def singleRunPostProcessing(self, PPoutpath, folder):
"""Generates a single yield histogram for the run_type
Args:
PPoutpath (string) - output path to place data in
folder (string) - full filepath to folder containing runs
"""
res = gen_summary(folder)
self.res = res
self.dist_plot([res['detected']],PPoutpath=PPoutpath,folder=folder,legtext=[''])
def multiRunPostProcessing(self, PPoutpath, folders):
"""Generates a yield histograms for the provided run_types
Args:
PPoutpath (string) - output path to place data in
folders (string) - full filepaths to folders containing runs of each run_type
"""
resList = list()
for folder in folders:
resList.append(gen_summary(folder)['detected'])
self.resList = resList
self.dist_plot(resList,PPoutpath=PPoutpath,folder=folder)
'Dean15May18RS09CXXfZ01OB40PP01SU01',\ 'Dean15May18RS09CXXfZ01OB41PP01SU01',\ 'Dean15May18RS09CXXfZ01OB42PP01SU01',\ 'Dean15May18RS09CXXfZ01OB43PP01SU01',\ 'Dean15May18RS09CXXfZ01OB44PP01SU01',\ 'Dean15May18RS09CXXfZ01OB45PP01SU01',\ 'Dean15May18RS09CXXfZ01OB46PP01SU01',\ 'Dean15May18RS09CXXfZ01OB47PP01SU01',\ 'Dean15May18RS09CXXfZ01OB48PP01SU01',\ 'Dean15May18RS09CXXfZ01OB49PP01SU01',\ 'Dean15May18RS09CXXfZ01OB50PP01SU01',\ 'Dean15May18RS09CXXfZ01OB51PP01SU01',\ 'Dean15May18RS09CXXfZ01OB52PP01SU01',\ 'Dean15May18RS09CXXfZ01OB53PP01SU01',\ 'Dean2May18RS09CXXfZ01OB01PP01SU01']#replace with more recent run res1 = gen_summary(pathRuns + t[0]) res2 = gen_summary(pathRuns + t[1]) res3 = gen_summary(pathRuns + t[2]) res4 = gen_summary(pathRuns + t[3]) res5 = gen_summary(pathRuns + t[4]) res6 = gen_summary(pathRuns + t[5]) res7 = gen_summary(pathRuns + t[6]) res8 = gen_summary(pathRuns + t[7]) res9 = gen_summary(pathRuns + t[8]) res10 = gen_summary(pathRuns + t[9]) res11 = gen_summary(pathRuns + t[10]) res12 = gen_summary(pathRuns + t[11]) res13 = gen_summary(pathRuns + t[12]) res14 = gen_summary(pathRuns + t[13]) res15 = gen_summary(pathRuns + t[14]) res16 = gen_summary(pathRuns + t[15])
for f in pklfiles:
myStr = '/'.join(f.split('/')[0:-1])
if not myStr in pklfiles2: #Ensures no duplicates added
pklfiles2.append(myStr)
##########
#### Get Date
date = unicode(datetime.datetime.now())
date = ''.join(
c + '_'
for c in re.split('-|:| ', date)[0:-1]) #Removes seconds from date
####################################3
#### Search Through All Files
for f in pklfiles2:
out = gen_summary(f, includeUniversePlanetPop=False)
Rps = list()
detected = list()
Mps = list()
#tottime = list()
starinds = list()
smas = list()
ps = list()
es = list()
WAs = list()
SNRs = list()
fZs = list()
fEZs = list()
dMags = list()
rs = list()
"""
from EXOSIMS.util.read_ipcluster_ensemble import gen_summary
import numpy as np
import math
from scipy.stats import ks_2samp
from scipy.stats import anderson
PPoutpath1 = '/home/dean/Documents/SIOSlab/EXOSIMSres/WFIRSTCompSpecPriors_WFIRSTcycle6core_3momaxC/WFIRSTcycle6core_CKL2_PPKL2'
folder1 = '/home/dean/Documents/SIOSlab/EXOSIMSres/WFIRSTCompSpecPriors_WFIRSTcycle6core_3momaxC/WFIRSTcycle6core_CKL2_PPKL2'
PPoutpath2 = '/home/dean/Documents/SIOSlab/EXOSIMSres/WFIRSTCompSpecPriors_WFIRSTcycle6core_3momaxC/WFIRSTcycle6core_CSAG13_PPKL2'
folder2 = '/home/dean/Documents/SIOSlab/EXOSIMSres/WFIRSTCompSpecPriors_WFIRSTcycle6core_3momaxC/WFIRSTcycle6core_CSAG13_PPKL2'
#Generate Summaries
res1 = gen_summary(folder1)
res2 = gen_summary(folder2)
rcounts = []
res = [res1['detected'], res2['detected']]
for el in res:
rcounts.append(np.array([np.unique(r).size for r in el]).astype(float))#unique detections
out = ks_2samp(rcounts[0], rcounts[1])
#out2 = anderson()
## Quick proof distributions are different
# X0 = np.random.normal(loc=1.,scale=1.,size=(1000))
'auto_2019_05_18_13_50__HabExTimeSweep_HabEx_CSAG13_PPSAG13_8',\ 'auto_2019_05_18_13_50__HabExTimeSweep_HabEx_CSAG13_PPSAG13_9',\ 'auto_2019_05_18_13_50__HabExTimeSweep_HabEx_CSAG13_PPSAG13_10'] # res1 = gen_summary(pathRuns + t[0]) # res2 = gen_summary(pathRuns + t[1]) # res3 = gen_summary(pathRuns + t[2]) # res4 = gen_summary(pathRuns + t[3]) # res5 = gen_summary(pathRuns + t[4]) # res6 = gen_summary(pathRuns + t[5]) # res7 = gen_summary(pathRuns + t[6]) # res8 = gen_summary(pathRuns + t[7]) # res9 = gen_summary(pathRuns + t[8]) # res10 = gen_summary(pathRuns + t[9]) rcounts = list() rcounts.append(calcStatistics(gen_summary(pathRuns + t[0])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[1])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[2])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[3])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[4])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[5])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[6])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[7])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[8])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[9])['detected'])) rcounts.append(calcStatistics(gen_summary(pathRuns + t[10])['detected'])) # 1mo, 0.08333yr=08 # 2mo, 0.1666yr=09 # 3mo, 0.25yr=10 # 4mo, 0.33333yr=11
#res2 = gen_summary('/home/dean/drk94_starkAYO_JTWINJTWINnov10_2017')
#11/9/2017 c-JTWIN pp-KeplerLike
#res1 = gen_summary('/home/dean/drk94_radDos0JTWINKeplernov6_2017')
#res2 = gen_summary('/home/dean/drk94_SLSQPJTWINKeplernov4_2017')
#res3 = gen_summary('/home/dean/drk94_SLSQPstaticJTWINKeplernov3_2017')
#res4 = gen_summary('/home/dean/drk94_starkAYOJTWINKeplernov9_2017')
#2/14/2018 c-KeplerLike New implementation
#res1 = gen_summary('/home/dean/drk94_starkAYO_fZnoDecnoCompnov14_2017')
#res2 = gen_summary('/home/dean/Feb13_2018starkAYOstaticFunctionalTest')
#2/15/2018 c-KeplerLike New implementation compared to Last master commit
res1 = gen_summary('/home/dean/Feb15_2018starkAYOstaticmasterFunctionalTest')
res2 = gen_summary('/home/dean/Feb13_2018starkAYOstaticFunctionalTest')
res3 = gen_summary('/home/dean/Feb16_2018SLSQPmasterFunctionalTest')
#res1 = gen_summary('/home/dean/wfirst_radDos0_SLSQP')
#res2 = gen_summary('/home/dean/drk94_SLSQP_CbyTnov7_2017')
#dir1 = "/home/dean/drk94_starkAYOoct20_2017"
#out = gen_summary(dir1)
#print('Done starkAYO')
#dir2 = "/home/dean/wfirst_radDos0"
#out2 = gen_summary(dir2)
def singleRunPostProcessing(self, PPoutpath, folder):
"""Generates a single yield histogram for the run_type
Args:
PPoutpath (string) - output path to place data in
folder (string) - full filepath to folder containing runs
"""
if not os.path.exists(folder):#Folder must exist
raise ValueError('%s not found'%folder)
if not os.path.exists(PPoutpath):#PPoutpath must exist
raise ValueError('%s not found'%PPoutpath)
outspecfile = os.path.join(folder,'outspec.json')
if not os.path.exists(outspecfile):#outspec file not found
raise ValueError('%s not found'%outspecfile)
#Extract Data from folder containing pkl files
out = gen_summary(folder)#out contains information on the detected planets
allres = read_all(folder)# contains all drm from all missions in folder
#Convert Extracted Data to x,y
x, y = self.extractXY(out, allres)
# Define the x and y data for detected planets
det_Rps = np.concatenate(out['Rps']).ravel() # Planet Radius in Earth Radius of detected planets
det_smas = np.concatenate(out['smas']).ravel()
#Create Mission Object To Extract Some Plotting Limits
sim = EXOSIMS.MissionSim.MissionSim(outspecfile, nopar=True)
ymax = np.nanmax(sim.PlanetPhysicalModel.ggdat['radii']).to('earthRad').value
################
#Create Figure and define gridspec
fig2 = plt.figure(2, figsize=(8.5,4.5))
gs = gridspec.GridSpec(3,5, width_ratios=[6,1,0.3,6,1.25], height_ratios=[0.2,1,4])
gs.update(wspace=0.06, hspace=0.06) # set the spacing between axes.
plt.rc('axes',linewidth=2)
plt.rc('lines',linewidth=2)
plt.rcParams['axes.linewidth']=2
plt.rc('font',weight='bold')
#What the plot layout looks like
###-----------------------------------
# | gs[0] gs[1] gs[2] gs[3] gs[4] |
# | gs[5] gs[6] gs[7] gs[8] gs[9] |
# | gs[10] gs[11] gs[12] gs[13] gs[14]|
###-----------------------------------
ax1 = plt.subplot(gs[5+5])#2D histogram of planet pop
ax2 = plt.subplot(gs[0+5])#1D histogram of a
ax3 = plt.subplot(gs[6+5])#1D histogram of Rp
ax4 = plt.subplot(gs[8+5])#2D histogram of detected Planet Population
ax5 = plt.subplot(gs[3+5])#1D histogram of detected planet a
ax6 = plt.subplot(gs[9+5])#1D histogram of detected planet Rp
TXT1 = plt.subplot(gs[1+5])
TXT4 = plt.subplot(gs[4+5])
axCBAR = plt.subplot(gs[0:5])
# Set up default x and y limits
print(min(x))
xlims = [min(x),max(x)]# of aPOP
ylims = [min(y),ymax]#max(y)]# of RpPOp
xmin = xlims[0]
xmax = xlims[1]
ymin = ylims[0]
ymax = ylims[1]
# Make the 'main' temperature plot
# Define the number of bins
nxbins = 50# a bins
nybins = 50# Rp bins
nbins = 100
xbins = np.logspace(start = np.log10(xmin), stop = np.log10(xmax), num = nxbins)
ybins = np.logspace(start = np.log10(ymin), stop = np.log10(ymax), num = nybins)
xcenter = (xbins[0:-1]+xbins[1:])/2.0
ycenter = (ybins[0:-1]+ybins[1:])/2.0
aspectratio = 1.0*(xmax - 0)/(1.0*ymax - 0)
H, xedges,yedges = np.histogram2d(x,y,bins=(xbins,ybins),normed=True)
X = xcenter
Y = ycenter
Z = H
#To calculate area under H
# tmpx = np.diff(xedges)
# tmpy = np.diff(yedges)
# tmpmat = np.transpose(np.asarray(tmpx,ndmin))*tmpy
# #test to be sure it is correct
# tmpmat[1,2] == tmpx[1]*tmpy[2] #this should be true
# np.sum(tmpmat*H) #this should equal1
# Plot the temperature data
xcents = np.diff(xbins)/2.+xbins[:-1]
ycents = np.diff(ybins)/2.+ybins[:-1]
#Plots the contour lines for ax1
cax = ax1.contourf(xcents, ycents, H.T, extent=[xmin, xmax, ymin, ymax], cmap='jet', locator=ticker.LogLocator())
CS4 = ax1.contour(cax, colors=('k',), linewidths=(1,), origin='lower', locator=ticker.LogLocator())
#Add Colorbar
cbar = fig2.colorbar(cax, cax=axCBAR, orientation='horizontal')#pad=0.05,
plt.rcParams['axes.titlepad']=-10
axCBAR.set_xlabel('Joint Probability Density: Universe (Left) Detected Planets (Right)', weight='bold', labelpad=-35)
axCBAR.tick_params(axis='x',direction='in',labeltop=True,labelbottom=False)#'off'
cbar.add_lines(CS4)
HDET, xedgesDET, yedgesDET = np.histogram2d(det_smas,det_Rps,bins=(xbins,ybins),normed=True)
caxDET = ax4.contourf(xcents,ycents,HDET.T, extent=[xmin, xmax, ymin, ymax], cmap='jet', locator=ticker.LogLocator())
CS42 = ax4.contour(caxDET, colors=('k',), linewidths=(1,), origin='lower', locator=ticker.LogLocator())
#Set axes scales to log
ax1.set_xscale('log')
ax1.set_yscale('log')
ax2.set_xscale('log')
ax3.set_yscale('log')
ax4.set_xscale('log')
ax4.set_yscale('log')
ax5.set_xscale('log')
ax6.set_yscale('log')
#Plot the axes labels
ax1.set_xlabel('Universe Pop.\nSemi-Major Axis, $a$, in ($AU$)',weight='bold', multialignment='center')
ax1.set_ylabel('Planet Radius $R_{p}$, in ($R_{\oplus}$)',weight='bold', multialignment='center')
ax4.set_xlabel('Detected Planet Pop.\nSemi-Major Axis, $a$, in ($AU$)',weight='bold', multialignment='center')
#Set up the histogram bins
xbins = np.logspace(start = np.log10(xmin), stop = np.log10(xmax), num = nxbins)
ybins = np.logspace(start = np.log10(ymin), stop = np.log10(ymax), num = nybins)
#Plot the universe planet pop histograms
#*note len(out) should equal len(all_res)
#Universe SMA Hist
n2, bins2, patches2 = plt.subplot(gs[4+5]).hist(x, bins=xbins, color = 'black', alpha=0., histtype='step',normed=True)#,density=True)#1D histogram of universe a
center2 = (bins2[:-1] + bins2[1:]) / 2
width2=np.diff(bins2)
ax2.bar(center2, n2*(len(x)/float(len(out['smas']))), align='center', width=width2, color='black', fill='black')
#Detected SMA Hist
n5, bins5, patches5 = plt.subplot(gs[4+5]).hist(det_smas, bins=xbins, color = 'black', alpha=0., histtype='step',normed=True)#,density=True)#1D histogram of detected planet a
center5 = (bins5[:-1] + bins5[1:]) / 2
width5=np.diff(bins5)
ax5.bar(center5, n5*(len(det_smas)/float(len(out['smas']))), align='center', width=width5, color='black', fill='black')
#Universe Rp Hist
n3, bins3, patches3 = plt.subplot(gs[4+5]).hist(y, bins=ybins, color = 'black', alpha=0., histtype='step',normed=True)#,density=True)#1D histogram of detected planet a
center3 = (bins3[:-1] + bins3[1:]) / 2
width3=np.diff(bins3)
ax3.barh(center3, n3*(len(y)/float(len(out['Rps']))), width3, align='center', color='black')
#aDetected Rp Hist
n6, bins6, patches6 = plt.subplot(gs[4+5]).hist(det_Rps, bins=ybins, color = 'black', alpha=0., histtype='step',normed=True)#,density=True)#1D histogram of detected planet a
center6 = (bins6[:-1] + bins6[1:]) / 2
width6=np.diff(bins6)
ax6.barh(center6, n6*(len(det_Rps)/float(len(out['Rps']))), width6, align='center', color='black')
#Label Histograms
ax2.set_ylabel(r'$\frac{{a\ Freq.}}{{{}\ sims}}$'.format(len(out['Rps'])),weight='bold', multialignment='center')
ax3.set_xlabel(r'$\frac{{R_P\ Freq.}}{{{}\ sims}}$'.format(len(out['Rps'])),weight='bold', multialignment='center')
ax6.set_xlabel(r'$\frac{{R_P\ Freq.}}{{{}\ sims}}$'.format(len(out['Rps'])),weight='bold', multialignment='center')
#Set plot limits
ax1.set_xlim(xlims)
ax1.set_ylim(ylims)
ax2.set_xlim(xlims)
ax3.set_ylim(ylims)
ax4.set_xlim(xlims)
ax4.set_ylim(ylims)
ax5.set_xlim(xlims)
ax6.set_ylim(ylims)
#Remove xticks on x-histogram and remove yticks on y-histogram
ax2.set_xticks([])
ax3.set_yticks([])
ax4.set_yticks([])
ax5.set_xticks([])
ax6.set_yticks([])
# Remove the inner axes numbers of the histograms
nullfmt = NullFormatter()
ax2.xaxis.set_major_formatter(nullfmt)
ax3.yaxis.set_major_formatter(nullfmt)
ax4.yaxis.set_major_formatter(nullfmt)
ax5.xaxis.set_major_formatter(nullfmt)
ax6.yaxis.set_major_formatter(nullfmt)
axCBAR.yaxis.set_major_formatter(nullfmt)
fig2.subplots_adjust(bottom=0.15, top=0.75)
TXT1.text(0.0, 0.15, 'Num.\nUniverse\nPlanets:\n%s'%("{:,}".format(len(x))), weight='bold', horizontalalignment='left', fontsize=6)
TXT4.text(0.0, 0.15, 'Num.\nDetected\nPlanets:\n%s'%("{:,}".format(len(det_Rps))), weight='bold', horizontalalignment='left', fontsize=6)
TXT1.axis('off')
TXT4.axis('off')
TXT1.xaxis.set_visible(False)
TXT1.yaxis.set_visible(False)
TXT4.xaxis.set_visible(False)
TXT4.yaxis.set_visible(False)
# Save to a File
date = unicode(datetime.datetime.now())
date = ''.join(c + '_' for c in re.split('-|:| ',date)[0:-1])#Removes seconds from date
fname = 'RpvsSMAdetections_' + folder.split('/')[-1] + '_' + date
plt.savefig(os.path.join(PPoutpath, fname + '.png'), format='png', dpi=500)
plt.savefig(os.path.join(PPoutpath, fname + '.svg'))
plt.savefig(os.path.join(PPoutpath, fname + '.eps'), format='eps', dpi=500)
plt.savefig(os.path.join(PPoutpath, fname + '.pdf'), format='pdf', dpi=500)
#### Apply Grid to Detected Planet Pop
#create coarse grid and calculate total numbers in each bin
acoarse1 = np.logspace(np.log10(xlims[0]),np.log10(xlims[1]),6)
Rcoarse1 = np.logspace(np.log10(ylims[0]),np.log10(ylims[1]),6)
#Calculate 2d Histogram for input bins
hcoarse1 = np.histogram2d(np.hstack(det_smas), np.hstack(det_Rps),bins=[acoarse1,Rcoarse1],normed=False)[0]
#Plot Vertical and Horizontal Lines
for R in Rcoarse1:
ax4.plot(xlims,[R]*2,'k--')
for a in acoarse1:
ax4.plot([a]*2,ylims,'k--')
accents1 = np.sqrt(acoarse1[:-1]*acoarse1[1:])#SMA centers for text
Rccents1 = np.sqrt(Rcoarse1[:-1]*Rcoarse1[1:])#Rp centers for text
#Plot Text
for i in np.arange(len(Rccents1)):
for j in range(len(accents1)):
tmp1 = ax4.text(accents1[j],Rccents1[i],u'%2.2f'%(hcoarse1[j,i]/len(out['smas'])),horizontalalignment='center',verticalalignment='center',weight='bold', color='white', fontsize=8)
tmp1.set_path_effects([PathEffects.withStroke(linewidth=2, foreground='k')])
#### Apply Grid to Universe Planet Population
acoarse2 = np.logspace(np.log10(xlims[0]),np.log10(xlims[1]),6)
Rcoarse2 = np.logspace(np.log10(ylims[0]),np.log10(ylims[1]),6)
#Calculate 2d Histogram for input bins
hcoarse2 = np.histogram2d(np.hstack(x), np.hstack(y),bins=[acoarse2,Rcoarse2],normed=False)[0]
#Plot Vertical and Horizontal Lines
for R in Rcoarse2:
ax1.loglog(xlims,[R]*2,'k--')
for a in acoarse2:
ax1.loglog([a]*2,ylims,'k--')
accents2 = np.sqrt(acoarse2[:-1]*acoarse2[1:])#SMA centers for text
Rccents2 = np.sqrt(Rcoarse2[:-1]*Rcoarse2[1:])#Rp centers for text
#Plot Text
for i in np.arange(len(Rccents2)):
for j in np.arange(len(accents2)):
tmp2 = ax1.text(accents2[j],Rccents2[i],u'%2.2f'%(hcoarse2[j,i]/len(out['smas'])),horizontalalignment='center',verticalalignment='center',weight='bold', color='white', fontsize=8)
tmp2.set_path_effects([PathEffects.withStroke(linewidth=2, foreground='k')])
# Save to a File
date = unicode(datetime.datetime.now())
date = ''.join(c + '_' for c in re.split('-|:| ',date)[0:-1])#Removes seconds from date
fname = 'RpvsSMAdetectionsGridOverlay_' + folder.split('/')[-1] + '_' + date
plt.savefig(os.path.join(PPoutpath, fname + '.png'), format='png', dpi=500, bbox_inches='tight')
plt.savefig(os.path.join(PPoutpath, fname + '.svg'), bbox_inches='tight')
plt.savefig(os.path.join(PPoutpath, fname + '.eps'), format='eps', dpi=500, bbox_inches='tight')
plt.savefig(os.path.join(PPoutpath, fname + '.pdf'), format='pdf', dpi=500, bbox_inches='tight')
plt.show(block=False)
###### Write Data File On This Plot
lines = []
lines.append("Number of Simulations (Count): " + str(len(out['Rps'])))
# Universe Plot Limits
lines.append('Universe plot xmin (AU): ' + str(xlims[0]) + '\n')
lines.append('Universe plot xmax (AU): ' + str(xlims[1]) + '\n')
lines.append('Universe plot ymin (Re): ' + str(ylims[0]) + '\n')
lines.append('Universe plot ymax (Re): ' + str(ylims[1]) + '\n')
lines.append('Universe plot xmin (AU): ' + str(xlims[0]) + '\n')
lines.append('Universe plot xmax (AU): ' + str(xlims[1]) + '\n')
lines.append('Universe plot ymin (Re): ' + str(ylims[0]) + '\n')
lines.append('Universe plot ymax (Re): ' + str(ylims[1]) + '\n')
lines.append('Universe plot max text grid val (Count/Sim): ' + str(np.amax(hcoarse1)) + '\n')
lines.append('Universe plot Total Detections (Count): ' + str(len(x)) + '\n')
lines.append('Universe plot Total Detections per Sim (Count/Sim): ' + str(len(x)/len(out['Rps'])) + '\n')
lines.append('Universe Grid Data: xmin_grid (AU), xmax_grid (AU), ymin_grid (Re), ymax_grid (Re), grid_value (Count/Sim)\n')
maxBinij = [0,0]
maxBinVal = 0.
for i in np.arange(len(Rccents2)):
for j in np.arange(len(accents2)):
lines.append(", ".join([str(acoarse2[i]),str(acoarse2[i+1]),str(Rcoarse2[j]),str(Rcoarse2[j+1]),str(hcoarse2[j,i]/len(out['smas']))]) + '\n')
if hcoarse2[j,i]/len(out['smas']) > maxBinVal:
maxBinVal = hcoarse2[j,i]/len(out['smas'])
maxBinij = [i,j]
lines.append('Universe plot Maximum Grid Value (Count/Sim): ' + str(maxBinVal))
lines.append('Universe plot Maximum Grid i,j (index, index): ' + str(maxBinij[0]) + ', ' + str(maxBinij[1]))
# Detected Planet Population Limits
lines.append('Detected plot xmin (AU): ' + str(xlims[0]) + '\n')
lines.append('Detected plot xmax (AU): ' + str(xlims[1]) + '\n')
lines.append('Detected plot ymin (Re): ' + str(ylims[0]) + '\n')
lines.append('Detected plot ymax (Re): ' + str(ylims[1]) + '\n')
lines.append('Detected plot xmin (AU): ' + str(xlims[0]) + '\n')
lines.append('Detected plot xmax (AU): ' + str(xlims[1]) + '\n')
lines.append('Detected plot ymin (Re): ' + str(ylims[0]) + '\n')
lines.append('Detected plot ymax (Re): ' + str(ylims[1]) + '\n')
lines.append('Detected plot max text grid val (Count/Sim): ' + str(np.amax(hcoarse2)) + '\n')
lines.append('Detected plot Total Detections (Count): ' + str(len(det_Rps)) + '\n')
lines.append('Detected plot Total Detections per Sim (Count/Sim): ' + str(len(x)/len(out['Rps'])) + '\n')
lines.append('Detected Grid Data: xmin_grid (AU), xmax_grid (AU), ymin_grid (Re), ymax_grid (Re), grid_value (Count/Sim)\n')
maxBinij = [0,0]
maxBinVal = 0.
for i in np.arange(len(Rccents1)):
for j in np.arange(len(accents1)):
lines.append(", ".join([str(acoarse1[i]),str(acoarse1[i+1]),str(Rcoarse1[j]),str(Rcoarse1[j+1]),str(hcoarse1[j,i]/len(out['smas']))]) + '\n')
if hcoarse2[j,i]/len(out['smas']) > maxBinVal:
maxBinVal = hcoarse1[j,i]/len(out['smas'])
maxBinij = [i,j]
lines.append('Detected plot Maximum Grid Value (Count/Sim): ' + str(maxBinVal))
lines.append('Detected plot Maximum Grid i,j (index, index): ' + str(maxBinij[0]) + ', ' + str(maxBinij[1]))
#### Save Data File
fname = 'RpvsSMAdetectionsDATA_' + folder.split('/')[-1] + '_' + date
with open(os.path.join(PPoutpath, fname + '.txt'), 'w') as g:
g.write("\n".join(lines))
del out
del allres
if not os.path.exists(folder): #Folder must exist
raise ValueError('%s not found' % folder)
if not os.path.exists(PPoutpath): #PPoutpath must exist
raise ValueError('%s not found' % PPoutpath)
outspecfile = os.path.join(folder, 'outspec.json')
if not os.path.exists(outspecfile): #outspec file not found
raise ValueError('%s not found' % outspecfile)
try:
with open(outspecfile, 'rb') as g:
outspec = json.load(g, encoding='latin1')
except:
vprint('Failed to open outspecfile %s' % outspecfile)
pass
#Extract Data from folder containing pkl files
out = gen_summary(folder) #out contains information on the detected planets
#allres = read_all(folder)# contains all drm from all missions in folder
outspec['cachedir'] = '/home/dean/.EXOSIMS/cache'
#Create Mission Object To Extract Some Plotting Limits
sim = EXOSIMS.MissionSim.MissionSim(scriptfile=None, nopar=True, **outspec)
#DELETE? ymax = np.nanmax(sim.PlanetPhysicalModel.ggdat['radii']).to('earthRad').value
SU = sim.SimulatedUniverse
TL = sim.TargetList
#I care about
#SU.r
#SU.dmag
#SU.S
