Utilities

lfd.analysis.profiles.utils.get_rabina_profile(angle, useCV2=False)[source]

Returns image of projected Rabina profile as a function of angle between the observers line of sight and the velocity vector of the meteor.

Parameters:
  • angle (float) – angle, in radians, between LOS and vector of the meteor.
  • useCV2 (bool, optional) – If True will use cv2 to open the Rabina profile, otherwise matplotlib. Default is False.

Notes

Pre-rendered Rabina profiles exist for angles in 0.1 increments from 0 to 1.5 inclusive. This is approximately every 6 degrees from 0 to 90 degrees. See gen_rabina_profile.py in persistent cache on how to pre-render Rabina profiles for other angles. Profiles are searched for in both ephemeral and persistent cache locations. If multiple matching files are found, those from peristent cache only are returned.

Raises:
  • ValueError: – when the given angle was not in the range 0-1.5 (step 0.1) inclusive.
  • FileNotFoundError: – when the searched for Rabina profile was not found. This can happen only when a 3rd party has manually cleared the persistent cache.
lfd.analysis.profiles.utils.meshgrid(data, x, y, tgt, fold=None, axes=True)[source]

Given a structured numpy array returns two 1D arrays and one 2D array. The two 1D arrays contain elements of data and represent the x and y axes, respectively, of the returned 2D. Similar to numpy’s meshgrid. The 2D array containes target column data reshaped such that it matches the chosen x and y. Optionally, if there are multiple additional columns with which the target data varies with, it is possible to “fold” over those columns; that is subselect only those elements of the target data that correspond to rows in the structured numpy array for which element values equal the given fold values.

Parameters:
  • data (np.array) – A structured array of data
  • x (str) – A string representing the column that will be selected as the x axis. Must be a valid dtype name of data.
  • y (str) – A string representing the column that will be selected as the y axis. Must be a valid dtype name of data.
  • tgt (str) – A string representing the column that will be reshaped into 2D array matching the entries of x and y.
  • fold (dict) – An optional dictionary of key:value pairs that represent column names and values on which to fold the total data on. Folding subselects particular target column elements of data based matched values provided in the dictionary.
  • axes (bool) – If True, default, both axes and grid are returned. If false, only the gridded data is returned.
Returns:

  • x (np.array) – A 1D array containing data elements that represent the x axis of the targeted reshaped data
  • y (np.array) – A 1D array containing data elements that represent the y axis of the targeted reshaped data
  • gridded (np.array) – A 2D array

Examples

>>> data
   array(
     [(3., 1., 5., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 8.1, 8.1, 3.5),
      (3., 1., 6., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 9.8, 9.8, 1.5),
      (3., 2., 5., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 4.5, 4.5, 2.6),
      (3., 2., 6., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 4.4, 4.4, 1.5),
      (4., 1., 5., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 8.1, 8.1, 3.5),
      (4., 1., 6., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 9.8, 9.8, 1.5),
      (4., 2., 5., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 4.5, 4.5, 2.6),
      (4., 2., 6., 'DiskSource', 'GausKolmogorov', 'FluxPerAngle', 1.43, 4.4, 4.4, 1.5)],
 dtype=[('fwhm', '<f8'), ('h', '<f8'), ('radius', '<f8'), ('source', '<U12'),
 ('seeing', '<U14'), ('defocus', '<U12'), ('sfwhm', '<f8'), ('dfwhm', '<f8'),
 ('ofwhm', '<f8'), ('depth', '<f8')])

>>> x, y, z = meshgrid(meas, 'h', 'fwhm', 'ofwhm', fold={'radius':5})
>>> x
array([1., 2.])
>>> y
array([3., 4.])
>>> z
array([[8.1, 4.5],
     [8.1, 4.5]])