defer some expensive imports

ecoscope/analysis/ecograph.py

@@ -6,7 +6,6 @@
 import numpy as np
 import pandas as pd
 import rasterio
-import sklearn.base
 from affine import Affine
 from shapely.geometry import shape
 from skimage.draw import line
@@ -119,6 +118,7 @@ def to_geotiff(self, feature, output_path, individual="all", interpolation=None,
         transform : sklearn.base.TransformerMixin or None
             A feature transform method (Default : None)
         """
+        from sklearn.base import TransformerMixin
 
         if feature in self.features:
             if individual == "all":
@@ -130,7 +130,7 @@ def to_geotiff(self, feature, output_path, individual="all", interpolation=None,
         else:
             raise ValueError("This feature was not computed by EcoGraph")
 
-        if isinstance(transform, sklearn.base.TransformerMixin):
+        if isinstance(transform, TransformerMixin):
             nan_mask = ~np.isnan(feature_ndarray)
             feature_ndarray[nan_mask] = transform.fit_transform(feature_ndarray[nan_mask].reshape(-1, 1)).reshape(
                 feature_ndarray[nan_mask].shape

ecoscope/analysis/seasons.py

@@ -4,7 +4,6 @@
 import numpy as np
 import pandas
 import shapely
-import sklearn.mixture
 from scipy.stats import norm
 from sklearn.preprocessing import LabelEncoder
 
@@ -50,7 +49,9 @@ def std_ndvi_vals(aoi=None, img_coll=None, nir_band=None, red_band=None, img_sca
 
 
 def val_cuts(vals, num_seasons=2):
-    distr = sklearn.mixture.GaussianMixture(n_components=num_seasons, max_iter=500)
+    from sklearn.mixture import GaussianMixture
+
+    distr = GaussianMixture(n_components=num_seasons, max_iter=500)
     vals = vals["NDVI"].to_numpy().reshape(-1, 1)
     distr.fit(vals)
     mu_vars = np.array(

ecoscope/analysis/speed.py

@@ -1,7 +1,6 @@
 import typing
 
 import geopandas as gpd
-import mapclassify
 import pandas as pd
 import shapely
 
@@ -65,12 +64,23 @@ def apply_classification(x, k, cls_method="natural_breaks", multiples=[-2, -1, 1
         The multiples of the standard deviation to add/subtract from the sample mean to define the bins. defaults=
     """
 
+    from mapclassify import classifiers
+
+    classification_methods = {
+        "equal_interval": classifiers.EqualInterval,
+        "natural_breaks": classifiers.NaturalBreaks,
+        "quantile": classifiers.Quantiles,
+        "std_mean": classifiers.StdMean,
+        "max_breaks": classifiers.MaximumBreaks,
+        "fisher_jenks": classifiers.FisherJenks,
+    }
+
     classifier = classification_methods.get(cls_method)
     if not classifier:
         return
 
     map_classifier = classifier(x, multiples) if cls_method == "std_mean" else classifier(x, k)
-    edges, _, _ = mapclassify.classifiers._format_intervals(map_classifier, fmt="{:.2f}")
+    edges, _, _ = classifiers._format_intervals(map_classifier, fmt="{:.2f}")
     return [float(i) for i in edges]
 
 
@@ -82,12 +92,3 @@ def apply_classification(x, k, cls_method="natural_breaks", multiples=[-2, -1, 1
     "#fc8d59",
     "#d73027",
 ]
-
-classification_methods = {
-    "equal_interval": mapclassify.EqualInterval,
-    "natural_breaks": mapclassify.NaturalBreaks,
-    "quantile": mapclassify.Quantiles,
-    "std_mean": mapclassify.StdMean,
-    "max_breaks": mapclassify.MaximumBreaks,
-    "fisher_jenks": mapclassify.FisherJenks,
-}

ecoscope/base/base.py

@@ -1,6 +1,5 @@
 import warnings
 
-import astroplan
 import astropy
 import geopandas as gpd
 import numpy as np
@@ -394,6 +393,7 @@ def get_daynight_ratio(self, n_grid_points=150) -> pd.Series:
         pd.Series:
             Daynight ratio for each unique individual subject in the grouby_col column.
         """
+        import astroplan
 
         locations = to_EarthLocation(self.geometry.to_crs(crs=self.estimate_utm_crs()).centroid)
 

ecoscope/mapping/map.py

@@ -13,7 +13,6 @@
 import matplotlib as mpl
 import numpy as np
 import pandas as pd
-import datashader as ds
 import rasterio
 import selenium.webdriver
 from branca.colormap import StepColormap
@@ -509,6 +508,7 @@ def add_datashader_gdf(
         kwargs
             Additional kwargs passed to datashader.transfer_functions.shade
         """
+        import datashader as ds
 
         gdf = gdf.to_crs(epsg=4326)
         bounds = gdf.geometry.total_bounds

ecoscope/plotting/plot.py

@@ -3,7 +3,6 @@
 import plotly.graph_objs as go
 import shapely
 from plotly.subplots import make_subplots
-from sklearn.neighbors import KernelDensity
 
 
 class EcoPlotData:
@@ -243,6 +242,8 @@ def speed(trajectory):
 
 
 def plot_seasonal_dist(ndvi_vals, cuts, bandwidth=0.05, output_file=None):
+    from sklearn.neighbors import KernelDensity
+
     x = ndvi_vals.sort_values().to_numpy().reshape(-1, 1)
     kde = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(x)
     dens = np.exp(kde.score_samples(x))