One way to retrieve data from a SQLite database is to use the select command. While this section is not a comprehensive SQL tutorial, it provides common use cases to allow you to get started. Here we provide a few detailed examples of the common ways in which the output of MOSAIC can be queried for further processing. Interacting with the data through the Structured Query Language (SQL) is a flexible approach to further analyze or plot the output. MOSAIC stores the output of an analysis in a SQLite database as described in the Database Structure and Query Syntax section. As seen from the figure below, the settings file is in the JSON format as described in the Settings File documentation. This allows any database opened with the MOSAIC GUI to retrieve settings that correspond to the analysis results in the file. The analysissettings table contains a single text entry that stores the settings file for the analysis. If event time-series storage was requested, then the TimeSeries column will store the ionic current data for that entry in binary format. eInvalidFitParameters) is stored and all other columns (except TimeSeries) are set to -1. If a failure occurred the corresponding error code (e.g. The ProcessingStatus column is a text field that should read normal if the fit for a particular event was successful. 2Ī typical metadata table for the ADEPT 2-State algorithm is shown below. Select BlockDepth from metadata where ProcessingStatus = 'normal' select BlockDepth from metadata where ProcessingStatus = 'normal' and ResTime > 0. The column names for ADEPT differ from this list. For example, the column names for the ADEPT 2-State algorithm are shown below. The column names describe the metadata and are unique to the processing algorithm used. The parameters describing each event (or metadata) are stored in individual rows of the metadata table in the database file. MOSAIC processes individual blockade events from a time-series of ionic current. The metadata table contains the primary output of the analysis. Two tables most relevant to the analysis ( metadata and analysissettings) are discussed in detail below. Databases output by MOSAIC contain four tables: i) analysisinfo contains general information about the anlysis such as the data path, analysis algorithm etc., ii) analysissettings contains a JSON formatted string with the analysis settings, iii) metadata holds the output of the analysis, and iv) metadata_t lists the data types for each column in metadata. MOSAIC outputs databases with multiple tables as seen from the figure below. Databased generated by MOSAIC can be inspected using a database viewer, for example the open source DB browser for SQLite. SQLite databases store data in tables similar to spreadsheets, where each table is analogous to a sheet in an Excel spreadsheet. 20140929 for Sep 29, 2014) and is the analysis start time (e.g. Each analysis creates a new database file named eventMD-.sqlite, where is the date the analysis was performed (e.g. Database files are stored in the same directory as the data being processed. The following screenshot shows the addressbook example application after running the test script.MOSAIC stores the output of an analysis in a SQLite database. The addEntry function takes the fetched data as arguments and adds them into the addressbook. To use this code properly you have to start the application and create a new addressbook as pre-condition. Test.log("added " + id + " entries in the addressbook application") test.log(id + forename + surname + email + phone)ĪddEntry(forename, surname, email, phone) Test.log("Result is not valid, maybe no entries in database?") Var result = conn.query("SELECT id, forename, surname, email, phone FROM addressbook ")
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