--- /dev/null
+# 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]))
+ }
Code Review / csit.git / blobdiff