h5analysis.LoadData.Load2d

class h5analysis.LoadData.Load2d

Bases: object

Class to load generic 2d (x,y,z) image data of a detector.

Methods

add(config, file, x_stream, detector, *args)	Add specified images for selected streams.
background_1d(config, file, x_stream, ...[, ...])	Subtracts the defined data from all loaded data
background_2d(config, file, x_stream, ...[, ...])	Subtracts the defined data from all loaded data
export(filename[, split_files])	Export and write data to specified file.
exportWidgetStep()	Helper function for exporter widget.
exporter()	Interactive exporter widget.
get_data()	Make data available in memory as exported to file.
hline(pos, **kwargs)	Draw a horizontal line in the plot.
label(pos_x, pos_y, text, **kwargs)	Draw a text box in the plot.
load(config, file, x_stream, detector, ...)	Load one or multiple specific scan(s) for selected streams.
plot([title, kind, xlabel, ylabel, zlabel, ...])	Plot all data assosciated with class instance/object.
save_plot(fname, **kwargs)	Create a matplotlib plot window
show_fluorescence(element, siegbahn_symbol)	Draw a line in the plot for the requested fluorescence line.
stitch(config, file, x_stream, detector, *args)	Stitch specified scans for selected image.
subtract(config, file, x_stream, detector, ...)	Subtract specified images for selected streams.
vline(pos, **kwargs)	Draw a vertical line in the plot.
xlim(lower, upper)	Set x-axis limits applied to data stream.
ylim(lower, upper)	Set y-axis limits applied to data stream.

add(config, file, x_stream, detector, *args, tempclass=None, **kwargs)

Add specified images for selected streams.

Parameters:

• See loader function.

• Adds all scans specified in *args.

background_1d(config, file, x_stream, y_stream, *args, axis='y', **kwargs)

Subtracts the defined data from all loaded data

Parameters:

• config (dict) – h5 configuration

• file (string) – file name

• x_stream (string) – h5 key or alias of 1d stream

• y_stream (string) – h5 key or alias of 1d stream

• *args (int) – scans

• **kwargs –

  axis: string

  <<x>> or <<y>> axis for subtraction direction

  norm: boolean

  normalizes to [0,1]

  xoffset: list

  fitting offset (x-stream)

  xcoffset: float

  constant offset (x-stream)

  yoffset: list

  fitting offset (y-stream)

  ycoffset: float

  constant offset (y-stream)

  grid_x: list

  grid data evenly with [start,stop,delta]

  savgol: tuple

  (window length, polynomial order, derivative)

  binsize: int

  puts data in bins of specified size

  legend_items: dict

  dict[scan number] = description for legend

background_2d(config, file, x_stream, detector, *args, tempclass=None, **kwargs)

Subtracts the defined data from all loaded data

Parameters:

• config (dict) – h5 configuration

• file (string) – file name

• x_stream (string) – h5 key or alias of 1d stream

• detector (string) – alias of the MCA detector

• *args (int) – scans

• **kwargs –

  norm: boolean

  normalizes to [0,1]

  xoffset: list

  fitting offset (x-stream)

  xcoffset: float

  constant offset (x-stream)

  yoffset: list

  fitting offset (y-stream)

  ycoffset: float

  constant offset (y-stream)

  grid_x: list

  grid data evenly with [start,stop,delta]

  savgol: tuple

  (window length, polynomial order, derivative)

  binsize: int

  puts data in bins of specified size

  legend_items: dict

  dict[scan number] = description for legend

  binsize_x: int

  puts data in bins of specified size in the horizontal direction

  binsize: int

  puts data in bins of specified size in the vertical direction

export(filename, split_files=False)

Export and write data to specified file.

Parameters:

• filename (string)

• split_files (Boolean) – Sets whether scans are exported appended to one file (False), or separately (True)

exportWidgetStep()

Helper function for exporter widget.

exporter()

Interactive exporter widget.

get_data()

Make data available in memory as exported to file.

Returns:

• f (string.IO object) – Motor and Detector Scales. Pandas Data Series. 1) Rewind memory with f.seek(0) 2) Load with pandas.read_csv(f,skiprows=3)

• g (string.IO object) – Actual gridded detector image. 1) Rewind memory with g.seek(0) 2) Load with numpy.genfromtxt(g,skip_header=4)

• raw_data (list) – List of lists with series data, series header, and matrix_data

hline(pos, **kwargs)

Draw a horizontal line in the plot.

Parameters:

• pos (float)

• **kwargs (dict, optional) – See bokeh manual for available options.

label(pos_x, pos_y, text, **kwargs)

Draw a text box in the plot.

Parameters:

• pos_x (float)

• pos_y (float)

• text (string)

• **kwargs (dict, optional) – See bokeh manual for available options.

load(config, file, x_stream, detector, *args, **kwargs)

Load one or multiple specific scan(s) for selected streams.

Parameters:

• config (dict) – h5 configuration

• file (string) – filename

• x_stream (string) – h5 sca key or alias of the x-stream

• detector (string) – alias of the MCA detector

• arg (int) – scan number

• **kwargs –

  norm: boolean

  Can be boolean or None (as False)

  xoffset: list of tuples

  fitted offset (x-stream)

  xcoffset: float

  constant offset (x-stream)

  yoffset: list of tuples

  fitted offset (y-stream)

  ycoffset: float

  constant offset (y-stream)

  grid_x: list

  grid equally spaced in x with [start, stop, delta]

  grid_y: list

  grid equally spaced in y with [start, stop, delta]

  norm_by: string

  norm MCA by defined h5 key or SCA alias

  binsize_x: int

  puts data in bins of specified size in the horizontal direction

  binsize: int

  puts data in bins of specified size in the vertical direction

plot(title=None, kind='Image', xlabel=None, ylabel=None, zlabel=None, plot_height=600, plot_width=600, vmin=None, vmax=None, colormap='linear', norm=False, xprec=None, yprec=None, **kwargs)

Plot all data assosciated with class instance/object.

Parameters:

• title (string, optional)

• kind (string, optional)

• xlabel (string, optional)

• ylabel (string, optional)

• zlabel (string, optional)

• plot_height (int, optional)

• plot_width (int, optional)

• vmin (float, optional)

• vmax (float, optional)

• colormap (string) – Use: “linear” or “log”

• norm (boolean) – to normalize the plot to the maximum

• xprec (int, optional) – Specifies the forced floating point X precision of the hover tool

• yprec (int, optional) – Specifies the forced floating point Y precision of the hover tool

• kwargs – all bokeh figure key-word arguments

save_plot(fname, **kwargs)

Create a matplotlib plot window

fname: string

path and file name of the exported file

kwargs:

figsize: tuple

determines size of plot

x_minor_ticks: float

distance between minor ticks on primary axis

x_major_ticks: float

distance between major ticks on primary axis

y_minor_ticks: float

distance between minor ticks on secondary axis

y_major_ticks: float

distance between major ticks on secondary axis

top: Boolean

Display ticks on top of the plot

right: Boolean

Display ticks on the right of the plot

fontsize_axes: string or int

Set the fontsize of the axes ticks

fontsize_labels: string or int

Set fontsize of the axis labels

fontsize_title: string or int

Set fontsize of the title

title_pad: int

Padding between title and the top of the plot

xlabel: string

Label of the primary axis

ylabel: string

Label of the secondary axis

title: string

Title displayed at the top of the plot

xlim: tuple

Limits the visible x-range

ylim: tuple

Limits the visible y-range

cmap: string

name of matplotlib colourmap

levels: int

determines how many levels the z data should be binned in

aspect: [equal, auto]

Set the axis to scale or stretch figszize

colorbar: Boolean

Display a colorbar

zlabel: string

Label of the colorbar

fontsize_colorbar: string or int

Fontsize of the colorbar ticks

data_format: string, [pdf,svg,png]

Sets the output data format and matplotlib backend used

show_fluorescence(element, siegbahn_symbol, orientation='v', **kwargs)

Draw a line in the plot for the requested fluorescence line.

Parameters:

• element (string) – IUPAC element abbreviation

• siegbahn_symbol (string) – Siegbahn symbol for requested energy transition

• orientation ([‘v’,’h’]) – Determines if a vertical or horizontal line is drawn

• **kwargs (dict, optional) – See bokeh manual for available options.

stitch(config, file, x_stream, detector, *args, average=True, adjust_scale=False, tempclass=None, **kwargs)

Stitch specified scans for selected image.

Parameters:

• See loader function.

• Stitches all scans specified in *args.

• kwargs –

  average: Boolean

  For overlap, whether the first scan takes precedence (False) or if overlap is averaged (True)

  adjust_scale: Boolean

  Adjusts the intensity of consecutive scans to match the precessors intensity in the overlap Automatically sets average True

subtract(config, file, x_stream, detector, minuend, subtrahend, tempclass=None, **kwargs)

Subtract specified images for selected streams.

Parameters:

• See loader function.

• Subtracts all imnages from the first element.

vline(pos, **kwargs)

Draw a vertical line in the plot.

Parameters:

• pos (float)

• **kwargs (dict, optional) – See bokeh manual for available options.

xlim(lower, upper)

Set x-axis limits applied to data stream.

Parameters:

• lower (float)

• upper (float)

ylim(lower, upper)

Set y-axis limits applied to data stream.

Parameters:

• lower (float)

• upper (float)