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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()

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

Saving (and loading) a numpy array (e.g. image) to (from) a file

If you want to save one array (max 2 dimensions), you can use binary numpy file or ascii file:

Code Block

import numpy as np

# binary file .npy format
np.save("filename.npy", array)
array = np.load("filename.npy")

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

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.

You can save an array or several into an HDF5 file (example from pyana):

Code Block

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()

Or you can group your datasets any other way you find useful, of course.

A comparison with MatLab.

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="28cb1a8a-38d1-4476-899d-dc9b03df6524"><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

Test

array of limits from graphical input

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

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