def df_sampling_ld(csv,n_samples):
"""
Extract from the list a new list with n_samples domain names
ARGS:
- csv: path of csv where extract the samples
- n_samples: number of samples to extract
"""
#check the presence of file out
if os.path.exists('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv'):
print('removing old csv')
os.remove('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv')
else:
print('no old csv exists')
df = pd.read_csv(csv, low_memory=False,names=['domain'])
print(df)
df_out = df.sample(n_samples)
df_out.to_csv('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv', header=None)
df_final = pd.read_csv('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv', low_memory=False, names=['index','domain'], index_col=['index'])
df_final_out = df_final.sort_values(by=['index'])
print(df_final)
df_final_out['domain'].to_csv('samplesort_'+str(csv.split('/')[-1].split('.')[0])+'.csv',header=None,index=False)
def sampling_ld(csv,n_samples):
"""
Extract from the list a new list with n_samples domain names.. dont'use it, error 'kill 9 ' too memery usage
Use df_sampling_ld
ARGS:
- csv: path of csv where extract the samples
- n_samples: number of samples to extract
"""
#check the presence of file out
if os.path.exists('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv'):
print('removing old csv')
os.remove('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv')
else:
print('no old csv exists')
list_dom = extract_doml(csv)
print('sampling...')
new_list = random.sample(list_dom,n_samples)
print('Writing..')
with open('sample_'+str(csv.split('/')[-1].split('.')[0])+'.csv', mode='a') as csv_out:
writer = csv.writer(csv_out)
for x in new_list:
writer.writerrow([x])
def lambda_handler(event, context):
stream_name = "dsoaws-data-stream"
s3 = boto3.client('s3')
response = s3.get_object(
Bucket="sagemaker-us-east-1-806570384721",
Key="data/amazon_reviews_us_Digital_Software_v1_00_noheader.csv")
csv = str(response['Body'].read().decode('UTF-8'))
lines = csv.split("\n")
for line in csv.split("\n"):
val = line.split(",")
data = json.dumps(getRating(val[0], val[1]))
kinesis.put_record(StreamName=stream_name,
Data=data,
PartitionKey="reviews")
return "complete"
def deserialize(self, s):
unseenRecords = set(self.fields)
for (name, strValue) in zip(self.fields, csv.split(s)):
setattr(self, name, self.converters[name](strValue))
unseenRecords.remove(name)
for name in unseenRecords:
setattr(self, name, self.defaults[name])
def get_yahoo_prices (exchange, ticker, start, end):
yahoo_ticker = ticker.replace (".", "-")
url = urltemplate % (yahoo_ticker, exchange, start.day, start.month-1, start.year, end.day, end.month-1, end.year)
try:
csv = scraperwiki.scrape (url)
except:
print 'Cannot find %s:%s' % (exchange, ticker)
return
first = True
alldata = []
for line in csv.split("\n"):
if first:
headers = [h.replace (" ", "") for h in line.split (",")]
first = False
elif line == '':
continue
else:
data = {}
raw = line.split(",")
data['Ticker'] = ticker
data['Source'] = 'Yahoo'
data['Exchange'] = exchange
data['Date'] = convert_date_iso (raw[0])
data['Open'] = float (raw[1])
data['High'] = float (raw[2])
data['Low'] = float (raw[3])
data['Close'] = float (raw[4])
data['Volume'] = float (raw[5])
data['AdjClose'] = float (raw[6])
alldata.append (data)
scraperwiki.sqlite.save (unique_keys=["Ticker", "Source", "Exchange", "Date"], data=alldata)
def parse_csv_string(csv):
csv_list = csv.split('\n')
parsed = []
for line in csv_list:
if line != '':
parsed.append(line.split(','))
return parsed
def get_intended_ratio(csv_name):
csv = os.path.basename(csv_name)
#ratio = float(csv.split('-')[-1][0:3].replace('_','.'))
ratio = csv.split('-')[-1][0:3].replace('_', '.')
print(ratio)
return ratio
def get_data (exchange, ticker, start, end):
yahoo_ticker = get_ticker (ticker)
url = url_template % (yahoo_ticker, exchange, start.day, start.month-1, start.year, end.day, end.month-1, end.year)
try:
csv = scraperwiki.scrape (url)
except:
print 'Cannot find %s:%s' % (exchange, ticker)
return
first = True
alldata = []
for line in csv.split("\n"):
if first:
headers = [h.replace (" ", "") for h in line.split (",")]
first = False
elif line == '':
continue
else:
tds = line.split(',')
data = \
{
'Source':'Yahoo',
'Type':'',
'Currency': None,
'Ticker':ticker,
'DeclarationDate':None,
'ExDivDate': convert_date (tds[0]),
'RecordDate':None,
'PayDate':None,
'Amount':float(tds[1]),
'Url':url
}
alldata.append (data)
scraperwiki.sqlite.save (unique_keys=["Source", "Ticker", "ExDivDate", "Type"], data=alldata)
def deserialize(self, s):
unseenRecords = set(self.fields)
for (name, strValue) in zip(self.fields, csv.split(s)):
setattr(self, name, self.converters[name](strValue))
unseenRecords.remove(name)
for name in unseenRecords:
setattr(self, name, self.defaults[name])
def to_csv(self,data):
email = '*****@*****.**'
json = str(data)
filename = 'json_csv'
sys.stdout.write('Status: 200 OK\n')
sys.stdout.write('Content-Type: text/csv; charset=utf-8\n')
sys.stdout.write('Content-Disposition: attachment; filename=' + filename + '\n\n')
url = 'https://json-csv.com/api/getcsv'
post_fields = {'email': email, 'json': json}
request = Request(url, urlencode(post_fields).encode())
csv = urlopen(request).read().decode()
for l in csv.split('\r\n'):
print (l)
"""outputdata=open('output.cvs','w')
def CsvVerify(self, csv):
separator = csv.split(".")
for separator_field in separator:
if separator_field == separator[(len(separator) - 1)]:
if separator_field == "csv":
return csv
else:
return False
def get_csvs(dataset_name):
csv_list = []
for csv in os.listdir(app.config["CSV_FOLDER"]):
e_name = dataset_name.split(".h5")[0]
ce_name = csv.split("_")[0]
if (ce_name in e_name):
csv_list.append(csv)
return json.dumps(csv_list)
def reName():
path = "E://scripts//4//pick-up-sku//"
csvList = os.listdir(path)
for csv in csvList:
l = csv.split('-')
l[7] = "400" + l[7]
newName = path + "-".join(l)
oldName = path + csv
os.rename(oldName, newName)
def main(dir):
dir = dir.replace("\\", "/")
# 图片转换成xml
#convert_to_xml(dir)
# xml提取信息到csv
convert_to_csv(dir)
# 处理csv生成新xml需要的属性值
csv_paths = glob.glob(dir + "/*.csv")
for csv in csv_paths:
loc_list = deal_csv(csv)
xml_origin_name = str(os.path.abspath(os.getcwd(
))) + '/xml_04_origin/' + csv.split("/")[-1][:-4] + '_origin.xml'
xml_last_name = str(os.path.abspath(os.getcwd(
))) + '/xml_04_last/' + csv.split("/")[-1][:-4] + '_last.xml'
try:
generate_xml(xml_origin_name, xml_last_name, loc_list)
except Exception as e:
raise e
def createcsv(csv, f):
try:
dataLines = csv.decode().split('\r\n')
except er:
dataLines = csv.split('\r\n')
with f:
writer = csv.writer(f)
for row in dataLines:
writer.writerow(row)
def analyseCSV(self):
csvFiles = locate("*tif_RESULTS.csv",self.imgDir)
for csv in csvFiles:
(mean,stdev) = self.calcIntensity(csv)
(bgMean,areaBG) = self.calcBGIntensity(csv)
csvName = csv.split('/')[-1][:-12]
print csvName, mean,bgMean
ofile.write("\t".join([csv, csvName, str(mean),str(stdev),str(bgMean), str(areaBG),"\n"]))
print mean, stdev, csvName
def read_and_draw(list_csv, anno_path, read_path, save_path, type='bmp'):
"""
标记预测框并保存
:param anno_lines: 标记索引 list[ 'img_name','left','top','x1','y1','x2','y2','catagory' ]
:param read_path: 原始图片路径
:param save_path: 处理后保存路径
:param type: 图片格式
:return: None
"""
list_csv = [csv for csv in list_csv if csv]
for csv in tqdm(list_csv):
anno_lines = read_csv(
os.path.join(anno_path,
csv.split(".")[0] + '.csv')) # 读取标注csv
im = cv2.imread(os.path.join(read_path,
csv.split(".")[0] + '.' + type))
name, show_img = draw(im, anno_lines)
# 保存
cv2.imwrite(os.path.join(save_path, str(name) + '.' + type), show_img)
def plotVSWR(csv, fig=None, freq_range=None, expect_linear=False, **kwargs):
'''
Plots the VSWR from a csv (assuming the csv contains log magnitude data).
If fig is given then it will plot on that figure with the predefined axis/title/etc. If label
is given as one of the optional kwargs then the plot will be given this label, otherwise it will pull one from csv. Will
always plot in MHz, so ensure that if fig is given that it accounts for this.
Parameters
---------
csv : str
The path/filename of the csv to be plotted. It should lead to a csv file that was made
using the field fox in the Log Magnitude setting.
fig : matlotlib.pyplot.figure(), optional
The input figure, to be plotted on. If not given then a figure will be created.
freq_range : tuple of floats
The range for which to plot the frequencies. (min_freq,max_freq), given in MHz.
expect_linear : bool
If true then the csv is expected to contain linear magnitudes.
**kwargs
Should contain only additional plotting kwargs, such as label.
Returns
-------
fig : matlotlib.pyplot.figure()
The plotted figure object.
'''
if fig is None:
new_plot = True
fig = pylab.figure()
else:
new_plot = False
if numpy.isin('label', list(kwargs.keys())) == False:
kwargs['label'] = 'VSWR For ' + csv.split('/')[-1].replace('.csv', '')
if expect_linear:
freqs, lin = readerFieldFox(csv)
VSWR = linToVSWR(lin)
else:
freqs, LM = readerFieldFox(csv)
VSWR = linToVSWR(logMagToLin(LM))
if freq_range is not None:
cut = numpy.logical_and(freqs / 1e6 > freq_range[0],
freqs / 1e6 < freq_range[1])
else:
cut = numpy.ones_like(freqs, dtype=bool)
pylab.plot(freqs[cut] / 1e6, VSWR[cut], **kwargs)
if new_plot == True:
pylab.legend()
pylab.xlabel('Frequency (MHz)')
pylab.ylabel('VSWR')
return fig
def build_acq_date_list(in_csv_list):
"""Builds an array made up of julian dates paired with csv files."""
print "Building acquisition date list..."
acq_date_list = []
for csv in in_csv_list:
csv_filepath_list = csv.split('.')
acq_date = csv_filepath_list[0][-7:-4]
acq_year = csv_filepath_list[0][-11:-7]
row = [acq_date, acq_year, csv]
acq_date_list.append(row)
return acq_date_list
def qcewCreateDataRows(csv):
dataRows = []
try:
dataLines = csv.decode().split('\r\n')
except:
dataLines = csv.split('\r\n')
for row in dataLines:
dataRows.append(
list(map(lambda x: x.replace('\"', ''), row.split(','))))
return dataRows
def get_au_images(au):
"""
Get au value for each frame for each user
"""
au_path = "./data/bp4d/AU/Project/AU%d"%au
csv_files = filter(lambda x: x.rsplit('.')[-1] == 'csv', os.listdir(au_path))
image_au_codes = dict()
for csv in csv_files:
meta, _ = csv.split('.')
image_au_codes[meta] = get_frame_au_map( au_path + "/" + csv)
return image_au_codes
def build_julian_csv_array(in_csv_list):
"""Builds an array made up of julian dates paired with csv files."""
print "Building acquisition date list..."
julian_csv_array = []
for csv in in_csv_list:
csv_filepath_list = csv.split('.')
acq_date = csv_filepath_list[0][-7:-4]
acq_year = csv_filepath_list[0][-11:-7]
row = [acq_date, acq_year, csv]
julian_csv_array.append(row)
return julian_csv_array
def crawler():
csvs = []
# existing volume csv files by date
volcsvs = list(
set([
volcsv.split('_')[0] for volcsv in listdir(DATA_DIR)
if volcsv.endswith("csv")
]))
headers = requests.utils.default_headers()
headers.update({
'User-Agent':
'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0'
# 'Referer': t.getRef()
})
try:
r = requests.get(BASE_URL + VOLUME_URI, headers=headers,
verify=False) #requestsを使って、webから取得
# print(r.headers['Set-Cookie'])
soup = BeautifulSoup(r.text, 'lxml') #要素を抽出 (lxml)
# csv file uri: a要素を全て取得
allcsv = []
for table in soup.find_all('table'):
for tr in table.find_all('tr'):
allcsv.extend([
a['href'] for a in tr.find_all('a', href=True)
if a['href'].endswith('.csv')
])
# download and rename saving
for csv in allcsv:
csvfile = csv.split('/')[-1]
if csvfile.split('_')[0] not in volcsvs: # not downloaded yet
print("sleeping 2s ...: " + BASE_URL + csv)
sleep(2)
response = requests.get(BASE_URL + csv)
if response.ok:
csvpath = DATA_DIR + csvfile
csvs.append(csvpath)
with open(
csvpath,
mode='wb+') as f: # write, binary, allow creation
f.write(response.content)
return csvs
except Exception as e:
print("error: {0}".format(e), file=sys.stderr)
exitCode = 2
def insertdata(data, date):
team1 = data[0]
team1_id = get_team_id(team1)
team1_players = data[1]
team1_players_id = [str(i) for i in get_player_id(team1_players, data[13][:5])]
team2 = data[2]
team2_id = get_team_id(team2)
team2_players = data[3]
team2_players_id = [str(i) for i in get_player_id(team2_players, data[13][5:])]
form = data[4]
matchid = data[6]
winner = data[7]
#INSERT Match
conn.execute("INSERT OR IGNORE INTO Match VALUES("+matchid+", '"+date+"', "+team1_id+", "+team2_id+", '"+winner.replace("'", "''")+"', '"+form+"', \
'"+team1_players_id[0]+"', '"+team1_players_id[1]+"', '"+team1_players_id[2]+"', '"+team1_players_id[3]+"', '"+team1_players_id[4]+"', \
'"+team2_players_id[0]+"', '"+team2_players_id[1]+"', '"+team2_players_id[2]+"', '"+team2_players_id[3]+"', '"+team2_players_id[4]+"', \
'"+create_string(data[9])+"', '"+create_string(data[10])+"','"+create_string(data[11])+"','"+create_string(data[12])+"')")
for csv in get_file_names('C:\Users\QQ\Dropbox\csgo stats\database\csv'):
#INSERT Round
Map = csv.split("_")[1].split(".")[0]
demo_data = read_csv('csv/'+csv)
if len(demo_data[0]) < 10:
continue
demo_team = assign_players(demo_data[0], data[1]+data[3])
demo_team1, demo_team2 = demo_team[:5], demo_team[5:]
for row in demo_data[1]:
if row[0] != '0' and int(row[5]) == 0 and int(row[6]) == 0:
continue
if row[26].lower() in demo_team1:
team_won = team1_id
elif row[26].lower() in demo_team2:
team_won = team2_id
else:
print "UNABLE TO RECOGNIZE WINNING TEAM!"
team_won = '-1'
conn.execute("INSERT OR IGNORE INTO Round VALUES("+matchid+", '"+Map+"', '"+row[0]+"', '"+row[25]+"', "+team_won+",\
"+row[3]+", "+row[4]+", "+row[5]+", "+row[6]+", "+row[7]+", "+row[8]+", "+row[9]+", "+row[10]+","+row[11]+","+row[12]+", "+row[19]+","+row[20]+", '"+row[21]+"')")
#INSERT Playerstats
pkills = [i.lower() for i in row[22].split(":")]
pdeaths = [i.lower() for i in row[23].split(":")]
passists = [i.lower() for i in row[24].split(":")]
for p in range(5):
conn.execute("INSERT OR IGNORE INTO Playerstats VALUES("+team1_players_id[p]+", "+matchid+", '"+Map+"', '"+row[0]+"', \
"+str(pkills.count( demo_team1[p] ))+","+str(pdeaths.count( demo_team1[p] ))+", "+str(passists.count( demo_team1[p] ))+")" )
conn.execute("INSERT OR IGNORE INTO Playerstats VALUES("+team2_players_id[p]+", "+matchid+", '"+Map+"', '"+row[0]+"', \
"+str(pkills.count( demo_team2[p] ))+","+str(pdeaths.count( demo_team2[p] ))+", "+str(passists.count( demo_team2[p] ))+")" )
conn.commit()
def __init__(self, csv):
self.mapping = {
'title': 6,
'authors': 2,
'year': 18,
'abstract': 24,
'doi': 11,
'keywords': 10,
}
self.source = 'ACM'
self.csv = csv.split('\r\n')
self.author_delim = ' and '
self.keyword_delim = ','
def findLatestExport(courseExportDir):
exportFiles = os.listdir(courseExportDir)
exportList = []
for csv in exportFiles:
if 'export' in csv:
csvDate = csv.split('_')
exportList.append(csvDate[-1])
sortedList = sorted(exportList, reverse=True)
for csv_r in exportFiles:
if "_export_{}".format(sortedList[0]) in csv_r:
print(sortedList[0])
return os.path.join(courseExportDir, csv_r)
else:
return print('Previous export cannot be found.')
def __init__(self, csv):
self.mapping = {
'title': 0,
'authors': 1,
'year': 5,
'abstract': 10,
'doi': 13,
'keywords': 16,
}
self.source = 'IEEE'
from unidecode import unidecode
csv = unidecode(unicode(csv, encoding="utf-8"))
self.csv = csv.split('\n')
self.author_delim = ';'
self.keyword_delim = ';'
def createMultiX(directory,listOfCSV,listOfComb):
titleList = []
rTable = 'Resistance Table.csv'
rt = {'Filename':filename,
'Ohmic': olist,
'NonOhmic' : nolist,
'Tasr' : tasr,
'Area Corrected Ohmic' : acohmic,
'Area Corrected Non Ohmic' : acnonohmic,
'Area Corrected Tasr' : actasr}
dt = pd.DataFrame(data=rt)
dt.to_csv(rTable,index = False)
for csv in listOfCSV:
csvSplitList = csv.split('_')
print(csvSplitList)
for val in csvSplitList:
try:
if val != 'aging' or val != 'preaging':
intTemp = (int(val))
except ValueError:
print ('Was Not a Temp')
stringName=(str(intTemp))
titleList.append(stringName)
dirpath = os.chdir(directory)
fileN = "Multi X-Axis Support.csv"
'''
dataf = { '1': listOfComb[0]}
df = pd.DataFrame(data=dataf)
df.to_csv(fileN, index = False)
for i in range(len(listOfComb)):
df = pd.read_csv(fileN)
df['test',i] = listOfComb[i]
df.to_csv(fileN)
'''
for i in range(len(listOfComb)):
if i ==0:
dataf = {titleList[0] : listOfComb[i]}
df = pd.DataFrame(data=dataf)
if i !=0:
head = titleList[i]
df[head] = listOfComb[i]
df.to_csv(fileN, index = False)
def parseCSV( csv ):
data = []
lines = csv.split( "\n" )
keys = lines [ 0 ].split( ',' )
for l in lines:
values = l.split( ',' )
d = {}
for k,v in zip( keys, values ):
d[k] = v
data.append( d )
# The next two lines are dependant on the report we are using
del data[0] # This line takes out the keys line from the data set
del data[len(data) -1] # This line takes out a blank data line at the end
return data
def analyseCSV(self):
csvFiles = locate("*tif_RESULTS.csv", self.imgDir)
for csv in csvFiles:
(mean, stdev) = self.calcIntensity(csv)
(bgMean, areaBG) = self.calcBGIntensity(csv)
csvName = csv.split('/')[-1][:-12]
print csvName, mean, bgMean
ofile.write("\t".join([
csv, csvName,
str(mean),
str(stdev),
str(bgMean),
str(areaBG), "\n"
]))
print mean, stdev, csvName
def file_finder(self, data):
path = os.getcwd()
self.result_path = os.path.join(path, data)
reward_files = [files for files in glob.glob(os.path.join(path, data, 'result*'))]
data_dict = {}
self.modelnumber=None
if len(reward_files)!=0:
sorted_reward_files = []
for i in range(len(reward_files)):
csv = reward_files[i].split('result')[-1]
sorted_reward_files.append(int(csv.split('.')[0]))
sorted_reward_files.sort()
data_dict['result'] = 'result' + str(sorted_reward_files[-1]) + '.csv'
data_dict['traveltime'] = 'traveltime' + str(sorted_reward_files[-1]) + '.csv'
#data_dict['algo_timer'] = 'algo_timer' + str(sorted_reward_files[-1]) + '.csv'
self.modelnumber = sorted_reward_files[-1] + 10000
print('MODEL NUMBER BEING USED IS: ', self.modelnumber)
return data_dict, bool(data_dict)
def downloadData(self, symbol, range=None): """ Return a csv object with symbol data. csv object is iterable by csv.reader """ #Holds the base Google finance URL base_data_url = "http://www.google.com/finance/historical?output=csv&q=" #Create full URL for downloading desired data download_url = base_data_url + symbol if (range != None): download_url = download_url + "&startdate=" + range[0] + "&enddate=" + range[1] #Download csv = (requests.get(download_url)).content csv = csv.split() #format string to be iterable for csv.reader return csv
def generateSheets(listOfCSV, fileName):
fileName = sg.PopupGetText("enter a name for the combined Excel file", 'Excel File Name')
writer = pd.ExcelWriter(fileName+'.xlsx', engine = 'xlsxwriter')
for csv in listOfCSV:
csvSplitList = csv.split('_')
print(csvSplitList)
for val in csvSplitList:
try:
if val != 'aging' or val != 'preaging':
intTemp = (int(val))
except ValueError:
print ('Was Not a Temp')
stringName=(str(intTemp))
#createMultiX()
df = pd.read_csv(csv)
df.to_excel(writer, sheet_name=stringName)
writer.save()
def parseHeaderLine(aLine):
tokens = aLine.split()
outFilename = tokens[1] + ".tsv"
fhOut = open(outFilename, 'w')
i1 = aLine.find("(")
i2 = aLine.find(")")
csv = aLine[i1 + 1:i2]
tokens = csv.split(", ")
print(tokens)
nTokens = len(tokens)
fhOut.write('\t'.join(tokens))
fhOut.write('\n')
return (nTokens, tokens, fhOut)
def convert_csvtojson(self, csv):
try:
array = csv.split('\n')
header = array[0].split(',')
ordered = []
array.pop(0)
i = 0
for line in array:
vals = line.split(',')
ii=0
orderedvals = OrderedDict()
for val in vals:
orderedvals[header[ii]] = vals[ii]
ii += 1
ordered.append(orderedvals)
i += 1
jsonstr = json.dumps(ordered)
return jsonstr
except:
return '{}'
def Process(File_Name,Symbols):
file = open(File_Name)
fields = []
entries = 0
for line in file:
current_line = line.strip()+"\n",
if (current_line[0][0]=="_"):
if (current_line[0].find(".")<0):
#print current_line[0]
csv = current_line[0].replace('\t',',').replace(' ',',').replace('\n','')
field = csv.split(',')
if field[0] in Symbols:
fields.append(field)
entries += 1
#break
#print entries, " Symbols Found"
return fields
def Process(File_Name, Symbols):
file = open(File_Name)
fields = []
entries = 0
for line in file:
current_line = (line.strip() + "\n",)
if current_line[0][0] == "_":
if current_line[0].find(".") < 0:
# print current_line[0]
csv = current_line[0].replace("\t", ",").replace(" ", ",").replace("\n", "")
field = csv.split(",")
if field[0] in Symbols:
fields.append(field)
entries += 1
# break
print entries, " Symbols Found"
return fields
def no_repeats(csv, sci):
#print(type(csv))
#print('\n')
#print('csv',10*" ",csv)
#print('sci',10*" ",sci)
csv=csv.replace('\""', '')
csv=csv.replace(' ', ' ')
sci=sci.replace('\""', '')
sci=sci.replace(' ', ' ')
new_csv=[]
new_sci=[]
final_draft=[]
if not csv:
#print ('final 1',sci)
return sci
if not sci:
#print ('final 2',sci)
return csv
if sci in csv:
#print('passed')
return False
if (sci + '.') in csv:
#print('passed')
return False
#FIXME: I haven't been able to seperate properly if there are abbre.
temp_csv=list(csv.split('. '))
#print('csv 1',10*" ",temp_csv)
#for ele in temp_csv:
#if '.' in ele:
#new_csv.append(ele.replace('.',''))
#else:
#new_csv.append(ele)
temp_sci=list(sci.split ('. '))
#for ele in temp_sci:
#if '.' in ele:
#new_sci.append(ele.replace('.',''))
#else:
#new_sci.append(ele)
temp2_csv=[]
for ele in temp_csv:
temp2_csv.append(ele.lower())
for ele1 in temp_sci:
ele=ele1.lower()
if ele not in temp2_csv:
final_draft.append(ele1)
final_draft=final_draft+temp_csv
final_draft=str(final_draft).strip('[]')
final_draft=final_draft.replace(',',', ')
final_draft=final_draft.replace(', ',', ')
final_draft=final_draft.replace(r'\\','')#need two \\ to make it valid to read one
final_draft=final_draft.replace(r'\\"', '')
final_draft=final_draft.replace(r'\\\\', '')
final_draft=final_draft.replace('.,','.')
#comma.join(final_draft)
#print(new_sci)
final_draft=final_draft.replace('S, J., Costa, V., Psaroulis, V., Arzoglou, L,','S. J., Costa, V., Psaroulis, V., Arzoglou, L.')
#print('final',10*" ",final_draft)
return final_draft
def csv_to_dictionary(csv):
'''
Transform input from "special" csv into a python dictionary with namedtuples
as values. Note, "strings" of interest are hardcoded!
Also, fix the re-definition of the function transform(). See
<http://stackoverflow.com/q/30204197/1172302>
'''
# split input in rows
rows = csv.split('\n')
dictionary = {} # empty dictionary
fields = rows.pop(0).split('|')[1:] # header
strings = ('TIRS10', 'TIRS11')
if any(string in fields for string in strings):
def transform(row):
'''
Transform an input row in to a named tuple, then feed it in to a
dictionary.
'''
# split row in elements
elements = row.split('|')
# key: 1st column, replace
key = replace_dot_comma_space(elements[0])
# namedtuple
ect = namedtuple(key, [fields[0], fields[1]])
# feed namedtuples
ect.TIRS10 = is_number(elements[1])
ect.TIRS11 = is_number(elements[2])
# feed dictionary
dictionary[key] = dictionary.get(key, ect)
strings = ('b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7')
if any(string in fields for string in strings):
def transform(row):
'''
Transform an input row in to a named tuple, then feed it in to a
dictionary.
'''
# split row in elements
elements = row.split('|')
# key: 1st column, replace
key = replace_dot_comma_space(elements[0])
# *** small modification for the CWV field ***
fields[0] = 'cwv'
# named tuples
cwv = namedtuple(key,
[replace_dot_comma_space(fields[0]),
replace_dot_comma_space(fields[1]),
replace_dot_comma_space(fields[2]),
replace_dot_comma_space(fields[3]),
replace_dot_comma_space(fields[4]),
replace_dot_comma_space(fields[5]),
replace_dot_comma_space(fields[6]),
replace_dot_comma_space(fields[7]),
replace_dot_comma_space(fields[8]),
replace_dot_comma_space(fields[9])])
# feed named tuples
cwv.subrange = to_tuple(elements[1])
cwv.b0 = is_number(elements[2])
cwv.b1 = is_number(elements[3])
cwv.b2 = is_number(elements[4])
cwv.b3 = is_number(elements[5])
cwv.b4 = is_number(elements[6])
cwv.b5 = is_number(elements[7])
cwv.b6 = is_number(elements[8])
cwv.b7 = is_number(elements[9])
cwv.rmse = is_number(elements[10])
dictionary[key] = dictionary.get(key, cwv) # feed dictionary
map(transform, rows)
return dictionary
def post(self):
if str(self.request.get('upload')) != 'True':
check = ''
filedisabled = 'disabled'
inputdatadisabled = ''
raw_data = self.request.get('inputdata')
split_line = raw_data.split('\r\n')
split_value = []
for line in split_line:
split_value.append(line.split(', '))
data = []
for line in split_value:
new_line = []
new_line.append(str(line[0]))
new_line.append(float(line[1]))
new_line.append(float(line[2]))
data.append(new_line)
else:
check = 'checked'
filedisabled = ''
inputdatadisabled = 'disabled'
csv = self.request.get('csv')
split_line = csv.split('\r\n')[:-1]
split_value = []
for line in split_line:
split_value.append(line.split(','))
data = []
for line in split_value:
new_line = []
new_line.append(line[0])
new_line.append(float(line[1]))
new_line.append(float(line[2]))
data.append(new_line)
self.response.out.write('''
<html>
<head>
<title>Graphing Results</title>
<link href="http://ajax.googleapis.com/ajax/libs/jqueryui/1.8.15/themes/ui-lightness/jquery-ui.css" rel="stylesheet" type="text/css"/>
<script src="//ajax.googleapis.com/ajax/libs/jquery/1.6.2/jquery.min.js" type="text/javascript"></script>
<script src="http://ajax.googleapis.com/ajax/libs/jqueryui/1.8.15/jquery-ui.min.js"></script>
<script type="text/javascript" src="https://www.google.com/jsapi"></script>
<script type="text/javascript ">
google.load('visualization', '1', {packages:['table']});
google.load('visualization', '1', {packages:['corechart']});
google.setOnLoadCallback(drawTable);
google.setOnLoadCallback(drawColumnChart);
var data = '''+str(data)+''';
function drawTable() {
var table = new google.visualization.DataTable();
table.addColumn('string', 'Date');
table.addColumn('number', 'Principal ($)');
table.addColumn('number', 'Interest ($)');
table.addRows(data.length);
for (var i=0;i<data.length;i++) {
table.setCell(i, 0, data[i][0]);
table.setCell(i, 1, data[i][1]);
table.setCell(i, 2, data[i][2]); }
var formatter = new google.visualization.NumberFormat();
formatter.format(table, 1);
formatter.format(table, 2);
var datatable = new google.visualization.Table(document.getElementById('table_div'));
datatable.draw(table, {width:500}); }
function drawColumnChart() {
var coldata = new google.visualization.DataTable();
coldata.addColumn('string', 'Date');
coldata.addColumn('number', 'Principal');
coldata.addColumn('number', 'Interest');
coldata.addRows(data.length);
for (var i=0;i<data.length;i++) {
coldata.setValue(i, 0, data[i][0]);
coldata.setValue(i, 1, data[i][1]);
coldata.setValue(i, 2, data[i][2]); }
var formatter = new google.visualization.NumberFormat({prefix:'$'});
formatter.format(coldata, 1);
formatter.format(coldata, 2);
var columnchart = new google.visualization.ColumnChart(document.getElementById('columnchart_div'));
columnchart.draw(coldata, {width:1500, height:700, title:'Cash Flow', isStacked:true,
hAxis:{title:'Date'}, legend:'right', vAxis:{title:'Payment', format:'$#,###'}}); }
function filecheck(){
if (document.getElementById('uploadfield').checked == true) {
document.getElementById('filefield').disabled = false;
document.getElementById('inputdatafield').disabled = true; }
else {
document.getElementById('filefield').disabled = true;
document.getElementById('inputdatafield').disabled = false; } }
</script>
<script>
function home() {window.location="/"; }
function logout() {location.replace("'''+users.create_logout_url("/")+'''"); }
$(document).ready(function() {
$("#tabs").tabs();
$("button").button();
$("#accordion1").accordion({active: false, collapsible: true});
$("#accordion2").accordion({collapsible: true, autoHeight: false}); });
</script>
</head>
<body style="font-size:62.5%;">
<button type="button" onclick="home();");">Back to Home</button>
<button type="button" onclick="logout();");">Sign Out</button>
<div id="accordion1">
<h3><a href="#">Cash Flow Grapher</a></h3>
<div>
<form action="/grapher" enctype="multipart/form-data" method="post">
<table style="text-align:center; font-size:12px;" align="center" cellpadding="5">
<tr><td>Input Data<br />(date, principal, interest)</td><td><textarea id="inputdatafield" name="inputdata" rows="10" cols="50" '''+inputdatadisabled+'''>'''+self.request.get('inputdata')+'''</textarea></td></tr>
<tr><td colspan="2"><p style="font-size:10px;">Every line requires all three values separated by commas. Enter 0 if there is no principal and/or interest.<br />
Make sure there is no extra empty line at end of dataset.</p></td></tr>
<tr><td>Upload CSV File <input id="uploadfield" onclick="filecheck();" type="checkbox" name="upload" value="True" '''+check+''' /></td>
<td><input id="filefield" type="file" name="csv" '''+filedisabled+''' /></td></tr>
<tr><td colspan="2"><p style="font-size:10px;">Must be a CSV file with three columns in this order: date, principal, interest.<br />
Remove all headers and extra data. Remove all number formatting.</p></td></tr>
<tr><td colspan="2"><button type="submit">Graph Cash Flow</button></td></tr>
</table>
</form>
</div>
</div>
<div id="accordion2">
<h3><a href="#">Results</a></h3>
<div>
<div id="tabs">
<ul>
<li><a href="#table_div">Cash Flow</a></li>
<li><a href="#columnchart_div">Time Chart of Cash Flow</a></li>
</ul>
<div id="table_div" align="center"></div><br />
<div id="columnchart_div" align="center"></div>
</div>
</div>
</div>
</body>
</html>
''')
def post(self):
if str(self.request.get('upload')) != 'True':
check = ''
filedisabled = 'disabled'
inputdatadisabled = ''
raw_data = self.request.get('inputdata')
split_line = raw_data.split('\r\n')
split_value = []
for line in split_line:
split_value.append(line.split(', '))
data = []
for line in split_value:
new_line = []
new_line.append(str(line[0]))
new_line.append(float(line[1]))
new_line.append(float(line[2]))
data.append(new_line)
else:
check = 'checked'
filedisabled = ''
inputdatadisabled = 'disabled'
csv = self.request.get('csv')
split_line = csv.split('\r\n')[:-1]
split_value = []
for line in split_line:
split_value.append(line.split(','))
data = []
for line in split_value:
new_line = []
new_line.append(line[0])
new_line.append(float(line[1]))
new_line.append(float(line[2]))
data.append(new_line)
self.response.out.write('''
<html>
<head>
<title>Graphing Results</title>
<style type="text/css">
body {font-family:arial}
</style>
<script type="text/javascript" src="https://www.google.com/jsapi"></script>
<script type="text/javascript ">
google.load('visualization', '1', {packages:['table']});
google.load('visualization', '1', {packages:['corechart']});
google.setOnLoadCallback(drawTable);
google.setOnLoadCallback(drawColumnChart);
var data = '''+str(data)+''';
function drawTable()
{
var table = new google.visualization.DataTable();
table.addColumn('string', 'Date');
table.addColumn('number', 'Principal ($)');
table.addColumn('number', 'Interest ($)');
table.addRows(data.length);
for (var i=0;i<data.length;i++){
table.setCell(i, 0, data[i][0]);
table.setCell(i, 1, data[i][1]);
table.setCell(i, 2, data[i][2]);}
var formatter = new google.visualization.NumberFormat();
formatter.format(table, 1);
formatter.format(table, 2);
var datatable = new google.visualization.Table(document.getElementById('table_div'));
datatable.draw(table, {width:400, page:'enable', pageSize:60});
}
function drawColumnChart()
{
var coldata = new google.visualization.DataTable();
coldata.addColumn('string', 'Date');
coldata.addColumn('number', 'Principal');
coldata.addColumn('number', 'Interest');
coldata.addRows(data.length);
for (var i=0;i<data.length;i++){
coldata.setValue(i, 0, data[i][0]);
coldata.setValue(i, 1, data[i][1]);
coldata.setValue(i, 2, data[i][2]);}
var formatter = new google.visualization.NumberFormat({prefix:'$'});
formatter.format(coldata, 1);
formatter.format(coldata, 2);
var columnchart = new google.visualization.ColumnChart(document.getElementById('columnchart_div'));
columnchart.draw(coldata, {width:1500, height:700, title:'Cash Flow', isStacked:true,
hAxis:{title:'Date'}, legend:'top', vAxis:{title:'Payment', format:'$#,###'}})
}
function filecheck(){
if (document.getElementById('uploadfield').checked == true){
document.getElementById('filefield').disabled = false;
document.getElementById('inputdatafield').disabled = true;}
else {
document.getElementById('filefield').disabled = true;
document.getElementById('inputdatafield').disabled = false;}}
</script>
</head>
<body>
<p><a href="/">Back to Home</a></p>
<h1 style="text-align:center;">Cash Flow Grapher</h1>
<form action="/grapher" enctype="multipart/form-data" method="post">
<table align="center" cellpadding="10">
<tr><th>Input Data<br />(date, principal, interest)</th><th><textarea id="inputdatafield" name="inputdata" rows="10" cols="50" '''+inputdatadisabled+'''>'''+self.request.get('inputdata')+'''</textarea></th></tr>
<tr><td colspan="2"><p style="font-size:10px; text-align:center;">Every line requires all three values separated by commas. Enter 0 if there is no principal and/or interest.<br />
Make sure there is no extra empty line at end of dataset.</p></td></tr>
<tr><th>Upload CSV File <input id="uploadfield" onclick="filecheck();" type="checkbox" name="upload" value="True" '''+check+''' /></th>
<th><input id="filefield" type="file" name="csv" '''+filedisabled+''' /></th></tr>
<tr><td colspan="2"><p style="font-size:10px; text-align:center;">Must be a CSV file with three columns in this order: date, principal, interest.<br />
Remove all headers and extra data from the file. Remove all number formatting.</p></td></tr>
<tr><th colspan="2"><input type="submit" value="Graph Cash Flow" /></th></tr>
</table>
</form>
<br /><hr />
<h1>Results</h1>
<a href="#outputdata">Output Data</a><br />
<a href="#columnchart">Time Series Cash Flow Chart</a><br /><br />
<a name="outputdata"><a href="#top">Back to Top</a></a>
<h2>Cash Flow</h2>
<div id="table_div"></div><br />
<a name="columnchart"><a href="#top">Back to Top</a></a>
<h2>Time Chart of Cash Flow</h2>
<div id="columnchart_div"></div>
</body>
</html>
''')