NumPy Integration

class ndarray:
    """
    Distributed array with numpy-compatible API.
    
    N-dimensional array object that supports all NumPy operations
    with automatic distribution across multiple workers for
    scalable array computing.
    """

# Boolean types
bool_: type          # Boolean type

# Integer types
int8: type           # 8-bit signed integer
int16: type          # 16-bit signed integer  
int32: type          # 32-bit signed integer
int64: type          # 64-bit signed integer
int_: type           # Default integer type
intc: type           # C integer type
intp: type           # Pointer-sized integer

uint8: type          # 8-bit unsigned integer
uint16: type         # 16-bit unsigned integer
uint32: type         # 32-bit unsigned integer
uint64: type         # 64-bit unsigned integer
uint: type           # Unsigned integer

# Floating point types
float16: type        # 16-bit floating point
float32: type        # 32-bit floating point
float64: type        # 64-bit floating point
double: type         # Double precision floating point
floating: type       # Floating point type

# Complex types
complex64: type      # 64-bit complex type
complex128: type     # 128-bit complex type
complexfloating: type # Complex floating point type

# String and character types
bytes_: type         # Bytes type
str_: type           # String type
character: type      # Character type

# Date and time types
datetime64: type     # 64-bit datetime type
timedelta64: type    # Time delta type

# Other types
object_: type        # Object type
void: type           # Void type
flexible: type       # Flexible type
generic: type        # Generic numpy type
inexact: type        # Inexact numeric type
number: type         # Number type
signedinteger: type  # Signed integer type
unsignedinteger: type # Unsigned integer type

# Type information classes
dtype: type          # Data type class
finfo: type          # Floating point type information

pi: float            # Pi constant (3.14159...)
e: float             # Euler's number (2.71828...)
inf: float           # Positive infinity
nan: float           # Not a Number
newaxis: object      # New axis constant for array indexing

class errstate:
    """Error state context manager for controlling floating-point error handling."""

class ndindex:
    """Multi-dimensional index iterator for arrays."""

# Exceptions
class AxisError(Exception):
    """Exception raised for axis-related errors."""

# Submodules providing specialized functionality
fft      # Fast Fourier Transform functions
linalg   # Linear algebra functions  
random   # Random number generation
special  # Special mathematical functions

# Array creation functions
def array(object, dtype=None, **kwargs): ...
def zeros(shape, dtype=float, **kwargs): ...
def ones(shape, dtype=None, **kwargs): ...
def empty(shape, dtype=float, **kwargs): ...
def full(shape, fill_value, dtype=None, **kwargs): ...
def arange(start, stop=None, step=1, dtype=None, **kwargs): ...
def linspace(start, stop, num=50, **kwargs): ...
def logspace(start, stop, num=50, **kwargs): ...
def eye(N, M=None, k=0, dtype=float, **kwargs): ...
def identity(n, dtype=None, **kwargs): ...

# Array manipulation functions
def reshape(a, newshape, **kwargs): ...
def flatten(a, **kwargs): ...
def ravel(a, **kwargs): ...
def transpose(a, axes=None): ...
def concatenate(arrays, axis=0, **kwargs): ...
def stack(arrays, axis=0, **kwargs): ...
def split(ary, indices_or_sections, axis=0): ...
def squeeze(a, axis=None): ...
def expand_dims(a, axis): ...

# Mathematical functions
def add(x1, x2, **kwargs): ...
def subtract(x1, x2, **kwargs): ...
def multiply(x1, x2, **kwargs): ...
def divide(x1, x2, **kwargs): ...
def power(x1, x2, **kwargs): ...
def sqrt(x, **kwargs): ...
def exp(x, **kwargs): ...
def log(x, **kwargs): ...
def sin(x, **kwargs): ...
def cos(x, **kwargs): ...
def tan(x, **kwargs): ...

# Statistical functions
def mean(a, axis=None, **kwargs): ...
def median(a, axis=None, **kwargs): ...
def std(a, axis=None, **kwargs): ...
def var(a, axis=None, **kwargs): ...
def sum(a, axis=None, **kwargs): ...
def min(a, axis=None, **kwargs): ...
def max(a, axis=None, **kwargs): ...
def argmin(a, axis=None, **kwargs): ...
def argmax(a, axis=None, **kwargs): ...

# Logical functions
def all(a, axis=None, **kwargs): ...
def any(a, axis=None, **kwargs): ...
def logical_and(x1, x2, **kwargs): ...
def logical_or(x1, x2, **kwargs): ...
def logical_not(x, **kwargs): ...
def where(condition, x=None, y=None): ...

# Sorting and searching
def sort(a, axis=-1, **kwargs): ...
def argsort(a, axis=-1, **kwargs): ...
def searchsorted(a, v, **kwargs): ...
def unique(ar, **kwargs): ...

# Input/output functions
def save(file, arr, **kwargs): ...
def load(file, **kwargs): ...
def savetxt(fname, X, **kwargs): ...
def loadtxt(fname, **kwargs): ...

import xorbits
import xorbits.numpy as np

xorbits.init()

# Array creation (same as NumPy)
arr = np.array([1, 2, 3, 4, 5])
zeros_arr = np.zeros((1000, 1000))
ones_arr = np.ones((500, 500))
random_arr = np.random.random((1000, 1000))

# Mathematical operations (same as NumPy)
result = np.sqrt(arr + 10)
matrix_mult = np.dot(zeros_arr, ones_arr)

# Statistical operations
mean_val = np.mean(random_arr)
std_val = np.std(random_arr, axis=0)

# Array manipulation
reshaped = np.reshape(arr, (5, 1))
transposed = np.transpose(matrix_mult)

# All NumPy operations work the same way
complex_result = np.fft.fft(np.sin(np.linspace(0, 2*np.pi, 1000)))

# Execute computation
computed = xorbits.run(complex_result)

xorbits.shutdown()

import xorbits.numpy as np
import xorbits

xorbits.init()

# Matrix operations
A = np.random.random((1000, 1000))
B = np.random.random((1000, 1000))

# Linear algebra operations
dot_product = np.dot(A, B)
eigenvals = np.linalg.eigvals(A)
determinant = np.linalg.det(A)
inverse = np.linalg.inv(A)

# Execute computations
results = xorbits.run(dot_product, eigenvals, determinant)

xorbits.shutdown()

import xorbits.numpy as np
import xorbits

xorbits.init()

# Signal processing with FFT
signal = np.sin(2 * np.pi * np.linspace(0, 1, 10000))
fft_result = np.fft.fft(signal)
frequencies = np.fft.fftfreq(len(signal))

# Execute computation
computed_fft = xorbits.run(fft_result)

xorbits.shutdown()