Rewrite ascii using rich

README.md

@@ -159,7 +159,7 @@ Rationale (for both): physt is dumb, but precise.
 - (optional) astropy - additional binning algorithms
 - (optional) folium - map plotting
 - (optional) vega3 - for vega in-line in IPython notebook (note that to generate vega JSON, this is not necessary)
-- (optional) xtermcolor - for ASCII color maps
+- (optional) rich - console output including plots
 - (testing) pytest
 - (docs) sphinx, sphinx_rtd_theme, ipython
 

pyproject.toml

@@ -39,7 +39,7 @@ plotly = ["plotly"]
 # vega3 = ["vega3"]
 folium = ["folium"]
 # root = ["uproot3"]  # TODO: Update to uproot4
-terminal = ["xtermcolor", "rich"]
+terminal = ["rich"]
 all = [
     "physt[astropy,dev,dask,pandas,polars,xarray,matplotlib,plotly,folium,terminal]"
 ]

src/physt/plotting/ascii.py

@@ -5,17 +5,24 @@
 """
 from __future__ import annotations
 
+import numpy as np
+import rich.console
+import rich.color
+from rich.style import Style
+from rich.text import Text
 import typing
-from contextlib import suppress
+
+from physt.plotting.common import get_value_format
 
 if typing.TYPE_CHECKING:
     from physt.types import Histogram1D, Histogram2D
 
 
-types: typing.Tuple[str, ...] = ("hbar",)
+types: typing.Tuple[str, ...] = ("hbar", "map")
 
 dims = {
     "hbar": [1],
+    "map": [2],
 }
 
 
@@ -29,70 +36,110 @@ def hbar(h1: "Histogram1D", width: int = 80, show_values: bool = False) -> None:
             print("#" * data[i])
 
 
-with suppress(ImportError):
-    import xtermcolor
+SUPPORTED_CMAPS = ("Greys", "Greys_r")
+DEFAULT_CMAP = SUPPORTED_CMAPS[1]
+
+SHADING_CHARS = " ░▒▓█"
+"""Characters used for shading in the ASCII map."""
 
-    SUPPORTED_CMAPS = ("Greys", "Greys_r")
-    DEFAULT_CMAP = SUPPORTED_CMAPS[1]
+FULL_SQUARE_CHAR = SHADING_CHARS[-1]
 
-    def map(h2: "Histogram2D", **kwargs) -> None:
-        """Heat map.
 
-        Note: Available only if xtermcolor present.
-        """
+def map(
+        h2: "Histogram2D",
+        use_color: typing.Optional[bool] = None,
+        **kwargs) -> None:
+    """Heat map."""
 
-        # Value format
-        val_format = kwargs.pop("value_format", ".2f")
-        if isinstance(val_format, str):
+    console = rich.console.Console()
+    color_system = console.color_system
+    if use_color is None:
+        use_color = bool(color_system)
 
-            def value_format(val):
-                return ("{0:" + val_format + "}").format(val)
+    # Value format
+    value_format = get_value_format(kwargs.pop("value_format", ".2f"))
+    cmap_data = _get_cmap_data(h2.frequencies, kwargs)
 
-        data = (h2.frequencies / h2.frequencies.max() * 255).astype(int)
+    if use_color:
+        def _render_cell(value: float) -> Text:
+            color = rich.color.Color.from_rgb(
+                    int(255 * value), int(255 * value), int(255 * value)
+            )
+            return Text(FULL_SQUARE_CHAR, style=Style(color=color))
 
         # Colour map
         cmap = kwargs.pop("cmap", DEFAULT_CMAP)
         if cmap == "Greys":
-            data = 255 - data
-            colorbar_range = range(h2.shape[1] + 1, -1, -1)
+            cmap_data = 1.0 - cmap_data
+            colorbar_range = np.arange(h2.shape[1] + 1, -1, -1) / h2.shape[1]
         elif cmap == "Greys_r":
-            colorbar_range = range(h2.shape[1] + 2)
+            colorbar_range = np.arange(h2.shape[1] + 1) / h2.shape[1]
         else:
             raise ValueError(
                 f"Unsupported colormap: {cmap}, select from: {SUPPORTED_CMAPS}"
             )
-        colors = (65536 + 256 + 1) * data
 
-        print(
-            (value_format(h2.get_bin_right_edges(0)[-1]) + " →").rjust(
-                h2.shape[1] + 2, " "
-            )
+    else:
+        def _render_cell(value: float) -> Text:
+            return Text(SHADING_CHARS[int(np.clip(value * (len(SHADING_CHARS)), 0, len(SHADING_CHARS) - 1))])
+
+        colorbar_range = np.arange(h2.shape[1] + 1) / h2.shape[1]
+
+    console.print(
+        (value_format(h2.get_bin_right_edges(0)[-1]) + " →").rjust(
+            h2.shape[1] + 2, " "
         )
-        print("+" + "-" * h2.shape[1] + "+")
-        for i in range(h2.shape[0] - 1, -1, -1):
-            line_frags = [
-                xtermcolor.colorize("█", bg=0, rgb=colors[i, j])
-                for j in range(h2.shape[1])
-            ]
-            line = "|" + "".join(line_frags) + "|"
-            if i == h2.shape[0] - 1:
-                line += value_format(h2.get_bin_right_edges(1)[-1]) + " ↑"
-            if i == 0:
-                line += value_format(h2.get_bin_left_edges(1)[0]) + " ↓"
-            print(line)
-        print("+" + "-" * h2.shape[1] + "+")
-        print("←", value_format(h2.get_bin_left_edges(0)[0]))
-        colorbar_frags = [
-            xtermcolor.colorize(
-                "█", bg=0, rgb=(65536 + 256 + 1) * int(j * 255 / (h2.shape[1] + 2))
-            )
-            for j in colorbar_range
+    )
+    console.print("+" + "-" * h2.shape[1] + "+")
+    for i in range(h2.shape[0] - 1, -1, -1):
+        line_frags = ["|"]
+        line_frags += [
+            _render_cell(cmap_data[i, j])
+            for j in range(h2.shape[1])
         ]
-        colorbar = "".join(colorbar_frags)
-        print()
-        print("↓", 0)
-        print(colorbar)
-        print(str(h2.frequencies.max()).rjust(h2.shape[1], " "), "↑")
-
-    types = types + ("map",)
-    dims["map"] = [2]
+        line_frags.append("|")
+        if i == h2.shape[0] - 1:
+            line_frags.append(value_format(h2.get_bin_right_edges(1)[-1]) + " ↑")
+        if i == 0:
+            line_frags.append(value_format(h2.get_bin_left_edges(1)[0]) + " ↓")
+        console.print(*line_frags, sep="")
+    console.print("+" + "-" * h2.shape[1] + "+")
+    console.print("←", value_format(h2.get_bin_left_edges(0)[0]))
+    colorbar_frags = [
+        _render_cell(j)
+        for j in colorbar_range
+    ]
+    console.print("↓", 0, sep="")
+    console.print(*colorbar_frags, sep="")
+    console.print(str(h2.frequencies.max()).rjust(h2.shape[1], " "), "↑")
+
+
+def _get_cmap_data(data, kwargs) -> np.ndarray:
+    """Get normalized values to be used with a colormap.
+
+    Parameters
+    ----------
+    data : array_like
+    cmap_min : Optional[float] or "min"
+        By default 0. If "min", minimum value of the data.
+    cmap_max : Optional[float]
+        By default, maximum value of the data
+    cmap_normalize : Optional[str]
+
+    Returns
+    -------
+    normalized_data : array_like
+    """
+    norm = kwargs.pop("cmap_normalize", None)
+    if norm == "log":
+        cmap_max = np.log(kwargs.pop("cmap_max", data.max()))
+        cmap_min = np.log(kwargs.pop("cmap_min", data[data > 0].min()))
+        return (np.log(data) - cmap_min) / (cmap_max - cmap_min)
+    elif not norm:
+        cmap_max = kwargs.pop("cmap_max", data.max())
+        cmap_min = kwargs.pop("cmap_min", 0)
+        if cmap_min == "min":
+            cmap_min = data.min()
+        return (data - cmap_min) / (cmap_max - cmap_min)
+    else:
+        raise ValueError(f"Unsupported normalization: {norm}")

src/physt/plotting/common.py

@@ -288,3 +288,5 @@ def __call__(self, h1: Histogram1D, min_: float, max_: float) -> TickCollection:
         ticks = self.get_time_ticks(h1, level, min_, max_)
         tick_labels = self.format_time_ticks(ticks, level=level)
         return ticks, tick_labels
+
+