get_layer() - Code Metrics - Inspection of "Get Layer Function" - Unidata/MetPy - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#444)

unknown

created 2017-06-26 17:44 UTC

get_layer() F

↳ Parent: Project

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
cc	11
c	1
b	0
f	0
dl	0
loc	95
rs	3.1764

How to fix Long Method Complexity

# Copyright (c) 2008-2017 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Contains a collection of generally useful calculation tools."""

import functools

import numpy as np
import numpy.ma as ma
from scipy.spatial import cKDTree

from . import height_to_pressure_std, pressure_to_height_std
from ..package_tools import Exporter
from ..units import check_units, units

exporter = Exporter(globals())


@exporter.export
def resample_nn_1d(a, centers):
    """Return one-dimensional nearest-neighbor indexes based on user-specified centers.

    Parameters
    ----------
    a : array-like
        1-dimensional array of numeric values from which to
        extract indexes of nearest-neighbors
    centers : array-like
        1-dimensional array of numeric values representing a subset of values to approximate

    Returns
    -------
        An array of indexes representing values closest to given array values

    """
    ix = []
    for center in centers:
        index = (np.abs(a - center)).argmin()
        if index not in ix:
            ix.append(index)
    return ix


@exporter.export
def nearest_intersection_idx(a, b):
    """Determine the index of the point just before two lines with common x values.

    Parameters
    ----------
    a : array-like
        1-dimensional array of y-values for line 1
    b : array-like
        1-dimensional array of y-values for line 2

    Returns
    -------
        An array of indexes representing the index of the values
        just before the intersection(s) of the two lines.

    """
    # Difference in the two y-value sets
    difference = a - b

    # Determine the point just before the intersection of the lines
    # Will return multiple points for multiple intersections
    sign_change_idx, = np.nonzero(np.diff(np.sign(difference)))

    return sign_change_idx


@exporter.export
def find_intersections(x, a, b, direction='all'):
    """Calculate the best estimate of intersection.

    Calculates the best estimates of the intersection of two y-value
    data sets that share a common x-value set.

    Parameters
    ----------
    x : array-like
        1-dimensional array of numeric x-values
    a : array-like
        1-dimensional array of y-values for line 1
    b : array-like
        1-dimensional array of y-values for line 2
    direction : string
        specifies direction of crossing. 'all', 'increasing' (a becoming greater than b),
        or 'decreasing' (b becoming greater than a).

    Returns
    -------
        A tuple (x, y) of array-like with the x and y coordinates of the
        intersections of the lines.

    """
    # Find the index of the points just before the intersection(s)
    nearest_idx = nearest_intersection_idx(a, b)
    next_idx = nearest_idx + 1

    # Determine the sign of the change
    sign_change = np.sign(a[next_idx] - b[next_idx])

    # x-values around each intersection
    _, x0 = _next_non_masked_element(x, nearest_idx)
    _, x1 = _next_non_masked_element(x, next_idx)

    # y-values around each intersection for the first line
    _, a0 = _next_non_masked_element(a, nearest_idx)
    _, a1 = _next_non_masked_element(a, next_idx)

    # y-values around each intersection for the second line
    _, b0 = _next_non_masked_element(b, nearest_idx)
    _, b1 = _next_non_masked_element(b, next_idx)

    # Calculate the x-intersection. This comes from finding the equations of the two lines,
    # one through (x0, a0) and (x1, a1) and the other through (x0, b0) and (x1, b1),
    # finding their intersection, and reducing with a bunch of algebra.
    delta_y0 = a0 - b0
    delta_y1 = a1 - b1
    intersect_x = (delta_y1 * x0 - delta_y0 * x1) / (delta_y1 - delta_y0)

    # Calculate the y-intersection of the lines. Just plug the x above into the equation
    # for the line through the a points. One could solve for y like x above, but this
    # causes weirder unit behavior and seems a little less good numerically.
    intersect_y = ((intersect_x - x0) / (x1 - x0)) * (a1 - a0) + a0

    # Make a mask based on the direction of sign change desired
    if direction == 'increasing':
        mask = sign_change > 0
    elif direction == 'decreasing':
        mask = sign_change < 0
    elif direction == 'all':
        return intersect_x, intersect_y
    else:
        raise ValueError('Unknown option for direction: {0}'.format(str(direction)))
    return intersect_x[mask], intersect_y[mask]


@exporter.export
def interpolate_nans(x, y, kind='linear'):
    """Interpolate NaN values in y.

    Interpolate NaN values in the y dimension. Works with unsorted x values.

    Parameters
    ----------
    x : array-like
        1-dimensional array of numeric x-values
    y : array-like
        1-dimensional array of numeric y-values
    kind : string
        specifies the kind of interpolation x coordinate - 'linear' or 'log'

    Returns
    -------
        An array of the y coordinate data with NaN values interpolated.

    """
    x_sort_args = np.argsort(x)
    x = x[x_sort_args]
    y = y[x_sort_args]
    nans = np.isnan(y)
    if kind == 'linear':
        y[nans] = np.interp(x[nans], x[~nans], y[~nans])
    elif kind == 'log':
        y[nans] = np.interp(np.log(x[nans]), np.log(x[~nans]), y[~nans])
    else:
        raise ValueError('Unknown option for kind: {0}'.format(str(kind)))
    return y[x_sort_args]


def _next_non_masked_element(a, idx):
    """Return the next non masked element of a masked array.

    If an array is masked, return the next non-masked element (if the given index is masked).
    If no other unmasked points are after the given masked point, returns none.

    Parameters
    ----------
    a : array-like
        1-dimensional array of numeric values
    idx : integer
        index of requested element

    Returns
    -------
        Index of next non-masked element and next non-masked element

    """
    try:
        next_idx = idx + a[idx:].mask.argmin()
        if ma.is_masked(a[next_idx]):
            return None, None
        else:
            return next_idx, a[next_idx]
    except (AttributeError, TypeError, IndexError):
        return idx, a[idx]


def delete_masked_points(*arrs):
    """Delete masked points from arrays.

    Takes arrays and removes masked points to help with calculations and plotting.

    Parameters
    ----------
    arrs : one or more array-like
        source arrays

    Returns
    -------
    arrs : one or more array-like
        arrays with masked elements removed

    """
    if any(hasattr(a, 'mask') for a in arrs):
        keep = ~functools.reduce(np.logical_or, (np.ma.getmaskarray(a) for a in arrs))
        return tuple(ma.asarray(a[keep]) for a in arrs)
    else:
        return arrs


@exporter.export
def reduce_point_density(points, radius, priority=None):
    r"""Return a mask to reduce the density of points in irregularly-spaced data.

    This function is used to down-sample a collection of scattered points (e.g. surface
    data), returning a mask that can be used to select the points from one or more arrays
    (e.g. arrays of temperature and dew point). The points selected can be controlled by
    providing an array of ``priority`` values (e.g. rainfall totals to ensure that
    stations with higher precipitation remain in the mask).

    Parameters
    ----------
    points : (N, K) array-like
        N locations of the points in K dimensional space
    radius : float
        minimum radius allowed between points
    priority : (N, K) array-like, optional
        If given, this should have the same shape as ``points``; these values will
        be used to control selection priority for points.

    Returns
    -------
        (N,) array-like of boolean values indicating whether points should be kept. This
        can be used directly to index numpy arrays to return only the desired points.

    Examples
    --------
    >>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1.)
    array([ True, False,  True], dtype=bool)
    >>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1.,
    ... priority=np.array([0.1, 0.9, 0.3]))
    array([False,  True, False], dtype=bool)

    """
    # Handle 1D input
    if points.ndim < 2:
        points = points.reshape(-1, 1)

    # Make a kd-tree to speed searching of data.
    tree = cKDTree(points)

    # Need to use sorted indices rather than sorting the position
    # so that the keep mask matches *original* order.
    if priority is not None:
        # Need to sort the locations in decreasing priority.
        sorted_indices = np.argsort(priority)[::-1]
    else:
        # Take advantage of iterator nature of range here to avoid making big lists
        sorted_indices = range(len(points))

    # Keep all points initially
    keep = np.ones(len(points), dtype=np.bool)

    # Loop over all the potential points
    for ind in sorted_indices:
        # Only proceed if we haven't already excluded this point
        if keep[ind]:
            # Find the neighbors and eliminate them
            neighbors = tree.query_ball_point(points[ind], radius)
            keep[neighbors] = False

            # We just removed ourselves, so undo that
            keep[ind] = True

    return keep


@exporter.export
def log_interp(x, xp, fp, **kwargs):
    r"""Interpolates data with logarithmic x-scale.

    Interpolation on a logarithmic x-scale for interpolation values in pressure coordintates.

    Parameters
    ----------
    x : array-like
        The x-coordinates of the interpolated values.

    xp : array-like
        The x-coordinates of the data points.

    fp : array-like
        The y-coordinates of the data points, same length as xp.

    Returns
    -------
    array-like
        The interpolated values, same shape as x.

    Examples
    --------
    >>> x_log = np.array([1e3, 1e4, 1e5, 1e6])
    >>> y_log = np.log(x_log) * 2 + 3
    >>> x_interp = np.array([5e3, 5e4, 5e5])
    >>> metpy.calc.log_interp(x_interp, x_log, y_log)
    array([ 20.03438638,  24.63955657,  29.24472675])

    """
    sort_args = np.argsort(xp)

    if hasattr(x, 'units'):
        x = x.m

    if hasattr(xp, 'units'):
        xp = xp.m

    interpolated_vals = np.interp(np.log(x), np.log(xp[sort_args]), fp[sort_args], **kwargs)

    if hasattr(fp, 'units'):
        interpolated_vals = interpolated_vals * fp.units

    return interpolated_vals


def _get_bound_pressure_height(pressure, bound, heights=None, interpolate=True):
    """Calculate the bounding pressure and height in a layer.

    Given pressure, optional heights, and a bound, return either the closest pressure/height
    or interpolated pressure/height. If no heights are provided, a standard atmosphere is
    assumed.

    Parameters
    ----------
    pressure : `pint.Quantity`
        Atmospheric pressures
    bound : `pint.Quantity`
        Bound to retrieve (in pressure or height)
    heights : `pint.Quantity`
        Atmospheric heights associated with the pressure levels
    interpolate : boolean
        Interpolate the bound or return the nearest

    Returns
    -------
    `pint.Quantity`
        The bound pressure and height.

    """
    # Bound is given in pressure
    if bound.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
        # If the bound is in the pressure data, we know the pressure bound exactly
        if bound in pressure:

            bound_pressure = bound
            # If we have heights, we know the exact height value, otherwise return standard
            # atmosphere height for the pressure
            if heights is not None:
                idx = np.where(pressure == bound_pressure)
                bound_height = heights[idx]
            else:
                bound_height = pressure_to_height_std(bound_pressure)
        # If bound is not in the data, return the nearest or interpolated values
        else:
            if interpolate:
                bound_pressure = bound  # Use the user specified bound
                if heights is not None:  # Interpolate heights from the height data
                    bound_height = log_interp(bound_pressure, pressure, heights)
                else:  # If not heights given, use the standard atmosphere
                    bound_height = pressure_to_height_std(bound_pressure)
            else:  # No interpolation, find the closest values
                idx = (np.abs(pressure - bound)).argmin()
                bound_pressure = pressure[idx]
                if heights is not None:
                    bound_height = heights[idx]
                else:
                    bound_height = pressure_to_height_std(bound_pressure)

    # Bound is given in length
    elif bound.dimensionality == {'[length]': 1.0}:

        # If there is height data, see if we have the bound or need to interpolate/find nearest
        if heights is not None:
            if bound in heights:  # Bound is in the height data
                bound_height = bound
                idx = np.where(heights == bound)
                bound_pressure = pressure[idx]
            else:  # Bound is not in the data
                if interpolate:
                    bound_height = bound
                    bound_pressure = height_to_pressure_std(bound_height)
                else:
                    idx = (np.abs(heights - bound)).argmin()
                    bound_pressure = pressure[idx]
                    bound_height = heights[idx]
        else:  # Don't have heights, so assume a standard atmosphere
            bound_height = bound
            bound_pressure = height_to_pressure_std(bound)
            # If interpolation is on, this is all we need, if not, we need to go back and
            # find the pressure closest to this and refigure the bounds
            if interpolate is False:
                idx = (np.abs(pressure - bound_pressure)).argmin()
                bound_pressure = pressure[idx]
                bound_height = pressure_to_height_std(bound_pressure)

    # Bound has invalid units
    else:
        raise ValueError('Bound must be specified in units of length or pressure.')

    return bound_pressure, bound_height


@exporter.export
@check_units('[pressure]')
def get_layer(p, *args, **kwargs):
    r"""Return an atmospheric layer from upper air data with the requested bottom and depth.

    This function will subset an upper air dataset to contain only the specified layer. The
    bottom of the layer can be specified with a pressure or height above the surface
    pressure. The bottom defaults to the surface pressure. The depth of the layer can be
    specified in terms of pressure or height above the bottom of the layer. If the top and
    bottom of the layer are not in the data, they are interpolated by default.

    Parameters
    ----------
    p : array-like
        Atmospheric pressure profile
    *args : array-like
        Atmospheric variable(s) measured at the given pressures
    heights: array-like
        Atmospheric heights corresponding to the given pressures
    bottom : `pint.Quantity`
        The bottom of the layer as a pressure or height above the surface pressure
    depth : `pint.Quantity`
        The thickness of the layer as a pressure or height above the bottom of the layer
    interpolate : bool
        Interpolate the top and bottom points if they are not in the given data

    Returns
    -------
    `pint.Quantity, pint.Quantity`
        The pressure and data variables of the layer

    """
    # Pop off keyword arguments
    heights = kwargs.pop('heights', None)
    bottom = kwargs.pop('bottom', None)
    depth = kwargs.pop('depth', 100 * units.hPa)
    interpolate = kwargs.pop('interpolate', True)

    # Make sure pressure and datavar are the same length
    for datavar in args:
        if len(p) != len(datavar):
            raise ValueError('Pressure and data variable must have the same length.')

    # If the bottom is not specified, make it the surface pressure
    if bottom is None:
        bottom = p[0]

    bottom_pressure, bottom_height = _get_bound_pressure_height(p, bottom, heights=heights,
                                                                interpolate=interpolate)

    # Calculate the top if whatever units depth is in
    if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
        top = bottom_pressure - depth
    elif depth.dimensionality == {'[length]': 1}:
        top = bottom_height + depth
    else:
        raise ValueError('Depth must be specified in units of length or pressure')

    top_pressure, _ = _get_bound_pressure_height(p, top, heights=heights,
                                                          interpolate=interpolate)

    ret = []  # returned data variables in layer

    # Ensure pressures are sorted in ascending order
    sort_inds = np.argsort(p)
    p = p[sort_inds]

    # Mask based on top and bottom pressure
    inds = (p <= bottom_pressure) & (p >= top_pressure)
    p_interp = p[inds]

    # Interpolate pressures at bounds if necessary and sort
    if interpolate:
        # If we don't have the bottom or top requested, append them
        if top_pressure not in p_interp:
            p_interp = np.sort(np.append(p_interp, top_pressure)) * p.units
        if bottom_pressure not in p_interp:
            p_interp = np.sort(np.append(p_interp, bottom_pressure)) * p.units

    ret.append(p_interp[::-1])

    for datavar in args:
        # Ensure that things are sorted in ascending order
        datavar = datavar[sort_inds]

        if interpolate:
            # Interpolate for the possibly missing bottom/top values
            datavar_interp = log_interp(p_interp, p, datavar)
            datavar = datavar_interp
        else:
            datavar = datavar[inds]

        ret.append(datavar[::-1])

    return ret


1		# Copyright (c) 2008-2017 MetPy Developers.
2		# Distributed under the terms of the BSD 3-Clause License.
3		# SPDX-License-Identifier: BSD-3-Clause
4		"""Contains a collection of generally useful calculation tools."""
5
6		import functools
7
8		import numpy as np
9		import numpy.ma as ma
10		from scipy.spatial import cKDTree
11
12		from . import height_to_pressure_std, pressure_to_height_std
13		from ..package_tools import Exporter
14		from ..units import check_units, units
15
16		exporter = Exporter(globals())
17
18
19		@exporter.export
20		def resample_nn_1d(a, centers):
21		"""Return one-dimensional nearest-neighbor indexes based on user-specified centers.
22
23		Parameters
24		----------
25		a : array-like
26		1-dimensional array of numeric values from which to
27		extract indexes of nearest-neighbors
28		centers : array-like
29		1-dimensional array of numeric values representing a subset of values to approximate
30
31		Returns
32		-------
33		An array of indexes representing values closest to given array values
34
35		"""
36		ix = []
37		for center in centers:
38		index = (np.abs(a - center)).argmin()
39		if index not in ix:
40		ix.append(index)
41		return ix
42
43
44		@exporter.export
45		def nearest_intersection_idx(a, b):
46		"""Determine the index of the point just before two lines with common x values.
47
48		Parameters
49		----------
50		a : array-like
51		1-dimensional array of y-values for line 1
52		b : array-like
53		1-dimensional array of y-values for line 2
54
55		Returns
56		-------
57		An array of indexes representing the index of the values
58		just before the intersection(s) of the two lines.
59
60		"""
61		# Difference in the two y-value sets
62		difference = a - b
63
64		# Determine the point just before the intersection of the lines
65		# Will return multiple points for multiple intersections
66		sign_change_idx, = np.nonzero(np.diff(np.sign(difference)))
67
68		return sign_change_idx
69
70
71		@exporter.export
72		def find_intersections(x, a, b, direction='all'):
73		"""Calculate the best estimate of intersection.
74
75		Calculates the best estimates of the intersection of two y-value
76		data sets that share a common x-value set.
77
78		Parameters
79		----------
80		x : array-like
81		1-dimensional array of numeric x-values
82		a : array-like
83		1-dimensional array of y-values for line 1
84		b : array-like
85		1-dimensional array of y-values for line 2
86		direction : string
87		specifies direction of crossing. 'all', 'increasing' (a becoming greater than b),
88		or 'decreasing' (b becoming greater than a).
89
90		Returns
91		-------
92		A tuple (x, y) of array-like with the x and y coordinates of the
93		intersections of the lines.
94
95		"""
96		# Find the index of the points just before the intersection(s)
97		nearest_idx = nearest_intersection_idx(a, b)
98		next_idx = nearest_idx + 1
99
100		# Determine the sign of the change
101		sign_change = np.sign(a[next_idx] - b[next_idx])
102
103		# x-values around each intersection
104		_, x0 = _next_non_masked_element(x, nearest_idx)
105		_, x1 = _next_non_masked_element(x, next_idx)
106
107		# y-values around each intersection for the first line
108		_, a0 = _next_non_masked_element(a, nearest_idx)
109		_, a1 = _next_non_masked_element(a, next_idx)
110
111		# y-values around each intersection for the second line
112		_, b0 = _next_non_masked_element(b, nearest_idx)
113		_, b1 = _next_non_masked_element(b, next_idx)
114
115		# Calculate the x-intersection. This comes from finding the equations of the two lines,
116		# one through (x0, a0) and (x1, a1) and the other through (x0, b0) and (x1, b1),
117		# finding their intersection, and reducing with a bunch of algebra.
118		delta_y0 = a0 - b0
119		delta_y1 = a1 - b1
120		intersect_x = (delta_y1 * x0 - delta_y0 * x1) / (delta_y1 - delta_y0)
121
122		# Calculate the y-intersection of the lines. Just plug the x above into the equation
123		# for the line through the a points. One could solve for y like x above, but this
124		# causes weirder unit behavior and seems a little less good numerically.
125		intersect_y = ((intersect_x - x0) / (x1 - x0)) * (a1 - a0) + a0
126
127		# Make a mask based on the direction of sign change desired
128		if direction == 'increasing':
129		mask = sign_change > 0
130		elif direction == 'decreasing':
131		mask = sign_change < 0
132		elif direction == 'all':
133		return intersect_x, intersect_y
134		else:
135		raise ValueError('Unknown option for direction: {0}'.format(str(direction)))
136		return intersect_x[mask], intersect_y[mask]
137
138
139		@exporter.export
140		def interpolate_nans(x, y, kind='linear'):
141		"""Interpolate NaN values in y.
142
143		Interpolate NaN values in the y dimension. Works with unsorted x values.
144
145		Parameters
146		----------
147		x : array-like
148		1-dimensional array of numeric x-values
149		y : array-like
150		1-dimensional array of numeric y-values
151		kind : string
152		specifies the kind of interpolation x coordinate - 'linear' or 'log'
153
154		Returns
155		-------
156		An array of the y coordinate data with NaN values interpolated.
157
158		"""
159		x_sort_args = np.argsort(x)
160		x = x[x_sort_args]
161		y = y[x_sort_args]
162		nans = np.isnan(y)
163		if kind == 'linear':
164		y[nans] = np.interp(x[nans], x[~nans], y[~nans])
165		elif kind == 'log':
166		y[nans] = np.interp(np.log(x[nans]), np.log(x[~nans]), y[~nans])
167		else:
168		raise ValueError('Unknown option for kind: {0}'.format(str(kind)))
169		return y[x_sort_args]
170
171
172		def _next_non_masked_element(a, idx):
173		"""Return the next non masked element of a masked array.
174
175		If an array is masked, return the next non-masked element (if the given index is masked).
176		If no other unmasked points are after the given masked point, returns none.
177
178		Parameters
179		----------
180		a : array-like
181		1-dimensional array of numeric values
182		idx : integer
183		index of requested element
184
185		Returns
186		-------
187		Index of next non-masked element and next non-masked element
188
189		"""
190		try:
191		next_idx = idx + a[idx:].mask.argmin()
192		if ma.is_masked(a[next_idx]):
193		return None, None
194		else:
195		return next_idx, a[next_idx]
196		except (AttributeError, TypeError, IndexError):
197		return idx, a[idx]
198
199
200		def delete_masked_points(*arrs):
201		"""Delete masked points from arrays.
202
203		Takes arrays and removes masked points to help with calculations and plotting.
204
205		Parameters
206		----------
207		arrs : one or more array-like
208		source arrays
209
210		Returns
211		-------
212		arrs : one or more array-like
213		arrays with masked elements removed
214
215		"""
216		if any(hasattr(a, 'mask') for a in arrs):
217		keep = ~functools.reduce(np.logical_or, (np.ma.getmaskarray(a) for a in arrs))
218		return tuple(ma.asarray(a[keep]) for a in arrs)
219		else:
220		return arrs
221
222
223		@exporter.export
224		def reduce_point_density(points, radius, priority=None):
225		r"""Return a mask to reduce the density of points in irregularly-spaced data.
226
227		This function is used to down-sample a collection of scattered points (e.g. surface
228		data), returning a mask that can be used to select the points from one or more arrays
229		(e.g. arrays of temperature and dew point). The points selected can be controlled by
230		providing an array of ``priority`` values (e.g. rainfall totals to ensure that
231		stations with higher precipitation remain in the mask).
232
233		Parameters
234		----------
235		points : (N, K) array-like
236		N locations of the points in K dimensional space
237		radius : float
238		minimum radius allowed between points
239		priority : (N, K) array-like, optional
240		If given, this should have the same shape as ``points``; these values will
241		be used to control selection priority for points.
242
243		Returns
244		-------
245		(N,) array-like of boolean values indicating whether points should be kept. This
246		can be used directly to index numpy arrays to return only the desired points.
247
248		Examples
249		--------
250		>>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1.)
251		array([ True, False, True], dtype=bool)
252		>>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1.,
253		... priority=np.array([0.1, 0.9, 0.3]))
254		array([False, True, False], dtype=bool)
255
256		"""
257		# Handle 1D input
258		if points.ndim < 2:
259		points = points.reshape(-1, 1)
260
261		# Make a kd-tree to speed searching of data.
262		tree = cKDTree(points)
263
264		# Need to use sorted indices rather than sorting the position
265		# so that the keep mask matches original order.
266		if priority is not None:
267		# Need to sort the locations in decreasing priority.
268		sorted_indices = np.argsort(priority)[::-1]
269		else:
270		# Take advantage of iterator nature of range here to avoid making big lists
271		sorted_indices = range(len(points))
272
273		# Keep all points initially
274		keep = np.ones(len(points), dtype=np.bool)
275
276		# Loop over all the potential points
277		for ind in sorted_indices:
278		# Only proceed if we haven't already excluded this point
279		if keep[ind]:
280		# Find the neighbors and eliminate them
281		neighbors = tree.query_ball_point(points[ind], radius)
282		keep[neighbors] = False
283
284		# We just removed ourselves, so undo that
285		keep[ind] = True
286
287		return keep
288
289
290		@exporter.export
291		def log_interp(x, xp, fp, **kwargs):
292		r"""Interpolates data with logarithmic x-scale.
293
294		Interpolation on a logarithmic x-scale for interpolation values in pressure coordintates.
295
296		Parameters
297		----------
298		x : array-like
299		The x-coordinates of the interpolated values.
300
301		xp : array-like
302		The x-coordinates of the data points.
303
304		fp : array-like
305		The y-coordinates of the data points, same length as xp.
306
307		Returns
308		-------
309		array-like
310		The interpolated values, same shape as x.
311
312		Examples
313		--------
314		>>> x_log = np.array([1e3, 1e4, 1e5, 1e6])
315		>>> y_log = np.log(x_log) * 2 + 3
316		>>> x_interp = np.array([5e3, 5e4, 5e5])
317		>>> metpy.calc.log_interp(x_interp, x_log, y_log)
318		array([ 20.03438638, 24.63955657, 29.24472675])
319
320		"""
321		sort_args = np.argsort(xp)
322
323		if hasattr(x, 'units'):
324		x = x.m
325
326		if hasattr(xp, 'units'):
327		xp = xp.m
328
329		interpolated_vals = np.interp(np.log(x), np.log(xp[sort_args]), fp[sort_args], **kwargs)
330
331		if hasattr(fp, 'units'):
332		interpolated_vals = interpolated_vals * fp.units
333
334		return interpolated_vals
335
336
337		def _get_bound_pressure_height(pressure, bound, heights=None, interpolate=True):
338		"""Calculate the bounding pressure and height in a layer.
339
340		Given pressure, optional heights, and a bound, return either the closest pressure/height
341		or interpolated pressure/height. If no heights are provided, a standard atmosphere is
342		assumed.
343
344		Parameters
345		----------
346		pressure : `pint.Quantity`
347		Atmospheric pressures
348		bound : `pint.Quantity`
349		Bound to retrieve (in pressure or height)
350		heights : `pint.Quantity`
351		Atmospheric heights associated with the pressure levels
352		interpolate : boolean
353		Interpolate the bound or return the nearest
354
355		Returns
356		-------
357		`pint.Quantity`
358		The bound pressure and height.
359
360		"""
361		# Bound is given in pressure
362		if bound.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
363		# If the bound is in the pressure data, we know the pressure bound exactly
364	View Code Duplication	if bound in pressure:
		0 ignored issues – show Duplication introduced 2017-06-26 15:07 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
365		bound_pressure = bound
366		# If we have heights, we know the exact height value, otherwise return standard
367		# atmosphere height for the pressure
368		if heights is not None:
369		idx = np.where(pressure == bound_pressure)
370		bound_height = heights[idx]
371		else:
372		bound_height = pressure_to_height_std(bound_pressure)
373		# If bound is not in the data, return the nearest or interpolated values
374		else:
375		if interpolate:
376		bound_pressure = bound # Use the user specified bound
377		if heights is not None: # Interpolate heights from the height data
378		bound_height = log_interp(bound_pressure, pressure, heights)
379		else: # If not heights given, use the standard atmosphere
380		bound_height = pressure_to_height_std(bound_pressure)
381		else: # No interpolation, find the closest values
382		idx = (np.abs(pressure - bound)).argmin()
383		bound_pressure = pressure[idx]
384		if heights is not None:
385		bound_height = heights[idx]
386		else:
387		bound_height = pressure_to_height_std(bound_pressure)
388
389		# Bound is given in length
390	View Code Duplication	elif bound.dimensionality == {'[length]': 1.0}:
		0 ignored issues – show Duplication introduced 2017-06-26 15:07 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
391		# If there is height data, see if we have the bound or need to interpolate/find nearest
392		if heights is not None:
393		if bound in heights: # Bound is in the height data
394		bound_height = bound
395		idx = np.where(heights == bound)
396		bound_pressure = pressure[idx]
397		else: # Bound is not in the data
398		if interpolate:
399		bound_height = bound
400		bound_pressure = height_to_pressure_std(bound_height)
401		else:
402		idx = (np.abs(heights - bound)).argmin()
403		bound_pressure = pressure[idx]
404		bound_height = heights[idx]
405		else: # Don't have heights, so assume a standard atmosphere
406		bound_height = bound
407		bound_pressure = height_to_pressure_std(bound)
408		# If interpolation is on, this is all we need, if not, we need to go back and
409		# find the pressure closest to this and refigure the bounds
410		if interpolate is False:
411		idx = (np.abs(pressure - bound_pressure)).argmin()
412		bound_pressure = pressure[idx]
413		bound_height = pressure_to_height_std(bound_pressure)
414
415		# Bound has invalid units
416		else:
417		raise ValueError('Bound must be specified in units of length or pressure.')
418
419		return bound_pressure, bound_height
420
421
422		@exporter.export
423		@check_units('[pressure]')
424		def get_layer(p, args, *kwargs):
425		r"""Return an atmospheric layer from upper air data with the requested bottom and depth.
426
427		This function will subset an upper air dataset to contain only the specified layer. The
428		bottom of the layer can be specified with a pressure or height above the surface
429		pressure. The bottom defaults to the surface pressure. The depth of the layer can be
430		specified in terms of pressure or height above the bottom of the layer. If the top and
431		bottom of the layer are not in the data, they are interpolated by default.
432
433		Parameters
434		----------
435		p : array-like
436		Atmospheric pressure profile
437		*args : array-like
438		Atmospheric variable(s) measured at the given pressures
439		heights: array-like
440		Atmospheric heights corresponding to the given pressures
441		bottom : `pint.Quantity`
442		The bottom of the layer as a pressure or height above the surface pressure
443		depth : `pint.Quantity`
444		The thickness of the layer as a pressure or height above the bottom of the layer
445		interpolate : bool
446		Interpolate the top and bottom points if they are not in the given data
447
448		Returns
449		-------
450		`pint.Quantity, pint.Quantity`
451		The pressure and data variables of the layer
452
453		"""
454		# Pop off keyword arguments
455		heights = kwargs.pop('heights', None)
456		bottom = kwargs.pop('bottom', None)
457		depth = kwargs.pop('depth', 100 * units.hPa)
458		interpolate = kwargs.pop('interpolate', True)
459
460		# Make sure pressure and datavar are the same length
461		for datavar in args:
462		if len(p) != len(datavar):
463		raise ValueError('Pressure and data variable must have the same length.')
464
465		# If the bottom is not specified, make it the surface pressure
466		if bottom is None:
467		bottom = p[0]
468
469		bottom_pressure, bottom_height = _get_bound_pressure_height(p, bottom, heights=heights,
470		interpolate=interpolate)
471
472		# Calculate the top if whatever units depth is in
473		if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
474		top = bottom_pressure - depth
475		elif depth.dimensionality == {'[length]': 1}:
476		top = bottom_height + depth
477		else:
478		raise ValueError('Depth must be specified in units of length or pressure')
479
480		top_pressure, _ = _get_bound_pressure_height(p, top, heights=heights,
481		interpolate=interpolate)
482
483		ret = [] # returned data variables in layer
484
485		# Ensure pressures are sorted in ascending order
486		sort_inds = np.argsort(p)
487		p = p[sort_inds]
488
489		# Mask based on top and bottom pressure
490		inds = (p <= bottom_pressure) & (p >= top_pressure)
491		p_interp = p[inds]
492
493		# Interpolate pressures at bounds if necessary and sort
494		if interpolate:
495		# If we don't have the bottom or top requested, append them
496		if top_pressure not in p_interp:
497		p_interp = np.sort(np.append(p_interp, top_pressure)) * p.units
498		if bottom_pressure not in p_interp:
499		p_interp = np.sort(np.append(p_interp, bottom_pressure)) * p.units
500
501		ret.append(p_interp[::-1])
502
503		for datavar in args:
504		# Ensure that things are sorted in ascending order
505		datavar = datavar[sort_inds]
506
507		if interpolate:
508		# Interpolate for the possibly missing bottom/top values
509		datavar_interp = log_interp(p_interp, p, datavar)
510		datavar = datavar_interp
511		else:
512		datavar = datavar[inds]
513
514		ret.append(datavar[::-1])
515
516		return ret
517

Unidata / MetPy

Pull Request — master (#444)

get_layer() F

Complexity

Size

Duplication

Importance

How to fix Long Method Complexity

Long Method

Complexity

Duplication Side-by-Side

Filter issues like