def run(self):
try:
yield 'Loading dataframe...'
self.data_frame = df_fileloader.load_agenda(self.frontur)
yield 'Selecting airport'
self.data_frame = em.select_airport(self.data_frame, self.airport)
if self.aliases:
yield 'Substituting values'
em.substitute_values(self.data_frame, self.aliases)
yield 'Merging plane data'
self.data_frame = em.add_plane_data(self.data_frame, self.planes)
yield 'Formatting days'
self.data_frame = em.format_dates(self.data_frame)
yield 'Selecting days'
self.data_frame = em.select_days(self.data_frame,
self.available_days)
self.fully_loaded = True
yield 'Finished.'
import io
buf = io.StringIO()
self.data_frame.info(buf=buf)
yield buf.getvalue()
except Exception as err:
yield str(err)
finally:
self.execution_end = True
def add_plane_data(
data_frame: pandas.DataFrame,
file_path: str,
target_col: str = const.DF_PLANE_COL_NAME) -> pandas.DataFrame:
"""Merges DataFrame with information about the flight planes
Args:
data_frame (pandas.DataFrame): Source DataFrame
file_path (str): Source file path
target_col (str): Target column to merge
Returns:
pandas.DataFrame: Source DataFrame with aditional information
"""
planes = df_fileloader.load_agenda(file_path)
data_frame[target_col] = data_frame[target_col].astype(str)
planes[target_col] = planes[target_col].astype(str)
data_frame = pandas.merge(data_frame,
planes,
how='outer',
on=[target_col],
indicator=True)
unmatched = data_frame.query('_merge == "left_only"').groupby(
[target_col]).size().reset_index(name='count')
if not unmatched.empty:
err_msg = 'There\'s missing information about the following planes:'
for index, row in unmatched.iterrows():
err_msg += '\n {} with {} ocurrences.'.format(
row[target_col], row['count'])
utility.eprint(err_msg)
return
return data_frame.query('_merge == "both"').drop(columns=['_merge'])
def run(self):
try:
yield 'Dataframe load'
self.data_frame = df_fileloader.load_agenda(self.filename)
yield 'Calculating results... (this may take a few minutes)'
self.data_frame = df_solver(self.data_frame,
no_groups=True,
parameters=self.solver_parameters)
yield 'Finished'
import io
buf = io.StringIO()
self.data_frame.info(buf=buf)
yield buf.getvalue()
self.fully_loaded = True
except Exception as err:
yield str(err)
finally:
self.execution_end = True
def solver(infile: str, outfile: str):
"""
·______ ______ ______ __ __ ______ __ __ ______
/\ ___\ /\ == \ /\ __ \ /\ "-.\ \ /\__ _\ /\ \/\ \ /\ == \
\ \ __\ \ \ __< \ \ \/\ \ \ \ \-. \ \/_/\ \/ \ \ \_\ \ \ \ __<
·\ \_\ \ \_\ \_\ \ \_____\ \ \_\\\\"\_\ \ \_\ \ \_____\ \ \_\ \_\\
··\/_/ \/_/ /_/ \/_____/ \/_/ \/_/ \/_/ \/_____/ \/_/ /_/
Program that selects the optimum amount of flights of an airport to interview.
\f
Args:
infile (str): Path to file with FronTur flights
outfile (str): Output path with selected FronTur flights
"""
data_frame = df_fileloader.load_agenda(infile)
import pandas
import json
with open(const.REQ_INTERVIEWS_FILE_PATH) as jfile:
data = json.load(jfile)
data_frame = df_solver(data_frame,
no_groups=True,
parameters={
'workday_time':
pandas.Timedelta(hours=8).seconds,
'rest_time':
pandas.Timedelta(minutes=10).seconds,
'execution_time_limit':
pandas.Timedelta(minutes=15).seconds,
'country_kwargs': {
'plane_kwargs': {
'seats_used':
0.8,
'poll_success':
0.6,
'poll_time':
pandas.Timedelta(seconds=30).seconds
},
'interviews': data
}
})
if outfile:
df_fileloader.save_agenda(outfile, data_frame)
def process(infile: str, airport: str, days: str, planes: str,
substitutions: str, outfile: str):
"""
·______ ______ ______ __ __ ______ __ __ ______
/\ ___\ /\ == \ /\ __ \ /\ "-.\ \ /\__ _\ /\ \/\ \ /\ == \
\ \ __\ \ \ __< \ \ \/\ \ \ \ \-. \ \/_/\ \/ \ \ \_\ \ \ \ __<
·\ \_\ \ \_\ \_\ \ \_____\ \ \_\\\\"\_\ \ \_\ \ \_____\ \ \_\ \_\\
··\/_/ \/_/ /_/ \/_____/ \/_/ \/_/ \/_/ \/_____/ \/_/ /_/
Program that processes various information from a file
with concrete information about flights of an airport.
\f
Args:
infile (str): Path to file with FronTur flights
outfile (str): Output path with parsed FronTur flights
airport (str): Target airport string identifier code
days (str): Path to json file with correct parameters of the assigned interview days
planes (str): Path to file with the neccesary information of the planes
substitutions (str): Optional json file with value conversions
"""
data_frame = df_fileloader.load_agenda(infile)
data_frame = em.select_airport(data_frame, airport)
if substitutions != '.':
em.substitute_values(data_frame, substitutions)
data_frame = em.add_plane_data(data_frame, const.PLANES_DATA_FILE_PATH)
data_frame = em.format_dates(data_frame)
data_frame = em.select_days(data_frame, days)
print(data_frame, '\n-------------\n',
data_frame.groupby(['Destino']).sum(), '\n-------------\n',
data_frame.groupby(['Pais']).sum())
if outfile:
df_fileloader.save_agenda(outfile, data_frame)
def test_load_csv_agenda(mock_method, ext, tmpdir):
file = tmpdir.join('filename.' + ext)
actual = utility_fileloader.load_agenda(file.strpath)
assert actual == 'csv'