def on_enter(self):
# change the navigation bar title
ab = self.manager.nav_bar
ab.build_valuation_advanced_nav_bar()
ab.set_title('Single Run: Set Parameters')
data_manager = App.get_running_app().data_manager
MODEL_PARAMS = data_manager.get_valuation_model_params(self.iso)
if not MODEL_PARAMS:
popup = WarningPopup()
popup.bind(on_dismiss=partial(ab.go_to_screen, 'load_data'))
popup.dismiss_button.text = 'Go back'
popup.popup_text.text = 'We need a market area in the "Select Data" screen selected first to populate this area.'
popup.open()
def __init__(self, write_directory=None, write_function=None, chooser_description=None, format_description=None, data_validation_function=None, **kwargs):
super(DataImporter, self).__init__(**kwargs)
if write_directory is None:
self.write_directory = ""
else:
self.write_directory = write_directory
if write_function is None:
def _write_time_series_csv(fname, dataframe):
"""Writes a generic time series dataframe to a two-column csv.
The data is inferred to be at an hourly time resolution for one standard year.
Parameters
----------
fname : str
Name of the file to be saved without an extension
dataframe : Pandas DataFrame
DataFrame containing a single Series of the data
Returns
-------
str
The save destination of the resulting file.
"""
save_destination = os.path.join(self.write_directory, fname + ".csv")
data_column_name = dataframe.columns[0]
datetime_start = datetime(2019, 1, 1, 0)
hour_range = pd.date_range(start=datetime_start, periods=len(dataframe), freq="H")
dataframe["DateTime"] = hour_range
dataframe[["DateTime", data_column_name]].to_csv(save_destination, index=False)
return save_destination
self.write_function = _write_time_series_csv
else:
self.write_function = write_function
if chooser_description is None:
self.chooser_description = "Select a .csv file to import data from."
else:
self.chooser_description = chooser_description
file_chooser_screen = self.screen_manager.get_screen("FileChooser")
file_chooser_screen.file_chooser_body_text.text = self.chooser_description
if format_description is None:
self.format_description = "Specify the data column."
else:
self.format_description = format_description
format_analyzer_screen = self.screen_manager.get_screen("FormatAnalyzer")
format_analyzer_screen.format_analyzer_body_text.text = self.format_description
if data_validation_function is None:
def _default_data_validation_function(dataframe, data_column_name):
if len(dataframe) != 8760:
raise ValueError("The length of the time series must be 8760 (got {0}).".format(len(dataframe)))
data_column = dataframe[data_column_name]
try:
data_column.astype("float")
except ValueError:
raise ValueError("The selected data column could not be interpeted as numeric float values.")
self.data_validation_function = _default_data_validation_function
else:
self.data_validation_function = data_validation_function
# Bind DataImporter dismissal to successful data import.
completion_popup = WarningPopup()
completion_popup.title = "Success!"
completion_popup.popup_text.text = "Data successfully imported."
completion_popup.bind(on_dismiss=self.dismiss)
self.screen_manager.completion_popup = completion_popup