fruitbat.utils

The collection of utility functions for Fruitbat.

fruitbat.utils.check_keys_in_dict(dictionary, keys)[source]

Checks that a list of keys exist in a dictionary.

Parameters:
  • dictionary (dict) – The input dictionary.
  • keys (list of strings) – The keys that the dictionary must contain.
Returns:

bool – Returns True is all required keys exist in the dictionary. Otherwise a KeyError is raised.
fruitbat.utils.check_type(value_name, value, dtype, desire=True)[source]

Checks the type of a variable and raises an error if not the desired type.

Parameters:
  • value_name (str) – The name of the variable that will be printed in the error message.
  • value – The value of the variable
  • dtype (dtype) – The data type to compare with isinstance
  • desire (boolean, optional) – If desire = True, then the error will be raised if value does not have a data type of dtype. If desire = False, then the error will be raised if value does have a data type of dtype.
Returns:

None
fruitbat.utils.calc_mean_from_pdf(x, pdf, dx=None)[source]

Calculates the mean of a probability density function

Parameters:
  • x (np.ndarray) – The x values.
  • pdf (np.ndarray) – The value of the PDF at x.
  • dx (np.ndarray or None, optional) – The spacing between the x bins. If None, then the bins are assumed to be linearly spaced.
Returns:

mean (float) – The mean of the PDF.
fruitbat.utils.calc_median_from_pdf(x, pdf)[source]

Calculates the median of a PDF.

Parameters:
  • x (np.ndarray) – The x values.
  • pdf (np.ndarray) – The value of the PDF at x.
Returns:

median (float) – The median of the PDF.
fruitbat.utils.calc_std_from_pdf(x, pdf, dx=None)[source]

Calculates the standard deviation from a probability density function.

Parameters:
  • x (np.ndarray) – The x values.
  • pdf (np.ndarray) – The value of the PDF at x.
  • dx (np.ndarray or None, optional) – The spacing between the x bins. If None, then the bins are assumed to be linearly spaced.
Returns:

std (float) – The standard deviation of the PDF.
fruitbat.utils.calc_variance_from_pdf(x, pdf, dx=None)[source]

Calculates the variance from a probability density function.

Parameters:
  • x (np.ndarray) – The x values.
  • pdf (np.ndarray) – The value of the PDF at x.
  • dx (np.ndarray or None, optional) – The spacing between the x bins. If None, then the bins are assumed to be linearly spaced.
Returns:

variance (float) – The variance of the PDF.
fruitbat.utils.calc_z_from_pdf_percentile(x, pdf, percentile)[source]
Parameters:
  • x (np.ndarray) – The x values of the PDF.
  • pdf (np.ndarray) – The value of the PDF at x.
  • percentile (float) – The percentile of the PDF.
Returns:

redshift (float) – The redshift at the given percentile.
fruitbat.utils.normalise_to_pdf(hist, bin_widths)[source]
fruitbat.utils.redshift_prior(zbins, prior='uniform')[source]
fruitbat.utils.sigma_to_pdf_percentiles(sigma)[source]

Looks up the percentile range of Gaussian for a given standard deviation.

Parameters:sigma ([1, 2, 3, 4, 5]) – The standard deviation to calculate a percentile.
Returns:
  • Lower (float) – The lower percentile
  • Higher (float) – The higher percentile

Example

>>> sigma_to_pdf_percentiles(1)
(0.158655254, 0.841344746)