#ifndef MATPLOTPLUSPLUS_HISTOGRAM_H

#define MATPLOTPLUSPLUS_HISTOGRAM_H

#include <matplot/detail/config.h>

#include <matplot/core/axes_object.h>

#include <matplot/core/figure_type.h>

#include <matplot/core/line_spec.h>

#include <matplot/util/common.h>

#include <matplot/util/concepts.h>

#include <matplot/util/handle_types.h>

#include <array>

#include <cmath>

#include <map>

namespace matplot {

class axes_type;

class MATPLOT_EXPORTS histogram : public axes_object {

public:

// source code in 'edit histcount'

enum class binning_algorithm {

};

enum class normalization {

};

public:

explicit histogram(class axes_type *parent);

histogram(class axes_type *parent, const std::vector<double> &data,

size_t n_bins,

enum histogram::normalization normalization_alg =

histogram::normalization::count);

histogram(class axes_type *parent, const std::vector<double> &data,

const std::vector<double> &edges,

enum histogram::normalization normalization_alg =

histogram::normalization::count);

histogram(class axes_type *parent, const std::vector<double> &data,

binning_algorithm algorithm = binning_algorithm::automatic,

histogram::normalization normalization_alg =

histogram::normalization::count);

/// If we receive an axes_handle, we can convert it to a raw

/// pointer because there is no ownership involved here

template <class... Args>

histogram(const axes_handle &parent, Args&&... args)

: histogram(parent.get(), std::forward<Args>(args)...) {}

virtual ~histogram() = default;

public /* xlim object virtual functions */:

// std::string set_variables_string() override;

std::string plot_string() override;

std::string legend_string(std::string_view title) override;

std::string data_string() override;

// std::string unset_variables_string() override;

double xmax() override;

double xmin() override;

double ymax() override;

double ymin() override;

enum axes_object::axes_category axes_category() override;

public /* useful functions for histograms */:

/// Increase number of bins

size_t morebins(double bin_increase = 0.1);

/// Decrease number of bins

size_t fewerbins(double bin_decrease = 0.1);

/// Find appropriate edges given a fixed number of bins and target bin

/// width If nbins == 0, round the bin width and create

/// (xmax-xmin)/bin_width bins If nbins != 0, bin width is set to

/// (xmax-xmin)/nbins and create nbins bins

static std::vector<double> bin_picker(double xmin, double xmax,

size_t nbins, double bin_width);

/// Find appropriate edges with a given algorithm

static std::vector<double> histogram_edges(

const std::vector<double> &data, double minx, double maxx,

binning_algorithm algorithm = binning_algorithm::automatic,

bool hard_limits = false);

/// Count points within each pair of edges

static std::vector<size_t>

histogram_count(const std::vector<double> &values,

const std::vector<double> &edges);

/// Normalize the instagram count

static std::vector<double>

histogram_normalize(const std::vector<size_t> &bin_count,

const std::vector<double> &bin_edges,

size_t data_size,

enum normalization normalization_algorithm);

public /* getters and setters */:

const std::vector<double> &data() const;

class histogram &data(const std::vector<double> &data);

const std::vector<double> &values() const;

class histogram &values(const std::vector<double> &values);

const std::vector<size_t> &bin_counts() const;

class histogram &bin_counts(const std::vector<size_t> &bin_counts);

size_t num_bins();

class histogram &num_bins(size_t num_bins);

binning_algorithm algorithm() const;

class histogram &algorithm(binning_algorithm algorithm);

std::vector<double> &bin_edges();

class histogram &bin_edges(const std::vector<double> &bin_edges);

double bin_width() const;

class histogram &bin_width(double bin_width);

double bin_limits_min() const;

class histogram &bin_limits_min(double bin_limits_min);

double bin_limits_max() const;

class histogram &bin_limits_max(double bin_limits_max);

normalization normalization() const;

class histogram &normalization(enum normalization normalization);

const color_array &face_color() const;

class histogram &face_color(const color_array &face_color);

class histogram &face_color(std::initializer_list<float> face_color);

class histogram &face_color(std::string_view color);

class histogram &face_alpha(float alpha);

class histogram &edge_alpha(float alpha);

bool manual_face_color() const;

class histogram &manual_face_color(bool manual_face_color);

const color_array &edge_color() const;

class histogram &edge_color(const color_array &edge_color);

class histogram &edge_color(std::initializer_list<float> face_color);

class histogram &edge_color(std::string_view edge_color);

const line_spec &edge_style() const;

class histogram &edge_style(const line_spec &edge_style);

float line_width() const;

class histogram &line_width(float line_width);

bool vertical_orientation() const;

class histogram &vertical_orientation(bool vertical_orientation);

bool visible() const;

class histogram &visible(bool visible);

float bar_width() const;

class histogram &bar_width(float bar_width);

bool polar() const;

class histogram &polar(bool polar);

bool stairs_only() const;

class histogram &stairs_only(bool stairs_only);

/// Algorithms to determine histogram edges

static std::vector<double> scotts_rule(const std::vector<double> &x,

double minx, double maxx,

bool hard_limits);

static std::vector<double> fd_rule(const std::vector<double> &x,

double minx, double maxx,

bool hard_limits);

static std::vector<double> integers_rule(const std::vector<double> &x,

double minx, double maxx,

bool hard_limits);

static std::vector<double> sqrt_rule(const std::vector<double> &x,

double minx, double maxx,

bool hard_limits);

static std::vector<double> sturges_rule(const std::vector<double> &x,

double minx, double maxx,

bool hard_limits);

static std::vector<double> automatic_rule(const std::vector<double> &x,

double minx, double maxx,

bool hard_limits);

public /* getters and setters bypassing the histogram_spec */:

private:

/// If the user has not set the face color,

/// we get a color from the xlim

void maybe_update_face_color();

/// Make sure we have already calculated the normalized values

/// for the current parameters. We reset the normalized values

/// every time the user changes the algorithm.

void make_sure_data_is_preprocessed();

protected:

// original data

std::vector<double> data_;

// normalized values (in the simplest case, values_ = bin_counts_)

std::vector<double> values_;

// number of elements in each bin

std::vector<size_t> bin_counts_;

// target num of bins (0 indicates no target: the algorithm should

// decide)

size_t num_bins_{0};

// algorithm we use to create the bins

binning_algorithm algorithm_;

// where edges start

std::vector<double> bin_edges_;

// binning mode

enum binning_mode_type {

};

binning_mode_type binning_mode_{binning_mode_type::use_algorithm};

// bin width (if zero, the algorithm determines the bin width)

double bin_width_{0.0};

// bin limits

double bin_limits_min_;

double bin_limits_max_;

// normalization algorithm

enum normalization normalization_ { normalization::count };

// color and style

color_array face_color_{0.4f, 0, 0, 0};

bool manual_face_color_{false};

color_array edge_color_{0, 0, 0, 0};

bool manual_edge_color_{false};

line_spec edge_style_{"-"};

float line_width_{0.5};

bool vertical_orientation_{true};

float bar_width_{1.0};

bool polar_{false};

bool stairs_only_{false};

// True if visible

bool visible_{true};

};

} // namespace matplot

#endif // MATPLOTPLUSPLUS_HISTOGRAM_H