from collections import defaultdict
from pathlib import Path
import numpy as np
import plotly
import plotly.graph_objects as go
from quacc.plot.base import BasePlot
class PlotCfg:
def __init__(self, mode, lwidth, font=None, legend=None, template="seaborn"):
self.mode = mode
self.lwidth = lwidth
self.legend = {} if legend is None else legend
self.font = {} if font is None else font
self.template = template
web_cfg = PlotCfg("lines+markers", 2)
png_cfg = PlotCfg(
"lines",
5,
legend=dict(
orientation="h",
yanchor="bottom",
xanchor="right",
y=1.02,
x=1,
font=dict(size=24),
),
font=dict(size=24),
# template="ggplot2",
)
_cfg = png_cfg
class PlotlyPlot(BasePlot):
__themes = defaultdict(
lambda: {
"template": _cfg.template,
}
)
__themes = __themes | {
"dark": {
"template": "plotly_dark",
},
}
def __init__(self, theme=None):
self.theme = PlotlyPlot.__themes[theme]
def hex_to_rgb(self, hex: str, t: float | None = None):
hex = hex.lstrip("#")
rgb = [int(hex[i : i + 2], 16) for i in [0, 2, 4]]
if t is not None:
rgb.append(t)
return f"{'rgb' if t is None else 'rgba'}{str(tuple(rgb))}"
def get_colors(self, num):
match num:
case v if v > 10:
__colors = plotly.colors.qualitative.Light24
case _:
__colors = plotly.colors.qualitative.G10
def __generator(cs):
while True:
for c in cs:
yield c
return __generator(__colors)
def update_layout(self, fig, title, x_label, y_label):
fig.update_layout(
# title=title,
xaxis_title=x_label,
yaxis_title=y_label,
template=self.theme["template"],
font=_cfg.font,
legend=_cfg.legend,
)
def save_fig(self, fig, base_path, title) -> Path:
return None
def plot_delta(
self,
base_prevs,
columns,
data,
*,
stdevs=None,
pos_class=1,
title="default",
x_label="prevs.",
y_label="error",
legend=True,
) -> go.Figure:
fig = go.Figure()
if isinstance(base_prevs[0], float):
base_prevs = np.around([(1 - bp, bp) for bp in base_prevs], decimals=4)
x = [str(tuple(bp)) for bp in base_prevs]
line_colors = self.get_colors(len(columns))
for name, delta in zip(columns, data):
color = next(line_colors)
_line = [
go.Scatter(
x=x,
y=delta,
mode=_cfg.mode,
name=name,
line=dict(color=self.hex_to_rgb(color), width=_cfg.lwidth),
hovertemplate="prev.: %{x}<br>error: %{y:,.4f}",
)
]
_error = []
if stdevs is not None:
_col_idx = np.where(columns == name)[0]
stdev = stdevs[_col_idx].flatten()
_error = [
go.Scatter(
x=np.concatenate([x, x[::-1]]),
y=np.concatenate([delta - stdev, (delta + stdev)[::-1]]),
name=int(_col_idx[0]),
fill="toself",
fillcolor=self.hex_to_rgb(color, t=0.2),
line=dict(color="rgba(255, 255, 255, 0)"),
hoverinfo="skip",
showlegend=False,
)
]
fig.add_traces(_line + _error)
self.update_layout(fig, title, x_label, y_label)
return fig
def plot_diagonal(
self,
reference,
columns,
data,
*,
pos_class=1,
title="default",
x_label="true",
y_label="estim.",
legend=True,
) -> go.Figure:
fig = go.Figure()
x = reference
line_colors = self.get_colors(len(columns))
_edges = (np.min([np.min(x), np.min(data)]), np.max([np.max(x), np.max(data)]))
_lims = np.array([[_edges[0], _edges[1]], [_edges[0], _edges[1]]])
for name, val in zip(columns, data):
color = next(line_colors)
slope, interc = np.polyfit(x, val, 1)
y_lr = np.array([slope * _x + interc for _x in _lims[0]])
fig.add_traces(
[
go.Scatter(
x=x,
y=val,
customdata=np.stack((val - x,), axis=-1),
mode="markers",
name=name,
line=dict(color=self.hex_to_rgb(color, t=0.5)),
hovertemplate="true acc: %{x:,.4f}<br>estim. acc: %{y:,.4f}<br>acc err.: %{customdata[0]:,.4f}",
),
go.Scatter(
x=_lims[0],
y=y_lr,
mode="lines",
name=name,
line=dict(color=self.hex_to_rgb(color), width=3),
showlegend=False,
),
]
)
fig.add_trace(
go.Scatter(
x=_lims[0],
y=_lims[1],
mode="lines",
name="reference",
showlegend=False,
line=dict(color=self.hex_to_rgb("#000000"), dash="dash"),
)
)
self.update_layout(fig, title, x_label, y_label)
fig.update_layout(yaxis_scaleanchor="x", yaxis_scaleratio=1.0)
return fig
def plot_shift(
self,
shift_prevs,
columns,
data,
*,
counts=None,
pos_class=1,
title="default",
x_label="true",
y_label="estim.",
legend=True,
) -> go.Figure:
fig = go.Figure()
# x = shift_prevs[:, pos_class]
x = shift_prevs
line_colors = self.get_colors(len(columns))
for name, delta in zip(columns, data):
col_idx = (columns == name).nonzero()[0][0]
color = next(line_colors)
fig.add_trace(
go.Scatter(
x=x,
y=delta,
customdata=np.stack((counts[col_idx],), axis=-1),
mode=_cfg.mode,
name=name,
line=dict(color=self.hex_to_rgb(color), width=_cfg.lwidth),
hovertemplate="shift: %{x}<br>error: %{y}"
+ "<br>count: %{customdata[0]}"
if counts is not None
else "",
)
)
self.update_layout(fig, title, x_label, y_label)
return fig
def plot_fit_scores(
self,
train_prevs,
scores,
*,
pos_class=1,
title="default",
x_label="prev.",
y_label="position",
legend=True,
) -> go.Figure:
fig = go.Figure()
# x = train_prevs
x = [str(tuple(bp)) for bp in train_prevs]
fig.add_trace(
go.Scatter(
x=x,
y=scores,
mode="lines+markers",
showlegend=False,
),
)
self.update_layout(fig, title, x_label, y_label)
return fig