{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integer and Float Primitives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing fixed length primitive types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "from byteclasses.print import byteclass_info, byteclass_inspect\n",
    "from byteclasses.types.primitives.floats import Double, Float, Float16, Float32, Float64, Half\n",
    "from byteclasses.types.primitives.integers import (\n",
    "    Int,\n",
    "    Int8,\n",
    "    Int16,\n",
    "    Int32,\n",
    "    Int64,\n",
    "    Long,\n",
    "    LongLong,\n",
    "    Ptr16,\n",
    "    Ptr32,\n",
    "    Ptr64,\n",
    "    Short,\n",
    "    UInt,\n",
    "    UInt8,\n",
    "    UInt16,\n",
    "    UInt32,\n",
    "    UInt64,\n",
    "    ULong,\n",
    "    ULongLong,\n",
    "    UnderflowError,\n",
    "    UShort,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixed Size Primitive Instantiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_integer_types = [Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64, Long, ULong]\n",
    "fixed_integer_type_aliases = [Ptr16, Ptr32, Ptr64, Short, UShort, Int, UInt, LongLong, ULongLong]\n",
    "fixed_float_types = [Float16, Float32, Float64]\n",
    "fixed_float_type_aliases = [Half, Float, Double]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, type_cls in enumerate(\n",
    "    chain(fixed_integer_types, fixed_integer_type_aliases, fixed_float_types, fixed_float_type_aliases)\n",
    "):\n",
    "    var = type_cls(i)\n",
    "    byteclass_info(var)\n",
    "    byteclass_inspect(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numeric byteclasses can be used in math operations just like normal numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var1 = UInt8(1)\n",
    "var2 = UInt8(2)\n",
    "print(var1, var2, var1 + var2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var1 = UInt8(1)\n",
    "var2 = 2\n",
    "print(var1, var2, var1 + var2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var1 = 1\n",
    "var2 = UInt8(2)\n",
    "print(var1, var2, var1 + var2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each primitive class has built-in bounds checks and will raise an `OverflowError` or `UnderflowError` as appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    _ = Int8(128)\n",
    "except OverflowError as err:\n",
    "    print(err)\n",
    "\n",
    "try:\n",
    "    _ = UInt8(-1)\n",
    "except UnderflowError as err:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Override Overflow Protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "overflow_var = Int8(128, allow_overflow=True)\n",
    "byteclass_inspect(overflow_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "underflow_var = UInt8(-1, allow_overflow=True)\n",
    "byteclass_inspect(underflow_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attaching to external data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data = bytearray(b\"\\x00\\x01\\x02\\x03\")\n",
    "mv = memoryview(my_data)\n",
    "my_var1 = Int32()\n",
    "my_var2 = UInt32()\n",
    "print(my_data, my_var1, my_var2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any byteclass instance can be attached to a memoryview of equal size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_var1.attach(mv)\n",
    "my_var2.attach(mv)\n",
    "print(my_data, my_var1, my_var2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changes to data are also represented in any attached byteclass instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mv[:] = b\"\\x04\\x05\\x06\\x07\"\n",
    "print(my_data, my_var1, my_var2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changes to a primitive's value or data attribute are also applied to the attached data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_var1.value = my_var1.max\n",
    "print(my_data, my_var1, my_var2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changes to a primitive's value or data attribute are also applied to the attached data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_var2.value = my_var2.max\n",
    "print(my_data, my_var1, my_var2)"
   ]
  }
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