PYME.Analysis.points.ripleys module¶
- PYME.Analysis.points.ripleys.mc_points_from_mask(mask, n_points, three_d=True, coord_origin=(0, 0, 0))¶
Calculate coordinate positions in nm from a Monte-Carlo-sampled mask.
- Parameters
- masknp.array
Mask image (array of 0s and 1s)
- n_pointsint
Number of points to return.
- three_dbool
Return 2D or 3D coordinates
- coord_origin3-tuple
Offset in nm of the x, y, and z coordinates w.r.t. the camera origin (used to make sure mask aligns)
- PYME.Analysis.points.ripleys.mc_sampling_statistics(K, n_points, n_bins, bin_size, mask, three_d, significance=0.05, n_sim=20, threaded=False, sampling=5.0, coord_origin=(0, 0, 0))¶
Calculates simulation envelope and significance of clustering on a mask by simulating random uniform distributions on the mask using Monte-Carlo sampling.
- Parameters
- Knp.array
Ripley’s K-values for real points to be compared to simulations. Expects output from ripleys_k.
- n_pointsint
Number of real points. Defines size of simulations.
- n_binsint
Number of spatial bins
- bin_size: float
Width of spatial bins (nm)
- maskPYME.IO.image.ImageStack
a mask of allowing the Ripleys to be computed within a given area
- three_dbool
Indicates if the real points are in 2D or 3D space
- n_simint
Number of Monte-Carlo simulations to run. More simulations = more statistical power.
- threadedbool
Calculate pairwise distances using multithreading (faster)
- samplingfloat
spacing (in nm) of samples from mask / region.
- coord_origin3-tuple
Offset in nm of the x, y, and z coordinates w.r.t. the camera origin (used to make sure mask aligns)
- PYME.Analysis.points.ripleys.points_from_mask(mask, sampling, three_d=True, coord_origin=(0, 0, 0))¶
Calculate coordinate positions in nm from a regularly-sampled mask.
- Parameters
- masknp.array
Mask image (array of 0s and 1s)
- samplingfloat
Sampling rate of mask image in nm.
- three_dbool
Return 2D or 3D coordinates
- coord_origin3-tuple
Offset in nm of the x, y, and z coordinates w.r.t. the camera origin (used to make sure mask aligns)
- PYME.Analysis.points.ripleys.ripleys_dh(bb, K, d=2)¶
Derivative of the H-function.
- Parameters
- bbnp.array
Histogram bins associated with K.
- Knp.array
Ripley’s K-function, calculated from PYME.Analysis.points.spatial_descriptive.ripleys_k
- dint
Dimension of the input data to calculate K (2 or 3).
- PYME.Analysis.points.ripleys.ripleys_dl(bb, K, d=2)¶
Derivative of the H-function.
- Parameters
- bbnp.array
Histogram bins associated with K.
- Knp.array
Ripley’s K-function, calculated from PYME.Analysis.points.spatial_descriptive.ripleys_k
- dint
Dimension of the input data to calculate K (2 or 3).
- PYME.Analysis.points.ripleys.ripleys_h(bb, K, d=2)¶
Normalizes Ripley’s K-function to an H-function such that > 0 indicates clustering and H < 0 indicates dispersion.
- Parameters
- bbnp.array
Histogram bins associated with K.
- Knp.array
Ripley’s K-function, calculated from PYME.Analysis.points.spatial_descriptive.ripleys_k
- dint
Dimension of the input data to calculate K (2 or 3).
- PYME.Analysis.points.ripleys.ripleys_k(x, y, n_bins, bin_size, mask=None, bbox=None, z=None, threaded=False, sampling=5.0, coord_origin=(0, 0, 0))¶
Ripley’s K-function for examining clustering and dispersion of points within a region R, where R is defined by a mask (2D or 3D) of the data.
- xnp.array
x-position of raw data
- ynp.array
y-position of raw data
- n_binsint
Number of spatial bins
- bin_size: float
Width of spatial bins (nm)
- maskPYME.IO.image.ImageStack
a mask of allowing the Ripleys to be computed within a given area
- bboxoptional, a tuple (x0, y0, x1, y1), or (x0, y0, z0, x1, y1, z1)
bounding box of the region to consider if no mask provided (defaults to min and max of supplied data)
- zoptional, np.array
z-position of raw data
- threadedbool
Calculate pairwise distances using multithreading (faster)
- samplingfloat
spacing (in nm) of samples from mask / region.
- coord_origin3-tuple
Offset in nm of the x, y, and z coordinates w.r.t. the camera origin (used to make sure mask aligns)
- PYME.Analysis.points.ripleys.ripleys_k_from_mask_points(x, y, xu, yu, n_bins, bin_size, mask_area, area_per_mask_point, z=None, zu=None, threaded=False)¶
Ripley’s K-function for examining clustering and dispersion of points within a region R, where R is defined by a mask (2D or 3D) of the data.
- Parameters
- xnp.array
x-position of raw data
- ynp.array
y-position of raw data
- xunp.array
x-position of simulated uniform random data over region R
- yunp.array
y-position of simulated uniform random data over region R
- n_binsint
Number of spatial bins
- bin_size: float
Width of spatial bins (nm)
- mask_areafloat
Area of region R (sum of the mask)
- area_per_mask_pointfloat
Scaling factor for weight calculations
- znp.array
z-position of raw data
- zunp.array
z-position of simulated uniform random data over region R
- threadedbool
Calculate pairwise distances using multithreading (faster)
- PYME.Analysis.points.ripleys.ripleys_l(bb, K, d=2)¶
Normalizes Ripley’s K-function to an L-function.
- Parameters
- bbnp.array
Histogram bins associated with K.
- Knp.array
Ripley’s K-function, calculated from PYME.Analysis.points.spatial_descriptive.ripleys_k
- dint
Dimension of the input data to calculate K (2 or 3).