Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc. --- now on pip: `pip install arrgh`

printarr

Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.

Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh

printarr.output.txt

       name | dtype         | shape         | type          | device | min         | max         | mean       
--------------------------------------------------------------------------------------------------------------
     [None] | None          | N/A           | NoneType      |        | N/A         | N/A         | N/A        
    intval1 | int           | scalar        | int           |        |      7      |      7      |      7     
    intval2 | int           | scalar        | int           |        |     -3      |     -3      |     -3     
  floatval0 | float         | scalar        | float         |        |     42      |     42      |     42     
  floatval1 | float         | scalar        | float         |        | 5.5e-12     | 5.5e-12     | 5.5e-12    
  floatval2 | float         | scalar        | float         |        | 7.72324e+44 | 7.72324e+44 | 7.72324e+44
     npval1 | int64         | [100]         | numpy.ndarray |        |      0      |     99      |   49.5     
     npval2 | int64         | [10000]       | numpy.ndarray |        |      0      |   9999      | 4999.5     
     npval3 | uint64        | [10000]       | numpy.ndarray |        |      0      |   9999      | 4999.5     
     npval4 | float32       | [100, 10, 10] | numpy.ndarray |        |      0      |   9999      | 4999.5     
[temporary] | float32       | [10, 8]       | numpy.ndarray |        |      2      |     99      |   50.5     
     npval5 | int64         | []            | numpy.int64   |        |   9999      |   9999      |   9999     
  torchval1 | torch.float32 | [1000, 12, 3] | torch.Tensor  | cpu    | -4.08445    | 3.90982     | 0.00404567 
  torchval2 | torch.float32 | [1000, 12, 3] | torch.Tensor  | cuda:0 | -3.87309    | 3.90342     | 0.00339224 
  torchval3 | torch.int64   | [1000]        | torch.Tensor  | cpu    |      0      |    999      | N/A        
  torchval4 | torch.int64   | []            | torch.Tensor  | cpu    |      0      |      0      | N/A    

printarr.py

def printarr(*arrs, float_width=6):
    """
    Print a pretty table giving name, shape, dtype, type, and content information for input tensors or scalars.

    Call like: printarr(my_arr, some_other_arr, maybe_a_scalar). Accepts a variable number of arguments.

    Inputs can be:
        - Numpy tensor arrays
        - Pytorch tensor arrays
        - Jax tensor arrays
        - Python ints / floats
        - None

    It may also work with other array-like types, but they have not been tested.

    Use the `float_width` option specify the precision to which floating point types are printed.

    Author: Nicholas Sharp (nmwsharp.com)
    Canonical source: https://gist.github.com/nmwsharp/54d04af87872a4988809f128e1a1d233
    License: This snippet may be used under an MIT license, and it is also released into the public domain. 
             Please retain this docstring as a reference.
    """
    
    frame = inspect.currentframe().f_back
    default_name = "[temporary]"

    ## helpers to gather data about each array
    def name_from_outer_scope(a):
        if a is None:
            return '[None]'
        name = default_name
        for k, v in frame.f_locals.items():
            if v is a:
                name = k
                break
        return name
    def dtype_str(a):
        if a is None:
            return 'None'
        if isinstance(a, int):
            return 'int'
        if isinstance(a, float):
            return 'float'
        return str(a.dtype)
    def shape_str(a):
        if a is None:
            return 'N/A'
        if isinstance(a, int):
            return 'scalar'
        if isinstance(a, float):
            return 'scalar'
        return str(list(a.shape))
    def type_str(a):
        return str(type(a))[8:-2] # TODO this is is weird... what's the better way?
    def device_str(a):
        if hasattr(a, 'device'):
            device_str = str(a.device)
            if len(device_str) < 10:
                # heuristic: jax returns some goofy long string we don't want, ignore it
                return device_str
        return ""
    def format_float(x):
        return f"{x:{float_width}g}"
    def minmaxmean_str(a):
        if a is None:
            return ('N/A', 'N/A', 'N/A')
        if isinstance(a, int) or isinstance(a, float): 
            return (format_float(a), format_float(a), format_float(a))

        # compute min/max/mean. if anything goes wrong, just print 'N/A'
        min_str = "N/A"
        try: min_str = format_float(a.min())
        except: pass
        max_str = "N/A"
        try: max_str = format_float(a.max())
        except: pass
        mean_str = "N/A"
        try: mean_str = format_float(a.mean())
        except: pass

        return (min_str, max_str, mean_str)

    try:

        props = ['name', 'dtype', 'shape', 'type', 'device', 'min', 'max', 'mean']

        # precompute all of the properties for each input
        str_props = []
        for a in arrs:
            minmaxmean = minmaxmean_str(a)
            str_props.append({
                'name' : name_from_outer_scope(a),
                'dtype' : dtype_str(a),
                'shape' : shape_str(a),
                'type' : type_str(a),
                'device' : device_str(a),
                'min' : minmaxmean[0],
                'max' : minmaxmean[1],
                'mean' : minmaxmean[2],
            })

        # for each property, compute its length
        maxlen = {}
        for p in props: maxlen[p] = 0
        for sp in str_props:
            for p in props:
                maxlen[p] = max(maxlen[p], len(sp[p]))

        # if any property got all empty strings, don't bother printing it, remove if from the list
        props = [p for p in props if maxlen[p] > 0]

        # print a header
        header_str = ""
        for p in props:
            prefix =  "" if p == 'name' else " | "
            fmt_key = ">" if p == 'name' else "<"
            header_str += f"{prefix}{p:{fmt_key}{maxlen[p]}}"
        print(header_str)
        print("-"*len(header_str))
            
        # now print the acual arrays
        for strp in str_props:
            for p in props:
                prefix =  "" if p == 'name' else " | "
                fmt_key = ">" if p == 'name' else "<"
                print(f"{prefix}{strp[p]:{fmt_key}{maxlen[p]}}", end='')
            print("")

    finally:
        del frame

printarr_test.py

if __name__ == "__main__":

    ## test it!

    # plain python vlaues
    noneval = None
    intval1 = 7
    intval2 = -3
    floatval0 = 42.0
    floatval1 = 5.5 * 1e-12
    floatval2 = 7.7232412351231231234 * 1e44

    # numpy values
    import numpy as np
    npval1 = np.arange(100)
    npval2 = np.arange(10000)
    npval3 = np.arange(10000).astype(np.uint64)
    npval4 = np.arange(10000).astype(np.float32).reshape(100,10,10)
    npval5 = np.arange(10000)[-1]

    # torch values 
    torchval1 = None
    torchval2 = None
    torchval3 = None
    torchval4 = None
    try:
        import torch
        torchval1 = torch.randn((1000,12,3))
        torchval2 = torch.randn((1000,12,3)).cuda()
        torchval3 = torch.arange(1000)
        torchval4 = torch.arange(1000)[0]
    except ModuleNotFoundError:
        pass
    
    # jax values 
    jaxval1 = None
    jaxval2 = None
    jaxval3 = None
    jaxval4 = None
    try:
        import jax
        import jax.numpy as jnp
        jaxval1 = jnp.linspace(0,1,10000)
        jaxval2 = jnp.linspace(0,1,10000).reshape(100,10,10)
        jaxval3 = jnp.arange(1000)
        jaxval4 = jnp.arange(1000)[0]
    except ModuleNotFoundError:
        pass
        
        
    printarr(noneval, 
             intval1, intval2, \
             floatval0, floatval1, floatval2, \
             npval1, npval2, npval3, npval4, npval4[0,:,2:], npval5, \
             torchval1, torchval2, torchval3, torchval4, \
             jaxval1, jaxval2, jaxval3, jaxval4, \
    )

Hi,

Thanks for this! I ran into an issue when all arrays were on CPU - in this case, the header for "device" was actually longer than the longest value in the rows, causing columns to misalign.

Adding

# Account for possibility that header is longer than any of the values
maxlen = {p: max(maxlen[p], len(p)) for p in props}

to line 111 fixed this, though there is probably a nicer way.

Thanks @afspies! I incorporated that fix into the version now on pip.

This gist is now a small package on pip! pip install arrgh https://github.com/nmwsharp/arrgh

Awesome! Thanks for your work on this :)