10147868343

Committed 29 Jul 2024 04:09PM UTC coverage: 73.608% (+1.4%) from 72.251%

Build # 10147868343

Build Type

push

github

Committed by

bjmorgan

Commit Message

Added orientation plotting

Run Details

43 of 44 new or added lines in 1 file covered. (97.73%)

608 of 826 relevant lines covered (73.61%)

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97.73

/polyhedral_analysis/plotting.py

import numpy as np
from scipy.stats import gaussian_kde
import matplotlib.pyplot as plt
from cmcrameri import cm
from typing import List, Tuple, Optional, Dict, Any

def _process_orientation_data(orientations: List[Tuple[float, float]]) -> Dict[str, Any]:
    """
    Process orientation data to prepare for plotting.

    Args:
        orientations (List[Tuple[float, float]]): List of (theta, phi) tuples.

    Returns:
        Dict[str, Any]: Dictionary containing processed data for plotting.

    Raises:
        ValueError: If orientations is empty, contains invalid data, or results in singular covariance matrix.
    """
    orientations_array = np.array(orientations)
    
    if orientations_array.size == 0:
        raise ValueError("The orientations list is empty.")
    
    if orientations_array.shape[1] != 2:
        raise ValueError("Each orientation should be a tuple of (theta, phi).")

    phi_grid, theta_grid = np.mgrid[-180:180:100j, 0:180:100j]
    positions = np.vstack([phi_grid.ravel(), theta_grid.ravel()])

    values = orientations_array.T[::-1]  # Swap and transpose
    
    try:
        kernel = gaussian_kde(values, bw_method=0.1)
        z = np.reshape(kernel(positions).T, phi_grid.shape)
    except np.linalg.LinAlgError:
        raise ValueError("The data resulted in a singular covariance matrix. Consider adding more varied data points.")

    return {
        'phi_grid': phi_grid,
        'theta_grid': theta_grid,
        'z': z
    }

def plot_orientation_distribution(orientations: List[Tuple[float, float]],
                                  title: Optional[str] = None,
                                  figsize: Tuple[int, int] = (10, 8),
                                  cmap = cm.lipari,
                                  fontsize: Optional[int] = None,
                                  plot: bool = False) -> plt.Figure:
    """
    Create a contour map of the probability distribution for (phi, theta) orientations.
    
    Args:
        orientations (List[Tuple[float, float]]): List of (theta, phi) tuples.
        title (Optional[str], optional): Title for the plot. Defaults to None.
        figsize (Tuple[int, int], optional): Figure size as (width, height). Defaults to (10, 8).
        cmap (matplotlib.colors.Colormap, optional): Colormap to use for the contour plot. Defaults to cm.lipari.
        fontsize (Optional[int], optional): Font size for labels and title. If None, uses matplotlib defaults.
        plot (bool, optional): Whether to display the plot immediately. Defaults to False.
    
    Returns:
        matplotlib.figure.Figure: The generated figure object.

    Raises:
        ValueError: If orientations is empty or contains invalid data.
    """
    processed_data = _process_orientation_data(orientations)

    fig, ax = plt.subplots(figsize=figsize)

    contour = ax.contourf(processed_data['phi_grid'], processed_data['theta_grid'], 
                          processed_data['z'], levels=20, cmap=cmap)
    if title:
        ax.set_title(title, fontsize=fontsize)
    ax.set_xlabel(r'$\phi$ (degrees)', fontsize=fontsize)
    ax.set_ylabel(r'$\theta$ (degrees)', fontsize=fontsize)
    if fontsize:
        ax.tick_params(axis='both', which='major', labelsize=fontsize)

    cbar = fig.colorbar(contour)
    cbar.set_label('Probability Density', fontsize=fontsize)
    if fontsize:
        cbar.ax.tick_params(labelsize=fontsize)
        cbar.ax.yaxis.get_offset_text().set_fontsize(fontsize)

    cbar.formatter.set_powerlimits((0, 0))
    cbar.update_ticks()

    ax.set_xlim(-180, 180)
    ax.set_ylim(0, 180)

    plt.tight_layout()
    
    if plot:
        plt.show()
    else:
        plt.close(fig)  # Close the figure to prevent it from being displayed
    
    return fig

# Example usage:
# orientations = [(theta1, phi1), (theta2, phi2), ...]
# fig = plot_orientation_distribution(orientations, title="Optional Title", fontsize=14)
# plt.show()

1	import numpy as np	4✔
2	from scipy.stats import gaussian_kde	4✔
3	import matplotlib.pyplot as plt	4✔
4	from cmcrameri import cm	4✔
5	from typing import List, Tuple, Optional, Dict, Any	4✔
6
7	def _process_orientation_data(orientations: List[Tuple[float, float]]) -> Dict[str, Any]:	4✔
8	"""
9	Process orientation data to prepare for plotting.
10
11	Args:
12	orientations (List[Tuple[float, float]]): List of (theta, phi) tuples.
13
14	Returns:
15	Dict[str, Any]: Dictionary containing processed data for plotting.
16
17	Raises:
18	ValueError: If orientations is empty, contains invalid data, or results in singular covariance matrix.
19	"""
20	orientations_array = np.array(orientations)	4✔
21
22	if orientations_array.size == 0:	4✔
23	raise ValueError("The orientations list is empty.")	4✔
24
25	if orientations_array.shape[1] != 2:	4✔
26	raise ValueError("Each orientation should be a tuple of (theta, phi).")	4✔
27
28	phi_grid, theta_grid = np.mgrid[-180:180:100j, 0:180:100j]	4✔
29	positions = np.vstack([phi_grid.ravel(), theta_grid.ravel()])	4✔
30
31	values = orientations_array.T[::-1] # Swap and transpose	4✔
32
33	try:	4✔
34	kernel = gaussian_kde(values, bw_method=0.1)	4✔
35	z = np.reshape(kernel(positions).T, phi_grid.shape)	4✔
36	except np.linalg.LinAlgError:	4✔
37	raise ValueError("The data resulted in a singular covariance matrix. Consider adding more varied data points.")	4✔
38
39	return {	4✔
40	'phi_grid': phi_grid,
41	'theta_grid': theta_grid,
42	'z': z
43	}
44
45	def plot_orientation_distribution(orientations: List[Tuple[float, float]],	4✔
46	title: Optional[str] = None,
47	figsize: Tuple[int, int] = (10, 8),
48	cmap = cm.lipari,
49	fontsize: Optional[int] = None,
50	plot: bool = False) -> plt.Figure:
51	"""
52	Create a contour map of the probability distribution for (phi, theta) orientations.
53
54	Args:
55	orientations (List[Tuple[float, float]]): List of (theta, phi) tuples.
56	title (Optional[str], optional): Title for the plot. Defaults to None.
57	figsize (Tuple[int, int], optional): Figure size as (width, height). Defaults to (10, 8).
58	cmap (matplotlib.colors.Colormap, optional): Colormap to use for the contour plot. Defaults to cm.lipari.
59	fontsize (Optional[int], optional): Font size for labels and title. If None, uses matplotlib defaults.
60	plot (bool, optional): Whether to display the plot immediately. Defaults to False.
61
62	Returns:
63	matplotlib.figure.Figure: The generated figure object.
64
65	Raises:
66	ValueError: If orientations is empty or contains invalid data.
67	"""
68	processed_data = _process_orientation_data(orientations)	4✔
69
70	fig, ax = plt.subplots(figsize=figsize)	4✔
71
72	contour = ax.contourf(processed_data['phi_grid'], processed_data['theta_grid'],	4✔
73	processed_data['z'], levels=20, cmap=cmap)
74	if title:	4✔
75	ax.set_title(title, fontsize=fontsize)	4✔
76	ax.set_xlabel(r'$\phi$ (degrees)', fontsize=fontsize)	4✔
77	ax.set_ylabel(r'$\theta$ (degrees)', fontsize=fontsize)	4✔
78	if fontsize:	4✔
79	ax.tick_params(axis='both', which='major', labelsize=fontsize)	4✔
80
81	cbar = fig.colorbar(contour)	4✔
82	cbar.set_label('Probability Density', fontsize=fontsize)	4✔
83	if fontsize:	4✔
84	cbar.ax.tick_params(labelsize=fontsize)	4✔
85	cbar.ax.yaxis.get_offset_text().set_fontsize(fontsize)	4✔
86
87	cbar.formatter.set_powerlimits((0, 0))	4✔
88	cbar.update_ticks()	4✔
89
90	ax.set_xlim(-180, 180)	4✔
91	ax.set_ylim(0, 180)	4✔
92
93	plt.tight_layout()	4✔
94
95	if plot:	4✔
NEW 96	plt.show()	×
97	else:
98	plt.close(fig) # Close the figure to prevent it from being displayed	4✔
99
100	return fig	4✔
101
102	# Example usage:
103	# orientations = [(theta1, phi1), (theta2, phi2), ...]
104	# fig = plot_orientation_distribution(orientations, title="Optional Title", fontsize=14)
105	# plt.show()

bjmorgan / polyhedral-analysis / 10147868343

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