change deprecated delim_whitespace to sep keyword

MichielsenM · Apr 21, 2024 · b19fd7b · b19fd7b
1 parent c673e91
commit b19fd7b
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Showing 8 changed files with 13 additions and 21 deletions.
diff --git a/foam/additional_constraints.py b/foam/additional_constraints.py
@@ -51,7 +51,7 @@ def surface_constraint(
     evolution_step: float
         Change in the evolutionary parameter from one step to the next (negative if quantity decreases, e.g. central hydrogen content Xc)
     """
-    obs_dataframe = pd.read_table(observations_file, delim_whitespace=True, header=0, index_col="index")
+    obs_dataframe = pd.read_table(observations_file, sep="\s+", header=0, index_col="index")
     dataframe_theory = pd.read_hdf(merit_values_file)
 
     if "Teff" in obs_dataframe.columns:

diff --git a/foam/build_optimised_pattern.py b/foam/build_optimised_pattern.py
@@ -96,7 +96,7 @@ def construct_theoretical_puls_pattern(
         Number of worker processes to use in multiprocessing. The default 'None' will use the number returned by os.cpu_count().
     """
     # Read in the files with observed and theoretical frequencies as pandas DataFrames
-    obs_dataframe = pd.read_table(observations_file, delim_whitespace=True, header=0, index_col="index")
+    obs_dataframe = pd.read_table(observations_file, sep="\s+", header=0, index_col="index")
     theory_dataframe = pd.read_hdf(pulsation_grid_file)
 
     obs = np.asarray(obs_dataframe[which_observable])

diff --git a/foam/functions_for_mesa.py b/foam/functions_for_mesa.py
@@ -44,10 +44,8 @@ def read_mesa_file(file_path, index_col=None):
         return sf.read_hdf5(file_path)
 
     else:  # assumes the default MESA output format
-        header_df = pd.read_table(file_path, delim_whitespace=True, nrows=1, header=1)
-        data_df = pd.read_table(
-            file_path, delim_whitespace=True, skiprows=3, header=1, index_col=index_col
-        )
+        header_df = pd.read_table(file_path, sep="\s+", nrows=1, header=1)
+        data_df = pd.read_table(file_path, sep="\s+", skiprows=3, header=1, index_col=index_col)
 
         header = {}
         for k in header_df.keys():
@@ -87,9 +85,7 @@ def calculate_number_densities(hist_file):
 
     average_atomic_mass = inverse_average_atomic_mass ** (-1)
     for key in element_list.keys():
-        number_densities.update(
-            {key.replace("_per_Mass_tot", "_per_N_tot"): element_list[key] * average_atomic_mass}
-        )
+        number_densities.update({key.replace("_per_Mass_tot", "_per_N_tot"): element_list[key] * average_atomic_mass})
 
     return number_densities
 
@@ -122,13 +118,9 @@ def extract_surface_grid(
     # Make list of extra observables requested by the user
     if additional_observables is None:
         additional_observables = []
-    extras_to_be_extracted = [
-        x for x in additional_observables if x not in ["logTeff", "logL", "logg", "age"]
-    ]
+    extras_to_be_extracted = [x for x in additional_observables if x not in ["logTeff", "logL", "logg", "age"]]
 
-    extract_func = partial(
-        info_from_profiles, parameters=parameters, extra_header_items=extras_to_be_extracted
-    )
+    extract_func = partial(info_from_profiles, parameters=parameters, extra_header_items=extras_to_be_extracted)
     # Glob all the files, then iteratively send them to a pool of processors
     profiles = glob.iglob(mesa_profiles)
     with multiprocessing.Pool(nr_cpu) as p:

diff --git a/foam/maximum_likelihood_estimator.py b/foam/maximum_likelihood_estimator.py
@@ -59,7 +59,7 @@ def calculate_likelihood(
     elif "P" or "dP" in observables:
         observed_quantity = "period"
     # Read in the observed data and make an array of the observed observables
-    obs_dataframe = pd.read_table(obs_path, delim_whitespace=True, header=0, index_col="index")
+    obs_dataframe = pd.read_table(obs_path, sep="\s+", header=0, index_col="index")
     obs, obs_err, file_suffix_observables = create_obs_observables_array(obs_dataframe, observables)
 
     # set the name of the output file

diff --git a/foam/pipeline/pipe4_AICc.py b/foam/pipeline/pipe4_AICc.py
@@ -31,7 +31,7 @@
     if merit == "CS":
         df_AICc = pd.DataFrame(data=[], columns=["method", "AICc"])
     elif merit == "MD":
-        df_AICc = pd.read_table(f"V_matrix/{config.star}_determinant_conditionNr.tsv", delim_whitespace=True, header=0)
+        df_AICc = pd.read_table(f"V_matrix/{config.star}_determinant_conditionNr.tsv", sep="\s+", header=0)
 
     for grid in config.grids:
         for method in config.pattern_methods:

diff --git a/foam/pipeline/pipe5_best_model_errors.py b/foam/pipeline/pipe5_best_model_errors.py
@@ -29,7 +29,7 @@ def likelihood_chi2(chi2):
 
 def likelihood_md(md):
     """Likelihood function of the mahalanobis distance"""
-    df_aicc_md = pd.read_table(f"V_matrix/{config.star}_determinant_conditionNr.tsv", delim_whitespace=True, header=0)
+    df_aicc_md = pd.read_table(f"V_matrix/{config.star}_determinant_conditionNr.tsv", sep="\s+", header=0)
     ln_det_v = float((df_aicc_md.loc[df_aicc_md["method"] == f"{config.star}_{analysis}", "ln(det(V))"]).iloc[0])
     return np.exp(-0.5 * (md + config.k * np.log(2 * np.pi) + ln_det_v))
 

diff --git a/foam/pipeline/pipe7_table_best_models.py b/foam/pipeline/pipe7_table_best_models.py
@@ -19,7 +19,7 @@
 for merit in config.merit_functions:
     # Get the pre-calculated AICc values from another file
     df_AICc = pd.read_table(
-        f"{directory_prefix}output_tables/{config.star}_AICc-values_{merit}.tsv", delim_whitespace=True, header=0
+        f"{directory_prefix}output_tables/{config.star}_AICc-values_{merit}.tsv", sep='\s+', header=0
     )
     with open(f"{directory_prefix}output_tables/{config.star}_best-model-table_{merit}.txt", "w") as outfile:
         params = ""

diff --git a/foam/plot_tools.py b/foam/plot_tools.py
@@ -160,7 +160,7 @@ def corner_plot(
     }
     CustomCMap = LinearSegmentedColormap("CustomMap", cdict)
     # theoretical models within the error ellipse
-    dataframe_theory_error_ellipse = pd.read_hdf(merit_values_file_error_ellipse, delim_whitespace=True, header=0)
+    dataframe_theory_error_ellipse = pd.read_hdf(merit_values_file_error_ellipse, sep="\s+", header=0)
     dataframe_theory_error_ellipse = dataframe_theory_error_ellipse.sort_values(
         "meritValue", ascending=False
     )  # Order from high to low, to plot lowest values last
@@ -333,7 +333,7 @@ def corner_plot(
 
     # Observations
     if n_sigma_box != None:
-        obs_dataframe = pd.read_table(observations_file, delim_whitespace=True, header=0, index_col="index")
+        obs_dataframe = pd.read_table(observations_file, sep="\s+", header=0, index_col="index")
         if (("logL" in obs_dataframe.columns) or ("logg" in obs_dataframe.columns)) and (
             "Teff" in obs_dataframe.columns
         ):