Merge branch 'master' into 223/event_dates

.github/workflows/publish.yml

@@ -74,7 +74,7 @@ jobs:
         name: pip-dist
         path: dist/
     - name: Sign the dists with Sigstore
-      uses: sigstore/gh-action-sigstore-python@v2.1.1
+      uses: sigstore/gh-action-sigstore-python@v3.0.0
       with:
         inputs: >-
           ./dist/*.tar.gz

ecoscope/io/__init__.py

@@ -1,15 +1,20 @@
 from ecoscope.io import earthranger, eetools, raster, utils
 from ecoscope.io.earthranger import EarthRangerIO
-from ecoscope.io.async_earthranger import AsyncEarthRangerIO
 from ecoscope.io.utils import download_file
 
 __all__ = [
     "earthranger",
     "EarthRangerIO",
-    "AsyncEarthRangerIO",
     "download_file",
     "earthranger_utils",
     "eetools",
     "raster",
     "utils",
 ]
+
+try:
+    from ecoscope.io.async_earthranger import AsyncEarthRangerIO
+
+    __all__ += ["AsyncEarthRangerIO"]
+except ImportError:
+    pass

ecoscope/io/async_earthranger.py

@@ -2,11 +2,18 @@
 import geopandas as gpd
 import pandas as pd
 import asyncio
-from erclient.client import AsyncERClient
+
+import ecoscope
 from ecoscope.io.utils import to_hex
 from ecoscope.io.earthranger_utils import clean_kwargs, to_gdf, clean_time_cols
 
-import ecoscope
+try:
+    from erclient.client import AsyncERClient
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
+        'Missing optional dependencies required by this module. \
+         Please run pip install ecoscope["async_earthranger"]'
+    )
 
 
 class AsyncEarthRangerIO(AsyncERClient):

ecoscope/plotting/__init__.py

@@ -7,6 +7,17 @@
     plot_seasonal_dist,
     speed,
     stacked_bar_chart,
+    pie_chart,
 )
 
-__all__ = ["EcoPlotData", "add_seasons", "ecoplot", "mcp", "nsd", "speed", "plot_seasonal_dist", "stacked_bar_chart"]
+__all__ = [
+    "EcoPlotData",
+    "add_seasons",
+    "ecoplot",
+    "mcp",
+    "nsd",
+    "speed",
+    "plot_seasonal_dist",
+    "stacked_bar_chart",
+    "pie_chart",
+]

ecoscope/plotting/plot.py

@@ -325,3 +325,42 @@ def stacked_bar_chart(data: EcoPlotData, agg_function: str, stack_column: str, l
 
     fig.update_layout(barmode="stack")
     return fig
+
+
+def pie_chart(
+    data: pd.DataFrame, value_column: str, label_column: str = None, style_kwargs: dict = {}, layout_kwargs: dict = None
+):
+    """
+    Creates a pie chart from the provided dataframe
+    Parameters
+    ----------
+    data: pd.Dataframe
+        The data to plot
+    value_column: str
+        The name of the dataframe column to pull slice values from
+        If the column contains non-numeric values, it is assumed to be categorical
+            and the pie slices will be a count of the occurrences of the category
+    label_column: str
+        The name of the dataframe column to label slices with, required if the data in value_column is numeric
+    style_kwargs: dict
+        Additional style kwargs passed to go.Pie()
+    layout_kwargs: dict
+        Additional kwargs passed to plotly.go.Figure(layout)
+    Returns
+    -------
+    fig : plotly.graph_objects.Figure
+        The plotly bar chart
+    """
+
+    if pd.api.types.is_numeric_dtype(data[value_column]):
+        if label_column is not None:
+            labels = data[label_column]
+            values = data[value_column]
+        else:
+            raise ValueError("numerical values require a label column to")
+    else:  # assume categorical
+        labels = data[value_column].unique()
+        values = data[value_column].value_counts()
+
+    fig = go.Figure(data=go.Pie(labels=labels, values=values, **style_kwargs), layout=layout_kwargs)
+    return fig

pyproject.toml

@@ -25,7 +25,7 @@ classifiers=[
 dependencies = [
     "backoff",
     "earthengine-api",
-    "earthranger-client @ git+https://github.com/PADAS/er-client@v1.2.3",
+    "earthranger-client",
     "geopandas<=0.14.2",
     "pyproj",
     "rasterio",
@@ -50,6 +50,9 @@ analysis = [
     "scikit-image",
     "scikit-learn",
 ]
+async_earthranger = [
+    "earthranger-client @ git+https://github.com/PADAS/er-client@v1.2.3",
+]
 mapping = [
     "lonboard @ git+https://github.com/wildlife-dynamics/lonboard@77c56d30a9c2dd96fd863e910bf62952cfa36da8",
     "matplotlib",

tests/test_ecoplot.py

@@ -1,9 +1,25 @@
+import pytest
 import numpy as np
 import pandas as pd
-from ecoscope.plotting.plot import EcoPlotData, ecoplot, mcp, nsd, speed, stacked_bar_chart
+from ecoscope.plotting.plot import EcoPlotData, ecoplot, mcp, nsd, speed, stacked_bar_chart, pie_chart
 from ecoscope.base import Trajectory
 
 
+@pytest.fixture
+def chart_df():
+    df = pd.DataFrame(
+        {
+            "id": [1, 2, 3, 4],
+            "category": ["A", "B", "B", "B"],
+            "type": ["A", "B", "C", "D"],
+            "value": [25, 40, 65, 150],
+            "time": ["2024-07-22", "2024-07-22", "2024-07-22", "2024-07-21"],
+        }
+    )
+    df.set_index("id", inplace=True)
+    return df
+
+
 def test_ecoplot(movebank_relocations):
     traj = Trajectory.from_relocations(movebank_relocations)
     epd = EcoPlotData(traj.groupby("groupby_col"), "segment_start", "speed_kmhr", line=dict(color="blue"))
@@ -48,21 +64,12 @@ def test_speed(movebank_relocations):
     len(figure.data[0].y) == len(traj) * 4
 
 
-def test_stacked_bar_chart_categorical():
-    df = pd.DataFrame(
-        {
-            "id": [1, 2, 3, 4],
-            "category": ["A", "B", "B", "B"],
-            "time": ["2024-07-22", "2024-07-22", "2024-07-22", "2024-07-21"],
-        }
-    )
-    df.set_index("id", inplace=True)
-
+def test_stacked_bar_chart_categorical(chart_df):
     groupby_style = {"A": {"marker_color": "red"}, "B": {"marker_color": "blue"}}
     style = {"marker_line_color": "black", "xperiodalignment": "middle"}
     layout_kwargs = {"plot_bgcolor": "gray", "xaxis_dtick": 86400000}
 
-    gb = df.groupby(["time", "category"])
+    gb = chart_df.groupby(["time", "category"])
     epd = EcoPlotData(gb, "time", "category", groupby_style=groupby_style, **style)
     chart = stacked_bar_chart(epd, agg_function="count", stack_column="category", layout_kwargs=layout_kwargs)
 
@@ -81,22 +88,12 @@ def test_stacked_bar_chart_categorical():
     assert chart.data[1].marker.color == "blue"
 
 
-def test_stacked_bar_chart_numerical():
-    df = pd.DataFrame(
-        {
-            "id": [1, 2, 3, 4],
-            "category": ["A", "B", "B", "B"],
-            "value": [25, 40, 65, 150],
-            "time": ["2024-07-22", "2024-07-22", "2024-07-22", "2024-07-21"],
-        }
-    )
-    df.set_index("id", inplace=True)
-
+def test_stacked_bar_chart_numerical(chart_df):
     groupby_style = {"A": {"marker_color": "yellow"}, "B": {"marker_color": "green"}}
     style = {"marker_line_color": "black", "xperiodalignment": "middle"}
     layout_kwargs = {"plot_bgcolor": "gray", "xaxis_dtick": 86400000}
 
-    gb = df.groupby(["time", "category"])
+    gb = chart_df.groupby(["time", "category"])
     epd = EcoPlotData(gb, "time", "value", groupby_style=groupby_style, **style)
     chart = stacked_bar_chart(epd, agg_function="sum", stack_column="category", layout_kwargs=layout_kwargs)
 
@@ -112,3 +109,27 @@ def test_stacked_bar_chart_numerical():
     assert chart.data[0].xperiodalignment == chart.data[1].xperiodalignment == "middle"
     assert chart.data[0].marker.line.color == chart.data[1].marker.line.color == "black"
     assert chart.data[1].marker.color == "green"
+
+
+def test_pie_chart_categorical(chart_df):
+    layout = {"piecolorway": ["red", "green", "blue"]}
+    style = {"marker_line_color": "#000000", "marker_line_width": 2, "textinfo": "value"}
+    chart = pie_chart(chart_df, value_column="category", style_kwargs=style, layout_kwargs=layout)
+
+    assert chart.layout["piecolorway"] == ("red", "green", "blue")
+    assert set(chart.data[0].labels) == set(["A", "B"])
+    assert set(chart.data[0].values) == set([1, 3])
+    assert chart.data[0].marker.line.color == "#000000"
+    assert chart.data[0].marker.line.width == 2
+
+
+def test_pie_chart_numerical(chart_df):
+    layout = {"piecolorway": ["red", "green", "blue"]}
+    style = {"marker_line_color": "#000000", "marker_line_width": 2, "textinfo": "value"}
+    chart = pie_chart(chart_df, value_column="value", label_column="type", style_kwargs=style, layout_kwargs=layout)
+
+    assert chart.layout["piecolorway"] == ("red", "green", "blue")
+    assert set(chart.data[0].labels) == set(chart_df.type)
+    assert set(chart.data[0].values) == set(chart_df.value)
+    assert chart.data[0].marker.line.color == "#000000"
+    assert chart.data[0].marker.line.width == 2