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⚡️ Speed up function _facet_grid_color_numerical by 14% #117

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⚡️ Speed up function _facet_grid_color_numerical by 14% #117

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260 changes: 130 additions & 130 deletions plotly/figure_factory/_facet_grid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

import math
from numbers import Number
import pandas as pd

pd = optional_imports.get_module("pandas")

Expand Down Expand Up @@ -40,13 +41,12 @@ def _is_flipped(num):


def _return_label(original_label, facet_labels, facet_var):
# Optimized: use get for dicts and avoid type checks when possible
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
return facet_labels.get(original_label, original_label)
if isinstance(facet_labels, str):
return f"{facet_var}: {original_label}"
return original_label


def _legend_annotation(color_name):
Expand All @@ -68,45 +68,47 @@ def _legend_annotation(color_name):
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)
l = (1 - (num_of_lanes - 1) * SUBPLOT_SPACING) / num_of_lanes
# Minimize branching, inline fast paths
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
annotation = {
"textangle": 0,
"xanchor": "center", "yanchor": "middle",
"x": (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l,
"y": 1.03
}
else: # "row"
annotation = {
"textangle": 90,
"xanchor": "center", "yanchor": "middle",
"x": 1.03,
"y": (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l
}
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,
annotation = {
"textangle": 270,
"xanchor": "center", "yanchor": "bottom",
"x": (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l,
"y": 1.0
}
else: # "row"
annotation = {
"textangle": 0,
"xanchor": "left", "yanchor": "middle",
"x": 1.0,
"y": (lane - 1) * (l + SUBPLOT_SPACING) + 0.5 * l
}
# Only one dict build
annotation.update(dict(
showarrow=False,
xref="paper",
yref="paper",
text=str(text),
font=dict(size=13, color=AXIS_TITLE_COLOR),
)
return annotation_dict
))
return annotation


def _axis_title_annotation(text, x_or_y_axis):
Expand Down Expand Up @@ -174,9 +176,14 @@ def _add_shapes_to_fig(fig, annot_rect_color, flipped_rows=False, flipped_cols=F


def _make_trace_for_scatter(trace, trace_type, color, **kwargs_marker):
if trace_type in ["scatter", "scattergl"]:
# Fast path: avoid list allocation, avoid redundant code
if trace_type == "scatter" or trace_type == "scattergl":
trace["mode"] = "markers"
trace["marker"] = dict(color=color, **kwargs_marker)
# Direct dict construction is fastest
if kwargs_marker:
trace["marker"] = {**{"color": color}, **kwargs_marker}
else:
trace["marker"] = {"color": color}
return trace


Expand Down Expand Up @@ -372,137 +379,130 @@ def _facet_grid_color_numerical(
):

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,
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 = []

df_color_values = df[color_name].values # cache for fast lookups

# Case 1: Not faceted
if not facet_row and not facet_col:
trace = dict(
type=trace_type,
marker=dict(color=df[color_name], colorscale=colormap, showscale=True),
marker=dict(color=df_color_values, colorscale=colormap, showscale=True),
**kwargs_trace,
)
if x:
trace["x"] = df[x]
trace["x"] = df[x].values
if y:
trace["y"] = df[y]
trace = _make_trace_for_scatter(
trace, trace_type, df[color_name], **kwargs_marker
)

trace["y"] = df[y].values
trace = _make_trace_for_scatter(trace, trace_type, df_color_values, **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):
# Case 2: single facet (row or col)
elif (facet_row and not facet_col) or (not facet_row and facet_col):
group_field = facet_row if facet_row else facet_col
is_row_facet = facet_row is not None
# groupby is a generator, convert once
groups_by_facet = list(df.groupby(group_field, sort=False))
colorbar_dict = dict(x=1.15) # create once
color_key = df_color_values

for j, (facet_value, group) in enumerate(groups_by_facet):
marker = dict(
color=color_key,
colorscale=colormap,
showscale=True,
colorbar=colorbar_dict,
)
trace = dict(
type=trace_type,
marker=dict(
color=df[color_name],
colorscale=colormap,
showscale=True,
colorbar=dict(x=1.15),
),
marker=marker,
**kwargs_trace,
)
if x:
trace["x"] = group[1][x]
trace["x"] = group[x].values
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,
)
trace["y"] = group[y].values
trace = _make_trace_for_scatter(trace, trace_type, color_key, **kwargs_marker)

row_idx = j + 1 if is_row_facet else 1
col_idx = 1 if is_row_facet else j + 1
fig.append_trace(trace, row_idx, col_idx)

labels = facet_row_labels if is_row_facet else facet_col_labels
label = _return_label(facet_value, labels, group_field)
annotation = _annotation_dict(
label,
num_of_rows - j if is_row_facet else j + 1,
num_of_rows if is_row_facet else num_of_cols,
SUBPLOT_SPACING,
"row" if is_row_facet else "col",
flipped=flipped_rows,
)
annotations.append(annotation)

elif facet_row and facet_col:
groups_by_facets = list(df.groupby([facet_row, facet_col]))
# Case 3: double facet (row and col)
else:
groups_by_facets = list(df.groupby([facet_row, facet_col], sort=False))
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()

# Prepare empty DataFrame for missing facets just once
empty_df = pd.DataFrame([[None, None, None]], columns=[x, y, color_name])
colorbar_dict = dict(x=1.15)

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]
group = tuple_to_facet_group.get((x_val, y_val), empty_df)
# Avoid .tolist() and values comparisons, optimize empty check
is_group_nonempty = not (group.shape[0] == 1 and group.isnull().all(axis=None))
if is_group_nonempty:
marker = dict(
color=df_color_values,
colorscale=colormap,
showscale=(row_count == 0),
colorbar=colorbar_dict,
)

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),
),
marker=marker,
**kwargs_trace,
)

else:
trace = dict(type=trace_type, showlegend=False, **kwargs_trace)

if x:
trace["x"] = group[x]
trace["x"] = group[x].values
if y:
trace["y"] = group[y]
trace = _make_trace_for_scatter(
trace, trace_type, df[color_name], **kwargs_marker
)

trace["y"] = group[y].values
trace = _make_trace_for_scatter(trace, trace_type, df_color_values, **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(col_values[col_count], facet_col_labels, facet_col)
annotation = _annotation_dict(
label,
col_count + 1,
num_of_cols,
SUBPLOT_SPACING,
row_col="col",
flipped=flipped_cols,
)
annotations.append(annotation)

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,
)
annotation = _annotation_dict(
row_values[row_count],
num_of_rows - row_count,
num_of_rows,
SUBPLOT_SPACING,
row_col="row",
flipped=flipped_rows,
)
annotations.append(annotation)

return fig, annotations

Expand Down