solarwindpy.plotting.orbits.OrbitHist1D

class OrbitHist1D(orbit, x, **kwargs)

Bases: OrbitPlot, Hist1D

__init__(orbit, x, **kwargs)

Create a one-dimensional histogram.

Parameters:

• x (pandas.Series) – Data from which to create bins.

• y (pandas.Series or None, optional) – Values to aggregate in bins of x. If None, counts of x are used.

• logx (bool, optional) – If True, compute bins in logarithmic space.

• axnorm ({"t", "d", None}, optional) – Normalisation applied to the histogram. "t" uses total counts and "d" yields a density.

• clip_data (bool, optional) – Remove extreme values at the 0.001 and 0.999 percentiles before binning or aggregation.

• nbins (int or array-like, optional) – Binning strategy passed to numpy.histogram_bin_edges() or pandas.cut() depending on the input type.

• bin_precision (int, optional) – Precision for decimal bin edges.

agg(**kwargs)

Perform the aggregation along the agg axes.

If either of the count limits specified in clim are not None, apply them.

fcn allows you to specify a specific function for aggregation. Otherwise, automatically choose “count” or “mean” based on the uniqueness of the aggregated values.

make_plot(ax=None, fcn=None, **kwargs)

Make a plot on ax.

If ax is None, create a mpl.subplots axis.

**kwargs passed directly to ax.plot.

drawstyle defaults to steps-mid

fcn passed to self.agg. Only one function is allow b/c we don’t yet handle uncertainties.

property agg_axes

The axis to aggregate into, e.g. the z variable in an (x, y, z) heatmap.

property alim

property axnorm

Data normalization in plot.

Not mpl.colors.Normalize instance. That is passed as a kwarg to make_plot.

calc_bins_intervals(nbins=101, precision=None)

Calculate histogram bins.

nbins: int, str, array-like

If int, use np.histogram to calculate the bin edges. If str and nbins == “knuth”, use astropy.stats.knuth_bin_width to calculate optimal bin widths. If str and nbins != “knuth”, use np.histogram(data, bins=nbins) to calculate bins. If array-like, treat as bins.

precision: int or None

Precision at which to store intervals. If None, default to 3.

property categoricals

property clim

property clip

static clip_data(data, clip)

construct_cdf(only_plotted=True)

Convert the obsered measuremets.

Returns:

cdf – “x” column is the value of the measuremnt. “position” column is the normalized position in the cdf. To plot the cdf:

cdf.plot(x=”x”, y=”cdf”)

Return type:

pd.DataFrame

property cut

property data

property edges

get_plotted_data_boolean_series()

Return a boolean pd.Series identifying each plotted measurement.

The series shares the same index as the stored data. To align with a different index you may need to adjust the returned series.

get_subset_above_threshold(threshold, fcn='count')

Get the subset of data above a given threshold using fcn to.

aggregate. If axnorm set, this is used.

property grouped

joint.groupby with appropriate axes passes.

property intervals

property joint

Combines the categorical and continuous data for Groupby.

property labels

property log

property logger

make_cut()

Calculate the Categorical quantities for the aggregation axes.

property orbit

property path

Path for saving figure.

set_alim(lower=None, upper=None)

Set the minimum (lower) and maximum (upper) allowed value when.

aggregating. This is different from clim because it uses the agg_fcn. So behavior will change based on axnorm, etc.

set_axnorm(new)

The method by which the gridded data is normalized.

key	description
d	Density normalize
t	Total normalize

set_clim(lower=None, upper=None)

Set the minimum (lower) and maximum (upper) allowed number of.

counts per bin to return after calling agg().

set_data(x, y, clip)

set_labels(**kwargs)

Set or update x, y, or z labels. Any label not specified in kwargs.

is propagated from self.labels.<x, y, or z>.

set_log(x=None, y=None)

set_orbit(new)

IntervalIndex corresponding to the times we want to subset the orbit.

set_path(*args, orbit=None, **kwargs)

Set path information, accounting for orbit info.

take_data_in_yrange_across_x(ranges_by_x, get_x_bounds, get_y_bounds)

Take data within y-ranges across x-values.

Parameters:

• ranges_by_x (iterable) – An iterable with keys used to get the left and right bounds for the data and values used to get the top and bottom bounds for the data.

• get_x_bounds (function) – First argument is one key of ranges_by_x and returns left, right. Second argument is a kwarg (expected_logx) boolean to transform the returned values according to whether or not the keys are \(log(x)\) or \(x\) in a manner that matches data stored in Hist2D.

• get_y_bounds (functions) – Takes on value of ranges_by_x and returns top, bottom. Second argument Second argument is a kwarg (expected_logx) boolean to transform the returned values according to whether or not the keys are \(log(y)\) or \(y\) in a manner that matches data stored in Hist2D.

Returns:

taken – Array of indices for selecting data in interval.

Return type:

np.ndarray 1D