Python Matplotlib自定义投影：如何变换点

我正在使用Matplotlib的自定义投影，不知道如何在投影中进行向量变换（注意：自定义投影是具有赤道方向的Lambert方位角等面积投影）

在我的示例中，我想将向北倾斜30°的点（表示该点位于赤道的60°N）转换为向东倾斜30°的点（表示位于本初子午线以东60°）。我想用一个向量变换矩阵来做这个，以便将来用这个程序做更复杂的计算。但我真的不明白如何正确地获得变换向量的长度（或者获得该点的正确经度和纬度）

我也在研究这个例子，但它使用了一种稍微不同的转换方法：

测试文件：

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from numpy import pi, sin, cos, sqrt, tan, arctan2, arccos

#Internal imports
import projection

def transformVector(geom, raxis, rot):
    """
    Input:
    geom: single point geometry (vector)
    raxis: rotation axis as a vector (vector)
    ([0][1][2]) = (x,y,z) = (Longitude, Latitude, Down)
    rot: rotation in radian

    Returns:
    Array: a vector that has been transformed
    """
    sr = sin(rot)
    cr = cos(rot)
    omcr = 1.0 - cr
    tf = np.array([
        [cr + raxis[0]**2 * omcr,
        -raxis[2] * sr + raxis[0] * raxis[1] * omcr,
        raxis[1] * sr + raxis[0] * raxis[2] * omcr],
        [raxis[2] * sr + raxis[1] * raxis[0] * omcr,
        cr + raxis[1]**2 * omcr,
        -raxis[0] * sr + raxis[1] * raxis[2] * omcr],
        [-raxis[1] * sr + raxis[2] * raxis[0] * omcr,
        raxis[0] * sr + raxis[2] * raxis[1] * omcr,
        cr + raxis[2]**2 * omcr]])

    ar = np.dot(geom, tf)
    return ar

def sphericalToVector(inp_ar):
    """
    Convert a spherical measurement into a vector in cartesian space
    [0] = x (+) east (-) west
    [1] = y (+) north (-) south
    [2] = z (+) down
    """
    ar = np.array([0.0, 0.0, 0.0])
    ar[0] = sin(inp_ar[0]) * cos(inp_ar[1])
    ar[1] = cos(inp_ar[0]) * cos(inp_ar[1])
    ar[2] = sin(inp_ar[1])
    return ar

def vectorToGeogr(vect):
    """
    Returns:
    Array with the components [0] longitude, [1] latitude
    """
    ar = np.array([0.0, 0.0])
    ar[0] = np.arctan2(vect[0], vect[2])
    ar[1] = np.arctan2(vect[1], vect[2])
    ar = ar * pi/2
    return ar

def plotPoint(dip):
    """
    Testfunction for converting, transforming and plotting a point
    """
    plt.subplot(111, projection="lmbrt_equ_area_equ_aspect")

    #Convert to radians
    dip_rad = np.radians(dip)

    #Set rotation to azimuth and convert dip to latitude on north-south axis
    rot = dip_rad[0]
    dip_lat = pi/2 - dip_rad[1]
    plt.plot(0, dip_lat, "ro")
    print(dip_lat, rot)

    #Convert the dip into a vector along the north-south axis
    #x = 0, y = dip
    vect = sphericalToVector([0, dip_lat])
    print(vect, np.linalg.norm(vect))

    #Transfrom the dip to its proper azimuth
    tvect = transformVector(vect, [0,0,1], rot)
    print(tvect, np.linalg.norm(tvect))

    #Transform the vector back to geographic coordinates
    geo = vectorToGeogr(tvect)
    print(geo)
    plt.plot(geo[0], geo[1], "bo")

    plt.grid(True)
    plt.show()

datapoint = np.array([090.0,30])
plotPoint(datapoint)

自定义投影：

import matplotlib
from matplotlib.axes import Axes
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import NullLocator, Formatter, FixedLocator
from matplotlib.transforms import Affine2D, BboxTransformTo, Transform
from matplotlib.projections import register_projection
import matplotlib.spines as mspines
import matplotlib.axis as maxis
import matplotlib.pyplot as plt
import numpy as np
from numpy import pi, sin, cos, sqrt, arctan2
# This example projection class is rather long, but it is designed to
# illustrate many features, not all of which will be used every time.
# It is also common to factor out a lot of these methods into common
# code used by a number of projections with similar characteristics
# (see geo.py).

class LambertAxes(Axes):
    """
    A custom class for the Lambert azimuthal equal-area projection
    with equatorial aspect. In geosciences this is also referre to
    as a "Schmidt plot". For more information see:
    http://pubs.er.usgs.gov/publication/pp1395
    """
    # The projection must specify a name.  This will be used be the
    # user to select the projection, i.e. ``subplot(111,
    # projection='lmbrt_equ_area_equ_aspect')``.
    name = 'lmbrt_equ_area_equ_aspect'

    def __init__(self, *args, **kwargs):
        Axes.__init__(self, *args, **kwargs)
        self.set_aspect(1, adjustable='box', anchor='C')
        self.cla()

    def _init_axis(self):
        self.xaxis = maxis.XAxis(self)
        self.yaxis = maxis.YAxis(self)
        # Do not register xaxis or yaxis with spines -- as done in
        # Axes._init_axis() -- until LambertAxes.xaxis.cla() works.
        # self.spines['hammer'].register_axis(self.yaxis)
        self._update_transScale()

    def cla(self):
        """
        Override to set up some reasonable defaults.
        """
        # Don't forget to call the base class
        Axes.cla(self)

        # Set up a default grid spacing
        self.set_longitude_grid(10)
        self.set_latitude_grid(10)
        self.set_longitude_grid_ends(80)

        # Turn off minor ticking altogether
        self.xaxis.set_minor_locator(NullLocator())
        self.yaxis.set_minor_locator(NullLocator())

        # Do not display ticks -- we only want gridlines and text
        self.xaxis.set_ticks_position('none')
        self.yaxis.set_ticks_position('none')

        # The limits on this projection are fixed -- they are not to
        # be changed by the user.  This makes the math in the
        # transformation itself easier, and since this is a toy
        # example, the easier, the better.
        Axes.set_xlim(self, -pi/2, pi/2)
        Axes.set_ylim(self, -pi, pi)

    def _set_lim_and_transforms(self):
        """
        This is called once when the plot is created to set up all the
        transforms for the data, text and grids.
        """
        # There are three important coordinate spaces going on here:
        #
        #    1. Data space: The space of the data itself
        #
        #    2. Axes space: The unit rectangle (0, 0) to (1, 1)
        #       covering the entire plot area.
        #
        #    3. Display space: The coordinates of the resulting image,
        #       often in pixels or dpi/inch.

        # This function makes heavy use of the Transform classes in
        # ``lib/matplotlib/transforms.py.`` For more information, see
        # the inline documentation there.

        # The goal of the first two transformations is to get from the
        # data space (in this case longitude and latitude) to axes
        # space.  It is separated into a non-affine and affine part so
        # that the non-affine part does not have to be recomputed when
        # a simple affine change to the figure has been made (such as
        # resizing the window or changing the dpi).

        # 1) The core transformation from data space into
        # rectilinear space defined in the LambertEqualAreaTransform class.
        self.transProjection = self.LambertEqualAreaTransform()

        # 2) The above has an output range that is not in the unit
        # rectangle, so scale and translate it so it fits correctly
        # within the axes.  The peculiar calculations of xscale and
        # yscale are specific to a Aitoff-Hammer projection, so don't
        # worry about them too much.
        xscale = sqrt(2.0) * sin(0.5 * pi)
        yscale = sqrt(2.0) * sin(0.5 * pi)
        self.transAffine = Affine2D() \
            .scale(0.5 / xscale, 0.5 / yscale) \
            .translate(0.5, 0.5)

        # 3) This is the transformation from axes space to display
        # space.
        self.transAxes = BboxTransformTo(self.bbox)

        # Now put these 3 transforms together -- from data all the way
        # to display coordinates.  Using the '+' operator, these
        # transforms will be applied "in order".  The transforms are
        # automatically simplified, if possible, by the underlying
        # transformation framework.
        self.transData = \
            self.transProjection + \
            self.transAffine + \
            self.transAxes

        # The main data transformation is set up.  Now deal with
        # gridlines and tick labels.

        # Longitude gridlines and ticklabels.  The input to these
        # transforms are in display space in x and axes space in y.
        # Therefore, the input values will be in range (-xmin, 0),
        # (xmax, 1).  The goal of these transforms is to go from that
        # space to display space.  The tick labels will be offset 4
        # pixels from the equator.
        self._xaxis_pretransform = \
            Affine2D() \
            .scale(1.0, pi) \
            .translate(0.0, -pi)
        self._xaxis_transform = \
            self._xaxis_pretransform + \
            self.transData
        self._xaxis_text1_transform = \
            Affine2D().scale(1.0, 0.0) + \
            self.transData + \
            Affine2D().translate(0.0, 4.0)
        self._xaxis_text2_transform = \
            Affine2D().scale(1.0, 0.0) + \
            self.transData + \
            Affine2D().translate(0.0, -4.0)

        # Now set up the transforms for the latitude ticks.  The input to
        # these transforms are in axes space in x and display space in
        # y.  Therefore, the input values will be in range (0, -ymin),
        # (1, ymax).  The goal of these transforms is to go from that
        # space to display space.  The tick labels will be offset 4
        # pixels from the edge of the axes ellipse.
        yaxis_stretch = Affine2D().scale(pi * 2.0, 1.0).translate(-pi, 0.0)
        yaxis_space = Affine2D().scale(1.0, 1.0)
        self._yaxis_transform = \
            yaxis_stretch + \
            self.transData
        yaxis_text_base = \
            yaxis_stretch + \
            self.transProjection + \
            (yaxis_space + \
             self.transAffine + \
             self.transAxes)
        self._yaxis_text1_transform = \
            yaxis_text_base + \
            Affine2D().translate(-8.0, 0.0)
        self._yaxis_text2_transform = \
            yaxis_text_base + \
            Affine2D().translate(8.0, 0.0)

    def get_xaxis_transform(self,which='grid'):
        """
        Override this method to provide a transformation for the
        x-axis grid and ticks.
        """
        assert which in ['tick1','tick2','grid']
        return self._xaxis_transform

    def get_xaxis_text1_transform(self, pixelPad):
        """
        Override this method to provide a transformation for the
        x-axis tick labels.

        Returns a tuple of the form (transform, valign, halign)
        """
        return self._xaxis_text1_transform, 'bottom', 'center'

    def get_xaxis_text2_transform(self, pixelPad):
        """
        Override this method to provide a transformation for the
        secondary x-axis tick labels.

        Returns a tuple of the form (transform, valign, halign)
        """
        return self._xaxis_text2_transform, 'top', 'center'

    def get_yaxis_transform(self,which='grid'):
        """
        Override this method to provide a transformation for the
        y-axis grid and ticks.
        """
        assert which in ['tick1','tick2','grid']
        return self._yaxis_transform

    def get_yaxis_text1_transform(self, pixelPad):
        """
        Override this method to provide a transformation for the
        y-axis tick labels.

        Returns a tuple of the form (transform, valign, halign)
        """
        return self._yaxis_text1_transform, 'center', 'right'

    def get_yaxis_text2_transform(self, pixelPad):
        """
        Override this method to provide a transformation for the
        secondary y-axis tick labels.

        Returns a tuple of the form (transform, valign, halign)
        """
        return self._yaxis_text2_transform, 'center', 'left'

    def _gen_axes_patch(self):
        """
        Override this method to define the shape that is used for the
        background of the plot.  It should be a subclass of Patch.

        In this case, it is a Circle (that may be warped by the axes
        transform into an ellipse).  Any data and gridlines will be
        clipped to this shape.
        """
        return Circle((0.5, 0.5), 0.5)

    def _gen_axes_spines(self):
        return {'lmbrt_equ_area_equ_aspect':mspines.Spine.circular_spine(self,
                                                      (0.5, 0.5), 0.5)}

    # Prevent the user from applying scales to one or both of the
    # axes.  In this particular case, scaling the axes wouldn't make
    # sense, so we don't allow it.
    def set_xscale(self, *args, **kwargs):
        if args[0] != 'linear':
            raise NotImplementedError
        Axes.set_xscale(self, *args, **kwargs)

    def set_yscale(self, *args, **kwargs):
        if args[0] != 'linear':
            raise NotImplementedError
        Axes.set_yscale(self, *args, **kwargs)

    # Prevent the user from changing the axes limits.  In our case, we
    # want to display the whole sphere all the time, so we override
    # set_xlim and set_ylim to ignore any input.  This also applies to
    # interactive panning and zooming in the GUI interfaces.
    def set_xlim(self, *args, **kwargs):
        Axes.set_xlim(self, -pi, pi)
        Axes.set_ylim(self, -pi, pi)
    set_ylim = set_xlim

    def format_coord(self, lon, lat):
        """
        Override this method to change how the values are displayed in
        the status bar.

        In this case, we want them to be displayed in degrees N/S/E/W.
        """
        lon = np.degrees(lon)
        lat = np.degrees(lat)

        #if lat >= 0.0:
        #    ns = 'N'
        #else:
        #    ns = 'S'
        #if lon >= 0.0:
        #    ew = 'E'
        #else:
        #    ew = 'W'
        return "{0} / {1}".format(round(lon,1), round(lat,1))

    class DegreeFormatter(Formatter):
        """
        This is a custom formatter that converts the native unit of
        radians into (truncated) degrees and adds a degree symbol.
        """
        def __init__(self, round_to=1.0):
            self._round_to = round_to

        def __call__(self, x, pos=None):
            degrees = (x / pi) * 180.0
            degrees = round(degrees / self._round_to) * self._round_to
            return "%d\u00b0" % degrees

    def set_longitude_grid(self, degrees):
        """
        Set the number of degrees between each longitude grid.

        This is an example method that is specific to this projection
        class -- it provides a more convenient interface to set the
        ticking than set_xticks would.
        """
        # Set up a FixedLocator at each of the points, evenly spaced
        # by degrees.
        number = (360.0 / degrees) + 1
        self.xaxis.set_major_locator(
            plt.FixedLocator(
                np.linspace(-pi, pi, number, True)[1:-1]))
        # Set the formatter to display the tick labels in degrees,
        # rather than radians.
        self.xaxis.set_major_formatter(self.DegreeFormatter(degrees))

    def set_latitude_grid(self, degrees):
        """
        Set the number of degrees between each longitude grid.

        This is an example method that is specific to this projection
        class -- it provides a more convenient interface than
        set_yticks would.
        """
        # Set up a FixedLocator at each of the points, evenly spaced
        # by degrees.
        number = (180.0 / degrees) + 1
        self.yaxis.set_major_locator(
            FixedLocator(
                np.linspace(-pi / 2.0, pi / 2.0, number, True)[1:-1]))
        # Set the formatter to display the tick labels in degrees,
        # rather than radians.
        self.yaxis.set_major_formatter(self.DegreeFormatter(degrees))

    def set_longitude_grid_ends(self, degrees):
        """
        Set the latitude(s) at which to stop drawing the longitude grids.

        Often, in geographic projections, you wouldn't want to draw
        longitude gridlines near the poles.  This allows the user to
        specify the degree at which to stop drawing longitude grids.

        This is an example method that is specific to this projection
        class -- it provides an interface to something that has no
        analogy in the base Axes class.
        """
        longitude_cap = degrees * (pi / 180.0)
        # Change the xaxis gridlines transform so that it draws from
        # -degrees to degrees, rather than -pi to pi.
        self._xaxis_pretransform \
            .clear() \
            .scale(1.0, longitude_cap * 2.0) \
            .translate(0.0, -longitude_cap)

    def get_data_ratio(self):
        """
        Return the aspect ratio of the data itself.

        This method should be overridden by any Axes that have a
        fixed data ratio.
        """
        return 1.0

    # Interactive panning and zooming is not supported with this projection,
    # so we override all of the following methods to disable it.
    def can_zoom(self):
        """
        Return True if this axes support the zoom box
        """
        return False
    def start_pan(self, x, y, button):
        pass
    def end_pan(self):
        pass
    def drag_pan(self, button, key, x, y):
        pass

    class LambertEqualAreaTransform(Transform):
        """
        The basic transformation class.
        """
        input_dims = 2
        output_dims = 2
        is_separable = False

        def transform_non_affine(self, ll):
            """
            Override the transform_non_affine method to implement the custom
            transform.

            The input and output are Nx2 numpy arrays.
            """
            xi = ll[:, 0:1]
            yi  = ll[:, 1:2]

            k = 1 + np.absolute(cos(yi) * cos(xi))
            k = 2 / k

            if np.isposinf(k[0]) == True:
                k[0] = 1e+15

            if np.isneginf(k[0]) == True:
                k[0] = -1e+15

            if k[0] == 0:
                k[0] = 1e-15

            k = sqrt(k)

            x = k * cos(yi) * sin(xi)
            y = k * sin(yi)

            return np.concatenate((x, y), 1)

        # This is where things get interesting.  With this projection,
        # straight lines in data space become curves in display space.
        # This is done by interpolating new values between the input
        # values of the data.  Since ``transform`` must not return a
        # differently-sized array, any transform that requires
        # changing the length of the data array must happen within
        # ``transform_path``.
        def transform_path_non_affine(self, path):
            ipath = path.interpolated(path._interpolation_steps)
            return Path(self.transform(ipath.vertices), ipath.codes)
        transform_path_non_affine.__doc__ = \
                Transform.transform_path_non_affine.__doc__

        if matplotlib.__version__ < '1.2':
            # Note: For compatibility with matplotlib v1.1 and older, you'll
            # need to explicitly implement a ``transform`` method as well.
            # Otherwise a ``NotImplementedError`` will be raised. This isn't
            # necessary for v1.2 and newer, however.
            transform = transform_non_affine

            # Similarly, we need to explicitly override ``transform_path`` if
            # compatibility with older matplotlib versions is needed. With v1.2
            # and newer, only overriding the ``transform_path_non_affine``
            # method is sufficient.
            transform_path = transform_path_non_affine
            transform_path.__doc__ = Transform.transform_path.__doc__

        def inverted(self):
            return LambertAxes.InvertedLambertEqualAreaTransform()
        inverted.__doc__ = Transform.inverted.__doc__

    class InvertedLambertEqualAreaTransform(Transform):
        #This is not working yet !!!
        input_dims = 2
        output_dims = 2
        is_separable = False

        def transform_non_affine(self, xy):
            x = xy[:, 0:1]
            y = xy[:, 1:2]

            #quarter_x = 0.25 * x
            #half_y = 0.5 * y
            #z = sqrt(1.0 - quarter_x*quarter_x - half_y*half_y)

            #longitude = 2 * np.arctan((z*x) / (2.0 * (2.0*z*z - 1.0)))

            r = sqrt(2)
            p = sqrt(x**2 * y**2)
            c = 2 * np.arcsin(p / (2 * r))
            phi1 = pi/2
            lbd0 = 0
            #print(x,y)
            if y[0] == 0:
                lat = 0
            else:
                lat = np.arcsin(cos(c) * sin(phi1) + (y * sin(c) * cos(phi1 / p)))
            #if phi == phi1:
            #    lon = lbd0 + np.arctan(x / (-y))
            #elif phi == -phi1:
            #    lon = lbd0 + np.arctan(x / y)
            #else:
            #    lon = lbd0 + np.arctan(x * sin(c) / (p * cos(phi1) * cos(c) - y * sin(phi1) * sin(c)))
            if x[0] == 0:
                lon = 0
            else:
                lon = lbd0 + np.arctan(x * sin(c) / (p * cos(phi1) * cos(c) - y * sin(phi1) * sin(c)))
            return np.concatenate((lon, lat), 1)
        transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

        # As before, we need to implement the "transform" method for
        # compatibility with matplotlib v1.1 and older.
        if matplotlib.__version__ < '1.2':
            transform = transform_non_affine

        def inverted(self):
            # The inverse of the inverse is the original transform... ;)
            return LambertAxes.LambertEqualAreaTransform()
        inverted.__doc__ = Transform.inverted.__doc__

# Now register the projection with matplotlib so the user can select
# it.
register_projection(LambertAxes)

导入matplotlib
从matplotlib.axes导入轴
从matplotlib.patches导入圆
从matplotlib.path导入路径
从matplotlib.ticker导入NullLocator、格式化程序、FixedLocator
从matplotlib.transforms导入仿射2D、BboxTransformTo、Transform
从matplotlib.projections导入寄存器\u projection
将matplotlib.spines作为mspines导入
将matplotlib.axis导入为maxis
将matplotlib.pyplot作为plt导入
将numpy作为np导入
从numpy进口pi、sin、cos、sqrt、arctan2
#这个示例projection类相当长，但其设计目的是
#演示许多功能，并不是每次都会使用所有功能。
#将这些方法中的许多分解成通用的方法也是很常见的
#许多具有类似特征的投影所使用的代码
#（见geo.py）。
类LambertAxes（轴）：
"""
Lambert方位角等面积投影的自定义类
赤道面。在地球科学中，这也是指
作为“施密特图”。有关更多信息，请参阅：
http://pubs.er.usgs.gov/publication/pp1395
"""
#投影必须指定一个名称。这将被用作
#用户选择投影，即``子地块（111，
#投影='lmbrt\u相等面积\u相等方面'）``。
名称='lmbrt_eq_area_eq_aspect'
定义初始化（self，*args，**kwargs）：
轴。u u初始化（self，*args，**kwargs）
自我设置方面（1，可调class='box'，锚点class='C'）
self.cla（）
定义初始轴（自）：
self.xaxis=maxis.xaxis（self）
self.yaxis=maxis.yaxis（self）
#不要将xaxis或yaxis注册为脊椎，如中所述
#Axes.\u init\u axis（）--直到LambertAxes.xaxis.cla（）工作。
#self.spines['hammer'].寄存器_轴（self.yaxis）
self.\u update\u transScale（）
def cla（自我）：
"""
重写以设置一些合理的默认值。
"""
#别忘了调用基类
轴类（自身）
#设置默认栅格间距
自设置经度网格（10）
自设置纬度网格（10）
自设置经度网格端点（80）
#完全关闭小滴答声
self.xaxis.set_minor_定位器（NullLocator（））
self.yaxis.set\u minor\u定位器（NullLocator（））
#不要显示记号——我们只需要网格线和文本
self.xaxis.set_ticks_position（'none'））
self.yaxis.set\u ticks\u position（'none'）
#这个投影的限制是固定的——它们不是固定的
#可由用户更改。这使得数学变得复杂
#转换本身更容易，因为这是一个玩具
#举个例子，越简单越好。
轴设置（自，-pi/2，pi/2）
轴。设置y（自，-pi，pi）
定义集和变换（自）：
"""
当创建绘图以设置所有
数据、文本和网格的变换。
"""
#这里有三个重要的坐标空间：
#
#    1. 数据空间：数据本身的空间
#
#    2. 轴空间：单位矩形（0,0）到（1,1）
#覆盖整个地块。
#
#    3. 显示空间：生成图像的坐标，
#通常以像素或dpi/英寸为单位。
#此函数大量使用中的转换类
#``lib/matplotlib/transforms.py.``有关详细信息，请参阅
#这里有内联文档。
#前两个转换的目标是从
#数据空间（在本例中为经度和纬度）到轴
#空间。它分为非仿射部分和仿射部分，因此
#非仿射部分在以下情况下不必重新计算：
#对图形进行了简单的仿射更改（例如
#调整窗口大小或更改dpi）。
#1）从数据空间到
#LambertEqualAreTransform类中定义的直线空间。
self.transProjection=self.lambertequalaretransform（）
#2）上述装置的输出范围不在装置内
#矩形，所以缩放和平移它，使其正确匹配
#在轴线内。xscale和xscale的特殊计算
#yscale特定于艾托夫锤投影，因此不要
#太担心他们了。
xscale=sqrt（2.0）*sin（0.5*pi）
yscale=sqrt（2.0）*sin（0.5*pi）
self.transafine=Affine2D（）\
.刻度（0.5/x刻度，0.5/y刻度）\
.翻译（0.5,0.5）
#3）这是从轴空间到显示的转换
#空间。
self.transAxes=BboxTransformTo（self.bbox）
#现在，将这3种转换放在一起--从数据一直到
#显示坐标。使用“+”运算符，这些
#变换将“按顺序”应用。这些变换是
#如有可能，由基础
#转换框架。
self.transData=\
x : east-west (east-positive)
y : north-south (north-positive)
z : up-down (down-positive)

x : towards the equator/prime-meridian intersection
y : towards the equator/90 intersection
z : towards the north pole

for lat in range(-90, 100, 10):
    for lon in range(-180, 190, 10):
        point = np.radians([lon, lat])
        round_trip = vectorToGeogr(sphericalToVector(point))
        assert np.allclose(point, round_trip)

def sphericalToVector(inp_ar):
    ar = np.array([0.0, 0.0, 0.0])
    ar[0] = sin(inp_ar[0]) * cos(inp_ar[1])
    ar[1] = cos(inp_ar[0]) * cos(inp_ar[1])
    ar[2] = sin(inp_ar[1])
    return ar

def sphericalToVector(inp_ar):
    ar = np.array([0.0, 0.0, 0.0])
    ar[0] = -sin(inp_ar[1]) 
    ar[1] = sin(inp_ar[0]) * cos(inp_ar[1]) 
    ar[2] = cos(inp_ar[0]) * cos(inp_ar[1])
    return ar

def vectorToGeogr(vect):
    ar = np.array([0.0, 0.0])
    ar[0] = np.arctan2(vect[0], vect[2])
    ar[1] = np.arctan2(vect[1], vect[2])
    ar = ar * pi/2
    return ar

def vectorToGeogr(vect):
    ar = np.array([0.0, 0.0])
    ar[0] = np.arctan2(vect[1], vect[2])
    ar[1] = np.arcsin(-vect[0] / np.linalg.norm(vect))
    return ar