Removed plot_collar_voltage function from ecoscope.plotting module

ecoscope/plotting/plot.py

@@ -286,155 +286,6 @@ def speed(trajectory):
     return fig
 
 
-def plot_collar_voltage(
-    relocations,
-    start_time,
-    extract_fn=extract_voltage,
-    output_folder=None,
-    layout_kwargs=None,
-    hline_kwargs=None,
-):
-    # @TODO Complete black-box re-write
-    assigned_range = (
-        relocations["extra__subjectsource__assigned_range"]
-        .apply(pd.Series)
-        .add_prefix("extra.extra.subjectsource__assigned_range.")
-    )
-    relocations = relocations.merge(assigned_range, right_index=True, left_index=True)
-
-    groups = relocations.groupby(by=["extra__subject__id", "extra__subjectsource__id"])
-
-    for group, dataframe in groups:
-        subject_name = relocations.loc[relocations["extra__subject__id"] == group[0]]["extra__subject__name"].unique()[
-            0
-        ]
-
-        dataframe["extra__subjectsource__assigned_range__upper"] = pd.to_datetime(
-            dataframe["extra__subjectsource__assigned_range"].str["upper"],
-            errors="coerce",
-        )
-        subjectsource_upperbound = dataframe["extra__subjectsource__assigned_range__upper"].unique()
-
-        is_source_active = subjectsource_upperbound >= start_time or pd.isna(subjectsource_upperbound)[0]
-        if is_source_active:
-            logger.info(subject_name)
-
-            dataframe = dataframe.sort_values(by=["fixtime"])
-            dataframe["voltage"] = np.array(dataframe.apply(extract_fn, axis=1), dtype=np.float64)
-
-            time = dataframe[dataframe.fixtime >= start_time].fixtime.tolist()
-            voltage = dataframe[dataframe.fixtime >= start_time].voltage.tolist()
-
-            # Calculate the historic voltage
-            hist_voltage = dataframe[dataframe.fixtime <= start_time].voltage.tolist()
-            if hist_voltage:
-                volt_upper, volt_lower = np.nanpercentile(hist_voltage, [97.5, 2.5])
-                hist_voltage_mean = np.nanmean(hist_voltage)
-            else:
-                volt_upper, volt_lower = np.nan, np.nan
-                hist_voltage_mean = None
-            volt_diff = volt_upper - volt_lower
-            volt_upper = np.full((len(time)), volt_upper, dtype=np.float32)
-            volt_lower = np.full((len(time)), volt_lower, dtype=np.float32)
-
-            if np.all(volt_diff == 0):
-                # jitter = np.random.random_sample((len(volt_upper,)))
-                volt_upper = volt_upper + 0.025 * max(volt_upper)
-                volt_lower = volt_lower - 0.025 * max(volt_lower)
-
-            if not any(hist_voltage or voltage):
-                continue
-
-            try:
-                lower_y = min(np.nanmin(np.array(hist_voltage)), np.nanmin(np.array(voltage)))
-                upper_y = max(np.nanmax(np.array(hist_voltage)), np.nanmax(np.array(voltage)))
-            except ValueError:
-                lower_y = min(hist_voltage or voltage)
-                upper_y = max(hist_voltage or voltage)
-            finally:
-                lower_y = lower_y - 0.1 * lower_y
-                upper_y = upper_y + 0.1 * upper_y
-
-            if not len(voltage):
-                continue
-
-            if not layout_kwargs:
-                layout = go.Layout(
-                    xaxis={"title": "Time"},
-                    yaxis={"title": "Collar Voltage"},
-                    margin={"l": 40, "b": 40, "t": 50, "r": 50},
-                    hovermode="closest",
-                )
-            else:
-                layout = go.Layout(**layout_kwargs)
-
-            # Add the current voltage
-            trace = go.Scatter(
-                x=time,
-                y=voltage,
-                fill=None,
-                showlegend=True,
-                mode="lines",
-                line={
-                    "width": 1,
-                    "shape": "spline",
-                },
-                line_color="rgb(0,0,246)",
-                marker={
-                    "colorscale": "Viridis",
-                    "color": voltage,
-                    "colorbar": dict(title="Colorbar"),
-                    "cmax": np.max(voltage),
-                    "cmin": np.min(voltage),
-                },
-                name=subject_name,
-            )
-
-            # Add the historical lower HPD value
-            trace_lower = go.Scatter(
-                x=time,
-                y=volt_lower,
-                fill=None,
-                line_color="rgba(255,255,255,0)",
-                mode="lines",
-                showlegend=False,
-            )
-
-            # Add the historical max HPD value
-            trace_upper = go.Scatter(
-                x=time,
-                y=volt_upper,
-                fill="tonexty",  # fill area between trace0 and trace1
-                mode="lines",
-                fillcolor="rgba(0,176,246,0.2)",
-                line_color="rgba(255,255,255,0)",
-                showlegend=True,
-                name="Historic 2.5% - 97.5%",
-            )
-
-            fig = go.Figure(layout=layout)
-            fig.add_trace(trace_lower)
-            fig.add_trace(trace_upper)
-            fig.add_trace(trace)
-            if hist_voltage_mean:
-                if not hline_kwargs:
-                    fig.add_hline(
-                        y=hist_voltage_mean,
-                        line_dash="dot",
-                        line_width=1.5,
-                        line_color="Red",
-                        annotation_text="Historic Mean",
-                        annotation_position="right",
-                    )
-                else:
-                    fig.add_hline(**hline_kwargs)
-            fig.update_layout(yaxis=dict(range=[lower_y, upper_y]))
-            if output_folder:
-                fig.write_image(os.path.join(f"{output_folder}/_{group}_{str(uuid.uuid4())[:4]}.png"))
-            else:
-                fig.show()
-
-
 def plot_seasonal_dist(ndvi_vals, cuts, bandwidth=0.05, output_file=None):
     x = ndvi_vals.sort_values().to_numpy().reshape(-1, 1)
     kde = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(x)