# Hello and welcome! This data-set is supplied to allow you to evaluate Scan-o-Matic without the need to setup a full system with the required hardware (router, power manager, scanners and fixtures). You should however really have installed the `scanomatic` package. Checkout the github wiki if you have not. ## Files ### SoMSuppData.scan.instructions An example of how the instructions to the scanning procedure get stored per each experiment run. The text-file is fairly easy to read as it is, but it also contains some speciic python code so that it can safely be read by Scan-o-Matic. If you wish to explore the contents of the file in python: from scanomatic.models.factories import ScanningModelFactory scanning_model = ScanningModelFactory.serializer.load( "SoMSuppData.scan.instructions")[0] If you are using ipyton, the returned object will have all its data easily listed by auto-complete. ### SoMSuppData_X_Y.tiff These are the original images captured by the scanner exactly as they were captured. X: There's 218 images enumerated from 0 - 217. Y: For each image, the duration (since project start) when the image was captured ### SoMSuppData.project.compile.instructions The instructions given about what files to include into the compilation and other compilation instructions. ### SoMSuppData.project.compilation The results of image registration and data from the grayscales. ### SoMSuppData.project.compilation.log Log file detailing what happend during the last compialition ## Analysis files For the sake of those who don't want to try out the actual Scan-o-Matic, a pre-run analysis and feature extraction is included in the folder `analysis`. The file `analysis.log` contains logging output from the procedure. The files `analysis.xml` and `analysis_slimmed.xml` contains the analysis output in xml-format. This is not what Scan-o-Matic uses for its own feature extraction (because reading it is slow), but exists as compatibility output for those who'd like to work with the data outside python. These are described elsewhere. ### grid___origin_plate_X.svg These images show a visual representation of where the grid was placed. ### grid_plate___X.npy and grid_size___X.npy Contains data representations of how the gridding was done as described in plate image section coordinates. ### image_X_data.npy These is the population sizes of all colonies at time index X. ### time_data.npy This is the vector of actual time since experiment start that each time index correspond to. ### curves_raw.npy and corves_smooth.npy This is the `image_X_data.npy` for all time indices rearranged as colony growth curves: `[plate][X, Y, timeIndex]` where X and Y represent positions on plate. Raw is the colony/population size as measured, Smooth is the same after smoothing. ### phenotypes.extraction.log Log-file for the phenotype extraction ### phenotype_params.npy The parameter-settings used when extracting phenotypes ### phenotypes_filter.npy Data-filter for which positions have been marked as non-valid data due to missing experiment, no growht or failed experiment. ### phenotypes_raw.csv and pheontypes_raw.csv.npy The phenotypes produced without and quality control The csv is a tab delimited data, plate, and position. The npy is numpy arrays of the same data. ### phenotypes_raw_qc.csv and phenotypes_raw_qc.csv.npy The phenotypes as produced but having removed those that were not correctly extracted or refered to positions without data. ### phenotypes_normed_defualt_qc.csv and phenotypes_normed_defualt_qc.csvb.npy The phenotypes as normalized. ## Analysis_focus_example files This is a focus-analysis of only one colony. It's position is shown on the grid image. The files that exist in the general analysis-folder exists here too and are not further explained. They aren't that useful though: Please note though, that in the current version, they only contain data from the specified position. ### 3d_growth.avi This is a visualisation of the growth of the colony throughout the experiment. It is based on the data-files in this folder. ### grid_cell_T_X_Y_Z.*.npy These are specific files on a certain colony at time index T, plate X (enumerate from 0), and position Y, Z on the plate (enumerate from 0). ### grid_cell_T_X_Y_Z.raw.image.npy The part of the actual image used for analysis of this colony that time. ### grid_cell_T_X_Y_Z.calibrated.image.npy The image section transformed with the grayscale calibration polynomial. ### grid_cell_T_X_Y_Z.image.cells.npy The number of cells in each pixel according to the cell count tranformation polynomial. ### grid_cell_T_X_Y_Z.blob.filter.npy The part of the area designated as the colony ### grid_cell_T_X_Y_Z.blob.trash.current.npy and grid_cell_T_X_Y_Z.trash.old.npy The features detected in the area that were not considered colony `old` being the previous iteration or culmulative if aggregating trash is toggled or not. ### grid_cell_T_X_Y_Z.background.filter.npy The part of the area designated as non-feature background. ### 3d_growth.avi Demonstration video with a 3D representation of the colony. Height axis slightly exaggerated on 3D plot. It shows the grayscale calibrated pixel values in the left panel. Rotating 3D representation in the middle The growth-curve produced by the colony and the right edge of the green field marking where the current left and center panel are showning data from.