#include "nmDataSensitive.h"
#include <cmath>
#include <limits>

nmDataSensitive::VariableSampling::VariableSampling()
	: m_varGroup("")
	, m_varName("")
	, m_enabled(false)
	, m_mode(MODE_AUTOMATIC)
	, m_log(false)
	, m_number(0)
{
    m_modelValue = nmDataAttribute("Model value", "", "");
    m_minValue   = nmDataAttribute("Minimum",    "", "");
    m_maxValue   = nmDataAttribute("Maximum",    "", "");
	m_values.clear();
}

// 序列化 VariableSampling 为 RapidJSON Value
rapidjson::Value nmDataSensitive::VariableSampling::ToJsonValue(
	rapidjson::Document::AllocatorType& allocator) const
{
	rapidjson::Value obj(rapidjson::kObjectType);

	// 字符串字段
	obj.AddMember("VarGroup",
		rapidjson::Value(m_varGroup.toStdString().c_str(), allocator).Move(),
		allocator);
	obj.AddMember("VarName",
		rapidjson::Value(m_varName.toStdString().c_str(), allocator).Move(),
		allocator);

	// 简单类型
	obj.AddMember("Enabled", m_enabled, allocator);
	obj.AddMember("Mode", static_cast<int>(m_mode), allocator);
	obj.AddMember("Log", m_log, allocator);
	obj.AddMember("Number", m_number, allocator);

	// nmDataAttribute 成员
	obj.AddMember("ModelValue", m_modelValue.ToJsonValue(allocator), allocator);
	obj.AddMember("Minimum", m_minValue.ToJsonValue(allocator),   allocator);
	obj.AddMember("Maximum", m_maxValue.ToJsonValue(allocator),   allocator);

	// values 数组
	rapidjson::Value arr(rapidjson::kArrayType);
	for (int i = 0; i < m_values.size(); ++i) {
		arr.PushBack(m_values.at(i), allocator);
	}
	obj.AddMember("Values", arr, allocator);

	return obj;
}

// 从 RapidJSON Value 反序列化到 VariableSampling
void nmDataSensitive::VariableSampling::FromJsonValue(const rapidjson::Value& jsonValue)
{
	if (jsonValue.HasMember("VarGroup") && jsonValue["VarGroup"].IsString()) {
		m_varGroup = QString::fromUtf8(jsonValue["VarGroup"].GetString());
	}
	if (jsonValue.HasMember("VarName") && jsonValue["VarName"].IsString()) {
		m_varName = QString::fromUtf8(jsonValue["VarName"].GetString());
	}
	if (jsonValue.HasMember("Enabled") && jsonValue["Enabled"].IsBool()) {
		m_enabled = jsonValue["Enabled"].GetBool();
	}
	if (jsonValue.HasMember("Mode") && jsonValue["Mode"].IsInt()) {
		int m = jsonValue["Mode"].GetInt();
		m_mode = (m == MODE_MANUAL) ? MODE_MANUAL : MODE_AUTOMATIC;
	}
	if (jsonValue.HasMember("Log") && jsonValue["Log"].IsBool()) {
		m_log = jsonValue["Log"].GetBool();
	}
	if (jsonValue.HasMember("Number") && jsonValue["Number"].IsInt()) {
		m_number = jsonValue["Number"].GetInt();
	}

	if (jsonValue.HasMember("ModelValue") && jsonValue["ModelValue"].IsObject()) {
		m_modelValue.FromJsonValue(jsonValue["ModelValue"]);
	}
	if (jsonValue.HasMember("Minimum") && jsonValue["Minimum"].IsObject()) {
		m_minValue.FromJsonValue(jsonValue["Minimum"]);
	}
	if (jsonValue.HasMember("Maximum") && jsonValue["Maximum"].IsObject()) {
		m_maxValue.FromJsonValue(jsonValue["Maximum"]);
	}

	m_values.clear();
	if (jsonValue.HasMember("Values") && jsonValue["Values"].IsArray()) {
		const rapidjson::Value& arr = jsonValue["Values"];
		for (rapidjson::SizeType i = 0; i < arr.Size(); ++i) {
			if (arr[i].IsNumber()) {
				m_values.append(arr[i].GetDouble());
			}
		}
	}
}

// varGroup
QString nmDataSensitive::VariableSampling::getVarGroup() const
{
	return m_varGroup;
}

void nmDataSensitive::VariableSampling::setVarGroup(const QString& group)
{
	m_varGroup = group;
}

// varName
QString nmDataSensitive::VariableSampling::getVarName() const
{
	return m_varName;
}

void nmDataSensitive::VariableSampling::setVarName(const QString& name)
{
	m_varName = name;
}

// enabled
bool nmDataSensitive::VariableSampling::getEnabled() const
{
	return m_enabled;
}

void nmDataSensitive::VariableSampling::setEnabled(bool e)
{
	m_enabled = e;
}

// mode
nmDataSensitive::VariableSampling::Mode
	nmDataSensitive::VariableSampling::getMode() const
{
	return m_mode;
}

void nmDataSensitive::VariableSampling::setMode(Mode m)
{
	m_mode = m;
}

// log
bool nmDataSensitive::VariableSampling::getLog() const
{
	return m_log;
}

void nmDataSensitive::VariableSampling::setLog(bool l)
{
	m_log = l;
}

// number
int nmDataSensitive::VariableSampling::getNumber() const
{
	return m_number;
}

void nmDataSensitive::VariableSampling::setNumber(int n)
{
	m_number = n;
}

// modelValue
nmDataAttribute& nmDataSensitive::VariableSampling::getModelValue()
{
	return m_modelValue;
}

void nmDataSensitive::VariableSampling::setModelValue(const nmDataAttribute& attr)
{
	m_modelValue = attr;
}

// minValue
nmDataAttribute& nmDataSensitive::VariableSampling::getMinValue()
{
	return m_minValue;
}

void nmDataSensitive::VariableSampling::setMinValue(const nmDataAttribute& attr)
{
	m_minValue = attr;
}

// maxValue
nmDataAttribute& nmDataSensitive::VariableSampling::getMaxValue()
{
	return m_maxValue;
}

void nmDataSensitive::VariableSampling::setMaxValue(const nmDataAttribute& attr)
{
	m_maxValue = attr;
}

// values
QVector<double>& nmDataSensitive::VariableSampling::getValues()
{
	return m_values;
}

void nmDataSensitive::VariableSampling::setValues(const QVector<double>& vals)
{
	m_values = vals;
}

QVector<double> nmDataSensitive::VariableSampling::samplingValues() const
{
	// 表格显示精度不参与采样，包含零或负数的对数范围按线性处理。
	if (m_mode == MODE_MANUAL) return m_values;
	QVector<double> values;
	if (m_number <= 0) return values;
	const double minimum = m_minValue.getValue().toDouble();
	const double maximum = m_maxValue.getValue().toDouble();
	const bool logarithmic = m_log && minimum > 0.0 && maximum > 0.0;
	for (int i = 0; i < m_number; ++i) {
		const double t = m_number == 1 ? 0.0 : double(i) / (m_number - 1);
		double value = logarithmic
			? std::exp((1.0 - t) * std::log(minimum) + t * std::log(maximum))
			: (1.0 - t) * minimum + t * maximum;
		if (m_number == 1) value = m_modelValue.getValue().toDouble();
		else if (i == 0) value = minimum;
		else if (i == m_number - 1) value = maximum;
		values.append(value);
	}
	return values;
}


nmDataSensitive::nmDataSensitive()
	: m_calcType(CALC_DETERMINISTIC)
	, m_totalModelCount(0)
	, m_usesBaseUnits(false)
	, m_variables()
{
}

// 拷贝构造
nmDataSensitive::nmDataSensitive(const nmDataSensitive& other)
{
	*this = other;
}

// 赋值运算符
nmDataSensitive& nmDataSensitive::operator=(const nmDataSensitive& other)
{
	if (this != &other) {
		m_calcType        = other.m_calcType;
		m_totalModelCount = other.m_totalModelCount;
		m_usesBaseUnits   = other.m_usesBaseUnits;
		m_variables       = other.m_variables;
	}
	return *this;
}

nmDataSensitive::~nmDataSensitive()
{
}

// 序列化 nmDataSensitive 为 RapidJSON Value
rapidjson::Value nmDataSensitive::ToJsonValue(
	rapidjson::Document::AllocatorType& allocator) const
{
	rapidjson::Value root(rapidjson::kObjectType);
	// 旧配置中的采样值是显示单位，新配置统一保存业务基准单位。
	root.AddMember("UsesBaseUnits", m_usesBaseUnits, allocator);

	// 顶部 Calculation type
	root.AddMember("CalculationType",
		static_cast<int>(m_calcType),
		allocator);

	// 总模型数
	root.AddMember("TotalModelCount",
		m_totalModelCount,
		allocator);

	// 变量数组
	rapidjson::Value arr(rapidjson::kArrayType);
	for (int i = 0; i < m_variables.size(); ++i) {
		arr.PushBack(m_variables.at(i).ToJsonValue(allocator), allocator);
	}
	root.AddMember("Variables", arr, allocator);

	return root;
}

// 从 RapidJSON Value 反序列化数据到 nmDataSensitive
void nmDataSensitive::FromJsonValue(const rapidjson::Value& jsonValue)
{
	m_usesBaseUnits = jsonValue.HasMember("UsesBaseUnits") &&
		jsonValue["UsesBaseUnits"].IsBool() && jsonValue["UsesBaseUnits"].GetBool();
	if (jsonValue.HasMember("CalculationType") && jsonValue["CalculationType"].IsInt()) {
		int t = jsonValue["CalculationType"].GetInt();
		if (t == CALC_DETERMINISTIC_MULTIVARIATE)
			m_calcType = CALC_DETERMINISTIC_MULTIVARIATE;
		else
			m_calcType = CALC_DETERMINISTIC;
	}

	if (jsonValue.HasMember("TotalModelCount") && jsonValue["TotalModelCount"].IsUint64()) {
		m_totalModelCount = jsonValue["TotalModelCount"].GetUint64();
	}

	m_variables.clear();
	if (jsonValue.HasMember("Variables") && jsonValue["Variables"].IsArray()) {
		const rapidjson::Value& arr = jsonValue["Variables"];
		for (rapidjson::SizeType i = 0; i < arr.Size(); ++i) {
			if (!arr[i].IsObject())
				continue;

			VariableSampling vs;
			vs.FromJsonValue(arr[i]);
			m_variables.append(vs);
		}
	}
}

// === Calculation type ===
nmDataSensitive::CalculationType nmDataSensitive::getCalculationType() const
{
	return m_calcType;
}

void nmDataSensitive::setCalculationType(CalculationType type)
{
	m_calcType = type;
}

// === 总模型数 ===
quint64 nmDataSensitive::getTotalModelCount() const
{
	return m_totalModelCount;
}

void nmDataSensitive::setTotalModelCount(quint64 count)
{
	m_totalModelCount = count;
}

quint64 nmDataSensitive::calculateModelCount(bool* overflow) const
{
	// 未选择变量时为零；两种确定性方法分别累计和、积。
	if (overflow) *overflow = false;
	quint64 count = 0;
	bool selected = false;
	for (int i = 0; i < m_variables.size(); ++i) {
		const VariableSampling& variable = m_variables[i];
		if (!variable.m_enabled) continue;
		const quint64 number = variable.m_number > 0 ? variable.m_number : 0;
		if (!number) return 0;
		const quint64 limit = (std::numeric_limits<quint64>::max)();
		if (m_calcType == CALC_DETERMINISTIC) {
			if (count > limit - number) { if (overflow) *overflow = true; return 0; }
			count += number;
		} else {
			if (!selected) count = 1;
			if (count > limit / number) { if (overflow) *overflow = true; return 0; }
			count *= number;
		}
		selected = true;
	}
	return count;
}

bool nmDataSensitive::getRunValues(quint64 runIndex, QVector<double>& values) const
{
	// 按编号解码组合，不预先展开笛卡尔积，也不删除重复的基准组合。
	bool overflow = false;
	const quint64 count = calculateModelCount(&overflow);
	if (overflow || runIndex >= count) return false;
	values.clear();
	for (int i = 0; i < m_variables.size(); ++i)
		values.append(m_variables[i].m_modelValue.getValue().toDouble());
	if (m_calcType == CALC_DETERMINISTIC) {
		for (int i = 0; i < m_variables.size(); ++i) {
			const VariableSampling& variable = m_variables[i];
			if (!variable.m_enabled) continue;
			const QVector<double> samples = variable.samplingValues();
			if (samples.size() != variable.m_number) return false;
			if (runIndex < quint64(samples.size())) {
				values[i] = samples[int(runIndex)];
				return true;
			}
			runIndex -= samples.size();
		}
	} else {
		// Saphir 按第一个选中参数最快变化的顺序列出多变量组合。
		for (int i = 0; i < m_variables.size(); ++i) {
			const VariableSampling& variable = m_variables[i];
			if (!variable.m_enabled) continue;
			const QVector<double> samples = variable.samplingValues();
			if (samples.isEmpty() || samples.size() != variable.m_number) return false;
			values[i] = samples[int(runIndex % samples.size())];
			runIndex /= samples.size();
		}
		return true;
	}
	return false;
}

bool nmDataSensitive::usesBaseUnits() const
{
	return m_usesBaseUnits;
}

void nmDataSensitive::setUsesBaseUnits(bool enabled)
{
	// 单位标记随配置保存，避免再次打开时重复转换。
	m_usesBaseUnits = enabled;
}

// === 变量列表 ===
QList<nmDataSensitive::VariableSampling>& nmDataSensitive::getVariables()
{
	return m_variables;
}

const QList<nmDataSensitive::VariableSampling>& nmDataSensitive::getVariables() const
{
	// 求解任务只读取采样配置，不取得可写参数列表。
	return m_variables;
}

void nmDataSensitive::setVariables(const QList<VariableSampling>& vars)
{
	m_variables = vars;
}

int nmDataSensitive::getVariableCount() const
{
	return m_variables.size();
}

nmDataSensitive::VariableSampling& nmDataSensitive::getVariable(int index)
{
	return m_variables[index];
}