From f51f9d9526d5260d7a19057615fa97ec12950bb9 Mon Sep 17 00:00:00 2001 From: Eidan Rosado <19523360+EdyVision@users.noreply.github.com> Date: Wed, 28 Dec 2022 12:36:24 -0700 Subject: [PATCH] Er.package edits (#27) * Package docs and example analysis updates * Version bump --- .zenodo.json | 2 +- CITATION.cff | 4 +- README.md | 2 +- docs/DETECTION_AND_ANALYSIS.md | 78 ++++++++----- docs/UC1_Converting_Existing_Detections.png | Bin 0 -> 21957 bytes ...tin_Service_for_Detection_and_Analysis.png | Bin 0 -> 17787 bytes notebooks/pii-analysis-ms-presidio.ipynb | 103 ++++++++++-------- pii_codex/models/microsoft_presidio_pii.py | 2 +- pii_codex/services/analysis_service.py | 6 + pii_codex/services/assessment_service.py | 8 +- pyproject.toml | 4 +- 11 files changed, 124 insertions(+), 85 deletions(-) create mode 100644 docs/UC1_Converting_Existing_Detections.png create mode 100644 docs/UC2_Using_Presidio_Builtin_Service_for_Detection_and_Analysis.png diff --git a/.zenodo.json b/.zenodo.json index f9d83dd..bdf63f6 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -1,6 +1,6 @@ { "access_right": "open", - "version": "0.4.2", + "version": "0.4.3", "creators": [ { "orcid": "0000-0003-0665-098X", diff --git a/CITATION.cff b/CITATION.cff index 5f8e78e..f908581 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -5,6 +5,6 @@ authors: given-names: Eidan J. orcid: https://orcid.org/0000-0003-0665-098X title: "pii-codex: a Python library for PII detection, categorization, and severity assessment" -version: 0.4.2 +version: 0.4.3 doi: 10.5281/zenodo.7212576 -date-released: 2022-12-26 +date-released: 2022-12-28 diff --git a/README.md b/README.md index b365f5e..b254646 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,6 @@ Sample output (results object converted to `dict` from notebook): "analysis": [ { "pii_type_detected": "PERSON", - "sanitized_text: "Hi! My name is ", "risk_level": 3, "risk_level_definition": "Identifiable", "cluster_membership_type": "Financial Information", @@ -85,6 +84,7 @@ Sample output (results object converted to `dict` from notebook): ], "index": 0, "risk_score_mean": 3, + "sanitized_text: "Hi! My name is ", }, ... ], diff --git a/docs/DETECTION_AND_ANALYSIS.md b/docs/DETECTION_AND_ANALYSIS.md index 0508a75..81fc3ca 100644 --- a/docs/DETECTION_AND_ANALYSIS.md +++ b/docs/DETECTION_AND_ANALYSIS.md @@ -8,7 +8,44 @@ The following are not integrated into the service, but have PII type mapping and
  • Azure PII Detection Cognitive Skill (Requires Azure Account) [docs]
    • -In the case you are using the built-in Presidio functionality, you can call the analysis service as follows: +For those using pre-detected results, adapters are provided to convert types and results to the expected DetectionResult/DetectionResultItem format (see diagram below): + +![Converting And Analyzing Existing Detections](UC1_Converting_Existing_Detections.png) + +To supply the analyzer module with a collection of pre-detected results from your own Microsoft Presidio, Azure, or AWS Comprehend analysis process, you will need to first convert the detection to a set of DetectionResult objects to feed into the analyzer as follows: + +```python +from typing import List +from pii_codex.models.common import ( + AnalysisProviderType, +) +from presidio_analyzer import RecognizerResult +from pii_codex.services.analysis_service import PIIAnalysisService +from pii_codex.services.adapters.detection_adapters.presidio_detection_adapter import PresidioPIIDetectionAdapter +from pii_codex.models.analysis import DetectionResult + +presidio_detection_service = PresidioPIIDetectionAdapter() + +list_of_detections: List[RecognizerResult] = [] # your list of detections +converted_detections: List[DetectionResult] = presidio_detection_service.convert_analyzed_collection( + pii_detections=list_of_detections + ) +pii_analysis_service = PIIAnalysisService( + analysis_provider=AnalysisProviderType.PRESIDIO.name +) # If you don't intend to use presidio, override the analysis_provider value + +results = pii_analysis_service.analyze_detection_collection( + detection_collection=converted_detections, + collection_name="Data Set Label", # this is more for those that intend to find a way to label collections + collection_type="SAMPLE" # defaults to POPULATION, input used for standard deviation and variance calculations +) +``` + +The other two detection adapters available are AWSComprehendPIIDetectionAdapter and AzurePIIDetectionAdapter. + +
    + +In the case you require the built-in Presidio functionality, you can call the analysis service as follows: ```python from pii_codex.services.analysis_service import PIIAnalysisService @@ -24,6 +61,11 @@ results = pii_analysis_service.analyze_collection( ) ``` +This functionality can easily take a singular text item or a collection of them and runs through the presidio analysis and assessment service files as presented in the diagram below. + +![Converting And Analyzing Text with Presidio Builtin](UC2_Using_Presidio_Builtin_Service_for_Detection_and_Analysis.png) + + For those analyzing social media posts, you can also supply metadata per text sample to be analyzed in a dataframe. ```python @@ -56,32 +98,6 @@ results = pii_analysis_service.analyze_collection( ) ``` -To supply the analyzer module with a collection of pre-detected results from your own Microsoft Presidio, Azure, or AWS Comprehend analysis process, you will need to first convert the detection to a set of DetectionResult objects to feed into the analyzer as follows: - -```python -from typing import List -from presidio_analyzer import RecognizerResult -from pii_codex.services.analysis_service import PIIAnalysisService -from pii_codex.services.adapters.detection_adapters.presidio_detection_adapter import PresidioPIIDetectionAdapter -from pii_codex.models.analysis import DetectionResult - -presidio_detection_service = PresidioPIIDetectionAdapter() - -list_of_detections: List[RecognizerResult] = [] # your list of MSFT Presidio detections -converted_detections: List[DetectionResult] = presidio_detection_service.convert_analyzed_collection( - pii_detections=list_of_detections - ) -pii_analysis_service = PIIAnalysisService() - -results = pii_analysis_service.analyze_detection_collection( - detection_collection=converted_detections, - collection_name="Data Set Label", # this is more for those that intend to find a way to label collections - collection_type="SAMPLE" # defaults to POPULATION, input used for standard deviation and variance calculations -) -``` - -The other two detection services available are AWSComprehendPIIDetectionAdapter and AzurePIIDetectionAdapter. - Sample output: ``` @@ -93,7 +109,6 @@ Sample output: "analysis": [ { "pii_type_detected": "PERSON", - "sanitized_text": "Hi! My name is ", "risk_level": 3, "risk_level_definition": "Identifiable", "cluster_membership_type": "Financial Information", @@ -108,6 +123,7 @@ Sample output: ], "index": 0, "risk_score_mean": 3, + "sanitized_text": "Hi! My name is ", }, { "analysis": [ @@ -153,6 +169,7 @@ Sample output: ], "index": 1, "risk_score_mean": 2.6666666666666665, + "sanitized_text": "Hi! My phone number is 555-555-5555. You can also reach me by email at email@example.com", }, { "analysis": [ @@ -168,6 +185,7 @@ Sample output: ], "index": 2, "risk_score_mean": 1, + "sanitized_text": "Hi! What is the title of this book?", }, { "analysis": [ @@ -187,6 +205,7 @@ Sample output: ], "index": 3, "risk_score_mean": 2, + "sanitized_text": "", }, { "analysis": [ @@ -202,6 +221,7 @@ Sample output: ], "index": 4, "risk_score_mean": 1, + "sanitized_text": "Hi! I have a cat too.", }, ], "detection_count": 5, @@ -229,4 +249,4 @@ Sample output: ``` -Check out full analysis example in the notebook: notebooks/pii-batch-analysis-ms-presidio. \ No newline at end of file +Check out full analysis example in the notebook: notebooks/pii-analysis-ms-presidio. \ No newline at end of file diff --git a/docs/UC1_Converting_Existing_Detections.png b/docs/UC1_Converting_Existing_Detections.png new file mode 100644 index 0000000000000000000000000000000000000000..5047eb8f161ff5e993ff63d31528cf150554aadd GIT binary patch literal 21957 zcmdqIbyQo=`#%V!Ev48SD9~aRiWMuxB~YU{6fYLq;u_q7mIB3#7YoHDK#E&}y9IZG z69^JKIGcVx-_P&+-95Yi?4GlG&gPt)duQ%rcjnGK^E~sK(2uHe?3knK~i;MRT4y)>0X6KjEb4t$6&eF1qVO8+dENDeloqupdXWvlo;K&*p^QQ`d z+1jaYXl?H3gXET0*S8LjP1PdWvkJ=Q7nWN(`hH~;UR_<)!rL0#da?`43Sl)xu$so$~J8yN3@S^78URq0s*R z{(lT`G5A`-uS#<734YvRmd6)J9OU(!2?!ph{{0gwxCX}KD~VkcRAhVa 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'Contact Information',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Stand Alone PII',\n 'nist_category': 'Directly PII',\n 'entity_type': 'PHONE_NUMBER',\n 'score': 0.75,\n 'start': 22,\n 'end': 34},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 2,\n 'risk_score_mean': 3,\n 'sanitized_text': 'Oh I do! My number is . Where is the residence hall?'},\n {'analysis': [{'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.85,\n 'start': 40,\n 'end': 42},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 3,\n 'risk_score_mean': 2.5,\n 'sanitized_text': 'The dorm is over at 123 Dark Data Lane, , 11111'},\n {'analysis': [{'pii_type_detected': None,\n 'risk_level': 1,\n 'risk_level_definition': 'Non-Identifiable',\n 'cluster_membership_type': None,\n 'hipaa_category': None,\n 'dhs_category': None,\n 'nist_category': None},\n {'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.0,\n 'start': 0,\n 'end': 0},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 4,\n 'risk_score_mean': 2,\n 'sanitized_text': \"Cool, I'll be there!\"}],\n 'detection_count': 15,\n 'risk_scores': [2, 2.6, 3, 2.5, 2],\n 'risk_score_mean': 2.42,\n 'risk_score_mode': 2,\n 'risk_score_median': 2.5,\n 'risk_score_standard_deviation': 0.38157568056677826,\n 'risk_score_variance': 0.1456,\n 'detected_pii_types': {'EMAIL_ADDRESS',\n 'LOCATION',\n 'PHONE_NUMBER',\n 'SCREEN_NAME',\n 'URL'},\n 'detected_pii_type_frequencies': {'LOCATION': 4,\n 'SCREEN_NAME': 5,\n 'EMAIL_ADDRESS': 1,\n 'PHONE_NUMBER': 2,\n 'URL': 1}}" + "text/plain": "{'collection_name': 'PII Collection 1',\n 'collection_type': 'POPULATION',\n 'analyses': [{'analysis': [{'pii_type_detected': None,\n 'risk_level': 1,\n 'risk_level_definition': 'Non-Identifiable',\n 'cluster_membership_type': None,\n 'hipaa_category': None,\n 'dhs_category': None,\n 'nist_category': None},\n {'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.0,\n 'start': 0,\n 'end': 0},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 0,\n 'risk_score_mean': 2,\n 'sanitized_text': 'I attend the University of Central Florida, how about you?'},\n {'analysis': [{'pii_type_detected': 'NRP',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'NRP',\n 'score': 0.85,\n 'start': 5,\n 'end': 13},\n {'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.0,\n 'start': 0,\n 'end': 0},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 1,\n 'risk_score_mean': 2.3333333333333335,\n 'sanitized_text': 'As a , I promise to uphold....'},\n {'analysis': [{'pii_type_detected': 'NRP',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'NRP',\n 'score': 0.85,\n 'start': 5,\n 'end': 13},\n {'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.0,\n 'start': 0,\n 'end': 0},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 2,\n 'risk_score_mean': 2.3333333333333335,\n 'sanitized_text': 'As a , I can tell you that....'},\n {'analysis': [{'pii_type_detected': 'EMAIL_ADDRESS',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Stand Alone PII',\n 'nist_category': 'Directly PII',\n 'entity_type': 'EMAIL_ADDRESS',\n 'score': 1.0,\n 'start': 72,\n 'end': 92},\n {'pii_type_detected': 'PHONE_NUMBER',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Contact Information',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Stand Alone PII',\n 'nist_category': 'Directly PII',\n 'entity_type': 'PHONE_NUMBER',\n 'score': 0.75,\n 'start': 43,\n 'end': 55},\n {'pii_type_detected': 'URL',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Community Interaction',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Linkable',\n 'nist_category': 'Linkable',\n 'entity_type': 'URL',\n 'score': 0.5,\n 'start': 83,\n 'end': 92},\n {'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.0,\n 'start': 0,\n 'end': 0},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 3,\n 'risk_score_mean': 2.6,\n 'sanitized_text': 'If anyone needs trig help, my phone number and my email is '},\n {'analysis': [{'pii_type_detected': 'PHONE_NUMBER',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Contact Information',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Stand Alone PII',\n 'nist_category': 'Directly PII',\n 'entity_type': 'PHONE_NUMBER',\n 'score': 0.75,\n 'start': 22,\n 'end': 34},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 4,\n 'risk_score_mean': 3,\n 'sanitized_text': 'Oh I do! My number is . Where is the residence hall?'},\n {'analysis': [{'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.85,\n 'start': 40,\n 'end': 42},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 5,\n 'risk_score_mean': 2.5,\n 'sanitized_text': 'The dorm is over at 123 Dark Data Lane, , 11111'},\n {'analysis': [{'pii_type_detected': None,\n 'risk_level': 1,\n 'risk_level_definition': 'Non-Identifiable',\n 'cluster_membership_type': None,\n 'hipaa_category': None,\n 'dhs_category': None,\n 'nist_category': None},\n {'pii_type_detected': 'LOCATION',\n 'risk_level': 2,\n 'risk_level_definition': 'Semi-Identifiable',\n 'cluster_membership_type': 'Secure Identifiers',\n 'hipaa_category': 'Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Linkable',\n 'entity_type': 'LOCATION',\n 'score': 0.0,\n 'start': 0,\n 'end': 0},\n {'pii_type_detected': 'SCREEN_NAME',\n 'risk_level': 3,\n 'risk_level_definition': 'Identifiable',\n 'cluster_membership_type': 'Personal Preferences',\n 'hipaa_category': 'Not Protected Health Information',\n 'dhs_category': 'Not Mentioned',\n 'nist_category': 'Directly PII',\n 'entity_type': 'SCREEN_NAME',\n 'score': 0.0,\n 'start': 0,\n 'end': 0}],\n 'index': 6,\n 'risk_score_mean': 2,\n 'sanitized_text': \"Cool, I'll be there!\"}],\n 'detection_count': 21,\n 'risk_scores': [2, 2.3333333333333335, 2.3333333333333335, 2.6, 3, 2.5, 2],\n 'risk_score_mean': 2.395238095238095,\n 'risk_score_mode': 2,\n 'risk_score_median': 2.3333333333333335,\n 'risk_score_standard_deviation': 0.32485824572972455,\n 'risk_score_variance': 0.1055328798185941,\n 'detected_pii_types': {'EMAIL_ADDRESS',\n 'LOCATION',\n 'NRP',\n 'PHONE_NUMBER',\n 'SCREEN_NAME',\n 'URL'},\n 'detected_pii_type_frequencies': {'LOCATION': 6,\n 'SCREEN_NAME': 7,\n 'NRP': 2,\n 'EMAIL_ADDRESS': 1,\n 'PHONE_NUMBER': 2,\n 'URL': 1}}" }, "execution_count": 3, "metadata": {}, @@ -189,9 +199,11 @@ "source": [ "\n", "analysis_results = pii_analysis_service.analyze_collection(\n", - " data=pd.DataFrame({\n", + " data=pd.DataFrame.from_dict({\n", " \"text\": [\n", " \"I attend the University of Central Florida, how about you?\",\n", + " \"As a democrat, I promise to uphold....\",\n", + " \"As a Catholic, I can tell you that....\",\n", " \"If anyone needs trig help, my phone number 555-555-5555 and my email is example123@email.com\",\n", " \"Oh I do! My number is 777-777-7777. Where is the residence hall?\",\n", " \"The dorm is over at 123 Dark Data Lane, OH, 11111\",\n", @@ -200,12 +212,13 @@ " \"metadata\": [\n", " {\"location\": True, \"url\": False, \"screen_name\": True},\n", " {\"location\": True, \"url\": False, \"screen_name\": True},\n", + " {\"location\": True, \"url\": False, \"screen_name\": True},\n", + " {\"location\": True, \"url\": False, \"screen_name\": True},\n", " {\"location\": False, \"url\": False, \"screen_name\": True}, # Not all social media posts will have location metadata\n", " {\"location\": False, \"url\": False, \"screen_name\": True},\n", " {\"location\": True, \"url\": False, \"screen_name\": True},\n", " ]\n", " }),\n", - " language_code=\"en\",\n", " collection_type=\"population\",\n", " collection_name=\"PII Collection 1\"\n", ")\n", @@ -234,13 +247,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "outputs": [ { "data": { - "text/plain": "['I attend the University of Central Florida, how about you?',\n 'If anyone needs trig help, my phone number and my email is ',\n 'Oh I do! My number is . Where is the residence hall?',\n 'The dorm is over at 123 Dark Data Lane, , 11111',\n \"Cool, I'll be there!\"]" + "text/plain": "['I attend the University of Central Florida, how about you?',\n 'As a , I promise to uphold....',\n 'As a , I can tell you that....',\n 'If anyone needs trig help, my phone number and my email is ',\n 'Oh I do! My number is . Where is the residence hall?',\n 'The dorm is over at 123 Dark Data Lane, , 11111',\n \"Cool, I'll be there!\"]" }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -258,14 +271,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "outputs": [ { "data": { - "text/plain": " 0\ncollection_name PII Collection 1\ncollection_type POPULATION\ndetection_count 15\nrisk_scores [2, 2.6, 3, 2.5, 2]\nrisk_score_mean 2.42\nrisk_score_mode 2\nrisk_score_median 2.5\nrisk_score_standard_deviation 0.381576\nrisk_score_variance 0.1456\ndetected_pii_types {EMAIL_ADDRESS, LOCATION, SCREEN_NAME, URL, PH...\ndetected_pii_type_frequencies {'LOCATION': 4, 'SCREEN_NAME': 5, 'EMAIL_ADDRE...", - "text/html": "
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    0
    collection_namePII Collection 1
    collection_typePOPULATION
    detection_count15
    risk_scores[2, 2.6, 3, 2.5, 2]
    risk_score_mean2.42
    risk_score_mode2
    risk_score_median2.5
    risk_score_standard_deviation0.381576
    risk_score_variance0.1456
    detected_pii_types{EMAIL_ADDRESS, LOCATION, SCREEN_NAME, URL, PH...
    detected_pii_type_frequencies{'LOCATION': 4, 'SCREEN_NAME': 5, 'EMAIL_ADDRE...
    \n
    " + "text/plain": " 0\ncollection_name PII Collection 1\ncollection_type POPULATION\ndetection_count 21\nrisk_scores [2, 2.3333333333333335, 2.3333333333333335, 2....\nrisk_score_mean 2.395238\nrisk_score_mode 2\nrisk_score_median 2.333333\nrisk_score_standard_deviation 0.324858\nrisk_score_variance 0.105533\ndetected_pii_types {SCREEN_NAME, LOCATION, URL, EMAIL_ADDRESS, NR...\ndetected_pii_type_frequencies {'LOCATION': 6, 'SCREEN_NAME': 7, 'NRP': 2, 'E...", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    0
    collection_namePII Collection 1
    collection_typePOPULATION
    detection_count21
    risk_scores[2, 2.3333333333333335, 2.3333333333333335, 2....
    risk_score_mean2.395238
    risk_score_mode2
    risk_score_median2.333333
    risk_score_standard_deviation0.324858
    risk_score_variance0.105533
    detected_pii_types{SCREEN_NAME, LOCATION, URL, EMAIL_ADDRESS, NR...
    detected_pii_type_frequencies{'LOCATION': 6, 'SCREEN_NAME': 7, 'NRP': 2, 'E...
    \n
    " }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -290,14 +303,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "outputs": [ { "data": { - "text/plain": " analysis index risk_score_mean \\\n0 [{'pii_type_detected': None, 'risk_level': 1, ... 0 2.0 \n1 [{'pii_type_detected': 'EMAIL_ADDRESS', 'risk_... 1 2.6 \n2 [{'pii_type_detected': 'PHONE_NUMBER', 'risk_l... 2 3.0 \n3 [{'pii_type_detected': 'LOCATION', 'risk_level... 3 2.5 \n4 [{'pii_type_detected': None, 'risk_level': 1, ... 4 2.0 \n\n sanitized_text collection_name \n0 I attend the University of Central Florida, ho... PII Collection 1 \n1 If anyone needs trig help, my phone number . Where is the... PII Collection 1 \n3 The dorm is over at 123 Dark Data Lane, \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    analysisindexrisk_score_meansanitized_textcollection_name
    0[{'pii_type_detected': None, 'risk_level': 1, ...02.0I attend the University of Central Florida, ho...PII Collection 1
    1[{'pii_type_detected': 'EMAIL_ADDRESS', 'risk_...12.6If anyone needs trig help, my phone number <RE...PII Collection 1
    2[{'pii_type_detected': 'PHONE_NUMBER', 'risk_l...23.0Oh I do! My number is <REDACTED>. Where is the...PII Collection 1
    3[{'pii_type_detected': 'LOCATION', 'risk_level...32.5The dorm is over at 123 Dark Data Lane, <REDAC...PII Collection 1
    4[{'pii_type_detected': None, 'risk_level': 1, ...42.0Cool, I'll be there!PII Collection 1
    \n" + "text/plain": " analysis index risk_score_mean \\\n0 [{'pii_type_detected': None, 'risk_level': 1, ... 0 2.000000 \n1 [{'pii_type_detected': 'NRP', 'risk_level': 2,... 1 2.333333 \n2 [{'pii_type_detected': 'NRP', 'risk_level': 2,... 2 2.333333 \n3 [{'pii_type_detected': 'EMAIL_ADDRESS', 'risk_... 3 2.600000 \n4 [{'pii_type_detected': 'PHONE_NUMBER', 'risk_l... 4 3.000000 \n5 [{'pii_type_detected': 'LOCATION', 'risk_level... 5 2.500000 \n6 [{'pii_type_detected': None, 'risk_level': 1, ... 6 2.000000 \n\n sanitized_text collection_name \n0 I attend the University of Central Florida, ho... PII Collection 1 \n1 As a , I promise to uphold.... PII Collection 1 \n2 As a , I can tell you that.... PII Collection 1 \n3 If anyone needs trig help, my phone number . Where is the... PII Collection 1 \n5 The dorm is over at 123 Dark Data Lane, \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    analysisindexrisk_score_meansanitized_textcollection_name
    0[{'pii_type_detected': None, 'risk_level': 1, ...02.000000I attend the University of Central Florida, ho...PII Collection 1
    1[{'pii_type_detected': 'NRP', 'risk_level': 2,...12.333333As a <REDACTED>, I promise to uphold....PII Collection 1
    2[{'pii_type_detected': 'NRP', 'risk_level': 2,...22.333333As a <REDACTED>, I can tell you that....PII Collection 1
    3[{'pii_type_detected': 'EMAIL_ADDRESS', 'risk_...32.600000If anyone needs trig help, my phone number <RE...PII Collection 1
    4[{'pii_type_detected': 'PHONE_NUMBER', 'risk_l...43.000000Oh I do! My number is <REDACTED>. Where is the...PII Collection 1
    5[{'pii_type_detected': 'LOCATION', 'risk_level...52.500000The dorm is over at 123 Dark Data Lane, <REDAC...PII Collection 1
    6[{'pii_type_detected': None, 'risk_level': 1, ...62.000000Cool, I'll be there!PII Collection 1
    \n" }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -319,12 +332,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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\n" + "image/png": 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YWGRmZlqty8jIQGxsLAAgKioKoaGhVmXy8/Oxb98+SxkiIpKIklvoy3vdf+enrbJFRZUr64C2bdsiIiICmzZtsqzbtGkT6tevjzZt2liVNZvNSE1NRVRUFDQaDaKjo7Fx40bLdpPJhBEjRli2N2nSBIsWLbKqY+jQoejXrx/effddhIWFoU6dOhg7dqxd4zcCAwORkZGBAQMGoEmTJnjyySexePFiHDx4EBcuXLC5b0FBAV5++WUsX74ctWrVqvQxvY1dCcvYsWPx73//G+vWrYO/vz9yc3ORm5uLolIda/DgwUhJSbG8f/PNN7FlyxYsWLAAx48fx6xZs3DgwAEkJSUBuJutjRs3DvPmzcNXX32FX375BYMHD0Z4eDj69evnnE9JRETuUaNGxa/n77tUHRxccdmePa3LRkaWX85Bw4cPt7rss2rVKgwbNqxMudTUVKxduxbLli3D0aNHMX78eLzyyivYsWMHgLsJTb169fDZZ5/h119/xYwZMzB16lR8+umnVvVs374dp0+fxvbt27FmzRqkpaUhLS3Nsn3WrFmIjIy06zPcvn0bgiCgZs2aNsuNHTsWvXv3trq5RYrsuiRUcr2va9euVutXr16NoUOHAgAuXLhgdbqoY8eOWLduHaZNm4apU6eicePG2Lx5s9VA3UmTJkGr1eL111/HrVu30KlTJ2zZssXj10aJiKhqeuWVV5CSkoLz588DAHbv3o3169cjKyvLUkav12P+/Pn47rvvLGf8H330UezatQsffvghunTpAoVCYXUTSFRUFLKzs/Hpp59iwIABlvW1atXC4sWLIZfL0bRpU/Tu3RuZmZkYOXIkAKBu3bpo2LBhpePX6XSYPHkyBg0aZHPs5vr163Ho0CHs37+/0nV7K7sSlpLbmmwp/Y9d4sUXX8SLL75Y4T6CIGDOnDmYM2eOPeEQEZG3KSioeNv9U7L/8XyZct0/TuLcOYdDKk9QUBB69+6NtLQ0iKKI3r17o27dulZlTp06hcLCQvTo0cNqvcFgsLp0tGTJEqxatQoXLlxAUVERDAZDmTGYLVq0sLqdNywsDL/88ovlfVJSkuXKw4MYjUYMGDAAoiiWGThc2sWLF/Hmm29Wmbtc+SwhG/z8/GAwGJCeno5cTRGkc48Q0T3FGjWWZX8CABDMAGz8nhA9NHse/+CMsg8x98rw4cMtScKSJUvKbC/4I/n65ptv8Mgjj1htK7nxY/369ZgwYQIWLFiA2NhY+Pv745133sG+ffusyt8/uFQQBIfmMylJVs6fP4/vv//e5tmVgwcP4urVq2jbtq1lnclkws6dO7F48WLo9XqHn+vjCUxYiIioWkpMTITBYIAgCEhISCizvXnz5lCpVLhw4QK6dOlSbh27d+9Gx44dMWbMGMu606dPuyTekmTl5MmT2L59+wMfPNm9e3erszjA3du6mzZtismTJ0sqWQGYsBARUTUll8tx7Ngxy/L9/P39MWHCBIwfPx5msxmdOnXC7du3sXv3bgQEBGDIkCFo3Lgx1q5di61btyIqKgoff/wx9u/fX6m7aEtbvHgxvvjiizJ31ZYwGo144YUXcOjQIXz99dcwmUyWyVVr164NpVIJ4G6S8qc//QlJSUnw9/cvM7Grn58f6tSpY3PCV2/FhMUGnU6Hl19+Gbm5uRg07y/w0Sg9HRKR3eR6A56Z/S8AwPbpYwBoPBsQkRd50GSjc+fORVBQEFJTU3HmzBnUrFkTbdu2xdSpUwEAf/nLX3D48GEMHDgQgiBg0KBBGDNmDL79Y3bpyrp+/brNMzOXLl3CV199BQBlxsds377dcjPM6dOncf36dbuOLRWCWJmRtF4uPz8fgYGBuH37ttNnuq3xx21zi75bDaWfdAYtjXpSWg+NNBqNSE9PR69evSQ1ydayvevt3kdfrMO4zLu3Ty7svhoqH9f2K58iHV575u7xVn63GnULakmunaVIqn26MnQ6Hc6ePYuoqCiPD+Y0m83Iz89HQECA1032VpU8bDtX1Gfs+f3mvy4RERF5PSYsRERE5PWYsBAREZHXY8JCREREXo8JCxEREXk9JixERETk9TgPiw2+vr64efMmtm7diptqx6d/JvKkYrUKK76/+1Rak1LFqfmJSJKYsNggCAL8/PygVqshCHyWEEmUIKBYc3feA4F5NxFJFC8JEREROVFWVhYEQcCtW7cAAGlpaahZs6ZHY6oKmLDYoNfrMWLECCxatAhGg9HT4RA5RGYwotvcpeg2dylk7MdUzQ0dOhSCIGDUqFFlto0dOxaCIGDo0KFOPebAgQPxv//9z6l1Vsa5c+cwYsQIREVFQaPRoGHDhpg5cyYMBoPN/f7yl7+gYcOG0Gg0CAoKQt++fXH8+HHL9hs3biAxMRHh4eFQqVSIiIhAUlIS8vPzXfp5mLDYUFxcjI8//hjbt2+H2WTydDhEDpGZTGiSvhNN0ndCxn5MhIiICKxfvx5FRUWWdTqdDuvWrUP9+vWdfjyNRoPg4GCn1/sgx48fh9lsxocffoijR4/i/fffx7JlyyzPQapIu3btsHr1ahw7dgxbt26FKIpITEyE6Y/vD5lMhr59++Krr77C//73P6SlpeG7774rNwl0JiYsRERUrbRt2xYRERHYtGmTZd2mTZtQv359tGnTxqqs2WxGamqq5SxFdHQ0Nm7caFUmPT0djz32GDQaDbp164Zz585Zbb//ktDp06fRt29fhISEoEaNGnjiiSfw3XffWe0TGRmJ+fPnY/jw4fD390f9+vXx0Ucf2fU5ExMTsXr1asTHx+PRRx/Fc889hwkTJlh97vK8/vrrePrppxEZGYm2bdti3rx5uHjxIi5cuAAAqFWrFkaPHo327dujQYMG6N69O8aMGYMffvjBrvjsxYSFiIgemiiK0Bq0Hnk58gzf4cOHY/Xq1Zb3q1atwrBhw8qUS01Nxdq1a7Fs2TIcPXoU48ePxyuvvIIdO3YAAC5evIj+/fujT58+yMnJwWuvvYYpU6bYPHZBQQF69eqFzMxMHD58GImJiejTp48lISixYMECtG/fHocPH8aYMWMwevRonDhxwrK9a9eudl++un37NmrXrl3p8lqtFqtXr0ZUVBQeeeSRcstcvnwZmzZtQpcuXeyKxV68S4iIiB5aobEQNVJreOTYv435DYEItGufV155BSkpKTh//jwAYPfu3Vi/fj2ysrIsZfR6PebPn4/vvvsOsbGxAIBHH30Uu3btwocffoguXbpg6dKlaNiwIRYsWAAAaNKkCX755Rf84x//qPDY0dHRiI6OtryfO3cuvvjiC3z11VdISkqyrO/VqxfGjBkDAJg8eTLef/99bN++HU2aNAEA1K9fH2FhYZX+zKdOncI///lPvPvuuw8s+69//QuTJk2CVqtFkyZNsHXrViiVSqsygwYNwpdffomioiL06dMHK1asqHQsjuAZFiIiqnaCgoLQu3dvpKWlYfXq1ejduzfq1q1rVebUqVMoLCxEjx49UKNGDctr7dq1OH36NADg2LFjiImJsdqvJLmpSEFBASZMmIBmzZqhZs2aqFGjBo4dO1bmDEurVq0sy4IgIDQ0FFevXrWsW7t2LVJTUyv1eS9duoTExES8+OKLGDly5APLv/zyyzh8+DB27NiBxx57DC+99BJ0Op1Vmffffx+HDh3Cl19+idOnTyM5OblSsTiKZ1iIiOih+Sp8UZDi/lkJzWYziouKHdp3+PDhljMaS5YsKbO9oODu5/nmm2/KXA5RqVQOHRMAJkyYgIyMDLz77rto1KgRNBoNXnjhhTJ37ygUCqv3giDAbLZ/MqXLly+jW7du6NixY6XHwQQGBiIwMBCNGzfGk08+iVq1auHrr7/G8OHDLWVCQ0MRGhqKpk2bonbt2ujcuTOmT59u11kfezBhISKihyYIAvyUfm4/rtlsRr7OsdtpExMTYTAYIAgCEhISymxv3rw5VCoVLly4UOH4jGbNmuGrr76yWrd3716bx929ezeGDh2KP/3pTwDuJkb3D9R1lkuXLqFbt26WO39kMvsvrIiiCFEUbd4OXZJI6fV6h2N9ECYsNvj6+uLSpUv47rvvUKhWPHgHIi9UrFYhLf1DyzKn5ie6Sy6X49ixY5bl+/n7+2PChAkYP348zGYzOnXqhNu3b2P37t0ICAjAkCFDMGrUKCxYsAATJ07Ea6+9hoMHDyItLc3mcRs3boxNmzahT58+EAQB06dPd+jMyeDBg/HII49UeFno0qVL6Nq1Kxo0aIB3330X165ds2wLDQ21lOnevTvWrl2LDh064MyZM9iwYQPi4+MRFBSE3377DW+99RY0Gg169OgB4O5dUXl5eXjiiSdQo0YNHD16FBMnTsRTTz2FyMhIuz9HZdmdau3cuRN9+vRBeHg4BEHA5s2bbZYvmaTn/leLFi0sZWbNmlVme9OmTe3+MM4mCAKCgoIQGBgIQRA8HQ6RYwQBuloB0NUKANiPiawEBAQgICCgwu1z587F9OnTkZqaimbNmiExMRHffPMNoqKiANwd+Pr5559j8+bNiI6OxrJlyzB//nybx3zvvfdQq1YtdOzYEX369EFCQgLatm1rd+wXLlzAlStXKtyekZGBU6dOITMzE/Xq1UNYWJjlVcJoNOLEiRMoLCwEAKjVavzwww/o1asXGjVqhIEDB8Lf3x+7du1CUFAQgLvzyixfvhydOnVCs2bNMH78eDz33HP4+uuv7f4M9hBEO+8H+/bbb7F79260a9cO/fv3xxdffIF+/fpVWP727dtWk/MUFxcjOjoab7zxBmbNmgXgbsKyceNGq/vQfXx8ygyAqkh+fj4CAwNx+/Ztmx3PEUajEenp6cgNKoIooSHKo558ydMh2KWknXv16lXmuq03W7Z3vd376It1GJd59/bJhd1XQ+WjdnZYFRLMQOg1jeTaWYqk2qcrQ6fT4ezZs4iKioJa7b7+Wx6z2Yz8/HwEBAQ4dLmDKudh27miPmPP77fdl4R69uyJnj17Vrp8ycCdEps3b8bNmzfL3O/u4+NjOUXlLfR6PcaNG4fz58+j16RB8OFlIZIgmcGIjos+BgBkv/EqAI1nAyIicoDbx7CsXLkScXFxaNCggdX6kydPIjw8HGq1GrGxsUhNTa1wimS9Xm81sKfk+QVGoxFGo/OelVJUVIRly5YBAHqOHwBBKZ2ExZnt4A4l8UotbkeefiyI1suufoKy3GjC45syAAA/jvozAOm1sxRJtU9XhtFohCiKMJvNDo29cKaSiwQl8ZBrPGw7m81miKIIo9FoNV7Inr8PtyYsly9fxrfffot169ZZrY+JiUFaWhqaNGmCK1euYPbs2ejcuTOOHDkCf3//MvWkpqZi9uzZZdZv27YNvr6+Tou39D3nwb9roC707KlPe6Snp3s6BIdkZGR4OgS7hDpwtkJnujeOJOSaBmq5a/uVXHfveMG/a2BSS6+dpawqtnXJGfGCgoIHPkjPXe7cuePpEKoFR9vZYDCgqKgIO3fuRHHxvdvQS8bOVIZbE5Y1a9agZs2aZca8lL7E1KpVK8TExKBBgwb49NNPMWLEiDL1pKSkWE1Qk5+fj4iICMTHxzt1DItWq7UsX61dBKWf/dM/e8rwJ573dAh2MRqNyMjIQI8ePSR1vX/V/s/t3kdvupcI5wUVQSV3bb/yKbp3vKu1i1CnUC25dpYiqfbpytDpdLh48SJq1Kjh8TEsoijizp078Pf3580RLvSw7azT6aDRaPD000+XGcNSWW5LWERRxKpVq/Dqq6+Wmd73fjVr1sRjjz2GU6dOlbtdpVKVO2mPQqFw6hdD6bpEGSQ16FaqX5DO/jd0NUf6hFjqbKoouL5fla6/ZFlq7SxlVbGtTSYTBEGATCbz+EDXkssTJfGQazxsO8tkMgiCUObvwZ6/Dbf96+7YsQOnTp0q94zJ/QoKCnD69GmXzZZHRERE0mJ3wlJQUICcnBzk5OQAAM6ePYucnBzLMxBSUlIwePDgMvutXLkSMTExePzxx8tsmzBhAnbs2IFz585hz549+NOf/gS5XI5BgwbZGx4RERFVQXZfEjpw4AC6detmeV8ylmTIkCFIS0vDlStXyjzA6fbt2/j888+xaNGicuv87bffMGjQINy4cQNBQUHo1KkT9u7da5mkhoiIiKo3uxOWrl27wtZcc+VNSRwYGGhzJPD69fZPvuUOGo0G//vf/7B9+3aYVLbH3RB5q2KVEv/Z9IFlmVPzE5EUcYSSDTKZDJGRkQgJCeFgLpIumQx3woJwJywIYD8mcrmsrCwIgoBbt255OpQqhd9eRERUbZQ8327UqFFlto0dOxaCIGDo0KHuD+wBjEYjJk+ejJYtW8LPzw/h4eEYPHgwLl++7OnQ3IYJiw0GgwFTpkxBWloaio3FD96ByAvJjMV48p//wZP//A9k7MdEiIiIwPr1662ec6fT6bBu3boKZ1j3tMLCQhw6dAjTp0/HoUOHsGnTJpw4cQLPPfecp0NzGyYsNhiNRrz33nvYvHkzTMX8oidpkhUXo/W6r9F63deQsR8ToW3btoiIiMCmTZss6zZt2oT69eujTZs2VmX1ej3++te/Ijg4GGq1Gp06dcL+/futyqSnp+Oxxx6DRqNBt27dcO7cuTLH3LVrFzp37gyNRoOIiAj89a9/tZqc9EECAwORkZGBAQMGoEmTJnjyySexePFiHDx4sMyNLlUVExYiInIarVZb4av0404eVLb02Q9bZR01fPhwrF692vJ+1apVZR7KCwCTJk3C559/jjVr1uDQoUNo1KgREhIS8PvvvwMALl68iP79+6NPnz7IycnBa6+9hilTpljVcfr0aSQmJuL555/Hzz//jA0bNmDXrl1ISkqylJk1axYiIyPt+gy3b9+GIAioWbOmXftJFRMWIiJymho1alT4ev5560eGBAcHV1i29CNbACAyMrLcco565ZVXsGvXLpw/fx7nz5/H7t278corr1iV0Wq1WLp0Kd555x307NkTzZs3x/Lly6HRaLBy5UoAwNKlS9GwYUMsWLAATZo0wcsvv1xmDExqaipefvlljBs3Do0bN0bHjh3xwQcfYO3atZYkrm7dumjYsGGl49fpdJg8eTIGDRrk1EfSeDO3P62ZiIjI04KCgtC7d2+kpaVBFEX07t0bdevWtSpz+vRpGI1GPPXUU5Z1CoUCHTp0wLFjxwAAx44dQ0xMjNV+sbGxVu9/+ukn/Pzzz/jPf/5jWVfy1OOzZ8+iWbNmSEpKsjrjYovRaMSAAQMgiiKWLl1q1+eWMiYsRETkNAUFFU/0I5fLrd5fvXq1wrL3TyVR3riQhzV8+HBLkrBkyRKn11+ioKAAf/nLX/DXv/61zDZ7B/mWJCvnz5/H999/X23OrgBMWIiIyIn8/PzcWrbkoXyOSExMhMFggCAISEhIKLO9YcOGUCqV2L17Nxo0aADgbsKwf/9+jBs3DgDQrFkzfPXVV1b77d271+p927Zt8euvv6JRo0YOx1py7AEDBuDkyZPYvn076tSp81D1SQ3HsBARUbUkl8tx7Ngx/Prrr2XO/gB3k6TRo0dj4sSJ2LJlC3799VeMHDkShYWFlgf5jho1CidPnsTEiRNx4sQJrFu3rsyM75MnT8aePXuQlJSEnJwcnDx5El9++aXVJaDFixeje/fuFcZqNBrxwgsv4MCBA/jPf/4Dk8mE3Nxc5ObmwmAwOKdBvBzPsNig0Whw+PBh/PDDD/Dh1PwkUcUqJTb8523LMqfmJ7rnQZdU3nrrLZjNZrz66qu4c+cO2rdvj61bt6JWrVoA7l7S+fzzzzF+/Hj885//RIcOHTB//nwMHz7cUkerVq2wY8cO/P3vf0fnzp0hiiIaNmyIgQMHWspcv34dp0+frjCOS5cuWc7ktG7d2mrb9u3b0bVrVzs/ufQwYbFBJpOhRYsWOH/+PHJlRaj4CUpEXkwmw81HIwAAguNnz4mqhPKed1fa5s2brd6r1Wp88MEH+OCDDyrc59lnn8Wzzz5rte7+W6SfeOIJbNu2rcI6Zs2ahVmzZlW4PTIy0uZz/KoDXhIiIiIir8eExQaDwYA5c+bgk08+4dT8JFkyYzHar9iI9is2cmp+IpIsXhKywWg0Yt68eQCAjiMTIVexuUh6ZMXFaL/ycwDAT4OefUBpIiLvxDMsRERE5PWYsBARkUOq+yBQqjxn9BUmLEREZBeFQgEAKCws9HAkJBUlfaWk7ziCgzKIiMgucrkcNWvWtEyt7+vrC0EQPBKL2WyGwWCATqcrM50/OY+j7SyKIgoLC3H16lXUrFmz3An6KosJCxER2S00NBSA7ecBuYMoiigqKoJGo/FY0lQdPGw716xZ09JnHMWEhYiI7CYIAsLCwhAcHAyj0eixOIxGI3bu3Imnn376oS43kG0P084KheKhzqyUYMJig1qtxp49e7B7924olJyan6TJpFTi85XzLMtEziSXy53yY/Qwxy8uLoZarWbC4kLe0M5MWGyQy+Vo3749rl69ilw5p+YnaRLlMlxr3hAAp+YnIumye4TSzp070adPH4SHh0MQhDLPXbhfVlYWBEEo88rNzbUqt2TJEkRGRkKtViMmJgY//vijvaERERFRFWV3wqLVahEdHY0lS5bYtd+JEydw5coVyys4ONiybcOGDUhOTsbMmTNx6NAhREdHIyEhweODuQwGAxYsWIAvvviCU/OTZMmMxYj+938R/e//cmp+IpIsuy8J9ezZEz179rT7QMHBwahZs2a529577z2MHDnS8nTLZcuW4ZtvvsGqVaswZcoUu4/lLEajESkpKQCAtq925dT8JEmy4mLELlkHAPj1Tz08HA0RkWPc9gvcunVr6PV6PP7445g1axaeeuopAHfPYhw8eNCSGACATCZDXFwcsrOzy61Lr9dDr9db3ufn5wO4m2A4c7R66boEs7Su/3ty1L4jSuKVWtyO9AlBtF52db8qXX/JstTaWYqk2qelhu3sHq5qZ3vqc3nCEhYWhmXLlqF9+/bQ6/VYsWIFunbtin379qFt27a4fv06TCYTQkJCrPYLCQnB8ePHy60zNTUVs2fPLrN+27Zt8PX1dVrsOp3Oshz8uwbqQrXT6na19PR0T4fgkIyMDE+HYJdQaOzeR2e6N4dByDUN1HLX9iu57t7xgn/XwKSWXjtLGdvaPdjO7uHsdrZntmSXJyxNmjRBkyZNLO87duyI06dP4/3338fHH3/sUJ0pKSlITk62vM/Pz0dERATi4+MREBDw0DGX0Gq1luWrtYug9JPOfULDn3je0yHYxWg0IiMjAz169JDUrYmr9n9u9z56071EOC+oCCq5a/uVT9G9412tXYQ6hWrJtbMUSbVPSw3b2T1c1c4lV0gqwyODMjp06IBdu3YBAOrWrQu5XI68vDyrMnl5eRXOiqdSqaBSqcqsVygUTm3I0nWJsrsvqZDqH66z/w1dzZE+IZa6RCMKru9XpesvWZZaO0sZ29o92M7u4crf2QfxyE9wTk4OwsLCAABKpRLt2rVDZmamZbvZbEZmZiZiY2M9ER4RERF5GbvPsBQUFODUqVOW92fPnkVOTg5q166N+vXrIyUlBZcuXcLatWsBAAsXLkRUVBRatGgBnU6HFStW4Pvvv8e2bdssdSQnJ2PIkCFo3749OnTogIULF0Kr1VruGiIiIqLqze6E5cCBA+jWrZvlfclYkiFDhiAtLQ1XrlzBhQsXLNsNBgP+9re/4dKlS/D19UWrVq3w3XffWdUxcOBAXLt2DTNmzEBubi5at26NLVu2lBmI625qtRoZGRnYu3cvp+YnyTIplfhqyXTLMhGRFNmdsHTt2hWiWPEgwbS0NKv3kyZNwqRJkx5Yb1JSEpKSkuwNx6Xkcjm6dOkCrVbLqflJskS5DJfbNgcgrVvziYhKk9AwUiIiIqqumLDYYDQasXTpUqSnp8NUzCnNSZpkxcVosXEbWmzcBhn7MRFJFOeat8FgMODNN98EACwaEAulks1F0iMzFqPzgtUAgP/1fNrD0RAROYZnWIiIiMjrMWEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx/t0bVCpVNi8eTMOHDgAHz4FlCTKpFAg/d2JlmUiIiliwmKDj48PevXqBQDI9eHU/CRNoo8cF55qC4BT8xORdPGSEBEREXk9Jiw2GI1GrF27FpmZmZyanyRLVlyMJt/sQJNvdnBqfiKSLF4SssFgMOC1114DACzqu5pT85MkyYzF6DZvGQDgTNcYD0dDROQYnmEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx4SFiIiIvB7v07VBpVJh3bp1OHz4MKfmJ8kyKRTYNu9NyzIRkRQxYbHBx8cHL7zwAnx9fTk1P0mW6CPHme5PAuDU/EQkXbwkRERERF7P7oRl586d6NOnD8LDwyEIAjZv3myz/KZNm9CjRw8EBQUhICAAsbGx2Lp1q1WZWbNmQRAEq1fTpk3tDc3piouLsXHjRuzevRumYpOnwyFyiFBswqOZe/Fo5l4I7MdEJFF2JyxarRbR0dFYsmRJpcrv3LkTPXr0QHp6Og4ePIhu3bqhT58+OHz4sFW5Fi1a4MqVK5bXrl277A3N6fR6Pf785z/jnXfeQbHR6OlwiBwiNxoRP20R4qctgpz9mIgkyu4xLD179kTPnj0rXX7hwoVW7+fPn48vv/wS//3vf9GmTZt7gfj4IDQ01N5wiIiIqBpw+6Bbs9mMO3fuoHbt2lbrT548ifDwcKjVasTGxiI1NRX169cvtw69Xg+9Xm95n5+fD+Du05WNTvw/yNJ1CWZpDVh0Zju4Q0m8UovbkT4hiNbLru5XpesvWZZaO0uRVPu01LCd3cNV7WxPfW5PWN59910UFBRgwIABlnUxMTFIS0tDkyZNcOXKFcyePRudO3fGkSNH4O/vX6aO1NRUzJ49u8z6bdu2wdfX12mx6nQ6y3Lw7xqoC9VOq9vV0tPTPR2CQzIyMjwdgl1CobF7H51JsCyHXNNALXdtv5Lr7h0v+HcNTGrptbOUsa3dg+3sHs5u58LCwkqXdWvCsm7dOsyePRtffvklgoODLetLX2Jq1aoVYmJi0KBBA3z66acYMWJEmXpSUlKQnJxseZ+fn4+IiAjEx8cjICDAafFqtVrL8tXaRVD6SefG5uFPPO/pEOxiNBqRkZGBHj16QCGhuUJW7f/c7n30pnuJcF5QEVRy1/Yrn6J7x7tauwh1CtWSa2cpkmqflhq2s3u4qp1LrpBUhtsSlvXr1+O1117DZ599hri4OJtla9asicceewynTp0qd7tKpYJKpSqzXqFQOLUhS9clyu6+pEKqf7jO/jd0NUf6hFjqEo0ouL5fla6/ZFlq7SxlbGv3YDu7hyt/Zx/ELT/Bn3zyCYYNG4ZPPvkEvXv3fmD5goICnD59GmFhYW6IjoiIiLyd3WdYCgoKrM58nD17Fjk5Oahduzbq16+PlJQUXLp0CWvXrgVw9zLQkCFDsGjRIsTExCA3NxcAoNFoEBgYCACYMGEC+vTpgwYNGuDy5cuYOXMm5HI5Bg0a5IzP6DClUokVK1bgp59+go+CkwKTNJkVPtg+bZRlmYhIiuw+w3LgwAG0adPGcktycnIy2rRpgxkzZgAArly5ggsXLljKf/TRRyguLsbYsWMRFhZmeb355puWMr/99hsGDRqEJk2aYMCAAahTpw727t2LoKCgh/18D0WhUGDw4MHo3r075D78oidpMvv44ETvLjjRuwvM7MdEJFF2f3t17doVoljxIMG0tDSr91lZWQ+sc/369faGQURERNWIhIaRul9xcTHS09Nx4MABTs1PkiUUm1B/9yHU332IU/MTkWTx/LANer0e/fr1AwAs6roaSqXcswEROUBuNKLXhHcAACu/W+3haIiIHMMzLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PWYsBAREZHX423NNiiVSixatAhHjx7l1PwkWWaFD3742zDLMhGRFPHbywaFQoHRo0cjPT0duT5FqHh+XyLvZfbxwdEX4gEAgvkBhYmIvBQvCREREZHXY8Jig8lkwo4dO/DLL7/AbOL/mpI0CSYzwg/9ivBDv0JgPyYiieIlIRt0Oh169OgBAFj03WooFWoPR0RkP7nBgOfGzgVQMjW/n2cDIiJyAM+wEBERkddjwkJERERejwkLEREReT0mLEREROT1mLAQERGR12PCQkRERF6PtzXboFAokJqaiuPHj0Puw6YiaTL7+CB77J8ty0REUsRvLxuUSiX+9re/3Z2aX8Gp+UmazAof/PRKHwCcmp+IpIuXhIiIiMjrMWGxwWQy4cCBAzh58iSn5ifJEkxmBP16GkG/nubU/EQkWXYnLDt37kSfPn0QHh4OQRCwefPmB+6TlZWFtm3bQqVSoVGjRkhLSytTZsmSJYiMjIRarUZMTAx+/PFHe0NzOp1Oh44dO2LixIkwGgyeDofIIXKDAc+PmIbnR0yDnP2YiCTK7oRFq9UiOjoaS5YsqVT5s2fPonfv3ujWrRtycnIwbtw4vPbaa9i6daulzIYNG5CcnIyZM2fi0KFDiI6ORkJCAq5evWpveERERFQF2T3otmfPnujZs2elyy9btgxRUVFYsGABAKBZs2bYtWsX3n//fSQkJAAA3nvvPYwcORLDhg2z7PPNN99g1apVmDJlir0hEhERURXj8ruEsrOzERcXZ7UuISEB48aNAwAYDAYcPHgQKSkplu0ymQxxcXHIzs4ut069Xg+9Xm95n5+fDwAwGo0wGo1Oi710XYJZWndYOLMd3KEkXqnF7UifEETrZVf3q9L1lyxLrZ2lSKp9WmrYzu7hqna2pz6XJyy5ubkICQmxWhcSEoL8/HwUFRXh5s2bMJlM5ZY5fvx4uXWmpqZi9uzZZdZv27YNvr6+Totdp9NZloN/10BdqHZa3a6Wnp7u6RAckpGR4ekQ7BIKjd376EyCZTnkmgZquWv7lVx373jBv2tgUkuvnaWMbe0ebGf3cHY7FxYWVrqsJOdhSUlJQXJysuV9fn4+IiIiEB8fj4CAAKcdR6vVWpav1i6C0k86M7EMf+J5T4dgF6PRiIyMDPTo0QMKhcLT4VTaqv2f272P3nQvEc4LKoJK7tp+5VN073hXaxehTqG6WrSzpwlmIOSGBnl1iiBK6H5Mfne4h9T6dEl/dnY7l1whqQyXJyyhoaHIy8uzWpeXl4eAgABoNBrI5XLI5fJyy4SGhpZbp0qlgkqlKrNeoVA4tSFL1yXKIKkvHSn94Zbm7H9DV3OkT4ilLtGIguv7Ven6S5arQzt7C353uAf7tHu48nf2QVzeZLGxscjMzLRal5GRgdjYWAB3Z5Nt166dVRmz2YzMzExLGU9RKBSYNm0aBg4cyKn5SbLMPj44MOJ5HBjxPKfmJyLJsvvbq6CgAKdOnbK8P3v2LHJyclC7dm3Ur18fKSkpuHTpEtauXQsAGDVqFBYvXoxJkyZh+PDh+P777/Hpp5/im2++sdSRnJyMIUOGoH379ujQoQMWLlwIrVZruWvIU5RKJWbMmMGp+UnSzAofHHjtBQDSGjhORFSa3QnLgQMH0K1bN8v7krEkQ4YMQVpaGq5cuYILFy5YtkdFReGbb77B+PHjsWjRItSrVw8rVqyw3NIMAAMHDsS1a9cwY8YM5ObmonXr1tiyZUuZgbhERERUPdmdsHTt2hWiWPG5hvJmse3atSsOHz5ss96kpCQkJSXZG45Lmc1mHD16FBcuXIBPnToQZBK96EjVm9mMWucuAQBu1X/Ew8EQETmGF7RtKCoqQps2bQAAi75bDaWPdG5rJirhozdg4MuTAAArv1sNwM+zAREROYCnDIiIiMjrMWEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx9uabVAoFEhOTsaZM2c4NT9JltnHBzl/ftayTEQkRfz2skGpVOKtt97i1PwkaWaFD/a+8TIATs1PRNLFS0JERETk9Ziw2GA2m3Hu3Dnk5eXBbOb/mpJEmc3wv3IN/leuAezHRCRRvCRkQ1FRER577DEAnJqfpMtHb8DL/f8KgFPzE5F08QwLEREReT0mLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PV4W7MNPj4+GDVqFM6fPw+ZXO7pcIgcYpbLcaR/D8syEZEUMWGxQaVS4YMPPrg7Nb+SU/OTNJmVCuyaOBwAp+YnIuniJSEiIiLyekxYbBBFEdeuXcPt27chijy/QhIlilDfzIf6Zj7AfkxEEsVLQjYUFhbikUceAfDH1Px+nJqfpMdHp8fQXn8BUDI1v69nAyIicoBDZ1iWLFmCyMhIqNVqxMTE4Mcff6ywbNeuXSEIQplX7969LWWGDh1aZntiYqIjoREREVEVZPcZlg0bNiA5ORnLli1DTEwMFi5ciISEBJw4cQLBwcFlym/atAkGg8Hy/saNG4iOjsaLL75oVS4xMRGrV6+2vFepVPaGRkRERFWU3WdY3nvvPYwcORLDhg1D8+bNsWzZMvj6+mLVqlXllq9duzZCQ0Mtr4yMDPj6+pZJWFQqlVW5WrVqOfaJiIiIqMqx6wyLwWDAwYMHkZKSYlknk8kQFxeH7OzsStWxcuVKvPTSS/Dzs37EfVZWFoKDg1GrVi0888wzmDdvHurUqVNuHXq9Hnq93vI+Pz8fAGA0GmE0Gu35SDaVrkswS+uWUGe2gzuUxCu1uB3pE4JovezqflW6/pLl6tDOnlYSs9Ril1rfqE7fHZ7kqu8Oe+qzK2G5fv06TCYTQkJCrNaHhITg+PHjD9z/xx9/xJEjR7By5Uqr9YmJiejfvz+ioqJw+vRpTJ06FT179kR2djbk5Ux0lZqaitmzZ5dZv23bNvj6Om9AoU6nsywH/66BulA6g27T09M9HYJDMjIyPB2CXUKhsXsfnUmwLIdc00Atd22/kuvuHS/4dw1M6urRzt4i5Ia0Yud3h3tItU87u50LCwsrXdatdwmtXLkSLVu2RIcOHazWv/TSS5blli1bolWrVmjYsCGysrLQvXv3MvWkpKQgOTnZ8j4/Px8RERGIj49HQECA0+LVarWW5au1i6D0k84tocOfeN7TIdjFaDQiIyMDPXr0gEKh8HQ4lbZq/+d276M33UuE84KKoJK7tl/5FN073tXaRahTqK4W7expgvluspJXpwiihCaQ4HeHe0itT5f0Z2e3c8kVksqwK2GpW7cu5HI58vLyrNbn5eUhNDTU5r5arRbr16/HnDlzHnicRx99FHXr1sWpU6fKTVhUKlW5g3IVCoVTG1Kj0eDVV1/Fb7/9BkEhl9SXjpT+cEtz9r+hqznSJ8RSp4JFwbE67GFSyHGi19OWZaB6tLO3EGXSil9K/aI09mn3cHY721OXXU2mVCrRrl07ZGZmWtaZzWZkZmYiNjbW5r6fffYZ9Ho9XnnllQce57fffsONGzcQFhZmT3hOp1KpsHLlSrz55ptQKKXzh0BUmlmpwPbpo7F9+miY2Y+JSKLszvGSk5OxfPlyrFmzBseOHcPo0aOh1WoxbNgwAMDgwYOtBuWWWLlyJfr161dmIG1BQQEmTpyIvXv34ty5c8jMzETfvn3RqFEjJCQkOPixiIiIqCqxewzLwIEDce3aNcyYMQO5ublo3bo1tmzZYhmIe+HCBchk1nnQiRMnsGvXLmzbtq1MfXK5HD///DPWrFmDW7duITw8HPHx8Zg7d67H52IRRRFarRY6ne6PqfmFB+5D5HVEET66u3fVmZSc34iIpMmhQbdJSUlISkoqd1tWVlaZdU2aNKnwWTwajQZbt251JAyXKywstMwHw6n5Sap8dHq89szdM6Ccmp+IpEqiw36IiIioOmHCQkRERF6PCQsRERF5PSYsRERE5PWYsBAREZHXY8JCREREXs+tzxKSGrlcjv79+yM3N7fM3DJEUiHKZDjdLcayTEQkRUxYbFCr1Vi/fj3S09ORqyqCdB59SHSPSaVExvxxAKT3SHsiohL83y0iIiLyekxYiIiIyOsxYbFBq9VCqVSiX79+0BfpPB0OkUN8inQYFTsIo2IHwYf9mIgkigkLEREReT0mLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PU4060NcrkcPXv2xNWrVzk1P0mWKJPhfMfWlmUiIiliwmKDWq3Gl19+yan5SdJMKiW+XTAZAKfmJyLp4v9uERERkddjwkJERERejwmLDVqtFjVr1sTAgQM5NT9Jlk+RDiO6DcWIbkM5NT8RSRbHsDxAYWGhp0MgemgKnd7TIRARPRSHzrAsWbIEkZGRUKvViImJwY8//lhh2bS0NAiCYPVSq9VWZURRxIwZMxAWFgaNRoO4uDicPHnSkdCIiIioCrI7YdmwYQOSk5Mxc+ZMHDp0CNHR0UhISMDVq1cr3CcgIABXrlyxvM6fP2+1/e2338YHH3yAZcuWYd++ffDz80NCQgJ0Op6+JiIiIgcSlvfeew8jR47EsGHD0Lx5cyxbtgy+vr5YtWpVhfsIgoDQ0FDLKyQkxLJNFEUsXLgQ06ZNQ9++fdGqVSusXbsWly9fxubNmx36UERERFS12DWGxWAw4ODBg0hJSbGsk8lkiIuLQ3Z2doX7FRQUoEGDBjCbzWjbti3mz5+PFi1aAADOnj2L3NxcxMXFWcoHBgYiJiYG2dnZeOmll8rUp9frodffuyafn58PADAajTAajfZ8JJtK1yWYpTWHhTPbwR1K4pVa3I70CUG0XnZ1vypdf8lydWhnTyuJWWqxS61vVKfvDk9y1XeHPfXZlbBcv34dJpPJ6gwJAISEhOD48ePl7tOkSROsWrUKrVq1wu3bt/Huu++iY8eOOHr0KOrVq4fc3FxLHffXWbLtfqmpqZg9e3aZ9du2bYOvr689H8mm0pekgn/XQF2otlHau6Snp3s6BIdkZGR4OgS7hEJj9z46k2BZDrmmgVru2n4l1907XvDvGpjU1aOdvUXIDWnFzu8O95Bqn3Z2O9tzY4vL7xKKjY1FbGys5X3Hjh3RrFkzfPjhh5g7d65DdaakpCA5OdnyPj8/HxEREYiPj0dAQMBDx1yiqKgInTt3xu+//45rdfVQaKQz1+3wJ573dAh2MRqNyMjIQI8ePaBQKDwdTqWt2v+53fvoTfcS4bygIqjkru1Xcr0Bl9s0u3u8unrULVBXi3b2NMF8N1nJq1MEUUITSPC7wz2k1qdL+rOz27nkCkll2JWw1K1bF3K5HHl5eVbr8/LyEBoaWqk6FAoF2rRpg1OnTgGAZb+8vDyEhYVZ1dm6dety61CpVFCpVOXW7cyGVCgUyMzMvDs1v0ZaXzpS+sMtzdn/hq7mSJ8QS50KFgXH6rBHsUaJr/41A8Afp3ULqkc7ewtRJq34pdQvSmOfdg9X/M5Wll1NplQq0a5dO2RmZlrWmc1mZGZmWp1FscVkMuGXX36xJCdRUVEIDQ21qjM/Px/79u2rdJ1ERERUtdl9SSg5ORlDhgxB+/bt0aFDByxcuBBarRbDhg0DAAwePBiPPPIIUlNTAQBz5szBk08+iUaNGuHWrVt45513cP78ebz22msA7t5BNG7cOMybNw+NGzdGVFQUpk+fjvDwcPTr1895n5SIiIgky+6EZeDAgbh27RpmzJiB3NxctG7dGlu2bLEMmr1w4QJkpR5hf/PmTYwcORK5ubmoVasW2rVrhz179qB58+aWMpMmTYJWq8Xrr7+OW7duoVOnTtiyZUuZCebcTavVIjIyEgaDAXM3LYLSTzqDbolK+BTp8HL/vwIA1m38AJDoYD8iqt4cGnSblJSEpKSkcrdlZWVZvX///ffx/vvv26xPEATMmTMHc+bMcSQcl7p+/bqnQyB6aJpbdzwdAhHRQ5HosB8iIiKqTpiwEBERkddjwkJERERejwkLEREReT0mLEREROT1XD41v5TJZDK0a9cOt2/fhiBjbkfSJAoyXG326N1l9mMikigmLDZoNBpkZ2ffnZpfVQTpPEmI6B6TWolNq/4PgPSeEEtEVIL/u0VERERejwkLEREReT0mLDYUFhaicePGGDlyJAw6vafDIXKIj06Pl//0Bl7+0xvwYT8mIoniGBYbRFHE+fPnLctEkiSK8M+9blkmIpIinmEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx7uEbBAEAc2aNUNBQQEEQfB0OESOEQT8HvWIZZmISIqYsNjg6+uLn3766e7U/GpOzU/SVKxW4dN17wL4Y2r+O56Nh4jIEbwkRERERF6PCQsRERF5PSYsNhQWFiI6OhpvvPEGp+YnyfLR6THgzxMw4M8TODU/EUkWx7DYIIoijh07ZlkmkiRRRO2zlyzLRERSxDMsRERE5PUcSliWLFmCyMhIqNVqxMTE4Mcff6yw7PLly9G5c2fUqlULtWrVQlxcXJnyQ4cOhSAIVq/ExERHQiMiIqIqyO6EZcOGDUhOTsbMmTNx6NAhREdHIyEhAVevXi23fFZWFgYNGoTt27cjOzsbERERiI+Px6VLl6zKJSYm4sqVK5bXJ5984tgnIiIioirH7oTlvffew8iRIzFs2DA0b94cy5Ytg6+vL1atWlVu+f/85z8YM2YMWrdujaZNm2LFihUwm83IzMy0KqdSqRAaGmp51apVy7FPRERERFWOXYNuDQYDDh48iJSUFMs6mUyGuLg4ZGdnV6qOwsJCGI1G1K5d22p9VlYWgoODUatWLTzzzDOYN28e6tSpU24der0eev29ux3y8/MBAEajEUaj0Z6PZFPpugTzH5NuSYQz28EdSuKVWtyO9AlBtF52db8qXX/JcnVoZ08riVlqsUutb1Sn7w5PctV3hz312ZWwXL9+HSaTCSEhIVbrQ0JCcPz48UrVMXnyZISHhyMuLs6yLjExEf3790dUVBROnz6NqVOnomfPnsjOzoZcLi9TR2pqKmbPnl1m/bZt2+Dr62vPR7JJr9cjKCgIABBy0xeqQpXT6na19PR0T4fgkIyMDE+HYJdQaOzeR2e6Nz1+yDUN1HK1M0MqQ66XofCPfhx80xcmVfVoZ28RckNasfO7wz2k2qed3c6FhYWVLuvW25rfeustrF+/HllZWVCr731Jv/TSS5blli1bolWrVmjYsCGysrLQvXv3MvWkpKQgOTnZ8j4/P98yNiYgIMCpMT/77LPIyMhAXp0iiLIip9btSsOfeN7TIdjFaDQiIyMDPXr0gEKh8HQ4lbZq/+d276M36SzLeUFFUMldf6vxx5s/AAAIZjNCbqBatLOnCea7ycrd7w5PR1N5/O5wD6n16ZL+7Ox2LrlCUhl2JSx169aFXC5HXl6e1fq8vDyEhoba3Pfdd9/FW2+9he+++w6tWrWyWfbRRx9F3bp1cerUqXITFpVKBZWq7NkOhULhsg4ryiCpLx0p/eGW5sp/Q1dwpE+IpU4Fi4Jn+lV1aGdvwe8O92Cfdg9nt7M9ddnVZEqlEu3atbMaMFsygDY2NrbC/d5++23MnTsXW7ZsQfv27R94nN9++w03btxAWFiYPeERERFRFWV3jpecnIzly5djzZo1OHbsGEaPHg2tVothw4YBAAYPHmw1KPcf//gHpk+fjlWrViEyMhK5ubnIzc1FQUEBAKCgoAATJ07E3r17ce7cOWRmZqJv375o1KgREhISnPQxHVNUVITY2FhMmDABBr3Bo7EQOUquM6D/8L+j//C/Q85+TEQSZfcYloEDB+LatWuYMWMGcnNz0bp1a2zZssUyEPfChQuQye7lQUuXLoXBYMALL7xgVc/MmTMxa9YsyOVy/Pzzz1izZg1u3bqF8PBwxMfHY+7cueVe9nEns9mMgwcPAgBEs8SGdBP9QRDNCD525u4y+zERSZRDg26TkpKQlJRU7rasrCyr9+fOnbNZl0ajwdatWx0Jg4iIiKoJiQ77ISIiouqECQsRERF5PSYsRERE5PWYsBAREZHXc+tMt1JUt25dGAy8FZSkraimv6dDICJ6KExYbPDz88Ply5eRnp6OXE0RXD+BOpHzFWvUWPPtRwD+eIBZgWfjISJyBC8JERERkddjwkJERERejwmLDUVFRYiLi8Pf//53Ts1PkiXXGfDcmDl4bswcTs1PRJLFMSw2mM1m7Ny5EwCn5ifpEkQzwg8fu7vMfkxEEsUzLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PV4l9AD+Pr6wmQyeToMoodiVKs8HQIR0UNhwmKDn58fbt26xan5SdKKNWqs3J4GgFPzE5F08ZIQEREReT0mLEREROT1mLDYoNPp0LdvX8ydOxdGTmlOEiXXG9Dzb/9Az7/9g1PzE5FkcQyLDSaTCd9++y0AYDCnNCeJEsxmNNiTY1kmIpIinmEhIiIir8eEhYiIiLyeQwnLkiVLEBkZCbVajZiYGPz44482y3/22Wdo2rQp1Go1WrZsifT0dKvtoihixowZCAsLg0ajQVxcHE6ePOlIaERERFQF2Z2wbNiwAcnJyZg5cyYOHTqE6OhoJCQk4OrVq+WW37NnDwYNGoQRI0bg8OHD6NevH/r164cjR45Yyrz99tv44IMPsGzZMuzbtw9+fn5ISEiATqdz/JMRERFRlWF3wvLee+9h5MiRGDZsGJo3b45ly5bB19cXq1atKrf8okWLkJiYiIkTJ6JZs2aYO3cu2rZti8WLFwO4e3Zl4cKFmDZtGvr27YtWrVph7dq1uHz5MjZv3vxQH46IiIiqBrvuEjIYDDh48CBSUlIs62QyGeLi4pCdnV3uPtnZ2UhOTrZal5CQYElGzp49i9zcXMTFxVm2BwYGIiYmBtnZ2XjppZfK1KnX66HX6y3vb9++DQD4/fffYTQa7flINmm1Wsuy7k4RzBK6w+LGjRueDsEuRqMRhYWFuHHjBhQKhafDqTTdnUK79zGY9MAfJw/1BUUQ5a7tVz5FeuT/say7U4TCIlW1aGdPE8xAYaEI3Z0iiBIaLcjvDveQWp8u6c/Obuc7d+4AuHvy4kHsSliuX78Ok8mEkJAQq/UhISE4fvx4ufvk5uaWWz43N9eyvWRdRWXul5qaitmzZ5dZHxUVVbkP4oDJfxrjsrpdYTxGeDoEqoTJb7mnX71RsiCxfkzux+8O8oQ7d+4gMDDQZhlJzsOSkpJiddbGbDbj999/R506dSAIglOPlZ+fj4iICFy8eBEBAQFOrZvuYTu7B9vZfdjW7sF2dg9XtbMoirhz5w7Cw8MfWNauhKVu3bqQy+XIy8uzWp+Xl4fQ0NBy9wkNDbVZvuS/eXl5CAsLsyrTunXrcutUqVRQqayfPluzZk17PordAgIC+MfgBmxn92A7uw/b2j3Yzu7hinZ+0JmVEnZdWVUqlWjXrh0yMzMt68xmMzIzMxEbG1vuPrGxsVblASAjI8NSPioqCqGhoVZl8vPzsW/fvgrrJCIiourF7ktCycnJGDJkCNq3b48OHTpg4cKF0Gq1GDZsGABg8ODBeOSRR5CamgoAePPNN9GlSxcsWLAAvXv3xvr163HgwAF89NFHAABBEDBu3DjMmzcPjRs3RlRUFKZPn47w8HD069fPeZ+UiIiIJMvuhGXgwIG4du0aZsyYgdzcXLRu3RpbtmyxDJq9cOECZLJ7J246duyIdevWYdq0aZg6dSoaN26MzZs34/HHH7eUmTRpErRaLV5//XXcunULnTp1wpYtW6BWq53wER+OSqXCzJkzy1yCIudiO7sH29l92NbuwXZ2D29oZ0GszL1ERERERB4kodkBiIiIqLpiwkJERERejwkLEREReT0mLEREROT1qnXCkpqaiieeeAL+/v4IDg5Gv379cOLEiQfu99lnn6Fp06ZQq9Vo2bIl0tPT3RCtdDnSzsuXL0fnzp1Rq1Yt1KpVC3Fxcfjxxx/dFLE0OdqfS6xfvx6CIHA6gUpwtK1v3bqFsWPHIiwsDCqVCo899hi/P2xwtJ0XLlyIJk2aQKPRICIiAuPHj4dOp3NDxNK0dOlStGrVyjIpXGxsLL799lub+3jid7BaJyw7duzA2LFjsXfvXmRkZMBoNCI+Pt7qoYf327NnDwYNGoQRI0bg8OHD6NevH/r164cjR464MXJpcaSds7KyMGjQIGzfvh3Z2dmIiIhAfHw8Ll265MbIpcWRdi5x7tw5TJgwAZ07d3ZDpNLnSFsbDAb06NED586dw8aNG3HixAksX74cjzzyiBsjlxZH2nndunWYMmUKZs6ciWPHjmHlypXYsGEDpk6d6sbIpaVevXp46623cPDgQRw4cADPPPMM+vbti6NHj5Zb3mO/gyJZXL16VQQg7tixo8IyAwYMEHv37m21LiYmRvzLX/7i6vCqjMq08/2Ki4tFf39/cc2aNS6MrGqpbDsXFxeLHTt2FFesWCEOGTJE7Nu3r3sCrEIq09ZLly4VH330UdFgMLgxsqqlMu08duxY8ZlnnrFal5ycLD711FOuDq9KqVWrlrhixYpyt3nqd7Ban2G53+3btwEAtWvXrrBMdnY24uLirNYlJCQgOzvbpbFVJZVp5/sVFhbCaDTatU91V9l2njNnDoKDgzFiBJ/S66jKtPVXX32F2NhYjB07FiEhIXj88ccxf/58mEwmd4UpeZVp544dO+LgwYOWS8hnzpxBeno6evXq5ZYYpc5kMmH9+vXQarUVPh7HU7+DknxasyuYzWaMGzcOTz31lNUsvPfLzc21zOpbIiQkBLm5ua4OsUqobDvfb/LkyQgPDy/zR0Llq2w779q1CytXrkROTo77gqtiKtvWZ86cwffff4+XX34Z6enpOHXqFMaMGQOj0YiZM2e6MWJpqmw7//nPf8b169fRqVMniKKI4uJijBo1ipeEHuCXX35BbGwsdDodatSogS+++ALNmzcvt6ynfgeZsPxh7NixOHLkCHbt2uXpUKo0R9r5rbfewvr165GVleUVj2uQgsq08507d/Dqq69i+fLlqFu3rhujq1oq26fNZjOCg4Px0UcfQS6Xo127drh06RLeeecdJiyVUNl2zsrKwvz58/Gvf/0LMTExOHXqFN58803MnTsX06dPd1O00tOkSRPk5OTg9u3b2LhxI4YMGYIdO3ZUmLR4hEsvOEnE2LFjxXr16olnzpx5YNmIiAjx/ffft1o3Y8YMsVWrVi6Kruqwp51LvPPOO2JgYKC4f/9+F0ZWtVS2nQ8fPiwCEOVyueUlCIIoCIIol8vFU6dOuSli6bKnTz/99NNi9+7drdalp6eLAES9Xu+qEKsEe9q5U6dO4oQJE6zWffzxx6JGoxFNJpOrQqxyunfvLr7++uvlbvPU72C1HsMiiiKSkpLwxRdf4Pvvv0dUVNQD94mNjUVmZqbVuoyMjAqv9ZFj7QwAb7/9NubOnYstW7agffv2Lo5S+uxt56ZNm+KXX35BTk6O5fXcc8+hW7duyMnJQUREhJsilx5H+vRTTz2FU6dOwWw2W9b973//Q1hYGJRKpSvDlSxH2rmwsNDqAbwAIJfLLfVR5ZjNZuj1+nK3eex30KXpkJcbPXq0GBgYKGZlZYlXrlyxvAoLCy1lXn31VXHKlCmW97t37xZ9fHzEd999Vzx27Jg4c+ZMUaFQiL/88osnPoIkONLOb731lqhUKsWNGzda7XPnzh1PfARJcKSd78e7hCrHkba+cOGC6O/vLyYlJYknTpwQv/76azE4OFicN2+eJz6CJDjSzjNnzhT9/f3FTz75RDxz5oy4bds2sWHDhuKAAQM88REkYcqUKeKOHTvEs2fPij///LM4ZcoUURAEcdu2baIoes/vYLVOWACU+1q9erWlTJcuXcQhQ4ZY7ffpp5+Kjz32mKhUKsUWLVqI33zzjXsDlxhH2rlBgwbl7jNz5ky3xy8Vjvbn0piwVI6jbb1nzx4xJiZGVKlU4qOPPir+3//9n1hcXOze4CXEkXY2Go3irFmzxIYNG4pqtVqMiIgQx4wZI968edPt8UvF8OHDxQYNGohKpVIMCgoSu3fvbklWRNF7fgcFUeQ5MiIiIvJu1XoMCxEREUkDExYiIiLyekxYiIiIyOsxYSEiIiKvx4SFiIiIvB4TFiIiIvJ6TFiIiIjI6zFhISIiIq/HhIWI7HLu3DkIgoCcnBynliUisoUJCxFZDB06FIIgQBAEKBQKREVFYdKkSdDpdJYyERERuHLlCh5//HGXxFBYWIiUlBQ0bNgQarUaQUFB6NKlC7788kuXHI+IpMHH0wEQkXdJTEzE6tWrYTQacfDgQQwZMgSCIOAf//gHgLtPvg0NDXXZ8UeNGoV9+/bhn//8J5o3b44bN25gz549uHHjhsuOaTAY+MRkIi/HMyxEZEWlUiE0NBQRERHo168f4uLikJGRYdl+/2Wemzdv4uWXX0ZQUBA0Gg0aN26M1atXl1u3yWTC8OHD0bRpU1y4cKHcMl999RWmTp2KXr16ITIyEu3atcMbb7yB4cOHW8ro9XpMnjwZERERUKlUaNSoEVauXGnZvmPHDnTo0AEqlQphYWGYMmUKiouLLdu7du2KpKQkjBs3DnXr1kVCQgIA4MiRI+jZsydq1KiBkJAQvPrqq7h+/bplv40bN6Jly5bQaDSoU6cO4uLioNVq7W9kIrIbExYiqtCRI0ewZ88em2cfpk+fjl9//RXffvstjh07hqVLl6Ju3bplyun1erz44ovIycnBDz/8gPr165dbX2hoKNLT03Hnzp0Kjzl48GB88skn+OCDD3Ds2DF8+OGHqFGjBgDg0qVL6NWrF5544gn89NNPWLp0KVauXIl58+ZZ1bFmzRoolUrs3r0by5Ytw61bt/DMM8+gTZs2OHDgALZs2YK8vDwMGDAAAHDlyhUMGjQIw4cPx7Fjx5CVlYX+/fuDz48lchOXPw+aiCRjyJAholwuF/38/ESVSiUCEGUymbhx40ZLmbNnz4oAxMOHD4uiKIp9+vQRhw0bVm59JWV/+OEHsXv37mKnTp3EW7du2Yxhx44dYr169USFQiG2b99eHDdunLhr1y7L9hMnTogAxIyMjHL3nzp1qtikSRPRbDZb1i1ZskSsUaOGaDKZRFEUxS5duoht2rSx2m/u3LlifHy81bqLFy+KAMQTJ06IBw8eFAGI586dsxk/EbkGz7AQkZVu3bohJycH+/btw5AhQzBs2DA8//zzFZYfPXo01q9fj9atW2PSpEnYs2dPmTKDBg2CVqvFtm3bEBgYaPP4Tz/9NM6cOYPMzEy88MILOHr0KDp37oy5c+cCAHJyciCXy9GlS5dy9z927BhiY2MhCIJl3VNPPYWCggL89ttvlnXt2rWz2u+nn37C9u3bUaNGDcuradOmAIDTp08jOjoa3bt3R8uWLfHiiy9i+fLluHnzps3PQkTOw4SFiKz4+fmhUaNGiI6OxqpVq7Bv3z6r8SH369mzJ86fP4/x48fj8uXL6N69OyZMmGBVplevXvj555+RnZ1dqRgUCgU6d+6MyZMnY9u2bZgzZw7mzp0Lg8EAjUbzUJ+vhJ+fn9X7goIC9OnTBzk5OVavkydP4umnn4ZcLkdGRga+/fZbNG/eHP/85z/RpEkTnD171inxEJFtTFiIqEIymQxTp07FtGnTUFRUVGG5oKAgDBkyBP/+97+xcOFCfPTRR1bbR48ejbfeegvPPfccduzYYXcczZs3R3FxMXQ6HVq2bAmz2VxhPc2aNUN2drbV2JLdu3fD398f9erVq/AYbdu2xdGjRxEZGYlGjRpZvUqSG0EQ8NRTT2H27Nk4fPgwlEolvvjiC7s/DxHZjwkLEdn04osvQi6XY8mSJeVunzFjBr788kucOnUKR48exddff41mzZqVKffGG29g3rx5ePbZZ7Fr164Kj9e1a1d8+OGHOHjwIM6dO4f09HRMnToV3bp1Q0BAACIjIzFkyBAMHz4cmzdvxtmzZ5GVlYVPP/0UADBmzBhcvHgRb7zxBo4fP44vv/wSM2fORHJyMmSyir/yxo4di99//x2DBg3C/v37cfr0aWzduhXDhg2DyWTCvn37MH/+fBw4cAAXLlzApk2bcO3atXI/KxE5HxMWIrLJx8cHSUlJePvtt8u9hVepVCIlJQWtWrWyXDpZv359uXWNGzcOs2fPRq9evcod6wIACQkJWLNmDeLj49GsWTO88cYbSEhIsCQkALB06VK88MILGDNmDJo2bYqRI0daYnvkkUeQnp6OH3/8EdHR0Rg1ahRGjBiBadOm2fyc4eHh2L17N0wmE+Lj49GyZUuMGzcONWvWhEwmQ0BAAHbu3IlevXrhsccew7Rp07BgwQL07Nmzsk1JRA9BEEXek0dERETejWdYiIiIyOsxYSEiIiKvx4SFiIiIvB4TFiIiIvJ6TFiIiIjI6zFhISIiIq/HhIWIiIi8HhMWIiIi8npMWIiIiMjrMWEhIiIir8eEhYiIiLze/wP/xTPliywnOwAAAABJRU5ErkJggg==\n" }, "metadata": {}, "output_type": "display_data" @@ -348,7 +361,7 @@ "ax_box.axvline(median, color='green', linestyle='-')\n", "ax_box.axvline(mode, color='black', linestyle='--')\n", "\n", - "ax_hist.hist(analyses_collection_df[\"risk_scores\"], alpha = 0.7)\n", + "ax_hist.hist(analyses_collection_df[\"risk_scores\"], alpha = 0.8)\n", "ax_hist.axvline(mean, color='r', linestyle='--', label=f\"Mean: {round(mean, 2)}\")\n", "ax_hist.axvline(median, color='green', linestyle='-', label=f\"Median: {round(median, 2)}\")\n", "ax_hist.axvline(mode, color='black', linestyle='--', label=f\"Mode: {mode}\")\n", @@ -369,14 +382,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "outputs": [ { "data": { - "text/plain": " Collection Detected PII Type Count\n0 PII Collection 1 LOCATION 4\n1 PII Collection 1 SCREEN_NAME 5\n2 PII Collection 1 EMAIL_ADDRESS 1\n3 PII Collection 1 PHONE_NUMBER 2\n4 PII Collection 1 URL 1", - "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    CollectionDetected PII TypeCount
    0PII Collection 1LOCATION4
    1PII Collection 1SCREEN_NAME5
    2PII Collection 1EMAIL_ADDRESS1
    3PII Collection 1PHONE_NUMBER2
    4PII Collection 1URL1
    \n
    " + "text/plain": " Collection Detected PII Type Count\n0 PII Collection 1 LOCATION 6\n1 PII Collection 1 SCREEN_NAME 7\n2 PII Collection 1 NRP 2\n3 PII Collection 1 EMAIL_ADDRESS 1\n4 PII Collection 1 PHONE_NUMBER 2\n5 PII Collection 1 URL 1", + "text/html": "
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    CollectionDetected PII TypeCount
    0PII Collection 1LOCATION6
    1PII Collection 1SCREEN_NAME7
    2PII Collection 1NRP2
    3PII Collection 1EMAIL_ADDRESS1
    4PII Collection 1PHONE_NUMBER2
    5PII Collection 1URL1
    \n
    " }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -402,19 +415,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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\n" + "image/png": 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qefPmyeFwuLWtxYsXKzw8XLt379bIkSM1f/58eXt7m+ZUr15dy5cvN42VLFnS9D0qKkrPPvusaaxs2bIu+6tZs6b69Omj0aNHmx4sctdLL72ke+65R08//XSOy+Pi4tS7d2/n9969e6tly5Z65ZVXVKKE+V/AevXqqWrVqvrwww+1bt06Pfzww/Lyyvkfi9weZ5bo6GiNGDHC+T05OVmhoaHap3PyUcZVj/NG5qFLfxju0Vll2l2MzeiFGf3IRi/M6Ec2emGW1Y+2bdu65Ji8kHWl8WoKXaiULj2w0rZtW7Vt21Zjx47Vo48+qnHjxmnmzJmSpH379qlZs2ZX3U5oaKiqVq2qqlWrKj09XQ888IB2795tuqzu4+OjKlWqXHE7AQEBV52TZcKECapWrZqWLVuWq/k5ufvuu9WuXTtFR0e7PD2+Z88ebd26Vdu2bTPdR5mRkaFFixZp4MCBLtvr37+/XnvtNe3Zs0fbtm277H7dOU5J8vX1dblFQZIy//yAXvwVvTCjH9nohRn9yEYvzLy9vfMlVOZ2mzfE7Qg1a9bU+fPnde+99+rmm2++7EMsf39Q56+6du0qLy8vvf766/lU5SWhoaEaOnSoxowZo4yMaz9bN2XKFH388cfasmWLaTw2NlZ33323vvnmGyUmJjo/I0aMuOzl8l69emnXrl2qXbu2atasec01AQCAoqtQnak8efKkunXrpv79+6tu3boqUaKEtm/frqlTp6pz584qXry45s2bp27duqlTp056/PHHVaVKFf3+++96//33dfToUS1atCjHbRuGoccff1zjx4/XP//5TxUrdumev/T0dJf3PBqGYbrsm5KS4jLH19dXpUqVynFf0dHReuutt3To0CF17979mnpRp04dRUVFafbs2c6xtLQ0/fvf/9bEiRNdnjB/9NFHNX36dH377bfOey2zlCpVSsePH7/qf4m4e5wAAKDoKFRnKv39/dW0aVPNmDFDd999t2rXrq2xY8dq4MCBevXVVyVJnTt31ubNm+Xt7a1evXopPDxcPXv2VFJSkiZNmnTF7fft21dpaWnObUnSt99+q3Llypk+lSpVMq331ltvuczp2bPnZfcTFBSkUaNG6eLFixa6IU2cOFGZmdkn/pcvX66TJ0/qgQcecJlbo0YN1ahR47JnKwMDA1W8ePEr7s/d4wQAAEWH4XD36RbgGiUnJysgIEDPvTdXPsVusrscW3lIqq0S2s1N5vTib+hHNnphRj+y0QuzrH5ERkbm24M6AQEBSkpKcnlY+e91AAAAAJYQKguAo0ePOl/sntMnp1+3AQAAKEgK1YM6N6ry5csrMTHxissBAAAKMkJlAeDl5eXW+x8BAAAKGi5/AwAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDLD4XA47C4CRUNycrICAgL0+++/Kzg42O5ybJWWlqaVK1cqMjJS3t7edpdjK3phRj+y0Qsz+pGNXpjldz+y/v5OSkpSyZIlLzuPM5UAAACwjFAJAAAAywiVAAAAsIxQCQAAAMsIlQAAALCMUAkAAADLCJUAAACwjFAJAAAAy645VP7xxx/av3+/0tPT87IeAAAAFEJuh8qUlBQNGDBAxYoVU61atXT06FFJ0rBhwzRlypQ8LxAAAAAFn9uhMjo6Wt98843Wr18vPz8/53hERIQWL16cp8UBAACgcPByd4Vly5Zp8eLFuuOOO2QYhnO8Vq1a+v777/O0ONyYnl+8WF43+V194g3MU4ZaBgVp1DvvKEMOu8uxVVYvAACFm9tnKn/77TeVKVPGZfz8+fOmkAkAAICiw+1Q2ahRI33yySfO71lBct68eWrWrFneVQYAAIBCw+3L35MnT9Z9992nPXv2KD09XbNmzdKePXu0efNmbdiwIT9qBAAAQAHn9pnKu+66S4mJiUpPT1edOnW0evVqlSlTRlu2bFHDhg3zo0YAAAAUcG6fqZSk2267TW+99VZe1wIAAIBC6ppCZUZGhpYuXaq9e/dKkmrWrKnOnTvLy+uaNgcAAIBCzu0U+O2336pTp046ceKEqlevLkl66aWXVLp0aX388ceqXbt2nhcJAACAgs3teyofffRR1apVSz/99JN27NihHTt26Mcff1TdunX12GOP5UeNAAAAKODcPlOZmJio7du3q1SpUs6xUqVK6cUXX1Tjxo3ztDgAAAAUDm6fqaxWrZp++eUXl/Fff/1VVapUyZOiAAAAULi4HSpjYmL0+OOP68MPP9RPP/2kn376SR9++KGGDx+ul156ScnJyc4PAAAAiga3L3/ff//9kqSHHnrI+Ws6Dsel3y7u2LGj87thGMrIyMirOgEAAFCAuR0qP/vsM37jGwAAACZuh8pWrVrlQxkAAAAozNy+p7Jy5cqaOHGijh49mh/1AAAAoBByO1Q+8cQTWrJkiW699Va1bdtWixYtUmpqan7UBgAAgELC7VA5fPhwJSYmatu2bapRo4aGDRumcuXKaejQodqxY0d+1AgAAIACzu1QmeX222/X7NmzdezYMY0bN07z5s1T48aNVb9+fcXFxTmfCAcAAMCNz+0HdbKkpaVp6dKlio+PV0JCgu644w4NGDBAP/30k8aMGaM1a9bovffey8taAQAAUEDlOlQuWLBA3bt317fffqv4+HgtXLhQHh4e6tOnj2bMmKHw8HDn3AceeICfbAQAAChCch0qH3nkEbVv316NGzdW27ZtNWfOHHXp0kXe3t4ucytXrqwePXrkaaEAAAAouHJ9T2XWPZI//PCDPv30U3Xr1i3HQClJxYsXV3x8fN5UiGvWr18/GYahKVOmmMaXLVvmfIH9+vXrZRiG81O6dGlFRkZq165dOW7LMAz5+PioSpUqmjhxotLT06/b8QAAgILLrQd1DMNQpUqV8qsW5AM/Pz+99NJLOn369BXn7d+/X8ePH9eqVauUmpqqDh066I8//jDNad++vY4fP66DBw/qqaee0vjx4/Xyyy/nZ/kAAKCQcOtBnTZt2sjL68qr8FqhgiUiIkLfffedYmJiNHXq1MvOK1OmjAIDAxUSEqLhw4erU6dO2rdvn+rWreuc4+vrq5CQEEnS4MGDtXTpUi1fvlzR0dE5bjM1NdX0DtPk5GRJkqckTxXtn/r0NP0vvZAuPfyH7D7QD3rxd/QjG70wy+9+5Ha7boXKdu3ayd/f/5oKgj08PT01efJk9erVS48//rgqVKhwxflJSUlatGiRJMnHx+eKc2+66SadPHnysstjYmI0YcIEl/FmpUqpWLFiuaj+xndXUJDdJRQYCQkJdpdQoNCPbPTCjH5koxdm+dWPlJSUXM1zK1Q+88wzKlOmzDUVBPs88MADql+/vsaNG6fY2Ngc52SFzfPnz0uSOnXqZHqi/68cDofWrl2rVatWadiwYZfdb3R0tEaMGOH8npycrNDQUG05fVpeFy9e6+HcEDx1KVD+79QpZdhdjM2yetG2bdvL3qddlKSlpSkhIYF+iF78Hf3IRi/M8rsfWVcarybXoTLrwQ4UTi+99JLuuecePf300zku37Rpk4oVK6atW7dq8uTJeuONN1zmrFixQv7+/kpLS1NmZqZ69eql8ePHX3afvr6+8vX1dRnPkGSoqL8c/9K/TxmSMuiFJMnb25u/HP6CfmSjF2b0Ixu9MMuvfuR2m7kOlfxCTuF29913q127doqOjla/fv1clleuXFmBgYGqXr26fv31V3Xv3l0bN240zWndurXmzJkjHx8flS9f/qr31wIAgKIj109/Hzp0SKVLl87PWpDPpkyZoo8//lhbtmy54rwhQ4Zo9+7dWrp0qWm8ePHiqlKliipWrEigBAAAJrkOlZUqVeISeCFXp04dRUVFafbs2VecV6xYMQ0cOFDjxo3jDDUAAMgVt95TicJv4sSJyszMvOq8oUOHau/evfrggw+uQ1UAAKCw4xrmDWz+/PkuY2FhYaZ3R7Zq1SrHs5GhoaGm91LltC0AAIAsnKkEAACAZbk+U7lz585czfvrL7AAAACgaMh1qKxfv74Mw8jxUmnWuGEYysgo6q9yBgAAKHpyHSoPHTqUn3UAAACgEMt1qKxUqVJ+1gEAAIBCjHsqAQAAYFme3FOZhXsqAQAAiibuqQQAAIBl3FMJAAAAy3L98vPz589r8ODBuuWWW1S6dGn16NFDv/32W37WBgAAgEIi16Fy7Nix+ve//637779fvXr10meffabHHnssP2sDAABAIZHry99Lly5VfHy8unXrJknq06eP7rjjDqWnp8vLi58QBwAAKMpyfabyp59+UvPmzZ3fGzZsKG9vbx07dixfCgMAAEDhketQmZmZKW9vb9OYl5cXrxACAABA7i9/OxwOtWnTxnSpOyUlRR07dpSPj49zbMeOHXlbIQAAAAq8XIfKcePGuYx17tw5T4sBAABA4WQpVAIAAACSG6ESyCsTu3dXcHCw3WXYKi0tTStXrtRLvXu73Ktc1GT1AgBQuOX6QR0AAADgcgiVAAAAsIxQCQAAAMsIlQAAALAsVw/qzJ49O9cbfPzxx6+5GAAAABROuQqVM2bMMH3/7bfflJKSosDAQEnSmTNnVKxYMZUpU4ZQCQAAUATl6vL3oUOHnJ8XX3xR9evX1969e3Xq1CmdOnVKe/fu1e23364XXnghv+sFAABAAeT2PZVjx47VK6+8ourVqzvHqlevrhkzZui5557L0+IAAABQOLgdKo8fP6709HSX8YyMDP3yyy95UhQAAAAKF7dDZZs2bfTPf/5TO3bscI599dVXGjx4sCIiIvK0OAAAABQObofKuLg4hYSEqFGjRvL19ZWvr6+aNGmismXLat68eflRIwAAAAo4t3/7u3Tp0lq5cqUOHDigffv2SZLCw8NVrVq1PC8ON6Yn3npHHr5+dpdhKy/DUOfKZe0uAwCAPON2qMwSFhYmh8Oh2267TV5e17wZAAAA3ADcvvydkpKiAQMGqFixYqpVq5aOHj0qSRo2bJimTJmS5wUCAACg4HM7VEZHR+ubb77R+vXr5eeXfQkzIiJCixcvztPiAAAAUDi4fd162bJlWrx4se644w4ZhuEcr1Wrlr7//vs8LQ4AAACFg9tnKn/77TeVKVPGZfz8+fOmkAkAAICiw+1Q2ahRI33yySfO71lBct68eWrWrFneVQYAAIBCw+3L35MnT9Z9992nPXv2KD09XbNmzdKePXu0efNmbdiwIT9qBAAAQAHn9pnKu+66S4mJiUpPT1edOnW0evVqlSlTRlu2bFHDhg3zo0YAAAAUcNf0gsnbbrtNb731Vl7XAgAAgELK7TOVnp6e+vXXX13GT548KU9PzzwpCgAAAIWL26HS4XDkOJ6amiofHx/LBQEAAKDwyfXl79mzZ0u69LT3vHnz5O/v71yWkZGhjRs3Kjw8PO8rBAAAQIGX61A5Y8YMSZfOVL7xxhumS90+Pj4KCwvTG2+8kfcVAgAAoMDLdag8dOiQJKl169ZasmSJSpUqlW9FAQAAoHBx++nvdevW5UcdAAAAKMTcflDnwQcf1EsvveQyPnXqVHXr1i1PigIAAEDh4nao3LhxoyIjI13G77vvPm3cuDFPigIAAEDh4naoPHfuXI6vDvL29lZycnKeFAUAAIDCxe1QWadOHS1evNhlfNGiRapZs2aeFAUAAIDCxe0HdcaOHat//OMf+v7773XPPfdIktauXauFCxfqgw8+yPMCAQAAUPC5faayY8eOWrZsmb777jv93//9n5566in99NNPWrNmjbp06eLWtvr16yfDMFw+7du3lySFhYXJMAwtWrTIZd1atWrJMAzNnz/fZVlMTIw8PT318ssvuyybP3++AgMDL/v9WmzZskWenp7q0KGDy7LDhw+bjq1EiRKqVauWhgwZooMHD7rUljXP09NTpUqVUtOmTTVx4kQlJSWZ5v61d97e3qpcubJGjhypixcvmubl1N+/9/Stt95SvXr15O/vr8DAQDVo0EAxMTHO5SkpKYqOjtZtt90mPz8/lS5dWi1bttRHH31kqW8AAODG4faZSknq0KFDjgHqWrRv317x8fGmMV9fX+f/Dw0NVXx8vHr06OEc27p1q06cOKHixYvnuM24uDiNHDlScXFxeuaZZ/KkziuJjY3VsGHDFBsbq2PHjql8+fIuc9asWaNatWopJSVFu3bt0qxZs1SvXj19/PHHatOmjXNeyZIltX//fjkcDp05c0abN29WTEyM4uPj9fnnn5u2ndW7tLQ0ffXVV+rbt68Mw3B5Oj8+Pt4Z1LNkBem4uDgNHz5cs2fPVsuWLZWamqqdO3dq9+7dzrmDBg3SF198oVdeeUU1a9bUyZMntXnzZp08eTIv2gcAAG4A1xQqz5w5ow8//FA//PCDnn76aQUFBWnHjh0qW7asbrnlFre25evrq5CQkMsuj4qK0owZM/Tjjz8qNDRU0qUgFBUVpQULFrjM37Bhgy5cuKCJEydqwYIF2rx5s+688073DtAN586d0+LFi7V9+3adOHFC8+fP15gxY1zmBQcHO4/z1ltvVceOHdWmTRsNGDBA33//vfMXigzDcM4rV66catSooY4dO6pWrVoaOXKk3nnnHec2/9q70NBQRUREKCEhwSVUBgYGXrbHy5cv10MPPaQBAwY4x2rVquUyZ9asWc6n/sPCwtSwYUO3+gQAAG5sbofKnTt3KiIiQgEBATp8+LAeffRRBQUFacmSJTp69GiOQc+KsmXLql27dnr77bf13HPPKSUlRYsXL9aGDRty3FdsbKx69uwpb29v9ezZU7GxsfkaKt9//32Fh4erevXq6t27t4YPH67o6GgZhnHF9Tw8PPTEE0/ogQce0FdffaUmTZpcdm6ZMmUUFRWluLg4ZWRkmH4iM8vu3bu1efNmVapUya36Q0JCtGHDBh05cuSy64aEhGjlypX6xz/+oRIlSuR626mpqUpNTXV+z3o7gJdhyOMq/bnRef15/GlpaTZXYr+sHtCLS+hHNnphRj+y0Quz/O5HbrfrdqgcMWKE+vXrp6lTp5oCRmRkpHr16uXu5rRixQr5+/ubxsaMGWM629e/f3899dRTevbZZ/Xhhx/qtttuU/369V22lZycrA8//FBbtmyRJPXu3VstWrTQrFmzXPaRV2JjY9W7d29Jly5HJyUlacOGDWrVqtVV1w0PD5d06b7LK4XKrLlnz57VyZMnVaZMGUnZvUtPT1dqaqo8PDz06quvuqzbs2dPlyC6Z88eVaxYUePGjdM//vEPhYWFqVq1amrWrJkiIyPVtWtXeXhcuuV27ty5ioqKUnBwsOrVq6e77rpLXbt2VfPmza9Yc0xMjCZMmOAy3q5iaRUrVuyK6xYVCQkJdpdQYNALM/qRjV6Y0Y9s9MIsv/qRkpKSq3luh8ovv/xSb775psv4LbfcohMnTri7ObVu3Vpz5swxjQUFBZm+d+jQQf/85z+1ceNGxcXFqX///jlua+HChbrttttUr149SVL9+vVVqVIlLV682HR5N6/s379f27Zt09KlSyVJXl5e6t69u2JjY3MVKh0OhyRd9azm5eZm9e78+fOaMWOGvLy89OCDD7qsO2PGDEVERJjGsu7NLFeunLZs2aLdu3dr48aN2rx5s/r27at58+bp008/lYeHh+6++2798MMP2rp1qzZv3qy1a9dq1qxZmjBhgsaOHXvZmqOjozVixAjn9+TkZIWGhmrV0d/k4et31WO+kXkZhjqElVHbtm3l7e1tdzm2SktLU0JCAr34E/3IRi/M6Ec2emGW3/3I7XvI3Q6Vvr6+OW78wIEDKl26tLubU/HixVWlSpUrzvHy8tLDDz+scePG6YsvvnCGuL+LjY3Vt99+Ky+v7MPKzMxUXFxcvoTK2NhYpaenmx6ecTgc8vX11auvvqqAgIArrr93715JUuXKla+6r71796pkyZIKDg52jv21d3FxcapXr55iY2NdjjUkJOSqPa5du7Zq166t//u//9OgQYPUokULbdiwQa1bt5Z06eX2LVq0UIsWLTRq1ChNmjRJEydO1KhRo3J8Gb506Z+Vvz50lSXd4ZDHnyG5qPP29uYPxD/RCzP6kY1emNGPbPTCLL/6kdttuv1KoU6dOmnixInO6+uGYejo0aMaNWpUjmfJ8kr//v21YcMGde7cWaVKlXJZvmvXLm3fvl3r169XYmKi87N+/Xpt2bJF+/bty9N60tPTtWDBAk2bNs20v2+++Ubly5fXwoULr7h+ZmamZs+ercqVK6tBgwZXnPvrr7/qvffeU5cuXZyXpP/Ow8NDY8aM0XPPPacLFy5c83FJcr7E/vz581eck56e7vIKIwAAUDS5faZy2rRp6tq1q8qUKaMLFy6oZcuWOnHihJo1a6YXX3zR7QJSU1NdLpt7eXnp5ptvNo3VqFFDv//++2XvxYuNjVWTJk109913uyxr3LixYmNjc3xvpSRlZGQoMTHRNObr66saNWpctu4VK1bo9OnTGjBggMsZyQcffFCxsbEaNGiQc+zkyZM6ceKEUlJStHv3bs2cOVPbtm3TJ598Yrrf0eFw6MSJE85XCm3ZskWTJ09WQECApkyZctl6JKlbt2565pln9Nprr+npp592jp85c8alxyVKlFDx4sU1ePBglS9fXvfcc48qVKig48ePa9KkSSpdurSaNWsmSWrVqpV69uypRo0aKTg4WHv27NGYMWPUunVrlSxZ8oo1AQCAosHtUBkQEKCEhAR9/vnn+uabb3Tu3DndfvvtLvfs5dann36qcuXKmcaqV6+e45nFv176/as//vhD77zzjkaNGpXj8gcffFDTpk3T5MmTc1x+7tw5l7OFt912m7777rvL1h0bG+t8Cj6n/U2dOlU7d+50hq6s/hQrVkyVKlVS69atNXfuXJfL0snJySpXrpwMw1DJkiVVvXp19e3bV0888cRVA5yXl5eGDh2qqVOnavDgwc73eD7yyCMuc2NiYjR69GhFREQoLi5Oc+bM0cmTJ3XzzTerWbNmWrt2rbPfWU/fjxkzRikpKSpfvrzuv/9+Pf/881esBwAAFB2Gw+HezW0LFixQ9+7dXe6V++OPP7Ro0SL16dMnTwvEjSM5OVkBAQGKipnJgzqGoc6VyyoyMrLI3w+UlpamlStX0os/0Y9s9MKMfmSjF2b53Y+sv7+TkpKueILL7XsqH3nkEZefDJSks2fP5nhGDAAAADc+t0Olw+HI8RU4P/3001Wfdi5Mjh49Kn9//8t+jh49aneJAAAABUau76ls0KCBDMOQYRhq06aN6bU9GRkZOnTokMvvSxdm5cuXd3l45+/LAQAAcEmuQ2WXLl0kSYmJiWrXrp3pF2p8fHwUFhaWr68Uut68vLyu+m5HAAAAXJLrUDlu3DhJUlhYmLp37y4/v6L9oAUAAACyuX1PZd++fXXx4kXNmzdP0dHROnXqlCRpx44d+vnnn/O8QAAAABR8br+ncufOnc73Mx4+fFgDBw5UUFCQlixZoqNHj2rBggX5UScAAAAKMLfPVD755JPq16+fDh48aLoEHhkZqY0bN+ZpcQAAACgc3D5TuX37ds2dO9dl/JZbbnH5KUAAAAAUDW6fqfT19VVycrLL+IEDB1S6dOk8KQoAAACFi9uhslOnTpo4caLS0tIkSYZh6OjRoxo1atQN9UohAAAA5J7boXLatGk6d+6cypQpowsXLqhly5aqUqWKSpQooRdffDE/agQAAEAB5/Y9lQEBAUpISND//vc/7dy5U+fOndPtt9+uiIiI/KgPAAAAhYDboTLLXXfdpbvuuisvawEAAEAh5VaozMzM1Pz587VkyRIdPnxYhmGocuXK6tq1qx5++GEZhpFfdQIAAKAAy/U9lQ6HQ506ddKjjz6qn3/+WXXq1FGtWrV05MgR9evXTw888EB+1gkAAIACLNdnKufPn6+NGzdq7dq1at26tWnZZ599pi5dumjBggXq06dPnhcJAACAgi3XoXLhwoUaM2aMS6CUpHvuuUejR4/Wu+++S6jEVc0a2FvBwcF2l2GrtLQ0rVy50u4yAADIM7m+/L1z5061b9/+ssvvu+8+ffPNN3lSFAAAAAqXXIfKU6dOqWzZspddXrZsWZ0+fTpPigIAAEDhkutQmZGRIS+vy18t9/T0VHp6ep4UBQAAgMIl1/dUOhwO9evXT76+vjkuT01NzbOiAAAAULjkOlT27dv3qnN4SAcAAKBoynWojI+Pz886AAAAUIjl+p5KAAAA4HIIlQAAALCMUAkAAADLCJUAAACwjFAJAAAAywiVAAAAsCzXrxQC8sqgaW/L8PGzuwxbeXkY6l6vvPrGzFN6psPucmyV1QsAQOHGmUoAAABYRqgEAACAZYRKAAAAWEaoBAAAgGWESgAAAFhGqAQAAIBlhEoAAABYRqgEAACAZYRKAAAAWEaoBAAAgGWESgAAAFhGqAQAAIBlhEoAAABYRqgEAACAZYRKAAAAWEaoBAAAgGWESgAAAFhGqAQAAIBlhEoAAABYRqgEAACAZbaGyn79+skwDBmGIR8fH1WpUkUTJ05Uenq61q9fL8MwdObMGZf1wsLCNHPmTNPY5s2bFRkZqVKlSsnPz0916tTR9OnTlZGRYZpnGIb8/Px05MgR03iXLl3Ur1+/HGv766d9+/a5OrawsDAZhqGtW7eaxocPH65WrVqZ9tOlSxeX9f9+/PPnz5dhGKpRo4bL3A8++ECGYSgsLMw5ljU/6+Pv76+GDRtqyZIlpnVbtWqV43EOGjTIOeev4yVLllTjxo310Ucf5aoPAACgaLD9TGX79u11/PhxHTx4UE899ZTGjx+vl19+2a1tLF26VC1btlSFChW0bt067du3T0888YQmTZqkHj16yOFwmOYbhqHnn38+17X99bNw4cJc1+Xn56dRo0a5dSxXUrx4cf3666/asmWLaTw2NlYVK1Z0mV+yZEln3V9//bXatWunhx56SPv37zfNGzhwoMtxTp061TQnPj5ex48f1/bt29W8eXN17dpVu3btyrNjAwAAhZuX3QX4+voqJCREkjR48GAtXbpUy5cvV7NmzXK1/vnz5zVw4EB16tRJc+fOdY4/+uijKlu2rDp16qT3339f3bt3dy4bOnSopk+frmeeeUa1a9fOVW3X4rHHHtMbb7yhlStXKjIy8pq3k8XLy0u9evVSXFycsz8//fST1q9fryeffNIl8BqG4aw/JCREkyZN0r/+9S/t3LlT1atXd84rVqzYVY8zMDBQISEhCgkJ0QsvvKBZs2Zp3bp1qlOnzmXXSU1NVWpqqvN7cnKyJMnTw5CHh+Hewd9gvP48fq8i3gcpuwdpaWk2V1IwZPWBftCLv6Mf2eiFWX73I7fbtT1U/t1NN92kkydP5nr+6tWrdfLkST399NMuyzp27Khq1app4cKFplDZvHlzHThwQKNHj9aKFSvypO6cVK5cWYMGDVJ0dLTat28vDw/rJ4b79++vVq1aadasWSpWrJjmz5+v9u3bq2zZsldcLyMjQwsWLJAk3X777de8//T0dMXGxkqSfHx8rjg3JiZGEyZMcBnvXCtExYoVu+YabiQP1ilndwkFRkJCgt0lFCj0Ixu9MKMf2eiFWX71IyUlJVfzCkyodDgcWrt2rVatWqVhw4Y5xytUqOAy968Hd+DAAUnK8V5DSQoPD3fO+auYmBjVrVtXmzZtUosWLXJcd8WKFfL39zeNjRkzRmPGjLn6Af3pueeeU3x8vN599109/PDDuV7vcho0aKBbb71VH374oR5++GHNnz9f06dP1w8//OAyNykpyVn/hQsX5O3trblz5+q2224zzXv99dc1b94809ibb76pqKgo5/eePXvK09NTFy5cUGZmpsLCwvTQQw9dsdbo6GiNGDHC+T05OVmhoaH66NsT8vDxc/vYbyReHoYerFNO/9l1XOmZjquvcAPL6kXbtm3l7e1tdzm2S0tLU0JCAv0Qvfg7+pGNXpjldz+yrjReje2hMiu4paWlKTMzU7169dL48eP15ZdfSpI2bdqkEiVKmNb564MuWf5+3+TV1KxZU3369NHo0aP1+eef5zindevWmjNnjmksKCjIrf2ULl1aTz/9tJ5//nnT2VIr+vfvr/j4eFWsWFHnz59XZGSkXn31VZd5JUqU0I4dOyRdCuJr1qzRoEGDFBwcrI4dOzrnRUVF6dlnnzWt+/cznzNmzFBERIR++OEHPfnkk5o9e/ZVe+Hr6ytfX1+X8YxMhzKLeJDKkp7pKPKhMou3tzd/OfwF/chGL8zoRzZ6YZZf/cjtNm0PlVnBzcfHR+XLl5eXl7mkypUrKzAw0DT21znVqlWTJO3du1d33nmny/b37t2rmjVr5rjvCRMmqFq1alq2bFmOy4sXL64qVaq4cTQ5GzFihF5//XW9/vrrLstKlizp8iS6JJ05c0aenp4qXry4y7KoqCiNHDlS48eP18MPP+zSsyweHh6m+uvWravVq1frpZdeMoXKgICAqx5nSEiIqlSpoipVqig+Pl6RkZHas2ePypQpc8X1AABA0WD7099Zwa1ixYqXDUdXcu+99yooKEjTpk1zWbZ8+XIdPHhQPXv2zHHd0NBQDR06VGPGjHF59VBe8vf319ixY/Xiiy/q7NmzpmXVq1fXt99+a3qgRZJ27NihypUr5/hfB0FBQerUqZM2bNig/v37u1VL1iVsK5o0aaKGDRvqxRdftLQdAABw47A9VFpVvHhxvfnmm/roo4/02GOPaefOnTp8+LBiY2PVr18/de3a9Yr3/kVHR+vYsWNas2aNy7LU1FSdOHHC9Pn999+vqc7HHntMAQEBeu+990zjUVFRMgxDffr00VdffaXvvvtOcXFxmjlzpp566qnLbm/+/Pn6/fffFR4eftk5DofDWfehQ4c0d+5crVq1Sp07dzbNS0lJcTnO06dPX/F4hg8frjfffFM///xzLo4eAADc6Ap9qJSkrl27at26dTp69KhatGih6tWra8aMGXr22We1aNEiGcblX9sSFBSkUaNG6eLFiy7LPv30U5UrV870ueuuu66pRm9vb73wwgsu+wkMDNSmTZuUlpamTp06qX79+po9e7amT5+uf/7zn5fd3k033aTg4OAr7jM5OdlZd40aNTRt2jRNnDjR5f7Jt956y+U4L3d2N0v79u1VuXJlzlYCAABJkuFw9wkX4BolJycrICBAXaOnyeDpb3WvV16LvzlW5B/UyepFZGQkN9zr0lOcWe+2Ler9oBdm9CMbvTDL735k/f2dlJSkkiVLXnbeDXGmEgAAAPYiVF6Dd999V/7+/jl+atWqZXd5AAAA153trxQqjDp16qSmTZvmuIzT8AAAoCgiVF6DEiVKuLyQHQAAoCjj8jcAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAs87K7ABQ9bzzVV8HBwXaXYau0tDStXLlSb0c/Km9vb7vLsVVWLwAAhRtnKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBm//Y3rbsiEWHl4+dpdhq28PA09eFclu8sAACDPcKYSAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmh8gbXqlUrDR8+3GV8/vz5CgwMlCSNHz9ehmHIMAx5enoqNDRUjz32mE6dOmVaJywsTDNnzsz/ogEAQKHjZXcBKBhq1aqlNWvWKCMjQ3v37lX//v2VlJSkxYsX210aAAAoBAiVkCR5eXkpJCREknTLLbeoW7duio+Pt7kqAABQWBAq4eLw4cNatWqVfHx8LG0nNTVVqampzu/JycmSJC9PycPTsLTtws7rz+NPS0uzuRL7ZfWAXlxCP7LRCzP6kY1emOV3P3K7XUIlJEm7du2Sv7+/MjIydPHiRUnS9OnTLW0zJiZGEyZMcBnv0CRUxYoVs7TtG0VCQoLdJRQY9MKMfmSjF2b0Ixu9MMuvfqSkpORqHqESkqTq1atr+fLlunjxot555x0lJiZq2LBhlrYZHR2tESNGOL8nJycrNDRUn2z7UR5eflZLLtS8PA11blZRbdu2lbe3t93l2CotLU0JCQn04k/0Ixu9MKMf2eiFWX73I+tK49UQKm9wJUuWVFJSksv4mTNnFBAQ4Pzu4+OjKlWqSJKmTJmiDh06aMKECXrhhReued++vr7y9fV1GU/PkDwMxzVv90bi7e3NH4h/ohdm9CMbvTCjH9nohVl+9SO32+SVQje46tWra8eOHS7jO3bsULVq1S673nPPPad//etfOnbsWH6WBwAAbhCEyhvc4MGDdeDAAT3++OPauXOn9u/fr+nTp2vhwoV66qmnLrtes2bNVLduXU2ePNk0/vPPPysxMdH0OX36dH4fBgAAKOAIlTe4W2+9VRs3btS+ffsUERGhpk2b6v3339cHH3yg9u3bX3HdJ598UvPmzdOPP/7oHPvXv/6lBg0amD6ffPJJfh8GAAAo4Linsgho3LixVq9efdnl48eP1/jx413Ge/TooR49eji/Hz58OB+qAwAANwLOVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACzzsrsAFD2vjRug4OBgu8uwVVpamlauXGl3GQAA5BnOVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALONBHVw3DodDknT27Fl5e3vbXI290tLSlJKSouTkZHpBL0zoRzZ6YUY/stELs/zuR3JysqTsv8cvh1CJ6+bkyZOSpMqVK9tcCQAAcNfZs2cVEBBw2eWESlw3QUFBkqSjR49e8R/KoiA5OVmhoaH68ccfVbJkSbvLsRW9MKMf2eiFGf3IRi/M8rsfDodDZ8+eVfny5a84j1CJ68bD49ItvAEBAfwh8KeSJUvSiz/RCzP6kY1emNGPbPTCLD/7kZuTQTyoAwAAAMsIlQAAALCMUInrxtfXV+PGjZOvr6/dpdiOXmSjF2b0Ixu9MKMf2eiFWUHph+G42vPhAAAAwFVwphIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEtfFa6+9prCwMPn5+alp06batm2b3SXZYuPGjerYsaPKly8vwzC0bNkyu0uyTUxMjBo3bqwSJUqoTJky6tKli/bv3293WbaZM2eO6tat63x5cbNmzfTf//7X7rIKhClTpsgwDA0fPtzuUmwxfvx4GYZh+oSHh9tdlm1+/vln9e7dW8HBwbrppptUp04dbd++3e6ybBEWFubyz4ZhGBoyZIgt9RAqke8WL16sESNGaNy4cdqxY4fq1aundu3a6ddff7W7tOvu/Pnzqlevnl577TW7S7Hdhg0bNGTIEG3dulUJCQlKS0vTvffeq/Pnz9tdmi0qVKigKVOm6KuvvtL27dt1zz33qHPnzvr222/tLs1WX375pd58803VrVvX7lJsVatWLR0/ftz5+d///md3SbY4ffq0mjdvLm9vb/33v//Vnj17NG3aNJUqVcru0mzx5Zdfmv65SEhIkCR169bNlnp4pRDyXdOmTdW4cWO9+uqrkqTMzEyFhoZq2LBhGj16tM3V2ccwDC1dulRdunSxu5QC4bffflOZMmW0YcMG3X333XaXUyAEBQXp5Zdf1oABA+wuxRbnzp3T7bffrtdff12TJk1S/fr1NXPmTLvLuu7Gjx+vZcuWKTEx0e5SbDd69Gh9/vnn2rRpk92lFEjDhw/XihUrdPDgQRmGcd33z5lK5Ks//vhDX331lSIiIpxjHh4eioiI0JYtW2ysDAVNUlKSpEtBqqjLyMjQokWLdP78eTVr1szucmwzZMgQdejQwfTnR1F18OBBlS9fXrfeequioqJ09OhRu0uyxfLly9WoUSN169ZNZcqUUYMGDfTWW2/ZXVaB8Mcff+idd95R//79bQmUEqES+ez3339XRkaGypYtaxovW7asTpw4YVNVKGgyMzM1fPhwNW/eXLVr17a7HNvs2rVL/v7+8vX11aBBg7R06VLVrFnT7rJssWjRIu3YsUMxMTF2l2K7pk2bav78+fr00081Z84cHTp0SC1atNDZs2ftLu26++GHHzRnzhxVrVpVq1at0uDBg/X444/r7bfftrs02y1btkxnzpxRv379bKvBy7Y9A8CfhgwZot27dxfZ+8SyVK9eXYmJiUpKStKHH36ovn37asOGDUUuWP7444964oknlJCQID8/P7vLsd19993n/P9169ZV06ZNValSJb3//vtF7taIzMxMNWrUSJMnT5YkNWjQQLt379Ybb7yhvn372lydvWJjY3XfffepfPnyttXAmUrkq5tvvlmenp765ZdfTOO//PKLQkJCbKoKBcnQoUO1YsUKrVu3ThUqVLC7HFv5+PioSpUqatiwoWJiYlSvXj3NmjXL7rKuu6+++kq//vqrbr/9dnl5ecnLy0sbNmzQ7Nmz5eXlpYyMDLtLtFVgYKCqVaum7777zu5Srrty5cq5/EdWjRo1iuztAFmOHDmiNWvW6NFHH7W1DkIl8pWPj48aNmyotWvXOscyMzO1du3aIn2vGCSHw6GhQ4dq6dKl+uyzz1S5cmW7SypwMjMzlZqaancZ112bNm20a9cuJSYmOj+NGjVSVFSUEhMT5enpaXeJtjp37py+//57lStXzu5SrrvmzZu7vHrswIEDqlSpkk0VFQzx8fEqU6aMOnToYGsdXP5GvhsxYoT69u2rRo0aqUmTJpo5c6bOnz+vRx55xO7Srrtz586Zzi4cOnRIiYmJCgoKUsWKFW2s7PobMmSI3nvvPX300UcqUaKE8x7bgIAA3XTTTTZXd/1FR0frvvvuU8WKFXX27Fm99957Wr9+vVatWmV3adddiRIlXO6tLV68uIKDg4vkPbdPP/20OnbsqEqVKunYsWMaN26cPD091bNnT7tLu+6efPJJ3XnnnZo8ebIeeughbdu2TXPnztXcuXPtLs02mZmZio+PV9++feXlZXOscwDXwSuvvOKoWLGiw8fHx9GkSRPH1q1b7S7JFuvWrXNIcvn07dvX7tKuu5z6IMkRHx9vd2m26N+/v6NSpUoOHx8fR+nSpR1t2rRxrF692u6yCoyWLVs6nnjiCbvLsEX37t0d5cqVc/j4+DhuueUWR/fu3R3fffed3WXZ5uOPP3bUrl3b4evr6wgPD3fMnTvX7pJstWrVKockx/79++0uxcF7KgEAAGAZ91QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAUcSdOnNCwYcN06623ytfXV6GhoerYsaPWrl17XeswDEPLli27rvsEkHds/uVxAICdDh8+rObNmyswMFAvv/yy6tSpo7S0NK1atUpDhgzRvn377C4RQCHBb38DQBEWGRmpnTt3av/+/SpevLhp2ZkzZxQYGKijR49q2LBhWrt2rTw8PNS+fXu98sorKlu2rCSpX79+OnPmjOks4/Dhw5WYmKj169dLklq1aqW6devKz89P8+bNk4+PjwYNGqTx48dLksLCwnTkyBHn+pUqVdLhw4fz89AB5DEufwNAEXXq1Cl9+umnGjJkiEuglKTAwEBlZmaqc+fOOnXqlDZs2KCEhAT98MMP6t69u9v7e/vtt1W8eHF98cUXmjp1qiZOnKiEhARJ0pdffilJio+P1/Hjx53fARQeXP4GgCLqu+++k8PhUHh4+GXnrF27Vrt27dKhQ4cUGhoqSVqwYIFq1aqlL7/8Uo0bN871/urWratx48ZJkqpWrapXX31Va9euVdu2bVW6dGlJl4JsSEiIhaMCYBfOVAJAEZWbu5/27t2r0NBQZ6CUpJo1ayowMFB79+51a39169Y1fS9Xrpx+/fVXt7YBoOAiVAJAEVW1alUZhmH5YRwPDw+XgJqWluYyz9vb2/TdMAxlZmZa2jeAgoNQCQBFVFBQkNq1a6fXXntN58+fd1l+5swZ1ahRQz/++KN+/PFH5/iePXt05swZ1axZU5JUunRpHT9+3LRuYmKi2/V4e3srIyPD7fUAFAyESgAowl577TVlZGSoSZMm+s9//qODBw9q7969mj17tpo1a6aIiAjVqVNHUVFR2rFjh7Zt26Y+ffqoZcuWatSokSTpnnvu0fbt27VgwQIdPHhQ48aN0+7du92uJSwsTGvXrtWJEyd0+vTpvD5UAPmMUAkARditt96qHTt2qHXr1nrqqadUu3ZttW3bVmvXrtWcOXNkGIY++ugjlSpVSnfffbciIiJ06623avHixc5ttGvXTmPHjtXIkSPVuHFjnT17Vn369HG7lmnTpikhIUGhoaFq0KBBXh4mgOuA91QCAADAMs5UAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAsv8HqLEJM9A9jl4AAAAASUVORK5CYII=\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "sns.barplot(data=detected_types_df, y=\"Detected PII Type\", x=\"Count\", alpha = 0.7)\n", + "sns.barplot(data=detected_types_df, y=\"Detected PII Type\", x=\"Count\", alpha = 0.8)\n", "plt.grid()" ], "metadata": { diff --git a/pii_codex/models/microsoft_presidio_pii.py b/pii_codex/models/microsoft_presidio_pii.py index d2d77ff..230fbe4 100644 --- a/pii_codex/models/microsoft_presidio_pii.py +++ b/pii_codex/models/microsoft_presidio_pii.py @@ -12,7 +12,7 @@ class MSFTPresidioPIIType(Enum): ABA_ROUTING_NUMBER: str = "ABA_ROUTING_NUMBER" IP_ADDRESS: str = "IP_ADDRESS" DATE: str = "DATE_TIME" - ADDRESS: str = "ADDRESS" + ADDRESS: str = "LOCATION" AGE: str = "AGE" PERSON: str = "PERSON" CREDIT_CARD_NUMBER: str = "CREDIT_CARD" diff --git a/pii_codex/services/analysis_service.py b/pii_codex/services/analysis_service.py index 5a82812..fd1ec9b 100644 --- a/pii_codex/services/analysis_service.py +++ b/pii_codex/services/analysis_service.py @@ -37,6 +37,12 @@ def __init__( pii_token_replacement_value: str = DEFAULT_TOKEN_REPLACEMENT_VALUE, analysis_provider: str = AnalysisProviderType.PRESIDIO.name, ): + """ + PIIAnalysisService constructor. + @param pii_token_replacement_value: PII Token replacement string (default is ) + @param analysis_provider: Default provider is PRESIDIO, pass in another analysis provider + when using the adapters. + """ self._analysis_provider = analysis_provider self._language_code = "en" self._pii_assessment_service = PIIAssessmentService() diff --git a/pii_codex/services/assessment_service.py b/pii_codex/services/assessment_service.py index 3023b33..b8d69e3 100644 --- a/pii_codex/services/assessment_service.py +++ b/pii_codex/services/assessment_service.py @@ -12,8 +12,8 @@ class PIIAssessmentService: @timed_operation def assess_pii_type(self, detected_pii_type: str) -> RiskAssessment: """ - Assesses a singular detected PII type given a type name string from commmon.PIIType enum - @param detected_pii_type: type name strings from commmon.PIIType enum + Assesses a singular detected PII type given a type name string from common.PIIType enum + @param detected_pii_type: type name strings from common.PIIType enum @return: RiskAssessment """ return PII_MAPPER.map_pii_type(detected_pii_type) @@ -23,8 +23,8 @@ def assess_pii_type_list( self, detected_pii_types: List[str] ) -> List[RiskAssessment]: """ - Assesses a list of detected PII types given an array of type name strings from commmon.PIIType enum - @param detected_pii_types: array type name strings from commmon.PIIType + Assesses a list of detected PII types given an array of type name strings from common.PIIType enum + @param detected_pii_types: array type name strings from common.PIIType enum (e.g. ["PHONE_NUMBER", "US_SOCIAL_SECURITY_NUMBER"]) @return: List[RiskAssessment] """ diff --git a/pyproject.toml b/pyproject.toml index 0cc1587..a55fe9d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,13 @@ [tool.poetry] name = "pii-codex" -version = "0.4.2" +version = "0.4.3" description = "" authors = ["Eidan J. Rosado"] license = "BSD 3-Clause" readme = "README.md" homepage = "https://github.com/EdyVision/pii-codex" repository = "https://github.com/EdyVision/pii-codex" -keywords = ["PII", "PII topology", "risk categories", "personal identifiable information"] +keywords = ["PII", "PII topology", "risk categories", "personal identifiable information", "risk assessment"] classifiers = [ "Development Status :: 1 - Planning", "Intended Audience :: Science/Research",