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z)rXD9-y=~P_{6`t>bKfroih{M!1v-ZVz|xGJl8H+#d1YSFW6##YPq5@{bci%WQtG^>~2pTqvvU2~ZJ`->0 zoCy!PIx~^vaxF$Hgfs+1yy}A9(AKD6Bn$Kn>(@ zHHmgoE4*~#u|r=?4eFtA*llB5e`hzqUBtf1!G< zJ8KfEzG}#wIAOThH(ccdj>D?K)XXkSEr?ccy}nagukc0^1HW&y({N>?cI^A}IhUH? ztTIw0fW7k^%;ULyur*TJvF4(2SK(}zS0|$@ZgQ&h@B_qMOQ9ZaSb=SKY)?@%!7v0W zu6z!LadfTSIRk(xrDy~YK;rB#+SH_}J~7Vl8Gx5Iyu7@+9P02qH5)5Yo1Vs>Lt(8R zfPs!E+tvi`f9Q2ZMbfeR@4$o*3G=^7o*=r3QlI8CWcIrEc9GkSP{ z$ + + + + + + + + + +Age reference plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + +
+ + +
+ + + +
+ +
+
+

Age reference plots

+
+ +
+
+ Age and sex reference plots for visualizing datasets +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

get_gam_expectiles

+
+
 get_gam_expectiles (X:numpy.ndarray, y:numpy.ndarray,
+                     expectiles:List[float]=[0.03, 0.1, 0.5, 0.9, 0.97])
+
+

*Fit Expectile Generalized Additive Models (GAMs) for given expectiles.

+

Args: X (ndarray): Feature data for the model. y (ndarray): Target variable for the model. expectiles (List[float]): List of expectiles to fit. Default is [0.03, 0.1, 0.5, 0.9, 0.97].

+

Returns: Tuple[ndarray, Dict[str, ndarray]]: A tuple containing a grid of X values for prediction and a dictionary with expectiles as keys and their corresponding model predictions as values.*

+
+

source

+
+
+

AgeRefPlot

+
+
 AgeRefPlot (data:pandas.core.frame.DataFrame, val_col:str,
+             age_col:str='age_at_research_stage', sex_col:str='sex',
+             sex:Optional[int]=None, val_color:Optional[str]=None,
+             ax_main:Optional[matplotlib.axes._axes.Axes]=None,
+             ax_agehist:Optional[matplotlib.axes._axes.Axes]=None,
+             ax_valhist:Optional[matplotlib.axes._axes.Axes]=None,
+             age_bins:Optional[numpy.ndarray]=None,
+             val_bins:Optional[numpy.ndarray]=None, linear_fit:bool=True,
+             expectiles:Optional[List]=[0.03, 0.1, 0.5, 0.9, 0.97],
+             thresholds:Optional[List]=None, top_disp_perc:float=99,
+             bottom_disp_perc:float=1, robust:bool=True, scale:float=1.0,
+             transform:Optional[Callable]=None, make_fig:bool=True)
+
+

*Initializes the AgeRefPlot class.

+

Args: data (pd.DataFrame): A pandas DataFrame containing the data. val_col (str): The name of the value column in the DataFrame. age_col (str): The name of the age column in the DataFrame. sex_col (str): The name of the sex column in the DataFrame. sex (Optional[int], optional): The sex to filter the data by. 0 for females and 1 for males. Defaults to None. val_color (Optional[str], optional): The color to use for the value plot. Defaults to None. ax_main (Optional[plt.Axes], optional): The main axis for the plot. Defaults to None. ax_agehist (Optional[plt.Axes], optional): The axis for the age histogram. Defaults to None. ax_valhist (Optional[plt.Axes], optional): The axis for the value histogram. Defaults to None. age_bins (Optional[np.ndarray], optional): The age bins for the histograms. Defaults to None. val_bins (Optional[np.ndarray], optional): The value bins for the histograms. Defaults to None. linear_fit (bool, optional): Whether to perform a linear fit on the data. Defaults to True. expectiles (Optional[List], optional): Whether to calculate and shpe gam expectiles or not. Defaults to [0.03, 0.1, 0.5, 0.9, 0.97]. top_disp_perc (float, optional): The top percentile to use for display. Defaults to 99. bottom_disp_perc (float, optional): The bottom percentile to use for display. Defaults to 1. robust (bool, optional): Whether to use a robust regression method (HuberRegressor) instead of ordinary least squares for linear_fit. Defaults to True. scale (float, optional): The scaling factor for the value column. Defaults to 1. transform (Optional[Callable], optional): The transformation function to apply to the value column. Defaults make_fig (bool, optional): Whether to create a new figure if axes are not provided. Defaults to True.*

+
+
data = generate_synthetic_data(n=1000)
+
+
+refplot = AgeRefPlot(data, "val1")
+refplot.plot()
+refplot.plot_thresholds([-np.inf, 50, 120, np.inf], cmap='RdYlGn_r')
+
+
+
+

+
+
+
+
+
+

source

+
+
+

GenderAgeRefPlot

+
+
 GenderAgeRefPlot (data:pandas.core.frame.DataFrame, val_col:str,
+                   age_col:str='age_at_research_stage', sex_col:str='sex',
+                   age_bins:Optional[numpy.ndarray]=None,
+                   val_bins:Optional[numpy.ndarray]=None,
+                   linear_fit:bool=True, expectiles:Optional[List]=[0.03,
+                   0.1, 0.5, 0.9, 0.97], top_disp_perc:float=99,
+                   bottom_disp_perc:float=1, robust:bool=True,
+                   scale:float=1.0, transform:Optional[Callable]=None)
+
+

*Initializes the GenderAgeRefPlot class.

+

Args: data (pd.DataFrame): The input data containing age, value, and gender columns. val_col (str): The name of the value column in the data. age_col (str): The name of the age column in the DataFrame. sex_col (str): The name of the sex column in the DataFrame. age_bins (np.ndarray, optional): An array of age bin edges. val_bins (np.ndarray, optional): An array of value bin edges. linear_fit (bool, optional): Whether to fit a linear regression line. Defaults to True. expectiles (Optional[List], optional): Whether to calculate and shpe gam expectiles or not. Defaults to [0.03, 0.1, 0.5, 0.9, 0.97]. top_disp_perc (float, optional): The top percentile for data display. Defaults to 99. bottom_disp_perc (float, optional): The bottom percentile for data display. Defaults to 1. robust (bool, optional): Whether to use a robust linear regression. Defaults to True. scale (float, optional): The scaling factor for the data. Defaults to 1. transform (Callable, optional): An optional function to apply to the data. Defaults to None.*

+
+
gender_refplots = GenderAgeRefPlot(data, "val1")
+gender_refplots.plot()
+gender_refplots.plot_thresholds([-np.inf, 50, 120, np.inf], cmap='RdYlGn')
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/basic_analysis.html b/basic_analysis.html new file mode 100644 index 0000000..e6f7ea4 --- /dev/null +++ b/basic_analysis.html @@ -0,0 +1,857 @@ + + + + + + + + + + +Basic analysis – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Basic analysis

+
+ +
+
+ Basic analysis tools +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

custom_describe

+
+
 custom_describe (df:pandas.core.frame.DataFrame)
+
+

*Generates a custom summary statistics dataframe for mixed data types.

+

Args: df: The input pandas DataFrame

+

Returns: A pandas DataFrame containing the summary statistics*

+
+
data = generate_synthetic_data(n=100)
+
+custom_describe(data[["date_of_research_stage", "sex", "val2"]])
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
date_of_research_stagesexval2
count100100.0100.0
unique992.0100.0
most_frequentNaN0.0-3.137486
min2020-01-04 00:00:000.0-3.137486
max2023-06-25 00:00:001.041.432775
meanNaN0.4619.091836
medianNaN0.019.601733
stdNaN0.50090810.401328
+ +
+
+
+
+

source

+
+
+

assign_nearest_research_stage

+
+
 assign_nearest_research_stage (dataset:pandas.core.frame.DataFrame,
+                                population:pandas.core.frame.DataFrame,
+                                max_days:int=60,
+                                stages:List[str]=['visit'],
+                                agg:Optional[str]='first')
+
+

*Assign the nearest research stage to each record in a dataset.

+

Args: dataset (pd.DataFrame): The dataset containing records to be assigned research stages. population (pd.DataFrame): The population data with participant_id, cohort, research_stage, and research_stage_date. max_days (int, optional): The maximum number of days allowed between the collection date and research stage date. Defaults to 60. stages (List[str], optional): The list of types of research stages to consider. Defaults to [‘visit’]. agg (Union[str, None], optional): The aggregation function to be used when (optionally) aggregating multiple rows from the same research stage. The rows are already sorted by distance from the date of the research stage. Can be ‘first’ (closest), ‘last’ (farthest), ‘mean’, ‘min’, ‘max’, or None. Defaults to ‘first’.

+

Returns: pd.DataFrame: The dataset with the nearest research stage assigned to each record.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/basic_plots.html b/basic_plots.html new file mode 100644 index 0000000..6094462 --- /dev/null +++ b/basic_plots.html @@ -0,0 +1,979 @@ + + + + + + + + + + +Basic plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Basic plots

+
+ +
+
+ Basic plots for visualizing datasets +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

get_gender_indices

+
+
 get_gender_indices (df, gender='male', gender_col='sex')
+
+

*Returns the indices of the requested gender from a specified column in a pandas DataFrame.

+

Parameters: - df: pandas DataFrame. - gender: str, ‘male’ or ‘female’, the gender to filter by. - gender_col: str, the name of the column containing gender information.

+

Returns: - indices: Index object with the indices of the rows matching the requested gender.*

+
+

source

+
+
+

data_histplot

+
+
 data_histplot (data:pandas.core.frame.DataFrame, col:str,
+                feature_str:Optional[str]=None, gender_col:str='sex',
+                plot_both_genders:bool=True, ax=None)
+
+

*Plots a histogram from a DataFrame for a specific column.

+

Args: data (pd.DataFrame): The DataFrame containing the data to plot. col (str): The name of the column to plot. feature_str (Optional[str], optional): The name of the feature to plot. Defaults to None. gender_col (str, optional): The name of the column containing gender information. Defaults to “sex”. plot_both_genders (bool, optional): Whether to plot both genders or just one. Defaults to True. ax ([type], optional): The axis to plot on. Defaults to None.*

+
+
# Generate synthetic data
+data = generate_synthetic_data(n=1000)
+
+
+
data_histplot(data=data, col="val1", plot_both_genders=False)
+
+
+
+

+
+
+
+
+
+
data_histplot(data=data, col="val1")
+
+
+
+

+
+
+
+
+
+

source

+
+
+

data_ecdfplot

+
+
 data_ecdfplot (data:pandas.core.frame.DataFrame, col:str,
+                feature_str:Optional[str]=None, gender_col:str='sex',
+                plot_both_genders:bool=True, ax=None)
+
+

*Plots an empirical cumulative distribution function (ECDF) from a DataFrame for a specific column.

+

Args: data (pd.DataFrame): The DataFrame containing the data to plot. col (str): The name of the column to plot. feature_str (Optional[str], optional): The name of the feature to plot. Defaults to None. gender_col (str, optional): The name of the column containing gender information. Defaults to “sex”. plot_both_genders (bool, optional): Whether to plot both genders or just one. Defaults to True. ax ([type], optional): The axis to plot on. Defaults to None.*

+
+
data_ecdfplot(data=data, col="val1", plot_both_genders=False)
+
+
+
+

+
+
+
+
+
+
data_ecdfplot(data=data, col="val1")
+
+
+
+

+
+
+
+
+
+

source

+
+
+

hist_ecdf_plots

+
+
 hist_ecdf_plots (data:pandas.core.frame.DataFrame, col:str,
+                  feature_str:Optional[str]=None, gender_col:str='sex',
+                  plot_both_genders:bool=True)
+
+

*Plots histograms and empirical cumulative distribution functions (ECDFs) from a DataFrame for a specific column.

+

Args: data: The input DataFrame containing the data to plot. col: The column name to plot. feature_str: The title of the plot. If not provided, the column name will be used. gender_col: The column name indicating sex (default is ‘sex’ - female:0; male:1). plot_both_genders (bool, optional): Whether to plot both genders or just one. Defaults to True.

+

Returns: None*

+
+
hist_ecdf_plots(data=data, col="val1")
+
+
+
+

+
+
+
+
+
+

source

+
+
+

plot_stats

+
+
 plot_stats (data:pandas.core.frame.DataFrame, col:str,
+             ax:matplotlib.axes._axes.Axes, color:str,
+             x_position:float=0.3, label:Optional[str]='All')
+
+

*Adds a text box to an axis object with summary statistics for a given column in a pandas DataFrame.

+

Args: data (pd.DataFrame): The pandas DataFrame containing the data to calculate statistics for. col (str): The name of the column to calculate statistics for. ax (plt.Axes): The axis object to add the text box to. color (str): The color of the text box. x_position (float, optional): The x position of the text box. Defaults to 0.3. label (Optional[str], optional): The label to display in the text box. Defaults to “All”.*

+
+

source

+
+
+

plot_hist_stats

+
+
 plot_hist_stats (data:pandas.core.frame.DataFrame, col:str,
+                  feature_str:Optional[str]=None, gender_col:str='sex',
+                  plot_both_genders:bool=True)
+
+

*Plots a histogram of a given column in a pandas DataFrame and adds summary statistics to the plot.

+

Args: data (pd.DataFrame): The pandas DataFrame containing the data to plot. col (str): The name of the column to plot. feature_str (Optional[str], optional): A string describing the feature being plotted. Defaults to None. gender_col (str, optional): The name of the column containing gender information. Defaults to “sex”. plot_both_genders (bool, optional): Whether to plot statistics separately for males and females. Defaults to True.*

+
+
# Generate synthetic data
+data = generate_synthetic_data(n=1000)
+
+
+
plot_hist_stats(data, "val1", plot_both_genders=False)
+
+
+
+

+
+
+
+
+
+
plot_hist_stats(data, "val1")
+
+
+
+

+
+
+
+
+
+

source

+
+
+

plot_data_collection

+
+
 plot_data_collection (data:pandas.core.frame.DataFrame,
+                       date_col:str='collection_date',
+                       feature_str:Optional[str]=None,
+                       ax:Optional[matplotlib.axes._axes.Axes]=None)
+
+

*Plots a histogram of the specified column in a pandas DataFrame and excludes the last point from the plot.

+

Args: data (pd.DataFrame): The pandas DataFrame containing the data to plot. date_col (str, optional): The name of the column containing the dates. Defaults to “collection_date”. feature_str (Optional[str], optional): The name of the feature to plot. If None, the name of the date column will be used. Defaults to None. ax (Optional[plt.Axes], optional): The axis object to plot on. If None, a new figure and axis will be created. Defaults to None.*

+
+
plot_data_collection(data, date_col="date_of_research_stage", feature_str="val1")
+
+
+
+

+
+
+
+
+
+

source

+
+
+

show_fundus

+
+
 show_fundus (fname:str)
+
+
+

source

+
+
+

plot_horizontal_count_bars

+
+
 plot_horizontal_count_bars (data, column_name, hue=None, n=20)
+
+

*Function to plot horizontal bar charts with counts.

+

Parameters: - data (pd.DataFrame): DataFrame containing the data - y (str): Column name for the y-axis - hue (str, optional): Column name for the hue (default is None) - n (int, optional): Number of top categories to display (default is None, showing all)

+

Returns: - ax (Axes object): The plot*

+
+
# Generate categorical synthetic data
+categorical_data = generate_categorical_synthetic_data(n=1000)
+
+
+
plot_horizontal_count_bars(categorical_data, 'val1', hue='val2', n=3)
+
+
+
+

+
+
+
+
+
+
plot_horizontal_count_bars(categorical_data, 'val1', hue=None)
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/blandaltman_plots.html b/blandaltman_plots.html new file mode 100644 index 0000000..7afcfdf --- /dev/null +++ b/blandaltman_plots.html @@ -0,0 +1,774 @@ + + + + + + + + + +Bland-Altman plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Bland-Altman plots

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

bland_altman_triple_plot

+
+
 bland_altman_triple_plot (data:pandas.core.frame.DataFrame, m1_col:str,
+                           m2_col:str, feature_str:str='',
+                           scale:str='linear')
+
+

*Generates a triple plot consisting of a scatter correlation plot, Bland-Altman plot, and a percentage Bland-Altman plot.

+

Args: data (pd.DataFrame): A pandas DataFrame containing the data. m1_col (str): The name of the first measurement column in the DataFrame. m2_col (str): The name of the second measurement column in the DataFrame. feature_str (str, optional): A string to include in the title of the plots. Defaults to ““. scale (str, optional): The scale of the axes. Defaults to”linear”.

+

Returns: None*

+
+
data = generate_synthetic_data(n=1000)
+
+bland_altman_triple_plot(data=data, m1_col="val1",m2_col="val2")
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/bulk_data_loader.html b/bulk_data_loader.html new file mode 100644 index 0000000..a8ae323 --- /dev/null +++ b/bulk_data_loader.html @@ -0,0 +1,772 @@ + + + + + + + + + + +Bulk Data Loader – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Bulk Data Loader

+
+ +
+
+ Class for managing all loading functions and utilitis for bulk data +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

get_function_for_field_type

+
+
 get_function_for_field_type (field_type)
+
+
+

source

+
+
+

load_image

+
+
 load_image (fname:str)
+
+

Display a fundus image from an input file path. Args: fname (str): The file path to the fundus image.

+
+

source

+
+
+

parse_gtf_attributes

+
+
 parse_gtf_attributes (attributes_str:str)
+
+

*Parse attribute column and return a dictionary.

+

Parameters: attributes_str (str): The attributes as a semicolon-separated string.

+

Returns: dict: A dictionary where the keys are the attribute names and the values are the attribute values.*

+
+

source

+
+
+

read_gtf

+
+
 read_gtf (filename:str)
+
+

*Read a GTF file with error handling for lines with too many fields.

+

Parameters: filename (str): The path to the GTF file.

+

Returns: pd.DataFrame: A DataFrame where each row corresponds to a line in the GTF file and each column corresponds to a field or attribute.*

+
+

source

+
+
+

show_fundus

+
+
 show_fundus (fname:str)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/cgm_plots.html b/cgm_plots.html new file mode 100644 index 0000000..9f03882 --- /dev/null +++ b/cgm_plots.html @@ -0,0 +1,947 @@ + + + + + + + + + +CGM plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

CGM plots

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

CGMPlot

+
+
 CGMPlot (cgm_df:pandas.core.frame.DataFrame,
+          diet_df:Optional[pandas.core.frame.DataFrame]=None,
+          cgm_date_col:str='collection_timestamp', gluc_col:str='glucose',
+          diet_date_col:str='collection_timestamp',
+          diet_text_col:str='shortname_eng',
+          ax:Optional[matplotlib.axes._axes.Axes]=None, smooth:bool=False,
+          sleep_tuples:Optional[List[Tuple[pandas._libs.tslibs.timestamps.
+          Timestamp,pandas._libs.tslibs.timestamps.Timestamp]]]=None)
+
+

*Initialize a CGMPlot object.

+

Args: cgm_df (pd.DataFrame): DataFrame containing the glucose measurements. diet_df (Optional[pd.DataFrame], optional): DataFrame containing the diet data. Defaults to None. cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to “Date”. gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to “glucose”. diet_date_col (str, optional): Name of the date column in diet_df. Defaults to “Date”. diet_text_col (str, optional): Name of the text column in diet_df. Defaults to “shortname_eng”. ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None. smooth (bool, optional): Apply smoothing to the glucose curve. Defaults to False. sleep_tuples (Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]], optional): List of sleep start and end times. Defaults to None.*

+
+
cgm_df= pd.read_parquet("./examples/cgm/cgm.parquet")
+cgm_df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
glucose
participant_idcollection_timestampconnection_id
02020-05-25 10:48:00+03:001000001111.6
2020-05-25 11:03:00+03:00100000179.2
2020-05-25 11:18:00+03:00100000184.6
2020-05-25 11:33:00+03:001000001106.2
2020-05-25 11:48:00+03:001000001102.6
+ +
+
+
+
+
diet_df = pd.read_parquet("./examples/diet_logging/diet_logging.parquet")
+diet_df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
collection_timestampfood_idweightshortname_eng
participant_idcohort
010k2020-05-25 08:15:00+03:00100729440.0Coffee
10k2020-05-25 08:15:00+03:00100741787.0Yellow Cheese
10k2020-05-25 08:15:00+03:0010086246.0Almonds
10k2020-05-25 08:15:00+03:00101164212.0Brazil nuts
10k2020-05-25 10:05:00+03:001007118100.0Hummus
+ +
+
+
+
+
start_date = pd.to_datetime('2020-05-25', utc=True).tz_convert('Asia/Jerusalem')
+end_date = pd.to_datetime('2020-05-27',utc=True).tz_convert('Asia/Jerusalem')
+
+
+sample_days = cgm_df[(cgm_df.index.get_level_values('collection_timestamp') >= start_date) \
+                     & (cgm_df.index.get_level_values('collection_timestamp') <= end_date)]
+
+
+
cgmplt = CGMPlot(cgm_df=sample_days.reset_index(),
+                 cgm_date_col="collection_timestamp",
+                 gluc_col="glucose",
+                 diet_df=diet_df.iloc[9:],
+                 diet_date_col="collection_timestamp",
+                 smooth=True)
+cgmplt.plot()
+
+
+
+

+
+
+
+
+
+
+

AGP

+
+

source

+
+

AGP

+
+
 AGP (cgm_df:pandas.core.frame.DataFrame,
+      cgm_date_col:str='collection_timestamp', gluc_col:str='glucose',
+      ax:Optional[matplotlib.axes._axes.Axes]=None)
+
+

*Initialize an AGP object.

+

Args: cgm_df (pd.DataFrame): DataFrame containing the glucose measurements. cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to “collection_timestamp”. gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to “glucose”. ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None.*

+
+
agp = AGP(cgm_df=cgm_df.reset_index(), cgm_date_col="collection_timestamp", gluc_col="glucose")
+agp.plot()
+
+
+
+

+
+
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/cohort_selector.html b/cohort_selector.html new file mode 100644 index 0000000..0f7f872 --- /dev/null +++ b/cohort_selector.html @@ -0,0 +1,966 @@ + + + + + + + + + + +Cohort selector – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Cohort selector

+
+ +
+
+ Select participants based on any field. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

CohortSelector

+
+
 CohortSelector (base_path:str='nbs/examples/', cohort:str=None,
+                 errors:str='warn', **kwargs)
+
+

*Class for selecting a subset of a cohort’s data based on a query.

+

Args:

+
base_path (str, optional): Base path of the datasets. Defaults to DATASETS_PATH.
+cohort (str, optional): Name of the cohort. Defaults to COHORT.
+errors (str, optional): Error action. Defaults to ERROR_ACTION.
+**kwargs: Additional keyword arguments.
+

Attributes:

+
cohort (str): Name of the cohort.
+base_path (str): Base path of the datasets.
+errors (str): Error action.
+kwargs: Additional keyword arguments.
+ml (MetaLoader): MetaLoader object for loading metadata and data.*
+
+
cs = CohortSelector()
+
+

You may use the CohortSelector to select participants based on any fields. The selector will return a DataFrame with the selected sub-cohort, along with the fields that were used in the query.

+

For example, the following query selects participants who have moderate obstructive sleep apnea (AHI > 15) based on recordings of at least 4 hours of sleep.

+
+
ml = MetaLoader()
+
+
+
cs.select('15 < ahi < 20 & total_sleep_time > 4*3600')
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ahitotal_sleep_time
participant_idcohortresearch_stagearray_index
910k02_00_visit218.3923748.0
1510k00_00_visit019.5419230.0
3010k00_00_visit018.5225111.0
4210k00_00_visit117.8424966.0
4910k02_00_visit017.5821980.0
..................
90210k02_00_visit219.7824162.0
91410k02_00_visit217.5426479.0
93610k00_00_visit117.4320865.0
94110k00_00_visit017.8217606.0
96510k00_00_visit215.3824390.0
+ +

77 rows × 2 columns

+
+
+
+

You may also use the selector to filter on dates. Here we filter on dates of image collection in the fundus imaging dataset.

+

In addition, we may load additional fields that are not part of the query.

+
+
cs.select('fundus/collection_date > "2022-01-01"', add_fields='fundus_image_left')
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fundus_image_leftcollection_date
participant_idcohortresearch_stagearray_index
010k00_00_visit0/path/to/file2022-11-16
110k00_00_visit0/path/to/file2022-06-30
310k00_00_visit0/path/to/file2022-04-26
+ +
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/config.html b/config.html new file mode 100644 index 0000000..f2daee2 --- /dev/null +++ b/config.html @@ -0,0 +1,1038 @@ + + + + + + + + + + +Config – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Config

+
+ +
+
+ Configuration parameters and synthetic datasets creation +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

get_dictionary_properties_file_path

+
+
 get_dictionary_properties_file_path ()
+
+

*Get the file path for dictionary properties - TODO: move to config file or DB. At this point only includes field_type properties.

+

Args:

+

Returns: str: the path to the file*

+
+

source

+
+
+

get_data_coding_file_path

+
+
 get_data_coding_file_path ()
+
+

*Get the file path for dictionary properties - TODO: move to config file or DB. At this point only includes field_type properties.

+

Args:

+

Returns: str: the path to the file*

+
+

source

+
+
+

generate_synthetic_data

+
+
 generate_synthetic_data (n:int=1000)
+
+

*Generates a sample DataFrame containing age, gender, and value data.

+

Args: n: The number of rows in the generated DataFrame.

+

Returns: A pandas DataFrame with columns ‘age’, ‘gender’, and ‘val’.*

+
+

source

+
+
+

generate_synthetic_data_like

+
+
 generate_synthetic_data_like (df:pandas.core.frame.DataFrame, n:int=1000,
+                               random_seed:int=42)
+
+

*Generate a sample DataFrame containing the same columns as df, but with random data.

+

Args:

+
df: The DataFrame whose columns should be used.
+n: The number of rows in the generated DataFrame.
+

Returns: A pandas DataFrame with the same columns as df.*

+
+

source

+
+
+

generate_categorical_synthetic_data

+
+
 generate_categorical_synthetic_data (n:int=1000)
+
+

*Generates a sample DataFrame containing age, gender, and categorical value data.

+

Args: n: The number of rows in the generated DataFrame.

+

Returns: A pandas DataFrame with columns ‘age’, ‘gender’, and ‘val1’.*

+
+
data = generate_synthetic_data()
+data.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
date_of_research_stageage_at_research_stagesexval1val2
participant_id
02020-11-1654.4228281103.72147848.846734
12021-06-0865.2329480129.51228054.583974
22020-08-1642.4138631114.87885152.193946
32021-04-1357.8726181113.65311751.826225
42023-07-1770.6402331129.66993756.631272
+ +
+
+
+
+
generate_synthetic_data_like(data.head(), n=5)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
date_of_research_stageage_at_research_stagesexval1val2
participant_id
02020-08-1657.8726181113.65311748.846734
12021-04-1365.2329481103.72147856.631272
22023-07-1742.4138631129.66993754.583974
32021-06-0854.4228280114.87885152.193946
42020-11-1670.6402331129.51228051.826225
+ +
+
+
+
+
data = generate_categorical_synthetic_data()
+data.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
date_of_research_stageage_at_research_stagesexval1val2
participant_id
02021-09-2469.7885551EA
12021-03-0236.2899471CB
22022-06-1561.5019701CC
32020-07-2346.2992620BA
42021-03-0370.1270551BC
+ +
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/date_plots.html b/date_plots.html new file mode 100644 index 0000000..a8efcb9 --- /dev/null +++ b/date_plots.html @@ -0,0 +1,851 @@ + + + + + + + + + +Dates plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Dates plots

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

dates_dist_plot

+
+
 dates_dist_plot (df:pandas.core.frame.DataFrame, col:str,
+                  sampling_period:str='W-MON', ax:Union[ForwardRef('Extens
+                  ionArray'),numpy.ndarray,ForwardRef('Index'),ForwardRef(
+                  'Series'),list,range,NoneType]=None,
+                  date_col:str='collection_date',
+                  ylim:Optional[Tuple[float,float]]=None,
+                  quantiles:Optional[List[Tuple[float,str]]]=None)
+
+

*Creates a scatter plot of data points and their statistics based on a specified sampling period.

+

Args: df (pd.DataFrame): The input DataFrame containing the data. col (str): The column name in the DataFrame to plot. sampling_period (str, optional): The frequency to resample the data. Defaults to ‘W-MON’. ax (Optional[Axes], optional): A matplotlib axes object to plot on. Defaults to None. date_col (str, optional): The name of the date column in the DataFrame. Defaults to ‘collection_date’. ylim (Optional[Tuple[float, float]], optional): A tuple defining the y-axis limits. Defaults to None. quantiles (Optional[List[Tuple[float, str]]], optional): A list of tuples containing quantiles and their labels. Defaults to [(0.1, “10%”), (0.9, “90%”)].*

+
+
data = generate_synthetic_data()
+data.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
date_of_research_stageage_at_research_stagesexval1val2
participant_id
02022-12-0157.7770731150.21621256.936487
12020-07-2953.7707241117.60387547.152785
22020-09-3051.326393197.92895041.250308
32022-05-0661.2172760105.16993941.422605
42021-06-2945.835170054.73554026.292285
+ +
+
+
+
+
dates_dist_plot(data, col="val1", date_col="date_of_research_stage")
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/diet_plots.html b/diet_plots.html new file mode 100644 index 0000000..8a7cc3a --- /dev/null +++ b/diet_plots.html @@ -0,0 +1,862 @@ + + + + + + + + + +Diet logging plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Diet logging plots

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

draw_pie_chart

+
+
 draw_pie_chart (ax:matplotlib.axes._axes.Axes, x:float, y:float,
+                 data:List[float], size:float, palette:str='muted',
+                 alpha:float=0.7)
+
+

*Draw a pie chart as an inset (in absolute figure coordinates) within the given axes at the specified data coordinates. What this solves is the issue of y-axis and x-axis scaling being different, which distorts the pie chart when drawn directly on the axes.

+

Args: ax (plt.Axes): The axis on which to draw the pie chart. x (float): The x-coordinate in data coordinates where the pie chart’s center will be placed. y (float): The y-coordinate in data coordinates where the pie chart’s center will be placed. data (List[float]): The data values to be represented in the pie chart. size (float): The size (radius) of the pie chart in axes-relative coordinates. palette (str): The color palette to use for the pie slices.

+

Returns: List[plt.Patch]: A list of wedge objects representing the pie chart slices.*

+
+

source

+
+
+

extract_units

+
+
 extract_units (column_names:List[str])
+
+
+

source

+
+
+

prepare_meals

+
+
 prepare_meals (diet_log:pandas.core.frame.DataFrame,
+                participant_id:int=None, array_index:int=None,
+                time_range:Tuple[str,str]=None,
+                label:str='short_food_name', return_meals:bool=True,
+                return_summary:bool=False, agg_units:dict={'kcal': 'sum',
+                'g': 'sum', 'mg': 'sum', 'unknown': 'first'},
+                x_col:str='collection_timestamp')
+
+

*Prepare the diet log data for plotting meals and/or daily summaries.

+

Args: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. label (str): The name of the column in diet_log representing the labels for each meal. Default is ‘short_food_name’. return_meals (bool): If True, includes individual meals in the plot. Default is True. return_summary (bool): If True, includes a daily summary in the plot. Default is False. agg_units (dict): A dictionary mapping nutrient units to aggregation functions. x_col (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’.

+

Returns: pd.DataFrame: A dataframe containing the prepared data for plotting.*

+
+

source

+
+
+

plot_nutrient_lollipop

+
+
 plot_nutrient_lollipop (diet_log:pandas.core.frame.DataFrame,
+                         x:str='collection_timestamp',
+                         y:str='calories_kcal', size:str='total_g',
+                         label:str='short_food_name',
+                         participant_id:int=None, array_index:int=None,
+                         time_range:Tuple[str,str]=None, meals:bool=True,
+                         summary:bool=False, legend:bool=True,
+                         size_scale:float=5, second_y:bool=False,
+                         palette:str='muted', alpha:float=0.7,
+                         ax:matplotlib.axes._axes.Axes=None,
+                         figsize:Tuple[float,float]=(12, 3))
+
+

*Plot a lollipop chart with pie charts representing nutrient composition for each meal.

+

NOTE: The y-axis is scaled to match the units of the x-axis, to avoid distortion of the pie charts. Due to scaling, if you intend to change xlim after plotting, you must also provide date_range. Use the second_y of g.plot() option to plot it with other y-axis data.

+

Args: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. x (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’. y (str): The name of the column in diet_log representing the y-axis variable, such as calories. Default is ‘calories_kcal’. size (str): The name of the column in diet_log representing the size of the pie charts. Default is ‘total_g’. label (str): The name of the column in diet_log representing the labels for each meal. Default is ‘short_food_name’. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. meals (bool): If True, includes individual meals in the plot. Default is True. summary (bool): If True, includes a daily summary in the plot. Default is False. legend (bool): If True, includes a legend in the plot. Default is True. size_scale (float): The scaling factor for the size of the pie charts. Default is 5. second_y (bool): If True, plot will be done on a secondary y-axis in the plot. Default is False. palette (str): The color palette to use for the pie slices. Default is DEFAULT_PALETTTE. alpha (float): The transparency of the pie slices. Default is 0.7. ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None. figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).

+

Returns: None: The function creates a lollipop plot with pie charts on the specified or newly created axis.*

+
+

source

+
+
+

plot_nutrient_bars

+
+
 plot_nutrient_bars (diet_log:pandas.core.frame.DataFrame,
+                     x:str='collection_timestamp',
+                     label:str='short_food_name', participant_id:int=None,
+                     array_index:int=None, time_range:Tuple[str,str]=None,
+                     meals:bool=True, summary:bool=False,
+                     agg_units:dict={'kcal': 'sum', 'g': 'sum', 'mg':
+                     'sum'}, legend:bool=True,
+                     bar_width=numpy.timedelta64(15,'m'),
+                     palette:str='muted', alpha:float=0.7,
+                     ax:matplotlib.axes._axes.Axes=None,
+                     figsize:Tuple[float,float]=(14, 3))
+
+

*Plot a stacked bar chart representing nutrient intake for each meal over time.

+

Args: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. x (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’. label (str): The name of the column in diet_log representing the labels for each meal. Default is ‘short_food_name’. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. meals (bool): If True, includes individual meals in the plot. Default is True. summary (bool): If True, includes a daily summary in the plot. Default is False. agg_units (dict): A dictionary mapping nutrient units to aggregation functions. Only nutrients with units in this dictionary are plotted. legend (bool): If True, includes a legend in the plot. Default is True. bar_width (np.timedelta64): The width of the bars representing each meal on the time axis. Default is 15 minutes. palette (str): The color palette to use for the stacked bars. alpha (float): The transparency of the stacked bars. Default is 0.7. ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the bar chart. If None, a new axis is created. Default is None. figsize (Tuple[float, float]): The size of the figure to create. Default is (14, 3).

+

Returns: None: The function creates a stacked bar chart on the specified or newly created axis.*

+
+

source

+
+
+

add_size_legend

+
+
 add_size_legend (ax:matplotlib.axes._axes.Axes, sizes:List[int],
+                  size_scale:float, alpha:float, shift:int=0)
+
+

Add a size legend to a plot_meals_hbars plot using broken_barh.

+
+

source

+
+
+

plot_meals_hbars

+
+
 plot_meals_hbars (diet_log:pandas.core.frame.DataFrame,
+                   x:str='collection_timestamp',
+                   y:str='short_food_category', size:str='weight_g',
+                   hue:str='short_food_category', participant_id:int=None,
+                   array_index:int=None, time_range:Tuple[str,str]=None,
+                   y_include:List[str]=None, y_exclude:List[str]=None,
+                   rename_categories:dict={'beef, veal, lamb, and other
+                   meat products': 'meat products', 'milk, cream cheese
+                   and yogurts': 'milk products', 'nuts, seeds, and
+                   products': 'nuts and seeds', 'eggs and their products':
+                   'eggs', 'pulses and products': 'pulses', 'fruit juices
+                   and soft drinks': 'juices and soft drinks', 'low
+                   calories and diet drinks': 'low cal. drinks', 'poultry
+                   and its products': 'poultry', 'pasta, grains and side
+                   dishes': 'grains'}, legend:bool=True,
+                   size_legend:List[int]=[100, 200, 500],
+                   size_scale:float=5, palette:str='muted',
+                   alpha:float=0.7, ax:matplotlib.axes._axes.Axes=None,
+                   figsize:Tuple[float,float]=(12, 6))
+
+

*Plot a diet chart with bars representing meals and their size over time.

+

Args: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. x (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’. y (str): The name of the column in diet_log representing the y-axis variable, such as food categories. Default is ‘short_food_category’. size (str): The name of the column in diet_log representing the size of the bars. Default is ‘weight_g’. hue (str): The name of the column in diet_log representing the color of the bars. Default is ‘short_food_category’. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. y_include (List[str]): A list of strings representing the categories to include in the plot. Default is None. y_exclude (List[str]): A list of strings representing the categories to exclude from the plot. Default is None. rename_categories (dict): A dictionary mapping original food categories to shorter names. Default is SHORT_FOOD_CATEGORIES. legend (bool): If True, includes a legend in the plot. Default is True. size_legend (List[int]): A list of integers representing the sizes to include in the size legend. Default is [100, 200, 500]. size_scale (float): The scaling factor for the size of the bars. Default is 5. palette (str): The palette to use for the bars. alpha (float): The transparency of the bars. Default is 0.7. ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None. figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).*

+
+

source

+
+
+

plot_diet_cgm_sleep

+
+
 plot_diet_cgm_sleep (diet:pandas.core.frame.DataFrame=None,
+                      cgm:pandas.core.frame.DataFrame=None,
+                      sleep_events:pandas.core.frame.DataFrame=None,
+                      sleep_channels:pandas.core.frame.DataFrame=None,
+                      cgm_grid:List[int]=[0, 54, 70, 100, 140, 180],
+                      channel_filter:List[str]=['heart_rate', 'actigraph',
+                      'spo2'], participant_id=None, array_index=None,
+                      time_range:Tuple[str,str]=None, figsize=(14, 10),
+                      nutrient_kws:dict={}, meals_kws:dict={},
+                      cgm_kws:dict={}, events_kws:dict={},
+                      channels_kws:dict={})
+
+

*Plot diet, CGM and sleep data together.

+

Arg: diet (pd.DataFrame): Diet logging data. Set to None to remove from figure. cgm (pd.DataFrame): CGM data. Set to None to remove from figure. sleep_events (pd.DataFrame): Sleep events data. Set to None to remove from figure. sleep_channels (pd.DataFrame): Sleep channels data. Set to None to remove from figure. cgm_grid (List[int]): CGM grid lines. Default: [0, 54, 70, 100, 140, 180]. channel_filter (List[str]): Which sleep channels to include in the plot. Default: [‘heart_rate’, ‘actigraph’, ‘spo2’]. participant_id (int): Participant ID. array_index (int): Array index. time_range (Tuple[str, str]): Time range to plot. figsize (Tuple[int, int]): Figure size. nutrient_kws (dict): Keyword arguments for diet nutrients lollipop plot. meals_kws (dict): Keyword arguments for diet meals plot. cgm_kws (dict): Keyword arguments for CGM plot. events_kws (dict): Keyword arguments for sleep events plot. channels_kws (dict): Keyword arguments for sleep channels plot.

+

Returns: TimeSeriesFigure: Plot.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/ecg_analysis.html b/ecg_analysis.html new file mode 100644 index 0000000..e209399 --- /dev/null +++ b/ecg_analysis.html @@ -0,0 +1,732 @@ + + + + + + + + + + +ECG analysis – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

ECG analysis

+
+ +
+
+ ECG analysis +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

vis_ecg

+
+
 vis_ecg (values_df:pandas.core.frame.DataFrame)
+
+

*Visualize ECG data for 12 leads.

+

Args: values_df (pd.DataFrame): A DataFrame containing ECG data with 12 columns, one for each lead.

+

Returns: None: Displays a 3x4 grid of ECG plots for the 12 leads.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..3711365 --- /dev/null +++ b/index.html @@ -0,0 +1,810 @@ + + + + + + + + + + +pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

pheno-utils

+
+ +
+
+ Pheno Data Science utilities +
+
+ + +
+ + + + +
+ + + +
+ + + +

pheno-utils is a dynamic Python package developed by Pheno.AI, for handling our medical datasets. It simplifies data loading, enables effective merging, and offers intuitive visualization tools.

+
+

Install

+
pip install pheno_utils
+
+
+

How to use

+

Examples:

+
+
data = generate_synthetic_data(n=1000)
+hist_ecdf_plots(data=data, col="val1")
+
+
+
+

+
+
+
+
+
+
age_refplots = GenderAgeRefPlot(data, "val1")
+age_refplots.plot()
+
+
+
+

+
+
+
+
+
+
+

Setting Up pheno-utils

+

To use pheno-utils, you must have a config.json file. This file should be set up according to your filesystem and placed in the ~/.pheno/ directory.

+
+

If You’re Working on TRE

+

For those working in the Trusted Research Environment (TRE), you don’t need to worry about the config.json file. It will be automatically generated for you!

+
+
+

If You’re Working with Phenos’ S3 Bucket

+

If you’re working with Phenos’ S3 bucket, you’ll need to manually create the config.json file. You can do this by running the following Python script:

+
python config_setup/create_default_config.py -d s3://datasets_bucket_name
+
+
+

If you are working on local file system

+

If you are working on local file system, you’ll need to manually create the config.json file and locating it under ~/.pheno/ directory. Please use the config_setup/example_config.json as a template for your config.json file.

+ + +
+
+ +
+ +
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+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Metadata loader

+
+ +
+
+ Load dataset dictionaries and access all datasets and fields in a flexible manner. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

MetaLoader

+
+
 MetaLoader (base_path:str='nbs/examples/', cohort:str=None,
+             flexible_field_search:bool=False, errors:str='warn',
+             **kwargs)
+
+

*Class to load multiple dictionaries and allows to easily access the relevant fields.

+

Args:

+
base_path (str, optional): The base path where the data is stored. Defaults to DATASETS_PATH.
+cohort (str, optional): The name of the cohort within the dataset. Defaults to COHORT.
+flexible_field_search (bool, optional): Whether to allow regex field search. Defaults to False.
+errors (str, optional): Whether to raise an error or issue a warning if missing data is encountered.
+    Possible values are 'raise', 'warn' and 'ignore'. Defaults to 'raise'.
+**kwargs: Additional keyword arguments to pass to a DataLoader class.
+

Attributes:

+
dicts (pd.DataFrame): A dictionary of data dictionaries (dataframes) of all availbale datasets in the base_path.
+fields (list): A list of all fields.
+cohort (str): The name of the cohort being used.
+base_path (str): The base path where the data is stored.
+flexible_field_search (bool): Whether to allow regex field search.
+errors (str): Whether to raise an error or issue a warning if missing data is encountered.
+kwargs (dict): Additional keyword arguments to pass to a DataLoader class.*
+
+
# os.path.join(DATASETS_PATH, '*')
+os.listdir(DATASETS_PATH)
+
+
['cgm', 'diet_logging', 'events', 'fundus', 'sleep', 'metadata']
+
+
+

The MetaLoader can be used to query all availbale fields throughout all datasets. In the following example, 3 datasets are available.

+
+
ml = MetaLoader()
+ml
+
+
MetaLoader for: examples/*
+with 83 fields
+4 datasets:
+['cgm'
+ 'diet_logging'
+ 'fundus'
+ 'sleep']
+
+
+

The object contains only the data dictionaries (metadata) of these datasets, where the columns correspond to columns in the data tables of the dataset (e.g., fundus).

+
+
ml.dicts['fundus']
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tabular_field_namefundus_image_leftfundus_image_rightcollection_datefractal_dimension_leftfractal_dimension_right
datasetfundusfundusfundusfundusfundus
field_stringFundus image (left)Fundus image (right)Collection date (YYYY-MM-DD)Fractal dimension (left)Fractal dimension (right)
description_stringFundus image (left)Fundus image (right)Collection date (YYYY-MM-DD)Fractal dimension (left)Fractal dimension (right)
parent_dataframe<NA><NA><NA><NA><NA>
relative_locationfundus/fundus.parquetfundus/fundus.parquetfundus/fundus.parquetfundus/fundus.parquetfundus/fundus.parquet
value_typeTextTextDateContinuousContinuous
units<NA><NA>TimeTimeTime
sampling_rate<NA><NA><NA><NA><NA>
field_typeImage file (individual)Image file (individual)DataDataData
arraySingleSingleSingleSingleSingle
cohorts10K10K10K10K10K
data_typeimageimagetabulartabulartabular
debut2021-02-172021-02-172021-02-172021-02-172021-02-17
pandas_dtypestringstringdatetime64[ns]floatfloat
+ +
+
+
+

You can query fields from multiple datasets directly.

+
+
ml[['glucose', 'fundus_image_left', 'fundus/collection_date']]
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tabular_field_namecgm/glucosefundus/fundus_image_leftfundus/collection_date
datasetcgmfundusfundus
field_stringGlucoseFundus image (left)Collection date (YYYY-MM-DD)
description_stringcgm temporal glucose valuesFundus image (left)Collection date (YYYY-MM-DD)
parent_dataframe<NA><NA><NA>
relative_locationcgm/cgm.parquetfundus/fundus.parquetfundus/fundus.parquet
value_typeSeries data, continousTextDate
unitsmg/dl<NA>Time
sampling_rate15min<NA><NA>
field_typeContinuousImage file (individual)Data
arraySingleSingleSingle
cohorts10K10K10K
data_typetime seriesimagetabular
debut2018-12-272021-02-172021-02-17
pandas_dtypefloatstringdatetime64[ns]
+ +
+
+
+

Note that in the example above, for collection_date (that is common to all datasets) the dataset is specified in the prefix fundus/. Therefore, the loader returns the field from the fundus imaging dataset. Omitting this prefix will return all collection_date fields in the Human Phenotype Project.

+

You can then use the MetaLoader to load the actual data of fields from multiple datasets. Here we load glucose from the CGM dataset, and fundus_image_left from the fundus dataset.

+
+
df1 = ml.load(['glucose'])
+df1
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
glucose
participant_idcollection_timestampconnection_id
02020-05-25 10:48:00+03:001000001111.6
2020-05-25 11:03:00+03:00100000179.2
2020-05-25 11:18:00+03:00100000184.6
2020-05-25 11:33:00+03:001000001106.2
2020-05-25 11:48:00+03:001000001102.6
.........
2020-05-30 15:03:00+03:00100000190.0
2020-05-30 15:18:00+03:00100000199.0
2020-05-30 15:33:00+03:001000001106.2
2020-05-30 15:48:00+03:00100000199.0
2020-05-30 16:03:00+03:00100000184.6
+ +

502 rows × 1 columns

+
+
+
+
+
df2 = ml.load(['fundus_image_left'])
+df2
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fundus_image_left
participant_idcohortresearch_stagearray_index
010k00_00_visit0/path/to/file
110k00_00_visit0/path/to/file
210k00_00_visit0/path/to/file
310k00_00_visit0/path/to/file
410k00_00_visit0/path/to/file
+ +
+
+
+
+
df3 = ml.load(['glucose' ,'fundus_image_left'])
+df3
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
glucosefundus_image_left
participant_idcollection_timestampconnection_idcohortresearch_stage
02020-05-25 10:48:00+03:00100000110k00_00_visit111.6/path/to/file
2020-05-25 11:03:00+03:00100000110k00_00_visit79.2/path/to/file
2020-05-25 11:18:00+03:00100000110k00_00_visit84.6/path/to/file
2020-05-25 11:33:00+03:00100000110k00_00_visit106.2/path/to/file
2020-05-25 11:48:00+03:00100000110k00_00_visit102.6/path/to/file
..................
2020-05-30 16:03:00+03:00100000110k00_00_visit84.6/path/to/file
1NaTNaN10k00_00_visitNaN/path/to/file
2NaTNaN10k00_00_visitNaN/path/to/file
3NaTNaN10k00_00_visitNaN/path/to/file
4NaTNaN10k00_00_visitNaN/path/to/file
+ +

506 rows × 2 columns

+
+
+
+
+
ml.load(['glucose' ,'fundus_image_left', 'fundus/collection_date']).head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
glucosefundus_image_leftcollection_date
participant_idcollection_timestampconnection_idcohortresearch_stage
02020-05-25 10:48:00+03:00100000110k00_00_visit111.6/path/to/file2022-11-16
2020-05-25 11:03:00+03:00100000110k00_00_visit79.2/path/to/file2022-11-16
2020-05-25 11:18:00+03:00100000110k00_00_visit84.6/path/to/file2022-11-16
2020-05-25 11:33:00+03:00100000110k00_00_visit106.2/path/to/file2022-11-16
2020-05-25 11:48:00+03:00100000110k00_00_visit102.6/path/to/file2022-11-16
+ +
+
+
+

You may use more flexible search queries using regex and various properties of the fields. Both the get() method and load() method support the same syntax.

+
    +
  1. Example: get all bulk data fields.
    2. +
+
+
ml.get('Time series', flexible=True, prop='field_type')
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tabular_field_namecgm/cgm_filename
datasetcgm
field_stringCGM timeseries
description_stringName of the file containing the participants' ...
parent_dataframe<NA>
relative_locationcgm/cgm.parquet
value_typeText
units<NA>
sampling_rate<NA>
field_typeTime series file (individual)
arraySingle
cohorts10K
data_typetext
debut2018-12-27
pandas_dtypestring
+ +
+
+
+
+
ml.get('Time series file (individual)', prop='field_type')
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tabular_field_namecgm/cgm_filename
datasetcgm
field_stringCGM timeseries
description_stringName of the file containing the participants' ...
parent_dataframe<NA>
relative_locationcgm/cgm.parquet
value_typeText
units<NA>
sampling_rate<NA>
field_typeTime series file (individual)
arraySingle
cohorts10K
data_typetext
debut2018-12-27
pandas_dtypestring
+ +
+
+
+
+
ml.load('Image file (individual)', prop='field_type')
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fundus_image_leftfundus_image_right
participant_idcohortresearch_stagearray_index
010k00_00_visit0/path/to/file/path/to/file
110k00_00_visit0/path/to/file/path/to/file
210k00_00_visit0/path/to/file/path/to/file
310k00_00_visit0/path/to/file/path/to/file
410k00_00_visit0/path/to/file/path/to/file
+ +
+
+
+
    +
  1. Example: get all fields that include “mg” in their units
    2. +
+
+
ml.get('mg', flexible=True, prop='units')
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tabular_field_namecgm/1st qu_cgm/3rd qu_cgm/auccgm/ea1ccgm/glucosecgm/gmicgm/iqrcgm/madcgm/magcgm/mage...cgm/moddcgm/rangecgm/sdcgm/sdbcgm/sdbdmcgm/sddmcgm/sdhhmmcgm/sdwcgm/sdwshdiet_logging/sodium_mg
datasetcgmcgmcgmcgmcgmcgmcgmcgmcgmcgm...cgmcgmcgmcgmcgmcgmcgmcgmcgmdiet_logging
field_string1st quantile3rd quantileAUCeA1CGlucoseGMIIQRMADMAGMAGE...MODDRangeSDSDbSDbdmSDdmSDhhmmSDwSDwshSodium intake per food logged
description_stringFirst quantile of all glucose values.Third quantile of all glucose values.Hourly average AUC. This measure integrates, t...A linear transformation of the mean glucose va...cgm temporal glucose valuesA linear transformation of the mean glucose va...Interquartile range (IQR), calculated as the d...Median Absolute Deviation (MAD). This is a mea...Mean Absolute Glucose (MAG). This is a measure...Mean Amplitude of Glycemic Excursions (MAGE), ......Mean difference between glucose values obtaine...Difference between the maximum and minimum glu...Standard deviation of all glucose values.SD between days, within time points. Mean valu...SD between days, within time points, corrected...Horizontal SD. SD of the mean glucose values, ...SD between time points. Standard deviation of ...Vertical SD within days. Average value of the ...SD within series. Taking hour-long intervals t...Sodium intake per food logged
parent_dataframe<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
relative_locationcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquet...cgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetcgm/cgm.parquetdiet_logging/diet_logging.parquet
value_typeContinuousContinuousContinuousContinuousSeries data, continousContinuousContinuousContinuousContinuousContinuous...ContinuousContinuousContinuousContinuousContinuousContinuousContinuousContinuousContinuousContinuous
unitsmg/dlmg/dlmg/dl*hmg/dlmg/dlmg/dlmg/dlmg/dlmg/dlmg/dl...mg/dlmg/dlmg/dlmg/dlmg/dlmg/dlmg/dlmg/dlmg/dlmg
sampling_rate<NA><NA><NA><NA>15min<NA><NA><NA><NA><NA>...<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
field_typeDataDataDataDataContinuousDataDataDataDataData...DataDataDataDataDataDataDataDataDataData
arraySingleSingleSingleSingleSingleSingleSingleSingleSingleSingle...SingleSingleSingleSingleSingleSingleSingleSingleSingleSingle
cohorts10K10K10K10K10K10K10K10K10K10K...10K10K10K10K10K10K10K10K10K10K
data_typetabulartabulartabulartabulartime seriestabulartabulartabulartabulartabular...tabulartabulartabulartabulartabulartabulartabulartabulartabularTime Series
debut2018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-27...2018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272018-12-272019-09-01
pandas_dtypefloatfloatfloatfloatfloatfloatfloatfloatfloatfloat...floatfloatfloatfloatfloatfloatfloatfloatfloatfloat
+ +

14 rows × 24 columns

+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/pheno_loader.html b/pheno_loader.html new file mode 100644 index 0000000..9280aa3 --- /dev/null +++ b/pheno_loader.html @@ -0,0 +1,1487 @@ + + + + + + + + + + +Pheno loader – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Pheno loader

+
+ +
+
+ Class for loading datasets on the research platform +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

PhenoLoader

+
+
 PhenoLoader (dataset:str, base_path:str='nbs/examples/', cohort:str=None,
+              age_sex_dataset:str='events', skip_dfs:List[str]=[],
+              unique_index:bool=False, valid_dates:bool=False,
+              valid_stage:bool=False, flexible_field_search:bool=False,
+              squeeze:bool=False, errors:str='warn',
+              read_parquet_kwargs:Dict[str,Any]={},
+              preferred_language:str='english',
+              keep_undefined_research_stage:bool=False,
+              join_non_overlapping:bool=False)
+
+

*Class to load multiple tables from a dataset and allows to easily access their fields.

+

Args:

+
dataset (str): The name of the dataset to load.
+base_path (str, optional): The base path where the data is stored. Defaults to DATASETS_PATH.
+cohort (str, optional): The name of the cohort within the dataset. Defaults to COHORT.
+age_sex_dataset (str, optional): The name of the dataset to use for computing age and sex. Defaults to EVENTS_DATASET.
+skip_dfs (list, optional): A list of tables (or substrings that match to tables) to skip when loading the data. Defaults to [].
+unique_index (bool, optional): Whether to ensure the index of the data is unique. Defaults to False.
+valid_dates (bool, optional): Whether to ensure that all timestamps in the data are valid dates. Defaults to False.
+valid_stage (bool, optional): Whether to ensure that all research stages in the data are valid. Defaults to False.
+flexible_field_search (bool, optional): Whether to allow regex field search. Defaults to False.
+keep_undefined_research_stage (bool, optional): Whether to keep samples with undefined research stage. Defaults to False.
+join_non_overlapping (bool, optional): Whether to join tables with non-overlapping indices. Defaults to False.
+errors (str, optional): Whether to raise an error or issue a warning if missing data is encountered.
+    Possible values are 'raise', 'warn' and 'ignore'. Defaults to ERROR_ACTION.
+

Attributes:

+
dict (pd.DataFrame): The data dictionary for the dataset, containing information about each field.
+dfs (dict): A dictionary of dataframes, one for each table in the dataset.
+fields (list): A list of all fields in the dataset.
+dataset (str): The name of the dataset being used.
+cohort (str): The name of the cohort being used.
+base_path (str): The base path where the data is stored.
+dataset_path (str): The full path to the dataset being used.
+age_sex_dataset (str): The name of the dataset being used to compute age and sex.
+skip_dfs (list): A list of tables to skip when loading the data.
+unique_index (bool): Whether to ensure the index of the data is unique.
+valid_dates (bool): Whether to ensure that all timestamps in the data are valid dates.
+valid_stage (bool): Whether to ensure that all research stages in the data are valid.
+flexible_field_search (bool): Whether to allow regex field search.
+keep_undefined_research_stage (bool, optional): Whether to keep samples with undefined research stage.
+join_non_overlapping (bool): Whether to join tables with non-overlapping indices.
+errors (str): Whether to raise an error or issue a warning if missing data is encountered.
+preferred_language (str): The preferred language for the questionnaires.*
+

Use the dataset name to load the dataset. It may contain multiple tables. Age / sex will be added to the data by default. The default base_path is set to work on the research platform.

+
+
pl = PhenoLoader('fundus', errors='warn')
+pl
+
+
PhenoLoader for fundus with
+78 fields
+2 tables: ['fundus', 'age_sex']
+
+
+

The PhenoLoader class contains several usefull attributes

+

The data dictionary of the dataset displays the description of each field.

+
+
pl.dict.head(3)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
field_stringdescription_stringparent_dataframerelative_locationvalue_typeunitssampling_ratefield_typearraycohortsdata_typedebutpandas_dtype
tabular_field_name
fundus_image_leftFundus image (left)Fundus image (left)NaNfundus/fundus.parquetTextNaNNaNImage file (individual)Single10Kimage2021-02-17string
fundus_image_rightFundus image (right)Fundus image (right)NaNfundus/fundus.parquetTextNaNNaNImage file (individual)Single10Kimage2021-02-17string
collection_dateCollection date (YYYY-MM-DD)Collection date (YYYY-MM-DD)NaNfundus/fundus.parquetDateTimeNaNDataSingle10Ktabular2021-02-17datetime64[ns]
+ +
+
+
+
+
pl.dfs.keys()
+
+
dict_keys(['fundus', 'age_sex'])
+
+
+
+
import pandas as pd
+
+# Example DataFrame with MultiIndex
+data = {
+    'value': [10, 20, 30, 40]
+}
+tuples = [('A', 'x'), ('A', 'y'), ('B', 'x'), ('B', 'y')]
+index = pd.MultiIndex.from_tuples(tuples, names=['outer', 'inner'])
+df = pd.DataFrame(data, index=index)
+
+# Reset the index
+df_reset = df.reset_index()
+
+# Re-add the desired index levels ('outer', 'inner' in this case) as index
+df_final = df_reset.set_index(['outer', 'inner'])
+
+
+
pl.dfs['fundus'].head(3)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fundus_image_leftfundus_image_rightcollection_datefractal_dimension_leftfractal_dimension_rightartery_average_width_leftartery_average_width_rightartery_distance_tortuosity_leftartery_distance_tortuosity_rightartery_fractal_dimension_left...vein_fractal_dimension_leftvein_fractal_dimension_rightvein_squared_curvature_tortuosity_leftvein_squared_curvature_tortuosity_rightvein_tortuosity_density_leftvein_tortuosity_density_rightvein_vessel_density_leftvein_vessel_density_rightvessel_density_leftvessel_density_right
participant_idcohortresearch_stagearray_index
010k00_00_visit0/path/to/file/path/to/file2022-11-161.5649891.52088518430.28475119038.5477713.6681753.2711471.355673...1.4105531.40310814.2081956.0984320.7001870.6985460.0466450.0458640.0803770.078671
110k00_00_visit0/path/to/file/path/to/file2022-06-301.5423111.53415817315.39878019099.4895752.0954611.6347821.368933...1.3875271.3328648.9990698.7026820.7408060.7089110.0378960.0468530.0741970.064578
210k00_00_visit0/path/to/file/path/to/file2021-10-051.4820511.54509715375.86699319855.5768622.7764722.7470151.360404...1.4118811.40879113.1192279.9366690.6272810.6751000.0530220.0480630.0795150.082102
+ +

3 rows × 76 columns

+
+
+
+

All availbale fields (columns) in all tables can be listed.

+
+
pl.fields[:5]
+
+
['artery_average_width_left',
+ 'artery_average_width_right',
+ 'artery_distance_tortuosity_left',
+ 'artery_distance_tortuosity_right',
+ 'artery_fractal_dimension_left']
+
+
+
+
pl['vein_average_width_right']
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
vein_average_width_right
participant_idcohortresearch_stagearray_index
010k00_00_visit018436.428634
110k00_00_visit018888.160314
210k00_00_visit019013.865043
310k00_00_visit018809.012493
410k00_00_visit019428.986690
+ +
+
+
+

Access any of the fields (e.g., vein_average_width_right, age) or indices (e.g., research_stage) from any of the tables via the data loader API.

+
+
pl[['research_stage', 'vein_average_width_right', 'age', 'sex']]
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
research_stagevein_average_width_rightagesex
participant_idcohortresearch_stagearray_index
010k00_00_visit000_00_visit18436.42863443.50
110k00_00_visit000_00_visit18888.16031453.71
210k00_00_visit000_00_visit19013.86504326.20
310k00_00_visit000_00_visit18809.01249344.61
410k00_00_visit000_00_visit19428.98669050.30
+ +
+
+
+

Access time series or bulk data that is stored separately for each sample via the data loader API. In the following example, the data loader retrieves the relative path of each sample’s bulk file from the main table (where it is stored in the field fundus_image_left), converts it to an absolute path, and loads the file. This is repeated for 2 samples and returned as a list. In the case of parquet DataFrames, there is no need to define the load_func and multiple DFs are concatenated by deafult.

+
+
pl.dfs['fundus']['fundus_image_left']
+
+
participant_id  cohort  research_stage  array_index
+0               10k     00_00_visit     0              /path/to/file
+1               10k     00_00_visit     0              /path/to/file
+2               10k     00_00_visit     0              /path/to/file
+3               10k     00_00_visit     0              /path/to/file
+4               10k     00_00_visit     0              /path/to/file
+Name: fundus_image_left, dtype: object
+
+
+
+
pl.load_bulk_data('fundus_image_left', participant_id=[0, 1])
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+

You can perform flexible field search (with regex support), when initializing the PhenoLoader as follows:

+
+
pl = PhenoLoader('fundus', flexible_field_search=True)
+
+

For example, the following command will search for any field starting with “fractal”.

+
+
pl['^fractal']
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fractal_dimension_leftfractal_dimension_right
participant_idcohortresearch_stagearray_index
010k00_00_visit01.5649891.520885
110k00_00_visit01.5423111.534158
210k00_00_visit01.4820511.545097
310k00_00_visit01.5487731.539352
410k00_00_visit01.5549221.557029
+ +
+
+
+

You can summarize a field or set of fields by the following command

+
+
pl.describe_field(['fundus_image_right', 'collection_date'])
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fundus_image_rightcollection_date
field_stringFundus image (right)Collection date (YYYY-MM-DD)
description_stringFundus image (right)Collection date (YYYY-MM-DD)
parent_dataframeNaNNaN
relative_locationfundus/fundus.parquetfundus/fundus.parquet
value_typeTextDate
unitsNaNTime
sampling_rateNaNNaN
field_typeImage file (individual)Data
arraySingleSingle
cohorts10K10K
data_typeimagetabular
debut2021-02-172021-02-17
pandas_dtypestringdatetime64[ns]
count55
unique15
most_frequent/path/to/fileNaN
minNaN2021-10-05 00:00:00
maxNaN2022-11-16 00:00:00
meanNaNNaN
medianNaNNaN
stdNaNNaN
+ +
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/questionnaire_handler.html b/questionnaire_handler.html new file mode 100644 index 0000000..9839dd8 --- /dev/null +++ b/questionnaire_handler.html @@ -0,0 +1,803 @@ + + + + + + + + + + +Questionnaires Handler – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Questionnaires Handler

+
+ +
+
+ Class for handling questionnaires datasets on the research platform +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

transform_dataframe

+
+
 transform_dataframe (df:pandas.core.frame.DataFrame, transform_from:str,
+                      transform_to:str,
+                      dict_df:pandas.core.frame.DataFrame,
+                      mapping_df:pandas.core.frame.DataFrame)
+
+
+

source

+
+
+

transform_answers

+
+
 transform_answers (tab_field_name:str,
+                    orig_answer:pandas.core.series.Series,
+                    transform_from:str, transform_to:str,
+                    dict_df:pandas.core.frame.DataFrame,
+                    mapping_df:pandas.core.frame.DataFrame)
+
+
+

source

+
+
+

replace_values

+
+
 replace_values (row:pandas.core.series.Series, mapping_dict:dict)
+
+

*Replace values in a row with corresponding values from a mapping dictionary used for categorical multiple questions

+

Parameters: row (pd.Series): A Pandas Series or a list. Each element of the Series can be an individual value or a list of values. mapping_dict (dict): A dictionary where the keys represent original values and the values represent the values to replace with.

+

Returns: pd.Series, list, or float: Transformed row with values replaced according to the mapping dictionary. If the original value is a list or an ndarray, it returns a list. If the original value is NaN, it returns a float (np.nan).*

+
+

source

+
+
+

check_invalid_values

+
+
 check_invalid_values (series:pandas.core.series.Series,
+                       mapping_df:pandas.core.frame.DataFrame)
+
+

*Check if values in normalized_answer exist in code_df[code_from], excluding np.nan. This check is used to compare the data codings and actual values in the series to make sure there are no invalid values for categoircal single

+

Args: mapping_df (pd.DataFrame): A dataframe whereall the data codings are stored and the values represent the values to replace with. series (pd.Series): The normalized answer series.

+

Returns: None: Prints the invalid values found, if any.*

+
+

source

+
+
+

flatten_series

+
+
 flatten_series (series:pandas.core.series.Series)
+
+

*Flatten a Pandas Series into a list, where each element of the Series can be an individual value or a list of values.

+

Parameters: series (pd.Series): A Pandas Series where each element can be a single value or a list of values.

+

Returns: list: A flattened list containing all the individual elements from the Series, including those within lists.*

+
+

source

+
+
+

normalize_answers

+
+
 normalize_answers (orig_answer:pandas.core.series.Series, field_type:str)
+
+

*Normalize the answers to be strings. Need to handle nulls which become strings initially and want them to still be na.

+

Args: orig_answer (pd.Series): The original answer series.

+

Returns: pd.Series: The normalized answer series.*

+
+

source

+
+
+

convert_to_string

+
+
 convert_to_string (x)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..3c94a27 --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://pheno-ai.github.io/pheno-utils/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 0000000..8c5b541 --- /dev/null +++ b/search.json @@ -0,0 +1,206 @@ +[ + { + "objectID": "bulk_data_loader.html", + "href": "bulk_data_loader.html", + "title": "Bulk Data Loader", + "section": "", + "text": "source\n\nget_function_for_field_type\n\n get_function_for_field_type (field_type)\n\n\nsource\n\n\nload_image\n\n load_image (fname:str)\n\nDisplay a fundus image from an input file path. Args: fname (str): The file path to the fundus image.\n\nsource\n\n\nparse_gtf_attributes\n\n parse_gtf_attributes (attributes_str:str)\n\n*Parse attribute column and return a dictionary.\nParameters: attributes_str (str): The attributes as a semicolon-separated string.\nReturns: dict: A dictionary where the keys are the attribute names and the values are the attribute values.*\n\nsource\n\n\nread_gtf\n\n read_gtf (filename:str)\n\n*Read a GTF file with error handling for lines with too many fields.\nParameters: filename (str): The path to the GTF file.\nReturns: pd.DataFrame: A DataFrame where each row corresponds to a line in the GTF file and each column corresponds to a field or attribute.*\n\nsource\n\n\nshow_fundus\n\n show_fundus (fname:str)" + }, + { + "objectID": "cgm_plots.html", + "href": "cgm_plots.html", + "title": "CGM plots", + "section": "", + "text": "source\n\nCGMPlot\n\n CGMPlot (cgm_df:pandas.core.frame.DataFrame,\n diet_df:Optional[pandas.core.frame.DataFrame]=None,\n cgm_date_col:str='collection_timestamp', gluc_col:str='glucose',\n diet_date_col:str='collection_timestamp',\n diet_text_col:str='shortname_eng',\n ax:Optional[matplotlib.axes._axes.Axes]=None, smooth:bool=False,\n sleep_tuples:Optional[List[Tuple[pandas._libs.tslibs.timestamps.\n Timestamp,pandas._libs.tslibs.timestamps.Timestamp]]]=None)\n\n*Initialize a CGMPlot object.\nArgs: cgm_df (pd.DataFrame): DataFrame containing the glucose measurements. diet_df (Optional[pd.DataFrame], optional): DataFrame containing the diet data. Defaults to None. cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to “Date”. gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to “glucose”. diet_date_col (str, optional): Name of the date column in diet_df. Defaults to “Date”. diet_text_col (str, optional): Name of the text column in diet_df. Defaults to “shortname_eng”. ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None. smooth (bool, optional): Apply smoothing to the glucose curve. Defaults to False. sleep_tuples (Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]], optional): List of sleep start and end times. Defaults to None.*\n\ncgm_df= pd.read_parquet(\"./examples/cgm/cgm.parquet\")\ncgm_df.head()\n\n\n\n\n\n\n\n\n\n\nglucose\n\n\nparticipant_id\ncollection_timestamp\nconnection_id\n\n\n\n\n\n0\n2020-05-25 10:48:00+03:00\n1000001\n111.6\n\n\n2020-05-25 11:03:00+03:00\n1000001\n79.2\n\n\n2020-05-25 11:18:00+03:00\n1000001\n84.6\n\n\n2020-05-25 11:33:00+03:00\n1000001\n106.2\n\n\n2020-05-25 11:48:00+03:00\n1000001\n102.6\n\n\n\n\n\n\n\n\ndiet_df = pd.read_parquet(\"./examples/diet_logging/diet_logging.parquet\")\ndiet_df.head()\n\n\n\n\n\n\n\n\n\ncollection_timestamp\nfood_id\nweight\nshortname_eng\n\n\nparticipant_id\ncohort\n\n\n\n\n\n\n\n\n0\n10k\n2020-05-25 08:15:00+03:00\n1007294\n40.0\nCoffee\n\n\n10k\n2020-05-25 08:15:00+03:00\n1007417\n87.0\nYellow Cheese\n\n\n10k\n2020-05-25 08:15:00+03:00\n1008624\n6.0\nAlmonds\n\n\n10k\n2020-05-25 08:15:00+03:00\n1011642\n12.0\nBrazil nuts\n\n\n10k\n2020-05-25 10:05:00+03:00\n1007118\n100.0\nHummus\n\n\n\n\n\n\n\n\nstart_date = pd.to_datetime('2020-05-25', utc=True).tz_convert('Asia/Jerusalem')\nend_date = pd.to_datetime('2020-05-27',utc=True).tz_convert('Asia/Jerusalem')\n\n\nsample_days = cgm_df[(cgm_df.index.get_level_values('collection_timestamp') >= start_date) \\\n & (cgm_df.index.get_level_values('collection_timestamp') <= end_date)]\n\n\ncgmplt = CGMPlot(cgm_df=sample_days.reset_index(),\n cgm_date_col=\"collection_timestamp\",\n gluc_col=\"glucose\",\n diet_df=diet_df.iloc[9:],\n diet_date_col=\"collection_timestamp\",\n smooth=True)\ncgmplt.plot()\n\n\n\n\n\n\n\n\n\n\nAGP\n\nsource\n\nAGP\n\n AGP (cgm_df:pandas.core.frame.DataFrame,\n cgm_date_col:str='collection_timestamp', gluc_col:str='glucose',\n ax:Optional[matplotlib.axes._axes.Axes]=None)\n\n*Initialize an AGP object.\nArgs: cgm_df (pd.DataFrame): DataFrame containing the glucose measurements. cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to “collection_timestamp”. gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to “glucose”. ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None.*\n\nagp = AGP(cgm_df=cgm_df.reset_index(), cgm_date_col=\"collection_timestamp\", gluc_col=\"glucose\")\nagp.plot()", + "crumbs": [ + "Plots", + "CGM plots" + ] + }, + { + "objectID": "basic_analysis.html", + "href": "basic_analysis.html", + "title": "Basic analysis", + "section": "", + "text": "source\n\ncustom_describe\n\n custom_describe (df:pandas.core.frame.DataFrame)\n\n*Generates a custom summary statistics dataframe for mixed data types.\nArgs: df: The input pandas DataFrame\nReturns: A pandas DataFrame containing the summary statistics*\n\ndata = generate_synthetic_data(n=100)\n\ncustom_describe(data[[\"date_of_research_stage\", \"sex\", \"val2\"]])\n\n\n\n\n\n\n\n\ndate_of_research_stage\nsex\nval2\n\n\n\n\ncount\n100\n100.0\n100.0\n\n\nunique\n99\n2.0\n100.0\n\n\nmost_frequent\nNaN\n0.0\n-3.137486\n\n\nmin\n2020-01-04 00:00:00\n0.0\n-3.137486\n\n\nmax\n2023-06-25 00:00:00\n1.0\n41.432775\n\n\nmean\nNaN\n0.46\n19.091836\n\n\nmedian\nNaN\n0.0\n19.601733\n\n\nstd\nNaN\n0.500908\n10.401328\n\n\n\n\n\n\n\n\nsource\n\n\nassign_nearest_research_stage\n\n assign_nearest_research_stage (dataset:pandas.core.frame.DataFrame,\n population:pandas.core.frame.DataFrame,\n max_days:int=60,\n stages:List[str]=['visit'],\n agg:Optional[str]='first')\n\n*Assign the nearest research stage to each record in a dataset.\nArgs: dataset (pd.DataFrame): The dataset containing records to be assigned research stages. population (pd.DataFrame): The population data with participant_id, cohort, research_stage, and research_stage_date. max_days (int, optional): The maximum number of days allowed between the collection date and research stage date. Defaults to 60. stages (List[str], optional): The list of types of research stages to consider. Defaults to [‘visit’]. agg (Union[str, None], optional): The aggregation function to be used when (optionally) aggregating multiple rows from the same research stage. The rows are already sorted by distance from the date of the research stage. Can be ‘first’ (closest), ‘last’ (farthest), ‘mean’, ‘min’, ‘max’, or None. Defaults to ‘first’.\nReturns: pd.DataFrame: The dataset with the nearest research stage assigned to each record.*", + "crumbs": [ + "Analysis", + "Basic analysis" + ] + }, + { + "objectID": "diet_plots.html", + "href": "diet_plots.html", + "title": "Diet logging plots", + "section": "", + "text": "source\n\ndraw_pie_chart\n\n draw_pie_chart (ax:matplotlib.axes._axes.Axes, x:float, y:float,\n data:List[float], size:float, palette:str='muted',\n alpha:float=0.7)\n\n*Draw a pie chart as an inset (in absolute figure coordinates) within the given axes at the specified data coordinates. What this solves is the issue of y-axis and x-axis scaling being different, which distorts the pie chart when drawn directly on the axes.\nArgs: ax (plt.Axes): The axis on which to draw the pie chart. x (float): The x-coordinate in data coordinates where the pie chart’s center will be placed. y (float): The y-coordinate in data coordinates where the pie chart’s center will be placed. data (List[float]): The data values to be represented in the pie chart. size (float): The size (radius) of the pie chart in axes-relative coordinates. palette (str): The color palette to use for the pie slices.\nReturns: List[plt.Patch]: A list of wedge objects representing the pie chart slices.*\n\nsource\n\n\nextract_units\n\n extract_units (column_names:List[str])\n\n\nsource\n\n\nprepare_meals\n\n prepare_meals (diet_log:pandas.core.frame.DataFrame,\n participant_id:int=None, array_index:int=None,\n time_range:Tuple[str,str]=None,\n label:str='short_food_name', return_meals:bool=True,\n return_summary:bool=False, agg_units:dict={'kcal': 'sum',\n 'g': 'sum', 'mg': 'sum', 'unknown': 'first'},\n x_col:str='collection_timestamp')\n\n*Prepare the diet log data for plotting meals and/or daily summaries.\nArgs: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. label (str): The name of the column in diet_log representing the labels for each meal. Default is ‘short_food_name’. return_meals (bool): If True, includes individual meals in the plot. Default is True. return_summary (bool): If True, includes a daily summary in the plot. Default is False. agg_units (dict): A dictionary mapping nutrient units to aggregation functions. x_col (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’.\nReturns: pd.DataFrame: A dataframe containing the prepared data for plotting.*\n\nsource\n\n\nplot_nutrient_lollipop\n\n plot_nutrient_lollipop (diet_log:pandas.core.frame.DataFrame,\n x:str='collection_timestamp',\n y:str='calories_kcal', size:str='total_g',\n label:str='short_food_name',\n participant_id:int=None, array_index:int=None,\n time_range:Tuple[str,str]=None, meals:bool=True,\n summary:bool=False, legend:bool=True,\n size_scale:float=5, second_y:bool=False,\n palette:str='muted', alpha:float=0.7,\n ax:matplotlib.axes._axes.Axes=None,\n figsize:Tuple[float,float]=(12, 3))\n\n*Plot a lollipop chart with pie charts representing nutrient composition for each meal.\nNOTE: The y-axis is scaled to match the units of the x-axis, to avoid distortion of the pie charts. Due to scaling, if you intend to change xlim after plotting, you must also provide date_range. Use the second_y of g.plot() option to plot it with other y-axis data.\nArgs: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. x (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’. y (str): The name of the column in diet_log representing the y-axis variable, such as calories. Default is ‘calories_kcal’. size (str): The name of the column in diet_log representing the size of the pie charts. Default is ‘total_g’. label (str): The name of the column in diet_log representing the labels for each meal. Default is ‘short_food_name’. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. meals (bool): If True, includes individual meals in the plot. Default is True. summary (bool): If True, includes a daily summary in the plot. Default is False. legend (bool): If True, includes a legend in the plot. Default is True. size_scale (float): The scaling factor for the size of the pie charts. Default is 5. second_y (bool): If True, plot will be done on a secondary y-axis in the plot. Default is False. palette (str): The color palette to use for the pie slices. Default is DEFAULT_PALETTTE. alpha (float): The transparency of the pie slices. Default is 0.7. ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None. figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).\nReturns: None: The function creates a lollipop plot with pie charts on the specified or newly created axis.*\n\nsource\n\n\nplot_nutrient_bars\n\n plot_nutrient_bars (diet_log:pandas.core.frame.DataFrame,\n x:str='collection_timestamp',\n label:str='short_food_name', participant_id:int=None,\n array_index:int=None, time_range:Tuple[str,str]=None,\n meals:bool=True, summary:bool=False,\n agg_units:dict={'kcal': 'sum', 'g': 'sum', 'mg':\n 'sum'}, legend:bool=True,\n bar_width=numpy.timedelta64(15,'m'),\n palette:str='muted', alpha:float=0.7,\n ax:matplotlib.axes._axes.Axes=None,\n figsize:Tuple[float,float]=(14, 3))\n\n*Plot a stacked bar chart representing nutrient intake for each meal over time.\nArgs: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. x (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’. label (str): The name of the column in diet_log representing the labels for each meal. Default is ‘short_food_name’. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. meals (bool): If True, includes individual meals in the plot. Default is True. summary (bool): If True, includes a daily summary in the plot. Default is False. agg_units (dict): A dictionary mapping nutrient units to aggregation functions. Only nutrients with units in this dictionary are plotted. legend (bool): If True, includes a legend in the plot. Default is True. bar_width (np.timedelta64): The width of the bars representing each meal on the time axis. Default is 15 minutes. palette (str): The color palette to use for the stacked bars. alpha (float): The transparency of the stacked bars. Default is 0.7. ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the bar chart. If None, a new axis is created. Default is None. figsize (Tuple[float, float]): The size of the figure to create. Default is (14, 3).\nReturns: None: The function creates a stacked bar chart on the specified or newly created axis.*\n\nsource\n\n\nadd_size_legend\n\n add_size_legend (ax:matplotlib.axes._axes.Axes, sizes:List[int],\n size_scale:float, alpha:float, shift:int=0)\n\nAdd a size legend to a plot_meals_hbars plot using broken_barh.\n\nsource\n\n\nplot_meals_hbars\n\n plot_meals_hbars (diet_log:pandas.core.frame.DataFrame,\n x:str='collection_timestamp',\n y:str='short_food_category', size:str='weight_g',\n hue:str='short_food_category', participant_id:int=None,\n array_index:int=None, time_range:Tuple[str,str]=None,\n y_include:List[str]=None, y_exclude:List[str]=None,\n rename_categories:dict={'beef, veal, lamb, and other\n meat products': 'meat products', 'milk, cream cheese\n and yogurts': 'milk products', 'nuts, seeds, and\n products': 'nuts and seeds', 'eggs and their products':\n 'eggs', 'pulses and products': 'pulses', 'fruit juices\n and soft drinks': 'juices and soft drinks', 'low\n calories and diet drinks': 'low cal. drinks', 'poultry\n and its products': 'poultry', 'pasta, grains and side\n dishes': 'grains'}, legend:bool=True,\n size_legend:List[int]=[100, 200, 500],\n size_scale:float=5, palette:str='muted',\n alpha:float=0.7, ax:matplotlib.axes._axes.Axes=None,\n figsize:Tuple[float,float]=(12, 6))\n\n*Plot a diet chart with bars representing meals and their size over time.\nArgs: diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements. x (str): The name of the column in diet_log representing the x-axis variable, such as timestamps. Default is ‘collection_timestamp’. y (str): The name of the column in diet_log representing the y-axis variable, such as food categories. Default is ‘short_food_category’. size (str): The name of the column in diet_log representing the size of the bars. Default is ‘weight_g’. hue (str): The name of the column in diet_log representing the color of the bars. Default is ‘short_food_category’. participant_id (Optional[int]): The participant’s ID to filter the diet log. If None, no filtering is done. Default is None. time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be ‘YYYY-MM-DD’. Default is None. y_include (List[str]): A list of strings representing the categories to include in the plot. Default is None. y_exclude (List[str]): A list of strings representing the categories to exclude from the plot. Default is None. rename_categories (dict): A dictionary mapping original food categories to shorter names. Default is SHORT_FOOD_CATEGORIES. legend (bool): If True, includes a legend in the plot. Default is True. size_legend (List[int]): A list of integers representing the sizes to include in the size legend. Default is [100, 200, 500]. size_scale (float): The scaling factor for the size of the bars. Default is 5. palette (str): The palette to use for the bars. alpha (float): The transparency of the bars. Default is 0.7. ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None. figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).*\n\nsource\n\n\nplot_diet_cgm_sleep\n\n plot_diet_cgm_sleep (diet:pandas.core.frame.DataFrame=None,\n cgm:pandas.core.frame.DataFrame=None,\n sleep_events:pandas.core.frame.DataFrame=None,\n sleep_channels:pandas.core.frame.DataFrame=None,\n cgm_grid:List[int]=[0, 54, 70, 100, 140, 180],\n channel_filter:List[str]=['heart_rate', 'actigraph',\n 'spo2'], participant_id=None, array_index=None,\n time_range:Tuple[str,str]=None, figsize=(14, 10),\n nutrient_kws:dict={}, meals_kws:dict={},\n cgm_kws:dict={}, events_kws:dict={},\n channels_kws:dict={})\n\n*Plot diet, CGM and sleep data together.\nArg: diet (pd.DataFrame): Diet logging data. Set to None to remove from figure. cgm (pd.DataFrame): CGM data. Set to None to remove from figure. sleep_events (pd.DataFrame): Sleep events data. Set to None to remove from figure. sleep_channels (pd.DataFrame): Sleep channels data. Set to None to remove from figure. cgm_grid (List[int]): CGM grid lines. Default: [0, 54, 70, 100, 140, 180]. channel_filter (List[str]): Which sleep channels to include in the plot. Default: [‘heart_rate’, ‘actigraph’, ‘spo2’]. participant_id (int): Participant ID. array_index (int): Array index. time_range (Tuple[str, str]): Time range to plot. figsize (Tuple[int, int]): Figure size. nutrient_kws (dict): Keyword arguments for diet nutrients lollipop plot. meals_kws (dict): Keyword arguments for diet meals plot. cgm_kws (dict): Keyword arguments for CGM plot. events_kws (dict): Keyword arguments for sleep events plot. channels_kws (dict): Keyword arguments for sleep channels plot.\nReturns: TimeSeriesFigure: Plot.*" + }, + { + "objectID": "pheno_loader.html", + "href": "pheno_loader.html", + "title": "Pheno loader", + "section": "", + "text": "source\n\nPhenoLoader\n\n PhenoLoader (dataset:str, base_path:str='nbs/examples/', cohort:str=None,\n age_sex_dataset:str='events', skip_dfs:List[str]=[],\n unique_index:bool=False, valid_dates:bool=False,\n valid_stage:bool=False, flexible_field_search:bool=False,\n squeeze:bool=False, errors:str='warn',\n read_parquet_kwargs:Dict[str,Any]={},\n preferred_language:str='english',\n keep_undefined_research_stage:bool=False,\n join_non_overlapping:bool=False)\n\n*Class to load multiple tables from a dataset and allows to easily access their fields.\nArgs:\ndataset (str): The name of the dataset to load.\nbase_path (str, optional): The base path where the data is stored. Defaults to DATASETS_PATH.\ncohort (str, optional): The name of the cohort within the dataset. Defaults to COHORT.\nage_sex_dataset (str, optional): The name of the dataset to use for computing age and sex. Defaults to EVENTS_DATASET.\nskip_dfs (list, optional): A list of tables (or substrings that match to tables) to skip when loading the data. Defaults to [].\nunique_index (bool, optional): Whether to ensure the index of the data is unique. Defaults to False.\nvalid_dates (bool, optional): Whether to ensure that all timestamps in the data are valid dates. Defaults to False.\nvalid_stage (bool, optional): Whether to ensure that all research stages in the data are valid. Defaults to False.\nflexible_field_search (bool, optional): Whether to allow regex field search. Defaults to False.\nkeep_undefined_research_stage (bool, optional): Whether to keep samples with undefined research stage. Defaults to False.\njoin_non_overlapping (bool, optional): Whether to join tables with non-overlapping indices. Defaults to False.\nerrors (str, optional): Whether to raise an error or issue a warning if missing data is encountered.\n Possible values are 'raise', 'warn' and 'ignore'. Defaults to ERROR_ACTION.\nAttributes:\ndict (pd.DataFrame): The data dictionary for the dataset, containing information about each field.\ndfs (dict): A dictionary of dataframes, one for each table in the dataset.\nfields (list): A list of all fields in the dataset.\ndataset (str): The name of the dataset being used.\ncohort (str): The name of the cohort being used.\nbase_path (str): The base path where the data is stored.\ndataset_path (str): The full path to the dataset being used.\nage_sex_dataset (str): The name of the dataset being used to compute age and sex.\nskip_dfs (list): A list of tables to skip when loading the data.\nunique_index (bool): Whether to ensure the index of the data is unique.\nvalid_dates (bool): Whether to ensure that all timestamps in the data are valid dates.\nvalid_stage (bool): Whether to ensure that all research stages in the data are valid.\nflexible_field_search (bool): Whether to allow regex field search.\nkeep_undefined_research_stage (bool, optional): Whether to keep samples with undefined research stage.\njoin_non_overlapping (bool): Whether to join tables with non-overlapping indices.\nerrors (str): Whether to raise an error or issue a warning if missing data is encountered.\npreferred_language (str): The preferred language for the questionnaires.*\nUse the dataset name to load the dataset. It may contain multiple tables. Age / sex will be added to the data by default. The default base_path is set to work on the research platform.\n\npl = PhenoLoader('fundus', errors='warn')\npl\n\nPhenoLoader for fundus with\n78 fields\n2 tables: ['fundus', 'age_sex']\n\n\nThe PhenoLoader class contains several usefull attributes\nThe data dictionary of the dataset displays the description of each field.\n\npl.dict.head(3)\n\n\n\n\n\n\n\n\nfield_string\ndescription_string\nparent_dataframe\nrelative_location\nvalue_type\nunits\nsampling_rate\nfield_type\narray\ncohorts\ndata_type\ndebut\npandas_dtype\n\n\ntabular_field_name\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfundus_image_left\nFundus image (left)\nFundus image (left)\nNaN\nfundus/fundus.parquet\nText\nNaN\nNaN\nImage file (individual)\nSingle\n10K\nimage\n2021-02-17\nstring\n\n\nfundus_image_right\nFundus image (right)\nFundus image (right)\nNaN\nfundus/fundus.parquet\nText\nNaN\nNaN\nImage file (individual)\nSingle\n10K\nimage\n2021-02-17\nstring\n\n\ncollection_date\nCollection date (YYYY-MM-DD)\nCollection date (YYYY-MM-DD)\nNaN\nfundus/fundus.parquet\nDate\nTime\nNaN\nData\nSingle\n10K\ntabular\n2021-02-17\ndatetime64[ns]\n\n\n\n\n\n\n\n\npl.dfs.keys()\n\ndict_keys(['fundus', 'age_sex'])\n\n\n\nimport pandas as pd\n\n# Example DataFrame with MultiIndex\ndata = {\n 'value': [10, 20, 30, 40]\n}\ntuples = [('A', 'x'), ('A', 'y'), ('B', 'x'), ('B', 'y')]\nindex = pd.MultiIndex.from_tuples(tuples, names=['outer', 'inner'])\ndf = pd.DataFrame(data, index=index)\n\n# Reset the index\ndf_reset = df.reset_index()\n\n# Re-add the desired index levels ('outer', 'inner' in this case) as index\ndf_final = df_reset.set_index(['outer', 'inner'])\n\n\npl.dfs['fundus'].head(3)\n\n\n\n\n\n\n\n\n\n\n\nfundus_image_left\nfundus_image_right\ncollection_date\nfractal_dimension_left\nfractal_dimension_right\nartery_average_width_left\nartery_average_width_right\nartery_distance_tortuosity_left\nartery_distance_tortuosity_right\nartery_fractal_dimension_left\n...\nvein_fractal_dimension_left\nvein_fractal_dimension_right\nvein_squared_curvature_tortuosity_left\nvein_squared_curvature_tortuosity_right\nvein_tortuosity_density_left\nvein_tortuosity_density_right\nvein_vessel_density_left\nvein_vessel_density_right\nvessel_density_left\nvessel_density_right\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n2022-11-16\n1.564989\n1.520885\n18430.284751\n19038.547771\n3.668175\n3.271147\n1.355673\n...\n1.410553\n1.403108\n14.208195\n6.098432\n0.700187\n0.698546\n0.046645\n0.045864\n0.080377\n0.078671\n\n\n1\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n2022-06-30\n1.542311\n1.534158\n17315.398780\n19099.489575\n2.095461\n1.634782\n1.368933\n...\n1.387527\n1.332864\n8.999069\n8.702682\n0.740806\n0.708911\n0.037896\n0.046853\n0.074197\n0.064578\n\n\n2\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n2021-10-05\n1.482051\n1.545097\n15375.866993\n19855.576862\n2.776472\n2.747015\n1.360404\n...\n1.411881\n1.408791\n13.119227\n9.936669\n0.627281\n0.675100\n0.053022\n0.048063\n0.079515\n0.082102\n\n\n\n\n3 rows × 76 columns\n\n\n\nAll availbale fields (columns) in all tables can be listed.\n\npl.fields[:5]\n\n['artery_average_width_left',\n 'artery_average_width_right',\n 'artery_distance_tortuosity_left',\n 'artery_distance_tortuosity_right',\n 'artery_fractal_dimension_left']\n\n\n\npl['vein_average_width_right']\n\n\n\n\n\n\n\n\n\n\n\nvein_average_width_right\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n18436.428634\n\n\n1\n10k\n00_00_visit\n0\n18888.160314\n\n\n2\n10k\n00_00_visit\n0\n19013.865043\n\n\n3\n10k\n00_00_visit\n0\n18809.012493\n\n\n4\n10k\n00_00_visit\n0\n19428.986690\n\n\n\n\n\n\n\nAccess any of the fields (e.g., vein_average_width_right, age) or indices (e.g., research_stage) from any of the tables via the data loader API.\n\npl[['research_stage', 'vein_average_width_right', 'age', 'sex']]\n\n\n\n\n\n\n\n\n\n\n\nresearch_stage\nvein_average_width_right\nage\nsex\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n00_00_visit\n18436.428634\n43.5\n0\n\n\n1\n10k\n00_00_visit\n0\n00_00_visit\n18888.160314\n53.7\n1\n\n\n2\n10k\n00_00_visit\n0\n00_00_visit\n19013.865043\n26.2\n0\n\n\n3\n10k\n00_00_visit\n0\n00_00_visit\n18809.012493\n44.6\n1\n\n\n4\n10k\n00_00_visit\n0\n00_00_visit\n19428.986690\n50.3\n0\n\n\n\n\n\n\n\nAccess time series or bulk data that is stored separately for each sample via the data loader API. In the following example, the data loader retrieves the relative path of each sample’s bulk file from the main table (where it is stored in the field fundus_image_left), converts it to an absolute path, and loads the file. This is repeated for 2 samples and returned as a list. In the case of parquet DataFrames, there is no need to define the load_func and multiple DFs are concatenated by deafult.\n\npl.dfs['fundus']['fundus_image_left']\n\nparticipant_id cohort research_stage array_index\n0 10k 00_00_visit 0 /path/to/file\n1 10k 00_00_visit 0 /path/to/file\n2 10k 00_00_visit 0 /path/to/file\n3 10k 00_00_visit 0 /path/to/file\n4 10k 00_00_visit 0 /path/to/file\nName: fundus_image_left, dtype: object\n\n\n\npl.load_bulk_data('fundus_image_left', participant_id=[0, 1])\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYou can perform flexible field search (with regex support), when initializing the PhenoLoader as follows:\n\npl = PhenoLoader('fundus', flexible_field_search=True)\n\nFor example, the following command will search for any field starting with “fractal”.\n\npl['^fractal']\n\n\n\n\n\n\n\n\n\n\n\nfractal_dimension_left\nfractal_dimension_right\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n1.564989\n1.520885\n\n\n1\n10k\n00_00_visit\n0\n1.542311\n1.534158\n\n\n2\n10k\n00_00_visit\n0\n1.482051\n1.545097\n\n\n3\n10k\n00_00_visit\n0\n1.548773\n1.539352\n\n\n4\n10k\n00_00_visit\n0\n1.554922\n1.557029\n\n\n\n\n\n\n\nYou can summarize a field or set of fields by the following command\n\npl.describe_field(['fundus_image_right', 'collection_date'])\n\n\n\n\n\n\n\n\nfundus_image_right\ncollection_date\n\n\n\n\nfield_string\nFundus image (right)\nCollection date (YYYY-MM-DD)\n\n\ndescription_string\nFundus image (right)\nCollection date (YYYY-MM-DD)\n\n\nparent_dataframe\nNaN\nNaN\n\n\nrelative_location\nfundus/fundus.parquet\nfundus/fundus.parquet\n\n\nvalue_type\nText\nDate\n\n\nunits\nNaN\nTime\n\n\nsampling_rate\nNaN\nNaN\n\n\nfield_type\nImage file (individual)\nData\n\n\narray\nSingle\nSingle\n\n\ncohorts\n10K\n10K\n\n\ndata_type\nimage\ntabular\n\n\ndebut\n2021-02-17\n2021-02-17\n\n\npandas_dtype\nstring\ndatetime64[ns]\n\n\ncount\n5\n5\n\n\nunique\n1\n5\n\n\nmost_frequent\n/path/to/file\nNaN\n\n\nmin\nNaN\n2021-10-05 00:00:00\n\n\nmax\nNaN\n2022-11-16 00:00:00\n\n\nmean\nNaN\nNaN\n\n\nmedian\nNaN\nNaN\n\n\nstd\nNaN\nNaN", + "crumbs": [ + "Data Loaders", + "Pheno loader" + ] + }, + { + "objectID": "date_plots.html", + "href": "date_plots.html", + "title": "Dates plots", + "section": "", + "text": "source\n\ndates_dist_plot\n\n dates_dist_plot (df:pandas.core.frame.DataFrame, col:str,\n sampling_period:str='W-MON', ax:Union[ForwardRef('Extens\n ionArray'),numpy.ndarray,ForwardRef('Index'),ForwardRef(\n 'Series'),list,range,NoneType]=None,\n date_col:str='collection_date',\n ylim:Optional[Tuple[float,float]]=None,\n quantiles:Optional[List[Tuple[float,str]]]=None)\n\n*Creates a scatter plot of data points and their statistics based on a specified sampling period.\nArgs: df (pd.DataFrame): The input DataFrame containing the data. col (str): The column name in the DataFrame to plot. sampling_period (str, optional): The frequency to resample the data. Defaults to ‘W-MON’. ax (Optional[Axes], optional): A matplotlib axes object to plot on. Defaults to None. date_col (str, optional): The name of the date column in the DataFrame. Defaults to ‘collection_date’. ylim (Optional[Tuple[float, float]], optional): A tuple defining the y-axis limits. Defaults to None. quantiles (Optional[List[Tuple[float, str]]], optional): A list of tuples containing quantiles and their labels. Defaults to [(0.1, “10%”), (0.9, “90%”)].*\n\ndata = generate_synthetic_data()\ndata.head()\n\n\n\n\n\n\n\n\ndate_of_research_stage\nage_at_research_stage\nsex\nval1\nval2\n\n\nparticipant_id\n\n\n\n\n\n\n\n\n\n0\n2022-12-01\n57.777073\n1\n150.216212\n56.936487\n\n\n1\n2020-07-29\n53.770724\n1\n117.603875\n47.152785\n\n\n2\n2020-09-30\n51.326393\n1\n97.928950\n41.250308\n\n\n3\n2022-05-06\n61.217276\n0\n105.169939\n41.422605\n\n\n4\n2021-06-29\n45.835170\n0\n54.735540\n26.292285\n\n\n\n\n\n\n\n\ndates_dist_plot(data, col=\"val1\", date_col=\"date_of_research_stage\")", + "crumbs": [ + "Plots", + "Dates plots" + ] + }, + { + "objectID": "meta_loader.html", + "href": "meta_loader.html", + "title": "Metadata loader", + "section": "", + "text": "source\n\nMetaLoader\n\n MetaLoader (base_path:str='nbs/examples/', cohort:str=None,\n flexible_field_search:bool=False, errors:str='warn',\n **kwargs)\n\n*Class to load multiple dictionaries and allows to easily access the relevant fields.\nArgs:\nbase_path (str, optional): The base path where the data is stored. Defaults to DATASETS_PATH.\ncohort (str, optional): The name of the cohort within the dataset. Defaults to COHORT.\nflexible_field_search (bool, optional): Whether to allow regex field search. Defaults to False.\nerrors (str, optional): Whether to raise an error or issue a warning if missing data is encountered.\n Possible values are 'raise', 'warn' and 'ignore'. Defaults to 'raise'.\n**kwargs: Additional keyword arguments to pass to a DataLoader class.\nAttributes:\ndicts (pd.DataFrame): A dictionary of data dictionaries (dataframes) of all availbale datasets in the base_path.\nfields (list): A list of all fields.\ncohort (str): The name of the cohort being used.\nbase_path (str): The base path where the data is stored.\nflexible_field_search (bool): Whether to allow regex field search.\nerrors (str): Whether to raise an error or issue a warning if missing data is encountered.\nkwargs (dict): Additional keyword arguments to pass to a DataLoader class.*\n\n# os.path.join(DATASETS_PATH, '*')\nos.listdir(DATASETS_PATH)\n\n['cgm', 'diet_logging', 'events', 'fundus', 'sleep', 'metadata']\n\n\nThe MetaLoader can be used to query all availbale fields throughout all datasets. In the following example, 3 datasets are available.\n\nml = MetaLoader()\nml\n\nMetaLoader for: examples/*\nwith 83 fields\n4 datasets:\n['cgm'\n 'diet_logging'\n 'fundus'\n 'sleep']\n\n\nThe object contains only the data dictionaries (metadata) of these datasets, where the columns correspond to columns in the data tables of the dataset (e.g., fundus).\n\nml.dicts['fundus']\n\n\n\n\n\n\n\ntabular_field_name\nfundus_image_left\nfundus_image_right\ncollection_date\nfractal_dimension_left\nfractal_dimension_right\n\n\n\n\ndataset\nfundus\nfundus\nfundus\nfundus\nfundus\n\n\nfield_string\nFundus image (left)\nFundus image (right)\nCollection date (YYYY-MM-DD)\nFractal dimension (left)\nFractal dimension (right)\n\n\ndescription_string\nFundus image (left)\nFundus image (right)\nCollection date (YYYY-MM-DD)\nFractal dimension (left)\nFractal dimension (right)\n\n\nparent_dataframe\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n\n\nrelative_location\nfundus/fundus.parquet\nfundus/fundus.parquet\nfundus/fundus.parquet\nfundus/fundus.parquet\nfundus/fundus.parquet\n\n\nvalue_type\nText\nText\nDate\nContinuous\nContinuous\n\n\nunits\n<NA>\n<NA>\nTime\nTime\nTime\n\n\nsampling_rate\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n\n\nfield_type\nImage file (individual)\nImage file (individual)\nData\nData\nData\n\n\narray\nSingle\nSingle\nSingle\nSingle\nSingle\n\n\ncohorts\n10K\n10K\n10K\n10K\n10K\n\n\ndata_type\nimage\nimage\ntabular\ntabular\ntabular\n\n\ndebut\n2021-02-17\n2021-02-17\n2021-02-17\n2021-02-17\n2021-02-17\n\n\npandas_dtype\nstring\nstring\ndatetime64[ns]\nfloat\nfloat\n\n\n\n\n\n\n\nYou can query fields from multiple datasets directly.\n\nml[['glucose', 'fundus_image_left', 'fundus/collection_date']]\n\n\n\n\n\n\n\ntabular_field_name\ncgm/glucose\nfundus/fundus_image_left\nfundus/collection_date\n\n\n\n\ndataset\ncgm\nfundus\nfundus\n\n\nfield_string\nGlucose\nFundus image (left)\nCollection date (YYYY-MM-DD)\n\n\ndescription_string\ncgm temporal glucose values\nFundus image (left)\nCollection date (YYYY-MM-DD)\n\n\nparent_dataframe\n<NA>\n<NA>\n<NA>\n\n\nrelative_location\ncgm/cgm.parquet\nfundus/fundus.parquet\nfundus/fundus.parquet\n\n\nvalue_type\nSeries data, continous\nText\nDate\n\n\nunits\nmg/dl\n<NA>\nTime\n\n\nsampling_rate\n15min\n<NA>\n<NA>\n\n\nfield_type\nContinuous\nImage file (individual)\nData\n\n\narray\nSingle\nSingle\nSingle\n\n\ncohorts\n10K\n10K\n10K\n\n\ndata_type\ntime series\nimage\ntabular\n\n\ndebut\n2018-12-27\n2021-02-17\n2021-02-17\n\n\npandas_dtype\nfloat\nstring\ndatetime64[ns]\n\n\n\n\n\n\n\nNote that in the example above, for collection_date (that is common to all datasets) the dataset is specified in the prefix fundus/. Therefore, the loader returns the field from the fundus imaging dataset. Omitting this prefix will return all collection_date fields in the Human Phenotype Project.\nYou can then use the MetaLoader to load the actual data of fields from multiple datasets. Here we load glucose from the CGM dataset, and fundus_image_left from the fundus dataset.\n\ndf1 = ml.load(['glucose'])\ndf1\n\n\n\n\n\n\n\n\n\n\nglucose\n\n\nparticipant_id\ncollection_timestamp\nconnection_id\n\n\n\n\n\n0\n2020-05-25 10:48:00+03:00\n1000001\n111.6\n\n\n2020-05-25 11:03:00+03:00\n1000001\n79.2\n\n\n2020-05-25 11:18:00+03:00\n1000001\n84.6\n\n\n2020-05-25 11:33:00+03:00\n1000001\n106.2\n\n\n2020-05-25 11:48:00+03:00\n1000001\n102.6\n\n\n...\n...\n...\n\n\n2020-05-30 15:03:00+03:00\n1000001\n90.0\n\n\n2020-05-30 15:18:00+03:00\n1000001\n99.0\n\n\n2020-05-30 15:33:00+03:00\n1000001\n106.2\n\n\n2020-05-30 15:48:00+03:00\n1000001\n99.0\n\n\n2020-05-30 16:03:00+03:00\n1000001\n84.6\n\n\n\n\n502 rows × 1 columns\n\n\n\n\ndf2 = ml.load(['fundus_image_left'])\ndf2\n\n\n\n\n\n\n\n\n\n\n\nfundus_image_left\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n/path/to/file\n\n\n1\n10k\n00_00_visit\n0\n/path/to/file\n\n\n2\n10k\n00_00_visit\n0\n/path/to/file\n\n\n3\n10k\n00_00_visit\n0\n/path/to/file\n\n\n4\n10k\n00_00_visit\n0\n/path/to/file\n\n\n\n\n\n\n\n\ndf3 = ml.load(['glucose' ,'fundus_image_left'])\ndf3\n\n\n\n\n\n\n\n\n\n\n\n\nglucose\nfundus_image_left\n\n\nparticipant_id\ncollection_timestamp\nconnection_id\ncohort\nresearch_stage\n\n\n\n\n\n\n0\n2020-05-25 10:48:00+03:00\n1000001\n10k\n00_00_visit\n111.6\n/path/to/file\n\n\n2020-05-25 11:03:00+03:00\n1000001\n10k\n00_00_visit\n79.2\n/path/to/file\n\n\n2020-05-25 11:18:00+03:00\n1000001\n10k\n00_00_visit\n84.6\n/path/to/file\n\n\n2020-05-25 11:33:00+03:00\n1000001\n10k\n00_00_visit\n106.2\n/path/to/file\n\n\n2020-05-25 11:48:00+03:00\n1000001\n10k\n00_00_visit\n102.6\n/path/to/file\n\n\n...\n...\n...\n...\n...\n...\n\n\n2020-05-30 16:03:00+03:00\n1000001\n10k\n00_00_visit\n84.6\n/path/to/file\n\n\n1\nNaT\nNaN\n10k\n00_00_visit\nNaN\n/path/to/file\n\n\n2\nNaT\nNaN\n10k\n00_00_visit\nNaN\n/path/to/file\n\n\n3\nNaT\nNaN\n10k\n00_00_visit\nNaN\n/path/to/file\n\n\n4\nNaT\nNaN\n10k\n00_00_visit\nNaN\n/path/to/file\n\n\n\n\n506 rows × 2 columns\n\n\n\n\nml.load(['glucose' ,'fundus_image_left', 'fundus/collection_date']).head()\n\n\n\n\n\n\n\n\n\n\n\n\nglucose\nfundus_image_left\ncollection_date\n\n\nparticipant_id\ncollection_timestamp\nconnection_id\ncohort\nresearch_stage\n\n\n\n\n\n\n\n0\n2020-05-25 10:48:00+03:00\n1000001\n10k\n00_00_visit\n111.6\n/path/to/file\n2022-11-16\n\n\n2020-05-25 11:03:00+03:00\n1000001\n10k\n00_00_visit\n79.2\n/path/to/file\n2022-11-16\n\n\n2020-05-25 11:18:00+03:00\n1000001\n10k\n00_00_visit\n84.6\n/path/to/file\n2022-11-16\n\n\n2020-05-25 11:33:00+03:00\n1000001\n10k\n00_00_visit\n106.2\n/path/to/file\n2022-11-16\n\n\n2020-05-25 11:48:00+03:00\n1000001\n10k\n00_00_visit\n102.6\n/path/to/file\n2022-11-16\n\n\n\n\n\n\n\nYou may use more flexible search queries using regex and various properties of the fields. Both the get() method and load() method support the same syntax.\n\nExample: get all bulk data fields.\n\n\nml.get('Time series', flexible=True, prop='field_type')\n\n\n\n\n\n\n\ntabular_field_name\ncgm/cgm_filename\n\n\n\n\ndataset\ncgm\n\n\nfield_string\nCGM timeseries\n\n\ndescription_string\nName of the file containing the participants' ...\n\n\nparent_dataframe\n<NA>\n\n\nrelative_location\ncgm/cgm.parquet\n\n\nvalue_type\nText\n\n\nunits\n<NA>\n\n\nsampling_rate\n<NA>\n\n\nfield_type\nTime series file (individual)\n\n\narray\nSingle\n\n\ncohorts\n10K\n\n\ndata_type\ntext\n\n\ndebut\n2018-12-27\n\n\npandas_dtype\nstring\n\n\n\n\n\n\n\n\nml.get('Time series file (individual)', prop='field_type')\n\n\n\n\n\n\n\ntabular_field_name\ncgm/cgm_filename\n\n\n\n\ndataset\ncgm\n\n\nfield_string\nCGM timeseries\n\n\ndescription_string\nName of the file containing the participants' ...\n\n\nparent_dataframe\n<NA>\n\n\nrelative_location\ncgm/cgm.parquet\n\n\nvalue_type\nText\n\n\nunits\n<NA>\n\n\nsampling_rate\n<NA>\n\n\nfield_type\nTime series file (individual)\n\n\narray\nSingle\n\n\ncohorts\n10K\n\n\ndata_type\ntext\n\n\ndebut\n2018-12-27\n\n\npandas_dtype\nstring\n\n\n\n\n\n\n\n\nml.load('Image file (individual)', prop='field_type')\n\n\n\n\n\n\n\n\n\n\n\nfundus_image_left\nfundus_image_right\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n\n\n1\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n\n\n2\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n\n\n3\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n\n\n4\n10k\n00_00_visit\n0\n/path/to/file\n/path/to/file\n\n\n\n\n\n\n\n\nExample: get all fields that include “mg” in their units\n\n\nml.get('mg', flexible=True, prop='units')\n\n\n\n\n\n\n\ntabular_field_name\ncgm/1st qu_\ncgm/3rd qu_\ncgm/auc\ncgm/ea1c\ncgm/glucose\ncgm/gmi\ncgm/iqr\ncgm/mad\ncgm/mag\ncgm/mage\n...\ncgm/modd\ncgm/range\ncgm/sd\ncgm/sdb\ncgm/sdbdm\ncgm/sddm\ncgm/sdhhmm\ncgm/sdw\ncgm/sdwsh\ndiet_logging/sodium_mg\n\n\n\n\ndataset\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\n...\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ncgm\ndiet_logging\n\n\nfield_string\n1st quantile\n3rd quantile\nAUC\neA1C\nGlucose\nGMI\nIQR\nMAD\nMAG\nMAGE\n...\nMODD\nRange\nSD\nSDb\nSDbdm\nSDdm\nSDhhmm\nSDw\nSDwsh\nSodium intake per food logged\n\n\ndescription_string\nFirst quantile of all glucose values.\nThird quantile of all glucose values.\nHourly average AUC. This measure integrates, t...\nA linear transformation of the mean glucose va...\ncgm temporal glucose values\nA linear transformation of the mean glucose va...\nInterquartile range (IQR), calculated as the d...\nMedian Absolute Deviation (MAD). This is a mea...\nMean Absolute Glucose (MAG). This is a measure...\nMean Amplitude of Glycemic Excursions (MAGE), ...\n...\nMean difference between glucose values obtaine...\nDifference between the maximum and minimum glu...\nStandard deviation of all glucose values.\nSD between days, within time points. Mean valu...\nSD between days, within time points, corrected...\nHorizontal SD. SD of the mean glucose values, ...\nSD between time points. Standard deviation of ...\nVertical SD within days. Average value of the ...\nSD within series. Taking hour-long intervals t...\nSodium intake per food logged\n\n\nparent_dataframe\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n...\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n\n\nrelative_location\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\n...\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ncgm/cgm.parquet\ndiet_logging/diet_logging.parquet\n\n\nvalue_type\nContinuous\nContinuous\nContinuous\nContinuous\nSeries data, continous\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\n...\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\nContinuous\n\n\nunits\nmg/dl\nmg/dl\nmg/dl*h\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\n...\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg/dl\nmg\n\n\nsampling_rate\n<NA>\n<NA>\n<NA>\n<NA>\n15min\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n...\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n<NA>\n\n\nfield_type\nData\nData\nData\nData\nContinuous\nData\nData\nData\nData\nData\n...\nData\nData\nData\nData\nData\nData\nData\nData\nData\nData\n\n\narray\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\n...\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\nSingle\n\n\ncohorts\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n...\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n10K\n\n\ndata_type\ntabular\ntabular\ntabular\ntabular\ntime series\ntabular\ntabular\ntabular\ntabular\ntabular\n...\ntabular\ntabular\ntabular\ntabular\ntabular\ntabular\ntabular\ntabular\ntabular\nTime Series\n\n\ndebut\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n...\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2018-12-27\n2019-09-01\n\n\npandas_dtype\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\n...\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\nfloat\n\n\n\n\n14 rows × 24 columns", + "crumbs": [ + "Data Loaders", + "Metadata loader" + ] + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "pheno-utils", + "section": "", + "text": "pheno-utils is a dynamic Python package developed by Pheno.AI, for handling our medical datasets. It simplifies data loading, enables effective merging, and offers intuitive visualization tools.", + "crumbs": [ + "pheno-utils" + ] + }, + { + "objectID": "index.html#install", + "href": "index.html#install", + "title": "pheno-utils", + "section": "Install", + "text": "Install\npip install pheno_utils", + "crumbs": [ + "pheno-utils" + ] + }, + { + "objectID": "index.html#how-to-use", + "href": "index.html#how-to-use", + "title": "pheno-utils", + "section": "How to use", + "text": "How to use\nExamples:\n\ndata = generate_synthetic_data(n=1000)\nhist_ecdf_plots(data=data, col=\"val1\")\n\n\n\n\n\n\n\n\n\nage_refplots = GenderAgeRefPlot(data, \"val1\")\nage_refplots.plot()", + "crumbs": [ + "pheno-utils" + ] + }, + { + "objectID": "index.html#setting-up-pheno-utils", + "href": "index.html#setting-up-pheno-utils", + "title": "pheno-utils", + "section": "Setting Up pheno-utils", + "text": "Setting Up pheno-utils\nTo use pheno-utils, you must have a config.json file. This file should be set up according to your filesystem and placed in the ~/.pheno/ directory.\n\nIf You’re Working on TRE\nFor those working in the Trusted Research Environment (TRE), you don’t need to worry about the config.json file. It will be automatically generated for you!\n\n\nIf You’re Working with Phenos’ S3 Bucket\nIf you’re working with Phenos’ S3 bucket, you’ll need to manually create the config.json file. You can do this by running the following Python script:\npython config_setup/create_default_config.py -d s3://datasets_bucket_name\n\n\nIf you are working on local file system\nIf you are working on local file system, you’ll need to manually create the config.json file and locating it under ~/.pheno/ directory. Please use the config_setup/example_config.json as a template for your config.json file.", + "crumbs": [ + "pheno-utils" + ] + }, + { + "objectID": "blandaltman_plots.html", + "href": "blandaltman_plots.html", + "title": "Bland-Altman plots", + "section": "", + "text": "source\n\nbland_altman_triple_plot\n\n bland_altman_triple_plot (data:pandas.core.frame.DataFrame, m1_col:str,\n m2_col:str, feature_str:str='',\n scale:str='linear')\n\n*Generates a triple plot consisting of a scatter correlation plot, Bland-Altman plot, and a percentage Bland-Altman plot.\nArgs: data (pd.DataFrame): A pandas DataFrame containing the data. m1_col (str): The name of the first measurement column in the DataFrame. m2_col (str): The name of the second measurement column in the DataFrame. feature_str (str, optional): A string to include in the title of the plots. Defaults to ““. scale (str, optional): The scale of the axes. Defaults to”linear”.\nReturns: None*\n\ndata = generate_synthetic_data(n=1000)\n\nbland_altman_triple_plot(data=data, m1_col=\"val1\",m2_col=\"val2\")", + "crumbs": [ + "Plots", + "Bland-Altman plots" + ] + }, + { + "objectID": "basic_plots.html", + "href": "basic_plots.html", + "title": "Basic plots", + "section": "", + "text": "source\n\nget_gender_indices\n\n get_gender_indices (df, gender='male', gender_col='sex')\n\n*Returns the indices of the requested gender from a specified column in a pandas DataFrame.\nParameters: - df: pandas DataFrame. - gender: str, ‘male’ or ‘female’, the gender to filter by. - gender_col: str, the name of the column containing gender information.\nReturns: - indices: Index object with the indices of the rows matching the requested gender.*\n\nsource\n\n\ndata_histplot\n\n data_histplot (data:pandas.core.frame.DataFrame, col:str,\n feature_str:Optional[str]=None, gender_col:str='sex',\n plot_both_genders:bool=True, ax=None)\n\n*Plots a histogram from a DataFrame for a specific column.\nArgs: data (pd.DataFrame): The DataFrame containing the data to plot. col (str): The name of the column to plot. feature_str (Optional[str], optional): The name of the feature to plot. Defaults to None. gender_col (str, optional): The name of the column containing gender information. Defaults to “sex”. plot_both_genders (bool, optional): Whether to plot both genders or just one. Defaults to True. ax ([type], optional): The axis to plot on. Defaults to None.*\n\n# Generate synthetic data\ndata = generate_synthetic_data(n=1000)\n\n\ndata_histplot(data=data, col=\"val1\", plot_both_genders=False)\n\n\n\n\n\n\n\n\n\ndata_histplot(data=data, col=\"val1\")\n\n\n\n\n\n\n\n\n\nsource\n\n\ndata_ecdfplot\n\n data_ecdfplot (data:pandas.core.frame.DataFrame, col:str,\n feature_str:Optional[str]=None, gender_col:str='sex',\n plot_both_genders:bool=True, ax=None)\n\n*Plots an empirical cumulative distribution function (ECDF) from a DataFrame for a specific column.\nArgs: data (pd.DataFrame): The DataFrame containing the data to plot. col (str): The name of the column to plot. feature_str (Optional[str], optional): The name of the feature to plot. Defaults to None. gender_col (str, optional): The name of the column containing gender information. Defaults to “sex”. plot_both_genders (bool, optional): Whether to plot both genders or just one. Defaults to True. ax ([type], optional): The axis to plot on. Defaults to None.*\n\ndata_ecdfplot(data=data, col=\"val1\", plot_both_genders=False)\n\n\n\n\n\n\n\n\n\ndata_ecdfplot(data=data, col=\"val1\")\n\n\n\n\n\n\n\n\n\nsource\n\n\nhist_ecdf_plots\n\n hist_ecdf_plots (data:pandas.core.frame.DataFrame, col:str,\n feature_str:Optional[str]=None, gender_col:str='sex',\n plot_both_genders:bool=True)\n\n*Plots histograms and empirical cumulative distribution functions (ECDFs) from a DataFrame for a specific column.\nArgs: data: The input DataFrame containing the data to plot. col: The column name to plot. feature_str: The title of the plot. If not provided, the column name will be used. gender_col: The column name indicating sex (default is ‘sex’ - female:0; male:1). plot_both_genders (bool, optional): Whether to plot both genders or just one. Defaults to True.\nReturns: None*\n\nhist_ecdf_plots(data=data, col=\"val1\")\n\n\n\n\n\n\n\n\n\nsource\n\n\nplot_stats\n\n plot_stats (data:pandas.core.frame.DataFrame, col:str,\n ax:matplotlib.axes._axes.Axes, color:str,\n x_position:float=0.3, label:Optional[str]='All')\n\n*Adds a text box to an axis object with summary statistics for a given column in a pandas DataFrame.\nArgs: data (pd.DataFrame): The pandas DataFrame containing the data to calculate statistics for. col (str): The name of the column to calculate statistics for. ax (plt.Axes): The axis object to add the text box to. color (str): The color of the text box. x_position (float, optional): The x position of the text box. Defaults to 0.3. label (Optional[str], optional): The label to display in the text box. Defaults to “All”.*\n\nsource\n\n\nplot_hist_stats\n\n plot_hist_stats (data:pandas.core.frame.DataFrame, col:str,\n feature_str:Optional[str]=None, gender_col:str='sex',\n plot_both_genders:bool=True)\n\n*Plots a histogram of a given column in a pandas DataFrame and adds summary statistics to the plot.\nArgs: data (pd.DataFrame): The pandas DataFrame containing the data to plot. col (str): The name of the column to plot. feature_str (Optional[str], optional): A string describing the feature being plotted. Defaults to None. gender_col (str, optional): The name of the column containing gender information. Defaults to “sex”. plot_both_genders (bool, optional): Whether to plot statistics separately for males and females. Defaults to True.*\n\n# Generate synthetic data\ndata = generate_synthetic_data(n=1000)\n\n\nplot_hist_stats(data, \"val1\", plot_both_genders=False)\n\n\n\n\n\n\n\n\n\nplot_hist_stats(data, \"val1\")\n\n\n\n\n\n\n\n\n\nsource\n\n\nplot_data_collection\n\n plot_data_collection (data:pandas.core.frame.DataFrame,\n date_col:str='collection_date',\n feature_str:Optional[str]=None,\n ax:Optional[matplotlib.axes._axes.Axes]=None)\n\n*Plots a histogram of the specified column in a pandas DataFrame and excludes the last point from the plot.\nArgs: data (pd.DataFrame): The pandas DataFrame containing the data to plot. date_col (str, optional): The name of the column containing the dates. Defaults to “collection_date”. feature_str (Optional[str], optional): The name of the feature to plot. If None, the name of the date column will be used. Defaults to None. ax (Optional[plt.Axes], optional): The axis object to plot on. If None, a new figure and axis will be created. Defaults to None.*\n\nplot_data_collection(data, date_col=\"date_of_research_stage\", feature_str=\"val1\")\n\n\n\n\n\n\n\n\n\nsource\n\n\nshow_fundus\n\n show_fundus (fname:str)\n\n\nsource\n\n\nplot_horizontal_count_bars\n\n plot_horizontal_count_bars (data, column_name, hue=None, n=20)\n\n*Function to plot horizontal bar charts with counts.\nParameters: - data (pd.DataFrame): DataFrame containing the data - y (str): Column name for the y-axis - hue (str, optional): Column name for the hue (default is None) - n (int, optional): Number of top categories to display (default is None, showing all)\nReturns: - ax (Axes object): The plot*\n\n# Generate categorical synthetic data\ncategorical_data = generate_categorical_synthetic_data(n=1000)\n\n\nplot_horizontal_count_bars(categorical_data, 'val1', hue='val2', n=3)\n\n\n\n\n\n\n\n\n\nplot_horizontal_count_bars(categorical_data, 'val1', hue=None)", + "crumbs": [ + "Plots", + "Basic plots" + ] + }, + { + "objectID": "ecg_analysis.html", + "href": "ecg_analysis.html", + "title": "ECG analysis", + "section": "", + "text": "source\n\nvis_ecg\n\n vis_ecg (values_df:pandas.core.frame.DataFrame)\n\n*Visualize ECG data for 12 leads.\nArgs: values_df (pd.DataFrame): A DataFrame containing ECG data with 12 columns, one for each lead.\nReturns: None: Displays a 3x4 grid of ECG plots for the 12 leads.*", + "crumbs": [ + "Analysis", + "ECG analysis" + ] + }, + { + "objectID": "age_reference_plots.html", + "href": "age_reference_plots.html", + "title": "Age reference plots", + "section": "", + "text": "source\n\nget_gam_expectiles\n\n get_gam_expectiles (X:numpy.ndarray, y:numpy.ndarray,\n expectiles:List[float]=[0.03, 0.1, 0.5, 0.9, 0.97])\n\n*Fit Expectile Generalized Additive Models (GAMs) for given expectiles.\nArgs: X (ndarray): Feature data for the model. y (ndarray): Target variable for the model. expectiles (List[float]): List of expectiles to fit. Default is [0.03, 0.1, 0.5, 0.9, 0.97].\nReturns: Tuple[ndarray, Dict[str, ndarray]]: A tuple containing a grid of X values for prediction and a dictionary with expectiles as keys and their corresponding model predictions as values.*\n\nsource\n\n\nAgeRefPlot\n\n AgeRefPlot (data:pandas.core.frame.DataFrame, val_col:str,\n age_col:str='age_at_research_stage', sex_col:str='sex',\n sex:Optional[int]=None, val_color:Optional[str]=None,\n ax_main:Optional[matplotlib.axes._axes.Axes]=None,\n ax_agehist:Optional[matplotlib.axes._axes.Axes]=None,\n ax_valhist:Optional[matplotlib.axes._axes.Axes]=None,\n age_bins:Optional[numpy.ndarray]=None,\n val_bins:Optional[numpy.ndarray]=None, linear_fit:bool=True,\n expectiles:Optional[List]=[0.03, 0.1, 0.5, 0.9, 0.97],\n thresholds:Optional[List]=None, top_disp_perc:float=99,\n bottom_disp_perc:float=1, robust:bool=True, scale:float=1.0,\n transform:Optional[Callable]=None, make_fig:bool=True)\n\n*Initializes the AgeRefPlot class.\nArgs: data (pd.DataFrame): A pandas DataFrame containing the data. val_col (str): The name of the value column in the DataFrame. age_col (str): The name of the age column in the DataFrame. sex_col (str): The name of the sex column in the DataFrame. sex (Optional[int], optional): The sex to filter the data by. 0 for females and 1 for males. Defaults to None. val_color (Optional[str], optional): The color to use for the value plot. Defaults to None. ax_main (Optional[plt.Axes], optional): The main axis for the plot. Defaults to None. ax_agehist (Optional[plt.Axes], optional): The axis for the age histogram. Defaults to None. ax_valhist (Optional[plt.Axes], optional): The axis for the value histogram. Defaults to None. age_bins (Optional[np.ndarray], optional): The age bins for the histograms. Defaults to None. val_bins (Optional[np.ndarray], optional): The value bins for the histograms. Defaults to None. linear_fit (bool, optional): Whether to perform a linear fit on the data. Defaults to True. expectiles (Optional[List], optional): Whether to calculate and shpe gam expectiles or not. Defaults to [0.03, 0.1, 0.5, 0.9, 0.97]. top_disp_perc (float, optional): The top percentile to use for display. Defaults to 99. bottom_disp_perc (float, optional): The bottom percentile to use for display. Defaults to 1. robust (bool, optional): Whether to use a robust regression method (HuberRegressor) instead of ordinary least squares for linear_fit. Defaults to True. scale (float, optional): The scaling factor for the value column. Defaults to 1. transform (Optional[Callable], optional): The transformation function to apply to the value column. Defaults make_fig (bool, optional): Whether to create a new figure if axes are not provided. Defaults to True.*\n\ndata = generate_synthetic_data(n=1000)\n\n\nrefplot = AgeRefPlot(data, \"val1\")\nrefplot.plot()\nrefplot.plot_thresholds([-np.inf, 50, 120, np.inf], cmap='RdYlGn_r')\n\n\n\n\n\n\n\n\n\nsource\n\n\nGenderAgeRefPlot\n\n GenderAgeRefPlot (data:pandas.core.frame.DataFrame, val_col:str,\n age_col:str='age_at_research_stage', sex_col:str='sex',\n age_bins:Optional[numpy.ndarray]=None,\n val_bins:Optional[numpy.ndarray]=None,\n linear_fit:bool=True, expectiles:Optional[List]=[0.03,\n 0.1, 0.5, 0.9, 0.97], top_disp_perc:float=99,\n bottom_disp_perc:float=1, robust:bool=True,\n scale:float=1.0, transform:Optional[Callable]=None)\n\n*Initializes the GenderAgeRefPlot class.\nArgs: data (pd.DataFrame): The input data containing age, value, and gender columns. val_col (str): The name of the value column in the data. age_col (str): The name of the age column in the DataFrame. sex_col (str): The name of the sex column in the DataFrame. age_bins (np.ndarray, optional): An array of age bin edges. val_bins (np.ndarray, optional): An array of value bin edges. linear_fit (bool, optional): Whether to fit a linear regression line. Defaults to True. expectiles (Optional[List], optional): Whether to calculate and shpe gam expectiles or not. Defaults to [0.03, 0.1, 0.5, 0.9, 0.97]. top_disp_perc (float, optional): The top percentile for data display. Defaults to 99. bottom_disp_perc (float, optional): The bottom percentile for data display. Defaults to 1. robust (bool, optional): Whether to use a robust linear regression. Defaults to True. scale (float, optional): The scaling factor for the data. Defaults to 1. transform (Callable, optional): An optional function to apply to the data. Defaults to None.*\n\ngender_refplots = GenderAgeRefPlot(data, \"val1\")\ngender_refplots.plot()\ngender_refplots.plot_thresholds([-np.inf, 50, 120, np.inf], cmap='RdYlGn')", + "crumbs": [ + "Plots", + "Age reference plots" + ] + }, + { + "objectID": "timeseries_plots.html", + "href": "timeseries_plots.html", + "title": "Timeseries plots", + "section": "", + "text": "source\n\nfilter_df\n\n filter_df (df:pandas.core.frame.DataFrame, participant_id:int=None,\n array_index:int=None, time_range:Tuple[str,str]=None,\n x_start:str='collection_timestamp',\n x_end:str='collection_timestamp', unique:bool=False)\n\n*Reformat and filter a time series DataFrame based on participant ID, array index, and date range.\nArgs: df (pd.DataFrame): The DataFrame to filter. participant_id (int): The participant ID to filter by. array_index (int): The array index to filter by. time_range: The date range to filter by. Can be a tuple of two dates / times or two strings. x_start (str): The name of the column containing the start time. x_end (str): The name of the column containing the end time.\nReturns: pd.DataFrame: The filtered DataFrame*\n\nsource\n\n\nformat_xticks\n\n format_xticks (ax:matplotlib.axes._axes.Axes, xticks:Iterable=None,\n format:str='%d/%m\\n%H:%M', **kwargs)\n\nformat datestrings on x axis\n\nsource\n\n\nTimeSeriesFigure\n\n TimeSeriesFigure (figsize:tuple=(10, 6), padding:float=0.05)\n\n*Initialize a TimeSeriesFigure instance. This class is used to create and manage a figure with multiple axes for time series data.\nArgs: figsize (tuple): Size of the figure (width, height) in inches.*\n\nsource\n\n\nget_color_map\n\n get_color_map (data:pandas.core.frame.DataFrame, hue:str, palette:str)\n\n*Get a color map for a specific column in the data.\nArgs: data (pd.DataFrame): The data to get the color map from. hue (str): The column name to use for the color map. palette (str): The name of the colormap to use.\nReturns: pd.DataFrame: A DataFrame with the color map.*\n\nsource\n\n\nprepare_events\n\n prepare_events (events:pandas.core.frame.DataFrame, x_start:str,\n x_end:str, hue:str, label:str, participant_id:int,\n array_index:int, time_range:Tuple[str,str],\n y_include:Iterable[str], y_exclude:Iterable[str],\n add_columns:Iterable[str]=None, palette='muted')\n\n*Prepare events for plotting.\nArgs: events (pd.DataFrame): The events dataframe. x_start (str): The column name for the start time of the event. x_end (str): The column name for the end time of the event. hue (str): The column name for the color of the event. label (str): The column name for the label of the event. participant_id (int): The participant ID to filter events by. array_index (int): The array index to filter events by. time_range (Iterable[str]): The time range to filter events by. y_include (Iterable[str]): The list of values to include in the plot. y_exclude (Iterable[str]): The list of values to exclude from the plot. add_columns (Iterable[str]): Additional columns to include in the plot. palette (str): The name of the colormap to use for coloring events.\nReturns: Tuple[pd.DataFrame, pd.DataFrame]: The filtered events dataframe and the color map.*\n\nsource\n\n\nplot_events_fill\n\n plot_events_fill (events:pandas.core.frame.DataFrame,\n x_start:str='collection_timestamp',\n x_end:str='event_end', hue:str='channel',\n label:str=None, participant_id:Optional[int]=None,\n array_index:Optional[int]=None,\n time_range:Optional[Tuple[str,str]]=None,\n y_include:Optional[Iterable[str]]=None,\n y_exclude:Optional[Iterable[str]]=None,\n legend:bool=True, palette:str='muted',\n alpha:Optional[float]=0.5,\n ax:Optional[matplotlib.axes._axes.Axes]=None,\n figsize:Iterable[float]=[12, 6])\n\n*Plot events as filled regions on a time series plot.\nArgs: events (pd.DataFrame): The events dataframe. x_start (str): The column name for the start time of the event. x_end (str): The column name for the end time of the event. hue (str): The column name for the color of the event. label (str): The column name for the label of the event. participant_id (int): The participant ID to filter events by. array_index (int): The array index to filter events by. time_range (Iterable[str]): The time range to filter events by. y_include (Iterable[str]): The list of values to include in the plot. y_exclude (Iterable[str]): The list of values to exclude from the plot. legend (bool): Whether to show the legend. palette (str): The name of the palette to use for coloring events. alpha (float): The transparency of the filled regions. ax (plt.Axes): The axis to plot on. If None, a new figure is created. figsize (Tuple[float, float]): The size of the figure (width, height) in inches.*\n\nsource\n\n\nplot_events_bars\n\n plot_events_bars (events:pandas.core.frame.DataFrame,\n x_start:str='collection_timestamp',\n x_end:str='event_end', y:str='event',\n hue:str='channel', participant_id:Optional[int]=None,\n array_index:Optional[int]=None,\n time_range:Optional[Tuple[str,str]]=None,\n y_include:Optional[Iterable[str]]=None,\n y_exclude:Optional[Iterable[str]]=None,\n legend:bool=True, palette:str='muted',\n alpha:Optional[float]=0.7,\n ax:Optional[matplotlib.axes._axes.Axes]=None,\n figsize:Tuple[float,float]=(12, 6))\n\n*Plot events as bars on a time series plot.\nArgs: events (pd.DataFrame): The events dataframe. x_start (str): The column name for the start time of the event. x_end (str): The column name for the end time of the event. y (str): The column name for the y-axis values. hue (str): The column name for the color of the event. participant_id (int): The participant ID to filter events by. array_index (int): The array index to filter events by. time_range (Tuple[str, str]): The time range to filter events by. y_include (Iterable[str]): The list of values to include in the plot. y_exclude (Iterable[str]): The list of values to exclude from the plot. legend (bool): Whether to show the legend. palette (str): The name of the colormap to use for coloring events. alpha (float): The transparency of the bars. Default is 0.7. ax (plt.Axes): The axis to plot on. If None, a new figure is created. figsize (Tuple[float, float]): The size of the figure (width, height) in inches.*", + "crumbs": [ + "Plots", + "Timeseries plots" + ] + }, + { + "objectID": "questionnaire_handler.html", + "href": "questionnaire_handler.html", + "title": "Questionnaires Handler", + "section": "", + "text": "source\n\ntransform_dataframe\n\n transform_dataframe (df:pandas.core.frame.DataFrame, transform_from:str,\n transform_to:str,\n dict_df:pandas.core.frame.DataFrame,\n mapping_df:pandas.core.frame.DataFrame)\n\n\nsource\n\n\ntransform_answers\n\n transform_answers (tab_field_name:str,\n orig_answer:pandas.core.series.Series,\n transform_from:str, transform_to:str,\n dict_df:pandas.core.frame.DataFrame,\n mapping_df:pandas.core.frame.DataFrame)\n\n\nsource\n\n\nreplace_values\n\n replace_values (row:pandas.core.series.Series, mapping_dict:dict)\n\n*Replace values in a row with corresponding values from a mapping dictionary used for categorical multiple questions\nParameters: row (pd.Series): A Pandas Series or a list. Each element of the Series can be an individual value or a list of values. mapping_dict (dict): A dictionary where the keys represent original values and the values represent the values to replace with.\nReturns: pd.Series, list, or float: Transformed row with values replaced according to the mapping dictionary. If the original value is a list or an ndarray, it returns a list. If the original value is NaN, it returns a float (np.nan).*\n\nsource\n\n\ncheck_invalid_values\n\n check_invalid_values (series:pandas.core.series.Series,\n mapping_df:pandas.core.frame.DataFrame)\n\n*Check if values in normalized_answer exist in code_df[code_from], excluding np.nan. This check is used to compare the data codings and actual values in the series to make sure there are no invalid values for categoircal single\nArgs: mapping_df (pd.DataFrame): A dataframe whereall the data codings are stored and the values represent the values to replace with. series (pd.Series): The normalized answer series.\nReturns: None: Prints the invalid values found, if any.*\n\nsource\n\n\nflatten_series\n\n flatten_series (series:pandas.core.series.Series)\n\n*Flatten a Pandas Series into a list, where each element of the Series can be an individual value or a list of values.\nParameters: series (pd.Series): A Pandas Series where each element can be a single value or a list of values.\nReturns: list: A flattened list containing all the individual elements from the Series, including those within lists.*\n\nsource\n\n\nnormalize_answers\n\n normalize_answers (orig_answer:pandas.core.series.Series, field_type:str)\n\n*Normalize the answers to be strings. Need to handle nulls which become strings initially and want them to still be na.\nArgs: orig_answer (pd.Series): The original answer series.\nReturns: pd.Series: The normalized answer series.*\n\nsource\n\n\nconvert_to_string\n\n convert_to_string (x)" + }, + { + "objectID": "config.html", + "href": "config.html", + "title": "Config", + "section": "", + "text": "source\n\nget_dictionary_properties_file_path\n\n get_dictionary_properties_file_path ()\n\n*Get the file path for dictionary properties - TODO: move to config file or DB. At this point only includes field_type properties.\nArgs:\nReturns: str: the path to the file*\n\nsource\n\n\nget_data_coding_file_path\n\n get_data_coding_file_path ()\n\n*Get the file path for dictionary properties - TODO: move to config file or DB. At this point only includes field_type properties.\nArgs:\nReturns: str: the path to the file*\n\nsource\n\n\ngenerate_synthetic_data\n\n generate_synthetic_data (n:int=1000)\n\n*Generates a sample DataFrame containing age, gender, and value data.\nArgs: n: The number of rows in the generated DataFrame.\nReturns: A pandas DataFrame with columns ‘age’, ‘gender’, and ‘val’.*\n\nsource\n\n\ngenerate_synthetic_data_like\n\n generate_synthetic_data_like (df:pandas.core.frame.DataFrame, n:int=1000,\n random_seed:int=42)\n\n*Generate a sample DataFrame containing the same columns as df, but with random data.\nArgs:\ndf: The DataFrame whose columns should be used.\nn: The number of rows in the generated DataFrame.\nReturns: A pandas DataFrame with the same columns as df.*\n\nsource\n\n\ngenerate_categorical_synthetic_data\n\n generate_categorical_synthetic_data (n:int=1000)\n\n*Generates a sample DataFrame containing age, gender, and categorical value data.\nArgs: n: The number of rows in the generated DataFrame.\nReturns: A pandas DataFrame with columns ‘age’, ‘gender’, and ‘val1’.*\n\ndata = generate_synthetic_data()\ndata.head()\n\n\n\n\n\n\n\n\ndate_of_research_stage\nage_at_research_stage\nsex\nval1\nval2\n\n\nparticipant_id\n\n\n\n\n\n\n\n\n\n0\n2020-11-16\n54.422828\n1\n103.721478\n48.846734\n\n\n1\n2021-06-08\n65.232948\n0\n129.512280\n54.583974\n\n\n2\n2020-08-16\n42.413863\n1\n114.878851\n52.193946\n\n\n3\n2021-04-13\n57.872618\n1\n113.653117\n51.826225\n\n\n4\n2023-07-17\n70.640233\n1\n129.669937\n56.631272\n\n\n\n\n\n\n\n\ngenerate_synthetic_data_like(data.head(), n=5)\n\n\n\n\n\n\n\n\ndate_of_research_stage\nage_at_research_stage\nsex\nval1\nval2\n\n\nparticipant_id\n\n\n\n\n\n\n\n\n\n0\n2020-08-16\n57.872618\n1\n113.653117\n48.846734\n\n\n1\n2021-04-13\n65.232948\n1\n103.721478\n56.631272\n\n\n2\n2023-07-17\n42.413863\n1\n129.669937\n54.583974\n\n\n3\n2021-06-08\n54.422828\n0\n114.878851\n52.193946\n\n\n4\n2020-11-16\n70.640233\n1\n129.512280\n51.826225\n\n\n\n\n\n\n\n\ndata = generate_categorical_synthetic_data()\ndata.head()\n\n\n\n\n\n\n\n\ndate_of_research_stage\nage_at_research_stage\nsex\nval1\nval2\n\n\nparticipant_id\n\n\n\n\n\n\n\n\n\n0\n2021-09-24\n69.788555\n1\nE\nA\n\n\n1\n2021-03-02\n36.289947\n1\nC\nB\n\n\n2\n2022-06-15\n61.501970\n1\nC\nC\n\n\n3\n2020-07-23\n46.299262\n0\nB\nA\n\n\n4\n2021-03-03\n70.127055\n1\nB\nC", + "crumbs": [ + "Other", + "Config" + ] + }, + { + "objectID": "sleep_plots.html", + "href": "sleep_plots.html", + "title": "Sleep plots", + "section": "", + "text": "source\n\nget_channels_colors\n\n get_channels_colors (channels:pandas.core.frame.DataFrame,\n events:pandas.core.frame.DataFrame,\n participant_id:int=None, array_index:int=None,\n time_range:Tuple[str,str]=None,\n event_filter:Iterable[str]=None,\n palette:str='muted')\n\n\nsource\n\n\nplot_sleep_channels\n\n plot_sleep_channels (channels:pandas.core.frame.DataFrame,\n x:str='collection_timestamp', y:str='values',\n row:str='source', hue:str='channel_group',\n participant_id:int=None, array_index:int=None,\n time_range:Tuple[str,str]=None,\n y_include:Iterable[str]=None,\n y_exclude:Iterable[str]=None,\n rename_channels:dict={'actigraph': 'Actigraph',\n 'body_position': 'Body Position', 'heart_rate':\n 'Heart Rate', 'heart_rate_raw': 'Heart Rate Raw',\n 'pat_infra': 'PAT Infra', 'pat_amplitude': 'PAT\n Amplitude', 'pat_lpf': 'PAT LPF',\n 'respiratory_movement': 'Respiratory Mov.', 'spo2':\n 'SpO2', 'snore_db': 'Snore dB', 'pat_view': 'PAT\n View', 'sleep_stage': 'Sleep Stage'},\n discrete_events:Iterable[str]=['sleep_stage',\n 'body_position'], resample:str='1s',\n color_map:pandas.core.series.Series=None,\n palette:str='muted', fig:pheno_utils.timeseries_plot\n s.TimeSeriesFigure=None,\n ax:List[matplotlib.axes._axes.Axes]=None, height=1,\n **kwargs)\n\nplot channels data for a given participant and array_index\n\nsource\n\n\nplot_sleep\n\n plot_sleep (events:pandas.core.frame.DataFrame,\n channels:pandas.core.frame.DataFrame,\n participant_id:int=None, array_index:int=None,\n time_range:Tuple[str,str]=None,\n event_filter:Iterable[str]=None,\n channel_filter:Iterable[str]=['actigraph', 'pat_infra',\n 'body_position', 'snore_db', 'heart_rate', 'spo2'],\n event_height:float=1, channel_height:float=0.5,\n padding:float=-0.02, figsize:Tuple[float,float]=None,\n palette:str='muted')\n\n*Plot sleep events and channels data.\nArgs: events (pd.DataFrame): The sleep events dataframe. channels (pd.DataFrame): The sleep channels dataframe. participant_id (int): The participant id to filter the data. array_index (int): The array index to filter the data. time_range (Tuple[str, str]): The time range to filter the data. event_filter (Iterable[str]): The events to include in the plot. channel_filter (Iterable[str]): The channels to include in the plot. event_height (float): The relative height of the events subplot. channel_height (float): The relative height of each channel’s subplot. padding (float): The padding between subplots. figsize (Tuple[float, float]): The size of the figure. palette (str): The color palette to use.\nReturns: TimeSeriesFigure: The figure with the sleep events and channels data.*", + "crumbs": [ + "Plots", + "Sleep plots" + ] + }, + { + "objectID": "cohort_selector.html", + "href": "cohort_selector.html", + "title": "Cohort selector", + "section": "", + "text": "source\n\nCohortSelector\n\n CohortSelector (base_path:str='nbs/examples/', cohort:str=None,\n errors:str='warn', **kwargs)\n\n*Class for selecting a subset of a cohort’s data based on a query.\nArgs:\nbase_path (str, optional): Base path of the datasets. Defaults to DATASETS_PATH.\ncohort (str, optional): Name of the cohort. Defaults to COHORT.\nerrors (str, optional): Error action. Defaults to ERROR_ACTION.\n**kwargs: Additional keyword arguments.\nAttributes:\ncohort (str): Name of the cohort.\nbase_path (str): Base path of the datasets.\nerrors (str): Error action.\nkwargs: Additional keyword arguments.\nml (MetaLoader): MetaLoader object for loading metadata and data.*\n\ncs = CohortSelector()\n\nYou may use the CohortSelector to select participants based on any fields. The selector will return a DataFrame with the selected sub-cohort, along with the fields that were used in the query.\nFor example, the following query selects participants who have moderate obstructive sleep apnea (AHI > 15) based on recordings of at least 4 hours of sleep.\n\nml = MetaLoader()\n\n\ncs.select('15 < ahi < 20 & total_sleep_time > 4*3600')\n\n\n\n\n\n\n\n\n\n\n\nahi\ntotal_sleep_time\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n\n9\n10k\n02_00_visit\n2\n18.39\n23748.0\n\n\n15\n10k\n00_00_visit\n0\n19.54\n19230.0\n\n\n30\n10k\n00_00_visit\n0\n18.52\n25111.0\n\n\n42\n10k\n00_00_visit\n1\n17.84\n24966.0\n\n\n49\n10k\n02_00_visit\n0\n17.58\n21980.0\n\n\n...\n...\n...\n...\n...\n...\n\n\n902\n10k\n02_00_visit\n2\n19.78\n24162.0\n\n\n914\n10k\n02_00_visit\n2\n17.54\n26479.0\n\n\n936\n10k\n00_00_visit\n1\n17.43\n20865.0\n\n\n941\n10k\n00_00_visit\n0\n17.82\n17606.0\n\n\n965\n10k\n00_00_visit\n2\n15.38\n24390.0\n\n\n\n\n77 rows × 2 columns\n\n\n\nYou may also use the selector to filter on dates. Here we filter on dates of image collection in the fundus imaging dataset.\nIn addition, we may load additional fields that are not part of the query.\n\ncs.select('fundus/collection_date > \"2022-01-01\"', add_fields='fundus_image_left')\n\n\n\n\n\n\n\n\n\n\n\nfundus_image_left\ncollection_date\n\n\nparticipant_id\ncohort\nresearch_stage\narray_index\n\n\n\n\n\n\n0\n10k\n00_00_visit\n0\n/path/to/file\n2022-11-16\n\n\n1\n10k\n00_00_visit\n0\n/path/to/file\n2022-06-30\n\n\n3\n10k\n00_00_visit\n0\n/path/to/file\n2022-04-26", + "crumbs": [ + "Data Loaders", + "Cohort selector" + ] + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #fdfeff;--bs-navbar-hover-color: rgba(253, 253, 255, 0.8);--bs-navbar-disabled-color: rgba(253, 254, 255, 0.75);--bs-navbar-active-color: #fdfdff;--bs-navbar-brand-color: #fdfeff;--bs-navbar-brand-hover-color: #fdfdff;--bs-navbar-toggler-border-color: rgba(253, 254, 255, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfeff' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfeff' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 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.value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr 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.value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Fs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(Rs)?t:z.findOne(Rs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..ae0063a --- /dev/null +++ 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t(n(n({},e),{},{queries:r}))},e.getAlgoliaResults=Or,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/site_libs/quarto-search/fuse.min.js b/site_libs/quarto-search/fuse.min.js new file mode 100644 index 0000000..adc2835 --- /dev/null +++ b/site_libs/quarto-search/fuse.min.js @@ -0,0 +1,9 @@ +/** + * Fuse.js v6.6.2 - Lightweight fuzzy-search (http://fusejs.io) + * + * Copyright (c) 2022 Kiro Risk (http://kiro.me) + * All Rights Reserved. Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..810b754 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,71 @@ + + + + https://pheno-ai.github.io/pheno-utils/bulk_data_loader.html + 2024-09-06T03:33:57.538Z + + + https://pheno-ai.github.io/pheno-utils/cgm_plots.html + 2024-09-06T03:33:57.498Z + + + https://pheno-ai.github.io/pheno-utils/basic_analysis.html + 2024-09-06T03:33:57.502Z + + + https://pheno-ai.github.io/pheno-utils/diet_plots.html + 2024-09-06T03:33:57.442Z + + + https://pheno-ai.github.io/pheno-utils/pheno_loader.html + 2024-09-06T03:33:57.410Z + + + https://pheno-ai.github.io/pheno-utils/date_plots.html + 2024-09-06T03:33:57.126Z + + + https://pheno-ai.github.io/pheno-utils/meta_loader.html + 2024-09-06T03:33:57.062Z + + + https://pheno-ai.github.io/pheno-utils/index.html + 2024-09-06T03:33:56.982Z + + + https://pheno-ai.github.io/pheno-utils/blandaltman_plots.html + 2024-09-06T03:33:57.134Z + + + https://pheno-ai.github.io/pheno-utils/basic_plots.html + 2024-09-06T03:33:57.158Z + + + https://pheno-ai.github.io/pheno-utils/ecg_analysis.html + 2024-09-06T03:33:57.042Z + + + https://pheno-ai.github.io/pheno-utils/age_reference_plots.html + 2024-09-06T03:33:57.170Z + + + https://pheno-ai.github.io/pheno-utils/timeseries_plots.html + 2024-09-06T03:33:57.390Z + + + https://pheno-ai.github.io/pheno-utils/questionnaire_handler.html + 2024-09-06T03:33:57.246Z + + + https://pheno-ai.github.io/pheno-utils/config.html + 2024-09-06T03:33:57.338Z + + + https://pheno-ai.github.io/pheno-utils/sleep_plots.html + 2024-09-06T03:33:57.486Z + + + https://pheno-ai.github.io/pheno-utils/cohort_selector.html + 2024-09-06T03:33:57.498Z + + diff --git a/sleep_plots.html b/sleep_plots.html new file mode 100644 index 0000000..6591676 --- /dev/null +++ b/sleep_plots.html @@ -0,0 +1,780 @@ + + + + + + + + + +Sleep plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + +
+ +
+
+

Sleep plots

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

get_channels_colors

+
+
 get_channels_colors (channels:pandas.core.frame.DataFrame,
+                      events:pandas.core.frame.DataFrame,
+                      participant_id:int=None, array_index:int=None,
+                      time_range:Tuple[str,str]=None,
+                      event_filter:Iterable[str]=None,
+                      palette:str='muted')
+
+
+

source

+
+
+

plot_sleep_channels

+
+
 plot_sleep_channels (channels:pandas.core.frame.DataFrame,
+                      x:str='collection_timestamp', y:str='values',
+                      row:str='source', hue:str='channel_group',
+                      participant_id:int=None, array_index:int=None,
+                      time_range:Tuple[str,str]=None,
+                      y_include:Iterable[str]=None,
+                      y_exclude:Iterable[str]=None,
+                      rename_channels:dict={'actigraph': 'Actigraph',
+                      'body_position': 'Body Position', 'heart_rate':
+                      'Heart Rate', 'heart_rate_raw': 'Heart Rate Raw',
+                      'pat_infra': 'PAT Infra', 'pat_amplitude': 'PAT
+                      Amplitude', 'pat_lpf': 'PAT LPF',
+                      'respiratory_movement': 'Respiratory Mov.', 'spo2':
+                      'SpO2', 'snore_db': 'Snore dB', 'pat_view': 'PAT
+                      View', 'sleep_stage': 'Sleep Stage'},
+                      discrete_events:Iterable[str]=['sleep_stage',
+                      'body_position'], resample:str='1s',
+                      color_map:pandas.core.series.Series=None,
+                      palette:str='muted', fig:pheno_utils.timeseries_plot
+                      s.TimeSeriesFigure=None,
+                      ax:List[matplotlib.axes._axes.Axes]=None, height=1,
+                      **kwargs)
+
+

plot channels data for a given participant and array_index

+
+

source

+
+
+

plot_sleep

+
+
 plot_sleep (events:pandas.core.frame.DataFrame,
+             channels:pandas.core.frame.DataFrame,
+             participant_id:int=None, array_index:int=None,
+             time_range:Tuple[str,str]=None,
+             event_filter:Iterable[str]=None,
+             channel_filter:Iterable[str]=['actigraph', 'pat_infra',
+             'body_position', 'snore_db', 'heart_rate', 'spo2'],
+             event_height:float=1, channel_height:float=0.5,
+             padding:float=-0.02, figsize:Tuple[float,float]=None,
+             palette:str='muted')
+
+

*Plot sleep events and channels data.

+

Args: events (pd.DataFrame): The sleep events dataframe. channels (pd.DataFrame): The sleep channels dataframe. participant_id (int): The participant id to filter the data. array_index (int): The array index to filter the data. time_range (Tuple[str, str]): The time range to filter the data. event_filter (Iterable[str]): The events to include in the plot. channel_filter (Iterable[str]): The channels to include in the plot. event_height (float): The relative height of the events subplot. channel_height (float): The relative height of each channel’s subplot. padding (float): The padding between subplots. figsize (Tuple[float, float]): The size of the figure. palette (str): The color palette to use.

+

Returns: TimeSeriesFigure: The figure with the sleep events and channels data.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/timeseries_plots.html b/timeseries_plots.html new file mode 100644 index 0000000..4b1ec24 --- /dev/null +++ b/timeseries_plots.html @@ -0,0 +1,823 @@ + + + + + + + + + +Timeseries plots – pheno-utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Timeseries plots

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

filter_df

+
+
 filter_df (df:pandas.core.frame.DataFrame, participant_id:int=None,
+            array_index:int=None, time_range:Tuple[str,str]=None,
+            x_start:str='collection_timestamp',
+            x_end:str='collection_timestamp', unique:bool=False)
+
+

*Reformat and filter a time series DataFrame based on participant ID, array index, and date range.

+

Args: df (pd.DataFrame): The DataFrame to filter. participant_id (int): The participant ID to filter by. array_index (int): The array index to filter by. time_range: The date range to filter by. Can be a tuple of two dates / times or two strings. x_start (str): The name of the column containing the start time. x_end (str): The name of the column containing the end time.

+

Returns: pd.DataFrame: The filtered DataFrame*

+
+

source

+
+
+

format_xticks

+
+
 format_xticks (ax:matplotlib.axes._axes.Axes, xticks:Iterable=None,
+                format:str='%d/%m\n%H:%M', **kwargs)
+
+

format datestrings on x axis

+
+

source

+
+
+

TimeSeriesFigure

+
+
 TimeSeriesFigure (figsize:tuple=(10, 6), padding:float=0.05)
+
+

*Initialize a TimeSeriesFigure instance. This class is used to create and manage a figure with multiple axes for time series data.

+

Args: figsize (tuple): Size of the figure (width, height) in inches.*

+
+

source

+
+
+

get_color_map

+
+
 get_color_map (data:pandas.core.frame.DataFrame, hue:str, palette:str)
+
+

*Get a color map for a specific column in the data.

+

Args: data (pd.DataFrame): The data to get the color map from. hue (str): The column name to use for the color map. palette (str): The name of the colormap to use.

+

Returns: pd.DataFrame: A DataFrame with the color map.*

+
+

source

+
+
+

prepare_events

+
+
 prepare_events (events:pandas.core.frame.DataFrame, x_start:str,
+                 x_end:str, hue:str, label:str, participant_id:int,
+                 array_index:int, time_range:Tuple[str,str],
+                 y_include:Iterable[str], y_exclude:Iterable[str],
+                 add_columns:Iterable[str]=None, palette='muted')
+
+

*Prepare events for plotting.

+

Args: events (pd.DataFrame): The events dataframe. x_start (str): The column name for the start time of the event. x_end (str): The column name for the end time of the event. hue (str): The column name for the color of the event. label (str): The column name for the label of the event. participant_id (int): The participant ID to filter events by. array_index (int): The array index to filter events by. time_range (Iterable[str]): The time range to filter events by. y_include (Iterable[str]): The list of values to include in the plot. y_exclude (Iterable[str]): The list of values to exclude from the plot. add_columns (Iterable[str]): Additional columns to include in the plot. palette (str): The name of the colormap to use for coloring events.

+

Returns: Tuple[pd.DataFrame, pd.DataFrame]: The filtered events dataframe and the color map.*

+
+

source

+
+
+

plot_events_fill

+
+
 plot_events_fill (events:pandas.core.frame.DataFrame,
+                   x_start:str='collection_timestamp',
+                   x_end:str='event_end', hue:str='channel',
+                   label:str=None, participant_id:Optional[int]=None,
+                   array_index:Optional[int]=None,
+                   time_range:Optional[Tuple[str,str]]=None,
+                   y_include:Optional[Iterable[str]]=None,
+                   y_exclude:Optional[Iterable[str]]=None,
+                   legend:bool=True, palette:str='muted',
+                   alpha:Optional[float]=0.5,
+                   ax:Optional[matplotlib.axes._axes.Axes]=None,
+                   figsize:Iterable[float]=[12, 6])
+
+

*Plot events as filled regions on a time series plot.

+

Args: events (pd.DataFrame): The events dataframe. x_start (str): The column name for the start time of the event. x_end (str): The column name for the end time of the event. hue (str): The column name for the color of the event. label (str): The column name for the label of the event. participant_id (int): The participant ID to filter events by. array_index (int): The array index to filter events by. time_range (Iterable[str]): The time range to filter events by. y_include (Iterable[str]): The list of values to include in the plot. y_exclude (Iterable[str]): The list of values to exclude from the plot. legend (bool): Whether to show the legend. palette (str): The name of the palette to use for coloring events. alpha (float): The transparency of the filled regions. ax (plt.Axes): The axis to plot on. If None, a new figure is created. figsize (Tuple[float, float]): The size of the figure (width, height) in inches.*

+
+

source

+
+
+

plot_events_bars

+
+
 plot_events_bars (events:pandas.core.frame.DataFrame,
+                   x_start:str='collection_timestamp',
+                   x_end:str='event_end', y:str='event',
+                   hue:str='channel', participant_id:Optional[int]=None,
+                   array_index:Optional[int]=None,
+                   time_range:Optional[Tuple[str,str]]=None,
+                   y_include:Optional[Iterable[str]]=None,
+                   y_exclude:Optional[Iterable[str]]=None,
+                   legend:bool=True, palette:str='muted',
+                   alpha:Optional[float]=0.7,
+                   ax:Optional[matplotlib.axes._axes.Axes]=None,
+                   figsize:Tuple[float,float]=(12, 6))
+
+

*Plot events as bars on a time series plot.

+

Args: events (pd.DataFrame): The events dataframe. x_start (str): The column name for the start time of the event. x_end (str): The column name for the end time of the event. y (str): The column name for the y-axis values. hue (str): The column name for the color of the event. participant_id (int): The participant ID to filter events by. array_index (int): The array index to filter events by. time_range (Tuple[str, str]): The time range to filter events by. y_include (Iterable[str]): The list of values to include in the plot. y_exclude (Iterable[str]): The list of values to exclude from the plot. legend (bool): Whether to show the legend. palette (str): The name of the colormap to use for coloring events. alpha (float): The transparency of the bars. Default is 0.7. ax (plt.Axes): The axis to plot on. If None, a new figure is created. figsize (Tuple[float, float]): The size of the figure (width, height) in inches.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file
+ + +
+ + +

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