one_d¶

Class summary¶

MarkersDashesColors(**kwargs) Nice looking markers, dashed lines, and colors for matplotlib line plots

Function summary¶

`apply_dict_to_object`(obj, dic)	Apply a dict of properties to a matplotlib object.
`apply_markevery_to_sequence_of_lines`(lines)	Apply markevery property to sequence of matplotlib lines
`copy_dict`(source_dict, diffs)	Returns a copy of source_dict, updated with the new key-value pairs in diffs dict.
`iterable_method_call`(iterable, method, ...)	Call a method on each element of an iterable
`pleasing_defaults`()	Alter some matplotlib rcparams defaults to be visually more appealing
`plot_data_in_grid`(fig, data, gs[, gs_index, ...])	Make a subplot for each set of data
`plot_generic_loads`(load_triples, load_names)	Plot loads that come in load_vs_time-load_vs_depth-omega_phase form
`plot_single_material_vs_depth`(z_x, xlabels)	Plot side by side property vs depth graphs
`plot_vs_depth`(x, z[, line_labels, H, ...])	Plot z vs x for various t values
`plot_vs_time`(t, y[, line_labels, prop_dict])	Plot y vs t with some options
`rgb_shade`(rgb[, factor, scaled])	Apply shade (darken) to a red, green, blue (rgb) triplet
`rgb_tint`(rgb[, factor, scaled])	Apply tint (lighten) to a red, green, blue (rgb) triplet
`row_major_order_reverse_map`(shape[, ...])	Map an index to a position in a row-major ordered array by reversing dims
`save_figure`(fig[, fname, ext, dpi])	Save a figure to a file in multiple formats
`split_sequence_into_dict_and_nondicts`(*args)	Separate dict and non-dict items.
`xylabel_subplots`(fig[, y_axis_labels, ...])	Set x-axis label and y-axis label for each sub plot in figure

Module listing¶

Routines to produce some matplotlib plots for one dimesional data.

One dimensional data is basically x-y data. (i.e. no contour plot needed)

class geotecha.plotting.one_d.MarkersDashesColors(**kwargs)[source]¶

Bases: object

Nice looking markers, dashed lines, and colors for matplotlib line plots

Use this object to create a list of style dictionaries. Each style dict can be unpacked when passed to the matplotlib.plot command. Each dict contains the appropriate keywords to set the marker, dashes, and color properties. You can turn the list into a cycle using itertools.cycle To see the marker, dashes, and color options run the demo_options’ method. To choose a subset of styles see the `__call__ method. To view what the chosen styles actually look like run the construct_styles’ and the `demo_styles method

Parameters:

**kwargs : keyword arguments

key value pairs to override the default_marker. e.g. color=(1.0,0,0) would change the default line and marker color to red. markersize=8 would change all marker sizes to 8. Most likely value to change are color, markersize, alpha. Defult values of the default_marker are:

key value

markersize 5

markeredgecolor almost_black, ‘#262626’

markeredgewidth 1

markerfacecolor almost_black, ‘#262626’

alpha 0.9

color almost_black, ‘#262626’

Attributes

almost_black	(str) hex string representing the default color, almost black ‘#262626’.
color	(matplotlib color) Default color of markers and lines. Default color=`almost_black`.
dashes	(list of tuples) List of on, off tuples for use with matplotlib dashes. See construct_dashes method.
colors	(list of tuples) List of rgb tuples describing colors. See construct_dashes.
markers	(list of dict) List of dict describing marker properties to pass to matplotlib.plot command. See construct_markers method.
default_marker	(dict) Default properties for markers. These defaults will be overridden by the values in markers. Before initialization default_marker has the following keys.

Methods

`__call__`([markers, dashes, marker_colors, ...])	List of styles to unpack in matplotlib.plot for pleasing lines/markers
`construct_colors`()	Populate self.colors: a list of rgb colors
`construct_dashes`()	List of on, off tuples for use with matplotlib dashes
`construct_markers`()	Populate self.markers: a list of dict that define a matplotlib marker
`construct_styles`([markers, dashes, ...])	Calls self.__call__ and assigns results to self.styles
`demo_options`()	Show a figure with all the marker, dashes, color options available
`demo_styles`()	Show a figure of all the styles in self.styles
`merge_default_markers`()	Merge self.default_marker dict with each dict in self.markers
`shade_colors`([factor])	Darken all colors by factor
`tint_colors`([factor])	Lighten all colors by factor

almost_black = '#262626'¶

construct_colors()[source]¶: Populate self.colors: a list of rgb colors

construct_dashes()[source]¶: List of on, off tuples for use with matplotlib dashes

construct_markers()[source]¶

Populate self.markers: a list of dict that define a matplotlib marker

run self.merge_default_markers after running self.construct_markers so that default properties are applied properly.

construct_styles(markers=None, dashes=None, marker_colors=None, line_colors=None)[source]¶: Calls self.__call__ and assigns results to self.styles

demo_options()[source]¶: Show a figure with all the marker, dashes, color options available

demo_styles()[source]¶: Show a figure of all the styles in self.styles

merge_default_markers()[source]¶: Merge self.default_marker dict with each dict in self.markers

shade_colors(factor=1)[source]¶: Darken all colors by factor

tint_colors(factor=1)[source]¶: Lighten all colors by factor

geotecha.plotting.one_d.apply_dict_to_object(obj, dic)[source]¶

Apply a dict of properties to a matplotlib object.

Note the object must support set_<property_name> methods If obj and d are lists then the each dic will be applied to the corresponding obj.

Parameters:

obj : matplotlib object or list of

Object to set properties in. Typically a matplotlib.lines.Line2D instance.

dic : dict or list of dict

A dictionary or properties to apply to the object. e.g for a matplotlib.lines.Line2D dict keys might be ‘marker’, or ‘linestyle’ etc. i.e any kwarg you would pass to plt.plot. Note that if a key in dic does not correspond to a obj.set_key method then it will be ignored.

geotecha.plotting.one_d.apply_markevery_to_sequence_of_lines(lines, markevery=None, random_start=True, seed=None)[source]¶

Apply markevery property to sequence of matplotlib lines

Allows for a random start so that marker on different lines do not line up.

Parameters:

lines : sequence of matplotlib lines

Lines to apply markevery to.

markevery : int or float, optional

Value of markevery property. Default markevery=None, i.e. markevery will be turned off and all points will have markers.

random_start : True/False

If True then first marker shown will be random between start marker and the markevery property. Default random_start=True.

seed : int, optional

Random seed. Default seed=None which does not specify the random seed.

geotecha.plotting.one_d.copy_dict(source_dict, diffs)[source]¶

Returns a copy of source_dict, updated with the new key-value: pairs in diffs dict.

Parameters:

source_dict : dict

Source dictionary.

diffs : dict

Dictionary with which to update source_dict.

Returns:

out : dict

Shallow copy of source_dict updated with diffs dict.

References

http://stackoverflow.com/a/5551706/2530083

Examples

>>> d = copy_dict({'a':7, 'b':12}, {'c':13})
>>> d['a']; d['b']; d['c']
7
12
13

>>> d = copy_dict({'a':7, 'b':12}, {'a':21, 'c':13})
>>> d['a']; d['b']; d['c']
21
12
13

geotecha.plotting.one_d.iterable_method_call(iterable, method, unpack, *args)[source]¶

Call a method on each element of an iterable

iterable[i].method(arg)

Parameters:

iterable : sequence etc.

Iterable whos members will have thier attribute changed.

method : string

Method to call.

unpack : bool

If True then each member of *args will be unpacked before passing to method.

*args : positional arguments

If a single positional argument then all members of iterable will have their method called with the same single argument. If more that one positional argument then iterable[0].method will be called with args[0], iterable[1].method will be set to args[1] etc. Skip elements by having corresponding value of *args=None.

geotecha.plotting.one_d.pleasing_defaults()[source]¶

Alter some matplotlib rcparams defaults to be visually more appealing

References

Many of the ideas/defaults/code come from Olga Botvinnik [R123], and her prettyplot package [R124].

[R123]

(1, 2) http://blog.olgabotvinnik.com/post/58941062205/prettyplotlib-painlessly-create-beautiful-matplotlib

[R124]

(1, 2) http://olgabot.github.io/prettyplotlib/

geotecha.plotting.one_d.plot_data_in_grid(fig, data, gs, gs_index=None, sharex=None, sharey=None)[source]¶

Make a subplot for each set of data

Parameters:

fig : matplotlib.Figure

Figure to create subplots in.

data : sequence of sequence of 2 element sequence

data[i] = Data for the ith subplot. data[i][j] = jth (x, y) data set for the ith subplot. Each set of (x, y) data will be plotted using matplotlib.plot fn e.g. data=[([x0, y0],), ([x1, y1],), ([x2a, y2a], [x2b, x2b])] Note that data[i][j] will be split into list of all the non-dict items and a merged dict of all the dict items. Both the list and the merged dict will be unpacked and passed to the plot_type function. This allows passing of keyword arguments. If one of the dict keys is ‘plot_type’ it’s value should be a string indicating a method of matplotlib.Axes that can be used to create the subplot. If ‘plot_type’ is not found then the default matplotlib.Axes.plot will be used. Be careful when using () to group data if there is only one item in the (item) then they are just parentheses and you are just saying ‘item’; put a comma after the item to make it a tuple which is usually what you want when specifying data in this function.

gs : matplotlib.gridspec.GridSpec instance

Defines the grid in which subplots will be created.

gs_index : List of int or list of slice, optional

Specifies the position within gs that each data set will be plotted Positions can be specified by 1) an integer which will correspond to row-major ordering in the grid (e.g. for a 3x3 grid, index 3 will be second row, first column), or 2) a tuple of (row,column), or 3) a slice (e.g. index of np.s_[:1,:1] will span from first row, first column to second row, second column). Another slice method is slice(3,7) which will span from position 3 to position 7. Default gs_index=None subplots are added in row-major ordering.

sharex : sequence of int

Subplot index to share x-axis with. Default sharex=None i.e. no sharing. To skip a subplot put None as the corresponding element of sharex. If only one value is given and ther is more than one data set then all subplots will share the given axis. Note that the axis to share must already have been created.

sharey : sequence of int

Subplot index to share y-axis with. Default sharey=None i.e. no sharing. To skip a subplot put None as the corresponding element of sharey. If only one value is given and ther is more than one data set then all subplots will share the given axis. Note that the axis to share must already have been created.

Returns:

ax : List of matplotlib.pyplot.Axes instances. Don’t be

confused with normal meaning of ‘ax’, this ax is a list of Axes, not just one.

Notes

You may be wondering how to apply axes labels and such. Do something like [a.set_xlabel(v) for a, v in zip(fig.get_axes(), [xlabel1, xlabel2, ...])]

geotecha.plotting.one_d.plot_generic_loads(load_triples, load_names, ylabels=None, trange=None, H=1.0, RLzero=None, prop_dict={})[source]¶

Plot loads that come in load_vs_time-load_vs_depth-omega_phase form

For each load_triple (i.e. each load type) two plots will be made side by side: a load_vs_time plot; and a load_vs_depth plot. the different load types will appear one under the other.

::: Load Magnitude Depth ^ ^ | ..... | * | . | * | . . . . | * | . | * | . | * ———————>Time ———>Load factor

Parameters:

load_triples : list of list of 3 element tuples

(load_vs_time, load_vs_depth, load_omega_phase) PolyLines. load_triples[i] will be a list of load triples for the ith plot load_triples[i][j] will be the jth load triple for the ith plot. The load_vs_depth can also be a two element tuple containing a list/array of depth values and a list/array of load values. The load_vs_time can also be a two element tuple containing a list/array of time values and a list/array of load values.

load_names : list of string

string to prepend to legend entries for each load

ylabels : list of string, optional

y labels for each of the axes, Default ylabels=None i.e. y0, y1, y2 etc.

trange : 2 element tuple, optional

(tmin, tmax) max and min times to plot loads for. Default trange=None i.e. t limits will be worked out from data.

H : float, optional

Height of soil profile. Default H=1.0. Used to transform normalised depth to actual depth.

RLzero : float, optional

Reduced level of the top of the soil layer. If RLzero is not None then all depths (in plots and results) will be transformed to an RL by RL = RLzero - z*H. If RLzero is None (i.e. the default) then all actual depths will be reported z*H (i.e. positive numbers).

prop_dict : dict of dict, optional

Dictionary containing certain properties used to set various plot options.

prop_dict option description

fig_prop dict of prop to pass to plt.figure. Defaults include: figsize=(7.05, 1.57 * no.of.loads)

styles List of dict. Each dict is for one line. Each dict contains kwargs for plt.plot. SeeMarkersDashesColors. Defaults give black and white markersize 5.

time_axis_label Label for x axis in load_vs_time plots. Default=’Time’

depth_axis_label Label for y axis in load_vs_depth plot Default=”Depth, z” or “RL” depending on RLzero.

has_legend True or False. Default = True.

legend_prop dict of prop to pass to ax.legend. Defaults include: title=’Load’ fontsize=9

Returns:

fig : matplolib.Figure

Figure with plots.

geotecha.plotting.one_d.plot_single_material_vs_depth(z_x, xlabels, H=1.0, RLzero=None, prop_dict={})[source]¶

Plot side by side property vs depth graphs

x1           x2           x3
-----------> -----------> ------------>
|     .      |   .        |   .        |
|     .      |    .       |  .         |
|     .      |     .      |  .         |
|    .       |      .     |    .       |
|   .        |      .     |      .     |
|  .         |      .     |        .   |
v            v            v
depth

Parameters:

z_x : list of PolyLine

List of value_vs_depth PolyLines.

xlabels : list of string

List of x-axis labels.

H : float, optional

Height of soil profile. Default H=1.0. Used to transform normalised depth to actual depth.

RLzero : float, optional

Reduced level of the top of the soil layer. If RLzero is not None then all depths (in plots and results) will be transformed to an RL by RL = RLzero - z*H. If RLzero is None (i.e. the default) then all depths will be reported z*H (i.e. positive numbers).

prop_dict : dict of dict, optional

Dictionary containing certain properties used to set various plot options:

prop_dict option description

fig_prop dict of prop to pass to plt.figure. defaults include: figsize=(7.05, 4.4)

styles List of dict. Each dict is for one line. Each dict contains kwargs for plt.plot See MarkersDashesColors defaults give black and white markersize 5

xlabel x-axis label. default = ‘Time’

ylabel y-axis label. default = ‘Depth, z’ or ‘RL’ depending on RLzero.

geotecha.plotting.one_d.plot_vs_depth(x, z, line_labels=None, H=1.0, RLzero=None, prop_dict={})[source]¶

Plot z vs x for various t values

Originally used for plotting things like excess pore pressure vs depth

--------------------> value
|.*
| .  *
|  .    *
|   .     *
|  .    *
| .  *
v
depth

Parameters:

x : one or two dimensional ndarray

y values to plot. Basically plt.plot(t, y) will be used

z : one d array of float

Depth values.

line_labels : list of string, optional

Label for each line in y. Default line_labels=None, i.e. no line labels.

H : float, optional

Height of soil profile. Default H=1.0. Used to transform normalised depth to actual depth.

RLzero : float, optional

Reduced level of the top of the soil layer. If RLzero is not None then all depths (in plots and results) will be transformed to an RL by RL = RLzero - z*H. If RLzero is None (i.e. the default) then actual depths will be reported z*H (i.e. positive numbers).

prop_dict : dict of dict, optional

Dictionary containing certain properties used to set various plot options.

prop_dict option description

fig_prop dict of prop to pass to plt.figure. defaults include: figsize=(7.05, 4.4)

styles List of dict. Each dict is for one line. Each dict contains kwargs for plt.plot See MarkersDashesColors defaults give black and white markersize 5

xlabel x-axis label. Default = ‘x’.

ylabel y-axis label. Default = ‘Depth, z’ or ‘RL’. depending on RLzero.

has_legend True or False. default is True

legend_prop dict of prop to pass to ax.legend defaults include: title=’Depth interval’ fontsize=9

Returns:

fig : matplolib.Figure

Figure with plots.

geotecha.plotting.one_d.plot_vs_time(t, y, line_labels=None, prop_dict={})[source]¶

Plot y vs t with some options

Originally used for plotting things like average excess pore pressure vs time.

y
^
|           .......
|          .
|   . . . .  ***
|  . *      *   *
| .*   *****
--------------------->Time

Parameters:

t : np.array

Time values.

y : one or two dimensional ndarray

y values to plot. basically plt.plot(t,y) will be used.

line_labels : list of string, optional

Label for each line in y. Defaultline_labels=None, i.e. no labels.

prop_dict : dict of dict, optional

Dictionary containing certain properties used to set various plot options.

prop_dict option description

fig_prop dict of prop to pass to plt.figure. Defaults include: figsize=(7.05, 4.4)

styles List of dict. Each dict is for one line. Each dict contains kwargs for plt.plot See MarkersDashesColors defaults give black and white markersize 5

xlabel x-axis label. default=’Time’.

ylabel y-axis label. default = ‘y’.

has_legend True or False. default = True.

legend_prop dict of prop to pass to ax.legend Defaults include: title=’Depth interval’ fontsize=9

Returns:

fig : matplolib.Figure

Figure with plots.

geotecha.plotting.one_d.rgb_shade(rgb, factor=1, scaled=True)[source]¶

Apply shade (darken) to a red, green, blue (rgb) triplet

If rgba tuple is given the ‘a’ value is not altered.

Parameters:

rgb : tuple

Triplet of rgb values. Note can be an rgba value.

factor : float

Between 0 and 1. Factor by which to shade. Default factor=1.

scaled : bool

If True then assumes RGB values aare scaled between 0, 1. If False rgb values are between 0 and 255. Default scaled=True.

Returns:

rgb_new : tuple

New tuple of rgb (or rgba) values.

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