diff --git a/examples/mass_assessment/analysis.ipynb b/examples/mass_assessment/analysis.ipynb deleted file mode 100644 index 57b606ed..00000000 --- a/examples/mass_assessment/analysis.ipynb +++ /dev/null @@ -1,911 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import pandas as pd\n", - "from collections.abc import MutableMapping\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def flatten_dict(d: MutableMapping, sep: str= '.') -> MutableMapping:\n", - " \"\"\"Flatten a given nested python dict and return a dict\"\"\"\n", - " [flat_dict] = pd.json_normalize(d, sep=sep).to_dict(orient='records')\n", - " return flat_dict" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def read_data(results_path):\n", - " all_files = os.listdir(results_path)\n", - " print('Reading results from this path {}'.format(results_path))\n", - "\n", - " all_data = []\n", - " # read from file\n", - " for filec in all_files:\n", - " filepath = os.path.join(results_path, filec)\n", - " with open(filepath, 'r', encoding='utf-8') as fileo:\n", - " data = json.load(fileo)\n", - " all_data.append(data)\n", - "\n", - " # parse to dictionary\n", - " all_data_dict = {}\n", - " for repo_data in all_data:\n", - " datat = flatten_dict(repo_data)\n", - " repo_results = datat.pop('results')\n", - " for test_res in repo_results:\n", - " test_results = flatten_dict(test_res)\n", - " test_id = test_results['id']\n", - " for key, val in test_results.items():\n", - " datat[key + '_id{}'.format(test_id)] = val\n", - " # request.normalized_object_identifier not always there\n", - " datat['request.normalized_object_identifier'] = datat.get('request.normalized_object_identifier', None)\n", - " # append to table\n", - " for key, val in datat.items():\n", - " temp = all_data_dict.get(key, [])\n", - " temp.append(val)\n", - " all_data_dict[key] = temp\n", - "\n", - " df = pd.DataFrame(data=all_data_dict)\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading results from this path ./results/fair_ease\n" - ] - } - ], - "source": [ - "results_path = './results/fair_ease'\n", - "keys_dict = {\"metric_tests.FRSM-13-R1-1.metric_test_status_id13\": \"FRSM-13-R1-1\",\n", - " \"metric_tests.FRSM-13-R1-2.metric_test_status_id13\": \"FRSM-13-R1-2\",\n", - " \"metric_tests.FRSM-15-R1.1-1.metric_test_status_id15\": \"FRSM-15-R1.1-1\",\n", - " \"metric_tests.FRSM-15-R1.1-2.metric_test_status_id15\": \"FRSM-15-R1.1-2\",\n", - " \"metric_tests.FRSM-15-R1.1-3.metric_test_status_id15\": \"FRSM-15-R1.1-3\"\n", - " }\n", - "df_fe = read_data(results_path)\n", - "df_fe = df_fe[[\"resolved_url\"]+list(keys_dict.keys())].rename(columns=keys_dict).set_index(\"resolved_url\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"source": [ - "df_fe" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df_fe.to_csv(\"analysis_fair_ease.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_fe.apply(lambda x: x.value_counts()).T.sort_index(ascending=False).plot(\n", - " kind=\"barh\",\n", - " stacked=True,\n", - " color={\"fail\": \"orange\", \"pass\": \"yellowgreen\"},\n", - " xlabel=\"count\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading results from this path ./results/cessda\n" - ] - } - ], - "source": [ - "results_path = './results/cessda'\n", - "keys_dict = {\"metric_tests.FRSM-13-R1-CESSDA-1.metric_test_status_id13\": \"FRSM-13-R1-CESSDA-1\",\n", - " \"metric_tests.FRSM-13-R1-CESSDA-2.metric_test_status_id13\": \"FRSM-13-R1-CESSDA-2\",\n", - " \"metric_tests.FRSM-13-R1-CESSDA-3.metric_test_status_id13\": \"FRSM-13-R1-CESSDA-3\",\n", - " \"metric_tests.FRSM-15-R1.1-CESSDA-1.metric_test_status_id15\": \"FRSM-15-R1.1-CESSDA-1\",\n", - " \"metric_tests.FRSM-15-R1.1-CESSDA-2.metric_test_status_id15\": \"FRSM-15-R1.1-CESSDA-2\",\n", - " \"metric_tests.FRSM-15-R1.1-CESSDA-3.metric_test_status_id15\": \"FRSM-15-R1.1-CESSDA-3\"\n", - " }\n", - "df_c = read_data(results_path)\n", - "df_c = df_c[[\"resolved_url\"]+list(keys_dict.keys())].rename(columns=keys_dict).set_index(\"resolved_url\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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resolved_url
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https://github.com/cessda/cessda.code.nesstarfailfailfailpassfailfail
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https://github.com/cessda/eqb.colectica.issuesfailfailfailfailfailfail
https://github.com/cessda/cessda.cdc.aggregator.devguidefailfailfailfailfailfail
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"https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.documentation.... fail \n", - "https://github.com/cessda/cessda.metadata.harve... pass \n", - "https://github.com/cessda/cessda.training-resou... fail \n", - "https://github.com/cessda/cessda.cmv fail \n", - "https://github.com/cessda/cessda.code.nesstar fail \n", - "https://github.com/cessda/cessda.twitter.api.stats fail \n", - "https://github.com/cessda/cessda.cmv.console pass \n", - "https://github.com/cessda/cessda.cdc.aggregator... pass \n", - "https://github.com/cessda/cessda.resource-direc... fail \n", - "https://github.com/cessda/cessda.cvs.userguide fail \n", - "https://github.com/cessda/cessda.cmv.documentation fail \n", - "https://github.com/cessda/cessda.cdc.osmh-index... pass \n", - "https://github.com/cessda/cessda.code.dataverse fail \n", - "https://github.com/cessda/cessda.cmv.core pass \n", - "https://github.com/cessda/cessda.cdc.fuji.runner fail \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.cdc.userguide pass \n", - "https://github.com/cessda/eqb.colectica.issues fail \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.cvs.two pass \n", - "https://github.com/cessda/cessda.cdc.searchkit fail \n", - "https://github.com/cessda/cessda.cafe.cashier fail \n", - "https://github.com/cessda/cessda.dmeg-dag.issues fail \n", - "https://github.com/cessda/cessda.cmv.server pass \n", - "https://github.com/cessda/cessda.guidelines.public fail \n", - "\n", - " FRSM-15-R1.1-CESSDA-3 \n", - "resolved_url \n", - "https://github.com/cessda/cessda.cvs.contentguide fail \n", - "https://github.com/cessda/cessda.metadata.profiles fail \n", - "https://github.com/cessda/cessda.cdc.versions fail \n", - "https://github.com/cessda/cessda.cafe.waiter fail \n", - "https://github.com/cessda/maintenance.notification fail \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.cafe.coffee.ca... fail \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.documentation.... fail \n", - "https://github.com/cessda/cessda.metadata.harve... pass \n", - "https://github.com/cessda/cessda.training-resou... fail \n", - "https://github.com/cessda/cessda.cmv fail \n", - "https://github.com/cessda/cessda.code.nesstar fail \n", - "https://github.com/cessda/cessda.twitter.api.stats fail \n", - "https://github.com/cessda/cessda.cmv.console pass \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.resource-direc... fail \n", - "https://github.com/cessda/cessda.cvs.userguide fail \n", - "https://github.com/cessda/cessda.cmv.documentation fail \n", - "https://github.com/cessda/cessda.cdc.osmh-index... pass \n", - "https://github.com/cessda/cessda.code.dataverse fail \n", - "https://github.com/cessda/cessda.cmv.core pass \n", - "https://github.com/cessda/cessda.cdc.fuji.runner fail \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.cdc.userguide fail \n", - "https://github.com/cessda/eqb.colectica.issues fail \n", - "https://github.com/cessda/cessda.cdc.aggregator... fail \n", - "https://github.com/cessda/cessda.cvs.two pass \n", - "https://github.com/cessda/cessda.cdc.searchkit fail \n", - "https://github.com/cessda/cessda.cafe.cashier fail \n", - "https://github.com/cessda/cessda.dmeg-dag.issues fail \n", - "https://github.com/cessda/cessda.cmv.server pass \n", - "https://github.com/cessda/cessda.guidelines.public fail " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_c" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_c.apply(lambda x: x.value_counts()).T.sort_index(ascending=False).plot(\n", - " kind=\"barh\",\n", - " stacked=True,\n", - " color={\"fail\": \"orange\", \"pass\": \"yellowgreen\"},\n", - " xlabel=\"count\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_c.to_csv(\"analysis_cessda.csv\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "fuji", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/mass_assessment/analysis_cessda.csv b/examples/mass_assessment/analysis_cessda.csv deleted file mode 100644 index 623609b2..00000000 --- a/examples/mass_assessment/analysis_cessda.csv +++ /dev/null @@ -1,34 +0,0 @@ -resolved_url,FRSM-13-R1-CESSDA-1,FRSM-13-R1-CESSDA-2,FRSM-13-R1-CESSDA-3,FRSM-15-R1.1-CESSDA-1,FRSM-15-R1.1-CESSDA-2,FRSM-15-R1.1-CESSDA-3 -https://github.com/cessda/cessda.cvs.contentguide,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.metadata.profiles,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.cdc.versions,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.cafe.waiter,fail,pass,pass,pass,fail,fail -https://github.com/cessda/maintenance.notification,fail,fail,fail,fail,fail,fail -https://github.com/cessda/cessda.cdc.aggregator.oai-pmh-repo-handler,fail,fail,pass,pass,fail,fail -https://github.com/cessda/cessda.cafe.coffee.carsten,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.cdc.aggregator.doc-store,fail,fail,pass,pass,fail,fail -https://github.com/cessda/cessda.documentation.theme,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.metadata.harvester,fail,pass,pass,pass,pass,pass -https://github.com/cessda/cessda.training-resources.issues,fail,fail,fail,fail,fail,fail -https://github.com/cessda/cessda.cmv,fail,pass,pass,pass,fail,fail -https://github.com/cessda/cessda.code.nesstar,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.twitter.api.stats,fail,fail,pass,pass,fail,fail -https://github.com/cessda/cessda.cmv.console,fail,pass,pass,pass,pass,pass -https://github.com/cessda/cessda.cdc.aggregator.client,fail,fail,pass,pass,pass,fail -https://github.com/cessda/cessda.resource-directory.issues,fail,fail,fail,fail,fail,fail -https://github.com/cessda/cessda.cvs.userguide,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.cmv.documentation,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.cdc.osmh-indexer.cmm,fail,pass,pass,pass,pass,pass -https://github.com/cessda/cessda.code.dataverse,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.cmv.core,fail,pass,pass,pass,pass,pass -https://github.com/cessda/cessda.cdc.fuji.runner,fail,pass,pass,pass,fail,fail -https://github.com/cessda/cessda.cdc.aggregator.shared-library,fail,fail,pass,pass,fail,fail -https://github.com/cessda/cessda.cdc.userguide,fail,fail,fail,pass,pass,fail -https://github.com/cessda/eqb.colectica.issues,fail,fail,fail,fail,fail,fail -https://github.com/cessda/cessda.cdc.aggregator.devguide,fail,fail,fail,fail,fail,fail -https://github.com/cessda/cessda.cvs.two,fail,pass,pass,pass,pass,pass -https://github.com/cessda/cessda.cdc.searchkit,pass,pass,pass,pass,fail,fail -https://github.com/cessda/cessda.cafe.cashier,fail,fail,fail,pass,fail,fail -https://github.com/cessda/cessda.dmeg-dag.issues,fail,fail,fail,fail,fail,fail -https://github.com/cessda/cessda.cmv.server,fail,pass,pass,pass,pass,pass -https://github.com/cessda/cessda.guidelines.public,fail,fail,fail,pass,fail,fail diff --git a/examples/mass_assessment/analysis_fair_ease.csv b/examples/mass_assessment/analysis_fair_ease.csv deleted file mode 100644 index 2e27bb7a..00000000 --- a/examples/mass_assessment/analysis_fair_ease.csv +++ /dev/null @@ -1,5 +0,0 @@ -resolved_url,FRSM-13-R1-1,FRSM-13-R1-2,FRSM-15-R1.1-1,FRSM-15-R1.1-2,FRSM-15-R1.1-3 -https://github.com/HCBScienceProducts/CANYON-B,fail,fail,pass,fail,pass -https://github.com/ESSI-Lab/DAB,pass,fail,pass,fail,pass -https://github.com/gher-uliege/DIVAnd.jl,fail,fail,pass,fail,pass -https://github.com/Geomatys/examind-community,pass,fail,pass,fail,fail diff --git a/examples/mass_assessment/cessda_repos.txt b/examples/mass_assessment/cessda_repos.txt deleted file mode 100644 index c5608e5d..00000000 --- a/examples/mass_assessment/cessda_repos.txt +++ /dev/null @@ -1,33 +0,0 @@ -https://github.com/cessda/cessda.cafe.cashier -https://github.com/cessda/cessda.cafe.waiter -https://github.com/cessda/cessda.cafe.coffee.carsten -https://github.com/cessda/cessda.guidelines.public -https://github.com/cessda/cessda.documentation.theme -https://github.com/cessda/cessda.cmv.server -https://github.com/cessda/cessda.cmv.documentation -https://github.com/cessda/cessda.twitter.api.stats -https://github.com/cessda/cessda.cmv.core -https://github.com/cessda/cessda.cmv -https://github.com/cessda/cessda.code.dataverse -https://github.com/cessda/cessda.code.nesstar -https://github.com/cessda/cessda.cmv.console -https://github.com/cessda/cessda.cvs.contentguide -https://github.com/cessda/cessda.cvs.userguide -https://github.com/cessda/cessda.cvs.two -https://github.com/cessda/cessda.metadata.harvester -https://github.com/cessda/cessda.cdc.userguide -https://github.com/cessda/cessda.cdc.searchkit -https://github.com/cessda/cessda.cdc.osmh-indexer.cmm -https://github.com/cessda/cessda.cdc.fuji.runner -https://github.com/cessda/cessda.cdc.aggregator.shared-library -https://github.com/cessda/cessda.cdc.aggregator.oai-pmh-repo-handler -https://github.com/cessda/cessda.cdc.aggregator.doc-store -https://github.com/cessda/cessda.cdc.aggregator.devguide -https://github.com/cessda/cessda.cdc.aggregator.client -https://github.com/cessda/cessda.cdc.versions -https://github.com/cessda/cessda.metadata.profiles -https://github.com/cessda/maintenance.notification -https://github.com/cessda/eqb.colectica.issues -https://github.com/cessda/cessda.dmeg-dag.issues -https://github.com/cessda/cessda.training-resources.issues -https://github.com/cessda/cessda.resource-directory.issues diff --git a/examples/mass_assessment/fair_ease_repos.txt b/examples/mass_assessment/fair_ease_repos.txt deleted file mode 100644 index ee2adb96..00000000 --- a/examples/mass_assessment/fair_ease_repos.txt +++ /dev/null @@ -1,4 +0,0 @@ -https://github.com/gher-uliege/DIVAnd.jl -https://github.com/HCBScienceProducts/CANYON-B -https://github.com/Geomatys/examind-community -https://github.com/ESSI-Lab/DAB