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visualize_sensor.py
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# https://blog.csdn.net/qq_40206371/article/details/134698358
import folium
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.colors import to_hex
def visualize_sensor(dataset, key_index=None, indices=None, filename=None, ext=None):
if dataset.lower() == 'metr_la':
filepath = f'libcity/cache/graph_sensor_locations.csv'
df = pd.read_csv(filepath)
else:
filepath = 'libcity/cache/graph_sensor_locations_bay.csv'
df = pd.read_csv(filepath, names=['sensor_id', 'latitude', 'longitude'])
df.insert(0, 'index', range(len(df)), allow_duplicates=False)
if indices is None:
indices = range(len(df))
if key_index is None:
key_index = []
mean_latitude = df['latitude'].mean()
mean_longitude = df['longitude'].mean()
m = folium.Map(location=(mean_latitude, mean_longitude), zoom_start=12)
for data in df.iterrows():
tmp_index = int(data[1]['index'])
if tmp_index not in indices and tmp_index not in key_index:
continue
tmp_latitude = data[1]['latitude']
tmp_longitude = data[1]['longitude']
tmp_sensor_id = int(data[1]['sensor_id'])
icon = folium.Icon(color='red') if tmp_index in key_index else None
testing_result = '' if ext is None else ext[tmp_index]
folium.Marker(location=(tmp_latitude, tmp_longitude), tooltip=f'{tmp_index}',
popup=f'{tmp_sensor_id}={testing_result}:({tmp_latitude},{tmp_longitude})', icon=icon).add_to(m)
if filename is None:
m.save(f'map_{dataset}.html')
else:
m.save(filename)
def visualize_sensor_marked(dataset, key_index, filename=None, ext=None, scaled=0.1):
"""只突出标记最不显著的scaled比例的传感器"""
if dataset.lower() == 'metr_la':
filepath = f'libcity/cache/graph_sensor_locations.csv'
df = pd.read_csv(filepath)
else:
filepath = 'libcity/cache/graph_sensor_locations_bay.csv'
df = pd.read_csv(filepath, names=['sensor_id', 'latitude', 'longitude'])
df.insert(0, 'index', len(df), allow_duplicates=False)
mean_latitude = df['latitude'].mean()
mean_longitude = df['longitude'].mean()
sorted_items = sorted(ext.items(), key=lambda x: x[1])
indices = [item[0] for item in sorted_items[-int(scaled * len(sorted_items)):]]
m = folium.Map(location=(mean_latitude, mean_longitude), zoom_start=12)
for data in df.iterrows():
tmp_index = int(data[1]['index'])
tmp_latitude = data[1]['latitude']
tmp_longitude = data[1]['longitude']
tmp_sensor_id = int(data[1]['sensor_id'])
if tmp_index == key_index:
folium.Marker(location=(tmp_latitude, tmp_longitude), tooltip=f'{tmp_index}',
popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})',
icon=folium.Icon(color='red')).add_to(m)
# folium.CircleMarker(location=[tmp_latitude, tmp_longitude],
# radius=10,
# color='blue',
# fill=True,
# fill_color='blue',
# fill_opacity=1,
# tooltip=f'{tmp_index}',
# popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})').add_to(m)
elif tmp_index in indices:
folium.CircleMarker(location=[tmp_latitude, tmp_longitude],
radius=10,
color='red',
fill=True,
fill_color='red',
fill_opacity=1,
tooltip=f'{tmp_index}',
popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})').add_to(m)
else:
folium.CircleMarker(location=[tmp_latitude, tmp_longitude],
radius=10,
color='blue',
fill=True,
fill_color='blue',
fill_opacity=1,
tooltip=f'{tmp_index}',
popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})').add_to(m)
if filename is None:
m.save(f'map_{dataset}.html')
else:
m.save(filename)
def visualize_sensor_varying(dataset, key_index, ext, filename=None, adjust=False, scaled=None, speeds=None, normalized=False):
if dataset.lower() == 'metr_la':
filepath = f'libcity/cache/graph_sensor_locations.csv'
df = pd.read_csv(filepath)
else:
filepath = 'libcity/cache/graph_sensor_locations_bay.csv'
df = pd.read_csv(filepath, names=['sensor_id', 'latitude', 'longitude'])
df.insert(0, 'index', len(df), allow_duplicates=False)
mean_latitude = df['latitude'].mean()
mean_longitude = df['longitude'].mean()
# if adjust:
# sorted_items = sorted(ext.items(), key=lambda x: x[1])
# minVal = 1.1 * sorted_items[1][1] - 0.1 * sorted_items[-1][1]
# assert minVal > 0, f'diff={sorted_items[-1][1]}-{sorted_items[1][1]}'
# ext[sorted_items[0][0]] = minVal
# diff = max(ext.values()) - minVal
# ext = {key: 1 - (value - minVal) / diff for key, value in ext.items()}
if adjust:
sorted_items = sorted(ext.items(), key=lambda x: x[1])
minVal = sorted_items[0][1]
maxVal = 1.1 * sorted_items[-2][1] - 0.1 * minVal
ext[sorted_items[-1][0]] = maxVal
diff = maxVal - minVal
ext = {key: value - minVal / diff for key, value in ext.items()}
if scaled is not None:
sorted_items = sorted(ext.items(), key=lambda x: x[1])
n = len(sorted_items)
scaled_n = int(scaled * n)
for i in range(scaled_n):
ext[sorted_items[i][0]] = 0
for i in range(scaled_n):
ext[sorted_items[n - i - 1][0]] = 1
minVal = sorted_items[scaled_n][1]
maxVal = sorted_items[n - scaled_n - 1][1]
for i in range(scaled_n, n - scaled_n):
ext[sorted_items[i][0]] = (sorted_items[i][1] - minVal) / (maxVal - minVal)
if normalized:
minVal = min(ext.values())
maxVal = max(ext.values())
for k, v in ext.items():
ext[k] = (v - minVal) / (maxVal - minVal) + minVal
# 获取Viridis颜色映射的颜色列表
viridis_colors = [to_hex(c) for c in plt.cm.viridis(range(256))]
plasma_colors = [to_hex(c) for c in plt.cm.plasma(range(256))]
inferno_colors = [to_hex(c) for c in plt.cm.inferno(range(256))]
magma_colors = [to_hex(c) for c in plt.cm.magma(range(256))]
# 创建颜色映射，使用Viridis颜色映射
colormap = folium.LinearColormap(colors=viridis_colors, vmin=min(ext.values()), vmax=max(ext.values()))
# colormap = folium.LinearColormap(colors=['blue', 'red'], vmin=min(ext.values()), vmax=max(ext.values()))
m = folium.Map(location=(mean_latitude, mean_longitude), zoom_start=13)
for data in df.iterrows():
tmp_index = int(data[1]['index'])
tmp_latitude = data[1]['latitude']
tmp_longitude = data[1]['longitude']
tmp_sensor_id = int(data[1]['sensor_id'])
if tmp_index not in ext.keys():
print(f'Jump Sensor {tmp_index}.')
continue
if tmp_index == key_index:
folium.CircleMarker(location=[tmp_latitude, tmp_longitude],
radius=20,
color='red',
fill=True,
fill_color='red',
fill_opacity=1,
tooltip=f'{tmp_index}',
popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})').add_to(m)
# folium.Marker(location=(tmp_latitude, tmp_longitude), tooltip=f'{tmp_index}',
# popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})',
# radius=20,
# icon=folium.Icon(color='red')).add_to(m)
# folium.CircleMarker(location=[tmp_latitude, tmp_longitude],
# radius=10,
# color='blue',
# fill=True,
# fill_color='blue',
# fill_opacity=1,
# tooltip=f'{tmp_index}',
# popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude})').add_to(m)
else:
popup_content = '' if speeds is None else f':{speeds[tmp_index]}'
folium.CircleMarker(location=[tmp_latitude, tmp_longitude],
radius=20,
color=colormap(ext[tmp_index]),
fill=True,
fill_color=colormap(ext[tmp_index]),
fill_opacity=1,
tooltip=f'{tmp_index}',
popup=f'{tmp_sensor_id}={ext[tmp_index]}:({tmp_latitude},{tmp_longitude}){popup_content}').add_to(m)
# Add LinearColormap to the map
colormap.caption = 'Bayesian Evidence'
m.add_child(colormap)
# Add LayerControl to show/hide the color bar
folium.LayerControl().add_to(m)
if filename is None:
m.save(f'map_{dataset}.html')
else:
m.save(filename)
if __name__ == '__main__':
visualize_sensor('metr_la')
visualize_sensor('pems_bay')