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Copy pathGenerateFigures_DH20a_processing.py
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GenerateFigures_DH20a_processing.py
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'''
This is the code to generate the figures in
Doughty and Hill (2020) on Raman Processing
Before you run, make the current working directory of python the directory of this file
before you run this, you need to run process_ARS_initial.py and have the .rdat, .rmta, .csv, and .npy outputs.
'''
#Imports
########
## The following two imports should be uncommented to generate figures on a linux system.
#import matplotlib
#matplotlib.use('Agg')
#these imports are always needed
import matplotlib.pyplot as plt
import rs_tools as rt
import os
import numpy as np
import glob
import pandas as pd
import matplotlib.dates as dates
############# Helper Functions
def extract_banddiff(nrtd,limits=[-9999,-9999],limits_as_wavenumber = True, bin_wavenumber = None, make_plot=True,file_save='',vmax=0.1,vmin=-0.1,threshold=0):
'''
This extracts the difference between bands between the first and the last replicate.
I am going to go with the simple solution (first) - then we'll see what happens.
Inputs:
Outputs:
Example:
'''
import numpy as np
import matplotlib.pyplot as plt
#######################
#
# Determine limits
#
if limits[0] < 0 or limits[1] < 0:
#If any limits are less than zero, assume you want the whole spectrum
limits = [0,1023]
limits_as_wavenumber = False #
if limits_as_wavenumber==True:
try:
limit_upper = rt.find_nearest(limits[1],bin_wavenumber)
limit_lower = rt.find_nearest(limits[0],bin_wavenumber)
#print(limit_lower,limit_upper)
except:
print('Error (Extract_Banddiff): could not find bin index')
print('You likely need to supply wavenumber values')
return None
else:
limit_lower = limits[0]
limit_upper = limits[1] + 1 #note we add one
'''
#############################
#
# Determine Band Image Plot
#
num_rows,num_wns,num_replicates = nrtd.Image[0].Replicate_Data.shape
num_images = len(nrtd.Image)
diffcube = np.zeros((num_rows,num_images))
for i,Image in enumerate(nrtd.Image):
#Average the replicate data
repmean = np.nanmean(Image.Replicate_Data[:,limit_lower:limit_upper,:],axis=1)
bmean = np.nanmean(Image.Bleach_Data[:,limit_lower:limit_upper],axis=1)
#print repmean.shape
#print bmean.shape
allmeans = np.hstack((repmean,bmean[:,None]))
meanvalue = np.nanmean(allmeans,axis=1)
#print allmeans.shape
#print meanvalue.shape
nmm = np.nanmax(repmean,axis=1)
diffcube[:,i] = np.divide(np.subtract(nmm,bmean),meanvalue)
diffcube[meanvalue<=threshold,i] = np.NaN
'''
#############################
#
# Determine Band Image Plot
#
num_rows,num_wns,num_replicates = nrtd.Image[0].Replicate_Data.shape
num_images = len(nrtd.Image)
diffcube = np.zeros((num_rows,num_images))
for i,Image in enumerate(nrtd.Image):
meanvs = np.nanmean(Image.Replicate_Data[:,limit_lower:limit_upper,:],axis=1)
allmean = np.nanmean(meanvs,axis=1)
diffcube[:,i] = np.subtract(meanvs[:,-1],meanvs[:,0])
diffcube[allmean<=threshold,i] = np.NaN
output_band_image = diffcube
if make_plot:
fig = plt.figure()
ax = fig.add_subplot(111)
x,y = np.meshgrid(range(num_images),range(num_rows))
if vmax<0:
vmax=np.nanmax(output_band_image)
vmin=-1*vmax
imgplot= ax.pcolormesh(x,y,output_band_image,vmin=vmin,linewidth=0,cmap='bwr',vmax=vmax)
cbh = plt.colorbar(imgplot,orientation='vertical')
ax.set_title(nrtd.Summary.Save_Name + ' Rep2-Bleach' )
ax.set_xlabel('Spectrum Number')
ax.set_ylabel('Vertical Distance (micron)')
ax.set_xlim([0,np.nanmax(x)])
ax.set_ylim([0,np.nanmax(y)])
cbh.set_label('Mean Raman Intensity Change 1300-1650 cm-1 (a.u.)')
savename= file_save + '_dimg.png'
plt.savefig(savename,transparent=True)
return output_band_image
def plot_loc(nrtd,row,column,myax):
xv = np.zeros(len(nrtd.Image[0].Replicate_Name))
for Image in nrtd.Image:
xv[:] = Image.Replicate_Time
results = myax.plot(xv,Image.Replicate_Data[row,column,:],'.k')
return results
#############################################################################
#
#
#
#
# Figure making Functions
#
#
def make_Figure2():
import pdb
rt.plot_format(fs=10)
dir_plus = os.path.join("heights","*+*.csv")
dir_minus = os.path.join("heights","*-*.csv")
x_values = rt.get_rebs_calibration(cal_file='rn13_cal_20160825_paper.txt',cal_type='fit')
allfiles_pl = glob.glob(dir_plus)
allfiles_mn = glob.glob(dir_minus)
allfiles_pl.sort()
allfiles_mn.sort()
allfiles_mn.reverse()
allfiles = allfiles_mn+ allfiles_pl
#pdb.set_trace()
begin=True
legendvalue = []
for myfile in allfiles:
try:
mydata = np.loadtxt(myfile,delimiter=',',skiprows=3)
except:
pdb.set_trace()
if begin==True:
alldata = mydata
legendvalue = [myfile[26:29]]
begin=False
else:
alldata = np.dstack((alldata,mydata))
try:
legendvalue.append(myfile[26:29])
except:
pdb.set_trace()
mydata_zero = np.loadtxt(os.path.join("heights","RN14_20170508_H0.csv"),delimiter=',',skiprows=3)
mydata_zero_plt = mydata_zero[40,:]
#pdb.set_trace()
alldata_plot=alldata[40,:,:]
#alldata_plot=alldata[40,:,10:17]
x,y = alldata_plot.shape
#pdb.set_trace()
#Part 3: Final Plots
f,ax = plt.subplots(1,1,figsize=(4,4))
cc = [plt.cm.jet(i) for i in np.linspace(0, 1, 9)]
min_spc = 11
max_spc = 18
#TOp subplot
line_below = ax.plot(x_values,alldata_plot[:,0:min_spc],color=cc[0],alpha=0.8,lw=1)
h_line = [line_below[0]]
for i,idx in enumerate(range(min_spc,max_spc)):
myline = ax.plot(x_values,alldata_plot[:,idx],color=cc[i+1],alpha=0.8,lw=1)
h_line.append(myline[0])
line_above = ax.plot(x_values,alldata_plot[:,max_spc::],color=cc[-1],alpha=0.8,lw=1)
h_line.append(line_above[0])
import pdb
#pdb.set_trace()
from matplotlib.font_manager import FontProperties
fontP = FontProperties()
fontP.set_size('small')
legendvalue = [mylv + ' $\mu$m' for mylv in legendvalue]
legend_txt = ['<' + legendvalue[min_spc-1]] + legendvalue[min_spc:max_spc] + ['>'+legendvalue[max_spc]]
ax.legend(h_line,legend_txt,loc='lower center',ncol=3,prop=fontP,handlelength=1)
ax.set_xlabel('Raman Shift (cm$^{-1}$)')
ax.set_ylabel('Raman Intensity (A.U.)')
ax.set_xlim([400,3200])
ax.set_ylim([0,2500])
plt.tight_layout()
'''
#Lower subplot
x,y = np.meshgrid(rcal_wl,range(nrows))
imgplot = ax[1].pcolormesh(x,y,mdata,cmap='viridis')
ax[1].set_xlim([min(rcal_wl),max(rcal_wl)])
ax[1].set_ylim([0,127])
ax[1].text(660,110,'b.',bbox=dict(facecolor='white'))
cbh = plt.colorbar(imgplot,orientation='vertical')
cbh.set_label('Intensity (A.U.)')
ax[1].set_xlabel('Wavelength (nm)')
ax[1].set_ylabel('CCD Vertical axis along\n laser line (pixel nmbr)')
'''
plt.savefig("DH19a_Processing_Figure02.png")
def make_Figure4():
rt.plot_format(fs=10)
x_values = rt.get_rebs_calibration(cal_file='rn13_cal_20160825_paper.txt',cal_type='fit')
myDirectory = os.path.join('20160825r13','REBS RN 13_20160825_001238')
myCollection = rt.load_spotinfo(myDirectory)
myCollection = rt.clean_collection(myCollection)
myCollection = rt.use_bleach(myCollection)
myCollection = rt.remove_saturation(myCollection)
myCollection = rt.clean_badlocs(myCollection,rn=13)
myCollection = rt.add_binwn(myCollection,x_values)
myCollection = rt.remove_cosmic(myCollection,plot=False)
synthetic_bkr = rt.compute_bkr_collection(myCollection)
myCollection = rt.collection_subtract_bkr(myCollection, synthetic_bkr)
from copy import deepcopy
myCollection_1 = deepcopy(myCollection)
junk,banddiff = rt.detect_charring(myCollection_1,limits=[1300,1650],bin_wavenumber=x_values,make_plot=False)
#banddiff = extract_banddiff(myCollection,limits=[1300,1650],bin_wavenumber=x_values,make_plot=False)
num_rows,num_images = banddiff.shape
#import pdb
#pdb.set_trace()
f, (ax1,ax2,ax3) = plt.subplots(3, 1,figsize=(4.1,7))
#A
#=plt.figure(figsize=(4.1,7))
#ax1 = plt.subplot2grid((3,6), (0,0), colspan=6)
#ax2 = plt.subplot2grid((3,6), (1,0), colspan=6)
#cax = plt.subplot2grid((3,6), (1,5))
#ax3 = plt.subplot2grid((3,6), (2,0), colspan=6)
myim = 6
myrw = 19
ax1.plot(x_values,myCollection.Image[myim].Replicate_Data[myrw,:,:])
ax1.legend(['0-10s','10-20s','20-30s'])
ax1.set_xlabel('Raman Shift (cm$^{-1}$)')
ax1.set_ylabel('Raman Intensity (A.U.)')
ax1.set_xlim([300,3200])
ax1.text(450,0.4,'(a)')
#Download burning timeseries
#import pdb
#pdb.set_trace()
import matplotlib.colors as mcolors
colors1 = plt.cm.bwr(np.linspace(0, 1,220))
colors2 = plt.cm.bwr(np.linspace(0, 1,22))
colors = np.vstack((colors2[0:12,:],colors1[110::,:]))
cmap = mcolors.LinearSegmentedColormap.from_list('colormap', colors)
# generating a smoothly-varying LinearSegmentedColormap
#cmap=plt.cm.jet
#cmap.set_under([0.5,0.5,0.5])
cmap.set_under('w')
#FOr plotting purposes, normally we would do this:
#x,y = np.meshgrid(range(num_images),range(num_rows))
#im1= ax2.pcolormesh(x,y,banddiff,vmin=-0.05,linewidth=0,cmap='bwr',vmax=0.05)
#However, we need to represent that the x position spacing is two.
banddiff_nu = np.zeros((num_rows,num_images*2))
for i in range(num_images):
myind = i*2
banddiff_nu[:,myind] = banddiff[:,i]
banddiff_nu[:,myind+1] = -10
num_rows,num_cols = banddiff_nu.shape
import pdb
#pdb.set_trace()
x_nu,y_nu = np.meshgrid(range(num_cols),range(num_rows))
im1= ax2.pcolormesh(x_nu,y_nu,banddiff_nu,vmin=-1,linewidth=0,cmap=cmap,vmax=11)
'''
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
cax2 = inset_axes(ax2,
width="2%", # width = 10% of parent_bbox width
height="60%", # height : 50%
loc='center right',
borderpad = 6)
'''
#cbh = plt.colorbar(im1,orientation='vertical',cax=cax)
cbh = plt.colorbar(im1,ax=ax2,orientation='vertical')
#cax1.set_xtick([0.04,0,-0.04])
#cbh.set_label('r',fontsize=10)
ax2.set_xlabel('Stepping Axis ($\mu$m)')
ax2.set_ylabel('Laser Long Axis ($\mu$m)')
ax2.set_xlim([0,np.nanmax(x_nu)])
ax2.set_ylim([0,np.nanmax(y_nu)])
#ax2.arrow(myim*2.0+0.5,myrw+0.5,4,-5,head_starts_at_zero=True,head_width=0.01)
ax2.annotate("",xy=(myim*2.0+0.5,myrw+0.5),xytext=(myim*2.0+3.5,myrw-3.5),arrowprops=dict(arrowstyle="simple",color='C2'))
#ax2.scatter(np.array([myim*2.0 + 0.5]),np.array([myrw+0.5]),facecolors='none',edgecolors='k')
ax2.text(1.8,35,'(b)')
ax2.text(65,20,'r-1')
#rectangle=plt.Rectangle((17.5,4.2),7,35,fill=False)
#ax2.add_patch(rectangle)
#C
import pandas as pd
import matplotlib.dates as dates
from scipy.signal import savgol_filter,medfilt
my_df = pd.read_csv('TS_burncos.csv',parse_dates=[0])
mydt = my_df['DateTime'].values
vals_burn = my_df['nBurning'].values
#vals_burn_smooth = savgol_filter(vals_burn,21,4)
#vals_burn_smooth = medfilt(vals_burn,kernel_size=21)
ax3.plot(mydt,vals_burn,'k')
ax3.plot(mydt,vals_burn,'o',markerfacecolor='C2',markeredgecolor='k')
#ax3.plot(mydt,vals_burn_smooth)
ax3.text(pd.to_datetime('2016-08-25 1:15:00'),11,'(c)')
ax3.set_xlim(pd.to_datetime(['2016-08-25','2016-08-26']))
ax3.xaxis.set_major_locator(dates.HourLocator(interval=4))
ax3.xaxis.set_major_formatter(dates.DateFormatter('%H'))
ax3.set_xlabel('Hour of Day (EDT)')
ax3.set_ylabel('# Charring Spectra')
plt.subplots_adjust(hspace=0.1)
brntxt_1 = 'Total Number of Charring Spectra:' + str(np.nansum(vals_burn))
brntxt_2 = 'Average Number of Charring Spectra/collection:' + str(np.nanmean(vals_burn))
plt.tight_layout()
print(brntxt_1)
print(brntxt_2)
plt.savefig('DH19a_Processing_Figure04.png')
def make_Figure3():
rt.plot_format(fs=10)
#directory = os.path.join("20160825r13","REBS RN 13_20160825_033821")
directory = os.path.join("20160825r13","REBS RN 13_20160825_172824")
myCollection = rt.load_spotinfo(directory)
from copy import deepcopy
myCollection_raw = deepcopy(myCollection)
myCollection = rt.clean_collection(myCollection)
myCollection = rt.use_bleach(myCollection)
myCollection = rt.clean_badlocs(myCollection,rn=13)
myCollection = rt.remove_cosmic(myCollection,plot=False)
synthetic_bkr = rt.compute_bkr_collection(myCollection)
myCollection = rt.collection_subtract_bkr(myCollection, synthetic_bkr)
dc,dx,dz,t,fl,ft,rx = rt.collection_process(myCollection)
#imgno=25
#rowno = 33
imgno=20
rowno = 37
#myspec = dc[33,:,25]
myspec = dc[rowno,:,imgno]
myfluo = rt.background_als_core_nu(myspec,handle_end=True,p=0.001,lmb=1e6)
myfluo_1 = rt.background_als_core_nu(myspec,handle_end=True,p=0.001,lmb=1)
import pdb
f, ax = plt.subplots(3, sharex=True,figsize=(6,8))
myColors = rt.nicePalette()
#Top part of graph
#This shows speectrum, and estimated fluorescence
x_values = rt.get_rebs_calibration(cal_file='rn13_cal_20160825_paper.txt',cal_type='fit')
myrepdata = myCollection_raw.Image[imgno].Replicate_Data[rowno,:,:]
myblcdata = myCollection_raw.Image[imgno].Bleach_Data[rowno,:]
ax[0].plot(x_values,myblcdata,'C0',alpha=0.8,linewidth=2)
ax[0].plot(x_values,myrepdata[:,0],'C3',alpha=0.8,linewidth=1.2)
ax[0].plot(x_values,myrepdata[:,1],'C2',alpha=0.8,linewidth=0.7)
ax[0].axis([350,3175,0.01,0.07])
ax[0].legend(['Rep 1','Rep 2','Rep 3'],loc='upper right',frameon=False,ncol=1,prop={'size':12})
ax[0].text(1200,0.05,'(a) Raw Replicate Spectra')
ax[1].plot(x_values,myspec,color='k')
ax[1].plot(x_values,myfluo,color=myColors[1])
ax[1].plot(x_values,myfluo_1,color=myColors[4])
ax[1].fill_between(x_values,myfluo,facecolor=myColors[1], alpha=0.2,edgecolor="white")
ax[1].fill_between(x_values,myfluo_1,facecolor=myColors[4], alpha=0.2,edgecolor="white")
ax[1].axis([350,3175,0,0.029])
ax[1].legend(['Processed','$\lambda$=10$^6$','$\lambda$=1'],loc='center right',frameon=False,ncol=1,prop={'size':12})
ax[1].text(500,0.025,'(b) Processed Spectrum/Estimated Fluorescence')
#ax[1].text(1200,0.0016,'Est. Fluorescence = $F_{rem}$')
ax[1].set_ylabel('Raman Intensity (A.U.)')
#Remove fluorescence
specdiff = myspec-myfluo
specdiff_1 = myspec-myfluo_1
specdiff[0:94] = 0
specdiff_1[0:94] = 0
maxv = np.nanmax(specdiff)
maxx = x_values[specdiff==maxv][0]
ymainv = np.zeros(len(x_values))
ymainv[:] = maxv
maxv_1 = np.nanmax(specdiff_1)
maxx_1 = x_values[specdiff_1==maxv_1][0]
ymainv_1 = np.zeros(len(x_values))
ymainv_1[:] = maxv_1
#Make second part of plot showing the
#removed fluorescence and Rmax
ax[2].plot(x_values,specdiff,color=myColors[0])
ax[2].plot(x_values,specdiff_1,color=myColors[3])
import pdb
#pdb.set_trace()
xloc_0 = np.where(specdiff==maxv)[0][0]
arrloc = 900
xloc_0_arr = rt.find_nearest(arrloc,x_values)
xloc_1 = np.where(specdiff_1==maxv_1)[0][0]
arrloc_1 = 1900
xloc_1_arr = rt.find_nearest(arrloc_1,x_values)
ax[2].plot(x_values[xloc_0_arr:xloc_0],ymainv[xloc_0_arr:xloc_0],color=myColors[0],alpha=0.5)
ax[2].plot(x_values[xloc_1:xloc_1_arr],ymainv_1[xloc_1:xloc_1_arr],color=myColors[3],alpha=0.5)
ax[2].axis([350,3175,0,0.024])
ax[2].arrow(arrloc,0,0,maxv,length_includes_head=True,overhang=0.1,head_width=50,head_length=0.001,facecolor='k',zorder=2)
ax[2].arrow(arrloc_1,0,0,maxv_1,length_includes_head=True,overhang=0.1,head_width=50,head_length=0.001,facecolor='k',zorder=2)
ax[2].text(550,0.013,'$R_{max}$,\n $\lambda$=10$^6$')
ax[2].text(1850,0.004,'$R_{max}$,\n $\lambda$=1')
#pdb.set_trace()
'''
ax[2].annotate("$R_{max}$",
xy=(maxx, maxv), xycoords='data',
xytext=(1750, 0.012), textcoords='data',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"),
)
ax[2].annotate("$R_{max}$",
xy=(maxx_1, maxv_1), xycoords='data',
xytext=(1750, 0.012), textcoords='data',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"),
)
'''
#ax[2].set_ylabel('Fluorescence Removed \n Raman Intensity (A.U.)')
ax[2].text(500,0.021,'(c) Spectra after fluorescence subtraction')
ax[2].legend(['$\lambda$=10$^6$','$\lambda$=1'],loc='center right',frameon=False,ncol=1,prop={'size':12})
ax[2].set_xlabel('Wavenumber (cm$^{-1}$)')
f.subplots_adjust(hspace=0.05)
#pdb.set_trace()
plt.savefig('DH19a_Processing_Figure03.png')
def load_specdata_1(file2load):
with open(file2load,'r') as f:
myline = f.readline().strip().split()
lenmax = len(myline)
col_range = range(6,lenmax)
data = np.loadtxt(file2load,usecols=col_range,delimiter='\t')
return data
def get_spectrum(myDT,myX,myY,mymeta,mydata):
myspecind= int(mymeta[(mymeta['DateTime'] == myDT) & (mymeta['x']==myX) & (mymeta['y'] == myY)].index.values)
myspec = mydata[myspecind]
return myspec,myspecind
def make_Figure11():
import matplotlib.patches as patches
import matplotlib as mpl
####################################
#
# Grab base Data
#
#
alldata_base = "alldata_r13_TimeSeries_2_ClCsBkr"
file_rdat = alldata_base + ".rdat"
file_rmta = alldata_base + ".rmta"
rt.plot_format(fs=10)
rdat = np.loadtxt(file_rdat,delimiter=',')
dt_meta = pd.read_table(file_rmta,sep=',')
x_values = rdat[0,:]
rdatdata = rdat[1::,:]
#Loads the Fluorescence Removed Spectrum
print("Loaded Non Fl-subtracted data")
alldata_base = "alldata_r13_TimeSeries_5_ClCsBkrBrnFle6"
file_rdat = alldata_base + ".rdat"
file_rmta = alldata_base + ".rmta"
file_rflu = alldata_base + ".rflu"
rdat_f = np.loadtxt(file_rdat,delimiter=',')
dt_meta_f = pd.read_table(file_rmta,sep=',')
#x_values_f = rdat_f[0,:]
rdatdata_f = rdat_f[1::,:]
rfluo_f = np.loadtxt(file_rflu,delimiter=',')
#x_values_fluo = rfluo_f[0,:]
rdatfluo_f = rfluo_f[1::,:]
#####################
#
#Subset the data
#
#data_fluo_ini = dt_meta_f['fl']
data_rmax_ini = dt_meta_f['rmx']
data_fmax_ini = dt_meta_f['fla']
#data_fmax_ini = np.nanmax(rdatfluo_f[:,100::],axis=1)
#These are the data to use in calculating for the vs plots!!
data_rmax = data_rmax_ini[(data_rmax_ini>0)]
data_fmax = data_fmax_ini[(data_rmax_ini>0)]
from copy import deepcopy
nufluo = deepcopy(data_fmax)
nufluo[nufluo<0.0001] = 0.0001
data_fmax[data_fmax<0.001] = 0.001
data_rat = np.true_divide(data_rmax,data_fmax)
#import pdb
#pdb.set_trace()
#########################
#
#Get data for the subplots
#
spec_loc = ('2016-08-25 07:52:15',20,20)
spec_b,ind_b = get_spectrum(spec_loc[0],spec_loc[1],spec_loc[2],dt_meta,rdatdata)
spec_b_fsub = rdatdata_f[ind_b]
spec_b_fluo = rdatfluo_f[ind_b]
fl_b = data_fmax[ind_b]
rmx_b = dt_meta_f['rmx'][ind_b]
spec_loc = ('2016-08-25 10:46:14',8,10)
spec_c,ind_c = get_spectrum(spec_loc[0],spec_loc[1],spec_loc[2],dt_meta,rdatdata)
spec_c_fsub = rdatdata_f[ind_c]
spec_c_fluo = rdatfluo_f[ind_c]
fl_c = data_fmax[ind_c]
rmx_c = dt_meta_f['rmx'][ind_c]
spec_loc = ('2016-08-25 03:06:38',26,19)
spec_f,ind_f = get_spectrum(spec_loc[0],spec_loc[1],spec_loc[2],dt_meta,rdatdata)
spec_f_fsub = rdatdata_f[ind_f]
spec_f_fluo = rdatfluo_f[ind_f]
fl_f = data_fmax[ind_f]
rmx_f = dt_meta_f['rmx'][ind_f]
spec_loc = ('2016-08-25 01:16:20',14,13)
spec_e,ind_e = get_spectrum(spec_loc[0],spec_loc[1],spec_loc[2],dt_meta,rdatdata)
spec_e_fsub = rdatdata_f[ind_e]
spec_e_fluo = rdatfluo_f[ind_e]
fl_e = data_fmax[ind_e]
rmx_e = dt_meta_f['rmx'][ind_e]
spec_loc = ('2016-08-25 10:30:20',6,23)
#spec_loc = ('2016-08-25 03:06:38',0,4)
spec_d,ind_d = get_spectrum(spec_loc[0],spec_loc[1],spec_loc[2],dt_meta,rdatdata)
spec_d_fsub = rdatdata_f[ind_d]
spec_d_fluo = rdatfluo_f[ind_d]
fl_d = data_fmax[ind_d]
rmx_d = dt_meta_f['rmx'][ind_d]
spec_loc = ('2016-08-25 03:06:38',0,4)
#spec_loc = ('2016-08-25 10:30:20',6,23)
spec_g,ind_g = get_spectrum(spec_loc[0],spec_loc[1],spec_loc[2],dt_meta,rdatdata)
spec_g_fsub = rdatdata_f[ind_g]
spec_g_fluo = rdatfluo_f[ind_g]
fl_g = data_fmax[ind_g]
rmx_g = dt_meta_f['rmx'][ind_g]
###################################
#
# Make the plot!
#
f, ax = plt.subplots(figsize=(8,8))
#Main axis
axloc_a = [0.075,0.3,0.60,0.68]
ax_a = f.add_axes(axloc_a)
axloc_cb = [0.1,0.88,0.3,0.05]
ax_cb = f.add_axes(axloc_cb)
axloc_b = [0.69,0.80,0.25,0.2]
ax_b = f.add_axes(axloc_b)
axloc_c = [0.69,0.55,0.25,0.2]
ax_c = f.add_axes(axloc_c)
axloc_d = [0.69,0.30,0.25,0.2]
ax_d = f.add_axes(axloc_d)
axloc_e = [0.105,0.05,0.25,0.2]
ax_e = f.add_axes(axloc_e)
axloc_f = [0.415,0.05,0.25,0.2]
ax_f = f.add_axes(axloc_f)
axloc_g = [0.73,0.05,0.25,0.2]
ax_g = f.add_axes(axloc_g)
ax.axis('off')
mycmap = 'viridis_r'
Fig11Name='DH19a_Processing_Figure11.png'
lc = 'k'# [0.3,0.3,0.3]
la = 0.7
mlw = 1
myvmin = 0.3
myvmax=5
sct = ax_a.scatter(data_fmax,data_rmax,c=data_rat,cmap=mycmap,vmin=myvmin,vmax=myvmax,s=1,norm=mpl.colors.LogNorm())
ax_a.text(-0.04,0.55,'R$_{max}$ (A.U.)',transform = ax_a.transAxes,horizontalalignment='center',rotation=90)
ax_a.text(0.50,-0.04,'F$_{max}$ (A.U.)',transform = ax_a.transAxes,horizontalalignment='center')
ax_a.text(0.075,0.96,'(a)',transform = ax_a.transAxes)
ax_a.set_yscale('log')
ax_a.set_xscale('log')
ax_a.set_xlim([0.00095,1])
ax_a.set_ylim([0.00095,1])
ax_a.axvspan(0.00091,0.00103, alpha=0.5, color=[0.5,0.5,0.5])
ax_a.text(0.0007,0.0007,' ',bbox=dict(facecolor='white',edgecolor='white'))
ax_a.text(0.0007,0.0007,'<=10$^{-3}$',bbox=dict(facecolor=[0.5,0.5,0.5], alpha=0.5))
import pdb
#pdb.set_trace()
from scipy.signal import savgol_filter
ax_b.plot(x_values,spec_b,alpha=0.8)
ax_b.plot(x_values,spec_b_fsub,alpha=0.5)
ax_b.plot(x_values,spec_b_fluo,alpha=0.5)
ax_b.yaxis.tick_right()
ax_b.yaxis.set_label_position("right")
ax_b.set_xticks([500,1500,3000])
ax_b.set_xlim([450,3150])
ax_b.text(0.075,0.85,'(b)',transform = ax_b.transAxes)
line = mpl.lines.Line2D([fl_b,10],[rmx_b,0.46],color=lc,alpha=la,lw=1,ls='--')
plt.setp(ax_b.spines.values(), color=lc,alpha=la,lw=mlw)
plt.setp([ax_b.get_xticklines(), ax_b.get_yticklines()], color=lc,alpha=la,lw=mlw)
ax_a.add_line(line)
line.set_clip_on(False)
ax_c.plot(x_values,spec_c,alpha=0.8)
ax_c.plot(x_values,spec_c_fsub,alpha=0.5)
ax_c.plot(x_values,spec_c_fluo,alpha=0.5)
ax_c.yaxis.tick_right()
ax_c.yaxis.set_label_position("right")
ax_c.set_xticks([500,3000])
ax_c.set_xlim([450,3150])
ax_c.text(0.075,0.85,'(c)',transform = ax_c.transAxes)
plt.setp(ax_c.spines.values(), color=lc,alpha=la,lw=mlw)
plt.setp([ax_c.get_xticklines(), ax_c.get_yticklines()], color=lc,alpha=la,lw=mlw)
line = mpl.lines.Line2D([fl_c,10],[rmx_c,0.03],color=lc,alpha=la,lw=1,ls='--')
ax_a.add_line(line)
line.set_clip_on(False)
ax_d.plot(x_values,spec_d,alpha=0.8)
ax_d.plot(x_values,spec_d_fsub,alpha=0.5)
ax_d.plot(x_values,spec_d_fluo,alpha=0.5)
#ax_c.set_yticklabels('')
ax_d.yaxis.tick_right()
ax_d.yaxis.set_label_position("right")
#ax_c.set_ylabel('Raman Intensity (A.U.)')
ax_d.set_xticks([500,3000])
ax_d.set_xticklabels([' 500','3000'])
ax_d.set_xlim([450,3150])
#ax_d.text(0.3,-0.25,' Raman\nShift ($cm^{-1}$)',transform = ax_d.transAxes)
ax_d.text(0.075,0.72,'(d)',transform = ax_d.transAxes)
plt.setp(ax_d.spines.values(), color=lc,alpha=la,lw=mlw)
plt.setp([ax_d.get_xticklines(), ax_d.get_yticklines()], color=lc,alpha=la,lw=mlw)
line = mpl.lines.Line2D([fl_d,10],[rmx_d,0.001],color=lc,alpha=la,lw=1,ls='--')
ax_a.add_line(line)
line.set_clip_on(False)
ax_e.plot(x_values,spec_e,alpha=0.8)
ax_e.plot(x_values,spec_e_fsub,alpha=0.5)
ax_e.plot(x_values,spec_e_fluo,alpha=0.5)
spec_e_smoothed = savgol_filter(spec_e,15,2)
ax_e.plot(x_values,spec_e_smoothed,alpha=0.8)
#ax_d.set_yticklabels('')
#ax_d.set_ylabel('Raman Intensity (A.U.)')
ax_e.set_ylabel('Intensity (A.U.)')
ax_e.set_xticks([500,3000])
ax_e.set_xlim([450,3150])
ax_e.text(0.3,-0.25,' Raman\nShift ($cm^{-1}$)',transform = ax_e.transAxes)
ax_e.text(0.075,0.85,'(e)',transform = ax_e.transAxes)
ax_e.set_ylim([-.0009,0.002])
plt.setp(ax_e.spines.values(), color=lc,alpha=la,lw=mlw)
plt.setp([ax_e.get_xticklines(), ax_e.get_yticklines()], color=lc,alpha=la,lw=mlw)
line = mpl.lines.Line2D([fl_e,0.002],[rmx_e,0.0005],color=lc,alpha=la,lw=1,ls='--')
ax_a.add_line(line)
line.set_clip_on(False)
ax_f.plot(x_values,spec_f,alpha=0.8)
ax_f.plot(x_values,spec_f_fsub,alpha=0.5)
ax_f.plot(x_values,spec_f_fluo,alpha=0.5)
#ax_e.set_yticklabels('')
#ax_e.set_ylabel('Raman Intensity (A.U.)')
ax_f.set_xticks([500,3000])
ax_f.set_xlim([450,3150])
ax_f.text(0.3,-0.25,' Raman\nShift ($cm^{-1}$)',transform = ax_f.transAxes)
ax_f.text(0.075,0.85,'(f)',transform = ax_f.transAxes)
plt.setp(ax_f.spines.values(), color=lc,alpha=la,lw=mlw)
plt.setp([ax_f.get_xticklines(), ax_f.get_yticklines()], color=lc,alpha=la,lw=mlw)
line = mpl.lines.Line2D([fl_f,0.2],[rmx_f,0.0005],color=lc,alpha=la,lw=1,ls='--')
ax_a.add_line(line)
line.set_clip_on(False)
ax_g.plot(x_values,spec_g,alpha=0.8)
ax_g.plot(x_values,spec_g_fsub,alpha=0.5)
ax_g.plot(x_values,spec_g_fluo,alpha=0.5)
#ax_f.set_yticklabels('')
#ax_f.set_ylabel('Raman Intensity (A.U.)')
ax_g.set_xticks([500,3000])
ax_g.set_xlim([450,3150])
ax_g.text(0.3,-0.25,' Raman\nShift ($cm^{-1}$)',transform = ax_g.transAxes)
ax_g.text(0.8,0.85,'(g)',transform = ax_g.transAxes)
plt.setp(ax_g.spines.values(), color=lc,alpha=la,lw=mlw)
plt.setp([ax_g.get_xticklines(), ax_g.get_yticklines()], color=lc,alpha=la,lw=mlw)
line = mpl.lines.Line2D([fl_g,3],[rmx_g,0.0003],color=lc,alpha=la,lw=1,ls='--')
ax_a.add_line(line)
line.set_clip_on(False)
ax_cb.set_title('R$_{max}$/F$_{max}$')
cbh = plt.colorbar(sct,cax=ax_cb,orientation='horizontal')
#cbh.set_ticks([0.5,5])
lc = patches.Rectangle((1.25,0.007),0.05,0.001,linewidth=2,edgecolor='r',facecolor='none')
ax.add_patch(lc)
plt.savefig(Fig11Name)
def cluster_in_cluster(input_file_base,input_file_comp,output_file):
data_base = pd.read_csv(input_file_base)
dt_base = pd.to_datetime(data_base["myspc.DateTime"].values)
cv_base = data_base[["myspc.x","myspc.y","myspc.clusters"]].values
data_comp = pd.read_csv(input_file_comp)
dt_comp = pd.to_datetime(data_comp["myspc.DateTime"].values)
cv_comp = data_comp[["myspc.x","myspc.y","myspc.clusters"]].values
ml = cv_base.shape[0]
uv_base = np.zeros(ml) #Number of values in the base without an analog in 'comp'
uv_comp = np.zeros(cv_comp.shape[0]) #number of values in the comp wihtout an analog in 'base'
#Really bad - we are dynamically allocating an array
#Deal with it.
dt_sec = np.array([])
cv_sec = np.zeros(4)
cv_sec[:] = np.nan
for i in range(ml):
#Loop over every value in CV_base
myloc = [(dt_base[i]==dt_comp) & (cv_base[i,0] == cv_comp[:,0]) & (cv_base[i,1] == cv_comp[:,1])]
goodval = cv_comp[myloc[0],2]
if len(goodval) == 1:
dt_sec = np.append(dt_sec,dt_base[i])
savearr = np.array([cv_base[i,0],cv_base[i,1],cv_base[i,2],goodval])
cv_sec = np.vstack((cv_sec,savearr))
elif len(goodval) == 0:
uv_base[i] = 1
elif len(goodval) > 1:
raise ValueError('I found multiple matches - there is a problem with your archives')
max_input_clusters=np.nanmax(cv_base[:,2])
with open(output_file,"w") as f:
f.write("Comparison of Clusters\nfrom file:\n" + input_file_base + " \nin file:\n" + input_file_comp + "\n\n")
f.write("-----------------------------------------------\n")
for i in range(max_input_clusters):
mc = i+1
matched_arr_2 = 0
#Initial work: get number of clusters not matched, and number of clusters matched
total_arr = np.where(cv_base[:,2] == mc)[0]
matched_arr = np.where((uv_base == 0) & (cv_base[:,2] == mc))[0]
unmatched_arr = np.where((uv_base == 1) & (cv_base[:,2] == mc))[0]
f.write("Cluster " + str(mc) + "| Total:" + str(len(total_arr)) + "| Matched:" + str(len(matched_arr)) + " | Unmatched:" + str(len(unmatched_arr)) + "\n")
good_arr = np.where(cv_sec[:,2]==mc)
cv_sec_nu = cv_sec[good_arr[0],:]
good_subclusters = np.unique(cv_sec_nu[:,3])
for msc in good_subclusters:
nr,nc = cv_sec_nu[cv_sec_nu[:,3]==msc].shape
f.write("\t" + str(int(msc)) + ":" + str(int(nr)))
f.write("\n")
matched_arr_2 = matched_arr_2 + nr
#Get number of clusters that did not compare
f.write("\tSum:"+ str(matched_arr_2))
f.write("\n")
f.write("-----------------------------------------------\n")
def make_FigureS2():
''' Code to generate Figure S2: Cosmic Ray Removal
Essentially it does the standard load, but when
running the remove_cosmic subroutine, plot=True
So it dumps out the plots.
#This is the code to generate cosmic_data
master_folder = os.path.join('Data,'raw13')
n_cosmic = []
t_collection = []
day_list = ['20160825r13']
for day in day_list:
all_directories = glob.glob(os.path.join(master_folder,day,'REBS*'))
for i,directory in enumerate(all_directories):
print directory
try:
myCollection = rt.load_spotinfo(directory)
myCollection = rt.clean_collection(myCollection)
myCollection = rt.use_bleach(myCollection)
myCollection = rt.remove_saturation(myCollection)
myCollection = rt.remove_cosmic(myCollection,plot=False)
n_cosmic = np.append(n_cosmic,myCollection.Summary.nCosmic)
t_collection = np.append(t_collection,myCollection.Summary.Imaging_Start)
except:
continue
np.save('cosmic.txt',np.vstack((t_collection,n_cosmic)))
'''
from copy import deepcopy
myDirectory = os.path.join('20160825r13','REBS RN 13_20160825_082403')
myCollection = rt.load_spotinfo(myDirectory)
myCollection = rt.clean_collection(myCollection)
myCollection = rt.use_bleach(myCollection)
myCollection = rt.remove_saturation(myCollection)
myCollection = rt.clean_badlocs(myCollection,rn=13)
myCollection = rt.add_binwn(myCollection,rt.get_rebs_calibration(cal_file='rn13_cal_20160825_paper.txt',cal_type='fit'))
#Code below here is copied and slightly edited from remove_cosmic.
#Get Image 7
Image = myCollection.Image[7]
j = 27
k=2
oldrows = deepcopy(Image.Replicate_Data[j,:,:])
##THis is the part that shows you where the cosmic rays are removed
myRow = Image.Replicate_Data[j,:,k]
myComparison = np.vstack((Image.Replicate_Data[j,:,:k].transpose(),Image.Replicate_Data[j,:,k+1:].transpose()))
crl,sdb,sdc = rt.remove_cosmic_core(myRow,myComparison)
all_crl = np.hstack((crl,np.add(crl,1),np.add(crl,-1)))
Image.Replicate_Data[j,all_crl,k] = np.nan
Image.Replicate_Data[j,:,k] = rt.lininterp_nan(Image.Replicate_Data[j,:,k])
#Here we Generate the plot for Figure 1
cgy = "#a0a0a0"
co = "#f4a442"
cgn = "#2f9143"
x = rt.get_rebs_calibration(cal_file='rn13_cal_20160825_paper.txt',cal_type='fit')
f, ax = plt.subplots(3,figsize=(3.5,9))
ax[0].plot(x,oldrows[:,k],color=co)
ax[0].plot(x,Image.Replicate_Data[j,:,k],color=cgy)
ax[0].plot(x[crl],oldrows[crl,k],'o', markerfacecolor="None",markeredgecolor='k')
ax[0].set_ylim([0,np.nanmax(Image.Replicate_Data[j,100::,k])+0.01])
ax[0].text(400,0.035,'(a)',fontsize=10)
ax[0].set_ylabel('Intensity (A.U.)')
ax[1].plot(x,sdb,color=cgn)
ax[1].plot(x,sdc,color=cgy)
ax[1].set_ylim([-0.002,0.002])
ax[1].text(400,0.0015,'(b)',fontsize=10)
ax[1].set_ylabel('Second Derivative (A.U.)')
#remove_cosmic_core calcs
dr = sdb-sdc
z_score = rt.calculate_z_score(dr)
ax[2].plot(x,z_score,color=cgn)
ax[2].plot([x[0],x[-1]],[-5,-5],'k')
ax[2].set_ylim([-15,10])
ax[2].text(400,5,'(c)',fontsize=10)
ax[2].set_ylabel('Z-score')
ax[2].set_xlabel('Wavenumber (cm$^{-1}$)')
#Load the time-series of the cosmic rays removed
#Plot it here.
plt.tight_layout()
plt.savefig("Figure_S2.png")
def make_FigureS3():