Removed unused parts of summarise_herg_export.py

MichaelClerx · MichaelClerx · commit 5247d7e5d867 · 2025-11-12T23:37:18.000Z
diff --git a/pcpostprocess/scripts/run_herg_qc.py b/pcpostprocess/scripts/run_herg_qc.py
@@ -35,7 +35,7 @@ def starmap(n, func, iterable):
     """
     if n == 1:
         return [func(*args) for args in iterable]
-    else:
+    else:  # pragma: no cover
         with multiprocessing.Pool(n, maxtasksperchild=1) as pool:
             return pool.starmap(func, iterable)
 
@@ -1300,5 +1300,5 @@ def fit_func(x, args=None):
     return (d, b), f, peak_current if res else (np.nan, np.nan), np.nan, peak_current
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':  # pragma: no cover
     run_from_command_line()
diff --git a/pcpostprocess/scripts/summarise_herg_export.py b/pcpostprocess/scripts/summarise_herg_export.py
@@ -16,27 +16,6 @@
 from pcpostprocess.scripts.run_herg_qc import create_qc_table
 
 
-def get_wells_list(input_dir, experiment_name):
-    regex = re.compile(f"{experiment_name}-([a-z|A-Z|0-9]*)-([A-Z][0-9][0-9])-after")
-    wells = []
-
-    for f in filter(regex.match, os.listdir(input_dir)):
-        well = re.search(regex, f).groups(2)[1]
-        if well not in wells:
-            wells.append(well)
-    return list(np.unique(wells))
-
-
-def get_protocol_list(input_dir, experiment_name):
-    regex = re.compile(f"{experiment_name}-([a-z|A-Z|0-9]*)-([A-Z][0-9][0-9])-after")
-    protocols = []
-    for f in filter(regex.match, os.listdir(input_dir)):
-        well = re.search(regex, f).groups(3)[0]
-        if protocols not in protocols:  # TODO This has GOT to be a bug
-            protocols.append(well)
-    return list(np.unique(protocols))
-
-
 def run_from_command_line():  # pragma: no cover
     """
     Parses arguments from the command line and then ???
@@ -147,7 +126,6 @@ def run(data_path, output_path, experiment_name, reversal_potential=None,
     leak_parameters_df['passed QC'] = [
         well in passed_wells for well in leak_parameters_df.well]
 
-    # do_scatter_matrices(leak_parameters_df, qc_vals_df, output_path, reversal_potential)
     plot_histograms(leak_parameters_df, output_path, reversal_potential, figsize)
 
 
@@ -342,192 +320,6 @@ def label_func(p, s):
     plt.close(fig)
 
 
-def do_combined_plots(data_path, output_path, experiment_name,
-                      leak_parameters_df, passed_wells, figsize=None):
-    """
-    ???
-
-    @param data_path
-    @param output_path
-    @param experiment_name
-    @param leak_parameters_df
-    @param passed_wells
-    @param figsize
-
-    """
-    fig = plt.figure(figsize=figsize, constrained_layout=True)
-    ax = fig.subplots()
-
-    wells = [well for well in leak_parameters_df.well.unique() if well in passed_wells]
-
-    protocol_overlaid_dir = os.path.join(output_path, 'overlaid_by_protocol')
-    if not os.path.exists(protocol_overlaid_dir):
-        os.makedirs(protocol_overlaid_dir)
-
-    leak_parameters_df = leak_parameters_df[leak_parameters_df.well.isin(passed_wells)]
-
-    palette = sns.color_palette('husl', len(leak_parameters_df.groupby(['well', 'sweep'])))
-    for protocol in leak_parameters_df.protocol.unique():
-        pname = protocol
-        if pname == 'staircaseramp1':
-            pname = 'staircaseramp'
-        elif pname == 'staircaseramp1_2':
-            pname = 'staircaseramp_2'
-
-        times_fname = os.path.join(data_path, 'traces',
-                                   f'{experiment_name}-{pname}-times.csv')
-        try:
-            times = np.loadtxt(times_fname).astype(np.float64).flatten()
-        except FileNotFoundError:
-            continue
-
-        times = times.flatten().astype(np.float64)
-
-        reference_current = None
-
-        i = 0
-        for sweep in leak_parameters_df.sweep.unique():
-            for well in wells:
-                fname = f"{experiment_name}-{pname}-{well}-sweep{sweep}.csv"
-                try:
-                    data = pd.read_csv(os.path.join(data_path, 'traces', fname))
-
-                except FileNotFoundError:
-                    continue
-
-                current = data['current'].values.flatten().astype(np.float64)
-
-                if reference_current is None:
-                    reference_current = current
-
-                scaled_current = scale_to_reference(current, reference_current)
-                col = palette[i]
-                i += 1
-                ax.plot(times, scaled_current, color=col, alpha=.5, label=well)
-
-        fig_fname = f"{protocol}_overlaid_traces_scaled"
-        fig.suptitle(f"{protocol}: all wells")
-        ax.set_xlabel(r'time / ms')
-        ax.set_ylabel('current scaled to reference trace')
-        ax.legend()
-        fig.savefig(os.path.join(protocol_overlaid_dir, fig_fname))
-        ax.cla()
-
-    plt.close(fig)
-
-    palette = sns.color_palette('husl',
-                                len(leak_parameters_df.groupby(['protocol', 'sweep'])))
-
-    fig2 = plt.figure(figsize=figsize, constrained_layout=True)
-    axs2 = fig2.subplots(1, 2, sharey=True)
-
-    wells_overlaid_dir = os.path.join(output_path, 'overlaid_by_well')
-    if not os.path.exists(wells_overlaid_dir):
-        os.makedirs(wells_overlaid_dir)
-
-    for well in passed_wells:
-        i = 0
-        for sweep in leak_parameters_df.sweep.unique():
-            for protocol in leak_parameters_df.protocol.unique():
-                pname = protocol
-                if pname == 'staircaseramp1':
-                    pname = 'staircaseramp'
-                elif pname == 'staircaseramp1_2':
-                    pname = 'staircaseramp_2'
-
-                times_fname = f'{experiment_name}-{pname}-times.csv'
-                times = np.loadtxt(os.path.join(data_path, 'traces', times_fname))
-                times = times.flatten().astype(np.float64)
-
-                fname = f"{experiment_name}-{protocol}-{well}-sweep{sweep}.csv"
-                try:
-                    data = pd.read_csv(os.path.join(data_path, 'traces', fname))
-                except FileNotFoundError:
-                    continue
-
-                current = data['current'].values.flatten().astype(np.float64)
-
-                indices_pre_ramp = times < 3000
-
-                col = palette[i]
-                i += 1
-
-                label = f"{protocol}_sweep{sweep}"
-
-                axs2[0].plot(times[indices_pre_ramp], current[indices_pre_ramp], color=col, alpha=.5,
-                             label=label)
-
-                indices_post_ramp = times > (times[-1] - 2000)
-                post_times = times[indices_post_ramp].copy()
-                post_times = post_times - post_times[0] + 5000
-                axs2[1].plot(post_times, current[indices_post_ramp], color=col, alpha=.5,
-                             label=label)
-
-        axs2[0].legend()
-        axs2[0].set_title('before drug')
-        axs2[0].set_xlabel(r'time / ms')
-        axs2[1].set_title('after drug')
-        axs2[1].set_xlabel(r'time / ms')
-
-        axs2[0].set_ylabel('current / pA')
-        axs2[1].set_ylabel('current / pA')
-
-        fig2_fname = f"{well}_overlaid_traces"
-        fig2.suptitle(f"Leak ramp comparison: {well}")
-
-        fig2.savefig(os.path.join(wells_overlaid_dir, fig2_fname))
-        axs2[0].cla()
-        axs2[1].cla()
-
-    plt.close(fig2)
-
-
-def do_scatter_matrices(df, qc_df, output_path, reversal_potential=None):
-    """
-    ???
-
-    @param df
-    @param qc_df
-    @param output_path
-    @reversal_potential
-    """
-    grid = sns.pairplot(data=df, hue='passed QC', diag_kind='hist',
-                        plot_kws={'alpha': 0.4, 'edgecolor': None},
-                        hue_order=[True, False])
-    grid.savefig(os.path.join(output_path, 'scatter_matrix_by_QC'))
-
-    if reversal_potential is not None:
-        true_reversal = reversal_potential
-    else:
-        # TODO Clarify in plot label!
-        true_reversal = df['E_rev'].values.mean()
-
-    df['hue'] = df.E_rev.to_numpy() > true_reversal
-    grid = sns.pairplot(data=df, hue='hue', diag_kind='hist',
-                        plot_kws={'alpha': 0.4, 'edgecolor': None},
-                        hue_order=[True, False])
-    grid.savefig(os.path.join(output_path, 'scatter_matrix_by_reversal.png'))
-
-    # Now do artefact parameters only
-    if 'drug' in qc_df:
-        qc_df = qc_df[qc_df.drug == 'before']
-
-    first_sweep = sorted(list(qc_df.sweep.unique()))[0]
-    qc_df = qc_df[(qc_df.protocol == 'staircaseramp1') &
-                  (qc_df.sweep == first_sweep)]
-    if 'drug' in qc_df:
-        qc_df = qc_df[qc_df.drug == 'before']
-
-    qc_df = qc_df.set_index(['protocol', 'well', 'sweep'])
-    qc_df = qc_df[['Rseries', 'Cm', 'Rseal', 'passed QC']]
-    # qc_df['R_leftover'] = df['R_leftover']
-    grid = sns.pairplot(data=qc_df, diag_kind='hist', plot_kws={'alpha': .4,
-                                                                'edgecolor': None},
-                        hue='passed QC', hue_order=[True, False])
-
-    grid.savefig(os.path.join(output_path, 'scatter_matrix_QC_params_by_QC'))
-
-
 def plot_reversal_spread(df, output_path, figsize=None):
     """
     ???
@@ -860,105 +652,6 @@ def plot_histograms(df, output_path, reversal_potential=None, figsize=None):
     plt.close(fig)
 
 
-def overlay_reversal_plots(
-        data_path, output_path, experiment_name, leak_parameters_df, wells,
-        reversal_potential=None, figsize=None):
-    """
-    ???
-
-    @param data_path
-    @param output_path
-    @param experiment_name
-    @param leak_parameters_df
-    @param reversal_potential
-    @param figsize
-    """
-    fig = plt.figure(figsize=figsize, constrained_layout=True)
-    ax = fig.subplots()
-
-    palette = sns.color_palette('husl', len(leak_parameters_df.groupby(['protocol', 'sweep'])))
-
-    sub_dir = os.path.join(output_path, 'overlaid_reversal_plots')
-
-    if not os.path.exists(sub_dir):
-        os.makedirs(sub_dir)
-
-    protocols_to_plot = ['staircaseramp1']
-    sweeps_to_plot = [1]
-
-    for well in wells:
-        # Setup figure
-        if False in leak_parameters_df[leak_parameters_df.well == well]['passed QC'].values:
-            continue
-        i = 0
-        for protocol in protocols_to_plot:
-            if protocol == np.nan:
-                continue
-
-            pname = protocol
-            if pname == 'staircaseramp1':
-                pname = 'staircaseramp'
-            elif pname == 'staircaseramp1_2':
-                pname = 'staircaseramp_2'
-            voltage_fname = os.path.join(data_path, 'traces',
-                                         f'{experiment_name}-{pname}-voltages.csv')
-            voltages = pd.read_csv(voltage_fname)['voltage'].values.flatten()
-
-            times_fname = f"{experiment_name}-{pname}-times.csv"
-            times = np.loadtxt(os.path.join(data_path, 'traces', times_fname))
-            times = times.flatten().astype(np.float64)
-
-            # First, find the reversal ramp
-            json_name = os.path.join(data_path, 'traces', 'protocols',
-                                     f'{experiment_name}-{pname}.json')
-            with open(json_name, 'r') as f:
-                json_protocol = json.load(f)
-            v_protocol = VoltageProtocol.from_json(json_protocol)
-            ramps = v_protocol.get_ramps()
-            reversal_ramp = ramps[-1]
-            ramp_start, ramp_end = reversal_ramp[:2]
-
-            # Next extract steps
-            istart = np.argmax(times >= ramp_start)
-            iend = np.argmax(times > ramp_end)
-
-            if istart == 0 or iend == 0 or istart == iend:
-                raise Exception('Could not identify reversal ramp')
-
-            for sweep in sweeps_to_plot:
-                fname = f"{experiment_name}-{pname}-{well}-sweep{sweep}.csv"
-                try:
-                    data = pd.read_csv(os.path.join(data_path, 'traces', fname))
-                except FileNotFoundError:
-                    continue
-
-                # Plot voltage vs current
-                current = data['current'].values.astype(np.float64)
-
-                col = palette[i]
-
-                ax.scatter(voltages[istart:iend], current[istart:iend], label=protocol,
-                           color=col, s=1.2)
-
-                fitted_poly = np.poly1d(np.polyfit(voltages[istart:iend], current[istart:iend], 4))
-                ax.plot(voltages[istart:iend], fitted_poly(voltages[istart:iend]), color=col)
-                i += 1
-
-        if reversal_potential is not None:
-            ax.axvline(reversal_potential, linestyle='--', color='grey',
-                       label='Calculated Nernst potential')
-
-        ax.legend()
-        # Save figure
-        fig.savefig(os.path.join(sub_dir, f"overlaid_reversal_ramps_{well}"))
-
-        # Clear figure
-        ax.cla()
-
-    plt.close(fig)
-    return
-
-
 def scale_to_reference(trace, reference):
     def error2(p):
         return np.sum((p*trace - reference)**2)
@@ -1043,5 +736,5 @@ def create_attrition_table(qc_df, subtraction_df):
     return res_df
 
 
-if __name__ == "__main__":
+if __name__ == "__main__":  # pragma: no cover
     run_from_command_line()
