loadParameters.m.template

params.FILE_BASENAME = 'simseqtest1';

% Data loading a storage parameteres
params.deconvolutionImagesDir = '../simseq/simseqtest1/1_deconvolution';
params.colorCorrectionImagesDir = '../simseq/simseqtest1/2_color-correction';
params.normalizedImagesDir = '../simseq/simseqtest1/3_normalization';
params.registeredImagesDir = '../simseq/simseqtest1/4_registration';
params.punctaSubvolumeDir = '../simseq/simseqtest1/5_puncta-extraction';
params.basecallingResultsDir = '../simseq/simseqtest1/6_base-calling';
params.reportingDir = '../simseq/simseqtest1/logs/imgs';
params.logDir = '../simseq/simseqtest1/logs';

% Microscope parameters
params.XRES = .17;
params.YRES = .17;
params.ZRES = .4;


% Which round will be the reference for the warping?
params.REFERENCE_ROUND_WARP = 2;

%How many rounds of images need to be processed?
params.NUM_ROUNDS = 4; 
%When aligning against a library, which rounds in the ground truth are we
%matching to?
params.ALIGNMENT_ROUNDS = 1:params.NUM_ROUNDS;

% Does your experiment contain a round that contains a morphological signal
% that you would like to be included for later analysis? If so, uncomment
% this line. If your experiment does *not* have a morphological component,
% leave this uncommented
% params.MORPHOLOGY_ROUND = 1;
% params.MORPHOLOGY_CHAN_STR = 'ch04';

% base calling parameters
% we include groundtruth_visualcortex_tutorial.mat in the tutorial
% directory as an example
params.GROUND_TRUTH_DICT = 'groundtruth_visualcortex_tutorial.mat';
params.ISILLUMINA = true; %we used two sequencing chemistries, illumina is now the standard




% Used for color correction
% Assuming beads are at the top of the stack, when do they start?
params.BEAD_ZSTART = 1; %goes to :end
% What is the buffer from the edge of the image we use when calculating the color shift? 
params.COLOR_OFFSETS3D = [20,20,20];
% To avoid errors, or if we don't expect much color drift, we can manually set a max magnitude
params.COLOR_CORRECT_CLAMP = [4,4,4];

% What is the minimum size of a puncta (in voxels) to be considered?
params.PUNCTA_SIZE_THRESHOLD = 30;
% What is the maximum size?
params.PUNCTA_SIZE_MAX = 2000;

params.PUNCTARADIUS_BGESTIMATE = 5;

% Defines the cubic region around each puncta
params.PUNCTA_SIZE = 10; %Not a critical parameter, could be deprecated soon
params.CHAN_STRS = {'ch00','ch01','ch02','ch03'};
params.NUM_CHANNELS = length(params.CHAN_STRS);

params.SHIFT_CHAN_STRS = {'ch00','ch01SHIFT','ch02SHIFT','ch03SHIFT'};

%What is the color channel we will load from the registration outputs?
%TODO: Need to explain this more. I believe this is normally summedNorm
params.PUNCTA_CHANNEL_SEG = 'summedNorm'; 

%Base calling parameters
params.COLOR_VEC = [1,2,3,4]; %Which channels are we comparing? (in case of empty chan)
params.DISTANCE_FROM_CENTER = 2.5; %how far from the center of the puncta subvol?
params.THRESHOLD_EXPRESSION = 15; %If a transcript shows up fewer than this it's probably noise
params.BASECALLING_MAXHAMMING = 2; %What is the max allowable mismatch?
params.BASECALLING_PERFORMWHITENING = false; %Do we attempt to remove correlation in channels?

%Do we need to downsample the first few steps of the pipeline?
params.DO_DOWNSAMPLE = true;
params.DOWNSAMPLE_RATE = 4.;

%If the channel data come from very different distributions, applying a quantile Norm 
%can be useful when combining channel information for the registration. However, 
%it is highly memory and time intensive, using any other paramter but 'quantile' will
%simply sum the channels in the normalization step, and this can reduce comptuation time
%by 20-50%!
params.NORMALIZE_METHOD = 'linear';% 'linear' or 'quantile';

%If you re-run the same script, should it overwrite results?
%Currently this is only implemented for the puncta-extraction and base-calling steps
params.OVERWRITE_PREV_RESULTS = true;

% For puncta extraction:
% How many rounds do we allow there to be no data in? "missing" in this case
% means pixel values = 0, which only happens in the case of registered image warps
% leaving empty areas on the edges (depending on the magnitude of the translation etc.)
params.MAXNUM_MISSINGROUND=2;

% For basecalling, do we do an additional filtering step?
% This can be helpful to define a function that removes artifacts in the data. 
% If you define uncomment the line before the stage_base-calling will call whatever script
% you define after the @ sign
%params.BASECALLING_FILTERINGFUNCTION = @filter_HTAPP917;
%Do we expect artifacts in the data? If so, uncomment and set the values here
%params.BASECALLING_ARTIFACT_THRESH = [3000,4000,9000,1500];

params.BASECALLING_SIG_THRESH = 90; %which percentile do we take from the puncta to call the base

%use GPU CUDA or CPU
params.USE_GPU_CUDA = false;


%intermediate image format, 'tif' of 'h5' are supported
params.IMAGE_EXT = 'h5';

%input image format, 'tif' of 'h5' are supported
params.INPUT_IMAGE_EXT = 'h5';

%Use intermediate tmp files in color-correction and normalization
params.tempDir = '/mp/nvmeof0/tmp';

%If the background is high for the particular sample, this will perform a background substraction step to discover the puncta and extract pixel values
params.PUNCTAEXTRACT_DOBGSUBTRACT = false;


%----- define registration params ----%
% These are specific just to the regisration step
% Define parameters for registration, titled regparams for clarity
% by old convention, 'registered' refers to the thin plate spline registration
% 'affine' refers to the simpler linear transform
regparams.REGISTRATION_TYPE = 'affine';%'registered' or 'affine'

%When calculating RANSAC, do we consider all possible affine warps (true)
%Or just translation+scale (false)
regparams.AFFINE_FULL = true;

%Do we want to combine features from multiple channels?
%keypoints and descriptors
%NOTE: b
regparams.REGISTERCHANNELS_SIFT = {'summedNorm'}; %keypoints and descriptors
%keypoints only
%Note: ShapeContext is an additional description method that can be used for 
% tricky registrations. However, it is currently deprecated, just keep this {}
% -Dan G. 2020-09-09
regparams.REGISTERCHANNELS_SC = {};

%In the Murray et al 2015 this was {'Lectin', 'DAPI', 'Data}
%Which channels do we warp?
regparams.CHANNELS = {'ch00','ch01SHIFT','ch02SHIFT','ch03SHIFT','summedNorm'};

%Ideally this is at least 20, but the bare minimum to solve the affine tform is 6
regparams.NCORRESPONDENCES_MIN = 20; 
%Set a threshold for maximum displacement distance?
%(not appropriate for all datasets. Distance in pixels, -1 for ignore this)
regparams.DISTANCEQUANTILECUTOFF = .95;

% SCALE_PYRAMID: Create a set of blurs to create a "scale pyramid", or in
% non-computervision speak:
% Blurring by a progressive set of gaussians to adjust for puncta flucutations
% Using the documentation in fspecial3.m, the standard deviations of
% the gaussian is defined as SIZE/2/2.354 so that FWHM equals half filter
% size (http://en.wikipedia.org/wiki/FWHM).The blur_size values are chosen
% with assumptions of the width of the Lectin vessels
% (5-20 pixels at 2um/pix res) observed and assumption of minor scale
% disparities (ie <20%)
params.SCALE_PYRAMID = [4];

% parameters for 3DSIFT
sift_params.TwoPeak_Flag = true;  % Allow 3DSIFT to throw out points, Default: true
sift_params.TwoPeak_Thresh = .9;  % Allow 3DSIFT to throw out points, Default: true
sift_params.IndexSize = 2;  % Min: 1  Default: 2
sift_params.xyScale = 1;
sift_params.tScale = 1;

sift_params.Display_flag = 0;  % Display a sphere which can be rotated (Rotate 3D button) to view gradient directions
sift_params.Tessellation_flag = 1;  % Keep this as 1
sift_params.Tessellation_levels = 1;  % Min: zero  Default: 1
sift_params.Tessel_thresh = 3; % take the top nearest tesselations in GetGradOri / PlaceInIndex
sift_params.nFaces = 20 * ( 4 ^ sift_params.Tessellation_levels );  % Number of faces in the tessellation, not a parameter
sift_params.descriptor_len = sift_params.IndexSize *...
    sift_params.IndexSize * sift_params.IndexSize * sift_params.nFaces;

sift_params.Smooth_Flag = true;  % Adds gradient data to surrounding bins in final histogram
sift_params.Smooth_Var = 20;  % Determines amount of smoothing, Default: 20
sift_params.MaxIndexVal = 0.2;

% The rest of the variables are not modified often, but are included here
% for completeness sake
sift_params.IgnoreGradSign = 0;
sift_params.IndexSigma = 5.0;
sift_params.SigmaScaled = sift_params.IndexSigma * 0.5 * sift_params.IndexSize;
sift_params.MagFactor = 3;

sift_params.UseHistogramOri = 1;
sift_params.OriHistThresh = 0.8;

if (sift_params.UseHistogramOri)
    sift_params.OriSigma = 1.5;
else
    sift_params.OriSigma = 1.0;
end
sift_params.stream_num = 20;
sift_params.x_substream_stride = 256;
sift_params.y_substream_stride = 256;

%batch_processing parameters
params.WAIT_SEC = 10;
params.JOB_MAXTIME = 3600; %Any job should not take more than an hour

params.USABLE_MEM_RATE = 0.9;
params.MATLAB_PROC_CONTEXT = 800; %MiB

params.DOWN_SAMPLING_MAX_POOL_SIZE = 4;
%
%params.COLOR_CORRECTION_MAX_RUN_JOBS = 2;
params.COLOR_CORRECTION_MAX_THREADS = 10;
%
params.NORM_MAX_RUN_JOBS = 20;
params.NORM_DOWNSAMPLE_MAX_RUN_JOBS = 20;
%
params.CALC_DESC_MAX_RUN_JOBS = 30;
params.CALC_DESC_MAX_THREADS = 4;
%
params.CALC_CORR_MAX_RUN_JOBS = 1;
params.CALC_CORR_MAX_THREADS = 10;
%
params.REG_CORR_MAX_RUN_JOBS = 8;
params.REG_CORR_MAX_THREADS = 10;
%
params.AFFINE_MAX_RUN_JOBS = 8;
params.AFFINE_MAX_POOL_SIZE = 5;
params.AFFINE_MAX_THREADS = 10;
%
%params.TPS3DWARP_MAX_RUN_JOBS = 4;
%params.TPS3DWARP_MAX_POOL_SIZE = 10;
params.TPS3DWARP_MAX_THREADS = 10;

params.PUNCTA_MAX_POOL_SIZE = 1;

%cluster environment
params.NUM_LOGICAL_CORES = 40;