Notebook

In [1]:

%matplotlib inline
#Codes by Shucheng Yang
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pycbc.psd

from pycbc.types.timeseries import TimeSeries
from pycbc.types.frequencyseries import FrequencySeries
from pycbc import filter

from scipy.interpolate import interp1d #导入scipy库
import os
import time

def file2series(path):
    with open(path, 'r') as f:
        datafile = f.readlines()

    datafile = [np.fromstring(item.replace("D","e").replace('\n','').strip(), dtype= float, sep =' ') for item in datafile]
    datafile = np.array(datafile)
    ans = {}
    
    ans['timeVec'] = datafile[:,0]
    ans['n'] = len(ans['timeVec'])
    ans['sampIntrvl'] = ans['timeVec'][1]-ans['timeVec'][0]
    
    ans['hp'] = TimeSeries( datafile[:,5], delta_t=ans['sampIntrvl'], dtype = float,copy=True)
    ans['hc'] = TimeSeries( datafile[:,6], delta_t=ans['sampIntrvl'], dtype = float,copy=True)    
    return ans


#绘制波形图
def plotwave(hp,hc):
    plt.figure(figsize=(8,8/1.5))
    plt.plot(hp.sample_times, hp, label = '$h_{+}$')
    plt.plot(hc.sample_times, hc, label = '$h_{\\times}$')
    # plt.xlim(40,40.5)
    # plt.ylim(- 2e-22,2e-22)
    plt.xlabel("Time / s")
    plt.ylabel("$Strain$")
    plt.legend()
    plt.tight_layout()
    plt.grid(linestyle = "dotted", color = "#d3d3d3" , which="both")
    plt.show()


def stdf(h, f_max, freqIntrvl):
    #interp1d
    hf = h.to_frequencyseries()    
    freqVec = hf.sample_frequencies
    hf_value = np.array(hf)
    fit=interp1d(freqVec, hf_value,fill_value = "extrapolate")  
    #
    stdFreqVec = np.arange(0, f_max, freqIntrvl)
    hf_value_std = fit(stdFreqVec)
    stdf_series = pycbc.types.frequencyseries.FrequencySeries(hf_value_std, delta_f=freqIntrvl, epoch='', copy=True)
    return stdf_series

def overlap_match_func(hf1, hf2, psd, f_min, f_max):
    roverlap = filter.matchedfilter.overlap(hf1, hf2, psd=psd, low_frequency_cutoff=f_min , high_frequency_cutoff=f_max, normalized=True)  
    amplitude1 = filter.matchedfilter.sigmasq(hf1, psd=psd, low_frequency_cutoff=f_min, high_frequency_cutoff=f_max )
    amplitude2 = filter.matchedfilter.sigmasq(hf2, psd=psd, low_frequency_cutoff=f_min, high_frequency_cutoff=f_max )
    rmatch,nn =filter.matchedfilter.match(hf1, hf2, psd=psd, low_frequency_cutoff=f_min , high_frequency_cutoff=f_max,v1_norm=True ,v2_norm=True)
    rmatch = rmatch / np.sqrt(amplitude1 * amplitude2)
    return roverlap, rmatch

def compare_multi(filepath1, dirpath2, filename2, parse_file_name_func):
    #filepath1, 被比较的波形文件的路径
    #dirpath2, 不同参数波形文件的文件夹路径
    #filename2, 不同参数波形文件的文件名
    #parse_file_name_func， 从文件名提取参数的函数 
    
    #filepath1 波形导入
    ans1 = file2series(path1)
    hp1 = ans1["hp"]
    hc1 = ans1["hc"]
    timeVec1 = ans1["timeVec"]
    n1 = ans1["n"]
    sampIntrvl1 = ans1["sampIntrvl"]

    #filename2 提取参数
    a, nu = parse_file_name_func(filename2)
    
    #filepath1 波形导入
    ans2 = file2series( dirpath2 + filename2)
    hp2 = ans2["hp"]
    hc2 = ans2["hc"]
    timeVec2 = ans2["timeVec"]
    n2 = ans2["n"]
    sampIntrvl2 = ans2["sampIntrvl"]
    assert sampIntrvl1 == sampIntrvl2 
    sampIntrvl = sampIntrvl1
    n_min = min(n1,n2)          #采样点数(Sampling Number), 有时也称为信号长度(Length of Signal) 2^16为2的幂时，快速傅里叶变化效率最高
                                #n =  duration * sampFreqint = (duration / sampIntrvl)
    sampFreq = 1/sampIntrvl  #采样频率(Sampling frequency)，单位时间样本点个数，应大于 2f（即Nyquist频率)
    #duration = n/sampFreq     #信号持续时间(duration of signal) 2^16, 0.75d, 2^25 ,1.06yr
    #sampIntrvl = 1.0/sampFreq #采样周期(Sampling period)，隔多少时间取样一次，或步长
    

    freqIntrvl = 1.0 / (n_min * sampIntrvl) #傅里叶变换 频率分辨率(Frequency Interval) #freqIntrvl=1/duration=1/(n*sampIntrvl)=sampFreq/n  

    f_min = 20                              #低于此频率的psd将被设置为0
    f_max = sampFreq/2                      #信号模式的最大频率

    #示例，psd参见， https://dcc.ligo.org/LIGO-T1800044/public
    psd = pycbc.psd.from_string('aLIGOaLIGODesignSensitivityT1800044', n_min , freqIntrvl, f_min)


    hpf1 = stdf(hp1, f_max, freqIntrvl)
    hpf2 = stdf(hp2, f_max, freqIntrvl)

    hcf1 = stdf(hc1, f_max, freqIntrvl)
    hcf2 = stdf(hc2, f_max, freqIntrvl)

    overlap_hp, match_hp = overlap_match_func(hpf1, hpf2, psd, f_min, f_max)
    overlap_hc, match_hc = overlap_match_func(hcf1, hcf2, psd, f_min, f_max)

    return a, nu, match_hp, match_hc, overlap_hp, overlap_hc

In [2]:

#下面用于[文件名解析参数]的函数要根据实际情况进行修改################################################################################
def parse_file_name_func(filename):
    ##下面要根据实际情况修改
    anu = filename.replace("hbh60_NS14_a","").replace("_f20Hz_fisco.dat","").split("_nu")    
    a = float("0." + anu[0]) 
    nu = float("0.0" +anu[1])
    return a, nu

path1 = "/home/ysc/Desktop/nw5/heco60_NS14_a60s50_R9_nu250_f20Hz_fisco.dat"  #被比较的波形文件的路径
dirpath2 = "/home/ysc/Desktop/data/"                                          #不同参数波形文件的文件夹路径
##################################################################################################################################################

In [3]:

#将dirpath2下的文件的文件名转换成一个列表 file_name_list
file_name_list = os.listdir(dirpath2)
anslist = []


#计时
time0 = time.time()
for item in file_name_list:
    try:
        ans = compare_multi(path1, dirpath2, item, parse_file_name_func)
        print(ans)
        anslist.append(ans)
    except Exception:
        print(item) #如果打印文件名，则说明出错了，请查看上面一个代码框排错    
timet = time.time()
print("cost %4.2f s"%(timet-time0))


#排序, 转换为numpy序列
anslist = sorted(anslist)
ansarray = np.array(anslist)

#输出到文件 读取用np.loadtxt
np.savetxt("a_nu_match_hp_hc_overlap_hp_hc.data",ansarray, fmt='%s')

#结果分别存储给a,nu等，用于作图
a = ansarray[:,0]
nu = ansarray[:,1]
hp_match = ansarray[:,2]
hc_match = ansarray[:,3]
hp_overlap = ansarray[:,4]
hc_overlap = ansarray[:,5]

(0.59, 0.013, 0.12119046422362557, 0.12137455068170741, -0.059139567578239766, -0.061378184440501964)
(0.62, 0.0385, 0.21758513311907823, 0.21642386906921335, 0.1395709874566834, 0.13349575330638405)
(0.61, 0.022, 0.27683064249145795, 0.27926320857158576, -0.09130047480361635, -0.09238766204931068)
(0.7, 0.04, 0.24974500538211972, 0.2488102462344809, -0.15306255686672718, -0.1626028922270148)
(0.69, 0.0265, 0.645094790660162, 0.6446543924124662, -0.0010565426256887828, 0.0018073549291134062)
(0.54, 0.0115, 0.12340787520824789, 0.12211039291814481, 0.04646890718742447, 0.041670460808053755)
(0.7, 0.0115, 0.09133345495604435, 0.09109470211042893, -0.0021031648400363016, -0.006336742257936639)
(0.55, 0.013, 0.13762201675329327, 0.1381079551241598, 0.026494820232602582, 0.027286490240252096)
(0.56, 0.01, 0.10364326233499592, 0.10365185686918144, 0.06777752507493015, 0.06689683207390637)
(0.62, 0.0205, 0.20995020230593536, 0.20860740038053652, -0.069151663649318, -0.07245478686025933)
(0.59, 0.0115, 0.10643852301124217, 0.1076296744056171, -0.033754172104148356, -0.032367608287910306)
(0.51, 0.031, 0.20210700923804847, 0.19837334055976538, -0.00524021781732167, -0.007566782067433522)
(0.53, 0.0115, 0.12983700591734773, 0.1276446989616602, -0.038809520632069584, -0.03782779902554615)
(0.58, 0.034, 0.21428856724424722, 0.2103496094643983, 0.02228940858728011, 0.020942928178566658)
(0.6, 0.0115, 0.1029854436697672, 0.10296580929121, 0.05114282318507108, 0.05113975916114084)
(0.52, 0.0205, 0.4190681286542925, 0.4171653474648478, -0.20041456430037727, -0.20854997636292008)
(0.59, 0.04, 0.18553518112452388, 0.18665220381369962, 0.06788797072500258, 0.06156252429073366)
(0.54, 0.01, 0.10623683035267767, 0.10647892012823072, 0.08029639603744211, 0.07611647420519208)
(0.63, 0.013, 0.10768444683553151, 0.10797105745715646, -0.002815103877742053, -0.0010212483015520821)
(0.64, 0.037, 0.23785177043948086, 0.23394360884186033, 0.21998388105983113, 0.21470672053204726)
(0.58, 0.0235, 0.5714023292773834, 0.5704847126815322, 0.17359217325924167, 0.17183862733148697)
(0.64, 0.0355, 0.24505941638822495, 0.2437329698820278, 0.1600206954270197, 0.1591392031608739)
(0.7, 0.0235, 0.25397235383709293, 0.25918703735943455, 0.007766064870913648, 0.0015127989446040073)
(0.69, 0.025, 0.3335201210902333, 0.33644308383660887, 0.017935090081461817, 0.01814007205652677)
(0.62, 0.013, 0.11071677902050062, 0.11071185763109168, -0.03537366309796409, -0.03640097572037633)
(0.59, 0.0205, 0.24977989773853515, 0.24796844617807062, -0.13019804648879493, -0.12894807461897206)
(0.57, 0.0115, 0.11150128728688788, 0.11265261964147581, -0.07112804452322712, -0.07214143364275069)
(0.64, 0.0235, 0.29078746602912997, 0.29020000604087404, 0.037475690321873895, 0.04056845054353718)
(0.51, 0.01, 0.11759185740505192, 0.11670992811639222, -0.08726767587493543, -0.08591654427779093)
(0.57, 0.0145, 0.14824021626597064, 0.14723408573301577, 0.08678780863234366, 0.08625129845182707)
(0.65, 0.01, 0.08630717390675975, 0.08556135897136258, -0.010203515336568002, -0.012643213025702758)
(0.53, 0.0235, 0.7509780732684807, 0.755273114279476, 0.18051638089937405, 0.1756986047258646)
(0.54, 0.0355, 0.19138346778339613, 0.188484664542411, 0.011498334788031197, 0.012349371868805126)
(0.53, 0.016, 0.20575909406554246, 0.2055199984081956, 0.1056788800625932, 0.1012057644182053)
(0.7, 0.022, 0.20872046002679184, 0.20657183693967315, 0.0070963527922684785, 0.001299480810605796)
(0.53, 0.0175, 0.24415464635801903, 0.2441006905797504, -0.005800760923731614, -0.010539050279142528)
(0.67, 0.0115, 0.09139003312237856, 0.0908123842629312, 0.0012323832899918687, 0.002828308800070422)
(0.5, 0.0205, 0.4662835882953531, 0.4663436189732639, -0.12183009141468484, -0.12562057216909536)
(0.68, 0.037, 0.28295915295831553, 0.2818315665124183, 0.2550837531110224, 0.2595424656999343)
(0.7, 0.025, 0.3724520823878405, 0.37155260563342074, 0.04447506876253021, 0.04769332800826984)
(0.64, 0.0325, 0.3000666431540701, 0.30694303002794127, 0.05305996383534428, 0.04547433537778312)
(0.61, 0.019, 0.190638060111729, 0.19231075992829227, 0.08607013795844816, 0.08536935476940044)
(0.67, 0.037, 0.2847229202048577, 0.2832375484939852, 0.07665684373161213, 0.07991407162869027)
(0.58, 0.0385, 0.18648344184614885, 0.18868130065621797, -0.07644382673161397, -0.07272691724600781)
(0.69, 0.031, 0.45137178715681653, 0.44901716827367283, -0.15910992415203692, -0.16682148890281323)
(0.53, 0.01, 0.11385759437272717, 0.11357333693845402, 0.06414090511017438, 0.059561380905267795)
(0.54, 0.025, 0.4421208040647681, 0.44324237023881946, -0.013291177984161092, -0.012548577798687248)
(0.53, 0.037, 0.18124862460608301, 0.1775456432079549, 0.04614413484994326, 0.0412982646557586)
(0.58, 0.0145, 0.13925077883436407, 0.14066729367764, 0.08901603744172068, 0.08609632326177485)
(0.69, 0.037, 0.29327186645929565, 0.2909939424634881, 0.059881159706949266, 0.05435980579140671)
(0.7, 0.0205, 0.17680593877664513, 0.1741003622591056, -0.017464343252537273, -0.02238104636921748)
(0.52, 0.0295, 0.2208171058578402, 0.22201568178210324, -0.002436733883659509, 0.0009621088997947224)
(0.61, 0.016, 0.1448422932882041, 0.14425733843052682, 0.02575150103440259, 0.028461172315470264)
(0.68, 0.0265, 0.6996677216194196, 0.7047550830691108, 0.10219916320698716, 0.10214785623179837)
(0.6, 0.016, 0.14939568363817174, 0.14930214649639148, -0.06545846682329443, -0.06866407523871566)
(0.62, 0.025, 0.6118009984604965, 0.6181085938742221, 0.050361861170916664, 0.05329502091267177)
(0.58, 0.0115, 0.11010432951572784, 0.1097735887082642, -0.0478406520061797, -0.04704301840162858)
(0.59, 0.0265, 0.4142359077654169, 0.4224639588513666, 0.17061000352743916, 0.16422991645067747)
(0.68, 0.013, 0.09821437657950703, 0.09821095839147094, 0.004033665409112524, -0.00038536142856477777)
(0.5, 0.0385, 0.17101212555588782, 0.1716688848111951, -0.025809041997526375, -0.0226889986637866)
(0.68, 0.031, 0.4198250808246454, 0.4170386221769133, -0.1596534768143479, -0.1582374266422638)
(0.68, 0.0295, 0.511832314709998, 0.5076699624824956, 0.31538256264688863, 0.31637069735282264)
(0.59, 0.01, 0.09579232352850041, 0.09517421607183338, 0.020596288821102416, 0.016346642599806144)
(0.61, 0.031, 0.2866136926921124, 0.2840803758352071, 0.11501964617917808, 0.11102977580446874)
(0.6, 0.0175, 0.16810674683630236, 0.17011678428488924, 0.08674084556888481, 0.08654514121142132)
(0.51, 0.0295, 0.21656871898185087, 0.21388348778373353, 0.018091315938893234, 0.012415737259833326)
(0.69, 0.04, 0.271267091332884, 0.2697909611679889, 0.07723186640264793, 0.07643418490744056)
(0.64, 0.0115, 0.09664807733214768, 0.0967199877547896, -0.020954388634928437, -0.025140835728628266)
(0.6, 0.037, 0.2042974658860456, 0.2090964569364395, 0.0929215013971266, 0.08591541085556721)
(0.57, 0.0325, 0.22137107759356228, 0.21862396083609806, 0.049630268282994085, 0.04628373277540802)
(0.53, 0.0325, 0.19261951273321443, 0.1992837152118281, 0.05800290850684282, 0.04997113073995793)
(0.56, 0.0145, 0.15666392674078275, 0.15640356950884268, 0.12616098843985463, 0.1223790367465454)
(0.52, 0.0385, 0.17447875667081558, 0.17459579880938605, -0.006053203392096717, -0.0029504566667488056)
(0.63, 0.034, 0.2575915921480707, 0.26301238249921827, 0.005461485467533054, -0.00027622504233128134)
(0.55, 0.031, 0.21861070549768596, 0.216272383543179, 0.03421948766823841, 0.03332021787114305)
(0.6, 0.031, 0.26116265614640377, 0.2623967517490725, 0.09686400375720314, 0.09144992778732618)
(0.59, 0.0175, 0.17688655242048953, 0.17714348277537256, 0.08185946120893269, 0.07716947565310352)
(0.67, 0.025, 0.3483174817367832, 0.3466688956929144, 0.060325727344259124, 0.06147402557639403)
(0.51, 0.022, 0.601923506893896, 0.6049122480278452, 0.13791804697946838, 0.1407823256876878)
(0.67, 0.0175, 0.13656607995788572, 0.13534571825974961, 0.017370351014098683, 0.014118299135733385)
(0.52, 0.0115, 0.1373004740030509, 0.13537683759804975, 0.12679998259871442, 0.12238774547813566)
(0.58, 0.0205, 0.27738584451047726, 0.27709114941843044, -0.11834759587453722, -0.12234065303310936)
(0.69, 0.016, 0.11906614740036314, 0.11904097220847987, 0.016853186461434938, 0.01889090310689391)
(0.68, 0.016, 0.1221508992057365, 0.12138537923699144, 0.009215191595929215, 0.011385141863883385)
(0.63, 0.0235, 0.2954175334065528, 0.29564647277279116, 0.004052149892296597, 0.006168111650780272)
(0.61, 0.0265, 0.5596963272593274, 0.561388193176305, -0.10782458582577985, -0.11145817164650387)
(0.59, 0.0145, 0.13790660885678374, 0.13825393431771213, 0.0347934276648615, 0.03688127930673523)
(0.61, 0.04, 0.19592966519049165, 0.19896969364257755, 0.12897413466175645, 0.12126974319295987)
(0.57, 0.0175, 0.20036487692945035, 0.2003290160474027, 0.03874246208838217, 0.03288437218356917)
(0.58, 0.0175, 0.18387723335349265, 0.18603721852245209, -0.12320734071035692, -0.12339898429908543)
(0.59, 0.025, 0.8789318140975965, 0.8847616657236804, 0.5012227099814291, 0.49719531239494036)
(0.57, 0.0295, 0.26076611990504933, 0.2672223311713446, 0.08650176668698951, 0.07919105944294753)
(0.64, 0.04, 0.21107254886636376, 0.21452934373447272, 0.19165071647714993, 0.1858044554891873)
(0.69, 0.028, 0.6587916506613273, 0.6591108302417218, 0.20753347142111925, 0.21133881616261793)
(0.6, 0.0205, 0.23021217008234535, 0.23255144350311213, -0.11951256728754225, -0.12163657883944738)
(0.67, 0.0325, 0.3560901096292634, 0.35610404159298226, -0.2699297177212757, -0.27914624798785015)
(0.57, 0.0355, 0.19549443870390248, 0.2012439920481836, 0.07594817345655556, 0.06882555597601799)
(0.53, 0.028, 0.2533396023849137, 0.25087644305142714, 0.013998785421173755, 0.009729735378961631)
(0.55, 0.0175, 0.223246833654225, 0.22080267772292256, 0.12042133376950971, 0.11545183569255596)
(0.69, 0.0175, 0.13298649612884755, 0.1320724877330576, -0.012663866003327391, -0.017557509816730286)
(0.66, 0.034, 0.3138159888346322, 0.31227951224253836, 0.06515218956032715, 0.06848126175546738)
(0.68, 0.04, 0.2618703749947095, 0.2608019852229993, 0.12044992483318019, 0.12419527616732591)
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._hbh60_NS14_a60_nu250_f20Hz_fisco.dat
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(0.63, 0.025, 0.500226895497532, 0.4986743379656583, 0.08276517588561079, 0.08449157720328719)
(0.5, 0.0325, 0.18135796120462563, 0.18043150605940406, 0.01290091158306104, 0.0057236303006584525)
(0.66, 0.0265, 0.7371596595734102, 0.7406228062838217, 0.18887776838788647, 0.18298119092841275)
(0.51, 0.0325, 0.1920707064138163, 0.19178243734274777, -0.02092255125723646, -0.019490253221585)
(0.59, 0.0355, 0.20186518041574036, 0.20728770832783644, 0.10130293865468176, 0.09349345509235964)
(0.66, 0.0175, 0.1386223831853854, 0.14019520115915438, 0.032159633534897904, 0.032904041444818795)
(0.65, 0.025, 0.39545469815587275, 0.39510666700787933, -0.011041988119829935, -0.0177542697287867)
(0.53, 0.0145, 0.17111257675404618, 0.1719332568485969, -0.1608686254878661, -0.15884826576384511)
(0.64, 0.022, 0.2302134819258402, 0.229914636063181, 0.055577652520321943, 0.05585978738242847)
(0.51, 0.0145, 0.19126959535154414, 0.19309614851461346, -0.08174431457884977, -0.08495953383255016)
(0.57, 0.0235, 0.6072084259579408, 0.607900631017592, 0.570122222592446, 0.565658677894036)
(0.61, 0.0325, 0.25254219260294347, 0.2492842669691462, -0.059785508486854294, -0.060187988994761556)
(0.64, 0.0295, 0.4126320622085741, 0.4120332622837592, 0.312463219937546, 0.316038401952483)
(0.55, 0.01, 0.10882285644217302, 0.10889921942874473, 0.08296288305452072, 0.0794336222919122)
(0.67, 0.016, 0.12457977289339811, 0.12398268960281394, 0.025137140755005775, 0.025054966913802035)
(0.59, 0.028, 0.3226134139030034, 0.3299596735652543, 0.15725666255785706, 0.15054272859544804)
(0.59, 0.037, 0.19508134466857482, 0.2022181300328618, 0.09728470068602768, 0.08912255651228232)
(0.54, 0.019, 0.28732649861932275, 0.28380244812917527, 0.19958920583883866, 0.19408859860823577)
(0.51, 0.016, 0.2324432154364541, 0.2362062559038737, -0.21090517428656108, -0.21196800356126125)
(0.57, 0.013, 0.12949048148715023, 0.12874106751211006, -0.08183754523646987, -0.08303108903810358)
(0.58, 0.016, 0.162576277800838, 0.16334053860317232, -0.09917049001106375, -0.10061581523300929)
(0.7, 0.019, 0.15535788242503376, 0.15304996779974483, -0.01632220082166932, -0.020028374538103196)
(0.69, 0.0355, 0.3233285425065716, 0.32162684308052986, -0.20160790684244276, -0.20362274804606878)
(0.7, 0.0175, 0.136974734994595, 0.1386801187200681, 0.009775804374669657, 0.01241217495127839)
(0.57, 0.0205, 0.2853307209483595, 0.28594859350244, -0.1844039719597942, -0.18558375242589048)
(0.63, 0.0175, 0.15023420731413636, 0.1522190614676782, -0.00830787276487441, -0.00657040480698327)
(0.53, 0.0385, 0.176539389799992, 0.17322202451937158, 0.01850207067976121, 0.018196839192076483)
(0.66, 0.037, 0.26883595543713507, 0.26526368069708506, 0.2618717860914679, 0.25970719484039795)
(0.64, 0.028, 0.5081296748520001, 0.5073772971843802, 0.46737758662922096, 0.4720255767428943)
(0.52, 0.013, 0.15857991974523852, 0.1572590840152047, 0.1082827909208201, 0.10430629624390306)
(0.55, 0.0355, 0.18834793602731278, 0.19004646069523987, -0.047603213984395265, -0.04450400327815841)
(0.65, 0.013, 0.10323028055899175, 0.10345279420643885, 0.025521110458182387, 0.0271372503815048)
(0.68, 0.019, 0.15260685722279851, 0.15145480361966518, 0.00231541930220964, -0.0031904513124004033)
(0.53, 0.022, 0.5489773908207924, 0.5504470567085625, -0.2817181256017599, -0.28125338625473834)
cost 271.45 s

In [4]:

#绘图字体参数设置
matplotlib.rcParams.update({'font.size': 15, 'font.family': 'STIXGeneral', 'mathtext.fontset': 'stix'})

x = a
y = nu
z = hp_match

cmap = matplotlib.cm.get_cmap('jet')#viridis
normalize = matplotlib.colors.Normalize(vmin=0, vmax=1)
colors = [cmap(normalize(value)) for value in z]



fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot()
ax.scatter(x, y, color=colors)
ax.set_xlabel("$a$")
ax.set_ylabel("$\\nu$")
ax.set_xlim([0.49,0.71])
ax.set_ylim([0.01-0.001,0.04 + 0.001])
#ax.set_title("hp_match")
plt.xticks([0.5,0.55,0.6,0.65,0.7])
plt.yticks([0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04])

# Optionally add a colorbar
cax, item = matplotlib.colorbar.make_axes(ax)
cbar = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=normalize)

ax.grid(linestyle = "dotted", color = "#d3d3d3" , which="both")
plt.show()

In [7]:

x = a
y = nu
z = hp_match


xstep = (max(x)-min(x))/21/2 #调整刻度，符合科学性
ystep = (max(y)-min(y))/21/2


plt.figure(figsize=(7.32,5.55))
plt.hist2d(x,y, bins=[21,21], weights = z, cmap=plt.cm.jet)
plt.xlabel("$a$")
plt.ylabel("$\\nu$")
plt.xlim([0.50,0.70])
plt.ylim([0.01, 0.04])
plt.xticks(np.linspace(0.5+xstep, 0.7-xstep,5),["0.50","0.55", "0.60", "0.65", "0.70"])
plt.yticks(np.linspace(0.01+ystep, 0.04-ystep,7),["0.010", "0.015", "0.020", "0.025", "0.030", "0.035", "0.040"])

#add a colorbar
cbar =plt.colorbar()
cbar.ax.text(3.5, 0.45, 'match', rotation=90)
cbar.set_ticks([min(z), 0.3, 0.5, 0.7,max(z)])
cbar.set_ticklabels([round(min(z),2), "0.30", "0.50", "0.70",round(max(z),2)])

#plt.grid(linestyle = "dotted", color = "#d3d3d3" , which="both")
plt.tight_layout()
plt.savefig('hp_match.pdf', format = 'pdf', dpi = 300)     #保存图像
plt.show()