def get_bar_plot(parents, colors, labels, cnts, title, fn_bar):
from plotly.offline import plot as pplot
import plotly.graph_objs as pgo
pplot_para = {}
pplot_para['data'] = [
pgo.Bar(x=labels, y=cnts, text=parents, marker=dict(color=colors))
]
pplot_para['layout'] = pgo.Layout(title='distribution of {}'.format(title),
xaxis=dict(title=title),
yaxis=dict(title='frequency'))
pplot(pplot_para, filename=fn_bar)
data_cols = ['Retail/Recreation', 'Workplace', 'Parks', 'Residential', 'Grocery/Pharmacy']
google_state.columns.name='Category'
# State
fig = px.line(
google_state,
x=google_state.index,
y=data_cols,
title='Wisconsin Google Mobility',
range_y=[-90, 200],
)
fig.update_layout(legend_title_text='Category')
pplot(
fig,
filename='.\\docs\\assets\\plotly\\Mobility-Google-WI.html',
include_plotlyjs='cdn',
)
fig.write_image(
'.\\docs\\assets\\Mobility-Google-WI.png',
width=700,
height=600,
engine='kaleido',
)
#%% Comparison
category = ['Workplace', 'Retail/Recreation']
counties = ['Milwaukee County', 'Dane County', 'Brown County']
# take out whole-state
#%% Scatter plots
# Case % vs overcrowding
fig = px.scatter(
prisons,
x='Crowding %',
y='Case %',
title='Case % vs Crowding',
text='Scatter Name',
)
fig.update_traces(textposition='middle left')
pplot(fig,
filename='.\\docs\\assets\\plotly\\Prisons-Crowded.html',
include_plotlyjs='cdn',
)
save_png = '.\\docs\\assets\\Prisons-Crowding.png'
fig.write_image(
save_png,
width=700,
height=500,
engine='kaleido',
)
os.startfile(save_png)
# Case % vs size
fig = px.scatter(
prisons,
x='Population',
y1=1,
xref='x',
x0=date,
x1=date))
date = date + delta
fig.update_layout(shapes=dividers)
fig.add_annotation(text='Christmas',
x=datetime.datetime(2020, 12, 25, 0),
y=1400,
yanchor='bottom',
xanchor='center',
showarrow=False,
textangle=270)
pplot(fig, include_plotlyjs='cdn', filename=plotpath + '\\Christmas-WI.html')
save_png = '.\\docs\\assets\\Christmas-WI.png'
fig.write_image(
save_png,
width=700,
height=340,
engine='kaleido',
)
os.startfile(save_png)
#%% Christmas surge for Milwaukee
# get milwaukee test numbers by copying html of the dashboard and scraping it
# get this originally by doing a browser html inspection of the graph on the Milwaukee county dashboard,
# then copying it to a text file. The tooltips of the graph are stored in "aria-label" labels, so
# take out 'variable=' part of the axis titles
fig.for_each_annotation(
lambda a: a.update(
text=a.text.split("=")[-1],
font=dict(size=15),
)
)
fig.update_traces(textposition='outside')
# other layout
fig.update_layout(showlegend=False)
pplot(fig,
filename='.\\docs\\assets\\plotly\\Age-Covid.html',
include_plotlyjs='cdn',
)
# save_png = '.\\docs\\assets\\Age-Covid_2020-12-11.png'
save_png = '.\\docs\\assets\\Age-Covid.png'
fig.write_image(
save_png,
width=700,
height=700,
engine='kaleido',
)
os.startfile(save_png)
# interesting - ICU admissions are highest for late middle age. Elderly are
# admitted to ICU lower than proportional to their hospitalization status. I
# guess because it's less likely to do them any good.
# reindex on county name
countiesWI = countiesWI.set_index('NAME')
# sort by name
countiesWI = countiesWI.sort_index()
# add Region column
countiesWI['Region'] = region_map
# chloropleth by population from geopandas
# base = countiesWI.plot(column='Region', color=colors, edgecolor='w', linewidth=0.5)
# filter counties shapefile to WI, convert to JSON format string, then decode
# to dictionary with json.loads()
countiesJS = json.loads(countiesWI.to_json())
fig = px.choropleth(countiesWI,
geojson=countiesJS,
locations=countiesWI.index,
color='Region',
color_discrete_map=color_dict,
category_orders={'Region': list(color_dict.keys())},
title='WI Regions',
projection='mercator')
fig.update_geos(fitbounds='locations', visible=False)
fig.update_traces(marker_line_color='white')
pplot(
fig,
filename=plotpath + '\\Map-Region.html',
include_plotlyjs='cdn',
)
showscale=False),
row=2,
col=1)
fig.add_trace(go.Surface(x=X, y=Y, z=Z2, colorscale='Viridis',
showscale=False),
row=2,
col=2)
c1 = np.linspace(0.5, 2.5, nd)
c2 = np.linspace(10, 20, nd)
Z1 = np.zeros((nd, nd))
Z2 = np.zeros((nd, nd))
for i in range(nd):
for j in range(nd):
Z1[j, i] = R1.evaluate([c1[i], c2[j]])
Z2[j, i] = R2.evaluate([c1[i], c2[j]])
fig.add_trace(go.Surface(x=X, y=Y, z=Z1, colorscale='Viridis',
showscale=False),
row=3,
col=1)
fig.add_trace(go.Surface(x=X, y=Y, z=Z2, colorscale='Viridis',
showscale=False),
row=3,
col=2)
pplot(fig)
def get_band_html(vasprun_file, kpts_file):
vasp = VaspRun(vasprun_file)
rec_lat = vasp.recip_lat()
eigval_origin = vasp.read_eigenvals()[0]
origin_kpt = vasp.read_kpoints()
kpts = np.dot(origin_kpt, rec_lat)
kpt_path = np.zeros((np.shape(kpts)[0], 1))
kpt_path[1:] = np.linalg.norm(kpts[1:] - kpts[:-1], axis=1).reshape(
(-1, 1))
kpt_path[1:] = np.cumsum(kpt_path[1:]).reshape((-1, 1))
eigval_shape = np.shape(eigval_origin)
eigval = np.zeros((eigval_shape[0], eigval_shape[1] * 2))
for i in range(eigval_shape[1]):
eigval[:, 2 * i:2 * i + 2] = eigval_origin[:, i, :]
fermi, _, _ = vasp.read_dos()
labels, high_kpts = read_kpoints(kpts_file)
high_kpts = high_kpts @ rec_lat
high_kpts_path = np.zeros((np.shape(high_kpts)[0], 1))
high_kpts_path[1:] = np.linalg.norm(high_kpts[1:] - high_kpts[:-1],
axis=1).reshape((-1, 1))
high_kpts_path[1:] = np.cumsum(high_kpts_path[1:]).reshape((-1, 1))
fig = go.Figure()
for ii in range(eigval_shape[1]):
# import pdb; pdb.set_trace()
fig.add_trace(
go.Scatter(x=kpt_path.reshape((len(kpt_path), )),
y=eigval[:, 2 * ii] - fermi,
mode='lines',
line=dict(color='blue', width=2)))
annotations = []
for i, label in enumerate(labels):
annotations.append(
go.Annotation(x=high_kpts_path[i][0],
y=-5,
xref="x1",
yref="y1",
text=label,
xanchor="center",
yanchor="top",
showarrow=False))
fig.add_trace(
go.Scatter(x=[high_kpts_path[i][0], high_kpts_path[i][0]],
y=[-5, 5],
mode='lines',
line=dict(color='black', width=1)))
# In[4]:
bandxaxis = go.XAxis(title="k-points",
range=[0, kpt_path[-1]],
showgrid=True,
showline=True,
ticks="",
showticklabels=False,
mirror=True,
linewidth=2)
bandyaxis = go.YAxis(title="$E - E_f \quad / \quad \\text{eV}$",
range=[-5, 5],
showgrid=True,
showline=True,
zeroline=True,
mirror="ticks",
ticks="inside",
linewidth=2,
tickwidth=2,
zerolinewidth=2)
bandlayout = go.Layout(title="Bands diagram",
xaxis=bandxaxis,
yaxis=bandyaxis,
annotations=go.Annotations(annotations))
fig.update_layout(bandlayout)
fig.update(layout_showlegend=False)
# fig.show()
# graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
# In[5]:
# Get HTML representation of plotly.js and this figure
plot_div = pplot(fig, output_type='div', include_plotlyjs=False)
# Get id of html div element that looks like
# <div id="301d22ab-bfba-4621-8f5d-dc4fd855bb33" ... >
res = re.search('<div id="([^"]*)"', plot_div)
div_id = res.groups()[0]
# Build JavaScript callback for handling clicks
# and opening the URL in the trace's customdata
js_callback = """
<script>
var plot_element = document.getElementById("{div_id}");
plot_element.on('plotly_click', function(data){{
console.log(data);
var point = data.points[0];
if (point) {{
console.log(point.customdata);
window.open(point.customdata);
}}
}})
</script>
""".format(div_id=div_id)
# Build HTML string
html_str = """
<html>
<body>
{plot_div}
{js_callback}
<script type="text/javascript" async
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-MML-AM_SVG">
</script>
</body>
</html>
""".format(plot_div=plot_div, js_callback=js_callback)
return html_str, plot_div
tests['Tests 7-day'] = tests['Tests'].rolling(7).mean()
tests['Positives 7-day'] = tests['Positives'].rolling(7).mean()
tests['Positivity 7-day'] = tests['Positives 7-day'] / tests['Tests 7-day']
tests['Positivity'] = tests['Positives'] / tests['Tests']
tests['Positivity 7-day x10000'] = tests['Positivity 7-day'] * 10000
tests['Prevalence Index'] = np.sqrt(
tests['Positives 7-day'] * tests['Positivity 7-day']) * 200
tests['Weekday'] = tests.Date.apply(lambda d: d.weekday())
tests.plot(
x='Date',
y=['Positives 7-day', 'Positivity 7-day x10000', 'Prevalence Index'])
fig = px.bar(tests, x='Date', y=['Positives', 'Tests'], barmode='group')
pplot(fig, include_plotlyjs='cdn', filename=plotpath + '\\temp.html')
fig = px.line(tests, x='Date', y='Positives', color='Weekday')
pplot(fig, include_plotlyjs='cdn', filename=plotpath + '\\temp.html')
#%% Plot tests by result date
covid.plotly_twolines(
tests,
'Positivity',
'Positives',
plotcolors=['violet', 'steelblue'],
secondary_scale=1e4,
)
covid.plotly_casetest(
# filter counties shapefile to WI, convert to JSON format string, then decode
# to dictionary with json.loads()
countiesJS = json.loads(countiesWI.to_json())
#%% Choropleth maps
if do_choropleth:
fig = px.choropleth(countiesWI,
geojson=countiesJS,
locations=countiesWI.index,
color=plotcol,
color_continuous_scale=px.colors.sequential.Blues,
title='Cases by County',
projection='mercator')
fig.update_geos(fitbounds='locations', visible=False)
pplot(fig, filename='.\\plots\\plotly\\temp.html' )
fig2 = px.choropleth(countiesWI,
geojson=countiesJS,
locations=countiesWI.index,
color=plotcol2,
color_continuous_scale=px.colors.sequential.Oranges,
range_color=hosp_scale,
title='Hospitalizations by County',
projection='mercator')
fig2.update_geos(fitbounds='locations', visible=False)
pplot(fig2, filename='.\\plots\\plotly\\temp2.html' )
# # take out 'variable=' part of the axis titles
# fig.for_each_annotation(
# lambda a: a.update(
# text=a.text.split("=")[-1],
# font=dict(size=15),
# )
# # )
# fig.update_traces(textposition='outside')
fig.update_traces(marker_line_color='gray')
# other layout
# fig.update_layout(showlegend=False)
pplot(fig,
filename='.\\docs\\assets\\plotly\\Vaccination-Region.html',
include_plotlyjs='cdn',
)
save_png = '.\\docs\\assets\\Vaccination-Region.png'
fig.write_image(
save_png,
width=700,
height=550,
engine='kaleido',
)
os.startfile(save_png)
usa = pd.read_csv(url_all)
# convert dates
state['Date'] = pd.to_datetime(state['date'], format='%Y%m%d')
usa['Date'] = pd.to_datetime(usa['date'], format='%Y%m%d')
#%% Plot cases against total tests
# remember total tests only fully populated oct 29... doesn't seem to have
# caused a discontinuity though
# Cases / Tests line plot
covid.plotly_casetest(
sourcedata=state,
case_col='positiveIncrease',
test_col='totalTestResultsIncrease',
date_col='Date',
savefile='docs\\assets\\plotly\\Cases-Tests-WI-CTP.html',
)
#%% Plot WI vs. MN
compare = usa.loc[usa.state.apply(lambda s: s in ['WI', 'MN'])]
fig = px.line(compare, x='Date', y='totalTestResultsIncrease', color='state')
pplot(
fig,
filename='.\\docs\\assets\\plotly\\WI-MN.html',
include_plotlyjs='cdn',
)
