def get_ts_from_complete_data(complete_data, mask, keys, dates=None):
if keys == None:
keys = list(complete_data.keys())
common_dates = becgis.common_dates([complete_data[key][1] for key in keys])
becgis.assert_proj_res_ndv([complete_data[key][0] for key in keys])
MASK = becgis.open_as_array(mask, nan_values=True)
tss = dict()
for key in keys:
var_mm = np.array([])
for date in common_dates:
tif = complete_data[key][0][complete_data[key][1] == date][0]
DATA = becgis.open_as_array(tif, nan_values=True)
DATA[np.isnan(DATA)] = 0.0
DATA[np.isnan(MASK)] = np.nan
var_mm = np.append(var_mm, np.nanmean(DATA))
tss[key] = (common_dates, var_mm)
return tss
def calc_basinmean(perc_fh, lu_fh):
"""
Calculate the mean of a map after masking out the areas outside an basin defined by
its landusemap.
Parameters
----------
perc_fh : str
Filehandle pointing to the map for which the mean needs to be determined.
lu_fh : str
Filehandle pointing to landusemap.
Returns
-------
percentage : float
The mean of the map within the border of the lu_fh.
"""
output_folder = tf.mkdtemp()
perc_fh = becgis.match_proj_res_ndv(lu_fh, np.array([perc_fh]),
output_folder)
EWR = becgis.open_as_array(perc_fh[0], nan_values=True)
LULC = becgis.open_as_array(lu_fh, nan_values=True)
EWR[np.isnan(LULC)] = np.nan
percentage = np.nanmean(EWR)
shutil.rmtree(output_folder)
return percentage
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.open_as_array(rz_depth_fh, nan_values=True)
geo = becgis.get_geoinfo(rz_depth_fh)
root_storage_fhs = []
for rz_sm_fh in rz_sm_fhs:
root_depth_sm = becgis.open_as_array(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.create_geotiff(out_fh, root_storage, *geo)
root_storage_fhs.append(out_fh)
return root_storage_fhs
def calc_ETs(ET, lu_fh, sheet1_lucs):
"""
Calculates the sums of the values within a specified landuse category.
Parameters
----------
ET : ndarray
Array of the data for which the sum needs to be calculated.
lu_fh : str
Filehandle pointing to landusemap.
sheet1_lucs : dict
Dictionary with landuseclasses per category.
Returns
-------
et : dict
Dictionary with the totals per landuse category.
"""
LULC = becgis.open_as_array(lu_fh, nan_values=True)
et = dict()
for key in sheet1_lucs:
classes = sheet1_lucs[key]
mask = np.logical_or.reduce([LULC == value for value in classes])
et[key] = np.nansum(ET[mask])
return et
def get_ts_from_complete_data_spec(complete_data, lu_fh, keys, a, dates=None):
if keys == None:
keys = list(complete_data.keys())
common_dates = becgis.common_dates([complete_data[key][1] for key in keys])
becgis.assert_proj_res_ndv([complete_data[key][0] for key in keys])
MASK = becgis.open_as_array(lu_fh, nan_values=True)
lucs = lucs = gd.get_sheet4_6_classes()
gw_classes = list()
for subclass in [
'Forests', 'Rainfed Crops', 'Shrubland', 'Forest Plantations'
]:
gw_classes += lucs[subclass]
mask_gw = np.logical_or.reduce([MASK == value for value in gw_classes])
tss = dict()
for key in keys:
var_mm = np.array([])
for date in common_dates:
tif = complete_data[key][0][complete_data[key][1] == date][0]
DATA = becgis.open_as_array(tif, nan_values=True)
DATA[np.isnan(DATA)] = 0.0
DATA[np.isnan(MASK)] = np.nan
alpha = np.ones(np.shape(DATA)) * a
alpha[mask_gw] = 0.0
var_mm = np.append(var_mm, np.nanmean(DATA * alpha))
tss[key] = (common_dates, var_mm)
return tss
def fuel_wood(output_folder, lu_fh, AREA, ndm_fhs, fraction_fhs, ndmdates):
"""
Calculate natural livestock feed production
INPUTS
----------
lu_fh : str
filehandle for land use map
ndm_fhs: nd array
array of filehandles of NDM maps
abv_grnd_biomass_ratio: dict
dictionnary 'LULC':[above ground biomass]
"""
Data_Path_Fuel = "Fuel"
out_folder = os.path.join(output_folder, Data_Path_Fuel)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
area_ha = AREA * 100
LULC = RC.Open_tiff_array(lu_fh)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
fuel_classes = [1, 8, 9, 10, 11, 12, 13]
fuel_mask = np.zeros(LULC.shape)
for fc in fuel_classes:
fuel_mask[np.where(LULC == fc)] = 1
fuel_fhs_landscape = []
fuel_fhs_incremental = []
for d in range(len(ndm_fhs)):
ndm_fh = ndm_fhs[d]
fraction_fh = fraction_fhs[d]
yield_fract = RC.Open_tiff_array(fraction_fh)
date1 = ndmdates[d]
year = '%d' % date1.year
month = '%02d' % date1.month
# year = ndm_fh[-14:-10]
# month = ndm_fh[-9:-7]
out_fh_l = out_folder + '\\fuel_prod_landscape_%s_%s.tif' % (year,
month)
out_fh_i = out_folder + '\\fuel_prod_incremental_%s_%s.tif' % (year,
month)
NDM = becgis.open_as_array(ndm_fh, nan_values=True)
NDM_fuel_incremental = NDM * .05 * fuel_mask * yield_fract * area_ha / 1e6
NDM_fuel_landscape = NDM * .05 * fuel_mask * (
1 - yield_fract) * area_ha / 1e6
DC.Save_as_tiff(out_fh_i, NDM_fuel_incremental, geo_out)
DC.Save_as_tiff(out_fh_l, NDM_fuel_landscape, geo_out)
fuel_fhs_landscape.append(out_fh_l)
fuel_fhs_incremental.append(out_fh_i)
return fuel_fhs_landscape, fuel_fhs_incremental
def lu_type_sum(data_fh, lu_fh, AREA, lu_dict, convert=None):
LULC = RC.Open_tiff_array(lu_fh)
in_data = becgis.open_as_array(data_fh, nan_values=True)
# in_data = RC.Open_tiff_array(data_fh)
if convert == 'mm_to_km3':
in_data *= AREA / 1e6
out_data = {}
for lu_class in list(lu_dict.keys()):
mask = [LULC == value for value in lu_dict[lu_class]]
mask = (np.sum(mask, axis=0)).astype(bool)
out_data[lu_class] = np.nansum(in_data[mask])
return out_data
def recycle(output_folder, et_bg_fhs, recy_ratio, lu_fh, et_type):
Data_Path_rec = "temp_et_recycle"
out_folder = os.path.join(output_folder, Data_Path_rec)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
recycle_fhs = []
for et_fh in et_bg_fhs:
out_fh = out_folder + "\\recycled_et_" + et_type + et_fh[
-11:-4] + ".tif"
et = becgis.open_as_array(et_fh, nan_values=True)
et_recy = et * recy_ratio
DC.Save_as_tiff(out_fh, et_recy, geo_out)
recycle_fhs.append(out_fh)
return recycle_fhs
def get_timeseries_raster(ds1_fhs, ds1_dates, coordinates, output_fh, unit = 'm3/s'):
"""
Substract a timeseries from a set of raster files. Store results in a csv-file.
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.
coordinates : tuple
Tuple with the latitude and longitude, (lat, lon).
output_fh : str
Filehandle pointing to a csv-file.
unit : str, optional
String indicating the unit of the data, default is 'm3/s'.
"""
ds1_values = list()
xpixel, ypixel = pixelcoordinates(coordinates[0], coordinates[1], ds1_fhs[0])
if np.any([np.isnan(xpixel), np.isnan(ypixel)]):
print("Coordinates ({0}) not on the map".format(coordinates))
else:
for date in ds1_dates:
ds1_values.append(becgis.open_as_array(ds1_fhs[ds1_dates == date][0], nan_values = True)[ypixel, xpixel])
ds1_values = np.array(ds1_values)
csv_file = open(output_fh, 'wb')
writer = csv.writer(csv_file, delimiter=';')
writer.writerow(['lat:',coordinates[0], 'lon:', coordinates[1], unit])
writer.writerow(['datetime','year','month','day','data'])
for date in ds1_dates:
year = date.year
month = date.month
day = date.day
dt = datetime.datetime(year, month, day, 0,0,0)
data = ds1_values[ds1_dates == date][0]
writer.writerow([dt, year, month, day, data])
csv_file.close()
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.get_geoinfo(fn)
data = becgis.open_as_array(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[0:6] + '.tif'
fn = os.path.join(folder, filen)
becgis.create_geotiff(fn, data, *geo_info)
meta = becgis.sort_files(folder, [-10, -6], month_position=[-6, -4])[0:2]
return a, meta
def calc_non_utilizable(P, ET, fractions_fh):
"""
Calculate non utilizable outflow.
Parameters
----------
P : ndarray
Array with the volumes of precipitation per pixel.
ET : ndarray
Array with the volumes of evapotranspiration per pixel.
fractions_fh : str
Filehandle pointing to a map with fractions indicating how much of the
(P-ET) difference is non-utilizable.
Returns
-------
non_utilizable_runoff : float
The total volume of non_utilizable runoff.
"""
fractions = becgis.open_as_array(fractions_fh, nan_values=True)
non_utilizable_runoff = np.nansum((P - ET) * fractions)
return non_utilizable_runoff
def diagnosis_wp(metadata, complete_data, output_dir, waterpix):
output_dir = os.path.join(output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
LU = becgis.open_as_array(metadata['lu'], nan_values=True)
# S = SortWaterPix(waterpix, 'Supply_M', output_dir)
# becgis.match_proj_res_ndv(metadata['lu'], becgis.list_files_in_folder(S), os.path.join(output_dir, "s_matched"))
# complete_data['supply'] = becgis.sort_files(os.path.join(output_dir, "s_matched"), [-10,-6], month_position = [-6,-4])[0:2]
common_dates = becgis.common_dates([
complete_data['p'][1], complete_data['et'][1], complete_data['tr'][1],
complete_data['etb'][1]
])
becgis.assert_proj_res_ndv([
complete_data['p'][0], complete_data['et'][0], complete_data['tr'][0]
])
balance_km3 = np.array([])
p_km3 = np.array([])
et_km3 = np.array([])
ro_km3 = np.array([])
balance_mm = np.array([])
p_mm = np.array([])
et_mm = np.array([])
ro_mm = np.array([])
area = becgis.map_pixel_area_km(metadata['lu'])
for date in common_dates:
print(date)
P = complete_data['p'][0][complete_data['p'][1] == date][0]
ET = complete_data['et'][0][complete_data['et'][1] == date][0]
RO = complete_data['tr'][0][complete_data['tr'][1] == date][0]
factor = 0.001 * 0.001 * area
p = becgis.open_as_array(P, nan_values=True)
et = becgis.open_as_array(ET, nan_values=True)
ro = becgis.open_as_array(RO, nan_values=True)
p[np.isnan(LU)] = et[np.isnan(LU)] = ro[np.isnan(LU)] = np.nan
balance_km3 = np.append(
balance_km3,
np.nansum(p * factor) - np.nansum(et * factor) -
np.nansum(ro * factor))
p_km3 = np.append(p_km3, np.nansum(p * factor))
et_km3 = np.append(et_km3, np.nansum(et * factor))
ro_km3 = np.append(ro_km3, np.nansum(ro * factor))
balance_mm = np.append(balance_mm,
np.nanmean(p) - np.nanmean(et) - np.nanmean(ro))
p_mm = np.append(p_mm, np.nanmean(p))
et_mm = np.append(et_mm, np.nanmean(et))
ro_mm = np.append(ro_mm, np.nanmean(ro))
relative_storage = np.cumsum(balance_km3) / np.mean(p_km3)
##
# BASIC BASINSCALE WATERBALANCE (PRE-SHEETS)
##
fig = plt.figure(1, figsize=(9, 6))
plt.clf()
fig.patch.set_alpha(0.7)
ax2 = plt.gca()
ax = ax2.twinx()
ax2.bar(common_dates, relative_storage, width=25, color='#3ee871')
ax2.grid(b=True, which='Major', color='0.65', linestyle='--', zorder=0)
ax.bar([common_dates[0]], [0],
label='$\sum dS / \overline{P}$',
color='#3ee871')
ax.plot(common_dates, np.cumsum(balance_km3), label='$\sum dS$')
ax.plot(common_dates, np.cumsum(p_km3), label='$\sum (P)$')
ax.plot(common_dates,
np.cumsum(et_km3) + np.cumsum(ro_km3),
label='$\sum (ET + RO)$')
box = ax.get_position()
ax.set_position(
[box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
ax2.set_position(
[box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
ax.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.1),
fancybox=True,
shadow=True,
ncol=5)
plt.suptitle(
'$\sum P = {0:.1f}\;{4}, \\ \sum ET = {1:.1f}\;{4}, \sum RO = {2:.1f}\;{4}, \sum dS = {3:.1f}\;{4}$'
.format(np.sum(p_km3), np.sum(et_km3), np.sum(ro_km3),
np.sum(balance_km3), r"km^{3}"))
plt.title(
'{0}, ${5} = {2:.3f}\;{6}, {7} = {3:.3f}, dt = {4}\;months$'.format(
metadata['name'], np.sum(balance_km3), np.mean(balance_km3),
np.mean(relative_storage), len(p_km3), r"\overline{dS}", r"km^{3}",
r"\overline{\sum dS / \overline{P}}"))
plt.xlabel('Time')
ax2.set_ylabel('Relative Storage [months of $\overline{P}$]')
ax.set_ylabel('Stock [$km^{3}$]')
#plt.savefig(os.path.join(output_dir, 'balance_{0}'.format(metadata['name'])))
fig = plt.figure(2)
plt.clf()
ax2 = plt.gca()
ax = ax2.twinx()
ax2.plot(common_dates, p_mm, common_dates, et_mm, common_dates, ro_mm)
ax.plot(common_dates, np.cumsum(balance_mm), 'k')
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.open_as_array(lu_fh, nan_values = True)
DS1 = becgis.open_as_array(ds1_fhs[0], nan_values = True)
DS2 = becgis.open_as_array(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.get_geoinfo(lu_fh)
becgis.create_geotiff(lu_fh.replace('.tif','_.tif'), LUCS, driver, NDV, xsize, ysize, GeoT, Projection)
common_dates = becgis.common_dates([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.open_as_array(ds1_fhs[ds1_dates == date][0], nan_values = True)
DS2 = becgis.open_as_array(ds2_fhs[ds2_dates == date][0], nan_values = True)
for clss in selected_lucs:
if clss in list(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 list(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 list(class_dictionary.items())}
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.get_geoinfo(lu_fh)
becgis.create_geotiff(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.assert_proj_res_ndv([ds1_fhs, ds2_fhs])
if dataset_names is None:
dataset_names = ['DS1','DS2']
driver, NDV, xsize, ysize, GeoT, Projection = becgis.get_geoinfo(ds1_fhs[0])
common_dates = becgis.common_dates([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.open_as_array(ds1_fhs[ds1_dates == date][0], nan_values = True)
DS2 = becgis.open_as_array(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 list(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.create_geotiff(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.assert_proj_res_ndv([ds1_fhs])
no_of_stations = len(list(station_dict.keys()))
ds1_dates = becgis.convert_datetime_date(ds1_dates, out = 'datetime')
for i, station in enumerate(station_dict.keys()):
station_dates, station_values = unzip(station_dict[station])
common_dates = becgis.common_dates([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.open_as_array(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 list(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 list(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 list(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.get_geoinfo(ds1_fhs[0])
dummy_map = becgis.open_as_array(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(list(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.create_geotiff(fh, interpolation_field, driver, NDV, xsize, ysize, GeoT, Projection)
return results
def calc_sheet1(entries,
lu_fh,
sheet1_lucs,
recycling_ratio,
q_outflow,
q_out_avg,
output_folder,
q_in_sw,
q_in_gw=0.,
q_in_desal=0.,
q_out_sw=0.,
q_out_gw=0.):
"""
Calculate the required values to plot Water Accounting Plus Sheet 1.
Parameters
----------
entries : dict
Dictionary with several filehandles, also see examples below.
lu_fh : str
Filehandle pointing to the landuse map.
sheet1_lucs : dict
Dictionary sorting different landuse classes into categories.
recycling_ratio : float
Value indicating the recycling ratio.
q_outflow : float
The outflow of the basin.
q_out_avg : float
The longterm average outflow.
output_folder : str
Folder to store results.
q_in_sw : float, optional
Surfacewater inflow into the basin. Default is 0.0.
q_in_gw : float, optional
Groundwater inflow into the basin. Default is 0.0.
q_in_desal : float, optional
Desalinised water inflow into the basin. Default is 0.0.
q_out_sw : float, optional
Additional surfacewater outflow from basin. Default is 0.0.
q_out_gw : float, optional
Groundwater outflow from the basin. Default is 0.0.
Returns
-------
results : dict
Dictionary containing necessary variables for Sheet 1.
"""
results = dict()
LULC = becgis.open_as_array(lu_fh, nan_values=True)
P = becgis.open_as_array(entries['P'], nan_values=True)
ETgreen = becgis.open_as_array(entries['ETgreen'], nan_values=True)
ETblue = becgis.open_as_array(entries['ETblue'], nan_values=True)
pixel_area = becgis.map_pixel_area_km(lu_fh)
gray_water_fraction = calc_basinmean(entries['WPL'], lu_fh)
ewr_percentage = calc_basinmean(entries['EWR'], lu_fh)
P[np.isnan(LULC)] = ETgreen[np.isnan(LULC)] = ETblue[np.isnan(
LULC)] = np.nan
P, ETgreen, ETblue = np.array([P, ETgreen, ETblue]) * 0.000001 * pixel_area
ET = np.nansum([ETblue, ETgreen], axis=0)
results['et_advection'], results['p_advection'], results[
'p_recycled'], results['dS'] = calc_wb(P,
ET,
q_outflow,
recycling_ratio,
q_in_sw=q_in_sw,
q_in_gw=q_in_gw,
q_in_desal=q_in_desal,
q_out_sw=q_out_sw,
q_out_gw=q_out_gw)
results['non_recoverable'] = gray_water_fraction * (
q_outflow + q_out_sw
) # Mekonnen and Hoekstra (2015), Global Gray Water Footprint and Water Pollution Levels Related to Anthropogenic Nitrogen Loads to Fresh Water
results['reserved_outflow_demand'] = q_out_avg * ewr_percentage
results['other'] = 0.0
landscape_et = calc_ETs(ETgreen, lu_fh, sheet1_lucs)
incremental_et = calc_ETs(ETblue, lu_fh, sheet1_lucs)
results['manmade'] = incremental_et['Managed']
results['natural'] = incremental_et['Modified'] + incremental_et[
'Protected'] + incremental_et['Utilized']
other_fractions = {
'Modified': 0.00,
'Managed': 1.00,
'Protected': 0.00,
'Utilized': 0.00
}
non_recoverable_fractions = {
'Modified': 0.00,
'Managed': 1.00,
'Protected': 0.00,
'Utilized': 0.00
}
results['uf_plu'], results['uf_ulu'], results['uf_mlu'], results[
'uf_mwu'] = calc_utilizedflow(incremental_et, results['other'],
results['non_recoverable'],
other_fractions,
non_recoverable_fractions)
net_inflow = results['p_recycled'] + results[
'p_advection'] + q_in_sw + q_in_gw + q_in_desal + results['dS']
consumed_water = np.nansum(list(landscape_et.values())) + np.nansum(
list(incremental_et.values())
) + results['other'] + results['non_recoverable']
non_consumed_water = net_inflow - consumed_water
results['non_utilizable_outflow'] = min(
non_consumed_water,
max(0.0, calc_non_utilizable(P, ET, entries['Fractions'])))
results['reserved_outflow_actual'] = min(
non_consumed_water - results['non_utilizable_outflow'],
results['reserved_outflow_demand'])
results['utilizable_outflow'] = max(
0.0, non_consumed_water - results['non_utilizable_outflow'] -
results['reserved_outflow_actual'])
results['landscape_et_mwu'] = landscape_et['Managed']
results['landscape_et_mlu'] = landscape_et['Modified']
results['landscape_et_ulu'] = landscape_et['Utilized']
results['landscape_et_plu'] = landscape_et['Protected']
results['q_outflow'] = q_outflow
results['q_in_sw'] = q_in_sw
results['q_in_gw'] = q_in_gw
results['q_in_desal'] = q_in_desal
results['q_out_sw'] = q_out_sw
results['q_out_gw'] = q_out_gw
return results
def livestock_feed(output_folder, lu_fh, AREA, ndm_fhs, feed_dict, live_feed,
cattle_fh, fraction_fhs, ndmdates):
"""
Calculate natural livestock feed production
INPUTS
----------
lu_fh : str
filehandle for land use map
ndm_fhs: nd array
array of filehandles of NDM maps
ndm_dates: nd array
array of dates for NDM maps
feed_dict: dict
dictionnary 'pasture class':[list of LULC]
feed_pct: dict
dictionnary 'pasture class':[percent available as feed]
cattle_fh : str
filehandle for cattle map
"""
Data_Path_Feed = "Feed"
out_folder = os.path.join(output_folder, Data_Path_Feed)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
area_ha = AREA * 100
LULC = RC.Open_tiff_array(lu_fh)
# cattle = RC.Open_tiff_array(cattle_fh)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
f_pct = np.zeros(LULC.shape)
for lu_type in list(feed_dict.keys()):
classes = feed_dict[lu_type]
mask = np.logical_or.reduce([LULC == value for value in classes])
f_pct[mask] = live_feed[lu_type]
feed_fhs_landscape = []
feed_fhs_incremental = []
for d in range(len(ndm_fhs)):
ndm_fh = ndm_fhs[d]
fraction_fh = fraction_fhs[d]
date1 = ndmdates[d]
year = '%d' % date1.year
month = '%02d' % date1.month
yield_fract = RC.Open_tiff_array(fraction_fh)
out_fh_l = out_folder + '\\feed_prod_landscape_%s_%s.tif' % (year,
month)
out_fh_i = out_folder + '\\feed_prod_incremental_%s_%s.tif' % (year,
month)
# out_fh2 = out_folder+'\\Feed_prod_pH_%s_%s.tif' %(year, month)
NDM = becgis.open_as_array(ndm_fh, nan_values=True)
NDM_feed = NDM * f_pct
NDM_feed_incremental = NDM_feed * yield_fract * area_ha / 1e6
NDM_feed_landscape = (NDM_feed * (1 - yield_fract)) * area_ha / 1e6
DC.Save_as_tiff(out_fh_l, NDM_feed_landscape, geo_out)
DC.Save_as_tiff(out_fh_i, NDM_feed_incremental, geo_out)
# NDM_feed_perHead = NDM_feed / cattle
# DC.Save_as_tiff(out_fh2, NDM_feed, geo_out)
feed_fhs_landscape.append(out_fh_l)
feed_fhs_incremental.append(out_fh_i)
return feed_fhs_landscape, feed_fhs_incremental
