# Copyright (c) 2013,Vienna University of Technology, Department of Geodesy and Geoinformation
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'''
Created on Sep 24, 2013
@author: Christoph.Paulik@geo.tuwien.ac.at
'''
import itertools
import pytesmo.temporal_matching as temp_match
import pandas as pd
from distutils.version import LooseVersion
[docs]class BasicTemporalMatching(object):
"""
Temporal matching object
Parameters
----------
window : float
window size to use for temporal matching. A match in other will only
be found if it is +- window size days away from a point in reference
"""
def __init__(self, window=0.5):
self.window = window
[docs] def match(self, reference, *args):
"""
takes reference and other dataframe and returnes a joined Dataframe
in this case the reference dataset for the grid is also the
temporal reference dataset
"""
matched_datasets = temp_match.df_match(reference, *args, dropna=True,
dropduplicates=True,
window=self.window)
if type(matched_datasets) != tuple:
matched_datasets = [matched_datasets]
matched_data = pd.DataFrame(reference)
for match in matched_datasets:
if LooseVersion(pd.__version__) < LooseVersion('0.23'):
match = match.drop(('index', ''), axis=1)
else:
match = match.drop('index', axis=1)
match = match.drop('distance', axis=1)
matched_data = matched_data.join(match)
return matched_data.dropna(how='all')
[docs] def combinatory_matcher(self, df_dict, refkey, n=2):
"""
Basic temporal matcher that matches always one Dataframe to
the reference Dataframe resulting in matched DataFrame pairs.
If the input dict has the keys 'data1' and 'data2' then the
output dict will have the key ('data1', 'data2'). The new key
is stored as a tuple to avoid any issues with string concetanation.
During matching the column names of the dataframes will be
transformed into MultiIndex to ensure unique names.
Parameters
----------
df_dict: dict of pandas.DataFrames
dictionary containing the spatially colocated DataFrames.
refkey: string
key into the df_dict of the DataFrame that should be taken
as a reference.
n: int
number of datasets to match at once
Returns
-------
matched: dict of pandas.DataFrames
Dictionary containing matched DataFrames. The key is put
together from the keys of the input dict as a tuple of the
keys of the datasets this dataframe contains.
"""
matched = {}
keys = list(df_dict)
keys.pop(keys.index(refkey))
ref_df = df_dict[refkey]
ref_df = df_name_multiindex(ref_df, refkey)
for iterkeys in itertools.combinations(keys, n - 1):
match_list = []
match_key = []
for key in iterkeys:
other_df = df_dict[key]
other_df = df_name_multiindex(other_df, key)
match_list.append(other_df)
match_key.append(key)
matched_key = tuple([refkey] + sorted(match_key))
joined = self.match(ref_df,
*match_list)
if len(joined) != 0:
matched[matched_key] = joined
return matched
[docs]def df_name_multiindex(df, name):
"""
Rename columns of a DataFrame by using new column names that
are tuples of (name, column_name) to ensure unique column names
that can also be split again. This transforms the columns to a MultiIndex.
"""
d = {}
for c in df.columns:
d[c] = (name, c)
return df.rename(columns=d)