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Vasudevan, Rama K
pycroscopy
Commits
058f5a20
Commit
058f5a20
authored
Jun 28, 2017
by
Unknown
Browse files
Swapped default colormap to new 'viridis'
parent
ccec4dd1
Changes
2
Hide whitespace changes
Inline
Side-by-side
pycroscopy/viz/be_viz_utils.py
View file @
058f5a20
...
...
@@ -377,21 +377,21 @@ def jupyter_visualize_be_spectrograms(h5_main):
spatial_map
=
np
.
abs
(
np
.
reshape
(
h5_main
[:,
0
],
pos_dims
))
spectrogram
=
np
.
reshape
(
h5_main
[
0
],
(
num_udvs_steps
,
-
1
))
fig
,
axes
=
plt
.
subplots
(
ncols
=
3
,
figsize
=
(
12
,
4
))
# spatial_img = axes[0].imshow(np.abs(spatial_map), cmap=plt.cm.
jet
)
# spatial_img = axes[0].imshow(np.abs(spatial_map), cmap=plt.cm.
viridis
)
spatial_img
=
plot_map
(
axes
[
0
],
np
.
abs
(
spatial_map
),
origin
=
'lower'
,
cmap
=
cmap_jet_white_center
())
axes
[
0
].
set_xlabel
(
'X'
)
axes
[
0
].
set_ylabel
(
'Y'
)
main_vert_line
=
axes
[
0
].
axvline
(
x
=
int
(
0.5
*
spatial_map
.
shape
[
1
]),
color
=
'k'
)
main_hor_line
=
axes
[
0
].
axhline
(
y
=
int
(
0.5
*
spatial_map
.
shape
[
0
]),
color
=
'k'
)
amp_img
=
axes
[
1
].
imshow
(
np
.
abs
(
spectrogram
),
cmap
=
plt
.
cm
.
jet
,
amp_img
=
axes
[
1
].
imshow
(
np
.
abs
(
spectrogram
),
cmap
=
plt
.
cm
.
viridis
,
extent
=
[
freqs_2d
[
0
,
0
],
freqs_2d
[
-
1
,
0
],
spectrogram
.
shape
[
0
],
0
],
interpolation
=
'none'
)
axes
[
1
].
set_title
(
'Amplitude'
)
axes
[
1
].
set_xlabel
(
'Frequency (kHz)'
)
axes
[
1
].
set_ylabel
(
'BE step'
)
phase_img
=
axes
[
2
].
imshow
(
np
.
angle
(
spectrogram
),
cmap
=
plt
.
cm
.
jet
,
phase_img
=
axes
[
2
].
imshow
(
np
.
angle
(
spectrogram
),
cmap
=
plt
.
cm
.
viridis
,
extent
=
[
freqs_2d
[
0
,
0
],
freqs_2d
[
-
1
,
0
],
spectrogram
.
shape
[
0
],
0
],
interpolation
=
'none'
)
...
...
@@ -439,13 +439,13 @@ def jupyter_visualize_be_spectrograms(h5_main):
def
plot_spectrogram
(
data
,
freq_vals
):
fig
,
axes
=
plt
.
subplots
(
ncols
=
2
,
figsize
=
(
9
,
5
),
sharey
=
True
)
im_handles
=
list
()
im_handles
.
append
(
axes
[
0
].
imshow
(
np
.
abs
(
data
),
cmap
=
plt
.
cm
.
jet
,
im_handles
.
append
(
axes
[
0
].
imshow
(
np
.
abs
(
data
),
cmap
=
plt
.
cm
.
viridis
,
extent
=
[
freqs_2d
[
0
,
0
],
freqs_2d
[
-
1
,
0
],
data
.
shape
[
0
],
0
],
interpolation
=
'none'
))
axes
[
0
].
set_title
(
'Amplitude'
)
axes
[
0
].
set_ylabel
(
'BE step'
)
im_handles
.
append
(
axes
[
1
].
imshow
(
np
.
angle
(
data
),
cmap
=
plt
.
cm
.
jet
,
im_handles
.
append
(
axes
[
1
].
imshow
(
np
.
angle
(
data
),
cmap
=
plt
.
cm
.
viridis
,
extent
=
[
freqs_2d
[
0
,
0
],
freqs_2d
[
-
1
,
0
],
data
.
shape
[
0
],
0
],
interpolation
=
'none'
))
...
...
pycroscopy/viz/plot_utils.py
View file @
058f5a20
...
...
@@ -62,7 +62,7 @@ def cmap_jet_white_center():
Returns
-------
white_jet : matplotlib.colors.LinearSegmentedColormap object
color map object that can be used in place of plt.cm.
jet
color map object that can be used in place of plt.cm.
viridis
"""
# For red - central column is like brightness
# For blue - last column is like brightness
...
...
@@ -170,7 +170,7 @@ def cmap_hot_desaturated():
return
cmap_from_rgba
(
'hot_desaturated'
,
hot_desaturated
,
255
)
def
discrete_cmap
(
num_bins
,
base_cmap
=
plt
.
cm
.
jet
):
def
discrete_cmap
(
num_bins
,
base_cmap
=
plt
.
cm
.
viridis
):
"""
Create an N-bin discrete colormap from the specified input map
...
...
@@ -226,7 +226,7 @@ def _add_loop_parameters(axes, switching_coef_vec):
return
axes
def
rainbow_plot
(
ax
,
ao_vec
,
ai_vec
,
num_steps
=
32
,
cmap
=
plt
.
cm
.
jet
,
**
kwargs
):
def
rainbow_plot
(
ax
,
ao_vec
,
ai_vec
,
num_steps
=
32
,
cmap
=
plt
.
cm
.
viridis
,
**
kwargs
):
"""
Plots the input against the output waveform (typically loops).
The color of the curve changes as a function of time using the jet colorscheme
...
...
@@ -254,11 +254,11 @@ def rainbow_plot(ax, ao_vec, ai_vec, num_steps=32, cmap=plt.cm.jet, **kwargs):
ai_vec
[(
num_steps
-
1
)
*
pts_per_step
:],
color
=
cmap
(
255
*
num_steps
/
num_steps
),
**
kwargs
)
"""
CS3=plt.contourf([[0,0],[0,0]], range(0,310),cmap=plt.cm.
jet
)
CS3=plt.contourf([[0,0],[0,0]], range(0,310),cmap=plt.cm.
viridis
)
fig.colorbar(CS3)"""
def
plot_line_family
(
axis
,
x_axis
,
line_family
,
line_names
=
None
,
label_prefix
=
'Line'
,
label_suffix
=
''
,
cmap
=
plt
.
cm
.
jet
,
def
plot_line_family
(
axis
,
x_axis
,
line_family
,
line_names
=
None
,
label_prefix
=
'Line'
,
label_suffix
=
''
,
cmap
=
plt
.
cm
.
viridis
,
**
kwargs
):
"""
Plots a family of lines with a sequence of colors
...
...
@@ -760,7 +760,7 @@ def plot_cluster_h5_group(h5_group, y_spec_label, centroids_together=True):
###############################################################################
def
plot_cluster_results_together
(
label_mat
,
mean_response
,
spec_val
=
None
,
cmap
=
plt
.
cm
.
jet
,
def
plot_cluster_results_together
(
label_mat
,
mean_response
,
spec_val
=
None
,
cmap
=
plt
.
cm
.
viridis
,
spec_label
=
'Spectroscopic Value'
,
resp_label
=
'Response'
,
pos_labels
=
(
'X'
,
'Y'
),
pos_ticks
=
None
):
"""
...
...
@@ -860,9 +860,9 @@ def plot_cluster_results_together(label_mat, mean_response, spec_val=None, cmap=
"""divider = make_axes_locatable(ax_map)
cax = divider.append_axes("right", size="5%", pad=0.05) # space for colorbar
fig.colorbar(im, cax=cax, ticks=np.arange(num_clusters),
cmap=discrete_cmap(num_clusters, base_cmap=plt.cm.
jet
))
cmap=discrete_cmap(num_clusters, base_cmap=plt.cm.
viridis
))
ax_map.axis('tight')"""
pcol0
=
ax_map
.
pcolor
(
label_mat
,
cmap
=
discrete_cmap
(
num_clusters
,
base_cmap
=
plt
.
cm
.
jet
))
pcol0
=
ax_map
.
pcolor
(
label_mat
,
cmap
=
discrete_cmap
(
num_clusters
,
base_cmap
=
plt
.
cm
.
viridis
))
fig
.
colorbar
(
pcol0
,
ax
=
ax_map
,
ticks
=
np
.
arange
(
num_clusters
))
ax_map
.
axis
(
'tight'
)
ax_map
.
set_aspect
(
'auto'
)
...
...
@@ -933,7 +933,7 @@ def plot_cluster_results_separate(label_mat, cluster_centroids, max_centroids=4,
# First plot the labels map:
pcol0
=
fax1
.
pcolor
(
label_mat
,
cmap
=
discrete_cmap
(
cluster_centroids
.
shape
[
0
],
base_cmap
=
plt
.
cm
.
jet
))
base_cmap
=
plt
.
cm
.
viridis
))
fig501
.
colorbar
(
pcol0
,
ax
=
fax1
,
ticks
=
np
.
arange
(
cluster_centroids
.
shape
[
0
]))
fax1
.
axis
(
'tight'
)
fax1
.
set_aspect
(
'auto'
)
...
...
@@ -950,7 +950,7 @@ def plot_cluster_results_separate(label_mat, cluster_centroids, max_centroids=4,
for
ax
,
index
in
zip
(
axes_handles
[
1
:
max_centroids
+
1
],
np
.
arange
(
max_centroids
)):
if
cluster_centroids
.
ndim
==
2
:
ax
.
plot
(
spec_val
,
cluster_centroids
[
index
,
:],
color
=
plt
.
cm
.
jet
(
int
(
255
*
index
/
(
cluster_centroids
.
shape
[
0
]
-
1
))))
color
=
plt
.
cm
.
viridis
(
int
(
255
*
index
/
(
cluster_centroids
.
shape
[
0
]
-
1
))))
ax
.
set_xlabel
(
x_label
)
ax
.
set_ylabel
(
y_label
)
elif
cluster_centroids
.
ndim
==
3
:
...
...
@@ -1110,7 +1110,7 @@ def plot_2d_spectrogram(mean_spectrogram, freq, title, cmap=None, figure_path=No
title : String
Plot group name
cmap : matplotlib.colors.LinearSegmentedColormap object
color map. Default = plt.cm.
jet
color map. Default = plt.cm.
viridis
figure_path : String / Unicode
Absolute path of the file to write the figure to
...
...
@@ -1129,7 +1129,7 @@ def plot_2d_spectrogram(mean_spectrogram, freq, title, cmap=None, figure_path=No
"""cmap = kwargs.get('cmap')
kwargs.pop('cmap')"""
if
cmap
is
None
:
# unpack from kwargs instead
col_map
=
plt
.
cm
.
jet
# overriding default
col_map
=
plt
.
cm
.
viridis
# overriding default
freq
*=
1E-3
# to kHz
fig
,
ax
=
plt
.
subplots
(
nrows
=
2
,
ncols
=
1
,
sharex
=
True
)
...
...
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