def make_plot(climate_data='agmerra'):
sns.set()
sns.set_context("notebook")
sns.set_style('ticks')
colors = define_colors()
b_prec, b_tmax = fig_data(climate_data=climate_data)
#b_prec = pd.read_csv('../data/result/agmip_prec_bin.csv', index_col=0)
# Sample plot use step, show response of individual model but no names
palette = sns.color_palette()
x_txt = [str(i) for i in np.arange(-2.5, 3.6, 0.5)]
x_txt.insert(0, '')
x_txt.append('')
fig, ax = plt.subplots(1)
ax.plot([1, 15], [0, 0], color='k', lw=0.5, linestyle='--')
temp = append_value(b_prec['obs'])
l1, = ax.step(range(1, 16),
temp,
where='post',
color='#1A1834',
lw=3,
alpha=0.9)
temp = append_value(b_prec.iloc[:, 1::].median(axis=1))
l2, = ax.step(range(1, 16), temp, where='post', color=palette[1], lw=3)
# for i in range(1,13):
# b_prec.iloc[:,i]
# l3, = ax.step(range(1,16)+np.random.rand(15)/10, append_value(b_prec.iloc[:,i]), where='post', color=palette[1],
# lw=0.75, alpha=0.5,linestyle='-')
# ax.set_ylim(-0.5,0.5)
ax.set_ylim(-75, 75)
ax.set_xlim(1, 15)
ax.set(xticks=np.arange(1, 15, 1), xticklabels=(x_txt))
ax.set_ylabel('Yield change (%)', fontsize=12)
ax.set_xlabel('Precipitation anomaly ($\sigma$)', labelpad=15, fontsize=12)
ax.text(0.00 + 0.04,
-0.1,
'Extremely dry',
transform=ax.transAxes,
fontsize=10,
color=colors[0])
ax.text(0.25 + 0.04,
-0.1,
'Moderate dry',
transform=ax.transAxes,
fontsize=10,
color=colors[2])
ax.text(0.5 + 0.04,
-0.1,
'Moderate wet',
transform=ax.transAxes,
fontsize=10,
color=colors[6])
ax.text(0.75 + 0.04,
-0.1,
'Extremely wet',
transform=ax.transAxes,
fontsize=10,
color=colors[-1])
[
t.set_color(colors[0]) for i, t in enumerate(ax.xaxis.get_ticklabels())
if i < 3
]
[
t.set_color(colors[-1])
for i, t in enumerate(ax.xaxis.get_ticklabels()) if i >= 11
]
# max and min range
p1 = ax.fill_between(range(1, 16),
append_value(b_prec.iloc[:, 1::].min(axis=1)),
append_value(b_prec.iloc[:, 1::].max(axis=1)),
alpha=0.3,
color=palette[1],
step='post')
# interquantile range
p2 = ax.fill_between(range(1, 16),
append_value(b_prec.iloc[:, 1::].quantile(0.25,
axis=1)),
append_value(b_prec.iloc[:, 1::].quantile(0.75,
axis=1)),
alpha=0.5,
color=palette[1],
step='post')
ax.legend([l1, l2, p2, p1],
['Obs', 'AgMIP median', 'AgMIP interquantile', 'AgMIP range'],
loc='upper left')
plt.subplots_adjust(bottom=0.2)
plt.savefig('../figure/figure4_%s.pdf' % climate_data)
print('Figure saved for %s' % climate_data)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
import seaborn as sns
from load_prism_data import load_gs_climate
from plot_figure1 import define_colors
from plot_figure3 import plot_scatter_sns, figure_data
colors = define_colors()
level = 'State'
rain_state_w, drought_state_w = figure_data(level=level)
# Load tile drainage data
tile = pd.read_csv('../../data/US_tile/drainage_shapefile.csv',
usecols=['STATE_NAME', 'FIPS', 'tileDra'],
dtype={'FIPS': str})
tile_st = tile.groupby('STATE_NAME').mean().sort_values(
by='tileDra', ascending=False).reset_index().copy()
tile_st.rename(columns={'STATE_NAME': 'State'}, inplace=True)
tile_st['State'] = tile_st['State'].str.upper()
tile_st.loc[:, 'tileDra'] = tile_st.loc[:, 'tileDra'] * 100
data_m = rain_state_w.merge(tile_st, on='State')
#print(data_m)
#color_var={'Prec_mean_weighted':colors[-1], 'Tmax_mean_weighted':colors[0],
# 'Area':'#525252', soilvar+'_weighted':'#995D12'}
def make_plot():
# Load and combine data
bin_yield = pd.read_csv(
'/home/yanli/Project/corn_extreme/data/result/bin_yield.csv',
dtype={'FIPS': str})
hrain = pd.read_csv(
'/home/yanli/Project/corn_extreme/data/result/heavy_rain_percent.csv',
dtype={'FIPS': str})
bin_yield_hrain = bin_yield.merge(hrain, on=['FIPS', 'Year'])
colors = define_colors()
# Begin plot
fig, ax = plt.subplots(1, figsize=(8, 6))
(bin_yield_hrain.groupby('Prec_sigma_bin').mean().
loc[:, slice('HPrec_percent_0', 'HPrec_percent_8')] * 100).plot(
legend=False, ax=ax, marker='.')
legend_txt = [
'<0$\sigma$', '(0$\sigma$,0.5$\sigma$)', '(0.5$\sigma$,1$\sigma$)',
'(1$\sigma$,1.5$\sigma$)', '(1.5$\sigma$,2$\sigma$)',
'(2$\sigma$,2.5$\sigma$)', '(2.5$\sigma$,3$\sigma$)',
'(3$\sigma$,3.5$\sigma$)', '>3.5$\sigma$ (Heavyrain)'
]
ax.legend(ax.lines, (legend_txt),
loc='upper center',
frameon=False,
title='Daily rainfall intensity',
ncol=2)
ax.set_xlabel('Precipitation anomaly of growning season', fontsize=12)
ax.set_ylabel('Percentage (%)', fontsize=12)
ax.set_title(
'Contribution of daily rainfall of different intensities to \n growing season total rainfall'
)
x_txt = [str(i) for i in np.arange(-2.5, 3.6, 0.5)]
x_txt.insert(0, '')
x_txt.append('')
ax.set_xlim([1.5, 15.5])
ax.set(xticks=np.arange(1.5, 15.5, 1), xticklabels=(x_txt))
ax.text(0.0,
-0.15,
'Extreme dry',
transform=ax.transAxes,
fontsize=10,
color=colors[0])
ax.text(0.25,
-0.15,
'Moderate dry',
transform=ax.transAxes,
fontsize=10,
color=colors[2])
ax.text(0.5,
-0.15,
'Moderate wet',
transform=ax.transAxes,
fontsize=10,
color=colors[6])
ax.text(0.8,
-0.15,
'Extreme wet',
transform=ax.transAxes,
fontsize=10,
color=colors[-1])
plt.subplots_adjust(bottom=0.15, top=0.9)
plt.savefig('../figure/figure_heavyrainf_percent.pdf')
print('Heavy rain precent figure saved')