def makeHoverCarpet(carpets, collabels, rowlabels, maxval, extralabels):
"""
makes a 3 carpet heatmap
"""
if (len(rowlabels) < 1):
print('no wells to show')
return
hv.extension('bokeh', width=90)
sprowlabels = [
rowlabels[i] + ',' + extralabels[i] for i in range(len(rowlabels))
]
heatmap1 = hv.HeatMap(
(collabels, sprowlabels, carpets[0])).opts(title=targets[0] + ' dRn')
heatmap2 = hv.HeatMap(
(collabels, sprowlabels, carpets[1])).opts(title=targets[1] + ' dRn')
heatmap3 = hv.HeatMap(
(collabels, sprowlabels, carpets[2])).opts(title=targets[2] + ' dRn')
tooltips = [('Well', '@y'), ('Cycle', '@x'), ('dRn', '@z')]
hover = HoverTool(tooltips=tooltips)
layout = heatmap1 + heatmap2 + heatmap3
layout.opts(
opts.HeatMap(tools=[hover],
cmap='jet',
colorbar=True,
invert_yaxis=True,
clim=(0.0, maxval),
width=400,
height=800,
xlabel='Cycle',
ylabel='Wells'))
return layout
def plot_adjacency_matrix(G,
save=False,
filename=None,
weight="weighted_score",
log=False,
width=200,
height=200,
fontsize=10,
fontscale=1.5):
"""Flattens and plots an adjecency matric for a given graph.
If a split graph is provided, plots adjacency matrix for each interaction.
Weight can be "weighted_score" or "score".
Returns: Holoviews heatmap
"""
import numpy as np
import holoviews as hv
from holoviews import opts
import networkx as nx
hv.extension('bokeh')
# Create adjecency matrix and sort index and columns by name
matrix = nx.to_pandas_adjacency(G, weight=weight)
if log:
matrix = np.log10(matrix)
matrix = matrix.sort_index(ascending=True)
matrix = matrix[matrix.index.values]
matrix = matrix.stack()
matrix = matrix.reset_index(name="values")
matrix.columns = ["source", "target", "values"]
# Plot the matrix as a heatmap
heatmap = hv.HeatMap(matrix, kdims=["target", "source"])
# Rotate X lables and show the color bar
heatmap.opts(
opts.HeatMap(tools=["hover"],
xrotation=90,
colorbar=True,
width=width,
height=height,
fontsize=fontsize,
fontscale=fontscale))
if save is True:
hv.save(heatmap, filename)
return heatmap
def setLibrary(library='bokeh'):
## Use Bokeh by default
if (library == 'bokeh'):
hv.extension('bokeh')
hv.archive.auto(filename_formatter="{obj:.7}") ## For notebooks
opts.defaults(
opts.Scatter(tools=['hover'], width=700, height=700, padding=0.05),
opts.HeatMap(tools=['hover'],
width=700,
height=700,
labelled=[],
xrotation=45,
colorbar=True,
cmap=('Blues')))
# opts.HeatMap(tools=['hover'], width=700, height=700, labelled=[],
# xrotation=45, colorbar=True, cmap=('Blues')))
## The library that Bokeh uses to export to SVG is not longer supported
## and so cannot be exported to SVG
elif (library == 'matplotlib'):
hv.extension('matplotlib')
hv.output(fig='svg')
opts.defaults(
opts.Scatter(fig_size=300, padding=0.05),
opts.HeatMap(fig_size=300,
labelled=[],
xrotation=45,
colorbar=True,
cmap=('Blues')))
else:
raise NotImplementedError("Unknown plotting library.")
def get_heatmap(data,
lim,
title,
kdim='x',
vdim='Iteration',
height=200):
return hv.HeatMap(data, kdims=[kdim, vdim]).opts(
opts.HeatMap(tools=['hover'],
colorbar=True,
width=350,
height=height,
invert_yaxis=True,
ylabel='Iteration',
title=title,
clim=(-lim, lim),
cmap='RdBu'))
def plot_connectivity_matrix(connectivity_matrix,
atlas_labels,
threshold=None,
dst_dir=None,
filename=None):
'''Plot a connectivity matrix. This function is very similar to nilearn.plotting.plot_matrix
but uses the bokeh backend and is therefore interactive
Parameters
----------
connectivity_matrix : np.array
A symmetric connectivity matrix.
atlas_labels : pd.Series or list
A list-like object providing names of each atlas region.
threshold : float or int, optional
Apply a threshold to the connectivity matrix before plotting. Values
lower than this threshold will be set to 0.
dst_dir : str, optional
Name of the output directory. The default is None.
filename : str, optional
Name of the file (must be provided including the extenstion).
The default is None.
Returns
-------
connectogram_plot : holoviews.element.raster.HeatMap
The the connectivity matrix plot object.
'''
# copy matrix
connectivity_matrix = connectivity_matrix.copy()
# convert to pd.DataFrame for further processing
connectivity_matrix_df = pd.DataFrame(data=connectivity_matrix,
columns=atlas_labels,
index=atlas_labels)
# Ensure that index name has the default name 'Index'
if connectivity_matrix_df.index.name:
connectivity_matrix_df.index.name = None
# stack connectivity_matrix
connectivity_matrix_stacked = connectivity_matrix_df.stack().reset_index()
connectivity_matrix_stacked.columns = ['source', 'target', 'value']
if threshold:
connectivity_matrix_stacked['value'].where(
connectivity_matrix_stacked['value'] >= threshold, 0, inplace=True)
connectivity_matrix_stacked_ds = hv.Dataset(connectivity_matrix_stacked,
['source', 'target'])
heatmap = hv.HeatMap(connectivity_matrix_stacked_ds)
heatmap.opts(
opts.HeatMap(tools=['hover'],
colorbar=True,
xaxis='bare',
yaxis='bare',
cmap='blues_r'))
# save plot
if dst_dir:
if not filename:
raise ValueError('Please provide a filename')
dst_path = dst_dir + filename
hv.save(heatmap, dst_path)
# FIXME: this doesn't work for me in Spyder
show(hv.render(heatmap))
return heatmap
from holoviews import opts
import panel as pn
import altair as alt
from altair import datum
pn.extension('vega')
#alt.renderers.enable('altair_viewer')
alt.data_transformers.disable_max_rows()
hv.extension("bokeh")
medal_count_year_withCate = pd.read_csv("medal_count_year_withCate.csv")
medal_count_year_top10 = medal_count_year_withCate.loc[medal_count_year_withCate["Order"]<=10]
year_unique = medal_count_year_top10["Year"].unique().tolist()
name_unique = medal_count_year_top10["name"].unique().tolist()
heatmap = hv.HeatMap(medal_count_year_top10, ["Year", "name"],["Medal"])
heatmap.opts(opts.HeatMap(radial=True,colorbar=True, start_angle=np.pi/2, width=500, height=500,
yticks=None,xticks=year_unique,tools=['hover'],toolbar='above'))
slider_year = alt.binding_range(min=1896, max=2016, step=4, name='Year:')
selector_year = alt.selection_single(fields=['Year'],bind=slider_year, init={'Year': 2016})
select_country = alt.selection(type="single", fields=['Year'])
olympic_bar = alt.Chart(medal_count_year_withCate).mark_bar(opacity=0.8).encode(
x=alt.X(field="name", type='nominal', title="country",sort = '-y',
axis=alt.Axis(labelFontSize=10,titleFontSize=15,labelAngle=-45)),
y=alt.Y(field="MedalByCate",type="quantitative",aggregate='sum',stack='zero',title="Total Medals",
axis=alt.Axis(labelFontSize=10,titleFontSize=15)),
color=alt.Color(field = 'MedalCate', type = 'nominal',
scale = alt.Scale(domain = ["Gold","Silver","Bronze"],range=['gold', 'silver','sienna']),
legend=alt.Legend(title="Medal Category",labelFontSize = 15,symbolSize = 30,titleFontSize=10)),
order=alt.Order('MedalCateOrder',sort='ascending')
)
edge_color='Gender',
edge_line_width=0,
node_alpha=1.0,
node_width=40,
node_sort=True,
width=1000,
height=800,
bgcolor="snow",
title="Distribution of the population of people with a dry cough")
#show(hv.render(sankey))
heatmap = hv.HeatMap(sym_fin,
vdims=['Dry.Cough']).sort().aggregate(function=np.sum)
heatmap.opts(
opts.HeatMap(tools=['hover'],
colorbar=True,
width=400,
height=500,
toolbar='above'))
#hv_ds = hv.Dataset(data=sym_fin, kdims=["Age", "Gender"], vdims=['Difficulty.in.Breathing'])
#sankey.data=hv_ds.data
#hv_ds.to(hv.Sankey)
hv_ds = hv.Dataset(data=sym_fin,
kdims=["Age", "Gender"],
vdims=['Difficulty.in.Breathing'])
sankey2 = hv_ds.to(hv.Sankey)
sankey2.opts(
cmap='Colorblind',
label_position='left',
edge_color='Gender',