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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.

Starting an interactive session

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title	Starting iPython
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[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.
?         -> 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.

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title	load the library module
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In [1]: from pymatlab import *

Generally, it is recommended to load library modules with 'import pymatlab' and access all its methods and classes with pyamatlab.function. In an interactive session it may be easier to have access to the contents of pymatlab in your immediate workspace by doing 'from pymatlab import *'.

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title	Workspace contents	List the workspace contents ('who' or 'whos')
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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.

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Plot filtered IPIMB data with limits from graphical input

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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)

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draws the selected events with red dots.
Image Added

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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()

Table of comparison (MATLAB vs MatPlotLib)

MatLab

MatPlotLib

Comments

Loglog plot of one array vs. another

Code Block
% % % 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')

Loglog plot of one array vs. another

Code Block

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')

channels is a 4xN array of floats, where N is the number of events. Each column corresponds to one out of four Ipimb channels.

Note that the arrays are indexed with 1,2,3,4 in MatLab and 0,1,2,3 in MatPlotLib/NumPy/Python.

<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="e297189e2718f6e5-5cd93a53-4d1344e4-8d4cbe31-8d49c70a5ed50c95b595b93f"><ac:plain-text-body><![CDATA[Note also the use of paranthesis, array() in MatLab, array[] in MatPlotLib.

]]></ac:plain-text-body></ac:structured-macro>

test

Test

array of limits from graphical input

Code Block
axes(a1) hold on lims(1:2,:) = ginput(2); axes(a2) hold on lims(3:4,:) = ginput(2);

Code Block
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)

In MatLab, lims is an expandable array that holds limits as set by input from mouse click on the plot (ginput).
NumPy arrays cannot be expanded, so I've declared a 4x2 array of zeros to start with, then fill it with ginput().

filter

Code Block

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 = (channels(:,3)>min(lims(3:4,3)))&(channels(:,3)<max(lims(3:4,3)))
fbool4 = (channels(:,4)>min(lims(3:4,4)))&(channels(:,4)<max(lims(3:4,4)));
fbool = fbool3&fbool4
loglog(channels(fbool,3),channels(fbool,4),'or')

Code Block

fbools0 = (channels[:,0]>lims[:,0].min())&(channels[:,0]<lims[:,0].max())
fbools1 = (channels[:,1]>lims[:,1].min())&(channels[:,1]<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

Comment

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Starting an interactive session

Plot filtered IPIMB data with limits from graphical input

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Table of comparison (MATLAB vs MatPlotLib)