2016-05-12 14:24:31 +02:00
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# dispersiveXanes
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2016-05-12 22:32:34 +02:00
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```python
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2016-06-06 10:24:37 +02:00
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# INSTALL THE STUFF
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2016-11-22 13:06:51 +01:00
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# 1. go to psexport (and not pslogin or psana)
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# 2. Go in a folder where you will install the things (cd ~/my_folder)
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# 3. git clone https://github.com/marcocamma/x3py.git
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# 4. git clone https://git.ipr.univ-rennes1.fr/mcammara/dispersiveXanes.git
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# 5. if you want to update go to the right folder (for example cd ~/my_folder/dispersiveXanes) and type git pull.
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2016-06-06 10:24:37 +02:00
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2016-05-12 22:32:34 +02:00
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# BEFORE GETTING STARTED
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# 1. if at LCLS load the anaconda session
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# source ~marcoc/setups/ana-marco3k-setup.sh
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2016-06-06 10:24:37 +02:00
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# 2. go in the right folder (cd ~/my_folder/dispersiveXanes)
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# → THIS IS IMPORTANT: change the experiment name in xanes_analyzeRun (line ~22)
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# GET STARTED
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# 1. start ipython (ipython3)
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# 2. tell python to use look for modules in the folder
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# 3. import sys; sys.path.insert(0,"~/my_folder")
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2016-05-12 22:32:34 +02:00
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# there are two files:
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2016-06-06 10:24:37 +02:00
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# 1. alignment.py (deals with images)
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2016-05-12 22:32:34 +02:00
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# 2. xanes_analyzeRun.py (deals with run and images reading)
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%matplotlib nbagg
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import matplotlib
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import matplotlib.pylab as plt
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matplotlib.style.use("ggplot")
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import pprint
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import numpy as np
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np.warnings.simplefilter('ignore')
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import xanes_analyzeRun
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import alignment
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#Doing first alignment on "hole"
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# define starting parameters for analysis; passed directly to iminuit so things like
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# limits, or fix_scalex=True, etc. can be used
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pars = dict( scalex = 0.6, intensity = 0.1, iblur1=2,fix_iblur1 = False )
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# default parameters can be found in alignment.g_fit_default_kw
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# you can have a look by uncommenting the following line:
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# pprint.pprint(alignment.g_fit_default_kw)
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# define the run object
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#### NOTE : for mec run swapx=True,swapy=False
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r = xanes_analyzeRun.AnalyzeRun(190,initAlign=pars,swapx=True,swapy=False)
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# data are d.spec1 and d.spec2 (spec1 is the one **upbeam**)
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# align one shot
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# show = True: show only output; showInit=True: show also starting parameters
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r0fit=r.doShot(shot=0,calib=0,showInit=True,doFit=True)
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# save as default transformation for run (used when reloading without initAlign keywork)
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r.saveTransform();
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# do more shots without fitting (using last r.initAlign)
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# the return value is a list with lots of stuff for each shot
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res = r.doShots(slice(100),doFit=False)
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print(list(res.keys()))
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print(list(res["parameters"].keys()))
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alignment.plotRatios(res["ratio"])
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ref = np.nanmedian(res["ratio"],axis=0)
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trash = plt.xlim(400,600)
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trash = plt.ylim(0,2)
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# analyze another run using previous alignment
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rShot = xanes_analyzeRun.AnalyzeRun(192,initAlign="mecl3616_init_pars/run0190_transform.npy",swapx=True,swapy=False)
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out = rShot.doShots(slice(0,5))
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ratios = out["ratio"]
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plt.figure()
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for i,r in enumerate(ratios):
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plt.plot(r/ref,label="Shot %d"%i)
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trash = plt.ylim(0,1)
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trash = plt.legend(loc=2)
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# save results in hdf file for Andy's happiness
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rShot.save(overwrite=True)
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```
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