downgrade to Altair 4.2.2

app.py

@@ -5,15 +5,21 @@ from typing import List
 from urllib.request import pathname2url
 from xml.dom import minidom
 
+from folium.plugins import BeautifyIcon
+from folium.features import DivIcon
+
+# import folium.plugins as plugins
+
 import numpy as np
 import pandas as pd
 from scipy.signal import find_peaks
 import streamlit as st
 
-# import folium
-# from streamlit_folium import st_folium
+import folium
+from streamlit_folium import st_folium
 import altair as alt
 from io import StringIO
+import branca
 
 
 def get_gpx(uploaded_file):
@@ -176,49 +182,129 @@ def generate_height_profile_json(df: pd.DataFrame) -> str:
     df["smoothed_grade_color"] = df["smoothed_grade"].map(grade_to_color)
     # df["grade_color"] = df["grade"].map(grade_to_color)
 
+    # elevation = (
+    #     alt.Chart(
+    #         df[["distance_from_start", "smoothed_elevation", "smoothed_grade_color"]]
+    #     )
+    #     .mark_bar()
+    #     .encode(
+    #         x=alt.X("distance_from_start")
+    #         .axis(
+    #             grid=False,
+    #             tickCount=10,
+    #             labelExpr="datum.label + ' km'",
+    #             title=None,
+    #         )
+    #         .scale(domain=(0, total_distance_round)),
+    #         y=alt.Y("smoothed_elevation").axis(
+    #             domain=False,
+    #             ticks=False,
+    #             tickCount=5,
+    #             labelExpr="datum.label + ' m'",
+    #             title=None,
+    #         ),
+    #         color=alt.Color("smoothed_grade_color").scale(None),
+    #     )
+    # )
+    max_elevation = df["elev"].max().round(-1)
     elevation = (
-        alt.Chart(
-            df[["distance_from_start", "smoothed_elevation", "smoothed_grade_color"]]
+        alt.Chart(df)
+        .mark_area(
+            color=alt.Gradient(
+                gradient="linear",
+                stops=[
+                    alt.GradientStop(color="lightgrey", offset=0),
+                    alt.GradientStop(color="darkgrey", offset=1),
+                ],
+                x1=1,
+                x2=1,
+                y1=1,
+                y2=0,
+            ),
+            line={"color": "darkgreen"},
         )
-        .mark_bar()
         .encode(
-            x=alt.X("distance_from_start")
-            .axis(
-                grid=False,
-                tickCount=10,
-                labelExpr="datum.label + ' km'",
-                title=None,
-            )
-            .scale(domain=(0, total_distance_round)),
-            y=alt.Y("smoothed_elevation").axis(
-                domain=False,
-                ticks=False,
-                tickCount=5,
-                labelExpr="datum.label + ' m'",
-                title=None,
+            x=alt.X(
+                "distance_from_start",
+                axis=alt.Axis(
+                    domain=False,
+                    ticks=False,
+                    tickCount=10,
+                    labelExpr="datum.label + ' km'",
+                ),
+                scale=alt.Scale(domain=(0, total_distance_round)),
+            ),
+            y=alt.Y(
+                "elev",
+                axis=alt.Axis(
+                    domain=False,
+                    ticks=False,
+                    tickCount=5,
+                    labelExpr="datum.label + ' m'",
+                ),
+                scale=alt.Scale(domain=(0, max_elevation)),
             ),
-            color=alt.Color("smoothed_grade_color").scale(None),
         )
     )
 
     df_peaks_filtered = find_climbs(df)
+    # line_peaks = (
+    #     alt.Chart(df_peaks_filtered[["distance_from_start", "elev", "max_elevation"]])
+    #     .mark_rule(color="red")
+    #     .encode(
+    #         x=alt.X("distance_from_start:Q").scale(domain=(0, total_distance_round)),
+    #         y="elev",
+    #         y2="max_elevation",
+    #     )
+    # )
     line_peaks = (
         alt.Chart(df_peaks_filtered[["distance_from_start", "elev", "max_elevation"]])
         .mark_rule(color="red")
         .encode(
-            x=alt.X("distance_from_start:Q").scale(domain=(0, total_distance_round)),
+            x=alt.X(
+                "distance_from_start:Q",
+                scale=alt.Scale(domain=(0, total_distance_round)),
+            ),
             y="elev",
             y2="max_elevation",
         )
     )
+    df_annot = (
+        df_peaks_filtered.reset_index(drop=True)
+        .assign(number=lambda df_: df_.index + 1)
+        .assign(circle_pos=lambda df_: df_["max_elevation"] + 20)[
+            ["distance_from_start", "max_elevation", "circle_pos", "number"]
+        ]
+    )
+    # annotation = (
+    #     alt.Chart(df_annot)
+    #     .mark_text(align="center", baseline="bottom", fontSize=16, dy=-10)
+    #     .encode(
+    #         x=alt.X("distance_from_start:Q").scale(domain=(0, total_distance_round)),
+    #         y="max_elevation",
+    #         text="number",
+    #     )
+    # )
+    annotation = (
+        alt.Chart(df_annot)
+        .mark_text(align="center", baseline="bottom", fontSize=16, dy=-10)
+        .encode(
+            x=alt.X(
+                "distance_from_start:Q",
+                scale=alt.Scale(domain=(0, total_distance_round)),
+            ),
+            y="max_elevation",
+            text="number",
+        )
+    )
     chart = (
-        (elevation + line_peaks).properties(width="container")
-        # .properties(height=100, width=750)
+        (elevation + line_peaks + annotation)
+        .properties(width="container")
         .configure_view(
             strokeWidth=0,
         )
     )
-    return chart
+    return chart, df_peaks_filtered
 
 
 gpx_file = st.file_uploader("Upload gpx file", type=["gpx"])
@@ -226,14 +312,45 @@ gpx_file = st.file_uploader("Upload gpx file", type=["gpx"])
 if gpx_file is not None:
     ave_lat, ave_lon, lon_list, lat_list, h_list = get_gpx(gpx_file)
     df = pd.DataFrame({"lon": lon_list, "lat": lat_list, "elev": h_list})
-    # route_map = folium.Map(
-    #     location=[ave_lat, ave_lon],
-    #     zoom_start=12,
-    # )
-    # folium.PolyLine(
-    #     list(zip(lat_list, lon_list)), color="red", weight=2.5, opacity=1
-    # ).add_to(route_map)
-    # st_data = st_folium(route_map, height=450, width=850)
-    chart = generate_height_profile_json(df)
+    route_map = folium.Map(
+        location=[ave_lat, ave_lon],
+        zoom_start=12,
+    )
+    folium.PolyLine(
+        list(zip(lat_list, lon_list)), color="red", weight=2.5, opacity=1
+    ).add_to(route_map)
+
+    chart, df_peaks = generate_height_profile_json(df)
+    for index, row in df_peaks.reset_index(drop=True).iterrows():
+        icon = BeautifyIcon(
+            icon="arrow-down",
+            icon_shape="marker",
+            number=str(index + 1),
+            border_color="red",
+            background_color="white",
+        )
+        icon_div = DivIcon(
+            icon_size=(150, 36),
+            icon_anchor=(7, 20),
+            html=f"<div style='font-size: 18pt; color : black'>{index+1}</div>",
+        )
+        length = (
+            f"{row['length']:.1f} km"
+            if row["length"] >= 1
+            else f"{row['length']*1000:.0f} m"
+        )
+        popup_text = f"""Lenght: {length}<br>
+                Avg. grade: {row['grade']/1000:.1f}%"""
+        popup = folium.Popup(popup_text, min_width=100, max_width=200)
+        folium.Marker(
+            [row["lat"], row["lon"]],
+            popup=popup,
+            icon=icon_div,
+        ).add_to(route_map)
+        route_map.add_child(folium.CircleMarker([row["lat"], row["lon"]], radius=15))
+
+    st.table(df_peaks)
+
+    st_data = st_folium(route_map, height=450, width=850)
 
     st.altair_chart(chart, use_container_width=True)

poetry.lock

@@ -2,26 +2,25 @@
 
 [[package]]
 name = "altair"
-version = "5.0.1"
-description = "Vega-Altair: A declarative statistical visualization library for Python."
+version = "4.2.2"
+description = "Altair: A declarative statistical visualization library for Python."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "altair-5.0.1-py3-none-any.whl", hash = "sha256:9f3552ed5497d4dfc14cf48a76141d8c29ee56eae2873481b4b28134268c9bbe"},
-    {file = "altair-5.0.1.tar.gz", hash = "sha256:087d7033cb2d6c228493a053e12613058a5d47faf6a36aea3ff60305fd8b4cb0"},
+    {file = "altair-4.2.2-py3-none-any.whl", hash = "sha256:8b45ebeaf8557f2d760c5c77b79f02ae12aee7c46c27c06014febab6f849bc87"},
+    {file = "altair-4.2.2.tar.gz", hash = "sha256:39399a267c49b30d102c10411e67ab26374156a84b1aeb9fcd15140429ba49c5"},
 ]
 
 [package.dependencies]
+entrypoints = "*"
 jinja2 = "*"
 jsonschema = ">=3.0"
 numpy = "*"
 pandas = ">=0.18"
 toolz = "*"
-typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-dev = ["black (<24)", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pytest", "pytest-cov", "ruff", "types-jsonschema", "types-setuptools", "vega-datasets", "vl-convert-python"]
-doc = ["docutils", "geopandas", "jinja2", "myst-parser", "numpydoc", "pillow", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+dev = ["black", "docutils", "flake8", "ipython", "m2r", "mistune (<2.0.0)", "pytest", "recommonmark", "sphinx", "vega-datasets"]
 
 [[package]]
 name = "attrs"
@@ -278,6 +277,17 @@ files = [
     {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"},
 ]
 
+[[package]]
+name = "entrypoints"
+version = "0.4"
+description = "Discover and load entry points from installed packages."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"},
+    {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"},
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.1.2"
@@ -1493,4 +1503,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "748a07a0f37a12766536420ac445825e1cabd04ca2fb89f4038c9dd55e1cd57b"
+content-hash = "b34d5fefcbbd82b4d635d5e7d866bde0aa6dbf2f7d5a59bb4ab832bac56622a2"

pyproject.toml

@@ -11,10 +11,10 @@ readme = "README.md"
 python = ">=3.10,<3.13"
 streamlit = "1.24.0"
 scipy = "1.11.1"
-altair = "5.0.1"
 watchdog = "^3.0.0"
 folium = "^0.14.0"
 streamlit-folium = "^0.12.0"
+altair = "4.2.2"
 
 
 [tool.poetry.group.dev.dependencies]

requirements.txt

@@ -1,6 +1,6 @@
-streamlit==1.24.0
-# folium==0.14.0
-# streamlit-folium==0.12.0
-# altair==5.0.1
+# streamlit==1.24.0
+folium==0.14.0
+streamlit-folium==0.12.0
+# altair==4.2.2
 # pandas==2.0.3
 scipy==1.11.1