def test_init_with_id(self):
html = """
<body>
<table id='wanted'>
<tr>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td>3</td>
<td>4</td>
</tr>
</table>
<table id='unwanted'>
<tr>
<td>unwanted</td>
</tr>
</table>
</body>
"""
soup = BeautifulSoup(html, 'html.parser')
extractor = Extractor(soup, id_='wanted').parse()
self.assertEqual(
extractor.return_list(),
[[u'1', u'2'], [u'3', u'4']]
)
def test_tune_parameter(f, numbers):
"""
:param f: the category like 10th has 'A' while 'B' is for science etc. given as first letter of the seat number
:param numbers: the remaining 7 letters of the seat numbers all provided in a list e.g:[3413535,1355151,3153153..]
:return: None . This function is just used to tune the values in the main function so we can get indexes of marks
etc as table structure may change.
"""
goto('http://www.gseb.org/')
for loop in numbers:
driver.find_element_by_xpath(
"//select[@name='drpInitChar']/option[text()='" + f +
"']").click()
username = driver.find_element_by_name("studentnumber")
seat_no = loop
username.send_keys(seat_no)
go = driver.find_element_by_name("go")
go.click()
time.sleep(1)
driver.switch_to.frame("marksheet")
soup = BeautifulSoup(driver.page_source)
table = soup.find("table", attrs={"class": "maintbl"})
extractor = Extractor(table)
extractor.parse()
l = extractor.return_list()
print(l)
class TestComplexExtractor(unittest.TestCase):
def setUp(self):
html = """
<table>
<tr>
<td rowspan=2>1</td>
<td>2</td>
<td>3</td>
</tr>
<tr>
<td colspan=2>4</td>
</tr>
<tr>
<td colspan=3>5</td>
</tr>
</table>
"""
self.extractor = Extractor(html)
self.extractor.parse()
def test_return_list(self):
self.assertEqual(
self.extractor.return_list(),
[[u'1', u'2', u'3'], [u'1', u'4', u'4'], [u'5', u'5', u'5']]
)
def get_gseb_results_10(numbers, output_file_path, subjects):
goto('http://gseb.org/indexssc.html')
df = pd.DataFrame()
for loop in numbers:
detail = {}
username = driver.find_element_by_name("studentnumber")
seat_no = loop
detail['seat_no'] = seat_no
username.send_keys(seat_no)
go = driver.find_element_by_name("go")
go.click()
time.sleep(1)
driver.switch_to.frame("marksheet")
soup = BeautifulSoup(driver.page_source)
table = soup.find("table", attrs={"class": "maintbl"})
extractor = Extractor(table)
extractor.parse()
l = extractor.return_list()
detail['Name'] = find(l[0][0], "Name: ")
detail['result'] = find(l[1][0],
"Result: ").strip('School Index: 55.224')
for i in range(len(subjects)):
detail[subjects[i] + ' in external'] = l[i + 3][1]
detail[subjects[i] + ' in internal'] = l[i + 3][2]
detail[subjects[i] + ' in total'] = l[i + 3][3]
detail[subjects[i] + ' grade'] = l[i + 3][4]
detail['Total'] = int(l[9][1].split()[0])
detail['Overall Grade'] = l[17][0]
detail['Percentile Rank'] = l[17][2]
df = df.append(detail, ignore_index=True)
driver.switch_to_default_content()
driver.find_element_by_name("studentnumber").clear()
print(df)
df.to_csv(output_file_path, index=False)
driver.close()
def _fetch_air_quality_routine(self, day: datetime):
"""
Populate the air quality of the provinces
Fetches data from `http://www.arpab.it/aria/qa.asp`
:param day: The day of which the air quality wants to be known (instance of `~datetime`)
"""
super()._fetch_air_quality_routine(day)
res = requests.get('http://www.arpab.it/aria/qa.asp',
params=[
('giorno', day.strftime('%d/%m/%Y'))
]
)
soup = BeautifulSoup(res.text, 'html.parser')
table = soup.select_one('.tabellenav')
if table is not None:
extractor = Extractor(table)
extractor.parse()
table_data = extractor.return_list()[1:]
for province in self.provinces:
province_rows = [x for idx, x in enumerate(table_data)
if idx in self.province_stations[province.short_name]]
for indicator, key in self.indicator_map.items():
values = [self.extract_float(x[key]) for x in province_rows
if self.extract_float(x[key]) is not None]
if len(values) > 0:
setattr(province.quality, indicator, round(mean(values), 2))
if self.on_quality_fetched is not None: self.on_quality_fetched(self)
def get_text_data(html):
extractor = Extractor(html)
extractor.parse()
table = extractor.return_list()
del table[0]
for i in range(len(table)):
a = table[i]
del a[0]
del a[3]
return table
def parseTable(self, table):
self.parseHeader(table)
# skip if header not found
if self.headers == []:
return []
# parse the table, split merged cell and fill in value
extractor = Extractor(str(table), transformer=unicode)
extractor.parse()
raw_data = extractor.return_list()
# fill the empty cell by previous row value
for i in xrange(0, len(raw_data)):
for j in xrange(0, len(raw_data[i])):
if raw_data[i][j].strip() == '':
raw_data[i][j] = raw_data[i - 1][j]
# select rows if there time qualifier appeared
raw_data = [
a for a in raw_data
if (''.join(a)).find(u'上午') != -1 or (''.join(a)).find(u'下午') != -1
]
# assigning columns with header as key
data = []
# printJson( self.headers )
for i in xrange(0, len(raw_data)):
data.append({})
raw_data[i] = self.unique(raw_data[i]) # handle colspan here
if len(self.headers) != len(
raw_data[i]
): # skip unformat row, likely to be the ending row
continue
for j in xrange(0, len(raw_data[i])):
data[i][self.headers[j]] = raw_data[i][j]
# mergeing cells content if the case no. is the same
content = []
for i in xrange(0, len(data)):
if not (u'案件號碼' in data[i] or u'案件編號' in data[i]):
continue
if content == [] or \
( u'案件號碼' in data[i] and content[-1][u'案件號碼'].strip() != data[i][u'案件號碼'].strip() and content[-1][u'案件號碼'].strip().strip(u'─') != '' ) or \
( u'案件編號' in data[i] and content[-1][u'案件編號'].strip() != data[i][u'案件編號'].strip() and content[-1][u'案件編號'].strip().strip(u'─') != '' ):
content.append(data[i])
else:
#print json.dumps(content[-1], ensure_ascii=False, indent=4)
#print json.dumps(data[i], ensure_ascii=False, indent=4)
for k, v in data[i].iteritems():
if content[-1][k] != data[i][k]:
content[-1][k] += data[i][k]
# done
return content
def parseHeader(self, table):
if "案件編號" in str(table) or "案件號碼" in str(table):
extractor = Extractor(str(table), transformer=unicode)
extractor.parse()
headerTable = extractor.return_list()
for i in xrange(0, len(headerTable)):
if (''.join(headerTable[i])).find(u'案件號碼') != -1 or (''.join(
headerTable[i])).find(u'案件編號') != -1:
self.headers = headerTable[i]
self.headers = self.unique(self.headers)
for i in xrange(0, len(self.headers)):
self.headers[i] = re.sub('[A-Za-z]', '',
self.headers[i]).strip()
def get_gseb_results_12(f, seat_numbers, output_file_path, subjects):
"""
:param f: The category of student in which he belongs which is metioned as first letter of seat number like='A' for std.10
:param seat_numbers: The list of seat numbers of all stuents e.g.: [3513611,3136144,7724523]
:param output_file_path: file address where the output result sheet is to be stored e.g. "D:/10_results/result.csv"
:param subjects: the list of subjects e.g. ['English','Maths',...]
:return: Nothing but csv is saved in the same folder
"""
goto('http://www.gseb.org/')
df = pd.DataFrame()
temp = []
for loop in seat_numbers:
detail = {}
driver.find_element_by_xpath(
"//select[@name='drpInitChar']/option[text()='" + f +
"']").click()
username = driver.find_element_by_name("studentnumber")
seat_no = loop
detail['seat_no'] = seat_no
username.send_keys(seat_no)
go = driver.find_element_by_name("go")
go.click()
time.sleep(1)
driver.switch_to.frame("marksheet")
soup = BeautifulSoup(driver.page_source)
table = soup.find("table", attrs={"class": "maintbl"})
extractor = Extractor(table)
extractor.parse()
l = extractor.return_list()
detail['Name'] = find(l[0][0], "Name: ")
detail['result'] = find(l[2][0],
"Result: ").strip('School Index: 27.109')
for i in range(len(subjects)):
ttt = 5 - int(l[4 + i][0][-5:].find(
re.findall(r"[A-Z]", l[4 + i][0][-5:])[0]))
detail[subjects[i] + ' in total'] = l[4 + i][0][-5:-ttt]
detail[subjects[i] + ' grade'] = l[4 + i][0][-ttt:]
detail['Total'] = int(l[11][0][-3:])
detail['Overall Grade'] = find(l[1][0], 'Grade: ')
detail['Percentile Rank'] = find(
l[1][0], 'Percentile: ').split()[0].strip('Grade:')
temp.append(detail)
driver.switch_to_default_content()
driver.find_element_by_name("studentnumber").clear()
df = df.append(temp, ignore_index=True)
print(df)
df.to_csv(output_file_path, index=False)
driver.close()
def get_low_currency_price_banks(currency):
url = f'https://www.findrate.tw/{currency}/#.XRjSkVMkveR'
result = requests.get(url)
result.encoding = 'UTF-8'
html = BeautifulSoup(result.text, 'html.parser')
tables = html.findAll('table')
extractor = Extractor(tables[1])
extractor.parse()
currency_table = extractor.return_list()
columns = currency_table.pop(0)[1:]
indices = [data.pop(0).rstrip() for data in currency_table]
df = pandas.DataFrame(currency_table, columns=columns, index=indices)
top8_cash_sell = df.sort_values(['現鈔賣出']).iloc[:8]
top8_spot_sell = df.sort_values(['即期賣出']).iloc[:8]
return (top8_cash_sell, top8_spot_sell)
def main():
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('-u', nargs=1, default=None, help='URL')
parser.add_argument('-f', nargs=1, default=None, help='HTML Content Input File')
parser.add_argument('-o', nargs=1, default=None, help='Output File')
parser.add_argument('-s', nargs=1, default=None, help='SKU Outline')
parser.add_argument('-t', nargs=1, default=None, help='Which table in order of appearance to pull')
args = parser.parse_args()
url = args.u[0]
sku = args.s[0]
if url:
xhtml = url_get_contents(url).decode('utf-8')
else:
xhtml = readFile('sample.html')
soup = BeautifulSoup(xhtml, 'lxml')
tables = soup.find_all('table', attrs={"class":'chart'})
cleanTables = []
print("{} tables found".format(len(tables)))
print("==== Table Information =====")
for i, table in enumerate(tables):
extractor = Extractor(table).parse()
table = extractor.return_list()
print("Table: {} Rows: {} Cols: {}".format(i, len(table), len(table[0])))
cleanTables.append(table)
try:
tableIndex = int(args.t[0])
except:
tableIndex = int(input("Which table do you want to process with SKU: {}\n".format(sku)))
products = processTable(cleanTables[tableIndex], sku)
outputProductSkus(products)
print('='*64)
print('{}Now without those pesky duplicates'.format(' '*16))
print('='*64)
products = combineWeightRanges(products)
outputProductSkus(products)
class TestExtractorTransformer(unittest.TestCase):
def setUp(self):
html = """
<table>
<tr>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td>3</td>
<td>4</td>
</tr>
</table>
"""
self.extractor = Extractor(html, transformer=int)
self.extractor.parse()
def test_config_transformer(self):
self.assertEqual(self.extractor.return_list(), [[1, 2], [3, 4]])
def data_table(s):
"""[summary]
Args:
s ([type]): [description]
"""
table_doc = s.find_all('table')
extractor = Extractor(table_doc[0])
extractor = extractor.parse()
tabla = extractor.return_list()
tabla_columns = tabla[0]
tabla_datos = tabla[1:]
final = []
for fila in tabla_datos:
for element in fila:
a = [w.strip() for w in fila]
final.append(a)
df = pd.DataFrame(final, columns=tabla_columns)
return (df)
class TestSimpleExtractor(unittest.TestCase):
def setUp(self):
html = """
<table>
<tr>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td>3</td>
<td>4</td>
</tr>
</table>
"""
self.extractor = Extractor(html)
self.extractor.parse()
def test_return_list(self):
self.assertEqual(self.extractor.return_list(),
[[u'1', u'2'], [u'3', u'4']])
def _fetch_air_quality_routine(self, day: datetime):
"""
Populate the air quality of the provinces
Fetches data from `http://www.arpa.veneto.it/arpavinforma/bollettini/aria/aria_dati_validati_storico.php`
:param day: The day of which the air quality wants to be known (instance of `~datetime`)
"""
super()._fetch_air_quality_routine(day)
for province in self.provinces:
data = {
'provincia': province.name.lower(),
'giorno': day.strftime('%d'),
'mese': day.strftime('%m'),
'anno': day.strftime('%Y'),
'Vai': 'Visualizza il bollettino'
}
response = requests.post(
'http://www.arpa.veneto.it/arpavinforma/bollettini/aria/aria_dati_validati_storico.php',
data=data)
soup = BeautifulSoup(response.text, 'html.parser')
table = soup.select_one('#ariadativalidati table')
if table:
extractor = Extractor(table)
extractor.parse()
table_data = extractor.return_list()[3:]
province.quality.co = self.indicator_value(table_data, 'co')
province.quality.so2 = self.indicator_value(table_data, 'so2')
province.quality.no2 = self.indicator_value(table_data, 'no2')
province.quality.o3 = self.indicator_value(table_data, 'o3')
province.quality.pm10 = self.indicator_value(
table_data, 'pm10')
province.quality.pm25 = self.indicator_value(
table_data, 'pm25')
province.quality.c6h6 = self.indicator_value(
table_data, 'c6h6')
if self.on_quality_fetched is not None: self.on_quality_fetched(self)
def scores(indx):
table = all_tables[indx] # Grab the first table
extractor = Extractor(table)
extractor.parse()
sias_list = extractor.return_list()
sias_list = sias_list[:-3]
sias_len = len(sias_list)
sias_names = sias_list[sias_len - 1]
sias_names = sias_names[1:]
num_item = len(sias_names)
sias_final = [[0] * num_item for _ in range(num_item)]
for i in range(sias_len - 1):
row = sias_list[i]
row = row[1:]
for j in range(len(row)):
sias_final[i][j] = row[j]
return sias_final
def get_tables(tables):
res = []
for t in tables:
labels = t.find_all('span', attrs={'class': 'label'})
captions = t.find_all('span', attrs={'class': 'captions'})
legend = t.find_all('p', attrs={'class': 'legend'})
footnotes = t.find_all('dl', attrs={'class': 'footnotes'})
for t2 in t.find_all('table'):
extractor = Extractor(t2)
extractor.parse()
content = extractor.return_list()
tab = {
'label':
labels[0].text if labels else '',
'caption':
captions[0].text.replace(labels[0].text, '')
if captions else '',
'legend': [x.text for x in legend] if legend else '',
'footnote': [x.text for x in footnotes] if footnotes else '',
'content':
content
}
res.append(tab)
return res
def extract_table(href: str) -> list:
# from given reference download all the tables = class .table and put them in the list, which is returned
def get_name(html_local): # the only way found to get the location name
soup_local = BeautifulSoup(
html_local, "html.parser"
) # I didnt know how to transfer the BeautifulSoup object in
vse = soup_local.find_all('h3')
for item in vse:
a = item.text.split(':')
if 'Obec' in a[0]:
obec = a[1].strip(' "\n"')
return obec
# from given reference download all the tables = class .table and put them in the list, which is returned
html = stahni_html(href)
district_name = get_name(html)
soup = BeautifulSoup(html, "html.parser")
tables = []
#using Extractor to get tables from the html code
for a_elem in soup.select('.table'):
extractor = Extractor(a_elem)
extractor.parse()
tables.append(extractor.return_list())
return tables, district_name
def html_table_converter(self, offer_table):
extractor = Extractor(offer_table)
extractor.parse()
table_list = extractor.return_list()
return table_list
def get_lists(soup: BeautifulSoup) -> List[List[str]]:
extractor = Extractor(soup)
extractor.parse()
return extractor.return_list()
def destination():
error = request.args['error']
text = request.args['text']
number_input = request.args['number_input']
req = requests.get('https://www.google.com/search?q='+text, headers={'User-Agent': 'Mozilla/5.0'})
req.raise_for_status()
soup_doc = BeautifulSoup(req.text, 'html.parser')
linkelements = soup_doc.select('.r a')
page = Request(("https://google.com" + linkelements[int(number_input)-1].get('href')), headers={'User-Agent': 'Mozilla/5.0'})
page_response = urlopen(page)
url = "https://google.com" + linkelements[int(number_input)-1].get('href')
html_read = page_response.read()
soup = BeautifulSoup(html_read, 'html.parser')
if error == '0' and request.method != 'POST':
sample = 'Press enter to search for HTML attributes. 1 to navigate back, 2 to navigate to home page'
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("text.mp3")
os.system("mpg321 text.mp3")
if request.method == 'POST':
if request.form['btn'] == 'Home':
return redirect(url_for('index', error = 1))
if request.form['btn'] == 'Back':
return redirect(url_for('link_number', text=text, error=1))
if request.form['btn'] == 'search audio':
if error == '0':
sample = 'Speak html attributes like title, paragraph, bold, links, tables, etcetra.'
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("text1.mp3")
os.system("mpg321 text1.mp3")
error = 1
r = sr.Recognizer()
with sr.Microphone() as source:
print("Say something or else say 'exit' to exit!")
r.adjust_for_ambient_noise(source, duration = 1)
audio = r.listen(source)
try :
text1 = r.recognize_google(audio)
print(text1)
if text1 == 'title':
sample = 'Title of the given page is '+soup.title.string
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("text.mp3")
os.system("mpg321 text.mp3")
elif text1 == 'bold' :
sample = 'Bold attributes of the given page are'
for x in soup.find_all('b'):
sample = sample +", "+ x.string
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("welcome.mp3")
os.system("mpg321 welcome.mp3")
elif text1 == 'links' :
sample = 'links to other pages are'
for x in soup.find_all('a'):
sample = sample +", "+ x.string
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("welcome.mp3")
os.system("mpg321 welcome.mp3")
elif text1 == 'para' :
sample = 'pragraphs of the given page is'
for x in soup.find_all('p'):
sample = sample +", "+ x.string
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("welcome.mp3")
os.system("mpg321 welcome.mp3")
elif text1 == 'tables' or text == 'table' :
sample = 'Tables of the given page are : '
extractor = Extractor(soup)
extractor.parse()
table_list = extractor.return_list()
print(len(table_list))
for rows in range(len(table_list)):
if rows >0 :
if len(table_list[rows]) != len(table_list[0]):
break
for columns in range(len(table_list[rows])):
sample += table_list[0][columns]+" is "+table_list[rows][columns]+", "
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("welcome.mp3")
os.system("mpg321 welcome.mp3")
else:
sample = 'Unable to recognize HTML attribute, press enter and speak again'
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("text.mp3")
os.system("mpg321 text.mp3")
return redirect(url_for('destination', text=text, number_input=number_input, error=1))
except :
sample = 'Unable to recognize you! , press enter and speak again'
myobj = gTTS(text=sample, lang='en', slow=False)
myobj.save("text.mp3")
os.system("mpg321 text.mp3")
return redirect(url_for('destination', text=text, number_input=number_input, error=1))
return render_template('destination.html', url=url)
def _fetch_air_quality_routine(self, day: datetime):
"""
Populate the air quality of the provinces.
Data is fetched from https://www.arpae.it/qualita-aria/bollettino-qa/{date} where {date}
is the date of interest in the format YYYYMMDD
:param day: The day of which the air quality wants to be known (instance of `~datetime`)
"""
super()._fetch_air_quality_routine(day)
date_fmt = day.strftime('%Y%m%d')
res = requests.get(
f'https://www.arpae.it/qualita-aria/bollettino-qa/{date_fmt}')
if res.status_code == 200:
soup = BeautifulSoup(res.text, 'html.parser')
table_rows = '\n'.join(
[str(x) for x in soup.select('.tabella table tbody tr')])
big_table = f'<table>{table_rows}</table>'
extractor = Extractor(big_table)
extractor.parse()
table = extractor.return_list()
for province in self.provinces:
province_rows = [
x for x in table if x[0] == province.short_name
]
so2 = [
self.extract_float(x[9]) for x in province_rows
if self.extract_float(x[9]) is not None
]
no2 = [
self.extract_float(x[4]) for x in province_rows
if self.extract_float(x[4]) is not None
]
co = [
self.extract_float(x[8]) for x in province_rows
if self.extract_float(x[8]) is not None
]
pm10 = [
self.extract_float(x[2]) for x in province_rows
if self.extract_float(x[2]) is not None
]
pm25 = [
self.extract_float(x[3]) for x in province_rows
if self.extract_float(x[3]) is not None
]
o3 = [
self.extract_float(x[6]) for x in province_rows
if self.extract_float(x[6]) is not None
]
c6h6 = [
self.extract_float(x[7]) for x in province_rows
if self.extract_float(x[7]) is not None
]
if len(so2) > 0: province.quality.so2 = round(mean(so2), 2)
if len(no2) > 0: province.quality.no2 = round(mean(no2), 2)
if len(co) > 0: province.quality.co = round(mean(co), 2)
if len(pm10) > 0: province.quality.pm10 = round(mean(pm10), 2)
if len(pm25) > 0: province.quality.pm25 = round(mean(pm25), 2)
if len(o3) > 0: province.quality.o3 = round(mean(o3), 2)
if len(c6h6) > 0: province.quality.c6h6 = round(mean(c6h6), 2)
if self.on_quality_fetched is not None: self.on_quality_fetched(self)
def read_shifts_from_html_pages(rawtoi1, rawtoi2, teamid1, teamid2, season,
game):
"""
Aggregates information from two html pages given into a dataframe with one row per second and one col per player.
:param rawtoi1: str, html page of shift log for team id1
:param rawtoi2: str, html page of shift log for teamid2
:param teamid1: int, team id corresponding to rawtoi1
:param teamid2: int, team id corresponding to rawtoi1
:param season: int, the season
:param game: int, the game
:return: dataframe
"""
from html_table_extractor.extractor import Extractor
dflst = []
for rawtoi, teamid in zip((rawtoi1, rawtoi2), (teamid1, teamid2)):
extractor = Extractor(rawtoi)
extractor.parse()
tables = extractor.return_list()
ids = []
periods = []
starts = []
ends = []
durationtime = []
teams = []
i = 0
while i < len(tables):
# A convenient artefact of this package: search for [p, p, p, p, p, p, p, p]
if len(tables[i]) == 8 and helpers.check_number_last_first_format(
tables[i][0]):
pname = helpers.remove_leading_number(tables[i][0])
pname = helpers.flip_first_last(pname)
pid = players.player_as_id(pname)
i += 2 # skip the header row
while re.match('\d{1,2}',
tables[i][0]): # First entry is shift number
# print(tables[i])
shiftnum, per, start, end, dur, ev = tables[i]
# print(pname, pid, shiftnum, per, start, end)
ids.append(pid)
periods.append(int(per))
starts.append(start[:start.index('/')].strip())
ends.append(end[:end.index('/')].strip())
durationtime.append(helpers.mmss_to_secs(dur))
teams.append(teamid)
i += 1
i += 1
else:
i += 1
startmin = [x[:x.index(':')] for x in starts]
startsec = [x[x.index(':') + 1:] for x in starts]
starttimes = [
1200 * (p - 1) + 60 * int(m) + int(s) + 1
for p, m, s in zip(periods, startmin, startsec)
]
# starttimes = [0 if x == 1 else x for x in starttimes]
endmin = [x[:x.index(':')] for x in ends]
endsec = [x[x.index(':') + 1:] for x in ends]
# There is an extra -1 in endtimes to avoid overlapping start/end
endtimes = [
1200 * (p - 1) + 60 * int(m) + int(s)
for p, m, s in zip(periods, endmin, endsec)
]
durationtime = [e - s for s, e in zip(starttimes, endtimes)]
df = pd.DataFrame({
'PlayerID': ids,
'Period': periods,
'Start': starttimes,
'End': endtimes,
'Team': teams,
'Duration': durationtime
})
dflst.append(df)
return _finish_toidf_manipulations(pd.concat(dflst), season, game)
def parse_html_table(html):
extractor = Extractor(html)
extractor.parse()
return extractor.return_list()
def set_indicator_value(self, day: datetime, indicator: str) -> float:
"""
Populates the indicator specified in the provinces
fetched data from `http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub3AccessoDatiAria.asp?Tipo=DatiGiorno`
:param day: The day of interest
:param indicator: The indicator of interest
"""
if indicator not in self.indicator_map: return
data = {
'Giorni': day.strftime('%d'),
'Mesi': day.strftime('%m'),
'Anni': day.strftime('%Y'),
'TipoTema': 'SENSORI',
'Tipo': 'DatiGiorno',
'Anno': day.strftime('%Y'),
'Mese': day.strftime('%m'),
'Giorno': day.strftime('%d'),
'DataIniz': day.strftime('%d/%m/%Y'),
'CodTema': 'SENSORI'
}
res = requests.post('http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub3AccessoDatiAria13.asp',
data=data)
soup = BeautifulSoup(res.text, 'html.parser')
# a unique needs to be provided when a request is made, it is sent to the user in form of an hidden field
try:
id_richiesta = soup.find_all('input', {'name': 'Id_Richiesta'})[0]['value']
except:
# data for the selected day not available
return
map_data = self.indicator_map[indicator]
res = requests.get('http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub3AccessoDatiAria131.asp',
params = (
('Anno', day.strftime('%Y')),
('CodParam', map_data['CodParam']),
('SiglaParam', map_data['SiglaParam']),
('Azione', 'LISTA_STAZIONI'),
('CodTema', 'SENSORI'),
('DataIniz', day.strftime('%d/%m/%Y')),
('Id_Richiesta', id_richiesta)
)
)
t = '</TR><TR>'.join(res.text.split('</TR>'))
soup = BeautifulSoup(t, 'html.parser')
table = soup.select('table')[0]
extractor = Extractor(table)
extractor.parse()
# remove header
table_data = extractor.return_list()[1:]
if len(table_data) > 0:
# remove any row after the first blank
table_data = table_data[:next(idx for idx, y in enumerate(table_data) if len(y) == 0)]
for province in self.provinces:
values = list()
for x in table_data:
if province.short_name in x[1]:
try:
values.append(float(x[map_data['table_idx']].strip()))
except:
pass
if len(values) != 0:
setattr(province.quality, indicator, round(float(sum(values) / len(values)), 2))
def _fetch_air_quality_routine(self, day: datetime):
"""
Populate the air quality of the provinces.
Data is fetched from http://www.arpa.umbria.it/monitoraggi/aria/Default.aspx
:param day: The day of which the air quality wants to be known (instance of `~datetime`)
"""
super()._fetch_air_quality_routine(day)
date_fmt = day.strftime('%d/%m/%Y')
data = {
'__EVENTTARGET': 'ctl00$Content$txtData',
'__EVENTARGUMENT': '',
'__LASTFOCUS': '',
'__VIEWSTATE':
'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',
'__VIEWSTATEGENERATOR': 'A373F38E',
'__PREVIOUSPAGE':
'5rDzdOLdhSojgNkWU0aySKgUcCP-WXzqaXaRNPbAb-Ekcs1vVl_yJf9liwnKWXEk15jl_Z8YIAJ86zswapmkHfDz2MMg9vQnDDQypfObingUmLuVVTMztw73FN9-55lI0',
'__EVENTVALIDATION':
'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',
'ctl00$Content$txtData': date_fmt,
'ctl00$Content$Grafico1$cboStazioni': '3_1',
'ctl00$Content$Grafico1$cboInquinante': 'SO224H'
}
res = requests.post(
'http://www.arpa.umbria.it/monitoraggi/aria/Default.aspx',
data=data)
if res.status_code == 200:
soup = BeautifulSoup(res.text, 'html.parser')
html_table = soup.select_one('#ctl00_Content_TabellaDati')
extractor = Extractor(html_table)
extractor.parse()
table = extractor.return_list()[2:]
html_table = soup.select_one(
'#ctl00_Content_TabellaDatiAltreStazioni')
extractor = Extractor(html_table)
extractor.parse()
table.extend(extractor.return_list()[2:])
for province in self.provinces:
province_rows = [
x for x in table
if x[0].split(' - ')[0].lower() == province.name.lower()
]
so2 = [
self.extract_float(x[1]) for x in province_rows
if self.extract_float(x[1]) is not None
]
no2 = [
self.extract_float(x[3]) for x in province_rows
if self.extract_float(x[3]) is not None
]
co = [
self.extract_float(x[4]) for x in province_rows
if self.extract_float(x[4]) is not None
]
pm10 = [
self.extract_float(x[7]) for x in province_rows
if self.extract_float(x[7]) is not None
]
pm25 = [
self.extract_float(x[9]) for x in province_rows
if self.extract_float(x[9]) is not None
]
o3 = [
self.extract_float(x[5]) for x in province_rows
if self.extract_float(x[5]) is not None
]
c6h6 = [
self.extract_float(x[7]) for x in province_rows
if self.extract_float(x[7]) is not None
]
if len(so2) > 0: province.quality.so2 = round(mean(so2), 2)
if len(no2) > 0: province.quality.no2 = round(mean(no2), 2)
if len(co) > 0: province.quality.co = round(mean(co), 2)
if len(pm10) > 0: province.quality.pm10 = round(mean(pm10), 2)
if len(pm25) > 0: province.quality.pm25 = round(mean(pm25), 2)
if len(o3) > 0: province.quality.o3 = round(mean(o3), 2)
if len(c6h6) > 0: province.quality.c6h6 = round(mean(c6h6), 2)
if self.on_quality_fetched is not None: self.on_quality_fetched(self)
def sync_conference(conf_id=3):
conf = ConferenceInterface.objects.get(pk=conf_id)
route = "forms/1/entries"
expires = arrow.utcnow().replace(minutes=+10).timestamp
string_to_sign = str("{}:{}:{}:{}".format(conf.api_key, "GET", route, expires))
sig = _calculate_signature(string_to_sign, conf.private_key)
req = requests.get(
conf.url, params={"api_key": conf.api_key, "signature": sig, "expires": expires}
)
data = json.loads(req.content)
for entry in data.get("response", {}).get("entries", []):
html = requests.get(
"{}/wp-json/conference/v1/entry/{}/{}".format(
settings.WP_URL, entry.get("form_id"), entry.get("id")
),
auth=(settings.WP_BASIC_AUTH_USER, settings.WP_BASIC_AUTH_PASS),
)
html = html.json().get("html")
table = BeautifulSoup(html, "html.parser").find_all(
"table", class_="entry-products"
)
extractor = Extractor(table[0], transformer=unicode)
extractor.parse()
table_data = extractor.return_list()
products = []
total_amount = ""
for item in table_data:
product_name, amount, price, total = item
if price == "Total":
total_amount = total
continue
product_name = product_name.strip()
amount = int(amount)
price = float(price.replace(u"$", "").strip().replace(",", "."))
products.append({"name": product_name, "amount": amount, "price": price})
doc = pq(html)
billing_html = ""
for val in doc(".entry-view-field-name"):
if val.text == "Billing address details":
doc(val).parents("tr").next_all().find("a").remove()
billing_html = doc(val).parents("tr").next_all().find("td").html()
if entry.get("34"):
billing_html += "<br/>" + entry.get("34")
billing_html = (
billing_html.replace("<br/>", "\n").replace("<p>", "").replace("</p>", "")
)
if "wire" in entry.get("21"):
payment_type = "wire"
else:
payment_type = "group"
registration, is_created = ConferenceRegistration.objects.get_or_create(
interface=conf,
form_id=entry.get("form_id"),
entry_id=entry.get("id"),
defaults={
"ticket_type": entry.get("9"),
"payment_type": payment_type,
"source_url": entry.get("source_url"),
"entry_created": arrow.get(
entry.get("date_created").replace(" ", "T")
).datetime,
"name": u"{} {}".format(entry.get("1.3"), entry.get("1.6")),
"email": entry.get("2"),
"organization": entry.get("6"),
"billing_html": billing_html,
"total_amount": total_amount,
"products": products,
},
)
def table2lists(table):
extractor = Extractor(table)
extractor.parse()
return extractor.return_list()
def sync_conference(conf_id=3):
conf = ConferenceInterface.objects.get(pk=conf_id)
route = "forms/1/entries"
expires = arrow.utcnow().replace(minute=+10).timestamp
string_to_sign = str("{}:{}:{}:{}".format(conf.api_key, "GET", route,
expires))
sig = _calculate_signature(string_to_sign, conf.private_key)
req = requests.get(
conf.url,
params={
"api_key": conf.api_key,
"signature": sig,
"expires": expires,
"paging[page_size]": 20,
},
)
data = json.loads(req.content)
for entry in data.get("response", {}).get("entries", []):
if entry["status"] == "trash":
continue
html = requests.get(
"{}/wp-json/conference/v1/entry/{}/{}".format(
settings.WP_URL, entry.get("form_id"), entry.get("id")),
auth=(settings.WP_BASIC_AUTH_USER, settings.WP_BASIC_AUTH_PASS),
)
html = html.json().get("html")
table = BeautifulSoup(html,
"html.parser").find_all("table",
class_="entry-products")
extractor = Extractor(table[0], transformer=unicode)
extractor.parse()
table_data = extractor.return_list()
products = []
total_amount = ""
for item in table_data:
product_name, amount, price, total = item
if price == "Total":
total_amount = total
continue
product_name = product_name.strip()
amount = int(amount)
price = float(price.replace(u"$", "").strip().replace(",", "."))
products.append({
"name": product_name,
"amount": amount,
"price": price
})
doc = pq(html)
billing_html = ""
for val in doc(".entry-view-field-name"):
if val.text == "Billing address details":
doc(val).parents("tr").next_all().find("a").remove()
billing_html = doc(val).parents("tr").next_all().find(
"td").html()
if entry.get("34"):
billing_html += "<br/>" + entry.get("34")
billing_html = (billing_html.replace("<br/>",
"\n").replace("<p>", "").replace(
"</p>", ""))
if "wire" in entry.get("21"):
payment_type = "wire"
else:
payment_type = "group"
if entry.get("53"):
is_group = True
else:
is_group = False
registration, is_created = ConferenceRegistration.objects.get_or_create(
interface=conf,
form_id=entry.get("form_id"),
entry_id=entry.get("id"),
defaults={
"ticket_type":
entry.get("9"),
"payment_type":
payment_type,
"source_url":
entry.get("source_url"),
"entry_created":
arrow.get(entry.get("date_created").replace(" ",
"T")).datetime,
"name":
u"{} {}".format(entry.get("1.3"), entry.get("1.6")),
"email":
entry.get("2"),
"organization":
entry.get("6"),
"billing_html":
billing_html,
"total_amount":
total_amount,
"products":
products,
"is_group":
is_group,
},
)
