void DelayDetection::putData(SFLDataFormat *inputData, int nbSamples)
{
// Machine may already got a spkr and is waiting for mic or computing correlation
if (nbSpkrSampleStored_ == WINDOW_SIZE)
return;
if ((nbSpkrSampleStored_ + nbSamples) > WINDOW_SIZE)
nbSamples = WINDOW_SIZE - nbSpkrSampleStored_;
if (nbSamples) {
float tmp[nbSamples];
float down[nbSamples];
convertInt16ToFloat32(inputData, tmp, nbSamples);
memcpy(spkrReference_ + nbSpkrSampleStored_, tmp, nbSamples * sizeof(float));
downsampleData(tmp, down, nbSamples, downsamplingFactor_);
bandpassFilter(down, nbSamples / downsamplingFactor_);
memcpy(spkrReferenceDown_+ (nbSpkrSampleStored_ / downsamplingFactor_), down, (nbSamples / downsamplingFactor_) * sizeof(float));
nbSpkrSampleStored_ += nbSamples;
}
// Update the state
internalState_ = WaitForMic;
}
/* calculate enhanced psd and return preview
* used for protocol preprocess_particles*/
PyObject *
xmipp_bandPassFilter(PyObject *obj, PyObject *args, PyObject *kwargs)
{
PyObject *pyStrFn, *pyImage;
double w1, w2, raised_w;
int dim;
FileName fn;
if (PyArg_ParseTuple(args, "OOdddi", &pyImage, &pyStrFn, &w1, &w2, &raised_w, &dim))
{
FILTER_TRY()
bandpassFilter(data, w1, w2, raised_w);
FILTER_CATCH()
}
void shakeData::loadKNET(string filename)
{
file=filename;
ifstream input(ofToDataPath(file).c_str());
string buffer;
int lineCount=0;
double scale;
double averg=0;
vector<double> val;
while(lineCount<18){
getline(input, buffer);
string result;
KNETresults rslt=getVal(buffer,result);
switch(rslt){
case FREQ:
sampFreq=ofToInt(result);
break;
case SCALE_AMOUNT:
scale=ofToInt(result.substr(0,result.find_first_of("(")));
scale/=ofToInt(result.substr(result.find_last_of("/")+1));
break;
case MAX_ACCEL:
break;
default:
break;
}
lineCount++;
}
while(!input.eof()){
getline(input, buffer);
for(int i=0; i<8; i++){
if(buffer.length()>(i+1)*9){
val.push_back(ofToInt(buffer.substr(i*9,9)));
}
}
}
input.close();
double a1,a0;
linearRegression(val,a1,a0);
for(unsigned int i=0; i<val.size(); i++){
val[i]-=a0+a1*i;
val[i]*=scale;
}
bandpassFilter(val,sampFreq,3);
for(unsigned int i=0; i<val.size(); i++){
uData.push_back(dataPoint(val[i],1/sampFreq));
}
processData();
}
uint16_t stepDetector(pedometer_data_t data[], uint16_t dataLen) {
// Create new data object to contained normalized value
fixed_t* normData = malloc(dataLen*sizeof(fixed_t));
uint16_t i;
for (i = 0; i < dataLen; i++) {
// Norm is simply sum of squares, do not take sqrt for precision purposes and
// also subtract gravity
normData[i] = sumSqrs(data[i]) - GRAVITY_OFFSET;
}
// Filter data with band pass filter, note modifies normData
bandpassFilter(normData, dataLen);
// Count and return steps
uint16_t nSteps = countSteps(normData, dataLen);
// Clean-up memory
free(normData);
return nSteps;
}