Calibrate UPPER LEDs using CaliQ PAR sensor


Each of the LEDs is calibrated to determine the light intensity it produces in the chamber (measured in uE of Photosynthetically Active Radiation, PAR) for all lights on main board and leaf clip. This calibration can be performed using the external RGBC sensor. For IR or UV LEDs (which are outside of the PAR range), the calibration values are set to slope = 1 y intercept = 0, so that the user inputted value is equal to the DAC setting inside the device hardware. This may cause the maximum intensity for these LEDs to be above levels which can be sustained by the LED - so be careful when using very high values for long periods of time.
// The protocol runs a series of light DAC settings to the LEDs. 
// There are two outputs: the DAC setting itself, and the the ext_light (measured values of PAR) and the 

// The macro aims to figure out what setting we need to send to the DAC to obtain a given light intensity
// So, after getting the data in the proper format, the results are sent into MathPOLYREG. In the past,
// the programers used a 4th order polinomial, which is quite excessive.
  
var output = {};
 
 
var number_intensities=0;
var temp=[]; 
var t=-1;
var test_led=0;
var num_leds=0;
var prev_led=NaN;
var light_in =0;

for (var j=0; j<json.set.length; j++){
  	// cycle through the data, find out how many times the protocols were repeated
    // and how many LEDs were used 
	
  if(json.set[j].dac_led != 'undefined') {
    
      var temp2 = json.set[j].dac_led[0]; 
  
 	if (temp2>0) {
      
      if (temp2 != prev_led) {
         num_leds += 1; 
         prev_led=temp2;
        light_in=0; 
      } else if (prev_led>0) {
        light_in+=1;
        test_led=temp2; 
        
      }
      
    }
}
}
//output.vidx=json.set[1].v_idx;
 
/*
   
for (var j=0; j<json.set.length; j++){  // cycle through the data, find out how many times the protocols were repeated
  										// by counting the times the index for protocol 0 appears
  temp = json.set[j].label; 
	if (temp=="@s1"){    // the first value of the ind contains the set index. 
  		number_intensities+=1; 
    } 
  var temp2 = json.set[j].dac_led[0]; 
	if (temp2==temp2){    // the first value of the ind contains the set index. 
  		t+=1; 
    } 
  
}      
output.light_in=light_in;

output.num_leds=num_leds;
output.prot_id=t; //json.set.length;    

return output;
*/

set_repeats=num_leds; //json.set[json.set.length-1].dac_led[0];   //4; //temp[0] +1;  

number_intensities=light_in+1;  //number_intensities/set_repeats;
 
output.number_intensities=number_intensities; //json.set_repeats;  //v_arrays[0].join();  

var protocol_repeats=1;
output.set_repeats = set_repeats;   
output.protocol_repeats = protocol_repeats;    

 
  
//var test2 = json.set[0].setting;
//var test3 = json.set[0].ext_light[4];
 
var LED=[]; 
var settings=[];
var ext_lights = [];
var ext_lights_c=[];

var indexes =[];
var responseArrays=[];
var models=[];
var settings=[];

var aArray = [];
var bArray = [];
var cArray = [];
var R2Array = [];
output.number_intensities=number_intensities;

output.LEDs=LED.join();
 
output["toDevice"] = "set_par_to_dac_calibration+"; // command to send calibration data to device  

 
for (var j=0; j<output.set_repeats; j++){  // cycle through the set releats, each one for a different LED
    LED.push(json.set[j*number_intensities].dac_led[0]);  //recall that the data is zero indexed, but the LEDs can 
  												   // be indexes arbitrarity.
	var setting=[]; // stores the LED output voltage settings
 	var ext_light=[]; // stored the output values for the external PAR sensor, which presumably have been calibrated
  	var responseArray=[]; // stored the same data in different format [x1,y1,x2,y2,...] for the fitting
	var ext_light_c=[];
  
	for (var k=1; k < number_intensities; k++){ 
      var index=j*output.number_intensities + k; 
      
      var av_ext_light=0;
      	 
      for (var use_pt =0; use_pt<protocol_repeats; use_pt++){
      	var pt = (5*use_pt)+4;
        av_ext_light+=json.set[index].qpar[pt]/protocol_repeats;   
      }
      
      if ((av_ext_light > 20) && (json.set[index].dac_led[1]<3095)) {       
         
            setting.push(json.set[index].dac_led[1]);
            ext_light.push(MathROUND(av_ext_light,3)); //json.set[index].ext_light[pt]);  // the measured PAR values for each setting
			indexes.push(k); 
       	 	   
        var tempvar = [json.set[index].dac_led[1], av_ext_light];  

        	responseArray.push(tempvar);     
      		     
      	} 
    }  
     ext_lights.push(ext_light);
     ext_lights_c.push(ext_light_c);
     settings.push(setting); // the settings used for each mesurements

  /* 
  
 
  To use the :

        var a = 1000;
        var b = 40;
        var c = 10;
        var eq = NonLinearRegression( data, {"equation": '( c + a / ( 1 + b / x ) )', initial: [ a, b, c]} )
  
  */
  	// the issue is that the best fits are very sensitive to starting parameters, AND
    // the better fits tend to be numbers much bigger than we can handle.
    // At present, the fit routines do not allow us to limit the range of paramters.
    // as a cludge, I run through a few different starting conditions and 
    // find the one that has the best R2 and sufficiently low paramter values
  
    var aa = [200, 1000, 2000, 4000, 5000, 5000]; //set up a range os 
    var bb = [100, 500, 1000, 2000, 4000, 2500]; 
    var iters = [5,5,10,15,30, 30]; 
    var cc = -234;
    var bestR2 = 0;
    var MaxA = 1000000;
  
  var a = 0;
  var b = 0;
  var c = 0;
  var r2 = 0;
  for (var icIndex = 0; icIndex<aa.length; icIndex++){
   
  	var model = NonLinearRegression( responseArray, {"equation": '( c + a / ( 1 + b / x ) )', initial: [ aa[icIndex], bb[icIndex], cc ], RelaxF: 0.1, LeastAbs: false, Centered:false, Percentile: 60, iterations:iters[icIndex]} );
    //if ((Math.abs(model.r2) > bestR2) && (model.parameters[0].value < MaxA)){
     if ((Math.abs(model.r2) > bestR2) && (model.parameters[0].value < MaxA) && (model.parameters[0].value > 0)){
    a = model.parameters[0].value;    
   	b = model.parameters[1].value;  
  	c = model.parameters[2].value; 
    r2 = model.r2;
    //output["r2_" + icIndex]=model.r2;
    //output["a_" + j + "_" +  icIndex]=a;
	//output["iter_" + j + "_"+ icIndex]=model.iterations; 
    }
   } 
   

    // the model will extimate the setting needed to achieve a given measured light intensity! 
  	 models.push(model);
      aArray.push(a);
      bArray.push(b);
      cArray.push(c);
  		R2Array.push(r2);
 	responseArrays.push(responseArray);
}
 
output.LEDs=LED.join(); 
output.ext_light_all=ext_lights; 
output.settings=settings; //.join();  
//output.test=responseArray; 
  
var led_set=0; 
var residual=[]; 
var pset_intensity=[];

for (var led_set=0; led_set<set_repeats; led_set++){
 	//var a = models[led_set].parameters[0].value;  
  	//var b = models[led_set].parameters[1].value;  
  	//var c = models[led_set].parameters[2].value;  
	a = aArray[led_set];
	b = bArray[led_set];
	c = cArray[led_set];
  
   output["R2 " +led_set]=R2Array[led_set];   //models[led_set].r2;  

    var predicted=[];
    for (var i=0; i<settings[led_set].length; i++){
      predicted.push((c + a / ( 1.0 + b / settings[led_set][i])));
    }
   // var predicted_10=[];
   // for (var i=0; i<settings[led_set].length; i++){
   //   predicted_10.push((c + (a) / ( 1.0 + (b) / settings[led_set][i])));
   // } 
   
    //output.predicted=predicted;
 //   output.predicted_10=predicted_10; 
   	//output.tt=settings.length;
  	output["toDevice"] += LED[led_set];
  	output ["toDevice"] += "+";
  	output["toDevice"] += MathROUND(a,3); 
  	output ["toDevice"] += "+";
  	output["toDevice"] += MathROUND(b,3);
  	output ["toDevice"] += "+"; 
     var tt=MathMIN(predicted)/2;
  	output["toDevice"] += MathROUND(c-tt,3); 
  	//output["toDevice"] += MathROUND(c,3); 
  	output ["toDevice"] += "+"; 
}
output ["toDevice"] += "-1+";
output ["toDevice"] += "par_method+1+"; 
/*
var nonParLEDS=[5,6,7,8,9];
for (var ii =0; ii<nonParLEDS.length; ii++) {
  
    output["toDevice"] += nonParLEDS[ii];
  	output ["toDevice"] += "+";
  	output["toDevice"] += 10000; 
  	output ["toDevice"] += "+";
  	output["toDevice"] += 2000;
  	output ["toDevice"] += "+"; 
  	output["toDevice"] += -200; 
  	output ["toDevice"] += "+"; 

}
	var ii = 10;
    output["toDevice"] += ii;
  	output ["toDevice"] += "+";
  	output["toDevice"] += 10000; 
  	output ["toDevice"] += "+";
  	output["toDevice"] += 2000; 
  	output ["toDevice"] += "+"; 
  	output["toDevice"] += -500; 
  	output ["toDevice"] += "+"; 
*/
//output ["toDevice"] += "-1+";


return output;
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  "device_name": "MultispeQ",
  "device_version": "2",
  "device_id": "00:00:00:00",
  "device_battery": 101,
  "device_firmware": 2.009,
  "sample": [
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        "set_repeats": 4,
        "protocol_id": 1,
        "set": [
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}
Dave in cap img 4615
Created by

David M. Kramer


Protocol connections:
1
Latest Update:
Jan 2019