diff --git a/.github/workflows/build-and-publish-image.yml b/.github/workflows/build-and-publish-image-latest.yml similarity index 70% rename from .github/workflows/build-and-publish-image.yml rename to .github/workflows/build-and-publish-image-latest.yml index a756291..67a37bd 100644 --- a/.github/workflows/build-and-publish-image.yml +++ b/.github/workflows/build-and-publish-image-latest.yml @@ -6,17 +6,18 @@ on: push: branches: - main - - v3-dev jobs: build-and-publish-image: + if: github.repository == 'mitre/GrowthViz' runs-on: ubuntu-latest steps: - uses: actions/checkout@v2.5.0 - - name: Build and publish Docker image + - name: Build and publish latest Docker image uses: VaultVulp/gp-docker-action@1.5.0 with: github-token: ${{ secrets.GITHUB_TOKEN }} - image-name: mitre/growthviz + image-name: gv-image + image-tag: latest diff --git a/.github/workflows/build-and-publish-image-tag.yml b/.github/workflows/build-and-publish-image-tag.yml new file mode 100644 index 0000000..971cd78 --- /dev/null +++ b/.github/workflows/build-and-publish-image-tag.yml @@ -0,0 +1,23 @@ +# Adapted from: +# https://github.com/VaultVulp/gp-docker-action#complete-workflow-example +name: Build and publish + +on: + push: + tags: + - "*" + +jobs: + build-and-publish-image: + if: github.repository == 'mitre/GrowthViz' + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2.5.0 + + - name: Build and publish tagged Docker image + uses: VaultVulp/gp-docker-action@1.5.0 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + image-name: gv-image + extract-git-tag: true diff --git a/GrowthViz-adults.ipynb b/GrowthViz-adults.ipynb index ce74dc2..317b8ef 100644 --- a/GrowthViz-adults.ipynb +++ b/GrowthViz-adults.ipynb @@ -378,7 +378,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [], "source": [ @@ -415,13 +415,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "mdf = sumstats.add_mzscored_to_merged_df_adults(merged_df, percentiles_wide) \n", "mdf['wtz'] = (mdf['weight'] - mdf['Mean_weight'])/mdf['sd_weight']\n", "mdf['htz'] = (mdf['height'] - mdf['Mean_height'])/mdf['sd_height']\n", - "mdf['BMIz'] = (mdf['bmi'] - mdf['Mean_bmi'])/mdf['sd_bmi']\n", + "mdf['bmiz'] = (mdf['bmi'] - mdf['Mean_bmi'])/mdf['sd_bmi']\n", "mdf.head()\n", "\n", "col_opt = {\n", @@ -438,7 +440,7 @@ " 'weight_cat': { 'width': 80 },\n", " 'wtz': { 'width': 50 },\n", " 'bmi': { 'width': 40 },\n", - " 'BMIz': { 'width': 30 },\n", + " 'bmiz': { 'width': 30 },\n", "}\n", "g = qgrid.show_grid(charts.top_ten(mdf, 'weight'), precision=3, column_options=col_opt, column_definitions=col_def)\n", "ind_out = widgets.Output()\n", @@ -458,7 +460,7 @@ " charts.overlap_view_adults(obs, subjid, 'WEIGHTKG', True, True, wt_percentiles, bmi_percentiles, ht_percentiles)\n", " display(plt.show()) \n", "g.on('selection_changed', handle_selection_change) \n", - "widgets.VBox([g, ind_out])\n" + "widgets.VBox([g, ind_out])" ] }, { @@ -496,7 +498,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [], "source": [ @@ -628,17 +630,22 @@ "def edge25(obs, category, group, sort_order, param):\n", " filtered_by_cat = obs[(obs.clean_cat == category) & (obs.param == param)]\n", " # get list of relevant IDs\n", - " filtered_sum = filtered_by_cat.groupby('subjid', as_index=False).agg(max_measure=('measurement', 'max'), min_measure=('measurement', 'min'), start_age=('age', 'min'), axis_range=('range', 'mean'))\n", + " filtered_sum = filtered_by_cat.groupby('subjid', as_index=False).agg(max_measure=('measurement', 'max'), \n", + " min_measure=('measurement', 'min'), \n", + " start_age=('ageyears', 'min'), \n", + " axis_range=('range', 'mean'))\n", " if group == 'largest':\n", " filtered_sum = filtered_sum.nlargest(25, 'max_measure')\n", " else:\n", " filtered_sum = filtered_sum.nsmallest(25, 'min_measure')\n", " filtered_sum.sort_values(by=[sort_order, 'subjid'], inplace=True)\n", - " fig = charts.five_by_five_view(obs, filtered_sum.subjid.values, param, wt_percentiles, ht_percentiles, bmi_percentiles, 'dotted')\n", + " fig = charts.five_by_five_view(obs, filtered_sum.subjid.values, param, wt_percentiles, ht_percentiles, \n", + " bmi_percentiles, 'dotted')\n", " plt.show()\n", " \n", - "interact(edge25, obs = fixed(obs_wbmi_mult), category = obs.clean_cat.unique(), \n", - " group = ['largest', 'smallest'], sort_order = ['max_measure', 'min_measure', 'start_age', 'axis_range'], param = ['WEIGHTKG', 'HEIGHTCM', 'BMI'])" + "interact(edge25, obs=fixed(obs_wbmi_mult), category=obs.clean_cat.unique(), \n", + " group=['largest', 'smallest'], sort_order=['max_measure', 'min_measure', 'start_age', 'axis_range'], \n", + " param=['WEIGHTKG', 'HEIGHTCM', 'BMI']);" ] }, { @@ -658,10 +665,11 @@ "source": [ "all_ids = obs_wbmi['subjid'].unique()\n", "val = 2431 if 2431 in all_ids else np.random.choice(all_ids, size=1, replace=False)\n", - "interact(charts.param_with_percentiles, merged_df = fixed(obs_wbmi),\n", - " subjid = widgets.Dropdown(options=all_ids, value=val,\n", - " description='Subject ID:',disabled=False), \n", - " param = ['BMI', 'WEIGHTKG', 'HEIGHTCM'], wt_df = fixed(wt_percentiles), ht_df = fixed(ht_percentiles), bmi_df = fixed(bmi_percentiles))" + "interact(charts.param_with_percentiles, merged_df=fixed(obs_wbmi),\n", + " subjid=widgets.Dropdown(options=all_ids, value=val,\n", + " description='Subject ID:', disabled=False), \n", + " param=['BMI', 'WEIGHTKG', 'HEIGHTCM'], wt_df=fixed(wt_percentiles), \n", + " ht_df=fixed(ht_percentiles), bmi_df=fixed(bmi_percentiles));" ] }, { @@ -833,7 +841,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/GrowthViz-adults.py b/GrowthViz-adults.py index c479739..d945b8b 100644 --- a/GrowthViz-adults.py +++ b/GrowthViz-adults.py @@ -253,7 +253,7 @@ mdf = sumstats.add_mzscored_to_merged_df_adults(merged_df, percentiles_wide) mdf['wtz'] = (mdf['weight'] - mdf['Mean_weight'])/mdf['sd_weight'] mdf['htz'] = (mdf['height'] - mdf['Mean_height'])/mdf['sd_height'] -mdf['BMIz'] = (mdf['bmi'] - mdf['Mean_bmi'])/mdf['sd_bmi'] +mdf['bmiz'] = (mdf['bmi'] - mdf['Mean_bmi'])/mdf['sd_bmi'] mdf.head() col_opt = { @@ -270,7 +270,7 @@ 'weight_cat': { 'width': 80 }, 'wtz': { 'width': 50 }, 'bmi': { 'width': 40 }, - 'BMIz': { 'width': 30 }, + 'bmiz': { 'width': 30 }, } g = qgrid.show_grid(charts.top_ten(mdf, 'weight'), precision=3, column_options=col_opt, column_definitions=col_def) ind_out = widgets.Output() @@ -397,17 +397,22 @@ def all_charts(subjid=val): def edge25(obs, category, group, sort_order, param): filtered_by_cat = obs[(obs.clean_cat == category) & (obs.param == param)] # get list of relevant IDs - filtered_sum = filtered_by_cat.groupby('subjid', as_index=False).agg(max_measure=('measurement', 'max'), min_measure=('measurement', 'min'), start_age=('age', 'min'), axis_range=('range', 'mean')) + filtered_sum = filtered_by_cat.groupby('subjid', as_index=False).agg(max_measure=('measurement', 'max'), + min_measure=('measurement', 'min'), + start_age=('ageyears', 'min'), + axis_range=('range', 'mean')) if group == 'largest': filtered_sum = filtered_sum.nlargest(25, 'max_measure') else: filtered_sum = filtered_sum.nsmallest(25, 'min_measure') filtered_sum.sort_values(by=[sort_order, 'subjid'], inplace=True) - fig = charts.five_by_five_view(obs, filtered_sum.subjid.values, param, wt_percentiles, ht_percentiles, bmi_percentiles, 'dotted') + fig = charts.five_by_five_view(obs, filtered_sum.subjid.values, param, wt_percentiles, ht_percentiles, + bmi_percentiles, 'dotted') plt.show() -interact(edge25, obs = fixed(obs_wbmi_mult), category = obs.clean_cat.unique(), - group = ['largest', 'smallest'], sort_order = ['max_measure', 'min_measure', 'start_age', 'axis_range'], param = ['WEIGHTKG', 'HEIGHTCM', 'BMI']) +interact(edge25, obs=fixed(obs_wbmi_mult), category=obs.clean_cat.unique(), + group=['largest', 'smallest'], sort_order=['max_measure', 'min_measure', 'start_age', 'axis_range'], + param=['WEIGHTKG', 'HEIGHTCM', 'BMI']); # # Visualizing Changes in Trajectory @@ -419,10 +424,11 @@ def edge25(obs, category, group, sort_order, param): all_ids = obs_wbmi['subjid'].unique() val = 2431 if 2431 in all_ids else np.random.choice(all_ids, size=1, replace=False) -interact(charts.param_with_percentiles, merged_df = fixed(obs_wbmi), - subjid = widgets.Dropdown(options=all_ids, value=val, - description='Subject ID:',disabled=False), - param = ['BMI', 'WEIGHTKG', 'HEIGHTCM'], wt_df = fixed(wt_percentiles), ht_df = fixed(ht_percentiles), bmi_df = fixed(bmi_percentiles)) +interact(charts.param_with_percentiles, merged_df=fixed(obs_wbmi), + subjid=widgets.Dropdown(options=all_ids, value=val, + description='Subject ID:', disabled=False), + param=['BMI', 'WEIGHTKG', 'HEIGHTCM'], wt_df=fixed(wt_percentiles), + ht_df=fixed(ht_percentiles), bmi_df=fixed(bmi_percentiles)); # # Summary Statistics diff --git a/GrowthViz-pediatrics.ipynb b/GrowthViz-pediatrics.ipynb index 4e17f28..38cdfd6 100644 --- a/GrowthViz-pediatrics.ipynb +++ b/GrowthViz-pediatrics.ipynb @@ -162,7 +162,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following cell shows what the first five rows look like in the CSV file" + "The following cell shows what the first five rows look like in the CSV file." ] }, { @@ -261,15 +261,6 @@ "obs = processdata.keep_age_range(obs_full, 'pediatrics')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "obs.head()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -303,7 +294,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following cell loads in the [CDC Growth Chart Percentile Data Files](https://www.cdc.gov/growthcharts/percentile_data_files.htm). Functions coerce some values into numeric types. It also add an `age` column which is a decimal value representing age in years. Finally, `Sex` is transformed so that the values align with the values used in growthcleanr, 0 (male) or 1 (female). This data is used to plot percentile bands in visualizations in the tool. " + "The following cells load in height, weight, and BMI percentiles [WHO and CDC Growth Chart Percentile Data Files](https://www.cdc.gov/growthcharts/who_charts.htm). Functions coerce some values into numeric types. It also adds a `ageyears` column representing age in years. Finally, `Sex` is transformed so that the values align with the values used in growthcleanr, 0 (male) or 1 (female). This data is used to plot percentile bands in visualizations in the tool.\n", + "\n", + "For ages 2-4, a smoothing function is used to transition from WHO to CDC height percentiles. This smoothing addresses the focus of WHO on younger subjects and the transition from length as a stature measure in infants to height in older children." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_percentiles = processdata.setup_percentiles_pediatrics_new()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_ht_percentiles, df_wt_percentiles, _ = processdata.split_percentiles_pediatrics(df_percentiles)" ] }, { @@ -312,9 +323,7 @@ "metadata": {}, "outputs": [], "source": [ - "bmi_percentiles = processdata.setup_percentiles_pediatrics(\"bmiagerev.csv\")\n", - "wt_percentiles = processdata.setup_percentiles_pediatrics(\"wtage.csv\")\n", - "ht_percentiles = processdata.setup_percentiles_pediatrics(\"statage.csv\")" + "df_bmi_percentiles = processdata.setup_percentiles_pediatrics(\"bmiagerev.csv\")" ] }, { @@ -380,18 +389,19 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [], "source": [ - "mdf = sumstats.add_mzscored_to_merged_df_pediatrics(merged_df, wt_percentiles, ht_percentiles, bmi_percentiles)\n", + "mdf = sumstats.add_smoothed_zscore_to_merged_df_pediatrics(merged_df.copy(), df_percentiles)\n", + "\n", "col_opt = {\n", " 'width': 20,\n", "}\n", "col_def = {\n", " 'subjid': { 'width': 80 },\n", " 'sex': { 'width': 30 },\n", - " 'age': { 'width': 30 },\n", + " 'ageyears': { 'width': 30 },\n", " 'height': { 'width': 50 },\n", " 'height_cat': { 'width': 80 },\n", " 'htz': { 'width': 50 },\n", @@ -399,9 +409,10 @@ " 'weight_cat': { 'width': 80 },\n", " 'wtz': { 'width': 50 },\n", " 'bmi': { 'width': 40 },\n", - " 'BMIz': { 'width': 30 },\n", + " 'bmiz': { 'width': 30 },\n", "}\n", - "g = qgrid.show_grid(charts.top_ten(mdf, 'weight'), precision=3, column_options=col_opt, column_definitions=col_def)\n", + "g = qgrid.show_grid(charts.top_ten(mdf, 'weight'), precision=3, column_options=col_opt, \n", + " column_definitions=col_def)\n", "ind_out = widgets.Output()\n", "def handle_selection_change(_event, _widget):\n", " df = g.get_changed_df()\n", @@ -416,7 +427,8 @@ " with ind_out:\n", " if currSubj != lastSubj:\n", " ind_out.clear_output()\n", - " charts.overlap_view_pediatrics(obs, subjid, 'WEIGHTKG', True, True, wt_percentiles, ht_percentiles)\n", + " charts.overlap_view_pediatrics(obs, subjid, 'WEIGHTKG', True, True, \n", + " df_wt_percentiles, df_ht_percentiles)\n", " display(plt.show())\n", "g.on('selection_changed', handle_selection_change) \n", "widgets.VBox([g, ind_out])" @@ -449,13 +461,16 @@ "val = 5450 if 5450 in all_ids else np.random.choice(all_ids, size=1, replace=False)\n", "interactive(charts.overlap_view_pediatrics_show, \n", " obs_df=fixed(obs), \n", - " subjid=widgets.Dropdown(options=all_ids, value=val, description='Subject ID:', disabled=False), \n", + " subjid=widgets.Dropdown(options=all_ids, value=val, description='Subject ID:', \n", + " disabled=False), \n", " param=['HEIGHTCM', 'WEIGHTKG'], \n", - " include_carry_forward=widgets.Checkbox(value=True,description='Include Carry Forward',disabled=False,indent=False), \n", - " include_percentiles=widgets.Checkbox(value=True,description='Include Measurement Percentile Bands',disabled=False,indent=False),\n", - " wt_df=fixed(wt_percentiles), \n", - " ht_df=fixed(ht_percentiles), \n", - " bmi_df=fixed(bmi_percentiles)\n", + " include_carry_forward=widgets.Checkbox(value=True, description='Include Carry Forward', \n", + " disabled=False, indent=False), \n", + " include_percentiles=widgets.Checkbox(value=True, \n", + " description='Include Measurement Percentile Bands', \n", + " disabled=False, indent=False),\n", + " wt_df=fixed(df_wt_percentiles),\n", + " ht_df=fixed(df_ht_percentiles)\n", " )" ] }, @@ -465,7 +480,9 @@ "source": [ "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.\n", "\n", - "In this chart, the dark blue line represents all weight measurements for an individual and the dark red represents all height measurements for an individual. Any values marked for exclusion are represented with a black x. The yellow dashed line represents the trajectory with exclusions removed. Any carried forward values are represented by a blue triangle. All lines and symbols can be excluded by unchecking the checkboxes above the chart." + "In this chart, the dark blue line represents all weight measurements for an individual and the dark red represents all height measurements for an individual. Any values marked for exclusion are represented with a black x. The yellow dashed line represents the trajectory with exclusions removed. Any carried forward values are represented by a blue triangle. All lines and symbols can be excluded by unchecking the checkboxes above the chart.\n", + "\n", + "The slight shift in the red height percentile lines at age 2 results from the smoothing function which uses a weighted combination of WHO and CDC percentiles between 2-4 years of age, and acknowledging the shift from using length to standing height for the measurement at that age. This may appear more pronounced when the chart scale is adjusted to show measurements for a young subject." ] }, { @@ -486,7 +503,7 @@ " show_trajectory_with_exclusions=widgets.Checkbox(value=True,description='Show Trajectory with Exclusions',disabled=False,indent=False),\n", " include_carry_forward=widgets.Checkbox(value=True,description='Include Carry Forward',disabled=False,indent=False), \n", " include_percentiles=widgets.Checkbox(value=True,description='Include Measurement Percentile Bands',disabled=False,indent=False),\n", - " wt_df=fixed(wt_percentiles), ht_df=fixed(ht_percentiles))" + " wt_df=fixed(df_wt_percentiles), ht_df=fixed(df_ht_percentiles))" ] }, { @@ -546,7 +563,8 @@ "metadata": {}, "outputs": [], "source": [ - "charts.five_by_five_view(obs, sample, 'HEIGHTCM', wt_percentiles, ht_percentiles, bmi_percentiles, 'solid')" + "charts.five_by_five_view(obs, sample, 'HEIGHTCM', df_wt_percentiles, \n", + " df_ht_percentiles, df_bmi_percentiles, 'solid')" ] }, { @@ -566,10 +584,14 @@ "metadata": {}, "outputs": [], "source": [ - "top_weight_extreme_ewma_ids = merged_df[merged_df.weight_cat == 'Exclude-EWMA-Extreme'].sort_values('weight', ascending=False).head(50)['subjid'].unique()\n", + "top_weight_extreme_ewma_ids = merged_df[merged_df.weight_cat == 'Exclude-EWMA-Extreme'] \\\n", + " .sort_values('weight', ascending=False) \\\n", + " .head(50)['subjid'].unique()\n", "if len(top_weight_extreme_ewma_ids) >= 1:\n", - " ewma_sample = np.random.choice(top_weight_extreme_ewma_ids, size=len(top_weight_extreme_ewma_ids), replace=False)\n", - " charts.five_by_five_view(obs, ewma_sample, 'WEIGHTKG', wt_percentiles, ht_percentiles, bmi_percentiles, 'solid')" + " ewma_sample = np.random.choice(top_weight_extreme_ewma_ids, \n", + " size=len(top_weight_extreme_ewma_ids), replace=False)\n", + " charts.five_by_five_view(obs, ewma_sample, 'WEIGHTKG', df_wt_percentiles,\n", + " df_ht_percentiles, df_bmi_percentiles, 'solid')" ] }, { @@ -595,11 +617,13 @@ " filtered_by_cat = filtered_by_cat.nlargest(25, 'measurement')\n", " else:\n", " filtered_by_cat = filtered_by_cat.nsmallest(25, 'measurement')\n", - " fig = charts.five_by_five_view(obs, filtered_by_cat.subjid.values, param, wt_percentiles, ht_percentiles, bmi_percentiles, 'solid')\n", + " fig = charts.five_by_five_view(obs, filtered_by_cat.subjid.values, param, \n", + " df_wt_percentiles, df_ht_percentiles, df_bmi_percentiles,\n", + " 'solid')\n", " plt.show()\n", " \n", "interact(edge25, obs = fixed(obs), category = obs.clean_cat.unique(), \n", - " sort_order = ['largest', 'smallest'], param = ['WEIGHTKG', 'HEIGHTCM'])" + " sort_order = ['largest', 'smallest'], param = ['WEIGHTKG', 'HEIGHTCM']);" ] }, { @@ -608,7 +632,9 @@ "source": [ "# Visualizing Changes in Trajectory\n", "\n", - "The `charts.bmi_with_percentiles` function displays a chart showing BMI for an individual over time. Black bands representing the 5th and 95th BMI percentile for age and sex are shown with the individual's BMI shown in blue. The plot on the left represents all values. The plot on the right is only included values." + "The `charts.bmi_with_percentiles` function displays a chart showing BMI for an individual over time. Black bands representing the 5th and 95th BMI percentile for age and sex are shown with the individual's BMI shown in blue. The plot on the left represents all values. The plot on the right is only included values.\n", + "\n", + "BMI percentile values are not included for subjects younger than two years old." ] }, { @@ -620,7 +646,7 @@ "all_ids = obs['subjid'].unique()\n", "val = 46717134 if 46717134 in all_ids else np.random.choice(all_ids, size=1, replace=False)\n", "interact(charts.bmi_with_percentiles, merged_df = fixed(merged_df), \n", - " bmi_percentiles = fixed(bmi_percentiles),\n", + " bmi_percentiles = fixed(df_bmi_percentiles),\n", " subjid = widgets.BoundedIntText(value=val,\n", " min=0,\n", " max=10000000000,\n", @@ -702,9 +728,10 @@ "| cleaned_obs | Original growthcleanr output read in from csv file |\n", "| obs_full | Slightly modified version of `cleaned_obs` in format needed for use in notebook |\n", "| obs | Patient observations within age range allowed for this notebook (18-80) |\n", - "| bmi_percentiles | BMI percentiles data for use in charts |\n", - "| wt_percentiles | Weight percentiles data for use in charts |\n", - "| ht_percentiles | Height percentiles data for use in charts |\n", + "| df_percentiles | Combined measure percentiles data for use in charts, split into three below |\n", + "| df_ht_percentiles | Height-specific percentiles data for use in charts |\n", + "| df_wt_percentiels | Weight-specific percentiles data for use in charts |\n", + "| df_bmi_percentiles | BMI-specific percentiles data for use in charts |\n", "| merged_df | Data by subject and age that contains height, weight, and BMI on one row |\n", "| mdf | Version of `merged_df` with added z-scores |" ] @@ -896,7 +923,7 @@ "metadata": {}, "outputs": [], "source": [ - "combined['rounded_age'] = np.around(combined.age).astype(int)" + "combined['rounded_age'] = np.around(combined.ageyears).astype(int)" ] }, { @@ -911,11 +938,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "p = sns.catplot(x='rounded_age', y='id', col='clean_value', data=count_by_age[count_by_age.clean_value != 'Include'], hue='run_name', col_wrap=3, kind=\"bar\")\n", - "p" + "p;" ] }, { @@ -950,7 +979,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/GrowthViz-pediatrics.py b/GrowthViz-pediatrics.py index 5b07cce..1e5042b 100644 --- a/GrowthViz-pediatrics.py +++ b/GrowthViz-pediatrics.py @@ -106,7 +106,7 @@ cleaned_obs = pd.read_csv("growthviz-data/sample-pediatrics-data.csv") -# The following cell shows what the first five rows look like in the CSV file +# The following cell shows what the first five rows look like in the CSV file. # In[ ]: @@ -159,34 +159,40 @@ obs = processdata.keep_age_range(obs_full, 'pediatrics') -# In[ ]: +# After that, `charts.weight_distr` creates two visualizations. The first shows a distribution of all of the included weights in the dataset. The second shows weights above a certain threshold to see whether there are spikes at a certain *Included* weights that might indicate that a commonly used scale maxes out at a certain value. This chart is restricted to values of 135kg or higher (rounded to the nearest KG) to make patterns in higher weights easier to identify. This potential issue is important to keep in mind when conducting an analysis. +# In[ ]: -obs.head() +charts.weight_distr(obs, 'all') -# After that, `charts.weight_distr` creates two visualizations. The first shows a distribution of all of the included weights in the dataset. The second shows weights above a certain threshold to see whether there are spikes at a certain *Included* weights that might indicate that a commonly used scale maxes out at a certain value. This chart is restricted to values of 135kg or higher (rounded to the nearest KG) to make patterns in higher weights easier to identify. This potential issue is important to keep in mind when conducting an analysis. # In[ ]: -charts.weight_distr(obs, 'all') +charts.weight_distr(obs, 'high') +# The following cells load in height, weight, and BMI percentiles [WHO and CDC Growth Chart Percentile Data Files](https://www.cdc.gov/growthcharts/who_charts.htm). Functions coerce some values into numeric types. It also adds a `ageyears` column representing age in years. Finally, `Sex` is transformed so that the values align with the values used in growthcleanr, 0 (male) or 1 (female). This data is used to plot percentile bands in visualizations in the tool. +# +# For ages 2-4, a smoothing function is used to transition from WHO to CDC height percentiles. This smoothing addresses the focus of WHO on younger subjects and the transition from length as a stature measure in infants to height in older children. + # In[ ]: -charts.weight_distr(obs, 'high') +df_percentiles = processdata.setup_percentiles_pediatrics_new() -# The following cell loads in the [CDC Growth Chart Percentile Data Files](https://www.cdc.gov/growthcharts/percentile_data_files.htm). Functions coerce some values into numeric types. It also add an `age` column which is a decimal value representing age in years. Finally, `Sex` is transformed so that the values align with the values used in growthcleanr, 0 (male) or 1 (female). This data is used to plot percentile bands in visualizations in the tool. +# In[ ]: + + +df_ht_percentiles, df_wt_percentiles, _ = processdata.split_percentiles_pediatrics(df_percentiles) + # In[ ]: -bmi_percentiles = processdata.setup_percentiles_pediatrics("bmiagerev.csv") -wt_percentiles = processdata.setup_percentiles_pediatrics("wtage.csv") -ht_percentiles = processdata.setup_percentiles_pediatrics("statage.csv") +df_bmi_percentiles = processdata.setup_percentiles_pediatrics("bmiagerev.csv") # In a previous cell, the tool creates the `obs` DataFrame. In that structure there is one measurement, either height or weight, per row. In this cell, the `processdata.setup_merged_df` function will create a DataFrame where a height observation and weight observation for the same `subjid` on the same `agedays` are combined into a single row. Several new columns are added to the resulting DataFrame: @@ -228,14 +234,15 @@ # In[ ]: -mdf = sumstats.add_mzscored_to_merged_df_pediatrics(merged_df, wt_percentiles, ht_percentiles, bmi_percentiles) +mdf = sumstats.add_smoothed_zscore_to_merged_df_pediatrics(merged_df.copy(), df_percentiles) + col_opt = { 'width': 20, } col_def = { 'subjid': { 'width': 80 }, 'sex': { 'width': 30 }, - 'age': { 'width': 30 }, + 'ageyears': { 'width': 30 }, 'height': { 'width': 50 }, 'height_cat': { 'width': 80 }, 'htz': { 'width': 50 }, @@ -243,9 +250,10 @@ 'weight_cat': { 'width': 80 }, 'wtz': { 'width': 50 }, 'bmi': { 'width': 40 }, - 'BMIz': { 'width': 30 }, + 'bmiz': { 'width': 30 }, } -g = qgrid.show_grid(charts.top_ten(mdf, 'weight'), precision=3, column_options=col_opt, column_definitions=col_def) +g = qgrid.show_grid(charts.top_ten(mdf, 'weight'), precision=3, column_options=col_opt, + column_definitions=col_def) ind_out = widgets.Output() def handle_selection_change(_event, _widget): df = g.get_changed_df() @@ -260,7 +268,8 @@ def handle_selection_change(_event, _widget): with ind_out: if currSubj != lastSubj: ind_out.clear_output() - charts.overlap_view_pediatrics(obs, subjid, 'WEIGHTKG', True, True, wt_percentiles, ht_percentiles) + charts.overlap_view_pediatrics(obs, subjid, 'WEIGHTKG', True, True, + df_wt_percentiles, df_ht_percentiles) display(plt.show()) g.on('selection_changed', handle_selection_change) widgets.VBox([g, ind_out]) @@ -283,19 +292,24 @@ def handle_selection_change(_event, _widget): 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), + 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) + 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(df_wt_percentiles), + ht_df=fixed(df_ht_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. # # In this chart, the dark blue line represents all weight measurements for an individual and the dark red represents all height measurements for an individual. Any values marked for exclusion are represented with a black x. The yellow dashed line represents the trajectory with exclusions removed. Any carried forward values are represented by a blue triangle. All lines and symbols can be excluded by unchecking the checkboxes above the chart. +# +# The slight shift in the red height percentile lines at age 2 results from the smoothing function which uses a weighted combination of WHO and CDC percentiles between 2-4 years of age, and acknowledging the shift from using length to standing height for the measurement at that age. This may appear more pronounced when the chart scale is adjusted to show measurements for a young subject. # In[ ]: @@ -310,7 +324,7 @@ def handle_selection_change(_event, _widget): show_trajectory_with_exclusions=widgets.Checkbox(value=True,description='Show Trajectory with Exclusions',disabled=False,indent=False), 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)) + wt_df=fixed(df_wt_percentiles), ht_df=fixed(df_ht_percentiles)) # # Visualizing Multiple Trajectories at Once @@ -343,7 +357,8 @@ def handle_selection_change(_event, _widget): # In[ ]: -charts.five_by_five_view(obs, sample, 'HEIGHTCM', wt_percentiles, ht_percentiles, bmi_percentiles, 'solid') +charts.five_by_five_view(obs, sample, 'HEIGHTCM', df_wt_percentiles, + df_ht_percentiles, df_bmi_percentiles, 'solid') # # Building a Different Sample @@ -355,10 +370,14 @@ def handle_selection_change(_event, _widget): # In[ ]: -top_weight_extreme_ewma_ids = merged_df[merged_df.weight_cat == 'Exclude-EWMA-Extreme'].sort_values('weight', ascending=False).head(50)['subjid'].unique() +top_weight_extreme_ewma_ids = merged_df[merged_df.weight_cat == 'Exclude-EWMA-Extreme'] \ + .sort_values('weight', ascending=False) \ + .head(50)['subjid'].unique() if len(top_weight_extreme_ewma_ids) >= 1: - ewma_sample = np.random.choice(top_weight_extreme_ewma_ids, size=len(top_weight_extreme_ewma_ids), replace=False) - charts.five_by_five_view(obs, ewma_sample, 'WEIGHTKG', wt_percentiles, ht_percentiles, bmi_percentiles, 'solid') + ewma_sample = np.random.choice(top_weight_extreme_ewma_ids, + size=len(top_weight_extreme_ewma_ids), replace=False) + charts.five_by_five_view(obs, ewma_sample, 'WEIGHTKG', df_wt_percentiles, + df_ht_percentiles, df_bmi_percentiles, 'solid') # ## Visualizing the Top/Bottom 25 for a Given Category @@ -374,16 +393,20 @@ 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') - fig = 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, + df_wt_percentiles, df_ht_percentiles, df_bmi_percentiles, + 'solid') plt.show() interact(edge25, obs = fixed(obs), category = obs.clean_cat.unique(), - sort_order = ['largest', 'smallest'], param = ['WEIGHTKG', 'HEIGHTCM']) + sort_order = ['largest', 'smallest'], param = ['WEIGHTKG', 'HEIGHTCM']); # # Visualizing Changes in Trajectory # # The `charts.bmi_with_percentiles` function displays a chart showing BMI for an individual over time. Black bands representing the 5th and 95th BMI percentile for age and sex are shown with the individual's BMI shown in blue. The plot on the left represents all values. The plot on the right is only included values. +# +# BMI percentile values are not included for subjects younger than two years old. # In[ ]: @@ -391,7 +414,7 @@ def edge25(obs, category, sort_order, param): all_ids = obs['subjid'].unique() val = 46717134 if 46717134 in all_ids else np.random.choice(all_ids, size=1, replace=False) interact(charts.bmi_with_percentiles, merged_df = fixed(merged_df), - bmi_percentiles = fixed(bmi_percentiles), + bmi_percentiles = fixed(df_bmi_percentiles), subjid = widgets.BoundedIntText(value=val, min=0, max=10000000000, @@ -459,9 +482,10 @@ def edge25(obs, category, sort_order, param): # | cleaned_obs | Original growthcleanr output read in from csv file | # | obs_full | Slightly modified version of `cleaned_obs` in format needed for use in notebook | # | obs | Patient observations within age range allowed for this notebook (18-80) | -# | bmi_percentiles | BMI percentiles data for use in charts | -# | wt_percentiles | Weight percentiles data for use in charts | -# | ht_percentiles | Height percentiles data for use in charts | +# | df_percentiles | Combined measure percentiles data for use in charts, split into three below | +# | df_ht_percentiles | Height-specific percentiles data for use in charts | +# | df_wt_percentiels | Weight-specific percentiles data for use in charts | +# | df_bmi_percentiles | BMI-specific percentiles data for use in charts | # | merged_df | Data by subject and age that contains height, weight, and BMI on one row | # | mdf | Version of `merged_df` with added z-scores | @@ -572,7 +596,7 @@ def on_button_clicked(b): # In[ ]: -combined['rounded_age'] = np.around(combined.age).astype(int) +combined['rounded_age'] = np.around(combined.ageyears).astype(int) # In[ ]: @@ -585,7 +609,7 @@ def on_button_clicked(b): p = sns.catplot(x='rounded_age', y='id', col='clean_value', data=count_by_age[count_by_age.clean_value != 'Include'], hue='run_name', col_wrap=3, kind="bar") -p +p; # In[ ]: diff --git a/NEWS.md b/NEWS.md index dd615ba..6a27f05 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,18 @@ +# GrowthViz 3.0.0-infants-beta - 2023-03-14 + +## Added + +- Support for infant subjects (#56, #61, #66) +- Automated container image builds for main/latest and tags (#59) +- Added dependabot checks for GitHub workflow action updates (#68) + +## Changed + +- Completed function documentation throughout (#65) +- Updated sample data to include excluded age ranges (#60) +- Bug fixes (#62, #63, #64) +- Added Python versions 3.10, 3.11 to CI workflow for testing (#58) + # GrowthViz 2.0.0 - 2021-06-30 ## Added diff --git a/growthviz-data/.DS_Store b/growthviz-data/.DS_Store deleted file mode 100644 index bbfb673..0000000 Binary files a/growthviz-data/.DS_Store and /dev/null differ diff --git a/growthviz-data/ext/growthfile_cdc_ext.csv.gz b/growthviz-data/ext/growthfile_cdc_ext.csv.gz new file mode 100644 index 0000000..6d74b16 Binary files /dev/null and b/growthviz-data/ext/growthfile_cdc_ext.csv.gz differ diff --git a/growthviz-data/ext/growthfile_who.csv.gz b/growthviz-data/ext/growthfile_who.csv.gz new file mode 100644 index 0000000..c68dde2 Binary files /dev/null and b/growthviz-data/ext/growthfile_who.csv.gz differ diff --git a/growthviz-data/ext/swaps.csv b/growthviz-data/ext/swaps.csv deleted file mode 100644 index d1955e7..0000000 --- a/growthviz-data/ext/swaps.csv +++ /dev/null @@ -1,21195 +0,0 @@ -subjid,sex,agedays,param,measurement -3930,0,794,WEIGHTKG,12.1926880159666 -3930,0,794,HEIGHTCM,86.36 -3930,0,1052,WEIGHTKG,14.0615077565091 -3930,0,1052,HEIGHTCM,91.44 -3930,0,1070,WEIGHTKG,31 -3930,0,1070,HEIGHTCM,235.4834 -3930,0,1494,WEIGHTKG,16.8964891590311 -3930,0,1494,HEIGHTCM,99.695 -22009,1,741,WEIGHTKG,9.7 -22009,1,741,HEIGHTCM,81 -22009,1,807,WEIGHTKG,10.4 -22009,1,807,HEIGHTCM,84 -22009,1,904,WEIGHTKG,10.7 -22009,1,904,HEIGHTCM,86 -22009,1,964,WEIGHTKG,10.8 -22009,1,964,HEIGHTCM,86.5 -22009,1,1161,WEIGHTKG,12 -22009,1,1161,HEIGHTCM,36 -22009,1,1266,WEIGHTKG,12.2 -22009,1,1266,HEIGHTCM,93 -23569,1,1291,WEIGHTKG,17.3773020048989 -23569,1,1291,HEIGHTCM,99.06 -23569,1,1378,WEIGHTKG,17.6902839517373 -23569,1,1378,HEIGHTCM,99.06 -23569,1,1410,WEIGHTKG,16.7830899029302 -23569,1,1410,HEIGHTCM,100.33 -23569,1,1494,WEIGHTKG,18.2799600834619 -23569,1,1494,HEIGHTCM,111.76 -23569,1,1569,WEIGHTKG,18.7698448698177 -23569,1,1569,HEIGHTCM,111.76 -23569,1,1633,WEIGHTKG,10.8863285856845 -23569,1,1633,HEIGHTCM,106.68 -23569,1,1959,WEIGHTKG,22.2262541957725 -23569,1,1959,HEIGHTCM,115.57 -23569,1,1972,WEIGHTKG,21.32 -23569,1,1972,HEIGHTCM,113 -23569,1,1987,WEIGHTKG,21.0922616347637 -23569,1,1987,HEIGHTCM,114.3 -23569,1,2007,WEIGHTKG,20.9788623786628 -23569,1,2007,HEIGHTCM,116.205 -23569,1,2149,WEIGHTKG,20.8654631225619 -23569,1,2149,HEIGHTCM,124.968 -23569,1,2324,WEIGHTKG,24.2130091626599 -23569,1,2324,HEIGHTCM,124.46 -23569,1,2591,WEIGHTKG,25.7 -23569,1,2591,HEIGHTCM,49 -23569,1,2667,WEIGHTKG,26.9980948924975 -23569,1,2667,HEIGHTCM,124.46 -23569,1,2897,WEIGHTKG,29.6017418125737 -23569,1,2897,HEIGHTCM,127.635 -23569,1,2933,WEIGHTKG,29.4838065862288 -23569,1,2933,HEIGHTCM,127.635 -23569,1,3023,WEIGHTKG,30.1188424203937 -23569,1,3023,HEIGHTCM,131.064 -23569,1,3197,WEIGHTKG,31.8107593214189 -23569,1,3197,HEIGHTCM,133.35 -23569,1,3200,WEIGHTKG,31.8107593214189 -23569,1,3200,HEIGHTCM,133.35 -23569,1,3393,WEIGHTKG,36.0019958269074 -23569,1,3393,HEIGHTCM,139.7 -23809,0,2834,WEIGHTKG,26.7 -23809,0,2834,HEIGHTCM,119.3 -23809,0,2879,WEIGHTKG,28.2 -23809,0,2879,HEIGHTCM,121.3 -23809,0,2926,WEIGHTKG,28.4 -23809,0,2926,HEIGHTCM,122 -23809,0,2932,WEIGHTKG,28.5 -23809,0,2932,HEIGHTCM,122 -23809,0,2963,WEIGHTKG,31.6 -23809,0,2963,HEIGHTCM,122.7 -23809,0,2998,WEIGHTKG,35.8 -23809,0,2998,HEIGHTCM,123 -23809,0,3033,WEIGHTKG,38.5 -23809,0,3033,HEIGHTCM,123 -23809,0,3047,WEIGHTKG,39.4 -23809,0,3047,HEIGHTCM,131 -23809,0,3075,WEIGHTKG,41.8 -23809,0,3075,HEIGHTCM,126 -23809,0,3138,WEIGHTKG,41.5 -23809,0,3138,HEIGHTCM,129 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-27265,1,787,HEIGHTCM,91.44 -27265,1,846,WEIGHTKG,12.0656808491336 -27265,1,846,HEIGHTCM,90.17 -27265,1,867,WEIGHTKG,11.4986845686292 -27265,1,867,HEIGHTCM,90.17 -27265,1,888,WEIGHTKG,11.7935226344915 -27265,1,888,HEIGHTCM,87.63 -27265,1,919,WEIGHTKG,12.3060872720675 -27265,1,919,HEIGHTCM,87.63 -27265,1,937,WEIGHTKG,12.4285584686564 -27265,1,937,HEIGHTCM,87.63 -27265,1,962,WEIGHTKG,14.5151047809126 -27265,1,962,HEIGHTCM,91.44 -27265,1,1022,WEIGHTKG,27.56 -27265,1,1022,HEIGHTCM,232.156 -27265,1,1109,WEIGHTKG,12.7007166832986 -27265,1,1109,HEIGHTCM,93.98 -27265,1,1129,WEIGHTKG,12.7007166832986 -27265,1,1129,HEIGHTCM,93.98 -27265,1,1156,WEIGHTKG,12.7007166832986 -27265,1,1156,HEIGHTCM,93.98 -27265,1,1172,WEIGHTKG,12.7007166832986 -27265,1,1172,HEIGHTCM,93.98 -27265,1,1243,WEIGHTKG,14.1476911911458 -27265,1,1243,HEIGHTCM,97.79 -27265,1,1274,WEIGHTKG,13.7802776013789 -27265,1,1274,HEIGHTCM,97.79 -27265,1,1324,WEIGHTKG,13.7802776013789 -27265,1,1324,HEIGHTCM,94.996 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-1328497,1,2654,WEIGHTKG,23.5 -1328497,1,2654,HEIGHTCM,127 -1328497,1,2795,WEIGHTKG,25.7 -1328497,1,2795,HEIGHTCM,129.54 -1330215,1,747,WEIGHTKG,9.0719404880704 -1330215,1,747,HEIGHTCM,81.28 -1330215,1,891,WEIGHTKG,9.97913453687744 -1330215,1,891,HEIGHTCM,83.82 -1330215,1,1115,WEIGHTKG,11.1131270978862 -1330215,1,1115,HEIGHTCM,224.79 -1330215,1,1199,WEIGHTKG,11.852490247664 -1330215,1,1199,HEIGHTCM,90 -1330215,1,1286,WEIGHTKG,11.9069218905924 -1330215,1,1286,HEIGHTCM,92.71 -1330215,1,1490,WEIGHTKG,13.2 -1330215,1,1490,HEIGHTCM,98 -1340256,1,3387,WEIGHTKG,26.3086274154042 -1340256,1,3387,HEIGHTCM,126.365 -1340256,1,3527,WEIGHTKG,27.2158214642112 -1340256,1,3527,HEIGHTCM,50.8 -1340256,1,3704,WEIGHTKG,27.8962170008165 -1340256,1,3704,HEIGHTCM,132.08 -1340256,1,3834,WEIGHTKG,28.6900117935226 -1340256,1,3834,HEIGHTCM,133.985 -1340256,1,3865,WEIGHTKG,28.5766125374218 -1340256,1,3865,HEIGHTCM,137.16 -1340256,1,4107,WEIGHTKG,31.0260364692008 -1340256,1,4107,HEIGHTCM,139.7 -1347096,1,1130,WEIGHTKG,18.0531615712601 -1347096,1,1130,HEIGHTCM,94.615 -1347096,1,1149,WEIGHTKG,13.7802776013789 -1347096,1,1149,HEIGHTCM,243.84 -1347096,1,1295,WEIGHTKG,14.2883062687109 -1347096,1,1295,HEIGHTCM,101.6 -1347096,1,1498,WEIGHTKG,15.594665698993 -1347096,1,1498,HEIGHTCM,104.14 -1347096,1,1879,WEIGHTKG,18.5974780005443 -1347096,1,1879,HEIGHTCM,109.22 -1347259,0,946,WEIGHTKG,13.7485258096707 -1347259,0,946,HEIGHTCM,86 -1347259,0,1009,WEIGHTKG,32.19 -1347259,0,1009,HEIGHTCM,226.06 -1347259,0,1044,WEIGHTKG,15 -1347259,0,1044,HEIGHTCM,90 -1347259,0,1233,WEIGHTKG,15.8 -1347259,0,1233,HEIGHTCM,95 -1347259,0,1597,WEIGHTKG,17.3 -1347259,0,1597,HEIGHTCM,102 -1361527,1,923,WEIGHTKG,17.7 -1361527,1,923,HEIGHTCM,98 -1361527,1,982,WEIGHTKG,18.5974780005443 -1361527,1,982,HEIGHTCM,39 -1361527,1,1115,WEIGHTKG,21.3 -1361527,1,1115,HEIGHTCM,103 -1361527,1,1300,WEIGHTKG,25.2 -1361527,1,1300,HEIGHTCM,108 diff --git a/growthviz/charts.py b/growthviz/charts.py index fc89697..4de772b 100644 --- a/growthviz/charts.py +++ b/growthviz/charts.py @@ -5,6 +5,9 @@ import numpy as np import pandas as pd +from .processdata import split_percentiles_pediatrics + + ADULTS_AGE_RANGES = pd.DataFrame( { "min": np.array([0, 18, 30, 40, 50, 60, 65, 80]), @@ -45,19 +48,20 @@ def weight_distr(df, mode): Create charts with overall and outlier weight distributions (included values only) Parameters: - df: (DataFrame) with subjid, param, measurement, age, sex, clean_value, clean_cat, include, - category, colors, patterns, and sort_order columns - mode: (str) indicates how many of the weights you want to use. If set to 'high', the function - will only use weights above a certain threshold. Otherwise, it displays all the weights. + df: (DataFrame) with subjid, param, measurement, age, sex, clean_value, and + include columns + mode: (str) indicates how many of the weights you want to use. If set to 'high', + the function will only use weights above a certain threshold. Otherwise, it + displays all the weights. """ - wgt_grp = df[(df["param"] == "WEIGHTKG") & (df["include"] == True)] + wgt_grp = df[(df["param"] == "WEIGHTKG") & (df["include"] is True)] if mode == "high": wgt_grp = wgt_grp.loc[wgt_grp["measurement"] >= 135] plt.title("Weights At or Above 135kg") else: plt.title("All Weights") if len(wgt_grp.index) == 0: - print("No included observations with weight (kg) >= 135") + print("No included observations with weight (kg) >= 135.") plt.close() else: round_col = wgt_grp.apply( @@ -66,7 +70,7 @@ def weight_distr(df, mode): wgt_grp = wgt_grp.assign(round_weight=round_col.values) wgt_grp_sum = wgt_grp.groupby("round_weight")["subjid"].count().reset_index() plt.rcParams["figure.figsize"] = [7, 5] - wgt_grp_sum_plot = plt.bar(wgt_grp_sum["round_weight"], wgt_grp_sum["subjid"]) + plt.bar(wgt_grp_sum["round_weight"], wgt_grp_sum["subjid"]) # Assure there is some breadth to the x-axis in case of just a few observations if wgt_grp["measurement"].max() - wgt_grp["measurement"].min() < 10: plt.xlim(wgt_grp["measurement"].min() - 5, wgt_grp["measurement"].max() + 5) @@ -78,11 +82,13 @@ def weight_distr(df, mode): def make_age_charts(df, mode): """ - Creates a chart with the age ranges in the dataset. Counts the number of subjids in each range. + Creates a chart with the age ranges in the dataset. Counts the number of subjids in + each range. Parameters: - df: (DataFrame) with subjid, param, measurement, age, sex, clean_value, clean_cat, include, - category, colors, patterns, and sort_order columns + df: (DataFrame) with subjid, param, measurement, ageyears, agedays, sex, + clean_value, clean_cat, include, category, colors, patterns, and + sort_order columns mode: (str) indicates whether you want the adults or pediatrics values. """ obs_grp = df @@ -94,8 +100,8 @@ def make_age_charts(df, mode): else: raise Exception("Valid modes are 'adults' and 'pediatrics'") - # Adds label, color, pattern and sort order columns to the dataframe based on the age of each - # row in the dataframe + # Adds label, color, pattern and sort order columns to the dataframe based on the + # age of each row in the dataframe def add_categories_to_frame(df_data, df_reference): categories = [] colors = [] @@ -105,7 +111,7 @@ def add_categories_to_frame(df_data, df_reference): for i in range(len(df_reference)): minVal = df_reference["min"][i] maxVal = df_reference["max"][i] - if df_data["age"][j] >= minVal and df_data["age"][j] < maxVal: + if df_data["ageyears"][j] >= minVal and df_data["ageyears"][j] < maxVal: categories.append(df_reference["label"][i]) colors.append(df_reference["color"][i]) patterns.append(df_reference["symbol"][i]) @@ -119,8 +125,8 @@ def add_categories_to_frame(df_data, df_reference): # Call the categorizing function on the data obs_grp = add_categories_to_frame(obs_grp, label_frame) - # Groups the new dataframe by category, sort order, colors and patterns. It then counts the - # number of subject ids in each group and sorts the values by sort order. + # Groups the new dataframe by category, sort order, colors and patterns. It then + # counts the number of subject ids in each group and sorts the values by sort order. obs_grp = ( obs_grp.groupby(["category", "sort_order", "colors", "patterns"])["subjid"] .count() @@ -162,19 +168,22 @@ def overlap_view_adults( ht_df, ): """ - Creates a chart showing the trajectory for an individual with all values present. All values will - be plotted with a blue line. Excluded values will be represented by a red x. A yellow dashed line - shows the resulting trajectory when excluded values are removed. + Creates a chart showing the trajectory for an individual with all values + present. All values will be plotted with a blue line. Excluded values will + be represented by a red x. A yellow dashed line shows the resulting + trajectory when excluded values are removed. Parameters: - obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value columns + obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value + columns subjid: (str) Id of the individuals to be plotted - param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or "WEIGHTKG" - include_carry_forward: (bool) If True, it will show carry forward values as a triangle and the - yellow dashed line will include carry forward values. If False, carry - forwards are excluded and will be shown as red x's. - include_percentiles: (bool) Controls whether the 5th and 95th percentile bands are displayed - on the chart + param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or + "WEIGHTKG" + include_carry_forward: (bool) If True, it will show carry forward values as + a triangle and the yellow dashed line will include carry forward values. If + False, carry forwards are excluded and will be shown as red x's. + include_percentiles: (bool) Controls whether the 5th and 95th percentile + bands are displayed on the chart wt_df: (DataFrame) with the CDC growth charts by age for weight bmi_df: (DataFrame) with the CDC growth charts by age for bmi ht_df: (DataFrame) with the CDC growth charts by age for height @@ -193,10 +202,10 @@ def overlap_view_adults( included_selected_param = selected_param[filter_excl] plt.rcParams["figure.figsize"] = [6, 4] selected_param_plot = selected_param.plot.line( - x="age", y="measurement", label="All Measurements", lw=3 + x="ageyears", y="measurement", label="All Measurements", lw=3 ) selected_param_plot.plot( - included_selected_param["age"], + included_selected_param["ageyears"], included_selected_param["measurement"], c="y", linestyle="-.", @@ -205,23 +214,23 @@ def overlap_view_adults( label="Included Only", ) selected_param_plot.scatter( - x=excluded_selected_param.age, + x=excluded_selected_param.ageyears, y=excluded_selected_param.measurement, c="r", marker="x", zorder=3, ) - xmin = math.floor(individual.age.min()) - xmax = math.ceil(individual.age.max()) + xmin = math.floor(individual.ageyears.min()) + xmax = math.ceil(individual.ageyears.max()) selected_param_plot.set_xlim(xmin, xmax) - if include_carry_forward == True: + if include_carry_forward is True: carry_forward = selected_param[ selected_param.clean_value == "Exclude-Carried-Forward" ] selected_param_plot.scatter( - x=carry_forward.age, y=carry_forward.measurement, c="c", marker="^" + x=carry_forward.ageyears, y=carry_forward.measurement, c="c", marker="^" ) - if include_percentiles == True: + if include_percentiles is True: if param == "WEIGHTKG": percentile_df = wt_df elif param == "BMI": @@ -287,7 +296,7 @@ def overlap_view_adults_show( """ Wraps overlap_view_adult with plt.show(). """ - plot = overlap_view_adults( + overlap_view_adults( obs_df, subjid, param, @@ -301,22 +310,31 @@ def overlap_view_adults_show( def overlap_view_pediatrics( - obs_df, subjid, param, include_carry_forward, include_percentiles, wt_df, ht_df + obs_df, + subjid, + param, + include_carry_forward, + include_percentiles, + wt_df=None, + ht_df=None, ): """ - Creates a chart showing the trajectory for an individual with all values present. All values will - be plotted with a blue line. Excluded values will be represented by a red x. A yellow dashed line - shows the resulting trajectory when excluded values are removed. + Creates a chart showing the trajectory for an individual with all values + present. All values will be plotted with a blue line. Excluded values will + be represented by a red x. A yellow dashed line shows the resulting + trajectory when excluded values are removed. Parameters: - obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value columns + obs_df: (DataFrame) with subjid, sex, age, measurement, param and + clean_value columns subjid: (str) Id of the individuals to be plotted - param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or "WEIGHTKG" - include_carry_forward: (bool) If True, it will show carry forward values as a triangle and the - yellow dashed line will include carry forward values. If False, carry - forwards are excluded and will be shown as red x's. - include_percentiles: (bool) Controls whether the 5th and 95th percentile bands are displayed - on the chart + param: (str) Whether to plot heights or weights. Expected values are + "HEIGHTCM" or "WEIGHTKG" + include_carry_forward: (bool) If True, it will show carry forward values as + a triangle and the yellow dashed line will include carry forward values. + If False, carry forwards are excluded and will be shown as red x's. + include_percentiles: (bool) Controls whether the 5th and 95th percentile + bands are displayed on the chart wt_df: (DataFrame) with the CDC growth charts by age for weight ht_df: (DataFrame) with the CDC growth charts by age for height @@ -332,32 +350,33 @@ def overlap_view_pediatrics( ) excluded_selected_param = selected_param[~filter_excl] included_selected_param = selected_param[filter_excl] - selected_param_plot = selected_param.plot.line(x="age", y="measurement") + selected_param_plot = selected_param.plot.line(x="ageyears", y="measurement") selected_param_plot.plot( - included_selected_param["age"], + included_selected_param["ageyears"], included_selected_param["measurement"], c="y", linestyle="-.", ) selected_param_plot.scatter( - x=excluded_selected_param.age, + x=excluded_selected_param.ageyears, y=excluded_selected_param.measurement, c="r", marker="x", ) - if include_carry_forward == True: + if include_carry_forward is True: carry_forward = selected_param[ selected_param.clean_value == "Exclude-Carried-Forward" ] selected_param_plot.scatter( - x=carry_forward.age, y=carry_forward.measurement, c="c", marker="^" + x=carry_forward.ageyears, y=carry_forward.measurement, c="c", marker="^" ) - if include_percentiles == True: + if include_percentiles is True: percentile_df = wt_df if param == "WEIGHTKG" else ht_df + percentile_window = percentile_df.loc[ (percentile_df.Sex == individual.sex.min()) - & (percentile_df.age > individual.age.min()) - & (percentile_df.age < individual.age.max()) + & (percentile_df.age > individual.ageyears.min()) + & (percentile_df.age < individual.ageyears.max()) ] selected_param_plot.plot( percentile_window.age, percentile_window.P5, color="k", zorder=1 @@ -369,12 +388,18 @@ def overlap_view_pediatrics( def overlap_view_pediatrics_show( - obs_df, subjid, param, include_carry_forward, include_percentiles, wt_df, ht_df + obs_df, + subjid, + param, + include_carry_forward, + include_percentiles, + wt_df=None, + ht_df=None, ): """ Wraps overlap_view_pediatrics with plt.show(). """ - plot = overlap_view_pediatrics( + overlap_view_pediatrics( obs_df, subjid, param, include_carry_forward, include_percentiles, wt_df, ht_df ) plt.show() @@ -392,21 +417,23 @@ def overlap_view_double_pediatrics( ht_df, ): """ - Creates a chart showing the trajectory for an individual with all values present. All values will - be plotted with a blue line. Excluded values will be represented by a red x. A yellow dashed line - shows the resulting trajectory when excluded values are removed. + Creates a chart showing the trajectory for an individual with all values present. + All values will be plotted with a blue line. Excluded values will be represented by + a red x. A yellow dashed line shows the resulting trajectory when excluded + values are removed. Parameters: - obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value columns + obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value + columns subjid: (str) Id of the individuals to be plotted show_all_measurements: (bool) indicates whether to show all measurements show_excluded_values: (bool) indicates whether to show the excluded values show_trajectory_with_exclusions: (bool) indicates whether to show the trajectory - include_carry_forward: (bool) If True, it will show carry forward values as a triangle and the - yellow dashed line will include carry forward values. If False, carry - forwards are excluded and will be shown as red x's. + include_carry_forward: (bool) If True, it will show carry forward values as a + triangle and the yellow dashed line will include carry forward values. + If False, carry forwards are excluded and will be shown as red x's. include_percentiles: (bool) Controls whether the percentile bands are displayed - on the chart + on the chart wt_df: (DataFrame) with the CDC growth charts by age for weight ht_df: (DataFrame) with the CDC growth charts by age for height """ @@ -431,18 +458,49 @@ def overlap_view_double_pediatrics( fig, ax1 = plt.subplots() color = "tab:red" color_secondary = "tab:blue" - ax1.set_ylim([50, 180]) - ax1.set_xlim([2, 20]) + + # Allow zooming in a bit if this is a young subject + maxage = individual["ageyears"].max() + if maxage < 10: + ax1_xlim = [0, maxage * 1.1] + # Find stature at P95 for maxage + pct_max = round( + ht_df.loc[ + (ht_df.Sex == individual.sex.min()) & (ht_df.age <= np.ceil(maxage)) + ]["P97"].max() + ) + # round up to nearest 20 tick + rounded_max = pct_max + 20 - (pct_max % 20) + ax1_ylim = [40, rounded_max] + else: + ax1_xlim = [0, 20] + ax1_ylim = [40, 180] + ax1.set_xlim(ax1_xlim) + ax1.set_ylim(ax1_ylim) ax1.set_xlabel("age (years)") ax1.set_ylabel("stature (cm)", color=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis - ax2.set_ylim([0, 160]) + # Adjust y-axis for weight also to use chart space well + if maxage < 10: + # Find weight at P97 for maxage + pct_max = round( + wt_df.loc[ + (wt_df.Sex == individual.sex.min()) & (wt_df.age <= np.ceil(maxage)) + ]["P97"].max() + ) + # bump to 2 x rounded up to nearest 10 tick to keep weight under height + rounded_max = 2 * (pct_max + 10 - (pct_max % 10)) + ax2_ylim = [0, rounded_max] + else: + ax2_ylim = [0, 160] + ax2.set_ylim(ax2_ylim) ax2.set_ylabel( "weight (kg)", color=color_secondary ) # we already handled the x-label with ax1 - if include_percentiles == True: + + if include_percentiles is True: percentile_window = wt_df.loc[wt_df.Sex == individual.sex.min()] ax2.plot(percentile_window.age, percentile_window.P5, color="lightblue") ax2.plot( @@ -476,29 +534,35 @@ def overlap_view_double_pediatrics( ) ax1.plot(percentile_window_ht.age, percentile_window_ht.P95, color="pink") - if show_all_measurements == True: - ax1.plot(height["age"], height["measurement"], color=color, label="stature") + if show_all_measurements is True: + # ax1.plot(height["age"], height["measurement"], color=color, label="stature") + ax1.plot( + height["ageyears"], height["measurement"], color=color, label="stature" + ) ax2.plot( - weight["age"], weight["measurement"], color=color_secondary, label="weight" + weight["ageyears"], + weight["measurement"], + color=color_secondary, + label="weight", ) - if show_excluded_values == True: + if show_excluded_values is True: ax1.scatter( - excluded_height.age, excluded_height.measurement, c="black", marker="x" + excluded_height.ageyears, excluded_height.measurement, c="black", marker="x" ) ax2.scatter( - excluded_weight.age, excluded_weight.measurement, c="black", marker="x" + excluded_weight.ageyears, excluded_weight.measurement, c="black", marker="x" ) - if show_trajectory_with_exclusions == True: + if show_trajectory_with_exclusions is True: ax1.plot( - included_height["age"], + included_height["ageyears"], included_height["measurement"], c="y", linestyle="-.", ) ax2.plot( - included_weight["age"], + included_weight["ageyears"], included_weight["measurement"], c="y", linestyle="-.", @@ -509,17 +573,17 @@ def overlap_view_double_pediatrics( fig.tight_layout() # otherwise the right y-label is slightly clipped - if include_carry_forward == True: + if include_carry_forward is True: carry_forward_height = height[height.clean_value == "Exclude-Carried-Forward"] carry_forward_weight = weight[weight.clean_value == "Exclude-Carried-Forward"] ax1.scatter( - x=carry_forward_height.age, + x=carry_forward_height.ageyears, y=carry_forward_height.measurement, c="c", marker="^", ) ax2.scatter( - x=carry_forward_weight.age, + x=carry_forward_weight.ageyears, y=carry_forward_weight.measurement, c="c", marker="^", @@ -532,21 +596,22 @@ def overlap_view_double_pediatrics( def mult_obs(obs): """ - Removes individuals from consideration for five_by_five_view() charts that only have one - observation in the data + Removes individuals from consideration for five_by_five_view() charts that + only have one observation in the data Parameters: - obs: (DataFrame) with subjid, param, measurement, age, sex, clean_value, clean_cat, include, - category, colors, patterns, and sort_order columns + obs: (DataFrame) with subjid, param, measurement, age, sex, clean_value, + clean_cat, include, category, colors, patterns, and sort_order columns Returns: - Dataframe where the individuals that only have one observation in the data are removed + Dataframe where the individuals that only have one observation in the data + are removed """ - obs["cat_count"] = obs.groupby(["subjid", "param"])["age"].transform("count") + obs["cat_count"] = obs.groupby(["subjid", "param"])["ageyears"].transform("count") obs["one_rec"] = np.where(obs["cat_count"] == 1, 1, 0) obs["any_ones"] = obs.groupby(["subjid"])["one_rec"].transform("max") - obs["max"] = obs.groupby("subjid")["age"].transform("max") - obs["min"] = obs.groupby("subjid")["age"].transform("min") + obs["max"] = obs.groupby("subjid")["ageyears"].transform("max") + obs["min"] = obs.groupby("subjid")["ageyears"].transform("min") obs["range"] = np.ceil(obs["max"]) - np.floor(obs["min"]) return obs[obs["any_ones"] == 0] @@ -576,7 +641,8 @@ def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle): Parameters: obs_df: (DataFrame) with subjid, measurement, param and clean_value columns subjids: (list) A list of the ids of the individuals to be plotted - param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or "WEIGHTKG" + param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or + "WEIGHTKG" wt_df: (DataFrame) with the CDC growth charts by age for weight ht_df: (DataFrame) with the CDC growth charts by age for height bmi_df: (DataFrame) with the CDC growth charts by age for bmi @@ -594,7 +660,7 @@ def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle): for x in range(nrows): try: subjid = subjids[x * 5 + y] - except IndexError as ie: + except IndexError: # No more subjects to render break individual = obs_df[obs_df.subjid == subjid] @@ -606,12 +672,12 @@ def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle): tgt = ax else: tgt = ax[y] - tgt.plot(selected_param.age, selected_param.measurement, marker=".") + tgt.plot(selected_param.ageyears, selected_param.measurement, marker=".") excluded_selected_param = selected_param[ selected_param.clean_value != "Include" ] tgt.scatter( - excluded_selected_param.age, + excluded_selected_param.ageyears, excluded_selected_param.measurement, c="r", marker="x", @@ -624,8 +690,8 @@ def five_by_five_view(obs_df, subjids, param, wt_df, ht_df, bmi_df, linestyle): percentile_df = ht_df percentile_window = percentile_df.loc[ (percentile_df.Sex == individual.sex.min()) - & (percentile_df.age >= math.floor(individual.age.min())) - & (percentile_df.age <= math.ceil(individual.age.max())) + & (percentile_df.age >= math.floor(individual.ageyears.min())) + & (percentile_df.age <= math.ceil(individual.ageyears.max())) ] tgt.plot( percentile_window.age, @@ -663,10 +729,10 @@ def bmi_with_percentiles(merged_df, bmi_percentiles, subjid): fig, ax = plt.subplots(1, 2) percentile_window = bmi_percentiles.loc[ (bmi_percentiles.Sex == individual.sex.min()) - & (bmi_percentiles.age > individual.age.min()) - & (bmi_percentiles.age < individual.age.max()) + & (bmi_percentiles.age > individual.ageyears.min()) + & (bmi_percentiles.age < individual.ageyears.max()) ] - ax[0].plot(individual.age, individual.bmi) + ax[0].plot(individual.ageyears, individual.bmi) ax[0].plot(percentile_window.age, percentile_window.P5, color="k") ax[0].plot(percentile_window.age, percentile_window.P95, color="k") @@ -674,7 +740,7 @@ def bmi_with_percentiles(merged_df, bmi_percentiles, subjid): ax[0].grid() ax[1].plot( - individual[individual.include_height & individual.include_weight].age, + individual[individual.include_height & individual.include_weight].ageyears, individual.loc[individual.include_height & individual.include_weight].bmi, ) ax[1].plot(percentile_window.age, percentile_window.P5, color="k") @@ -687,13 +753,15 @@ def bmi_with_percentiles(merged_df, bmi_percentiles, subjid): def param_with_percentiles(merged_df, subjid, param, wt_df, ht_df, bmi_df): """ - A version of bmi_with_percentiles() that provides the option of looking at wt or ht as well + A version of bmi_with_percentiles() that provides the option of looking at wt or ht + as well Parameters: merged_df: (DataFrame) with subjid, bmi, include_height, include_weight, rounded_age - and sex columns + and sex columns subjids: (list) A list of the ids of the individuals to be plotted - param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or "WEIGHTKG" + param: (str) Whether to plot heights or weights. Expected values are "HEIGHTCM" or + "WEIGHTKG" wt_df: (DataFrame) with the CDC growth charts by age for weight ht_df: (DataFrame) with the CDC growth charts by age for height bmi_df: (DataFrame) with the CDC growth charts by age for bmi @@ -708,10 +776,10 @@ def param_with_percentiles(merged_df, subjid, param, wt_df, ht_df, bmi_df): percentile_df = ht_df percentile_window = percentile_df.loc[ (percentile_df.Sex == individual.sex.min()) - & (percentile_df.age > individual.age.min()) - & (percentile_df.age < individual.age.max()) + & (percentile_df.age > individual.ageyears.min()) + & (percentile_df.age < individual.ageyears.max()) ] - ax[0].plot(individual.age, individual.measurement) + ax[0].plot(individual.ageyears, individual.measurement) ax[0].plot(percentile_window.age, percentile_window.P5, color="k") ax[0].plot(percentile_window.age, percentile_window.P95, color="k") @@ -719,7 +787,7 @@ def param_with_percentiles(merged_df, subjid, param, wt_df, ht_df, bmi_df): ax[0].grid() included_individual = individual[individual.clean_cat.isin(["Include"])] - ax[1].plot(included_individual.age, included_individual.measurement) + ax[1].plot(included_individual.ageyears, included_individual.measurement) ax[1].plot(percentile_window.age, percentile_window.P5, color="k") ax[1].plot(percentile_window.age, percentile_window.P95, color="k") @@ -742,31 +810,33 @@ def top_ten( Displays the top ten records depending on the criteria passed in Parameters: - merged_df: (DataFrame) with subjid, bmi, height, weight, include_height, include_weight, - rounded_age and sex columns + merged_df: (DataFrame) with subjid, bmi, height, weight, include_height, + include_weight, rounded_age and sex columns field: (str) What field to sort on. Expected values are "height", "weight" and "bmi" age: (list) Two elements containing the minimum and maximum ages that should be included in the statistics. None if no age filtering desired. sex: (int) 1 - Female, 0 - Male, None - no sex filtering - wexclusion: (list) of weight exclusions to filter on. None - no weight exclusion filtering - hexclusion: (list) of height exclusions to filter on. None - no height exclusion filtering + wexclusion: (list) of weight exclusions to filter on. None - no weight exclusion + filtering + hexclusion: (list) of height exclusions to filter on. None - no height exclusion + filtering order: (str) Sort order - Expected values are "smallest" and "largest" out: (ipywidgets.Output) displays the results Returns: - If out is None, it will return a DataFrame. If out is provided, results will be displayed - in the notebook. + If out is None, it will return a DataFrame. If out is provided, results will be + displayed in the notebook. """ working_set = merged_df - if age != None: + if age is not None: working_set = working_set.loc[ working_set.rounded_age.ge(age[0]) & working_set.rounded_age.le(age[1]) ] - if sex != None: + if sex is not None: working_set = working_set[working_set.sex == sex] - if wexclusion != None: + if wexclusion is not None: working_set = working_set[working_set.weight_cat.isin(wexclusion)] - if hexclusion != None: + if hexclusion is not None: working_set = working_set[working_set.height_cat.isin(hexclusion)] # if order == 'largest': # working_set = working_set.nlargest(10, field) @@ -776,7 +846,7 @@ def top_ten( columns=["include_height", "include_weight", "include_both", "rounded_age"] ) working_set["sex"] = working_set.sex.replace(0, "M").replace(1, "F") - working_set["age"] = working_set.age.round(decimals=2) + working_set["age"] = working_set.ageyears.round(decimals=2) working_set["height"] = working_set.height.round(decimals=1) working_set["weight"] = working_set.weight.round(decimals=1) working_set["weight_cat"] = working_set.weight_cat.str.replace("Exclude-", "") @@ -793,10 +863,10 @@ def top_ten( "weight_cat", "wtz", "bmi", - "BMIz", + "bmiz", ] ] - if out == None: + if out is None: return working_set else: out.clear_output() diff --git a/growthviz/compare.py b/growthviz/compare.py index 8133875..f5c52ea 100644 --- a/growthviz/compare.py +++ b/growthviz/compare.py @@ -130,7 +130,7 @@ def subject_stats_comparison(combined_df): for rn in combined_df.run_name.unique(): total_subjects = combined_df[combined_df.run_name == rn].subjid.nunique() only_exclusions = combined_df[ - (combined_df.run_name == rn) & (combined_df.include == False) + (combined_df.run_name == rn) & (combined_df.include is False) ] percent_with_exclusion = ( only_exclusions.subjid.nunique() / total_subjects diff --git a/growthviz/processdata.py b/growthviz/processdata.py index 9e8021f..703f93c 100644 --- a/growthviz/processdata.py +++ b/growthviz/processdata.py @@ -1,8 +1,9 @@ -from IPython.display import FileLinks -import matplotlib as mpl -import matplotlib.pyplot as plt +from pathlib import Path + import numpy as np import pandas as pd +from scipy.stats import norm +from IPython.display import FileLinks def setup_individual_obs_df(obs_df): @@ -10,24 +11,25 @@ def setup_individual_obs_df(obs_df): Standardizes adults and pediatrics files for clean processing in GrowthViz notebooks Parameters: - obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value columns + obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value + columns Returns: DataFrame with updated columns """ df = obs_df.copy() df.rename(columns={"clean_res": "clean_value"}, inplace=True) - df["age"] = df["agedays"] / 365.25 - df.drop(columns=["agedays"], inplace=True) + df["ageyears"] = df["agedays"] / 365.25 df["clean_cat"] = df["clean_value"].astype("category") df["include"] = df.clean_value.eq("Include") col_list = [ "id", "subjid", + "agedays", + "ageyears", + "sex", "param", "measurement", - "age", - "sex", "clean_value", "clean_cat", "include", @@ -102,6 +104,96 @@ def setup_percentiles_adults(percentiles): return dta +def setup_percentiles_pediatrics_new(): + """ + Process pediatrics growth data and return one big DataFrames with each of + height, weight, and BMI percentiles as well as weighting values to support + smoothing z scores for pediatric subjects between 2-4. + + Returns: + Combined DataFrame with all (BMI, height, weight) percentiles and weighting + values + """ + df_cdc = pd.read_csv(Path("growthviz-data/ext/growthfile_cdc_ext.csv.gz")) + df_who = pd.read_csv(Path("growthviz-data/ext/growthfile_who.csv.gz")) + df = df_cdc.merge(df_who, on=["agedays", "sex"], how="left") + + # Add weighting columns to support smoothing between 2-4yo + df = df.assign(ageyears=lambda r: (r["agedays"] / 365.25)) + df["cdcweight"] = 0 + df.loc[df["ageyears"].between(2, 4, inclusive="left"), "cdcweight"] = ( + df["ageyears"] - 2 + ) + df["whoweight"] = 0 + df.loc[df["ageyears"].between(2, 4, inclusive="left"), "whoweight"] = ( + 4 - df["ageyears"] + ) + + PERCENTILES = [0.03, 0.05, 0.10, 0.25, 0.50, 0.75, 0.85, 0.90, 0.95, 0.97] + + # Compute percentiles for the full set of vars + for s in ["who", "cdc"]: + pvars = ["ht", "wt"] + if s == "cdc": + pvars.append("bmi") + for p in pvars: + for pct in PERCENTILES: + lvar = f"{s}_{p}_l" + mvar = f"{s}_{p}_m" + svar = f"{s}_{p}_s" + tvar = f"{s}_{p}_p{int(pct * 100)}" + df.loc[df[lvar] == 0, tvar] = df[mvar] * (df[svar] ** norm.ppf(pct)) + df.loc[df[lvar] != 0, tvar] = df[mvar] * ( + 1 + (df[lvar] * df[svar] * norm.ppf(pct)) + ) ** (1 / df[lvar]) + + # Add smoothed percentiles + for p in ["ht", "wt"]: + for pct in PERCENTILES: + cdc_var = f"cdc_{p}_p{int(pct * 100)}" + who_var = f"who_{p}_p{int(pct * 100)}" + s_var = f"s_{p}_p{int(pct * 100)}" + df.loc[df["ageyears"] <= 2, s_var] = df[who_var] + df.loc[df["ageyears"].between(2, 4, inclusive="neither"), s_var] = ( + (df[who_var] * df["whoweight"]) + (df[cdc_var] * df["cdcweight"]) + ) / 2 + df.loc[df["ageyears"] >= 4, s_var] = df[cdc_var] + + return df + + +def split_percentiles_pediatrics(df): + """ + Return (height, weight, BMI) percentile DataFrames, with column names + aligned to chart var names. + + Parameters: + DataFrame produced by setup_percentiles_pediatrics_new() + + Returns: + Tuple of (height, weight, BMI) percentile DataFrames with columns renamed + """ + df.rename(columns={"ageyears": "age", "sex": "Sex"}, inplace=True) + cols = ["Sex", "agedays", "age"] + + ht_cols = cols.copy() + ht_cols.extend([col for col in df.columns if "s_ht_p" in col]) + df_ht = df[ht_cols] + df_ht.columns = [c.replace("s_ht_p", "P") for c in df_ht] + + wt_cols = cols.copy() + wt_cols.extend([col for col in df.columns if "s_wt_p" in col]) + df_wt = df[wt_cols] + df_wt.columns = [c.replace("s_wt_p", "P") for c in df_wt] + + bmi_cols = cols.copy() + bmi_cols.extend([col for col in df.columns if "s_bmi_p" in col]) + df_bmi = df[bmi_cols] + df_bmi.columns = [c.replace("s_bmi_p", "P") for c in df_bmi] + + return (df_ht, df_wt, df_bmi) + + def setup_percentiles_pediatrics(percentiles_file): """ Processes pediatrics percentiles from CDC @@ -135,20 +227,27 @@ def setup_percentiles_pediatrics(percentiles_file): def keep_age_range(df, mode): """ - Returns specified age range + Returns specified range of ages in years, removing extraneous columns as well Parameters: - df: (DataFrame) with subjid, param, measurement, age, sex, clean_value, clean_cat, include, - category, colors, patterns, and sort_order columns - mode: (str) indicates whether you want the "adults" (18-80) or "pediatrics" (0-25) values + df: (DataFrame) with subjid, param, measurement, ageyears, agedays, sex, + clean_value, and include columns + mode: (str) indicates whether you want the "adults" (18-80) or "pediatrics" (0-25) + values Returns: DataFrame with filtered ages, unchanged if invalid mode is specified """ + # Note: this is a side effect; just the simplest place to remove these + cols_to_drop = [] + for extra_col in ["category", "colors", "patterns", "sort_order"]: + if extra_col in df.columns: + cols_to_drop.append(extra_col) + df = df.drop(columns=cols_to_drop) if mode == "adults": - return df[df["age"].between(18, 80, inclusive="both")] + return df[df["ageyears"].between(18, 80, inclusive="both")] elif mode == "pediatrics": - return df[df["age"].between(0, 25, inclusive="both")] + return df[df["ageyears"].between(0, 25, inclusive="both")] else: return df @@ -158,7 +257,8 @@ def setup_merged_df(obs_df): Merges together weight and height data for calculating BMI Parameters: - obs_df: (DataFrame) with subjid, sex, age, measurement, param and clean_value columns + obs_df: (DataFrame) with subjid, sex, agedays, ageyears, measurement, param and + clean_value columns Returns: DataFrame with merged data @@ -168,7 +268,9 @@ def setup_merged_df(obs_df): obs_df.loc[obs_df.param == "HEIGHTCM", "weight"] = np.NaN heights = obs_df[obs_df.param == "HEIGHTCM"] weights = obs_df[obs_df.param == "WEIGHTKG"] - merged = heights.merge(weights, on=["subjid", "age", "sex"], how="outer") + merged = heights.merge( + weights, on=["subjid", "agedays", "ageyears", "sex"], how="outer" + ) only_needed_columns = merged.drop( columns=[ "param_x", @@ -197,7 +299,7 @@ def setup_merged_df(obs_df): clean_column_names["bmi"] = clean_column_names["weight"] / ( (clean_column_names["height"] / 100) ** 2 ) - clean_column_names["rounded_age"] = np.around(clean_column_names.age) + clean_column_names["rounded_age"] = np.around(clean_column_names.ageyears) clean_column_names["include_both"] = ( clean_column_names["include_height"] & clean_column_names["include_weight"] ) @@ -206,7 +308,8 @@ def setup_merged_df(obs_df): def exclusion_information(obs): """ - Provides a count and percentage of growthcleanr categories by measurement type (param). + Provides a count and percentage of growthcleanr categories by measurement type + (param). Parameters: obs: a DataFrame, in the format output by setup_individual_obs_df @@ -238,7 +341,8 @@ def exclusion_information(obs): def label_incl(row): """ - Categorizes BMI calculations as Include, Implausible, or unable to calculate (Only Wt or Ht) + Categorizes BMI calculations as Include, Implausible, or unable to calculate (Only + Wt or Ht) Parameters: row: (Series) dataframe row @@ -246,7 +350,7 @@ def label_incl(row): Returns: Category (str) for BMI calculation """ - if row["include_both"] == True: + if row["include_both"] is True: return "Include" elif (row["weight_cat"] == "Implausible") | (row["height_cat"] == "Implausible"): return "Implausible" @@ -261,8 +365,8 @@ def setup_bmi_adults(merged_df, obs): Parameters: merged_df: (DataFrame) with subjid, bmi, include_height, include_weight, rounded_age and sex columns - obs: (DataFrame) with subjid, param, measurement, age, sex, clean_value, clean_cat, include, - category, colors, patterns, and sort_order columns + obs: (DataFrame) with subjid, param, measurement, age, sex, clean_value, clean_cat, + include, category, colors, patterns, and sort_order columns Returns: DataFrame with appended values @@ -272,7 +376,7 @@ def setup_bmi_adults(merged_df, obs): "id", "subjid", "sex", - "age", + "ageyears", "rounded_age", "bmi", "weight_cat", @@ -324,15 +428,17 @@ def export_to_csv(da_locals, selection_widget, out): def clean_swapped_values(merged_df): """ - This function will look in a DataFrame for rows where the height_cat and weight_cat are set to - "Swapped-Measurements" (or the adult equivalent). It will then swap the height and weight values - for those rows, and recalculate BMIs based on these changes. It will also create two new columns: - postprocess_height_cat and postprocess_weight_cat. The values for these columns are copied from - the original categories except in the case where swaps are fixed when it is set to + This function will look in a DataFrame for rows where the height_cat and weight_cat + are set to "Swapped-Measurements" (or the adult equivalent). It will then swap the + height and weight values for those rows, and recalculate BMIs based on these + changes. It will also create two new columns: postprocess_height_cat and + postprocess_weight_cat. The values for these columns are copied from the original + categories except in the case where swaps are fixed when it is set to "Include-Fixed-Swap". Parameters: - merged_df: (DataFrame) with subjid, height, weight, include_height and include_weight columns + merged_df: (DataFrame) with subjid, height, weight, include_height and + include_weight columns Returns: The cleaned DataFrame @@ -368,20 +474,21 @@ def clean_swapped_values(merged_df): def clean_unit_errors(merged_df): """ - This function will look in a DataFrame for rows where the height_cat and weight_cat are set to - "Unit-Error-High" or "Unit-Error-Low". It will then multiply / divide the height and weight - values to convert them. It will also create two new columns: postprocess_height_cat and - postprocess_weight_cat. The values for these columns are copied from the original categories - except in the case where unit errors are fixed when it is set to "Include-UH" or "Include-UL" - respectively. - - At present, the adult algorithm does not specify high or low unit errors, rather it only flags - "Exclude-Adult-Unit-Errors", so this function only works with pediatrics data. If growthcleanr - adds high and low designations for adult unit errors, a comparable set of conditions could be - added here to accommodate adult data. + This function will look in a DataFrame for rows where the height_cat and weight_cat + are set to "Unit-Error-High" or "Unit-Error-Low". It will then multiply / divide + the height and weight values to convert them. It will also create two new columns: + postprocess_height_cat and postprocess_weight_cat. The values for these columns + are copied from the original categories except in the case where unit errors are + fixed when it is set to "Include-UH" or "Include-UL" respectively. + + At present, the adult algorithm does not specify high or low unit errors, rather it + only flags "Exclude-Adult-Unit-Errors", so this function only works with pediatrics + data. If growthcleanr adds high and low designations for adult unit errors, a + comparable set of conditions could be added here to accommodate adult data. Parameters: - merged_df: (DataFrame) with subjid, height, weight, include_height and include_weight columns + merged_df: (DataFrame) with subjid, height, weight, include_height and + include_weight columns Returns: The cleaned DataFrame diff --git a/growthviz/sumstats.py b/growthviz/sumstats.py index f534198..33d0e1a 100644 --- a/growthviz/sumstats.py +++ b/growthviz/sumstats.py @@ -1,5 +1,6 @@ -from IPython.display import Markdown +import math import numpy as np +from IPython.display import Markdown def setup_percentile_zscore_adults(percentiles_clean): @@ -24,7 +25,8 @@ def label_param(row): param_col = dta_forz_long.apply(lambda row: label_param(row), axis=1) dta_forz_long = dta_forz_long.assign(param2=param_col.values) - # preserving some capitalization to maintain compatibility with pediatric percentiles data + # preserving some capitalization to maintain compatibility with pediatric + # percentiles data dta_forz = dta_forz_long.pivot_table( index=["Sex", "age"], columns="param2", values=["Mean", "sd"], aggfunc="first" ) @@ -69,6 +71,7 @@ def add_mzscored_to_merged_df_pediatrics( Returns: merged Dataframe """ + merged_df = calculate_modified_zscore_pediatrics( merged_df, wt_percentiles, "weight" ) @@ -76,9 +79,23 @@ def add_mzscored_to_merged_df_pediatrics( merged_df, ht_percentiles, "height" ) merged_df = calculate_modified_zscore_pediatrics(merged_df, bmi_percentiles, "bmi") + return merged_df +def add_smoothed_zscore_to_merged_df_pediatrics(df_merged, df_percentiles): + """ + Adds smoothed Z score calculations to pediatrics data + + Parameters: + df_merged: (DataFrame) merged subject observations including height, weight, + and bmi + df_percentiles: (DataFrame) combined WHO and CDC percentiles + """ + df_merged = calculate_smoothed_zscore_pediatrics(df_merged, df_percentiles) + return df_merged + + def bmi_stats( merged_df, out=None, @@ -92,9 +109,9 @@ def bmi_stats( include_missing=False, ): """ - Computes summary statistics for BMI. Clean values are for BMIs computed when both the height - and weight values are categorized by growthcleanr as "Include". Raw values are computed for - all observations. Information is provided by age and sex. + Computes summary statistics for BMI. Clean values are for BMIs computed when both + the height and weight values are categorized by growthcleanr as "Include". Raw + values are computed for all observations. Information is provided by age and sex. Parameters: merged_df: (DataFrame) with bmi, rounded_age and sex columns @@ -106,14 +123,14 @@ def bmi_stats( include_mean_diff: (bool) Whether to include the difference between the raw and clean mean value column include_count: (bool) Whether to include the count column - age_range: (list) Two elements containing the minimum and maximum ages that should be - included in the statistics - include_missing: (bool) Whether to include the missing (0) heights and weights that impact - raw columns + age_range: (list) Two elements containing the minimum and maximum ages that should + be included in the statistics + include_missing: (bool) Whether to include the missing (0) heights and weights that + impact raw columns Returns: - If out is None, it will return a DataFrame. If out is provided, results will be displayed - in the notebook. + If out is None, it will return a DataFrame. If out is provided, results will be + displayed in the notebook. """ # Incoming data is float, not int merged_df["rounded_age"] = merged_df["rounded_age"].astype(int) @@ -169,7 +186,7 @@ def bmi_stats( merged_stats = merged_stats.rename( columns={"std_raw": "sd_raw", "std_clean": "sd_clean"} ) - if out == None: + if out is None: return merged_stats else: # Clear output on first update and all subsequent updates, see @@ -184,11 +201,13 @@ def bmi_stats( def calculate_modified_zscore_pediatrics(merged_df, percentiles, category): """ - Adds a column to the provided DataFrame with the modified Z score for the provided category + Adds a column to the provided DataFrame with the modified Z score for the provided + category Parameters: merged_df: (DataFrame) with subjid, sex, weight and age columns - percentiles: (DataFrame) CDC growth chart DataFrame with L, M, S values for the desired category + percentiles: (DataFrame) CDC growth chart DataFrame with L, M, S values for the + desired category category: (str) name of category Returns @@ -199,17 +218,78 @@ def calculate_modified_zscore_pediatrics(merged_df, percentiles, category): pct_cpy["M"] * np.power((1 + pct_cpy["L"] * pct_cpy["S"] * 2), (1 / pct_cpy["L"])) ) - pct_cpy["M"] - # Calculate an age in months by rounding and then adding 0.5 to have values that match the - # growth chart - merged_df["agemos"] = np.around(merged_df["age"] * 12) + 0.5 + # Calculate an age in months by rounding and then adding 0.5 to have values that + # match the growth chart + merged_df["agemos"] = np.around(merged_df["ageyears"] * 12) + 0.5 mswpt = merged_df.merge( pct_cpy[["Agemos", "M", "Sex", "half_of_two_z_scores"]], how="left", left_on=["sex", "agemos"], right_on=["Sex", "Agemos"], ) - z_column_name = {"weight": "wtz", "height": "htz", "bmi": "BMIz"} + z_column_name = {"weight": "wtz", "height": "htz", "bmi": "bmiz"} mswpt[z_column_name[category]] = (mswpt[category] - mswpt["M"]) / mswpt[ "half_of_two_z_scores" ] return mswpt.drop(columns=["Agemos", "Sex", "M", "half_of_two_z_scores"]) + + +def calculate_smoothed_zscore_pediatrics(df_merged, df_percentiles): + """ + Add column to provided DataFrame with smoothed Z scores + + Parameters: + df_merged: (DataFrame) with subjid, sex, weight, and age columns + df_percentiles: (DataFrame) growth chart w/WHO and CDC L, M, S values for + each measurement type + + Returns: + DataFrame with smoothed zscore column for each measurement type + """ + df_pct = df_percentiles.copy() + + # Merge z scores into observations + df = df_merged.merge( + df_pct, + how="left", + left_on=["agedays", "ageyears", "sex"], + right_on=["agedays", "age", "Sex"], + ) + + for p, param in (("ht", "height"), ("wt", "weight"), ("bmi", "bmi")): + cdc_l_var = f"cdc_{p}_l" + cdc_m_var = f"cdc_{p}_m" + cdc_s_var = f"cdc_{p}_s" + cdc_csd_pos_var = f"cdc_{p}_csd_pos" + cdc_csd_neg_var = f"cdc_{p}_csd_neg" + cdc_z_var = f"cdc_{p}_z" + who_z_var = f"who_{p}_z" + s_z_var = f"{p}z" + + # Assign CDC z scores + df[cdc_z_var] = np.where( + df[cdc_l_var] != 0, + ( + (((df[param] / df[cdc_m_var]) ** df[cdc_l_var]) - 1) + / (df[cdc_l_var] * df[cdc_s_var]) + ), + (np.log(df[param] / df[cdc_m_var]) / df[cdc_s_var]), + ) + + # Assign WHO z scores + df.loc[df[param] == df[cdc_m_var], who_z_var] = 0 + df.loc[df[param] > df[cdc_m_var], who_z_var] = (df[param] - df[cdc_m_var]) / ( + df[cdc_csd_pos_var] / 2 + ) + df.loc[df[param] < df[cdc_m_var], who_z_var] = (df[param] - df[cdc_m_var]) / ( + df[cdc_csd_neg_var] / 2 + ) + + # Assign z scores, smoothing between 2-4 + df.loc[df["ageyears"] <= 2, s_z_var] = df[who_z_var] + df.loc[df["ageyears"].between(2, 4, inclusive="neither"), s_z_var] = ( + (df[who_z_var] * df["whoweight"]) + (df[cdc_z_var] * df["cdcweight"]) + ) / 2 + df.loc[df["ageyears"] >= 4, s_z_var] = df[cdc_z_var] + + return df diff --git a/requirements.txt b/requirements.txt index 9fbe480..be7b14e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,7 @@ ipywidgets~=7.0 +jupyter-server<2.0.0 matplotlib>=3.3.4 pandas>=1.2.2 qgrid>=1.3.1 +scipy seaborn>=0.11.1 diff --git a/tests/test_processdata.py b/tests/test_processdata.py index bda3369..e4bed4e 100644 --- a/tests/test_processdata.py +++ b/tests/test_processdata.py @@ -6,7 +6,6 @@ class DataTestCase: - SAMPLE_DATA = "path/to/sample/data.csv" MODE = "adults or pediatrics" @@ -22,7 +21,8 @@ def test_setup_individual_obs(self): "subjid", "param", "measurement", - "age", + "agedays", + "ageyears", "sex", "clean_value", "clean_cat", @@ -45,7 +45,7 @@ def setup_keep_merge(self, df): def test_no_nans(self): merge_df = self.setup_keep_merge(self.df) - self.assertEqual(0, merge_df["age"].isnull().sum()) + self.assertEqual(0, merge_df["ageyears"].isnull().sum()) self.assertEqual(0, merge_df["sex"].isnull().sum()) def test_cols(self): @@ -55,14 +55,15 @@ def test_cols(self): [ "id", "subjid", - "age", + "agedays", + "ageyears", "sex", - "height_cat", "include_height", "height", - "weight_cat", + "height_cat", "include_weight", "weight", + "weight_cat", "bmi", "include_both", "rounded_age", @@ -77,13 +78,11 @@ def test_sex(self): class AdultDataTestCase(DataTestCase, unittest.TestCase): - SAMPLE_DATA = "growthviz-data/sample-adults-data.csv" MODE = "adults" class PediatricDataTestCase(DataTestCase, unittest.TestCase): - SAMPLE_DATA = "growthviz-data/sample-pediatrics-data.csv" MODE = "pediatrics" @@ -98,7 +97,7 @@ def test_vdsmeasures_data(self): def test_setup_percentiles_adults(self): setup_df = processdata.setup_percentiles_adults(self.df) - self.assertTrue(setup_df["age"].between(18, 110, inclusive=True).all()) + self.assertTrue(setup_df["age"].between(18, 110, inclusive="both").all()) self.assertEqual(0, setup_df["sd"].isnull().sum()) self.assertEqual(0, setup_df["P5"].isnull().sum()) self.assertEqual(0, setup_df["P95"].isnull().sum()) @@ -111,5 +110,11 @@ def setUp(self): def test_setup_percentiles_peds_bmi(self): for fn in self.fns: setup_df = processdata.setup_percentiles_pediatrics(fn) - self.assertTrue(setup_df["age"].between(2, 20.1, inclusive=True).all()) + self.assertTrue(setup_df["age"].between(2, 20.1, inclusive="both").all()) self.assertEqual(setup_df["P50"].all(), setup_df["M"].all()) + + def test_setup_percentiles_peds_ht_wt(self): + df_percentiles = processdata.setup_percentiles_pediatrics_new() + df_ht, df_wt, _ = processdata.split_percentiles_pediatrics(df_percentiles) + self.assertTrue(df_ht["age"].between(0, 21.1, inclusive="both").all()) + self.assertTrue(df_wt["age"].between(0, 21.1, inclusive="both").all())