API Documentation
DearEIS includes an API that is primarily intended for batch processing (e.g., importing data into a project or exporting results/plots/tables).
>>> import deareis # The main functions, classes, etc.
>>> from deareis import mpl # Plotting functions based on matplotlib.
Warning
DearEIS provides wrapper functions for some of pyimpspec’s functions since DearEIS uses classes that store the relevant arguments/settings in a way that can be serialized and deserialized as part of project files. Consequently, the API might feel somewhat cumbersome to use for some tasks where these settings classes must be instantiated and using pyimpspec directly might be more convenient.
DearEIS also implements subclasses or entirely new classes to contain some of the information that pyimpspec’s various result classes would contain. This has been done so that various results can be serialized and deserialized as part of project files.
However, DearEIS’ classes can in several cases be used directly with functions from pyimpspec (e.g., the various plotting functions) since pyimpspec checks for the presence of attributes and/or methods with specific names rather than whether or not an object is an instance of some class.
Note
The API makes use of multiple processes where possible to perform tasks in parallel.
Functions that implement this parallelization have a num_procs keyword argument that can be used to override the maximum number of processes allowed.
Using this keyword argument should not be necessary for most users under most circumstances.
Call the get_default_num_procs() function to get the automatically determined value for your system.
There is also a set_default_num_procs() function that can be used to set a global override rather than using the num_procs keyword argument when calling various functions.
If NumPy is linked against a multithreaded linear algebra library like OpenBLAS or MKL, then this may in some circumstances result in unusually poor performance despite heavy CPU utilization.
It may be possible to remedy the issue by specifying a lower number of processes via the num_procs keyword argument and/or limiting the number of threads that, e.g., OpenBLAS should use by setting the appropriate environment variable (e.g., OPENBLAS_NUM_THREADS).
Again, this should not be necessary for most users and reporting this as an issue to the DearEIS or pyimpspec repository on GitHub would be preferred.
- deareis.get_default_num_procs()
Get the default number of parallel processes that pyimpspec would try to use. NumPy may be using libraries that are multithreaded, which can lead to poor performance or system responsiveness when combined with pyimpspec’s use of multiple processes. This function attempts to return a reasonable number of processes depending on the detected libraries (and relevant environment variables):
OpenBLAS (
OPENBLAS_NUM_THREADS)MKL (
MKL_NUM_THREADS)
If none of the libraries listed above are detected because some other library is used, then the value returned by
multiprocessing.cpu_count()is used.- Return type:
int
- deareis.set_default_num_procs(num_procs)
Override the default number of parallel process that pyimpspec should use. Setting the value to less than one disables any previous override.
- Parameters:
num_procs (int) – If the value is greater than zero, then the value is used as the number of processes to use. Otherwise, any previous override is disabled.