Reference for flowtracks.graphics¶
Various specialized graphing routines. The Probability Density Function
graphing is best accessed by calling pdf_graph()
on the raw data, but
you can generate the PDF from the data separately (e.g. using
pdf_bins()
) and calling generalized_histogram_disp()
on the
result.
The other facility here is a function to plot a timedependent 3D vector as
3 component subplots, which is another customary presentation in fluid
dynamics circles. See plot_vectors()
.

flowtracks.graphics.
generalized_histogram_disp
(hist, bin_edges, log_bins=False, log_density=False, marker='o')[source]¶ Draws a given histogram according to the visual custom of the fluid dynamics community.
Arguments
hist
: an array containing the number of values (or density) for each bin.bin_edges
: the start value of each bin, same length ashist
.log_bins
: indicates that the bin edges are logspaced.log_densify
: Show the log of the probability density value. May cause problems iflog_bins
is True.marker
: marker style for matplotlib.
Returns
the list of lines drawn, Matplotlib objects.

flowtracks.graphics.
pdf_bins
(data, num_bins, log_bins=False)[source]¶ Generate a PDF of the given data possibly with logarithmic bins, ready for using in a histogram plot.
Arguments
data
: the samples to histogram.bins
: the number of bins in the histogram.log_bins
: if True, the bin edges are equally spaced on the log scale, otherwise they are linearly spaced (a normal histogram). If True,data
should not contain zeros.
Returns
hist
: num_binslenght array of density values for each bin.bin_edges
: array of size num_bins + 1 with the edges of the bins including the ending limit of the bins.

flowtracks.graphics.
pdf_graph
(data, num_bins, log=False, log_density=False, marker='o')[source]¶ Draw a PDF of the given data, according to the visual custom of the fluid dynamics community, and possibly with logarithmic bins.
Arguments
data
: the samples to histogram.bins
: the number of bins in the histogram.log
: if True, the bin edges are equally spaced on the log scale, otherwise they are linearly spaced (a normal histogram). If True,data
should not contain zeros.log_density
: Show the log of the probability density value. Only if log is False.marker
: override the circle marker with any string acceptable to matplotlib.

flowtracks.graphics.
plot_vectors
(vecs, indep, xlabel, fig=None, marker='', ytick_dens=None, yticks_format=None, unit_str='', common_scale=None, arrows=None, arrow_color=None)[source]¶ Plot 3D vectors as 3 subplots sharing the same independent axis.
Arguments
vecs
: an (n,3) array, with n vectors to plot against the independent variable.indep
: the corresponding n values of the independent variable.xlabel
: label for the independent axis.fig
: an optional figure object to use. If None, one will be created.ytick_dens
: if not None, place this many yticks on each subplot, instead of the automatic tick marks.yticks_format
: a pyplot formatter object.unit_str
: a string to add to the Y labels representing the vector’s units.arrows
: an (n,3) array of values to represent as vertical arrows attached to each trajectory point.arrow_color
: a matplotlib color spec for the arrow bodies.
Returns
fig
: the figure object used for plotting.