def plot_01(axs, x, y, c, xlabel, ylabel):
r, p = get_pearsonr(x, y)
p = axs.scatter(x, y, c=c, cmap='jet', marker='.', label='r= %.2f' % r + funcs_lzh.funcstar(p))
axs.set_xlabel(xlabel)
axs.set_ylabel(ylabel)
axs.legend()
return p
def scatterplot(ax,x,y,c,xlim_range,ylim_range):
r, p = get_pearsonr(x, y)
label = 'r= %.2f' % r + funcs_lzh.funcstar(p)
p=ax.scatter(x, y, c=c, vmin=0, vmax=1, cmap='jet', marker='o', label=label)
ax.set_xlim(xlim_range)
ax.set_ylim(ylim_range)
return p
def plot_02(axs, x, y, color, llabel):
r, p = get_pearsonr(x, y)
axs.scatter(x,
y,
color=color,
marker='.',
label=llabel + 'r= %.2f' % r + funcs_lzh.funcstar(p))
def scatterplot1(ax, x, y, xlim_range, ylim_range, color):
r, p = get_pearsonr(x, y)
label = 'r= %.2f' % r + funcs_lzh.funcstar(p)
p = ax.scatter(x,
y,
marker='.',
facecolor='',
edgecolor=color,
label=label)
ax.set_xlim(xlim_range)
ax.set_ylim(ylim_range)
return p
def scatterplot1(ax,x,y,xlim_range,ylim_range,marker,color,range_label):
reg = linreg.fit(x.values.reshape(-1, 1), y)
a, b = linreg.coef_, linreg.intercept_
pred = reg.predict(x.values.reshape(-1, 1))
text = 'y=' + '%.2f' % a + 'x' + '+' + '%.2f' % b
ax.plot(x.values.reshape(-1, 1), pred,
color=color, linewidth=1, label='')
r, p = get_pearsonr(x, y)
label = 'R$^2$= %.2f' % np.square(r) + funcs_lzh.funcstar(p)
p=ax.scatter(x, y, marker=marker, facecolor='',edgecolor=color,label=label+', '+range_label)
ax.set_xlim(xlim_range)
ax.set_ylim(ylim_range)
return p
temp.loc[i, 'r'] = r
temp.loc[i, 'p'] = p
box_data.append(daymean.loc[idx, box_label])
print(temp)
axs[flag].bar(temp['idx'],
temp['r'],
bottom=0,
color='k',
width=0.5,
alpha=0.5,
label='r of SIF$_{yield}$-LUE')
for num in temp['idx']:
axs[flag].text(temp.loc[num - 1, 'idx'],
0.5,
funcs_lzh.funcstar(temp.loc[num - 1, 'p']),
color='r',
horizontalalignment='center')
axs[flag].set_ylabel('r of SIF$_{yield}$-LUE', **axis_font)
axs[flag].set_ylim([-0.5, 2])
axs[flag].set_yticklabels(['-0.5', '0', '0.5', '1', '', ''])
par = axs[flag].twinx()
bp1 = par.boxplot(box_data, labels=temp['idx'], showmeans=True)
par.set_ylabel(box_label, **axis_font)
par.set_ylim([-2, 1.5])
par.set_yticklabels(['', '', '', '', '0', '0.5', '1', '1.5'])
for ax in [axs[flag], par]:
ax.tick_params(labelsize=ticklabelsize)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Arial') for label in labels]
daymean['scaled_Ta'] = MaxMinNormalization(daymean['Ta'], np.max(daymean['Ta']), np.min(daymean['Ta']))
daymean['eci'] = daymean['scaled_APAR'] + daymean['scaled_VPD'] + daymean['scaled_Ta']
daymean['ECI'] = MaxMinNormalization(daymean['eci'], np.max(daymean['eci']), np.min(daymean['eci']))
daymean['sify'] = daymean['SIFyieldgndvi']
daymean['lue'] = daymean['LUEgndvi']
# 分阶段
vege = daymean[daymean['doy'] <= a2]
repro = daymean[(daymean['doy'] > a2) & (daymean['doy'] <= a3)]
ripen = daymean[daymean['doy'] > a3]
fig, axs = plt.subplots(2, 4, figsize=(30, 8.5))
fig.subplots_adjust(left=0.07,right=0.85)
r,p=get_pearsonr(vege['GPP'],vege['SFM'])
label='r= %.2f'%r+funcs_lzh.funcstar(p)
axs[0, 0].scatter(vege['GPP'], vege['SFM'], c=vege['ECI'],vmin=0, vmax=1, cmap='jet', marker='o',label=label)
axs[0,0].set_xlabel('GPP $[μmol CO_2/m^2/s]$',**axis_font)
axs[0,0].set_ylabel('SIF $[mW/m^2/nm/sr]$',**axis_font)
axs[0,0].set_xlim([0,100])
axs[0,0].set_ylim([0,2.5])
r,p=get_pearsonr(repro['GPP'],repro['SFM'])
label='r= %.2f'%r+funcs_lzh.funcstar(p)
axs[0, 1].scatter(repro['GPP'], repro['SFM'], c=repro['ECI'],vmin=0, vmax=1, cmap='jet', marker='o',label=label)
axs[0,1].set_xlabel('GPP $[μmol CO_2/m^2/s]$',**axis_font)
axs[0,1].set_xlim([0,100])
axs[0,1].set_ylim([0,2.5])
r,p=get_pearsonr(ripen['GPP'],ripen['SFM'])
label='r= %.2f'%r+funcs_lzh.funcstar(p)
axs[0, 2].scatter(ripen['GPP'], ripen['SFM'], c=ripen['ECI'],vmin=0, vmax=1, cmap='jet', marker='o',label=label)
axs[0,2].set_xlabel('GPP $[μmol CO_2/m^2/s]$',**axis_font)
PAR = data['PARd_North']
NetPAR = data['NetRad_North']
LAIgreen_from_NDVI = data['LAI_green_fromNDVI']
#
plt.subplots(figsize=(10, 5))
temp = pd.concat([SIF_A_ifld, GPP], axis=1)
# print(temp)
temp = temp.dropna()
x = temp['SIF_A_ifld_mean']
y = temp['GPP_MR_f_Subcanopy']
a, b, pred_y = funcs_lzh.get_lingre(x.values.reshape(-1, 1), y)
r, p = pearsonr(x, y)
r2 = '%.2f' % np.square(r)
text_line = 'y=' + '%.2f' % a + 'x' + '+' + '%.2f' % b
text_rsqu = '$R^2$= ' + r2 + funcs_lzh.funcstar(p)
plt.scatter(SIF_A_ifld,
GPP,
edgecolors='k',
facecolors='',
label='SIFA_ifld ' + text_rsqu)
plt.plot(x, pred_y, label=text_line, color='k')
#
temp = pd.concat([SIF_A_sfm, GPP], axis=1)
temp = temp.dropna()
x = temp['SIF_A_sfm_mean']
y = temp['GPP_MR_f_Subcanopy']
a, b, pred_y = funcs_lzh.get_lingre(x.values.reshape(-1, 1), y)
r, p = pearsonr(x, y)
r2 = '%.2f' % np.square(r)
text_line = 'y=' + '%.2f' % a + 'x' + '+' + '%.2f' % b
a1 = 192
a2 = 205
a3 = 255
a4 = 283
# daymean
filepath=r'D:\Shared_Folder\Lzh py\SifAnalysis\SIF_GPP_reviewer\data'
data = pd.ExcelFile(
filepath + '\\' + r'leaf data_top and canopy_20180311_corn.xlsx')
data = data.parse(0)
data=pd.concat([data['Canopy_SIF'],data['Canopy_GPP']
,data['PAR'],data['LAI'],data['Cab'] # chl a+b(mg/cm2)
,data['SIFyield760']],axis=1)
data=data.dropna()
print(data)
data['psify']=data['Canopy_SIF']/data['PAR']
data['plue']=data['Canopy_GPP']/data['PAR']
data['leafsify']=data['SIFyield760']*1000
## fig
fig,axs=plt.subplots(2,4,figsize=(15,8))
r,p=get_pearsonr(data['plue'],data['psify'])
label='r= '+'%.2f'%r+funcs_lzh.funcstar(p)
axs[0,0].scatter(data['plue'],data['psify'],edgecolors='k'
,facecolors='',marker='.',label=label)
axs[0,0].yaxis.set_major_formatter(fromatter)
axs[0,0].set_ylabel('psify [-]')
axs[0,0].set_xlabel('plue [-]')
axs[0,0].legend()
plt.show()
axis=1)
daymean = daymean.dropna()
# 去掉structural and pigment effects
meanMTVI2 = np.mean(daymean['MTVI2'])
daymean['scaled_MTVI2'] = daymean['MTVI2'] / meanMTVI2
daymean['rsify'] = daymean['sify'] / daymean['scaled_MTVI2']
daymean['rpri'] = daymean['PRI'] / daymean['scaled_MTVI2']
# 分阶段
vege = daymean[daymean['DOY'] <= a2]
repro = daymean[(daymean['DOY'] > a2) & (daymean['DOY'] <= a3)]
ripen = daymean[daymean['DOY'] > a3]
## SIFyield-LUE & PRI-LUE的关系
fig, axs = plt.subplots(1, 2, figsize=(8, 3.5))
fig.subplots_adjust(left=0.1, bottom=0.15, right=0.92, top=0.92, wspace=0.33)
r, p = get_pearsonr(daymean['lue'], daymean['sify'])
label = 'R$^2$= %.2f' % np.square(r) + funcs_lzh.funcstar(p)
p = axs[0].scatter(daymean['lue'],
daymean['sify'],
c=daymean['ECI'],
vmin=0,
vmax=1,
cmap='jet',
marker='o',
label=label,
edgecolor='k')
axs[0].set_ylim([-0.001, 0.003])
axs[0].set_ylabel('$SIF_{yield}$', **axis_font)
r, p = get_pearsonr(daymean['lue'], daymean['PRI'])
label = 'R$^2$= %.2f' % np.square(r) + funcs_lzh.funcstar(p)
p = axs[1].scatter(daymean['lue'],
daymean['PRI'],
