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 sum_ts(flow_csvs):
flows = list()
dates = list()
for cv in flow_csvs:
coordinates, flow_ts, station_name, unit = pwv.create_dict_entry(cv)
flow_dates, flow_values = list(zip(*flow_ts))
flow_dates = becgis.convert_datetime_date(flow_dates)
if unit == 'm3/s':
flow_values = np.array([
flow_values[flow_dates == date] * 60 * 60 * 24 *
calendar.monthrange(date.year, date.month)[1] / 1000**3
for date in flow_dates
])[:, 0]
flows.append(flow_values)
dates.append(flow_dates)
common_dates = becgis.common_dates(dates)
data = np.zeros(np.shape(common_dates))
for flow_values, flow_dates in zip(flows, dates):
add_data = np.array([
np.array(flow_values)[flow_dates == date][0]
for date in common_dates
])
data += add_data
return data, common_dates
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 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 create_sheet7(complete_data, metadata, output_dir, global_data, data):
template_m = get_path('sheet7m_svg')
template_y = get_path('sheet7y_svg')
lu_fh = metadata['lu']
AREA = becgis.map_pixel_area_km(lu_fh)
output_folder = os.path.join(output_dir, metadata['name'], 'sheet7')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
recy_ratio = metadata['recycling_ratio']
date_list2 = becgis.common_dates([
complete_data['etb'][1], complete_data['tr'][1],
complete_data['recharge'][1]
])
date_list = becgis.convert_datetime_date(date_list2, out='datetime')
live_feed, feed_dict, abv_grnd_biomass_ratio, fuel_dict, sheet7_lulc_classes, c_fractions = get_sheet7_classes(
)
# year:fish production
#avg 2003-2012: 375375 2013:528000 2014:505005 <http://www.fao.org/3/a-i5555e.pdf>
#http://www.fao.org/fishery/statistics/global-production/en
# fish_production = {'2000':245600,
# '2001':385000,
# '2002':360300,
# '2003':308750,
# '2004':250000,
# '2005':324000,
# '2006':422000,
# '2007':395000,
# '2008':365000,
# '2009':390000}
# Select and project population to LULC map
pop_fh = global_data['population_tif']
# pop_temp = os.path.join(output_folder, 'temp_pop')
# pop_fh = becgis.match_proj_res_ndv(lu_fh, pop_fh, pop_temp)
# Select and project cattle to LULC map
cattle_fh = [global_data["cattle"]]
cattle_temp = os.path.join(output_folder, 'temp_cattle')
cattle_fh = becgis.match_proj_res_ndv(lu_fh, cattle_fh, cattle_temp)[0]
ndm_fhs = []
ro_fhs = []
et_blue_fhs = []
et_green_fhs = []
p_fhs = []
dry_bf_fhs = []
gw_rchg_fhs = []
for d in date_list2:
ndm_fhs.extend(complete_data['ndm'][0][complete_data['ndm'][1] == d])
ro_fhs.extend(complete_data['tr'][0][complete_data['tr'][1] == d])
et_blue_fhs.extend(
complete_data['etb'][0][complete_data['etb'][1] == d])
et_green_fhs.extend(
complete_data['etg'][0][complete_data['etg'][1] == d])
p_fhs.extend(complete_data['p'][0][complete_data['p'][1] == d])
dry_bf_fhs.extend(complete_data['bf'][0][complete_data['bf'][1] == d])
gw_rchg_fhs.extend(
complete_data['recharge'][0][complete_data['recharge'][1] == d])
# Make fraction maps to split feed and fuel yields in landscape and incremental ET
fraction_fhs = split_yield(output_folder,
p_fhs,
et_blue_fhs,
et_green_fhs,
ab=(1.0, 1.0))
# calculate feed production and return filehandles of saved tif files
feed_fhs_landscape, feed_fhs_incremental = livestock_feed(
output_folder, lu_fh, AREA, ndm_fhs, feed_dict, live_feed, cattle_fh,
fraction_fhs, date_list2)
# calculate fuel production and return filehandles of saved tif files
fuel_fhs_landscape, fuel_fhs_incremental = fuel_wood(
output_folder, lu_fh, AREA, ndm_fhs, fraction_fhs, date_list2)
# calculate root_storage and return filehandles of saved tif files
rz_depth_fh = global_data['root_depth']
rz_depth_tif = becgis.match_proj_res_ndv(lu_fh, np.array([rz_depth_fh]),
tf.mkdtemp())[0]
rz_sm_fhs = complete_data['rzsm'][0]
root_storage_fhs = root_zone_storage_Wpx(output_folder, rz_sm_fhs,
rz_depth_tif)
atm_recy_landscape_fhs = recycle(output_folder, et_green_fhs, recy_ratio,
lu_fh, 'landscape')
atm_recy_incremental_fhs = recycle(output_folder, et_blue_fhs, recy_ratio,
lu_fh, 'incremental')
class Vividict(dict):
def __missing__(self, key):
value = self[key] = type(self)()
return value
results = Vividict()
for d in date_list:
datestr1 = "%04d_%02d" % (d.year, d.month)
datestr2 = "%04d%02d" % (d.year, d.month)
ystr = "%04d" % (d.year)
mstr = "%02d" % (d.month)
ro_fh = ro_fhs[np.where(
[datestr2 in ro_fhs[i] for i in range(len(ro_fhs))])[0][0]]
feed_fh_landscape = feed_fhs_landscape[np.where([
datestr1 in feed_fhs_landscape[i]
for i in range(len(feed_fhs_landscape))
])[0][0]]
feed_fh_incremental = feed_fhs_incremental[np.where([
datestr1 in feed_fhs_incremental[i]
for i in range(len(feed_fhs_incremental))
])[0][0]]
fuel_fh_landscape = fuel_fhs_landscape[np.where([
datestr1 in fuel_fhs_landscape[i]
for i in range(len(fuel_fhs_landscape))
])[0][0]]
fuel_fh_incremental = fuel_fhs_incremental[np.where([
datestr1 in fuel_fhs_incremental[i]
for i in range(len(fuel_fhs_incremental))
])[0][0]]
baseflow_fh = dry_bf_fhs[np.where(
[datestr2 in dry_bf_fhs[i] for i in range(len(dry_bf_fhs))])[0][0]]
gw_recharge_fh = gw_rchg_fhs[np.where([
datestr2 in gw_rchg_fhs[i] for i in range(len(gw_rchg_fhs))
])[0][0]]
root_storage_fh = root_storage_fhs[np.where([
datestr2 in root_storage_fhs[i]
for i in range(len(root_storage_fhs))
])[0][0]]
atm_recy_landscape_fh = atm_recy_landscape_fhs[np.where([
datestr2 in atm_recy_landscape_fhs[i]
for i in range(len(atm_recy_landscape_fhs))
])[0][0]]
atm_recy_incremental_fh = atm_recy_incremental_fhs[np.where([
datestr2 in atm_recy_incremental_fhs[i]
for i in range(len(atm_recy_incremental_fhs))
])[0][0]]
results[ystr][mstr]['tot_runoff'] = lu_type_sum(ro_fh,
lu_fh,
AREA,
sheet7_lulc_classes,
convert='mm_to_km3')
# results['fish'] =
results[ystr][mstr]['feed_incremental'] = lu_type_sum(
feed_fh_incremental, lu_fh, AREA, sheet7_lulc_classes)
results[ystr][mstr]['feed_landscape'] = lu_type_sum(
feed_fh_landscape, lu_fh, AREA, sheet7_lulc_classes)
results[ystr][mstr]['fuel_incremental'] = lu_type_sum(
fuel_fh_incremental, lu_fh, AREA, sheet7_lulc_classes)
results[ystr][mstr]['fuel_landscape'] = lu_type_sum(
fuel_fh_landscape, lu_fh, AREA, sheet7_lulc_classes)
results[ystr][mstr]['baseflow'] = lu_type_sum(baseflow_fh,
lu_fh,
AREA,
sheet7_lulc_classes,
convert='mm_to_km3')
results[ystr][mstr]['gw_rech'] = lu_type_sum(gw_recharge_fh,
lu_fh,
AREA,
sheet7_lulc_classes,
convert='mm_to_km3')
results[ystr][mstr]['root_storage'] = lu_type_sum(root_storage_fh,
lu_fh,
AREA,
sheet7_lulc_classes,
convert='mm_to_km3')
results[ystr][mstr]['atm_recycl_landscape'] = lu_type_sum(
atm_recy_landscape_fh,
lu_fh,
AREA,
sheet7_lulc_classes,
convert='mm_to_km3')
results[ystr][mstr]['atm_recycl_incremental'] = lu_type_sum(
atm_recy_incremental_fh,
lu_fh,
AREA,
sheet7_lulc_classes,
convert='mm_to_km3')
output_fh = output_folder + "\\sheet7_monthly\\sheet7_" + datestr1 + ".csv"
create_csv(results[ystr][mstr], output_fh)
output = output_folder + '\\sheet7_monthly\\sheet7_' + datestr1 + '.pdf'
create_sheet7_svg(metadata['name'],
datestr1,
output_fh,
output,
template=template_m)
fhs = hl.create_csv_yearly(os.path.join(output_folder, "sheet7_monthly"),
os.path.join(output_folder, "sheet7_yearly"),
7,
metadata['water_year_start_month'],
year_position=[-11, -7],
month_position=[-6, -4],
header_rows=1,
header_columns=3,
minus_header_colums=-1)
for csv_fh in fhs:
year = csv_fh[-8:-4]
create_sheet7_svg(metadata['name'],
year,
csv_fh,
csv_fh.replace('.csv', '.pdf'),
template=template_y)
def create_sheet1(complete_data, metadata, output_dir, global_data):
output_folder = os.path.join(output_dir, metadata['name'], 'sheet1')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
output_fh_in = False
output_fh_in, output_fh_out = create_sheet1_in_outflows(
os.path.join(output_dir, metadata['name'], "sheet5", "sheet5_monthly"),
metadata, output_folder)
outflow_values, outflow_dates = sum_ts(np.array([output_fh_out]))
transfer_values, transfer_dates = get_transfers(
os.path.join(output_dir, metadata['name'], "sheet5", "sheet5_monthly"))
if output_fh_in:
inflow_values, inflow_dates = sum_ts(np.array([output_fh_in]))
# Calculate the average longterm outflow.
q_out_avg = np.nanmean(outflow_values)
# Open a dictionary specyfing the landuseclasses.
sheet1_lucs = gd.get_sheet1_classes()
# Determine for what dates all the required data is available.
if output_fh_in:
common_dates = becgis.common_dates([
complete_data['p'][1], complete_data['etb'][1],
complete_data['etg'][1], outflow_dates, inflow_dates
])
else:
common_dates = becgis.common_dates([
complete_data['tr'][1], complete_data['p'][1],
complete_data['etb'][1], complete_data['etg'][1]
]) #, outflow_dates])
# Create list to store results.
all_results = list()
for date in common_dates:
# Summurize some data in a dictionary.
entries = {
'Fractions':
complete_data['fractions'][0][complete_data['fractions'][1] ==
date][0],
'WPL':
global_data["wpl_tif"],
'EWR':
global_data["environ_water_req"],
'P':
complete_data['p'][0][complete_data['p'][1] == date][0],
'ETblue':
complete_data['etb'][0][complete_data['etb'][1] == date][0],
'ETgreen':
complete_data['etg'][0][complete_data['etg'][1] == date][0]
}
# Select the required outflow value.
q_outflow = outflow_values[outflow_dates == date][0]
q_transfer = np.array(transfer_values)[np.array(transfer_dates) ==
date][0]
if output_fh_in:
q_inflow = inflow_values[inflow_dates == date][0]
else:
q_inflow = 0.0
# Calculate the sheet values.
results = calc_sheet1(entries,
metadata['lu'],
sheet1_lucs,
metadata['recycling_ratio'],
q_outflow,
q_out_avg,
output_folder,
q_in_sw=q_inflow,
q_out_sw=q_transfer)
# Save the results of the current month.
all_results.append(results)
# Create the csv-file.
output_fh = os.path.join(
output_folder, 'sheet1_monthly',
'sheet1_{0}_{1}.csv'.format(date.year,
str(date.month).zfill(2)))
create_csv(results, output_fh)
# Plot the actual sheet.
create_sheet1_png(metadata['name'],
'{0}-{1}'.format(date.year,
str(date.month).zfill(2)),
'km3/month',
output_fh,
output_fh.replace('.csv', '.pdf'),
template=get_path('sheet1_svg'),
smart_unit=True)
# Create some graphs.
plot_storages(all_results, common_dates, metadata['name'], output_folder)
plot_parameter(all_results, common_dates, metadata['name'], output_folder,
'utilizable_outflow')
# Create yearly csv-files.
yearly_csv_fhs = hl.create_csv_yearly(os.path.split(output_fh)[0],
os.path.join(output_folder,
"sheet1_yearly"),
1,
metadata['water_year_start_month'],
year_position=[-11, -7],
month_position=[-6, -4],
header_rows=1,
header_columns=3)
# Plot yearly sheets.
for csv_fh in yearly_csv_fhs:
create_sheet1_png(metadata['name'],
csv_fh[-8:-4],
'km3/year',
csv_fh,
csv_fh.replace('.csv', '.pdf'),
template=get_path('sheet1_svg'),
smart_unit=True)
return complete_data, all_results
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')
