def test_DataManager_get_data():
datasets = setup_TestDatasets()
dm = DataManager(datasets,
'DS1',
read_ts_names={f'DS{i}': 'read'
for i in range(1, 4)})
data = dm.get_data(1, 1, 1)
assert sorted(list(data)) == ['DS1', 'DS2', 'DS3']
class Validation(object):
"""
Class for the validation process.
Parameters
----------
datasets : dict of dicts, or :py:class:`pytesmo.validation_framework.data_manager.DataManager`
:Keys: string, datasets names
:Values: dict, containing the following fields
'class': object
Class containing the method read_ts for reading the data.
'columns': list
List of columns which will be used in the validation process.
'args': list, optional
Args for reading the data.
'kwargs': dict, optional
Kwargs for reading the data
'grids_compatible': boolean, optional
If set to True the grid point index is used directly when
reading other, if False then lon, lat is used and a nearest
neighbour search is necessary.
'use_lut': boolean, optional
If set to True the grid point index (obtained from a
calculated lut between reference and other) is used when
reading other, if False then lon, lat is used and a
nearest neighbour search is necessary.
'lut_max_dist': float, optional
Maximum allowed distance in meters for the lut calculation.
spatial_ref: string
Name of the dataset used as a spatial, temporal and scaling reference.
temporal and scaling references can be changed if needed. See the optional parameters
``temporal_ref`` and ``scaling_ref``.
metrics_calculators : dict of functions
The keys of the dict are tuples with the following structure: (n, k) with n >= 2
and n>=k. n must be equal to the number of datasets now.
n is the number of datasets that should be temporally matched to the
reference dataset and k is how many columns the metric calculator will get at once.
What this means is that it is e.g. possible to temporally match 3 datasets with
3 columns in total and then give the combinations of these columns to the metric
calculator in sets of 2 by specifying the dictionary like:
.. code::
{ (3, 2): metric_calculator}
The values are functions that take an input DataFrame with the columns 'ref'
for the reference and 'n1', 'n2' and
so on for other datasets as well as a dictionary mapping the column names
to the names of the original datasets. In this way multiple metric calculators
can be applied to different combinations of n input datasets.
temporal_matcher: function, optional
function that takes a dict of dataframes and a reference_key.
It performs the temporal matching on the data and returns a dictionary
of matched DataFrames that should be evaluated together by the metric calculator.
temporal_window: float, optional
Window to allow in temporal matching in days. The window is allowed on both
sides of the timestamp of the temporal reference data.
Only used with the standard temporal matcher.
temporal_ref: string, optional
If the temporal matching should use another dataset than the spatial reference
as a reference dataset then give the dataset name here.
period : list, optional
Of type [datetime start, datetime end]. If given then the two input
datasets will be truncated to start <= dates <= end.
masking_datasets : dict of dictionaries
Same format as the datasets with the difference that the read_ts method of these
datasets has to return pandas.DataFrames with only boolean columns. True means that the
observations at this timestamp should be masked and False means that it should be kept.
scaling : string, None or class instance
- If set then the data will be scaled into the reference space using the
method specified by the string using the
:py:class:`pytesmo.validation_framework.data_scalers.DefaultScaler` class.
- If set to None then no scaling will be performed.
- It can also be set to a class instance that implements a
``scale(self, data, reference_index, gpi_info)`` method. See
:py:class:`pytesmo.validation_framework.data_scalers.DefaultScaler` for an example.
scaling_ref : string, optional
If the scaling should be done to another dataset than the spatial reference then
give the dataset name here.
Methods
-------
calc(job)
Takes either a cell or a gpi_info tuple and performs the validation.
get_processing_jobs()
Returns processing jobs that this process can understand.
"""
def __init__(self,
datasets,
spatial_ref,
metrics_calculators,
temporal_matcher=None,
temporal_window=1 / 24.0,
temporal_ref=None,
masking_datasets=None,
period=None,
scaling='lin_cdf_match',
scaling_ref=None):
if type(datasets) is DataManager:
self.data_manager = datasets
else:
self.data_manager = DataManager(datasets, spatial_ref, period)
self.temp_matching = temporal_matcher
if self.temp_matching is None:
self.temp_matching = temporal_matchers.BasicTemporalMatching(
window=temporal_window).combinatory_matcher
self.temporal_ref = temporal_ref
if self.temporal_ref is None:
self.temporal_ref = self.data_manager.reference_name
self.metrics_c = metrics_calculators
for n, k in self.metrics_c:
if n < len(self.data_manager.datasets.keys()):
raise ValueError('n must be equal to the number of datasets')
self.masking_dm = None
if masking_datasets is not None:
# add temporal reference dataset to the masking datasets since it
# is necessary for temporally matching the masking datasets to the
# common time stamps. Use _reference here to make a clash with the
# names of the masking datasets unlikely
masking_datasets.update(
{'_reference': datasets[self.temporal_ref]})
self.masking_dm = DataManager(masking_datasets,
'_reference',
period=period)
if type(scaling) == str:
self.scaling = DefaultScaler(scaling)
else:
self.scaling = scaling
self.scaling_ref = scaling_ref
if self.scaling_ref is None:
self.scaling_ref = self.data_manager.reference_name
self.luts = self.data_manager.get_luts()
def calc(self, gpis, lons, lats, *args):
"""
The argument iterables (lists or numpy.ndarrays) are processed one after the other in
tuples of the form (gpis[n], lons[n], lats[n], arg1[n], ..).
Parameters
----------
gpis: iterable
The grid point indices is an identificator by which the
spatial reference dataset can be read. This is either a list
or a numpy.ndarray or any other iterable containing this indicator.
lons: iterable
Longitudes of the points identified by the gpis. Has to be the same size as gpis.
lats: iterable
latitudes of the points identified by the gpis. Has to be the same size as gpis.
args: iterables
any addiational arguments have to have the same size as the gpis iterable. They are
given to the metrics calculators as metadata. Common usage is e.g. the long name
or network name of an in situ station.
Returns
-------
compact_results : dict of dicts
:Keys: result names, combinations of
(referenceDataset.column, otherDataset.column)
:Values: dict containing the elements returned by metrics_calculator
"""
results = {}
if len(args) > 0:
gpis, lons, lats, args = args_to_iterable(gpis,
lons,
lats,
*args,
n=3)
else:
gpis, lons, lats = args_to_iterable(gpis, lons, lats)
for gpi_info in zip(gpis, lons, lats, *args):
df_dict = self.data_manager.get_data(gpi_info[0], gpi_info[1],
gpi_info[2])
# if no data is available continue with the next gpi
if len(df_dict) == 0:
continue
matched_data, result, used_data = self.perform_validation(
df_dict, gpi_info)
# add result of one gpi to global results dictionary
for r in result:
if r not in results:
results[r] = []
results[r] = results[r] + result[r]
compact_results = {}
for key in results.keys():
compact_results[key] = {}
for field_name in results[key][0].keys():
entries = []
for result in results[key]:
entries.append(result[field_name][0])
compact_results[key][field_name] = \
np.array(entries, dtype=results[key][0][field_name].dtype)
return compact_results
def perform_validation(self, df_dict, gpi_info):
"""
Perform the validation for one grid point index and return the
matched datasets as well as the calculated metrics.
Parameters
----------
df_dict: dict of pandas.DataFrames
DataFrames read by the data readers for each dataset
gpi_info: tuple
tuple of at least, (gpi, lon, lat)
Returns
-------
matched_n: dict of pandas.DataFrames
temporally matched data stored by (n, k) tuples
results: dict
Dictonary of calculated metrics stored by dataset combinations tuples.
used_data: dict
The DataFrame used for calculation of each set of metrics.
"""
results = {}
used_data = {}
matched_n = {}
if self.masking_dm is not None:
ref_df = df_dict[self.temporal_ref]
masked_ref_df = self.mask_dataset(ref_df, gpi_info)
if len(masked_ref_df) == 0:
return matched_n, results, used_data
df_dict[self.temporal_ref] = masked_ref_df
matched_n = self.temporal_match_datasets(df_dict)
for n, k in self.metrics_c:
n_matched_data = matched_n[(n, k)]
if len(n_matched_data) == 0:
continue
result_names = get_result_combinations(self.data_manager.ds_dict,
n=k)
for data, result_key in self.k_datasets_from(
n_matched_data, result_names):
if len(data) == 0:
continue
# at this stage we can drop the column multiindex and just use
# the dataset name
if LooseVersion(pd.__version__) < LooseVersion('0.23'):
data.columns = data.columns.droplevel(level=1)
else:
data = data.rename(columns=lambda x: x[0])
if self.scaling is not None:
# get scaling index by finding the column in the
# DataFrame that belongs to the scaling reference
scaling_index = data.columns.tolist().index(
self.scaling_ref)
try:
data = self.scaling.scale(data, scaling_index,
gpi_info)
except ValueError:
continue
# Drop the scaling reference if it was not in the intended
# results
if self.scaling_ref not in [key[0] for key in result_key]:
data = data.drop(columns=[self.scaling_ref])
# Rename the columns to 'ref', 'k1', 'k2', ...
rename_dict = {}
f = lambda x: "k{}".format(x) if x > 0 else 'ref'
for i, r in enumerate(result_key):
rename_dict[r[0]] = f(i)
data.rename(columns=rename_dict, inplace=True)
if result_key not in results.keys():
results[result_key] = []
metrics_calculator = self.metrics_c[(n, k)]
used_data[result_key] = data
metrics = metrics_calculator(data, gpi_info)
results[result_key].append(metrics)
return matched_n, results, used_data
def mask_dataset(self, ref_df, gpi_info):
"""
Mask the temporal reference dataset with the data read
through the masking datasets.
Parameters
----------
gpi_info: tuple
tuple of at least, (gpi, lon, lat)
Returns
-------
mask: numpy.ndarray
boolean array of the size of the temporal reference read
"""
matched_masking = self.temporal_match_masking_data(ref_df, gpi_info)
# this will only be one element since n is the same as the
# number of masking datasets
result_names = get_result_names(self.masking_dm.ds_dict,
'_reference',
n=2)
choose_all = pd.DataFrame(index=ref_df.index)
for data, result in self.k_datasets_from(matched_masking,
result_names,
include_scaling_ref=False):
if len(data) == 0:
continue
for key in result:
if key[0] != '_reference':
# this is necessary since the boolean datatype might have
# been changed to float 1.0 and 0.0 issue with temporal
# resampling that is not easily resolved since most
# datatypes have no nan representation.
choose = pd.Series((data[key] == False), index=data.index)
choose = choose.reindex(index=choose_all.index,
fill_value=True)
choose_all[key] = choose.copy()
choosing = choose_all.apply(np.all, axis=1)
return ref_df[choosing]
def temporal_match_masking_data(self, ref_df, gpi_info):
"""
Temporal match the masking data to the reference DataFrame
Parameters
----------
ref_df: pandas.DataFrame
Reference data
gpi_info: tuple or list
contains, (gpi, lon, lat)
Returns
-------
matched_masking: dict of pandas.DataFrames
Contains temporally matched masking data. This dict has only one key
being a tuple that contains the matched datasets.
"""
# read only masking datasets and use the already read reference
masking_df_dict = self.masking_dm.get_other_data(
gpi_info[0], gpi_info[1], gpi_info[2])
masking_df_dict.update({'_reference': ref_df})
matched_masking = self.temp_matching(masking_df_dict,
'_reference',
n=2)
return matched_masking
def temporal_match_datasets(self, df_dict):
"""
Temporally match all the requested combinations of datasets.
Parameters
----------
df_dict: dict of pandas.DataFrames
DataFrames read by the data readers for each dataset
Returns
-------
matched_n: dict of pandas.DataFrames
for each (n, k) in the metrics calculators the n temporally
matched dataframes
"""
matched_n = {}
for n, k in self.metrics_c:
matched_data = self.temp_matching(df_dict, self.temporal_ref, n=n)
matched_n[(n, k)] = matched_data
return matched_n
def k_datasets_from(self,
n_matched_data,
result_names,
include_scaling_ref=True):
"""
Extract k datasets from n temporally matched ones.
This is used to send combinations of k datasets to
metrics calculators expecting only k datasets.
Parameters
----------
n_matched_data: dict of pandas.DataFrames
DataFrames in which n datasets were temporally matched.
The key is a tuple of the dataset names.
result_names: list
result names to extract
include_scaling_ref: boolean, optional
if set the scaling reference will always be included.
Should only be disabled for getting the masking datasets
Yields
------
data: pd.DataFrame
pandas DataFrame with k columns extracted from the
temporally matched datasets
result: tuple
Tuple describing which datasets and columns are in
the returned data. ((dataset_name, column_name), (dataset_name2, column_name2))
"""
for result in result_names:
result_extract = result
if self.scaling is not None and include_scaling_ref:
# always make sure the scaling reference is included in the results
# otherwise the scaling will fail
scaling_ref_column = self.data_manager.datasets[
self.scaling_ref]['columns'][0]
scaling_result_name = (self.scaling_ref, scaling_ref_column)
if scaling_result_name not in result:
result_extract = result + (scaling_result_name, )
data = self.get_data_for_result_tuple(n_matched_data,
result_extract)
yield data, result
def get_data_for_result_tuple(self, n_matched_data, result_tuple):
"""
Extract a dataframe for a given result tuple from the
matched dataframes.
Parameters
----------
n_matched_data: dict of pandas.DataFrames
DataFrames in which n datasets were temporally matched.
The key is a tuple of the dataset names.
result_tuple: tuple
Tuple describing which datasets and columns should be
extracted. ((dataset_name, column_name), (dataset_name2, column_name2))
Returns
-------
data: pd.DataFrame
pandas DataFrame with columns extracted from the
temporally matched datasets
"""
# find the key into the temporally matched dataset by combining the
# dataset parts of the result_names
dskey = []
for i, r in enumerate(result_tuple):
dskey.append(r[0])
dskey = tuple(dskey)
if len(list(n_matched_data)[0]) == len(dskey):
# we should have an exact match of datasets and
# temporal matches
try:
# still need to make sure that dskey is in the right order and
# contains all the same datasets as the n_matched_data
if sorted(dskey) == sorted(list(n_matched_data.keys())[0]):
dskey = list(n_matched_data.keys())[0]
data = n_matched_data[dskey]
except KeyError:
# if not then temporal matching between two datasets was
# unsuccessful
return []
else:
# more datasets were temporally matched than are
# requested now so we select a temporally matched
# dataset that has the first key in common with the
# temporal reference.
# This guarantees that we only select columns from dataframes for
# which the temporal reference dataset was included in the temporal
# matching
first_match = [
key for key in n_matched_data if self.temporal_ref == key[0]
]
found_key = None
for key in first_match:
for dsk in dskey:
if dsk not in key:
continue
found_key = key
data = n_matched_data[found_key]
# extract only the relevant columns from matched DataFrame
data = data[[x for x in result_tuple]]
# drop values if one column is NaN
data = data.dropna()
return data
def get_processing_jobs(self):
"""
Returns processing jobs that this process can understand.
Returns
-------
jobs : list
List of cells or gpis to process.
"""
jobs = []
if self.data_manager.reference_grid is not None:
if type(self.data_manager.reference_grid) is CellGrid:
cells = self.data_manager.reference_grid.get_cells()
for cell in cells:
(cell_gpis, cell_lons, cell_lats
) = self.data_manager.reference_grid.grid_points_for_cell(
cell)
jobs.append([cell_gpis, cell_lons, cell_lats])
else:
gpis, lons, lats = self.data_manager.reference_grid.get_grid_points(
)
jobs = [gpis, lons, lats]
return jobs
def test_DataManager_get_data():
datasets = setup_TestDatasets()
dm = DataManager(datasets, 'DS1')
data = dm.get_data(1, 1, 1)
assert sorted(list(data)) == ['DS1', 'DS2', 'DS3']
class Validation(object):
"""
Class for the validation process.
Parameters
----------
datasets : dict of dicts, or pytesmo.validation_framwork.data_manager.DataManager
Keys: string, datasets names
Values: dict, containing the following fields
'class': object
Class containing the method read_ts for reading the data.
'columns': list
List of columns which will be used in the validation process.
'args': list, optional
Args for reading the data.
'kwargs': dict, optional
Kwargs for reading the data
'grids_compatible': boolean, optional
If set to True the grid point index is used directly when
reading other, if False then lon, lat is used and a nearest
neighbour search is necessary.
'use_lut': boolean, optional
If set to True the grid point index (obtained from a
calculated lut between reference and other) is used when
reading other, if False then lon, lat is used and a
nearest neighbour search is necessary.
'lut_max_dist': float, optional
Maximum allowed distance in meters for the lut calculation.
spatial_ref: string
Name of the dataset used as a spatial, temporal and scaling reference.
temporal and scaling references can be changed if needed. See the optional parameters
``temporal_ref`` and ``scaling_ref``.
metrics_calculators : dict of functions
The keys of the dict are tuples with the following structure: (n, k) with n >= 2
and n>=k. n is the number of datasets that should be temporally matched to the
reference dataset and k is how many columns the metric calculator will get at once.
What this means is that it is e.g. possible to temporally match 3 datasets with
3 columns in total and then give the combinations of these columns to the metric
calculator in sets of 2 by specifying the dictionary like:
.. code::
{ (3, 2): metric_calculator}
The values are functions that take an input DataFrame with the columns 'ref'
for the reference and 'n1', 'n2' and
so on for other datasets as well as a dictionary mapping the column names
to the names of the original datasets. In this way multiple metric calculators
can be applied to different combinations of n input datasets.
temporal_matcher: function, optional
function that takes a dict of dataframes and a reference_key.
It performs the temporal matching on the data and returns a dictionary
of matched DataFrames that should be evaluated together by the metric calculator.
temporal_window: float, optional
Window to allow in temporal matching in days. The window is allowed on both
sides of the timestamp of the temporal reference data.
Only used with the standard temporal matcher.
temporal_ref: string, optional
If the temporal matching should use another dataset than the spatial reference
as a reference dataset then give the dataset name here.
period : list, optional
Of type [datetime start, datetime end]. If given then the two input
datasets will be truncated to start <= dates <= end.
masking_datasets : dict of dictionaries
Same format as the datasets with the difference that the read_ts method of these
datasets has to return pandas.DataFrames with only boolean columns. True means that the
observations at this timestamp should be masked and False means that it should be kept.
scaling : string, None or class instance
- If set then the data will be scaled into the reference space using the
method specified by the string using the
:py:class:`pytesmo.validation_framework.data_scalers.DefaultScaler` class.
- If set to None then no scaling will be performed.
- It can also be set to a class instance that implements a
``scale(self, data, reference_index, gpi_info)`` method. See
:py:class:`pytesmo.validation_framework.data_scalers.DefaultScaler` for an example.
scaling_ref : string, optional
If the scaling should be done to another dataset than the spatial reference then
give the dataset name here.
Methods
-------
calc(job)
Takes either a cell or a gpi_info tuple and performs the validation.
get_processing_jobs()
Returns processing jobs that this process can understand.
"""
def __init__(self, datasets, spatial_ref, metrics_calculators,
temporal_matcher=None, temporal_window=1 / 24.0,
temporal_ref=None,
masking_datasets=None,
period=None,
scaling='lin_cdf_match', scaling_ref=None):
if type(datasets) is DataManager:
self.data_manager = datasets
else:
self.data_manager = DataManager(datasets, spatial_ref, period)
self.temp_matching = temporal_matcher
if self.temp_matching is None:
self.temp_matching = temporal_matchers.BasicTemporalMatching(
window=temporal_window).combinatory_matcher
self.temporal_ref = temporal_ref
if self.temporal_ref is None:
self.temporal_ref = self.data_manager.reference_name
self.metrics_c = metrics_calculators
self.masking_dm = None
if masking_datasets is not None:
# add temporal reference dataset to the masking datasets since it
# is necessary for temporally matching the masking datasets to the
# common time stamps. Use _reference here to make a clash with the
# names of the masking datasets unlikely
masking_datasets.update(
{'_reference': datasets[self.temporal_ref]})
self.masking_dm = DataManager(masking_datasets, '_reference',
period=period)
if type(scaling) == str:
self.scaling = DefaultScaler(scaling)
else:
self.scaling = scaling
self.scaling_ref = scaling_ref
if self.scaling_ref is None:
self.scaling_ref = self.data_manager.reference_name
self.luts = self.data_manager.get_luts()
def calc(self, gpis, lons, lats, *args):
"""
The argument iterables (lists or numpy.ndarrays) are processed one after the other in
tuples of the form (gpis[n], lons[n], lats[n], arg1[n], ..).
Parameters
----------
gpis : iterable
The grid point indices is an identificator by which the
spatial reference dataset can be read. This is either a list
or a numpy.ndarray or any other iterable containing this indicator.
lons: iterable
Longitudes of the points identified by the gpis. Has to be the same size as gpis.
lats: iterable
latitudes of the points identified by the gpis. Has to be the same size as gpis.
args: iterables
any addiational arguments have to have the same size as the gpis iterable. They are
given to the metrics calculators as metadata. Common usage is e.g. the long name
or network name of an in situ station.
Returns
-------
compact_results : dict of dicts
Keys: result names, combinations of
(referenceDataset.column, otherDataset.column)
Values: dict containing the elements returned by metrics_calculator
"""
results = {}
if len(args) > 0:
gpis, lons, lats, args = args_to_iterable(gpis,
lons,
lats,
*args,
n=3)
else:
gpis, lons, lats = args_to_iterable(gpis, lons, lats)
for gpi_info in zip(gpis, lons, lats, *args):
df_dict = self.data_manager.get_data(gpi_info[0],
gpi_info[1],
gpi_info[2])
# if no data is available continue with the next gpi
if len(df_dict) == 0:
continue
matched_data, result, used_data = self.perform_validation(
df_dict, gpi_info)
# add result of one gpi to global results dictionary
for r in result:
if r not in results:
results[r] = []
results[r] = results[r] + result[r]
compact_results = {}
for key in results.keys():
compact_results[key] = {}
for field_name in results[key][0].keys():
entries = []
for result in results[key]:
entries.append(result[field_name][0])
compact_results[key][field_name] = \
np.array(entries, dtype=results[key][0][field_name].dtype)
return compact_results
def perform_validation(self,
df_dict,
gpi_info):
"""
Perform the validation for one grid point index and return the
matched datasets as well as the calculated metrics.
Parameters
----------
df_dict: dict of pandas.DataFrames
DataFrames read by the data readers for each dataset
gpi_info: tuple
tuple of at least, (gpi, lon, lat)
Returns
-------
matched_n: dict of pandas.DataFrames
temporally matched data stored by (n, k) tuples
results: dict
Dictonary of calculated metrics stored by dataset combinations tuples.
used_data: dict
The DataFrame used for calculation of each set of metrics.
"""
results = {}
used_data = {}
matched_n = {}
if self.masking_dm is not None:
ref_df = df_dict[self.temporal_ref]
masked_ref_df = self.mask_dataset(ref_df,
gpi_info)
if len(masked_ref_df) == 0:
return matched_n, results, used_data
df_dict[self.temporal_ref] = masked_ref_df
matched_n = self.temporal_match_datasets(df_dict)
for n, k in self.metrics_c:
n_matched_data = matched_n[(n, k)]
if len(n_matched_data) == 0:
continue
result_names = get_result_names(self.data_manager.ds_dict,
self.temporal_ref,
n=k)
for data, result_key in self.k_datasets_from(n_matched_data,
result_names):
if len(data) == 0:
continue
# at this stage we can drop the column multiindex and just use
# the dataset name
if LooseVersion(pd.__version__) < LooseVersion('0.23'):
data.columns = data.columns.droplevel(level=1)
else:
data = data.rename(columns=lambda x: x[0])
if self.scaling is not None:
# get scaling index by finding the column in the
# DataFrame that belongs to the scaling reference
scaling_index = data.columns.tolist().index(self.scaling_ref)
try:
data = self.scaling.scale(data,
scaling_index,
gpi_info)
except ValueError:
continue
# Rename the columns to 'ref', 'k1', 'k2', ...
rename_dict = {}
f = lambda x: "k{}".format(x) if x > 0 else 'ref'
for i, r in enumerate(result_key):
rename_dict[r[0]] = f(i)
data.rename(columns=rename_dict, inplace=True)
if result_key not in results.keys():
results[result_key] = []
metrics_calculator = self.metrics_c[(n, k)]
used_data[result_key] = data
metrics = metrics_calculator(data, gpi_info)
results[result_key].append(metrics)
return matched_n, results, used_data
def mask_dataset(self, ref_df, gpi_info):
"""
Mask the temporal reference dataset with the data read
through the masking datasets.
Parameters
----------
gpi_info: tuple
tuple of at least, (gpi, lon, lat)
Returns
-------
mask: numpy.ndarray
boolean array of the size of the temporal reference read
"""
matched_masking = self.temporal_match_masking_data(ref_df, gpi_info)
# this will only be one element since n is the same as the
# number of masking datasets
result_names = get_result_names(self.masking_dm.ds_dict,
'_reference',
n=2)
choose_all = pd.DataFrame(index=ref_df.index)
for data, result in self.k_datasets_from(matched_masking,
result_names):
if len(data) == 0:
continue
for key in result:
if key[0] != '_reference':
# this is necessary since the boolean datatype might have
# been changed to float 1.0 and 0.0 issue with temporal
# resampling that is not easily resolved since most
# datatypes have no nan representation.
choose = pd.Series((data[key] == False), index=data.index)
choose = choose.reindex(index=choose_all.index,
fill_value=True)
choose_all[key] = choose.copy()
choosing = choose_all.apply(np.all, axis=1)
return ref_df[choosing]
def temporal_match_masking_data(self, ref_df, gpi_info):
"""
Temporal match the masking data to the reference DataFrame
Parameters
----------
ref_df: pandas.DataFrame
Reference data
gpi_info: tuple or list
contains, (gpi, lon, lat)
Returns
-------
matched_masking: dict of pandas.DataFrames
Contains temporally matched masking data. This dict has only one key
being a tuple that contains the matched datasets.
"""
# read only masking datasets and use the already read reference
masking_df_dict = self.masking_dm.get_other_data(gpi_info[0],
gpi_info[1],
gpi_info[2])
masking_df_dict.update({'_reference': ref_df})
matched_masking = self.temp_matching(masking_df_dict,
'_reference',
n=2)
return matched_masking
def temporal_match_datasets(self, df_dict):
"""
Temporally match all the requested combinations of datasets.
Parameters
----------
df_dict: dict of pandas.DataFrames
DataFrames read by the data readers for each dataset
Returns
-------
matched_n: dict of pandas.DataFrames
for each (n, k) in the metrics calculators the n temporally
matched dataframes
"""
matched_n = {}
for n, k in self.metrics_c:
matched_data = self.temp_matching(df_dict,
self.temporal_ref,
n=n)
matched_n[(n, k)] = matched_data
return matched_n
def k_datasets_from(self, n_matched_data, result_names):
"""
Extract k datasets from n temporally matched ones.
This is used to send combinations of k datasets to
metrics calculators expecting only k datasets.
Parameters
----------
n_matched_data: dict of pandas.DataFrames
DataFrames in which n datasets were temporally matched.
The key is a tuple of the dataset names.
result_names: list
result names to extract
Yields
------
data: pd.DataFrame
pandas DataFrame with k columns extracted from the
temporally matched datasets
result: tuple
Tuple describing which datasets and columns are in
the returned data. ((dataset_name, column_name), (dataset_name2, column_name2))
"""
for result in result_names:
data = self.get_data_for_result_tuple(n_matched_data, result)
yield data, result
def get_data_for_result_tuple(self, n_matched_data, result_tuple):
"""
Extract a dataframe for a given result tuple from the
matched dataframes.
Parameters
----------
n_matched_data: dict of pandas.DataFrames
DataFrames in which n datasets were temporally matched.
The key is a tuple of the dataset names.
result_tuple: tuple
Tuple describing which datasets and columns should be
extracted. ((dataset_name, column_name), (dataset_name2, column_name2))
Returns
-------
data: pd.DataFrame
pandas DataFrame with columns extracted from the
temporally matched datasets
"""
# find the key into the temporally matched dataset by combining the
# dataset parts of the result_names
dskey = []
for i, r in enumerate(result_tuple):
dskey.append(r[0])
dskey = tuple(dskey)
if len(list(n_matched_data)[0]) == len(dskey):
# we should have an exact match of datasets and
# temporal matches
try:
data = n_matched_data[dskey]
except KeyError:
# if not then temporal matching between two datasets was
# unsuccessful
return []
else:
# more datasets were temporally matched than are
# requested now so we select a temporally matched
# dataset that has the first key in common with the
# requested one ensuring that it was used as a
# reference and also has the rest of the requested
# datasets in the key
first_match = [
key for key in n_matched_data if dskey[0] == key[0]]
found_key = None
for key in first_match:
for dsk in dskey[1:]:
if dsk not in key:
continue
found_key = key
data = n_matched_data[found_key]
# extract only the relevant columns from matched DataFrame
data = data[[x for x in result_tuple]]
# drop values if one column is NaN
data = data.dropna()
return data
def get_processing_jobs(self):
"""
Returns processing jobs that this process can understand.
Returns
-------
jobs : list
List of cells or gpis to process.
"""
jobs = []
if self.data_manager.reference_grid is not None:
if type(self.data_manager.reference_grid) is CellGrid:
cells = self.data_manager.reference_grid.get_cells()
for cell in cells:
(cell_gpis,
cell_lons,
cell_lats) = self.data_manager.reference_grid.grid_points_for_cell(cell)
jobs.append([cell_gpis, cell_lons, cell_lats])
else:
gpis, lons, lats = self.data_manager.reference_grid.get_grid_points()
jobs = [gpis, lons, lats]
return jobs
def test_DataManager_get_data():
datasets = setup_TestDatasets()
dm = DataManager(datasets, 'DS1')
data = dm.get_data(1, 1, 1)
assert sorted(list(data)) == ['DS1', 'DS2', 'DS3']
