def get_scores(year, month ,day):
home_teams = []
away_teams = []
home_scores = []
away_scores = []
games = Boxscores(datetime(year, month, day))
all_games = games.games[str(month)+'-'+str(day)+'-'+str(year)]
for game in all_games:
home_teams.append(game['home_name'])
away_teams.append(game['away_name'])
home_scores.append(game['home_score'])
away_scores.append(game['away_score'])
name = str(year)+"_"+str(month)+"_"+str(day)+"_scores.csv"
with open(name, 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(["home_team","away_team","home_score","away_score"])
for i,team in enumerate(home_teams):
writer.writerow([home_teams[i],away_teams[i],home_scores[i],away_scores[i]])
path = os.getcwd()
os.rename(path+"\\"+name, path+"\\"+"scores\\"+name)
return home_teams,away_teams,home_scores,away_scores
def _force(self):
if config.sections["CALCULATOR"].force:
num_forces = np.array(pt.shared_arrays['a'].num_atoms)*3
if pt.shared_arrays['configs_per_group'].testing:
testing = -1 * np.sum(num_forces[-pt.shared_arrays['configs_per_group'].testing:])
else:
testing = 0
a, b, w = self._make_abw(pt.shared_arrays['a'].force_index, num_forces.tolist())
config_indicies,energy_list,force_list,stress_list = self._config_error()
if config.sections["SOLVER"].detailed_errors and self.weighted == "Unweighted":
from csv import writer
true, pred = b, a @ self.fit
if pt.shared_arrays['configs_per_group'].testing:
ConfigType = ['Training'] * (
np.shape(true)[0] - np.sum(num_forces[-pt.shared_arrays['configs_per_group'].testing:])) + \
['Testing'] * (np.sum(num_forces[-pt.shared_arrays['configs_per_group'].testing:]))
else:
ConfigType = ['Training'] * np.shape(true)[0]
with open('detailed_force_errors.dat', 'w') as f:
writer = writer(f, delimiter=' ')
writer.writerow(['FileName Type True-Ref Predicted-Ref Difference(Pred-True)'])
writer.writerows(zip(force_list,ConfigType, true, pred, pred-true))
self._errors([[0, testing]], ['*ALL'], "Force", a, b, w)
if testing != 0:
self._errors([[testing, 0]], ['*ALL'], "Force_testing", a, b, w)
def _energy(self):
if config.sections["CALCULATOR"].energy:
testing = -1 * pt.shared_arrays['configs_per_group'].testing
a, b, w = self._make_abw(pt.shared_arrays['a'].energy_index, 1)
pt.single_print(np.shape(a))
config_indicies, energy_list, force_list, stress_list = self._config_error(
)
self._errors([[0, testing]], ['*ALL'], "Energy", a, b, w)
if config.sections[
"SOLVER"].detailed_errors and self.weighted == "Unweighted":
from csv import writer
true, pred = b, a @ self.fit
ConfigType = ['Training'] * (np.shape(true)[0]-pt.shared_arrays['configs_per_group'].testing) + \
['Testing'] * (pt.shared_arrays['configs_per_group'].testing)
with open('detailed_energy_errors.dat', 'w') as f:
writer = writer(f, delimiter=' ')
writer.writerow([
'FileName Type True-Ref Predicted-Ref Difference(Pred-True)'
])
writer.writerows(
zip(energy_list, ConfigType, true, pred, pred - true))
if testing != 0:
self._errors([[testing, 0]], ['*ALL'], "Energy_testing", a, b,
w)
def create_csv_file(self, writer, word=None):
'''takes a CSV writer as parameter'''
if self.n > 0:
writer.writerow([word, self.n])
if self.d:
for node in self.d:
self.d[node].create_csv_file(writer, node)
def write_to_csv(logfile, smoothed_value, threshold_flag):
# open csv file to record data
f = open(logfile, "a")
writer = writer(f)
record_time = datetime.now().strftime("%X")
writer.writerow([record_time, smoothed_value, threshold_flag])
f.close()
def csv(self, response, archive, options, **params):
response.setHeader('Content-Type', 'application/vns.ms-excel')
response.setHeader('Content-Disposition',
'attachment; filename=events.csv')
from csv import writer
writer = writer(response)
wroteHeader = False
for fields, evt in self._query(archive, **params):
data = []
if not wroteHeader:
writer.writerow(fields)
wroteHeader = True
details = evt.get(DETAILS_KEY)
for field in fields:
val = evt.get(field, '')
if field in ("lastTime", "firstTime", "stateChange") and val:
val = self._timeformat(val, options)
elif field == DETAILS_KEY and val:
# ZEN-ZEN-23871: add all details in one column
val = json.dumps(val)
elif not (val or val is 0) and details:
# ZEN-27617: fill in value for requested field in details
val = details.get(field, '')
data.append(
str(val).replace('\n', ' ').strip()
if (val or val is 0) else ''
)
writer.writerow(data)
def top_up(account, ammount):
### function tops up a selected account###
# shutil to merge temp into the old file
import shutil
import csv
filename = "bank.csv"
temp = "temp_bank.csv"
with open(filename, "r") as csvFile:
reader = csv.DictReader(csvFile)
with open(temp, "w") as temp:
fieldnames = ["acc_name", "balance"]
writer = csv.DictWriter(temp, fieldnames=fieldnames)
writer.writeheader()
for line in reader:
if line["acc_name"] == str(account):
line["balance"] = int(line["balance"]) + int(ammount)
writer.writerow(line)
else:
writer.writerow(line)
shutil.move(temp.name, filename)
def csv(self, response, archive, options, **params):
response.setHeader('Content-Type', 'application/vns.ms-excel')
response.setHeader('Content-Disposition',
'attachment; filename=events.csv')
from csv import writer
writer = writer(response)
wroteHeader = False
for fields, evt in self._query(archive, **params):
data = []
if not wroteHeader:
writer.writerow(fields)
wroteHeader = True
details = evt.get(DETAILS_KEY)
for field in fields:
val = evt.get(field, '')
if field in ("lastTime", "firstTime", "stateChange") and val:
val = self._timeformat(val, options)
elif field == DETAILS_KEY and val:
# ZEN-ZEN-23871: add all details in one column
val = json.dumps(val)
elif not (val or val is 0) and details:
# ZEN-27617: fill in value for requested field in details
val = details.get(field, '')
data.append(
str(val).replace('\n', ' ').strip() if (
val or val is 0) else '')
writer.writerow(data)
def open_csv_first(logfile):
# open csv file to record data
if not path.exists(logfile, ):
f = open(logfile, "a")
writer = csv.writer(f)
writer.writerow(["Time", "Value", "Over Threshold"])
f.close()
def set_data(self):
with open(self.filename, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=self.data[0].headers)
writer.writeheader()
for el in self.data:
writer.writerow(el.data)
def AddProductToProductList():
print("fuckit")
print(ProductNameEntry.get())
print(productPriceEntry.get())
with open('productlist.csv', 'a+', newline='') as file:
writer = csv.writer(file)
row = [ProductNameEntry.get(), productPriceEntry.get()]
writer.writerow(row)
def writeNewLinksCsv(self, newLink):
with open(self.wd + "barchartLinks.csv", 'a+', newline='') as linksCSV:
writer = csv.writer(linksCSV, delimiter=',')
if newLink not in self.csvLinks:
writer.writerow(
[self.indexId, newLink,
self.title]) #str(newLinks[i]).split(self.subDomain)[1]])
self.indexId += 1
def export_selected_objects(self, request, queryset): response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename="reps.csv"' writer = csv.writer(response) writer.writerow(['Name', 'Company', 'Alumni', 'Present']) for rep in queryset.all(): writer.writerow([rep.rep, rep.company, rep.is_alumni, rep.is_present]) return response
def UpdateAllProducts():
print('Update all products')
with open('productlist.csv', 'w', newline='') as file:
writer = csv.writer(file)
for name, price in zip(ManageProductsList, ManageProductsPriceList):
print(name.get(), price.get())
if(name.get() != ''):
writer.writerow([name.get(), price.get()])
def append_dict_as_row(csv_file, hours_data, fields):
try:
with open(csv_file, 'a+', newline='') as csvfile:
writer = DictWriter(csvfile, fieldnames=fields)
writer.writeheader()
for data in hours_data:
writer.writerow(data)
except IOError:
print("I/O error")
def export_selected_objects(self, request, queryset):
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="reps.csv"'
writer = csv.writer(response)
writer.writerow(['Name', 'Company', 'Alumni', 'Present'])
for rep in queryset.all():
writer.writerow(
[rep.rep, rep.company, rep.is_alumni, rep.is_present])
return response
def out_put_symbols_csv():
"""
Outpur symbols to symbols.csv
"""
from csv import writer
with open("symbols.csv", "w") as output_file:
writer = writer(output_file)
writer.writerow(["name", "symbol"])
for name, symbol in SYMBOLS.items():
writer.writerow([name, symbol])
def save():
with open(output_path, "a", newline='') as f:
writer = csv.writer(f)
position = mylist
xr, yr = position[0][0], position[0][1]
xl, yl = position[1][0], position[1][1]
list_of_elem = [counter, xr, yr, xl, yl]
writer.writerow(list_of_elem)
print("saved ", counter)
def add_details(Subject_ID, Subject_Name, Modules, SLO, Expected_Outcome):
df = pd.read_csv("Data.csv")
columns = list(df.head(0))
ID = int(columns[-1]) + 1
df[ID] = ""
df.to_csv("Data.csv", index=False)
fields = [ID, Subject_ID, Subject_Name, Modules, SLO, Expected_Outcome]
with open('Subjects.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(fields)
def windDataLogging(windDirRaw, windDirNow, windDirAvg):
with open('wind.csv', 'a', newline='') as f:
writer = csv.writer(f)
writer.writerow([
time.time(), windDirRaw,
round(windDirNow, 2),
round(windDirAvg, 2)
])
return (True)
def __call__(self, value, system):
fout = StringIO()
writer = UnicodeWriter(fout, quoting=QUOTE_ALL)
writer.writerow(value['header'])
writer.writerows(value['rows'])
resp = system['request'].response
resp.content_type = 'text/csv'
resp.content_disposition = 'attachment;filename="report.csv"'
return fout.getvalue()
def csv_message(message, state='error', url=None, code=200):
keys = ['message', 'state', 'url']
response = Response(mimetype='text/csv', status=code)
writer = DictWriter(response.stream, keys)
writer.writerow(dict(zip(keys, keys)))
writer.writerow({'message': message.encode('utf-8'),
'state': state,
'url': url.encode('utf-8') if url else ''})
if url is not None:
response.headers['Location'] = url
return response
def billScrape(soup, writer, billPage):
sponsor_text = ''
for sponsor in soup.find_all("table", class_="standard01"):
sponsor_text = sponsor.find("a", target="_blank").get_text()
#print(sponsor_text)
title_text = ''
for title in soup.find_all("h1", class_="legDetail"):
title_text = title.get_text()
#print(title_text)
name_text = ''
for name in soup.find_all("h2", class_="primary"):
# name_text = name.get_text()
name_text = name.contents[1]
print(name_text)
#for tracker in soup.find_all("li", class_="first selected last"):
#tracker_text = tracker.get_text()
#print(tracker_text)
tracker2_text = ''
for tracker2 in soup.find_all("h3", class_="currentVersion"):
tracker2_text = tracker2.find("span").get_text()
# print(tracker2_text)
index = billPage.find('?')
# print(billPage[0:index-1]+'/text?format=txt')
billTextUrl = billPage[0:index - 1] + '/text?format=txt'
billTextGet = requests.get(billTextUrl)
soupBillText = BeautifulSoup(billTextGet.text, 'html.parser')
# billTextLink = [0]
# for billTextUrl in soup.find_all("ul", _class="cdg-summary-wrapper-list"):
# billTextUrl2 = billTextUrl.find("a", href=True)
# billTextLink = billTextUrl2['href']
# print(billTextLink)
# billTextSearch = requests.get(billTextLink)
# soupBillText = BeautifulSoup(billTextSearch.text, 'html.parser')
writer.writerow([name_text, sponsor_text, title_text, tracker2_text])
billText = soupBillText.find('pre', id='billTextContainer')
if billText is not None:
billText2 = billText.get_text()
BillTxt = open(name_text + ".txt", "a")
BillTxt.write(billText2)
BillTxt.close()
def register_student(self, list_of_elem):
with open('student_info.csv', 'a+', newline='') as write_obj:
fieldnames = ['ID', 'Name', 'Amount', 'Amount Remaining']
writer = csv.DictWriter(write_obj,
fieldnames=fieldnames,
delimiter='|')
writer.writeheader()
writer.writerow({
'ID': list_of_elem[0],
'Name': list_of_elem[1],
'Amount': list_of_elem[2],
'Amount Remaining': list_of_elem[3]
})
def csv_message(message, state='error', url=None, code=200):
keys = ['message', 'state', 'url']
response = Response(mimetype='text/csv', status=code)
writer = DictWriter(response.stream, keys)
writer.writerow(dict(zip(keys, keys)))
writer.writerow({
'message': message.encode('utf-8'),
'state': state,
'url': url.encode('utf-8') if url else ''
})
if url is not None:
response.headers['Location'] = url
return response
def log_stock_pick_CSV(data):
fields = [
'date',
'stock',
'Adj Close',
]
fileName = config.STOCK_RESULTS
try:
with open(fileName, 'a+', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fields)
# writer.writeheader()
writer.writerow(data)
except Exception as e:
print("Exception in log_stock_pick: " + e)
def write_rows(writer, rows):
'''Write a batch of row data to the csv writer'''
for row in rows:
try:
writer.writerow(row)
except UnicodeEncodeError: # pragma: no cover
# Python 2 csv does badly with unicode outside of ASCII
new_row = []
for item in row:
if isinstance(item, text_type):
new_row.append(item.encode('utf-8'))
else:
new_row.append(item)
writer.writerow(new_row)
def button(update, context):
query = update.callback_query
if query.data == "1":
record = ["spam", SMS]
with open('dataset/bot_dataset.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(record)
elif query.data == "0":
record = ["ham", SMS]
with open('dataset/bot_dataset.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(record)
print(record)
query.edit_message_text(text="Thanks for your help! You're helping me to become the best version of myself")
def add_user_data_to_csv(
file,
mix,
email,
):
""" used to append test data one wants to keep to a csv file """
with open(file, 'a', newline='') as csvfile:
fieldnames = ['DATE', 'MIX', 'EMAIL']
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
dialect="excel")
today = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
writer.writerow({'DATE': today, 'MIX': mix, 'EMAIL': email})
csvfile.close()
def del_users(first, last):
global count
with open('users.csv') as file:
reader = csv.reader(file)
rows = list(reader)
with open('users.csv', 'w') as file:
writer = csv.writer(file)
for row in rows:
if row[0] == first and row[1] == last:
count += 1
else:
writer.writerow(row)
return f'Users updated: {count}.'
def main():
pgtoken = "0"
# countrylist = []
# sessionlist = []
# clean csv if exist as used append function to do one by one export
clean = []
with open('venv/Scripts/pagination1.csv', 'w', newline='') as myfile:
writer = csv.writer(myfile)
writer.writerows(clean)
while pgtoken is not None:
print(pgtoken)
analytics = initialize_analyticsreporting()
response = get_report(analytics, pgtoken)
headerlist, cdict, sdict = print_response(response)
print(cdict, sdict)
# for i in range(len(cdict.keys())):
# col = list(cdict.values())[i]
# print(col)
# col1=list(cdict.values())[0]
# col2 = list(cdict.values())[1]
# col3 = list(cdict.values())[2]
# accumulate data if export all record to csv
# countrylist.append(c)
# sessionlist.append(s)
# store_response =(zip(cdict.values(),slist))
# # # store_response[pgtoken] = newdict
# print(store_response)
if pgtoken == "0":
with open('venv/Scripts/pagination1.csv', 'a',
newline='') as myfile:
writer = csv.writer(myfile)
writer.writerow(headerlist)
writer.writerows(zip(*cdict.values(), *sdict.values()))
else:
with open('venv/Scripts/pagination1.csv', 'a',
newline='') as myfile:
writer = csv.writer(myfile)
writer.writerows(zip(*cdict.values(), *sdict.values()))
pgtoken = response['reports'][0].get(
'nextPageToken') # update the pageToken
def scrape(url):
page = requests.get(url)
tree = html.fromstring(page.content)
title2 = str(lxml.html.parse(url).find(".//title").text)
title2 = title2.replace('-' + title2.split("-", 1)[1], '')
price = tree.xpath("//span[@itemprop='price']//text()")
i = 0
for span in tree.cssselect('span'):
clas = span.get('class')
rel = span.get('rel')
if clas == "packaging-des":
if rel != None:
if i == 0:
weight = rel
elif i == 1:
dim = str(rel)
i = i + 1
weight = weight
height = dim.split("|", 3)[0]
length = dim.split("|", 3)[1]
width = dim.split("|", 3)[2]
# Sometimes aliexpress doesn't list a price
# This dumps a 0 into price in that case to stop the errors
if len(price) == 1:
price = float(str(price[0]))
elif len(price) == 0:
price = int(0)
for inpu in tree.cssselect('input'):
if inpu.get("id") == "hid-product-id":
sku = inpu.get('value')
for meta in tree.cssselect('meta'):
name = meta.get("name")
prop = meta.get("property")
content = meta.get('content')
if prop == 'og:image':
image = meta.get('content')
if name == 'keywords':
keywords = meta.get('content')
if name == 'description':
desc = meta.get('content')
listvar = (
[str(title2), str(name), '', '', str(desc), 'publish', '', '', '0', '1', 'open', str(sku), 'no', 'no', 'visible',
'', 'instock', 'no', 'no', str(price * 2), str(price * 1.5), str(weight), str(length), str(width), str(height),
'taxable', '', '', '', 'no', '', '', '', '', '', '', '', '', '', str(keywords), str(image), '', 'simple', '', '',
'', '0', '', '', '', '', '', '', '', ''])
with open("output.csv", 'ab') as f:
writer = csv.writer(f)
writer.writerow(listvar)
def csv(self, response, archive, **params):
response.setHeader('Content-Type', 'application/vns.ms-excel')
response.setHeader('Content-Disposition', 'attachment; filename=events.csv')
from csv import writer
writer = writer(response)
wroteHeader = False
for fields, evt in self._query(archive, **params):
if not wroteHeader:
writer.writerow(fields)
wroteHeader = True
data = []
for field in fields:
val = evt.get(field, '')
data.append(str(val).replace('\n',' ').strip() if val or val is 0 else '')
writer.writerow(data)
def csv(self, response, archive, **params):
response.setHeader('Content-Type', 'application/vns.ms-excel')
response.setHeader('Content-Disposition', 'attachment; filename=events.csv')
from csv import writer
writer = writer(response)
wroteHeader = False
for fields, evt in self._query(archive, **params):
if not wroteHeader:
writer.writerow(fields)
wroteHeader = True
data = []
for field in fields:
val = evt.get(field, '')
data.append(str(val).replace('\n',' ').strip() if val or val is 0 else '')
writer.writerow(data)
def write_result(self):
if self.result:
from csv import writer
with open(self.output, 'w') as bench_file:
writer = writer(bench_file)
writer.writerow(
['Method', 'Dataset', 'Cat', 'Size.B', 'Memory.KB',
'Elapsed.s', 'User.s', 'System.s'])
writer.writerows(self.result)
# Move obtained results to the result/ directory
make_path('result')
for file in os.listdir(current_dir):
if file.endswith('.csv') or file.endswith('.svg') or \
file.endswith('.pos') or file.endswith('.fil'):
shutil.copy(file, 'result/')
remove_path(file)
def csv(response, devices, fields):
response.setHeader('Content-Type', 'application/vns.ms-excel')
response.setHeader('Content-Disposition', 'attachment; filename=devices.csv')
from csv import writer
writer = writer(response)
writer.writerow(fields)
for device in devices:
data = []
for field in fields:
value = device.get(field, '')
if isinstance(value, list):
value = "|".join([v.get('name') for v in value])
if isinstance(value, dict):
value = event(value) if field == 'events' else value.get('name')
if not (value or value is 0):
value = ''
data.append(str(value).strip())
writer.writerow(data)
def stats(self, data):
"""Produce stats for count of lamps and densities. """
from csv import writer
p = self.partitions.find_or_new(table='streetlights')
p.database.attach(neighborhoods,'nb')
name = data['name']
# The areas are in square feet. WTF?
feetperm = 3.28084
feetperkm = feetperm * 1000
with open(self.filesystem.path('extracts',name), 'wb') as f:
writer = writer(f)
writer.writerow(['count', 'neighborhood','area-sqft','area-sqm','area-sqkm', 'density-sqkm',])
for row in p.database.query("""
SELECT count(streetlights_id) as count, objectid, cpname, shape_area
FROM streetlights, {nb}.communities
WHERE streetlights.neighborhood_id = {nb}.communities.objectid
GROUP BY {nb}.communities.objectid
"""):
n = float(row['count'])
area = float(row['shape_area'])
writer.writerow([
n,
row['cpname'].title(),
area,
area / (feetperm * feetperm),
area / (feetperkm * feetperkm),
n / (area / (feetperkm * feetperkm))
])
foundTargetGrammar = True
if foundTargetGrammar and currGrammar != targetGrammar:
foundAllTargetSentences = True
i += 1
# Set up the sample learner and dictionary used to record
# which sentences trigger which parameters
sentenceParameterTriggers = defaultdict(lambda: [])
sampleLearner = Child()
oldGrammar = [0,0,0,0,0,0,1,0,0,0,1,0,1]
# After processing each sentence, the sample learner's
# grammar will be compared to the old one. Differences will
# be noted and the sentence that caused the change along with the
# morphed parameter will be added to the dictionary
for sentence in selectedSentences:
sentenceStr = sentence.rsplit('\t', 3)[2]
sampleLearner.consumeSentence(sentence)
sampleLearner.setParameters()
for i, parameter in enumerate(oldGrammar):
if parameter != sampleLearner.grammar[i] and (not 'p{}'.format(i+1) in sentenceParameterTriggers[sentenceStr]):
sentenceParameterTriggers[sentenceStr].append('p{}'.format(i+1))
oldGrammar = [0,0,0,0,0,0,1,0,0,0,1,0,1]
# The output file will opened and the corresponding
# sentences and parameters added line by line
with open(outputFile, 'a+') as outFile:
writer = writer(outFile)
for key in sentenceParameterTriggers:
writer.writerow((key, sentenceParameterTriggers[key]))
month,
day,
date(year,month,day).weekday(),
hour,
minute,
second))
writefile = 'texting_database.csv'
with open( writefile, 'w' ) as f:
writer = writer(f)
writer.writerow(('ID',
'Phone Number',
'Name',
'Sent',
'Year'
'Month'
'Day',
'Day of Week',
'Hour',
'Minute',
'Second'))
for (index,text) in enumerate(database):
entry = database[index]
writer.writerow(entry)
#phonebook = {}
#for text in body:
# #build up the phonebook with numbers and associated texts
# x = sms.clean_phone(sms.get_phone(text))
# z = sms.get_name(text)
# if x not in phonebook:
def PeakFit(separation, array, fileName):
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from math import floor
from csv import writer
from os import path
from time import strftime, localtime
x = array[:,0] # 2-theta or radial distance. Refer to Fit2D output (chi plot).
y = array[:,1] # intensity from Fti2D output (chi plot).
done = False
while not done:
n = int(raw_input('Please enter the number of peaks to fit: ')) #ADD RAISE EXCEPTION FOR DATA OUT OF BOUND ???????
if(len(x) < n * separation):
print ('Index out of bound. Input a smaller value.')
else:
done = True
# sigma = int(raw_input('Please the half maximum width of peaks to fit: '))
center = [] # placeholders
height = []
popt_array = [["center","height","baseline"]]
for i in range(n):
center.append(x[separation * (i + 1) - i/6]) # Defining starting point for peak fits.
height.append(y[separation * (i + 1)- i/6]) # Defining starting point for peak fits.
# print center # For Debugging.
# print height # For Debugging.
print '%15s %15s %15s'%("mu","height","baseline")
plt.figure()
for i in range(n):
xdata = x[i*separation + int(floor(0.5 * separation)) - i/6 : (i + 2) * separation - int(floor(0.5 * separation))- i/6] # defining the X-range of the peak.
ydata = y[i*separation + int(floor(0.5 * separation)) - i/6 : (i + 2) * separation - int(floor(0.5 * separation))- i/6] # defining the Y-range of the peak.
par = [center[i], height[i], 50] # Random initialization of Gaussian fit function.
# The 4 parameters correspond to sigma, center, A and C in function definition in line 88.
popt, pcov = curve_fit(GaussianModel,xdata, ydata, par) # popt is the optimum set of parameters for the Gaussian fit.
# pcov is the covariance of the parameters. (not using in our case)
popt_array.append(popt) # writing popt to list to make a csv file with fit parameters and data.
print "%15.10f %15.10f %15.10f" % (popt[0],popt[1],popt[2])
plt.plot(xdata, ydata) # Plotting raw data.
plt.plot(np.arange(min(xdata),max(xdata),0.001), GaussianModel(np.arange(min(xdata),max(xdata),0.001), *popt)) # Plotting fit data.
# Saving peak fit plot and raw peak plot as overlaid png.
plt.savefig(fileName+'peak'+'.svg')
plt.show()
# plt.close()
outFileName = 'fitted_'+ fileName + '_' + strftime("%b%d%H%M",localtime()) +'.csv'
#
with open(outFileName, 'wb') as outfile:
writer = writer(outfile) # These lines are for writing out a csv file after removing the header from the chiplot.
for row in popt_array:
writer.writerow(row)
print 'The fit data has been output to ' + outFileName + ' in current directory.'