fmflow.utils package#

Module contents#

class CStructReader(structure, ignored='$.', byteorder='<', encoding='utf-8')[source]#

Bases: object

Read a binary file to unpack values in a C structure.

C structures can be expressed as a Python list that stores name, type, and shape of each member in a C structure. For example, the following C structure:

struct structure {
int a[2];
float b[2][3];
char c[10];
};

will be expressed as the following Python list:

structure = [
    ('a', 'i', 2),
    ('b', 'd', (2,3)),
    ('c', '10s')
]

where the first element of each member is name, the second one is the format character (https://docs.python.jp/3/library/struct.html#format-characters) that represents C type in Python, and the third one (optional) is shape.

Example

>>> structure = [('a', 'i', 2), ('b', 'd', (2,3)), ('c', '10s')]
>>> reader = fm.utils.CStructReader(structure)
>>> with open('binaryfile', 'rb') as f:
...     reader.read(f)
>>> reader.data
OrderedDict([
    ('a', array([0, 1])),
    ('b', array([[0, 1, 2],[3, 4, 5]])),
    ('c', 'some text')
])

size#

A byte size of the structure.

Type:: int

data#

An ordered dictionary that stores unpacked values.

Type:: OrderedDict

jsondata#

A JSON string that stores unpacked values.

Type:: OrderedDict

params#

Stored information about the structure, ignored, and byteorder.

Type:: dict

References

https://docs.python.jp/3/library/struct.html#module-struct

property data#: An ordered dictionary that stores unpacked values.

property jsondata#: An JSON string that stores unpacked values.

read(f)[source]#

Sequentially read a file object to unpack values in a C structure.

Values are stored in the C structure reader instance as an ordered dictionary. Use data or jsondata attributes to access them.

Parameters:: f (file) – A binary file object to be read. It must be open`ed with `b option.

class Convergence(threshold=0.001, n_maxiters=300, n_miniters=2, *, centering=False, reuseable=True, raise_exception=False)[source]#

Bases: object

property status#

class DatetimeParser(outputiso=True, cutoffsec=True, encoding='utf-8')[source]#: Bases: object

class StructureReader(structure, skipname='$.', byteorder='<', encoding='utf-8')[source]#

Bases: object

property data#

read(f)[source]#

copy_function(func, name=None)[source]#

Copy a function object with different name.

Parameters:

func (function) – A function to be copied.
name (string, optional) – A name of the new function. If not spacified, the same name of func will be used.

Returns:

A new function with different name.

Return type:

newfunc (function)

ctype_to_tform(ctype, shape=None)[source]#

Convert Python C-type to FITS format.

Parameters:

ctype (str) – A C-type format string in Python.
shape (int or tuple of int, optional) – shape of the C-type.

Returns:

A format string of FITS (TFORM).

Return type:

tform (str)

References: https://docs.python.org/3.6/library/struct.html http://docs.astropy.org/en/stable/io/fits/usage/table.html

dtype_to_tform(dtype, shape=None)[source]#

Convert NumPy dtype to FITS format.

Parameters:

dtype (str) – A dtype string of NumPy.
shape (int or tuple of int, optional) – shape of the C-type.

Returns:

A format string of FITS (TFORM).

Return type:

tform (str)

References: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html http://docs.astropy.org/en/stable/io/fits/usage/table.html

fmgf(array, sigma)[source]#

Apply the FMGF (Fast M-estimation based Gaussian Filter) to a 1D array.

Parameters:

array (numpy.ndarray) – A 1D array.
sigma (int) – Standard deviation for Gaussian kernel.

Returns:

A 1D array to which the FMGF is applied.

Return type:

filtered (numpy.ndarray)

References

Journal of the Japan Society for Precision Engineering Vol.76 (2010) No.6 P684-688 A Proposal of Robust Gaussian Filter by Using Fast M-Estimation Method http://doi.org/10.2493/jjspe.76.684

gaussian(x, x0=0.0, fwhm=1.0, ampl=1.0)[source]#

Return Gaussian with given parameters.

y = ampl * exp(-4ln2 * (x-x0)^2 / fwhm^2)

Parameters:

x (numpy.ndarray) – An input 1D array.
x0 (float) – Center of the Gaussian.
fwhm (float) – FWHM of the Gaussian.
ampl (float) – Amplitude of the Gaussian.

Returns:

An output 1D Gaussian.

Return type:

y (numpy.ndarray)

get_filename()[source]#: Get filename by a dialog window.

ignore_numpy_errors()[source]#

Return a context manager where all numpy errors are ignored.

This function is intended to be used as a with statement like:

>>> with ignore_numpy_errors():
...     np.arange(10) / 0 # no errors are displayed

lorentzian(x, x0=0.0, fwhm=1.0, ampl=1.0)[source]#

Return Lorentzian with given parameters.

y = ampl / (1 + 4(x-x0)^2 / fwhm^2)

Parameters:

x (numpy.ndarray) – An input 1D array.
x0 (float) – Center of the Lorentzian.
fwhm (float) – FWHM of the Lorentzian.
ampl (float) – Amplitude of the Lorentzian.

Returns:

An output 1D Lorentzian.

Return type:

y (numpy.ndarray)

mad(array, axis=None, keepdims=False)[source]#

Compute the median absolute deviation (MAD) along the given axis.

Parameters:

array (numpy.ndarray) – An input array.
axis (int, optional) – Axis along which the MADs are computed. The default is to compute the MAD along a flattened version of the array.
keepdims (bool, optional) – If True, the axes which are reduced are left in the result as dimensions with size one.

Returns:

A new array holding the result.

Return type:

mad (numpy.ndarray)

one_thread_per_process()[source]#

Return a context manager where only one thread is allocated to a process.

This function is intended to be used as a with statement like:

>>> with process_per_thread():
...     do_something() # one thread per process

Notes

This function only works when MKL (Intel Math Kernel Library) is installed and used in, for example, NumPy and SciPy. Otherwise this function does nothing.

pseudovoigt(x, x0=0.0, fwhm=1.0, ampl=1.0, frac=0.5)[source]#

Return Pseudo-Voigt with given parameters.

y = frac * gaussian(x, x0, fwhm) + (1-frac) * lorentzian(x, x0, fwhm)

Parameters:

x (numpy.ndarray) – An input 1D array.
x0 (float) – Center of the Pseudo-Voigt.
fwhm (float) – FWHM of the Pseudo-Voigt.
ampl (float) – Amplitude of the Pseudo-Voigt.
frac (float) – Fraction of Gaussian and Lorentzian. Must be 0 <= frac <= 1.

Returns:

An output 1D Pseudo-Voigt.

Return type:

y (numpy.ndarray)

rollrows(array, shifts)[source]#

Roll 2D array elements of each row by a given shifts.

Parameters:

array (numpy.ndarray) – A 2D array.
shifts (int of list of int) – The number(s) of places by which elements of each row are shifted.

Returns:

An output rolled array.

Return type:

array (numpy.ndarray)

slicewhere(condition)[source]#

Return slices of regions that fulfill condition.

Example

>>> cond = [False, True, True, False, False, True, False]
>>> fm.utils.slicewhere(cond)
[slice(1L, 3L, None), slice(5L, 6L, None)]

Parameters:: condition (numpy.ndarray) – An array of booleans.
Returns:: List of slice objects.
Return type:: slices (list of slice)