Updating pediatrics notebook and chart generation for new sample set; tweaked five by five to handle <25 subjects; specifying plt.show() on some chart functions to initiate chart refresh, refs #37

Author: dchud

.gitignore

@@ -145,3 +145,4 @@ cython_debug/
 *.swo
 *.swp
 .DS_Store
+output

GrowthViz-pediatrics.ipynb

@@ -251,13 +251,17 @@ def handle_selection_change(_event, _widget):
 
 
 all_ids = obs['subjid'].unique()
-val = 47085108 if 47085108 in all_ids else np.random.choice(all_ids, size=1, replace=False)
-interactive(charts.overlap_view_pediatrics, obs_df=fixed(obs), 
+val = 5450 if 5450 in all_ids else np.random.choice(all_ids, size=1, replace=False)
+interactive(charts.overlap_view_pediatrics_show, 
+            obs_df=fixed(obs), 
             subjid=widgets.Dropdown(options=all_ids, value=val, description='Subject ID:', disabled=False), 
             param=['HEIGHTCM', 'WEIGHTKG'], 
             include_carry_forward=widgets.Checkbox(value=True,description='Include Carry Forward',disabled=False,indent=False), 
             include_percentiles=widgets.Checkbox(value=True,description='Include Measurement Percentile Bands',disabled=False,indent=False),
-            wt_df=fixed(wt_percentiles), ht_df=fixed(ht_percentiles), bmi_df=fixed(bmi_percentiles))
+            wt_df=fixed(wt_percentiles), 
+            ht_df=fixed(ht_percentiles), 
+            bmi_df=fixed(bmi_percentiles)
+           )
 
 
 # The cell below also creates a plot for an individual modeled after the [CDC paper growth charts](https://www.cdc.gov/growthcharts/data/set1clinical/cj41c021.pdf). It shows both the weight trajectory and height trajectory. The lighter bands in the diagram background represent the 5th through 95th percentile values for age and sex for the given measurement type.
@@ -268,8 +272,9 @@ def handle_selection_change(_event, _widget):
 
 
 all_ids = obs['subjid'].unique()
-val = 47085108 if 47085108 in all_ids else np.random.choice(all_ids, size=1, replace=False)
-interactive(charts.overlap_view_double_pediatrics, obs_df=fixed(obs), 
+val = 5446 if 5446 in all_ids else np.random.choice(all_ids, size=1, replace=False)
+interactive(charts.overlap_view_double_pediatrics, 
+            obs_df=fixed(obs), 
             subjid=widgets.Dropdown(options=all_ids, value=val, description='Subject ID:', disabled=False),
             show_all_measurements=widgets.Checkbox(value=True,description='Show All Measurements',disabled=False,indent=False),
             show_excluded_values=widgets.Checkbox(value=True,description='Show Excluded Values (x)',disabled=False,indent=False),
@@ -340,7 +345,8 @@ def edge25(obs, category, sort_order, param):
         filtered_by_cat = filtered_by_cat.nlargest(25, 'measurement')
     else:
         filtered_by_cat = filtered_by_cat.nsmallest(25, 'measurement')
-    return charts.five_by_five_view(obs, filtered_by_cat.subjid.values, param, wt_percentiles, ht_percentiles, bmi_percentiles, 'solid')
+    fig = charts.five_by_five_view(obs, filtered_by_cat.subjid.values, param, wt_percentiles, ht_percentiles, bmi_percentiles, 'solid')
+    plt.show()
 
 interact(edge25, obs = fixed(obs), category = obs.clean_cat.unique(), 
          sort_order = ['largest', 'smallest'], param = ['WEIGHTKG', 'HEIGHTCM'])

GrowthViz-pediatrics.py

@@ -151,7 +151,10 @@ If not using Anaconda, specific versions of packages can be found in `requiremen
 
 By default when you reach Step 6 of the [Simple Install](#simple-install)
 instructions above the notebook will use sample data loaded from the `.csv`
-files located in the GrowthViz-master project.
+files located in the GrowthViz project. This is the same synthetic sample data
+that is packaged with
+[growthcleanr](https://github.com/carriedaymont/growthcleanr), cleaned and
+then separated into pediatric and adult sets for GrowthViz.
 
 To ensure that all of the necessary example files are present, run the
 `check_setup.py` script.

README.md

@@ -29,7 +29,7 @@ def weight_distr(df, mode):
         plt.ylabel("Total Patient Observations")
         plt.xlabel("Recorded Weight (Kg)")
         plt.grid()
-        return wgt_grp_sum_plot
+        plt.show()
 
 
 def overlap_view_adults(
@@ -215,6 +215,18 @@ def overlap_view_pediatrics(
     return selected_param_plot
 
 
+def overlap_view_pediatrics_show(
+    obs_df, subjid, param, include_carry_forward, include_percentiles, wt_df, ht_df
+):
+    """
+    Wraps overlap_view_pediatrics with plt.show().
+    """
+    plot = overlap_view_pediatrics(
+        obs_df, subjid, param, include_carry_forward, include_percentiles, wt_df, ht_df
+    )
+    plt.show()
+
+
 def overlap_view_double_pediatrics(
     obs_df,
     subjid,
@@ -359,7 +371,7 @@ def overlap_view_double_pediatrics(
 
     # Reset figsize to default
     plt.rcParams["figure.figsize"] = [6.4, 4.8]
-    return fig
+    plt.show()
 
 
 def mult_obs(obs):
@@ -375,6 +387,24 @@ def mult_obs(obs):
     return obs[obs["any_ones"] == 0]
 
 
+def five_by_five_shape(n):
+    """
+    Determines shape of five by five view, allowing for fewer than 25 observations.
+
+    Parameters:
+    n: length of subject list to display
+
+    Returns:
+    Dimensions of grid/subplots as (nrows, ncols)
+    """
+    if n // 5 == 0:
+        return (1, n % 5)
+    elif n % 5 > 0:
+        return ((n // 5) + 1, 5)
+    else:
+        return (n // 5, 5)
+
+
 def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle):
     """
     Creates a small multiples plot showing the growth trend for 25 individuals
@@ -384,17 +414,32 @@ def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle):
     subjids: An list of the ids of the individuals to be plotted
     param: (String) Whether to plot heights or weights. Expected values are "HEIGHTCM" or "WEIGHTKG"
     """
-    fig, ax = plt.subplots(5, 5)
-    for y in range(5):
-        for x in range(5):
-            subjid = subjids[x * 5 + y]
+    if len(subjids) == 0:
+        print("No matching subjects found.")
+        return
+    nrows, ncols = five_by_five_shape(len(subjids))
+    fig, ax = plt.subplots(nrows, ncols)
+    for y in range(ncols):
+        for x in range(nrows):
+            try:
+                subjid = subjids[x * 5 + y]
+            except IndexError as ie:
+                # No more subjects to render
+                break
             individual = obs_df[obs_df.subjid == subjid]
             selected_param = individual[individual.param == param]
-            ax[x, y].plot(selected_param.age, selected_param.measurement, marker=".")
+            # Indexing varies by dimensionality, so simplify
+            if nrows > 1:
+                tgt = ax[x, y]
+            elif len(subjids) == 1:
+                tgt = ax
+            else:
+                tgt = ax[y]
+            tgt.plot(selected_param.age, selected_param.measurement, marker=".")
             excluded_selected_param = selected_param[
                 selected_param.clean_value != "Include"
             ]
-            ax[x, y].scatter(
+            tgt.scatter(
                 excluded_selected_param.age,
                 excluded_selected_param.measurement,
                 c="r",
@@ -411,22 +456,23 @@ def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle):
                 & (percentile_df.age >= math.floor(individual.age.min()))
                 & (percentile_df.age <= math.ceil(individual.age.max()))
             ]
-            ax[x, y].plot(
+            tgt.plot(
                 percentile_window.age,
                 percentile_window.P5,
                 color="k",
                 linestyle=linestyle,
                 zorder=1,
             )
-            ax[x, y].plot(
+            tgt.plot(
                 percentile_window.age,
                 percentile_window.P95,
                 color="k",
                 linestyle=linestyle,
                 zorder=1,
             )
-            ax[x, y].set(title=subjid)
-    fig.set_size_inches(20, 12)
+            tgt.set(title=subjid)
+    # Set size dynamically to average out about the same
+    fig.set_size_inches(4 * ncols, 2.4 * nrows)
     return plt.tight_layout()
 
 
@@ -465,7 +511,7 @@ def bmi_with_percentiles(merged_df, bmi_percentiles, subjid):
 
     ax[1].set(xlabel="age (y)", ylabel="BMI", title="BMI Cleaned")
     ax[1].grid()
-    return plt
+    plt.show()
 
 
 def param_with_percentiles(merged_df, subjid, param, wt_df, ht_df, bmi_df):
@@ -499,7 +545,7 @@ def param_with_percentiles(merged_df, subjid, param, wt_df, ht_df, bmi_df):
 
     ax[1].set(xlabel="age (y)", ylabel="", title=(param + " Cleaned"))
     ax[1].grid()
-    return plt
+    plt.show()
 
 
 def top_ten(

README.pdf

@@ -95,7 +95,7 @@ def setup_percentiles_pediatrics(percentiles_file):
     Processes pediatrics percentiles from CDC
     """
     percentiles = pd.read_csv(
-        f'growthviz-data/ext/{percentiles_file}',
+        f"growthviz-data/ext/{percentiles_file}",
         dtype={
             "Agemos": float,
             "P5": float,
@@ -332,11 +332,9 @@ def export_to_csv(da_locals, selection_widget, out):
     Saves out csv file of dataframe
     """
     df_name = selection_widget.value
-    da_locals[df_name].to_csv(
-        "growthviz-data/output/{}.csv".format(df_name), index=False
-    )
+    da_locals[df_name].to_csv("output/{}.csv".format(df_name), index=False)
     out.clear_output()
-    out.append_display_data(FileLinks("growthviz-data/output"))
+    out.append_display_data(FileLinks("output"))
 
 
 def clean_swapped_values(merged_df):

growthviz-data/sample-adults-data.csv

@@ -1,5 +1,5 @@
 ipywidgets
-matplotlib>=3.3.4 
-pandas>=1.2.2 
+matplotlib>=3.3.4
+pandas>=1.2.2
 qgrid>=1.3.1
-seaborn>=0.11.1 
+seaborn>=0.11.1