#ifndef FIRCOMPLEX_H
#define FIRCOMPLEX_H

#include <vector>
#include <complex>
#include "../../../Assertions.h"

/**
 * FIR filter using complex convolution
 */
class FIRComplex {

	/** signal's sample-rate */
	int sRate_hz;

	/** the created convolution kernel */
	std::vector<std::complex<float>> kernel;

	/** incoming data */
	std::vector<std::complex<float>> data;

public:

	/** ctor with signal's sample-rate */
	FIRComplex(const int sRate_hz) : sRate_hz(sRate_hz) {
		;
	}

	/** get the internal kernel */
	const std::vector<std::complex<float>> getKernel() const {
		return kernel;
	}

	/** configure as lowpass with the given cutoff and 2*size+1 */
	void lowPass(const int cutOff_hz, const int size) {
		this->kernel = getLowpass(cutOff_hz, sRate_hz, size);
	}

	/** shift the constructed filter by the given hz-rate */
	void shiftBy(const int shift_hz) {
		shiftKernel(shift_hz, sRate_hz);
	}

	/** filter the given incoming real data */
	std::vector<std::complex<float>> append(const std::vector<float>& newData) {

		// append to local buffer (as we need some history)
		//data.insert(data.end(), newData.begin(), newData.end());
		for (const float f : newData) {
			data.push_back(std::complex<float>(f, 0));	// real = value, imag = 0;
		}

		return processLocalBuffer();

	}

	/** filter the given incoming complex data */
	std::vector<std::complex<float>> append(const std::vector<std::complex<float>>& newData) {

		// append to local buffer (as we need some history)
		data.insert(data.end(), newData.begin(), newData.end());

		return processLocalBuffer();

	}

	/** filter the given incoming real value */
	std::complex<float> append(const float val) {
		data.push_back(std::complex<float>(val, 0));
		auto tmp = processLocalBuffer();
		if (tmp.size() == 0) {return std::complex<float>(NAN, NAN);}
		if (tmp.size() == 1) {return tmp[0];}
		throw Exception("FIRComplex:: detected invalid result");
	}

	/** filter the given incoming real value */
	std::complex<float> append(const std::complex<float> c) {
		data.push_back(c);
		auto tmp = processLocalBuffer();
		if (tmp.size() == 0) {return std::complex<float>(NAN, NAN);}
		if (tmp.size() == 1) {return tmp[0];}
		throw Exception("FIRComplex:: detected invalid result");
	}

	void dumpKernel(const std::string& file, const std::string& varName) {

		std::ofstream out(file);
		out << "# name: " << varName << "\n";
		out << "# type: complex matrix\n";
		out << "# rows: " << kernel.size() << "\n";
		out << "# columns: 1\n";

		for (const std::complex<float> c : kernel) {
			out << "(" << c.real() << "," << c.imag() << ")" << "\n";
		}
		out.close();

	}


private:

	std::vector<std::complex<float>> processLocalBuffer() {

		// sanity check
		Assert::isNot0(kernel.size(), "FIRComplex:: kernel not yet configured!");

		// number of processable elements (due to filter size)
		const int processable = data.size() - kernel.size() + 1 - kernel.size()/2;

		// nothing to-do?
		if (processable <= 0) {return std::vector<std::complex<float>>();}

		// result-vector
		std::vector<std::complex<float>> res;
		res.resize(processable);

		// fire
		convolve(data.data(), res.data(), processable);

		// drop processed elements from the local buffer
		data.erase(data.begin(), data.begin() + processable);

		// done
		return res;

	}

	template <typename T> void convolve(const std::complex<float>* src, T* dst, const size_t cnt) {

		const size_t ks = kernel.size();

		for (size_t i = 0; i < cnt; ++i) {
			T t = T();
			for (size_t j = 0; j < ks; ++j) {
				t += src[j+i] * kernel[j];
			}
			if (t != t) {throw std::runtime_error("detected NaN");}
			dst[i] = t;
		}

	}


//	template <typename T> void convolve(const float* src, T* dst, const size_t cnt) {

//		const size_t ks = kernel.size();

//		for (size_t i = 0; i < cnt; ++i) {
//			T t = T();
//			for (size_t j = 0; j < ks; ++j) {
//				t += std::complex<float>(src[j+i], 0) * kernel[j];
//			}
//			if (t != t) {throw std::runtime_error("detected NaN");}
//			dst[i] = t;
//		}

//	}

	/** get a value from the hamming window */
	static double winHamming(const double t, const double size) {
		return 0.54 - 0.46 * std::cos(2 * M_PI * t / size);
	}

	/** frequency shift the kernel by multiplying with a frequency */
	void shiftKernel(const int shift_hz, const int sRate_hz) {
		for (size_t i = 0; i < kernel.size(); ++i) {
			const float t = (float) i / (float) sRate_hz;
			const float real = std::cos(t * 2 * M_PI * shift_hz);
			const float imag = std::sin(t * 2 * M_PI * shift_hz);
			kernel[i] = kernel[i] * std::complex<float>(real, imag);
		}
	}

	// https://dsp.stackexchange.com/questions/4693/fir-filter-gain
	/** normalize using the DC-part of the kernel */
	static void normalizeDC(std::vector<std::complex<float>>& kernel) {
		std::complex<float> sum;
		for (auto f : kernel) {sum += f;}
		for (auto& f : kernel) {f /= sum;}
	}

	// https://dsp.stackexchange.com/questions/4693/fir-filter-gain
	static void normalizeAC(std::vector<std::complex<float>>& kernel, const float freq) {
		throw std::runtime_error("TODO");
	}

	/** build a lowpass filter kernel */
	static std::vector<std::complex<float>> getLowpass(const int cutOff, const int sRate, const int n) {

		std::vector<std::complex<float>> kernel;

		for (int i = -n; i <= +n; ++i) {

			const double t = (double) i / (double) sRate;
			const double tmp = 2 * M_PI * cutOff * t;
			const double val = (tmp == 0) ? (1) : (std::sin(tmp) / tmp);
			const double win = winHamming(i+n, n*2);
			const double res = val * win;// * 0.5f;	// why 0.5?
			if (res != res) {throw std::runtime_error("detected NaN");}
			kernel.push_back( std::complex<float>(res, 0) );

		}

		// important!!! normalize so the original frequencies stay at 0dB
		normalizeDC(kernel);	// dc works for low-pass filter only as this one contains DC!

		return kernel;

	}

};

#endif // FIRCOMPLEX_H