Cleanup

pyproject.toml

@@ -75,7 +75,7 @@ ttfsc = "ttfsc:cli"
 
 # https://docs.astral.sh/ruff
 [tool.ruff]
-line-length = 88
+line-length = 125
 target-version = "py38"
 src = ["src"]
 
@@ -112,7 +112,7 @@ skip-magic-trailing-comma = false  # default is false
 # https://mypy.readthedocs.io/en/stable/config_file.html
 [tool.mypy]
 files = "src/**/"
-strict = true
+strict = false
 disallow_any_generics = false
 disallow_subclassing_any = false
 show_error_codes = true

src/ttfsc/__init__.py

@@ -1,4 +1,4 @@
-"""Use Fourier shell correlation to estimate the resolution of cryo-EM images and volumes"""
+"""Use Fourier shell correlation to estimate the resolution of cryo-EM images and volumes."""
 
 from importlib.metadata import PackageNotFoundError, version
 
@@ -9,4 +9,6 @@
 __author__ = "Johannes Elferich"
 __email__ = "[email protected]"
 
-from .cli import cli
+from ._cli import cli
+
+__all__ = ["cli"]

src/ttfsc/cli.py → src/ttfsc/_cli.py

@@ -1,57 +1,60 @@
 from pathlib import Path
-from typing import Optional, Annotated
+from typing import Annotated, Optional
 
 import mrcfile
-import typer
 import torch
-from torch_fourier_shell_correlation import fsc
+import typer
 from rich import print as rprint
+from torch_fourier_shell_correlation import fsc
+
+cli = typer.Typer(name="ttfsc", no_args_is_help=True, add_completion=False)
 
-cli = typer.Typer(name='ttctf', no_args_is_help=True, add_completion=False)
 
 @cli.command(no_args_is_help=True)
 def ttfsc_cli(
     map1: Annotated[Path, typer.Argument(show_default=False)],
     map2: Annotated[Path, typer.Argument(show_default=False)],
-    pixel_spacing_angstroms: Annotated[Optional[float], typer.Option('--pixel-spacing-angstroms', show_default=False, help="Pixel spacing in Å/px, taken from header if not set")] = None,
-    plot: Annotated[bool, typer.Option('--plot')] = True,
-    plot_with_matplotlib: Annotated[bool, typer.Option('--plot-with-matplotlib')] = False,
-    fsc_threshold: Annotated[float, typer.Option('--fsc-treshold', show_default=False, help="FSC treshold")] = 0.143,
-    #mask: Annotated[]
-):
+    pixel_spacing_angstroms: Annotated[
+        Optional[float],
+        typer.Option(
+            "--pixel-spacing-angstroms", show_default=False, help="Pixel spacing in Å/px, taken from header if not set"
+        ),
+    ] = None,
+    plot: Annotated[bool, typer.Option("--plot")] = True,
+    plot_with_matplotlib: Annotated[bool, typer.Option("--plot-with-matplotlib")] = False,
+    fsc_threshold: Annotated[float, typer.Option("--fsc-threshold", show_default=False, help="FSC threshold")] = 0.143,
+    # mask: Annotated[]
+) -> None:
     with mrcfile.open(map1) as f:
         map1_tensor = torch.tensor(f.data)
         if pixel_spacing_angstroms is None:
             pixel_spacing_angstroms = f.voxel_size.x
     with mrcfile.open(map2) as f:
         map2_tensor = torch.tensor(f.data)
-
-
+
     frequency_pixels = torch.fft.rfftfreq(map1_tensor.shape[0])
     resolution_angstroms = (1 / frequency_pixels) * pixel_spacing_angstroms
     fsc_values = fsc(map1_tensor, map2_tensor)
 
-    estimated_resolution_frequency_pixel = float(frequency_pixels[(fsc_values < fsc_threshold).nonzero()[0]-1])
-    estimated_resolution_angstrom = float(resolution_angstroms[(fsc_values < fsc_threshold).nonzero()[0]-1])
+    estimated_resolution_frequency_pixel = float(frequency_pixels[(fsc_values < fsc_threshold).nonzero()[0] - 1])
+    estimated_resolution_angstrom = float(resolution_angstroms[(fsc_values < fsc_threshold).nonzero()[0] - 1])
     rprint(f"Estimated resolution using {fsc_threshold} criterion: {estimated_resolution_angstrom:.2f} Å")
 
     if plot:
-        from .plotting import plot_matplotlib, plot_plottile
+        from ._plotting import plot_matplotlib, plot_plottile
+
         if plot_with_matplotlib:
             plot_matplotlib(
                 fsc_values=fsc_values,
                 resolution_angstroms=resolution_angstroms,
                 estimated_resolution_angstrom=estimated_resolution_angstrom,
-                fsc_threshold=fsc_threshold
+                fsc_threshold=fsc_threshold,
             )
         else:
             plot_plottile(
                 fsc_values=fsc_values,
                 frequency_pixels=frequency_pixels,
                 pixel_spacing_angstroms=pixel_spacing_angstroms,
                 estimated_resolution_frequency_pixel=estimated_resolution_frequency_pixel,
-                fsc_threshold=fsc_threshold
+                fsc_threshold=fsc_threshold,
             )
-
-
-

src/ttfsc/_plotting.py

@@ -0,0 +1,53 @@
+import torch
+
+
+def plot_matplotlib(
+    fsc_values: torch.Tensor, resolution_angstroms: torch.Tensor, estimated_resolution_angstrom: float, fsc_threshold: float
+) -> None:
+    from matplotlib import pyplot as plt
+
+    plt.hlines(0, resolution_angstroms[1], resolution_angstroms[-2], "black")
+    plt.plot(resolution_angstroms, fsc_values, label="FSC (unmasked)")
+    plt.xlabel("Resolution (Å)")
+    plt.ylabel("Correlation")
+    plt.xscale("log")
+    plt.xlim(resolution_angstroms[1], resolution_angstroms[-2])
+    plt.ylim(-0.05, 1.05)
+    plt.hlines(fsc_threshold, resolution_angstroms[1], estimated_resolution_angstrom, "red", "--")
+    plt.vlines(estimated_resolution_angstrom, -0.05, fsc_threshold, "red", "--")
+    plt.legend()
+    plt.tight_layout()
+    plt.show()
+
+
+def plot_plottile(
+    fsc_values: torch.Tensor,
+    frequency_pixels: torch.Tensor,
+    pixel_spacing_angstroms: float,
+    estimated_resolution_frequency_pixel: float,
+    fsc_threshold: float,
+) -> None:
+    import plotille
+
+    fig = plotille.Figure()
+    fig.width = 60
+    fig.height = 20
+    fig.set_x_limits(float(frequency_pixels[1]), float(frequency_pixels[-1]))
+    fig.set_y_limits(0, 1)
+
+    def resolution_callback(x: float, _: float) -> str:
+        return f"{(1 / x) * pixel_spacing_angstroms:.2f}"
+
+    fig.x_ticks_fkt = resolution_callback
+    fig.plot(frequency_pixels[1:].numpy(), fsc_values[1:].numpy(), lc="blue", label="FSC")
+
+    fig.plot(
+        [float(frequency_pixels[1].numpy()), estimated_resolution_frequency_pixel],
+        [fsc_threshold, fsc_threshold],
+        lc="red",
+        label=" ",
+    )
+    fig.plot(
+        [estimated_resolution_frequency_pixel, estimated_resolution_frequency_pixel], [0, fsc_threshold], lc="red", label=" "
+    )
+    print(fig.show(legend=True))