Ruff (#103)

Author: fsoubelet

docs/conf.py

@@ -14,6 +14,11 @@
 import sys
 import warnings
 
+from sphinx_gallery.scrapers import matplotlib_scraper
+from sphinx_gallery.sorting import ExampleTitleSortKey
+
+import pyhdtoolkit
+
 # ignore numpy warnings, see:
 # https://stackoverflow.com/questions/40845304/runtimewarning-numpy-dtype-size-changed-may-indicate-binary-incompatibility
 warnings.filterwarnings("ignore", message="numpy.dtype size changed")
@@ -31,11 +36,6 @@
 if str(TOPLEVEL_DIR) not in sys.path:
     sys.path.insert(0, str(TOPLEVEL_DIR))
 
-from sphinx_gallery.scrapers import matplotlib_scraper
-from sphinx_gallery.sorting import ExampleTitleSortKey
-
-import pyhdtoolkit
-
 # This is to tell Sphinx how to print some specific type annotations
 # See: https://stackoverflow.com/a/67483317
 # See: https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#confval-autodoc_type_aliases

examples/demo_lhc_rigid_waist_shift.py

@@ -179,12 +179,12 @@
     madx.call("acc-models-lhc/lhc.seq")
     lhc.make_lhc_beams(madx, energy=6800)
     madx.call("acc-models-lhc/operation/optics/R2022a_A30cmC30cmA10mL200cm.madx")
-    madx.command.use(sequence=f"lhcb1")
+    madx.command.use(sequence="lhcb1")
 
-    lhc.re_cycle_sequence(madx, sequence=f"lhcb1", start=f"MSIA.EXIT.B1")
-    madx.command.use(sequence=f"lhcb1")
-    lhc.make_lhc_thin(madx, sequence=f"lhcb1", slicefactor=4)
-    lhc.add_markers_around_lhc_ip(madx, sequence=f"lhcb1", ip=1, n_markers=1000, interval=0.001)
+    lhc.re_cycle_sequence(madx, sequence="lhcb1", start="MSIA.EXIT.B1")
+    madx.command.use(sequence="lhcb1")
+    lhc.make_lhc_thin(madx, sequence="lhcb1", slicefactor=4)
+    lhc.add_markers_around_lhc_ip(madx, sequence="lhcb1", ip=1, n_markers=1000, interval=0.001)
     madx.command.twiss()
     initial_twiss = madx.table.twiss.dframe()
 
@@ -206,7 +206,7 @@
 
 initial_twiss.name = initial_twiss.name.apply(lambda x: x[:-2])
 twiss_df.name = twiss_df.name.apply(lambda x: x[:-2])
-ip_s = twiss_df.s[f"ip1"]
+ip_s = twiss_df.s["ip1"]
 slimits = (ip_s - 10, ip_s + 10)
 
 around_ip = twiss_df[twiss_df.s.between(*slimits)]
@@ -285,12 +285,12 @@
 # Manipulating the equation to determine the waist yields:
 # :math:`w = L^{*} - \sqrt{\beta_0 \beta_w - \beta_w^2}`
 
-q1_right_s = twiss_df[twiss_df.name.str.contains(f"mqxa.1r1")].s[0]  # to calculate from the right Q1
-q1_left_s = twiss_df[twiss_df.name.str.contains(f"mqxa.1l1")].s[-1]  # to calculate from the left Q1
+q1_right_s = twiss_df[twiss_df.name.str.contains("mqxa.1r1")].s[0]  # to calculate from the right Q1
+q1_left_s = twiss_df[twiss_df.name.str.contains("mqxa.1l1")].s[-1]  # to calculate from the left Q1
 
 L_star = ip_s - q1_left_s  # we calculate from left Q1
 # beta0 = twiss_df[twiss_df.name.str.contains(f"mqxa.1r1")].betx[0]  # to calculate from the right
-beta0 = twiss_df[twiss_df.name.str.contains(f"mqxa.1l1")].betx[-1]  # to calculate from the left
+beta0 = twiss_df[twiss_df.name.str.contains("mqxa.1l1")].betx[-1]  # to calculate from the left
 betaw = around_ip.betx.min()
 
 ###############################################################################

examples/demo_lhc_setup.py

@@ -15,7 +15,6 @@
 """
 # sphinx_gallery_thumbnail_number = 3
 import matplotlib.pyplot as plt
-import numpy as np
 
 from pyhdtoolkit.cpymadtools import coupling, lhc, twiss
 from pyhdtoolkit.plotting.aperture import plot_physical_apertures

examples/demo_stats_fitting.py

@@ -147,7 +147,7 @@ def chi_dist(num: int, meas_used: int) -> np.ndarray:
 best_fit_func, best_fit_params = fitting.best_fit_distribution(data, 200, ax)
 ax.set_ylim(data_y_lim)
 
-ax.set_title(f"All Fitted Distributions")
+ax.set_title("All Fitted Distributions")
 ax.set_ylabel("Normed Hist Counts")
 plt.legend()
 plt.show()
@@ -188,7 +188,7 @@ def chi_dist(num: int, meas_used: int) -> np.ndarray:
     fontsize=25,
 )
 plt.vlines(pdf.idxmax(), ymin=0, ymax=max(pdf.to_numpy()), linestyles="--", color="indianred")
-ax.set_title(f"Best fit to distribution:\n" + dist_str)
+ax.set_title("Best fit to distribution:\n" + dist_str)
 ax.set_ylabel("Normed Hist Counts")
 ax.set_xlabel("x")
 ax.legend()
@@ -242,7 +242,7 @@ def chi_dist(num: int, meas_used: int) -> np.ndarray:
 # Find best fit candidate
 best_fit_func, best_fit_params = fitting.best_fit_distribution(chi_data, 200, ac)
 ac.set_ylim(dataYLim)
-ac.set_title(f"All Fitted Distributions")
+ac.set_title("All Fitted Distributions")
 ac.set_ylabel("Normed Hist Counts")
 plt.legend()
 

pyhdtoolkit/__init__.py

@@ -7,7 +7,7 @@
 :copyright: (c) 2019-2020 by Felix Soubelet.
 :license: MIT, see LICENSE for more details.
 """
-from . import cpymadtools, maths, models, optics, plotting, utils, version
+from . import cpymadtools, maths, models, optics, plotting, utils, version  # noqa: F401
 
 __title__ = "pyhdtoolkit"
 __description__ = "An all-in-one toolkit package to easy my Python work in my PhD."

pyhdtoolkit/cpymadtools/coupling.py

@@ -201,7 +201,7 @@ def get_cminus_from_coupling_rdts(
 
             >>> complex_cminus = get_cminus_from_coupling_rdts(madx, patterns=["^BPM.*B[12]$"])
     """
-    logger.debug(f"Getting coupling RDTs at selected elements thoughout the machine")
+    logger.debug("Getting coupling RDTs at selected elements thoughout the machine")
     twiss_with_rdts = get_pattern_twiss(madx, patterns=patterns, columns=MONITOR_TWISS_COLUMNS)
     twiss_with_rdts.columns = twiss_with_rdts.columns.str.upper()  # optics_functions needs capitalized names
     twiss_with_rdts[["F1001", "F1010"]] = coupling_via_cmatrix(twiss_with_rdts, output=["rdts"])

pyhdtoolkit/cpymadtools/lhc/__init__.py

@@ -28,7 +28,7 @@
             from pyhdtoolkit.cpymadtools.lhc._setup import LHCSetup
             # use this now
 """
-from ._coupling import correct_lhc_global_coupling, get_lhc_bpms_twiss_and_rdts
+from ._coupling import get_lhc_bpms_twiss_and_rdts
 from ._elements import add_markers_around_lhc_ip, install_ac_dipole_as_kicker, install_ac_dipole_as_matrix
 from ._errors import misalign_lhc_ir_quadrupoles, misalign_lhc_triplets
 from ._misc import (

pyhdtoolkit/cpymadtools/lhc/_elements.py

@@ -209,8 +209,8 @@ def install_ac_dipole_as_matrix(madx: Madx, /, deltaqx: float, deltaqy: float, b
     madx.input(f"vacmap43 = 2 * (cos(2*pi*{q2_dipole}) - cos(2*pi*{q2})) / (betyac * sin(2*pi*{q2}));")
 
     logger.debug("Defining matrix elements for transverse planes")
-    madx.input(f"hacmap: matrix, l=0, rm21=hacmap21;")
-    madx.input(f"vacmap: matrix, l=0, rm43=vacmap43;")
+    madx.input("hacmap: matrix, l=0, rm21=hacmap21;")
+    madx.input("vacmap: matrix, l=0, rm43=vacmap43;")
 
     logger.debug(f"Installing AC Dipole matrix with driven tunes of Qx_D = {q1_dipole:.5f}  |  Qy_D = {q2_dipole:.5f}")
     madx.command.seqedit(sequence=f"lhcb{beam:d}")

pyhdtoolkit/cpymadtools/lhc/_routines.py

@@ -1,12 +1,11 @@
-import numpy as np
-
 """
 .. _lhc-routines:
 
 **Routine Utilities**
 
 The functions below are routines mimicking manipulations that would be done in the ``LHC``.
 """
+import numpy as np
 import tfs
 
 from cpymad.madx import Madx

pyhdtoolkit/cpymadtools/matching.py

@@ -6,7 +6,7 @@
 
 Module with functions to perform ``MAD-X`` matchings through a `~cpymad.madx.Madx` object.
 """
-from typing import Dict, Optional, Sequence, Tuple
+from typing import Optional, Sequence
 
 from cpymad.madx import Madx
 from loguru import logger

pyhdtoolkit/cpymadtools/ptc.py

@@ -315,7 +315,7 @@ def ptc_twiss(
     icase = kwargs.pop("icase", 6)
     normal = kwargs.pop("normal", True)
 
-    logger.debug(f"Creating PTC universe")
+    logger.debug("Creating PTC universe")
     madx.ptc_create_universe()
 
     logger.trace("Creating PTC layout")
@@ -448,7 +448,7 @@ def ptc_track_particle(
         logger.debug(f"Using sequence '{sequence}' for tracking")
         madx.use(sequence=sequence)
 
-    logger.debug(f"Creating PTC universe")
+    logger.debug("Creating PTC universe")
     madx.ptc_create_universe()
 
     logger.trace("Creating PTC layout")

pyhdtoolkit/cpymadtools/tune.py

@@ -203,7 +203,7 @@ def get_footprint_patches(dynap_dframe: tfs.TfsDataFrame) -> matplotlib.collecti
             "Cannot group tune points according to starting angles and amplitudes. Try changing "
             "the 'AMPLITUDE' value in the provided TfsDataFrame's headers."
         )
-        raise
+        raise tune_grouping_error
 
     logger.debug("Determining polygon vertices")
     sx = A.shape[0] - 1

pyhdtoolkit/cpymadtools/twiss.py

@@ -76,6 +76,7 @@ def get_pattern_twiss(
             ...     ],
             ... )
     """
+    columns = columns or DEFAULT_TWISS_COLUMNS
     logger.trace("Clearing 'TWISS' flag")
     madx.select(flag="twiss", clear=True)
 

pyhdtoolkit/cpymadtools/utils.py

@@ -60,10 +60,10 @@ def export_madx_table(
         logger.debug(f"Setting NAME column as index and filtering extracted table with regex pattern '{pattern}'")
         dframe = dframe.set_index("NAME").filter(regex=pattern, axis="index").reset_index()
     if "NAME" not in dframe.headers:
-        logger.debug(f"No 'NAME' header found, adding a default value 'EXPORT'")
+        logger.debug("No 'NAME' header found, adding a default value 'EXPORT'")
         dframe.headers["NAME"] = "EXPORT"
     if "TYPE" not in dframe.headers:
-        logger.debug(f"No 'TYPE' header found, adding a default value 'EXPORT'")
+        logger.debug("No 'TYPE' header found, adding a default value 'EXPORT'")
         dframe.headers["TYPE"] = "EXPORT"
     logger.debug("Writing to disk")
     tfs.write(file_path, dframe, **kwargs)

pyhdtoolkit/maths/stats_fitting.py

@@ -11,7 +11,7 @@
 from typing import Dict, Tuple, Union
 
 import matplotlib
-import matplotlib.pyplot as plt  # if omitted, get AttributeError: module 'matplotlib' has no attribute 'axes'
+import matplotlib.pyplot as plt  # noqa: F401 | if omitted, get AttributeError: module 'matplotlib' has no attribute 'axes'
 import numpy as np
 import pandas as pd
 import scipy.stats as st

pyhdtoolkit/models/beam.py

@@ -79,4 +79,4 @@ def __repr__(self) -> str:
         )
 
     def __str__(self) -> str:
-        return f""
+        return ""

pyhdtoolkit/optics/ripken.py

@@ -80,7 +80,7 @@ def _beam_size(coordinates_distribution: np.ndarray, method: str = "std") -> flo
         return coordinates_distribution.std()
     elif method == "rms":
         return np.sqrt(np.mean(np.square(coordinates_distribution)))
-    raise NotImplementedError(f"Invalid method provided")
+    raise NotImplementedError("Invalid method provided")
 
 
 def _add_beam_size_to_df(df: tfs.TfsDataFrame, geom_emit_x: float, geom_emit_y: float) -> tfs.TfsDataFrame:

pyhdtoolkit/plotting/aperture.py

@@ -148,7 +148,7 @@ def plot_aperture(
     aperture_df = aperture_df[aperture_df.s.between(*xlimits)] if xlimits else aperture_df
 
     # Create a subplot for the lattice patches (takes a third of figure)
-    figure = plt.gcf()
+    # figure = plt.gcf()
     quadrupole_patches_axis = plt.subplot2grid((3, 3), (0, 0), colspan=3, rowspan=1)
     plot_machine_layout(
         madx,
@@ -246,7 +246,7 @@ def plot_physical_apertures(
         logger.error(f"'plane' argument should be 'x', 'horizontal', 'y' or 'vertical' not '{plane}'")
         raise ValueError("Invalid 'plane' argument.")
 
-    logger.debug(f"Plotting real element apertures")
+    logger.debug("Plotting real element apertures")
     axis, kwargs = maybe_get_ax(**kwargs)
 
     if xlimits is not None:

pyhdtoolkit/plotting/crossing.py

@@ -83,7 +83,7 @@ def plot_two_lhc_ips_crossings(
 
     # ----- Plotting figure ----- #
     logger.debug(f"Plotting crossing schemes for IP{first_ip} and IP{second_ip}")
-    figure = plt.gcf()
+    # figure = plt.gcf()
     first_ip_x_axis = plt.subplot2grid((2, 2), (0, 0), colspan=1, rowspan=1)
     first_ip_y_axis = plt.subplot2grid((2, 2), (1, 0), colspan=1, rowspan=1)
     second_ip_x_axis = plt.subplot2grid((2, 2), (0, 1), colspan=1, rowspan=1)

pyhdtoolkit/plotting/lattice.py

@@ -146,7 +146,7 @@ def plot_latwiss(
     xlimits = (twiss_df.s.min(), twiss_df.s.max()) if xlimits is None else xlimits
 
     # Create a subplot for the lattice patches (takes a third of figure)
-    figure = plt.gcf()
+    # figure = plt.gcf()
     quadrupole_patches_axis = plt.subplot2grid((3, 3), (0, 0), colspan=3, rowspan=1)
     plot_machine_layout(
         madx,

pyhdtoolkit/plotting/sbs/coupling.py

@@ -263,6 +263,6 @@ def _plot_sbs_coupling_rdt_component(
     )
     if model_df is not None and isinstance(model_df, tfs.TfsDataFrame):
         # If model dataframe is given, find S location of IP and highlight it
-        logger.debug(f"Plotting the IP location in the segment.")
+        logger.debug("Plotting the IP location in the segment.")
         ips = find_ip_s_from_segment_start(segment_df=segment_df, model_df=model_df, ip=ip)
         ax.axvline(ips, ls="--", color="grey")

pyhdtoolkit/plotting/sbs/phase.py

@@ -52,8 +52,8 @@ def plot_phase_segment_one_beam(
             ...     sbs_phasex, sbs_phasey, model=b2_model_tfs, ip=5, figsize=(8, 8)
             ... )
     """
-    logger.debug(f"Plotting the phase for both planes over the segment.")
-    legend_bbox_to_anchor = kwargs.pop("bbox_to_anchor", (0.535, 0.97))
+    logger.debug("Plotting the phase for both planes over the segment.")
+    # legend_bbox_to_anchor = kwargs.pop("bbox_to_anchor", (0.535, 0.97))
     figure, (ax1, ax2) = plt.subplots(2, 1, **kwargs)
 
     plot_phase_segment(ax1, segment_df=phase_x, model_df=model, plane="x", ip=ip)
@@ -118,7 +118,7 @@ def plot_phase_segment_both_beams(
             ...     bbox_to_anchor=(0.535, 0.94),
             ... )
     """
-    logger.debug(f"Plotting the phase for both planes over the segment.")
+    logger.debug("Plotting the phase for both planes over the segment.")
     legend_bbox_to_anchor = kwargs.pop("bbox_to_anchor", (0.535, 0.97))
     figure, ((b1x, b2x), (b1y, b2y)) = plt.subplots(2, 2, sharex=True, sharey="row", **kwargs)
 
@@ -212,6 +212,6 @@ def plot_phase_segment(
     )
     if model_df is not None and isinstance(model_df, tfs.TfsDataFrame):
         # If model dataframe is given, find S location of IP and highlight it
-        logger.debug(f"Plotting the IP location in the segment.")
+        logger.debug("Plotting the IP location in the segment.")
         ips = find_ip_s_from_segment_start(segment_df=segment_df, model_df=model_df, ip=ip)
         ax.axvline(ips, ls="--", color="grey")