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set_params(nbins=None)[source]
Set parameters within this locator.
|
matplotlib.ticker_api#matplotlib.ticker.FixedLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the locations of the ticks. Note Because the values are fixed, vmin and vmax are not used in this method.
|
matplotlib.ticker_api#matplotlib.ticker.FixedLocator.tick_values
|
classmatplotlib.ticker.FormatStrFormatter(fmt)[source]
Bases: matplotlib.ticker.Formatter Use an old-style ('%' operator) format string to format the tick. The format string should have a single variable format (%) in it. It will be applied to the value (not the position) of the tick. Negative numeric values will use a dash not a unicode minus, use mathtext to get a unicode minus by wrappping the format specifier with $ (e.g. "$%g$").
|
matplotlib.ticker_api#matplotlib.ticker.FormatStrFormatter
|
classmatplotlib.ticker.Formatter[source]
Bases: matplotlib.ticker.TickHelper Create a string based on a tick value and location. staticfix_minus(s)[source]
Some classes may want to replace a hyphen for minus with the proper unicode symbol (U+2212) for typographical correctness. This is a helper method to perform such a replacement when it is enabled via rcParams["axes.unicode_minus"] (default: True).
format_data(value)[source]
Return the full string representation of the value with the position unspecified.
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
format_ticks(values)[source]
Return the tick labels for all the ticks at once.
get_offset()[source]
locs=[]
set_locs(locs)[source]
Set the locations of the ticks. This method is called before computing the tick labels because some formatters need to know all tick locations to do so.
|
matplotlib.ticker_api#matplotlib.ticker.Formatter
|
staticfix_minus(s)[source]
Some classes may want to replace a hyphen for minus with the proper unicode symbol (U+2212) for typographical correctness. This is a helper method to perform such a replacement when it is enabled via rcParams["axes.unicode_minus"] (default: True).
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.fix_minus
|
format_data(value)[source]
Return the full string representation of the value with the position unspecified.
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.format_data
|
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.format_data_short
|
format_ticks(values)[source]
Return the tick labels for all the ticks at once.
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.format_ticks
|
get_offset()[source]
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.get_offset
|
locs=[]
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.locs
|
set_locs(locs)[source]
Set the locations of the ticks. This method is called before computing the tick labels because some formatters need to know all tick locations to do so.
|
matplotlib.ticker_api#matplotlib.ticker.Formatter.set_locs
|
classmatplotlib.ticker.FuncFormatter(func)[source]
Bases: matplotlib.ticker.Formatter Use a user-defined function for formatting. The function should take in two inputs (a tick value x and a position pos), and return a string containing the corresponding tick label. get_offset()[source]
set_offset_string(ofs)[source]
|
matplotlib.ticker_api#matplotlib.ticker.FuncFormatter
|
get_offset()[source]
|
matplotlib.ticker_api#matplotlib.ticker.FuncFormatter.get_offset
|
set_offset_string(ofs)[source]
|
matplotlib.ticker_api#matplotlib.ticker.FuncFormatter.set_offset_string
|
classmatplotlib.ticker.IndexLocator(base, offset)[source]
Bases: matplotlib.ticker.Locator Place a tick on every multiple of some base number of points plotted, e.g., on every 5th point. It is assumed that you are doing index plotting; i.e., the axis is 0, len(data). This is mainly useful for x ticks. Place ticks every base data point, starting at offset. set_params(base=None, offset=None)[source]
Set parameters within this locator
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.IndexLocator
|
set_params(base=None, offset=None)[source]
Set parameters within this locator
|
matplotlib.ticker_api#matplotlib.ticker.IndexLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.IndexLocator.tick_values
|
classmatplotlib.ticker.LinearLocator(numticks=None, presets=None)[source]
Bases: matplotlib.ticker.Locator Determine the tick locations The first time this function is called it will try to set the number of ticks to make a nice tick partitioning. Thereafter the number of ticks will be fixed so that interactive navigation will be nice Use presets to set locs based on lom. A dict mapping vmin, vmax->locs propertynumticks
set_params(numticks=None, presets=None)[source]
Set parameters within this locator.
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
view_limits(vmin, vmax)[source]
Try to choose the view limits intelligently.
|
matplotlib.ticker_api#matplotlib.ticker.LinearLocator
|
set_params(numticks=None, presets=None)[source]
Set parameters within this locator.
|
matplotlib.ticker_api#matplotlib.ticker.LinearLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.LinearLocator.tick_values
|
view_limits(vmin, vmax)[source]
Try to choose the view limits intelligently.
|
matplotlib.ticker_api#matplotlib.ticker.LinearLocator.view_limits
|
classmatplotlib.ticker.Locator[source]
Bases: matplotlib.ticker.TickHelper Determine the tick locations; Note that the same locator should not be used across multiple Axis because the locator stores references to the Axis data and view limits. MAXTICKS=1000
nonsingular(v0, v1)[source]
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification.
raise_if_exceeds(locs)[source]
Log at WARNING level if locs is longer than Locator.MAXTICKS. This is intended to be called immediately before returning locs from __call__ to inform users in case their Locator returns a huge number of ticks, causing Matplotlib to run out of memory. The "strange" name of this method dates back to when it would raise an exception instead of emitting a log.
set_params(**kwargs)[source]
Do nothing, and raise a warning. Any locator class not supporting the set_params() function will call this.
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
view_limits(vmin, vmax)[source]
Select a scale for the range from vmin to vmax. Subclasses should override this method to change locator behaviour.
|
matplotlib.ticker_api#matplotlib.ticker.Locator
|
MAXTICKS=1000
|
matplotlib.ticker_api#matplotlib.ticker.Locator.MAXTICKS
|
nonsingular(v0, v1)[source]
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification.
|
matplotlib.ticker_api#matplotlib.ticker.Locator.nonsingular
|
raise_if_exceeds(locs)[source]
Log at WARNING level if locs is longer than Locator.MAXTICKS. This is intended to be called immediately before returning locs from __call__ to inform users in case their Locator returns a huge number of ticks, causing Matplotlib to run out of memory. The "strange" name of this method dates back to when it would raise an exception instead of emitting a log.
|
matplotlib.ticker_api#matplotlib.ticker.Locator.raise_if_exceeds
|
set_params(**kwargs)[source]
Do nothing, and raise a warning. Any locator class not supporting the set_params() function will call this.
|
matplotlib.ticker_api#matplotlib.ticker.Locator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.Locator.tick_values
|
view_limits(vmin, vmax)[source]
Select a scale for the range from vmin to vmax. Subclasses should override this method to change locator behaviour.
|
matplotlib.ticker_api#matplotlib.ticker.Locator.view_limits
|
classmatplotlib.ticker.LogFormatter(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]
Bases: matplotlib.ticker.Formatter Base class for formatting ticks on a log or symlog scale. It may be instantiated directly, or subclassed. Parameters
basefloat, default: 10.
Base of the logarithm used in all calculations.
labelOnlyBasebool, default: False
If True, label ticks only at integer powers of base. This is normally True for major ticks and False for minor ticks.
minor_thresholds(subset, all), default: (1, 0.4)
If labelOnlyBase is False, these two numbers control the labeling of ticks that are not at integer powers of base; normally these are the minor ticks. The controlling parameter is the log of the axis data range. In the typical case where base is 10 it is the number of decades spanned by the axis, so we can call it 'numdec'. If numdec <= all, all minor ticks will be labeled. If all < numdec <= subset, then only a subset of minor ticks will be labeled, so as to avoid crowding. If numdec > subset then no minor ticks will be labeled.
linthreshNone or float, default: None
If a symmetric log scale is in use, its linthresh parameter must be supplied here. Notes The set_locs method must be called to enable the subsetting logic controlled by the minor_thresholds parameter. In some cases such as the colorbar, there is no distinction between major and minor ticks; the tick locations might be set manually, or by a locator that puts ticks at integer powers of base and at intermediate locations. For this situation, disable the minor_thresholds logic by using minor_thresholds=(np.inf, np.inf), so that all ticks will be labeled. To disable labeling of minor ticks when 'labelOnlyBase' is False, use minor_thresholds=(0, 0). This is the default for the "classic" style. Examples To label a subset of minor ticks when the view limits span up to 2 decades, and all of the ticks when zoomed in to 0.5 decades or less, use minor_thresholds=(2, 0.5). To label all minor ticks when the view limits span up to 1.5 decades, use minor_thresholds=(1.5, 1.5). base(base)[source]
Change the base for labeling. Warning Should always match the base used for LogLocator
format_data(value)[source]
Return the full string representation of the value with the position unspecified.
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
label_minor(labelOnlyBase)[source]
Switch minor tick labeling on or off. Parameters
labelOnlyBasebool
If True, label ticks only at integer powers of base.
set_locs(locs=None)[source]
Use axis view limits to control which ticks are labeled. The locs parameter is ignored in the present algorithm.
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatter
|
base(base)[source]
Change the base for labeling. Warning Should always match the base used for LogLocator
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatter.base
|
format_data(value)[source]
Return the full string representation of the value with the position unspecified.
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatter.format_data
|
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatter.format_data_short
|
label_minor(labelOnlyBase)[source]
Switch minor tick labeling on or off. Parameters
labelOnlyBasebool
If True, label ticks only at integer powers of base.
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatter.label_minor
|
set_locs(locs=None)[source]
Use axis view limits to control which ticks are labeled. The locs parameter is ignored in the present algorithm.
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatter.set_locs
|
classmatplotlib.ticker.LogFormatterExponent(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]
Bases: matplotlib.ticker.LogFormatter Format values for log axis using exponent = log_base(value).
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatterExponent
|
classmatplotlib.ticker.LogFormatterMathtext(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]
Bases: matplotlib.ticker.LogFormatter Format values for log axis using exponent = log_base(value).
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatterMathtext
|
classmatplotlib.ticker.LogFormatterSciNotation(base=10.0, labelOnlyBase=False, minor_thresholds=None, linthresh=None)[source]
Bases: matplotlib.ticker.LogFormatterMathtext Format values following scientific notation in a logarithmic axis.
|
matplotlib.ticker_api#matplotlib.ticker.LogFormatterSciNotation
|
classmatplotlib.ticker.LogitFormatter(*, use_overline=False, one_half='\x0crac{1}{2}', minor=False, minor_threshold=25, minor_number=6)[source]
Bases: matplotlib.ticker.Formatter Probability formatter (using Math text). Parameters
use_overlinebool, default: False
If x > 1/2, with x = 1-v, indicate if x should be displayed as $overline{v}$. The default is to display $1-v$.
one_halfstr, default: r"frac{1}{2}"
The string used to represent 1/2.
minorbool, default: False
Indicate if the formatter is formatting minor ticks or not. Basically minor ticks are not labelled, except when only few ticks are provided, ticks with most space with neighbor ticks are labelled. See other parameters to change the default behavior.
minor_thresholdint, default: 25
Maximum number of locs for labelling some minor ticks. This parameter have no effect if minor is False.
minor_numberint, default: 6
Number of ticks which are labelled when the number of ticks is below the threshold. format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
set_locs(locs)[source]
Set the locations of the ticks. This method is called before computing the tick labels because some formatters need to know all tick locations to do so.
set_minor_number(minor_number)[source]
Set the number of minor ticks to label when some minor ticks are labelled. Parameters
minor_numberint
Number of ticks which are labelled when the number of ticks is below the threshold.
set_minor_threshold(minor_threshold)[source]
Set the threshold for labelling minors ticks. Parameters
minor_thresholdint
Maximum number of locations for labelling some minor ticks. This parameter have no effect if minor is False.
set_one_half(one_half)[source]
Set the way one half is displayed. one_halfstr, default: r"frac{1}{2}"
The string used to represent 1/2.
use_overline(use_overline)[source]
Switch display mode with overline for labelling p>1/2. Parameters
use_overlinebool, default: False
If x > 1/2, with x = 1-v, indicate if x should be displayed as $overline{v}$. The default is to display $1-v$.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter
|
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter.format_data_short
|
set_locs(locs)[source]
Set the locations of the ticks. This method is called before computing the tick labels because some formatters need to know all tick locations to do so.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter.set_locs
|
set_minor_number(minor_number)[source]
Set the number of minor ticks to label when some minor ticks are labelled. Parameters
minor_numberint
Number of ticks which are labelled when the number of ticks is below the threshold.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter.set_minor_number
|
set_minor_threshold(minor_threshold)[source]
Set the threshold for labelling minors ticks. Parameters
minor_thresholdint
Maximum number of locations for labelling some minor ticks. This parameter have no effect if minor is False.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter.set_minor_threshold
|
set_one_half(one_half)[source]
Set the way one half is displayed. one_halfstr, default: r"frac{1}{2}"
The string used to represent 1/2.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter.set_one_half
|
use_overline(use_overline)[source]
Switch display mode with overline for labelling p>1/2. Parameters
use_overlinebool, default: False
If x > 1/2, with x = 1-v, indicate if x should be displayed as $overline{v}$. The default is to display $1-v$.
|
matplotlib.ticker_api#matplotlib.ticker.LogitFormatter.use_overline
|
classmatplotlib.ticker.LogitLocator(minor=False, *, nbins='auto')[source]
Bases: matplotlib.ticker.MaxNLocator Determine the tick locations for logit axes Place ticks on the logit locations Parameters
nbinsint or 'auto', optional
Number of ticks. Only used if minor is False.
minorbool, default: False
Indicate if this locator is for minor ticks or not. propertyminor
nonsingular(vmin, vmax)[source]
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification.
set_params(minor=None, **kwargs)[source]
Set parameters within this locator.
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.LogitLocator
|
nonsingular(vmin, vmax)[source]
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification.
|
matplotlib.ticker_api#matplotlib.ticker.LogitLocator.nonsingular
|
set_params(minor=None, **kwargs)[source]
Set parameters within this locator.
|
matplotlib.ticker_api#matplotlib.ticker.LogitLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.LogitLocator.tick_values
|
classmatplotlib.ticker.LogLocator(base=10.0, subs=(1.0,), numdecs=4, numticks=None)[source]
Bases: matplotlib.ticker.Locator Determine the tick locations for log axes Place ticks on the locations : subs[j] * base**i Parameters
basefloat, default: 10.0
The base of the log used, so ticks are placed at base**n.
subsNone or str or sequence of float, default: (1.0,)
Gives the multiples of integer powers of the base at which to place ticks. The default places ticks only at integer powers of the base. The permitted string values are 'auto' and 'all', both of which use an algorithm based on the axis view limits to determine whether and how to put ticks between integer powers of the base. With 'auto', ticks are placed only between integer powers; with 'all', the integer powers are included. A value of None is equivalent to 'auto'.
numticksNone or int, default: None
The maximum number of ticks to allow on a given axis. The default of None will try to choose intelligently as long as this Locator has already been assigned to an axis using get_tick_space, but otherwise falls back to 9. base(base)[source]
Set the log base (major tick every base**i, i integer).
nonsingular(vmin, vmax)[source]
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification.
set_params(base=None, subs=None, numdecs=None, numticks=None)[source]
Set parameters within this locator.
subs(subs)[source]
Set the minor ticks for the log scaling every base**i*subs[j].
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
view_limits(vmin, vmax)[source]
Try to choose the view limits intelligently.
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator
|
base(base)[source]
Set the log base (major tick every base**i, i integer).
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator.base
|
nonsingular(vmin, vmax)[source]
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification.
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator.nonsingular
|
set_params(base=None, subs=None, numdecs=None, numticks=None)[source]
Set parameters within this locator.
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator.set_params
|
subs(subs)[source]
Set the minor ticks for the log scaling every base**i*subs[j].
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator.subs
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator.tick_values
|
view_limits(vmin, vmax)[source]
Try to choose the view limits intelligently.
|
matplotlib.ticker_api#matplotlib.ticker.LogLocator.view_limits
|
classmatplotlib.ticker.MaxNLocator(nbins=None, **kwargs)[source]
Bases: matplotlib.ticker.Locator Find nice tick locations with no more than N being within the view limits. Locations beyond the limits are added to support autoscaling. Parameters
nbinsint or 'auto', default: 10
Maximum number of intervals; one less than max number of ticks. If the string 'auto', the number of bins will be automatically determined based on the length of the axis.
stepsarray-like, optional
Sequence of nice numbers starting with 1 and ending with 10; e.g., [1, 2, 4, 5, 10], where the values are acceptable tick multiples. i.e. for the example, 20, 40, 60 would be an acceptable set of ticks, as would 0.4, 0.6, 0.8, because they are multiples of 2. However, 30, 60, 90 would not be allowed because 3 does not appear in the list of steps.
integerbool, default: False
If True, ticks will take only integer values, provided at least min_n_ticks integers are found within the view limits.
symmetricbool, default: False
If True, autoscaling will result in a range symmetric about zero.
prune{'lower', 'upper', 'both', None}, default: None
Remove edge ticks -- useful for stacked or ganged plots where the upper tick of one axes overlaps with the lower tick of the axes above it, primarily when rcParams["axes.autolimit_mode"] (default: 'data') is 'round_numbers'. If prune=='lower', the smallest tick will be removed. If prune == 'upper', the largest tick will be removed. If prune == 'both', the largest and smallest ticks will be removed. If prune is None, no ticks will be removed.
min_n_ticksint, default: 2
Relax nbins and integer constraints if necessary to obtain this minimum number of ticks. default_params={'integer': False, 'min_n_ticks': 2, 'nbins': 10, 'prune': None, 'steps': None, 'symmetric': False}
set_params(**kwargs)[source]
Set parameters for this locator. Parameters
nbinsint or 'auto', optional
see MaxNLocator
stepsarray-like, optional
see MaxNLocator
integerbool, optional
see MaxNLocator
symmetricbool, optional
see MaxNLocator
prune{'lower', 'upper', 'both', None}, optional
see MaxNLocator
min_n_ticksint, optional
see MaxNLocator
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
view_limits(dmin, dmax)[source]
Select a scale for the range from vmin to vmax. Subclasses should override this method to change locator behaviour.
|
matplotlib.ticker_api#matplotlib.ticker.MaxNLocator
|
default_params={'integer': False, 'min_n_ticks': 2, 'nbins': 10, 'prune': None, 'steps': None, 'symmetric': False}
|
matplotlib.ticker_api#matplotlib.ticker.MaxNLocator.default_params
|
set_params(**kwargs)[source]
Set parameters for this locator. Parameters
nbinsint or 'auto', optional
see MaxNLocator
stepsarray-like, optional
see MaxNLocator
integerbool, optional
see MaxNLocator
symmetricbool, optional
see MaxNLocator
prune{'lower', 'upper', 'both', None}, optional
see MaxNLocator
min_n_ticksint, optional
see MaxNLocator
|
matplotlib.ticker_api#matplotlib.ticker.MaxNLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.MaxNLocator.tick_values
|
view_limits(dmin, dmax)[source]
Select a scale for the range from vmin to vmax. Subclasses should override this method to change locator behaviour.
|
matplotlib.ticker_api#matplotlib.ticker.MaxNLocator.view_limits
|
classmatplotlib.ticker.MultipleLocator(base=1.0)[source]
Bases: matplotlib.ticker.Locator Set a tick on each integer multiple of a base within the view interval. set_params(base)[source]
Set parameters within this locator.
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
view_limits(dmin, dmax)[source]
Set the view limits to the nearest multiples of base that contain the data.
|
matplotlib.ticker_api#matplotlib.ticker.MultipleLocator
|
set_params(base)[source]
Set parameters within this locator.
|
matplotlib.ticker_api#matplotlib.ticker.MultipleLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.MultipleLocator.tick_values
|
view_limits(dmin, dmax)[source]
Set the view limits to the nearest multiples of base that contain the data.
|
matplotlib.ticker_api#matplotlib.ticker.MultipleLocator.view_limits
|
classmatplotlib.ticker.NullFormatter[source]
Bases: matplotlib.ticker.Formatter Always return the empty string.
|
matplotlib.ticker_api#matplotlib.ticker.NullFormatter
|
classmatplotlib.ticker.NullLocator[source]
Bases: matplotlib.ticker.Locator No ticks tick_values(vmin, vmax)[source]
Return the locations of the ticks. Note Because the values are Null, vmin and vmax are not used in this method.
|
matplotlib.ticker_api#matplotlib.ticker.NullLocator
|
tick_values(vmin, vmax)[source]
Return the locations of the ticks. Note Because the values are Null, vmin and vmax are not used in this method.
|
matplotlib.ticker_api#matplotlib.ticker.NullLocator.tick_values
|
classmatplotlib.ticker.PercentFormatter(xmax=100, decimals=None, symbol='%', is_latex=False)[source]
Bases: matplotlib.ticker.Formatter Format numbers as a percentage. Parameters
xmaxfloat
Determines how the number is converted into a percentage. xmax is the data value that corresponds to 100%. Percentages are computed as x / xmax * 100. So if the data is already scaled to be percentages, xmax will be 100. Another common situation is where xmax is 1.0.
decimalsNone or int
The number of decimal places to place after the point. If None (the default), the number will be computed automatically.
symbolstr or None
A string that will be appended to the label. It may be None or empty to indicate that no symbol should be used. LaTeX special characters are escaped in symbol whenever latex mode is enabled, unless is_latex is True.
is_latexbool
If False, reserved LaTeX characters in symbol will be escaped. convert_to_pct(x)[source]
format_pct(x, display_range)[source]
Format the number as a percentage number with the correct number of decimals and adds the percent symbol, if any. If self.decimals is None, the number of digits after the decimal point is set based on the display_range of the axis as follows:
display_range decimals sample
>50 0 x = 34.5 => 35%
>5 1 x = 34.5 => 34.5%
>0.5 2 x = 34.5 => 34.50%
... ... ... This method will not be very good for tiny axis ranges or extremely large ones. It assumes that the values on the chart are percentages displayed on a reasonable scale.
propertysymbol
The configured percent symbol as a string. If LaTeX is enabled via rcParams["text.usetex"] (default: False), the special characters {'#', '$', '%', '&', '~', '_', '^', '\', '{', '}'} are automatically escaped in the string.
|
matplotlib.ticker_api#matplotlib.ticker.PercentFormatter
|
convert_to_pct(x)[source]
|
matplotlib.ticker_api#matplotlib.ticker.PercentFormatter.convert_to_pct
|
format_pct(x, display_range)[source]
Format the number as a percentage number with the correct number of decimals and adds the percent symbol, if any. If self.decimals is None, the number of digits after the decimal point is set based on the display_range of the axis as follows:
display_range decimals sample
>50 0 x = 34.5 => 35%
>5 1 x = 34.5 => 34.5%
>0.5 2 x = 34.5 => 34.50%
... ... ... This method will not be very good for tiny axis ranges or extremely large ones. It assumes that the values on the chart are percentages displayed on a reasonable scale.
|
matplotlib.ticker_api#matplotlib.ticker.PercentFormatter.format_pct
|
classmatplotlib.ticker.ScalarFormatter(useOffset=None, useMathText=None, useLocale=None)[source]
Bases: matplotlib.ticker.Formatter Format tick values as a number. Parameters
useOffsetbool or float, default: rcParams["axes.formatter.useoffset"] (default: True)
Whether to use offset notation. See set_useOffset.
useMathTextbool, default: rcParams["axes.formatter.use_mathtext"] (default: False)
Whether to use fancy math formatting. See set_useMathText.
useLocalebool, default: rcParams["axes.formatter.use_locale"] (default: False).
Whether to use locale settings for decimal sign and positive sign. See set_useLocale. Notes In addition to the parameters above, the formatting of scientific vs. floating point representation can be configured via set_scientific and set_powerlimits). Offset notation and scientific notation Offset notation and scientific notation look quite similar at first sight. Both split some information from the formatted tick values and display it at the end of the axis. The scientific notation splits up the order of magnitude, i.e. a multiplicative scaling factor, e.g. 1e6. The offset notation separates an additive constant, e.g. +1e6. The offset notation label is always prefixed with a + or - sign and is thus distinguishable from the order of magnitude label. The following plot with x limits 1_000_000 to 1_000_010 illustrates the different formatting. Note the labels at the right edge of the x axis. (Source code, png, pdf) format_data(value)[source]
Return the full string representation of the value with the position unspecified.
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
get_offset()[source]
Return scientific notation, plus offset.
get_useLocale()[source]
Return whether locale settings are used for formatting. See also ScalarFormatter.set_useLocale
get_useMathText()[source]
Return whether to use fancy math formatting. See also ScalarFormatter.set_useMathText
get_useOffset()[source]
Return whether automatic mode for offset notation is active. This returns True if set_useOffset(True); it returns False if an explicit offset was set, e.g. set_useOffset(1000). See also ScalarFormatter.set_useOffset
set_locs(locs)[source]
Set the locations of the ticks. This method is called before computing the tick labels because some formatters need to know all tick locations to do so.
set_powerlimits(lims)[source]
Set size thresholds for scientific notation. Parameters
lims(int, int)
A tuple (min_exp, max_exp) containing the powers of 10 that determine the switchover threshold. For a number representable as \(a \times 10^\mathrm{exp}\) with \(1 <= |a| < 10\), scientific notation will be used if exp <= min_exp or exp >= max_exp. The default limits are controlled by rcParams["axes.formatter.limits"] (default: [-5, 6]). In particular numbers with exp equal to the thresholds are written in scientific notation. Typically, min_exp will be negative and max_exp will be positive. For example, formatter.set_powerlimits((-3, 4)) will provide the following formatting: \(1 \times 10^{-3}, 9.9 \times 10^{-3}, 0.01,\) \(9999, 1 \times 10^4\). See also ScalarFormatter.set_scientific
set_scientific(b)[source]
Turn scientific notation on or off. See also ScalarFormatter.set_powerlimits
set_useLocale(val)[source]
Set whether to use locale settings for decimal sign and positive sign. Parameters
valbool or None
None resets to rcParams["axes.formatter.use_locale"] (default: False).
set_useMathText(val)[source]
Set whether to use fancy math formatting. If active, scientific notation is formatted as \(1.2 \times 10^3\). Parameters
valbool or None
None resets to rcParams["axes.formatter.use_mathtext"] (default: False).
set_useOffset(val)[source]
Set whether to use offset notation. When formatting a set numbers whose value is large compared to their range, the formatter can separate an additive constant. This can shorten the formatted numbers so that they are less likely to overlap when drawn on an axis. Parameters
valbool or float
If False, do not use offset notation. If True (=automatic mode), use offset notation if it can make the residual numbers significantly shorter. The exact behavior is controlled by rcParams["axes.formatter.offset_threshold"] (default: 4). If a number, force an offset of the given value. Examples With active offset notation, the values 100_000, 100_002, 100_004, 100_006, 100_008 will be formatted as 0, 2, 4, 6, 8 plus an offset +1e5, which is written to the edge of the axis.
propertyuseLocale
Return whether locale settings are used for formatting. See also ScalarFormatter.set_useLocale
propertyuseMathText
Return whether to use fancy math formatting. See also ScalarFormatter.set_useMathText
propertyuseOffset
Return whether automatic mode for offset notation is active. This returns True if set_useOffset(True); it returns False if an explicit offset was set, e.g. set_useOffset(1000). See also ScalarFormatter.set_useOffset
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter
|
format_data(value)[source]
Return the full string representation of the value with the position unspecified.
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.format_data
|
format_data_short(value)[source]
Return a short string version of the tick value. Defaults to the position-independent long value.
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.format_data_short
|
get_offset()[source]
Return scientific notation, plus offset.
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.get_offset
|
get_useLocale()[source]
Return whether locale settings are used for formatting. See also ScalarFormatter.set_useLocale
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.get_useLocale
|
get_useMathText()[source]
Return whether to use fancy math formatting. See also ScalarFormatter.set_useMathText
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.get_useMathText
|
get_useOffset()[source]
Return whether automatic mode for offset notation is active. This returns True if set_useOffset(True); it returns False if an explicit offset was set, e.g. set_useOffset(1000). See also ScalarFormatter.set_useOffset
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.get_useOffset
|
set_locs(locs)[source]
Set the locations of the ticks. This method is called before computing the tick labels because some formatters need to know all tick locations to do so.
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.set_locs
|
set_powerlimits(lims)[source]
Set size thresholds for scientific notation. Parameters
lims(int, int)
A tuple (min_exp, max_exp) containing the powers of 10 that determine the switchover threshold. For a number representable as \(a \times 10^\mathrm{exp}\) with \(1 <= |a| < 10\), scientific notation will be used if exp <= min_exp or exp >= max_exp. The default limits are controlled by rcParams["axes.formatter.limits"] (default: [-5, 6]). In particular numbers with exp equal to the thresholds are written in scientific notation. Typically, min_exp will be negative and max_exp will be positive. For example, formatter.set_powerlimits((-3, 4)) will provide the following formatting: \(1 \times 10^{-3}, 9.9 \times 10^{-3}, 0.01,\) \(9999, 1 \times 10^4\). See also ScalarFormatter.set_scientific
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.set_powerlimits
|
set_scientific(b)[source]
Turn scientific notation on or off. See also ScalarFormatter.set_powerlimits
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.set_scientific
|
set_useLocale(val)[source]
Set whether to use locale settings for decimal sign and positive sign. Parameters
valbool or None
None resets to rcParams["axes.formatter.use_locale"] (default: False).
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.set_useLocale
|
set_useMathText(val)[source]
Set whether to use fancy math formatting. If active, scientific notation is formatted as \(1.2 \times 10^3\). Parameters
valbool or None
None resets to rcParams["axes.formatter.use_mathtext"] (default: False).
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.set_useMathText
|
set_useOffset(val)[source]
Set whether to use offset notation. When formatting a set numbers whose value is large compared to their range, the formatter can separate an additive constant. This can shorten the formatted numbers so that they are less likely to overlap when drawn on an axis. Parameters
valbool or float
If False, do not use offset notation. If True (=automatic mode), use offset notation if it can make the residual numbers significantly shorter. The exact behavior is controlled by rcParams["axes.formatter.offset_threshold"] (default: 4). If a number, force an offset of the given value. Examples With active offset notation, the values 100_000, 100_002, 100_004, 100_006, 100_008 will be formatted as 0, 2, 4, 6, 8 plus an offset +1e5, which is written to the edge of the axis.
|
matplotlib.ticker_api#matplotlib.ticker.ScalarFormatter.set_useOffset
|
classmatplotlib.ticker.StrMethodFormatter(fmt)[source]
Bases: matplotlib.ticker.Formatter Use a new-style format string (as used by str.format) to format the tick. The field used for the tick value must be labeled x and the field used for the tick position must be labeled pos.
|
matplotlib.ticker_api#matplotlib.ticker.StrMethodFormatter
|
classmatplotlib.ticker.SymmetricalLogLocator(transform=None, subs=None, linthresh=None, base=None)[source]
Bases: matplotlib.ticker.Locator Determine the tick locations for symmetric log axes. Parameters
transformSymmetricalLogTransform, optional
If set, defines the base and linthresh of the symlog transform.
base, linthreshfloat, optional
The base and linthresh of the symlog transform, as documented for SymmetricalLogScale. These parameters are only used if transform is not set.
subssequence of float, default: [1]
The multiples of integer powers of the base where ticks are placed, i.e., ticks are placed at [sub * base**i for i in ... for sub in subs]. Notes Either transform, or both base and linthresh, must be given. set_params(subs=None, numticks=None)[source]
Set parameters within this locator.
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
view_limits(vmin, vmax)[source]
Try to choose the view limits intelligently.
|
matplotlib.ticker_api#matplotlib.ticker.SymmetricalLogLocator
|
set_params(subs=None, numticks=None)[source]
Set parameters within this locator.
|
matplotlib.ticker_api#matplotlib.ticker.SymmetricalLogLocator.set_params
|
tick_values(vmin, vmax)[source]
Return the values of the located ticks given vmin and vmax. Note To get tick locations with the vmin and vmax values defined automatically for the associated axis simply call the Locator instance: >>> print(type(loc))
<type 'Locator'>
>>> print(loc())
[1, 2, 3, 4]
|
matplotlib.ticker_api#matplotlib.ticker.SymmetricalLogLocator.tick_values
|
view_limits(vmin, vmax)[source]
Try to choose the view limits intelligently.
|
matplotlib.ticker_api#matplotlib.ticker.SymmetricalLogLocator.view_limits
|
classmatplotlib.ticker.TickHelper[source]
Bases: object axis=None
create_dummy_axis(**kwargs)[source]
set_axis(axis)[source]
set_bounds(vmin, vmax)[source]
[Deprecated] Notes Deprecated since version 3.5:
set_data_interval(vmin, vmax)[source]
[Deprecated] Notes Deprecated since version 3.5:
set_view_interval(vmin, vmax)[source]
[Deprecated] Notes Deprecated since version 3.5:
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper
|
axis=None
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper.axis
|
create_dummy_axis(**kwargs)[source]
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper.create_dummy_axis
|
set_axis(axis)[source]
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper.set_axis
|
set_bounds(vmin, vmax)[source]
[Deprecated] Notes Deprecated since version 3.5:
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper.set_bounds
|
set_data_interval(vmin, vmax)[source]
[Deprecated] Notes Deprecated since version 3.5:
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper.set_data_interval
|
set_view_interval(vmin, vmax)[source]
[Deprecated] Notes Deprecated since version 3.5:
|
matplotlib.ticker_api#matplotlib.ticker.TickHelper.set_view_interval
|
matplotlib.tight_bbox Helper module for the bbox_inches parameter in Figure.savefig. matplotlib.tight_bbox.adjust_bbox(fig, bbox_inches, fixed_dpi=None)[source]
Temporarily adjust the figure so that only the specified area (bbox_inches) is saved. It modifies fig.bbox, fig.bbox_inches, fig.transFigure._boxout, and fig.patch. While the figure size changes, the scale of the original figure is conserved. A function which restores the original values are returned.
matplotlib.tight_bbox.process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None)[source]
A function that needs to be called when figure dpi changes during the drawing (e.g., rasterizing). It recovers the bbox and re-adjust it with the new dpi.
|
matplotlib.tight_bbox_api
|
matplotlib.tight_bbox.adjust_bbox(fig, bbox_inches, fixed_dpi=None)[source]
Temporarily adjust the figure so that only the specified area (bbox_inches) is saved. It modifies fig.bbox, fig.bbox_inches, fig.transFigure._boxout, and fig.patch. While the figure size changes, the scale of the original figure is conserved. A function which restores the original values are returned.
|
matplotlib.tight_bbox_api#matplotlib.tight_bbox.adjust_bbox
|
matplotlib.tight_bbox.process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None)[source]
A function that needs to be called when figure dpi changes during the drawing (e.g., rasterizing). It recovers the bbox and re-adjust it with the new dpi.
|
matplotlib.tight_bbox_api#matplotlib.tight_bbox.process_figure_for_rasterizing
|
matplotlib.tight_layout Routines to adjust subplot params so that subplots are nicely fit in the figure. In doing so, only axis labels, tick labels, axes titles and offsetboxes that are anchored to axes are currently considered. Internally, this module assumes that the margins (left margin, etc.) which are differences between Axes.get_tightbbox and Axes.bbox are independent of Axes position. This may fail if Axes.adjustable is datalim as well as such cases as when left or right margin are affected by xlabel. matplotlib.tight_layout.auto_adjust_subplotpars(fig, renderer, nrows_ncols, num1num2_list, subplot_list, ax_bbox_list=None, pad=1.08, h_pad=None, w_pad=None, rect=None)[source]
[Deprecated] Return a dict of subplot parameters to adjust spacing between subplots or None if resulting axes would have zero height or width. Note that this function ignores geometry information of subplot itself, but uses what is given by the nrows_ncols and num1num2_list parameters. Also, the results could be incorrect if some subplots have adjustable=datalim. Parameters
nrows_ncolstuple[int, int]
Number of rows and number of columns of the grid.
num1num2_listlist[tuple[int, int]]
List of numbers specifying the area occupied by the subplot
subplot_listlist of subplots
List of subplots that will be used to calculate optimal subplot_params.
padfloat
Padding between the figure edge and the edges of subplots, as a fraction of the font size.
h_pad, w_padfloat
Padding (height/width) between edges of adjacent subplots, as a fraction of the font size. Defaults to pad.
recttuple[float, float, float, float]
[left, bottom, right, top] in normalized (0, 1) figure coordinates. Notes Deprecated since version 3.5.
matplotlib.tight_layout.get_renderer(fig)[source]
matplotlib.tight_layout.get_subplotspec_list(axes_list, grid_spec=None)[source]
Return a list of subplotspec from the given list of axes. For an instance of axes that does not support subplotspec, None is inserted in the list. If grid_spec is given, None is inserted for those not from the given grid_spec.
matplotlib.tight_layout.get_tight_layout_figure(fig, axes_list, subplotspec_list, renderer, pad=1.08, h_pad=None, w_pad=None, rect=None)[source]
Return subplot parameters for tight-layouted-figure with specified padding. Parameters
figFigure
axes_listlist of Axes
subplotspec_listlist of SubplotSpec
The subplotspecs of each axes.
rendererrenderer
padfloat
Padding between the figure edge and the edges of subplots, as a fraction of the font size.
h_pad, w_padfloat
Padding (height/width) between edges of adjacent subplots. Defaults to pad.
recttuple[float, float, float, float], optional
(left, bottom, right, top) rectangle in normalized figure coordinates that the whole subplots area (including labels) will fit into. Defaults to using the entire figure. Returns
subplotspec or None
subplotspec kwargs to be passed to Figure.subplots_adjust or None if tight_layout could not be accomplished.
|
matplotlib.tight_layout_api
|
matplotlib.tight_layout.auto_adjust_subplotpars(fig, renderer, nrows_ncols, num1num2_list, subplot_list, ax_bbox_list=None, pad=1.08, h_pad=None, w_pad=None, rect=None)[source]
[Deprecated] Return a dict of subplot parameters to adjust spacing between subplots or None if resulting axes would have zero height or width. Note that this function ignores geometry information of subplot itself, but uses what is given by the nrows_ncols and num1num2_list parameters. Also, the results could be incorrect if some subplots have adjustable=datalim. Parameters
nrows_ncolstuple[int, int]
Number of rows and number of columns of the grid.
num1num2_listlist[tuple[int, int]]
List of numbers specifying the area occupied by the subplot
subplot_listlist of subplots
List of subplots that will be used to calculate optimal subplot_params.
padfloat
Padding between the figure edge and the edges of subplots, as a fraction of the font size.
h_pad, w_padfloat
Padding (height/width) between edges of adjacent subplots, as a fraction of the font size. Defaults to pad.
recttuple[float, float, float, float]
[left, bottom, right, top] in normalized (0, 1) figure coordinates. Notes Deprecated since version 3.5.
|
matplotlib.tight_layout_api#matplotlib.tight_layout.auto_adjust_subplotpars
|
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