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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 Numpy.Models;
namespace Numpy
{
public partial class NumPy
{
/// <summary>
/// Load arrays or pickled objects from .npy, .npz or pickled files.<br></br>
///
/// Notes
/// </summary>
/// <param name="file">
/// The file to read.<br></br>
/// File-like objects must support the
/// seek() and read() methods.<br></br>
/// Pickled files require that the
/// file-like object support the readline() method as well.
/// </param>
/// <param name="mmap_mode">
/// If not None, then memory-map the file, using the given mode (see
/// numpy.memmap for a detailed description of the modes).<br></br>
/// A
/// memory-mapped array is kept on disk.<br></br>
/// However, it can be accessed
/// and sliced like any ndarray.<br></br>
/// Memory mapping is especially useful
/// for accessing small fragments of large files without reading the
/// entire file into memory.
/// </param>
/// <param name="allow_pickle">
/// Allow loading pickled object arrays stored in npy files.<br></br>
/// Reasons for
/// disallowing pickles include security, as loading pickled data can
/// execute arbitrary code.<br></br>
/// If pickles are disallowed, loading object
/// arrays will fail.<br></br>
///
/// Default: True
/// </param>
/// <param name="fix_imports">
/// Only useful when loading Python 2 generated pickled files on Python 3,
/// which includes npy/npz files containing object arrays.<br></br>
/// If fix_imports
/// is True, pickle will try to map the old Python 2 names to the new names
/// used in Python 3.
/// </param>
/// <param name="encoding">
/// What encoding to use when reading Python 2 strings.<br></br>
/// Only useful when
/// loading Python 2 generated pickled files in Python 3, which includes
/// npy/npz files containing object arrays.<br></br>
/// Values other than ‘latin1’,
/// ‘ASCII’, and ‘bytes’ are not allowed, as they can corrupt numerical
/// data.<br></br>
/// Default: ‘ASCII’
/// </param>
/// <returns>
/// Data stored in the file.<br></br>
/// For .npz files, the returned instance
/// of NpzFile class must be closed to avoid leaking file descriptors.
/// </returns>
public NDarray load(string file, MemMapMode mmap_mode = null, bool? allow_pickle = true, bool? fix_imports = true, string encoding = "ASCII")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
file,
});
var kwargs=new PyDict();
if (mmap_mode!=null) kwargs["mmap_mode"]=ToPython(mmap_mode);
if (allow_pickle!=true) kwargs["allow_pickle"]=ToPython(allow_pickle);
if (fix_imports!=true) kwargs["fix_imports"]=ToPython(fix_imports);
if (encoding!="ASCII") kwargs["encoding"]=ToPython(encoding);
dynamic py = __self__.InvokeMethod("load", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Save an array to a binary file in NumPy .npy format.<br></br>
///
/// Notes
///
/// For a description of the .npy format, see numpy.lib.format.
/// </summary>
/// <param name="file">
/// File or filename to which the data is saved.<br></br>
/// If file is a file-object,
/// then the filename is unchanged.<br></br>
/// If file is a string or Path, a .npy
/// extension will be appended to the file name if it does not already
/// have one.
/// </param>
/// <param name="arr">
/// Array data to be saved.
/// </param>
/// <param name="allow_pickle">
/// Allow saving object arrays using Python pickles.<br></br>
/// Reasons for disallowing
/// pickles include security (loading pickled data can execute arbitrary
/// code) and portability (pickled objects may not be loadable on different
/// Python installations, for example if the stored objects require libraries
/// that are not available, and not all pickled data is compatible between
/// Python 2 and Python 3).<br></br>
///
/// Default: True
/// </param>
/// <param name="fix_imports">
/// Only useful in forcing objects in object arrays on Python 3 to be
/// pickled in a Python 2 compatible way.<br></br>
/// If fix_imports is True, pickle
/// will try to map the new Python 3 names to the old module names used in
/// Python 2, so that the pickle data stream is readable with Python 2.
/// </param>
public void save(string file, NDarray arr, bool? allow_pickle = true, bool? fix_imports = true)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
file,
arr,
});
var kwargs=new PyDict();
if (allow_pickle!=true) kwargs["allow_pickle"]=ToPython(allow_pickle);
if (fix_imports!=true) kwargs["fix_imports"]=ToPython(fix_imports);
dynamic py = __self__.InvokeMethod("save", pyargs, kwargs);
}
/// <summary>
/// Save several arrays into a single file in uncompressed .npz format.<br></br>
///
/// If arguments are passed in with no keywords, the corresponding variable
/// names, in the .npz file, are ‘arr_0’, ‘arr_1’, etc.<br></br>
/// If keyword
/// arguments are given, the corresponding variable names, in the .npz
/// file will match the keyword names.<br></br>
///
/// Notes
///
/// The .npz file format is a zipped archive of files named after the
/// variables they contain.<br></br>
/// The archive is not compressed and each file
/// in the archive contains one variable in .npy format.<br></br>
/// For a
/// description of the .npy format, see numpy.lib.format.<br></br>
///
/// When opening the saved .npz file with load a NpzFile object is
/// returned.<br></br>
/// This is a dictionary-like object which can be queried for
/// its list of arrays (with the .files attribute), and for the arrays
/// themselves.
/// </summary>
/// <param name="file">
/// Either the file name (string) or an open file (file-like object)
/// where the data will be saved.<br></br>
/// If file is a string or a Path, the
/// .npz extension will be appended to the file name if it is not
/// already there.
/// </param>
/// <param name="args">
/// Arrays to save to the file.<br></br>
/// Since it is not possible for Python to
/// know the names of the arrays outside savez, the arrays will be saved
/// with names “arr_0”, “arr_1”, and so on.<br></br>
/// These arguments can be any
/// expression.
/// </param>
/// <param name="kwds">
/// Arrays to save to the file.<br></br>
/// Arrays will be saved in the file with the
/// keyword names.
/// </param>
public void savez(string file, NDarray[] args = null, Dictionary<string, NDarray> kwds = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
file,
});
var kwargs=new PyDict();
if (args!=null) kwargs["args"]=ToPython(args);
if (kwds!=null) kwargs["kwds"]=ToPython(kwds);
dynamic py = __self__.InvokeMethod("savez", pyargs, kwargs);
}
/// <summary>
/// Save several arrays into a single file in compressed .npz format.<br></br>
///
/// If keyword arguments are given, then filenames are taken from the keywords.<br></br>
///
/// If arguments are passed in with no keywords, then stored file names are
/// arr_0, arr_1, etc.<br></br>
///
/// Notes
///
/// The .npz file format is a zipped archive of files named after the
/// variables they contain.<br></br>
/// The archive is compressed with
/// zipfile.ZIP_DEFLATED and each file in the archive contains one variable
/// in .npy format.<br></br>
/// For a description of the .npy format, see
/// numpy.lib.format.<br></br>
///
/// When opening the saved .npz file with load a NpzFile object is
/// returned.<br></br>
/// This is a dictionary-like object which can be queried for
/// its list of arrays (with the .files attribute), and for the arrays
/// themselves.
/// </summary>
/// <param name="file">
/// Either the file name (string) or an open file (file-like object)
/// where the data will be saved.<br></br>
/// If file is a string or a Path, the
/// .npz extension will be appended to the file name if it is not
/// already there.
/// </param>
/// <param name="args">
/// Arrays to save to the file.<br></br>
/// Since it is not possible for Python to
/// know the names of the arrays outside savez, the arrays will be saved
/// with names “arr_0”, “arr_1”, and so on.<br></br>
/// These arguments can be any
/// expression.
/// </param>
/// <param name="kwds">
/// Arrays to save to the file.<br></br>
/// Arrays will be saved in the file with the
/// keyword names.
/// </param>
public void savez_compressed(string file, NDarray[] args = null, Dictionary<string, NDarray> kwds = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
file,
});
var kwargs=new PyDict();
if (args!=null) kwargs["args"]=ToPython(args);
if (kwds!=null) kwargs["kwds"]=ToPython(kwds);
dynamic py = __self__.InvokeMethod("savez_compressed", pyargs, kwargs);
}
/// <summary>
/// Save an array to a text file.<br></br>
///
/// Notes
///
/// Further explanation of the fmt parameter
/// (%[flag]width[.precision]specifier):
///
/// This explanation of fmt is not complete, for an exhaustive
/// specification see [1].<br></br>
///
/// References
/// </summary>
/// <param name="fname">
/// If the filename ends in .gz, the file is automatically saved in
/// compressed gzip format.<br></br>
/// loadtxt understands gzipped files
/// transparently.
/// </param>
/// <param name="X">
/// Data to be saved to a text file.
/// </param>
/// <param name="fmt">
/// A single format (%10.5f), a sequence of formats, or a
/// multi-format string, e.g.<br></br>
/// ‘Iteration %d – %10.5f’, in which
/// case delimiter is ignored.<br></br>
/// For complex X, the legal options
/// for fmt are:
/// </param>
/// <param name="delimiter">
/// String or character separating columns.
/// </param>
/// <param name="newline">
/// String or character separating lines.
/// </param>
/// <param name="header">
/// String that will be written at the beginning of the file.
/// </param>
/// <param name="footer">
/// String that will be written at the end of the file.
/// </param>
/// <param name="comments">
/// String that will be prepended to the header and footer strings,
/// to mark them as comments.<br></br>
/// Default: ‘# ‘, as expected by e.g.<br></br>
///
/// numpy.loadtxt.
/// </param>
/// <param name="encoding">
/// Encoding used to encode the outputfile.<br></br>
/// Does not apply to output
/// streams.<br></br>
/// If the encoding is something other than ‘bytes’ or ‘latin1’
/// you will not be able to load the file in NumPy versions < 1.14. Default
/// is ‘latin1’.
/// </param>
public void savetxt(string fname, NDarray X, string[] fmt = null, string delimiter = " ", string newline = "\n", string header = "", string footer = "", string comments = null, string encoding = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
fname,
X,
});
var kwargs=new PyDict();
if (fmt!=null) kwargs["fmt"]=ToPython(fmt);
if (delimiter!=" ") kwargs["delimiter"]=ToPython(delimiter);
if (newline!="\n") kwargs["newline"]=ToPython(newline);
if (header!="") kwargs["header"]=ToPython(header);
if (footer!="") kwargs["footer"]=ToPython(footer);
if (comments!=null) kwargs["comments"]=ToPython(comments);
if (encoding!=null) kwargs["encoding"]=ToPython(encoding);
dynamic py = __self__.InvokeMethod("savetxt", pyargs, kwargs);
}
/*
/// <summary>
/// Load data from a text file, with missing values handled as specified.<br></br>
///
/// Each line past the first skip_header lines is split at the delimiter
/// character, and characters following the comments character are discarded.<br></br>
///
/// Notes
///
/// References
/// </summary>
/// <param name="fname">
/// File, filename, list, or generator to read.<br></br>
/// If the filename
/// extension is gz or bz2, the file is first decompressed.<br></br>
/// Note
/// that generators must return byte strings in Python 3k.<br></br>
/// The strings
/// in a list or produced by a generator are treated as lines.
/// </param>
/// <param name="dtype">
/// Data type of the resulting array.<br></br>
///
/// If None, the dtypes will be determined by the contents of each
/// column, individually.
/// </param>
/// <param name="comments">
/// The character used to indicate the start of a comment.<br></br>
///
/// All the characters occurring on a line after a comment are discarded
/// </param>
/// <param name="delimiter">
/// The string used to separate values.<br></br>
/// By default, any consecutive
/// whitespaces act as delimiter.<br></br>
/// An integer or sequence of integers
/// can also be provided as width(s) of each field.
/// </param>
/// <param name="skiprows">
/// skiprows was removed in numpy 1.10. Please use skip_header instead.
/// </param>
/// <param name="skip_header">
/// The number of lines to skip at the beginning of the file.
/// </param>
/// <param name="skip_footer">
/// The number of lines to skip at the end of the file.
/// </param>
/// <param name="converters">
/// The set of functions that convert the data of a column to a value.<br></br>
///
/// The converters can also be used to provide a default value
/// for missing data: converters = {3: lambda s: float(s or 0)}.
/// </param>
/// <param name="missing">
/// missing was removed in numpy 1.10. Please use missing_values
/// instead.
/// </param>
/// <param name="missing_values">
/// The set of strings corresponding to missing data.
/// </param>
/// <param name="filling_values">
/// The set of values to be used as default when the data are missing.
/// </param>
/// <param name="usecols">
/// Which columns to read, with 0 being the first.<br></br>
/// For example,
/// usecols = (1, 4, 5) will extract the 2nd, 5th and 6th columns.
/// </param>
/// <param name="names">
/// If names is True, the field names are read from the first line after
/// the first skip_header lines.<br></br>
/// This line can optionally be proceeded
/// by a comment delimiter.<br></br>
/// If names is a sequence or a single-string of
/// comma-separated names, the names will be used to define the field names
/// in a structured dtype.<br></br>
/// If names is None, the names of the dtype
/// fields will be used, if any.
/// </param>
/// <param name="excludelist">
/// A list of names to exclude.<br></br>
/// This list is appended to the default list
/// [‘return’,’file’,’print’].<br></br>
/// Excluded names are appended an underscore:
/// for example, file would become file_.
/// </param>
/// <param name="deletechars">
/// A string combining invalid characters that must be deleted from the
/// names.
/// </param>
/// <param name="defaultfmt">
/// A format used to define default field names, such as “f%i” or “f_%02i”.
/// </param>
/// <param name="autostrip">
/// Whether to automatically strip white spaces from the variables.
/// </param>
/// <param name="replace_space">
/// Character(s) used in replacement of white spaces in the variables
/// names.<br></br>
/// By default, use a ‘_’.
/// </param>
/// <param name="case_sensitive">
/// If True, field names are case sensitive.<br></br>
///
/// If False or ‘upper’, field names are converted to upper case.<br></br>
///
/// If ‘lower’, field names are converted to lower case.
/// </param>
/// <param name="unpack">
/// If True, the returned array is transposed, so that arguments may be
/// unpacked using x, y, z = loadtxt(...)
/// </param>
/// <param name="usemask">
/// If True, return a masked array.<br></br>
///
/// If False, return a regular array.
/// </param>
/// <param name="loose">
/// If True, do not raise errors for invalid values.
/// </param>
/// <param name="invalid_raise">
/// If True, an exception is raised if an inconsistency is detected in the
/// number of columns.<br></br>
///
/// If False, a warning is emitted and the offending lines are skipped.
/// </param>
/// <param name="max_rows">
/// The maximum number of rows to read.<br></br>
/// Must not be used with skip_footer
/// at the same time.<br></br>
/// If given, the value must be at least 1.<br></br>
/// Default is
/// to read the entire file.
/// </param>
/// <param name="encoding">
/// Encoding used to decode the inputfile.<br></br>
/// Does not apply when fname is
/// a file object.<br></br>
/// The special value ‘bytes’ enables backward compatibility
/// workarounds that ensure that you receive byte arrays when possible
/// and passes latin1 encoded strings to converters.<br></br>
/// Override this value to
/// receive unicode arrays and pass strings as input to converters.<br></br>
/// If set
/// to None the system default is used.<br></br>
/// The default value is ‘bytes’.
/// </param>
/// <returns>
/// Data read from the text file.<br></br>
/// If usemask is True, this is a
/// masked array.
/// </returns>
public NDarray genfromtxt(string fname, Dtype dtype = null, string comments = null, string delimiter = null, int? skiprows = null, int? skip_header = 0, int? skip_footer = 0, variable converters = null, variable missing = null, variable missing_values = null, variable filling_values = null, sequence usecols = null, {None names = null, sequence excludelist = null, string deletechars = null, string defaultfmt = "f%i", bool? autostrip = false, char replace_space = "_", {True case_sensitive = true, bool? unpack = null, bool? usemask = false, bool? loose = true, bool? invalid_raise = true, int? max_rows = null, string encoding = "bytes")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
fname,
});
var kwargs=new PyDict();
if (dtype!=null) kwargs["dtype"]=ToPython(dtype);
if (comments!=null) kwargs["comments"]=ToPython(comments);
if (delimiter!=null) kwargs["delimiter"]=ToPython(delimiter);
if (skiprows!=null) kwargs["skiprows"]=ToPython(skiprows);
if (skip_header!=0) kwargs["skip_header"]=ToPython(skip_header);
if (skip_footer!=0) kwargs["skip_footer"]=ToPython(skip_footer);
if (converters!=null) kwargs["converters"]=ToPython(converters);
if (missing!=null) kwargs["missing"]=ToPython(missing);
if (missing_values!=null) kwargs["missing_values"]=ToPython(missing_values);
if (filling_values!=null) kwargs["filling_values"]=ToPython(filling_values);
if (usecols!=null) kwargs["usecols"]=ToPython(usecols);
if (names!=null) kwargs["names"]=ToPython(names);
if (excludelist!=null) kwargs["excludelist"]=ToPython(excludelist);
if (deletechars!=null) kwargs["deletechars"]=ToPython(deletechars);
if (defaultfmt!="f%i") kwargs["defaultfmt"]=ToPython(defaultfmt);
if (autostrip!=false) kwargs["autostrip"]=ToPython(autostrip);
if (replace_space!="_") kwargs["replace_space"]=ToPython(replace_space);
if (case_sensitive!=true) kwargs["case_sensitive"]=ToPython(case_sensitive);
if (unpack!=null) kwargs["unpack"]=ToPython(unpack);
if (usemask!=false) kwargs["usemask"]=ToPython(usemask);
if (loose!=true) kwargs["loose"]=ToPython(loose);
if (invalid_raise!=true) kwargs["invalid_raise"]=ToPython(invalid_raise);
if (max_rows!=null) kwargs["max_rows"]=ToPython(max_rows);
if (encoding!="bytes") kwargs["encoding"]=ToPython(encoding);
dynamic py = __self__.InvokeMethod("genfromtxt", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
*/
/// <summary>
/// Construct an array from a text file, using regular expression parsing.<br></br>
///
/// The returned array is always a structured array, and is constructed from
/// all matches of the regular expression in the file.<br></br>
/// Groups in the regular
/// expression are converted to fields of the structured array.<br></br>
///
/// Notes
///
/// Dtypes for structured arrays can be specified in several forms, but all
/// forms specify at least the data type and field name.<br></br>
/// For details see
/// doc.structured_arrays.
/// </summary>
/// <param name="file">
/// File name or file object to read.
/// </param>
/// <param name="regexp">
/// Regular expression used to parse the file.<br></br>
///
/// Groups in the regular expression correspond to fields in the dtype.
/// </param>
/// <param name="dtype">
/// Dtype for the structured array.
/// </param>
/// <param name="encoding">
/// Encoding used to decode the inputfile.<br></br>
/// Does not apply to input streams.
/// </param>
/// <returns>
/// The output array, containing the part of the content of file that
/// was matched by regexp.<br></br>
/// output is always a structured array.
/// </returns>
public NDarray fromregex(string file, string regexp, Dtype dtype, string encoding = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
file,
regexp,
dtype,
});
var kwargs=new PyDict();
if (encoding!=null) kwargs["encoding"]=ToPython(encoding);
dynamic py = __self__.InvokeMethod("fromregex", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Write array to a file as text or binary (default).<br></br>
///
/// Data is always written in ‘C’ order, independent of the order of a.<br></br>
///
/// The data produced by this method can be recovered using the function
/// fromfile().<br></br>
///
/// Notes
///
/// This is a convenience function for quick storage of array data.<br></br>
///
/// Information on endianness and precision is lost, so this method is not a
/// good choice for files intended to archive data or transport data between
/// machines with different endianness.<br></br>
/// Some of these problems can be overcome
/// by outputting the data as text files, at the expense of speed and file
/// size.<br></br>
///
/// When fid is a file object, array contents are directly written to the
/// file, bypassing the file object’s write method.<br></br>
/// As a result, tofile
/// cannot be used with files objects supporting compression (e.g., GzipFile)
/// or file-like objects that do not support fileno() (e.g., BytesIO).
/// </summary>
/// <param name="fid">
/// An open file object, or a string containing a filename.
/// </param>
/// <param name="sep">
/// Separator between array items for text output.<br></br>
///
/// If “” (empty), a binary file is written, equivalent to
/// file.write(a.tobytes()).
/// </param>
/// <param name="format">
/// Format string for text file output.<br></br>
///
/// Each entry in the array is formatted to text by first converting
/// it to the closest Python type, and then using “format” % item.
/// </param>
public void tofile(string fid, string sep, string format)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
fid,
sep,
format,
});
var kwargs=new PyDict();
dynamic py = __self__.InvokeMethod("tofile", pyargs, kwargs);
}
/*
/// <summary>
/// Return the array as a (possibly nested) list.<br></br>
///
/// Return a copy of the array data as a (nested) Python list.<br></br>
///
/// Data items are converted to the nearest compatible Python type.<br></br>
///
/// Notes
///
/// The array may be recreated, a = np.array(a.tolist()).
/// </summary>
/// <returns>
/// The possibly nested list of array elements.
/// </returns>
public List<T> tolist<T>()
{
//auto-generated code, do not change
var __self__=self;
dynamic py = __self__.InvokeMethod("tolist");
return ToCsharp<List<T>>(py);
}
*/
/*
/// <summary>
/// Return a string representation of an array.<br></br>
///
/// Notes
///
/// If a formatter is specified for a certain type, the precision keyword is
/// ignored for that type.<br></br>
///
/// This is a very flexible function; array_repr and array_str are using
/// array2string internally so keywords with the same name should work
/// identically in all three functions.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="max_line_width">
/// The maximum number of columns the string should span.<br></br>
/// Newline
/// characters splits the string appropriately after array elements.
/// </param>
/// <param name="precision">
/// Floating point precision.<br></br>
/// Default is the current printing
/// precision (usually 8), which can be altered using set_printoptions.
/// </param>
/// <param name="suppress_small">
/// Represent very small numbers as zero.<br></br>
/// A number is “very small” if it
/// is smaller than the current printing precision.
/// </param>
/// <param name="separator">
/// Inserted between elements.
/// </param>
/// <param name="suffix">
/// The length of the prefix and suffix strings are used to respectively
/// align and wrap the output.<br></br>
/// An array is typically printed as:
///
/// The output is left-padded by the length of the prefix string, and
/// wrapping is forced at the column max_line_width - len(suffix).<br></br>
///
/// It should be noted that the content of prefix and suffix strings are
/// not included in the output.
/// </param>
/// <param name="formatter">
/// If not None, the keys should indicate the type(s) that the respective
/// formatting function applies to.<br></br>
/// Callables should return a string.<br></br>
///
/// Types that are not specified (by their corresponding keys) are handled
/// by the default formatters.<br></br>
/// Individual types for which a formatter
/// can be set are:
///
/// Other keys that can be used to set a group of types at once are:
/// </param>
/// <param name="threshold">
/// Total number of array elements which trigger summarization
/// rather than full repr.
/// </param>
/// <param name="edgeitems">
/// Number of array items in summary at beginning and end of
/// each dimension.
/// </param>
/// <param name="sign">
/// Controls printing of the sign of floating-point types.<br></br>
/// If ‘+’, always
/// print the sign of positive values.<br></br>
/// If ‘ ‘, always prints a space
/// (whitespace character) in the sign position of positive values.<br></br>
/// If
/// ‘-‘, omit the sign character of positive values.
/// </param>
/// <param name="floatmode">
/// Controls the interpretation of the precision option for
/// floating-point types.<br></br>
/// Can take the following values:
/// </param>
/// <param name="legacy">
/// If set to the string ‘1.13’ enables 1.13 legacy printing mode.<br></br>
/// This
/// approximates numpy 1.13 print output by including a space in the sign
/// position of floats and different behavior for 0d arrays.<br></br>
/// If set to
/// False, disables legacy mode.<br></br>
/// Unrecognized strings will be ignored
/// with a warning for forward compatibility.
/// </param>
/// <returns>
/// String representation of the array.
/// </returns>
public string array2string(NDarray a, int? max_line_width = null, int? precision = null, bool? suppress_small = null, string separator = " ", string prefix = "", string suffix = "", dict of callables formatter = null, int? threshold = null, int? edgeitems = null, string sign = null, string floatmode = null, string or False legacy = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (max_line_width!=null) kwargs["max_line_width"]=ToPython(max_line_width);
if (precision!=null) kwargs["precision"]=ToPython(precision);
if (suppress_small!=null) kwargs["suppress_small"]=ToPython(suppress_small);
if (separator!=" ") kwargs["separator"]=ToPython(separator);
if (prefix!="") kwargs["prefix"]=ToPython(prefix);
if (suffix!="") kwargs["suffix"]=ToPython(suffix);
if (formatter!=null) kwargs["formatter"]=ToPython(formatter);
if (threshold!=null) kwargs["threshold"]=ToPython(threshold);
if (edgeitems!=null) kwargs["edgeitems"]=ToPython(edgeitems);
if (sign!=null) kwargs["sign"]=ToPython(sign);
if (floatmode!=null) kwargs["floatmode"]=ToPython(floatmode);
if (legacy!=null) kwargs["legacy"]=ToPython(legacy);
dynamic py = __self__.InvokeMethod("array2string", pyargs, kwargs);
return ToCsharp<string>(py);
}
*/
/// <summary>
/// Return the string representation of an array.
/// </summary>
/// <param name="arr">
/// Input array.
/// </param>
/// <param name="max_line_width">
/// The maximum number of columns the string should span.<br></br>
/// Newline
/// characters split the string appropriately after array elements.
/// </param>
/// <param name="precision">
/// Floating point precision.<br></br>
/// Default is the current printing precision
/// (usually 8), which can be altered using set_printoptions.
/// </param>
/// <param name="suppress_small">
/// Represent very small numbers as zero, default is False.<br></br>
/// Very small
/// is defined by precision, if the precision is 8 then
/// numbers smaller than 5e-9 are represented as zero.
/// </param>
/// <returns>
/// The string representation of an array.
/// </returns>
public string array_repr(NDarray arr, int? max_line_width = null, int? precision = null, bool? suppress_small = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
arr,
});
var kwargs=new PyDict();
if (max_line_width!=null) kwargs["max_line_width"]=ToPython(max_line_width);
if (precision!=null) kwargs["precision"]=ToPython(precision);
if (suppress_small!=null) kwargs["suppress_small"]=ToPython(suppress_small);
dynamic py = __self__.InvokeMethod("array_repr", pyargs, kwargs);
return ToCsharp<string>(py);
}
/// <summary>
/// Return a string representation of the data in an array.<br></br>
///
/// The data in the array is returned as a single string.<br></br>
/// This function is
/// similar to array_repr, the difference being that array_repr also
/// returns information on the kind of array and its data type.
/// </summary>
/// <param name="a">
/// Input array.
/// </param>
/// <param name="max_line_width">
/// Inserts newlines if text is longer than max_line_width.<br></br>
/// The
/// default is, indirectly, 75.
/// </param>
/// <param name="precision">
/// Floating point precision.<br></br>
/// Default is the current printing precision
/// (usually 8), which can be altered using set_printoptions.
/// </param>
/// <param name="suppress_small">
/// Represent numbers “very close” to zero as zero; default is False.<br></br>
///
/// Very close is defined by precision: if the precision is 8, e.g.,
/// numbers smaller (in absolute value) than 5e-9 are represented as
/// zero.
/// </param>
public void array_str(NDarray a, int? max_line_width = null, int? precision = null, bool? suppress_small = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (max_line_width!=null) kwargs["max_line_width"]=ToPython(max_line_width);
if (precision!=null) kwargs["precision"]=ToPython(precision);
if (suppress_small!=null) kwargs["suppress_small"]=ToPython(suppress_small);
dynamic py = __self__.InvokeMethod("array_str", pyargs, kwargs);
}
/*
/// <summary>
/// Format a floating-point scalar as a decimal string in positional notation.<br></br>
///
/// Provides control over rounding, trimming and padding.<br></br>
/// Uses and assumes
/// IEEE unbiased rounding.<br></br>
/// Uses the “Dragon4” algorithm.
/// </summary>
/// <param name="x">
/// Value to format.
/// </param>
/// <param name="precision">
/// Maximum number of digits to print.<br></br>
/// May be None if unique is
/// True, but must be an integer if unique is False.
/// </param>
/// <param name="unique">
/// If True, use a digit-generation strategy which gives the shortest
/// representation which uniquely identifies the floating-point number from
/// other values of the same type, by judicious rounding.<br></br>
/// If precision
/// was omitted, print out all necessary digits, otherwise digit generation
/// is cut off after precision digits and the remaining value is rounded.<br></br>
///
/// If False, digits are generated as if printing an infinite-precision
/// value and stopping after precision digits, rounding the remaining
/// value.
/// </param>
/// <param name="fractional">
/// If True, the cutoff of precision digits refers to the total number
/// of digits after the decimal point, including leading zeros.<br></br>
///
/// If False, precision refers to the total number of significant
/// digits, before or after the decimal point, ignoring leading zeros.
/// </param>
/// <param name="trim">
/// Controls post-processing trimming of trailing digits, as follows:
/// </param>
/// <param name="sign">
/// Whether to show the sign for positive values.
/// </param>
/// <param name="pad_left">
/// Pad the left side of the string with whitespace until at least that
/// many characters are to the left of the decimal point.
/// </param>
/// <param name="pad_right">
/// Pad the right side of the string with whitespace until at least that
/// many characters are to the right of the decimal point.
/// </param>
/// <returns>
/// The string representation of the floating point value
/// </returns>
public string format_float_positional(python float or numpy floating scalar x, non-negative integer or None precision = null, bool? unique = true, bool? fractional = true, one of ‘k’ trim = "k", bool? sign = false, non-negative integer pad_left = null, non-negative integer pad_right = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
x,
});
var kwargs=new PyDict();
if (precision!=null) kwargs["precision"]=ToPython(precision);
if (unique!=true) kwargs["unique"]=ToPython(unique);
if (fractional!=true) kwargs["fractional"]=ToPython(fractional);
if (trim!="k") kwargs["trim"]=ToPython(trim);
if (sign!=false) kwargs["sign"]=ToPython(sign);
if (pad_left!=null) kwargs["pad_left"]=ToPython(pad_left);
if (pad_right!=null) kwargs["pad_right"]=ToPython(pad_right);
dynamic py = __self__.InvokeMethod("format_float_positional", pyargs, kwargs);
return ToCsharp<string>(py);
}
*/
/*
/// <summary>
/// Format a floating-point scalar as a decimal string in scientific notation.<br></br>
///
/// Provides control over rounding, trimming and padding.<br></br>
/// Uses and assumes
/// IEEE unbiased rounding.<br></br>
/// Uses the “Dragon4” algorithm.
/// </summary>
/// <param name="x">
/// Value to format.
/// </param>
/// <param name="precision">
/// Maximum number of digits to print.<br></br>
/// May be None if unique is
/// True, but must be an integer if unique is False.
/// </param>
/// <param name="unique">
/// If True, use a digit-generation strategy which gives the shortest
/// representation which uniquely identifies the floating-point number from
/// other values of the same type, by judicious rounding.<br></br>
/// If precision
/// was omitted, print all necessary digits, otherwise digit generation is
/// cut off after precision digits and the remaining value is rounded.<br></br>
///
/// If False, digits are generated as if printing an infinite-precision
/// value and stopping after precision digits, rounding the remaining
/// value.
/// </param>
/// <param name="trim">
/// Controls post-processing trimming of trailing digits, as follows:
/// </param>
/// <param name="sign">
/// Whether to show the sign for positive values.
/// </param>
/// <param name="pad_left">
/// Pad the left side of the string with whitespace until at least that
/// many characters are to the left of the decimal point.
/// </param>
/// <param name="exp_digits">
/// Pad the exponent with zeros until it contains at least this many digits.<br></br>
///
/// If omitted, the exponent will be at least 2 digits.
/// </param>
/// <returns>
/// The string representation of the floating point value
/// </returns>
public string format_float_scientific(python float or numpy floating scalar x, non-negative integer or None precision = null, bool? unique = true, one of ‘k’ trim = "k", bool? sign = false, non-negative integer pad_left = null, non-negative integer exp_digits = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
x,
});
var kwargs=new PyDict();
if (precision!=null) kwargs["precision"]=ToPython(precision);
if (unique!=true) kwargs["unique"]=ToPython(unique);
if (trim!="k") kwargs["trim"]=ToPython(trim);
if (sign!=false) kwargs["sign"]=ToPython(sign);
if (pad_left!=null) kwargs["pad_left"]=ToPython(pad_left);
if (exp_digits!=null) kwargs["exp_digits"]=ToPython(exp_digits);
dynamic py = __self__.InvokeMethod("format_float_scientific", pyargs, kwargs);
return ToCsharp<string>(py);
}
*/
/// <summary>
/// Create a memory-map to an array stored in a binary file on disk.<br></br>
///
/// Memory-mapped files are used for accessing small segments of large files