104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
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import sys
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sys.path.insert(0,"../../../")
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import collections
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import dispersiveXanes_utils as utils
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import xppl37_spectra
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import xanes_analyzeRun
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import trx
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import datastorage as ds
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nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
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nice_colors = "#1f78b4 #a6cee3 #b2df8a #33a02c".split()
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gradual_colors = ['#014636', '#016c59', '#02818a', '#3690c0', '#67a9cf', '#a6bddb', '#d0d1e6']#, '#ece2f0']
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def get_data(run,threshold=0.02,force=False):
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if run == 80:
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refCalibs=slice(1,None,2)
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elif run == 84:
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refCalibs=slice(None,None,2)
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elif run == 76:
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refCalibs=slice(None,None)
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else:
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refCalibs=slice(None,None,2)
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fname = "../data/fig_fel_modes_run%04d.h5" % run
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if not os.path.isfile(fname) or force:
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r = xanes_analyzeRun.AnalyzeRun(run=run)
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r.load()
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E = r.E
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calibs = list(r.results.keys())
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calibs.sort()
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calibs = calibs[refCalibs]
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p1 = np.vstack( [r.results[c].p1 for c in calibs] )
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p2 = np.vstack( [r.results[c].p2 for c in calibs] )
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#temp = ds.DataStorage( E=E,p1=p1.astype(np.float16),p2=p2.astype(np.float16))
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temp = ds.DataStorage( E=E,p1=p1,p2=p2)
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_,_,Abs = xppl37_spectra.calcAbs( temp, threshold=0.02 )
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temp.Abs = Abs #.astype(np.float16)
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temp.info="Abs calculated with threshold = 0.02"
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temp.save(fname)
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data = ds.read(fname)
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# nan is saved as -1 for masked arrays
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for k in data.keys():
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try:
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data[k][data[k]==-1] =np.nan
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except (TypeError,AttributeError):
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pass
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print("Run %d → nshots = %d"%(run,len(data.p1)))
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p1,p2,Abs = xppl37_spectra.calcAbs( data, threshold=threshold )
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data.Abs = Abs
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return data
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def fig_fel_modes(shots_per_run = slice(10,13),showAv=True,force=False,threshold=0.02,smootWidth=0.3):
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runs = [28,39,54,76,80,84]
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runs = [80,76,84]
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#runs = [28,54,76,80,84]
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figsize = [6,8]
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fig,axes = plt.subplots( len(runs),2 , sharex=True, sharey=True,figsize=figsize)
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#fig,axes = plt.subplots( 2,len(runs) , sharex=True, sharey='row')
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#axes = axes.T
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for run,ax in zip(runs,axes):
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data = get_data(run,threshold=threshold,force=force)
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E = data.E
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s2 = data.p2[shots_per_run]
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s1 = data.p1[shots_per_run]
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norm = s2.max()*1.1
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if showAv: ax[0].fill_between(E,0,data.p2.mean(0)/norm,color='#d95f0e',alpha=0.4)
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for ispectrum,(spectrum1,spectrum2,a) in enumerate(zip(s1,s2,data.Abs)):
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c = nice_colors[ispectrum]
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ax[0].axhline(ispectrum,ls='--',lw=0.5,color=c)
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# ax[1].axhline(ispectrum,ls='--',lw=0.5,color=c)
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ax[0].plot(E,spectrum1/norm+ispectrum,lw=2,color=c)
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ax[1].axhline(0.25+ispectrum,ls='--',lw=1,color=c)
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# smooth does not work with nan's...
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#if smootWidth > 0:
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# a = xppl37_spectra.smoothSpectra(E,a,res=smootWidth)[0]
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ax[1].plot(E,a+0.25+ispectrum,lw=2,color=c)
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noise = np.nanstd(a)
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ax[1].text(7135,0.4+ispectrum,"σ = %.2f"%noise)
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ax[0].set_ylabel("Spectrum (a.u.) (run %d)"%run)
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ax[1].set_ylabel("Absorption (run %d)"%run)
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ax[0].grid(color="0.8",lw=0.5)
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ax[1].grid(axis='x',color="0.8",lw=0.5)
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tosave = np.vstack( (data.p2.mean(0)/norm,s2/norm) )
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trx.utils.saveTxt("../data/fig_fel_modes_run%04d_spectra.txt"%run,E,tosave,info="E average_spectrum spectra")
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trx.utils.saveTxt("../data/fig_fel_modes_run%04d_abs.txt"%run,E,data.Abs[shots_per_run],info="# threshold = %.2f\n# E Abs"%threshold)
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axes[0,0].set_yticks(())
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ax[0].set_xlim(7070,7180)
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ax[0].set_ylim(-0.2,3.2)
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axes[-1,0].set_xlabel("Energy (keV)")
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axes[-1,1].set_xlabel("Energy (keV)")
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#plt.subplots_adjust(left=0.07,right=0.95
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plt.tight_layout()
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plt.savefig("fig_fel_modes.png",transparent=True,dpi=300)
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plt.savefig("fig_fel_modes.pdf",transparent=True)
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if __name__ == "__main__": fig_fel_modes()
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