Merge branch 'daredevil' of github.com:arafune/arpes into daredevil

resources/example_configuration/local_config.example.py

@@ -6,6 +6,5 @@
         "marginal_width": 300,
         "palette": "magma",
     },
-    "xarray_repr_mod": True,
     "DEBUG": True,
 }

src/arpes/_typing.py

@@ -377,7 +377,7 @@ class QPushButtonARGS(TypedDict, total=False):
 #
 
 
-class MPLPlotKwagsBasic(TypedDict, total=False):
+class MPLPlotKwargsBasic(TypedDict, total=False):
     """Kwargs for Axes.plot & Axes.fill_between."""
 
     agg_filter: Callable[[NDArray[np.float_], int], tuple[NDArray[np.float_], int, int]]
@@ -408,10 +408,10 @@ class MPLPlotKwagsBasic(TypedDict, total=False):
     visible: bool
 
 
-class MPLPlotKwargs(MPLPlotKwagsBasic, total=False):
+class MPLPlotKwargs(MPLPlotKwargsBasic, total=False):
     scalex: bool
     scaley: bool
-
+    fmt: str
     dash_capstyle: CapStyleType
     dash_joinstyle: JoinStyleType
     dashes: Sequence[float | None]
@@ -446,7 +446,6 @@ class ColorbarParam(TypedDict, total=False):
     alpha: float
     orientation: None | Literal["vertical", "horizontal"]
     ticklocation: Literal["auto", "right", "top", "bottom"]
-    drawedge: bool
     extend: Literal["neither", "both", "min", "max"]
     extendfrac: None | Literal["auto"] | float | tuple[float, float] | list[float]
     spacing: Literal["uniform", "proportional"]

src/arpes/analysis/band_analysis.py

@@ -327,7 +327,7 @@ def resolve_partial_bands_from_description(
                 "band": band,
                 "name": f"{name}_{i}",
                 "params": _build_params(
-                    old_params=params,
+                    params=params,
                     center=band_center,
                     center_stray=params.get("stray", stray),
                     marginal=marginal,

src/arpes/analysis/deconvolution.py

@@ -139,13 +139,15 @@ def make_psf(
     sigmas: dict[Hashable, float],
     *,
     fwhm: bool = True,
+    clip: float | None = None,
 ) -> xr.DataArray:
     """Produces an n-dimensional gaussian point spread function for use in deconvolve_rl.
 
     Args:
         data (DataType): input data
         sigmas (dict[str, float]): sigma values for each dimension.
         fwhm (bool): if True, sigma is FWHM, not the standard deviation.
+        clip (float | bool): clip the region by sigma-unit.
 
     Returns:
         The PSF to use.
@@ -184,11 +186,15 @@ def make_psf(
 
     coords_for_pdf_pos = np.stack(coords, axis=-1)  # point distribution function (pdf)
     logger.debug(f"shape of coords_for_pdf_pos: {coords_for_pdf_pos.shape}")
-    return xr.DataArray(
+    psf = xr.DataArray(
         multivariate_normal(mean=np.zeros(len(sigmas)), cov=cov).pdf(
             coords_for_pdf_pos,
         ),
         dims=data.dims,
         coords=psf_coords,
         name="PSF",
     )
+    if clip:
+        clipping_region = {k: slice(-clip * v, clip * v) for k, v in sigmas.items()}
+        return psf.sel(clipping_region)
+    return psf

src/arpes/analysis/savitzky_golay.py

@@ -294,7 +294,9 @@ def savitzky_golay_array(
     half_window = (window_size - 1) // 2
     # precompute coefficients
     b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window + 1)])
-    m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
+    b_inv = np.linalg.pinv(b)
+    assert isinstance(b_inv, np.matrix)
+    m = b_inv.A[deriv] * rate**deriv * factorial(deriv)
     # pad the signal at the extremes with
     # values taken from the signal itself
     firstvals = y[0] - np.abs(y[1 : half_window + 1][::-1] - y[0])

src/arpes/analysis/xps.py

@@ -38,7 +38,6 @@ def local_minima(a: NDArray[np.float_], promenance: int = 3) -> NDArray[np.float
     Returns:
         A mask where the local minima are True and other values are False.
     """
-    conditions = a == a
     for i in range(1, promenance + 1):
         current_conditions = np.r_[[False] * i, a[i:] < a[:-i]] & np.r_[a[:-i] < a[i:], [False] * i]
         conditions = conditions & current_conditions

src/arpes/plotting/fermi_edge.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Unpack
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -19,9 +19,10 @@
 if TYPE_CHECKING:
     from pathlib import Path
 
-    from _typeshed import Incomplete
     from numpy.typing import NDArray
 
+    from arpes._typing import MPLPlotKwargs
+
 __all__ = ["fermi_edge_reference", "plot_fit"]
 
 
@@ -107,16 +108,16 @@ def plot_fit(
 
 @save_plot_provenance
 def fermi_edge_reference(
-    data: xr.DataArray,
+    data_arr: xr.DataArray,
     title: str = "",
     ax: Axes | None = None,
     out: str | Path = "",
-    **kwargs: Incomplete,
+    **kwargs: Unpack[MPLPlotKwargs],
 ) -> Path | Axes:
     """Fits for and plots results for the Fermi edge on a piece of data.
 
     Args:
-        data: The data, this should be of type DataArray<lmfit.model.ModelResult>
+        data_arr: The data, this should be of type DataArray<lmfit.model.ModelResult>
         title: A title to attach to the plot
         ax:  The axes to plot to, if not specified will be generated
         out:  Where to save the plot
@@ -129,10 +130,10 @@ def fermi_edge_reference(
         "Not automatically correcting for slit shape distortions to the Fermi edge",
         stacklevel=2,
     )
-    assert isinstance(data, xr.DataArray)
+    assert isinstance(data_arr, xr.DataArray)
     sum_dimensions: set[str] = {"cycle", "phi", "kp", "kx"}
-    sum_dimensions.intersection_update(set(data.dims))
-    summed_data = data.sum(*list(sum_dimensions))
+    sum_dimensions.intersection_update(set(data_arr.dims))
+    summed_data = data_arr.sum(*list(sum_dimensions))
 
     broadcast_dimensions = [str(d) for d in summed_data.dims if str(d) != "eV"]
     msg = f"Could not product fermi edge reference. Too many dimensions: {broadcast_dimensions}"
@@ -155,18 +156,17 @@ def fermi_edge_reference(
         _, ax = plt.subplots(figsize=(8, 5))
 
     if not title:
-        title = data.S.label.replace("_", " ")
+        title = data_arr.S.label.replace("_", " ")
 
     centers.plot(ax=ax, **kwargs)
     widths.plot(ax=ax, **kwargs)
 
     if isinstance(ax, Axes):
-        ax.set_xlabel(label_for_dim(data, ax.get_xlabel()))
-        ax.set_ylabel(label_for_dim(data, ax.get_ylabel()))
+        ax.set_xlabel(label_for_dim(data_arr, ax.get_xlabel()))
+        ax.set_ylabel(label_for_dim(data_arr, ax.get_ylabel()))
         ax.set_title(title, font_size=14)
 
     if out:
         plt.savefig(path_for_plot(out), dpi=400)
         return path_for_plot(out)
-
     return ax

src/arpes/plotting/stack_plot.py

@@ -39,10 +39,10 @@
     from pathlib import Path
 
     from matplotlib.figure import Figure
-    from matplotlib.typing import ColorType, RGBAColorType
+    from matplotlib.typing import ColorType, RGBAColorType, RGBColorType
     from numpy.typing import NDArray
 
-    from arpes._typing import LegendLocation, MPLPlotKwagsBasic, XrTypes
+    from arpes._typing import LegendLocation, MPLPlotKwargsBasic
 __all__ = (
     "stack_dispersion_plot",
     "flat_stack_plot",
@@ -217,7 +217,7 @@ def flat_stack_plot(  # noqa: PLR0913
     title: str = "",
     out: str | Path = "",
     loc: LegendLocation = "upper left",
-    **kwargs: Unpack[MPLPlotKwagsBasic],
+    **kwargs: Unpack[MPLPlotKwargsBasic],
 ) -> Path | tuple[Figure | None, Axes]:
     """Generates a stack plot with all the lines distinguished by color rather than offset.
 
@@ -313,7 +313,7 @@ def flat_stack_plot(  # noqa: PLR0913
 
 @save_plot_provenance
 def stack_dispersion_plot(  # noqa: PLR0913
-    data: XrTypes,
+    data: xr.DataArray,
     *,
     stack_axis: str = "",
     ax: Axes | None = None,
@@ -326,7 +326,7 @@ def stack_dispersion_plot(  # noqa: PLR0913
     negate: bool = False,
     figsize: tuple[float, float] = (7, 7),
     title: str = "",
-    **kwargs: Unpack[MPLPlotKwagsBasic],
+    **kwargs: Unpack[MPLPlotKwargsBasic],
 ) -> Path | tuple[Figure | None, Axes]:
     """Generates a stack plot with all the lines distinguished by offset (and color).
 
@@ -573,7 +573,7 @@ def _color_for_plot(
     color: Colormap | ColorType,
     i: int,
     num_plot: int,
-) -> RGBAColorType:
+) -> RGBAColorType | RGBColorType:
     if isinstance(color, Colormap):
         cmap = color
         return cmap(np.abs(i / num_plot))

src/arpes/plotting/tof.py

@@ -11,7 +11,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Unpack
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -27,7 +27,7 @@
     import xarray as xr
     from matplotlib.figure import Figure
 
-    from arpes._typing import DataType
+    from arpes._typing import MPLPlotKwargs, MPLPlotKwargsBasic
 
 __all__ = (
     "plot_with_std",
@@ -37,37 +37,41 @@
 
 @save_plot_provenance
 def plot_with_std(
-    data: DataType,
+    data_set: xr.Dataset,  # dat_vars is used,
     name_to_plot: str = "",
     ax: Axes | None = None,
     out: str | Path = "",
-    **kwargs: tuple[int, int] | float | str,
+    figsize: tuple[float, float] = (7, 5),
+    **kwargs: Unpack[MPLPlotKwargs],
 ) -> Path | tuple[Figure | None, Axes]:
     """Makes a fill-between line plot with error bars from associated statistical errors.
 
     Args:
-       data(xr.Dataset): ARPES data that 'mean_and_deviation' is applied.
+       data_set (xr.Dataset): ARPES data that 'mean_and_deviation' is applied.
        name_to_plot(str): data name to plot, in most case "spectrum" is used.
        ax: Matplotlib Axes object
        out: (str | Path): Path name to output figure.
+       figsize (tuple[float, float]): figure size
        **kwargs: pass to subplots if figsize is set as tuple, other kwargs are pass to
            ax.fill_between/xr.DataArray.plot
     """
     if not name_to_plot:
-        var_names = [k for k in data.data_vars if "_std" not in str(k)]
+        var_names = [k for k in data_set.data_vars if "_std" not in str(k)]
         assert len(var_names) == 1
         name_to_plot = str(var_names[0])
-        assert (name_to_plot + "_std") in data.data_vars, "Has 'mean_and_deviation' been applied?"
+        assert (
+            name_to_plot + "_std"
+        ) in data_set.data_vars, "Has 'mean_and_deviation' been applied?"
 
     fig: Figure | None = None
     if ax is None:
-        fig, ax = plt.subplots(figsize=kwargs.pop("figsize", (7, 5)))
+        fig, ax = plt.subplots(figsize=figsize)
     assert isinstance(ax, Axes)
 
-    data.data_vars[name_to_plot].plot(ax=ax, **kwargs)
-    x, y = data.data_vars[name_to_plot].G.to_arrays()
+    data_set.data_vars[name_to_plot].plot(ax=ax, **kwargs)
+    x, y = data_set.data_vars[name_to_plot].G.to_arrays()
 
-    std = data.data_vars[name_to_plot + "_std"].values
+    std = data_set.data_vars[name_to_plot + "_std"].values
     ax.fill_between(x, y - std, y + std, alpha=0.3, **kwargs)
 
     if out:
@@ -85,7 +89,8 @@ def scatter_with_std(
     name_to_plot: str = "",
     ax: Axes | None = None,
     out: str | Path = "",
-    **kwargs: tuple[int, int] | float | str,
+    figsize: tuple[float, float] = (7, 5),
+    **kwargs: Unpack[MPLPlotKwargsBasic],
 ) -> Path | tuple[Figure | None, Axes]:
     """Makes a scatter plot of data with error bars generated from associated statistical errors.
 
@@ -94,6 +99,8 @@ def scatter_with_std(
         name_to_plot(str): data name to plot, in most case "spectrum" is used.
         ax: Matplotlib Axes object
         out: (str | Path): Path name to output figure.
+        figsize (tuple[float, float]): tuple for figure size.
+        fmt (str): THe form at for the data points/lines.
         **kwargs: pass to subplots if figsize is set as tuple, other kwargs are pass to ax.errorbar
     """
     if not name_to_plot:
@@ -106,7 +113,7 @@ def scatter_with_std(
 
     fig: Figure | None = None
     if ax is None:
-        fig, ax = plt.subplots(figsize=kwargs.pop("figsize", (7, 5)))
+        fig, ax = plt.subplots(figsize=figsize)
     assert isinstance(ax, Axes)
     x, y = data.data_vars[name_to_plot].G.to_arrays()
 

src/arpes/plotting/utils.py

@@ -995,7 +995,7 @@ def temperature_colorbar(
     low: float = 0,
     high: float = 300,
     ax: Axes | None = None,
-    **kwargs: Incomplete,
+    **kwargs: Unpack[ColorbarParam],
 ) -> colorbar.Colorbar:
     """Generates a colorbar suitable for temperature data with fixed extent."""
     assert isinstance(ax, Axes)
@@ -1017,7 +1017,7 @@ def delay_colorbar(
     low: float = -1,
     high: float = 1,
     ax: Axes | None = None,
-    **kwargs: Incomplete,
+    **kwargs: Unpack[ColorbarParam],
 ) -> colorbar.Colorbar:
     assert isinstance(ax, Axes)
     """Generates a colorbar suitable for delay data.
@@ -1037,7 +1037,7 @@ def temperature_colorbar_around(
     central: float,
     temperature_range: float = 50,
     ax: Axes | None = None,
-    **kwargs: Incomplete,
+    **kwargs: Unpack[ColorbarParam],
 ) -> colorbar.Colorbar:
     """Generates a colorbar suitable for temperature axes around a central value."""
     assert isinstance(ax, Axes)
@@ -1122,7 +1122,7 @@ def generic_colorbarmap_for_data(
     ax: Axes,
     *,
     keep_ticks: bool = True,
-    **kwargs: Incomplete,
+    **kwargs: Unpack[ColorbarParam],
 ) -> tuple[colorbar.Colorbar, Callable[..., RGBAColorType]]:
     """Generates a colorbar and colormap which is useful in general context.
 

src/arpes/provenance.py

@@ -77,6 +77,8 @@ class PROVENANCE(TypedDict, total=False):
     #
     old_axis: str
     new_axis: str
+    #
+    occupation_ratio: float
 
 
 def attach_id(data: XrTypes) -> None: