mixed_layer() - Code Metrics - Inspection of "Layer helpers" - Unidata/MetPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#444)

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created 2017-06-02 18:02 UTC

mixed_layer() B

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dl	0
loc	30
rs	8.8571

# 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 ..calc import pressure_at_height_above_pressure
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 is 'linear':
        y[nans] = np.interp(x[nans], x[~nans], y[~nans])
    elif kind is '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
@check_units('[pressure]')
def get_layer(p, datavar, 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
    if len(p) != len(datavar):
        raise ValueError('Pressure and data variable must have the same length.')

    # If no bottom is specified, use the first pressure value
    if not bottom:
        bottom_pressure = p[0]

    # If bottom is specified as a height (AGL), convert to pressure
    if bottom:
        if bottom.dimensionality == {'[length]': 1.0}:
            bottom_pressure = pressure_at_height_above_pressure(p[0], bottom)
        if bottom.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
            bottom_pressure = bottom

    # Calculate the pressure at the top of the layer
    if depth.dimensionality == {'[length]': 1.0}:
        top_pressure = pressure_at_height_above_pressure(bottom_pressure, depth)
    else:
        top_pressure = bottom_pressure - depth

    # 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.')

        # Mask the pressure values we have data for in the layer
    inds = (p <= bottom_pressure) & (p >= top_pressure)
    p_interp = p[inds]

    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)) * units.hPa
        if bottom_pressure not in p_interp:
            p_interp = np.sort(np.append(p_interp, bottom_pressure)) * units.hPa

        # Interpolate for the possibly missing bottom/top pressure values
        sort_args = np.argsort(p)
        datavar_interp = np.interp(p_interp, p[sort_args], datavar[sort_args]) * units.degC
        p = p_interp
        datavar = datavar_interp
    else:
        datavar = datavar[inds]
        p = p_interp

    return p, datavar


@exporter.export
@check_units('[pressure]')
def mixed_layer(p, datavar, bottom=None, depth=100 * units.hPa, interpolate=True):
    r"""Mix a variable over a layer using pressure as a mass-average.

    This function will integrate a data variable with respect to pressure and using the
    mean value theorem determine the average value.

    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`
        The mixed value of the data variable.

    """
    p_layer, datavar_layer = get_layer(p, datavar, bottom, depth, interpolate)
    actual_depth = abs(p_layer[0] - p_layer[-1])
    return (1. / actual_depth.m) * np.trapz(datavar_layer, p_layer) * datavar.units


@exporter.export
@check_units('[pressure]', '[temperature]', '[temperature]')
def mixed_parcel(p, T, Td, parcel_start_pressure=p[0], bottom=None, depth=100 * units.hPa,
                 interpolate=True):
    r"""Calculate the properties of a parcel mixed from a layer.

    Determines the properties of an air parcel that is the result of complete mixing of a
    given atmospheric layer.

    Parameters
    ----------
    p : array-like
        Atmospheric pressure profile
    T : array-like
        Atmospheric temperature profile
    Td : array-like
        Atmospheric dewpoint profile
    parcel_start_pressure : `pint.Quantity`
        Pressure at which the mixed parcel should begin
    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, pint.Quantity`
        The pressure, temperature, and dewpoint of the mixed parcel.

    """
    # Calculate the potential temperature and mixing ratio over the layer
    theta = mpcalc.potential_temperature(p, T)
    mixing_ratio = mpcalc.saturation_mixing_ratio(p, Td)

    # Mix the variables over the layer
    mean_theta = mixed_layer(p, theta, bottom, depth, interpolate)
    mean_mixing_ratio = mixed_layer(p, mixing_ratio, bottom, depth, interpolate)

    # Convert back to temperature
    mean_temperature = mean_theta / mpcalc.potential_temperature(parcel_start_pressure,
                                                                 1 * units.degK)

    # Convert back to dewpoint
    mean_vapor_pressure = mpcalc.vapor_pressure(parcel_start_pressure, mean_mixing_ratio)
    mean_dewpoint = mpcalc.dewpoint(mean_vapor_pressure)

    return parcel_start_pressure, mean_temperature.to(T.units), mean_dewpoint.to(Td.units)

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 ..calc import pressure_at_height_above_pressure
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 is 'linear':
164			y[nans] = np.interp(x[nans], x[~nans], y[~nans])
165			elif kind is '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			@check_units('[pressure]')
292			def get_layer(p, datavar, bottom=None, depth=100 * units.hPa, interpolate=True):
293			r"""Return an atmospheric layer from upper air data with the requested bottom and depth.
294
295			This function will subset an upper air dataset to contain only the specified layer. The
296			bottom of the layer can be specified with a pressure or height above the surface
297			pressure. The bottom defaults to the surface pressure. The depth of the layer can be
298			specified in terms of pressure or height above the bottom of the layer. If the top and
299			bottom of the layer are not in the data, they are interpolated by default.
300
301			Parameters
302			----------
303			p : array-like
304			Atmospheric pressure profile
305			datavar : array-like
306			Atmospheric variable measured at the given pressures
307			bottom : `pint.Quantity`
308			The bottom of the layer as a pressure or height above the surface pressure
309			depth : `pint.Quantity`
310			The thickness of the layer as a pressure or height above the bottom of the layer
311			interpolate : bool
312			Interpolate the top and bottom points if they are not in the given data
313
314			Returns
315			-------
316			`pint.Quantity, pint.Quantity`
317			The pressure and data variable of the layer
318
319			"""
320			# Make sure pressure and datavar are the same length
321			if len(p) != len(datavar):
322			raise ValueError('Pressure and data variable must have the same length.')
323
324			# If no bottom is specified, use the first pressure value
325			if not bottom:
326			bottom_pressure = p[0]
327
328			# If bottom is specified as a height (AGL), convert to pressure
329			if bottom:
330			if bottom.dimensionality == {'[length]': 1.0}:
331			bottom_pressure = pressure_at_height_above_pressure(p[0], bottom)
332			if bottom.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
333			bottom_pressure = bottom
334
335			# Calculate the pressure at the top of the layer
336			if depth.dimensionality == {'[length]': 1.0}:
337			top_pressure = pressure_at_height_above_pressure(bottom_pressure, depth)
338			else:
339			top_pressure = bottom_pressure - depth
340
341			# Handle top or bottom values that are invalid
342			if bottom_pressure > p[0]:
343			raise ValueError(
344			'Bottom of layer pressure is greater than the largest pressure in data.')
345			if top_pressure < p[-1]:
346			raise ValueError('Top of layer pressure is less than the lowest pressure in data.')
347			if bottom_pressure < top_pressure:
348			raise ValueError(
349			'Pressure at the top of the layer is greater than that at the bottom.')
350
351			# Mask the pressure values we have data for in the layer
352			inds = (p <= bottom_pressure) & (p >= top_pressure)
353			p_interp = p[inds]
354
355			if interpolate:
356			# If we don't have the bottom or top requested, append them
357			if top_pressure not in p_interp:
358			p_interp = np.sort(np.append(p_interp, top_pressure)) * units.hPa
359			if bottom_pressure not in p_interp:
360			p_interp = np.sort(np.append(p_interp, bottom_pressure)) * units.hPa
361
362			# Interpolate for the possibly missing bottom/top pressure values
363			sort_args = np.argsort(p)
364			datavar_interp = np.interp(p_interp, p[sort_args], datavar[sort_args]) * units.degC
365			p = p_interp
366			datavar = datavar_interp
367			else:
368			datavar = datavar[inds]
369			p = p_interp
370
371			return p, datavar
372
373
374			@exporter.export
375			@check_units('[pressure]')
376			def mixed_layer(p, datavar, bottom=None, depth=100 * units.hPa, interpolate=True):
377			r"""Mix a variable over a layer using pressure as a mass-average.
378
379			This function will integrate a data variable with respect to pressure and using the
380			mean value theorem determine the average value.
381
382			Parameters
383			----------
384			p : array-like
385			Atmospheric pressure profile
386			datavar : array-like
387			Atmospheric variable measured at the given pressures
388			bottom : `pint.Quantity`
389			The bottom of the layer as a pressure or height above the surface pressure
390			depth : `pint.Quantity`
391			The thickness of the layer as a pressure or height above the bottom of the layer
392			interpolate : bool
393			Interpolate the top and bottom points if they are not in the given data
394
395			Returns
396			-------
397			`pint.Quantity`
398			The mixed value of the data variable.
399
400			"""
401			p_layer, datavar_layer = get_layer(p, datavar, bottom, depth, interpolate)
402			actual_depth = abs(p_layer[0] - p_layer[-1])
403			return (1. / actual_depth.m) * np.trapz(datavar_layer, p_layer) * datavar.units
404
405
406			@exporter.export
407			@check_units('[pressure]', '[temperature]', '[temperature]')
408			def mixed_parcel(p, T, Td, parcel_start_pressure=p[0], bottom=None, depth=100 * units.hPa,
409			interpolate=True):
410			r"""Calculate the properties of a parcel mixed from a layer.
411
412			Determines the properties of an air parcel that is the result of complete mixing of a
413			given atmospheric layer.
414
415			Parameters
416			----------
417			p : array-like
418			Atmospheric pressure profile
419			T : array-like
420			Atmospheric temperature profile
421			Td : array-like
422			Atmospheric dewpoint profile
423			parcel_start_pressure : `pint.Quantity`
424			Pressure at which the mixed parcel should begin
425			bottom : `pint.Quantity`
426			The bottom of the layer as a pressure or height above the surface pressure
427			depth : `pint.Quantity`
428			The thickness of the layer as a pressure or height above the bottom of the layer
429			interpolate : bool
430			Interpolate the top and bottom points if they are not in the given data
431
432			Returns
433			-------
434			`pint.Quantity, pint.Quantity, pint.Quantity`
435			The pressure, temperature, and dewpoint of the mixed parcel.
436
437			"""
438			# Calculate the potential temperature and mixing ratio over the layer
439			theta = mpcalc.potential_temperature(p, T)
440			mixing_ratio = mpcalc.saturation_mixing_ratio(p, Td)
441
442			# Mix the variables over the layer
443			mean_theta = mixed_layer(p, theta, bottom, depth, interpolate)
444			mean_mixing_ratio = mixed_layer(p, mixing_ratio, bottom, depth, interpolate)
445
446			# Convert back to temperature
447			mean_temperature = mean_theta / mpcalc.potential_temperature(parcel_start_pressure,
448			1 * units.degK)
449
450			# Convert back to dewpoint
451			mean_vapor_pressure = mpcalc.vapor_pressure(parcel_start_pressure, mean_mixing_ratio)
452			mean_dewpoint = mpcalc.dewpoint(mean_vapor_pressure)
453
454			return parcel_start_pressure, mean_temperature.to(T.units), mean_dewpoint.to(Td.units)

Unidata / MetPy

Pull Request — master (#444)

mixed_layer() B

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