1]
train = readcsv('prostate-training-data.csv',1)
print("file train: ",train)
test = readcsv('prostate-test-Vi-Du.csv',0)
#print("row cua test:", len(test[2]))
print("file test:",test)
colnum = train[1]
#tim max chuan hoa
print("colnum :",colnum )
print("max cua train: ", maxcol(colnum, train[2]))
print("max cua test: ", maxcol(colnum, test[2]))
maxchuanhoa = maxall(colnum,test[2],train[2])
print("max chuan hoa",maxchuanhoa)
if stopflag:
return templist
else:
return checkContiguous(ctg, newRegionDict, regiondict)
# --------------------------------------------------------------------------
# MAIN
if __name__ == "__main__":
start_time = datetime.now()
print "begin at " + str(start_time)
filePath = "C:/_DATA/CancerData/NewEnglandDiseaseData/"
datacsv = "C:/_DATA/CancerData/NewEnglandDiseaseData/format/simundernull9999.csv"
r = readcsv.readcsv(datacsv)
data = r.getData()
pop = data[:, 1]
ctgcsv = "C:/_DATA/CancerData/SatScan/NortheeaternUS_re_contiguity.ctg"
ctgdic = readctg(ctgcsv)
## read image via matplotlib
# im = mpimg.imread(imagefile)
# num_row, num_col = im.shape
# num_row, num_col = int(num_row), int(num_col)
# im.shape = (-1,1)
# imagedata = im[:,0].tolist()
## global variable
alpha = 1
beta = 0.1
import config import readcsv import datacleaning import featurescaling import algorithm import predict import writecsv import numpy data = readcsv.readcsv(config.trainingset) config.data = data config.row_ = data.shape[0] config.col_ = data.shape[1] datacleaning.cabin(config.data, 10) # Problem specific data cleaning datacleaning.gender(config.data, 3) # Problem specific data cleaning feature = config.allowed_feature config.cdata = datacleaning.reduce_features(config.row_, len(feature), feature, config.data) config.cdata = datacleaning.polynomial_feature_cubic(config.cdata) crow_ = (config.cdata).shape[0] ccol_ = (config.cdata).shape[1] featurescaling.featurescaling(config.cdata) feature = config.outputlabel y = datacleaning.get_output_label(config.row_, feature, config.data) config.y = y theta = numpy.zeros((ccol_, 1)) print "training....."
parser.add_argument('--csvcolumnseparator', default=',')
parser.add_argument('--csvemptycell', default='')
parser.add_argument('--csvcolorformat', default='html')
parser.add_argument('--nullcolor', default=__NULL_COLOR)
parser.add_argument('--topdepth', '-t', type=float, default=np.nan)
parser.add_argument('--bottomdepth', '-b', type=float, default=np.nan)
parser.add_argument('--topshift', type=float, default=0.0)
parser.add_argument('--bottomshift', type=float, default=0.0)
parser.add_argument('--width', type=int)
parser.add_argument('--height', type=int)
parser.add_argument('--outputfilename', '-o')
args = parser.parse_args()
layersheader, layersdata = readcsv.readcsv(args.layersfilename,
delimiter=args.csvcolumnseparator,
headerlines=args.layersskiplines,
onlystr=True)
layercodes = layersdata[args.layerscodecolumn - 1]
tops = layersdata[args.layerstopcolumn - 1]
bottoms = layersdata[args.layersbottomcolumn - 1]
if tops[0] == args.csvemptycell:
tops[0] = np.nan
if bottoms[-1] == args.csvemptycell:
bottoms[-1] = np.nan
tops = np.array(list(map(float, tops)))
bottoms = np.array(list(map(float, bottoms)))
if args.topdepth is not np.nan and args.bottomdepth is not np.nan:
n = args.height
Duczmal_d = [95,99,109,110,113,117,129,142,148,150,159,160,164,180,182]
Duczmal_e = [84,95,99,102,109,110,113,116,117,129,142,148,150,159,160,164,180,182,232,233,236]
#Duczmal_f = []
Duczmal_j = [61,76,78,89,94,98,106,111,119,121,123,131,136,151,154,157,158,160,161,162,163,164,165,167,168,169,\
170,171,172,173,174,175,178,179,180,181,182,183,184,185,227,228,230,231,232,233,234,235,236,237,238,239,240,241,242]
Duczmal_k = Duczmal_j + [84,88,90,91,92,93,95,96,99,102,114,118,135,139,140,144,145,146,147,148,150,153,156,159]
Duczmal_k.remove(169)
clustername = ['6mixed16','6rural16','6urban16','Duczmal_a','Duczmal_b',\
'Duczmal_c','Duczmal_d','Duczmal_e','Duczmal_j','Duczmal_k']#'Duczmal_f','Duczmal_g','Duczmal_h',\
##'Duczmal_i','Duczmal_j']
clusterIDlist = [mixed, rural, urban, Duczmal_a, Duczmal_b, Duczmal_c, Duczmal_d, Duczmal_e, Duczmal_j, Duczmal_k]
tractcsv = 'C:/_DATA/CancerData/NewEnglandDiseaseData/tract/tractinfo_intonly.csv'
r = readcsv.readcsv(tractcsv)
data = r.getData()
for clusteridx in xrange(len(clustername)):
clustertype = clustername[clusteridx]
cluster = clusterIDlist[clusteridx]
cluster_tract = []
for i in xrange(len(data[:,-2])):
if int(data[i,-2]) in cluster:
cluster_tract.append(i)
print clustertype, cluster_tract
print "end at " + str(finish_time)
print 'Time for whole procedure: ' + str(finish_time - start_time)
print datadict
for key in sorted(datadict):
#print "%s: %s" % (key, datadict[key])
print key, '\t', datadict[key]
'''
def weightedchoice(cumsumhere, totalsum):
y = random.randint(totalsum)
filepath = 'C:/_DATA/CancerData/NewEnglandDiseaseData/'
tractcsv = filepath + 'tract/tractinfo_intonly.csv'
countycsv = filepath + 'tract/countyinfo_intonly.csv'
r = readcsv.readcsv(tractcsv)
tractdata = r.getData()
r = readcsv.readcsv(countycsv)
countydata = r.getData()
#countydict = {}
countytractid = []
countytractpop = []
for i in xrange(len(countydata)):
#countydict[int(countydata[i,4])] = countydata[i,0]
countytractid.append([])
countytractpop.append([])
for i in xrange(len(tractdata)):
#id = countydict[int(tractdata[i,6])]
Duczmal_d = [95,99,109,110,113,117,129,142,148,150,159,160,164,180,182]
Duczmal_e = [84,95,99,102,109,110,113,116,117,129,142,148,150,159,160,164,180,182,232,233,236]
#Duczmal_f = []
Duczmal_j = [61,76,78,89,94,98,106,111,119,121,123,131,136,151,154,157,158,160,161,162,163,164,165,167,168,169,\
170,171,172,173,174,175,178,179,180,181,182,183,184,185,227,228,230,231,232,233,234,235,236,237,238,239,240,241,242]
Duczmal_k = Duczmal_j + [84,88,90,91,92,93,95,96,99,102,114,118,135,139,140,144,145,146,147,148,150,153,156,159]
Duczmal_k.remove(169)
clustername = ['6mixed16','6rural16','6urban16','Duczmal_a','Duczmal_b',\
'Duczmal_c','Duczmal_d','Duczmal_e','Duczmal_j','Duczmal_k']#'Duczmal_f','Duczmal_g','Duczmal_h',\
##'Duczmal_i','Duczmal_j']
clusterIDlist = [mixed, rural, urban, Duczmal_a, Duczmal_b, Duczmal_c, Duczmal_d, Duczmal_e, Duczmal_j, Duczmal_k]
popcsv = 'C:/_DATA/CancerData/NewEnglandDiseaseData/format/pop.csv'
r = readcsv.readcsv(popcsv)
pop = r.getData()
pop = pop[:,1]
sumpop = sum(pop)
power = [] # the p-value of one unit in the true cluster is less than 0.05
power2 = [] # as long as the p-value of one unit is less than 0.05 (no matter if it belongs to true cluster)
ppv = []
sensitivity = []
misclassification = []
for clusteridx in xrange(len(clustername)):
#for clusteridx in xrange(1):
#clusteridx = 3
clustertype = clustername[clusteridx]
cluster = clusterIDlist[clusteridx]
self.power0[pid] = 1
self.power1[pid] += 1
return self.power0, self.power1
#--------------------------------------------------------------------------
#MAIN
if __name__ == "__main__":
# modified
hot_16 = [9, 10, 12, 13, 14, 17, 20, 23, 26, 27, 28, 33, 34, 35, 38, 41,
95, 99, 109, 110, 114, 115, 117, 119, 125, 126, 127, 128, 130,
131, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 146, 147,
149, 151, 152, 156, 157, 237 ]
rural = set([9,10,12,13,14,17,20,23,26,27,28,33,34,35,38,41])
mixed = set([95,99,109,110,114,115,117,119,126,131,139,140,142,146,147,237])
urban = set([125,127,128,130,133,134,136,137,138,141,143,149,151,152,156,157])
hot_16_set = [rural, mixed, urban]
#print hot_16
filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/LLR/'
pvaluecsv = filePath + 'LLR_EBS_high_0_pvalue.csv'
r = readcsv.readcsv(pvaluecsv)
#print r
data = r.getData()
pvalue = data[:,-1]
s = SSS_Power(range(len(pvalue)), hot_16_set)
print s.getMeasure(pvalue)
#print 'sigma =', s.getMeasure(pvalue)
import json
import simplejson
import sys, os
sys.path.append(os.path.join(os.path.dirname(sys.argv[0]), '..', 'common'))
import readcsv
def putjson(data, filename):
jsondata = simplejson.dumps(data, separators=(',', ':'))
fd = open(filename, 'w')
fd.write(jsondata)
fd.close()
csvfile = 'C:/Users/Hu/Downloads/temp/cnty_pts_lnglat.csv'
r = readcsv.readcsv(csvfile, 'float')
cntypts = r.getData()
cntypts_object = []
for f in cntypts:
t = {}
t['id'] = int(f[0])
t['lng'] = f[1]
t['lat'] = f[2]
cntypts_object.append(t)
putjson(cntypts_object, 'C:/_DATA/migration_census_2000/cnty/cnty_pts_lnglat.json')
#--------------------------------------------------------------------------
#MAIN
if __name__ == "__main__":
start_time = datetime.now()
print "begin at " + str(start_time)
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/LLR_buildonly/'
filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16/LLR_buildonly/'
datacsv = filePath + 'minpvalue_Unsmoothed_corrected.csv'
#datacsv = filePath + 'minpvalue_corrected.csv'
r = readcsv.readcsv(datacsv, 'float')
data = r.getData()
data = data * 10
repeatTime = len(data[0,:])
header = ''
fileLoc = datacsv[:-4] + '_2.csv'
for i in xrange(repeatTime):
header += 'data' + str(i) + ','
np.savetxt(fileLoc, data, delimiter=',', comments='', header = header[:-1], fmt = '%s')
finish_time = datetime.now()
print "end at " + str(finish_time)
print 'Time for whole procedure: ' + str(finish_time - start_time)
print "========================================================================"
import simplejson
import sys, os
sys.path.append(os.path.join(os.path.dirname(sys.argv[0]), '..', 'common'))
import readcsv
import numpy as np
def putjson(data, filename):
jsondata = simplejson.dumps(data, separators=(',', ':'))
fd = open(filename, 'w')
fd.write(jsondata)
fd.close()
#csvfile = 'C:/_DATA/migration_census_2000/cnty/sig_flowtable_above65_allm.csv'
#csvfile = 'C:/_DATA/migration_census_2000/cnty/sig_flowtable_allage_allm.csv'
csvfile = 'C:/_DATA/migration_census_2009/cnty/sig_flowtable_allage_1m.csv'
r = readcsv.readcsv(csvfile, 'int')
flowdata = r.getData() # oid, did, volume
#csvfile = 'C:/_DATA/migration_census_2000/cnty/cnty_pts_lnglat.csv'
#txtfile = 'C:/_DATA/migration_census_2000/cnty/cnty_pts_lnglat.txt'
txtfile = 'C:/_DATA/migration_census_2009/cnty/cnty_pts_lnglat.txt'
#r = readcsv.readcsv(csvfile, 'float')
#cntypnts = r.getData() # id, x, y
cntypnts = [line.strip() for line in open(txtfile)]
output = '{"type":"FeatureCollection","features":[\n'
i = 0
flowdata = flowdata[np.argsort(flowdata[:,2]),:]
for f in flowdata:
output += '{"type":"Feature","id":"' + str(i) +'","properties":{"volume":' + str(f[2])
output += '},"geometry":{"type":"LineString","coordinates":[['
print '75Q:', temp_3Q
return [temp_mean, temp_1Q, temp_median, temp_3Q]
#--------------------------------------------------------------------------
#MAIN
if __name__ == "__main__":
start_time = datetime.now()
print "begin at " + str(start_time)
filePath = 'C:/_DATA/CancerData/SatScan/mult6000/'
originaldatacsv = filePath + 'TPFP_original_summary.csv'
# satscan_rate satscan_rate_error satscan_elliptic_rate satscan_elliptic_rate_error LLR_rate LLR_rate_error
r = readcsv.readcsv(originaldatacsv)
originaldata = r.getData()
modifieddatacsv = filePath + 'TPFP_modified_summary.csv'
# satscan_rate satscan_rate_error satscan_elliptic_rate satscan_elliptic_rate_error LLR_rate LLR_rate_error
r = readcsv.readcsv(modifieddatacsv)
modifieddata = r.getData()
interval = np.arange(0,1,0.001)
#np.savetxt(fileLoc, zip(np.mean(intervalvalue,0),np.std(intervalvalue,0)), delimiter=',', comments='', header = 'rate,error', fmt = '%s')
fig = plt.figure()
ax = fig.add_subplot(111)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
def column(col,table):
#for row in range(0,rownum):
#print(len(table))
for row in range(0,len(table)):
#print(row)
yield math.fabs(table[row][col])
def maxcolumn(colnum,table):
for col in range(0,colnum-1):
#print(col)
yield max([x for x in column(col,table)])
def maxcol(colnum,table):
return [x for x in maxcolumn(colnum,table)]
def maxall(colnum,test,train):
testmax = maxcol(colnum,test)
trainmax = maxcol(colnum,train)
_maxall = []
for col in range(0,colnum-1):
_maxall.append(max(testmax[col],trainmax[col]))
return _maxall
if __name__ == '__main__':
rownum,colnum,table = readcsv(file='prostate-training-data.csv')
#print(x for x in collumn(rownum,colnum,table))
print(maxcol(rownum,colnum,table))
def test_readcsv(self):
# on test si la sortie de la fonction test_readcsv est de type class
self.assertIsInstance(type(readcsv('Region')), type)
# on test que la sortie est != de None
self.assertIsNotNone(readcsv('Region'))
urban = [105,107,112,120,122,125,127,128,130,133,134,141,143,149,152,155]
hot_16 = mixed + rural + urban
'''
## modified risk area
hot_16 = [9, 10, 12, 13, 14, 17, 20, 23, 26, 27, 28, 33, 34, 35, 38, 41,
95, 99, 109, 110, 114, 115, 117, 119, 125, 126, 127, 128, 130,
131, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 146, 147,
149, 151, 152, 156, 157, 237 ]
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16/'
filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/'
datacsv = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/three16_format_modify.csv'
#datacsv = 'C:/_DATA/CancerData/SatScan/mult6000/three16_format.csv'
r = readcsv.readcsv(datacsv)
data = r.getData()
pop = data[:,1]
datacsv = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/three16_lebs_rate_newCTG.csv'
r = readcsv.readcsv(datacsv)
smoothdata = r.getData()
ctgcsv = 'C:/_DATA/CancerData/SatScan/NortheeaternUS_re_contiguity.ctg'
ctgdic = readctg(ctgcsv)
## read image via matplotlib
#im = mpimg.imread(imagefile)
#num_row, num_col = im.shape
#num_row, num_col = int(num_row), int(num_col)
#im.shape = (-1,1)
#imagedata = im[:,0].tolist()
Duczmal_k.remove(169)
clusteridx = 9
clusterlist = ['6mixed16','6rural16','6urban16','Duczmal_a','Duczmal_b',\
'Duczmal_c','Duczmal_d','Duczmal_e','Duczmal_j','Duczmal_k']#'Duczmal_f','Duczmal_g','Duczmal_h',\
##'Duczmal_i','Duczmal_j']
clusterIDlist = [mixed, rural, urban, Duczmal_a, Duczmal_b, Duczmal_c, Duczmal_d, Duczmal_e, Duczmal_j, Duczmal_k]
clustertype = clusterlist[clusteridx]
cluster = clusterIDlist[clusteridx]
print 'Cluster type: ', clustertype
#
filePath = 'C:/_DATA/CancerData/NewEnglandDiseaseData/'
datacsv = filePath + 'format/' + clustertype + '.csv'
r = readcsv.readcsv(datacsv, 'int')
#print randomdatacsv
data = r.getData()
pop = data[:,1]
randomLLRcsv = filePath + 'LLR_buildonly/Random/maxllr.csv'
r = readcsv.readcsv(randomLLRcsv)
randomLLR = r.getData()
randomLLR = randomLLR[:,0]
randomCount = len(randomLLR) + 1
repeatTime = len(data[0,:])-2
#repeatTime = 100
numUnit = len(pop)
maxPvaluelist = []
Duczmal_d = [95,99,109,110,113,117,129,142,148,150,159,160,164,180,182]
Duczmal_e = [84,95,99,102,109,110,113,116,117,129,142,148,150,159,160,164,180,182,232,233,236]
#Duczmal_f = []
Duczmal_j = [61,76,78,89,94,98,106,111,119,121,123,131,136,151,154,157,158,160,161,162,163,164,165,167,168,169,\
170,171,172,173,174,175,178,179,180,181,182,183,184,185,227,228,230,231,232,233,234,235,236,237,238,239,240,241,242]
Duczmal_k = Duczmal_j + [84,88,90,91,92,93,95,96,99,102,114,118,135,139,140,144,145,146,147,148,150,153,156,159]
Duczmal_k.remove(169)
clustername = ['6mixed16','6rural16','6urban16','Duczmal_a','Duczmal_b',\
'Duczmal_c','Duczmal_d','Duczmal_e','Duczmal_j','Duczmal_k']#'Duczmal_f','Duczmal_g','Duczmal_h',\
##'Duczmal_i','Duczmal_j']
clusterIDlist = [mixed, rural, urban, Duczmal_a, Duczmal_b, Duczmal_c, Duczmal_d, Duczmal_e, Duczmal_j, Duczmal_k]
popcsv = 'C:/_DATA/CancerData/NewEnglandDiseaseData/format/pop.csv'
r = readcsv.readcsv(popcsv)
pop = r.getData()
pop = pop[:,1]
sumpop = sum(pop)
power = [] # the p-value of one unit in the true cluster is less than 0.05
power2 = [] # as long as the p-value of one unit is less than 0.05 (no matter if it belongs to true cluster)
ppv = []
sensitivity = []
misclassification = []
for clusteridx in xrange(len(clustername)):
#for clusteridx in xrange(1):
#clusteridx = 9
clustertype = clustername[clusteridx]
cluster = clusterIDlist[clusteridx]
import readcsv
import datetime
import pandas as pd
import calculate
CSV_NAME = '平衡型基金_NAV_Return.csv'
test_list_of_fund = ['21719942', '42334401A', '42334401B']
test_ratio_of_fund = [0.2, 0.5, 0.3]
CD_NUMBER = 0.6 / 52 #better to depend on real number, not a stable one
if __name__ == '__main__':
# start = datetime.datetime.strptime("2016/5/6","%Y/%m/%d")
# end = datetime.datetime.strptime("2019/4/26","%Y/%m/%d")
start = datetime.datetime.strptime("2016/5/6", "%Y/%m/%d")
end = datetime.datetime.strptime("2016/12/6", "%Y/%m/%d")
df = readcsv.readcsv(CSV_NAME, start, end, CD_NUMBER)
print("finish df")
# print(df)
# cor = calculate.cal_cor(df,"normal")
# print(cor.head(10))
# print("===============")
# cor_down = calculate.cal_cor(df,"downside")
# print(cor_down)
# print("finish cor")
risk = calculate.cal_co_risk(test_list_of_fund, test_ratio_of_fund, df)
print(risk)
print("--------------------------------------------------------------------------------------------------------------------------------")
print("Chave primaria já existe: " + str(ViolacaoPK), end='')
print("Comando excluido da lista:\n" + sql_insert)
# Retira o comando problematico da lista e reexecuta a função
insert_list.remove(sql_insert)
execute_insert_list(insert_list)
except psycopg2.InternalError as ErroInterno:
print("Erro interno: " + str(ErroInterno))
print("--------------------------------------------------------------------------------------------------------------------------------")
# Recupera uma lista de dicionários com os dados a serem inseridos no banco...
insert_list = construct_insert_list( readcsv(CSV_PATH), TABLE_NAME) # ...e constroi uma lista de inserts com esta lista
execute_insert_list(insert_list)
"""
TIPOS DE DADOS A SEREM TRATADOS
- Com aspas
1. string
2. date
- Sem aspas
1. Numericos
2. boolean
OBS: Dados do CSV que tenham alguma função dentro dos comandos do banco de dados(como aspas) devem ser tratados, por exemplo:
- Tratar aspas simple substituindo-as por aspas duplas
"""
import config
import readcsv
import datacleaning
import featurescaling
import algorithm
import predict
import writecsv
import numpy
data = readcsv.readcsv(config.trainingset)
config.data = data
config.row_ = data.shape[0]
config.col_ = data.shape[1]
datacleaning.cabin(config.data, 10) #Problem specific data cleaning
datacleaning.gender(config.data, 3) #Problem specific data cleaning
feature = config.allowed_feature
config.cdata = datacleaning.reduce_features(config.row_, len(feature), feature,
config.data)
config.cdata = datacleaning.polynomial_feature_cubic(config.cdata)
crow_ = (config.cdata).shape[0]
ccol_ = (config.cdata).shape[1]
featurescaling.featurescaling(config.cdata)
feature = config.outputlabel
y = datacleaning.get_output_label(config.row_, feature, config.data)
config.y = y
theta = numpy.zeros((ccol_, 1))
print "training....."
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16/satscan/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16/satscan/elliptic/'
filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16/LLR_buildonly/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/redcap/three16/LLR/EBS/constraint/5p_improved/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/satscan/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/satscan/elliptic/'
#filePath = 'C:/_DATA/CancerData/SatScan/mult6000/three16_modify/LLR_buildonly/'
#minpvaluecsv = filePath + 'minpvalue_atleast2counties.csv'
#minpvaluecsv = filePath + 'minpvalue2.csv'
minpvaluecsv = filePath + 'minpvalue_corrected_2.csv'
#minpvaluecsv = filePath + 'minpvalue_Unsmoothed_atleast2counties.csv'
#minpvaluecsv = filePath + 'seg_minpvalue_beta0.1.csv'
r = readcsv.readcsv(minpvaluecsv)
minpvalue = r.getData()
repeatTime = 1000
#pvalueLevel = -1 # -1: min pvalue, -2: last level
numUnit = 255
sigma = []
s = measureSigma.SSS_sigma(range(numUnit), hot_16)
row = 0
count = 0
for repeat in xrange(repeatTime):
row += 1
if int(row/10) > 0:
#print count*10
row = 0
parser.add_argument('--data_dir', help='Path to dataset images')
parser.add_argument('--output_dir',
help='Path to directory for saving outputs')
args = parser.parse_args()
train = "train_fluid_intelligence_household.csv"
valid = "valid_fluid_intelligence_household.csv"
test = "test_fluid_intelligence_household.csv"
if not os.path.isdir(args.output_dir):
try:
os.mkdir(args.output_dir)
except:
raise Exception('Could not create output directory')
csv_train = readcsv(args.data_dir, train)
csv_valid = readcsv(args.data_dir, valid)
csv_test = readcsv(args.data_dir, test)
for key, value in csv_train.items():
print(key, value)
for key, value in csv_valid.items():
print(key, value)
for key, value in csv_test.items():
print(key, value)
print('saving train dict!')
np.save(os.path.join(args.output_dir, 'csv_train_target_dti.npy'),
csv_train)
