Add historic time series plot

ecoscope/io/earthranger.py

@@ -3,25 +3,24 @@
 import math
 import typing
 
+import ecoscope
 import geopandas as gpd
 import numpy as np
 import pandas as pd
 import pytz
 import requests
-from erclient.client import ERClient, ERClientException, ERClientNotFound
-from shapely.geometry import shape
-from tqdm.auto import tqdm
-
-import ecoscope
 from ecoscope.io.earthranger_utils import (
     clean_kwargs,
     clean_time_cols,
     dataframe_to_dict,
     format_iso_time,
+    pack_columns,
     to_gdf,
     to_hex,
-    pack_columns,
 )
+from erclient.client import ERClient, ERClientException, ERClientNotFound
+from shapely.geometry import shape
+from tqdm.auto import tqdm
 
 
 class EarthRangerIO(ERClient):

ecoscope/plotting/plot.py

@@ -5,9 +5,9 @@
 from ecoscope.base.utils import color_tuple_to_css
 
 try:
-    from sklearn.neighbors import KernelDensity
     import plotly.graph_objs as go
     from plotly.subplots import make_subplots
+    from sklearn.neighbors import KernelDensity
 except ModuleNotFoundError:
     raise ModuleNotFoundError(
         'Missing optional dependencies required by this module. \
@@ -386,3 +386,98 @@ def pie_chart(
 
     fig = go.Figure(data=go.Pie(labels=labels, values=values, **style_kwargs), layout=layout_kwargs)
     return fig
+
+
+def draw_historic_timeseries(
+    df: pd.DataFrame,
+    current_value_column: str,
+    current_value_title: str,
+    historic_min_column: str = None,
+    historic_max_column: str = None,
+    historic_band_title: str = "Historic Min-Max",
+    historic_mean_column: str = None,
+    historic_mean_title: str = "Historic Mean",
+    layout_kwargs: dict = None,
+):
+    """
+    Creates a timeseries plot compared with historical values
+    Parameters
+    ----------
+    df: pd.Dataframe
+        The data to plot
+    current_value_column: str
+        The name of the dataframe column to pull slice values from
+    current_value_title: str
+        The title shown in the plot legend for current value
+    historic_min_column: str
+        The name of the dataframe column to pull historic min values from. historic_min_column and historic_max_column should exist together.
+    historic_max_column: str
+        The name of the dataframe column to pull historic max values from. historic_min_column and historic_max_column should exist together.
+    historic_band_title: str
+        The title shown in the plot legend for historic band
+    historic_mean_column: str
+        The name of the dataframe column to pull historic mean values from
+    current_value_title: str
+        The title shown in the plot legend for historic mean values
+    layout_kwargs: dict
+        Additional kwargs passed to plotly.go.Figure(layout)
+    Returns
+    -------
+    fig : plotly.graph_objects.Figure
+        The plotly bar chart
+    """
+
+    fig = go.Figure(layout=layout_kwargs)
+
+    if historic_max_column and historic_min_column:
+        # add the upper bound
+        fig.add_trace(
+            go.Scatter(
+                x=df.img_date,
+                y=df[historic_max_column],
+                fill=None,
+                mode="lines",
+                line_color="green",
+                name="",
+                showlegend=False,
+            )
+        )
+
+        # lower band
+        fig.add_trace(
+            go.Scatter(
+                x=df.img_date,
+                y=df[historic_min_column],
+                fill="tonexty",
+                mode="lines",
+                line_color="green",
+                name=historic_band_title,
+            )
+        )
+
+    if historic_mean_column:
+        # add the historic mean
+        fig.add_trace(
+            go.Scatter(
+                x=df.img_date,
+                y=df[historic_mean_column],
+                fill=None,
+                mode="lines",
+                line=dict(color="green", dash="dot"),
+                name=historic_mean_title,
+            )
+        )
+
+    # add current NDVI values
+    fig.add_trace(
+        go.Scatter(
+            x=df.img_date,
+            y=df[current_value_column],
+            fill=None,
+            mode="lines",
+            line_color="navy",
+            name=current_value_title,
+        )
+    )
+
+    return fig

tests/test_ecoplot.py

@@ -1,9 +1,19 @@
-import pytest
 import numpy as np
 import pandas as pd
-from ecoscope.plotting.plot import EcoPlotData, ecoplot, mcp, nsd, speed, stacked_bar_chart, pie_chart
-from ecoscope.base import Trajectory
+import pytest
+
 from ecoscope.analysis.classifier import apply_color_map
+from ecoscope.base import Trajectory
+from ecoscope.plotting.plot import (
+    EcoPlotData,
+    draw_historic_timeseries,
+    ecoplot,
+    mcp,
+    nsd,
+    pie_chart,
+    speed,
+    stacked_bar_chart,
+)
 
 
 @pytest.fixture
@@ -175,3 +185,39 @@ def test_pie_chart_numerical(chart_df):
         "rgba(0, 0, 255, 1.0)",
         "rgba(255, 255, 255, 1.0)",
     )
+
+
+def test_draw_historic_timeseries():
+    df = pd.DataFrame(
+        {
+            "min": [0.351723, 0.351723, 0.303219, 0.303219, 0.342149, 0.342149],
+            "max": [0.667335, 0.667335, 0.708183, 0.708183, 0.727095, 0.727095],
+            "mean": [0.472017, 0.472017, 0.543062, 0.543062, 0.555547, 0.555547],
+            "NDVI": [0.609656, 0.671868, 0.680008, 0.586662, 0.612170, np.nan],
+            "img_date": pd.to_datetime(
+                [
+                    "2023-11-09",
+                    "2023-11-25",
+                    "2023-12-11",
+                    "2023-12-27",
+                    "2024-01-09",
+                    "2024-01-25",
+                ]
+            ),
+        }
+    )
+
+    chart = draw_historic_timeseries(
+        df,
+        current_value_column="NDVI",
+        current_value_title="NDVI",
+        historic_min_column="min",
+        historic_max_column="max",
+        historic_mean_column="mean",
+    )
+
+    assert len(chart.data) == 4
+    assert chart.data[0].showlegend is False
+    assert chart.data[1].name == "Historic Min-Max"
+    assert chart.data[2].name == "Historic Mean"
+    assert chart.data[3].name == "NDVI"