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

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Pull Request — master (#444)

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created 2017-06-22 14:16 UTC

get_layer() F

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137

Duplication

Lines	0
Ratio	0 %

Importance

Changes	5
Bugs	0	Features	0

Metric	Value
cc	25
c	5
b	0
f	0
dl	0
loc	137
rs	2

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


@exporter.export
@check_units('[pressure]')
def get_layer(p, *args, heights=None, bottom=None, depth=100 * units.hPa, interpolate=True):
    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
    datavar : array-like
        Atmospheric variable measured at 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 variable of the layer

    """
    # 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.')

    # Bottom of layer calculations

    # If no bottom is specified, use the first pressure value
    if not bottom:
        bottom_pressure = p[0]
        if heights is not None:
            bottom_height = heights[0]
        else:
            bottom_height = pressure_to_height_std(bottom_pressure)

    # If the bottom pressure is specified
    if bottom:
        # in pressure units, assign it
        if bottom.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
            bottom_pressure = bottom
            if heights is not None:
                bottom_height = log_interp(bottom_pressure, p, heights)
            else:
                bottom_height = pressure_to_height_std(bottom_pressure)
        # in height units
        if bottom.dimensionality == {'[length]': 1.0}:
            bottom_height = bottom
            if heights is not None:
                bottom_pressure = np.interp(np.array([bottom_height.m]), heights,
                                            p)[0] * p.units
            else:
                bottom_pressure = height_to_pressure_std(bottom_height)

    # Top of layer calculations

    # Depth is in pressure
    if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
        top_pressure = bottom_pressure - depth
        if heights is not None:
            top_height = log_interp(top_pressure, p, heights)
        else:
            top_height = pressure_to_height_std(top_pressure)

    # Depth is in height
    if depth.dimensionality == {'[length]': 1.0}:
        top_height = bottom_height + depth
        if heights is not None:
            top_pressure = np.interp(np.array([top_height.m]), heights, p)[0] * p.units
        else:
            top_pressure = height_to_pressure_std(top_height)

    # Handle top or bottom values that are invalid
    if bottom_pressure > p[0]:
        raise ValueError(
            'Bottom of layer pressure is greater than the largest pressure in data.')
    if top_pressure < p[-1]:
        raise ValueError('Top of layer pressure is less than the lowest pressure in data.')
    if bottom_pressure < top_pressure:
        raise ValueError(
            'Pressure at the top of the layer is greater than that at the bottom.')

    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

    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])

    # If heights are given, return the height range as well as pressure range
    if heights is not None:
        heights_clipped = heights[((heights >= bottom_height) & (heights <= top_height))]

        if bottom_height not in heights:
            heights_clipped = np.sort(np.append(heights_clipped,
                                                bottom_height)) * heights.units
        if top_height not in heights:
            heights_clipped = np.sort(np.append(heights_clipped, top_height)) * heights.units

        ret.insert(0, heights_clipped)

    ret.insert(0, p_interp[::-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			@exporter.export
338			@check_units('[pressure]')
339			def get_layer(p, args, heights=None, bottom=None, depth=100 units.hPa, interpolate=True):
340			r"""Return an atmospheric layer from upper air data with the requested bottom and depth.
341
342			This function will subset an upper air dataset to contain only the specified layer. The
343			bottom of the layer can be specified with a pressure or height above the surface
344			pressure. The bottom defaults to the surface pressure. The depth of the layer can be
345			specified in terms of pressure or height above the bottom of the layer. If the top and
346			bottom of the layer are not in the data, they are interpolated by default.
347
348			Parameters
349			----------
350			p : array-like
351			Atmospheric pressure profile
352			datavar : array-like
353			Atmospheric variable measured at the given pressures
354			bottom : `pint.Quantity`
355			The bottom of the layer as a pressure or height above the surface pressure
356			depth : `pint.Quantity`
357			The thickness of the layer as a pressure or height above the bottom of the layer
358			interpolate : bool
359			Interpolate the top and bottom points if they are not in the given data
360
361			Returns
362			-------
363			`pint.Quantity, pint.Quantity`
364			The pressure and data variable of the layer
365
366			"""
367			# Make sure pressure and datavar are the same length
368			for datavar in args:
369			if len(p) != len(datavar):
370			raise ValueError('Pressure and data variable must have the same length.')
371
372			# Bottom of layer calculations
373
374			# If no bottom is specified, use the first pressure value
375			if not bottom:
376			bottom_pressure = p[0]
377			if heights is not None:
378			bottom_height = heights[0]
379			else:
380			bottom_height = pressure_to_height_std(bottom_pressure)
381
382			# If the bottom pressure is specified
383			if bottom:
384			# in pressure units, assign it
385			if bottom.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
386			bottom_pressure = bottom
387			if heights is not None:
388			bottom_height = log_interp(bottom_pressure, p, heights)
389			else:
390			bottom_height = pressure_to_height_std(bottom_pressure)
391			# in height units
392			if bottom.dimensionality == {'[length]': 1.0}:
393			bottom_height = bottom
394			if heights is not None:
395			bottom_pressure = np.interp(np.array([bottom_height.m]), heights,
396			p)[0] * p.units
397			else:
398			bottom_pressure = height_to_pressure_std(bottom_height)
399
400			# Top of layer calculations
401
402			# Depth is in pressure
403			if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
404			top_pressure = bottom_pressure - depth
405			if heights is not None:
406			top_height = log_interp(top_pressure, p, heights)
407			else:
408			top_height = pressure_to_height_std(top_pressure)
409
410			# Depth is in height
411			if depth.dimensionality == {'[length]': 1.0}:
412			top_height = bottom_height + depth
413			if heights is not None:
414			top_pressure = np.interp(np.array([top_height.m]), heights, p)[0] * p.units
415			else:
416			top_pressure = height_to_pressure_std(top_height)
417
418			# Handle top or bottom values that are invalid
419			if bottom_pressure > p[0]:
420			raise ValueError(
421			'Bottom of layer pressure is greater than the largest pressure in data.')
422			if top_pressure < p[-1]:
423			raise ValueError('Top of layer pressure is less than the lowest pressure in data.')
424			if bottom_pressure < top_pressure:
425			raise ValueError(
426			'Pressure at the top of the layer is greater than that at the bottom.')
427
428			ret = [] # returned data variables in layer
429
430			# Ensure pressures are sorted in ascending order
431			sort_inds = np.argsort(p)
432			p = p[sort_inds]
433
434			# Mask based on top and bottom pressure
435			inds = (p <= bottom_pressure) & (p >= top_pressure)
436			p_interp = p[inds]
437
438			# Interpolate pressures at bounds if necessary and sort
439			if interpolate:
440			# If we don't have the bottom or top requested, append them
441			if top_pressure not in p_interp:
442			p_interp = np.sort(np.append(p_interp, top_pressure)) * p.units
443			if bottom_pressure not in p_interp:
444			p_interp = np.sort(np.append(p_interp, bottom_pressure)) * p.units
445
446			for datavar in args:
447			# Ensure that things are sorted in ascending order
448			datavar = datavar[sort_inds]
449
450			if interpolate:
451			# Interpolate for the possibly missing bottom/top values
452			datavar_interp = log_interp(p_interp, p, datavar)
453			datavar = datavar_interp
454			else:
455			datavar = datavar[inds]
456
457			ret.append(datavar[::-1])
458
459			# If heights are given, return the height range as well as pressure range
460			if heights is not None:
461			heights_clipped = heights[((heights >= bottom_height) & (heights <= top_height))]
462
463			if bottom_height not in heights:
464			heights_clipped = np.sort(np.append(heights_clipped,
465			bottom_height)) * heights.units
466			if top_height not in heights:
467			heights_clipped = np.sort(np.append(heights_clipped, top_height)) * heights.units
468
469			ret.insert(0, heights_clipped)
470
471			ret.insert(0, p_interp[::-1])
472
473			return ret
474

Unidata / MetPy

Pull Request — master (#444)

get_layer() F

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