munge¶
A collection of functions for doing common processing tasks.
-
dragons.munge.munge.describe(arr, **kwargs)[source]¶ Run scipy.stats.describe and produce legible output.
- Args:
- arr : ndarray
- Numpy ndarray
- Kwargs:
- passed to scipy.stats.describe
- Returns:
- output of scipy.stats.describe
-
dragons.munge.munge.edges_to_centers(edges, width=False)[source]¶ Convert evenly spaced bin edges to centers.
- Args:
- edges : ndarray
- bin edges
- Kwargs:
- width : bool
- also return the bin width
- Returns:
- centers : ndarray
- bin centers (size = edges.size-1)
- bin_width : float
- only returned if width = True
-
dragons.munge.munge.mass_function(mass, volume, bins, range=None, poisson_uncert=False, return_edges=False, **kwargs)[source]¶ Generate a mass function.
- Args:
- mass : array
- an array of ‘masses’
- volume : float
- volume of simulation cube/subset
- bins : int or list or str
- If bins is a string, then it must be one of:
- ‘blocks’ : use bayesian blocks for dynamic bin widths‘knuth’ : use Knuth’s rule to determine bins‘scott’ : use Scott’s rule to determine bins‘freedman’ : use the Freedman-diaconis rule to determine bins
- Kwargs:
- range : len=2 list or array
- range of data to be used for mass function
- poisson_uncert : bool
- return poisson uncertainties in output array (default: False)
- return_edges : bool
- return the bin_edges (default: False)
- **kwargs
- passed to np.histogram call
- Returns:
array of [bin centers, mass function vals]
If poisson_uncert=True then array has 3rd column with uncertainties.
If return_edges=True then the bin edges are also returned.
- Notes:
- The code to generate the bin_widths is taken from astroML.hist
-
dragons.munge.munge.ndarray_to_dataframe(arr, drop_vectors=False)[source]¶ Convert numpy ndarray to a pandas DataFrame, dealing with N(>1) dimensional datatypes.
- Args:
- arr : ndarray
- Numpy ndarray
- Kwargs:
- drop_vectors : bool
- only include single value datatypes in output DataFrame
- Returns:
- df : DataFrame
- Pandas DataFrame
-
dragons.munge.munge.power_spectrum(grid, side_length, n_bins)[source]¶ Calculate the dimensionless power spectrum of a grid (G):
\[\begin{split}\Delta = \frac{k^3}{2\pi^2 V} <|\hat G|^2>\end{split}\]- Args:
- grid : ndarray
- The grid from which to construct the power spectrum
- side_length : float
- The side length of the grid (assumes all side lengths are equal)
- n_bins : int
- The number of k bins to use
- Returns:
- kmean : ndarray
- The mean wavenumber of each bin
- power : ndarray
- The dimensionless power (\(\Delta\))
- uncert : ndarray
- The standard deviation of the power within each k bin
-
dragons.munge.munge.pretty_print_dict(d, fmtlen=30)[source]¶ Pretty print a dictionary, dealing with recursion.
- Args:
- d : dict
- the dictionary to print
- fmtlen : int
- maximum length of dictionary key for print formatting
-
dragons.munge.munge.smooth_grid(grid, side_length, radius, filt='tophat')[source]¶ Smooth a grid by convolution with a filter.
- Args:
- grid : ndarray
- The grid to be smoothed
- side_length : float
- The side length of the grid (assumes all side lengths are equal)
- radius : float
- The radius of the smoothing filter
- Kwargs:
- filt : string
- The name of the filter. Currently only “tophat” (real space) is implemented. More filters will be added over time.
- Returns:
- smoothed_grid : ndarray
- The smoothed grid
-
dragons.munge.regrid.regrid()¶ Downgrade the resolution of a 3 dimensional grid.
- Args:
old_grid (np.ndarray[float32, ndim=3]) : Grid to be resampled
n_cell (int) : n cells per dimension of new grid
- Returns:
- New, degraded resolution grid.