Fix all remaining ruff check error

docs/swapi/figure_src/figure_utils.py

@@ -12,13 +12,15 @@
 REPO_ROOT = Path(__file__).resolve().parents[3]
 sys.path.insert(0, str(REPO_ROOT))
 
+# ruff: disable[E402]
 from imap_l3_processing.constants import (
     METERS_PER_KILOMETER,
     PROTON_CHARGE_COULOMBS,
     PROTON_MASS_KG,
 )
 from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+# ruff: enable[E402]
 
 FIGURES_DIR = REPO_ROOT / "docs" / "swapi" / "figures"
 

docs/swapi/figure_src/plot_pui_cutoff_staircase.py

@@ -28,6 +28,7 @@
 REPO_ROOT = Path(__file__).resolve().parents[3]
 sys.path.insert(0, str(REPO_ROOT))
 
+# ruff: disable[E402]
 import matplotlib
 
 matplotlib.use("Agg")
@@ -59,6 +60,7 @@
 from imap_l3_processing.swapi.l3a.utils import (
     velocity_components_to_angles_in_instrument_frame,
 )
+# ruff: enable[E402]
 
 _SW_SPEED_KMS = 450.0
 _SW_AZIMUTH_DEG = 0.0

docs/swapi/figure_src/plot_pui_distribution.py

@@ -22,6 +22,7 @@
 REPO_ROOT = Path(__file__).resolve().parents[3]
 sys.path.insert(0, str(REPO_ROOT))
 
+# ruff: disable[E402]
 import matplotlib
 
 matplotlib.use("Agg")
@@ -38,6 +39,7 @@
     FittingParameters,
     VasyliunasSiscoeDistribution,
 )
+# ruff: enable[E402]
 
 _CUTOFF_SPEED_KMS = 450.0
 _SW_SPEED_INERTIAL_KMS = 450.0

docs/swapi/figure_src/plot_pui_reference_comparison.py

@@ -21,6 +21,7 @@
 REPO_ROOT = Path(__file__).resolve().parents[3]
 sys.path.insert(0, str(REPO_ROOT))
 
+# ruff: disable[E402]
 import matplotlib
 
 matplotlib.use("Agg")
@@ -30,6 +31,7 @@
 
 from figure_utils import FIGURES_DIR
 from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E
+# ruff: enable[E402]
 
 _REFERENCE_CSV_PATH = (
     REPO_ROOT / "tests" / "test_data" / "swapi" / "pui_count_rate_reference.csv"

imap_l3_processing/glows/l3a/glows_toolkit/l0_data_histogram.py

@@ -67,7 +67,7 @@ def deserialize(self, reader):
 
         # additional padding when odd number of bins?
         if self.data["number_of_bins_per_histogram"] % 2 == 1:
-            aux = reader.read_uint8()
+            _ = reader.read_uint8()
 
         return self.data
 

imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_direct_events.py

@@ -205,8 +205,8 @@ def _save_data_to_json_file(self):
         data = copy.deepcopy(self.data)
 
         for key in fmt["arrays"].keys():
-            if (
-                type(data[key]) == np.ndarray
+            if isinstance(
+                data[key], np.ndarray
             ):  # check if we have more than one direct event
                 data[key] = (
                     "["

imap_l3_processing/glows/l3a/glows_toolkit/l2_data_histogram.py

@@ -172,7 +172,7 @@ def _check_l1b_bad_time_flags(self, l1b_data):
         ):
             cmp_result = 1
 
-        if l1b_data["flags"]["is_night"] == True:
+        if l1b_data["flags"]["is_night"]:
             print("(night", self.var["is_night_counter"], cmp_result, end=") ")
 
         if cmp_result != 0:
@@ -351,7 +351,7 @@ def is_new_observational_day(self, l1b_data):
         ):  # we are at the second (or later) l1b file
             if (
                 self.var["is_night_for_previous_l1b"] == 1
-                and l1b_data["flags"]["is_night"] == False
+                and not l1b_data["flags"]["is_night"]
             ):  # new observational day
                 is_new_observ_day = True
 
@@ -465,7 +465,7 @@ def process_l1b_data_files(self, l1b_data, pointing_counter):
             # increment good-time counter for L1b files
             self.var["l1b_file_counter_good_time"] += 1
 
-        if l1b_data["flags"]["is_night"] == True:
+        if l1b_data["flags"]["is_night"]:
             # increment is-night-flag-raised counter for L1b files
             self.var["is_night_counter"] += 1
 

imap_l3_processing/glows/l3a/glows_toolkit/l3a_data.py

@@ -140,11 +140,6 @@ def _compute_time_dependent_bckgrd(self, anc_input_from_instr_team):
             fh.close()
 
             # read L3a lightcurve start dates and array of corrections
-            identifiers = np.loadtxt(
-                anc_input_from_instr_team["time_dependent_bckgrd"],
-                usecols=[0],
-                dtype=str,
-            )
             start_dates = np.loadtxt(
                 anc_input_from_instr_team["time_dependent_bckgrd"],
                 usecols=[1],

imap_l3_processing/glows/l3bc/l3bc_toolkit/funcs.py

@@ -520,7 +520,7 @@ def read_raw_OMNI_data(ext_dependencies, t_window=None):
         ext_dependencies["omni_raw_data"], usecols=(0, 1, 2, 5, 23, 24, 27, 30, 31, 34)
     )
     omni_raw_date = time_from_yday(omni_raw[:, (0, 1, 2)])
-    if t_window == None:
+    if t_window is None:
         out = omni_raw
     else:
         idx_ini = np.abs(omni_raw_date - t_window[0]).argmin()

imap_l3_processing/glows/l3d/science/toolkit/funcs.py

@@ -520,7 +520,7 @@ def read_raw_OMNI_data(ext_dependencies, t_window=None):
         ext_dependencies["omni_raw_data"], usecols=(0, 1, 2, 5, 23, 24, 27, 30, 31, 34)
     )
     omni_raw_date = time_from_yday(omni_raw[:, (0, 1, 2)])
-    if t_window == None:
+    if t_window is None:
         out = omni_raw
     else:
         idx_ini = np.abs(omni_raw_date - t_window[0]).argmin()

imap_l3_processing/glows/l3d/science/toolkit/l3d_SolarParamHistory.py

@@ -115,7 +115,6 @@ def __init__(self, anc_input_from_instr_team, ext_dependencies):
         self.ini_data = {}
         self.ini_data["CR_last"] = None
         self.ini_data["label"] = list(anc_input_from_instr_team["WawHelioIon"].keys())
-        N = len(self.ini_data["label"])
         self.ini_data["time"] = {}
         for k in anc_input_from_instr_team["WawHelioIon"]:
             self.ini_data["time"][k] = None

imap_l3_processing/maps/mpfit.py

@@ -835,6 +835,9 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
               If nocovar is set or MPFIT terminated abnormally, then .covar is set to
               a scalar with value None.
 
+           .catch_msg
+              String describing the last operation attempted (which may have failed).
+
            .errmsg
               A string error or warning message is returned.
 
@@ -869,12 +872,13 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         self.perror = None
         self.status = 0  # Invalid input flag set while we check inputs
         self.debug = debug
+        self.catch_msg = ""
         self.errmsg = ""
         self.nfev = 0
         self.damp = damp
         self.dof = 0
 
-        if fcn == None:
+        if fcn is None:
             self.errmsg = "Usage: parms = mpfit('myfunt', ... )"
             return
 
@@ -1070,7 +1074,6 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
             if (nprint > 0) and (iterfunct is not None):
                 if ((self.niter - 1) % nprint) == 0:
-                    mperr = 0
                     xnew0 = self.params.copy()
 
                     dof = numpy.max([len(fvec) - len(x), 0])
@@ -1103,7 +1106,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
             # Calculate the jacobian matrix
             self.status = 2
-            catch_msg = "calling MPFIT_FDJAC2"
+            self.catch_msg = "calling MPFIT_FDJAC2"
             fjac = self.fdjac2(
                 fcn,
                 x,
@@ -1125,7 +1128,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
             # Determine if any of the parameters are pegged at the limits
             if qanylim:
-                catch_msg = "zeroing derivatives of pegged parameters"
+                self.catch_msg = "zeroing derivatives of pegged parameters"
                 whlpeg = (numpy.nonzero(qllim & (x == llim)))[0]
                 nlpeg = len(whlpeg)
                 whupeg = (numpy.nonzero(qulim & (x == ulim)))[0]
@@ -1149,7 +1152,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
             # On the first iteration if "diag" is unspecified, scale
             # according to the norms of the columns of the initial jacobian
-            catch_msg = "rescaling diagonal elements"
+            self.catch_msg = "rescaling diagonal elements"
             if self.niter == 1:
                 if (rescale == 0) or (len(diag) < n):
                     diag = wa2.copy()
@@ -1164,7 +1167,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     delta = factor
 
             # Form (q transpose)*fvec and store the first n components in qtf
-            catch_msg = "forming (q transpose)*fvec"
+            self.catch_msg = "forming (q transpose)*fvec"
             wa4 = fvec.copy()
             for j in range(n):
                 lj = ipvt[j]
@@ -1192,7 +1195,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
             # if ct GT 0 then goto, FAIL_OVERFLOW
 
             # Compute the norm of the scaled gradient
-            catch_msg = "computing the scaled gradient"
+            self.catch_msg = "computing the scaled gradient"
             gnorm = 0.0
             if self.fnorm != 0:
                 for j in range(n):
@@ -1216,7 +1219,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
             # Beginning of the inner loop
             while 1:
                 # Determine the levenberg-marquardt parameter
-                catch_msg = "calculating LM parameter (MPFIT_)"
+                self.catch_msg = "calculating LM parameter (MPFIT_)"
                 [fjac, par, wa1, wa2] = self.lmpar(
                     fjac, ipvt, diag, qtf, delta, wa1, wa2, par=par
                 )
@@ -1236,7 +1239,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
                     if qanylim:
                         # Do not allow any steps out of bounds
-                        catch_msg = "checking for a step out of bounds"
+                        self.catch_msg = "checking for a step out of bounds"
                         if nlpeg > 0:
                             wa1[whlpeg] = numpy.clip(wa1[whlpeg], 0.0, numpy.max(wa1))
                         if nupeg > 0:
@@ -1299,16 +1302,15 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 self.params[ifree] = wa2
 
                 # Evaluate the function at x+p and calculate its norm
-                mperr = 0
-                catch_msg = "calling " + str(fcn)
+                self.catch_msg = "calling " + str(fcn)
                 [self.status, wa4] = self.call(fcn, self.params, functkw)
                 if self.status < 0:
                     self.errmsg = 'WARNING: premature termination by "' + fcn + '"'
                     return
                 fnorm1 = self.enorm(wa4)
 
                 # Compute the scaled actual reduction
-                catch_msg = "computing convergence criteria"
+                self.catch_msg = "computing convergence criteria"
                 actred = -1.0
                 if (0.1 * fnorm1) < self.fnorm:
                     actred = -((fnorm1 / self.fnorm) ** 2) + 1.0
@@ -1399,7 +1401,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 if ~numpy.all(
                     numpy.isfinite(wa1) & numpy.isfinite(wa2) & numpy.isfinite(x)
                 ) or ~numpy.isfinite(ratio):
-                    errmsg = """ERROR: parameter or function value(s) have become
+                    self.errmsg = """ERROR: parameter or function value(s) have become
 						'infinite; check model function for over- 'and underflow"""
                     self.status = -16
                     break
@@ -1410,7 +1412,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 break
         # End of outer loop.
 
-        catch_msg = "in the termination phase"
+        self.catch_msg = "in the termination phase"
         # Termination, either normal or user imposed.
         if len(self.params) == 0:
             return
@@ -1419,9 +1421,9 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         else:
             self.params[ifree] = x
         if (nprint > 0) and (self.status > 0):
-            catch_msg = "calling " + str(fcn)
+            self.catch_msg = "calling " + str(fcn)
             [status, fvec] = self.call(fcn, self.params, functkw)
-            catch_msg = "in the termination phase"
+            self.catch_msg = "in the termination phase"
             self.fnorm = self.enorm(fvec)
 
         if (self.fnorm is not None) and (fnorm1 is not None):
@@ -1440,7 +1442,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         ):
             sz = fjac.shape
             if (n > 0) and (sz[0] >= n) and (sz[1] >= n) and (len(ipvt) >= n):
-                catch_msg = "computing the covariance matrix"
+                self.catch_msg = "computing the covariance matrix"
                 cv = self.calc_covar(fjac[0:n, 0:n], ipvt[0:n])
                 cv.shape = [n, n]
                 nn = len(xall)
@@ -1452,7 +1454,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     self.covar[ifree, ifree[i]] = cv[:, i]
 
                 # Compute errors in parameters
-                catch_msg = "computing parameter errors"
+                self.catch_msg = "computing parameter errors"
                 self.perror = numpy.zeros(nn, dtype=float)
                 d = numpy.diagonal(self.covar)
                 wh = (numpy.nonzero(d >= 0))[0]
@@ -1464,7 +1466,6 @@ def __str__(self):
         return {
             "params": self.params,
             "niter": self.niter,
-            "params": self.params,
             "covar": self.covar,
             "perror": self.perror,
             "status": self.status,
@@ -1559,9 +1560,6 @@ def parinfo(self, parinfo=None, key="a", default=None, n=0):
                 values.append(default)
 
         # Convert to numeric arrays if possible
-        test = default
-        if type(default) is list:
-            test = default[0]
         values = numpy.asarray(values)
         return values
 
@@ -1624,7 +1622,6 @@ def fdjac2(
 
         # Compute analytical derivative if requested
         if autoderivative == 0:
-            mperr = 0
             fjac = numpy.zeros(nall, dtype=float)
             fjac[ifree] = 1.0  # Specify which parameters need derivatives
             [status, fp] = self.call(fcn, xall, functkw, fjac=fjac)
@@ -1694,7 +1691,6 @@ def fdjac2(
                 # COMPUTE THE TWO-SIDED DERIVATIVE
                 xp[ifree[j]] = xall[ifree[j]] - h[j]
 
-                mperr = 0
                 [status, fm] = self.call(fcn, xp, functkw)
                 if status < 0:
                     return None
@@ -2001,7 +1997,6 @@ def qrsolv(self, r, ipvt, diag, qtb, sdiag):
         if self.debug:
             print("Entering qrsolv...")
         sz = r.shape
-        m = sz[0]
         n = sz[1]
 
         # copy r and (q transpose)*b to preserve input and initialize s.
@@ -2176,7 +2171,6 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
         dwarf = self.machar.minnum
         machep = self.machar.machep
         sz = r.shape
-        m = sz[0]
         n = sz[1]
 
         # Compute and store in x the gauss-newton direction.  If the

imap_l3_processing/predicted_ephemeris_tracker.py

@@ -1,4 +1,4 @@
-from typing import Callable
+from typing import Callable, ParamSpec, TypeVar
 
 from imap_data_access import SPICEFilePath
 from spiceypy import spiceypy, KernelPool, SpiceyError
@@ -25,7 +25,10 @@ def __init__(self):
             except SPICEFilePath.InvalidImapFileError:
                 self.kernels_without_predict.append(kernel)
 
-    def run[T, **P](self, func: Callable[P, T], *args: P.args, **kwargs: P.kwargs) -> T:
+    T = TypeVar("T")
+    P = ParamSpec("P")
+
+    def run(self, func: Callable[P, T], *args: P.args, **kwargs: P.kwargs) -> T:
         if self.used_predict or not self.predict_kernel_available:
             result = func(*args, **kwargs)
         else: