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interactive_histogram.py
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173 lines (136 loc) · 6 KB
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import plotly.figure_factory as ff
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Add histogram data
my_var = 'BURDEN2'
intel_df = df[(df.Label == 'Intel')]
my_test_input = 'rh-min-low'
nsteps = 10
fig = go.Figure()
gnu_df = df[df.Label == 'GNU']
def make_interact_dist(my_var,nsteps,my_test = my_test_input):
init_year = 0
test_df = df[df.Simulation == my_test]
mean_init_intel = intel_df[intel_df.nyear == init_year].mean()[my_var]
std_init_intel = intel_df[intel_df.nyear == init_year].std()[my_var]
mean_init_gnu = gnu_df[gnu_df.nyear == init_year].mean()[my_var]
std_init_gnu = gnu_df[gnu_df.nyear == init_year].std()[my_var]
fig = make_subplots(rows=1, cols=1,shared_xaxes=True)#, subplot_titles = ['Mean %f' % (mean_init_intel) +', Std %f' % (std_init_intel),'Mean %f' % (mean_init_gnu) +', Std %f' % (std_init_gnu)]
#xmin = np.nanmin((intel_df.min()[my_var],gnu_df.min()[my_var]))
#xmax = np.nanmax((intel_df.max()[my_var],gnu_df.max()[my_var]))
xmin = np.nanmin((intel_df.min()[my_var],gnu_df.min()[my_var],test_df.min()[my_var]))
xmax = np.nanmax((intel_df.max()[my_var],gnu_df.max()[my_var],test_df.max()[my_var]))
nbins = 40
bin_size = (xmax - xmin)/nbins
xbins = np.linspace(xmin,xmax,nbins+1)
print(xmin,xmax,np.percentile((xmin,xmax),80))
intel_ymax = []
gnu_ymax = []
intel_mean = []
intel_std = []
gnu_mean = []
gnu_std = []
for step in np.arange(0, nsteps, 1):
my_year = step
x1 = np.array(intel_df[intel_df.nyear == my_year][my_var])
x2 = np.array(gnu_df[gnu_df.nyear == my_year][my_var])
#x3 = np.array(test_df[test_df.nyear == my_year][my_var])
# Create distplot with custom bin_size
#fig = ff.create_distplot(hist_data, group_labels)#, bin_size=[.05, 0.05]
counts_intel, bins = np.histogram(x1,bins = xbins,density =True)
counts_gnu, bins = np.histogram(x2,bins = xbins,density =True)
counts_intel = counts_intel/np.sum(counts_intel)
counts_gnu = counts_gnu/np.sum(counts_gnu)
intel_ymax.append(counts_intel)
gnu_ymax.append(counts_intel)
intel_mean.append(x1.mean())
intel_std.append(x1.std())
gnu_mean.append(x2.mean())
gnu_std.append(x2.std())
### Decide ymax
ymax_intel = np.nanmax(intel_ymax)
ymax_gnu = np.nanmax(gnu_ymax)
ymax = np.nanmax([ymax_intel,ymax_gnu])
print(intel_std)
for step in np.arange(0, nsteps, 1):
my_year = step
x1 = np.array(intel_df[intel_df.nyear == my_year][my_var])
x2 = np.array(gnu_df[gnu_df.nyear == my_year][my_var])
x3 = np.array(test_df[(df.Simulation == my_test) & (df.nyear == my_year)][my_var])[0]
fig.add_trace(go.Histogram(visible=False,x=x1, histnorm='probability',name = 'Intel',xbins=dict(
start=xmin,
end=xmax,
size=bin_size),
autobinx=False
),row=1, col=1)
fig.add_trace(go.Histogram(visible=False,x=x2, histnorm='probability',name = 'GNU',xbins=dict(
start=xmin,
end=xmax,
size=bin_size),
autobinx=False
),row=1, col=1)
#fig.add_annotation(
# visible = False,
# x=np.percentile((xmin,xmax),20),
# y=ymax_intel*0.95,
# text="Mean: %f" % ((x1.mean())),
# showarrow= False
# )
#fig.add_annotation(
# visible = False,
# x=np.percentile((xmin,xmax),20),
# y=ymax_intel*0.8,
# text="Std: %f" % ((x1.std())),
# showarrow= False
# )
fig.add_trace(go.Scatter(visible=False,x=[x3, x3], y=[0,ymax],
mode="lines",name = my_test, line=dict(color="black",dash='dash') ),row=1,col=1)
fig.add_trace(go.Scatter(visible=False,x=[x1.mean(), x1.mean()], y=[0,ymax],
mode="lines",name = 'Mean Intel',line=dict(color="blue",dash='dash') ),row=1,col= 1)
fig.add_trace(go.Scatter(visible=False,x=[x2.mean(), x2.mean()], y=[0,ymax],
mode="lines",name = 'Mean GNU',line=dict(color="red",dash='dash') ),row=1,col= 1)
fig.update_traces(opacity=0.75)
#fig['layout']['annotations'][0].update(text='Mean %f' % (x1.mean()) +', Std %f' % (x1.std()))
#fig['layout']['annotations'][1].update(text='Mean %f' % (x2.mean()) +', Std %f' % (x2.std()))
fig.data[0].visible = True
fig.data[1].visible = True
fig.data[2].visible = True
fig.data[3].visible = True
fig.data[4].visible = True
#fig.layout.annotations[0].visible = False
#fig.layout.annotations[1].visible = False
# Create and add slider
years = []
for i in range(0, len(fig.data), 5):
year = dict(
method="update",
label = 'Year {}'.format(int(i/5)+1),
args=[{"visible": [False] * len(fig.data)},
{"title": "Distribution across ensemble for year " + str(int(i/5)+1)},
{"annotation": ['Mean %f' % my_mean for my_mean in (intel_mean)]}
], # layout attribute
)
year["args"][0]["visible"][i] =True # Toggle i'th trace to "visible"
year["args"][0]["visible"][i+1] =True # Toggle i'th trace to "visible"
year["args"][0]["visible"][i+2] =True # Toggle i'th trace to "visible"
year["args"][0]["visible"][i+3] =True # Toggle i'th trace to "visible"
year["args"][0]["visible"][i+4] =True # Toggle i'th trace to "visible"
#for new_idx in range(len(fig.layout.annotations)):
# fig.layout.annotations[new_idx].visible = False
years.append(year)
sliders = [dict(
active=0,
currentvalue={"prefix": "This is "},
pad={"t": nsteps},
steps=years
)]
fig.update_layout(
sliders=sliders
)
fig.update_xaxes(range=[xmin, xmax])
fig.update_yaxes(range=[0, ymax*1.1],row=1,col=1)
#fig.update_yaxes(range=[0, ymax_gnu*1.2],row=2,col=1)
fig.show()
return fig
my_fig = make_interact_dist(my_var = 'SNOWHLND',nsteps = 10,my_test = 'rh-min-low')
#my_fig.write_html(local_dir+"/interactive_distribution.html")