def test_masking_adapter():
for col in (None, "x"):
ds = TestDataset("", n=20)
ds_mask = MaskingAdapter(ds, "<", 10, col)
data_masked = ds_mask.read_ts()
data_masked2 = ds_mask.read()
nptest.assert_almost_equal(
data_masked["x"].values,
np.concatenate(
[np.ones((10), dtype=bool),
np.zeros((10), dtype=bool)]),
)
nptest.assert_almost_equal(
data_masked2["x"].values,
np.concatenate(
[np.ones((10), dtype=bool),
np.zeros((10), dtype=bool)]),
)
if col is None:
nptest.assert_almost_equal(data_masked["y"].values,
np.ones((20), dtype=bool))
nptest.assert_almost_equal(data_masked2["y"].values,
np.ones((20), dtype=bool))
def test_adapters_with_ascat():
ascat_data_folder = os.path.join(
os.path.dirname(__file__),
"..",
"test-data",
"sat",
"ascat",
"netcdf",
"55R22",
)
ascat_grid_folder = os.path.join(
os.path.dirname(__file__),
"..",
"test-data",
"sat",
"ascat",
"netcdf",
"grid",
)
grid_fname = os.path.join(ascat_grid_folder, "TUW_WARP5_grid_info_2_1.nc")
ascat_reader = AscatGriddedNcTs(
ascat_data_folder,
"TUW_METOP_ASCAT_WARP55R22_{:04d}",
grid_filename=grid_fname,
)
ascat_anom = AnomalyAdapter(ascat_reader, window_size=35, columns=["sm"])
data = ascat_anom.read_ts(12.891455, 45.923004)
assert data is not None
assert np.any(data["sm"].values != 0)
data = ascat_anom.read(12.891455, 45.923004)
assert data is not None
assert np.any(data["sm"].values != 0)
ascat_self = SelfMaskingAdapter(ascat_reader, ">", 0, "sm")
data2 = ascat_self.read_ts(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2["sm"].values > 0)
data2 = ascat_self.read(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2["sm"].values > 0)
ascat_mask = MaskingAdapter(ascat_reader, ">", 0, "sm")
data3 = ascat_mask.read_ts(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3["sm"].values)
data3 = ascat_mask.read(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3["sm"].values)
ascat_clim = AnomalyClimAdapter(ascat_reader, columns=["sm"])
data4 = ascat_clim.read_ts(12.891455, 45.923004)
assert data4 is not None
assert np.any(data["sm"].values != 0)
data4 = ascat_clim.read(12.891455, 45.923004)
assert data4 is not None
assert np.any(data["sm"].values != 0)
def test_masking_adapter():
ds = TestDataset('', n=20)
ds_mask = MaskingAdapter(ds, '<', 10)
data_masked = ds_mask.read_ts()
nptest.assert_almost_equal(data_masked['x'].values,
np.concatenate([np.ones((10), dtype=bool),
np.zeros((10), dtype=bool)]))
nptest.assert_almost_equal(
data_masked['y'].values, np.ones((20), dtype=bool))
def test_masking_adapter():
ds = TestDataset('', n=20)
ds_mask = MaskingAdapter(ds, '<', 10)
data_masked = ds_mask.read_ts()
nptest.assert_almost_equal(
data_masked['x'].values,
np.concatenate([np.ones((10), dtype=bool),
np.zeros((10), dtype=bool)]))
nptest.assert_almost_equal(data_masked['y'].values,
np.ones((20), dtype=bool))
def test_timezone_removal():
tz_reader = TestTimezoneReader()
reader_anom = AnomalyAdapter(tz_reader, window_size=35, columns=["data"])
assert reader_anom.read_ts(0) is not None
reader_self = SelfMaskingAdapter(tz_reader, ">", 0, "data")
assert reader_self.read_ts(0) is not None
reader_mask = MaskingAdapter(tz_reader, ">", 0, "data")
assert reader_mask.read_ts(0) is not None
reader_clim = AnomalyClimAdapter(tz_reader, columns=["data"])
assert reader_clim.read_ts(0) is not None
def test_adapters_with_ascat():
ascat_data_folder = os.path.join(os.path.dirname(__file__), '..',
'test-data', 'sat', 'ascat', 'netcdf',
'55R22')
ascat_grid_folder = os.path.join(os.path.dirname(__file__), '..',
'test-data', 'sat', 'ascat', 'netcdf',
'grid')
ascat_reader = AscatSsmCdr(ascat_data_folder,
ascat_grid_folder,
grid_filename='TUW_WARP5_grid_info_2_1.nc')
ascat_anom = AnomalyAdapter(ascat_reader, window_size=35, columns=['sm'])
data = ascat_anom.read_ts(12.891455, 45.923004)
assert data is not None
assert np.any(data['sm'].values != 0)
data = ascat_anom.read(12.891455, 45.923004)
assert data is not None
assert np.any(data['sm'].values != 0)
ascat_self = SelfMaskingAdapter(ascat_reader, '>', 0, 'sm')
data2 = ascat_self.read_ts(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2['sm'].values > 0)
data2 = ascat_self.read(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2['sm'].values > 0)
ascat_mask = MaskingAdapter(ascat_reader, '>', 0, 'sm')
data3 = ascat_mask.read_ts(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3['sm'].values)
data3 = ascat_mask.read(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3['sm'].values)
ascat_clim = AnomalyClimAdapter(ascat_reader, columns=['sm'])
data4 = ascat_clim.read_ts(12.891455, 45.923004)
assert data4 is not None
assert np.any(data['sm'].values != 0)
data4 = ascat_clim.read(12.891455, 45.923004)
assert data4 is not None
assert np.any(data['sm'].values != 0)
# # Masking datasets are datasets that return a pandas DataFrame with boolean values. `True` means that the observation # should be masked, `False` means it should be kept. All masking datasets are temporally matched in pairs to the # temporal reference dataset. Only observations for which all masking datasets have a value of `False` are kept for # further validation. # # The masking datasets have the same format as the dataset dictionary and can be specified in the Validation class # with the `masking_datasets` keyword. # # ### Masking adapter # # To easily transform an existing dataset into a masking dataset `pytesmo` offers a adapter class that calls the # `read_ts` method of an existing dataset and creates a masking dataset based on an operator, a given threshold, and (optionally) a column name. # In[12]: from pytesmo.validation_framework.adapters import MaskingAdapter ds_mask = MaskingAdapter(ismn_reader, '<', 0.2, 'soil moisture') print(ds_mask.read_ts(ids[0]).head()) # ### Self-masking adapter # `pytesmo` also has a class that masks a dataset "on-the-fly", based on one of the columns it contains and an operator and a threshold. In contrast to the masking adapter mentioned above, the output of the self-masking adapter is the masked data, not the the mask. The self-masking adapter wraps a data reader, which must have a `read_ts` or `read` method. Calling its `read_ts`/`read` method will return the masked data - more precisely a DataFrame with only rows where the masking condition is true. # In[13]: from pytesmo.validation_framework.adapters import SelfMaskingAdapter ds_mask = SelfMaskingAdapter(ismn_reader, '<', 0.2, 'soil moisture') print(ds_mask.read_ts(ids[0]).head())
# ```python # from pytesmo.validation_framework import start_validation # # # Note that before starting the validation you must start a controller # # and engines, for example by using: ipcluster start -n 4 # # This command will launch a controller and 4 engines on the local machine. # # Also, do not forget to change the setup_code path to your current setup. # # setup_code = "my_validation.py" # start_validation(setup_code) # ``` # ## Masking datasets # # Masking datasets are datasets that return a pandas DataFrame with boolean values. `True` means that the observation should be masked, `False` means it should be kept. All masking datasets are temporally matched in pairs to the temporal reference dataset. Only observations for which all masking datasets have a value of `False` are kept for further validation. # # The masking datasets have the same format as the dataset dictionary and can be specified in the Validation class with the `masking_datasets` keyword. # # ### Masking adapter # # To easily transform an existing dataset into a masking dataset `pytesmo` offers a adapter class that calls the `read_ts` method of an existing dataset and performs the masking based on an operator and a given threshold. # In[12]: from pytesmo.validation_framework.adapters import MaskingAdapter ds_mask = MaskingAdapter(ismn_reader, '<', 0.2) print ds_mask.read_ts(ids[0])['soil moisture'].head()
# further validation. # # The masking datasets have the same format as the dataset dictionary and can be specified in the Validation class # with the `masking_datasets` keyword. # # ### Masking adapter # # To easily transform an existing dataset into a masking dataset `pytesmo` offers a adapter class that calls the # `read_ts` method of an existing dataset and creates a masking dataset based on an operator, a given threshold, and (optionally) a column name. # In[12]: from pytesmo.validation_framework.adapters import MaskingAdapter ds_mask = MaskingAdapter(ismn_reader, '<', 0.2, 'soil moisture') print(ds_mask.read_ts(ids[0]).head()) # ### Self-masking adapter # `pytesmo` also has a class that masks a dataset "on-the-fly", based on one of the columns it contains and an operator and a threshold. In contrast to the masking adapter mentioned above, the output of the self-masking adapter is the masked data, not the the mask. The self-masking adapter wraps a data reader, which must have a `read_ts` or `read` method. Calling its `read_ts`/`read` method will return the masked data - more precisely a DataFrame with only rows where the masking condition is true. # In[13]: from pytesmo.validation_framework.adapters import SelfMaskingAdapter ds_mask = SelfMaskingAdapter(ismn_reader, '<', 0.2, 'soil moisture') print(ds_mask.read_ts(ids[0]).head())
def getdata():
"""
handles the get request, which should contain the arguments listes under
parameters
Parameters
----------
station_id: int
id of station in database
scaling: string
chosen scaling method , for available choices see general.times_eries.scaling
snow_depth: float
mask snow depth greater than this value
st_l1: float
mask surface temperature layer1 lower than this value
air_temp: float
mask 2m air temperature lower than this value
ssf_masking: boolean
use SSF for masking true or false
"""
station_id = request.args.get('station_id')
scaling = request.args.get('scaling')
if scaling == 'noscale':
scaling = None
masking_ids = request.args.getlist('masking_ds[]')
masking_ops = request.args.getlist('masking_op[]')
masking_values = request.args.getlist('masking_values[]')
masking_values = [float(x) for x in masking_values]
anomaly = request.args.get('anomaly')
if anomaly == 'none':
anomaly = None
(depth_from,
depth_to,
sensor_id) = get_station_first_sm_layer(app.config['ISMN_PATH'],
station_id)
lon, lat = get_station_lonlat(app.config['ISMN_PATH'],
station_id)
start, end = get_station_start_end(app.config['ISMN_PATH'],
station_id, "soil moisture",
depth_from, depth_to)
period = [start, end]
masking_data = {'labels': [], 'data': []}
masking_meta = get_masking_metadata()
masking_masked_dict = None
if len(masking_ids) > 0:
# prepare masking datasets
masking_ds_dict = get_masking_ds_dict(masking_ids)
masking_masked_dict = {}
for masking_ds, masking_op, masking_value in zip(masking_ids,
masking_ops,
masking_values):
masking_masked_dict[masking_ds] = dict(masking_ds_dict[masking_ds])
new_cls = MaskingAdapter(masking_masked_dict[masking_ds]['class'],
masking_op,
masking_value)
masking_masked_dict[masking_ds]['class'] = new_cls
# use DataManager for reading masking datasets
masking_dm = DataManager(masking_ds_dict, masking_ids[0],
period=period)
masking_data = {}
valid_masking_ids = []
for mds in masking_ids:
mdata = masking_dm.read_ds(mds, lon, lat)
if mdata is not None:
masking_data[mds] = mdata
valid_masking_ids.append(mds)
else:
masking_data[mds] = pd.DataFrame()
if len(valid_masking_ids) > 1:
masking_data = BasicTemporalMatching(window=1.0).combinatory_matcher(
masking_data, masking_ids[0], n=len(masking_ids))
if len(masking_data) > 0:
labels, values = masking_data[
masking_data.keys()[0]].to_dygraph_format()
elif len(valid_masking_ids) == 1:
masking_data = masking_data[valid_masking_ids[0]]
labels, values = masking_data.to_dygraph_format()
else:
labels = [None]
values = None
for i, label in enumerate(labels):
for mid in masking_meta:
if masking_meta[mid]['variable']['name'] in label:
labels[i] = masking_meta[mid]['long_name']
masking_data = {'labels': labels, 'data': values}
ismn_iface = prepare_station_interface(app.config['ISMN_PATH'],
station_id,
"soil moisture",
depth_from, depth_to, sensor_id)
validation_ds_dict = get_validation_ds_dict()
validation_ds_dict.update({'ISMN': {'class': ismn_iface,
'columns': ['soil moisture']}})
if anomaly is not None:
adapter = {'climatology': AnomalyClimAdapter,
'average': AnomalyAdapter}
for dataset in validation_ds_dict:
validation_ds_dict[dataset]['class'] = adapter[
anomaly](validation_ds_dict[dataset]['class'],
columns=validation_ds_dict[dataset]['columns'])
mcalc = BasicMetricsPlusMSE(other_name='k1',
calc_tau=True).calc_metrics
process = Validation(validation_ds_dict, 'ISMN',
temporal_ref='cci',
scaling=scaling,
metrics_calculators={(2, 2): mcalc},
masking_datasets=masking_masked_dict,
period=period,
temporal_window=1)
df_dict = process.data_manager.get_data(1,
lon,
lat)
matched_data, result, used_data = process.perform_validation(
df_dict, (1, lon, lat))
res_key = list(result)[0]
data = used_data[res_key]
result = result[res_key][0]
# rename data to original names
rename_dict = {}
f = lambda x: "k{}".format(x) if x > 0 else 'ref'
for i, r in enumerate(res_key):
rename_dict[f(i)] = " ".join(r)
data.rename(columns=rename_dict, inplace=True)
labels, values = data.to_dygraph_format()
validation_datasets = {'labels': labels, 'data': values}
statistics = {'kendall': {'v': '%.2f' % result['tau'], 'p': '%.4f' % result['p_tau']},
'spearman': {'v': '%.2f' % result['rho'], 'p': '%.4f' % result['p_rho']},
'pearson': {'v': '%.2f' % result['R'], 'p': '%.4f' % result['p_R']},
'bias': '%.4f' % result['BIAS'],
'rmsd': {'rmsd': '%.4f' % np.sqrt(result['mse']),
'rmsd_corr': '%.4f' % np.sqrt(result['mse_corr']),
'rmsd_bias': '%.4f' % np.sqrt(result['mse_bias']),
'rmsd_var': '%.4f' % np.sqrt(result['mse_var'])},
'mse': {'mse': '%.4f' % result['mse'],
'mse_corr': '%.4f' % result['mse_corr'],
'mse_bias': '%.4f' % result['mse_bias'],
'mse_var': '%.4f' % result['mse_var']}}
scaling_options = {'noscale': 'No scaling',
'porosity': 'Scale using porosity',
'linreg': 'Linear Regression',
'mean_std': 'Mean - standard deviation',
'min_max': 'Minimum,maximum',
'lin_cdf_match': 'Piecewise <br> linear CDF matching',
'cdf_match': 'CDF matching'}
if scaling is None:
scaling = 'noscale'
masking_option_return = {}
for mid, mops, mval in zip(masking_ids,
masking_ops,
masking_values):
masking_option_return[mid] = {'op': mops,
'val': mval,
'name': masking_meta[mid]['long_name']}
settings = {'scaling': scaling_options[scaling],
'masking': masking_option_return}
output_data = {'validation_data': validation_datasets, 'masking_data': masking_data,
'statistics': statistics, 'settings': settings}
status = 1
if status == -1:
data = 'Error'
else:
data = jsonify(output_data)
resp = make_response(data)
resp.headers['Access-Control-Allow-Origin'] = '*'
return resp