I am trying to write this matplot function and plot a graph using plotly instead of matplot but i am unable to do so. Here is the function for the plot:
def plot_one_signal_sequentially(
mres_all,
y_sort,
case_no,
avg_window_size=10,
dpi=150,
opacity_color=0.10,
opacity_grid=0.10,
m_avg=True,
caption="MRE",
y_label="MRE",
legend_label="MRE",
save_name="save.png",
):
"""
Plot a trend of a signal.
"""
# plot parameters
colors = ["#33a02c", "#fdbf6f", "#e31a1c"] # green, orange, red
failed_reg = ["Healthy", "Degraded", "Failed"]
pad_size = 0
x_min = -pad_size
# create pallette for color of trend lines
pal = px.colors.sequential.Cubehelix(6, rot=-0.25, gamma=0.7)
tool_class_change_index = np.where(y_sort[:-1] != y_sort[1:])[0]
tool_class_change_index = tool_class_change_index - avg_window_size
tool_class_change_index = np.concatenate(
([0], tool_class_change_index, [np.shape(y_sort)[0] - avg_window_size + 1])
)
indexer = (
np.arange(2)[None, :]
+ np.arange(np.shape(tool_class_change_index)[0] - 1)[:, None]
)
# establish shaded region
shade_reg = tool_class_change_index[indexer]
x_max = len(y_sort) - avg_window_size + pad_size
# define colour palette and seaborn style for plot
sns.set(style="white", context="notebook")
fig, axes = plt.subplots(
1, 1, dpi=150, figsize=(7, 2.5), sharex=True, constrained_layout=True,
)
x = moving_average(mres_all, n=1)
x2 = moving_average(mres_all, n=avg_window_size)
y_avg = np.array([i for i in range(len(x2))]) + avg_window_size
axes.plot(y_avg, x2, linewidth=1.5, alpha=1, color=pal[5], label="Rolling average")
axes.plot(x, linewidth=0.5, alpha=0.5, color="grey", label=legend_label)
y_min = np.min(x)
y_max = np.max(x)
y_pad = (np.abs(y_min) + np.abs(y_max)) * 0.02
axes.spines["top"].set_visible(False)
axes.spines["right"].set_visible(False)
axes.spines["left"].set_visible(False)
axes.spines["bottom"].set_visible(True)
axes.grid(alpha=opacity_grid, linewidth=0.5)
axes.tick_params(axis="x", labelsize=7.5)
axes.tick_params(axis="y", labelsize=7.5)
axes.spines["bottom"].set_linewidth(0.5)
# colors
axes.set_ylim(y_min - y_pad, y_max + y_pad)
axes.set_xlim(x_min, x_max)
axes.tick_params(axis="y", labelsize=7.5)
axes.spines["bottom"].set_linewidth(0.5)
for region in range(len(shade_reg)):
f = failed_reg[region % 3]
c = colors[region % 3]
axes.fill_between(
x=shade_reg[region],
y1=y_min - y_pad,
y2=y_max + y_pad,
color=c,
alpha=opacity_color,
linewidth=0,
zorder=1,
)
# for text
axes.text(
x=(
shade_reg[region][0] + (shade_reg[region][1] - shade_reg[region][0]) / 2
),
y=y_max + y_pad - y_max * 0.1,
s=f,
horizontalalignment="center",
verticalalignment="center",
size=8.5,
color=c,
rotation="horizontal",
weight="semibold",
alpha=1,
)
# axis label
axes.set_xlabel("Sample Number", fontsize=7.5)
axes.set_ylabel(y_label, fontsize=7.5)
fig.suptitle(caption, fontsize=8.5)
plt.legend(
loc="center left", bbox_to_anchor=(0.02, 0.6), fontsize=7.5,
)
plt.show()
case = 13
caption = "Input Space Mean Reconstruction Error Trend for Case Number {} ".format(case)
legend_label = "MRE"
X_sort, y_sort = sorted_x(X, dfy, case)
mre_all, mre_avg = build_mre_scores(beta_vae_model, X_sort)
plot_one_signal_sequentially(
mre_avg,
y_sort,
case,
avg_window_size=10,
opacity_color=0.10,
opacity_grid=0.10,
m_avg=True,
caption=caption,
y_label="Mean Reconstruction Error",
legend_label=legend_label,
)
