refactor(births): rename births to buddings

compiler.py

@@ -179,20 +179,20 @@ class ExperimentCompiler(Compiler):
         names_signals = {
             "dvol": "postprocessing/dsignal/postprocessing_savgol_extraction_general_None_volume",
             "bud_dvol": "postprocessing/bud_metric/postprocessing_dsignal_postprocessing_savgol_extraction_general_None_volume",
-            "births": "postprocessing/births/extraction_general_None_volume",
+            "buddings": "postprocessing/buddings/extraction_general_None_volume",
         }
         operations = {
             "dvol": ("dvol", "max"),
             "bud_dvol": ("bud_dvol", "max"),
-            "births": ("births", "sum"),
-            "births_mean": ("births", "mean"),
+            "buddings": ("buddings", "sum"),
+            "buddings_mean": ("buddings", "mean"),
         }
 
         input_signals = {
             k: self.grouper.concat_signal(v) for k, v in names_signals.items()
         }
 
-        ids = input_signals["births"].index
+        ids = input_signals["buddings"].index
         for v in input_signals.values():
             ids = ids.intersection(v.index)
 
@@ -271,31 +271,31 @@ class ExperimentCompiler(Compiler):
 
     def compile_growth_metrics(
         self,
-        min_nbirths: int = 2,
+        min_nbuddings: int = 2,
     ):
-        """Filter mothers with n number of births and get their metrics.
+        """Filter mothers with n number of buddings and get their metrics.
 
-        Select cells with at least two recorded births
+        Select cells with at least two recorded buddings
         """
         names_signals = {
             "dvol": "postprocessing/dsignal/postprocessing_savgol_extraction_general_None_volume",
             "bud_dvol": "postprocessing/bud_metric/postprocessing_dsignal_postprocessing_savgol_extraction_general_None_volume",
-            "births": "postprocessing/births/extraction_general_None_volume",
+            "buddings": "postprocessing/buddings/extraction_general_None_volume",
         }
         operations = {
             "dvol": ("dvol", "max"),
             "bud_dvol": ("bud_dvol", "max"),
-            "births": ("births", "sum"),
+            "buddings": ("buddings", "sum"),
             "cycle_length_mean": (
-                "births",
+                "buddings",
                 lambda x: np.diff(np.where(x)[0]).mean(),
             ),
             "cycle_length_min": (
-                "births",
+                "buddings",
                 lambda x: np.diff(np.where(x)[0]).min(),
             ),
             "cycle_length_median": (
-                "births",
+                "buddings",
                 lambda x: np.median(np.diff(np.where(x)[0])),
             ),
         }
@@ -303,7 +303,7 @@ class ExperimentCompiler(Compiler):
         input_signals = {
             k: self.grouper.concat_signal(v) for k, v in names_signals.items()
         }
-        ids = self.get_shared_ids(input_signals, min_nbirths=min_nbirths)
+        ids = self.get_shared_ids(input_signals, min_nbuddings=min_nbuddings)
 
         compiled_df = pd.DataFrame(
             {
@@ -316,18 +316,18 @@ class ExperimentCompiler(Compiler):
         return compiled_df
 
     def get_shared_ids(
-        self, input_signals: Dict[str, pd.DataFrame], min_nbirths: int = None
+        self, input_signals: Dict[str, pd.DataFrame], min_nbuddings: int = None
     ):
         """Get the intersection id of multiple signals.
 
-        "births" must be one the keys in input_signals to use the
-        argument min_nbirths.
+        "buddings" must be one the keys in input_signals to use the
+        argument min_nbuddings.
         """
         ids = list(input_signals.values())[0].index
-        if min_nbirths is not None:
+        if min_nbuddings is not None:
             ids = (
-                input_signals["births"]
-                .loc[input_signals["births"].sum(axis=1) >= min_nbirths]
+                input_signals["buddings"]
+                .loc[input_signals["buddings"].sum(axis=1) >= min_nbuddings]
                 .index
             )
         for v in input_signals.values():

core/multisignal/align.py

@@ -48,7 +48,7 @@ class alignParameters(ParametersABC):
         Specifies the number of events required for each cell.  For example, if
         events_at_least is 2, then it will discard time series (from the DataFrame)
         that have less than 2 events.  As a more pratical example: discarding
-        flavin time series that derive from cells with less than 2 births
+        flavin time series that derive from cells with less than 2 buddings
         identified.
     """
 

core/processes/births.py → core/processes/buddings.py

@@ -12,7 +12,7 @@ from postprocessor.core.processes.lineageprocess import (
 )
 
 
-class birthsParameters(LineageProcessParameters):
+class buddingsParameters(LineageProcessParameters):
     """Parameter class to obtain birth events.
 
     Parameters
@@ -29,14 +29,14 @@ class birthsParameters(LineageProcessParameters):
     _defaults = {"lineage_location": "postprocessing/lineage_merged"}
 
 
-class births(LineageProcess):
+class buddings(LineageProcess):
     """
-    Calculate births in a trap assuming one mother per trap
+    Calculate buddings in a trap assuming one mother per trap
 
-    returns a pandas series with the births
+    returns a pandas series with the buddings
     """
 
-    def __init__(self, parameters: birthsParameters):
+    def __init__(self, parameters: buddingsParameters):
         super().__init__(parameters)
 
     def load_lineage(self, lineage):
@@ -60,12 +60,12 @@ class births(LineageProcess):
         mothers = signal.loc[
             set(signal.index).intersection(traps_mothers.keys())
         ]
-        births = pd.DataFrame(
+        buddings = pd.DataFrame(
             np.zeros((mothers.shape[0], signal.shape[1])).astype(bool),
             index=mothers.index,
             columns=signal.columns,
         )
-        births.columns.names = ["timepoint"]
+        buddings.columns.names = ["timepoint"]
         for mother_id, daughters in traps_mothers.items():
             daughters_idx = set(
                 fvi.loc[
@@ -74,9 +74,9 @@ class births(LineageProcess):
                     )
                 ].values
             ).difference({0})
-            births.loc[
+            buddings.loc[
                 mother_id,
                 daughters_idx,
             ] = True
 
-        return births
+        return buddings

core/processor.py

@@ -55,7 +55,7 @@ class PostProcessorParameters(ParametersABC):
             },
             "processes": [
                 [
-                    "births",
+                    "buddings",
                     [
                         "/extraction/general/None/volume",
                     ],

routines/heatmap.py

@@ -15,7 +15,7 @@ class _HeatmapPlotter(BasePlotter):
         self,
         trace_df,
         trace_name,
-        births_df,
+        buddings_df,
         cmap,
         unit_scaling,
         xtick_step,
@@ -27,7 +27,7 @@ class _HeatmapPlotter(BasePlotter):
         super().__init__(trace_name, unit_scaling, xlabel, plot_title)
         # Define attributes from arguments
         self.trace_df = trace_df
-        self.births_df = births_df
+        self.buddings_df = buddings_df
         self.cmap = cmap
         self.xtick_step = xtick_step
         self.scale = scale
@@ -83,16 +83,16 @@ class _HeatmapPlotter(BasePlotter):
         ax.xaxis.set_major_locator(
             ticker.MultipleLocator(self.xtick_step / self.unit_scaling)
         )
-        # Overlay births, if present
-        if self.births_df is not None:
+        # Overlay buddings, if present
+        if self.buddings_df is not None:
             # Must be masked array for transparency
-            births_array = self.births_df.to_numpy()
-            births_heatmap_mask = np.ma.masked_where(
-                births_array == 0, births_array
+            buddings_array = self.buddings_df.to_numpy()
+            buddings_heatmap_mask = np.ma.masked_where(
+                buddings_array == 0, buddings_array
             )
             # Overlay
             ax.imshow(
-                births_heatmap_mask,
+                buddings_heatmap_mask,
                 interpolation="none",
             )
         # Draw colour bar
@@ -104,7 +104,7 @@ class _HeatmapPlotter(BasePlotter):
 def heatmap(
     trace_df,
     trace_name,
-    births_df=None,
+    buddings_df=None,
     cmap=cm.RdBu,
     unit_scaling=1,
     xtick_step=60,
@@ -123,7 +123,7 @@ def heatmap(
         Time series of traces (rows = cells, columns = time points).
     trace_name : string
         Name of trace being plotted, e.g. 'flavin'.
-    births_df : pandas.DataFrame
+    buddings_df : pandas.DataFrame
         Birth mask (rows = cells, columns = time points).  Elements should be
         0 or 1.
     cmap : matplotlib ColorMap
@@ -160,7 +160,7 @@ def heatmap(
     plotter = _HeatmapPlotter(
         trace_df,
         trace_name,
-        births_df,
+        buddings_df,
         cmap,
         unit_scaling,
         xtick_step,

routines/single_birth_plot.py

@@ -6,7 +6,7 @@ from postprocessor.routines.single_plot import _SinglePlotter
 
 
 class _SingleBirthPlotter(_SinglePlotter):
-    """Draw a line plot of a single time series, but with births overlaid"""
+    """Draw a line plot of a single time series, but with buddings overlaid"""
 
     def __init__(
         self,
@@ -33,7 +33,7 @@ class _SingleBirthPlotter(_SinglePlotter):
             xlabel,
             plot_title,
         )
-        # Add some more attributes useful for births
+        # Add some more attributes useful for buddings
         self.birth_mask = birth_mask
         self.birth_color = birth_color
         self.birth_linestyle = birth_linestyle
@@ -71,7 +71,7 @@ def single_birth_plot(
     plot_title="",
     ax=None,
 ):
-    """Plot time series of trace, overlaid with births
+    """Plot time series of trace, overlaid with buddings
 
     Parameters
     ----------
@@ -82,18 +82,18 @@ def single_birth_plot(
     trace_name : string
         Name of trace being plotted, e.g. 'flavin'.
     birth_mask : array_like
-        Mask to indicate where births are. Expect values of '0' and '1' or
+        Mask to indicate where buddings are. Expect values of '0' and '1' or
         'False' and 'True' in the elements.
     unit_scaling : int or float
         Unit scaling factor, e.g. 1/60 to convert minutes to hours.
     trace_color : string
         matplotlib colour string for the trace
     birth_color : string
-        matplotlib colour string for the vertical lines indicating births
+        matplotlib colour string for the vertical lines indicating buddings
     trace_linestyle : string
         matplotlib linestyle argument for the trace
     birth_linestyle : string
-        matplotlib linestyle argument for the vertical lines indicating births
+        matplotlib linestyle argument for the vertical lines indicating buddings
     xlabel : string
         x axis label.
     plot_title : string