In [1]:
%pylab notebook
import matplotlib.pyplot as plt
import numpy as np


Populating the interactive namespace from numpy and matplotlib

In [3]:
%cd /vagrant/source/charistools/test
%ls


/vagrant/source/charistools/test
20010629.MODICE.SOI.EGI.before_fix.txt  test_basin.500.melt.png
all_tests.py                            test_basin.500.tri.imshow.png
all_tests.pyc                           test_calibration_modelEnv_config.ini
calibration_modelEnv_config.ini         test_calibration_modelEnv_config.ini~
calibration_modelEnv_config.ini~        test_convertors.py
compare/                                test_convertors.pyc
h23v05.1.v5.dat.npy                     test.dat
h23v05.1.v6.dat.npy                     test_files/
h23v05.2.v5.dat.npy                     test_hypsometry.py
h23v05.2.v6.dat.npy                     test_hypsometry.pyc
h23v05.3.v5.dat.npy                     test_meltModels.py
h23v05.3.v6.dat.npy                     test_meltModels.pyc
h24v05.1.v5.dat.npy                     test_model_env.ini~
h24v05.1.v6.dat.npy                     test_modelEnv.py
h24v05.2.v5.dat.npy                     test_modelEnv.pyc
h24v05.2.v6.dat.npy                     test_modis_tile_bounds.py
h24v05.3.v5.dat.npy                     test_modis_tile_bounds.pyc
h24v05.3.v6.dat.npy                     test_readers.py
__init__.py                             test_readers.pyc
__init__.pyc                            test_run_calibration_stats.py
melt_model_config.ini                   test_run_calibration_stats.py~
modis_tiles_config.ini                  test_run_calibration_stats.pyc
sca_by_elev.barplot.png                 test_run_forcings_by_drainageid.py
sca_by_elev.imshow.png                  test_run_forcings_by_drainageid.py~
SOI_by_elev.2001.0100m.txt              test_run_forcings_by_drainageid.pyc
test_ablation_bug.py                    test_run_forcings_env.ini
test_ablation_bug.py~                   test_run_forcings_env.ini~
test_ablation_bug.pyc                   test_timeSeries.py
test_basin.500.imshow.png               test_timeSeries.py~
test_basin.500.input_plot.png           test_timeSeries.pyc

In [32]:
h23_v5 = np.load("h23v05.1.v5.dat.npy")
h23_v6 = np.load("h23v05.1.v6.dat.npy")
albedo_h23_v5 = np.load("h23v05.1.v5.dat.albedo.npy")
albedo_h23_v6 = np.load("h23v05.1.v6.dat.albedo.npy")

In [33]:
print(np.amin(h23_v5), np.amax(h23_v5))
print(len(h23_v5[h23_v5 != 0.]))


(0.0, 1.0)
3674

In [34]:
print(np.amin(h23_v6), np.amax(h23_v6))
print(len(h23_v6[h23_v6 != 0.]))


(0.0, 1.0)
3962

In [35]:
fig, ax = plt.subplots(1,2)
ax.shape
ax[0].imshow(h23_v5)
ax[1].imshow(h23_v6)


Out[35]:
<matplotlib.image.AxesImage at 0x7fa194f681d0>

In [36]:
diff = h23_v6 - h23_v5

In [37]:
len(diff[diff != 0])


Out[37]:
288

In [38]:
diff[diff != 0]


Out[38]:
array([ 0.79999995,  0.75      ,  0.88      ,  0.83999997,  0.81999999,
        0.81999999,  0.83999997,  0.77999997,  0.74000001,  0.75999999,
        0.81999999,  0.78999996,  0.70999998,  0.81      ,  0.88999999,
        0.71999997,  0.71999997,  0.81999999,  0.85999995,  0.89999998,
        0.78999996,  0.81      ,  0.83999997,  0.81999999,  0.83999997,
        0.79999995,  0.34999999,  0.78999996,  0.88      ,  0.47      ,
        0.78999996,  0.59999996,  0.84999996,  0.71999997,  0.74000001,
        0.74000001,  0.87      ,  0.84999996,  0.88      ,  0.37      ,
        0.29999998,  0.22999999,  0.75      ,  0.63      ,  0.66999996,
        0.63999999,  0.35999998,  0.55000001,  0.62      ,  0.70999998,
        0.85999995,  0.78999996,  0.71999997,  0.71999997,  0.82999998,
        0.75      ,  0.72999996,  0.74000001,  0.75      ,  0.69      ,
        0.81999999,  0.74000001,  0.71999997,  0.72999996,  0.76999998,
        0.70999998,  0.78999996,  0.88      ,  0.56999999,  0.53999996,
        0.51999998,  0.28999999,  0.28999999,  0.53999996,  0.50999999,
        0.56999999,  0.31999999,  0.17      ,  0.47999999,  0.48999998,
        0.42999998,  0.59999996,  0.56      ,  0.41      ,  0.63999999,
        0.64999998,  0.22999999,  0.22      ,  0.51999998,  0.57999998,
        0.78999996,  0.78999996,  0.74000001,  0.74000001,  0.75999999,
        0.77999997,  0.56999999,  0.52999997,  0.57999998,  0.59999996,
        0.88      ,  0.72999996,  0.5       ,  0.77999997,  0.89999998,
        0.88999999,  0.61000001,  0.69999999,  0.75999999,  0.82999998,
        0.68000001,  0.66999996,  0.57999998,  0.79999995,  0.56999999,
        0.42999998,  0.53999996,  0.79999995,  0.63      ,  0.72999996,
        0.74000001,  0.22999999,  0.47      ,  0.69      ,  0.77999997,
        0.76999998,  0.71999997,  0.69      ,  0.68000001,  0.64999998,
        0.42999998,  0.66999996,  0.68000001,  0.88      ,  0.88999999,
        0.75999999,  0.77999997,  0.63999999,  0.77999997,  0.75999999,
        0.81      ,  0.70999998,  0.72999996,  0.75999999,  0.72999996,
        0.75999999,  0.76999998,  0.77999997,  0.72999996,  0.69999999,
        0.78999996,  0.68000001,  0.76999998,  0.79999995,  0.69999999,
        0.77999997,  0.81999999,  0.77999997,  0.74000001,  0.74000001,
        0.75      ,  0.70999998,  0.75      ,  0.83999997,  0.55000001,
        0.81      ,  0.63      ,  0.48999998,  0.84999996,  0.82999998,
        0.75999999,  0.81      ,  0.79999995,  0.76999998,  0.70999998,
        0.26999998,  0.76999998,  0.82999998,  0.81      ,  0.94999999,
        0.94      ,  0.82999998,  0.83999997,  0.75999999,  0.88      ,
        0.74000001,  0.79999995,  0.41      ,  0.56      ,  0.39999998,
        0.32999998,  0.75      ,  0.76999998,  0.75999999,  0.11      ,
        0.62      ,  0.75999999,  0.76999998,  0.74000001,  0.88      ,
        0.74000001,  0.72999996,  0.69999999,  0.88999999,  0.91999996,
        0.76999998,  0.71999997,  0.74000001,  0.74000001,  0.70999998,
        0.69999999,  0.69      ,  0.69999999,  0.84999996,  0.34      ,
        0.75      ,  0.76999998,  0.69999999,  0.59999996,  0.69      ,
        0.69      ,  0.64999998,  0.68000001,  0.72999996,  0.74000001,
        0.29999998,  0.16      ,  0.14999999,  0.19      ,  0.19      ,
        0.81999999,  0.74000001,  0.65999997,  0.75      ,  0.52999997,
        0.72999996,  0.69999999,  0.55000001,  0.5       ,  0.28      ,
        0.78999996,  0.88999999,  0.41999999,  0.63999999,  0.85999995,
        0.82999998,  0.69999999,  0.72999996,  0.81      ,  0.77999997,
        0.61000001,  0.25999999,  0.32999998,  0.69      ,  0.81999999,
        0.77999997,  0.69      ,  0.23999999,  0.12      ,  0.37      ,
        0.48999998,  0.57999998,  0.63      ,  0.57999998,  0.65999997,
        0.41999999,  0.56999999,  0.65999997,  0.75      ,  0.69      ,
        0.55000001,  0.62      ,  0.70999998,  0.70999998,  0.78999996,
        0.75999999,  0.75999999,  0.45999998,  0.47999999,  0.45999998,
        0.72999996,  0.48999998,  0.71999997,  0.95999998,  0.85999995,
        0.77999997,  0.94      ,  0.94999999], dtype=float32)

In [23]:
3962 - 3674


Out[23]:
288

In [24]:
fig, ax = plt.subplots()
plt.imshow(diff)


Out[24]:
<matplotlib.image.AxesImage at 0x7fa194fdd190>

In [39]:
idx = diff != 0
len(diff[idx])


Out[39]:
288

In [40]:
h23_v5[idx]


Out[40]:
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.], dtype=float32)

In [41]:
h23_v6[idx]


Out[41]:
array([ 0.79999995,  0.75      ,  0.88      ,  0.83999997,  0.81999999,
        0.81999999,  0.83999997,  0.77999997,  0.74000001,  0.75999999,
        0.81999999,  0.78999996,  0.70999998,  0.81      ,  0.88999999,
        0.71999997,  0.71999997,  0.81999999,  0.85999995,  0.89999998,
        0.78999996,  0.81      ,  0.83999997,  0.81999999,  0.83999997,
        0.79999995,  0.34999999,  0.78999996,  0.88      ,  0.47      ,
        0.78999996,  0.59999996,  0.84999996,  0.71999997,  0.74000001,
        0.74000001,  0.87      ,  0.84999996,  0.88      ,  0.37      ,
        0.29999998,  0.22999999,  0.75      ,  0.63      ,  0.66999996,
        0.63999999,  0.35999998,  0.55000001,  0.62      ,  0.70999998,
        0.85999995,  0.78999996,  0.71999997,  0.71999997,  0.82999998,
        0.75      ,  0.72999996,  0.74000001,  0.75      ,  0.69      ,
        0.81999999,  0.74000001,  0.71999997,  0.72999996,  0.76999998,
        0.70999998,  0.78999996,  0.88      ,  0.56999999,  0.53999996,
        0.51999998,  0.28999999,  0.28999999,  0.53999996,  0.50999999,
        0.56999999,  0.31999999,  0.17      ,  0.47999999,  0.48999998,
        0.42999998,  0.59999996,  0.56      ,  0.41      ,  0.63999999,
        0.64999998,  0.22999999,  0.22      ,  0.51999998,  0.57999998,
        0.78999996,  0.78999996,  0.74000001,  0.74000001,  0.75999999,
        0.77999997,  0.56999999,  0.52999997,  0.57999998,  0.59999996,
        0.88      ,  0.72999996,  0.5       ,  0.77999997,  0.89999998,
        0.88999999,  0.61000001,  0.69999999,  0.75999999,  0.82999998,
        0.68000001,  0.66999996,  0.57999998,  0.79999995,  0.56999999,
        0.42999998,  0.53999996,  0.79999995,  0.63      ,  0.72999996,
        0.74000001,  0.22999999,  0.47      ,  0.69      ,  0.77999997,
        0.76999998,  0.71999997,  0.69      ,  0.68000001,  0.64999998,
        0.42999998,  0.66999996,  0.68000001,  0.88      ,  0.88999999,
        0.75999999,  0.77999997,  0.63999999,  0.77999997,  0.75999999,
        0.81      ,  0.70999998,  0.72999996,  0.75999999,  0.72999996,
        0.75999999,  0.76999998,  0.77999997,  0.72999996,  0.69999999,
        0.78999996,  0.68000001,  0.76999998,  0.79999995,  0.69999999,
        0.77999997,  0.81999999,  0.77999997,  0.74000001,  0.74000001,
        0.75      ,  0.70999998,  0.75      ,  0.83999997,  0.55000001,
        0.81      ,  0.63      ,  0.48999998,  0.84999996,  0.82999998,
        0.75999999,  0.81      ,  0.79999995,  0.76999998,  0.70999998,
        0.26999998,  0.76999998,  0.82999998,  0.81      ,  0.94999999,
        0.94      ,  0.82999998,  0.83999997,  0.75999999,  0.88      ,
        0.74000001,  0.79999995,  0.41      ,  0.56      ,  0.39999998,
        0.32999998,  0.75      ,  0.76999998,  0.75999999,  0.11      ,
        0.62      ,  0.75999999,  0.76999998,  0.74000001,  0.88      ,
        0.74000001,  0.72999996,  0.69999999,  0.88999999,  0.91999996,
        0.76999998,  0.71999997,  0.74000001,  0.74000001,  0.70999998,
        0.69999999,  0.69      ,  0.69999999,  0.84999996,  0.34      ,
        0.75      ,  0.76999998,  0.69999999,  0.59999996,  0.69      ,
        0.69      ,  0.64999998,  0.68000001,  0.72999996,  0.74000001,
        0.29999998,  0.16      ,  0.14999999,  0.19      ,  0.19      ,
        0.81999999,  0.74000001,  0.65999997,  0.75      ,  0.52999997,
        0.72999996,  0.69999999,  0.55000001,  0.5       ,  0.28      ,
        0.78999996,  0.88999999,  0.41999999,  0.63999999,  0.85999995,
        0.82999998,  0.69999999,  0.72999996,  0.81      ,  0.77999997,
        0.61000001,  0.25999999,  0.32999998,  0.69      ,  0.81999999,
        0.77999997,  0.69      ,  0.23999999,  0.12      ,  0.37      ,
        0.48999998,  0.57999998,  0.63      ,  0.57999998,  0.65999997,
        0.41999999,  0.56999999,  0.65999997,  0.75      ,  0.69      ,
        0.55000001,  0.62      ,  0.70999998,  0.70999998,  0.78999996,
        0.75999999,  0.75999999,  0.45999998,  0.47999999,  0.45999998,
        0.72999996,  0.48999998,  0.71999997,  0.95999998,  0.85999995,
        0.77999997,  0.94      ,  0.94999999], dtype=float32)

In [43]:
albedo_h23_v5[idx]


Out[43]:
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.], dtype=float32)

In [44]:
albedo_h23_v6[idx]


Out[44]:
array([-99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
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       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
       -99., -99., -99., -99., -99., -99., -99., -99., -99., -99., -99.,
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In [ ]: