# coding: utf-8

# # Intro

# Name:

#

# ASR_2019_Proposal_MPL_SASZe

#

# Purpose:

#

# Make some figures to be put into the proposal, showing MPL data of aerosol over clouds, and examples of SASZe data.

#

# Input:

#

# none at command line

#

# Output:

#

# figures and save files...

#

# Keywords:

#

# none

#

# Dependencies:

#

# - Sp_parameters.py : for Sp class definition, and for defining the functions used to build parameters

# - matplotlib

# - mpltools

# - numpy

# - scipy : for saving and reading

# - plotting_utils (user defined plotting routines)

# - hdf5storage

#

# Needed Files:

#

# - file.rc : for consistent creation of look of matplotlib figures

# - ARM netcdf files for MPL and SASZe

#

# Modification History:

#

# Written: by Samuel LeBlanc, Halfway to Tahoe, 2019-03-08

# # Load the python modules and setup paths

# In[1]:

import numpy as np

import hdf5storage as hs

import os

import write_utils as wu

import scipy.io as sio

import matplotlib.pyplot as plt

# In[2]:

import load_utils as lu

from path_utils import getpath

# In[3]:

get_ipython().magic(u'matplotlib notebook')

# In[4]:

import scipy.stats as st

import Sun_utils as su

import plotting_utils as pu

# In[5]:

import Sp_parameters as sp

# In[6]:

fp = getpath('LASIC')

fp

# # Load the files

# ## Load the MPL files

# In[7]:

daystr = '20160814'

# In[9]:

ff = fp+'data/MPL/asi30smplcmask1zwangM1.c1.{}.00000?.cdf'.format(daystr)

# In[8]:

fl = os.listdir(fp+'data/MPL/')

# In[9]:

for i,f in enumerate(fl):

if daystr in f:

fo = f

# In[10]:

fo

# In[11]:

mpl,mplh = lu.load_netcdf(fp+'data/MPL/'+fo,everything=True)

# In[14]:

mplh['qc_backscatter']

# In[15]:

mplh['backscatter']

# In[16]:

mplh['background_signal']

# In[20]:

mplh['time']

# In[23]:

mplh['height']

# ## Load the SASZe data

# In[158]:

fs = os.listdir(fp+'data/SASZe/')

# In[159]:

for i,f in enumerate(fs):

if daystr in f:

if 'vis' in f:

fgv = f

if 'nir' in f:

fgn = f

# In[160]:

vis,vish = lu.load_netcdf(fp+'data/SASZe/'+fgv,everything=True)

# In[162]:

vish['zenith_radiance']

# In[165]:

vish['wavelength']

# In[163]:

vish['time']

# In[161]:

nir,nirh = lu.load_netcdf(fp+'data/SASZe/'+fgn,everything=True)

# In[166]:

nirh['zenith_radiance']

# ### Build the full spectra from vis and nir

# In[243]:

vis['wavelength'][100:1200]

# In[244]:

nir['wavelength'][15:]

# In[286]:

rad = np.append(vis['zenith_radiance'][:,100:1200],nir['zenith_radiance'][:,15:]*2.0,axis=1)

wvl = np.append(vis['wavelength'][100:1200],nir['wavelength'][15:])

# In[223]:

wvl.shape

# In[224]:

rad.shape

# In[287]:

s = {'zenlambda':wvl,'rad':rad,'utc':vis['time']/3600.0,'good':np.where(vis['solar_zenith']<70.0)[0]}

# In[288]:

sr = sp.Sp(s)

# ## Load the LUT for zenith spectra from ORACLES

# In[255]:

fpo = getpath('ORACLES')

# In[256]:

fpo

# In[263]:

model = hs.loadmat(fpo+'model/v3_ORACLES_lut.mat')

# In[259]:

model.keys()

# In[267]:

1.0/np.cos(model['sza']*np.pi/180.0)

# In[270]:

sptemp = {}

sptemp['tau'] = model['tau']

sptemp['ref'] = model['ref']

sptemp['zout'] = model['zout']

sptemp['sza'] = model['sza']

sptemp['phase'] = model['phase']

sptemp['irr_dn_diff'] = model['irr_dn_diff'][:,:,:,:,:,1]

sptemp['irr_dn'] = model['irr_dn'][:,:,:,:,:,1]

sptemp['irr_up'] = model['irr_up'][:,:,:,:,:,1]

sptemp['wvl'] = [model['wvl']]

sptemp['rad'] = model['rad'][:,:,:,:,:,1]

# In[271]:

lut = sp.Sp(sptemp)

# In[319]:

lut.tau

# In[321]:

lut.ref,lut.ref.shape

# In[323]:

lut.norm[0,:,0,4,16]

# # MPL plotting

# In[35]:

mpl['backscatter'][0,:]

# In[12]:

bsc = mpl['backscatter'].T

bsc[bsc <0.005] = np.nan

bsc[bsc >10.005] = np.nan

# In[13]:

ib = np.where(mpl['cloud_base']>0)[0]

iafter = np.append(ib[np.where(np.diff(ib,1)>1)[0]]+1,ib[-1]+1)

ibefore = np.append(ib[0]-1,ib[np.where(np.diff(ib,1)>1)[0]+1]-1)

# In[14]:

len(iafter),len(ibefore)

# In[140]:

ib

# In[150]:

mpl['time'][iafter]/3600.0

# In[ ]:

bsc

# In[99]:

bsc.shape

# In[105]:

mpl['height'][70]

# In[129]:

x

# In[15]:

#plt.figure()

fig,ax = plt.subplots(1,1,figsize=(12,3))

pc = ax.pcolorfast(mpl['time']/3600.0,mpl['height'],bsc[:-1,:-1],vmax=5.0,vmin=0.0005,cmap='viridis')

ax.plot(mpl['time']/3600.0,mpl['cloud_base'],'.r',label='cloud base')

pre,post = [],[]

for x in np.where(mpl['cloud_base']>0)[0]:

ax.axvline(mpl['time'][x]/3600.0,color='#DDDDDD',alpha=0.3,linewidth=5.0)

ax.axvline(x,color='#DDDDDD',alpha=0.3,linewidth=5.0,label='cloud event')

for i,y in enumerate(iafter):

if bsc[70,ibefore[i]]>0.001 and bsc[70,y]>0.001:

#print i,mpl['time'][ibefore[i]]/3600.0,mpl['time'][y]/3600.0

ax.axvspan(mpl['time'][ibefore[i]]/3600.0,mpl['time'][y]/3600.0,color='r',alpha=0.2)

ax.axvline(mpl['time'][ibefore[i]]/3600.0,color='g',alpha=0.5)

ax.axvline(mpl['time'][y]/3600.0,color='g',alpha=0.5)

ax.axvspan(mpl['time'][ibefore[i]]/3600.0,mpl['time'][y]/3600.0,color='r',alpha=0.2,label='AAC event')

ax.axvline(mpl['time'][y]/3600.0,color='g',alpha=0.5,label='Before/After cloud')

#for x in mpl['time'][bsc[70,:]>0.1]/3600.0:

# ax.axvline(x,color='r',alpha=0.05,linewidth=5.0)

#ax.axvline(x,color='r',alpha=0.05,linewidth=5.0,label='AAC event')

#ax.plot(mpl['time']/3600.0,mpl['cloud_top'],'.r',label='cloud top')

plt.legend(frameon=True,numpoints=1)

aa = plt.colorbar(pc)

#aa.set_label('Total attenuated backscatter\n[counts/microsecond]')

aa.set_label('Lidar signal')

ax.set_ylabel('Altitude [km]')

ax.set_xlabel('Time UTC [H]')

ax.set_xlim([19,22])

ax.set_ylim([0,4.5])

ax.set_title('MPL - Ascension Island, 2016-08-14')

plt.tight_layout()

plt.savefig(fp+'data/MPL_ASI_AAC_events.png',dpi=600,transparent=True)

# # SASZe plotting

# In[186]:

np.argmin(abs(sr.utc-19.45))

# In[208]:

np.argmin(abs(sr.utc-14.57))

# In[268]:

vis['solar_zenith'][23760]

# In[265]:

1.0/np.cos(vis['solar_zenith'][23760]*np.pi/180.0)

# In[251]:

plt.figure()

plt.plot(sr.utc,sr.sp[:,400])

# In[298]:

import matplotlib.ticker as ticker

from mpltools import color

# In[304]:

def frmt(x,pos):

return '{:5.2f}'.format(x)

# In[310]:

len(range(23000,25000,50))

# In[356]:

plt.figure(figsize=(10,3))

color.cycle_cmap(41,cmap=plt.cm.plasma,ax=plt.gca())

for j in xrange(23000,25000,50):

plt.plot(sr.wvl,sr.norm[j,:])

scalarmap = plt.cm.ScalarMappable(cmap=plt.cm.plasma)

scalarmap.set_array(sr.utc[range(23000,25000,50)])

cba = plt.colorbar(scalarmap,format=ticker.FuncFormatter(frmt))

cba.set_label('UTC [h]')

plt.ylim(0,1)

plt.xlim(340,1750)

plt.title('SASZe normalized spectra, Ascension Island, 2016-08-14')

plt.xlabel('Wavelength [nm]')

plt.ylabel('Normalized Radiance')

plt.tight_layout()

#plt.plot(lut.wvl,lut.norm[0,:,0,8,20],'k-',lw=3,

# label='Modeled, $\\tau$={:2.1f}, r$_{{eff}}$={:2.0f}$\\mu$m'.format(lut.tau[20],lut.ref[8]))

plt.plot(lut.wvl,lut.norm[0,:,0,4,16],'-',color='k',lw=3,alpha=0.8,

label='Modeled, $\\tau$={:2.1f}, r$_{{eff}}$={:2.0f}$\\mu$m'.format(lut.tau[16],lut.ref[4]))

plt.plot(lut.wvl,lut.norm[0,:,0,4,1],'-',color='lightgrey',lw=3,alpha=0.8,

label='Modeled, $\\tau$={:2.1f}, r$_{{eff}}$={:2.0f}$\\mu$m'.format(lut.tau[1],lut.ref[4]))

plt.legend(handletextpad=0.08,labelspacing=0.05,frameon=True)

plt.savefig(fp+'data/SASZe_sample_spectra_with_model.png',dpi=600,transparent=True)

# In[184]:

fig = sp.plt_zenrad(sr)

plt.ylim(0,0.5)

#plt.figure()

# In[253]:

sp.plt_norm_zenrad(sr)