""" Upset Plots =========== """ # %% # `Upset plots `_ can be generated using the :py:func:`~isaricanalytics.visualisation.fig_upset` function, which returns a :py:class:`Plotly Go Figure ` object. # %% # The plot below was generated using a synthetic dataset of patient treatment complications for Dengue, consisting of five complications and their patient counts, as well as counts for the intersections (conjoint occurrences) of the complication subsets. # # The dataset is given below as a table (but can also be loaded from the `docs/sources/plot-gallery/examples/csv/upset.csv `_ file). # # .. list-table:: Dengue patient treatment complications - patient counts # :header-rows: 1 # :widths: auto # # * - Complication # - Patient Count # * - Shock # - 891 # * - Meningitis # - 781 # * - Acute renal injury / acute renal failure # - 757 # * - Cardiac arrest # - 709 # * - Focal neurological signs # - 702 # # and the second showing the intersections between these subsets: # # .. list-table:: Dengue patient treatment complications - intersection counts # :header-rows: 1 # :widths: auto # # * - Complication Intersections # - Patient Count # * - Shock, Meningitis, Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs # - 256 # * - Shock, Meningitis, Acute renal injury / acute renal failure, Cardiac arrest # - 121 # * - Shock, Meningitis, Acute renal injury / acute renal failure, Focal neurological signs # - 115 # * - Shock, Meningitis, Cardiac arrest, Focal neurological signs # - 86 # * - Shock, Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs # - 67 # * - Shock, Meningitis, Focal neurological signs # - 37 # * - Shock, Acute renal injury / acute renal failure, Cardiac arrest # - 37 # * - Shock, Acute renal injury / acute renal failure, Focal neurological signs # - 34 # * - Shock, Meningitis, Cardiac arrest # - 31 # * - Shock, Meningitis, Acute renal injury / acute renal failure # - 30 # * - Meningitis, Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs # - 30 # * - Shock, Cardiac arrest, Focal neurological signs # - 26 # * - Meningitis, Acute renal injury / acute renal failure, Focal neurological signs # - 18 # * - Shock, Meningitis # - 15 # * - Meningitis, Acute renal injury / acute renal failure, Cardiac arrest # - 15 # * - Shock, Acute renal injury / acute renal failure # - 14 # * - Shock, Cardiac arrest # - 11 # * - Shock, Focal neurological signs # - 9 # * - Meningitis, Cardiac arrest, Focal neurological signs # - 8 # * - Meningitis, Acute renal injury / acute renal failure # - 7 # * - Meningitis, Cardiac arrest # - 6 # * - Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs # - 6 # * - Acute renal injury / acute renal failure, Cardiac arrest # - 5 # * - Meningitis, Focal neurological signs # - 3 # * - Meningitis, # - 3 # * - Cardiac arrest, Focal neurological signs # - 3 # * - Shock, # - 2 # * - Acute renal injury / acute renal failure, Focal neurological signs # - 2 # * - Focal neurological signs, # - 2 # * - Cardiac arrest, # - 1 # # The :py:func:`~isaricanalytics.visualisation.fig_upset` function expects these tables in the form of **a pair** of dataframes, the first dataframe containing the complication counts data with the following columns (in no particular order): # # * ``"index"`` - a label internal to the function denoting the complication, and prefixed with ``"compl"``, e.g. ``"compl_shock"`` for shock, ``"compl_meningitis"`` for meningitis, ``"compl_acuterenal"`` for acute renal injury / failure etc. # * ``"label"`` - a descriptive label for the complication # * ``"short_label"`` - a more concise descriptive label for the complication, which could be the same as the value of ``"label"`` # * ``"count"`` - the complication count # # and the second dataframe containing the complication intersection data with the following columns (in no particular order): # # * ``"index"`` - a string form of a tuple of the labels described above for the complications, e.g. ``"('compl_shock', 'compl_meningitis', 'compl_acuterenal', 'compl_cardiarrest', 'compl_focalneuro')"`` for the intersection of shock, meningitis, acute renal injury / failure, cardiac arrest, focal neurological signs. # * ``"label"`` - a string form of the descriptive labels associated with the complications, e.g. ``"('Shock ', 'Meningitis', 'Acute renal injury / acute renal failure', 'Cardiac arrest', 'Focal neurological signs')"`` # * ``"count"`` - the complication intersection count # # Here are the Python steps you need to generate the plot using the :py:func:`~isaricanalytics.visualisation.fig_upset` function: import pandas as pd from isaricanalytics.visualisation import fig_upset # Load the CSV data from a string buffer counts = pd.read_csv("./csv/upset_counts.csv") intersections = pd.read_csv("./csv/upset_intersections.csv") # Create and display the figure fig = fig_upset( (counts, intersections), title="Upset Plot of Dengue Patient Treatment Complications", ) fig.update_layout(autosize=True) fig # %% # # .. note:: # # Note that the counts and intersections dataframes are provided in a :py:class:`tuple` object. # # .. note:: # # Any dataframe or CSV column names, or dictionary field labels, in the # example above that are not specific to the dataset must be as given, # otherwise the function may throw an exception or return an incorrect figure. # # The figure height and width parameters can be set using the ``height`` # and ``width`` parameters, but it may be more convenient to let Plotly handle # this using the figure layout `autosize `_ # parameter. Refer to the :py:func:`~isaricanalytics.visualisation.fig_upset` # function docstring for more information.