from typing import Optional
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import seaborn as sns
from . import stats
[docs]
def plot_bar(
df: pd.DataFrame,
ddg_cols: str,
error_cols: str,
exp_col: str = "exp",
exp_error_col: str = "dexp",
name_col: str = "edge",
title: str = "",
filename: Optional[str] = None,
):
"""
Creates a plotly barplot. It takes a pandas.Dataframe df as input and plots
horizontal bars grouping the values in the rows together. The columns which
will be used are specified by ddg_cols (DDG values),
error_cols (corresponding errors), exp_col (column with exp. values),
exp_error_col (column with exp. errors) and name_col (column which will be
used as y axis tick labels).
"""
# create color palette
colors = sns.color_palette(palette="bright")
num_edges = df.shape[0]
num_bars_per_edge = len(ddg_cols)
height = 20 * (num_bars_per_edge + 0.3) * num_edges
exp_size = height / num_edges / 2.0
alim = (
np.max(
np.fabs(df.loc[:, ddg_cols + [exp_col]].values)
+ np.fabs(df.loc[:, error_cols + [exp_error_col]].values)
)
* 1.05
)
fig = go.Figure()
# add data
for i, (col, ecol) in enumerate(zip(ddg_cols, error_cols)):
fig.add_trace(
go.Bar(
x=df.loc[:, col].values,
y=df[name_col].values,
error_x=dict(
type="data", # value of error bar given in data coordinates
array=df.loc[:, ecol].values,
visible=True,
),
name=col,
marker=dict(color=f"rgba{colors[i]}", line=None),
orientation="h",
)
)
if exp_col is not None:
fig.add_trace(
go.Scatter(
x=df.loc[:, exp_col].values,
y=df[name_col].values,
name="experiment",
mode="markers",
marker=dict(
symbol="line-ns",
color="black",
size=exp_size,
line_width=4,
),
)
)
fig.add_trace(
go.Scatter(
x=df.loc[:, exp_col].values - df.loc[:, exp_error_col].values,
y=df[name_col].values,
name="ExpErrors1",
mode="markers",
marker=dict(
symbol="line-ns",
color="black",
size=exp_size,
line_width=2,
),
showlegend=False,
)
)
fig.add_trace(
go.Scatter(
x=df.loc[:, exp_col].values + df.loc[:, exp_error_col].values,
y=df[name_col].values,
name="ExpErrors2",
mode="markers",
marker=dict(
symbol="line-ns",
color="black",
size=exp_size,
line_width=2,
),
showlegend=False,
)
)
fig.update_layout(
title=title,
xaxis=dict(
title=r"$\Delta\Delta G\, \mathrm{[kcal\,mol^{-1}]}$",
titlefont_size=16,
tickfont_size=14,
range=(-alim, alim),
),
yaxis=dict(
title="Edge",
titlefont_size=16,
tickfont_size=14,
range=(-0.5, num_edges - 0.5),
),
width=800,
height=height,
legend=dict(
x=1.0,
y=1.0,
bgcolor="rgba(255, 255, 255, 0)",
bordercolor="rgba(255, 255, 255, 0)",
font_size=16,
),
barmode="group",
bargap=0.3, # gap between bars of adjacent location coordinates.
bargroupgap=0.0, # gap between bars of the same location coordinate.
)
if filename is None:
fig.show()
elif filename.find(".html"):
fig.write_html(filename)
else:
fig.write_image(filename)
def _master_plot(
x: np.ndarray,
y: np.ndarray,
title: str = "",
xerr: Optional[np.ndarray] = None,
yerr: Optional[np.ndarray] = None,
method_name: str = "",
target_name: str = "",
plot_type: str = "",
guidelines: bool = True,
origins: bool = True,
statistics: list = ["RMSE", "MUE"],
filename: Optional[str] = None,
bootstrap_x_uncertainty: bool = False,
bootstrap_y_uncertainty: bool = False,
statistic_type: str = "mle",
):
nsamples = len(x)
ax_min = min(min(x), min(y)) - 0.5
ax_max = max(max(x), max(y)) + 0.5
fig = go.Figure()
# x = 0 and y = 0 axes through origin
if origins:
# x=0
fig.add_trace(
go.Scatter(
x=[0, 0],
y=[ax_min, ax_max],
line_color="black",
mode="lines",
showlegend=False,
)
)
# y =0
fig.add_trace(
go.Scatter(
x=[ax_min, ax_max],
y=[0, 0],
line_color="black",
mode="lines",
showlegend=False,
)
)
if guidelines:
small_dist = 0.5
fig.add_trace(
go.Scatter(
x=[ax_min, ax_max, ax_max, ax_min],
y=[
ax_min + 2.0 * small_dist,
ax_max + 2.0 * small_dist,
ax_max - 2.0 * small_dist,
ax_min - 2.0 * small_dist,
],
name="1 kcal/mol margin",
hoveron="points+fills",
hoverinfo="name",
fill="toself",
mode="lines",
line_width=0,
fillcolor="rgba(0, 0, 0, 0.2)",
showlegend=False,
)
)
fig.add_trace(
go.Scatter(
x=[ax_min, ax_max, ax_max, ax_min],
y=[
ax_min + small_dist,
ax_max + small_dist,
ax_max - small_dist,
ax_min - small_dist,
],
name=".5 kcal/mol margin",
hoveron="points+fills",
hoverinfo="name",
fill="toself",
mode="lines",
line_width=0,
fillcolor="rgba(0, 0, 0, 0.2)",
showlegend=False,
)
)
# diagonal
fig.add_trace(
go.Scatter(
x=[ax_min, ax_max],
y=[ax_min, ax_max],
line_color="black",
mode="lines",
showlegend=False,
)
)
# 2.372 kcal / mol = 4 RT
clr = np.abs(x - y) / 2.372
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode="markers",
name=f"{target_name},{method_name}",
marker=dict(symbol="circle", color=clr, colorscale="BlueRed"),
error_x=dict(
type="data", # value of error bar given in data coordinates
array=xerr,
visible=True,
),
error_y=dict(
type="data", # value of error bar given in data coordinates
array=yerr,
visible=True,
),
showlegend=False,
)
)
# stats and title
string = []
if statistic_type not in ['mle', 'mean']:
raise ValueError(f"Unknown statistic type {statistic_type}")
for statistic in statistics:
bss = stats.bootstrap_statistic(x,
y,
xerr,
yerr,
statistic=statistic,
include_true_uncertainty=bootstrap_x_uncertainty,
include_pred_uncertainty=bootstrap_y_uncertainty)
string.append(
f"{statistic + ':':5s}{bss[statistic_type]:5.2f} [95%: {bss['low']:5.2f}, {bss['high']:5.2f}]"
)
stats_string = "<br>".join(string)
long_title = f"{title}<br>{target_name} (N = {nsamples})<br>{stats_string}"
# figure layout
fig.update_layout(
title=dict(
text=long_title,
font_family="monospace",
x=0.0,
y=0.95,
font_size=14,
),
xaxis=dict(
title=f"Experimental {plot_type} [kcal mol<sup>-1</sup>]",
titlefont_size=14,
tickfont_size=12,
range=(ax_min, ax_max),
),
yaxis=dict(
title=f"Calculated {plot_type} {method_name} [kcal mol<sup>-1</sup>]",
titlefont_size=14,
tickfont_size=12,
range=(ax_min, ax_max),
),
width=400,
height=400
# legend=dict(
# x=1.0,
# y=1.0,
# bgcolor='rgba(255, 255, 255, 0)',
# bordercolor='rgba(255, 255, 255, 0)',
# font_size=12
# )
)
if filename is None:
fig.show()
elif filename.find(".html") > 0:
fig.write_html(filename)
else:
fig.write_image(filename)