sample

Handle data associated with a sample.

class sample.Sample(input_arr, **kwargs)

Handle the measured data along with metadata.

This class is a subclass of the numpy.ndarray class with additional methods and attributes that are specific to small-angle X-ray scattering experiments on biological samples.

It can handle various operations such as addition and subtraction of sample data or numpy array, scaling by a scalar or an array, indexing, broadcasting, reshaping, binning, sliding average or data cleaning.

Parameters

input_arr (np.ndarray, list, tuple or scalar) – Input array corresponding to sample scattering data.
kwargs (dict (optional)) –
Additional keyword arguments either for np.asarray() or for sample metadata. The metadata are:
- filename, the name of the file used to extract the data.
- errors, the errors associated with scattering data.
- time, the experimental time.
- elution_volume, if available, the elution volume of the HPLC.
- I0, the fitted intensity at q = 0.
- I0_std, the uncertainty on I0 value(s).
- rg, the gyration radius.
- rg_std, the uncertainty on Rg value(s).
- wavelength, the wavelength of the X-ray beam.
- name, the name for the sample.
- temperature, the temperature(s) used experimentally.
- concentration, the concentration of the sample.
- pressure, the pressure used experimentally.
- buffer, a description of the buffer used experimentally.
- q, the values for the momentum transfer q.
- detector, the detector used.
- beamline, the name of the beamline used.
- flow_rate, the flow rate inside the capillary.
- observable, for 2D dataset the name of the attribute
  corresponding to the first axis.

Note

The errors metadata is special as it is updated for various operations that are performed on the data array such as indexing or for the use of universal functions. For instance, indexing of the data will be performed on errors as well if its shape is the same as for the data. Also, addition, subtraction and other universal functions will lead to automatic error propagation. Some other metadata might change as well, like q, but only for the use of methods specific of the Sample class and not for methods inherited from numpy.

Examples

A sample can be created using the following:

>>> s1 = Sample(
...     np.arange(5),
...     dtype='float32',
...     errors=np.array([0.1, 0.2, 0.12, 0.14, 0.15])
... )

>>> buffer = Sample(
...     [0., 0.2, 0.4, 0.3, 0.1],
...     dtype='float32',
...     errors=np.array([0.1, 0.2, 0.05, 0.1, 0.2])
... )

where my_data, my_errors and q_values are numpy arrays. A buffer subtraction can be performed using:

>>> s1 = s1 - buffer
Sample([0. , 0.80000001, 1.60000002, 2.70000005, 3.9000001], dtype=float32)

where buffer1 is another instance of Sample. The error propagation is automatically performed and the other attributes are taken from the first operand (here s1). Other operations such as scaling can be performed using:

>>> s1 = 0.8 * s1
Sample([0. , 0.80000001, 1.60000002, 2.4000001, 3.20000005], dtype=float32)

You can transform another Sample instance into a column vector and look how broadcasting and error propagation work:

>>> s2 = Sample(
...     np.arange(5, 10),
...     dtype='float32',
...     errors=np.array([0.1, 0.3, 0.05, 0.1, 0.2])
... )
>>> s2 = s2[:, np.newaxis]
>>> res = s1 * s2
>>> res.errors
array([[0.5       , 1.00498756, 0.63245553, 0.76157731, 0.85      ],
       [0.6       , 1.23693169, 0.93722996, 1.23109707, 1.5       ],
       [0.7       , 1.40089257, 0.84593144, 0.99141313, 1.06887792],
       [0.8       , 1.60312195, 0.98061205, 1.15948264, 1.26491106],
       [0.9       , 1.81107703, 1.1516944 , 1.3955644 , 1.56923548]])

property T

The transposed array.

Same as self.transpose().

Examples

>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1.,  2.],
       [ 3.,  4.]])
>>> x.T
array([[ 1.,  3.],
       [ 2.,  4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1.,  2.,  3.,  4.])
>>> x.T
array([ 1.,  2.,  3.,  4.])