import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import os
from matplotlib.cm import ScalarMappable
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from pathlib import Path
from polsartools.utils.utils import read_bin, time_it
# from .halpha_plot_dp import get_feas_bounds
def get_feas_bounds():
c1l = np.array([[ 0, 0.00000000e+00],
[5.68070458e-02, 8.91089109e-01],
[1.00217413e-01, 1.76470588e+00],
[1.38247415e-01, 2.62135922e+00],
[1.72657031e-01, 3.46153846e+00],
[2.04304187e-01, 4.28571429e+00],
[2.33706941e-01, 5.09433962e+00],
[2.61215910e-01, 5.88785047e+00],
[2.87086073e-01, 6.66666667e+00],
[3.11512289e-01, 7.43119266e+00],
[3.34648976e-01, 8.18181818e+00],
[3.56621927e-01, 8.91891892e+00],
[3.77535871e-01, 9.64285714e+00],
[3.97479533e-01, 1.03539823e+01],
[4.16529162e-01, 1.10526316e+01],
[4.34751078e-01, 1.17391304e+01],
[4.52203543e-01, 1.24137931e+01],
[4.68938188e-01, 1.30769231e+01],
[4.85001106e-01, 1.37288136e+01],
[5.00433715e-01, 1.43697479e+01],
[5.15273445e-01, 1.50000000e+01],
[5.29554291e-01, 1.56198347e+01],
[5.43307267e-01, 1.62295082e+01],
[5.56560782e-01, 1.68292683e+01],
[5.69340953e-01, 1.74193548e+01],
[5.81671866e-01, 1.80000000e+01],
[5.93575801e-01, 1.85714286e+01],
[6.05073426e-01, 1.91338583e+01],
[6.16183955e-01, 1.96875000e+01],
[6.26925297e-01, 2.02325581e+01],
[6.37314175e-01, 2.07692308e+01],
[6.47366238e-01, 2.12977099e+01],
[6.57096154e-01, 2.18181818e+01],
[6.66517697e-01, 2.23308271e+01],
[6.75643820e-01, 2.28358209e+01],
[6.84486721e-01, 2.33333333e+01],
[6.93057905e-01, 2.38235294e+01],
[7.01368234e-01, 2.43065693e+01],
[7.09427979e-01, 2.47826087e+01],
[7.17246861e-01, 2.52517986e+01],
[7.24834092e-01, 2.57142857e+01],
[7.32198407e-01, 2.61702128e+01],
[7.39348101e-01, 2.66197183e+01],
[7.46291058e-01, 2.70629371e+01],
[7.53034772e-01, 2.75000000e+01],
[7.59586381e-01, 2.79310345e+01],
[7.65952682e-01, 2.83561644e+01],
[7.72140158e-01, 2.87755102e+01],
[7.78154991e-01, 2.91891892e+01],
[7.84003086e-01, 2.95973154e+01],
[7.89690082e-01, 3.00000000e+01],
[7.95221372e-01, 3.03973510e+01],
[8.00602113e-01, 3.07894737e+01],
[8.05837242e-01, 3.11764706e+01],
[8.10931485e-01, 3.15584416e+01],
[8.15889374e-01, 3.19354839e+01],
[8.20715250e-01, 3.23076923e+01],
[8.25413279e-01, 3.26751592e+01],
[8.29987458e-01, 3.30379747e+01],
[8.34441626e-01, 3.33962264e+01],
[8.38779468e-01, 3.37500000e+01],
[8.43004527e-01, 3.40993789e+01],
[8.47120210e-01, 3.44444444e+01],
[8.51129792e-01, 3.47852761e+01],
[8.55036424e-01, 3.51219512e+01],
[8.58843141e-01, 3.54545455e+01],
[8.62552864e-01, 3.57831325e+01],
[8.66168407e-01, 3.61077844e+01],
[8.69692481e-01, 3.64285714e+01],
[8.73127701e-01, 3.67455621e+01],
[8.76476587e-01, 3.70588235e+01],
[8.79741571e-01, 3.73684211e+01],
[8.82925000e-01, 3.76744186e+01],
[8.86029137e-01, 3.79768786e+01],
[8.89056170e-01, 3.82758621e+01],
[8.92008211e-01, 3.85714286e+01],
[8.94887299e-01, 3.88636364e+01],
[8.97695407e-01, 3.91525424e+01],
[9.00434441e-01, 3.94382022e+01],
[9.03106244e-01, 3.97206704e+01],
[9.05712598e-01, 4.00000000e+01],
[9.08255228e-01, 4.02762431e+01],
[9.10735802e-01, 4.05494505e+01],
[9.13155936e-01, 4.08196721e+01],
[9.15517194e-01, 4.10869565e+01],
[9.17821089e-01, 4.13513514e+01],
[9.20069090e-01, 4.16129032e+01],
[9.22262616e-01, 4.18716578e+01],
[9.24403046e-01, 4.21276596e+01],
[9.26491714e-01, 4.23809524e+01],
[9.28529914e-01, 4.26315789e+01],
[9.30518901e-01, 4.28795812e+01],
[9.32459893e-01, 4.31250000e+01],
[9.34354071e-01, 4.33678756e+01],
[9.36202579e-01, 4.36082474e+01],
[9.38006531e-01, 4.38461538e+01],
[9.39767004e-01, 4.40816327e+01],
[9.41485048e-01, 4.43147208e+01],
[9.43161680e-01, 4.45454545e+01],
[9.44797887e-01, 4.47738693e+01],
[9.46394630e-01, 4.50000000e+01],
[9.47952842e-01, 4.52238806e+01],
[9.49473429e-01, 4.54455446e+01],
[9.50957272e-01, 4.56650246e+01],
[9.52405226e-01, 4.58823529e+01],
[9.53818125e-01, 4.60975610e+01],
[9.55196779e-01, 4.63106796e+01],
[9.56541973e-01, 4.65217391e+01],
[9.57854475e-01, 4.67307692e+01],
[9.59135029e-01, 4.69377990e+01],
[9.60384360e-01, 4.71428571e+01],
[9.61603173e-01, 4.73459716e+01],
[9.62792156e-01, 4.75471698e+01],
[9.63951976e-01, 4.77464789e+01],
[9.65083284e-01, 4.79439252e+01],
[9.66186714e-01, 4.81395349e+01],
[9.67262883e-01, 4.83333333e+01],
[9.68312392e-01, 4.85253456e+01],
[9.69335827e-01, 4.87155963e+01],
[9.70333757e-01, 4.89041096e+01],
[9.71306738e-01, 4.90909091e+01],
[9.72255313e-01, 4.92760181e+01],
[9.73180009e-01, 4.94594595e+01],
[9.74081340e-01, 4.96412556e+01],
[9.74959809e-01, 4.98214286e+01],
[9.75815904e-01, 5.00000000e+01],
[9.76650102e-01, 5.01769912e+01],
[9.77462869e-01, 5.03524229e+01],
[9.78254658e-01, 5.05263158e+01],
[9.79025911e-01, 5.06986900e+01],
[9.79777061e-01, 5.08695652e+01],
[9.80508528e-01, 5.10389610e+01],
[9.81220724e-01, 5.12068966e+01],
[9.81914050e-01, 5.13733906e+01],
[9.82588897e-01, 5.15384615e+01],
[9.83245647e-01, 5.17021277e+01],
[9.83884675e-01, 5.18644068e+01],
[9.84506343e-01, 5.20253165e+01],
[9.85111008e-01, 5.21848739e+01],
[9.85699017e-01, 5.23430962e+01],
[9.86270709e-01, 5.25000000e+01],
[9.86826416e-01, 5.26556017e+01],
[9.87366462e-01, 5.28099174e+01],
[9.87891161e-01, 5.29629630e+01],
[9.88400825e-01, 5.31147541e+01],
[9.88895753e-01, 5.32653061e+01],
[9.89376242e-01, 5.34146341e+01],
[9.89842580e-01, 5.35627530e+01],
[9.90295048e-01, 5.37096774e+01],
[9.90733922e-01, 5.38554217e+01],
[9.91159471e-01, 5.40000000e+01],
[9.91571959e-01, 5.41434263e+01],
[9.91971643e-01, 5.42857143e+01],
[9.92358774e-01, 5.44268775e+01],
[9.92733599e-01, 5.45669291e+01],
[9.93096359e-01, 5.47058824e+01],
[9.93447289e-01, 5.48437500e+01],
[9.93786618e-01, 5.49805447e+01],
[9.94114574e-01, 5.51162791e+01],
[9.94431375e-01, 5.52509653e+01],
[9.94737238e-01, 5.53846154e+01],
[9.95032374e-01, 5.55172414e+01],
[9.95316988e-01, 5.56488550e+01],
[9.95591284e-01, 5.57794677e+01],
[9.95855460e-01, 5.59090909e+01],
[9.96109708e-01, 5.60377358e+01],
[9.96354218e-01, 5.61654135e+01],
[9.96589175e-01, 5.62921348e+01],
[9.96814763e-01, 5.64179104e+01],
[9.97031157e-01, 5.65427509e+01],
[9.97238532e-01, 5.66666667e+01],
[9.97437060e-01, 5.67896679e+01],
[9.97626906e-01, 5.69117647e+01],
[9.97808234e-01, 5.70329670e+01],
[9.97981205e-01, 5.71532847e+01],
[9.98145975e-01, 5.72727273e+01],
[9.98302698e-01, 5.73913043e+01],
[9.98451525e-01, 5.75090253e+01],
[9.98592603e-01, 5.76258993e+01],
[9.98726076e-01, 5.77419355e+01],
[9.98852087e-01, 5.78571429e+01],
[9.98970773e-01, 5.79715302e+01],
[9.99082272e-01, 5.80851064e+01],
[9.99186715e-01, 5.81978799e+01],
[9.99284234e-01, 5.83098592e+01],
[9.99374957e-01, 5.84210526e+01],
[9.99459009e-01, 5.85314685e+01],
[9.99536513e-01, 5.86411150e+01],
[9.99607590e-01, 5.87500000e+01],
[9.99672358e-01, 5.88581315e+01],
[9.99730933e-01, 5.89655172e+01],
[9.99783428e-01, 5.90721649e+01],
[9.99829955e-01, 5.91780822e+01],
[9.99870624e-01, 5.92832765e+01],
[9.99905540e-01, 5.93877551e+01],
[9.99934810e-01, 5.94915254e+01],
[9.99958537e-01, 5.95945946e+01],
[9.99976821e-01, 5.96969697e+01],
[9.99989761e-01, 5.97986577e+01],
[9.99997456e-01, 5.98996656e+01],
[1.00000000e+00, 6.00000000e+01]])
c22l = np.array([
[np.nan,90],
[0.637490666922012,89.9100899100899],
[0.642780051326664,89.8203592814371],
[0.64758493789284,89.7308075772681],
[0.652073091598438,89.6414342629482],
[0.656324368447387,89.5522388059701],
[0.660385905549124,89.4632206759443],
[0.664288917260044,89.3743793445878],
[0.668055637075812,89.2857142857142],
[0.671702725080799,89.197224975223],
[0.675243140923087,89.1089108910891],
[0.678687260183894,89.0207715133531],
[0.682043581735929,88.9328063241106],
[0.685319197953429,88.8450148075024],
[0.688520119602884,88.7573964497041],
[0.69165150759842,88.6699507389162],
[0.694717842804856,88.5826771653543],
[0.697723053318171,88.4955752212389],
[0.700670611766267,88.4086444007858],
[0.703563610976874,88.3218842001962],
[0.706404823715649,88.235294117647],
[0.709196750482267,88.1488736532811],
[0.71194165821052,88.0626223091976],
[0.714641611940935,87.9765395894428],
[0.717298500994026,87.890625],
[0.719914060789865,87.8048780487804],
[0.722489891184453,87.719298245614],
[0.725027471992373,87.6338851022395],
[0.727528176216381,87.5486381322957],
[0.729993281393007,87.4635568513119],
[0.732423979378592,87.378640776699],
[0.73482138483533,87.29388942774],
[0.737186542626655,87.2093023255813],
[0.739520434292086,87.1248789932236],
[0.741823983740734,87.0406189555125],
[0.744098062278127,86.9565217391304],
[0.746343493061373,86.8725868725868],
[0.748561055061879,86.7888138862102],
[0.750751486601976,86.7052023121387],
[0.75291548852138,86.6217516843118],
[0.755053727020791,86.5384615384615],
[0.757166836222881,86.4553314121037],
[0.759255420485009,86.3723608445297],
[0.761320056493135,86.2895493767976],
[0.763361295162268,86.2068965517241],
[0.765379663365375,86.1244019138756],
[0.767375665509718,86.0420650095602],
[0.769349784977149,85.9598853868194],
[0.771302485442781,85.8778625954198],
[0.773234212084631,85.7959961868446],
[0.775145392695341,85.7142857142857],
[0.777036438705702,85.6327307326355],
[0.778907746128593,85.551330798479],
[0.780759696430966,85.4700854700854],
[0.782592657340606,85.3889943074003],
[0.784406983593702,85.3080568720379],
[0.786203017628564,85.2272727272727],
[0.787981090230271,85.1466414380321],
[0.789741521130544,85.0661625708884],
[0.791484619566666,84.9858356940509],
[0.793210684802909,84.9056603773584],
[0.794920006617574,84.8256361922714],
[0.796612865758444,84.7457627118644],
[0.79828953436918,84.6660395108184],
[0.799950276388959,84.5864661654135],
[0.801595347927417,84.5070422535211],
[0.803224997616808,84.4277673545966],
[0.804839466943062,84.3486410496719],
[0.80643899055734,84.2696629213483],
[0.808023796569481,84.1908325537885],
[0.809594106824666,84.1121495327102],
[0.81115013716447,84.0336134453781],
[0.812692097673412,83.955223880597],
[0.814220192911991,83.8769804287045],
[0.815734622137124,83.7988826815642],
[0.817235579510849,83.7209302325581],
[0.818723254298042,83.6431226765799],
[0.820197831053887,83.5654596100278],
[0.821659489801753,83.4879406307977],
[0.823108406202082,83.4105653382761],
[0.824544751712857,83.3333333333333],
[0.825968693742175,83.2562442183163],
[0.827380395793399,83.1792975970425],
[0.828780017603347,83.1024930747922],
[0.830167715273929,83.0258302583025],
[0.831543641397619,82.9493087557603],
[0.832907945177117,82.8729281767955],
[0.834260772539548,82.7966881324746],
[0.835602266245496,82.7205882352941],
[0.836932565993171,82.6446280991735],
[0.838251808517971,82.5688073394495],
[0.839560127687712,82.4931255728689],
[0.840857654593732,82.4175824175824],
[0.842144517638125,82.3421774931381],
[0.843420842617274,82.2669104204753],
[0.844686752801916,82.1917808219177],
[0.845942369013886,82.1167883211678],
[0.847187809699736,82.0419325432999],
[0.848423191001369,81.9672131147541],
[0.849648626823857,81.8926296633302],
[0.850864228900568,81.8181818181818],
[0.852070106855741,81.7438692098092],
[0.853266368264641,81.6696914700544],
[0.854453118711399,81.5956482320942],
[0.85563046184465,81.5217391304347],
[0.856798499431088,81.4479638009049],
[0.857957331407016,81.3743218806509],
[0.859107055927998,81.30081300813],
[0.860247769416691,81.2274368231046],
[0.861379566608956,81.1541929666366],
[0.862502540598302,81.081081081081],
[0.863616782878763,81.008100810081],
[0.864722383386256,80.9352517985611],
[0.865819430538506,80.8625336927223],
[0.866908011273582,80.7899461400358],
[0.867988211087122,80.7174887892376],
[0.869060114068288,80.6451612903225],
[0.870123802934516,80.5729632945389],
[0.871179359065106,80.5008944543828],
[0.872226862533699,80.4289544235924],
[0.873266392139697,80.3571428571428],
[0.874298025438653,80.2854594112399],
[0.875321838771693,80.2139037433154],
[0.876337907293989,80.1424755120213],
[0.877346305002334,80.0711743772241],
[0.878347104761853,80],
[0.879340378331882,79.9289520426287],
[0.880326196391042,79.8580301685891],
[0.881304628561553,79.7872340425531],
[0.882275743432804,79.7165633303808],
[0.883239608584211,79.646017699115],
[0.884196290607398,79.5755968169761],
[0.885145855127715,79.5053003533568],
[0.886088366825122,79.4351279788172],
[0.887023889454463,79.3650793650793],
[0.887952485865159,79.295154185022],
[0.888874218020325,79.225352112676],
[0.889789147015348,79.1556728232189],
[0.890697333095935,79.0861159929701],
[0.891598835675656,79.0166812993854],
[0.892493713352995,78.9473684210526],
[0.893382023927933,78.8781770376862],
[0.894263824418069,78.8091068301225],
[0.895139171074307,78.7401574803149],
[0.896008119396114,78.6713286713286],
[0.896870724146366,78.6026200873362],
[0.897727039365803,78.5340314136125],
[0.898577118387097,78.46556233653],
[0.89942101384855,78.397212543554],
[0.900258777707433,78.3289817232375],
[0.90109046125298,78.2608695652173],
[0.901916115119046,78.1928757602084],
[0.902735789296443,78.1249999999999],
[0.903549533144957,78.0572419774501],
[0.904357395405065,77.9896013864817],
[0.905159424209364,77.9220779220779],
[0.905955667093698,77.8546712802768],
[0.906746171008029,77.7873811581676],
[0.907530982327027,77.720207253886],
[0.908310146860409,77.6531492666091],
[0.909083709863021,77.5862068965517],
[0.909851716044683,77.5193798449612],
[0.910614209579789,77.4526678141135],
[0.911371234116693,77.3860705073086],
[0.912122832786855,77.3195876288659],
[0.912869048213788,77.2532188841201],
[0.913609922521786,77.1869639794168],
[0.914345497344451,77.1208226221079],
[0.915075813833028,77.0547945205479],
[0.915800912664543,76.9888793840889],
[0.91652083404976,76.9230769230769],
[0.917235617740953,76.8573868488471],
[0.917945303039508,76.7918088737201],
[0.918649928803355,76.7263427109974],
[0.919349533454226,76.6609880749574],
[0.920044154984765,76.595744680851],
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[0.929265940259966,75.6938603868797],
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[0.999906810435944,60.6060606060605],
[0.999918949606537,60.5652759084791],
[0.999930224852035,60.5245460659044],
[0.999940640221957,60.4838709677419],
[0.99995019974473,60.4432505036937],
[0.999958907427822,60.4026845637583],
[0.99996676725788,60.3621730382293],
[0.999973783200858,60.3217158176943],
[0.999979959202154,60.2813127930341],
[0.999985299186735,60.2409638554216],
[0.999989807059272,60.200668896321],
[0.999993486704266,60.1604278074866],
[0.999996341986175,60.1202404809619],
[0.999998376749542,60.0801068090787],
[0.999999594819121,60.0400266844562],
[1,60]
])
c21l = np.array([[0,0],
[0.1,1]])
return c1l,c22l,c21l
[docs]
@time_it
def plot_h_a_alpha_fp(H, A, alpha, ppath = None, cmap='jet',
cbar = True, norm='', vmin=None,vmax=None,
grey_region=True,zone_lines=True,
zone_line_color = 'k', gridsize=200
):
"""
Generates and saves a density plot of entropy (H) versus alpha (degrees) for full-pol data,
including optional zone lines, zone IDs, and grey regions.
Example:
--------
>>> plot_h_a_alpha_fp(h, A, alpha, ppath="HAAlpha_plot.png", cmap='jet', colorbar=True, norm='log')
This will generates a H/A/Alpha plot from the input arrays and save it as HAAlpha_plot.png, using the 'jet' colormap and logarithmic normalization
Parameters:
-----------
h : path or array-like
path to the Entropy file or Array representing entropy values.
A : array-like
path to the Anisotropy file or Array representing Anisotropy values.
alpha : array-like
path to the Alpha file or Array representing alpha values in degrees.
ppath : str, optional
Path to save the generated plot. If a folder is given, the plot is saved as 'halpha_plot_fp.png' inside that folder.
If the file already exists, it will be overwritten.
cmap : str, optional
Colormap used for the hexbin plot. Defaults to 'viridis'.
colorbar : bool, optional
If True, displays a colorbar representing sample count. Defaults to True.
norm : str, optional
If set to 'log', applies logarithmic normalization to the hexbin plot.
grey_region : bool, optional
If True, fills non-feasible regions of the plot with a grey color to indicate feasible boundaries. Defaults to True.
zone_lines : bool, optional
If True, adds dashed lines to mark different entropy-alpha zones. Defaults to True.
zone_line_color : str, optional
Color used for zone boundary lines. Defaults to 'k' (black).
zone_ids : bool, optional
If True, labels predefined zones with numerical identifiers. Defaults to True.
gridsize : int, optional
Number of hexagonal bins used in the plot. Higher values result in finer binning. Defaults to 300.
Returns:
--------
None
Displays the plot and optionally saves it to the specified location.
Notes:
------
- Uses `get_feas_bounds()` to obtain feasible boundary curves for plotting.
- If `norm` is 'log', a `LogNorm()` normalization is applied.
"""
if isinstance(H, (str, os.PathLike, Path)):
H = read_bin(H)
if isinstance(alpha, (str, os.PathLike, Path)):
alpha = read_bin(alpha)
if isinstance(A, (str, os.PathLike, Path)):
A = read_bin(A)
# --- Drop NaNs and zeros---
# valid_mask = ~(
# ((H == 0) & (A == 0) & (alpha == 0)) | # all three are zero
# (np.isnan(H) & np.isnan(A) & np.isnan(alpha)) # all three are NaN
# )
valid_mask = ~(
((H == 0) & (A == 0) & (alpha == 0)) | # all three are zero
(np.isnan(H) | np.isnan(A) | np.isnan(alpha)) # any one is NaN
)
h_vals = H[valid_mask].flatten()
a_vals = A[valid_mask].flatten()
alpha_vals = alpha[valid_mask].flatten()
fs = 15
if cbar:
fig = plt.figure(figsize=(7.5, 7))
fig.subplots_adjust(left=0.18)
else:
fig = plt.figure(figsize=(7, 7))
fig.subplots_adjust(left=0.15)
ax = fig.add_subplot(111, projection='3d')
get_cmap = plt.get_cmap(cmap)
# --- Histogram surfaces ---
hist_top, xH, yA = np.histogram2d(h_vals, a_vals, bins=gridsize)
hist_front, _, yAlpha = np.histogram2d(h_vals, alpha_vals, bins=[gridsize, 90])
hist_side, _, _ = np.histogram2d(a_vals, alpha_vals, bins=[gridsize, 90])
# --- Combine all for global normalization ---
all_counts = np.concatenate([
hist_top.T.flatten(),
hist_front.T.flatten(),
hist_side.T.flatten()
])
all_counts = all_counts[all_counts > 0] # Drop zeros for log norm
if vmin is not None:
vmin = np.min(all_counts)
if vmax is not None:
vmax = np.max(all_counts)
if vmin==0:
vmin=1
norm_option = mcolors.LogNorm(vmin=vmin, vmax=vmax) if norm == 'log' else mcolors.Normalize(vmin=vmin, vmax=vmax)
# --- Top Surface (H vs A @ α = 90°) ---
xHc = (xH[:-1] + xH[1:]) / 2
yAc = (yA[:-1] + yA[1:]) / 2
X, Y = np.meshgrid(xHc, yAc)
face_top = norm_option(hist_top.T)
rgba_top = get_cmap(face_top)
rgba_top[..., -1] = np.where(hist_top.T == 0, 0.0, 1.0)
ax.plot_surface(X, Y, np.ones_like(X) * 90, facecolors=rgba_top, rstride=1, cstride=1, shade=False,zorder=0)
# --- Front Surface (H vs α @ A = 0) ---
xHc = (xH[:-1] + xH[1:]) / 2
yAlphc = (yAlpha[:-1] + yAlpha[1:]) / 2
X, Y = np.meshgrid(xHc, yAlphc)
face_front = norm_option(hist_front.T)
rgba_front = get_cmap(face_front)
rgba_front[..., -1] = np.where(hist_front.T == 0, 0.0, 1.0)
ax.plot_surface(X, np.zeros_like(X), Y, facecolors=rgba_front, rstride=1, cstride=1, shade=False,zorder=0)
# --- Side Surface (A vs α @ H = 0) ---
xAc = (xH[:-1] + xH[1:]) / 2 # Reusing xH for bin centers of A for simplicity
yAlphc = (yAlpha[:-1] + yAlpha[1:]) / 2
X, Y = np.meshgrid(xAc, yAlphc)
face_side = norm_option(hist_side.T)
rgba_side = get_cmap(face_side)
rgba_side[..., -1] = np.where(hist_side.T == 0, 0.0, 1.0)
ax.plot_surface(np.zeros_like(X), X, Y, facecolors=rgba_side, rstride=1, cstride=1, shade=False,zorder=0)
# --- Wireframes ---
lw = 1
zv = np.arange(0, 91, 10)
ax.plot(np.zeros_like(zv), np.zeros_like(zv), zv, 'k-', linewidth=lw,zorder=10)
ax.plot(np.ones_like(zv), np.zeros_like(zv), zv, 'k-', linewidth=lw,zorder=10)
xv = np.linspace(0, 1, 11)
ax.plot(np.zeros_like(xv), xv, np.ones_like(xv) * 90, 'k-', linewidth=lw,zorder=10)
ax.plot(xv, np.zeros_like(xv), np.ones_like(xv) * 90, 'k-', linewidth=lw,zorder=10)
ax.plot(xv, np.zeros_like(xv), np.ones_like(xv) * 0, 'k-', linewidth=lw,zorder=10)
ax.plot(xv, np.ones_like(xv), np.ones_like(xv) * 90, 'k-', linewidth=lw,zorder=10)
ax.plot(np.ones_like(xv), xv, np.ones_like(xv) * 90, 'k-', linewidth=lw,zorder=10)
# --- Axes and view ---
ax.set_xlim(1, 0)
ax.set_ylim(1, 0)
ax.set_zlim(0, 90)
ax.view_init(elev=25, azim=45)
ax.set_xlabel("Entropy, H", labelpad=10, fontsize=fs)
ax.set_ylabel("Anisotropy, A", labelpad=10, fontsize=fs)
ax.set_zlabel("Alpha (°)", labelpad=2, fontsize=fs)
ax.tick_params(axis='both', which='major',
# length=20, width=1,
labelsize=fs)
# Tick length and width
ax.xaxis._axinfo['tick']['inward_factor'] = 0.2
ax.xaxis._axinfo['tick']['outward_factor'] = 0.25
ax.xaxis._axinfo['tick']['size'] = 20
ax.yaxis._axinfo['tick']['inward_factor'] = 0.2
ax.yaxis._axinfo['tick']['outward_factor'] = 0.25
ax.yaxis._axinfo['tick']['size'] = 20
ax.zaxis._axinfo['tick']['inward_factor'] = 0.2
ax.zaxis._axinfo['tick']['outward_factor'] = 0.25
ax.zaxis._axinfo['tick']['size'] = 20
ax.grid(False)
if zone_lines:
ax.plot(np.zeros_like(zv)+0.5, np.zeros_like(zv), zv, color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
ax.plot(np.zeros_like(zv)+0.9, np.zeros_like(zv), zv, color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
zone_xv = np.linspace(0, 0.5, 11)
ax.plot(zone_xv, np.zeros_like(zone_xv), np.ones_like(zone_xv) * 42.5,color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
ax.plot(zone_xv, np.zeros_like(zone_xv), np.ones_like(zone_xv) * 47.5,color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
zone_xv = np.linspace(0.5, 1, 11)
ax.plot(zone_xv, np.zeros_like(zone_xv), np.ones_like(zone_xv) * 40, color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
zone_xv = np.linspace(0.5, 0.9, 11)
ax.plot(zone_xv, np.zeros_like(zone_xv), np.ones_like(zone_xv) * 50, color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
zone_xv = np.linspace(0.9, 1.0, 11)
ax.plot(zone_xv, np.zeros_like(zone_xv), np.ones_like(zone_xv) * 60, color=zone_line_color, ls='-', linewidth=lw-0.5,zorder=10)
c1l,c22l,c21l = get_feas_bounds()
if grey_region:
Y_front = 0 # y-coordinate of the front XZ plane
# --- poly1: fill below curve toward z = 0 ---
xs = c1l[:, 0]
zs = c1l[:, 1]
ys = np.ones_like(xs) * Y_front
# Create polygon from curve down to z=0
verts1 = list(zip(xs, ys, zs)) + list(zip(xs[::-1], ys[::-1], np.zeros_like(zs)))
poly1 = Poly3DCollection([verts1], color='#dbdbdb', alpha=1.0, zorder=0)
ax.add_collection3d(poly1)
# --- poly2: fill above curve toward z = 90 ---
xs = c22l[:, 0]
zs = c22l[:, 1]
ys = np.ones_like(xs) * Y_front
# Create polygon from curve up to z=90
verts2 = list(zip(xs, ys, zs)) + list(zip(xs[::-1], ys[::-1], np.ones_like(zs) * 90))
poly2 = Poly3DCollection([verts2], color='#dbdbdb', alpha=1.0, zorder=0)
ax.add_collection3d(poly2)
ax.plot(c1l[:,0],np.zeros_like(c1l[:,0]), c1l[:,1],'k-',linewidth=0.5,zorder=10)
# plt.plot(c21l[:,0],c21l[:,1],'k-',linewidth=0.3)
ax.plot(c22l[:,0],np.zeros_like(c22l[:,0]), c22l[:,1],'k-',linewidth=0.5,zorder=10)
# --- Unified Colorbar ---
if cbar:
sm = ScalarMappable(cmap=cmap, norm=norm_option)
sm.set_array(all_counts)
# fig.colorbar(sm, ax=ax, shrink=0.7, pad=0.04, label='# samples')
cb = fig.colorbar(sm, ax=ax, shrink=0.7, pad=0.04)
cb.set_label('# samples', fontsize=fs) # Label font size
cb.ax.tick_params(labelsize=fs)
plt.tight_layout()
if ppath is not None:
if os.path.isdir(ppath):
ppath = os.path.join(ppath, 'haalpha_plot_fp.png')
plt.savefig(ppath,dpi=300,bbox_inches='tight',pad_inches=0.3)
# plt.show()
