figures_v1
This commit is contained in:
Marco Cammarata
parent
b53354da9b
commit
23e631e3bd
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import sys
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sys.path.insert(0,"../../../")
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import numpy as np
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import matplotlib.pyplot as plt
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import xanes_analyzeRun
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def run80():
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pars = dict( transx = 60, transy = -20, scalex = 0.9, scaley = 1.1, rotation = -0.03 )
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r=xanes_analyzeRun.AnalyzeRun(80,initAlign=pars)
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# NOTE: calib0 has sample in, so alignment has to be done with odd calibcycles
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r.doShots(shots=slice(3,10),calib=1,doFit=True)
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r.analyzeScan()
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r.save(overwrite=True)
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def run82():
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pars = dict( rotation = -0.03, scaley = 0.85, transx = 48, transy = 25.8 )
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r=xanes_analyzeRun.AnalyzeRun(82,initAlign=pars)
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r.doShots(shots=slice(5,10),calib=0,doFit=True)
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r.analyzeScan()
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r.save(overwrite=True)
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def run84():
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# run 84 is IN/OUT scan and theta scan
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r=xanes_analyzeRun.AnalyzeRun(84)
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r.analyzeScan(calibsToFit='even', nImagesToFit=5)
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r.save(overwrite=True)
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@ -0,0 +1,877 @@
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#calib2 target2_RT
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7.3916571339748 0.906394205387994
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7.82088423 0.382290098389458
|
||||
7.82400465 0.380210098389458
|
||||
7.82712746 0.377720498389458
|
||||
7.83025312 0.375316898389458
|
||||
7.83339596 0.372908498389458
|
||||
7.83652687 0.370708098389458
|
||||
7.83967447 0.368110098389458
|
||||
7.84283972 0.365290898389458
|
||||
7.84599257 0.363111298389458
|
||||
7.84916353 0.360150898389458
|
||||
7.85233593 0.357358498389458
|
||||
7.85552645 0.354880098389458
|
||||
7.85871983 0.352119698389458
|
||||
7.86191511 0.348837298389458
|
||||
7.86511374 0.345962098389458
|
||||
7.86832952 0.342442498389458
|
||||
7.87154818 0.340040898389458
|
||||
7.87476921 0.337241698389458
|
||||
7.87799311 0.334465698389458
|
||||
7.88123417 0.331460098389458
|
||||
7.88447857 0.329004498389458
|
||||
7.88772535 0.326027298389458
|
||||
7.89098978 0.322916098389458
|
||||
7.89425755 0.319540498389458
|
||||
7.89752769 0.316738498389458
|
||||
7.9008007 0.314084098389458
|
||||
7.90409088 0.311810098389458
|
||||
7.90738392 0.309446098389458
|
||||
7.91068029 0.307140898389458
|
||||
7.91399431 0.304106098389458
|
||||
7.91729593 0.301492098389458
|
||||
7.92061567 0.298506898389458
|
||||
7.92395306 0.296130098389458
|
||||
7.92727852 0.293153298389458
|
||||
7.9306221 0.290319698389458
|
||||
7.93396759 0.288179698389458
|
||||
7.93731737 0.285890498389458
|
||||
7.94068384 0.283201698389458
|
||||
7.94405365 0.280762098389458
|
||||
7.94742632 0.278813298389458
|
||||
7.95080233 0.275940498389458
|
||||
7.95419645 0.273586098389458
|
||||
7.95759249 0.270887298389458
|
||||
7.96099281 0.268601698389458
|
||||
7.96439552 0.266184898389458
|
||||
7.96781635 0.263566898389458
|
||||
7.97124052 0.261264098389458
|
||||
7.97466707 0.258779698389458
|
||||
7.97809696 0.256287698389458
|
||||
7.98154497 0.253673298389458
|
||||
7.98499632 0.251136498389458
|
||||
7.98845053 0.248524498389458
|
||||
7.99192333 0.246064498389458
|
||||
7.99538374 0.243000498389458
|
||||
7.99886274 0.240316498389458
|
||||
8.00234413 0.237296498389458
|
||||
8.00584507 0.235004498389458
|
||||
8.00933361 0.232504498389458
|
||||
8.01283932 0.230012498389458
|
||||
8.01634979 0.227616498389458
|
||||
8.01987839 0.225064498389458
|
||||
8.02339363 0.222080498389458
|
||||
8.0269289 0.219612498389458
|
||||
8.03046703 0.216904498389458
|
||||
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|
||||
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|
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8.04113007 0.208740498389458
|
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8.04469585 0.205852498389458
|
||||
8.04826546 0.203108498389458
|
||||
8.05183792 0.199920498389458
|
||||
8.05542946 0.197300498389458
|
||||
8.05902481 0.194796498389458
|
||||
8.06262207 0.191452498389458
|
||||
8.06623936 0.188580498389458
|
||||
8.06984425 0.186156498389458
|
||||
8.07346725 0.182920498389458
|
||||
8.07709408 0.180528498389458
|
||||
8.08072472 0.177624498389458
|
||||
8.08437443 0.175192498389458
|
||||
8.08801079 0.172852498389458
|
||||
8.09166622 0.170144498389458
|
||||
8.09534168 0.167548498389458
|
||||
8.09900475 0.164664498389458
|
||||
8.10268593 0.162172498389458
|
||||
8.10635567 0.160428498389458
|
||||
8.11004448 0.158220498389458
|
||||
8.11375237 0.155584498389458
|
||||
8.11744785 0.152932498389458
|
||||
8.12116241 0.150068498389458
|
||||
8.12488079 0.147552498389458
|
||||
8.12860298 0.145132498389458
|
||||
8.13232803 0.142168498389458
|
||||
8.13607311 0.139104498389458
|
||||
8.13982105 0.136792498389458
|
||||
8.14355659 0.134440498389458
|
||||
8.14732838 0.131940498389458
|
||||
8.15108681 0.129424498389458
|
||||
8.15486526 0.126712498389458
|
||||
8.15863132 0.123936498389458
|
||||
8.16241646 0.121528498389458
|
||||
8.16622162 0.118820498389458
|
||||
8.17001438 0.116136498389458
|
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8.17382622 0.113816498389458
|
||||
8.17762661 0.111112498389458
|
||||
8.18144608 0.108304498389458
|
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8.18527031 0.105476498389458
|
||||
8.18911266 0.102916498389458
|
||||
8.19294357 0.100232498389458
|
||||
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|
||||
8.20064735 0.0947964983894583
|
||||
8.20450592 0.0918764983894583
|
||||
8.20838356 0.0887204983894583
|
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8.2122488 0.0866364983894583
|
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8.21613407 0.0835964983894583
|
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8.22002316 0.0806604983894583
|
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8.22781181 0.0752724983894583
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8.23172855 0.0727124983894583
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8.23564911 0.0701444983894583
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8.24743271 0.0621124983894584
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8.25136852 0.0591364983894583
|
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8.25532436 0.0568404983894583
|
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8.25926781 0.0542844983894583
|
||||
8.26323223 0.0516004983894583
|
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8.26721573 0.0487284983894583
|
||||
8.27118778 0.0462804983894583
|
||||
8.2751627 0.0435484983894582
|
||||
8.27915859 0.0409284983894583
|
||||
8.2831583 0.0384084983894583
|
||||
8.28716183 0.0361284983894583
|
||||
8.29116917 0.0335084983894584
|
||||
8.29519749 0.0306284983894583
|
||||
8.29921341 0.0281524983894583
|
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8.30324936 0.0252244983894583
|
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8.30728912 0.0225844983894583
|
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8.31133366 0.0201124983894584
|
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8.315382 0.0170924983894583
|
||||
8.31943417 0.0144364983894583
|
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8.32350731 0.0116324983894583
|
||||
8.32758427 0.00902449838945829
|
||||
8.33166504 0.00640849838945829
|
||||
8.33575058 0.00384049838945835
|
||||
8.33983994 0.00111649838945826
|
||||
8.34393311 -0.00127150161054175
|
||||
8.34804726 -0.00401950161054163
|
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8.35216618 -0.00691550161054169
|
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8.35627174 -0.00932750161054168
|
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8.36041546 -0.0118635016105417
|
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8.36454678 -0.0149875016105417
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8.36868191 -0.0176635016105417
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8.37283802 -0.0202675016105417
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8.37698174 -0.0227275016105416
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8.38114548 -0.0255075016105417
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8.38531494 -0.0278755016105417
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8.38948822 -0.0304195016105417
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8.39366531 -0.0334595016105417
|
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8.39786434 -0.0361035016105417
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8.40205002 -0.0388035016105417
|
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8.40625763 -0.0414155016105417
|
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8.4104681 -0.0438995016105417
|
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8.4146843 -0.0466515016105417
|
||||
8.4189043 -0.0490795016105417
|
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8.42314625 -0.0518115016105417
|
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8.42737484 -0.0546515016105417
|
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8.43162537 -0.0572195016105416
|
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8.43586254 -0.0599475016105417
|
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8.44012165 -0.0628395016105417
|
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8.44438553 -0.0653515016105417
|
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8.44867039 -0.0681035016105417
|
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8.45294285 -0.0709435016105417
|
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8.45722008 -0.0734315016105417
|
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8.46151829 -0.0759235016105417
|
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8.46582127 -0.0787675016105417
|
||||
8.47012806 -0.0814155016105417
|
||||
8.47444057 -0.0839915016105417
|
||||
8.4787569 -0.0865595016105416
|
||||
8.48307705 -0.0896235016105416
|
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8.48742008 -0.0921595016105417
|
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8.49174976 -0.0945955016105417
|
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8.49610138 -0.0972515016105417
|
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8.50045872 -0.0998435016105416
|
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8.50480175 -0.102287501610542
|
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8.50918484 -0.105159501610542
|
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8.51355457 -0.107935501610542
|
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8.51792908 -0.110683501610542
|
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8.52232647 -0.113435501610542
|
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8.52671051 -0.115767501610542
|
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8.53111649 -0.118399501610542
|
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8.53552723 -0.121263501610542
|
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8.53994274 -0.124015501610542
|
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8.54436302 -0.126567501610542
|
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8.54878807 -0.128851501610542
|
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8.55321693 -0.131259501610542
|
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8.55766964 -0.134259501610542
|
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8.56210804 -0.136947501610542
|
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8.56656933 -0.139703501610542
|
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8.57103539 -0.142195501610542
|
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8.57550621 -0.144707501610542
|
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8.5799818 -0.147199501610542
|
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8.58446217 -0.149699501610542
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8.58894825 -0.152459501610542
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8.59345531 -0.154903501610542
|
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8.59794998 -0.157759501610542
|
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8.60246754 -0.160291501610542
|
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8.60698986 -0.162655501610542
|
||||
8.61149979 -0.165463501610542
|
||||
8.61603165 -0.168275501610542
|
||||
8.62056828 -0.171131501610542
|
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8.62512875 -0.173671501610542
|
||||
8.62967491 -0.176155501610542
|
||||
8.6342268 -0.178579501610542
|
||||
8.63880062 -0.181339501610542
|
||||
#
|
||||
#
|
||||
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|
After Width: | Height: | Size: 376 KiB |
|
|
@ -0,0 +1,103 @@
|
|||
import sys
|
||||
sys.path.insert(0,"../../../")
|
||||
import collections
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import dispersiveXanes_utils as utils
|
||||
import xppl37_spectra
|
||||
import xanes_analyzeRun
|
||||
import trx
|
||||
import datastorage as ds
|
||||
|
||||
|
||||
nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
|
||||
nice_colors = "#1f78b4 #a6cee3 #b2df8a #33a02c".split()
|
||||
gradual_colors = ['#014636', '#016c59', '#02818a', '#3690c0', '#67a9cf', '#a6bddb', '#d0d1e6']#, '#ece2f0']
|
||||
|
||||
def get_data(run,threshold=0.02,force=False):
|
||||
if run == 80:
|
||||
refCalibs=slice(1,None,2)
|
||||
elif run == 84:
|
||||
refCalibs=slice(None,None,2)
|
||||
elif run == 76:
|
||||
refCalibs=slice(None,None)
|
||||
else:
|
||||
refCalibs=slice(None,None,2)
|
||||
fname = "../data/fig_fel_modes_run%04d.h5" % run
|
||||
if not os.path.isfile(fname) or force:
|
||||
r = xanes_analyzeRun.AnalyzeRun(run=run)
|
||||
r.load()
|
||||
E = r.E
|
||||
calibs = list(r.results.keys())
|
||||
calibs.sort()
|
||||
calibs = calibs[refCalibs]
|
||||
p1 = np.vstack( [r.results[c].p1 for c in calibs] )
|
||||
p2 = np.vstack( [r.results[c].p2 for c in calibs] )
|
||||
#temp = ds.DataStorage( E=E,p1=p1.astype(np.float16),p2=p2.astype(np.float16))
|
||||
temp = ds.DataStorage( E=E,p1=p1,p2=p2)
|
||||
_,_,Abs = xppl37_spectra.calcAbs( temp, threshold=0.02 )
|
||||
temp.Abs = Abs #.astype(np.float16)
|
||||
temp.info="Abs calculated with threshold = 0.02"
|
||||
temp.save(fname)
|
||||
data = ds.read(fname)
|
||||
# nan is saved as -1 for masked arrays
|
||||
for k in data.keys():
|
||||
try:
|
||||
data[k][data[k]==-1] =np.nan
|
||||
except (TypeError,AttributeError):
|
||||
pass
|
||||
print("Run %d → nshots = %d"%(run,len(data.p1)))
|
||||
p1,p2,Abs = xppl37_spectra.calcAbs( data, threshold=threshold )
|
||||
data.Abs = Abs
|
||||
return data
|
||||
|
||||
|
||||
def fig_fel_modes(shots_per_run = slice(10,13),showAv=True,force=False,threshold=0.02,smootWidth=0.3):
|
||||
runs = [28,39,54,76,80,84]
|
||||
runs = [80,76,84]
|
||||
#runs = [28,54,76,80,84]
|
||||
figsize = [6,8]
|
||||
fig,axes = plt.subplots( len(runs),2 , sharex=True, sharey=True,figsize=figsize)
|
||||
#fig,axes = plt.subplots( 2,len(runs) , sharex=True, sharey='row')
|
||||
#axes = axes.T
|
||||
for run,ax in zip(runs,axes):
|
||||
data = get_data(run,threshold=threshold,force=force)
|
||||
E = data.E
|
||||
s2 = data.p2[shots_per_run]
|
||||
s1 = data.p1[shots_per_run]
|
||||
norm = s2.max()*1.1
|
||||
if showAv: ax[0].fill_between(E,0,data.p2.mean(0)/norm,color='#d95f0e',alpha=0.4)
|
||||
for ispectrum,(spectrum1,spectrum2,a) in enumerate(zip(s1,s2,data.Abs)):
|
||||
c = nice_colors[ispectrum]
|
||||
ax[0].axhline(ispectrum,ls='--',lw=0.5,color=c)
|
||||
# ax[1].axhline(ispectrum,ls='--',lw=0.5,color=c)
|
||||
ax[0].plot(E,spectrum1/norm+ispectrum,lw=2,color=c)
|
||||
ax[1].axhline(0.25+ispectrum,ls='--',lw=1,color=c)
|
||||
# smooth does not work with nan's...
|
||||
#if smootWidth > 0:
|
||||
# a = xppl37_spectra.smoothSpectra(E,a,res=smootWidth)[0]
|
||||
ax[1].plot(E,a+0.25+ispectrum,lw=2,color=c)
|
||||
noise = np.nanstd(a)
|
||||
ax[1].text(7135,0.4+ispectrum,"σ = %.2f"%noise)
|
||||
ax[0].set_ylabel("Spectrum (a.u.) (run %d)"%run)
|
||||
ax[1].set_ylabel("Absorption (run %d)"%run)
|
||||
ax[0].grid(color="0.8",lw=0.5)
|
||||
ax[1].grid(axis='x',color="0.8",lw=0.5)
|
||||
tosave = np.vstack( (data.p2.mean(0)/norm,s2/norm) )
|
||||
trx.utils.saveTxt("../data/fig_fel_modes_run%04d_spectra.txt"%run,E,tosave,info="E average_spectrum spectra")
|
||||
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)
|
||||
|
||||
axes[0,0].set_yticks(())
|
||||
ax[0].set_xlim(7070,7180)
|
||||
ax[0].set_ylim(-0.2,3.2)
|
||||
axes[-1,0].set_xlabel("Energy (keV)")
|
||||
axes[-1,1].set_xlabel("Energy (keV)")
|
||||
#plt.subplots_adjust(left=0.07,right=0.95
|
||||
plt.tight_layout()
|
||||
plt.savefig("fig_fel_modes.png",transparent=True,dpi=300)
|
||||
plt.savefig("fig_fel_modes.pdf",transparent=True)
|
||||
|
||||
if __name__ == "__main__": fig_fel_modes()
|
||||
|
||||
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 339 KiB |
|
|
@ -0,0 +1,144 @@
|
|||
import sys
|
||||
sys.path.insert(0,"../../../")
|
||||
import collections
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import gridspec
|
||||
|
||||
import dispersiveXanes_utils as utils
|
||||
import xppl37_spectra
|
||||
import xanes_analyzeRun
|
||||
import mcutils as mc
|
||||
import trx
|
||||
import datastorage as ds
|
||||
|
||||
|
||||
nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
|
||||
nice_colors = "#1f78b4 #a6cee3 #b2df8a #33a02c".split()
|
||||
gradual_colors = ['#014636', '#016c59', '#02818a', '#3690c0', '#67a9cf', '#a6bddb', '#d0d1e6']#, '#ece2f0']
|
||||
|
||||
def get_data(run=82,calib=1,threshold=0.02,force=False):
|
||||
fname = "../data/fig_fe_xas_run%04d.h5" % run
|
||||
if not os.path.isfile(fname) or force:
|
||||
E,p1,p2,Abs=xppl37_spectra.calcAbsForRun(run,merge_calibs=True,threshold=threshold)
|
||||
temp = ds.DataStorage( E=E,p1=p1,p2=p2,Abs=Abs)
|
||||
temp.info="Abs calculated with threshold = %.3f" % threshold
|
||||
temp.save(fname)
|
||||
data = ds.read(fname)
|
||||
# nan is saved as -1 for masked arrays
|
||||
for k in data.keys():
|
||||
try:
|
||||
data[k][data[k]==-1] =np.nan
|
||||
except TypeError:
|
||||
pass
|
||||
return data
|
||||
|
||||
|
||||
def get_ref():
|
||||
E,data=np.loadtxt("../data/Fe_ref.txt",unpack=True)
|
||||
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)
|
||||
|
||||
def get_1b():
|
||||
E,data=np.loadtxt("../data/Fe_1bunch.txt",unpack=True)
|
||||
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)
|
||||
|
||||
def fig_fe_xas(run=82,shots = slice(100,105),showAv=True,force=False,threshold=0.02,smoothWidth=0.3):
|
||||
ref = get_ref()
|
||||
color_ss = '#08519c'
|
||||
color_av = '#238b45'
|
||||
color_av_all = '#d95f0e'
|
||||
shifty = 1
|
||||
data = get_data(run,force=force)
|
||||
E = data.E;p1=data.p1;p2=data.p2;Abs=data.Abs
|
||||
p1_sum = p1.sum(-1)
|
||||
p1_av = np.nanmean(p1,axis=0)
|
||||
p2_av = np.nanmean(p2,axis=0)
|
||||
# somehow nanmedian screws up when array is too big ... so using nanmean
|
||||
abs_av = np.nanmean(Abs,axis=0)
|
||||
n = 2**np.arange(4)
|
||||
av = np.nanmedian(Abs[:],0)
|
||||
av = xppl37_spectra.smoothSpectra(E,av,res=smoothWidth)
|
||||
for ni in n:
|
||||
aa = np.nanmedian(Abs[:ni],0)
|
||||
aa = xppl37_spectra.smoothSpectra(E,aa,res=smoothWidth)
|
||||
print(ni,np.nanstd(aa-av))
|
||||
|
||||
|
||||
p1 = p1[shots]; p2=p2[shots]; Abs = Abs[shots]
|
||||
if smoothWidth > 0:
|
||||
Abs = xppl37_spectra.smoothSpectra(E,Abs,res=smoothWidth)
|
||||
idx = E< 7080
|
||||
Abs[:,idx]=np.nan
|
||||
#fig,ax = plt.subplots(1,3,sharex=True,sharey=False,squeeze=False,figsize=[6,4])
|
||||
figure = plt.figure(figsize = [7,5])
|
||||
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 1.5],)
|
||||
ax = []
|
||||
ax.append( plt.subplot(gs[0]) )
|
||||
ax.append( plt.subplot(gs[1],sharex=ax[0],sharey=ax[0]) )
|
||||
ax.append( plt.subplot(gs[2],sharex=ax[0]) )
|
||||
|
||||
#ax = ax[0]
|
||||
normalization = np.nanmax( p1 )
|
||||
to_save = []
|
||||
for ishot,(s1,s2,a) in enumerate(zip(p1,p2,Abs)):
|
||||
s1_norm = s1/normalization
|
||||
s2_norm = s2/normalization
|
||||
ax[0].axhline(ishot*shifty,ls='--',color="0.9")
|
||||
ax[1].axhline(ishot*shifty,ls='--',color=color_ss)
|
||||
if showAv:
|
||||
ax[1].plot(E,np.nanmedian(Abs,0)+ishot*shifty,color=color_av_all,lw=1,zorder=10,alpha=0.8)
|
||||
ax[0].plot(E,s1_norm+ishot*shifty,ls = '-' ,color='0.8',lw=2)
|
||||
ax[0].plot(E,s2_norm+ishot*shifty,ls = '-' ,color='0.3',lw=2)
|
||||
ax[1].plot(E,a+ishot*shifty,color=color_ss,lw=2)
|
||||
#ax[1].plot(ref.E,ref.data+ishot*shifty,color=color_av_all,lw=2,zorder=100)
|
||||
to_save.append(s1_norm)
|
||||
to_save.append(s2_norm)
|
||||
to_save.append(a)
|
||||
ax[0].set_title("Run %s"%str(run))
|
||||
ax[1].set_ylabel("Sample Absorption")
|
||||
ax[0].set_ylabel("Normalized Spectra")
|
||||
|
||||
ax[2].plot(E,xppl37_spectra.smoothSpectra(E,Abs[-1],res=smoothWidth)[0],color=color_ss,label="1 shot LCLS")
|
||||
ax[2].plot(E,xppl37_spectra.smoothSpectra(E,np.nanmedian(Abs,axis=0),res=smoothWidth)[0],color=color_av_all,label="5 shots LCLS")
|
||||
ref = get_1b()
|
||||
ax[2].plot(ref.E,ref.data-0.05,color=nice_colors[-2],label="1 shot ESRF")
|
||||
ref = get_ref()
|
||||
ax[2].plot(ref.E,ref.data-0.05,color=nice_colors[-1],label="ref ESRF")
|
||||
ax[2].legend()
|
||||
|
||||
ref = get_1b()
|
||||
rr = mc.interpolate(ref.E,ref.data,E)
|
||||
to_save.insert(0,rr)
|
||||
ref = get_ref()
|
||||
rr = mc.interpolate(ref.E,ref.data,E)
|
||||
to_save.insert(1,rr)
|
||||
to_save.insert(2,xppl37_spectra.smoothSpectra(E,np.nanmedian(Abs,axis=0),res=smoothWidth)[0])
|
||||
|
||||
to_save = np.vstack(to_save)
|
||||
info = "# threshold=%.2f; smoothWidth=%.2f eV" %(threshold,smoothWidth)
|
||||
info += "\n#E esrf_1b esrf_ref abs_average_over_shots nshots x (spectro1 spectro2 abs)"
|
||||
trx.utils.saveTxt("../data/fig_fe_xas_spectra_run%04d.txt"%run,E,to_save,info=info)
|
||||
ax[0].set_xlabel("Energy (keV)")
|
||||
ax[1].set_xlabel("Energy (keV)")
|
||||
ax[2].set_xlabel("Energy (keV)")
|
||||
ax[0].grid(axis='x',color="0.7",lw=0.5)
|
||||
ax[1].grid(axis='x',color="0.7",lw=0.5)
|
||||
ax[0].set_xlim(7070,7180)
|
||||
ax[1].set_xlim(7070,7180)
|
||||
ax[2].set_xlim(7090,7180)
|
||||
ax[0].set_yticks( () )
|
||||
ax[1].set_yticks( () )
|
||||
ax[2].set_yticks( () )
|
||||
ax[0].set_ylim(-0.1,len(Abs)+0.2)
|
||||
ax[1].set_ylim(-0.1,len(Abs)+0.2)
|
||||
ax[2].set_ylim(0,0.7)
|
||||
ax[2].grid(color="0.7",lw=0.5)
|
||||
plt.tight_layout()
|
||||
|
||||
plt.savefig("fig_fe_xas.png",transparent=True,dpi=300)
|
||||
plt.savefig("fig_fe_xas.pdf",transparent=True)
|
||||
|
||||
|
||||
#if __name__ == "__main__": fig_fe_xas()
|
||||
|
||||
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 346 KiB |
|
|
@ -0,0 +1,151 @@
|
|||
import sys
|
||||
sys.path.insert(0,"../../../")
|
||||
import collections
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import gridspec
|
||||
|
||||
import dispersiveXanes_utils as utils
|
||||
import xppl37_spectra
|
||||
import xanes_analyzeRun
|
||||
import mcutils as mc
|
||||
import trx
|
||||
import datastorage as ds
|
||||
|
||||
|
||||
nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
|
||||
nice_colors = "#1f78b4 #a6cee3 #b2df8a #33a02c".split()
|
||||
gradual_colors = ['#014636', '#016c59', '#02818a', '#3690c0', '#67a9cf', '#a6bddb', '#d0d1e6']#, '#ece2f0']
|
||||
gradual_colors="#fec44f #fe9929 #ec7014 #cc4c02 #8c2d04".split()
|
||||
|
||||
def get_data(runs=(155,156),threshold=0.02,force=False):
|
||||
run_hash = "_".join(map(str,runs))
|
||||
fname = "../data/fig_fe_xas_runs_%s.h5" % run_hash
|
||||
if not os.path.isfile(fname) or force:
|
||||
E,p1,p2,Abs=xppl37_spectra.calcAbsForRun(runs,merge_calibs=True,threshold=threshold)
|
||||
temp = ds.DataStorage( E=E,p1=p1,p2=p2,Abs=Abs)
|
||||
temp.info="Abs calculated with threshold = %.3f" % threshold
|
||||
temp.save(fname)
|
||||
data = ds.read(fname)
|
||||
# nan is saved as -1 for masked arrays
|
||||
for k in data.keys():
|
||||
try:
|
||||
data[k][data[k]==-1] =np.nan
|
||||
except TypeError:
|
||||
pass
|
||||
# for runs 155,156 the vernier stopped working after shots ~ 2000
|
||||
if runs == (155,156):
|
||||
data.p1=data.p1[:2000]
|
||||
data.p2=data.p2[:2000]
|
||||
data.Abs=data.Abs[:2000]
|
||||
return data
|
||||
|
||||
|
||||
def get_ref():
|
||||
E,data=np.loadtxt("../data/Fe_ref.txt",unpack=True)
|
||||
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)
|
||||
|
||||
def get_1b():
|
||||
E,data=np.loadtxt("../data/Fe_1bunch.txt",unpack=True)
|
||||
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)
|
||||
|
||||
def fig_fe_exas(run=(155,156),first=7,period=70,nSpectra=5,force=False,threshold=0.1,smoothWidth=1.0,i0_filter=0.1):
|
||||
ref = get_ref()
|
||||
color_ss = '#08519c'
|
||||
color_av = '#238b45'
|
||||
color_av_all = '#d95f0e'
|
||||
shifty = 1
|
||||
data = get_data(run,force=force)
|
||||
E = data.E;p1=data.p1;p2=data.p2;Abs=data.Abs
|
||||
E = (E-7100)*1.938+7133
|
||||
p1_sum = p1.sum(-1)
|
||||
if i0_filter is not None:
|
||||
m = np.percentile(p1_sum,i0_filter*100)
|
||||
idx = p1_sum>m
|
||||
p1=p1[idx]
|
||||
p2=p2[idx]
|
||||
Abs=Abs[idx]
|
||||
print(idx.sum(),idx.shape[0])
|
||||
p1_av = np.nanmean(p1,axis=0)
|
||||
p2_av = np.nanmean(p2,axis=0)
|
||||
shots = slice(first,None,period)
|
||||
|
||||
|
||||
p1 = p1[shots]; p2=p2[shots]; Abs = Abs[shots]
|
||||
if smoothWidth > 0:
|
||||
Abs = xppl37_spectra.smoothSpectra(E,Abs,res=smoothWidth)
|
||||
|
||||
figure = plt.figure(figsize = [8,5])
|
||||
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 1.5],wspace=0.15,right=0.97,left=0.05)
|
||||
ax = []
|
||||
ax.append( plt.subplot(gs[0]) )
|
||||
ax.append( plt.subplot(gs[1],sharex=ax[0],sharey=ax[0]) )
|
||||
ax.append( plt.subplot(gs[2],sharex=ax[0]) )
|
||||
|
||||
#fig,ax = plt.subplots(1,3,sharex=True,sharey=True,squeeze=False,figsize=[6,8])
|
||||
#ax = ax[0]
|
||||
normalization = np.nanmax( p1[:nSpectra] )
|
||||
to_save = []
|
||||
ref = get_ref()
|
||||
for ishot,(s1,s2,a) in enumerate(zip(p1[:nSpectra],p2[:nSpectra],Abs[:nSpectra])):
|
||||
s1_norm = s1/normalization
|
||||
s2_norm = s2/normalization
|
||||
ax[0].axhline(ishot*shifty,ls='--',color="0.9")
|
||||
ax[1].axhline(ishot*shifty,ls='--',color=color_ss)
|
||||
ax[1].plot(E,np.nanmedian(Abs[:nSpectra],0)+ishot*shifty,color=color_av_all,lw=2,zorder=10,alpha=0.8)
|
||||
ax[0].plot(E,s1_norm+ishot*shifty,ls = '-' ,color='0.8',lw=2)
|
||||
ax[0].plot(E,s2_norm+ishot*shifty,ls = '-' ,color='0.3',lw=2)
|
||||
ax[1].plot(E,a+ishot*shifty,color=color_ss,lw=2)
|
||||
to_save.append(s1_norm)
|
||||
to_save.append(s2_norm)
|
||||
to_save.append(a)
|
||||
ax[1].plot(ref.E,ref.data+ishot*shifty,color=nice_colors[-2],lw=2,zorder=100)
|
||||
nmax = int(np.floor(np.log(len(Abs))/np.log(2)))+1
|
||||
print(nmax)
|
||||
n = 2**np.arange(nmax)
|
||||
for i,ni in enumerate(n):
|
||||
ax[2].plot(E,np.nanmedian(Abs[:ni],axis=0)+(len(n)-i)*0.2,color=gradual_colors[i],label = "%d shots"%ni)
|
||||
to_save.insert(i,np.nanmedian(Abs[:ni],axis=0))
|
||||
ax[2].plot(ref.E,ref.data,color=nice_colors[-2],lw=2,zorder=100,label="ref ESRF")
|
||||
ax[2].legend()
|
||||
ax[0].set_title("Run %s"%str(run))
|
||||
ax[1].set_ylabel("Sample Absorption")
|
||||
ax[2].set_ylabel("Sample Absorption")
|
||||
ax[0].set_ylabel("Normalized Spectra")
|
||||
to_save.insert(i+1,np.nanmedian(Abs[:nSpectra],axis=0))
|
||||
|
||||
ref = get_1b()
|
||||
rr = mc.interpolate(ref.E,ref.data,E)
|
||||
to_save.insert(0,rr)
|
||||
ref = get_ref()
|
||||
rr = mc.interpolate(ref.E,ref.data,E)
|
||||
to_save.insert(1,rr)
|
||||
|
||||
to_save = np.vstack(to_save)
|
||||
info = "# threshold=%.2f; smoothWidth=%.2f eV" %(threshold,smoothWidth)
|
||||
info += "\n#E esrf_1b esrf_ref "+" ".join(["av_%d_shots"%ni for ni in n]) +" av_%d_shots " % nSpectra + "+ nshots x (spectro1 spectro2 abs)"
|
||||
run_hash = "_".join(map(str,run))
|
||||
trx.utils.saveTxt("../data/fig_fe_exas_spectra_runs_%s.txt"%run_hash,E,to_save,info=info)
|
||||
ax[0].set_xlabel("Energy (keV)")
|
||||
ax[1].set_xlabel("Energy (keV)")
|
||||
ax[2].set_xlabel("Energy (keV)")
|
||||
ax[0].grid(axis='x',color="0.7",lw=0.5)
|
||||
ax[1].grid(axis='x',color="0.7",lw=0.5)
|
||||
ax[2].grid(color="0.7",lw=0.5)
|
||||
ax[0].set_xlim(7060,7300)
|
||||
ax[0].set_yticks( () )
|
||||
ax[1].set_yticks( () )
|
||||
ax[2].set_yticks( () )
|
||||
ax[0].set_ylim(-0.1,nSpectra+0.2)
|
||||
ax[1].set_ylim(-0.1,nSpectra+0.2)
|
||||
ax[2].set_ylim(-0.1,1.7)
|
||||
#ax[2].grid(color="0.7",lw=0.5)
|
||||
#plt.tight_layout()
|
||||
|
||||
plt.savefig("fig_fe_exas.png",transparent=True,dpi=300)
|
||||
plt.savefig("fig_fe_exas.pdf",transparent=True)
|
||||
|
||||
|
||||
#if __name__ == "__main__": fig_fe_xas()
|
||||
|
||||
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 293 KiB |
|
|
@ -0,0 +1,136 @@
|
|||
import sys
|
||||
sys.path.insert(0,"../../../")
|
||||
import collections
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import gridspec
|
||||
|
||||
import dispersiveXanes_utils as utils
|
||||
import xppl37_spectra
|
||||
import xanes_analyzeRun
|
||||
import mcutils as mc
|
||||
import trx
|
||||
import datastorage as ds
|
||||
|
||||
|
||||
nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
|
||||
nice_colors = "#1f78b4 #a6cee3 #b2df8a #33a02c".split()
|
||||
gradual_colors = ['#014636', '#016c59', '#02818a', '#3690c0', '#67a9cf', '#a6bddb', '#d0d1e6']#, '#ece2f0']
|
||||
|
||||
def get_data(run=127,threshold=0.05,force=False):
|
||||
fname = "../data/fig_focusing_run%04d.h5" % run
|
||||
if not os.path.isfile(fname) or force:
|
||||
# this functions splits the run based on FOM
|
||||
data=xppl37_spectra.calcSpectraForRun(run)
|
||||
E = data.run.E
|
||||
ref = ds.DataStorage(E=E, p1=data.p1[0],p2=data.p2[0] )
|
||||
sample = ds.DataStorage(E=E, p1=data.p1[1],p2=data.p2[1] )
|
||||
|
||||
_,_,Abs = xppl37_spectra.calcAbs(ref,ref,threshold=threshold)
|
||||
ref.Abs = Abs
|
||||
|
||||
_,_,Abs = xppl37_spectra.calcAbs(ref,sample,threshold=threshold)
|
||||
sample.Abs = Abs
|
||||
|
||||
temp = ds.DataStorage( ref=ref,sample=sample)
|
||||
temp.info="Abs calculated with threshold = %.3f" % threshold
|
||||
temp.save(fname)
|
||||
data = ds.read(fname)
|
||||
ref = data.ref
|
||||
sample = data.sample
|
||||
_,_,Abs = xppl37_spectra.calcAbs(ref,ref,threshold=threshold)
|
||||
ref.Abs = Abs
|
||||
_,_,Abs = xppl37_spectra.calcAbs(ref,sample,threshold=threshold)
|
||||
sample.Abs = Abs
|
||||
data = ds.DataStorage( ref=ref,sample=sample)
|
||||
data["threshold"]=threshold
|
||||
return data
|
||||
|
||||
|
||||
def get_ref():
|
||||
E,data=np.loadtxt("../data/Fe_ref.txt",unpack=True)
|
||||
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)
|
||||
|
||||
def get_1b():
|
||||
E,data=np.loadtxt("../data/Fe_1bunch.txt",unpack=True)
|
||||
return ds.DataStorage(E=E*1e3,data=data/2.05+0.07)
|
||||
|
||||
def fig_focusing(run=127,force=False,threshold=0.05,smoothWidth=0.3,i0_monitor=0.1):
|
||||
ref = get_ref()
|
||||
color_ss = '#08519c'
|
||||
color_av = '#238b45'
|
||||
color_av_all = '#d95f0e'
|
||||
shifty = 1
|
||||
data = get_data(run,force=force,threshold=threshold)
|
||||
E = data.ref.E
|
||||
shots = range(30,35)
|
||||
figure = plt.figure(figsize = [7,5])
|
||||
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1],)
|
||||
ax = []
|
||||
ax.append( plt.subplot(gs[0]) )
|
||||
ax.append( plt.subplot(gs[1],sharex=ax[0],sharey=ax[0]) )
|
||||
# ax.append( plt.subplot(gs[2],sharex=ax[0]) )
|
||||
|
||||
if i0_monitor is not None:
|
||||
i0_ref = np.nanmean(data.ref.p1,axis=1)
|
||||
idx = i0_ref>np.percentile(i0_ref,i0_monitor)
|
||||
data.ref.Abs = data.ref.Abs[idx]
|
||||
|
||||
i0_ref = np.nanmean(data.sample.p1,axis=1)
|
||||
idx = i0_ref>np.percentile(i0_ref,i0_monitor)
|
||||
data.sample.Abs = data.sample.Abs[idx]
|
||||
|
||||
ref = data.ref.Abs[shots]
|
||||
sam = data.sample.Abs[shots]
|
||||
if smoothWidth > 0:
|
||||
ref = xppl37_spectra.smoothSpectra(E,ref,res=smoothWidth)
|
||||
sam = xppl37_spectra.smoothSpectra(E,sam,res=smoothWidth)
|
||||
idx = E>7150
|
||||
sam[:,idx]=np.nan
|
||||
ref[:,idx]=np.nan
|
||||
av_ref = np.nanmedian(ref,0)
|
||||
av_sam = np.nanmedian(sam,0)
|
||||
for ishot,shot in enumerate(shots):
|
||||
ax[0].axhline(ishot*shifty,ls='--',color="0.9")
|
||||
ax[1].axhline(ishot*shifty,ls='--',color="0.9")
|
||||
ax[0].plot(E,av_ref+ishot*shifty,color=color_av_all,lw=1,zorder=10)
|
||||
ax[1].plot(E,av_sam+ishot*shifty,color=color_av_all,lw=1,zorder=10)
|
||||
ax[0].plot(E,ref[ishot]+ishot*shifty,ls = '-' ,color=color_ss,lw=2)
|
||||
ax[0].text(7125,ishot+0.2,"σ = %.2f"%np.nanstd(ref[ishot]))
|
||||
ax[1].plot(E,sam[ishot]+ishot*shifty,ls = '-' ,color=color_ss,lw=2)
|
||||
#to_save.append(s1_norm)
|
||||
#to_save.append(s2_norm)
|
||||
#to_save.append(a)
|
||||
ax[0].set_title("Run %s"%str(run))
|
||||
ax[0].set_ylabel("No sample Absorption")
|
||||
ax[1].set_ylabel("Sample Absorption")
|
||||
|
||||
ax[0].set_xlabel("Energy (keV)")
|
||||
ax[1].set_xlabel("Energy (keV)")
|
||||
ax[0].grid(axis='x',color="0.7",lw=0.5)
|
||||
ax[1].grid(axis='x',color="0.7",lw=0.5)
|
||||
ax[0].set_xlim(7080,7150)
|
||||
ax[0].set_yticks( () )
|
||||
ax[1].set_yticks( () )
|
||||
ax[0].set_ylim(-0.3,len(ref)+0.2)
|
||||
plt.tight_layout()
|
||||
|
||||
plt.savefig("fig_focusing.png",transparent=True,dpi=300)
|
||||
plt.savefig("fig_focusing.pdf",transparent=True)
|
||||
|
||||
to_save = np.vstack( (E,np.nanmedian(ref,0),ref) )
|
||||
info = "# threshold=%.2f; smoothWidth=%.2f eV" %(threshold,smoothWidth)
|
||||
info += "\n#E abs_average_over_shots shots ..."
|
||||
trx.utils.saveTxt("../data/fig_focusing_run%04d_ref.txt"%run,E,to_save,info=info)
|
||||
|
||||
to_save = np.vstack( (E,np.nanmedian(sam,0),sam) )
|
||||
info = "# threshold=%.2f; smoothWidth=%.2f eV" %(threshold,smoothWidth)
|
||||
info += "\n#E abs_average_over_shots shots ..."
|
||||
trx.utils.saveTxt("../data/fig_focusing_run%04d_sam.txt"%run,E,to_save,info=info)
|
||||
|
||||
|
||||
|
||||
|
||||
#if __name__ == "__main__": fig_fe_xas()
|
||||
|
||||
Loading…
Reference in New Issue