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Python 3.6.4\r\n1. Linux cinnamon 18.3 or Windows10 64 bit\r\n1. pandas==0.22.0\r\n1. numpy==1.14.0\r\n1. matplotlib==2.1.2\r\n1. psycopg2==2.7.4\r\n1. os\r\n1. pprint\r\n1. datetime\r\n1. re\r\n1. math\r\n\r\n### Application Description\r\nETL flat data exported from a Chemical Analyzer instrument (TOF-MS).  While the data is still in memory data is cleansed and linked so that primary and foreign keys can be generated prior to loading into a Postgres managed database using a Snowflake schema.  A CLI is implemented to query the DB to generate customizable data visualizations.\r\n\r\n### Database Schema\r\n![Schema](https://github.com/kitestring/DetectorStressQuantification/blob/master/DB_Setup/DetectorStress_Schema.png)\r\n\r\n### Command Line Interface (CLI) Example\r\n![CLI](https://github.com/kitestring/DetectorStressQuantification/blob/master/ExampleOutput/CLI_Example.png)\r\n\r\n### To maintain confidentiality the data examples shown below are not “real” and are simulated data sets.\r\n\r\n### Detector Measurement Ion Statistics Plot examle\r\n![Data Set #1 Example](https://github.com/kitestring/DetectorStressQuantification/blob/master/ExampleOutput/DM_API_Analysis_Upgraded.png)\r\n\r\n### System Performance vs Detector Voltage Offset\r\n![Data Set #2 Example](https://github.com/kitestring/DetectorStressQuantification/blob/master/ExampleOutput/Eicosane_5_0pg_plots.png)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkitestring%2Fdetectorstressquantification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkitestring%2Fdetectorstressquantification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkitestring%2Fdetectorstressquantification/lists"}