Manually labeling features in a crowed plot can be very time consuming. Functions in this module can be used to automatically place labels without the labels overlapping each other. This is useful, for example, in creating plots of spectrum with lines identified with labels.
Examples illustrating all these features are shown at http://phn.github.com/lineid_plot . Source for these are included in the doc/index.rst file as embedded code in ..plot: reStructuredText directives.
$ pip install lineid_plot
$ easy_install lineid_plot
To download the entire repository either clone the repository or use the Download button. To download just the lineid_plot.py file, click on the file and then download the raw version.
Detailed examples are provided at http://phn.github.com/lineid_plot .
A basic plot can be created by calling the function plot_line_ids(), and passing labels and x-axis locations of features.
>>> import numpy as np >>> from matplotlib import pyplot as plt >>> import lineid_plot >>> wave = 1240 + np.arange(300) * 0.1 >>> flux = np.random.normal(size=300) >>> line_wave = [1242.80, 1260.42, 1264.74, 1265.00, 1265.2, 1265.3, 1265.35] >>> line_label1 = ['N V', 'Si II', 'Si II', 'Si II', 'Si II', 'Si II', 'Si II'] >>> lineid_plot.plot_line_ids(wave, flux, line_wave, line_label1) >>> plt.show()
The plot_line_ids() function also accepts Axes and/or Figure instances where labels are to be draw.
>>> import numpy as np >>> from matplotlib import pyplot as plt >>> import lineid_plot >>> wave = 1240 + np.arange(300) * 0.1 >>> flux = np.random.normal(size=300) >>> line_wave = [1242.80, 1260.42, 1264.74, 1265.00, 1265.2, 1265.3, 1265.35] >>> line_flux = np.interp(line_wave, wave, flux) >>> line_label1 = ['N V', 'Si II', 'Si II', 'Si II', 'Si II', 'Si II', 'Si II'] >>> label1_sizes = np.array([12, 12, 12, 12, 12, 12, 12]) >>> fig = plt.figure(1) >>> ax = fig.add_axes([0.1,0.06, 0.85, 0.35]) >>> ax.plot(wave, flux) >>> lineid_plot.plot_line_ids(wave, flux, line_wave, line_label1, ax=ax) >>> ax1 = fig.add_axes([0.1, 0.55, 0.85, 0.35]) >>> ax1.plot(wave, flux) >>> lineid_plot.plot_line_ids(wave, flux, line_wave, line_label1, ax=ax1)
Each of the boxes and the lines extending to the flux level have their label property set to a unique value. These can be used to quickly identify them.
>>> for i in ax.texts: ....: print i.get_label() ....: N V Si II_num_1 Si II_num_2 Si II_num_3 Si II_num_4 Si II_num_5 Si II_num_6 >>> for i in ax.lines: ....: print i.get_label() ....: _line0 N V_line Si II_num_1_line Si II_num_2_line Si II_num_3_line Si II_num_4_line Si II_num_5_line Si II_num_6_line
The label _line0 corresponds to the data plot and was assigned by Matplotlib.
The placements are calculated using a simple, iterative algorithm adapted from the procedure lineid_plot in the NASA IDL Astronomy User's Library. Matplotlib makes most of the other computations such as extracting width of label boxes, re-positioning them etc., very easy.
The main function in the module is plot_line_ids(). Labeled plots can be created by passing the x and y coordinates, for example wavelength and flux, along with the x coordinates of the features and their labels. The x coordinates are adjusted until the labels, of given size, do not overlap, or when the iteration limit is reached.
Users can provide the Axes instance or the Figure instance on which plots are to be made. If an Axes instance is provided, then the data is not plotted; only the labels are marked. This allows the user to separate plotting from labeling. For example, the user can create multiple Axes on a figure and then pass the Axes on which labels are to be marked. No changes are made to the existing layout.
The y axis locations of labels and annotation points i.e., arrow tips, can also be passed to the plot_line_ids() function. Minor changes can be passed using the box_axes_space keyword, where as major changes can be passed using the arrow_tip and box_loc keywords. The former is in figure fraction units and the latter two are in data coordinates. The latter two can be specified separately for each label. This is very useful in crowded regions. These features along with the ability to pass an Axes instance gives the program a lot of flexibility.
An extension line from the annotation point to the y data value at the location of the identification i.e., flux level at the line, is drawn by default. The flux at the line is calculated using linear interpolation. This can be turned off using the extend keyword. This keyword can be set separately for each feature.
The plot_line_ids() function will return the Axes and Figure instances used. Additional customizations, such as manual adjustments to positions, can be carried out using these references. To easily identify the labels, each label box and extension lines have their label property set to a string that depends on the label text provided. Identifying the Matplotlib objects corresponding to these and customizing them are made easy by the many features provided by Matplotlib.
The maximum iterations to use can be supplied using the max_iter keyword. The adjustments to be made in each iteration and when to change the adjustment factor can also be supplied. The defaults for these should be enough for most cases.
Released under BSD; see http://www.opensource.org/licenses/bsd-license.php.
For comments and suggestions, email to user prasanthhn in the gmail.com domain.