🚨 modifications from pre-commit run --all-files

Author: elenafuengar

.github/workflows/manual_tests_CPU.yml

@@ -37,5 +37,3 @@ jobs:
     - name: Run pytest
       run:  |
         python -m pytest --cov=bihc --cov-report=term-missing -vv
-
-

.github/workflows/nightly_tests_CPU_p3.11.yml

@@ -37,4 +37,3 @@ jobs:
     - name: Run pytest
       run:  |
         python -m pytest --cov=bihc --cov-report=term-missing -vv
-

.github/workflows/nightly_tests_CPU_p3.12.yml

@@ -37,4 +37,3 @@ jobs:
     - name: Run pytest
       run:  |
         python -m pytest --cov=bihc --cov-report=term-missing -vv
-

.github/workflows/publish.yml

@@ -59,4 +59,4 @@ jobs:
       - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          verbose: true
+          verbose: true

.github/workflows/publish_to_testPyPI.yaml

@@ -111,4 +111,4 @@ jobs:
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           repository-url: https://test.pypi.org/legacy/
-          verbose: true
+          verbose: true

.readthedocs.yaml

@@ -32,4 +32,4 @@ sphinx:
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
 python:
   install:
-    - requirements: docs/requirements.txt
+    - requirements: docs/requirements.txt

CITATION.cff

@@ -8,7 +8,7 @@ authors:
   given-names: "Leonardo"
   orcid: "https://orcid.org/0000-0002-1405-2739"
 title: "BIHC"
-version: 0.0.8.10 
-doi: 
+version: 0.0.8.10
+doi:
 date-released: 01-01-2025
 url: "https://github.com/ImpedanCEI/BIHC"

MANIFEST.in

@@ -4,4 +4,4 @@ include pyproject.toml
 include *.md
 
 # Include the license file
-include LICENSE.txt
+include LICENSE.txt

README.md

@@ -20,7 +20,7 @@ For specific needs, please contact the maintainers :woman_technologist: :man_tec
 There is a [paper about `bihc`](10.18429/JACoW-HB2023-THBP52), presented at 8th ICFA Advanced Beam Dynamics Workshop on High-Intensity and High-Brightness Hadron Beam (0ct. 2023).
 If you are using `bihc` in your scientific research, please help our scientific
 visibility by citing our work:
-> [1] E. de la Fuente, L. Sito, F. Giordano, G. Rumolo, B. Salvant, and C. Zannini, “A Python Package to Compute Beam-Induced Heating in Particle Accelerators and Applications,” JACoW, vol. HB2023, pp. 611–614, 2024, doi: [https://doi.10.18429/JACoW-HB2023-THBP52](https://jacow.org/hb2023/papers/thbp52.pdf). 
+> [1] E. de la Fuente, L. Sito, F. Giordano, G. Rumolo, B. Salvant, and C. Zannini, “A Python Package to Compute Beam-Induced Heating in Particle Accelerators and Applications,” JACoW, vol. HB2023, pp. 611–614, 2024, doi: [https://doi.10.18429/JACoW-HB2023-THBP52](https://jacow.org/hb2023/papers/thbp52.pdf).
 
 Bibtex:
 ```
@@ -34,28 +34,28 @@ Bibtex:
     year = "2024"
 }
 ```
-:mag_right: About `bihc` python package 
+:mag_right: About `bihc` python package
 ---
 
-`bihc` is a computational package that integrates over a decade of experience in beam-induced heating calculations from the Impedance and Coherent Effects Section (see [^1], [^3], [^4], [^5], [^6], [^7]) into a comprehensive and flexible Python-based tool. 
+`bihc` is a computational package that integrates over a decade of experience in beam-induced heating calculations from the Impedance and Coherent Effects Section (see [^1], [^3], [^4], [^5], [^6], [^7]) into a comprehensive and flexible Python-based tool.
 
 The package has been presented at the 68th ICFA Advanced Beam Dynamics Workshop on High-Intensity and High-Brightness Hadron Beam (0ct. 2023)[^8], and is under continuous development to face the beam-induce heating challenges that become more relevant as the beam total intensity and bunch length is pushed.
 
 `bihc` has been succesfully employed to assess the mitigation strategy for the CERN-SPS Beam Wire Scanners after the wire failure in 2023, and was extensively used to study the CERN-LHC Warm Vacuum modules limitations in intensity and bunch length for the 2024 run.
 
-[^1]: B. Salvant et al., “Beam induced heating”, 2012, [Online]. Available: https://cds.cern.ch/record/1975499 
+[^1]: B. Salvant et al., “Beam induced heating”, 2012, [Online]. Available: https://cds.cern.ch/record/1975499
 
-[^3]: C. Zannini, et al. "Power Loss Calculation in Separated and Common Beam Chambers of the LHC". Proceedings of the 5th Int. Particle Accelerator Conf., vol. IPAC2014, 2014, p. 3 pages, 1.928 MB. DOI.org (Datacite), https://doi.org/10.18429/JACOW-IPAC2014-TUPRI061. 
+[^3]: C. Zannini, et al. "Power Loss Calculation in Separated and Common Beam Chambers of the LHC". Proceedings of the 5th Int. Particle Accelerator Conf., vol. IPAC2014, 2014, p. 3 pages, 1.928 MB. DOI.org (Datacite), https://doi.org/10.18429/JACOW-IPAC2014-TUPRI061.
 
-[^4]: C. Zannini, "Electromagnetic Simulation of CERN accelerator Components and Experimental Applications", 2013. [Online]. Available: https://cds.cern.ch/record/1561199 
+[^4]: C. Zannini, "Electromagnetic Simulation of CERN accelerator Components and Experimental Applications", 2013. [Online]. Available: https://cds.cern.ch/record/1561199
 
-[^5]: C. Zannini, “Multiphysics Simulations of Impedance Effects in Accelerators,” CERN Yellow Rep. Conf. Proc., vol. 1, pp. 141–144, 2018, doi: 10.23732/CYRCP-2018-001.141. 
+[^5]: C. Zannini, “Multiphysics Simulations of Impedance Effects in Accelerators,” CERN Yellow Rep. Conf. Proc., vol. 1, pp. 141–144, 2018, doi: 10.23732/CYRCP-2018-001.141.
 
 [^6]: G. Rumolo, “Beam Instabilities”, 21 pages contribution to the CAS - CERN Accelerator School: Advanced Accelerator Physics Course, Trondheim, Norway, 2014, doi: 10.5170/CERN-2014-009.199. Available; https://cds.cern.ch/record/1982422
 
 [^7]: F. Giordano, ‘Simulation Analysis and Machine Learning Based Detection of Beam-Induced Heating in Particle Accelerator at CERN’, University of Naples Federico II, 2020.
 
-[^8]: L. Sito, E. de la Fuente, F. Giordano, G. Rumolo, B. Salvant, and C. Zannini, “A Python Package to Compute Beam-Induced Heating in Particle Accelerators and Applications,” in Proc. 68th Adv. Beam Dyn. Workshop High-Intensity High-Brightness Hadron Beams (HB’23), Geneva, Switzerland, Apr. 2024, no. 68, pp. 611–614. doi: 10.18429/JACoW-HB2023-THBP52. 
+[^8]: L. Sito, E. de la Fuente, F. Giordano, G. Rumolo, B. Salvant, and C. Zannini, “A Python Package to Compute Beam-Induced Heating in Particle Accelerators and Applications,” in Proc. 68th Adv. Beam Dyn. Workshop High-Intensity High-Brightness Hadron Beams (HB’23), Geneva, Switzerland, Apr. 2024, no. 68, pp. 611–614. doi: 10.18429/JACoW-HB2023-THBP52.
 
 ## :zap: Installation
 This section explains how to set up the environment to start using BIHC package for power loss computations.
@@ -72,7 +72,7 @@ git clone git@github.com:ImpedanCEI/BIHC.git
 git clone https://github.com/ImpedanCEI/BIHC.git
 ```
 
-#### Users: pip install 
+#### Users: pip install
 ```
 pip install bihc
 ```
@@ -89,10 +89,10 @@ In your /user or /work directory, do:
 # If miniconda is not installed
 # Get, install and activate miniconda
 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
-bash Miniconda3-latest-Linux-x86_64.sh 
+bash Miniconda3-latest-Linux-x86_64.sh
 source miniconda3/bin/activate
 
-# Get standard packages 
+# Get standard packages
 # (to have all spark functionalities pandas needs to be installed before pytimber)
 pip install numpy scipy matplotlib ipython pandas
 
@@ -105,11 +105,11 @@ pip install pytimber
 # Change python package index back to default
 pip uninstall acc-py-pip-config
 ```
-Test the installation with 
+Test the installation with
 ```
 $ ipython
 import pytimber
-ldb = pytimber.LoggingDB(source="nxcals") 
+ldb = pytimber.LoggingDB(source="nxcals")
 ldb.search('LHC%BEAM_ENERGY%')
 ldb.get(ldb.search('LHC%BEAM_ENERGY%')[0], t1='2022-06-15 15:10:30.0000')
 ```
@@ -120,4 +120,3 @@ ldb.get(ldb.search('LHC%BEAM_ENERGY%')[0], t1='2022-06-15 15:10:30.0000')
 [![nightly_tests_CPU_py3.12](https://github.com/ImpedanCEI/BIHC/actions/workflows/nightly_tests_CPU_p3.12.yml/badge.svg)](https://github.com/ImpedanCEI/BIHC/actions/workflows/nightly_tests_CPU_p3.12.yml)
 [![nightly_tests_CPU_py3.13](https://github.com/ImpedanCEI/BIHC/actions/workflows/nightly_tests_CPU_p3.13.yml/badge.svg)](https://github.com/ImpedanCEI/BIHC/actions/workflows/nightly_tests_CPU_p3.13.yml)
 [![nightly_tests_CPU_py3.14](https://github.com/ImpedanCEI/BIHC/actions/workflows/nightly_tests_CPU_p3.14.yml/badge.svg)](https://github.com/ImpedanCEI/BIHC/actions/workflows/nightly_tests_CPU_p3.14.yml)
-

bihc/beam.py

@@ -116,7 +116,6 @@ def __init__(
         exp=2.5,
         verbose=False,
     ):
-
         self.M = M  # Default max numebr of buckets
         self.A_GLOBAL = A
         self.BUNCH_LENGTH_GLOBAL = bunchLength / 4  # Bunch lenght (sigma) [s]
@@ -130,7 +129,9 @@ def __init__(
         self.exp = exp
         self.J = 1
         self.fillMode = fillMode
-        self._fillNumber = fillNumber  # Fill number relative to a particular fill of the machine
+        self._fillNumber = (
+            fillNumber  # Fill number relative to a particular fill of the machine
+        )
 
         self._isSpectrumReady = False
         self.isATimberFill = False
@@ -292,9 +293,7 @@ def spectrum(self):
                 "! When using spectrum == 'user', set the spectrum data via:\n \
                    >>> beam.setSpectrum(newSpectrum)\n"
             )
-            return (
-                self._spectrum
-            )  # spectrum must be provided through the setter
+            return self._spectrum  # spectrum must be provided through the setter
 
         else:
             if self._spectrumtype == "numeric":
@@ -342,41 +341,31 @@ def spectrum(self):
                     )
 
                 elif self._bunchShape == "GAUSSIAN":
-                    for p in progressbar(
-                        range(len(S)), "Computing analytic FFT: ", 20
-                    ):
+                    for p in progressbar(range(len(S)), "Computing analytic FFT: ", 20):
                         lambdas[p] = np.exp(
-                            -(p * p * wrev * wrev * sigma * sigma)
-                            / (2 * c * c)
+                            -(p * p * wrev * wrev * sigma * sigma) / (2 * c * c)
                         )
                         S[p] = np.abs(
-                            A
-                            * lambdas[p]
-                            * np.sum(an * np.exp(1j * p * wrev * n * t0))
+                            A * lambdas[p] * np.sum(an * np.exp(1j * p * wrev * n * t0))
                         )
 
                 elif self._bunchShape == "COS2":
                     for p in progressbar(
                         range(1, len(S)), "Computing analytic FFT: ", 20
                     ):  # TODO fix
-                        Fc = (
-                            (F**2) * (sigmacos**2) * ((p * wrev) ** 2) / (c**2)
-                        )
+                        Fc = (F**2) * (sigmacos**2) * ((p * wrev) ** 2) / (c**2)
                         lambdas[p] = (
                             -1.14
                             * np.sqrt(2 * np.pi)
                             / np.pi
                             / (sigmacos * p * wrev / c * (-2 + Fc))
                             * (np.sqrt(2 / np.pi))
                             * np.sin(
-                                (np.pi * sigmacos * p * wrev * F)
-                                / (np.sqrt(2) * c)
+                                (np.pi * sigmacos * p * wrev * F) / (np.sqrt(2) * c)
                             )
                         )
                         S[p] = np.abs(
-                            A
-                            * lambdas[p]
-                            * np.sum(an * np.exp(1j * p * wrev * n * t0))
+                            A * lambdas[p] * np.sum(an * np.exp(1j * p * wrev * n * t0))
                         )
 
                 elif self._bunchShape == "PARABOLIC":
@@ -389,17 +378,11 @@ def spectrum(self):
                         lambdas[p] = (
                             -3
                             * c**3
-                            / (
-                                (np.sqrt(5) ** 3)
-                                * (sigmapar**3)
-                                * (p * wrev) ** 3
-                            )
+                            / ((np.sqrt(5) ** 3) * (sigmapar**3) * (p * wrev) ** 3)
                             * CosSin
                         )
                         S[p] = np.abs(
-                            A
-                            * lambdas[p]
-                            * np.sum(an * np.exp(1j * p * wrev * n * t0))
+                            A * lambdas[p] * np.sum(an * np.exp(1j * p * wrev * n * t0))
                         )
 
                 f = (
@@ -475,10 +458,7 @@ def _setBunches(self):
                 self.filledSlots += 1  # TODO: how to match the bunch length
                 if self._bunchShape == "BINOMIAL":
                     lambda_0 = (2 * gamma(1.5 + self.exp)) / (
-                        (
-                            self._bunchLength[i]
-                            * (2 * np.sqrt(3 + 2 * self.exp))
-                        )
+                        (self._bunchLength[i] * (2 * np.sqrt(3 + 2 * self.exp)))
                         * np.sqrt(np.pi)
                         * gamma(1 + self.exp)
                     )  # normalization factor
@@ -487,10 +467,7 @@ def _setBunches(self):
                         - 4
                         * (
                             (tTemp - self.phi[i])
-                            / (
-                                self._bunchLength[i]
-                                * (2 * np.sqrt(3 + 2 * self.exp))
-                            )
+                            / (self._bunchLength[i] * (2 * np.sqrt(3 + 2 * self.exp)))
                         )
                         ** 2
                     )  # Binomial definition from RF-BR
@@ -529,10 +506,7 @@ def _setBunches(self):
                 elif self._bunchShape == "COS2":
                     tc = self._bunchLength[i] * 2.77  # 4*0.854
                     sTemp = (
-                        1
-                        / tc
-                        * (np.cos(np.pi * (tTemp - self.phi[i]) / (2 * tc)))
-                        ** 2
+                        1 / tc * (np.cos(np.pi * (tTemp - self.phi[i]) / (2 * tc))) ** 2
                     )  # cos^2 function
                     mask = (np.abs(tTemp - self.phi[i])) < self.l
                     mask2 = abs(tTemp) < tc
@@ -544,10 +518,7 @@ def _setBunches(self):
                     # sTemp=(1-(1/(4*0.744653*self._bunchLength[i]**2))*(tTemp- self.phi[i])**2)  #Parabolic function
                     self.exp = 1
                     lambda_0 = (2 * gamma(1.5 + self.exp)) / (
-                        (
-                            self._bunchLength[i]
-                            * (2 * np.sqrt(3 + 2 * self.exp))
-                        )
+                        (self._bunchLength[i] * (2 * np.sqrt(3 + 2 * self.exp)))
                         * np.sqrt(np.pi)
                         * gamma(1 + self.exp)
                     )  # normalization factor
@@ -556,15 +527,10 @@ def _setBunches(self):
                         - 4
                         * (
                             (tTemp - self.phi[i])
-                            / (
-                                self._bunchLength[i]
-                                * (2 * np.sqrt(3 + 2 * self.exp))
-                            )
+                            / (self._bunchLength[i] * (2 * np.sqrt(3 + 2 * self.exp)))
                         )
                         ** 2
-                    ) ** (
-                        self.exp
-                    )  # Parabolic definition, same as Binomial with exp=1
+                    ) ** (self.exp)  # Parabolic definition, same as Binomial with exp=1
                     mask = (np.abs(tTemp - self.phi[i])) < self.l
                     mask2 = sTemp > 0
                     sTemp = sTemp * mask * mask2
@@ -586,8 +552,7 @@ def _Cq(q):
                             return np.sqrt(np.pi)
                         elif q >= 1.005827 and q < 3.0:
                             return (
-                                np.sqrt(np.pi)
-                                * Gamma((3.0 - q) / 2.0 / (q - 1.0))
+                                np.sqrt(np.pi) * Gamma((3.0 - q) / 2.0 / (q - 1.0))
                             ) / (np.sqrt(q - 1.0) * Gamma(1.0 / (q - 1.0)))
                         else:
                             raise Exception("q>3.0")
@@ -607,9 +572,7 @@ def _eq(x, q):
                     q = self.q  # [TODO] pass 1 q-val per bunch
                     b = 1 / ((self._bunchLength[i] ** 2) * (5 - 3 * q))
                     mu = self.phi[i]
-                    sTemp = (
-                        np.sqrt(b) / _Cq(q) * _eq(-b * (tTemp - mu) ** 2, q)
-                    )
+                    sTemp = np.sqrt(b) / _Cq(q) * _eq(-b * (tTemp - mu) ** 2, q)
 
                     mask = (np.abs(tTemp - self.phi[i])) < self.l
                     mask2 = sTemp > 0
@@ -640,9 +603,7 @@ def _eq(x, q):
         t = np.linspace(np.min(tn), np.max(tn), len(s))
 
         self.totalBeamCharge = self.Np * e * self.filledSlots
-        if (
-            self.Np_arr is not None
-        ):  # weigth the longitudinal profile amplitudes
+        if self.Np_arr is not None:  # weigth the longitudinal profile amplitudes
             for i, Npi in enumerate(self.Np_arr):
                 s[i * self.ppbk : (i + 1) * self.ppbk] = (
                     s[i * self.ppbk : (i + 1) * self.ppbk] * Npi / self.Np
@@ -668,9 +629,7 @@ def _eq(x, q):
         self.profile_1_bunch = profile_1_bunch
         self.longitudinalProfile = [t, s]
 
-    def setBeamFromFillNumber(
-        self, fillNumber, fillMode="FLATTOP", beamNumber=1
-    ):
+    def setBeamFromFillNumber(self, fillNumber, fillMode="FLATTOP", beamNumber=1):
         """Set beam from fill number
 
         Retrieves beam fill information from Timber provided a fill Number
@@ -776,15 +735,9 @@ def setBeamFromFillNumber(
 
         self._bunchLength = self._bunchLength[0 : self.M]
         if self.verbose:
-            print(
-                f"Avg. bunch length off all bunches: {np.mean(self._bunchLength)}"
-            )
-            print(
-                f"Max. bunch length off all bunches: {np.max(self._bunchLength)}"
-            )
-            print(
-                f"Min. bunch length off all bunches: {np.min(self._bunchLength)}"
-            )
+            print(f"Avg. bunch length off all bunches: {np.mean(self._bunchLength)}")
+            print(f"Max. bunch length off all bunches: {np.max(self._bunchLength)}")
+            print(f"Min. bunch length off all bunches: {np.min(self._bunchLength)}")
 
         self.phi = self.phi[0 : self.M]
         self.setNpFromFillNumber()
@@ -850,9 +803,9 @@ def setBeamFromLPC(self):
 
                     if start_line:
                         try:
-                            self._fillingScheme[
-                                int((int(row[0]) - 1) / 10)
-                            ] = True  # we take beam one
+                            self._fillingScheme[int((int(row[0]) - 1) / 10)] = (
+                                True  # we take beam one
+                            )
                         except Exception:
                             break
 
@@ -877,16 +830,12 @@ def setCustomBeamWithFillingScheme(self):
             )
         elif len(self._fillingScheme) < self.M:
             padding = [False] * (self.M - len(self._fillingScheme))
-            self._fillingScheme = np.concatenate(
-                (self._fillingScheme, padding), axis=0
-            )
+            self._fillingScheme = np.concatenate((self._fillingScheme, padding), axis=0)
 
         self._bunchLength = np.zeros(
             self.M
         )  # std vector of a single turn in the machine
-        self.A = np.zeros(
-            self.M
-        )  # amplitude vector of a single turn in the machine
+        self.A = np.zeros(self.M)  # amplitude vector of a single turn in the machine
         self.phi = np.zeros(self.M)
         for j in range(self.M):
             if self._fillingScheme[j]: