diff options
Diffstat (limited to 'resources/tools/dash/app/pal/utils')
| -rw-r--r-- | resources/tools/dash/app/pal/utils/constants.py | 6 | ||||
| -rw-r--r-- | resources/tools/dash/app/pal/utils/utils.py | 154 |
2 files changed, 150 insertions, 10 deletions
diff --git a/resources/tools/dash/app/pal/utils/constants.py b/resources/tools/dash/app/pal/utils/constants.py index 1c84ba14a6..7a836e79f3 100644 --- a/resources/tools/dash/app/pal/utils/constants.py +++ b/resources/tools/dash/app/pal/utils/constants.py @@ -253,7 +253,11 @@ class Constants: # The default job displayed when the page is loaded first time. NEWS_DEFAULT_JOB = "csit-vpp-perf-mrr-daily-master-2n-icx" - # Time period for regressions and progressions. + # Time period for regressions and progressions. Be CAREFULL with this + # number. Setting it too high causes long processing time during the + # application start-up. + # If NEWS_TIME_PERIOD = 180, it takes approx. 35 minutes to calculate + # annomalies for all tests. NEWS_TIME_PERIOD = 21 # [days] ############################################################################ diff --git a/resources/tools/dash/app/pal/utils/utils.py b/resources/tools/dash/app/pal/utils/utils.py index 70fb02e189..9e4eeeb892 100644 --- a/resources/tools/dash/app/pal/utils/utils.py +++ b/resources/tools/dash/app/pal/utils/utils.py @@ -14,6 +14,7 @@ """Function used by Dash applications. """ +import pandas as pd import dash_bootstrap_components as dbc from numpy import isnan @@ -80,7 +81,7 @@ def get_color(idx: int) -> str: its index. :param idx: Index of the color. - :type idex: int + :type idx: int :returns: Color defined by hex code. :trype: str """ @@ -183,26 +184,161 @@ def get_date(s_date: str) -> datetime: 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])) + } |