"""Function used by Dash applications.
"""
+import pandas as pd
import dash_bootstrap_components as dbc
from numpy import isnan
its index.
:param idx: Index of the color.
- :type idex: int
+ :type idx: int
:returns: Color defined by hex code.
:trype: str
"""
return datetime(int(s_date[0:4]), int(s_date[5:7]), int(s_date[8:10]))
-def gen_new_url(parsed_url: dict, params: dict) -> str:
+def gen_new_url(url_components: dict, params: dict) -> str:
"""Generate a new URL with encoded parameters.
- :param parsed_url: Dictionary with URL elements. It should contain "scheme",
- "netloc" and "path".
- :param params: URL parameters to be encoded to the URL.
+ :param url_components: Dictionary with URL elements. It should contain
+ "scheme", "netloc" and "path".
+ :param url_components: URL parameters to be encoded to the URL.
:type parsed_url: dict
:type params: dict
:returns Encoded URL with parameters.
:rtype: str
"""
- if parsed_url:
+ if url_components:
return url_encode(
{
- "scheme": parsed_url.get("scheme", ""),
- "netloc": parsed_url.get("netloc", ""),
- "path": parsed_url.get("path", ""),
+ "scheme": url_components.get("scheme", ""),
+ "netloc": url_components.get("netloc", ""),
+ "path": url_components.get("path", ""),
"params": params
}
)
else:
return str()
+
+
+def get_duts(df: pd.DataFrame) -> list:
+ """Get the list of DUTs from the pre-processed information about jobs.
+
+ :param df: DataFrame with information about jobs.
+ :type df: pandas.DataFrame
+ :returns: Alphabeticaly sorted list of DUTs.
+ :rtype: list
+ """
+ return sorted(list(df["dut"].unique()))
+
+
+def get_ttypes(df: pd.DataFrame, dut: str) -> list:
+ """Get the list of test types from the pre-processed information about
+ jobs.
+
+ :param df: DataFrame with information about jobs.
+ :param dut: The DUT for which the list of test types will be populated.
+ :type df: pandas.DataFrame
+ :type dut: str
+ :returns: Alphabeticaly sorted list of test types.
+ :rtype: list
+ """
+ return sorted(list(df.loc[(df["dut"] == dut)]["ttype"].unique()))
+
+
+def get_cadences(df: pd.DataFrame, dut: str, ttype: str) -> list:
+ """Get the list of cadences from the pre-processed information about
+ jobs.
+
+ :param df: DataFrame with information about jobs.
+ :param dut: The DUT for which the list of cadences will be populated.
+ :param ttype: The test type for which the list of cadences will be
+ populated.
+ :type df: pandas.DataFrame
+ :type dut: str
+ :type ttype: str
+ :returns: Alphabeticaly sorted list of cadences.
+ :rtype: list
+ """
+ return sorted(list(df.loc[(
+ (df["dut"] == dut) &
+ (df["ttype"] == ttype)
+ )]["cadence"].unique()))
+
+
+def get_test_beds(df: pd.DataFrame, dut: str, ttype: str, cadence: str) -> list:
+ """Get the list of test beds from the pre-processed information about
+ jobs.
+
+ :param df: DataFrame with information about jobs.
+ :param dut: The DUT for which the list of test beds will be populated.
+ :param ttype: The test type for which the list of test beds will be
+ populated.
+ :param cadence: The cadence for which the list of test beds will be
+ populated.
+ :type df: pandas.DataFrame
+ :type dut: str
+ :type ttype: str
+ :type cadence: str
+ :returns: Alphabeticaly sorted list of test beds.
+ :rtype: list
+ """
+ return sorted(list(df.loc[(
+ (df["dut"] == dut) &
+ (df["ttype"] == ttype) &
+ (df["cadence"] == cadence)
+ )]["tbed"].unique()))
+
+
+def get_job(df: pd.DataFrame, dut, ttype, cadence, testbed):
+ """Get the name of a job defined by dut, ttype, cadence, test bed.
+ Input information comes from the control panel.
+
+ :param df: DataFrame with information about jobs.
+ :param dut: The DUT for which the job name will be created.
+ :param ttype: The test type for which the job name will be created.
+ :param cadence: The cadence for which the job name will be created.
+ :param testbed: The test bed for which the job name will be created.
+ :type df: pandas.DataFrame
+ :type dut: str
+ :type ttype: str
+ :type cadence: str
+ :type testbed: str
+ :returns: Job name.
+ :rtype: str
+ """
+ return df.loc[(
+ (df["dut"] == dut) &
+ (df["ttype"] == ttype) &
+ (df["cadence"] == cadence) &
+ (df["tbed"] == testbed)
+ )]["job"].item()
+
+
+def generate_options(opts: list) -> list:
+ """Return list of options for radio items in control panel. The items in
+ the list are dictionaries with keys "label" and "value".
+
+ :params opts: List of options (str) to be used for the generated list.
+ :type opts: list
+ :returns: List of options (dict).
+ :rtype: list
+ """
+ return [{"label": i, "value": i} for i in opts]
+
+
+def set_job_params(df: pd.DataFrame, job: str) -> dict:
+ """Create a dictionary with all options and values for (and from) the
+ given job.
+
+ :param df: DataFrame with information about jobs.
+ :params job: The name of job for and from which the dictionary will be
+ created.
+ :type df: pandas.DataFrame
+ :type job: str
+ :returns: Dictionary with all options and values for (and from) the
+ given job.
+ :rtype: dict
+ """
+
+ l_job = job.split("-")
+ return {
+ "job": job,
+ "dut": l_job[1],
+ "ttype": l_job[3],
+ "cadence": l_job[4],
+ "tbed": "-".join(l_job[-2:]),
+ "duts": generate_options(get_duts(df)),
+ "ttypes": generate_options(get_ttypes(df, l_job[1])),
+ "cadences": generate_options(get_cadences(df, l_job[1], l_job[3])),
+ "tbeds": generate_options(
+ get_test_beds(df, l_job[1], l_job[3], l_job[4]))
+ }
Code Review / csit.git / blobdiff