def load(input: Dict) -> "Cohort":
if input["output_type"] == "patients":
return Cohort(input["name"], input["name"],
read_data_frame(input["output_path"]))
else:
return Cohort(
input["name"],
"subjects with event {}".format(input["name"]),
read_data_frame(input["population_path"]),
read_data_frame(input["output_path"]),
)
def from_json(json_content: Dict) -> "SingleTable":
"""
Build single table from metadata.
Parameters
----------
json_content : Dict, single table part in metadata.
See Also
--------
FlatTableCollection.from_json(json_file) : FlatTableCollection.
FlatTable.from_json(json_content, single_tables) : FlatTable.
Notes
-----
Generally, this method is called by FlatTableCollection.from_json(json_file).
We got a single table by FlatTable.single_tables.get(single_table_name).
"""
path = "{}/{}".format(json_content["output_path"],
json_content["output_table"])
return SingleTable(
json_content["output_table"],
read_data_frame(path),
json_content["output_table"],
)
def from_json(json_content: Dict,
single_tables: Dict[str, SingleTable]) -> "FlatTable":
"""
Build flat table from metadata.
Parameters
----------
json_content : Dict, flat table part in metadata.
single_tables: Dict, single tables in this flat table.
See Also
--------
FlatTableCollection.from_json(json_file) : FlatTableCollection.
SingleTable.from_json(json_content) : SingleTable.
Notes
-----
Generally, this method is called by FlatTableCollection.from_json(json_file).
We got a flat table by FlatTableCollection.get(flat_table_name).
"""
path = "{}/{}".format(json_content["output_path"],
json_content["output_table"])
return FlatTable(
json_content["output_table"],
read_data_frame(path),
json_content["output_table"],
json_content["join_keys"],
single_tables,
)
def compare_stats_patients_on_months(
figure: Figure,
his_patients: Dict[str, str],
show=False,
show_func=print,
save_path=None,
years: List[int] = None,
) -> Figure:
"""
This method is used to compare histories of patients on months.
Parameters
----------
figure: matplotlib.figure.Figure, users can define it like plt.figure() or plt.gcf().
his_patients: Dict, a dict of paths of patients stats
show: {False, True}, optional,
If show the pandas table of confidence degree, default first when optional.
show_func: optional
Function to show a pandas table, print by default.
save_path: str, optional
the HDFS path to persist the pandas table, None by default, the save data can be
used in stat history api.
years: a list of special years in which the data will be loaded, default is None.
Examples
--------
This is an example to illustrate how to use the function in jupyter.
>>> his = {"A":"/path/A", "B":"/path/B"}
... compare_stats_patients_on_months(plt.gcf(), his, show=True, show_func=display)
... plt.show()
"""
data = {
name: read_data_frame(path)
for (name, path) in his_patients.items()
}
cohort = HistoryTable.build("Histories of patients",
"Histories of patients", data)
return _compare_stats_each_year_on_months(
figure,
cohort,
show=show,
show_func=show_func,
save_path=save_path,
title="History of patients",
ylabel="number of patients",
years=years,
)