mod naismith’s rule
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deng
@ -69,7 +69,10 @@ class HikingAssistant:
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all_points = gpx_processor.get_all_points()
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distances, elevations = gpx_processor.get_elevation_profile_data()
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gradients = gpx_processor.get_gradients()
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estimated_time = gpx_processor.calculate_naismith_time()
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estimated_time = gpx_processor.calculate_naismith_time(
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horizontal_speed=self._config['app']['estimated_time']['horizontal_speed'],
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vertical_speed=self._config['app']['estimated_time']['vertical_speed'],
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)
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# Cache all processed data
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st.session_state.gpx_file_key = file_key
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@ -127,7 +130,7 @@ class HikingAssistant:
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st.metric(
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label='預估行進時間',
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value=f'{estimated_time // 60}h {estimated_time % 60}m',
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help='依據[Naismith’s Rule](https://en.wikipedia.org/wiki/Naismith%27s_rule)計算',
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help='基於[Naismith’s Rule](https://en.wikipedia.org/wiki/Naismith%27s_rule)修正後計算',
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)
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# Map section
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@ -6,5 +6,8 @@ app:
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elevation_threshold: 2100
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warning_text: 此路線海拔較高，請留意[高山症](https://www.ysnp.gov.tw/StaticPage/MountainSickness)發生風險
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emoji: 💊
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estimated_time:
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horizontal_speed: 3
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vertical_speed: 400
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weather:
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forecast_days: 7
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@ -4,7 +4,7 @@
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# date: 20251127
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import gpxpy
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import gpxpy.gpx
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import numpy as np
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from geopy.distance import geodesic
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@ -19,10 +19,23 @@ class GPXProcessor:
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"""
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self.gpx_file = gpx_file
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self.gpx = None
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self.time = []
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self.points = []
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self.elevations = []
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self.elevations = [] # unit: m
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self.distances = [] # unit: km
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def _get_sample_rate(self):
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"""Get the median time interval between points.
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Returns:
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float: Median sample time interval in seconds
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"""
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if len(self.time) < 2:
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return 0
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diff = np.diff(self.time)
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median_sample_time = np.median(diff).total_seconds()
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return median_sample_time
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def validate_and_parse(self):
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"""Validate and parse the GPX file.
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@ -43,8 +56,9 @@ class GPXProcessor:
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for track in self.gpx.tracks:
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for segment in track.segments:
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for point in segment.points:
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self.points.append((point.latitude, point.longitude))
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self.elevations.append(point.elevation if point.elevation else 0)
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self.time.append(point.time)
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self.points.append((round(point.latitude, 6), round(point.longitude, 6)))
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self.elevations.append(round(point.elevation, 1) if point.elevation else 0)
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return len(self.points) > 0
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@ -70,41 +84,34 @@ class GPXProcessor:
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return total_distance
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def calculate_elevation_gain_loss(self, threshold=3.0):
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def calculate_elevation_gain_loss(self, threshold=1.0):
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"""Calculate total elevation gain and loss with noise filtering.
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GPS altitude data is notoriously inaccurate. Small fluctuations (noise) can
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accumulate to create inflated elevation gain/loss values. This method uses
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a threshold to filter out GPS noise.
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a threshold to filter out GPS noise and downsampling to reduce computation.
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Args:
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threshold: Minimum elevation change in meters to be counted (default: 3.0)
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threshold: Minimum elevation change in meters to be counted (default: 1.0)
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This filters out GPS noise while preserving real elevation changes.
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Returns:
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tuple: (elevation_gain, elevation_loss) in meters
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"""
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if len(self.elevations) < 2:
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window_size = int(120 / self._get_sample_rate())
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if len(self.elevations) <= window_size:
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return 0.0, 0.0
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gain = 0.0
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loss = 0.0
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# Use cumulative approach to avoid missing real elevation changes
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# Track cumulative change since last significant threshold crossing
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cumulative_change = 0.0
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for i in range(window_size, len(self.elevations), window_size):
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diff = self.elevations[i] - self.elevations[i - window_size]
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for i in range(1, len(self.elevations)):
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diff = self.elevations[i] - self.elevations[i - 1]
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cumulative_change += diff
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# Only count if cumulative change exceeds threshold
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if cumulative_change >= threshold:
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gain += cumulative_change
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cumulative_change = 0.0
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elif cumulative_change <= -threshold:
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loss += abs(cumulative_change)
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cumulative_change = 0.0
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if diff >= threshold:
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gain += diff
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elif diff <= -threshold:
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loss += abs(diff)
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return gain, loss
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@ -169,12 +176,12 @@ class GPXProcessor:
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return gradients
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def calculate_naismith_time(self):
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def calculate_naismith_time(self, horizontal_speed=5, vertical_speed=600):
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"""Calculate estimated hiking time using Naismith's Rule.
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Naismith's Rule:
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- Base time: 1 hour per 5 km of horizontal distance
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- Add time: 1 hour per 600 meters of ascent
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- Horizontal speed: 1 hour per 5 km of horizontal distance
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- Vertical speed: 1 hour per 600 meters of ascent
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Returns:
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int: Estimated time in minutes
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@ -187,11 +194,8 @@ class GPXProcessor:
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elevation_gain, _ = self.calculate_elevation_gain_loss()
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# Naismith's Rule calculation
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# 1 hour per 5 km = 0.2 hours per km
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time_for_distance = total_distance * 0.2
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# 1 hour per 600 meters of ascent
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time_for_ascent = elevation_gain / 600.0
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time_for_distance = total_distance / horizontal_speed
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time_for_ascent = elevation_gain / vertical_speed
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total_time = int((time_for_distance + time_for_ascent) * 60)
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@ -32,17 +32,17 @@ class MapRenderer:
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center_lat = sum(p[0] for p in points) / len(points)
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center_lon = sum(p[1] for p in points) / len(points)
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# Create map ojb
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# Create map obj
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route_map = folium.Map(
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location=[center_lat, center_lon],
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zoom_start=13,
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tiles=None, # Don't add default tiles, we'll add custom ones
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tiles=None,
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)
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# Add OpenStreetMap layer
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folium.TileLayer(
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tiles='OpenStreetMap',
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name='OpenStreetMap',
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name='平面（OpenStreetMap）',
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overlay=False,
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control=True,
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show=tile_layer == 'OpenStreetMap',
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@ -52,7 +52,7 @@ class MapRenderer:
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folium.TileLayer(
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tiles='https://tile.happyman.idv.tw/map/moi_osm/{z}/{x}/{y}.png',
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attr='© <a href="https://rudy.basecamp.tw/">魯地圖</a>',
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name='魯地圖 (Rudy Map)',
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name='等高線（魯地圖）',
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overlay=False,
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control=True,
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show=tile_layer == 'RudyMap',
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@ -80,7 +80,7 @@ class MapRenderer:
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fill=True,
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fill_color='blue',
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fill_opacity=0.7,
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popup='起點',
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popup=start_point,
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tooltip='起點',
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).add_to(route_map)
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@ -93,7 +93,7 @@ class MapRenderer:
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fill=True,
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fill_color='blue',
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fill_opacity=0.7,
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popup='終點',
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popup=end_point,
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tooltip='終點',
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).add_to(route_map)
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