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Diffstat (limited to 'resources/tools/dash/app/pal/utils/utils.py')
| -rw-r--r-- | resources/tools/dash/app/pal/utils/utils.py | 344 |
1 files changed, 0 insertions, 344 deletions
diff --git a/resources/tools/dash/app/pal/utils/utils.py b/resources/tools/dash/app/pal/utils/utils.py deleted file mode 100644 index 9e4eeeb892..0000000000 --- a/resources/tools/dash/app/pal/utils/utils.py +++ /dev/null @@ -1,344 +0,0 @@ -# Copyright (c) 2022 Cisco and/or its affiliates. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Function used by Dash applications. -""" - -import pandas as pd -import dash_bootstrap_components as dbc - -from numpy import isnan -from dash import dcc -from datetime import datetime - -from ..jumpavg import classify -from ..utils.constants import Constants as C -from ..utils.url_processing import url_encode - - -def classify_anomalies(data): - """Process the data and return anomalies and trending values. - - Gather data into groups with average as trend value. - Decorate values within groups to be normal, - the first value of changed average as a regression, or a progression. - - :param data: Full data set with unavailable samples replaced by nan. - :type data: OrderedDict - :returns: Classification and trend values - :rtype: 3-tuple, list of strings, list of floats and list of floats - """ - # NaN means something went wrong. - # Use 0.0 to cause that being reported as a severe regression. - bare_data = [0.0 if isnan(sample) else sample for sample in data.values()] - # TODO: Make BitCountingGroupList a subclass of list again? - group_list = classify(bare_data).group_list - group_list.reverse() # Just to use .pop() for FIFO. - classification = list() - avgs = list() - stdevs = list() - active_group = None - values_left = 0 - avg = 0.0 - stdv = 0.0 - for sample in data.values(): - if isnan(sample): - classification.append("outlier") - avgs.append(sample) - stdevs.append(sample) - continue - if values_left < 1 or active_group is None: - values_left = 0 - while values_left < 1: # Ignore empty groups (should not happen). - active_group = group_list.pop() - values_left = len(active_group.run_list) - avg = active_group.stats.avg - stdv = active_group.stats.stdev - classification.append(active_group.comment) - avgs.append(avg) - stdevs.append(stdv) - values_left -= 1 - continue - classification.append("normal") - avgs.append(avg) - stdevs.append(stdv) - values_left -= 1 - return classification, avgs, stdevs - - -def get_color(idx: int) -> str: - """Returns a color from the list defined in Constants.PLOT_COLORS defined by - its index. - - :param idx: Index of the color. - :type idx: int - :returns: Color defined by hex code. - :trype: str - """ - return C.PLOT_COLORS[idx % len(C.PLOT_COLORS)] - - -def show_tooltip(tooltips:dict, id: str, title: str, - clipboard_id: str=None) -> list: - """Generate list of elements to display a text (e.g. a title) with a - tooltip and optionaly with Copy&Paste icon and the clipboard - functionality enabled. - - :param tooltips: Dictionary with tooltips. - :param id: Tooltip ID. - :param title: A text for which the tooltip will be displayed. - :param clipboard_id: If defined, a Copy&Paste icon is displayed and the - clipboard functionality is enabled. - :type tooltips: dict - :type id: str - :type title: str - :type clipboard_id: str - :returns: List of elements to display a text with a tooltip and - optionaly with Copy&Paste icon. - :rtype: list - """ - - return [ - dcc.Clipboard(target_id=clipboard_id, title="Copy URL") \ - if clipboard_id else str(), - f"{title} ", - dbc.Badge( - id=id, - children="?", - pill=True, - color="white", - text_color="info", - class_name="border ms-1", - ), - dbc.Tooltip( - children=tooltips.get(id, str()), - target=id, - placement="auto" - ) - ] - - -def label(key: str) -> str: - """Returns a label for input elements (dropdowns, ...). - - If the label is not defined, the function returns the provided key. - - :param key: The key to the label defined in Constants.LABELS. - :type key: str - :returns: Label. - :rtype: str - """ - return C.LABELS.get(key, key) - - -def sync_checklists(options: list, sel: list, all: list, id: str) -> tuple: - """Synchronize a checklist with defined "options" with its "All" checklist. - - :param options: List of options for the cheklist. - :param sel: List of selected options. - :param all: List of selected option from "All" checklist. - :param id: ID of a checklist to be used for synchronization. - :returns: Tuple of lists with otions for both checklists. - :rtype: tuple of lists - """ - opts = {v["value"] for v in options} - if id =="all": - sel = list(opts) if all else list() - else: - all = ["all", ] if set(sel) == opts else list() - return sel, all - - -def list_tests(selection: dict) -> list: - """Transform list of tests to a list of dictionaries usable by checkboxes. - - :param selection: List of tests to be displayed in "Selected tests" window. - :type selection: list - :returns: List of dictionaries with "label", "value" pairs for a checkbox. - :rtype: list - """ - if selection: - return [{"label": v["id"], "value": v["id"]} for v in selection] - else: - return list() - - -def get_date(s_date: str) -> datetime: - """Transform string reprezentation of date to datetime.datetime data type. - - :param s_date: String reprezentation of date. - :type s_date: str - :returns: Date as datetime.datetime. - :rtype: datetime.datetime - """ - return datetime(int(s_date[0:4]), int(s_date[5:7]), int(s_date[8:10])) - - -def gen_new_url(url_components: dict, params: dict) -> str: - """Generate a new URL with encoded parameters. - - :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 url_components: - return url_encode( - { - "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])) - } |