streamplot Namespace Reference

Classes
class	DomainMap
class	Grid
class	InvalidIndexError
class	StreamMask
class	StreamplotSet
class	TerminateTrajectory

Functions
def	streamplot (axes, x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, maxlength=4.0, transform=None, zorder=2, start_points=None, integration_direction='both')
def	get_integrator (u, v, dmap, minlength, maxlength, integration_direction)
def	_integrate_rk12 (x0, y0, dmap, f, maxlength)
def	_euler_step (xf_traj, yf_traj, dmap, f)
def	interpgrid (a, xi, yi)
def	_gen_starting_points (shape)

Variables
list	__all__ = ['streamplot']

Detailed Description

Streamline plotting for 2D vector fields.

Function Documentation

_euler_step()

def streamplot._euler_step (   xf_traj,   yf_traj,   dmap,   f  )

private

Simple Euler integration step that extends streamline to boundary.

Definition at line 556 of file streamplot.py.

_gen_starting_points()

def streamplot._gen_starting_points (   shape )

private

Yield starting points for streamlines.

Trying points on the boundary first gives higher quality streamlines.
This algorithm starts with a point on the mask corner and spirals inward.
This algorithm is inefficient, but fast compared to rest of streamplot.

Definition at line 623 of file streamplot.py.

_integrate_rk12()

def streamplot._integrate_rk12 (   x0,   y0,   dmap,   f,   maxlength  )

private

2nd-order Runge-Kutta algorithm with adaptive step size.

This method is also referred to as the improved Euler's method, or Heun's
method. This method is favored over higher-order methods because:

1. To get decent looking trajectories and to sample every mask cell
   on the trajectory we need a small timestep, so a lower order
   solver doesn't hurt us unless the data is *very* high resolution.
   In fact, for cases where the user inputs
   data smaller or of similar grid size to the mask grid, the higher
   order corrections are negligible because of the very fast linear
   interpolation used in `interpgrid`.

2. For high resolution input data (i.e. beyond the mask
   resolution), we must reduce the timestep. Therefore, an adaptive
   timestep is more suited to the problem as this would be very hard
   to judge automatically otherwise.

This integrator is about 1.5 - 2x as fast as both the RK4 and RK45
solvers in most setups on my machine. I would recommend removing the
other two to keep things simple.

Definition at line 464 of file streamplot.py.

get_integrator()

def streamplot.get_integrator	(		u,
			v,
			dmap,
			minlength,
			maxlength,
			integration_direction
	)

Definition at line 402 of file streamplot.py.

interpgrid()

def streamplot.interpgrid	(		a,
			xi,
			yi
	)

Fast 2D, linear interpolation on an integer grid

Definition at line 583 of file streamplot.py.

streamplot()

def streamplot.streamplot	(		axes,
			x,
			y,
			u,
			v,
			density = 1,
			linewidth = None,
			color = None,
			cmap = None,
			norm = None,
			arrowsize = 1,
			arrowstyle = '-|>',
			minlength = 0.1,
			maxlength = 4.0,
			transform = None,
			zorder = 2,
			start_points = None,
			integration_direction = 'both'
	)

Draws streamlines of a vector flow.

*x*, *y* : 1d arrays
    an *evenly spaced* grid.
*u*, *v* : 2d arrays
    x and y-velocities. Number of rows should match length of y, and
    the number of columns should match x.
*density* : float or 2-tuple
    Controls the closeness of streamlines. When `density = 1`, the domain
    is divided into a 30x30 grid---*density* linearly scales this grid.
    Each cell in the grid can have, at most, one traversing streamline.
    For different densities in each direction, use [density_x, density_y].
*linewidth* : numeric or 2d array
    vary linewidth when given a 2d array with the same shape as velocities.
*color* : matplotlib color code, or 2d array
    Streamline color. When given an array with the same shape as
    velocities, *color* values are converted to colors using *cmap*.
*cmap* : :class:`~matplotlib.colors.Colormap`
    Colormap used to plot streamlines and arrows. Only necessary when using
    an array input for *color*.
*norm* : :class:`~matplotlib.colors.Normalize`
    Normalize object used to scale luminance data to 0, 1. If None, stretch
    (min, max) to (0, 1). Only necessary when *color* is an array.
*arrowsize* : float
    Factor scale arrow size.
*arrowstyle* : str
    Arrow style specification.
    See :class:`~matplotlib.patches.FancyArrowPatch`.
*minlength* : float
    Minimum length of streamline in axes coordinates.
*maxlength* : float
    Maximum length of streamline in axes coordinates.
*start_points*: Nx2 array
    Coordinates of starting points for the streamlines.
    In data coordinates, the same as the ``x`` and ``y`` arrays.
*integration_direction* : ['foward','backward','both']
    Integrate the streamline in forward, backward or both directions.
*zorder* : int
    any number

Returns:

    *stream_container* : StreamplotSet
        Container object with attributes

            - lines: `matplotlib.collections.LineCollection` of streamlines

            - arrows: collection of `matplotlib.patches.FancyArrowPatch`
              objects representing arrows half-way along stream
              lines.

        This container will probably change in the future to allow changes
        to the colormap, alpha, etc. for both lines and arrows, but these
        changes should be backward compatible.

Definition at line 25 of file streamplot.py.

Variable Documentation

__all__

list streamplot.__all__ = ['streamplot']

private

Definition at line 19 of file streamplot.py.