def main(window):
window = int(window)
years = range(2006, 2015)
fullvcm = []
fullnprof = []
for year in years:
mask = 'out.{:02d}/{:04d}/*.nc4'.format(window, year)
print mask
try:
vcm = da.read_nc(mask, vcm_name, axis='file')
except ValueError:
print 'No monthlies for {:04d}, skipping'.format(year)
continue
# vcm.reset_axis(filename_to_datetime, 'file')
nprof = da.read_nc(mask, 'nprof', axis='file')
# nprof.reset_axis(filename_to_datetime, 'file')
fullvcm.append(vcm)
fullnprof.append(nprof)
vcm = da.concatenate(fullvcm, axis='file')
nprof = da.concatenate(fullnprof, axis='file')
print vcm, nprof
np.savez('series_%d.npz' % window,
vcm=vcm,
nprof=nprof,
altmin=vcm.labels[1],
time=vcm.labels[0],
lon=vcm.labels[2])
def test_concatenate_2d():
a = DimArray([[ 1, 2, 3],
[11, 22, 33]], axes=[[0,1],[2,1,0]])
b = DimArray([[44, 55, 66],
[4, 5, 6]], axes=[[1,0],[2,1,0]])
c0_got = concatenate((a, b), axis=0)
c0_got_ds = concatenate_ds(_make_datasets(a, b), axis=0)
c0 = DimArray([[ 1, 2, 3],
[11, 22, 33],
[44, 55, 66],
[4, 5, 6]], axes=[[0,1,1,0],[2,1,0]])
assert_equal_dimarrays(c0_got, c0)
assert_equal_dimarrays(c0_got_ds['a'], c0)
# axis "x0" is not aligned !
with pytest.raises(ValueError):
c1_got = concatenate((a, b), axis=1)
print(c1_got)
c1_got = concatenate((a, b), axis=1, align=True, sort=True)
c1_got_ds = concatenate_ds(_make_datasets(a, b), axis=1, align=True, sort=True)
c1 = DimArray([[ 1, 2, 3, 4, 5, 6],
[11, 22, 33, 44, 55, 66]], axes=[[0,1],[2,1,0,2,1,0]])
assert_equal_dimarrays(c1_got, c1)
assert_equal_dimarrays(c1_got_ds['a'], c1)
def test_concatenate_1d():
a = DimArray([1,2,3], dims=['x0'])
b = DimArray([11,22,33], dims=['x0'])
c = DimArray([1, 2, 3, 11, 22, 33], axes=[[0, 1, 2, 0, 1, 2]], dims=['x0'])
# a = DimArray([1,2,3], axes=[['a','b','c']])
# b = DimArray([4,5,6], axes=[['d','e','f']])
# c = DimArray([1, 2, 3, 4, 5, 6], axes=[['a','b','c','d','e','f']])
c_got = concatenate((a, b))
c_got_ds = concatenate_ds(_make_datasets(a, b))
assert_equal_dimarrays(c_got, c)
assert_equal_dimarrays(c_got_ds['a'], c)
def main(window):
window = int(window)
years = range(2006,2015)
fullvcm = []
fullnprof = []
for year in years:
mask = 'out.{:02d}/{:04d}/*.nc4'.format(window, year)
print mask
try:
vcm = da.read_nc(mask, vcm_name, axis='file')
except ValueError:
print 'No monthlies for {:04d}, skipping'.format(year)
continue
# vcm.reset_axis(filename_to_datetime, 'file')
nprof = da.read_nc(mask, 'nprof', axis='file')
# nprof.reset_axis(filename_to_datetime, 'file')
fullvcm.append(vcm)
fullnprof.append(nprof)
vcm = da.concatenate(fullvcm, axis='file')
nprof = da.concatenate(fullnprof, axis='file')
print vcm, nprof
np.savez('series_%d.npz' % window, vcm=vcm, nprof=nprof, altmin=vcm.labels[1], time=vcm.labels[0], lon=vcm.labels[2])
import __settings
model_dict = __settings.model_dict
masks = da.read_nc('masks/srex_mask_' + model_dict[model]['grid'] + '.nc')
all_files = sorted(
glob.glob(in_path + scenario + '/monEKE*_' + scenario + '*.nc'))
big_merge = {}
big_merge['eke'] = da.read_nc(all_files[0])['eke'][:, 0:, :]
big_merge['run_id'] = da.read_nc(all_files[0])['eke'][:, 0:, :].copy()
big_merge['run_id'].values = 0
for i_run, file_name in enumerate(all_files[1:]):
print(file_name)
big_merge['eke'] = da.concatenate(
(big_merge['eke'], da.read_nc(file_name)['eke'][:, 0:, :]))
tmp = da.read_nc(file_name)['eke'][:, 0:, :].copy()
tmp.values = i_run + 1
big_merge['run_id'] = da.concatenate((big_merge['run_id'], tmp))
for region in [
'EAS', 'TIB', 'CAS', 'WAS', 'MED', 'CEU', 'ENA', 'CNA', 'WNA', 'NAS',
'NEU', 'CGI', 'ALA'
]:
mask = masks[region][0:, :]
lats = np.where(np.nanmax(mask, axis=1) != 0)[0]
lons = np.where(np.nanmax(mask, axis=0) != 0)[0]
da.Dataset({
key: val.ix[:, lats, lons]
for key, val in big_merge.items()
'/stats_' + corWith_name + '*' +
scenario + '*_' + state + '.nc')
tmp_4 = {}
for file_name in all_files:
region = file_name.split('_')[-2]
if region != 'NHml':
tmp = da.stack(da.read_nc(file_name),
axis='statistic',
align=True)
tmp_4[region] = tmp.mean(axis=(-2, -1))
tmp_4[region].values = np.nanmean(tmp, axis=(-2, -1))
tmp_3_ = da.stack(tmp_3, align=True, axis='region')
tmp_4_ = da.stack(tmp_4, align=True, axis='region')
tmp_2[corWith_name] = da.concatenate((tmp_3_, tmp_4_),
align=True,
axis='statistic')
tmp_1[state] = da.stack(tmp_2, axis='corWith', align=True)
tmp_0[model] = da.stack(tmp_1, axis='state', align=True)
result[scenario] = da.stack(tmp_0, axis='model', align=True)
da.Dataset({
'summary_cor': da.stack(result, axis='sceanrio', align=True)
}).write_nc('data/cor_reg_summary.nc')
#
'grid_60.0_244.0', 'grid_60.0_246.0', 'grid_60.0_250.0', 'grid_60.0_252.0',
'grid_64.0_238.0', 'grid_58.0_248.0', 'grid_58.0_250.0', 'grid_56.0_256.0',
'grid_56.0_258.0', 'grid_58.0_258.0', 'grid_54.0_260.0', 'grid_54.0_262.0',
'grid_52.0_262.0', 'grid_50.0_262.0', 'grid_52.0_264.0', 'grid_50.0_264.0',
'grid_48.0_268.0', 'grid_48.0_270.0', 'grid_48.0_272.0', 'grid_48.0_274.0',
'grid_48.0_276.0', 'grid_50.0_272.0', 'grid_46.0_268.0', 'grid_46.0_270.0',
'grid_46.0_272.0', 'grid_46.0_274.0', 'grid_46.0_276.0', 'grid_46.0_278.0',
'grid_46.0_280.0', 'grid_44.0_272.0', 'grid_44.0_274.0', 'grid_44.0_276.0',
'grid_44.0_278.0', 'grid_44.0_280.0', 'grid_44.0_282.0', 'grid_44.0_284.0',
'grid_42.0_272.0', 'grid_42.0_274.0', 'grid_42.0_276.0', 'grid_42.0_278.0',
'grid_42.0_280.0', 'grid_42.0_282.0'
]
slr = da.read_nc('data/slr.nc')['slr']
slr_copied = da.read_nc('data/slr_copied.nc')['slr']
slr = da.concatenate((slr, slr_copied), axis='ID')
stations = da.read_nc('data/stations.nc')['stations']
stations_copied = da.read_nc('data/stations_copied.nc')['stations']
stations = da.concatenate((stations, stations_copied), axis='name')
station_lons, station_lats, station_names = [], [], []
grid_xmin, grid_xmax, grid_ymin, grid_ymax, grid_names = [], [], [], [], []
for name in list(set(stations.name)):
if stations[name, 'tide']:
station_names.append(name)
station_lons.append(float(stations[name, 'lon']))
station_lats.append(float(stations[name, 'lat']))
if stations[name, 'tide'] == False:
if name not in inland_grids:
import matplotlib.pylab as plt
import dimarray as da
from statsmodels.sandbox.stats import multicomp
os.chdir(
'/Users/peterpfleiderer/Documents/Projects/Scripts/allgemeine_scripte')
import plot_map as plot_map
reload(plot_map)
from plot_map import col_conv
os.chdir('/Users/peterpfleiderer/Documents/Projects/HAPPI_persistence')
sum_dict = {}
tmp_1 = da.read_nc('data/MIROC5_SummaryMeanQu.nc')['SummaryMeanQu']
tmp_2 = da.read_nc('data/MIROC5_SummaryKS.nc')['SummaryKS']
sum_dict['MIROC5'] = da.concatenate((tmp_1, tmp_2), axis='type')
tmp_1 = da.read_nc('data/NORESM1_SummaryMeanQu.nc')['SummaryMeanQu']
tmp_2 = da.read_nc('data/NORESM1_SummaryKS.nc')['SummaryKS']
sum_dict['NORESM1'] = da.concatenate((tmp_1, tmp_2), axis='type')
tmp_1 = da.read_nc('data/ECHAM6-3-LR_SummaryMeanQu.nc')['SummaryMeanQu']
tmp_2 = da.read_nc('data/ECHAM6-3-LR_SummaryKS.nc')['SummaryKS']
sum_dict['ECHAM6-3-LR'] = da.concatenate((tmp_1, tmp_2), axis='type')
tmp_1 = da.read_nc('data/CAM4-2degree_SummaryMeanQu.nc')['SummaryMeanQu']
tmp_2 = da.read_nc('data/CAM4-2degree_SummaryKS.nc')['SummaryKS']
sum_dict['CAM4-2degree'] = da.concatenate((tmp_1, tmp_2), axis='type')
tmp_1 = da.read_nc(
'../HadGHCND_persistence/data/HadGHCND_SummaryMeanQu.nc')['SummaryMeanQu']
datevar.append(num2date(nc_rcp85.variables['time'][:],units = nc_rcp85.variables['time'].units,calendar = nc_rcp85.variables['time'].calendar))
year=np.array([int(str(date).split("-")[0]) for date in datevar[0][:]])
var_in=nc_rcp85.variables[var_name][:,:,:]
if var_in.mean()>150:
var_in-=273.15
input_rcp85=da.DimArray(var_in[:,:,:].squeeze(), axes=[year, lat, lon],dims=['year', 'lat', 'lon'] )
datevar = []
datevar.append(num2date(nc_hist.variables['time'][:],units = nc_hist.variables['time'].units,calendar = nc_hist.variables['time'].calendar))
year=np.array([int(str(date).split("-")[0]) for date in datevar[0][:]])
var_in=nc_hist.variables[var_name][:,:,:]
if var_in.mean()>150:
var_in-=273.15
input_hist=da.DimArray(var_in[:,:,:].squeeze(), axes=[year, lat, lon],dims=['year', 'lat', 'lon'] )
input_data=da.concatenate((input_hist, input_rcp85), axis='year')
cmip5_dict[model][var]=pdf.PDF_Processing(var)
cmip5_dict[model][var].mask_for_ref_period_data_coverage(input_data,ref_period,check_ref_period_only=False,target_periods=target_periods)
# Derive time slices
cmip5_dict[model][var].derive_time_slices(ref_period,target_periods,period_names)
cmip5_dict[model][var].derive_distributions()
for change in levels:
if len(cmip5_dict[model][var]._distributions['global'][str(change)]-cmip5_dict[model][var]._distributions['global']['ref'])>0:
cmip5_dict[model][var].derive_pdf_difference('ref',str(change),pdf_method=pdf_method,bin_range=varin_dict[var]['cut_interval'],relative_diff=False)
else:
print(cmip5_dict[model][var]._distributions['global'][str(change)]-cmip5_dict[model][var]._distributions['global']['ref'])
break