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Composite.py
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# -*- coding: utf-8 -*-
"""
Created on Sun May 6 19:44:39 2018
@author: noahb
"""
import matplotlib.pyplot as plt
from netCDF4 import Dataset, num2date, MFDataset
import numpy as np
from mpl_toolkits.basemap import Basemap,maskoceans,interp,shiftgrid
from scipy import io
from scipy import stats,signal
from datetime import datetime
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
from matplotlib.ticker import MultipleLocator
from colormap import Colormap
import glob
#from gridrad.py import read_file
def read_file(infile):
# Import python libraries
import sys
import os
import numpy as np
import netCDF4
# Check to see if file exists
if not os.path.isfile(infile):
print('File "' + infile + '" does not exist. Returning -2.')
return -2
# Check to see if file has size of zero
if os.stat(infile).st_size == 0:
print('File "' + infile + '" contains no valid data. Returning -1.')
return -1
from netCDF4 import Dataset
from netCDF4 import Variable
# Open GridRad netCDF file
id = Dataset(infile, "r", format="NETCDF4")
# Read global attributes
Analysis_time = str(id.getncattr('Analysis_time' ))
Analysis_time_window = str(id.getncattr('Analysis_time_window' ))
File_creation_date = str(id.getncattr('File_creation_date' ))
Grid_scheme = str(id.getncattr('Grid_scheme' ))
Algorithm_version = str(id.getncattr('Algorithm_version' ))
Algorithm_description = str(id.getncattr('Algorithm_description' ))
Data_source = str(id.getncattr('Data_source' ))
Data_source_URL = str(id.getncattr('Data_source_URL' ))
NOAA_wct_export_Version = str(id.getncattr('NOAA_wct-export_Version'))
Authors = str(id.getncattr('Authors' ))
Project_sponsor = str(id.getncattr('Project_sponsor' ))
Project_name = str(id.getncattr('Project_name' ))
# Read list of merged files
file_list = (id.variables['files_merged'])[:]
files_merged = ['']*(id.dimensions['File'].size)
for i in range(0,id.dimensions['File'].size):
for j in range(0,id.dimensions['FileRef'].size):
files_merged[i] += str(file_list[i,j])
# Read longitude dimension
x = id.variables['Longitude']
x = {'values' : x[:], \
'long_name' : str(x.long_name), \
'units' : str(x.units), \
'delta' : str(x.delta), \
'n' : len(x[:])}
# Read latitude dimension
y = id.variables['Latitude']
y = {'values' : y[:], \
'long_name' : str(y.long_name), \
'units' : str(y.units), \
'delta' : str(y.delta), \
'n' : len(y[:])}
# Read altitude dimension
z = id.variables['Altitude']
z = {'values' : z[:], \
'long_name' : str(z.long_name), \
'units' : str(z.units), \
'delta' : str(z.delta), \
'n' : len(z[:])}
# Read observation and echo counts
nobs = (id.variables['Nradobs' ])[:]
necho = (id.variables['Nradecho'])[:]
index = (id.variables['index' ])[:]
# Read reflectivity variables
Z_H = id.variables['Reflectivity' ]
wZ_H = id.variables['wReflectivity']
zdr = id.variables['DifferentialReflectivity']
kdp = id.variables['DifferentialPhase']
wzdr = id.variables['wDifferentialReflectivity']
wkdp = id.variables['wDifferentialPhase']
# Create arrays to store binned values
values = np.zeros(x['n']*y['n']*z['n'])
wvalues = np.zeros(x['n']*y['n']*z['n'])
kvalues = np.zeros(x['n']*y['n']*z['n'])
kwvalues = np.zeros(x['n']*y['n']*z['n'])
zvalues = np.zeros(x['n']*y['n']*z['n'])
zwvalues = np.zeros(x['n']*y['n']*z['n'])
values[:] = float('nan')
# Add values to arrays
values[index[:]] = (Z_H)[:]
wvalues[index[:]] = (wZ_H)[:]
kvalues[index[:]] = (kdp)[:]
kwvalues[index[:]] = (wkdp)[:]
zvalues[index[:]] = (zdr)[:]
zwvalues[index[:]] = (wzdr)[:]
# Reshape arrays to 3-D GridRad domain
values = values.reshape((z['n'], y['n'] ,x['n']))
wvalues = wvalues.reshape((z['n'], y['n'] ,x['n']))
kvalues = kvalues.reshape((z['n'], y['n'] ,x['n']))
kwvalues = kwvalues.reshape((z['n'], y['n'] ,x['n']))
zvalues = zvalues.reshape((z['n'], y['n'] ,x['n']))
zwvalues = zwvalues.reshape((z['n'], y['n'] ,x['n']))
Z_H = {'values' : values, \
'long_name' : str(Z_H.long_name), \
'units' : str(Z_H.units), \
'missing' : float('nan'), \
'wvalues' : wvalues, \
'wlong_name' : str(wZ_H.long_name), \
'wunits' : str(wZ_H.units), \
'wmissing' : wZ_H.missing_value, \
'n' : values.size}
zdr = {'values' : zvalues, \
'long_name' : str(zdr.long_name), \
'units' : str(zdr.units), \
'missing' : float('nan'), \
'wvalues' : zwvalues, \
'wlong_name' : str(wzdr.long_name), \
'wunits' : str(wzdr.units), \
'wmissing' : wzdr.missing_value, \
'n' : values.size}
kdp = {'values' : kvalues, \
'long_name' : str(kdp.long_name), \
'units' : str(kdp.units), \
'missing' : float('nan'), \
'wvalues' : kwvalues, \
'wlong_name' : str(wkdp.long_name), \
'wunits' : str(wkdp.units), \
'wmissing' : wkdp.missing_value, \
'n' : values.size}
# Close netCDF4 file
id.close()
# Return data dictionary
return {'name' : 'GridRad analysis for ' + Analysis_time, \
'x' : x, \
'y' : y, \
'z' : z, \
'Z_H' : Z_H, \
'zdr' : zdr, \
'kdp' : kdp, \
'nobs' : nobs, \
'necho' : necho, \
'file' : infile, \
'files_merged' : files_merged, \
'Analysis_time' : Analysis_time, \
'Analysis_time_window' : Analysis_time_window, \
'File_creation_date' : File_creation_date, \
'Grid_scheme' : Grid_scheme, \
'Algorithm_version' : Algorithm_version, \
'Algorithm_description' : Algorithm_description, \
'Data_source' : Data_source, \
'Data_source_URL' : Data_source_URL, \
'NOAA_wct_export_Version' : NOAA_wct_export_Version, \
'Authors' : Authors, \
'Project_sponsor' : Project_sponsor, \
'Project_name' : Project_name}
#%%
files2 = glob.glob('nexrad*.nc')[:]
zdr = np.ones((145,528//3,672//3))*np.nan
kdp = np.ones((145,528//3,672//3))*np.nan
zh = np.ones((145,528//3,672//3))*np.nan
for n,j in enumerate(files2):
data = read_file(j)
zdr[n,:,:] = data['zdr']['values'][4,::3,::3]
kdp[n,:,:] = data['kdp']['values'][4,::3,::3]
zh[n,:,:] = data['Z_H']['values'][4,::3,::3]
#%%
ncFile = 'apcp.2017.nc'
narr = {}
with Dataset(ncFile,'r') as nc:
lon = nc.variables['lon'][:]
#lon2 = lon+360
lat = nc.variables['lat'][:]
lam = nc.variables['Lambert_Conformal'][:]
time = nc.variables['time'][:]
precip = nc.variables['apcp'][:].T #shape: 349/277/2920 = IJT
#narr['lat'],narr['lon']=np.meshgrid(lat,lon)
timeUnits = nc.variables['time'].units
tmpDates = num2date(time,timeUnits,calendar='gregorian')
narr['date'] = np.asarray([datetime(d.year,d.month,d.day) for d in tmpDates])
narr['day'] = np.asarray([d.day for d in narr['date']])
narr['month'] = np.asarray([d.month for d in narr['date']])
narr['year'] = np.asarray([d.year for d in narr['date']])
precip2 = np.where(precip.mask,np.nan,precip.data)
aug_index = np.where((narr['month']==8) & (narr['day']>25) & (narr['day']<28))[0]
precip_aug = precip2[:,:,aug_index] #349, 277, 248
I,J,T = precip_aug.shape
precip_squish = precip_aug.reshape(I*J,T,order='F') #(96673, 16)
precip = precip_squish.T
#Standardize precip
precip_ano = precip-np.nanmean(precip,0)
precip = precip_ano/np.nanstd(precip,0)
nanremove = np.where(~np.isnan(precip[0,:]))[0]
precipraw = precip[:,nanremove] ###raw precip with no nans (not anomalies)
precip3 = precip[:,nanremove] #Goes into EOF machine
precip_ano_no_nan = precip_ano[:,nanremove] #Goes into regression
#%%
preciptotal = np.zeros((2,96673))*np.nan
for i in range(0,2):
preciptotal[i,:] = np.sum(precip[i*8:8*i+8,:],axis = 0)
T2 = 2
I2 = 277
J2 = 349
preciptotal2 = preciptotal.reshape(T2,I2,J2)
#%%
cmin = 0; cmax = 10.; cint = 0.5; clevs = np.round(np.arange(cmin,cmax,cint),2)
nlevs = len(clevs) - 1; cmap = plt.get_cmap(name='bwr',lut=nlevs)
#plt.figure()
#plt.figure(figsize=(24,16))
#cornpmm, lon = shiftgrid(180., corrnpmm, lon, start=False)
#lon, lat = np.meshgrid(np.linspace(-180, 180, 360), np.linspace(-90, 90, 180))
xlim = np.array([-101,-92]); ylim = np.array([26,33])
#parallels = np.arange(23.,35.,1.)
# labels = [left,right,top,bottom]
m = Basemap(projection='cyl',lon_0=np.mean(xlim),lat_0=np.mean(ylim),llcrnrlat=ylim[0],urcrnrlat=ylim[1],llcrnrlon=xlim[0],urcrnrlon=xlim[1],resolution='l')
m.drawcoastlines(); m.drawstates(), m.drawcountries(), m.drawstates() #m.drawparallels(parallels,labels=[True,True,True,True], size = 20) #m.fillcontinents(color='Black');m.drawparallels(np.arange(-180.,180.,30.), labels = [1,0,0,0]);m.drawmeridians(np.arange(-180,180,30),labels = [0,0,0,1])
#xCoord,yCoord = m(lat2, lon2) ###Add lat-lon here
cs = m.contourf(lon,lat,preciptotal2[0],clevs,cmap='GnBu',extend='both') #plot lat, lon, and North Pacific SST Anomalies
#x2star,y2star = m(obs25['lon'],obs25['lat'])
#m.plot(x2star,y2star,'g*',markersize=2)
cbar = m.colorbar(cs,size='2%')
cbar.ax.set_ylabel('in',weight='bold',name='Calibri',size=14)
cticks = []
for i in clevs:
cticks.append(int(i)) if i.is_integer() else cticks.append(i)
cbar.set_ticks(clevs[::4])
cbar.set_ticklabels(cticks[::4])
for i in cbar.ax.yaxis.get_ticklabels():
i.set_family('Calibri')
i.set_size(14)
plt.title('Total Precipitation (in) 2017082600Z-2017082800Z',name='Calibri',weight='bold',size=16)
x2star,y2star = m(-97.3964,27.8006)
m.plot(x2star,y2star,'ro',markersize=7, label = 'Corpus Christi')
x3star,y3star = m(-95.3698,29.7604)
m.plot(x3star,y3star,'ko',markersize=7, label = 'Houston')
plt.legend(loc = 4)
plt.show(block=False)
#%%
###Take three hour averages of zdr and kdp (16 3-hour averages)
#zdravg = np.zeros((16,354816))
#kdpavg = np.zeros((16,354816))
zdravg = np.ones_like(zdr)[:16,:,:]
kdpavg = np.ones_like(kdp)[:16,:,:]
for i in range(0,16):
zdravg[i,:,:] = np.nanmean(zdr[i*9:9*i+9,:],0)
kdpavg[i,:,:] = np.nanmean(kdp[i*9:9*i+9,:],0)
###Standardize the data
zdr3 = (zdravg-np.nanmean(zdravg,0))/np.nanstd(zdravg,0)
kdp3 = (kdpavg-np.nanmean(kdpavg,0))/np.nanstd(kdpavg,0) #(16,354816)
T,J,I = zdravg.shape
#Import the lat-lon for radar
radarlon = data['x']['values'][::3]-360
radarlat = data['y']['values'][::3]
radarlat2,radarlon2 = np.meshgrid(radarlat,radarlon)
zh1 = zh[0,:,:] #8/26/00Z
zh2 = zh[72,:,:] #8/27/00Z
zh3 = zh[-1,:,:] #8/28/00Z
zdralone1 = zdr[0,:,:]
zdralone2 = zdr[72,:,:]
zdralone3 = zdr[-1,:,:]
#%%
cmin = 0; cmax = 75.; cint = 2; clevs = np.round(np.arange(cmin,cmax,cint),2)
nlevs = len(clevs) - 1; cmap = plt.get_cmap(name='bwr',lut=nlevs)
#plt.figure()
#plt.figure(figsize=(24,16))
#cornpmm, lon = shiftgrid(180., corrnpmm, lon, start=False)
#lon, lat = np.meshgrid(np.linspace(-180, 180, 360), np.linspace(-90, 90, 180))
xlim = np.array([-101,-92]); ylim = np.array([26,33])
#parallels = np.arange(23.,35.,1.)
# labels = [left,right,top,bottom]
m = Basemap(projection='cyl',lon_0=np.mean(xlim),lat_0=np.mean(ylim),llcrnrlat=ylim[0],urcrnrlat=ylim[1],llcrnrlon=xlim[0],urcrnrlon=xlim[1],resolution='l')
m.drawcoastlines(); m.drawstates(), m.drawcountries(), m.drawstates() #m.drawparallels(parallels,labels=[True,True,True,True], size = 20) #m.fillcontinents(color='Black');m.drawparallels(np.arange(-180.,180.,30.), labels = [1,0,0,0]);m.drawmeridians(np.arange(-180,180,30),labels = [0,0,0,1])
#xCoord,yCoord = m(lat2, lon2) ###Add lat-lon here
cs = m.contourf(radarlon2,radarlat2,zh1.T,clevs,cmap='gist_ncar',extend='both') #plot lat, lon, and North Pacific SST Anomalies
#x2star,y2star = m(obs25['lon'],obs25['lat'])
#m.plot(x2star,y2star,'g*',markersize=2)
cbar = m.colorbar(cs,size='2%')
cbar.ax.set_ylabel('dBZ',weight='bold',name='Calibri',size=14)
cticks = []
for i in clevs:
cticks.append(int(i)) if i.is_integer() else cticks.append(i)
cbar.set_ticks(clevs[::4])
cbar.set_ticklabels(cticks[::4])
for i in cbar.ax.yaxis.get_ticklabels():
i.set_family('Calibri')
i.set_size(14)
plt.title('3 km Reflectivity (dBZ) 2017082600Z',name='Calibri',weight='bold',size=16)
x2star,y2star = m(-97.3964,27.8006)
m.plot(x2star,y2star,'r*',markersize=10, label = 'Corpus Christi')
x3star,y3star = m(-95.3698,29.7604)
m.plot(x3star,y3star,'k*',markersize=10, label = 'Houston')
plt.legend(loc = 4)
plt.show(block=False)
#%%
def diff_reflect():
diff_reflect_cdict ={
'red':((0.000, 0.000, 0.000),
(0.333, 1.000, 1.000),
(0.417, 0.000, 0.000),
(0.500, 0.000, 0.000),
(0.583, 1.000, 1.000),
(0.750, 1.000, 1.000),
(0.833, 1.000, 1.000),
(1.000, 1.000, 1.000)),
'green': ((0.000, 0.000, 0.000),
(0.333, 1.000, 1.000),
(0.417, 0.000, 0.000),
(0.500, 1.000, 1.000),
(0.583, 1.000, 1.000),
(0.750, 0.000, 0.000),
(0.833, 0.000, 0.000),
(1.000, 1.000, 1.000)),
'blue': ((0.000, 0.000, 0.000),
(0.333, 1.000, 1.000),
(0.417, 1.000, 1.000),
(0.500, 1.000, 1.000),
(0.583, 0.000, 0.000),
(0.750, 0.000, 0.000),
(0.833, 1.000, 1.000),
(1.000, 1.000, 1.000))}
diff_reflect_coltbl = Colormap('DIFF_REFLECT_COLTBL',diff_reflect_cdict) #You will need to import this
return diff_reflect_coltbl
zdrcmap = diff_reflect
#%%
cmin = -6; cmax = 6.; cint = 0.5; clevs = np.round(np.arange(cmin,cmax,cint),2)
nlevs = len(clevs) - 1; cmap = plt.get_cmap(name='bwr',lut=nlevs)
#plt.figure()
#plt.figure(figsize=(24,16))
#cornpmm, lon = shiftgrid(180., corrnpmm, lon, start=False)
#lon, lat = np.meshgrid(np.linspace(-180, 180, 360), np.linspace(-90, 90, 180))
xlim = np.array([-101,-92]); ylim = np.array([26,33])
#parallels = np.arange(23.,35.,1.)
# labels = [left,right,top,bottom]
m = Basemap(projection='cyl',lon_0=np.mean(xlim),lat_0=np.mean(ylim),llcrnrlat=ylim[0],urcrnrlat=ylim[1],llcrnrlon=xlim[0],urcrnrlon=xlim[1],resolution='l')
m.drawcoastlines(); m.drawstates(), m.drawcountries(), m.drawstates() #m.drawparallels(parallels,labels=[True,True,True,True], size = 20) #m.fillcontinents(color='Black');m.drawparallels(np.arange(-180.,180.,30.), labels = [1,0,0,0]);m.drawmeridians(np.arange(-180,180,30),labels = [0,0,0,1])
#xCoord,yCoord = m(lat2, lon2) ###Add lat-lon here
cs = m.contourf(radarlon2,radarlat2,zdralone1.T,clevs,cmap='gist_ncar',extend='both') #plot lat, lon, and North Pacific SST Anomalies
#x2star,y2star = m(obs25['lon'],obs25['lat'])
#m.plot(x2star,y2star,'g*',markersize=2)
cbar = m.colorbar(cs,size='2%')
cbar.ax.set_ylabel('dB',weight='bold',name='Calibri',size=14)
cticks = []
for i in clevs:
cticks.append(int(i)) if i.is_integer() else cticks.append(i)
cbar.set_ticks(clevs[::4])
cbar.set_ticklabels(cticks[::4])
for i in cbar.ax.yaxis.get_ticklabels():
i.set_family('Calibri')
i.set_size(14)
plt.title('3 km $Z_{DR}$ (dB) 2017082600Z',name='Calibri',weight='bold',size=16)
x2star,y2star = m(-97.3964,27.8006)
m.plot(x2star,y2star,'ro',markersize=7, label = 'Corpus Christi')
x3star,y3star = m(-95.3698,29.7604)
m.plot(x3star,y3star,'ko',markersize=7, label = 'Houston')
plt.legend(loc = 4)
plt.show(block=False)
#%%
T,I,J = zdravg.shape
zdravg2 = zdravg.reshape((T,I*J), order = 'F')
kdpavg2 = kdpavg.reshape((T,I*J), order = 'F')
newzdr = np.zeros((16,39424))
newkdp = np.zeros((16,39424))
for n in range(0,16):
for i in range(0,39424):
if precip_ano[n,i]>=0.05:
newzdr[n,i] = zdravg2[n,i]
newkdp[n,i] = kdpavg2[n,i]
else:
newzdr[n,i] = np.nan
newkdp[n,i] = np.nan
zdrmean = np.nanmean(newzdr, axis=0)
kdpmean = np.nanmean(newkdp, axis=0)
I2 = 176
J2 = 224
zdrmean1 = zdrmean.reshape((I2,J2), order = 'F')
kdpmean1 = kdpmean.reshape((I2,J2), order = 'F')
cmin = -5; cmax = 5.; cint = 0.25; clevs = np.round(np.arange(cmin,cmax,cint),2)
nlevs = len(clevs) - 1; cmap = plt.get_cmap(name='bwr',lut=nlevs)
#plt.figure()
#plt.figure(figsize=(24,16))
#cornpmm, lon = shiftgrid(180., corrnpmm, lon, start=False)
#lon, lat = np.meshgrid(np.linspace(-180, 180, 360), np.linspace(-90, 90, 180))
xlim = np.array([-101,-92]); ylim = np.array([26,33])
#parallels = np.arange(23.,35.,1.)
# labels = [left,right,top,bottom]
m = Basemap(projection='cyl',lon_0=np.mean(xlim),lat_0=np.mean(ylim),llcrnrlat=ylim[0],urcrnrlat=ylim[1],llcrnrlon=xlim[0],urcrnrlon=xlim[1],resolution='l')
m.drawcoastlines(); m.drawstates(), m.drawcountries(), m.drawstates() #m.drawparallels(parallels,labels=[True,True,True,True], size = 20) #m.fillcontinents(color='Black');m.drawparallels(np.arange(-180.,180.,30.), labels = [1,0,0,0]);m.drawmeridians(np.arange(-180,180,30),labels = [0,0,0,1])
#xCoord,yCoord = m(lat2, lon2) ###Add lat-lon here
cs = m.contourf(radarlon2,radarlat2,zdrmean1.T,clevs,cmap='RdPu',extend='both') #plot lat, lon, and North Pacific SST Anomalies
#x2star,y2star = m(obs25['lon'],obs25['lat'])
#m.plot(x2star,y2star,'g*',markersize=2)
cbar = m.colorbar(cs,size='2%')
cbar.ax.set_ylabel('dB',weight='bold',name='Calibri',size=14)
cticks = []
for i in clevs:
cticks.append(int(i)) if i.is_integer() else cticks.append(i)
cbar.set_ticks(clevs[::4])
cbar.set_ticklabels(cticks[::4])
for i in cbar.ax.yaxis.get_ticklabels():
i.set_family('Calibri')
i.set_size(14)
plt.title('3 km $Z_{DR}$ (dB) (Precip anomalies > 0.05")',name='Calibri',weight='bold',size=16)
x2star,y2star = m(-97.3964,27.8006)
m.plot(x2star,y2star,'bo',markersize=7, label = 'Corpus Christi')
x3star,y3star = m(-95.3698,29.7604)
m.plot(x3star,y3star,'ko',markersize=7, label = 'Houston')
plt.legend(loc = 4)
plt.show(block=False)
cmin = -2.; cmax = 2.; cint = 0.1; clevs = np.round(np.arange(cmin,cmax,cint),2)
nlevs = len(clevs) - 1; cmap = plt.get_cmap(name='bwr',lut=nlevs)
#plt.figure()
#plt.figure(figsize=(24,16))
#cornpmm, lon = shiftgrid(180., corrnpmm, lon, start=False)
#lon, lat = np.meshgrid(np.linspace(-180, 180, 360), np.linspace(-90, 90, 180))
xlim = np.array([-101,-92]); ylim = np.array([26,33])
#parallels = np.arange(23.,35.,1.)
# labels = [left,right,top,bottom]
m = Basemap(projection='cyl',lon_0=np.mean(xlim),lat_0=np.mean(ylim),llcrnrlat=ylim[0],urcrnrlat=ylim[1],llcrnrlon=xlim[0],urcrnrlon=xlim[1],resolution='l')
m.drawcoastlines(); m.drawstates(), m.drawcountries(), m.drawstates() #m.drawparallels(parallels,labels=[True,True,True,True], size = 20) #m.fillcontinents(color='Black');m.drawparallels(np.arange(-180.,180.,30.), labels = [1,0,0,0]);m.drawmeridians(np.arange(-180,180,30),labels = [0,0,0,1])
#xCoord,yCoord = m(lat2, lon2) ###Add lat-lon here
cs = m.contourf(radarlon2,radarlat2,kdpmean1.T,clevs,cmap='Greens',extend='both') #plot lat, lon, and North Pacific SST Anomalies
#x2star,y2star = m(obs25['lon'],obs25['lat'])
#m.plot(x2star,y2star,'g*',markersize=2)
cbar = m.colorbar(cs,size='2%')
cbar.ax.set_ylabel('deg/km',weight='bold',name='Calibri',size=14)
cticks = []
for i in clevs:
cticks.append(int(i)) if i.is_integer() else cticks.append(i)
cbar.set_ticks(clevs[::4])
cbar.set_ticklabels(cticks[::4])
for i in cbar.ax.yaxis.get_ticklabels():
i.set_family('Calibri')
i.set_size(14)
plt.title('3 km $K_{DP}$ (deg/km) (Precip anomalies > 0.05")',name='Calibri',weight='bold',size=16)
x2star,y2star = m(-97.3964,27.8006)
m.plot(x2star,y2star,'ro',markersize=7, label = 'Corpus Christi')
x3star,y3star = m(-95.3698,29.7604)
m.plot(x3star,y3star,'ko',markersize=7, label = 'Houston')
plt.legend(loc = 4)
plt.show(block=False)
###Test for significance
zdrmean2 = np.nanmean(newzdr)
kdpmean2 = np.nanmean(newkdp)
print('The mean Kdp value when precipitation anomalies exceed 0.05" in 3-hours is',kdpmean2,'deg/km')
print('The mean Zdr value when precipitation anomalies exceed 0.05" in 3-hours is',zdrmean2,'dB')
zdrvariance = (np.nanstd(newzdr))**2
kdpvariance = (np.nanstd(newkdp))**2
print('The variance of Kdp when precipitation anomalies exceed 0.05" in 3-hours is',kdpvariance,'deg/km')
print('The variance of Zdr when precipitation anomalies exceed 0.05" in 3-hours is',zdrvariance,'dB')
###two tail t-test
##Null hypothesis:
T,I,J = zdr3.shape
zdrcomp = zdr3.reshape(T,I*J) #zdr for all precip
zdrsig = scipy.stats.ttest_ind(zdrcomp,newzdr, nan_policy='omit', axis = 0)[1]
print(zdrsig)