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plotOperations.py
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#!/usr/bin/env python
import epygram
import numpy as np
#import copy
import matplotlib.pyplot as plt
import matplotlib.colors as colors
#import warnings
#warnings.filterwarnings("ignore",category=np.VisibleDeprecationWarning)
#epygram.init_env()
##########################################################
# Functions
#
def create_fig(rows,cols,figsize,proj):
# Create matplotlib subplot handle
#
fig,ax=plt.subplots(nrows=rows,ncols=cols,figsize=figsize,\
subplot_kw={'projection': proj})
#plt.subplots_adjust(hspace=0.5,wspace=0.15)
return fig,ax
########################################################
def init_fig2(data,nrows,ncols,figsize):
# Init figure handles
fig,ax=create_fig(nrows,ncols,figsize,data.geometry.default_cartopy_CRS())
return fig,ax
########################################################
def plot_multi_member3(fig,ax,data,plottype,masked,scalesMatched,monoColor,dExtra=[],\
subZone=0,manualBounds=[]):
# Plot option for plotting multiple members for a chosen
# variable/level array.
#
# Plotting from AllData
#
# Unroll over the dimensions indicated by dim
for i in range(0,data.shape[0]):
for j in range(0,data.shape[1]):
# Choose plot options according to plottype
if plottype == "multiMember":
if monoColor:
# Special treatment for radar case
plot_in_fig2(fig,ax[i,j],data[i,j],dataBounds=[manualBounds[j]],subZone=subZone,\
inum=j)
#plot_in_fig2(fig,ax[i],data[i,j],dataBounds=manualBounds,subZone=subZone,\
# inum=j)
else:
if not scalesMatched:
dataBounds=[]
else:
dataBounds=[dExtra[j]]
if manualBounds:
dataBounds=[manualBounds[0][j]]
#plot_in_fig(fig,ax[i,j],data[i,j],scalesMatched=True,dataBounds=[0,dExtra[j]])
#plot_in_fig(fig,ax[i],data[i,j],scalesMatched=True,dataBounds=manualBounds)
plot_in_fig(fig,ax[i,j],data[i,j],scalesMatched=scalesMatched,masked=masked,\
dataBounds=dataBounds,subZone=subZone)
#else:
# plot_in_fig(fig,ax[i,j],data[i,j])
elif plottype == "multiMemberDiff":
if i==0:
if manualBounds:
plot_in_fig(fig,ax[i,j],data[i,j],dataBounds=[manualBounds[0][j]],subZone=subZone)
else:
plot_in_fig(fig,ax[i,j],data[i,j],subZone=subZone)
else:
if not scalesMatched:
dataBounds=[]
else:
dataBounds=[dExtra[0][j],dExtra[1][j]]
if manualBounds:
dataBounds=[manualBounds[1][j],-1*manualBounds[1][j]]
plot_in_fig(fig,ax[i,j],data[i,j],colmap='RdBu_r',center=True,\
scalesMatched=scalesMatched,masked=masked,dataBounds=dataBounds,\
subZone=subZone)
elif plottype == "multiMemberDiff2":
if i==0:
plot_in_fig(fig,ax[i,j],data[i,0],dataBounds=[3*10**-5])
else:
if not scalesMatched:
dataBounds=[]
else:
dataBounds=[dExtra[0][j],dExtra[1][j]]
plot_in_fig(fig,ax[i,j],data[i,j],colmap='RdBu_r',center=True,\
scalesMatched=scalesMatched,masked=masked,dataBounds=dataBounds)
elif plottype == "multiMemberStd":
if i==0:
plot_in_fig(fig,ax[i,j],data[i,j])
elif i==1:
# Mean diff from ctrl
dDiff=data[i,j]-data[0,j]
if masked:
dataBounds=[dDiff.max(),dDiff.min()]
else:
dataBounds=[]
plot_in_fig(fig,ax[i,j],dDiff,colmap="RdBu_r",center=True,\
masked=masked,dataBounds=dataBounds)
else:
if masked:
dataBounds=[data[i,j].max()]
else:
dataBounds=[]
plot_in_fig(fig,ax[i,j],data[i,j],colmap="Reds",center=True,\
masked=masked,dataBounds=dataBounds)
elif plottype == "multiMemberStdMasked":
if i==1:
plot_in_fig(fig,ax[i,j],data[i,j],colmap="RdBu",center=True)
else:
plot_in_fig(fig,ax[i,j],data[i,j],colmap="Greens")
elif plottype == "multiExpDiff":
if i==0:
plot_in_fig(fig,ax[i,j],data[i,j],subZone=subZone)
else:
if not scalesMatched:
dataBounds=[]
else:
dataBounds=[dExtra[0][j],dExtra[1][j]]
plot_in_fig(fig,ax[i,j],data[i,j],colmap="RdBu_r",center=True,\
masked=masked,dataBounds=dataBounds,subZone=subZone)
elif plottype == "multiExpStd":
if i==0:
plot_in_fig(fig,ax[i,j],data[i,j],subZone=subZone)
else:
if not scalesMatched:
dataBounds=[]
else:
dataBounds=[dExtra[0][j]]
plot_in_fig(fig,ax[i,j],data[i,j],colmap="Reds_r",\
masked=masked,dataBounds=dataBounds,subZone=subZone)
########################################################
def plot_in_fig(fig,ax,data,colmap='plasma',center=False,scalesMatched=False,masked=False,dataBounds=[],subZone=0):
# Use epygram cartopy function to plot the data
#
title="{0}\n Min{1} Max {2}\n Mean {3}".\
format(data.fid['FA'],format(data.min(),'.3e'),\
format(data.max(),'.3e'),format(data.mean(),'.3e'))
if len(dataBounds)==0:
data.cartoplot(fig=fig,ax=ax,colormap=colmap,center_cmap_on_0=center,title=title)
else:
if len(dataBounds)==1:
# Try if the element contains two values. Used for fields that are
# displaced w.r.t. to zero (e.g. temp in [K])
try:
minmax=(dataBounds[0][0],dataBounds[0][1])
except:
minmax=(0.,dataBounds[0])
else:
minmax=(dataBounds[1],dataBounds[0])
if not masked:
data.cartoplot(fig=fig,ax=ax,colormap=colmap,center_cmap_on_0=center,\
plot_method='contourf',\
contourf_kw={'extend':'both'},\
minmax=minmax,
title=title)
else:
if len(dataBounds)==1:
# Modify the colormap to start from darker colors
cmap=plt.cm.RdPu(np.linspace(0,1,20))
cmap=colors.ListedColormap(cmap[14:,:-1])
# Create a white color to start of the colmap
my_cmap=cmap(np.arange(cmap.N))
my_cmap[:,-1]=np.linspace(0,1,cmap.N)
my_cmap=colors.ListedColormap(my_cmap)
colmap=my_cmap
colorbounds=np.linspace(0,dataBounds[0],7)
# Offscale smallest value
colorbounds[1]=colorbounds[1]*0.1
#colorbounds[2]=colorbounds[2]*0.5
extend='max'
else:
absMin=min(dataBounds[0],-1*dataBounds[1])
colorbounds=np.linspace(-1*absMin,absMin,10)
# Offscale the innermost two values
colorbounds[4]=colorbounds[4]*0.4
colorbounds[5]=colorbounds[5]*0.4
extend='both'
data.cartoplot(fig=fig,ax=ax,center_cmap_on_0=center,\
plot_method='contourf',\
contourf_kw={'extend':extend,'cmap':colmap},\
colorbounds=colorbounds,
title=title)
if subZone==1:
ax.set_extent([14, 27, 54, 63])
elif subZone==2:
ax.set_extent([1, 14, 54, 63])
elif subZone==3:
ax.set_extent([14, 33, 63, 74])
elif subZone==4:
ax.set_extent([-4, 15, 63, 74])
elif subZone==5:
ax.set_extent([17, 30, 57, 66])
elif subZone==6:
ax.set_extent([1, 14, 56, 66])
elif subZone==7:
ax.set_extent([16, 18, 59, 63])
#print(data.fid['FA'])
########################################################
def plot_in_fig2(fig,ax,data,colmap='plasma',center=False,scalesMatched=False,masked=False,dataBounds=[],subZone=0,inum=0):
# Special case for radar phase
# Use epygram cartopy function to plot the data
#
title="{0}\n Min{1} Max {2}\n Mean {3}".\
format(data.fid['FA'],format(data.min(),'.3e'),\
format(data.max(),'.3e'),format(data.mean(),'.3e'))
colorbounds=np.linspace(0,dataBounds[0],6)
# Generate monocolor colourpmaps
if inum==0:
colmap="Blues"
cmap=plt.cm.Blues(np.linspace(0,1,18))
elif inum==1:
colmap="Greens"
cmap=plt.cm.Greens(np.linspace(0,1,18))
elif inum==2:
colmap="Reds"
cmap=plt.cm.Reds(np.linspace(0,1,18))
# Modify the colormap to start from darker colors
cmap=colors.ListedColormap(cmap[13:,:-1])
# Create a white color to start of the colmap
my_cmap=cmap(np.arange(cmap.N))
my_cmap[:,-1]=np.linspace(0,1,cmap.N)
my_cmap=colors.ListedColormap(my_cmap)
colmap=my_cmap
#data.cartoplot(fig=fig,ax=ax,colormap=colmap,center_cmap_on_0=center,\
# plot_method='contourf',\
# contourf_kw={'extend':'both'},\
# colorbounds=colorbounds,\
# minmax=minmax,
# title=title)
data.cartoplot(fig=fig,ax=ax,center_cmap_on_0=center,\
plot_method='contourf',\
contourf_kw={'extend':'max','cmap':colmap},\
colorbounds=colorbounds,\
title=title)
if subZone==1:
ax.set_extent([14, 27, 54, 63])
elif subZone==2:
ax.set_extent([1, 14, 54, 63])
elif subZone==3:
ax.set_extent([14, 33, 63, 74])
elif subZone==4:
ax.set_extent([-4, 15, 63, 74])
elif subZone==5:
ax.set_extent([17, 30, 57, 66])
elif subZone==6:
ax.set_extent([1, 14, 56, 66])
elif subZone==7:
ax.set_extent([16, 18, 59, 63])
#print(data.fid['FA'])