def test_get_coordinates():
ncs = [local_path(TESTDATA['cordex_tasmax_2006_nc']),
local_path(TESTDATA['cordex_tasmax_2007_nc'])]
lats, lons = utils.get_coordinates(ncs, unrotate=False)
assert 1 == len(lats.shape)
lats, lons = utils.get_coordinates(ncs)
assert 103 == len(lats)
assert 106 == len(lons)
def fieldmean(resource):
"""
calculating of a weighted field mean
:param resource: str or list of str containing the netCDF files pathes
:return list: averaged values
"""
from flyingpigeon.utils import get_values, get_coordinates
from numpy import radians, average, cos, sqrt
data = get_values(resource) # np.squeeze(ds.variables[variable][:])
dim = data.shape
if len(data.shape) == 3:
# TODO if data.shape == 2 , 4 ...
lats, lons = get_coordinates(resource, unrotate=True)
if len(lats.shape) == 2:
# TODO: calcult weighed average with 2D lats (rotated pole coordinates)
lats, lons = get_coordinates(resource)
if dim[0] == len(lats):
lat_index = 0
elif dim[1] == len(lats):
lat_index = 1
elif dim[2] == len(lats):
lat_index = 2
else:
LOGGER.exception(
'length of latitude is not matching values dimensions')
lat_w = sqrt(cos(lats * radians(1)))
meanLon = average(data, axis=lat_index, weights=lat_w)
meanTimeserie = average(meanLon, axis=1)
print('fieldmean calculated')
else:
print('not 3D shaped data. Average can not be calculated')
return meanTimeserie
def plot_extend(resource, file_extension='png'):
"""
plots the extend (domain) of the values stored in a netCDF file:
:parm resource: path to netCDF file
:param file_extension: file format of the graphic. if file_extension=None a matplotlib figure will be returned
:return graphic: graphic in specified format
"""
import matplotlib.patches as mpatches
lats, lons = utils.get_coordinates(resource, unrotate=True)
# box_top = 45
# x, y = [-20, -20, 45, 45, -44], [-45, box_top, box_top, -45, -45]
xy = np.array([[np.min(lons), np.min(lats)],
[np.max(lons), np.min(lats)],
[np.max(lons), np.max(lats)],
[np.min(lons), np.max(lats)]])
fig = plt.figure(figsize=(20, 10), dpi=600, facecolor='w', edgecolor='k')
projection = ccrs.Robinson()
# ccrs.Orthographic(central_longitude=np.mean(xy[:, 0]),
# central_latitude=np.mean(xy[:, 1]),
# globe=None) # Robinson()
ax = plt.axes(projection=projection)
ax.stock_img()
ax.coastlines()
ax.add_patch(mpatches.Polygon(xy, closed=True, transform=ccrs.PlateCarree(), color='coral', alpha=0.6))
# ccrs.Geodetic()
ax.gridlines()
plt.show()
if file_extension is None:
map_graphic = fig
else:
map_graphic = fig2plot(fig=fig, file_extension=file_extension)
plt.close()
return map_graphic
def get_PAmask(coordinates=[], nc=None):
"""
generates a matrix with 1/0 values over land areas. (NaN for water regions)
:param coordinates: 2D array with lat lon coordinates representing tree observation
:param domain: region (default='EUR-11')
:return : PAmask
"""
from scipy import spatial
import numpy as np
import numpy.ma as ma
# from netCDF4 import Dataset
# from flyingpigeon import config
# DIR_MASKS = config.masks_dir()
from flyingpigeon.utils import get_variable
from flyingpigeon.utils import get_coordinates, get_values # unrotate_pole,
lats, lons = np.array(get_coordinates(nc)) # unrotate_pole(nc, write_to_file=False))
sftlf = get_values(nc)[0, :, :]
#
# sftlf[sftlf.mask is True] = 0
# sftlf[sftlf.mask is False] = np.nan
domain = sftlf.shape
lats1D = np.array(lats).ravel()
lons1D = np.array(lons).ravel()
tree = spatial.KDTree(zip(lats1D, lons1D))
l, i = tree.query(coordinates)
PA = np.zeros(len(lats1D))
PA[i] = 1
#
PAmask = PA.reshape(domain)
PAmask[sftlf.mask] = np.nan
return PAmask
def fieldmean(resource):
"""
calculating of a weighted field mean
:param resource: str or list of str containing the netCDF files pathes
:return list: timeseries of the averaged values per timepstep
"""
from flyingpigeon.utils import get_values, get_coordinates, get_index_lat
from numpy import radians, average, cos, sqrt, array
data = get_values(resource) # np.squeeze(ds.variables[variable][:])
dim = data.shape
LOGGER.debug(data.shape)
if len(data.shape) == 3:
# TODO if data.shape == 2 , 4 ...
lats, lons = get_coordinates(resource, unrotate=False)
lats = array(lats)
if len(lats.shape) == 2:
lats = lats[:, 0]
else:
LOGGER.debug('Latitudes not reduced to 1D')
# TODO: calculat weighed average with 2D lats (rotated pole coordinates)
# lats, lons = get_coordinates(resource, unrotate=False)
# if len(lats.shape) == 2:
# lats, lons = get_coordinates(resource)
lat_index = get_index_lat(resource)
LOGGER.debug('lats dimension %s ' % len(lats.shape))
LOGGER.debug('lats index %s' % lat_index)
lat_w = sqrt(cos(lats * radians(1)))
meanLon = average(data, axis=lat_index, weights=lat_w)
meanTimeserie = average(meanLon, axis=1)
LOGGER.debug('fieldmean calculated')
else:
LOGGER.error('not 3D shaped data. Average can not be calculated')
return meanTimeserie
def map_spatial_analog(ncfile, variable='dissimilarity', cmap='viridis', title='Spatial analog'):
"""Return a matplotlib Figure instance showing a map of the dissimilarity measure.
"""
import netCDF4 as nc
from flyingpigeon import utils
from mpl_toolkits.axes_grid import make_axes_locatable
import matplotlib.axes as maxes
try:
var = utils.get_values(ncfile, variable)
LOGGER.info('Data loaded')
lats, lons = utils.get_coordinates(ncfile, variable=variable, unrotate=False)
if len(lats.shape) == 1:
cyclic_var, cyclic_lons = add_cyclic_point(var, coord=lons)
lons = cyclic_lons.data
var = cyclic_var
with nc.Dataset(ncfile) as D:
V = D.variables[variable]
lon, lat = map(float, V.target_location.split(','))
LOGGER.info('Lat and lon loaded')
except Exception as e:
msg = 'Failed to get data for plotting: {0}\n{1}'.format(ncfile, e)
LOGGER.exception(msg)
raise Exception(msg)
try:
fig = plt.figure(facecolor='w', edgecolor='k')
fig.subplots_adjust(top=.95, bottom=.05, left=.03, right=.95)
ax = plt.axes(
projection=ccrs.Robinson(central_longitude=int(np.mean(lons))))
divider = make_axes_locatable(ax)
cax = divider.new_horizontal("4%", pad=0.15, axes_class=maxes.Axes)
fig.add_axes(cax)
ax.plot(lon, lat, marker='o', mfc='#292421', ms=13, transform=ccrs.PlateCarree())
ax.plot(lon, lat, marker='o', mfc='#ffffff', ms=7, transform=ccrs.PlateCarree())
cs = ax.contourf(lons, lats, var, 60,
transform=ccrs.PlateCarree(),
cmap=cmap, interpolation='nearest')
ax.coastlines(color='k', linewidth=.8)
ax.set_title(title)
cb = plt.colorbar(cs, cax=cax, orientation='vertical')
cb.set_label(u"– Dissimilarity +") # ha='left', va='center')
cb.set_ticks([])
except:
msg = 'failed to plot graphic'
LOGGER.exception(msg)
LOGGER.info('Plot created and figure saved')
return fig
def map_robustness(signal, high_agreement_mask, low_agreement_mask,
variable=None, cmap='seismic', title=None,
file_extension='png'):
"""
generates a graphic for the output of the ensembleRobustness process for a lat/long file.
:param signal: netCDF file containing the signal difference over time
:param highagreement:
:param lowagreement:
:param variable:
:param cmap: default='seismic',
:param title: default='Model agreement of signal'
:returns str: path/to/file.png
"""
from flyingpigeon import utils
from numpy import mean, ma
if variable is None:
variable = utils.get_variable(signal)
try:
var_signal = utils.get_values(signal)
mask_l = utils.get_values(low_agreement_mask)
mask_h = utils.get_values(high_agreement_mask)
# mask_l = ma.masked_where(low < 0.5, low)
# mask_h = ma.masked_where(high < 0.5, high)
# mask_l[mask_l == 0] = np.nan
# mask_h[mask_h == 0] = np.nan
LOGGER.info('data loaded')
lats, lons = utils.get_coordinates(signal, unrotate=True)
if len(lats.shape) == 1:
cyclic_var, cyclic_lons = add_cyclic_point(var_signal, coord=lons)
mask_l, cyclic_lons = add_cyclic_point(mask_l, coord=lons)
mask_h, cyclic_lons = add_cyclic_point(mask_h, coord=lons)
lons = cyclic_lons.data
var_signal = cyclic_var
LOGGER.info('lat lon loaded')
minval = round(np.nanmin(var_signal))
maxval = round(np.nanmax(var_signal)+.5)
LOGGER.info('prepared data for plotting')
except:
msg = 'failed to get data for plotting'
LOGGER.exception(msg)
raise Exception(msg)
try:
fig = plt.figure(facecolor='w', edgecolor='k')
ax = plt.axes(projection=ccrs.Robinson(central_longitude=int(mean(lons))))
norm = MidpointNormalize(midpoint=0)
cs = plt.contourf(lons, lats, var_signal, 60, norm=norm, transform=ccrs.PlateCarree(),
cmap=cmap, interpolation='nearest')
cl = plt.contourf(lons, lats, mask_l, 1, transform=ccrs.PlateCarree(), colors='none', hatches=[None, '/'])
ch = plt.contourf(lons, lats, mask_h, 1, transform=ccrs.PlateCarree(), colors='none', hatches=[None, '.'])
# artists, labels = ch.legend_elements()
# plt.legend(artists, labels, handleheight=2)
# plt.clim(minval,maxval)
ax.coastlines()
ax.gridlines()
# ax.set_global()
if title is None:
plt.title('%s with Agreement' % variable)
else:
plt.title(title)
plt.colorbar(cs)
plt.annotate('// = low model ensemble agreement', (0, 0), (0, -10),
xycoords='axes fraction', textcoords='offset points', va='top')
plt.annotate('.. = high model ensemble agreement', (0, 0), (0, -20),
xycoords='axes fraction', textcoords='offset points', va='top')
graphic = fig2plot(fig=fig, file_extension=file_extension)
plt.close()
LOGGER.info('Plot created and figure saved')
except:
msg = 'failed to plot graphic'
LOGGER.exception(msg)
return graphic
def map_robustness(signal,
high_agreement_mask,
low_agreement_mask,
cmap='seismic',
title=None):
"""
generates a graphic for the output of the ensembleRobustness process for a lat/long file.
:param signal: netCDF file containing the signal difference over time
:param highagreement:
:param lowagreement:
:param variable:
:param cmap: default='seismic',
:param title: default='Model agreement of signal'
:returns str: path/to/file.png
"""
try:
# from flyingpigeon.utils import get_values
from cartopy.util import add_cyclic_point
# from flyingpigeon.utils import get_coordinates
var_signal = utils.get_values(signal)
mask_l = utils.get_values(low_agreement_mask)
mask_h = utils.get_values(high_agreement_mask)
mask_l.mask = var_signal.mask
mask_h.mask = var_signal.mask
mask_l.mask[mask_l.data is 0] = False
mask_h.mask[mask_h.data is 0] = False
LOGGER.debug('values loaded')
lats, lons = utils.get_coordinates(signal)
#
# cyclic_var, cyclic_lons = add_cyclic_point(var_signal, coord=lons)
# mask_l, cyclic_lons = add_cyclic_point(mask_l, coord=lons)
# mask_h, cyclic_lons = add_cyclic_point(mask_h, coord=lons)
#
# lons = cyclic_lons.data
# var_signal = cyclic_var
#
LOGGER.debug('coordinates loaded')
#
minval = round(np.nanmin(var_signal))
maxval = round(np.nanmax(var_signal) + .5)
LOGGER.info('prepared data for plotting')
except:
msg = 'failed to get data for plotting'
LOGGER.exception(msg)
try:
fig = plt.figure(facecolor='w',
edgecolor='k') # figsize=(20,10), dpi=600,
ax = plt.axes(projection=ccrs.Robinson(central_longitude=0))
norm = MidpointNormalize(midpoint=0)
cs = plt.contourf(lons,
lats,
var_signal,
60,
transform=ccrs.PlateCarree(),
norm=norm,
cmap=cmap,
interpolation='nearest')
cl = plt.contourf(lons,
lats,
mask_l,
60,
transform=ccrs.PlateCarree(),
colors='none',
hatches=['//'])
ch = plt.contourf(lons,
lats,
mask_h,
60,
transform=ccrs.PlateCarree(),
colors='none',
hatches=['.'])
# plt.clim(minval, maxval)
ax.coastlines()
# ax.set_global()
if title is None:
plt.title('Robustness')
else:
plt.title(title)
plt.colorbar(cs)
plt.annotate('// = low model ensemble agreement', (0, 0), (0, -10),
xycoords='axes fraction',
textcoords='offset points',
va='top')
plt.annotate('.. = high model ensemble agreement', (0, 0), (0, -20),
xycoords='axes fraction',
textcoords='offset points',
va='top')
_, graphic = mkstemp(dir='.', suffix='.png')
fig.savefig(graphic)
plt.close()
LOGGER.info('Plot created and figure saved')
except:
msg = 'failed to plot graphic'
LOGGER.exception(msg)
_, graphic = mkstemp(dir='.', suffix='.png')
return graphic