aerosandbox.tools.pretty_plots.plots#

Submodules#

Package Contents#

Functions#

eng_string(x[, unit, format, si, add_space_after_number])

Taken from: https://stackoverflow.com/questions/17973278/python-decimal-engineering-notation-for-mili-10e-3-and-micro-10e-6/40691220

contour(*args[, levels, colorbar, linelabels, cmap, ...])

An analogue for plt.contour and plt.tricontour and friends that produces a much prettier default graph.

eng_string(x[, unit, format, si, add_space_after_number])

Taken from: https://stackoverflow.com/questions/17973278/python-decimal-engineering-notation-for-mili-10e-3-and-micro-10e-6/40691220

pie

plot_color_by_value

plot_smooth(*args[, color, label, function_of, ...])

Plots a curve that interpolates a 2D dataset. Same as matplotlib.pyplot.plot(), with the following changes:

plot_with_bootstrapped_uncertainty(x, y[, ci, ...])

Attributes#

x

data

x

x

x

aerosandbox.tools.pretty_plots.plots.eng_string(x, unit='', format='%.3g', si=True, add_space_after_number=None)[source]#

Taken from: https://stackoverflow.com/questions/17973278/python-decimal-engineering-notation-for-mili-10e-3-and-micro-10e-6/40691220

Returns float/int value <x> formatted in a simplified engineering format - using an exponent that is a multiple of 3.

Parameters:

  • x (float) – The value to be formatted. Float or int.

  • unit (str) – A unit of the quantity to be expressed, given as a string. Example: Newtons -> “N”

  • format – A printf-style string used to format the value before the exponent.

  • si – if true, use SI suffix for exponent. (k instead of e3, n instead of e-9, etc.)

  • add_space_after_number (bool) –

Return type:

str

Examples:

With format=’%.2f’:
1.23e-08 -> 12.30e-9

123 -> 123.00

1230.0 -> 1.23e3

-1230000.0 -> -1.23e6

With si=True:

1230.0 -> “1.23k”

-1230000.0 -> “-1.23M”

With unit=”N” and si=True:

1230.0 -> “1.23 kN”

-1230000.0 -> “-1.23 MN”

aerosandbox.tools.pretty_plots.plots.contour(*args, levels=31, colorbar=True, linelabels=True, cmap=None, alpha=0.7, extend='neither', linecolor='k', linewidths=0.5, extendrect=True, linelabels_format=eng_string, linelabels_fontsize=8, max_side_length_nondim=np.inf, colorbar_label=None, x_log_scale=False, y_log_scale=False, z_log_scale=False, mask=None, drop_nans=None, contour_kwargs=None, contourf_kwargs=None, colorbar_kwargs=None, linelabels_kwargs=None, **kwargs)[source]#

An analogue for plt.contour and plt.tricontour and friends that produces a much prettier default graph.

Can take inputs with either contour or tricontour syntax.

See syntax here:

https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tricontour.html https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tricontourf.html

Parameters:

  • X – If dataset is gridded, follow contour syntax. Otherwise, follow tricontour syntax.

  • Y – If dataset is gridded, follow contour syntax. Otherwise, follow tricontour syntax.

  • Z – If dataset is gridded, follow contour syntax. Otherwise, follow tricontour syntax.

  • levels (Union[int, List, numpy.ndarray]) – See contour docs.

  • colorbar (bool) – Should we draw a colorbar?

  • linelabels (bool) – Should we add line labels?

  • cmap – What colormap should we use?

  • alpha (float) – What transparency should all plot elements be?

  • extend (str) – See contour docs.

  • linecolor – What color should the line labels be?

  • linewidths (float) – See contour docs.

  • extendrect (bool) – See colorbar docs.

  • linelabels_format (Union[str, Callable[[float], str]]) – See ax.clabel docs.

  • linelabels_fontsize (float) – See ax.clabel docs.

  • contour_kwargs (Dict) – Additional keyword arguments for contour.

  • contourf_kwargs (Dict) – Additional keyword arguments for contourf.

  • colorbar_kwargs (Dict) – Additional keyword arguments for colorbar.

  • linelabels_kwargs (Dict) – Additional keyword arguments for the line labels (ax.clabel).

  • **kwargs – Additional keywords, which are passed to both contour and contourf.

  • max_side_length_nondim (float) –

  • colorbar_label (str) –

  • x_log_scale (bool) –

  • y_log_scale (bool) –

  • z_log_scale (bool) –

  • mask (numpy.ndarray) –

  • drop_nans (bool) –

Returns: A tuple of (contour, contourf, colorbar) objects.

aerosandbox.tools.pretty_plots.plots.x[source]#
aerosandbox.tools.pretty_plots.plots.eng_string(x, unit='', format='%.3g', si=True, add_space_after_number=None)[source]#

Taken from: https://stackoverflow.com/questions/17973278/python-decimal-engineering-notation-for-mili-10e-3-and-micro-10e-6/40691220

Returns float/int value <x> formatted in a simplified engineering format - using an exponent that is a multiple of 3.

Parameters:

  • x (float) – The value to be formatted. Float or int.

  • unit (str) – A unit of the quantity to be expressed, given as a string. Example: Newtons -> “N”

  • format – A printf-style string used to format the value before the exponent.

  • si – if true, use SI suffix for exponent. (k instead of e3, n instead of e-9, etc.)

  • add_space_after_number (bool) –

Return type:

str

Examples:

With format=’%.2f’:
1.23e-08 -> 12.30e-9

123 -> 123.00

1230.0 -> 1.23e3

-1230000.0 -> -1.23e6

With si=True:

1230.0 -> “1.23k”

-1230000.0 -> “-1.23M”

With unit=”N” and si=True:

1230.0 -> “1.23 kN”

-1230000.0 -> “-1.23 MN”

aerosandbox.tools.pretty_plots.plots.pie(values, names, colors=None, label_format=lambda name, value, percentage: ..., sort_by=None, startangle=0.0, center_text=None, x_labels=1.25, y_max_labels=1.3, arm_length=20, arm_radius=5)#
Parameters:

  • values (Union[numpy.ndarray, List[float]]) –

  • names (List[str]) –

  • colors (Union[numpy.ndarray, List[str]]) –

  • label_format (Callable[[str, float, float], str]) –

  • sort_by (Union[numpy.ndarray, List[float], str, None]) –

  • startangle (float) –

  • center_text (str) –

  • x_labels (float) –

  • y_max_labels (float) –

aerosandbox.tools.pretty_plots.plots.data[source]#
aerosandbox.tools.pretty_plots.plots.plot_color_by_value(x, y, *args, c, cmap='turbo', colorbar=False, colorbar_label=None, clim=None, **kwargs)#

Uses same syntax as matplotlib.pyplot.plot, except that c is now an array-like that maps to a specific color pulled from cmap. Makes lines that are multicolored based on this c value.

Parameters:

  • x (numpy.ndarray) – Array of x-points.

  • y (numpy.ndarray) – Array of y-points.

  • *args – Args that will be passed into matplotlib.pyplot.plot(). Example: “.-” for a dotted line.

  • c (numpy.ndarray) – Array of values that will map to colors. Must be the same length as x and y.

  • cmap – The colormap to use.

  • colorbar (bool) – Whether or not to display the colormap. [bool]

  • colorbar_label (str) – The label to add to the colorbar. Only applies if the colorbar is created. [str]

  • clim (Tuple[float, float]) – A tuple of (min, max) that assigns bounds to the colormap. Computed from the range of c if not given.

  • **kwargs – Kwargs that will be passed into matplotlib.pyplot.plot()

Returns:

aerosandbox.tools.pretty_plots.plots.x[source]#
aerosandbox.tools.pretty_plots.plots.plot_smooth(*args, color=None, label=None, function_of=None, resample_resolution=500, drop_nans=False, **kwargs)[source]#
Plots a curve that interpolates a 2D dataset. Same as matplotlib.pyplot.plot(), with the following changes:

  • uses B-splines to draw a smooth curve rather than a jagged polyline

  • By default, plots in line format fmt=’.-’ rather than fmt=’-’.

Other than that, almost all matplotlib.pyplot.plot() syntax can be used. See syntax here: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html

Example usage:
>>> import aerosandbox.numpy as np
>>>
>>> t = np.linspace(0, 1, 12)  # Parametric variable
>>> x = np.cos(2 * np.pi * t)
>>> y = np.cos(2 * np.pi * t ** 4) - t
>>>
>>> plot_smooth(
>>>     x, y, 'o--', color='purple'
>>> )
>>> plt.show()

  • Note: a true 2D interpolation is performed - it is not assumed y is a function of x, or vice versa. This can,

in rare cases, cause single-valuedness to not be preserved in cases where it logically should. If this is the case, you need to perform the interpolation yourself without plot_smooth().

Parameters:

  • *args –

    Same arguments as matplotlib.pyplot.plot(). Notes on standard plot() syntax:

    Call signatures: >>> plot([x], y, [fmt], *, data=None, **kwargs) >>> plot([x], y, [fmt], [x2], y2, [fmt2], …, **kwargs)

    Examples: >>> plot(x, y) # plot x and y using default line style and color >>> plot(x, y, ‘bo’) # plot x and y using blue circle markers >>> plot(y) # plot y using x as index array 0..N-1 >>> plot(y, ‘r+’) # ditto, but with red plusses

  • color – Specifies the color of any line and/or markers that are plotted (as determined by the fmt).

  • label – Attaches a label to this line. Use plt.legend() to display.

  • resample_resolution (int) – The number of points to use when resampling the interpolated curve.

  • **kwargs – Same keyword arguments as matplotlib.pyplot.plot().

  • function_of (str) –

  • drop_nans (bool) –

Return type:

Tuple[aerosandbox.numpy.ndarray, aerosandbox.numpy.ndarray]

Returns: A tuple (x, y) of the resampled points on the interpolated curve. Both x and y are 1D ndarrays.

aerosandbox.tools.pretty_plots.plots.x[source]#
aerosandbox.tools.pretty_plots.plots.plot_with_bootstrapped_uncertainty(x, y, ci=0.95, x_stdev=0.0, y_stdev=None, color=None, draw_data=True, label_line='Best Estimate', label_ci=True, label_data='Raw Data', line_alpha=0.9, ci_to_alpha_mapping=lambda ci: ..., n_bootstraps=2000, n_fit_points=500, spline_degree=3, normalize=True, x_log_scale=False, y_log_scale=False)[source]#
Parameters:

  • x (numpy.ndarray) –

  • y (numpy.ndarray) –

  • ci (Optional[Union[float, Iterable[float], numpy.ndarray]]) –

  • x_stdev (Union[None, float]) –

  • y_stdev (Union[None, float]) –

  • color (Optional[Union[str, Tuple[float]]]) –

  • draw_data (bool) –

  • label_line (Union[bool, str]) –

  • label_ci (bool) –

  • label_data (Union[bool, str]) –

  • line_alpha (float) –

  • ci_to_alpha_mapping (Callable[[float], float]) –

  • normalize (bool) –

  • x_log_scale (bool) –

  • y_log_scale (bool) –

aerosandbox.tools.pretty_plots.plots.x[source]#