#######################################################################################################################
if r!=0: #Masks out for STD
if (len(np.std(original_obs, axis=1)[(np.std(original_obs, axis=1))< 0.03e12])!=0):
print ("STD mask in effect")
for i in range(len(auto)):
if np.std(original_obs, axis=1)[i] < 0.03e12:
boot_auto[i,:]=-1 # 0 will be seen as significant
auto[i,:]=0
#%%
fig, ax = plt.subplots(figsize=(4,9))
if pstyle == "pc":
#pcparams=dict(clevs=np.arange(-0.15,1.05,0.1),cmap='acccbar') #CCCMA
pcparams=dict(clevs=np.arange(-0.8,1,0.1),cmap=jet3) #Mitch's
pc=rpl.add_pc(ax,range(13),range(37),auto.transpose(),**pcparams)
elif pstyle == "cf":
pcparams=dict(clevs=np.arange(-0.8,1,0.1),cmap=jet3,latlon=False)
pc=rpl.add_cf(ax,range(1,13),range(1,37),auto.transpose(),**pcparams)
for month in range(len(boot_auto[:,0])):
for lead in range(len(boot_auto[0,:])):
if boot_auto[month,lead]>0: #MUST BE CHANGED BACK to >=!!!!!!
plt.scatter((month+0.5),(lead+0.5), color = 'black', s=20)
plt.title("%s Obs AutoCorr" % region_titles[(r-1)], fontsize=16)
plt.colorbar(pc)
ax.invert_xaxis
ax.invert_yaxis
ax.set_xticks(np.arange(len(calendar.month_name[1:13]))+0.5)
ax.set_xticklabels(calendar.month_name[1:13], rotation=90)
ax.set_yticks(np.arange(36)+0.5)
Created on Sat Feb 20 12:51:48 2021
@author: josmarti
"""
import rms_plots as rpl
import numpy as np
from matplotlib import colors
import matplotlib.pyplot as plt
import calendar
version = raw_input("Version?\n")
skill = np.load('/home/josmarti/Data/GeoACC_%s.npy' % version,
allow_pickle=True).astype(float)
ACC = np.nanmean(np.nanmean(skill, axis=2), axis=2)
rpl.register_rms_cmaps()
fig, ax = plt.subplots()
pcparams = dict(clevs=np.arange(-0.15, 1.05, 0.1), cmap='acccbar')
pc = rpl.add_pc(ax, range(13), range(13), ACC, **pcparams)
plt.colorbar(pc)
plt.title("ACC Means for %s" % version)
ax.invert_xaxis
ax.invert_yaxis
ax.set_xticks(np.arange(len(calendar.month_name[1:13])) + 0.5)
ax.set_xticklabels(calendar.month_name[1:13], rotation=90)
ax.set_yticks(np.arange(len(calendar.month_name[1:13])) + 0.5)
ax.set_yticklabels(
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'])
plt.xlabel("Predicted Month")
plt.ylabel("Lead (Months)")
plt.show()
np.std(pm_std, axis=1)[(np.std(pm_std, axis=1) * 1e15) /
np.sum(area) < 0.8]) != 0
and std_mask):
print("Perfect STD mask in effect")
for i in range(len(auto)):
if ((np.std(pm_std, axis=1)[i] * 1e15) /
np.sum(area)) < 0.8:
boot_auto[i, :] = -1 # 0 will be seen as significant
auto[i, :] = 0
if auto_corr:
fig, ax = plt.subplots(figsize=(4, 9))
if pstyle == "pc":
#pcparams=dict(clevs=np.arange(-0.15,1.05,0.1),cmap='acccbar') #CCCMA
pcparams = dict(clevs=np.arange(-0.8, 1, 0.1), cmap=jet3) #Mitch's
pc = rpl.add_pc(ax, range(13), range(37), auto.transpose(), **pcparams)
elif pstyle == "cf":
pcparams = dict(clevs=np.arange(-0.15, 1.05, 0.1),
cmap='acccbar',
latlon=False)
pc = rpl.add_cf(ax, range(1, 13), range(1, 37), ACC, **pcparams)
for month in range(len(boot_auto[:, 0])):
for lead in range(len(boot_auto[0, :])):
if boot_auto[month, lead] > 0: #MUST BE CHANGED BACK to >=!!!!!!
plt.scatter((month + 0.5), (lead + 0.5), color='black', s=20)
if thesis_figures:
plt.title("%s Obs" % region_titles[r], fontsize=16)
else:
if r != 0:
plt.title("%s Detrended Obs Autocorrelation" % region, fontsize=16)
else: