Source code for gr.pygr.mlab

# coding: utf-8
"""
This module offers a simple, matlab-style API built on top of the gr package.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals


import functools
import sys
import warnings
import numpy as np
import gr
import gr3


try:
    basestring
except NameError:
    basestring = str


def _close_gks_on_error(func):
    """
    Wrap an API function to make sure GKS is closed on error.

    Not closing GKS after an error occurred during plotting could lead to
    an unexpected GKS state, e.g. printing of more than one output page.

    :param func: the mlab API function to wrap
    :return: the wrapped API function
    """
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception:
            gr.emergencyclosegks()
            raise
    return wrapper


[docs]@_close_gks_on_error def plot(*args, **kwargs): """ Draw one or more line plots. This function can receive one or more of the following: - x values and y values, or - x values and a callable to determine y values, or - y values only, with their indices as x values :param args: the data to plot **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = 2*x+4 >>> # Plot x and y >>> mlab.plot(x, y) >>> # Plot x and a callable >>> mlab.plot(x, lambda x: x**3 + x**2 + x) >>> # Plot y, using its indices for the x values >>> mlab.plot(y) """ global _plt _plt.kwargs.update(kwargs) if _plt.kwargs['ax']: _plt.args += _plot_args(args) else: _plt.args = _plot_args(args) _plot_data(kind='line')
[docs]@_close_gks_on_error def oplot(*args, **kwargs): """ Draw one or more line plots over another plot. This function can receive one or more of the following: - x values and y values, or - x values and a callable to determine y values, or - y values only, with their indices as x values :param args: the data to plot **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = 2*x+4 >>> # Draw the first plot >>> mlab.plot(x, y) >>> # Plot graph over it >>> mlab.oplot(x, lambda x: x**3 + x**2 + x) """ global _plt _plt.kwargs.update(kwargs) _plt.args += _plot_args(args) _plot_data(kind='line')
[docs]@_close_gks_on_error def step(*args, **kwargs): """ Draw one or more step or staircase plots. This function can receive one or more of the following: - x values and y values, or - x values and a callable to determine y values, or - y values only, with their indices as x values :param args: the data to plot :param where: pre, mid or post, to decide where the step between two y values should be placed **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = 2*x+4 >>> # Plot x and y >>> mlab.step(x, y) >>> # Plot x and a callable >>> mlab.step(x, lambda x: x**3 + x**2 + x) >>> # Plot y, using its indices for the x values >>> mlab.step(y) >>> # Use next y step directly after x each position >>> mlab.step(y, where='pre') >>> # Use next y step between two x positions >>> mlab.step(y, where='mid') >>> # Use next y step immediately before next x position >>> mlab.step(y, where='post') """ global _plt if 'where' in kwargs: _plt.kwargs['step_where'] = kwargs.pop('where') _plt.kwargs.update(kwargs) if _plt.kwargs['ax']: _plt.args += _plot_args(args) else: _plt.args = _plot_args(args) _plot_data(kind='step')
[docs]@_close_gks_on_error def scatter(*args, **kwargs): """ Draw one or more scatter plots. This function can receive one or more of the following: - x values and y values, or - x values and a callable to determine y values, or - y values only, with their indices as x values Additional to x and y values, you can provide values for the markers' size and color. Size values will determine the marker size in percent of the regular size, and color values will be used in combination with the current colormap. :param args: the data to plot **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = 0.2*x+0.4 >>> # Plot x and y >>> mlab.scatter(x, y) >>> # Plot x and a callable >>> mlab.scatter(x, lambda x: 0.2*x + 0.4) >>> # Plot y, using its indices for the x values >>> mlab.scatter(y) >>> # Plot a diagonal with increasing size and color >>> x = np.linspace(0, 1, 11) >>> y = np.linspace(0, 1, 11) >>> s = np.linspace(50, 400, 11) >>> c = np.linspace(0, 255, 11) >>> mlab.scatter(x, y, s, c) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyac') _plot_data(kind='scatter')
[docs]@_close_gks_on_error def quiver(x, y, u, v, **kwargs): """ Draw a quiver plot. This function draws arrows to visualize a vector for each point of a grid. :param x: the X coordinates of the grid :param y: the Y coordinates of the grid :param u: the U component for each point on the grid :param v: the V component for each point on the grid **Usage examples:** >>> # Create example data >>> x = np.linspace(-1, 1, 30) >>> y = np.linspace(-1, 1, 20) >>> u = np.repeat(x[np.newaxis, :], len(y), axis=0) >>> v = np.repeat(y[:, np.newaxis], len(x), axis=1) >>> # Draw arrows on grid >>> mlab.quiver(x, y, u, v) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args((x, y, u, v), fmt='xyuv') _plot_data(kind='quiver')
[docs]@_close_gks_on_error def polar(*args, **kwargs): """ Draw one or more polar plots. This function can receive one or more of the following: - angle values and radius values, or - angle values and a callable to determine radius values :param args: the data to plot **Usage examples:** >>> # Create example data >>> angles = np.linspace(0, 2*math.pi, 40) >>> radii = np.linspace(0, 2, 40) >>> # Plot angles and radii >>> mlab.polar(angles, radii) >>> # Plot angles and a callable >>> mlab.polar(angles, lambda radius: math.cos(radius)**2) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args) _plot_data(kind='polar')
[docs]@_close_gks_on_error def trisurf(*args, **kwargs): """ Draw a triangular surface plot. This function uses the current colormap to display a series of points as a triangular surface plot. It will use a Delaunay triangulation to interpolate the z values between x and y values. If the series of points is concave, this can lead to interpolation artifacts on the edges of the plot, as the interpolation may occur in very acute triangles. :param x: the x coordinates to plot :param y: the y coordinates to plot :param z: the z coordinates to plot **Usage examples:** >>> # Create example point data >>> x = np.random.uniform(-4, 4, 100) >>> y = np.random.uniform(-4, 4, 100) >>> z = np.sin(x) + np.cos(y) >>> # Draw the triangular surface plot >>> mlab.trisurf(x, y, z) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyzc') _plot_data(kind='trisurf')
[docs]@_close_gks_on_error def tricont(x, y, z, *args, **kwargs): """ Draw a triangular contour plot. This function uses the current colormap to display a series of points as a triangular contour plot. It will use a Delaunay triangulation to interpolate the z values between x and y values. If the series of points is concave, this can lead to interpolation artifacts on the edges of the plot, as the interpolation may occur in very acute triangles. :param x: the x coordinates to plot :param y: the y coordinates to plot :param z: the z coordinates to plot **Usage examples:** >>> # Create example point data >>> x = np.random.uniform(-4, 4, 100) >>> y = np.random.uniform(-4, 4, 100) >>> z = np.sin(x) + np.cos(y) >>> # Draw the triangular contour plot >>> mlab.tricont(x, y, z) """ global _plt _plt.kwargs.update(kwargs) args = [x, y, z] + list(args) _plt.args = _plot_args(args, fmt='xyzc') _plot_data(kind='tricont')
[docs]@_close_gks_on_error def stem(*args, **kwargs): """ Draw a stem plot. This function can receive one or more of the following: - x values and y values, or - x values and a callable to determine y values, or - y values only, with their indices as x values :param args: the data to plot **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = 0.2*x+0.4 >>> # Plot x and y >>> mlab.stem(x, y) >>> # Plot x and a callable >>> mlab.stem(x, lambda x: x**3 + x**2 + x + 6) >>> # Plot y, using its indices for the x values >>> mlab.stem(y) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args) _plot_data(kind='stem')
def _hist(x, nbins=0, weights=None): x = np.array(x) x_min = x.min() x_max = x.max() if nbins <= 1: nbins = int(np.round(3.3 * np.log10(len(x)))) + 1 binned_x = np.array(np.floor((x - x_min) / (x_max - x_min) * nbins), dtype=int) binned_x[binned_x == nbins] = nbins - 1 counts = np.bincount(binned_x, weights=weights) edges = np.linspace(x_min, x_max, nbins + 1) return counts, edges
[docs]@_close_gks_on_error def histogram(x, num_bins=0, weights=None, **kwargs): r""" Draw a histogram. If **num_bins** is 0, this function computes the number of bins as :math:`\text{round}(3.3\cdot\log_{10}(n))+1` with n as the number of elements in x, otherwise the given number of bins is used for the histogram. :param x: the values to draw as histogram :param num_bins: the number of bins in the histogram :param weights: weights for the x values **Usage examples:** >>> # Create example data >>> x = np.random.uniform(-1, 1, 100) >>> # Draw the histogram >>> mlab.histogram(x) >>> # Draw the histogram with 19 bins >>> mlab.histogram(x, num_bins=19) >>> # Draw the histogram with weights >>> weights = np.ones_like(x) >>> weights[x < 0] = 2.5 >>> mlab.histogram(x, weights=weights) """ global _plt _plt.kwargs.update(kwargs) hist, bins = _hist(x, nbins=num_bins, weights=weights) _plt.args = [(np.array(bins), np.array(hist), None, None, "")] _plot_data(kind='hist')
[docs]@_close_gks_on_error def contour(*args, **kwargs): """ Draw a contour plot. This function uses the current colormap to display a either a series of points or a two-dimensional array as a contour plot. It can receive one or more of the following: - x values, y values and z values, or - N x values, M y values and z values on a MxN grid, or - N x values, M y values and a callable to determine z values If a series of points is passed to this function, their values will be interpolated on a grid. For grid points outside the convex hull of the provided points, a value of 0 will be used. :param args: the data to plot :param levels: Number of contour lines **Usage examples:** >>> # Create example point data >>> x = np.random.uniform(-4, 4, 100) >>> y = np.random.uniform(-4, 4, 100) >>> z = np.sin(x) + np.cos(y) >>> # Draw the contour plot >>> mlab.contour(x, y, z) >>> # Create example grid data >>> x = np.linspace(-2, 2, 40) >>> y = np.linspace(0, np.pi, 20) >>> z = np.sin(x[np.newaxis, :]) + np.cos(y[:, np.newaxis]) >>> # Draw the contour plot >>> mlab.contour(x, y, z, levels=10) >>> # Draw the contour plot using a callable >>> mlab.contour(x, y, lambda x, y: np.sin(x) + np.cos(y)) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyzc') _plot_data(kind='contour')
[docs]@_close_gks_on_error def contourf(*args, **kwargs): """ Draw a filled contour plot. This function uses the current colormap to display a either a series of points or a two-dimensional array as a filled contour plot. It can receive one or more of the following: - x values, y values and z values, or - N x values, M y values and z values on a MxN grid, or - N x values, M y values and a callable to determine z values If a series of points is passed to this function, their values will be interpolated on a grid. For grid points outside the convex hull of the provided points, a value of 0 will be used. :param args: the data to plot :param levels: Number of contour lines **Usage examples:** >>> # Create example point data >>> x = np.random.uniform(-4, 4, 100) >>> y = np.random.uniform(-4, 4, 100) >>> z = np.sin(x) + np.cos(y) >>> # Draw the filled contour plot >>> mlab.contourf(x, y, z) >>> # Create example grid data >>> x = np.linspace(-2, 2, 40) >>> y = np.linspace(0, np.pi, 20) >>> z = np.sin(x[np.newaxis, :]) + np.cos(y[:, np.newaxis]) >>> # Draw the filled contour plot >>> mlab.contourf(x, y, z, levels=10) >>> # Draw the filled contour plot using a callable >>> mlab.contourf(x, y, lambda x, y: np.sin(x) + np.cos(y)) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyzc') _plot_data(kind='contourf')
[docs]@_close_gks_on_error def hexbin(*args, **kwargs): """ Draw a hexagon binning plot. This function uses hexagonal binning and the the current colormap to display a series of points. It can receive one or more of the following: - x values and y values, or - x values and a callable to determine y values, or - y values only, with their indices as x values :param args: the data to plot **Usage examples:** >>> # Create example data >>> x = np.random.normal(0, 1, 100000) >>> y = np.random.normal(0, 1, 100000) >>> # Draw the hexbin plot >>> mlab.hexbin(x, y) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args) _plot_data(kind='hexbin')
[docs]@_close_gks_on_error def heatmap(data, **kwargs): """ Draw a heatmap. This function uses the current colormap to display a two-dimensional array as a heatmap. The array is drawn with its first value in the upper left corner, so in some cases it may be necessary to flip the columns (see the example below). By default the function will use the row and column indices for the x- and y-axes, so setting the axis limits is recommended. Also note that the values in the array must lie within the current z-axis limits so it may be necessary to adjust these limits or clip the range of array values. :param data: the heatmap data **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = np.linspace(0, np.pi, 20) >>> z = np.sin(x[:, np.newaxis]) + np.cos(y[np.newaxis, :]) >>> # Draw the heatmap >>> mlab.heatmap(z[::-1, :], xlim=(-2, 2), ylim=(0, np.pi)) """ global _plt data = np.array(data, copy=False) if len(data.shape) != 2: raise ValueError('expected 2-D array') _plt.kwargs.update(kwargs) xlim = _plt.kwargs.get('xlim', None) ylim = _plt.kwargs.get('ylim', None) _plt.args = [(xlim, ylim, data, None, "")] _plot_data(kind='heatmap')
@_close_gks_on_error def polar_heatmap(data, **kwargs): """ Draw a polar heatmap. This function uses the current colormap to display a two-dimensional array mapped to a disk using polar coordinates. The array is mapped by interpreting the rows as the angle and the columns as the radius. By default the function will use an inner radius of 0 and the number of columns as the outer radius and draw a complete circle, so setting the axis limits is recommended. Also note that the values in the array must lie within the current z-axis limits so it may be necessary to adjust these limits or clip the range of array values. :param data: the heatmap data **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 100) >>> y = np.linspace(0, np.pi, 200) >>> z = np.sin(x[:, np.newaxis]) + np.cos(y[np.newaxis, :]) >>> # Draw the heatmap >>> mlab.polar_heatmap(z, rlim=(1, 10), philim=(0, np.pi / 2)) """ global _plt data = np.array(data, copy=False) if len(data.shape) != 2: raise ValueError('expected 2-D array') _plt.kwargs.update(kwargs) rlim = _plt.kwargs.get('rlim', None) philim = _plt.kwargs.get('philim', None) _plt.args = [(rlim, philim, data, None, "")] _plot_data(kind='polar_heatmap')
[docs]@_close_gks_on_error def shade(*args, **kwargs): """ Draw a point or line based heatmap. This function uses the current colormap to display a series of points or polylines. For line data, NaN values can be used as separator. :param args: the data to plot :param xform: the transformation type used for color mapping The available transformation types are: +----------------+---+--------------------+ |XFORM_BOOLEAN | 0|boolean | +----------------+---+--------------------+ |XFORM_LINEAR | 1|linear | +----------------+---+--------------------+ |XFORM_LOG | 2|logarithmic | +----------------+---+--------------------+ |XFORM_LOGLOG | 3|double logarithmic | +----------------+---+--------------------+ |XFORM_CUBIC | 4|cubic | +----------------+---+--------------------+ |XFORM_EQUALIZED | 5|histogram equalized | +----------------+---+--------------------+ **Usage examples:** >>> # Create point data >>> x = np.random.normal(size=100000) >>> y = np.random.normal(size=100000) >>> mlab.shade(x, y) >>> # Create line data with NaN as polyline separator >>> x = np.concatenate((np.random.normal(size=10000), [np.nan], np.random.normal(loc=5, size=10000))) >>> y = np.concatenate((np.random.normal(size=10000), [np.nan], np.random.normal(loc=5, size=10000))) >>> mlab.shade(x, y) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xys') _plot_data(kind='shade')
[docs]@_close_gks_on_error def wireframe(*args, **kwargs): """ Draw a three-dimensional wireframe plot. This function uses the current colormap to display a either a series of points or a two-dimensional array as a wireframe plot. It can receive one or more of the following: - x values, y values and z values, or - N x values, M y values and z values on a MxN grid, or - N x values, M y values and a callable to determine z values If a series of points is passed to this function, their values will be interpolated on a grid. For grid points outside the convex hull of the provided points, a value of 0 will be used. :param args: the data to plot **Usage examples:** >>> # Create example point data >>> x = np.random.uniform(-4, 4, 100) >>> y = np.random.uniform(-4, 4, 100) >>> z = np.sin(x) + np.cos(y) >>> # Draw the wireframe plot >>> mlab.wireframe(x, y, z) >>> # Create example grid data >>> x = np.linspace(-2, 2, 40) >>> y = np.linspace(0, np.pi, 20) >>> z = np.sin(x[np.newaxis, :]) + np.cos(y[:, np.newaxis]) >>> # Draw the wireframe plot >>> mlab.wireframe(x, y, z) >>> # Draw the wireframe plot using a callable >>> mlab.wireframe(x, y, lambda x, y: np.sin(x) + np.cos(y)) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyzc') _plot_data(kind='wireframe')
[docs]@_close_gks_on_error def surface(*args, **kwargs): """ Draw a three-dimensional surface plot. This function uses the current colormap to display a either a series of points or a two-dimensional array as a surface plot. It can receive one or more of the following: - x values, y values and z values, or - N x values, M y values and z values on a MxN grid, or - N x values, M y values and a callable to determine z values If a series of points is passed to this function, their values will be interpolated on a grid. For grid points outside the convex hull of the provided points, a value of 0 will be used. :param args: the data to plot **Usage examples:** >>> # Create example point data >>> x = np.random.uniform(-4, 4, 100) >>> y = np.random.uniform(-4, 4, 100) >>> z = np.sin(x) + np.cos(y) >>> # Draw the surface plot >>> mlab.surface(x, y, z) >>> # Create example grid data >>> x = np.linspace(-2, 2, 40) >>> y = np.linspace(0, np.pi, 20) >>> z = np.sin(x[np.newaxis, :]) + np.cos(y[:, np.newaxis]) >>> # Draw the surface plot >>> mlab.surface(x, y, z) >>> # Draw the surface plot using a callable >>> mlab.surface(x, y, lambda x, y: np.sin(x) + np.cos(y)) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyzc') _plot_data(kind='surface')
[docs]@_close_gks_on_error def bar(y, *args, **kwargs): """ Draw a two-dimensional bar plot. This function uses a list of y values to create a two dimensional bar plot. It can receive the following parameters: - a list of y values, or - a list of same length lists which contain y values (multiple bars) - a list of colors - one or more of the following key value pairs: - a parameter bar_width - a parameter edge_width - a parameter bar_color (either an int or a list of rgb values) - a parameter edge_color (either an int or a list of rgb values) - a list of xnotations - a list ind_bar_color, ind_edge_color which can be one color pack or a list with multiple ones for individual bars (color pack: first value: list with the indices or one index as an int, second value: color (rgb list)) - a list ind_edge_width can be one width pack or a list with multiple ones for individual bars (width pack: first value: list with the indices or one index as an int, second value: width (float)) - a style parameter (only for multi-bar) All parameters except for xnotations, ind_bar_color, ind_edge_color and ind_edge_width will be saved for the next bar plot (set to None for default) :param y: the data to plot :param args: the list of colors **Usage examples:** >>> # Create example data >>> y = np.random.uniform(0, 10, 5) >>> # Draw the bar plot >>> mlab.bar(y) >>> # Draw the bar plot with locations specified by x >>> x = ['a', 'b', 'c', 'd', 'e'] >>> mlab.bar(y, xnotations=x) >>> # Draw the bar plot with different bar_width, edge_width, edge_color and bar_color >>> # (bar_width: float in (0;1], edge_width: float value bigger or equal to 0, color: int in [0;1255] or rgb list) >>> mlab.bar(y, bar_width=0.6, edge_width=2, bar_color=999, edge_color=3) >>> mlab.bar(y, bar_color=[0.66, 0.66, 0.66], edge_color=[0.33, 0.33, 0.33]) >>> # Draw the bar plot with bars that have individual bar_color, edge_color, edge_with >>> mlab.bar(y, ind_bar_color=[1, [0.4, 0.4, 0.4]], ind_edge_color=[[1, 2], [0.66, 0.33, 0.33]], ind_edge_width=[[1, 2], 3]) >>> # Draw the bar plot with bars that have multiple individual colors >>> mlab.bar(y, ind_bar_color=[[1, [1, 0.3, 0]], [[2, 3], [1, 0.5, 0]]]) >>> # Draw the bar plot with default bar_width, edge_width, edge_color and bar_color >>> mlab.bar(y, bar_width=None, edge_width=None, bar_color=None, edge_color=None) >>> # Draw the bar plot with colorlist >>> mlab.bar(y, [989, 982, 980, 981, 996]) >>> # Create example sublist data >>> yy = np.random.rand(3, 3) >>> # Draw the bar plot lined / stacked / with colorlist >>> mlab.bar(yy, bar_style='lined') >>> mlab.bar(yy, bar_style='stacked') >>> mlab.bar(yy, [989, 998, 994]) """ global _plt _plt.kwargs.update(kwargs) if 'bar_style' not in _plt.kwargs: _plt.kwargs['bar_style'] = 'stacked' if isinstance(y[0], list) or (isinstance(y, np.ndarray) and len(y.shape) == 2): if isinstance(y, np.ndarray) and len(y.shape) > 2: raise IndexError('Numpy array has to be of dimension 2 or lower!') _plt.kwargs['multi_bar'] = True if _plt.kwargs['bar_style'] == 'lined': new_arg = [] for ls in y: new_arg.append(max(ls)) else: new_arg = [] for ls in y: new_arg.append(sum(ls)) new_args = [new_arg, y] else: _plt.kwargs['multi_bar'] = False new_args = [y, None] if args: c = args[0] if not isinstance(c, list): raise TypeError('C has to be of type list!') if _plt.kwargs['multi_bar']: if len(c) != len(y[0]): raise IndexError('The length of c has to equal the length of the sublists when using a multi-bar!') else: if len(c) != len(y): raise IndexError('The length of c has to equal the amount of y-values!') for color in c: if isinstance(color, int): if color < 0: raise ValueError('The values in c have to be bigger or equal to 0!') if isinstance(color, list): if len(color) != 3: raise IndexError("RGB list has to contain 3 values!") rgb = color for v in rgb: if v < 0 or v > 1: raise ValueError('The values of a rgb color have to be in [1;0]!') new_args.append(c) _plt.args = _plot_args(new_args, fmt='y') _plot_data(kind='bar')
[docs]def polar_histogram(*args, **kwargs): """ Draw a polar histogram plot. This function uses certain input values to display a polar histogram. It must receive only one of the following: - theta: a list containing angles between 0 and 2 * pi - bin_counts: a list containing integer values for the height of bins It can also receive: - normalization: type of normalization in which the histogram will be displayed + count: the default normalization. The height of each bar is the number of observations in each bin. + probability: The height of each bar is the relative number of observations, (number of observations in bin/total number of observations). The sum of the bar heights is 1. + countdensity: The height of each bar is the number of observations in bin/width of bin. + pdf: Probability density function estimate. The height of each bar is, (number of observations in the bin)/(total number of observations * width of bin). The area of each bar is the relative number of observations. The sum of the bar areas is 1 + cumcount: The height of each bar is the cumulative number of observations in each bin and all previous bins. The height of the last bar is numel (theta). + cdf: Cumulative density function estimate. The height of each bar is equal to the cumulative relative number of observations in the bin and all previous bins. The height of the last bar is 1. - stairs: Boolean, display the histogram outline only - face_alpha: float value between 0 and 1 inclusive. Transparency of histogram bars. A value of 1 means fully opaque and 0 means completely transparent (invisible). Default is 0.75 - face_color: Histogram bar color. Either an integer between 0 and 1008 or a list with three floats between 0 and 1 representing an RGB Triplet - edge_color: Histogram bar edgecolor. Either an integer between 0 and 1008 or a list with three floats between 0 and 1 representing an RGB Triplet - num_bins: Number of bins specified as a positive integer. If no bins are given, the polarhistogram will automatically calculate the number of bins - philim: a tuple containing two angle values [min, max]. This option plots a histogram using the input values (theta) that fall between min and max inclusive. - rlim: a tuple containing two values between 0 and 1 (min, max). This option plots a histogram with bins starting at min and ending at max - bin_edges: a list of angles between 0 and 2 * pi which specify the Edges of each bin/bar. NOT COMPATIBLE with bin_limits. When used with bin_counts: len(bin_edges) == len(bin_counts) + 1 - bin_width: Width across the top of bins, specified as a float less than 2 * pi - colormap: A triple tuple used to create a Colormap. (x, y, size). x = index for the first Colormap. Y for the second. size for the size of the colormap. each component must be given! At least (None, None, None) - draw_edges: Boolean, draws the edges of each bin when using a colormap. Only works with a colormap :param args: a list **Usage examples:** >>> # create a theta list >>> theta = [0, 0.6, 1, 1.3, np.pi/2, np.pi, np.pi*2, np.pi*2] >>> polar_histogram(theta) >>> # create bin_counts >>> bc = [1, 2, 6, 9] >>> polar_histogram(theta, bin_counts=bc) >>> #create bin_edges >>> be = [1, 2, np.pi*1.3, np.pi] >>> polar_histogram(theta, bin_edges=be) >>> polar_histogram(theta, normalization='cdf') >>> polar_histogram(theta, colormap=(0,0,1000)) """ global _plt if _plt.kwargs.get('ax', False) is False: temp_ax = False else: temp_ax = True _plt.kwargs['ax'] = True _plt.args = _plot_args(args, fmt='xys') def find_max(classes, normalization): if normalization == 'cumcount': sum = 0 for x in range(len(classes)): sum += len(classes[x]) return sum else: max = len(classes[0]) for x in range(len(classes)): if max < len(classes[x]): max = len(classes[x]) return max _plt.kwargs.update(kwargs) classes = [] gr.setlinecolorind(1) angles = [] # Color Map if _plt.kwargs.get('colormap') is not None: is_colormap = True colormap = _plt.kwargs['colormap'] if isinstance(colormap, tuple): colormap = _create_colormap(colormap) else: is_colormap = False if _plt.kwargs.get('draw_edges') is True and not is_colormap: raise ValueError('draw_edges only usable with a colormap') # bin_edges if _plt.kwargs.get('bin_edges', None) is not None: binedges = _plt.kwargs['bin_edges'] is_binedges = True else: is_binedges = False # normalization if _plt.kwargs.get('normalization') is not None: normalization = _plt.kwargs['normalization'] else: normalization = 'count' _plt.kwargs['normalization'] = 'count' # Bincounts or theta if _plt.kwargs.get('theta') is not None: theta = _plt.kwargs['theta'] else: theta = args[0] # theta check if len(theta) == 0: raise ValueError('List is empty') for obj in theta: if isinstance(obj, float): has_theta = True break elif isinstance(obj, int): has_theta = False if not has_theta: if is_binedges: if len(binedges) is not len(theta) + 1: raise ValueError('Number bin_edges must be number of Bincounts + 1') for x in range(len(theta)): classes.append([]) if theta[x] == 0: classes[x].append(None) continue for y in range(theta[x]): classes[x].append(y) num_bins = len(theta) width = 2 * np.pi / num_bins if is_colormap: if not is_binedges: angles = np.linspace(0, np.pi * 2, num_bins + 1) # Philim for bincounts if _plt.kwargs.get('philim', None) is not None: binlimits = _plt.kwargs['philim'] if binlimits[0] is None: binlimits = (0, binlimits[1]) if binlimits[1] is None: binlimits = (binlimits[0], 2 * np.pi) if not is_binedges: binedges = np.linspace(binlimits[0], binlimits[1], num_bins + 1) is_binedges = True _plt.kwargs['temp_bin_edges'] = binedges else: binedges = [angle for angle in binedges if (binlimits[0] <= angle <= binlimits[1])] if len(binedges) != num_bins + 1: raise ValueError('The given binedges is not compatible with philim since the number of angles,' ' which are in between the philims, does not equal len(bincounts) + 1 ') is_binedges = True _plt.kwargs['temp_bin_edges'] = binedges # No bin_counts else: # Number of bins if _plt.kwargs.get('num_bins', None) is not None: num_bins = _plt.kwargs['num_bins'] if num_bins < 1: raise ValueError('Number of num_bins must be 1 or larger') else: # Auto generated num_bins num_bins = min(int(len(theta) / 2 - 1), 200) if is_binedges: num_bins = len(binedges) - 1 start = 0 # Bin Width --> will overwrite the number of bins but it will not exceed 200 if _plt.kwargs.get('bin_width') is not None: width = _plt.kwargs['bin_width'] if not width < np.pi * 2 or not width > 0: raise ValueError('bin_width not correct! Must be between 0 and 2*pi') num_bins = int(2 * np.pi / width) if num_bins > 200: num_bins = 200 width = 2 * np.pi / num_bins else: width = 2 * np.pi / num_bins # Philim if _plt.kwargs.get('philim', None) is not None: is_binlimits = True binlimits = _plt.kwargs['philim'] if binlimits[0] is None: binlimits = (0, binlimits[1]) if binlimits[1] is None: binlimits = (binlimits[0], 2 * np.pi) if not is_binedges: if _plt.kwargs.get('num_bins', None) is None: num_bins = max(int(num_bins * (binlimits[1] - binlimits[0]) / (np.pi * 2)), 3) binedges = np.linspace(binlimits[0], binlimits[1], num_bins + 1) is_binedges = True _plt.kwargs['temp_bin_edges'] = binedges else: binedges = [angle for angle in binedges if (binlimits[0] <= angle <= binlimits[1])] is_binedges = True _plt.kwargs['temp_bin_edges'] = binedges num_bins = len(binedges) - 1 else: is_binlimits = False # grouping the data from given theta if is_binedges: for x in range(len(binedges)): start = binedges[x] classes.append([]) for y in range(len(theta)): try: if start < theta[y] <= binedges[x + 1]: if is_binlimits: if binlimits[0] <= theta[y] <= binlimits[1]: classes[x].append(theta[y]) else: classes[x].append(theta[y]) except Exception: pass if len(classes[x]) == 0: classes[x].append(None) # no Binedges else: # Optional Bin Limits if is_binlimits: theta = [angle for angle in theta if binlimits[0] <= angle <= binlimits[1]] interval = 2 * np.pi / num_bins for x in range(num_bins): classes.append([]) for y in range(len(theta)): if start <= theta[y] < (start + interval): classes[x].append(theta[y]) if is_colormap: # angles list for colormap angles.append([start, start + interval]) if len(classes[x]) == 0: classes[x].append(None) start += interval _plt.kwargs['classes'] = classes # calc total total = 0 for temp in classes: total += len(temp) - temp.count(None) if is_binedges: if normalization == 'pdf': norm_factor = total elif normalization == 'countdensity': norm_factor = 1 binwidths = [] classes = [temp for temp in classes if len(temp) > 0] for i in range(len(binedges) - 1): binwidths.append(binedges[i + 1] - binedges[i]) binwidths.append(binedges[-1] - binedges[-2]) if normalization == 'countdensity' or normalization == 'pdf': bin_value = [len(x) / (norm_factor * binwidths[i]) for (i, x) in enumerate(classes)] exp = 0 if normalization == 'probability' or normalization == 'pdf': if normalization == 'probability': maximum = find_max(classes, normalization) / total elif normalization == 'pdf': if is_binedges: maximum = max(bin_value) else: maximum = find_max(classes, normalization) / (total * width) exp = round(np.log10(maximum) + 0.5) border = round((maximum * (10 ** (abs(exp) + 1))) / (10 ** (round(abs(exp) + 1)))) while border < maximum or border * 10 ** (abs(exp) + 2) % 4 != 0: border += 10 ** (exp - 1) elif normalization == 'cdf': border = 1 else: # interval for x-Axis if is_binedges: if normalization == 'countdensity': maximum = round(max(bin_value) + 0.49) else: maximum = int(find_max(classes, normalization)) else: if normalization == 'countdensity': maximum = int(find_max(classes, normalization) / width) else: maximum = int(find_max(classes, normalization)) border = maximum outer = True e = 1 while True and outer: if border <= 40: while border % 4 != 0: border += 1 break else: for x in range(9): border += x * 10 ** (e - 1) if border % 4 == 0: outer = False break e += 1 # calc colormap image if normalization == 'probability': norm_factor = total elif normalization == 'countdensity': norm_factor = width elif normalization == 'pdf': norm_factor = total * width elif normalization == 'cdf': norm_factor = total elif normalization == 'count': norm_factor = 1 elif normalization == 'cumcount': norm_factor = 1 else: raise ValueError("Incorrect normalization Value") _plt.kwargs['norm_factor'] = norm_factor if is_colormap: # r_lim if _plt.kwargs.get('rlim', None) is not None: if not has_theta: pass r_lim = _plt.kwargs['rlim'] if r_lim[0] is None: r_lim = (0, r_lim[1]) if r_lim[1] is None: r_lim = (r_lim[0], 1) else: r_lim = (0, 1) height = 2000 width = height center = width / 2 cumulative = [] length = 0 max_radius = center * 0.8 r_min = r_lim[0] * max_radius r_max = r_lim[1] * max_radius del max_radius if normalization == 'cumcount' or normalization == 'cdf': for temp in classes: if temp[0] is None: if len(cumulative) > 0: cumulative.append(cumulative[-1]) else: cumulative.append(0) else: length += len(temp) / norm_factor / border * 0.8 * center cumulative.append(length) factor_angle_b = len(colormap[0]) / (2 * np.pi) Y, X = np.ogrid[:height, :width] radiusc = np.sqrt((X - center) ** 2 + (Y - center) ** 2) angle_c = np.arctan2(Y - center, X - center) angle_c[angle_c < 0] += 2 * np.pi angle_b = angle_c * factor_angle_b factor_radiusb2 = (len(colormap) - 1) / (center * 2 ** (1 / 2)) radiusb2 = radiusc * factor_radiusb2 colormap = np.array(colormap) lineardata = colormap[radiusb2.astype(np.int), angle_b.astype(np.int)].flatten() if is_binedges: if normalization == 'pdf': norm_factor = total if normalization == 'countdensity': norm_factor = 1 binwidths = [] for i in range(len(classes)): if len(classes[i]) < 1: classes.pop(i) for i in range(len(binedges) - 1): binwidths.append(binedges[i + 1] - binedges[i]) binwidths.append(binedges[-1] - binedges[-2]) if normalization == 'countdensity' or normalization == 'pdf': bin_value = np.array( [len(x) / (norm_factor * binwidths[i]) / border * 0.8 * center if x[0] is not None else 0 for (i, x) in enumerate(classes)]) else: bin_value = np.array([len(x) / norm_factor / border * 0.8 * center if x[0] is not None else 0 for x in classes]) # no bin_edges else: bin_value = np.array([len(x) / norm_factor / border * 0.8 * center if x[0] is not None else 0 for x in classes]) if not is_binedges: if normalization == 'cdf' or normalization == 'cumcount': boolmap = np.zeros((height, width)) for (angle1, angle2), radius in zip(angles, cumulative): tempmap = angle1 <= angle_c tempmap = np.logical_and(angle_c <= angle2, tempmap) tempmap = np.logical_and(radiusc <= radius, tempmap) tempmap = np.logical_and(radiusc <= r_max, tempmap) tempmap = np.logical_and(r_min <= radiusc, tempmap) boolmap = np.logical_or(tempmap, boolmap) else: boolmap = np.zeros((height, width)) for (angle1, angle2), radius in zip(angles, bin_value): tempmap = angle1 <= angle_c tempmap = np.logical_and(angle_c <= angle2, tempmap) tempmap = np.logical_and(radiusc <= radius, tempmap) tempmap = np.logical_and(radiusc <= r_max, tempmap) tempmap = np.logical_and(r_min <= radiusc, tempmap) boolmap = np.logical_or(tempmap, boolmap) # Binedges else: angles = [] for i in range(len(binedges) - 1): angles.append([]) angles[i].append(binedges[i]) angles[i].append(binedges[i + 1]) if normalization == 'cdf' or normalization == 'cumcount': boolmap = np.zeros((height, width)) for (angle1, angle2), radius in zip(angles, cumulative): tempmap = angle1 <= angle_c tempmap = np.logical_and(angle_c <= angle2, tempmap) tempmap = np.logical_and(radiusc <= radius, tempmap) tempmap = np.logical_and(radiusc <= r_max, tempmap) tempmap = np.logical_and(r_min <= radiusc, tempmap) boolmap = np.logical_or(tempmap, boolmap) else: boolmap = np.zeros((height, width)) for (angle1, angle2), radius in zip(angles, bin_value): tempmap = angle1 <= angle_c tempmap = np.logical_and(angle_c <= angle2, tempmap) tempmap = np.logical_and(radiusc <= radius, tempmap) tempmap = np.logical_and(radiusc <= r_max, tempmap) tempmap = np.logical_and(r_min <= radiusc, tempmap) boolmap = np.logical_or(tempmap, boolmap) lineardata[np.logical_not(boolmap.flatten())] = 0 _plt.kwargs['temp_colormap'] = (height, width, lineardata) _plt.kwargs['border_exp'] = (border, exp) if is_binedges: if _plt.kwargs['normalization'] == 'pdf': _plt.kwargs['norm_factor'] = total _plot_data(kind='polar_histogram') _plt.kwargs['ax'] = temp_ax del temp_ax
[docs]@_close_gks_on_error def plot3(*args, **kwargs): """ Draw one or more three-dimensional line plots. :param x: the x coordinates to plot :param y: the y coordinates to plot :param z: the z coordinates to plot **Usage examples:** >>> # Create example data >>> x = np.linspace(0, 30, 1000) >>> y = np.cos(x) * x >>> z = np.sin(x) * x >>> # Plot the points >>> mlab.plot3(x, y, z) """ global _plt _plt.kwargs.update(kwargs) _plt.args = _plot_args(args, fmt='xyac') _plot_data(kind='plot3')
[docs]@_close_gks_on_error def scatter3(x, y, z, c=None, *args, **kwargs): """ Draw one or more three-dimensional scatter plots. Additional to x, y and z values, you can provide values for the markers' color. Color values will be used in combination with the current colormap. :param x: the x coordinates to plot :param y: the y coordinates to plot :param z: the z coordinates to plot :param c: the optional color values to plot **Usage examples:** >>> # Create example data >>> x = np.random.uniform(-1, 1, 100) >>> y = np.random.uniform(-1, 1, 100) >>> z = np.random.uniform(-1, 1, 100) >>> c = np.random.uniform(1, 1000, 100) >>> # Plot the points >>> mlab.scatter3(x, y, z) >>> # Plot the points with colors >>> mlab.scatter3(x, y, z, c) """ global _plt _plt.kwargs.update(kwargs) args = [x, y, z] + list(args) if c is not None: args.append(c) _plt.args = _plot_args(args, fmt='xyac') _plot_data(kind='scatter3')
[docs]@_close_gks_on_error def isosurface(v, **kwargs): """ Draw an isosurface. This function can draw an isosurface from a three-dimensional numpy array. Values greater than the isovalue will be seen as outside the isosurface, while values less than the isovalue will be seen as inside the isosurface. :param v: the volume data :param isovalue: the isovalue **Usage examples:** >>> # Create example data >>> x = np.linspace(-1, 1, 40)[:, np.newaxis, np.newaxis] >>> y = np.linspace(-1, 1, 40)[np.newaxis, :, np.newaxis] >>> z = np.linspace(-1, 1, 40)[np.newaxis, np.newaxis, :] >>> v = 1-(x**2 + y**2 + z**2)**0.5 >>> # Draw the isosurace. >>> mlab.isosurface(v, isovalue=0.2) """ global _plt _plt.kwargs.update(kwargs) _plt.args = [(None, None, v, None, '')] _plot_data(kind='isosurface')
[docs]@_close_gks_on_error def volume(v, **kwargs): """ Draw a volume. This function can draw a three-dimensional numpy array using volume rendering. The volume data is reduced to a two-dimensional image using an emission or absorption model or by a maximum intensity projection. After the projection the current colormap is applied to the result. :param v: the volume data :param algorithm: the algorithm used to reduce the volume data. Available algorithms are "maximum", "emission" and "absorption". :param dmin: the minimum data value when applying the colormap :param dmax: the maximum data value when applying the colormap **Usage examples:** >>> # Create example data >>> x = np.linspace(-1, 1, 40)[:, np.newaxis, np.newaxis] >>> y = np.linspace(-1, 1, 40)[np.newaxis, :, np.newaxis] >>> z = np.linspace(-1, 1, 40)[np.newaxis, np.newaxis, :] >>> v = 1 - (x**2 + y**2 + z**2)**0.5 - np.random.uniform(0, 0.25, (40, 40, 40)) >>> # Draw the 3d volume data >>> mlab.volume(v) >>> # Draw the 3d volume data using an emission model >>> mlab.volume(v, algorithm='emission', dmin=0.1, dmax=0.4) """ global _plt _plt.kwargs.update(kwargs) nz, ny, nx = v.shape _plt.args = [(np.arange(nx + 1), np.arange(nz + 1), np.arange(ny + 1), v, '')] _plot_data(kind='volume')
[docs]@_close_gks_on_error def imshow(image, **kwargs): """ Draw an image. This function can draw an image either from reading a file or using a two-dimensional array and the current colormap. :param image: an image file name or two-dimensional array **Usage examples:** >>> # Create example data >>> x = np.linspace(-2, 2, 40) >>> y = np.linspace(0, np.pi, 20) >>> z = np.sin(x[np.newaxis, :]) + np.cos(y[:, np.newaxis]) >>> # Draw an image from a 2d array >>> mlab.imshow(z) >>> # Draw an image from a file >>> mlab.imshow("example.png") """ global _plt _plt.kwargs.update(kwargs) _plt.args = [(None, None, image, None, "")] _plot_data(kind='imshow')
@_close_gks_on_error def size(*size): """ Set the size of the output window. The size can be passed in several formats, allowing both pixel sizes and metric sizes, as well as mixes of those. If nothing is passed to this function, the default of 600 by 450 pixels is restored. :param size: aseries of values or a tuple containing: - two numbers (width and height as pixels), or - two strings (width and height as another unit), or - two numbers and a string (width, height and their unit), or - a number, a string, another number and another string (width and height, along with their units) **Usage examples:** >>> # Set the size to 640 by 480 pixels >>> mlab.size(600, 450) >>> # Set the size to 640 by 480 pixels using a tuple >>> mlab.size((600, 450)) >>> # Reset size to its default of 600 by 450 pixels >>> mlab.size() >>> # Set the size to 6 centimeters by 200 pixels >>> mlab.size("6cm", 200) >>> # Set the size to 120 millimeters by 9 centimeters >>> mlab.size("120mm", "9cm") >>> # Set the size to 4 by 3 inches >>> mlab.size(4, 3, "in") >>> # Set the size to 0.5 feet by 0.1 meters >>> mlab.size(0.5, "ft", 0.1, "m") """ if len(size) == 1 and hasattr(size[0], '__getitem__'): size = size[0] if len(size) == 0: size = (600, 450) _plot_data(size=size)
[docs]@_close_gks_on_error def title(title=""): """ Set the plot title. The plot title is drawn using the extended text function :py:func:`gr.textext`. You can use a subset of LaTeX math syntax, but will need to escape certain characters, e.g. parentheses. For more information see the documentation of :py:func:`gr.textext`. :param title: the plot title **Usage examples:** >>> # Set the plot title to "Example Plot" >>> mlab.title("Example Plot") >>> # Clear the plot title >>> mlab.title() """ _plot_data(title=title)
[docs]@_close_gks_on_error def xlabel(x_label=""): """ Set the x-axis label. The axis labels are drawn using the extended text function :py:func:`gr.textext`. You can use a subset of LaTeX math syntax, but will need to escape certain characters, e.g. parentheses. For more information see the documentation of :py:func:`gr.textext`. :param x_label: the x-axis label **Usage examples:** >>> # Set the x-axis label to "x" >>> mlab.xlabel("x") >>> # Clear the x-axis label >>> mlab.xlabel() """ _plot_data(xlabel=x_label)
[docs]@_close_gks_on_error def ylabel(y_label=""): r""" Set the y-axis label. The axis labels are drawn using the extended text function :py:func:`gr.textext`. You can use a subset of LaTeX math syntax, but will need to escape certain characters, e.g. parentheses. For more information see the documentation of :py:func:`gr.textext`. :param y_label: the y-axis label **Usage examples:** >>> # Set the y-axis label to "y\(x\)" >>> mlab.ylabel("y\(x\)") >>> # Clear the y-axis label >>> mlab.ylabel() """ _plot_data(ylabel=y_label)
[docs]@_close_gks_on_error def zlabel(z_label=""): r""" Set the z-axis label. The axis labels are drawn using the extended text function :py:func:`gr.textext`. You can use a subset of LaTeX math syntax, but will need to escape certain characters, e.g. parentheses. For more information see the documentation of :py:func:`gr.textext`. :param z_label: the z-axis label **Usage examples:** >>> # Set the z-axis label to "z(x, y)" >>> mlab.zlabel("z\(x, y\)") >>> # Clear the z-axis label >>> mlab.zlabel() """ _plot_data(zlabel=z_label)
[docs]@_close_gks_on_error def dlabel(d_label=""): r""" Set the volume intensity label. This label is drawn using the extended text function :py:func:`gr.textext`. You can use a subset of LaTeX math syntax, but will need to escape certain characters, e.g. parentheses. For more information see the documentation of :py:func:`gr.textext`. :param d_label: the volume intensity label **Usage examples:** >>> # Set the volume intensity label to "Intensity" >>> mlab.dlabel("Intensity") >>> # Clear the volume intensity label >>> mlab.dlabel() """ _plot_data(dlabel=d_label)
[docs]@_close_gks_on_error def xlim(x_min=None, x_max=None, adjust=True): """ Set the limits for the x-axis. The x-axis limits can either be passed as individual arguments or as a tuple of (**x_min**, **x_max**). Setting either limit to **None** will cause it to be automatically determined based on the data, which is the default behavior. :param x_min: - the x-axis lower limit, or - **None** to use an automatic lower limit, or - a tuple of both x-axis limits :param x_max: - the x-axis upper limit, or - **None** to use an automatic upper limit, or - **None** if both x-axis limits were passed as first argument :param adjust: whether or not the limits may be adjusted **Usage examples:** >>> # Set the x-axis limits to -1 and 1 >>> mlab.xlim(-1, 1) >>> # Set the x-axis limits to -1 and 1 using a tuple >>> mlab.xlim((-1, 1)) >>> # Reset the x-axis limits to be determined automatically >>> mlab.xlim() >>> # Reset the x-axis upper limit and set the lower limit to 0 >>> mlab.xlim(0, None) >>> # Reset the x-axis lower limit and set the upper limit to 1 >>> mlab.xlim(None, 1) """ if x_max is None and x_min is not None: try: x_min, x_max = x_min except TypeError: pass _plot_data(xlim=(x_min, x_max), adjust_xlim=adjust)
[docs]@_close_gks_on_error def ylim(y_min=None, y_max=None, adjust=True): """ Set the limits for the y-axis. The y-axis limits can either be passed as individual arguments or as a tuple of (**y_min**, **y_max**). Setting either limit to **None** will cause it to be automatically determined based on the data, which is the default behavior. :param y_min: - the y-axis lower limit, or - **None** to use an automatic lower limit, or - a tuple of both y-axis limits :param y_max: - the y-axis upper limit, or - **None** to use an automatic upper limit, or - **None** if both y-axis limits were passed as first argument :param adjust: whether or not the limits may be adjusted **Usage examples:** >>> # Set the y-axis limits to -1 and 1 >>> mlab.ylim(-1, 1) >>> # Set the y-axis limits to -1 and 1 using a tuple >>> mlab.ylim((-1, 1)) >>> # Reset the y-axis limits to be determined automatically >>> mlab.ylim() >>> # Reset the y-axis upper limit and set the lower limit to 0 >>> mlab.ylim(0, None) >>> # Reset the y-axis lower limit and set the upper limit to 1 >>> mlab.ylim(None, 1) """ if y_max is None and y_min is not None: try: y_min, y_max = y_min except TypeError: pass _plot_data(ylim=(y_min, y_max), adjust_ylim=adjust)
[docs]@_close_gks_on_error def zlim(z_min=None, z_max=None, adjust=True): """ Set the limits for the z-axis. The z-axis limits can either be passed as individual arguments or as a tuple of (**z_min**, **z_max**). Setting either limit to **None** will cause it to be automatically determined based on the data, which is the default behavior. :param z_min: - the z-axis lower limit, or - **None** to use an automatic lower limit, or - a tuple of both z-axis limits :param z_max: - the z-axis upper limit, or - **None** to use an automatic upper limit, or - **None** if both z-axis limits were passed as first argument :param adjust: whether or not the limits may be adjusted **Usage examples:** >>> # Set the z-axis limits to -1 and 1 >>> mlab.zlim(-1, 1) >>> # Set the z-axis limits to -1 and 1 using a tuple >>> mlab.zlim((-1, 1)) >>> # Reset the z-axis limits to be determined automatically >>> mlab.zlim() >>> # Reset the z-axis upper limit and set the lower limit to 0 >>> mlab.zlim(0, None) >>> # Reset the z-axis lower limit and set the upper limit to 1 >>> mlab.zlim(None, 1) """ if z_max is None and z_min is not None: try: z_min, z_max = z_min except TypeError: pass _plot_data(zlim=(z_min, z_max), adjust_zlim=adjust)
@_close_gks_on_error def rlim(r_min=None, r_max=None, adjust=True): """ Set the limits for the radii in polar plots. The inner and outer radius can either be passed as individual arguments or as a tuple of (**r_min**, **r_max**). Setting either limit to **None** will cause it to be automatically determined based on the data, which is the default behavior. **r_min** and **r_max** must both be greater or equal 0. :param r_min: - the inner radius, or - **None** to use an automatic inner radius, or - a tuple of both radii :param r_max: - the outer radius, or - **None** to use an automatic outer radius, or - **None** if both radii were passed as first argument :param adjust: whether or not the radii may be adjusted **Usage examples:** >>> # Set the radii in polar plots to 0 and 1 >>> mlab.rlim(0, 1) >>> # Set the radii in polar plots to 0 and 1 using a tuple >>> mlab.rlim((0, 1)) >>> # Reset the radii to be determined automatically >>> mlab.rlim() >>> # Reset the outer radius and set the inner radius to 0 >>> mlab.rlim(0, None) >>> # Reset the inner radius and set the outer radius to 1 >>> mlab.rlim(None, 1) """ if r_max is None and r_min is not None: try: r_min, r_max = r_min except TypeError: pass _plot_data(rlim=(r_min, r_max), adjust_rlim=adjust) @_close_gks_on_error def philim(phi_min=None, phi_max=None, adjust=True): """ Set the start and end angle for polar plots in radians. The angles can either be passed as individual arguments or as a tuple of (**phi_min**, **phi_max**). Setting either angle to **None** will cause it to be automatically determined based on the data, which is the default behavior. :param phi_min: - the start angle in radians, or - **None** to use an automatic start angle, or - a tuple of both angles :param phi_max: - the end angle in radians, or - **None** to use an automatic end angle, or - **None** if both angles were passed as first argument :param adjust: whether or not the angles may be adjusted **Usage examples:** >>> # Set the angle limits to 0 and 2 Pi >>> mlab.philim(0, 2*np.pi) >>> # Set the angle limits to 0 and 2 Pi using a tuple >>> mlab.philim((0, 2*np.pi)) >>> # Reset the angle limits to be determined automatically >>> mlab.philim() >>> # Reset the end angle and set the start angle to 0 >>> mlab.philim(0, None) >>> # Reset the start angle and set end angle to 2 Pi >>> mlab.philim(None, 2*np.pi) """ if phi_max is None and phi_min is not None: try: phi_min, phi_max = phi_min except TypeError: pass _plot_data(philim=(phi_min, phi_max), adjust_philim=adjust)
[docs]@_close_gks_on_error def xlog(xlog=True): """ Enable or disable a logarithmic scale for the x-axis. :param xlog: whether or not the x-axis should be logarithmic **Usage examples:** >>> # Enable a logarithic x-axis >>> mlab.xlog() >>> # Disable it again >>> mlab.xlog(False) """ _plot_data(xlog=xlog)
[docs]@_close_gks_on_error def ylog(ylog=True): """ Enable or disable a logarithmic scale for the y-axis. :param ylog: whether or not the y-axis should be logarithmic **Usage examples:** >>> # Enable a logarithic y-axis >>> mlab.ylog() >>> # Disable it again >>> mlab.ylog(False) """ _plot_data(ylog=ylog)
[docs]@_close_gks_on_error def zlog(zlog=True): """ Enable or disable a logarithmic scale for the z-axis. :param zlog: whether or not the z-axis should be logarithmic **Usage examples:** >>> # Enable a logarithic z-axis >>> mlab.zlog() >>> # Disable it again >>> mlab.zlog(False) """ _plot_data(zlog=zlog)
[docs]@_close_gks_on_error def xflip(xflip=True): """ Enable or disable x-axis flipping/reversal. :param xflip: whether or not the x-axis should be flipped **Usage examples:** >>> # Flips/Reverses the x-axis >>> mlab.xflip() >>> # Restores the x-axis >>> mlab.xflip(False) """ _plot_data(xflip=xflip)
[docs]@_close_gks_on_error def yflip(yflip=True): """ Enable or disable y-axis flipping/reversal. :param yflip: whether or not the y-axis should be flipped **Usage examples:** >>> # Flips/Reverses the y-axis >>> mlab.yflip() >>> # Restores the y-axis >>> mlab.yflip(False) """ _plot_data(yflip=yflip)
[docs]@_close_gks_on_error def zflip(zflip=True): """ Enable or disable z-axis flipping/reversal. :param zflip: whether or not the z-axis should be flipped **Usage examples:** >>> # Flips/Reverses the z-axis >>> mlab.zflip() >>> # Restores the z-axis >>> mlab.zflip(False) """ _plot_data(zflip=zflip)
@_close_gks_on_error def rflip(rflip=True): """ Enable or disable flipping/reversal of the radius. :param rflip: whether or not the inner and outer radius should be flipped **Usage examples:** >>> # Flips/Reverses the inner and outer radius >>> mlab.rflip() >>> # Restores the radius >>> mlab.rflip(False) """ _plot_data(rflip=rflip) @_close_gks_on_error def phiflip(phiflip=True): """ Enable or disable flipping/reversal of the polar angles. :param zflip: whether or not the start and end angle should be flipped **Usage examples:** >>> # Flips/Reverses the start and end angle >>> mlab.phiflip() >>> # Restores the angles >>> mlab.phiflip(False) """ _plot_data(phiflip=phiflip)
[docs]@_close_gks_on_error def colormap(colormap=''): """ Get or set the colormap for the current plot or enable manual colormap control. :param colormap: - The name of a gr colormap - One of the gr colormap constants (**gr.COLORMAP_...**) - A list of red-green-blue tuples as colormap - A dict mapping a normalized position to the corresponding red-green-blue tuple - **None**, if the colormap should use the current colors set by :py:func:`gr.setcolorrep` - No parameter or an empty string (default) to get the colormap as a list of red-green-blue tuples **Usage examples:** >>> # Use one of the built-in colormap names >>> mlab.colormap('viridis') >>> # Use one of the built-in colormap constants >>> mlab.colormap(gr.COLORMAP_BWR) >>> # Use a list of red-green-blue tuples as colormap >>> mlab.colormap([(0, 0, 1), (1, 1, 1), (1, 0, 0)]) >>> # Use a dict mapping a normalized position to the corresponding red-green-blue tuple as colormap >>> mlab.colormap({0.0: (0, 0, 1), 0.25: (1, 1, 1), 1.0: (1, 0, 0)}) >>> # Use a custom colormap using gr.setcolorrep directly >>> for i in range(256): ... gr.setcolorrep(1.0-i/255.0, 1.0, i/255.0) ... >>> mlab.colormap(None) >>> # Get the current colormap as list of red-green-blue tuples >>> colormap = mlab.colormap() """ if colormap == '': _set_colormap() return [(c[0], c[1], c[2]) for c in _colormap()] _plot_data(colormap=colormap)
@_close_gks_on_error def field_of_view(field_of_view): """ Set the vertical field of view of the current 3D plot. Depending on how the field of view is set, a different projection will be used for 3D plots. Setting field of view to a value between 0 and 180 degrees (exclusively) will use a perspective projection with the given vertical field of view. Setting field of view to 0 or NaN will use an orthographic projection and setting Field of view to None will use a custom projection with limited camera positioning. By default, a field of view set to None will be assumed. :param field_of_view: the vertical field of view or None **Usage examples:** >>> # Create example data >>> x = np.random.uniform(0, 1, 100) >>> y = np.random.uniform(0, 1, 100) >>> z = np.random.uniform(0, 1, 100) >>> # Set the field of view to 20 degrees and draw an example plot >>> mlab.field_of_view(20) >>> mlab.plot3(x, y, z) >>> # Select an orthographic projection instead >>> mlab.field_of_view(0) >>> mlab.plot3(x, y, z) >>> # Restore the default projection >>> mlab.field_of_view(None) >>> mlab.plot3(x, y, z) """ _plot_data(tilt=tilt)
[docs]@_close_gks_on_error def tilt(tilt): """ Set the 3d axis tilt of the current plot. How the tilt is interpreted depends on the current field_of_view setting. For the default projection, with field_of_view set to None, the tilt can be any value between 0 and 90, and controls the angle between the viewer and the X-Y-plane. For the orthographic or perspective projection, with field_of_view set to a value between 0 and 180 or to NaN, the tilt can be any value between 0 and 180 and controls the angle between the viewer and the z-axis. :param tilt: the 3d axis tilt **Usage examples:** >>> # Create example data >>> x = np.random.uniform(0, 1, 100) >>> y = np.random.uniform(0, 1, 100) >>> z = np.random.uniform(0, 1, 100) >>> # Set the tilt and draw an example plot >>> mlab.tilt(45) >>> mlab.plot3(x, y, z) """ _plot_data(tilt=tilt)
[docs]@_close_gks_on_error def rotation(rotation): """ Set the 3d axis rotation of the current plot. The rotation controls the angle between the viewer projected onto the X-Y-plane and the x-axis, setting the camera position for 3D plots in combination with the tilt setting. The range of values for the rotation depends on the current field_of_view setting. For the default projection, with field_of_view set to None, the rotation can be any value between 0 and 90 degrees. For the orthographic or perspective projection, with field_of_view set to a value between 0 and 180 or to NaN, the rotation can be any value between 0 and 360 degrees. :param rotation: the 3d axis rotation **Usage examples:** >>> # Create example data >>> x = np.random.uniform(0, 1, 100) >>> y = np.random.uniform(0, 1, 100) >>> z = np.random.uniform(0, 1, 100) >>> # Set the rotation and draw an example plot >>> mlab.rotation(45) >>> mlab.plot3(x, y, z) """ _plot_data(rotation=rotation)
[docs]@_close_gks_on_error def legend(*labels, **kwargs): r""" Set the labels and location for the legend of the current plot. The labels for the legend are drawn using the extended text function :py:func:`gr.textext`. You can use a subset of LaTeX math syntax, but will need to escape certain characters, e.g. parentheses. For more information see the documentation of :py:func:`gr.textext`. :param labels: the labels for each graph in the current plot :param location: the location of the legend (from 1 to 10) **Usage examples:** >>> # Set the labels for the two graphs "f(x)" and "g(x)" >>> mlab.legend("f\(x\)", "g\(x\)") >>> # Set the labels and draws the legend in the lower right corner >>> mlab.legend("f\(x\)", "g\(x\)", location=4) >>> # Resets the legend >>> mlab.legend() """ if not all(isinstance(label, basestring) for label in labels): raise TypeError('list of strings expected') _plot_data(labels=labels, **kwargs)
[docs]@_close_gks_on_error def savefig(filename): """ Save the current figure to a file. This function draw the current figure using one of GR's workstation types to create a file of the given name. Which file types are supported depends on the installed workstation types, but GR usually is built with support for .png, .jpg, .pdf, .ps, .gif and various other file formats. :param filename: the filename the figure should be saved to **Usage examples:** >>> # Create a simple plot >>> mlab.plot(range(100), lambda x: 1/(x+1)) >>> # Save the figure to a file >>> mlab.savefig("example.png") """ gr.beginprint(filename) _plot_data() gr.endprint()
[docs]@_close_gks_on_error def figure(**kwargs): """ Create a new figure with the given settings. Settings like the current colormap, title or axis limits as stored in the current figure. This function creates a new figure, restores the default settings and applies any settings passed to the function as keyword arguments. **Usage examples:** >>> # Restore all default settings >>> mlab.figure() >>> # Restore all default settings and set the title >>> mlab.figure(title="Example Figure") """ global _plt _plt = _Figure() _plt.kwargs.update(kwargs) return _plt
[docs]@_close_gks_on_error def hold(flag): """ Set the hold flag for combining multiple plots. The hold flag prevents drawing of axes and clearing of previous plots, so that the next plot will be drawn on top of the previous one. :param flag: the value of the hold flag **Usage examples:** >>> # Create example data >>> x = np.linspace(0, 1, 100) >>> # Draw the first plot >>> mlab.plot(x, lambda x: x**2) >>> # Set the hold flag >>> mlab.hold(True) >>> # Draw additional plots >>> mlab.plot(x, lambda x: x**4) >>> mlab.plot(x, lambda x: x**8) >>> # Reset the hold flag >>> mlab.hold(False) """ global _plt _plt.kwargs['ax'] = flag _plt.kwargs['clear'] = not flag
[docs]@_close_gks_on_error def subplot(num_rows, num_columns, subplot_indices): """ Set current subplot index. By default, the current plot will cover the whole window. To display more than one plot, the window can be split into a number of rows and columns, with the current plot covering one or more cells in the resulting grid. Subplot indices are one-based and start at the upper left corner, with a new row starting after every **num_columns** subplots. :param num_rows: the number of subplot rows :param num_columns: the number of subplot columns :param subplot_indices: - the subplot index to be used by the current plot - a pair of subplot indices, setting which subplots should be covered by the current plot **Usage examples:** >>> # Set the current plot to the second subplot in a 2x3 grid >>> mlab.subplot(2, 3, 2) >>> # Set the current plot to cover the first two rows of a 4x2 grid >>> mlab.subplot(4, 2, (1, 4)) >>> # Use the full window for the current plot >>> mlab.subplot(1, 1, 1) """ global _plt x_min = y_min = 1 x_max = y_max = 0 if isinstance(subplot_indices, int): subplot_indices = (subplot_indices,) for subplot_index in subplot_indices: row = num_rows - (subplot_index - 1.0) // num_columns column = (subplot_index - 1.0) % num_columns + 1 x_min = min(x_min, (column - 1) / num_columns) x_max = max(x_max, column / num_columns) y_min = min(y_min, (row - 1) / num_rows) y_max = max(y_max, row / num_rows) _plt.kwargs['subplot'] = [x_min, x_max, y_min, y_max] _plt.kwargs['clear'] = (subplot_indices[0] == 1) _plt.kwargs['update'] = (subplot_indices[-1] == num_rows * num_columns)
class _Figure(object): def __init__(self, width=600, height=450): self.args = [] self.kwargs = { 'size': (width, height), 'ax': False, 'subplot': [0, 1, 0, 1], 'clear': True, 'update': True } _plt = _Figure() _gr3_available = None def _gr3_is_available(): global _gr3_available if _gr3_available is None: try: gr3.init() except gr3.GR3_Exception: _gr3_available = False else: _gr3_available = True return _gr3_available def _colormap(): rgba = np.ones((256, 4), np.float32) for color_index in range(256): color = gr.inqcolor(1000 + color_index) rgba[color_index, 0] = (color % 256) / 255.0 rgba[color_index, 1] = ((color >> 8) % 256) / 255.0 rgba[color_index, 2] = ((color >> 16) % 256) / 255.0 return rgba def _set_colormap(): global _plt if 'cmap' in _plt.kwargs: warnings.warn('The parameter "cmap" has been replaced by "colormap". The value of "cmap" will be ignored.', stacklevel=3) colormap = _plt.kwargs.get('colormap', gr.COLORMAP_VIRIDIS) if colormap is None: return if isinstance(colormap, int): gr.setcolormap(colormap) return if hasattr(colormap, 'upper'): colormap_name = 'COLORMAP_' + colormap.upper() colormap = getattr(gr, colormap_name) gr.setcolormap(colormap) return if isinstance(colormap, dict): positions, colors = zip(*sorted(list(colormap.items()))) if isinstance(colormap, tuple): if isinstance(colormap[0], int): gr.setcolormap(colormap[0]) elif isinstance(colormap[1], int): gr.setcolormap(colormap[1]) else: gr.setcolormap(1) return else: positions = None colors = colormap gr.setcolormapfromrgb(colors, positions) def _interpret_size(size, dpi, default_size=(600, 450)): units = { 'px': (1, 0), 'in': (0, 1), '"': (0, 1), 'ft': (0, 12), '\'': (0, 12), 'mm': (0, 0.1 / 2.54), 'cm': (0, 1 / 2.54), 'dm': (0, 10 / 2.54), 'm': (0, 100 / 2.54) } def pixels_per_unit(unit, dpi): return units[unit][0] + units[unit][1] * dpi def interpret_length(length, dpi, default_length): if length is None: return default_length if is_floatish(length): return float(length) if hasattr(length, 'endswith'): for unit in units: if length.endswith(unit) and is_floatish(length[:-len(unit)]): length_value = float(length[:-len(unit)]) return length_value * pixels_per_unit(unit, dpi) print("Unable to interpret length '{}', falling back to default value: {} pixels".format(length, default_length), file=sys.stderr) return default_length def is_floatish(number): try: float(number) return True except ValueError: return False except TypeError: return False if len(size) == 2: width = interpret_length(size[0], dpi, default_size[0]) height = interpret_length(size[1], dpi, default_size[1]) return width, height if len(size) == 3 and is_floatish(size[0]) and is_floatish(size[1]) and size[2] in units: width_value = float(size[0]) height_value = float(size[1]) unit = size[2] width = width_value * pixels_per_unit(unit, dpi) height = height_value * pixels_per_unit(unit, dpi) return width, height if len(size) == 4 and is_floatish(size[0]) and size[1] in units and is_floatish(size[2]) and size[3] in units: width_value = float(size[0]) unit = size[1] width = width_value * pixels_per_unit(unit, dpi) height_value = float(size[2]) unit = size[3] height = height_value * pixels_per_unit(unit, dpi) return width, height print("Unable to interpret size '{}', falling back to default size: {} x {} pixels".format(repr(size), default_size[0], default_size[1]), file=sys.stderr) return default_size def _set_viewport(kind, subplot): global _plt metric_width, metric_height, pixel_width, pixel_height = gr.inqdspsize() if 'figsize' in _plt.kwargs: horizontal_pixels_per_inch = pixel_width * 0.0254 / metric_width vertical_pixels_per_inch = pixel_height * 0.0254 / metric_height width = _plt.kwargs['figsize'][0] * horizontal_pixels_per_inch height = _plt.kwargs['figsize'][1] * vertical_pixels_per_inch else: dpi = pixel_width / metric_width * 0.0254 width, height = _interpret_size(_plt.kwargs['size'], dpi) viewport = [0, 0, 0, 0] vp = subplot[:] if width > height: aspect_ratio = height / width metric_size = metric_width * width / pixel_width gr.setwsviewport(0, metric_size, 0, metric_size * aspect_ratio) gr.setwswindow(0, 1, 0, aspect_ratio) vp[2] *= aspect_ratio vp[3] *= aspect_ratio else: aspect_ratio = width / height metric_size = metric_height * height / pixel_height gr.setwsviewport(0, metric_size * aspect_ratio, 0, metric_size) gr.setwswindow(0, aspect_ratio, 0, 1) vp[0] *= aspect_ratio vp[1] *= aspect_ratio if kind in ('wireframe', 'surface', 'plot3', 'scatter3', 'trisurf', 'volume'): if kind in ('surface', 'trisurf', 'volume'): extent = min(vp[1] - vp[0] - 0.1, vp[3] - vp[2]) else: extent = min(vp[1] - vp[0], vp[3] - vp[2]) vp0 = 0.5 * (vp[0] + vp[1] - extent) vp1 = 0.5 * (vp[0] + vp[1] + extent) vp2 = 0.5 * (vp[2] + vp[3] - extent) vp3 = 0.5 * (vp[2] + vp[3] + extent) vp = (vp0, vp1, vp2, vp3) viewport[0] = vp[0] + 0.125 * (vp[1] - vp[0]) viewport[1] = vp[0] + 0.925 * (vp[1] - vp[0]) viewport[2] = vp[2] + 0.125 * (vp[3] - vp[2]) viewport[3] = vp[2] + 0.925 * (vp[3] - vp[2]) if width > height: viewport[2] += (1 - (subplot[3] - subplot[2])**2) * 0.02 colorbar = _plt.kwargs.get('colorbar', None) if colorbar or kind in ('contour', 'contourf', 'heatmap', 'polar_heatmap', 'hexbin', 'quiver'): viewport[1] -= 0.1 gr.setviewport(*viewport) _plt.kwargs['viewport'] = viewport _plt.kwargs['vp'] = vp _plt.kwargs['ratio'] = aspect_ratio if 'backgroundcolor' in _plt.kwargs: gr.savestate() gr.selntran(0) gr.setfillintstyle(gr.INTSTYLE_SOLID) gr.setfillcolorind(_plt.kwargs['backgroundcolor']) if width > height: gr.fillrect(subplot[0], subplot[1], subplot[2] * aspect_ratio, subplot[3] * aspect_ratio) else: gr.fillrect(subplot[0] * aspect_ratio, subplot[1] * aspect_ratio, subplot[2], subplot[3]) gr.selntran(1) gr.restorestate() if kind == 'polar': x_min, x_max, y_min, y_max = viewport x_center = 0.5 * (x_min + x_max) y_center = 0.5 * (y_min + y_max) r = 0.5 * min(x_max - x_min, y_max - y_min) gr.setviewport(x_center - r, x_center + r, y_center - r, y_center + r) def _fix_minmax(v_min, v_max): if v_min == v_max: if v_min == 0: v_min -= 0.1 v_max += 0.1 else: v_min -= 0.1 * np.abs(v_min) v_max += 0.1 * np.abs(v_max) return v_min, v_max def _minmax(kind=None): global _plt x_min = y_min = z_min = float('infinity') x_max = y_max = z_max = float('-infinity') x_step = y_step = float('-infinity') for x, y, z, c, spec in _plt.args: if x is None and kind in ('heatmap',): x_min = -0.5 x_max = z.shape[1] - 0.5 elif x is None and kind in ('polar_heatmap',): x_min = 0 x_max = z.shape[0] else: x_min = min(np.nanmin(x), x_min) x_max = max(np.nanmax(x), x_max) if y is None and kind in ('heatmap',): y_min = -0.5 y_max = z.shape[0] - 0.5 elif y is None and kind in ('polar_heatmap',): y_min = 0 y_max = z.shape[1] else: y_min = min(np.nanmin(y), y_min) y_max = max(np.nanmax(y), y_max) if z is not None and kind not in ('bar', ): z_min = min(np.nanmin(z), z_min) z_max = max(np.nanmax(z), z_max) if kind in ('quiver',): if len(x) > 1: x_step = max(np.abs(x[1:] - x[:-1]).max(), x_step) if len(y) > 1: y_step = max(np.abs(y[1:] - y[:-1]).max(), y_step) if kind in ('quiver',): if x_step is not None and x_step > 0: x_min -= x_step x_max += x_step if y_step is not None and y_step > 0: y_min -= y_step y_max += y_step # Use vector length for colormap x, y, u, v, spec = _plt.args[0] lengths_squared = u**2 + v**2 z_min = np.sqrt(np.min(lengths_squared)) z_max = np.sqrt(np.max(lengths_squared)) if kind in ('bar'): x_min -= 1 x_max += 1 else: x_min, x_max = _fix_minmax(x_min, x_max) y_min, y_max = _fix_minmax(y_min, y_max) z_min, z_max = _fix_minmax(z_min, z_max) x_range = _plt.kwargs.get('xlim', (x_min, x_max)) y_range = _plt.kwargs.get('ylim', (y_min, y_max)) z_range = _plt.kwargs.get('zlim', (z_min, z_max)) r_range = _plt.kwargs.get('rlim', (0, y_max)) phi_range = _plt.kwargs.get('philim', (None, None)) # Replace None with values determined above if x_range[0] is None: x_range = (x_min, x_range[1]) if x_range[1] is None: x_range = (x_range[0], x_max) if y_range[0] is None: y_range = (y_min, y_range[1]) if y_range[1] is None: y_range = (y_range[0], y_max) if z_range[0] is None: z_range = (z_min, z_range[1]) if z_range[1] is None: z_range = (z_range[0], z_max) if r_range[0] is None: r_range = (0, r_range[1]) if r_range[1] is None: r_range = (r_range[0], y_max) if phi_range[0] is None: phi_range = (0, phi_range[1]) else: phi_range = (np.degrees(phi_range[0]), phi_range[1]) if phi_range[1] is None: phi_range = (phi_range[0], 360) else: phi_range = (phi_range[0], np.degrees(phi_range[1])) _plt.kwargs['xrange'] = x_range _plt.kwargs['yrange'] = y_range _plt.kwargs['zrange'] = z_range _plt.kwargs['rrange'] = r_range _plt.kwargs['phirange'] = phi_range def _set_window(kind): global _plt scale = 0 if kind != 'polar': scale |= gr.OPTION_X_LOG if _plt.kwargs.get('xlog', False) else 0 scale |= gr.OPTION_Y_LOG if _plt.kwargs.get('ylog', False) else 0 scale |= gr.OPTION_Z_LOG if _plt.kwargs.get('zlog', False) else 0 scale |= gr.OPTION_FLIP_X if _plt.kwargs.get('xflip', False) else 0 scale |= gr.OPTION_FLIP_Y if _plt.kwargs.get('yflip', False) else 0 scale |= gr.OPTION_FLIP_Z if _plt.kwargs.get('zflip', False) else 0 _minmax(kind) if kind in ('wireframe', 'surface', 'plot3', 'scatter3', 'polar', 'trisurf', 'volume'): major_count = 2 else: major_count = 5 x_min, x_max = _plt.kwargs['xrange'] if not scale & gr.OPTION_X_LOG: if _plt.kwargs.get('adjust_xlim', True) and kind not in ('heatmap',): x_min, x_max = gr.adjustlimits(x_min, x_max) if kind in ('bar'): x_tick = 1 if 'xnotations' in _plt.kwargs: x_major_count = 0 else: x_major_count = 1 else: x_major_count = major_count x_tick = gr.tick(x_min, x_max) / x_major_count else: x_tick = x_major_count = 1 if not scale & gr.OPTION_FLIP_X: xorg = (x_min, x_max) else: xorg = (x_max, x_min) _plt.kwargs['xaxis'] = x_tick, xorg, x_major_count y_min, y_max = _plt.kwargs['yrange'] if kind in ('hist', 'bar') and 'ylim' not in _plt.kwargs: if scale & gr.OPTION_Y_LOG: y_min = 1 else: y_min = 0 if not scale & gr.OPTION_Y_LOG: if _plt.kwargs.get('adjust_ylim', True) and kind not in ('heatmap',): y_min, y_max = gr.adjustlimits(y_min, y_max) y_major_count = major_count y_tick = gr.tick(y_min, y_max) / y_major_count else: y_tick = y_major_count = 1 if not scale & gr.OPTION_FLIP_Y: yorg = (y_min, y_max) else: yorg = (y_max, y_min) _plt.kwargs['yaxis'] = y_tick, yorg, y_major_count _plt.kwargs['window'] = (x_min, x_max, y_min, y_max) if kind in ('polar', 'polar_heatmap'): phi_min, phi_max = _phase_wrapped_philim(adjust=_plt.kwargs.get('adjust_philim', True)) r_min, r_max = _plt.kwargs['rrange'] if _plt.kwargs.get('adjust_rlim', True): r_min, r_max = gr.adjustlimits(r_min, r_max) r_rrel_min = r_min / r_max angles = [a for a in (0, 90, 180, 270, 360) if phi_min < a < phi_max] + [phi_min, phi_max] bbox = [1, -1, 1, -1] for angle in angles: sinf = np.sin(np.radians(angle)) cosf = np.cos(np.radians(angle)) min_x = min(cosf * 1.12, r_rrel_min * cosf) max_x = max(cosf * 1.12, r_rrel_min * cosf) min_y = min(sinf * 1.12, r_rrel_min * sinf) max_y = max(sinf * 1.12, r_rrel_min * sinf) bbox[0] = min(bbox[0], min_x) bbox[1] = max(bbox[1], max_x) bbox[2] = min(bbox[2], min_y) bbox[3] = max(bbox[3], max_y) viewport = _plt.kwargs['viewport'] vp = _plt.kwargs['vp'] viewport_aspect = (viewport[1] - viewport[0]) / (viewport[3] - viewport[2]) vp_aspect = (vp[1] - vp[0]) / (vp[3] - vp[2]) vp_aspect = vp_aspect / viewport_aspect width = (bbox[1] - bbox[0]) / vp_aspect height = bbox[3] - bbox[2] aspect = width / height if aspect > 1: d = (width - height) / 2 bbox[3] += d bbox[2] -= d else: d = (height - width) / 2 bbox[1] += d bbox[0] -= d gr.setwindow(*bbox) else: gr.setwindow(x_min, x_max, y_min, y_max) if kind in ('wireframe', 'surface', 'plot3', 'scatter3', 'trisurf', 'volume'): z_min, z_max = _plt.kwargs['zrange'] if not scale & gr.OPTION_Z_LOG: if _plt.kwargs.get('adjust_zlim', True): z_min, z_max = gr.adjustlimits(z_min, z_max) z_major_count = major_count z_tick = gr.tick(z_min, z_max) / z_major_count else: z_tick = z_major_count = 1 if not scale & gr.OPTION_FLIP_Z: zorg = (z_min, z_max) else: zorg = (z_max, z_min) _plt.kwargs['zaxis'] = z_tick, zorg, z_major_count rotation = _plt.kwargs.get('rotation', 40) tilt = _plt.kwargs.get('tilt', 70) gr.setspace(z_min, z_max, rotation, tilt) fov = _plt.kwargs.get('field_of_view', None) if fov is not None: gr.setwindow3d(x_min, x_max, y_min, y_max, z_min, z_max) gr.setwindow(-1, 1, -1, 1) rotation %= 360 tilt %= 380 tilt = min(max(tilt, 0), 180) gr.setspace3d(-rotation, tilt, fov, 0) _plt.kwargs['scale'] = scale gr.setscale(scale) def _phase_wrapped_philim(phirange=None, adjust=False): if phirange is None: phi_min, phi_max = _plt.kwargs.get('phirange', (0, 360)) else: phi_min, phi_max = phirange if phi_min == 0 and phi_max == 360: return phi_min, phi_max phi_min -= np.floor(phi_min / 360.) * 360 phi_max -= np.floor(phi_max / 360.) * 360 if abs(phi_min - phi_max) < 1e-6: phi_max += 360 if phi_min > phi_max: phi_max, phi_min = phi_min, phi_max if adjust: phi_min, phi_max = gr.adjustlimits(phi_min / 3., phi_max / 3.) phi_min *= 3 phi_max *= 3 if phi_max > 360: phi_max = 360 if phi_min > 360: phi_min = 360 return phi_min, phi_max def _draw_axes(kind, pass_=1): global _plt viewport = _plt.kwargs['viewport'] vp = _plt.kwargs['vp'] x_tick, x_org, x_major_count = _plt.kwargs['xaxis'] y_tick, y_org, y_major_count = _plt.kwargs['yaxis'] # enforce uniform axis labels for logarithmic labels # otherwise the labels will switch between decimal and exponential notation if _plt.kwargs['scale'] & gr.OPTION_X_LOG: x_tick = 10 if _plt.kwargs['scale'] & gr.OPTION_Y_LOG: y_tick = 10 gr.setlinecolorind(1) gr.setlinewidth(1) diag = ((viewport[1] - viewport[0])**2 + (viewport[3] - viewport[2])**2)**0.5 charheight = max(0.018 * diag, 0.012) gr.setcharheight(charheight) ticksize = 0.0075 * diag if kind in ('wireframe', 'surface', 'plot3', 'scatter3', 'trisurf', 'volume'): z_tick, z_org, z_major_count = _plt.kwargs['zaxis'] fov = _plt.kwargs.get('field_of_view', None) if fov is None: if pass_ == 1: gr.grid3d(x_tick, 0, z_tick, x_org[0], y_org[1], z_org[0], 2, 0, 2) gr.grid3d(0, y_tick, 0, x_org[0], y_org[1], z_org[0], 0, 2, 0) else: gr.axes3d(x_tick, 0, z_tick, x_org[0], y_org[0], z_org[0], x_major_count, 0, z_major_count, -ticksize) gr.axes3d(0, y_tick, 0, x_org[1], y_org[0], z_org[0], 0, y_major_count, 0, ticksize) else: rotation = _plt.kwargs.get('rotation', 40) tilt = _plt.kwargs.get('tilt', 70) rotation %= 360 tilt %= 380 tilt = min(max(tilt, 0), 180) zi = 0 if 0 <= tilt <= 90 else 1 if pass_ == 1: if 0 <= rotation < 90: gr.grid3d(x_tick, 0, z_tick, x_org[0], y_org[1], z_org[zi], 2, 0, 2) gr.grid3d(0, y_tick, 0, x_org[0], y_org[1], z_org[zi], 0, 2, 0) elif 90 <= rotation < 180: gr.grid3d(x_tick, 0, z_tick, x_org[1], y_org[1], z_org[zi], 2, 0, 2) gr.grid3d(0, y_tick, 0, x_org[1], y_org[1], z_org[zi], 0, 2, 0) elif 180 <= rotation < 270: gr.grid3d(x_tick, 0, z_tick, x_org[1], y_org[0], z_org[zi], 2, 0, 2) gr.grid3d(0, y_tick, 0, x_org[1], y_org[0], z_org[zi], 0, 2, 0) else: gr.grid3d(x_tick, 0, z_tick, x_org[0], y_org[0], z_org[0], 2, 0, 2) gr.grid3d(0, y_tick, 0, x_org[0], y_org[0], z_org[zi], 0, 2, 0) else: if 0 <= rotation < 90: gr.axes3d(x_tick, 0, z_tick, x_org[0], y_org[0], z_org[zi], x_major_count, 0, z_major_count, -ticksize) gr.axes3d(0, y_tick, 0, x_org[1], y_org[0], z_org[zi], 0, y_major_count, 0, ticksize) elif 90 <= rotation < 180: gr.axes3d(0, 0, z_tick, x_org[0], y_org[1], z_org[zi], 0, 0, z_major_count, -ticksize) gr.axes3d(x_tick, y_tick, 0, x_org[0], y_org[0], z_org[zi], x_major_count, y_major_count, 0, -ticksize) elif 180 <= rotation < 270: gr.axes3d(x_tick, 0, z_tick, x_org[1], y_org[1], z_org[zi], x_major_count, 0, z_major_count, ticksize) gr.axes3d(0, y_tick, 0, x_org[0], y_org[0], z_org[zi], 0, y_major_count, 0, -ticksize) else: gr.axes3d(0, 0, z_tick, x_org[1], y_org[0], z_org[zi], 0, 0, z_major_count, -ticksize) gr.axes3d(x_tick, y_tick, 0, x_org[1], y_org[1], z_org[zi], x_major_count, y_major_count, 0, ticksize) else: if kind in ('heatmap', 'shade'): ticksize = -ticksize if kind not in ('shade',): gr.grid(x_tick, y_tick, 0, 0, x_major_count, y_major_count) gr.axes(x_tick, y_tick, x_org[0], y_org[0], x_major_count, y_major_count, ticksize) gr.axes(x_tick, y_tick, x_org[1], y_org[1], -x_major_count, -y_major_count, -ticksize) if 'title' in _plt.kwargs: gr.savestate() gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_TOP) gr.textext(0.5 * (viewport[0] + viewport[1]), vp[3], _plt.kwargs['title']) gr.restorestate() if kind in ('wireframe', 'surface', 'plot3', 'scatter3', 'trisurf', 'volume'): x_label = _plt.kwargs.get('xlabel', '') y_label = _plt.kwargs.get('ylabel', '') z_label = _plt.kwargs.get('zlabel', '') gr.titles3d(x_label, y_label, z_label) else: if 'xlabel' in _plt.kwargs: gr.savestate() gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_BOTTOM) gr.textext(0.5 * (viewport[0] + viewport[1]), vp[2] + 0.5 * charheight, _plt.kwargs['xlabel']) gr.restorestate() if 'ylabel' in _plt.kwargs: gr.savestate() gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_TOP) gr.setcharup(-1, 0) gr.textext(vp[0] + 0.5 * charheight, 0.5 * (viewport[2] + viewport[3]), _plt.kwargs['ylabel']) gr.restorestate() if kind in ('bar'): if 'xnotations' in _plt.kwargs: x_notations = _plt.kwargs.pop('xnotations') yval = _plt.args[0][2] if _plt.kwargs['multi_bar'] else _plt.args[0][1] if len(x_notations) == len(yval): window = _plt.kwargs['window'] gr.setcharheight(charheight) gr.settextalign(2, 1) for i in range(1, len(x_notations) + 1): x = viewport[0] + ((viewport[1] - viewport[0]) * i) / (window[1] - window[0]) y = viewport[2] - 0.5 * charheight gr.textext(x, y, x_notations[i - 1]) else: raise IndexError('The length of xnotations has to equal the amount of y-values!') def _draw_polar_axes(): global _plt viewport = _plt.kwargs['viewport'] diag = ((viewport[1] - viewport[0])**2 + (viewport[3] - viewport[2])**2)**0.5 charheight = max(0.018 * diag, 0.012) r_min, r_max = _plt.kwargs['rrange'] if _plt.kwargs.get('adjust_rlim', True): r_min, r_max = gr.adjustlimits(r_min, r_max) phi_min, phi_max = _phase_wrapped_philim(adjust=_plt.kwargs.get('adjust_philim', True)) gr.savestate() gr.setcharheight(charheight) gr.setlinetype(gr.LINETYPE_SOLID) tick = gr.tick(phi_min / 6., phi_max / 6.) * 1.5 n = int(round((phi_max - phi_min) / tick + 0.5)) for i in range(n + 1): angle_label = phi_min + i * tick if _plt.kwargs.get('phiflip', False): angle = phi_max - i * tick else: angle = angle_label sinf = np.sin(np.radians(angle)) cosf = np.cos(np.radians(angle)) pline = np.array([r_min / r_max, 1]) if phi_min <= angle <= phi_max: if i % 2 == 0 and not (i == n and phi_max % 360 == phi_min % 360): gr.setlinecolorind(88) gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_HALF) x, y = gr.wctondc(1.1 * cosf, 1.1 * sinf) gr.textext(x, y, "%g\xb0" % angle_label) else: gr.setlinecolorind(90) else: angle = np.clip(angle, phi_min, phi_max) gr.setlinecolorind(88) sinf = np.sin(np.radians(angle)) cosf = np.cos(np.radians(angle)) gr.polyline(cosf * pline, sinf * pline) tick = 0.5 * gr.tick(r_min, r_max) n = int(round((r_max - r_min) / tick + 0.5)) for i in range(n + 1): r = (r_min + i * tick) / r_max gr.setlinecolorind(88) r = np.clip(r, 0, 1) if i % 2 == 1 and r <= 1: gr.setlinecolorind(90) gr.drawarc(-r, r, -r, r, phi_min, phi_max) if i % 2 == 0 and not r > 1: gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_HALF) sinf = np.sin(np.radians(phi_min)) cosf = np.cos(np.radians(phi_min)) x, y = gr.wctondc(r * cosf + sinf * 0.05, r * sinf - cosf * 0.05) if _plt.kwargs.get('rflip', False): r_label = r_max - i * tick else: r_label = r_min + i * tick gr.text(x, y, "%g" % r_label) gr.restorestate() def _draw_legend(): global _plt viewport = _plt.kwargs['viewport'] num_labels = len(_plt.kwargs['labels']) location = _plt.kwargs.get('location', 1) gr.savestate() gr.selntran(0) gr.setscale(0) w = 0 for label in _plt.kwargs['labels']: tbx, tby = gr.inqtextext(0, 0, label) w = max(w, tbx[2]) num_lines = len(_plt.args) h = (num_lines + 1) * 0.03 if location in (8, 9, 10): px = 0.5 * (viewport[0] + viewport[1] - w) elif location in (2, 3, 6): px = viewport[0] + 0.11 else: px = viewport[1] - 0.05 - w if location in (5, 6, 7, 10): py = 0.5 * (viewport[2] + viewport[3] + h) - 0.03 elif location in (3, 4, 8): py = viewport[2] + h else: py = viewport[3] - 0.06 gr.setfillintstyle(gr.INTSTYLE_SOLID) gr.setfillcolorind(0) gr.fillrect(px - 0.08, px + w + 0.02, py + 0.03, py - 0.03 * num_lines) gr.setlinetype(gr.LINETYPE_SOLID) gr.setlinecolorind(1) gr.setlinewidth(1) gr.drawrect(px - 0.08, px + w + 0.02, py + 0.03, py - 0.03 * num_lines) i = 0 gr.uselinespec(" ") for (x, y, z, c, spec) in _plt.args: gr.savestate() mask = gr.uselinespec(spec) if mask in (0, 1, 3, 4, 5): gr.polyline([px - 0.07, px - 0.01], [py, py]) if mask & 2: gr.polymarker([px - 0.06, px - 0.02], [py, py]) gr.restorestate() gr.settextalign(gr.TEXT_HALIGN_LEFT, gr.TEXT_VALIGN_HALF) if i < num_labels: gr.textext(px, py, _plt.kwargs['labels'][i]) i += 1 py -= 0.03 gr.selntran(1) gr.restorestate() def _colorbar(off=0.0, colors=256, label_name='zlabel'): global _plt gr.savestate() viewport = _plt.kwargs['viewport'] zmin, zmax = _plt.kwargs['zrange'] zlog = _plt.kwargs.get('zlog', None) gr.setviewport(viewport[1] + 0.02 + off, viewport[1] + 0.05 + off, viewport[2], viewport[3]) gr.setwindow(0, 1, 0, 1) if colors == 1: data = [1000] else: data = [1000 + int(255 * i / (colors - 1)) for i in range(colors)] gr.setlinecolorind(1) gr.setscale(0) if _plt.kwargs['scale'] & gr.OPTION_FLIP_Z: gr.cellarray(0, 1, 0, 1, 1, colors, data) else: gr.cellarray(0, 1, 1, 0, 1, colors, data) diag = ((viewport[1] - viewport[0]) ** 2 + (viewport[3] - viewport[2]) ** 2) ** 0.5 charheight = max(0.016 * diag, 0.012) gr.setcharheight(charheight) if 0 < zmin < zmax and zlog: gr.setwindow(0, 1, zmin, zmax) if _plt.kwargs['scale'] & gr.OPTION_FLIP_Z: gr.setscale(gr.OPTION_Y_LOG | gr.OPTION_FLIP_Y) else: gr.setscale(gr.OPTION_Y_LOG) gr.axes(0, 2, 1, zmin, 0, 1, 0.005) elif zmin <= zmax and not zlog: if _plt.kwargs['scale'] & gr.OPTION_FLIP_Z: gr.setscale(gr.OPTION_FLIP_Y) else: gr.setscale(0) ztick = 0.5 * gr.tick(zmin, zmax) gr.setwindow(0, 1, zmin, zmax) gr.axes(0, ztick, 1, zmin, 0, 1, 0.005) else: if zmin == float('inf') and zmax != float('-inf') and not (zmax < 0 and zlog): labels = ("min", str(zmax)) elif zmax == float('-inf') and zmin != float('inf') and not (zmin < 0 and zlog): labels = (str(zmin), "max") else: labels = ("min", "max") def axeslbl_callback(x, y, svalue, value, labels=labels): gr.text(x, y, labels[int(value)]) if _plt.kwargs['scale'] & gr.OPTION_FLIP_Z: gr.setscale(gr.OPTION_FLIP_Y) else: gr.setscale(0) gr.axeslbl(0, 1, 1, 0, 0, 1, 0.005, 0, axeslbl_callback) label = _plt.kwargs.get(label_name, None) if label: diag = ((viewport[1] - viewport[0])**2 + (viewport[3] - viewport[2])**2)**0.5 charheight = max(0.018 * diag, 0.012) gr.setcharheight(charheight) gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_BASE) gr.textext(viewport[1] + 0.035 + off, viewport[3] + 0.01, label) gr.restorestate() def _plot_data(**kwargs): global _plt _plt.kwargs.update(kwargs) colorbar = _plt.kwargs.get('colorbar', None) if not _plt.args: return kind = _plt.kwargs.get('kind', 'line') if _plt.kwargs['clear']: gr.clearws() if kind in ('imshow', 'isosurface'): _set_viewport(kind, _plt.kwargs['subplot']) elif not _plt.kwargs['ax']: _set_viewport(kind, _plt.kwargs['subplot']) _set_window(kind) if kind == 'polar': _draw_polar_axes() elif kind == 'polar_heatmap': pass else: _draw_axes(kind) elif kind in ('polar', 'polar_heatmap'): _set_viewport(kind, _plt.kwargs['subplot']) _set_window(kind) _set_colormap() if colorbar and kind not in ('quiver', 'hexbin', 'contour', 'contourf', 'surface', 'trisurf', 'heatmap', 'volume' 'polar_heatmap'): _colorbar() gr.uselinespec(" ") for x, y, z, c, spec in _plt.args: gr.savestate() if 'alpha' in _plt.kwargs: gr.settransparency(_plt.kwargs['alpha']) if kind == 'line': mask = gr.uselinespec(spec) if mask in (0, 1, 3, 4, 5): gr.polyline(x, y) if mask & 2: gr.polymarker(x, y) if kind == 'step': mask = gr.uselinespec(spec) if mask in (0, 1, 3, 4, 5): where = _plt.kwargs.get('step_where', 'mid') if where == 'pre': n = len(x) x_step_boundaries = np.zeros(2 * n - 1) y_step_values = np.zeros(2 * n - 1) x_step_boundaries[0] = x[0] x_step_boundaries[1::2] = x[:-1] x_step_boundaries[2::2] = x[1:] y_step_values[0] = y[0] y_step_values[1::2] = y[1:] y_step_values[2::2] = y[1:] elif where == 'post': n = len(x) x_step_boundaries = np.zeros(2 * n - 1) y_step_values = np.zeros(2 * n - 1) x_step_boundaries[0::2] = x x_step_boundaries[1::2] = x[1:] x_step_boundaries[-1] = x[-1] y_step_values[0::2] = y y_step_values[1::2] = y[:-1] y_step_values[-1] = y[-1] else: n = len(x) x_step_boundaries = np.zeros(2 * n) x_step_boundaries[0] = x[0] x_step_boundaries[1:-1][0::2] = (x[1:] + x[:-1]) / 2 x_step_boundaries[1:-1][1::2] = (x[1:] + x[:-1]) / 2 x_step_boundaries[-1] = x[-1] y_step_values = np.zeros(2 * n) y_step_values[0::2] = y y_step_values[1::2] = y gr.polyline(x_step_boundaries, y_step_values) if mask & 2: gr.polymarker(x, y) elif kind == 'scatter': gr.setmarkertype(gr.MARKERTYPE_SOLID_CIRCLE) if z is not None or c is not None: if c is not None: c_min = c.min() c_ptp = c.ptp() for i in range(len(x)): if z is not None: gr.setmarkersize(z[i] / 100.0) if c is not None: c_index = 1000 + int(255 * (c[i] - c_min) / c_ptp) gr.setmarkercolorind(c_index) gr.polymarker([x[i]], [y[i]]) else: gr.polymarker(x, y) elif kind == 'bar': _plot_bar() elif kind == 'polar_histogram': _plot_polar_histogram() elif kind == 'quiver': u = z v = c gr.quiver(len(x), len(y), x, y, u, v, True) if colorbar or colorbar is None: _colorbar(0.05) elif kind == 'stem': gr.setlinecolorind(1) gr.polyline(_plt.kwargs['window'][:2], [0, 0]) gr.setmarkertype(gr.MARKERTYPE_SOLID_CIRCLE) gr.uselinespec(spec) for xi, yi in zip(x, y): gr.polyline([xi, xi], [0, yi]) gr.polymarker(x, y) elif kind == 'hist': y_min = _plt.kwargs['window'][2] for i in range(1, len(y) + 1): gr.setfillcolorind(989) gr.setfillintstyle(gr.INTSTYLE_SOLID) gr.fillrect(x[i - 1], x[i], y_min, y[i - 1]) gr.setfillcolorind(1) gr.setfillintstyle(gr.INTSTYLE_HOLLOW) gr.fillrect(x[i - 1], x[i], y_min, y[i - 1]) elif kind == 'contour': z_min, z_max = _plt.kwargs['zrange'] gr.setspace(z_min, z_max, 0, 90) num_levels = _plt.kwargs.get('levels', 20) if x.shape == y.shape == z.shape: x, y, z = gr.gridit(x, y, z, 200, 200) z = np.array(z) z_min, z_max = _plt.kwargs.get('zlim', (np.min(z), np.max(z))) else: z = np.ascontiguousarray(z) if _plt.kwargs['scale'] & gr.OPTION_Z_LOG: h = [np.exp(np.log(z_min) + i / num_levels * (np.log(z_max) - np.log(z_min))) for i in range(num_levels)] else: h = [z_min + i / num_levels * (z_max - z_min) for i in range(num_levels)] z.shape = np.prod(z.shape) gr.contour(x, y, h, z, 1000) if colorbar or colorbar is None: _colorbar(colors=num_levels) elif kind == 'contourf': z_min, z_max = _plt.kwargs['zrange'] gr.setspace(z_min, z_max, 0, 90) scale = _plt.kwargs['scale'] num_levels = _plt.kwargs.get('levels', 20) gr.setscale(scale) if x.shape == y.shape == z.shape: x, y, z = gr.gridit(x, y, z, 200, 200) z = np.array(z) z_min, z_max = _plt.kwargs.get('zlim', (np.min(z), np.max(z))) else: z = np.ascontiguousarray(z) if _plt.kwargs['scale'] & gr.OPTION_Z_LOG: h = [np.exp(np.log(z_min) + i / num_levels * (np.log(z_max) - np.log(z_min))) for i in range(num_levels)] else: h = [z_min + i / num_levels * (z_max - z_min) for i in range(num_levels)] z.shape = np.prod(z.shape) if colorbar or colorbar is None: _colorbar(colors=num_levels) gr.setlinecolorind(1) gr.contourf(x, y, h, z, 0) elif kind == 'hexbin': nbins = _plt.kwargs.get('nbins', 40) cntmax = gr.hexbin(x, y, nbins) if cntmax > 0: _plt.kwargs['zrange'] = (0, cntmax) if colorbar or colorbar is None: _colorbar() elif kind == 'heatmap': x_min, x_max, y_min, y_max = _plt.kwargs['window'] if x is not None: x_min, x_max = x if y is not None: y_min, y_max = y height, width = z.shape cmap = _colormap() icmap = np.zeros(256, np.uint32) for i in range(256): r, g, b, a = cmap[i] icmap[i] = (int(r * 255) << 0) + (int(g * 255) << 8) + (int(b * 255) << 16) + (int(a * 255) << 24) z_min, z_max = _plt.kwargs.get('zlim', (np.min(z), np.max(z))) if z_max < z_min: z_max, z_min = z_min, z_max if _plt.kwargs.get('zlog', False): z = np.log(z) z_min = np.log(z_min) z_max = np.log(z_max) if z_max > z_min: data = (z - z_min) / (z_max - z_min) * 255 else: data = np.zeros((height, width)) rgba = np.zeros((height, width), np.uint32) for x in range(width): for y in range(height): if 0 <= data[y, x] <= 255: rgba[y, x] = icmap[int(data[y, x])] else: # make invalid values transparent rgba[y, x] = 0 y_min, y_max = y_max, y_min gr.drawimage(x_min, x_max, y_min, y_max, width, height, rgba) if colorbar or colorbar is None: _colorbar() elif kind == 'polar_heatmap': height, width = z.shape z_min, z_max = _plt.kwargs.get('zlim', (np.min(z), np.max(z))) if z_max < z_min: z_max, z_min = z_min, z_max if _plt.kwargs.get('zlog', False): z = np.log(z) z_min = np.log(z_min) z_max = np.log(z_max) if z_max > z_min: data = (1000 + (z - z_min) / (z_max - z_min) * 255).astype(np.int) else: data = np.zeros((height, width), dtype=np.int) if y is not None: phi_range = np.degrees(y) else: phi_range = None phi_min, phi_max = _phase_wrapped_philim(phi_range) if _plt.kwargs.get('phiflip', False): phi_min_adj, phi_max_adj = _phase_wrapped_philim(adjust=_plt.kwargs.get('adjust_philim', True)) phi_offset = phi_max_adj + phi_min_adj - phi_max - phi_min phi_min, phi_max = phi_max + phi_offset, phi_min + phi_offset if x is not None: r_range = np.array(x) else: r_range = np.array(_plt.kwargs['rrange']) r_min, r_max = _plt.kwargs['rrange'] relative_r_min = r_min / r_max r_range = ((r_range - r_min) / (r_max - r_min) * (1 - relative_r_min)) + relative_r_min if _plt.kwargs.get('rflip', False): r_range = 1 + relative_r_min - r_range gr.polarcellarray(0, 0, phi_min, phi_max, r_range[0], r_range[1], width, height, data) _draw_polar_axes() if colorbar or colorbar is None: _colorbar() elif kind == 'wireframe': if x.shape == y.shape == z.shape: x, y, z = gr.gridit(x, y, z, 50, 50) z = np.array(z) else: z = np.ascontiguousarray(z) z.shape = np.prod(z.shape) gr.setfillcolorind(0) gr.surface(x, y, z, gr.OPTION_FILLED_MESH) _draw_axes(kind, 2) elif kind == 'surface': if x.shape == y.shape == z.shape: x, y, z = gr.gridit(x, y, z, 200, 200) z = np.array(z) else: z = np.ascontiguousarray(z) z.shape = np.prod(z.shape) if _plt.kwargs.get('accelerate', True) and _gr3_is_available(): gr3.clear() gr3.surface(x, y, z, gr.OPTION_COLORED_MESH) else: gr.surface(x, y, z, gr.OPTION_COLORED_MESH) _draw_axes(kind, 2) if colorbar or colorbar is None: _colorbar(0.05) elif kind == 'plot3': gr.polyline3d(x, y, z) _draw_axes(kind, 2) elif kind == 'scatter3': gr.setmarkertype(gr.MARKERTYPE_SOLID_CIRCLE) if c is not None: c_min = c.min() c_ptp = c.ptp() for i in range(len(x)): c_index = 1000 + int(255 * (c[i] - c_min) / c_ptp) gr.setmarkercolorind(c_index) gr.polymarker3d([x[i]], [y[i]], [z[i]]) else: gr.polymarker3d(x, y, z) _draw_axes(kind, 2) elif kind == 'imshow': _plot_img(z) elif kind == 'isosurface': _plot_iso(z) elif kind == 'volume': algorithm = _plt.kwargs.get('algorithm', 'maximum').lower() if algorithm == 'emission': _algorithm = 0 elif algorithm == 'absorption': _algorithm = 1 elif algorithm in ('mip', 'maximum'): _algorithm = 2 else: raise ValueError('Invalid volume algorithm. Use "emission", "absorption" or "maximum".') dmin = _plt.kwargs.get('dmin', -1) dmax = _plt.kwargs.get('dmax', -1) if dmin is None: dmin = -1 if dmax is None: dmax = -1 width, height, device_pixel_ratio = gr.inqvpsize() gr.setpicturesizeforvolume(int(width * device_pixel_ratio), int(height * device_pixel_ratio)) dmin, dmax = gr.volume(c, algorithm=_algorithm, dmin=dmin, dmax=dmax) prev_zrange = _plt.kwargs.get('zrange', None) _plt.kwargs['zrange'] = (dmin, dmax) if colorbar or colorbar is None: _colorbar(0.05, label_name='dlabel') _plt.kwargs['zrange'] = prev_zrange _draw_axes(kind, 2) elif kind == 'polar': gr.uselinespec(spec) _plot_polar(x, y) elif kind == 'trisurf': gr.trisurface(x, y, z) _draw_axes(kind, 2) if colorbar or colorbar is None: _colorbar(0.05) elif kind == 'tricont': zmin, zmax = _plt.kwargs['zrange'] levels = np.linspace(zmin, zmax, 20) gr.tricontour(x, y, z, levels) elif kind == 'shade': xform = _plt.kwargs.get('xform', 5) if np.any(np.isnan(x)): gr.shadelines(x, y, xform=xform) else: gr.shadepoints(x, y, xform=xform) gr.restorestate() if kind in ('line', 'step', 'scatter', 'stem') and 'labels' in _plt.kwargs: _draw_legend() if _plt.kwargs['update']: gr.updatews() if gr.isinline(): return gr.show() def _plot_img(image): global _plt if isinstance(image, basestring): width, height, data = gr.readimage(image) if width == 0 or height == 0: return else: image = np.array(image) height, width = image.shape data = np.array(1000 + (1.0 * image - image.min()) / image.ptp() * 255, np.int32) if _plt.kwargs['clear']: gr.clearws() if not _plt.kwargs['ax']: _set_viewport('line', _plt.kwargs['subplot']) viewport = _plt.kwargs['viewport'] vp = _plt.kwargs['vp'] if width * (viewport[3] - viewport[2]) < height * (viewport[1] - viewport[0]): w = width / height * (viewport[3] - viewport[2]) x_min = max(0.5 * (viewport[0] + viewport[1] - w), viewport[0]) x_max = min(0.5 * (viewport[0] + viewport[1] + w), viewport[1]) y_min = viewport[2] y_max = viewport[3] else: h = height / width * (viewport[1] - viewport[0]) x_min = viewport[0] x_max = viewport[1] y_min = max(0.5 * (viewport[3] + viewport[2] - h), viewport[2]) y_max = min(0.5 * (viewport[3] + viewport[2] + h), viewport[3]) _set_colormap() gr.selntran(0) if isinstance(image, basestring): gr.drawimage(x_min, x_max, y_min, y_max, width, height, data) else: gr.cellarray(x_min, x_max, y_min, y_max, width, height, data) if 'title' in _plt.kwargs: gr.savestate() gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_TOP) gr.textext(0.5 * (viewport[0] + viewport[1]), vp[3], _plt.kwargs['title']) gr.restorestate() gr.selntran(1) def _plot_iso(v): global _plt if not _gr3_is_available(): raise RuntimeError("Unable to initialize GR3, please ensure that your system supports OpenGL") viewport = _plt.kwargs['viewport'] if viewport[3] - viewport[2] < viewport[1] - viewport[0]: width = viewport[3] - viewport[2] center_x = 0.5 * (viewport[0] + viewport[1]) x_min = max(center_x - 0.5 * width, viewport[0]) x_max = min(center_x + 0.5 * width, viewport[1]) y_min = viewport[2] y_max = viewport[3] else: height = viewport[1] - viewport[0] center_y = 0.5 * (viewport[2] + viewport[3]) x_min = viewport[0] x_max = viewport[1] y_min = max(center_y - 0.5 * height, viewport[2]) y_max = min(center_y + 0.5 * height, viewport[3]) gr.selntran(0) usable_vs = v[np.abs(v) != np.inf] if np.prod(usable_vs.shape) == 0: return v_max = usable_vs.max() v_min = usable_vs.min() if v_min == v_max: return uint16_max = np.iinfo(np.uint16).max if v.dtype == np.uint16: isovalue = int(_plt.kwargs.get('isovalue', 0.5 * uint16_max)) else: isovalue = _plt.kwargs.get('isovalue', 0.5) isovalue = int((isovalue - v_min) / (v_max - v_min) * uint16_max) v = (np.clip(v, v_min, v_max) - v_min) / (v_max - v_min) * uint16_max values = v.astype(np.uint16) nx, ny, nz = v.shape rotation = np.radians(_plt.kwargs.get('rotation', 40)) tilt = np.radians(_plt.kwargs.get('tilt', 70)) gr3.clear() mesh = gr3.createisosurfacemesh(values, (2 / (nx - 1), 2 / (ny - 1), 2 / (nz - 1)), (-1., -1., -1.), isovalue) color = _plt.kwargs.get('color', (0.0, 0.5, 0.8)) gr3.setbackgroundcolor(1, 1, 1, 0) gr3.drawmesh(mesh, 1, (0, 0, 0), (0, 0, 1), (0, 1, 0), color, (1, 1, 1)) r = 2.5 if tilt != 0: up = (0, 1, 0) else: up = (0, 0, 1) gr3.cameralookat( r * np.sin(tilt) * np.sin(rotation), r * np.cos(tilt), r * np.sin(tilt) * np.cos(rotation), 0, 0, 0, up[0], up[1], up[2] ) gr3.drawimage(x_min, x_max, y_min, y_max, 500, 500, gr3.GR3_Drawable.GR3_DRAWABLE_GKS) gr3.deletemesh(mesh) gr.selntran(1) def _plot_polar(phi, rho): global _plt r_min, r_max = _plt.kwargs['rrange'] if _plt.kwargs.get('adjust_rlim', True): r_min, r_max = gr.adjustlimits(r_min, r_max) phi_min, phi_max = np.radians(_phase_wrapped_philim(adjust=_plt.kwargs.get('adjust_philim', True))) phi = np.fmod(phi, 2 * np.pi) split_rho = np.logical_or(rho < r_min, rho > r_max) split_phi = np.logical_or(phi < phi_min, phi > phi_max) split = np.where(np.logical_or(split_phi, split_rho))[0].astype(np.int) relative_r_min = r_min / r_max rho = ((rho - r_min) / (r_max - r_min) * (1 - relative_r_min)) + relative_r_min if _plt.kwargs.get('rflip', False): rho = 1 + relative_r_min - rho if _plt.kwargs.get('phiflip', False): phi = phi_max + phi_min - phi for r, a in zip(np.split(rho, split), np.split(phi, split)): if not len(r) > 1: continue x = r * np.cos(a) y = r * np.sin(a) gr.polyline(x, y) def _convert_to_array(obj, may_be_2d=False, xvalues=None, always_flatten=False): global _plt if callable(obj): if xvalues is None: raise TypeError('object is callable, but xvalues is None') if len(xvalues.shape) == 1: a = np.fromiter((obj(x) for x in xvalues), np.float64) else: a = np.fromiter((obj(x, y) for x, y in xvalues), np.float64) else: a = obj try: if obj.__iter__() is obj: a = np.fromiter(obj, np.float64, len(xvalues)) except (AttributeError, TypeError): pass try: a = np.array(a, copy=False) except TypeError: raise TypeError("expected a sequence, but got '{}'".format(type(obj))) if always_flatten: # Only contiguous arrays can be flattened this way a = np.ascontiguousarray(a) a.shape = np.prod(a.shape) # Ensure a shape of (n,) or (n, 2) if may_be_2d is True dimension = sum(i != 1 for i in a.shape) if may_be_2d and dimension > 2: raise TypeError("expected a 1d- or 2d-sequence, but got shape {}".format(a.shape)) if not may_be_2d and dimension > 1: raise TypeError("expected a 1d-sequence, but got shape {}".format(a.shape)) if dimension == 0: dimension = 1 a.shape = [1] else: a.shape = [i for i in a.shape if i != 1] if dimension == 2: if 2 not in a.shape: raise TypeError("expected a sequence of pairs, but got shape {}".format(a.shape)) if a.shape[0] == 2 and a.shape[1] != 2: a = a.T n = len(a) assert a.shape == (n,) or a.shape == (n, 2) if a.dtype == complex: if dimension != 1: raise TypeError("expected a sequence of complex values, but got shape {}".format(a.shape)) a = np.hstack((np.real(a), np.imag(a))) dimension = 2 elif a.dtype != np.float64: try: a = a.astype(np.float64) except (TypeError, ValueError): raise TypeError("expected a sequence of real values, but got '{}'".format(a.dtype)) return a def _plot_bar(): global _plt kwargs = _plt.kwargs args = _plt.args[0] multi_bar = kwargs['multi_bar'] style = kwargs['bar_style'] # Default bar_width = 1 edgewidth = 1 edgecolor = [0, 0, 0] std_colors = [989, 982, 980, 981, 996, 983, 995, 988, 986, 990, 991, 984, 992, 993, 994, 987, 985, 997, 998, 999] color = std_colors[0] color_save_spot = 1000 # Args if multi_bar: y_values = args[2] colorlist = args[3] else: y_values = args[1] colorlist = args[3] # Width if kwargs.get('bar_width'): bar_width = kwargs['bar_width'] if not isinstance(bar_width, (int, float)): raise TypeError('Bar_width has to be an int or float!') elif bar_width > 1 or bar_width <= 0: raise ValueError('Bar_width has to be in (0;1]!') # Color if kwargs.get('bar_color'): color = kwargs['bar_color'] if isinstance(kwargs['bar_color'], int): if not color >= 0 or not color < 1256: raise ValueError('Bar_color has to be in [0;1256)!') elif isinstance(kwargs['bar_color'], list) and len(kwargs['bar_color']) == 3: for c in color: if c < 0 or c > 1: raise ValueError('The values of bar_color have to be in [1;0]!') else: raise TypeError('Bar_color has to be an int or list of RGB values!') # EdgeColor if kwargs.get('edge_color'): edgecolor = kwargs['edge_color'] if isinstance(kwargs['edge_color'], int): if not edgecolor >= 0 or not edgecolor < 1256: raise ValueError('Edge_color has to be in [0;1256)!') elif isinstance(kwargs['edge_color'], list) and len(kwargs['edge_color']) == 3: for c in edgecolor: if c < 0 or c > 1: raise ValueError('The values of edge_color have to be in [1;0]!') else: raise TypeError('Edge_color has to be an int or list of RGB values!') # EdgeWidth if kwargs.get('edge_width'): if not isinstance(kwargs['edge_width'], (float, int)): raise TypeError('Edge_width has to be of type int or float') edgewidth = kwargs['edge_width'] if edgewidth < 0: raise ValueError('Edge_width has to be bigger or equal to 0!') # Change individual color pos_indcolor = [] changecolor = False indcolors = [None for i in range(len(y_values) + 1)] if 'ind_bar_color' in kwargs: changecolor = True indcolor = kwargs.pop('ind_bar_color') if not isinstance(indcolor, list): raise TypeError('Ind_bar_color has to be a list!') if not indcolor: raise IndexError('Ind_bar_color can`t be empty!') if isinstance(indcolor[0], list) and not indcolor[0]: raise IndexError('Ind_bar_color[0] can`t be empty!') try: if isinstance(indcolor[0], list) and isinstance(indcolor[0][1], int) or isinstance(indcolor[0], int): has_pairs = False elif isinstance(indcolor[0], list) and isinstance(indcolor[0][1], list): has_pairs = True else: raise TypeError('Ind_bar_color[0] has to be an int or list!') except IndexError: raise IndexError('Ind_bar_color[0] has to be a colorpack, a list bigger than 1 or an int!') if not has_pairs: indcolor = [indcolor] for colorpack in indcolor: if not isinstance(colorpack[0], (list, int)) or not isinstance(colorpack[1], list): raise TypeError('Colorpack[0] has to be an int or list and colorpack[1] has to be a list!') if not len(colorpack[1]) == 3: raise IndexError('Colorpack[1] has to be of length 3!') for c in colorpack[1]: if c < 0 or c > 1: raise ValueError('The values in colorpack[1] have to be in [0;1]!') indcolor_rgb = [colorpack[1][0], colorpack[1][1], colorpack[1][2]] if isinstance(colorpack[0], list): pos_indcolor.extend(colorpack[0]) for index in colorpack[0]: indcolors[index] = indcolor_rgb elif isinstance(colorpack[0], int): pos_indcolor.append(colorpack[0]) indcolors[colorpack[0]] = indcolor_rgb # Change individual edgecolor pos_indedgecolor = [] changeedgecolor = False indedgecolors = [None for i in range(len(y_values) + 1)] if 'ind_edge_color' in kwargs: changeedgecolor = True indedgecolor = kwargs.pop('ind_edge_color') if not isinstance(indedgecolor, list): raise TypeError('Ind_edge_color has to be a list!') try: if isinstance(indedgecolor[0], list) and isinstance(indedgecolor[0][1], int) or isinstance(indedgecolor[0], int): has_pairs = False elif isinstance(indedgecolor[0], list) and isinstance(indedgecolor[0][1], list): has_pairs = True else: raise TypeError('Ind_edge_color[0] has to be an int or list!') except IndexError: raise IndexError('Ind_edge_color[0] has to be a colorpack, a list bigger than 1 or an int!') if not has_pairs: indedgecolor = [indedgecolor] for colorpack in indedgecolor: if not isinstance(colorpack[0], (list, int)): raise TypeError('Colorpack[0] has to be an int or list!') if not isinstance(colorpack[1], list): raise TypeError('Colorpack[1] has to be a list!') if len(colorpack[1]) != 3: raise IndexError('Colorpack[1] has to be of length 3!') for c in colorpack[1]: if c < 0 or c > 1: raise ValueError('The values in colorpack[1] have to be in [0;1]!') indedgecolor_rgb = [colorpack[1][0], colorpack[1][1], colorpack[1][2]] if isinstance(colorpack[0], list): pos_indedgecolor.extend(colorpack[0]) for index in colorpack[0]: indedgecolors[index] = indedgecolor_rgb elif isinstance(colorpack[0], int): pos_indedgecolor.append(colorpack[0]) indedgecolors[colorpack[0]] = indedgecolor_rgb # Change individual edge width pos_indedgewidth = [] changeedgewidth = False indedgewidths = [None for i in range(len(y_values) + 1)] if 'ind_edge_width' in kwargs: indedgewidth = kwargs.pop('ind_edge_width') if not isinstance(indedgewidth, list): raise TypeError('Ind_edge_width has to be of type list!') if isinstance(indedgewidth[1], (float, int)) and isinstance(indedgewidth[0], (int, list)): has_pairs = False elif isinstance(indedgewidth[1], list): has_pairs = True else: raise TypeError('Ind_edge_width[0] has to be of type int or list and Ind_edge_width[1] of type int ' 'or float!') if not has_pairs: indedgewidth = [indedgewidth] for widthpack in indedgewidth: if isinstance(widthpack[1], (float, int)) and isinstance(widthpack[0], (int, list)): if not widthpack[1] >= 0: raise ValueError('Widthpack[1] has to be bigger or equal to 0!') changeedgewidth = True ind_edgewidth = widthpack[1] if isinstance(widthpack[0], list): pos_indedgewidth.extend(widthpack[0]) for index in widthpack[0]: indedgewidths[index] = ind_edgewidth elif isinstance(widthpack[0], int): pos_indedgewidth.append(widthpack[0]) indedgewidths[widthpack[0]] = ind_edgewidth # Draw bars wfac = bar_width * 0.8 gr.setfillintstyle(1) for a, y in enumerate(y_values): x = a + 1 if style == 'stacked' and multi_bar: c = 0 for i in range(0, len(y), 1): if changecolor and (i + 1 in pos_indcolor): current_rgb = indcolors[i + 1] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) else: if colorlist: if isinstance(colorlist[i], int): gr.setfillcolorind(colorlist[i]) else: current_rgb = colorlist[i] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) else: gr.setfillcolorind(std_colors[i % len(std_colors)]) gr.fillrect((x - 0.5 * bar_width), (x + 0.5 * bar_width), c, (y[i] + c)) c = y[i] + c elif style == 'lined' and multi_bar: bar_width = wfac / (len(y)) for i in range(0, len(y), 1): if changecolor and (i + 1 in pos_indcolor): current_rgb = indcolors[i + 1] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) else: if colorlist: if isinstance(colorlist[i], int): gr.setfillcolorind(colorlist[i]) else: current_rgb = colorlist[i] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) else: gr.setfillcolorind(std_colors[i % len(std_colors)]) gr.fillrect((x - 0.5 * wfac + bar_width * i), (x - 0.5 * wfac + bar_width + bar_width * i), 0, y[i]) else: if changecolor and (x in pos_indcolor): current_rgb = indcolors[x] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) else: if colorlist: if isinstance(colorlist[a], int): gr.setfillcolorind(colorlist[a]) else: current_rgb = colorlist[a] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) else: if isinstance(color, int): gr.setfillcolorind(color) else: current_rgb = color gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setfillcolorind(color_save_spot) gr.fillrect((x - 0.5 * bar_width), (x + 0.5 * bar_width), 0, y) # Draw edges gr.setfillintstyle(0) for a, y in enumerate(y_values): x = a + 1 if style == 'stacked' and multi_bar: c = 0 for i in range(0, len(y), 1): if changeedgecolor and (i + 1 in pos_indedgecolor): current_rgb = indedgecolors[i + 1] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setlinecolorind(color_save_spot) else: if isinstance(edgecolor, int): gr.setlinecolorind(edgecolor) else: current_rgb = edgecolor gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setlinecolorind(color_save_spot) if changeedgewidth and (i + 1 in pos_indedgewidth): gr.setlinewidth(indedgewidths[i + 1]) else: gr.setlinewidth(edgewidth) gr.drawrect((x - 0.5 * bar_width), (x + 0.5 * bar_width), c, (y[i] + c)) c = y[i] + c elif style == 'lined' and multi_bar: bar_width = wfac / (len(y)) for i in range(0, len(y), 1): if changeedgecolor and (i + 1 in pos_indedgecolor): current_rgb = indedgecolors[i + 1] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setlinecolorind(color_save_spot) else: if isinstance(edgecolor, int): gr.setlinecolorind(edgecolor) else: current_rgb = edgecolor gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setlinecolorind(color_save_spot) if changeedgewidth and (i + 1 in pos_indedgewidth): gr.setlinewidth(indedgewidths[i + 1]) else: gr.setlinewidth(edgewidth) gr.drawrect((x - 0.5 * wfac + bar_width * i), (x - 0.5 * wfac + bar_width + bar_width * i), 0, y[i]) else: if changeedgecolor and (x in pos_indedgecolor): current_rgb = indedgecolors[x] gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setlinecolorind(color_save_spot) else: if isinstance(edgecolor, int): gr.setlinecolorind(edgecolor) else: current_rgb = edgecolor gr.setcolorrep(color_save_spot, current_rgb[0], current_rgb[1], current_rgb[2]) gr.setlinecolorind(color_save_spot) if changeedgewidth and (x in pos_indedgewidth): gr.setlinewidth(indedgewidths[x]) else: gr.setlinewidth(edgewidth) gr.drawrect((x - 0.5 * bar_width), (x + 0.5 * bar_width), 0, y) def _create_colormap(tup): if len(tup) != 3: raise ValueError('colormap must be a triple tuple!') if tup[2] is None: size = 700 else: size = tup[2] if tup[0] is None and tup[1] is None: gr.setcolormap(0) COLORMAP_Y = np.array(_colormap() * 255, dtype=int) COLORMAP_X = np.zeros((256, 4)) factor = 1 elif tup[0] is not None and tup[1] is None: if isinstance(tup[0], int): gr.setcolormap(tup[0]) elif isinstance(tup[0], str): gr.setcolormap(gr.COLORMAPS[tup[0]]) else: raise ValueError('Invalid colormap parameter') COLORMAP_X = np.array(_colormap() * 255, dtype=int) COLORMAP_Y = np.zeros((256, 4)) factor = 1 elif tup[0] is None and tup[1] is not None: if isinstance(tup[1], int): gr.setcolormap(tup[1]) elif isinstance(tup[1], str): gr.setcolormap(gr.COLORMAPS[tup[1]]) else: raise ValueError('Invalid colormap parameter') COLORMAP_X = np.zeros((256, 4)) COLORMAP_Y = np.array(_colormap() * 255, dtype=int) factor = 1 else: if isinstance(tup[0], int): gr.setcolormap(tup[0]) elif isinstance(tup[0], str): gr.setcolormap(gr.COLORMAPS[tup[0]]) else: raise ValueError('Invalid colormap parameter') COLORMAP_X = np.array(_colormap() * 255, dtype=int) if isinstance(tup[1], int): gr.setcolormap(tup[1]) elif isinstance(tup[1], str): gr.setcolormap(gr.COLORMAPS[tup[1]]) else: raise ValueError('Invalid colormap parameter') COLORMAP_Y = np.array(_colormap() * 255, dtype=int) factor = 2 size_range = range(256) lins_float = np.linspace(0, 255, num=size) r1 = np.array(np.interp(lins_float, size_range, COLORMAP_X[size_range, 0])).reshape(1, -1) r2 = np.array(np.interp(lins_float, size_range, COLORMAP_Y[size_range, 0])).reshape(-1, 1) r12 = np.array((r1 + r2) / factor, dtype=np.int) g1 = np.array(np.interp(lins_float, size_range, COLORMAP_X[size_range, 1])).reshape(1, -1) g2 = np.array(np.interp(lins_float, size_range, COLORMAP_Y[size_range, 1])).reshape(-1, 1) g12 = np.left_shift(np.array((g1 + g2) / factor, dtype=np.int), 8) b1 = np.array(np.interp(lins_float, size_range, COLORMAP_X[size_range, 2])).reshape(1, -1) b2 = np.array(np.interp(lins_float, size_range, COLORMAP_Y[size_range, 2])).reshape(-1, 1) b12 = np.left_shift(np.array((b1 + b2) / factor, dtype=np.int), 16) a1 = np.array(np.interp(lins_float, size_range, COLORMAP_X[size_range, 3])).reshape(1, -1) a2 = np.array(np.interp(lins_float, size_range, COLORMAP_Y[size_range, 3])).reshape(-1, 1) a12 = np.left_shift(np.array((a1 + a2) / factor, dtype=np.int), 24) pixmap = np.array(r12 | g12 | b12 | a12) return np.ascontiguousarray(pixmap) def _plot_polar_histogram(): def moivre(r, x, n): list1 = [1, 0] if n != 0: list1[0] = r ** (1 / n) * (np.cos((2 * x * np.pi) / n)) list1[1] = r ** (1 / n) * (np.sin((2 * x * np.pi) / n)) return list1 global _plt edgecolor = 1 facecolor = 989 facealpha = 0.75 temp_face = None temp_edge = None vp = _plt.kwargs['subplot'] if vp[1] - vp[0] > 0.99 and vp[3] - vp[2] > 0.99: if 'title' in _plt.kwargs: gr.savestate() gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_TOP) gr.textext(0.5 * (vp[0] + vp[1]), 0.95, _plt.kwargs['title']) gr.restorestate() vp = [0.1, 0.9, 0.1, 0.9] else: vp[3] = vp[2] + vp[1] - vp[0] _set_viewport('polar_histogram', vp) vp = gr.inqviewport() vp[3] = vp[2] + vp[1] - vp[0] temp_add = (vp[1] - vp[0]) * 0.25 if 'title' in _plt.kwargs: factor = 0.3 vp[0] += temp_add * 2 * factor vp[1] -= temp_add * 2 * factor gr.savestate() gr.settextalign(gr.TEXT_HALIGN_CENTER, gr.TEXT_VALIGN_TOP) gr.textext(0.5 * (vp[0] + vp[1]), vp[3] - temp_add * 4 * 0.25, _plt.kwargs['title']) gr.restorestate() vp[3] -= temp_add * 4 * factor del factor else: vp[1] -= temp_add vp[3] -= temp_add del temp_add gr.setviewport(vp[0], vp[1], vp[2], vp[3]) gr.setlinewidth(1) gr.setwindow(0, 1, 0, 1) convert = 180 / np.pi norm_factor = _plt.kwargs['norm_factor'] normalization = _plt.kwargs['normalization'] classes = _plt.kwargs['classes'] del _plt.kwargs['classes'] if _plt.kwargs.get('bin_edges', None) is not None: is_binedges = True binedges = _plt.kwargs['bin_edges'] else: is_binedges = False if _plt.kwargs.get('temp_bin_edges', None) is not None: is_binedges = True binedges = _plt.kwargs['temp_bin_edges'] del _plt.kwargs['temp_bin_edges'] temp = _plt.kwargs['border_exp'] border = temp[0] exp = temp[1] del temp del _plt.kwargs['border_exp'] gr.settransparency(0.8) num_bins = len(classes) if _plt.kwargs.get('rlim', None) is not None: r_min = _plt.kwargs['rlim'] r_max = r_min[1] r_min = r_min[0] is_rlim = True else: is_rlim = False # face_color either a number or an rgb triplet [x,y,z] if 'face_color' in _plt.kwargs: facecolor = _plt.kwargs['face_color'] if isinstance(facecolor, list): if len(facecolor) == 3: temp_face = gr.inqcolor(1004) gr.setcolorrep(1004, facecolor[0], facecolor[1], facecolor[2]) facecolor = 1004 else: raise ValueError('RGB Triplet not correct') elif not isinstance(facecolor, int): raise ValueError('face_color not correct') # face_alpha if 'face_alpha' in _plt.kwargs: facealpha = _plt.kwargs['face_alpha'] if not (0 <= facealpha <= 1): raise ValueError('face_alpha not correct') # edge_color if 'edge_color' in _plt.kwargs: edgecolor = _plt.kwargs['edge_color'] if isinstance(edgecolor, list): if len(edgecolor) == 3: temp_edge = gr.inqcolor(1005) gr.setcolorrep(1005, edgecolor[0], edgecolor[1], edgecolor[2]) edgecolor = 1005 elif not isinstance(edgecolor, int): raise ValueError('Incorrect Color Value. Either Integer or rgb triplet in a list') # drawing the circles + x-Axis numbers gr.settextalign(1, 1) window_width = (vp[1] - vp[0]) center_x = window_width / 2 + vp[0] center_y = (vp[3] - vp[2]) / 2 + vp[2] for x in range(4): gr.drawarc((0.1 + x * 0.1), (0.9 - 0.1 * x), (0.1 + x * 0.1), (0.9 - 0.1 * x), 0, 360) if normalization == 'count' or normalization == 'cumcount': gr.text(center_x * 1.02, center_y + (x + 1) * window_width * 0.8 / 2 / 4, str(int((x + 1) * border / 4))) else: if normalization == 'probability': gr.text(center_x * 1.02, center_y + (x + 1) * window_width * 0.8 / 2 / 4, str(round(((x + 1) * border / 4) * 10 ** (exp + 2 + int(num_bins / 25))) / 10 ** ( exp + 2 + int(num_bins / 25)))) else: gr.text(center_x * 1.02, center_y + (x + 1) * window_width * 0.8 / 2 / 4, str(round(((x + 1) * border / 4) * 10 ** (exp + 3)) / 10 ** (exp + 3))) # drawinng lines + angle number = 0 gr.settextalign(2, 3) for x in range(12): liste = moivre(0.4 ** 12, x, 12) gr.polyline([0.5, 0.5 + (1 * liste[0])], [0.5, 0.5 + (1 * liste[1])]) gr.text(center_x + (liste[0]) * window_width * 1.15, center_y + liste[1] * window_width * 1.15, str(number)) number += 30 gr.settransparency(facealpha) length = 0 # colormap if 'temp_colormap' in _plt.kwargs: triple = _plt.kwargs['temp_colormap'] del _plt.kwargs['temp_colormap'] gr.drawimage(0, 1, 1, 0, triple[0], triple[1], triple[2]) # draw_edges for Colormap: if draw_edges is given, the edges will be drawn if 'draw_edges' in _plt.kwargs: if _plt.kwargs['draw_edges'] is True: gr.setlinecolorind(edgecolor) gr.setlinewidth(1.5) if is_binedges: if normalization == 'countdensity': norm_factor = 1 if normalization == 'countdensity' or normalization == 'pdf': binwidths = [] classes = [temp for temp in classes if len(temp) > 0] for i in range(len(binedges) - 1): binwidths.append(binedges[i + 1] - binedges[i]) binwidths.append(binedges[-1] - binedges[-2]) bin_value = [len(x) / (norm_factor * binwidths[i]) if x[0] is not None else 0 for (i, x) in enumerate(classes)] else: bin_value = [len(x) / (norm_factor) if x[0] is not None else 0 for (i, x) in enumerate(classes)] else: bin_value = [len(x) / norm_factor if x is not None else 0 for x in classes] length = 0 mlist = [] for x in range(len(classes)): if normalization == 'cumcount' or normalization == 'cdf': if classes[x][0] is None: pass else: length = len(classes[x]) / norm_factor + length elif classes[x][0] is None: continue elif normalization == 'pdf' or normalization == 'countdensity': length = bin_value[x] else: length = len(classes[x]) / norm_factor r = (length / border * 0.4) ** (num_bins * 2) liste = moivre(r, (2 * x), num_bins * 2) rect = np.sqrt(liste[0] ** 2 + liste[1] ** 2) if is_rlim: liste2 = moivre(r, (2 * x + 2), (num_bins * 2)) mlist.append(liste) mlist.append(liste2) r_min_list = moivre((r_min * 0.4) ** (num_bins * 2), (x * 2), num_bins * 2) r_min_list2 = moivre((r_min * 0.4) ** (num_bins * 2), (x * 2 + 2), num_bins * 2) for kaman in (-1, -2): temporary = abs(np.sqrt(mlist[kaman][0]**2 + mlist[kaman][1]**2)) if temporary > (r_max * 0.4): factor = abs(r_max * 0.4 / temporary) mlist[kaman][0] *= factor mlist[kaman][1] *= factor del temporary gr.settransparency(1) gr.setfillintstyle(0) gr.setfillcolorind(edgecolor) if is_binedges: if is_rlim: rect = int(rect * 10000) rect = rect / 10000 if round(rect, 3) > r_min * 0.4: try: gr.drawarc(0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), 0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), binedges[x] * convert, binedges[x + 1] * convert) gr.drawarc(0.5 - r_min * 0.4, 0.5 + r_min * 0.4, 0.5 - r_min * 0.4, 0.5 + r_min * 0.4, binedges[x] * convert, binedges[x + 1] * convert) gr.polyline([0.5 + r_min * 0.4 * np.cos(binedges[x]), 0.5 + min(rect, r_max * 0.4) * np.cos(binedges[x])], [0.5 + r_min * 0.4 * np.sin(binedges[x]), 0.5 + min(rect, r_max * 0.4) * np.sin(binedges[x])]) gr.polyline([0.5 + r_min * 0.4 * np.cos(binedges[x + 1]), 0.5 + min(rect, r_max * 0.4) * np.cos(binedges[x + 1])], [0.5 + r_min * 0.4 * np.sin(binedges[x + 1]), 0.5 + min(rect, r_max * 0.4) * np.sin(binedges[x + 1])]) except Exception: pass # no rlim else: try: gr.fillarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, binedges[x] * convert, binedges[x + 1] * convert) except Exception: pass # no binedges else: if is_rlim: rect = int(rect * 10000) rect = rect / 10000 if round(rect, 3) > r_min * 0.4: gr.drawarc(0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), 0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), x * (360 / num_bins), (x + 1) * (360 / num_bins)) gr.drawarc(0.5 - r_min * 0.4, 0.5 + r_min * 0.4, 0.5 - r_min * 0.4, 0.5 + r_min * 0.4, x * (360 / num_bins), (x + 1) * (360 / num_bins)) gr.polyline([0.5 + r_min_list[0], 0.5 + mlist[2 * x][0]], [0.5 + r_min_list[1], 0.5 + mlist[2 * x][1]]) gr.polyline([0.5 + r_min_list2[0], 0.5 + mlist[2 * x + 1][0]], [0.5 + r_min_list2[1], 0.5 + mlist[2 * x + 1][1]]) # Normal (no rlim) else: gr.fillarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, x * (360 / num_bins), (x + 1) * (360 / num_bins)) del mlist # No Colormap else: if is_binedges: if normalization == 'pdf': pass elif normalization == 'countdensity': norm_factor = 1 binwidths = [] classes = [bin for bin in classes if len(bin) > 0] for i in range(len(binedges) - 1): binwidths.append(binedges[i + 1] - binedges[i]) binwidths.append(binedges[-1] - binedges[-2]) if normalization == 'countdensity' or normalization == 'pdf': bin_value = [len(x) / (norm_factor * binwidths[i]) for (i, x) in enumerate(classes)] else: bin_value = [len(x) / norm_factor if x[0] is not None else 0 for x in classes] # No binedges else: if normalization == 'countdensity': bin_value = [len(x) / norm_factor if x[0] is not None else 0 for x in classes] elif normalization == 'pdf': bin_value = [len(x) / (norm_factor) if x[0] is not None else 0 for x in classes] else: bin_value = [len(x) / norm_factor if x[0] is not None else 0 for x in classes] # no stairs if _plt.kwargs.get('stairs', False) is False: mlist = [] for x in range(len(classes)): if normalization == 'cumcount' or normalization == 'cdf': if classes[x][0] is None: pass else: length = len(classes[x]) / norm_factor + length elif normalization == 'pdf' or normalization == 'countdensity': length = bin_value[x] elif classes[x][0] is None: continue else: length = len(classes[x]) / norm_factor r = (length / border * 0.4) ** (num_bins * 2) liste = moivre(r, (2 * x), num_bins * 2) rect = np.sqrt(liste[0] ** 2 + liste[1] ** 2) gr.setfillcolorind(facecolor) gr.settransparency(facealpha) gr.setfillintstyle(1) if is_rlim: liste2 = moivre(r, (2 * x + 2), (num_bins * 2)) mlist.append(liste) mlist.append(liste2) r_min_list = moivre((r_min * 0.4) ** (num_bins * 2), (x * 2), num_bins * 2) r_min_list2 = moivre((r_min * 0.4) ** (num_bins * 2), (x * 2 + 2), num_bins * 2) for kaman in (-1, -2): temporary = abs(np.sqrt(mlist[kaman][0] ** 2 + mlist[kaman][1] ** 2)) if temporary > (r_max * 0.4): factor = abs(r_max * 0.4 / temporary) mlist[kaman][0] *= factor mlist[kaman][1] *= factor del temporary r = length / border * 0.4 if r > r_max * 0.4: r = r_max * 0.4 if is_binedges: if is_rlim: try: if r > r_min * 0.4: start_angle = binedges[x] end_angle = binedges[x + 1] diff_angle = end_angle - start_angle num_angle = int(diff_angle / (0.2 / convert)) * 1j phi_array = np.array( np.ogrid[start_angle: end_angle:num_angle], dtype=np.float) arc_1_x = [r * np.cos(phi) + 0.5 for phi in phi_array] arc_1_y = [r * np.sin(phi) + 0.5 for phi in phi_array] arc_2_x = [r_min * 0.4 * np.cos(phi) + 0.5 for phi in phi_array] arc_2_y = [r_min * 0.4 * np.sin(phi) + 0.5 for phi in phi_array] line_1_x = [0.5 + r_min * 0.4 * np.cos(binedges[x]), 0.5 + min(rect, r_max * 0.4) * np.cos(binedges[x])] line_1_y = [0.5 + r_min * 0.4 * np.sin(binedges[x]), 0.5 + min(rect, r_max * 0.4) * np.sin(binedges[x])] line_2_x = [0.5 + r_min * 0.4 * np.cos(binedges[x + 1]), 0.5 + min(rect, r_max * 0.4) * np.cos(binedges[x + 1])] line_2_y = [0.5 + r_min * 0.4 * np.sin(binedges[x + 1]), 0.5 + min(rect, r_max * 0.4) * np.sin(binedges[x + 1])] gr.setfillintstyle(1) gr.fillarea(np.hstack(( line_1_x, arc_1_x, line_2_x[::-1], arc_2_x[::-1],)), np.hstack(( line_1_y, arc_1_y, line_2_y[::-1], arc_2_y[::-1],)) ) gr.setfillintstyle(0) gr.setfillcolorind(edgecolor) gr.fillarea(np.hstack(( line_1_x, arc_1_x, line_2_x[::-1], arc_2_x[::-1],)), np.hstack(( line_1_y, arc_1_y, line_2_y[::-1], arc_2_y[::-1],)) ) except Exception: pass pass else: try: gr.fillarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, binedges[x] * convert, binedges[x + 1] * convert) except Exception: pass gr.settransparency(1) gr.setfillintstyle(0) gr.setfillcolorind(edgecolor) try: gr.fillarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, binedges[x] * convert, binedges[x + 1] * convert) except Exception: pass # no binedges else: if is_rlim: try: if r > r_min * 0.4: start_angle = x * (360 / num_bins) / convert end_angle = (x + 1) * (360 / num_bins) / convert diff_angle = end_angle - start_angle num_angle = int(diff_angle / (0.2 / convert)) * 1j phi_array = np.array( np.ogrid[start_angle: end_angle:num_angle], dtype=np.float) arc_1_x = [r * np.cos(phi) + 0.5 for phi in phi_array] arc_1_y = [r * np.sin(phi) + 0.5 for phi in phi_array] arc_2_x = [r_min * 0.4 * np.cos(phi) + 0.5 for phi in phi_array] arc_2_y = [r_min * 0.4 * np.sin(phi) + 0.5 for phi in phi_array] line_1_x = [0.5 + r_min_list[0], 0.5 + mlist[2 * x][0]] line_1_y = [0.5 + r_min_list[1], 0.5 + mlist[2 * x][1]] line_2_x = [0.5 + r_min_list2[0], 0.5 + mlist[2 * x + 1][0]] line_2_y = [0.5 + r_min_list2[1], 0.5 + mlist[2 * x + 1][1]] gr.setfillintstyle(1) gr.fillarea(np.hstack(( line_1_x, arc_1_x, line_2_x[::-1], arc_2_x[::-1],)), np.hstack(( line_1_y, arc_1_y, line_2_y[::-1], arc_2_y[::-1],)) ) gr.setfillintstyle(0) gr.setfillcolorind(edgecolor) gr.fillarea(np.hstack(( line_1_x, arc_1_x, line_2_x[::-1], arc_2_x[::-1],)), np.hstack(( line_1_y, arc_1_y, line_2_y[::-1], arc_2_y[::-1],)) ) except Exception: pass # no rlim else: gr.fillarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, x * (360 / num_bins), (x + 1) * (360 / num_bins)) gr.settransparency(1) gr.setfillintstyle(0) gr.setfillcolorind(edgecolor) gr.fillarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, x * (360 / num_bins), (x + 1) * (360 / num_bins)) # stairs else: gr.setlinewidth(2.3) gr.setlinecolorind(edgecolor) # With given bin_edges if is_binedges: mlist = [] rectlist = [] for x in range(len(classes)): if normalization == 'cumcount' or normalization == 'cdf': if classes[x][0] is None: pass else: length = len(classes[x]) / norm_factor + length elif normalization == 'pdf' or normalization == 'countdensity': length = bin_value[x] elif classes[x][0] is None: length = 0 else: length = len(classes[x]) / norm_factor r = (length / border * 0.4) ** (num_bins * 2) liste = moivre(r, (2 * x), num_bins * 2) liste2 = moivre(r, (2 * x + 2), (num_bins * 2)) mlist.append(liste) mlist.append(liste2) rect = np.sqrt(liste[0] ** 2 + liste[1] ** 2) if is_rlim: for kaman in (-1, -2): temporary = abs(np.sqrt(mlist[kaman][0]**2 + mlist[kaman][1]**2)) if temporary > (r_max * 0.4): factor = abs(r_max * 0.4 / temporary) mlist[kaman][0] *= factor mlist[kaman][1] *= factor del temporary if rect < r_min * 0.4: rectlist.append(r_min * 0.4) elif rect > r_max * 0.4: rectlist.append(r_max * 0.4) else: rectlist.append(rect) else: rectlist.append(rect) if is_rlim: rect = int(rect * 10000) rect = rect / 10000 if round(rect, 3) > r_min * 0.4: try: gr.drawarc(0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), 0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), binedges[x] * convert, binedges[x + 1] * convert) gr.drawarc(0.5 - r_min * 0.4, 0.5 + r_min * 0.4, 0.5 - r_min * 0.4, 0.5 + r_min * 0.4, binedges[x] * convert, binedges[x + 1] * convert) except Exception: pass else: try: gr.drawarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, binedges[x] * convert, binedges[x + 1] * convert) except Exception: pass if is_rlim: startx = max(rectlist[0] * np.cos(binedges[0]), r_min * 0.4 * np.cos(binedges[0])) starty = max(rectlist[0] * np.sin(binedges[0]), r_min * 0.4 * np.sin(binedges[0])) for x in range(len(binedges)): if not (binedges[0] == 0 and binedges[len(binedges) - 1] == 2 * np.pi and ( x == len(binedges) - 1 or x == 0)): try: gr.polyline([0.5 + startx, 0.5 + rectlist[x] * np.cos(binedges[x])], [0.5 + starty, 0.5 + rectlist[x] * np.sin(binedges[x])]) except Exception: pass try: startx = (rectlist[x] * np.cos(binedges[x + 1])) starty = (rectlist[x] * np.sin(binedges[x + 1])) except Exception: pass gr.polyline([0.5 + r_min * 0.4 * np.cos(binedges[0]), 0.5 + rectlist[0] * np.cos(binedges[0])], [0.5 + r_min * 0.4 * np.sin(binedges[0]), 0.5 + rectlist[0] * np.sin(binedges[0])]) gr.polyline([0.5 + r_min * 0.4 * np.cos(binedges[-1]), 0.5 + rectlist[-1] * np.cos(binedges[-1])], [0.5 + r_min * 0.4 * np.sin(binedges[-1]), 0.5 + rectlist[-1] * np.sin(binedges[-1])]) # no rlim else: startx = 0 starty = 0 for x in range(len(binedges)): pass try: gr.polyline([0.5 + startx, 0.5 + rectlist[x] * np.cos(binedges[x])], [0.5 + starty, 0.5 + rectlist[x] * np.sin(binedges[x])]) startx = (rectlist[x] * np.cos(binedges[x + 1])) starty = (rectlist[x] * np.sin(binedges[x + 1])) except Exception: pass if binedges[0] == 0 and binedges[-1] == 2 * np.pi: gr.polyline([0.5 + rectlist[0] * np.cos(binedges[0]), 0.5 + startx], [0.5 + rectlist[0] * np.sin(binedges[0]), 0.5 + starty]) else: gr.polyline([0.5 + rectlist[-1] * np.cos(binedges[-1]), 0.5], [0.5 + rectlist[-1] * np.sin(binedges[-1]), 0.5]) # Normal stairs (no bin_edges) else: mlist = [] for x in range(len(classes)): if normalization == 'cumcount' or normalization == 'cdf': if classes[x][0] is None: pass else: length = length + len(classes[x]) / norm_factor elif classes[x][0] is None: length = 0 else: length = len(classes[x]) / norm_factor r = (length / border * 0.4) ** (num_bins * 2) liste = moivre(r, (2 * x), num_bins * 2) liste2 = moivre(r, (2 * x + 2), (num_bins * 2)) mlist.append(liste) mlist.append(liste2) rect = np.sqrt(liste[0] ** 2 + liste[1] ** 2) gr.setfillcolorind(edgecolor) if is_rlim: for kaman in (-1, -2): temporary = abs(np.sqrt(mlist[kaman][0] ** 2 + mlist[kaman][1] ** 2)) if temporary > (r_max * 0.4): factor = abs(r_max * 0.4 / temporary) mlist[kaman][0] *= factor mlist[kaman][1] *= factor del temporary rect = int(rect * 10000) rect = rect / 10000 if round(rect, 3) > r_min * 0.4: gr.drawarc(0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), 0.5 - min(rect, r_max * 0.4), 0.5 + min(rect, r_max * 0.4), x * (360 / num_bins), (x + 1) * (360 / num_bins)) gr.drawarc(0.5 - r_min * 0.4, 0.5 + r_min * 0.4, 0.5 - r_min * 0.4, 0.5 + r_min * 0.4, x * (360 / num_bins), (x + 1) * (360 / num_bins)) # no rlim else: gr.drawarc(0.5 - rect, 0.5 + rect, 0.5 - rect, 0.5 + rect, x * (360 / num_bins), (x + 1) * (360 / num_bins)) if is_rlim: for x in range(len(classes) * 2): if x > 1 and x % 2 == 0: rect1 = np.sqrt(mlist[x][0]**2 + mlist[x][1]**2) rect1 = round(int(rect1 * 10000) / 10000, 3) rect2 = np.sqrt(mlist[x - 1][0]**2 + mlist[x - 1][1]**2) rect2 = round(int(rect2 * 10000) / 10000, 3) if rect1 < (r_min * 0.4) and rect2 < (r_min * 0.4): continue if rect1 < r_min * 0.4: mlist[x][0] = r_min * 0.4 * np.cos(np.pi / len(classes) * x) mlist[x][1] = r_min * 0.4 * np.sin(np.pi / len(classes) * x) if rect2 < r_min * 0.4: mlist[x - 1][0] = r_min * 0.4 * np.cos(np.pi / len(classes) * x) mlist[x - 1][1] = r_min * 0.4 * np.sin(np.pi / len(classes) * x) gr.polyline([0.5 + mlist[x][0], 0.5 + mlist[x - 1][0]], [0.5 + mlist[x][1], 0.5 + mlist[x - 1][1]]) mlist[-1][0] = max(mlist[-1][0], r_min * 0.4 * np.cos(0)) mlist[-1][1] = max(mlist[-1][1], r_min * 0.4 * np.sin(0)) mlist[0][0] = max(mlist[0][0], r_min * 0.4 * np.cos(0)) mlist[0][1] = max(mlist[0][1], r_min * 0.4 * np.sin(0)) gr.polyline([0.5 + mlist[-1][0], 0.5 + mlist[0][0]], [0.5 + mlist[-1][1], 0.5 + mlist[0][1]]) else: for x in range(len(classes) * 2): if x > 1 and x % 2 == 0: gr.polyline([0.5 + mlist[x][0], 0.5 + mlist[x - 1][0]], [0.5 + mlist[x][1], 0.5 + mlist[x - 1][1]]) gr.polyline([0.5 + mlist[-1][0], 0.5 + mlist[0][0]], [0.5 + mlist[-1][1], 0.5 + mlist[0][1]]) if temp_edge is not None: red = (temp_edge % 256) / 255 green = ((temp_edge >> 8) % 256) / 255 blue = ((temp_edge >> 16) % 256) / 255 gr.setcolorrep(1004, red, green, blue) if temp_face is not None: red = (temp_face % 256) / 255 green = ((temp_face >> 8) % 256) / 255 blue = ((temp_face >> 16) % 256) / 255 gr.setcolorrep(1004, red, green, blue) gr.updatews() def _plot_args(args, fmt='xys'): global _plt args = list(args) parsed_args = [] while args: # Try to read x, y, z and c x = y = z = c = None if fmt == 'xyuv': if len(args) == 4: x, y, u, v = args x = _convert_to_array(x) y = _convert_to_array(y) u = _convert_to_array(u, always_flatten=True) v = _convert_to_array(v, always_flatten=True) if u.shape != (len(x) * len(y),): raise TypeError('expected an array of len(y) * len(x) u values') if v.shape != (len(x) * len(y),): raise TypeError('expected an array of len(y) * len(x) v values') parsed_args.append((x, y, u.reshape(len(y), len(x)), v.reshape(len(y), len(x)), "")) break else: raise TypeError('expected x, y, u and v') if fmt == 'xyzc' and len(args) == 1: try: a = np.array(args[0]) if max(a.shape) == 1: a.shape = [1, 1] else: a.shape = [i for i in a.shape if i != 1] if len(a.shape) == 1: a.shape = [len(a), 1] if a.dtype == complex: raise TypeError() x = np.arange(1, a.shape[1] + 1) y = np.arange(1, a.shape[0] + 1) z = a.astype(np.float64) args.pop(0) except TypeError: x = y = z = c = None if x is None: a = _convert_to_array(args.pop(0), may_be_2d=True) if len(a.shape) == 2: x, y = a[:, 0], a[:, 1] else: if fmt == 'xys': try: x = a y = _convert_to_array(args[0], xvalues=x) args.pop(0) except (TypeError, IndexError): y = a x = np.arange(1, len(a) + 1) elif fmt == 'y': y = a x = np.arange(1, len(a) + 1) elif fmt == 'xyac' or fmt == 'xyzc': try: x = a if fmt == 'xyac' and len(args) >= 1: y = _convert_to_array(args[0], xvalues=x) if len(args) >= 2: y = _convert_to_array(args[0]) xy_y, xy_x = np.meshgrid(y, x) xy_x.shape = np.prod(xy_x.shape) xy_y.shape = np.prod(xy_y.shape) xy = np.stack((xy_x, xy_y), axis=1) if fmt == 'xyzc': z = _convert_to_array(args[1], xvalues=xy, always_flatten=True) if z.shape != x.shape or z.shape != y.shape: z.shape = (len(y), len(x)) else: z = _convert_to_array(args[1], xvalues=x) if len(args) >= 3: if fmt == 'xyzc': c = _convert_to_array(args[2], xvalues=xy, always_flatten=True) c.shape = (len(y), len(x)) else: c = _convert_to_array(args[2], xvalues=x) except TypeError: pass if y is None: raise TypeError('expected callable or sequence of real values') for v in (y, z, c): if v is None: break args.pop(0) else: raise TypeError("Invalid format: '{}'".format(fmt)) # Remove unused values if z is None: if len(x) > len(y): x = x[:len(y)] elif len(x) < len(y): y = y[:len(x)] else: if fmt == 'xyzc': if z.shape[0] > len(y): z = z[:len(y), :] elif z.shape[0] < len(y): y = y[:z.shape[0]] if len(z.shape) > 1 and z.shape[1] > len(x): z = z[:, len(x)] elif len(z.shape) > 1 and z.shape[1] < len(x): x = x[:z.shape[1]] if c is not None: if c.shape[0] > len(y): c = c[:len(y), :] elif c.shape[0] < len(y): y = y[:c.shape[0]] z = z[:c.shape[0], 0] if c.shape[1] > len(x): c = c[:, len(x)] elif c.shape[1] < len(x): x = x[:c.shape[1]] z = z[:, :c.shape[1]] if z is not None: z = np.ascontiguousarray(z) if c is not None: c = np.ascontiguousarray(c) else: if len(x) > len(y): x = x[:len(y)] else: y = y[:len(x)] if len(z) > len(x): z = z[:len(x)] else: x = x[:len(z)] y = y[:len(z)] if c is not None: if len(c) > len(z): c = c[:len(z)] else: x = x[:len(c)] y = y[:len(c)] z = z[:len(c)] # Try to read the spec if available spec = "" if fmt == 'xys' and len(args) > 0 and isinstance(args[0], basestring): spec = args.pop(0) if fmt == 'y' and args: z = args.pop(0) if args: c = args.pop(0) parsed_args.append((x, y, z, c, spec)) return parsed_args