def test_single_percentile_data():
n = 1000
x = np.arange(n, dtype=np.float)
y = np.ones(n)
s = scaling.lin_cdf_match(y, x)
nptest.assert_almost_equal(s, np.full_like(s, np.nan))
s = scaling.cdf_match(y, x)
nptest.assert_almost_equal(s, np.full_like(s, np.nan))
def test_single_percentile_data():
n = 1000
x = np.arange(n, dtype=np.float)
y = np.ones(n)
s = scaling.lin_cdf_match(y, x)
nptest.assert_almost_equal(s, np.full_like(s, np.nan))
s = scaling.cdf_match(y, x)
nptest.assert_almost_equal(s, np.full_like(s, np.nan))
def compare_ssm_index(index, warp_gpi, sm_dataset, start=None, end=None,
weekly=True, plot=False):
df = ssm_iwmi.IWMI_read_csv()
iwmi_bare, iwmi_crop = ssm_iwmi.IWMI_ts_index(df, index)
if sm_dataset == 'cci':
sm_ts = ssm_TUW.read_CCI(warp_gpi, start, end)
if sm_dataset == 'ascat':
sm_ts = ssm_TUW.read_ASCAT_ssm(warp_gpi)
if sm_dataset == 'ers':
sm_ts = ssm_TUW.read_ERS_ssm(warp_gpi, args=['sm'], start=None,
end=None)
if weekly == True:
iwmi_bare_weekly = iwmi_bare.resample('W', how='mean').dropna()
iwmi_crop_weekly = iwmi_crop.resample('W', how='mean').dropna()
sm_ts_weekly = sm_ts.resample('W', how='mean').dropna()
# correlation of bare and crop
if len(iwmi_bare) != 0 and len(iwmi_crop) != 0:
iwmi_bare_weekly.columns.values[0] = 1
match_bare_crop = temp_match.matching(iwmi_crop_weekly, iwmi_bare_weekly)
corr_crop_bare = metrics.spearmanr(match_bare_crop.iloc[:, 0], match_bare_crop.iloc[:, 1])[0]
print corr_crop_bare
if len(iwmi_bare) == 0:
iwmi_bare_weekly = iwmi_bare_weekly
else:
match_bare = temp_match.matching(sm_ts_weekly, iwmi_bare_weekly)
iwmi_bare_weekly_match = match_bare.iloc[:,1]
sm_ts_weekly_bare = match_bare.iloc[:,0]
iwmi_bare_resc = scaling.lin_cdf_match(iwmi_bare_weekly.iloc[:,0],
sm_ts_weekly)
iwmi_bare_weekly = pd.DataFrame(iwmi_bare_resc,
index=iwmi_bare_weekly.index)
corr_bare = metrics.spearmanr(iwmi_bare_weekly_match,
sm_ts_weekly_bare)[0]
if len(iwmi_crop) == 0:
iwmi_crop_weekly = iwmi_crop_weekly
else:
match_crop = temp_match.matching(sm_ts_weekly, iwmi_crop_weekly)
iwmi_crop_weekly_match = match_crop.iloc[:, 1]
sm_ts_weekly_crop = match_crop.iloc[:,0]
iwmi_crop_resc = scaling.lin_cdf_match(iwmi_crop_weekly.iloc[:,0],
sm_ts_weekly)
iwmi_crop_weekly = pd.DataFrame(iwmi_crop_resc,
index=iwmi_crop_weekly.index)
corr_crop = metrics.spearmanr(iwmi_crop_weekly_match,
sm_ts_weekly_crop)[0]
else:
if len(iwmi_bare) == 0:
iwmi_bare_resc = iwmi_bare
else:
iwmi_bare_resc = scaling.lin_cdf_match(iwmi_bare, sm_ts)
if len(iwmi_crop) == 0:
iwmi_crop_resc = iwmi_crop
else:
iwmi_crop_resc = scaling.lin_cdf_match(iwmi_crop, sm_ts)
iwmi_bare = pd.DataFrame(iwmi_bare_resc,
index=iwmi_bare.index)
iwmi_crop = pd.DataFrame(iwmi_crop_resc,
index=iwmi_crop.index)
if plot == True:
if weekly == True:
sm_ts_plot = sm_ts_weekly
iwmi_bare_plot = iwmi_bare_weekly
iwmi_crop_plot = iwmi_crop_weekly
else:
sm_ts_plot = sm_ts
iwmi_bare_plot = iwmi_bare
iwmi_crop_plot = iwmi_crop
ax = sm_ts_plot.plot(color='b')
if len(iwmi_crop_plot) != 0:
iwmi_crop_plot.plot(color='r', ax=ax)
if len(iwmi_bare_plot) != 0:
iwmi_bare_plot.plot(color='g', ax=ax)
plt.legend([sm_dataset+' ts', 'iwmi crop, index '+str(index),
'iwmi bare, index '+str(index)])
if sm_dataset in ['ers', 'ascat']:
plt.ylabel('degree of saturation [%]')
if 'corr_crop_bare' in locals():
plt.title('corr_bare_crop ='+str(round(corr_crop_bare, 3)))
plt.ylim([0, 140])
else:
plt.ylabel('volumetric soil moisture [m3/m3]')
if 'corr_bare' in locals() and 'corr_crop' in locals():
plt.title('corr_bare = '+str(round(corr_bare,3))+
' corr_crop = '+str(round(corr_crop,3))+
'\n corr_bare_crop = '+str(round(corr_crop_bare,3)))
elif 'corr_bare' in locals():
plt.title('corr_bare = '+str(round(corr_bare,3)))
elif 'corr_crop' in locals():
plt.title('corr_crop = '+str(round(corr_crop,3)))
plt.ylim([0, 100])
plt.grid()
#plt.show()
plt.savefig(os.path.join(root.x, 'staff', 'ipfeil', 'iwmi_plots',
sm_dataset,
sm_dataset+'_cdf_'+str(index)+'.png'))
plt.clf()
return iwmi_bare_plot, iwmi_crop_plot, sm_ts_plot
amsre_dat_obj = datasets.DatasetTs(amsre_datfile)
amsre_gpi_data = amsre_dat_obj.read_ts(gpi)
tmi_dat_obj = datasets.DatasetTs(tmi_datfile)
tmi_gpi_data = tmi_dat_obj.read_ts(gpi)
print amsre_gpi_data.dtype, amsre_gpi_data.shape
print tmi_gpi_data.dtype, tmi_gpi_data.shape
# In CDF evaluation should be only values without Nan
wh = amsre_gpi_data['sm'] > -100
amsre_gpi_data['sm'][wh]
wh2 = tmi_gpi_data['sm'] > -100
tmi_gpi_data
scalled_data = scaling.lin_cdf_match(tmi_gpi_data['sm'][wh2],
amsre_gpi_data['sm'][wh])
print tmi_gpi_data['sm'].shape
print scalled_data.shape
# Estimating the parameters
in_data = tmi_gpi_data['sm'][wh2]
scale_to = amsre_gpi_data['sm'][wh]
print "In data:", in_data
print "Scale to data:", scale_to
percentiles = [0, 5, 10, 30, 50, 70, 90, 95, 100]
in_data_pctl = np.array(np.percentile(in_data, percentiles))
scale_to_pctl = np.array(np.percentile(scale_to, percentiles))
uniq_ind = np.unique(in_data_pctl, return_index=True)[1]