crikit.utils package

Submodules

crikit.utils.breadcrumb module

crikit.utils.datacheck module

Functions to check and (if needed) convert input variables

Created on Sat Jun 18 00:16:27 2016

@author: chc

crikit.utils.datacheck._rng_is_pix_vec(rng, vec_size=None)[source]

Make sure rng is a vector, unless None (then returns None).

Parameters

rng (int, list, tuple, or ndarray(1D)) –

  • int: number of pixels in rng

  • list, tuple, ndarray with length 2: Start and end of rng

  • ndarray (length > 2): actual rng vector

Returns

rng – Array of pixel range

Return type

ndarray (1D)

crikit.utils.general module

General utilities

expand_1d_to_ndim_data : Match 1D data array dimensionality to that of another array

expand_1d_to_ndim : Expand 1D data array dimensionality to ndim

find_nearestGiven a vector and a value, find the index and value

of the closest match

padWrapper around numpy.pad that also returns a window defining the

original signal

Notes

crikit.utils.general.arange_nonzero(start, stop, dtype=<class 'float'>)[source]

Similar to numpy arange but only returns non-zero elements

crikit.utils.general.expand_1d_to_ndim(data, ndim)[source]

Make 1D array into ndim dimensions

crikit.utils.general.expand_1d_to_ndim_data(data, data_to_match)[source]

Make 1D data array equal in dimensions to data_to_match

crikit.utils.general.find_nearest(np_vec, to_find=0)[source]

Given a vector and a value (or list/vector of values), find the index and value of the closest match

Parameters
  • np_vec (numpy.ndarray) – Numpy array (list) of values

  • to_find (int, float, numpy.ndarray, or list) –

Returns

out – Closest value (nearest_value) and index (index)

Return type

tuple (nearest_value(s), index(es))

crikit.utils.general.lin_from_row_col(row, col, sh)[source]

Convert a col and row counter to 1D linear count

crikit.utils.general.mean_nd_to_1d(data, axis=-1)[source]

Take the mean of an nd array, except axis, returning a 1D array

crikit.utils.general.np_fcn_nd_to_1d(fcn, data, axis=-1)[source]

Take in an n-dimensional array and return a 1D version operated on by fcn. Works with many numpy functions that can take an “axis” parameter

crikit.utils.general.pad(y, pad_width, mode)[source]

Pad array with either constants or edge values.

Note: For N-D arrays, pads the -1 axis

Parameters
  • y (ndarray) – Input array

  • pad_width (int) – Size of padding on each side of y

  • mode (str) – ‘constant’ (0), ‘edge’ currently accepted

Returns

Padded array and window. Window defines the region of the original signal

Return type

y_pad, window

crikit.utils.general.pad_dual(y, edge_pad_width, constant_pad_width)[source]

Pad array with edge values followed by constant 0’s.

Note: For N-D arrays, pads the -1 axis

Parameters
  • y (ndarray) – Input array

  • edge_pad_width (int) – Size of edge-value padding on each side of y

  • constant_pad_width (int) – Size of 0-padding on each side of y after edge-value padding

Returns

Padded array and window. Window defines the region of the original signal

Return type

y_pad, window

crikit.utils.general.pad_edge_mean(y, pad_width, n_edge=1, axis=-1)[source]

Pad data y with edge-values or near-edge mean values along axis

Parameters
  • y (ndarray) – Input array

  • pad_width (int) – Size of padding on each side of y

  • n_edge (int) – Number of edge points to average for the pad value

  • axis (int) – Axis to pad

Returns

  • (y_pad, window)

  • y_pad (ndarray) – Padded y

  • window (ndarray (1D)) – Mask with 0’s for pad regions, 1’s for original size

crikit.utils.general.row_col_from_lin(ct, sh)[source]

Convert a 1D counter into a col and row counter

crikit.utils.general.std_nd_to_1d(data, axis=-1)[source]

Take the mean of an nd array, except axis, returning a 1D array

crikit.utils.mplstyle module

Created on Thu Jun 30 10:23:03 2016

@author: chc

class crikit.utils.mplstyle.CrikitMplStyle[source]

Bases: object

_base_crikit = {'axes.prop_cycle': cycler('color', [array([0.12156863, 0.46666667, 0.70588235]), array([1. , 0.49803922, 0.05490196]), array([0.17254902, 0.62745098, 0.17254902]), array([0.83921569, 0.15294118, 0.15686275]), array([0.58039216, 0.40392157, 0.74117647]), array([0.54901961, 0.3372549 , 0.29411765]), array([0.89019608, 0.46666667, 0.76078431]), array([0.49803922, 0.49803922, 0.49803922]), array([0.7372549 , 0.74117647, 0.13333333]), array([0.09019608, 0.74509804, 0.81176471])]), 'font.family': ['sans-serif'], 'font.sans-serif': ['Arial', 'Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Avant Garde', 'sans-serif'], 'image.cmap': 'viridis', 'image.interpolation': 'none'}
_paper_fullwidth = {'axes.labelsize': 8.8, 'axes.titlesize': 9.6, 'figure.figsize': [6.4, 4.4], 'grid.linewidth': 0.8, 'legend.fontsize': 8.8, 'lines.linewidth': 1.4, 'lines.markeredgewidth': 0.0, 'lines.markersize': 5.6, 'patch.linewidth': 0.24, 'xtick.labelsize': 8.0, 'xtick.major.pad': 5.6, 'xtick.major.width': 0.8, 'xtick.minor.width': 0.4, 'ytick.labelsize': 8.0, 'ytick.major.pad': 5.6, 'ytick.major.width': 0.8, 'ytick.minor.width': 0.4}
_paper_halfwidth = {'axes.labelsize': 8.8, 'axes.titlesize': 9.6, 'figure.figsize': [3.3, 4.4], 'grid.linewidth': 0.8, 'legend.fontsize': 8.0, 'lines.linewidth': 1.4, 'lines.markeredgewidth': 0.0, 'lines.markersize': 5.6, 'patch.linewidth': 0.24, 'xtick.labelsize': 8.0, 'xtick.major.pad': 5.6, 'xtick.major.width': 0.8, 'xtick.minor.width': 0.4, 'ytick.labelsize': 8.0, 'ytick.major.pad': 5.6, 'ytick.major.width': 0.8, 'ytick.minor.width': 0.4}
_poster = {'axes.labelsize': 17.6, 'axes.titlesize': 19.2, 'figure.figsize': [12.8, 8.8], 'grid.linewidth': 1.6, 'legend.fontsize': 16.0, 'lines.linewidth': 2.8, 'lines.markeredgewidth': 0.0, 'lines.markersize': 11.2, 'patch.linewidth': 0.48, 'xtick.labelsize': 16.0, 'xtick.major.pad': 11.2, 'xtick.major.width': 1.6, 'xtick.minor.width': 0.8, 'ytick.labelsize': 16.0, 'ytick.major.pad': 11.2, 'ytick.major.width': 1.6, 'ytick.minor.width': 0.8}
_tableau10 = array([[0.12156863, 0.46666667, 0.70588235], [1. , 0.49803922, 0.05490196], [0.17254902, 0.62745098, 0.17254902], [0.83921569, 0.15294118, 0.15686275], [0.58039216, 0.40392157, 0.74117647], [0.54901961, 0.3372549 , 0.29411765], [0.89019608, 0.46666667, 0.76078431], [0.49803922, 0.49803922, 0.49803922], [0.7372549 , 0.74117647, 0.13333333], [0.09019608, 0.74509804, 0.81176471]])
_tableau10_med = array([[0.44705882, 0.61960784, 0.80784314], [1. , 0.61960784, 0.29019608], [0.40392157, 0.74901961, 0.36078431], [0.92941176, 0.4 , 0.36470588], [0.67843137, 0.54509804, 0.78823529], [0.65882353, 0.47058824, 0.43137255], [0.92941176, 0.59215686, 0.79215686], [0.63529412, 0.63529412, 0.63529412], [0.80392157, 0.8 , 0.36470588], [0.42745098, 0.8 , 0.85490196]])
_tableau20 = array([[0.12156863, 0.46666667, 0.70588235], [0.68235294, 0.78039216, 0.90980392], [1. , 0.49803922, 0.05490196], [1. , 0.73333333, 0.47058824], [0.17254902, 0.62745098, 0.17254902], [0.59607843, 0.8745098 , 0.54117647], [0.83921569, 0.15294118, 0.15686275], [1. , 0.59607843, 0.58823529], [0.58039216, 0.40392157, 0.74117647], [0.77254902, 0.69019608, 0.83529412], [0.54901961, 0.3372549 , 0.29411765], [0.76862745, 0.61176471, 0.58039216], [0.89019608, 0.46666667, 0.76078431], [0.96862745, 0.71372549, 0.82352941], [0.49803922, 0.49803922, 0.49803922], [0.78039216, 0.78039216, 0.78039216], [0.7372549 , 0.74117647, 0.13333333], [0.85882353, 0.85882353, 0.55294118], [0.09019608, 0.74509804, 0.81176471], [0.61960784, 0.85490196, 0.89803922]])
basic_fullwidth = {'axes.labelsize': 8.8, 'axes.prop_cycle': cycler('color', [array([0.12156863, 0.46666667, 0.70588235]), array([1. , 0.49803922, 0.05490196]), array([0.17254902, 0.62745098, 0.17254902]), array([0.83921569, 0.15294118, 0.15686275]), array([0.58039216, 0.40392157, 0.74117647]), array([0.54901961, 0.3372549 , 0.29411765]), array([0.89019608, 0.46666667, 0.76078431]), array([0.49803922, 0.49803922, 0.49803922]), array([0.7372549 , 0.74117647, 0.13333333]), array([0.09019608, 0.74509804, 0.81176471])]), 'axes.titlesize': 9.6, 'figure.figsize': [6.4, 4.4], 'font.family': ['sans-serif'], 'font.sans-serif': ['Arial', 'Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Avant Garde', 'sans-serif'], 'grid.linewidth': 0.8, 'image.cmap': 'viridis', 'image.interpolation': 'none', 'legend.fontsize': 8.8, 'lines.linewidth': 1.4, 'lines.markeredgewidth': 0.0, 'lines.markersize': 5.6, 'patch.linewidth': 0.24, 'xtick.labelsize': 8.0, 'xtick.major.pad': 5.6, 'xtick.major.width': 0.8, 'xtick.minor.width': 0.4, 'ytick.labelsize': 8.0, 'ytick.major.pad': 5.6, 'ytick.major.width': 0.8, 'ytick.minor.width': 0.4}
basic_halfwidth = {'axes.labelsize': 8.8, 'axes.prop_cycle': cycler('color', [array([0.12156863, 0.46666667, 0.70588235]), array([1. , 0.49803922, 0.05490196]), array([0.17254902, 0.62745098, 0.17254902]), array([0.83921569, 0.15294118, 0.15686275]), array([0.58039216, 0.40392157, 0.74117647]), array([0.54901961, 0.3372549 , 0.29411765]), array([0.89019608, 0.46666667, 0.76078431]), array([0.49803922, 0.49803922, 0.49803922]), array([0.7372549 , 0.74117647, 0.13333333]), array([0.09019608, 0.74509804, 0.81176471])]), 'axes.titlesize': 9.6, 'figure.figsize': [3.3, 4.4], 'font.family': ['sans-serif'], 'font.sans-serif': ['Arial', 'Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Avant Garde', 'sans-serif'], 'grid.linewidth': 0.8, 'image.cmap': 'viridis', 'image.interpolation': 'none', 'legend.fontsize': 8.0, 'lines.linewidth': 1.4, 'lines.markeredgewidth': 0.0, 'lines.markersize': 5.6, 'patch.linewidth': 0.24, 'xtick.labelsize': 8.0, 'xtick.major.pad': 5.6, 'xtick.major.width': 0.8, 'xtick.minor.width': 0.4, 'ytick.labelsize': 8.0, 'ytick.major.pad': 5.6, 'ytick.major.width': 0.8, 'ytick.minor.width': 0.4}
basic_poster = {'axes.labelsize': 17.6, 'axes.prop_cycle': cycler('color', [array([0.12156863, 0.46666667, 0.70588235]), array([1. , 0.49803922, 0.05490196]), array([0.17254902, 0.62745098, 0.17254902]), array([0.83921569, 0.15294118, 0.15686275]), array([0.58039216, 0.40392157, 0.74117647]), array([0.54901961, 0.3372549 , 0.29411765]), array([0.89019608, 0.46666667, 0.76078431]), array([0.49803922, 0.49803922, 0.49803922]), array([0.7372549 , 0.74117647, 0.13333333]), array([0.09019608, 0.74509804, 0.81176471])]), 'axes.titlesize': 19.2, 'figure.figsize': [12.8, 8.8], 'font.family': ['sans-serif'], 'font.sans-serif': ['Arial', 'Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Avant Garde', 'sans-serif'], 'grid.linewidth': 1.6, 'image.cmap': 'viridis', 'image.interpolation': 'none', 'legend.fontsize': 16.0, 'lines.linewidth': 2.8, 'lines.markeredgewidth': 0.0, 'lines.markersize': 11.2, 'patch.linewidth': 0.48, 'xtick.labelsize': 16.0, 'xtick.major.pad': 11.2, 'xtick.major.width': 1.6, 'xtick.minor.width': 0.8, 'ytick.labelsize': 16.0, 'ytick.major.pad': 11.2, 'ytick.major.width': 1.6, 'ytick.minor.width': 0.8}

crikit.utils.roi module

Created on Wed Jun 15 23:33:41 2016

@author: chc

crikit.utils.roi.pts_in_path(path)[source]

Return points (pixels) that fall within path (but not on boundary)

crikit.utils.roi.pts_to_verts(xvec, yvec)[source]

Convert points to vertices, i.e., convert from 2 1D arrays (or list) of x- and y-coordinates to a list-of-lists of [x,y] pairs

crikit.utils.roi.verts_to_path(verts, isclosed=True)[source]

Convert vertices to paths

crikit.utils.roi.verts_to_points_in_roi(verts)[source]

Vertice list defining ROI in, points within returned.

Module contents