`utils.utils` — Utility Functions¶

The utils.utils module includes a collection of utility functions

New in version 0.1.0.

autocnet.utils.utils.array_to_poly(array)[source]¶

Generate a geojson geom :param array: 2-D array of size (n, 2) of x, y coordinates :type array: array-like

Returns: geom – geojson containing the necessary data to construct a poly gon
Return type: GeoJson

autocnet.utils.utils.bytescale(data, cmin=None, cmax=None, high=255, low=0)[source]¶

This is pulled directly from scipy.misc as they are deprecating bytescale.

Byte scales an array (image). Byte scaling means converting the input image to uint8 dtype and scaling the range to (low, high) (default 0-255). If the input image already has dtype uint8, no scaling is done. This function is only available if Python Imaging Library (PIL) is installed. :param data: PIL image data array. :type data: ndarray :param cmin: Bias scaling of small values. Default is data.min(). :type cmin: scalar, optional :param cmax: Bias scaling of large values. Default is data.max(). :type cmax: scalar, optional :param high: Scale max value to high. Default is 255. :type high: scalar, optional :param low: Scale min value to low. Default is 0. :type low: scalar, optional

Returns: img_array – The byte-scaled array.
Return type: uint8 ndarray

Examples

>>> from scipy.misc import bytescale
>>> img = np.array([[ 91.06794177,   3.39058326,  84.4221549 ],
...                 [ 73.88003259,  80.91433048,   4.88878881],
...                 [ 51.53875334,  34.45808177,  27.5873488 ]])
>>> bytescale(img)
array([[255,   0, 236],
       [205, 225,   4],
       [140,  90,  70]], dtype=uint8)
>>> bytescale(img, high=200, low=100)
array([[200, 100, 192],
       [180, 188, 102],
       [155, 135, 128]], dtype=uint8)
>>> bytescale(img, cmin=0, cmax=255)
array([[91,  3, 84],
       [74, 81,  5],
       [52, 34, 28]], dtype=uint8)

autocnet.utils.utils.calculate_slope(x1, x2)[source]¶

Calculates the 2-dimensional slope between the points in two dataframes each containing two columns [‘x’, ‘y’] The slope is calculated from x1 to x2.

Parameters

x1 (dataframe) – Each row is a point with columns [‘x’, ‘y’]
x2 (dataframe) – Each row is a point with columns [‘x’, ‘y’]

Returns

A dataframe with the slope between the points in x1 and x2 for each row.

Return type

dataframe

autocnet.utils.utils.cartesian(arrays, out=None)[source]¶

Generate a cartesian product of input arrays. :param arrays: 1-D arrays to form the cartesian product of. :type arrays: list of array-like :param out: Array to place the cartesian product in. :type out: ndarray

Returns

out (ndarray) – 2-D array of shape (M, len(arrays)) containing cartesian products formed of input arrays.
from scikit-learn
https (//github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py)

autocnet.utils.utils.checkbandnumbers(bands, checkbands)[source]¶

Given a list of input bands, check that the passed tuple contains those bands.

In case of THEMIS, we check for band 9 as band 9 is the temperature band required to derive thermal temperature. We also check for band 10 which is required for TES atmosphere calculations.

Parameters

bands (tuple) – of bands in the input image
checkbands (list) – of bands to check against

Returns

True if the bands are present, else False

Return type

bool

autocnet.utils.utils.checkdeplaid(incidence)[source]¶

Given an incidence angle, select the appropriate deplaid method.

Parameters: incidence (float) – incidence angle extracted from the campt results.

autocnet.utils.utils.checkmonotonic(iterable, piecewise=False)[source]¶

Check if a given iterable is monotonically increasing.

Parameters

iterable (iterable) – Any Python iterable object
piecewise (boolean) – If false, return a boolean for the entire iterable, else return a list with elementwise monotinicy checks

Returns

monotonic – A boolean list of all True if monotonic, or including an inflection point

Return type

bool/list

autocnet.utils.utils.compare_dicts(d, o)[source]¶

Given two dictionaries, compare them with support for np.ndarray and pd.DataFrame objects

Parameters

d (dict) – first dict to compare
o (dict) – second dict to compare

Examples

>>> d = {'a':0}
>>> o = {'a':0}
>>> compare_dicts(d, o)
True
>>> d['a'] = 1
>>> compare_dicts(d,o)
False
>>> d['a'] = np.arange(3)
>>> o['a'] = np.arange(3)
>>> compare_dicts(d,o)
True

autocnet.utils.utils.compute_depression(input_dem, scale_factor=1, curvature_percentile=75)[source]¶

Compute depressions and return a new image with larges depressions filled in.

Parameters

input_dem (np.array, rd.rdarray) – 2d array of elevation DNs, a DEM
scale_factor (float) – Value to scale the erotion of planform curvatures by
curvature_percentile (float) – what percentile of the curvature to keep, lower values results in bigger blobs

Returns

dem (rd.rdarray) – Dem with filled depressions
mask (np.array) – Change mask, true on pixels that have been changed

autocnet.utils.utils.create_decorator(dec, **namespace)[source]¶: Create a decorator function using arbirary params. The objects passed in can be used in the body. Originally designed with the idea of automatically updating one object after the decorated object was modified.

autocnet.utils.utils.crossform(a)[source]¶

Return the cross form, e.g. a in the cross product of a b. :param a: (3,) vector :type a: ndarray

Returns: a – (3,3)
Return type: ndarray

autocnet.utils.utils.decorate_class(cls, decorator, exclude=[], *args, **kwargs)[source]¶

Decorates a class with a give docorator. Returns a subclass with dectorations applied

Parameters

cls (Class) – A class to be decorated
decorator (callable) – callable to wrap cls’s methods with
exclude (list) – list of method names to exclude from being decorated
args (list, dict) – Parameters to pass into decorator
kwargs (list, dict) – Parameters to pass into decorator

autocnet.utils.utils.find_in_dict(obj, key)[source]¶

Recursively find an entry in a dictionary

Parameters

obj (dict) – The dictionary to search
key (str) – The key to find in the dictionary

Returns

item – The value from the dictionary

Return type

obj

autocnet.utils.utils.find_nested_in_dict(data, key_list)[source]¶

Traverse a list of keys into a dict.

Parameters

data (dict) – The dictionary to be traversed
key_list (list) – The list of keys to be travered. Keys are traversed in the order they are entered in the list

Returns

value – The value in the dict

Return type

object

autocnet.utils.utils.generate_dem(alpha=1.0, size=800, scales=[160, 80, 32, 16, 8, 4, 2, 1], scale_factor=5)[source]¶

Produces a random DEM

Parameters

alpha (float) – Controls height variation. Lower number makes a shallower and noisier DEM, higher values create smoother DEM with large peaks and valleys. Reccommended range = (0, 1.5]
size (int) – size of DEM, output DEM is in the shape of (size, size)
scale_factor (float) – Scalar to multiply the slope degradation by, higher values = more erosion. Recommended to increase proportionately with alpha (higher alphas mean you might want higher scale_factor)

Returns

dem – DEM array in the shape (size, size)

Return type

np.array

autocnet.utils.utils.getnearest(iterable, value)[source]¶

Given an iterable, get the index nearest to the input value

Parameters

iterable (iterable) – An iterable to search
value (int, float) – The value to search for

Returns

The index into the list

Return type

int

autocnet.utils.utils.hillshade(img, azi=255, min_slope=20, max_slope=100, min_bright=0, grayscale=False)[source]¶

hillshade a DEM, based on IDL code by Colin Dundas

Parameters

img (np.array) – DEM to hillshade
azi (float) – Sun azimuth
min_slope (float) – minimum slope value
max_slope (float) – maximum slope value
min_bright (float) – minimum brightness
grayscale (bool) – whether or not to produce grayscale image

Returns

dem – hillshaded DEM

Return type

np.array

autocnet.utils.utils.import_func(func)[source]¶

Imports a function from the autocnet package.

Parameters: func (str) – import path. For example, to import the place_points_in_overlap function, this func can be called with: ‘spatial.overlap.place_points_in_overlap’
Returns: func – The function object for use.
Return type: obj

autocnet.utils.utils.make_homogeneous(points)[source]¶

Convert a set of points (n x dim array) to: homogeneous coordinates.

Parameters: points (arraylike) – n x m array of points, where n is the number of points.
Returns: n x m + 1 array of homogeneous points
Return type: arraylike

autocnet.utils.utils.methodispatch(func)[source]¶

New dispatch decorator that looks at the second argument to avoid self

Parameters

func (Object) – Function object to be dispatched
Returns –
wrapper (Object) – Wrapped function call chosen by the dispatcher
---------- –

autocnet.utils.utils.normalize_vector(line)[source]¶

Normalize a standard form line

Parameters: line (ndarray) – Standard form of a line (Ax + By + C = 0)
Returns: line – The normalized line
Return type: ndarray

Examples

>>> x = np.array([3, 1, 2])
>>> nv = normalize_vector(x)
>>> print(np.round(nv, 6))  # For doc test float percision
[0.801784 0.267261 0.534522]

autocnet.utils.utils.rasterize_polygon(shape, vertices, dtype=<class 'bool'>)[source]¶

Simple tool to convert poly into a boolean numpy array.

source: https://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array

Parameters

shape (tuple) – size of the array in (y,x) format
vertices (np.array, list) – array of vertices in [[x0, y0], [x1, y1]…] format
dtype (type) – datatype of output mask

Returns

mask – mask with filled polygon set to true

Return type

np.array

autocnet.utils.utils.remove_field_name(a, name)[source]¶

Given a numpy structured array, remove a column and return a copy of the remainder of the array

Parameters

a (ndarray) – Numpy structured array
name (str) – of the index (column) to be removed

Returns

b – Numpy structured array with the ‘name’ column removed

Return type

ndarray

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utils.utils — Utility Functions¶

`utils.utils` — Utility Functions¶