自定义Matplotlib投影:Schmidt投影 [英] Custom Matplotlib projection: Schmidt projection

问题描述

我正在尝试修改这个自定义投影示例:

• ).

  我实际上正在寻找的投影是具有 赤道纵横比 的投影.

  变换需要的两个公式是(后面的代码不需要radius):

    y = R * k'* sin(phi)x = R * k' * cos(phi) sin(lambda - lambda0)

  k为:

    k = sqrt(2/(1 + cos(phi)cos(lambda-lambda0))

  我遇到了一些错误，结果是无穷大的值并被零除，因此我添加了一些检查.仍然获得了一些奇怪的标签位置，但是在这个问题中这可能变得不合时宜了.我现在运行的非常粗糙的代码是:

    def transform_non_affine(self，ll):xi = ll[:, 0:1]yi = ll [:, 1:2]k = 1 + np.absolute(cos(yi) * cos(xi))k = 2/k如果 np.isposinf(k[0]) == True:k [0] = 1e + 15如果np.isneginf(k [0])==真:k[0] = -1e+15如果 k[0] == 0:k[0] = 1e-15k =平方根(k)x = k * cos(yi)* sin(xi)y = k * sin(yi)返回np.concatenate((x，y)，1)

  I am trying to modify this custom-projection example:

  • http://matplotlib.org/examples/api/custom_projection_example.html

  to display a Schmidt plot. The mathematics behind the projection are explained e.g. here:

  • https://bearspace.baylor.edu/Vince_Cronin/www/StructGeol/StructLabBk3.html

  I made some modifications of the example which brought me closer to the solution but I am still doing something wrong. Anything I change within the function transform_non_affine makes the plot look worse. It would be great if somebody could explain to me how this function can be modified.

  I also looked at the example at

  • https://github.com/joferkington/mplstereonet/blob/master/mplstereonet/stereonet_transforms.py

  but couldn't really figure out how to translate that into the example.

      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.
          """
          longitude = ll[:, 0:1]
          latitude  = ll[:, 1:2]
  
          # Pre-compute some values
          half_long = longitude / 2.0
          cos_latitude = np.cos(latitude)
          sqrt2 = np.sqrt(2.0)
  
          alpha = 1.0 + cos_latitude * np.cos(half_long)
          x = (2.0 * sqrt2) * (cos_latitude * np.sin(half_long)) / alpha
          y = (sqrt2 * np.sin(latitude)) / alpha
  
          return np.concatenate((x, y), 1)
  

  The whole code can be run and shows the result:

  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, LambertAxes
  import matplotlib.spines as mspines
  import matplotlib.axis as maxis
  import matplotlib.pyplot as plt
  import numpy as np
  
  class SchmidtProjection(Axes):
      '''Class defines the new projection'''
      name = 'SchmidtProjection'
  
      def __init__(self, *args, **kwargs):
          '''Call self, set aspect ratio and call default values'''
          Axes.__init__(self, *args, **kwargs)
          self.set_aspect(1.0, adjustable='box', anchor='C')
          self.cla()
  
      def _init_axis(self):
          '''Initialize axis'''
          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 HammerAxes.xaxis.cla() works.
          # self.spines['hammer'].register_axis(self.yaxis)
          self._update_transScale()
  
      def cla(self):
          '''Calls Axes.cla and overrides some functions to set new defaults'''
          Axes.cla(self)
  
          self.set_longitude_grid(10)
          self.set_latitude_grid(10)
          self.set_longitude_grid_ends(80)
  
          self.xaxis.set_minor_locator(NullLocator())
          self.yaxis.set_minor_locator(NullLocator())
          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, -np.pi, np.pi)
          Axes.set_ylim(self, -np.pi, np.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 SchmidtTransform class.
          self.transProjection = self.SchmidtTransform()
  
          #Plot should extend 180° = pi/2 NS and EW
          xscale = np.pi/2
          yscale = np.pi/2
  
          #The radius of the circle (0.5) is divided by the scale.
          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, np.pi) \
              .translate(0.0, -np.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(np.pi * 2.0, 1.0).translate(-np.pi, 0.0)
          yaxis_space = Affine2D().scale(1.0, 1.1)
          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 set_rotation(self, rotation):
          """Set the rotation of the stereonet in degrees clockwise from North."""
          self._rotation = np.radians(90)
          self._polar.set_theta_offset(self._rotation + np.pi / 2.0)
          self.transData.invalidate()
          self.transAxes.invalidate()
          self._set_lim_and_transforms()
  
      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 {'SchmidtProjection':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, -np.pi, np.pi)
          Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0)
      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 = lon * (180.0 / np.pi)
          lat = lat * (180.0 / np.pi)
          if lat >= 0.0:
              ns = 'N'
          else:
              ns = 'S'
          if lon >= 0.0:
              ew = 'E'
          else:
              ew = 'W'
          #return '%f°%s, %f°%s' % (abs(lat), ns, abs(lon), ew)
          coord_string = ("{0} / {1}".format(round(lon, 2), round(lat,2)))
          return coord_string
  
      class LatitudeFormatter(Formatter):
          """
          Custom formatter for Latitudes
          """
          def __init__(self, round_to=1.0):
              self._round_to = round_to
  
          def __call__(self, x, pos=None):
              degrees = np.degrees(x)
              degrees = round(degrees / self._round_to) * self._round_to
              return "%d°" % degrees
  
      class LongitudeFormatter(Formatter):
          """
          Custom formatter for Longitudes
          """
          def __init__(self, round_to=1.0):
              self._round_to = round_to
  
          def __call__(self, x, pos=None):
              degrees = np.degrees(x)
              degrees = round(degrees / self._round_to) * self._round_to
              return ""
  
      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(-np.pi, np.pi, number, True)[1:-1]))
          # Set the formatter to display the tick labels in degrees,
          # rather than radians.
          self.xaxis.set_major_formatter(self.LongitudeFormatter(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(-np.pi / 2.0, np.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.LatitudeFormatter(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 = np.radians(degrees)
          # 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
  
      # Now, the transforms themselves.
      class SchmidtTransform(Transform):
          """
          The base Hammer transform.
          """
          input_dims = 2
          output_dims = 2
          is_separable = False
  
          def __init__(self):
              """
              Create a new transform.  Resolution is the number of steps to
              interpolate between each input line segment to approximate its path in
              projected space.
              """
              Transform.__init__(self)
              self._resolution = 10
              self._center_longitude = 0
              self._center_latitude = 0
  
          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.
              """
              longitude = ll[:, 0:1]
              latitude  = ll[:, 1:2]
  
              # Pre-compute some values
              half_long = longitude / 2.0
              cos_latitude = np.cos(latitude)
              sqrt2 = np.sqrt(2.0)
  
              alpha = 1.0 + cos_latitude * np.cos(half_long)
              x = (2.0 * sqrt2) * (cos_latitude * np.sin(half_long)) / alpha
              y = (sqrt2 * np.sin(latitude)) / alpha
  
              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 SchmidtProjection.InvertedSchmidtTransform()
          inverted.__doc__ = Transform.inverted.__doc__
  
      class InvertedSchmidtTransform(Transform):
          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 = np.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)))
              latitude = np.arcsin(y*z)
  
              return np.concatenate((longitude, latitude), 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):
              return SchmidtProjection.SchmidtTransform()
          inverted.__doc__ = Transform.inverted.__doc__
  
  # Now register the projection with matplotlib so the user can select
  # it.
  register_projection(SchmidtProjection)
  
  if __name__ == '__main__':
      plt.subplot(111, projection="SchmidtProjection")
      plt.grid(True)
      plt.show()
  

  Edit 1

  This is the closest I get to the wanted solution:

  With this code:

  class SchmidtTransform(Transform):
  input_dims = 2
  output_dims = 2
  is_separable = False
  
  def __init__(self):
      Transform.__init__(self)
      self._resolution = 100
      self._center_longitude = 0
      self._center_latitude = 0
  
  def transform_non_affine(self, ll):
      longitude = ll[:, 0:1]
      latitude  = ll[:, 1:2]
  
      clong = self._center_longitude
      clat = self._center_latitude
  
      cos_lat = np.cos(latitude)
      sin_lat = np.sin(latitude)
  
      diff_long = longitude - clong
      cos_diff_long = np.cos(diff_long)
      inner_k = (1.0 + np.sin(clat)*sin_lat + np.cos(clat)*cos_lat*cos_diff_long)
  
      # Prevent divide-by-zero problems
      inner_k = np.where(inner_k == 0.0, 1e-15, inner_k)
  
      k = np.sqrt(2.0 / inner_k)
  
      x = k*cos_lat*np.sin(diff_long)
      y = k*(np.cos(clat)*sin_lat - np.sin(clat)*cos_lat*cos_diff_long)
  
      return np.concatenate((x, y), 1)
  

  Is there maybe a way to just do this with a regular transformation matrix? I can get the math to work with a transformation matrix, but I don't really understand at which place of the projection code I have to change what.

  解决方案

  I could figure out the next step by reading the chapter about Lambert azimuthal equal-area projections in Map projections: A Working Manual - John Parr Snyder 1987 - Page 182 and following (http://pubs.er.usgs.gov/publication/pp1395).

  The projection I was actually looking for was the one with Equatorial aspect.

  The two formulas that are required for the transformation are (radius is not required for the later code):

  y = R * k' * sin(phi)
  x = R * k' * cos(phi) sin(lambda - lambda0)
  

  With k being:

  k = sqrt( 2 / (1 + cos(phi) cos(lambda - lambda0))
  

  I got some errors, which turned out to be infinite values and divisions by zero, so I added some checks. Still getting some weird label placements, but that might be going off-topic in this question. The very rough code I have running now is:

      def transform_non_affine(self, ll):
          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)
  

  这篇关于自定义Matplotlib投影:Schmidt投影的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！