Address pytest warnings (#2745)

* Fix pandas inplace warnings

* Use ruff again to fix invalid-escape-sequence warnings

* Close regression data after yielding it

* Close files in test_complete_plasma

* Add a backlash for the docstring

* Remove extra backslash

* Use raw strings

* Use raw strings

.ci-helpers/update_credits.py

@@ -25,7 +25,7 @@ def generate_zenodo():
             f"https://zenodo.org/api/records/{CONCEPT_DOI}", headers=headers
         )
         response.encoding = "utf-8"
-        citation = re.findall("@software{(.*)\,", response.text)
+        citation = re.findall(r"@software{(.*)\,", response.text)
         specific_doi = citation[0].lstrip(f"kerzendorf_wolfgang_{year}_")
         doi_org_url = f"https://doi.org/10.5281/zenodo.{specific_doi}"
         doi_badge = f"https://img.shields.io/badge/DOI-10.5281/zenodo.{specific_doi}-blue"

docs/physics/pyplot/plot_mu_in_out_packet.py

@@ -7,8 +7,8 @@
 mu_out = y / 500.0 - 1
 v = 1.1e4 * u.km / u.s
 doppler_fac = (1 - mu_in * v / const.c) / (1 - mu_out * v / const.c)
-xlabel("$\mu_{\\rm in}$")
-ylabel("$\mu_{\\rm out}$")
+xlabel("$\\mu_{\\rm in}$")
+ylabel("$\\mu_{\\rm out}$")
 imshow(np.rot90(doppler_fac), extent=[-1, 1, -1, 1], cmap="bwr")
 colorbar()
 show()

tardis/analysis.py

@@ -206,8 +206,8 @@ def update_last_interaction_filter(self):
             ]
         ]
         self.last_line_in_table["count"] = last_line_in_count
-        self.last_line_in_table.sort_values(
-            by="count", ascending=False, inplace=True
+        self.last_line_in_table = self.last_line_in_table.sort_values(
+            by="count", ascending=False
         )
         self.last_line_out_table = self.last_line_out.reset_index()[
             [
@@ -219,8 +219,8 @@ def update_last_interaction_filter(self):
             ]
         ]
         self.last_line_out_table["count"] = last_line_out_count
-        self.last_line_out_table.sort_values(
-            by="count", ascending=False, inplace=True
+        self.last_line_out_table = self.last_line_out_table.sort_values(
+            by="count", ascending=False
         )
 
     def plot_wave_in_out(self, fig, do_clf=True, plot_resonance=True):

tardis/analysis/opacities.py

@@ -285,11 +285,11 @@ def _calc_expansion_opacity(self):
         The expansion opacity formalism, in the particular version of Blinnikov
         et al. 1998, is used. In the supernova ejecta case (assuming perfect
         homologous expansion), the formula for the expansion opacity in the
-        interval $[\nu, \nu+\Delta \nu]$ simplifies to
-        \[
-          \chi_{\mathrm{exp}} = \frac{\nu}{\Delta \nu} \frac{1}{c t} \sum_j
-          \left(1 - \exp(-\tau_{\mathrm{S},j})\right)
-        \]
+        interval $[\nu, \nu+\\Delta \nu]$ simplifies to
+        \\[
+          \\chi_{\\mathrm{exp}} = \frac{\nu}{\\Delta \nu} \frac{1}{c t} \\sum_j
+          \\left(1 - \\exp(-\tau_{\\mathrm{S},j})\right)
+        \\]
         The summation involves all lines in the frequency bin.
 
         Returns
@@ -326,7 +326,7 @@ def _calc_expansion_opacity(self):
         return kappa_exp.to("1/cm")
 
     def _calc_thomson_scattering_opacity(self):
-        """Calculate the Thomson scattering opacity for each grid cell
+        r"""Calculate the Thomson scattering opacity for each grid cell
 
         \[
           \chi_{\mathrm{Thomson}} = n_{\mathrm{e}} \sigma_{\mathrm{T}}

tardis/io/atom_data/base.py

@@ -411,7 +411,7 @@ def prepare_atom_data(
             )
         ]
 
-        self.lines.sort_values(by="wavelength", inplace=True)
+        self.lines = self.lines.sort_values(by="wavelength")
 
         self.lines_index = pd.Series(
             np.arange(len(self.lines), dtype=int),

tardis/io/model/readers/blondin_toymodel.py

@@ -52,7 +52,7 @@ def read_blondin_toymodel(fname):
         header=None,
         names=columns,
     )
-    raw_blondin_csv.set_index("idx", inplace=True)
+    raw_blondin_csv = raw_blondin_csv.set_index("idx")
 
     blondin_csv = raw_blondin_csv.loc[
         :,
@@ -76,7 +76,7 @@ def read_blondin_toymodel(fname):
     }
     rename_col_dict.update({item: item[2:] for item in blondin_csv.columns[3:]})
     rename_col_dict["X_56Ni0"] = "Ni56"
-    blondin_csv.rename(columns=rename_col_dict, inplace=True)
+    blondin_csv = blondin_csv.rename(columns=rename_col_dict)
     blondin_csv.iloc[:, 3:] = blondin_csv.iloc[:, 3:].divide(
         blondin_csv.iloc[:, 3:].sum(axis=1), axis=0
     )

tardis/io/tests/test_decay.py

@@ -26,7 +26,7 @@ def test_simple_decay(simple_abundance_model):
 @pytest.fixture
 def raw_abundance_simple():
     abundances = pd.DataFrame([[0.2, 0.2], [0.1, 0.1]], index=[28, 30])
-    abundances.index.rename("atomic_number", inplace=True)
+    abundances.index = abundances.index.rename("atomic_number")
     return abundances
 
 

tardis/model/matter/decay.py

@@ -106,7 +106,7 @@ def decay(self, t):
             )
         decayed_inventories = [item.decay(t_second) for item in inventories]
         df = IsotopicMassFraction.from_inventories(decayed_inventories)
-        df.sort_index(inplace=True)
+        df = df.sort_index()
         assert (
             df.ge(0.0).all().all()
         ), "Negative abundances detected. Please make sure your input abundances are correct."

tardis/plasma/properties/atomic.py

@@ -620,7 +620,7 @@ def _filter_atomic_property(self, zeta_data, selected_atoms):
             updated_index = pd.DataFrame(updated_index)
             updated_dataframe["atomic_number"] = np.array(updated_index[0])
             updated_dataframe["ion_number"] = np.array(updated_index[1])
-            updated_dataframe.fillna(1.0, inplace=True)
+            updated_dataframe = updated_dataframe.fillna(1.0)
             return updated_dataframe
 
     def _set_index(self, zeta_data):

tardis/plasma/tests/test_complete_plasmas.py

@@ -167,8 +167,9 @@ def plasma(
     ):
         config["atom_data"] = str(chianti_he_db_fpath)
         sim = Simulation.from_config(config)
-        self.regression_data.sync_hdf_store(sim.plasma, update_fname=False)
-        return sim.plasma
+        data = self.regression_data.sync_hdf_store(sim.plasma, update_fname=False)
+        yield sim.plasma
+        data.close()
 
     @pytest.mark.parametrize("attr", combined_properties)
     def test_plasma_properties(self, plasma, attr):

tardis/spectrum/formal_integral_cuda.py

@@ -93,7 +93,7 @@ def cuda_formal_integral(
     pp : array(float64, 1d, C)
         Impact parameter arrays
     exp_tau : array(float64, 1d, C)
-        $\exp{-tau}$ array to speed up computation
+        $\\exp{-tau}$ array to speed up computation
     I_nu array(floatt64, 2d, C)
         Radiative intensity per unit frequency per impact parameter
     z : array(float64, 2d, C)

tardis/transport/montecarlo/packet_source.py

@@ -226,8 +226,8 @@ def create_packet_nus(self, no_of_packets, l_samples=1000):
 
     def create_packet_mus(self, no_of_packets):
         """
-        Create zero-limb-darkening packet :math:`\mu` distributed
-        according to :math:`\\mu=\\sqrt{z}, z \isin [0, 1]`
+        Create zero-limb-darkening packet :math:`\\mu` distributed
+        according to :math:`\\mu=\\sqrt{z}, z \\isin [0, 1]`
 
         Parameters
         ----------
@@ -340,7 +340,7 @@ def create_packets(self, no_of_packets):
 
     def create_packet_mus(self, no_of_packets):
         """
-        Create zero-limb-darkening packet :math:`\mu^\prime` distributed
+        Create zero-limb-darkening packet :math:`\\mu^\\prime` distributed
         according to :math:`\\mu^\\prime=2 \\frac{\\mu^\\prime + \\beta}{2 \\beta + 1}`.
         The complicated distribution is due to the fact that the inner boundary
         on which the packets are initialized is not comoving with the material.

tardis/util/base.py

@@ -578,7 +578,7 @@ def convert_abundances_format(fname, delimiter=r"\s+"):
     """
     df = pd.read_csv(fname, delimiter=delimiter, comment="#", header=None)
     # Drop shell index column
-    df.drop(df.columns[0], axis=1, inplace=True)
+    df = df.drop(df.columns[0], axis=1)
     # Assign header row
     df.columns = [Z_DICT[i] for i in range(1, df.shape[1] + 1)]
     return df

tardis/visualization/plot_util.py

@@ -28,7 +28,7 @@ def axis_label_in_latex(label_text, unit, only_text=True):
     # If present, place s^{-1} just after erg
     if "erg" in unit_in_latex and "s^{-1}" in unit_in_latex:
         constituent_units = (
-            re.compile("\\\mathrm\{(.*)\}")
+            re.compile(r"\\mathrm\{(.*)\}")
             .findall(unit_in_latex)[0]
             .split("\\,")
         )

tardis/visualization/tools/sdec_plot.py

@@ -746,8 +746,8 @@ def _calculate_plotting_data(
                 ),
             )
             # Then drop all of the individual columns for species included in 'other'
-            self.total_luminosities_df.drop(
-                sorted_list.keys()[~mask], inplace=True, axis=1
+            self.total_luminosities_df = self.total_luminosities_df.drop(
+                sorted_list.keys()[~mask], axis=1
             )
             # Repeat this for the emission and absorption dfs
             # This will require creating a temporary list that includes 'noint' and 'escatter'
@@ -770,9 +770,8 @@ def _calculate_plotting_data(
             # Need to add a new value to the mask array for the 'other' column just added
             mask = np.insert(mask, 0, True)
             # Then drop all of the individual columns for species included in 'other'
-            self.emission_luminosities_df.drop(
+            self.emission_luminosities_df = self.emission_luminosities_df.drop(
                 self.emission_luminosities_df.keys()[~mask],
-                inplace=True,
                 axis=1,
             )
 
@@ -792,9 +791,8 @@ def _calculate_plotting_data(
             # Need to add a new value to the mask array for the 'other' column just added
             mask = np.insert(mask, 0, True)
             # Then drop all of the individual columns for species included in 'other'
-            self.absorption_luminosities_df.drop(
+            self.absorption_luminosities_df = self.absorption_luminosities_df.drop(
                 self.absorption_luminosities_df.keys()[~mask],
-                inplace=True,
                 axis=1,
             )
 
@@ -813,8 +811,8 @@ def _calculate_plotting_data(
                 ].sum(axis=1),
             )
             # Then drop all of the individual columns for elements included in 'other'
-            self.total_luminosities_df.drop(
-                sorted_list.keys()[nelements:], inplace=True, axis=1
+            self.total_luminosities_df = self.total_luminosities_df.drop(
+                sorted_list.keys()[nelements:], axis=1
             )
             # If nelements is included then create a new column which is the sum
             # of all other elements, i.e. those that aren't in the top contributing nelements
@@ -826,8 +824,8 @@ def _calculate_plotting_data(
                 ].sum(axis=1),
             )
             # Then drop all of the individual columns for elements included in 'other'
-            self.emission_luminosities_df.drop(
-                sorted_list.keys()[nelements:], inplace=True, axis=1
+            self.emission_luminosities_df = self.emission_luminosities_df.drop(
+                sorted_list.keys()[nelements:], axis=1
             )
             # If nelements is included then create a new column which is the sum
             # of all other elements, i.e. those that aren't in the top contributing nelements
@@ -839,8 +837,8 @@ def _calculate_plotting_data(
                 ].sum(axis=1),
             )
             # Then drop all of the individual columns for elements included in 'other'
-            self.absorption_luminosities_df.drop(
-                sorted_list.keys()[nelements:], inplace=True, axis=1
+            self.absorption_luminosities_df = self.absorption_luminosities_df.drop(
+                sorted_list.keys()[nelements:], axis=1
             )
             # Get the list of species in the model
             # Index from 1: to avoid the 'other' column

tardis/visualization/widgets/custom_abundance.py

@@ -175,9 +175,9 @@ def from_csvy(cls, fpath):
 
         # Combine elements and isotopes to one DataFrame
         abundance["mass_number"] = ""
-        abundance.set_index("mass_number", append=True, inplace=True)
+        abundance = abundance.set_index("mass_number", append=True)
         abundance = pd.concat([abundance, isotope_abundance])
-        abundance.sort_index(inplace=True)
+        abundance = abundance.sort_index()
 
         return cls(
             density_t_0=time_0,
@@ -222,9 +222,9 @@ def from_yml(cls, fpath, atom_data=None):
 
         # Combine elements and isotopes to one DataFrame
         abundance["mass_number"] = ""
-        abundance.set_index("mass_number", append=True, inplace=True)
+        abundance = abundance.set_index("mass_number", append=True)
         abundance = pd.concat([abundance, isotopic_mass_fraction])
-        abundance.sort_index(inplace=True)
+        abundance = abundance.sort_index()
 
         return cls(
             density_t_0=density_t_0,
@@ -259,7 +259,7 @@ def from_hdf(cls, fpath):
         velocity = np.append(v_inner, v_outer[len(v_outer) - 1]) * u.cm / u.s
 
         abundance["mass_number"] = ""
-        abundance.set_index("mass_number", append=True, inplace=True)
+        abundance = abundance.set_index("mass_number", append=True)
 
         return cls(
             density_t_0=density_t_0,
@@ -288,9 +288,9 @@ def from_simulation(cls, sim):
 
         # integrate element and isotope to one DataFrame
         abundance["mass_number"] = ""
-        abundance.set_index("mass_number", append=True, inplace=True)
+        abundance = abundance.set_index("mass_number", append=True)
         abundance = pd.concat([abundance, isotope_abundance])
-        abundance.sort_index(inplace=True)
+        abundance = abundance.sort_index()
 
         velocity = sim.simulation_state.velocity
         density_t_0 = sim.simulation_state.time_explosion
@@ -787,7 +787,7 @@ def on_btn_add_shell(self, obj):
         if end_index < len(v_vals) and np.isclose(v_vals[end_index], v_end):
             # New shell will overwrite the original shell that ends at v_end.
             v_vals = np.delete(v_vals, end_index)
-            self.data.abundance.drop(max(0, end_index - 1), 1, inplace=True)
+            self.data.abundance = self.data.abundance.drop(max(0, end_index - 1), 1)
 
         # Insert new velocities calculate new densities according
         # to new velocities through interpolation.
@@ -807,10 +807,9 @@ def on_btn_add_shell(self, obj):
         # Change abundances after adding new shell.
         if start_index != end_index:
             self.data.abundance.insert(start_index, "", new_shell_abundances)
-            self.data.abundance.drop(
+            self.data.abundance = self.data.abundance.drop(
                 self.data.abundance.iloc[:, start_index : end_index - 1],
                 1,
-                inplace=True,
             )
         else:
             if start_index == 0:
@@ -1046,7 +1045,7 @@ def on_btn_add_element(self, obj):
             z = nuc.Z
             self.data.abundance.loc[(z, mass_no), :] = 0
 
-        self.data.abundance.sort_index(inplace=True)
+        self.data.abundance = self.data.abundance.sort_index()
 
         # Add new BoundedFloatText control and Checkbox control.
         item = ipw.BoundedFloatText(min=0, max=1, step=0.01)
@@ -1466,7 +1465,7 @@ def write_csv_portion(self, path):
             first_row = [0] * self.no_of_elements
             data.loc[-1] = first_row
             data.index += 1  # shifting index
-            data.sort_index(inplace=True)
+            data = data.sort_index()
 
             formatted_v = pd.Series(self.data.velocity.value).apply(
                 lambda x: "%.3e" % x

tardis/visualization/widgets/shell_info.py

@@ -93,7 +93,7 @@ def element_count(self, shell_num):
         """
         element_count_data = self.abundance[shell_num - 1].copy()
         element_count_data.index.name = "Z"
-        element_count_data.fillna(0, inplace=True)
+        element_count_data = element_count_data.fillna(0)
         return pd.DataFrame(
             {
                 "Element": element_count_data.index.map(
@@ -128,7 +128,7 @@ def ion_count(self, atomic_num, shell_num):
         element_num_density = self.number_density.loc[atomic_num, shell_num - 1]
         ion_count_data = ion_num_density / element_num_density  # Normalization
         ion_count_data.index.name = "Ion"
-        ion_count_data.fillna(0, inplace=True)
+        ion_count_data = ion_count_data.fillna(0)
         return pd.DataFrame(
             {
                 "Species": ion_count_data.index.map(
@@ -170,7 +170,7 @@ def level_count(self, ion, atomic_num, shell_num):
         level_count_data = level_num_density / ion_num_density  # Normalization
         level_count_data.index.name = "Level"
         level_count_data.name = f"Frac. Ab. (Ion={ion})"
-        level_count_data.fillna(0, inplace=True)
+        level_count_data = level_count_data.fillna(0)
         return level_count_data.map("{:.6e}".format).to_frame()