Source code for plotnine.layer

from copy import copy, deepcopy

import pandas as pd

from .exceptions import PlotnineError
from .utils import array_kind, ninteraction
from .utils import check_required_aesthetics, defaults
from .mapping.aes import aes, NO_GROUP
from .mapping.evaluation import stage, evaluate

class Layers(list):
    List of layers

    During the plot building pipeline, many operations are
    applied at all layers in the plot. This class makes those
    tasks easier.

    def __iadd__(self, other):
        return Layers(super().__iadd__(other))

    def __add__(self, other):
        return Layers(super().__add__(other))

    def __radd__(self, other, inplace=False):
        Add layers to ggplot object
        from .ggplot import ggplot
        if isinstance(other, ggplot):
            other = other if inplace else deepcopy(other)
            for obj in self:
                other += obj
            msg = "Cannot add Layers to object of type {!r}".format
            raise PlotnineError(msg(type(other)))
        return other

    def __getitem__(self, key):
        result = super().__getitem__(key)
        if not isinstance(key, int):
            result = Layers(result)
        return result

    def data(self):
        return [ for l in self]

    def setup(self, plot):
        for l in self:

    def setup_data(self):
        for l in self:

    def draw(self, layout, coord):
        # If zorder is 0, it is left to MPL
        for i, l in enumerate(self, start=1):
            l.zorder = i
            l.draw(layout, coord)

    def compute_aesthetics(self, plot):
        for l in self:

    def compute_statistic(self, layout):
        for l in self:

    def map_statistic(self, plot):
        for l in self:

    def compute_position(self, layout):
        for l in self:

    def use_defaults(self, data=None, aes_modifiers=None):
        for l in self:
            l.use_defaults(data, aes_modifiers)

    def transform(self, scales):
        for l in self:
   = scales.transform_df(

    def train(self, scales):
        for l in self:
   = scales.train_df(

    def map(self, scales):
        for l in self:
   = scales.map_df(

    def finish_statistics(self):
        for l in self:

    def update_labels(self, plot):
        for l in self:

[docs]class layer: """ Layer When a ``geom`` or ``stat`` is added to a :class:`~plotnine.ggplot` object, it creates a single layer. This class is a representation of that layer. Parameters ---------- geom : geom, optional geom to used to draw this layer. stat : stat, optional stat used for the statistical transformation of data in this layer data : dataframe, optional Data plotted in this layer. If ``None``, the data from the :class:`~plotnine.ggplot` object will be used. mapping : aes, optional Aesthetic mappings. position : position, optional Position object to adjust the geometries in this layer. inherit_aes : bool, optional If ``True`` inherit from the aesthetic mappings of the :class:`~plotnine.ggplot` object. Default ``True``. show_legend : bool or None, optional Whether to make up and show a legend for the mappings of this layer. If ``None`` then an automatic/good choice is made. Default is ``None``. raster : bool, optional (default: False) If ``True``, draw onto this layer a raster (bitmap) object even if the final image format is vector. Notes ----- There is no benefit to manually creating a layer. You should always use a ``geom`` or ``stat``. """ def __init__(self, geom=None, stat=None, data=None, mapping=None, position=None, inherit_aes=True, show_legend=None, raster=False): self.geom = geom self.stat = stat = data self.mapping = mapping self.position = position self.inherit_aes = inherit_aes self.show_legend = show_legend self.raster = raster self._active_mapping = {} self.zorder = 0
[docs] @staticmethod def from_geom(geom): """ Create a layer given a :class:`geom` Parameters ---------- geom : geom `geom` from which a layer will be created Returns ------- out : layer Layer that represents the specific `geom`. """ kwargs = geom._kwargs lkwargs = {'geom': geom, 'mapping': geom.mapping, 'data':, 'stat': geom._stat, 'position': geom._position} layer_params = ('inherit_aes', 'show_legend', 'raster') for param in layer_params: if param in kwargs: lkwargs[param] = kwargs[param] elif param in geom.DEFAULT_PARAMS: lkwargs[param] = geom.DEFAULT_PARAMS[param] return layer(**lkwargs)
def __radd__(self, gg): """ Add layer to ggplot object """ try: gg.layers.append(self) except AttributeError: msg = "Cannot add layer to object of type {!r}".format raise PlotnineError(msg(type(gg))) return gg def __deepcopy__(self, memo): """ Deep copy without copying the dataframe """ cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result old = self.__dict__ new = result.__dict__ for key, item in old.items(): if key == 'data': new[key] = old[key] else: new[key] = deepcopy(old[key], memo) return result
[docs] def setup(self, plot): """ Prepare layer for the plot building Give the layer access to the data, mapping and environment """ self._make_layer_data( self._make_layer_mapping(plot.mapping) self._make_layer_environments(plot.environment)
def _make_layer_data(self, plot_data): """ Generate data to be used by this layer Parameters ---------- plot_data : dataframe ggplot object data """ if plot_data is None: plot_data = pd.DataFrame() # Each layer that does not have data gets a copy of # of the If the has data it is replaced # by copy so that we do not alter the users data if is None: try: = plot_data.copy() except AttributeError: _geom_name = self.geom.__class__.__name__ _data_name = plot_data.__class__.__name__ raise PlotnineError( f"{_geom_name} layer expects a dataframe, " f"but it got {_data_name} instead." ) elif callable( = if not isinstance(, pd.DataFrame): raise PlotnineError( "Data function must return a dataframe" ) else: = def _make_layer_mapping(self, plot_mapping): """ Create the aesthetic mappings to be used by this layer Parameters ---------- plot_mapping : aes ggplot object mapping """ if self.inherit_aes: self.mapping = defaults(self.mapping, plot_mapping) # aesthetics set as parameters override the same # aesthetics set as mappings, so we can ignore # those in the mapping for ae in self.geom.aes_params: if ae in self.mapping: del self.mapping[ae] # Set group as a mapping if set as a parameter if 'group' in self.geom.aes_params: group = self.geom.aes_params['group'] # Double quote str so that it evaluates to itself if isinstance(group, str): group = f'"{group}"' self.mapping['group'] = stage(start=group) def _make_layer_environments(self, plot_environment): """ Create the aesthetic mappings to be used by this layer Parameters ---------- plot_environment : ~patsy.Eval.EvalEnvironment Namespace in which to execute aesthetic expressions. """ self.geom.environment = plot_environment self.stat.environment = plot_environment
[docs] def compute_aesthetics(self, plot): """ Return a dataframe where the columns match the aesthetic mappings. Transformations like 'factor(cyl)' and other expression evaluation are made in here """ evaled = evaluate(self.mapping._starting,, plot.environment) evaled_aes = aes(**{col: col for col in evaled}) plot.scales.add_defaults(evaled, evaled_aes) if len( == 0 and len(evaled) > 0: # No data, and vectors suppled to aesthetics evaled['PANEL'] = 1 else: evaled['PANEL'] =['PANEL'] data = add_group(evaled) = data.sort_values('PANEL', kind='mergesort')
[docs] def compute_statistic(self, layout): """ Compute & return statistics for this layer """ data = if not len(data): return type(data)() params = self.stat.setup_params(data) data = self.stat.use_defaults(data) data = self.stat.setup_data(data) data = self.stat.compute_layer(data, params, layout) = data
[docs] def map_statistic(self, plot): """ Mapping aesthetics to computed statistics """ data = if not len(data): return type(data)() # Mixin default stat aesthetic mappings aesthetics = defaults(self.mapping, self.stat.DEFAULT_AES) stat_data = evaluate(aesthetics._calculated, data, plot.environment) if not len(stat_data): return # (see stat_spoke for one exception) if self.stat.retransform: stat_data = plot.scales.transform_df(stat_data) # When there are duplicate columns, we use the computed # ones in stat_data columns = data.columns.difference(stat_data.columns) = pd.concat([data[columns], stat_data], axis=1) # Add any new scales, if needed new = {ae: ae for ae in stat_data.columns} plot.scales.add_defaults(, new)
[docs] def setup_data(self): """ Prepare/modify data for plotting """ data = if len(data) == 0: return type(data)() data = self.geom.setup_data(data) check_required_aesthetics( self.geom.REQUIRED_AES, set(data.columns) | set(self.geom.aes_params), self.geom.__class__.__name__ ) = data
[docs] def compute_position(self, layout): """ Compute the position of each geometric object in concert with the other objects in the panel """ if len( == 0: return params = self.position.setup_params( data = self.position.setup_data(, params) data = self.position.compute_layer(data, params, layout) = data
[docs] def draw(self, layout, coord): """ Draw geom Parameters ---------- layout : Layout Layout object created when the plot is getting built coord : coord Type of coordinate axes """ params = copy(self.geom.params) params.update(self.stat.params) params['zorder'] = self.zorder params['raster'] = self.raster = self.geom.handle_na( # At this point each layer must have the data # that is created by the plot build process self.geom.draw_layer(, layout, coord, **params)
[docs] def use_defaults(self, data=None, aes_modifiers=None): """ Prepare/modify data for plotting Parameters ---------- data : dataframe, optional Data aes_modifiers : dict Expression to evaluate and replace aesthetics in the data. """ if data is None: data = if aes_modifiers is None: aes_modifiers = self.mapping._scaled return self.geom.use_defaults(data, aes_modifiers)
[docs] def finish_statistics(self): """ Prepare/modify data for plotting """ self.stat.finish_layer(, self.stat.params)
def add_group(data): if len(data) == 0: return data if 'group' not in data: disc = discrete_columns(data, ignore=['label']) if disc: data['group'] = ninteraction(data[disc], drop=True) else: data['group'] = NO_GROUP else: data['group'] = ninteraction(data[['group']], drop=True) return data def discrete_columns(df, ignore): """ Return a list of the discrete columns in the dataframe `df`. `ignore` is a list|set|tuple with the names of the columns to skip. """ lst = [] for col in df: if array_kind.discrete(df[col]) and (col not in ignore): # Some columns are represented as object dtype # but may have compound structures as values. try: hash(df[col].iloc[0]) except TypeError: continue lst.append(col) return lst