Making plotting functions easier to read

docs/examples/plot_0_contours.py

@@ -25,7 +25,7 @@
 # Here's a convenience function for you
 chain2 = Chain.from_covariance(
     [3.0, 1.0],
-    [[1.0, -0.7], [-0.7, 1.5]],
+    [[1.0, -1], [-1, 2]],
     columns=["A", "B"],
     name="Another contour!",
     color="#065f46",
@@ -43,7 +43,7 @@
 # let's add markers and truth values.
 
 c.add_marker(location={"A": 0, "B": 2}, name="A point", color="orange", marker_style="P", marker_size=50)
-c.add_truth(Truth(location={"A": 0, "B": 1}))
+c.add_truth(Truth(location={"A": 0, "B": 5}))
 fig = c.plotter.plot()
 
 
@@ -81,7 +81,7 @@
 # Notice that Chain is a child of ChainConfig
 # So you could override base properties like line weights... but not samples
 c.set_override(ChainConfig(sigmas=[0, 1, 2, 3]))
-c.add_truth(Truth(location={"A": 0, "B": 1}, color="#500724"))
+c.add_truth(Truth(location={"A": 0, "B": 5}, color="#500724"))
 
 # And if we want to change the plot itself in some way, we can do that via
 c.set_plot_config(

src/chainconsumer/__init__.py

@@ -5,7 +5,7 @@
 from .chainconsumer import ChainConsumer
 from .color_finder import colors
 from .examples import make_sample
-from .plotter import PlotConfig
+from .plotting.config import PlotConfig
 from .truth import Truth
 
 __all__ = ["ChainConsumer", "Chain", "ChainConfig", "Truth", "PlotConfig", "make_sample", "Bound", "colors"]

src/chainconsumer/examples.py

@@ -15,7 +15,7 @@ def make_sample(
     diag = np.sqrt(np.diag(cov))
     outer = np.outer(diag, diag)
     cor = cov / outer
-    means = np.arange(num_dimensions) * 1.0
+    means = np.arange(num_dimensions) * 5.0
     if randomise_mean:
         means += gen.uniform(-1, 1, num_dimensions)
     norm = mv(mean=means, cov=cor)

src/chainconsumer/helpers.py

@@ -75,8 +75,8 @@ def get_smoothed_bins(
 
 def get_smoothed_histogram2d(
     chain: Chain,
-    col1: str,
-    col2: str,
+    px: str,
+    py: str,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:  # pragma: no cover
     """Returns a smoothed 2D histogram of two parameters.
 
@@ -88,8 +88,8 @@ def get_smoothed_histogram2d(
     Returns:
         tuple[np.ndarray, np.ndarray, np.ndarray]: The histogram, x bin enters, y bin centers
     """
-    x = chain.get_data(col1)
-    y = chain.get_data(col2)
+    x = chain.get_data(px)
+    y = chain.get_data(py)
     w = chain.weights
 
     if chain.grid: