pyTypes

JavaScript Objects that emulate python objects.

Abstract Class PyType

All pyTypes descend from an abstract class, PyType. Generally, unless you are defining a new pyType, there should be no reason to deal with PyType directly.

API

Attributes

• sessionId:string. ID of Session
• uid:string. Unique id of command
• type:string. level-1 python type (coarse)
• pyType:string. level-2 python type (precise)
• date:Date. Date/time received.
• value:[object,array,string,number]. The value associated with a particular pyType. See full docs for exact fields.
• output:bool. If true, is output.

Methods

• get value(). Alias for this._v.
• setSessionId(id). Sets the session id associated with the object.
• get asObject(). Converts the es6 class to a plain object that is suitable for, e.g., redux.
• fromObject(obj). Takes a plain object and sets the properties of the object using this object.

Creating a new pyType

New pyTypes can be created by extending PyType. See the example below as well as yapij-js/unpackers/pyTypes.js:

import { PyType } from "yapij-js/unpackers/pyTypes.js";

class NpArray extends PyType {
  constructor(uid, { b, s, t }) {
    super(uid, "np.ndarray", "np.ndarray");
    this._v = { b, s, t };
  }

  // Optional accessors to elements.
  get values() {
    return this._v.b;
  }

  get shape() {
    return this._v.s;
  }

  get dtype() {
    return this._v.t;
  }

  inspect() {
    return `NpArray [ ... ] (${this._v.t}, ${this._v.s})`;
  }
}

Particular pyTypes

Built-in Types

Str

Values. (string) The string

Dict

Values. (object) The dict.

Float

Values. (number) The float.

Int

Values. (number) The int.

Bool

Values. (boolean) The boolean

List

Values. (array) Members of list.

PySet

Values. (array) Members of set.

Tuple

Values. (array) Members of tuple.

Complex

Values. (Object)

• r:number: Real part of number
• i:number: Complex part of number

Callable

Values. (string) String returned by python from callable.__str__. Usually something like a signature or function name.

Exception

Values. (Object)

• tb:string: Traceback returned by exception
• lineno:number: Line number in file where occurred.
• args:any: Other args

Warning

Values. (Object)

• file:string: File where warning arose.
• lineno:number: Line number in file where occurred.
• msg:string: Warning message.
• source:string: Info about where warning coming up.

NumPy

NpArray

Values. (Object)

• b:array: Values of array.
• s:array[number,number]: Shape of array.
• t:string: NumPy type of array values.

Pandas

PandasDataFrame

Values. (Same as PandasAbstractFrame)

PandasSeries

Values. (Same as PandasAbstractFrame)

Special Types

These types provide handling for all other cases.

DocString

When help is called in the python process, this returns a detailed object. The attribute str will format the output similarly to python and iPython shells.

UnknownPyType

If python process lacks a handler for a type, it is processed here. The type will be the actual type of the object.

UnhandledType

If output is received but it is not recognizeable as a pre-specified packer type, it is filed as an UnhandledType. If these types are popping up, there is probably an IPC error.

Meta Types

This types provide templates that are extended by particular

ListLike

For list-like objects (list, tuple, set, etc.).

BuiltInTypes

For simple built-ins like str, int, float, etc.

PandasAbstractFrame

Values. (Object)

• d:array: Values of underlying data array.
• i:array[..[number,string]]: Indices/row names of
• c:array[...string]: Column names/name of series.
• s:array[number,number]: Shape of object.

Values are identical across children.

Getter method values differs from value in that values returns the underlying data array while value returns this._v.

Examples

A script to get a full set of examples

var { Server } = require("./src/index.js");

const outFile =
  "C:/Users/us57144/Dropbox/grantThornton/projects/macro_model/yapij/yapij-re/devMode/samples/samplePyTypes.js";

// Set up the python script
var script = `
import warnings
import contextlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
plt.plot(np.arange(0,np.pi*3,.1), np.sin(np.arange(0,np.pi*3,.1)))

df = pd.DataFrame(np.random.randn(4,3), columns=['a', 'b', 'c'])

warnings.warn('This is a warning')

print(1)
print(1.23)
print(1.23 + 3j)

print(iter((1,2,3)))

print([1,2,"a", 5])
print((1,2,"a", 5))
print(range(5))

print('a')

print(b'a')
print(bytearray(b'abcd'))
print(memoryview(b'abcdefg'))

print({1,2,"a", 5})
print(frozenset({1,2,3}))

print({"g": 5, "h": "a"})

print(contextlib.redirect_stdout)

print(pd)
print(np.max)
print(df.max)
print(compile('1+1', '<str>', 'eval'))
print(type(1))
print(None)
print(...)
print(NotImplemented)
print(True)

print(np.random.randn(4,3))
print(pd.Series(np.random.randn(3)))
print(df)
print(fig)`;

// Initialize server
var s = new Server();

s.on("session.status", (th, msg) => null);
s.on("session.output", (th, msg) => null);
s.on("session.input", (th, msg) => null);

// Start the server
var ss = s.start().then(
  () => {
    console.log("Started");
  },
  e => {
    throw e;
  }
);

var main = s.exec(script);
var exc = s.exec('raise SyntaxError("This is an error!")');

var ex = "";
for (let ii = 0; ii < main.out.length; ii++) {
  if (!(main.out[ii]._v == "\n" || main.out[ii]._v == " ")) {
    ex += `\n\nexport const ${main.out[ii].pyType} = ${JSON.stringify(
      main.out[ii].asObject,
      null,
      2
    )};`;
  }
}

ex += `\n\nexport const ${exc.out[0].pyType} = ${JSON.stringify(
  exc.out[0].asObject,
  null,
  2
)};`;

ex = ex.replace(/"date": "([\w\-\:\.]+)",/gi, '"date": new Date("$1"),');

if (outFile) {
  const fs = require("fs");
  fs.writeFile(outFile, ex, err => {
    if (err) throw err;
  });
}

console.log(ex);

Examples of pyTypes as objects

export const Int = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "int",
  pyType: "Int",
  value: 1,
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Float = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "float",
  pyType: "Float",
  value: 1.23,
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Complex = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "complex",
  pyType: "Complex",
  value: {
    r: 1.23,
    i: 3
  },
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Iterator = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "iter",
  pyType: "Iterator",
  value: "<tuple_iterator object at 0x000001F411B37940>",
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const List = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "list",
  pyType: "List",
  value: [1, 2, "a", 5],
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Tuple = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "tuple",
  pyType: "Tuple",
  value: [1, 2, "a", 5],
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Range = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "range",
  pyType: "Range",
  value: "range(0, 5)",
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Str = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "str",
  pyType: "Str",
  value: "a",
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const Bytes = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "bytes",
  pyType: "Bytes",
  value: "b'a'",
  date: new Date("2018-11-09T19:13:39.173Z"),
  output: true
};

export const ByteArray = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "bytearray",
  pyType: "ByteArray",
  value: "bytearray(b'abcd')",
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const MemoryView = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "memoryview",
  pyType: "MemoryView",
  value: {
    l: [97, 98, 99, 100, 101, 102, 103],
    s: [7],
    b: 7
  },
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const PySet = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "set",
  pyType: "PySet",
  value: [1, 2, 5, "a"],
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const FrozenSet = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "frozenset",
  pyType: "FrozenSet",
  value: [1, 2, 3],
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const Dict = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "dict",
  pyType: "Dict",
  value: {
    g: 5,
    h: "a"
  },
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const ContextManager = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "ContextManager",
  pyType: "ContextManager",
  value: "<class 'contextlib.redirect_stdout'>",
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const Module = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "module",
  pyType: "Module",
  value: {
    n: "pandas",
    dir: [
      "Categorical",
      "CategoricalIndex",
      "DataFrame",
      "DateOffset",
      "DatetimeIndex",
      "ExcelFile",
      "ExcelWriter",
      "Expr",
      "Float64Index",
      "Grouper",
      "HDFStore",
      "Index",
      "IndexSlice",
      "Int64Index",
      "Interval",
      "IntervalIndex",
      "MultiIndex",
      "NaT",
      "Panel",
      "Period",
      "PeriodIndex",
      "RangeIndex",
      "Series",
      "SparseArray",
      "SparseDataFrame",
      "SparseSeries",
      "Term",
      "TimeGrouper",
      "Timedelta",
      "TimedeltaIndex",
      "Timestamp",
      "UInt64Index",
      "WidePanel",
      "_DeprecatedModule",
      "__builtins__",
      "__cached__",
      "__doc__",
      "__docformat__",
      "__file__",
      "__loader__",
      "__name__",
      "__package__",
      "__path__",
      "__spec__",
      "__version__",
      "_hashtable",
      "_lib",
      "_libs",
      "_np_version_under1p10",
      "_np_version_under1p11",
      "_np_version_under1p12",
      "_np_version_under1p13",
      "_np_version_under1p14",
      "_np_version_under1p15",
      "_tslib",
      "_version",
      "api",
      "bdate_range",
      "compat",
      "concat",
      "core",
      "crosstab",
      "cut",
      "date_range",
      "datetime",
      "datetools",
      "describe_option",
      "errors",
      "eval",
      "factorize",
      "get_dummies",
      "get_option",
      "get_store",
      "groupby",
      "infer_freq",
      "interval_range",
      "io",
      "isna",
      "isnull",
      "json",
      "lib",
      "lreshape",
      "match",
      "melt",
      "merge",
      "merge_asof",
      "merge_ordered",
      "notna",
      "notnull",
      "np",
      "offsets",
      "option_context",
      "options",
      "pandas",
      "parser",
      "period_range",
      "pivot",
      "pivot_table",
      "plot_params",
      "plotting",
      "pnow",
      "qcut",
      "read_clipboard",
      "read_csv",
      "read_excel",
      "read_feather",
      "read_fwf",
      "read_gbq",
      "read_hdf",
      "read_html",
      "read_json",
      "read_msgpack",
      "read_parquet",
      "read_pickle",
      "read_sas",
      "read_sql",
      "read_sql_query",
      "read_sql_table",
      "read_stata",
      "read_table",
      "reset_option",
      "scatter_matrix",
      "set_eng_float_format",
      "set_option",
      "show_versions",
      "test",
      "testing",
      "timedelta_range",
      "to_datetime",
      "to_msgpack",
      "to_numeric",
      "to_pickle",
      "to_timedelta",
      "tools",
      "tseries",
      "tslib",
      "unique",
      "util",
      "value_counts",
      "wide_to_long"
    ],
    doc:
      '\npandas - a powerful data analysis and manipulation library for Python\n=====================================================================\n\n**pandas** is a Python package providing fast, flexible, and expressive data\nstructures designed to make working with "relational" or "labeled" data both\neasy and intuitive. It aims to be the fundamental high-level building block for\ndoing practical, **real world** data analysis in Python. Additionally, it has\nthe broader goal of becoming **the most powerful and flexible open source data\nanalysis / manipulation tool available in any language**. It is already well on\nits way toward this goal.\n\nMain Features\n-------------\nHere are just a few of the things that pandas does well:\n\n  - Easy handling of missing data in floating point as well as non-floating\n    point data.\n  - Size mutability: columns can be inserted and deleted from DataFrame and\n    higher dimensional objects\n  - Automatic and explicit data alignment: objects can be explicitly aligned\n    to a set of labels, or the user can simply ignore the labels and let\n    `Series`, `DataFrame`, etc. automatically align the data for you in\n    computations.\n  - Powerful, flexible group by functionality to perform split-apply-combine\n    operations on data sets, for both aggregating and transforming data.\n  - Make it easy to convert ragged, differently-indexed data in other Python\n    and NumPy data structures into DataFrame objects.\n  - Intelligent label-based slicing, fancy indexing, and subsetting of large\n    data sets.\n  - Intuitive merging and joining data sets.\n  - Flexible reshaping and pivoting of data sets.\n  - Hierarchical labeling of axes (possible to have multiple labels per tick).\n  - Robust IO tools for loading data from flat files (CSV and delimited),\n    Excel files, databases, and saving/loading data from the ultrafast HDF5\n    format.\n  - Time series-specific functionality: date range generation and frequency\n    conversion, moving window statistics, moving window linear regressions,\n    date shifting and lagging, etc.\n'
  },
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const Callable = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "callable",
  pyType: "Callable",
  value: {
    n: "amax",
    s: "<function amax at 0x000001F40FAEAEA0>"
  },
  date: new Date("2018-11-09T19:13:39.189Z"),
  output: true
};

export const Method = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "method",
  pyType: "Method",
  value: {
    n: "max",
    s:
      "<bound method DataFrame.max of           a         b         c\n0  0.319831 -0.059300  0.692662\n1  0.484981 -0.280187  2.064612\n2 -0.035997  0.552478 -0.826798\n3 -1.351537 -0.445281 -0.381492>"
  },
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const Code = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "Code",
  pyType: "Code",
  value: '<code object <module> at 0x000001F411B66420, file "<str>", line 1>',
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const Type = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "type",
  pyType: "Type",
  value: "<class 'int'>",
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const NoneType = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "None",
  pyType: "NoneType",
  value: "None",
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const Ellipsis = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "ellipsis",
  pyType: "Ellipsis",
  value: "...",
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const NotImplemented = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "NotImplemented",
  pyType: "NotImplemented",
  value: "NotImplemented",
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const Bool = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "bool",
  pyType: "Bool",
  value: true,
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const NpArray = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "numpy.ndarray",
  pyType: "NpArray",
  value: {
    values: [
      [0.6948521930581089, 0.08245255912669233, -2.4087481285008683],
      [0.5221359653590188, -1.3272575190130476, 0.9612348635606618],
      [0.899421369444219, 1.3748677169863266, 1.209352685283758],
      [-2.0905962313322592, 1.6097380516745405, -0.41523735012084784]
    ],
    shape: [4, 3],
    dtype: "float64"
  },
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const PandasSeries = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "pandas.Series",
  pyType: "PandasSeries",
  value: {
    d: [0.691473538282211, -1.1512948650158192, 2.7940517960441986],
    i: {
      v: ["0", "1", "2"],
      t: "RangeIndex"
    },
    c: "None",
    s: [3]
  },
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const PandasDataFrame = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "pandas.DataFrame",
  pyType: "PandasDataFrame",
  value: {
    d: [
      [
        0.3198306816710428,
        0.48498110717769044,
        -0.0359969367823809,
        -1.3515371869222568
      ],
      [
        -0.059299773886911884,
        -0.28018684722932885,
        0.5524780147547433,
        -0.4452809100699413
      ],
      [
        0.6926623265875399,
        2.0646117368092898,
        -0.8267979730680277,
        -0.38149179911950704
      ]
    ],
    i: {
      v: ["0", "1", "2", "3"],
      t: "RangeIndex"
    },
    c: {
      v: ["a", "b", "c"],
      t: ["f", "f", "f"]
    },
    s: [4, 3]
  },
  date: new Date("2018-11-09T19:13:39.204Z"),
  output: true
};

export const Figure = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "matplotlib.figure.Figure",
  pyType: "Figure",
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  date: new Date("2018-11-09T19:13:39.267Z"),
  output: true
};

export const Warning = {
  uid: "biZrI1J09",
  sessionId: "kUx3v2_W5",
  type: "<class 'UserWarning'>",
  pyType: "Warning",
  value: {
    type: "<class 'UserWarning'>",
    file: "None",
    lineno: 13,
    msg: "This is a warning",
    source: "None"
  },
  date: new Date("2018-11-09T19:13:39.267Z"),
  output: true
};

export const Exception = {
  uid: "PazD2U6AEl",
  sessionId: "kUx3v2_W5",
  type: "SyntaxError",
  pyType: "Exception",
  value: {
    type: "SyntaxError",
    tb: [
      "Traceback (most recent call last):\n",
      '  File "C:\\Temp\\yapijReVenv\\lib\\site-packages\\yapij\\context.py", line 40, in catch_output\n    yield\n',
      "  File \"C:\\Temp\\yapijReVenv\\lib\\site-packages\\yapij\\session.py\", line 126, in _done_callback\n    raise fut.result()['error']\n",
      '  File "C:\\Temp\\yapijReVenv\\lib\\site-packages\\yapij\\session.py", line 145, in exec_one_command\n    exec(code, {}, env)\n',
      '  File "<str>", line 1, in <module>\n',
      '  File "<string>", line None\n',
      "SyntaxError: This is an error!\n"
    ],
    args: ["This is an error!"],
    lineno: 40
  },
  date: new Date("2018-11-09T19:13:39.267Z"),
  output: true
};