hyperspy.api module¶
All public packages, functions and classes are available in this module.
When starting HyperSpy using the hyperspy
script (e.g. by executing
hyperspy
in a console, using the context menu entries or using the links in
the Start Menu
, the api
package is imported in the user
namespace as hs
, i.e. by executing the following:
>>> import hyperspy.api as hs
(Note that code snippets are indicated by three greater-than signs)
We recommend to import the HyperSpy API as above also when doing it manually.
The docstring examples assume that hyperspy has been imported as hs,
numpy as np
and matplotlib.pyplot
as plt
.
Functions:
- create_model
- Create a model for curve fitting.
- get_configuration_directory_path
- Return the configuration directory path.
- load
- Load data into BaseSignal instances from supported files.
- preferences
- Preferences class instance to configure the default value of different parameters. It has a CLI and a GUI that can be started by execting its gui method i.e. preferences.gui().
- stack
- Stack several signals.
- interactive
- Define operations that are automatically recomputed on event changes.
- set_log_level
- Convenience function to set HyperSpy’s the log level.
The api
package contains the following submodules/packages:
signals
- Signal classes which are the core of HyperSpy. Use this modules to create Signal instances manually from numpy arrays. Note that to load data from supported file formats is more convenient to use the load function.
model
- Contains the
components
module with components that can be used to create a model for curve fitting.eds
- Functions for energy dispersive X-rays data analysis.
material
- Useful functions for materials properties and elements database that includes physical properties and X-rays and EELS energies.
plot
- Plotting functions that operate on multiple signals.
datasets
- Example datasets.
roi
- Region of interests (ROIs) that operate on BaseSignal instances and include widgets for interactive operation.
samfire
- SAMFire utilities (strategies, Pool, fit convergence tests)
For more details see their doctrings.