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RUINSapp.py
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# -*- coding: utf-8 -*-
import streamlit as st
import pandas as pd
import numpy as np
import xarray as xr
import seaborn as sns
sns.set_style('whitegrid', {'grid.linestyle': u'--'})
from sdm import SDM
#import statsmodels.api as sm
#import altair as alt
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
from PIL import Image
from pathlib import Path
st.title('RUINS climate data app')
# helper functions
#@st.cache(persist=True)
def load_alldata():
weather = xr.load_dataset('data/weather.nc')
climate = xr.load_dataset('data/cordex_coast.nc')
# WARNING - bug fix for now:
# 'HadGEM2-ES' model runs are problematic and will be removed for now
# The issue is with the timestamp and requires revision of the ESGF reading routines
kys = [s for s in list(climate.keys()) if 'HadGEM2-ES' not in s] #remove all entries of HadGEM2-ES (6 entries)
climate = climate[kys]
return weather, climate
weather, climate = load_alldata()
def load_data(sel='Weather',regagg=None):
#Read data from netcdf files and return xarray
if sel == 'Weather':
data = xr.load_dataset('data/weather.nc')
elif sel == 'CORDEX':
data = xr.load_dataset('data/cordex_coast.nc')
else:
if regagg == 'North Sea Coast':
data = xr.load_dataset('data/cordex_coast.nc')
data.filter_by_attrs(RCP=sel)
return data
def read_markdown_file(markdown_file):
return Path(markdown_file).read_text()
def kde(data, cmdata='none', split_ts=1, cplot=True, eq_period=True):
# plot of kde with stripes
from sklearn.neighbors import KernelDensity
cxx = ['#E69F00', '#009E73', '#0072B2', '#D55E00', '#CC79A7']
cxx2 = ['#8c6bb1', '#810f7c']
data = data[~np.isnan(data)]
x_d = np.linspace(np.min(data) * 0.9, np.max(data) * 1.1, len(data))
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde.fit(data[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
if cplot & (split_ts == 1):
plt.fill_between(x_d, np.exp(logprob), alpha=0.4, facecolor='grey')
lp = np.exp(logprob)
xd = x_d
if type(cmdata) != str:
fig, (ax, cax, cax2) = plt.subplots(ncols=3, figsize=(10.5, 2.3), gridspec_kw={"width_ratios": [1, 0.02, 0.02]})
x_d2 = np.linspace(np.min(cmdata) * 0.9, np.max(cmdata) * 1.1, len(cmdata))
# instantiate and fit second KDE model
kde2 = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde2.fit(cmdata[:, None])
# score_samples returns the log of the probability density
logprob2 = kde2.score_samples(x_d2[:, None])
if cplot & (split_ts == 1):
ax.fill_between(x_d2, np.exp(logprob2), alpha=0.4, facecolor='grey')
lp2 = np.exp(logprob2)
xd2 = x_d2
else:
fig, (ax, cax) = plt.subplots(ncols=2, figsize=(10.5, 2.3), gridspec_kw={"width_ratios": [1, 0.06]})
if split_ts > 1:
if eq_period:
spliti = [0, len(data) - 40, len(data) - 20, len(data)]
else:
spliti = np.linspace(0, len(data), split_ts + 1).astype(int)
for i in np.arange(split_ts):
datax = data.iloc[spliti[i]:spliti[i + 1]]
x_d = np.linspace(np.min(datax) * 0.9, np.max(datax) * 1.1, len(datax))
try:
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde.fit(datax[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
if cplot:
ax.fill_between(x_d, np.exp(logprob), alpha=0.4, facecolor=cxx[i],
label='-'.join([str(datax.index.year.min()), str(datax.index.year.max())]))
except:
pass
if type(cmdata) != str:
# add climate model data
csplit_ts = [2040, 2080]
for i in np.arange(2):
datax = cmdata.loc[(cmdata.index.year >= csplit_ts[i]) & (cmdata.index.year < csplit_ts[i] + 20)]
x_d = np.linspace(np.min(datax) * 0.9, np.max(datax) * 1.1, len(datax))
try:
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=1.0, kernel='gaussian')
kde.fit(datax[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
if cplot:
ax.fill_between(x_d, np.exp(logprob), alpha=0.4, facecolor=cxx2[i],
label='-'.join([str(datax.index.year.min()), str(datax.index.year.max())]))
except:
pass
ax.legend(loc=1, ncol=5)
if cplot:
# cmap = plt.cm.get_cmap('cividis_r')
cmap = plt.cm.get_cmap('viridis_r')
colors = plt.cm.cividis_r(np.linspace(0, 1, len(data)))
colorsx = cmap(np.arange(cmap.N))
for i in np.arange(len(data)):
ax.plot([data.iloc[i], data.iloc[i]], [0, np.max(lp) * 0.9], c=colors[i])
labcb = 'Year'
if type(cmdata) != str:
cmap2 = plt.cm.get_cmap('plasma')
colors2 = plt.cm.plasma(np.linspace(0, 1, len(cmdata)))
colorsx2 = cmap2(np.arange(cmap2.N))
for i in np.arange(len(cmdata)):
ax.plot([cmdata.iloc[i], cmdata.iloc[i]], [0, np.max(lp) * 0.9], c=colors2[i])
cbar2 = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap2), cax=cax2, label=labcb,
ticks=[0, 0.2, 0.4, 0.6, 0.8, 1], fraction=0.0027, anchor=(1.0, 0.1))
cbar2.ax.set_yticklabels(
np.round(np.linspace(cmdata.index.year.min(), cmdata.index.year.max(), 6)).astype(int).astype(str))
labcb = ''
cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), cax=cax, label=labcb, ticks=[0, 0.2, 0.4, 0.6, 0.8, 1])
cbar.ax.set_yticklabels(
np.round(np.linspace(data.index.year.min(), data.index.year.max(), 6)).astype(int).astype(
str)) # vertically oriented colorbar
ax.set_ylabel('Occurrence (KDE)')
return ax
def yrplot_hm(sr, ref=[1980, 2000], ag='sum', qa=0.95, cbar_title='Temperature anomaly (K)', cmx='coolwarm', cmxeq=True, li=False):
# plot of heatmap with monthyl and annual stripes
yrs = sr.index.year.unique()
dummy = np.zeros((len(yrs), 14)) * np.nan
dummy = pd.DataFrame(dummy)
dummy.index = yrs
dummy.columns = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D', '', 'Year']
for i in sr.index:
dummy.iloc[i.year - yrs[0], i.month - 1] = sr.loc[i]
for i in yrs:
if ag == 'sum':
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].sum()
cmx = 'coolwarm_r'
elif ag == 'min':
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].min()
elif ag == 'max':
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].max()
else: # ag == 'mean'
dummy.iloc[i - yrs[0], 13] = sr.loc[sr.index.year == i].mean()
if ref == None:
pass
else:
refx = dummy.loc[ref[0]:ref[1]].mean(axis=0)
dummy = dummy - refx
if cmxeq:
vxU = dummy.abs().quantile(qa).quantile(qa)
vxL = -1. * vxU
else:
vxU = dummy.quantile(qa).quantile(qa)
vxL = dummy.quantile(1. - qa).quantile(1. - qa)
if ag == 'sum':
dummy.iloc[:, 13] = dummy.iloc[:, 13] / 12
plt.figure(figsize=(8,len(dummy)/15.))
ax = sns.heatmap(dummy, cmap=cmx, vmin=vxL, vmax=vxU, cbar_kws={'label': cbar_title})
if ref == None:
pass
else:
ax.add_patch(
plt.Rectangle((0, ref[0] - yrs[0]), 12, ref[1] - ref[0], fill=True, edgecolor='red', facecolor='gray', lw=2,
alpha=0.3, clip_on=False))
ax.add_patch(
plt.Rectangle((13, ref[0] - yrs[0]), 1, ref[1] - ref[0], fill=True, edgecolor='red', facecolor='gray', lw=2,
alpha=0.3, clip_on=False))
ax.annotate('Reference period', (0.5, ref[1] - yrs[0] - 2), color='white', weight='bold', ha='left',
va='bottom', alpha=0.6)
if type(li) == int:
ax.axhline(li - yrs[0], color='k', ls='--', lw=1, alpha=0.5)
ax.annotate(' >> observed', (12.5, li - yrs[0] - 0.5), color='k', ha='center', va='bottom', alpha=0.6,
rotation=90.)
ax.annotate('modelled << ', (12.5, li - yrs[0] + 0.5), color='k', ha='center', va='top', alpha=0.6,
rotation=90.)
# ax.add_patch(plt.Rectangle((0, li-yrs[0]), 12, 0, fill=False, edgecolor='k', ls='--', lw=1, alpha=0.5, clip_on=False))
ax.set_ylabel('Year')
ax.set_xlabel('Month ')
return
def monthlyx(dy, dyx=1, ylab='T (°C)', clab1='Monthly Mean in Year', clab2='Monthly Max in Year', pls='cividis_r'):
cmap = plt.cm.get_cmap(pls)
colors = cmap(np.linspace(0, 1, len(dy.index.year.unique())+1))
colorsx = cmap(np.arange(cmap.N))
idx1 = dy.index.year - dy.index.year.min()
idx1m = dy.index.month
if type(dyx) == int:
pass
else:
idx2 = (dyx.index.year - dyx.index.year.min()).astype(int)
idx2m = dyx.index.month
cmap1 = plt.cm.get_cmap('gist_heat_r')
colors1 = plt.cm.gist_heat_r(np.linspace(0, 1, len(dyx.index.year.unique()) + 1))
colorsx1 = cmap1(np.arange(cmap1.N))
for i in dy.columns:
plt.scatter(idx1m + (np.random.rand(len(dy)) - 1.5), dy[i].values.astype(np.float), c=colors[idx1], alpha=0.6, s=2)
if type(dyx) == int:
pass
else:
for i in dyx.columns:
plt.scatter(idx2m + (np.random.rand(len(dyx)) - 1.5), dyx[i].values.astype(np.float), c=colors1[idx2], alpha=0.6, s=2)
cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), label=clab1, ticks=[0, 0.2, 0.4, 0.6, 0.8, 1])
cbar.ax.set_yticklabels(np.round(np.linspace(dy.index.year.min(), dy.index.year.max(), 6)).astype(int).astype(str))
if type(dyx) == int:
pass
else:
cbar1 = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap1), label=clab2, ticks=[0, 0.2, 0.4, 0.6, 0.8, 1])
cbar1.ax.set_yticklabels(
np.round(np.linspace(dyx.index.year.min(), dyx.index.year.max(), 6)).astype(int).astype(str))
plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.ylabel(ylab)
return
def plt_map(sel='all',cm='none'):
import plotly.graph_objs as go
import plotly.express as px
dummy = xr.open_dataset('data/CORDEXgrid.nc')
dummy5 = xr.open_dataset('data/CMIP5grid.nc')
stats = pd.read_csv('data/stats.csv', index_col=0)
stats['ms'] = 15.
stats['color'] = 'gray'
mapbox_access_token = 'pk.eyJ1IjoiY29qYWNrIiwiYSI6IkRTNjV1T2MifQ.EWzL4Qk-VvQoaeJBfE6VSA'
px.set_mapbox_access_token(mapbox_access_token)
nodexy = pd.DataFrame([dummy.lon.values.ravel(), dummy.lat.values.ravel()]).T
nodexy.columns = ['lon', 'lat']
nodexy['hov'] = 'CORDEX grid'
def lin_grid(fig, dummy, chex='#d95f0e', opc=0.7):
x1, x2 = np.shape(dummy.lon)
lond = np.diff(dummy.lon.values)
latd = np.diff(dummy.lat.values)
lond1 = np.diff(dummy.lon.values, axis=0)
latd1 = np.diff(dummy.lat.values, axis=0)
for i in np.arange(x2)[1:-1]:
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[:, i] - 0.5 * lond[:, i - 1],
lat=dummy.lat.values[:, i] - 0.5 * latd[:, i - 1],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[:, i] + 0.5 * lond[:, i - 1],
lat=dummy.lat.values[:, i] + 0.5 * latd[:, i - 1],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
for i in np.arange(x1)[1:-1]:
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[i, :] - 0.5 * lond1[i - 1, :],
lat=dummy.lat.values[i, :] - 0.5 * latd1[i - 1, :],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
fig.add_trace(go.Scattermapbox(
mode='lines',
lon=dummy.lon.values[i, :] + 0.5 * lond1[i - 1, :],
lat=dummy.lat.values[i, :] + 0.5 * latd1[i - 1, :],
line={'color': chex, 'width': 1},
hoverinfo='skip',
opacity=opc))
return fig
def add_stats(sel='all'):
if sel == 'all':
ac = '#2c7fb8'
stats1 = stats
else:
ac = 'gray'
fig.add_trace(go.Scattermapbox(
lat=stats.lat,
lon=stats.lon,
mode='markers',
marker=go.scattermapbox.Marker(
size=10,
color=ac,
opacity=0.8
),
text=stats['Station name']
))
ac = '#df65b0'
if sel == 'krummhoern':
stats1 = stats.loc[stats.krummhoern == True]
elif sel == 'coast':
stats1 = stats.loc[stats.coast == True]
elif sel == 'niedersachsen':
stats1 = stats.loc[stats.niedersachsen == True]
elif sel == 'inland':
stats1 = stats.loc[stats.inland == True]
else:
try:
stats1 = pd.DataFrame(stats.loc[sel]).T
except:
ac = 'gray'
stats1 = pd.DataFrame(stats.loc['Norderney']).T
fig.add_trace(go.Scattermapbox(
lat=stats1.lat,
lon=stats1.lon,
mode='markers',
marker=go.scattermapbox.Marker(
size=10,
color=ac,
opacity=0.8
),
text=stats1['Station name']
))
return fig
def add_cmpx(cm):
maskcordex_coast = [[10, 4], [11, 4], [11, 5], [11, 6], [11, 7], [11, 8], [10, 8], [9, 8], [8, 8], [8, 9],
[8, 10], [9, 10], [10, 10], [10, 11], [11, 12], [12, 12], [12, 13], [12, 14], [13, 14],
[14, 14], [15, 14], [16, 14], [17, 14], [18, 14], [19, 15], [20, 15], [21, 14], [22, 14],
[22, 13],
[9, 4], [10, 5], [10, 6], [10, 7], [10, 8], [9, 7], [8, 7], [7, 7], [7, 8], [7, 9], [9, 9],
[10, 12], [11, 13], [11, 14], [14, 15], [15, 15], [16, 15], [17, 15], [18, 15], [19, 16],
[20, 16], [21, 15], [22, 15],
[11, 4], [12, 4], [12, 5], [12, 6], [12, 7], [12, 8], [11, 10], [11, 11], [13, 12],
[13, 13], [14, 13], [15, 13], [16, 13], [17, 13], [18, 13], [19, 14], [20, 14], [21, 13]]
for cc in maskcordex_coast:
fig.add_trace(go.Scattermapbox(
lat=[dummy.lat.values[tuple(cc)]],
lon=[dummy.lon.values[tuple(cc)]],
mode='markers',
marker=go.scattermapbox.Marker(
size=8,
color='#fec44f',
opacity=0.8
),
hoverinfo='skip'
))
return fig
# fig = go.Scattermapbox(lon = nodexy.lon, lat= nodexy.lat, mode='markers', marker_symbol='square', marker_size=15)
#fig = px.scatter_mapbox(nodexy, lat='lat', lon='lon', center={'lat': 53.0, 'lon': 8.3}, zoom=5, opacity=0.1, hover_data=['hov'])
fig = px.scatter_mapbox(stats, lat='lat', lon='lon', center={'lat': 53.0, 'lon': 8.6}, zoom=5, size='ms', opacity=0.8, color='color', hover_data=['Station name', 'lat', 'lon'], size_max=10)
if cm != 'none':
fig = lin_grid(fig, dummy)
fig = lin_grid(fig, dummy5, '#2c7fb8')
fig = add_cmpx(cm)
fig = add_stats(sel)
fig.update_layout(showlegend=False,width=300, height=350,margin=dict(l=10, r=10, b=10, t=10)) # ,center={'lat':54.0,'lon':8.3}, zoom=7)
st.sidebar.plotly_chart(fig)
return
def applySDM(wdata, data, meth='rel', cdf_threshold=0.9999999, lower_limit=0.1):
'''apply structured distribution mapping to climate data and return unbiased version of dataset'''
from sdm import SDM
data_ub = data.copy()
for k in data_ub.columns:
data_col = data_ub[k].dropna()
overlapx = pd.concat(
[wdata.loc[data_col.index[0]:wdata.index[-1]], data_col.loc[data_col.index[0]:wdata.index[-1]]], axis=1)
overlapx.columns = ['obs', 'cm']
overlapx = overlapx.dropna()
try:
data_ub[k] = SDM(overlapx.obs, overlapx.cm, data_col, meth, cdf_threshold, lower_limit)
except:
data_ub[k] = data_ub[k] * np.nan
data_ub[data_ub == 0.0000000] = np.nan
data_ub = data_ub.loc[data_ub.index[0]:pd.to_datetime('2099-12-31 23:59:59')]
return data_ub
def climate_indi(ts, indi='Summer days (Tmax ≥ 25°C)'):
'''
Calculate climate indicator days.
Input time series of meteorological data
'''
if pd.infer_freq(ts.index) != 'D':
print('Please provide daily data.')
return
if indi == 'Summer days (Tmax ≥ 25°C)': # summer days
return (ts.Tmax >= 25.).groupby(ts.index.year).sum()
elif indi == 'Ice days (Tmax < 0°C)': # ice days
return (ts.Tmax < 0.).groupby(ts.index.year).sum()
elif indi == 'Frost days (Tmin < 0°C)': # frost days
return (ts.Tmin < 0.).groupby(ts.index.year).sum()
elif indi == 'Hot days (Tmax ≥ 30°C)': # hot days
return (ts.Tmax >= 30.).groupby(ts.index.year).sum()
elif indi == 'Tropic nights (Tmin ≥ 20°C)': # tropic night
return (ts.Tmin >= 20.).groupby(ts.index.year).sum()
elif indi == 'Rainy days (Precip ≥ 1mm)': # rainy days
return (ts.Prec >= 1.).groupby(ts.index.year).sum()
else:
print('Nothing calculated.')
return
def climate_indices(stati='coast',cliproj=True):
cindi = ['Ice days (Tmax < 0°C)', 'Frost days (Tmin < 0°C)', 'Summer days (Tmax ≥ 25°C)', 'Hot days (Tmax ≥ 30°C)','Tropic nights (Tmin ≥ 20°C)', 'Rainy days (Precip ≥ 1mm)']
ci_topic = st.selectbox('Select Index:', cindi)
if ci_topic == 'Rainy days (Precip ≥ 1mm)':
vari = 'Prec'
meth = 'rel'
elif (ci_topic == 'Frost days (Tmin < 0°C)') | (ci_topic == 'Tropic nights (Tmin ≥ 20°C)'):
vari = 'Tmin'
meth = 'abs'
else:
vari = 'Tmax'
meth = 'abs'
w1 = weather[stati].sel(vars=vari).to_dataframe().dropna()
w1.columns = ['bla', vari]
plt.figure(figsize=(10,2.5))
wi = climate_indi(w1, ci_topic).astype(int)
wi.plot(style='.', color='steelblue', label='Coast weather')
wi.rolling(10, center=True).mean().plot(color='steelblue', label='Rolling mean\n(10 years)')
if cliproj:
c1 = climate.sel(vars=vari).to_dataframe()
c1 = c1[c1.columns[c1.columns != 'vars']]
c2 = applySDM(w1[vari], c1, meth=meth)
firstitem = True
for j in c2.columns:
dummyt = pd.DataFrame(c2[j])
dummyt.columns = [vari]
ci = pd.DataFrame(climate_indi(dummyt, ci_topic).astype(int))
ci.columns = [j]
if firstitem:
ci[j].plot(style='.', color='gray', alpha=0.1, label='all climate projections')
cid = ci
firstitem = False
else:
ci[j].plot(style='.', color='gray', alpha=0.1, label='_')
cid = pd.concat([cid, ci], axis=1)
rcpx = np.array([x[-5:] for x in cid.columns.values])
for n in np.unique(rcpx):
nx = n
if n == np.unique(rcpx)[-1]:
nx = n + '\n(Rolling of 5 years)'
cid[cid.columns[rcpx == n]].mean(axis=1).rolling(5, center=True).mean().plot(label='Mean of ' + nx)
plt.legend(ncol=2)
plt.ylabel('Number of days')
plt.title(ci_topic)
st.pyplot()
if ci_topic == 'Ice days (Tmax < 0°C)':
st.markdown('''Number of days in one year which persistently remain below 0°C air temperature.''')
elif ci_topic == 'Frost days (Tmin < 0°C)':
st.markdown('''Number of days in one year which reached below 0°C air temperature.''')
elif ci_topic == 'Summer days (Tmax ≥ 25°C)':
st.markdown('''Number of days in one year which reached or exceeded 25°C air temperature.''')
elif ci_topic == 'Hot days (Tmax ≥ 30°C)':
st.markdown('''Number of days in one year which reached or exceeded 30°C air temperature.''')
elif ci_topic == 'Tropic nights (Tmin ≥ 20°C)':
st.markdown('''Number of days in one year which persistently remained above 20°C air temperature.''')
elif ci_topic == 'Rainy days (Precip ≥ 1mm)':
st.markdown('''Number of days in one year which received at least 1 mm precipitation.''')
return
def weather_explorer():
#weather = load_data('Weather')
#aspects = ['Annual', 'Monthly', 'Season']
#w_aspect = st.sidebar.selectbox('Temporal aggegate:', aspects)
#cliproj = st.sidebar.checkbox('add climate projections',False)
statios = list(weather.keys())
stat1 = st.selectbox('Select station/group (see map in sidebar for location):', statios)
aspects = ['Annual', 'Monthly'] # , 'Season']
w_aspect = st.selectbox('Select temporal aggegate:', aspects)
cliproj = st.checkbox('add climate projections (for coastal region)',False)
if cliproj:
plt_map(stat1, 'CORDEX')
st.sidebar.markdown(
'''Map with available stations (<span style="color:blue">blue dots</span>) and selected reference station (<span style="color:magenta">magenta highlight</span>). The climate model grid is given in <span style="color:orange">orange</span> with the selected references as filled dots.''',
unsafe_allow_html=True)
else:
plt_map(stat1)
st.sidebar.markdown(
'''Map with available stations (<span style="color:blue">blue dots</span>) and selected reference station (<span style="color:magenta">magenta highlight</span>).''',
unsafe_allow_html=True)
if w_topic == 'Warming':
navi_vars = ['Maximum Air Temperature', 'Mean Air Temperature', 'Minimum Air Temperature']
navi_var = st.sidebar.radio("Select variable:", options=navi_vars)
if navi_var[:4] == 'Mini':
vari = 'Tmin'
afu = np.min
ag = 'min'
elif navi_var[:4] == 'Maxi':
vari = 'Tmax'
afu = np.max
ag = 'max'
else:
vari = 'T'
afu = np.mean
ag = 'mean'
if w_aspect == 'Annual':
wdata = weather[stat1].sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe()[stat1]
wdata = wdata[~np.isnan(wdata)]
allw = weather.sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe().iloc[:, 1:]
dataLq = float(np.floor(allw.min().quantile(0.22)))
datamin = float(np.min([dataLq, np.round(allw.min().min(), 1)]))
if cliproj:
rcps = ['rcp26', 'rcp45', 'rcp85']
rcp = st.selectbox(
'RCP (Mean over all projections will be shown. For more details go to section "Climate Projections"):',
rcps)
data = climate.filter_by_attrs(RCP=rcp).sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe()
data = data[data.columns[data.columns != 'vars']]
data_ub = applySDM(wdata, data, meth='abs')
dataUq = float(np.ceil(data_ub.max().quantile(0.76)))
datamax = float(np.max([dataUq, np.round(data_ub.max().max(), 1)]))
else:
dataUq = float(np.ceil(allw.max().quantile(0.76)))
datamax = float(np.max([dataUq,np.round(allw.max().max(), 1)]))
datarng = st.slider('Adjust data range on x-axis of plot:', min_value=datamin, max_value=datamax, value=(dataLq, dataUq), step=0.1, key='drangew')
if cliproj:
ax = kde(wdata, data_ub.mean(axis=1), split_ts=3)
else:
ax = kde(wdata, split_ts=3)
ax.set_title(stat1 + ' Annual ' + navi_var)
ax.set_xlabel('T (°C)')
ax.set_xlim(datarng[0],datarng[1])
st.pyplot()
sndstat = st.checkbox('Show second station for comparison')
if sndstat:
stat2 = st.selectbox('Select second station:', [x for x in statios if x != stat1])
wdata2 = weather[stat2].sel(vars=vari).resample(time='1Y').apply(afu).to_dataframe()[stat2]
ax2 = kde(wdata2, split_ts=3)
ax2.set_title(stat2 + ' Annual ' + navi_var)
ax2.set_xlabel('T (°C)')
ax2.set_xlim(datarng[0],datarng[1])
st.pyplot()
expl_md = read_markdown_file('explainer/stripes.md')
st.markdown(expl_md, unsafe_allow_html=True)
elif w_aspect == 'Monthly':
wdata = weather[stat1].sel(vars=vari).resample(time='1M').apply(afu).to_dataframe()[stat1]
wdata = wdata[~np.isnan(wdata)]
ref_yr = st.slider('Reference period for anomaly calculation:', min_value=int(wdata.index.year.min()), max_value=2020,value=(max(1980, int(wdata.index.year.min())), 2000))
if cliproj:
rcps = ['rcp26', 'rcp45', 'rcp85']
rcp = st.selectbox('RCP (Mean over all projections will be shown. For more details go to section "Climate Projections"):', rcps)
data = climate.filter_by_attrs(RCP=rcp).sel(vars=vari).resample(time='1M').apply(afu).to_dataframe()
data = data[data.columns[data.columns != 'vars']]
#ub = st.sidebar.checkbox('Apply SDM bias correction',True)
ub = True # simplify here and automatically apply bias correction
if ub:
data_ub = applySDM(wdata, data, meth='abs')
yrplot_hm(pd.concat([wdata.loc[wdata.index[0]:data.index[0] - pd.Timedelta('1M')], data_ub.mean(axis=1)]),ref_yr, ag, li=2006)
else:
yrplot_hm(pd.concat([wdata.loc[wdata.index[0]:data.index[0] - pd.Timedelta('1M')], data.mean(axis=1)]), ref_yr, ag, li=2006)
plt.title(stat1 + ' ' + navi_var + ' anomaly to ' + str(ref_yr[0]) + '-' + str(ref_yr[1]))
st.pyplot()
else:
yrplot_hm(wdata,ref_yr,ag)
plt.title(stat1 + ' ' + navi_var + ' anomaly to ' + str(ref_yr[0]) + '-' + str(ref_yr[1]))
st.pyplot()
sndstat = st.checkbox('Compare to a second station?')
if sndstat:
stat2 = st.selectbox('Select second station:', [x for x in statios if x != stat1])
data2 = weather[stat2].sel(vars=vari).resample(time='1M').apply(afu).to_dataframe()[stat2]
data2 = data2[~np.isnan(data2)]
ref_yr2 = list(ref_yr)
if ref_yr2[1]<data2.index.year.min():
ref_yr2[0] = data2.index.year.min()
ref_yr2[1] = ref_yr2[0]+10
if ref_yr2[0]<data2.index.year.min():
ref_yr2[0] = data2.index.year.min()
if ref_yr2[1] - ref_yr2[0] < 10:
ref_yr2[1] = ref_yr2[0] + 10
yrplot_hm(data2, ref_yr2, ag)
plt.title(stat2 + ' ' + navi_var + ' anomaly to ' + str(ref_yr2[0]) + '-' + str(ref_yr2[1]))
st.pyplot()
expl_md = read_markdown_file('explainer/stripes_m.md')
st.markdown(expl_md, unsafe_allow_html=True)
elif w_topic == 'Weather Indices':
climate_indices(stat1,cliproj)
if cliproj:
st.markdown(
'''RCPs are scenarios about possible greenhouse gas concentrations by the year 2100. RCP2.6 is a world in which little further greenhouse gasses are emitted -- similar to the Paris climate agreement from 2015. RCP8.5 was intendent to explore a rather risky, more worst-case future with further increased emissions. RCP4.5 is one candidate of a more moderate greenhouse gas projection, which might be more likely to resemble a realistic situation. It is important to note that the very limited differentiation between RCP scenarios have been under debate for several years. One outcome is the definition of Shared Socioeconomic Pathways (SSPs) for which today, however, there are not very many model runs awailable. For more information, please check with the [Climatescenario Primer](https://climatescenarios.org/primer/), [CarbonBrief](https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change) and this [NatureComment](https://www.nature.com/articles/d41586-020-00177-3)''',
unsafe_allow_html=True)
def climate_explorer():
cliprojs = ["Global", "Regional"]
cliproj = st.sidebar.radio("Climate Model Scaling:", options=cliprojs)
expl_md = read_markdown_file('explainer/climatescale.md')
st.sidebar.markdown(expl_md, unsafe_allow_html=True)
if cliproj=='Regional':
regaggs = ['North Sea Coast', 'Krummhörn', 'Niedersachsen', 'Inland']
regagg = st.sidebar.selectbox('Spatial aggregation:', regaggs)
if regagg=='North Sea Coast':
climate = xr.load_dataset('data/cordex_coast.nc')
climate.filter_by_attrs(RCP='rcp45')
elif regagg=='Krummhörn':
climate = xr.load_dataset('data/cordex_krummh.nc')
st.subheader('Climate Model Comparison')
crefs = ['Weather Data', 'all RCPs', 'RCP2.6', 'RCP4.5','RCP8.5']
cref1 = st.sidebar.selectbox('Select first reference:', options=crefs)
if cref1=='Weather Data':
data1 = load_data('Weather')
data1 = data1.coast
drngx1 = (1980,2000)
elif cref1 == 'all RCPs':
data1 = load_data('CORDEX')
drngx1 = (2050, 2070)
elif cref1 == 'RCP2.6':
data1 = load_data('rcp26','North Sea Coast')
drngx1 = (2050, 2070)
elif cref1 == 'RCP4.5':
data1 = load_data('rcp45', 'North Sea Coast')
drngx1 = (2050, 2070)
elif cref1 == 'RCP8.5':
data1 = load_data('rcp85','North Sea Coast')
drngx1 = (2050, 2070)
drng1 = [pd.to_datetime(data1.isel(time=0, vars=1).time.values).year, pd.to_datetime(data1.isel(time=-1, vars=1).time.values).year]
datarng1 = st.sidebar.slider('Data range', drng1[0], drng1[1], drngx1, key='dr1')
cref2 = st.sidebar.selectbox('Select second reference:', options=crefs)
if cref2 == 'Weather Data':
data2 = load_data('Weather')
drngx2 = (1980, 2000)
elif cref2 == 'all RCPs':
data2 = load_data('CORDEX')
drngx2 = (2050, 2070)
elif cref2 == 'RCP2.6':
data2 = load_data('rcp26','North Sea Coast')
drngx2 = (2050, 2070)
elif cref2 == 'RCP4.5':
data2 = load_data('rcp45', 'North Sea Coast')
drngx2 = (2050, 2070)
elif cref2 == 'RCP8.5':
data2 = load_data('rcp85','North Sea Coast')
drngx2 = (2050, 2070)
drng2 = [pd.to_datetime(data2.isel(time=0, vars=1).time.values).year, pd.to_datetime(data2.isel(time=-1, vars=1).time.values).year]
datarng2 = st.sidebar.slider('Data range', drng2[0], drng2[1], drngx2, key='dr2')
if w_topic == 'Warming':
navi_vars = ['Maximum Air Temperature', 'Mean Air Temperature', 'Minimum Air Temperature']
navi_var = st.sidebar.radio("Select variable:", options=navi_vars)
if navi_var[:4] == 'Mini':
vari = 'Tmin'
afu = np.min
ag = 'min'
elif navi_var[:4] == 'Maxi':
vari = 'Tmax'
afu = np.max
ag = 'max'
else:
vari = 'T'
afu = np.mean
ag = 'mean'
dyp = data1.sel(vars=vari).to_dataframe().resample('1M').apply(afu)
dyp = dyp.loc[(dyp.index.year>=datarng1[0]) & (dyp.index.year<datarng1[1]),dyp.columns[dyp.columns!='vars']]
dyp2 = data2.sel(vars=vari).to_dataframe().resample('1M').apply(afu)
dyp2 = dyp2.loc[(dyp2.index.year >= datarng2[0]) & (dyp2.index.year < datarng2[1]),dyp2.columns[dyp2.columns!='vars']]
monthlyx(dyp, dyp2, 'Temperature (°C)', 'Monthly '+ag+' in Year ('+cref1+')', 'Monthly '+ag+' in Year ('+cref2+')')
st.pyplot()
def cropmodel(data,crop='wheat',rcp='rcp85',name='croprunname'):
import get_climate_data.cli_crop as clc
#read co2 concentrations
if rcp == 'rcp85':
CO2 = pd.read_csv('data/RCP85_MIDYEAR_CONCENTRATIONS.DAT', skiprows=38, delim_whitespace=True, index_col=0).CO2EQ
elif rcp == 'rcp45':
CO2 = pd.read_csv('data/RCP45_MIDYEAR_CONCENTRATIONS.DAT', skiprows=38, delim_whitespace=True, index_col=0).CO2EQ
else:
CO2 = pd.read_csv('data/RCP45_MIDYEAR_CONCENTRATIONS.DAT', skiprows=38, delim_whitespace=True, index_col=0).CO2EQ * 0. + 400.
if crop == 'maize':
x = np.array([9.375e+00, 3.198e+01, 1.973e+00, 8.700e+01, 1.144e+01, 3.630e+01, 7.260e-02, 1.237e+00, 2.180e+03, 1.501e-01, 5.230e-01, 5.678e+01, 6.970e+02])
elif crop == 'meadow':
x = np.array([6.543e+00, 1.238e+01, 1.029e+00, 8.706e+01, 1.510e+01, 3.253e+01, 1.199e+00, 1.535e+03, 7.784e+03, 6.530e+03, 8.030e+03, 8.092e+03, 5.884e+03])
elif crop == 'wheat':
x = np.array([1.257e+00, 1.276e+01, 1.101e+00, 1.010e+02, 2.578e+01, 2.769e+01, 3.416e-01, 4.940e-01, 1.906e+03, 1.921e-01, 4.595e-01, 6.066e+01, 5.360e+02])
else:
print('ERROR: Crop not specified with parameters.')
yields = clc.cli_SC(data, x, CO2, nme=name)
return yields
def ub_climate(cdata, wdata, ub=True):
varis = ['T', 'Tmax', 'Tmin', 'aP', 'Prec', 'RH', 'Rs', 'u2', 'EToHG']
firstitem = True
for vari in varis:
data = cdata.sel(vars=vari).to_dataframe()
data = data[data.columns[data.columns != 'vars']]
if (vari == 'T') | (vari == 'Tmax') | (vari == 'Tmin'):
meth = 'abs'
else:
meth = 'rel'
if ub:
wdatax = wdata.sel(vars=vari).to_dataframe().iloc[:, -1].dropna()
data_ub = applySDM(wdatax, data, meth=meth)
else:
data_ub = data
data_ubx = data_ub.mean(axis=1)
data_ubx.columns = [vari]
if firstitem:
data_ubc = data_ubx
firstitem = False
else:
data_ubc = pd.concat([data_ubc, data_ubx], axis=1)
data_ubc.columns = varis
return data_ubc
def get_turbine(pfi, plotit=False):
dummy = pd.read_csv(pfi)
v_start = dummy.loc[3].astype(np.float).values[0]
v_max = dummy.loc[2].astype(np.float).values[0]
P_v = dummy.loc[4:28].astype(np.float)
P_v.index = np.arange(int(np.ceil(v_max)) + 1)[1:]
if plotit:
P_v.plot()
return [P_v, v_start, v_max]
def P_wind(wind, pfi):
[P_v, v_start, v_max] = get_turbine(pfi, False)
# stephours = (wind.index[2] - wind.index[1]).days * 24
# stephours = 1.
def interp(val):
if (val >= v_max) | (val < v_start):
return 0.
elif ~np.isnan(val):
if np.ceil(val) == np.floor(val):
return P_v.loc[int(np.floor(val))].values[0]
else:
x1 = P_v.loc[int(np.floor(val))].values[0]
x2 = P_v.loc[int(np.ceil(val))].values[0]
return ((val - np.floor(val)) * x2 + (1. - (val - np.floor(val))) * x1)
else:
return np.nan
ip_vec = np.vectorize(interp)
def get_windpower(u2):
val = np.fmax(-1. * np.cos(np.arange(0., 2 * np.pi, 0.27)) + u2, 0.)
return np.sum(ip_vec(val))
return wind.apply(get_windpower)
def water_proj():
#hsim = pd.read_csv('data/hsim.csv',index_col=0)
#hsim.index = pd.to_datetime(hsim.index)
hsim_collect = pd.read_csv('data/hsim_collect.csv', index_col=0)
hsim_collect.index = pd.to_datetime(hsim_collect.index)
all_vals = np.ravel(hsim_collect.values)[~np.isnan(np.ravel(hsim_collect.values))]
perci = st.slider('Adjust percentile of extreme events:', min_value=90., max_value=99.9999, value=99., key='perci')
firstitem = True
for i in hsim_collect.columns:
hc = hsim_collect.loc[hsim_collect[i] > np.percentile(all_vals, perci), i].resample('1Y').count()
hc.name = i
if firstitem:
h_count = hc
firstitem = False
else:
h_count = pd.concat([h_count, hc], axis=1)
fig = plt.figure(constrained_layout=True,figsize=(10,2.5))
gs = fig.add_gridspec(1, 5)
ax = fig.add_subplot(gs[0, :-1])
ax1 = ax.twinx()
ax2 = fig.add_subplot(gs[0, -1])
all_vals = np.ravel(hsim_collect.values)[~np.isnan(np.ravel(hsim_collect.values))]
sns.distplot(all_vals, ax=ax2)
ax2.vlines(np.percentile(all_vals, perci), 0., 0.06, colors='red')
ax2.text(np.percentile(all_vals, perci), 0.065, str(perci) + '%', c='red', ha='center')
ax2.set_xlim(-3, np.percentile(all_vals, 99.98))
for i in hsim_collect.columns:
hsim_collect[i].plot(style='.', label='_nolegend_', c='gray', alpha=0.2, ax=ax)
try:
hsim_collect.loc[hsim_collect[i] > np.percentile(all_vals, perci), i].plot(style='.', label='_nolegend_',
c='red', alpha=0.2, ax=ax)
except:
pass
(h_count.sum(axis=1) / len(h_count.columns)).rolling(7).mean().plot(ax=ax1, grid=False, ls='--', label='all')
cc = [x for x in h_count.columns if x.split('.')[-1] == 'rcp45']
(h_count[cc].sum(axis=1) / len(cc)).rolling(7).mean().plot(ax=ax1, grid=False, label='rcp45')
cc = [x for x in h_count.columns if x.split('.')[-1] == 'rcp85']
(h_count[cc].sum(axis=1) / len(cc)).rolling(7).mean().plot(ax=ax1, grid=False, label='rcp85')
ax1.legend()
ax.set_ylabel('Q (mm/day)')
ax1.set_ylabel('days w/ Q>' + str(np.round(perci / 100., 3)) + ' percentile\n7 year rolling mean')
st.pyplot()
def management_explorer():
rcps = ['rcp45', 'rcp85'] # 'rcp26',
rcp = st.sidebar.selectbox('RCP:', rcps)
data = climate.filter_by_attrs(RCP=rcp)
ub = st.sidebar.checkbox('Apply SDM bias correction', True)
cdata = ub_climate(data, weather['krummhoern'], ub)
if w_topic == 'Agriculture':
crops = ['wheat', 'maize', 'meadow']
crop = st.sidebar.selectbox('Crop:', crops)
yieldx = cropmodel(cdata,crop,rcp,name=crop+' @ '+rcp)
yieldx.rolling(7).mean().plot()
st.pyplot()
elif w_topic == 'Wind Energy':
storeddata = st.checkbox('Use pre-calculated data (calculation not yet optimized - very slow)', True)
if storeddata:
cwind_p = pd.read_csv('data/windpowerx.csv', index_col=0)
cwind_p.index = pd.to_datetime(cwind_p.index)
plt.figure(figsize=(10, 2.5))
cwind_p[[x for x in cwind_p.columns if rcp in x][::3]].mean(axis=1).plot(label='Enercon 3MW')
cwind_p[[x for x in cwind_p.columns if rcp in x][1::3]].mean(axis=1).plot(label='9x Enercon 0.33MW')
cwind_p[[x for x in cwind_p.columns if rcp in x][2::3]].mean(axis=1).plot(label='0.4x Enercon 7.5MW')
else:
wpengine = ['Enercon 3MW', 'Vestas 3MW', '9x Enercon 0.33MW', '13x Vestas 0.23MW', '0.4x Enercon 7.5MW']
#wpengine = ['Enercon 3MW', '9x Enercon 0.33MW', '0.4x Enercon 7.5MW']
turbine = st.sidebar.selectbox('Select turbine:', wpengine)
plt.figure(figsize=(10,2.5))
if turbine == 'Enercon 3MW':
pfi = 'data/pow/Enercon E-115 3000kW (MG).pow'
P_wind(cdata.u2, pfi).resample('1Y').sum().rolling(7).mean().plot(label=turbine)
elif turbine == 'Vestas 3MW':
pfi = 'data/pow/Vestas 112m 3MW (MT).pow'
P_wind(cdata.u2, pfi).resample('1Y').sum().rolling(7).mean().plot(label=turbine)
elif turbine == '9x Enercon 0.33MW':
pfi = 'data/pow/Enercon E33_33.4m _330kw(MT).pow'
(P_wind(cdata.u2, pfi).resample('1Y').sum()*9.).rolling(7).mean().plot(label=turbine)
elif turbine == '13x Vestas 0.23MW':
pfi = 'data/pow/Vestas V29-225kw(MT).pow'
(P_wind(cdata.u2, pfi).resample('1Y').sum() * 13.).rolling(7).mean().plot(label=turbine)
elif turbine == '0.4x Vestas 7.5MW':
pfi = 'data/pow/Enercon E-126_127m_7500kW (MT).pow'
(P_wind(cdata.u2, pfi).resample('1Y').sum() * 0.4).rolling(7).mean().plot(label=turbine)
plt.legend()
plt.ylabel('Wh/yr')
st.pyplot()
elif w_topic == 'Extreme Events':
water_proj()
# main app