def set_matplotlib_colors_89pct(min_tb=105, max_tb=280):
if min_tb >= 125 or max_tb <= 265:
raise ('89pct range must include 125 and 265')
from geoips2.image_utils.colormaps import create_linear_segmented_colormap
transition_vals = [(min_tb, 125), (125, 150), (150, 175), (175, 212),
(212, 230), (230, 250), (250, 265), (265, max_tb)]
# (280, max_tb)]
transition_colors = [('orange', 'chocolate'), ('chocolate', 'indianred'),
('indianred', 'firebrick'), ('firebrick', 'red'),
('gold', 'yellow'), ('lime', 'limegreen'),
('deepskyblue', 'blue'), ('navy', 'slateblue')]
# ('magenta', 'white')]
ticks = [int(xx[0]) for xx in transition_vals]
#special selection of label
# ticks = [min_tb, 125, 150, 175, 200, 225, 250, 275, max_tb]
# selection of min and max values for colormap if needed
min_tb = transition_vals[0][0]
max_tb = transition_vals[-1][1]
ticks = ticks + [int(max_tb)]
LOG.info('Setting cmap')
mpl_cmap = create_linear_segmented_colormap('89pct_cmap', min_tb, max_tb,
transition_vals,
transition_colors)
LOG.info('Setting norm')
from matplotlib.colors import Normalize
mpl_norm = Normalize(vmin=min_tb, vmax=max_tb)
cbar_label = 'TB (K)'
# Must be uniform or proportional, None not valid for Python 3
cbar_spacing = 'proportional'
mpl_tick_labels = None
mpl_boundaries = None
# from geoips2.image_utils.mpl_utils import create_colorbar
# only create colorbar for final imagery
# cbar = create_colorbar(fig, mpl_cmap, mpl_norm, ticks, cbar_label=cbar_label)
mpl_colors_info = {
'cmap': mpl_cmap,
'norm': mpl_norm,
'cbar_ticks': ticks,
'cbar_tick_labels': mpl_tick_labels,
'cbar_label': cbar_label,
'boundaries': mpl_boundaries,
'cbar_spacing': cbar_spacing,
'colorbar': True,
'cbar_full_width': True
}
# return cbar, min_tb, max_tb
return mpl_colors_info
def set_matplotlib_colors_37H(min_tb=125, max_tb=300):
if min_tb >= 185 or max_tb <= 280:
raise ('89pct range must include 125 and 265')
from geoips2.image_utils.colormaps import create_linear_segmented_colormap
transition_vals = [(min_tb, 185), (185, 210), (210, 240), (240, 260),
(260, 280), (280, max_tb)]
transition_colors = [('lightyellow', 'darkmagenta'), ('purple', 'cyan'),
('cyan', 'yellow'), ('yellow', 'red'),
('red', 'darkred'), ('silver', 'black')]
ticks = [xx[0] for xx in transition_vals]
#special selection of label
# ticks = [125, 150, 175, 200, 225, 250, 275, 300]
# selection of min and max values for colormap if needed
min_tb = transition_vals[0][0]
max_tb = transition_vals[-1][1]
ticks = ticks + [int(max_tb)]
LOG.info('Setting cmap')
mpl_cmap = create_linear_segmented_colormap('37h_cmap', min_tb, max_tb,
transition_vals,
transition_colors)
LOG.info('Setting norm')
from matplotlib.colors import Normalize
mpl_norm = Normalize(vmin=min_tb, vmax=max_tb)
cbar_label = 'TB (K)'
# Must be uniform or proportional, None not valid for Python 3
cbar_spacing = 'proportional'
mpl_tick_labels = None
mpl_boundaries = None
# from geoips2.image_utils.mpl_utils import create_colorbar
# only create colorbar for final imagery
# cbar = create_colorbar(fig, mpl_cmap, mpl_norm, ticks, cbar_label=cbar_label)
mpl_colors_info = {
'cmap': mpl_cmap,
'norm': mpl_norm,
'cbar_ticks': ticks,
'cbar_tick_labels': mpl_tick_labels,
'cbar_label': cbar_label,
'boundaries': mpl_boundaries,
'cbar_spacing': cbar_spacing,
'colorbar': True,
'cbar_full_width': True
}
# return cbar, min_tb, max_tb
return mpl_colors_info
def set_matplotlib_colors_150H(min_tb=110, max_tb=310):
from geoips2.image_utils.colormaps import create_linear_segmented_colormap
transition_vals = [(min_tb, 130), (130, 160), (160, 180), (180, 210),
(210, 230), (230, 250), (250, 270), (270, 290),
(290, max_tb)]
transition_colors = [('black', 'blue'), ('blue', 'cyan'),
('cyan', 'green'), ('green', 'yellow'),
('yellow', 'orange'), ('orange', 'red'),
('red', 'maroon'), ('maroon', 'darkmagenta'),
('darkmagenta', 'white')]
#ticks = [xx[0] for xx in transition_vals]
#special selection of label
ticks = [110, 130, 150, 170, 190, 210, 230, 250, 270, 290, 310]
# selection of min and max values for colormap if needed
min_tb = transition_vals[0][0]
max_tb = transition_vals[-1][1]
LOG.info('Setting cmap')
mpl_cmap = create_linear_segmented_colormap('150h_cmap', min_tb, max_tb,
transition_vals,
transition_colors)
LOG.info('Setting norm')
from matplotlib.colors import Normalize
mpl_norm = Normalize(vmin=min_tb, vmax=max_tb)
cbar_label = 'TB (K)'
# Must be uniform or proportional, None not valid for Python 3
cbar_spacing = 'proportional'
mpl_tick_labels = None
mpl_boundaries = None
# from geoips2.image_utils.mpl_utils import create_colorbar
# only create colorbar for final imagery
# cbar = create_colorbar(fig, mpl_cmap, mpl_norm, ticks, cbar_label=cbar_label)
mpl_colors_info = {
'cmap': mpl_cmap,
'norm': mpl_norm,
'cbar_ticks': ticks,
'cbar_tick_labels': mpl_tick_labels,
'cbar_label': cbar_label,
'boundaries': mpl_boundaries,
'cbar_spacing': cbar_spacing,
'colorbar': True
}
return mpl_colors_info
def set_matplotlib_colors_37pct(min_tb=230, max_tb=280):
from geoips2.image_utils.colormaps import create_linear_segmented_colormap
transition_vals = [(min_tb, 240), (240, 260), (260, max_tb)]
transition_colors = [('cyan', 'yellow'), ('yellow', 'red'),
('red', 'darkred')]
#special selection of label
ticks = [230, 240, 250, 260, 270, 280]
# selection of min and max values for colormap if needed
min_tb = transition_vals[0][0]
max_tb = transition_vals[-1][1]
LOG.info('Setting cmap')
mpl_cmap = create_linear_segmented_colormap('37pct_cmap', min_tb, max_tb,
transition_vals,
transition_colors)
LOG.info('Setting norm')
from matplotlib.colors import Normalize
mpl_norm = Normalize(vmin=min_tb, vmax=max_tb)
cbar_label = 'TB (K)'
# Must be uniform or proportional, None not valid for Python 3
cbar_spacing = 'proportional'
mpl_tick_labels = None
mpl_boundaries = None
# from geoips2.image_utils.mpl_utils import create_colorbar
# only create colorbar for final imagery
# cbar = create_colorbar(fig, mpl_cmap, mpl_norm, ticks, cbar_label=cbar_label)
mpl_colors_info = {
'cmap': mpl_cmap,
'norm': mpl_norm,
'cbar_ticks': ticks,
'cbar_tick_labels': mpl_tick_labels,
'cbar_label': cbar_label,
'boundaries': mpl_boundaries,
'cbar_spacing': cbar_spacing,
'colorbar': True
}
# return cbar, min_tb, max_tb
return mpl_colors_info
def set_matplotlib_colors_winds(min_wind_speed=0, max_wind_speed=200):
''' Generate appropriate matplotlib colors for plotting standard wind speeds. set_matplotlib_colors_winds
contains hard coded transition values for different colors, in order to have consistent imagery across all
sensors / products
Args:
min_wind_speed (float) : Default 0
max_wind_speed (float) : Default 200
Returns:
mpl_colors_info (dict) Specifies matplotlib Colors parameters for use in both plotting and colorbar generation
See geoips2.image_utils.mpl_utils.create_colorbar for field descriptions.
For Wind Speed colors, the following general fields are used:
mpl_colors_info = {'cmap': (LinearSegmentedColormap)
'norm': (Normalize)
'cbar_ticks': (list of transition values for wind speeds)
'cbar_tick_labels': (None)
'cbar_label': ('Surface Wind (knots)')
'boundaries': (None)
'cbar_spacing': ('proportional')
'colorbar': (True)}
'''
from geoips2.image_utils.colormaps import create_linear_segmented_colormap
transition_vals = [
(min_wind_speed, 34),
(34, 50),
(50, 64),
(64, 80),
# (64, 72),
# (72, 80),
(80, 100),
(100, 120),
(120, 150),
(150, max_wind_speed)
]
transition_colors = [
('lightblue', 'blue'),
('yellow', 'orange'),
('red', 'red'),
# ('thistle', 'thistle'),
# ('firebrick', 'firebrick'),
# ('fuchsia', 'fuchsia'),
# ('mediumvioletred', 'mediumvioletred'),
('rebeccapurple', 'rebeccapurple'),
# ('purple', 'rebeccapurple'),
# ('rebeccapurple', 'rebeccapurple'),
# ('mediumvioletred', 'mediumvioletred'),
('palevioletred', 'palevioletred'),
('silver', 'silver'),
('gray', 'gray'),
('dimgray', 'dimgray')
]
ticks = [xx[0] for xx in transition_vals]
min_wind_speed = transition_vals[0][0]
max_wind_speed = transition_vals[-1][1]
LOG.info('Setting cmap')
mpl_cmap = create_linear_segmented_colormap('windspeed_cmap',
min_wind_speed, max_wind_speed,
transition_vals,
transition_colors)
LOG.info('Setting norm')
from matplotlib.colors import Normalize
mpl_norm = Normalize(vmin=min_wind_speed, vmax=max_wind_speed)
cbar_label = 'Surface Wind (knots)'
# Must be uniform or proportional, None not valid for Python 3
cbar_spacing = 'proportional'
mpl_tick_labels = None
mpl_boundaries = None
# from geoips2.image_utils.mpl_utils import create_colorbar
# only create colorbar for final imagery
# cbar = create_colorbar(fig, mpl_cmap, mpl_norm, ticks, cbar_label=cbar_label)
mpl_colors_info = {
'cmap': mpl_cmap,
'norm': mpl_norm,
'cbar_ticks': ticks,
'cbar_tick_labels': mpl_tick_labels,
'cbar_label': cbar_label,
'boundaries': mpl_boundaries,
'cbar_spacing': cbar_spacing,
'colorbar': True
}
# return cbar, min_wind_speed, max_wind_speed
return mpl_colors_info
def set_matplotlib_colors_IR(min_tb=-90, max_tb=30):
# for Infrared images at 11 um. Unit: Celsius
if min_tb > -90 or max_tb < 30:
raise ('Infrared TB range must include -90 and 30')
from geoips2.image_utils.colormaps import create_linear_segmented_colormap
transition_vals = [(min_tb, -80), (-80, -70), (-70, -50), (-50, -40),
(-40, -30), (-30, -15), (-15, 0), (0, 15), (15, max_tb)]
transition_colors = [('darkorange', 'yellow'), ('darkred', 'red'),
('green', 'palegreen'), ('navy', 'royalblue'),
('royalblue', 'deepskyblue'),
('whitesmoke', 'silver'), ('silver', 'grey'),
('grey', 'dimgrey'), ('dimgrey', 'black')]
#ticks = [int(xx[0]) for xx in transition_vals]
#special selection of label
ticks = [
min_tb, -80, -70, -60, -50, -40, -30, -20, -10, 0, 10, 20, 30, max_tb
]
# selection of min and max values for colormap if needed
min_tb = transition_vals[0][0]
max_tb = transition_vals[-1][1]
ticks = ticks + [int(max_tb)]
LOG.info('Setting cmap')
mpl_cmap = create_linear_segmented_colormap('89pct_cmap', min_tb, max_tb,
transition_vals,
transition_colors)
LOG.info('Setting norm')
from matplotlib.colors import Normalize
mpl_norm = Normalize(vmin=min_tb, vmax=max_tb)
cbar_label = '11um BT (C)'
# Must be uniform or proportional, None not valid for Python 3
cbar_spacing = 'proportional'
mpl_tick_labels = None
mpl_boundaries = None
# from geoips2.image_utils.mpl_utils import create_colorbar
# only create colorbar for final imagery
# cbar = create_colorbar(fig, mpl_cmap, mpl_norm, ticks, cbar_label=cbar_label)
mpl_colors_info = {
'cmap': mpl_cmap,
'norm': mpl_norm,
'cbar_ticks': ticks,
'cbar_tick_labels': mpl_tick_labels,
'cbar_label': cbar_label,
'boundaries': mpl_boundaries,
'cbar_spacing': cbar_spacing,
'colorbar': True,
'cbar_full_width': True
}
# return cbar, min_tb, max_tb
return mpl_colors_info