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dashboard.py
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dashboard.py
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# app is here: https://share.streamlit.io/ouranosinc/info-crue-cmip6/main/dashboard.py
import streamlit as st
import holoviews as hv
from pathlib import Path
import pandas as pd
import numpy as np
import xarray as xr
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import glob
import hvplot.xarray
from matplotlib import colors
st.set_page_config(layout="wide")
st.title('Info-Crue CMIP6')
@st.cache(hash_funcs={xr.core.dataset.Dataset: id},ttl=60)
def load_zarr(path):
return xr.open_zarr(path,decode_timedelta= False)
@st.cache(hash_funcs={xr.core.dataset.Dataset: id},ttl=60)
def load_nc(path):
return xr.open_dataset(path,decode_timedelta= False)
useCat = st.checkbox("use catalog (only for local version)")
if useCat:
tab1, tab2, tab3, tab4 = st.tabs(["Diagnostiques", "Niveaux de réchauffement global", "Horizons Temporels", "Sélection vs tout"])
else:
tab1, tab2 = st.tabs(
["Diagnostiques", "Niveaux de réchauffement global"])
with tab1:
#useCat=True
#load data
if useCat:
cols = st.columns(2)
from xscen.config import CONFIG, load_config
from xscen.catalog import ProjectCatalog
load_config('paths_neree.yml', 'config.yml', verbose=(__name__ == '__main__'), reset=True)
#pcat = ProjectCatalog(CONFIG['paths']['dashboard_catalog'])
pcat = ProjectCatalog(CONFIG['paths']['project_catalog'])
# choose id
option_id = cols[0].selectbox('Id',pcat.search(type=['simulations', 'simulation'], processing_level = 'diag-improved').df.id.unique())
option_domain = cols[1].selectbox('Domain', pcat.search(type=['simulations', 'simulation']).df.domain.unique())
#load all properties from ref, sim, scen
ref = pcat.search( processing_level='diag-ref-prop', domain=option_domain).to_dask(xarray_open_kwargs={'decode_timedelta':False})
sim = pcat.search(id= option_id, processing_level='diag-sim-prop', domain=option_domain).to_dask(xarray_open_kwargs={'decode_timedelta':False})
scen = pcat.search(id= option_id, processing_level='diag-scen-prop', domain=option_domain).to_dask(xarray_open_kwargs={'decode_timedelta':False})
#get bias
bias_sim = pcat.search(id=option_id, processing_level='diag-sim-meas', domain=option_domain).to_dask(xarray_open_kwargs={'decode_timedelta':False})
bias_scen = pcat.search(id=option_id, processing_level='diag-scen-meas', domain=option_domain).to_dask(xarray_open_kwargs={'decode_timedelta':False})
#get measures summary
hm = pcat.search( id=option_id,processing_level='diag-heatmap', domain=option_domain).to_dask()
imp = pcat.search(id=option_id,processing_level='diag-improved', domain=option_domain).to_dask()
else:
cols = st.columns(3)
#option_id = st.selectbox('id',[x[30:-5] for x in glob.glob('dashboard_data/diag_scen_bias_*')])
ids = [x[30:-6] for x in glob.glob('dashboard_data/diag-scen-meas_*')]
models = sorted(set([y.split('_')[3] for y in ids ]))
option_model = cols[0].selectbox('Models', models)
ids_of_model = [x for x in ids if option_model in x]
exps = sorted(set([y.split('_')[4] for y in ids_of_model]))
option_ssp = cols[1].selectbox('Experiments',exps)
ids_of_model_exps = [x for x in ids if option_model in x and option_ssp in x]
members = sorted(set([y.split('_')[5] for y in ids_of_model_exps]))
option_member = cols[2].selectbox('Members', members)
option_id = [x for x in ids if option_model in x and option_ssp in x and option_member in x][0]
ref = load_nc(f'dashboard_data/diag_ref_ECMWF_ERA5-Land_NAM_qc.nc')
sim = load_nc(f'dashboard_data/diag-sim-prop_{option_id}_qc.nc')
scen = load_nc(f'dashboard_data/diag-scen-prop_{option_id}_qc.nc')
bias_sim = load_nc(f'dashboard_data/diag-sim-meas_{option_id}_qc.nc')
bias_scen = load_nc(f'dashboard_data/diag-scen-meas_{option_id}_qc.nc')
hm = load_nc(f'dashboard_data/diag-heatmap_{option_id}_qc.nc')
imp = load_nc(f'dashboard_data/diag-improved_{option_id}_qc.nc')
cols2=st.columns(2)
# choose properties
option_var = cols2[0].selectbox('Input Variables',[ 'Maximum Temperature','Minimum Temperature', 'Precipitation'])
varlong2short = {'Minimum Temperature':'tasmin', 'Maximum Temperature':'tasmax', 'Precipitation':'pr' }
props_of_var= [x for x in scen.data_vars if f'{varlong2short[option_var]},' in scen[x].attrs['history'] ]
def show_long_name(name):
return f"{scen[name].attrs['long_name']} ({name})"
option_prop = cols2[1].selectbox('Properties of the variable',sorted(props_of_var), format_func = show_long_name)
prop_sim = sim[option_prop]
prop_ref = ref[option_prop]
prop_scen = scen[option_prop]
bias_scen_prop = bias_scen[option_prop]
bias_sim_prop = bias_sim[option_prop]
if 'season' in prop_sim.coords:
option_season = cols2[1].selectbox('Season',prop_sim.season.values)
prop_sim = prop_sim.sel(season=option_season)
prop_ref = prop_ref.sel(season=option_season)
prop_scen = prop_scen.sel(season=option_season)
bias_scen_prop = bias_scen_prop.sel(season=option_season)
bias_sim_prop = bias_sim_prop.sel(season=option_season)
#colormap
maxi_prop = max(prop_ref.max().values, prop_scen.max().values, prop_sim.max().values)
mini_prop = min(prop_ref.min().values, prop_scen.min().values, prop_sim.min().values)
maxi_bias = max(abs(bias_scen_prop).max().values, abs(bias_sim_prop).max().values)
cmap='viridis_r' if option_var == 'Precipitation' else 'plasma'
cmap_bias ='BrBG' if option_var == 'Precipitation' else 'coolwarm'
long_name=prop_sim.attrs['long_name']
col1, col2, col3 = st.columns([6,3,4])
w, h = 300, 300
wb, hb = 400, 300
measure_name = bias_sim_prop.attrs['long_name']
# fix range of colorbar
if 'ratio' in measure_name : #center around 1 for ratio
maxi_rat = maxi_bias
mini_rat = max(abs(bias_scen_prop).min().values, abs(bias_sim_prop).min().values)
max_deviation_from_1 = max(abs(1-maxi_rat),abs(1-mini_rat))
mini, maxi = 1-max_deviation_from_1, 1+max_deviation_from_1
else: # center around 0 for bias
mini, maxi = -maxi_bias, maxi_bias
col1.write(hv.render(prop_ref.hvplot(title=f'REF\n{long_name}',width=600, height=616, cmap=cmap, clim=(mini_prop,maxi_prop))))
col2.write(hv.render(prop_sim.hvplot(width=w, height=h, title=f'SIM', cmap=cmap, clim=(mini_prop,maxi_prop)).opts(colorbar=False)))
col2.write(hv.render(prop_scen.hvplot(width=w, height=h, title=f'SCEN', cmap=cmap, clim=(mini_prop,maxi_prop)).opts(colorbar=False)))
col3.write(hv.render(bias_sim_prop.hvplot(width=wb, height=hb, title=f'SIM {measure_name}', cmap=cmap_bias,clim=(mini, maxi))))
col3.write(hv.render(bias_scen_prop.hvplot(width=wb, height=hb, title=f'SCEN {measure_name}', cmap=cmap_bias,clim=(mini,maxi))))
#plot the heat map
fig_hmap, ax = plt.subplots(figsize=(7,3))
cmap=plt.cm.RdYlGn_r
norm = colors.BoundaryNorm(np.linspace(0,1,4), cmap.N)
im = ax.imshow(hm.heatmap.values, cmap=cmap, norm=norm)
ax.set_xticks(ticks = np.arange(len(hm.properties)), labels=hm.properties.values, rotation=45,ha='right')
ax.set_yticks(ticks = np.arange(len(hm.datasets)), labels=[x.split('.')[2].split('-')[1] for x in hm.datasets.values])
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size='15%', pad=0.4)
fig_hmap.add_axes(cax)
cbar = fig_hmap.colorbar(im, cax=cax, ticks=[0, 1], orientation='horizontal')
cbar.ax.set_xticklabels(['best', 'worst'])
plt.title('Normalised mean measure of properties')
fig_hmap.tight_layout()
#plot improved
percent_better= imp.improved_grid_points.values
percent_better=np.reshape(np.array(percent_better), (1, len(hm.properties)))
fig_per, ax = plt.subplots(figsize=(7, 3))
cmap=plt.cm.RdYlGn
norm = colors.BoundaryNorm(np.linspace(0,1,100), cmap.N)
im = ax.imshow(percent_better, cmap=cmap, norm=norm)
ax.set_xticks(ticks=np.arange(len(hm.properties)), labels= imp.properties.values, rotation=45,ha='right')
ax.set_yticks(ticks=np.arange(1), labels=[''])
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size='15%', pad=0.4)
fig_per.add_axes(cax)
cbar = fig_per.colorbar(im, cax=cax, ticks=np.arange(0,1.1,0.1), orientation='horizontal')
plt.title('Fraction of grid cells of scen that improved or stayed the same compared to sim')
fig_per.tight_layout()
col1, col2 = st.columns([1,1])
col1.write(fig_hmap)
col2.write(fig_per)
with tab2:
cols = st.columns([3,1,1])
#option_type= cols[0].selectbox('Type',['delta', 'absolute'])
# load data
if useCat:
#from xscen.config import CONFIG, load_config
#from xscen.catalog import ProjectCatalog
#load_config('paths_neree.yml', 'config.yml', verbose=(__name__ == '__main__'),reset=True)
#pcat = ProjectCatalog(CONFIG['paths']['project_catalog'])
# get warminglevel
wl15 = pcat.search(processing_level='delta-+1.5C-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
wl2 = pcat.search(processing_level='delta-+2C-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
wl3 = pcat.search(processing_level='delta-+3C-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
#ensemble_sizes= {x.horizon.values[0]:x.horizon.attrs['ensemble_size'] for x in wls.values()}
#wl = xr.concat(wls.values(), dim='horizon')
else:
ensemble_sizes={}
wl15 = load_zarr(f'dashboard_data/ensemble-deltas-1.5_CMIP6_ScenarioMIP_qc.zarr')
wl2 = load_zarr(f'dashboard_data/ensemble-deltas-2_CMIP6_ScenarioMIP_qc.zarr')
wl3 = load_zarr(f'dashboard_data/ensemble-deltas-3_CMIP6_ScenarioMIP_qc.zarr')
#ensemble_sizes[f"+{h}C"]=cur_wl.horizon.attrs['ensemble_size']
#wls.append(cur_wl)
#wl = xr.concat(wls, dim='horizon')
#choose data
def show_long_name(name):
for var in wl2.data_vars:
if f"{name}_delta" in var:
return f"{wl2[var].attrs['long_name']} ({name})"
option_ind_wl = cols[0].selectbox('Indicateurs',set([x.split('_delta')[0] for x in wl2.data_vars]), format_func = show_long_name)
option_stats_wl = cols[1].selectbox('Statistiques', ['mean', 'max','min', 'stdev','p10', 'p50', 'p190'])
option_season_wl = cols[2].selectbox('Saisons', wl2.season.values)
complete_var = f"{option_ind_wl}_delta_1991_2020_{option_stats_wl}"
#plot data
cmap = 'viridis_r' if 'precipitation' in wl2[complete_var].attrs['standard_name'] else 'plasma'
vmin = np.min([cur_wl[complete_var].sel(season=option_season_wl).min().values for cur_wl in [wl15, wl2, wl3]])
vmax = np.max([cur_wl[complete_var].sel(season=option_season_wl).max().values for cur_wl in [wl15, wl2, wl3]])
col3 = st.columns(3)
for i, cur_wl in enumerate([wl15, wl2, wl3]):
select_wl = cur_wl[complete_var].sel(season=option_season_wl).isel(horizon=0)
col3[i].write(
hv.render(select_wl.hvplot(cmap=cmap,
width=450,
height=350,
clim=(vmin, vmax))))
col3[i].write(f"Nombre de réalisations de l'ensemble: {cur_wl.horizon.attrs['ensemble_size']}")
#st.write( f"Warming levels are calculated from the {select_wl.horizon.attrs['baseline']} baseline.")
# load data
if useCat:
with tab3:
cols = st.columns([3,1,1])
# load data
if useCat:
# get warminglevel
s1 = pcat.search(processing_level='delta-ssp126-2081-2100-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
s2 = pcat.search(processing_level='delta-ssp245-2081-2100-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
s3 = pcat.search(processing_level='delta-ssp370-2081-2100-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
s4 = pcat.search(processing_level='delta-ssp585-2081-2100-selection').to_dask(xarray_open_kwargs={'decode_timedelta':False})
else:
ensemble_sizes={}
# wl15 = load_zarr(f'dashboard_data/ensemble-deltas-1.5_CMIP6_ScenarioMIP_qc.zarr')
# wl2 = load_zarr(f'dashboard_data/ensemble-deltas-2_CMIP6_ScenarioMIP_qc.zarr')
# wl3 = load_zarr(f'dashboard_data/ensemble-deltas-3_CMIP6_ScenarioMIP_qc.zarr')
#ensemble_sizes[f"+{h}C"]=cur_wl.horizon.attrs['ensemble_size']
#wls.append(cur_wl)
#wl = xr.concat(wls, dim='horizon')
#choose data
def show_long_name(name):
for var in s2.data_vars:
if name in var:
return f"{s2[var].attrs['long_name']} ({name})"
option_ind_s = cols[0].selectbox('Indicateurs ',set([x.split('_delta')[0] for x in s2.data_vars]), format_func = show_long_name)
option_stats_s = cols[1].selectbox('Statistiques ', [ 'mean', 'max', 'min', 'stdev','p10', 'p50', 'p90'])
option_season_s = cols[2].selectbox('Saisons ', s2.season.values)
complete_var = f"{option_ind_s}_delta_1991_2020_{option_stats_s}"
#plot data
cmap = 'viridis_r' if 'precipitation' in s2[complete_var].attrs['standard_name'] else 'plasma'
vmin = np.min([cur_wl[complete_var].sel(season=option_season_s).min().values for cur_wl in [s1,s2,s3,s4]])
vmax = np.max([cur_wl[complete_var].sel(season=option_season_s).max().values for cur_wl in [s1,s2,s3,s4]])
col3 = st.columns(4)
names = ['ssp1-2.6', 'ssp2-4.5', 'ssp3-7.0', 'ssp5-8.5']
for i, cur_wl in enumerate([s1,s2,s3,s4]):
select_wl = cur_wl[complete_var].sel(season=option_season_s).isel(horizon=0)
col3[i].write(names[i])
col3[i].write(
hv.render(select_wl.hvplot(cmap=cmap,width=400,
height=350,
clim=(vmin, vmax))))
col3[i].write(f"Nombre de réalisations de l'ensemble: {cur_wl.attrs['ensemble_size']}")
with tab4:
col = st.columns([1,5,1,1])
if useCat:
option_ens = col[0].selectbox('ensemble', ['+1.5C', '+2C','+3C' ,'ssp126-2081-2100','ssp245-2081-2100','ssp370-2081-2100','ssp585-2081-2100'])
option_can = 'include'
if option_ens in ['ssp245-2081-2100','ssp370-2081-2100']:
option_can = col[0].selectbox('CanESM5', ['include', 'exclude'])
can=''
r=''
if option_can== 'exclude':
can = 'NoCanESM5'
option_r = col[0].selectbox('members', ['all members', 'only r1'])
if option_r == 'only r1':
r = 'r1'
selection = pcat.search(processing_level=f'delta-{option_ens}-selection{can}{r}').to_dask(xarray_open_kwargs={'decode_timedelta':False})
all = pcat.search(processing_level=f'delta-{option_ens}-all{r}').to_dask(xarray_open_kwargs={'decode_timedelta':False})
diff = pcat.search(processing_level=f'{option_ens}-selection{can}{r}VSall{r}').to_dask(xarray_open_kwargs={'decode_timedelta':False})
pvalues=None
if len(pcat.search(processing_level=f'p-{option_ens}-selection{can}{r}VSall{r}').df)>0:
pvalues = pcat.search(processing_level=f'p-{option_ens}-selection{can}{r}VSall{r}').to_dask(xarray_open_kwargs={'decode_timedelta':False})
if 'baseline' in selection.horizon.attrs:
st.write(f"Warming levels are calculated from the {selection.horizon.attrs['baseline']} baseline")
def show_long_name_ens(name):
return f"{all[f'{name}_mean'].attrs['long_name']} ({name})"
option_ind_ens = col[1].selectbox('Indicateur',
set(['_'.join(x.split('_')[:-1]) for x in
all.data_vars]),
format_func=show_long_name_ens)
option_stats_ens = col[2].selectbox('Statistique',[ 'mean', 'max', 'min', 'stdev','p10', 'p50', 'p90'])
option_season_ens = col[3].selectbox('Saison', all.season.values)
complete_var = f"{option_ind_ens}_{option_stats_ens}"
col_fig = st.columns(3)
select_haus = selection[complete_var].sel(season=option_season_ens).isel(horizon=0)
select_all = all[complete_var].sel(season=option_season_ens).isel(horizon=0)
select_diff = diff[complete_var].sel(season=option_season_ens).isel(horizon=0)
#plot data
cmap = 'viridis_r' if 'precipitation' in select_all.attrs['standard_name'] else 'plasma'
cmap_bias = 'BrBG' if 'precipitation' in select_all.attrs[
'standard_name'] else 'coolwarm'
vmin = np.min([cur.min().values for cur in [select_all, select_haus]])
vmax = np.max([cur.max().values for cur in [select_all, select_haus]])
col_fig[0].write('Selection')
col_fig[0].write(hv.render(select_haus.hvplot(cmap=cmap,width=450,height=350,clim=(vmin, vmax))))
col_fig[0].write(f"Nombre de réalisations de l'ensemble: {selection.attrs['ensemble_size']}")
col_fig[1].write('All')
col_fig[1].write( hv.render(select_all.hvplot(cmap=cmap, width=450,height=350, clim=(vmin, vmax))))
col_fig[1].write(f"Nombre de réalisations de l'ensemble: {all.attrs['ensemble_size']}")
col_fig[2].write('(Selection - All)/All')
diff_plot= select_diff.hvplot(cmap=cmap_bias,clim =(- abs(select_diff).max(),abs(select_diff).max()),width=450,height=350)
col_fig[2].write( hv.render(diff_plot))
if pvalues:
select_p = pvalues[option_ind_ens].sel(season=option_season_ens).isel(horizon=0)
levels = [0, 0.05, 10]
colors = ['#43A047', '#E53935']
col_fig[2].write( hv.render(select_p.hvplot(width=450,height=350).options(color_levels=levels, cmap=colors)))
# # test panel
# # https://github.com/holoviz/panel/issues/1074
# # https://github.com/streamlit/streamlit/issues/927
# # import panel as pn
# # import hvplot.xarray
# #
# # hvplot_plot = scen['mean-pr'].hvplot()
# # hvplot_pane = pn.pane.HoloViews(hvplot_plot, name="Holoviews Plot")
# # tabs = pn.Tabs(hvplot_pane)
# # st.bokeh_chart(tabs.get_root())
# #
# # def plot_var(variable):
# # return scen[variable].hvplot()
# #
# # pan = pn.interact(plot_var, variable=list(scen.data_vars))
# # st.write(hv.render(pan).get_root(), backend='bokeh')
# #
# #
# #
# #
# # st.write(fig_hmap)
# # st.write(fig_per)