from __future__ import absolute_import from plotly import colors, exceptions, optional_imports from plotly.figure_factory import utils from plotly.tools import make_subplots import math from numbers import Number pd = optional_imports.get_module('pandas') TICK_COLOR = '#969696' AXIS_TITLE_COLOR = '#0f0f0f' AXIS_TITLE_SIZE = 12 GRID_COLOR = '#ffffff' LEGEND_COLOR = '#efefef' PLOT_BGCOLOR = '#ededed' ANNOT_RECT_COLOR = '#d0d0d0' LEGEND_BORDER_WIDTH = 1 LEGEND_ANNOT_X = 1.05 LEGEND_ANNOT_Y = 0.5 MAX_TICKS_PER_AXIS = 5 THRES_FOR_FLIPPED_FACET_TITLES = 10 GRID_WIDTH = 1 VALID_TRACE_TYPES = ['scatter', 'scattergl', 'histogram', 'bar', 'box'] CUSTOM_LABEL_ERROR = ( "If you are using a dictionary for custom labels for the facet row/col, " "make sure each key in that column of the dataframe is in your facet " "labels. The keys you need are {}" ) def _is_flipped(num): if num >= THRES_FOR_FLIPPED_FACET_TITLES: flipped = True else: flipped = False return flipped def _return_label(original_label, facet_labels, facet_var): if isinstance(facet_labels, dict): label = facet_labels[original_label] elif isinstance(facet_labels, str): label = '{}: {}'.format(facet_var, original_label) else: label = original_label return label def _legend_annotation(color_name): legend_title = dict( textangle=0, xanchor='left', yanchor='middle', x=LEGEND_ANNOT_X, y=1.03, showarrow=False, xref='paper', yref='paper', text='factor({})'.format(color_name), font=dict( size=13, color='#000000' ) ) return legend_title def _annotation_dict(text, lane, num_of_lanes, SUBPLOT_SPACING, row_col='col', flipped=True): l = (1 - (num_of_lanes - 1) * SUBPLOT_SPACING) / (num_of_lanes) if not flipped: xanchor = 'center' yanchor = 'middle' if row_col == 'col': x = (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l y = 1.03 textangle = 0 elif row_col == 'row': y = (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l x = 1.03 textangle = 90 else: if row_col == 'col': xanchor = 'center' yanchor = 'bottom' x = (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l y = 1.0 textangle = 270 elif row_col == 'row': xanchor = 'left' yanchor = 'middle' y = (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l x = 1.0 textangle = 0 annotation_dict = dict( textangle=textangle, xanchor=xanchor, yanchor=yanchor, x=x, y=y, showarrow=False, xref='paper', yref='paper', text=str(text), font=dict( size=13, color=AXIS_TITLE_COLOR ) ) return annotation_dict def _axis_title_annotation(text, x_or_y_axis): if x_or_y_axis == 'x': x_pos = 0.5 y_pos = -0.1 textangle = 0 elif x_or_y_axis == 'y': x_pos = -0.1 y_pos = 0.5 textangle = 270 if not text: text = '' annot = {'font': {'color': '#000000', 'size': AXIS_TITLE_SIZE}, 'showarrow': False, 'text': text, 'textangle': textangle, 'x': x_pos, 'xanchor': 'center', 'xref': 'paper', 'y': y_pos, 'yanchor': 'middle', 'yref': 'paper'} return annot def _add_shapes_to_fig(fig, annot_rect_color, flipped_rows=False, flipped_cols=False): shapes_list = [] for key in fig['layout'].to_plotly_json().keys(): if 'axis' in key and fig['layout'][key]['domain'] != [0.0, 1.0]: shape = { 'fillcolor': annot_rect_color, 'layer': 'below', 'line': {'color': annot_rect_color, 'width': 1}, 'type': 'rect', 'xref': 'paper', 'yref': 'paper' } if 'xaxis' in key: shape['x0'] = fig['layout'][key]['domain'][0] shape['x1'] = fig['layout'][key]['domain'][1] shape['y0'] = 1.005 shape['y1'] = 1.05 if flipped_cols: shape['y1'] += 0.5 shapes_list.append(shape) elif 'yaxis' in key: shape['x0'] = 1.005 shape['x1'] = 1.05 shape['y0'] = fig['layout'][key]['domain'][0] shape['y1'] = fig['layout'][key]['domain'][1] if flipped_rows: shape['x1'] += 1 shapes_list.append(shape) fig['layout']['shapes'] = shapes_list def _make_trace_for_scatter(trace, trace_type, color, **kwargs_marker): if trace_type in ['scatter', 'scattergl']: trace['mode'] = 'markers' trace['marker'] = dict(color=color, **kwargs_marker) return trace def _facet_grid_color_categorical(df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker): fig = make_subplots(rows=num_of_rows, cols=num_of_cols, shared_xaxes=True, shared_yaxes=True, horizontal_spacing=SUBPLOT_SPACING, vertical_spacing=SUBPLOT_SPACING, print_grid=False) annotations = [] if not facet_row and not facet_col: color_groups = list(df.groupby(color_name)) for group in color_groups: trace = dict( type=trace_type, name=group[0], marker=dict( color=colormap[group[0]], ), **kwargs_trace ) if x: trace['x'] = group[1][x] if y: trace['y'] = group[1][y] trace = _make_trace_for_scatter( trace, trace_type, colormap[group[0]], **kwargs_marker ) fig.append_trace(trace, 1, 1) elif (facet_row and not facet_col) or (not facet_row and facet_col): groups_by_facet = list( df.groupby(facet_row if facet_row else facet_col) ) for j, group in enumerate(groups_by_facet): for color_val in df[color_name].unique(): data_by_color = group[1][group[1][color_name] == color_val] trace = dict( type=trace_type, name=color_val, marker=dict( color=colormap[color_val], ), **kwargs_trace ) if x: trace['x'] = data_by_color[x] if y: trace['y'] = data_by_color[y] trace = _make_trace_for_scatter( trace, trace_type, colormap[color_val], **kwargs_marker ) fig.append_trace(trace, j + 1 if facet_row else 1, 1 if facet_row else j + 1) label = _return_label( group[0], facet_row_labels if facet_row else facet_col_labels, facet_row if facet_row else facet_col ) annotations.append( _annotation_dict( label, num_of_rows - j if facet_row else j + 1, num_of_rows if facet_row else num_of_cols, SUBPLOT_SPACING, 'row' if facet_row else 'col', flipped_rows) ) elif facet_row and facet_col: groups_by_facets = list(df.groupby([facet_row, facet_col])) tuple_to_facet_group = {item[0]: item[1] for item in groups_by_facets} row_values = df[facet_row].unique() col_values = df[facet_col].unique() color_vals = df[color_name].unique() for row_count, x_val in enumerate(row_values): for col_count, y_val in enumerate(col_values): try: group = tuple_to_facet_group[(x_val, y_val)] except KeyError: group = pd.DataFrame([[None, None, None]], columns=[x, y, color_name]) for color_val in color_vals: if group.values.tolist() != [[None, None, None]]: group_filtered = group[group[color_name] == color_val] trace = dict( type=trace_type, name=color_val, marker=dict( color=colormap[color_val], ), **kwargs_trace ) new_x = group_filtered[x] new_y = group_filtered[y] else: trace = dict( type=trace_type, name=color_val, marker=dict( color=colormap[color_val], ), showlegend=False, **kwargs_trace ) new_x = group[x] new_y = group[y] if x: trace['x'] = new_x if y: trace['y'] = new_y trace = _make_trace_for_scatter( trace, trace_type, colormap[color_val], **kwargs_marker ) fig.append_trace(trace, row_count + 1, col_count + 1) if row_count == 0: label = _return_label(col_values[col_count], facet_col_labels, facet_col) annotations.append( _annotation_dict(label, col_count + 1, num_of_cols, SUBPLOT_SPACING, row_col='col', flipped=flipped_cols) ) label = _return_label(row_values[row_count], facet_row_labels, facet_row) annotations.append( _annotation_dict(label, num_of_rows - row_count, num_of_rows, SUBPLOT_SPACING, row_col='row', flipped=flipped_rows) ) return fig, annotations def _facet_grid_color_numerical(df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker): fig = make_subplots(rows=num_of_rows, cols=num_of_cols, shared_xaxes=True, shared_yaxes=True, horizontal_spacing=SUBPLOT_SPACING, vertical_spacing=SUBPLOT_SPACING, print_grid=False) annotations = [] if not facet_row and not facet_col: trace = dict( type=trace_type, marker=dict( color=df[color_name], colorscale=colormap, showscale=True, ), **kwargs_trace ) if x: trace['x'] = df[x] if y: trace['y'] = df[y] trace = _make_trace_for_scatter( trace, trace_type, df[color_name], **kwargs_marker ) fig.append_trace(trace, 1, 1) if (facet_row and not facet_col) or (not facet_row and facet_col): groups_by_facet = list( df.groupby(facet_row if facet_row else facet_col) ) for j, group in enumerate(groups_by_facet): trace = dict( type=trace_type, marker=dict( color=df[color_name], colorscale=colormap, showscale=True, colorbar=dict(x=1.15), ), **kwargs_trace ) if x: trace['x'] = group[1][x] if y: trace['y'] = group[1][y] trace = _make_trace_for_scatter( trace, trace_type, df[color_name], **kwargs_marker ) fig.append_trace( trace, j + 1 if facet_row else 1, 1 if facet_row else j + 1 ) labels = facet_row_labels if facet_row else facet_col_labels label = _return_label( group[0], labels, facet_row if facet_row else facet_col ) annotations.append( _annotation_dict( label, num_of_rows - j if facet_row else j + 1, num_of_rows if facet_row else num_of_cols, SUBPLOT_SPACING, 'row' if facet_row else 'col', flipped=flipped_rows) ) elif facet_row and facet_col: groups_by_facets = list(df.groupby([facet_row, facet_col])) tuple_to_facet_group = {item[0]: item[1] for item in groups_by_facets} row_values = df[facet_row].unique() col_values = df[facet_col].unique() for row_count, x_val in enumerate(row_values): for col_count, y_val in enumerate(col_values): try: group = tuple_to_facet_group[(x_val, y_val)] except KeyError: group = pd.DataFrame([[None, None, None]], columns=[x, y, color_name]) if group.values.tolist() != [[None, None, None]]: trace = dict( type=trace_type, marker=dict( color=df[color_name], colorscale=colormap, showscale=(row_count == 0), colorbar=dict(x=1.15), ), **kwargs_trace ) else: trace = dict( type=trace_type, showlegend=False, **kwargs_trace ) if x: trace['x'] = group[x] if y: trace['y'] = group[y] trace = _make_trace_for_scatter( trace, trace_type, df[color_name], **kwargs_marker ) fig.append_trace(trace, row_count + 1, col_count + 1) if row_count == 0: label = _return_label(col_values[col_count], facet_col_labels, facet_col) annotations.append( _annotation_dict(label, col_count + 1, num_of_cols, SUBPLOT_SPACING, row_col='col', flipped=flipped_cols) ) label = _return_label(row_values[row_count], facet_row_labels, facet_row) annotations.append( _annotation_dict(row_values[row_count], num_of_rows - row_count, num_of_rows, SUBPLOT_SPACING, row_col='row', flipped=flipped_rows) ) return fig, annotations def _facet_grid(df, x, y, facet_row, facet_col, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker): fig = make_subplots(rows=num_of_rows, cols=num_of_cols, shared_xaxes=True, shared_yaxes=True, horizontal_spacing=SUBPLOT_SPACING, vertical_spacing=SUBPLOT_SPACING, print_grid=False) annotations = [] if not facet_row and not facet_col: trace = dict( type=trace_type, marker=dict( color=marker_color, line=kwargs_marker['line'], ), **kwargs_trace ) if x: trace['x'] = df[x] if y: trace['y'] = df[y] trace = _make_trace_for_scatter( trace, trace_type, marker_color, **kwargs_marker ) fig.append_trace(trace, 1, 1) elif (facet_row and not facet_col) or (not facet_row and facet_col): groups_by_facet = list( df.groupby(facet_row if facet_row else facet_col) ) for j, group in enumerate(groups_by_facet): trace = dict( type=trace_type, marker=dict( color=marker_color, line=kwargs_marker['line'], ), **kwargs_trace ) if x: trace['x'] = group[1][x] if y: trace['y'] = group[1][y] trace = _make_trace_for_scatter( trace, trace_type, marker_color, **kwargs_marker ) fig.append_trace(trace, j + 1 if facet_row else 1, 1 if facet_row else j + 1) label = _return_label( group[0], facet_row_labels if facet_row else facet_col_labels, facet_row if facet_row else facet_col ) annotations.append( _annotation_dict( label, num_of_rows - j if facet_row else j + 1, num_of_rows if facet_row else num_of_cols, SUBPLOT_SPACING, 'row' if facet_row else 'col', flipped_rows ) ) elif facet_row and facet_col: groups_by_facets = list(df.groupby([facet_row, facet_col])) tuple_to_facet_group = {item[0]: item[1] for item in groups_by_facets} row_values = df[facet_row].unique() col_values = df[facet_col].unique() for row_count, x_val in enumerate(row_values): for col_count, y_val in enumerate(col_values): try: group = tuple_to_facet_group[(x_val, y_val)] except KeyError: group = pd.DataFrame([[None, None]], columns=[x, y]) trace = dict( type=trace_type, marker=dict( color=marker_color, line=kwargs_marker['line'], ), **kwargs_trace ) if x: trace['x'] = group[x] if y: trace['y'] = group[y] trace = _make_trace_for_scatter( trace, trace_type, marker_color, **kwargs_marker ) fig.append_trace(trace, row_count + 1, col_count + 1) if row_count == 0: label = _return_label(col_values[col_count], facet_col_labels, facet_col) annotations.append( _annotation_dict(label, col_count + 1, num_of_cols, SUBPLOT_SPACING, row_col='col', flipped=flipped_cols) ) label = _return_label(row_values[row_count], facet_row_labels, facet_row) annotations.append( _annotation_dict(label, num_of_rows - row_count, num_of_rows, SUBPLOT_SPACING, row_col='row', flipped=flipped_rows) ) return fig, annotations def create_facet_grid(df, x=None, y=None, facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type='scatter', scales='fixed', dtick_x=None, dtick_y=None, show_boxes=True, ggplot2=False, binsize=1, **kwargs): """ Returns figure for facet grid. :param (pd.DataFrame) df: the dataframe of columns for the facet grid. :param (str) x: the name of the dataframe column for the x axis data. :param (str) y: the name of the dataframe column for the y axis data. :param (str) facet_row: the name of the dataframe column that is used to facet the grid into row panels. :param (str) facet_col: the name of the dataframe column that is used to facet the grid into column panels. :param (str) color_name: the name of your dataframe column that will function as the colormap variable. :param (str|list|dict) colormap: the param that determines how the color_name column colors the data. If the dataframe contains numeric data, then a dictionary of colors will group the data categorically while a Plotly Colorscale name or a custom colorscale will treat it numerically. To learn more about colors and types of colormap, run `help(plotly.colors)`. :param (bool) color_is_cat: determines whether a numerical column for the colormap will be treated as categorical (True) or sequential (False). Default = False. :param (str|dict) facet_row_labels: set to either 'name' or a dictionary of all the unique values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (str|dict) facet_col_labels: set to either 'name' or a dictionary of all the values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (int) height: the height of the facet grid figure. :param (int) width: the width of the facet grid figure. :param (str) trace_type: decides the type of plot to appear in the facet grid. The options are 'scatter', 'scattergl', 'histogram', 'bar', and 'box'. Default = 'scatter'. :param (str) scales: determines if axes have fixed ranges or not. Valid settings are 'fixed' (all axes fixed), 'free_x' (x axis free only), 'free_y' (y axis free only) or 'free' (both axes free). :param (float) dtick_x: determines the distance between each tick on the x-axis. Default is None which means dtick_x is set automatically. :param (float) dtick_y: determines the distance between each tick on the y-axis. Default is None which means dtick_y is set automatically. :param (bool) show_boxes: draws grey boxes behind the facet titles. :param (bool) ggplot2: draws the facet grid in the style of `ggplot2`. See http://ggplot2.tidyverse.org/reference/facet_grid.html for reference. Default = False :param (int) binsize: groups all data into bins of a given length. :param (dict) kwargs: a dictionary of scatterplot arguments. Examples 1: One Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_one_way_facet') ``` Example 2: Two Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_row='drv', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_two_way_facet') ``` Example 3: Categorical Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mtcars, x='mpg', y='wt', facet_col='cyl', color_name='cyl', color_is_cat=True, ) py.iplot(fig, filename='facet_grid_mpg_default_colors') ``` Example 4: Sequential Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv') fig = ff.create_facet_grid( tips, x='total_bill', y='tip', facet_row='sex', facet_col='smoker', color_name='size', colormap='Viridis', ) py.iplot(fig, filename='facet_grid_tips_sequential_colors') ``` Example 5: Custom labels ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', y='mpg', facet_col='cyl', facet_col_labels={4: "$\\alpha$", 6: '$\\beta$', 8: '$\sqrt[y]{x}$'}, ) py.iplot(fig, filename='facet_grid_mtcars_custom_labels') ``` Example 6: Other Trace Type ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', facet_col='cyl', trace_type='histogram', ) py.iplot(fig, filename='facet_grid_mtcars_other_trace_type') ``` """ if not pd: raise exceptions.ImportError( "'pandas' must be installed for this figure_factory." ) if not isinstance(df, pd.DataFrame): raise exceptions.PlotlyError( "You must input a pandas DataFrame." ) # make sure all columns are of homogenous datatype utils.validate_dataframe(df) if trace_type in ['scatter', 'scattergl']: if not x or not y: raise exceptions.PlotlyError( "You need to input 'x' and 'y' if you are you are using a " "trace_type of 'scatter' or 'scattergl'." ) for key in [x, y, facet_row, facet_col, color_name]: if key is not None: try: df[key] except KeyError: raise exceptions.PlotlyError( "x, y, facet_row, facet_col and color_name must be keys " "in your dataframe." ) # autoscale histogram bars if trace_type not in ['scatter', 'scattergl']: scales = 'free' # validate scales if scales not in ['fixed', 'free_x', 'free_y', 'free']: raise exceptions.PlotlyError( "'scales' must be set to 'fixed', 'free_x', 'free_y' and 'free'." ) if trace_type not in VALID_TRACE_TYPES: raise exceptions.PlotlyError( "'trace_type' must be in {}".format(VALID_TRACE_TYPES) ) if trace_type == 'histogram': SUBPLOT_SPACING = 0.06 else: SUBPLOT_SPACING = 0.015 # seperate kwargs for marker and else if 'marker' in kwargs: kwargs_marker = kwargs['marker'] else: kwargs_marker = {} marker_color = kwargs_marker.pop('color', None) kwargs.pop('marker', None) kwargs_trace = kwargs if 'size' not in kwargs_marker: if ggplot2: kwargs_marker['size'] = 5 else: kwargs_marker['size'] = 8 if 'opacity' not in kwargs_marker: if not ggplot2: kwargs_trace['opacity'] = 0.6 if 'line' not in kwargs_marker: if not ggplot2: kwargs_marker['line'] = {'color': 'darkgrey', 'width': 1} else: kwargs_marker['line'] = {} # default marker size if not ggplot2: if not marker_color: marker_color = 'rgb(31, 119, 180)' else: marker_color = 'rgb(0, 0, 0)' num_of_rows = 1 num_of_cols = 1 flipped_rows = False flipped_cols = False if facet_row: num_of_rows = len(df[facet_row].unique()) flipped_rows = _is_flipped(num_of_rows) if isinstance(facet_row_labels, dict): for key in df[facet_row].unique(): if key not in facet_row_labels.keys(): unique_keys = df[facet_row].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys) ) if facet_col: num_of_cols = len(df[facet_col].unique()) flipped_cols = _is_flipped(num_of_cols) if isinstance(facet_col_labels, dict): for key in df[facet_col].unique(): if key not in facet_col_labels.keys(): unique_keys = df[facet_col].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys) ) show_legend = False if color_name: if isinstance(df[color_name].iloc[0], str) or color_is_cat: show_legend = True if isinstance(colormap, dict): utils.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary." ) else: # use default plotly colors for dictionary default_colors = utils.DEFAULT_PLOTLY_COLORS colormap = {} j = 0 for val in df[color_name].unique(): if j >= len(default_colors): j = 0 colormap[val] = default_colors[j] j += 1 fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) elif isinstance(df[color_name].iloc[0], Number): if isinstance(colormap, dict): show_legend = True utils.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary." ) fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) elif isinstance(colormap, list): colorscale_list = colormap utils.validate_colorscale(colorscale_list) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) elif isinstance(colormap, str): if colormap in colors.PLOTLY_SCALES.keys(): colorscale_list = colors.PLOTLY_SCALES[colormap] else: raise exceptions.PlotlyError( "If 'colormap' is a string, it must be the name " "of a Plotly Colorscale. The available colorscale " "names are {}".format(colors.PLOTLY_SCALES.keys()) ) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) else: colorscale_list = colors.PLOTLY_SCALES['Reds'] fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) else: fig, annotations = _facet_grid( df, x, y, facet_row, facet_col, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) if not height: height = max(600, 100 * num_of_rows) if not width: width = max(600, 100 * num_of_cols) fig['layout'].update(height=height, width=width, title='', paper_bgcolor='rgb(251, 251, 251)') if ggplot2: fig['layout'].update(plot_bgcolor=PLOT_BGCOLOR, paper_bgcolor='rgb(255, 255, 255)', hovermode='closest') # axis titles x_title_annot = _axis_title_annotation(x, 'x') y_title_annot = _axis_title_annotation(y, 'y') # annotations annotations.append(x_title_annot) annotations.append(y_title_annot) # legend fig['layout']['showlegend'] = show_legend fig['layout']['legend']['bgcolor'] = LEGEND_COLOR fig['layout']['legend']['borderwidth'] = LEGEND_BORDER_WIDTH fig['layout']['legend']['x'] = 1.05 fig['layout']['legend']['y'] = 1 fig['layout']['legend']['yanchor'] = 'top' if show_legend: fig['layout']['showlegend'] = show_legend if ggplot2: if color_name: legend_annot = _legend_annotation(color_name) annotations.append(legend_annot) fig['layout']['margin']['r'] = 150 # assign annotations to figure fig['layout']['annotations'] = annotations # add shaded boxes behind axis titles if show_boxes and ggplot2: _add_shapes_to_fig(fig, ANNOT_RECT_COLOR, flipped_rows, flipped_cols) # all xaxis and yaxis labels axis_labels = {'x': [], 'y': []} for key in fig['layout']: if 'xaxis' in key: axis_labels['x'].append(key) elif 'yaxis' in key: axis_labels['y'].append(key) string_number_in_data = False for var in [v for v in [x, y] if v]: if isinstance(df[var].tolist()[0], str): for item in df[var]: try: int(item) string_number_in_data = True except ValueError: pass if string_number_in_data: for x_y in axis_labels.keys(): for axis_name in axis_labels[x_y]: fig['layout'][axis_name]['type'] = 'category' if scales == 'fixed': fixed_axes = ['x', 'y'] elif scales == 'free_x': fixed_axes = ['y'] elif scales == 'free_y': fixed_axes = ['x'] elif scales == 'free': fixed_axes = [] # fixed ranges for x_y in fixed_axes: min_ranges = [] max_ranges = [] for trace in fig['data']: if trace[x_y] is not None and len(trace[x_y]) > 0: min_ranges.append(min(trace[x_y])) max_ranges.append(max(trace[x_y])) while None in min_ranges: min_ranges.remove(None) while None in max_ranges: max_ranges.remove(None) min_range = min(min_ranges) max_range = max(max_ranges) range_are_numbers = (isinstance(min_range, Number) and isinstance(max_range, Number)) if range_are_numbers: min_range = math.floor(min_range) max_range = math.ceil(max_range) # extend widen frame by 5% on each side min_range -= 0.05 * (max_range - min_range) max_range += 0.05 * (max_range - min_range) if x_y == 'x': if dtick_x: dtick = dtick_x else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS ) elif x_y == 'y': if dtick_y: dtick = dtick_y else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS ) else: dtick = 1 for axis_title in axis_labels[x_y]: fig['layout'][axis_title]['dtick'] = dtick fig['layout'][axis_title]['ticklen'] = 0 fig['layout'][axis_title]['zeroline'] = False if ggplot2: fig['layout'][axis_title]['tickwidth'] = 1 fig['layout'][axis_title]['ticklen'] = 4 fig['layout'][axis_title]['gridwidth'] = GRID_WIDTH fig['layout'][axis_title]['gridcolor'] = GRID_COLOR fig['layout'][axis_title]['gridwidth'] = 2 fig['layout'][axis_title]['tickfont'] = { 'color': TICK_COLOR, 'size': 10 } # insert ranges into fig if x_y in fixed_axes: for key in fig['layout']: if '{}axis'.format(x_y) in key and range_are_numbers: fig['layout'][key]['range'] = [min_range, max_range] return fig