sxdm package¶
Submodules¶
sxdm.alignment module¶
-
sxdm.alignment.
alignment_function
(self)¶ Allows the user to align the scans based on Fluorescence or Region Of Interest.
Sets alignment variables and stores them in designated .h5 file. Easier to reload alignment data or redo alignment data
- Parameters
self (
(SXDMFrameset)
) – the sxdmframeset object- Returns
- Return type
Nothing
-
sxdm.alignment.
reset_dxdy
(self)¶ Resets the dxdy movements used for alignment of the frames
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
- Return type
Nothing
sxdm.background module¶
-
sxdm.background.
scan_background
(self, amount2ave=3, multiplier=1)¶ Create background images for each scan.
Takes the first “amount2ave” and last “amount2ave” images of each scan and average them together to each scan. Create a dictionary of these background images.
- Parameters
self (
(SXDMFrameset)
) –amount2ave (
(int)
) – amount of images from the beginning and enf of each scan to average togethermultiplier (
(int/float)
) – multiplier for each background scan
- Returns
- Return type
A dictionary of background images with entries for each scan number
-
sxdm.background.
scan_background_finder
(destination, background_dic)¶ The destination is the scan numbers associated with a given pixel location. This will take all scans in a pixel location and return a numpy array of their appropriate background images.
- Parameters
destination (
(numpy array)
) – the output of h5get_image_destination(self, pixel) list of scan numbers which the user wants to get the background images forbackground_dic (
(dic)
) – the dictionary otuput from the scan_background() function
- Returns
- Return type
A numpy array of background images corresponding to the scans in the destination input
sxdm.chi_determination module¶
-
class
sxdm.chi_determination.
Chi_FiguresClass
¶ Bases:
object
A class function that allows the code to store figure data. Makes it easier to transfer data between figures
-
sxdm.chi_determination.
broadening_in_pixles
(self)¶ Determine the instrumental broadening in pixels as well as the focal length and numberical aperature
- Returns
Nothing
Sets
====
self.focal_length_mm
self.NA_mrads
self.broadening_in_pix
-
sxdm.chi_determination.
chi_function
(self)¶ Runs all code necessary to determine the chi bounds
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
Nothing
-
sxdm.chi_determination.
chis
(self)¶ Calculates the bounds for the detector
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
Nothing
Prints and Sets
======
self.chi value,
self.focal_length,
self.NA_mrads (numberical aperature),
self.broadening_in_pix of the detector
-
sxdm.chi_determination.
closebtn_press
(event, self, chi_figures)¶ Set the chi angle difference, position difference, and image dimension upon closing the figure
- Parameters
(matplotlib event) (event) – matplotlib event
(SXDMFrameset) (self) – the SXDMFrameset)
(Chi_FiguresClass) (chi_figures) – the chi_figuresclass
- Returns
- Return type
Nothing - runs chis() function. see documentation for values set
-
sxdm.chi_determination.
closebtn_start
(chi_figures, btn_ax)¶ Set up the close button
- Parameters
(Chi_FiguresClass) (chi_figures) – the chi_figuresclass
(matplotlib_axis) (btn_ax) – the figure axis corresponding to the close button
- Returns
- Return type
Nothing
-
sxdm.chi_determination.
display_first_images
(chi_figures)¶ From the chi figure class set up the first set of figures
- Returns
- Return type
Nothing
-
sxdm.chi_determination.
display_second_images
(chi_figures)¶ Display the second figure
- Parameters
(Chi_FiguresClass) (chi_figures) – the chi_figuresclass
- Returns
- Return type
Nothing
-
sxdm.chi_determination.
first_chi_figure_setup
(self)¶ Setting up the chi figure GUI
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
- Return type
The Chi_FigureClass object created by the function
-
sxdm.chi_determination.
idx_tb_setup
(vmin_spot_ax, vmax_spot_ax)¶ Set up the index textboxes DEPRECIATED
- Returns
- Return type
position 1 and position 2 textboxes for index values
-
sxdm.chi_determination.
minmax_tb_setup
(vmin_spot_ax, vmax_spot_ax)¶ Set up the minmax textboxes
- Parameters
(matplotlib axis) (vmax_spot_ax) – the figure axis that corresponds to the vmin value of the spot diffraction
(matplotlib axis) – the figure axis that corresponds to the vmax value of the spot diffraction
- Returns
- Return type
vmin and vmax textboxes
-
sxdm.chi_determination.
pos_tb_setup
(pos1_ax, pos2_ax)¶ Set up the position text boxes
- Parameters
(matplotlib axis) (pos2_ax) – the figure axis corresponding to the vertical line position of the first (left) image
(matplotlib axis) – the figure axis corresponding to the vertical line position of the second (right) image
- Returns
- Return type
position 1 and position 2 textboxes for index values
-
sxdm.chi_determination.
return_chi_images
(file, chi_figures)¶ Return the detector movement data
- Parameters
(str) (file) – the file the user would like to find the detector images from
(Chi_FigureClass) (chi_figures) – the figure class. used to make it easier to move data around
- Returns
- Return type
Nothing - sets chi_fiures.images value
-
sxdm.chi_determination.
return_chi_images_loc
(file, chi_figures, detector_channel_loc='detector_channels/detector_scan/Main_Scan', zfill_num=4)¶ Grabs the image location for the detector sweep scan
- Parameters
file (
(str)
) – the .h5 file associated with the datasetchi_figures (
(Chi_FigureClass)
) – the Chi_FigureClass allowing transfer of data from figure to figuredetector_channel_loc (
(str)
) – the h5 group location to the detector sweep scan used for chi bounds determinationzfill_num (
(int)
) – the integer value for how many digits the scan number must have
- Returns
- Return type
Nothing - sets the chi_figures.scan_theta and chi_figures.images_location valuess
-
sxdm.chi_determination.
second_change
(event, chi_figures)¶ When the second figure values change, update the second figure
- Parameters
(matplotlib event) (event) – matplotlib event
(Chi_FiguresClass) (chi_figures) – the chi_figuresclass
- Returns
- Return type
Nothing
-
sxdm.chi_determination.
second_chi_figure_setup
(chi_figures)¶ Setting up the second figure
- Parameters
(Chi_FigureClass) (chi_figures) – the chi_figuresclass
- Returns
- Return type
Nothing
-
sxdm.chi_determination.
vs_change
(event, chi_figures)¶ When the user changes the vmin or vmax update the figures
- Parameters
(matplotlib event) (event) – matplotlib event
(Chi_FiguresClass) (chi_figures) – the chi_figuresclass
- Returns
- Return type
Nothing
sxdm.clicks module¶
-
sxdm.clicks.
check_mouse_ax
(event, self)¶ Grabs the mouse axes position
- Parameters
(matplotlib event) (event) – the matpotlib event
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
Nothing - just checks which axis the mouse is in
-
sxdm.clicks.
fig1_click
(event, self, fig, images)¶ Deal with event clicks in the first figure and redraw plots
- Parameters
(matplotlib event) (event) – matplotlib event
(SXDMFrameset) (self) – the sxdmframeset
(matplotlib figure) (fig) – the figure to be clicked on
(nd.array) (images) – the images to display
- Returns
- Return type
Nothing - deals with the clicks on the first image
-
sxdm.clicks.
fig_leave
(event, self)¶ If the user leaves an axis set the self.viewer_currentax to None :param event (matplotlib event): the matplotlib event :param self (SXDMFrameset): the sxdmframeset
- Returns
- Return type
Nothing
-
sxdm.clicks.
onclick_1
(event, self)¶ Deal with click events for first figure. Only allows for 30 figures
Once you click on figure one open up a second figure with the figure you just clicked on, but larger
- Parameters
event (
(matplotlib event)
) – not 1100% sure. Needed to be the first param in a matplotlib eventself (
(SXDMFrameset)
) – An SXDMFrameset which allows the function to store click values
- Returns
- Return type
Nothing
-
sxdm.clicks.
onclick_2
(event, self)¶ Determine the pixel location the user has clicked on.
From the second figure that popped up, once you click on it, determine the location of that click
- Parameters
(matplotlib event) (event) – Unsure. Needed to be the first variable in a matplotlib event click
(SXDMFrameset) (self) – An SXDMFrameset that allows the clicks to be saved
-
sxdm.clicks.
save_alignment
(event, self)¶ Allow the alignment click information to be saved to self.file
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
Nothing. It just stores /dxdy to the self.file as well
as adding alingment_group and alignment_subgroup attributes
sxdm.det_chan module¶
-
sxdm.det_chan.
add_column
(im, max_column)¶ Add a numpy.nan column to an image array based on how many columns in the max amount of columns
- Parameters
(a 2D array) (im) – the image array to add columns to
(int) (max_column) – the final amount of columns the user want to make the im array
- Returns
- Return type
a new im array with the User selected max_column
-
sxdm.det_chan.
add_row
(im, max_row)¶ Add a numpy.nan rows to an image array based on how many rows in the max amount of rows
- Parameters
(a 2D array) (im) – the image array to add columns to
(int) (max_row) – the final amount of rows the user want to make the im array
- Returns
- Return type
a new im array with the User selected max_row
-
sxdm.det_chan.
del_det_chan
(file)¶ Deletes the /detector_channels group :param file (str): the .h5 file you would like to delete the detector_channel group from
- Returns
- Return type
Nothing
-
sxdm.det_chan.
det_dim_fix
(array, max_row, max_column)¶ Add columns and rows to image matrix to make everything the same dimensions
- Parameters
(np.ndarray) (array) – a 3D array of images
(int) (max_column) – the max amount of rows the User would like to make all the images
(int) – the max amount of columns the User would like to make all the images
- Returns
- Return type
the 3D image array with dimensions (#images, max_row, max_column)
-
sxdm.det_chan.
disp_det_chan
(file)¶ Allows the user to quicky see what all values are set to in the detector_channel group
- Parameters
(str) (file) – the .h5 file you would like to delete the detector_channel group from
- Returns
prints the current values of the detector_channel group. If no values are currently set it displays something the
User can copy and paste into a cell to set the appropriate dectector values
-
sxdm.det_chan.
max_dims
(array)¶ Get the max dimensions for an array of 2D images
- Parameters
(np.ndarray) (array) – a 3 dimensional array
- Returns
- Return type
the max rows and the max columns round for the 3 dimensionsal array
-
sxdm.det_chan.
return_det
(file, scan_numbers, group='fluor', default=False, dim_correction=True)¶ Returns all information for a given detector channel for an array of scan numbers
- Parameters
file (
(str)
) – the .h5 file locationscan_numbers (
(np.array)
) – an array of scan numbers the user wants to get a specific detector channel information forgroup (
(str)
) – a string corresponding to a group value that the user wants to returndefault (
(bool)
) – If True this will default to the first acceptable detector_channel in the hdf5 filedim_correction (
(bool)
) – If False this will not add rows and columns to the returned array to make them all the same shape
- Returns
- Return type
The specified detector channel for all specified scan numbers, the .mda detector value
-
sxdm.det_chan.
setup_det_chan
(file, fluor, roi, detector_scan, filenumber, sample_theta, hybrid_x, hybrid_y, mis, xrf)¶ Sets detector channel information and stores it in the .h5 file
- Parameters
file (
(str)
) – the hdf file pathfluor (
(dic)
) – a dictionary entry with all the Fluorescence images names as well as their corresponding .mda detector channel valeroi (
(dic)
) – a dictionary entry with all the Region of Interest images names as well as their corresponding .mda detector channel valedetector_scan (
(int)
) – the dectector channel that corresponds to the scanning of the detector - used to determine detector dimensionsfilenumber (
(int)
) – the dectector channel that corresponds to the image file numberssample_theta (
(int)
) – the detector channel that corresponds to the sample thetahybrid_x (
(int)
) – the detector channel that corresponds to the hybrid_xhybrid_y (
(int)
) – the detector channel that corresponds to the hybrid_ymis (
(dic)
) – a miscellaneous dictionary with entries of detector channels that might be usefull. ex. 2Theta, Ring_Currentxrf (
(dic)
) – an x-ray fluorescence dictionary with entries of the detector channels CORRESPOND TO THE DETECTOR CHANNELS UNDER THE ‘xrf’ HEADING THE THE HDF FILE!!!
- Returns
- Return type
Nothing
-
sxdm.det_chan.
space_check
(fluor, roi, detector_scan, filenumber, sample_theta, hybrid_x, hybrid_y, mis, xrf)¶ Based on the input variables this checks to see if the user has put any spaces into their group names.
Warns the user that they are using spaces
- Parameters
fluor (
(dic)
) – a dictionary entry with all the Fluorescence images names as well as their corresponding .mda detector channel valueroi (
(dic)
) – a dictionary entry with all the Region of Interest images names as well as their corresponding .mda detector channel valedetector_scan (
(int)
) – the dectector channel that corresponds to the scanning of the detector - used to determine detector dimensionsfilenumber (
(int)
) – the dectector channel that corresponds to the image file numberssample_theta (
(int)
) – the detector channel that corresponds to the sample thetahybrid_x (
(int)
) – the detector channel that corresponds to the hybrid_xhybrid_y (
(int)
) – the detector channel that corresponds to the hybrid_ymis (
(dic)
) – a miscellaneous dictionary with entries of detector channels that might be usefull. ex. 2Theta, Ring_Currentxrf (
(dic)
) – an x-ray fluorescence dictionary with entries of the detector channels CORRESPOND TO THE DETECTOR CHANNELS UNDER THE ‘xrf’ HEADING THE THE HDF FILE!!!
- Returns
- Return type
(bool) on whether or not there is a space in any of the dictionary entries given by the user
-
sxdm.det_chan.
true_filenumbers
(file, scan_numbers, shapes)¶ Since the .mda files do not store the correct file_names/image_names inside of it. This creates an appropriate file number for each pixel in a scan
- Parameters
(str) (file) – the location of the .h5 file
(nd.array) (shapes) – array of all the scan numbers the User wishes to create image numbers for
(nd.array) – the shapes of all the scans
Returns –
======= –
nd.array with the .tif image numbers/locations for each voxel of the scan (an) –
sxdm.generalize module¶
-
sxdm.generalize.
general_analysis_multi
(self, rows, columns, analysis_function, analysis_input, bkg_multiplier=0)¶ Runs the centroid analysis in a pool.map function
- Parameters
self (
(SXDMFrameset)
) – the sxdmframeset objectrows (
(int
ortup)
) – the total amount of rows the User wants to analyze or a tuple of the rowscolumns (
(int
ortup)
) – the total amount of columns the User wants to analyze or a tuple of the columnsmed_blur_distance (
(int)
) – the median blur distance - must be oddmed_blur_height (
(int)
) – the median blur heightstdev (
(int)
) – the standard deviation used to segment databkg_multiplier (
(int)
) – the multiplier to the background imagesanalysis_function (
(func)
) – a function to be passed into the analysis. the program will find the summed diffraction pattern for each pixel, for each scan number of the dataset, background subtract, and pass the summed diffraction into the first analysis_function argument.analysis_input (
(user defined)
) – an static input array, value, etc passed into the second argument in the analysis_function.
- Returns
- Return type
a pooled results from the centroid_pixel_analysis_multi() function
-
sxdm.generalize.
general_pixel_analysis_multi
(row, column, image_array, scan_numbers, background_dic, file, analysis_function, analysis_input)¶ The analysis done on a single pixel
- Parameters
row (
(int)
) – the row the User wants to do analysis oncolumn (
(int)
) – the column the User wants to do analysis onimage_array (
(nd.array)
) – the image location array - can be created with create_imagearray(self)scan_numbers (
(nd.array)
) – the list of scan numbers usedbackground_dic (
(dic)
) – the background scan dictionary entry - can be made with scan_backgrounnd(self)file (
(str)
) – the hdf5 file locationanalysis_function (
(func)
) – a function to be passed into the analysis. the program will find the summed diffraction pattern for each pixel, for each scan number of the dataset, background subtract, and pass the summed diffraction into the first analysis_function argument.analysis_input (
(user defined)
) – an static input array, value, etc passed into the second argument in the analysis_function.
- Returns
- Return type
the analysis results as an nd.array
-
sxdm.generalize.
general_pooled_return
(results, user_val, user_acceptable_values)¶ Makes it easy to return values from the pooled results from the multi.analysis function
- Parameters
(n dimensional array) (results) – the output from the analysis function
(str) (user_val) – a string that defines what the user wants to be returned. Type ‘help’ for all acceptable values
(list) (user_acceptable_values) – an array of all the analysis_function outputs in the order the user has set them as.
- Returns
- Return type
An n dimensional array consisting of the user selected data output from the multi.analysis function
-
sxdm.generalize.
general_pre_multi
(inputs, image_array, scan_numbers, background_dic, file, analysis_function, analysis_input)¶ Allows Python to run the roi analysis in a pool.map function
- Parameters
inputs (
(nd.array)
) – the iterable inputs of rows and columnsimage_array (
(nd.array)
) – the total locations of all the images - created by create_imagearray(self)scan_numbers (
(nd.array)
) – the list of all the scan numbersbackground_dic (
(dic)
) – the dictionary of all the background images - created by scan_background(self)file (
(str)
) – the full hdf5 file locationanalysis_function (
(func)
) – a function to be passed into the analysis. the program will find the summed diffraction pattern for each pixel, for each scan number of the dataset, background subtract, and pass the summed diffraction into the first analysis_function argument.analysis_input (
(user defined)
) – an static input array, value, etc passed into the second argument in the analysis_function.
- Returns
- Return type
the results from the general_pixel_analysis_multi() function
sxdm.h5 module¶
-
sxdm.h5.
close_h5
(hdf)¶ Closing and opened hdf5 file
- Parameters
(str) (file) – the path to the hdf5 file
- Returns
- Return type
Closes an opened hdf5 file
-
sxdm.h5.
h5create_dataset
(file, ds_path, ds_data)¶ Creates a dataset in the user defined group with data equal to the user defined data
- Parameters
file (
(str)
) – the user defined hdf5 fileds_path (
(str)
) – the group path to the dataset inside the hdf5 fileds_data (
(nd.array)
) – a numpy array the user would like to store
- Returns
- Return type
Nothing
-
sxdm.h5.
h5create_file
(loc, name)¶ Creates hdf5 file
- Parameters
loc (
(str)
) – the location of the hdf5 filename (
(str)
) – the name of the hdf5 file WITHOUT .h5 at the end
- Returns
- Return type
Nothing
-
sxdm.h5.
h5create_group
(file, group)¶ Creates a group based on the Users group input
- Parameters
(str) (group) – the user defined hdf5 file
(str) – the group the user would like to create inside the file
- Returns
- Return type
Nothing
-
sxdm.h5.
h5del_data
(file, group)¶ Sets all data equal to zero for a given dataset.
- Parameters
file (
(str)
) – the hdf5 file pathgroup (
(str)
) – the group location in the hdf5 the user wants to delete the data for
- Returns
- Return type
Nothing
-
sxdm.h5.
h5del_group
(file, group)¶ Deletes the user defined group. DOES NOT REDUCE FILE SIZE
- Parameters
file (
(str)
) – the user defined hdf5 filegroup (
(str)
) – the group inside the hdf5 file the user would like to delete
- Returns
- Return type
Nothing
-
sxdm.h5.
h5delete_file
(file)¶ Deletes the file set by the user
- Parameters
file (
(str)
) – the full location of the hdf5 file the user would like to delete- Returns
- Return type
Nothing
-
sxdm.h5.
h5get_image_destination
(self, pixel)¶ Determine where all the .tif images are for all the scan numbers disregarding the nan values
- Parameters
(SXDMFramset) (self) – the sxdmframset
(str array) (pixel) – the image number from each scan that corresponds to a certain pixel
- Returns
- Return type
All of the diffraction FULL image locations for each scan in a 3D array - excluding the np.nan’s
-
sxdm.h5.
h5get_image_destination_v2
(self, pixel)¶ Determine where all the .tif images are for all the scan numbers disregarding the nan values
- Parameters
(SXDMFramset) (self) – the sxdmframset
(str array) (pixel) – the image number from each scan that corresponds to a certain pixel
- Returns
- Return type
All of the diffraction FULL image locations for each scan in a 3D array - excluding the np.nan’s
-
sxdm.h5.
h5grab_data
(file, data_loc)¶ Returns the data stored in the user defined group
- Parameters
(str) (data_loc) – the user defined hdf5 file
(str) – the group the user would like to pull data from
- Returns
- Return type
the data stored int the user defined location
-
sxdm.h5.
h5group_list
(file, group_name='base')¶ Displays all group members for a user defined group
- Parameters
(str) (group_name) – the path to the hdf5 file
(str) – the path to the group the user wants the Keys for. Set to ‘base’ if you want the top most group
- Returns
- Return type
a list of all the subgroups inside the user defined group
-
sxdm.h5.
h5images_wra
(file, scan, im_loc, im_name, delete=False, import_type='uint32')¶ Used to import/delete .tif images into the .h5 file
- Parameters
(str) (import_type) – the user defined hdf5 file
(nd.array) (scan) – the scan numbers the user wants to import
(str) – the location of the image file
(bool) (delete) – set to True if the user would like to delete the data in the hdf5 file
(str) – string passed into imageio.imread(image).astype(import_type)
- Returns
- Return type
Nothing
-
sxdm.h5.
h5path_exists
(file, loc)¶ See if the group the user selected exists
- Parameters
(str) (loc) – the path to the hdf5 file
(str) – the location to the group the user wishes to check the status of
- Returns
- Return type
a bool (True or False) on whether or not the group exists
-
sxdm.h5.
h5read_attr
(file, loc, attribute_name)¶ Read an attribute from a user selected group and attribute name
- Parameters
(str) (attribute_name) – the path to the hdf5 file
(str) – the location to the group inside the hdf5 file
(str) – the name of the attribute
- Returns
- Return type
Attribute value
-
sxdm.h5.
h5replace_data
(file, group, data)¶ Replaces all data in a dataset.
Helps with space savings since deleting groups does not decrease the file size. Easier to replace data
- Parameters
(str) (group) – the user defined hdf5 file
(str) – the user defined group in the hdf5 file
(nd.array) (data) – the data the user would like to sub in
- Returns
- Return type
Nothing
-
sxdm.h5.
h5set_attr
(file, loc, attribute_name, attribute_val)¶ Set and attribute for a User selected group
- Parameters
(str) (attribute_name) – the path to the hdf5 file
(str) – the group location in the hdf5 file
(str) – the name the user wants to set the attribute value of
(str or int) (attribute_val) – the value of the attribute
- Returns
- Return type
Nothing
-
sxdm.h5.
open_h5
(file)¶ Opening an hdf5 file
- Parameters
(str) (file) – the path to the hdf5 file
- Returns
- Return type
The opened hdf5 file
sxdm.importer module¶
-
sxdm.importer.
images_group_exsist
(file, scan)¶ See if the images group exists.
-
sxdm.importer.
import_images
(file, images_loc, scans=False, fill_num=4, import_type='uint32', delimiter_function=<function delimiter_func>, force_reimport=False)¶ Allows the user to import all .tif images into the .h5 file
- Parameters
(str) (import_type) – the user defined hdf5 file
(str) – the location of the images folder from the beamline
(nd.array) (scans) – the scans the user would like to import
(int) (fill_num) – the amount of numbers in the images folders names
(str) – a string value passed into imageio.imread().astype(import_type)
(function) (delimiter_function) – a function which determines the image number. redefine this if 26 - ID -C naming scheme changes
(bool) (force_reimport) – set to True if you would like to force reimport images
- Returns
- Return type
Nothing
-
sxdm.importer.
import_mda
(mda_path, hdf5_save_directory, hdf5_save_filename, single_file=False, maxdims=2)¶ Allows the User to import all .mda image and line scan data into an hdf5 format
- Parameters
(str) (single_file) – the string path to the folder holding the .mda files
(str) – the path location where you would like to save your data to EXAMPLE: ‘/home/Desktop’
(str) – the file inside that path in which you would like to save data to (DO NOT INCLUDE “.h5”) EXAMPLE: ‘test’
(str) – the location of the file to import
(int) (maxdims) – the maximum dimensions for the readMDA function to import from the .mda file
- Returns
- Return type
Nothing - Will Not Reimport Files
sxdm.mis module¶
-
sxdm.mis.
array2dic
(array)¶ Turn a numpy array into a dictionary
- Parameters
(numpy array) (array) – a numpy array of form [(num1, num2), (num3, num4)] that will be turned into a dictionary
- Returns
a dictionary of form {0
- Return type
(num1
,num2)
,1
:(num3
,num4)``
}``
-
sxdm.mis.
array_shift
(self, arrays2shift, centering_idx)¶ Translate a 3D stack of numpy arrays to align based on set centering values
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
(numpy array) (centering_idx) – the 3 dimensional array the user wants to center around one of the indexes
(numpy array) – the dxdy values (translation values) for each matrix in the array
- Returns
- Return type
the shifted array based on the dxdy values
-
sxdm.mis.
centering_det
(self, group='fluor', center_around=False, summed=False, default=False)¶ Return a detector that has been centered around a set value
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
(str) (group) – the group name the user would like to center to - acc_vals = fluor and roi
(bool) (default) – if this equals -1 then there will be no centering adjustments
(bool) – if True it will sum together all scans after centering
(bool) – if True it will set the fluor image to center on or roi image to view to the first index
- Returns
an array of the fluor images or the roi images for all the SXDMFrameset scans centered around
the users value
-
sxdm.mis.
create_file
(file)¶ Create an hdf5 file
- Parameters
(str) (file) – the path of the hdf5 file the user would like to create
- Returns
- Return type
Nothing
-
sxdm.mis.
create_rois
(self)¶ Take the self.roi_results and make them into something more useful
- Parameters
(SXDMFrameset) (self) – the sxdmframset
- Returns
the region of interst maps for each scan, the region of interest maps for the user defined
sub regions of interest
-
sxdm.mis.
delimiter_func
(string)¶ The delimiter function used in the image importer Used to just get the number of the image from the name of the image/mda file
- Parameters
(str) (string) – the string the user wants to place the delimiter function on
- Returns
- Return type
a cropped string based on the delimiter function
-
sxdm.mis.
dic2array
(dic)¶ Turn a dictionary into a numpy array
- Parameters
(dictionary) (dic) – a dictionary of form {0: (num1, num2), 1: (num3, num4)}
- Returns
- Return type
a numpy array of form [(num1, num2), (num3, num4)]
-
sxdm.mis.
figure_size_finder
(images)¶ Determines the amount of boxes to place in the alignment viewer
- Parameters
(nd.array) (images) – the numpy array
- Returns
- Return type
the amount of images to make axes for in plt.subplot((return, return))
-
sxdm.mis.
get_idx4roi
(pix, destination, scan_numbers)¶ Based on the pixel being loaded, this returns the index of each scan used in the self.scan_numbers
- Parameters
(array of strings) (scan_numbers) – the image numbers for each scan for a given pixel
(array of strings) – full diffraction image locations for each scan for a given pixel
(array of strings) – the self.scan_numbers value
- Returns
- Return type
an array of index values for a master roi used to store values in the correct position
-
sxdm.mis.
grab_dxdy
(self)¶ Return the dxdy movements for each scan
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
the dxdy values stored in the self.file
-
sxdm.mis.
grab_fov_dimensions
(self)¶ Returns the image dimensions for the User
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
the np.shape() of the fluorescence images - which are identical for the the entire field of view
-
sxdm.mis.
image_numbers
(self)¶ Grab the image numbers
- Parameters
(SXDMFrameset) (self) – the sxdmframset
- Returns
- Return type
all of the diffraction image numbers for each scan in a 3D matrix
-
sxdm.mis.
load_variable_pickle
(file)¶
-
sxdm.mis.
median_blur_numpy
(input_array, median_blur_distance, cut_off_value_above_mean, with_low=False)¶ Median Blur a 1D array. Used for eliminating hot or dead pixels
- Parameters
(1D array) (input_array) – a one dimensional numpy array
(int) (median_blur_distance) – the chunk size of numbers to to check for a median blur corrections
- Returns
- Return type
a 1 dimensional numpy array that has been median blurred
-
sxdm.mis.
median_blur_selective
(input_array, median_blur_distance, cut_off_value_above_mean, with_low=False)¶ Allows for the user to have a selective median blur for individual spots. Meaning it will not assign a median blur to the entire spectra.
- Parameters
(nd.array) (input_array) – the 1D spectral array
(int) (cut_off_value_above_mean) – a value that assigns how large of a median blur the User wants to walk through.
(int) – a value above the mean value for the med_blur_distance in which the value gets attributed as the median of that data chunk
(bool) (with_low) – True median blurs on values way above or way below the mean. Rather than just way above.
- Returns
- Return type
An 1D spectral array with appropriate values median blurred
-
sxdm.mis.
order_dir
(path)¶ For a selected path return the ordered filenames. Meant for ordering images inside of a folder
- Parameters
(str) (path) – the path to the folder you would like to order the contents of
- Returns
- Return type
the full image location, image name
-
sxdm.mis.
ram_check
()¶ Check how much RAM is being used. If it’s over 90% then the analysis function stop loading information
- Returns
- Return type
the percent of RAM usage
-
sxdm.mis.
resolution_check
(self, user_resolution_um=0.0005)¶ Check if all X dimensions for all scans are equal to each other. Also checks in all y dimensions for all scans are equal to each other. Then checks if the x and y are equal to one another.
- Parameters
(SXDMFramset) (self) – the sxdmframset
(float) (user_resolution_um) – the resolution in um the user would like the scan dimensions for
- Returns
- Return type
Nothing - sets resolution values
-
sxdm.mis.
results_2dsum
(self)¶ Returns the 2d summed diffraction pattern from the results self.saved_file without loading all the diffraction data. This works on the self.results variable.
Problems - Uses a lot of RAM
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
- Return type
2d image array of the summed diffraction pattern
-
sxdm.mis.
save_variable_pickle
(array_of_vars, file)¶
-
sxdm.mis.
scan_num_convert
(scan_numbers, fill_num=4)¶ Convert the users int scan numbers into strings
- Parameters
(str or int) (scan_numbers) – takes an array of str values or int values and zfills them accordingly
(int) (fill_num) – the string fill number the user would like to use
- Returns
- Return type
the scan numbers in an array of strings with the user selected fill number
-
sxdm.mis.
set_centering
(self)¶ Set the centering values for each scan
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
The centering index - sets all centering data
-
sxdm.mis.
shape_check
(self)¶ Check to see if the imported hybrids have the same shape
- Parameters
(SXDMFrameset) (self) – the sxdmframeset used
- Returns
- Return type
Nothing. Prints Important Data
-
sxdm.mis.
show_hybrid_dimensions
(self)¶ Shows the hybrid x and hybrid y image dimensions
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
- Return type
prints the hybrid x an y dimensions
-
sxdm.mis.
tif_separation
(string, func=<function delimiter_func>)¶ Separating the image number from the full name stored in the images folder through the user of the delimiter_func
- Parameters
(str) (string) – the string the user wants to place the delimiter function on
(function) (func) – the delimiter function for the string. Makes it easier to change in later versions
- Returns
- Return type
the image string name of just the number
-
sxdm.mis.
total_rows_int_tup
(input, difference=False)¶ Allows the user to return the true starting and ending values for a scan. Useful for when scans do not start at zero.
- Parameters
(int or tuple) (input) – the int or tuple input of the analysis functions
(bool) (difference) – if the User would like to take the difference between the starting and ending points
- Returns
- Return type
Either the starting and ending values for a scan or the difference between them
-
sxdm.mis.
val_check
(array, resolution)¶ Check the resolution of the x and y dimensions of the scans
- Parameters
(nd.array) (array) – array of all the scan x or y dimensions
(float) (resolution) – a numbers in microns that sets the resolution check of the x and y dimensions
- Returns
- Return type
bool (True or False) on whether or not the scan dimensions are within the set resolution
-
sxdm.mis.
zfill_scan
(scan_list, fill_num)¶ Change int to str and fill them with the user set number of zeros
- Parameters
(str) (scan_list) – a list string of the scans the user would like to import
(int) (fill_num) – the amount of numbers each number should have. fill_num=4 turns ‘40’ into ‘0040’
- Returns
- Return type
A list of scan numbers with corrected zfill values
sxdm.multi module¶
-
sxdm.multi.
centroid_map_analysis
(self, rows, columns, med_blur_distance=4, med_blur_height=10, stdev_min=35, multiplier=1, center_around=False)¶ Calculates spot diffraction and data needed to make 2theta/chi/roi maps
- Parameters
self (
(SXDMFramset)
) – the sxdmframsetrows (
(int)
) – the total number of rows you want to iterate throughcolumns (
(int)
) – the total number of columns you want to iterate throughmed_blur_distance (
(int)
) – the amount of values to scan for median blurmed_blur_height – (int) the height cut off for the median blur
stdev_min (
(int)
) – standard deviation above the mean of signal to ignoremultiplier (
(int)
) – multiplier for the background signal to be subtractedcenter_around (
(int)
) – the index of the scan you would like to center around
- Returns
A numpy matrix of every pixel asked. Each pixel contains
row_column,(row, column), summed_dif, ttheta, chi,
ttheta_centroid_finder, ttheta_centroid,
chi_centroid_finder, chi_centroid, full_roi
-
sxdm.multi.
create_imagearray
(self, center_around=False)¶ Creates the self.image_array variable needed for pixel_analysis
- Parameters
(SXDMFrameset) (self) – the sxdmframset
(bool) (center_around) – setting this to True allows the user to default to the first image index to center around
- Returns
- Return type
Nothing - sets the self.image_array value
-
sxdm.multi.
initialize_vectorize
(num_rows, num_columns)¶ - Places the row and column number next to each other in an iterable array.
Used for starmap multiprocessing pixels.
- Parameters
num_rows (
(int)
) – Total number of rows the user wants to calculatenum_columns (
(int)
) – Total number of columns the user wants to calculate
- Returns
- Return type
An iterable array of pixel locations to be passed into the multiprocessing pixel analysis
-
sxdm.multi.
iterations
(self, num_rows, num_columns)¶ - Places the row and column number next to each other in an iterable array.
Used for starmap multiprocessing pixels.
- Parameters
self (
(SXDMFrameset)
) –num_rows (
(int)
) – Total number of rows the user wants to calculatenum_columns (
(int)
) – Total number of columns the user wants to calculate
- Returns
- Return type
An iterable array of pixel locations to be passed into the multiprocessing pixel analysis
-
sxdm.multi.
pooled_return
(results, user_val)¶ Makes it easy to return values from the pooled results from the multi.analysis function
- Parameters
(n dimensional array) (results) – the output from the analysis function
(str) (user_val) – a string that defines what the user wants to be returned
- Returns
- Return type
An n dimensional array consisting of the user selected data output from the multi.analysis function
-
sxdm.multi.
roi_analysis
(self, rows, columns, med_blur_distance=4, med_blur_height=10, stdev_min=35, multiplier=1, center_around=False, diff_segmentation=True)¶ Calculates region of interest for each scan as well as the region of interest maps for user defined sub region of interests
- Parameters
self (
(SXDMFramset)
) – the sxdmframsetrows (
(int)
) – the total number of rows you want to iterate throughcolumns (
(int)
) – the total number of columns you want to iterate throughmed_blur_distance (
(int)
) – the amount of values to scan for median blurmed_blur_height – (int) the height cut off for the median blur
stdev_min (
(int)
) – standard deviation above the mean of signal to ignoremultiplier (
(int)
) – multiplier for the background signal to be subtractedcenter_around (
(int)
) – the index of the scan you would like to center arounddiff_segmentation (
(bool)
) – if set to True this will initiate the user sub region of interest analysis
- Returns
A numpy matrix of every pixel asked. Each pixel contains
[(row, column), idxs, – raw_scan_data, corr_scan_data, scan_data_roi_vals, summed_data, corr_summed_data, summed_data_roi_vals]
sxdm.multi_update module¶
-
sxdm.multi_update.
centroid_analysis_multi
(self, rows, columns, med_blur_distance=9, med_blur_height=100, stdev=35, bkg_multiplier=0)¶ Runs the centroid analysis in a pool.map function
- Parameters
self (
(SXDMFrameset)
) – the sxdmframeset objectrows (
(int
ortup)
) – the total amount of rows the User wants to analyze or a tuple of the rowscolumns (
(int
ortup)
) – the total amount of columns the User wants to analyze or a tuple of the columnsmed_blur_distance (
(int)
) – the median blur distance - must be oddmed_blur_height (
(int)
) – the median blur heightstdev (
(int)
) – the standard deviation used to segment databkg_multiplier (
(int)
) – the multiplier to the background images
- Returns
- Return type
a pooled results from the centroid_pixel_analysis_multi() function
-
sxdm.multi_update.
centroid_pixel_analysis_multi
(row, column, median_blur_distance, median_blur_height, stdev_min, image_array, scan_numbers, background_dic, file)¶ The analysis done on a single pixel :param row: the row the User wants to do analysis on :type row:
(int)
:param column: the column the User wants to do analysis on :type column:(int)
:param median_blur_distance: the amount of values to scan for median blur :type median_blur_distance:(int)
:param median_blur_height: the height cut off for the median blur :type median_blur_height:(int)
:param stdev_min: standard deviation above the mean of signal to ignore :type stdev_min:(int)
:param image_array: the image location array - can be created with create_imagearray(self) :type image_array:(nd.array)
:param scan_numbers: the list of scan numbers used :type scan_numbers:(nd.array)
:param background_dic: the background scan dictionary entry - can be made with scan_backgrounnd(self) :type background_dic:(dic)
:param file: the hdf5 file location :type file:(str)
- Returns
- Return type
the analysis results as an nd.array
-
sxdm.multi_update.
centroid_pre_analysis
(inputs, meds_d, meds_h, st, image_array, scan_numbers, background_dic, file)¶ Allows Python to run the centroid analysis in a pool.map function
- Parameters
inputs (
(nd.array)
) – the iterable inputs of rows and columnsmeds_d (
(int)
) – the median blur distance valuemeds_h (
(int)
) – the median_blur height valueimage_array (
(nd.array)
) – the total locations of all the images - created by create_imagearray(self)scan_numbers (
(nd.array)
) – the list of all the scan numbersbackground_dic (
(dic)
) – the dictionary of all the background images - created by scan_background(self)file (
(str)
) – the full hdf5 file locationdiff_segments (
(bool)
) – if True this will run the segmentation analysis as well
- Returns
- Return type
the results from the centroid_pixel_analysis_multi() function
-
sxdm.multi_update.
h5get_image_destination_multi
(scan_numbers, pixel)¶ Determine where all the .tif images are for all the scan numbers disregarding the nan values
- Parameters
(nd.array) (scan_numbers) – the scan numbers the user wants to get
(str array) (pixel) – the image number from each scan that corresponds to a certain pixel
- Returns
- Return type
All of the diffraction FULL image locations for each scan in a 3D array - excluding the np.nan’s
-
sxdm.multi_update.
roi_analysis_multi
(self, rows, columns, med_blur_distance=9, med_blur_height=100, bkg_multiplier=0, diff_segments=True)¶ Runs the region of interest analysis in a pool.map function
- Parameters
self (
(SXDMFrameset)
) – the sxdmframeset objectrows (
(int
ortup)
) – the total amount of rows the User wants to analyze or a tuple of the rowscolumns (
(int
ortup)
) – the total amount of columns the User wants to analyze or a tuple of the columnsmed_blur_distance (
(int)
) – the median blur distance - must be oddmed_blur_height (
(int)
) – the median blur heightbkg_multiplier (
(int)
) – the multiplier to the background imagesdiff_segments (
(bool)
) – if True this will analyze the roi segmentations
- Returns
- Return type
a pooled results from the roi_pixel_analysis_multi() function
-
sxdm.multi_update.
roi_pixel_analysis_multi
(row, column, median_blur_distance, median_blur_height, image_array, scan_numbers, background_dic, file, diff_segments=False)¶ The analysis done on a single pixel - this will get the new roi for each theta and be able to segment out diffraction areas and create the roi for them :param row: the total number of rows you want to iterate through :type row:
(int)
:param column: the total number of columns you want to iterate through :type column:(int)
:param median_blur_distance: the amount of values to scan for median blur :type median_blur_distance:(int)
:param median_blur_height: the height cut off for the median blur :type median_blur_height:(int)
:param image_array: the location for all the pixels - can be created through create_imagearray(self) :type image_array:(nd.array)
:param scan_numbers: the list of scan numbers :type scan_numbers:(array)
:param background_dic: the dictionary for the background images - created through scan_background(self) :type background_dic:(dic)
:param file: the location to the hdf5 file :type file:(str)
:param diff_segments: array used for segmenting the diffraction patterns :type diff_segments:(array)
- Returns
the analysis results as an nd.array
[(row, column), idxs, – raw_scan_data, corr_scan_data, scan_data_roi_vals, summed_data, corr_summed_data, summed_data_roi_vals]
-
sxdm.multi_update.
roi_pre_analysis
(inputs, meds_d, meds_h, image_array, scan_numbers, background_dic, file, diff_segments)¶ Allows Python to run the roi analysis in a pool.map function
- Parameters
inputs (
(nd.array)
) – the iterable inputs of rows and columnsmeds_d (
(int)
) – the median blur distance valuemeds_h (
(int)
) – the median_blur height valueimage_array (
(nd.array)
) – the total locations of all the images - created by create_imagearray(self)scan_numbers (
(nd.array)
) – the list of all the scan numbersbackground_dic (
(dic)
) – the dictionary of all the background images - created by scan_background(self)file (
(str)
) – the full hdf5 file locationdiff_segments (
(bool)
) – if True this will run the segmentation analysis as well
- Returns
- Return type
the results from the roi_pixel_analysis_multi() function
-
sxdm.multi_update.
sum_pixel_multi
(file, images_loc)¶ Sum a pixel :param file (str): the full file location of the hdf5 file :param image_loc (list of str): the full image location in the hdf5 file
- Returns
- Return type
all images set in the image_loc variable
sxdm.pixel module¶
-
sxdm.pixel.
centroid_finder
(oneDarray_start, stdev_min=35)¶ Determine the centroid function :param oneDarray_start (numpy array): a one dimensions numpy array :param stdev_min (int): the standard deviation minimum the user would like to section the data off with
- Returns
- Return type
the corrected one dimensional array and the centroid of the array
-
sxdm.pixel.
centroid_pixel_analysis
(self, row, column, median_blur_distance, median_blur_height, stdev_min)¶ The analysis done on a single pixel :param self (SXDMFrameset): the sxdmframset :param rows: the total number of rows you want to iterate through :type rows:
(int)
:param columns: the total number of columns you want to iterate through :type columns:(int)
:param med_blur_distance: the amount of values to scan for median blur :type med_blur_distance:(int)
:param med_blur_height: the height cut off for the median blur :type med_blur_height:(int)
:param stdev_min: standard deviation above the mean of signal to ignore :type stdev_min:(int)
:param bkg_multiplier: multiplier for the background signal to be subtracted :type bkg_multiplier:(int)
- Returns
the analysis results as an nd.array
# For a given row and column
[
(row_index, column_index),
summed diffraction pattern (set to zero to save RAM. User can change in source code in /pixel.py),
two theta centroid value (float)
chi centroid value (float)
two theta cropped signal (nd.array)
full two theta signal (nd.array)
chi cropped signal (nd.array)
full chi signal (nd.array)
summed region of interest value (float)
]
-
sxdm.pixel.
chi_maths
(summed_dif, median_blur_distance, median_blur_height, stdev_min, q=False)¶ Determine the centroid of the chi axis :param summed_dif (nd.array image): a 2D summed diffraction image :param median_blur_distance (int): the amount of values to take the median of :param median_blur_height (int): the value above the median to replace with the median value :param stdev_min (int): the standard deviation above the noise to consider the signal valid :param q (bool): used for an old multi processing function - unused now - might be put in at a later date
- Returns
the edited (median blured) sum down the x axis
the centroid of the data
the cropped data to find the centroid
the edited (median blured) sum down the x axis as a numpy array
-
sxdm.pixel.
grab_pix
(array, row, column, int_convert=False)¶ Return a pixel at a given row and column value :param array (nd.array): a 3 dimensional numpy array :param row (int): the row the user would like to grab :param column (int): the column the user would like to grab :param int_convert (bool): if the user would like to change np.nans to integers set this to True
- Returns
- Return type
All data associated with the set row and column for the 3 dimensional array
-
sxdm.pixel.
pixel_diffraction_grab
(self, image_array, row, column)¶ Returns the diffraction image for a specific scan location (row, column)
- Parameters
self (
(SXDMFrameset object)
) – the sxdmframset objectimage_array (
(nd.array)
) – the array that holds all the diffraction image locations self.im_array()row (
(int)
) – the row the User would like to return data forcolumn (
(int)
) – the column the User would like to return data for
- Returns
- Return type
Summed diffraction image for a single pixel
-
sxdm.pixel.
roi_pixel_analysis
(self, row, column, median_blur_distance, median_blur_height, diff_segments=False)¶ The analysis done on a single pixel - this will get the new roi for each theta and be able to segment out diffraction areas and create the roi for them :param self (SXDMFrameset): the sxdmframset :param rows: the total number of rows you want to iterate through :type rows:
(int)
:param columns: the total number of columns you want to iterate through :type columns:(int)
:param med_blur_distance: the amount of values to scan for median blur :type med_blur_distance:(int)
:param med_blur_height: the height cut off for the median blur :type med_blur_height:(int)
:param diff_segments: array used for segmenting the diffraction patterns :type diff_segments:(array)
- Returns
the analysis results as an nd.array
[(row, column), idxs, – raw_scan_data, corr_scan_data, scan_data_roi_vals, summed_data, corr_summed_data, summed_data_roi_vals]
-
sxdm.pixel.
segment_diffraction_roi
(diffraction)¶ Creates a roi value and the raw data analysis for the ROI maps
- Parameters
(image) (diffraction) – the summed diffraction image
- Returns
- Return type
ttheta, ttheta_copy, np.sum(ttheta_copy), scan_roi_val
-
sxdm.pixel.
sum_pixel
(self, images_loc)¶ Sum a pixel :param self (SXDMFrameset): the sxdmframset :param image_loc (list of str): the full image location in the hdf5 file
- Returns
- Return type
all images set in the image_loc variable
-
sxdm.pixel.
sum_pixel_v2
(images_loc, file)¶ Sum a pixel :param image_loc (list of str): the full image location in the hdf5 file :param file: the opened hdf file :type file:
(hdf file)
- Returns
- Return type
all images set in the image_loc variable
-
sxdm.pixel.
theta_maths
(summed_dif, median_blur_distance, median_blur_height, stdev_min, q=False)¶ Determine the centroid of the theta axis :param summed_dif (nd.array image): a 2D summed diffraction image :param median_blur_distance (int): the amount of values to take the median of :param median_blur_height (int): the value above the median to replace with the median value :param stdev_min (int): the standard deviation above the noise to consider the signal valid :param q (bool): used for an old multi processing function - unused now - might be put in at a later date
- Returns
the edited (median blured) sum down the y axis
the centroid of the data
the cropped data to find the centroid
the edited (median blured) sum down the y axis as a numpy array
sxdm.postprocess module¶
-
sxdm.postprocess.
centroid_roi_map
(results, map_type='chi_centroid')¶ Returns the centroid or roi based on the Centroid Analysis :param results (nd.array): self.results or the output of self.analysis :param map_type (str): the value the user would like to return acceptable values are chi_centroid, ttheta_centroid, or full_roi
- Returns
- Return type
the user selected map in an nd.array
-
sxdm.postprocess.
maps_correct
(user_map, new_bounds)¶ Takes the centroid_rou_map() function output and gives it new bounds :param user_map (nd.array): the output of the centroid_roi_map function :param new_bounds (np.linspace): np.linspace(lowerbound, higherbound, dim of image)
- Returns
nd.array of the user_map, but with new bounds rather than with the dimensions
of the diffraction image
-
sxdm.postprocess.
pixel_analysis_return
(results, row, column, show_accep_vals=False)¶ Easy return of the results based on the input row and column value :param results (nd.array): self.results or output of self.analysis :param row (int): the row the user would like to look at :param column (int): the column the user would like to look at
- Returns
A dictionary with entries
’row_column’,
’summed_dif’,
’ttheta’,
’chi’,
’ttheta_corr’,
’chi_corr’,
’ttheta_cent’,
’chi_cent’,
’roi’
-
sxdm.postprocess.
saved_return
(file, group, summed_dif_return=False)¶ Load saved data :param file (str): a user defined hdf5 file :param group (str): the group the user would like to import :param summed_dif_return (bool): if True this will import all data. it is set to False because this import take up a lot of RAM
- Returns
- Return type
a nd.array thay can be set to the self.results value
-
sxdm.postprocess.
twodsummed
(results)¶ Returns the summed diffraction pattern for the analysis output :param results (nd.array): the self.results or output of self.analysis
- Returns
- Return type
the summed diffraction patter from the analysis output
sxdm.preprocess module¶
-
sxdm.preprocess.
gaus_check
(self, center_around=False, default=False)¶ Determines the intensity change over all group scans
- Parameters
(SXDMFrameset) (self) – the sxdmframset
- Returns
the x and y values for a plot that shows intensity vs scan angle.
also auto displays the figure
-
sxdm.preprocess.
init_dxdy
(self)¶ Initializes a group that scan store dxdy data
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
Nothing - creates a blank self.dxdy_store variable
-
sxdm.preprocess.
initialize_experimental_attrs
(self)¶ Initialized the Kev and detector’s theta position.
Sets attributes of Kev and detector theta position to the current user defined group
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
Nothing - sets Kev and detector_theta attributes
-
sxdm.preprocess.
initialize_group
(self)¶ Initialized the group assigned by the user to the user defined file.
From the scan numbers the program determines the scan_theta as well checking if identical settings have already been imported. If so the program reloads saved settings
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
- Returns
- Return type
Nothing - sets /scan_numbers and /scan_theta
-
sxdm.preprocess.
initialize_saving
(self)¶ Initialize the ability to save data
- Parameters
(SXDMFrameset) (self) – the sxdmframset
- Returns
- Return type
Nothing - creates the filename_savedata.h5 file to save data to
-
sxdm.preprocess.
initialize_scans
(self, scan_numbers=False, fill_num=4)¶ Initialize all necessities for each scan
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
(nd.array of int) (scan_numbers) – the list of scan numbers the user wants to create [178, 178, …]
(int) (fill_num) – the zfill number for the scan number integers
- Returns
- Return type
Nothing - sets self.scan_numbers, self.det_smpl_theta, and self.scan_theta
-
sxdm.preprocess.
initialize_zoneplate_data
(self, reset=False)¶ Initialize values for the zoneplate used as well as detector pixel size.
Asks the user what the parameters of the zone plate are and detector pixel size. If they have been already set then the program displays the current values when setting up SXDMFrameset object
- Parameters
(SXDMFrameset) (self) – the sxdmframset
(bool) (reset) – if True this allows the user to reset all values
- Returns
- Return type
Nothing - sets values inside /zone_plate/
-
sxdm.preprocess.
max_det_val
(self, detector='fluor')¶ Used to determine the max values for a given detector channel
- Parameters
(SXDMFrameset) (self) – the sxdmframeset
(str) (detector) – value to be passed into the return_det() function
- Returns
- Return type
the max values for all scans for a given detector channel input
sxdm.roi module¶
-
sxdm.roi.
add_rois
(rois, types='scan')¶ Add the create_rois output and return the correct plots
- Parameters
rois (
(nd.array)
) – the rois the user would like to add togethertypes (
(str)
) – either ‘scan’ or ‘bounding’ - depends on what roi’s the User sets for the rois variable
- Returns
- Return type
1d.array of the roi’s summed
-
sxdm.roi.
bounding_box_setup
(figure_class)¶ Set up the viewer figure for what bounding boxes have been selected
- Parameters
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing - sets figure_class.axes
-
sxdm.roi.
bounding_box_total_setup
(user_class)¶ Complete setup of the summed diffraction pattern with the bounding boxes on top of them
- Parameters
user_class (
(SXDMFrameset)
) – the sxdmframeset object- Returns
- Return type
Nothing - sets up all the bounding box figures and loads all items
-
sxdm.roi.
bounding_roi_figure_setup
(figure_class)¶ Initiates the bounding box figure with axes
- Parameters
(ROI_FiguresClass) (figure_class) – the roi_figuresclass object
- Returns
- Return type
Nothing
-
sxdm.roi.
bounding_slider_setup
(figure_class, user_class)¶ Sets up the bounding figure slider inside the roi figures
- Parameters
(ROI_FiguresClass) (figure_class) – the roi_figuresclass object
(SXDMFrameset) (user_class) – the sxdmframeset object
- Returns
- Return type
Nothing
-
sxdm.roi.
bounding_tb_setup
(figure_class)¶ Sets up all the textboxes and buttons
- Parameters
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing
-
sxdm.roi.
close_all
(event)¶
-
sxdm.roi.
create_bounding_box_rois
(fs)¶
-
sxdm.roi.
create_total_linescan
(fs)¶
-
sxdm.roi.
display_left_roi
(figure_class, user_class, types='scan')¶ Sets up the display for the left roi plot
- Parameters
figure_class (
(ROI_FigureClass)
) – the roi_figureclass objectuser_class (
(SXDMFrameset)
) – the sxdmframeset objecttypes (
(str)
) – either ‘scan’ or ‘bounding’
- Returns
- Return type
Nothing - displays the left roi figure
-
sxdm.roi.
display_left_roi_reload
(val, figure_class, user_class, types='scan')¶ Reloads the left roi
- Parameters
val (
(matplotlib event)
) – the val matplotlib eventfigure_class (
(ROI_FigureClass)
) – the roi_figureclass objectuser_class (
(SXDMFrameset)
) – the sxdmframeset objecttypes (
(str)
) – either ‘scan’ or ‘bounding’
- Returns
- Return type
Nothing - reloads the left image
-
sxdm.roi.
display_right_roi
(figure_class, im)¶ Displays the right roi plot
- Parameters
figure_class (
(ROI_FigureClass)
) – the roi_figureclass objectim (
(nd.array)
) – the image array to be displayed
- Returns
- Return type
Nothing - displays the right roi image
-
sxdm.roi.
display_right_roi_reload
(val, figure_class, im)¶ Load the Right ROI data
- Parameters
val (
(matplotlib event)
) – the val matplotlib eventfigure_class (
(ROI_FigureClass)
) – the roi_figureclass objectim (
(nd.array)
) – the image to be displayed
- Returns
- Return type
Nothing - reloads the right figure data
-
sxdm.roi.
display_summed_ims
(figure_class, user_class)¶ Show the summed diffraction pattern with all assigned bounding boxes
- Parameters
figure_class (
(ROI_FigureClass)
) – the roi_figureclass objectuser_class (
(SXDMFrameset)
) – the sxdmframeset object
- Returns
- Return type
Nothing - displays the summed diffraction pattern along with the User selected bounding boxes
-
sxdm.roi.
display_summed_ims_reload
(val, figure_class, user_class)¶ Allows the summed diffraction images to be displayed
- Parameters
val (
(matplotlib val)
) – matplotlib variablefigure_class (
(ROI_FigureClass)
) – the roifigureclass objectuser_class (
(SXDMFrameset)
) – the sxdmframeset object
- Returns
- Return type
Nothing - reloads the summed diffraction images in the figure
-
sxdm.roi.
grab_int_v_scan
(user_class, types='scan')¶ Grab the data needed for the gaus plot
- Parameters
user_class (
(SXDMFrameset)
) – the sxdmframeset objecttypes (
(str)
) – either ‘scan’ or ‘bounding’ - determines which plots the User would like to get information for
- Returns
- Return type
Nothing - sets up the x and y values for the top plot of either the Scan ROI or the Bounding Box ROI
-
sxdm.roi.
initiate_roi_viewer
(user_class)¶ Sets up all the ROI viewer figures
- Parameters
(SXDMFrameset) (user_class) – the sxdmframeset object
- Returns
- Return type
Nothing
-
sxdm.roi.
on_scan_click
(event, figure_class, user_class, types='scan')¶ Obtains all the raw scan data for a user clicked pixel
- Parameters
(matplotlib event) (event) – the matplotlib event
(ROI_FiguresClass) (figure_class) – the roi_figuresclass object
(SXDMFrameset) (user_class) – the sxdmframeset object
(str) (types) – either ‘scan’ or ‘bounding’
- Returns
- Return type
Nothing - obtains all data and diplays data to appropriate location
-
sxdm.roi.
right_roi
(user_class, types='scan')¶ Plots the right roi plot
- Parameters
user_class (
(SXDMFrameset)
) – the sxdmframeset objecttypes (
(str)
) – either ‘scan’ or ‘bounding’ - depends on which figure is being called
- Returns
- Return type
The entire summed region of interest for either the scans or the bounding boxes
-
sxdm.roi.
scan_roi_figure_setup
(figure_class)¶ Initiates the scan roi figure with proper axes locations current_roi_slider_val
- Parameters
(ROI_FiguresClass) (figure_class) – the roi_figuresclass object
- Returns
- Return type
Nothing
-
sxdm.roi.
scan_slider_setup
(figure_class, user_class)¶ Sets up the scan figure slider inside the roi figures
- Parameters
(ROI_FiguresClass) (figure_class) – the roi_figuresclass object
(SXDMFrameset) (user_class) – the sxdmframeset object
- Returns
- Return type
Nothing
-
sxdm.roi.
start_bounding_roi
(user_class)¶ Displays the bounding box roi with aproptiate textboxes
- Parameters
(SXDMFrameset) (user_class) – the sxdmframeset object
- Returns
- Return type
Nothing
-
sxdm.roi.
start_scan_roi
(user_class)¶ Displays the scan roi figure with appropriate settings
- Parameters
(SXDMFrameset) (user_class) – the sxdmframeset object
- Returns
- Return type
Nothing
-
sxdm.roi.
textbox_setup
(figure_class)¶ Sets up textboxes for the scan roi and the bounding box roi figures
- Parameters
(ROI_FiguresClass) (figure_class) – the roi_figuresclass object
- Returns
- Return type
Nothing
-
sxdm.roi.
top_plot_reload
(val, figure_class, user_class, types='scan')¶ Reloads the gaus plot
- Parameters
val (
(matplotlib val)
) – the value for matplot lib eventsfigure_class (
(ROIFigure_Class)
) – the roifigure_class objectsuser_class (
(SXDMFrameset)
) – the sxdmframeset objecttypes (
(str)
) – either ‘scan’ or ‘bounding’ - determines which plots need to be reloaded
- Returns
- Return type
Nothing - reloads the top plot in the roi figures
-
sxdm.roi.
top_plot_start
(figure_class, user_class, types='scan')¶ Initiate the gaus plot
- Parameters
figure_class (
(ROI_FigureClass)
) – the roi_figureclass objectuser_class (
(SXDMFrameset)
) – the sxdmframeset objecttypes (
(str)
) – either ‘scan’ or ‘bounding’ allow the User to choose which figure they are working on
- Returns
- Return type
Nothing
sxdm.roi_bounding module¶
-
class
sxdm.roi_bounding.
ROI_FiguresClass
¶ Bases:
object
A class function which will make it easier to move variable in and out of functions
-
sxdm.roi_bounding.
bounding_figure_setup
(figure_class)¶ Set up the viewer figure
- Parameters
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing - sets figure_class.axes
-
sxdm.roi_bounding.
contbtn_click
(event, figure_class, user_class)¶ Close the bounding box figure once complete
- Parameters
(matplotlib event) (event) – the matplotlib event
(matplotlib figure) (fig) – the matplotlib figure to close
- Returns
- Return type
Nothing
-
sxdm.roi_bounding.
line_select_callback
(eclick, erelease, figure_class)¶ Creates interactive bounding boxes for roi analysis
- Parameters
(event) (erelease) – the click event
(event) – the release event
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing
-
sxdm.roi_bounding.
rec_select_setup
(figure_class)¶ Initiate the rectangle interactive
- Parameters
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing
-
sxdm.roi_bounding.
rm_box_click
(event, figure_class)¶ Removed the last bounding box the user made
- Parameters
(matplotlib event) (event) – the matploblib event
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing
-
sxdm.roi_bounding.
start_bounding_box
(summed_diff_pattern, user_class)¶ Starts the ROI bounding box GUI
- Returns
- Return type
Nothing
-
sxdm.roi_bounding.
textbox_btn_dropdown_setup
(figure_class)¶ Sets up all the textboxes and buttons
- Parameters
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing
-
sxdm.roi_bounding.
vs_change
(text, figure_class)¶ Change the vmin and vmax of a plot
- Parameters
(matplotlib textbox) (text) – the matplotlib textbox
(ROI_FigureClass) (figure_class) – the roi_figureclass object
- Returns
- Return type
Nothing
sxdm.summed2d module¶
-
sxdm.summed2d.
summed2d_all_data
(self, bkg_multiplier=0)¶ Loads the summed diffraction patter without using a large amount of RAM
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
(int) (bkg_multiplier) – the multiplier for the background images
- Returns
- Return type
the 2D summed diffraction pattern
-
sxdm.summed2d.
summed2d_all_data_v2
(self, bkg_multiplier=0)¶ Loads the summed diffraction patter without using a large amount of RAM
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
(int) (bkg_multiplier) – the multiplier for the background images
- Returns
- Return type
the 2D summed diffraction pattern
sxdm.SXDM module¶
-
class
sxdm.SXDM.
SXDMFrameset
(file, dataset_name, scan_numbers=False, fill_num=4, restart_zoneplate=False, median_blur_algorithm='scipy')¶ Bases:
object
-
alignment
(reset=False)¶ Allows the user to align all scans based on Fluorescence Maps or ROI Maps
- Parameters
(bool) (reset) – if True this will clear all the alignment data. Use this when adding new scans to a group
- Returns
- Return type
Nothing - sets alignment data
-
centroid_analysis
(rows, columns, med_blur_distance=9, med_blur_height=10, stdev_min=25, bkg_multiplier=1, slow=False, change_center=False, cores=0)¶ Calculates spot diffraction and data needed to make 2theta/chi/roi maps
- Parameters
rows (
(int
,tup)
) – the total number of rows you want to iterate through or a tuple of the rows to iterate throughcolumns (
(int
,tup)
) – the total number of columns you want to iterate through or a tuple of the columns to iterate throughmed_blur_distance (
(int)
) – the amount of values to scan for median blurmed_blur_height (
(int)
) – the height cut off for the median blurstdev_min (
(int)
) – standard deviation above the mean of signal to ignorebkg_multiplier (
(int)
) – multiplier for the background signal to be subtracted
- Returns
- Return type
the analysis results in the form of self.results
-
centroid_viewer
(default_extents=False)¶ Allow the user to view the data in a convenient format
- Parameters
(bool) (diffraction_load) – if True this will load all necessary data for the diffraction will take up at least 2gb of RAM
- Returns
- Return type
Nothing - displays viewer
-
chi_determination
()¶ Determine the axis bounds for the diffraction images
-
frame_shape
()¶ Returns the total imported scans shape (scan_number, rows, columns)
- Parameters
(SXDMFrameset) (self) –
- Returns
- Return type
the frame set scan dimensions
-
gaus_checker
(center_around=False, default=False)¶ Plots total diffraction intensity vs scan angle for a set ROI
- Parameters
(bool) (default) – if True this will default the centerind index to the first index (0)
(bool) – if True this will default the roi to check the gaus of to the first user defined ROI
- Returns
- Return type
Nothing - displays the total intensity vs scan theta rocking curve
-
image_data_dimensions
()¶ Determine the CCD image dimensions
- Parameters
- SXDMFrameset (self) – the sxdmframeset
- Returns
- Return type
the image dimensions of the CCD camera
-
ims_array
(amount2ave=3, multiplier=1, center_around=False)¶ Creates the Scan Background and the Image Array
- Parameters
- int (amount2ave) – Take the first (amount2ave) to the last (amount2ave) pixels and use them as a background
- float (multiplier) – A number to be applied to the background image
- Returns
- Return type
Creates the background image and the image array used for many proceses
-
region_of_interest
(rows, columns, med_blur_distance=9, med_blur_height=100, bkg_multiplier=1, diff_segmentation=True, slow=False, cores=0)¶ Create a region of interest map for each scan and center the region of interest maps. If the diff segmentation is True this will also create a region of interest map based on a user defined sub region of interests
- Parameters
(int) or (tup) (columns) – the total amount of rows the user wants to do analysis on
(int) or (tup) – the total amount of columns the user wants to do analysis on
(int) (bkg_multiplier) – the amount of values to scan for median blur
(int) – the height cut off for the median blur
(int) – multiplier for the background signal to be subtracted
(bool) (diff_segmentation) – if set to True this will determine region of interests maps for sub roi’s set by the user
- Returns
- [(row, column), idxs,
raw_scan_data, corr_scan_data, scan_data_roi_vals, summed_data, corr_summed_data, summed_data_roi_vals]
- Return type
the roi results in self.roi_results
-
reload_save
(summed_dif_return=True)¶ Allow the user to reload Centroid saved data
- Parameters
(bool) (summed_dif_return) – if True this will load all necessary data for the diffraction will take up at least 2gb of RAM
- Returns
- Return type
Nothing
-
roi_segmentation
(bkg_multiplier=1, restart=False)¶ Allows the User to create a summed diffraction pattern bounding box for region_of_interest analysis
- Parameters
(int) (bkg_multiplier) – the scaler applied to the background images
(bool) (restart) – if True this clears all bounding boxes when loading the segmentation data
- Returns
- Return type
Nothing
-
roi_viewer
()¶ Takes the self.roi_results variable and displays it in a GUI format
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
- Return type
Nothing
-
save
()¶ Save self.results (Centroid Data) to _savedata.h5
Unable to efficiently save ROI data
-
sxdm.viewer module¶
-
class
sxdm.viewer.
FiguresClass
¶ Bases:
object
A class function which will make it easier to move variable in and out of functions
-
sxdm.viewer.
analysis_change
(text, self)¶ When analysis values change, make the plots change as well to fit analysis parameters :param text (textbox text event): textbox text event :param self (SXDMFrameset): the sxdmframeset
- Returns
- Return type
Nothing - loads figure data
-
sxdm.viewer.
btn_setup
(reproocessbtn_ax, savingbtn_ax)¶ Set up the reprocess and saving button
- Parameters
reproocessbtn_ax (
(matplotlib axis)
) – the reproocess button figure axissavingbtn_ax (
(matplotlib axis)
) – the saving button figure axis
- Returns
- Return type
reprocess and saving button
-
sxdm.viewer.
convert_x_axis
(intensity_values, extents, centroid_value, type='ttheta')¶
-
sxdm.viewer.
figure_setup
()¶ Set up the viewer figure
- Returns
- Return type
All axes for the figure
-
sxdm.viewer.
image_array_bkg_check
(self)¶ Warns the User if they have not started the create_imagearray() or scan_background() functions
- Parameters
self –
- Returns
-
sxdm.viewer.
load_dynamic_data
(results, vmin_spot, vmax_spot, spot_dif_ax, ttheta_centroid_ax, chi_centroid_ax, med_blur_distance, med_blur_height, stdev_min, row, column, self)¶ Load the dynamic data in the viewer based on the user inputs :param results (nd.array): the output of self.analysis or the self.results value :param vmin_spot (int): the vmin for the summed diffraction spot image :param vmax_spot (int): the vmax for the summed diffraction spot image :param spot_dif_ax (matplotlib axis): the figure axis for the diffraction spot image :param ttheta_centroid_ax (matplotlib axis): the figure axis for the 1D two theta plot :param chi_centroid_ax (matplotlib axis): the figure axis for the 1D chi plot :param med_blur_distance (int): the integer value used in the median_blur() function :param med_blur_height (int): the integer value used in the median_blur() function :param stdev_min (int): the standard deviation value used in the median_blur() segmentation function :param row (int): the row the user would like to load the data for :param column (int): the bolumn the user would like to load the data for :param self (SXDMFramset): the sxdmframset
- Returns
- Return type
Nothing - loads figure data
-
sxdm.viewer.
load_static_data
(results, vmin_sum, vmax_sum, fluor_ax, roi_ax, summed_dif_ax, ttheta_map_ax, chi_map_ax, fluor_image, user_class=False)¶ Load all the static data for the viewer :param results (nd.array): the output of the self.analysis function or the self.results value :param vmin_sum (int): the vmin for the summed diffraction image :param vmax_sum (int): the vmax for the summed diffraction image :param fluor_ax (matplotlib axis): the figure axis for the fluorescence image :param roi_ax (matplotlib axis): the figure axis for the region of interest image :param summed_dif_ax (matplotlib axis): the figure axis for the summed diffraction image :param ttheta_map_ax (matplotlib axis): the figure axis for the 2theta centroid map :param chi_map_ax (matplotlib axis): the figure axis for the chi centroid map :param fluor_image (image array): the fluorescence image the user would like to display in the figure :param user_class (SXDMFrameset): the sxdmframeset object or False
- Returns
- Return type
Nothing - displays figure images
-
sxdm.viewer.
make_black
(self)¶ Reset figures to black when everything is up to date :param self (SXDMFrameset): the sxdmframeset
- Returns
- Return type
Nothing - loads figure formatting
-
sxdm.viewer.
make_pink
(self)¶ Show the user which values have not been saved by turning them pink :param self (SXDMFrameset): the sxdmframeset
- Returns
- Return type
Nothing - loads figure formatting
-
sxdm.viewer.
make_red
(self)¶ Show the user which figures are not current by turning them red :param self (SXDMFrameset): the sxdmframeset
- Returns
- Return type
Nothing - loads figure formatting
-
sxdm.viewer.
reload_some_static_data
(results, roi_ax, ttheta_map_ax, chi_map_ax)¶ Reloading some of the static data when you save/reload image :param results (nd.array): the output of the self.analysis function or the self.results value :param roi_ax (matplotlib axis): the figure axis for the region of interest map :param ttheta_map_ax (matplotlib axis): the figure axis for the 2theta centroid map :param chi_map_ax (matplotlib axis): the figure axis for the chi centroid map
- Returns
- Return type
Nothing - displays figure images
-
sxdm.viewer.
reprocessbtn_click
(event, user_class, figure_class)¶ Calls the self.analysis function once the reprocessed button is clicked :param event (matplotlib event): matplotlib event :param user_class (SXDMFrameset): the sxdmframeset :param figure_class (FigureClass): the figureclass
- Returns
- Return type
Nothing - loads figure formatting
-
sxdm.viewer.
run_viewer
(user_class, fluor_image)¶ Function that compiles functions to get the viewer working :param user_class (SXDMFrameset): the sxdmframeset :param fluor_image (image): the fluorescence image the user would like to load into the viewer
- Returns
- Return type
Nothing - loads figure data
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sxdm.viewer.
savingbtn_click
(event, user_class, figure_class)¶ Calls the self.save function and reloads data to the viewer :param event (matplotlib event): matplotlib event :param user_class (SXDMFrameset): the sxdmframeset :param figure_class (FigureClass): the figureclass
- Returns
- Return type
Nothing - loads figure formatting
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sxdm.viewer.
spot_change
(text, self)¶ When the user changes texbox value spots on the image, reload all the data for the new spot :param text (texbox text event): textbox text event :param self (CurrentFigure Class): the currecnt figure class
- Returns
- Return type
Nothing - loads figure data
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sxdm.viewer.
spot_dif_ram_save
(self)¶ Allows the program to take all the user_class.image_array images and sum them together to get a single summed diffraction image
- Parameters
(SXDMFrameset) (self) – the sxdmframeset object
- Returns
- Return type
the summed diffraction image for the entire FOV set by the create_imagearray() function
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sxdm.viewer.
sum_error
()¶ If there is an error loading data, import psyduck
- Returns
- Return type
psyduck image
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sxdm.viewer.
tb_setup
(vmin_spot_ax, vmax_spot_ax, vmin_sum_ax, vmax_sum_ax, med_blur_dis_ax, med_blur_h_ax, stdev_ax, multiplier_ax, self)¶ Set up all textboxes :param vmin_spot_ax (matplotlib axis): the figure axis that goes to the vmin change for the spot diffraction :param vmax_spot_ax (matplotlib axis): the figure axis that goes to the vmax change for the spot diffraction :param vmin_sum_ax (matplotlib axis): the figure axis that goes to the vmin change for the summed diffraction :param vmax_spot_ax (matplotlib axis): the figure axis that goes to the vmax change for the summed diffraction :param med_blur_dis_ax (matplotlib axis): the figure axis that goes to the median blur distance value :param med_blur_h_ax (matplotlib axis): the figure axis that goes to the median blur height value :param stdev_ax (matplotlib axis): the figure axis that goes to the standard deviation value :param multiplier_ax (matplotlib axis): the figure axis that goes to the background multiplier value :param self (SXDMFrameset): the sxdmframset
- Returns
- Return type
All textboxes associated with these axis
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sxdm.viewer.
viewer_mouse_click
(event, self)¶ Based on what is clicked in the figure change the viewer accordingly :param event (matplotlib event): matplotlib event :param self (FigureClass): the figureclass object
- Returns
- Return type
Nothing - loads figure formatting