Commit 5ffa4753 authored by Unknown's avatar Unknown
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Merge branch 'master' into cades_dev

parents 251a84e5 3f4d4c5a
......@@ -87,7 +87,7 @@ Journal Papers using pycroscopy
 
4. `Direct Imaging of the Relaxation of Individual Ferroelectric Interfaces in a Tensile-Strained Film <http://onlinelibrary.wiley.com/doi/10.1002/aelm.201600508/full>`_ by L. Li et al.; Advanced Electronic Materials (2017), jupyter notebook `here 4 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/BE_Processing.ipynb>`_
5. Feature extraction via similarity search: application to atom finding and denosing in electon and scanning probe microscopy imaging by S. Somnath et al.; submitted (2017), jupyter notebook `here 5 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/Image_Cleaning_Atom_Finding.ipynb>`_
5. Feature extraction via similarity search: application to atom finding and denosing in electon and scanning probe microscopy imaging by S. Somnath et al.; under review at Advanced Structural and Chemical Imaging (2017), jupyter notebook `here 5 <http://nbviewer.jupyter.org/github/pycroscopy/pycroscopy/blob/master/jupyter_notebooks/Image_Cleaning_Atom_Finding.ipynb>`_
6. Many more coming soon....
......
......@@ -75,6 +75,7 @@ Documentation
* Switch from static examples to dynamic jupyter notebook like examples:
* Work will be needed after examples are done
* Include examples in documentation
* Links to references for all functions and methods used in our workflows.
Formatting changes
------------------
......
%% Cell type:markdown id: tags:
# Band Excitation data procesing using pycroscopy
### Suhas Somnath, Chris R. Smith, Stephen Jesse
The Center for Nanophase Materials Science and The Institute for Functional Imaging for Materials <br>
Oak Ridge National Laboratory<br>
2/10/2017
%% Cell type:markdown id: tags:
## Configure the notebook
%% Cell type:code id: tags:
``` python
# !pip install -U numpy matplotlib Ipython ipywidgets pycroscopy
!pip install -U numpy matplotlib Ipython ipywidgets pycroscopy
# Ensure python 3 compatibility
from __future__ import division, print_function, absolute_import
# Import necessary libraries:
# General utilities:
import sys
import os
# Computation:
import numpy as np
import h5py
# Visualization:
# import ipympl
import matplotlib.pyplot as plt
import matplotlib.widgets as mpw
from IPython.display import display
from IPython.display import display, clear_output, HTML
import ipywidgets as widgets
# Finally, pycroscopy itself
sys.path.append('..')
import pycroscopy as px
# set up notebook to show plots within the notebook
% matplotlib inline
% matplotlib notebook
# Make Notebook take up most of page width
display(HTML(data="""
<style>
div#notebook-container { width: 95%; }
div#menubar-container { width: 65%; }
div#maintoolbar-container { width: 99%; }
</style>
"""))
```
%% Cell type:markdown id: tags:
## Set some basic parameters for computation
This notebook performs some functional fitting whose duration can be substantially decreased by using more memory and CPU cores. We have provided default values below but you may choose to change them if necessary.
%% Cell type:code id: tags:
``` python
max_mem = 1024*8 # Maximum memory to use, in Mbs. Default = 1024
max_cores = None # Number of logical cores to use in fitting. None uses all but 2 available cores.
```
%% Cell type:markdown id: tags:
## Make the data pycroscopy compatible
Converting the raw data into a pycroscopy compatible hierarchical data format (HDF or .h5) file gives you access to the fast fitting algorithms and powerful analysis functions within pycroscopy
#### H5 files:
* are like smart containers that can store matrices with data, folders to organize these datasets, images, metadata like experimental parameters, links or shortcuts to datasets, etc.
* are readily compatible with high-performance computing facilities
* scale very efficiently from few kilobytes to several terabytes
* can be read and modified using any language including Python, Matlab, C/C++, Java, Fortran, Igor Pro, etc.
#### You can load either of the following:
* Any .mat or .txt parameter file from the original experiment
* A .h5 file generated from the raw data using pycroscopy - skips translation
You can select desired file type by choosing the second option in the pull down menu on the bottom right of the file window
%% Cell type:code id: tags:
``` python
input_file_path = px.io_utils.uiGetFile(caption='Select translated .h5 file or raw experiment data',
file_filter='Parameters for raw BE data (*.txt *.mat *xls *.xlsx);; \
Translated file (*.h5)')
(data_dir, data_name) = os.path.split(input_file_path)
if input_file_path.endswith('.h5'):
# No translation here
h5_path = input_file_path
force = False # Set this to true to force patching of the datafile.
tl = px.LabViewH5Patcher()
hdf = tl.translate(h5_path, force_patch=force)
else:
# Set the data to be translated
data_path = input_file_path
(junk, base_name) = os.path.split(data_dir)
# Check if the data is in the new or old format. Initialize the correct translator for the format.
if base_name == 'newdataformat':
(junk, base_name) = os.path.split(junk)
translator = px.BEPSndfTranslator(max_mem_mb=max_mem)
else:
translator = px.BEodfTranslator(max_mem_mb=max_mem)
if base_name.endswith('_d'):
base_name = base_name[:-2]
# Translate the data
h5_path = translator.translate(data_path, show_plots=True, save_plots=False)
hdf = px.ioHDF5(h5_path)
print('Working on:\n' + h5_path)
h5_main = px.hdf_utils.getDataSet(hdf.file, 'Raw_Data')[0]
```
%% Cell type:markdown id: tags:
##### Inspect the contents of this h5 data file
The file contents are stored in a tree structure, just like files on a conventional computer.
The data is stored as a 2D matrix (position, spectroscopic value) regardless of the dimensionality of the data. Thus, the positions will be arranged as row0-col0, row0-col1.... row0-colN, row1-col0.... and the data for each position is stored as it was chronologically collected
The main dataset is always accompanied by four ancillary datasets that explain the position and spectroscopic value of any given element in the dataset.
%% Cell type:code id: tags:
``` python
print('Datasets and datagroups within the file:\n------------------------------------')
px.io.hdf_utils.print_tree(hdf.file)
print('\nThe main dataset:\n------------------------------------')
print(h5_main)
print('\nThe ancillary datasets:\n------------------------------------')
print(hdf.file['/Measurement_000/Channel_000/Position_Indices'])
print(hdf.file['/Measurement_000/Channel_000/Position_Values'])
print(hdf.file['/Measurement_000/Channel_000/Spectroscopic_Indices'])
print(hdf.file['/Measurement_000/Channel_000/Spectroscopic_Values'])
print('\nMetadata or attributes in a datagroup\n------------------------------------')
for key in hdf.file['/Measurement_000'].attrs:
print('{} : {}'.format(key, hdf.file['/Measurement_000'].attrs[key]))
```
%% Cell type:markdown id: tags:
## Get some basic parameters from the H5 file
This information will be vital for futher analysis and visualization of the data
%% Cell type:code id: tags:
``` python
h5_pos_inds = px.hdf_utils.getAuxData(h5_main, auxDataName='Position_Indices')[-1]
pos_sort = px.hdf_utils.get_sort_order(np.transpose(h5_pos_inds))
pos_dims = px.hdf_utils.get_dimensionality(np.transpose(h5_pos_inds), pos_sort)
pos_labels = np.array(px.hdf_utils.get_attr(h5_pos_inds, 'labels'))[pos_sort]
print(pos_labels, pos_dims)
parm_dict = hdf.file['/Measurement_000'].attrs
is_ckpfm = hdf.file.attrs['data_type'] == 'cKPFMData'
if is_ckpfm:
num_write_steps = parm_dict['VS_num_DC_write_steps']
num_read_steps = parm_dict['VS_num_read_steps']
num_fields = 2
```
%% Cell type:markdown id: tags:
## Visualize the raw data
Use the sliders below to visualize spatial maps (2D only for now), and spectrograms.
For simplicity, all the spectroscopic dimensions such as frequency, excitation bias, cycle, field, etc. have been collapsed to a single slider.
%% Cell type:code id: tags:
``` python
px.be_viz_utils.jupyter_visualize_be_spectrograms(h5_main)
```
%% Cell type:code id: tags:
``` python
sho_fit_points = 5 # The number of data points at each step to use when fitting
h5_sho_group = px.hdf_utils.findH5group(h5_main, 'SHO_Fit')
sho_fitter = px.BESHOmodel(h5_main, parallel=True)
if len(h5_sho_group) == 0:
print('No SHO fit found. Doing SHO Fitting now')
h5_sho_guess = sho_fitter.do_guess(strategy='complex_gaussian', processors=max_cores, options={'num_points':sho_fit_points})
h5_sho_fit = sho_fitter.do_fit(processors=max_cores)
else:
print('Taking previous SHO results already present in file')
h5_sho_guess = h5_sho_group[-1]['Guess']
try:
h5_sho_fit = h5_sho_group[-1]['Fit']
except KeyError:
print('Previously computed guess found. Now computing fit')
h5_sho_fit = sho_fitter.do_fit(processors=max_cores, h5_guess=h5_sho_guess)
```
%% Cell type:markdown id: tags:
## Visualize the SHO results
Here, we visualize the parameters for the SHO fits. BE-line (3D) data is visualized via simple spatial maps of the SHO parameters while more complex BEPS datasets (4+ dimensions) can be visualized using a simple interactive visualizer below.
You can choose to visualize the guesses for SHO function or the final fit values from the first line of the cell below.
Use the sliders below to inspect the BE response at any given location.
%% Cell type:code id: tags:
``` python
h5_sho_spec_inds = px.hdf_utils.getAuxData(h5_sho_fit, auxDataName='Spectroscopic_Indices')[0]
sho_spec_labels = px.io.hdf_utils.get_attr(h5_sho_spec_inds,'labels')
if is_ckpfm:
# It turns out that the read voltage index starts from 1 instead of 0
# Also the VDC indices are NOT repeating. They are just rising monotonically
write_volt_index = np.argwhere(sho_spec_labels == 'write_bias')[0][0]
read_volt_index = np.argwhere(sho_spec_labels == 'read_bias')[0][0]
h5_sho_spec_inds[read_volt_index, :] -= 1
h5_sho_spec_inds[write_volt_index, :] = np.tile(np.repeat(np.arange(num_write_steps), num_fields), num_read_steps)
(Nd_mat, success, nd_labels) = px.io.hdf_utils.reshape_to_Ndims(h5_sho_fit, get_labels=True)
print('Reshape Success: ' + str(success))
print(nd_labels)
print(Nd_mat.shape)
```
%% Cell type:code id: tags:
``` python
use_sho_guess = False
use_static_viz_func = False
if use_sho_guess:
sho_dset = h5_sho_guess
else:
sho_dset = h5_sho_fit
data_type = px.io.hdf_utils.get_attr(hdf.file, 'data_type')
if data_type == 'BELineData' or len(pos_dims) != 2:
use_static_viz_func = True
step_chan = None
else:
vs_mode = px.io.hdf_utils.get_attr(h5_main.parent.parent, 'VS_mode')
if vs_mode not in ['AC modulation mode with time reversal',
'DC modulation mode']:
use_static_viz_func = True
else:
if vs_mode == 'DC modulation mode':
step_chan = 'DC_Offset'
else:
step_chan = 'AC_Amplitude'
if not use_static_viz_func:
try:
# use interactive visualization
px.be_viz_utils.jupyter_visualize_beps_sho(sho_dset, step_chan)
except:
raise
print('There was a problem with the interactive visualizer')
use_static_viz_func = True
if use_static_viz_func:
else:
# show plots of SHO results vs. applied bias
px.be_viz_utils.visualize_sho_results(sho_dset, show_plots=True,
save_plots=False)
```
%% Cell type:markdown id: tags:
## Fit loops to a function
This is applicable only to DC voltage spectroscopy datasets from BEPS. The PFM hysteresis loops in this dataset will be projected to maximize the loop area and then fitted to a function.
Note: This computation generally takes a while for reasonably sized datasets.
%% Cell type:code id: tags:
``` python
# Do the Loop Fitting on the SHO Fit dataset
loop_success = False
h5_loop_group = px.hdf_utils.findH5group(h5_sho_fit, 'Loop_Fit')
if len(h5_loop_group) == 0:
try:
loop_fitter = px.BELoopModel(h5_sho_fit, parallel=True)
print('No loop fits found. Fitting now....')
h5_loop_guess = loop_fitter.do_guess(processors=max_cores, max_mem=max_mem)
h5_loop_fit = loop_fitter.do_fit(processors=max_cores, max_mem=max_mem)
loop_success = True
except ValueError:
print('Loop fitting is applicable only to DC spectroscopy datasets!')
else:
loop_success = True
print('Taking previously computed loop fits')
h5_loop_guess = h5_loop_group[-1]['Guess']
h5_loop_fit = h5_loop_group[-1]['Fit']
h5_loop_group = h5_loop_fit.parent
```
%% Cell type:markdown id: tags:
## Prepare datasets for visualization
%% Cell type:code id: tags:
``` python
# Prepare some variables for plotting loops fits and guesses
# Plot the Loop Guess and Fit Results
if loop_success:
h5_projected_loops = h5_loop_guess.parent['Projected_Loops']
h5_proj_spec_inds = px.hdf_utils.getAuxData(h5_projected_loops,
auxDataName='Spectroscopic_Indices')[-1]
h5_proj_spec_vals = px.hdf_utils.getAuxData(h5_projected_loops,
auxDataName='Spectroscopic_Values')[-1]
# reshape the vdc_vec into DC_step by Loop
sort_order = px.hdf_utils.get_sort_order(h5_proj_spec_inds)
dims = px.hdf_utils.get_dimensionality(h5_proj_spec_inds[()],
sort_order[::-1])
vdc_vec = np.reshape(h5_proj_spec_vals[h5_proj_spec_vals.attrs['DC_Offset']], dims).T
#Also reshape the projected loops to Positions-DC_Step-Loop
# Also reshape the projected loops to Positions-DC_Step-Loop
proj_nd, _ = px.hdf_utils.reshape_to_Ndims(h5_projected_loops)
proj_3d = np.reshape(proj_nd, [h5_projected_loops.shape[0],
proj_nd.shape[2], -1])
```
%% Cell type:markdown id: tags:
## Visualize Loop fits
%% Cell type:code id: tags:
``` python
use_static_plots = False
if loop_success:
if not use_static_plots:
try:
px.be_viz_utils.jupyter_visualize_beps_loops(h5_projected_loops, h5_loop_guess, h5_loop_fit)
except:
print('There was a problem with the interactive visualizer')
use_static_plots = True
if use_static_plots:
for iloop in range(h5_loop_guess.shape[1]):
fig, ax = px.be_viz_utils.plot_loop_guess_fit(vdc_vec[:, iloop], proj_3d[:, :, iloop],
h5_loop_guess[:, iloop], h5_loop_fit[:, iloop],
title='Loop {} - All Positions'.format(iloop))
```
%% Cell type:markdown id: tags:
## Loop Parameters
We will now load the loop parameters caluculated from the fit and plot them.
%% Cell type:code id: tags:
``` python
loop_mets = h5_loop_group[-1]['Fit_Loop_Parameters']
px.viz.be_viz_utils.jupyter_visualize_parameter_maps(loop_mets)
h5_loop_parameters = h5_loop_group['Fit_Loop_Parameters']
px.viz.be_viz_utils.jupyter_visualize_parameter_maps(h5_loop_parameters)
```
%% Cell type:code id: tags:
``` python
map_parm = 'Work of Switching'
plot_cycle = 0
plot_position = (int(pos_dims[0]/2), int(pos_dims[1]/2))
plot_bias_step = 0
px.viz.be_viz_utils.plot_loop_sho_raw_comparison(h5_loop_parameters, map_parm, plot_cycle, plot_position, plot_bias_step)
# display(px.viz.plot_utils.save_fig_filebox_button(fig, 'plot.png'))
```
%% Cell type:markdown id: tags:
## Save and close
* Save the .h5 file that we are working on by closing it. <br>
* Also, consider exporting this notebook as a notebook or an html file. <br> To do this, go to File >> Download as >> HTML
* Finally consider saving this notebook if necessary
%% Cell type:code id: tags:
``` python
hdf.close()
```
......
......@@ -77,6 +77,11 @@ class BELoopModel(Model):
Returns
-------
None
Notes
-----
Quantitative mapping of switching behavior in piezoresponse force microscopy, Stephen Jesse, Ho Nyung Lee,
and Sergei V. Kalinin, Review of Scientific Instruments 77, 073702 (2006); doi: http://dx.doi.org/10.1063/1.2214699
"""
......
......@@ -604,6 +604,7 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
Whether or not to print debugging statements
sort_dims : bool
If True, the data is sorted so that the dimensions are in order from fastest to slowest
If False, the data is kept in the original order
If `get_labels` is also True, the labels are sorted as well.
Returns
......@@ -627,7 +628,8 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
generate dummy values for them.
"""
pos_labs = np.array(['Positions'])
spec_labs = np.array(['Spectral_Step'])
if h5_pos is None:
"""
Get the Position datasets from the references if possible
......@@ -636,23 +638,28 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
try:
h5_pos = h5_main.file[h5_main.attrs['Position_Indices']]
ds_pos = h5_pos[()]
pos_labs = get_attr(h5_pos, 'labels')
except KeyError:
print('No position datasets found as attributes of {}'.format(h5_main.name))
if len(h5_main.shape) > 1:
ds_pos = np.arange(h5_main.shape[0], dtype=np.uint8).reshape(-1, 1)
pos_labs = np.array(['Position Dimension {}'.format(ipos) for ipos in range(ds_pos.shape[1])])
else:
ds_pos = np.array(0, dtype=np.uint8).reshape(-1, 1)
except:
raise
else:
ds_pos = np.arange(h5_main.shape[0], dtype=np.uint32).reshape(-1, 1)
pos_labs = np.array(['Position Dimension {}'.format(ipos) for ipos in range(ds_pos.shape[1])])
elif isinstance(h5_pos, h5py.Dataset):
"""
Position Indices dataset was provided
"""
ds_pos = h5_pos[()]
pos_labs = get_attr(h5_pos, 'labels')
elif isinstance(h5_pos, np.ndarray):
ds_pos = h5_pos
pos_labs = np.array(['Position Dimension {}'.format(ipos) for ipos in range(ds_pos.shape[1])])
else:
raise TypeError('Position Indices must be either h5py.Dataset or None')
......@@ -666,25 +673,29 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
try:
h5_spec = h5_main.file[h5_main.attrs['Spectroscopic_Indices']]
ds_spec = h5_spec[()]
spec_labs = get_attr(h5_spec, 'labels')
except KeyError:
print('No spectroscopic datasets found as attributes of {}'.format(h5_main.name))
if len(h5_main.shape) > 1:
ds_spec = np.arange(h5_main.shape[1], dtype=np.uint8).reshape([1, -1])
spec_labs = np.array(['Spectral Dimension {}'.format(ispec) for ispec in range(ds_spec.shape[0])])
else:
ds_spec = np.array(0, dtype=np.uint8).reshape([1, 1])
except:
raise
else:
ds_spec = np.arange(h5_main.shape[1], dtype=np.uint8).reshape([1, -1])
spec_labs = np.array(['Spectral Dimension {}'.format(ispec) for ispec in range(ds_spec.shape[0])])
elif isinstance(h5_spec, h5py.Dataset):
"""
Spectroscopic Indices dataset was provided
"""
ds_spec = h5_spec[()]
spec_labs = get_attr(h5_spec, 'labels')
elif isinstance(h5_spec, np.ndarray):
ds_spec = h5_spec
spec_labs = np.array(['Spectral Dimension {}'.format(ispec) for ispec in range(ds_spec.shape[0])])
else:
raise TypeError('Spectroscopic Indices must be either h5py.Dataset or None')
......@@ -695,9 +706,9 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
spec_sort = get_sort_order(ds_spec)
if verbose:
print('Position dimensions:', get_attr(h5_pos, 'labels'))
print('Position dimensions:', pos_labs)
print('Position sort order:', pos_sort)
print('Spectroscopic Dimensions:', get_attr(h5_spec, 'labels'))
print('Spectroscopic Dimensions:', spec_labs)
print('Spectroscopic sort order:', spec_sort)
'''
......@@ -707,14 +718,11 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
spec_dims = get_dimensionality(ds_spec, spec_sort)
if verbose:
print('\nPosition dimensions (sort applied):', get_attr(h5_pos, 'labels')[pos_sort])
print('\nPosition dimensions (sort applied):', pos_labs[pos_sort])
print('Position dimensionality (sort applied):', pos_dims)
print('Spectroscopic dimensions (sort applied):', get_attr(h5_spec, 'labels')[spec_sort])
print('Spectroscopic dimensions (sort applied):', spec_labs[spec_sort])
print('Spectroscopic dimensionality (sort applied):', spec_dims)
all_labels = np.hstack((get_attr(h5_pos, 'labels')[pos_sort][::-1],
get_attr(h5_spec, 'labels')[spec_sort][::-1]))
ds_main = h5_main[()]
"""
......@@ -725,6 +733,7 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
"""
try:
ds_Nd = np.reshape(ds_main, pos_dims[::-1] + spec_dims[::-1])
except ValueError:
warn('Could not reshape dataset to full N-dimensional form. Attempting reshape based on position only.')
try:
......@@ -738,6 +747,9 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False, verbo
except:
raise
all_labels = np.hstack((pos_labs[pos_sort][::-1],
spec_labs[spec_sort][::-1]))
if verbose:
print('\nAfter first reshape, labels are', all_labels)
print('Data shape is', ds_Nd.shape)
......@@ -1601,3 +1613,21 @@ def get_unit_values(h5_spec_ind, h5_spec_val, dim_names=None):
unit_values[dim_name] = h5_spec_val[desired_row_ind, intersections]
return unit_values
def get_source_dataset(h5_group):
"""
Find the name of the source dataset used to create the input `h5_group`
Parameters
----------
h5_group : h5py.Datagroup
Child group whose source dataset will be returned
Returns
-------
h5_source : h5py.Dataset
"""
h5_parent_group = h5_group.parent
h5_source = h5_parent_group[h5_group.name.split('/')[-1].split('-')[0]]
return h5_source
This diff is collapsed.
......@@ -333,7 +333,7 @@ def plot_line_family(axis, x_axis, line_family, line_names=None, label_prefix='L
color=cmap(int(255 * line_ind / (num_lines - 1))), **kwargs)
def plot_map(axis, data, stdevs=2, origin='lower', **kwargs):
def plot_map(axis, data, stdevs=None, origin='lower', **kwargs):
"""
Plots a 2d map with a tight z axis, with or without color bars.
Note that the direction of the y axis is flipped if the color bar is required
......@@ -344,8 +344,8 @@ def plot_map(axis, data, stdevs=2, origin='lower', **kwargs):
Axis to plot this map onto
data : 2D real numpy array
Data to be plotted
stdevs : unsigned int (Optional. Default = 2)
Number of standard deviations to consider for plotting
stdevs : unsigned int (Optional. Default = None)
Number of standard deviations to consider for plotting. If None, full range is plotted.
origin : str
Where should the origin of the image data be located. 'lower' sets the origin to the
bottom left, 'upper' sets it to the upper left.
......@@ -354,29 +354,32 @@ def plot_map(axis, data, stdevs=2, origin='lower', **kwargs):
Returns
-------
"""
data_mean = np.mean(data)
data_std = np.std(data)
if stdevs is not None:
data_mean = np.mean(data)
data_std = np.std(data)
plt_min = data_mean - stdevs * data_std
plt_max = data_mean + stdevs * data_std
else:
plt_min = np.min(data)
plt_max = np.max(data)
im = axis.imshow(data, interpolation='none',
vmin=data_mean - stdevs * data_std,
vmax=data_mean + stdevs * data_std,
vmin=plt_min,
vmax=plt_max,
origin=origin,
**kwargs)
# axis.set_aspect('auto')
return im
def single_img_cbar_plot(fig, axis, img, show_xy_ticks=None, show_cbar=True,
x_size=1, y_size=1, num_ticks=4, cbar_label=None,
tick_font_size=14, **kwargs):
def single_img_cbar_plot(axis, img, show_xy_ticks=None, show_cbar=True, x_size=1, y_size=1, num_ticks=4,
cbar_label=None, tick_font_size=14, **kwargs):
"""
Plots an image within the given axis with a color bar + label and appropriate X, Y tick labels.
This is particularly useful to get readily interpretable plots for papers
Parameters
----------
fig : matplotlib.figure object
Handle to figure
axis : matplotlib.axis object
Axis to plot this image onto
img : 2D numpy array with real values
......@@ -1489,3 +1492,56 @@ def save_fig_filebox_button(fig, filename):
save_button.on_click(_save_fig)
return widget_box
def export_fig_data(fig, basename='junk', ext='.dat', include_images=False):
"""
Parameters
----------
fig
basename
include_images
Returns
-------
"""
# Get the data from the figure
axes = fig.get_axes()
axes_dict = dict()
for ax in axes:
ax_dict = dict()
ims = ax.get_images()
if len(ims) != 0 and include_images:
im_dict = dict()
for im in ims:
im_dict[im.get_label()] = im.get_array().data
ax_dict['Images'] = im_dict
lines = ax.get_lines()
if len(lines) != 0:
line_dict = dict()
xlab = ax.get_xlabel()
ylab = ax.get_ylabel()
if xlab == '':
xlab = 'X Data'
if ylab == '':
ylab = 'Y Data'
for line in lines:
line_dict[line.get_label()] = {xlab: line.get_xdata(),
ylab: line.get_ydata()}
ax_dict['Lines'] = line_dict
if ax_dict != dict():
axes_dict[ax.get_title()] = ax_dict
basename = os.path.abspath(basename)
folder, _ = os.path.split(basename)
\ No newline at end of file
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