Switch out black for Ruff for formatting

michellab · Jun 13, 2024 · 8b45f71 · 8b45f71
1 parent cae2d38
commit 8b45f71
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diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml
@@ -18,7 +18,7 @@ on:
 jobs:
   # Check with ruff and black
   lint_and_format:
-    name: Check for style and formatting violations with Ruff and Black
+    name: Check for style and formatting violations with Ruff
     runs-on: ubuntu-latest
     steps:
       - name: Check out code
@@ -32,13 +32,13 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install ruff black
+          pip install ruff
 
-      - name: Run Ruff
+      - name: Check style with Ruff
         run: ruff check .
 
-      - name: Check Black formatting
-        run: black --check .
+      - name: Check formatting with Ruff
+        run: ruff format --check .
 
   test:
     name: Test on ${{ matrix.os }}, Python ${{ matrix.python-version }}

diff --git a/README.md b/README.md
@@ -5,6 +5,7 @@ a3fe
 [![codecov](https://codecov.io/gh/michellab/a3fe/graph/badge.svg?token=5IGO8SCRRQ)](https://codecov.io/gh/michellab/a3fe)
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 [![Documentation Status](https://readthedocs.org/projects/a3fe/badge/?version=latest)](https://a3fe.readthedocs.io/en/latest/?badge=latest)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <img src="./a3fe_logo.png" alt="Alt text" style="width: 50%; height: 50%;">
 

diff --git a/a3fe/analyse/autocorrelation.py b/a3fe/analyse/autocorrelation.py
@@ -1,7 +1,7 @@
 """Functionality to calculate autocorrelation
 
-Aknowledgement: "get_statistical_inefficiency" is copied almost exactly from 
-"statisticalInefficiency_multiscale" here: 
+Aknowledgement: "get_statistical_inefficiency" is copied almost exactly from
+"statisticalInefficiency_multiscale" here:
 https://github.com/choderalab/automatic-equilibration-detection/blob/master/examples/liquid-argon/equilibration.py
 The license and original authorship are preserved below:
 

diff --git a/a3fe/analyse/detect_equil.py b/a3fe/analyse/detect_equil.py
@@ -29,7 +29,10 @@
 )
 
 
-def check_equil_block_gradient(lam_win: "LamWindow", run_nos: _Optional[_List[int]]) -> _Tuple[bool, _Optional[float]]:  # type: ignore # noqa: F821
+def check_equil_block_gradient(
+    lam_win: "LamWindow",  # type: ignore # noqa: F821
+    run_nos: _Optional[_List[int]],
+) -> _Tuple[bool, _Optional[float]]:
     """
     Check if the ensemble of simulations at the lambda window is
     equilibrated based on the ensemble gradient between averaged blocks.
@@ -149,7 +152,10 @@ def check_equil_block_gradient(lam_win: "LamWindow", run_nos: _Optional[_List[in
     return equilibrated, equil_time
 
 
-def check_equil_chodera(lam_win: "LamWindow", run_nos: _Optional[_List[int]] = None) -> _Tuple[bool, _Optional[float]]:  # type: ignore # noqa: F821
+def check_equil_chodera(
+    lam_win: "LamWindow",  # type: ignore # noqa: F821
+    run_nos: _Optional[_List[int]] = None,
+) -> _Tuple[bool, _Optional[float]]:
     """
     Check if the ensemble of simulations at the lambda window is
     equilibrated based Chodera's method of maximising the number
@@ -337,7 +343,9 @@ def check_equil_multiwindow(
     # Write out data
     with open(f"{output_dir}/check_equil_multiwindow.txt", "w") as ofile:
         ofile.write(f"Equilibrated: {equilibrated}\n")
-        ofile.write(f"Overall gradient {overall_grad_dg} +/- {conf_int[1] - overall_grad_dg} kcal mol^-1 ns^-1\n")  # type: ignore
+        ofile.write(
+            f"Overall gradient {overall_grad_dg} +/- {conf_int[1] - overall_grad_dg} kcal mol^-1 ns^-1\n"
+        )  # type: ignore
         ofile.write(f"Fractional equilibration time: {fractional_equil_time} \n")
         ofile.write(f"Equilibration time: {equil_time} ns\n")
         ofile.write(f"Run numbers: {run_nos}\n")
@@ -549,13 +557,14 @@ def check_equil_multiwindow_modified_geweke(
             last_slice_means,
             equal_var=False,  # Welches t-test
             alternative="two-sided",
-        )[
-            1
-        ]  # First value is the t statistic - we want p
+        )[1]  # First value is the t statistic - we want p
 
         # Store results - note that time is the per-run time
         p_vals_and_times.append(
-            (p_value, overall_times[0][0])
+            (
+                p_value,
+                overall_times[0][0],
+            )
         )  # second value is the equilibration time
 
         # Check if the p-value is greater than the cutoff
@@ -704,13 +713,14 @@ def check_equil_multiwindow_paired_t(
             first_slice_means,
             last_slice_means,
             alternative="two-sided",
-        )[
-            1
-        ]  # First value is the t statistic - we want p
+        )[1]  # First value is the t statistic - we want p
 
         # Store results - note that time is the per-run time
         p_vals_and_times.append(
-            (p_value, overall_times[0][0])
+            (
+                p_value,
+                overall_times[0][0],
+            )
         )  # second value is the equilibration time
 
         # Check if the p-value is greater than the cutoff
@@ -765,7 +775,8 @@ def check_equil_multiwindow_paired_t(
 
 
 def dummy_check_equil_multiwindow(
-    lam_win: "LamWindow", run_nos: _Optional[_List[int]] = None  # type: ignore # noqa: F821
+    lam_win: "LamWindow",  # type: ignore # noqa: F821
+    run_nos: _Optional[_List[int]] = None,
 ) -> _Tuple[bool, _Optional[float]]:
     """
     Becuse "check_equil_multiwindow" checks multiple windows at once and sets the _equilibrated

diff --git a/a3fe/analyse/plot.py b/a3fe/analyse/plot.py
@@ -96,7 +96,9 @@ def general_plot(
     fig, ax = _plt.subplots(figsize=(8, 6))
     ax.plot(x_vals, y_avg, label="Mean", linewidth=2)
     for i, entry in enumerate(y_vals):
-        ax.plot(x_vals, entry, alpha=0.5, label=f"run {run_nos[i] if run_nos else i+1}")
+        ax.plot(
+            x_vals, entry, alpha=0.5, label=f"run {run_nos[i] if run_nos else i + 1}"
+        )
     if vline_val is not None:
         ax.axvline(x=vline_val, color="red", linestyle="dashed")
     if hline_val is not None:
@@ -198,9 +200,11 @@ def plot_gradient_stats(
             edgecolor="black",
             yerr=gradients_data.sems_overall,
         )
-        ax.set_ylabel(
-            r"$\langle \frac{\mathrm{d}h}{\mathrm{d}\lambda}\rangle _{\lambda} $ / kcal mol$^{-1}$"
-        ),
+        (
+            ax.set_ylabel(
+                r"$\langle \frac{\mathrm{d}h}{\mathrm{d}\lambda}\rangle _{\lambda} $ / kcal mol$^{-1}$"
+            ),
+        )
 
     elif plot_type == "stat_ineff":
         ax.bar(
@@ -219,9 +223,11 @@ def plot_gradient_stats(
             width=0.02,
             edgecolor="black",
         )
-        ax.set_ylabel(
-            r"$\sqrt{t}$SEM($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $) / kcal mol$^{-1}$ ns$^{1/2}$"
-        ),
+        (
+            ax.set_ylabel(
+                r"$\sqrt{t}$SEM($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $) / kcal mol$^{-1}$ ns$^{1/2}$"
+            ),
+        )
         ax.legend()
         # Get second y axis so we can plot on different scales
         ax2 = ax.twinx()
@@ -253,9 +259,11 @@ def plot_gradient_stats(
         # Add vertical lines at optimal lambda vals
         for lam_val in optimal_lam_vals:
             ax2.axvline(x=lam_val, color="black", linestyle="dashed", linewidth=0.5)
-        ax2.set_ylabel(
-            r"Integrated $\sqrt{t}$SEM($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $) / kcal mol$^{-1}$ ns$^{1/2}$"
-        ),
+        (
+            ax2.set_ylabel(
+                r"Integrated $\sqrt{t}$SEM($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $) / kcal mol$^{-1}$ ns$^{1/2}$"
+            ),
+        )
 
     elif plot_type == "pred_best_simtime":
         # Calculate the predicted optimum simulation time
@@ -277,7 +285,7 @@ def plot_gradient_stats(
             width=0.02,
             edgecolor="black",
         )
-        ax.set_ylabel(r"Predicted most efficient runtimes per run /  ns"),
+        (ax.set_ylabel(r"Predicted most efficient runtimes per run /  ns"),)
         ax.legend()
 
     elif plot_type == "integrated_var":
@@ -288,9 +296,11 @@ def plot_gradient_stats(
             width=0.02,
             edgecolor="black",
         )
-        ax.set_ylabel(
-            r"(Var($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $))$^{1/2}$ / kcal mol$^{-1}$"
-        ),
+        (
+            ax.set_ylabel(
+                r"(Var($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $))$^{1/2}$ / kcal mol$^{-1}$"
+            ),
+        )
         ax.legend()
         # Get second y axis so we can plot on different scales
         ax2 = ax.twinx()
@@ -318,9 +328,11 @@ def plot_gradient_stats(
         # Add vertical lines at optimal lambda vals
         for lam_val in optimal_lam_vals:
             ax2.axvline(x=lam_val, color="black", linestyle="dashed", linewidth=0.5)
-        ax2.set_ylabel(
-            r"Integrated (Var($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $))$^{1/2}$ / kcal mol$^{-1}$"
-        ),
+        (
+            ax2.set_ylabel(
+                r"Integrated (Var($\frac{\mathrm{d}h}{\mathrm{d}\lambda} $))$^{1/2}$ / kcal mol$^{-1}$"
+            ),
+        )
         ax2.legend()
         ax2.legend()
 
@@ -374,7 +386,7 @@ def plot_gradient_hists(
                     bins=50,
                     density=True,
                     alpha=0.5,
-                    label=f"Run {run_nos[j] if run_nos else j+1}",
+                    label=f"Run {run_nos[j] if run_nos else j + 1}",
                 )
             ax.legend()
             ax.set_title(f"$\lambda$ = {gradients_data.lam_vals[i]}")
@@ -455,7 +467,7 @@ def plot_gradient_timeseries(
                     gradients_data.times[i],
                     gradients,
                     alpha=0.5,
-                    label=f"Run {run_nos[j] if run_nos else j+1}",
+                    label=f"Run {run_nos[j] if run_nos else j + 1}",
                 )
             ax.legend()
             ax.set_title(f"$\lambda$ = {gradients_data.lam_vals[i]}")
@@ -709,7 +721,7 @@ def plot_overlap_mats(
     for i in range(n_runs):
         plot_overlap_mat(
             ax=axs[i],
-            name=f"Run {i+1}" if not predicted else "Predicted",
+            name=f"Run {i + 1}" if not predicted else "Predicted",
             mbar_file=mbar_outfiles[i] if mbar_outfiles else None,
             predicted=predicted,
             gradient_data=gradient_data,
@@ -891,9 +903,11 @@ def _plot_mbar_gradient_convergence_single_run(
 
     # Labels
     ax.set_xlabel(r"$\lambda$")
-    ax.set_ylabel(
-        r"$\langle \frac{\mathrm{d}h}{\mathrm{d}\lambda}\rangle _{\lambda} $ / kcal mol$^{-1}$"
-    ),
+    (
+        ax.set_ylabel(
+            r"$\langle \frac{\mathrm{d}h}{\mathrm{d}\lambda}\rangle _{\lambda} $ / kcal mol$^{-1}$"
+        ),
+    )
     ax.set_title(run_name)
 
     # Return the colour mapper so we can add it to the plot
@@ -1053,7 +1067,7 @@ def plot_rmsds(
             ax.set_xlabel("Time (ns)")
             ax.set_ylabel(r"RMSD ($\AA$)")
             for j, rmsd in enumerate(rmsds):
-                ax.plot(times, rmsd, label=f"Run {j+1}")
+                ax.plot(times, rmsd, label=f"Run {j + 1}")
             ax.legend()
 
             # If we have equilibration data, plot this
@@ -1069,7 +1083,7 @@ def plot_rmsds(
     group_selection_name = (
         "none" if not group_selection else group_selection.replace(" ", "")
     )
-    name = f"{output_dir}/rmsd_{selection.replace(' ','')}_{group_selection_name}"  # Use selection string to make sure save name is unique
+    name = f"{output_dir}/rmsd_{selection.replace(' ', '')}_{group_selection_name}"  # Use selection string to make sure save name is unique
     fig.savefig(
         name, dpi=300, bbox_inches="tight", facecolor="white", transparent=False
     )

diff --git a/a3fe/analyse/process_grads.py b/a3fe/analyse/process_grads.py
@@ -577,7 +577,10 @@ def get_time_series_multiwindow(
     # simulation time for the whole calculation
     n_runs = len(run_nos)
     overall_dgs = _np.zeros(
-        [n_runs, 100]
+        [
+            n_runs,
+            100,
+        ]
     )  # One point for each % of the total simulation time
     overall_times = _np.zeros([n_runs, 100])
     for lam_win in lambda_windows:
@@ -696,15 +699,16 @@ def get_time_series_multiwindow_mbar(
     n_runs = len(run_nos)
     n_points = 100
     overall_dgs = _np.zeros(
-        [n_runs, n_points]
+        [
+            n_runs,
+            n_points,
+        ]
     )  # One point for each % of the total simulation time
     overall_times = _np.zeros([n_runs, n_points])
     start_and_end_fracs = [
         (i, i + (end_frac - start_frac) / n_points)
         for i in _np.linspace(start_frac, end_frac, n_points + 1)
-    ][
-        :-1
-    ]  # Throw away the last point as > 1
+    ][:-1]  # Throw away the last point as > 1
     # Round the values to avoid floating point errors
     start_and_end_fracs = [
         (round(x[0], 5), round(x[1], 5)) for x in start_and_end_fracs

diff --git a/a3fe/analyse/waters.py b/a3fe/analyse/waters.py
@@ -148,7 +148,7 @@ def get_av_waters_lambda_window(
     avg_close_waters = _np.full(n_runs, _np.nan)
     for i, run_no in enumerate(run_nos):
         print_fn(
-            f"Calculating average number of waters for run {i+1} of {n_runs} for lambda window {lam_val}"
+            f"Calculating average number of waters for run {i + 1} of {n_runs} for lambda window {lam_val}"
         )
         sim = simulations[run_no - 1]
         avg_close_waters[i] = get_av_waters_simulation(

diff --git a/a3fe/read/_process_bss_systems.py b/a3fe/read/_process_bss_systems.py
@@ -5,7 +5,9 @@
 from sire.legacy import Mol as _SireMol
 
 
-def rename_lig(bss_system: _BSS._SireWrappers._system.System, new_name: str = "LIG") -> None:  # type: ignore
+def rename_lig(
+    bss_system: _BSS._SireWrappers._system.System, new_name: str = "LIG"
+) -> None:  # type: ignore
     """Rename the ligand in a BSS system.
 
     Parameters

diff --git a/a3fe/read/_process_somd_files.py b/a3fe/read/_process_somd_files.py
@@ -271,9 +271,7 @@ def write_truncated_sim_datafile(
         raise ValueError(f"No data found in simfile: {simfile}.")
     start_reading_idx = (
         # + 1 and -1 because no data is written at t = 0
-        round((final_idx - start_data_idx + 1) * fraction_initial)
-        + start_data_idx
-        - 1
+        round((final_idx - start_data_idx + 1) * fraction_initial) + start_data_idx - 1
     )
     end_reading_idx = (
         round((final_idx - start_data_idx + 1) * fraction_final) + start_data_idx - 1

diff --git a/a3fe/read/_read_exp_dgs.py b/a3fe/read/_read_exp_dgs.py
@@ -1,5 +1,5 @@
 """Functionality to read experimental free energies from a supplied csv file.
-   This must have the columns: calc_base_dir, name, exp_dg, exp_er"""
+This must have the columns: calc_base_dir, name, exp_dg, exp_er"""
 
 import os as _os
 

diff --git a/a3fe/run/_simulation_runner.py b/a3fe/run/_simulation_runner.py
@@ -420,7 +420,7 @@ def analyse(
             )
             for i in range(self.ensemble_size):
                 ofile.write(
-                    f"Free energy from run {i+1}: {dg_overall[i]: .3f} +/- {er_overall[i]:.3f} kcal/mol\n"
+                    f"Free energy from run {i + 1}: {dg_overall[i]: .3f} +/- {er_overall[i]:.3f} kcal/mol\n"
                 )
             ofile.write(
                 "Errors are 95 % C.I.s based on the assumption of a Gaussian distribution of free energies\n"
@@ -831,7 +831,9 @@ def update_paths(self, old_sub_path: str, new_sub_path: str) -> None:
         if hasattr(self, "virtual_queue"):
             # Virtual queue may have already been updated
             if new_sub_path not in self.virtual_queue.log_dir:  # type: ignore
-                self.virtual_queue.log_dir = self.virtual_queue.log_dir.replace(old_sub_path, new_sub_path)  # type: ignore
+                self.virtual_queue.log_dir = self.virtual_queue.log_dir.replace(
+                    old_sub_path, new_sub_path
+                )  # type: ignore
                 self.virtual_queue._set_up_logging()  # type: ignore
 
         # Update the paths of any sub-simulation runners

diff --git a/a3fe/run/_utils.py b/a3fe/run/_utils.py
@@ -1,4 +1,4 @@
-""""Utilities for SimulationRunners."""
+""" "Utilities for SimulationRunners."""
 
 from __future__ import annotations
 
@@ -29,7 +29,8 @@ def check_has_wat_and_box(system: _BSS._SireWrappers._system.System) -> None:  #
 
 
 def get_simtime(
-    sim_runner: "SimulationRunner", run_nos: _Optional[_List[int]] = None  # noqa: F821
+    sim_runner: "SimulationRunner",  # noqa: F821
+    run_nos: _Optional[_List[int]] = None,
 ) -> float:
     """
     Get the simulation time of a sub simulation runner, in ns. This function