...

• Write your own application, or be patient and wait for our interactive framework solution.

Data visualization with NumPy (arrays) and MatPlotLib (plots).

This is not meant to be documentation or a tutorial for matplotlib or numpy. Just a place to document stuff that I have a hard time finding explained elsewhere.

• Code Block
  1 none title Inspecting objects
  for attr_name in dir(obj): attribute = getattr(obj, attr_name) print attr_name, ": ", attribute

  This is really just simple python. But since 'matplotlib' documentation can be frustratingly non-verbose about what functions and attributes are available for its various classes/objects, I found this is a useful way to inspect what an object knows about itself.

...

import numpy as np

np.save("filename.npy", array)
array = np.load("filename.npy")

np.savetxt("filename.dat", array)
array = loadtxt("filename.dat")

Because of this limitation, this page isn't really what it pretends to be ("How to access XTC data from Python"). But this page is a placeholder, and attempts to explore some of the functionality that we can use with XTC files later. The interactive analysis in (I)Python is the same in the end.

Numpy and HDF5 files

You can store numpy arrays from a pyana job (reads XTC) and store them in simple numpy files or HDF5 files. Here are some examples:

• Code Block
  none none title saving and loading
  import numpy as np np.save("filename.npy", array) array = np.load("filename.npy") np.savetxt("filename.dat", array) array = loadtxt("filename.dat")

  This example shows saving and loading of a binary numpy file (.npy) and an ascii file (.dat).
  This only works with single arrays (max 2 dimensions).
  If you need to save multiple events/shots in the same file you will need to do some tricks (e.g. flatten the array and stack 1d arrays into 2d arrays where axis2 represent event number). Or you could save as an HDF5 file.

• Code Block
  none none title saving to HDF5
  import h5py def beginjob(self,evt,env): self.ofile = h5py.File("outputfile.hdf5", 'w') # open for writing (overwrites existing file) self.shot_counter = 0 def event(self,evt,env) # example: store several arrays from one shot in a group labeled with shot (event) number self.shot_counter += 1 group = self.ofile.create_group("Shot%d" % self.shot_counter) image1_source = "CxiSc1-0|TM6740-1" image2_source = "CxiSc1-0|TM6740-2" frame = evt.getFrameValue(image1_source) image1 = frame.data() frame = evt.getFrameValue(image2_source) image2 = frame.data() dataset1 = group.create_dataset("%s"%image1_source,data=image1) dataset2 = group.create_dataset("%s"%image2_source,data=image2) def endjob(self,env) self.ofile.close()

  This example is shown in a pyana setting. The HDF5 file is declared and opened in beginjob, datasets created for each event, and the file is closed in the endjob method.
  Or you can group your datasets any other way you find useful, of course.

IPython used "like" MATLAB

Of course MATLAB is much more than this, but here's what we've started with. Here are some examples with IPython based on matlab functions provided by XPP. Thanks to H. Lemke for matlab examples and advice. A python module pymatlab.py defines a number of functions to use in this analysis example.

[ofte@psana0XXX myrelease]$ ipython
Python 2.4.3 (#1, Nov  3 2010, 12:52:40)
Type "copyright", "credits" or "license" for more information.

IPython 0.9.1 -- An enhanced Interactive Python.
?         -

...

import h5py

def beginjob(self,evt,env):
    self.ofile = h5py.File("outputfile.hdf5", 'w') # open for writing (overwrites existing file)
    self.shot_counter = 0

def event(self,evt,env)
    # example: store several arrays from one shot in a group labeled with shot (event) number
    self.shot_counter += 1
    group = self.ofile.create_group("Shot%d" % self.shot_counter)

    image1_source = "CxiSc1-0|TM6740-1"
    image2_source = "CxiSc1-0|TM6740-2"

    frame = evt.getFrameValue(image1_source)
    image1 = frame.data()
    frame = evt.getFrameValue(image2_source)
    image2 = frame.data()

    dataset1 = group.create_dataset("%s"%image1_source,data=image1)
    dataset2 = group.create_dataset("%s"%image2_source,data=image2)

def endjob(self,env)
    self.ofile.close()

...

Examples from MATLAB

These examples are mostly python rewrites of matlab functions provided by XPP. Thanks to H. Lemke for matlab examples and advice.

• The "library": pymatlab.py
  ... a module implementing in python some of the tools written by Henrik/XPP for matlab. For those familiar with the XPP matlab tools, the functions here should be intuitive to use. Only a few functions have been implemented thus far... (feel free to contribute).

...

> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object'. ?object also works, ?? prints more.

...

In [1]: from pymatlab import *

...

In [

...

2]: 

...

who
H5getobjnames  

...

...

ScanInput 

...

...

...

...

...

...

...

ScanOutput      filtvec findmovingmotor
getSTDMEANfrac_from_startpoint  get_filter get_limits       get_limits_automatic
get_limits_channelhist  get_limits_correlation  get_limits_corrfrac
h5py    np      plt     rdXPPdata       runexpNO2fina       scan       scaninput

...

In [3]: whos
Variable                         Type          Data/Info
--------------------------------------------------------
H5getobjnames                    function      <function H5getobjnames at 0x2b57de8>
ScanInput                        type          <class 'pymatlab.ScanInput'>
ScanOutput                       type          <class 'pymatlab.ScanOutput'>
filtvec                          function      <function filtvec at 0x2b57f50>
findmovingmotor                  function      <function findmovingmotor at 0x2b57d70>
getSTDMEANfrac_from_startpoint   function      <function getSTDMEANfrac_<...>_startpoint at 0x2b581b8>
get_filter                       function      <function get_filter at 0x2b57ed8>
get_limits                       function      <function get_limits at 0x2b58050>
get_limits_automatic             function      <function get_limits_automatic at 0x2b58230>
get_limits_channelhist           function      <function get_limits_channelhist at 0x2b582a8>
get_limits_correlation           function      <function get_limits_correlation at 0x2b580c8>
get_limits_corrfrac              function      <function get_limits_corrfrac at 0x2b58140>
h5py                             module        <module 'h5py' from '/reg<...>ython/h5py/__init__.pyc'>
np                               module        <module 'numpy' from '/re<...>thon/numpy/__init__.pyc'>
plt                              module        <module 'matplotlib.pyplo<...>n/matplotlib/pyplot.pyc'>
rdXPPdata                        function      <function rdXPPdata at 0x2b57c80>
runexpNO2fina                    function      <function runexpNO2fina at 0x2b57e60>
scan                             ScanOutput    <pymatlab.ScanOutput object at 0x2b60536bee90>
scaninput                        ScanInput     <pymatlab.ScanInput object at 0x2b60536b4e90>

...

Like in MATLAB, who gives you a short list of workspace contents, whos gives you a longer list of workspace contents.

• Example 1)
  Select limits from graphical input and plot filtered IPIMB
  Here's a log from a session that produces a loglog plot (blue dots) of two IPIMB channels, selects limits from graphical inpu (mouse click),
  draws the selected events with red dots.
  Image Modified

In [1]: from pymatlab import *
Pretend this is matlab

In [2]: whos
Variable                         Type        Data/Info
------------------------------------------------------
H5getobjnames                    function    <function H5getobjnames at 0x1379fde8>
ScanInput                        type        <class 'pymatlab.ScanInput'>
ScanOutput                       type        <class 'pymatlab.ScanOutput'>
filtvec                          function    <function filtvec at 0x1379ff50>
findmovingmotor                  function    <function findmovingmotor at 0x1379fd70>
getSTDMEANfrac_from_startpoint   function    <function getSTDMEANfrac_<...>startpoint at 0x137a01b8>
get_filter                       function    <function get_filter at 0x1379fed8>
get_limits                       function    <function get_limits at 0x137a0050>
get_limits_automatic             function    <function get_limits_automatic at 0x137a0230>
get_limits_channelhist           function    <function get_limits_channelhist at 0x137a02a8>
get_limits_correlation           function    <function get_limits_correlation at 0x137a00c8>
get_limits_corrfrac              function    <function get_limits_corrfrac at 0x137a0140>
h5py                             module      <module 'h5py' from '/reg<...>ython/h5py/__init__.pyc'>
np                               module      <module 'numpy' from '/re<...>thon/numpy/__init__.pyc'>
plt                              module      <module 'matplotlib.pyplo<...>n/matplotlib/pyplot.pyc'>
rdXPPdata                        function    <function rdXPPdata at 0x1379fc80>
runexpNO2fina                    function    <function runexpNO2fina at 0x1379fe60>

In [3]: scaninput = ScanInput()

In [4]: scaninput.fina = "/reg/d/psdm/XPP/xpp23410/hdf5/xpp23410-r0107.h5"

In [5]: scan = rdXPPdata(scaninput)
Reading XPP data from  /reg/d/psdm/XPP/xpp23410/hdf5/xpp23410-r0107.h5
Found pv control object  fs2:ramp_angsft_target
Found scan vector  [ 2800120.  2800240.  2800360.  2800480.  2800600.  2800720.  2800840.
  2800960.  2801080.  2801200.  2801320.  2801440.  2801560.  2801680.
  2801800.  2801920.  2802040.  2802160.  2802280.  2802400.  2802520.
  2802640.  2802760.  2802880.  2803000.  2803120.  2803240.  2803360.
  2803480.  2803600.  2803720.  2803840.  2803960.  2804080.  2804200.
  2804320.  2804440.  2804560.  2804680.  2804800.  2804920.  2805040.
  2805160.  2805280.]
Fetching data to correlate with motor
['IPM1', 'IPM2']
(44, 120, 4)

In [6]: channels = np.concatenate(scan.scandata,axis=0)

In [7]: channels.shape
Out[7]: (5280, 4)

In [8]: get_limits(channels,1,"correlation")
4 channels a 5280 events
indexes that pass filter:  (array([   1,    5,    8, ..., 5266, 5272, 5273]),)
Out[8]:
array([[ 0.00086654,  0.01604564],
       [ 0.67172102,  0.71968567],
       [ 0.00194716,  0.01447819],
       [ 0.80365403,  0.73463468]])

In [9]: plt.draw()

...

%
%
%
a1 = subplot(121);
loglog(channels(:,1),channels(:,2),'o')
xlabel('CH0')
ylabel('CH1')
a2 = subplot(122);
loglog(channels(:,3),channels(:,4),'o')
xlabel('CH2')
ylabel('CH3')

import matplotlib.pyplot as plt
import numpy as np

a1 = plt.subplot(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')

axes(a1)
hold on
lims(1:2,:) = ginput(2);

axes(a2)
hold on
lims(3:4,:) = ginput(2);

lims = np.zeros((4,2),dtype="float")

plt.axes(a1)
plt.hold(True)
lims[0:2,:] = plt.ginput(2)

plt.axes(a2)
plt.hold(True)
lims[2:4,:] = plt.ginput(2)

(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')

axes(a1)
hold on
lims(1:2,:) = ginput(2);

axes(a2)
hold on
lims(3:4,:) = ginput(2);

lims = np.zeros((4,2),dtype="float")

plt.axes(a1)
plt.hold(True)
lims[0:2,:] = plt.ginput(2)

plt.axes(a2)
plt.hold(True)
lims[2:4,:] = plt.ginput(2)

fbool1 = (channels(:,1)>min(lims(1:2,1)))&(channels(:,1)<max(lims(1:2,1)))
fbool2 = (channels(:,2)>min(lims(1:2,2)))&(channels(:,2)<max(lims(1:2,2)));
fbool = fbool1&fbool2
loglog(channels(fbool,1),channels(fbool,2),'or')

fbool3

fbool1 = (channels(:,13)>min(lims(13:24,13)))&(channels(:,13)<max(lims(13:24,13)))
fbool2fbool4 = (channels(:,24)>min(lims(13:24,24)))&(channels(:,24)<max(lims(13:24,24)));
fbool = fbool1fbool3&fbool2fbool4
loglog(channels(fbool,13),channels(fbool,24),'or')

fbool3 

fbools0 = (channels([:,3)>min(lims(3:4,3)))0]>lims[:,0].min())&(channels([:,3)<max(lims(3:4,3)))
fbool40]<lims[:,0].max())
fbools1 = (channels([:,4)>min(lims(3:4,4)1]>lims[:,1].min())&(channels([:,4)<max(lims(3:4,4)));
fbool = fbool3&fbool4
loglog(channels(fbool,3),channels(fbool,4),'or') 

fbools01]<lims[:,1].max())
fbools = fbools0 & fbools1

fbools2 = (channels[:,02]>lims[:,02].min())&(channels[:,02]<lims[:,02].max())
fbools1fbools3 = (channels[:,13]>lims[:,13].min())&(channels[:,13]<lims[:,1].max())
fbools = fbools0 & fbools1

fbools2 = (channels[:,2]>lims[:,2].min())&(channels[:,2]<lims[:,2].max())
fbools3 = (channels[:,3]>lims[:,3].min())&(channels[:,3]<lims[:,3].max())
fbools = fbools2&fbools3

:,3].max())
fbools = fbools2&fbools3

Data visualization with NumPy (arrays) and MatPlotLib (plots).

This is not meant to be documentation or a tutorial for matplotlib or numpy. Just a place to document stuff that I have a hard time finding explained elsewhere.

• Code Block
  1 none title Inspecting objects
  for attr_name in dir(obj): attribute = getattr(obj, attr_name) print attr_name, ": ", attribute

  This is really just simple python. But since 'matplotlib' documentation can be frustratingly non-verbose about what functions and attributes are available for its various classes/objects, I found this is a useful way to inspect what an object knows about itself.