def _draw_time_series_plot(evaluation, plot_config):
""""""
time_range_info = plot_config['time_range']
ref_ds = evaluation.ref_dataset
target_ds = evaluation.target_datasets
if time_range_info == 'monthly':
ref_ds.values, ref_ds.times = utils.calc_climatology_monthly(ref_ds)
for t in target_ds:
t.values, t.times = utils.calc_climatology_monthly(t)
else:
logger.error('Invalid time range provided. Only monthly is supported '
'at the moment')
return
if evaluation.subregions:
for bound_count, bound in enumerate(evaluation.subregions):
results = []
labels = []
subset = dsp.subset(bound,
ref_ds,
subregion_name="R{}_{}".format(
bound_count, ref_ds.name))
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
for t in target_ds:
subset = dsp.subset(bound,
t,
subregion_name="R{}_{}".format(
bound_count, t.name))
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
plots.draw_time_series(np.array(results), ref_ds.times, labels,
'R{}'.format(bound_count),
**plot_config.get('optional_args', {}))
else:
results = []
labels = []
results.append(utils.calc_time_series(ref_ds))
labels.append(ref_ds.name)
for t in target_ds:
results.append(utils.calc_time_series(t))
labels.append(t.name)
plots.draw_time_series(np.array(results), ref_ds.times, labels,
'time_series',
**plot_config.get('optional_args', {}))
def _run_subregion_evaluation(self):
results = []
for target in self.target_datasets:
results.append([])
for metric in self.metrics:
results[-1].append([])
for subregion in self.subregions:
# Subset the reference and target dataset with the
# subregion information.
new_ref = DSP.subset(subregion, self.ref_dataset)
new_tar = DSP.subset(subregion, target)
run_result = metric.run(new_ref, new_tar)
results[-1][-1].append(run_result)
return results
def _run_subregion_evaluation(self):
results = []
for target in self.target_datasets:
results.append([])
for metric in self.metrics:
results[-1].append([])
for subregion in self.subregions:
# Subset the reference and target dataset with the
# subregion information.
new_ref = DSP.subset(subregion, self.ref_dataset)
new_tar = DSP.subset(subregion, target)
run_result = metric.run(new_ref, new_tar)
results[-1][-1].append(run_result)
return results
def test_subset(self):
subset = dp.subset(self.target_dataset, self.subregion)
self.assertEqual(subset.lats.shape[0], 82)
self.assertSequenceEqual(list(np.array(range(-81, 82, 2))),
list(subset.lats))
self.assertEqual(subset.lons.shape[0], 162)
self.assertEqual(subset.times.shape[0], 37)
self.assertEqual(subset.values.shape, (37, 82, 162))
def test_subset(self):
subset = dp.subset(self.subregion, self.target_dataset)
self.assertEqual(subset.lats.shape[0], 82)
self.assertSequenceEqual(list(np.array(range(-81, 82, 2))),
list(subset.lats))
self.assertEqual(subset.lons.shape[0], 162)
self.assertEqual(subset.times.shape[0], 37)
self.assertEqual(subset.values.shape, (37, 82, 162))
def _run_subregion_evaluation(self):
results = []
new_refs = [DSP.subset(s, self.ref_dataset) for s in self.subregions]
for target in self.target_datasets:
results.append([])
new_targets = [DSP.subset(s, target) for s in self.subregions]
for metric in self.metrics:
results[-1].append([])
for i in range(len(self.subregions)):
new_ref = new_refs[i]
new_tar = new_targets[i]
run_result = metric.run(new_ref, new_tar)
results[-1][-1].append(run_result)
return convert_evaluation_result(results, subregion=True)
def _run_subregion_unary_evaluation(self):
unary_results = []
if self.ref_dataset:
new_refs = [DSP.subset(s, self.ref_dataset) for s in self.subregions]
new_targets = [
[DSP.subset(s, t) for s in self.subregions]
for t in self.target_datasets
]
for metric in self.unary_metrics:
unary_results.append([])
for i in range(len(self.subregions)):
unary_results[-1].append([])
if self.ref_dataset:
unary_results[-1][-1].append(metric.run(new_refs[i]))
for t in range(len(self.target_datasets)):
unary_results[-1][-1].append(metric.run(new_targets[t][i]))
return convert_unary_evaluation_result(unary_results, subregion = True)
def test_subset_without_start_index(self):
self.subregion = ds.Bounds(
lat_min=-81, lat_max=81,
lon_min=-161, lon_max=161,
)
subset = dp.subset(self.target_dataset, self.subregion)
times = np.array([datetime.datetime(year, month, 1)
for year in range(2000, 2010)
for month in range(1, 13)])
self.assertEqual(subset.lats.shape[0], 82)
self.assertSequenceEqual(list(np.array(range(-81, 82, 2))),
list(subset.lats))
self.assertEqual(subset.lons.shape[0], 162)
self.assertEqual(subset.values.shape, (120, 82, 162))
self.assertEqual(subset.times.shape[0], 120)
np.testing.assert_array_equal(subset.times, times)
def test_subset_without_start_index(self):
self.subregion = ds.Bounds(
-81, 81,
-161, 161,
)
subset = dp.subset(self.target_dataset, self.subregion)
times = np.array([datetime.datetime(year, month, 1)
for year in range(2000, 2010)
for month in range(1, 13)])
self.assertEqual(subset.lats.shape[0], 82)
self.assertSequenceEqual(list(np.array(range(-81, 82, 2))),
list(subset.lats))
self.assertEqual(subset.lons.shape[0], 162)
self.assertEqual(subset.values.shape, (120, 82, 162))
self.assertEqual(subset.times.shape[0], 120)
np.testing.assert_array_equal(subset.times, times)
if time_info['maximum_overlap_period']:
start_time, end_time = utils.get_temporal_overlap([ref_dataset]+model_datasets)
print 'Maximum overlap period'
print 'start_time:', start_time
print 'end_time:', end_time
if temporal_resolution == 'monthly' and end_time.day !=1:
end_time = end_time.replace(day=1)
if ref_data_info['data_source'] == 'rcmed':
min_lat = np.max([min_lat, ref_dataset.lats.min()])
max_lat = np.min([max_lat, ref_dataset.lats.max()])
min_lon = np.max([min_lon, ref_dataset.lons.min()])
max_lon = np.min([max_lon, ref_dataset.lons.max()])
bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
ref_dataset = dsp.subset(bounds,ref_dataset)
if ref_dataset.temporal_resolution() != temporal_resolution:
ref_dataset = dsp.temporal_rebin(ref_dataset, temporal_resolution)
for idata,dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.subset(bounds,dataset)
if dataset.temporal_resolution() != temporal_resolution:
model_datasets[idata] = dsp.temporal_rebin(dataset, temporal_resolution)
# Temporaly subset both observation and model datasets for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']
ref_dataset = dsp.temporal_subset(month_start, month_end,ref_dataset,average_each_year)
for idata,dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.temporal_subset(month_start, month_end,dataset,average_each_year)
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX,
START, END)
""" Step 3: Resample Datasets so they are the same shape """
print("Resampling datasets ...")
print("... on units")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
print("... temporal")
CRU31 = dsp.temporal_rebin(CRU31, temporal_resolution='monthly')
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.water_flux_unit_conversion(
target_datasets[member])
target_datasets[member] = dsp.temporal_rebin(target_datasets[member],
temporal_resolution='monthly')
target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member])
#Regrid
print("... regrid")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member],
new_lats, new_lons)
#find the mean values
#way to get the mean. Note the function exists in util.py as def calc_climatology_year(dataset):
CRU31.values = utils.calc_temporal_mean(CRU31)
""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print("Working with the rcmed interface to get CRU3.1 Daily Precipitation")
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(
10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
""" Step 3: Processing datasets so they are the same shape ... """
print("Processing datasets so they are the same shape")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[
member])
target_datasets[member] = dsp.normalize_dataset_datetimes(
target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(
target_datasets[member], new_lats, new_lons)
def test_out_of_dataset_bounds_start(self):
self.subregion.start = datetime.datetime(1999, 1, 1)
with self.assertRaises(ValueError):
dp.subset(self.subregion, self.target_dataset)
def _draw_time_series_plot(evaluation, plot_config):
""""""
time_range_info = plot_config['time_range']
ref_ds = evaluation.ref_dataset
target_ds = evaluation.target_datasets
if time_range_info == 'monthly':
ref_ds.values, ref_ds.times = utils.calc_climatology_monthly(ref_ds)
for t in target_ds:
t.values, t.times = utils.calc_climatology_monthly(t)
else:
logger.error(
'Invalid time range provided. Only monthly is supported '
'at the moment'
)
return
if evaluation.subregions:
for bound_count, bound in enumerate(evaluation.subregions):
results = []
labels = []
subset = dsp.subset(
bound,
ref_ds,
subregion_name="R{}_{}".format(bound_count, ref_ds.name)
)
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
for t in target_ds:
subset = dsp.subset(
bound,
t,
subregion_name="R{}_{}".format(bound_count, t.name)
)
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
plots.draw_time_series(np.array(results),
ref_ds.times,
labels,
'R{}'.format(bound_count),
**plot_config.get('optional_args', {}))
else:
results = []
labels = []
results.append(utils.calc_time_series(ref_ds))
labels.append(ref_ds.name)
for t in target_ds:
results.append(utils.calc_time_series(t))
labels.append(t.name)
plots.draw_time_series(np.array(results),
ref_ds.times,
labels,
'time_series',
**plot_config.get('optional_args', {}))
def test_out_of_dataset_bounds_lat_min(self):
self.subregion.lat_min = -90
with self.assertRaises(ValueError):
dp.subset(self.target_dataset, self.subregion)
LON_MAX = 55 START = datetime.datetime(1999, 1, 1) END = datetime.datetime(2000, 12, 1) SEASON_MONTH_START = 1 SEASON_MONTH_END = 12 EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END) # Normalize the time values of our datasets so they fall on expected days # of the month. For example, monthly data will be normalized so that: # 15 Jan 2014, 15 Feb 2014 => 1 Jan 2014, 1 Feb 2014 ref_dataset = dsp.normalize_dataset_datetimes(ref_dataset, "monthly") target_dataset = dsp.normalize_dataset_datetimes(target_dataset, "monthly") # Subset down the evaluation datasets to our selected evaluation bounds. target_dataset = dsp.subset(EVAL_BOUNDS, target_dataset) ref_dataset = dsp.subset(EVAL_BOUNDS, ref_dataset) # Do a monthly temporal rebin of the evaluation datasets. target_dataset = dsp.temporal_rebin(target_dataset, datetime.timedelta(days=30)) ref_dataset = dsp.temporal_rebin(ref_dataset, datetime.timedelta(days=30)) # Spatially regrid onto a 1 degree lat/lon grid within our evaluation bounds. new_lats = np.arange(LAT_MIN, LAT_MAX, 1.0) new_lons = np.arange(LON_MIN, LON_MAX, 1.0) target_dataset = dsp.spatial_regrid(target_dataset, new_lats, new_lons) ref_dataset = dsp.spatial_regrid(ref_dataset, new_lats, new_lons) # Load the datasets for the evaluation. mean_bias = metrics.MeanBias() # These versions of the metrics require seasonal bounds prior to running
if time_info['maximum_overlap_period']:
start_time, end_time = utils.get_temporal_overlap([ref_dataset]+model_datasets)
print 'Maximum overlap period'
print 'start_time:', start_time
print 'end_time:', end_time
if temporal_resolution == 'monthly' and end_time.day !=1:
end_time = end_time.replace(day=1)
if ref_data_info['data_source'] == 'rcmed':
min_lat = np.max([min_lat, ref_dataset.lats.min()])
max_lat = np.min([max_lat, ref_dataset.lats.max()])
min_lon = np.max([min_lon, ref_dataset.lons.min()])
max_lon = np.min([max_lon, ref_dataset.lons.max()])
bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
ref_dataset = dsp.subset(ref_dataset, bounds)
if ref_dataset.temporal_resolution() != temporal_resolution:
ref_dataset = dsp.temporal_rebin(ref_dataset, temporal_resolution)
for idata,dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.subset(dataset, bounds)
if dataset.temporal_resolution() != temporal_resolution:
model_datasets[idata] = dsp.temporal_rebin(dataset, temporal_resolution)
# Temporaly subset both observation and model datasets for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']
ref_dataset = dsp.temporal_subset(ref_dataset,month_start, month_end,average_each_year)
for idata,dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.temporal_subset(dataset,month_start, month_end,average_each_year)
max_lon = np.min([max_lon, dataset.lons.max()])
if not 'boundary_type' in space_info:
bounds = Bounds(lat_min=min_lat,
lat_max=max_lat,
lon_min=min_lon,
lon_max=max_lon,
start=start_time,
end=end_time)
else:
bounds = Bounds(boundary_type=space_info['boundary_type'],
start=start_time,
end=end_time)
for i, dataset in enumerate(datasets):
datasets[i] = dsp.subset(dataset, bounds)
if dataset.temporal_resolution() != temporal_resolution:
datasets[i] = dsp.temporal_rebin(dataset, temporal_resolution)
# Temporally subset both observation and model datasets
# for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']
# For now we will treat the first listed dataset as the reference dataset for
# evaluation purposes.
for i, dataset in enumerate(datasets):
datasets[i] = dsp.temporal_subset(dataset, month_start, month_end,
average_each_year)
import ocw.dataset as ds
import ocw.data_source.local as local
import ocw.dataset_processor as dsp
import ocw.plotter as plotter
import numpy as np
import numpy.ma as ma
''' data source: https://dx.doi.org/10.6084/m9.figshare.3753321.v1
AOD_monthly_2000-Mar_2016-FEB_from_MISR_L3_JOINT.nc is publicly available.'''
dataset = local.load_file('AOD_monthly_2000-MAR_2016-FEB_from_MISR_L3_JOINT.nc',
'nonabsorbing_ave')
''' Subset the data for East Asia'''
Bounds = ds.Bounds(lat_min=20, lat_max=57.7, lon_min=90, lon_max=150)
dataset = dsp.subset(dataset, Bounds)
'''The original dataset includes nonabsorbing AOD values between March 2000 and February 2015.
dsp.temporal_subset will extract data in September-October-November.'''
dataset_SON = dsp.temporal_subset(
dataset, month_start=9, month_end=11, average_each_year=True)
ny, nx = dataset_SON.values.shape[1:]
# multi-year mean aod
clim_aod = ma.zeros([3, ny, nx])
clim_aod[0, :] = ma.mean(dataset_SON.values, axis=0) # 16-year mean
clim_aod[1, :] = ma.mean(dataset_SON.values[-5:, :],
axis=0) # the last 5-year mean
clim_aod[2, :] = dataset_SON.values[-1, :] # the last year's value
parameter_id,
min_lat,
max_lat,
min_lon,
max_lon,
start_time,
end_time)
""" Step 3: Resample Datasets so they are the same shape """
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s" % (knmi_dataset.values.shape,))
print("Our two datasets have a mis-match in time. We will subset on time to %s years\n" % YEARS)
# Create a Bounds object to use for subsetting
new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
knmi_dataset = dsp.subset(new_bounds, knmi_dataset)
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,))
print("Temporally Rebinning the Datasets to a Single Timestep")
# To run FULL temporal Rebinning use a timedelta > 366 days. I used 999 in this example
knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=999))
cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=999))
print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape,))
print("CRU31_Dataset.values shape: %s \n\n" % (cru31_dataset.values.shape,))
""" Spatially Regrid the Dataset Objects to a 1/2 degree grid """
# Using the bounds we will create a new set of lats and lons on 1 degree step
new_lons = np.arange(min_lon, max_lon, 0.5)
""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print("Working with the rcmed interface to get CRU3.1 Daily Precipitation")
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
""" Step 3: Processing datasets so they are the same shape ... """
print("Processing datasets so they are the same shape")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member])
target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
target_datasets[member] = dsp.normalize_dataset_datetimes(target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member], new_lats, new_lons)
#find climatology monthly for obs and models
CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))
""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print("Working with the rcmed interface to get CRU3.1 Daily Precipitation")
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
""" Step 3: Resample Datasets so they are the same shape """
print("Resampling datasets")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
CRU31 = dsp.temporal_rebin(CRU31, datetime.timedelta(days=30))
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member])
target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
target_datasets[member] = dsp.temporal_rebin(target_datasets[member], datetime.timedelta(days=30))
""" Spatially Regrid the Dataset Objects to a user defined grid """
# Using the bounds we will create a new set of lats and lons
print("Regridding datasets")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member], new_lats, new_lons)
#make the model ensemble
""" Step 3: Resample Datasets so they are the same shape """
print("Temporally Rebinning the Datasets to an Annual Timestep")
# To run annual temporal Rebinning use a timedelta of 360 days.
knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=360))
wrf311_dataset = dsp.temporal_rebin(wrf311_dataset, datetime.timedelta(days=360))
cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=360))
# Running Temporal Rebin early helps negate the issue of datasets being on different
# days of the month (1st vs. 15th)
# Create a Bounds object to use for subsetting
new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
# Subset our model datasets so they are the same size
knmi_dataset = dsp.subset(new_bounds, knmi_dataset)
wrf311_dataset = dsp.subset(new_bounds, wrf311_dataset)
""" Spatially Regrid the Dataset Objects to a 1/2 degree grid """
# Using the bounds we will create a new set of lats and lons on 1/2 degree step
new_lons = np.arange(min_lon, max_lon, 0.5)
new_lats = np.arange(min_lat, max_lat, 0.5)
# Spatially regrid datasets using the new_lats, new_lons numpy arrays
knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons)
wrf311_dataset = dsp.spatial_regrid(wrf311_dataset, new_lats, new_lons)
cru31_dataset = dsp.spatial_regrid(cru31_dataset, new_lats, new_lons)
# Generate an ensemble dataset from knmi and wrf models
ensemble_dataset = dsp.ensemble([knmi_dataset, wrf311_dataset])
]
model_dataset_season = [
dsp.temporal_subset(dataset,
month_start,
month_end,
average_each_year=True)
for dataset in model_dataset_subset
]
""" Spatial subset of obs_dataset and generate time series """
obs_timeseries = np.zeros([nyear, n_region
]) # region index 0-6: NW, SW, NGP, SGP, MW, NE, SE
model_timeseries = np.zeros([nmodel, nyear, n_region])
for iregion in np.arange(n_region):
obs_timeseries[:, iregion] = utils.calc_time_series(
dsp.subset(obs_dataset_season, regional_bounds[iregion]))
for imodel in np.arange(nmodel):
model_timeseries[imodel, :, iregion] = utils.calc_time_series(
dsp.subset(model_dataset_season[imodel], regional_bounds[iregion]))
year = np.arange(nyear)
regional_trends_obs = np.zeros(n_region)
regional_trends_obs_error = np.zeros(n_region)
regional_trends_model = np.zeros([nmodel, n_region])
regional_trends_model_error = np.zeros([nmodel, n_region])
regional_trends_ens = np.zeros(n_region)
regional_trends_ens_error = np.zeros(n_region)
for iregion in np.arange(n_region):
regional_trends_obs[iregion], regional_trends_obs_error[
model_datasets)
print 'Maximum overlap period'
print 'start_time:', start_time
print 'end_time:', end_time
if temporal_resolution == 'monthly' and end_time.day != 1:
end_time = end_time.replace(day=1)
if ref_data_info['data_source'] == 'rcmed':
min_lat = np.max([min_lat, ref_dataset.lats.min()])
max_lat = np.min([max_lat, ref_dataset.lats.max()])
min_lon = np.max([min_lon, ref_dataset.lons.min()])
max_lon = np.min([max_lon, ref_dataset.lons.max()])
bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
if ref_dataset.lats.ndim != 2 and ref_dataset.lons.ndim != 2:
ref_dataset = dsp.subset(bounds, ref_dataset)
for idata, dataset in enumerate(model_datasets):
if dataset.lats.ndim != 2 and dataset.lons.ndim != 2:
model_datasets[idata] = dsp.subset(bounds, dataset)
# Temporaly subset both observation and model datasets for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']
ref_dataset = dsp.temporal_subset(month_start, month_end, ref_dataset,
average_each_year)
for idata, dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.temporal_subset(month_start, month_end,
dataset, average_each_year)
def test_subset_name_propagation(self):
subset_name = 'foo_subset_name'
subset = dp.subset(self.target_dataset, self.subregion, subset_name)
self.assertEqual(subset.name, subset_name)
SEASON_MONTH_START = 1
SEASON_MONTH_END = 12
EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
# Normalize the time values of our datasets so they fall on expected days
# of the month. For example, monthly data will be normalized so that:
# 15 Jan 2014, 15 Feb 2014 => 1 Jan 2014, 1 Feb 2014
ref_dataset = dsp.normalize_dataset_datetimes(ref_dataset, 'monthly')
target_datasets = [
dsp.normalize_dataset_datetimes(target, 'monthly')
for target in target_datasets
]
# Subset down the evaluation datasets to our selected evaluation bounds.
ref_dataset = dsp.subset(EVAL_BOUNDS, ref_dataset)
target_datasets = [
dsp.subset(EVAL_BOUNDS, target) for target in target_datasets
]
# Do a monthly temporal rebin of the evaluation datasets.
ref_dataset = dsp.temporal_rebin(ref_dataset, datetime.timedelta(days=30))
target_datasets = [
dsp.temporal_rebin(target, datetime.timedelta(days=30))
for target in target_datasets
]
# Spatially regrid onto a 1 degree lat/lon grid within our evaluation bounds.
new_lats = np.arange(LAT_MIN, LAT_MAX, 1.0)
new_lons = np.arange(LON_MIN, LON_MAX, 1.0)
ref_dataset = dsp.spatial_regrid(ref_dataset, new_lats, new_lons)
target_datasets.append(local.load_file(FILE_3, varName, name='UCT'))
# Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module
print('Working with the rcmed interface to get CRU3.1 Daily Precipitation')
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(
10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
# Step 3: Processing datasets so they are the same shape
print('Processing datasets so they are the same shape')
CRU31 = dsp.water_flux_unit_conversion(CRU31)
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
target_datasets[member] = dsp.normalize_dataset_datetimes(
target_datasets[member], 'monthly')
print('... spatial regridding')
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] =\
dsp.spatial_regrid(target_datasets[member], new_lats, new_lons)
# Find climatology monthly for obs and models.
CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
target_datasets.append(local.load_file(FILE_1, varName, name="KNMI"))
target_datasets.append(local.load_file(FILE_2, varName, name="REGCM"))
target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))
""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print("Working with the rcmed interface to get CRU3.1 Daily Precipitation")
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX,
START, END)
""" Step 3: Processing datasets so they are the same shape ... """
print("Processing datasets so they are the same shape")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(EVAL_BOUNDS, target_datasets[member])
target_datasets[member] = dsp.water_flux_unit_conversion(
target_datasets[member])
target_datasets[member] = dsp.normalize_dataset_datetimes(
target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member],
new_lats, new_lons)
#find climatology monthly for obs and models
def test_out_of_dataset_bounds_lon_max(self):
self.subregion.lon_max = 180
with self.assertRaises(ValueError):
dp.subset(self.subregion, self.target_dataset)
import ssl
if hasattr(ssl, '_create_unverified_context'):
ssl._create_default_https_context = ssl._create_unverified_context
# rectangular boundary
min_lat = 15.75
max_lat = 55.75
min_lon = -125.75
max_lon = -66.75
start_time = datetime(1998,1,1)
end_time = datetime(1998,12,31)
TRMM_dataset = rcmed.parameter_dataset(3, 36, min_lat, max_lat, min_lon, max_lon,
start_time, end_time)
Cuba_and_Bahamas_bounds = Bounds(boundary_type='countries', countries=['Cuba','Bahamas'])
TRMM_dataset2 = dsp.subset(TRMM_dataset, Cuba_and_Bahamas_bounds, extract=False) # to mask out the data over Mexico and Canada
plotter.draw_contour_map(ma.mean(TRMM_dataset2.values, axis=0), TRMM_dataset2.lats, TRMM_dataset2.lons, fname='TRMM_without_Cuba_and_Bahamas')
NCA_SW_bounds = Bounds(boundary_type='us_states', us_states=['CA','NV','UT','AZ','NM','CO'])
TRMM_dataset3 = dsp.subset(TRMM_dataset2, NCA_SW_bounds, extract=True) # to mask out the data over Mexico and Canada
plotter.draw_contour_map(ma.mean(TRMM_dataset3.values, axis=0), TRMM_dataset3.lats, TRMM_dataset3.lons, fname='TRMM_NCA_SW')
def test_out_of_dataset_bounds_end(self):
self.subregion.end = datetime.datetime(2011, 1, 1)
with self.assertRaises(ValueError):
dp.subset(self.subregion, self.target_dataset)
min_lat = 15.75
max_lat = 55.75
min_lon = -125.75
max_lon = -66.75
start_time = datetime(1998, 1, 1)
end_time = datetime(1998, 12, 31)
TRMM_dataset = rcmed.parameter_dataset(3, 36, min_lat, max_lat, min_lon,
max_lon, start_time, end_time)
Cuba_and_Bahamas_bounds = Bounds(boundary_type='countries',
countries=['Cuba', 'Bahamas'])
# to mask out the data over Mexico and Canada
TRMM_dataset2 = dsp.subset(TRMM_dataset,
Cuba_and_Bahamas_bounds,
extract=False)
plotter.draw_contour_map(ma.mean(TRMM_dataset2.values, axis=0),
TRMM_dataset2.lats,
TRMM_dataset2.lons,
fname='TRMM_without_Cuba_and_Bahamas')
NCA_SW_bounds = Bounds(boundary_type='us_states',
us_states=['CA', 'NV', 'UT', 'AZ', 'NM', 'CO'])
# to mask out the data over Mexico and Canada
TRMM_dataset3 = dsp.subset(TRMM_dataset2, NCA_SW_bounds, extract=True)
plotter.draw_contour_map(ma.mean(TRMM_dataset3.values, axis=0),
TRMM_dataset3.lats,
TRMM_dataset3.lons,
knmi_dataset.name = "knmi"
wrf_dataset.name = "wrf"
# Date values from loaded datasets might not always fall on reasonable days.
# With monthly data, we could have data falling on the 1st, 15th, or some other
# day of the month. Let's fix that real quick.
##########################################################################
knmi_dataset = dsp.normalize_dataset_datetimes(knmi_dataset, 'monthly')
wrf_dataset = dsp.normalize_dataset_datetimes(wrf_dataset, 'monthly')
# We're only going to run this evaluation over a years worth of data. We'll
# make a Bounds object and use it to subset our datasets.
##########################################################################
subset = Bounds(lat_min=-45, lat_max=42, lon_min=-24, lon_max=60,
start=datetime.datetime(1989, 1, 1), end=datetime.datetime(1989, 12, 1))
knmi_dataset = dsp.subset(knmi_dataset, subset)
wrf_dataset = dsp.subset(wrf_dataset, subset)
# Temporally re-bin the data into a monthly timestep.
##########################################################################
knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution='monthly')
wrf_dataset = dsp.temporal_rebin(wrf_dataset, temporal_resolution='monthly')
# Spatially regrid the datasets onto a 1 degree grid.
##########################################################################
# Get the bounds of the reference dataset and use it to create a new
# set of lat/lon values on a 1 degree step
# Using the bounds we will create a new set of lats and lons on 1 degree step
min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries()
new_lons = numpy.arange(min_lon, max_lon, 1)
new_lats = numpy.arange(min_lat, max_lat, 1)
print("Fetching data from RCMED...")
cru31_dataset = rcmed.parameter_dataset(dataset_id, parameter_id, min_lat,
max_lat, min_lon, max_lon, start_time,
end_time)
""" Step 3: Resample Datasets so they are the same shape """
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" %
(cru31_dataset.values.shape, ))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s" %
(knmi_dataset.values.shape, ))
print(
"Our two datasets have a mis-match in time. We will subset on time to %s years\n"
% YEARS)
# Create a Bounds object to use for subsetting
new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
knmi_dataset = dsp.subset(new_bounds, knmi_dataset)
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" %
(cru31_dataset.values.shape, ))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" %
(knmi_dataset.values.shape, ))
print("Temporally Rebinning the Datasets to a Single Timestep")
# To run FULL temporal Rebinning use a timedelta > 366 days. I used 999 in this example
knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=999))
cru31_dataset = dsp.temporal_rebin(cru31_dataset, datetime.timedelta(days=999))
print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape, ))
print("CRU31_Dataset.values shape: %s \n\n" % (cru31_dataset.values.shape, ))
""" Spatially Regrid the Dataset Objects to a 1/2 degree grid """
# Using the bounds we will create a new set of lats and lons on 1 degree step
""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print(
"Working with the rcmed interface to get CRU3.1 Monthly Mean Precipitation"
)
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX,
START, END)
""" Step 3: Processing Datasets so they are the same shape """
print("Processing datasets ...")
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
print("... on units")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
target_datasets[member] = dsp.water_flux_unit_conversion(
target_datasets[member])
target_datasets[member] = dsp.normalize_dataset_datetimes(
target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member],
new_lats, new_lons)
# find the total annual mean. Note the function exists in util.py as def
target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))
""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print("Working with the rcmed interface to get CRU3.1 Monthly Mean Precipitation")
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
""" Step 3: Processing Datasets so they are the same shape """
print("Processing datasets ...")
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
print("... on units")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[member])
target_datasets[member] = dsp.normalize_dataset_datetimes(target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member], new_lats, new_lons)
#find the total annual mean. Note the function exists in util.py as def calc_climatology_year(dataset):
_,CRU31.values = utils.calc_climatology_year(CRU31)
for member, each_target_dataset in enumerate(target_datasets):
knmi_dataset.name = "knmi" wrf_dataset.name = "wrf" # Date values from loaded datasets might not always fall on reasonable days. # With monthly data, we could have data falling on the 1st, 15th, or some other # day of the month. Let's fix that real quick. ################################################################################ knmi_dataset = dsp.normalize_dataset_datetimes(knmi_dataset, 'monthly') wrf_dataset = dsp.normalize_dataset_datetimes(wrf_dataset, 'monthly') # We're only going to run this evaluation over a years worth of data. We'll # make a Bounds object and use it to subset our datasets. ################################################################################ subset = Bounds(-45, 42, -24, 60, datetime.datetime(1989, 1, 1), datetime.datetime(1989, 12, 1)) knmi_dataset = dsp.subset(subset, knmi_dataset) wrf_dataset = dsp.subset(subset, wrf_dataset) # Temporally re-bin the data into a monthly timestep. ################################################################################ knmi_dataset = dsp.temporal_rebin(knmi_dataset, datetime.timedelta(days=30)) wrf_dataset = dsp.temporal_rebin(wrf_dataset, datetime.timedelta(days=30)) # Spatially regrid the datasets onto a 1 degree grid. ################################################################################ # Get the bounds of the reference dataset and use it to create a new # set of lat/lon values on a 1 degree step # Using the bounds we will create a new set of lats and lons on 1 degree step min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries() new_lons = numpy.arange(min_lon, max_lon, 1) new_lats = numpy.arange(min_lat, max_lat, 1)
# under the License.
import ocw.dataset as ds
import ocw.data_source.local as local
import ocw.dataset_processor as dsp
import ocw.plotter as plotter
import numpy as np
import numpy.ma as ma
''' data source: https://dx.doi.org/10.6084/m9.figshare.3753321.v1
AOD_monthly_2000-Mar_2016-FEB_from_MISR_L3_JOINT.nc is publicly available.'''
dataset = local.load_file(
'AOD_monthly_2000-MAR_2016-FEB_from_MISR_L3_JOINT.nc', 'nonabsorbing_ave')
''' Subset the data for East Asia'''
Bounds = ds.Bounds(lat_min=20, lat_max=57.7, lon_min=90, lon_max=150)
dataset = dsp.subset(dataset, Bounds)
'''The original dataset includes nonabsorbing AOD values between March 2000 and February 2015.
dsp.temporal_subset will extract data in September-October-November.'''
dataset_SON = dsp.temporal_subset(dataset,
month_start=9,
month_end=11,
average_each_year=True)
ny, nx = dataset_SON.values.shape[1:]
# multi-year mean aod
clim_aod = ma.zeros([3, ny, nx])
clim_aod[0, :] = ma.mean(dataset_SON.values, axis=0) # 16-year mean
clim_aod[1, :] = ma.mean(dataset_SON.values[-5:, :],
axis=0) # the last 5-year mean
ssl._create_default_https_context = ssl._create_unverified_context
# rectangular boundary
min_lat = 15.75
max_lat = 55.75
min_lon = -125.75
max_lon = -66.75
start_time = datetime(1998, 1, 1)
end_time = datetime(1998, 12, 31)
TRMM_dataset = rcmed.parameter_dataset(3, 36, min_lat, max_lat, min_lon, max_lon,
start_time, end_time)
Cuba_and_Bahamas_bounds = Bounds(
boundary_type='countries', countries=['Cuba', 'Bahamas'])
# to mask out the data over Mexico and Canada
TRMM_dataset2 = dsp.subset(
TRMM_dataset, Cuba_and_Bahamas_bounds, extract=False)
plotter.draw_contour_map(ma.mean(TRMM_dataset2.values, axis=0), TRMM_dataset2.lats,
TRMM_dataset2.lons, fname='TRMM_without_Cuba_and_Bahamas')
NCA_SW_bounds = Bounds(boundary_type='us_states', us_states=[
'CA', 'NV', 'UT', 'AZ', 'NM', 'CO'])
# to mask out the data over Mexico and Canada
TRMM_dataset3 = dsp.subset(TRMM_dataset2, NCA_SW_bounds, extract=True)
plotter.draw_contour_map(ma.mean(TRMM_dataset3.values, axis=0),
TRMM_dataset3.lats, TRMM_dataset3.lons, fname='TRMM_NCA_SW')
max_lon = np.min([max_lon, dataset.lons.max()])
if not 'boundary_type' in space_info:
bounds = Bounds(lat_min=min_lat,
lat_max=max_lat,
lon_min=min_lon,
lon_max=max_lon,
start=start_time,
end=end_time)
else:
bounds = Bounds(boundary_type=space_info['boundary_type'],
start=start_time,
end=end_time)
for i, dataset in enumerate(datasets):
datasets[i] = dsp.subset(dataset, bounds)
if dataset.temporal_resolution() != temporal_resolution:
datasets[i] = dsp.temporal_rebin(datasets[i], temporal_resolution)
# Temporally subset both observation and model datasets
# for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']
# For now we will treat the first listed dataset as the reference dataset for
# evaluation purposes.
for i, dataset in enumerate(datasets):
datasets[i] = dsp.temporal_subset(dataset, month_start, month_end,
average_each_year)
LON_MAX = 55
START = datetime.datetime(1999, 1, 1)
END = datetime.datetime(2000, 12, 1)
SEASON_MONTH_START = 1
SEASON_MONTH_END = 12
EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)
# Normalize the time values of our datasets so they fall on expected days
# of the month. For example, monthly data will be normalized so that:
# 15 Jan 2014, 15 Feb 2014 => 1 Jan 2014, 1 Feb 2014
ref_dataset = dsp.normalize_dataset_datetimes(ref_dataset, "monthly")
target_dataset = dsp.normalize_dataset_datetimes(target_dataset, "monthly")
# Subset down the evaluation datasets to our selected evaluation bounds.
target_dataset = dsp.subset(EVAL_BOUNDS, target_dataset)
ref_dataset = dsp.subset(EVAL_BOUNDS, ref_dataset)
# Do a monthly temporal rebin of the evaluation datasets.
target_dataset = dsp.temporal_rebin(target_dataset,
datetime.timedelta(days=30))
ref_dataset = dsp.temporal_rebin(ref_dataset, datetime.timedelta(days=30))
# Spatially regrid onto a 1 degree lat/lon grid within our evaluation bounds.
new_lats = np.arange(LAT_MIN, LAT_MAX, 1.0)
new_lons = np.arange(LON_MIN, LON_MAX, 1.0)
target_dataset = dsp.spatial_regrid(target_dataset, new_lats, new_lons)
ref_dataset = dsp.spatial_regrid(ref_dataset, new_lats, new_lons)
# Load the datasets for the evaluation.
mean_bias = metrics.MeanBias()
def test_out_of_dataset_bounds_start(self):
self.subregion.start = datetime.datetime(1999, 1, 1)
with self.assertRaises(ValueError):
dp.subset(self.target_dataset, self.subregion)
def test_subset_name(self):
subset = dp.subset(self.target_dataset, self.subregion)
self.assertEqual(subset.name, self.name)
max_lon = np.min([max_lon, dataset.lons.max()])
if not 'boundary_type' in space_info:
bounds = Bounds(lat_min=min_lat,
lat_max=max_lat,
lon_min=min_lon,
lon_max=max_lon,
start=start_time,
end=end_time)
else:
bounds = Bounds(boundary_type=space_info['boundary_type'],
start=start_time,
end=end_time)
for i, dataset in enumerate(obs_datasets):
obs_datasets[i] = dsp.subset(dataset, bounds)
if dataset.temporal_resolution() != temporal_resolution:
obs_datasets[i] = dsp.temporal_rebin(dataset, temporal_resolution)
for i, dataset in enumerate(model_datasets):
model_datasets[i] = dsp.subset(dataset, bounds)
if dataset.temporal_resolution() != temporal_resolution:
model_datasets[i] = dsp.temporal_rebin(dataset, temporal_resolution)
# Temporally subset both observation and model datasets
# for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']
# TODO: Fully support multiple observation / reference datasets.
parameter_id,
min_lat,
max_lat,
min_lon,
max_lon,
start_time,
end_time)
""" Step 3: Resample Datasets so they are the same shape """
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s" % (knmi_dataset.values.shape,))
print("Our two datasets have a mis-match in time. We will subset on time to %s years\n" % YEARS)
# Create a Bounds object to use for subsetting
new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
knmi_dataset = dsp.subset(knmi_dataset, new_bounds)
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" % (cru31_dataset.values.shape,))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" % (knmi_dataset.values.shape,))
print("Temporally Rebinning the Datasets to a Single Timestep")
# To run FULL temporal Rebinning
knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution = 'full')
cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution = 'full')
print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape,))
print("CRU31_Dataset.values shape: %s \n\n" % (cru31_dataset.values.shape,))
""" Spatially Regrid the Dataset Objects to a 1/2 degree grid """
# Using the bounds we will create a new set of lats and lons on 0.5 degree step
new_lons = np.arange(min_lon, max_lon, 0.5)
# With monthly data, we could have data falling on the 1st, 15th, or some other
# day of the month. Let's fix that real quick.
##########################################################################
knmi_dataset = dsp.normalize_dataset_datetimes(knmi_dataset, 'monthly')
wrf_dataset = dsp.normalize_dataset_datetimes(wrf_dataset, 'monthly')
# We're only going to run this evaluation over a years worth of data. We'll
# make a Bounds object and use it to subset our datasets.
##########################################################################
subset = Bounds(lat_min=-45,
lat_max=42,
lon_min=-24,
lon_max=60,
start=datetime.datetime(1989, 1, 1),
end=datetime.datetime(1989, 12, 1))
knmi_dataset = dsp.subset(knmi_dataset, subset)
wrf_dataset = dsp.subset(wrf_dataset, subset)
# Temporally re-bin the data into a monthly timestep.
##########################################################################
knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution='monthly')
wrf_dataset = dsp.temporal_rebin(wrf_dataset, temporal_resolution='monthly')
# Spatially regrid the datasets onto a 1 degree grid.
##########################################################################
# Get the bounds of the reference dataset and use it to create a new
# set of lat/lon values on a 1 degree step
# Using the bounds we will create a new set of lats and lons on 1 degree step
min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries()
new_lons = numpy.arange(min_lon, max_lon, 1)
new_lats = numpy.arange(min_lat, max_lat, 1)
def test_out_of_dataset_bounds_lon_max(self):
self.subregion.lon_max = 180
with self.assertRaises(ValueError):
dp.subset(self.target_dataset, self.subregion)
end_time = min([end_time, dataset_end])
cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution='annual')
dataset_start, dataset_end = cru31_dataset.temporal_boundaries()
start_time = max([start_time, dataset_start])
end_time = min([end_time, dataset_end])
print("Time Range is: %s to %s" % (start_time.strftime("%Y-%m-%d"),
end_time.strftime("%Y-%m-%d")))
# Create a Bounds object to use for subsetting
new_bounds = Bounds(lat_min=min_lat, lat_max=max_lat, lon_min=min_lon,
lon_max=max_lon, start=start_time, end=end_time)
# Subset our model datasets so they are the same size
knmi_dataset = dsp.subset(knmi_dataset, new_bounds)
wrf311_dataset = dsp.subset(wrf311_dataset, new_bounds)
# Spatially Regrid the Dataset Objects to a 1/2 degree grid.
# Using the bounds we will create a new set of lats and lons on 1/2 degree step
new_lons = np.arange(min_lon, max_lon, 0.5)
new_lats = np.arange(min_lat, max_lat, 0.5)
# Spatially regrid datasets using the new_lats, new_lons numpy arrays
knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons)
wrf311_dataset = dsp.spatial_regrid(wrf311_dataset, new_lats, new_lons)
cru31_dataset = dsp.spatial_regrid(cru31_dataset, new_lats, new_lons)
# Generate an ensemble dataset from knmi and wrf models
ensemble_dataset = dsp.ensemble([knmi_dataset, wrf311_dataset])
def test_out_of_dataset_bounds_end(self):
self.subregion.end = datetime.datetime(2011, 1, 1)
with self.assertRaises(ValueError):
dp.subset(self.target_dataset, self.subregion)
def test_subset_name(self):
subset = dp.subset(self.subregion, self.target_dataset)
self.assertEqual(subset.name, self.name)
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" %
(cru31_dataset.values.shape, ))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s" %
(knmi_dataset.values.shape, ))
print(
"Our two datasets have a mis-match in time. We will subset on time to %s years\n"
% YEARS)
# Create a Bounds object to use for subsetting
new_bounds = Bounds(lat_min=min_lat,
lat_max=max_lat,
lon_min=min_lon,
lon_max=max_lon,
start=start_time,
end=end_time)
knmi_dataset = dsp.subset(knmi_dataset, new_bounds)
print("CRU31_Dataset.values shape: (times, lats, lons) - %s" %
(cru31_dataset.values.shape, ))
print("KNMI_Dataset.values shape: (times, lats, lons) - %s \n" %
(knmi_dataset.values.shape, ))
print("Temporally Rebinning the Datasets to a Single Timestep")
# To run FULL temporal Rebinning
knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution='full')
cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution='full')
print("KNMI_Dataset.values shape: %s" % (knmi_dataset.values.shape, ))
print("CRU31_Dataset.values shape: %s \n\n" % (cru31_dataset.values.shape, ))
""" Spatially Regrid the Dataset Objects to a 1/2 degree grid """
# Using the bounds we will create a new set of lats and lons on 0.5 degree step
average_each_year=True)
""" Temporal subset of model_dataset """
model_dataset_subset = [dsp.temporal_slice(dataset,start_time=start_date, end_time=end_date)
for dataset in model_dataset]
model_dataset_season = [dsp.temporal_subset(dataset, month_start, month_end,
average_each_year=True) for dataset in model_dataset_subset]
""" Spatial subset of obs_dataset and generate time series """
obs_timeseries = np.zeros([nyear, n_region]) # region index 0-6: NW, SW, NGP, SGP, MW, NE, SE
model_timeseries = np.zeros([nmodel, nyear, n_region])
for iregion in np.arange(n_region):
obs_timeseries[:, iregion] = utils.calc_time_series(
dsp.subset(obs_dataset_season, regional_bounds[iregion]))
for imodel in np.arange(nmodel):
model_timeseries[imodel, :, iregion] = utils.calc_time_series(
dsp.subset(model_dataset_season[imodel], regional_bounds[iregion]))
year = np.arange(nyear)
regional_trends_obs = np.zeros(n_region)
regional_trends_obs_error = np.zeros(n_region)
regional_trends_model = np.zeros([nmodel, n_region])
regional_trends_model_error = np.zeros([nmodel, n_region])
regional_trends_ens = np.zeros(n_region)
regional_trends_ens_error = np.zeros(n_region)
for iregion in np.arange(n_region):
regional_trends_obs[iregion], regional_trends_obs_error[iregion] = utils.calculate_temporal_trend_of_time_series(
""" Step 3: Resample Datasets so they are the same shape """
print("Temporally Rebinning the Datasets to an Annual Timestep")
# To run annual temporal Rebinning,
knmi_dataset = dsp.temporal_rebin(knmi_dataset, temporal_resolution = 'annual')
wrf311_dataset = dsp.temporal_rebin(wrf311_dataset, temporal_resolution = 'annual')
cru31_dataset = dsp.temporal_rebin(cru31_dataset, temporal_resolution = 'annual')
# Running Temporal Rebin early helps negate the issue of datasets being on different
# days of the month (1st vs. 15th)
# Create a Bounds object to use for subsetting
new_bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)
# Subset our model datasets so they are the same size
knmi_dataset = dsp.subset(knmi_dataset, new_bounds)
wrf311_dataset = dsp.subset(wrf311_dataset, new_bounds)
""" Spatially Regrid the Dataset Objects to a 1/2 degree grid """
# Using the bounds we will create a new set of lats and lons on 1/2 degree step
new_lons = np.arange(min_lon, max_lon, 0.5)
new_lats = np.arange(min_lat, max_lat, 0.5)
# Spatially regrid datasets using the new_lats, new_lons numpy arrays
knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons)
wrf311_dataset = dsp.spatial_regrid(wrf311_dataset, new_lats, new_lons)
cru31_dataset = dsp.spatial_regrid(cru31_dataset, new_lats, new_lons)
# Generate an ensemble dataset from knmi and wrf models
ensemble_dataset = dsp.ensemble([knmi_dataset, wrf311_dataset])
def test_out_of_dataset_bounds_lat_min(self):
self.subregion.lat_min = -90
with self.assertRaises(ValueError):
dp.subset(self.subregion, self.target_dataset)
def test_subset_name_propagation(self):
subset_name = 'foo_subset_name'
subset = dp.subset(self.subregion, self.target_dataset, subset_name)
self.assertEqual(subset.name, subset_name)
file_path='./data/WRF24_2010_summer/',
filename_pattern=['wrf2dout*'])
# Step 2: Load the spatial filter (Bukovsky region mask).
Bukovsky_mask = Bounds(
boundary_type='user',
user_mask_file='Bukovsky_regions.nc',
mask_variable_name='Bukovsky',
longitude_name='lon',
latitude_name='lat')
# Step 3: Spatial subset the WRF data (for Northern Great Plains, user_mask_values=[10]).
WRF_dataset_filtered = \
dsp.subset(WRF_dataset, Bukovsky_mask, user_mask_values=[10])
# Step 4: Analyze the wet spells.
duration1, peak1, total1 = \
metrics.wet_spell_analysis(GPM_dataset_filtered, threshold=0.1, nyear=1, dt=0.5)
duration2, peak2, total2 =\
metrics.wet_spell_analysis(WRF_dataset_filtered.values, threshold=0.1, nyear=1, dt=0.5)
# Step 5: Calculate the joint PDF(JPDF) of spell_duration and peak_rainfall.
histo2d_GPM = \
metrics.calc_joint_histogram(data_array1=duration1, data_array2=peak1,
bins_for_data1=np.append(np.arange(25)+0.5, [48.5, 120.5]),
bins_for_data2=[0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10, 20, 30])
histo2d_GPM = histo2d_GPM/np.sum(histo2d_GPM) * 100.