def root_zone_storage_Wpx(output_folder, rz_sm_fhs, rz_depth_fh):
Data_Path_RZ = "RZstor"
out_folder = os.path.join(output_folder, Data_Path_RZ)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
root_depth = becgis.OpenAsArray(rz_depth_fh, nan_values=True)
geo = becgis.GetGeoInfo(rz_depth_fh)
root_storage_fhs = []
for rz_sm_fh in rz_sm_fhs:
root_depth_sm = becgis.OpenAsArray(rz_sm_fh, nan_values=True)
root_storage = root_depth * root_depth_sm
out_fh = os.path.join(out_folder, 'RZ_storage_mm_%s' %(rz_sm_fh[-10:]))
becgis.CreateGeoTiff(out_fh, root_storage, *geo)
root_storage_fhs.append(out_fh)
return root_storage_fhs
def correct_var(metadata,
complete_data,
output_dir,
formula,
new_var,
slope=False,
bounds=(0, [1.0, 1., 12.])):
var = split_form(formula)[0][-1]
a, x0 = calc_var_correction(metadata,
complete_data,
output_dir,
formula=formula,
slope=slope,
plot=True,
bounds=bounds)
for date, fn in zip(complete_data[var][1], complete_data[var][0]):
geo_info = becgis.GetGeoInfo(fn)
data = becgis.OpenAsArray(fn, nan_values=True)
x = calc_delta_months(x0, date)
fraction = a[0] * (np.cos(
(x - a[2]) * (np.pi / 6)) * 0.5 + 0.5) + (a[1] * (1 - a[0]))
data *= fraction
folder = os.path.join(output_dir, metadata['name'], 'data', new_var)
if not os.path.exists(folder):
os.makedirs(folder)
bla = os.path.split(fn)[1].split('_')[-1]
filen = 'supply_sw_' + bla[0:4] + '_' + bla[4:6] + '.tif'
fn = os.path.join(folder, filen)
becgis.CreateGeoTiff(fn, data, *geo_info)
meta = becgis.SortFiles(folder, [-11, -7], month_position=[-6, -4])[0:2]
return a, meta
def compare_rasters2rasters_per_lu(ds1_fhs,
ds1_dates,
ds2_fhs,
ds2_dates,
lu_fh,
output_dir,
dataset_names=["DS1", "DS2"],
class_dictionary=None,
no_of_classes=6):
"""
Compare two raster datasets with eachother per different landuse categories.
Parameters
----------
ds1_fhs : ndarray
Array with strings pointing to maps of dataset 1.
ds1_dates : ndarray
Array with same shape as ds1_fhs, containing datetime.date objects.
ds2_fhs : ndarray
Array with strings pointing to maps of dataset 2.
ds2_dates : ndarray
Array with same shape as ds2_fhs, containing datetime.date objects.
lu_fh : str
Pointer to a landusemap.
output_dir : str
Map to save results.
dataset_names : list, optional
List with two strings describing the names of the two datasets.
class_dictionary : dict
Dictionary specifying all the landuse categories.
no_of_classes : int
The 'no_of_classes' most dominant classes in the the lu_fh are compared, the rest is ignored.
"""
LUCS = becgis.OpenAsArray(lu_fh, nan_values=True)
DS1 = becgis.OpenAsArray(ds1_fhs[0], nan_values=True)
DS2 = becgis.OpenAsArray(ds2_fhs[0], nan_values=True)
DS1[np.isnan(DS2)] = np.nan
LUCS[np.isnan(DS1)] = np.nan
classes, counts = np.unique(LUCS[~np.isnan(LUCS)], return_counts=True)
counts_sorted = np.sort(counts)[-no_of_classes:]
selected_lucs = [
classes[counts == counter][0] for counter in counts_sorted
]
driver, NDV, xsize, ysize, GeoT, Projection = becgis.GetGeoInfo(lu_fh)
becgis.CreateGeoTiff(lu_fh.replace('.tif', '_.tif'), LUCS, driver, NDV,
xsize, ysize, GeoT, Projection)
common_dates = becgis.CommonDates([ds1_dates, ds2_dates])
ds1_totals = np.array([])
ds2_totals = np.array([])
DS1_per_class = dict()
DS2_per_class = dict()
for date in common_dates:
DS1 = becgis.OpenAsArray(ds1_fhs[ds1_dates == date][0],
nan_values=True)
DS2 = becgis.OpenAsArray(ds2_fhs[ds2_dates == date][0],
nan_values=True)
for clss in selected_lucs:
if clss in DS1_per_class.keys():
DS1_per_class[clss] = np.append(DS1_per_class[clss],
np.nanmean(DS1[LUCS == clss]))
else:
DS1_per_class[clss] = np.array([np.nanmean(DS1[LUCS == clss])])
if clss in DS2_per_class.keys():
DS2_per_class[clss] = np.append(DS2_per_class[clss],
np.nanmean(DS2[LUCS == clss]))
else:
DS2_per_class[clss] = np.array([np.nanmean(DS2[LUCS == clss])])
ds1_totals = np.append(ds1_totals, np.nanmean(DS1))
ds2_totals = np.append(ds2_totals, np.nanmean(DS2))
print("Finished {0}, going to {1}".format(date, common_dates[-1]))
for clss in selected_lucs:
if class_dictionary is None:
plot_scatter_series(DS1_per_class[clss], DS2_per_class[clss],
dataset_names[0], dataset_names[1], clss,
output_dir)
else:
cats = {v[0]: k for k, v in class_dictionary.iteritems()}
plot_scatter_series(DS1_per_class[clss], DS2_per_class[clss],
dataset_names[0], dataset_names[1], cats[clss],
output_dir)
plot_scatter_series(ds1_totals, ds2_totals, dataset_names[0],
dataset_names[1], "Total Area", output_dir)
if class_dictionary is not None:
output_fh = os.path.join(output_dir, 'landuse_percentages.png')
driver, NDV, xsize, ysize, GeoT, Projection = becgis.GetGeoInfo(lu_fh)
becgis.CreateGeoTiff(lu_fh.replace('.tif', '_.tif'), LUCS, driver, NDV,
xsize, ysize, GeoT, Projection)
becgis.plot_category_areas(lu_fh.replace('.tif', '_.tif'),
class_dictionary,
output_fh,
area_treshold=0.01)
os.remove(lu_fh.replace('.tif', '_.tif'))
def compare_rasters2rasters(ds1_fhs,
ds1_dates,
ds2_fhs,
ds2_dates,
output_dir=None,
dataset_names=None,
data_treshold=0.75):
"""
Compare two series of raster maps by computing
the relative bias, RMAE, Pearson-correlation coefficient and
the Nash-Sutcliffe coefficient per pixel.
Parameters
----------
ds1_fhs : list
list pointing to georeferenced raster files of dataset 1.
ds1_dates : list
list corresponding to ds1_fhs specifying the dates.
ds2_fhs : list
list pointing to georeferenced raster files of dataset 2.
ds2_dates : list
list corresponding to ds2_fhs specifying the dates.
quantity_unit : list, optional
list of two strings describing the quantity and unit of the data. e.g. ['Precipitation', 'mm/month'].
dataset_names : list, optional
list of strings describing the names of the datasets. e.g. ['CHIRPS', 'ERA-I'].
output_dir : list, optional
directory to store some results, i.e. (1) a graph of the spatially averaged datasets trough time and the
bias and (2) 4 geotiffs showing the bias, nash-sutcliffe coefficient, pearson coefficient and rmae per pixel.
data_treshold : float, optional
pixels with less than data_treshold * total_number_of_samples actual values are set to no-data, i.e. pixels with
too few data points are ignored.
Returns
-------
results : dict
dictionary with four keys (relative bias, RMAE, Pearson-correlation coefficient and
the Nash-Sutcliffe) with 2dnarrays of the values per pixel.
Examples
--------
>>> results = compare_rasters2rasters(ds1_fhs, ds1_dates, ds2_fhs, ds2_dates,
output_dir = r"C:/Desktop/", quantity_unit = ["P", "mm/month"],
dataset_names = ["CHIRPS", "TRMM"])
"""
becgis.AssertProjResNDV([ds1_fhs, ds2_fhs])
if dataset_names is None:
dataset_names = ['DS1', 'DS2']
driver, NDV, xsize, ysize, GeoT, Projection = becgis.GetGeoInfo(ds1_fhs[0])
common_dates = becgis.CommonDates([ds1_dates, ds2_dates])
diff_sum = np.zeros((ysize, xsize))
non_nans = np.zeros((ysize, xsize))
progress = 0
samples = len(common_dates)
for date in common_dates:
DS1 = becgis.OpenAsArray(ds1_fhs[ds1_dates == date][0],
nan_values=True)
DS2 = becgis.OpenAsArray(ds2_fhs[ds2_dates == date][0],
nan_values=True)
DS1[np.isnan(DS2)] = np.nan
DS2[np.isnan(DS1)] = np.nan
non_nans[~np.isnan(DS1)] += np.ones((ysize, xsize))[~np.isnan(DS1)]
diff = (DS1 - DS2)**2
diff_sum[~np.isnan(DS1)] += diff[~np.isnan(DS1)]
progress += 1
print "progress: {0} of {1} finished".format(progress, samples)
diff_sum[non_nans <= data_treshold * samples] = np.nan
results = dict()
results['rmse'] = np.where(non_nans == 0., np.nan,
np.sqrt(diff_sum / non_nans))
startdate = common_dates[0].strftime('%Y%m%d')
enddate = common_dates[-1].strftime('%Y%m%d')
path = os.path.join(output_dir, 'spatial_errors')
if not os.path.exists(path):
os.makedirs(path)
if output_dir is not None:
for varname in results.keys():
fh = os.path.join(
path,
'{0}_{1}_vs_{2}_{3}_{4}.tif'.format(varname, dataset_names[0],
dataset_names[1],
startdate, enddate))
becgis.CreateGeoTiff(fh, results[varname], driver, NDV, xsize,
ysize, GeoT, Projection)
return results
def compare_rasters2stations(ds1_fhs,
ds1_dates,
station_dict,
output_dir,
station_names=None,
quantity_unit=None,
dataset_names=None,
method='cubic',
min_records=1):
"""
Compare a series of raster maps with station time series by computing
the relative bias, RMAE, Pearson-correlation coefficient and
the Nash-Sutcliffe coefficient for each station.
Parameters
----------
ds1_fhs : 1dnarray
List containing filehandles to georeferenced raster files.
ds1_dates : 1dnarray
List containing datetime.date or datetime.datetime objects corresponding
to the filehandles in ds1_fhs. Lenght should be equal to ds1_fhs.
station_dict : dictionary
Dictionary containing coordinates of stations and timeseries. See examples
below for an example
output_dir : str, optional
Directory to store several results, i.e. (1) a csv file to load in a GIS program,
(2) interpolated maps showing the various error indicators spatially and (3)
scatter plots for all the stations.
station_names : dictionary, optional
Dictionary containing names of the respective stations which can be added to the csv-file, see
Examples for more information.
quantity_unit : list, optional
List of two strings describing the quantity and unit of the data.
dataset_name : list, optional
List of strings describing the names of the datasets.
method : str, optional
Method used for interpolation of the error-indicators, i.e.: 'linear', 'nearest' or 'cubic' (default).
Returns
-------
results : dictionary
Dictionary containing several error indicators per station.
Examples
--------
>>> station_dict = {(lat1, lon1): [(datetime.date(year, month, day), data_value),
(datetime.date(year, month, day), data_value),
etc.],
(lat2, lon2): [(datetime.date(year, month, day), data_value),
(datetime.date(year, month, day), data_value),
etc.],
etc.}
>>> station_names = {(lat1,lon1): 'stationname1', (lat2,lon2): 'stationname2', etc.}
>>> results = compare_rasters2stations(ds1_fhs, ds1_dates, station_dict, output_dir = r"C:/Desktop",
station_names = None, quantity_unit = ["P", "mm/month"],
dataset_names = ["CHIRPS", "Meteo Stations"],
method = 'cubic')
"""
results = dict()
pixel_coordinates = list()
if dataset_names is None:
dataset_names = ['Spatial', 'Station']
if quantity_unit is not None:
quantity_unit[1] = r'[' + quantity_unit[1] + r']'
else:
quantity_unit = ['data', '']
becgis.AssertProjResNDV([ds1_fhs])
no_of_stations = len(station_dict.keys())
ds1_dates = becgis.ConvertDatetimeDate(ds1_dates, out='datetime')
for i, station in enumerate(station_dict.keys()):
station_dates, station_values = unzip(station_dict[station])
common_dates = becgis.CommonDates([ds1_dates, station_dates])
sample_size = common_dates.size
if sample_size >= min_records:
ds1_values = list()
xpixel, ypixel = pixelcoordinates(station[0], station[1],
ds1_fhs[0])
if np.any([np.isnan(xpixel), np.isnan(ypixel)]):
print "Skipping station ({0}), cause its not on the map".format(
station)
continue
else:
for date in common_dates:
ds1_values.append(
becgis.OpenAsArray(ds1_fhs[ds1_dates == date][0],
nan_values=True)[ypixel, xpixel])
common_station_values = [
station_values[station_dates == date][0]
for date in common_dates
]
results[station] = pairwise_validation(ds1_values,
common_station_values)
results[station] += (sample_size, )
pixel_coordinates.append((xpixel, ypixel))
#m, b = np.polyfit(ds1_values, common_station_values, 1)
path_scatter = os.path.join(output_dir, 'scatter_plots')
if not os.path.exists(path_scatter):
os.makedirs(path_scatter)
path_ts = os.path.join(output_dir, 'time_series')
if not os.path.exists(path_ts):
os.makedirs(path_ts)
path_int = os.path.join(output_dir, 'interp_errors')
if not os.path.exists(path_int):
os.makedirs(path_int)
xlabel = '{0} {1} {2}'.format(dataset_names[0],
quantity_unit[0],
quantity_unit[1])
ylabel = '{0} {1} {2}'.format(dataset_names[1],
quantity_unit[0],
quantity_unit[1])
if station_names is not None:
title = station_names[station]
fn = os.path.join(
path_scatter,
'{0}_vs_{1}.png'.format(station_names[station],
dataset_names[0]))
fnts = os.path.join(
path_ts,
'{0}_vs_{1}.png'.format(station_names[station],
dataset_names[0]))
else:
title = station
fn = os.path.join(
path_scatter,
'{0}_vs_station_{1}.png'.format(dataset_names[0], i))
fnts = os.path.join(
path_ts,
'{0}_vs_station_{1}.png'.format(dataset_names[0], i))
suptitle = 'pearson: {0:.5f}, rmse: {1:.5f}, ns: {2:.5f}, bias: {3:.5f}, n: {4:.0f}'.format(
results[station][0], results[station][1],
results[station][2], results[station][3],
results[station][4])
plot_scatter_series(ds1_values,
common_station_values,
xlabel,
ylabel,
title,
fn,
suptitle=suptitle,
dates=common_dates)
xaxis_label = '{0} {1}'.format(quantity_unit[0],
quantity_unit[1])
xlabel = '{0}'.format(dataset_names[0])
ylabel = '{0}'.format(dataset_names[1])
plot_time_series(ds1_values,
common_station_values,
common_dates,
xlabel,
ylabel,
xaxis_label,
title,
fnts,
suptitle=suptitle)
print "station {0} ({3}) of {1} finished ({2} matching records)".format(
i + 1, no_of_stations, sample_size, title)
else:
print "____station {0} of {1} skipped____ (less than {2} matching records)".format(
i + 1, no_of_stations, min_records)
continue
n = len(results)
csv_filename = os.path.join(
output_dir,
'{0}stations_vs_{1}_indicators.csv'.format(n, dataset_names[0]))
with open(csv_filename, 'wb') as csv_file:
writer = csv.writer(csv_file, delimiter=';')
writer.writerow([
'longitude', 'latitude', 'station_id', 'pearson', 'rmse',
'nash_sutcliffe', 'bias', 'no_of_samples'
])
for station in results.keys():
writer.writerow([
station[1], station[0], station_names[station],
results[station][0], results[station][1], results[station][2],
results[station][3], results[station][4]
])
rslt = {
'Relative Bias': list(),
'RMSE': list(),
'Pearson Coefficient': list(),
'Nash-Sutcliffe Coefficient': list(),
'Number Of Samples': list()
}
for value in results.values():
rslt['Relative Bias'].append(value[3])
rslt['RMSE'].append(value[1])
rslt['Pearson Coefficient'].append(value[0])
rslt['Nash-Sutcliffe Coefficient'].append(value[2])
rslt['Number Of Samples'].append(value[4])
for key, value in rslt.items():
title = '{0}'.format(key)
print title
if key is 'RMSE':
xlabel = '{0} [mm/month]'.format(key)
else:
xlabel = key
value = np.array(value)
value = value[(~np.isnan(value)) & (~np.isinf(value))]
suptitle = 'mean: {0:.5f}, std: {1:.5f}, n: {2}'.format(
np.nanmean(value), np.nanstd(value), n)
print value
plot_histogram(value[(~np.isnan(value)) & (~np.isinf(value))],
title,
xlabel,
output_dir,
suptitle=suptitle)
driver, NDV, xsize, ysize, GeoT, Projection = becgis.GetGeoInfo(ds1_fhs[0])
dummy_map = becgis.OpenAsArray(ds1_fhs[0])
grid = np.mgrid[0:ysize, 0:xsize]
var_names = ['pearson', 'rmse', 'ns', 'bias', 'no_of_samples']
for i, var in enumerate(unzip(results.values())):
xy = np.array(pixel_coordinates)[~np.isnan(var)]
z = var[~np.isnan(var)]
interpolation_field = interpolate.griddata(xy,
z, (grid[1], grid[0]),
method=method,
fill_value=np.nanmean(z))
interpolation_field[dummy_map == NDV] = NDV
fh = os.path.join(
path_int,
'{0}_{1}stations_vs_{2}.tif'.format(var_names[i], len(xy),
dataset_names[0]))
becgis.CreateGeoTiff(fh, interpolation_field, driver, NDV, xsize,
ysize, GeoT, Projection)
return results