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Use this notebook to obtain "expected" values for

test_geom_imshow_nan_values.py

test suite.

In [1]:
import numpy as np
from lets_plot import *

LetsPlot.setup_html()
In [2]:
LetsPlot.set_theme(flavor_solarized_light())
In [3]:
arr = np.array([
    [50., 150., 200.],
    [200., 100., 50.]
    ])
In [4]:
ggplot() + geom_imshow(arr)

Normalization: 0.00015306472778320312
Clipping: 7.295608520507812e-05
image_2d: 0.00016307830810546875
png.Writer: 0.00023698806762695312
base64: 0.00018596649169921875
Out[4]:
In [5]:
# With NaN values

arr_nan = np.array([
    [50., np.nan, 200.],
    [np.nan, 100., 50.]
    ])
In [6]:
ggplot() + geom_imshow(arr_nan)

LA add alpha: 5.1021575927734375e-05
Normalization: 0.0002892017364501953
Clipping: 5.698204040527344e-05
image_2d: 6.29425048828125e-05
png.Writer: 0.00018787384033203125
base64: 2.7179718017578125e-05
Out[6]:

NaN values + cmap

In [7]:
ggplot() + geom_imshow(arr_nan, cmap="magma")

Normalization: 0.0001461505889892578
Clipping: 0.00010395050048828125
image_2d: 3.528594970703125e-05
png.Writer: 0.00505375862121582
base64: 0.00016117095947265625
Out[7]:
In [8]:
_.as_dict()
Out[8]:
{'mapping': {},
 'data_meta': {},
 'theme': {'flavor': 'solarized_light'},
 'kind': 'plot',
 'scales': [],
 'layers': [{'geom': 'image',
   'mapping': {},
   'data_meta': {},
   'href': 'data:image/png;base64,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',
   'xmin': -0.5,
   'ymin': -0.5,
   'xmax': 2.5,
   'ymax': 1.5}],
 'metainfo_list': []}
In [ ]: