#ifndef _HPP_VCONNECTION
#define _HPP_VCONNECTION
#include <stdexcept>
#include "connection.hpp"
 

/**
@page WeightFileFormatDescription Weight file format description

- FileFormatStringLength (64 bit)
- FileFormatString
- FileData
- File Data is Chunk based. Chunk has the following form:
    - Chunk Name Length: 1 byte
    - Chunk Name
    - Chunk Length: 64 bit integer
    - Chunk Data
- HeaderChunk:
    - Chunk Name: "Header"
    - Chunk Length: 64 bit integer
    - NSource, NSx, NSy, NTarget, NTx, NTy
    - SourceNameLength, SourceName
    - TargetNameLength, TargetName
- SourceNrChunk:
    - Chunk Name: „SourceNr“,
    - Chunk Length: 64 bit integer
    - sizeof(T_NNeurons)
    - Nweights,
    - SourceNr-Array
- TargetNrChunk:
    - Chunk Name: „TargeteNr“,
    - Chunk Length: 64 bit integer
    - sizeof(T_NNeurons)
    - Nweights,
    - TargeteNr-Array
- WeightsChunk:
    - Chunk Name: „Weights“
    - Chunk Length: 64 bit integer
    - sizeof(T_Weights)
    - NWeights
    - Weights-Array
- DelaysChunk:
    - Chunk Name: „Delays“
    - Chunk Length: 64 bit integer
    - sizeof(T_Delays)
    - NWeights
    - Delays-Array
*/


// forward declaration
class VecNormalize;
class layer;
class Matrix4D;

Connection* LoadConnection(const char* filename);

// Zugriff auf vector <int> 10% - 15% schneller als Zugriff auf 
// Element von vector <class synapse>
// daher SynWeights, SynSourceNr, ...


class NotSupportedWithMemsave: public domain_error
{
public:
    explicit NotSupportedWithMemsave(const string& what_arg): domain_error(what_arg){};
};

class RequestedDelayTooLarge: public std::out_of_range
{
public:
    explicit RequestedDelayTooLarge (const std::string& what_arg): out_of_range(what_arg) {};
    explicit RequestedDelayTooLarge ()
        : std::out_of_range("requested Delay is larger than DMAX") {};
    explicit RequestedDelayTooLarge (long RequestedDelay)
        : std::out_of_range("requested Delay=" + stringify( RequestedDelay ) + " is larger than DMAX=" + stringify(DMAX)) {};
};

class RangeConnectionParameters: public ParameterSet
{
public:
    RangeConnectionParameters()
        : Range(0), Strength(0), MaxDelay(1), MinDelay(0), Cyclic(false), Connectivity(1)
    {}
    RangeConnectionParameters(float _Range, float _Strength)
        : Range(_Range), Strength(_Strength), Connectivity(1), MaxDelay(1), MinDelay(0), Cyclic(false)
    {}
    RangeConnectionParameters(float _Range,
                              float _Strength,
                              float _MaxDelay,
                              float _MinDelay,
                              float _Connectivity,
                              bool _Cyclic)
        : Range(_Range),
        Strength(_Strength),
        MaxDelay(_MaxDelay),
        MinDelay(_MinDelay),
        Connectivity(_Connectivity),
        Cyclic(_Cyclic)
    {}
    float Range;
    float Strength;
    float MaxDelay;
    float MinDelay;
    float Connectivity; 
    bool Cyclic;
};


/*! \brief Klasse VecConnection
 *         Neu-Implementierung der connection mit std::vector statt c-Arrays
 *
 *  Detailed description starts here.
 */
class VecConnection : public Connection
{
	friend class VecNormalize;
protected:
     veclearning* learnobj;
     int Observe_s, Observe_m;
	string FileTypeString;  // identifyer for loading weight file correctly (e.g. in IDL)
public:
    VecConnection();
    VecConnection(layer*, layer*,
                  csimInputChannel _InputChannel=csimInputChannel_AMPA, bool _nonself=true);
    ~VecConnection();
    virtual int proceede(int TotalTime);
    virtual int prepare(int TotalTime);
    int SetMinMaxDelay(float _MaxDelay, float _MinDelay);
    int SetupDelaysArray();
    int SetupPreSynNrArray();
    virtual int CleanupArrays();
    int SetRandomDelays();
    int DeleteSynapse(int SynNr);
    virtual void SetName(const char*);
    
    virtual int Save();
    virtual int Save(int nr);
    virtual int Save(const string&);
    virtual void Save_VecConnection_2_0(const string& SaveWeightFileName);
    virtual void Save_VecConnection_1_0(const string& SaveWeightFileName);
        
    int Load();
    int Load(const string& FileName);
    int Load(const string& FileName, const string& DirName);
    int Load_VecConnection_1_0(const string& FileNameWithDir);
    void Load_VecConnection_2_0(const string& FileName);
    void Load_VecConnection_2_1(const string& FileName);
        
    virtual long calcMemoryConsumption();
    float GetSourceWeights(int SourceNr, vector<float>& WeightMatrix);
    float GetSourceWeights(int SourceNr, vector<Synapse>& SynList, int& MaxDelay);
    float GetTargetWeights(int TargetNr, vector<float>& WeightMatrix);
    float GetTargetWeights(int TargetNr, vector<Synapse>& SynList, int& MaxDelay);
    float GetMaxWeight();
    float GetMinWeight();
    int GetMaxDelay();
    
    void GetWeightMatrix4D(Matrix4D& matrix);
    int DeleteLowWeights(float threshold);
    int SetAllWeights(float WeightValue);
    int MultiplyTargetWeights(int TargetNr, float Factor);
    int MultiplyAllTargetWeights(float Factor);
    int ScrambleSynTargets();
    float GetWeightSum(int TargetNr, bool quadratic);
    float GetMeanWeight();
    int Rewire(float minWeight, float maxConnectivity);
    virtual int WriteSimInfo(fstream &fw);
    virtual int WriteSimInfo(fstream &fw, const string &ChildInfo);
    
    int SetLearn(bool);
    int SetLearnObj(veclearning*);
    
    VecConnectionInfo GetConnectionInfo();
    
    virtual int DeleteSynapseArrays();
    virtual int PushBackNewSynapse(int SourceNr, int TargetNr, float Weight, int Delay);
    virtual int ReserveSynapses(int nsyn);
    int ConnectFull(float MaxWeight,
                    float MaxDelay =20,
                    float MinDelay =0,
                    bool RandomWeights=false);
    int ConnectRandomIncomming(double _connectivity,
                               float _InitialWeights,
                               float _maxDelay=20,
                               float minDelay=0,
                               bool RandomDelays=true);
    int ConnectRandomIncomming(int _NIncommingConnections,
                               float _InitialWeights,
                               float _maxDelay=20,
                               float minDelay=0,
                               bool RandomDelays=true);
    int ConnectSelf(float MaxWeight, float MaxDelay =20, float MinDelay =0);
    int ConnectGaussian(float Sigma, float MaxWeight,
                        float MaxDelay =20, float MinDelay=0, bool Cyclic=false);
    void ConnectCircular(const RangeConnectionParameters& Paras);
     // Delay-Typen als enum: random, distance-dependent (todo?)
    int SetNewWeights(float IncommingConnectivity, float InitialWeights);
    int SetNewWeights(vector<int>* NNewWeights, float InitialWeights);
    
    bool hasEqualSynapses(const VecConnection& OtherVecCon);
    
protected:
    void updateNSynapses();
    
	/** @brief number of valid synapses\n (!=SynWeights.size(), 
	    because deleted synapses are still stored in thevectors)
	*/
	int NSynapses;				
	vector <float> SynWeights;  	//!< strength of synaptic weights
	vector <T_NNeurons> SynTargetNr;	//!< index of target neuron
    vector <T_NNeurons> SynSourceNr;	//!< index of source neuron
    vector <T_Delays> SynDelays;		//!< synaptic delay of the synapse.
							//!< If synapse i is deleted, SynDelays[i]==-1

	//! helper array for finding all synapses of a postsynaptic neuron
	vector < vector <T_NSynapses> > PreSynNr;  
	
	//! helper array for finding all synapses with a given SourceNr and DelayIndex		
	//! delays[SourceNr][DelayIndex][Index] == SynapseNr										//
	vector < vector <vector <T_NSynapses> > > delays;  	
};

/*! \brief Klasse VecDepressiveConnection
 *        
 *
 * Depressive Synapses 
 *      Tsodyks M, Pawelzik K, Markram H. 
 *      Neural networks with dynamic synapses. 
 *      Neural Comput. 1998 May 15;10(4):821-35. 
 */
class VecDepressiveConnection : public VecConnection
{
protected:
	float U_se_fac;  	//!< efficacy reduction factor after transmitter release (after spike)
	float U_SE;	  	//!< proportion of transmitter per spike
     float TauRec;    	//!< recovery dynamics in ms
	float E0; 		//!< initialize Efficacy;
public:
    VecDepressiveConnection(
                             layer*, layer*, csimInputChannel =csimInputChannel_AMPA, 
                             float _TauRec=20, float U_se=0.2, float InitializeFrequency=0);
//      ~VecDepressiveConnection(); // es werden keine dynamischen Objekte angelegt, daher kein Destruktor noetig
    virtual int DeleteSynapseArrays();
    virtual int PushBackNewSynapse(int SourceNr, int TargetNr, float Weight, int Delay);
    virtual int ReserveSynapses(int nsyn);
    virtual int proceede(int =0);
    virtual int prepare(int =0);
    virtual int reset(int t);
    virtual void SetParameter(ParaType p, double value);
    virtual int WriteSimInfo(fstream &fw);
    virtual float InitializeEfficacy(float frequency);
    vector <int> LastEpsp;
    vector <float> Efficacy;    
    virtual long calcMemoryConsumption();
    
};


#endif /*_HPP_VCONNECTION */