def _varName_toMultiIndex(index_varDictionnary: pd.Index) -> pd.MultiIndex:
long_names = index_varDictionnary.tolist()
splits = [
long_name.strip('\\').split('\\') for long_name in long_names
]
multi_index = pd.MultiIndex.from_tuples(splits)
return multi_index
def to_omx(file: str, matrices: Dict[str, MATRIX_TYPES], zone_index: pd.Index = None, title: str = '',
descriptions: Dict[str, str] = None, attrs: Dict[str, dict] = None, mapping: str = 'zone_numbers'):
"""Creates a new (or overwrites an old) OMX file with a collection of matrices.
Args:
file: OMX to write.
matrices: Collection of matrices to write. MUST be a dict, to permit the encoding of matrix metadata,
and must contain the same types: all Numpy arrays, all Series, or all DataFrames. Checking is done to
ensure that all items have the same shape and labels.
zone_index: Override zone labels to use. Generally only useful if writing a dict of raw Numpy arrays.
title: The title saved in the OMX file.
descriptions: A dict of descriptions (one for each given matrix), or None to not use.
attrs: A dict of dicts (one for each given matrix), or None to not use
mapping: Name of the mapping internal to the OMX file
"""
matrices, zone_index = _prep_matrix_dict(matrices, zone_index)
if descriptions is None:
descriptions = {name: '' for name in matrices.keys()}
if attrs is None:
attrs = {name: None for name in matrices.keys()}
file = str(file) # Converts from Path
with omx.open_file(file, mode='w', title=title) as omx_file:
omx_file.create_mapping(mapping, zone_index.tolist())
for name, array in matrices.items():
description = descriptions[name]
attr = attrs[name]
omx_file.create_matrix(name, obj=np.ascontiguousarray(array), title=description, attrs=attr)
return
def _validate_indexer(indexer: pd.Index) -> Iterable:
"""Ensure `indexer` can be used as an indexer on another index.
https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
"""
if not isinstance(indexer, pd.MultiIndex) and pd.isna(indexer).any():
# isna is not defined for MultiIndex
return indexer.tolist()
return indexer
def find_overlap_in_dataset(data_in: np.ndarray, list_1: pd.Index, list_2: list):
if not isinstance(list_1, pd.Index):
raise TypeError("{} is supposed to be of type [pd.Index]. "
"Wrong format instead: {}".format(list_1, type(list_1)))
else:
overlap = list(set(list_1.tolist()).intersection(list_2))
found_bool = list_1.isin(overlap)
return found_bool, data_in.X[:, found_bool]
def build_autocomplete_grph_driver(rtplot: Figure, plots: List, ms_plot: Figure, patchsources: Dict[str, Tuple],
source: ColumnDataSource, default_county: str,
counties: pd.Index) -> Tuple[CDSView, AutocompleteInput]:
choices = AutocompleteInput(completions=counties.tolist(), case_sensitive=False, value=default_county,
title='Search for county or select from table:', name="county_input", width_policy='fit',
css_classes=['autocomplete_input'], min_width=250, align="start")
someargs = dict(source=source, rtplot=rtplot, rtxaxis=rtplot.xaxis[0], rtyaxis=rtplot.yaxis[0],
ms_plot=ms_plot, ms_plot_xaxis=ms_plot.xaxis[0], ms_plot_yaxis0=ms_plot.yaxis[0],
plots=plots, choices=choices, patchsources=patchsources)
someargs['xaxes'], someargs['yaxes'], someargs['plots'] = [], [], []
for p in plots:
someargs['xaxes'].append(p['plot'].xaxis[0])
someargs['yaxes'].append(p['plot'].yaxis[0])
someargs['plots'].append(p['plot'])
callback = CustomJS(args=someargs, code=constants.autocomplete_input_code)
choices.js_on_change('value', callback)
js_filter = CustomJSFilter(args=dict(choices=choices), code=constants.cdsview_jsfilter_code)
view = CDSView(source=source, filters=[js_filter])
return view, choices
def convert_pandas_index_to_array(value: pd.Index):
if isinstance(value, pd.Index):
return value.tolist()
