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Merge pull request #9 from sede-open/bugfix_polar_scatter_plot
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Bugfix polar scatter plot
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@mattj89
mattj89 authored May 31, 2024
2 parents 20e6cd1 + 388fd6d commit 318a268
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Showing 2 changed files with 121 additions and 41 deletions.
2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ build-backend = "poetry.core.masonry.api"

[tool.poetry]
name = "pyelq-sdk"
version = "1.0.5"
version = "1.0.6"
description = "Package for detection, localization and quantification code."
authors = ["Bas van de Kerkhof", "Matthew Jones", "David Randell"]
homepage = "https://sede-open.github.io/pyELQ/"
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160 changes: 120 additions & 40 deletions src/pyelq/meteorology.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,7 +124,7 @@ def plot_polar_hist(self, nof_sectors: int = 16, nof_divisions: int = 5, templat
Args:
nof_sectors (int, optional): The number of wind direction sectors into which the data is binned.
nof_divisions (int, optional): The number of wind speed divisions into which the data is binned.
template (go.update_layout): A layout template which can be applied to the plot. Defaults to None.
template (object): A layout template which can be applied to the plot. Defaults to None.
Returns:
fig (go.Figure): A plotly go figure containing the trace of the rose plot.
Expand Down Expand Up @@ -195,13 +195,19 @@ def plot_polar_hist(self, nof_sectors: int = 16, nof_divisions: int = 5, templat

return fig

def plot_polar_scatter(self, fig, sensor_object: SensorGroup, template: object = None) -> go.Figure():
def plot_polar_scatter(self, fig: go.Figure, sensor_object: SensorGroup, template: object = None) -> go.Figure():
"""Plots a scatter plot of concentration with respect to wind direction in polar Coordinates.
This function implements the polar scatter functionality for a (single) Meteorology object. Assuming the all
Sensors in the SensorGroup are consistent with the Meteorology object.
Note we do plot the sensors which do not contain any values when present in the SensorGroup to keep consistency
in plot colors.
Args:
fig (go.Figure): A plotly figure onto which traces can be drawn.
sensor_object (SensorGroup): SensorGroup object which contains the concentration information
template (go.update_layout): A layout template which can be applied to the plot. Defaults to None.
template (object): A layout template which can be applied to the plot. Defaults to None.
Returns:
fig (go.Figure): A plotly go figure containing the trace of the rose plot.
Expand All @@ -218,53 +224,21 @@ def plot_polar_scatter(self, fig, sensor_object: SensorGroup, template: object =
)
else:
theta = self.wind_direction
color_idx = i % len(sensor_object.color_map)

fig.add_trace(
go.Scatterpolar(
r=sensor.concentration,
theta=theta,
mode="markers",
name=sensor_key,
marker={"color": sensor_object.color_map[i]},
marker={"color": sensor_object.color_map[color_idx]},
)
)
if sensor.concentration.size > 0:
max_concentration = np.maximum(np.nanmax(sensor.concentration), max_concentration)

max_concentration = np.maximum(np.nanmax(sensor.concentration), max_concentration)

ticktext = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
polar_dict = {
"radialaxis": {"tickangle": 0, "range": [0.0, 1.01 * max_concentration]},
"radialaxis_angle": 0,
"angularaxis": {
"tickmode": "array",
"ticktext": ticktext,
"direction": "clockwise",
"rotation": 90,
"tickvals": list(np.linspace(0, 360 - (360 / 8), 8)),
},
}

fig.add_annotation(
x=1,
y=1,
yref="paper",
xref="paper",
xanchor="right",
yanchor="top",
align="left",
font={"size": 18, "color": "#000000"},
showarrow=False,
borderwidth=2,
borderpad=10,
bgcolor="#ffffff",
bordercolor="#000000",
opacity=0.8,
text="<b>Radial Axis:</b> Wind<br>speed in m/s.",
)

fig.update_layout(polar=polar_dict)
fig.update_layout(template=template)
fig.update_layout(title="Measured Concentration against Wind Direction.")
fig = set_plot_polar_scatter_layout(max_concentration=max_concentration, fig=fig, template=template)

return fig

Expand Down Expand Up @@ -301,3 +275,109 @@ def calculate_wind_speed_from_uv(self):
"""Calculate wind speed from the u and v components for each member of the group."""
for met in self.values():
met.calculate_wind_speed_from_uv()

def plot_polar_scatter(self, fig: go.Figure, sensor_object: SensorGroup, template: object = None) -> go.Figure():
"""Plots a scatter plot of concentration with respect to wind direction in polar coordinates.
This function implements the polar scatter functionality for a MeteorologyGroup object. It assumes each object
in the SensorGroup has an associated Meteorology object in the MeteorologyGroup.
Note we do plot the sensors which do not contain any values when present in the SensorGroup to keep consistency
in plot colors.
Args:
fig (go.Figure): A plotly figure onto which traces can be drawn.
sensor_object (SensorGroup): SensorGroup object which contains the concentration information
template (object): A layout template which can be applied to the plot. Defaults to None.
Returns:
fig (go.Figure): A plotly go figure containing the trace of the rose plot.
Raises
ValueError: When there is a sensor key which is not present in the MeteorologyGroup.
"""
max_concentration = 0

for i, (sensor_key, sensor) in enumerate(sensor_object.items()):
if sensor_key not in self.keys():
raise ValueError(f"Key {sensor_key} not found in MeteorologyGroup.")
temp_met_object = self[sensor_key]
if sensor.concentration.shape != temp_met_object.wind_direction.shape:
warnings.warn(
f"Concentration values for sensor {sensor_key} are of shape "
+ f"{sensor.concentration.shape}, but wind_direction values for meteorology object {sensor_key} "
f"has shape {temp_met_object.wind_direction.shape}. It will not be plotted on the polar scatter "
f"plot."
)
else:
theta = temp_met_object.wind_direction
color_idx = i % len(sensor_object.color_map)

fig.add_trace(
go.Scatterpolar(
r=sensor.concentration,
theta=theta,
mode="markers",
name=sensor_key,
marker={"color": sensor_object.color_map[color_idx]},
)
)

if sensor.concentration.size > 0:
max_concentration = np.maximum(np.nanmax(sensor.concentration), max_concentration)

fig = set_plot_polar_scatter_layout(max_concentration=max_concentration, fig=fig, template=template)

return fig


def set_plot_polar_scatter_layout(max_concentration: float, fig: go.Figure(), template: object) -> go.Figure:
"""Helper function to set the layout of the polar scatter plot.
Helps avoid code duplication.
Args:
max_concentration (float): The maximum concentration value used to update radial axis range.
fig (go.Figure): A plotly figure onto which traces can be drawn.
template (object): A layout template which can be applied to the plot.
Returns:
fig (go.Figure): A plotly go figure containing the trace of the rose plot.
"""
ticktext = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
polar_dict = {
"radialaxis": {"tickangle": 0, "range": [0.0, 1.01 * max_concentration]},
"radialaxis_angle": 0,
"angularaxis": {
"tickmode": "array",
"ticktext": ticktext,
"direction": "clockwise",
"rotation": 90,
"tickvals": list(np.linspace(0, 360 - (360 / 8), 8)),
},
}

fig.add_annotation(
x=1,
y=1,
yref="paper",
xref="paper",
xanchor="right",
yanchor="top",
align="left",
font={"size": 18, "color": "#000000"},
showarrow=False,
borderwidth=2,
borderpad=10,
bgcolor="#ffffff",
bordercolor="#000000",
opacity=0.8,
text="<b>Radial Axis:</b> Wind<br>speed in m/s.",
)

fig.update_layout(polar=polar_dict)
fig.update_layout(template=template)
fig.update_layout(title="Measured Concentration against Wind Direction.")
return fig

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