Class hierarchy¶

The properties of the graph (figure in Matplotlib parlance) are defined in an object of the pplot.Figure class.

Each figure can have one or more panels, whose properties are defined by objects of the pplot.Panel class. Panels are arranged vertically in the figure and share the same independent axis. The limits of the independent axis of the figure result from the union of the limits of the independent axis of all the panels. The independent axis is shown by default in the bottom-most panel although it can be configured to be in any panel or panels.

Each panel can have one or more data series, whose properties are defined by objects of the pplot.Series class. A series can be associated with either the primary or secondary dependent axis of the panel. The limits of the primary and secondary dependent axis of the panel result from the union of the primary and secondary dependent data points of all the series associated with each axis. The primary axis is shown on the left of the panel and the secondary axis is shown on the right of the panel. Axes can be linear or logarithmic.

The data for a series is defined by a source. Two data sources are provided: the pplot.BasicSource class provides basic data validation and minimum/maximum independent variable range bounding. The pplot.CsvSource class builds upon the functionality of the pplot.BasicSource class and offers a simple way of accessing data from a comma-separated values (CSV) file. Other data sources can be programmed by inheriting from the pplot.functions.DataSource abstract base class (ABC). The custom data source needs to implement the following methods: __str__, _set_indep_var and _set_dep_var. The latter two methods set the contents of the independent variable (an increasing real Numpy vector) and the dependent variable (a real Numpy vector) of the source, respectively.

Figure 1: Example diagram of the class hierarchy of a figure. In this particular example the figure consists of 3 panels. Panel 1 has a series whose data comes from a basic source, panel 2 has three series, two of which come from comma-separated values (CSV) files and one that comes from a basic source. Panel 3 has one series whose data comes from a basic source.

Axes tick marks¶

Axes tick marks are selected so as to create the most readable graph. Two global variables control the actual number of ticks, pplot.constants.MIN_TICKS and pplot.constants.SUGGESTED_MAX_TICKS. In general the number of ticks are between these two bounds; one or two more ticks can be present if a data series uses interpolation and the interpolated curve goes above (below) the largest (smallest) data point. Tick spacing is chosen so as to have the most number of data points “on grid”. Engineering notation (i.e. 1K = 1000, 1m = 0.001, etc.) is used for the axis tick marks.

Example¶

# plot_example_1.py
from __future__ import print_function
import os
import sys
import matplotlib
import numpy
import pplot

def main(fname, no_print):
    """
    Example of how to use the pplot library
    to generate presentation-quality plots
    """
    ###
    # Series definition (Series class)
    ###
    # Extract data from a comma-separated (csv)
    # file using the CsvSource class
    wdir = os.path.dirname(__file__)
    csv_file = os.path.join(wdir, 'data.csv')
    series1_obj = [pplot.Series(
        data_source=pplot.CsvSource(
            fname=csv_file,
            rfilter={'value1':1},
            indep_col_label='value2',
            dep_col_label='value3',
            indep_min=None,
            indep_max=None,
            fproc=series1_proc_func,
            fproc_eargs={'xoffset':1e-3}
        ),
        label='Source 1',
        color='k',
        marker='o',
        interp='CUBIC',
        line_style='-',
        secondary_axis=False
    )]
    # Literal data can be used with the BasicSource class
    series2_obj = [pplot.Series(
        data_source=pplot.BasicSource(
            indep_var=numpy.array([0e-3, 1e-3, 2e-3]),
            dep_var=numpy.array([4, 7, 8]),
        ),
        label='Source 2',
        color='r',
        marker='s',
        interp='STRAIGHT',
        line_style='--',
        secondary_axis=False
    )]
    series3_obj = [pplot.Series(
        data_source=pplot.BasicSource(
            indep_var=numpy.array([0.5e-3, 1e-3, 1.5e-3]),
            dep_var=numpy.array([10, 9, 6]),
        ),
        label='Source 3',
        color='b',
        marker='h',
        interp='STRAIGHT',
        line_style='--',
        secondary_axis=True
    )]
    series4_obj = [pplot.Series(
        data_source=pplot.BasicSource(
            indep_var=numpy.array([0.3e-3, 1.8e-3, 2.5e-3]),
            dep_var=numpy.array([8, 8, 8]),
        ),
        label='Source 4',
        color='g',
        marker='D',
        interp='STRAIGHT',
        line_style=None,
        secondary_axis=True
    )]
    ###
    # Panels definition (Panel class)
    ###
    panel_obj = pplot.Panel(
        series=series1_obj+series2_obj+series3_obj+series4_obj,
        primary_axis_label='Primary axis label',
        primary_axis_units='-',
        secondary_axis_label='Secondary axis label',
        secondary_axis_units='W',
        legend_props={'pos':'lower right', 'cols':1}
    )
    ###
    # Figure definition (Figure class)
    ###
    fig_obj = pplot.Figure(
        panels=panel_obj,
        indep_var_label='Indep. var.',
        indep_var_units='S',
        log_indep_axis=False,
        fig_width=4*2.25,
        fig_height=3*2.25,
        title='Library pplot Example'
    )
    # Save figure
    output_fname = os.path.join(wdir, fname)
    if not no_print:
        print('Saving image to file {0}'.format(output_fname))
    fig_obj.save(output_fname)

def series1_proc_func(indep_var, dep_var, xoffset):
    """ Process data 1 series """
    return (indep_var*1e-3)-xoffset, dep_var

Figure 2: plot_example_1.png generated by plot_example_1.py

Global variables¶

pplot.constants.AXIS_LABEL_FONT_SIZE = 18¶

Axis labels font size in points

Type:	integer

pplot.constants.LINE_WIDTH = 2.5¶

Series line width in points

Type:	float

pplot.constants.LEGEND_SCALE = 1.5¶

Scale factor for panel legend. The legend font size in points is equal to the axis font size divided by the legend scale

Type:	number

pplot.constants.MARKER_SIZE = 14¶

Series marker size in points

Type:	integer

pplot.constants.MIN_TICKS = 6¶

Minimum number of ticks desired for the independent and dependent axis of a panel

Type:	integer

pplot.constants.PRECISION = 10¶

Number of mantissa significant digits used in all computations

Type:	integer

pplot.constants.SUGGESTED_MAX_TICKS = 10¶

Maximum number of ticks desired for the independent and dependent axis of a panel. It is possible for a panel to have more than SUGGESTED_MAX_TICKS in the dependent axis if one or more series are plotted with an interpolation function and at least one interpolated curve goes above or below the maximum and minimum data points of the panel. In this case the panel will have SUGGESTED_MAX_TICKS+1 ticks if some interpolation curve is above the maximum data point of the panel or below the minimum data point of the panel; or the panel will have SUGGESTED_MAX_TICKS+2 ticks if some interpolation curve(s) is(are) above the maximum data point of the panel and below the minimum data point of the panel

Type:	integer

pplot.constants.TITLE_FONT_SIZE = 24¶

Figure title font size in points

Type:	integer

Functions¶

pplot.parameterized_color_space(param_list, offset=0, color_space='binary')¶

Computes a color space where lighter colors correspond to lower parameter values

Parameters:	param_list (list) – Parameter values offset (OffsetRange) – Offset of the first (lightest) color color_space (ColorSpaceOption) – Color palette (case sensitive)
Return type:	Matplotlib color map

Raises:

RuntimeError (Argument `color_space` is not valid) RuntimeError (Argument `offset` is not valid) RuntimeError (Argument `param_list` is not valid) TypeError (Argument `param_list` is empty) ValueError (Argument `color_space` is not one of ‘binary’, ‘Blues’, ‘BuGn’, ‘BuPu’, ‘GnBu’, ‘Greens’, ‘Greys’, ‘Oranges’, ‘OrRd’, ‘PuBu’, ‘PuBuGn’, ‘PuRd’, ‘Purples’, ‘RdPu’, ‘Reds’, ‘YlGn’, ‘YlGnBu’, ‘YlOrBr’ or ‘YlOrRd’ (case insensitive))

Classes¶

class pplot.functions.DataSource¶

Bases: object

Abstract base class for data sources. The following example is a minimal implementation of a data source class:

# plot_example_2.py
import pplot

class MySource(pplot.DataSource, object):
    def __init__(self):
        super(MySource, self).__init__()

    def __str__(self):
        return super(MySource, self).__str__()

    def _set_dep_var(self, dep_var):
        super(MySource, self)._set_dep_var(dep_var)

    def _set_indep_var(self, indep_var):
        super(MySource, self)._set_indep_var(indep_var)

    dep_var = property(
        pplot.DataSource._get_dep_var, _set_dep_var
    )

    indep_var = property(
        pplot.DataSource._get_indep_var, _set_indep_var
    )

Warning

The abstract methods listed below need to be defined in a child class

__str__()¶

Pretty prints the stored independent and dependent variables. For example:

>>> from __future__ import print_function
>>> import numpy, docs.support.plot_example_2
>>> obj = docs.support.plot_example_2.MySource()
>>> obj.indep_var = numpy.array([1, 2, 3])
>>> obj.dep_var = numpy.array([-1, 1, -1])
>>> print(obj)
Independent variable: [ 1.0, 2.0, 3.0 ]
Dependent variable: [ -1.0, 1.0, -1.0 ]

_set_dep_var(dep_var)¶

Sets the dependent variable (casting to float type). For example:

>>> import numpy, docs.support.plot_example_2
>>> obj = docs.support.plot_example_2.MySource()
>>> obj.dep_var = numpy.array([-1, 1, -1])
>>> obj.dep_var
array([-1.,  1., -1.])

_set_indep_var(indep_var)¶

Sets the independent variable (casting to float type). For example:

>>> import numpy, docs.support.plot_example_2
>>> obj = docs.support.plot_example_2.MySource()
>>> obj.indep_var = numpy.array([1, 2, 3])
>>> obj.indep_var
array([ 1.,  2.,  3.])

class pplot.BasicSource(indep_var, dep_var, indep_min=None, indep_max=None)¶

Bases: pplot.functions.DataSource

Objects of this class hold a given data set intended for plotting. It is a convenient way to plot manually-entered data or data coming from a source that does not export to a comma-separated values (CSV) file.

Parameters:	indep_var (IncreasingRealNumpyVector) – Independent variable vector dep_var (RealNumpyVector) – Dependent variable vector indep_min (RealNum or None) – Minimum independent variable value. If None no minimum thresholding is applied to the data indep_max – Maximum independent variable value. If None no maximum thresholding is applied to the data
Return type:	pplot.BasicSource

Raises:

RuntimeError (Argument `dep_var` is not valid) RuntimeError (Argument `indep_max` is not valid) RuntimeError (Argument `indep_min` is not valid) RuntimeError (Argument `indep_var` is not valid) ValueError (Argument `indep_min` is greater than argument `indep_max`) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements)

__str__()¶

Prints source information. For example:

# plot_example_4.py
import numpy, pplot

def create_basic_source():
    obj = pplot.BasicSource(
        indep_var=numpy.array([1, 2, 3, 4]),
        dep_var=numpy.array([1, -10, 10, 5]),
        indep_min=2, indep_max=3
    )
    return obj

>>> from __future__ import print_function
>>> import docs.support.plot_example_4
>>> obj = docs.support.plot_example_4.create_basic_source()
>>> print(obj)
Independent variable minimum: 2
Independent variable maximum: 3
Independent variable: [ 2.0, 3.0 ]
Dependent variable: [ -10.0, 10.0 ]

dep_var¶

Gets or sets the dependent variable data

Type:	RealNumpyVector

Raises:	(when assigned) RuntimeError (Argument `dep_var` is not valid) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements)

indep_max¶

Gets or sets the maximum independent variable limit. If None no maximum thresholding is applied to the data

Type:	RealNum or None

Raises:	(when assigned) RuntimeError (Argument `indep_max` is not valid) ValueError (Argument `indep_min` is greater than argument `indep_max`) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding)

indep_min¶

Gets or sets the minimum independent variable limit. If None no minimum thresholding is applied to the data

Type:	RealNum or None

Raises:	(when assigned) RuntimeError (Argument `indep_min` is not valid) ValueError (Argument `indep_min` is greater than argument `indep_max`) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding)

indep_var¶

Gets or sets the independent variable data

Type:	IncreasingRealNumpyVector

Raises:	(when assigned) RuntimeError (Argument `indep_var` is not valid) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements)

class pplot.CsvSource(fname, indep_col_label, dep_col_label, rfilter=None, indep_min=None, indep_max=None, fproc=None, fproc_eargs=None)¶

Bases: pplot.functions.DataSource

Objects of this class hold a data set from a CSV file intended for plotting. The raw data from the file can be filtered and a callback function can be used for more general data pre-processing

Parameters:

fname (FileNameExists) – Comma-separated values file name indep_col_label (string) – Independent variable column label (case insensitive) dep_col_label (string) – Dependent variable column label (case insensitive) rfilter (CsvRowFilter or None) – Row filter specification. If None no row filtering is performed indep_min – Minimum independent variable value. If None no minimum thresholding is applied to the data indep_max – Maximum independent variable value. If None no maximum thresholding is applied to the data fproc (Function or None) – Data processing function. If None no processing function is used fproc_eargs (dictionary or None) – Data processing function extra arguments. If None no extra arguments are passed to the processing function (if defined)

Return type:

pplot.CsvSource

Note

The row where data starts in the comma-separated file is auto-detected as the first row that has a number (integer or float) in at least one of its columns

Raises:

OSError (File [fname] could not be found) RuntimeError (Argument `dep_col_label` is not valid) RuntimeError (Argument `dep_var` is not valid) RuntimeError (Argument `fname` is not valid) RuntimeError (Argument `fproc_eargs` is not valid) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Argument `fproc` is not valid) RuntimeError (Argument `indep_col_label` is not valid) RuntimeError (Argument `indep_max` is not valid) RuntimeError (Argument `indep_min` is not valid) RuntimeError (Argument `indep_var` is not valid) RuntimeError (Argument `rfilter` is not valid) RuntimeError (Column headers are not unique in file [fname]) RuntimeError (File [fname] has no valid data) RuntimeError (File [fname] is empty) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Argument `fproc` (function [func_name]) does not have at least 2 arguments) ValueError (Argument `indep_min` is greater than argument `indep_max`) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding) ValueError (Argument `rfilter` is empty) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements) ValueError (Column [col_name] (dependent column label) could not be found in comma-separated file [fname] header) ValueError (Column [col_name] (independent column label) could not be found in comma-separated file [fname] header) ValueError (Column [col_name] in row filter not found in comma- separated file [fname] header) ValueError (Column [column_identifier] not found) ValueError (Extra argument `*[arg_name]*` not found in argument `fproc` (function [func_name]) definition) ValueError (Filtered dependent variable is empty) ValueError (Filtered independent variable is empty) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

__str__()¶

Prints source information. For example:

# plot_example_3.py
import pmisc, pcsv

def cwrite(fobj, data):
    fobj.write(data)

def write_csv_file(file_handle):
    cwrite(file_handle, 'Col1,Col2\n')
    cwrite(file_handle, '0E-12,10\n')
    cwrite(file_handle, '1E-12,0\n')
    cwrite(file_handle, '2E-12,20\n')
    cwrite(file_handle, '3E-12,-10\n')
    cwrite(file_handle, '4E-12,30\n')

# indep_var is a Numpy vector, in this example time,
# in seconds. dep_var is a Numpy vector
def proc_func1(indep_var, dep_var):
    # Scale time to pico-seconds
    indep_var = indep_var/1e-12
    # Remove offset
    dep_var = dep_var-dep_var[0]
    return indep_var, dep_var

def create_csv_source():
    with pmisc.TmpFile(write_csv_file) as fname:
        obj = pplot.CsvSource(
            fname=fname,
            indep_col_label='Col1',
            dep_col_label='Col2',
            indep_min=2E-12,
            fproc=proc_func1
        )
    return obj

>>> from __future__ import print_function
>>> import docs.support.plot_example_3
>>> obj = docs.support.plot_example_3.create_csv_source()
>>> print(obj)  
File name: ...
Row filter: None
Independent column label: Col1
Dependent column label: Col2
Processing function: proc_func1
Processing function extra arguments: None
Independent variable minimum: 2e-12
Independent variable maximum: +inf
Independent variable: [ 2.0, 3.0, 4.0 ]
Dependent variable: [ 0.0, -30.0, 10.0 ]

dep_col_label¶

Gets or sets the dependent variable column label (column name)

Type:	string

Raises:

(when assigned) RuntimeError (Argument `dep_col_label` is not valid) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Column [col_name] (dependent column label) could not be found in comma-separated file [fname] header) ValueError (Column [col_name] in row filter not found in comma- separated file [fname] header) ValueError (Filtered dependent variable is empty) ValueError (Filtered independent variable is empty) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

dep_var¶

Gets the dependent variable Numpy vector

fname¶

Gets or sets the comma-separated values file from which data series is to be extracted. It is assumed that the first line of the file contains unique headers for each column

Type:	string

Raises:

(when assigned) OSError (File [fname] could not be found) RuntimeError (Argument `dep_var` is not valid) RuntimeError (Argument `fname` is not valid) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Argument `indep_var` is not valid) RuntimeError (Argument `rfilter` is not valid) RuntimeError (Column headers are not unique in file [fname]) RuntimeError (File [fname] has no valid data) RuntimeError (File [fname] is empty) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding) ValueError (Argument `rfilter` is empty) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements) ValueError (Column [col_name] (dependent column label) could not be found in comma-separated file [fname] header) ValueError (Column [col_name] (independent column label) could not be found in comma-separated file [fname] header) ValueError (Column [col_name] in row filter not found in comma- separated file [fname] header) ValueError (Column [column_identifier] not found) ValueError (Filtered dependent variable is empty) ValueError (Filtered independent variable is empty) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

fproc¶

Gets or sets the data processing function pointer. The processing function is useful for “light” data massaging, like scaling, unit conversion, etc.; it is called after the data has been retrieved from the comma-separated values file and the resulting filtered data set has been bounded (if applicable). If None no processing function is used.

When defined the processing function is given two arguments, a Numpy vector containing the independent variable array (first argument) and a Numpy vector containing the dependent variable array (second argument). The expected return value is a two-item Numpy vector tuple, its first item being the processed independent variable array, and the second item being the processed dependent variable array. One valid processing function could be:

# indep_var is a Numpy vector, in this example time,
# in seconds. dep_var is a Numpy vector
def proc_func1(indep_var, dep_var):
    # Scale time to pico-seconds
    indep_var = indep_var/1e-12
    # Remove offset
    dep_var = dep_var-dep_var[0]
    return indep_var, dep_var

Type:	Function or None

Raises:

(when assigned) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Argument `fproc` is not valid) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Argument `fproc` (function [func_name]) does not have at least 2 arguments) ValueError (Extra argument `*[arg_name]*` not found in argument `fproc` (function [func_name]) definition) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

fproc_eargs¶

Gets or sets the extra arguments for the data processing function. The arguments are specified by key-value pairs of a dictionary, for each dictionary element the dictionary key specifies the argument name and the dictionary value specifies the argument value. The extra parameters are passed by keyword so they must appear in the function definition explicitly or keyword variable argument collection must be used (**kwargs, for example). If None no extra arguments are passed to the processing function (if defined)

Type:	dictionary or None

Raises:

(when assigned) RuntimeError (Argument `fproc_eargs` is not valid) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Extra argument `*[arg_name]*` not found in argument `fproc` (function [func_name]) definition) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

For example:

# plot_example_5.py
import sys, pmisc, pcsv
if sys.hexversion < 0x03000000:
    from pplot.compat2 import _write
else:
    from pplot.compat3 import _write

def write_csv_file(file_handle):
    _write(file_handle, 'Col1,Col2\n')
    _write(file_handle, '0E-12,10\n')
    _write(file_handle, '1E-12,0\n')
    _write(file_handle, '2E-12,20\n')
    _write(file_handle, '3E-12,-10\n')
    _write(file_handle, '4E-12,30\n')

def proc_func2(indep_var, dep_var, par1, par2):
    return (indep_var/1E-12)+(2*par1), dep_var+sum(par2)

def create_csv_source():
    with pmisc.TmpFile(write_csv_file) as fname:
        obj = pplot.CsvSource(
            fname=fname,
            indep_col_label='Col1',
            dep_col_label='Col2',
            fproc=proc_func2,
            fproc_eargs={'par1':5, 'par2':[1, 2, 3]}
        )
    return obj

>>> from __future__ import print_function
>>> import docs.support.plot_example_5
>>> obj = docs.support.plot_example_5.create_csv_source()
>>> print(obj)  
File name: ...
Row filter: None
Independent column label: Col1
Dependent column label: Col2
Processing function: proc_func2
Processing function extra arguments: None
Independent variable minimum: -inf
Independent variable maximum: +inf
Independent variable: [ 10, 11, 12, 13, 14 ]
Dependent variable: [ 16, 6, 26, -4, 36 ]

indep_col_label¶

Gets or sets the independent variable column label (column name)

Type:	string

Raises:

(when assigned) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Argument `indep_col_label` is not valid) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Column [col_name] (independent column label) could not be found in comma-separated file [fname] header) ValueError (Column [col_name] in row filter not found in comma- separated file [fname] header) ValueError (Filtered dependent variable is empty) ValueError (Filtered independent variable is empty) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

indep_max¶

Gets or sets the maximum independent variable limit. If None no maximum thresholding is applied to the data

Type:	RealNum or None

Raises:	(when assigned) RuntimeError (Argument `indep_max` is not valid) ValueError (Argument `indep_min` is greater than argument `indep_max`) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding)

indep_min¶

Gets or sets the minimum independent variable limit. If None no minimum thresholding is applied to the data

Type:	RealNum or None

Raises:	(when assigned) RuntimeError (Argument `indep_min` is not valid) ValueError (Argument `indep_min` is greater than argument `indep_max`) ValueError (Argument `indep_var` is empty after `indep_min`/`indep_max` range bounding)

indep_var¶

Gets the independent variable Numpy vector

rfilter¶

Gets or sets the row filter. If None no row filtering is performed

Type:	CsvRowFilter or None

Raises:

(when assigned) RuntimeError (Argument `fproc` (function [func_name]) returned an illegal number of values) RuntimeError (Argument `rfilter` is not valid) RuntimeError (Processing function [func_name] raised an exception when called with the following arguments: \n indep_var: [indep_var_value] \n dep_var: [dep_var_value] \n fproc_eargs: [fproc_eargs_value] \n Exception error: [exception_error_message]) TypeError (Argument `fproc` (function [func_name]) return value is not valid) TypeError (Processed dependent variable is not valid) TypeError (Processed independent variable is not valid) ValueError (Argument `rfilter` is empty) ValueError (Column [col_name] in row filter not found in comma- separated file [fname] header) ValueError (Filtered dependent variable is empty) ValueError (Filtered independent variable is empty) ValueError (Processed dependent variable is empty) ValueError (Processed independent and dependent variables are of different length) ValueError (Processed independent variable is empty)

class pplot.Series(data_source, label, color='k', marker='o', interp='CUBIC', line_style='-', secondary_axis=False)¶

Bases: object

Specifies a series within a panel

Parameters:

data_source (pplot.BasicSource, pplot.CsvSource or others conforming to the data source specification) – Data source object label (string) – Series label, to be used in the panel legend color (polymorphic) – Series color. All Matplotlib colors are supported marker (string or None) – Marker type. All Matplotlib marker types are supported. None indicates no marker interp (InterpolationOption or None) – Interpolation option (case insensitive), one of None (no interpolation) ‘STRAIGHT’ (straight line connects data points), ‘STEP’ (horizontal segments between data points), ‘CUBIC’ (cubic interpolation between data points) or ‘LINREG’ (linear regression based on data points). The interpolation option is case insensitive line_style (LineStyleOption or None) – Line style. All Matplotlib line styles are supported. None indicates no line secondary_axis (boolean) – Flag that indicates whether the series belongs to the panel primary axis (False) or secondary axis (True)

Raises:

RuntimeError (Argument `color` is not valid) RuntimeError (Argument `data_source` does not have an `dep_var` attribute) RuntimeError (Argument `data_source` does not have an `indep_var` attribute) RuntimeError (Argument `data_source` is not fully specified) RuntimeError (Argument `interp` is not valid) RuntimeError (Argument `label` is not valid) RuntimeError (Argument `line_style` is not valid) RuntimeError (Argument `marker` is not valid) RuntimeError (Argument `secondary_axis` is not valid) TypeError (Invalid color specification) ValueError (Argument `interp` is not one of [‘STRAIGHT’, ‘STEP’, ‘CUBIC’, ‘LINREG’] (case insensitive)) ValueError (Argument `line_style` is not one of [‘-‘, ‘–’, ‘-.’, ‘:’]) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements) ValueError (At least 4 data points are needed for CUBIC interpolation)

__str__()¶

Print series object information

color¶

Gets or sets the series line and marker color. All Matplotlib colors are supported

Type:	polymorphic

Raises:	(when assigned) RuntimeError (Argument `color` is not valid) TypeError (Invalid color specification)

data_source¶

Gets or sets the data source object. The independent and dependent data sets are obtained once this attribute is set. To be valid, a data source object must have an indep_var attribute that contains a Numpy vector of increasing real numbers and a dep_var attribute that contains a Numpy vector of real numbers

Type:	pplot.BasicSource, pplot.CsvSource or others conforming to the data source specification

Raises:	(when assigned) RuntimeError (Argument `data_source` does not have an `dep_var` attribute) RuntimeError (Argument `data_source` does not have an `indep_var` attribute) RuntimeError (Argument `data_source` is not fully specified) ValueError (Arguments `indep_var` and `dep_var` must have the same number of elements) ValueError (At least 4 data points are needed for CUBIC interpolation)

interp¶

Gets or sets the interpolation option, one of None (no interpolation) 'STRAIGHT' (straight line connects data points), 'STEP' (horizontal segments between data points), 'CUBIC' (cubic interpolation between data points) or 'LINREG' (linear regression based on data points). The interpolation option is case insensitive

Type:	InterpolationOption or None

Raises:	(when assigned) RuntimeError (Argument `interp` is not valid) ValueError (Argument `interp` is not one of [‘STRAIGHT’, ‘STEP’, ‘CUBIC’, ‘LINREG’] (case insensitive)) ValueError (At least 4 data points are needed for CUBIC interpolation)

label¶

Gets or sets the series label, to be used in the panel legend if the panel has more than one series

Type:	string

Raises:	(when assigned) RuntimeError (Argument `label` is not valid)

line_style¶

Sets or gets the line style. All Matplotlib line styles are supported. None indicates no line

Type:	LineStyleOption

Raises:	(when assigned) RuntimeError (Argument `line_style` is not valid) ValueError (Argument `line_style` is not one of [‘-‘, ‘–’, ‘-.’, ‘:’])

marker¶

Gets or sets the series marker type. All Matplotlib marker types are supported. None indicates no marker

Type:	string or None

Raises:	(when assigned) RuntimeError (Argument `marker` is not valid)

secondary_axis¶

Sets or gets the secondary axis flag; indicates whether the series belongs to the panel primary axis (False) or secondary axis (True)

Type:	boolean

Raises:	(when assigned) RuntimeError (Argument `secondary_axis` is not valid)

class pplot.Panel(series=None, primary_axis_label='', primary_axis_units='', primary_axis_ticks=None, secondary_axis_label='', secondary_axis_units='', secondary_axis_ticks=None, log_dep_axis=False, legend_props=None, display_indep_axis=False)¶

Bases: object

Defines a panel within a figure

Parameters:

series (pplot.Series or list of pplot.Series or None) – One or more data series primary_axis_label (string) – Primary dependent axis label primary_axis_units (string) – Primary dependent axis units primary_axis_ticks (list, Numpy vector or None) – Primary dependent axis tick marks. If not None overrides automatically generated tick marks if the axis type is linear. If None automatically generated tick marks are used for the primary axis secondary_axis_label (string) – Secondary dependent axis label secondary_axis_units (string) – Secondary dependent axis units secondary_axis_ticks (list, Numpy vector or None) – Secondary dependent axis tick marks. If not None overrides automatically generated tick marks if the axis type is linear. If None automatically generated tick marks are used for the secondary axis log_dep_axis (boolean) – Flag that indicates whether the dependent (primary and /or secondary) axis is linear (False) or logarithmic (True) legend_props (dictionary or None) – Legend properties. See pplot.Panel.legend_props. If None the legend is placed in the best position in one column display_indep_axis (boolean) – Flag that indicates whether the independent axis is displayed (True) or not (False)

Raises:

RuntimeError (Argument `display_indep_axis` is not valid) RuntimeError (Argument `legend_props` is not valid) RuntimeError (Argument `log_dep_axis` is not valid) RuntimeError (Argument `primary_axis_label` is not valid) RuntimeError (Argument `primary_axis_ticks` is not valid) RuntimeError (Argument `primary_axis_units` is not valid) RuntimeError (Argument `secondary_axis_label` is not valid) RuntimeError (Argument `secondary_axis_ticks` is not valid) RuntimeError (Argument `secondary_axis_units` is not valid) RuntimeError (Argument `series` is not valid) RuntimeError (Legend property `cols` is not valid) RuntimeError (Series item [number] is not fully specified) TypeError (Legend property `pos` is not one of [‘BEST’, ‘UPPER RIGHT’, ‘UPPER LEFT’, ‘LOWER LEFT’, ‘LOWER RIGHT’, ‘RIGHT’, ‘CENTER LEFT’, ‘CENTER RIGHT’, ‘LOWER CENTER’, ‘UPPER CENTER’, ‘CENTER’] (case insensitive)) ValueError (Illegal legend property `*[prop_name]*`) ValueError (Series item [number] cannot be plotted in a logarithmic axis because it contains negative data points)

__bool__()¶

Returns True if the panel has at least a series associated with it, False otherwise

Note

This method applies to Python 3.x

__iter__()¶

Returns an iterator over the series object(s) in the panel. For example:

# plot_example_6.py
from __future__ import print_function
import numpy, pplot

def panel_iterator_example(no_print):
    source1 = pplot.BasicSource(
        indep_var=numpy.array([1, 2, 3, 4]),
        dep_var=numpy.array([1, -10, 10, 5])
    )
    source2 = pplot.BasicSource(
        indep_var=numpy.array([100, 200, 300, 400]),
        dep_var=numpy.array([50, 75, 100, 125])
    )
    series1 = pplot.Series(
        data_source=source1,
        label='Goals'
    )
    series2 = pplot.Series(
        data_source=source2,
        label='Saves',
        color='b',
        marker=None,
        interp='STRAIGHT',
        line_style='--'
    )
    panel = pplot.Panel(
        series=[series1, series2],
        primary_axis_label='Time',
        primary_axis_units='sec',
        display_indep_axis=True
    )
    if not no_print:
        for num, series in enumerate(panel):
            print('Series {0}:'.format(num+1))
            print(series)
            print('')
    else:
        return panel

>>> import docs.support.plot_example_6 as mod
>>> mod.panel_iterator_example(False)
Series 1:
Independent variable: [ 1.0, 2.0, 3.0, 4.0 ]
Dependent variable: [ 1.0, -10.0, 10.0, 5.0 ]
Label: Goals
Color: k
Marker: o
Interpolation: CUBIC
Line style: -
Secondary axis: False

Series 2:
Independent variable: [ 100.0, 200.0, 300.0, 400.0 ]
Dependent variable: [ 50.0, 75.0, 100.0, 125.0 ]
Label: Saves
Color: b
Marker: None
Interpolation: STRAIGHT
Line style: --
Secondary axis: False

__nonzero__()¶

Returns True if the panel has at least a series associated with it, False otherwise

Note

This method applies to Python 2.x

__str__()¶

Prints panel information. For example:

>>> from __future__ import print_function
>>> import docs.support.plot_example_6 as mod
>>> print(mod.panel_iterator_example(True))
Series 0:
   Independent variable: [ 1.0, 2.0, 3.0, 4.0 ]
   Dependent variable: [ 1.0, -10.0, 10.0, 5.0 ]
   Label: Goals
   Color: k
   Marker: o
   Interpolation: CUBIC
   Line style: -
   Secondary axis: False
Series 1:
   Independent variable: [ 100.0, 200.0, 300.0, 400.0 ]
   Dependent variable: [ 50.0, 75.0, 100.0, 125.0 ]
   Label: Saves
   Color: b
   Marker: None
   Interpolation: STRAIGHT
   Line style: --
   Secondary axis: False
Primary axis label: Time
Primary axis units: sec
Secondary axis label: not specified
Secondary axis units: not specified
Logarithmic dependent axis: False
Display independent axis: True
Legend properties:
   cols: 1
   pos: BEST

display_indep_axis¶

Gets or sets the independent axis display flag; indicates whether the independent axis is displayed (True) or not (False)

Type:	boolean

Raises:	(when assigned) RuntimeError (Argument `display_indep_axis` is not valid)

legend_props¶

Gets or sets the panel legend box properties; this is a dictionary that has properties (dictionary key) and their associated values (dictionary values). Currently supported properties are:

• pos (string) – legend box position, one of 'BEST', 'UPPER RIGHT', 'UPPER LEFT', 'LOWER LEFT', 'LOWER RIGHT', 'RIGHT', 'CENTER LEFT', 'CENTER RIGHT', 'LOWER CENTER', 'UPPER CENTER' or 'CENTER' (case insensitive)
• cols (integer) – number of columns of the legend box

If None the default used is {'pos':'BEST', 'cols':1}

Note

No legend is shown if a panel has only one series in it or if no series has a label

Type:	dictionary

Raises:	(when assigned) RuntimeError (Argument `legend_props` is not valid) RuntimeError (Legend property `cols` is not valid) TypeError (Legend property `pos` is not one of [‘BEST’, ‘UPPER RIGHT’, ‘UPPER LEFT’, ‘LOWER LEFT’, ‘LOWER RIGHT’, ‘RIGHT’, ‘CENTER LEFT’, ‘CENTER RIGHT’, ‘LOWER CENTER’, ‘UPPER CENTER’, ‘CENTER’] (case insensitive)) ValueError (Illegal legend property `*[prop_name]*`)

log_dep_axis¶

Gets or sets the panel logarithmic dependent (primary and/or secondary) axis flag; indicates whether the dependent (primary and/or secondary) axis is linear (False) or logarithmic (True)

Type:	boolean

Raises:	(when assigned) RuntimeError (Argument `log_dep_axis` is not valid) RuntimeError (Argument `series` is not valid) RuntimeError (Series item [number] is not fully specified) ValueError (Series item [number] cannot be plotted in a logarithmic axis because it contains negative data points)

primary_axis_label¶

Gets or sets the panel primary dependent axis label

Type:	string

Raises:	(when assigned) RuntimeError (Argument `primary_axis_label` is not valid)

primary_axis_scale¶

Gets the scale of the panel primary axis, None if axis has no series associated with it

Type:	float or None

primary_axis_ticks¶

Gets the primary axis (scaled) tick locations, None if axis has no series associated with it

Type:	list or None

primary_axis_units¶

Gets or sets the panel primary dependent axis units

Type:	string

Raises:	(when assigned) RuntimeError (Argument `primary_axis_units` is not valid)

secondary_axis_label¶

Gets or sets the panel secondary dependent axis label

Type:	string

Raises:	(when assigned) RuntimeError (Argument `secondary_axis_label` is not valid)

secondary_axis_scale¶

Gets the scale of the panel secondary axis, None if axis has no series associated with it

Type:	float or None

secondary_axis_ticks¶

Gets the secondary axis (scaled) tick locations, None if axis has no series associated with it

Type:	list or None with it

secondary_axis_units¶

Gets or sets the panel secondary dependent axis units

Type:	string

Raises:	(when assigned) RuntimeError (Argument `secondary_axis_units` is not valid)

series¶

Gets or sets the panel series, None if there are no series associated with the panel

Type:	pplot.Series, list of pplot.Series or None

Raises:	(when assigned) RuntimeError (Argument `series` is not valid) RuntimeError (Series item [number] is not fully specified) ValueError (Series item [number] cannot be plotted in a logarithmic axis because it contains negative data points)

class pplot.Figure(panels=None, indep_var_label='', indep_var_units='', indep_axis_ticks=None, fig_width=None, fig_height=None, title='', log_indep_axis=False)¶

Bases: object

Generates presentation-quality plots

Parameters:

panels (pplot.Panel or list of pplot.Panel or None) – One or more data panels indep_var_label (string) – Independent variable label indep_var_units (string) – Independent variable units indep_axis_ticks (list, Numpy vector or None) – Independent axis tick marks. If not None overrides automatically generated tick marks if the axis type is linear. If None automatically generated tick marks are used for the independent axis fig_width (PositiveRealNum or None) – Hard copy plot width in inches. If None the width is automatically calculated so that there is no horizontal overlap between any two text elements in the figure fig_height – Hard copy plot height in inches. If None the height is automatically calculated so that there is no vertical overlap between any two text elements in the figure title (string) – Plot title log_indep_axis (boolean) – Flag that indicates whether the independent axis is linear (False) or logarithmic (True)

Raises:

RuntimeError (Argument `fig_height` is not valid) RuntimeError (Argument `fig_width` is not valid) RuntimeError (Argument `indep_axis_ticks` is not valid) RuntimeError (Argument `indep_var_label` is not valid) RuntimeError (Argument `indep_var_units` is not valid) RuntimeError (Argument `log_indep_axis` is not valid) RuntimeError (Argument `panels` is not valid) RuntimeError (Argument `title` is not valid) RuntimeError (Figure object is not fully specified) RuntimeError (Figure size is too small: minimum width [min_width], minimum height [min_height]) TypeError (Panel [panel_num] is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

__bool__()¶

Returns True if the figure has at least a panel associated with it, False otherwise

Note

This method applies to Python 3.x

__iter__()¶

Returns an iterator over the panel object(s) in the figure. For example:

# plot_example_7.py
from __future__ import print_function
import numpy, pplot

def figure_iterator_example(no_print):
    source1 = pplot.BasicSource(
        indep_var=numpy.array([1, 2, 3, 4]),
        dep_var=numpy.array([1, -10, 10, 5])
    )
    source2 = pplot.BasicSource(
        indep_var=numpy.array([100, 200, 300, 400]),
        dep_var=numpy.array([50, 75, 100, 125])
    )
    series1 = pplot.Series(
        data_source=source1,
        label='Goals'
    )
    series2 = pplot.Series(
        data_source=source2,
        label='Saves',
        color='b',
        marker=None,
        interp='STRAIGHT',
        line_style='--'
    )
    panel1 = pplot.Panel(
        series=series1,
        primary_axis_label='Average',
        primary_axis_units='A',
        display_indep_axis=False
    )
    panel2 = pplot.Panel(
        series=series2,
        primary_axis_label='Standard deviation',
        primary_axis_units=r'$\sqrt{{A}}$',
        display_indep_axis=True
    )
    figure = pplot.Figure(
        panels=[panel1, panel2],
        indep_var_label='Time',
        indep_var_units='sec',
        title='Sample Figure'
    )
    if not no_print:
        for num, panel in enumerate(figure):
            print('Panel {0}:'.format(num+1))
            print(panel)
            print('')
    else:
        return figure

>>> import docs.support.plot_example_7 as mod
>>> mod.figure_iterator_example(False)
Panel 1:
Series 0:
   Independent variable: [ 1.0, 2.0, 3.0, 4.0 ]
   Dependent variable: [ 1.0, -10.0, 10.0, 5.0 ]
   Label: Goals
   Color: k
   Marker: o
   Interpolation: CUBIC
   Line style: -
   Secondary axis: False
Primary axis label: Average
Primary axis units: A
Secondary axis label: not specified
Secondary axis units: not specified
Logarithmic dependent axis: False
Display independent axis: False
Legend properties:
   cols: 1
   pos: BEST

Panel 2:
Series 0:
   Independent variable: [ 100.0, 200.0, 300.0, 400.0 ]
   Dependent variable: [ 50.0, 75.0, 100.0, 125.0 ]
   Label: Saves
   Color: b
   Marker: None
   Interpolation: STRAIGHT
   Line style: --
   Secondary axis: False
Primary axis label: Standard deviation
Primary axis units: $\sqrt{{A}}$
Secondary axis label: not specified
Secondary axis units: not specified
Logarithmic dependent axis: False
Display independent axis: True
Legend properties:
   cols: 1
   pos: BEST

__nonzero__()¶

Returns True if the figure has at least a panel associated with it, False otherwise

Note

This method applies to Python 2.x

__str__()¶

Prints figure information. For example:

>>> from __future__ import print_function
>>> import docs.support.plot_example_7 as mod
>>> print(mod.figure_iterator_example(True))    
Panel 0:
   Series 0:
      Independent variable: [ 1.0, 2.0, 3.0, 4.0 ]
      Dependent variable: [ 1.0, -10.0, 10.0, 5.0 ]
      Label: Goals
      Color: k
      Marker: o
      Interpolation: CUBIC
      Line style: -
      Secondary axis: False
   Primary axis label: Average
   Primary axis units: A
   Secondary axis label: not specified
   Secondary axis units: not specified
   Logarithmic dependent axis: False
   Display independent axis: False
   Legend properties:
      cols: 1
      pos: BEST
Panel 1:
   Series 0:
      Independent variable: [ 100.0, 200.0, 300.0, 400.0 ]
      Dependent variable: [ 50.0, 75.0, 100.0, 125.0 ]
      Label: Saves
      Color: b
      Marker: None
      Interpolation: STRAIGHT
      Line style: --
      Secondary axis: False
   Primary axis label: Standard deviation
   Primary axis units: $\sqrt{{A}}$
   Secondary axis label: not specified
   Secondary axis units: not specified
   Logarithmic dependent axis: False
   Display independent axis: True
   Legend properties:
      cols: 1
      pos: BEST
Independent variable label: Time
Independent variable units: sec
Logarithmic independent axis: False
Title: Sample Figure
Figure width: ...
Figure height: ...

save(fname, ftype='PNG')¶

Saves the figure to a file

Parameters:	fname (FileName) – File name ftype (string) – File type, either ‘PNG’ or ‘EPS’ (case insensitive). The PNG format is a raster format while the EPS format is a vector format

Raises:	RuntimeError (Argument `fname` is not valid) RuntimeError (Argument `ftype` is not valid) RuntimeError (Figure object is not fully specified) RuntimeError (Unsupported file type: [file_type])

show()¶

Displays the figure

Raises:	RuntimeError (Figure object is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

axes_list¶

Gets the Matplotlib figure axes handle list or None if figure is not fully specified. Useful if annotations or further customizations to the panel(s) are needed. Each panel has an entry in the list, which is sorted in the order the panels are plotted (top to bottom). Each panel entry is a dictionary containing the following key-value pairs:

• number (integer) – panel number, panel 0 is the top-most panel
• primary (Matplotlib axis object) – axis handle for the primary axis, None if the figure has not primary axis
• secondary (Matplotlib axis object) – axis handle for the secondary axis, None if the figure has no secondary axis

Type:	list

fig¶

Gets the Matplotlib figure handle. Useful if annotations or further customizations to the figure are needed. None if figure is not fully specified

Type:	Matplotlib figure handle or None

fig_height¶

Gets or sets the height (in inches) of the hard copy plot, None if figure is not fully specified.

Type:	PositiveRealNum or None

Raises:	(when assigned) RuntimeError (Argument `fig_height` is not valid)

fig_width¶

Gets or sets the width (in inches) of the hard copy plot, None if figure is not fully specified

Type:	PositiveRealNum or None

Raises:	(when assigned) RuntimeError (Argument `fig_width` is not valid)

indep_axis_scale¶

Gets the scale of the figure independent axis, None if figure is not fully specified

Type:	float or None if figure has no panels associated with it

indep_axis_ticks¶

Gets the independent axis (scaled) tick locations, None if figure is not fully specified

Type:	list

indep_var_label¶

Gets or sets the figure independent variable label

Type:	string or None

Raises:	(when assigned) RuntimeError (Argument `indep_var_label` is not valid) RuntimeError (Figure object is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

indep_var_units¶

Gets or sets the figure independent variable units

Type:	string or None

Raises:	(when assigned) RuntimeError (Argument `indep_var_units` is not valid) RuntimeError (Figure object is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

log_indep_axis¶

Gets or sets the figure logarithmic independent axis flag; indicates whether the independent axis is linear (False) or logarithmic (True)

Type:	boolean

Raises:	(when assigned) RuntimeError (Argument `log_indep_axis` is not valid) RuntimeError (Figure object is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

panels¶

Gets or sets the figure panel(s), None if no panels have been specified

Type:	pplot.Panel, list of pplot.panel or None

Raises:

(when assigned) RuntimeError (Argument `fig_height` is not valid) RuntimeError (Argument `fig_width` is not valid) RuntimeError (Argument `panels` is not valid) RuntimeError (Figure object is not fully specified) RuntimeError (Figure size is too small: minimum width [min_width], minimum height [min_height]) TypeError (Panel [panel_num] is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

title¶

Gets or sets the figure title

Type:	string or None

Raises:	(when assigned) RuntimeError (Argument `title` is not valid) RuntimeError (Figure object is not fully specified) ValueError (Figure cannot be plotted with a logarithmic independent axis because panel [panel_num], series [series_num] contains negative independent data points)

Contracts pseudo-types¶