from plotly import exceptions, optional_imports import plotly.colors as clrs from plotly.figure_factory import utils from plotly.subplots 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; **this function is deprecated**, since plotly.express functions should be used instead, for example >>> import plotly.express as px >>> tips = px.data.tips() >>> fig = px.scatter(tips, ... x='total_bill', ... y='tip', ... facet_row='sex', ... facet_col='smoker', ... color='size') :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.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', ... ) >>> fig.show() Example 2: Two Way Faceting >>> 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', ... ) >>> fig.show() Example 3: Categorical Coloring >>> import plotly.figure_factory as ff >>> import pandas as pd >>> mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') >>> mtcars.cyl = mtcars.cyl.astype(str) >>> fig = ff.create_facet_grid( ... mtcars, ... x='mpg', ... y='wt', ... facet_col='cyl', ... color_name='cyl', ... color_is_cat=True, ... ) >>> fig.show() """ if not pd: raise 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): clrs.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 = clrs.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 clrs.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 clrs.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 clrs.PLOTLY_SCALES.keys(): colorscale_list = clrs.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(clrs.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 = clrs.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