# %% [markdown]
# # Final plot:
# %%
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from ar6_ch6_rcmipfigs.utils.plot import get_cmap_dic, get_ls_dic, trans_scen2plotlabel, get_scenario_c_dic, \
get_scenario_ls_dic
figsize = [7, 4.5]
s_y = '2021'
s_y2 = '2015'
e_y = '2100'
e_y2 = '2100'
# fig, axs = plt.subplots(1,2, figsize=[18,5])
cdic = get_cmap_dic(ds_DT[scenario].values)
lsdic = get_scenario_ls_dic()#get_ls_dic(ds_DT[climatemodel].values)
s_y = '2021'
e_y = '2100'
s_y2 = '2015'
e_y2 = '2100'
cdic = get_scenario_c_dic()
# fig, axs = plt.subplots(3,2, figsize=[20,20])
def fix_ax(ax):
ls = pd.date_range('2010-1-1', periods=100, freq='Y')[0::10]
ax.set_xticks(list(ls))
ax.set_xlim(s_y2, e_y2) # '2015','2100')
ax.spines['right'].set_visible(False)
rn_dic_cols_o = {}
for key in rename_dic_cols.keys():
rn_dic_cols_o[rename_dic_cols[key]] = key
# %% [markdown]
# ### Open ERF dataset:
# %%
ds = xr.open_dataset(PATH_DATASET)
ds # ['Delta T']
# %% [markdown]
# ### Overview plots
# %%
cols = get_cmap_dic(ds['variable'].values)
# %%
fig, axs = plt.subplots(2, sharex=True, figsize=[6, 6])
ax_erf = axs[0]
ax_dT = axs[1]
for v in ds['variable'].values:
ds.sel(variable=v)['Delta T'].plot(ax=ax_dT, label=v, c=cols[v])
ds.sel(variable=v)['ERF'].plot(ax=ax_erf, c=cols[v])
ds.sum('variable')['Delta T'].plot(ax=ax_dT, label='Sum', c='k', linewidth=2)
ds.sum('variable')['ERF'].plot(ax=ax_erf, c='k', linewidth=2)
ax_dT.set_title('Temperature change')
ax_erf.set_title('ERF')
ax_erf.set_ylabel('ERF [W m$^{-2}$]')
return fig2, com_axs, all_ax
#get_fig_ax_tot
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from ar6_ch6_rcmipfigs.utils.misc_func import make_folders
figsize = [6, 4]
s_y = '2021'
s_y2 = '2015'
e_y = '2100'
e_y2 = '2100'
# fig, axs = plt.subplots(1,2, figsize=[18,5])
cdic = get_cmap_dic(ds_DT[scenario].values)
lsdic = get_scenario_ls_dic() #get_ls_dic(ds_DT[climatemodel].values)
s_y = '2021'
e_y = '2100'
s_y2 = '2015'
e_y2 = '2100'
cdic = get_scenario_c_dic()
SMALL_SIZE = 11
MEDIUM_SIZE = 11
BIGGER_SIZE = 16
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
years,
ref_year_uncertainty,
sts='p05-p50')
tabel_dT_sum_slcf_95 = table_of_sts(ds_uncertainty,
scenarios_fl,
[sum_v],
['Sum SLCFs'],
years,
ref_year_uncertainty,
sts='p95-p50')
# %% [markdown]
# ### colors
# %%
cdic = get_cmap_dic(variables_erf_comp) # , palette='colorblind'):
ls = [cdic[key] for key in variables_erf_comp]
# %%
def plt_stacked(axs, _table_dT_anthrop, _table_dT_slcfs,
_table_dT_anthrop_5th, _table_dT_anthrop_95th,
_table_dT_sum_slcf_5, _table_dT_sum_slcf_95):
t**s = ['Change in GSAT in 2040 relative to 2019', 'Change in GSAT in 2100 relative to 2019']
for _yr, _ax, _tit in zip(years, axs, t**s):
# Pick out year and do various renames:
# Total antropogenic
_tot_anthrop = _table_dT_anthrop.loc[_yr].rename(scn_dic, axis=1)
# Sum SLCFs
# uncertainty:
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
s_y = '2021'
s_y2 = '2000'
e_y = '2100'
e_y2 = '2100'
scenarios_ss = ['ssp126', 'ssp245', 'ssp585']
ref_var_erf = 'Effective Radiative Forcing|Anthropogenic'
ref_var_dt = new_varname(ref_var_erf, name_deltaT)
# make subset and ref to year s_y:
_ds = ds_DT.sel(scenario=scenarios_ss, time=slice(s_y2, e_y2)) - ds_DT.sel(
scenario=scenarios_ss, time=slice(s_y, s_y)).squeeze()
cdic1 = get_cmap_dic(variables_erf_comp, palette='bright')
cdic2 = get_cmap_dic(variables_dt_comp, palette='bright')
cdic = dict(**cdic1, **cdic2)
first = True
for ref_var, varl in zip([ref_var_dt],
[variables_dt_comp, variables_erf_comp]):
fig, ax = plt.subplots(1, figsize=[7, 4.5])
ax.plot(_ds['time'],
np.zeros(len(_ds['time'])),
c='k',
alpha=0.5,
linestyle='dashed')
# print(ref_var)
for scn in scenarios_ss[:]:
# print(scn)
# subtract year
# dic_ds[key]['Delta T']
# %% [markdown]
# ## Double check historical $\Delta$ T:
#
# %%
from matplotlib.ticker import (MultipleLocator)
from ar6_ch6_rcmipfigs.utils.plot import get_cmap_dic
# %%
ls_vars = ['aerosol-total', 'ch4', 'co2', 'other_wmghg', 'o3','HFCs']
# %%
cdic = get_cmap_dic(ls_vars)
# %%
ds
# %%
fig, ax = plt.subplots(figsize=[6, 7])
ds_hist = ds.sel(year=slice(1750, 2019), percentile=recommendation, scenario='ssp119', variable=ls_vars)
for var in ds_hist.variable.values:
ds_hist.sel(variable=var)[name_deltaT].plot(label=var, linestyle='dashed', linewidth=2, c=cdic[var])
plt.legend(title='Variable', bbox_to_anchor=(1.05, 1), loc='upper left') # , prop=fontP)
ax.yaxis.set_major_locator(MultipleLocator(.25))
ax.yaxis.set_minor_locator(MultipleLocator(.05))
ax.tick_params(right=True, labelright=True)