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Get required modules imported





In [1]:



    


import copy
import numpy as NP
from scipy import stats
from astropy.stats import sigma_clipped_stats, histogram
import matplotlib.pyplot as PLT
import matplotlib.colors as PLTC
import matplotlib.ticker as PLTick
import yaml, argparse, warnings
import progressbar as PGB
import prisim
from prisim import bispectrum_phase as BSP
from IPython.core.debugger import set_trace
%matplotlib inline



    











In [2]:



    


print(prisim.__githash__)



    


















    






d3592b0a9fc91290adb87f61dd9f82cf8dbb5db9

Read YAML file containing input parameters





In [3]:



    


inparmsfile = '/lustre/aoc/users/nthyagar/codes/mine/python/projects/closure/combine_pol_multiday_EQ28_data_RA_1.6_transit_closure_PS_analysis_parms.yaml'
with open(inparmsfile, 'r') as parms_file:
    parms = yaml.safe_load(parms_file)



    











In [4]:



    


print(parms)



    


















    






{'plot': {'2d': {'kbin_max': None, 'kbintype': 'linear', 'action': True, 'kbin_min': None, 'kprll_min_rms': 0.85, 'num_kbins': None}, '2': {'datapool': ['whole'], 'nsigma': 1.0, 'spw': [0, 1], 'action': True, 'statistic': 'median', 'units': 'K', 'errtype': ['ssdiff'], 'sampling': 'resampled'}, '2c_err': {'action': True}, '2c': {'kprll_min': 0.85, 'action': True, 'diagoffsets_b': [[[0], [1], [0]], [[0], [1], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]], [[0], [1], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]], [[0], [-1, 0, 1], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]]], 'diagoffsets_a': [[[0], [0]], [[0], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]], [[0], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]], [[0], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]]], 'incohax_b': [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]], 'incohax_a': [[1, 3], [1, 3], [1, 3], [1, 3]]}}, 'dirStruct': {'infiles_b': ['xCPDPS_collapse_axes_123_EQ28XX_LST_1.6hr.hdf5', 'xCPDPS_collapse_axes_123_EQ28YY_LST_1.6hr.hdf5'], 'infiles_a': ['xCPDPS_collapse_axes_13_EQ28XX_LST_1.6hr.hdf5', 'xCPDPS_collapse_axes_13_EQ28YY_LST_1.6hr.hdf5'], 'datadirs': ['/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX/PS/', '/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/YY/PS/'], 'figdir': 'figures/', 'err_infiles_b': ['xCPDPS_collapse_axes_123_EQ28XX_LST_1.6hr_errinfo.hdf5', 'xCPDPS_collapse_axes_123_EQ28YY_LST_1.6hr_errinfo.hdf5'], 'modelinfo': {'infiles_b': [['xCPDPS_collapse_axes_123_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5', 'xCPDPS_collapse_axes_123_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5'], ['xCPDPS_collapse_axes_123_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5', 'xCPDPS_collapse_axes_123_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5']], 'infiles_a': [['xCPDPS_collapse_axes_13_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5', 'xCPDPS_collapse_axes_13_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5'], ['xCPDPS_collapse_axes_13_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5', 'xCPDPS_collapse_axes_13_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless.hdf5']], 'err_infiles_b': [['xCPDPS_collapse_axes_123_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5', 'xCPDPS_collapse_axes_123_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5'], ['xCPDPS_collapse_axes_123_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5', 'xCPDPS_collapse_axes_123_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5']], 'err_infiles_a': [['xCPDPS_collapse_axes_13_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5', 'xCPDPS_collapse_axes_13_EQ28XX_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5'], ['xCPDPS_collapse_axes_13_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5', 'xCPDPS_collapse_axes_13_EQ28XX_HI_21cmfast_FaintGalaxies_fiducial_GLEAM_all_RA_1.6_FOV_30_ephemeris_HERA61_noiseless_errinfo.hdf5']], 'mdldirs': [['/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/models/bispectrum_phase/PS/', '/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/models/bispectrum_phase/PS/'], ['/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/models/bispectrum_phase/PS/', '/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/models/bispectrum_phase/PS/']], 'model_labels': ['FG', 'FG+HI']}, 'err_infiles_a': ['xCPDPS_collapse_axes_13_EQ28XX_LST_1.6hr_errinfo.hdf5', 'xCPDPS_collapse_axes_13_EQ28YY_LST_1.6hr_errinfo.hdf5'], 'plotfile_pfx': 'EQ28_dualpol_data', 'outdir': '/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/'}}

Parse YAML file and obtain input parameters





In [5]:



    


datadirs = parms['dirStruct']['datadirs']
infiles_a = parms['dirStruct']['infiles_a']
infiles_a_errinfo = parms['dirStruct']['err_infiles_a']
infiles_b = parms['dirStruct']['infiles_b']
infiles_b_errinfo = parms['dirStruct']['err_infiles_b']
model_labels = parms['dirStruct']['modelinfo']['model_labels']
mdldirs = parms['dirStruct']['modelinfo']['mdldirs']
mdl_infiles_a = parms['dirStruct']['modelinfo']['infiles_a']
mdl_infiles_a_errinfo = parms['dirStruct']['modelinfo']['err_infiles_a']
mdl_infiles_b = parms['dirStruct']['modelinfo']['infiles_b']
mdl_infiles_b_errinfo = parms['dirStruct']['modelinfo']['err_infiles_b']
outdir = parms['dirStruct']['outdir']
figdir = outdir + parms['dirStruct']['figdir']
plotfile_pfx = parms['dirStruct']['plotfile_pfx']

Initialize data cross-power spectra





In [6]:



    


xcpdps_a = []
excpdps_a = []
xcpdps_b = []
excpdps_b = []
for fileind,indir in enumerate(datadirs):
    infile_a = indir + infiles_a[fileind]
    infile_a_errinfo = indir + infiles_a_errinfo[fileind]
    infile_b = indir + infiles_b[fileind]
    infile_b_errinfo = indir + infiles_b_errinfo[fileind]

    xcpdps_a += [BSP.read_CPhase_cross_power_spectrum(infile_a)]
    excpdps_a += [BSP.read_CPhase_cross_power_spectrum(infile_a_errinfo)]
    xcpdps_b += [BSP.read_CPhase_cross_power_spectrum(infile_b)]
    excpdps_b += [BSP.read_CPhase_cross_power_spectrum(infile_b_errinfo)]

Incoherently average cross-power spectra of two polarizations

Incoherently average cross-power spectra of two polarizations of data





In [7]:



    


xcpdps_a_avg_pol, excpdps_a_avg_pol = BSP.incoherent_cross_power_spectrum_average(xcpdps_a, excpdps=excpdps_a, diagoffsets=None)
xcpdps_b_avg_pol, excpdps_b_avg_pol = BSP.incoherent_cross_power_spectrum_average(xcpdps_b, excpdps=excpdps_b, diagoffsets=None)

Initialize and average cross-power spectra of models from two polarizations





In [8]:



    


models_xcpdps_a_avg_pol = []
models_excpdps_a_avg_pol = []
models_xcpdps_b_avg_pol = []
models_excpdps_b_avg_pol = []
for mdlind, model in enumerate(model_labels):
    mdl_xcpdps_a = []
    mdl_excpdps_a = []
    mdl_xcpdps_b = []
    mdl_excpdps_b = []
    for fileind,mdldir in enumerate(mdldirs[mdlind]):
        mdl_infile_a = mdldir + mdl_infiles_a[mdlind][fileind]
        mdl_infile_a_errinfo = mdldir + mdl_infiles_a_errinfo[mdlind][fileind]
        mdl_infile_b = mdldir + mdl_infiles_b[mdlind][fileind]
        mdl_infile_b_errinfo = mdldir + mdl_infiles_b_errinfo[mdlind][fileind]

        mdl_xcpdps_a += [BSP.read_CPhase_cross_power_spectrum(mdl_infile_a)]
        mdl_excpdps_a += [BSP.read_CPhase_cross_power_spectrum(mdl_infile_a_errinfo)]
        mdl_xcpdps_b += [BSP.read_CPhase_cross_power_spectrum(mdl_infile_b)]
        mdl_excpdps_b += [BSP.read_CPhase_cross_power_spectrum(mdl_infile_b_errinfo)]
    mdl_xcpdps_a_avg_pol, mdl_excpdps_a_avg_pol = BSP.incoherent_cross_power_spectrum_average(mdl_xcpdps_a, excpdps=mdl_excpdps_a, diagoffsets=None)
    models_xcpdps_a_avg_pol += [mdl_xcpdps_a_avg_pol]
    models_excpdps_a_avg_pol += [mdl_excpdps_a_avg_pol]
    mdl_xcpdps_b_avg_pol, mdl_excpdps_b_avg_pol = BSP.incoherent_cross_power_spectrum_average(mdl_xcpdps_b, excpdps=mdl_excpdps_b, diagoffsets=None)
    models_xcpdps_b_avg_pol += [mdl_xcpdps_b_avg_pol]
    models_excpdps_b_avg_pol += [mdl_excpdps_b_avg_pol]

Gather info on plots to be made





In [9]:



    


plot_info = parms['plot']
plots = [key for key in plot_info if plot_info[key]['action']]
print(plots)



    


















    






['2d', '2', '2c_err', '2c']

Plot 2: Plot closure phase delay power spectra





In [17]:



    


if ('2' in plots) or ('2a' in plots) or ('2b' in plots) or ('2c' in plots) or ('2d' in plots):

    sampling = plot_info['2']['sampling']
    statistic = plot_info['2']['statistic']
    datapool = plot_info['2']['datapool']
    pspec_unit_type = plot_info['2']['units']

    if pspec_unit_type == 'K':
        pspec_unit = 'mK2 Mpc3'
    else:
        pspec_unit = 'Jy2 Mpc'

    spw = plot_info['2']['spw']
    if spw is None:
        spwind = NP.arange(xcpdps2_a[sampling]['z'].size)
    else:
        spwind = NP.asarray(spw)

    if statistic is None:
        statistic = ['mean', 'median']
    else:
        statistic = [statistic]
        
    ps_errtype = plot_info['2']['errtype']
    errshade = {}
    for errtype in ps_errtype:
        if errtype.lower() == 'ssdiff':
            errshade[errtype] = '0.8'
        elif errtype.lower() == 'psdiff':
            errshade[errtype] = '0.6'

    nsigma = plot_info['2']['nsigma']

    mdl_colrs = ['red', 'green', 'blue', 'cyan', 'gray', 'orange']
    mdl_mrkrs = ['o', '+']

Plot 2c: Plot PS after incoherent averaging over redundant triads, LST diff range, and/or other polarization.





In [11]:



    


if ('2c' in plots) or ('2d' in plots):
        kprll_min_for_rms = plot_info['2c']['kprll_min']
        if kprll_min_for_rms is None:
            kprll_min_for_rms = 0.0
        avg_incohax_a = plot_info['2c']['incohax_a']
        diagoffsets_incohax_a = plot_info['2c']['diagoffsets_a']
        diagoffsets_a = []
        avg_incohax_b = plot_info['2c']['incohax_b']
        diagoffsets_incohax_b = plot_info['2c']['diagoffsets_b']
        diagoffsets_b = []
        for combi,incax_comb in enumerate(avg_incohax_a):
            diagoffsets_a += [{}]
            for incaxind,incax in enumerate(incax_comb):
                diagoffsets_a[-1][incax] = NP.asarray(diagoffsets_incohax_a[combi][incaxind])
        xcpdps_a_avg_pol_diag, excpdps_a_avg_pol_diag = BSP.incoherent_cross_power_spectrum_average(xcpdps_a_avg_pol, excpdps=excpdps_a_avg_pol, diagoffsets=diagoffsets_a)

        models_xcpdps_a_avg_pol_diag = []
        models_excpdps_a_avg_pol_diag = []

        for combi,incax_comb in enumerate(avg_incohax_b):
            diagoffsets_b += [{}]
            for incaxind,incax in enumerate(incax_comb):
                diagoffsets_b[-1][incax] = NP.asarray(diagoffsets_incohax_b[combi][incaxind])

        xcpdps_b_avg_pol_diag, excpdps_b_avg_pol_diag = BSP.incoherent_cross_power_spectrum_average(xcpdps_b_avg_pol, excpdps=excpdps_b_avg_pol, diagoffsets=diagoffsets_b)
        models_xcpdps_b_avg_pol_diag = []
        models_excpdps_b_avg_pol_diag = []

        if len(model_labels) > 0:
            progress = PGB.ProgressBar(widgets=[PGB.Percentage(), PGB.Bar(marker='-', left=' |', right='| '), PGB.Counter(), '/{0:0d} Models '.format(len(model_labels)), PGB.ETA()], maxval=len(model_labels)).start()

            for i in range(len(model_labels)):
                model_xcpdps_a_avg_pol_diag, model_excpdps_a_avg_pol_diag = BSP.incoherent_cross_power_spectrum_average(models_xcpdps_a_avg_pol[i], excpdps=models_excpdps_a_avg_pol[i], diagoffsets=diagoffsets_a)
                models_xcpdps_a_avg_pol_diag += [copy.deepcopy(model_xcpdps_a_avg_pol_diag)]
                models_excpdps_a_avg_pol_diag += [copy.deepcopy(model_excpdps_a_avg_pol_diag)]

                model_xcpdps_b_avg_pol_diag, model_excpdps_b_avg_pol_diag = BSP.incoherent_cross_power_spectrum_average(models_xcpdps_b_avg_pol[i], excpdps=models_excpdps_b_avg_pol[i], diagoffsets=diagoffsets_b)
                models_xcpdps_b_avg_pol_diag += [copy.deepcopy(model_xcpdps_b_avg_pol_diag)]
                models_excpdps_b_avg_pol_diag += [copy.deepcopy(model_excpdps_b_avg_pol_diag)]

                progress.update(i+1)
            progress.finish()



    


















    






100% |-----------------------------------------------| 2/2 Models Time: 0:00:07

















In [12]:



    


print(excpdps_a_avg_pol_diag['resampled']['errinfo']['median'][2].shape)
        print(excpdps_b_avg_pol_diag['resampled']['errinfo']['median'][2].shape)
#         print('==========================')
#         print(excpdps_a_avg_pol_diag['resampled']['errinfo']['median'][2])
#         print('--------------------------')
#         print(excpdps_b_avg_pol_diag['resampled']['errinfo']['median'][2])



    


















    






(2, 1, 3, 1, 103)
(2, 1, 3, 1, 103)

















In [13]:



    


if '2c' in plots:

#             ylim = {0: [-1e4, 1e9], 1: [-1e4, 1e8]} # keys are spw indices            
#             linthreshy = 1e0
            ylim = {0: [-1e10, 1e16], 1: [-1e10, 1e14]} # keys are spw indices
            linthreshy = 9e5

            lstind = [0]
            triadind = [0]
            dayind = [0]
            dayind_models = NP.zeros(len(model_labels), dtype=int).reshape(1,-1)

            for stat in statistic:
                for zind in spwind:
                    kprll_ind_for_rms = NP.where(NP.abs(excpdps_b_avg_pol_diag[sampling]['kprll'][zind,:]) >= kprll_min_for_rms)[0]
                    kprll_for_rms = excpdps_b_avg_pol_diag[sampling]['kprll'][zind,kprll_ind_for_rms]
                    for lind in lstind:
                        for di,dind in enumerate(dayind):
                            for combi in range(len(diagoffsets_b)):
                                maxabsvals = []
                                minabsvals = []
                                maxvals = []
                                minvals = []

                                fig, axs = PLT.subplots(nrows=1, ncols=len(datapool), sharex=True, sharey=True, figsize=(4.0*len(datapool), 3.6))
                                if len(datapool) == 1:
                                    axs = [axs]
                                for dpoolind,dpool in enumerate(datapool):
                                    for trno,trind in enumerate(triadind):
#                                         if len(model_labels) > 0:
#                                             for mdlind, mdl in enumerate(model_labels):
#                                                 if dpool in models_xcpdps_b_avg_pol_diag[mdlind][sampling]:
#                                                     psval = (2/3.0) * models_xcpdps_b_avg_pol_diag[mdlind][sampling][dpool][stat][combi][zind,lind,dayind_models[di][mdlind],trind,:].to(pspec_unit).value
#                                                     maxabsvals += [NP.abs(psval.real).max()]
#                                                     minabsvals += [NP.abs(psval.real).min()]
#                                                     maxvals += [psval.real.max()]
#                                                     minvals += [psval.real.min()]
#                                                     axs[dpoolind].plot(models_xcpdps_b_avg_pol_diag[mdlind][sampling]['kprll'][zind,:], psval.real, ls='none', marker='.', ms=3, color=mdl_colrs[mdlind], label='{0}'.format(mdl))

                                        if dpool in xcpdps_b_avg_pol_diag[sampling]:
                                            psval = (2/3.0) * xcpdps_b_avg_pol_diag[sampling][dpool][stat][combi][zind,lind,dind,trind,:].to(pspec_unit).value
                                            ps_ssdiff = (2/3.0) * excpdps_b_avg_pol_diag[sampling]['errinfo'][stat][combi][zind,lind,:,trind,kprll_ind_for_rms].to(pspec_unit).value # Single RMS across all k_prll bins
                                            psrms_ssdiff = (2/3.0) * NP.nanstd(excpdps_b_avg_pol_diag[sampling]['errinfo'][stat][combi][zind,lind,:,trind,:], axis=0).to(pspec_unit).value # RMS per k_prll bin
                                            if 2 in avg_incohax_b[combi]:
                                                ind_dayax_in_incohax = avg_incohax_b[combi].index(2)
                                                if 0 in diagoffsets_incohax_b[combi][ind_dayax_in_incohax]:
                                                    rms_inflation_factor = 2.0 * NP.sqrt(2.0)
                                                else:
                                                    rms_inflation_factor = NP.sqrt(2.0)
                                            else:
                                                rms_inflation_factor = NP.sqrt(2.0)
                                            psrms_psdiff = (2/3.0) * (xcpdps_a_avg_pol_diag[sampling][dpool][stat][combi][zind,lind,1,1,trind,:] - xcpdps_a_avg_pol_diag[sampling][dpool][stat][combi][zind,lind,0,0,trind,:]).to(pspec_unit).value
                                            psrms_psdiff = NP.abs(psrms_psdiff.real) / rms_inflation_factor

                                            psrms_max = NP.amax(NP.vstack((psrms_ssdiff, psrms_psdiff)), axis=0)

                                            maxabsvals += [NP.abs(psval.real + nsigma*psrms_max).max()]
                                            minabsvals += [NP.abs(psval.real).min()]
                                            maxvals += [(psval.real + nsigma*psrms_max).max()]
                                            minvals += [(psval.real - nsigma*psrms_max).min()]

                                            for errtype in ps_errtype:
                                                if errtype.lower() == 'ssdiff':
                                                    axs[dpoolind].errorbar(xcpdps_b_avg_pol_diag[sampling]['kprll'][zind,:], psval.real, yerr=nsigma*psrms_ssdiff, xerr=None, ecolor=errshade[errtype.lower()], ls='none', marker='.', ms=4, color='black')
                                                    psmean_ssdiff_across_kbins, psmedian_ssdiff_across_kbins, psrms_ssdiff_across_kbins = sigma_clipped_stats(NP.vstack((ps_ssdiff.real, ps_ssdiff.imag)))
                                                    pos_kprll_ind_for_rms = NP.where(kprll_for_rms >= 0.0)[0]
                                                    neg_kprll_ind_for_rms = NP.where(kprll_for_rms <= 0.0)[0]
                                                    axs[dpoolind].hlines([-psrms_ssdiff_across_kbins, psrms_ssdiff_across_kbins], kprll_for_rms[pos_kprll_ind_for_rms].min(), kprll_for_rms[pos_kprll_ind_for_rms].max(), linestyles='--', lw=1.5, color='0.25')
                                                    axs[dpoolind].hlines([-psrms_ssdiff_across_kbins, psrms_ssdiff_across_kbins], kprll_for_rms[neg_kprll_ind_for_rms].min(), kprll_for_rms[neg_kprll_ind_for_rms].max(), linestyles='--', lw=1.5, color='0.25')
                                                elif errtype.lower() == 'psdiff':
                                                    axs[dpoolind].errorbar(xcpdps_b_avg_pol_diag[sampling]['kprll'][zind,:], psval.real, yerr=nsigma*psrms_psdiff, xerr=None, ecolor=errshade[errtype.lower()], ls='none', marker='.', ms=4, color='black', label='FG+N')

#                                         legend = axs[dpoolind].legend(loc='center', bbox_to_anchor=(0.5,0.3), shadow=False, fontsize=8)
                                        if trno == 0:
                                            axs[dpoolind].text(0.95, 0.97, r'$z=$'+' {0:.1f}'.format(xcpdps_b_avg_pol_diag[sampling]['z'][zind]), transform=axs[dpoolind].transAxes, fontsize=8, weight='medium', ha='right', va='top', color='black')

#                                         axt = axs[dpoolind].twiny()
#                                         axt.set_xlim(1e6*xcpdps_b_avg_pol_diag[sampling]['lags'].min(), 1e6*xcpdps_b_avg_pol_diag[sampling]['lags'].max())

                                    axs[dpoolind].axhline(y=0, xmin=0, xmax=1, ls='-', lw=1, color='black')

                                    minvals = NP.asarray(minvals)
                                    maxvals = NP.asarray(maxvals)
                                    minabsvals = NP.asarray(minabsvals)
                                    maxabsvals = NP.asarray(maxabsvals)
#                                     axs[dpoolind].set_xlim(0.99*xcpdps_b_avg_pol_diag[sampling]['kprll'][zind,:].min(), 1.01*xcpdps_b_avg_pol_diag[sampling]['kprll'][zind,:].max())
#                                     axs[dpoolind].set_xlim(-2.5, 2.5)
#                                     if NP.min(minvals) < 0.0:
#                                         axs[dpoolind].set_ylim(1.5*NP.min(minvals), 2*NP.max(maxabsvals))
#                                     else:
#                                         axs[dpoolind].set_ylim(0.5*NP.min(minvals), 2*NP.max(maxabsvals))
                                    axs[dpoolind].set_ylim(ylim[zind])                                    
                                    
#                                     axs[dpoolind].set_yscale('symlog', linthreshy=10**NP.floor(NP.log10(NP.min(minabsvals[minabsvals > 0.0]))))
#                                     tickloc = PLTick.SymmetricalLogLocator(linthresh=10**NP.floor(NP.log10(NP.min(minabsvals[minabsvals > 0.0]))), base=100.0)
                                    axs[dpoolind].set_yscale('symlog', linthreshy=linthreshy)
                                    tickloc = PLTick.SymmetricalLogLocator(linthresh=linthreshy, base=100.0)

                                    axs[dpoolind].yaxis.set_major_locator(tickloc)
                                    yticklocs = NP.asarray(axs[dpoolind].get_yticks())
                                    ytickspacings = NP.diff(yticklocs)
                                    tickinds_to_remove = NP.setdiff1d(NP.where(NP.abs(yticklocs)<=linthreshy)[0], NP.argmin(NP.abs(yticklocs)))
                                    if tickinds_to_remove.size > 0:
                                        new_yticklocs = yticklocs[NP.setdiff1d(NP.arange(yticklocs.size), tickinds_to_remove)]
                                        axs[dpoolind].yaxis.set_major_locator(PLTick.FixedLocator((new_yticklocs.tolist())))
                                    axs[dpoolind].grid(color='0.8', which='both', linestyle=':', lw=1)

                                fig.subplots_adjust(top=0.85)
                                fig.subplots_adjust(bottom=0.16)
                                fig.subplots_adjust(left=0.22)
                                fig.subplots_adjust(right=0.98)

                                big_ax = fig.add_subplot(111)
                                big_ax.set_facecolor('none') # matplotlib.__version__ >= 2.0.0
                                # big_ax.set_axis_bgcolor('none') # matplotlib.__version__ < 2.0.0
                                big_ax.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
                                big_ax.set_xticks([])
                                big_ax.set_yticks([])
                                big_ax.set_xlabel(r'$\kappa_\parallel$'+' [pseudo '+r'$h$'+' Mpc'+r'$^{-1}$'+']', fontsize=12, weight='medium', labelpad=20)
                                if pspec_unit_type == 'K':
                                    big_ax.set_ylabel(r'$\frac{2}{3}\, P_\nabla(\kappa_\parallel)$ [pseudo mK$^2h^{-3}$ Mpc$^3$]', fontsize=12, weight='medium', labelpad=40)
                                else:
                                    big_ax.set_ylabel(r'$\frac{2}{3}\, P_\nabla(\kappa_\parallel)$ [pseudo Jy$^2h^{-1}$ Mpc]', fontsize=12, weight='medium', labelpad=40)

                                big_axt = big_ax.twiny()
                                big_axt.set_xticks([])
#                                 big_axt.set_xlabel(r'$\tau$'+' ['+r'$\mu$'+'s]', fontsize=12, weight='medium', labelpad=20)

                                PLT.savefig(figdir + '{0}_symlog_incoh_avg_real_cpdps_z_{1:.1f}_{2}_{3}_dlst_{4:.1f}s_comb_{5:0d}.pdf'.format(plotfile_pfx, xcpdps_b_avg_pol_diag[sampling]['z'][zind], stat, sampling, 3.6e3*xcpdps_b_avg_pol_diag['dlst'][0], combi), bbox_inches=0)

Plot 2c_err: PS Error analysis after incoherent averaging over redundant triads, LST diff range





In [35]:



    


print(len(excpdps_b))
        print(excpdps_b[0]['resampled']['errinfo']['median'].shape)
        print(NP.hstack((xcpdps_b_avg_pol_diag[sampling]['lags'].reshape(-1,1), xcpdps_b_avg_pol_diag[sampling]['kprll'][zind,:].reshape(-1,1))))
        print(xcpdps_b_avg_pol_diag[sampling]['kprll'][zind,:])



    


















    






2
(2, 2, 3, 61, 103)
[[-5.12000000e-06 -2.87254795e+00]
 [-5.02000000e-06 -2.81644350e+00]
 [-4.92000000e-06 -2.76033905e+00]
 [-4.82000000e-06 -2.70423459e+00]
 [-4.72000000e-06 -2.64813014e+00]
 [-4.62000000e-06 -2.59202569e+00]
 [-4.52000000e-06 -2.53592124e+00]
 [-4.42000000e-06 -2.47981678e+00]
 [-4.32000000e-06 -2.42371233e+00]
 [-4.22000000e-06 -2.36760788e+00]
 [-4.12000000e-06 -2.31150343e+00]
 [-4.02000000e-06 -2.25539898e+00]
 [-3.92000000e-06 -2.19929452e+00]
 [-3.82000000e-06 -2.14319007e+00]
 [-3.72000000e-06 -2.08708562e+00]
 [-3.62000000e-06 -2.03098117e+00]
 [-3.52000000e-06 -1.97487672e+00]
 [-3.42000000e-06 -1.91877226e+00]
 [-3.32000000e-06 -1.86266781e+00]
 [-3.22000000e-06 -1.80656336e+00]
 [-3.12000000e-06 -1.75045891e+00]
 [-3.02000000e-06 -1.69435445e+00]
 [-2.92000000e-06 -1.63825000e+00]
 [-2.82000000e-06 -1.58214555e+00]
 [-2.72000000e-06 -1.52604110e+00]
 [-2.62000000e-06 -1.46993665e+00]
 [-2.52000000e-06 -1.41383219e+00]
 [-2.42000000e-06 -1.35772774e+00]
 [-2.32000000e-06 -1.30162329e+00]
 [-2.22000000e-06 -1.24551884e+00]
 [-2.12000000e-06 -1.18941439e+00]
 [-2.02000000e-06 -1.13330993e+00]
 [-1.92000000e-06 -1.07720548e+00]
 [-1.82000000e-06 -1.02110103e+00]
 [-1.72000000e-06 -9.64996577e-01]
 [-1.62000000e-06 -9.08892125e-01]
 [-1.52000000e-06 -8.52787673e-01]
 [-1.42000000e-06 -7.96683220e-01]
 [-1.32000000e-06 -7.40578768e-01]
 [-1.22000000e-06 -6.84474316e-01]
 [-1.12000000e-06 -6.28369864e-01]
 [-1.02000000e-06 -5.72265412e-01]
 [-9.20000000e-07 -5.16160960e-01]
 [-8.20000000e-07 -4.60056508e-01]
 [-7.20000000e-07 -4.03952055e-01]
 [-6.20000000e-07 -3.47847603e-01]
 [-5.20000000e-07 -2.91743151e-01]
 [-4.20000000e-07 -2.35638699e-01]
 [-3.20000000e-07 -1.79534247e-01]
 [-2.20000000e-07 -1.23429795e-01]
 [-1.20000000e-07 -6.73253426e-02]
 [-2.00000000e-08 -1.12208904e-02]
 [ 8.00000000e-08  4.48835617e-02]
 [ 1.80000000e-07  1.00988014e-01]
 [ 2.80000000e-07  1.57092466e-01]
 [ 3.80000000e-07  2.13196918e-01]
 [ 4.80000000e-07  2.69301370e-01]
 [ 5.80000000e-07  3.25405822e-01]
 [ 6.80000000e-07  3.81510275e-01]
 [ 7.80000000e-07  4.37614727e-01]
 [ 8.80000000e-07  4.93719179e-01]
 [ 9.80000000e-07  5.49823631e-01]
 [ 1.08000000e-06  6.05928083e-01]
 [ 1.18000000e-06  6.62032535e-01]
 [ 1.28000000e-06  7.18136987e-01]
 [ 1.38000000e-06  7.74241440e-01]
 [ 1.48000000e-06  8.30345892e-01]
 [ 1.58000000e-06  8.86450344e-01]
 [ 1.68000000e-06  9.42554796e-01]
 [ 1.78000000e-06  9.98659248e-01]
 [ 1.88000000e-06  1.05476370e+00]
 [ 1.98000000e-06  1.11086815e+00]
 [ 2.08000000e-06  1.16697260e+00]
 [ 2.18000000e-06  1.22307706e+00]
 [ 2.28000000e-06  1.27918151e+00]
 [ 2.38000000e-06  1.33528596e+00]
 [ 2.48000000e-06  1.39139041e+00]
 [ 2.58000000e-06  1.44749487e+00]
 [ 2.68000000e-06  1.50359932e+00]
 [ 2.78000000e-06  1.55970377e+00]
 [ 2.88000000e-06  1.61580822e+00]
 [ 2.98000000e-06  1.67191267e+00]
 [ 3.08000000e-06  1.72801713e+00]
 [ 3.18000000e-06  1.78412158e+00]
 [ 3.28000000e-06  1.84022603e+00]
 [ 3.38000000e-06  1.89633048e+00]
 [ 3.48000000e-06  1.95243493e+00]
 [ 3.58000000e-06  2.00853939e+00]
 [ 3.68000000e-06  2.06464384e+00]
 [ 3.78000000e-06  2.12074829e+00]
 [ 3.88000000e-06  2.17685274e+00]
 [ 3.98000000e-06  2.23295720e+00]
 [ 4.08000000e-06  2.28906165e+00]
 [ 4.18000000e-06  2.34516610e+00]
 [ 4.28000000e-06  2.40127055e+00]
 [ 4.38000000e-06  2.45737500e+00]
 [ 4.48000000e-06  2.51347946e+00]
 [ 4.58000000e-06  2.56958391e+00]
 [ 4.68000000e-06  2.62568836e+00]
 [ 4.78000000e-06  2.68179281e+00]
 [ 4.88000000e-06  2.73789726e+00]
 [ 4.98000000e-06  2.79400172e+00]
 [ 5.08000000e-06  2.85010617e+00]]
[-2.87254795 -2.8164435  -2.76033905 -2.70423459 -2.64813014 -2.59202569
 -2.53592124 -2.47981678 -2.42371233 -2.36760788 -2.31150343 -2.25539898
 -2.19929452 -2.14319007 -2.08708562 -2.03098117 -1.97487672 -1.91877226
 -1.86266781 -1.80656336 -1.75045891 -1.69435445 -1.63825    -1.58214555
 -1.5260411  -1.46993665 -1.41383219 -1.35772774 -1.30162329 -1.24551884
 -1.18941439 -1.13330993 -1.07720548 -1.02110103 -0.96499658 -0.90889212
 -0.85278767 -0.79668322 -0.74057877 -0.68447432 -0.62836986 -0.57226541
 -0.51616096 -0.46005651 -0.40395206 -0.3478476  -0.29174315 -0.2356387
 -0.17953425 -0.12342979 -0.06732534 -0.01122089  0.04488356  0.10098801
  0.15709247  0.21319692  0.26930137  0.32540582  0.38151027  0.43761473
  0.49371918  0.54982363  0.60592808  0.66203254  0.71813699  0.77424144
  0.83034589  0.88645034  0.9425548   0.99865925  1.0547637   1.11086815
  1.1669726   1.22307706  1.27918151  1.33528596  1.39139041  1.44749487
  1.50359932  1.55970377  1.61580822  1.67191267  1.72801713  1.78412158
  1.84022603  1.89633048  1.95243493  2.00853939  2.06464384  2.12074829
  2.17685274  2.2329572   2.28906165  2.3451661   2.40127055  2.457375
  2.51347946  2.56958391  2.62568836  2.68179281  2.73789726  2.79400172
  2.85010617]

















In [15]:



    


if '2c_err' in plots:
            lstind = [0]
            triadind = [0]
            dayind = [0]
            
            for stat in statistic:
                print(stat)
                print('=======================================')
                for zind in spwind:
                    kprll_ind_current_for_rms = NP.where(NP.abs(excpdps_b_avg_pol_diag[sampling]['kprll'][zind,:]) >= kprll_min_for_rms)[0]
                    kprll_current_for_rms = excpdps_b_avg_pol_diag[sampling]['kprll'][zind,kprll_ind_current_for_rms]
#                     kprll_ind_other0_for_rms = NP.where(NP.abs(excpdps_b[0][sampling]['kprll'][zind,:]) >= kprll_min_for_rms)[0]
#                     kprll_other0_for_rms = excpdps_b[0][sampling]['kprll'][zind,kprll_ind_other0_for_rms]
#                     kprll_ind_other1_for_rms = NP.where(NP.abs(excpdps_b[1][sampling]['kprll'][zind,:]) >= kprll_min_for_rms)[0]
#                     kprll_other1_for_rms = excpdps_b[1][sampling]['kprll'][zind,kprll_ind_other1_for_rms]
                    for lind in lstind:
                        for dpoolind,dpool in enumerate(datapool):
                            for di,dind in enumerate(dayind):
                                for trno,trind in enumerate(triadind):
                                    for combi in range(len(diagoffsets_b)):
                                        ps_ssdiff_current = (2/3.0) * excpdps_b_avg_pol_diag[sampling]['errinfo'][stat][combi][zind,lind,:,trind,kprll_ind_current_for_rms].to(pspec_unit).value
#                                         ps_ssdiff_other0 = (2/3.0) * excpdps_b[0][sampling]['errinfo'][stat][combi][zind,lind,:,trind,kprll_ind_other0_for_rms].to(pspec_unit).value
#                                         ps_ssdiff_other1 = (2/3.0) * excpdps_b[1][sampling]['errinfo'][stat][combi][zind,lind,:,trind,kprll_ind_other1_for_rms].to(pspec_unit).value
                                        hist_current_real, hist_current_real_bins = histogram(ps_ssdiff_current.real.ravel(), bins='knuth', density=False)
#                                         hist_other0_real, hist_other0_real_bins = histogram(ps_ssdiff_other0.real.ravel(), bins='knuth', density=False)
#                                         hist_other1_real, hist_other1_real_bins = histogram(ps_ssdiff_other1.real.ravel(), bins='knuth', density=False)
                                        hist_current_imag, hist_current_imag_bins = histogram(ps_ssdiff_current.imag.ravel(), bins='knuth', density=False)
#                                         hist_other0_imag, hist_other0_imag_bins = histogram(ps_ssdiff_other0.imag.ravel(), bins='knuth', density=False)
#                                         hist_other1_imag, hist_other1_imag_bins = histogram(ps_ssdiff_other1.imag.ravel(), bins='knuth', density=False)
                                        hist_current, hist_current_bins = histogram(NP.vstack((ps_ssdiff_current.real,ps_ssdiff_current.imag)).ravel(), bins='knuth', density=False)
#                                         hist_other0, hist_other0_bins = histogram(NP.vstack((ps_ssdiff_other0.real,ps_ssdiff_other0.imag)).ravel(), bins='knuth', density=False)
#                                         hist_other1, hist_other1_bins = histogram(NP.vstack((ps_ssdiff_other1.real,ps_ssdiff_other1.imag)).ravel(), bins='knuth', density=False)

                                        current_real_mean, current_real_median, current_real_std = sigma_clipped_stats(ps_ssdiff_current.real) 
                                        current_imag_mean, current_imag_median, current_imag_std = sigma_clipped_stats(ps_ssdiff_current.imag)
                                        current_mean, current_median, current_std = sigma_clipped_stats(NP.vstack((ps_ssdiff_current.real, ps_ssdiff_current.imag)))
#                                         other0_real_mean, other0_real_median, other0_real_std = sigma_clipped_stats(ps_ssdiff_other0.real) 
#                                         other0_imag_mean, other0_imag_median, other0_imag_std = sigma_clipped_stats(ps_ssdiff_other0.imag)
#                                         other0_mean, other0_median, other0_std = sigma_clipped_stats(NP.vstack((ps_ssdiff_other0.real, ps_ssdiff_other0.imag)))
#                                         other1_real_mean, other1_real_median, other1_real_std = sigma_clipped_stats(ps_ssdiff_other1.real) 
#                                         other1_imag_mean, other1_imag_median, other1_imag_std = sigma_clipped_stats(ps_ssdiff_other1.imag)
#                                         other1_mean, other1_median, other1_std = sigma_clipped_stats(NP.vstack((ps_ssdiff_other1.real, ps_ssdiff_other1.imag)))

#                                         real_kval, real_pval = stats.ks_2samp(ps_ssdiff_current.real.ravel(), ps_ssdiff_other.real.ravel())
#                                         imag_kval, imag_pval = stats.ks_2samp(ps_ssdiff_current.imag.ravel(), ps_ssdiff_other.imag.ravel())
#                                         kval, pval = stats.ks_2samp(NP.vstack((ps_ssdiff_other.real, ps_ssdiff_other.imag)).ravel(), NP.vstack((ps_ssdiff_current.real, ps_ssdiff_current.imag)).ravel())

                                        print('===================')
                                        print('SpW: {0}, Diagcomb: {1}'.format(zind, combi))
                                        print('-------------------')
                                        print('Current (real): Mean = {0},\t Median = {1},\t RMS = {2}'.format(current_real_mean, current_real_median, current_real_std))
#                                         print('Other0 (real): Mean = {0},\t Median = {1},\t RMS = {2}'.format(other0_real_mean, other0_real_median, other0_real_std))
#                                         print('Other1 (real): Mean = {0},\t Median = {1},\t RMS = {2}'.format(other1_real_mean, other1_real_median, other1_real_std))
#                                         print('K-S (real): k-val = {0},\t p-val = {1}'.format(real_kval, real_pval))

                                        print('\nCurrent (imag): Mean = {0},\t Median = {1},\t RMS = {2}'.format(current_imag_mean, current_imag_median, current_imag_std))
#                                         print('Other0 (imag): Mean = {0},\t Median = {1},\t RMS = {2}'.format(other0_imag_mean, other0_imag_median, other0_imag_std))
#                                         print('Other1 (imag): Mean = {0},\t Median = {1},\t RMS = {2}'.format(other1_imag_mean, other1_imag_median, other1_imag_std))
#                                         print('K-S (imag): k-val = {0},\t p-val = {1}'.format(imag_kval, imag_pval))

                                        print('\nCurrent: Mean = {0},\t Median = {1},\t RMS = {2}'.format(current_mean, current_median, current_std))
#                                         print('Other0: Mean = {0},\t Median = {1},\t RMS = {2}'.format(other0_mean, other0_median, other0_std))
#                                         print('Other1: Mean = {0},\t Median = {1},\t RMS = {2}'.format(other1_mean, other1_median, other1_std))
#                                         print('K-S: k-val = {0},\t p-val = {1}'.format(kval, pval))

#                                         fig, axs = PLT.subplots(nrows=3, sharex=True, sharey=True, figsize=(4.0, 4.5))
#                                         hist_current_real, hist_current_real_bins, patches = axs[0].hist(ps_ssdiff_current.real.ravel(), bins=hist_current_real_bins, density=False, histtype='step', ls='-', color='black', lw=2, label='{0} (real)'.format(current_label))
#                                         hist_other_real, hist_other_real_bins, patches = axs[0].hist(ps_ssdiff_other.real.ravel(), bins=hist_other_real_bins, density=False, histtype='step', ls='-', color='gray', lw=2, label='{0} (real)'.format(other_label))
#                                         hist_current_imag, hist_current_imag_bins, patches = axs[1].hist(ps_ssdiff_current.imag.ravel(), bins=hist_current_imag_bins, density=False, histtype='step', ls='-', color='black', lw=2, label='{0} (imag)'.format(current_label))
#                                         hist_other_imag, hist_other_imag_bins, patches = axs[1].hist(ps_ssdiff_other.imag.ravel(), bins=hist_other_imag_bins, density=False, histtype='step', ls='-', color='gray', lw=2, label='{0} (imag)'.format(other_label))
#                                         hist_current, hist_current_bins, patches = axs[2].hist(NP.vstack((ps_ssdiff_current.real,ps_ssdiff_current.imag)).ravel(), bins=hist_current_bins, density=False, histtype='step', ls='-', color='black', lw=2, label='{0}'.format(current_label))
#                                         hist_other, hist_other_bins, patches = axs[2].hist(NP.vstack((ps_ssdiff_other.real,ps_ssdiff_other.imag)).ravel(), bins=hist_current_bins, density=False, histtype='step', ls='-', color='gray', lw=2, label='{0}'.format(other_label))
                                        
#                                         axs[0].legend(loc='upper right')
#                                         axs[1].legend(loc='upper right')
#                                         axs[2].legend(loc='upper right')
#                                         axs[2].set_xlim(-5*other_std, 5*other_std)
                     
#                                         fig.subplots_adjust(hspace=0, wspace=0)



    


















    






median
=======================================
===================
SpW: 0, Diagcomb: 0
-------------------
Current (real): Mean = -1518178091.43,	 Median = -2014600620.45,	 RMS = 4561390886.12

Current (imag): Mean = 106884080.395,	 Median = 64712829.9024,	 RMS = 1808517073.64

Current: Mean = -828806775.812,	 Median = -1115032234.55,	 RMS = 2405509441.43
===================
SpW: 0, Diagcomb: 1
-------------------
Current (real): Mean = -1633421838.71,	 Median = -2062364468.79,	 RMS = 4484633547.19

Current (imag): Mean = 73651423.0512,	 Median = 138983611.694,	 RMS = 754978383.845

Current: Mean = -680320222.906,	 Median = -494389696.381,	 RMS = 1532426608.65
===================
SpW: 0, Diagcomb: 2
-------------------
Current (real): Mean = -1637088349.03,	 Median = -2059613054.46,	 RMS = 4485827081.84

Current (imag): Mean = 72033343.0204,	 Median = 143494348.678,	 RMS = 772854868.053

Current: Mean = -680560714.512,	 Median = -503330810.063,	 RMS = 1582281096.42
===================
SpW: 0, Diagcomb: 3
-------------------
Current (real): Mean = -1637088349.03,	 Median = -2059613054.46,	 RMS = 4485827081.84

Current (imag): Mean = 72033343.0204,	 Median = 143494348.678,	 RMS = 772854868.053

Current: Mean = -680560714.512,	 Median = -503330810.063,	 RMS = 1582281096.42
===================
SpW: 1, Diagcomb: 0
-------------------
Current (real): Mean = 30878387.7033,	 Median = 27114324.0916,	 RMS = 75503331.5219

Current (imag): Mean = 17769742.8257,	 Median = -1223560.00554,	 RMS = 342551463.937

Current: Mean = 28734067.7116,	 Median = 25964947.0393,	 RMS = 211405114.32
===================
SpW: 1, Diagcomb: 1
-------------------
Current (real): Mean = 4782978.20595,	 Median = 4440309.35637,	 RMS = 21082306.684

Current (imag): Mean = 1125622.92494,	 Median = -1827729.90522,	 RMS = 70266965.0194

Current: Mean = 4113016.0018,	 Median = 2946572.2136,	 RMS = 50444117.2568
===================
SpW: 1, Diagcomb: 2
-------------------
Current (real): Mean = 2792641.64086,	 Median = 2838172.96423,	 RMS = 22256673.7889

Current (imag): Mean = -244065.67212,	 Median = -568640.155783,	 RMS = 73014224.1848

Current: Mean = 696110.691131,	 Median = 1973070.86316,	 RMS = 51060597.0014
===================
SpW: 1, Diagcomb: 3
-------------------
Current (real): Mean = 2792641.64086,	 Median = 2838172.96423,	 RMS = 22256673.7889

Current (imag): Mean = -244065.67212,	 Median = -568640.155783,	 RMS = 73014224.1848

Current: Mean = 696110.691131,	 Median = 1973070.86316,	 RMS = 51060597.0014

Plot 2d: Plot PS after incoherent averaging over k-bins, in standard PS and Delta^2 units





In [16]:



    


if '2d' in plots:
            kbin_min = plot_info['2d']['kbin_min']
            kbin_max = plot_info['2d']['kbin_max']
            num_kbins = plot_info['2d']['num_kbins']
            kbintype = plot_info['2d']['kbintype']
            kprll_min_for_rms = plot_info['2d']['kprll_min_rms']
            if kprll_min_for_rms is None:
                kprll_min_for_rms = 0.0
            
            if (kbin_min is None) or (kbin_max is None):
                kbins = None
            else:
                if num_kbins is None:
                    raise ValueError('Input num_kbins must be set if kbin range is provided')
                if kbintype == 'linear':
                    kbins = NP.linspace(kbin_min, kbin_max, num=num_kbins, endpoint=True)
                elif kbintype == 'log':
                    if kbin_min > 0.0:
                        kbins = NP.geomspace(kbin_min, kbin_max, num=num_kbins, endpoint=True)
                    elif kbin_min == 0.0:
                        eps_k = 1e-3
                        kbins = NP.geomspace(kbin_min+eps_k, kbin_max, num=num_kbins, endpoint=True)
                    else:
                        eps_k = 1e-3
                        kbins_pos = NP.geomspace(eps_k, kbin_max, num=num_kbins, endpoint=True)
                        ind_kbin_thresh = NP.argmin(kbins_pos[kbins_pos >= NP.abs(kbin_min)])
                        kbins_neg = -1 * kbins_pos[:ind_kbin_thresh+1][::-1]
                        kbins = NP.hstack((kbins_neg, kbins_pos))
                else:
                    raise ValueError('Input kbintype must be set to "linear" or "log"')
            xcpdps_a_avg_pol_diag_kbin = BSP.incoherent_kbin_averaging(xcpdps_a_avg_pol_diag, kbins=kbins, kbintype=kbintype)
            excpdps_a_avg_pol_diag_kbin = BSP.incoherent_kbin_averaging(excpdps_a_avg_pol_diag, kbins=kbins, kbintype=kbintype)
            models_xcpdps_a_avg_pol_diag_kbin = []
            models_excpdps_a_avg_pol_diag_kbin = []

            xcpdps_b_avg_pol_diag_kbin = BSP.incoherent_kbin_averaging(xcpdps_b_avg_pol_diag, kbins=kbins, kbintype=kbintype)
            excpdps_b_avg_pol_diag_kbin = BSP.incoherent_kbin_averaging(excpdps_b_avg_pol_diag, kbins=kbins, kbintype=kbintype)
            models_xcpdps_b_avg_pol_diag_kbin = []
            models_excpdps_b_avg_pol_diag_kbin = []

            if len(model_labels) > 0:
                for i in range(len(model_labels)):
                    models_xcpdps_a_avg_pol_diag_kbin += [BSP.incoherent_kbin_averaging(models_xcpdps_a_avg_pol_diag[i], kbins=kbins, kbintype=kbintype)]
                    models_excpdps_a_avg_pol_diag_kbin += [BSP.incoherent_kbin_averaging(models_excpdps_a_avg_pol_diag[i], kbins=kbins, kbintype=kbintype)]
                    models_xcpdps_b_avg_pol_diag_kbin += [BSP.incoherent_kbin_averaging(models_xcpdps_b_avg_pol_diag[i], kbins=kbins, kbintype=kbintype)]
                    models_excpdps_b_avg_pol_diag_kbin += [BSP.incoherent_kbin_averaging(models_excpdps_b_avg_pol_diag[i], kbins=kbins, kbintype=kbintype)]



    


















    






/lustre/aoc/users/nthyagar/src/miniconda2/envs/PRISim-dev/lib/python2.7/site-packages/scipy/stats/_binned_statistic.py:607: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
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/lustre/aoc/users/nthyagar/src/miniconda2/envs/PRISim-dev/lib/python2.7/site-packages/astropy/units/quantity.py:641: RuntimeWarning: invalid value encountered in true_divide
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...
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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...
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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=1...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=0...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...
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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=0...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=1...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=0...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...
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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...
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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=0...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=0...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=0...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=0...
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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...
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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=0...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=1...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=0...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...
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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=0...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=1...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=1...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...
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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=whole, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=whole, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=whole, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=whole, stat=median, LST-Day-Triad combination=3, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=1, spw=1...
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Processing datapool=submodel, stat=mean, LST-Day-Triad combination=2, spw=1...
Processing datapool=submodel, stat=mean, LST-Day-Triad combination=3, spw=0...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=0, spw=1...
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Processing datapool=submodel, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=submodel, stat=median, LST-Day-Triad combination=3, spw=1...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=0, spw=1...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=residual, stat=mean, LST-Day-Triad combination=2, spw=1...
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Processing datapool=residual, stat=mean, LST-Day-Triad combination=3, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=1, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=residual, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=0...
Processing datapool=residual, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=0, spw=1...
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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=1, spw=1...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=2, spw=1...










    






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Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=0...
Processing datapool=errinfo, stat=mean, LST-Day-Triad combination=3, spw=1...
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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=0, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=1, spw=1...










    






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Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=0...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=2, spw=1...
Processing datapool=errinfo, stat=median, LST-Day-Triad combination=3, spw=0...
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In [29]:



    


ylim = {0: [-1e11, 1e16], 1: [-1e10, 1e14]} # keys are spw indices
            linthreshy = 9e5
#             ylim = {0: [-1e4, 1e9], 1: [-1e4, 1e8]} # keys are spw indices
#             linthreshy = 1e0

            lstind = [0]
            triadind = [0]
            dayind = [0]
            dayind_models = NP.zeros(len(model_labels), dtype=int).reshape(1,-1)

            for stat in statistic:
                for zind in spwind:
                    print('\n===================')
                    for lind in lstind:
                        for di,dind in enumerate(dayind):
                            for pstype in ['PS', 'Del2']:
                                for combi in range(len(diagoffsets_b)):
                                    print('\nSpW: {0}, Diagcomb: {1}'.format(zind, combi))
                                    maxabsvals = []
                                    minabsvals = []
                                    maxvals = []
                                    minvals = []
                                    if pstype == 'Del2':
                                        fig, axs = PLT.subplots(nrows=1, ncols=len(datapool), sharex=True, sharey=True, figsize=(4.0*len(datapool), 6.0))
                                    else:
                                        fig, axs = PLT.subplots(nrows=1, ncols=len(datapool), sharex=True, sharey=True, figsize=(4.0*len(datapool), 3.6))
                                    if len(datapool) == 1:
                                        axs = [axs]
                                    for dpoolind,dpool in enumerate(datapool):
                                        for trno,trind in enumerate(triadind):
                                            if pstype == 'Del2':
                                                if len(model_labels) > 0:
                                                    for mdlind, mdl in enumerate(model_labels):
                                                        if dpool in models_xcpdps_b_avg_pol_diag_kbin[mdlind][sampling]:
                                                            if pstype == 'PS':
                                                                psval = (2/3.0) * models_xcpdps_b_avg_pol_diag_kbin[mdlind][sampling][dpool][stat][pstype][combi][zind,lind,dayind_models[di][mdlind],trind,:].to(pspec_unit).value
                                                            else:
                                                                psval = (2/3.0) * models_xcpdps_b_avg_pol_diag_kbin[mdlind][sampling][dpool][stat][pstype][combi][zind,lind,dayind_models[di][mdlind],trind,:].to('mK2').value
                                                            kval = models_xcpdps_b_avg_pol_diag_kbin[mdlind][sampling]['kbininfo'][dpool][stat][combi][zind,lind,dayind_models[di][mdlind],trind,:].to('Mpc-1').value
                                                            maxabsvals += [NP.nanmin(NP.abs(psval.real))]
                                                            minabsvals += [NP.nanmin(NP.abs(psval.real))]
                                                            maxvals += [NP.nanmax(psval.real)]
                                                            minvals += [NP.nanmin(psval.real)]
#                                                             axs[dpoolind].plot(kval, psval.real, ls='none', marker='.', ms=3, color=mdl_colrs[mdlind], label='{0}'.format(mdl))
                                                            axs[dpoolind].plot(kval, psval.real, ls='-', lw=1, marker=mdl_mrkrs[mdlind], ms=6, mfc='none', mec='black', color='black', label='{0}'.format(mdl))

                                            if dpool in xcpdps_b_avg_pol_diag_kbin[sampling]:
                                                kval = xcpdps_b_avg_pol_diag_kbin[sampling]['kbininfo'][dpool][stat][combi][zind,lind,dind,trind,:].to('Mpc-1').value
                                                kprll_ind_for_rms = NP.where(NP.abs(kval) >= kprll_min_for_rms)[0]
                                                kprll_for_rms = kval[kprll_ind_for_rms]
                                                if pstype == 'PS':
                                                    psval = (2/3.0) * xcpdps_b_avg_pol_diag_kbin[sampling][dpool][stat][pstype][combi][zind,lind,dind,trind,:].to(pspec_unit).value
                                                    ps_ssdiff = (2/3.0) * excpdps_b_avg_pol_diag_kbin[sampling]['errinfo'][stat][pstype][combi][zind,lind,:,trind,kprll_ind_for_rms].to(pspec_unit).value # Single RMS across all k_prll bins
                                                    psrms_ssdiff = (2/3.0) * NP.nanstd(excpdps_b_avg_pol_diag_kbin[sampling]['errinfo'][stat][pstype][combi][zind,lind,:,trind,:], axis=0).to(pspec_unit).value # RMS per k_prll bin
                                                    psrms_psdiff = (2/3.0) * (xcpdps_a_avg_pol_diag_kbin[sampling][dpool][stat][pstype][combi][zind,lind,1,1,trind,:] - xcpdps_a_avg_pol_diag_kbin[sampling][dpool][stat][pstype][combi][zind,lind,0,0,trind,:]).to(pspec_unit).value
                                                else:
                                                    psval = (2/3.0) * xcpdps_b_avg_pol_diag_kbin[sampling][dpool][stat][pstype][combi][zind,lind,dind,trind,:].to('mK2').value
                                                    psrms_ssdiff = (2/3.0) * NP.nanstd(excpdps_b_avg_pol_diag_kbin[sampling]['errinfo'][stat][pstype][combi][zind,lind,:,trind,:], axis=0).to('mK2').value
                                                    psrms_psdiff = (2/3.0) * (xcpdps_a_avg_pol_diag_kbin[sampling][dpool][stat][pstype][combi][zind,lind,1,1,trind,:] - xcpdps_a_avg_pol_diag_kbin[sampling][dpool][stat][pstype][combi][zind,lind,0,0,trind,:]).to('mK2').value
#                                                     del2rms_ssdiff = ps_ssdiff * kprll_for_rms**3 / (2 * NP.pi**2)
                                                    
                                                if 2 in avg_incohax_b[combi]:
                                                    ind_dayax_in_incohax = avg_incohax_b[combi].index(2)
                                                    if 0 in diagoffsets_incohax_b[combi][ind_dayax_in_incohax]:
                                                        rms_inflation_factor = 2.0 * NP.sqrt(2.0)
                                                    else:
                                                        rms_inflation_factor = NP.sqrt(2.0)
                                                else:
                                                    rms_inflation_factor = NP.sqrt(2.0)
                                                psrms_psdiff = NP.abs(psrms_psdiff.real) / rms_inflation_factor
                                                psrms_max = NP.amax(NP.vstack((psrms_ssdiff, psrms_psdiff)), axis=0)

                                                maxabsvals += [NP.nanmax(NP.abs(psval.real + nsigma*psrms_max.real))]
                                                minabsvals += [NP.nanmin(NP.abs(psval.real))]
                                                maxvals += [NP.nanmax(psval.real + nsigma*psrms_max.real)]
                                                minvals += [NP.nanmin(psval.real - nsigma*psrms_max.real)]
                                                
                                                for errtype in ps_errtype:
                                                    if errtype.lower() == 'ssdiff':
                                                        axs[dpoolind].errorbar(kval, psval.real, yerr=nsigma*psrms_ssdiff, xerr=None, ecolor=errshade[errtype.lower()], ls='none', marker='o', ms=4, color='black', label='Data')
                                                        if pstype == 'PS':
                                                            psmean_ssdiff_across_kbins, psmedian_ssdiff_across_kbins, psrms_ssdiff_across_kbins = sigma_clipped_stats(NP.vstack((ps_ssdiff.real, ps_ssdiff.imag)))
                                                            print('-------------------')
#                                                             print('Current (real): Mean = {0},\t Median = {1},\t RMS = {2}'.format(current_real_mean, current_real_median, current_real_std))
#                                                             print('\nCurrent (imag): Mean = {0},\t Median = {1},\t RMS = {2}'.format(current_imag_mean, current_imag_median, current_imag_std))
                                                            print('\nCurrent: Mean = {0},\t Median = {1},\t RMS = {2}'.format(psmean_ssdiff_across_kbins, psmedian_ssdiff_across_kbins, psrms_ssdiff_across_kbins))
                                                            axs[dpoolind].hlines([-psrms_ssdiff_across_kbins, psrms_ssdiff_across_kbins], kprll_for_rms.min(), kprll_for_rms.max(), linestyles='--', lw=1.5, color='0.25')
                                                        if pstype == 'Del2':
                                                            del2rms_ssdiff_across_kbins =  psrms_ssdiff_across_kbins * kprll_for_rms**3 / (2 * NP.pi**2)
                                                            axs[dpoolind].plot(kprll_for_rms, del2rms_ssdiff_across_kbins, ls='--', lw=2, color='0.25', label='Noise r.m.s')
                                                            axs[dpoolind].plot(kprll_for_rms, -del2rms_ssdiff_across_kbins, ls='--', lw=2, color='0.25')
                                                    elif errtype.lower() == 'psdiff':
                                                        axs[dpoolind].errorbar(kval, psval.real, yerr=nsigma*psrms_psdiff, xerr=None, ecolor=errshade[errtype.lower()], ls='none', marker='.', ms=4, color='black', label='Data')
                                            if pstype == 'Del2':
                                                legend = axs[dpoolind].legend(loc='center', bbox_to_anchor=(0.5,0.3), shadow=False, fontsize=8)
                                            if trno == 0:
                                                if pstype == 'PS':
                                                    axs[dpoolind].text(0.95, 0.97, r'$z=$'+' {0:.1f}'.format(xcpdps_b_avg_pol_diag_kbin['resampled']['z'][zind]), transform=axs[dpoolind].transAxes, fontsize=8, weight='medium', ha='right', va='top', color='black')
                                                else:
                                                    axs[dpoolind].text(0.03, 0.02, r'$z=$'+' {0:.1f}'.format(xcpdps_b_avg_pol_diag_kbin['resampled']['z'][zind]), transform=axs[dpoolind].transAxes, fontsize=8, weight='medium', ha='left', va='bottom', color='black')                                                    
                                                
                                        axs[dpoolind].axhline(y=0, xmin=0, xmax=1, ls='-', lw=1, color='black')
                                        
                                        minvals = NP.asarray(minvals)
                                        maxvals = NP.asarray(maxvals)
                                        minabsvals = NP.asarray(minabsvals)
                                        maxabsvals = NP.asarray(maxabsvals)
                                        axs[dpoolind].set_xlim(0.99*NP.nanmin(xcpdps_b_avg_pol_diag_kbin['resampled']['kbininfo']['kbin_edges'][zind].to('Mpc-1').value), 1.01*NP.nanmax(xcpdps_b_avg_pol_diag_kbin['resampled']['kbininfo']['kbin_edges'][zind].to('Mpc-1').value))
                                        if pstype == 'PS':
                                            axs[dpoolind].set_ylim(ylim[zind]) 
                                            axs[dpoolind].set_yscale('symlog', linthreshy=linthreshy)
                                            tickloc = PLTick.SymmetricalLogLocator(linthresh=linthreshy, base=100.0)
                                        else:    
                                            if NP.min(minvals) < 0.0:
                                                axs[dpoolind].set_ylim(1.5*NP.nanmin(minvals), 2*NP.nanmax(maxabsvals))
                                            else:
                                                axs[dpoolind].set_ylim(0.5*NP.nanmin(minvals), 2*NP.nanmax(maxabsvals))
                                            axs[dpoolind].set_yscale('symlog', linthreshy=10**NP.floor(NP.log10(NP.min(minabsvals[minabsvals > 0.0]))))
                                            tickloc = PLTick.SymmetricalLogLocator(linthresh=10**NP.floor(NP.log10(NP.min(minabsvals[minabsvals > 0.0]))), base=100.0)
                                        axs[dpoolind].yaxis.set_major_locator(tickloc)
                                        
                                        yticklocs = NP.asarray(axs[dpoolind].get_yticks())
                                        ytickspacings = NP.diff(yticklocs)
                                        if pstype == 'PS':
                                            tickinds_to_remove = NP.setdiff1d(NP.where(NP.abs(yticklocs)<=linthreshy)[0], NP.argmin(NP.abs(yticklocs)))
                                        if pstype == 'Del2':
#                                             tickinds_to_remove = NP.asarray([])
                                            tickinds_to_remove = NP.setdiff1d(NP.where(NP.abs(yticklocs) <= 10**NP.floor(NP.log10(NP.min(minabsvals[minabsvals > 0.0]))))[0], NP.argmin(NP.abs(yticklocs)))
                                        if tickinds_to_remove.size > 0:
                                            new_yticklocs = yticklocs[NP.setdiff1d(NP.arange(yticklocs.size), tickinds_to_remove)]
                                            axs[dpoolind].yaxis.set_major_locator(PLTick.FixedLocator((new_yticklocs.tolist())))
                                        
                                        axs[dpoolind].grid(color='0.8', which='both', linestyle=':', lw=1)

                                    axs[dpoolind].set_xlim(-0.05, 2.65)
                                    axs[dpoolind].set_xlim(left=-0.05)
                                    axs[dpoolind].set_xlabel(r'$\kappa_\parallel$'+' [pseudo '+r'$h$'+' Mpc'+r'$^{-1}$'+']', fontsize=12, weight='medium')
                                    if pstype == 'PS':
                                        axs[dpoolind].set_ylabel(r'$\frac{2}{3}\, P_\nabla(\kappa_\parallel)$ [pseudo mK$^2h^{-3}$ Mpc$^3$]', fontsize=12, weight='medium')
                                    else:
                                        axs[dpoolind].set_ylabel(r'$\frac{2}{3}\, \Delta_\nabla^2(\kappa_\parallel)$ [pseudo mK$^2$]', fontsize=12, weight='medium')

                                    
                                    fig.subplots_adjust(top=0.85)
                                    fig.subplots_adjust(bottom=0.16)
                                    fig.subplots_adjust(left=0.227)
                                    fig.subplots_adjust(right=0.98)

#                                     big_ax = fig.add_subplot(111)
#                                     big_ax.set_facecolor('none') # matplotlib.__version__ >= 2.0.0
#                                     # big_ax.set_axis_bgcolor('none') # matplotlib.__version__ < 2.0.0
#                                     big_ax.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
#                                     big_ax.set_xticks([])
#                                     big_ax.set_yticks([])
#                                     big_ax.set_xlabel(r'$k_\parallel$'+' ['+r'$h$'+' Mpc'+r'$^{-1}$'+']', fontsize=12, weight='medium', labelpad=20)
#                                     if pstype == 'PS':
#                                         big_ax.set_ylabel(r'$\frac{1}{3}\, P_\nabla(k_\parallel)$ [K$^2h^{-3}$ Mpc$^3$]', fontsize=12, weight='medium', labelpad=40)
#                                     else:
#                                         big_ax.set_ylabel(r'$\frac{1}{3}\, \Delta_\nabla^2(k_\parallel)$ [K$^2$]', fontsize=12, weight='medium', labelpad=40)

#                                     big_axt = big_ax.twiny()
#                                     big_axt.set_xticks([])
#                                     big_axt.set_xlabel(r'$\tau$'+' ['+r'$\mu$'+'s]', fontsize=12, weight='medium', labelpad=20)

                                    if pstype == 'PS':
                                        PLT.savefig(figdir + '{0}_symlog_incoh_kbin_avg_real_cpdps_z_{1:.1f}_{2}_{3}_dlst_{4:.1f}s_comb_{5:0d}.pdf'.format(plotfile_pfx, xcpdps_a_avg_pol_diag_kbin[sampling]['z'][zind], stat, sampling, 3.6e3*xcpdps_b_avg_pol_diag_kbin['dlst'][0], combi), bbox_inches=0)
                                        print(figdir + '{0}_symlog_incoh_kbin_avg_real_cpdps_z_{1:.1f}_{2}_{3}_dlst_{4:.1f}s_comb_{5:0d}.pdf'.format(plotfile_pfx, xcpdps_a_avg_pol_diag_kbin[sampling]['z'][zind], stat, sampling, 3.6e3*xcpdps_b_avg_pol_diag_kbin['dlst'][0], combi))
                                    else:
                                        PLT.savefig(figdir + '{0}_symlog_incoh_kbin_avg_real_cpDel2_z_{1:.1f}_{2}_{3}_dlst_{4:.1f}s_comb_{5:0d}.pdf'.format(plotfile_pfx, xcpdps_a_avg_pol_diag_kbin[sampling]['z'][zind], stat, sampling, 3.6e3*xcpdps_b_avg_pol_diag_kbin['dlst'][0], combi), bbox_inches=0)
                                        print(figdir + '{0}_symlog_incoh_kbin_avg_real_cpDel2_z_{1:.1f}_{2}_{3}_dlst_{4:.1f}s_comb_{5:0d}.pdf'.format(plotfile_pfx, xcpdps_a_avg_pol_diag_kbin[sampling]['z'][zind], stat, sampling, 3.6e3*xcpdps_b_avg_pol_diag_kbin['dlst'][0], combi))



    


















    






===================

SpW: 0, Diagcomb: 0
-------------------

Current: Mean = -664090138.006,	 Median = -900075236.769,	 RMS = 2040633107.77
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_10.2_median_resampled_dlst_60.0s_comb_0.pdf

SpW: 0, Diagcomb: 1
-------------------

Current: Mean = -637399716.456,	 Median = -193337675.17,	 RMS = 1399102589.63
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_10.2_median_resampled_dlst_60.0s_comb_1.pdf

SpW: 0, Diagcomb: 2
-------------------

Current: Mean = -668584212.558,	 Median = -248477353.353,	 RMS = 1444806709.23
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_10.2_median_resampled_dlst_60.0s_comb_2.pdf

SpW: 0, Diagcomb: 3
-------------------

Current: Mean = -668584212.558,	 Median = -248477353.353,	 RMS = 1444806709.23
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_10.2_median_resampled_dlst_60.0s_comb_3.pdf

SpW: 0, Diagcomb: 0










    






/lustre/aoc/users/nthyagar/src/miniconda2/envs/PRISim-dev/lib/python2.7/site-packages/ipykernel_launcher.py:49: RuntimeWarning: invalid value encountered in greater_equal










    






/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_10.2_median_resampled_dlst_60.0s_comb_0.pdf

SpW: 0, Diagcomb: 1
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_10.2_median_resampled_dlst_60.0s_comb_1.pdf

SpW: 0, Diagcomb: 2
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_10.2_median_resampled_dlst_60.0s_comb_2.pdf

SpW: 0, Diagcomb: 3
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_10.2_median_resampled_dlst_60.0s_comb_3.pdf

===================

SpW: 1, Diagcomb: 0
-------------------

Current: Mean = 31339603.683,	 Median = 34405394.5292,	 RMS = 160537572.793
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_7.7_median_resampled_dlst_60.0s_comb_0.pdf

SpW: 1, Diagcomb: 1
-------------------

Current: Mean = 4022683.56564,	 Median = 3236571.3817,	 RMS = 38931911.46
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_7.7_median_resampled_dlst_60.0s_comb_1.pdf

SpW: 1, Diagcomb: 2
-------------------

Current: Mean = 1835965.83649,	 Median = 1273980.942,	 RMS = 37172217.754
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_7.7_median_resampled_dlst_60.0s_comb_2.pdf

SpW: 1, Diagcomb: 3
-------------------

Current: Mean = 1835965.83649,	 Median = 1273980.942,	 RMS = 37172217.754
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpdps_z_7.7_median_resampled_dlst_60.0s_comb_3.pdf

SpW: 1, Diagcomb: 0
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_7.7_median_resampled_dlst_60.0s_comb_0.pdf

SpW: 1, Diagcomb: 1
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_7.7_median_resampled_dlst_60.0s_comb_1.pdf

SpW: 1, Diagcomb: 2
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_7.7_median_resampled_dlst_60.0s_comb_2.pdf

SpW: 1, Diagcomb: 3
/lustre/aoc/projects/hera/nthyagar/data/HERA/IDR2.1/ClosurePhase/Binned_Data_0100_0220/EQ28/XX_YY/figures/EQ28_dualpol_data_symlog_incoh_kbin_avg_real_cpDel2_z_7.7_median_resampled_dlst_60.0s_comb_3.pdf










    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    
























In [24]:



    


print(pstype)
print(pspec_unit)
print(psval.unit)



    


















    






Del2
mK2 Mpc3










    






---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-24-4ab5ecec5c93> in <module>()
      1 print(pstype)
      2 print(pspec_unit)
----> 3 print(psval.unit)

AttributeError: 'numpy.ndarray' object has no attribute 'unit'
















In [38]:



    


1e-6 * 1e6 * (1.0)**3 / (2*NP.pi**2)



    


















    
Out[38]:








0.05066059182116889

















In [ ]:

Content source: nithyanandan/PRISim

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