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// Copyright (c) 2019 by the SciSharp Team
// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion
using System;
using System.Collections;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using Python.Runtime;
using Python.Included;
using Numpy.Models;
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Returns True if cast between data types can occur according to the
/// casting rule. If from is a scalar or array scalar, also returns
/// True if the scalar value can be cast without overflow or truncation
/// to an integer.
///
/// Notes
///
/// Starting in NumPy 1.9, can_cast function now returns False in ‘safe’
/// casting mode for integer/float dtype and string dtype if the string dtype
/// length is not long enough to store the max integer/float value converted
/// to a string. Previously can_cast in ‘safe’ mode returned True for
/// integer/float dtype and a string dtype of any length.
/// </summary>
/// <param name="from_">
/// Data type, scalar, or array to cast from.
/// </param>
/// <param name="to">
/// Data type to cast to.
/// </param>
/// <param name="casting">
/// Controls what kind of data casting may occur.
/// </param>
/// <returns>
/// True if cast can occur according to the casting rule.
/// </returns>
public static bool can_cast(Dtype from_, Dtype to, string casting = "safe")
=> NumPy.Instance.can_cast(from_, to, casting:casting);
/// <summary>
/// Returns the data type with the smallest size and smallest scalar
/// kind to which both type1 and type2 may be safely cast.
/// The returned data type is always in native byte order.
///
/// This function is symmetric, but rarely associative.
///
/// Notes
///
/// Starting in NumPy 1.9, promote_types function now returns a valid string
/// length when given an integer or float dtype as one argument and a string
/// dtype as another argument. Previously it always returned the input string
/// dtype, even if it wasn’t long enough to store the max integer/float value
/// converted to a string.
/// </summary>
/// <param name="type1">
/// First data type.
/// </param>
/// <param name="type2">
/// Second data type.
/// </param>
/// <returns>
/// The promoted data type.
/// </returns>
public static Dtype promote_types(Dtype type1, Dtype type2)
=> NumPy.Instance.promote_types(type1, type2);
/// <summary>
/// For scalar a, returns the data type with the smallest size
/// and smallest scalar kind which can hold its value. For non-scalar
/// array a, returns the vector’s dtype unmodified.
///
/// Floating point values are not demoted to integers,
/// and complex values are not demoted to floats.
///
/// Notes
/// </summary>
/// <param name="a">
/// The value whose minimal data type is to be found.
/// </param>
/// <returns>
/// The minimal data type.
/// </returns>
public static Dtype min_scalar_type(NDarray a)
=> NumPy.Instance.min_scalar_type(a);
/*
/// <summary>
/// Returns the type that results from applying the NumPy
/// type promotion rules to the arguments.
///
/// Type promotion in NumPy works similarly to the rules in languages
/// like C++, with some slight differences. When both scalars and
/// arrays are used, the array’s type takes precedence and the actual value
/// of the scalar is taken into account.
///
/// For example, calculating 3*a, where a is an array of 32-bit floats,
/// intuitively should result in a 32-bit float output. If the 3 is a
/// 32-bit integer, the NumPy rules indicate it can’t convert losslessly
/// into a 32-bit float, so a 64-bit float should be the result type.
/// By examining the value of the constant, ‘3’, we see that it fits in
/// an 8-bit integer, which can be cast losslessly into the 32-bit float.
///
/// Notes
///
/// The specific algorithm used is as follows.
///
/// Categories are determined by first checking which of boolean,
/// integer (int/uint), or floating point (float/complex) the maximum
/// kind of all the arrays and the scalars are.
///
/// If there are only scalars or the maximum category of the scalars
/// is higher than the maximum category of the arrays,
/// the data types are combined with promote_types
/// to produce the return value.
///
/// Otherwise, min_scalar_type is called on each array, and
/// the resulting data types are all combined with promote_types
/// to produce the return value.
///
/// The set of int values is not a subset of the uint values for types
/// with the same number of bits, something not reflected in
/// min_scalar_type, but handled as a special case in result_type.
/// </summary>
/// <param name="arrays_and_dtypes">
/// The operands of some operation whose result type is needed.
/// </param>
/// <returns>
/// The result type.
/// </returns>
public static Dtype result_type(list of arrays and dtypes arrays_and_dtypes)
=> NumPy.Instance.result_type(arrays_and_dtypes);
*/
/// <summary>
/// Return a scalar type which is common to the input arrays.
///
/// The return type will always be an inexact (i.e. floating point) scalar
/// type, even if all the arrays are integer arrays. If one of the inputs is
/// an integer array, the minimum precision type that is returned is a
/// 64-bit floating point dtype.
///
/// All input arrays except int64 and uint64 can be safely cast to the
/// returned dtype without loss of information.
/// </summary>
/// <param name="array2">
/// Input arrays.
/// </param>
/// <param name="array1">
/// Input arrays.
/// </param>
/// <returns>
/// Data type code.
/// </returns>
public static Dtype common_type(NDarray array2, NDarray array1)
=> NumPy.Instance.common_type(array2, array1);
/// <summary>
/// Return the scalar dtype or NumPy equivalent of Python type of an object.
/// </summary>
/// <param name="rep">
/// The object of which the type is returned.
/// </param>
/// <param name="@default">
/// If given, this is returned for objects whose types can not be
/// determined. If not given, None is returned for those objects.
/// </param>
/// <returns>
/// The data type of rep.
/// </returns>
public static Dtype obj2sctype(object rep, object @default = null)
=> NumPy.Instance.obj2sctype(rep, @default:@default);
/// <summary>
/// Create a data type object.
///
/// A numpy array is homogeneous, and contains elements described by a
/// dtype object. A dtype object can be constructed from different
/// combinations of fundamental numeric types.
/// </summary>
/// <param name="align">
/// Add padding to the fields to match what a C compiler would output
/// for a similar C-struct. Can be True only if obj is a dictionary
/// or a comma-separated string. If a struct dtype is being created,
/// this also sets a sticky alignment flag isalignedstruct.
/// </param>
/// <param name="copy">
/// Make a new copy of the data-type object. If False, the result
/// may just be a reference to a built-in data-type object.
/// </param>
public static void dtype(bool? align = null, bool? copy = null)
=> NumPy.Instance.dtype(align:align, copy:copy);
/// <summary>
/// Class to convert formats, names, titles description to a dtype.
///
/// After constructing the format_parser object, the dtype attribute is
/// the converted data-type:
/// dtype = format_parser(formats, names, titles).dtype
/// </summary>
/// <param name="formats">
/// The format description, either specified as a string with
/// comma-separated format descriptions in the form 'f8, i4, a5', or
/// a list of format description strings in the form
/// ['f8', 'i4', 'a5'].
/// </param>
/// <param name="names">
/// The field names, either specified as a comma-separated string in the
/// form 'col1, col2, col3', or as a list or tuple of strings in the
/// form ['col1', 'col2', 'col3'].
/// An empty list can be used, in that case default field names
/// (‘f0’, ‘f1’, …) are used.
/// </param>
/// <param name="titles">
/// Sequence of title strings. An empty list can be used to leave titles
/// out.
/// </param>
/// <param name="aligned">
/// If True, align the fields by padding as the C-compiler would.
/// Default is False.
/// </param>
/// <param name="byteorder">
/// If specified, all the fields will be changed to the
/// provided byte-order. Otherwise, the default byte-order is
/// used. For all available string specifiers, see dtype.newbyteorder.
/// </param>
public static void format_parser(string[] formats, string[] names, string[] titles, bool? aligned = null, string byteorder = null)
=> NumPy.Instance.format_parser(formats, names, titles, aligned:aligned, byteorder:byteorder);
/// <summary>
/// Machine limits for floating point types.
///
/// Notes
///
/// For developers of NumPy: do not instantiate this at the module level.
/// The initial calculation of these parameters is expensive and negatively
/// impacts import times. These objects are cached, so calling finfo()
/// repeatedly inside your functions is not a problem.
/// </summary>
/// <param name="dtype">
/// Kind of floating point data-type about which to get information.
/// </param>
public static void finfo(Dtype dtype)
=> NumPy.Instance.finfo(dtype);
/// <summary>
/// Machine limits for integer types.
/// </summary>
/// <param name="int_type">
/// The kind of integer data type to get information about.
/// </param>
public static void iinfo(Dtype int_type)
=> NumPy.Instance.iinfo(int_type);
/// <summary>
/// Diagnosing machine parameters.
///
/// References
/// </summary>
/// <param name="float_conv">
/// Function that converts an integer or integer array to a float
/// or float array. Default is float.
/// </param>
/// <param name="int_conv">
/// Function that converts a float or float array to an integer or
/// integer array. Default is int.
/// </param>
/// <param name="float_to_float">
/// Function that converts a float array to float. Default is float.
/// Note that this does not seem to do anything useful in the current
/// implementation.
/// </param>
/// <param name="float_to_str">
/// Function that converts a single float to a string. Default is
/// lambda v:'%24.16e' %v.
/// </param>
/// <param name="title">
/// Title that is printed in the string representation of MachAr.
/// </param>
public static void MachAr(Delegate float_conv = null, Delegate int_conv = null, Delegate float_to_float = null, Delegate float_to_str = null, string title = null)
=> NumPy.Instance.MachAr(float_conv:float_conv, int_conv:int_conv, float_to_float:float_to_float, float_to_str:float_to_str, title:title);
/// <summary>
/// Determines whether the given object represents a scalar data-type.
/// </summary>
/// <param name="rep">
/// If rep is an instance of a scalar dtype, True is returned. If not,
/// False is returned.
/// </param>
/// <returns>
/// Boolean result of check whether rep is a scalar dtype.
/// </returns>
public static bool issctype(object rep)
=> NumPy.Instance.issctype(rep);
/// <summary>
/// Returns True if first argument is a typecode lower/equal in type hierarchy.
/// </summary>
/// <param name="arg2">
/// dtype or string representing a typecode.
/// </param>
/// <param name="arg1">
/// dtype or string representing a typecode.
/// </param>
public static bool issubdtype(Dtype arg2, Dtype arg1)
=> NumPy.Instance.issubdtype(arg2, arg1);
/// <summary>
/// Determine if the first argument is a subclass of the second argument.
/// </summary>
/// <param name="arg2">
/// Data-types.
/// </param>
/// <param name="arg1">
/// Data-types.
/// </param>
/// <returns>
/// The result.
/// </returns>
public static bool issubsctype(Dtype arg2, Dtype arg1)
=> NumPy.Instance.issubsctype(arg2, arg1);
/*
/// <summary>
/// Determine if a class is a subclass of a second class.
///
/// issubclass_ is equivalent to the Python built-in issubclass,
/// except that it returns False instead of raising a TypeError if one
/// of the arguments is not a class.
/// </summary>
/// <param name="arg1">
/// Input class. True is returned if arg1 is a subclass of arg2.
/// </param>
/// <param name="arg2">
/// Input class. If a tuple of classes, True is returned if arg1 is a
/// subclass of any of the tuple elements.
/// </param>
/// <returns>
/// Whether arg1 is a subclass of arg2 or not.
/// </returns>
public static bool issubclass_(class arg1, class or tuple of classes. arg2)
=> NumPy.Instance.issubclass_(arg1, arg2);
*/
/*
/// <summary>
/// Determine common type following standard coercion rules.
/// </summary>
/// <param name="array_types">
/// A list of dtypes or dtype convertible objects representing arrays.
/// </param>
/// <param name="scalar_types">
/// A list of dtypes or dtype convertible objects representing scalars.
/// </param>
/// <returns>
/// The common data type, which is the maximum of array_types ignoring
/// scalar_types, unless the maximum of scalar_types is of a
/// different kind (dtype.kind). If the kind is not understood, then
/// None is returned.
/// </returns>
public static Dtype find_common_type(sequence array_types, sequence scalar_types)
=> NumPy.Instance.find_common_type(array_types, scalar_types);
*/
/// <summary>
/// Return a description for the given data type code.
/// </summary>
/// <param name="@char">
/// Data type code.
/// </param>
/// <returns>
/// Description of the input data type code.
/// </returns>
public static string typename(string @char)
=> NumPy.Instance.typename(@char);
/// <summary>
/// Return the string representation of a scalar dtype.
/// </summary>
/// <param name="sctype">
/// If a scalar dtype, the corresponding string character is
/// returned. If an object, sctype2char tries to infer its scalar type
/// and then return the corresponding string character.
/// </param>
/// <returns>
/// The string character corresponding to the scalar type.
/// </returns>
public static string sctype2char(object sctype)
=> NumPy.Instance.sctype2char(sctype);
/// <summary>
/// Return the character for the minimum-size type to which given types can
/// be safely cast.
///
/// The returned type character must represent the smallest size dtype such
/// that an array of the returned type can handle the data from an array of
/// all types in typechars (or if typechars is an array, then its
/// dtype.char).
/// </summary>
/// <param name="typechars">
/// If a list of strings, each string should represent a dtype.
/// If array_like, the character representation of the array dtype is used.
/// </param>
/// <param name="typeset">
/// The set of characters that the returned character is chosen from.
/// The default set is ‘GDFgdf’.
/// </param>
/// <param name="@default">
/// The default character, this is returned if none of the characters in
/// typechars matches a character in typeset.
/// </param>
/// <returns>
/// The character representing the minimum-size type that was found.
/// </returns>
public static string mintypecode(string[] typechars, string[] typeset = null, string @default = "d")
=> NumPy.Instance.mintypecode(typechars, typeset:typeset, @default:@default);
}
}