def test_LogColorMapper():
mapper = LogColorMapper()
yield (
check_properties_existence,
mapper,
["palette", "low", "high", "nan_color"],
)
def get_color_mapper(column, df, palette='Viridis256'):
'''
Return a color mapper instace for a given category or continuous
properties
Args:
column : str
name of the color that should be color mapped
df : pandas.DataFrame
data frame
'''
cmaps = {
'block': 'Set1_4',
'period': 'Dark2_7',
'name_series': 'Spectral10',
'group_name': viridis(18),
'is_radioactive': d3['Category10'][4][2:],
'is_monoisotopic': d3['Category10'][4][2:],
'goldschmidt_class': 'Set2_4',
'geochemical_class': d3['Category10'][10],
}
if column in cmaps.keys():
factors = list(df[column].unique())
if any(x is None for x in factors):
factors = sorted([x for x in factors if x is not None]) + [None]
elif column == 'group_name':
factors = [
str(x)
for x in sorted(int(s) for s in factors if s != 'f block')
] + ['f block']
else:
factors = sorted(factors)
ccm = CategoricalColorMapper(palette=cmaps[column],
factors=factors,
nan_color='#ffffff')
elif column == 'value':
if df[column].skew() > SKEW_THRS:
ccm = LogColorMapper(palette=palette,
low=df[column].min(),
high=df[column].max(),
nan_color='#ffffff')
else:
ccm = LinearColorMapper(palette=palette,
low=df[column].min(),
high=df[column].max(),
nan_color='#ffffff')
else:
ccm = None
return ccm
def get_color_mapper(column, df, palette="Viridis256"):
"""
Return a color mapper instace for a given category or continuous
properties
Args:
column : str
name of the color that should be color mapped
df : pandas.DataFrame
data frame
"""
cmaps = {
"block": "Set1_4",
"period": "Dark2_7",
"name_series": "Spectral10",
"group_name": viridis(18),
"is_radioactive": d3["Category10"][4][2:],
"is_monoisotopic": d3["Category10"][4][2:],
"goldschmidt_class": "Set2_4",
"geochemical_class": d3["Category10"][10],
}
if column in cmaps.keys():
factors = list(df[column].unique())
if any(x is None for x in factors):
factors = sorted([x for x in factors if x is not None]) + [None]
elif column == "group_name":
factors = [str(x) for x in sorted(int(s) for s in factors
if s != "f block")] + ["f block"]
else:
factors = sorted(factors)
ccm = CategoricalColorMapper(palette=cmaps[column], factors=factors,
nan_color="#ffffff")
elif column == "value":
if df[column].skew() > SKEW_THRS:
ccm = LogColorMapper(palette=palette, low=df[column].min(),
high=df[column].max(), nan_color="#ffffff")
else:
ccm = LinearColorMapper(palette=palette, low=df[column].min(),
high=df[column].max(), nan_color="#ffffff")
else:
ccm = None
return ccm
def _set_video(self, vid_path: Union[Path, str]):
self.tif = tifffile.TiffFile(vid_path)
self.current_frame = 0
self.frame = self.tif.asarray(key=self.current_frame)
# this is basically used for vmin mvax
self.color_mapper = LogColorMapper(
palette=auto_colormap(256, 'gnuplot2', output='bokeh'),
low=np.nanmin(self.frame),
high=np.nanmax(self.frame)
)
self.image_glyph.data_source.data['image'] = [self.frame]
self.image_glyph.glyph.color_mapper = self.color_mapper
# shows the file size in gigabytes
self.label_filesize.update(value=str(os.path.getsize(vid_path) / 1024 / 1024 / 1024))
def log_cmap(field_name,
palette,
low,
high,
low_color=None,
high_color=None,
nan_color="gray"):
''' Create a ``DataSpec`` dict to apply a client-side ``LogColorMapper``
transformation to a ``ColumnDataSource`` column.
Args:
field_name (str) : a field name to configure ``DataSpec`` with
palette (seq[color]) : a list of colors to use for colormapping
low (float) : a minimum value of the range to map into the palette.
Values below this are clamped to ``low``.
high (float) : a maximum value of the range to map into the palette.
Values above this are clamped to ``high``.
low_color (color, optional) : color to be used if data is lower than
``low`` value. If None, values lower than ``low`` are mapped to the
first color in the palette. (default: None)
high_color (color, optional) : color to be used if data is higher than
``high`` value. If None, values higher than ``high`` are mapped to
the last color in the palette. (default: None)
nan_color (color, optional) : a default color to use when mapping data
from a column does not succeed (default: "gray")
'''
return field(
field_name,
LogColorMapper(palette=palette,
low=low,
high=high,
nan_color=nan_color,
low_color=low_color,
high_color=high_color))
tools=[hover, tap, zoom, pan])
plot.title.text = "VirtualDive"
plot.title.text_font_size = "25px"
plot.title_location = "right"
plot.title.align = "right"
plot.api_key = "AIzaSyAX0RhQ5JTdQAjveEADHzBXbxkVLYCiPps"
#color_mapper = CategoricalColorMapper(factors=['hi', 'lo'], palette=[RdBu3[2], RdBu3[0]])
#color_mapper = LogColorMapper(palette="Viridis5", low=min_median_house_value, high=max_median_house_value)
paletteinvert = palette[::-1]
color_mapper = CategoricalColorMapper(palette=["magenta", "cyan"],
factors=['No', 'Yes'])
color_mapper2 = LogColorMapper(palette=fire)
protectedannulus = Annulus(x="lon",
y="lat",
inner_radius=4000,
outer_radius=7000,
fill_color="cyan",
fill_alpha=0.3,
line_color=None)
unprotectedannulus = Annulus(x="lon",
y="lat",
inner_radius=4000,
outer_radius=7000,
fill_color={
'field': 'color',
TOOLS = "pan,wheel_zoom,reset,hover,save,tap"
# init figure
p = figure(title="Plotting USarray stations",
toolbar_location="left", x_axis_label='Longitude', y_axis_label='Latitude', tools="lasso_select",plot_width=750, plot_height=500)
# Draw state lines
p.patches(state_xs, state_ys, fill_alpha=0.0,
line_color="#884444", line_width=1.5)
# Now group these values together into a lists of x (longitude) and y (latitude)
x = locations["longitude"].tolist()
y = locations["latitude"].tolist()
custom_colors = ['#f2f2f2', '#fee5d9', '#fcbba1', '#fc9272', '#fb6a4a', '#de2d26']
color_mapper = LogColorMapper(palette=custom_colors)
data = dict(x=x, y=y,
rate=array_vals.iloc[0].tolist(),**d)
s1 = ColumnDataSource(data)
#creating stations over the map of United States
p.circle('x', 'y', source=s1,
fill_color={'field': 'rate', 'transform': color_mapper},
fill_alpha=2,size=7)
#Creating the spectrogram
sp = figure(title="Spectrogram", x_range=(0, 14401), y_range=(0, 438),x_axis_label='Time in seconds', y_axis_label='Stations by distance from origin',plot_width=450, plot_height=250)
sp.image(image=[h], x=0, y=0, dw=14401, dh=438, palette="Spectral11")
p_img.border_fill_alpha = 0.4
p_img.axis.major_tick_out = 0
p_img.axis.major_tick_in = 5
p_img.axis.minor_tick_out = 0
p_img.axis.minor_tick_in = 3
p_img.axis.major_tick_line_color = "white"
p_img.axis.minor_tick_line_color = "white"
if imgdict['observatory'] == 'SDO' and imgdict['instrument'] == 'AIA':
palette = getAIApalette(wavelngth)
colormap_jet = cm.get_cmap("jet") # choose any matplotlib colormap here
# palette = [colors.rgb2hex(m) for m in colormap_jet(np.arange(colormap_jet.N))]
clrange = DButil.sdo_aia_scale_dict(wavelength=wavelngth,
imagetype=imagetype)
if clrange['log']:
colormapper = LogColorMapper(palette=palette,
low=clrange['low'],
high=clrange['high'])
else:
colormapper = LinearColorMapper(palette=palette,
low=clrange['low'],
high=clrange['high'])
else:
palette = bp.viridis(256)
colormapper = LinearColorMapper(palette=palette)
r_img = p_img.image(image=[zz],
x=xbd[0],
y=ybd[0],
dw=xbd[-1] - xbd[0] + dx,
dh=ybd[-1] - ybd[0] + dy,
color_mapper=colormapper)
def PlotMap(self,
DrawLimb=True,
DrawGrid=True,
grid_spacing=15 * u.deg,
ignore_coord=False,
title=None,
tools=None,
x_range=None,
y_range=None,
palette=None,
imagetype='image',
*args,
**kwargs):
"""Plot the map using the bokeh.plotting interface
"""
source_image = self.ImageSource()
if not title:
title = self.smap.name
if not tools:
tools = 'pan,wheel_zoom,save,reset'
if not x_range:
x_range = self.x_range
if not y_range:
y_range = self.y_range
if not palette:
if self.smap.observatory == 'SDO' and self.smap.instrument[
0:3] == 'AIA':
wavelngth = '{:.0f}'.format(self.smap.wavelength.value)
# clmap = cm.get_cmap("sdoaia" + wavelngth) # choose any matplotlib colormap here
# palette = [colors.rgb2hex(m).encode("ascii").upper() for m in clmap(np.arange(clmap.N))]
palette = getAIApalette(wavelngth)
clrange = DButil.sdo_aia_scale_dict(wavelength=wavelngth,
imagetype=imagetype)
print clrange
if clrange['log']:
colormapper = LogColorMapper(palette=palette,
low=clrange['low'],
high=clrange['high'])
else:
colormapper = LinearColorMapper(palette=palette,
low=clrange['low'],
high=clrange['high'])
else:
palette = bokehpalette_jet
colormapper = LinearColorMapper(palette=palette)
else:
colormapper = LinearColorMapper(palette=palette)
if ignore_coord:
x_range = [0 - 0.5, self.smap.data.shape[0] + 0.5]
y_range = [0 - 0.5, self.smap.data.shape[1] + 0.5]
p_xaxis_visible = False
p_yaxis_visible = False
p_xaxis_axislabel = ''
p_yaxis_axislabel = ''
x0 = 0 - 0.5
y0 = 0 - 0.5
dw = self.smap.data.shape[0]
dh = self.smap.data.shape[1]
else:
p_xaxis_visible = True
p_yaxis_visible = True
p_xaxis_axislabel = 'X-position [arcsec]'
p_yaxis_axislabel = 'Y-position [arcsec]'
x0 = self.x
y0 = self.y
dw = self.dw
dh = self.dh
p_image = figure(tools=tools,
x_range=x_range,
y_range=y_range,
title=title,
plot_height=self.plot_height,
plot_width=self.plot_width,
*args,
**kwargs)
p_image.xaxis.axis_label = p_xaxis_axislabel
p_image.yaxis.axis_label = p_yaxis_axislabel
p_image.xaxis.visible = p_xaxis_visible
p_image.yaxis.visible = p_yaxis_visible
r_img = p_image.image(image=source_image['data'],
x=x0,
y=y0,
dw=dw,
dh=dh,
color_mapper=colormapper)
if DrawLimb:
p_image.line(x='x',
y='y',
line_color='white',
line_dash='solid',
source=self.DrawLimbSource())
if DrawGrid:
p_image.multi_line(
xs='xs',
ys='ys',
line_color='white',
line_dash='dotted',
source=self.DrawGridSource(grid_spacing=grid_spacing))
return p_image, r_img
def map_tab(map_cov, map_df, min_density, max_density):
# Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson=map_cov)
palette = RdYlGn[11]
low = min_density
high = max_density
color_mapper = LogColorMapper(palette=palette,
low=low,
high=high,
nan_color='whitesmoke',
low_color='whitesmoke')
#Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
location=(0, 0),
orientation='horizontal',
ticker=LogTicker(),
border_line_color=None,
title='Density of confirmed cases')
#Create figure object.
p = figure(title='COVID-19 spread map',
plot_height=900,
plot_width=1500,
toolbar_location='below',
toolbar_sticky=False,
background_fill_color="whitesmoke")
p.title.text_font = "helvetica"
p.title.text_font_size = "20px"
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
#Add patch renderer to figure.
patches = p.patches('xs',
'ys',
source=geosource,
fill_color={
'field': 'ConfirmedDensity',
'transform': color_mapper
},
line_color='black',
line_width=0.25,
fill_alpha=0.5)
p.renderers.append(patches)
#Specify figure layout.
p.add_layout(color_bar, 'below')
#create hoover
hover = HoverTool(tooltips=[('Country', '@{NAME_EN}'),
('Population', '@Population'),
('Confirmed', '@Confirmed'),
('Deaths', '@Deaths'),
('Recovered', '@Recovered'),
('Conf. density', '@ConfirmedDensity'),
('Deaths density', '@DeathsDensity'),
('Recovered density', '@RecoveredDensity')],
renderers=[patches])
p.add_tools(hover)
# Input coronavirus data
pointsource = ColumnDataSource(map_df)
# color palette
# palette_p = brewer['Blues'][9]
# palette_p = palette_p[::-1]
# palette_p[1::]
# color_mapper_p = LinearColorMapper(palette=palette_p, low=0, high=17)
# add points virus
point = p.circle(
'Long',
'Lat',
source=pointsource,
size='Size',
#color={'field': 'Size','transform': color_mapper_p},
color='lightskyblue',
fill_alpha=0.3,
line_alpha=0.3)
p.renderers.append(point)
# create hoover
hover_p = HoverTool(tooltips=[('Country', '@{Country/Region}'),
('Province', '@{Province/State}'),
('Confirmed', '@Confirmed'),
('Deaths', '@Deaths'),
('Recovered', '@Recovered')],
renderers=[point])
p.add_tools(hover_p)
# Add legend at fixed positions
location, orientation, side = 'bottom_left', "horizontal", "center"
legend = Legend(items=[("Nr. of confirmed cases", [point])],
location=location,
orientation=orientation,
border_line_color="black",
background_fill_alpha=0,
border_line_alpha=0)
p.add_layout(legend, side)
# Layout setup
layout = p
tab = Panel(child=layout, title='COVID-19 Map')
return tab
