More work following on from BenchmarkData changes

Author: gavinmacaulay

docs/usage.md

@@ -281,7 +281,7 @@ Note that the `parameters()` call does not return all of the parameters needed b
 
 ## Benchmark model TS
 
-[Jech et al., (2015)](https://doi.org/10.1121/1.4937607) presented _benchmark_ model runs for the reference models. The TS results from these benchmarks are available in echoSMs via the [`BenchmarkData`](api_reference.md#echosms.BenchmarkData) class. This class reads the CSV-formatted file of benchmark values. Methods in the `BenchmarkData` class provide a list of the benchmark names, access to individual TS and frequency/angle datasets, and also the frequency and angle datasets as Pandas DataFrames.
+[Jech et al., (2015)](https://doi.org/10.1121/1.4937607) presented _benchmark_ model runs for the reference models. The TS results from these benchmarks are available in echoSMs via the [`BenchmarkData`](api_reference.md#echosms.BenchmarkData) class. This class reads the CSV-formatted files of benchmark values and provides methods that list the benchmark names, return individual TS and frequency/angle datasets, and also provide the entire datasets as Pandas DataFrames.
 
 ```py
     from echosms import BenchmarkData

src/echosms/benchmarkdata.py

@@ -67,8 +67,12 @@ def __init__(self):
 
         self.freq_dataset['frequency (kHz)'] *= 1e3  # want Hz not kHz
 
-        self.angle_dataset.set_index('angle (deg)', inplace=True)
-        self.freq_dataset.set_index('frequency (kHz)', inplace=True)
+        # Remove units from the column names (we have the echoSMs units convention instead)
+        self.freq_dataset.rename(columns={'frequency (kHz)': 'frequency'}, inplace=True)
+        self.angle_dataset.rename(columns={'angle (deg)': 'angle'}, inplace=True)
+
+        self.angle_dataset.set_index('angle', inplace=True)
+        self.freq_dataset.set_index('frequency', inplace=True)
 
     def angle_names(self) -> list:
         """Provide the model names for the angle benchmark data.
@@ -142,6 +146,6 @@ def freq_as_dataframe(self) -> pd.DataFrame:
         Returns
         -------
         :
-            Dataframe containing the benchmark data. 
+            Dataframe containing the benchmark data.
         """
         return self.freq_dataset

src/example_code.py

@@ -106,7 +106,7 @@ def plot_compare_angle(theta1, ts1, label1, theta2, ts2, label2, title):
     bm_f, bm_ts = bm.freq_data(name)
     not_nan = ~np.isnan(bm_ts)
 
-    m['f'] = bm_f[not_nan]*1e3  # [Hz]
+    m['f'] = bm_f[not_nan]
     bm_ts = bm_ts[not_nan]
 
     # No benchmark TS is available for this model, so add some sensible frequencies in
@@ -186,7 +186,7 @@ def plot_compare_angle(theta1, ts1, label1, theta2, ts2, label2, title):
 m_f, bm_ts = bm.freq_data(name)
 not_nan = ~np.isnan(bm_ts)
 
-m['f'] = bm_f[not_nan]*1e3  # [Hz]
+m['f'] = bm_f[not_nan]
 bm_ts = bm_ts[not_nan]
 
 mod = KAModel()
@@ -240,8 +240,7 @@ def plot_compare_angle(theta1, ts1, label1, theta2, ts2, label2, title):
     m.pop('b', None)
 
     # Get benchmark model for comparison
-    bm_f, bm_ts = bm.freq_data(name)
-    f = bm_f * 1e3
+    f, bm_ts = bm.freq_data(name)
 
     m |= {'theta': 90, 'phi': 0, 'a': a, 'rv_pos': rv_pos, 'rv_tan': rv_tan, 'f': f}
 
@@ -455,7 +454,7 @@ def plot_compare_angle(theta1, ts1, label1, theta2, ts2, label2, title):
 m['rho'] = [m['medium_rho'], m['target_rho']]
 m['c'] = [m['medium_c'], m['target_c']]
 bm_f, bm_ts = bm.freq_data(name)
-m['f'] = bm_f * 1e3  # [Hz]
+m['f'] = bm_f
 # remove unneeded parameters
 m = {k: v for k, v in m.items()
      if k not in ['boundary_type', 'a', 'medium_rho', 'medium_c', 'target_rho', 'target_c']}
@@ -471,7 +470,7 @@ def plot_compare_angle(theta1, ts1, label1, theta2, ts2, label2, title):
 mss = MSSModel()
 mm = rm.parameters(name)
 bm_f, bm_ts = bm.freq_data(name)
-mm['f'] = bm_f * 1e3  # [Hz]
+mm['f'] = bm_f
 mm['theta'] = 90.0
 
 mss_ts = mss.calculate_ts(mm, progress=True)
@@ -494,7 +493,7 @@ def plot_compare_angle(theta1, ts1, label1, theta2, ts2, label2, title):
 m['rho'] = [m['medium_rho'], m['target_rho']]
 m['c'] = [m['medium_c'], m['target_c']]
 bm_f, bm_ts = bm.freq_data(name)
-m['f'] = bm_f * 1e3  # [Hz]
+m['f'] = bm_f
 # remove unneeded parameters
 m = {k: v for k, v in m.items()
      if k not in ['boundary_type', 'a', 'b', 'medium_rho', 'medium_c', 'target_rho', 'target_c']}