Commit 0df7717d by Somnath, Suhas Committed by GitHub

### First dump

parent 726baf0a
 """ ====================================================================================== Plot utilities ====================================================================================== **Suhas Somnath** 10/12/2017 This is a short walkthrough of useful plotting utilities available in pycroscopy. Some of these functions fill gaps in the defult matplotlib package, some were developed for scientific applications, and others were developed specifically for handling pycroscopy datasets. These functions have been developed to substantially simplify the generation of high quality figures for journal publications. """ # Ensure python 3 compatibility: from __future__ import division, print_function, absolute_import, unicode_literals # plotting utilities and our own pycroscopy: import numpy as np import matplotlib.pyplot as plt import pycroscopy as px ################################################################################################ # 1D plot utilities # =========================== # plot_loops # ---------- # This function is particularly useful when we need to plot a 1D signal acquired at multiple locations. # The function is rather flexible and can take on several optional arguments that will be alluded to below # In the below example, we are simply simulating sine waveforms for different frequencies (think of these as # different locations on a sample) x_vec = np.linspace(0, 2*np.pi, 256) # The different frequencies: freqs = np.linspace(0.5, 5, 9) # Generating the signals at the different "positions" y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) px.plot_utils.plot_loops(x_vec, y_mat) ################################################################################################ # Frequently, we may need to compare signals from two different datasets for the same positions # The same plot_loops function can be used for this purpose even if the signal lengths / resolutions are different x_vec_1 = np.linspace(0, 2*np.pi, 256) x_vec_2 = np.linspace(0, 2*np.pi, 32) freqs = np.linspace(0.5, 5, 9) y_mat_1 = np.array([np.sin(freq * x_vec_1) for freq in freqs]) y_mat_2 = np.array([np.cos(freq * x_vec_2) for freq in freqs]) px.plot_utils.plot_loops([x_vec_1, x_vec_2], [y_mat_1, y_mat_2], title='Sine and Cosine of different resolutions') ################################################################################################ # plot_line_family # ------------------ # Often there is a need to visualize multiple spectra or signals on the same plot. plot_line_family # is a handy function ideally suited for this purpose and it is highly configurable for different styles and purposes # A few example applications include visualizing X ray / IR spectra (with y offsets), centroids from clustering # algorithms x_vec = np.linspace(0, 2*np.pi, 256) freqs = range(1, 5) y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) freq_strs = [str(_) for _ in freqs] fig, axes = plt.subplots(ncols=3, figsize=(12, 4)) px.plot_utils.plot_line_family(axes[0], x_vec, y_mat) axes[0].set_title('Basic line family') # Option suitable for visualiing spectra with y offsets: px.plot_utils.plot_line_family(axes[1], x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5) axes[1].legend() axes[1].set_title('Line family with legend') # Option highly suited for visualizing the centroids from a clustering algorithm: px.plot_utils.plot_line_family(axes[2], x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True) axes[2].set_title('Line family with colorbar') ################################################################################################ # rainbow_plot # ------------ # This function is ideally suited for visualizing a signal that varies as a function of time or when # the directionality of the signal is important num_pts = 1024 t_vec = np.linspace(0, 10*np.pi, num_pts) fig, axis = plt.subplots() px.plot_utils.rainbow_plot(axis, np.cos(t_vec)*np.linspace(0, 1, num_pts), np.sin(t_vec)*np.linspace(0, 1, num_pts), num_steps=32) ################################################################################################ # cbar_for_line_plot # ------------------ # Note that from the above plot it may not be clear if the signal is radiating outwards or spiraling inwards. # In these cases it helps to add a colorbar. However, colorbars can typically only be added for 2D images. # In such cases we can use a handy function: cbar_for_line_plot num_pts = 1024 t_vec = np.linspace(0, 10*np.pi, num_pts) fig, axis = plt.subplots(figsize=(6,5)) px.plot_utils.rainbow_plot(axis, np.cos(t_vec)*np.linspace(0, 1, num_pts), np.sin(t_vec)*np.linspace(0, 1, num_pts), num_steps=32) cbar = px.plot_utils.cbar_for_line_plot(axis, 10) cbar.set_label('Time (sec)') ################################################################################################ # plot_scree # ------------------ # One of the results of applying Singular Value Decomposition is the variance or statistical significance # of the resultant components. This data is best visualized via a log-log plot and plot_scree is available # exclusively to vvisualizethis kind of data scree = np.exp(-1 * np.arange(100)) px.plot_utils.plot_scree(scree, color='r') ################################################################################################ # 2D plot utilities # =========================== # Colormaps # --------- # pycroscopy has a handful of colormaps suited for different applications. # # cmap_jet_white_center # ~~~~~~~~~~~~~~~~~~~~~ # This is the standard jet colormap with a white center instead of green. This is a good colormap for images with # divergent data (data that diverges slightly both positively and negatively from the mean). # One example target is the ronchigrams from scanning transmission electron microscopy (STEM) # # cmap_hot_desaturated # ~~~~~~~~~~~~~~~~~~~~~ # This is a desaturated version of the standard jet colormap # # discrete_cmap # ~~~~~~~~~~~~~~~~~~~~~ # This function helps create a discretized version of the provided colormap. This is ideally suited when the data # only contains a few discrete values. One popular application is the visualization of labels from a clustering # algorithm x_vec = np.linspace(0, 2*np.pi, 256) y_vec = np.sin(x_vec) test = y_vec * np.atleast_2d(y_vec).T fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(10, 10)) for axis, title, cmap in zip(axes.flat, ['Jet', 'Jet with white center', 'Jet desaturated', 'Jet discretized'], [plt.cm.jet, px.plot_utils.cmap_jet_white_center(), px.plot_utils.cmap_hot_desaturated(), px.plot_utils.discrete_cmap(8, cmap='jet')]): im_handle = axis.imshow(test, cmap=cmap) cbar = plt.colorbar(im_handle, ax=axis, orientation='vertical', fraction=0.046, pad=0.04, use_gridspec=True) axis.set_title(title) fig.tight_layout() ################################################################################################ # make_linear_alpha_cmap # -------- # On certain occasions we may want to superimpose one image with another. However, this is not possible # by default since colormaps involve solid colors. This function allows one to plot multiple images using # a transparent-to-solid colormap. Here we will demonstrate this by plotting blobs representing atomic columns # over some background intensity. num_pts = 256 fig, axis = plt.subplots() axis.hold(True) # Prepare some backround signal x_mat, y_mat = np.meshgrid(np.linspace(-0.2*np.pi, 0.1*np.pi, num_pts), np.linspace(0, 0.25*np.pi, num_pts)) background_distortion = 0.2 * (x_mat + y_mat + np.sin(0.25 * np.pi * x_mat)) # plot this signal in grey axis.imshow(background_distortion, cmap='Greys') # prepare the signal of interest (think of this as intensities in a HREM dataset) x_vec = np.linspace(0, 6*np.pi, num_pts) y_vec = np.sin(x_vec)**2 atom_intensities = y_vec * np.atleast_2d(y_vec).T # prepare the transparent-to-solid colormap solid_color = plt.cm.jet(0.8) translucent_colormap = px.plot_utils.make_linear_alpha_cmap('my_map', solid_color, 1, min_alpha=0, max_alpha=1) # plot the atom intensities using the custom colormap im_handle = axis.imshow(atom_intensities, cmap=translucent_colormap) cbar = plt.colorbar(im_handle, ax=axis, orientation='vertical', fraction=0.046, pad=0.04, use_gridspec=True) ################################################################################################ # plot_map # -------- # This function adds several popularly used features to the basic image plotting function in matplotlib including: # * easy addition of a colorbar # * easy modification of the x and y tick values # * clipping the colorbar to N standard deviations of the mean x_vec = np.linspace(0, 6*np.pi, 256) y_vec = np.sin(x_vec)**2 atom_intensities = y_vec * np.atleast_2d(y_vec).T fig, axes = plt.subplots(ncols=2, figsize=(10, 5)) # Standard imshow plot for reference axes[0].imshow(atom_intensities, origin='lower') axes[0].set_title('Standard imshow') # Now plot_map with some options enabled: px.plot_utils.plot_map(axes[1], atom_intensities, stdevs=1.5, num_ticks=4, x_size=3, y_size=6, cbar_label='intensity (a. u.)', tick_font_size=16) axes[1].set_title('plot_map') fig.tight_layout() ################################################################################################ # set_tick_font_size # ------------------ # Adjusting the font sizes of the tick marks is often necessary for preparing figures for journal papers. # However, adjusting the tick sizes is actually tedious in python and this function makes this easier. test = np.random.rand(10, 10) fig, axes = plt.subplots(ncols=2, figsize=(8, 4)) for axis, title in zip(axes, ['Default', 'Custom']): axis.imshow(test) axis.set_title(title + ' tick size') # only changing the tick font size on the second plot: px.plot_utils.set_tick_font_size(axes[1], 24) fig.tight_layout() ################################################################################################ # get_cmap_object # --------------- # This function is useful more for developers writing their own plotting functions that need to manipulate the # colormap object. This function makes it easy to ensure that you are working on the colormap object and not the # string name of the colormap (both of which are accepted by most matplotlib functions). # Here we simply compare the returned values when passing both the colormap object and the string name of the colormap px.plot_utils.get_cmap_object('jet') == px.plot_utils.get_cmap_object(plt.cm.jet)
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