diff --git a/README.md b/README.md index 415295293..e723f5cc9 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,7 @@ Most probably, you don't want to fully process the certification artifacts by yo ```python from sec_certs.dataset import CCDataset -dset = CCDataset.from_web_latest() # now you can inspect the object, certificates are held in dset.certs +dset = CCDataset.from_web() # now you can inspect the object, certificates are held in dset.certs df = dset.to_pandas() # Or you can transform the object into Pandas dataframe dset.to_json( './latest_cc_snapshot.json') # You may want to store the snapshot as json, so that you don't have to download it again diff --git a/docs/api/dataset.md b/docs/api/dataset.md index 714b3a799..a3b8a8367 100644 --- a/docs/api/dataset.md +++ b/docs/api/dataset.md @@ -25,6 +25,14 @@ The examples related to this package can be found in the [common criteria notebo :members: ``` +## ProtectionProfileDataset + +```{eval-rst} +.. currentmodule:: sec_certs.dataset +.. autoclass:: ProtectionProfileDataset + :members: +``` + ## FIPSDataset ```{eval-rst} diff --git a/docs/api/sample.md b/docs/api/sample.md index e404fd47a..1690ea336 100644 --- a/docs/api/sample.md +++ b/docs/api/sample.md @@ -17,10 +17,19 @@ The examples related to this package can be found in the [common criteria notebo :members: ``` +## ProtectionProfile + +```{eval-rst} +.. currentmodule:: sec_certs.sample +.. autoclass:: ProtectionProfile + :members: +``` + ## FIPSCertificate ```{eval-rst} .. currentmodule:: sec_certs.sample .. autoclass:: FIPSCertificate :members: -``` \ No newline at end of file +``` + diff --git a/docs/index.md b/docs/index.md index f97933439..cb3720389 100644 --- a/docs/index.md +++ b/docs/index.md @@ -41,6 +41,7 @@ search_examples.md :maxdepth: 1 Demo Common Criteria +Protection Profiles FIPS-140 FIPS-140 IUT FIPS-140 MIP diff --git a/docs/quickstart.md b/docs/quickstart.md index 685423ba6..8371163fb 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -8,7 +8,7 @@ ```python from sec_certs.dataset.cc import CCDataset -dset = CCDataset.from_web_latest() +dset = CCDataset.from_web() ``` to obtain to obtain freshly processed dataset from [sec-certs.org](https://sec-certs.org). @@ -21,7 +21,7 @@ to obtain to obtain freshly processed dataset from [sec-certs.org](https://sec-c ```python from sec_certs.dataset.fips import FIPSDataset -dset = FIPSDataset.from_web_latest() +dset = FIPSDataset.from_web() ``` to obtain to obtain freshly processed dataset from [sec-certs.org](https://sec-certs.org). diff --git a/docs/user_guide.md b/docs/user_guide.md index 9d330c6c1..8e6e2f4ba 100644 --- a/docs/user_guide.md +++ b/docs/user_guide.md @@ -16,11 +16,11 @@ Our tool can seamlessly download the required NVD datasets when needed. We suppo The following two keys control the behaviour: ```yaml -preferred_source_nvd_datasets: "api" # set to "sec-certs" to fetch them from sec-certs.org +preferred_source_aux_datasets: "api" # set to "sec-certs" to fetch them from sec-certs.org nvd_api_key: null # or the actual key value ``` -If you aim to fetch the sources from NVD, we advise you to get an [NVD API key](https://nvd.nist.gov/developers/request-an-api-key) and set the `nvd_api_key` setting accordingly. The download from NVD will work even without API key, it will just be slow. No API key is needed when `preferred_source_nvd_datasets: "sec-certs"` +If you aim to fetch the sources from NVD, we advise you to get an [NVD API key](https://nvd.nist.gov/developers/request-an-api-key) and set the `nvd_api_key` setting accordingly. The download from NVD will work even without API key, it will just be slow. No API key is needed when `preferred_source_aux_datasets: "sec-certs"` ## Inferring inter-certificate reference context diff --git a/notebooks/cc/cert_id_eval.ipynb b/notebooks/cc/cert_id_eval.ipynb index 2af43c548..97e608a13 100644 --- a/notebooks/cc/cert_id_eval.ipynb +++ b/notebooks/cc/cert_id_eval.ipynb @@ -64,7 +64,7 @@ }, "outputs": [], "source": [ - "dset = CCDataset.from_web_latest()\n" + "dset = CCDataset.from_web()\n" ] }, { diff --git a/notebooks/cc/chain_of_trust_plots.ipynb b/notebooks/cc/chain_of_trust_plots.ipynb index 708502a8d..378cce57d 100644 --- a/notebooks/cc/chain_of_trust_plots.ipynb +++ b/notebooks/cc/chain_of_trust_plots.ipynb @@ -1,9 +1,11 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Plots from the \"Chain of Trust\" paper" + "metadata": {}, + "source": [ + "# Plots from the \"Chain of Trust\" paper" + ] }, { "cell_type": "code", @@ -79,7 +81,7 @@ "figure_width = 3.5\n", "figure_height = 2.5\n", "\n", - "dset = CCDataset.from_web_latest()\n", + "dset = CCDataset.from_web()\n", "df = dset.to_pandas()" ] }, diff --git a/notebooks/cc/cpe_eval.ipynb b/notebooks/cc/cpe_eval.ipynb index 088d46593..3e581b792 100644 --- a/notebooks/cc/cpe_eval.ipynb +++ b/notebooks/cc/cpe_eval.ipynb @@ -18,7 +18,8 @@ "from sec_certs.dataset import CCDataset\n", "import pandas as pd\n", "import json\n", - "import tempfile" + "import tempfile\n", + "from sec_certs.utils.label_studio_utils import to_label_studio_json" ] }, { @@ -42,7 +43,7 @@ } ], "source": [ - "dset = CCDataset.from_web_latest()\n", + "dset = CCDataset.from_web()\n", "df = dset.to_pandas()\n", "\n", "eval_digests = pd.read_csv(\"./../../data/cpe_eval/random.csv\", sep=\";\").set_index(\"dgst\").index\n", @@ -58,7 +59,7 @@ "with tempfile.TemporaryDirectory() as tmp_dir:\n", " dset.root_dir = tmp_dir\n", " dset.certs = {x.dgst: x for x in dset if x.dgst in eval_certs.index.tolist()}\n", - " dset.to_label_studio_json(\"./label_studio_input_data.json\", update_json=False)" + " to_label_studio_json(dset, \"./label_studio_input_data.json\")" ] }, { diff --git a/notebooks/cc/reference_annotations/train_validation_test_split.ipynb b/notebooks/cc/reference_annotations/train_validation_test_split.ipynb index ff005f64d..8bf1d5423 100644 --- a/notebooks/cc/reference_annotations/train_validation_test_split.ipynb +++ b/notebooks/cc/reference_annotations/train_validation_test_split.ipynb @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset = CCDataset.from_web_latest()\n", + "dset = CCDataset.from_web()\n", "df = dset.to_pandas()\n", "reference_rich_certs = {x.dgst for x in dset if (x.heuristics.st_references.directly_referencing and x.state.st_txt_path) or (x.heuristics.report_references.directly_referencing and x.state.report_txt_path)}\n", "df = df.loc[df.index.isin(reference_rich_certs)]\n", @@ -57,7 +57,7 @@ " json.dump(x_valid.tolist(), handle, indent=4)\n", "\n", "with open(\"../../../data/reference_annotations_split/test.json\", \"w\") as handle:\n", - " json.dump(x_test, handle, indent=4) " + " json.dump(x_test, handle, indent=4)" ] } ], diff --git a/notebooks/cc/scheme_eval.ipynb b/notebooks/cc/scheme_eval.ipynb index 7ffc6f163..9150cb8cc 100644 --- a/notebooks/cc/scheme_eval.ipynb +++ b/notebooks/cc/scheme_eval.ipynb @@ -24,7 +24,8 @@ "from sec_certs.model import CCSchemeMatcher\n", "from sec_certs.sample.cc_certificate_id import canonicalize\n", "from sec_certs.sample.cc_scheme import CCScheme, EntryType\n", - "from sec_certs.configuration import config" + "from sec_certs.configuration import config\n", + "from sec_certs.dataset.auxiliary_dataset_handling import CCSchemeDatasetHandler" ] }, { @@ -56,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset.auxiliary_datasets.scheme_dset = schemes\n", + "dset.aux_handlers[CCSchemeDatasetHandler].dset = schemes\n", "\n", "count_was = 0\n", "count_is = 0\n", @@ -161,7 +162,7 @@ " rate = len(assigned)/len(total) * 100 if len(total) != 0 else 0\n", " rate_list = rates.setdefault(country, [])\n", " rate_list.append(rate)\n", - " \n", + "\n", " print(f\"{country}: {len(assigned)} assigned out of {len(total)} -> {rate:.1f}%\")\n", " total_active = total[total[\"status\"] == \"active\"]\n", " assigned_active = assigned[assigned[\"status\"] == \"active\"]\n", diff --git a/notebooks/cc/temporal_trends.ipynb b/notebooks/cc/temporal_trends.ipynb index 16cbc1625..7b6aaee74 100644 --- a/notebooks/cc/temporal_trends.ipynb +++ b/notebooks/cc/temporal_trends.ipynb @@ -1,9 +1,11 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Temporal trends in the CC ecosystem" + "metadata": {}, + "source": [ + "# Temporal trends in the CC ecosystem" + ] }, { "cell_type": "code", @@ -39,7 +41,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset = CCDataset.from_web_latest()\n", + "dset = CCDataset.from_web()\n", "df = dset.to_pandas()" ] }, diff --git a/notebooks/cc/vulnerabilities.ipynb b/notebooks/cc/vulnerabilities.ipynb index 176b3e697..05f537046 100644 --- a/notebooks/cc/vulnerabilities.ipynb +++ b/notebooks/cc/vulnerabilities.ipynb @@ -33,6 +33,7 @@ "import warnings\n", "from pathlib import Path\n", "import tempfile\n", + "from sec_certs.dataset.auxiliary_dataset_handling import CVEDatasetHandler, CPEDatasetHandler, CCMaintenanceUpdateDatasetHandler\n", "from sec_certs.dataset import CCDataset, CCDatasetMaintenanceUpdates, CVEDataset, CPEDataset\n", "from sec_certs.utils.pandas import (\n", " compute_cve_correlations,\n", @@ -81,16 +82,12 @@ "cpe_dset: CPEDataset = CPEDataset.from_json(\"/path/to/cpe_dataset.json\")\n", "\n", "# # Remote instantiation (takes approx. 10 minutes to complete)\n", - "# dset: CCDataset = CCDataset.from_web_latest(path=\"dset\", auxiliary_datasets=True)\n", + "# dset: CCDataset = CCDataset.from_web(path=\"dset\", auxiliary_datasets=True)\n", + "# dset.load_auxiliary_datasets()\n", "\n", - "# print(\"Downloading dataset of maintenance updates\")\n", - "# main_dset: CCDatasetMaintenanceUpdates = CCDatasetMaintenanceUpdates.from_web_latest()\n", - "\n", - "# print(\"Downloading CPE dataset\")\n", - "# cpe_dset: CPEDataset = dset.auxiliary_datasets.cpe_dset\n", - "\n", - "# print(\"Downloading CVE dataset\")\n", - "# cve_dset: CVEDataset = dset.auxiliary_datasets.cve_dset" + "# main_dset: CCDatasetMaintenanceUpdates = dset.aux_handlers[CCMaintenanceUpdateDatasetHandler].dset\n", + "# cpe_dset: CPEDataset = dset.aux_handlers[CPEDatasetHandler].dset\n", + "# cve_dset: CVEDataset = dset.aux_handlers[CVEDatasetHandler].dset" ] }, { diff --git a/notebooks/examples/cc.ipynb b/notebooks/examples/cc.ipynb index 84308596c..ee166593c 100644 --- a/notebooks/examples/cc.ipynb +++ b/notebooks/examples/cc.ipynb @@ -44,7 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset = CCDataset.from_web_latest()\n", + "dset = CCDataset.from_web()\n", "print(len(dset)) # Print number of certificates in the dataset" ] }, @@ -188,7 +188,7 @@ "source": [ "## Assign dataset with CPE records and compute vulnerabilities\n", "\n", - "*Note*: The data is already computed on dataset obtained with `from_web_latest()`, this is just for illustration. \n", + "*Note*: The data is already computed on dataset obtained with `from_web()`, this is just for illustration. \n", "*Note*: This may likely not run in Binder, as the corresponding `CVEDataset` and `CPEDataset` instances take a lot of memory." ] }, @@ -212,7 +212,7 @@ "The following piece of code roughly corresponds to `$ sec-certs cc all` CLI command -- it fully processes the CC pipeline. This will create a folder in current working directory where the outputs will be stored. \n", "\n", "```{warning}\n", - "It's not good idea to run this from notebook. It may take several hours to finish. We recommend using `from_web_latest()` or turning this into a Python script.\n", + "It's not good idea to run this from notebook. It may take several hours to finish. We recommend using `from_web()` or turning this into a Python script.\n", "```" ] }, @@ -231,8 +231,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Advanced usage\n", "There are more notebooks available showcasing more advanced usage of the tool.\n", diff --git a/notebooks/examples/est_solution.ipynb b/notebooks/examples/est_solution.ipynb index fd0f98fd7..0368c592c 100644 --- a/notebooks/examples/est_solution.ipynb +++ b/notebooks/examples/est_solution.ipynb @@ -57,7 +57,7 @@ ], "source": [ "# Download the dataset and see how many certificates it contains\n", - "dataset = CCDataset.from_web_latest()\n", + "dataset = CCDataset.from_web()\n", "print(f\"The downloaded CCDataset contains {len(dataset)} certificates\")" ] }, @@ -80,7 +80,9 @@ "attachments": {}, "cell_type": "markdown", "metadata": {}, - "source": "## 2. Turn the dataset into a [pandas](https://pandas.pydata.org/) dataframe -- a data structure suitable for further data analysis." + "source": [ + "## 2. Turn the dataset into a [pandas](https://pandas.pydata.org/) dataframe -- a data structure suitable for further data analysis." + ] }, { "cell_type": "code", @@ -495,7 +497,7 @@ } ], "source": [ - "# Show arbitrary subset that we've defined earlier \n", + "# Show arbitrary subset that we've defined earlier\n", "eal6_or_more.head()" ] }, diff --git a/notebooks/examples/fips.ipynb b/notebooks/examples/fips.ipynb index 54849f83d..aeb6bcce9 100644 --- a/notebooks/examples/fips.ipynb +++ b/notebooks/examples/fips.ipynb @@ -38,7 +38,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset: FIPSDataset = FIPSDataset.from_web_latest()\n", + "dset: FIPSDataset = FIPSDataset.from_web()\n", "print(len(dset))" ] }, @@ -87,7 +87,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Dissect a single certificate" + "source": [ + "## Dissect a single certificate" + ] }, { "cell_type": "code", @@ -128,7 +130,7 @@ "## Create new dataset and fully process it\n", "\n", "```{warning}\n", - "It's not good idea to run this from notebook. It may take several hours to finish. We recommend using `from_web_latest()` or turning this into a Python script.\n", + "It's not good idea to run this from notebook. It may take several hours to finish. We recommend using `from_web()` or turning this into a Python script.\n", "```" ] }, @@ -147,8 +149,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Advanced usage\n", "There are more notebooks available showcasing more advanced usage of the tool.\n", diff --git a/notebooks/examples/model.ipynb b/notebooks/examples/model.ipynb index e5e8966db..453facf7a 100644 --- a/notebooks/examples/model.ipynb +++ b/notebooks/examples/model.ipynb @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset: CCDataset = CCDataset.from_web_latest()" + "dset: CCDataset = CCDataset.from_web()" ] }, { diff --git a/notebooks/examples/protection_profiles.ipynb b/notebooks/examples/protection_profiles.ipynb new file mode 100644 index 000000000..f1de21a83 --- /dev/null +++ b/notebooks/examples/protection_profiles.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Protection profiles example\n", + "\n", + "This notebook illustrated basic functionality of the `ProtectionProfileDataset` class that holds protection profiles bound to Common Criteria certified products. The object that holds a single profile is called `ProtectionProfile`. \n", + "\n", + "Note that there exists a front end to this functionality at [sec-certs.org/cc](https://sec-certs.org/cc/). Before reinventing the wheel, it's good idea to check our web. Maybe you don't even need to run the code, but just use our web instead. \n", + "\n", + "For full API documentation of the `CCDataset` class go to the [dataset](../../api/dataset) docs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sec_certs.dataset import ProtectionProfileDataset\n", + "from sec_certs.sample import ProtectionProfile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get fresh dataset snapshot from mirror\n", + "\n", + "There's no need to do full processing of the dataset by yourself, unless you modified `sec-certs` code. You can simply fetch the processed version from the web. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dset = ProtectionProfileDataset.from_web()\n", + "print(len(dset)) # Print number of protection profiles in the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Do some basic dataset serialization\n", + "\n", + "The dataset can be saved/loaded into/from `json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dset.to_json(\"./pp.json\")\n", + "new_dset: ProtectionProfileDataset = ProtectionProfileDataset.from_json(\"./pp.json\")\n", + "assert dset == new_dset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple dataset manipulation\n", + "\n", + "The samples of the dataset are stored in a dictionary that maps sample's primary key (we call it `dgst`) to the `ProtectionProfile` object. The primary key of the protection profile is simply a hash of the attributes that make the sample unique.\n", + "\n", + "You can iterate over the dataset which is handy when selecting some subset of protection profiles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for pp in dset:\n", + " pass\n", + "\n", + "# Get only collaborative protection profiles\n", + "collaborative_pps = [x for x in dset if x.web_data.is_collaborative]\n", + "\n", + "# Get protection_profiles from 2015 and newer\n", + "from datetime import date\n", + "newer_than_2015 = [x for x in dset if x.web_data.not_valid_before > date(2014, 12, 31)]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dissect a single protection profiles\n", + "\n", + "The `ProtectionProfile` is basically a data structure that holds all the data we keep about a protection profile. Other classes (`ProtectionProfile` or `model` package members) are used to transform and process the samples. You can see all its attributes at [API docs](https://seccerts.org/docs/api/sample.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select a protection profile and print some attributes\n", + "pp: ProtectionProfile = dset[\"b02ed76d2545326a\"]\n", + "print(f\"{pp.name=}\")\n", + "print(f\"{pp.web_data.not_valid_before=}\")\n", + "print(f\"{pp.pdf_data=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Serialize a single protection profile\n", + "\n", + "Again, a protection profile can be (de)serialized into/from json. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp.to_json(\"./pp.json\")\n", + "new_pp = pp.from_json(\"./pp.json\")\n", + "assert pp == new_pp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create new dataset and fully process it\n", + "\n", + "The following piece of code roughly corresponds to `$ sec-certs pp all` CLI command -- it fully processes the PP pipeline. This will create a folder in current working directory where the outputs will be stored. \n", + "\n", + "```{warning}\n", + "It's not good idea to run this from notebook. It may take several hours to finish. We recommend using `from_web()` or turning this into a Python script.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dset = ProtectionProfileDataset()\n", + "dset.get_certs_from_web()\n", + "dset.process_auxiliary_datasets()\n", + "dset.download_all_artifacts()\n", + "dset.convert_all_pdfs()\n", + "dset.analyze_certificates()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/fips/icmc_plots.ipynb b/notebooks/fips/icmc_plots.ipynb index 9f2090d49..9bac802a1 100644 --- a/notebooks/fips/icmc_plots.ipynb +++ b/notebooks/fips/icmc_plots.ipynb @@ -28,7 +28,7 @@ "metadata": {}, "outputs": [], "source": [ - "dataset = FIPSDataset.from_web_latest()\n", + "dataset = FIPSDataset.from_web()\n", "print(f\"The loaded FIPSDataset contains {len(dataset)} certificates\")\n", "df = dataset.to_pandas().loc[lambda _df: _df[\"name\"].notna()]" ] diff --git a/notebooks/fips/in_process.ipynb b/notebooks/fips/in_process.ipynb index fb04ea798..311b5731d 100644 --- a/notebooks/fips/in_process.ipynb +++ b/notebooks/fips/in_process.ipynb @@ -1,10 +1,12 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# FIPS IUT and MIP queues", - "id": "6212ee2f4518283e" + "id": "6212ee2f4518283e", + "metadata": {}, + "source": [ + "# FIPS IUT and MIP queues" + ] }, { "cell_type": "code", @@ -52,7 +54,7 @@ }, "outputs": [], "source": [ - "fips = FIPSDataset.from_web_latest()\n" + "fips = FIPSDataset.from_web()\n" ] }, { @@ -70,7 +72,7 @@ "metadata": {}, "outputs": [], "source": [ - "iut_dset = IUTDataset.from_web_latest()" + "iut_dset = IUTDataset.from_web()" ] }, { @@ -253,7 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "mip_dset = MIPDataset.from_web_latest()" + "mip_dset = MIPDataset.from_web()" ] }, { @@ -374,7 +376,7 @@ " print(\"Average seen for\", np.mean(mip_local_df.loc[mip_local_df.status == status].seen_for))\n", " print(\"Average seen for (FIPS 140-2)\", np.mean(mip_local_df.loc[(mip_local_df.status == status) & (mip_local_df.standard == \"FIPS 140-2\")].seen_for))\n", " print(\"Average seen for (FIPS 140-3)\", np.mean(mip_local_df.loc[(mip_local_df.status == status) & (mip_local_df.standard == \"FIPS 140-3\")].seen_for))\n", - " \n", + "\n", " print(\"Only not present:\")\n", " print(\"Average seen for\", np.mean(mip_local_df.loc[~(mip_local_df.present) & (mip_local_df.status == status)].seen_for))\n", " print(\"Average seen for (FIPS 140-2)\", np.mean(mip_local_df.loc[~(mip_local_df.present) & (mip_local_df.status == status) & (mip_local_df.standard == \"FIPS 140-2\")].seen_for))\n", diff --git a/notebooks/fips/references.ipynb b/notebooks/fips/references.ipynb index c6306b035..3a7bea16d 100644 --- a/notebooks/fips/references.ipynb +++ b/notebooks/fips/references.ipynb @@ -82,7 +82,7 @@ } ], "source": [ - "dset = FIPSDataset.from_web_latest()" + "dset = FIPSDataset.from_web()" ] }, { @@ -143,7 +143,7 @@ " \"embodiment\",\n", " \"year_from\",\n", " \"related_cves\",\n", - " \"module_directly_referenced_by\", \n", + " \"module_directly_referenced_by\",\n", " \"module_indirectly_referenced_by\",\n", " \"module_directly_referencing\",\n", " \"module_indirectly_referencing\",\n", @@ -193,7 +193,7 @@ "n_certs: int = df.shape[0]\n", "n_referencing_certs: int = df[df[\"outgoing_direct_references_count\"] > 0].shape[0]\n", "ratio: float = round(n_referencing_certs / n_certs, 2)\n", - " \n", + "\n", "print(f\"Total ratio of referencing certs in dataset: {ratio}\")" ] }, @@ -283,7 +283,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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\n", + "image/png": 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AGDp06E3braxevRouLi4YPnw4gNJ8eOqppyxXjpYtW4YePXqgbdu2AEq3+eDBgyu837Zt21raCkyfPt3yudWWsv1z4sSJltwLCwsr1+D1ww8/tFzNcXJyQv/+/S1Xbq+nbFtMmTLF8tiECROwa9cuxMbGIj09HbIsWxpKenl5YenSpZZlDQAsO19hYeFN38iFCxegKIrlCw4AiouL4efnh5SUFMtlQm9vbxiNxpuuryYuXLhQYacr+xEAACUlJZg7dy4iIyNhNBqRk5OD1NTUG64zJiYGxcXFWLp0abl7qz4+PhUaE1xPXl4eEhISsG7dOmzbtq1cPfn5+QCAjh07Ijg4GD///DOmT5+On3/+GSNGjCj33pKSksrtIJmZmQgODq5SDQAQGRlZoQFMkyZNkJSUdN3n9OvXD2FhYRg5ciT69u2L4cOH4+67767S65X9mKiKJk2alPt3s2bNsGnTpio/v6ouXLgAX19fODo6Wh4LDAyELMu4ePFiuQZKjRo1svx/Z2dn5OXlXXe9BoMBX375Jfbt2wedTmf58k9LSyu3ja7+AnNwcKjwOk5OTpbXKdv3mjdvbvm7Xq9HQEAAzp8/X+71b1TrrWz3yMhIy2uVKftxVRXXNqqpynF0s+O3qoYOHYq33noLf/75J4YPH47169dX6V73tftrVY7byly4cKHcpXWg9NK4wWBASUmJ5Xvw2s+oOs+9dnuX1Vu2Dr1eb/nuBUrb3Tz00EM3/QyuXW/Z+8zNzUVqamqFWwQ32ycuXLhQ4Tldu3a94fvNycmp0Ki0ss+qNlV2rAGwXM4GgKSkJMybNw+ZmZkwGo3l7ktfz4ULF6DT6fDss89aHjObzQgICEBKSgq6deuGUaNG4V//+hfCw8MxfPjwct/7BgAIDg6Gq6srLl68iEGDBt30zXh6et70bEav1990PXVt6tSpCA4OxsqVK2FnZ4f9+/dXaScFgFmzZlXpF+aNTJky5YaN0UaNGoV169Zh+vTp+PPPPys0Ohg5ciRmzJhRoxqqy97eHsuXL8fx48exbt06vPbaa1i9ejXWrFljaSl9PXW5zcvur9Wlq+uXJAniBl3058+fjx07duD777+Ht7c3AKBNmzYVnlPZZ6LTletteMPXqWqtdaE66732PZWpjePoZpydnTFkyBCsW7cOPXr0QFFRUYVQqMz19tebHbeVCQkJueXvxOo+t2y73Mp+c62rt9vN9vmrX7smevfujfnz599wGbXzIz4+HlOmTMGMGTMsjcYWLVqEAwcOVOn5K1asuO57WLBgAaZOnYp169Zh4cKF+OKLL7B27Vq4ubmVdnvS6/WYOHEiNm/eXOkKpk2bZvkAQ0JCkJqaWu4XeVnrbLPZfNNCr96gNzoDqYqQkBBcvny53GObN2/GxYsXkZmZiYsXL+KOO+6wXDIrKSm5aT3NmzeHvb09oqKiyi23evVqHDx4sEp1ubi4oEmTJhXWsWnTJvz++++Wf48YMQKxsbFYs2YNWrRoUe6WQUhISIXn79u3r1qXrFu2bFnh80lMTLzhcyIjI3H+/Hl06tQJc+fOxffff49jx45ZLt9e/XkVFxdX+plWxbV1XL582dLoAyj/ax1Apf2kq7Ivle2vV1/9iY2NhV6vt1xGuxWHDh1Cjx49LGF89e2aW1W278XExFgek2UZ8fHx1WpQeCvbvWXLlpbXKpOQkFDl17xWVY6jGx2/QPmwMJlMN/yMx44diz179mDJkiUYNWrULdVc1eP2WiEhIYiJiYGiKJbH0tPT8cYbb9z0NWvy3KvXce22y8jIqFHvCVdXV/j6+lZ7PwoJCakwrsOJEyewfft2y9+v/XzPnz9fpe5DV+8PiqKgoKDgps+5nrL989r398UXX6CwsBAnT55EUVERhg0bZvnbtd91V9djNptRVFSEkJAQKIqC6Ojocsu+/vrryMzMRHJyMo4ePYqQkBDMnj0bv/76K1JSUrB3797SdZY94ZlnnoGDgwMWLFhgCVYhBFatWoULFy5YfiWMGDECfn5+5a57f/XVV9DpdDc9gwJKr5nn5OTAbDZXaMVZXdOmTcPff/9tuZeSlJSE9957D15eXvDw8ICPjw/2799vWb6yjvFl9aSnp+Phhx+Gg4MDHnnkEaxZswbZ2dkASlvprly5slpf4NOmTcOGDRssX2oZGRlYvHgxQkJCLMsEBASge/fumD9/foXPYtq0adiyZYslCAsKCrBw4cJqXbKePHkyjhw5YukMf/nyZcuBcT3Hjx/Hp59+avmlbDabYWdnZ7nE7OXlZflc3nnnHezevbvK9Vz7OmV1RUZGYseOHeUuY4WGhuLo0aMASi/hbd26tcI6qrIvjRgxAo0aNbKcgZSUlGDlypUYP358jfrTtmzZEseOHbMEfW0MulC2733zzTeWL5t169ZBp9NhwoQJVV5PZdt9z549N3xO7969y3VRk2W5Qgvl6qjKcXSj4xcovRKXm5sLIQS++uqrG97H7d69OwICArB+/XoMGTLkluuuynF7rcmTJ6OwsNBSnxACn3zyieV93EhNnlumZ8+e6NKlC5YvX2557Msvv0RKSkqV11GZhx56CBs2bEBmZiYA4Pfff0dWVtYNnzN58mTk5uZa2gmYTCbMnz/fkg1Tp07F6dOnsWvXLgClx+OHH35400vBQOlleJ1Oh5ycHJw8ebJGgwtdfayVHcM7duzAn3/+CUdHRwQHB1taUwOl95yvvX9cto2ys7Pxxx9/4MMPP7RsiyVLllh+ZP3222+4dOkSPD09ER0djQULFljCXVEUCCEsl84lcdU1iuLiYnz++efYtWsXjEYjTCYTWrVqhenTp5e7d1DWjSQ5ORnu7u4IDg7Giy++CCcnJ6xbtw5Lly5FfHw8OnfujGeffRZhYWGW527evBkLFy6Eu7s7Ro4ciZYtW1q6qnTq1AmzZs3C+vXrLfdwgoODMXfuXKxduxabN29GTk5Oua4bV3eb0Ov1ePbZZy2NaA4dOoR58+ZBURQEBARYumuEh4fjww8/hJeXF1atWoU1a9bA1dUVU6dOxV133QWz2YwPP/wQf/75J3x8fGA0GvHCCy+gQ4cOlW7cxx57DMeOHYObmxtGjx5tucy8fPly/PDDD/Dw8IBer8cTTzyB22+/vdxzf/jhByxatAjbtm2rcNnvp59+wrJly+Ds7AxJkjBx4kSMHDkSubm5eOqpp3Ds2DEEBATgoYceum5f8R9++AFLliyxdH/x9PTEhg0b0K1bN8yfP9/S7SkgIACTJ09Gnz59sHDhQiQmJsLBwQHFxcV48sknLY0q0tPT8cQTT8BoNMLV1RWLFy/G7NmzsXPnTri5uSEkJMSybS5evIi5c+fiwIEDaNu2LZ588kls374dO3bsQI8ePeDi4oLIyEhLt6epU6da6r58+TJmzpwJRVHQvHlztGnTBosWLULnzp2xZMkSODs7V2lfCgsLQ3R0NN58801kZmaipKQEYWFhmD17NhwcHLBjxw68//77OHv2LMLDw7Fo0SLMnz8ff/75J9zc3Mp1x7pacnIy5syZg6ioKISEhKBdu3ZYvHgx2rZti1mzZuHUqVP49ttvkZOTg0GDBuHxxx/Hyy+/bKnt7bffxtKlS7Flyxa4ubnh/vvvx+OPP15pt6eXX37Zck+4rEtYQEAAnn76aej1enz00UeIj49H165d8dVXX5Xb7mXd3VxdXXHu3LlyX9rXKuv2lJeXh0aNGmHo0KGYM2eO5bPcsmVLuWNw3rx5eOGFFyzbd8CAAeW6wlTlOLrR8WsymfDEE08gNzcXjo6O+PDDDzFr1qxKjzWgdJCfsvt+N3L1Z9itW7cKozxd77i9ustUQEAAmjZtimXLlgEo7erz7rvvorCwEI6OjggLC8Ozzz4LvV6PpUuXWvaF0NBQzJs3r9y9y6o+d9CgQXj11Vfx1FNPWT7zWbNmoU+fPpauNjExMXByckLbtm3x8ssvw2g0Wro9AcCAAQMwefJkvP766+X2xZ9++skyAt+IESPw4osvwmw2491338Vff/2FFi1aoGfPnti1axf69OmDJ5988rqfb9n7KSkpgU6nw+jRoy3dmgBg586dWLhwIXQ6HYxGI4YMGWJpTPjWW29Z2pIEBwdbjvUy//3vf7F9+3Y4OTlh9uzZiIiIwHfffWfJmyVLllS6jaZMmYKIiAi4ublZumyVdcvbvn07PDw84OLigtdff92Sdd9++y2WLl2Kxo0bW66E7dmzB/3798f7778PoLTbXWRkJBwcHPD2228jODgYaWlpeOutt3Du3Dl4e3vDx8cHc+bMgbe3N1JTU/HBBx/g3LlzcHZ2RkFBASZNmmS5RVIukInI+pWUlKCoqMjSjxYAXn31VQghbhpW1uzVV1/FmDFjyjUioluXl5cHOzu7ciORDRkyBE8//TRGjhypYmW2q/KWGERktaKiovDMM89YGsIlJyfjr7/+sskv0f379+PIkSPIycnBhQsXGMa1aMOGDeVuTe7atQtZWVno16+filXZtpvf9CUiq+Lr6wtPT09MmDABjo6OMJlMeOmllxAeHq52abUuNzcXc+fOha+vb52MddCQdezYEe+//z4mTpwISZJgMBiwbNkyy4hTVPt4yZqIiEgDeMmaiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpgEHtAojoH0IIyIoAAOh1EiRJqrV1y4qAEAJCAJIE6CQJOl3trZ+IaoaBTFSPZFmBAGDQ/3NxSlEEcvJNSM8pRFpmITJyipCRU4TM3GIUmWSUlMgoMSsokRWYyv6/5b/Sf5vMCsxmBTqdBEd7A5wcDHByMJb+r70Bjg5GODv887ijvQHOjka4O9uhsY8zvN0dLeGsKAKKIqDX1+4PAiK6MUkIIdQugsjWlJ3plgVvYbEZ0QnZSEjLR3JGwVX/5SMjuwiKykehQS/B19MJTXyc0djHGY29ndHE1wVNG7nA19MRel3p+1BEaVhf/YOCiGoHA5mohioL38i4LJyPzcTFuGxcvJyFpIx8WOuRptNJ8PVwRGOf0pBuGeCOdkFeCPB1gSRJkBUFACyhTUS3hoFMVE2yokAnlV7OLSw24+LlLJy/nImLcVmIvJxt1eFbHY72BrRs6o7WgZ5o09wTHYJ94OpsV+EHChFVDQOZqArMsgKDXofiEhnHzqXg0JlkHL+YhsS0fLVL05QAXxe0b+mN9sHe6BTiC083ByhXrsezARnRjTGQiSohKwoklLZCTkzLx/5TiTh0JgWnLqXDLCtql2c1/Lyc0L2dH/p0bIJ2Qd6QpNL70Ly8TVQRA5noirKz4BKzjIiLaTh4OhmHzyYjKb1A7dJsgpuzHbq380efjo3RpU0jGPQ6y2dORAxkauBkWYFer0NeYQl2HInDgdNJOBmZjuISWe3SbJqjvQHd2jZC7w6NEX6bP+ztDAxnavAYyNTglA2OAQBHz6Xgj/0xOHA6mZeiVWI06NApxBe9OjRG7w6N4eJkB1lReFmbGhwGMjUYZWdgyRn52Lw3BlsOXUZGTpHaZdFVDHoJPds3xvDbg9A+2MdyBYOoIWAgk01TFAFJAkxmBTuPxuHPA7E4HZWhdllUBYF+rri7dwsMDg+EvVEPIdhSm2wbA5lsUtnZ8NnoDPy+Pwa7jyegsNisdll0Cxzs9BjQtSlG9A1GoL8b7zWTzWIgk02RZQWSJGH7kTj8uPUCYpJy1S6JalHbFp4Y3jsIt3cOuDI4CzjeNtkMBjLZBEURMMsKNu+NxoYdkUjNLFS7JKpD7i52GBzeHKP7t4Srsx0kMJjJ+jGQyaopikCRyYwN2yPxy65LyC0oUbskqkf2Rj2G9m6B++5sDScHI+8xk1VjIJNVKgviH7dexC+7LqGgiPeHGzJHewNG3B6M8XeEwM6oh57BTFaIgUxWRVEECk1mrGMQUyWcHY0Y078lRg9oBYNeYl9msioMZLIKsqxAEQI//H0BP+2IZBDTDbk522H8oBDcc3swdBLYl5msAgOZNK1sxKY9EQlY9vNJNtaiavFyc8CEO1vj7p7NATCYSdsYyKRJZbtlQlo+Pv0xAscvpKpcEVmzRp6O+Nc9t+H2zgGQFcF7zKRJDGTSHFlWUCIrWP3bWfyy6xJkhbso1Y7OrX3x9PhOaOTlBB27SZHGMJBJM8rGLf77YCxW/HoaWbnFapdENsig12HswFa4f3BrSJLEUb9IMxjIpDohBCRJQlR8Nj7+8TjOxWSqXRI1AH5eTnhyXEd0a+sHRRHsw0yqYyCTqmRZQXGJjC83nsKf+2PAq9NU3/p3bYppYzvC0U7PRl+kKgYyqUYIgVOX0vHemsNIz+Y0iKQedxc7PD66A/p1acqzZVINA5nqnSwrAICVv53Bhm0XeVZMmtE91A/PTOgMN2c7ni1TvWMgU71SFIGkjHwsWHkIkfHZapdDVIGLoxEvTOqK7u381S6FGhgGMtWLssuAv+2Jwhc/n0Jxiax2SUQ3NLp/SzxyTzsA4BCcVC8YyFTnZFlBYbEZC789igOnktQuh6jK2jT3xMsPh8PdhZewqe4xkKnOHTufgg++PoJM9ismK+TqZMQLk7ohLNRP7VLIxjGQqU7IigIhgC83nsIvuy6BexlZM0kCRvdvhUeGt4OA4CVsqhMMZKp1sqygoNiMucv2cZAPsimhLbzw4sPd4c5W2FQHGMhUq2RFQWJaPl5fuhcpnJmJbJCbsx1mTuqKrm15CZtqFwOZao0QAkfPp+Ldrw6isJjzFZPtkiTgvjtb44G7Qy1DvxLVFAOZas0vuy7h859OQuFIH9RA3NE9EDMmdAYAju5FNcZAphopC9+lG07g191RKldDVP+6tW2Elx8Jh14n8b4y1QgDmW5Z2bzF76w4iCPnUtQuh0g1Ic08MPfxXnCyNzCU6ZYxkOmWyLKCjNxi/OfzvYhNylW7HCLVNfZ2xrwne8PbzYGhTLeEgUzVJisCkXFZeOOLfcjOM6ldDpFmeLjY4z9Te6JFEzf2VaZqYyBTtSiKwInINLz5xX6OR01UCQc7PV5+JBydQnzZ0IuqhYFMVaYoAkfPp+Ct5QdQYlbULodIs/Q6Cc9M6Iw7ugeqXQpZEQYyVYmiCBw8nYx3Vx6EWWYYE1XFw8PbYfygELXLICthULsA0j5FCOw7mYgFqw5BZh9joir76tfT0OskjBnQSu1SyAqw1QHdkKII7I1IwHyGMdEt+XLjKfy+Lxq8GEk3w0Cm6yq7TP3f1Yc5+hZRDXyy9jh2HovncUQ3xECmSimKwNFzKXh35UGeGRPVkCKAD74+gsNnkxnKdF0MZKpAvtK16a0VB9iAi6iWyIrAO18dxKlL6ZAVHldUEQOZypFlBediMvDmF/vZtYmolpWYFbzxxT5ExmVD5o9dugYDmSxkWUFqViEH/SCqQ0UmGa8t3Yu4lDyGMpXDQCYApfeMi0tkvL50L/IKS9Quh8im5ReW4JUlu5GcWcBQJgsGMkEIAQGBt1YcQEJavtrlEDUI2XkmvPLpbmTnmRjKBICBTAAkScInayMQcSFN7VKIGpS0rCL8Z9leyIpg62tiIDd0Qghs2H4Rf+yPUbsUogYpKiEHH3x9hBNREAO5IZMVBYfPpmD5xlNql0LUoO2OSMA3f5zlaF4NHAO5gZJlBfEp+Viw6hB4pYxIfd/8cQ77TyZxIJ4GjIHcAMmKgvwiM/6zbC8Ki81ql0NEAIQA3v/6MBJS2R2qoWIgNzBCCAgFeGPZPqRmFqpdDhFdpcgkY96X+1EiK1B4+brBYSA3MJIk4dN1x3EuNlPtUoioEglp+aWNvCQ28mpoGMgNiCwr2HciEX/sj1W7FCK6gb0nEvHj1gts5NXAMJAbCEURyC0w4aPvj6pdChFVwcpNZ3A6KoP3kxsQBnIDodNJeP/rI8gt4LCYRNZAUQTeXXkQhSaZg4Y0EAzkBkBRSgf/OHY+Ve1SiKgasnKL8fHaYxw0pIFgINs4WVZwOSUXKzedUbsUIroFu44lYE9EAi9dNwAMZBsnBLBg5SHObUxkxT758TgKi83sCmXjGMg27ouNJxGbnKt2GURUA9l5Jiz+4Ti7Qtk4BrKNkmUFR86l4JddUWqXQkS1YHdEAnYfj+elaxvGQLZBiiJQWGzGwm+OqF0KEdWiT36MQAEvXdssBrIN0ukkfLz2OLJyi9UuhYhqUU6+CYt/OMZL1zaKgWxjZFnBycg07DqeoHYpRFQH9kQkYtcxXrq2RQxkG1M6VnWE2mUQUR36dN2VS9ccMMSmMJBtiKwo+GXXJcQmsVU1kS3LyTfh4x+Oc8AQG8NAthGKECgoMuPr38+qXQoR1YPdEQk4HZXOS9c2hIFsI3SShOUbTyG/yKx2KURUT5b9dBJ6Pb/GbQW3ZA2UlJTg888/R+fOnXH+/PnrLrdp0yY8+eSTeOWVVzBnzhyUlNTuBA+yrCAyLgt/HeS0ikQNyYXLWdh+NI5nyTaCgVwD33//PcLCwlBYWHjdZZKTk/H222/jvffew1tvvQWdToc1a9bUah16vQ6f/BgBdk0kanhW/noaPPRtAwO5Bh544AF06dLlhsts2rQJXbt2hbOzMwBg4MCBWL9+fa3VIMsK/j4Yi/OxmbW2TiKyHimZhfh5RyRktri2egzkOhYfHw8fHx/Lv729vREXF1dr6zeZFaz45XStrY+IrM/3f19AkckMwctkVo2BbMWEEFiz+Syy8jgiF1FDll9Ygq83s4eFtWMg17GAgACkpaVZ/p2eno6AgIAar1cRApk5xfh1NyePICJg054opGQWcrAQK8ZArgN79+5FdHQ0AGDYsGE4cuQI8vPzAQBbt27F6NGja/waEoBv/zwHM1tXEhEAsyywfOMpDhZixRjINXDo0CG88cYbAIDPPvsMv/32GwBg+fLl+PvvvwEAfn5+mD17NmbOnImXX34ZZrMZkydPrtHrKkIgM7cYfx5gNyci+sfuiAScj81kNygrJQm2ArA6QggsWReBTXui1S6FiDTmtmBvvPv07WqXQbeAZ8hWRhECWbnF+GM/z46JqKJTl9Jx4XImZIVnydaGgWxlJADf/32e946J6LrWbb0IvY5f79aGW8zK5BeW4E+eHRPRDew5kYi0rEL2S7YyDGQroigCP+2IRHGJrHYpRKRhiiKwYftFDqlpZRjIVsQsK+x3TERV8sf+WBSb+OPdmjCQrYQsK9i8Nxq5BbU7UxQR2abCYjM2741mFygrwkC2EpIk4acdkWqXQURWZOPOS5AkDhRiLRjIVsAsK9h/KhEpmdef5pGI6FqpWYXYeTyevTKsBAPZChj0OvY7JqJbsmFbJAx6ftVbA24lK5CVV4wj51LULoOIrNDFuCycjkrnvWQrwEDWOFlW8NeBWM7gQkS3bN3Wi9DzLFnzuIU0Tq/X4S9OIkFENXDwTDJy8jlvutYxkDVMUQTOxWQgPjVP7VKIyIopisCWQ5d52VrjGMgaJknAH/tj1C6DiGzAlkOXedla47h1NKzErGDnsQS1yyAiGxCVkIPY5FwoHN9asxjIGmWWFew6Ho/CYrPapRCRjfjrQAw4wLV2MZA1yqDX4U825iKiWrT9SDw4cJd2MZA1SAiBlMwCnLqUrnYpRGRDMnKKcCY6g90oNYqBrEGKEPhzfwx4q4eIatv2o/EAz5I1iYGsQXqdDvtOJqldBhHZoD0RCcxjjTKoXQBVlJVbjOjEHLXLKKcoOwHp5/9ASX4aIJX+jnNr2g2eQbdD0ukBAGnn/kDO5UPQ2zmWe67O6IhmvabdcP0FaZGIP7gcds7eFf5WUpABR+9gBHT/FwDAlJ+OlBPrYC7OBQB4hwyCa5PO5Z6Tl3QSKac2okX/mdAZ7G7pPRPZoqzcYpyOykBoCy/odIxmLWEga4xZVnDgtLbOjksKMxG37zM4+7ZB837PQdIZUJgZi7i9SyCb8uEbOsyyrHebu+DeLOyWXsfBvSma9S4f3IpsxqW/5sEtoKvlsaSj38DJpyV82g5FcU4iYnctgp1rY9i7+pU+x1yElJM/oVGHMQxjokrsOBqH0CAvtcuga/CStcYY9DocOpOsdhnl5CefhVJSCM+W/SDpSn/DOXoGwsk3BDlxh2vlNexcG8ErZFCFx/OSTkDS6eDi3x4AIJcUoSgrFq5NugAA7N0aw87VDwVpFy3PSTv7Oxw8AuHi165WaiOyNXtPJELH5taaw0DWGFlRcOx8qtpllHflwBVK+WH3hKKgtlqeGexd4ezbusLj2TH74N4s3HJZvGyydSHkq+qQLY8XZV1GTvwRNGo/slbqIrJFmbnFuJycq3YZdA0GsoYoisCZ6AzNDQbiFtAZdq5+SD//J+SSQgBAXvIZFKRdgGdwv3LLFqSeQ9y+pYjevhDR2z9AysmfYC66tfvhprwUFGbGwL15D8tjOoM9HL1bIit6N4RQUJB2ESX5aXDybQOhyEiOWAvv1nfB4OB+62+YqAE4ei4FZo5trSm8h6wxB09r63I1AOgMDmja8wkkR/yAyD/mQm/nBKEo8Os4vtz9Yp2+9H6tf5dJMNi7oKQgA4lH1iBm54cIvP0ZGB09qvW6WTH74NyoDYyOnuUeb9xlIlJPb0T0tveht3NCk+6PwM7ZGxmR2yDpDPBo0QvZsQeQFb0HAODRog/cA7vX7EMgsjERF9Mwsl9LtcugqzCQNUSnkzR3/xgATHmpiNv/ORzcm6Ll4Negt3NCYWYsEg+vgrkoB95X7v16tRpQ7nlGJy/4dboXMds/QMaFv+HXcVyVX1ORS5ATdwSNu9xf4W8GBzc07vpAucdKCjKQcWELmvaahsL0S0g9swnN+84AAMTs/BBGZy84efPLh6jMycg0KIpgS2sN4SVrDcnMKUJskvbu66Sd+x3mwmz4dRwPvZ0TgNJGXZ7B/ZB+bjMKM6Kv+1x7V3/oDA4ozKzerFV5iRHQGx3gVMl95cokn1gP98BwOLg3QU78Ubj4t4PRyQtGJy+4+LVDTtzRar0+ka3LLzIjKjEbgiMQaQYDWSPMsoL9p7TV3amMKTcJejtnSxiXsXPxBQBL2JqLrvNjQpKq3fgrK2Y/3AN7QJJuvovmxB+DKS8F3q3vulJHNgz2bpa/GxzcYC7KrtbrEzUER8+lQuYwmprBQNYIg16Hw2e1d7kaAPR2LpBLCqCYi8s9XlKQeeXvpUF96a83KwSfKT8dSkkhHDyalntcNuVDKJU3XivOTUJxdhzcmt38vq9cUojU0z+jUftRlj7HBntXyKb8cq9lsHe96bqIGpqIi6kwcI5kzeCW0AhFETh+IU3tMirlGXw7IARSTm2EUEq7G5ny05F5aTsMjh5w8e9gWTbt7GYocgmA0rBMObEOOoMDvFr908e4pCADl/56C/EHv6r09bJj9sPFvz0M9i43rS3tzK9w9Awq1+fYxb898pJPQzYVQDYVIC/5DFwad7jBWogaptNRGZAVtrTWCjbq0oj41DzNdXcq4+LfHk17Po6MyK2I3v5+6eAgQoFzo7bwajUIeqMDAMC/y0TkJkQgdudHgAQoJUVw9GqBwNunWy5vA4CkM0JndITBwa3CaylyCXLijyCg+yM3raswIwq5CRFoMWDmNfXehuLcZFze8wkAwDOoD1z8QmvwCRDZpmKTjPOxWWjb3NPSl5/UIwne0VedWVaw7XAcPvyODY+IqH5NGtIGE+5oDT0vXauOW0ADdJKEyPgstcsgogYo4kIaw1gjuBU0QKeTcDEuS+0yiKgBOhuTCZkjdmkCA1kDFEUgKkFb0y0SUcNglhUkpOXffEGqcwxkDUhMz0exSb75gkREdeBSQjZbW2sAA1llsqzgXEym2mUQUQMWm5gLsHmv6hjIKpMk3j8mInXFJuewYZcGcAuoTKeTEMlAJiIVxSRqbwz9hoiBrDIhBC7Fc5xlIlJPckY+Ssxsx6I2BrLKktLzUcQGXUSkIkUAcSl5apfR4DGQVSTLCs7FskEXEakvKiEHZvZHVhUDWUUCQEIq+/8RkfpiknLA4azVxUBWkUGvQ0pmgdplEBEhNikXeh0jQU389FWWnM5AJiL1xSRxtEC1MZBVlswzZCLSgNTMQhSXsIGpmhjIKlIUgfTsIrXLICICAGTlFqtdQoOm6UBOSEhQu4Q6lZlbBEXheHVEpA1ZuTxBUJOmA/npp59Wu4Q6lZpZqHYJREQWGTnFUARPEtRiqM8Xe+ihh6q1fExMTB1Voj5FEUjLZiATkXZk5xVDUQR0evZ/UkO9niGfOHECQogq/2fLZEUgM4f3a4hIO7Lzijnrk4rq9Qy5efPmWLVqVZWXHz16dN0VozJJAjJyeL+GiLQjO88EnY5nx2qp1zPkhQsX1uny1sSg1yGTDSiISEOy84sZyCqq10AOCgqq9PHExERs2LABa9asAQBERUXdcHlbwTNkItKSrDzeRlNTvV6yvpaiKHjzzTfx3XffQVEU+Pj44IEHHsDHH3+M2NhYLFu2DG5ubmqWWKfyCkrULoGIyCKb/ZBVpWq3pyVLluDPP//E9OnTsXjxYnh4eAAAFixYgF69euF///ufmuXVuRIzZ1YhIu3IzjepXUKDpuoZ8s8//4w1a9agefPmAICPP/4YAKDT6TBjxgyMHz9ezfLqHKc6IyItyWEgq0rVM2S9Xm8J48r+VlJi25d0eYZMRFqiKAL5hbb9vatlqgayyWTC5cuXK/3b5cuXYTLZ9q81BjIRaU2Ryax2CQ2Wqpeshw0bhkmTJmHKlCno2rUrSkpKcP78eZw+fRqffPIJRo4cqWZ5dY6XrIlIa2SOr68aVQN5+vTpiIyMxPz58yFJEoQQGDVqFADgrrvuwpNPPqlmeXWOZ8hEpDWc8EY9qgay0WjE4sWLsW/fPuzZsweZmZnw9PTE7bffjvDwcDVLqxclZs49SkTawkBWj6qBvH79eowZMwY9e/ZEz5491SxFFWaZOz4RaQsvWatH1UBeuHAhgoKC0LlzZzXLUAUvV1NtsTPo8N6MfsgvKuHUeVRjPh6OapfQYKkayLIs4/3330dGRgbGjRuH0aNHw8vLS82S6o3MBl1USx4e3g4tmrhBkv4Zgzi3OI/hTLfEzsixrNWiaiD369cP77zzDmJiYrBu3TqMHz8e7du3x7hx49CvX79yXzC2hi2sqbas+u00Boc3haODPcxyCQx6I8yKjG1Re7Eteh8Sc5PVLpGsyPt3v4Zm7o3VLqNBkoSGJh5WFAU7duzADz/8gFOnTmHMmDF49tln1S6rTuTkF+OB1zarXQbZiOaNXfHRc/1gSr6E3ONb4NZ9GAzeAdDr9IjMiMGWS3uwJ/YQ8ksK1C6VNO5/Q/+DJm5+apfRIKk+MMjV0tLScO7cOZw/fx5JSUn4+uuvVaqs7tkbVb04QTYmJjEXi9aegH3jVrD3D0b80udx+f1HkLXvZwTaueOxbvfj89EL8ELvqejauD10kqqHPmmYXsd9Qy2qpsJ9992HH3/8EVu2bMHatWuxa9cuyLKMnj174rnnnsPgwYPVLK9O2dvpoZMANmik2vLXwVjcFuyFO8PvQnFiJHKP/YWMv79Cxt9fwegbCK8BkxAW1AE9m3VFbnEetkXvw/aofYjNjle7dNIQnaRXu4QGS9VL1mFhYXBwcEBaWhr8/f0xduxYjB07Fk2bNlWrpHp1/5xNHDeWat3i/xuAwEYuSFg1B8Xx5yv83bldH3j0HA1Do0Do9QbEZsVjS9Qe7Io5iJziXBUqJi1ZMvIdeDl6qF1Gg6RqILdv3x6DBg3C+PHj0bdvX5tuxFWZKfP+QGpmodplkI1xsNPhq9fugp1ShPhlMyHnZVa+oMEOnn3GwqXjIOhdPSGEwLGk09gWtReHE07ArHBM44bo81EL4O7gqnYZDZKqgfzQQw9h5cqVar286p55byuiE3PULoNs0NWNvBJWvQrINw5Xg6cfvAY8AIeWXWCwd0KBqRA7Yw5gW/ReRGbE1FPVpAVrxn8Eo96odhkNkmZaWScmJiIzMxPt2rWDoijQNYCGBbMX78TpqAy1yyAbdWf3QMyY0Am5x/5C2m+fVfl5jq26wfP28TD4B8OgNyAxNxlbLu3Bjpj9yCzMrsOKSW0OBnusHPc/tctosFRv6rtp0yb873//w+XLl+Hj44OdO3fi3//+N/z9/fHvf/9b7fLqlJMDf4VS3SnXyCvpEnKP/lml5xVePIzCi4cBnQHuPUfCu8tgTOw4ChM7jsLJ5HPYGrUXB+OPwSRrt/2DKbMQ5z4+ADuviqNOBQxrDZcWHtd97ukP9kBn1EHSlz8pkIvMKMkuwm2zbofB2Q5CEUj8KxI559IgSRKcA93RZGgIdMZ/GkXJxWacW7QfTUe0gVsbn1p7f3XF3cFN7RIaNFUD+ffff8fMmTPRo0cP9O/fHzt27AAAzJgxA2+88Qa+/PJLTJkyRc0S65Szg+q/h8jGffjdMYQ080Dg3VNhSolFcfy5qj9ZMSN7zzpk71kHnas3vAdMRGjrcHTsFYoiczF2xx7C9qh9OJt2se7eQA04NnFFqyldb+m5wZM7wc6zfJjH/XoepoxCGJztAADphxOQcyYNIdPCoDPoELn8KJK3R6PxnS0tz0n6+xKcmrpZRRgDgAfvHatK1evCn332GT7++GOsWLECr7zyCpycnAAAzZs3x8KFC/HLL7+oWV6dEkLwDJnqxf99tAOFxWb43zsbehfPW1qHkpuO1I2Lcfn9h5Cw6jWI+IvoHxiON+6YiY/vmYfxtw2Hr7N3LVeujoChIdBfCd0yiklG5vEkeHcPsDyWdzEDbm28obc3QNLr4N6+EXIv/HMLqiA+B5nHkxAwrHW91V5TbvYMZDWpGsgFBQUYNGhQpX9zc3ODotju8JKyIuDEM2SqB0UmBbM+3g3YO8Pv3tmAvmb7XVHsKSSufg2x8ych7fcv4F5UjHHthuLje+Zh7qCZGBDUCw4G+1qqvv65h/pCb1e+L27WyWTo7fVwa33Vjw4dIK4eSEAWlm9UISu4/NNZ+A8KhtHNej4LDwc3aKRZUYOkaiKYTCYUFxfD3r7iDltQUIDsbBtuQCJ4D5nqT0xiLhavPYEZEzrBZ8hjSNu0pBbWqiDn0CbkHNoEnZMbPPvdh1ahfdC2+4N4rNtE7Lt8BNuj9+FkyjlVvuTNeSbE/ngaxWkFMBeWwMHXGT49m8K1ZfUnsEk/lADvsABIun+6Zrq18UHin5Hw7RMIvb0BGceT4HFbIwBA6t446PS6cmfU1sDdwQ2ykGGQeLKgBlU/9bCwMEydOhWzZ8/GbbfdZnk8ISEBb7zxBnr16qVidXXPxZGBTPXnr4OxaBfshcHhg0tH8qpiI6+qUApykL75c6Rv/hx2jVvBa8BE9A7sgn4teiCzMLv+J7rQSYAAvLo1gUsLDyglMlJ2xODSymNoOrItvLs1qfKqCpNyUZiYixYTO5R73LOTP8x5JYhaHQEIAbe2Pmh0eyBMmYVI3h6FVlO6ojijAAmbLsCUVQSnZu4IGBYCvb12w479j9WlarenlJQUTJw4EQkJCXBwcIDZbIaTkxNycnIQGBiIr7/+Gt7etnFf6lqKEDh8JhlvfLFf7VKogVk0cwCa+7sgYeWr1WvkdQtcOg2CR48R0Gtkoovznx1EcXohbpt1O3SGqt2xi9t4DubCErSY0L5Ky19adRwOvk5oPLglzn60Dz69msGnewBifjgFnb0BgWNCa/IW6tQLvacivGlnjnWuElU/9UaNGmHDhg148skn0aZNGzRp0gRBQUF4+umnsXbtWpsNYwDQSRKaNuKvUap//15U80ZeVZV3fAvibjDRRZd6nujCuak7lCIzilLyqrS8bJKRGZEEnypees48kYyi1Hz4DQpG/uUclOSaSi9163Xw6dEUWRFJ5e87a4yXowfDWEWqXztxdXXFjBkzMGPGjAp/e/HFF/Huu++qUFX98PV05AQTVO+KTAr+vWg3PnqhH/zunY2ElXNuOpJXTSmmgutOdJFTnIfttTzRhVxkhqSXyvUJBlB6KRsAqnjMZZ1IhtHNHi5BN//hIheWIOG3C2g2qi30dnqU5BZD72iwnIkbXe0hZAFzQQmMLnY3WZs6PBzd1S6hQavXQN6wYUO1lj9w4EDdFKIRBr0OPh6OSOF41lTPYpNzseiHCDx7X2f4DJmKtE2f1ttrl6TGIvmH0h/azu36wKPXaAxrNQAj2txZaxNdxG86D3tvJ/j1b1Hu8cL4HOiMOjj4OgMAFLMCxSTD4FR5e470g/FVbpiV8GcknAPdLX2Ojc52kIvMEIqApJNgLigBdNJ1X0sL3Oxd1C6hQavXQH7xxRertXxDmGyiia8LA5lU8fehy2gX7I27etyJ4sSLtdrIq6ryT+9G/undlokuGncchIc6j8ODncbWeKKL9IPx8LitEex9Ssc3SDsQh/zYbDQe3BK6K92aotZEID8mC22f6VFhIJCChFwUp+bDq5P/zd9HTBayTqag7fQelsecmrlB72BA1slkeHb0R8axRLiH+pRrqa0lRp3Bqrur2YJ6DeSWLVti6dKlVVpWCIEnnniijitSlyIEGns74xhS1S6FGqhF3x9D62YeaH73VJhSY1EcV7eNvK7LbELm9m+Ruf1by0QXnVp2QbcmHVBgKsSOmP3YHr2vyhNd+PYJhN7BgOjvTgCQIBeVwOhqj8Bx7eB5VcAaXe1gcDJCuvbSNoD0Q/Hw6OAH/U16QwhZweWfz6HxHeX7HOuMegRN7Ii4X88hZUcM7Dwc0HS0dht0+bs2UruEBq9eW1l/8cUXePTRR+tseWtjNivYuOsSvtx4Su1SqAGzM+iw8vW74CCKEHej6RpVcO1EFwm5ydjKiS7qRK9mXfF876lql9GgaWa2p4ZIEQKHTifjzS/Z9YnUFejnio9e6IeSlKh6aeRVbVcmunDrMhh699J7tNYy0YW1uPe24RjTbigMuopXC6h+MJBVlpCWhyfe+VvtMohwR1gzPHtfZ+Qe31Kvjbyqq2yiC4fW4TA6OFvFRBfW4PlejyG8aRfoG8DUt1qlerenhq6RpxO7PpEmlGvklRSJ3CN/qF1SpcomugAAh8Db4NnvPvQPDMcdwX2Qmp+OrVF7sT16H1Lz01Wu1Lq08GzKMFYZz5A1YMq8P5DKltakEZaRvFa9ql4jr2rTwS3sbriFDYXe0w96nR5nUi9ia9Qe7Lt8BEXmYrUL1DS9pMPq8R9BX8PL1fPmzUN+fj7c3Nxw9uxZTJ48GYMHD66w3KZNm7Bx40Z4eXlBkiS8/vrrMBq12x2svjCQNWDOkj04foEtrUkbLI28UFzayCs34+ZP0hCdkxu8+k2EY2gvGBxdUKKYVZ/oQuuaujXGB0Nfq/F65s+fj9mzZwMA9u7di+eeew7795dvI5OcnIxx48bh999/h7OzM1577TUEBwfjkUceqfHrWzten1CZogi0aMwhNEk7TGYFsxbvBuyc4D++5tM11jelIAdpmz/D5YWPIH7FSzDHnELvpl3w6oBnsWTEO5jYYRQau/qpXaamBHk2q5X1lIUxAERHR6NNmzYVltm0aRO6du0KZ+fSwVkGDhyI9evX18rrWzvrOtJskBACrQPrdjxhouqKTc7FRz9E4Ln7OsPn7seR9usnapd0S0wJF5D0zZsA/pnoYmTbwRjT7m7VJ7rQkmDPQJgVMwy6mkfC6dOn8emnnyIxMREff/xxhb/Hx8fDx8fH8m9vb2/ExcXV+HVtAQNZZXq9Du2CbHcSDbJeWw5dxm3BXrirxx2l0zUe+V3tkmok7/gW5B3fAp2dEzz63ovA9v3wWLf78a+uE3Aw/ji2Re3F8aTTUISidqn1rqVXc+il2unu1K5dOyxatAi7d+/GpEmTsHHjRjg5OdXKum0dA1kDfDwc4eZsh5x8k9qlEJWz6PvjCGnmiRZDHoUpJQbFcWfVLqnGKpvoontQB/Sqo4kutE6ChBaezWo8VLEsyygqKrJciu7Tpw/y8/Nx8uRJhIeHW5YLCAjA0aNHLf9OT09HQEDVxgu3dbyHrBFteNmaNOr/PtyBgqIr0zW6eqldTq0qm+gidsEDSF7/AewzUzCs1QC8d/ccvDdkDoa1HgQ3e9tu49HIxadWxrBOTEzEa6/90zAsOTkZ+fn5CAgIwN69exEdHQ0AGDZsGI4cOYL8/HwAwNatWzF69Ogav74tYCtrDTDLCtZuuYA1m63/7INsUzM/Fyx6oT/MqTGI/+pl7Y3kVZuuTHTh0nEQ9K6eEELUeKILLevdLAzP9a75EMV5eXmYM2cOHB0d4ebmhosXL2LMmDG455578Pjjj6NHjx6WoZA3btyIX3/9FV5epT/w/vOf/8DOTptTUtYnBrIGCCFwMjIdL3+6W+1SiK5rYLemeP7+LsiN2Gq1jbyqq2yiC/uWXWC0d7qliS60bmrYJAwM6lUrDbqoZhjIGmEqkXHfK7/CLHNzkHZNv7cThvRsgdTfllp9I6/q+meiiyAY9Eabmeji0xFvw9uJt8y0gIGsIf9etANno7Uz0w5RZT58YQCCGrsgYdVrNtHIq9psaKILfxdffDT8DbXLoCsYyBohKwpW/3YWa7dcULsUohuy9pG8atP1JrrYFrUX59Ii1S7vpoa06o9/dZ0AncT2vVrAQNYIRRE4fiEVry3dq3YpRDd1dSOvhK9egbCis8K64tC8PTz7ToAxoDUMBiNS89Ox5dIe7Ijeh9QCbf5omXX7k+jS+LYaj2FNtYOBrCHFJjMmvLIJCqd+IivwTyOvbUj7teKITA2XDm5hQ+EWdremJ7rQSzqsGLsQ9ga2btYKBrLGzF68E6ejtPlrmuhaZY280jYvRc7hhtXIqyq0PNFFW59WeOOOmaq9PlXEQNYQs6zgl12X8MXPp9QuhajKyhp5Ja5+HUWXz6hdjmbZNQmBV//7YRfYDgaDHTILs7Etai+2Re9DYm5yvddzX/sRGB06hJerNYSBrDFpWYX415vanBieqDL/NPIyIW7ZCw26kVdVlU10ofcOgF6nx8X0aGyN2oM9sYfrbaKLdwe/hKBaGDKTag8DWYOe/WAbLsVbb79GanjYyOvW6Oyc4NFvApxv6wuDsztkIeNgfESdT3ThbOeEL0e/xzDWGAayxsiygh84jCZZobJGXnkntiH1Fzbyqq6yiS7sgzrAYHSo04kuejbtihf6TK3VdVLNMZA1RgiBhNR8TJv/t9qlEFXbU+M7YWivFkjb/DlyDm9Wuxyr5dyuDzx6jYbeNxAGvQGxWfHYErUHu2IOIqc4t8brfyLsAfQP6gUD7x9rCgNZo56c/zfiUvLULoOo2j58oT+CGrsicfV/UHT5tNrlWDeDfelEF50GQu9SOtHF0cRT2Ba1F0cST97SRBeSJGHpyHfh7uBWBwVTTTCQNUhWBFb/doajdpFVKt/Iaybk3PRbWk9kRiFWH0tBfK4J+iu3Ou9o6Ykxod7Q625+73PLpSz8ei4DJllBnkmGk1GPwa08MDq0dLhLWRH46mgy9sXlQicBtzVywuNhjWFv+GfUqoISGU/8dAHTezZBj6bqBpjB07/0kvaViS7yTYXYeQsTXdzWqDVeH/h8HVZKt4qBrEGKEIhKyMZzH2xXuxSiW1LTRl4peSY8/ctFhDVxxQt9AmDU63AurQCzfo/CyLbeeLSb/w2f/9XRZByIy8XrAwPRyMUOsiLwyYEEJOSY8M5dQQCAX89lYP2ZNHw0vCXs9Dq8+EcU2vs545Eufpb1LDmQiLSCEswZEFj9D6EO1WSiiye6T0b/Fj15uVqDOICpBukkCS0DPODr4ah2KUS35HJyHhZ+dwx2fkHwGfpEtZ9/ID4XeSYF427zgVFf+jXVxscJXRq7YMulrBs+93xaIb47kYrnegegkUvpKFR6nYQHO/vhgU6NLMsdScxDj6aucDLqYdBJ6NvcHYfj/7k/ey6tAH9fysKT4Y2rXX9dK7x4GAkrXkLsgslI37oGvrKEiR1H4dMRb2NO/xnoE9gddnpjhecZdQb0btaNYaxRnABToxRFoGeHxti485LapRDdku1H4nFbkDeG9h6I4sSL1WrkpbvSHUe+5gKerAgoN7mo99uFDHg6GhDiXf4HrYeDAR4O/3zl6SRAvqpXkSzEP6+rCHy0NwEPdm4Eb6eKwaYZihnZe9Yhe8+6KxNdTEJo6+7o2Cu00okuujRuD0ejg8pF0/UwkDXs9k5NGMhk1T75MQKtAz0RfNcUmFJiq9zIa0CQO34+m47Vx1Mwu28zuNjpcSAuF0cT8/DwVZeUK3MqpQD+LnbYFZONn86kI6vIDCejHoOCPTCyrZel722Ppq5YcSQZ427zgZNRh78js3B789L7xOvPpMOol3BPG6+afQD1SMlNR+rGRQD+meiif2A47gjuY5noItS3FWRF5uhcGsV7yBo39e0/kZRePyP3ENUFO4MOX71+Fxyr2cgrq9CM/+2Nx8H4XLjbGyALgcfC/DG4pecNnzfm69LQb+5hj9cGBMLT0YD9cbl4a/tl3B3iiad7NAFQ2sXwx9Pp2HIpC0II9GjmhsmdGiG9oARPbbyIBUOCYK/XYcnBRCTnmRDq64Rp4Y3hZLSmMKs40QVpFwNZw2RZwU87IrH8F3YdIesW4OuCj/+v6o284nOK8fKf0QjxdsSzvQLgaq/H2dQCvLU9FsPbeOH+Do2u+9zhq05CEcB/hwShvZ+z5fH3dsVhy6UsLB/bGn4u15/h6NW/oxHobo8pXf3x2IbzGB3qg3vaeOGdHZfhZNThhT5Nq/8BaIBbjxHwvuNhjs6lYWzUpWF6vQ5DeraAnYGbiaxbfGoeFn5b9UZeK48mIzW/BM/2agJX+9Kzura+ThjbzgdfHU3BqZT86z7XyVh6vLT0Kn+vtKWXAwSA8+mF133u9qhsxGYV48FOfjiTWoD0AjOGtfaEXidhZFsvbI3Kvuk9bK1ybd8fgHXW3lDwm17jnB2N6NslQO0yiGps+9F4bN4XA9dOA+EWNvSGy0ZlFcPdQQ9X+/LNXJq62wMAzqReP1QD3UuD+NppxcsabF0vT/NMMj47lIinejSGg1GHtIISuNnrLa28vZyMMCsC2UXyDWvXIrtGzWHvHwRJ4le+lnHraJyiCIy4PVjtMohqxSc/RiAyLgveg/8Fh2btrruch4MeOcUyCkvKh19yngkA4HblrFlWBLIKy49W1bOZKwAgKrOo3OMxWUWQALT2rrw74ZeHk9DO18kyAIinowF5JhnylWTPKZKhl/55bWvi2mkQhGx9PyQaGgayxul0Elo29UBIMw+1SyGqFbMW7UR+kRl+986C3tW70mVGh/pACGDpwSSYrwRiYq4JP55KQyNnI/oElobmx/sT8MDaszid8k/DxxFtvBHgaoevjiWj4Eqgn0srwNaobAxr7QV/14r3j0+l5GNHdHa5Psehvk5wsdNjR3TpQBt/RWaiV6BblUYJ0xLJ6ADXTndA0lvfD4mGho26rIAsK9h6JA4ffntU7VKIaoWlkVdabGkjL7OpwjIRSXn4/mQakvJMMOokyALo7O+M+zv4wutK3+CVx5Lxy9kMLBgShBae/9wzzigowZdHknE8KQ8OBh10koS7QzwxKtTbcum6jFkReHrjRQxv44WRbcv/QDiXVoBP9ieiWFbg52KH53sFwMPRunqLuoUNhfddj7IxlxVgIFuJErOCh+duRm4B55kl29C/SwBmTuqKvJM7LP1nqZZJOgQ+/Sn0bl68f2wFuIWshF4n4c5wbY2nS1QT24/G47e9MXDtOOCmjbzo1ji3CYfB3YdhbCW4layEJAH33B4MXnUiW/LpurJGXlPgEHj9Rl50azx6jYFQ2JjLWjCQrYQkSWjk6YSuba4/IAKRNfq/RTuRX1gCv/GzoHfzUbscm2HftA3sm7SCxNG5rAYD2YrIsoLR/VuqXQZRrTKbFfzfol2A0RH+974IyXD9UbSo6jx6joaQzTdfkDSDgWxF9HodOrduhHZB1jPgPVFVWEbyatT8lqZrpPIMnv5wat0dkt66WoQ3dAxkKyPLCh4eznttZHu2H43HJksjr2Fql2PV3MPvAYRy8wVJUxjIVkav16FdkDc6t/ZVuxSiWrdkXQQulo3kxUZet0Tv4gm3znfy3rEVYiBbIVnhWTLZrn+zkVeNePadAOj41W6NuNWskF6nQ6umHgi/zV/tUohqndmsYOainWzkdQuMXk3gyrNjq8VAtlKyouDhYaHsl0w2KSE1H+9/w0Ze1eU5YNL1p7MizWMgWym9TodAfzf06dRE7VKI6sTOY/H4dU80G3lVkV3jlnAJ7cVJJKwYA9mKKYrAQ0PbQWdls88QVdVn60/gwuXMK428blO7HE3zHvQQp1i0cgxkK6bTSWjs44yB3ZqpXQpRnZm1eBcbed2EY1BHOLZoz7NjK8dAtnKKEJh8d1sY9DxLJtv0TyMvB/hPeImNvCqQ4DXoIY5ZbQMYyFZOJ0nwdnfA0N5BapdCVGcsjbx8A+EzbJra5WiKc2gv2PsHsWW1DWAg24gHh4bC09Ve7TKI6oylkVeH/nDrPlztcjRBMjrAe/C/IBSOymULGMg2QJIk2Bl0eGxUe7VLIapTlkZedz4Ch+bc3z37TYDe2QMSBwKxCdyKNkKv16Ffl6boGMJGL2TbZi3ehbxCE/zG/RsGt4Y7hKydXxDcw0cwjG0It6QNkRUF08d3gkHPzUq265/pGh3gN6GBjuQl6eA7/CkAHATElvCb24bodTr4eTtj7MBWapdCVKcSUvPx3tdHrzTyelLtcuqdW7chsG8czIZcNoaBbGN0koSJg9ugaSMXtUshqlO7jifglz3RcO3Qr0E18tK7esFr4GQIDpFpcxjINkiSgOfu78JxrsnmLW2Ajby8Bz8KyWCExAPc5jCQbZBer0Ob5l4Y3od9k8n2NaRGXk6tusEltCcvVdsoBrKNEkLgkXtuQyNPR7VLIapTZrOCmR/utPlGXpK9E3yGTWOfYxvGQLZRkiRBr5PwzIQuapdCVOcS0wvw3zVHbLqRl8/dU6F3dmc3JxvGLWvDDHodOrf2xah+LdUuhajO7Y5IxMbdUTbZyMu53e1wbd+Pl6ptHAO5AfjXiHZoE+ipdhlEde7zDSdxPta2GnkZ3HzhO2wahOClalvHQG4gXn4kHK5ORrXLIKpzsz/ehbwCG2nkJengO/pZSEY7SBK/rm0dt3ADoNfp4O5ih5mTurErFNk8s1nBzI92QthAIy/PvvfCoWlbXqpuIBjIDYRer0O3UD+MHcBRvMj2JaYX4L2yRl7DrbORl0Pz9vC4/V72N25AGMgNzEPD2qFdkJfaZRDVOUsjr/b94B5+j9rlVIvOyQ1+Y14AeN+4QWEgNzACwEsPd4ebs/VexiOqqrJGXl53PGw9jbwkHfxGPw+dowsvVTcwkuCAqA2OLCuIuJiG1z/fC259snUGgw4rXx0MJ4OM+GUzYc5OVbukG/Ie/C+4dR9e40vVmZmZWLBgAZycnCBJEuLi4vDSSy+hefPmFZbdtGkTNm7cCC8vL0iShNdffx1GIxuB1jeeITdA+iv9k+8d1FrtUojqnKWRl94OfhNe0nQjL9fOd8I9/J5auW+clJQEe3t7vPrqq5gzZw769OmDOXPmVFguOTkZb7/9Nt577z289dZb0Ol0WLNmTY1fn6qPgdxASZKEB+5ui46tfNQuhajOJaYXlE7X6NMMPsOfUrucSjkE3gafoY/X2ixOoaGheP311y3/btasGZKTkysst2nTJnTt2hXOzs4AgIEDB2L9+vW1UgNVDwO5ARMQeOVf4Qj0c1W7FKI6908jr75wDx+hdjnlGDz84HfvbABSrbaqvnpdW7ZswaRJkyosEx8fDx+ff36Ye3t7Iy4urtZqoKpjIDdgep0O9kY93pzWGz4eDmqXQ1TnPt9wEudiM+B150NwaNFB7XIAAJKdI/zvnwOd0aHOxqnetm0bioqK8PDDD9fJ+ql2MJAbOL1eB3dnO7z5RG84O7IRB9m+FxftRG7+lZG83FUeyUvSwW/sTBg9/SDp66ZF9bZt2/D333/jnXfeqfTsOyAgAGlpaZZ/p6enIyAgoE5qoRtjIBP0eh0aezvjtUd7wM7AXYJsm1kBXvhwJ4TBXvVGXl6DHoRjcOc6697022+/YdeuXXjjjTeg1+sxb948AMDevXsRHR0NABg2bBiOHDmC/Px8AMDWrVsxevToOqmHbozdnshCUQQOnErCO18dgMK9gmxc7w6N8eJD3ZB/Zi9SNiys99d3D78H3oP/VWfrP3v2LMaOHQtPz38mlsnNzUVERAQef/xx9OjRA48++igAYOPGjfj111/h5VU6aNB//vMf2NlptzW6rWIgUzlCCGzaE40l6yLULoWozj028jaM6t8K6X+uQPaBjfX2uq5dBsN32LR6ez2yDrw+SeVIkoThfYIw4Q72USbbt+znUzgXU7+NvFxu6wufoU/UWvcmsh0MZKrUg8NCcUf3QLXLIKpzLy6uv0ZeTq3D4TtyBgBw0giqgIFMlRJCYMaEzggL9VO7FKI6ZWnkVccjeTkGdYLf2JmAxDCmyjGQqVJlXxgvPxKO7gxlsnHJGQVYsOYo7Hyawveep2t9/Q7NQuE/4SVA0kGS+LVLleOeQdel00nQ6yS8MiUct3duonY5RHVq74lE/LwzCi633Q73HrU3kpd945bwv38OoNPX2cAfZBu4d9AN6XSlQ/nNmhyGu3pUnCWGyJYs+/kUzsZkwOuOh+DYomON12ffJASNH/gPJIORYUw3xW5PVCVCCEiShGU/ncRPOyLVLoeozhh0wFevD4GzQUb8sv+DOTvlltbjGNwZfvfOhqTTc15jqhL+ZKMqKbun/Nio9ph4VxuVqyGqO1c38vK/xUZezu36wP++lyHpDAxjqjIGMlXbpCFt8djI9mqXQVRnkjMKsGD1ERh9AuA7Ynq1nuvW7W40Gv08IEm8TE3Vwr2Fbsmo/i0xY0Jn6Nh7g2zU3pNJpY282vWBe4+RVXqOZ98J8Ll7KiRJYmtqqjbuMXTL7gwPxL8nh8GgZyqTbfqnkdeDcAy6USMvCd5DHoNnv/vqrTayPWzURTWiKALHLqTi3a8OorDYrHY5RLXupo28dAY0GjUDzqG9OeAH1QgDmWpMVhQkpRdg7uf7kJier3Y5RLXOz8sJS2YNgJKViPjlL0KUFAMAdE5u8L/3RdgHhPASNdUY9yCqMb1OB38vJyx8oT86hag84TtRHbA08vIOsIzkZefXAk0fex/2TVoxjKlW8AyZao1yZRLlZT+dxMZdl1Suhqj2TRlxG8YMaIXckzvg3LYn+xhTrWIgU534+2AsPll7HCazonYpRLVGp5Pw5ZzB8HZ3hBAKz4ypVjGQqU4oikBsci7eWr4fSekFapdDVGPuLnaY/WB3tG/pzcZbVCcYyFRnZFmByazgv6sP4eDpZLXLIbplbQI98cq/wuHmbAe9nmfFVDcYyFSnFEVAp5Pw/V/nseb3s5b7zETWYlifIDw+qj0glTZgJKorDGSqF0IInInOwP++OcquUWQVvNwcMGNCZ3QL9bNMrkJUlxjIVG9kWYGsCKz45TR+2X0J3PNIqwZ2a4ppYzvC3qjnJWqqNwxkUsXpqHSeLZPmeLraY/qEzghv5w9FCOh4Vkz1iIFMqrCcLf96Gr/s4tkyqa9/16Z4clxHOPCsmFTCQCbVnYnOwMJvjiAxjWfLVP88XOzx9L2d0LN9Y0sjRCI1MJBJdWVny1/9ehobebZM9ej2zk3w9PjOcLTjWTGpj4FMmsKzZaoPHi72eHJcR/Tu2IT3ikkzGMikKbKsQAhgw45IrP37PPKLOKUj1R57ox6j+rXEhDtDYNDreFZMmsJAJk2SFYHCYjO+/v0sftsTBbPM3ZRunU4CBnRrhkeGt4O7iz3vE5MmMZBJs8p2zZTMQizfeAq7IxJUroisUccQH0wd1QEtGrux0RZpGgOZNK/sS/R8bCaW/XQSZ6Iz1C6JrECgnyumjLgN3UL9IMsKL0+T5jGQyWqUfanuO5GI5b+cQgIbflElPFzt8cCQtrirR3MIIRjEZDUYyGR1zLICSQJ+2xONb/44h5x8k9olkQY42hswom8wJtzBBltknRjIZLVkRYEsC/y+PwYbd1ziMJwNlLe7A0b0Dcaw3kGwN+p5j5isFgOZrJ4sK9DpJOw/lYT12y7idBTvMTcEQU3cMGZAK/TrEgCAUyOS9WMgk80wywoMeh0i47Lw49aL2BORAJnzL9sUSQK6tmmEcQND0KGVDxtrkU1hIJPNkRUFep0OGdlF2LDjIv7YF8MBRqyc0aDDgK5NMW5QCAJ8XRjEZJMYyGSzhBAQAigxy9i8LwYbd15CckaB2mVRNbg522ForxYY2a8lXJ2MEAK8R0w2i4FMDYIsK5AkCREXU/H3wcvYezIRxSZZ7bKoEga9Dt3b+eGOsECEtWsECRJDmBoEBjI1KGWXs4tLZOw+Ho8th+Jw4mIqeKtZfW0CPTEorBkGdGsKJwcjL0tTg8NApgarrBFYVm4xdhyNw+6IBJyJzuD0j/UoOMAdt3dqgv5dm6KRp5NlmxA1RAxkIpQP553H4rHreDzDuY60aOyGvp0D0L9rU/h5OfFMmOgKBjLRNcrCOSe/GEfOpSDiQhoiLqaxQdgt8nZ3QMdWvugU4oOubRrB082BIUxUCQYy0Q2YZQV6nQRJkpCeXYij51IRcTEVERfTkJ5dpHZ5muTmbIf2Lb3RKcQXXds0gr+3MwDwcjTRTTCQiarh6lBJSs/H0XMpiIhMw4mLacjOa5hjajvaG9AuyMsSwM0buwFgABNVFwOZqAauDp2E1DxciMtCbFLulf9ykJSebzMtuCUJaOTphEB/VzT3d0Pzxq4IbuKBgEbO0Ot0DGCiGmIgE9UiWVEAAcv90RKzgoS0PEQlZCM2KRcxV4I6OaNA0w3GvNwc0NzfFYGW4HVHUz9X2Bv1AEr7dQPgfWCiWsRAJqoH1waYqURGfGoeUjMLkJVnQk6+Cdl5xVf+MyE7vxg5eaWPmcxKrdSg10lwd7GHu4sdPFzsr/x/e3i4/vOYl5sDGvs4w8nBCABQFAFFCJ75EtUDBjKRioQQkBVxZUjIymcsKjbJyC0oDee8whLIioCiKJAVAVkuDUxcGVJSp5Ng0EvQ63Qw6HUwGnXwcLGHm7OdJWSvJisKFKX0cnRZ4zUiUgcDmciKlY3XDQCQAAlgqBJZKQYyERGRBvDGEBERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiINYCATERFpAAOZiIhIAxjIREREGsBAJiIi0gAGMhERkQYwkImIiDSAgUxERKQBDGQiIiIN+H9op8z/GjWBtgAAAABJRU5ErkJggg==", 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" ] @@ -384,7 +384,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -418,7 +418,7 @@ "outputs": [ { "data": { - "image/png": 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vvYQJEyZAp9NhyZIl2Lt3L4KCgiCTyfDOO++gdevWgnHpdDrMmzcPhw4dQlRUFOrXr49Lly6hsLAQr7zyCqKjo/HVV18hISEBXbp0wdKlS/HFF19g79698PHxwSuvvIJXX30Ve/fuxaJFi+Dj44OQkBD4+Phg165d6NChA2bPns13TG3RogWmTJmCfv368TGMHDkSw4cPx+DBg/nbLl26hKVLl6KgoAAymQwGgwHTp09H+/bt+SW6qampaNeuHVasWIHNmzdjx44d8PDwgFqtRtOmTTFr1ix+rv9BDy7RDQkJwUcffVRuiS4Xb8uWLTF//vwK51i5ciX/GsXExGDevHnl/rO5dOkSFixYAJVKBXd3d3Tq1AlvvfVWhSW6MTExePPNN9GlSxcolUp88cUX+OeffxAcHAxPT0/MmDEDkZGRuH79OmbPns2/BwYPHsy/h7jfqarHfv755/jjjz+wdetWAMCgQYPwwQcfAABWrVqFP/74Az4+PnBxccGMGTP4EaKHvb++/vprHDt2DJ6enigqKkLnzp3x7rvvCg75l33+zPkev3nzJj7++GPk5uaiSZMmaNy4MVatWoW2bdvi008/xebNm7F7924AwLPPPovnn38eH3zwAX/dTz/9VLCYlptinDhxIv7++2/cuXNHcIluTV8/U7Zt24YdO3Zg9erVFe5bvXo1Nm3aBD8/P0gkEkycOBHdunUDYCwSnjVrFu7cuYNmzZqhZcuWWLZsGVq0aIH33nsPV69erfT9S2yLkhBCrOT555/H2rVr+Q9yQuwRl4RwvWmsZcWKFQgLC8OgQYOsel1iWzQdQ4gFrVu3DgUFBTh16hRat25NCQghZdy8eRP79u2DVqvFgQMHyo0cEudAq2MIsaC0tDQMGzYMAQEBWLJkia3DIaRSXMdUbtn1jz/+aNHEWa1W45NPPkFwcDDGjh1LSboToukYQgghhNgETccQQgghxCYoCSGEEEKITVASQgghhBCboCSEEEIIITZR51fHDBw48KH7FxBCCCHEviQnJ9f9JCQiIgIrVqywdRiEEEIIqYZJkybRdAwhhBBCbIOSEEIIIYTYBCUhhBBCCLEJSkIIIYQQYhOUhBBCCCHEJigJIYQQQohNUBJCCCGEEJugJIQQQgghNkFJCCGEEEJsgpIQQgghhNgEJSGEEEIIsQlKQgghhBBiE5SEEEIIIcQmKAkhhNRZ+UoNrt/JAWPM1qEQQmqAkhBCSJ21eMM5vLfsKBKScm0dCiGkBigJIYTUSXoDw5VbWQCAO/cVNo6GEFITlIQQQuqktMxCqDV6AEBmrsrG0RBCaoKSEEJInXQzOY//e0Zuke0CIYTUGCUhhJA6KTElj/87jYQQUjdREkIIqZMSy4yEZOZREkJIXURJCCGkztHrDbidVlqMmqNQQac32DAiQkhNUBJCCKlzUjIKUazRw91VAqlEDAMDchRqW4dFCKkmSkIIIXUOV5TaJMwPwX7uAKg4lZC6iJIQQkidc6ukKLVZhB+C/Y1JCNWFEFL3SG0dACGEVNfNkiSkabgfCoo0AGiFDCF1EY2EEELqFJ3egDupxqLUphF+CPH3AEDTMYTURZSEEELqlGR5ATQ6AzzcpKgf6MnXhNB0DCF1DyUhhJA6hesP0jTcD2KxqLQmhEZCCKlzKAkhhNQpZetBAPDTMZm5KjDGbBQVIaQmKAkhhNQpZUdCACCoZDpGrdGjUKW1UVRGJy/fx6wVx5GtoKkhQqqCkhBCSJ2h1RmQdD8fgLEoFQBcZBL4ebsCADJybDsls+PoLVy8mYWjF1JtGgchdQUlIYSQOuNeej60OgM83WWoF+jB324vxanp2cYk6F56gU3jIKSuoCSEEFJnJPL1IL4QiUT87Vxxqi2X6Wp1en4ahpIQQqqGkhBCSJ1x84F6EE7Z4lRbychVgauLvScvoCJZQqqAkhBCSJ1R2q7dv9zt/HSMDZOQ9Gwl/3dVsc7mU0OE1AWUhBBC6gStTl+hKJUTzI2E5NluOkb+QFEsTckQ8nCUhBBC6oSk+/nQ6Rm8PWQIKakB4ZQ2LLPlSAglIYRUl8U3sMvNzcWXX34JDw8PiEQipKSkYMaMGYiMjKzw2N27d2Pnzp0ICAiASCTC7NmzIZPJLB0iIaQOSEwp2S8m3K9cUSpQOh2TW1AMjVYPF5nE6vFx0zF+Xq7IKyzGPXm+1WMgpK6x+EhIeno6XF1d8dFHH2HWrFl44oknMGvWrAqPk8vl+Pzzz7Fo0SJ89tlnEIvFWLdunaXDI4TUEXyTsgemYgDAx9MFri7GxCPLRo3C5CUjIZ1iQgHQSAghVWHxJCQmJgazZ8/mf46IiIBcLq/wuN27d6NDhw7w9PQEAPTq1Qvbtm2zdHjEQVy8kYlz/2XYOgxiQQ92Si1LJBKVFqfmWD8JYYwhPcc4EtLlEWMSkiwvgMFAK2QIqYxVakLKDp0eOHAAr7zySoXHpKamIigoiP85MDAQKSkp1giP1HFanQFzfz6NT386jSK1bdt2E8vQaPW4my5clMoJsWFxakGRFkVqHQCgbbNgSCViqDV6m/YtIaQusHhNSFmHDh2CWq3G6NGjq3Xcrl27sGvXLsH7hEZViHMpVGmg0eoBGAsTI+tTHZGjSbqfD72BwdfLhR/xeJAti1O5epAAH1d4uMkQHuKFpPv5uCcvQL1AT6vHQ0hdIZiE/PHHH3j22Wcr3H7y5En88MMPmD59Olq1alWtCx06dAj79+/H/PnzKxSVAUBYWBjOnz/P/5ydnY2wsDAAQGxsLGJjYwXPO2nSpGrFQRwP9w0UMLbtjqzvY8NoiCWUbVIm9PkBlO2aav0khKsHCQ0wJhwNQ72NSUh6Abq0rGf1eAipKwSnY9asWSP44ObNm6N///74+OOPq3WRPXv24NixY5g7dy4kEgnmzZsHwJjUJCUlAQAGDBiAc+fOQak0fqM4ePAghgwZUq3rEOekLLNzKjWIckyV1YNwgv1sNx3D1YNw+9k0rOcNwLjXDSHEtGrVhAQEBOCll16CVlv1efeEhARMmzYNe/bsQbdu3fDEE0/g999/BwCsXr0a+/fvBwCEhobi/fffx7Rp0/Dhhx9Cp9MhLi6uOuERJ1W2DiST5uAdEr9njIl6EMDGIyEljcq4qRc+CZHTChlCKsNPx+zbt49PCNLS0jBjxgzBA9LT06t1gRYtWuDatWuC961cubLcz4MGDcKgQYOqdX5ClA9MxxDHotbo+P/Mm1WShHCFqVl5KhgMDGKx8LSNJXA1IaUjIcYpwWR5odVjIaQu4ZOQ1NRUnD59GgCgVCr5v5clk8kQHh6OTz/91HoREvIQqjIjIVmUhDicpLR8GAwMft6uCPBxM/m4QF83iEXG1VIKZTH8vU0/1tzSH6gJqRfoCZlUDI1WD3lOEeoHUXEqIUL4JGT06NH8qpUhQ4Zg+/bttoqJkGopNxJiw7bdxDKqUpQKAFKJGAE+bshSqJGZq7JaEqLTG/gROG4kRCIWITzEC3fS8nEvPZ+SEEJMEKwJWbx4sbXjIKTGisoUpmYrVNBTgyiHksjvnOv30MfyG9lZMRnlpn9kUnG5xCeyZEqG6kIIMU0wCWncuHGlB7388ssWCYaQmig7EqLTM+QVqG0YDTE3vii1kpUxHK6HiDWbhHH1IKEBHuVqP0pXyFASQogpJpuVnT59GidPnkRWVhb0en25+27fvm3xwAipqge7pGbmqRDoK9zQitQt6mIdUkpGEqLCfR/6eL5hmRVrgx5cGcNpGEpJCCEPI5iEfP/991iyZAnc3d3h51dxHraoiJZBEvuhfCAJycpTARU3aSZ10O00BQwMCPBxq1JiWTodY82RkJIkJMCj3O3cCpmUjALoDQwSWiFDSAWCScimTZvw3XffoXfv3oIHURMxYk+KVMbpGLFYBIOBUXGqA+GalFWlHgQAQmzQK4SfjnlgJCQ0wAMuMolxhUy2Eg2CvawWEyF1hWBNiJeXl8kEBACWL19usYAIqS5uJCQs2PifAPUKcRw3S+pBoqpQDwLYpjA1nZ+OKT8SIhaLEBFqTDzu0pQMIYIEk5DmzZsjKyvL5EH79u2zWECEVBdXE8KtRqCuqY6juiMhXGFqQZEG6mLdQx5tHnK+UVnFZbh8XYic2rcTIkRwOqZfv35466230K9fPzRu3BgeHuUz/PXr11d7J1xCLIXbwK5RfR8cu5hGDcscRJFai9TMQgBVK0oFAE93GTzdpFCqdcjMUyGiJAmwlEKVFgVFxiQ49IGaEKC0LoSKUwkRJpiETJ06FQBw9uxZAChXmMoYq7RhECHWxi3R5XbPpekYx3A7VQHGgCBft2o1Hgv294Dyfj4ycossnoRwoyC+Xi5wd634cUrLdAmpnGAS0rBhQ36n2wcxxvDRRx9ZNChCqkqnN0CjNS4h56ZjFIUaFGv1cJVJbBkaqaWqbFonJNjfHUn3861SF8LXgwQId0TlpmNSMgqh1xsgkVRrz1BCHJ5gEtK3b1906dLF5EFDhw61WECEVIeyTLfUEH93uLtKoCrWIytPhTBajVCnJSYrANQgCfGzXq8QObdnTGDFqRjAuKmeq4sExRo97mcrER5i2ZEZQuoawbT8vffeq/SgwYMHWyQYQqqLqwdxc5FAIhEjyM/6fSKIZSSm5AIAmoX7V+s4boWMNbqmpueYLkoFuBUyxsSDVsgQUlGNxgbfeOMNc8dBSI1wy3M93GQASr8FU3Fq3aZUaZGaafwPvqpFqRyuV4g1pmPkJhqVlUWdUwkxTXA6ZtSoUZUedPfuXYsEQ0h1FfFJiPGtHGzF/4CI5dxKzQNgTCh8vVyrdWywFUfD0itZnsuJ5ItTaZkuIQ8STEIuX76MVq1albtNqVQiJSUFMpmswn2E2IqypFuq5wMjIbRCpm6raT0IAIQElIyGKdQWbZeuNzB+ysdUTQhQZpku7aZLSAWCSUhkZCTi4+Mr3K7VarFmzRqEhYVZPDBCqoJGQhxTdXbOfZCftxskYhH0BobcfDWC/CyzmWG2QgWdnkEqEVW6rw03HZOWWQid3gAprZAhhCf4r2Hjxo2CD5bJZHjttdewYcMGiwZFSFVxhake7txISMlQPI2E1Glcp9SaJCESsYhPPCxZnMrVgwT7e1Q62hJcsmpLp2dIK2m+RggxEkxCXF1Nz8FqtVqkpqZaLCBCqoMbCeGmY4LKTMcwxmwWF6m5wiIN7pfUWtRkOgawzogYXw9SSVEqYGz2GMG3b6cpGULKEpyO2b59e4XbGGNQKBTYv38/TccQu8F1S+WmY4L8jJ01NVo98pWaahc1Etu7lWKsBwkN8IC3h0uNzhFsjZEQfuM600WpnIahPrhxL8+4QqatxUIipM4RTEI++OADwQeLRCK0b9/eZDdVQqyNHwkpmY6RSSXw93ZFbkExMvNUlITUQTdr2Cm1rBB/y0/LpWcL754rhNq3EyJMMAmJiorCypUry90mkUgQEBAAF5eafTMhxBK4jqncSAhgHIrPLShGZq6qRjUFxLa4otRmtXjtrDIdU9KoLLQKIyGR/AoZWqZLSFmCScj48eNpyoXUCVxhKlcTAhjrQm7cy6OGZXUUX5Rai5EQrmuqJXuFVKVRGYcbCUnLVEKrM0AmpRUyhAAmClPL7g1z9+5dnD9/nhqUEbukVAuMhNAKmTorX6nhay2iajMSYuF+MapiHfIKiwFUrSYk0NcNHm5S6A20QoaQsgRHQgDg8OHD+Oyzz5CcnMzf1rBhQ8ycORM9evSwSnCEPEzRA23bgbJD8bR/TF3DTcXUD/KEl7us8gdXgktCitQ6FKq0tTqXEC5R8nKX8fVIlRGJRGgY6o2Eu7m4l16AyPo+Zo2HkLpKcCTk1KlTmDx5Mtzc3DB8+HBMmDABw4cPh6urKyZPnozTp09bO05CBAlNx1DX1LrrlhnqQQDAzVUKH09j/ZolktHSdu0Pn4rhcJ1T71JdCCE8wZGQ5cuX48MPP8SIESMq3LdhwwYsXboUXbt2tXhwhDwMPxLiXr4wFaBN7OqimyX1ILWZiuEE+7sjX6lBZq4KjRtUbxO8h+FGQqpSlMqhFTKEVCQ4EpKeni6YgADAyy+/jPT0dIsGRUhV6A0MqmI9gIqFqQCQk6+GTm+wSWykZviVMbUoSuWEWLA4taqNysqi3XQJqUgwCdHr9ZUeZDDQBzuxPVXJKAhQvibE19MVMqkYjAHZCrUtQiM1oCgs5pfURoXXfuTCktNypT1Cqj8Scj9bCa2u8s9YQpyFYBISFRWFxYsXV0hG9Ho9lixZgqioKKsER0hluG6pLlJxuSWP4jJ7h1Bxat3BjYKEBXuVSypripuWy7BArxB5TvVrQgJ83ODpLoPBwJCSQStkCAFM1IS89dZbiIuLw+bNm9GyZUv4+vpCoVDg+vXrKCwsxPr1660dJyEVlNaDVPwPK9jPHfezlFQXUofUZtM6IZbqFWIwsNIeIdUYCeFWyFxPysG99AKz16kQUhcJjoS0atUK8fHxaNKkCY4fP45du3bh+PHjaNy4MeLj49GyZUtrx0lIBVy3VE+3irl0EK2QqXNumqFJWVml+8eY9z2QW6CGRmcoN+JWVdyUzN10WiFDCFBJn5DWrVsjPj4earUaCoUCvr6+cHNzs2ZshFSKW57rLjB0b4223cS8bpmxKBUoLUzNLVCbtUspVw8S5OcOqaR656QVMoSU99B/QW5ubggNDYWbmxsKC2kek9gPrluq0EgIdU2tW3Lz1chSqCESAU3CzDNN4evlUqZA2XzvA74epBorYziRodweMpSEEAKYSEJ27NiBLl26oHfv3uVuHzduHGbMmAGNRmOV4AipDDcSIlTESF1T6xauKDU8xAvuriYHaKtFJBKVrpAx44hYTepBONxISHq2EsVaWiFDiGASsnPnTgwcOBDbtm0rd/vy5cvBGMOSJUusEhwhlSniR0KEC1MBalhWVySmKACYryiVw/cKyTNfMpqewyUh1R8J8fN2hbeHDIwBKTQaQohwEiKXyzFr1iz4+pYfFg0ODsbcuXNx7NgxqwRHSGW4wtSy3VI5XBKiVOv4xxH7Ze6VMRxLLNMtbVRW/ZEQkUjEt2+nKRlCKmlWJpFIBA9wcXGBTqezaFCEVIXQvjEcN1cpvD2Mt9NoiP1LTMkFYL6VMZzSZbrmTEK4lu3VHwkBqHMqIWUJJiEikQhXr14VPODKlSsWDYiQqlIK7KBbFhWn1g3ZChVy8oshFgFNzNw7I9jMTeuKtXrk5Bu78NakJgSgFTKElCVYAfbyyy9j7NixGDZsGFq3bg0/Pz/k5eXh8uXL2LJlC9566y1rx0lIBaUjIcKFjMH+7ridpqAkxM7dKqkHiQj1hpuZilI5IQHmnY7JKKkH8XArHWmrLj4Jod10CRFOQkaMGIGUlBT88ssvYIwBABhjEIvFGD16tMnN7QixptKaEOH/DKh1e91gzp1zH1R2NIwxBpFIVKvzcfUgoQEeNT5Xw5JluvKcIqg1Ori5mDfxIqQuMfnuf//99/HKK6/gxIkTyM3Nhb+/Px5//HFERERYMz5CTOLbtpv49mzJDcyI+Zhz59wHBfkZGyxqtHrkKzXw9XKt1fnkOTVfnsvx83aFr5cLFIUapMgLzV4HQ0hdUmkKHhERgRdffPGhJ/n5558xbtw4swVFSFVwG9h5mhgJoa6p9o8xxichlvjPWCaVIMDHFTn5xh16a5uE8EWpNWhUVlbDUB9cLszCPXk+JSHEqZmlj/HOnTvNcRpCqkXFF6aaGgmhwlR7l61QI6+gGGKxyGIbupW+D2o/Lccvz63FSAhAxamEcMyShHB1I4RYi8HAUFRseokuUFoTkqNQQW+g96g94upBGoZ6w1Um3BagtszZK0Rei0ZlZZVuZEdJCHFuZklCalvsRUh1qTU6cLmvqcLUAB9XiMUi6PQMeQVqK0ZHqsqS9SAcc/UKYYyZbySE6xVCDcuIkzPPtpKEWJlSZRwFkUpEcDGxO6pEIkagr7EwkaZk7JMl60E4XIFyRi1XSSkKNVBr9BCJgJCS0ZWa4rqmZuQUQVVMzR+J86IkhNRJRWUalVU2EmeJDcyIeTDGLNauvSwuYahtIppesntuoI8bZNLaTR35eLrAz9tYJJtMoyHEiVESQuokZSWb15XFFSVS63b7k5mrQr5SA4lYhEb1fSx2ndLpmNqNhJS2a6/dVAyH2rcTQoWppI7iuqUKbV5XFtcngqZj7A83FRNZ3wcuFipKBUpHQhSFGhRr9TU+jzyHqwepXVEqp7RzKiUhxHmZJQlp1aqVOU5DSJXx3VJdHzISYqZvwcT8+HoQC07FAMY+Mu6uxiSnNiNi8uzaNyori6sLuZtO7duJ86pyEpKUlIR9+/YhIyOjwn3z5s0za1CEPAy3PNdUjxBOsJnqAYj5cctzLd2sSyQSIahkWo7b+6UmuOmYerVsVMah6Rhi7xhjOH3lPvIKii12DcEkZMuWLejTpw++++47AMCRI0cQGxuLKVOmYMCAAbh06ZLFAiKkKopKRkJMdUvlUGGqfSpblNrMwiMhgHmKU9NzzLM8lxNZMh2TlafiC60JsSf/Xpdj3up/sHL7ZYtdQzAJ2blzJ0aOHIlXX30VAPDNN9+gUaNG2Lp1KyZPnowlS5ZYLCBCqkL5kG6pHC4JyVfWrh6AmJc8pwiFKi2kEhEi63tb/HrctFxNl+lqdQZ+KifUTDUhXh4uCPAxrpChuhBij67ezgbw8C97tSGYhOTl5WHMmDFwcXHB3bt3ce3aNUyZMgUtW7bEuHHjBKdkCLEmrjD1YatjzFUPQMyLqwdpVN+n1stdqyKklvsIZeYWgTHA1UUCv1ruP1MWt6MuTckQe3QrRQEAaBpumS0VABNJiFhcevPff/8Nb29v9O7dm79NKqWtp4ltKcv0CalM2XoAKk61H3x/kAh/q1yPGxGraSJaduM6c3aIpj1kiL1ijOFWah4AICrMz2LXEUxCXF1d8e+//yIjIwPr16/H008/DRcXFwCAXC6HTkcd/ohtFam4HXQfnhDTbrr2hTGGK7eMw7yWXhnDqe10DF8PEmCeehBOaRJCK2SIfcnIVaGgyPJTpoKf4JMnT8bYsWOh0+ng7u7O14Zs2rQJP/74I7p3726xgAipiqqOhAC1/xZMzOvP43fw371cyKRitIsOtso1uUQ0K08Fg4FBLK7eaEbp8lzz1INw+OkYqgkhduZWyZRpw3qWnTIVTEK6d++O3bt349q1a2jbti3q1asHAAgPD8frr7+Ozp07WywgQqqiqIodU4EyK2QoCbG5u/fz8fPOqwCAMbEtEWqm5a4PE+jjxm9mmFugRqBv9fZ+4UZCzFWUyokoGQnJVqhRqNLCy4IFgIRUh7X6+Jgcy46IiEBERES52x577DGLBkNIVSnVVesTAtB0jL0o1uqxcO2/0OoM6NgiBIO6NbHatbnNDDNzVcjMU1U/CTFzozKOl7sMgb5uyFaokZxegJjGAWY9PyE1dSvVWJQaZcGiVKCSZmX5+fn4/vvvMXr0aMTFxQEANmzYgGvXrlk0IEKqQlXFJbpA6f4xmXlUmGpLa3Zdxd30Avh5ueLtlzqYtcCzKmraM4YxhvRsribE/CM3fNMyOdWFEPvAGOOnYyw9EiKYhKSkpGDQoEFYsmQJrl27huTkZOODxWJMmDAB586ds2hQhFSGMcaPhFRl/XrZkRDa58g2zlxLx65jdwAAb7/cnt9B1pqCa7hKqlCl5ZeEh1ggCYmsT8t0iX3JVqihKNRALBbx709LEUxCFi5ciI4dO+LQoUM4c+YM/P2Ny+hefPFFLFu2DMuWLbNoUIRUplijh8FgTCaqUpga6GvcxE6jMyBfqbFobKSi3Hw1lmw8DwAY3L0JOrYItUkcIQE1GwnhRkECfFzh5mL+9gTUvp3YG64epGGoN1wtuLkkYKIm5MqVK9i7dy/fL6TssGm7du2gUCgsGhQhleFWxojFIri5PPwfiEwqgb+3K3ILipGZp4KvGZtNkcoZDAzfbDwPRaEGjer7YPTAljaLhZuOyah2EsL1CDFvPQindDddmo4h9oFrUmbpehDAxEiIVCot17DsQbm5uRYLiJCH4YbGPVylVa4roOJU29h57DbOJWTARSrG9LiOcLHwt6rK8DsqV7M2SF6y6Z25V8ZwIkpGQnLyi1FYRCN1xPastTIGMJGE+Pn5YcOGDYIHbNu2jV+yS4gt8D1CqrGckYpTre9OmgJrdhkL2ccNboXIepadW36YmiaipUWplhkJ8XCT8bHdpSkZYgduW6FTKkdwOuaNN97ApEmTsH79enTo0AGZmZlYsGABrl27hrNnz2LVqlUWD4wQU/huqVVYGcMpbValtkhMpDy1RoeFa/+FTm9Al5b1MODxRrYOiZ+OMRaaaqtUTwRYrlFZWQ1DvZGZq8I9eQEeaRJosevUVEpGAf48fgfP925W7eXNpG7JyVcjJ78YYhHQuIHlvzgIjoT06NEDS5YsgVKpxMaNG5GVlYU1a9YgNTUVS5cupX4hxKaq0y2VE8Qvz6SREGv4eedVJMsL4e/tiqkvtrP6clwhHm4yvhlYdRrX8S3bzdwjpKyG9bgVMvZXF6LTG7DglzPYdewOfthmuS3diX3gluaGhXjDzdXy+8SZvEK/fv3Qr18/3LlzB7m5ufD390fjxo1rdBGtVos1a9Zg+fLl+P333xEdHS34uDZt2sDbu7RH/VdffYVHH320RtckjquoGj1CONQ11XpOX7mPPSeSAAD/93IHuyoEDvZ3R6FKi8xcVZWmh/R6A1/IaumREMA+V8jsPHqbnyY6efk+bibnopmVNh4k1pdohZ1zy3rop3jjxo1rnHxwfv/9d3Tq1AkqVeX/AQwYMAALFiyo1bWI4+MKU6vSsp1DhanWka1QYcnGCwCAIU9GoX3zENsG9IAQfw/cScuv8ohYZsleMzKpGP7ebhaLy153083KU2H9/xIAGHcQlucUYe2eBMyZQKPhjoobCYmy0uaSgtMxhw8fxnPPPYeXX3653O1jx46tUY+QESNGoH379g993M2bN7FgwQLMnTsXv/32GzWWIoKUNRoJMX6LzS1QQ6c3WCQuZ2cwMHyz4TwKijRo0sAXowbE2DqkCqq7TFfOL8/1qPamd9XBrZDJKyyGorDYYteprh//uAy1Ro+YRgGYO/ExSMQinPsvA1duZdk6NGIh1uqUyhH8FN+8eTPq16+PqVOnlrt9+vTp+Prrr/Hjjz/itddeM3swQ4cOxYgRI2AwGDBlyhTk5+djwoQJ2LVrF3bt2iV4jFwuN3scxL4VVaNbKsfXywUyqRhanQHZCrXVNk5zJtsP38KFm5lwkUkwPa6jRXferCl+mW4VkxB+4zoLv1/cXaUICfBARk4R7skL0NoOprD+vS7HiUv3IRaL8PqwNmgQ5IV+XSPx18kkxO+5jgVvdLOLWh9iPnkFxchSqCGyUlEqYCIJSUpKwubNm+HqWv4fwiOPPIIlS5YgLi7OIknIiBEjABjbww8ZMgRLly7FhAkTEBsbi9jYWMFjJk2aZPY4iH1TqqpfmCoSiRDk5477WUpk5hZREmJmiSl5iN9jXI772rOt+G/29oaflqviUm2uR4gli1I5DUO9jUlIegFaRwVZ/HqVKdbq8cO2SwCMXW4bNzDWB7zULxr7z9zDtTs5OPdfhs263xLLuFWyNLdBkFe1Pl9rw2RHsgcTEI6Xlxf0er3ZA8nOzkZBQel8qEwmQ3Gx/QxLEvvBFaZWZ4kuQMWplqIu1mHR2rPQ6Rkea10fTz8aaeuQTOKSkKpOx6RbYXkuJ5KvC7H9CpnN+28iPbsIAT5uePmp5vztgb7uGPiEsUZw7Z7rNGXuYKzZKZUjmIRoNBqkpKQIHpCcnAyNxjxd/U6ePImkpCQAxjqUHTt28PedOnWKlgITQXzH1Gpm6lScahmrdlxBamYhAnzcMGW4fSzHNSWkZDomR6GCvgq1QVyjMku1bC+rtH27bYtT0zILsfnATQDAa0NaVfh39nzvZnB3lSAxRYGTl+/bIkRiIdbslMoR/Co5aNAgjBo1CuPHj0fr1q3h6+sLhUKBS5cu4eeff8awYcOqdZF///0Xu3fvBgD88MMP6Nu3L5555hmsXr0aXbt2xfjx4xETE4Ovv/4ad+7cgUajgUajwcyZM2v/GxKHwxWmVqcmBCgtTs2ikRCzOXEpDf87dRciEfDOKx3g4+li65Aq5eflCqlEDJ3eWBv0sF1xrTkS0jDU9rvpMsawYusl6PQGdGgegifaNKjwGF8vVwzuHoWN+25g7V8J6NqqPiQWLNol1nMr1fojIYJJyKRJk3Djxg18+umn5b7VMMbw9NNPV7sOo1OnTujUqRM+/vjjcrevXLmS/3tMTAx+/PHHap2XOCeuY2p1VscAZRqWURJiFll5Kiz9/QIAYGjPpmjbLNi2AVWBWCxCsJ877mcrkZmnqjQJUaq0KCjZy8UaNUThoV4QiYB8pQZ5BcXw87Z+ceqxi2k4fyMTMqkYE4e2NjmqNaRnU+w6fgfJ8gIcOZ+CXh0jrBxnKlZsvYSJQ9qge/swq17bUeUrNcgoqYFqYoV27RzBT3GpVIpvv/0Wp0+fxvHjx/lmZd26dUOXLl2sFhwhQoqKq1+YCpSdjqGuqbWlNzAs3nAOhSotmob7YkR/+1uOa0qwf0kSklsEwHSLdK4o1dfLxSpFem4uUoQGeCA9uwj35Pnw87ZuUlek1mLVH8aOqM/3boYGQV4mH+vlLsOwXk3x6+7rWP+/BHRvFwapxPSmp+aUllWIbzeeh6pYjx//uIzOLUOt0tnT0XH7xdQP9OQ7C1tDpa9c165d0bVr1wq3FxYWwsvL9BuUEEtS1nAkhApTzWfrwZu4lJgFVxcJpsd1gkxqnf+AzCGoir1CSutBrLeSqmGojzEJSS9Am6bWTULW/+8/5OQXo36gJ57v3eyhjx/UrQl2HLmN9Owi7PvnHvo/1sjiMWp1BixcexaqYuPiiNyCYuw8dhvD+wh34SZVl2iDolSgktUxlRk5cqS54yCkSjRaPd9srDodU4HSJKRIreOX+ZLqu3EvF+v+MnbRnDikNcKC69YXEq449WHJKL881wpFqRxbdU69k6bAzmO3AQATh7aGi+zhPV7cXKUY3seYrGzc+x80WvOvmnzQ+v8lIDE5D17uMr4Z3paDiSgsMs9iCWdm7U6pHMGvknq9Hn/88QdOnjyJrKysCkty7969a5XgCHkQV5QqEhkbPFWHm6sU3h4yFBRpkZWnqnZhKwFUxTosWncWegPDE20aoG+XhrYOqdpKl+lWPi3Hj4RYoSiVY4sVMgYDw3ebL8JQ8ppWp/dH/8caYduhRGQp1NhzMgnP9oiyWJwXb2Ziy0Hjqp03X2iHrq3q49C5FNxLL8DWQ4kYNaClxa7tDG5Zec8YjuBIyIIFCzBr1ixcu3YNWq0WjLFyfwixFW55rrurtEZttLkVMjQlUzM/77yK+1lKBPm6Ycrwtna9HNeUkCou1U63YqMyTmSZ3XSt9Vm778w9JNzNhburBK8NaVWtY11kErxU0kdk0/4bUBXrLBEiFIXF+Hr9OTAGPP1oJB5v0wASsQijnjGOhvxx5DZy8tUWubYzKFRpcb8k6bZmUSpgYiRk79692LJlC2JihIvNhgwZYsmYCDGpJt1Sywr2d8ftNAUVp9aAorAY+/4xjoK+/XIHeHnY93JcU7jW7Vl5RWCMmUyk5CUfytZYnssJD/GCWAQUFGmRV1AMfx/LbZoHGF/TNbuuAgBeeboFAn3dq32OPp0bYsuBRNzPVmLn0dt4oa956zMYY1j6+wXk5KsRHuKFVweXJkpdHqmHFpH+SLibi417/8Prw9qa9drOgitKDQnwsPoye8GRkICAAJMJCABs2rTJYgERUpmadkvlUHFqzR0+lwKdnqFpuG+dWI5rCleYqirWo9BEbZDewCDPMb5HrFkT4iKT8CMv1qgL+eXPaygo0qJRfR8M6takRueQSsR45WnjaMjWQ4kmn9Oa2nMyCaevpkMqEWP6iI7lVsKIRCJ+GuZ/p+7yU2ikevhOqWHWnYoBTCQh7dq1w+3bt00e9M0331gqHkIqpaxht1RO6d4hlIRUB2MMe/+5BwDo28V+27JXhatMAr+SDeJMTcnkKIy7LUvEIgT6VX90oDa4upC7csu2b79+J4d/TV8f1gaSWiyx7d4+HA3reUOp0mLboURzhYi76fn46Y8rAIDRA1sKFk22bhqE9tHB0BsY1v0vwWzXdia26JTKEXzXNWvWDG+99RY+++wzrF+/Htu3by/3Z8+ePdaOkxAAQJGqZt1SOXzDMmrdXi23UhRIup8PmVSMJx2gOVTQQ4pTud1zQwI8rN4NtGE9y3dO1esN+G7LRQBAvy4N0bKx6X4pVSERixDXvwUAYMeRW8grqP2+XxqtHovWnoVGZ0CHFiEY3N30SA03GnL4XAqS7tt+7526xhZ7xnAEx7TnzJkDALh586bgQXWxGI04hqKSwjePGjYnosLUmvm7pBbksdb162wtSFkh/u5ITM4zmYzKbdAjhNMw1PLLdHceu4Ok+/nw9pBh9EDzrCp5tFV9NI3wQ2JyHjYfuIlXn61ekeuDVu+6iqT7+fDzcsXbL7WvtBC9aYQfnmjbAMcvpiF+93V8NL5ifysirEitRVpWIQAgyspFqYCJJCQqKqpcS/WyGGOYOHGiRYMixBRuJMSjhiMh3HRMdp4KegOjPS+qoFirx5Fzxg0t+9XBJblCHpaM2mJlDKfsMt3KCmdrKluhwvr/XQcAjB74CHy9zNMeXiQSYWT/GMz+8SR2n7iDIU9G8SOP1XXmWjp2HbsDAHjrpfbw9354gW5c/xY4efk+/rmWjut3chDTOKBG13Y2d9LywRgQ5Otmk60CBKdjhg8fjrCwMME/4eHhGD9+vLXjJARAaU1ITQtT/X3cIBaLoDcw5BXQkr6qOHn5PpRqHYL93a3exdNSQh4yHSPnNq6zwUhIeIgXxGIRlCqtRZad/vjHFaiK9WgR6W/2pLJ982A80iQQWp0BG/fdqNE5cvLV+Oa38wCAwT2aoFNM1fqWhId4o08n4x42v+65Ru0kqijRRk3KOIJJyJgxYyo9aOjQoZaIhZCH4lbH1LQwVSIWIdDX+K2KpmSqhluW27dzwxr1ZrFH3IhYlonpmHR+ea71R0JkUgnql1z33+sZMBjM95/puYQMHL+YBrEImPx8W7O/niKRCCNLenfsPV391SqGkj2J8pUaNG7ggzHVnCp6+akWkEnFuHIrG+f/y6zWsc7KVp1SOSbLoZOTkzFr1iz07dsXffr0AQAsW7YMhw4dslZshFSgrOUSXaDMMl0qTn0oeU4RLt7Mgkhk7AfhKLjpGNOFqcbbrdkttawmJUsll226gNc+34tfd1/DvfTaFVxqtHqs2HYJABDbvQkaN7BMEeIjTQLRoXkI9AaG9dVcrfLHkVu4cCMTLjIJ3o3rBJn04e3jywr2d8fAJxoDMI6GmDOBc1SJNuqUyhFMQhISEjBkyBDs3r0bXl5e/LBWixYtMHfuXBw8eNCqQRLCKeI2r6tFy3W+HoCSkIfaf8a4hLNt02CbFGlaCjcSkltQDK2u/LYU6mIdv7rDFiMhgHE5ap/OEXB3lSIjV4VN+2/ijYUH8dbXh7D9cGKNpmm2HLiJ+1lKBPi4YcTTLSwQdam4Z4znN7ZVr1rylJiSh193XwMAvPZsK0SUFOhW1/O9m8HdVYpbKQocv5RWo3M4C3WxDqkZxgJouxoJWbRoEV544QWcOHEC27dvh4+PcclY3759sWrVKvz0009WDZIQTulISC2SEL5XCHVNrYzBwLDvDNcbxHFGQQDAx9OF36TtwWk5buM6L3eZVbc0Lys0wANvv9QB8XP6472RndD1kXqQiEW4narATzuuYuzc/+GjH05g/5l7/BRlZdKyCrHpgHG146vPtqrxdGZVNYvwx2Ot64MxVKl3h6pYh0Vr/4VOz/BY6/p4+tGa96Lx9XLFc08a97BZ99d16Es2vCQV3UnLh4EBAT6uCLBwd15TBMe0k5KSsGrVKv7nstXZTZo0gUpF3yCJbZTWhNRiOoarB6CakEpdSsxEZq5xo79HW9e3dThmJRKJEOLvjpSMQmTmqtAgqHQn4HQbtGs3xVUmQfd2YejeLgyKwmIcv5SGQ2dTcD0pBxduZOLCjUx8t+USHn2kHp7sGI4OzUMgfaDpGGMMP2y9DK3OgHbRwejWtoFVYh/RvwVOXbmPE5fuIzElr9JGWD9uv4zUTCUCfd0wZXi7Wq8IevbJKOw6fgepmUrsO5Ncq6TGkd0qaddu7f1iyhIcCXlYVXF2drZFgiHkYfjVMbX4hhpErdurhOum+WT7MLhWYWv3usZUbRBfD2LFdu1V4evligGPN8aXb3bHjx/2RVz/FggL9oJGq8eRC6n49KfTGD3nf1ix9RISknL4z/ETl+7j3H8ZkErEeH1oG6v1eYqs54Mn24cDANbuuW7ycccupmLvP/cgEgHTXulolr1LPNxkGN7HuIfNb38noFirf8gRzsmWnVI5gklIw4YNsWjRImi1FYf5li1bhqgoy23XTEhlyu6iW1N1qTDVYGAVahasobBIg5OX7wMA+tXxNu2mcBvZPbiZoZzvEWL7kRBT6gV64sV+zfH9+72x+O0nMbhHE/h5uyJfqcGfx+/g3aVHMXH+fqz96zp+/OMyAGOtRINgr4ec2bxefro5xGIRziZk4Nqdil9eM3KLsGzTRT6+1k2DzHbtAY83QpCfO7IUauw+fsds53UktuyUyhFMQt5++238+uuv6N69O1599VWkpaXhzTffRL9+/bBq1Sq888471o6TEOj0BmhKvtHUZiSE+88nX6mBWmOZrcfNgTGGj344gXHz9lo9YTp8PhVanQGN6vvY9APKkkJM7CPETceE2qgotTpEIhGaRvjhtWdbY81HT2HOa4+hV8dwuLlIcD9biY17byBboUa9QA8836eZ1eNrEOTF9yKJ33O93Ci73sDw1bqzUKq0aN7QH6+YuVjWRSbBK08ZN9bbtP9mlWpnnEmxVo97cmNRqt2NhLRt2xZr165F06ZNceLECSgUCuzfvx/16tVDfHw8HnnkEWvHSQiUZXbnrGnbdsC4vJcbSclW2G/DsqT7+biUmIW8gmLE77lm1WtzvUH6dWnosNs0BJtoWJZuw0ZltSGRiNGhRQjeeaUj4j/pj2kjOqJTTCgCfd0w9YX2NptSe7Fvc0glxt4dF26U9u7YtP8Grt3JgburFNPjOlaoZTGH3p0iEB7ihYIiDbYdumX289dld+/nw2Bg8PVy4Xsn2YLJT/I2bdpg7dq1UKvVUCgU8PX1hZub7QIlhJuKcXOR1GrHT5FIhCA/dyTLC5CZW4QwKw9RV9XhklbpAHDwbAoGd49C0wg/i1/3TpoCiSkKSCUiPNkh3OLXs5XS6ZjSkRDGGL9vjK2W55qDm6sUPTuEo6cdvH7B/u4Y8Hgj7Dh6G/F7rqNddDASknKx4e//ABh38LXUcy2RiBHXPwYLfj2DP44kIrZbY7O1qa/rynZKteUXDcFP8s6dO6Nr165ITk6Gm5sbQkNDKQEhNqesZbfUsvhlunZaF8IYw9ELqQCMO7kCwE87r1ilFTVXkNr1kfoO/YEdXKZAmXtecwuKodEZIBaVvkdI7T3fpxlcXSS4mZyHg2eTsWjdvzAYGHp2CEevjhEWvfbjbeqjabgvVMV6/L6/Zq3kHRFfDxJm2+lWk6tjtmzZgogIy745CKkObk7X073mUzGcYDtfIZOQlIuMXBXcXaWYO+ExuJS0oj59Nd2i19Xq9Dh0NhmA4/UGeVCgrztEIkCrMyCv0NicjKsHCfL3sMj0gLPy93bD4O5NAADf/HYeGbkqhAZ44PVhbSx+bZFIhFEDjO3fdx9PQkYO9QcC7GNlDGAiCWnatCnCw00P4yUkVK8VLyHmoOS6pTrBSMjh88apmEdb1UNYsBeeLWm+tGbXVegs2Hzp9NV0FBRpEejrhvbNQyx2HXsgk4r5Bk3c+6Cu1oPUBUN7NoWnmxSMAWKxCNPjOlq8aRqnXXQw2jQNgk5v4KeBnJlWp+c72dqqUypHMAnp1q0bDh8+bPKgGTNmWCwgQkwpMkO3VA43EmKPDcv0egOOXzS2m+ZqMp7v3Qx+Xq5IzVRiz4kki12bm4rp3SkCEgfZrK4yDy7XlvM9QigJMTcvDxd+BczoAS3RIjLAatcWiUQYOcC4sd6Bf+8huWRViLO6e78AOj2Dt4eMXyVmK4Lj2gaDAR9//DGio6PRtGlTeHqWLxrKzKTdCYn1cYWptemWyuH3j7HD1u0XE7OQV1gMH08XtG0WDMA4+vPK083x3ZZL2PD3f+jVKcLsLcUzc1U4/18GAMefiuGE+Hsg4W4u/z6w5e65zmBwjyj07hQBL4/aNySrrhaRAXi0VT2cupKOtX9dx4zRXaweg73gOqVGhdm2KBUwkYR89913AAC5XI6jR49WuN/WQRPnVGShwlTGmF29p7lVMU+0bVCuLuGprpHYeewOkuUF+H3fDYwbZN6l8gfO3gNjQKuowHJtzB3Zg9NydaFRWV1niwSEE/dMDE5fTceJS/dx414uohv62ywWW0q0gyZlHMHpmBYtWiAhIcHkn+bNm1s7TkL4lu3mGAkJ9HWDSARodAbkKzW1Pp+5aLR6nLpi7FTKtbzmSCRiPvHYefQ2/63dHAwGhn0lUzH9nGQUBCidjuF6hdBIiGOLrOfDr8aJ3226lbyju1Vmea6tCSYhEyZMqPSg6dOnWyQYQipTujqm9iMhMqkE/t7G5af2tELm3+tyFKl1CPJzR0yjinPmHVuEoF2zYOj0Bvxqxg/Rq3eykZ5dBHdXKR5vbZ0NzuxBcAA3LaeCRqvnm9dRTYjjeuXpFpBKRLhwMxMXbzpfaYFOb0DSfWNRqq1XxgAmkpABAwbwf9fr9cjJySl3f7du3SwbFSECuI6p5hgJAcpsZGdHK2S4VTE92oVBLFAYKhKJMG7wIxCJgKMXUpFwN6fCY2qCGwXp0T4MbrXoRlvX8CMhOSp+KsbdVWqWTdSIfQoN8ED/RxsBAH7dfc0qvXfsSbK8AFqdAZ5uUruYdjS5EP7cuXMYO3Ys2rdvj2effRYAMGfOHKxfv95qwRFSFleYao7VMYD9FacWqbU4c00OAJV2Km3cwBd9OxunTH76o/YNzIrUWhwrWY3jLAWpnJCSrqkFRRrcLVmyWC/Qw65qhIj5vdAvGm4uEty4l4dTVyzbe8feJCbnAbB9p1SOYBJy+vRpjBo1CqmpqejevTtcXY3D1oMGDcKmTZuwdetWqwZJCGDejqmA/fUKOXXlPrQ6A8JDvNC4gU+ljx3RvwVcXSRIuJuL45fSanXdoxdSodHqERHqheZOVqjn6S7jR9au3DLu8kr1II7P39sNg3sYe+/E77kOvcF5RkNupRqLUpvYuFMqRzAJWbp0KT744AP8/fffWL58Oby9vQEAHTp0wA8//IDffvvNqkESApi3Yypgf71CDp8ztmnv0T78od9QAn3dMaxnUwDAml3XoNXpa3xdrjdI386RdvHNyNq40ZDLt7IAUD2Is3iuZ1N4ucuQLC/A9kOJtg7HauylUypHMAnJzMxEXFyc4AEhISHQ6ex3+3PiuMzZMRUoUxNiB0mIorAYF0qK5J5sH1alY57r2RQBPq6Q5xRh17E7NbruvfR8/Hc3F2KxCL062X6zM1vg3gf30o0NrKhbqnPwcpfhxX7RAIA1f17DtxvPQ6OteTJfF+j1BtxJ4zql2vFIiFarNTnPrNPpKhSqEmINqmLzdUwF7Gs65tjFNBgMDE0j/NCgirv6urlKEdff2AVy474bNVpqzI2CdI4Jhb+3c25S+eBGdaE0HeM0BnePwqgBMRCLjP8W3l9+zKH3lknJKIRGq4e7q8RuegEJJiExMTF49913kZ2dXe52lUqFOXPmoG3btlYJjhCO3sCgKjZ+SzHX6hiuMDW3QA2tznL7sVQF16CsqqMgnN6dG6JRfR8oVVr8trd6e2Lo9AYcLNmszpl6gzyIm47h2MOKAWIdYrEIw/tE45PXHoO3hwyJyXl4e/FhXLiRYevQLILrlNokzE9w9Z0tCCYh06dPx9GjR/Hkk0/imWeewd27dzF06FB069YN+/btw7Rp06wdJ3FyqpJ6EMB80zG+Xi6QScVgDMhW2G40JCO3CNeTciASAd3bVS8JkYhFGD/Y2MBs9/E7SM0srPKxZ67JoSjUwM/bFR1jQqt1XUfC1QYBgEhUMSkhjq998xAs/r+eiAr3RUGRBrNXnsSm/Tccbvku3ynVTopSgZIkRKFQIC2ttMK+cePG2LJlCwYOHIiCggJoNBpkZGTgqaeewubNm9GwofN+ayK2wXVLdZGKIZOaZ4t1kUhkF8WpR88bC1JbNQlCoG/1N5NqFx2CTjGh0BsY1uy6WuXjuN4gfTpFOPW29WWTjkAfN7jIJDaMhthKaIAHvpjSHX07N4SBAb/uvo75v5zhC+IdgT11SuVIAWDy5MkoKirC5s2bIZEY/wGGh4fjiy++sGlwhHD4fWPMvGlbkJ870rKUNi1O5RuUVXMqpqyxsS1x7r8MnLqSjsu3stA6KqjSx+fkq/FvgrEnSZ/Ozv2lomxNCNWDODdXmQRTX2yH6Eh/rNx2CScv38e99ALMHNsFEaHetg6vVvQGhtup9rNnDEcMADk5OeUSkHHjxlV60J49eywfGSFlcN1SPc1UD8KxdXFqsrwAd9LyIZWI8HibmrdLb1jPB093jQQA/LzjCgwP6Xtw8N9kGAwMMY0C6vyHa235+7hBUjI/TstziUgkwjOPNcKCN7oh0NcNqZmFmLbkMI5frF0/HltLyyyEWqOHi0yC8BD7+TcvBoxPulhcOhz7sNUvK1eutGxUhDygSG3e5bmc0q6ptklCuFGQ9s1Dat0q/JWnW8DdVYrEFAV/XiGMsdLeIE5ckMqRiEUILJmWo0ZlhNM8MgDf/F9PtI4KgqpYjwW/nsHqnVeh19u2iL2muKmYJg18+KTbHkgBICoqCnFxcejYsSNcXFyQlZWF5cuXmyzKycx0vk1/iG1x3VLNtTyXw42E2KImhDGGI2UalNWWn7crhvdphl93X8evu6/j8TYN4CpQ35CQlIvUzEK4ukjQra3zbFZXmQaBnsjIKUJ4FZdHE+fg5+2KTyc+hl92X8e2Q4nYeigRiSl5eG9kJ/h6udo6vGrhOqXaS5MyjhQAPv74Y3zyySf47bffkJ9vbGSydOlSkwc5Y1dFYltF3OZ1ZuqWyindxM76vQFuJufhfrYSri4SdH2knlnOObhHFPacTEJmrgp/HL6FF/pGV3jM3n/uAgC6tW1g9pGlumrc4Efwz7V0PNraPK8DcRwSiRjjBj2C6IZ+WPLbeVxKzMLbXx/CjDFdEF2HtjlI5ItS7aceBChJQoKDg7F8+XL+xiFDhmD79u0mDxoyZIil4yKknKJi825exwm2YdfUIyWrYrq2rAd3M+1c6yqTYNQzMfhq/TlsPnAD/bo2LNeETFWsw7GLxuv26xJplms6gsYNfNG4gX19OBP70q1tGCJCvTF/zT9IzVTi/WXHMGloazxdsiOvPTOUK0r1s20wDxBclzdhwoRKD3rY/YSYG1eY6m7uwtSSJKRIreOvYQ16A8PRC9xUTM1XxQjp0T4cTSP8oCrWY8P/yjcwO34xDapiPeoHeaJl4wCzXpcQRxdZzwdfvfUkHm1VDzq9Acs2XawT7d7Ts5UoUusgk4rtrhBdMAkZMGAA/3e9Xl+hULXs/YRYA1eYau6REDdXKbw9jAWh1hwNuXo7Czn5ani6y9ChRYhZzy0WizB+kLGB2f9OJeFeyRb1ALDvjLEgtV+XhjStSkgNeLrLMGN0lzrV7v1WSZOyxg187K4nkMlozp07h7Fjx6J9+/Z49tlnAQBz5szB+vXrrRYcIRyuMNUSNQy2KE7lpmKeaNMAMqn5m2O1igrCo63qwcCA1buuAQBSMwtx9XY2xCKgd6cIs1+TEGdRvt27C9/u/fx/9tnuna8HCfOzaRxCBJOQ06dPY9SoUUhNTUX37t3h6mqsAh40aBA2bdqErVu3WjVIQkpHQsw7HQOUqQuxUnGqVmfgew6YeyqmrDGxj0AiFuHf63JcuJGB/SWjIB1ahNaoMyshpLz2zUPwzf89iaZcu/cfT2Ljvv8e2qfH2rg9Y+ytHgQwkYQsXboUH3zwAf7++28sX74c3t7GOaQOHTrghx9+wG+//WbVIAlRqizTMRWwfnHq+f8yUKjSIsDHFa0e0tm0NsKCvTDgicYAgJ92XMX+M8bN6qg3CCHmE1LS7v2prpFgDFi7JwGf/nwahUXV39XaEhhj/HSMva2MAUwkIZmZmYiLixM8ICQkBDqdzqJBEfKgIrVlOqYC1u+ayjUS69YuzOJNg17q1xye7jIk3c9HTr4aPp4u6NKSlqESYk4uMgnefKEdpr7QDi5SMf69Lsdbiw/z0yC2JM8pQqFKC6lEhMh6PrYOpwLBJESr1ZpsVKbT6R7aUZUQc1NaqGMqYN2uqepiHU5fTQcAPGmGBmUP4+PpghfL9Arp1THCbBsAEkLK69c1El++2R31Aj2QkVOE95Yexd+n79o0Jm4UJLK+j13+2xeMKCYmBu+++y6ys7PL3a5SqTBnzhy0bdvWKsERwlFxIyEWmI4JsuJ0zOmr6SjW6FE/0BPNIvwsfj0AiO3WGGHBXpBKxHiqK03FEGJJUeF+WPz2k+jSsh60OgOW/n4B3248j2IbLePl6kHsrVMqR3Bse/r06XjppZfw119/ISIiAnK5HEOHDsXdu3fh4uKCjRs3WjtO4sQMBsY3K/MwU1OvsrjpmOw8FfQGZtEpEm5VTI/2YVZbIiuTSrBwancUFmlRP4j2RiHE0rw8XDBzbBdsPnAT6/66jr3/3MOtFAVmjOls9f2JEpPzAABRYfZXDwKYGAlp3LgxNm/ejIEDB6KgoAAajQYZGRl46qmnsHnzZjRsSN+miPWoNTpws4OWKEz193GDWCyC3sCQV6A2+/k5BUUanPtPDsCyq2KEeHu4UAJCiBWJxSK80DcacyY8Bh9PF9xOU+DtxYfxT8l0rDUwxvg9Y+xxZQxgYiSksLAQ/v7+WLBgATU0IjanVBlHQaQSEVwsMKcpEYsQ5OuGjFwVMnNVFlu+euJSGnR6hkb1fdDQDgvECCHm1y46BEve6YkFv57Bf3dz8enPpzG8TzOM6B9j8cL0zDwV8pUaSMQiNKpvn585gp/onTp1Qp8+fXD//n1rx0NIBUVlGpVZKikO9rd8cSo3FfNkB8sXpBJC7EeQnzvmT+6G2G7GJfOb9t/E7JUnkFdQbNHrckWpDet5w0VgR217IJiE+Pv7Y9++fWjQgLb5Jran5JfnWm7H1yBfyy7TzVaocPlWFgCgRzvrTsUQQmxPJhVj4nNtMH1ER7i6SHDxZhbeXnwICUmWW216y447pXJM1oRwDcqEHDlyxGIBEfIgrluqh7v5i1I5fK+QPMt0TT16IQ2MATGNAhAS4GGRaxBC7N+THcLx1Vs9EBbshWyFGh8sP4adR2+bbItRG1w9SFM7bFLGEUxCBg4cWOkeMYsXL7ZYQIQ8iOuWasmREEs3LDtS0qDsSSsXpBJC7E9kPR98/XYPPNG2AfQGhpXbL2PRurNQFZuvEShjrHTPGDstSgVMFKZeuXIFx48fx9q1a9G0aVN4epavqk9LS7NKcIQAZWtCLDgSUtIrJEth/iQkLasQN5PzIBaL8ERbSkIIIcYat/dHdsKORrexeudVHDmfijtp+ZgxujMiQk3PRFRVTr4aeQXFEIuARg3ssygVMJGE7Ny5EyEhIVCr1bhy5UqF+4uK7HfLYuJ4iizYLZXDNyyzwEgIV5DatmkQ/LxdzX5+QkjdJBKJ8GyPKDQN98OX8WeQLC/AtCWH8cxjjdE2OhgtGwfAzaVmX764otTwUO8an8MaBCNr2rQptm/fbvKgIUOGWCgcQipSWmMkpGR1TL5SA7VGZ7Z/tIyx0qkYWhVDCBHwSJNAfPNOTyyMP4vLt7Kw9VAith5KhFQiRkyjALSNDkLbZsFoFu4HiaRqbQq4olR77ZTKEfyknTVrVqUHffnll+V+LioqgocHFdsRy+BGQixZE+LpJoW7qxSqYh2y8lQID6n9cCgAJN3PR7K8EDKpGI+1rm+WcxJCHI+/txs+nfgYjl9Kw7n/MnDxRiayFGpcvpWFy7eysHZPAjzcpGgdFYQ2zYLQrlkwIkK9TbYtSOR2zrXTTqkcwSSkU6dOlR4UHR1d7ucRI0Zg27Zt5ouKkDKUZfqEWIpIJEKwvzvupRcgM9d8Scjhc8ZRkE4xoRaNnxBS90kkYvRoH44e7cPBGENalhIXb2biwo1MXE7MQqFKi9NX0/lNMAN8XNGmaTDaNjP+4QrsgdI9Y+y5KBUwkYRUlyWWFhHCKSrpmOppwSW6gLE49V56AbLM1LDMYGA4coEalBFCqk8kEiEs2AthwV4Y8Hhj6A0Md1IVuHAzExdvZuLa7Wzk5Bfj0LkUHCr5stMgyBNto4MRHeGPbIUaIhHQpC6OhFQXtXYnlmSNkRDA/LvpJtzNQWauCu6uUnSKCTXLOQkhzkkiFqFphB+aRvjh+d7NoNHqkXA3BxdvZuHijUzcTM5FWpYSaVlK7EESACAs2AvuFtj005zsOzpCULpE15I1IYD5e4VwUzGPta4PVzttmUwIqZtcZBK0aRqMNk2DMfKZGChVWly5lVUyUpKFZHkButWBlgBWSUK0Wi3WrFmD5cuX4/fff69QU8LZvXs3du7ciYCAAIhEIsyePRsyGc2jOzulFTqmAkCwH7d/TO2XoOv1Bhy/ZOyn82R7moohhFiWp7sMXVvVR9dWxgJ4rU4PmdT+v/yYf0tSAb///js6deoElcr0N0y5XI7PP/8cixYtwmeffQaxWIx169ZZIzxi54qs0DEVKB0JMUdNyMWbWVAUauDr5YK2zYJqfT5CCKmOupCAAFYaCRkxYsRDH7N792506NCB787aq1cvfPPNNxgzZoyFoxP273U55NnK2p9IJEL75sFoEORV+3M5IcYYioq5ZmWWL0wFgIxcFf48drtW5zpx2bgD9RNtGlR5XT8hhDgbu1kdk5qaiqCg0m+MgYGBSElJqfV5ayItqxBzVp0y2/ka1ffB0um9zHY+Z1Ks0cNgML6/LF2YGujrDolYBK3OgBXbLpvlnD1oKoYQQkwSTEJu3bqFqKioCrcnJCTgwIEDeOWVV+Dn58ffPm/ePIsFCAC7du3Crl27BO+Ty+Vmv15ogCeG9WqK9Jza1QbodAacvpqOlIwCGAwMYjGtIqoubmWMWCyCm4tlhxdlUjHeeL4tzv6XYZbzNa7vg5aNA8xyLkIIcUSCScj06dMFm4/JZDLcunUL06ZNw08//cTf3qpVq1oHEhYWhvPnz/M/Z2dnIyzMWNkbGxuL2NhYweMmTZpU62s/SCIWYUzsI7U+j15vwND3d0KnZ8grLEaAj5sZonMu/L4xrlKrLAXv1zUS/bpGWvw6hBBCTBSmmppeiYqKwldffYWsrCyzXPzkyZNISkoCAAwYMADnzp2DUmmswzh48GCd36NGIhHziUdmLm36VxN8jxB3WiVFCCGOhh8JSUhIQEJCAgAgPz9fcAM7xhjS09NRWFhYrYv8+++/2L17NwDghx9+QN++ffHMM89g9erV6Nq1K8aPH4/Q0FC8//77mDZtGgICjEPYcXFxNf297EawvweyFGpk5qnQnL5gVxvfLdXCRamEEEKsj/9k37dvH5YtWwbA2AH1gw8+EDzAzc0NM2fOrNZFOnXqhE6dOuHjjz8ud/vKlSvL/Txo0CAMGjSoWue2d8F+7rgO8yz7dEbW6pZKCCHE+vgkZPTo0XjuuefAGMPEiRMrJAgAIJVKERQUBImkbqw/tgd8K3AzdeF0NtbqlkoIIcT6+CTE29sb3t7GnUMnTZrEF4WS2uFbgdNISI0oVdbplkoIIcT6BAtTY2NjUVhYWKH24+7du1YJypEE8yMhVJhaE0XFNBJCCCGOSjAJ2bBhAzp16lShPmPGjBkYOXIk8vPzrRKcIwj2N+5HkpWntnEkdRO/RJcKUwkhxOEIJiF79uzB//3f/+HAgQPlbv/ll1/QoUMHLFq0yCrBOQKuJiSvsBjFWr2No6l7lCoqTCWEEEclmIQoFApMnDixQnMomUyGt99+GxcvXrRKcI7A20MG15JOn9lUF1JtpYWpNBJCCCGORjAJKS4uNnmASCSCWk1TC1UlEonK1IVQElJdpdMxNBJCCCGORjAJ8fT0xNGjRwUPOHr0KL/TLakaPgnJo+LU6uL6hHhSx1RCCHE4gmPcr776Kl5//XX07t0brVu3hp+fH/Ly8nD58mUcOHAAX375pbXjrNO44tRMKk6tNq5jKhWmEkKI4xH8ZB8wYABycnLw9ddf4++//+Zv9/DwwIwZMzBgwACrBegIgmiZbo0pqVkZIYQ4LJNfL+Pi4vDcc8/h/PnzyM3Nhb+/P9q3b09TMTVQOh1DNSHVRTUhhBDiuCod4/b09ES3bt2sFYvD4rumUmFqtWi0euj0BgCAJ3VMJYQQh2Pykz0/Px/r1q3DqVOnoNfrsXbtWmzYsAFt2rTBI488Ys0Y6zwuCclSqMAYq7D0mQjjpmJEIsDNhZIQQghxNIKf7CkpKRgxYgTkcjm8vb3h4WEsrBSLxZg4cSK+/fZbdOjQwaqB1mVBvsYkpFijR0GRFj6eLjaOqG7gpmLcXaUQiylxI4QQRyO4RHfhwoXo2LEjDh06hDNnzsDf3x8A8OKLL2LZsmVYtmyZVYOs61xkEvh5uQKg4tTqoG6phBDi2ARHQq5cuYK9e/dCLDbmKGWnD9q1aweFQmGd6BxIkL878gqLkZmnQlS4n63DqROoWyohhDg2wZEQqVTKJyBCcnNzLRaQo6KuqdWnpJUxhBDi0AQzDT8/P2zYsEHwgG3btqFevXoWDcoR8cWptEy3yopU1C2VEEIcmeA49xtvvIFJkyZh/fr16NChAzIzM7FgwQJcu3YNZ8+exapVq6wdZ51HvUKqr3QkhKZjCCHEEQmOhPTo0QNLliyBUqnExo0bkZWVhTVr1iA1NRVLly7FY489Zu0467xgv5LW7VSYWmUq6pZKCCEOzeRXzH79+qFfv364c+cO3zG1cePG1ozNofANy2gkpMpoJIQQQhyb4Kf7c889BwBYvnw5GjduTMmHGXDTMbn5auj0Bkglpgt/iRG3OoYKUwkhxDEJJiG3b9/GqlWrUL9+fWvH47B8vVwhlYig0zPkKNQICfCwdUh2T0lLdAkhxKEJfh1v3rw5OnfubLK9eE5OjkWDckRisah0N12akqmSIlXJdAytjiGEEIckmIS0adMGly9fNnnQ+PHjLRaQI6Pi1OpRUmEqIYQ4NMFx7qioKEyfPh2PPfYYmjVrBk9Pz3L3U8fUmqHi1OoprQmh6RhCCHFEgp/uc+bMAQDcvXtX8CDaBbZmqFdI9XCrY6hZGSGEOCaTIyErV64UPIAxhokTJ1o0KEcVRK3bq6WINrAjhBCHJpiEDB8+HGFhYSYPopqQmqHW7VWn1Rmg0RkA0OoYQghxVIKFqWPGjKn0oKIiKqysidJN7Oj5exiuHgQA3GkkhBBCHBL/FbO4uBgSiQRSqRRpaWmVHvTbb78hLi7O4sE5Gm46RqnWoUitpWmGShSV1IO4uUggEVMNEiGEOCI+CRk4cCDCwsLwyy+/oHfv3lR8agEebjJ4usugVGmRmadCZD1KQkxRUrdUQghxeHwS8tRTTyEoKAgAUL9+fUydOlXwAMYYli1bZp3oHFCwn7sxCclVIbKej63DsVvcdIynO9WDEEKIo+I/4d977z3+xkcffZTfP0bImTNnLBuVAwv2d0fS/XxapvsQSq5bKo2EEEKIwxIsTJ0/f36lB02ePNkiwTgDrjiVVshUroi6pRJCiMOr0VaupqZqyMMF+1Pr9qpQUrdUQghxeFIAGDVqVLUOMtVJlTwcbWJXNSrqlkoIIQ5PCgCXL19Gq1atyt2RmJgIjUaDhg0bwsvLCwUFBUhOTobBYEDr1q1tEqwjCKauqVXCtWynmhBCCHFcUgCIjIxEfHw8f+POnTtx/fp1TJ06FW5ubvztarUaS5YsQXh4uPUjdRBc19RshQoGA4OYemAIKq0JoekYQghxVGIAWL58ebkb169fj/fee69cAgIAbm5ueP/997Ft2zbrRehgAn3cIBYBOj1DXmGxrcOxW8qSfWPcKQkhhBCHJQZQYZ+Y9PT0Sg/KysqyXEQOTiIRI8DHmNxRcappXMdUWh1DCCGOS3B1jLu7O3744QcwxsrdbjAYsGLFCnh5eVklOEfFr5Ch4lSTqGMqIYQ4PsGx7qlTp+L//u//sHbtWrRs2RI+Pj5QKBS4du0acnJysGTJEmvH6VCC/dxxHVScWhnqmEoIIY5P8BO+f//+CAgIwJIlS3D8+HHodDpIpVK0a9cOixcvRufOna0dp0PhilOpYZlp1DGVEEIcn8mvmV26dMG6detgMBiQm5sLf39/iMU16m1GHkC9Qh6OOqYSQojje2hWIRaLERgYWGkCEhcXZ9agHF1prxAqTBWiNzCoNXoA1DGVEEIcmVmGNgoLC81xGqdBhamVU5WMggDUMZUQQhyZWZIQkYgablUHVxOiKNSgWKu3cTT2h+uW6iKTQCqhKUBCCHFU9AlvA17uMri5SAAA2TQaUkERbV5HCCFOgZIQGxCJRKXFqbRMtwKuWyq1bCeEEMdGSYiN8MWpeVSc+qAi2ryOEEKcAiUhNsIXp9JISAVKWp5LCCFOwSxJyIPt3cnDccWptEKmoqKS6RgP6pZKCCEOTTAJSU5OrtZJpk6dapZgnEmQLyUhpihp8zpCCHEKgklIdZOK3r17myUYZ8KPhNB0TAVFtHkdIYQ4BcHx7qSkJIwaNcrkQSKRCB4eHnjkkUfwwgsvICQkxGIBOqqy0zGMMeq1UkYRPxJC0zGEEOLIBEdCWrVqhcuXL+Pq1asoKCgAYwz5+fm4evUqbt68CYPBgNTUVPz8888YNGgQEhMTrR13ncdNx2i0euQrNTaOxr5whake1C2VEEIcmuBXzWeeeQatWrXCW2+9BTc3N/52tVqNb7/9Fi1btkRsbCyKiorw9ddfY9GiRVixYoXVgnYELjIJ/LxdkVdQjKw8FXy9XG0dkt3gl+i60kgIIYQ4MsGRkO3bt+P9998vl4AAgJubG9577z2sW7cOAODh4YEPP/wQ//33n+UjdUC0m64wpYpGQgghxBkIJiGZmZmVHpSenl56ArEYvr6+5o3KSQRT11RBRWrqmEoIIc5AMAlxd3fHDz/8UKH/h8FgwIoVK+Dp6cnfdu/ePRQXF1s2SgdFvUKEKaljKiGEOAXBr5pTp07F//3f/2Ht2rVo2bIlfHx8oFAocO3aNeTk5GDJkiUAgNWrV2PlypXo1auXVYN2FMF+xq6pWZSElMOPhNB0DCGEODTBJKR///4ICAjAkiVLcPz4ceh0OkilUrRr1w6LFy9G586dAQAdO3bEl19+iaZNm1o1aEdROh1D+8dwDAYGVTE3EkLTMYQQ4shMfsp36dIF69atg8FgQG5uLvz9/SEWl5+9adOmjcUDdGQ0HVORWqMDNwtIHVMJIcSxPXTvGLFYjMDAwHIJyI8//mjRoJwFNxKSk6+GTm+wcTT2QakyjoJIJWK4yCQ2joYQQoglmRwJYYwhOTkZmZmZMBjK/we5efNmvPbaaxYPztH5erlCKhFDpzcgW6FGaICHrUOyudJ6EJqKIYQQRyf4SX/p0iVMmzYNKSkpFe6jFuPmIxaLEOznjvvZSmTlqSgJQZluqa40FUMIIY5OMAn55JNPEBMTg3feeadCLQhjDB999JHVAnR0QSVJiLE4NdDW4dgc3y2VRkIIIcThCX7SKxQKbN261eRBI0eOtFhAzoaKU8vjuqVSUSohhDg+wSQkIiKi0oN69uxZrYukpaVh3rx5CAoKglwux7Rp0xAdHV3hcW3atIG3tzf/81dffYVHH320Wteqa6hranlcTQgtzyWEEMcnuDpm4sSJWLhwIRQKheBBU6dOrdZFPvnkE8TGxmLu3Ll4/fXXMX36dMHHDRgwAMePH+f/OHoCAtBIyIOoWyohhDgPwa+bM2fOREFBAVavXg0/Pz+4u7uXuz8jI6PKF8jNzcWRI0ewePFiAEC7du0gl8tx/fp1xMTElHvszZs3sWDBAmg0GkRHR+PFF190+CJYbhM76ppqRN1SCSHEeQgmIUqlEn379hU8gDGGgwcPVvkCaWlpcHd3L7ffTFBQEFJSUiokIUOHDsWIESNgMBgwZcoU5OfnY8KECdi1axd27doleH65XF7lWOwRdU0tjy9MpekYQghxeIKf9PXr18f8+fNNHvTCCy9YJJgRI0YAMDZIGzJkCJYuXYoJEyYgNjYWsbGxgsdMmjTJIrFYCzcSolTrUKTWOv00hFJNhamEEOIsBGtCNm7cWOlB69evr/IFGjRoAJVKBaVSyd+WnZ2NsLCwco/Lzs5GQUEB/7NMJnOK3Xk93GTwKpl6oLoQoEhFNSGEEOIsBJMQV1fXSg8aPnx4lS/g7++P7t274/DhwwCACxcuIDg4GC1btsTJkyeRlJQEADh8+DB27NjBH3fq1Ck89thjVb5OXcYXp9IKmdJmZTQdQwghDo//pF+3bh38/f0xYMAAzJgxo9KD0tLSqnWRTz75BPPmzcOpU6eQnp6OhQsXAgBWr16Nrl27Yvz48YiJicHXX3+NO3fuQKPRQKPRYObMmTX4leqeID933EnLp5EQlClMpZEQQghxeHwSsnz5coSFhWHAgAHYuXMnQkJCTB5UVFS9IsqwsDB8//33FW5fuXIl//eYmBin3RiPilNLKaljKiGEOA3+k37r1q1wcXEBADRt2hTbt283edCQIUMsHZdTCfY37hlDIyFAEXVMJYQQp8EnIfXq1eNvnDt3bqUHPex+Uj3B1CsEgHH5d1ExLdElhBBnIfhJ36ZNG/7vd+/eRU5ODgICAhAZGVnhflJ7QdS6HQBQrNHDYGAAaCSEEEKcgcmvm4cPH8Znn32G5ORk/raGDRti5syZ6NGjh1WCcxbc6phshQp6A4NE7NhdYk3hVsaIxSK4ukhsHA0hhBBLE1yie+rUKUyePBlubm4YPnw4JkyYgOHDh8PV1RWTJ0/G6dOnrR2nQwv0cYNYBOj0DHkFaluHYzNct1RPN6nDt+snhBBiYiRk+fLl+PDDD/kOpmVt2LABS5cuRdeuXS0enLOQSMQI8HVHVp4KWXkqBPq6P/wgB1TaI4SmYgghxBkIjoSkp6cLJiAA8PLLLyM9Pd2iQTkjfpmuExenlnZLpaJUQghxBoJJiF6vr/Qgg8FgkWCcWTAVp9JICCGEOBnBJCQqKgqLFy+ukIzo9XosWbIEUVFRVgnOmfCt2515JIS6pRJCiFMRHPd+6623EBcXh82bN6Nly5bw9fWFQqHA9evXUVhYWK0N7EjVUNdUQKmibqmEEOJMBD/tW7Vqhfj4eHz55Zc4fvw4DAYDxGIxOnbsiPfeew8tW7a0dpwOj+ua6swNy4qKaSSEEEKcicmvnK1bt0Z8fDzUajUUCgV8fX3h5uZmzdicShAVpvJLdKkwlRBCnINgTcgvv/zC/93NzQ2hoaGUgFgYVxOiKNSgWFt5YbCjUtK+MYQQ4lQEv3Ju2rQJTz31FBhjggeJRCJ4eHjA19fXosE5Ey93GdxcJFBr9MjKUyEs2MvWIVkdV5jq4U5JCCGEOAPBJCQxMRG9e/d+6MH169fHhAkT8NJLL5k9MGcjEokQ7O+OZHkhsnKdNQkp7ZhKCCHE8Ql+2n/wwQf44Ycf0KdPH0RHR8Pb2xv5+fn477//cPr0aYwbNw5arRb//fcfFixYAJlMhmHDhlk7docT5GtMQjLznHOFDPUJIYQQ5yKYhCQkJGDFihVo27ZthfsuXryIbdu24ZNPPgEADBs2DHPnzqUkxAy4FTLO2rCMOqYSQohzESxMvXHjhmACAgBt27bF5cuX+Z87deoEtdp5N10zJ2dvWKakZmWEEOJUBJOQ1NRUFBYWCh5QUFCAlJSUcre5urqaPzIn5Myt2xljpYWplIQQQohTEExC2rdvjzFjxuDIkSPIycmBXq9HTk4ODh8+jLFjx6Jjx478Y+Pj4+Hi4mK1gB2ZM4+EaHUG6PTG1Vie1DGVEEKcguCn/ccff4yxY8di4sSJFe6LjIzEsmXLAADvvPMO/vnnH4wePdqyUTqJsg3LGGMQiUQ2jsh6uKkYkQhwc6EkhBBCnIHgp32DBg3w559/Yvv27Th//jwyMjIQEhKCDh064Nlnn4VUajzs66+/tmqwji7I15iEaLR65Cs18PVynmkuvluqqxRisfMkX4QQ4sxMfuWUSqV4/vnn8fzzz/O3KRQKZGVloV69elYJztm4yCTw83ZFXkExMvNUTpWEcN1SqVEZIYQ4D8GakLfeekvwwZcvX0b//v2xcuVKiwblzLjiVGfbyK6IVsYQQojTEUxC7t69K/jgbt264ejRo9ixY4dFg3JmQU66QkZZMh3j7kr1IIQQ4iwEk5DKCiKLiopQXFxssYCcnbOukCniNq+j6RhCCHEa/NfOZcuWYfny5fwdMTExJg/q27evZaNyYsF+XNdU52rdzo2EULdUQghxHvwnfpcuXQAYm0Zt3LhRcFM6qVSK8PBwPPXUU9aL0Mk47UgI1YQQQojTKZeEcInI7du3MWXKFJsF5cyctzCVRkIIIcTZCNaELF68uNKD0tLSLBIMKU1CcvLV0OkNNo7GeviREKoJIYQQpyGYhDzMG2+8Ye44SAlfL1dIJWIwBmQrnGdjQCXtG0MIIU5HcOx71KhRlR5kagkvqT2xWIRgP3fcz1YiM7cIoQEetg7JKopUxukYT5qOIYQQpyE4EnL58mUwxsr9KSwsREJCAm7duoVWrVpZO06nwhWnOlNdCD8SQtMxhBDiNAS/dkZGRiI+Pr7C7VqtFmvWrEFYWJjFA3NmZTeycxZcTYgHNSsjhBCnITgSsnHjRsEHy2QyvPbaa9iwYYNFg3J2wU7YNZXrE0KFqYQQ4jwEkxBXV9Mbp2m1WqSmplosIOKcvUK4jqlUmEoIIc5DcOx7+/btFW5jjEGhUGD//v00HWNhXNdUZ6kJ0eoM0OiMy5GpMJUQQpyH4Cf+Bx98IPhgkUiE9u3bY968eRYNytnxIyFO0rqdqwcBAHcaCSGEEKchmIRERUVh5cqV5W6TSCQICAiAi4uLVQJzZlxhqlKtg1Kldfg6iSJ+B10JJGLTmycSQghxLII1IePHj0dYWBjCwsKg0+mQkZGB4uJiSkCsxN1VCq+SxMMZpmSoURkhhDgnwZGQoUOH4vDhw/jss8+QnJzM3x4REYFZs2ahR48eVgvQWQX7u6NQpUVmngqR9X1sHY5FFVESQgghTklwJOTUqVOYPHky3NzcMHz4cEyYMAHDhw+Hm5sbJk+ejNOnT1s7TqfDFac6wwoZJXVLJYQQpyT4qb98+XJ8+OGHGDFiRIX7NmzYgKVLl6Jr164WD86ZBfm5AXCO4lQaCSGEEOckOBKSnp4umIAAwMsvv4z09HSLBkWAYH8nGgnhkxAaCSGEEGcimITo9fpKDzIYnGeLeVtxpq6pRdQtlRBCnJJgEhIVFYXFixdXSEb0ej2WLFmCqKgoqwTnzJxpEzsuCaHpGEIIcS6C499vvfUW4uLisHnzZrRs2RK+vr5QKBS4fv06CgsLsX79emvH6XS4XiHZChX0BubQ/TO4mhAqTCWEEOciOBLSqlUrxMfHo0mTJjh+/Dh27dqF48ePo3HjxoiPj0fLli2tHafTCfRxg1gE6PQMeQVqW4djUUraN4YQQpySya+erVu3Rnx8PNRqNRQKBXx9feHm5mbN2JyaRCJGgK87svJUyMxTIdDX3dYhWUxpTQiNhBBCiDMRHAkpy83NDaGhoZSA2ICzFKdSx1RCCHFOD01CiO04S3FqaU0IJSGEEOJMKAmxY/xIiIMnIVzHVHcqTCWEEKdCSYgdK52OceyuqTQSQgghzomSEDvmDF1T9XoD1BpjPxrqmEoIIc6FkhA75gw1IapiHf936phKCCHOhZIQO8Y1LFMUalCsrbyVfl2lLFme6yKTQCqhtyMhhDgT+tS3Y17uMri5SAA47mgIdUslhBDnRUmIHROJRPyUjKMWp1K3VEIIcV6UhNi5YL+S4lQHbVhG3VIJIcR5URJi5xy9OJW6pRJCiPOiJMTOBTl4w7IifjqGRkIIIcTZUBJi5xx9/xhudQw1KiOEEOdDSYid4wtT8xyzMLWIpmMIIcRpURJi5/jC1Dw1GGM2jsb8+MJUmo4hhBCnQ0mInQvycwMAaLR65Cs1No7G/PjCVOqWSgghToeSEDsnk0rg5+0KwDGLU2kkhBBCnBclIXWAIxenUrMyQghxXpSE1AGOXJxa2radkhBCCHE2VhkDT0tLw7x58xAUFAS5XI5p06YhOjq6wuN2796NnTt3IiAgACKRCLNnz4ZMRv85ccWpWXlqG0diftwSXXeajiGEEKdjlZGQTz75BLGxsZg7dy5ef/11TJ8+vcJj5HI5Pv/8cyxatAifffYZxGIx1q1bZ43w7B7fsMwB94/hR0KoMJUQQpyOxb9+5ubm4siRI1i8eDEAoF27dpDL5bh+/TpiYmL4x+3evRsdOnSAp6cnAKBXr1745ptvMGbMGEuHaPe46Zg7afk4ej7VxtGYDwODqtg4EkIdUwkhxPlY/JM/LS0N7u7ufHIBAEFBQUhJSSmXhKSmpiIoKIj/OTAwECkpKZYOr04IKUlCUjML8eXaf20cjfmJRFQTQgghzqhOfP3ctWsXdu3aJXifXC63cjTWFxXmh9gnGuOevMDWoVhEh+YhcJFJbB0GIYQQK7N4EtKgQQOoVCoolUp+NCQ7OxthYWHlHhcWFobz58/zP5d9TGxsLGJjYwXPP2nSJAtFbj/EYhEmDm1j6zAIIYQQs7J4Yaq/vz+6d++Ow4cPAwAuXLiA4OBgtGzZEidPnkRSUhIAYMCAATh37hyUSiUA4ODBgxgyZIilwyOEEEKIjVhlOuaTTz7BvHnzcOrUKaSnp2PhwoUAgNWrV6Nr164YP348QkND8f7772PatGkICAgAAMTFxVkjPEIIIYTYgIjV8V3RJk2ahBUrVtg6DEIIIYRUw6RJk6hjKiGEEEJsg5IQQgghhNgEJSGEEEIIsQlKQgghhBBiE5SEEEIIIcQmKAkhhBBCiE1QEkIIIYQQm6AkhBBCCCE2QUkIIYQQQmyCkhBCCCGE2AQlIYQQQgixCUpCCCGEEGITdX4Du4EDByIiIsLWYViFXC5HaGiorcNwePQ8Wwc9z9ZBz7P10HNdPcnJyXU/CXEmtGOwddDzbB30PFsHPc/WQ8919dF0DCGEEEJsgpIQQgghhNgEJSGEEEIIsQlKQgghhBBiE5SEEEIIIcQmpLYOwBnl5ubiyy+/hIeHB0QiEVJSUjBjxgxERkYiPz8fs2fPhpeXFzIyMjB+/Hh06dIFANC/f3/MnDkTAJCTk4PBgwfjmWeeAQAwxvDVV19BLpejuLgYnTp1wqhRo2z2O9qLmjzXsbGx0Gg0mDNnDoCKz/Xp06cxefJkuLm58dc5ePAgXFxcbPI72oOaPs8AsG/fPnz++ecYN24c4uLi+HPSe7oiSzzP9H6uqKaf0RKJBB988AH8/Pxw69YtjBw5Ej169ABA72eTGLG6a9eusdmzZ/M///rrrywuLo4xxtgnn3zCfvjhB8YYY+np6eyJJ55garWaMcbYjz/+yD7++GPGGGOFhYWsW7duLCMjgzHG2O7du9n48eMZY4zpdDo2cOBAduXKFWv9SnbLEs/1qVOn2JYtW6z4W9i/mj7Px48fZ1u3bmVxcXEsPj6+3DnpPV2RJZ5nej9XVNPn+aWXXmJ6vZ4xxlhCQgJr27YtKyoqYozR+9kUmo6xgZiYGMyePZv/OSIiAnK5HACwY8cO9OzZEwAQGhqKkJAQHD16FADwxx9/8Pd5enqiffv2+PPPPyvcJ5FI0L17d2zfvt0qv489s8RzDQD79+/HggUL8Mknn+DkyZPW+WXsWE2f58cffxzPPfec4DnpPV2RJZ5ngN7PD6rp87xu3TqIxWL+GJVKhfz8fAD0fjaFpmNsRCQS8X8/cOAAXnnlFeTl5aGwsBBBQUH8fUFBQUhJSQEApKamlrsvMDCw0vvOnj1r6V+jTjD3c92gQQO8+OKL6NGjBxQKBZ577jl89dVXaN++vZV+I/tUk+e5MvSeFmbu55nez8Jq8jxzCQhgnNLq06cP30GV3s/CaCTExg4dOgS1Wo3Ro0fbOhSHZ67nOiIigp/n9fX1Re/evcuNkjg7ek9bB72fraMmz3NKSgo2bdqEzz77zIKROQZKQmzo0KFD2L9/P+bPnw+RSAQ/Pz94enoiKyuLf0xWVhbCwsIAAGFhYeXuy87ORnh4uMn7uOOIeZ/rpKSkcueWyWRQq9WW/yXqgOo+z5Wh97Rp5nye6f1sWk2e53v37mH+/Pn46quv4O/vz99O72dhlITYyJ49e3Ds2DHMnTsXEokE8+bNAwAMHjwYhw4dAmDcDCkjI4P/llL2PqVSifPnz2PAgAEV7tPr9Th69CiGDBlizV/Jbpn7uV6xYgUSExMBAAaDAadPn8bjjz9u3V/KDtXkea4MvaeFmft5pvezsJo8z4mJiVi4cCHmz5+PwMBA7N69G+fOnatwHL2fS9EGdjaQkJCAoUOHlsuSCwoKcOnSJSgUCnz88cfw8fGBXC7H2LFj8dhjjwEANBoNZs+eDZFIhJycHAwaNAgDBw4EYFz+9eWXXyIzMxMajQYdOnTAmDFjbPHr2RVLPNd//vkntm3bhqioKMjlcjRv3hyvv/66TX4/e1HT5zkrKwvfffcd9u7di8jISHTr1g2TJk0CQO9pIZZ4nun9XFFNn+dHH30UBoMBMpkMAKBWq/Hdd9+ha9eu9H42gZIQQgghhNgETccQQgghxCYoCSGEEEKITVASQgghhBCboCSEEEIIITZBSQghhBBCbIKSEEIIIYTYBO0dQwipU7Zu3Yqff/4ZIpEIxcXFGDVqVLmt6QkhdQclIYSQOuPatWv48MMPsXLlSvTo0QN//fUX34WSEFL3UBJCCKkzzpw5A8YYunbtCgB4+umn0adPHxtHRQipKaoJIYTUGfn5+QAAV1dXAMbt1rkW2YSQuofathPiZPLz8zFixAjcuHEDgYGBiImJwU8//QQAGDlyJK5fvw5fX1989tlnaNWqFb7++mscOnQILi4ukEgkeOWVVzBixAj+fDk5Ofjuu+9w5swZiMVi6HQ6tGzZEtOmTUNISAgA4N9//8Wnn36KW7duITY2Fq1atcKff/6JO3fuIDc3F2fOnIGPj0+lcY8cORK3b99GVlYWWrRoAQB4/fXXsXfvXpw9exb3799HfHw84uPjce/ePSQkJGDUqFGYOXMmAGDdunXYsGEDtFotNBoNHn/8cbzzzjsIDAwEAHzxxRfYt28f7t27h6VLl+Lvv/9GQkICioqK8MYbb2DYsGH46aef8Pfff+P+/ft4+umn8f7770MqpQFlQmqMEUKc0oABA9jLL79c7ja9Xs969erFMjIymEajYc8//zwbMGAAy8rKYowxduHCBdamTRv2ww8/8MecP3+ePf300yw7O5sxxphGo2Fz5sxhzz33HNPpdOXO36tXL/bEE0+wtWvXMsYYUygUrFOnTkyhUFQp5m+//ZZFR0dXuH3Lli0sOjqajRkzhmVmZvKPnTdvHmOMsQULFrAOHTqwCxcuMMYYKywsZHFxceypp55iBQUF/HlOnTrFoqOj2YgRI/jfee3atax58+Zs4cKF7Pz584wxxq5fv86aN2/OtmzZUqW4CSHCaDqGECc1dOhQnD17FklJSfxtx44dQ3R0NIKDg7Fjxw5cunQJU6ZM4UcL2rZti4EDB2LFihVQqVQAgOjoaKxevRoBAQEAAJlMhhEjRuDq1au4evVqhet6eXnxIyk+Pj7Ytm0bvLy8zPI7DRs2DEFBQQCAcePGYdKkSbh37x7WrFmDYcOGoW3btgAAT09PfPDBB0hKSsKaNWsqnKdv37787zxgwAAwxnDr1i20a9cOANCiRQs0bdoUJ06cMEvchDgrSkIIcVLPPvsspFIptmzZwt+2detWPP/88wCA48ePAwA6duxY7rjo6GgolUpcvnwZAODh4YELFy5g7NixiI2NxbPPPos333wTAHDv3r0K123WrFm5n8PDwyEWm+ejqOy5PT09ERgYiBMnTsBgMPAJCOeRRx6Bi4sLjh07VuE8jRo14v/u5+dX4Tbu9szMTLPETYizoslMQpxUUFAQunfvju3bt+Ptt99GQUEBLly4gIULFwIAcnNzAQCvvfZauePUajWCgoL4ItFNmzZh1qxZ+OKLL/Dss89CJBIhJSUFffr0gUajqXBdT09Pi/1OQufmfg9fX98K9/n6+iInJ6fC7e7u7vzfRSIRAGOyVZZIJILBYKhVvIQ4O0pCCHFiw4YNw8GDB3Hs2DEkJyejf//+/GoTf39/AMDatWvh7e1t8hxbtmxBs2bNMGTIEGuEXG3c76FQKCrcp1AoEB4ebu2QCCElaDqGECfWs2dP+Pv7Y8uWLdi6dSuGDRvG39etWzcAxgZhZRUUFGDKlCnIy8sDAGg0Gn60gGNP0xRPPPEExGIxLl68WO72a9euQaPR8L8nIcT6KAkhxInJZDIMHjwY+/btg1QqLVdTMWjQILRv3x4LFy5EdnY2AONUzGeffQaxWMzXSvTu3Rs3b97EgQMH+Md8//33Vv9dTImIiMCYMWOwdetWXLp0CQBQVFSEL774Ao0aNcKYMWNsGyAhToz6hBDi5P777z8MHjwYc+bMwUsvvVTuvsLCQixZsgT79++Hp6cnxGIxevTogSlTpvANwzQaDb799lvs2rUL3t7eCAgIQM+ePbFgwQLUr18fffv2xfDhw/Hee+/h1q1b8PDwQP369fHOO+/gySefrHKcD/YJ8fHxQXx8PObMmYODBw/i/v37iIqKQsOGDbFixYoKx69duxYbNmyATqdDcXExHn/8cUybNo1fBfP9999j69atuHfvHho2bIgXX3wRrVq1wvz585GQkICgoCB07twZ8+bNw4gRI/ii24YNG2LNmjX8tA8hpOooCSHEyWk0GnTv3h379u2rtPaDEELMjaZjCHFyBw8eRPfu3SkBIYRYHSUhhDihH3/8ETt27IDBYMCaNWvKtWEnhBBroSW6hDghT09PzJ8/HytXrkTfvn3Rvn17m8bz2muvISMjw+T948ePx+DBg60YESHEGqgmhBBCCCE28f9pNZpTdG6hQgAAAABJRU5ErkJggg==\n", + "image/png": 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", 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sWYPffvtNelxSUoImTZpIj/38/JCbm4uSkhIMGDAAABAdHY1NmzZJ/zrbu3cvzp8/L/VTeOmll5Ceno6ff/4ZQGmThK1JqyrSvHlzHDhwACUlJQCAX375pSa3WKHo6Gjs2rVL+kNw9uxZxMfH47XXXpPl/MHBwYiMjMSqVaukPkKHDx/GL7/8Uq4J5PZy7d69G4mJiQCAgoICLFq0qFyzjtzHWg0dOhR5eXnYsWMHgNJmgvnz50tNGq+++ir++usv6XNjMpmwePFiqblu8+bN2L59u3Q+60iaO5V506ZNUjNZYmIiVq1aBX9/f7i6umLkyJH45ptvpACxceNGKJVKvPjiiwBKh5s3atQIR44cAVDaJGm9/6rw8fGBUqlEbm4uTpw4galTp9rcb8aMGUhLS5Me3/6dqSrr5+TLL7+E2WwGAKxfv17q4/Lwww8jLCwMy5cvh8ViAQDs2LEDFy5cqHAIfHJyMl577TWMHj0a169fR0JCAhISErBt2zZpn+HDh0sjzIDSEW8XLlyoUu2qq6srhg8fju+++076jG/YsEEaOUa1j0I4st6Z6Ibjx49jwYIF+PPPPxESEoJ+/fpJTQFLly7Ftm3bcPHiRXTt2hWTJ09GUlKSNKy5a9eumDt3Lo4dO4avvvpKGtYbGBiImTNnSv8i27lzJxYtWgRvb29ERUVJv8ysw1C1Wq00rLl79+5S2azDmr29vaHT6dCqVSts2LBBCh8TJkxAfHw8vLy80KJFizIdXc+ePYtZs2ZJfS9CQkKwatUqdOjQAe+++y7Wr18vzSfRv39/afiq9b6WLFmCiRMn4sCBA9DpdDb791jdPqx5zJgx6NevH4Cbw6cBoGnTppg9e3a5P/y3Dqvt0KFDmdcIKP1X//z58/Hnn38iMDAQHh4emDZtGh588MEyQ6I7dOiAbt264e233wZQ+kd/1apV8PDwgEKhwCuvvCJ1hr7Ta1eVY4cNG4a2bdvi3//+t/TaLV68GH5+fjh+/DjmzZsHk8kEpVKJAQMGSMOWASA+Ph6LFi2CUqmERqPB008/jZEjRwIAfv31V3z66afSH1dXV1dMnz5dGhJty6pVq7B582Z4eXnBxcUF06ZNk2pQbA1rnj59Olq3bi0dv2fPHsTGxiIgIADt27dHRkYG9u7di169euHf//43hg0bhqNHj0Kn02Hs2LFQqVT4+OOPkZqaik6dOuHLL78EUNp8t2fPHri7u2PKlCno3LlzubJ++OGH+O233+Dh4YGCggJ06dIFkydPRl5enjSsWafTYejQoXB3d8fKlSuRmpqKLl264PPPP8c//vEP/PHHH9DpdIiOjsaAAQOk4e/Hjh2Dt7c36tevj5iYGHh5eQEobar597//jdOnT8Pf3x8BAQGYMWOG1FH5duPHj5f+wXCrrl27lplnxfoZ1Gg0MBqNmDJlSpkhyleuXMGUKVOQnp4u/S4JDQ3FzJkzpX3MZjMWLVqEPXv2SO/fpEmT7tgXjpyHgYXoFkIIXL9+vcy//pYvX44//vhDGklERESOxyYholsUFBRgyJAh0vDI3NxcbN269a7DpYmIyL44SojoFi4uLujYsaM0d0ZRURFeeOEFqQ8MERE5B5uEiIiIqNZjkxARERHVegwsREREVOsxsBAREVGtx8BCREREtd59NUromWeeqfK6KERERORcycnJ91dgCQ4OxvLly51dDCIiIqqC6OhoNgkRERFR7cfAQkRERLUeAwsRERHVegwsREREVOsxsBAREVGtx8BCREREtR4DCxEREdV6DCxERERU6zGwEBERUa3HwEJERES1HgMLERER1XoMLERERFTrMbAQERFRrcfAQkRERLUeA0sN5RcYsWnPeWTlFjm7KERERHUWA0sN/d+hFHy25QTW7z7r7KIQERHVWQwsNeSmVQEAUvR5Ti4JERFR3cXAUkNBfu4AAH1WgZNLQkREVHcxsNRQfT8PAEBadiEsFuHk0hAREdVNDCw1FODtCqVSgRKzBdl57HhLRERkDwwsNaRSKRHg4wYAuJbJZiEiIiJ7UDviInPnzoXBYICXlxcSExMxdOhQ9OnTB0uWLMFXX30Flaq042rbtm2xcuVKAIDRaMTs2bMBAFlZWYiKikJERAQAQAiBhQsXQq/Xo7i4GOHh4Rg+fLgjbsWmBn7uSMsqQFp2AdrC32nlICIiqqscElg0Gg1iY2MBAPv27cNbb72FPn36AADWr1+Pxo0blztmzZo1UKvVmD17NgwGA/r27Yvw8HAEBgZi586dSExMxKpVq2A2m9G/f3907twZbdu2dcTtlFOfHW+JiIjsyiFNQlOmTJF+vnTpElq2bCk9/vzzzzF//ny8++67uHz5svT85s2b0atXLwCAh4cHwsLCsH379nLbVCoVevbsiU2bNtn9PioijRRikxAREZFdOKSGBQD++usvfPLJJ7h69SqWLVsGAAgPD0eDBg0QEhKChIQEDB48GDt27EC9evWQmpqKgIAA6Xh/f3+kpKQAgM1thw4dctStlMMaFiIiIvtyWGBp06YNlixZgt9//x2DBw/G1q1b0b17d2l7+/bt4evri3379uGpp56q9nW2bduGbdu22dym1+urfd47kQJLNgMLERGRPdg9sJjNZhQVFcHDo3S+kh49esBgMODEiRMIDAxESEiItK9Go0FRUenQYJ1Oh4yMDGlbZmYmOnXqVOE2nU4HAIiMjERkZKTNskRHR8t7czdYA0vG9UKYzRaoVBx8RUREJCe7/2W9evUqZs2aJT3W6/UwGAzQ6XSYNm0aTCYTACAjIwPJycno3LkzACAqKgpxcXEAAIPBgCNHjqBfv37ltpnNZsTHx2PAgAH2vpUK+dZzhUathMUikH690GnlICIiqqvsXsPi4+MDs9mMadOmwcvLC+fOnUNsbCx0Oh26deuGiRMnolGjRkhKSsK8efOkmpIRI0YgJiYG06dPR1ZWFqZOnYqgoCAAQEREBBISEjBp0iQYjUYMGjQI7dq1s/etVEipVCDI1w2p6QakZReggb+H08pCRERUF9k9sHh6euKjjz6yuW3ChAkVHufi4iINhb6dQqEoM/KoNgjydUdquqF0pFBzZ5eGiIiobmFnC5nUv1GrwpFCRERE8mNgkQmHNhMREdkPA4tMGFiIiIjsh4FFJgHepQsgZuVyxWYiIiK5MbDIxM21tP9ykbHEySUhIiKqexhYZOLqUrridGGx2cklISIiqnsYWGTipi2tYTGazDBbhJNLQ0REVLcwsMjEGlgAoJjNQkRERLJiYJGJRq2EUqkAABQWM7AQERHJiYFFJgqFAm43+rEUGdmPhYiISE4MLDJyvdEsxBoWIiIieTGwyMjV5cbQZgYWIiIiWTGwyMhNyyYhIiIie2BgkRGbhIiIiOyDgUVGbBIiIiKyDwYWGVnnYinkPCxERESyYmCRkXV6/iJOz09ERCQrBhYZWWtYuAAiERGRvBhYZMROt0RERPbBwCKjmys2M7AQERHJiYFFRlKTEPuwEBERyYqBRUbWYc0cJURERCQvBhYZ3axhYWAhIiKSEwOLjNgkREREZB8MLDJyvbGWEJuEiIiI5MXAIiM2CREREdkHA4uMpLWEWMNCREQkKwYWGVmbhIqMZlgswsmlISIiqjsYWGTkdqOGRQjAaGLHWyIiIrkwsMjIRaOCQlH6MzveEhERyYeBRUZKpYIrNhMREdkBA4vM2PGWiIhIfgwsMuOKzURERPJjYJGZteMtm4SIiIjkw8AiM852S0REJD8GFpm5crZbIiIi2TGwyOxmkxADCxERkVwYWGRmbRIqYGAhIiKSDQOLzKQaFiM73RIREcmFgUVm7MNCREQkP7UjLjJ37lwYDAZ4eXkhMTERQ4cORZ8+fZCbm4uYmBh4enoiLS0No0ePRteuXQEARqMRs2fPBgBkZWUhKioKERERAAAhBBYuXAi9Xo/i4mKEh4dj+PDhjriVu3LjPCxERESyc0hg0Wg0iI2NBQDs27cPb731Fvr06YNFixahdevWGDNmDPR6PZ5//nns2rULWq0Wa9asgVqtxuzZs2EwGNC3b1+Eh4cjMDAQO3fuRGJiIlatWgWz2Yz+/fujc+fOaNu2rSNu545uXbGZiIiI5OGQJqEpU6ZIP1+6dAktW7YEAGzZsgW9evUCANSvXx9BQUGIj48HAGzevFna5uHhgbCwMGzfvr3cNpVKhZ49e2LTpk2OuJW7svZhYQ0LERGRfBxSwwIAf/31Fz755BNcvXoVy5Ytw/Xr15Gfn4+AgABpn4CAAKSkpAAAUlNTy2zz9/e/47ZDhw456E7uTOrDwonjiIiIZOOwwNKmTRssWbIEv//+OwYPHoyvv/7aLtfZtm0btm3bZnObXq+3yzVv5cZOt0RERLKze2Axm80oKiqCh4cHAKBHjx4wGAxISkqCh4cHMjIy4OfnBwDIyMiATqcDAOh0OmRkZEjnyczMRKdOnSrcZj0uMjISkZGRNssSHR0t/w3extXlxtT8XEuIiIhINnbvw3L16lXMmjVLeqzX62EwGKDT6RAVFYW4uDjp+bS0NDz22GMAUGabwWDAkSNH0K9fv3LbzGYz4uPjMWDAAHvfSqWwSYiIiEh+dq9h8fHxgdlsxrRp0+Dl5YVz584hNjYWOp0OEyZMwKxZszBz5kzo9XosWLAAWq0WADBixAjExMRg+vTpyMrKwtSpUxEUFAQAiIiIQEJCAiZNmgSj0YhBgwahXbt29r6VSmGTEBERkfzsHlg8PT3x0Ucf2dzm7e2NxYsX29zm4uIiDYW+nUKhKDPyqDaRmoSMZgghoFAonFwiIiKiex9nupWZtYbFYhEwlVicXBoiIqK6gYFFZlqXm5VWnIuFiIhIHgwsMlMpFXBRl76snO2WiIhIHgwsduCiKe3HYjQxsBAREcmBgcUOGFiIiIjkxcBiB1opsLDTLRERkRwYWOxAoyl9WVnDQkREJA8GFjuwNgkVlzCwEBERyYGBxQ6sTUImNgkRERHJgoHFDqzDmovZJERERCQLBhY74CghIiIieTGw2AEDCxERkbwYWOzARcMmISIiIjkxsNiBtYaFix8SERHJg4HFDrRsEiIiIpIVA4sdaDhKiIiISFYMLHbAqfmJiIjkxcBiB9IoIc50S0REJAsGFjvgsGYiIiJ5MbDYgXWmWzYJERERyYOBxQ5Yw0JERCQvBhY7YGAhIiKSFwOLHXCUEBERkbwYWOxAw6n5iYiIZMXAYgdaDmsmIiKSFQOLHUhrCbFJiIiISBYMLHbA1ZqJiIjkxcBiBy5qjhIiIiKSEwOLHVibhMwWAbOZzUJEREQ1xcBiB9YmIQAwljCwEBER1RQDix1Ym4QANgsRERHJgYHFDpRKBTRqdrwlIiKSCwOLndxcAJGBhYiIqKYYWOzEhdPzExERyYaBxU5cONstERGRbBhY7IQrNhMREcmHgcVOrEOb2SRERERUcwwsdmId2sxRQkRERDXHwGInWmkBRAYWIiKimmJgsRNrH5ZiNgkRERHVmNreF8jOzsb7778Pd3d3KBQKpKSkYNq0aXjwwQexZMkSfPXVV1CpSv+4t23bFitXrgQAGI1GzJ49GwCQlZWFqKgoREREAACEEFi4cCH0ej2Ki4sRHh6O4cOH2/tWqkSj4TwsREREcrF7YLl27Rq0Wi1mzpwJAFi7di1mzJiBtWvXAgDWr1+Pxo0blztuzZo1UKvVmD17NgwGA/r27Yvw8HAEBgZi586dSExMxKpVq2A2m9G/f3907twZbdu2tfftVJqWo4SIiIhkY/cmodatWyMmJkZ6HBwcDL1eLz3+/PPPMX/+fLz77ru4fPmy9PzmzZvRq1cvAICHhwfCwsKwffv2cttUKhV69uyJTZs22ftWquTmPCxsEiIiIqopu9ewAIBCoZB+3r17NwYPHgwACA8PR4MGDRASEoKEhAQMHjwYO3bsQL169ZCamoqAgADpOH9/f6SkpACAzW2HDh1yxK1UmgubhIiIiGTjkMBiFRcXh6KiIowYMQIA0L17d2lb+/bt4evri3379uGpp56q9jW2bduGbdu22dx2a82OvVmHNTOwEBER1ZzDAktcXBx27dqF2NhYqcbl4sWLCAkJkfbRaDQoKioCAOh0OmRkZEjbMjMz0alTpwq36XQ6AEBkZCQiIyNtliE6Olrem7qDm6OEGFiIiIhqyiHDmnfs2IHffvsNc+bMgUqlwty5cwEA06ZNg8lkAgBkZGQgOTkZnTt3BgBERUUhLi4OAGAwGHDkyBH069ev3Daz2Yz4+HgMGDDAEbdSadobTUIm9mEhIiKqMbvXsCQmJuLtt9+Gr68vduzYAQDIy8vDjBkz0K1bN0ycOBGNGjVCUlIS5s2bJ9WUjBgxAjExMZg+fTqysrIwdepUBAUFAQAiIiKQkJCASZMmwWg0YtCgQWjXrp29b6VKWMNCREQkH7sHllatWuGvv/6yuW3ChAkVHufi4oLY2Fib2xQKBaZMmSJL+exFwz4sREREsuFMt3ai5SghIiIi2TCw2Ik0Dwun5iciIqoxBhY7uTlxHGtYiIiIaoqBxU44NT8REZF8GFjsxLr4IVdrJiIiqjkGFjthDQsREZF8GFjsxNqHxcTAQkREVGMMLHZiXUuITUJEREQ1x8BiJ9bVmkvMFpgtwsmlISIiurcxsNiJtUkIYLMQERFRTTGw2MmtgcXIBRCJiIhqhIHFTlRKBdQqBQCOFCIiIqopBhY74gKIRERE8mBgsSPrXCzFDCxEREQ1wsBiR9aRQib2YSEiIqoRBhY7cmENCxERkSwYWOzIhX1YiIiIZMHAYkfWJiEGFiIiopphYLEjF2kBRPZhISIiqgkGFjty4YrNREREsmBgsSM2CREREcmDgcWObo4SYpMQERFRTTCw2JF14jhTCWtYiIiIaoKBxY44DwsREZE8GFjsyEVt7cPCJiEiIqKaYGCxI44SIiIikgcDix2xSYiIiEgeDCx2xMUPiYiI5MHAYkdaNgkRERHJgoHFjjRqNgkRERHJgYHFjljDQkREJA+bgSU/P9/mzpcuXcLmzZthMpnsWqi6Qpqan31YiIiIasRmYBk2bJjNnQ0GA7755hv885//tGuh6goOayYiIpKHzcAihLC5c9u2bfHtt9/i/Pnzdi1UXeGiZmAhIiKSg9r6w5UrV5CamgoAKCwsxMGDB8sFFyEErl27VmGTEZXF1ZqJiIjkIQWWjRs3YunSpVAoFABsNwsJIaBUKvH66687roT3sJtNQuzDQkREVBNSYHnuuefQtWtXCCEwc+ZMzJ07t/zOajV0Oh3q16/v0ELeqzhKiIiISB5SYNHpdNDpdACAl156CV27dnVaoeoKzS2jhIQQUu0VERERVY3NTrejR4++40GffvqpXQpT11hrWAAObSYiIqoJdUUbhBBITk5Geno6LJayf2zXr1+PV1991e6Fu9e53BJYTCZzmQBDRERElWczsBw/fhxvv/02UlJSym2ratNGdnY23n//fbi7u0OhUCAlJQXTpk3Dgw8+iNzcXMTExMDT0xNpaWkYPXq01BRlNBoxe/ZsAEBWVhaioqIQEREhlWHhwoXQ6/UoLi5GeHg4hg8fXuWbtze1SgmlUgGLRaDYZIanswtERER0j7IZWP71r3+hdevWmDhxInx9faFU3mw5snbKraxr165Bq9VKx6xduxYzZszA2rVrsWjRIrRu3RpjxoyBXq/H888/j127dkGr1WLNmjVQq9WYPXs2DAYD+vbti/DwcAQGBmLnzp1ITEzEqlWrYDab0b9/f3Tu3Blt27at4cshP61GicJiM0cKERER1YDNwJKTk4ONGzdWeFBFM+Ha0rp1a8TExEiPg4ODodfrAQBbtmzBN998AwCoX78+goKCEB8fjyeffBKbN2/GxIkTAQAeHh4ICwvD9u3bMXLkSGzevBm9evUCAKhUKvTs2RObNm2qlYFFo1bdCCwcKURERFRdNjvdBgcH3/Ega1iorFubkHbv3o3Bgwfj+vXryM/PR0BAgLQtICBAaoZKTU0ts83f379S22obaS6WEgYWIiKi6rJZw/Laa69hwYIFGDNmDLy9vcttHz9+PH788ccqXywuLg5FRUUYMWIEcnJyql7aSti2bRu2bdtmc5u1ZseRtNJst2wSIiIiqi6bgeWdd95BXl4evvjiC/j4+MDNza3M9rS0tCpfKC4uDrt27UJsbCwUCgV8fHzg4eGBjIwM+Pn5AQAyMjKkuWB0Oh0yMjKk4zMzM9GpU6cKt1mPi4yMRGRkpM0yREdHV7ncNWWtYSlmkxAREVG12WwSMhgMePLJJ9G/f388/vjj6Nq1q/Rfly5d4O7uXqWL7NixA7/99hvmzJkDlUolzaIbFRWFuLg4AKW1H2lpaXjsscfKbTMYDDhy5Aj69etXbpvZbEZ8fDwGDBhQxVt3DC6ASEREVHM2a1gaNmyI2NjYCg968cUXK32BxMREvP322/D19cWOHTsAAHl5eZgxYwYmTJiAWbNmYebMmdDr9ViwYAG0Wi0AYMSIEYiJicH06dORlZWFqVOnIigoCAAQERGBhIQETJo0CUajEYMGDUK7du0qXSZHcuH0/ERERDWmELcvyQyguLhYCg51SXR0NJYvX+7Qa/7r0304lJiGN18Kw5NdH3DotYmIiOqC6Oho201CdwsrU6dOtUuB6iKOEiIiIqo5m01CmzZtuuNBf/75pz3KUiexDwsREVHN2QwsFdWgcLXhqnO5MayZo4SIiIiqz2ZgadasGVauXFnmOYPBgPPnz2PLli0YNWqUQwpXF1gXPDRxHhYiIqJqsxlYxo4dK81rcqvQ0FD07NkTU6dORZcuXexeuLqA87AQERHVnM1Ot9b5Tmzx9PREUlKS3QpU12ikmW4ZWIiIiKrLZg1LRXJycrBjxw4UFxfbqzx1jlaah4VNQkRERNVlM7C0atWqwg62SqUS//rXv+xZpjqFw5qJiIhqzmZgCQgIwMsvv1zmOaVSiYCAAHTt2hVNmjRxRNnqBM50S0REVHM2A0vHjh0xbtw4R5elTnJRc7VmIiKimrLZ6Xbp0qWOLkedxVFCRERENVdhp1uDwYAvv/wS8fHxyMrKgp+fHx577DEMHz4cHh4ejizjPU2ah4V9WIiIiKrNZmDJysrC4MGDcenSJbi4uMDb2xtXr17FkSNHsHXrVqxbtw5+fn6OLus9yUXDJiEiIqKastkk9OGHHyIoKAgbN27E8ePHER8fj+PHj2Pjxo0ICgrCokWLHF3OexabhIiIiGrOZg3L3r17sX37dri5uZV5vk2bNvjPf/6DyMhIhxSuLuDih0RERDVns4ZFq9WWCytW7u7u0Gq1di1UXeLCmW6JiIhqzGZgUavVSEhIsHlAQkICVCqVXQtVl9ycOI59WIiIiKrLZpPQyy+/jFGjRmHQoEFo3749fHx8cP36dakfy5tvvunoct6ztLdMHCeEqHAGYSIiIqqYzcAyZMgQpKSk4Msvv4QQAgAghIBSqcSIESMwZMgQhxbyXqa5EViEAErMFmjUrJ0iIiKqqgrnYZkyZQoGDx6MvXv3Ijs7G76+vnjkkUcQHBzsyPLd87Sam61uxSYGFiIiouqQAktJSQn27NkDAGjQoAHatm2L4OBgvPTSSwCACxcuQK/XM7BUkVqlhEJRWsNiMpkBN42zi0RERHTPkf75f+DAAYwdOxaTJk3Cvn37yu2Ynp6OoUOH4oMPPnBoAe91CoWCc7EQERHVkBRYdu/ejfbt22PXrl34xz/+UW7Hbt264ZtvvsGWLVuwa9cuhxbyXndzAUQGFiIiouqQAsvBgwcRGxt7xyn3w8LCsHDhQnz99dcOKVxdIQ1t5vT8RERE1SIFlry8PDRv3vyuB3Tp0gWZmZl2LVRdc3MuFtawEBERVYcUWOrVq1fpgziXSHnW4d+23DoXiyPdqUxERET3EmmUkMVigclkgkZz51EsJpMJJpPJ7gW71ygUCuw9fgU5+cXlthUUlb5e+45fxdUMg0PK4+2pxSMPNXLItYiIiOxNCiwdO3bEN998g+HDh9/xgK+//hphYWF2L9i9KCe/GNl55QOLtZ4jx2B7OxEREd2ZFFhGjRqF5557DtevX8fQoUPLdb7NzMzEunXrsG7dOmzcuNHhBb2XqZSlTWhmC5toiIiIqkMKLA8++CDmzZuHyZMnY/ny5WjcuDH8/f0BlIaVlJQUaLVafPTRR5w8rorUqtKuQmYzAwsREVF1lJma/6mnnkKTJk3wn//8B/Hx8bh8+TIAwMPDA08//TTeeOMNNG3a1CkFvZcpb9SwlFg4rJmIiKg6yq0lFBoaio8++ghCCGRnZwMAfH19OTKoBqQmIdawEBERVUuFix8qFIo7TiJHlWdtErKwDwsREVG1KO++C9WUik1CRERENcLA4gAqdrolIiKqEQYWB+CwZiIiopphYHGAm51u2SRERERUHQwsDiA1CbGGhYiIqFoYWBxArbrR6ZY1LERERNXCwOIAKiU73RIREdVEhfOwyMlkMmH16tVYtmwZvv/+e4SGhgIAlixZgq+++goqlQoA0LZtW6xcuRIAYDQaMXv2bABAVlYWoqKiEBERAQAQQmDhwoXQ6/UoLi5GeHj4XRdtdCbWsBAREdWMQwLL999/j/DwcBQWFpbbtn79ejRu3Ljc82vWrIFarcbs2bNhMBjQt29fhIeHIzAwEDt37kRiYiJWrVoFs9mM/v37o3Pnzmjbtq0jbqfK1OzDQkREVCMOaRIaMmQIwsLCbG77/PPPMX/+fLz77rvS2kUAsHnzZvTq1QtA6VpGYWFh2L59e7ltKpUKPXv2xKZNm+x5CzWiYg0LERFRjTikhqUi4eHhaNCgAUJCQpCQkIDBgwdjx44dqFevHlJTUxEQECDt6+/vj5SUFACwue3QoUMOL39lcbVmIiKimnFqYOnevbv0c/v27eHr64t9+/bhqaeeqvY5t23bhm3bttncptfrq33empCm5mcNCxERUbU4NbBcvHgRISEh0mONRoOioiIAgE6nQ0ZGhrQtMzMTnTp1qnCbTqcDAERGRiIyMtLm9aKjo2W/h8q4dR4WIQRXviYiIqoipw5rnjZtGkwmEwAgIyMDycnJ6Ny5MwAgKioKcXFxAACDwYAjR46gX79+5baZzWbEx8djwIABji5+pVmbhAB2vCUiIqoOh9SwHDx4ED/99BMAYMWKFXjyyScRERGBbt26YeLEiWjUqBGSkpIwb948qaZkxIgRiImJwfTp05GVlYWpU6ciKCgIABAREYGEhARMmjQJRqMRgwYNQrt27RxxK9Vi7XQLlPZjUaucWBgiIqJ7kEIIcd/8kz86OhrLly+32/l37L2I7Lxim9u++98ZWCwCUT2bwsNNY7cyWPnW0yLikZC770hERFTLRUdHc6ZbR1FLKzaz4y0REVFVMbA4iLXjbQmHNhMREVUZA4uDWKfnN3NoMxERUZUxsDgIa1iIiIiqj4HFQaQaFvZhISIiqjIGFgdRKVnDQkREVF0MLA5ycz0h1rAQERFVFQOLg9xcsZk1LERERFXFwOIgaqnTLWtYiIiIqoqBxUFU0sRxrGEhIiKqKgYWB2ENCxERUfUxsDgIJ44jIiKqPgYWB+HEcURERNXHwOIgXPyQiIio+hhYHIQ1LERERNXHwOIg7MNCRERUfQwsDsIaFiIioupjYHEQaR4W1rAQERFVGQOLg0jzsHDiOCIioipjYHEQLn5IRERUfQwsDsLFD4mIiKqPgcVBpBoWiwVCMLQQERFVBQOLg1g73QoBWBhYiIiIqoSBxUGsNSwAYGazEBERUZUwsDiIUqmAorSShSs2ExERVREDiwPdHCnEGhYiIqKqYGBxIOv0/CVcAJGIiKhKGFgcSKVkDQsREVF1MLA40M25WFjDQkREVBUMLA7EPixERETVw8DiQNYmIdawEBERVQ0DiwPd7HTLGhYiIqKqYGBxIC6ASEREVD0MLA7ETrdERETVw8DiQOx0S0REVD0MLA5kXQCRE8cRERFVDQOLA7GGhYiIqHoYWByIfViIiIiqh4HFgVjDQkREVD0MLA6kUnHiOCIioupQO+IiJpMJq1evxrJly/D9998jNDQUAJCbm4uYmBh4enoiLS0No0ePRteuXQEARqMRs2fPBgBkZWUhKioKERERAAAhBBYuXAi9Xo/i4mKEh4dj+PDhjriVGlHf6HRr5sRxREREVeKQwPL9998jPDwchYWFZZ5ftGgRWrdujTFjxkCv1+P555/Hrl27oNVqsWbNGqjVasyePRsGgwF9+/ZFeHg4AgMDsXPnTiQmJmLVqlUwm83o378/OnfujLZt2zridqqNNSxERETV45AmoSFDhiAsLKzc81u2bEGvXr0AAPXr10dQUBDi4+MBAJs3b5a2eXh4ICwsDNu3by+3TaVSoWfPnti0aZO9b6PGrFPzsw8LERFR1TitD8v169eRn5+PgIAA6bmAgACkpKQAAFJTU8ts8/f3r9S22kyqYeE8LERERFXikCYhR9q2bRu2bdtmc5ter3dwacqS+rCwSYiIiKhKnBZYfHx84OHhgYyMDPj5+QEAMjIyoNPpAAA6nQ4ZGRnS/pmZmejUqVOF26zHRUZGIjIy0uY1o6Oj7XIvlaVWl9awmErYJERERFQVTh3WHBUVhbi4OACltR9paWl47LHHym0zGAw4cuQI+vXrV26b2WxGfHw8BgwY4ODSV53mlk63QjC0EBERVZZDalgOHjyIn376CQCwYsUKPPnkk4iIiMCECRMwa9YszJw5E3q9HgsWLIBWqwUAjBgxAjExMZg+fTqysrIwdepUBAUFAQAiIiKQkJCASZMmwWg0YtCgQWjXrp0jbqVGNOqb+bDELKBRK5xYGiIionuHQtxH/9SPjo7G8uXL7Xb+HXsvIjuvuMLtQgh8/78zsAig/2NN4e6qsVtZfOtpEfFIiN3OT0RE5CjR0dGc6daRFAoF1GoVAMBUwo63RERElcXA4mDWfiwMLERERJXHwOJg1n4sJg5tJiIiqjQGFgezBpYS1rAQERFVGgOLg0k1LAwsRERElcbA4mBqBhYiIqIqY2BxMPZhISIiqjoGFge7OUrI7OSSEBER3TsYWByMnW6JiIiqjoHFwdiHhYiIqOoYWBxMw5luiYiIqoyBxcGkPizsdEtERFRpDCwOxnlYiIiIqo6BxcHY6ZaIiKjqGFgcjDUsREREVcfA4mAcJURERFR1DCwOZu10axECZgtDCxERUWUwsDiYtYYFYC0LERFRZTGwOJhSoYBapQDAjrdERESVxcDiBOx4S0REVDUMLE6gVnG2WyIioqpgYHECqYaFs90SERFVCgOLE7BJiIiIqGoYWJyAgYWIiKhqGFic4GZgMTu5JERERPcGBhYnUN+YPK7ELJxcEiIionsDA4sTsIaFiIioahhYnIB9WIiIiKqGgcUJGFiIiIiqhoHFCTRqThxHRERUFQwsTmBdsZlrCREREVUOA4sTqDnTLRERUZUwsDjBvdSHxWIR+PrnRPz7iz+QV2B0dnGIiOg+pXZ2Ae5H90pgsVgE/rPhGH7enwQA8Pb8C+Ne6OjcQhER0X2JNSxOIPVhMVsgRO2dPG75j8fx8/4kKBSlj3/en4TEpCznFoqIiO5LDCxOYK1hAUpDS2106Wouduy9BKUCmPBKJzzRJRgA8Mn64zDX0jITEVHdxcDiBEqlAsobtRa1tVnoj5NXAQDhrRugd+dg/D2yLTzdNLhwJQe7DiY7uXRERHS/YWBxAoVCcXOkUC0NLAdO6gEAXdrUBwB4e2ox4PFmAIA/T15zWrmIiOj+xMDiJFpN6eRxxabat55Qdl4RziRnA7gZWACgU6sgAEDC+Qw2CxERkUMxsDiJ1uVGYDHWvsBy6JQeQgDNG3vD39tNer6pzgeebhoUFJXgbMp15xWQiIjuO04f1jx16lTEx8dLj5944gnMmTMHAJCbm4uYmBh4enoiLS0No0ePRteuXQEARqMRs2fPBgBkZWUhKioKERERjr+BanJ1KX3pi2phYPnzL2tzUIMyz6uUCrRvHoB9CVdx7Gw6Wj3o54ziERHRfcjpgQUAfv/9d5vPL1q0CK1bt8aYMWOg1+vx/PPPY9euXdBqtVizZg3UajVmz54Ng8GAvn37Ijw8HIGBgQ4uffXcrGEpcXJJyjKazDhyOg0A0PW2wAIAHUMDSwPLmQy89GRLRxePiIjuU7WiSejDDz/E/PnzMW/ePGRmZkrPb9myBb169QIA1K9fH0FBQVJtzObNm6VtHh4eCAsLw/bt2x1d9GpzvdGHpbbVsJw4n4kioxl+Xlo01XmX296hRWkgPHUpC0W1LGwREVHd5fQalr/97W8ICwtDYGAgfvnlF4wcORI//vgj8vPzkZ+fj4CAAGnfgIAApKSkAABSU1PLbPP395e23Qu0N5qEalsflr8ulgbGjqFBUFrHXt+iUYAHAnzckHG9EKcuZiGsZZCji0hERPchpweWp556qszPU6dOxenTp6HT6ap1vm3btmHbtm02t+n1+mqd0x5cXaw1LLWrluJs8nUAQMsHfW1uVygU6NAiALsOJOPY2XQGFiIicginB5aLFy8iJCREeqzRaFBUVAQfHx94eHggIyMDfn6lnTszMjKkIKPT6ZCRkSEdl5mZiU6dOiEyMhKRkZE2rxUdHW3HO6karUvtaxISQuDM5dLhzKHBtgMLUNostOtAMk6cz6xwHyIiIjk5vQ/L5MmTpZ8TExOhVCrRsmVpZ86oqCjExcUBKK0dSUtLw2OPPVZum8FgwJEjR9CvXz+Hlr0mXGthk9DVTAPyC01Qq5R4sKFXhftZa18uXMmptUsLEBFR3eL0GpYWLVrg7bffhr+/P5KSkrB06VJ4enoCACZMmIBZs2Zh5syZ0Ov1WLBgAbRaLQBgxIgRiImJwfTp05GVlYWpU6ciKOjeaZ6QRgmZzLAIAaWifH8RRztz+ToAoJnOu8x6R7dr6O8BD1c1DEUluHwtz2bnXCIiIjk5PbDExsZWuM3b2xuLFy+2uc3FxeWOx9Z21pluAcBoNMNV6/S3AmdvzG7b4gGfO+6nUCjQPNgHx85m4GzydQYWIiKyO6c3Cd2vlEoFXGrZ0OazN2pYQh+ouP+KVfPGPgCAc5zxloiIHICBxYlcXWrPekIlZgvO3wgflQksLW50yj13o1aGiIjInhhYnEhbi4Y2X76WB2OJBR5uGjT097jr/s2DfQAAl67mwlTi/MBFRER1GwOLE7nWogUQrcOZWzT2sTlh3O2CfN1Qz12DErNA0tU8exePiIjucwwsTlSbFkCUAstdOtxaKRQKqR8LV24mIiJ7Y2BxIutIodqwAOL5lBwAQIsbTT2VYW0WOndjdlwiIiJ7YWBxItdaMtut0WRG0rVcAECzG7UmldGCgYWIiByEgcWJassCiJeu5sJsEfDycEGgj1ulj2veuHSkUNK13Fox0omIiOouBhYnqi0LIFqHMzdv7ANFFWbcDfBxhbenC8wWgUtXcuxUOiIiIgYWp6otCyCeu9F/pVnjqs1Ye2vHW+s5iIiI7IGBxYmso4RMJRaYLcJp5bDOVluV/itW7HhLRESOwMDiRC4aJawtMM7qx2IqMePyjQ63zasRWFpwin4iInIABhYnUigUTh/anHQ1DyVmgXruGgT5Vr7DrZW1huXytVyn98UhIqK6i4HFyaR+LE4aZXNrc1BVOtxa+Xu7wbeeFhYBXEzNlbl0REREpRhYnMzVyUObpcCiq1qH21tZa1nOpnAhRCIisg8GFiezDm0uLHZOc4o0pLkKM9zeTurHwo63RERkJwwsTubppgEA5BcYZT2vq4sKQtx55FGxyYxLV6vf4dZKGilUyaHNdysXERHR7dTOLsD9rp6HCwAg12CS9bwuGhUUCgX2Hr+CnPxim/tcyTCgxCzg7qrG4UR9tfqwAEBBUWnZk/V52PLrOWjUqgr39fbU4pGHGlXrOkREdP9iYHGyeu6lgSVP5hoWq5z8YmTn2Q4sF2/MTuvn5Yrr+TW7vptWjcLiElxIzUWQn3uNzkVERHQ7Ngk5mdeNGpbC4hKYSiwOvXbG9UIAqNL6QRUJ8HYFAKTfOCcREZGcGFiczEWjkuZisVctiy1CCKRfLwIABMgQWAJ9S2tV0rMZWIiISH4MLLWAtR9LnsFxgSWvwASjyQylUgFfL22Nz2eddC79eiEsTlxmgIiI6iYGllrA2iyU68AaFmtzkL+XK1TKmn8MvOtpoVErUWK24HoFnXyJiIiqi4GlFqjnXjq02ZE1LNbAIkdzEAAoFQqpL0xadoEs5yQiIrJiYKkFrDUsjuzDki5zYAGAQGuzEPuxEBGRzBhYagHr0OZcg8khk6oZTWbk3qjNCfBxle28QTc63qZlF3JyOCIikhUDSy3g6a6BAkCJ2YIiB6wpdC3TAKC0Zse6lpEcfL1coVIqygQiIiIiOTCw1AIqpRIebo7rx5KSlg8AaBToIet5VUqF1MSUlsV+LEREJB8GllqinoNGClksAlcySmtYGgd6yn5+6yy31msQERHJgYGllpCm6LdzDUtadgFMJRZoNSr4y9jh1io4qDQEXcs0wGiyf/MWERHdHxhYagkvj9ImIXvPYZKaXlrz0SjQA8pqLnZ4J96eWnh7usAibjY9ERER1RQDSy1x6wibEjutKSSEQOqNENE4SP7mIKsHGngBAC7r8+x2DSIiur8wsNQSXh4u8HDTwGIRuJZln/4fOflGGIpMUCoVaOAnb4fbWz1Qvx6A0mahYgeMeiIiorqPgaWWUCgU0N3oBGtttpHbxas5AIAGfu5Qq+331nt5uMCnnhZCAClp90Yti6nEzLljiIhqMfkm4aAa0wV64MzlbKSm50MIAYWMfUyMJjPOJZcGluaNfWQ7b0UeqF8P1/OKcelqHpo54HpVlV9gxM/7kxB3OAXp1wthKDTBp54WnVoG4eF2DfFwuwayvv5ERFQzDCy1SKCvOzRqJYqNZmTmFMk6bf7Z5OsoMVvg7eki+/wrtjRp6IWE8xlIyy5AWlaBNNzZ2cxmC37YfRYbdp8tN0nf9bxi7D6YjN0Hk9G6iR/+0b8dQh/wdVJJiYjoVgwstYhKqUBDfw9c1uchNT1ftsBSYrbgdFI2AKB1Ez+H1Bx4uGnQvLEPziZfx9Gz6ejT9QGn11hcSc/Hwq8P4czl6wBKQ1VUz6Zo+aAvvD21SLqWiwN/6bFz3yWcupSFtxf/igGPN8Pwfq2hUaucWnYiovsdA0stowvyxGV9HlLS8vFQ8wBZ/shfSM1BsckMD1cNHrwxgscR2jb1x4XUHGTmFCElLR/BNzrjOpoQAjv3J+GzLSdQbDTDw02D6IEP4fEwXZnX96HmgXioeSAGPN4Ma346hd0Hk7Fpz3kcO5uOSUM6S6OfiIjI8djptpZpFOABlVKBXIMRZ5Ov1/h8GdcLcfRMOgCgdRNfKJWOq+Vw06rRqokfAODY2XSY7DRc+04ycwox57M/8J/1x1BsNOOh5gFY8nZv9OrUuMIw6O/thgmvdMLMUd3g5eGCi1dyMWHRHmz//SI75hIROQkDSy3jolGhY2ggAODomfQazXybnVeEn/Zegtki0MDf3SmdX1s38YXWRYW8AhN+PZLisNBisQj8vP8SXn9/Nw6e0kOjVuIf/dvh3dceQaBv+aY2W0Gka9sGWDqpNzq1CoKxxILlG48jZuU+JDtwfhk5ApIQAmaLgNns+MBIRCSXe7pJ6MqVK5g7dy4CAgKg1+vx9ttvIzQ01NnFqrEWwT5IScuHPqsA+09cRa/OwdBUcRhyWnYBvl95FvmFJtRz16DHQ40cWrtipVGr8HiYDrsPpiAtuxA79l1Cr87B8LqxdpLcLBaBvQlX8N1/z+DS1VwAQOgDPhj/Utgdm8MUCgX2Hr+CHBszDXdrUx/uWjX2JVzFkTPpGLdgN9o29Uf7ZgHwqae1y30ApbMGP/JQo0rvX1BkwvmUHJxNvo7zKddxJSMfadmFZVbO9nDTwLeeFo2DPNFU54Nmjb3RTOcNPy9Xp/cxIiK6k3s6sPzrX//CgAED0K9fPxw9ehSTJk3Cli1bnF2sGlMoFOjWtgF+2nsJGTlF2P77BXRoEYjgoHp3nD/FYhHIzClE0rU8nEu5DiEArUaFx8Iaw0XjvE6j/t5u6NVJh7jDKUhJy8c//v0Lono2w2NhOjQOqlfjIJWdW4TzqTk4eEqPfQlXkZVbBKC0SWrw0y3xbM9mUFXiGjn5xcjOs700QnD9evDx1OLImXSkpucj4XwmEs5nor6fOxoFeqC+rzu8PbUOCYVmswXXsgqQdDUXSdfykHQtF5eu5FRq/h5DoQmGQhNS0vKx/8Q16XkfTy2aNPJCSCNvNGnohZBGXmjg7wE3rXN/RQghkF9owvW8YmTnFSErpwhZucXIyi1Cdl4RCopKUFhcItUeqdVK1HN3gaebBl4eLqjnXjonkG89V/h6aeFTTwsvD22lPg9EVLsoxD3aKJ+dnY3u3bvj0KFD8PAoHabbrVs3rF69Gq1bt7Z5THR0NJYvX263Mu3Ye7HCP3jVoc8qwJ8nryG/0AQAUKB0UjY3VzU0aiWUCgUsFgGT2YLC4hIYCk0oMd98O3s81AjNdF4wmWvHW5xxvRBHTqchI6dIes7DVY1GgZ7wqaeFh5sGaqUSKpUCKqUCapUSAqVBzGIRsAgBU4kFxUYzCopMyM4rRmZOEfJuW+Haw02D/j2b4tmeTeHpXvmanMq+f9cyDTidlF1uRWoFAHdXDdxcVVCrlGX+q6jywtbzQpTes9ZFhQAfNxQbzcgrMCIn34hcgxH5hUZU9K0N8nVD82AfNG/sgwfq10OQnzt8PLVQqZQQQiDXYJRC7fmU6zifmoMUfR4sFZxP66KCj6f1D70LNGolVErljf8vfY/UNz6LAgI3/lfalHXLzwIou+3GfVp/NplL39dioxlFxtIQkpNvRE5+McwVFa6alIrS2iufelq4adVwdVFD66KC1kUFjUoJpVIBhUIBhQJQlvl/hfR+ld5e6Q1ZbrlXy+33LW75f9zyGKXvsdksUGK2wGS2oKTEghLzjf9KSr/XZvPN50wlpfsqFIDqlu9J6X+l5Varbvx827ab+5b+rFQqynzXlNZ9Vcoqh7mKPos33vXbn6xg34rOXbUDqnKeCj9VcpXR5r41P8cdz2OjlFX9C19RWQQAi1nALIT0O9l84/ey9bGbVo1/9G+HBv7yT50RHR1979awXLlyBW5ublJYAYCAgACkpKRUGFjszdtT3uYB33patGjsjWPnMpBwPhOGQhNyDEbk3KFfi6uLCo2DPNGjgw6D/taiwmYOZ/Ctp0XnVkFQKhXY9ttFnL6cDUNRSY07FysUgC7QE62b+KF7+4boGBpYrWHIlX3/fOtp0bqJH3INRpxPuY7UdAOuZhpgKrHAUGSCochU5WtXlVqlRKCvGwJ93BDk64ZAXzc09PeAu6umzH5pWaXz4NyugZ87Gvi5o8dDjWAqMSMtuxD6rAKkZRdAn1WItOwCKUDoswqgt3EOR/Jw08DHUwt/b1f4ebnC18sVfl5aeLhq4OaqLg23QqCkRCCv0Ii8AiPyDCbkFRil2pns3GLkGIphEUB2XsW1aURUfQ+3a2iXwALcwzUsJ0+exNChQ3HkyBHpuWeeeQaPPvookpKSbB7z119/oU2bNnYpj16vR/369e1y7tqA93fvq+v3yPu799X1e+T9VV9ycjIg7lFZWVmiZcuWIj8/X3quW7du4uTJk04pz2uvveaU6zoK7+/eV9fvkfd376vr98j7q5l7dlizr68vevbsiT179gAAjh49isDAQLvVoBAREZHz3LN9WIDSUUJz587F/v37ce3aNSxYsMDZRSIiIiI7uKcDi06nwyeffOLsYhAREZGd3bNNQkRERHT/YGAhIiKiWo+BpYauXLmC119/HUVFRXjttddw5swZZxdJViaTCZ9++il+//33Ondv2dnZmDZtGt59912YTCZER0dXOCT+XjZ37lxMmzYNZrMZI0aMwH//+19nF8kuPDw80LJlS2cXQ3ZTp05Fjx49cPDgQfTo0QOzZs1ydpFkV1RUhAULFsBisWDWrFkYPXq0s4skm5SUFISFhZV5D9u1a4dLly45u2iy2rNnD65evYrY2FhER0dj586dsl/jnu7DUhvU1eUBrL7//nuEh4fDaKz+Ioy11bVr16DVajFz5kwAwNq1azFjxgysXbvWySWTl0ajQWxsLABg3759eOutt9CnTx8nl0peZ86cQV6e4xaldLTff//d2UWwqw8//BD9+/dH27ZtAQCHDx92conko1Kp8Prrr+PVV18FAOTm5mLcuHFo0qSJcwsms2nTpmHhwoXo3r07Ll++jH79+qFXr15wdXWV7RoMLDWQnZ2NX3/9FYsWLQIAdOzYEXq9HqdOnXLabLtyGzJkiLOLYDetW7dGTEyM9Dg4OBh6vd6JJbKPKVOmSD9funSpztVCmEwmfPTRR5g4caI0zUFd8+GHH8JkMkEIgVdffRX+/v7OLpJsioqKEBcXhxYtWmDnzp3Iz8/HyJEjnV0s2TRs2FAKKwCwYcMGDBw40Iklso/69esjMzMTAJCeng6VSgWLRd4V4hlYaqA2Lg9AVXPrCsW7d+/G4MGDnVga+/nrr7/wySef4OrVq1i2bJmziyOrpUuXYtiwYfD09HR2Uezib3/7G8LCwhAYGIhffvkFI0eOxI8//gi1um78+k5NTUVSUhIUCgXefvttHDt2DMOGDcPOnTvh7u7u7OLJymKx4Oeff8aaNWucXRTZLVq0CBMnTsTevXtx/PhxLF68WPb3j31YiADExcWhqKgII0aMcHZR7KJNmzZYsmQJJkyYgMGDB6OgwLlrA8nl8OHDKCwsRPfu3Z1dFLt56qmnEBgYKP2cmpqK06dPO7lU8jEYShcR7du3LwCgQ4cOcHFxwaFDh5xZLLv49ddf8fDDD8PFpfKLst4LioqKMHr0aEyfPh3vvfcelixZgiVLlkjvrVwYWGqgUaNGKCwsLPOmZGZmQqfTObFUVFVxcXHYtWsXYmNjy9S41AVms7nM57NHjx4wGAw4ceKEE0sln127diE3NxezZs2SmmZnzZqFn3/+2cklk8/FixfLPNZoNCgqKqpg73uPde0ZpfLmnyONRoPi4rq3OOU333yDV155xdnFkN2ZM2eQlZWF8PBwAEBISAiKiopk73vFwFIDXB7g3rdjxw789ttvmDNnDlQqFebOnevsIsnq6tWrZUaV6PV6GAyGOhOqJ0+ejHnz5mHOnDmYMGECAGDOnDl4+umnnVwy+UyePFn6OTExEUqlsk71Q6pfvz46d+6MAwcOACj9jGZlZSEsLMzJJZPXxYsX4ebmVicXP2zcuDFKSkqQmpoKAMjPz8e1a9fQsGFDWa9zz67WXFukpqZi7ty5CAwMxLVr1zBx4kS0atXK2cWSzcGDB/HTTz/hq6++QmRkJJ588klEREQ4u1iySExMxMCBA+Hr6ys9l5eXh+PHjzuxVPLKz8/HjBkz4ObmBi8vL5w7dw7PPfccIiMjnV00Wf3xxx/YuHEjNm3ahCFDhuCVV15BixYtnF0sWUybNg1GoxH+/v5ISkrCmDFj0LlzZ2cXS1apqalYsGAB6tevj9TUVLzyyivo0aOHs4slq7lz5+Lpp59Gly5dnF0Uu/j555+xfv16hISE4NKlS3jkkUdk7zzNwEJERES1HpuEiIiIqNZjYCEiIqJaj4GFiIiIaj0GFiIiIqr1GFiIiIio1mNgISIiolqvbixGQUR3lZKSgrFjx+Ly5cto165dnVuVWg5paWmYM2cOzp8/DyEE/Pz8sGzZsjJz9RCRc7CGheq8P/74A0uWLHHIta5evYru3btj3bp1DrleVTRu3BibN29Gu3btym17//338cQTT6CwsNAJJQNyc3OxZMkSnDp1yinXt5o9ezYyMzOxdetWbNu2DQaDQfb1UMi5Vq9ejf/973/OLgZVAwML1Xl//vknli5d6pBrubi4oFGjRvD29nbI9eTi7++PRo0aQaVSOeX6ubm5WLp0qdMDy59//onOnTtDrVZDrVZj/fr1dWYZAyq1Zs0aBpZ7FJuEiGTk7++PDRs2OLsYVTZ69GiMHj3a2cVwutzcXGi1WumxRqNxYmmI6Facmp9qpRMnTuCjjz7C+fPnoVQq0aBBA4wbNw7du3fHhg0b8Nlnn+H8+fOIjY3FwIEDUVRUhJdeeqlc/4y33noLBw4cQEZGhrTG04MPPoiPP/4YQOkaJu+++y4OHz4MnU6HFi1aoHXr1pg3bx6aNWuGF198UVoP405lAoBjx45h1qxZOH/+PCIjIzFv3jypXFevXoWnpyfmzp2LFStWIDk5GT4+PoiJiUGHDh3K3PvOnTuxePFiFBQUIDAwEM899xx27tyJEydO4IEHHsCSJUvwwAMPVOp1vPVcDRs2xODBg/HDDz8AgPQaTZs2Dfv27cPVq1exa9cuNG7cGKtXr8b333+P8+fPIyYmBhcuXMCxY8dw+vRphISEYPPmzQCAX3/9FUuXLkV2djbMZjPatWuHyZMnIzg4uEw5fvrpJ6xatQr5+fnQarXw8fFB37598fLLL2P79u1Yvnw5zp8/j4YNG0q1UwsXLkTz5s3veH8HDx7Eu+++K73m7dq1w/bt23Hx4kVkZ2fjwIED8PLywoULF/DBBx/g1KlTUKvVCAwMxPjx4/Hwww9L5VuxYgUSExMREBCAgIAA+Pr6YvXq1QBQpeNff/11KJVK/P777zh79iwUCgUOHjwofUYWLVqEy5cvAyhd1XbSpElo3bo1AJR73c+ePYtjx44hMzMTffr0wZQpU8qEKJPJhBUrVmDr1q1Qq9VQKBRo0qQJBg0ahF69ekn7VeZ9io+Px7Jly1BcXAyz2QxfX188++yzGDRoUKU+a1ZfffUVvv76a1gsFqjVatSvXx/PPvss+vfvL+1zt+9SVb7j8+fPx//+9z9cvnwZH3/8Mf773//i9OnTMBgMGDRoEF5//XXpPZwwYQLOnz8Pd3d3aWG+v//97xgwYECV7pGcRBDVMsePHxcPPfSQ+OCDD4TFYhFCCLFu3TrRunVr8X//939CCCGSk5NFaGio2LBhQ5ljhw4dKoYOHVrmuY8//liEhoaWu47RaBRPPfWUeO6550ROTo4QQogjR46Ibt26idDQUJGcnFylMln17t1bTJkypcxzU6ZMEZ06dRLz5s0TFotFmM1m8cYbb4i//e1voqSkRNrvjz/+EC1bthTLli0TQghhsVhEbGys6NChQ7n7uhtb55o3b57o1q1buXNt2LCh3D1bX+M+ffqIY8eOCSGE2L9/v4iKihJCCPHLL7+IVq1aiXXr1gkhhDCZTOKtt94SPXv2FNnZ2dJ51q5dK9q0aSPi4uKkcnzyySciNDRUet0rej8rq3fv3qJHjx5SWXJyckR4eLjIyckRly9fFl27dhUTJkwQJpNJCCHEmjVrRNu2bcWBAwfKnCc0NFR8/PHHZZ6r6vGPPvqo2L17t3Rsly5dhBBCHDt2TLRr107MmzdP2n/evHkiLCxMXLp0SXrO+lr069dPnDt3TgghxMmTJ0WrVq3EDz/8UOZ6b7zxhnj88cdFUlKSEEKIgoICER0dLb1HQlTufbp8+bJo27at2L9/v3TcF198IXr37n3nF/428+bNE507d5Y+L0ajUcTExIjOnTtL+1T2u1SV7/j+/ftFaGioePnll4VerxdCCPG///1PhIaGir1795bZ19b3k+4N7MNCtc77778Pd3d3jB8/HgqFAgAwZMgQNGvWDHPnzpXtOps3b8alS5cwfvx4eHl5AQA6duyIJ554wi5lMhgMePXVV6FQKKBUKtG3b1+kpKQgOTlZ2mfx4sUICAjAa6+9BgBQKBR48803oVRW/atq61zjx4+H0Wis0nkefvhhPPTQQwCALl26YNmyZRBCIDY2Fk2bNsWQIUMAAGq1Gv/85z+h1+vx1VdfAShdLXrhwoXo3bs3Hn/8cakcY8aMQYMGDaTXUg6enp5SWby8vPDjjz/C09MTS5cuRV5eHqZOnQq1urQVfOjQoWjYsGGlOmNX9fjQ0FD07t0bABAcHIz169cDuPkZeuutt6R9x48fDyEEVqxYUe48Dz/8MJo1awYAaNOmDUJCQrBv3z5p+x9//IGff/4Zo0aNkmrc3Nzc8Oabb8LNzQ0AKv0+nTx5EiaTCU2aNJHOP3jwYAwcOPCur4/V5cuXsXr1ajz//PPS50Wj0eCtt96Cp6entJ89v999+vRBUFAQAOBvf/sb3N3dsX///hqdk2oPBhaqVQoLC3Hw4EG0adOmXP+BsLAwJCcn4+LFi7Jc6/DhwwCA9u3bl3k+NDTULmXy8fGBn5+f9Ng6VDYjIwMAYDabcezYMbRu3bpM51c3N7dKNwFZyXmuFi1aSD8rlUo0btwYFy9eRGpqKjp37lxm34YNG6JevXrSH4kjR46goKBA+gN263n27NmDevXqVakslS0nUDoqyto0ExwcLP0hA0pDU4sWLXDo0CGYTKY7nreqx99ejgceeACFhYU4dOgQ2rVrV6aPjPX9sPVHtWnTpmUe+/r6Sp8VAPjtt98AoNxr26pVK3z77bcAUOn3qUOHDnB3d8dLL72ElStX4tKlS3BxccG4cePu+Nrcau/evbBYLOXK4+Pjg7i4OAD2/37f+popFAp4e3sjPT292uej2oWdbqlWyc3NhcVigY+PT7lt1ueysrJQv379Gl8rLS0NAKTaFavb/4hWtkwhISF3vJ67u3uZx9ZaE7PZDADIzs6GyWQqVx5bZbobOc/l4eFh8/wAsGvXLhw7dqzMNjc3N5SUlJTZzxGjpmyV01oGg8FQpg8FAOTl5cHb2xu5ubnw9/ev8LxVPd5WOayfoYSEhHLnycnJsVnTZK0lsVIqlbBYLGXKBdz5ta3s+9SwYUNs2LABq1atwooVK7Bw4UK0adMGb731llQzdjeVKY9c36WK3O01o3sbAwvVKl5eXlAqlbh+/Xq5bdbn/Pz8pFoDcVufcYPBUOEfrttZ/8Wck5ODgIAA6fnc3NxqlammfH19odFokJOTU25bbm6uzfDhiHNVdH4AeOaZZzB9+vS77merHI7i6+uLgIAAbNq0ySnHAzc/Q9YmNTlU5rWt7PsElNZOvPfee4iJicGuXbuwdOlSvP7669i6dWu52p7qlqcq3yU5vuNUt7BJiGoVNzc3hIeH49SpU+Wq2o8ePYrg4GCEhITA398fCoWizC9Ho9FYpj+IlbXfgfUXX3x8PK5fv45OnToBABISEsrsf+bMmWqVqaZUKhU6dOiAU6dOSf/yBUqr0W3dl6POZUtISAh0Op3NeVO+++47qW9EWFgY3N3dcfz48TL7GI1GvPLKK1L1v7V5wPoenT9/XrY5WR599FEkJSWVmwDu8OHD+Ne//mX344Gbn6HExMRy/+L/3//+V62JDR999FEAKPfanjx5EiNHjoTFYqn0+7Rv3z5p9JhWq0W/fv2wYMEClJSU4Ny5c5UqT48ePaBUKsuVJz09HS+88ALy8vKq9F2qyne8KtRqtfQ5y8rKwt69e2t0PnIcBhaqdSZPnoz8/HwsXbpU+sXyzTff4Ny5c5gxYwaA0gnawsLCsGvXLqkT6erVq23Om9G4cWMAwLVr15CXl4dx48ahoKAA/fv3R5MmTbBkyRKpVuXo0aM2+xNUpkxyePPNN5GZmYmVK1cCKP0D/vHHH8PFxUWWcy1evLjcv1irQ6FQ4J133sHBgwfLzDtz9OhRfPzxx1I/Bk9PT7z99tv4v//7P+zZswcAYLFYsGzZMqhUqjJ/nFxdXXHt2jUAwLJly7Br164alxMAxo0bBxcXF7z33nvSH0nrFPx3GzYtx/FWkydPRnp6utRpGSgdavvee++hTZs2Vb6vbt264emnn8bnn38uDZO2dnIOCwuDUqms9Pt09epVrFixAnq9Xtrnjz/+gIeHR7lh9xUJDg7GyJEjsWHDBukfAUajER988AFCQkKkpsjKfpeq8h2visaNG0ufs19++QXLly+v0fnIcTgPC9VKJ06cwKJFi3DhwgUoFAo0aNAAb7zxhjRPA1D6r/BZs2YhNTUVOp0OgwcPxrfffivNV7Js2TI0btwYxcXFmDhxIhITE6HVavHss8/i//2//wcAuHLlCubMmYPDhw+jcePGaNeuHVq2bIk5c+Zg9+7dZWY5vVuZbp2HxTrPww8//IAhQ4YgKSkJBQUFaNasGZYsWYK4uDisXbsWly9fxgMPPIAXXngBY8aMAQD8/PPPWLx4MQwGQ4Vzp1SW9Vz5+fkICAjAwIED8fPPP5eZ0+WTTz6R5mFp1qwZXn75ZXh4eEjzYFjnRvn888/L9fX4/fffsXTpUly7dg2+vr7w8vLCuHHjEB4eXma/7du3Y9WqVTAYDNBqtejQoQMmTZpUpi/Dd999hxUrVsDDwwMBAQFYuHDhXZvaTp8+jX/+859lXvOJEyeW63dx6dIlfPjhhzh69Cj8/PygVqvx0ksv4YUXXgBgex6W1157Df369avU8fHx8fjggw/KHD9v3jxpfhWrhIQEfPTRRzh79iwCAgLg6uqKUaNG4cknnwRQdv6Rhg0bIiIiAhMmTMALL7wghZIHHngAa9euhZeXlzQPy5YtW6DRaKBSqfDMM8/g1VdfLTOy7G7vU3JyMj7//HMcOHAAKpUKFosFgYGBeOONNxAWFnbH9+B269atw9dffw0hBNRqNR599FG89dZbZTobV+b7DVTuO75161Zs3LhR+i4NGTIETz75JMaOHSt9Lpo2bSp1RD5y5AhmzJgBhUIBjUaD2bNnl+soTLUTAwvRbb744gvMmzcP+/fvrzWL3j377LPQ6XT81yAR3bfYJET3tQkTJpTrU3DmzBk0bNjQKWHl2LFj+Pzzz8s8V1BQgJSUlHL/Wiciup8wsNB97c8//yzTzHLw4EH89NNPePXVV51SnpycHKxcuRJJSUkASvt7fPjhh1Cr1Xj55ZedUiYiotqATUJ0X1u5ciV27twJo9GI4uJiuLq6Yvjw4VLfBEe7cuUKlixZgsOHD0Or1SInJwetWrXC22+/LU1ot2vXLmktJFs8PDzw9ddfO6rIdpOQkHDXDs1fffVVmVlUyT7eeecdnDhxosLtzz77LP7xj384sER0P2JgISIiolrv/wPW6ik1zM5HhAAAAABJRU5ErkJggg==\n", + "image/png": 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", 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" ] @@ -500,7 +500,7 @@ "outputs": [ { "data": { - "image/png": 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O4cOHNXbsWF26dElFixbVn3/+6XQ4NrectYuEhAQVLVpUKSkpKlas2DX3ndv27kx8fLxOnTqVpU1Ur149y2vb1dfjlectJ8dypVKlSkmSEhMTc1xrp06dNHz4cIWFhem+++7Td99959B2r3TlEHGef7rm/yOQ3IB9+/apVKlSDuNuV2rUqJG2bt2qr7/+WitWrFCPHj00ZswY+fn5XXfbme/y/y4XF5cs0zLHIfPLleOamfs3BfQ1N9OnT7/uGPPV5za7cdj85Ox5yU5u623RooWmTp16zWVu5DkKCAhQz549tWrVKi1YsEAfffSRli1bluMx/fyS2+O1Q0REhAYMGKCXXnrJumk2ODg4S+9ddubPn5/tMUybNk0DBw7UypUrNXPmTH388cf6/PPPdccddzhd/uGHH9a0adMUGxurEiVKqEaNGqpTp46++eYbGWOsd+I3Ijfn+ddff9XgwYM1c+ZMqxcwMDAwz14rateunW3vck72XZDt3dn1eKVrHcvVSpYsqdq1a+vIkSM53n/btm1VokQJrV27Vqmpqapfv76KFy9+3fUy76XJawzZ5FJcXJxWrVql3r17Z3sRbtq0ScWKFVOXLl20YMEC6/Pcma5seNd7J3EtmZ9WkDLeHZ4/f97qjr/y3W+mK79TQHL8g5eamqqkpCSn+6ldu7b++usvh2knTpywuqFvRPXq1eXm5uaw3bS0NEVERORqu5ldzMeOHXOYPmvWrDzpLbrWOb7eealVq5bOnj3rcF6v3F5eql27dpZzcPjwYc2ePTtX27m6bcbHxys0NFRVq1bVkCFDtGHDBpUoUUKbNm3Kto6zZ886vEs7ceKEihQpYg235PR4MocjMu3bt0/bt2+35t/o8V7Z7tPT03Xp0qUc13W1zHYcHh7uMP3jjz9WYmKifvvtNyUlJalDhw7WvKu/x8jZdVi7dm2lp6fr+PHjDssGBQUpNjZWkZGR2rt3r2rXrq0RI0Zo3bp1ioqK0vfff59tra1bt1ZsbKzef/9960bmNm3aaNu2bdq9e3eW3r6rXdk2EhMTb/gNzk8//SQXFxc9+eST1rRrnZPk5GQlJyfnaNuZ12R6ero1LSYmRuPHj8/Rvq/X3nPymunp6akqVar87dfM6x2LM35+ftqyZYt1u8CVJkyYoJdeeslhmoeHh9q0aaP169fryy+/zNWnRRMSEhyG/PICgSQXwsPD9cILL6hWrVrZdmtJ0sKFC62xWimj4V7Zm1KuXDldvHhRqamp6tat2w3X8/nnn1sXxMKFC1WxYkXrQqtevbpKlixpfenN999/n2UII/N+hAsXLujrr7/WrFmznO7H399fW7ZssV4c//jjD4WGhuqFF1644dpLlCih5557TkuWLLH+IKxcuVKurq7q2bNnjrdTrVo1derUSXPnzrUuwp9//llff/11li7TG3Gtc3y989KpUyeVLFnSGvNNSkrS559//rdrcmbgwIHav3+/1e5SUlI0a9as6w4NXO3qtnn+/HmNGzdOCQkJkjJ6U9LS0rLtHezcubMqVqxovatLSUnRwoUL9fTTT+dqHN7Pz09xcXHasGGDpIw/SlOnTlXRokX/9vGWKVNGrq6uunjxon777be/1f18ZTvODGHffvutNm3aJHd3d9WsWdP6NI2U0QauvlfC2XX44IMPqlGjRgoJCbH+IG3YsEF//vmnypYtq+PHj2vatGnWH9P09HQZY67Z5suVK6f7779fS5cutYYoHn74Ye3bt0+enp7XfWdcrlw5636jl156SX/++WduT5ekjKCelpZm9RLFxsZm6TEqW7as4uLiZIzRggUL9N///jdH2/bz81NiYqK1vDFG//nPf6xzfL19X6+95+Y1c9WqVYqJiZEk7dy5U0ePHs3Vx2SvdyzO9OrVS02bNtWoUaMcwtJXX32ljRs3OgwBZurUqZPOnj2r0NBQ+fr65ri+2NhYffzxxzlePidcTEH1qd9CwsLCNH36dO3evVt33323KlSooOTkZLm4uKhz587q1auX9cK4Zs0a62O/vr6+mjhxon799VctXrzY+ihohQoVNGbMGGu8cOPGjZo5c6ZKly6tLl26qEmTJgoKCtKvv/6q+++/X126dLGGd+bNm6dly5YpIiJCDRs21KhRozRp0iTt3r1bgYGB2rFjh2JiYmSM0YQJE9SgQQPrOFasWKEPP/xQlStX1kMPPaTQ0FBFRESoc+fOevXVVyVJw4YN09GjR1WiRAm99dZb+vzzz7Vx40ZdvHhRTZo0sW7QvPrjrYMGDbLe9U2aNEnr16+XJHXt2lVPP/20AgMDreOZMGFClm8nlZx/7PeNN95QvXr1rI9d/vLLL/L29lbVqlU1d+5cp89XQkKCpk6dqt27d6tChQry8PDQyJEjVb16dR04cMDh3L711luaM2eOtm7dqjvuuEP9+vWTl5eX3n//fescjxs3TkFBQTk6x9c6L9L/PvZbpEgReXl56aGHHtLkyZOttvL+++9rx44dkqRHHnlEfn5+ua43849/aGioZs6cKVdXVxUrVkxPPvmk9dHTnD5HV7fNHj16aMaMGfr555/l4eGhhIQEtWvXToMGDcr2+jl+/LgmTJig2NhYpaSkqGnTphoxYoRKlChhfew383pp27Ztlu9ByBQWFqYpU6YoJSVFrq6u6tatm/Wx3twcb82aNRUSEmL1GkoZQ3zbt29XyZIlNWLECIWFhTlcZyEhIU7b34ABAxQWFqY77rjDuo5SU1M1c+ZMbd++XWXKlJGnp6eCgoJUuXJlSdLSpUs1Z84cVa5cWeXLl5eU8Qfq4Ycf1jvvvCMp63VYs2ZNRUdHa9KkSTp06JDKly8vLy8vjR49WuXLl9fZs2c1Y8YMHTp0SB4eHtbN00899VS2z4uUcbPl119/bX2zanp6ulq0aGENU0gZf5SHDh1qHbufn5/8/Px09OhRvfLKKypVqpSqVq2qadOmqV+/ftZyL774oooUKaL33ntPERERaty4sfUR46sFBwdrxYoVql69uipUqKCzZ8/qyJEj6ty5swIDA5WcnKwXXnhBcXFxcnd316xZs7L8IV6wYIGWLFliPWcfffSRSpQoYbWbxMREubu7q2nTpnr55ZcdPvab3b5feuml67Z3Z8+VMx9//LFWrVolNzc362O/zZs3lyS9+uqrCg0Nta7p+vXra9KkSdZ1kXm81zsWZ9LT07Vo0SJt2LDBut+kUqVKGjp0qNNeyuTkZLVs2VI9evTIEs4XL16sxYsX6+jRo6pXr55DaE1OTlapUqVyPKSUEwQSAABgO4ZsAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2u+1+y6Zjx462/8YGAADIndsukFSrVk0hISF2lwEAAHKBIRsAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQS4BZg0tPtLgE3EdoDbkdF7S4AwPW5uLrq2NqPlBhz2u5SYDP38pV1d6eBdpcB5DkCCXCLSIw5rcTIE3aXAQD5giEbAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xXN7x3ExsZq2rRpKlmypFxcXHTy5EmNHDlS1atX18WLFxUUFCRPT09FRUXp+eefl6+vryQpOTlZ48aNkySdO3dOXbp0Ufv27fO7XAAAYIN8DyRnzpyRm5ubxowZI0n69NNPNXr0aH366aeaOXOm6tWrp0GDBikyMlJPPfWUtmzZIjc3Ny1cuFBFixbVuHHjlJCQoHbt2qlp06aqUKFCfpcMAAAKWL4P2dSrV09BQUHW42rVqikyMlKStHr1aj3yyCOSpEqVKqlixYoKDQ2VJH355ZfWPA8PDzVq1Ejr1q3L73IBAIANCuQeEhcXF+v/W7duVZ8+fXT+/HnFx8fLy8vLmufl5aWTJ09KkiIiIhzmlS9f3poHAABuL/k+ZHOlbdu2KSkpSf3799eFCxdueDtr167V2rVrnc7L7H0BAAC3jgILJNu2bdOWLVs0efJkubi4qEyZMvLw8FB0dLTKlSsnSYqOjpa3t7ckydvbW9HR0db6MTExaty4sSSpU6dO6tSpk9P9+Pv75/ORAACAvFYgQzYbNmzQd999p/Hjx6tIkSKaOHGiJKlLly7atm2bpIyejaioKLVu3TrLvISEBO3du1cdOnQoiHIBAEABczHGmPzcwcGDB9WjRw+VLVvWmhYXF6ewsDBduHBBY8eO1R133KHIyEj961//UvPmzSVlfOw3KChILi4uOnfunDp37qyOHTted3/+/v4KCQnJt+MB7LJ/wXglRp6wuwzYzL3SXbqn/1i7ywDyXL4P2dStW1f79+93Oq906dKaNWuW03nFixfX5MmT87M0AABwk+CbWgEAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsVLYidpKSkaP78+Xr//fe1fPly+fj4SJKCg4O1ePFiFSlSRJJUv359zZkzR5KUnJyscePGSZLOnTunLl26qH379gVRLgAAKGAFEkiWL1+upk2bKjExMcu8zz//XFWrVs0yfeHChSpatKjGjRunhIQEtWvXTk2bNlWFChUKomQAAFCACiSQ9O3bN9t5n3zyidzc3JScnKz+/fvrrrvukiR9+eWXCggIkCR5eHioUaNGWrdunZ577rmCKBkAABSgAgkk2WnatKnuvPNO3X333dq3b5/69OmjDRs2qFSpUoqIiJCXl5e1bPny5XXy5ElJ0tq1a7V27Vqn24yMjCyQ2gEAQN6xNZA0b97c+n+DBg1UtmxZff/993riiSeuuV6nTp3UqVMnp/P8/f3ztEYAAJD/bP2UzbFjxxweFytWTElJSZIkb29vRUdHW/NiYmKc3msCAABufbYGkpEjRyolJUWSFB0drfDwcDVp0kSS1KVLF23btk2SlJCQoL1796pDhw52lQoAAPJRgQzZ7NmzR+vXr5ckffjhh2rbtq3at2+vZs2aKSAgQFWqVNFff/2lKVOmyNvbW5LUv39/BQUF6Y033tC5c+cUGBioihUrFkS5AACggLkYY4zdReQlf39/hYSE2F0GkOf2LxivxMgTdpcBm7lXukv39B9rdxlAnuObWgEAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsJ3TQBIfH+904ePHj+vLL79USkpKvhYFAAAKF6eBpF+/fk4XTkhI0JIlSzR8+PB8LQoAABQuTgOJMcbpwvXr19fSpUt19OjRfC0KAAAULkUz/3Pq1ClFRERIkhITE7Vnz54swcQYozNnzmQ7pAMAAHAjrECycuVKzZ49Wy4uLpKcD9sYY+Tq6qrBgwcXXIUAAOC2ZwWS7t27y9fXV8YYjRkzRhMnTsy6cNGi8vb2VqVKlQq0SAAAcHuzAom3t7e8vb0lSb169ZKvr69tRQEAgMLF6U2tzz///DVX+uijj/KlGAAAUDgVzW6GMUbh4eE6e/as0tPTHeZ9/vnnGjhwYL4XBwAACgengSQsLEzDhg3TyZMns8wzxlg3vgIAAOQFp4HkzTffVL169RQQEKCyZcvK1fV/IzuZN70CAADkFaeB5MKFC1q5cmW2K2X3Ta4AAAA3wulNrdWqVbvmSo888kh+1AIAAAopp4HkhRde0PTp03XhwgWnK7300kv5WhQAAChcnA7ZjBo1SnFxcZo3b57KlCkjd3d3h/lRUVEFUhwAACgcnAaShIQEtW3b1ukKxhh98803+VoUAAAoXJwGksqVK2vy5MnZrtSzZ898KwgAABQ+Tu8hWbZs2TVXWr58eb4UAwAACiengcTNze2aKwUGBuZLMQAAoHByOmSzatWqa660e/fu/KgFAAAUUk4DSXY9IHxlPAAAyA9OA0mtWrU0Z84ch2kJCQk6evSoVq9erQEDBhRIcQAAoHBwGkhefPFFeXt7Z5nu4+OjVq1aKTAwUA888EC+FwcAAAoHpze1dujQIdsVPD099ddff+VbQQAAoPBx2kOSnQsXLmjDhg26fPlyftUDAAAKIaeBpG7dutnewOrq6qo333wzP2sCAACFjNNA4uXlpWeeecZhmqurq7y8vOTr66saNWoURG0AAKCQcBpIGjZsqCFDhhR0LQAAoJByelPr7NmzC7oOAABQiGV7U2tCQoIWLFig0NBQnTt3TuXKlVPr1q317LPPysPDoyBrBAAAtzmngeTcuXPq06ePjh8/ruLFi6t06dI6ffq09u7dqzVr1mjRokUqV65cQdcKAABuU06HbGbMmKGKFStq5cqVCgsLU2hoqMLCwrRy5UpVrFhRM2fOLOg6AQDAbcxpD8nOnTu1bt06ubu7O0y/55579J///EedOnUqkOIAAEDh4LSHxM3NLUsYyVSyZEm5ubnla1EAAKBwcRpIihYtqn379jldYd++fSpSpEi+FgUAAAoXp0M2zzzzjAYMGKCnn35aDRo0UJkyZXT+/HnrPpKXX365oOsEAAC3MaeBpG/fvjp58qQWLFggY4wkyRgjV1dX9e/fX3379i3QIgEAwO0t2+8hGTFihPr06aOdO3cqNjZWZcuWVYsWLVStWrWCrA8AABQCViBJTU3V9u3bJUl33nmn6tevr2rVqqlXr16SpD///FORkZEEEgAAkOesm1p//PFHvfjii3rttdf0/fffZ1nw7Nmz8vPz09tvv12gBQIAgNufFUi2bt2qBg0aaMuWLfr3v/+dZcFmzZppyZIlWr16tbZs2VKgRQIAgNubFUj27NmjyZMnX/Mr4Rs1aqR33nlHn332WYEUBwAACgcrkMTFxekf//jHdVd44IEHFBMTk69FAQCAwsW6qbVUqVI5XsnFxSVXO0lJSdH8+fP1/vvva/ny5fLx8ZEkXbx4UUFBQfL09FRUVJSef/55+fr6SpKSk5M1btw4SRk/9telSxe1b98+V/sFAAC3BiuQpKenKyUlRcWKFbvmCikpKUpJScnVTpYvX66mTZsqMTHRYfrMmTNVr149DRo0SJGRkXrqqae0ZcsWubm5aeHChSpatKjGjRunhIQEtWvXTk2bNlWFChVytW8AAHDzs4ZsGjZsqCVLllx3hc8++0yNGjXK1U769u3rdJ3Vq1frkUcekSRVqlRJFStWVGhoqCTpyy+/tOZ5eHioUaNGWrduXa72CwAAbg1WD8mAAQPUvXt3nT9/Xn5+fllubo2JidGiRYu0aNEirVy58m/v+Pz584qPj5eXl5c1zcvLSydPnpQkRUREOMwrX768NQ8AANxerEBSvXp1TZkyRa+//rpCQkJUtWpVlS9fXlJGGDl58qTc3Nz07rvv2v7laGvXrtXatWudzouMjCzgagAAwN/l8NXxTzzxhGrUqKH//Oc/Cg0N1YkTJyRlDJk8+eSTGjp0qGrWrJknOy5Tpow8PDwUHR1t9cZER0fL29tbkuTt7a3o6Ghr+ZiYGDVu3FiS1KlTJ3Xq1Mnpdv39/fOkPgAAUHCy/JaNj4+P3n33XRljFBsbK0kqW7Zsrj9ZkxNdunTRtm3b5OPjo8jISEVFRal169YO89q0aaOEhATt3btXo0ePzvMaAACA/Vyzm+Hi4qJy5cqpXLlyfzuM7NmzR+PHj5ckffjhh9qwYYMk6dVXX9Xvv/+uMWPGaMyYMZo+fbrc3NwkSf3791dycrLeeOMNDRs2TIGBgapYseLfqgMAANycXIwxxu4i8pK/v79CQkLsLgPIc/sXjFdi5Am7y4DN3CvdpXv6j7W7DCDPZdtDAgAAUFAIJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsVtbuAwMBAhYaGWo8fe+wxjR8/XpJ08eJFBQUFydPTU1FRUXr++efl6+trV6kAACCf2B5IJGnHjh1Op8+cOVP16tXToEGDFBkZqaeeekpbtmyRm5tbAVcIAADy000RSGbMmKGUlBQZYzRw4ECVL19ekrR69WotWbJEklSpUiVVrFhRoaGhatu2rZ3lAgCAPGZ7IHn00UfVqFEjVahQQV9//bWee+45ffHFF4qPj1d8fLy8vLysZb28vHTy5EkbqwUAAPnB9kDyxBNPOPw/MDBQhw4dkre3d7brrF27VmvXrnU6LzIyMs9rBAAA+cv2QHLs2DHdfffd1uNixYopKSlJZcqUkYeHh6Kjo1WuXDlJUnR0tLy9vfX444+rU6dOTrfn7+9fIHUDAIC8Y/vHfl9//XXr/wcPHpSrq6vq1KkjSerSpYu2bdsmKaPnIyoqSq1bt7ajTAAAkI9s7yGpXbu2hg0bpvLly+uvv/7S7Nmz5enpKUl69dVXNXbsWI0ZM0aRkZGaPn06n7ABAOA2ZHsgmTx5crbzSpcurVmzZhVgNQAAwA62D9kAAAAQSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkAAAANsRSAAAgO0IJAAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYrancB13Pq1ClNnDhRXl5eioyM1LBhw+Tj45Nv+0tPN3J1dcm37ePWQ5sAgPx30weSN998U926dVOHDh30yy+/6LXXXtPq1avzbX+uri56f8kORURdyLd94NbhXbG0Xuz9kN1lAMBt76YOJLGxsfr22281c+ZMSVLDhg0VGRmpAwcOqF69evm234ioCzoeEZtv2weAW116erpcXRn1R4a8aA83dSA5deqU3N3d5eHhYU3z8vLSyZMn8zWQeFcsnW/bxq3lZmoL7uUr210CbgI3SztwdXXV6l++UnT8ObtLgc28PMupS8Mn//Z2XIwxJg/qyRe///67/Pz8tHfvXmtax44d1bJlS/31119O19m/f7/uueeegirxthYZGalKlSrZXQZgoU3iZkObzDs3dQ9JlSpVlJiYqISEBKuXJCYmRl27diV0FAB/f3+FhITYXQZgoU3iZkObzDs39QBg2bJl1apVK23fvl2S9Msvv6hChQqEEQAAbjM3dQ+JlPEpm4kTJ2rXrl06c+aMpk+fbndJAAAgj930gcTb21sffPCB3WUAAIB8dFMP2QAAgMKBQAIAAGxHIMmB0NBQ9evXT/fee6/Cw8OzzH/hhRfUsmVLvf/++9fczqVLlzRs2DA9+uij1rTAwECtXLnSenz48GH17t07222kp6dr7ty5evXVVzV69GgFBATo5Zdf1u7du51u/0oXLlzQ448/rvT09OsdsiSpU6dOkqQ9e/aoX79+at68ucaOHWv969Gjh06ePJmjbWWaNGmS6tSpk6t1rvbBBx+oUaNGTvd9rXnZOXjwoPr166fAwMC/VVd+Kqxt8ErffPONYmJiFBQUpNdff10DBw7UwYMHJUknT57Mdp836syZM/L391e/fv2uudyRI0dUt25dHTt2zJp26tSpHK2Lv++bb77RSy+95LRdXK8t58T1nsvM10ln7LpWblkGObJixQrz2GOPmTfeeMNhelhYmGnbtq3x8/PL0XbCw8NNmzZtrMcjRowwK1assB6npaWZiIiIbNcfN26cmTp1qsO0jz/+2IwYMcLp9q924sSJHNV5tRUrVpju3bs7TFu0aJE5c+ZMrrfl4+NzQzVcqU2bNiY8PDzX87KzYsUK6xzerApzG7x8+bJp3ry5uXTpkjVt+vTpZsOGDTna543atWvXdc/r5MmTTceOHc20adNyvS7+nuu1i+u15Zy60efSrtfrW9VNf1PrzWTQoEGaOHGi/P39Va1aNUnSZ599pp49e+rbb7+VJB09elQTJkzQnXfeqSlTpujLL7/U22+/rbffflvNmjVz2N727dv166+/6syZM/rtt9/Ut29f/fe//9W8efN06NChLPs/efKkVqxYoZ07dzpM79Onj3bv3u0wLTg4WLt371ZKSoo+/PBDlS5dWiEhIZozZ45Wr16tuLg4TZ06Venp6apRo4bi4uKUlJSkKVOmqHTp6387aWBgoKZMmSJJ2rBhg95++201b95ciYmJ+v333/Xss88qOTlZW7du1eXLlzVnzhyH7S5cuFD79+/X8ePH9fLLL6t58+bW9GPHjqlEiRK6cOGC3njjDXl6eurQoUMaP368KleurBo1ashc8X1+15oXERGh4OBgVahQQadOnVLHjh2tdyTTp0/X77//rurVq98yX4FdWNvg5cuXFR8fr6ioKFWvXl2S9OKLLzrs6/z58xo/fryqVq2qAQMGaPjw4brjjjvk6uqq1NRUjRo1SkWKFNHo0aO1evVqDR06VLt371ZERISmTJmi++67T5I0f/58bdq0SbVq1VKpUqWu+XwkJyfrxIkTGjx4sCZNmqRXX31VRYs6f1nduHGjNm3apEqVKun06dN67bXX5O3tLX9/f33zzTcaO3asNm/erHPnzmn27NnW85vdNYHrt4tp06ZZbXnPnj0KCgpSzZo15enpqYMHD+qee+7RpEmTJEmnT5/WyJEjVaFCBZUqVUo///yzvL29NWrUqCz7Xb9+vXbs2KGyZcvq9OnTGjFihCpWrOiwzM30en3LsDsR3SpWrFhhdu3aZV5//XXrHWpYWJiZO3eu+fTTTx3S89Xvtv38/MyuXbuMMdd/d2pM9j0I69evN0888cQ16wwPDzf33HOPOXLkiDHGmFdeecUsW7bMmn9l70HmO+7k5GRjjDFvvfWWefPNN7M9/gceeMC88sor5pVXXsmS6t977z3j5+dn0tPTzV9//WXuu+8+s3PnTmOMMQEBAQ41+Pj4WPMOHDhgHnzwQZOQkGB27txpnn32WWu5GTNmmHfeeccYY0y3bt3MunXrjDHGHD9+3NStW9c6jmvN6927t/nxxx+NMcbExcWZZs2amdjYWLN582bTsWNHk5qaaowx5vXXX78lekgKcxsMDg42DRs2NIMHDzZLly41sbGxDvu8uk2uXr3a+v+bb75ptZHMGpYvX26MMeazzz4zAQEBxpiM9tisWTOTkJBgjDHm3XffveY74/Xr15vly5eby5cvG19fX7Np0yZr3pXvqo8ePWoeeughk5SUZIwxZt26daZPnz7Wsj4+PmbHjh3GmIx3+DNmzDDGmGteE8hwrXZhjGNbXrFihWnbtq1JSkoyqamppmXLlubYsWPGGGNefPFF8+GHHxpjjLlw4YJp1KiRdc1c+VweOXLEtG/f3qSlpRljjFm+fLnVfq5k57Vyq7o13hbeRAYPHqw1a9YoPDxcS5YsUZ8+ffJtXz///LMef/xxPf7445o/f75cXFxytF7ZsmVVq1YtSVL16tUVFRWV7bL33XefihUrJklq1qxZljR/papVq2rmzJmaOXOm0+Nu2LChXFxcVK1aNSUlJalJkybZ1pA5r27dukpPT9ehQ4f07bff6ty5c9Y9KseOHdPFixcVFxen/fv364EHHrC2V758eUm65rz4+Hj99NNPWrFihcaOHatp06apRo0aOn36tH744Qc1btxYRYoUkSQ1btz4Omf15lFY2+CQIUP09ddfq2XLltq4caMeffRRff/999luNy0tTYGBgZo0aZJ+//13h3s8pP+1wRo1aujs2bOSpB9++EH169dXyZIlJV2/Xaxfv17t27dX8eLF1alTJ/33v/91utzOnTtVp04dubm5SZKaNm2qPXv2KCEhwVqmadOmkhzPV3bXBP4nt+2iQYMGcnNzU5EiRVStWjWH597X11eSdMcdd6h27dpO1//++++VlJSkN998U2PHjtWuXbuUmJiYZTm7X69vRQzZ5FKNGjXUvn17DR8+XI8//rjc3d2zLOPq6upwI1JKSsoN7atx48batGmT9TgiIkKRkZEOX6UvSYmJiTp16pTVqIsXL27NK1KkiMMQRl7597//nWVa5n4zL8QrH+e0hvvvv1/jx4+XJBljlJiYeM2bunJy0b/yyivWb00kJSWpWLFiOX6xuBkV1jaY+TtVvXv3Vu/evTVjxgwtWLDAGu670t69ezV16lRt3rxZHh4eCg4OztKOMmt0dXW16stNu4iIiNDx48c1a9YsSdL58+cVGhp6w79tklnP1efL2TWB/8lNu5Cyts3MdpGb57569erWcyLJIVhmatCgwU3zen2roIfkBgwePFjGmGzv3vby8rJSd1JSkv78889st1W8eHGlpaXp9OnTCg0NveZ+vb299fTTT2f53YQZM2Zo8+bNuTyKDPv27VNqaqqkjHcILVq0yPG669at008//XRD+/35558lZXzCxdXVVXXq1FHr1q21e/duq54tW7bok08+kaenp+rXr2+Nu544cULR0dGSdN15TZs21XfffScp4473gQMHKikpSQ8++KB+/vlnpaWlSZLDDzjeCgpjGxwxYkSWUFG5csYv37q5uVnP5YoVKxQTEyM3NzcrrJ06dSpHtfj6+ur333/XpUuXJF27XaxcuVJvvfWWRo0apVGjRumdd95R/fr1HT6xlKlFixY6dOiQLl++LCnjk2tNmzZ1+EPlTHbXBP7nWu0iNx588EHrdSQuLk5HjhxxulyLFi20f/9+xcXFScp4DZs4cWKW5W621+tbAT0kObBnzx6tXr1aHh4e8vLyUq1atbR06VJJGd13Gzdu1F9//aVPPvlEAwYMULNmzfTpp58qMDDQutFy4cKFqlq1qnXzXUhIiPz9/dW2bVvNmzdP27Zt07/+9S/rRtEpU6Y4/RjqqFGj9NlnnykgIEClSpVSfHy8ateurUGDBik1NdXa/oIFC9SwYUPrd4BatmypH374QefPn1dwcLCCgoIkSbVq1dKkSZMUExOj5ORka//Ojv/06dMO7wr+/PNP/d///Z/27Nlj7ad169bW/9977z09/PDD1uPmzZtbF+KPP/6o9evX6/Dhw5oxY4ZKliyp5s2bq1+/fgoICLB+WHHkyJGSpKlTp+rNN9/Ud999pzJlyqhChQrWcVxr3rRp0zR58mQdOHBAiYmJev755+Xh4aE2bdro559/1r///W/dfffdSk1N1f79+7Vhwwa1b9/+7zeaPFbY26CUMSQ4dOhQVapUSZcuXVJKSopGjx4tKSOA+fj4aOzYsUpJSdGECRO0bt06DRw4UD4+PgoPD9cff/yhli1bau/evVYNI0eO1MKFC3X8+HGtWrVK3bp104svvqiBAweqbt26Sk5O1vHjx7VkyRKH8PfVV19p1apVKleunBo0aCBJ1kdNFy9erMqVK2vTpk0O644ePVqBgYGqWLGiIiMjNW3aNEkZf6CkjOulS5cuWr16tSIjI/XNN9+oTZs22V4TyHCtdnFlW/7nP/9pndvt27fr/PnzOn78uBYuXKjatWtr1KhRCgwM1MGDB1WxYkXVrFlTxYoV06lTp6w2kvlcvvnmmxo+fLiqV6+uCxcu6PXXX3dam13Xyq3KxRTm/qFCbuXKldq9e/dt16hx66AN4maxf/9+ValSRWXKlFFKSoratWunFStWqEyZMnaXJqlwXCv0kBRS+/fv1xdffKGoqCitX79eHTp0sLskFDK0QdxMzp49qw8++EBVq1ZVVFSUhg0bdtOEkcJyrdBDAgAAbMdNrQAAwHYEEgAAYDsCCQAAsB2BBAAA2I5AAuBvmzt3rjp06KA6deo4/WKwvLJx40Z17dpVderUUXBwcL7tB0DB42O/wG0qKSlJvXr1UnR0tKKjo1WrVi3rdzAyXbp0SXfeeac+/fTTv7Wvf//732rXrp0ee+yxv7Wd62nXrp3atWunOnXqZJn3z3/+U1WqVLG+yv1mdODAAW3evFn9+/fXHXfcYXc5wE2FQALcpkqUKKEvv/xSwcHBmj17tubMmaOqVas6LPPDDz9o9uzZNlWYtypXrpzlJ+BvNgcOHNDs2bPVvXt3AglwFQIJUIj5+PgoICDA7jLyxHvvvWd3CQD+BgIJUEg9+uijWrhwoRo1aqQTJ05o6NChOnHihO69914988wzWrJkiY4fP6577rlHkydPVnx8vKZOnaojR47Iw8ND48aN03333Zdlu4mJiRo7dqx+++03hYeHq0mTJho7dqyqVKnisNzixYu1ZMkSpaSkKDk5WS1atFBAQIDKly9vLZOenq73339fy5Ytk6enpypVqqThw4c7bCctLU09evTQ6dOn5enpqa1bt0qShg0bpp9++kmnT5/Wp59+qgULFujo0aOSpMDAQLVq1UozZ87Uzp07FRsbq549e+r//u//HLYdHx+vGTNmaNu2bSpevLiKFCmiPn36qG/fvpKU5bz16tVLS5cuVXh4uKpVq6YJEybo7rvvliS99dZb+vrrryVJgwYNUrFixeTu7m79JhFQ6BkAt7X33nvP+Pj4mPDwcIfpbdq0yTLNz8/PNG/e3MyfP98YY0xcXJx57LHHzKBBg8zbb79tUlJSTHp6uhk6dKhp27atSU1NtdYNDw83Pj4+pk2bNmbv3r3GGGPOnz9vunXrZp588kmTkpJiLTtlyhTTuHFj88svvxhjjImPjzd+fn7miSeeMHFxcdZywcHBpn79+mbnzp1WPUOHDjU+Pj7mvffec6h9xIgRpk2bNg7TVqxYYXx8fMyQIUNMQkKCte97773XzJo1yxw7dswYY8zWrVuNj4+P2bVrl7VucnKyefrpp02HDh1MdHS0McaYX375xdx3333mww8/dHrePvroI2OMMZcvXzY9e/Y0ffr0cVrP1ecdgDF8ygYoJAYNGqSuXbta/6Kiopwul5aWZvUAeHp66uGHH9a2bdvUvn17FS1aVC4uLurQoYNOnDihkydPZlm/efPmatiwoSSpdOnSGjJkiI4dO6ZVq1ZJyuhVmD9/vp566indf//9kiQPDw8FBgbq+PHjmj9/vqSMn4D/5JNP9Oijj6p58+ZWPc8++2yuj71Lly4qWbKkJKljx45KTk7WxYsXVaNGDUlSmzZtVLJkSe3cudNaZ/Xq1QoLC9OQIUOsXpv7779fHTt2VEhIiBITEx32kZqaqv79+0uSihcvrrZt2+qnn35ScnJyrusFCiMCCVBIzJkzR19++aX1L7sbQKtWraqiRf83mlu6dGlJsv54S7J+dOzs2bNZ1r/6EzCZwzp79+6VJO3cuVPp6elWGMlUv359FS9eXN99950k6dChQ0pISFCDBg2uuf2cuLL2zOPJHErJVKZMGYfj2bFjhySpSZMmDsv5+PgoISFB+/btc5herVo1h08xlS1bVsYYxcTE5LpeoDDiHhKgkMq81+JqmT0JmVxcXLJMz5yWlpaWZX1PT0+Hx5kBIDIyUpIUGxvrMP3qZc+dOydJVg/O1cuVKlXKad3X4u7unqX2K6dlTk9PT7ceZ9Y5cOBAh+WSkpLk5eWlixcvOky/+ry5uma833N2jgBkRSABkKfi4uIcHp8/f16SVKlSJUkZPQeSdOHChSzrXrhwwfpocmYPTub6ma4OAvkls85FixbdUAgCkDsM2QCF2OnTp9W9e/c83ebhw4cdHoeFhUmSGjVqJEl66KGH5Orqql9//dVhuf379ys5OVktW7aUlDE04+Hhod9+++2a288vmXXs37/fYXpcXJyGDBmSJSjlROZQmDFGkrRnzx6dOXPm7xUK3CYIJEAhlpaW5rSn4u/YunWrfvnlF0kZPR7vv/++7r77bnXr1k1Sxr0Wzz33nFauXGmFlUuXLmnq1KmqUaOGnnvuOUkZQzMDBgzQ1q1b9f3330vK+BjunDlz8rTe7HTu3FmNGjXS9OnTrftAkpKSNGnSJLm6ulr30eRGZu9PZGSkUlNT9dprryk8PDwvywZuWS4mM6oDuK0kJiaqY8eOunjxouLi4lSpUiWHm1WljE+GFC1aVCtWrNBzzz2nEydOSJLuuusuLV68WKNHj9aPP/6o6Oho1a1bVyNHjtRvv/2mZcuW6cSJE7rrrrvUo0cPFStWTCtXrtTRo0cVGBio3377TUePHtWpU6fUuHFjp99DsmjRIi1ZskSpqam6fPmyWrRooWHDhmX5HpL//Oc/Wrp0qTw8PFSuXDm98sorevbZZ+Xl5aWaNWtq/vz51veQXLp0SbVq1VJQUJDWrFmjb775RqdPn1atWrXk7+8vSQoJCdHRo0dVuXJlPfHEE3rmmWf06quv6ujRoypZsqSqV6+u//73v5IyAtCsWbO0ZcsWeXh4yNXVVa1bt9aQIUPk5uam2NjYLOdt2bJlmj17ttauXWvte9CgQVYgGzNmjHbs2CF3d3f5+voqKCgoX55/4FZDIAEAALZjyAYAANiOQAIAAGxHIAEAALYjkAAAANv9P+BP0e7/BW4pAAAAAElFTkSuQmCC\n", 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", "text/plain": [ "
" ] @@ -542,17 +542,17 @@ " \"\"\"\n", " if column not in df.columns:\n", " return None\n", - " \n", + "\n", " data = []\n", " indices = []\n", - " \n", + "\n", " for tpl in series.items():\n", " index = tpl[0]\n", " count = tpl[1]\n", " amount_in_category = df[df[column] == index].shape[0]\n", " indices.append(index)\n", " data.append(round(count / amount_in_category, 3))\n", - " \n", + "\n", " return Series(index=indices, data=data)" ] }, @@ -564,7 +564,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -639,7 +639,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -980,7 +980,7 @@ "for _, rows in active_referencing_certs.iterrows():\n", " referencing_ids = rows[\"module_directly_referencing\"]\n", " intersection = referencing_ids & historical_cert_ids\n", - " \n", + "\n", " if intersection:\n", " active_certs_referencing_historical.append(rows.cert_id)\n", "\n", @@ -1027,15 +1027,15 @@ "\n", "for _, cert in df[df[\"cert_id\"].isin(active_certs_referencing_historical)].iterrows():\n", " cert_id = cert[\"cert_id\"]\n", - " \n", + "\n", " for referenced_cert_id in cert[\"module_directly_referencing\"]:\n", " referenced_cert_id_int = int(referenced_cert_id)\n", " related_cves = get_cert_property(df, referenced_cert_id_int, \"related_cves\")\n", - " \n", + "\n", " if not pd.isna(related_cves):\n", " print(f\"Active certificate {cert_id} is referencing historical certificate {referenced_cert_id} with assigned CVE\")\n", " active_cert_referencing_historical_with_cves.append((cert_id, referenced_cert_id_int))\n", - " \n", + "\n", "active_cert_referencing_historical_with_cves" ] }, @@ -1074,12 +1074,12 @@ "source": [ "def get_cert_ids_referencing_lower_level_cert(level_referencing_certs_df: DataFrame, lower_cert_ids: set[str]) -> list[int]:\n", " cert_ids = []\n", - " \n", + "\n", " for _, cert in level_referencing_certs_df.iterrows():\n", " if cert[\"module_directly_referencing\"] & lower_cert_ids:\n", " cert_ids.append(cert[\"cert_id\"])\n", - " \n", - " return cert_ids " + "\n", + " return cert_ids" ] }, { @@ -1106,7 +1106,7 @@ } ], "source": [ - "LEVEL2: int = 2 \n", + "LEVEL2: int = 2\n", "below_level2_cert_ids: set[str] = cert_level_ids[1]\n", "level2_ref_certs = referencing_certs[referencing_certs[\"level\"] == LEVEL2]\n", "level2_referencing_lower_level = get_cert_ids_referencing_lower_level_cert(level2_ref_certs, below_level2_cert_ids)\n", @@ -1198,12 +1198,12 @@ "def get_embodiment_references(df: DataFrame, embodiment: str) -> dict[str, int]:\n", " result: dict[str, int] = {}\n", " sub_df = df[(df[\"embodiment\"] == embodiment) & (df[\"outgoing_direct_references_count\"] > 0)]\n", - " \n", + "\n", " for references in sub_df[\"module_directly_referencing\"]:\n", " for cert_id in references:\n", " referenced_embodiment: str = get_cert_property(df, cert_id, \"embodiment\")\n", " result[referenced_embodiment] = result.get(referenced_embodiment, 0) + 1\n", - " \n", + "\n", " return result" ] }, @@ -1230,7 +1230,7 @@ } ], "source": [ - "final_embodiment_statistics: dict[str, dict[str, int]] = {} \n", + "final_embodiment_statistics: dict[str, dict[str, int]] = {}\n", "\n", "for embodiment in df[\"embodiment\"].unique():\n", " final_embodiment_statistics[embodiment] = get_embodiment_references(df, embodiment)\n", @@ -1246,7 +1246,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1283,7 +1283,7 @@ "def get_type_references(df: DataFrame, cert_type: str) -> dict[str, int]:\n", " result = {}\n", " sub_df = df[(df[\"type\"] == cert_type) & (df[\"outgoing_direct_references_count\"] > 0)]\n", - " \n", + "\n", " for references in sub_df[\"module_directly_referencing\"]:\n", " for cert_id in references:\n", " referenced_type: str = get_cert_property(df, cert_id, \"type\")\n", @@ -1337,7 +1337,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1372,7 +1372,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1436,20 +1436,20 @@ ], "source": [ "result: list[tuple[int, int]] = []\n", - "cross_references_df: DataFrame = df[(df[\"incoming_direct_references_count\"] > 0) & (df[\"outgoing_direct_references_count\"] > 0)] \n", + "cross_references_df: DataFrame = df[(df[\"incoming_direct_references_count\"] > 0) & (df[\"outgoing_direct_references_count\"] > 0)]\n", "\n", "\n", "for _, cert in cross_references_df.iterrows():\n", " referenced_by = cert[\"module_directly_referenced_by\"]\n", " referencing = cert[\"module_directly_referencing\"]\n", " cert_id = cert[\"cert_id\"]\n", - " \n", + "\n", " intersection: set[str] = referenced_by & referencing\n", - " \n", - " \n", + "\n", + "\n", " for another_cert_id in intersection:\n", " another_cert_id_int = int(another_cert_id)\n", - " \n", + "\n", " if not (another_cert_id_int, cert_id) in result:\n", " result.append((cert_id, int(another_cert_id)))\n", "\n", @@ -1726,7 +1726,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1760,7 +1760,7 @@ "outputs": [ { "data": { - "image/png": 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A0dFR1/zkVZch40zflTmGLvvie+XFcf6qsRETE5NjHfHx8ShevDgA4Pbt26hQoYLu/QMAVapUyTPvq7yqVkPGqaFe3IbZ63hZXn1CQkKg1WoxadIk3bT09HQ4OjoiOjoa1atXz3Odxiyb12sTGhqKlJSUXJ+jhlw99bLX+/bt27muvqhSpUqeh1Oz81hZWeW44qxMmTK68/9CQkKQmZmJESNG6OanpKTAwcEB0dHRuuXKlStn0PYsXbo0xo4di9mzZ+PHH39E37594eHhkeP9qDQ2Fq+Q/ZdVXvr375/nOQt5sbKyeuV6hBG3F2nSpAmOHTuGw4cPw8/PD4MGDcLs2bN1x1XN6XWzvbicvufJTy3Z5zcY87q+KPuXy8OHD3NMj42NRZs2bfQuU7NmTZQsWRK3bt3Sew6GPu7u7lizZg0OHDgAe3t7dO3a1aDlnm8ygKwGdteuXTh16hR27dqFjz76CF26dMHatWsNer78ymvsPH8praEMGWd5vWeMXVbfuTCv0qZNGyxbtszgx7/OOrLlt9bXXdfzjM2b7dtvv33l2M5rvrHLajSafI33vJ7THMqWLZtrPL/ImM9Ib29vvP3227rzjTZv3oydO3caPQ4NxUMh+VC7dm3cuXMnx7RTp0698lCIMezs7FClShWEhobmmH7v3j3dyYZHjhxB0aJF0b9/f2zbtg2jRo3Czp07dY99fjDo21sAZGW5f/9+jmlXr17F8ePHdfP11VCjRg2j/wrKr7xqyX49TKV06dKoV68e/vnnH920hIQE3L17N8/GwsrKCu+++26ef/WPGzcu1wd09erV0ahRI+zbtw/79+/X7d41RHR0NM6ePQsg64TDBw8eoHXr1lixYgXWr1+P3377DY8ePdK7rKlf19q1awNArjGzZs0a3L59+6XLve44U2KMGjI2XlxHcHAw1q9fDwCoVasWYmJichwui4iIMHj9xjBknD7/eZCYmGj0L99X5c1rmczMTNy9ezfH9Llz5+b5flRi2WxOTk4oXrw4wsLCckzfunWrUXsTX1SrVq1cz/ngwYOXLlO7dm1kZGToDk0AQFxcHL777jvd/JiYmByf1dlXlaWnp7+yphc/7xMSEhAYGIiqVatiwoQJOHjwIKytrXHkyBGDMr4ONhb5MG7cOBw7dkx3HC8pKQmrVq165aGQ11nPnj17dH8pnzx5Erdv38Z//vMfAMD27dvx559/6h6fnp6ON954Q/d/e3t7xMfHIz09HQMHDtS7Dk9PTzx9+hQHDx4EkLV7f9myZbpDNv/3f/+HK1eu4MKFCwCyzuvw9/fPcYWEuYwbNw5Hjx7VfdCEhIQgMDAQY8eONfm6P/jgA+zevVv3gbZ9+3bUrl1bdza3PhMnToS1tTWWL1+u+2AQQmDHjh0ICQnRe4mbu7s7rl69ivj4eKP+qrhz5w78/PwAZF2pkP1hBWS9L8qWLZvnoSBTv67VqlWDu7s7tmzZojsf6MKFCzh8+HCuXdQv1vW640yJMWrI2Lh+/bpuDKalpWHNmjW6yy3d3d1hY2Oja2aSk5Pxyy+/5FrPlClTsGjRIoPr0seQcWpvb6871yj7ahhj1/GyvPq0atUKTZo0wYYNG5CZmQkAOHjwIP79999XXk6cn2WzWVtbY+TIkfjhhx90jcSJEydw5MiRPA9jGsLT0xMXLlzAlStXAABhYWE4efKkQXmyz5EDsq76i46OBgD069cPDg4O2LRpk27+tm3boNVqc1yunpcXP+8fP36M+fPn67azEAIZGRk5fkcoTSOU2FdkgU6cOIH169fr7ryZfYkjkHUtvbe3N86cOYO6detixowZCA4OxrZt2xAfH4+OHTti5cqVAIC9e/diy5YtsLW1hUajwbvvvpvniVm7du3Cpk2bEB4ejubNm8PHxwcTJ07MsZ7AwEDs3bsXANCnTx/dXeC2bt2KPXv2oHjx4rrLTVu3bg0A+PXXX/Hdd9/pLhGsUKECZs+erTs2eOjQIaxatQqlS5dG//798zxEcuXKFXz22WdIS0uDVqvFwIEDc1wyd/z4caxduxZarTbXZWybNm3Cjz/+iPj4eHTp0gVjxozBp59+isuXL6Nx48ZYsmQJNm3ahGPHjqFUqVJ45513ULduXaxcuRJBQUFo0aIF1q1bB29vb5w9exaOjo4YP348YmNjsWPHDsTHx6N9+/ZYtWoVgNyXRY4ZM0Z3QuzixYtx4MABAMCAAQMwZMgQzJgxQ1fLwoULcx2HBrJ+YYwePRrx8fEICgpC48aNUalSpVyHDn744Qf89NNPKF68OEqVKoUFCxboPRb9vJSUFGzevBl//vknihYtitTUVLz55puYMGGC7oTD58XExKBjx46YOXNmrnMnVq9eDX9/f4SFhaFx48Y55iUkJKBRo0b47LPPcOXKFaxbtw5Pnz5F0aJFkZmZialTp6JJkyZ51vmy1zX7EkQg6xDP/Pnzc/2CvnHjBubOnat7rV98vyUmJmLZsmU4c+YMKlSoAFtbW8ycORNOTk45LlVt3LgxWrZsiSlTpgB4+TibPHkyAgMDUapUKdSuXTvXyaaGLuvl5YX69etj8eLFuvfkmjVrYG9v/8qxERgYiFWrVkGr1aJo0aLo2bNnjkvbsy83tbKyQvny5dG2bVssXboULVq0wKJFi+Dk5IRBgwbB09Mzz/uoeHl54dKlS7qxYWVlhbVr1yI8PBxNmzbFtm3bALx8nAJZlwTPmjULZcqUQfPmzXWv8fOeH0M1a9bEhg0bdOcpvSpvXu+T2NhYLF68GDdv3kS5cuVQvnx5zJo1C+XKlcOJEydyfBa899576NWrl259xiy7bt06LFu2DEeOHEGpUqUwbNgwvP/++0hPT8eqVatw/PhxlClTBnZ2dpg7dy4qV66su9w0PDwcrq6u2LBhg+5y01e93j///DM2bNiAihUr6g593rx5M0fj8KLsy2dDQ0NhY2ODunXr4tNPP9XtVbp79y4WLVqEqKgolC5dGjVr1sSMGTNgY2OT4/eIq6srJk2apDtZF8j9eT9o0CD4+PjgwoULsLW1RWJiInr16pXrUnElFdrGgoiooDh69ChWrFiB3bt35+svaDKvtLQ0JCcno2TJkrpps2fPhhAi33ufLBkPhRARqezhw4f45ptv2FRYmDt37mDixInIyMgAkHXo6ffffzfp3W8tAfdYEBERvYZHjx5hwYIFuHfvHkqUKIHU1FR4enqysWBjQURERErhoRAiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSTIH9rpC+ffua7D7mREREZBoFtrGoVq3aa3/VLxEREamDh0KIiIhIMWwsiIiISDFsLIiIiEgxbCyIiIhIMWwsiIiISDFsLIiIiEgxbCyIiIhIMWwsiIiISDFsLIiIiEgxbCyIiIhIMWwsiIiISDFsLIiIiEgxbCyIiIhIMWwsiIiISDFsLIiIiEgxFtVYZGYKtUvIU0GujYiIyFyKqF2AMbRaDb744S+ERz9Ru5QcHCuWxvh326pdBhERkeosqrEAgPDoJ7gb/kjtMoiIiEgPizoUQkRERAUbGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUkwRc6zk+PHj2LFjB2rVqoXQ0FAMHDgQvXr1MseqiYiIyIzM0ljMnDkTK1euROvWrXHv3j306dMHnTp1grW1tTlWT0RERGZilkMhDg4OePjwIQAgJiYGVlZWyMzMNMeqiYiIyIzMssdi1apV8Pb2xsmTJ3HlyhWsWbMGNjY28Pf3h7+/v95loqKizFGa2YnMTGi0BffUFkPry8zMhLaA5jC0Nnm2hYBWqzFDRa/H0PrkeE8JaArwtjC0Pim2hciERlMwMwCG15cpBLSagvue0lefyRuL5ORkjB49GsuWLYObmxvu3LmDqVOnonnz5nB3d4e7u7ve5caNG2fq0lSh0Wpxx38znj18oHYpuZQoVxk13P/PoMdqtVpsPL4dEU8KVgNYpbQDxnYcbtBjNVotLn21EQkRBW9b2FWpDNcPxhr0WK1Wg70/nUZszFMTV2W88hVKYsDbLQ16rFarxYGdWxEXXbC2h33FyugzdLRBj9VoNQg9ch0pj5JMXJXxipe1gVP3egY9VqvVYv/+/YiLizNxVcaxt7dH3759DXqsRqNF0EU/JD2NNXFVxrMpWR51mww26LFajQa+Z28g6mnBe085lLSBZ3OXXNNN3lgEBwcjLi4Obm5uAIAaNWogOTkZf/31F3r06GHq1RdIzx4+wLOoe2qXkW8RT6IQ+vC+2mXkS0LEA8SHhqpdRr7FxjxFVMRjtcvIt7joB4iOCFO7jHxJeZSEZ7EJapeRb3FxcYiOjla7jHxJehqLhPiC1ai+jqinSQh/YjnvKZPvJ6patSrS09MRHh4OAEhISEBkZCQqV65s6lUTERGRmZl8j4W9vT0+//xzzJs3DzVq1MDdu3cxceJENGzY0NSrJiIiIjMzy8mbPXv2RM+ePc2xKiIiIlJRwT1lloiIiCwOGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlIMGwsiIiJSDBsLIiIiUkwRc6wkOTkZ69atQ0ZGBpKSkhAeHo6tW7eaY9VERERkRmZpLHx8fDBgwADUr18fAHDhwgVzrJaIiIjMzOSNRXJyMgICAlC7dm0cOnQICQkJGDlypKlXS0RERCoweWMRHh6O0NBQaDQaTJkyBZcvX4aXlxcOHTqEY8eOwd/fX+9yUVFRpi6NiIiIFGbyxiIxMREA0KtXLwBA48aNUaxYMZw/fx7u7u5wd3fXu9y4ceNMXRoREREpzORXhTg4OGStSPu/VRUtWhQpKSmmXjURERGZmVkai2bNmuHs2bMAsg5xxMXFoUmTJqZeNREREZmZWa4KWbFiBVasWIGTJ08iPDwcPj4+KFeunDlWTURERGZklsbC0dERq1evNseqiIiISEW88yYREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKYaNBRERESmGjQUREREpho0FERERKcaoxiI+Ph6RkZGmqoWIiIgsnN7GYtKkSXoffOXKFfTq1QubNm0yaVFERERkmfQ2FqGhoXof3K5dOwQGBuLXX381aVFERERkmfQ2FhqNJs8FkpKSkJKSYrKCiIiIyHIVyf5h/fr1+OKLL3QzXFxc8lyoW7dupq2KiIiILJKusWjRogUAQAiBnTt34p133sn94CJFULVqVfTo0cN8FRIREZHFyNFYZDcX//77LyZMmKBaUURERGSZ9J5jsWrVqpcu9Pfff5ukGCIiIrJsr3WDrOXLlytdBxEREUmgiL6JDx8+xIoVK/D3338jNjYWmZmZ5q6LiIiILJDexuK///0vbt++jS5duqBs2bLQav+3YyP75E4iIiKiF+ltLK5fv479+/ejZMmSehe6f/++SYsiIiIiy6T3HIuqVavm2VQAwLJly0xWEBEREVkuvY1Fr169cPDgwTwXGjFihMkKIiIiIsul91DIjRs3sGXLFnz11VeoWbMmSpQokWN+UFCQWYojIiIiy6K3sdi3bx8qVqyIhIQEXLlyJdf8pKQkkxdGRERElkdvY/Hmm29iz549eS40cOBAE5VDRERElkzvORazZs166UK8QRYRERHpo7excHNze+lCZ86cMUkxREREZNn0HgqJiIh46UI//vgjPD09TVIQERERWS69jUWXLl2g0WjMXQsRERFZOL2NReXKlfHRRx/lmJaYmIjbt2/jzJkz8PLyMktxREREZFn0Nha9e/eGh4eH3gVu376N77//3qRFERERkWXSe/LmtGnT8lygVq1auHTpkqnqISIiIgumt7F4mZCQEMTFxZmiFiIiIrJweg+FDB8+PNc0IQSePHmCf//9F++++67JCyMiIiLLo7exuHr1Kho0aJBjmpWVFWrXrg0vL688z78gIiKiwk1vY+Hk5IQdO3aYuxYiIiKycHrPsfj222/NXAYRERHJQO8eizJlygAAzp07h8DAQDx69Ahly5ZF+/btX3m7byIiIiq89DYWGRkZmDZtGg4cOAAhhG76pk2b0KdPH6xYsQJardEXlBAREZHk9DYWGzduxNmzZzF9+nS4urqiTJkyePz4MS5duoRvvvkGmzZtwrhx48xdKxERERVwehuLX3/9FTt27ICTk1OO6a6urujcuTPGjBnDxoKIiIhy0Xs8Q6vV5moqsjk5OcHKysqkRREREZFl0ttYpKam4tGjR3oXiIuLQ0pKikmLIiIiIsukt7Ho2rUr/vOf/+CPP/7Aw4cPkZ6ejocPH+LYsWMYPXo0unfvbu46iYiIyALoPcdi0qRJGDVqFD744ANoNJoc8xo3bpzrK9WJiIiIgDwaCxsbG/j6+mLfvn34888/dfexaNeuHfr164ciRfQuRkRERIWc3g7h0aNHuHDhAuzt7bFy5Urd9AMHDiA+Ph729vZmK5CIiIgsh95zLHx9fTFp0iTs3bs3x/TTp09j4MCBuHXrllmKIyIiIsuid4/FH3/8ga1bt6Jly5Y5ps+fPx+dO3fG8uXLsWnTJrMUSERERJZD7x4LIUSupiJbp06dEBMTY9KiiIiIyDLpbSyePHny0oVeNZ+IiIgKJ72NRa1atbB27VpkZGTkmJ6RkYE1a9agZs2aZimOiIiILEue97Hw9PTETz/9BBcXF5QuXRpPnjzBjRs3kJCQgO+//97cdRIREZEF0NtYNGjQADt27MDy5cvx119/ITMzE1qtFs2aNcO0adNQr149c9dJREREFiDPO101bNgQO3bsQHJyMp48eYLSpUvD2to6XyvbunUrli9fjps3b+breYiIiKhgeuUtNK2trfPdUABAcHAwTp8+ne/nISIiooJL78mbSktLS8Pq1avh7e1tjtURERGRSszSWKxfvx5eXl6ws7Mzx+qIiIhIJSb/NrELFy7g2bNnaN26Ne7fv59jnr+/P/z9/fUuFxUVZerSiIiISGEmbyyOHj2K+Ph4zJkzB4mJiQCAOXPmoG3btnB3d4e7u7ve5caNG2fq0oiIiEhhJm8sPvnkE93P9+/fh7+/PxYsWGDq1RIREZEKzHKOBZD1zajr1q0DACxYsAAhISHmWjURERGZicn3WGRr2bIlWrZsiWXLlplrlURERGRmZttjQURERPJjY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIopYuoVPHr0CMuXL4eNjQ00Gg3u37+PmTNnwsnJydSrJiIiIjMz+R6LyMhIFC9eHLNnz8asWbPQtm1bzJo1y9SrJSIiIhWYvLFwcXHB3Llzdf+vVq0aoqKiTL1aIiIiUoHJD4UAgEaj0f187NgxDBs2DADg7+8Pf39/vcuw+SAiIrI8ZmkssgUEBCA5ORkjRowAALi7u8Pd3V3vY8eNG2fO0oiIiEgBZrsqJCAgAEePHsXSpUtz7MEgIiIieZilsTh48CD+/PNPLFiwAFZWVli0aJE5VktERERmZvJDIUFBQZgyZQrKli2LgwcPAgCePn3KK0OIiIgkZPLGom7durh+/bqpV0NEREQFAO+8SURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREimFjQURERIphY0FERESKYWNBREREiilijpVERERg0aJFKF++PKKiojBlyhQ4OzubY9VERERkRmZpLObNm4eBAweiT58+uHTpEqZOnYpff/3VHKsmIiIiMzJ5Y/Ho0SOcOHECq1atAgC4uroiKioKN27cgIuLi9HP51ixtNIl5puxNZUoV9lEleSPsXVVKe1gokpen7E12VUpmNvC2LrKVyhpokryx9i67CsWvO1hbE3Fy9qYqJL8MbYue3t7E1Xy+oytyaZkeRNVkj/G1uVQsmC+p/KqSyOEEKZc8T///ANPT09cvHhRN61v3774+OOPkZKSAn9/f73LXb9+HfXq1TNZXVFRUXBwKHi/GI3FHAWHDBkAOXLIkAFgjoJEhgyAmXIIE7t27ZpwdXXNMa1Pnz7i8OHDpl71S40dO1bV9SuFOQoOGTIIIUcOGTIIwRwFiQwZhDBPDpNfFVKlShU8e/YMiYmJumkPHz6Eo6OjqVdNREREZmbyxqJs2bJo3749jh8/DgC4dOkSKlSoYNLDHERERKQOs10VsmjRIpw6dQqRkZFYsWKFOVZLREREZmaWxsLR0RFfffWVOVZFREREKuKdN4mIiEgxhbaxcHd3V7sERTBHwSFDBkCOHDJkAJijIJEhA2CeHCa/jwUREREVHoV2jwUREREpzywnb5Iyfv75Z/Ts2ROlSpVSu5R8SUxMhI2NDTQaDaKjo3Hy5Ek4OTmhSZMmapdmlLCwMBw8eBCRkZGws7NDzZo10adPHxQrVkzt0gwmw7aQZVwAWff42b9/P+7fvw8g68T3vn37onz5gnlran1kGBfh4eEoXbo07OzscOPGDRw6dAgpKSlo27Yt2rdvr3Z5BlFzXBT6PRY7d+5UuwSDLVmyBB9//DFmz56NoKAgtct5LTt37oSbmxt69+6NkJAQDB48GL6+vpgwYQJ8fX3VLs9gBw4cwMiRI/H333/j4MGDuH//Pg4dOoS+fftazLaRZVvIMC4A4NChQ/Dw8MC5c+eQnp6O9PR0nD9/HoMHD8bBgwfVLs8gMowLAJgxYwaioqJw8uRJfPDBB4iNjUVycjJWrFiBb7/9Vu3yDKLquDD5vT0LgICAgDz/WdJtWtu2bSuEEOL69eti5syZYtSoUeLAgQMiPT1d5coMN2rUKHHv3j1x/Phx0a9fPxEcHCyEECIxMdGitsW0adN0r/vjx4/FF198IYQQIjIyUnh7e6tZmsFk2RYyjAshhPjwww9FYmJirukJCQnigw8+UKEi48kwLoQQYvHixUIIIebNmyfi4+N10zMyMsTUqVPVKssoao6LQnEoZMaMGbCxsYHQc55qfHy8ChXlj4uLC5YsWYLHjx/jp59+wnvvvYcOHTrgww8/VLu0V3J2dka1atVQrVo17Nu3D7Vr1wYA2NjY4I033lC3OCPY29vDysoKAFC6dGndLesdHBwK5LdC6iPLtshmyeMCyLpLsY1N7m+LtLW1RdmyZVWoyHgyjAsA0Gg0AIBSpUqhZMn/fUOvVqu1mG2RTY1xUSgaiylTpqBChQro2LFjrnkLFy5UoSJllClTBmPGjMH777+Po0ePql2OQWJjY5GRkQErKytMnjw5x7yYmBiVqjJeeHg49u/fjzfeeAOnT5/WNa1hYWEWs8tXlm3xIkscF9nmz5+PTp066c6piI2NRUBAgLpFGUGGcQEA1apVw7Zt21C1alXMnDkTPXr0gFarxblz55Camqp2ea/FnOOiUJxjMWTIECQkJOidN2zYMDNX8/psbW2xaNGiXNO1Wi26d++uQkXGq127NtauXQsg6wvqACAhIQHvvPMOihcvrmZpRpkyZQq2b98OT09PnD59GmPGjAEAHDt2DL169VK5OsPIsi1kGBcAMHfuXFStWhUrV67E8OHDMXz4cKxcuRJVq1bF3Llz1S7PIDKMCwDw9PSEo6Mjfv31V5w4cQKTJ0/GkiVLULRoUcycOVPt8gyi5rjgfSyIiIhIMYXiUAgACCFw5coVPHjwAHZ2dqhRowa/ul1FQUFBukvqqlatirp166pcUeHz999/o2XLltBqLX/HZUJCAk6cOKEb37Vq1YKbm5vaZRlNhhxhYWEAsg4nREVF4eDBg3B0dLSovUeAHDlSU1MRGBiY4/LlDh06mPzS30KxxyI4OBjjx4/HgwcPkJGRAScnJ8TFxaFly5ZYtGgRSpcurXaJ+bZz504MHTpU7TJeKSQkBFOnTkVUVJTuOPLDhw9RsWJFrFixAs7OzipXmH+Wsi0aN26MevXqoUOHDhg6dKhFnVz3vDNnzsDb2xs2NjaIiIhA06ZN8ejRIxQtWhTr1q2zmD8gZMjx3XffYdmyZcjMzMSCBQvg6+uLChUq4M6dO+jdu3euc3kKKhlynD17Ft7e3qhYsWKOc3aio6OxcuVKtGjRwnQrN/l1JwXA5MmTxb///iuEEOLs2bPC19dXZGRkiN9++018+umnKldnOBkumx07dqy4dOlSrukXL160mAxCyLEt2rZtKzIyMsThw4fF+++/L6ZPny4uX76sdllG8/b21l0SGBoaKjZv3iyEEOLatWvik08+UbM0o8iQY+LEieLZs2ciIiJCDBgwQDx8+FAIIUR6eroYP368ytUZToYcY8eOFZGRkbmmR0ZGmvwzqlAcCnFwcECNGjUAAG5ubggMDIRWq0WPHj1w6tQplasznAyXzTo4OKBx48a5pru6usLBwUGFil6PDNsC+N+JXN27d0dISAh8fX2xcuVKDBo0CAMGDFC7PIM4ODjoLgmsXr06njx5AgCoX7++Rd2NU4Ycjo6OsLa2RuXKleHi4qLbC2ZlZYWqVauqXJ3hZMhRsWJFvZ+pDg4OqFixoknXXSgai/DwcMTExKBChQq4du0aHj9+DCDrvIvo6Gh1izOCDJfNxsbG4tChQ+jYsSNKlCgBAHj27BkCAgIQGxurcnWGk2FbvKh27dqYP38+nj59Cj8/P7XLMVhYWBguX74MJycnnD59Gs+ePQMAJCUl4c6dOypXZzgZcoSEhGDXrl2IjY3F3bt3sW/fPrRv3x63bt3C7du31S7PYDLkSEhIwJYtW/Revvz06VPTrtyk+0MKiOPHjwtXV1fRpEkT0aZNG90dBhcvXiymTZumcnXG8ff31zv91q1bZq7k9URERIgxY8aIunXriiZNmogmTZoIFxcXMWbMGBEeHq52eUax9G3RqFEjsXDhQrXLyLfLly+Ldu3aibp16wp3d3cREREhhBDCx8dH+Pj4qFyd4WTIcfnyZfHWW2+JiRMniri4ODF06FBRp04d0bp1a3H27Fm1yzOYDDni4+PF/PnzRbNmzUTdunVF3bp1RbNmzcT8+fPFkydPTLruQnHyJgA8efIEYWFhqFmzpt6725F5PX78GBEREQCy7qFQpkwZdQsii/fo0SOLuyuiPrLkyBYXF4eyZcvq7mZpqSw5R/ZhNXNdqFBoGgsg69j387/MLOW45Yt4qWbBkJqaiszMTFhbW+PZs2f466+/ULlyZdSvX1/t0oyWnJyM8+fPIyUlBa6urhZ7hYgsLHl76BsXVapUQb169dQuzSiy5FCFSfeHFBCRkZHigw8+EC4uLsLV1VW4uroKFxcXMW7cOL1nzRZUwcHBon///qJly5aib9++om/fvqJVq1aif//+4ubNm2qXl29///232iUY7NChQ6JBgwbC1dVVBAQEiMGDB4u+ffsKNzc3sX37drXLM8jo0aPFxYsXRWhoqOjQoYPo1q2b6N27t2jVqpU4efKk2uUZLDQ0VHh6eooePXqIbdu26aYvWrRIuLq6qliZcWTYHjKMCyHkyKHmuCgUjcWECRPE/v37c3xzYGJiovD39xcffvihipUZR4ZLNZOSkvL8N2fOHLXLM9gHH3wg7t27J86dOyfc3d3FnTt3hBBCPH361GK2RfbrvWDBghyXmUZGRoqJEyeqVZbRxo0bJ3bs2CECAgLEnDlzxPTp00VmZqYQ4n/f8GgJZNgeMowLIeTIoea4KBRXhdjb26NPnz45ptnY2KBv3744c+aMSlUZT4ZLNZs3b46MjIxc04UQ0Gg0mD9/vgpVGc/JyUn3zaCNGjXSfRuonZ2dxXwzaPa5Rra2tmjUqJFuuoODg0XcjCmbo6MjPD09AQAdO3ZEQEAA5s6diwULFqhcmXFk2B4yjAtAjhxqjotC0VhERUXh6tWraNiwYY7pV65cQWRkpEpVGU+GSzVHjhyJFi1aoFatWjmmCyGwfv16laoy3q1bt3DmzBnExsYiKCgIFy5cQNOmTfHgwQOLuTTQysoKN27cQPXq1bFr1y707NkTVlZWOHv2LKKiotQu77V16tQJGo0GixcvVrsUo8iwPWQYF4A8OZ5n1nFh0v0hBURQUJBwd3cXrVu3Fu7u7jl+vnHjhtrlGUyGSzWTkpLEhg0b9M47evSomat5fQEBAaJNmzbCw8ND3L17V3Tt2lU0adJENGrUSBw+fFjt8gySnJwslixZInr06CHq1KmjuyTtvffes5j3kxBCzJgxQ8yfPz/X9AMHDoh69eqpUNHrkWF7yDAuhJAjh5rjolBdFXL9+nWEh4cDyNpNZKln9/JSzYInJSUFwcHBcHR0tKgz+IGs2u/fv4/ExERUqlTJ5HflU9rjx48RGRmp9+qomzdvok6dOipU9fosfXs8z5LHxfMsMYea46JQNBYpKSkoXry42mUoRpbLZrOJ/39+BanvwYMHKFeunMm//dAUZBkXMuSQIQMgTw5zKxSNRaNGjdC9e3cMGzYMzZo1U7uc1xYVFYX58+cjICBA1yilpKSgY8eOmDdvnkWcwHn79m2sWbMGLi4uGDt2LKZMmYIjR47A0dERq1evhouLi9ol5tupU6fQqlUrtct4pXPnzmHJkiWoV68ePv30U4waNQqXLl2Cra0t1q9fj9atW6tdokFkGBeAHDlkyADIkyMvpv6MKhSNRbt27eDr6wtfX1/cvHkTAwcORL9+/Szur7KJEyeid+/e6NSpk+4M8qSkJPzxxx84cOAAvvjiC5UrfLVRo0bB1dUVoaGh0Gq1cHR0hLu7O4KDg7Fnzx5s2rRJ7RINkv09Dvp89tlnFnF1y8iRI9GvXz/cvXsX169fR69eveDu7o6QkBCsXbsWW7ZsUbtEg8gwLgA5csiQAZAjh5qfUYXiqhAAeOONNzBr1iwkJSVh9+7dGDFiBJo1a4Zhw4ahSpUqapdnEBkum33jjTfw0UcfQQiB9957DytWrAAAvPnmm7hw4YLK1RlOhstmnZ2dMXjwYADA+++/j7feegtA1h6+F6/aKchkGBeAHDlkyADIkUPNz6hC01hks7GxwXvvvYf33nsPJ0+exNKlS7Fu3Tq1yzKIDJfNFimS9ZbTaDTw8vJSuZrXJ8Nls+np6bqfR48enWNecnKyuct5bTKMC0COHDJkAOTIoeZnVKFoLJKTk3HgwIFcHWibNm3Qpk0blaoy3uTJkzF16lQ8fPgQ5cqVAwDdz9l/+Rd0kZGR2L59O4YPH47evXsDANLS0rB7926EhYWpXJ3hxo8fj+3bt6NDhw655vXo0UOFioz35MkT+Pj4wNvbW3c+RUpKCr788kuLuS8KIMe4AOTIIUMGQI4can5GFYpzLM6cOYMiRYqgadOmapeiCEu+bPbJkydITk7OcfJTSkoKDhw4ABcXF36hmhmJrFv6Q6vV6qalpaXh4sWLqFq1qsUcIsxmyePieTLkkCEDIE8OcysUjcWLfvvtN/Ts2VPtMvJNhhyXLl2Cq6ur2mXkmwzbQoYMAHMUJDJkAOTIYc4M2lc/RD4+Pj5ql6AIGXJMnz5d7RIUIcO2kCEDwBwFiQwZADlymDNDoWwsZNlJI0MOGTIAcuSQIQPAHAWJDBkAOXKYM0OhbCy8vb3VLkERMuQYNmyY2iUoQoZtIUMGgDkKEhkyAHLkMGeGQneORXJyMs6fP4+UlBS4urpazH3fnxcUFIT79+8DAKpWrWpRJzzeu3cP1atXV7sMRWVmZuLmzZtITk5GnTp1dDfUsURHjhxB9+7dcfv2bYu6l4UQAleuXMGDBw9gZ2eHGjVqWMxXjROZkhq/LwrF5abvv/8+JkyYAHt7e3h5eaF48eIoUqQIHj16BB8fH4u5dXFISAimTp2KqKgolC9fHkDWJVAVK1bEihUr4OzsrHKFr9anTx+4ublh0KBB6N27N4oWLap2Sa9l3rx5mDdvHuLi4jBixAhERESgaNGisLKywhdffGGxJ6T+9ddf6N69O3744QfMmjVL7XIMEhwcjPHjx+PBgwfIyMiAk5MT4uLi0LJlSyxatAilS5dWu8R848mDBYel5FDz90WhaCwcHR3h6uqKhQsXYt26dWjUqBGArJugLF682GIai5UrV2LBggVo3LhxjumXLl2Cj48PNmzYoFJlhitTpgzWrVsHPz8/eHl5oU2bNnjnnXcs7hscf//9d8ybNw+bN2/GRx99hO7duwPI+utg9erVFrEt6tatq/fL33788UdoNBqLaSw2bNiATZs2oUaNGjh37hxu3ryJd999F7///juWL1+OxYsXq12iQW7dupXnvCNHjljELzMZMgBy5FDz90WhaCyyd03b2trqmgoAcHBwsKjdpQ4ODrneJADg6upqUV+KU7JkSYwcORIjRozAiRMnMGfOHNjY2GDYsGFwc3NTuzyjZTcVQNYvaycnJxWrMdwnn3yCM2fOYMqUKbC1tYUQAqtXr8bkyZMt5m60QNa4qFGjBgDAzc0NgYGB0Gq16NGjB06dOqVydYZ76623kJycrPckO41Gg88//1yFqowjQwZAjhxq/r4oFI2FlZUVbty4gerVq2PXrl3o2bMnrKyscPbsWURFRaldnsFiY2Nx6NAhdOzYESVKlACQ9UUzAQEBFnWnxGwajQYdO3ZEx44dcffuXfj6+lpcY1GsWLEcd+cDsnY3WoLRo0ejQ4cOWLNmDYYPH44WLVrAzs4Ojo6OGDRokNrlGSw8PBwxMTGoUKECrl27hsePHwPIOu8iOjpa3eKMMH78eLi5uek9jLZw4ULzF/QaZMgAyJFDzd8XheLkzZSUFPj4+CAgIAChoaG63b/NmjXD8uXLLeYOgw8ePMC8efNw4sQJ3RslOTkZ7du3x9y5cy0iR5MmTTBhwoRc301haWbOnKn7uWvXrujWrRvu3r2LTz75BLVr18aSJUtUrM446enpWLduHVJSUvDkyRMsXbpU7ZKMcuLECUyaNAkajQYlSpTAt99+q9sGT548wbJly9Qu0SDp6en46aef9F4pdf78eTRr1kyFqowjQwZAjhxq/r4oFI1FtpSUFNy/fx+JiYmoVKmSxR3Xz/b48WNEREQAAKpUqYIyZcqoW5ARwsPDYWVlhUqVKqldCr3g4sWLOHr0KKZOnap2KUZ78uQJwsLCULNmTYu+KodIaWr8vihUjYUs4uPjc7xRSpUqpXJFxpMhAxUssbGxustNHR0dUaxYMbVLei0yjA0ZMgBy5FAjQ6E4x+JlTp06hVatWqldhkGioqIwf/58BAQEoHjx4gCy9sJ07NgR8+bNs4gTOGXI8CqW9J7KiyVliIyMxCeffIKzZ89Co9GgePHi0Gg0GDp0KLy9vS2mwZBhbMiQAZAjh6oZRCGQlJSU5785c+aoXZ7BJkyYIPbv3y8SExN10xITE4W/v7/48MMPVazMcDJkEEKO95QMGYQQwtvbWwQGBorIyEixe/dusXv3bhEXFye++eYbsWDBArXLM5gMY0OGDELIkUPNDIVij0Xz5s2RkZGRa7oQAhqNBvPnz1ehKuPZ29ujT58+OabZ2Nigb9++OHPmjEpVGUeGDIAc7ykZMgBAxYoV0a5dOwDAwIEDsWbNGpQtWxYjR47E3LlzVa7OcDKMDRkyAHLkUDNDoWgsRo4ciRYtWuS6RbEQAuvXr1epKuNFRUXh6tWraNiwYY7pV65cQWRkpEpVGUeGDIAc7ykZMgBZZ7+npqaiWLFiiIyMzHG5b1JSkoqVGUeGsSFDBkCOHGpmKBSNxfjx47F9+3Z06NAh17wePXqoUNHrmTx5MqZOnZrjvgnZP69YsULl6gwjQwZAjveUDBkAoFOnTmjfvj0qVqyI6OhofPnllwCARYsWISYmRuXqDCfD2JAhAyBHDjUz8KoQC/TPP//ozvJ1dHREvXr1VK7IeDJkoILj1q1buH37Nho1aoTKlSurXU6+yDA2ZMgAyJFDjQyFrrF4+vQpgKzbSlsyGXLIkAGQI4cMGQAgLi4OdnZ2FnMlSF5k2B4yZADkyGHuDIXiUEhiYiJWr16NPXv2ICEhAUDWC9y/f398/PHHsLOzU7lCw8iQQ4YMgBw5ZMgAZH1oLl68GAcPHkRqaiqArL/MRowYAS8vL5WrM5wM20OGDIAcOdTMUCj2WEyZMgV16tRB586dcxxr+uOPPxAUFAQfHx+VKzSMDDlkyADIkUOGDEDW7dVdXV3x5ptv4vTp0yhfvjzq1auHPXv2wM7ODh9//LHaJRpEhu0hQwZAjhxqZigUeyxsbW0xZsyYHNPs7e1Ru3ZtzJkzR6WqjCdDDhkyAHLkkCEDAJQpUwZDhw4FkPX9P2vXrsXbb7+NBg0a4L///a/K1RlOhu0hQwZAjhxqZtCa9NkLiOjoaL1nh0dHR1vUt5vKkEOGDIAcOWTIAOT8NtnExMQc39yo1VrOR5wM20OGDIAcOdTMUCj2WIwcORIeHh6oVKkSypcvDyDrewUiIyMt5tIhQI4cMmQA5MghQwYAqFOnDvr06YPq1avjxo0buptirVq1Cv/884/K1RlOhu0hQwZAjhxqZigU51gAWfdIP3HiBMLDwwFkndzVoUMH3T3ULYUMOWTIAMiRQ4YMABAYGIiQkBC4ubmhUaNGALJujqXVamFtba1ydYaTYXvIkAGQI4daGQpNY0FERESmZzkHIPNpz5492LhxY45bmS5evBgzZ85UsSrjyZBDhgyAHDlkyPAylnL75WwybA8ZMgBy5FArQ6HYY7FixQqcOnUKVatWxfXr1zFr1ix07NgRANCuXTv8+eefKldoGBlyyJABkCOHDBleZdGiRZg1a5baZRhEhu0hQwZAjhxqZigUJ29GRkbCz88PQNZZ4wsXLoQQAp06dVK3MCPJkEOGDIAcOWTIAABDhgzJc96DBw8sprGQYXvIkAGQI4eaGQpFY1GpUiXdz7a2tvjss88wb948izqpC5AjhwwZADlyyJAByPra9JYtW6JUqVK55v36668qVPR6ZNgeMmQA5MihZoZCcY7F/fv3cfXq1RzT5syZg7179+pudWoJZMghQwZAjhwyZACAWbNmISIiAh4eHrn+6fvm1oJKhu0hQwZAjhyqZhCFwMmTJ8WSJUtyTU9LSxPLli1ToaLXI0MOGTIIIUcOGTJky8zMVLuEfJNhe8iQQQg5cqiZoVCcvPmiyMjIHLuJLJUMOWTIAMiRQ4YMAHMUJDJkAOTIYc4MheJQyItGjBihdgmKkCGHDBkAOXLIkAFgjoJEhgyAHDnMmaFQNhay7KSRIYcMGQA5csiQAWCOgkSGDIAcOcyZoVA2Fl27dlW7BEXIkEOGDIAcOWTIADBHQSJDBkCOHObMUCjPsSAiIiLTKBR7LGbOnIk1a9bkmr5o0SJ8+OGHKlT0emTIIUMGQI4cMmQAmKMgkSEDIEcONTMUisaiWLFimDRpEtavX4/169dj7969ALKuf69cubLK1RlOhhwyZADkyCFDBoA5ChIZMgBy5FAzQ6FoLDQaDQBg4MCB+Oeff9C+fftc8yyBDDlkyADIkUOGDABzFCQyZADkyKFmhkLRWGSrWrUqbGxsYG9vr3Yp+SJDDhkyAHLkkCEDwBwFiQwZADlyqJGhUHxXyIMHD3D8+HEAQHR0tO5nIOu2p5ZChhwyZADkyCFDBoA5ChIZMgBy5FAzQ6G4KqRp06YoU6aM3nnx8fE4d+6ceQt6TTLkkCEDIEcOGTIAzFGQyJABkCOHqhlMesPwAmLFihV5zvPx8TFjJfkjQw4ZMgghRw4ZMgjBHAWJDBmEkCOHmhkKxTkWjo6Oec6bPHmyGSvJHxlyyJABkCOHDBkA5ihIZMgAyJFDzQyF4hyLr7/+Gvfv38fgwYNRs2ZNtct5bTLkkCEDIEcOGTIAzFGQyJABkCOHmhkKxTkWP/74I9q3bw8/Pz+EhoaiXbt26N27N6ytrdUuzSgy5JAhAyBHDhkyAMxRkMiQAZAjh5oZCkVj8bzMzEwEBgbi4MGDsLOzg4eHB+rXr692WUaTIYcMGQA5csiQAWCOgkSGDIAcOcydoVCcY/E8rVaL1q1bo1WrVjh79izeeusttUt6LTLkkCEDIEcOGTIAzFGQyJABkCOHuTMUinMs/vnnH9SvXx83btzAzz//DH9/f13X9uWXX6pdnsFkyCFDBkCOHDJkAJijIJEhAyBHDjUzFIpDIR4eHtBoNAgJCUGXLl0wZMgQtGvXzmJuzZpNhhwyZADkyCFDBoA5ChIZMgBy5FAzQ6HYY/H48WOMGDEC/fv3t+hbs8qQQ4YMgBw5ZMgAMEdBIkMGQI4camYoFOdYDB48GDdv3sT169d1027fvo1NmzapWJXxZMghQwZAjhwyZACYoyCRIQMgRw41MxSKxuLWrVuoU6cOWrRooZvm6OiI2NhYi3qjyJBDhgyAHDlkyAAwR0EiQwZAjhxqZigUjYWVlRVGjhyJYsWK6aZZW1vj008/RXBwsIqVGUeGHDJkAOTIIUMGgDkKEhkyAHLkUDNDoWgsbG1t85xXsmRJM1aSPzLkkCEDIEcOGTIAzFGQyJABkCOHmhkKRWORkJCQ57z4+HgzVpI/MuSQIQMgRw4ZMgDMUZDIkAGQI4eaGQpFY1G/fn18/vnnSE5O1k179uwZVq1ahWbNmqlYmXFkyCFDBkCOHDJkAJijIJEhAyBHDjUzFIr7WAghsHDhQuzatQuVKlUCAERFReGtt97Cp59+qnJ1hpMhhwwZADlyyJABYI6CRIYMgBw51MxQKBqLbOHh4bh69So0Gg0aNmyIKlWqqF3Sa5EhhwwZADlyyJABYI6CRIYMgBw51MhQqBoLIiIiMq1CcY4FERERmQcbCyIiIlIMGwsiIiJSDBsLIiIiUgwbCyIiIlJMofjadCJSXmhoKLZu3YpLly5Bo9EgISEBdnZ2aN68Obp27Yrg4GBs374d9+/fR9myZdGwYUNs3rw5x3MsWbIEv//+O54+fQpXV1d4eHhg48aNuH37NooWLYrq1avnWu+DBw8wY8YMDBo0yFxRicgIvNyUiIx2+PBhTJ8+HZMmTcKwYcN0X3QUHByMTz/9FFevXsUXX3yBJk2aoGPHjqhTpw78/Pz0PteCBQvg5OSEESNG6KZ16dIFjo6O2LFjR67Hz5gxAy1atGBjQVRA8VAIERklODgYU6dOxfDhw3N9e6KzszM2btwIa2trAEC5cuXQqVMnXLt2DUFBQbmeKyUlBb/99hv69+9v8PpHjhyJNm3a5D8IEZkEGwsiMsqXX36J1NRUeHl56Z1frlw5jB8/Hg4ODgCAwYMHAwB++eWXXI89fPgwmjdvjrJly75yvadPn4aXlxfq1q2ru0UxERU8bCyIyGCZmZk4ceIEqlativLly+f5uDFjxqBhw4YAgA4dOqBChQrYt28fUlNTczzul19+wZAhQ0xaMxGZF0/eJCKDPXr0CImJiXB2djZ4GSsrK3h4eGDTpk34/fff0adPHwBAWFgYwsLC8jysce3aNQwYMED3/6SkJO6pILIA3GNBRAbTaDSvtZy+wyF+fn7w8PCAVqv/Y6hBgwbYu3ev7t+iRYtea91EZF7cY0FEBitTpgxsbW0RGxtr1HJvvPEGmjVrhr///hvh4eGoXLky9u7dC19fX4Ofo2XLlmjZsqWxJRORmXGPBREZTKvVokOHDrh//z5iYmLyfNypU6dw+/btHNOGDBmCzMxM7Nq1C4GBgahRowYcHR1NXTIRmRkbCyIyysSJE2FtbZ3n3oaLFy9ixIgRePjwYY7pvXr1gq2tLXbt2oWff/75tU/a3Lx5c64bbRFRwcHGgoiMUqtWLaxZswa+vr749ttvc1zpcfbsWXz00UcYNWoUWrRokWM5Gxsb9O7dGxERETh79iy6dev2WutPTk5GcnJyvjIQkenwzptE9FpCQ0OxefNmXLx4EUWKFIEQAuXKlcOwYcPQvXt3vctcvHgR77zzDry8vDBr1qxc8/fs2YO1a9ciMjISVlZWqFChQq7HxMfHY8SIEZg4caLimYgo/9hYEBERkWJ4KISIiIgUw8aCiIiIFMPGgoiIiBTz/wCwTMJ4LzvEnQAAAABJRU5ErkJggg==\n", + "image/png": 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", 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" ] @@ -1825,7 +1825,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1859,7 +1859,7 @@ "outputs": [ { "data": { - "image/png": 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hvY8vyvtgS01N1Xs5Pj4+WLduHWJjY2FjY4P79++jZs2aOqd9/kqB59dDAOjWrRv27t2LDh06oF27dujatStat25d4HL1fV31ldcUPP/8Qgh89dVX0voCAC4uLlITVVBd+mxnuq6g0XfeF9eVF7fzl20bBXl+O3/xef/9918A0HrevOaqMF62rt++fRtOTk5a62iVKlWQnZ2NO3fuGPyHqly5clrvH5D7GuXk5EhNEJB7laSzszNiYmJ0Xg0XHh6O9PR0BAQEYNu2bdJwR0dH6bBnnhfXJUPmfX690PXevrjO5/3Rz/vs3bVrl9Y5ZpmZmUhOTgYA3L17N9/VMpUqVSrwMHHeMs3NzbVek7Jly0oNqr6vpa73QRc7OzuMGDECM2fOxM8//4wuXbqgZ8+e+Q7hvW5sUIwg77+bgnTr1q3AczoKYm5u/srlCANucdOgQQMcO3YMhw4dws6dO9GrVy/MnDlTOj/EmAqb7cX5dD1PUWrJO//DkNf1RXl/TB4/fqw1PC4uDk2bNtU5T9WqVWFra4s7d+7oPEdFFx8fH6xevRrBwcFwcHBAu3bt9JrvxcsaHRwcsGvXLoSGhmLXrl347LPP0LZtW6xZs0av5yuqgrad5y+h1pc+21lB64yh8+o6V6gwXqxHo9G8dP0rynJf97qu77KeZ29vX6hLa7/44ot8TcKLClqXDJ23MK/xkCFDXvvJ4K+iz2tpyGfkhAkT8NFHH0nnY23cuBG//PKLwduhIXiIR2bVq1dHWFiY1rDQ0NBXHuIxROnSpVGpUiWEh4drDb9//750Uufhw4dhYWGBbt264ccff8SQIUPwyy+/SNM+v1Hq2nsB5GZ58OCB1rBr167hxIkT0nhdNbzzzjt6dfGvU0G15L0excXOzg7vvfce/v77b2lYUlIS7t27V2CDYm5ujv79+xe4F8Lf3x+LFy/WGlalShXUrVsXgYGB2L9/P3x8fPSuMSYmBhcuXACQe2Lno0eP0KRJEyxduhTr1q3DwYMH8fTpU53zFvfrWr16dQDIt82sXr0ad+/efel8hd3OXsc2+qptozDy9uBERERIwx49elTo53uZ6tWrIzY2Vmsv3v3792Fubi4dpnr+MyI9PR2ZmZmFWsbzny95V65lZWXpnMfV1RWWlpb53p+tW7dK63BBijLvi3U//x4AuSfv37lzR/rsfXEZwcHBOHjwIACgWrVq+eZ/1ftYvXp1ZGdnS4dcAODJkyfSnqDCvJbPe/HzPikpCSEhIahcuTJGjx6NAwcOwMrKCocPH37lcxUFGxSZ+fv749ixY7h58yaA3MtUV65c+cpDPIVZzp49e6T/3M+cOYO7d+/ik08+AQBs3rwZp06dkqbPysqSjqkCuf9JJyQkICsrCz169NC5DF9fXyQmJkrHbzMyMrB48WJpF/V//vMfXL16FZcuXQKQe95LUFCQ1hUtxuLv74+jR4/i3r17AHJ3iYaEhGDEiBHFvuyRI0di9+7d0h/5zZs3o3r16tLZ97qMGTMGVlZWWLJkifQBI4TAli1bcPv2bQwdOjTfPD4+Prh27RoSEhIM+i8nLCwMO3fuBACcOHFCa/d3VlYW7O3tCzzEVdyv61tvvQUfHx98++230vlSly5dwqFDh/IdwnyxrsJuZ69jG33VtlEYVatWRfPmzbF582bk5OQAgNY/FXl27Nih1+XlL9O1a1eUL19e+o88MzMTmzdvRu/evaXDOw4ODtI5KwsXLsTp06cNXoazszMCAgKkYT/++CPMzMwKfJ2srKzg5+eHbdu2Scu+d+8eNm/e/Mrze4oy7/Py1vn79+8DyL2Cb9myZdJtBPI+ex8+fAggt5FYt26d1Gz7+vri0qVLuHr1KoDchvNVjauXlxcaNGggnUMI5F6lGRMTA6Bwr+XzXvy8f/bsGebMmSMdlhJCIDs7W+tvRHHQiOLah/cGOHnyJNatWyfdSTbv0lYg9/K/CRMm4Pz586hZsyamTJmCW7du4ccff0RCQgJatWqF5cuXAwD27t2Lb7/9FjY2NtBoNOjfv3+BJ6ju2rULAQEBiIyMROPGjbFixQqMGTNGazkhISHYu3cvAMDb2xvTp08HAHz33XfYs2cPLC0tpcuM807G2rdvH7Zt2yZdGurk5ISZM2dKx6X/97//YeXKlbCzs0O3bt0KPPRz9epVLFq0CJmZmTAzM0OPHj3Qv39/afyJEyewZs0amJmZ5bvMOCAgAD///DMSEhLQtm1bDB8+HNOmTcOVK1dQr149LFiwAAEBATh27BjKlCmDfv36oWbNmli+fDlu3rwJDw8PrF27FhMmTMCFCxfg4uKCUaNGIS4uDlu2bEFCQgJatGiBlStXAsh/Oezw4cOlE4+/+uorBAcHAwC6d++O3r17Y8qUKVIt8+bNy3c+DZD7h2fo0KFISEjAzZs3Ua9ePVSoUCHfIZGffvoJO3bsgKWlJcqUKYO5c+fmO0b+ovT0dGzcuBGnTp2ChYUFMjIy8O6772L06NE6zz2IjY1Fq1atMHXq1HznlqxatQpBQUGIiIhAvXr1tMYlJSWhbt26WLRoEa5evYq1a9ciMTERFhYWyMnJweeff44GDRoUWOfLXte8S1qB3D+wc+bMyfeH/saNG5g9e7b0Wr+4viUnJ2Px4sU4f/48nJycYGNjg6lTp8LV1VXrEuV69erB09MTEydOBPDy7Wz8+PEICQlBmTJlUL169Xwn9eo776BBg1C7dm189dVX0jq5evVqODg4FLhtPHv2TLrM2MXFBb6+vrC2ttbazr///nsMGzYM586dg4uLC/z9/dGjRw/ExsZi6tSpiIiIQOXKldG/f3+MGjUK27dvR6NGjQAAixYtwrNnz7Bo0SKd75e+6/q9e/cwb948PH36FJmZmXB3d8fkyZOl8xAeP36MESNGwMLCAra2tli3bl2+k8Gf//yqX78+xo4dq3W5bd6lzNHR0bCzs0PVqlUxZcoUWFtbS5fG5s3bv39/eHt7IysrC6tXr8bhw4fh6OgICwsLTJgwAXXq1Mn3Ody6dWuMHz9eWp4h877sM/z5y4zNzc0xduxY6UR/ANi0aRN+/fVXlC1bFubm5hgxYgSaN28ujf/111+xfv16ODk5wcXFBfb29tizZw8aNWpU4AnmsbGxmDdvHsLDw2FtbY2aNWti2rRp0t7ol72Wr3ofXvy879WrF1asWIFLly7BxsYGycnJ6NSpU75bBLxubFCIiEzUkydPtG7hHx0dLd1DyNnZGZGRkejbty82bdok/cdOZCp4iIeIyER9+eWXWpdXb9++HR4eHtJVJ/fv38fy5cvZnJBJ4h4UIiITtWfPHmzZsgXW1tbIzMxEpUqVMHXqVK1L7IlMFRsUIiIiUhwe4iEiIiLFYYNCREREisMGhYiIiBSHDQoREREpjqq/i6dLly7F+j0BRERE9PpFRESou0F56623sH79ernLICIiIgP4+/vzEA8REREpDxsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREivPGNSg5OULuEl5K6fUREREZQwm5CzA2MzMNvv7pNCJj4uUuJR+X8nYY1b+Z3GUQERHJ7o1rUAAgMiYe9yKfyl0GERERFeCNO8RDREREyscGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcUoYYyGZmZn44Ycf8PXXX2PHjh1wc3MDANStWxe2trbSdImJifjmm2/QrFmzfM/x4rTLly+Hl5dX8RdPRERERmeUBmXHjh1wd3dHamqq1vA+ffpg5syZAIDs7GwMHDgQnp6eOp/D29sbixYtKvZaiYiISH5GaVAGDhyoc3hecwIAR48eRYsWLVCihO6Sbt++jUWLFiEjIwNubm7o27cvNBpNsdRLRERE8jJKg6KPHTt2vHQPSa9evTBw4EDk5ORg9OjRSEhIwPDhwxEUFISgoCCd80RHRxdXuURERFSMFNGg3Lp1Cw4ODnB0dCxwmry9MGZmZujRowfWrl2L4cOHw8fHBz4+Pjrn8ff3L5Z6iYiIqHgp4iqerVu3FngYCAAeP36MxMRE6bGFhQXS09ONURoRERHJQPYGJSEhAWFhYahXr57W8LNnz+LevXsAgBMnTmDfvn3SuNDQUDRp0sSYZRIREZERGeUQz8WLFxEcHAwA2LBhA9q3b4/OnTsDAHbu3IkPP/ww3zybNm2Cp6cnhg4dilq1amHFihUICwtDRkYGMjIyMH36dGOUTkRERDLQCCGE3EUUF39/f6xfvz7f8Gmrg3Ev8qkMFb3c2y72WDDWW+4yiIiIZOXv7y//IR4iIiKiF7FBISIiIsVhg0JERESKwwbFRImcHLlLeCl968tRcA4l10ZEpHaKuFEbGU5jZoawoI1IffxI7lLyKVWuIt7x+Y9e05qZmWHDic14GK+su/5WsnPGiFYfy10GEdEbiw2KCUt9/Aip0fflLqPIHsZHI/zxA7nLICIiBeEhHiIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFKIiEjk5cpfwUvrWl5MjirmSwtO3thyFvxf61icU/F4A+ten5PdDybVRrhJyF0Bk6jRmZvjzmw1IevhI7lLyKV2pIuqPHKHXtGZmGuzdcQ5xsYnFXJVhHJ1s0f0jT72mNTMzQ/Av3+FJjPLeC4fyFeHdd6he02rMNAg/fB3pT1OKuSrDWdpbw7XDe3pNa2Zmhv379+PJkyfFXJVhHBwc0KVLF7nLoFdgg0L0GiQ9fISE8HC5yyiyuNhERD98JncZRfIk5hFiHkbIXUaRpT9NQWpcktxlFNmTJ08QExMjdxlkgniIh4iIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4RmlQMjMzsXHjRtSvXx+3bt2Shq9duxZeXl5o1qwZmjVrhuHDhxf4HKGhoRgxYgRmzpyJ8ePHIynJ9L9Ei4iIiHQzyrcZ79ixA+7u7khNTc037rfffkPlypVfOn9aWhomTpyIXbt2wdnZGQEBAVi1ahVmzJhRXCUTERGRjIzSoAwcOLDAcd9//z0sLS2RkZGBwYMHo0qVKvmmOXnyJJydneHs7AwAaN26NQYMGMAGhYiISKWM0qAUxN3dHRUqVMA777yDa9euYcCAAThw4ABsbW21pouMjISTk5P02NHREYmJiYiPj0dISAiCgoJ0Pn90dHSx1k9EROomRA40GuWerqlvfTlCwEyjMUJFhiuoNlkblCZNmki/16lTB/b29jh79iw++OADvZ/Dx8cHPj4+Osf5+/sXuUYiInpzaTRmuHl5J1IS4+QuJR9rW0fUbPChXtOaaTTYeuEGohNTirkqwzjbWsO3cS2d42RtUMLCwvDOO+9Ijy0sLJCWlpZvOhcXF8TGxkqP4+LiYGtrCzs7O6PUSUREb66UxDgkJTySu4wii05MQWS86VxgIut+q6lTpyIzMxNAbtMRERGBRo0aAQCuXr2Ka9euAQBatmyJqKgo6ZDN8ePH0b17d3mKJiIiomJnlD0oFy9eRHBwMABgw4YNaN++PTp37gxPT09MmDABlSpVQnh4OBYtWgQXFxcAwO7du6HRaFCnTh1YWVlh2bJlmDlzJpydnZGYmIi5c+cao3QiIiKSgVEaFHd3d7i7u2PWrFlaw8ePH1/gPLNnz9Z63LRpUzRt2rRY6iMiIiJlUe6pyURERPTGYoNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUpwSxlhIZmYmfvjhB3z99dfYsWMH3NzckJmZiYULFyIrKwtWVlYICwvDmDFjULduXZ3P0bFjRyQlJUmPJ02ahB49ehijfCIiIjIyozQoO3bsgLu7O1JTU6VhqampiIiIwMaNGwEAx44dw7hx43Ds2DGdz9GgQQMsWrTIGOUSERGRzIzSoAwcODDfsDJlymDDhg3S47feeguxsbHIycmBmVn+I0/R0dFYtGgRsrOz4eTkBD8/P5QsWbJY6yYiIiJ5yHoOyvONyO+//45+/frpbE4AoHPnzpg4cSKmT5+OmJgYzJ8/31hlEhERkZEZZQ/Kq/z111+4dOkSVq9eXeA0H330kfR7z5498fHHH2Pu3LkICgpCUFCQznmio6Nfe61ERERU/GRvUK5evYpNmzZhxYoVsLS01DlNYmIi0tPT4ejoCACwsLBARkYGcnJy4OPjAx8fH53z+fv7F1vdREREVHxkPcRz4cIFbN26FYsXL4a1tTW+++47PHr0CEBu43Lt2jUAwPXr1/H9999L84WGhsLT07PAw0FERERk2oyyB+XixYsIDg4GAGzYsAHt27eHp6cnhg0bhlKlSqFNmzYAgJSUFHTs2BEAsHv3bmg0GtSpUweVK1dGWFgYZs+ejRIlSiA2NhZfffWVMUonIiIiGRilQXF3d4e7uztmzZqlNfzKlSsFzjN79mzpdxcXF3zzzTfFVh8REREpC4+REBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBTHoAYlISEBUVFRxVULEREREYACGpSxY8fqnPjq1avo1KkTAgICirUoIiIierPpbFDCw8N1Tty8eXOEhIRg3759xVoUERERvdl0NigajabAGVJSUpCenl5sBRERERGVyPtl3bp1+Prrr6URtWrVKnCm9u3bF29VRERE9EaTGhQPDw8AgBACv/zyC/r165d/4hIlULlyZXzwwQfGq5CIiIjeOFoNSl6T8u+//2L06NGyFUVERERvNp3noKxcufKlM509e7ZYiiEiIiICCnmjtiVLlrzuOoiIiIgkJXQNfPz4MZYuXYqzZ88iLi4OOTk5xq6LiIiI3mA6G5Tp06fj7t27aNu2Lezt7WFm9n87WvJOoiUiIiIqLjoblOvXr2P//v2wtbXVOdODBw+KtSgiIiJ6s+k8B6Vy5coFNicAsHjx4mIriIiIiEhng9KpUyccOHCgwJkGDx5cbAURERER6TzEc+PGDXz77bf45ptvULVqVZQqVUpr/M2bN41SHBEREb2ZdDYogYGBKF++PJKSknD16tV841NSUoq9MCIiInpz6WxQ3n33XezZs6fAmXr06FFM5RAREREVcA7KjBkzXjoTb9RGRERExUlng+Lu7v7Smc6fP2/QQjIzM7Fx40bUr18ft27dkoYnJCRg/PjxmDlzJkaMGPHS5w0NDcWIESMwc+ZMjB8/HklJSQbVQERERKZD5yGehw8fvnSmn3/+Gb6+vnovZMeOHXB3d0dqaqrW8JUrV6JWrVoYPnw4oqOj8eGHH+Lo0aOwtLTUmi4tLQ0TJ07Erl274OzsjICAAKxateqVe3qIiIjINOlsUNq2bQuNRvPaFjJw4ECdw/ft24effvoJAODs7Izy5csjJCQE7du315ru5MmTcHZ2hrOzMwCgdevWGDBgABsUIiIildLZoFSsWBGfffaZ1rDk5GTcvXsX58+fx6BBg4q84GfPniEpKQmOjo7SMEdHR513qY2MjISTk5PWdImJiYiPj0dISAiCgoJ0LiM6OrrIdRIREZHx6WxQOnfujJ49e+qc4e7du9i+fXuxFmUIHx8f+Pj46Bzn7+9v5GqIiIjoddB5kuwXX3xR4AzVqlXDn3/+WeQFly1bFjY2NoiLi5OGxcXFwcXFJd+0Li4uiI2N1ZrO1tYWdnZ2Ra6DiIiIlEdng/Iyt2/fxpMnT17Lwrt164bjx48DyD0cExMTg5YtWwIArl69imvXrgEAWrZsiaioKOmQzfHjx9G9e/fXUgMREREpj85DPB9//HG+YUIIxMfH499//0X//v0NWsjFixcRHBwMANiwYQPat2+Pzp07Y/z48Zg1axZmzpyJ6OhoLF26VLqCZ/fu3dBoNKhTpw6srKywbNkyzJw5E87OzkhMTMTcuXMNzUpEREQmQmeDcu3aNbz//vtaw8zNzVG9enUMGjSowPNTCuLu7g53d3fMmjVLa7idnR1Wr16tc57Zs2drPW7atCmaNm1q0HKJiIjINOlsUFxdXbFlyxZj10JEREQEoIBzUH744Qcjl0FERET0f3TuQSlbtiyA3HNHQkJC8PTpU9jb26NFixavvA0+ERERUVHpbFCys7PxxRdfIDg4GEIIaXhAQAC8vb2xdOlSmJkZfAEQERERkV50NigbNmzAhQsXMHnyZNSvXx9ly5bFs2fP8Oeff2LTpk0ICAjgTdCIiIio2OhsUPbt24ctW7bA1dVVa3j9+vXRpk0bDB8+nA0KERERFRudx2nMzMzyNSd5XF1dYW5uXqxFERER0ZtNZ4OSkZGBp0+f6pzhyZMnSE9PL9aiiIiI6M2ms0Fp164dPvnkE/z+++94/PgxsrKy8PjxYxw7dgxDhw5Fhw4djF0nERERvUF0noMyduxYDBkyBCNHjoRGo9EaV69ePXz22WdGKY6IiIjeTDobFGtra2zduhWBgYE4deqUdB+U5s2bo2vXrihRQudsRERERK+Fzk7j6dOnuHTpEhwcHLB8+XJpeHBwMBISEuDg4GC0AomIiOjNo/MclK1bt2Ls2LHYu3ev1vBz586hR48euHPnjlGKIyIiojeTzj0ov//+O7777jt4enpqDZ8zZw7atGmDJUuWICAgwCgFEhER0ZtH5x4UIUS+5iRP69atERsbW6xFERER0ZtNZ4MSHx//0pleNZ6IiIioKHQ2KNWqVcOaNWuQnZ2tNTw7OxurV69G1apVjVIcERERvZkKvA+Kr68vduzYgVq1asHOzg7x8fG4ceMGkpKSsH37dmPXSURERG8QnQ3K+++/jy1btmDJkiU4ffo0cnJyYGZmhkaNGuGLL77Ae++9Z+w6iYiI6A1S4B3X6tSpgy1btiAtLQ3x8fGws7ODlZWVMWsjIiKiN9QrbwlrZWXFxoSIiIiMSudJskRERERyYoNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKwQSEiIiLFYYNCREREisMGhYiIiBSHDQoREREpDhsUIiIiUhw2KERERKQ4bFCIiIhIcdigEBERkeKUkLuABw8eoGvXrrC2tpaGxcfHIygoCG+//fZLp9u+fTtcXV2NWS4REREZgewNirm5OT799FP85z//AQAkJCRg9OjRWs1JniFDhmDMmDFGrpCIiIiMTfYGpWLFilJzAgA7d+5Er169dE578eJFLFq0COnp6WjcuDG8vb2NVSYREREZkewNyvNycnJw8OBBbN68Od84W1tb9OnTBz4+PkhPT8fAgQMhhIAQAkFBQTqfLzo6urhLJiIiomKgqAbl5MmT8PLyQsmSJfONs7Ozg4+PDwDA0tISXbp0QWBgINavXy8Nf5G/v3+x1ktERETFQ1FX8fz000/o37+/znEPHz5Eenq69NjCwkLrMREREamHYhqUsLAwlCpVCs7OztKwI0eOICYmBkDuuSmhoaHSuNDQUDRp0sTodRIREVHxU8whnm3btmHgwIFaw1avXo2RI0fC29sb7u7u+Pbbb3H69GnEx8ejYsWK8PPzk6dYIiIiKlaKaVBmzJiRb1hgYKD0e5MmTbjHhIiI6A2hmEM8RERERHnYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUp4TcBUyZMgUhISHS43bt2mHu3Lk6pw0ODkZgYCAcHByg0Wgwe/ZsWFhYGKtUIiIiMhLZGxQAOH369CuniY6OxoIFC3Dw4EHY2Nhg1qxZ2LZtG/z8/Iq/QCIiIjIqRTQoK1asQGZmJoQQ+M9//oNy5crlmyY4OBgNGzaEjY0NAKBNmzZYtWoVGxQiIiIVkr1Badu2LRo0aAAnJyccOnQIfn5+2L17N0qU0C4tMjISjo6O0uNy5crhwYMHCAoKQlBQkM7njo6OLtbaiYiIqHjI3qB88MEHWr9PmTIF//zzD2rXrq3X/D4+PvDx8dE5zt/f/7XUSERERMYl+1U8YWFhWo8tLCyQlpaWbzoXFxfExcVJjx8/fgwXF5dir4+IiIiMT/YGZdKkSdLvN2/ehJmZGWrUqAEAOHv2LO7duwcA8Pb2xqVLl5CcnAwA+P3339GjRw9jl0tERERGIPshnurVq2PixIkoV64cwsPDsW7dOpQuXRoAsGnTJnh6emLo0KFwdnbG5MmTMXHiRDg4OAAAfH195SydiIiIionsDcrChQsLHBcQEKD1uGvXrujatWtxl0REREQyk/0QDxEREdGL2KAQERGR4rBBISIiIsVhg0JERESKwwaFiIiIFIcNChERESkOGxQiIiJSHDYoREREpDhsUIiIiEhx2KAQERGR4rBBISIiIsVhg0JERESKwwaFiIiIFIcNChERESkOGxQiIiJSHDYoREREpDhsUIiIiEhx2KAQERGR4rBBISIiIsVhg0JERESKwwaFiIiIFIcNChERESkOGxQiIiJSHDYoREREpDhsUIiIiEhx2KAQERGR4rBBISIiIsVhg0JERESKwwaFiIiIFIcNChERESkOGxQiIiJSHDYoREREpDhsUIiIiEhx2KAQERGR4rBBISIiIsVhg0JERESKwwaFiIiIFIcNChERESlOCTkX/vTpUyxZsgTW1tbQaDR48OABpk6dCldX13zT1q1bF7a2ttLj5cuXw8vLy5jlEhERkZHI2qBERUXB0tISM2fOBABs2bIFM2bMwJYtW/JN6+3tjUWLFhm7RCIiIpKBrA1KrVq1MHv2bOnxW2+9hejoaJ3T3r59G4sWLUJGRgbc3NzQt29faDQaY5VKRERERiRrgwJAq8k4duwYBgwYoHO6Xr16YeDAgcjJycHo0aORkJCA4cOHIygoCEFBQTrnKajZISIiImWTvUHJc/z4caSlpWHw4ME6xw8cOBAAYGZmhh49emDt2rUYPnw4fHx84OPjo3Mef3//YquXiIiIio8iruI5fvw4jh49ioULF+o8bPP48WMkJiZKjy0sLJCenm7MEomIiMiIZG9QDhw4gFOnTmHu3LkwNzfH/PnzAQBnz57FvXv3AAAnTpzAvn37pHlCQ0PRpEkTOcolIiIiI5D1EM/NmzcxceJE2Nvb48CBAwCAxMREzJgxA5s2bYKnpyeGDh2KWrVqYcWKFQgLC0NGRgYyMjIwffp0OUsnIiKiYiRrg1KzZk1cv35d57iAgADp91q1amHjxo3GKouIiIhkJvshHiIiIqIXsUEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHHYoBAREZHisEEhIiIixWGDQkRERIrDBoWIiIgUhw0KERERKU4JuQt4+PAh5s+fD0dHR0RHR2PixIlwc3PLN11wcDACAwPh4OAAjUaD2bNnw8LCQoaKiYiIqLjJvgflyy+/hI+PD+bOnYuRI0fi888/zzdNdHQ0FixYgGXLluGrr76CmZkZtm3bJkO1REREZAyy7kF5+vQpTp48iZUrVwIA6tevj+joaNy4cQO1atWSpgsODkbDhg1hY2MDAGjTpg1WrVoFPz+/Qi3XpbxdkWsvDobWVapcxWKqpGgMrauSnXMxVVJ4htZUupIy3wtD63J0si2mSgrP0JocyivzvTC0Lkt762KqpGgMrcvBwaGYKik8Q2uytnUspkqKxtC6nG2Vt069rCaNEEIYsRYtf//9N3x9fXH58mVpWJcuXTBu3Dh06NBBGjZ//nzk5ORg1qxZAICrV6/ik08+wR9//IGgoCAEBQXpfP7r16/jvffeK9YM0dHRcHZW3h9YQ6khhxoyAMyhJGrIAKgjhxoyAMyhr4iICPnPQSkqHx8f+Pj4yLZ8f39/rF+/Xrblvy5qyKGGDABzKIkaMgDqyKGGDABzGELWc1AqVaqE1NRUJCcnS8MeP34MFxcXrelcXFwQFxf30mmIiIhIPWRtUOzt7dGiRQucOHECAPDnn3/CyckJ7733Hs6ePYt79+4BALy9vXHp0iWpkfn999/Ro0cPmaomIiKi4ib7IZ4vv/wS8+fPR2hoKKKiorB06VIAwKZNm+Dp6YmhQ4fC2dkZkydPxsSJE6WTm3x9feUsm4iIiIqR7A2Ki4sLvvnmm3zDAwICtB537doVXbt2NVZZREREJCPZ74NCRERE9CI2KEUk5xVEr5MacqghA8AcSqKGDIA6cqghA8AchpD1PihEREREunAPChERESmO7CfJmprk5GRYW1tDo9EgJiYGZ86cgaurKxo0aCB3aW+syMhIHD58GOnp6fDy8kK9evXkLskgEREROHDgAKKiolC6dGlUrVoV3t7eKFmypNyl6e327dtwdnZGmTJlcPPmTRw9ehSurq4mtzs7IyMDISEhePDgAYDck/hbtmxpUu9FHlPeLn799Vd07NgRZcqUkbuUIktPT0d8fDzKly8vDfvzzz+RmZmJxo0by1iZ/uTKwD0oBvjll1/g7u6Ozp074/bt2/jwww+xdetWjB49Glu3bpW7vCL75Zdf5C5Bb927dweQ+3UGffr0wblz5/D333/js88+w86dO2WuTn/BwcHw8/PD2bNnceDAATx48AD/+9//0KVLF9y8eVPu8vSybt069OrVC507d8b58+cxcuRI3Lp1Cxs2bMCqVavkLk9vFy5cQLt27fDf//4XZ86cwZkzZ/DNN9+gXbt2OH/+vNzl6UUt28WCBQswbtw4zJw502S2A11CQkLg5eWFVq1aYfTo0YiPjwcAPHz4EJ9++qnM1elH1gyC9DZkyBBx//59ceLECdG1a1dx69YtIYQQycnJYsSIETJXp5/jx48X+GMqGYQQolmzZkIIIb788ksRGRkpDU9LSxOjR4+WqyyDffHFFyIrK0sIIcSzZ8/E119/LYQQIioqSkyYMEHO0vQ2YsQIkZaWJm7fvi28vb1FdHS0EEKI7Oxsk3ovRowYIaKiovINj4qKMpltQy3bRV6O69evi6lTp4ohQ4aI4OBgaVsxFf/5z3/EP//8I1JSUkRQUJD45JNPxJMnT4QQ/5dR6eTMwEM8BnBzc8Nbb72Ft956C4GBgahevToAwNraGm+//ba8xelpypQpsLa2htBxbnRCQoIMFRWNlZUVKlWqJD22tLRE5cqVZazIMA4ODjA3NwcA2NnZSXdLdnZ2VuS3wOryzjvvwNLSEu+++y7q1asn7QY2MzMzqfeifPnyOr/8zNnZWWvXtikw9e0iT61atbBgwQI8e/YMO3bswMCBA9GyZUuT2ftQrVo1uLm5Acj9Itx69eph1qxZWLhwocyV6U/ODGxQDBAXF4fs7GyYm5tj/PjxWuNiY2NlqsowEydOhJOTE1q1apVv3Lx582SoqHAyMzNx4cIFPHnyBFevXkXdunUB5DZZeV+RYAoiIyOxf/9+vP322zh37pzUOEZERJjMru2HDx8iMTERtra2mD59ujQ8KysLkZGRMlZmmKSkJHz77bdo3bo1HB1zv8Y+Li4Ox48fR2JioszV6Uct28WLypYti+HDh2PYsGE4evSo3OXo7dmzZ1qPK1eujC+++ALTpk1DZmamPEUZSM4MPAfFANWrV8eaNWsAQPrvJCkpCf369YOlpaWcpemtd+/eSEpK0jluwIABRq6m8KZMmYIHDx7Ay8sLJUrk9tn37t3D+PHj0axZM5mr09/EiROxefNm+Pr64ty5cxg+fDgA4NixY+jUqZPM1emnZcuW+OmnnwAANjY2AHK3i08++QRVq1aVszSDzJkzBw8fPkS/fv3QpEkTNGnSBP369cPDhw8xZ84cucvTi1q2CxsbG8yfPz/fcDMzM3To0EGGigrH2dkZAwcO1Br21ltv4bPPPoOZmWn8+ZUzA++DQkT0grwTAe3s7GSuhOjNxQbFAGfPnoWnp6fJdL4FEULg6tWrePToEUqXLo133nkHLi4ucpdlsKSkJJw8eVLKUa1aNbi7u8td1htJTZfnmjq1bBcREREAcv9bj46OxoEDB1CpUiV88MEHMldWOElJSQgNDUVaWhoaNWqEihUryl2SXtLT01GiRAmYm5sjJiYGx48fR3p6Opo0aYJ33323WJfNBsUA9erVw3vvvYeWLVuib9++JnMS4/Nu3bqFUaNG4dGjR8jOzoarqyuePHkCT09PzJ8/32T+Yzx//jwmTJgAa2trPHz4EA0bNsTTp09hYWGBtWvXmmTD9aJffvkFffv2lbuMV7pw4QImTJiA8uXLa527ERMTg+XLl8PDw0PmCovu4MGD6Nixo9xlvJJatott27Zh8eLFyMnJwdy5c7F161Y4OTkhLCwMnTt3zncOoFL5+/tj/fr1iIiIwIABA2Bvbw8LCws8ePAAixcvRuvWreUu8ZUGDhyIadOmAQCGDRuGGjVqwMLCAjdv3sTkyZOL915HxXqNkMo0a9ZMZGdni0OHDolhw4aJyZMniytXrshdlkHGjx8v/v33XyGEEBcuXBBbt24V2dnZ4uDBg2LatGkyV6e/CRMmiISEBCGEEOHh4WLjxo1CCCH++usvMWnSJDlLM4gaLvtWw+W5Qghx+/btAn8mTpwod3l6Uct2MWbMGJGamioePnwounfvLh4/fiyEECIrK0uMGjVK5ur0l3cZ7rx588S1a9ek4bGxsWLkyJFylWWQefPmCSHyX7qempoqxo4dW6zL5lU8Bso7SatDhw64ffs2tm7diuXLl6NXr17STZKUzNnZGe+88w4AwN3dHSEhITAzM8MHH3yA0NBQmavTn7OzM2xtbQEAVapUkc4ZqF27tkndfVINl32r5fLcPn36IC0tTed7odFosGzZMhmqMoxatgsXFxdYWVmhYsWKqFWrlrS32tzc3CQvl7awsMD7778vPXZ0dESVKlVkrEh/FhYWAIAyZcpoXbpuZWWFChUqFOuy2aAUQfXq1TFnzhwkJiaazF0aIyMjERsbCycnJ/z111/SJWRCCMTExMhbnAEiIiJw5coVuLq64ty5c0hNTQUApKSkICwsTObq9KeGy77VcHkuAIwaNQru7u6oX79+vnGm8l6oZbu4ffs2du3ahbi4ONy7dw+BgYFo0aIF7ty5g7t378pdnt6ys7Px8OFDZGRkIDw8HK6urlrDTUHp0qVx5MgRuLm5Yd26dfjggw9gbm4uXc5enHgOigHq1auHPn36YMaMGXKXUmgnT57E2LFjodFoUKpUKfzwww+oXr06FixYgPj4eCxevFjuEvVy9epVjBo1CnFxcXj33XcREBCAihUrYuXKlQBgMseoAWD//v3o0qVLvuF3795FtWrVZKjIMImJiVi5ciX27dsn3WjOxsYG3bp1w7hx40zmP/esrCzs2LFD5+X2f/zxBxo1aiRDVYZRy3Zx9epVzJ8/HxUqVMCcOXMwcuRI/Pnnn3BwcMCaNWtM5qTfdevWSb+7u7vDy8sL9+7dw9SpU+Hp6Ylx48bJV5yehBDYvHkztmzZgsjISAghYGlpiU6dOmHq1KkoW7ZssS2bDcobKD4+HhEREahatSqsra3lLqdInj59Cnt7e7nLoP+Pl+cqgxq3iydPnsDe3h4ajUbuUt5YaWlpSElJMdoFIqZ9vayM0tLScPr0aRw7dqzYd3O9bnZ2dqhSpQru37+Pmzdvmsy5Drqo4UM4IyMDaWlpAIDU1FQcOXIEf//9t8xVFY6dnR3s7Oxw+PBhADCp3fH379+Xu4TXxt7e3qQ/o16UlpaGf//9F9HR0XKX8kazsrIy6tWrbFAMMGzYMPz555+4f/8+OnbsiC+//BLLli1Dly5dcPbsWbnL00t0dDQ+/fRTeHl5oX///ujfvz+8vLwwcuRI1Wz8Bw8elLsEvR08eBCNGjVCkyZNcOLECQwaNAirVq2Cn58ftmzZInd5hXb69GkAkO4wawq8vb3h5+eHffv2mcxtyF+khs8oADhy5Ag8PT3xySefICYmBj4+PhgzZgw6duyI4OBgucvT2/379zFo0CB07NgRmzdvloZ/9dVXaNCggYyV6U/WDMV6jZDKzJo1SwghxNy5c7UuL46KihJjxoyRqyyDjB49Wuzfv18kJydLw5KTk0VQUJD49NNPZazMMGq4JFQIIUaOHCnu378vLl68KHx8fERYWJgQQojExESTuUS3Ro0aombNmvl+8oabimbNmomEhASxadMm0bdvX7F69Wrpm5lNhRo+o4QQYvDgweLs2bNi+/btok+fPuL06dNCiNwcw4YNk7k6/fn7+4stW7aI48ePi1mzZonJkyeLnJwcIYTpfJuxnBl4FY8B8s7XsLGxkb6EC8i9tM9UboDk4OAAb29vrWHW1tbo0qULzp8/L1NVhlPDJaEA4OrqKn1Ddt26daVvxS5durTJfEP2pEmTcP78eUycOBE2NjYQQmDVqlUYP3481q5dK3d5BrG1tYWfnx8GDx6MkydPYtasWbC2tsaAAQNM4sRMNXxGAUDNmjXh5eUFLy8vnD17Fk2bNgWQm8MUThzP4+LiAl9fXwBAq1atcPz4ccyePRtz586VuTL9yZmBDYoBzM3NcePGDVSpUgW7du1Cx44dpcutTOXwSHR0NK5du4Y6depoDb969SqioqJkqspwargkFADu3LmD8+fPIy4uDjdv3sSlS5fQsGFDPHr0yGQuCx06dChatmyJ1atX4+OPP4aHhwdKly4NFxcX9OrVS+7yCkWj0aBVq1Zo1aoV7t27h61bt5pEg6KGzygAWpenjxw5UmucKZ8z17p1a2g0Gnz11Vdyl1JoxszAq3gMkJ6ejhUrVuD48eMIDw+XziZv1KgRlixZonUTG6X6559/8Pnnn+Px48coV64cAEi/L126FDVr1pS5Qv2o4ZJQADhx4gSmTZsGZ2dnrFy5EkOHDsWTJ0+QnZ2NZcuWmdQ3t2ZlZWHt2rVIT09HfHw8Fi5cKHdJBmnQoAFGjx6NoUOHyl1KoanhMwrIPb+hZMmSmDRpkjQsJSUFM2fOhBACK1askLE6/U2dOhWlSpXCrFmztIYfOHAAn3/+uUmcDC9nBjYohZCeno4HDx4gOTkZFSpUMKm7Zea5fv06IiMjAeTuwnvvvfdkroiA3HXr1q1bcHFxMcnvegKAy5cv4+jRo/j888/lLsUgkZGRMDc3L/a7YxqDGj6jXpSTk4NHjx7Bzs4OpUuXlrscvTx79gxRUVE6//H7559/UKNGDRmqMoycGdigFNGjR49Qrlw5k/nW1vT0dFhaWspdRrEwtfdCrRISEpCWlqaKP4qmuk4lJCRIdyqtVKmSydwsryBqWafUkMOYGXiZsQEuXryIXr16YcaMGUhJSUG/fv3Qpk0bNGnSxGQu4WvcuDEmTpyIP/74Q+5SikQN78WrmMp3I23YsAEAkJycjGHDhsHDwwOtWrVCly5dTOo+KGpYp9RyGwG1rFNqyCFrhmK9RkhlBg8eLH777TexbNkyMWTIELFjxw6RkpIirly5IoYOHSp3eXpp1qyZCAsLE/PmzRO+vr7it99+E+np6XKXZTA1vBdCCJGSklLgT94lo0qXd6nhypUrxaZNm8SzZ89EcnKyOHLkiMl8Y6sQ6lin1HIbAbWsU2rIIWcGXsVjADc3N3z44YcAcm+I1KdPHwBA3bp1TerSt7ffflv6L3H37t0YPHgwGjVqhAEDBpjMSXRqeS8aN26M7OzsfMOFENBoNJgzZ44MVRVOeno6/Pz8pMft2rXDuXPn5CvIQGpYp9RyG4E8pr5O5VFDDjkysEExQFZWlvT7i2f6592q3JRYW1tj4MCBGDhwIM6cOYOFCxeazH0r1PJe+Pn5wcPDI98fQCGE1heNmQIzMzOkpqaiVKlS0rC8Lw80BWpYp9RyG4E8pr5O5VFDDjky8BwUA8THx0uXtzVp0gRAble5cuVKxMXFyVma3tLS0nTeKrpp06Ym05wA6ngvgNz7udy4cQMuLi5aP5UrV8YHH3wgd3l6effddzF16lRERETg0KFDAHJPLh03bhxSU1Nlrk5/alinxo8fj2nTpqFp06bo2rUrunbtiqZNm2L69Okm803GgHrWKTXkkDMDr+IxgBACQgiYmf1fX5eZmYnLly+jcuXKJnF45Pz58yhRogQaNmwodylFoob3Qs0yMjIQGxtrUpeEqmmdUuNtBExxndJFDTmMlYF7UAyg0Wi0PrwOHjwICwsLeHh4mMyHl4eHh1ZzYkpfrPc8NbwXupjq+/G8gwcPomTJknBxcTGpD2A1rVPvvfceOnTogJycHFU0J6a6Tr1IDTmMmYENShGYyt0MX0YNGQDmUBI1ZADUkUMNGQDmUBJjZmCDUgRqODqmhgwAcyiJGjIA6sihhgwAcyiJMTOwQSmCCRMmyF1CkakhA8AcSqKGDIA6cqghA8AcSmLMDGxQiqBTp044fPgwAJjMXQGfl5aWBltbWxw7dgxPnjyRu5xCy8nJgaurKy5fvoyUlBS5yyk0NeRQyzqlhhxqyAAwh5IYOwPvg1JEp0+fRocOHfDTTz9hxowZcpfzSsOGDcPo0aPh4OCAQYMGwdLSEiVKlMDTp0+xYsUK6fJKpfvyyy/x5Zdf4smTJxg8eDAePnwICwsLmJub4+uvv0b9+vXlLlEvasihlnVKDTnUkAFgDiWRNUOx3qdWZWrUqCFq1qyZ7ydvuCnIu3363LlzxZUrV6ThUVFRYsyYMXKVZbC82y8vWrRIHDp0SBp+48YNMWLECLnKMpgacqhlnVJDDjVkEII5lETODDzEY4BJkyahZcuW2Lt3L44cOYLDhw+jS5cuOHr0KLp37y53eXqxtrYGANjY2KBu3brScGdnZ7i4uMhVVpF06NBB+r1mzZpwdXWVsZrCM9Ucalmn1JBDDRkA5lASOTOwQTHA0KFD8fnnn2PNmjWIjIxE5cqVUbp0abi4uKBXr15yl6cXc3Nz3LhxA1WqVMGuXbuQnJyMtLQ0hISEmNS3neYpWbIkHj9+rDXsxcemwJRzqGWdUkMONWQAmENJ5MzAO8kWQlZWFtauXYv09HTEx8dj4cKFcpekt/T0dKxYsQLHjx9HeHg4NBoNAKBRo0ZYsmSJydyQaurUqdLv7dq1Q/v27XHv3j1MmjQJ1atXx4IFC2SsTn9qyKGWdUoNOdSQAWAOJZEzAxuUIrh8+TKOHj2Kzz//XO5SDJaeno4HDx4gOTkZFSpUQPny5eUuiUycWtYpNeRQQwaAOZREjgxsUIiIiEhxeA7KaxIaGip3CUWmhgwAcyiJGjIA6sihhgwAcyhJcWdgg2KA1NTUAn8OHDggd3l6UUMGgDmURA0ZAHXkUEMGgDmURM4MPMRjgPfffx/Z2dn5hgshoNFocOPGDRmqMowaMgDMoSRqyACoI4caMgDMoSRyZuCdZA3g5+cHDw8PVKtWTWu4EALr1q2TqSrDqCEDwBxKooYMgDpyqCEDwBxKImuGYr0NnMqkpKSI9evX6xx39OhRI1dTOGrIIARzKIkaMgihjhxqyCAEcyiJnBl4iIeIiIgUhyfJFlJiYiISExPlLqNI1JABYA4lUUMGQB051JABYA4lMXYGnoNigOTkZKxatQp79uxBUlISAMDW1hbdunXDuHHjULp0aZkrfDU1ZACYQ0nUkAFQRw41ZACYQ0nkzMBDPAaYOHEiatSogTZt2qBcuXIAcr8v5ffff8fNmzexYsUKmSt8NTVkAJhDSdSQAVBHDjVkAJhDSeTMwD0oBrCxscHw4cO1hjk4OKB69eqYNWuWTFUZRg0ZAOZQEjVkANSRQw0ZAOZQEjkz8BwUA8TExCA2NlbncFP5Zko1ZACYQ0nUkAFQRw41ZACYQ0nkzMA9KAbw8/NDz549UaFCBTg6OgIA4uLiEBUVhaVLl8pcnX7UkAFgDiVRQwZAHTnUkAFgDiWRMwPPQTFQeno6Tp48icjISACAi4sLWrZsCUtLS5kr058aMgDMoSRqyACoI4caMgDMoSRyZWCDQkRERIrDc1AMtGfPHmzYsAFRUVHSsK+++gpTp06VsSrDqCEDwBxKooYMgDpyqCEDwBxKIlcG7kExwNKlSxEaGorKlSvj+vXrmDFjBlq1agUAaN68OU6dOiVzha+mhgwAcyiJGjIA6sihhgwAcyiJnBl4kqwBoqKisHPnTgC5N6+ZN28ehBBo3bq1vIUZQA0ZAOZQEjVkANSRQw0ZAOZQEjkz8BCPASpUqCD9bmNjg0WLFuH48eMIDQ2VsSrDqCEDwBxKooYMgDpyqCEDwBxKImcGNigGePDgAa5du6Y1bNasWdi7d690C2ClU0MGgDmURA0ZAHXkUEMGgDmURM4MPMRjgH79+iEoKAh16tSRhpmZmWHevHmwt7eXsTL9qSEDwBxKooYMgDpyqCEDwBxKImcGniRbBFFRUVq7v0yRGjIAzKEkasgAqCOHGjIAzKEkxszAQzxFMHjwYLlLKDI1ZACYQ0nUkAFQRw41ZACYQ0mMmYENShGoYeeTGjIAzKEkasgAqCOHGjIAzKEkxszABqUI2rVrJ3cJRaaGDABzKIkaMgDqyKGGDABzKIkxM/AcFCIiIlIc7kExwNSpU7F69ep8w+fPn49PP/1UhooMp4YMAHMoiRoyAOrIoYYMAHMoiZwZ2KAYoGTJkhg7dizWrVuHdevWYe/evQCAGTNmoGLFijJXpx81ZACYQ0nUkAFQRw41ZACYQ0nkzMAGxQAajQYA0KNHD/z9999o0aJFvnFKp4YMAHMoiRoyAOrIoYYMAHMoiZwZ2KAUQuXKlWFtbQ0HBwe5Syk0NWQAmENJ1JABUEcONWQAmENJ5MjAO8ka4NGjRzhx4gQAICYmRvodyL0dsClQQwaAOZREDRkAdeRQQwaAOZREzgy8iscADRs2RNmyZXWOS0hIwMWLF41bUCGoIQPAHEqihgyAOnKoIQPAHEoiawZBelu6dGmB41asWGHESgpPDRmEYA4lUUMGIdSRQw0ZhGAOJZEzA89BMYCLi0uB48aPH2/ESgpPDRkA5lASNWQA1JFDDRkA5lASOTPwHBQDfP/993jw4AE+/PBDVK1aVe5yCkUNGQDmUBI1ZADUkUMNGQDmUBI5M/AcFAP8/PPPaNGiBXbu3Inw8HA0b94cnTt3hpWVldyl6U0NGQDmUBI1ZADUkUMNGQDmUBI5M7BBKaScnByEhITgwIEDKF26NHr27InatWvLXZZB1JABYA4lUUMGQB051JABYA4lMXYGnoNSSGZmZmjSpAm8vLxw4cIF9OnTR+6SDKaGDABzKIkaMgDqyKGGDABzKImxM/AcFAP8/fffqF27Nm7cuIFff/0VQUFBUhf53//+V+7y9KKGDABzKIkaMgDqyKGGDABzKImcGXiIxwA9e/aERqPB7du30bZtW/Tu3RvNmzc3mVsWA+rIADCHkqghA6COHGrIADCHksiZgXtQDPDs2TMMHjwY3bp1M9lbFqshA8AcSqKGDIA6cqghA8AcSiJnBp6DYoAPP/wQ//zzD65fvy4Nu3v3LgICAmSsyjBqyAAwh5KoIQOgjhxqyAAwh5LImYENigHu3LmDGjVqwMPDQxrm4uKCuLg4k1nh1JABYA4lUUMGQB051JABYA4lkTMDGxQDmJubw8/PDyVLlpSGWVlZYdq0abh165aMlelPDRkA5lASNWQA1JFDDRkA5lASOTOwQTGAjY1NgeNsbW2NWEnhqSEDwBxKooYMgDpyqCEDwBxKImcGNigGSEpKKnBcQkKCESspPDVkAJhDSdSQAVBHDjVkAJhDSeTMwAbFALVr18ayZcuQlpYmDUtNTcXKlSvRqFEjGSvTnxoyAMyhJGrIAKgjhxoyAMyhJHJm4H1QDCCEwLx587Br1y5UqFABABAdHY0+ffpg2rRpMlenHzVkAJhDSdSQAVBHDjVkAJhDSeTMwAalECIjI3Ht2jVoNBrUqVMHlSpVkrskg6khA8AcSqKGDIA6cqghA8AcSiJHBjYoREREpDg8B4WIiIgUhw0KERERKQ4bFCIiIlIcNihERESkOGxQiIiISHFKyF0AEb3ZwsPD8d133+HPP/+ERqNBUlISSpcujcaNG6Ndu3a4desWNm/ejAcPHsDe3h516tTBxo0btZ5jwYIFOHLkCBITE1G/fn307NkTGzZswN27d2FhYYEqVarkW+6jR48wZcoU9OrVy1hRicgAvMyYiGRz6NAhTJ48GWPHjsWAAQOkLyS7desWpk2bhmvXruHrr79GgwYN0KpVK9SoUQM7d+7U+Vxz586Fq6srBg8eLA1r27YtXFxcsGXLlnzTT5kyBR4eHmxQiBSKh3iISBa3bt3C559/jo8//jjft6W6ublhw4YNsLKyAgCUK1cOrVu3xl9//YWbN2/me6709HQcPHgQ3bp103v5fn5+aNq0adGDEFGxYINCRLL473//i4yMDAwaNEjn+HLlymHUqFFwdnYGAHz44YcAgN9++y3ftIcOHULjxo1hb2//yuWeO3cOgwYNQs2aNaVbdxOR8rBBISKjy8nJwcmTJ1G5cmU4OjoWON3w4cNRp04dAEDLli3h5OSEwMBAZGRkaE3322+/oXfv3sVaMxEZF0+SJSKje/r0KZKTk+Hm5qb3PObm5ujZsycCAgJw5MgReHt7AwAiIiIQERFR4OGav/76C927d5cep6SkcM8JkQngHhQiMjqNRlOo+XQd5tm5cyd69uwJMzPdH2fvv/8+9u7dK/3Mnz+/UMsmIuPiHhQiMrqyZcvCxsYGcXFxBs339ttvo1GjRjh79iwiIyNRsWJF7N27F1u3btX7OTw9PeHp6WloyURkZNyDQkRGZ2ZmhpYtW+LBgweIjY0tcLrQ0FDcvXtXa1jv3r2Rk5ODXbt2ISQkBO+88w5cXFyKu2QiMjI2KEQkizFjxsDKyqrAvR+XL1/G4MGD8fjxY63hnTp1go2NDXbt2oVff/210CfHbty4Md8N34hIOdigEJEsqlWrhtWrV2Pr1q344YcftK7MuXDhAj777DMMGTIEHh4eWvNZW1ujc+fOePjwIS5cuID27dsXavlpaWlIS0srUgYiKj68kywRySo8PBwbN27E5cuXUaJECQghUK5cOQwYMAAdOnTQOc/ly5fRr18/DBo0CDNmzMg3fs+ePVizZg2ioqJgbm4OJyenfNMkJCRg8ODBGDNmzGvPRERFxwaFiIiIFIeHeIiIiEhx2KAQERGR4rBBISIiIsX5f8Kj6afQBt51AAAAAElFTkSuQmCC\n", + "image/png": 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", "text/plain": [ "
" ] @@ -1872,10 +1872,10 @@ "number_of_cves: int = 10\n", "counter: Counter = Counter()\n", "cve_rich_certs_df: DataFrame = not_referenced_df[not_referenced_df[\"related_cves\"].notna()]\n", - " \n", + "\n", "for cve_set in cve_rich_certs_df[\"related_cves\"]:\n", " counter.update(cve_set)\n", - " \n", + "\n", "not_referenced_by_df: DataFrame = pd.DataFrame.from_dict(counter, orient=\"index\").reset_index()\n", "not_referenced_by_df.columns = (\"CVE\", \"count\")\n", "not_referenced_by_df.sort_values(by=\"count\", ascending=False, inplace=True)\n", @@ -1919,7 +1919,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -2001,7 +2001,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -2034,7 +2034,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] diff --git a/notebooks/fips/temporal_trends.ipynb b/notebooks/fips/temporal_trends.ipynb index 2e91a9760..8b66b9536 100644 --- a/notebooks/fips/temporal_trends.ipynb +++ b/notebooks/fips/temporal_trends.ipynb @@ -1,9 +1,11 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Temporal trends in the FIPS-140 ecosystem" + "metadata": {}, + "source": [ + "# Temporal trends in the FIPS-140 ecosystem" + ] }, { "cell_type": "code", @@ -47,7 +49,7 @@ }, "outputs": [], "source": [ - "dset = FIPSDataset.from_web_latest()\n", + "dset = FIPSDataset.from_web()\n", "df = dset.to_pandas()" ] }, @@ -245,8 +247,8 @@ " rules_subset = rules_get_subset(examined_category)\n", " keywords = [x.split(\".\")[-1] for x in extract_key_paths(rules_subset, examined_category)]\n", " top_n_keywords = df_keywords.loc[:, keywords].sum().sort_values(ascending=False).head(10).index\n", - " \n", - " # Count number of non-zero rows for each year, weight by number of certificates issued in the given year. \n", + "\n", + " # Count number of non-zero rows for each year, weight by number of certificates issued in the given year.\n", " crypto = df_keywords.groupby(\"year_from\")[top_n_keywords].sum()\n", " crypto[\"n_certs\"] = df_keywords.groupby(\"year_from\").size()\n", " crypto.iloc[:,:-1] = crypto.iloc[:,:-1].div(crypto.n_certs, axis=0) * 100\n", @@ -291,7 +293,7 @@ "# df_algos = df.loc[:, [\"type\", \"level\", \"date_validation\", \"date_sunset\", \"year_from\", \"algorithms\"]].copy()\n", "# for algo, count in algo_types.most_common(14):\n", "# df_algos[algo] = df_algos.algorithms.apply(algo_present, args=(algo,))\n", - " \n", + "\n", "# crypto = df_algos.groupby(\"year_from\").sum()\n", "# crypto[\"n_certs\"] = df_algos.groupby(\"year_from\").size()\n", "# crypto.iloc[:,:-1] = crypto.iloc[:,:-1].div(crypto.n_certs, axis=0) * 100\n", diff --git a/notebooks/fips/vulnerabilities.ipynb b/notebooks/fips/vulnerabilities.ipynb index 3b3d21e3c..e0d336d85 100644 --- a/notebooks/fips/vulnerabilities.ipynb +++ b/notebooks/fips/vulnerabilities.ipynb @@ -1,10 +1,12 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Vulnerability analysis", - "id": "3a0981d008383c12" + "id": "3a0981d008383c12", + "metadata": {}, + "source": [ + "# Vulnerability analysis" + ] }, { "cell_type": "code", @@ -21,6 +23,7 @@ "from sec_certs.dataset.fips import FIPSDataset\n", "from sec_certs.dataset.cpe import CPEDataset\n", "from sec_certs.dataset.cve import CVEDataset\n", + "from sec_certs.dataset.auxiliary_dataset_handling import CPEDatasetHandler, CVEDatasetHandler\n", "from sec_certs.utils.pandas import expand_df_with_cve_cols\n", "import pandas as pd\n", "import seaborn as sns\n", @@ -37,7 +40,7 @@ "metadata": {}, "outputs": [], "source": [ - "dset = FIPSDataset.from_web_latest(path=\"dset\", auxiliary_datasets=True)" + "dset = FIPSDataset.from_web(path=\"dset\", auxiliary_datasets=True)" ] }, { @@ -47,8 +50,9 @@ "metadata": {}, "outputs": [], "source": [ - "cve_dset: CVEDataset = dset.auxiliary_datasets.cve_dset\n", - "cpe_dset: CPEDataset = dset.auxiliary_datasets.cpe_dset" + "dset.load_auxiliary_datasets()\n", + "cve_dset: CVEDataset = dset.aux_handlers[CVEDatasetHandler].dset\n", + "cpe_dset: CPEDataset = dset.aux_handlers[CPEDatasetHandler].dset" ] }, { @@ -181,14 +185,6 @@ "g = sns.relplot(data=df_cve_rich, x=\"level\", y=\"avg_cve_score\")\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c3c2ec4-3fab-48ad-aacb-6f54277abe66", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/latex_plotting.ipynb b/notebooks/latex_plotting.ipynb index 278f776af..1ec8ab61c 100644 --- a/notebooks/latex_plotting.ipynb +++ b/notebooks/latex_plotting.ipynb @@ -147,7 +147,7 @@ "figure_width = 2.3\n", "figure_height = 1.8\n", "\n", - "dset = CCDataset.from_web_latest() # local instantiation\n", + "dset = CCDataset.from_web() # local instantiation\n", "df = dset.to_pandas()" ] }, diff --git a/pyproject.toml b/pyproject.toml index 41a7e071d..377c65847 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ "matplotlib", "numpy", "pandas", - "pdftotext", + "pdftotext>=3.0.0", "pikepdf", "Pillow>=9.2.0", "pypdf[crypto]>=3.1.0", diff --git a/requirements/all_requirements.txt b/requirements/all_requirements.txt index f94889f9d..dbf881870 100644 --- a/requirements/all_requirements.txt +++ b/requirements/all_requirements.txt @@ -1,164 +1,160 @@ -accessible-pygments==0.0.4 +accelerate==1.3.0 + # via sentence-transformers +accessible-pygments==0.0.5 # via pydata-sphinx-theme -aiohappyeyeballs==2.4.0 +aiohappyeyeballs==2.4.4 # via aiohttp -aiohttp==3.10.11 +aiohttp==3.11.11 # via # datasets # fsspec -aiosignal==1.3.1 +aiosignal==1.3.2 # via aiohttp -alabaster==0.7.13 +alabaster==1.0.0 # via sphinx -alembic==1.12.1 +alembic==1.14.1 # via optuna -annotated-types==0.6.0 +annotated-types==0.7.0 # via pydantic -appnope==0.1.3 - # via - # ipykernel - # ipython -asttokens==2.4.1 +appnope==0.1.4 + # via ipykernel +asttokens==3.0.0 # via stack-data -async-timeout==4.0.3 - # via aiohttp -attrs==23.1.0 +attrs==25.1.0 # via # aiohttp # jsonschema # jupyter-cache # referencing -babel==2.13.1 +babel==2.16.0 # via # pydata-sphinx-theme # sphinx -beautifulsoup4==4.12.2 +beautifulsoup4==4.12.3 # via # pydata-sphinx-theme # sec-certs (./../pyproject.toml) -bleach==6.1.0 +bleach==6.2.0 # via panel -blis==0.7.11 +blis==1.2.0 # via thinc -bokeh==3.3.1 +bokeh==3.6.2 # via + # holoviews # panel # umap-learn -build==1.0.3 +build==1.2.2.post1 # via pip-tools catalogue==2.0.10 # via # spacy # srsly # thinc -catboost==1.2.2 +catboost==1.2.7 # via sec-certs (./../pyproject.toml) -certifi==2024.7.4 +certifi==2024.12.14 # via requests -cffi==1.16.0 +cffi==1.17.1 # via cryptography cfgv==3.4.0 # via pre-commit -charset-normalizer==3.3.2 +charset-normalizer==3.4.1 # via requests -click==8.1.7 +click==8.1.8 # via # dask # jupyter-cache - # nltk # pip-tools # sec-certs (./../pyproject.toml) # typer -cloudpathlib==0.16.0 +cloudpathlib==0.20.0 # via weasel -cloudpickle==3.0.0 +cloudpickle==3.1.1 # via dask -colorcet==3.0.1 +colorcet==3.1.0 # via # datashader # holoviews # umap-learn -colorlog==6.7.0 +colorlog==6.9.0 # via optuna -comm==0.2.0 +comm==0.2.2 # via # ipykernel # ipywidgets -confection==0.1.3 +confection==0.1.5 # via # thinc # weasel -contourpy==1.2.0 +contourpy==1.3.1 # via # bokeh # matplotlib -coverage[toml]==7.3.2 +coverage[toml]==7.6.10 # via # pytest-cov # sec-certs (./../pyproject.toml) -cryptography==43.0.1 +cryptography==44.0.0 # via pypdf cycler==0.12.1 # via matplotlib -cymem==2.0.8 +cymem==2.0.11 # via # preshed # spacy # thinc -dask==2023.11.0 +dask==2025.1.0 # via datashader -datasets==2.15.0 +datasets==3.2.0 # via # evaluate # sec-certs (./../pyproject.toml) + # sentence-transformers # setfit -datashader==0.16.0 +datashader==0.16.3 # via umap-learn dateparser==1.2.0 # via sec-certs (./../pyproject.toml) -debugpy==1.8.0 +debugpy==1.8.12 # via ipykernel decorator==5.1.1 # via ipython -deprecated==1.2.14 +deprecated==1.2.18 # via pikepdf -dill==0.3.7 +dill==0.3.8 # via # datasets # evaluate # multiprocess -distlib==0.3.7 +distlib==0.3.9 # via virtualenv -distro==1.8.0 +distro==1.9.0 # via tabula-py -docutils==0.19 +docutils==0.21.2 # via # myst-parser # pydata-sphinx-theme # sphinx -evaluate==0.4.1 +evaluate==0.4.3 # via setfit -exceptiongroup==1.2.2 - # via - # ipython - # pytest -executing==2.0.1 +executing==2.2.0 # via stack-data -fastjsonschema==2.19.0 +fastjsonschema==2.21.1 # via nbformat -filelock==3.13.1 +filelock==3.17.0 # via + # datasets # huggingface-hub # torch # transformers # virtualenv -fonttools==4.45.0 +fonttools==4.55.6 # via matplotlib -frozenlist==1.4.0 +frozenlist==1.5.0 # via # aiohttp # aiosignal -fsspec[http]==2023.10.0 +fsspec[http]==2024.9.0 # via # dask # datasets @@ -166,127 +162,133 @@ fsspec[http]==2023.10.0 # fsspec # huggingface-hub # torch -gprof2dot==2022.7.29 +gprof2dot==2024.6.6 # via pytest-profiling -graphviz==0.20.1 +graphviz==0.20.3 # via catboost -holoviews==1.18.1 +holoviews==1.20.0 # via umap-learn html5lib==1.1 # via sec-certs (./../pyproject.toml) -huggingface-hub==0.19.4 +huggingface-hub==0.27.1 # via + # accelerate # datasets # evaluate # sentence-transformers + # setfit # tokenizers # transformers -identify==2.5.32 +identify==2.6.6 # via pre-commit -idna==3.7 +idna==3.10 # via # requests # yarl -imageio==2.33.0 +imageio==2.37.0 # via scikit-image imagesize==1.4.1 # via sphinx -importlib-metadata==6.8.0 +importlib-metadata==8.6.1 # via # dask # jupyter-cache # myst-nb iniconfig==2.0.0 # via pytest -ipykernel==6.27.0 +ipykernel==6.29.5 # via # myst-nb # sec-certs (./../pyproject.toml) -ipython==8.17.2 +ipython==8.31.0 # via # ipykernel # ipywidgets # myst-nb # sec-certs (./../pyproject.toml) -ipywidgets==8.1.1 +ipywidgets==8.1.5 # via sec-certs (./../pyproject.toml) -jedi==0.19.1 +jedi==0.19.2 # via ipython -jinja2==3.1.4 +jinja2==3.1.5 # via # bokeh # myst-parser # spacy # sphinx # torch -joblib==1.3.2 +joblib==1.4.2 # via - # nltk # pynndescent # scikit-learn -jsonschema==4.20.0 +jsonschema==4.23.0 # via # nbformat # sec-certs (./../pyproject.toml) -jsonschema-specifications==2023.11.1 +jsonschema-specifications==2024.10.1 # via jsonschema -jupyter-cache==1.0.0 +jupyter-cache==1.0.1 # via myst-nb -jupyter-client==8.6.0 +jupyter-client==8.6.3 # via # ipykernel # nbclient -jupyter-core==5.5.0 +jupyter-core==5.7.2 # via # ipykernel # jupyter-client # nbclient # nbformat -jupyterlab-widgets==3.0.9 +jupyterlab-widgets==3.0.13 # via ipywidgets -kiwisolver==1.4.5 +kiwisolver==1.4.8 # via matplotlib -langcodes==3.3.0 +langcodes==3.5.0 # via spacy -lazy-loader==0.3 +language-data==1.3.0 + # via langcodes +lazy-loader==0.4 # via scikit-image -linkify-it-py==2.0.2 +linkify-it-py==2.0.3 # via panel -llvmlite==0.41.1 +llvmlite==0.44.0 # via # numba # pynndescent locket==1.0.0 # via partd -lxml==4.9.3 +lxml==5.3.0 # via # pikepdf # sec-certs (./../pyproject.toml) -mako==1.3.0 +mako==1.3.8 # via alembic -markdown==3.5.1 +marisa-trie==1.2.1 + # via language-data +markdown==3.7 # via panel markdown-it-py==3.0.0 # via # mdit-py-plugins # myst-parser # panel -markupsafe==2.1.3 + # rich +markupsafe==3.0.2 # via # jinja2 # mako -matplotlib==3.8.2 +matplotlib==3.10.0 # via # catboost # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) # umap-learn -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # ipykernel # ipython -mdit-py-plugins==0.4.0 +mdit-py-plugins==0.4.2 # via # myst-parser # panel @@ -296,17 +298,17 @@ memory-profiler==0.61.0 # via pytest-monitor mpmath==1.3.0 # via sympy -multidict==6.0.4 +multidict==6.1.0 # via # aiohttp # yarl multipledispatch==1.0.0 # via datashader -multiprocess==0.70.15 +multiprocess==0.70.16 # via # datasets # evaluate -murmurhash==1.0.10 +murmurhash==1.0.12 # via # preshed # spacy @@ -315,37 +317,36 @@ mypy==1.13.0 # via sec-certs (./../pyproject.toml) mypy-extensions==1.0.0 # via mypy -myst-nb==1.0.0 +myst-nb==1.1.2 # via sec-certs (./../pyproject.toml) -myst-parser==2.0.0 +myst-parser==4.0.0 # via myst-nb -nbclient==0.9.0 +nbclient==0.10.2 # via # jupyter-cache # myst-nb -nbformat==5.9.2 +nbformat==5.10.4 # via # jupyter-cache # myst-nb # nbclient -nest-asyncio==1.5.8 +nest-asyncio==1.6.0 # via ipykernel -networkx==3.2.1 +networkx==3.4.2 # via # scikit-image # sec-certs (./../pyproject.toml) # torch -nltk==3.9 - # via sentence-transformers -nodeenv==1.8.0 +nodeenv==1.9.1 # via pre-commit -numba==0.58.1 +numba==0.61.0 # via # datashader # pynndescent # umap-learn -numpy==1.26.2 +numpy==1.26.4 # via + # accelerate # blis # bokeh # catboost @@ -359,42 +360,43 @@ numpy==1.26.2 # numba # optuna # pandas - # pyarrow # pysankeybeta # scikit-image # scikit-learn # scipy # seaborn # sec-certs (./../pyproject.toml) - # sentence-transformers # spacy # tabula-py # thinc # tifffile - # torchvision # transformers # umap-learn # xarray -optuna==3.4.0 +optuna==4.2.0 # via sec-certs (./../pyproject.toml) -packaging==23.2 +packaging==24.2 # via + # accelerate # bokeh # build # dask # datasets + # datashader # evaluate # holoviews # huggingface-hub # ipykernel + # lazy-loader # matplotlib # optuna + # panel # pikepdf # plotly - # pydata-sphinx-theme # pytesseract # pytest # scikit-image + # setfit # setuptools-scm # spacy # sphinx @@ -402,7 +404,7 @@ packaging==23.2 # transformers # weasel # xarray -pandas==2.1.3 +pandas==2.2.3 # via # bokeh # catboost @@ -417,26 +419,26 @@ pandas==2.1.3 # tabula-py # umap-learn # xarray -panel==1.3.2 +panel==1.6.0 # via holoviews -param==2.0.1 +param==2.2.0 # via # datashader # holoviews # panel # pyct # pyviz-comms -parso==0.8.3 +parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask -pdftotext==2.2.2 +pdftotext==3.0.0 # via sec-certs (./../pyproject.toml) -pexpect==4.8.0 +pexpect==4.9.0 # via ipython -pikepdf==8.7.1 +pikepdf==9.5.1 # via sec-certs (./../pyproject.toml) -pillow==10.3.0 +pillow==11.1.0 # via # bokeh # datashader @@ -446,52 +448,51 @@ pillow==10.3.0 # pytesseract # scikit-image # sec-certs (./../pyproject.toml) - # torchvision -pip-tools==7.3.0 + # sentence-transformers +pip-tools==7.4.1 # via sec-certs (./../pyproject.toml) pkgconfig==1.5.5 # via sec-certs (./../pyproject.toml) -platformdirs==4.0.0 +platformdirs==4.3.6 # via # jupyter-core # virtualenv -plotly==5.18.0 +plotly==5.24.1 # via # catboost # sec-certs (./../pyproject.toml) -pluggy==1.3.0 +pluggy==1.5.0 # via pytest -pre-commit==3.5.0 +pre-commit==4.1.0 # via sec-certs (./../pyproject.toml) preshed==3.0.9 # via # spacy # thinc -prompt-toolkit==3.0.41 +prompt-toolkit==3.0.50 # via ipython -propcache==0.2.0 - # via yarl -psutil==5.9.6 +propcache==0.2.1 # via + # aiohttp + # yarl +psutil==6.1.1 + # via + # accelerate # ipykernel # memory-profiler # pytest-monitor # sec-certs (./../pyproject.toml) ptyprocess==0.7.0 # via pexpect -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data -pyarrow==14.0.1 - # via datasets -pyarrow-hotfix==0.6 +pyarrow==19.0.0 # via datasets -pycparser==2.21 +pycparser==2.22 # via cffi pyct==0.5.0 - # via - # colorcet - # datashader -pydantic==2.5.2 + # via datashader +pydantic==2.10.6 # via # confection # pydantic-settings @@ -499,63 +500,67 @@ pydantic==2.5.2 # spacy # thinc # weasel -pydantic-core==2.14.5 +pydantic-core==2.27.2 # via pydantic -pydantic-settings==2.1.0 +pydantic-settings==2.7.1 # via sec-certs (./../pyproject.toml) -pydata-sphinx-theme==0.14.3 +pydata-sphinx-theme==0.16.1 # via sphinx-book-theme -pygments==2.17.2 +pygments==2.19.1 # via # accessible-pygments # ipython # pydata-sphinx-theme + # rich # sphinx -pynndescent==0.5.11 +pynndescent==0.5.13 # via umap-learn -pyparsing==3.1.1 +pyparsing==3.2.1 # via matplotlib -pypdf[crypto]==3.17.1 +pypdf[crypto]==5.2.0 # via # pypdf # sec-certs (./../pyproject.toml) -pyproject-hooks==1.0.0 - # via build -pysankeybeta==1.4.1 +pyproject-hooks==1.2.0 + # via + # build + # pip-tools +pysankeybeta==1.4.2 # via sec-certs (./../pyproject.toml) -pytesseract==0.3.10 +pytesseract==0.3.13 # via sec-certs (./../pyproject.toml) -pytest==7.4.3 +pytest==8.3.4 # via # pytest-cov # pytest-monitor # pytest-profiling # sec-certs (./../pyproject.toml) -pytest-cov==4.1.0 +pytest-cov==6.0.0 # via sec-certs (./../pyproject.toml) pytest-monitor==1.6.6 # via sec-certs (./../pyproject.toml) -pytest-profiling==1.7.0 +pytest-profiling==1.8.1 # via sec-certs (./../pyproject.toml) -python-dateutil==2.8.2 +python-dateutil==2.9.0.post0 # via # dateparser # jupyter-client # matplotlib # pandas # sec-certs (./../pyproject.toml) -python-dotenv==1.0.0 +python-dotenv==1.0.1 # via pydantic-settings -pytz==2023.3.post1 +pytz==2024.2 # via # dateparser # pandas -pyviz-comms==3.0.0 +pyviz-comms==3.0.4 # via # holoviews # panel -pyyaml==6.0.1 +pyyaml==6.0.2 # via + # accelerate # bokeh # dask # datasets @@ -567,56 +572,55 @@ pyyaml==6.0.1 # pre-commit # sec-certs (./../pyproject.toml) # transformers -pyzmq==25.1.1 +pyzmq==26.2.0 # via # ipykernel # jupyter-client -rapidfuzz==3.5.2 +rapidfuzz==3.11.0 # via sec-certs (./../pyproject.toml) -referencing==0.31.0 +referencing==0.36.2 # via # jsonschema # jsonschema-specifications -regex==2023.10.3 +regex==2024.11.6 # via # dateparser - # nltk # transformers -requests==2.32.0 +requests==2.32.3 # via # datasets # datashader # evaluate - # fsspec # huggingface-hub # panel # pytest-monitor - # responses # sec-certs (./../pyproject.toml) # spacy # sphinx - # torchvision # transformers # weasel -responses==0.18.0 - # via evaluate -rpds-py==0.13.1 +rich==13.9.4 + # via typer +rpds-py==0.22.3 # via # jsonschema # referencing ruff==0.7.4 # via sec-certs (./../pyproject.toml) -safetensors==0.4.5 - # via transformers -scikit-image==0.22.0 +safetensors==0.5.2 + # via + # accelerate + # transformers +scikit-image==0.25.1 # via umap-learn -scikit-learn==1.5.0 +scikit-learn==1.6.1 # via # pynndescent # sec-certs (./../pyproject.toml) # sentence-transformers + # setfit # umap-learn -scipy==1.11.4 +scipy==1.15.1 # via # catboost # datashader @@ -626,42 +630,40 @@ scipy==1.11.4 # sec-certs (./../pyproject.toml) # sentence-transformers # umap-learn -seaborn==0.13.0 +seaborn==0.13.2 # via # pysankeybeta # sec-certs (./../pyproject.toml) # umap-learn -sentence-transformers==2.2.2 - # via setfit -sentencepiece==0.1.99 - # via sentence-transformers -setfit==0.7.0 +sentence-transformers[train]==3.4.0 + # via + # sentence-transformers + # setfit +setfit==1.1.1 # via sec-certs (./../pyproject.toml) -setuptools-scm==8.0.4 +setuptools-scm==8.1.0 # via sec-certs (./../pyproject.toml) -six==1.16.0 +shellingham==1.5.4 + # via typer +six==1.17.0 # via - # asttokens - # bleach # catboost # html5lib # pytest-profiling # python-dateutil -smart-open==6.4.0 - # via - # spacy - # weasel +smart-open==7.1.0 + # via weasel snowballstemmer==2.2.0 # via sphinx -soupsieve==2.5 +soupsieve==2.6 # via beautifulsoup4 -spacy==3.7.2 +spacy==3.8.4 # via sec-certs (./../pyproject.toml) spacy-legacy==3.0.12 # via spacy spacy-loggers==1.0.5 # via spacy -sphinx==6.2.1 +sphinx==8.1.3 # via # myst-nb # myst-parser @@ -670,35 +672,30 @@ sphinx==6.2.1 # sphinx-book-theme # sphinx-copybutton # sphinx-design - # sphinxcontrib-applehelp - # sphinxcontrib-devhelp - # sphinxcontrib-htmlhelp - # sphinxcontrib-qthelp - # sphinxcontrib-serializinghtml -sphinx-book-theme==1.0.1 +sphinx-book-theme==1.1.3 # via sec-certs (./../pyproject.toml) sphinx-copybutton==0.5.2 # via sec-certs (./../pyproject.toml) -sphinx-design==0.5.0 +sphinx-design==0.6.1 # via sec-certs (./../pyproject.toml) -sphinxcontrib-applehelp==1.0.7 +sphinxcontrib-applehelp==2.0.0 # via sphinx -sphinxcontrib-devhelp==1.0.5 +sphinxcontrib-devhelp==2.0.0 # via sphinx -sphinxcontrib-htmlhelp==2.0.4 +sphinxcontrib-htmlhelp==2.1.0 # via sphinx sphinxcontrib-jsmath==1.0.1 # via sphinx -sphinxcontrib-qthelp==1.0.6 +sphinxcontrib-qthelp==2.0.0 # via sphinx -sphinxcontrib-serializinghtml==1.1.9 +sphinxcontrib-serializinghtml==2.0.0 # via sphinx -sqlalchemy==2.0.23 +sqlalchemy==2.0.37 # via # alembic # jupyter-cache # optuna -srsly==2.4.8 +srsly==2.5.1 # via # confection # spacy @@ -706,53 +703,41 @@ srsly==2.4.8 # weasel stack-data==0.6.3 # via ipython -sympy==1.12 +sympy==1.13.1 # via torch -tabula-py==2.9.0 +tabula-py==2.10.0 # via sec-certs (./../pyproject.toml) tabulate==0.9.0 # via jupyter-cache -tenacity==8.2.3 +tenacity==9.0.0 # via plotly -thinc==8.2.1 +thinc==8.3.4 # via spacy -threadpoolctl==3.2.0 +threadpoolctl==3.5.0 # via scikit-learn -tifffile==2023.9.26 +tifffile==2025.1.10 # via scikit-image -tokenizers==0.15.0 +tokenizers==0.21.0 # via transformers -tomli==2.0.1 - # via - # build - # coverage - # mypy - # pip-tools - # pyproject-hooks - # pytest - # setuptools-scm -toolz==0.12.0 +toolz==1.0.0 # via # dask # datashader # partd -torch==2.1.1 +torch==2.5.1 # via + # accelerate # sentence-transformers - # torchvision -torchvision==0.16.1 - # via sentence-transformers -tornado==6.4.1 +tornado==6.4.2 # via # bokeh # ipykernel # jupyter-client -tqdm==4.66.3 +tqdm==4.67.1 # via # datasets # evaluate # huggingface-hub - # nltk # optuna # panel # sec-certs (./../pyproject.toml) @@ -760,7 +745,7 @@ tqdm==4.66.3 # spacy # transformers # umap-learn -traitlets==5.13.0 +traitlets==5.14.3 # via # comm # ipykernel @@ -771,82 +756,83 @@ traitlets==5.13.0 # matplotlib-inline # nbclient # nbformat -transformers==4.38.0 - # via sentence-transformers -typer==0.9.0 +transformers==4.48.1 + # via + # sentence-transformers + # setfit +typer==0.15.1 # via # spacy # weasel -types-python-dateutil==2.8.19.14 +types-python-dateutil==2.9.0.20241206 # via sec-certs (./../pyproject.toml) -types-pyyaml==6.0.12.12 +types-pyyaml==6.0.12.20241230 # via sec-certs (./../pyproject.toml) -types-requests==2.31.0.10 +types-requests==2.32.0.20241016 # via sec-certs (./../pyproject.toml) -typing-extensions==4.8.0 +typing-extensions==4.12.2 # via # alembic - # cloudpathlib # huggingface-hub + # ipython # mypy # myst-nb # panel # pydantic # pydantic-core # pydata-sphinx-theme - # setuptools-scm + # referencing # sqlalchemy # torch # typer -tzdata==2023.3 +tzdata==2025.1 # via pandas tzlocal==5.2 # via dateparser -uc-micro-py==1.0.2 +uc-micro-py==1.0.3 # via linkify-it-py -umap-learn[plot]==0.5.5 +umap-learn[plot]==0.5.7 # via sec-certs (./../pyproject.toml) -urllib3==2.2.2 +urllib3==2.3.0 # via # requests - # responses # types-requests -virtualenv==20.24.7 +virtualenv==20.29.1 # via pre-commit -wasabi==1.1.2 +wasabi==1.1.3 # via # spacy # thinc # weasel -wcwidth==0.2.12 +wcwidth==0.2.13 # via prompt-toolkit -weasel==0.3.4 +weasel==0.4.1 # via spacy webencodings==0.5.1 # via # bleach # html5lib -wheel==0.41.3 +wheel==0.45.1 # via # pip-tools # pytest-monitor -widgetsnbextension==4.0.9 +widgetsnbextension==4.0.13 # via ipywidgets -wrapt==1.16.0 - # via deprecated -xarray==2023.11.0 +wrapt==1.17.2 + # via + # deprecated + # smart-open +xarray==2025.1.1 # via datashader -xxhash==3.4.1 +xxhash==3.5.0 # via # datasets # evaluate -xyzservices==2023.10.1 - # via - # bokeh - # panel -yarl==1.17.2 +xyzservices==2025.1.0 + # via bokeh +yarl==1.18.3 # via aiohttp -zipp==3.19.1 +zipp==3.21.0 # via importlib-metadata # The following packages are considered to be unsafe in a requirements file: diff --git a/requirements/compile.sh b/requirements/compile.sh index 743006b24..ffb74e935 100755 --- a/requirements/compile.sh +++ b/requirements/compile.sh @@ -2,8 +2,8 @@ # See CONTRIBUTING.md for description -pip-compile --no-header -o requirements.txt ./../pyproject.toml -pip-compile --no-header --extra dev -o dev_requirements.txt ./../pyproject.toml -pip-compile --no-header --extra test -o test_requirements.txt ./../pyproject.toml -pip-compile --no-header --extra nlp -o nlp_requirements.txt ./../pyproject.toml -pip-compile --no-header --extra dev --extra test --extra nlp -o all_requirements.txt ./../pyproject.toml +pip-compile --upgrade --no-header -o requirements.txt ./../pyproject.toml +pip-compile --upgrade --no-header --extra dev -o dev_requirements.txt ./../pyproject.toml +pip-compile --upgrade --no-header --extra test -o test_requirements.txt ./../pyproject.toml +pip-compile --upgrade --no-header --extra nlp -o nlp_requirements.txt ./../pyproject.toml +pip-compile --upgrade --no-header --extra dev --extra test --extra nlp -o all_requirements.txt ./../pyproject.toml diff --git a/requirements/dev_requirements.txt b/requirements/dev_requirements.txt index 4db7d080a..1995d48e7 100644 --- a/requirements/dev_requirements.txt +++ b/requirements/dev_requirements.txt @@ -1,230 +1,228 @@ -accessible-pygments==0.0.4 +accessible-pygments==0.0.5 # via pydata-sphinx-theme -aiohappyeyeballs==2.4.0 +aiohappyeyeballs==2.4.4 # via aiohttp -aiohttp==3.10.11 +aiohttp==3.11.11 # via # datasets # fsspec -aiosignal==1.3.1 +aiosignal==1.3.2 # via aiohttp -alabaster==0.7.13 +alabaster==1.0.0 # via sphinx -annotated-types==0.6.0 +annotated-types==0.7.0 # via pydantic appnope==0.1.4 - # via - # ipykernel - # ipython -asttokens==2.4.1 + # via ipykernel +asttokens==3.0.0 # via stack-data -async-timeout==5.0.1 - # via aiohttp -attrs==23.1.0 +attrs==25.1.0 # via # aiohttp # jsonschema # jupyter-cache # referencing -babel==2.13.1 +babel==2.16.0 # via # pydata-sphinx-theme # sphinx -beautifulsoup4==4.12.2 +beautifulsoup4==4.12.3 # via # pydata-sphinx-theme # sec-certs (./../pyproject.toml) -blis==0.7.11 +blis==1.2.0 # via thinc -build==1.0.3 +build==1.2.2.post1 # via pip-tools catalogue==2.0.10 # via # spacy # srsly # thinc -certifi==2024.7.4 +certifi==2024.12.14 # via requests -cffi==1.16.0 +cffi==1.17.1 # via cryptography cfgv==3.4.0 # via pre-commit -charset-normalizer==3.3.2 +charset-normalizer==3.4.1 # via requests -click==8.1.7 +click==8.1.8 # via # jupyter-cache # pip-tools # sec-certs (./../pyproject.toml) # typer -cloudpathlib==0.16.0 +cloudpathlib==0.20.0 # via weasel -comm==0.2.0 +comm==0.2.2 # via # ipykernel # ipywidgets -confection==0.1.3 +confection==0.1.5 # via # thinc # weasel -contourpy==1.2.0 +contourpy==1.3.1 # via matplotlib -coverage[toml]==7.3.2 +coverage[toml]==7.6.10 # via # coverage # pytest-cov -cryptography==43.0.1 +cryptography==44.0.0 # via pypdf cycler==0.12.1 # via matplotlib -cymem==2.0.8 +cymem==2.0.11 # via # preshed # spacy # thinc -datasets==2.15.0 +datasets==3.2.0 # via sec-certs (./../pyproject.toml) dateparser==1.2.0 # via sec-certs (./../pyproject.toml) -debugpy==1.8.0 +debugpy==1.8.12 # via ipykernel decorator==5.1.1 # via ipython -deprecated==1.2.14 +deprecated==1.2.18 # via pikepdf -dill==0.3.7 +dill==0.3.8 # via # datasets # multiprocess -distlib==0.3.7 +distlib==0.3.9 # via virtualenv -distro==1.8.0 +distro==1.9.0 # via tabula-py -docutils==0.19 +docutils==0.21.2 # via # myst-parser # pydata-sphinx-theme # sphinx -exceptiongroup==1.2.2 - # via - # ipython - # pytest -executing==2.0.1 +executing==2.2.0 # via stack-data -fastjsonschema==2.19.0 +fastjsonschema==2.21.1 # via nbformat -filelock==3.13.1 +filelock==3.17.0 # via + # datasets # huggingface-hub # virtualenv -fonttools==4.45.0 +fonttools==4.55.6 # via matplotlib -frozenlist==1.4.0 +frozenlist==1.5.0 # via # aiohttp # aiosignal -fsspec[http]==2023.10.0 +fsspec[http]==2024.9.0 # via # datasets # fsspec # huggingface-hub -gprof2dot==2022.7.29 +gprof2dot==2024.6.6 # via pytest-profiling html5lib==1.1 # via sec-certs (./../pyproject.toml) -huggingface-hub==0.19.4 +huggingface-hub==0.27.1 # via datasets -identify==2.5.32 +identify==2.6.6 # via pre-commit -idna==3.7 +idna==3.10 # via # requests # yarl imagesize==1.4.1 # via sphinx -importlib-metadata==6.8.0 +importlib-metadata==8.6.1 # via # jupyter-cache # myst-nb iniconfig==2.0.0 # via pytest -ipykernel==6.27.0 +ipykernel==6.29.5 # via # myst-nb # sec-certs (./../pyproject.toml) -ipython==8.17.2 +ipython==8.31.0 # via # ipykernel # ipywidgets # myst-nb # sec-certs (./../pyproject.toml) -ipywidgets==8.1.1 +ipywidgets==8.1.5 # via sec-certs (./../pyproject.toml) -jedi==0.19.1 +jedi==0.19.2 # via ipython -jinja2==3.1.4 +jinja2==3.1.5 # via # myst-parser # spacy # sphinx -joblib==1.3.2 +joblib==1.4.2 # via scikit-learn -jsonschema==4.20.0 +jsonschema==4.23.0 # via # nbformat # sec-certs (./../pyproject.toml) -jsonschema-specifications==2023.11.1 +jsonschema-specifications==2024.10.1 # via jsonschema -jupyter-cache==1.0.0 +jupyter-cache==1.0.1 # via myst-nb -jupyter-client==8.6.0 +jupyter-client==8.6.3 # via # ipykernel # nbclient -jupyter-core==5.5.0 +jupyter-core==5.7.2 # via # ipykernel # jupyter-client # nbclient # nbformat -jupyterlab-widgets==3.0.9 +jupyterlab-widgets==3.0.13 # via ipywidgets -kiwisolver==1.4.5 +kiwisolver==1.4.8 # via matplotlib -langcodes==3.3.0 +langcodes==3.5.0 # via spacy -lxml==4.9.3 +language-data==1.3.0 + # via langcodes +lxml==5.3.0 # via # pikepdf # sec-certs (./../pyproject.toml) +marisa-trie==1.2.1 + # via language-data markdown-it-py==3.0.0 # via # mdit-py-plugins # myst-parser -markupsafe==2.1.3 + # rich +markupsafe==3.0.2 # via jinja2 -matplotlib==3.8.2 +matplotlib==3.10.0 # via # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # ipykernel # ipython -mdit-py-plugins==0.4.0 +mdit-py-plugins==0.4.2 # via myst-parser mdurl==0.1.2 # via markdown-it-py memory-profiler==0.61.0 # via pytest-monitor -multidict==6.0.4 +multidict==6.1.0 # via # aiohttp # yarl -multiprocess==0.70.15 +multiprocess==0.70.16 # via datasets -murmurhash==1.0.10 +murmurhash==1.0.12 # via # preshed # spacy @@ -233,33 +231,32 @@ mypy==1.13.0 # via sec-certs (./../pyproject.toml) mypy-extensions==1.0.0 # via mypy -myst-nb==1.0.0 +myst-nb==1.1.2 # via sec-certs (./../pyproject.toml) -myst-parser==2.0.0 +myst-parser==4.0.0 # via myst-nb -nbclient==0.9.0 +nbclient==0.10.2 # via # jupyter-cache # myst-nb -nbformat==5.9.2 +nbformat==5.10.4 # via # jupyter-cache # myst-nb # nbclient -nest-asyncio==1.5.8 +nest-asyncio==1.6.0 # via ipykernel -networkx==3.2.1 +networkx==3.4.2 # via sec-certs (./../pyproject.toml) -nodeenv==1.8.0 +nodeenv==1.9.1 # via pre-commit -numpy==1.26.2 +numpy==2.2.2 # via # blis # contourpy # datasets # matplotlib # pandas - # pyarrow # pysankeybeta # scikit-learn # scipy @@ -268,7 +265,7 @@ numpy==1.26.2 # spacy # tabula-py # thinc -packaging==23.2 +packaging==24.2 # via # build # datasets @@ -276,7 +273,6 @@ packaging==23.2 # ipykernel # matplotlib # pikepdf - # pydata-sphinx-theme # pytesseract # pytest # setuptools-scm @@ -284,48 +280,50 @@ packaging==23.2 # sphinx # thinc # weasel -pandas==2.1.3 +pandas==2.2.3 # via # datasets # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) # tabula-py -parso==0.8.3 +parso==0.8.4 # via jedi -pdftotext==2.2.2 +pdftotext==3.0.0 # via sec-certs (./../pyproject.toml) -pexpect==4.8.0 +pexpect==4.9.0 # via ipython -pikepdf==8.7.1 +pikepdf==9.5.1 # via sec-certs (./../pyproject.toml) -pillow==10.3.0 +pillow==11.1.0 # via # matplotlib # pikepdf # pytesseract # sec-certs (./../pyproject.toml) -pip-tools==7.3.0 +pip-tools==7.4.1 # via sec-certs (./../pyproject.toml) pkgconfig==1.5.5 # via sec-certs (./../pyproject.toml) -platformdirs==4.0.0 +platformdirs==4.3.6 # via # jupyter-core # virtualenv -pluggy==1.3.0 +pluggy==1.5.0 # via pytest -pre-commit==3.5.0 +pre-commit==4.1.0 # via sec-certs (./../pyproject.toml) preshed==3.0.9 # via # spacy # thinc -prompt-toolkit==3.0.41 +prompt-toolkit==3.0.50 # via ipython -propcache==0.2.0 - # via yarl -psutil==5.9.6 +propcache==0.2.1 + # via + # aiohttp + # yarl +psutil==6.1.1 # via # ipykernel # memory-profiler @@ -333,15 +331,13 @@ psutil==5.9.6 # sec-certs (./../pyproject.toml) ptyprocess==0.7.0 # via pexpect -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data -pyarrow==14.0.1 +pyarrow==19.0.0 # via datasets -pyarrow-hotfix==0.6 - # via datasets -pycparser==2.21 +pycparser==2.22 # via cffi -pydantic==2.5.2 +pydantic==2.10.6 # via # confection # pydantic-settings @@ -349,56 +345,59 @@ pydantic==2.5.2 # spacy # thinc # weasel -pydantic-core==2.14.5 +pydantic-core==2.27.2 # via pydantic -pydantic-settings==2.1.0 +pydantic-settings==2.7.1 # via sec-certs (./../pyproject.toml) -pydata-sphinx-theme==0.14.3 +pydata-sphinx-theme==0.16.1 # via sphinx-book-theme -pygments==2.17.2 +pygments==2.19.1 # via # accessible-pygments # ipython # pydata-sphinx-theme + # rich # sphinx -pyparsing==3.1.1 +pyparsing==3.2.1 # via matplotlib -pypdf[crypto]==3.17.1 +pypdf[crypto]==5.2.0 # via # pypdf # sec-certs (./../pyproject.toml) -pyproject-hooks==1.0.0 - # via build -pysankeybeta==1.4.1 +pyproject-hooks==1.2.0 + # via + # build + # pip-tools +pysankeybeta==1.4.2 # via sec-certs (./../pyproject.toml) -pytesseract==0.3.10 +pytesseract==0.3.13 # via sec-certs (./../pyproject.toml) -pytest==7.4.3 +pytest==8.3.4 # via # pytest-cov # pytest-monitor # pytest-profiling # sec-certs (./../pyproject.toml) -pytest-cov==4.1.0 +pytest-cov==6.0.0 # via sec-certs (./../pyproject.toml) pytest-monitor==1.6.6 # via sec-certs (./../pyproject.toml) -pytest-profiling==1.7.0 +pytest-profiling==1.8.1 # via sec-certs (./../pyproject.toml) -python-dateutil==2.8.2 +python-dateutil==2.9.0.post0 # via # dateparser # jupyter-client # matplotlib # pandas # sec-certs (./../pyproject.toml) -python-dotenv==1.0.0 +python-dotenv==1.0.1 # via pydantic-settings -pytz==2023.3.post1 +pytz==2024.2 # via # dateparser # pandas -pyyaml==6.0.1 +pyyaml==6.0.2 # via # datasets # huggingface-hub @@ -407,67 +406,67 @@ pyyaml==6.0.1 # myst-parser # pre-commit # sec-certs (./../pyproject.toml) -pyzmq==25.1.1 +pyzmq==26.2.0 # via # ipykernel # jupyter-client -rapidfuzz==3.5.2 +rapidfuzz==3.11.0 # via sec-certs (./../pyproject.toml) -referencing==0.31.0 +referencing==0.36.2 # via # jsonschema # jsonschema-specifications -regex==2024.9.11 +regex==2024.11.6 # via dateparser -requests==2.32.0 +requests==2.32.3 # via # datasets - # fsspec # huggingface-hub # pytest-monitor # sec-certs (./../pyproject.toml) # spacy # sphinx # weasel -rpds-py==0.13.1 +rich==13.9.4 + # via typer +rpds-py==0.22.3 # via # jsonschema # referencing ruff==0.7.4 # via sec-certs (./../pyproject.toml) -scikit-learn==1.5.0 +scikit-learn==1.6.1 # via sec-certs (./../pyproject.toml) -scipy==1.11.4 +scipy==1.15.1 # via # scikit-learn # sec-certs (./../pyproject.toml) -seaborn==0.13.0 +seaborn==0.13.2 # via # pysankeybeta # sec-certs (./../pyproject.toml) -setuptools-scm==8.0.4 +setuptools-scm==8.1.0 # via sec-certs (./../pyproject.toml) -six==1.16.0 +shellingham==1.5.4 + # via typer +six==1.17.0 # via - # asttokens # html5lib # pytest-profiling # python-dateutil -smart-open==6.4.0 - # via - # spacy - # weasel +smart-open==7.1.0 + # via weasel snowballstemmer==2.2.0 # via sphinx -soupsieve==2.5 +soupsieve==2.6 # via beautifulsoup4 -spacy==3.7.2 +spacy==3.8.4 # via sec-certs (./../pyproject.toml) spacy-legacy==3.0.12 # via spacy spacy-loggers==1.0.5 # via spacy -sphinx==6.2.1 +sphinx==8.1.3 # via # myst-nb # myst-parser @@ -476,32 +475,27 @@ sphinx==6.2.1 # sphinx-book-theme # sphinx-copybutton # sphinx-design - # sphinxcontrib-applehelp - # sphinxcontrib-devhelp - # sphinxcontrib-htmlhelp - # sphinxcontrib-qthelp - # sphinxcontrib-serializinghtml -sphinx-book-theme==1.0.1 +sphinx-book-theme==1.1.3 # via sec-certs (./../pyproject.toml) sphinx-copybutton==0.5.2 # via sec-certs (./../pyproject.toml) -sphinx-design==0.5.0 +sphinx-design==0.6.1 # via sec-certs (./../pyproject.toml) -sphinxcontrib-applehelp==1.0.7 +sphinxcontrib-applehelp==2.0.0 # via sphinx -sphinxcontrib-devhelp==1.0.5 +sphinxcontrib-devhelp==2.0.0 # via sphinx -sphinxcontrib-htmlhelp==2.0.4 +sphinxcontrib-htmlhelp==2.1.0 # via sphinx sphinxcontrib-jsmath==1.0.1 # via sphinx -sphinxcontrib-qthelp==1.0.6 +sphinxcontrib-qthelp==2.0.0 # via sphinx -sphinxcontrib-serializinghtml==1.1.9 +sphinxcontrib-serializinghtml==2.0.0 # via sphinx -sqlalchemy==2.0.23 +sqlalchemy==2.0.37 # via jupyter-cache -srsly==2.4.8 +srsly==2.5.1 # via # confection # spacy @@ -509,34 +503,25 @@ srsly==2.4.8 # weasel stack-data==0.6.3 # via ipython -tabula-py==2.9.0 +tabula-py==2.10.0 # via sec-certs (./../pyproject.toml) tabulate==0.9.0 # via jupyter-cache -thinc==8.2.1 +thinc==8.3.4 # via spacy -threadpoolctl==3.2.0 +threadpoolctl==3.5.0 # via scikit-learn -tomli==2.1.0 - # via - # build - # coverage - # mypy - # pip-tools - # pyproject-hooks - # pytest - # setuptools-scm -tornado==6.4.1 +tornado==6.4.2 # via # ipykernel # jupyter-client -tqdm==4.66.3 +tqdm==4.67.1 # via # datasets # huggingface-hub # sec-certs (./../pyproject.toml) # spacy -traitlets==5.13.0 +traitlets==5.14.3 # via # comm # ipykernel @@ -547,62 +532,64 @@ traitlets==5.13.0 # matplotlib-inline # nbclient # nbformat -typer==0.9.0 +typer==0.15.1 # via # spacy # weasel -types-python-dateutil==2.8.19.14 +types-python-dateutil==2.9.0.20241206 # via sec-certs (./../pyproject.toml) -types-pyyaml==6.0.12.12 +types-pyyaml==6.0.12.20241230 # via sec-certs (./../pyproject.toml) -types-requests==2.31.0.10 +types-requests==2.32.0.20241016 # via sec-certs (./../pyproject.toml) -typing-extensions==4.8.0 +typing-extensions==4.12.2 # via - # cloudpathlib # huggingface-hub + # ipython # mypy # myst-nb # pydantic # pydantic-core # pydata-sphinx-theme - # setuptools-scm + # referencing # sqlalchemy # typer -tzdata==2023.3 +tzdata==2025.1 # via pandas tzlocal==5.2 # via dateparser -urllib3==2.2.2 +urllib3==2.3.0 # via # requests # types-requests -virtualenv==20.24.7 +virtualenv==20.29.1 # via pre-commit -wasabi==1.1.2 +wasabi==1.1.3 # via # spacy # thinc # weasel -wcwidth==0.2.12 +wcwidth==0.2.13 # via prompt-toolkit -weasel==0.3.4 +weasel==0.4.1 # via spacy webencodings==0.5.1 # via html5lib -wheel==0.41.3 +wheel==0.45.1 # via # pip-tools # pytest-monitor -widgetsnbextension==4.0.9 +widgetsnbextension==4.0.13 # via ipywidgets -wrapt==1.16.0 - # via deprecated -xxhash==3.4.1 +wrapt==1.17.2 + # via + # deprecated + # smart-open +xxhash==3.5.0 # via datasets -yarl==1.17.2 +yarl==1.18.3 # via aiohttp -zipp==3.19.1 +zipp==3.21.0 # via importlib-metadata # The following packages are considered to be unsafe in a requirements file: diff --git a/requirements/nlp_requirements.txt b/requirements/nlp_requirements.txt index a8868053d..45258a506 100644 --- a/requirements/nlp_requirements.txt +++ b/requirements/nlp_requirements.txt @@ -1,36 +1,35 @@ -aiohappyeyeballs==2.4.0 +accelerate==1.3.0 + # via sentence-transformers +aiohappyeyeballs==2.4.4 # via aiohttp -aiohttp==3.10.11 +aiohttp==3.11.11 # via # datasets # fsspec -aiosignal==1.3.1 +aiosignal==1.3.2 # via aiohttp -alembic==1.12.1 +alembic==1.14.1 # via optuna -annotated-types==0.6.0 +annotated-types==0.7.0 # via pydantic -appnope==0.1.3 - # via - # ipykernel - # ipython -asttokens==2.4.1 +appnope==0.1.4 + # via ipykernel +asttokens==3.0.0 # via stack-data -async-timeout==4.0.3 - # via aiohttp -attrs==23.1.0 +attrs==25.1.0 # via # aiohttp # jsonschema # referencing -beautifulsoup4==4.12.2 +beautifulsoup4==4.12.3 # via sec-certs (./../pyproject.toml) -bleach==6.1.0 +bleach==6.2.0 # via panel -blis==0.7.11 +blis==1.2.0 # via thinc -bokeh==3.3.1 +bokeh==3.6.2 # via + # holoviews # panel # umap-learn catalogue==2.0.10 @@ -38,93 +37,92 @@ catalogue==2.0.10 # spacy # srsly # thinc -catboost==1.2.2 +catboost==1.2.7 # via sec-certs (./../pyproject.toml) -certifi==2024.7.4 +certifi==2024.12.14 # via requests -cffi==1.16.0 +cffi==1.17.1 # via cryptography -charset-normalizer==3.3.2 +charset-normalizer==3.4.1 # via requests -click==8.1.7 +click==8.1.8 # via # dask - # nltk # sec-certs (./../pyproject.toml) # typer -cloudpathlib==0.16.0 +cloudpathlib==0.20.0 # via weasel -cloudpickle==3.0.0 +cloudpickle==3.1.1 # via dask -colorcet==3.0.1 +colorcet==3.1.0 # via # datashader # holoviews # umap-learn -colorlog==6.7.0 +colorlog==6.9.0 # via optuna -comm==0.2.0 +comm==0.2.2 # via # ipykernel # ipywidgets -confection==0.1.3 +confection==0.1.5 # via # thinc # weasel -contourpy==1.2.0 +contourpy==1.3.1 # via # bokeh # matplotlib -cryptography==43.0.1 +cryptography==44.0.0 # via pypdf cycler==0.12.1 # via matplotlib -cymem==2.0.8 +cymem==2.0.11 # via # preshed # spacy # thinc -dask==2023.11.0 +dask==2025.1.0 # via datashader -datasets==2.15.0 +datasets==3.2.0 # via # evaluate + # sentence-transformers # setfit -datashader==0.16.0 +datashader==0.16.3 # via umap-learn dateparser==1.2.0 # via sec-certs (./../pyproject.toml) -debugpy==1.8.0 +debugpy==1.8.12 # via ipykernel decorator==5.1.1 # via ipython -deprecated==1.2.14 +deprecated==1.2.18 # via pikepdf -dill==0.3.7 +dill==0.3.8 # via # datasets # evaluate # multiprocess -distro==1.8.0 +distro==1.9.0 # via tabula-py -evaluate==0.4.1 +evaluate==0.4.3 # via setfit -exceptiongroup==1.2.2 - # via ipython -executing==2.0.1 +executing==2.2.0 # via stack-data -filelock==3.13.1 +filelock==3.17.0 # via + # datasets # huggingface-hub # torch # transformers -fonttools==4.45.0 +fonttools==4.55.6 # via matplotlib -frozenlist==1.4.0 +frozenlist==1.5.0 # via # aiohttp # aiosignal -fsspec[http]==2023.10.0 +fsspec[http]==2024.9.0 # via # dask # datasets @@ -132,137 +130,142 @@ fsspec[http]==2023.10.0 # fsspec # huggingface-hub # torch -graphviz==0.20.1 +graphviz==0.20.3 # via catboost -holoviews==1.18.1 +holoviews==1.20.0 # via umap-learn html5lib==1.1 # via sec-certs (./../pyproject.toml) -huggingface-hub==0.19.4 +huggingface-hub==0.27.1 # via + # accelerate # datasets # evaluate # sentence-transformers + # setfit # tokenizers # transformers -idna==3.7 +idna==3.10 # via # requests # yarl -imageio==2.33.0 +imageio==2.37.0 # via scikit-image -importlib-metadata==6.8.0 +importlib-metadata==8.6.1 # via dask -ipykernel==6.27.0 +ipykernel==6.29.5 # via sec-certs (./../pyproject.toml) -ipython==8.17.2 +ipython==8.31.0 # via # ipykernel # ipywidgets -ipywidgets==8.1.1 +ipywidgets==8.1.5 # via sec-certs (./../pyproject.toml) -jedi==0.19.1 +jedi==0.19.2 # via ipython -jinja2==3.1.4 +jinja2==3.1.5 # via # bokeh # spacy # torch -joblib==1.3.2 +joblib==1.4.2 # via - # nltk # pynndescent # scikit-learn -jsonschema==4.20.0 +jsonschema==4.23.0 # via sec-certs (./../pyproject.toml) -jsonschema-specifications==2023.11.1 +jsonschema-specifications==2024.10.1 # via jsonschema -jupyter-client==8.6.0 +jupyter-client==8.6.3 # via ipykernel -jupyter-core==5.5.0 +jupyter-core==5.7.2 # via # ipykernel # jupyter-client -jupyterlab-widgets==3.0.9 +jupyterlab-widgets==3.0.13 # via ipywidgets -kiwisolver==1.4.5 +kiwisolver==1.4.8 # via matplotlib -langcodes==3.3.0 +langcodes==3.5.0 # via spacy -lazy-loader==0.3 +language-data==1.3.0 + # via langcodes +lazy-loader==0.4 # via scikit-image -linkify-it-py==2.0.2 +linkify-it-py==2.0.3 # via panel -llvmlite==0.41.1 +llvmlite==0.44.0 # via # numba # pynndescent locket==1.0.0 # via partd -lxml==4.9.3 +lxml==5.3.0 # via # pikepdf # sec-certs (./../pyproject.toml) -mako==1.3.0 +mako==1.3.8 # via alembic -markdown==3.5.1 +marisa-trie==1.2.1 + # via language-data +markdown==3.7 # via panel markdown-it-py==3.0.0 # via # mdit-py-plugins # panel -markupsafe==2.1.3 + # rich +markupsafe==3.0.2 # via # jinja2 # mako -matplotlib==3.8.2 +matplotlib==3.10.0 # via # catboost # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) # umap-learn -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # ipykernel # ipython -mdit-py-plugins==0.4.0 +mdit-py-plugins==0.4.2 # via panel mdurl==0.1.2 # via markdown-it-py mpmath==1.3.0 # via sympy -multidict==6.0.4 +multidict==6.1.0 # via # aiohttp # yarl multipledispatch==1.0.0 # via datashader -multiprocess==0.70.15 +multiprocess==0.70.16 # via # datasets # evaluate -murmurhash==1.0.10 +murmurhash==1.0.12 # via # preshed # spacy # thinc -nest-asyncio==1.5.8 +nest-asyncio==1.6.0 # via ipykernel -networkx==3.2.1 +networkx==3.4.2 # via # scikit-image # sec-certs (./../pyproject.toml) # torch -nltk==3.9 - # via sentence-transformers -numba==0.58.1 +numba==0.61.0 # via # datashader # pynndescent # umap-learn -numpy==1.26.2 +numpy==1.26.4 # via + # accelerate # blis # bokeh # catboost @@ -276,46 +279,48 @@ numpy==1.26.2 # numba # optuna # pandas - # pyarrow # pysankeybeta # scikit-image # scikit-learn # scipy # seaborn # sec-certs (./../pyproject.toml) - # sentence-transformers # spacy # tabula-py # thinc # tifffile - # torchvision # transformers # umap-learn # xarray -optuna==3.4.0 +optuna==4.2.0 # via sec-certs (./../pyproject.toml) -packaging==23.2 +packaging==24.2 # via + # accelerate # bokeh # dask # datasets + # datashader # evaluate # holoviews # huggingface-hub # ipykernel + # lazy-loader # matplotlib # optuna + # panel # pikepdf # plotly # pytesseract # scikit-image + # setfit # setuptools-scm # spacy # thinc # transformers # weasel # xarray -pandas==2.1.3 +pandas==2.2.3 # via # bokeh # catboost @@ -330,26 +335,26 @@ pandas==2.1.3 # tabula-py # umap-learn # xarray -panel==1.3.2 +panel==1.6.0 # via holoviews -param==2.0.1 +param==2.2.0 # via # datashader # holoviews # panel # pyct # pyviz-comms -parso==0.8.3 +parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask -pdftotext==2.2.2 +pdftotext==3.0.0 # via sec-certs (./../pyproject.toml) -pexpect==4.8.0 +pexpect==4.9.0 # via ipython -pikepdf==8.7.1 +pikepdf==9.5.1 # via sec-certs (./../pyproject.toml) -pillow==10.3.0 +pillow==11.1.0 # via # bokeh # datashader @@ -359,12 +364,12 @@ pillow==10.3.0 # pytesseract # scikit-image # sec-certs (./../pyproject.toml) - # torchvision + # sentence-transformers pkgconfig==1.5.5 # via sec-certs (./../pyproject.toml) -platformdirs==4.0.0 +platformdirs==4.3.6 # via jupyter-core -plotly==5.18.0 +plotly==5.24.1 # via # catboost # sec-certs (./../pyproject.toml) @@ -372,29 +377,28 @@ preshed==3.0.9 # via # spacy # thinc -prompt-toolkit==3.0.41 +prompt-toolkit==3.0.50 # via ipython -propcache==0.2.0 - # via yarl -psutil==5.9.6 +propcache==0.2.1 # via + # aiohttp + # yarl +psutil==6.1.1 + # via + # accelerate # ipykernel # sec-certs (./../pyproject.toml) ptyprocess==0.7.0 # via pexpect -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data -pyarrow==14.0.1 - # via datasets -pyarrow-hotfix==0.6 +pyarrow==19.0.0 # via datasets -pycparser==2.21 +pycparser==2.22 # via cffi pyct==0.5.0 - # via - # colorcet - # datashader -pydantic==2.5.2 + # via datashader +pydantic==2.10.6 # via # confection # pydantic-settings @@ -402,43 +406,46 @@ pydantic==2.5.2 # spacy # thinc # weasel -pydantic-core==2.14.5 +pydantic-core==2.27.2 # via pydantic -pydantic-settings==2.1.0 +pydantic-settings==2.7.1 # via sec-certs (./../pyproject.toml) -pygments==2.17.2 - # via ipython -pynndescent==0.5.11 +pygments==2.19.1 + # via + # ipython + # rich +pynndescent==0.5.13 # via umap-learn -pyparsing==3.1.1 +pyparsing==3.2.1 # via matplotlib -pypdf[crypto]==3.17.1 +pypdf[crypto]==5.2.0 # via # pypdf # sec-certs (./../pyproject.toml) -pysankeybeta==1.4.1 +pysankeybeta==1.4.2 # via sec-certs (./../pyproject.toml) -pytesseract==0.3.10 +pytesseract==0.3.13 # via sec-certs (./../pyproject.toml) -python-dateutil==2.8.2 +python-dateutil==2.9.0.post0 # via # dateparser # jupyter-client # matplotlib # pandas # sec-certs (./../pyproject.toml) -python-dotenv==1.0.0 +python-dotenv==1.0.1 # via pydantic-settings -pytz==2023.3.post1 +pytz==2024.2 # via # dateparser # pandas -pyviz-comms==3.0.0 +pyviz-comms==3.0.4 # via # holoviews # panel -pyyaml==6.0.1 +pyyaml==6.0.2 # via + # accelerate # bokeh # dask # datasets @@ -446,52 +453,51 @@ pyyaml==6.0.1 # optuna # sec-certs (./../pyproject.toml) # transformers -pyzmq==25.1.1 +pyzmq==26.2.0 # via # ipykernel # jupyter-client -rapidfuzz==3.5.2 +rapidfuzz==3.11.0 # via sec-certs (./../pyproject.toml) -referencing==0.31.0 +referencing==0.36.2 # via # jsonschema # jsonschema-specifications -regex==2023.10.3 +regex==2024.11.6 # via # dateparser - # nltk # transformers -requests==2.32.0 +requests==2.32.3 # via # datasets # datashader # evaluate - # fsspec # huggingface-hub # panel - # responses # sec-certs (./../pyproject.toml) # spacy - # torchvision # transformers # weasel -responses==0.18.0 - # via evaluate -rpds-py==0.13.1 +rich==13.9.4 + # via typer +rpds-py==0.22.3 # via # jsonschema # referencing -safetensors==0.4.5 - # via transformers -scikit-image==0.22.0 +safetensors==0.5.2 + # via + # accelerate + # transformers +scikit-image==0.25.1 # via umap-learn -scikit-learn==1.5.0 +scikit-learn==1.6.1 # via # pynndescent # sec-certs (./../pyproject.toml) # sentence-transformers + # setfit # umap-learn -scipy==1.11.4 +scipy==1.15.1 # via # catboost # datashader @@ -501,43 +507,41 @@ scipy==1.11.4 # sec-certs (./../pyproject.toml) # sentence-transformers # umap-learn -seaborn==0.13.0 +seaborn==0.13.2 # via # pysankeybeta # sec-certs (./../pyproject.toml) # umap-learn -sentence-transformers==2.2.2 - # via setfit -sentencepiece==0.1.99 - # via sentence-transformers -setfit==0.7.0 +sentence-transformers[train]==3.4.0 + # via + # sentence-transformers + # setfit +setfit==1.1.1 # via sec-certs (./../pyproject.toml) -setuptools-scm==8.0.4 +setuptools-scm==8.1.0 # via sec-certs (./../pyproject.toml) -six==1.16.0 +shellingham==1.5.4 + # via typer +six==1.17.0 # via - # asttokens - # bleach # catboost # html5lib # python-dateutil -smart-open==6.4.0 - # via - # spacy - # weasel -soupsieve==2.5 +smart-open==7.1.0 + # via weasel +soupsieve==2.6 # via beautifulsoup4 -spacy==3.7.2 +spacy==3.8.4 # via sec-certs (./../pyproject.toml) spacy-legacy==3.0.12 # via spacy spacy-loggers==1.0.5 # via spacy -sqlalchemy==2.0.23 +sqlalchemy==2.0.37 # via # alembic # optuna -srsly==2.4.8 +srsly==2.5.1 # via # confection # spacy @@ -545,44 +549,39 @@ srsly==2.4.8 # weasel stack-data==0.6.3 # via ipython -sympy==1.12 +sympy==1.13.1 # via torch -tabula-py==2.9.0 +tabula-py==2.10.0 # via sec-certs (./../pyproject.toml) -tenacity==8.2.3 +tenacity==9.0.0 # via plotly -thinc==8.2.1 +thinc==8.3.4 # via spacy -threadpoolctl==3.2.0 +threadpoolctl==3.5.0 # via scikit-learn -tifffile==2023.9.26 +tifffile==2025.1.10 # via scikit-image -tokenizers==0.15.0 +tokenizers==0.21.0 # via transformers -tomli==2.0.1 - # via setuptools-scm -toolz==0.12.0 +toolz==1.0.0 # via # dask # datashader # partd -torch==2.1.1 +torch==2.5.1 # via + # accelerate # sentence-transformers - # torchvision -torchvision==0.16.1 - # via sentence-transformers -tornado==6.4.1 +tornado==6.4.2 # via # bokeh # ipykernel # jupyter-client -tqdm==4.66.3 +tqdm==4.67.1 # via # datasets # evaluate # huggingface-hub - # nltk # optuna # panel # sec-certs (./../pyproject.toml) @@ -590,7 +589,7 @@ tqdm==4.66.3 # spacy # transformers # umap-learn -traitlets==5.13.0 +traitlets==5.14.3 # via # comm # ipykernel @@ -599,66 +598,66 @@ traitlets==5.13.0 # jupyter-client # jupyter-core # matplotlib-inline -transformers==4.38.0 - # via sentence-transformers -typer==0.9.0 +transformers==4.48.1 + # via + # sentence-transformers + # setfit +typer==0.15.1 # via # spacy # weasel -typing-extensions==4.8.0 +typing-extensions==4.12.2 # via # alembic - # cloudpathlib # huggingface-hub + # ipython # panel # pydantic # pydantic-core - # setuptools-scm + # referencing # sqlalchemy # torch # typer -tzdata==2023.3 +tzdata==2025.1 # via pandas tzlocal==5.2 # via dateparser -uc-micro-py==1.0.2 +uc-micro-py==1.0.3 # via linkify-it-py -umap-learn[plot]==0.5.5 +umap-learn[plot]==0.5.7 # via sec-certs (./../pyproject.toml) -urllib3==2.2.2 - # via - # requests - # responses -wasabi==1.1.2 +urllib3==2.3.0 + # via requests +wasabi==1.1.3 # via # spacy # thinc # weasel -wcwidth==0.2.12 +wcwidth==0.2.13 # via prompt-toolkit -weasel==0.3.4 +weasel==0.4.1 # via spacy webencodings==0.5.1 # via # bleach # html5lib -widgetsnbextension==4.0.9 +widgetsnbextension==4.0.13 # via ipywidgets -wrapt==1.16.0 - # via deprecated -xarray==2023.11.0 +wrapt==1.17.2 + # via + # deprecated + # smart-open +xarray==2025.1.1 # via datashader -xxhash==3.4.1 +xxhash==3.5.0 # via # datasets # evaluate -xyzservices==2023.10.1 - # via - # bokeh - # panel -yarl==1.17.2 +xyzservices==2025.1.0 + # via bokeh +yarl==1.18.3 # via aiohttp -zipp==3.19.1 +zipp==3.21.0 # via importlib-metadata # The following packages are considered to be unsafe in a requirements file: diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a89cf985c..182393cb6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,130 +1,134 @@ -annotated-types==0.6.0 +annotated-types==0.7.0 # via pydantic appnope==0.1.4 - # via - # ipykernel - # ipython -asttokens==2.4.1 + # via ipykernel +asttokens==3.0.0 # via stack-data -attrs==23.1.0 +attrs==25.1.0 # via # jsonschema # referencing -beautifulsoup4==4.12.2 +beautifulsoup4==4.12.3 # via sec-certs (./../pyproject.toml) -blis==0.7.11 +blis==1.2.0 # via thinc catalogue==2.0.10 # via # spacy # srsly # thinc -certifi==2024.7.4 +certifi==2024.12.14 # via requests -cffi==1.16.0 +cffi==1.17.1 # via cryptography -charset-normalizer==3.3.2 +charset-normalizer==3.4.1 # via requests -click==8.1.7 +click==8.1.8 # via # sec-certs (./../pyproject.toml) # typer -cloudpathlib==0.16.0 +cloudpathlib==0.20.0 # via weasel -comm==0.2.0 +comm==0.2.2 # via # ipykernel # ipywidgets -confection==0.1.3 +confection==0.1.5 # via # thinc # weasel -contourpy==1.2.0 +contourpy==1.3.1 # via matplotlib -cryptography==43.0.1 +cryptography==44.0.0 # via pypdf cycler==0.12.1 # via matplotlib -cymem==2.0.8 +cymem==2.0.11 # via # preshed # spacy # thinc dateparser==1.2.0 # via sec-certs (./../pyproject.toml) -debugpy==1.8.0 +debugpy==1.8.12 # via ipykernel decorator==5.1.1 # via ipython -deprecated==1.2.14 +deprecated==1.2.18 # via pikepdf -distro==1.8.0 +distro==1.9.0 # via tabula-py -exceptiongroup==1.2.2 - # via ipython -executing==2.0.1 +executing==2.2.0 # via stack-data -fonttools==4.45.0 +fonttools==4.55.6 # via matplotlib html5lib==1.1 # via sec-certs (./../pyproject.toml) -idna==3.7 +idna==3.10 # via requests -ipykernel==6.27.0 +ipykernel==6.29.5 # via sec-certs (./../pyproject.toml) -ipython==8.17.2 +ipython==8.31.0 # via # ipykernel # ipywidgets -ipywidgets==8.1.1 +ipywidgets==8.1.5 # via sec-certs (./../pyproject.toml) -jedi==0.19.1 +jedi==0.19.2 # via ipython -jinja2==3.1.4 +jinja2==3.1.5 # via spacy -joblib==1.3.2 +joblib==1.4.2 # via scikit-learn -jsonschema==4.20.0 +jsonschema==4.23.0 # via sec-certs (./../pyproject.toml) -jsonschema-specifications==2023.11.1 +jsonschema-specifications==2024.10.1 # via jsonschema -jupyter-client==8.6.0 +jupyter-client==8.6.3 # via ipykernel -jupyter-core==5.5.0 +jupyter-core==5.7.2 # via # ipykernel # jupyter-client -jupyterlab-widgets==3.0.9 +jupyterlab-widgets==3.0.13 # via ipywidgets -kiwisolver==1.4.5 +kiwisolver==1.4.8 # via matplotlib -langcodes==3.3.0 +langcodes==3.5.0 # via spacy -lxml==4.9.3 +language-data==1.3.0 + # via langcodes +lxml==5.3.0 # via # pikepdf # sec-certs (./../pyproject.toml) -markupsafe==2.1.3 +marisa-trie==1.2.1 + # via language-data +markdown-it-py==3.0.0 + # via rich +markupsafe==3.0.2 # via jinja2 -matplotlib==3.8.2 +matplotlib==3.10.0 # via # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # ipykernel # ipython -murmurhash==1.0.10 +mdurl==0.1.2 + # via markdown-it-py +murmurhash==1.0.12 # via # preshed # spacy # thinc -nest-asyncio==1.5.8 +nest-asyncio==1.6.0 # via ipykernel -networkx==3.2.1 +networkx==3.4.2 # via sec-certs (./../pyproject.toml) -numpy==1.26.2 +numpy==2.2.2 # via # blis # contourpy @@ -138,7 +142,7 @@ numpy==1.26.2 # spacy # tabula-py # thinc -packaging==23.2 +packaging==24.2 # via # ipykernel # matplotlib @@ -148,21 +152,21 @@ packaging==23.2 # spacy # thinc # weasel -pandas==2.1.3 +pandas==2.2.3 # via # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) # tabula-py -parso==0.8.3 +parso==0.8.4 # via jedi -pdftotext==2.2.2 +pdftotext==3.0.0 # via sec-certs (./../pyproject.toml) -pexpect==4.8.0 +pexpect==4.9.0 # via ipython -pikepdf==8.7.1 +pikepdf==9.5.1 # via sec-certs (./../pyproject.toml) -pillow==10.3.0 +pillow==11.1.0 # via # matplotlib # pikepdf @@ -170,25 +174,25 @@ pillow==10.3.0 # sec-certs (./../pyproject.toml) pkgconfig==1.5.5 # via sec-certs (./../pyproject.toml) -platformdirs==4.0.0 +platformdirs==4.3.6 # via jupyter-core preshed==3.0.9 # via # spacy # thinc -prompt-toolkit==3.0.41 +prompt-toolkit==3.0.50 # via ipython -psutil==5.9.6 +psutil==6.1.1 # via # ipykernel # sec-certs (./../pyproject.toml) ptyprocess==0.7.0 # via pexpect -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data -pycparser==2.21 +pycparser==2.22 # via cffi -pydantic==2.5.2 +pydantic==2.10.6 # via # confection # pydantic-settings @@ -196,88 +200,91 @@ pydantic==2.5.2 # spacy # thinc # weasel -pydantic-core==2.14.5 +pydantic-core==2.27.2 # via pydantic -pydantic-settings==2.1.0 +pydantic-settings==2.7.1 # via sec-certs (./../pyproject.toml) -pygments==2.17.2 - # via ipython -pyparsing==3.1.1 +pygments==2.19.1 + # via + # ipython + # rich +pyparsing==3.2.1 # via matplotlib -pypdf[crypto]==3.17.1 +pypdf[crypto]==5.2.0 # via # pypdf # sec-certs (./../pyproject.toml) -pysankeybeta==1.4.1 +pysankeybeta==1.4.2 # via sec-certs (./../pyproject.toml) -pytesseract==0.3.10 +pytesseract==0.3.13 # via sec-certs (./../pyproject.toml) -python-dateutil==2.8.2 +python-dateutil==2.9.0.post0 # via # dateparser # jupyter-client # matplotlib # pandas # sec-certs (./../pyproject.toml) -python-dotenv==1.0.0 +python-dotenv==1.0.1 # via pydantic-settings -pytz==2023.3.post1 +pytz==2024.2 # via # dateparser # pandas -pyyaml==6.0.1 +pyyaml==6.0.2 # via sec-certs (./../pyproject.toml) -pyzmq==25.1.1 +pyzmq==26.2.0 # via # ipykernel # jupyter-client -rapidfuzz==3.5.2 +rapidfuzz==3.11.0 # via sec-certs (./../pyproject.toml) -referencing==0.31.0 +referencing==0.36.2 # via # jsonschema # jsonschema-specifications -regex==2024.9.11 +regex==2024.11.6 # via dateparser -requests==2.32.0 +requests==2.32.3 # via # sec-certs (./../pyproject.toml) # spacy # weasel -rpds-py==0.13.1 +rich==13.9.4 + # via typer +rpds-py==0.22.3 # via # jsonschema # referencing -scikit-learn==1.5.0 +scikit-learn==1.6.1 # via sec-certs (./../pyproject.toml) -scipy==1.11.4 +scipy==1.15.1 # via # scikit-learn # sec-certs (./../pyproject.toml) -seaborn==0.13.0 +seaborn==0.13.2 # via # pysankeybeta # sec-certs (./../pyproject.toml) -setuptools-scm==8.0.4 +setuptools-scm==8.1.0 # via sec-certs (./../pyproject.toml) -six==1.16.0 +shellingham==1.5.4 + # via typer +six==1.17.0 # via - # asttokens # html5lib # python-dateutil -smart-open==6.4.0 - # via - # spacy - # weasel -soupsieve==2.5 +smart-open==7.1.0 + # via weasel +soupsieve==2.6 # via beautifulsoup4 -spacy==3.7.2 +spacy==3.8.4 # via sec-certs (./../pyproject.toml) spacy-legacy==3.0.12 # via spacy spacy-loggers==1.0.5 # via spacy -srsly==2.4.8 +srsly==2.5.1 # via # confection # spacy @@ -285,23 +292,21 @@ srsly==2.4.8 # weasel stack-data==0.6.3 # via ipython -tabula-py==2.9.0 +tabula-py==2.10.0 # via sec-certs (./../pyproject.toml) -thinc==8.2.1 +thinc==8.3.4 # via spacy -threadpoolctl==3.2.0 +threadpoolctl==3.5.0 # via scikit-learn -tomli==2.1.0 - # via setuptools-scm -tornado==6.4.1 +tornado==6.4.2 # via # ipykernel # jupyter-client -tqdm==4.66.3 +tqdm==4.67.1 # via # sec-certs (./../pyproject.toml) # spacy -traitlets==5.13.0 +traitlets==5.14.3 # via # comm # ipykernel @@ -310,38 +315,40 @@ traitlets==5.13.0 # jupyter-client # jupyter-core # matplotlib-inline -typer==0.9.0 +typer==0.15.1 # via # spacy # weasel -typing-extensions==4.8.0 +typing-extensions==4.12.2 # via - # cloudpathlib + # ipython # pydantic # pydantic-core - # setuptools-scm + # referencing # typer -tzdata==2023.3 +tzdata==2025.1 # via pandas tzlocal==5.2 # via dateparser -urllib3==2.2.2 +urllib3==2.3.0 # via requests -wasabi==1.1.2 +wasabi==1.1.3 # via # spacy # thinc # weasel -wcwidth==0.2.12 +wcwidth==0.2.13 # via prompt-toolkit -weasel==0.3.4 +weasel==0.4.1 # via spacy webencodings==0.5.1 # via html5lib -widgetsnbextension==4.0.9 +widgetsnbextension==4.0.13 # via ipywidgets -wrapt==1.16.0 - # via deprecated +wrapt==1.17.2 + # via + # deprecated + # smart-open # The following packages are considered to be unsafe in a requirements file: # setuptools diff --git a/requirements/test_requirements.txt b/requirements/test_requirements.txt index 8b5b5f687..b5d833695 100644 --- a/requirements/test_requirements.txt +++ b/requirements/test_requirements.txt @@ -1,138 +1,140 @@ -annotated-types==0.6.0 +annotated-types==0.7.0 # via pydantic appnope==0.1.4 - # via - # ipykernel - # ipython -asttokens==2.4.1 + # via ipykernel +asttokens==3.0.0 # via stack-data -attrs==23.1.0 +attrs==25.1.0 # via # jsonschema # referencing -beautifulsoup4==4.12.2 +beautifulsoup4==4.12.3 # via sec-certs (./../pyproject.toml) -blis==0.7.11 +blis==1.2.0 # via thinc catalogue==2.0.10 # via # spacy # srsly # thinc -certifi==2024.7.4 +certifi==2024.12.14 # via requests -cffi==1.16.0 +cffi==1.17.1 # via cryptography -charset-normalizer==3.3.2 +charset-normalizer==3.4.1 # via requests -click==8.1.7 +click==8.1.8 # via # sec-certs (./../pyproject.toml) # typer -cloudpathlib==0.16.0 +cloudpathlib==0.20.0 # via weasel -comm==0.2.0 +comm==0.2.2 # via # ipykernel # ipywidgets -confection==0.1.3 +confection==0.1.5 # via # thinc # weasel -contourpy==1.2.0 +contourpy==1.3.1 # via matplotlib -coverage[toml]==7.3.2 +coverage[toml]==7.6.10 # via # pytest-cov # sec-certs (./../pyproject.toml) -cryptography==43.0.1 +cryptography==44.0.0 # via pypdf cycler==0.12.1 # via matplotlib -cymem==2.0.8 +cymem==2.0.11 # via # preshed # spacy # thinc dateparser==1.2.0 # via sec-certs (./../pyproject.toml) -debugpy==1.8.0 +debugpy==1.8.12 # via ipykernel decorator==5.1.1 # via ipython -deprecated==1.2.14 +deprecated==1.2.18 # via pikepdf -distro==1.8.0 +distro==1.9.0 # via tabula-py -exceptiongroup==1.2.2 - # via - # ipython - # pytest -executing==2.0.1 +executing==2.2.0 # via stack-data -fonttools==4.45.0 +fonttools==4.55.6 # via matplotlib html5lib==1.1 # via sec-certs (./../pyproject.toml) -idna==3.7 +idna==3.10 # via requests iniconfig==2.0.0 # via pytest -ipykernel==6.27.0 +ipykernel==6.29.5 # via sec-certs (./../pyproject.toml) -ipython==8.17.2 +ipython==8.31.0 # via # ipykernel # ipywidgets -ipywidgets==8.1.1 +ipywidgets==8.1.5 # via sec-certs (./../pyproject.toml) -jedi==0.19.1 +jedi==0.19.2 # via ipython -jinja2==3.1.4 +jinja2==3.1.5 # via spacy -joblib==1.3.2 +joblib==1.4.2 # via scikit-learn -jsonschema==4.20.0 +jsonschema==4.23.0 # via sec-certs (./../pyproject.toml) -jsonschema-specifications==2023.11.1 +jsonschema-specifications==2024.10.1 # via jsonschema -jupyter-client==8.6.0 +jupyter-client==8.6.3 # via ipykernel -jupyter-core==5.5.0 +jupyter-core==5.7.2 # via # ipykernel # jupyter-client -jupyterlab-widgets==3.0.9 +jupyterlab-widgets==3.0.13 # via ipywidgets -kiwisolver==1.4.5 +kiwisolver==1.4.8 # via matplotlib -langcodes==3.3.0 +langcodes==3.5.0 # via spacy -lxml==4.9.3 +language-data==1.3.0 + # via langcodes +lxml==5.3.0 # via # pikepdf # sec-certs (./../pyproject.toml) -markupsafe==2.1.3 +marisa-trie==1.2.1 + # via language-data +markdown-it-py==3.0.0 + # via rich +markupsafe==3.0.2 # via jinja2 -matplotlib==3.8.2 +matplotlib==3.10.0 # via # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # ipykernel # ipython -murmurhash==1.0.10 +mdurl==0.1.2 + # via markdown-it-py +murmurhash==1.0.12 # via # preshed # spacy # thinc -nest-asyncio==1.5.8 +nest-asyncio==1.6.0 # via ipykernel -networkx==3.2.1 +networkx==3.4.2 # via sec-certs (./../pyproject.toml) -numpy==1.26.2 +numpy==2.2.2 # via # blis # contourpy @@ -146,7 +148,7 @@ numpy==1.26.2 # spacy # tabula-py # thinc -packaging==23.2 +packaging==24.2 # via # ipykernel # matplotlib @@ -157,21 +159,21 @@ packaging==23.2 # spacy # thinc # weasel -pandas==2.1.3 +pandas==2.2.3 # via # pysankeybeta # seaborn # sec-certs (./../pyproject.toml) # tabula-py -parso==0.8.3 +parso==0.8.4 # via jedi -pdftotext==2.2.2 +pdftotext==3.0.0 # via sec-certs (./../pyproject.toml) -pexpect==4.8.0 +pexpect==4.9.0 # via ipython -pikepdf==8.7.1 +pikepdf==9.5.1 # via sec-certs (./../pyproject.toml) -pillow==10.3.0 +pillow==11.1.0 # via # matplotlib # pikepdf @@ -179,27 +181,27 @@ pillow==10.3.0 # sec-certs (./../pyproject.toml) pkgconfig==1.5.5 # via sec-certs (./../pyproject.toml) -platformdirs==4.0.0 +platformdirs==4.3.6 # via jupyter-core -pluggy==1.3.0 +pluggy==1.5.0 # via pytest preshed==3.0.9 # via # spacy # thinc -prompt-toolkit==3.0.41 +prompt-toolkit==3.0.50 # via ipython -psutil==5.9.6 +psutil==6.1.1 # via # ipykernel # sec-certs (./../pyproject.toml) ptyprocess==0.7.0 # via pexpect -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data -pycparser==2.21 +pycparser==2.22 # via cffi -pydantic==2.5.2 +pydantic==2.10.6 # via # confection # pydantic-settings @@ -207,94 +209,97 @@ pydantic==2.5.2 # spacy # thinc # weasel -pydantic-core==2.14.5 +pydantic-core==2.27.2 # via pydantic -pydantic-settings==2.1.0 +pydantic-settings==2.7.1 # via sec-certs (./../pyproject.toml) -pygments==2.17.2 - # via ipython -pyparsing==3.1.1 +pygments==2.19.1 + # via + # ipython + # rich +pyparsing==3.2.1 # via matplotlib -pypdf[crypto]==3.17.1 +pypdf[crypto]==5.2.0 # via # pypdf # sec-certs (./../pyproject.toml) -pysankeybeta==1.4.1 +pysankeybeta==1.4.2 # via sec-certs (./../pyproject.toml) -pytesseract==0.3.10 +pytesseract==0.3.13 # via sec-certs (./../pyproject.toml) -pytest==7.4.3 +pytest==8.3.4 # via # pytest-cov # sec-certs (./../pyproject.toml) -pytest-cov==4.1.0 +pytest-cov==6.0.0 # via sec-certs (./../pyproject.toml) -python-dateutil==2.8.2 +python-dateutil==2.9.0.post0 # via # dateparser # jupyter-client # matplotlib # pandas # sec-certs (./../pyproject.toml) -python-dotenv==1.0.0 +python-dotenv==1.0.1 # via pydantic-settings -pytz==2023.3.post1 +pytz==2024.2 # via # dateparser # pandas -pyyaml==6.0.1 +pyyaml==6.0.2 # via sec-certs (./../pyproject.toml) -pyzmq==25.1.1 +pyzmq==26.2.0 # via # ipykernel # jupyter-client -rapidfuzz==3.5.2 +rapidfuzz==3.11.0 # via sec-certs (./../pyproject.toml) -referencing==0.31.0 +referencing==0.36.2 # via # jsonschema # jsonschema-specifications -regex==2024.9.11 +regex==2024.11.6 # via dateparser -requests==2.32.0 +requests==2.32.3 # via # sec-certs (./../pyproject.toml) # spacy # weasel -rpds-py==0.13.1 +rich==13.9.4 + # via typer +rpds-py==0.22.3 # via # jsonschema # referencing -scikit-learn==1.5.0 +scikit-learn==1.6.1 # via sec-certs (./../pyproject.toml) -scipy==1.11.4 +scipy==1.15.1 # via # scikit-learn # sec-certs (./../pyproject.toml) -seaborn==0.13.0 +seaborn==0.13.2 # via # pysankeybeta # sec-certs (./../pyproject.toml) -setuptools-scm==8.0.4 +setuptools-scm==8.1.0 # via sec-certs (./../pyproject.toml) -six==1.16.0 +shellingham==1.5.4 + # via typer +six==1.17.0 # via - # asttokens # html5lib # python-dateutil -smart-open==6.4.0 - # via - # spacy - # weasel -soupsieve==2.5 +smart-open==7.1.0 + # via weasel +soupsieve==2.6 # via beautifulsoup4 -spacy==3.7.2 +spacy==3.8.4 # via sec-certs (./../pyproject.toml) spacy-legacy==3.0.12 # via spacy spacy-loggers==1.0.5 # via spacy -srsly==2.4.8 +srsly==2.5.1 # via # confection # spacy @@ -302,26 +307,21 @@ srsly==2.4.8 # weasel stack-data==0.6.3 # via ipython -tabula-py==2.9.0 +tabula-py==2.10.0 # via sec-certs (./../pyproject.toml) -thinc==8.2.1 +thinc==8.3.4 # via spacy -threadpoolctl==3.2.0 +threadpoolctl==3.5.0 # via scikit-learn -tomli==2.1.0 - # via - # coverage - # pytest - # setuptools-scm -tornado==6.4.1 +tornado==6.4.2 # via # ipykernel # jupyter-client -tqdm==4.66.3 +tqdm==4.67.1 # via # sec-certs (./../pyproject.toml) # spacy -traitlets==5.13.0 +traitlets==5.14.3 # via # comm # ipykernel @@ -330,38 +330,40 @@ traitlets==5.13.0 # jupyter-client # jupyter-core # matplotlib-inline -typer==0.9.0 +typer==0.15.1 # via # spacy # weasel -typing-extensions==4.8.0 +typing-extensions==4.12.2 # via - # cloudpathlib + # ipython # pydantic # pydantic-core - # setuptools-scm + # referencing # typer -tzdata==2023.3 +tzdata==2025.1 # via pandas tzlocal==5.2 # via dateparser -urllib3==2.2.2 +urllib3==2.3.0 # via requests -wasabi==1.1.2 +wasabi==1.1.3 # via # spacy # thinc # weasel -wcwidth==0.2.12 +wcwidth==0.2.13 # via prompt-toolkit -weasel==0.3.4 +weasel==0.4.1 # via spacy webencodings==0.5.1 # via html5lib -widgetsnbextension==4.0.9 +widgetsnbextension==4.0.13 # via ipywidgets -wrapt==1.16.0 - # via deprecated +wrapt==1.17.2 + # via + # deprecated + # smart-open # The following packages are considered to be unsafe in a requirements file: # setuptools diff --git a/src/sec_certs/cli.py b/src/sec_certs/cli.py index 4b613af47..6276aa2f9 100644 --- a/src/sec_certs/cli.py +++ b/src/sec_certs/cli.py @@ -15,6 +15,7 @@ from sec_certs.dataset.cc import CCDataset from sec_certs.dataset.dataset import Dataset from sec_certs.dataset.fips import FIPSDataset +from sec_certs.dataset.protection_profile import ProtectionProfileDataset from sec_certs.utils.helpers import warn_if_missing_poppler, warn_if_missing_tesseract logger = logging.getLogger(__name__) @@ -38,7 +39,7 @@ def __post_init__(self) -> None: if not hasattr(Dataset.DatasetInternalState, condition): raise ValueError(f"Precondition attribute {condition} is not member of `Dataset.DatasetInternalState`.") - def run(self, dset: CCDataset | FIPSDataset) -> None: + def run(self, dset: Dataset) -> None: for condition in self.preconditions: if not getattr(dset.state, condition): err_msg = ( @@ -59,14 +60,21 @@ def warn_missing_libs(): warn_if_missing_tesseract() +FRAMEWORK_TO_CONSTRUCTOR: dict[str, type[Dataset]] = { + "cc": CCDataset, + "fips": FIPSDataset, + "pp": ProtectionProfileDataset, +} + + def build_or_load_dataset( framework: str, inputpath: Path | None, to_build: bool, outputpath: Path | None, -) -> CCDataset | FIPSDataset: - constructor: type[CCDataset] | type[FIPSDataset] = CCDataset if framework == "cc" else FIPSDataset - dset: CCDataset | FIPSDataset +) -> Dataset: + constructor: type[Dataset] = FRAMEWORK_TO_CONSTRUCTOR[framework] + dset: Dataset if to_build: if not outputpath: @@ -138,7 +146,7 @@ def build_or_load_dataset( "framework", required=True, nargs=1, - type=click.Choice(["cc", "fips"], case_sensitive=False), + type=click.Choice(["cc", "fips", "pp"], case_sensitive=False), ) @click.argument( "actions", @@ -166,7 +174,7 @@ def build_or_load_dataset( "--input", "inputpath", type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), - help="If set, the actions will be performed on a CC dataset loaded from JSON from the input path.", + help="If set, the actions will be performed on a dataset loaded from JSON from the input path.", ) @click.option("-q", "--quiet", is_flag=True, help="If set, will not print to stdout") def main( @@ -202,8 +210,6 @@ def main( ) dset = build_or_load_dataset(framework, inputpath, "build" in actions_set, outputpath) - aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" - aux_dsets_to_handle += "CPE, CVE" processing_step: ProcessingStep for processing_step in [x for x in steps if x.name in actions_set]: diff --git a/src/sec_certs/configuration.py b/src/sec_certs/configuration.py index 09e245398..9ea63791c 100644 --- a/src/sec_certs/configuration.py +++ b/src/sec_certs/configuration.py @@ -54,12 +54,20 @@ class Configuration(BaseSettings): "https://sec-certs.org/cc/cc.tar.gz", description="URL from where to fetch the latest full archive of fully processed CC dataset.", ) + cc_maintenances_latest_full_archive: AnyHttpUrl = Field( + "https://sec-certs.org/cc/cc_mu.tar.gz", + description="URL from where to fetch the latest full archive of fully processed CC Maintenace updates dataset", + ) cc_maintenances_latest_snapshot: AnyHttpUrl = Field( "https://sec-certs.org/cc/maintenance_updates.json", description="URL from where to fetch the latest snapshot of CC maintenance updates", ) + pp_latest_full_archive: AnyHttpUrl = Field( + "https://sec-certs.org/pp/pp.tar.gz", + description="URL from where to fetch the latest full archive of fully processed PP dataset.", + ) pp_latest_snapshot: AnyHttpUrl = Field( - "https://sec-certs.org/static/pp.json", + "https://sec-certs.org/pp/pp.json", description="URL from where to fetch the latest snapshot of the PP dataset.", ) fips_latest_snapshot: AnyHttpUrl = Field( @@ -134,11 +142,11 @@ class Configuration(BaseSettings): description="If true, progress bars will be printed to stdout during computation.", ) nvd_api_key: Optional[str] = Field(None, description="NVD API key for access to CVEs and CPEs.") # noqa: UP007 - preferred_source_nvd_datasets: Literal["sec-certs", "api"] = Field( + preferred_source_remote_datasets: Literal["sec-certs", "origin"] = Field( "sec-certs", - description="If set to `sec-certs`, will fetch CPE and CVE datasets from sec-certs.org." - + " If set to `api`, will fetch these resources from NVD API. It is advised to set an" - + " `nvd_api_key` when setting this to `api`.", + description="If set to `sec-certs`, will fetch remote datasets from sec-certs.org." + + " If set to `origin`, will fetch these resources from their origin URL. It is advised to set an" + + " `nvd_api_key` when setting this to `origin`.", ) def _get_nondefault_keys(self) -> set[str]: diff --git a/src/sec_certs/constants.py b/src/sec_certs/constants.py index d134b3fdf..125c2265e 100644 --- a/src/sec_certs/constants.py +++ b/src/sec_certs/constants.py @@ -22,11 +22,50 @@ MIN_FIPS_HTML_SIZE = 64000 MIN_CC_HTML_SIZE = 5000000 +MIN_PP_HTML_SIZE = 200000 MIN_CC_CSV_SIZE = 700000 MIN_CC_PP_DATASET_SIZE = 2500000 CPE_VERSION_NA = "-" +CC_CAT_ABBREVIATIONS = [ + "AC", + "BD", + "BP", + "DP", + "DB", + "DD", + "IC", + "KM", + "MD", + "MF", + "NS", + "OS", + "OD", + "DG", + "TC", +] + +CC_CATEGORIES = [ + "Access Control Devices and Systems", + "Biometric Systems and Devices", + "Boundary Protection Devices and Systems", + "Data Protection", + "Databases", + "Detection Devices and Systems", + "ICs, Smart Cards and Smart Card-Related Devices and Systems", + "Key Management Systems", + "Mobility", + "Multi-Function Devices", + "Network and Network-Related Devices and Systems", + "Operating Systems", + "Other Devices and Systems", + "Products for Digital Signatures", + "Trusted Computing", +] + +CC_PORTAL_BASE_URL = "https://www.commoncriteriaportal.org" + RELEASE_CANDIDATE_REGEX: re.Pattern = re.compile(r"rc\d{0,2}$", re.IGNORECASE) FIPS_BASE_URL = "https://csrc.nist.gov" diff --git a/src/sec_certs/dataset/auxiliary_dataset_handling.py b/src/sec_certs/dataset/auxiliary_dataset_handling.py new file mode 100644 index 000000000..a8a376d4e --- /dev/null +++ b/src/sec_certs/dataset/auxiliary_dataset_handling.py @@ -0,0 +1,279 @@ +import gzip +import itertools +import json +import logging +import tempfile +from abc import ABC, abstractmethod +from collections.abc import Iterable +from pathlib import Path +from typing import Any, ClassVar + +from sec_certs import constants +from sec_certs.configuration import config +from sec_certs.dataset.cc_scheme import CCSchemeDataset +from sec_certs.dataset.cpe import CPEDataset +from sec_certs.dataset.cve import CVEDataset +from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset +from sec_certs.sample.cc import CCCertificate +from sec_certs.sample.cc_maintenance_update import CCMaintenanceUpdate +from sec_certs.utils import helpers +from sec_certs.utils.nvd_dataset_builder import CpeMatchNvdDatasetBuilder, CpeNvdDatasetBuilder, CveNvdDatasetBuilder +from sec_certs.utils.profiling import staged + +logger = logging.getLogger(__name__) + + +class AuxiliaryDatasetHandler(ABC): + RELATIVE_DIR: ClassVar[str | None] = None + + def __init__(self, aux_datasets_dir: str | Path) -> None: + self.aux_datasets_dir = Path(aux_datasets_dir) + self.dset: Any + + @property + def root_dir(self) -> Path: + if self.RELATIVE_DIR: + return self.aux_datasets_dir / Path(self.RELATIVE_DIR) + return self.aux_datasets_dir + + @property + @abstractmethod + def dset_path(self) -> Path: + raise NotImplementedError("Not meant to be implemented by base class") + + def set_local_paths(self, aux_datasets_dir: str | Path) -> None: + self.aux_datasets_dir = Path(aux_datasets_dir) + + def process_dataset(self, download_fresh: bool = False) -> None: + self.root_dir.mkdir(parents=True, exist_ok=True) + self._process_dataset_body(download_fresh) + + @abstractmethod + def load_dataset(self) -> None: + raise NotImplementedError("Not meant to be implemented by base class") + + @abstractmethod + def _process_dataset_body(self, download_fresh: bool = False) -> None: + raise NotImplementedError("Not meant to be implemented by base class") + + +class CPEDatasetHandler(AuxiliaryDatasetHandler): + @property + def dset_path(self) -> Path: + return self.root_dir / "cpe_dataset.json" + + @staged(logger, "Processing CPE dataset") + def _process_dataset_body(self, download_fresh: bool = False) -> None: + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing CPEDataset from json.") + self.load_dataset() + return + + if config.preferred_source_remote_datasets == "origin": + logger.info("Fetching new CPE records from NVD API") + with CpeNvdDatasetBuilder(api_key=config.nvd_api_key) as builder: + self.dset = builder.build_dataset() + else: + logger.info("Preparing CPEDataset from sec-certs.org.") + self.dset = CPEDataset.from_web(self.dset_path) + + self.dset.to_json() + self.dset.json_path = self.dset_path + + def load_dataset(self) -> None: + self.dset = CPEDataset.from_json(self.dset_path) + + +class CVEDatasetHandler(AuxiliaryDatasetHandler): + @property + def dset_path(self) -> Path: + return self.root_dir / "cve_dataset.json" + + @staged(logger, "Processing CVE dataset") + def _process_dataset_body(self, download_fresh: bool = False) -> None: + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing CVEDataset from json.") + self.load_dataset() + return + + if config.preferred_source_remote_datasets == "origin": + logger.info("Fetching new CVE records from NVD API.") + with CveNvdDatasetBuilder(api_key=config.nvd_api_key) as builder: + self.dset = builder.build_dataset() + else: + logger.info("Preparing CVEDataset from sec-certs.org.") + self.dset = CVEDataset.from_web(self.dset_path) + + self.dset.to_json() + self.dset.json_path = self.dset_path + + def load_dataset(self): + self.dset = CVEDataset.from_json(self.dset_path) + + +class CPEMatchDictHandler(AuxiliaryDatasetHandler): + @property + def dset_path(self) -> Path: + return self.root_dir / "cpe_match.json" + + @staged(logger, "Processing CPE Match dictionary") + def _process_dataset_body(self, download_fresh: bool = False) -> None: + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing CPE Match feed from json.") + self.load_dataset() + return + + if config.preferred_source_remote_datasets == "origin": + logger.info("Fetchnig CPE Match feed from NVD APi.") + with CpeMatchNvdDatasetBuilder(api_key=config.nvd_api_key) as builder: + self.dset = builder.build_dataset() + else: + logger.info("Preparing CPE Match feed from sec-certs.org.") + with tempfile.TemporaryDirectory() as tmp_dir: + dset_path = Path(tmp_dir) / "cpe_match_feed.json.gz" + if ( + not helpers.download_file( + config.cpe_match_latest_snapshot, + dset_path, + progress_bar_desc="Downloading CPE Match feed from web", + ) + == constants.RESPONSE_OK + ): + raise RuntimeError(f"Could not download CPE Match feed from {config.cpe_match_latest_snapshot}.") + with gzip.open(str(dset_path)) as handle: + json_str = handle.read().decode("utf-8") + self.dset = json.loads(json_str) + + with self.dset_path.open("w") as handle: + json.dump(self.dset, handle, indent=4) + + def load_dataset(self): + with self.dset_path.open("r") as handle: + self.dset = json.load(handle) + + +class FIPSAlgorithmDatasetHandler(AuxiliaryDatasetHandler): + @property + def dset_path(self) -> Path: + return self.root_dir / "algorithms.json" + + @staged(logger, "Processing FIPS Algorithms") + def _process_dataset_body(self, download_fresh: bool = False) -> None: + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing FIPSAlgorithmDataset from json.") + self.load_dataset() + return + + self.dset = FIPSAlgorithmDataset.from_web(self.dset_path) + self.dset.to_json() + self.dset.json_path = self.dset_path + + def load_dataset(self): + self.dset = FIPSAlgorithmDataset.from_json(self.dset_path) + + +class CCSchemeDatasetHandler(AuxiliaryDatasetHandler): + def __init__( + self, + aux_datasets_dir: str | Path = constants.DUMMY_NONEXISTING_PATH, + only_schemes: set[str] | None = None, + ): + self.aux_datasets_dir = Path(aux_datasets_dir) + self.only_schemes = only_schemes + self.dset: Any + + @property + def dset_path(self) -> Path: + return self.root_dir / "cc_scheme.json" + + @staged(logger, "Processing CC Schemes") + def _process_dataset_body(self, download_fresh: bool = False) -> None: + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing CCSchemeDataset from json.") + self.load_dataset() + return + + self.dset = CCSchemeDataset.from_web(self.dset_path, self.only_schemes) + self.dset.to_json() + self.dset.json_path = self.dset_path + + def load_dataset(self): + self.dset = CCSchemeDataset.from_json(self.dset_path) + + +class CCMaintenanceUpdateDatasetHandler(AuxiliaryDatasetHandler): + RELATIVE_DIR: ClassVar[str] = "maintenances" + + def __init__( + self, + aux_datasets_dir: str | Path = constants.DUMMY_NONEXISTING_PATH, + certs_with_updates: Iterable[CCCertificate] = [], + ) -> None: + self.aux_datasets_dir = Path(aux_datasets_dir) + self.certs_with_updates = certs_with_updates + self.dset: Any + + @property + def dset_path(self) -> Path: + return self.root_dir / "maintenance_updates.json" + + def load_dataset(self) -> None: + from sec_certs.dataset.cc import CCDatasetMaintenanceUpdates + + self.dset = CCDatasetMaintenanceUpdates.from_json(self.dset_path) + + @staged(logger, "Processing CC Maintenance updates") + def _process_dataset_body(self, download_fresh: bool = False): + from sec_certs.dataset.cc import CCDatasetMaintenanceUpdates + + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing CCDatasetMaintenanceUpdates from json.") + self.load_dataset() + return + + updates = list( + itertools.chain.from_iterable( + CCMaintenanceUpdate.get_updates_from_cc_cert(x) for x in self.certs_with_updates + ) + ) + self.dset = CCDatasetMaintenanceUpdates( + {x.dgst: x for x in updates}, + root_dir=self.dset_path.parent, + name="maintenance_updates", + ) + self.dset.download_all_artifacts() + self.dset.convert_all_pdfs() + self.dset.extract_data() + self.dset.to_json() + + +class ProtectionProfileDatasetHandler(AuxiliaryDatasetHandler): + RELATIVE_DIR: ClassVar[str] = "protection_profiles" + + def __init__(self, aux_datasets_dir: str | Path = constants.DUMMY_NONEXISTING_PATH): + self.aux_datasets_dir = Path(aux_datasets_dir) + + @property + def dset_path(self) -> Path: + return self.root_dir / "pp.json" + + def load_dataset(self) -> None: + from sec_certs.dataset.protection_profile import ProtectionProfileDataset + + self.dset = ProtectionProfileDataset.from_json(self.dset_path) + + @staged(logger, "Processing Protection profiles") + def _process_dataset_body(self, download_fresh: bool = False): + from sec_certs.dataset.protection_profile import ProtectionProfileDataset + + if not download_fresh and self.dset_path.exists(): + logger.info("Preparing ProtectionProfileDataset from json.") + self.load_dataset() + return + + self.dset_path.parent.mkdir(exist_ok=True, parents=True) + self.dset = ProtectionProfileDataset(root_dir=self.dset_path.parent) + self.dset.get_certs_from_web() + self.dset.download_all_artifacts() + self.dset.convert_all_pdfs() + self.dset.analyze_certificates() diff --git a/src/sec_certs/dataset/cc.py b/src/sec_certs/dataset/cc.py index 568e68cea..41bb62ac1 100644 --- a/src/sec_certs/dataset/cc.py +++ b/src/sec_certs/dataset/cc.py @@ -1,11 +1,8 @@ from __future__ import annotations -import itertools import locale import shutil -import tempfile from collections.abc import Iterator -from dataclasses import dataclass from datetime import datetime from pathlib import Path from typing import ClassVar, cast @@ -13,47 +10,47 @@ import numpy as np import pandas as pd from bs4 import BeautifulSoup, Tag +from pydantic import AnyHttpUrl -import sec_certs.utils.sanitization from sec_certs import constants from sec_certs.configuration import config -from sec_certs.dataset.cc_scheme import CCSchemeDataset -from sec_certs.dataset.cpe import CPEDataset -from sec_certs.dataset.cve import CVEDataset -from sec_certs.dataset.dataset import AuxiliaryDatasets, Dataset, logger -from sec_certs.dataset.protection_profile import ProtectionProfileDataset -from sec_certs.model import ( - ReferenceFinder, - SARTransformer, - TransitiveVulnerabilityFinder, +from sec_certs.dataset.auxiliary_dataset_handling import ( + AuxiliaryDatasetHandler, + CCMaintenanceUpdateDatasetHandler, + CCSchemeDatasetHandler, + CPEDatasetHandler, + CPEMatchDictHandler, + CVEDatasetHandler, + ProtectionProfileDatasetHandler, ) -from sec_certs.model.cc_matching import CCSchemeMatcher +from sec_certs.dataset.dataset import Dataset, logger +from sec_certs.heuristics.cc import ( + compute_cert_labs, + compute_eals, + compute_normalized_cert_ids, + compute_references, + compute_sars, + compute_scheme_data, + link_to_protection_profiles, +) +from sec_certs.heuristics.common import compute_cpe_heuristics, compute_related_cves, compute_transitive_vulnerabilities from sec_certs.sample.cc import CCCertificate -from sec_certs.sample.cc_certificate_id import CertificateId from sec_certs.sample.cc_maintenance_update import CCMaintenanceUpdate -from sec_certs.sample.cc_scheme import EntryType -from sec_certs.sample.protection_profile import ProtectionProfile from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers, sanitization from sec_certs.utils import parallel_processing as cert_processing from sec_certs.utils.profiling import staged -@dataclass -class CCAuxiliaryDatasets(AuxiliaryDatasets): - cpe_dset: CPEDataset | None = None - cve_dset: CVEDataset | None = None - pp_dset: ProtectionProfileDataset | None = None - mu_dset: CCDatasetMaintenanceUpdates | None = None - scheme_dset: CCSchemeDataset | None = None - - -class CCDataset(Dataset[CCCertificate, CCAuxiliaryDatasets], ComplexSerializableType): +class CCDataset(Dataset[CCCertificate], ComplexSerializableType): """ Class that holds CCCertificate. Serializable into json, pandas, dictionary. Conveys basic certificate manipulations and dataset transformations. Many private methods that perform internal operations, feel free to exploit them. """ + FULL_ARCHIVE_URL: ClassVar[AnyHttpUrl] = config.cc_latest_full_archive + SNAPSHOT_URL: ClassVar[AnyHttpUrl] = config.cc_latest_snapshot + def __init__( self, certs: dict[str, CCCertificate] = {}, @@ -61,7 +58,7 @@ def __init__( name: str | None = None, description: str = "", state: Dataset.DatasetInternalState | None = None, - auxiliary_datasets: CCAuxiliaryDatasets | None = None, + aux_handlers: dict[type[AuxiliaryDatasetHandler], AuxiliaryDatasetHandler] = {}, ): self.certs = certs self.timestamp = datetime.now() @@ -69,13 +66,21 @@ def __init__( self.name = name if name else type(self).__name__ + " dataset" self.description = description if description else datetime.now().strftime("%d/%m/%Y %H:%M:%S") self.state = state if state else self.DatasetInternalState() - - self.auxiliary_datasets: CCAuxiliaryDatasets = ( - auxiliary_datasets if auxiliary_datasets else CCAuxiliaryDatasets() - ) - + self.aux_handlers = aux_handlers self.root_dir = Path(root_dir) + if not self.aux_handlers: + self.aux_handlers[CPEDatasetHandler] = CPEDatasetHandler(self.auxiliary_datasets_dir) + self.aux_handlers[CVEDatasetHandler] = CVEDatasetHandler(self.auxiliary_datasets_dir) + self.aux_handlers[CPEMatchDictHandler] = CPEMatchDictHandler(self.auxiliary_datasets_dir) + self.aux_handlers[CCSchemeDatasetHandler] = CCSchemeDatasetHandler(self.auxiliary_datasets_dir) + self.aux_handlers[ProtectionProfileDatasetHandler] = ProtectionProfileDatasetHandler( + self.auxiliary_datasets_dir / "protection_profiles" + ) + self.aux_handlers[CCMaintenanceUpdateDatasetHandler] = CCMaintenanceUpdateDatasetHandler( + self.auxiliary_datasets_dir / "maintenances" + ) + def to_pandas(self) -> pd.DataFrame: """ Return self serialized into pandas DataFrame @@ -172,57 +177,27 @@ def certificates_txt_dir(self) -> Path: """ return self.certificates_dir / "txt" - @property - def pp_dataset_path(self) -> Path: - """ - Returns a path to the dataset of Protection Profiles - """ - return self.auxiliary_datasets_dir / "pp_dataset.json" - - @property - def mu_dataset_dir(self) -> Path: - """ - Returns directory that holds dataset of maintenance updates - """ - return self.auxiliary_datasets_dir / "maintenances" - - @property - def mu_dataset_path(self) -> Path: - """ - Returns a path to the dataset of maintenance updates - """ - return self.mu_dataset_dir / "maintenance_updates.json" - @property def reference_annotator_dir(self) -> Path: return self.root_dir / "reference_annotator" - @property - def scheme_dataset_path(self) -> Path: - """ - Returns a path to the scheme dataset - """ - return self.auxiliary_datasets_dir / "scheme_dataset.json" - - BASE_URL: ClassVar[str] = "https://www.commoncriteriaportal.org" - HTML_PRODUCTS_URL = { - "cc_products_active.html": BASE_URL + "/products/index.cfm", - "cc_products_archived.html": BASE_URL + "/products/index.cfm?archived=1", + "cc_products_active.html": constants.CC_PORTAL_BASE_URL + "/products/index.cfm", + "cc_products_archived.html": constants.CC_PORTAL_BASE_URL + "/products/index.cfm?archived=1", } - HTML_LABS_URL = {"cc_labs.html": BASE_URL + "/labs"} + HTML_LABS_URL = {"cc_labs.html": constants.CC_PORTAL_BASE_URL + "/labs"} CSV_PRODUCTS_URL = { - "cc_products_active.csv": BASE_URL + "/products/certified_products.csv", - "cc_products_archived.csv": BASE_URL + "/products/certified_products-archived.csv", + "cc_products_active.csv": constants.CC_PORTAL_BASE_URL + "/products/certified_products.csv", + "cc_products_archived.csv": constants.CC_PORTAL_BASE_URL + "/products/certified_products-archived.csv", } PP_URL = { - "cc_pp_active.html": BASE_URL + "/pps/", - "cc_pp_collaborative.html": BASE_URL + "/pps/collaborativePP.cfm?cpp=1", - "cc_pp_archived.html": BASE_URL + "/pps/index.cfm?archived=1", + "cc_pp_active.html": constants.CC_PORTAL_BASE_URL + "/pps/", + "cc_pp_collaborative.html": constants.CC_PORTAL_BASE_URL + "/pps/collaborativePP.cfm?cpp=1", + "cc_pp_archived.html": constants.CC_PORTAL_BASE_URL + "/pps/index.cfm?archived=1", } PP_CSV = { - "cc_pp_active.csv": BASE_URL + "/pps/pps.csv", - "cc_pp_archived.csv": BASE_URL + "/pps/pps-archived.csv", + "cc_pp_active.csv": constants.CC_PORTAL_BASE_URL + "/pps/pps.csv", + "cc_pp_archived.csv": constants.CC_PORTAL_BASE_URL + "/pps/pps-archived.csv", } @property @@ -257,46 +232,9 @@ def archived_csv_tuples(self) -> list[tuple[str, Path]]: """ return [(x, self.web_dir / y) for y, x in self.CSV_PRODUCTS_URL.items() if "archived" in y] - @classmethod - def from_web_latest( - cls, - path: str | Path | None = None, - auxiliary_datasets: bool = False, - artifacts: bool = False, - ) -> CCDataset: - """ - Fetches the fresh snapshot of CCDataset from sec-certs.org. - - Optionally stores it at the given path (a directory) and also downloads auxiliary datasets and artifacts (PDFs). - - .. note:: - Note that including the auxiliary datasets adds several gigabytes and including artifacts adds tens of gigabytes. - - :param path: Path to a directory where to store the dataset, or `None` if it should not be stored. - :param auxiliary_datasets: Whether to also download auxiliary datasets (CVE, CPE, CPEMatch datasets). - :param artifacts: Whether to also download artifacts (i.e. PDFs). - """ - return cls.from_web( - config.cc_latest_full_archive, - config.cc_latest_snapshot, - "Downloading CC", - path, - auxiliary_datasets, - artifacts, - ) - def _set_local_paths(self): super()._set_local_paths() - if self.auxiliary_datasets.pp_dset: - self.auxiliary_datasets.pp_dset.json_path = self.pp_dataset_path - - if self.auxiliary_datasets.mu_dset: - self.auxiliary_datasets.mu_dset.root_dir = self.mu_dataset_dir - - if self.auxiliary_datasets.scheme_dset: - self.auxiliary_datasets.scheme_dset.json_path = self.scheme_dataset_path - for cert in self: cert.set_local_paths( self.reports_pdf_dir, @@ -307,6 +245,35 @@ def _set_local_paths(self): self.certificates_txt_dir, ) + def process_auxiliary_datasets( + self, + download_fresh: bool = False, + processed_pp_dataset_root_dir: Path | None = None, + skip_schemes: bool = False, + **kwargs, + ) -> None: + if CCMaintenanceUpdateDatasetHandler in self.aux_handlers: + self.aux_handlers[CCMaintenanceUpdateDatasetHandler].certs_with_updates = [ # type: ignore + x for x in self if x.maintenance_updates + ] + if CCSchemeDatasetHandler in self.aux_handlers: + self.aux_handlers[CCSchemeDatasetHandler].only_schemes = {x.scheme for x in self} # type: ignore + + if processed_pp_dataset_root_dir: + if self.aux_handlers[ProtectionProfileDatasetHandler].root_dir.exists(): + logger.warning( + f"Overwriting PP Dataset at {self.aux_handlers[ProtectionProfileDatasetHandler].root_dir} with dataset from {processed_pp_dataset_root_dir}." + ) + shutil.copytree( + processed_pp_dataset_root_dir, + self.aux_handlers[ProtectionProfileDatasetHandler].root_dir, + dirs_exist_ok=True, + ) + + if skip_schemes: + self.aux_handlers[CCSchemeDatasetHandler].only_schemes = {} # type: ignore + super().process_auxiliary_datasets(download_fresh, **kwargs) + def _merge_certs(self, certs: dict[str, CCCertificate], cert_source: str | None = None) -> None: """ Merges dictionary of certificates into the dataset. Assuming they all are CommonCriteria certificates @@ -403,7 +370,7 @@ def map_ip_to_hostname(url: str) -> str: return url tokens = url.split("/") relative_path = "/" + "/".join(tokens[3:]) - return CCDataset.BASE_URL + relative_path + return constants.CC_PORTAL_BASE_URL + relative_path def _get_primary_key_str(row: Tag): return "|".join( @@ -474,13 +441,6 @@ def _get_primary_key_str(row: Tag): df_base = df_base.drop_duplicates(subset=["dgst"]) df_main = df_main.drop_duplicates() - profiles = { - x.dgst: { - ProtectionProfile(pp_name=y, pp_eal=None) - for y in sec_certs.utils.sanitization.sanitize_protection_profiles(x.protection_profiles) - } - for x in df_base.itertuples() - } updates: dict[str, set] = {x.dgst: set() for x in df_base.itertuples()} for x in df_main.itertuples(): updates[x.dgst].add( @@ -506,7 +466,7 @@ def _get_primary_key_str(row: Tag): x.st_link, None, None, - profiles.get(x.dgst, None), + None, updates.get(x.dgst, None), None, None, @@ -551,9 +511,9 @@ def _parse_table( ) -> dict[str, CCCertificate]: tables = soup.find_all("table", id=table_id) - if not len(tables) <= 1: + if len(tables) > 1: raise ValueError( - f'The "{file.name}" was expected to contain <1 element. Instead, it contains: {len(tables)}
elements.' + f'The "{file.name}" was expected to contain 0-1
element. Instead, it contains: {len(tables)}
elements.' ) if not tables: @@ -582,40 +542,8 @@ def _parse_table( cert_status = "active" if "active" in str(file) else "archived" - cc_cat_abbreviations = [ - "AC", - "BP", - "DP", - "DB", - "DD", - "IC", - "KM", - "MD", - "MF", - "NS", - "OS", - "OD", - "DG", - "TC", - ] - cc_table_ids = ["tbl" + x for x in cc_cat_abbreviations] - cc_categories = [ - "Access Control Devices and Systems", - "Boundary Protection Devices and Systems", - "Data Protection", - "Databases", - "Detection Devices and Systems", - "ICs, Smart Cards and Smart Card-Related Devices and Systems", - "Key Management Systems", - "Mobility", - "Multi-Function Devices", - "Network and Network-Related Devices and Systems", - "Operating Systems", - "Other Devices and Systems", - "Products for Digital Signatures", - "Trusted Computing", - ] - cat_dict = dict(zip(cc_table_ids, cc_categories)) + cc_table_ids = ["tbl" + x for x in constants.CC_CAT_ABBREVIATIONS] + cat_dict = dict(zip(cc_table_ids, constants.CC_CATEGORIES)) with file.open("r") as handle: soup = BeautifulSoup(handle, "html5lib") @@ -833,202 +761,25 @@ def extract_data(self) -> None: self._extract_pdf_frontpage() self._extract_pdf_keywords() - @staged( - logger, - "Computing heuristics: Deriving information about laboratories involved in certification.", - ) - def _compute_cert_labs(self) -> None: - certs_to_process = [x for x in self if x.state.report.is_ok_to_analyze()] - for cert in certs_to_process: - cert.compute_heuristics_cert_lab() - - @staged( - logger, - "Computing heuristics: Deriving information about certificate ids from artifacts.", - ) - def _compute_normalized_cert_ids(self) -> None: - for cert in self: - cert.compute_heuristics_cert_id() - - @staged( - logger, - "Computing heuristics: Transitive vulnerabilities in referenc(ed/ing) certificates.", - ) - def _compute_transitive_vulnerabilities(self): - transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: cert.heuristics.cert_id) - transitive_cve_finder.fit(self.certs, lambda cert: cert.heuristics.report_references) - - for dgst in self.certs: - transitive_cve = transitive_cve_finder.predict_single_cert(dgst) - - self.certs[dgst].heuristics.direct_transitive_cves = transitive_cve.direct_transitive_cves - self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves - - @staged(logger, "Computing heuristics: Matching scheme data.") - def _compute_scheme_data(self): - if self.auxiliary_datasets.scheme_dset: - for scheme in self.auxiliary_datasets.scheme_dset: - if certified := scheme.lists.get(EntryType.Certified): - certs = [cert for cert in self if cert.status == "active"] - matches, scores = CCSchemeMatcher.match_all(certified, scheme.country, certs) - for dgst, match in matches.items(): - self[dgst].heuristics.scheme_data = match - if archived := scheme.lists.get(EntryType.Archived): - certs = [cert for cert in self if cert.status == "archived"] - matches, scores = CCSchemeMatcher.match_all(archived, scheme.country, certs) - for dgst, match in matches.items(): - self[dgst].heuristics.scheme_data = match - - @staged(logger, "Computing heuristics: SARs") - def _compute_sars(self) -> None: - transformer = SARTransformer().fit(self.certs.values()) - for cert in self: - cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) - - @staged(logger, "Computing heuristics: certificate versions") - def _compute_cert_versions(self) -> None: - cert_ids = { - cert.dgst: CertificateId(cert.scheme, cert.heuristics.cert_id) - if cert.heuristics.cert_id is not None - else None - for cert in self - } - for cert in self: - cert.compute_heuristics_cert_versions(cert_ids) - - def _compute_heuristics(self) -> None: - self._compute_normalized_cert_ids() - super()._compute_heuristics() - self._compute_scheme_data() - self._compute_cert_versions() - self._compute_cert_labs() - self._compute_sars() - - @staged(logger, "Computing heuristics: references between certificates.") - def _compute_references(self) -> None: - def ref_lookup(kw_attr): - def func(cert): - kws = getattr(cert.pdf_data, kw_attr) - if not kws: - return set() - res = set() - for scheme, matches in kws["cc_cert_id"].items(): - for match in matches: - try: - canonical = CertificateId(scheme, match).canonical - res.add(canonical) - except Exception: - res.add(match) - return res - - return func - - for ref_source in ("report", "st"): - kw_source = f"{ref_source}_keywords" - dep_attr = f"{ref_source}_references" - - finder = ReferenceFinder() - finder.fit(self.certs, lambda cert: cert.heuristics.cert_id, ref_lookup(kw_source)) # type: ignore - - for dgst in self.certs: - setattr( - self.certs[dgst].heuristics, - dep_attr, - finder.predict_single_cert(dgst, keep_unknowns=False), - ) - - @serialize - def process_auxiliary_datasets(self, download_fresh: bool = False) -> None: - """ - Processes all auxiliary datasets needed during computation. On top of base-class processing, - CC handles protection profiles, maintenance updates and schemes. - """ - super().process_auxiliary_datasets(download_fresh) - self.auxiliary_datasets.pp_dset = self.process_protection_profiles(to_download=download_fresh) - self.auxiliary_datasets.mu_dset = self.process_maintenance_updates(to_download=download_fresh) - self.auxiliary_datasets.scheme_dset = self.process_schemes( - to_download=download_fresh, only_schemes={cert.scheme for cert in self} + def _compute_heuristics_body(self, skip_schemes: bool = False) -> None: + link_to_protection_profiles(self.certs.values(), self.aux_handlers[ProtectionProfileDatasetHandler].dset) + compute_cpe_heuristics(self.aux_handlers[CPEDatasetHandler].dset, self.certs.values()) + compute_related_cves( + self.aux_handlers[CPEDatasetHandler].dset, + self.aux_handlers[CVEDatasetHandler].dset, + self.aux_handlers[CPEMatchDictHandler].dset, + self.certs.values(), ) + compute_normalized_cert_ids(self.certs.values()) + compute_references(self.certs) + compute_transitive_vulnerabilities(self.certs) - @staged(logger, "Processing protection profiles.") - def process_protection_profiles( - self, to_download: bool = True, keep_metadata: bool = True - ) -> ProtectionProfileDataset: - """ - Downloads new snapshot of dataset with processed protection profiles (if it doesn't exist) and links PPs - with certificates within self. Assigns PPs to all certificates, based on name and fname match. - - :param bool to_download: If dataset should be downloaded or fetched from json, defaults to True - :param bool keep_metadata: If json related to the PP dataset should be kept on drive, defaults to True - :raises RuntimeError: When building of PPDataset fails - """ + if not skip_schemes: + compute_scheme_data(self.aux_handlers[CCSchemeDatasetHandler].dset, self.certs) - self.auxiliary_datasets_dir.mkdir(parents=True, exist_ok=True) - - if to_download or not self.pp_dataset_path.exists(): - pp_dataset = ProtectionProfileDataset.from_web(self.pp_dataset_path) - else: - pp_dataset = ProtectionProfileDataset.from_json(self.pp_dataset_path) - - # Map protection profiles to their name and file name for matching to certs. - pps = {(pp.pp_name, sanitization.sanitize_link_fname(pp.pp_link)): pp for pp in pp_dataset} - - for cert in self: - if cert.protection_profiles is None: - raise RuntimeError("Building of the dataset probably failed - this should not be happening.") - cert.protection_profiles = { - pps.get((x.pp_name, sanitization.sanitize_link_fname(x.pp_link)), x) for x in cert.protection_profiles - } - - if not keep_metadata: - self.pp_dataset_path.unlink() - - return pp_dataset - - @staged(logger, "Processing maintenace updates.") - def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMaintenanceUpdates: - """ - Downloads or loads from json a dataset of maintenance updates. Runs analysis on that dataset if it's not completed. - :return CCDatasetMaintenanceUpdates: the resulting dataset of maintenance updates - """ - self.mu_dataset_dir.mkdir(parents=True, exist_ok=True) - - if to_download or not self.mu_dataset_path.exists(): - maintained_certs: list[CCCertificate] = [x for x in self if x.maintenance_updates] - updates = list( - itertools.chain.from_iterable(CCMaintenanceUpdate.get_updates_from_cc_cert(x) for x in maintained_certs) - ) - update_dset = CCDatasetMaintenanceUpdates( - {x.dgst: x for x in updates}, - root_dir=self.mu_dataset_dir, - name="maintenance_updates", - ) - else: - update_dset = CCDatasetMaintenanceUpdates.from_json(self.mu_dataset_path) - - if not update_dset.state.artifacts_downloaded: - update_dset.download_all_artifacts() - if not update_dset.state.pdfs_converted: - update_dset.convert_all_pdfs() - if not update_dset.state.certs_analyzed: - update_dset.extract_data() - - return update_dset - - @staged(logger, "Processing CC scheme dataset.") - def process_schemes(self, to_download: bool = True, only_schemes: set[str] | None = None) -> CCSchemeDataset: - """ - Downloads or loads from json a dataset of CC scheme data. - """ - self.auxiliary_datasets_dir.mkdir(parents=True, exist_ok=True) - - if to_download or not self.scheme_dataset_path.exists(): - scheme_dset = CCSchemeDataset.from_web(only_schemes) - scheme_dset.to_json(self.scheme_dataset_path) - else: - scheme_dset = CCSchemeDataset.from_json(self.scheme_dataset_path) - - return scheme_dset + compute_cert_labs(self.certs.values()) + compute_eals(self.certs.values(), self.aux_handlers[ProtectionProfileDatasetHandler].dset) + compute_sars(self.certs.values()) class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): @@ -1037,6 +788,9 @@ class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): Should be used merely for actions related to Maintenance updates: download pdfs, convert pdfs, extract data from pdfs """ + FULL_ARCHIVE_URL: ClassVar[AnyHttpUrl] = config.cc_maintenances_latest_full_archive + SNAPSHOT_URL: ClassVar[AnyHttpUrl] = config.cc_maintenances_latest_snapshot + # Quite difficult to achieve correct behaviour with MyPy here, opting for ignore def __init__( self, @@ -1047,6 +801,7 @@ def __init__( state: CCDataset.DatasetInternalState | None = None, ): super().__init__(certs, root_dir, name, description, state) # type: ignore + self.aux_handlers = {} self.state.meta_sources_parsed = True @property @@ -1056,13 +811,19 @@ def certs_dir(self) -> Path: def __iter__(self) -> Iterator[CCMaintenanceUpdate]: yield from self.certs.values() # type: ignore - def _compute_heuristics(self) -> None: + def _compute_heuristics_body(self, skip_schemes: bool = False) -> None: raise NotImplementedError def compute_related_cves(self) -> None: raise NotImplementedError - def process_auxiliary_datasets(self, download_fresh: bool = False) -> None: + def process_auxiliary_datasets( + self, + download_fresh: bool = False, + processed_pp_dataset_root_dir: Path | None = None, + skip_schemes: bool = False, + **kwargs, + ) -> None: raise NotImplementedError def analyze_certificates(self) -> None: @@ -1097,31 +858,6 @@ def to_pandas(self) -> pd.DataFrame: df.maintenance_date = pd.to_datetime(df.maintenance_date, errors="coerce") return df.fillna(value=np.nan) - @classmethod - def from_web_latest( - cls, - path: str | Path | None = None, - auxiliary_datasets: bool = False, - artifacts: bool = False, - ) -> CCDatasetMaintenanceUpdates: - if auxiliary_datasets or artifacts: - raise ValueError( - "Maintenance update dataset does not support downloading artifacts or other auxiliary datasets." - ) - if path: - path = Path(path) - if not path.exists(): - path.mkdir(parents=True) - if not path.is_dir(): - raise ValueError("Path needs to be a directory.") - with tempfile.TemporaryDirectory() as tmp_dir: - dset_path = Path(tmp_dir) / "maintenance_updates.json" - helpers.download_file(config.cc_maintenances_latest_snapshot, dset_path) - dset = cls.from_json(dset_path) - if path: - dset.move_dataset(path) - return dset - def get_n_maintenances_df(self) -> pd.DataFrame: """ Returns a DataFrame with CCCertificate digest as an index, and number of registered maintenances as a value diff --git a/src/sec_certs/dataset/cc_scheme.py b/src/sec_certs/dataset/cc_scheme.py index 19f1a3def..e7f4433e8 100644 --- a/src/sec_certs/dataset/cc_scheme.py +++ b/src/sec_certs/dataset/cc_scheme.py @@ -50,7 +50,11 @@ def from_dict(cls, dct: Mapping) -> CCSchemeDataset: @classmethod def from_web( - cls, only_schemes: set[str] | None = None, enhanced: bool | None = None, artifacts: bool | None = None + cls, + json_path: str | Path = constants.DUMMY_NONEXISTING_PATH, + only_schemes: set[str] | None = None, + enhanced: bool | None = None, + artifacts: bool | None = None, ) -> CCSchemeDataset: schemes = {} for scheme, sources in CCScheme.methods.items(): @@ -60,4 +64,4 @@ def from_web( schemes[scheme] = CCScheme.from_web(scheme, sources.keys(), enhanced=enhanced, artifacts=artifacts) except Exception as e: logger.warning(f"Could not download CC scheme: {scheme} due to error {e}.") - return cls(schemes) + return cls(schemes, json_path=json_path) diff --git a/src/sec_certs/dataset/cpe.py b/src/sec_certs/dataset/cpe.py index efcb3ded1..c93ddd715 100644 --- a/src/sec_certs/dataset/cpe.py +++ b/src/sec_certs/dataset/cpe.py @@ -9,8 +9,8 @@ import pandas as pd -import sec_certs.configuration as config_module from sec_certs import constants +from sec_certs.configuration import config from sec_certs.dataset.json_path_dataset import JSONPathDataset from sec_certs.sample.cpe import CPE from sec_certs.serialization.json import ComplexSerializableType @@ -19,6 +19,10 @@ logger = logging.getLogger(__name__) +class CPEMatchDict(dict): + pass + + class CPEDataset(JSONPathDataset, ComplexSerializableType): """ Dataset of CPE records. Includes look-up dictionaries for fast search. @@ -78,13 +82,13 @@ def from_web(cls, json_path: str | Path = constants.DUMMY_NONEXISTING_PATH) -> C dset_path = Path(tmp_dir) / "cpe_dataset.json.gz" if ( not helpers.download_file( - config_module.config.cpe_latest_snapshot, + config.cpe_latest_snapshot, dset_path, progress_bar_desc="Downloading CPEDataset from web", ) == constants.RESPONSE_OK ): - raise RuntimeError(f"Could not download CPEDataset from {config_module.config.cpe_latest_snapshot}.") + raise RuntimeError(f"Could not download CPEDataset from {config.cpe_latest_snapshot}.") dset = cls.from_json(dset_path, is_compressed=True) dset.json_path = json_path diff --git a/src/sec_certs/dataset/dataset.py b/src/sec_certs/dataset/dataset.py index 8caeafcfe..bd7fff510 100644 --- a/src/sec_certs/dataset/dataset.py +++ b/src/sec_certs/dataset/dataset.py @@ -1,10 +1,6 @@ from __future__ import annotations -import gzip -import itertools -import json import logging -import re import shutil import tarfile import tempfile @@ -13,51 +9,37 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Generic, TypeVar, cast +from typing import Any, ClassVar, Generic, TypeVar, cast import pandas as pd +from pydantic import AnyHttpUrl from sec_certs import constants -from sec_certs.configuration import config -from sec_certs.dataset.cpe import CPEDataset -from sec_certs.dataset.cve import CVEDataset -from sec_certs.model.cpe_matching import CPEClassifier +from sec_certs.dataset.auxiliary_dataset_handling import AuxiliaryDatasetHandler from sec_certs.sample.certificate import Certificate -from sec_certs.sample.cpe import CPE from sec_certs.serialization.json import ( ComplexSerializableType, get_class_fullname, serialize, ) from sec_certs.utils import helpers -from sec_certs.utils.nvd_dataset_builder import ( - CpeMatchNvdDatasetBuilder, - CpeNvdDatasetBuilder, - CveNvdDatasetBuilder, -) from sec_certs.utils.profiling import staged -from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) - -@dataclass -class AuxiliaryDatasets: - cpe_dset: CPEDataset | None = None - cve_dset: CVEDataset | None = None - - CertSubType = TypeVar("CertSubType", bound=Certificate) -AuxiliaryDatasetsSubType = TypeVar("AuxiliaryDatasetsSubType", bound=AuxiliaryDatasets) DatasetSubType = TypeVar("DatasetSubType", bound="Dataset") -class Dataset(Generic[CertSubType, AuxiliaryDatasetsSubType], ComplexSerializableType, ABC): +class Dataset(Generic[CertSubType], ComplexSerializableType, ABC): """ Base class for dataset of certificates from CC and FIPS 140 schemes. Layouts public functions, the processing pipeline and common operations on the dataset and certs. """ + FULL_ARCHIVE_URL: ClassVar[AnyHttpUrl] + SNAPSHOT_URL: ClassVar[AnyHttpUrl] + @dataclass class DatasetInternalState(ComplexSerializableType): meta_sources_parsed: bool = False @@ -73,7 +55,7 @@ def __init__( name: str | None = None, description: str = "", state: DatasetInternalState | None = None, - auxiliary_datasets: AuxiliaryDatasetsSubType | None = None, + aux_handlers: dict[type[AuxiliaryDatasetHandler], AuxiliaryDatasetHandler] = {}, ): self.certs = certs @@ -82,12 +64,7 @@ def __init__( self.name = name if name else type(self).__name__.lower() + "_dataset" self.description = description if description else "No description provided" self.state = state if state else self.DatasetInternalState() - - if not auxiliary_datasets: - self.auxiliary_datasets = AuxiliaryDatasets() - else: - self.auxiliary_datasets = auxiliary_datasets - + self.aux_handlers = aux_handlers self.root_dir = Path(root_dir) @property @@ -98,7 +75,7 @@ def root_dir(self) -> Path: return self._root_dir @root_dir.setter - def root_dir(self: DatasetSubType, new_dir: str | Path) -> None: + def root_dir(self, new_dir: str | Path) -> None: """ This setter will only set the root dir and all internal paths so that they point to the new root dir. No data is being moved around. @@ -131,22 +108,6 @@ def certs_dir(self) -> Path: """ return self.root_dir / "certs" - @property - def cpe_dataset_path(self) -> Path: - return self.auxiliary_datasets_dir / "cpe_dataset.json" - - @property - def cpe_match_json_path(self) -> Path: - return self.auxiliary_datasets_dir / "cpe_match_feed.json" - - @property - def cve_dataset_path(self) -> Path: - return self.auxiliary_datasets_dir / "cve_dataset.json" - - @property - def nist_cve_cpe_matching_dset_path(self) -> Path: - return self.auxiliary_datasets_dir / "nvdcpematch-1.0.json" - @property def json_path(self) -> Path: return self.root_dir / (self.name + ".json") @@ -181,9 +142,9 @@ def __str__(self) -> str: @classmethod def from_web( # noqa cls: type[DatasetSubType], - archive_url: str, - snapshot_url: str, - progress_bar_desc: str, + archive_url: AnyHttpUrl | None = None, + snapshot_url: AnyHttpUrl | None = None, + progress_bar_desc: str | None = None, path: None | str | Path = None, auxiliary_datasets: bool = False, artifacts: bool = False, @@ -196,13 +157,20 @@ def from_web( # noqa .. note:: Note that including the auxiliary datasets adds several gigabytes and including artifacts adds tens of gigabytes. - :param archive_url: The URL of the full dataset archive. - :param snapshot_url: The URL of the full dataset snapshot. - :param progress_bar_desc: Description of the download progress bar. + :param archive_url: The URL of the full dataset archive. If `None` provided, defaults to `cls.FULL_ARCHIVE_URL`. + :param snapshot_url: The URL of the full dataset snapshot. If `None` provided, defaults to `cls.SNAPSHOT_URL`. + :param progress_bar_desc: Description of the download progress bar. If `None`, will pick reasonable default. :param path: Path to a directory where to store the dataset, or `None` if it should not be stored. :param auxiliary_datasets: Whether to also download auxiliary datasets (CVE, CPE, CPEMatch datasets). :param artifacts: Whether to also download artifacts (i.e. PDFs). """ + if not archive_url: + archive_url = cls.FULL_ARCHIVE_URL + if not snapshot_url: + snapshot_url = cls.SNAPSHOT_URL + if not progress_bar_desc: + progress_bar_desc = f"Downloading: {type(cls).__name__}" + if (artifacts or auxiliary_datasets) and path is None: raise ValueError("Path needs to be defined if artifacts or auxiliary datasets are to be downloaded.") if artifacts and not auxiliary_datasets: @@ -259,7 +227,7 @@ def to_dict(self) -> dict[str, Any]: } @classmethod - def from_dict(cls: type[DatasetSubType], dct: dict) -> DatasetSubType: + def from_dict(cls, dct: dict) -> Dataset: certs = {x.dgst: x for x in dct["certs"]} dset = cls(certs, name=dct["name"], description=dct["description"], state=dct["state"]) if len(dset) != (claimed := dct["n_certs"]): @@ -279,10 +247,8 @@ def from_json(cls: type[DatasetSubType], input_path: str | Path, is_compressed: return dset def _set_local_paths(self) -> None: - if self.auxiliary_datasets.cpe_dset: - self.auxiliary_datasets.cpe_dset.json_path = self.cpe_dataset_path - if self.auxiliary_datasets.cve_dset: - self.auxiliary_datasets.cve_dset.json_path = self.cve_dataset_path + for handler in self.aux_handlers.values(): + handler.set_local_paths(self.auxiliary_datasets_dir) def move_dataset(self, new_root_dir: str | Path) -> None: """ @@ -311,7 +277,7 @@ def copy_dataset(self, new_root_dir: str | Path) -> None: shutil.copytree(str(self.root_dir), str(new_root_dir), dirs_exist_ok=True) self.root_dir = new_root_dir - def _get_certs_by_name(self, name: str) -> set[CertSubType]: + def get_certs_by_name(self, name: str) -> set[CertSubType]: """ Returns list of certificates that match given name. """ @@ -321,22 +287,28 @@ def _get_certs_by_name(self, name: str) -> set[CertSubType]: def get_certs_from_web(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") + @staged(logger, "Processing auxiliary datasets") @serialize - @abstractmethod - def process_auxiliary_datasets(self, download_fresh: bool = False) -> None: + def process_auxiliary_datasets(self, download_fresh: bool = False, **kwargs) -> None: """ Processes all auxiliary datasets (CPE, CVE, ...) that are required during computation. """ logger.info("Processing auxiliary datasets.") - self.auxiliary_datasets_dir.mkdir(parents=True, exist_ok=True) - self.auxiliary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh) - self.auxiliary_datasets.cve_dset = self._prepare_cve_dataset(download_fresh) - - if download_fresh or not self.cpe_match_json_path.exists(): - self._prepare_cpe_match_dict(download_fresh=download_fresh) - + for handler in self.aux_handlers.values(): + handler.process_dataset(download_fresh) self.state.auxiliary_datasets_processed = True + def load_auxiliary_datasets(self) -> None: + logger.info("Loading auxiliary datasets into memory.") + for handler in self.aux_handlers.values(): + if not hasattr(handler, "dset"): + try: + handler.load_dataset() + except Exception: + logger.warning( + f"Failed to load auxiliary dataset bound to {handler}, some functionality may not work." + ) + @serialize def download_all_artifacts(self, fresh: bool = True) -> None: """ @@ -397,307 +369,24 @@ def analyze_certificates(self) -> None: self.state.certs_analyzed = True def _analyze_certificates_body(self) -> None: + logger.info("Extracting data and heuristics") self.extract_data() - self._compute_heuristics() + self.compute_heuristics() @abstractmethod def extract_data(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") - def _compute_heuristics(self) -> None: + @serialize + def compute_heuristics(self) -> None: logger.info("Computing various heuristics from the certificates.") - self.compute_cpe_heuristics() - self.compute_related_cves() - self._compute_references() - self._compute_transitive_vulnerabilities() + self.load_auxiliary_datasets() + self._compute_heuristics_body() @abstractmethod - def _compute_references(self) -> None: + def _compute_heuristics_body(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") - @abstractmethod - def _compute_transitive_vulnerabilities(self) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") - - @staged(logger, "Processing CPEDataset.") - def _prepare_cpe_dataset(self, download_fresh: bool = False) -> CPEDataset: - if not self.auxiliary_datasets_dir.exists(): - self.auxiliary_datasets_dir.mkdir(parents=True) - - if self.cpe_dataset_path.exists(): - logger.info("Preparing CPEDataset from json.") - cpe_dataset = CPEDataset.from_json(self.cpe_dataset_path) - else: - cpe_dataset = CPEDataset(json_path=self.cpe_dataset_path) - download_fresh = True - - if download_fresh: - if config.preferred_source_nvd_datasets == "api": - logger.info("Fetching new CPE records from NVD API.") - with CpeNvdDatasetBuilder(api_key=config.nvd_api_key) as builder: - cpe_dataset = builder.build_dataset(cpe_dataset) - cpe_dataset.to_json() - else: - logger.info("Preparing CPEDataset from sec-certs.org.") - cpe_dataset = CPEDataset.from_web(self.cpe_dataset_path) - - return cpe_dataset - - @staged(logger, "Processing CVEDataset.") - def _prepare_cve_dataset(self, download_fresh: bool = False) -> CVEDataset: - if not self.auxiliary_datasets_dir.exists(): - logger.info("Loading CVEDataset from json.") - self.auxiliary_datasets_dir.mkdir(parents=True) - - if self.cve_dataset_path.exists(): - logger.info("Preparing CVEDataset from json.") - cve_dataset = CVEDataset.from_json(self.cve_dataset_path) - else: - cve_dataset = CVEDataset(json_path=self.cve_dataset_path) - download_fresh = True - - if download_fresh: - if config.preferred_source_nvd_datasets == "api": - logger.info("Fetching new CVE records from NVD API.") - with CveNvdDatasetBuilder(api_key=config.nvd_api_key) as builder: - cve_dataset = builder.build_dataset(cve_dataset) - cve_dataset.to_json() - else: - logger.info("Preparing CVEDataset from sec-certs.org") - cve_dataset = CVEDataset.from_web(self.cve_dataset_path) - - return cve_dataset - - @staged(logger, "Processing CPE match dict.") - def _prepare_cpe_match_dict(self, download_fresh: bool = False) -> dict: - if self.cpe_match_json_path.exists(): - logger.info("Preparing CPE Match feed from json.") - with self.cpe_match_json_path.open("r") as handle: - cpe_match_dict = json.load(handle) - else: - cpe_match_dict = CpeMatchNvdDatasetBuilder._init_new_dataset() - download_fresh = True - - if download_fresh: - if config.preferred_source_nvd_datasets == "api": - logger.info("Fetching CPE Match feed from NVD APi.") - with CpeMatchNvdDatasetBuilder(api_key=config.nvd_api_key) as builder: - cpe_match_dict = builder.build_dataset(cpe_match_dict) - else: - logger.info("Preparing CPE Match feed from sec-certs.org.") - with tempfile.TemporaryDirectory() as tmp_dir: - dset_path = Path(tmp_dir) / "cpe_match_feed.json.gz" - if ( - not helpers.download_file( - config.cpe_match_latest_snapshot, - dset_path, - progress_bar_desc="Downloading CPE Match feed from web", - ) - == constants.RESPONSE_OK - ): - raise RuntimeError( - f"Could not download CPE Match feed from {config.cpe_match_latest_snapshot}." - ) - with gzip.open(str(dset_path)) as handle: - json_str = handle.read().decode("utf-8") - cpe_match_dict = json.loads(json_str) - with self.cpe_match_json_path.open("w") as handle: - json.dump(cpe_match_dict, handle, indent=4) - - return cpe_match_dict - - @serialize - @staged(logger, "Computing heuristics: Finding CPE matches for certificates") - def compute_cpe_heuristics(self) -> CPEClassifier: - """ - Computes matching CPEs for the certificates. - """ - WINDOWS_WEAK_CPES: set[CPE] = { - CPE( - "", - "cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x64:*", - "Microsoft Windows on X64", - ), - CPE( - "", - "cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x86:*", - "Microsoft Windows on X86", - ), - } - - def filter_condition(cpe: CPE) -> bool: - """ - Filters out very weak CPE matches that don't improve our database. - """ - if ( - cpe.title - and (cpe.version == "-" or cpe.version == "*") - and not any(char.isdigit() for char in cpe.title) - ): - return False - if ( - not cpe.title - and cpe.item_name - and (cpe.version == "-" or cpe.version == "*") - and not any(char.isdigit() for char in cpe.item_name) - ): - return False - if re.match(constants.RELEASE_CANDIDATE_REGEX, cpe.update): - return False - return cpe not in WINDOWS_WEAK_CPES - - if not self.auxiliary_datasets.cpe_dset: - self.auxiliary_datasets.cpe_dset = self._prepare_cpe_dataset() - - clf = CPEClassifier(config.cpe_matching_threshold, config.cpe_n_max_matches) - - if self.auxiliary_datasets.cpe_dset is None: - raise ValueError("CPE dataset cannot be None") - - clf.fit([x for x in self.auxiliary_datasets.cpe_dset if filter_condition(x)]) - - cert: CertSubType - for cert in tqdm(self, desc="Predicting CPE matches with the classifier"): - cert.compute_heuristics_version() - - cert.heuristics.cpe_matches = ( - clf.predict_single_cert(cert.manufacturer, cert.name, cert.heuristics.extracted_versions) - if cert.name - else None - ) - - return clf - - @serialize - def to_label_studio_json(self, output_path: str | Path) -> None: - cpe_dset = self._prepare_cpe_dataset() - - lst = [] - for cert in [x for x in self if x.heuristics.cpe_matches]: - dct = {"text": cert.label_studio_title} - candidates = [cpe_dset[x].title for x in cert.heuristics.cpe_matches] - candidates += ["No good match"] * (config.cpe_n_max_matches - len(candidates)) - options = ["option_" + str(x) for x in range(1, config.cpe_n_max_matches)] - dct.update(dict(zip(options, candidates))) - lst.append(dct) - - with Path(output_path).open("w") as handle: - json.dump(lst, handle, indent=4) - - @serialize - def load_label_studio_labels(self, input_path: str | Path) -> set[str]: - with Path(input_path).open("r") as handle: - data = json.load(handle) - - cpe_dset = self._prepare_cpe_dataset() - title_to_cpes_dict = cpe_dset.get_title_to_cpes_dict() - labeled_cert_digests: set[str] = set() - - logger.info("Translating label studio matches into their CPE representations and assigning to certificates.") - for annotation in tqdm(data, desc="Translating label studio matches"): - cpe_candidate_keys = {key for key in annotation if "option_" in key and annotation[key] != "No good match"} - - if "verified_cpe_match" not in annotation: - incorrect_keys: set[str] = set() - elif isinstance(annotation["verified_cpe_match"], str): - incorrect_keys = {annotation["verified_cpe_match"]} - else: - incorrect_keys = set(annotation["verified_cpe_match"]["choices"]) - - incorrect_keys = {x.lstrip("$") for x in incorrect_keys} - predicted_annotations = {annotation[x] for x in cpe_candidate_keys - incorrect_keys} - - cpes: set[CPE] = set() - for x in predicted_annotations: - if x not in title_to_cpes_dict: - logger.error(f"{x} not in dataset") - else: - to_update = title_to_cpes_dict[x] - if to_update and not cpes: - cpes = to_update - elif to_update and cpes: - cpes.update(to_update) - - # distinguish between FIPS and CC - if "\n" in annotation["text"]: - cert_name = annotation["text"].split("\nModule name: ")[1].split("\n")[0] - else: - cert_name = annotation["text"] - - certs = self._get_certs_by_name(cert_name) - labeled_cert_digests.update({x.dgst for x in certs}) - - for c in certs: - c.heuristics.verified_cpe_matches = {x.uri for x in cpes if x is not None} if cpes else None - - return labeled_cert_digests - - def enrich_automated_cpes_with_manual_labels(self) -> None: - """ - Prior to CVE matching, it is wise to expand the database of automatic CPE matches with those that were manually assigned. - """ - for cert in cast(Iterator[Certificate], self): - if not cert.heuristics.cpe_matches and cert.heuristics.verified_cpe_matches: - cert.heuristics.cpe_matches = cert.heuristics.verified_cpe_matches - elif cert.heuristics.cpe_matches and cert.heuristics.verified_cpe_matches: - cert.heuristics.cpe_matches = set(cert.heuristics.cpe_matches).union( - set(cert.heuristics.verified_cpe_matches) - ) - - def _get_all_cpes_in_dataset(self) -> set[CPE]: - if not self.auxiliary_datasets.cpe_dset: - raise ValueError( - "Cannot retrieve all cpes in dataset when cpe_dset is not set. You can prepare it with obj._prepare_cpe_dataset()" - ) - - cpe_matches = [ - [self.auxiliary_datasets.cpe_dset.cpes[y] for y in x.heuristics.cpe_matches] - for x in self - if x.heuristics.cpe_matches - ] - return set(itertools.chain.from_iterable(cpe_matches)) - - @serialize - @staged(logger, "Computing heuristics: CVEs in certificates.") - def compute_related_cves(self) -> None: - """ - Computes CVEs for the certificates, given their CPE matches. - """ - - if not self.auxiliary_datasets.cpe_dset: - self.auxiliary_datasets.cpe_dset = self._prepare_cpe_dataset() - - if not self.auxiliary_datasets.cve_dset: - self.auxiliary_datasets.cve_dset = self._prepare_cve_dataset() - - if self.auxiliary_datasets.cve_dset is None: - raise ValueError("CVE dataset cannot be None") - - if not self.auxiliary_datasets.cve_dset.look_up_dicts_built: - cpe_match_dict = self._prepare_cpe_match_dict() - all_cpes = self._get_all_cpes_in_dataset() - self.auxiliary_datasets.cve_dset.build_lookup_dict(cpe_match_dict, all_cpes) - - self.enrich_automated_cpes_with_manual_labels() - cpe_rich_certs = [x for x in cast(Iterator[Certificate], self) if x.heuristics.cpe_matches] - - if not cpe_rich_certs: - logger.error( - "No certificates with verified CPE match detected. You must run dset.manually_verify_cpe_matches() first. Returning." - ) - return - - cert: Certificate - for cert in tqdm(cpe_rich_certs, desc="Computing related CVES"): - related_cves = self.auxiliary_datasets.cve_dset.get_cves_from_matched_cpe_uris(cert.heuristics.cpe_matches) - cert.heuristics.related_cves = related_cves if related_cves else None - - n_vulnerable = len([x for x in cpe_rich_certs if x.heuristics.related_cves]) - n_vulnerabilities = sum([len(x.heuristics.related_cves) for x in cpe_rich_certs if x.heuristics.related_cves]) - logger.info( - f"In total, we identified {n_vulnerabilities} vulnerabilities in {n_vulnerable} vulnerable certificates." - ) - def get_keywords_df(self, var: str) -> pd.DataFrame: """ Get dataframe of keyword hits for attribute (var) that is member of PdfData class. diff --git a/src/sec_certs/dataset/fips.py b/src/sec_certs/dataset/fips.py index 0feb920f8..19e09af85 100644 --- a/src/sec_certs/dataset/fips.py +++ b/src/sec_certs/dataset/fips.py @@ -5,22 +5,29 @@ import logging import shutil from pathlib import Path -from typing import Final +from typing import ClassVar, Final import numpy as np import pandas as pd from bs4 import BeautifulSoup, NavigableString +from pydantic import AnyHttpUrl from sec_certs import constants from sec_certs.configuration import config -from sec_certs.dataset.cpe import CPEDataset -from sec_certs.dataset.cve import CVEDataset -from sec_certs.dataset.dataset import AuxiliaryDatasets, Dataset -from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset -from sec_certs.model.reference_finder import ReferenceFinder -from sec_certs.model.transitive_vulnerability_finder import ( - TransitiveVulnerabilityFinder, +from sec_certs.dataset.auxiliary_dataset_handling import ( + AuxiliaryDatasetHandler, + CPEDatasetHandler, + CPEMatchDictHandler, + CVEDatasetHandler, + FIPSAlgorithmDatasetHandler, ) +from sec_certs.dataset.dataset import Dataset +from sec_certs.heuristics.common import ( + compute_cpe_heuristics, + compute_related_cves, + compute_transitive_vulnerabilities, +) +from sec_certs.heuristics.fips import compute_references from sec_certs.sample.fips import FIPSCertificate from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers @@ -31,17 +38,14 @@ logger = logging.getLogger(__name__) -class FIPSAuxiliaryDatasets(AuxiliaryDatasets): - cpe_dset: CPEDataset | None = None - cve_dset: CVEDataset | None = None - algorithm_dset: FIPSAlgorithmDataset | None = None - - -class FIPSDataset(Dataset[FIPSCertificate, FIPSAuxiliaryDatasets], ComplexSerializableType): +class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType): """ Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. """ + FULL_ARCHIVE_URL: ClassVar[AnyHttpUrl] = config.fips_latest_full_archive + SNAPSHOT_URL: ClassVar[AnyHttpUrl] = config.fips_latest_snapshot + def __init__( self, certs: dict[str, FIPSCertificate] = {}, @@ -49,7 +53,7 @@ def __init__( name: str | None = None, description: str = "", state: Dataset.DatasetInternalState | None = None, - auxiliary_datasets: FIPSAuxiliaryDatasets | None = None, + aux_handlers: dict[type[AuxiliaryDatasetHandler], AuxiliaryDatasetHandler] = {}, ): self.certs = certs self.timestamp = datetime.datetime.now() @@ -57,12 +61,15 @@ def __init__( self.name = name if name else type(self).__name__ + " dataset" self.description = description if description else datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S") self.state = state if state else self.DatasetInternalState() - self.auxiliary_datasets: FIPSAuxiliaryDatasets = ( - auxiliary_datasets if auxiliary_datasets else FIPSAuxiliaryDatasets() - ) - + self.aux_handlers = aux_handlers self.root_dir = Path(root_dir) + if not self.aux_handlers: + self.aux_handlers[CPEDatasetHandler] = CPEDatasetHandler(self.auxiliary_datasets_dir) + self.aux_handlers[CVEDatasetHandler] = CVEDatasetHandler(self.auxiliary_datasets_dir) + self.aux_handlers[FIPSAlgorithmDatasetHandler] = FIPSAlgorithmDatasetHandler(self.auxiliary_datasets_dir) + self.aux_handlers[CPEMatchDictHandler] = CPEMatchDictHandler(self.auxiliary_datasets_dir) + LIST_OF_CERTS_HTML: Final[dict[str, str]] = { "fips_modules_active.html": constants.FIPS_ACTIVE_MODULES_URL, "fips_modules_historical.html": constants.FIPS_HISTORICAL_MODULES_URL, @@ -85,10 +92,6 @@ def policies_txt_dir(self) -> Path: def module_dir(self) -> Path: return self.certs_dir / "modules" - @property - def algorithm_dataset_path(self) -> Path: - return self.auxiliary_datasets_dir / "algorithms.json" - def __getitem__(self, item: str) -> FIPSCertificate: try: return super().__getitem__(item) @@ -110,6 +113,17 @@ def _extract_data_from_html_modules(self) -> None: ) self.update_with_certs(processed_certs) + def _compute_heuristics_body(self): + compute_cpe_heuristics(self.aux_handlers[CPEDatasetHandler].dset, self.certs.values()) + compute_related_cves( + self.aux_handlers[CPEDatasetHandler].dset, + self.aux_handlers[CVEDatasetHandler].dset, + self.aux_handlers[CPEMatchDictHandler].dset, + self.certs.values(), + ) + compute_references(self.certs) + compute_transitive_vulnerabilities(self.certs) + @serialize def extract_data(self) -> None: logger.info("Extracting various data from certification artifacts.") @@ -216,41 +230,9 @@ def _get_certificates_from_html(self, html_file: Path) -> list[FIPSCertificate]: return [FIPSCertificate(int(cert_id)) for cert_id in cert_ids] - @classmethod - def from_web_latest( - cls, - path: str | Path | None = None, - auxiliary_datasets: bool = False, - artifacts: bool = False, - ) -> FIPSDataset: - """ - Fetches the fresh snapshot of FIPSDataset from sec-certs.org. - - Optionally stores it at the given path (a directory) and also downloads auxiliary datasets and artifacts (PDFs). - - .. note:: - Note that including the auxiliary datasets adds several gigabytes and including artifacts adds tens of gigabytes. - - :param path: Path to a directory where to store the dataset, or `None` if it should not be stored. - :param auxiliary_datasets: Whether to also download auxiliary datasets (CVE, CPE, CPEMatch datasets). - :param artifacts: Whether to also download artifacts (i.e. PDFs). - """ - return cls.from_web( - config.fips_latest_full_archive, - config.fips_latest_snapshot, - "Downloading FIPS", - path, - auxiliary_datasets, - artifacts, - ) - def _set_local_paths(self) -> None: super()._set_local_paths() - if self.auxiliary_datasets.algorithm_dset: - self.auxiliary_datasets.algorithm_dset.json_path = self.algorithm_dataset_path - - cert: FIPSCertificate - for cert in self.certs.values(): + for cert in self: cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.module_dir) @serialize @@ -270,21 +252,6 @@ def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = Tru self._set_local_paths() self.state.meta_sources_parsed = True - @serialize - def process_auxiliary_datasets(self, download_fresh: bool = False) -> None: - super().process_auxiliary_datasets(download_fresh) - self.auxiliary_datasets.algorithm_dset = self._prepare_algorithm_dataset(download_fresh) - - @staged(logger, "Processing FIPSAlgorithm dataset.") - def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSAlgorithmDataset: - if not self.algorithm_dataset_path.exists() or download_fresh_algs: - alg_dset = FIPSAlgorithmDataset.from_web(self.algorithm_dataset_path) - alg_dset.to_json() - else: - alg_dset = FIPSAlgorithmDataset.from_json(self.algorithm_dataset_path) - - return alg_dset - @staged(logger, "Extracting Algorithms from policy tables") def _extract_algorithms_from_policy_tables(self): certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] @@ -306,52 +273,6 @@ def _extract_policy_pdf_metadata(self) -> None: ) self.update_with_certs(processed_certs) - @staged( - logger, - "Computing heuristics: Transitive vulnerabilities in referenc(ed/ing) certificates.", - ) - def _compute_transitive_vulnerabilities(self) -> None: - transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: str(cert.cert_id)) - transitive_cve_finder.fit(self.certs, lambda cert: cert.heuristics.policy_processed_references) - - for dgst in self.certs: - transitive_cve = transitive_cve_finder.predict_single_cert(dgst) - self.certs[dgst].heuristics.direct_transitive_cves = transitive_cve.direct_transitive_cves - self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves - - @staged(logger, "Computing heuristics: references between certificates.") - def _compute_references(self, keep_unknowns: bool = False) -> None: - # Previously, a following procedure was used to prune reference_candidates: - # - A set of algorithms was obtained via self.auxiliary_datasets.algorithm_dset.get_algorithms_by_id(reference_candidate) - # - If any of these algorithms had the same vendor as the reference_candidate, the candidate was rejected - # - The rationale is that if an ID appears in a certificate s.t. an algorithm with the same ID was produced by the same vendor, the reference likely refers to alg. - # - Such reference should then be discarded. - # - We are uncertain of the effectivity of such measure, disabling it for now. - for cert in self: - cert.prune_referenced_cert_ids() - - policy_reference_finder = ReferenceFinder() - policy_reference_finder.fit( - self.certs, - lambda cert: str(cert.cert_id), - lambda cert: cert.heuristics.policy_prunned_references, - ) - - module_reference_finder = ReferenceFinder() - module_reference_finder.fit( - self.certs, - lambda cert: str(cert.cert_id), - lambda cert: cert.heuristics.module_prunned_references, - ) - - for cert in self: - cert.heuristics.policy_processed_references = policy_reference_finder.predict_single_cert( - cert.dgst, keep_unknowns - ) - cert.heuristics.module_processed_references = module_reference_finder.predict_single_cert( - cert.dgst, keep_unknowns - ) - def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame( [x.pandas_tuple for x in self.certs.values()], diff --git a/src/sec_certs/dataset/fips_iut.py b/src/sec_certs/dataset/fips_iut.py index 369d81bf3..494358f66 100644 --- a/src/sec_certs/dataset/fips_iut.py +++ b/src/sec_certs/dataset/fips_iut.py @@ -54,7 +54,7 @@ def from_dict(cls, dct: Mapping) -> IUTDataset: return cls(dct["snapshots"]) @classmethod - def from_web_latest(cls) -> IUTDataset: + def from_web(cls) -> IUTDataset: """ Get the IUTDataset from sec-certs.org """ diff --git a/src/sec_certs/dataset/fips_mip.py b/src/sec_certs/dataset/fips_mip.py index 934cc84db..a5af3f4ad 100644 --- a/src/sec_certs/dataset/fips_mip.py +++ b/src/sec_certs/dataset/fips_mip.py @@ -56,7 +56,7 @@ def from_dict(cls, dct: Mapping) -> MIPDataset: return cls(dct["snapshots"]) @classmethod - def from_web_latest(cls) -> MIPDataset: + def from_web(cls) -> MIPDataset: """ Get the MIPDataset from sec-certs.org """ diff --git a/src/sec_certs/dataset/protection_profile.py b/src/sec_certs/dataset/protection_profile.py index af7733a80..b9f0d35c4 100644 --- a/src/sec_certs/dataset/protection_profile.py +++ b/src/sec_certs/dataset/protection_profile.py @@ -1,97 +1,376 @@ -from __future__ import annotations - -import json -import logging import shutil -import tempfile -from dataclasses import dataclass +from datetime import datetime from pathlib import Path +from typing import ClassVar, Literal + +from bs4 import BeautifulSoup +from pydantic import AnyHttpUrl from sec_certs import constants from sec_certs.configuration import config +from sec_certs.dataset.auxiliary_dataset_handling import AuxiliaryDatasetHandler +from sec_certs.dataset.dataset import Dataset, logger from sec_certs.sample.protection_profile import ProtectionProfile -from sec_certs.serialization.json import get_class_fullname +from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers +from sec_certs.utils import parallel_processing as cert_processing +from sec_certs.utils.profiling import staged -logger = logging.getLogger(__name__) - -@dataclass -class ProtectionProfileDataset: - pps: dict[tuple[str, str | None], ProtectionProfile] - _json_path: Path +class ProtectionProfileDataset(Dataset[ProtectionProfile], ComplexSerializableType): + FULL_ARCHIVE_URL: ClassVar[AnyHttpUrl] = config.pp_latest_full_archive + SNAPSHOT_URL: ClassVar[AnyHttpUrl] = config.pp_latest_snapshot def __init__( self, - pps: dict[tuple[str, str | None], ProtectionProfile], - json_path: str | Path = constants.DUMMY_NONEXISTING_PATH, - ) -> None: - self.pps = pps - self.json_path = Path(json_path) + certs: dict[str, ProtectionProfile] = {}, + root_dir: str | Path = constants.DUMMY_NONEXISTING_PATH, + name: str | None = None, + description: str = "", + state: Dataset.DatasetInternalState | None = None, + aux_handlers: dict[type[AuxiliaryDatasetHandler], AuxiliaryDatasetHandler] = {}, + ): + self.certs = certs + self.timestamp = datetime.now() + self.sha256_digest = "not implemented" + self.name = name if name else type(self).__name__ + " dataset" + self.description = description if description else datetime.now().strftime("%d/%m/%Y %H:%M:%S") + self.state = state if state else self.DatasetInternalState() + self.aux_handlers = aux_handlers + self.root_dir = Path(root_dir) + """ + Class for processing ProtectionProfile samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. + """ + + @property + def json_path(self) -> Path: + return self.root_dir / "pp.json" + + @property + def reports_dir(self) -> Path: + """ + Path to protection profile reports. + """ + return self.root_dir / "reports" + + @property + def pps_dir(self) -> Path: + """ + Path to actual protection profiles. + """ + return self.root_dir / "pps" + + @property + def reports_pdf_dir(self) -> Path: + """ + Path to pdfs of protection profile reports. + """ + return self.reports_dir / "pdf" + + @property + def reports_txt_dir(self) -> Path: + """ + Path to txts of protection profile reports. + """ + return self.reports_dir / "txt" @property - def json_path(self): - return self._json_path + def pps_pdf_dir(self) -> Path: + """ + Path to pdfs of protection profiles + """ + return self.pps_dir / "pdf" + + @property + def pps_txt_dir(self) -> Path: + """ + Path to txts of protection profiles. + """ + return self.pps_dir / "txt" + + def _compute_heuristics_body(self): + logger.info("Protection profile dataset has no heuristics to compute, skipping.") + + @property + def web_dir(self) -> Path: + """ + Path to directory with html sources downloaded from commoncriteriaportal.org + """ + return self.root_dir / "web" + + HTML_URL = { + "pp_active.html": constants.CC_PORTAL_BASE_URL + "/pps/index.cfm", + "pp_archived.html": constants.CC_PORTAL_BASE_URL + "/pps/index.cfm?archived=1", + "pp_collaborative.html": constants.CC_PORTAL_BASE_URL + "/pps/collaborativePP.cfm?cpp=1", + } + + @property + def active_html_tuples(self) -> list[tuple[str, Path]]: + return [(x, self.web_dir / y) for y, x in self.HTML_URL.items() if "active" in y] + + @property + def archived_html_tuples(self) -> list[tuple[str, Path]]: + return [(x, self.web_dir / y) for y, x in self.HTML_URL.items() if "archived" in y] + + @property + def collaborative_html_tuples(self) -> list[tuple[str, Path]]: + return [(x, self.web_dir / y) for y, x in self.HTML_URL.items() if "collaborative" in y] + + @serialize + @staged(logger, "Downloading and processing CSV and HTML files of certificates.") + def get_certs_from_web( + self, + to_download: bool = True, + keep_metadata: bool = True, + get_active: bool = True, + get_archived: bool = True, + get_collaborative: bool = True, + ) -> None: + """ + Fetches list of protection profiles together with metadata from commoncriteriaportal.org + """ + if to_download: + self._download_html_resources(get_active, get_archived, get_collaborative) + + logger.info("Adding HTML certificates to ProtectionProfile dataset.") + self.certs = self._get_all_certs_from_html(get_active, get_archived, get_collaborative) + logger.info(f"The resulting dataset has {len(self)} certificates.") + + if not keep_metadata: + shutil.rmtree(self.web_dir) + + self._set_local_paths() + self.state.meta_sources_parsed = True + + def _get_all_certs_from_html( + self, get_active: bool = True, get_archived: bool = True, get_collaborative: bool = True + ) -> dict[str, ProtectionProfile]: + html_sources = [] + if get_active: + html_sources.extend([x for x in self.HTML_URL if "active" in x]) + if get_archived: + html_sources.extend([x for x in self.HTML_URL if "archived" in x]) + if get_collaborative: + html_sources.extend([x for x in self.HTML_URL if "collaborative" in x]) + + new_certs = {} + for file in html_sources: + partial_certs = self._parse_single_html(self.web_dir / file) + logger.info(f"Parsed {len(partial_certs)} protection profiles from: {file}.") + new_certs.update(partial_certs) + return new_certs + + def _download_html_resources( + self, get_active: bool = True, get_archived: bool = True, get_collaborative: bool = True + ) -> None: + self.web_dir.mkdir(parents=True, exist_ok=True) + html_items = [] + if get_active: + html_items.extend(self.active_html_tuples) + if get_archived: + html_items.extend(self.archived_html_tuples) + if get_collaborative: + html_items.extend(self.collaborative_html_tuples) + + html_urls, html_paths = [x[0] for x in html_items], [x[1] for x in html_items] + + logger.info("Downloading required csv and html files.") + helpers.download_parallel(html_urls, html_paths) + + @staticmethod + def _parse_single_html(file: Path) -> dict[str, ProtectionProfile]: + def _parse_table( + soup: BeautifulSoup, + cert_status: Literal["active", "archived"], + table_id: str, + category_string: str, + is_collaborative: bool, + ) -> dict[str, ProtectionProfile]: + tables = soup.find_all("table", id=table_id) + if len(tables) > 1: + raise ValueError( + f'The "{file.name}" was expected to contain 0-1
element. Instead, it contains: {len(tables)}
elements.' + ) + + if not tables: + return {} + + body = list(tables[0].find_all("tr"))[1:] + table_certs = {} + for row in body: + try: + pp = ProtectionProfile.from_html_row(row, cert_status, category_string, is_collaborative) + table_certs[pp.dgst] = pp + except ValueError as e: + logger.error(f"Error when creating ProtectionProfile object: {e}") + + return table_certs + + cert_status: Literal["active", "archived"] = "active" if "active" in file.name else "archived" + is_collaborative = "collaborative" in file.name + cc_table_ids = ["tbl" + x for x in constants.CC_CAT_ABBREVIATIONS] + if is_collaborative: + cc_table_ids = [x + "1" for x in cc_table_ids] + cat_dict = dict(zip(cc_table_ids, constants.CC_CATEGORIES)) + + with file.open("r") as handle: + soup = BeautifulSoup(handle, "html5lib") + + certs = {} + for key, val in cat_dict.items(): + certs.update(_parse_table(soup, cert_status, key, val, is_collaborative)) + + return certs + + def _convert_all_pdfs_body(self, fresh=True): + self._convert_reports_to_txt(fresh) + self._convert_pps_to_txt(fresh) + + @staged(logger, "Converting PDFs of PP certification reports to text.") + def _convert_reports_to_txt(self, fresh: bool = True): + self.reports_txt_dir.mkdir(parents=True, exist_ok=True) + certs_to_process = [x for x in self if x.state.report.is_ok_to_convert(fresh)] + + if not fresh and certs_to_process: + logger.info( + f"Converting {len(certs_to_process)} PDFs of PP certification reports to text for which previous conversion failed." + ) + + cert_processing.process_parallel( + ProtectionProfile.convert_report_pdf, + certs_to_process, + progress_bar_desc="Converting PDFs of PP certification reports to text.", + ) + + @staged(logger, "Converting PDFs of actual Protection Profiles to text.") + def _convert_pps_to_txt(self, fresh: bool = True): + self.pps_txt_dir.mkdir(parents=True, exist_ok=True) + certs_to_process = [x for x in self if x.state.pp.is_ok_to_convert(fresh)] + + if not fresh and certs_to_process: + logger.info( + f"Converting {len(certs_to_process)} PDFs of actual Protection Profiles to text for which previous conversion failed." + ) + + cert_processing.process_parallel( + ProtectionProfile.convert_pp_pdf, + certs_to_process, + progress_bar_desc="Converting PDFs of actual Protection Profiles to text.", + ) + + def _download_all_artifacts_body(self, fresh=True): + self._download_reports(fresh) + self._download_pps(fresh) - @json_path.setter - def json_path(self, new_path: str | Path): - new_path = Path(new_path) - if new_path.is_dir(): - raise ValueError(f"Json path of {get_class_fullname(self)} cannot be a directory.") + @staged(logger, "Downloading PDFs of PP certification reports.") + def _download_reports(self, fresh: bool = True): + self.reports_pdf_dir.mkdir(parents=True, exist_ok=True) + certs_to_process = [x for x in self if x.state.report.is_ok_to_download(fresh) and x.web_data.report_link] - self._json_path = new_path + if not fresh and certs_to_process: + logger.info( + f"Downloading {len(certs_to_process)} PDFs of PP certification reports for which previous download failed." + ) - def move_dataset(self, new_json_path: str | Path) -> None: - logger.info(f"Moving {get_class_fullname(self)} dataset to {new_json_path}") - new_json_path = Path(new_json_path) - new_json_path.parent.mkdir(parents=True, exist_ok=True) + cert_processing.process_parallel( + ProtectionProfile.download_pdf_report, + certs_to_process, + progress_bar_desc="Downloading PDFs of PP certification reports.", + ) - if not self.json_path.exists(): - raise ValueError("Cannot move the PPDataset if the json path does not exist.") + @staged(logger, "Downloading PDFs of actual Protection Profiles.") + def _download_pps(self, fresh: bool = True): + self.pps_pdf_dir.mkdir(parents=True, exist_ok=True) + certs_to_process = [x for x in self if x.state.pp.is_ok_to_download(fresh) and x.web_data.pp_link] - shutil.move(str(self.json_path), str(new_json_path)) - self.json_path = new_json_path + if not fresh and certs_to_process: + logger.info( + f"Downloading {len(certs_to_process)} PDFs of actual Protection Profiles for which previous download failed." + ) - def __iter__(self): - yield from self.pps.values() + cert_processing.process_parallel( + ProtectionProfile.download_pdf_pp, + certs_to_process, + progress_bar_desc="Downloading PDFs of actual Protection Profiles.", + ) - def __getitem__(self, item: tuple[str, str | None]) -> ProtectionProfile: - return self.pps.__getitem__(item) + def extract_data(self): + """ + Extracts pdf metadata and keywords from converted text documents. + """ + logger.info("Extracting various data from certification artifacts.") + self._extract_pdf_metadata() + self._extract_pdf_keywords() - def __setitem__(self, key: tuple[str, str | None], value: ProtectionProfile): - self.pps.__setitem__(key, value) + @staged(logger, "Extracting metadata from certification artifacts.") + def _extract_pdf_metadata(self): + self._extract_report_metadata() + self._extract_pp_metadata() - def __contains__(self, key): - return key in self.pps + @staged(logger, "Extracting keywords from certification artifacts.") + def _extract_pdf_keywords(self): + self._extract_report_keywords() + self._extract_pp_keywords() - def __len__(self) -> int: - return len(self.pps) + def _extract_report_metadata(self): + certs_to_process = [x for x in self if x.state.report.is_ok_to_analyze()] + processed_certs = cert_processing.process_parallel( + ProtectionProfile.extract_report_pdf_metadata, + certs_to_process, + use_threading=False, + progress_bar_desc="Extracting metadata from PP certification reports.", + ) + self.update_with_certs(processed_certs) - @classmethod - def from_json(cls, json_path: str | Path): - with Path(json_path).open("r") as handle: - data = json.load(handle) - pps = [ProtectionProfile.from_old_api_dict(x) for x in data.values()] + def _extract_pp_metadata(self): + certs_to_process = [x for x in self if x.state.pp.is_ok_to_analyze()] + processed_certs = cert_processing.process_parallel( + ProtectionProfile.extract_pp_pdf_metadata, + certs_to_process, + use_threading=False, + progress_bar_desc="Extracting metadata from actual Protection Profiles.", + ) + self.update_with_certs(processed_certs) - dct = {} - for item in pps: - if (item.pp_name, item.pp_link) in dct: - logger.warning(f"Duplicate entry in PP dataset: {(item.pp_name, item.pp_link)}") - dct[(item.pp_name, item.pp_link)] = item + def _extract_report_keywords(self): + certs_to_process = [x for x in self if x.state.report.is_ok_to_analyze()] + processed_certs = cert_processing.process_parallel( + ProtectionProfile.extract_report_pdf_keywords, + certs_to_process, + use_threading=False, + progress_bar_desc="Extracting keywords from PP certification reports.", + ) + self.update_with_certs(processed_certs) - return cls(dct) + def _extract_pp_keywords(self): + certs_to_process = [x for x in self if x.state.pp.is_ok_to_analyze()] + processed_certs = cert_processing.process_parallel( + ProtectionProfile.extract_pp_pdf_keywords, + certs_to_process, + use_threading=False, + progress_bar_desc="Extracting keywords from actual Protection Profiles.", + ) + self.update_with_certs(processed_certs) - @classmethod - def from_web(cls, store_dataset_path: Path | None = None): - logger.info(f"Downloading static PP dataset from: {config.pp_latest_snapshot}") - if not store_dataset_path: - tmp = tempfile.TemporaryDirectory() - store_dataset_path = Path(tmp.name) / "pp_dataset.json" + def _set_local_paths(self): + super()._set_local_paths() - helpers.download_file(config.pp_latest_snapshot, store_dataset_path) - obj = cls.from_json(store_dataset_path) + for cert in self: + cert.set_local_paths(self.reports_pdf_dir, self.pps_pdf_dir, self.reports_txt_dir, self.pps_txt_dir) - if not store_dataset_path: - tmp.cleanup() + def process_auxiliary_datasets(self) -> None: + """ + Dummy method to adhere to `Dataset` interface. `ProtectionProfile` dataset has currently no auxiliary datasets. + This will just set the state `auxiliary_datasets_processed = True` + """ + logger.info("Protection Profile dataset has no auxiliary datasets to process, skipping.") + self.state.auxiliary_datasets_processed = True - return obj + def get_pp_by_pp_link(self, pp_link: str) -> ProtectionProfile | None: + """ + Given URL to PP pdf, will retrieve `ProtectionProfile` object in the dataset with the link, if such exists. + """ + for pp in self: + if pp.web_data.pp_link == pp_link: + return pp + return None diff --git a/src/sec_certs/heuristics/cc.py b/src/sec_certs/heuristics/cc.py new file mode 100644 index 000000000..435c69ed1 --- /dev/null +++ b/src/sec_certs/heuristics/cc.py @@ -0,0 +1,117 @@ +import logging +import re +from collections.abc import Iterable + +from sec_certs.cert_rules import security_level_csv_scan +from sec_certs.dataset.cc_scheme import CCSchemeDataset +from sec_certs.dataset.protection_profile import ProtectionProfileDataset +from sec_certs.model.cc_matching import CCSchemeMatcher +from sec_certs.model.reference_finder import ReferenceFinder +from sec_certs.model.sar_transformer import SARTransformer +from sec_certs.sample.cc import CCCertificate +from sec_certs.sample.cc_certificate_id import CertificateId +from sec_certs.sample.cc_scheme import EntryType +from sec_certs.utils.helpers import choose_lowest_eal +from sec_certs.utils.profiling import staged + +logger = logging.getLogger(__name__) + + +@staged(logger, "Computing heuristics: Linking certificates to protection profiles") +def link_to_protection_profiles( + certs: Iterable[CCCertificate], + pp_dset: ProtectionProfileDataset, +) -> None: + for cert in certs: + if cert.protection_profile_links: + pps = [pp_dset.get_pp_by_pp_link(x) for x in cert.protection_profile_links] + pp_digests = {x.dgst for x in pps if x} + cert.heuristics.protection_profiles = pp_digests if pp_digests else None + logger.info( + f"Linked {len([x for x in certs if x.heuristics.protection_profiles])} certificates to their protection profiles." + ) + + +@staged(logger, "Computing heuristics: references between certificates.") +def compute_references(certs: dict[str, CCCertificate]) -> None: + def ref_lookup(kw_attr): + def func(cert): + kws = getattr(cert.pdf_data, kw_attr) + if not kws: + return set() + res = set() + for scheme, matches in kws["cc_cert_id"].items(): + for match in matches: + try: + canonical = CertificateId(scheme, match).canonical + res.add(canonical) + except Exception: + res.add(match) + return res + + return func + + for ref_source in ("report", "st"): + kw_source = f"{ref_source}_keywords" + dep_attr = f"{ref_source}_references" + + finder = ReferenceFinder() + finder.fit(certs, lambda cert: cert.heuristics.cert_id, ref_lookup(kw_source)) # type: ignore + + for dgst in certs: + setattr(certs[dgst].heuristics, dep_attr, finder.predict_single_cert(dgst, keep_unknowns=False)) + + +@staged(logger, "Computing heuristics: Deriving information about certificate ids from artifacts.") +def compute_normalized_cert_ids(certs: Iterable[CCCertificate]) -> None: + for cert in certs: + cert.compute_heuristics_cert_id() + + +@staged(logger, "Computing heuristics: Matching scheme data.") +def compute_scheme_data(scheme_dset: CCSchemeDataset, certs: dict[str, CCCertificate]): + for scheme in scheme_dset: + if certified := scheme.lists.get(EntryType.Certified): + active_certs = [cert for cert in certs.values() if cert.status == "active"] + matches, _ = CCSchemeMatcher.match_all(certified, scheme.country, active_certs) + for dgst, match in matches.items(): + certs[dgst].heuristics.scheme_data = match + if archived := scheme.lists.get(EntryType.Archived): + archived_certs = [cert for cert in certs.values() if cert.status == "archived"] + matches, _ = CCSchemeMatcher.match_all(archived, scheme.country, archived_certs) + for dgst, match in matches.items(): + certs[dgst].heuristics.scheme_data = match + + +@staged(logger, "Computing heuristics: Deriving information about laboratories involved in certification.") +def compute_cert_labs(certs: Iterable[CCCertificate]) -> None: + for cert in certs: + cert.compute_heuristics_cert_lab() + + +@staged(logger, "Computing heuristics: SARs") +def compute_sars(certs: Iterable[CCCertificate]) -> None: + transformer = SARTransformer().fit(certs) + for cert in certs: + cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) + + +@staged(logger, "Computing heuristics: EALs") +def compute_eals(certs: Iterable[CCCertificate], pp_dataset: ProtectionProfileDataset) -> None: + def compute_cert_eal(cert: CCCertificate) -> str | None: + res = [x for x in cert.security_level if re.match(security_level_csv_scan, x)] + if res and len(res) == 1: + return res[0] + elif res and len(res) > 1: + raise ValueError(f"Expected single EAL in security_level field, got: {res}") + else: + if cert.heuristics.protection_profiles: + eals: set[str] = { + eal for x in cert.heuristics.protection_profiles if (eal := pp_dataset[x].web_data.eal) is not None + } + return choose_lowest_eal(eals) + else: + return None + + for cert in certs: + cert.heuristics.eal = compute_cert_eal(cert) diff --git a/src/sec_certs/heuristics/common.py b/src/sec_certs/heuristics/common.py new file mode 100644 index 000000000..8a2b47695 --- /dev/null +++ b/src/sec_certs/heuristics/common.py @@ -0,0 +1,132 @@ +import itertools +import logging +import re +from collections.abc import Iterable + +from tqdm import tqdm + +from sec_certs import constants +from sec_certs.configuration import config +from sec_certs.dataset.cpe import CPEDataset +from sec_certs.dataset.cve import CVEDataset +from sec_certs.dataset.dataset import CertSubType +from sec_certs.model.cpe_matching import CPEClassifier +from sec_certs.model.transitive_vulnerability_finder import TransitiveVulnerabilityFinder +from sec_certs.sample.cc import CCCertificate +from sec_certs.sample.certificate import Certificate +from sec_certs.sample.cpe import CPE +from sec_certs.sample.fips import FIPSCertificate +from sec_certs.utils.profiling import staged + +logger = logging.getLogger(__name__) + + +@staged(logger, "Computing heuristics: Finding CPE matches for certificates") +def compute_cpe_heuristics(cpe_dataset: CPEDataset, certs: Iterable[CertSubType]) -> None: + """ + Computes matching CPEs for the certificates. + """ + WINDOWS_WEAK_CPES: set[CPE] = { + CPE("", "cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x64:*", "Microsoft Windows on X64"), + CPE("", "cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x86:*", "Microsoft Windows on X86"), + } + + def filter_condition(cpe: CPE) -> bool: + """ + Filters out very weak CPE matches that don't improve our database. + """ + if cpe.title and (cpe.version == "-" or cpe.version == "*") and not any(char.isdigit() for char in cpe.title): + return False + if ( + not cpe.title + and cpe.item_name + and (cpe.version == "-" or cpe.version == "*") + and not any(char.isdigit() for char in cpe.item_name) + ): + return False + if re.match(constants.RELEASE_CANDIDATE_REGEX, cpe.update): + return False + return cpe not in WINDOWS_WEAK_CPES + + logger.info("Computing CPE heuristics.") + clf = CPEClassifier(config.cpe_matching_threshold, config.cpe_n_max_matches) + clf.fit([x for x in cpe_dataset if filter_condition(x)]) + + for cert in tqdm(certs, desc="Predicting CPE matches with the classifier"): + cert.compute_heuristics_version() + cert.heuristics.cpe_matches = ( + clf.predict_single_cert(cert.manufacturer, cert.name, cert.heuristics.extracted_versions) + if cert.name + else None + ) + + +def get_all_cpes_in_dataset(cpe_dset: CPEDataset, certs: Iterable[Certificate]) -> set[CPE]: + cpe_matches = [[cpe_dset.cpes[y] for y in x.heuristics.cpe_matches] for x in certs if x.heuristics.cpe_matches] + return set(itertools.chain.from_iterable(cpe_matches)) + + +def enrich_automated_cpes_with_manual_labels(certs: Iterable[Certificate]) -> None: + """ + Prior to CVE matching, it is wise to expand the database of automatic CPE matches with those that were manually assigned. + """ + for cert in certs: + if not cert.heuristics.cpe_matches and cert.heuristics.verified_cpe_matches: + cert.heuristics.cpe_matches = cert.heuristics.verified_cpe_matches + elif cert.heuristics.cpe_matches and cert.heuristics.verified_cpe_matches: + cert.heuristics.cpe_matches = set(cert.heuristics.cpe_matches).union( + set(cert.heuristics.verified_cpe_matches) + ) + + +@staged(logger, "Computing heuristics: CVEs in certificates.") +def compute_related_cves( + cpe_dset: CPEDataset, cve_dset: CVEDataset, cpe_match_dict: dict, certs: Iterable[Certificate] +) -> None: + """ + Computes CVEs for the certificates, given their CPE matches. + """ + + logger.info("Computing related CVEs") + if not cve_dset.look_up_dicts_built: + all_cpes = get_all_cpes_in_dataset(cpe_dset, certs) + cve_dset.build_lookup_dict(cpe_match_dict, all_cpes) + + enrich_automated_cpes_with_manual_labels(certs) + cpe_rich_certs = [x for x in certs if x.heuristics.cpe_matches] + + for cert in tqdm(cpe_rich_certs, desc="Computing related CVES"): + related_cves = cve_dset.get_cves_from_matched_cpe_uris(cert.heuristics.cpe_matches) + cert.heuristics.related_cves = related_cves if related_cves else None + + n_vulnerable = len([x for x in cpe_rich_certs if x.heuristics.related_cves]) + n_vulnerabilities = sum([len(x.heuristics.related_cves) for x in cpe_rich_certs if x.heuristics.related_cves]) + logger.info( + f"In total, we identified {n_vulnerabilities} vulnerabilities in {n_vulnerable} vulnerable certificates." + ) + + +@staged( + logger, + "Computing heuristics: Transitive vulnerabilities in referenc(ed/ing) certificates.", +) +def compute_transitive_vulnerabilities(certs: dict[str, CertSubType]) -> None: + logger.info("Computing transitive vulnerabilities") + if not certs: + return + + some_cert = next(iter(certs.values())) + + if isinstance(some_cert, FIPSCertificate): + transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: str(cert.cert_id)) + transitive_cve_finder.fit(certs, lambda cert: cert.heuristics.policy_processed_references) + elif isinstance(some_cert, CCCertificate): + transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: str(cert.heuristics.cert_id)) + transitive_cve_finder.fit(certs, lambda cert: cert.heuristics.report_references) + else: + raise ValueError("Members of `certs` object must be either FIPSCertificate or CCCertificate instances.") + + for cert in certs.values(): + transitive_cve = transitive_cve_finder.predict_single_cert(cert.dgst) + cert.heuristics.direct_transitive_cves = transitive_cve.direct_transitive_cves + cert.heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves diff --git a/src/sec_certs/heuristics/fips.py b/src/sec_certs/heuristics/fips.py new file mode 100644 index 000000000..4abecc4e7 --- /dev/null +++ b/src/sec_certs/heuristics/fips.py @@ -0,0 +1,42 @@ +import logging + +from sec_certs.model.reference_finder import ReferenceFinder +from sec_certs.sample.fips import FIPSCertificate +from sec_certs.utils.profiling import staged + +logger = logging.getLogger(__name__) + + +@staged(logger, "Computing heuristics: references between certificates.") +def compute_references(certs: dict[str, FIPSCertificate], keep_unknowns: bool = False) -> None: + # Previously, a following procedure was used to prune reference_candidates: + # - A set of algorithms was obtained via self.auxiliary_datasets.algorithm_dset.get_algorithms_by_id(reference_candidate) + # - If any of these algorithms had the same vendor as the reference_candidate, the candidate was rejected + # - The rationale is that if an ID appears in a certificate s.t. an algorithm with the same ID was produced by the same vendor, the reference likely refers to alg. + # - Such reference should then be discarded. + # - We are uncertain of the effectivity of such measure, disabling it for now. + logger.info("Computing references") + for cert in certs.values(): + cert.prune_referenced_cert_ids() + + policy_reference_finder = ReferenceFinder() + policy_reference_finder.fit( + certs, + lambda cert: str(cert.cert_id), + lambda cert: cert.heuristics.policy_prunned_references, + ) + + module_reference_finder = ReferenceFinder() + module_reference_finder.fit( + certs, + lambda cert: str(cert.cert_id), + lambda cert: cert.heuristics.module_prunned_references, + ) + + for cert in certs.values(): + cert.heuristics.policy_processed_references = policy_reference_finder.predict_single_cert( + cert.dgst, keep_unknowns + ) + cert.heuristics.module_processed_references = module_reference_finder.predict_single_cert( + cert.dgst, keep_unknowns + ) diff --git a/src/sec_certs/sample/cc.py b/src/sec_certs/sample/cc.py index a9aa2262a..ca096a6c2 100644 --- a/src/sec_certs/sample/cc.py +++ b/src/sec_certs/sample/cc.py @@ -17,13 +17,13 @@ import sec_certs.utils.extract import sec_certs.utils.pdf from sec_certs import constants -from sec_certs.cert_rules import SARS_IMPLIED_FROM_EAL, cc_rules, rules, security_level_csv_scan +from sec_certs.cert_rules import SARS_IMPLIED_FROM_EAL, cc_rules, rules from sec_certs.configuration import config from sec_certs.sample.cc_certificate_id import CertificateId, canonicalize, schemes from sec_certs.sample.certificate import Certificate, References, logger from sec_certs.sample.certificate import Heuristics as BaseHeuristics from sec_certs.sample.certificate import PdfData as BasePdfData -from sec_certs.sample.protection_profile import ProtectionProfile +from sec_certs.sample.document_state import DocumentState from sec_certs.sample.sar import SAR from sec_certs.serialization.json import ComplexSerializableType from sec_certs.serialization.pandas import PandasSerializableType @@ -43,8 +43,6 @@ class CCCertificate( the certificate can handle itself. `CCDataset` class then instrument this functionality. """ - cc_url = "https://www.commoncriteriaportal.org" - @dataclass(eq=True, frozen=True) class MaintenanceReport(ComplexSerializableType): """ @@ -76,86 +74,15 @@ def __lt__(self, other): return self.maintenance_date < other.maintenance_date @dataclass - class DocumentState(ComplexSerializableType): - download_ok: bool = False # Whether download went OK - convert_garbage: bool = False # Whether initial conversion resulted in garbage - convert_ok: bool = False # Whether overall conversion went OK (either pdftotext or via OCR) - extract_ok: bool = False # Whether extraction went OK - - pdf_hash: str | None = None - txt_hash: str | None = None - - _pdf_path: Path | None = None - _txt_path: Path | None = None - - def is_ok_to_download(self, fresh: bool = True) -> bool: - return True if fresh else not self.download_ok - - def is_ok_to_convert(self, fresh: bool = True) -> bool: - return self.download_ok if fresh else self.download_ok and not self.convert_ok - - def is_ok_to_analyze(self, fresh: bool = True) -> bool: - if fresh: - return self.download_ok and self.convert_ok - else: - return self.download_ok and self.convert_ok and not self.extract_ok - - @property - def pdf_path(self) -> Path: - if not self._pdf_path: - raise ValueError(f"pdf_path not set on {type(self)}") - return self._pdf_path - - @pdf_path.setter - def pdf_path(self, pth: str | Path | None) -> None: - self._pdf_path = Path(pth) if pth else None - - @property - def txt_path(self) -> Path: - if not self._txt_path: - raise ValueError(f"txt_path not set on {type(self)}") - return self._txt_path - - @txt_path.setter - def txt_path(self, pth: str | Path | None) -> None: - self._txt_path = Path(pth) if pth else None - - @property - def serialized_attributes(self) -> list[str]: - return [ - "download_ok", - "convert_garbage", - "convert_ok", - "extract_ok", - "pdf_hash", - "txt_hash", - ] - - @dataclass(init=False) class InternalState(ComplexSerializableType): """ Holds internal state of the certificate, whether downloads and converts of individual components succeeded. Also holds information about errors and paths to the files. """ - report: CCCertificate.DocumentState - st: CCCertificate.DocumentState - cert: CCCertificate.DocumentState - - def __init__( - self, - report: CCCertificate.DocumentState | None = None, - st: CCCertificate.DocumentState | None = None, - cert: CCCertificate.DocumentState | None = None, - ): - super().__init__() - self.report = report if report is not None else CCCertificate.DocumentState() - self.st = st if st is not None else CCCertificate.DocumentState() - self.cert = cert if cert is not None else CCCertificate.DocumentState() - - @property - def serialized_attributes(self) -> list[str]: - return ["report", "st", "cert"] + report: DocumentState = field(default_factory=DocumentState) + st: DocumentState = field(default_factory=DocumentState) + cert: DocumentState = field(default_factory=DocumentState) @dataclass class PdfData(BasePdfData, ComplexSerializableType): @@ -350,7 +277,6 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): next_certificates: list[str] | None = field(default=None) st_references: References = field(default_factory=References) report_references: References = field(default_factory=References) - # Contains direct outward references merged from both st, and report sources, annotated with ReferenceAnnotator # TODO: Reference meanings as Enum if we work with it further. annotated_references: dict[str, str] | None = field(default=None) @@ -358,6 +284,8 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): direct_transitive_cves: set[str] | None = field(default=None) indirect_transitive_cves: set[str] | None = field(default=None) scheme_data: dict[str, Any] | None = field(default=None) + protection_profiles: set[str] | None = field(default=None) + eal: str | None = field(default=None) @property def serialized_attributes(self) -> list[str]: @@ -388,6 +316,7 @@ def serialized_attributes(self) -> list[str]: "directly_referencing", "indirectly_referencing", "extracted_sars", + "protection_profile_links", "protection_profiles", "cert_lab", ] @@ -406,7 +335,7 @@ def __init__( st_link: str | None, cert_link: str | None, manufacturer_web: str | None, - protection_profiles: set[ProtectionProfile] | None, + protection_profile_links: set[str] | None, maintenance_updates: set[MaintenanceReport] | None, state: InternalState | None, pdf_data: PdfData | None, @@ -430,7 +359,7 @@ def __init__( self.st_link = sanitization.sanitize_link(st_link) self.cert_link = sanitization.sanitize_link(cert_link) self.manufacturer_web = sanitization.sanitize_link(manufacturer_web) - self.protection_profiles = protection_profiles + self.protection_profile_links = protection_profile_links self.maintenance_updates = maintenance_updates self.state = state if state else self.InternalState() self.pdf_data = pdf_data if pdf_data else self.PdfData() @@ -468,22 +397,6 @@ def older_dgst(self) -> str: raise RuntimeError("Certificate digest can't be computed, because information is missing.") return helpers.get_first_16_bytes_sha256(self.category + self.name + self.report_link) - @property - def eal(self) -> str | None: - """ - Returns EAL of certificate if it was extracted, None otherwise. - """ - res = [x for x in self.security_level if re.match(security_level_csv_scan, x)] - if res and len(res) == 1: - return res[0] - if res and len(res) > 1: - raise ValueError(f"Expected single EAL in security_level field, got: {res}") - else: - if self.protection_profiles: - return helpers.choose_lowest_eal({x.pp_eal for x in self.protection_profiles if x.pp_eal}) - else: - return None - @property def actual_sars(self) -> set[SAR] | None: """ @@ -492,8 +405,8 @@ def actual_sars(self) -> set[SAR] | None: :return Optional[Set[SAR]]: Set of actual SARs of a certificate, None if empty """ sars = {} - if self.eal: - sars = {x[0]: SAR(x[0], x[1]) for x in SARS_IMPLIED_FROM_EAL[self.eal[:4]]} + if self.heuristics.eal: + sars = {x[0]: SAR(x[0], x[1]) for x in SARS_IMPLIED_FROM_EAL[self.heuristics.eal[:4]]} if self.heuristics.extracted_sars: for sar in self.heuristics.extracted_sars: @@ -520,7 +433,7 @@ def pandas_tuple(self) -> tuple: self.manufacturer, self.scheme, self.security_level, - self.eal, + self.heuristics.eal, self.not_valid_before, self.not_valid_after, self.report_link, @@ -536,7 +449,8 @@ def pandas_tuple(self) -> tuple: self.heuristics.report_references.directly_referencing, self.heuristics.report_references.indirectly_referencing, self.heuristics.extracted_sars, - [x.pp_name for x in self.protection_profiles] if self.protection_profiles else np.nan, + self.protection_profile_links if self.protection_profile_links else np.nan, + self.heuristics.protection_profiles if self.heuristics.protection_profiles else np.nan, self.heuristics.cert_lab[0] if (self.heuristics.cert_lab and self.heuristics.cert_lab[0]) else np.nan, ) @@ -557,7 +471,13 @@ def merge(self, other: CCCertificate, other_source: str | None = None) -> None: # Prefer some values from the HTML # Links in CSV are currently (13.08.2024) broken. - html_preferred_attrs = {"protection_profiles", "maintenance_updates", "cert_link", "report_link", "st_link"} + html_preferred_attrs = { + "protection_profile_links", + "maintenance_updates", + "cert_link", + "report_link", + "st_link", + } for att, val in vars(self).items(): if (not val) or (other_source == "html" and att in html_preferred_attrs) or (att == "state"): @@ -575,7 +495,8 @@ def from_dict(cls, dct: dict) -> CCCertificate: """ new_dct = dct.copy() new_dct["maintenance_updates"] = set(dct["maintenance_updates"]) - new_dct["protection_profiles"] = set(dct["protection_profiles"]) + if dct["protection_profile_links"]: + new_dct["protection_profile_links"] = set(dct["protection_profile_links"]) new_dct["not_valid_before"] = ( date.fromisoformat(dct["not_valid_before"]) if isinstance(dct["not_valid_before"], str) @@ -615,16 +536,12 @@ def _html_row_get_manufacturer_web(cell: Tag) -> str | None: return None @staticmethod - def _html_row_get_protection_profiles(cell: Tag) -> set: - protection_profiles = set() + def _html_row_get_protection_profile_links(cell: Tag) -> set: + protection_profile_links = set() for link in list(cell.find_all("a")): if link.get("href") is not None and "/ppfiles/" in link.get("href"): - protection_profiles.add( - ProtectionProfile( - pp_name=str(link.contents[0]), pp_eal=None, pp_link=CCCertificate.cc_url + link.get("href") - ) - ) - return protection_profiles + protection_profile_links.add(constants.CC_PORTAL_BASE_URL + link.get("href")) + return protection_profile_links @staticmethod def _html_row_get_date(cell: Tag) -> date | None: @@ -643,16 +560,16 @@ def _html_row_get_report_st_links(cell: Tag) -> tuple[str | None, str | None]: if not title: continue if title.startswith("Certification Report"): - report_link = CCCertificate.cc_url + link.get("href") + report_link = constants.CC_PORTAL_BASE_URL + link.get("href") elif title.startswith("Security Target"): - security_target_link = CCCertificate.cc_url + link.get("href") + security_target_link = constants.CC_PORTAL_BASE_URL + link.get("href") return report_link, security_target_link @staticmethod def _html_row_get_cert_link(cell: Tag) -> str | None: links = cell.find_all("a") - return CCCertificate.cc_url + links[0].get("href") if links else None + return constants.CC_PORTAL_BASE_URL + links[0].get("href") if links else None @staticmethod def _html_row_get_maintenance_div(cell: Tag) -> Tag | None: @@ -675,9 +592,9 @@ def _html_row_get_maintenance_updates(main_div: Tag) -> set[CCCertificate.Mainte links = u.find_all("a") for link in links: if link.get("title").startswith("Maintenance Report:"): - main_report_link = CCCertificate.cc_url + link.get("href") + main_report_link = constants.CC_PORTAL_BASE_URL + link.get("href") elif link.get("title").startswith("Maintenance ST"): - main_st_link = CCCertificate.cc_url + link.get("href") + main_st_link = constants.CC_PORTAL_BASE_URL + link.get("href") else: logger.error("Unknown link in Maintenance part!") maintenance_updates.add( @@ -700,7 +617,7 @@ def from_html_row(cls, row: Tag, status: str, category: str) -> CCCertificate: manufacturer_web = CCCertificate._html_row_get_manufacturer_web(cells[1]) scheme = CCCertificate._html_row_get_scheme(cells[6]) security_level = CCCertificate._html_row_get_security_level(cells[5]) - protection_profiles = CCCertificate._html_row_get_protection_profiles(cells[0]) + protection_profile_links = CCCertificate._html_row_get_protection_profile_links(cells[0]) not_valid_before = CCCertificate._html_row_get_date(cells[3]) not_valid_after = CCCertificate._html_row_get_date(cells[4]) report_link, st_link = CCCertificate._html_row_get_report_st_links(cells[0]) @@ -721,7 +638,7 @@ def from_html_row(cls, row: Tag, status: str, category: str) -> CCCertificate: st_link, cert_link, manufacturer_web, - protection_profiles, + protection_profile_links, maintenances, None, None, diff --git a/src/sec_certs/sample/certificate.py b/src/sec_certs/sample/certificate.py index 74b1af96b..6fbf8af4d 100644 --- a/src/sec_certs/sample/certificate.py +++ b/src/sec_certs/sample/certificate.py @@ -30,8 +30,7 @@ def __bool__(self): class Heuristics: - cpe_matches: set[str] | None - related_cves: set[str] | None + pass class PdfData: @@ -87,7 +86,3 @@ def to_dict(self) -> dict[str, Any]: def from_dict(cls: type[T], dct: dict) -> T: dct.pop("dgst") return cls(**dct) - - @abstractmethod - def compute_heuristics_version(self) -> None: - raise NotImplementedError("Not meant to be implemented") diff --git a/src/sec_certs/sample/document_state.py b/src/sec_certs/sample/document_state.py new file mode 100644 index 000000000..a2cf1769b --- /dev/null +++ b/src/sec_certs/sample/document_state.py @@ -0,0 +1,61 @@ +from dataclasses import dataclass +from pathlib import Path + +from sec_certs.serialization.json import ComplexSerializableType + + +@dataclass +class DocumentState(ComplexSerializableType): + download_ok: bool = False # Whether download went OK + convert_garbage: bool = False # Whether initial conversion resulted in garbage + convert_ok: bool = False # Whether overall conversion went OK (either pdftotext or via OCR) + extract_ok: bool = False # Whether extraction went OK + + pdf_hash: str | None = None + txt_hash: str | None = None + + _pdf_path: Path | None = None + _txt_path: Path | None = None + + def is_ok_to_download(self, fresh: bool = True) -> bool: + return True if fresh else not self.download_ok + + def is_ok_to_convert(self, fresh: bool = True) -> bool: + return self.download_ok if fresh else self.download_ok and not self.convert_ok + + def is_ok_to_analyze(self, fresh: bool = True) -> bool: + if fresh: + return self.download_ok and self.convert_ok + else: + return self.download_ok and self.convert_ok and not self.extract_ok + + @property + def pdf_path(self) -> Path: + if not self._pdf_path: + raise ValueError(f"pdf_path not set on {type(self)}") + return self._pdf_path + + @pdf_path.setter + def pdf_path(self, pth: str | Path | None) -> None: + self._pdf_path = Path(pth) if pth else None + + @property + def txt_path(self) -> Path: + if not self._txt_path: + raise ValueError(f"txt_path not set on {type(self)}") + return self._txt_path + + @txt_path.setter + def txt_path(self, pth: str | Path | None) -> None: + self._txt_path = Path(pth) if pth else None + + @property + def serialized_attributes(self) -> list[str]: + return [ + "download_ok", + "convert_garbage", + "convert_ok", + "extract_ok", + "pdf_hash", + "txt_hash", + ] diff --git a/src/sec_certs/sample/fips_iut.py b/src/sec_certs/sample/fips_iut.py index 32c159f1d..c1c2900bf 100644 --- a/src/sec_certs/sample/fips_iut.py +++ b/src/sec_certs/sample/fips_iut.py @@ -147,21 +147,17 @@ def from_web(cls) -> IUTSnapshot: """ Get an IUT snapshot from the FIPS website right now. """ - iut_resp = requests.get(constants.FIPS_IUT_URL) + if config.preferred_source_remote_datasets == "origin": + iut_resp = requests.get(constants.FIPS_IUT_URL) + else: + iut_resp = requests.get(config.fips_iut_dataset) if iut_resp.status_code != 200: raise ValueError(f"Getting IUT snapshot failed: {iut_resp.status_code}") - snapshot_date = to_utc(datetime.now()) - return cls.from_page(iut_resp.content, snapshot_date) - - @classmethod - def from_web_latest(cls) -> IUTSnapshot: - """ - Get a IUT snapshot from sec-certs.org. - """ - iut_resp = requests.get(config.fips_iut_latest_snapshot) - if iut_resp.status_code != 200: - raise ValueError(f"Getting MIP snapshot failed: {iut_resp.status_code}") - with NamedTemporaryFile() as tmpfile: - tmpfile.write(iut_resp.content) - return cls.from_json(tmpfile.name) + if config.preferred_source_remote_datasets == "origin": + snapshot_date = to_utc(datetime.now()) + return cls.from_page(iut_resp.content, snapshot_date) + else: + with NamedTemporaryFile() as tmpfile: + tmpfile.write(iut_resp.content) + return cls.from_json(tmpfile.name) diff --git a/src/sec_certs/sample/fips_mip.py b/src/sec_certs/sample/fips_mip.py index c3fb177d6..875f04647 100644 --- a/src/sec_certs/sample/fips_mip.py +++ b/src/sec_certs/sample/fips_mip.py @@ -250,21 +250,17 @@ def from_web(cls) -> MIPSnapshot: """ Get a MIP snapshot from the FIPS website right now. """ - mip_resp = requests.get(constants.FIPS_MIP_URL) + if config.preferred_source_remote_datasets == "origin": + mip_resp = requests.get(constants.FIPS_MIP_URL) + else: + mip_resp = requests.get(config.fips_mip_dataset) if mip_resp.status_code != 200: raise ValueError(f"Getting MIP snapshot failed: {mip_resp.status_code}") - snapshot_date = to_utc(datetime.now()) - return cls.from_page(mip_resp.content, snapshot_date) - - @classmethod - def from_web_latest(cls) -> MIPSnapshot: - """ - Get a MIP snapshot from sec-certs.org. - """ - mip_resp = requests.get(config.fips_mip_latest_snapshot) - if mip_resp.status_code != 200: - raise ValueError(f"Getting MIP snapshot failed: {mip_resp.status_code}") - with NamedTemporaryFile() as tmpfile: - tmpfile.write(mip_resp.content) - return cls.from_json(tmpfile.name) + if config.preferred_source_remote_datasets == "origin": + snapshot_date = to_utc(datetime.now()) + return cls.from_page(mip_resp.content, snapshot_date) + else: + with NamedTemporaryFile() as tmpfile: + tmpfile.write(mip_resp.content) + return cls.from_json(tmpfile.name) diff --git a/src/sec_certs/sample/protection_profile.py b/src/sec_certs/sample/protection_profile.py index 4c26a1c70..ad5cea25f 100644 --- a/src/sec_certs/sample/protection_profile.py +++ b/src/sec_certs/sample/protection_profile.py @@ -1,55 +1,366 @@ from __future__ import annotations -import copy -import logging -from dataclasses import dataclass -from typing import Any +from dataclasses import dataclass, field +from datetime import date, datetime +from pathlib import Path +from typing import Any, Literal +from urllib.parse import unquote_plus, urlparse +import requests +from bs4 import Tag + +import sec_certs.utils.extract +import sec_certs.utils.pdf +from sec_certs import constants +from sec_certs.cert_rules import cc_rules +from sec_certs.configuration import config +from sec_certs.sample.certificate import Certificate, logger +from sec_certs.sample.certificate import Heuristics as BaseHeuristics +from sec_certs.sample.certificate import PdfData as BasePdfData +from sec_certs.sample.document_state import DocumentState from sec_certs.serialization.json import ComplexSerializableType -from sec_certs.utils import sanitization +from sec_certs.utils import cc_html_parsing, helpers, sanitization -logger = logging.getLogger(__name__) +class ProtectionProfile( + Certificate["ProtectionProfile", "ProtectionProfile.Heuristics", "ProtectionProfile.PdfData"], + ComplexSerializableType, +): + @dataclass + class Heuristics(BaseHeuristics, ComplexSerializableType): + pass -@dataclass(frozen=True) -class ProtectionProfile(ComplexSerializableType): - """ - Object for holding protection profiles. - """ + @dataclass + class PdfData(BasePdfData, ComplexSerializableType): + """ + Class to hold data related to PDF and txt files related to protection profiles. + """ - pp_name: str - pp_eal: str | None - pp_link: str | None = None - pp_ids: frozenset[str] | None = None + report_metadata: dict[str, Any] | None = field(default=None) + pp_metadata: dict[str, Any] | None = field(default=None) + report_keywords: dict[str, Any] | None = field(default=None) + pp_keywords: dict[str, Any] | None = field(default=None) + report_filename: str | None = field(default=None) + pp_filename: str | None = field(default=None) - def __post_init__(self): - super().__setattr__("pp_name", sanitization.sanitize_string(self.pp_name)) - super().__setattr__("pp_link", sanitization.sanitize_link(self.pp_link)) + def __bool__(self) -> bool: + return any(x is not None for x in vars(self)) - @classmethod - def from_dict(cls, dct: dict[str, Any]) -> ProtectionProfile: - new_dct = copy.deepcopy(dct) - new_dct["pp_ids"] = frozenset(new_dct["pp_ids"]) if new_dct["pp_ids"] else None - return cls(*tuple(new_dct.values())) + @dataclass(eq=True) + class WebData(ComplexSerializableType): + """ + Class to hold metadata about protection profiles found on commoncriteriaportal.org + """ + + category: str + status: Literal["active", "archived"] + is_collaborative: bool + name: str + version: str + security_level: set[str] + not_valid_before: date | None + not_valid_after: date | None + report_link: str | None + pp_link: str | None + scheme: str | None + maintenances: list[tuple[Any]] + + @property + def eal(self) -> str | None: + return helpers.choose_lowest_eal(self.security_level) + + @classmethod + def from_html_row( + cls, row: Tag, status: Literal["active", "archived"], category: str, is_collaborative: bool + ) -> ProtectionProfile.WebData: + """ + Given bs4 tag of html row (fetched from cc portal), will build the object. + """ + if is_collaborative: + return cls._from_html_row_collaborative(row, category) + return cls._from_html_row_classic_pp(row, status, category) + + @classmethod + def _from_html_row_classic_pp( + cls, row: Tag, status: Literal["active", "archived"], category: str + ) -> ProtectionProfile.WebData: + cells = list(row.find_all("td")) + if status == "active" and len(cells) != 6: + raise ValueError( + f"Unexpected number of
elements in PP html row. Expected: 6, actual: {len(cells)}" + ) + if status == "archived" and len(cells) != 7: + raise ValueError( + f"Unexpected number of elements in PP html row. Expected: 6, actual: {len(cells)}" + ) + + pp_link = cls._html_row_get_link(cells[0]) + pp_name = cls._html_row_get_name(cells[0]) + if not sanitization.sanitize_cc_link(pp_link): + raise ValueError(f"pp_link for PP {pp_name} is empty, cannot create PP record") + + mu_div = cc_html_parsing.html_row_get_maintenance_div(row) + maintenance_updates = cc_html_parsing.parse_maintenance_div(mu_div) if mu_div else [] + if maintenance_updates: + # Drop ST links, not filled in for PPs + maintenance_updates = [x[:3] for x in maintenance_updates] + + return cls( + category, + status, + False, + pp_name, + cls._html_row_get_version(cells[1]), + cls._html_row_get_security_level(cells[2]), + cls._html_row_get_date(cells[3]), + None if status == "active" else cls._html_row_get_date(cells[4]), + cls._html_row_get_link(cells[-1]), + pp_link, + cls._html_row_get_scheme(cells[-2]), + maintenance_updates, + ) + + @classmethod + def _from_html_row_collaborative(cls, row: Tag, category: str) -> ProtectionProfile.WebData: + cells = list(row.find_all("td")) + if len(cells) != 5: + raise ValueError( + f"Unexpected number of elements in collaborative PP html row. Expected: 5, actual: {len(cells)}" + ) + + pp_link = cls._html_row_get_collaborative_pp_link(cells[0]) + pp_name = cls._html_row_get_collaborative_name(cells[0]) + if not sanitization.sanitize_cc_link(pp_link): + raise ValueError(f"pp_link for PP {pp_name} is empty, cannot create PP record") + + return cls( + category, + "active", + True, + pp_name, + cls._html_row_get_version(cells[1]), + cls._html_row_get_security_level(cells[2]), + cls._html_row_get_date(cells[3]), + None, + cls._html_row_get_link(cells[-1]), + pp_link, + None, + [], + ) + + @staticmethod + def _html_row_get_date(cell: Tag) -> date | None: + text = cell.get_text() + extracted_date = datetime.strptime(text, "%Y-%m-%d").date() if text else None + return extracted_date + + @staticmethod + def _html_row_get_name(cell: Tag) -> str: + return cell.find_all("a")[0].string + + @staticmethod + def _html_row_get_link(cell: Tag) -> str: + return constants.CC_PORTAL_BASE_URL + cell.find_all("a")[0].get("href") + + @staticmethod + def _html_row_get_version(cell: Tag) -> str: + return cell.text + + @staticmethod + def _html_row_get_security_level(cell: Tag) -> set[str]: + return set(cell.stripped_strings) + + @staticmethod + def _html_row_get_scheme(cell: Tag) -> str | None: + schemes = list(cell.stripped_strings) + return schemes[0] if schemes else None + + @staticmethod + def _html_row_get_collaborative_name(cell: Tag) -> str: + return list(cell.stripped_strings)[0] + + @staticmethod + def _html_row_get_collaborative_pp_link(cell: Tag) -> str: + return constants.CC_PORTAL_BASE_URL + [x for x in cell.find_all("a") if x.string == "Protection Profile"][ + 0 + ].get("href") + + @dataclass + class InternalState(ComplexSerializableType): + """ + Class to hold internal state for each of the documents. + """ + + pp: DocumentState = field(default_factory=DocumentState) + report: DocumentState = field(default_factory=DocumentState) + + def __init__( + self, + web_data: WebData, + pdf_data: PdfData | None = None, + heuristics: Heuristics | None = None, + state: InternalState | None = None, + ): + super().__init__() + self.web_data: ProtectionProfile.WebData = web_data + self.pdf_data: ProtectionProfile.PdfData = pdf_data if pdf_data else ProtectionProfile.PdfData() + self.heuristics: ProtectionProfile.Heuristics = heuristics if heuristics else ProtectionProfile.Heuristics() + self.state: ProtectionProfile.InternalState = state if state else ProtectionProfile.InternalState() + + @property + def dgst(self) -> str: + """ + digest of thwe protection profile, formed as first 16 bytes of `category|name|version` fields from `WebData` object. + """ + return helpers.get_first_16_bytes_sha256( + "|".join([self.web_data.category, self.web_data.name, self.web_data.version]) + ) + + @property + def label_studio_title(self) -> str: + return self.web_data.name + + def merge(self, other: ProtectionProfile, other_source: str | None = None) -> None: + raise ValueError("Merging of PPs not implemented.") + + def set_local_paths( + self, + report_pdf_dir: str | Path | None, + pp_pdf_dir: str | Path | None, + report_txt_dir: str | Path | None, + pp_txt_dir: str | Path | None, + ) -> None: + """ + Adjusts local paths for various files. + """ + if report_pdf_dir: + self.state.report.pdf_path = Path(report_pdf_dir) / f"{self.dgst}.pdf" + if pp_pdf_dir: + self.state.pp.pdf_path = Path(pp_pdf_dir) / f"{self.dgst}.pdf" + if report_txt_dir: + self.state.report.txt_path = Path(report_txt_dir) / f"{self.dgst}.txt" + if pp_txt_dir: + self.state.pp.txt_path = Path(pp_txt_dir) / f"{self.dgst}.txt" @classmethod - def from_old_api_dict(cls, dct: dict[str, Any]) -> ProtectionProfile: - pp_name = sanitization.sanitize_string(dct["csv_scan"]["cc_pp_name"]) - pp_link = sanitization.sanitize_link(dct["csv_scan"]["link_pp_document"]) - pp_ids = frozenset(dct["processed"]["cc_pp_csvid"]) if dct["processed"]["cc_pp_csvid"] else None - eal_set = sanitization.sanitize_security_levels(dct["csv_scan"]["cc_security_level"]) + def from_html_row( + cls, row: Tag, status: Literal["active", "archived"], category: str, is_collaborative: bool + ) -> ProtectionProfile: + """ + Builds a `ProtectionProfile` object from html row obtained from cc portal html source. + """ + return cls(ProtectionProfile.WebData.from_html_row(row, status, category, is_collaborative)) + + @staticmethod + def download_pdf_report(cert: ProtectionProfile) -> ProtectionProfile: + """ + Downloads pdf of certification report for the given protection profile. + """ + exit_code: str | int + if not cert.web_data.report_link: + exit_code = "No link" + else: + exit_code = helpers.download_file( + cert.web_data.report_link, cert.state.report.pdf_path, proxy=config.cc_use_proxy + ) + if exit_code != requests.codes.ok: + error_msg = f"failed to download report from {cert.web_data.report_link}, code: {exit_code}" + logger.error(f"Cert dgst: {cert.dgst} " + error_msg) + cert.state.report.download_ok = False + else: + cert.state.report.download_ok = True + cert.state.report.pdf_hash = helpers.get_sha256_filepath(cert.state.report.pdf_path) + cert.pdf_data.report_filename = unquote_plus(str(urlparse(cert.web_data.report_link).path).split("/")[-1]) + return cert + + @staticmethod + def download_pdf_pp(cert: ProtectionProfile) -> ProtectionProfile: + """ + Downloads actual pdf of the given protection profile. + """ + exit_code: str | int + if not cert.web_data.pp_link: + exit_code = "No link" + else: + exit_code = helpers.download_file(cert.web_data.pp_link, cert.state.pp.pdf_path, proxy=config.cc_use_proxy) + if exit_code != requests.codes.ok: + error_msg = f"failed to download PP from {cert.web_data.pp_link}, code: {exit_code}" + logger.error(f"Cert dgst: {cert.dgst} " + error_msg) + cert.state.pp.download_ok = False + else: + cert.state.pp.download_ok = True + cert.state.pp.pdf_hash = helpers.get_sha256_filepath(cert.state.pp.pdf_path) + cert.pdf_data.pp_filename = unquote_plus(str(urlparse(cert.web_data.pp_link).path).split("/")[-1]) + return cert + + @staticmethod + def convert_report_pdf(cert: ProtectionProfile) -> ProtectionProfile: + """ + Converts certification reports from pdf to txt. + """ + ocr_done, ok_result = sec_certs.utils.pdf.convert_pdf_file( + cert.state.report.pdf_path, cert.state.report.txt_path + ) + cert.state.report.convert_garbage = ocr_done + cert.state.report.convert_ok = ok_result + if not ok_result: + logger.error(f"Cert dgst: {cert.dgst} failed to convert report pdf to txt") + else: + cert.state.report.txt_hash = helpers.get_sha256_filepath(cert.state.report.txt_path) + return cert - if not len(eal_set) <= 1: - raise ValueError("EAL field should have single value or should be empty.") + @staticmethod + def convert_pp_pdf(cert: ProtectionProfile) -> ProtectionProfile: + """ + Converts the actual protection profile from pdf to txt. + """ + ocr_done, ok_result = sec_certs.utils.pdf.convert_pdf_file(cert.state.pp.pdf_path, cert.state.pp.txt_path) + cert.state.pp.convert_garbage = ocr_done + cert.state.pp.convert_ok = ok_result + if not ok_result: + logger.error(f"Cert dgst: {cert.dgst} failed to convert PP pdf to txt") + else: + cert.state.pp.txt_hash = helpers.get_sha256_filepath(cert.state.pp.txt_path) + return cert - eal_str = list(eal_set)[0] if eal_set else None + @staticmethod + def extract_report_pdf_metadata(cert: ProtectionProfile) -> ProtectionProfile: + """ + Extracts various pdf metadata from the certification report. + """ + response, cert.pdf_data.report_metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.report.pdf_path) + cert.state.report.extract_ok = response == constants.RETURNCODE_OK + return cert - return cls(pp_name, eal_str, pp_link, pp_ids) + @staticmethod + def extract_pp_pdf_metadata(cert: ProtectionProfile) -> ProtectionProfile: + """ + Extracts various pdf metadata from the actual protection profile. + """ + response, cert.pdf_data.pp_metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.pp.pdf_path) + cert.state.pp.extract_ok = response == constants.RETURNCODE_OK + return cert - def __eq__(self, other: object) -> bool: - if not isinstance(other, ProtectionProfile): - return False - return self.pp_name == other.pp_name and self.pp_link == other.pp_link + @staticmethod + def extract_report_pdf_keywords(cert: ProtectionProfile) -> ProtectionProfile: + """ + Extracts keywords using regexes from the certification report. + """ + report_keywords = sec_certs.utils.extract.extract_keywords(cert.state.report.txt_path, cc_rules) + if report_keywords is None: + cert.state.report.extract_ok = False + else: + cert.pdf_data.report_keywords = report_keywords + return cert - def __lt__(self, other: ProtectionProfile) -> bool: - return self.pp_name < other.pp_name + @staticmethod + def extract_pp_pdf_keywords(cert: ProtectionProfile) -> ProtectionProfile: + """ + Extracts keywords using regexes from the actual protection profile. + """ + pp_keywords = sec_certs.utils.extract.extract_keywords(cert.state.pp.txt_path, cc_rules) + if pp_keywords is None: + cert.state.pp.extract_ok = False + else: + cert.pdf_data.pp_keywords = pp_keywords + return cert diff --git a/src/sec_certs/utils/cc_html_parsing.py b/src/sec_certs/utils/cc_html_parsing.py new file mode 100644 index 000000000..956353bf8 --- /dev/null +++ b/src/sec_certs/utils/cc_html_parsing.py @@ -0,0 +1,38 @@ +import logging +from datetime import datetime +from typing import Any + +from bs4 import Tag + +from sec_certs import constants + +logger = logging.getLogger(__name__) + + +def html_row_get_maintenance_div(cell: Tag) -> Tag | None: + divs = cell.find_all("div") + for d in divs: + if d.find("div") and d.stripped_strings and list(d.stripped_strings)[0] == "Maintenance Report(s)": + return d + return None + + +def parse_maintenance_div(main_div: Tag) -> list[tuple[Any, ...]]: + possible_updates = list(main_div.find_all("li")) + maintenance_updates = set() + for u in possible_updates: + text = list(u.stripped_strings)[0] + main_date = datetime.strptime(text.split(" ")[0], "%Y-%m-%d").date() if text else None + main_title = text.split("– ")[1] + main_report_link = None + main_st_link = None + links = u.find_all("a") + for link in links: + if link.get("title").startswith("Maintenance Report:"): + main_report_link = constants.CC_PORTAL_BASE_URL + link.get("href") + elif link.get("title").startswith("Maintenance ST"): + main_st_link = constants.CC_PORTAL_BASE_URL + link.get("href") + else: + logger.error("Unknown link in Maintenance part!") + maintenance_updates.add((main_date, main_title, main_report_link, main_st_link)) + return list(maintenance_updates) diff --git a/src/sec_certs/utils/helpers.py b/src/sec_certs/utils/helpers.py index 6a6a99541..d34e9f0eb 100644 --- a/src/sec_certs/utils/helpers.py +++ b/src/sec_certs/utils/helpers.py @@ -264,7 +264,18 @@ def choose_lowest_eal(eals: set[str] | None) -> str | None: if not eals: return None - matches = [(re.search(r"\d+", x)) for x in eals] - min_number = min([int(x.group()) for x in matches if x]) - candidates = [x for x in eals if str(min_number) in x] - return "EAL" + str(min_number) if len(candidates) == 2 else candidates[0] + eal_pattern = re.compile(r"(EAL(\d+)\+?)") + eal_entries = [] + + for s in eals: + match = eal_pattern.search(s) + if match: + full_match = match.group(1) + number = int(match.group(2)) + has_plus = "+" in full_match + eal_entries.append((number, has_plus, full_match)) + + if eal_entries: + eal_entries.sort(key=lambda x: (x[0], x[1])) + return eal_entries[0][2] + return None diff --git a/src/sec_certs/utils/label_studio_utils.py b/src/sec_certs/utils/label_studio_utils.py new file mode 100644 index 000000000..9c70b938c --- /dev/null +++ b/src/sec_certs/utils/label_studio_utils.py @@ -0,0 +1,78 @@ +import json +import logging +from pathlib import Path + +from tqdm import tqdm + +from sec_certs.configuration import config +from sec_certs.dataset.auxiliary_dataset_handling import CPEDatasetHandler +from sec_certs.dataset.dataset import Dataset +from sec_certs.sample.cpe import CPE + +logger = logging.getLogger(__name__) + + +def to_label_studio_json(dataset: Dataset, output_path: str | Path) -> None: + dataset.load_auxiliary_datasets() + cpe_dset = dataset.aux_handlers[CPEDatasetHandler].dset + + lst = [] + for cert in [x for x in dataset if x.heuristics.cpe_matches]: + dct = {"text": cert.label_studio_title} + candidates = [cpe_dset[x].title for x in cert.heuristics.cpe_matches] + candidates += ["No good match"] * (config.cpe_n_max_matches - len(candidates)) + options = ["option_" + str(x) for x in range(1, config.cpe_n_max_matches)] + dct.update(dict(zip(options, candidates))) + lst.append(dct) + + with Path(output_path).open("w") as handle: + json.dump(lst, handle, indent=4) + + +def load_label_studio_labels(dataset: Dataset, input_path: str | Path) -> set[str]: + with Path(input_path).open("r") as handle: + data = json.load(handle) + + dataset.load_auxiliary_datasets() + cpe_dset = dataset.aux_handlers[CPEDatasetHandler].dset + title_to_cpes_dict = cpe_dset.get_title_to_cpes_dict() + labeled_cert_digests: set[str] = set() + + logger.info("Translating label studio matches into their CPE representations and assigning to certificates.") + for annotation in tqdm(data, desc="Translating label studio matches"): + cpe_candidate_keys = {key for key in annotation if "option_" in key and annotation[key] != "No good match"} + + if "verified_cpe_match" not in annotation: + incorrect_keys: set[str] = set() + elif isinstance(annotation["verified_cpe_match"], str): + incorrect_keys = {annotation["verified_cpe_match"]} + else: + incorrect_keys = set(annotation["verified_cpe_match"]["choices"]) + + incorrect_keys = {x.lstrip("$") for x in incorrect_keys} + predicted_annotations = {annotation[x] for x in cpe_candidate_keys - incorrect_keys} + + cpes: set[CPE] = set() + for x in predicted_annotations: + if x not in title_to_cpes_dict: + logger.error(f"{x} not in dataset") + else: + to_update = title_to_cpes_dict[x] + if to_update and not cpes: + cpes = to_update + elif to_update and cpes: + cpes.update(to_update) + + # distinguish between FIPS and CC + if "\n" in annotation["text"]: + cert_name = annotation["text"].split("\nModule name: ")[1].split("\n")[0] + else: + cert_name = annotation["text"] + + certs = dataset.get_certs_by_name(cert_name) + labeled_cert_digests.update({x.dgst for x in certs}) + + for c in certs: + c.heuristics.verified_cpe_matches = {x.uri for x in cpes if x is not None} if cpes else None + + return labeled_cert_digests diff --git a/src/sec_certs/utils/nvd_dataset_builder.py b/src/sec_certs/utils/nvd_dataset_builder.py index 08f65d308..4e7162eb0 100644 --- a/src/sec_certs/utils/nvd_dataset_builder.py +++ b/src/sec_certs/utils/nvd_dataset_builder.py @@ -16,13 +16,13 @@ from requests import RequestException, Response from sec_certs import constants -from sec_certs.dataset.cpe import CPEDataset +from sec_certs.dataset.cpe import CPEDataset, CPEMatchDict from sec_certs.dataset.cve import CVEDataset from sec_certs.utils.parallel_processing import process_parallel logger = logging.getLogger(__name__) -DatasetType = TypeVar("DatasetType", CPEDataset, CVEDataset, dict) +DatasetType = TypeVar("DatasetType", CPEDataset, CVEDataset, CPEMatchDict) @dataclass @@ -320,7 +320,7 @@ def _init_new_dataset() -> CVEDataset: return CVEDataset() -class CpeMatchNvdDatasetBuilder(NvdDatasetBuilder[dict]): +class CpeMatchNvdDatasetBuilder(NvdDatasetBuilder[CPEMatchDict]): _ENDPOINT: Final[str] = "CPEMatch" _ENDPOINT_URL: Final[str] = "https://services.nvd.nist.gov/rest/json/cpematch/2.0" _RESULTS_PER_PAGE: Final[int] = 500 @@ -331,7 +331,7 @@ class CpeMatchNvdDatasetBuilder(NvdDatasetBuilder[dict]): "versionEndExcluding", ] - def _process_responses(self, responses: list[Response], dataset_to_fill: dict) -> dict: + def _process_responses(self, responses: list[Response], dataset_to_fill: CPEMatchDict) -> CPEMatchDict: timestamp = self._end_mod_date.isoformat() if self._end_mod_date else responses[-1].json()["timestamp"] match_strings = list(itertools.chain.from_iterable(response.json()["matchStrings"] for response in responses)) dataset_to_fill["timestamp"] = timestamp @@ -361,5 +361,5 @@ def _get_last_update_from_previous_data(self, previous_data: dict) -> datetime: return datetime.fromisoformat(previous_data["timestamp"]) @staticmethod - def _init_new_dataset() -> dict: - return {"timestamp": datetime.fromtimestamp(0).isoformat(), "match_strings": {}} + def _init_new_dataset() -> CPEMatchDict: + return CPEMatchDict({"timestamp": datetime.fromtimestamp(0).isoformat(), "match_strings": {}}) diff --git a/tests/cc/conftest.py b/tests/cc/conftest.py index 1f5050be1..91f8caab9 100644 --- a/tests/cc/conftest.py +++ b/tests/cc/conftest.py @@ -4,11 +4,19 @@ from pathlib import Path import pytest +import tests.data.cc.analysis import tests.data.cc.dataset +import tests.data.protection_profiles from sec_certs.dataset.cc import CCDataset +from sec_certs.dataset.protection_profile import ProtectionProfileDataset from sec_certs.sample.cc import CCCertificate -from sec_certs.sample.protection_profile import ProtectionProfile + + +@pytest.fixture(scope="module") +def pp_data_dir() -> Generator[Path, None, None]: + with resources.path(tests.data.protection_profiles, "") as path: + yield path @pytest.fixture(scope="module") @@ -17,13 +25,25 @@ def data_dir() -> Generator[Path, None, None]: yield path +@pytest.fixture(scope="module") +def analysis_data_dir() -> Generator[Path, None, None]: + with resources.path(tests.data.cc.analysis, "") as path: + yield path + + @pytest.fixture def toy_dataset() -> CCDataset: with resources.path(tests.data.cc.dataset, "toy_dataset.json") as path: return CCDataset.from_json(path) -@pytest.fixture(scope="module") +@pytest.fixture +def toy_pp_dataset() -> ProtectionProfileDataset: + with resources.path(tests.data.protection_profiles, "pp.json") as path: + return ProtectionProfileDataset.from_json(path) + + +@pytest.fixture def cert_one() -> CCCertificate: return CCCertificate( "active", @@ -34,11 +54,11 @@ def cert_one() -> CCCertificate: {"ALC_FLR.2", "EAL3+"}, date(2020, 6, 15), date(2025, 6, 15), - "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf", - "https://www.commoncriteriaportal.org/files/epfiles/ST%20-%20NetIQ%20Identity%20Manager%204.7.pdf", - "https://www.commoncriteriaportal.org/files/epfiles/Certifikat%20CCRA%20-%20NetIQ%20Identity%20Manager%204.7_signed.pdf", + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf", + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/epfiles/ST%20-%20NetIQ%20Identity%20Manager%204.7.pdf", + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/epfiles/Certifikat%20CCRA%20-%20NetIQ%20Identity%20Manager%204.7_signed.pdf", "https://www.netiq.com/", - set(), + None, set(), None, None, @@ -48,7 +68,6 @@ def cert_one() -> CCCertificate: @pytest.fixture(scope="module") def cert_two() -> CCCertificate: - pp = ProtectionProfile("sample_pp", None, pp_link="https://sample.pp") update = CCCertificate.MaintenanceReport( date(1900, 1, 1), "Sample maintenance", "https://maintenance.up", "https://maintenance.up" ) @@ -66,7 +85,7 @@ def cert_two() -> CCCertificate: "https://path.to/st/link", "https://path.to/cert/link", "https://path.to/manufacturer/web", - {pp}, + {"https://sample.pp"}, {update}, None, None, diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index ce83d7057..6591ca182 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -10,11 +10,18 @@ import tests.data.common from sec_certs.cert_rules import SARS_IMPLIED_FROM_EAL +from sec_certs.dataset.auxiliary_dataset_handling import ( + CPEDatasetHandler, + CPEMatchDictHandler, + CVEDatasetHandler, + ProtectionProfileDatasetHandler, +) from sec_certs.dataset.cc import CCDataset from sec_certs.dataset.cpe import CPEDataset from sec_certs.dataset.cve import CVEDataset +from sec_certs.heuristics.cc import compute_references +from sec_certs.heuristics.common import compute_related_cves, compute_transitive_vulnerabilities from sec_certs.sample.cc import CCCertificate -from sec_certs.sample.protection_profile import ProtectionProfile from sec_certs.sample.sar import SAR @@ -26,17 +33,23 @@ def analysis_data_dir() -> Generator[Path, None, None]: @pytest.fixture(scope="module") def processed_cc_dset( - analysis_data_dir: Path, cve_dataset: CVEDataset, cpe_dataset: CPEDataset, tmp_path_factory + analysis_data_dir: Path, cve_dataset: CVEDataset, cpe_dataset: CPEDataset, tmp_path_factory, pp_data_dir: Path ) -> CCDataset: tmp_dir = tmp_path_factory.mktemp("cc_dset") shutil.copytree(analysis_data_dir, tmp_dir, dirs_exist_ok=True) + shutil.copy(pp_data_dir / "pp.json", tmp_dir / "pp.json") cc_dset = CCDataset.from_json(tmp_dir / "vulnerable_dataset.json") - cc_dset.process_protection_profiles() + cc_dset.aux_handlers[ProtectionProfileDatasetHandler].root_dir.mkdir(parents=True, exist_ok=True) + shutil.copy(tmp_dir / "pp.json", cc_dset.aux_handlers[ProtectionProfileDatasetHandler].dset_path) + + cc_dset.aux_handlers[ProtectionProfileDatasetHandler].process_dataset() + cc_dset.aux_handlers[CPEMatchDictHandler].dset = {} + cc_dset.aux_handlers[CVEDatasetHandler].dset = cve_dataset + cc_dset.aux_handlers[CPEDatasetHandler].dset = cpe_dataset + cc_dset.extract_data() - cc_dset.auxiliary_datasets.cve_dset = cve_dataset - cc_dset.auxiliary_datasets.cpe_dset = cpe_dataset - cc_dset._compute_heuristics() + cc_dset._compute_heuristics_body(skip_schemes=True) return cc_dset @@ -66,7 +79,13 @@ def test_find_related_cves(processed_cc_dset: CCDataset, random_certificate: CCC random_certificate.heuristics.cpe_matches = { "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*" } - processed_cc_dset.compute_related_cves() + compute_related_cves( + processed_cc_dset.aux_handlers[CPEDatasetHandler].dset, + processed_cc_dset.aux_handlers[CVEDatasetHandler].dset, + {}, + processed_cc_dset.certs.values(), + ) + assert random_certificate.heuristics.related_cves == {"CVE-2017-1732", "CVE-2019-4513"} @@ -75,7 +94,14 @@ def test_find_related_cves_criteria_configuration(processed_cc_dset: CCDataset, "cpe:2.3:a:ibm:websphere_application_server:7.0:*:*:*:*:*:*:*", "cpe:2.3:o:ibm:zos:6.0.1:*:*:*:*:*:*:*", } - processed_cc_dset.compute_related_cves() + + compute_related_cves( + processed_cc_dset.aux_handlers[CPEDatasetHandler].dset, + processed_cc_dset.aux_handlers[CVEDatasetHandler].dset, + {}, + processed_cc_dset.certs.values(), + ) + assert random_certificate.heuristics.related_cves == {"CVE-2010-2325"} @@ -132,26 +158,6 @@ def test_keywords_heuristics(random_certificate: CCCertificate): assert extracted_keywords["cipher_mode"]["CBC"]["CBC"] == 2 -def test_protection_profile_matching(processed_cc_dset: CCDataset, random_certificate: CCCertificate): - artificial_pp: ProtectionProfile = ProtectionProfile( - "Korean National Protection Profile for Single Sign On V1.0", - "EAL1+", - pp_link="http://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - ) - - random_certificate.protection_profiles = {artificial_pp} - - expected_pp: ProtectionProfile = ProtectionProfile( - "Korean National Protection Profile for Single Sign On V1.0", - "EAL1+", - pp_link="http://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - pp_ids=frozenset(["KECS-PP-0822-2017 SSO V1.0"]), - ) - - processed_cc_dset.process_protection_profiles(to_download=False) - assert random_certificate.protection_profiles == {expected_pp} - - def test_single_record_references_heuristics(random_certificate: CCCertificate): # Single record in daset is not affecting nor affected by other records assert not random_certificate.heuristics.report_references.directly_referenced_by @@ -161,7 +167,8 @@ def test_single_record_references_heuristics(random_certificate: CCCertificate): def test_reference_dataset(reference_dataset: CCDataset): - reference_dataset._compute_references() + compute_references(reference_dataset.certs) + test_cert = reference_dataset["d1b238729b25d745"] assert test_cert.heuristics.report_references.directly_referenced_by == {"BSI-DSZ-CC-0370-2006"} @@ -174,12 +181,12 @@ def test_reference_dataset(reference_dataset: CCDataset): def test_direct_transitive_vulnerability_dataset(transitive_vulnerability_dataset: CCDataset): - transitive_vulnerability_dataset._compute_transitive_vulnerabilities() + compute_transitive_vulnerabilities(transitive_vulnerability_dataset.certs) assert transitive_vulnerability_dataset["11f77cb31b931a57"].heuristics.direct_transitive_cves == {"CVE-2013-5385"} def test_indirect_transitive_vulnerability_dataset(transitive_vulnerability_dataset: CCDataset): - transitive_vulnerability_dataset._compute_transitive_vulnerabilities() + compute_transitive_vulnerabilities(transitive_vulnerability_dataset.certs) assert transitive_vulnerability_dataset["11f77cb31b931a57"].heuristics.indirect_transitive_cves == {"CVE-2013-5385"} @@ -218,3 +225,28 @@ def test_eal_implied_sar_inference(random_certificate: CCCertificate): actual_sars = random_certificate.actual_sars eal_3_sars = {SAR(x[0], x[1]) for x in SARS_IMPLIED_FROM_EAL["EAL3"]} assert eal_3_sars.issubset(actual_sars) + + +def test_eal_inference(processed_cc_dset: CCDataset): + assert processed_cc_dset["ed91ff3e658457fd"].heuristics.eal == "EAL1" + assert processed_cc_dset["95e3850bef32f410"].heuristics.eal == "EAL1+" + + +def test_pp_linking(processed_cc_dset: CCDataset): + assert processed_cc_dset["ed91ff3e658457fd"].heuristics.protection_profiles == {"e315e3e834a61448"} + assert processed_cc_dset["95e3850bef32f410"].heuristics.protection_profiles == { + "b02ed76d2545326a", + "c8b175590bb7fdfb", + } + pp_dset = processed_cc_dset.aux_handlers[ProtectionProfileDatasetHandler].dset + assert processed_cc_dset["ed91ff3e658457fd"].protection_profile_links + assert processed_cc_dset["95e3850bef32f410"].protection_profile_links + assert ( + pp_dset["e315e3e834a61448"].web_data.pp_link in processed_cc_dset["ed91ff3e658457fd"].protection_profile_links + ) + assert ( + pp_dset["b02ed76d2545326a"].web_data.pp_link in processed_cc_dset["95e3850bef32f410"].protection_profile_links + ) + assert ( + pp_dset["c8b175590bb7fdfb"].web_data.pp_link in processed_cc_dset["95e3850bef32f410"].protection_profile_links + ) diff --git a/tests/cc/test_cc_aux_datasets.py b/tests/cc/test_cc_aux_datasets.py new file mode 100644 index 000000000..08d12902d --- /dev/null +++ b/tests/cc/test_cc_aux_datasets.py @@ -0,0 +1,215 @@ +from unittest.mock import mock_open + +import pytest + +from sec_certs.configuration import config +from sec_certs.dataset import ( + CCDatasetMaintenanceUpdates, + CCSchemeDataset, + CPEDataset, + CVEDataset, + FIPSAlgorithmDataset, + ProtectionProfileDataset, +) +from sec_certs.dataset.auxiliary_dataset_handling import ( + CCMaintenanceUpdateDatasetHandler, + CCSchemeDatasetHandler, + CPEDatasetHandler, + CPEMatchDictHandler, + CVEDatasetHandler, + FIPSAlgorithmDatasetHandler, + ProtectionProfileDatasetHandler, +) + + +@pytest.fixture +def temp_dir(tmp_path): + return tmp_path + + +@pytest.fixture +def mock_dset(): + return {"key": "value"} + + +def test_cpe_dataset_handler_set_local_paths(temp_dir): + handler = CPEDatasetHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +@pytest.mark.parametrize("preferred_source_aux_datasets", ["sec-certs", "origin"]) +def test_cpe_dataset_handler_process_dataset(preferred_source_aux_datasets, temp_dir, monkeypatch): + config.preferred_source_remote_datasets = preferred_source_aux_datasets + handler = CPEDatasetHandler(temp_dir) + mock_dset = CPEDataset() + + def mock_get_dset(path): + return mock_dset + + if preferred_source_aux_datasets == "sec-certs": + monkeypatch.setattr("sec_certs.dataset.cpe.CPEDataset.from_web", mock_get_dset) + else: + monkeypatch.setattr("sec_certs.utils.nvd_dataset_builder.CpeNvdDatasetBuilder.build_dataset", mock_get_dset) + + monkeypatch.setattr("sec_certs.dataset.cpe.CPEDataset.to_json", lambda x: None) + handler.process_dataset(download_fresh=True) + + assert handler.dset == mock_dset + assert handler.dset_path == temp_dir / "cpe_dataset.json" + + +def test_cve_dataset_handler_set_local_paths(temp_dir): + handler = CVEDatasetHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +@pytest.mark.parametrize("preferred_source_aux_datasets", ["sec-certs", "origin"]) +def test_cve_dataset_handler_process_dataset(preferred_source_aux_datasets, temp_dir, monkeypatch): + config.preferred_source_remote_datasets = preferred_source_aux_datasets + handler = CVEDatasetHandler(temp_dir) + mock_dset = CVEDataset() + + def mock_get_dset(path): + return mock_dset + + if preferred_source_aux_datasets == "sec-certs": + monkeypatch.setattr("sec_certs.dataset.cve.CVEDataset.from_web", mock_get_dset) + else: + monkeypatch.setattr("sec_certs.utils.nvd_dataset_builder.CveNvdDatasetBuilder.build_dataset", mock_get_dset) + monkeypatch.setattr("sec_certs.dataset.cve.CVEDataset.to_json", lambda x: None) + handler.process_dataset(download_fresh=True) + + assert handler.dset == mock_dset + assert handler.dset_path == temp_dir / "cve_dataset.json" + + +def test_cpe_match_dict_handler_set_local_paths(temp_dir): + handler = CPEMatchDictHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +@pytest.mark.parametrize("preferred_source_aux_datasets", ["sec-certs", "origin"]) +def test_cpe_match_dict_handler_process_dataset(preferred_source_aux_datasets, temp_dir, monkeypatch): + config.preferred_source_remote_datasets = preferred_source_aux_datasets + handler = CPEMatchDictHandler(temp_dir) + mock_dset = {"key": "value"} + mock_dset_str_single_quotes = '{"key": "value"}' + + def mock_get_dset(path): + return mock_dset + + def mock_download_file(url, path, progress_bar_desc): + return 200 + + if preferred_source_aux_datasets == "origin": + monkeypatch.setattr( + "sec_certs.utils.nvd_dataset_builder.CpeMatchNvdDatasetBuilder.build_dataset", mock_get_dset + ) + else: + monkeypatch.setattr("sec_certs.utils.helpers.download_file", mock_download_file) + monkeypatch.setattr("gzip.open", mock_open(read_data=(mock_dset_str_single_quotes.encode()))) + + handler.process_dataset(download_fresh=True) + + assert handler.dset == mock_dset + + +def test_fips_algorithm_dataset_handler_set_local_paths(temp_dir): + handler = FIPSAlgorithmDatasetHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +def test_fips_algorithm_dataset_handler_process_dataset(temp_dir, monkeypatch): + handler = FIPSAlgorithmDatasetHandler(temp_dir) + mock_dset = FIPSAlgorithmDataset() + + def mock_from_web(path): + return mock_dset + + monkeypatch.setattr("sec_certs.dataset.fips_algorithm.FIPSAlgorithmDataset.from_web", mock_from_web) + monkeypatch.setattr("sec_certs.dataset.fips_algorithm.FIPSAlgorithmDataset.to_json", lambda x: None) + handler.process_dataset(download_fresh=True) + assert handler.dset == mock_dset + assert handler.dset_path == temp_dir / "algorithms.json" + assert handler.dset.json_path == handler.dset_path + + +def test_cc_scheme_dataset_handler_set_local_paths(temp_dir): + handler = CCSchemeDatasetHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +def test_cc_scheme_dataset_handler_process_dataset(temp_dir, monkeypatch): + handler = CCSchemeDatasetHandler(temp_dir) + mock_dset = CCSchemeDataset(schemes={}) + + def mock_from_web(path, only_schemes): + return mock_dset + + monkeypatch.setattr("sec_certs.dataset.cc_scheme.CCSchemeDataset.from_web", mock_from_web) + monkeypatch.setattr("sec_certs.dataset.cc_scheme.CCSchemeDataset.to_json", lambda x: None) + handler.process_dataset(download_fresh=True) + assert handler.dset == mock_dset + assert handler.dset_path == temp_dir / "cc_scheme.json" + assert handler.dset.json_path == handler.dset_path + + +def test_cc_maintenance_update_dataset_handler_set_local_paths(temp_dir): + handler = CCMaintenanceUpdateDatasetHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +def test_cc_maintenance_update_dataset_handler_process_dataset(temp_dir, monkeypatch): + handler = CCMaintenanceUpdateDatasetHandler(temp_dir) + mock_dset = CCDatasetMaintenanceUpdates(root_dir=handler.dset_path.parent, name="maintenance_updates") + + monkeypatch.setattr( + "sec_certs.sample.cc_maintenance_update.CCMaintenanceUpdate.get_updates_from_cc_cert", + lambda x: [], + ) + monkeypatch.setattr("sec_certs.dataset.dataset.Dataset.download_all_artifacts", lambda x: None) + monkeypatch.setattr("sec_certs.dataset.dataset.Dataset.convert_all_pdfs", lambda x: None) + monkeypatch.setattr("sec_certs.dataset.cc.CCDataset.extract_data", lambda x: None) + monkeypatch.setattr("sec_certs.dataset.dataset.Dataset.to_json", lambda x: None) + handler.process_dataset(download_fresh=True) + assert handler.dset == mock_dset + + +def test_protection_profile_dataset_handler_set_local_paths(temp_dir): + handler = ProtectionProfileDatasetHandler(temp_dir) + new_path = temp_dir / "new_path" + handler.set_local_paths(new_path) + assert handler.aux_datasets_dir == new_path + + +def test_protection_profile_dataset_handler_process_dataset(temp_dir, monkeypatch): + handler = ProtectionProfileDatasetHandler(temp_dir) + mock_dset = ProtectionProfileDataset() + + monkeypatch.setattr( + "sec_certs.dataset.protection_profile.ProtectionProfileDataset.get_certs_from_web", lambda x: None + ) + monkeypatch.setattr( + "sec_certs.dataset.protection_profile.ProtectionProfileDataset.download_all_artifacts", lambda x: None + ) + monkeypatch.setattr( + "sec_certs.dataset.protection_profile.ProtectionProfileDataset.convert_all_pdfs", lambda x: None + ) + monkeypatch.setattr( + "sec_certs.dataset.protection_profile.ProtectionProfileDataset.analyze_certificates", lambda x: None + ) + monkeypatch.setattr("sec_certs.dataset.protection_profile.ProtectionProfileDataset.to_json", lambda x: None) + handler.process_dataset(download_fresh=True) + assert handler.dset == mock_dset diff --git a/tests/cc/test_cc_certificate.py b/tests/cc/test_cc_certificate.py index f90245e07..d24f4c303 100644 --- a/tests/cc/test_cc_certificate.py +++ b/tests/cc/test_cc_certificate.py @@ -60,7 +60,7 @@ def test_keyword_extraction(vulnerable_certificate: CCCertificate): def test_cert_link_escaping(cert_one: CCCertificate): assert ( cert_one.report_link - == "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf" + == "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf" ) @@ -79,3 +79,24 @@ def test_cert_to_json(cert_two: CCCertificate, tmp_path: Path, data_dir: Path): def test_cert_from_json(cert_two: CCCertificate, data_dir: Path): crt = CCCertificate.from_json(data_dir / "fictional_cert.json") assert cert_two == crt + + +def test_cert_old_dgst(cert_one: CCCertificate): + assert cert_one.old_dgst == "309ac2fd7f2dcf17" + with pytest.raises(RuntimeError): + cert_one.report_link = None + cert_one.old_dgst + + +def test_cert_dgst(cert_one: CCCertificate): + assert cert_one.dgst == "e3dcf91ef38ddbf0" + cert_one.name = None + with pytest.raises(RuntimeError): + cert_one.dgst + + +def test_cert_older_dgst(cert_one: CCCertificate): + assert cert_one.older_dgst == "916f4d199f78d70c" + cert_one.report_link = None + with pytest.raises(RuntimeError): + cert_one.older_dgst diff --git a/tests/cc/test_cc_dataset.py b/tests/cc/test_cc_dataset.py index bc1433aaf..4c988b5b3 100644 --- a/tests/cc/test_cc_dataset.py +++ b/tests/cc/test_cc_dataset.py @@ -6,6 +6,7 @@ import pytest from sec_certs import constants +from sec_certs.dataset import ProtectionProfileDataset from sec_certs.dataset.cc import CCDataset from sec_certs.sample.cc import CCCertificate @@ -106,6 +107,7 @@ def test_build_empty_dataset(): dset.get_certs_from_web(to_download=False, get_archived=False, get_active=False) assert len(dset) == 0 assert dset.state.meta_sources_parsed + assert not dset.state.auxiliary_datasets_processed assert not dset.state.artifacts_downloaded assert not dset.state.pdfs_converted assert not dset.state.certs_analyzed @@ -129,26 +131,23 @@ def test_build_dataset(data_dir: Path, cert_one: CCCertificate, toy_dataset: CCD assert dset == toy_dataset -def test_process_pp_dataset(toy_dataset: CCDataset): - with TemporaryDirectory() as tmp_dir: - toy_dataset.copy_dataset(tmp_dir) - toy_dataset.process_protection_profiles() - assert toy_dataset.pp_dataset_path.exists() - assert toy_dataset.pp_dataset_path.stat().st_size > constants.MIN_CC_PP_DATASET_SIZE - - @pytest.mark.xfail(reason="May fail due to error on CC server") -def test_download_csv_html_files(): +@pytest.mark.parametrize("dataset_class", ["CCDataset", "ProtectionProfileDataset"]) +def test_download_csv_html_files(dataset_class): with TemporaryDirectory() as tmp_dir: - dset = CCDataset({}, Path(tmp_dir), "sample_dataset", "sample dataset description") - dset._download_csv_html_resources(get_active=True, get_archived=False) + constructor = CCDataset if dataset_class == "CCDataset" else ProtectionProfileDataset + min_html_size = constants.MIN_CC_HTML_SIZE if dataset_class == "CCDataset" else constants.MIN_PP_HTML_SIZE + dset = constructor(root_dir=Path(tmp_dir)) + dset._download_html_resources(get_active=True, get_archived=False) for x in dset.active_html_tuples: assert x[1].exists() - assert x[1].stat().st_size >= constants.MIN_CC_HTML_SIZE - for x in dset.active_csv_tuples: - assert x[1].exists() - assert x[1].stat().st_size >= constants.MIN_CC_CSV_SIZE + assert x[1].stat().st_size >= min_html_size + + if dataset_class == "CCDataset": + for x in dset.active_csv_tuples: + assert x[1].exists() + assert x[1].stat().st_size >= constants.MIN_CC_CSV_SIZE def test_to_pandas(toy_dataset: CCDataset): diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py index 9c89c7483..05148cd77 100644 --- a/tests/cc/test_cc_maintenance_updates.py +++ b/tests/cc/test_cc_maintenance_updates.py @@ -6,7 +6,7 @@ import pytest import tests.data.cc.dataset -from sec_certs.dataset import CCDatasetMaintenanceUpdates +from sec_certs.dataset.cc import CCDatasetMaintenanceUpdates from sec_certs.sample.cc_maintenance_update import CCMaintenanceUpdate @@ -29,7 +29,7 @@ def test_methods_not_meant_to_be_implemented(): with pytest.raises(NotImplementedError): dset.analyze_certificates() with pytest.raises(NotImplementedError): - dset._compute_heuristics() + dset._compute_heuristics_body() with pytest.raises(NotImplementedError): dset.process_auxiliary_datasets() with pytest.raises(NotImplementedError): @@ -79,7 +79,7 @@ def test_to_pandas(mu_dset: CCDatasetMaintenanceUpdates): @pytest.mark.skip(reason="Will work only with fresh snapshot on sec-certs.org") def test_from_web(): - dset = CCDatasetMaintenanceUpdates.from_web_latest() + dset = CCDatasetMaintenanceUpdates.from_web() assert dset is not None assert len(dset) >= 492 # Contents as of November 2022, maintenances should not disappear assert "cert_8f08cacb49a742fb_update_559ed93dd80320b5" in dset # random cert verified to be present diff --git a/tests/cc/test_cc_protection_profiles.py b/tests/cc/test_cc_protection_profiles.py new file mode 100644 index 000000000..9bb475642 --- /dev/null +++ b/tests/cc/test_cc_protection_profiles.py @@ -0,0 +1,165 @@ +import json +import shutil +from pathlib import Path +from tempfile import TemporaryDirectory + +import pytest + +from sec_certs.dataset.protection_profile import ProtectionProfileDataset + + +def test_dataset_from_json(toy_pp_dataset: ProtectionProfileDataset, pp_data_dir: Path, tmp_path: Path): + toy_pp_dataset.to_json(tmp_path / "dset.json") + with (tmp_path / "dset.json").open("r") as handle: + data = json.load(handle) + + with (pp_data_dir / "pp.json").open("r") as handle: + template_data = json.load(handle) + + del data["timestamp"] + del template_data["timestamp"] + assert data == template_data + + +def test_dataset_to_json(toy_pp_dataset: ProtectionProfileDataset, pp_data_dir: Path, tmp_path: Path): + assert toy_pp_dataset == ProtectionProfileDataset.from_json(pp_data_dir / "pp.json") + compressed_path = tmp_path / "dset.json.gz" + toy_pp_dataset.to_json(compressed_path, compress=True) + decompressed_dataset = ProtectionProfileDataset.from_json(compressed_path, is_compressed=True) + assert toy_pp_dataset == decompressed_dataset + + +def test_build_empty_dataset(): + with TemporaryDirectory() as tmp_dir: + dset = ProtectionProfileDataset(root_dir=Path(tmp_dir)) + dset.get_certs_from_web(to_download=False, get_archived=False, get_active=False, get_collaborative=False) + + assert len(dset) == 0 + assert dset.state.meta_sources_parsed + assert not dset.state.auxiliary_datasets_processed + assert not dset.state.artifacts_downloaded + assert not dset.state.pdfs_converted + assert not dset.state.certs_analyzed + + +def test_get_certs_from_web(pp_data_dir: Path, toy_pp_dataset: ProtectionProfileDataset): + with TemporaryDirectory() as tmp_dir: + dataset_path = Path(tmp_dir) + (dataset_path / "web").mkdir() + shutil.copyfile(pp_data_dir / "pp_active.html", dataset_path / "web/pp_active.html") + + dset = ProtectionProfileDataset(root_dir=dataset_path) + dset.get_certs_from_web( + to_download=False, + get_active=True, + get_archived=False, + get_collaborative=False, + keep_metadata=False, + update_json=False, + ) + + assert len(list(dataset_path.iterdir())) == 0 + assert len(dset) == 3 + assert "b02ed76d2545326a" in dset.certs + assert dset == toy_pp_dataset + + +def test_download_and_convert_artifacts(toy_pp_dataset: ProtectionProfileDataset, tmpdir, pp_data_dir): + toy_pp_dataset.copy_dataset(tmpdir) + toy_pp_dataset.download_all_artifacts() + + template_pp_pdf_hashes = { + "c8b175590bb7fdfb": "f35ea732cfe303415080e0a95b9aa573ff9e02019e9ab971904c7530c2617b80", + "e315e3e834a61448": "605489cda568c32371d0aeb6841df0dc63277f57113f59a5a60f8a64a1661def", + "b02ed76d2545326a": "e88bddd8948a8624d3f350e4cb489f4b1b708e5f10e2c1402166cdfe08e5d32a", + } + template_report_pdf_hashes = { + "c8b175590bb7fdfb": "c7dbaec8c333431c65129a0f429cdea22aa244e971f79139fb0ae079d4805b29", + "e315e3e834a61448": "5f72a3ef0dce80b66c077a8a7482a1843c36e90113bd77827fba81c6e148d248", + "b02ed76d2545326a": "e4c2d590fce870cd14fe6571a3258bd094b1e66f83f5e4d4a53a28a96f27490e", + } + + if not all( + [ + toy_pp_dataset["c8b175590bb7fdfb"].state.pp.download_ok, + toy_pp_dataset["c8b175590bb7fdfb"].state.report.download_ok, + toy_pp_dataset["e315e3e834a61448"].state.pp.download_ok, + toy_pp_dataset["e315e3e834a61448"].state.report.download_ok, + toy_pp_dataset["b02ed76d2545326a"].state.pp.download_ok, + toy_pp_dataset["b02ed76d2545326a"].state.report.download_ok, + ] + ): + pytest.xfail(reason="Fail due to errror during download") + + toy_pp_dataset.convert_all_pdfs() + + for cert in toy_pp_dataset: + assert cert.state.pp.pdf_hash == template_pp_pdf_hashes[cert.dgst] + assert cert.state.report.pdf_hash == template_report_pdf_hashes[cert.dgst] + assert cert.state.report.convert_ok + assert cert.state.pp.convert_ok + assert cert.state.report.txt_path.exists() + assert cert.state.pp.txt_path.exists() + + template_report_txt_path = pp_data_dir / "reports/txt/b02ed76d2545326a.txt" + template_pp_txt_path = pp_data_dir / "pps/txt/b02ed76d2545326a.txt" + assert ( + abs( + toy_pp_dataset["b02ed76d2545326a"].state.report.txt_path.stat().st_size + - template_report_txt_path.stat().st_size + ) + < 1000 + ) + assert ( + abs(toy_pp_dataset["b02ed76d2545326a"].state.pp.txt_path.stat().st_size - template_pp_txt_path.stat().st_size) + < 1000 + ) + + +def test_keyword_extraction(toy_pp_dataset: ProtectionProfileDataset, pp_data_dir: Path, tmpdir): + toy_pp_dataset.state.artifacts_downloaded = True + toy_pp_dataset.state.pdfs_converted = True + toy_pp_dataset.state.auxiliary_datasets_processed = True + + toy_pp_dataset.copy_dataset(tmpdir) + + toy_pp_dataset["b02ed76d2545326a"].state.pp.download_ok = True + toy_pp_dataset["b02ed76d2545326a"].state.pp.convert_ok = True + toy_pp_dataset["b02ed76d2545326a"].state.report.download_ok = True + toy_pp_dataset["b02ed76d2545326a"].state.report.convert_ok = True + + toy_pp_dataset.analyze_certificates() + assert toy_pp_dataset.state.certs_analyzed + assert not toy_pp_dataset["c8b175590bb7fdfb"].state.pp.extract_ok + assert not toy_pp_dataset["e315e3e834a61448"].state.report.extract_ok + + report_keywords = toy_pp_dataset["b02ed76d2545326a"].pdf_data.report_keywords + assert report_keywords + assert "cc_protection_profile_id" in report_keywords + assert report_keywords["cc_protection_profile_id"]["BSI"]["BSI-CC-PP-0062-2010"] == 14 + + pp_keywords = toy_pp_dataset["b02ed76d2545326a"].pdf_data.pp_keywords + assert pp_keywords + assert "cc_security_level" in pp_keywords + assert pp_keywords["cc_security_level"]["EAL"]["EAL 2"] == 6 + assert "tee_name" in pp_keywords + assert pp_keywords["tee_name"]["IBM"]["SE"] == 1 + assert not pp_keywords["asymmetric_crypto"] + + pp_metadata = toy_pp_dataset["b02ed76d2545326a"].pdf_data.pp_metadata + assert pp_metadata + assert not pp_metadata["pdf_is_encrypted"] + assert "https://www.bsi.bund.de" in pp_metadata["pdf_hyperlinks"] + + report_metadata = toy_pp_dataset["b02ed76d2545326a"].pdf_data.report_metadata + assert report_metadata + assert "BSI-CC-PP-0062-2010" in report_metadata["/Title"] + + +def test_get_pp_by_pp_link(toy_pp_dataset: ProtectionProfileDataset): + pp = toy_pp_dataset.get_pp_by_pp_link( + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/pp0062b_pdf.pdf" + ) + assert pp + assert pp.dgst == "b02ed76d2545326a" + assert not toy_pp_dataset.get_pp_by_pp_link("https://some-random-url.com") diff --git a/tests/cc/test_cc_schemes.py b/tests/cc/test_cc_schemes.py index afaeda9ff..8808fff30 100644 --- a/tests/cc/test_cc_schemes.py +++ b/tests/cc/test_cc_schemes.py @@ -5,7 +5,9 @@ from requests import RequestException import sec_certs.sample.cc_scheme as CCSchemes +from sec_certs.dataset.auxiliary_dataset_handling import CCSchemeDatasetHandler from sec_certs.dataset.cc import CCDataset +from sec_certs.heuristics.cc import compute_scheme_data from sec_certs.model.cc_matching import CCSchemeMatcher from sec_certs.sample.cc import CCCertificate @@ -231,6 +233,7 @@ def test_matching(toy_dataset: CCDataset, canada_certified): def test_process_dataset(toy_dataset: CCDataset): - toy_dataset.auxiliary_datasets.scheme_dset = toy_dataset.process_schemes(True, only_schemes={"CA"}) - toy_dataset._compute_scheme_data() + toy_dataset.aux_handlers[CCSchemeDatasetHandler].only_schemes = {"CA"} # type: ignore + toy_dataset.aux_handlers[CCSchemeDatasetHandler].process_dataset() + compute_scheme_data(toy_dataset.aux_handlers[CCSchemeDatasetHandler].dset, toy_dataset.certs) assert toy_dataset["8f08cacb49a742fb"].heuristics.scheme_data is not None diff --git a/tests/data/cc/analysis/cc_full_dataset.json b/tests/data/cc/analysis/cc_full_dataset.json index ba64903ce..a2149b67b 100644 --- a/tests/data/cc/analysis/cc_full_dataset.json +++ b/tests/data/cc/analysis/cc_full_dataset.json @@ -35,18 +35,9 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0683b_pdf.pdf", "cert_link": null, "manufacturer_web": "https://www.ibm.com", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [ - { - "_type": "sec_certs.sample.protection_profile.ProtectionProfile", - "pp_name": "Korean National Protection Profile for Single Sign On V1.0", - "pp_eal": "EAL1+", - "pp_link": "https://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - "pp_ids": [ - "KECS-PP-0822-2017 SSO V1.0" - ] - } ] }, "maintenance_updates": { @@ -56,7 +47,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -65,7 +56,7 @@ "txt_hash": "35627594d3806ac3926ec47f466503fe27781533da12beb6f8705882fccf125e" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -74,7 +65,7 @@ "txt_hash": "c8b4c5667a3f60edc845051e5a31a2d17b9d9a11df9e56dd89681d25e727a622" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -730,8 +721,10 @@ }, "direct_transitive_cves": null, "indirect_transitive_cves": null, - "next_certificates": null, - "prev_certificates": null + "next_certificates": null, + "prev_certificates": null, + "protection_profiles": null, + "eal": null } } ] diff --git a/tests/data/cc/analysis/reference_dataset.json b/tests/data/cc/analysis/reference_dataset.json index 28234b7e6..4ea176de6 100644 --- a/tests/data/cc/analysis/reference_dataset.json +++ b/tests/data/cc/analysis/reference_dataset.json @@ -34,7 +34,7 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0517b.pdf", "cert_link": null, "manufacturer_web": "https://global.oce.com/", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [] }, @@ -45,7 +45,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -54,7 +54,7 @@ "txt_hash": "460e8010dbc8f5de5b87bf96fd45c71cfd9f3869f34ca6ac1ab02cbd70d2523f" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -63,7 +63,7 @@ "txt_hash": "81c53d1e5b1c2fcb129ce1053d13cd1308f7a556921f0b9024cedf75c6b2efb7" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -580,7 +580,9 @@ "direct_transitive_cves": null, "indirect_transitive_cves": null, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } }, { @@ -604,7 +606,7 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0370b.pdf", "cert_link": null, "manufacturer_web": "https://global.oce.com/", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [] }, @@ -615,7 +617,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -624,7 +626,7 @@ "txt_hash": "0535df1c56fb4f87153cbffee51ba4d77fac47a6f17f024aa7d9df461028bc65" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -633,7 +635,7 @@ "txt_hash": "926668bea7c427a4fcf82857bfc63420f3597b6bff39699927a58f335620eaac" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1228,7 +1230,9 @@ "direct_transitive_cves": null, "indirect_transitive_cves": null, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } }, { @@ -1252,7 +1256,7 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0325b.pdf", "cert_link": null, "manufacturer_web": "https://global.oce.com/", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [] }, @@ -1263,7 +1267,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1272,7 +1276,7 @@ "txt_hash": "11e1262fd8f5df1b140f5e8813883b71447503781399427b35adbbecd00b4d63" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1281,7 +1285,7 @@ "txt_hash": "179b07b4fc7402066a884edea494b28e324315108a5e0820184031f2e2062ad5" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1870,7 +1874,9 @@ "direct_transitive_cves": null, "indirect_transitive_cves": null, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } } ] diff --git a/tests/data/cc/analysis/transitive_vulnerability_dataset.json b/tests/data/cc/analysis/transitive_vulnerability_dataset.json index 586ac5a68..7ed779108 100644 --- a/tests/data/cc/analysis/transitive_vulnerability_dataset.json +++ b/tests/data/cc/analysis/transitive_vulnerability_dataset.json @@ -34,18 +34,10 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0874b_pdf.pdf", "cert_link": null, "manufacturer_web": "https://www.ibm.com", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [ - { - "_type": "sec_certs.sample.protection_profile.ProtectionProfile", - "pp_name": "Operating System Protection Profile, Version 2.0", - "pp_eal": "EAL4+", - "pp_link": "https://www.commoncriteriaportal.org/files/ppfiles/pp0067b_pdf.pdf", - "pp_ids": [ - "OSPP_V2.0" - ] - } + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/pp0067b_pdf.pdf" ] }, "maintenance_updates": { @@ -55,7 +47,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -64,7 +56,7 @@ "txt_hash": "9d360141a98e764b15855f519b456c4e4639f993c4f8b5ab67e9c8ae7fbfc9e4" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -73,7 +65,7 @@ "txt_hash": "66271d8bf0b581a2f189301438f2aee13ff3da0bb0bb180bcf518261eb695496" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1336,7 +1328,9 @@ ] }, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } }, { @@ -1360,7 +1354,7 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0875b_pdf.pdf", "cert_link": null, "manufacturer_web": "https://www.ibm.com", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [] }, @@ -1371,7 +1365,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1380,7 +1374,7 @@ "txt_hash": "dd120ba7667c2385839c96ee70c56f2a4d464fc95e3ea2818d31b3347d06fd4f" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -1389,7 +1383,7 @@ "txt_hash": "f7f7b8f31dddde3f0756cde8843061f01b606bdf266eca71dbcc56b3672d1db5" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -2287,7 +2281,9 @@ ] }, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } }, { @@ -2311,18 +2307,10 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/0948b_pdf.pdf", "cert_link": null, "manufacturer_web": "https://www.ibm.com", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [ - { - "_type": "sec_certs.sample.protection_profile.ProtectionProfile", - "pp_name": "Operating System Protection Profile, Version 2.0", - "pp_eal": "EAL4+", - "pp_link": "https://www.commoncriteriaportal.org/files/ppfiles/pp0067b_pdf.pdf", - "pp_ids": [ - "OSPP_V2.0" - ] - } + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/pp0067b_pdf.pdf" ] }, "maintenance_updates": { @@ -2332,7 +2320,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -2341,7 +2329,7 @@ "txt_hash": "0a7c65e3d11f082c8f75aba7de0079c0b1aa5e67bb28d4635cbcaa4cd200d1c2" }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -2350,7 +2338,7 @@ "txt_hash": "90b8e48add278faea4668eccba591d3992bf782669cca1b0a63bf6f21b514cd9" }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -3640,7 +3628,9 @@ "direct_transitive_cves": null, "indirect_transitive_cves": null, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } } ] diff --git a/tests/data/cc/analysis/vulnerable_dataset.json b/tests/data/cc/analysis/vulnerable_dataset.json index 01d720c0d..776db230b 100644 --- a/tests/data/cc/analysis/vulnerable_dataset.json +++ b/tests/data/cc/analysis/vulnerable_dataset.json @@ -26,7 +26,7 @@ "_type": "Set", "elements": [ "ALC_FLR.1", - "EAL3+" + "EAL1" ] }, "not_valid_before": "2014-12-05", @@ -35,12 +35,17 @@ "st_link": "http://www.commoncriteriaportal.org/files/epfiles/0683b_pdf.pdf", "cert_link": null, "manufacturer_web": "http://www.ibm.com", - "protection_profiles": [], + "protection_profile_links": { + "_type": "Set", + "elements": [ + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/HBYS_PP_07_09_2016_Updated.pdf" + ] + }, "maintenance_updates": [], "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": true, "convert_garbage": false, "convert_ok": true, @@ -49,7 +54,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": true, "convert_garbage": false, "convert_ok": true, @@ -58,7 +63,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -94,7 +99,9 @@ "cert_lab": null, "cert_id": null, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } }, { @@ -108,8 +115,7 @@ "security_level": { "_type": "Set", "elements": [ - "ALC_FLR.1", - "EAL3+" + "ALC_FLR.1" ] }, "not_valid_before": "2010-12-05", @@ -118,12 +124,18 @@ "st_link": "", "cert_link": null, "manufacturer_web": "http://www.ibm.com", - "protection_profiles": [], + "protection_profile_links": { + "_type": "Set", + "elements": [ + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/KECS-PP-0822-2017 Korean National PP for Single Sign On V1.0(eng).pdf", + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/pp0062b_pdf.pdf" + ] + }, "maintenance_updates": [], "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": true, "convert_garbage": false, "convert_ok": true, @@ -132,7 +144,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": true, "convert_garbage": false, "convert_ok": true, @@ -141,7 +153,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -177,7 +189,9 @@ "cert_lab": null, "cert_id": null, "next_certificates": null, - "prev_certificates": null + "prev_certificates": null, + "protection_profiles": null, + "eal": null } } ] diff --git a/tests/data/cc/certificate/fictional_cert.json b/tests/data/cc/certificate/fictional_cert.json index 8239c908f..327aaf46e 100644 --- a/tests/data/cc/certificate/fictional_cert.json +++ b/tests/data/cc/certificate/fictional_cert.json @@ -15,18 +15,12 @@ "not_valid_before": "1900-01-02", "not_valid_after": "1900-01-03", "manufacturer_web": "https://path.to/manufacturer/web", - "protection_profiles": { - "_type": "Set", - "elements": [ - { - "_type": "sec_certs.sample.protection_profile.ProtectionProfile", - "pp_name": "sample_pp", - "pp_eal": null, - "pp_link": "https://sample.pp", - "pp_ids": null - } - ] - }, + "protection_profile_links": { + "_type": "Set", + "elements": [ + "https://sample.pp" + ] + }, "maintenance_updates": { "_type": "Set", "elements": [ @@ -42,7 +36,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -51,7 +45,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -60,7 +54,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -112,7 +106,9 @@ "indirectly_referenced_by": null, "indirectly_referencing": null }, - "scheme_data": null + "scheme_data": null, + "protection_profiles": null, + "eal": null }, "report_link": "https://path.to/report/link", "st_link": "https://path.to/st/link", diff --git a/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json b/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json index 0c8b13067..d8de0f3ae 100644 --- a/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json +++ b/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json @@ -23,7 +23,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": true, "convert_garbage": false, "convert_ok": false, @@ -32,7 +32,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": true, "convert_garbage": false, "convert_ok": false, @@ -41,7 +41,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -93,7 +93,9 @@ "indirect_transitive_cves": null, "scheme_data": null, "prev_certificates": null, - "next_certificates": null + "next_certificates": null, + "protection_profiles": null, + "eal": null }, "related_cert_digest": "8f08cacb49a742fb", "maintenance_date": "2019-08-26" diff --git a/tests/data/cc/dataset/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json index 3395d3e85..d593f3829 100644 --- a/tests/data/cc/dataset/toy_dataset.json +++ b/tests/data/cc/dataset/toy_dataset.json @@ -35,7 +35,7 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/ST%20-%20NetIQ%20Identity%20Manager%204.7.pdf", "cert_link": "https://www.commoncriteriaportal.org/files/epfiles/Certifikat%20CCRA%20-%20NetIQ%20Identity%20Manager%204.7_signed.pdf", "manufacturer_web": "https://www.netiq.com/", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [] }, @@ -46,7 +46,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -55,7 +55,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -64,7 +64,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -116,7 +116,9 @@ "indirectly_referenced_by": null, "indirectly_referencing": null }, - "scheme_data": null + "scheme_data": null, + "protection_profiles": null, + "eal": null } }, { @@ -137,16 +139,10 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/Magic_SSO_V4.0-ST-v1.4_EN.pdf", "cert_link": null, "manufacturer_web": "https://www.dreamsecurity.com/", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [ - { - "_type": "sec_certs.sample.protection_profile.ProtectionProfile", - "pp_name": "Korean National Protection Profile for Single Sign On V1.0", - "pp_eal": "EAL1+", - "pp_link": "https://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - "pp_ids": null - } + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf" ] }, "maintenance_updates": { @@ -156,7 +152,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -165,7 +161,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -174,7 +170,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -226,7 +222,9 @@ "indirectly_referenced_by": null, "indirectly_referencing": null }, - "scheme_data": null + "scheme_data": null, + "protection_profiles": null, + "eal": null } }, { @@ -247,16 +245,10 @@ "st_link": "https://www.commoncriteriaportal.org/files/epfiles/383-4-450%20ST%20v1.3A.pdf", "cert_link": "https://www.commoncriteriaportal.org/files/epfiles/383-4-450%20CT%20v1.0a.pdf", "manufacturer_web": "https://www.fortinet.com/", - "protection_profiles": { + "protection_profile_links": { "_type": "Set", "elements": [ - { - "_type": "sec_certs.sample.protection_profile.ProtectionProfile", - "pp_name": "collaborative Protection Profile for Stateful Traffic Filter Firewalls v2.0 + Errata 20180314", - "pp_eal": null, - "pp_link": "https://www.commoncriteriaportal.org/files/ppfiles/CPP_FW_V2.0E.pdf", - "pp_ids": null - } + "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/CPP_FW_V2.0E.pdf" ] }, "maintenance_updates": { @@ -274,7 +266,7 @@ "state": { "_type": "sec_certs.sample.cc.CCCertificate.InternalState", "report": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -283,7 +275,7 @@ "txt_hash": null }, "st": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -292,7 +284,7 @@ "txt_hash": null }, "cert": { - "_type": "sec_certs.sample.cc.CCCertificate.DocumentState", + "_type": "sec_certs.sample.document_state.DocumentState", "download_ok": false, "convert_garbage": false, "convert_ok": false, @@ -344,7 +336,9 @@ "indirectly_referenced_by": null, "indirectly_referencing": null }, - "scheme_data": null + "scheme_data": null, + "protection_profiles": null, + "eal": null } } ] diff --git a/tests/data/protection_profiles/__init__.py b/tests/data/protection_profiles/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/data/protection_profiles/pp.json b/tests/data/protection_profiles/pp.json new file mode 100644 index 000000000..7b96cf8c0 --- /dev/null +++ b/tests/data/protection_profiles/pp.json @@ -0,0 +1,191 @@ +{ + "_type": "sec_certs.dataset.protection_profile.ProtectionProfileDataset", + "state": { + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", + "meta_sources_parsed": true, + "artifacts_downloaded": false, + "pdfs_converted": false, + "auxiliary_datasets_processed": false, + "certs_analyzed": false + }, + "timestamp": "2025-01-25 17:39:26.873380", + "sha256_digest": "not implemented", + "name": "ProtectionProfileDataset dataset", + "description": "25/01/2025 17:39:26", + "n_certs": 3, + "certs": [ + { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile", + "dgst": "c8b175590bb7fdfb", + "web_data": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.WebData", + "category": "Access Control Devices and Systems", + "status": "active", + "is_collaborative": false, + "name": "Korean National Protection Profile for Single Sign On V1.0", + "version": "V1.0", + "security_level": { + "_type": "Set", + "elements": [ + "ATE_FUN.1", + "EAL1+" + ] + }, + "not_valid_before": "2017-08-18", + "not_valid_after": null, + "report_link": "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/KECS-CR-17-58 Korean National PP for Single Sign On V1.0(eng).pdf", + "pp_link": "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/KECS-PP-0822-2017 Korean National PP for Single Sign On V1.0(eng).pdf", + "scheme": "KR", + "maintenances": [] + }, + "pdf_data": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.PdfData", + "report_metadata": null, + "pp_metadata": null, + "report_keywords": null, + "pp_keywords": null, + "report_filename": null, + "pp_filename": null + }, + "heuristics": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.Heuristics" + }, + "state": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.InternalState", + "pp": { + "_type": "sec_certs.sample.document_state.DocumentState", + "download_ok": false, + "convert_garbage": false, + "convert_ok": false, + "extract_ok": false, + "pdf_hash": null, + "txt_hash": null + }, + "report": { + "_type": "sec_certs.sample.document_state.DocumentState", + "download_ok": false, + "convert_garbage": false, + "convert_ok": false, + "extract_ok": false, + "pdf_hash": null, + "txt_hash": null + } + } + }, + { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile", + "dgst": "e315e3e834a61448", + "web_data": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.WebData", + "category": "Other Devices and Systems", + "status": "active", + "is_collaborative": false, + "name": "Protection Profile for Security Module of General-Purpose Health Informatics Software", + "version": "1.0", + "security_level": { + "_type": "Set", + "elements": [ + "EAL2" + ] + }, + "not_valid_before": "2016-09-20", + "not_valid_after": null, + "report_link": "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/HBYS_PP_CR.pdf", + "pp_link": "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/HBYS_PP_07_09_2016_Updated.pdf", + "scheme": "TR", + "maintenances": [] + }, + "pdf_data": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.PdfData", + "report_metadata": null, + "pp_metadata": null, + "report_keywords": null, + "pp_keywords": null, + "report_filename": null, + "pp_filename": null + }, + "heuristics": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.Heuristics" + }, + "state": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.InternalState", + "pp": { + "_type": "sec_certs.sample.document_state.DocumentState", + "download_ok": false, + "convert_garbage": false, + "convert_ok": false, + "extract_ok": false, + "pdf_hash": null, + "txt_hash": null + }, + "report": { + "_type": "sec_certs.sample.document_state.DocumentState", + "download_ok": false, + "convert_garbage": false, + "convert_ok": false, + "extract_ok": false, + "pdf_hash": null, + "txt_hash": null + } + } + }, + { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile", + "dgst": "b02ed76d2545326a", + "web_data": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.WebData", + "category": "Biometric Systems and Devices", + "status": "active", + "is_collaborative": false, + "name": "Fingerprint Spoof Detection Protection Profile based on Organisational Security Policies (FSDPP_OSP), Version 1.7", + "version": "1.7", + "security_level": { + "_type": "Set", + "elements": [ + "ALC_FLR.1", + "EAL2+" + ] + }, + "not_valid_before": "2010-02-25", + "not_valid_after": null, + "report_link": "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/pp0062a_pdf.pdf", + "pp_link": "https://www.commoncriteriaportal.org/nfs/ccpfiles/files/ppfiles/pp0062b_pdf.pdf", + "scheme": "DE", + "maintenances": [] + }, + "pdf_data": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.PdfData", + "report_metadata": null, + "pp_metadata": null, + "report_keywords": null, + "pp_keywords": null, + "report_filename": null, + "pp_filename": null + }, + "heuristics": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.Heuristics" + }, + "state": { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile.InternalState", + "pp": { + "_type": "sec_certs.sample.document_state.DocumentState", + "download_ok": false, + "convert_garbage": false, + "convert_ok": false, + "extract_ok": false, + "pdf_hash": null, + "txt_hash": null + }, + "report": { + "_type": "sec_certs.sample.document_state.DocumentState", + "download_ok": false, + "convert_garbage": false, + "convert_ok": false, + "extract_ok": false, + "pdf_hash": null, + "txt_hash": null + } + } + } + ] +} diff --git a/tests/data/protection_profiles/pp_active.html b/tests/data/protection_profiles/pp_active.html new file mode 100644 index 000000000..cc3708d29 --- /dev/null +++ b/tests/data/protection_profiles/pp_active.html @@ -0,0 +1,965 @@ + + + + + + + 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Protection ProfileVersionAssurance LevelIssuedSchemeCertified
 + +Access Control Devices and Systems – 7 Protection Profiles + +
+ +Korean National Protection Profile for Single Sign On V1.0 + +V1.0 +EAL1+ +
ATE_FUN.1 +		2017-08-18KR – KR
KR		 +Certification Report +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Protection ProfileVersionAssurance LevelIssuedSchemeCertified
 + +Biometric Systems and Devices – 6 Protection Profiles + +
+ +Fingerprint Spoof Detection Protection Profile based on Organisational Security Policies (FSDPP_OSP), Version 1.7 + +1.7 +EAL2+ +
ALC_FLR.1 +		2010-02-25DE – DE
DE		 +Certification Report +
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Protection ProfileVersionAssurance LevelIssuedSchemeCertified
 + +Other Devices and Systems – 81 Protection Profiles + +
+ +Protection Profile for Security Module of General-Purpose Health Informatics Software + +1.0 +EAL2 +2016-09-20TR – TR
TR		 +Certification Report +
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+
+ + diff --git a/tests/data/protection_profiles/pps/pdf/b02ed76d2545326a.pdf b/tests/data/protection_profiles/pps/pdf/b02ed76d2545326a.pdf new file mode 100644 index 000000000..9d986ad1d Binary files /dev/null and b/tests/data/protection_profiles/pps/pdf/b02ed76d2545326a.pdf differ diff --git a/tests/data/protection_profiles/pps/txt/b02ed76d2545326a.txt b/tests/data/protection_profiles/pps/txt/b02ed76d2545326a.txt new file mode 100644 index 000000000..4b575e23a --- /dev/null +++ b/tests/data/protection_profiles/pps/txt/b02ed76d2545326a.txt @@ -0,0 +1,944 @@ +Fingerprint Spoof Detection Protection Profile +based on Organisational Security Policies +FSDPP_OSP +v1.7 +Bundesamt für Sicherheit in der Informationstechnik +Postfach 20 03 63 +53133 Bonn +Tel.: +49 228 99 9582-0 +E-Mail: bsi@bsi.bund.de +Internet: https://www.bsi.bund.de +© Bundesamt für Sicherheit in der Informationstechnik 2009 + FSDPP_OSP +Table of content +1. PP introduction..................................................................................................................................4 +1.1 PP Reference.................................................................................................................................4 +1.2 PP Overview..................................................................................................................................4 +2. TOE Description................................................................................................................................5 +2.1 Protection of biometric systems.....................................................................................................5 +2.2 TOE configuration and TOE environment.....................................................................................6 +2.3 TOE boundary...............................................................................................................................6 +2.3.1 Physical boundary.....................................................................................................................7 +2.3.2 Logical boundary......................................................................................................................7 +3. Conformance Claims.........................................................................................................................9 +3.1 Conformance statement.................................................................................................................9 +3.2 CC Conformance Claims...............................................................................................................9 +3.3 PP Claim........................................................................................................................................9 +3.4 Package Claim...............................................................................................................................9 +4. Security Problem Definition ...........................................................................................................10 +4.1 External entities...........................................................................................................................10 +4.2 Assets..........................................................................................................................................10 +4.3 Assumptions................................................................................................................................11 +4.4 Threats.........................................................................................................................................11 +4.5 Organizational Security Policies..................................................................................................11 +5. Security Objectives..........................................................................................................................12 +5.1 Security Objectives for the TOE..................................................................................................12 +5.2 Security objectives for the operational environment....................................................................12 +5.3 Security Objectives rationale.......................................................................................................13 +5.3.1 Overview................................................................................................................................13 +5.3.2 Justification for coverage of assumptions...............................................................................14 +5.3.3 Justification for the coverage of organizational security policies............................................14 +6. Extended Component definition......................................................................................................16 +6.1 FPT_SPOD Biometric Spoof Detection......................................................................................16 +6.1.1 Biometric Spoof Detection (FPT_SPOD.1)............................................................................17 +6.1.2 Justification for the definition of functional family FPT_SPOD.............................................17 +7. Security Requirements.....................................................................................................................18 +7.1 Security Functional Requirements for the TOE...........................................................................18 +7.1.1 Security audit (FAU)..............................................................................................................19 +2 Bundesamt für Sicherheit in der Informationstechnik + FPSDPP_OSP +7.1.2 User data protection (FDP).....................................................................................................19 +7.1.3 Security management (FMT)..................................................................................................20 +7.1.4 Protection of the TSF (FPT)...................................................................................................21 +7.2 Security Assurance Requirements for the TOE...........................................................................22 +7.3 Security Requirements rationale..................................................................................................23 +7.3.1 Security Functional Requirements rationale...........................................................................23 +7.3.2 Security Assurance Requirements rationale............................................................................24 +8. Appendix.........................................................................................................................................26 +8.1 Glossary.......................................................................................................................................26 +8.2 References...................................................................................................................................27 +Bundesamt für Sicherheit in der Informationstechnik 3 + FSDPP_OSP +1. PP introduction +1.1 PP Reference +Title: Fingerprint Spoof Detection Protection Profile based on OSP (FSDPP_OSP) +Version 1.7 +Date November, 27th +2009 +Author Boris Leidner, Nils Tekampe, TÜV Informationstechnik GmbH +Registration Bundesamt für Sicherheit in der Informationstechnik (BSI) +Federal Office for Information Security Germany +Certification-ID BSI-CC-PP-0062 +CC-Version 3.1 Revision 3 +Keywords biometric; fingerprint-recognition; Protection Profile; spoof detection +1.2 PP Overview +Biometric systems that work based on fingerprints are often subject to a well known and easy kind of +attack: Attackers can use faked fingerprints (e.g. built out of gummy or silicone) that carry the +characteristics of a known user in order to get recognized by a biometric system. As an alternative a +user of a biometric system may use a faked finger in order to disguise their identity. Countermeasures +against those attacks may be implemented by a set of dedicated hardware and software, the so called +biometric spoof detection system. +In order to facilitate new mechanisms for spoof detection in fingerprint recognition systems and +thereby advancing innovative technologies in the area of security the project “LifeFinger I” has been +initiated by the Federal Office for Information Security. This Protection Profile forms part of this +project that has been conducted by the Bundesdruckerei GmbH. +The scope of this Protection Profile is to describe the functionality of a biometric spoof detection +system in terms of [CC] and to define functional and assurance requirements for the evaluation of such +systems. Chapter 2 gives a more detailed overview about the design of the TOE and its boundaries. +This Protection Profile thereby focuses on application cases for which it is sufficient to determine +whether the security functionality claimed by a TOE is working correctly without performing a +dedicated vulnerability assessment. Therefore, this PP is solely based on organizational security +policies and threats are completely omitted. The explicit assurance package for an evaluation without a +vulnerability assessment is defined in chapter 3.4. +When planning an evaluation according to this PP the ST author should also consider the Fingerprint +Spoof Detection Protection Profile [FSDPP] which is based on threats and not organizational security +policies only. In general, the use of the [FSDPP] should be the preferred option. +4 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +2. TOE Description +The Target of Evaluation (TOE) described in this PP is a system that provides fingerprint spoof +detection either as part of, or in front of a biometric system for fingerprint recognition. +The TOE determines whether a fingerprint presented to the biometric system is genuine or spoofed. +The term spoofed biometric characteristics hereby refers to artificially created fake fingers which are +currently known to circumvent fingerprint recognition systems. +For this purpose the spoof detection system acquires spoofing evidences for a presented fingerprint +using a sensor device. This sensor can either be part of the capture device that is used to capture the +biometric sample of the fingerprint (or even be identical to it) or be a separate sensor device (or more +than one) that is completely dedicated to spoof detection. +Beside the fingerprint spoof detection functionality every TOE that claims conformance to this PP +shall implement: +• Management functionality to modify security relevant parameters +• Quality control for management parameters +• Audit functionality for security relevant events +• Protection of residual and security relevant data. +2.1 Protection of biometric systems +Systems claiming compliance to this Protection Profile are developed to protect biometric systems for +fingerprint recognition against one specific kind of attacks: The use of well known faked +finger(prints). The following paragraphs introduce the core biometric processes of a biometric system +in order to improve the understanding of the direct environment of the TOE and to explain the +motivation of an attacker. +● Enrollment: +Often, the enrollment process is the first contact of a user with a biometric system. This +process is necessary because a biometric system has to be trained in order to verify the identity +of each user based on their fingerprint. +During the enrollment process the system captures the fingerprint image of a user and extracts +the features it is working with. These features are then combined with the identity of the user +to a biometric reference and stored as template in a database. +During enrollment an attacker could try to present faked finger(prints) to the capture device in +order to get enrolled with another biometric characteristic. When having success the attackers +identity would be associated with the fake fingerprint. The important thing to notice in this +context is that an attacker must not necessarily have to have any knowledge about the +biometric characteristic of another user to perform this attack. +● Biometric verification: +The objective of a verification process is to verify or refuse the claimed identity of a user +based on their fingerprint. Therefore the user has to claim an identity to the system. The +system retrieves the fingerprint reference record associated with this identity from the +database and captures the live fingerprint. If the fingerprint features that are extracted from the +live fingerprint image and the fingerprint reference from the database are similar enough, the +claimed identity of the user is considered to be verified. +During biometric verification an attacker could try to use a faked finger to get recognized by +the system as another user (this kind of attack is often referred to as impersonation). For such +an attack however, the attacker will have to know about the biometric characteristic of the +attacked user. +Bundesamt für Sicherheit in der Informationstechnik 5 + FSDPP_OSP +Another specific aspect for a spoof detection system that is used to protect a biometric +verification process is that a claimed identity is available. +● Biometric identification: +The objective of a biometric identification process is similar to a verification process. +However, in contrast to a verification process there is no claimed identity for the user. The +system directly captures the fingerprint of a user and compares it to all fingerprint references +in the database. If at least one reference is found to be similar enough according to the relevant +threshold settings, the system returns this as the found identity of the user. +In the identification scenario an attacker can have multiple aims: +○ An attacker could try to get identified as a specific enrolled user (i.e. using a fake finger of +that specific user). The reason for doing so may be that this attacked user has a specific +credential that the attacker is after. +○ An attacker could try to get identified as any enrolled user (i.e. using a faked fingerprint of +any enrolled user). This can be relevant for cases where all enrolled users for a system +have similar permissions. +○ An attacker who is enrolled in the system could try avoid identification (i.e. disguise their +identity) For such an attack the attacker may not need any knowledge about the biometric +characteristic of another user. +More information on how the environment contributes to the security problem addressed by the TOE +can be found in the Fingerprint Spoof Detection Evaluation Guidance [FSDEG]. +2.2 TOE configuration and TOE environment +A biometric spoof detection system in general could be realized in two major configurations: +● An integrated solution: All relevant parts of the TOE are integrated into one physical unit. +● A distributed solution: Relevant parts of the TOE are implemented in physically separated +parts. +This PP describes a biometric spoof detection system for fingerprints as an integrated solution but +should be applicable to distributed solutions as well. However, if applied to a distributed TOE +additional aspects of security shall be considered by the author of the Security Target in form of: +1. Assumptions for the TOE environment +2. Requirements for additional functionality: e. g. encrypted transmission +It is known that environmental factors may influence the performance and therewith the protection +provided by a spoof detection system. Therefore the author of a Security Target claiming compliance +to this PP shall clearly identify the relevant environmental factors and their acceptable range for the +operation of the TOE. More information about influencing factors can be found in [FSDEG]. +In general it should be noted that the TOE should not impact the functionality of the protected +biometric system (e.g. by a deterioration of image quality) beyond what is necessary for the desired +application. If a negative impact cannot be completely avoided this shall be clearly pointed out by the +ST author. +2.3 TOE boundary +A simplified model of a biometric spoof detection system and its boundaries is shown in Figure 1.The +following chapters provide more details about the physical and logical boundaries of the TOE. +6 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +2.3.1 Physical boundary +Figure 1: TOE demarcation +Spoof +Detection +Biometric +System +Biometric +Sample +(fingerprint +image) +TOE +Spoof Detection +parameters +Audit log +Audit data +Biometric sample +(fingerprint image), +Spoofing evidence +Add. sensors +Administrator +User +Capture +Device +Finger +Fingerprint +Other attributes +The TOE defined in this PP is limited to the biometric spoof detection system. This system shall +decide whether a provided fingerprint is spoofed or genuine. The TOE shall comprise all parts of a +product (hardware and software) that contribute to this functionality or any of the additional +functionality outlined in chapter 2.3.2. In particular these are: +• the capture device for capturing of fingerprint images +• additional sensor devices for acquisition of spoofing evidences (if applicable) +• necessary software (if applicable) +The spoofing evidences for a fingerprint can either be captured by the same sensor device being also +used for the biometric system (capture process) or using separate sensor devices. If separate sensor +devices are used, it has to be ensured that the same fingerprint is used for both processes. +The biometric system that is protected by the TOE resides in the environment. It can be, e. g., a +biometric identification system, a biometric verification system, or an enrollment system as described +in chapter 2.1. This means that all aspects about the security of the biometric systems (e.g. questions +about the error rates of this system) are out of scope for the evaluation of the TOE. +The TOE shall be able to generate audit data. This audit data can be used for quality assurance or +statistics. However, functionality for storage, protection and review of audit records is assumed to be +provided by the environment of the TOE. +Further the TOE may rely on access control mechanisms of the environment for its own protection and +the restriction of access to management functions offered by the TOE (e.g. for adjustment of important +parameters). Also for the implementation of management functions the TOE may partly rely on +functions of the environment (i.e. in form of a file import that involves the Operating System). +2.3.2 Logical boundary +The logical boundaries of the TOE can be defined by the functionality that it provides: +● Spoof detection: the TOE detects whether a presented fingerprint is spoofed or genuine. It +shall perform appropriate actions in case of a spoofed and in case of a genuine biometric +Bundesamt für Sicherheit in der Informationstechnik 7 + FSDPP_OSP +characteristic. It should be clearly mentioned that in the context of this PP a TOE is always +required to decide about the presented fingerprint in form of a yes/no decision. It is not +considered to be sufficient if a TOE would return a confidence value that would need further +interpretation by the environment. +● Management: the TOE provides functionality to manage its relevant parameters. This +specifically (but not only) refers to the parameters that are involved in the spoof detection +process (e.g. a threshold). The TOE ensures that only secure values for spoof detection +parameters are accepted to ensure the constant operation of the primary functionality. +● Residual Information Protection: in order to prevent the leakage of information the TOE +deletes relevant information if not longer in use. +● Audit: the TOE produces audit events for security relevant events. +The following functionality on the other hand may be provided by the environment to support the +operation of the TOE: +● Access control: the environment provides access control for the spoof detection parameters, +the life record, audit data and any software parts of the TOE. To perform access control, the +environment maintains roles for users and ensures their identification and authentication. +● Transmission / Storage: the environment provides a secure communication and storage for +data where security relevant data is transferred to or from the TOE. +● Auditing: the environment may provide additional audit functionality. In any case it will +provide reliable time stamps for auditing, storage for the audit records that are produced by the +TOE and mechanisms for review of audit logs. The developer will probably have to consider +privacy concerns (in case that personal information is part of the audit logs). Applicable data +protection laws and protection mechanisms might have to be considered. +● +Application Note: +To allow the application of this PP to a wide range of systems, several +functions are stated to be implemented in the environment. However, if a TOE +is able to provide those functions on its own the ST author should consider to +define those functions as part of the TOE. +8 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +3. Conformance Claims +3.1 Conformance statement +The PP requires strict conformance of any PPs/STs to this PP. A demonstrable conformance is not +allowed. +3.2 CC Conformance Claims +• This PP has been developed using Version 3.1 R3 of Common Criteria [CC]. +• The conformance of this Protection Profile is Common Criteria [CC] Part II extended (due +to the use of FPT_SPOD.1) +• The conformance of this Protection Profile is Common Criteria [CC] Part III conformant. +3.3 PP Claim +• This PP does not claim conformance to any other Protection Profile. +3.4 Package Claim +This PP does not claim conformance to any assurance package (i.e. EAL) as defined in Common +Criteria Part III. Instead, this PP defines an explicit assurance package that bases on EAL 2. However, +in contrast to EAL 2 as defined in part III of [CC], the assurance package in this PP does not contain +any AVA_VAN component. It further includes the assurance component ALC_FLR.1. +The reason for this explicit assurance level is to allow a purely functional evaluation of the +performance of a system for spoof detection. Such an evaluation will allow to determine whether the +functionality of a system for spoof detection is sufficient to recognize spoofed biometric +characteristics that are know for a certain biometric modality. +An evaluation using this explicit assurance level is deliberately ignoring the fact that an attacker could +try to circumvent the functionality of the TOE (e.g. by using different/innovative spoofed +characteristics) and focuses on the basic functionality of the TOE. A system claiming compliance to +this Protection Profile is therefore suitable for the use in application cases in which an assurance about +the basic functionality of a system is sufficient. To emphasize that this PP only deals with the pure +functionality of spoof detection, the definition of threats has been omitted and the PP is completely +based on organizational security policies. +The complete list of the assurance components of the explicit assurance package can be found in +chapter 7.2. +Bundesamt für Sicherheit in der Informationstechnik 9 + FSDPP_OSP +4. Security Problem Definition +4.1 External entities +The following external entities interact with the TOE: +TOE administrator: The TOE administrator is authorized to perform administrative TOE +operations and able to use the administrative functions of the TOE. +The administrator is also responsible for the installation and maintenance of +the TOE. +Depending on the concrete implementation of a TOE there may be more than +one administrator and consequently also more than one administrative role. +User: A person who uses a biometric system that is protected by the TOE to get +enrolled, identified or verified and is therefore checked by the biometric spoof +detection system. +4.2 Assets +The following assets are defined in the context of this Protection Profile. +Primary assets: The primary assets do not belong to the TOE itself. The primary scope of the +biometric spoof detection system is the protection of the biometric system +behind it. As such any asset that is protected by the biometric system can be +considered being a primary asset for the TOE. +Formally, the decision that is taken by the TOE (fake/no fake) can be +considered being the primary asset. +Secondary assets: Secondary assets (i.e. TSF data) are information which are used by the TOE to +provide its core services and which consequently will need to be protected. The +following assets should be explicitly mentioned for the TOE: +● Spoof detection parameters (SDP): These (configuration) data +include the settings necessary to detect a spoofed biometric +characteristic, e. g., temperature limits, general threshold settings, +typical movement patterns. These parameters may be specific for a +claimed identity. The parameters are partly produced during +development of the TOE but may be adjusted during installation, +maintenance and enrollment. The integrity and confidentiality of these +parameters will have to be protected. +● Spoofing evidence (SE): This data is acquired by the capture device +and/or separated dedicated sensor devices for the purpose of spoof +detection. The TOE decides about a finger being a fake or not based on +this data. The integrity and confidentiality of this data have to be +protected. +● Audit data (AD): This data comprises the audit information that is +generated by the TOE. The integrity, confidentiality and authenticity of +the information has to be protected. +10 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +4.3 Assumptions +A.BIO The spoof detection system addressed in this Protection Profile is a protection +mechanism against spoofing attacks. +The biometric system that is protected by the TOE therefore ensures that all +threats that are not related to spoof detection are appropriately handled. +Further, the biometric system ensures that the functionality of the TOE is +invoked/used in order to protected the biometric system against spoof attacks. +It is also assumed that the fingerprint sample that is acquired by the capture +devices belongs to the fingerprint that is used for spoof detection. +4.4 Threats +No threats have been defined in the Security Problem Definition of this PP as it is solely based on +organizational security policies. +4.5 Organizational Security Policies +OSP.SPOOF_DETECTION The TOE shall be able to detect whether a presented fingerprint is +spoofed or genuine. The spoof detection shall be adequate to detect +all artificial biometric characteristics listed and described in +[Toolbox]. +OSP.RESIDUAL The TOE shall ensure that no residual or unprotected security +relevant data remain in memory after operations are completed. +OSP.MANAGEMENT The TOE shall provide the necessary management functionality +for the modification of security relevant parameters for TOE +administrators. Only secure values shall be used for such +parameters. +OSP.AUDIT In order to +● generate statistics that can be used to adjust the parameters +for better quality (maintenance), +● trace modification, and +● trace possible attacks, +the TOE shall record security-relevant events. +Bundesamt für Sicherheit in der Informationstechnik 11 + FSDPP_OSP +5. Security Objectives +5.1 Security Objectives for the TOE +O.SPOOF_DETECTION The TOE shall be able to detect whether a presented fingerprint is +spoofed or genuine. +The spoofing evidence may be extracted from the data provided by the +same sensor that is used to acquire the biometric characteristic for +recognition (by the biometric system in the environment), or it may be +retrieved using sensors which are solely dedicated to spoof detection. +O.AUDIT The TOE shall produce audit records at least for the following security +relevant events: +● A use of the TOE where a faked fingerprint has been detected +● A use of the TOE where a genuine fingerprint has been +detected +● Every use of a management function +● All parameters modified by the management functions +O.RESIDUAL The TOE shall ensure that no residual or unprotected security relevant +data remain in memory after operations are completed. +O.MANAGEMENT The TOE shall provide the necessary management functionality for the +modification of security relevant parameters to TOE administrators +only. +As part of this management functionality the TOE shall only accept +secure values for security relevant parameters to ensure the correct +operation of the TOE. +5.2 Security objectives for the operational environment +OE.ADMINISTRATION The TOE administrator is well trained and non hostile. They read the +guidance documentation carefully, completely understands and +applies it. +The TOE administrator is responsible for the secure installation and +maintenance of the TOE and its platform and oversees the biometric +spoof detection system requirements. In particular, the administrator +shall ensure that all environmental factors (e. g., lighting, +electromagnetic fields) are within an acceptable range with respect to +the used capture and sensor devices. +The administrator assures that audit records of the TOE are regularly +reviewed in order to detect and prevent attacks being performed +against the TOE. +OE.PHYSICAL It shall be ensured that the TOE and its components are physically +protected against unauthorized access or modification. Physical +access to the hardware that is used by the TOE is only allowed for +authorized administrators. +This does not have to cover the capture device that has to be +accessible for every user. +12 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +OE.PLATFORM The platform the TOE runs on shall provide the TOE with services +necessary for its correct operation. Specifically the platform shall +• identify and authenticate TOE administrators, +• restrict to use the management functions of the TOE in order +to query, modify, delete, and clear security parameters which +are important for the operation of the TOE to TOE +administrators, +• provide access control for all secondary assets (spoof +detection parameters, spoofing evidence, and audit data) and +the software parts of the TOE, +• provide a secure communication and storage of information +where security relevant data is transferred to or from the +TOE, +• provide functionality for storage and review of audit +information and ensure that only authorized administrators +have access to the audit logs, +• provide reliable time stamps that can be used by the TOE, +and +• be free of malware like viruses, trojan horses, and other +malicious software. +OE.BIO The spoof detection system described in this Protection Profile is a +protection mechanism which ensures that spoofed fingerprints are +rejected by the TOE. The TOE only addresses the detection of spoof +attacks. +The biometric system that is protected by the TOE shall therefore +ensure that all threats that are not related to spoof detection are +appropriately handled. +Further, the biometric system shall ensure that the functionality of the +TOE is invoked/used in order to protected the biometric system +against spoof attacks. +5.3 Security Objectives rationale +5.3.1 Overview +The following table gives an overview of how the assumptions, threats, and organizational security +policies are addressed by the security objectives of the TOE. The text of the following sections +justifies this in more detail. Aspects of the TOE operational environment are marked grey. +Bundesamt für Sicherheit in der Informationstechnik 13 + FSDPP_OSP +O.SPOOF_DETECTION +O.AUDIT +O.RESIDUAL +O.MANAGEMENT +OE.ADMINISTRATION +OE.PHYSICAL +OE.PLATFORM +OE.BIO +OSP.SPOOF_DETECTION X X X X X +OSP.MANAGEMENT X X X X +OSP.RESIDUAL X X X X +OSP.AUDIT X X +A.BIO X +Table 1: Security Objectives Rationale +5.3.2 Justification for coverage of assumptions +The only assumption A.BIO is covered by security objective OE.BIO as directly follows. +5.3.3 Justification for the coverage of organizational security policies +5.3.3.1 OSP.SPOOF_DETECTION +The organisational security policy OSP.SPOOF_DETECTION is covered by the security objective +O.SPOOF_DETECTION which is supported by O.MANAGEMENT, OE.ADMINISTRATION, +OE.PHYSICAL, and OE.PLATFORM.. +O.SPOOF_DETECTION detects whether a presented fingerprint is spoofed or genuine, and +performs appropriate actions in case of a spoofed and in case of a genuine fingerprint. Therefore, a +spoofed fingerprint will not be used by the Biometric System being behind the TOE. This objective +covers the main part of the OSP. +O.MANAGEMENT provides necessary management functionality for the modification of security +relevant parameters to TOE administrators which are authenticated and authorized by the TOE +platform as stated in OE.PLATFORM. TOE administrators are well-trained and non-hostile +according to OE.ADMINISTRATION and will therefore unlikely misconfigure the spoof detection +functionality. All three objectives ensure that the spoof detection is securely managed and therefore +support that spoof detection performs as intended. +OE.PHYSICAL ensures that the TOE is physically protected against manipulation so that the spoof +detection functionality can not be compromised using physically means. +OE.PLATFORM further ensures that the platform for the TOE provides secure communication and +storage of data and ensures that the TOE is free of malware which could otherwise compromise the +spoof detection. +OE.ADMINISTRATION further ensures that environmental factors which influence the capture and +sensor devices are within acceptable ranges. It therefore supports that the spoof detection functionality +is not compromised by environmental conditions. +14 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +5.3.3.2 OSP.MANAGEMENT +OSP.MANAGEMENT is covered by the security objectives O.MANAGEMENT which is supported +by OE.ADMINISTRATION, OE.PHYSICAL, and OE.PLATFORM.. +O.MANAGEMENT provides the necessary management functionality to securely modify security +parameters. It comprises the main part to cover the OSP. It is supported by OE.PLATFORM which +ensures that only authenticated TOE administrators are authorized to manage the TOE. +OE.ADMINISTRATION thereby ensures that these TOE administrators are well-trained and non- +hostile so that misconfiguration is unlikely. +OE.PHYSICAL ensures that the TOE is physically protected against manipulation so that +management functionality can not be altered by physically means. +OE.PLATFORM further ensures that the platform for the TOE provides secure communication and +storage of data and ensures that the TOE is free of malware which could otherwise compromise the +management functionality. +5.3.3.3 OSP.RESIDUAL +OSP.RESIDUAL is covered by security objective O.RESIDUAL which is supported by +OE.ADMINISTRATION, OE.PHYSICAL, and OE.PLATFORM.. +O.RESIDUAL ensures that no residual or unprotected security relevant data remains after operations +are completed and therefore residual security relevant data from a previous usage of the TOE can not +be used by an attacker. It comprises the main part to cover the OSP. It is supported by +OE.PHYSICAL which ensures that the TOE is physically protected against manipulation and +therefore residual information can not be obtained via physical attacks. +OE.PLATFORM ensures that the TOE platform is free of malware and therefore does not +compromise functionality for residual information protection. OE.ADMINISTRATION supports that +as it ensures that the platform is securely installed by the TOE administrator. +5.3.3.4 OSP.AUDIT +The organizational security policy OSP.AUDIT is covered by O.AUDIT which is supported by +OE.PLATFORM.. +O.AUDIT ensures that the TOE generates audit records for security relevant events and therefore +comprises the main part to cover the OSP. +OE.PLATFROM ensures that the environment provides the time stamps necessary for audit, the +secure storage for audit data, and mechanisms for review of audit data. It therefore supports the task of +O.AUDIT. +Bundesamt für Sicherheit in der Informationstechnik 15 + FSDPP_OSP +6. Extended Component definition +The extended functional family FPT_SPOD (Biometric Spoof Detection) of the Class FPT (Protection +of the TSF) has been defined here to describe the core security function as provided by the TOE +described in this PP: The TOE shall prevent that a spoofed biometric characteristics can be used with a +biometric system that is protected by the TOE. The class FPT (Protection of the TSF) as defined in +part II of Common Criteria has been selected even if the functionality to be protected is not part of the +TOE. The following chapter contains the detailed definition. +6.1 FPT_SPOD Biometric Spoof Detection +Family behavior +This family defines functional requirements to detect spoofed biometric characteristics. +Component leveling: +FPT_SPOD Biometric Spoof Detection 1 +FPT_SPOD.1 Biometric Spoof Detection has four elements: +FPT_SPOD.1.1 FPT_SPOD.1.1 requires to provide spoof detection functionality for a specific +biometric characteristic. +FPT_SPOD.1.2 FPT_SPOD.1.2 defines actions to be performed if a spoofed biometric +characteristic is detected. +FPT_SPOD.1.3 FPT_SPOD.1.3 defines actions to be performed if a genuine biometric +characteristic is detected. +FPT_SPOD.1.4 FPT_SPOD.1.4 defines additional information returned with the feedback about +spoof status. +Management: FPT_SPOD.1 +The following actions could be considered for the management functions in FMT: +a) Management of the parameters used for spoofed detection. +Audit: FPT_SPOD.1 +The following actions should be auditable if FAU_GEN Security audit data generation is included in +the PP/ST: +a) Basic: spoof detected +b) Basic: no spoof detected +16 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +6.1.1 Biometric Spoof Detection (FPT_SPOD.1) +FPT_SPOD.1 Biometric Spoof Detection +FPT_SPOD.1.1 The TSF shall be able to detect whether a presented [assignment: biometric +characteristic] is spoofed or genuine. +FPT_SPOD.1.2 If a spoofed biometric characteristic is detected, the following action(s) shall be +performed: +● [assignment: list of actions] +FPT_SPOD.1.3 If a genuine biometric characteristic is detected, the following action(s) shall be +performed: +● [assignment: list of actions] +FPT_SPOD.1.4 Along with the feedback about the spoof status of the presented biometric +characteristic the TOE shall deliver the following information: +● [assignment: list of information] +Hierarchical to: No other components +Dependencies: FMT_MTD.3 Secure TSF data +FMT_SMF.1 Specification of Management Functions +6.1.2 Justification for the definition of functional family FPT_SPOD +Spoof detection functionality describes mechanisms that protect biometric systems like fingerprint +verification systems against threats of non-genuine biometric characteristics like fake fingers. It +therefore provides protection of the TSF which is subject of the functional class FPT. +There is no family in FPT that deals with detection of spoofing attacks or biometric functionality at all, +therefore a new family has been defined. +Bundesamt für Sicherheit in der Informationstechnik 17 + FSDPP_OSP +7. Security Requirements +This chapter describes the security functional and the assurance requirements which have to be +fulfilled by the TOE. +Those requirements comprise functional components from part II of [CC] and assurance components +from part III of [CC]. Further the extended requirement FPT_SPOD.1 as defined in chapter 6 is used. +The following notations are used to mark operations that have been performed: +● Selection operations (used to select one or more options provided by the [CC] in stating a +requirement.) are denoted by underlined text +● Assignment operation (used to assign a specific value to an unspecified parameter, such as the +length of a password) are denoted by italicized text. +● No Refinements have been performed +● No Iterations have been performed. +7.1 Security Functional Requirements for the TOE +The following table summarizes all security functional requirements of this PP: +Class FAU: Security Audit +FAU_GEN.1 Audit Data Generation +Class FDP: User Data Protection +FDP_RIP.2 Full residual information protection +Class FMT: Security Management +FMT_MTD.3 Secure TSF data +FMT_SMF.1 Specification of Management Functions +Class FPT: Protection of the TSF +FPT_SPOD.1 Spoof Detection +Table 2: Security Functional Requirements +18 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +7.1.1 Security audit (FAU) +7.1.1.1 Security audit data generation (FAU_GEN) +FAU_GEN.1 Audit data generation +FAU_GEN.1.1 The TSF shall be able to generate an audit record of the following auditable events: +a) Start-up and shutdown of the audit functions; +b) All auditable events for the [basic] level of audit; and +c) [modification of Spoof Detection Parameters, and +d) [assignment: other specifically defined auditable events]]. +FAU_GEN.1.2 The TSF shall record within each audit record at least the following information: +a) Date and time of the event, type of event, subject identity (if applicable), and the +outcome (success or failure) of the event; and +b) For each audit event type, based on the auditable event definitions of the +functional components included in the PP/ST, [assignment: other audit relevant +information]. +Hierarchical to: No other components +Dependencies: FPT_STM.1 +Application Note: According to the chosen level of audit and the SFRs contained in this PP the +TOE has to audit the following event per minimum: +● A use of the TOE where a faked fingerprint has been detected +(FPT_SPOD.1) +● A use of the TOE where a genuine fingerprint has been detected +(FPT_SPOD.1) +● Every use of a management function (FMT_SMF.1) +● All parameters rejected by the management functions (FMT_SMF.3) +If useful in the context of a concrete technology the ST author should consider +to audit additional information (e.g. a score or a claimed identity) together with +the first two events. +7.1.2 User data protection (FDP) +7.1.2.1 Residual information protection (FDP_RIP) +FDP_RIP.2 Full residual information protection +FDP_RIP.2.1 The TSF shall ensure that any previous information content of a resource is made +unavailable upon the [deallocation of the resource from] all objects. +Hierarchical to: FDP_RIP.1 +Dependencies: No dependencies +Bundesamt für Sicherheit in der Informationstechnik 19 + FSDPP_OSP +7.1.3 Security management (FMT) +7.1.3.1 Management of TSF data (FMT_MTD) +FMT_MTD.3 Secure TSF data +FMT_MTD.3.1 The TSF shall ensure that only secure values are accepted for [ +● [assignment: list of all spoof detection parameters] +● [assignment: list of other TSF data or none] +] +Hierarchical to: No other components +Dependencies: FMT_MTD.1 +Application Note: The assignment in FMT_MTD.3.1 (list of all spoof detection parameters) +represents the minimum of parameters for which the TOE has to ensure +secure settings. The objective O.MANAGEMENT however requires that the +TOE has to ensure secure values for all security relevant parameters. +As the list of those parameters depends on the concrete technology the ST +author shall add all security relevant parameters to this assignment. +7.1.3.2 Specification of Management Functions (FMT_SMF.1) +FMT_SMF.1 Specification of Management Functions +FMT_SMF.1.1 The TSF shall be capable of performing the following management +functions: [assignment: list of management functions to be provided by the +TSF]. +Hierarchical to: No other components +Dependencies: No dependencies +Application Note: The necessary management functions are highly depending on the necessary +information for the core functionality as defined in FPT_SPOD.1. The ST +author shall consider all relevant parameters and decide whether a +management function will be necessary for each. +20 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +7.1.4 Protection of the TSF (FPT) +7.1.4.1 Biometric Spoof Detection (FPT_SPOD.1) +FPT_SPOD.1 Biometric Spoof Detection +FPT_SPOD.1.1 The TSF shall be able to detect whether a presented [fingerprint] is spoofed or +genuine. +FPT_SPOD.1.2 If a spoofed biometric characteristic is detected, the following action(s) shall be +performed: +● [assignment: list of actions] +FPT_SPOD.1.3 If a genuine biometric characteristic is detected, the following action(s) shall be +performed: +● [assignment: list of actions] +FPT_SPOD.1.4 Along with the feedback about spoof status of the presented biometric +characteristic the TOE shall deliver the following information: +● [assignment: list of information] +Hierarchical to: No other components +Dependencies: FMT_MTD.3 Secure TSF data +FMT_SMF.1 Specification of Management Functions +Application Note: FPT_SPOD.1 represents the core functionality to be provided by the TOE. +Due to the special character of this technology additional guidance for +evaluation is provided in form of [FSDEG]. This guidance shall be applied +during evaluation. +Application Note: Please note that any use of residual information that remains on a sensor +device is considered being a spoofed characteristic in the context of this +SFR. +Application Note: In FPT_SPOD.1.4, the ST author should list all additional information that +shall be delivered by the spoof detection functionality to the integrating +biometric system. Such information could be an additional score value that +represents the likelihood that the presented biometric characteristic is +spoofed. However, the ST author should understand that such information is +sensitive as an attacker could use it to improve his attacks. Such information +shall not be visible to the user of the biometric system. +Bundesamt für Sicherheit in der Informationstechnik 21 + FSDPP_OSP +7.2 Security Assurance Requirements for the TOE +Due to the special character of the technology described in this PP, the following explicit assurance +package has been defined for the TOE based on EAL 2. In contrast to EAL 2, it does not contain +AVA_VAN.2 but is augmented by ALC_FLR.1. +The following table lists the assurance components which are chosen for this PP. +Assurance Class Assurance Component Title +Development ADV_ARC.1 Security architecture description +ADV_FSP.2 Security-enforcing functional specification +ADV_TDS.1 Basic Design +Guidance documents AGD_OPE.1 Operational User Guidance +AGD_PRE.1 Preparative Procedures +Life-cycle support ALC_CMC.2 Use of a CM system +ALC_CMS.2 Parts of the TOE CM coverage +ALC_DEL.1 Delivery procedures +ALC_FLR.1 Basic flaw remediation +Security Target Evaluation ASE_CCL.1 Conformance claims +ASE_ECD.1 Extended component definition +ASE_INT.1 ST introduction +ASE_OBJ.2 Security Objectives +ASE_REQ.2 Derived Security Requirements +ASE_SPD.1 Security problem definition +ASE_TSS.1 TOE summary specification +Tests ATE_COV.1 Evidence of coverage +ATE_FUN.1 Functional testing +ATE_IND.2 Independent testing - sample +Table 3: Assurance Requirements +Due to the special character of the technology described in this PP, the Spoof Detection Evaluation +Methodology [FSDEG] shall be applied during evaluation. This methodology will provide the +evaluator with additional information and guidance for some assurance requirements. +22 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +7.3 Security Requirements rationale +7.3.1 Security Functional Requirements rationale +7.3.1.1 Fulfillment of the Security Objectives +This chapter proves that the set of security requirements (TOE) is suited to fulfill the security +objectives described in chapter 4 and that each SFR can be traced back to the security objectives. At +least one security objective exists for each security requirement. +O.AUDIT +O. +RESIDUAL +O.MANAGEMENT +O.SPOOF_DETECTION +FAU_GEN.1 X +FDP_RIP.2 X +FMT_MTD.3 X +FMT_SMF.1 X +FPT_SPOD.1 X +Table 4:Fulfillment of Security Objectives +The following paragraphs contain more details on this mapping. +O.AUDIT +● FAU_GEN.1 defines that the TOE has to capture all the events as required by O.AUDIT. +O.RESIDUAL +● This objective is completely covered by FDP_RIP.2 as directly follows. +O.MANAGEMENT +● FMT_MTD.1 defines that the TOE only accepts secure values for spoof detection parameters +so that the spoof detection works correctly. +● FMT_SMF.1 ensures that the TOE provides the necessary management functionality +O.SPOOF_DETECTION +● FPT_SPOD.1 defines that the TOE is able to detect whether a presented fingerprint is +spoofed or genuine and therewith directly addresses this objective. +7.3.1.2 Fulfillment of the dependencies +The following table summarizes all TOE functional requirements dependencies of this PP and +demonstrates that they are fulfilled. +Bundesamt für Sicherheit in der Informationstechnik 23 + FSDPP_OSP +SFR Dependencies Fulfilled by +FAU_GEN.1 FPT_STM.1 See chapter 7.3.1.3 +FDP_RIP.2 - - +FMT_MTD.3 FMT_MTD.1 See chapter 7.3.1.3 +FMT_SMF.1 - - +FPT_SPOD.1 FMT_MTD.3 +FMT_SMF.1 +FMT_MTD.3 +FMT_SMF.1 +Table 5: Security Functional Requirements +7.3.1.3 Justification for missing dependencies +The functional component FAU_GEN.1 has an identified dependency on FPT_STM.1. This +dependency is not satisfied by any TOE functional requirement as the functionality of reliable time +stamps is provided by the TOE environment (OE.PLATFORM). +The functional component FMT_MTD.3 has an identified dependency on FMT_MTD.1. This +dependency is not satisfied by any TOE functional requirement as the functionality of restricting the +ability to query, modify, delete, and clear security parameters to TOE administrators is provided by the +TOE environment (see OE.PLATFORM). +7.3.2 Security Assurance Requirements rationale +Due to the special character of the technology described in this PP, an explicit assurance package has +been defined for the TOE. It has been chosen for this Protection Profile as it should focus on +application cases for which it is sufficient to determine whether the security functionality claimed by a +TOE is working correctly without performing a dedicated vulnerability assessment. +The defined assurance package has been developed based on EAL 2. In contrast to EAL 2, it does not +contain AVA_VAN.2 but has been augmented by the assurance component ALC_FLR.1. ALC_FLR.1 +has been included as spoof detection systems are supposed to have flaws that will be found in future +and that will then have to be addressed. +Additional guidance has been provided for some of the assurance components due to the special nature +of the biometric technology in form of [FSDEG]. +7.3.2.1 Dependencies of assurance components +The dependencies of the assurance requirements are fulfilled as shown in Table 6: +Assurance Class Assurance +Component +Dependencies Fulfillment +Development ADV_ARC.1 ADV_FSP.1, +ADV_TDS.1 +ADV_FSP.2, +ADV_TDS.1 +ADV_FSP.2 ADV_TDS.1 ADV_TDS.1 +ADV_TDS.1 ADV_FSP.2 ADV_FSP.2 +Guidance documents AGD_OPE.1 ADV_FSP.1 ADV_FSP.2 +AGD_PRE.1 No dependencies - +Life-cycle support ALC_CMC.2 ALC_CMS.1 ALC_CMS.2 +24 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +Assurance Class Assurance +Component +Dependencies Fulfillment +ALC_CMS.2 No dependencies - +ALC_DEL.1 No dependencies - +ALC_FLR.1 No dependencies - +Security Target +Evaluation +ASE_CCL.1 ASE_INT.1, +ASE_ECD.1, +ASE_REQ.1 +ASE_INT.1, +ASE_ECD.1, +ASE_REQ.2 +ASE_ECD.1 No dependencies - +ASE_INT.1 No dependencies - +ASE_OBJ.2 ASE_SPD.1 ASE_SPD.1 +ASE_REQ.2 ASE_OBJ.2, +ASE_ECD.1 +ASE_OBJ.2, +ASE_ECD.1 +ASE_SPD.1 No dependencies - +ASE_TSS.1 ASE_INT.1, +ASE_REQ.1 +ADV_FSP.1 +ASE_INT.1, +ASE_REQ.2 +ADV_FSP.2 +Tests ATE_COV.1 ADV_FSP.2, +ATE_FUN.1 +ADV_FSP.2, +ATE_FUN.1 +ATE_FUN.1 ATE_COV.1 ATE_COV.1 +ATE_IND.2 ADV_FSP.2, +AGD_OPE.1, +AGD_PRE.1, +ATE_COV.1, +ATE_FUN.1 +ADV_FSP.2, +AGD_OPE.1, +AGD_PRE.1, +ATE_COV.1, +ATE_FUN.1 +Table 6: Dependencies of assurance components +Bundesamt für Sicherheit in der Informationstechnik 25 + FSDPP_OSP +8. Appendix +8.1 Glossary +Term Description +AD Audit data +Audit data Content of the audit trace generated by the TOE. +Attacker An attacker in the context of this PP is any individual who is attempting to +subvert the operation of the biometric system protected by the TOE using a +faked fingerprint. +This does explicitly included cases in which users try to subvert the operation +of the TOE directly but in any case it is the final focus of an attacker to +subvert the operation of the protected biometric system using a faked +fingerprint. +Biometric A measurable physical characteristic or personal behavioral trait used to +recognize the identity of a user or verify a claimed identity. +Biometric +identification +Application in which a search of the enrolled database is performed, and a +candidate list of 0, 1 or more identifiers is returned. +Biometric system An automated system capable of capturing a biometric sample from a user, +extracting biometric data from the sample, comparing the data with one or +more biometric references, deciding on how well they match, and indicating +whether or not an identification or verification of identity has been achieved. +Note that in [CC] evaluation terms, a biometric system may be a product or +part of a system. +Biometric verification The objective of a verification process is to verify or refuse the claimed +identity of a user based on their biometric characteristic. +CC Common Criteria - Common Criteria for Information Technology Security +Evaluation +CEM Common Evaluation Methodology +EAL Evaluation Assurance Level +FAU Class of functional requirements for audit +FDP Class of functional requirements for data protection +FMT Class of functional requirements for management +FPT Class of functional requirements for TSF protection +Identification system Biometric system that provides an identification function (see also biometric +identification) +I&A Identification and authentication +LAN Local Area Network +OS Operating system +26 Bundesamt für Sicherheit in der Informationstechnik + FSDPP_OSP +Term Description +PP Protection Profile - An implementation-independent set of security +requirements for a category of TOEs that meet specific consumer needs. +SDP Spoof detection parameters +Sensor The physical hardware device used for biometric capture. Also called capture +device +SFR Security Functional Requirement +ST Security Target – A set of implementation-dependent security requirements +for a specific TOE. +Spoof detection +parameters +Settings (configuration data) necessary to detect a spoofed biometric +characteristic, e. g., temperature limits, thresholds, typical movement +patterns. +Spoofing evidence Information that is acquired from a biometric characteristic to decide whether +it is spoofed or genuine. +Threshold A parametric value used to convert a matching score to a decision. +TOE Target of Evaluation +TSF TOE Security Functionality. +Verification system A biometric system that provides verification functionality. +WAN Wide Area Network +WLAN Wireless Local Area Network +8.2 References +[FSDPP] Fingerprint Spoof Detection Protection Profile, version 1.8, November 2009 +[Toolbox] Standard Fake Finger Toolbox for Common Criteria evaluations of Spoof +Detection systems, as referenced in [FSDEG] +[FSDEG] Fingerprint Spoof Detection Evaluation Guidance, version 2.0 (or a more recent +version) +[CC] Common Criteria for Information Technology Security Evaluation – +● Part 1: Introduction and general model, dated +July 2009, version 3.1 R3 +● Part 2: Security functional requirements, dated July 2009, version 3.1, +R3 +● Part 3: Security assurance requirements, dated July 2009, version 3.1, +R3 +[CEM] Common Evaluation Methodology for Information Technology Security – +Evaluation Methodology, dated July 2009, version 3.1 R3 +Bundesamt für Sicherheit in der Informationstechnik 27 + diff --git a/tests/data/protection_profiles/reports/pdf/b02ed76d2545326a.pdf b/tests/data/protection_profiles/reports/pdf/b02ed76d2545326a.pdf new file mode 100644 index 000000000..9afbdc2a6 Binary files /dev/null and b/tests/data/protection_profiles/reports/pdf/b02ed76d2545326a.pdf differ diff --git a/tests/data/protection_profiles/reports/txt/b02ed76d2545326a.txt b/tests/data/protection_profiles/reports/txt/b02ed76d2545326a.txt new file mode 100644 index 000000000..81796a869 --- /dev/null +++ b/tests/data/protection_profiles/reports/txt/b02ed76d2545326a.txt @@ -0,0 +1,701 @@ +BSI-CC-PP-0062-2010 +for +Fingerprint Spoof Detection Protection Profile +based on Organisational Security Policies +(FSDPP_OSP), Version 1.7 +from +German Federal Office for +Information Security (BSI) + BSI - Bundesamt für Sicherheit in der Informationstechnik, Postfach 20 03 63, D-53133 Bonn +Phone +49 (0)228 99 9582-0, Fax +49 (0)228 9582-5477, Infoline +49 (0)228 99 9582-111 +Certification Report V1.0 ZS-01-01-F-414 V1.40 + BSI-CC-PP-0062-2010 +Common Criteria Protection Profile +Fingerprint Spoof Detection Protection Profile based on +Organisational Security Policies (FSDPP_OSP), +Version 1.7 +developed by the German Federal Office for Information Security (BSI) +Assurance Package claimed in the Protection Profile: +Common Criteria Part 3 conformant +ADV_ARC.1, ADV_FSP.2, ADV_TDS.1, AGD_OPE.1, +AGD_PRE.1, ALC_CMC.2, ALC_CMS.2, ALC_DEL.1, +ALC_FLR.1, ASE_CCL.1, ASE_ECD.1, ASE_INT.1, +ASE_OBJ.2, ASE_REQ.2, ASE_SPD.1, ASE_TSS.1, +ATE_COV.1, ATE_FUN.1, ATE_IND.2 +Common Criteria +Recognition +Arrangement +The Protection Profile identified in this certificate has been evaluated at an approved evaluation facility using +the Common Methodology for IT Security Evaluation (CEM), Version 3.1 for conformance to the Common +Criteria for IT Security Evaluation (CC), Version 3.1. +This certificate applies only to the specific version and release of the Protection Profile and in conjunction +with the complete Certification Report. +The evaluation has been conducted in accordance with the provisions of the certification scheme of the +German Federal Office for Information Security (BSI) and the conclusions of the evaluation facility in the +evaluation technical report are consistent with the evidence adduced. +This certificate is not an endorsement of the Protection Profile by the Federal Office for Information Security +or any other organisation that recognises or gives effect to this certificate, and no warranty of the Protection +Profile by the Federal Office for Information Security or any other organisation that recognises or gives effect +to this certificate, is either expressed or implied. +Bonn, 25. February 2010 +For the Federal Office for Information Security +Bernd Kowalski L.S. +Head of Department +Bundesamt für Sicherheit in der Informationstechnik +Godesberger Allee 185-189 - D-53175 Bonn - Postfach 20 03 63 - D-53133 Bonn +Phone +49 (0)228 99 9582-0 - Fax +49 (0)228 9582-5477 - Infoline +49 (0)228 99 9582-111 + Certification Report BSI-CC-PP-0062-2010 +This page is intentionally left blank. +4 / 28 + BSI-CC-PP-0062-2010 Certification Report +Preliminary Remarks +Under the BSIG1 +Act, the Federal Office for Information Security (BSI) has the task of +issuing certificates for information technology products as well as for Protection Profiles +(PP). +A PP defines an implementation-independent set of IT security requirements for a +category of products which are intended to meet common consumer needs for IT security. +The development and certification of a PP or the reference to an existent one gives +consumers the possibility to express their IT security needs without referring to a special +product. Product or system certifications can be based on Protection Profiles. For products +which have been certified based on a Protection Profile an individual certificate will be +issued. +Certification of the Protection Profile is carried out on the instigation of the BSI or a +sponsor. +A part of the procedure is the technical examination (evaluation) of the Protection Profile +according to Common Criteria [1]. +The evaluation is normally carried out by an evaluation facility recognised by the BSI or by +BSI itself. +The result of the certification procedure is the present Certification Report. This report +contains among others the certificate (summarised assessment) and the detailed +Certification Results. +1 +Act on the Federal Office for Information Security (BSI-Gesetz - BSIG) of 14 August 2009, +Bundesgesetzblatt I p. 2821 +5 / 28 + Certification Report BSI-CC-PP-0062-2010 +Contents +A Certification........................................................................................................................7 +1 Specifications of the Certification Procedure.................................................................7 +2 Recognition Agreements................................................................................................7 +2.1 International Recognition of CC - Certificates.........................................................8 +3 Performance of Evaluation and Certification..................................................................8 +4 Validity of the certification result.....................................................................................9 +5 Publication......................................................................................................................9 +B Certification Results.........................................................................................................11 +1 Protection Profile Overview..........................................................................................12 +2 Security Functional Requirements...............................................................................12 +3 Security Assurance Requirements...............................................................................13 +4 Results of the PP-Evaluation........................................................................................13 +5 Obligations and notes for the usage............................................................................14 +6 Protection Profile Document.........................................................................................14 +7 Definitions.....................................................................................................................14 +7.1 Acronyms...............................................................................................................14 +7.2 Glossary.................................................................................................................14 +8 Bibliography..................................................................................................................15 +C Excerpts from the Criteria................................................................................................17 +D Annexes...........................................................................................................................27 +6 / 28 + BSI-CC-PP-0062-2010 Certification Report +A Certification +1 Specifications of the Certification Procedure +The certification body conducts the procedure according to the criteria laid down in the +following: +● BSIG2 +● BSI Certification Ordinance3 +● BSI Schedule of Costs4 +● Special decrees issued by the Bundesministerium des Innern (Federal Ministry +of the Interior) +● DIN EN 45011 standard +● BSI certification: Procedural Description (BSI 7125) [3] +● Common Criteria for IT Security Evaluation (CC), Version 3.15 +[1] +● Common Methodology for IT Security Evaluation, Version 3.1[2] +● BSI certification: Application Notes and Interpretation of the Scheme (AIS) [7] +● Procedure for the Issuance of a PP certificate by the BSI +2 Recognition Agreements +In order to avoid multiple certification of the same Protection Profile in different countries a +mutual recognition of IT security certificates - as far as such certificates are based on CC - +under certain conditions was agreed. +2 +Act on the Federal Office for Information Security (BSI-Gesetz - BSIG) of 14 August 2009, +Bundesgesetzblatt I p. 2821 +3 +Ordinance on the Procedure for Issuance of a Certificate by the Federal Office for Information Security +(BSI-Zertifizierungsverordnung, BSIZertV) of 07 July 1992, Bundesgesetzblatt I p. 1230 +4 +Schedule of Cost for Official Procedures of the Bundesamt für Sicherheit in der Informationstechnik +(BSI-Kostenverordnung, BSI-KostV) of 03 March 2005, Bundesgesetzblatt I p. 519 +5 +Proclamation of the Bundesministerium des Innern of 12 February 2007 in the Bundesanzeiger dated +23 February 2007 +7 / 28 + Certification Report BSI-CC-PP-0062-2010 +2.1 International Recognition of CC - Certificates +An arrangement (Common Criteria Arrangement) on the mutual recognition of certificates +based on the CC evaluation assurance levels up to and including EAL 4 has been signed +in May 2000 (CCRA). It includes also the recognition of Protection Profiles based on the +CC. +As of January 2009 the arrangement has been signed by the national bodies of: Australia, +Austria, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, +India, Israel, Italy, Japan, Republic of Korea, Malaysia, The Netherlands, New Zealand, +Norway, Pakistan, Republic of Singapore, Spain, Sweden, Turkey, United Kingdom, +United States of America. The current list of signatory nations resp. approved certification +schemes can be seen on the web site: http://www.commoncriteriaportal.org +The Common Criteria Arrangement logo printed on the certificate indicates that this +certification is recognised under the terms of this agreement. +3 Performance of Evaluation and Certification +The certification body monitors each individual evaluation to ensure a uniform procedure, a +uniform interpretation of the criteria and uniform ratings. +The Fingerprint Spoof Detection Protection Profile based on Organisational Security +Policies (FSDPP_OSP), Version 1.7 has undergone the certification procedure at BSI. +The evaluation of the Fingerprint Spoof Detection Protection Profile based on +Organisational Security Policies (FSDPP_OSP), Version 1.7 was conducted by the ITSEF +SRC Security Research & Consulting GmbH. The evaluation was completed on +10 December 2009. The ITSEF SRC Security Research & Consulting GmbH is an +evaluation facility (ITSEF)6 +recognised by the certification body of BSI. +For this certification procedure the sponsor and applicant is: German Federal Office for +Information Security (BSI) +The PP was developed by: TÜV Informationstechnik GmbH +The certification is concluded with the comparability check and the production of this +Certification Report. This work was completed by the BSI. +6 +Information Technology Security Evaluation Facility +8 / 28 + BSI-CC-PP-0062-2010 Certification Report +4 Validity of the certification result +This Certification Report only applies to the version of the Protection Profile as indicated. +In case of changes to the certified version of the Protection Profile, the validity can be +extended to the new versions and releases, provided the sponsor applies for assurance +continuity (i.e. re-certification or maintenance) of the modified Protection Profile, in +accordance with the procedural requirements, and the evaluation does not reveal any +security deficiencies. +For the meaning of the assurance levels please refer to the excerpts from the criteria at +the end of the Certification Report. +5 Publication +The Fingerprint Spoof Detection Protection Profile based on Organisational Security +Policies (FSDPP_OSP), Version 1.7 has been included in the BSI list of the certified +Protection Profiles, which is published regularly (see also Internet: https://www.bsi.bund.de +and [4]). Further information can be obtained from BSI-Infoline +49 228 9582-111. +Further copies of this Certification Report can be requested from the sponsor7 +of the +Protection Profile. The Certification Report may also be obtained in electronic form at the +internet address stated above. +7 +Federal Office for Information Security (BSI) +Godesberger Allee 185-189 +53175 Bonn +9 / 28 + Certification Report BSI-CC-PP-0062-2010 +This page is intentionally left blank. +10 / 28 + BSI-CC-PP-0062-2010 Certification Report +B Certification Results +The following results represent a summary of +● the certified Protection Profile, +● the relevant evaluation results from the evaluation facility, and +● complementary notes and stipulations of the certification body. +11 / 28 + Certification Report BSI-CC-PP-0062-2010 +1 Protection Profile Overview +The Fingerprint Spoof Detection Protection Profile based on Organisational Security +Policies (FSDPP_OSP), Version 1.7 [6] is established by the German Federal Office for +Information Security (BSI) as a basis for the development of Security Targets in order to +perform a certification of an IT-product (TOE). +The Target of Evaluation (TOE) described in the Protection Profile (PP) is a system that +provides fingerprint spoof detection either as part of or in front of a biometric system for +fingerprint recognition. +The TOE determines whether a fingerprint presented to the biometric system is genuine or +spoofed. The term spoofed biometric characteristics hereby refers to artificially created +fake fingers which are currently known to circumvent fingerprint recognition systems. +For this purpose the spoof detection system acquires spoofing evidences for a presented +fingerprint using a sensor device. This sensor can either be part of the capture device that +is used to capture the biometric sample of the fingerprint (or even be identical to it) or be a +separate sensor device (or more than one) that is completely dedicated to spoof detection. +Beside the fingerprint spoof detection functionality every TOE that claims conformance to +the PP shall implement: +● Management functionality to modify security relevant parameters +● Quality control for management parameters +● Audit functionality for security relevant events +● Protection of residual and security relevant data +The assets to be protected by a TOE claiming conformance to this PP are defined in the +Protection Profile [6], chapter 4.2. Based on these assets the Security Problem Definition +is defined in terms of Assumptions and Organisational Security Policies. This is outlined in +the Protection Profile [6], chapters 4.3 to 4.5. +These Assumptions and Organisational Security Policies are split into Security Objectives +to be fulfilled by a TOE claiming conformance to this PP and Security Objectives to be +fulfilled by the operational environment of a TOE claiming conformance to this PP. These +Security Objectives are outlined in the PP [6], chapter 5. +The Protection Profile [6] requires a Security Target based on this PP or another PP +claiming this PP, to fulfil the CC requirements for strict conformance. +2 Security Functional Requirements +Based on the Security Objectives to be fulfilled by a TOE claiming conformance to this PP +the security policy is expressed by the set of Security Functional Requirements to be +implemented by a TOE. It covers the following issues: Biometric spoof detection, security +audit, residual information protection and TOE security management. +These TOE Security Functional Requirements (SFR) are outlined in the PP [6], chapter +7.1. They are selected from Common Criteria Part 2 and one of them is newly defined. +Thus the SFR claim is called: +Common Criteria Part 2 extended +12 / 28 + BSI-CC-PP-0062-2010 Certification Report +3 Security Assurance Requirements +Due to the special character of the technology described in this PP, an explicit assurance +package has been defined for the TOE. It has been chosen for this Protection Profile as +the PP should focus on application cases for which it is sufficient to determine whether the +Security Functionality claimed by a TOE is working correctly without performing a +dedicated vulnerability assessment. +The TOE security assurance package claimed in the Protection Profile is based entirely on +the assurance components defined in part 3 of the Common Criteria. Thus, this assurance +package is called: +Common Criteria Part 3 conformant +This explicit assurance package is based on EAL 2 and consists of the following +Assurance Families: +ADV_ARC.1, ADV_FSP.2, ADV_TDS.1, AGD_OPE.1, AGD_PRE.1, +ALC_CMC.2, ALC_CMS.2, ALC_DEL.1, ALC_FLR.1, ASE_CCL.1, +ASE_ECD.1, ASE_INT.1, ASE_OBJ.2, ASE_REQ.2, ASE_SPD.1, +ASE_TSS.1, ATE_COV.1, ATE_FUN.1, ATE_IND.2 +(for the definition and scope of assurance packages according to CC see part C or [1], +part 3 for details). +The only differences compared to EAL 2 are the omitted AVA_VAN.2 assurance +component and the added ALC_FLR.1 assurance component. +Additional guidance in form of the Fingerprint Spoof Detection Evaluation Guidance [8] +has been provided for some of the assurance components due to the special nature of +the biometric technology. +4 Results of the PP-Evaluation +The Evaluation Technical Report (ETR) [5] was provided by the ITSEF according to the +Common Criteria [1], the Methodology [2], the requirements of the Scheme [3] and all +interpretations and guidelines of the Scheme (AIS) [7] as relevant for the TOE. +As a result of the evaluation the verdict PASS is confirmed for the assurance components +of the class APE. +The following assurance components were used: +APE_INT.1 PP introduction +APE_CCL.1 Conformance claims +APE_SPD.1 Security problem definition +APE_OBJ.2 Security objectives +APE_ECD.1 Extended components definition +APE_REQ.2 Derived security requirements +The results of the evaluation are only applicable to the Protection Profile as defined in +chapter 1. +13 / 28 + Certification Report BSI-CC-PP-0062-2010 +5 Obligations and notes for the usage +The following aspects need to be fulfilled when using the Protection Profile: +Due to the special character of the technology described in this PP, the Fingerprint Spoof +Detection Evaluation Guidance [8] shall be applied during evaluation. This document will +provide the evaluator with additional information and guidance for some assurance +requirements. +6 Protection Profile Document +The Fingerprint Spoof Detection Protection Profile based on Organisational Security +Policies (FSDPP_OSP), Version 1.7 [6] is being provided within a separate document as +Annex A of this report. +7 Definitions +7.1 Acronyms +BSI Bundesamt für Sicherheit in der Informationstechnik / Federal Office for +Information Security, Bonn, Germany +CCRA Common Criteria Recognition Arrangement +CC Common Criteria for IT Security Evaluation +EAL Evaluation Assurance Level +FSDPP Fingerprint Spoof Detection Protection Profile +IT Information Technology +ITSEF Information Technology Security Evaluation Facility +OSP Organisational Security Policy +PP Protection Profile +SF Security Function +SFP Security Function Policy +ST Security Target +TOE Target of Evaluation +TSF TOE Security Functions +7.2 Glossary +Augmentation - The addition of one or more requirement(s) to a package. +Extension - The addition to an ST or PP of functional requirements not contained in part 2 +and/or assurance requirements not contained in part 3 of the CC. +Formal - Expressed in a restricted syntax language with defined semantics based on well- +established mathematical concepts. +Informal - Expressed in natural language. +14 / 28 + BSI-CC-PP-0062-2010 Certification Report +Object - An passive entity in the TOE, that contains or receives information, and upon +which subjects perform operations. +Protection Profile - An implementation-independent statement of security needs for a +TOE type. +Security Target - An implementation-dependent statement of security needs for a specific +identified TOE. +Semiformal - Expressed in a restricted syntax language with defined semantics. +Subject - An active entity in the TOE that performs operations on objects. +Target of Evaluation - A set of software, firmware and/or hardware possibly accompanied +by guidance. +TOE Security Functionality - A set consisting of all hardware, software, and firmware of +the TOE that must be relied upon for the correct enforcement of the SFRs. +8 Bibliography +[1] Common Criteria for Information Technology Security Evaluation, Version 3.1, +Part 1: Introduction and general model, Revision 3, July 2009 +Part 2: Security functional components, Revision 3, July 2009 +Part 3: Security assurance components, Revision 3, July 2009 +[2] Common Methodology for Information Technology Security Evaluation (CEM), +Evaluation Methodology, Version 3.1, Revision 3, July 2009 +[3] BSI certification: Procedural Description (BSI 7125) +[4] German IT Security Certificates (BSI 7148, BSI 7149), periodically updated list +published also on the BSI Website +[5] Evaluation Technical Report, Version 1.2, 09 December 2009, Evaluation Technical +Report (ETR) for a PP evaluation, Certification ID: BSI-CC-PP-0062, SRC Security +Research & Consulting GmbH (confidential document) +[6] Fingerprint Spoof Detection Protection Profile based on Organisational Security +Policies (FSDPP_OSP), BSI-CC-PP-0062, Version 1.7, 27 November 2009, Federal +Office for Information Security (BSI) +[7] Application Notes and Interpretations of the Scheme (AIS) as relevant for the TOE. +[8] Fingerprint Spoof Detection Evaluation Guidance, Version 2.0 (or a more recent +version), Federal Office for Information Security (BSI) +15 / 28 + Certification Report BSI-CC-PP-0062-2010 +This page is intentionally left blank. +16 / 28 + BSI-CC-PP-0062-2010 Certification Report +C Excerpts from the Criteria +CC Part1: +Conformance Claim (chapter 10.4) +„The conformance claim indicates the source of the collection of requirements that is met +by a PP or ST that passes its evaluation. This conformance claim contains a CC +conformance claim that: +● describes the version of the CC to which the PP or ST claims conformance. +● describes the conformance to CC Part 2 (security functional requirements) as either: +– CC Part 2 conformant - A PP or ST is CC Part 2 conformant if all SFRs in that +PP or ST are based only upon functional components in CC Part 2, or +– CC Part 2 extended - A PP or ST is CC Part 2 extended if at least one SFR in +that PP or ST is not based upon functional components in CC Part 2. +● describes the conformance to CC Part 3 (security assurance requirements) as either: +– CC Part 3 conformant - A PP or ST is CC Part 3 conformant if all SARs in that +PP or ST are based only upon assurance components in CC Part 3, or +– CC Part 3 extended - A PP or ST is CC Part 3 extended if at least one SAR in +that PP or ST is not based upon assurance components in CC Part 3. +Additionally, the conformance claim may include a statement made with respect to +packages, in which case it consists of one of the following: +● Package name Conformant - A PP or ST is conformant to a pre-defined package +(e.g. EAL) if: +– the SFRs of that PP or ST are identical to the SFRs in the package, or +– the SARs of that PP or ST are identical to the SARs in the package. +● Package name Augmented - A PP or ST is an augmentation of a predefined package +if: +– the SFRs of that PP or ST contain all SFRs in the package, but have at least +one additional SFR or one SFR that is hierarchically higher than an SFR in the +package. +– the SARs of that PP or ST contain all SARs in the package, but have at least +one additional SAR or one SAR that is hierarchically higher than an SAR in the +package. +Note that when a TOE is successfully evaluated to a given ST, any conformance claims of +the ST also hold for the TOE. A TOE can therefore also be e.g. CC Part 2 conformant. +Finally, the conformance claim may also include two statements with respect to Protection +Profiles: +● PP Conformant - A PP or TOE meets specific PP(s), which are listed as part of the +conformance result. +● Conformance Statement (Only for PPs) - This statement describes the manner in +which PPs or STs must conform to this PP: strict or demonstrable. For more +information on this Conformance Statement, see Annex D. +17 / 28 + Certification Report BSI-CC-PP-0062-2010 +CC Part 3: +Class APE: Protection Profile evaluation (chapter 10) +“Evaluating a PP is required to demonstrate that the PP is sound and internally consistent, +and, if the PP is based on one or more other PPs or on packages, that the PP is a correct +instantiation of these PPs and packages. These properties are necessary for the PP to be +suitable for use as the basis for writing an ST or another PP.” +Assurance Class Assurance Components +Class APE: Protection +Profile evaluation +APE_INT.1 PP introduction +APE_CCL.1 Conformance claims +APE_SPD.1 Security problem definition +APE_OBJ.1 Security objectives for the operational environment +APE_OBJ.2 Security objectives +APE_ECD.1 Extended components definition +APE_REQ.1 Stated security requirements +APE_REQ.2 Derived security requirements +APE: Protection Profile evaluation class decomposition +Class ASE: Security Target evaluation (chapter 11) +“Evaluating an ST is required to demonstrate that the ST is sound and internally +consistent, and, if the ST is based on one or more PPs or packages, that the ST is a +correct instantiation of these PPs and packages. These properties are necessary for the +ST to be suitable for use as the basis for a TOE evaluation.” +Assurance Class Assurance Components +Class ASE: Security +Target evaluation +ASE_INT.1 ST introduction +ASE_CCL.1 Conformance claims +ASE_SPD.1 Security problem definition +ASE_OBJ.1 Security objectives for the operational environment +ASE_OBJ.2 Security objectives +ASE_ECD.1 Extended components definition +ASE_REQ.1 Stated security requirements +ASE_REQ.2 Derived security requirements +ASE_TSS.1 TOE summary specification +ASE_TSS.2 TOE summary specification with architectural design +summary +ASE: Security Target evaluation class decomposition +18 / 28 + BSI-CC-PP-0062-2010 Certification Report +Security assurance components (chapter 7) +“The following sections describe the constructs used in representing the assurance +classes, families, and components.“ +“Each assurance class contains at least one assurance family.” +“Each assurance family contains one or more assurance components.” +The following table shows the assurance class decomposition. +Assurance Class Assurance Components +ADV: Development +ADV_ARC.1 Security architecture description +ADV_FSP.1 Basic functional specification +ADV_FSP.2 Security-enforcing functional specification +ADV_FSP.3 Functional specification with complete summary +ADV_FSP.4 Complete functional specification +ADV_FSP.5 Complete semi-formal functional specification with +additional error information +ADV_FSP.6 Complete semi-formal functional specification with +additional formal specification +ADV_IMP.1 Implementation representation of the TSF +ADV_IMP.2 Implementation of the TSF +ADV_INT.1 Well-structured subset of TSF internals +ADV_INT.2 Well-structured internals +ADV_INT.3 Minimally complex internals +ADV_SPM.1 Formal TOE security policy model +ADV_TDS.1 Basic design +ADV_TDS.2 Architectural design +ADV_TDS.3 Basic modular design +ADV_TDS.4 Semiformal modular design +ADV_TDS.5 Complete semiformal modular design +ADV_TDS.6 Complete semiformal modular design with formal high- +level design presentation +AGD: +Guidance documents +AGD_OPE.1 Operational user guidance +AGD_PRE.1 Preparative procedures +ALC: Life cycle support +ALC_CMC.1 Labelling of the TOE +ALC_CMC.2 Use of a CM system +ALC_CMC.3 Authorisation controls +ALC_CMC.4 Production support, acceptance procedures and +automation +ALC_CMC.5 Advanced support +ALC_CMS.1 TOE CM coverage +ALC_CMS.2 Parts of the TOE CM coverage +ALC_CMS.3 Implementation representation CM coverage +ALC_CMS.4 Problem tracking CM coverage +ALC_CMS.5 Development tools CM coverage +ALC_DEL.1 Delivery procedures +ALC_DVS.1 Identification of security measures +ALC_DVS.2 Sufficiency of security measures +19 / 28 + Certification Report BSI-CC-PP-0062-2010 +Assurance Class Assurance Components +ALC_FLR.1 Basic flaw remediation +ALC_FLR.2 Flaw reporting procedures +ALC_FLR.3 Systematic flaw remediation +ALC_LCD.1 Developer defined life-cycle model +ALC_LCD.2 Measurable life-cycle model +ALC_TAT.1 Well-defined development tools +ALC_TAT.2 Compliance with implementation standards +ALC_TAT.3 Compliance with implementation standards - all parts +ATE: Tests +ATE_COV.1 Evidence of coverage +ATE_COV.2 Analysis of coverage +ATE_COV.3 Rigorous analysis of coverage +ATE_DPT.1 Testing: basic design +ATE_DPT.2 Testing: security enforcing modules +ATE_DPT.3 Testing: modular design +ATE_DPT.4 Testing: implementation representation +ATE_FUN.1 Functional testing +ATE_FUN.2 Ordered functional testing +ATE_IND.1 Independent testing – conformance +ATE_IND.2 Independent testing – sample +ATE_IND.3 Independent testing – complete +AVA: Vulnerability +assessment +AVA_VAN.1 Vulnerability survey +AVA_VAN.2 Vulnerability analysis +AVA_VAN.3 Focused vulnerability analysis +AVA_VAN.4 Methodical vulnerability analysis +AVA_VAN.5 Advanced methodical vulnerability analysis +Assurance class decomposition +20 / 28 + BSI-CC-PP-0062-2010 Certification Report +Evaluation assurance levels (chapter 8) +“The Evaluation Assurance Levels (EALs) provide an increasing scale that balances the +level of assurance obtained with the cost and feasibility of acquiring that degree of +assurance. The CC approach identifies the separate concepts of assurance in a TOE at +the end of the evaluation, and of maintenance of that assurance during the operational use +of the TOE. +It is important to note that not all families and components from CC Part 3 are included in +the EALs. This is not to say that these do not provide meaningful and desirable +assurances. Instead, it is expected that these families and components will be considered +for augmentation of an EAL in those PPs and STs for which they provide utility.” +Evaluation assurance level (EAL) overview (chapter 8.1) +“Table 1 represents a summary of the EALs. The columns represent a hierarchically +ordered set of EALs, while the rows represent assurance families. Each number in the +resulting matrix identifies a specific assurance component where applicable. +As outlined in the next Section, seven hierarchically ordered evaluation assurance levels +are defined in the CC for the rating of a TOE's assurance. They are hierarchically ordered +inasmuch as each EAL represents more assurance than all lower EALs. The increase in +assurance from EAL to EAL is accomplished by substitution of a hierarchically higher +assurance component from the same assurance family (i.e. increasing rigour, scope, +and/or depth) and from the addition of assurance components from other assurance +families (i.e. adding new requirements). +These EALs consist of an appropriate combination of assurance components as described +in chapter 7 of this CC Part 3. More precisely, each EAL includes no more than one +component of each assurance family and all assurance dependencies of every component +are addressed. +While the EALs are defined in the CC, it is possible to represent other combinations of +assurance. Specifically, the notion of “augmentation” allows the addition of assurance +components (from assurance families not already included in the EAL) or the substitution +of assurance components (with another hierarchically higher assurance component in the +same assurance family) to an EAL. Of the assurance constructs defined in the CC, only +EALs may be augmented. The notion of an “EAL minus a constituent assurance +component” is not recognised by the standard as a valid claim. Augmentation carries with +it the obligation on the part of the claimant to justify the utility and added value of the +added assurance component to the EAL. An EAL may also be augmented with extended +assurance requirements. +21 / 28 + Certification Report BSI-CC-PP-0062-2010 +Assurance +Class +Assurance +Family +Assurance Components by +Evaluation Assurance Level +EAL1 EAL2 EAL3 EAL4 EAL5 EAL6 EAL7 +Development ADV_ARC 1 1 1 1 1 1 +ADV_FSP 1 2 3 4 5 5 6 +ADV_IMP 1 1 2 2 +ADV_INT 2 3 3 +ADV_SPM 1 1 +ADV_TDS 1 2 3 4 5 6 +Guidance AGD_OPE 1 1 1 1 1 1 1 +Documents AGD_PRE 1 1 1 1 1 1 1 +Life cycle +Support +ALC_CMC 1 2 3 4 4 5 5 +ALC_CMS 1 2 3 4 5 5 5 +ALC_DEL 1 1 1 1 1 1 +ALC_DVS 1 1 1 2 2 +ALC_FLR +ALC_LCD 1 1 1 1 2 +ALC_TAT 1 2 3 3 +Security Target +Evaluation +ASE_CCL 1 1 1 1 1 1 1 +ASE_ECD 1 1 1 1 1 1 1 +ASE_INT 1 1 1 1 1 1 1 +ASE_OBJ 1 2 2 2 2 2 2 +ASR_REQ 1 2 2 2 2 2 2 +ASE_SPD 1 1 1 1 1 1 +ASE_TSS 1 1 1 1 1 1 1 +Tests ATE_COV 1 2 2 2 3 3 +ATE_DPT 1 1 3 3 4 +ATE_FUN 1 1 1 1 2 2 +ATE_IND 1 2 2 2 2 2 3 +Vulnerability +assessment +AVA_VAN 1 2 2 3 4 5 5 +Table 1: Evaluation assurance level summary” +22 / 28 + BSI-CC-PP-0062-2010 Certification Report +Evaluation assurance level 1 (EAL1) - functionally tested (chapter 8.3) +“Objectives +EAL1 is applicable where some confidence in correct operation is required, but the threats +to security are not viewed as serious. It will be of value where independent assurance is +required to support the contention that due care has been exercised with respect to the +protection of personal or similar information. +EAL1 requires only a limited security target. It is sufficient to simply state the SFRs that the +TOE must meet, rather than deriving them from threats, OSPs and assumptions through +security objectives. +EAL1 provides an evaluation of the TOE as made available to the customer, including +independent testing against a specification, and an examination of the guidance +documentation provided. It is intended that an EAL1 evaluation could be successfully +conducted without assistance from the developer of the TOE, and for minimal outlay. +An evaluation at this level should provide evidence that the TOE functions in a manner +consistent with its documentation.” +Evaluation assurance level 2 (EAL2) - structurally tested (chapter 8.4) +“Objectives +EAL2 requires the co-operation of the developer in terms of the delivery of design +information and test results, but should not demand more effort on the part of the +developer than is consistent with good commercial practise. As such it should not require a +substantially increased investment of cost or time. +EAL2 is therefore applicable in those circumstances where developers or users require a +low to moderate level of independently assured security in the absence of ready +availability of the complete development record. Such a situation may arise when securing +legacy systems, or where access to the developer may be limited.” +Evaluation assurance level 3 (EAL3) - methodically tested and checked (chapter 8.5) +“Objectives +EAL3 permits a conscientious developer to gain maximum assurance from positive +security engineering at the design stage without substantial alteration of existing sound +development practises. +EAL3 is applicable in those circumstances where developers or users require a moderate +level of independently assured security, and require a thorough investigation of the TOE +and its development without substantial re-engineering.” +23 / 28 + Certification Report BSI-CC-PP-0062-2010 +Evaluation assurance level 4 (EAL4) - methodically designed, tested, and reviewed +(chapter 8.6) +“Objectives +EAL4 permits a developer to gain maximum assurance from positive security engineering +based on good commercial development practises which, though rigorous, do not require +substantial specialist knowledge, skills, and other resources. EAL4 is the highest level at +which it is likely to be economically feasible to retrofit to an existing product line. +EAL4 is therefore applicable in those circumstances where developers or users require a +moderate to high level of independently assured security in conventional commodity TOEs +and are prepared to incur additional security-specific engineering costs.” +Evaluation assurance level 5 (EAL5) - semiformally designed and tested (chapter 8.7) +“Objectives +EAL5 permits a developer to gain maximum assurance from security engineering based +upon rigorous commercial development practises supported by moderate application of +specialist security engineering techniques. Such a TOE will probably be designed and +developed with the intent of achieving EAL5 assurance. It is likely that the additional costs +attributable to the EAL5 requirements, relative to rigorous development without the +application of specialised techniques, will not be large. +EAL5 is therefore applicable in those circumstances where developers or users require a +high level of independently assured security in a planned development and require a +rigorous development approach without incurring unreasonable costs attributable to +specialist security engineering techniques.” +Evaluation assurance level 6 (EAL6) - semiformally verified design and tested +(chapter 8.8) +“Objectives +EAL6 permits developers to gain high assurance from application of security engineering +techniques to a rigorous development environment in order to produce a premium TOE for +protecting high value assets against significant risks. +EAL6 is therefore applicable to the development of security TOEs for application in high +risk situations where the value of the protected assets justifies the additional costs.” +Evaluation assurance level 7 (EAL7) - formally verified design and tested +(chapter 8.9) +“Objectives +EAL7 is applicable to the development of security TOEs for application in extremely high +risk situations and/or where the high value of the assets justifies the higher costs. Practical +application of EAL7 is currently limited to TOEs with tightly focused security functionality +that is amenable to extensive formal analysis.” +Class AVA: Vulnerability assessment (chapter 16) +“The AVA: Vulnerability assessment class addresses the possibility of exploitable +vulnerabilities introduced in the development or the operation of the TOE.” +24 / 28 + BSI-CC-PP-0062-2010 Certification Report +Vulnerability analysis (AVA_VAN) (chapter 16.1) +"Objectives +Vulnerability analysis is an assessment to determine whether potential vulnerabilities +identified, during the evaluation of the development and anticipated operation of the TOE +or by other methods (e.g. by flaw hypotheses or quantitative or statistical analysis of the +security behaviour of the underlying security mechanisms), could allow attackers to violate +the SFRs. +Vulnerability analysis deals with the threats that an attacker will be able to discover flaws +that will allow unauthorised access to data and functionality, allow the ability to interfere +with or alter the TSF, or interfere with the authorised capabilities of other users.” +25 / 28 + Certification Report BSI-CC-PP-0062-2010 +This page is intentionally left blank. +26 / 28 + BSI-CC-PP-0062-2010 Certification Report +D Annexes +List of annexes of this certification report +Annex A: Fingerprint Spoof Detection Protection Profile based on Organisational +Security Policies (FSDPP_OSP), Version 1.7 [6] provided within a separate +document. +27 / 28 + Certification Report BSI-CC-PP-0062-2010 +This page is intentionally left blank. +28 / 28 + diff --git a/tests/fips/conftest.py b/tests/fips/conftest.py index f2da80c4e..f9377c7dc 100644 --- a/tests/fips/conftest.py +++ b/tests/fips/conftest.py @@ -4,6 +4,9 @@ import tests.data.fips.dataset from sec_certs.dataset import CPEDataset, CVEDataset, FIPSDataset +from sec_certs.dataset.auxiliary_dataset_handling import CPEDatasetHandler, CPEMatchDictHandler, CVEDatasetHandler +from sec_certs.heuristics.common import compute_cpe_heuristics, compute_related_cves, compute_transitive_vulnerabilities +from sec_certs.heuristics.fips import compute_references @pytest.fixture(scope="module") @@ -27,15 +30,30 @@ def processed_dataset( ] toy_dataset.certs = {x.dgst: x for x in tested_certs} + cpe_handler = CPEDatasetHandler(toy_dataset.auxiliary_datasets_dir) + cpe_handler.dset = cpe_dataset + cve_handler = CVEDatasetHandler(toy_dataset.auxiliary_datasets_dir) + cve_handler.dset = cve_dataset + cpe_match_dict_handler = CPEMatchDictHandler(toy_dataset.auxiliary_datasets_dir) + cpe_match_dict_handler.dset = {} + toy_dataset.aux_handlers = { + CPEDatasetHandler: cpe_handler, + CVEDatasetHandler: cve_handler, + CPEMatchDictHandler: cpe_match_dict_handler, + } + toy_dataset.download_all_artifacts() toy_dataset.convert_all_pdfs() toy_dataset.extract_data() - toy_dataset._compute_references(keep_unknowns=True) - toy_dataset.auxiliary_datasets.cpe_dset = cpe_dataset - toy_dataset.auxiliary_datasets.cve_dset = cve_dataset - toy_dataset.compute_cpe_heuristics() - toy_dataset.compute_related_cves() - toy_dataset._compute_transitive_vulnerabilities() + compute_cpe_heuristics(toy_dataset.aux_handlers[CPEDatasetHandler].dset, toy_dataset.certs.values()) + compute_related_cves( + toy_dataset.aux_handlers[CPEDatasetHandler].dset, + toy_dataset.aux_handlers[CVEDatasetHandler].dset, + toy_dataset.aux_handlers[CPEMatchDictHandler].dset, + toy_dataset.certs.values(), + ) + compute_references(toy_dataset.certs, keep_unknowns=True) + compute_transitive_vulnerabilities(toy_dataset.certs) return toy_dataset diff --git a/tests/fips/test_fips_analysis.py b/tests/fips/test_fips_analysis.py index 61f56848a..54954c41a 100644 --- a/tests/fips/test_fips_analysis.py +++ b/tests/fips/test_fips_analysis.py @@ -2,7 +2,9 @@ import pytest +from sec_certs.dataset.auxiliary_dataset_handling import CPEDatasetHandler, CVEDatasetHandler from sec_certs.dataset.fips import FIPSDataset +from sec_certs.heuristics.common import compute_related_cves @pytest.mark.parametrize( @@ -104,11 +106,16 @@ def test_match_cpe(processed_dataset: FIPSDataset): def test_find_related_cves(processed_dataset: FIPSDataset): - assert processed_dataset.auxiliary_datasets.cve_dset - processed_dataset.auxiliary_datasets.cve_dset._cpe_uri_to_cve_ids_lookup[ + assert processed_dataset.aux_handlers[CVEDatasetHandler].dset + processed_dataset.aux_handlers[CVEDatasetHandler].dset._cpe_uri_to_cve_ids_lookup[ "cpe:2.3:o:redhat:enterprise_linux:7.1:*:*:*:*:*:*:*" ] = {"CVE-123456"} - processed_dataset.compute_related_cves() + compute_related_cves( + processed_dataset.aux_handlers[CPEDatasetHandler].dset, + processed_dataset.aux_handlers[CVEDatasetHandler].dset, + {}, + processed_dataset.certs.values(), + ) assert processed_dataset["2441"].heuristics.related_cves == {"CVE-123456"} @@ -117,7 +124,12 @@ def test_find_related_cves_criteria_configuration(processed_dataset: FIPSDataset "cpe:2.3:a:nalin_dahyabhai:vte:0.11.21:*:*:*:*:*:*:*", "cpe:2.3:a:gnome:gnome-terminal:2.2:*:*:*:*:*:*:*", } - processed_dataset.compute_related_cves() + compute_related_cves( + processed_dataset.aux_handlers[CPEDatasetHandler].dset, + processed_dataset.aux_handlers[CVEDatasetHandler].dset, + {}, + processed_dataset.certs.values(), + ) assert processed_dataset["2441"].heuristics.related_cves == {"CVE-2003-0070"} diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index 9b18b754f..81ad019d2 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -93,18 +93,18 @@ def test_download_and_convert_artifacts(toy_dataset: FIPSDataset, data_dir: Path toy_dataset.copy_dataset(tmp_dir) toy_dataset.download_all_artifacts() - if not crt.state.policy_download_ok or crt.state.module_download_ok: - pytest.xfail(reason="Fail due to error during download") + if not crt.state.policy_download_ok or not crt.state.module_download_ok: + pytest.xfail(reason="Fail due to error during download") - toy_dataset.convert_all_pdfs() + toy_dataset.convert_all_pdfs() - assert not crt.state.policy_convert_garbage - assert crt.state.policy_convert_ok - assert crt.state.policy_pdf_hash == "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1" - assert crt.state.policy_txt_path.exists() + assert not crt.state.policy_convert_garbage + assert crt.state.policy_convert_ok + assert crt.state.policy_pdf_hash == "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1" + assert crt.state.policy_txt_path.exists() - template_policy_txt_path = data_dir / "template_policy_184097a88a9b4ad9.txt" - assert abs(crt.state.policy_txt_path.stat().st_size - template_policy_txt_path.stat().st_size) < 1000 + template_policy_txt_path = data_dir / "template_policy_184097a88a9b4ad9.txt" + assert abs(crt.state.policy_txt_path.stat().st_size - template_policy_txt_path.stat().st_size) < 1000 def test_to_pandas(toy_dataset: FIPSDataset): diff --git a/tests/fips/test_fips_iut.py b/tests/fips/test_fips_iut.py index e3bfbc1cd..17597e1d0 100644 --- a/tests/fips/test_fips_iut.py +++ b/tests/fips/test_fips_iut.py @@ -8,6 +8,7 @@ import pytest import tests.data.fips.iut +from sec_certs.configuration import config from sec_certs.dataset.fips import FIPSDataset from sec_certs.dataset.fips_iut import IUTDataset from sec_certs.model.fips_matching import FIPSProcessMatcher @@ -31,22 +32,20 @@ def test_iut_dataset_from_dumps(data_dir: Path): assert len(dset) == 2 -def test_iut_dataset_from_web_latest(): - assert IUTDataset.from_web_latest() +def test_iut_dataset_from_web(): + assert IUTDataset.from_web() def test_iut_snapshot_from_dump(data_dump_path: Path): assert IUTSnapshot.from_dump(data_dump_path) -def test_iut_snapshot_from_web(): +@pytest.mark.parametrize("preferred_source", ["origin", "sec-certs"]) +def test_iut_snapshot_from_web(preferred_source): + config.preferred_source_remote_datasets = preferred_source assert IUTSnapshot.from_web() -def test_iut_snapshot_from_web_latest(): - assert IUTSnapshot.from_web_latest() - - def test_iut_matching(processed_dataset: FIPSDataset): entry = IUTEntry( module_name="Red Hat Enterprise Linux 7.1 OpenSSL Module", diff --git a/tests/fips/test_fips_mip.py b/tests/fips/test_fips_mip.py index 1f2d2d2f5..15b0d4bf9 100644 --- a/tests/fips/test_fips_mip.py +++ b/tests/fips/test_fips_mip.py @@ -8,6 +8,7 @@ import pytest import tests.data.fips.mip +from sec_certs.configuration import config from sec_certs.dataset.fips import FIPSDataset from sec_certs.dataset.fips_mip import MIPDataset from sec_certs.model.fips_matching import FIPSProcessMatcher @@ -32,7 +33,7 @@ def test_mip_dataset_from_dumps(data_dir: Path): def test_mip_flows(): - dset = MIPDataset.from_web_latest() + dset = MIPDataset.from_web() assert dset.compute_flows() @@ -40,14 +41,12 @@ def test_mip_snapshot_from_dump(data_dump_path: Path): assert MIPSnapshot.from_dump(data_dump_path) -def test_from_web(): +@pytest.mark.parametrize("preferred_source", ["sec-certs", "origin"]) +def test_from_web(preferred_source): + config.preferred_source_remote_datasets = preferred_source assert MIPSnapshot.from_web() -def test_from_web_latest(): - assert MIPSnapshot.from_web_latest() - - def test_mip_matching(processed_dataset: FIPSDataset): entry = MIPEntry( module_name="Red Hat Enterprise Linux 7.1 OpenSSL Module", diff --git a/tests/test_common.py b/tests/test_common.py index 7b29dd8d2..660659236 100644 --- a/tests/test_common.py +++ b/tests/test_common.py @@ -1,4 +1,7 @@ +import pytest + from sec_certs.cert_rules import cc_rules, fips_rules, rules +from sec_certs.utils.helpers import choose_lowest_eal def test_rules(): @@ -7,3 +10,18 @@ def test_rules(): for rule_group in rules: if rule_group not in ("cc_rules", "fips_rules", "cc_filename_cert_id"): assert rule_group in cc_rules or rule_group in fips_rules + + +@pytest.mark.parametrize( + "strings, expected", + [ + ({"EAL5", "EAL4+", "EAL3", "random", "EAL7+", "EAL2"}, "EAL2"), + ({"EAL1", "EAL1+", "EAL2", "EAL3+"}, "EAL1"), + ({"random", "no_match"}, None), + ({"EAL5+", "EAL6"}, "EAL5+"), + (set(), None), + ({"EAL100", "EAL10", "EAL20+"}, "EAL10"), + ], +) +def test_find_min_eal(strings, expected): + assert choose_lowest_eal(strings) == expected diff --git a/tests/test_cve_matching.py b/tests/test_cve_matching.py index c7e7da289..ed7ecc177 100644 --- a/tests/test_cve_matching.py +++ b/tests/test_cve_matching.py @@ -4,7 +4,9 @@ import pytest +from sec_certs.dataset.auxiliary_dataset_handling import CPEDatasetHandler, CPEMatchDictHandler, CVEDatasetHandler from sec_certs.dataset.cc import CCDataset +from sec_certs.heuristics.common import compute_cpe_heuristics, compute_related_cves @pytest.fixture(scope="module") @@ -12,11 +14,17 @@ def processed_cc_dataset() -> CCDataset: with tempfile.TemporaryDirectory() as tmp_dir: cc_dset = CCDataset(root_dir=tmp_dir) cc_dset.get_certs_from_web() - cc_dset._prepare_cpe_dataset() - cc_dset._prepare_cve_dataset() - cc_dset._prepare_cpe_match_dict() - cc_dset.compute_cpe_heuristics() - cc_dset.compute_related_cves() + cc_dset.aux_handlers[CPEDatasetHandler].process_dataset() + cc_dset.aux_handlers[CVEDatasetHandler].process_dataset() + cc_dset.aux_handlers[CPEMatchDictHandler].process_dataset() + + compute_cpe_heuristics(cc_dset.aux_handlers[CPEDatasetHandler].dset, cc_dset.certs.values()) + compute_related_cves( + cc_dset.aux_handlers[CPEDatasetHandler].dset, + cc_dset.aux_handlers[CVEDatasetHandler].dset, + cc_dset.aux_handlers[CPEMatchDictHandler].dset, + cc_dset.certs.values(), + ) return cc_dset diff --git a/tests/test_nvd_dataset_builder.py b/tests/test_nvd_dataset_builder.py index fda94d1fe..daf0270a6 100644 --- a/tests/test_nvd_dataset_builder.py +++ b/tests/test_nvd_dataset_builder.py @@ -41,6 +41,7 @@ def test_cpe_match_download_from_seccerts(): assert datetime.fromisoformat(cpe_match_dict["timestamp"]) > datetime.now() - timedelta(days=28) +@pytest.mark.xfail(reason="May fail due to NVD server errors.") @pytest.mark.parametrize( "default_dataset, builder_class", [ @@ -60,7 +61,7 @@ def get_dataset_len(dset) -> int: return len(dset) return len(dset["match_strings"]) - config.preferred_source_nvd_datasets = "api" + config.preferred_source_remote_datasets = "origin" with builder_class(api_key=config.nvd_api_key) as dataset_builder: dataset = dataset_builder._init_new_dataset() assert dataset == default_dataset