Basic methods to help plot and interpret experimental data
Note
The following examples are written for Linux. On Windows®, replace the path separators appropriately (from “/” to “\”).
Named tuple class for a constant physical quantity
The factor and then the offset are applied to the number to arrive at the quantity expressed in terms of the unit.
Return a nicely formatted representation string
Alias for field number 1
Alias for field number 0
Alias for field number 2
Alias for field number 3
Overlay arrows with annotations on top of a pre-plotted line.
Arguments:
Example:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from modelicares import *
>>> # Create a plot.
>>> figure('examples/add_arrows')
<matplotlib.figure.Figure object at 0x...>
>>> x = np.arange(100)
>>> p = plt.plot(x, np.sin(x/4.0))
>>> # Add arrows and annotations.
>>> add_arrows(p[0], x_locs=x.take(np.arange(20,100,20)),
... label="Incr. time", xstar_offset=-0.15)
>>> saveall()
Saved examples/add_arrows.pdf
Saved examples/add_arrows.png
>>> plt.show()
Add horizontal lines to a set of axes with optional labels.
Arguments:
ax: Axes (matplotlib.axes object)
positions: Positions (along the x axis)
labels: List of labels for the lines
**kwargs: Line properties (propagated to matplotlib.pyplot.axhline())
E.g., color='k', linestyle='--', linewidth=0.5
Example:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from modelicares import *
>>> # Create a plot.
>>> figure('examples/add_hlines')
<matplotlib.figure.Figure object at 0x...>
>>> x = np.arange(100)
>>> y = np.sin(x/4.0)
>>> plt.plot(x, y)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-1.2, 1.2])
(-1.2, 1.2)
>>> # Add horizontal lines and labels.
>>> add_hlines(positions=[min(y), max(y)], labels=["min", "max"],
... color='r', ls='--')
>>> saveall()
Saved examples/add_hlines.pdf
Saved examples/add_hlines.png
>>> plt.show()
Add vertical lines to a set of axes with optional labels.
Arguments:
ax: Axes (matplotlib.axes object)
positions: Positions (along the x axis)
labels: List of labels for the lines
**kwargs: Line properties (propagated to matplotlib.pyplot.axvline())
E.g., color='k', linestyle='--', linewidth=0.5
Example:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from modelicares import *
>>> # Create a plot.
>>> figure('examples/add_vlines')
<matplotlib.figure.Figure object at 0x...>
>>> x = np.arange(100)
>>> y = np.sin(x/4.0)
>>> plt.plot(x, y)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-1.2, 1.2])
(-1.2, 1.2)
>>> # Add horizontal lines and labels.
>>> add_vlines(positions=[25, 50, 75], labels=["A", "B", "C"],
... color='k', ls='--')
>>> saveall()
Saved examples/add_vlines.pdf
Saved examples/add_vlines.png
>>> plt.show()
Encode a series of PNG images as a MPG movie.
Arguments:
imagebase: Base filename for the PNG images
The images should be located in the current directory as an “imagebase**xx.png” sequence, where xx is a frame index.
fname: Filename for the movie
”.mpg” will be appended if necessary.
fps: Number of frames per second
clean: True, if the PNG images should be deleted afterward
Note
This method requires mencoder. On Linux, install it with the following command: sudo apt-get install mencoder. Currently, this method is not supported on Windows.
Example:
import matplotlib.pyplot as plt
from numpy.random import rand
from modelicares import *
# Create the frames.
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111)
for i in range(50): # 50 frames
ax.cla()
ax.imshow(rand(5,5), interpolation='nearest')
fname = '_tmp%02d.png' % i
print 'Saving frame', fname
fig.savefig(fname) # doctest: +ELLIPSIS
# Assemble the frames into a movie.
animate(clean=True)
Close all open figures.
This is a shortcut for the following:
>>> from matplotlib._pylab_helpers import Gcf
>>> Gcf.destroy_all()
Plot 2D scalar data on a color axis in 2D Cartesian coordinates.
This uses a uniform grid.
Arguments:
Example:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from modelicares import *
>>> figure('examples/color')
<matplotlib.figure.Figure object at 0x...>
>>> x, y = np.meshgrid(np.arange(0, 2*np.pi, 0.2),
... np.arange(0, 2*np.pi, 0.2))
>>> c = np.cos(x) + np.sin(y)
>>> ax = plt.subplot(111)
>>> color(ax, c)
<matplotlib.image.AxesImage object at 0x...>
>>> saveall()
Saved examples/color.pdf
Saved examples/color.png
>>> plt.show()
Convert the expression of a physical quantity between units.
Arguments:
Example:
>>> from modelicares import *
>>> T = 293.15 # Temperature in K
>>> T_degC = convert(Quantity(T, factor=1, offset=-273.15, unit='C'))
>>> print(str(T) + " K is " + str(T_degC) + " degC.")
293.15 K is 20.0 degC.
Expand a file path by replacing ‘~’ with the user directory and making the path absolute.
Example:
>>> from modelicares import *
>>> expand_path('~/Documents')
'/home/.../Documents'
>>> # on Linux or 'C:\Users\...\Documents' on Windows, where "..."
>>> # is the user id.
Create a figure and set its label.
Arguments:
Example:
>>> fig = figure("velocity_vs_time")
>>> plt.getp(fig, 'label')
'velocity_vs_time'
Note
The label property is used as the base filename in the saveall() method.
Flatten a nested dictionary.
Arguments:
Example:
>>> from modelicares import *
>>> flatten_dict(dict(a=1, b=dict(c=2, d='hello')))
{'a': 1, 'b.c': 2, 'b.d': 'hello'}
Flatten a nested list.
Arguments:
l: List (may be nested to an arbitrary depth)
If the type of l is not in ltypes, then it is placed in a list.
ltypes: Tuple (not list) of accepted indexable types
Example:
>>> from modelicares import *
>>> flatten_list([1, [2, 3, [4]]])
[1, 2, 3, 4]
Return the the pair of indices that bound a target value in a monotonically increasing vector.
Arguments:
Example:
>>> from modelicares import *
>>> get_indices([0,1,2],1.6)
(1, 2)
Return the exponent of 10 for which the significand of a number is within the range [1, 10).
Example:
>>> get_pow10(50)
1
Return the exponent of 1000 for which the significand of a number is within the range [1, 1000).
Example:
>>> get_pow1000(1e5)
1
Load a CSV file into a dictionary.
The strings from the header row are used as dictionary keys.
Arguments:
fname: Path and name of the file
header_row: Row that contains the keys (uses zero-based indexing)
first_data_row: First row of data (uses zero-based indexing)
If first_data_row is not provided, then it is assumed that the data starts just after the header row.
types: List of data types for each column
int and float data types will be cast into a numpy.array. If types is not provided, attempts will be made to cast each column into int, float, and str (in that order).
**kwargs: Propagated to csv.reader()
Example:
>>> from modelicares import *
>>> data = load_csv("examples/load_csv.csv", header_row=2)
>>> print("The keys are: %s" % data.keys())
The keys are: ['Price', 'Description', 'Make', 'Model', 'Year']
Plot 1D scalar data as points and/or line segments in 2D Cartesian coordinates.
This is similar to matplotlib.pyplot.plot() (and actually calls that method), but provides direct support for plotting an arbitrary number of curves.
Arguments:
y: y-axis data
This may contain multiple series.
x: x-axis data
If x is not provided, the y-axis data will be plotted versus its indices. If x is a single series, it will be used for all of the y-axis series. If it is a list of series, each x-axis series will be matched to a y-axis series.
ax: Axis onto which the data should be plotted.
If ax is None (default), axes are created.
label: List of labels of each series (to be used later for the legend if applied)
color: Single entry, list, or itertools.cycle of colors that will be used sequentially
Each entry may be a character, grayscale, or rgb value.
marker: Single entry, list, or itertools.cycle of markers that will be used sequentially
Use None for no marker. A good assortment is [“o”, “v”, “^”, “<”, “>”, “s”, “p”, “*”, “h”, “H”, “D”, “d”]. All of the possible entries are listed at: http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.lines.Line2D.set_marker.
dashes: Single entry, list, or itertools.cycle of dash styles that will be used sequentially
Each style is a tuple of on/off lengths representing dashes. Use (0,1) for no line and (1,0) for a solid line.
**kwargs: Propagated to matplotlib.pyplot.plot()
Returns: List of matplotlib.lines.Line2D objects
Example:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from modelicares import *
>>> figure('examples/plot')
<matplotlib.figure.Figure object at 0x...>
>>> ax = plt.subplot(111)
>>> plot([range(11), range(10, -1, -1)], ax=ax)
[[<matplotlib.lines.Line2D object at 0x...>], [<matplotlib.lines.Line2D object at 0x...>]]
>>> saveall()
Saved examples/plot.pdf
Saved examples/plot.png
>>> plt.show()
Plot 2D vector data as arrows in 2D Cartesian coordinates.
Uses a uniform grid.
Arguments:
Example:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from modelicares import *
>>> figure('examples/quiver')
<matplotlib.figure.Figure object at 0x...>
>>> x, y = np.meshgrid(np.arange(0, 2*np.pi, 0.2),
... np.arange(0, 2*np.pi, 0.2))
>>> u = np.cos(x)
>>> v = np.sin(y)
>>> ax = plt.subplot(111)
>>> quiver(ax, u, v)
<matplotlib.quiver.Quiver object at 0x...>
>>> saveall()
Saved examples/quiver.pdf
Saved examples/quiver.png
>>> plt.show()
Save all open figures as images in a format or list of formats.
The directory and base filenames are taken from the label property of the figures. If the label property is empty, then a directory dialog is opened to chose a directory. In that case, the figures are saved as a sequence of numbers.
Example:
>>> import matplotlib.pyplot as plt
>>> from modelicares import *
>>> figure('temp_plot')
<matplotlib.figure.Figure object at 0x...>
>>> plt.plot(range(10))
[<matplotlib.lines.Line2D object at 0x...>]
>>> saveall()
Saved temp_plot.pdf
Saved temp_plot.png
Note
The figure() method can be used to directly create a figure with a label.
Create an array of subplots and return their axes.
Arguments:
n_plots: Number of (sub)plots
n_rows: Number of rows of (sub)plots
title: Title for the figure
subtitles: List of subtitles (i.e., titles for each subplot) or None for no subtitles
label: Label for the figure
This will be used as a base filename if the figure is saved.
xlabel: Label for the x-axes (only shown for the subplots on the bottom row)
xticklabels: Labels for the x-axis ticks (only shown for the subplots on the bottom row)
If None, then the default is used.
xticks: Positions of the x-axis ticks
If None, then the default is used.
ylabel: Label for the y-axis (only shown for the subplots on the left column)
yticklabels: Labels for the y-axis ticks (only shown for the subplots on the left column)
If None, then the default is used.
yticks: Positions of the y-axis ticks
If None, then the default is used.
ctype: Type of colorbar (None, ‘vertical’, or ‘horizontal’)
clabel: Label for the color- or c-bar axis
margin_left: Left margin
margin_right: Right margin (ignored if cbar_orientation == 'vertical')
margin_bottom: Bottom margin (ignored if cbar_orientation == 'horizontal')
margin_top: Top margin
margin_cbar: Margin reserved for the colorbar (right margin if cbar_orientation == 'vertical' and bottom margin if cbar_orientation == 'horizontal')
wspace: The amount of width reserved for blank space between subplots
hspace: The amount of height reserved for white space between subplots
cbar_space: Space between the subplot rectangles and the colorbar
If cbar is None, then this is ignored.
cbar_width: Width of the colorbar if vertical (or height if horizontal)
If cbar is None, then this is ignored.
Returns:
Example:
>>> import matplotlib.pyplot as plt
>>> from modelicares import *
>>> setup_subplots(4, 2, label='examples/setup_subplots')
([<matplotlib.axes.AxesSubplot object at 0x...>, <matplotlib.axes.AxesSubplot object at 0x...>, <matplotlib.axes.AxesSubplot object at 0x...>, <matplotlib.axes.AxesSubplot object at 0x...>], 2)
>>> saveall()
Saved examples/setup_subplots.pdf
Saved examples/setup_subplots.png
>>> plt.show()
Apply an offset and a factor as necessary to the x axis.
Arguments:
ax: matplotlib.axes object
eagerness: Parameter to adjust how little of an offset is required before the label will be recentered
- 0: Offset is never applied.
- 1: Offset is always applied if it will help.
Example:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from texunit import label_number
>>> from modelicares import *
>>> # Generate some random data.
>>> x = np.linspace(55478, 55486, 100) # Small range and large offset
>>> xlabel = label_number('Time', 's')
>>> y = np.cumsum(np.random.random(100) - 0.5)
>>> # Plot the data.
>>> ax = setup_subplots(2, 2, label='examples/shift_scale_x')[0]
>>> for a in ax:
... a.plot(x, y)
... a.set_xlabel(xlabel)
[<matplotlib.lines.Line2D object at 0x...>]
<matplotlib.text.Text object at 0x...>
[<matplotlib.lines.Line2D object at 0x...>]
<matplotlib.text.Text object at 0x...>
>>> # Shift and scale the axes.
>>> ax[0].set_title('Original plot')
<matplotlib.text.Text object at 0x...>
>>> ax[1].set_title('After applying offset and factor')
<matplotlib.text.Text object at 0x...>
>>> shift_scale_x(ax[1])
>>> saveall()
Saved examples/shift_scale_x.pdf
Saved examples/shift_scale_x.png
>>> plt.show()
Apply an offset and a factor as necessary to the y axis.
Arguments:
ax: matplotlib.axes object
eagerness: Parameter to adjust how little of an offset is required before the label will be recentered
- 0: Offset is never applied.
- 1: Offset is always applied if it will help.
Example:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from texunit import label_number
>>> from modelicares import *
>>> # Generate some random data.
>>> x = range(100)
>>> y = np.cumsum(np.random.random(100) - 0.5)
>>> y -= y.min()
>>> y *= 1e-3
>>> y += 1e3 # Small magnitude and large offset
>>> ylabel = label_number('Velocity', 'mm/s')
>>> # Plot the data.
>>> ax = setup_subplots(2, 2, label='examples/shift_scale_y')[0]
>>> for a in ax:
... a.plot(x, y)
... a.set_ylabel(ylabel)
[<matplotlib.lines.Line2D object at 0x...>]
<matplotlib.text.Text object at 0x...>
[<matplotlib.lines.Line2D object at 0x...>]
<matplotlib.text.Text object at 0x...>
>>> # Shift and scale the axes.
>>> ax[0].set_title('Original plot')
<matplotlib.text.Text object at 0x...>
>>> ax[1].set_title('After applying offset and factor')
<matplotlib.text.Text object at 0x...>
>>> shift_scale_y(ax[1])
>>> saveall()
Saved examples/shift_scale_y.pdf
Saved examples/shift_scale_y.png
>>> plt.show()
A matplotlib subclass to draw an arrowhead on a line
Initialize the line and arrow.
Arguments:
Example:
>>> import matplotlib.pyplot as plt
>>> from modelicares import *
>>> fig = figure('examples/ArrowLine')
>>> ax = fig.add_subplot(111, autoscale_on=False)
>>> t = [-1,2]
>>> s = [0,-1]
>>> line = ArrowLine(t, s, color='b', ls='-', lw=2, arrow='>',
... arrowsize=20)
>>> ax.add_line(line)
<modelicares.base.ArrowLine object at 0x...>
>>> ax.set_xlim(-3, 3)
(-3, 3)
>>> ax.set_ylim(-3, 3)
(-3, 3)
>>> saveall()
Saved examples/ArrowLine.pdf
Saved examples/ArrowLine.png
>>> plt.show()
