import json
import logging
import os
import numpy as np
import xarray as xr
from dask import array as da
try:
from julia.api import Julia
julia_installed = True
except ImportError:
julia_installed = False
from tqdm.auto import tqdm
from . import __version__
from . import _py_bitinfo as pb
from .julia_helpers import install
already_ran = False
if not already_ran and julia_installed:
already_ran = install(quiet=True)
jl = Julia(compiled_modules=False, debug=False)
from julia import Main # noqa: E402
path_to_julia_functions = os.path.join(
os.path.dirname(__file__), "bitinformation_wrapper.jl"
)
Main.path = path_to_julia_functions
jl.using("BitInformation")
jl.using("Pkg")
jl.eval("include(Main.path)")
def bit_partitioning(dtype):
if dtype.kind == "f":
n_bits = np.finfo(dtype).bits
n_sign = 1
n_exponent = np.finfo(dtype).nexp
n_mantissa = np.finfo(dtype).nmant
elif dtype.kind == "i":
n_bits = np.iinfo(dtype).bits
n_sign = 1
n_exponent = 0
n_mantissa = n_bits - n_sign
elif dtype.kind == "u":
n_bits = np.iinfo(dtype).bits
n_sign = 0
n_exponent = 0
n_mantissa = n_bits - n_sign
else:
raise ValueError(f"dtype {dtype} neither known nor implemented.")
assert (
n_sign + n_exponent + n_mantissa == n_bits
), "The components of the datatype could not be safely inferred."
return n_bits, n_sign, n_exponent, n_mantissa
def get_bit_coords(dtype):
"""Get coordinates for bits based on dtype."""
n_bits, n_sign, n_exponent, n_mantissa = bit_partitioning(dtype)
coords = (
n_sign * ["±"]
+ [f"e{int(i)}" for i in range(1, n_exponent + 1)]
+ [f"m{int(i)}" for i in range(1, n_mantissa + 1)]
)
return coords
def dict_to_dataset(info_per_bit):
"""Convert keepbits dictionary to :py:class:`xarray.Dataset`."""
dsb = xr.Dataset()
for v in info_per_bit.keys():
dtype = np.dtype(info_per_bit[v]["dtype"])
dim = info_per_bit[v]["dim"]
dim_name = f"bit{dtype}"
dsb[v] = xr.DataArray(
info_per_bit[v]["bitinfo"],
dims=[dim_name],
coords={dim_name: get_bit_coords(dtype), "dim": dim},
name=v,
attrs={
"long_name": f"{v} bitwise information",
"units": 1,
},
).astype("float64")
# add metadata
dsb.attrs = {
"xbitinfo_description": "bitinformation calculated by xbitinfo.get_bitinformation wrapping bitinformation.jl",
"python_repository": "https://github.com/observingClouds/xbitinfo",
"julia_repository": "https://github.com/milankl/BitInformation.jl",
"reference_paper": "http://www.nature.com/articles/s43588-021-00156-2",
"xbitinfo_version": __version__,
"BitInformation.jl_version": get_julia_package_version("BitInformation"),
}
for c in dsb.coords:
if "bit" in c:
dsb.coords[c].attrs = {
"description": "name of the bits: '±' refers to the sign bit, 'e' to the exponents bits and 'm' to the mantissa bits."
}
dsb.coords["dim"].attrs = {
"description": "dimension of the source dataset along which the bitwise information has been analysed."
}
return dsb
def _jl_get_bitinformation(ds, var, axis, dim, kwargs={}):
X = ds[var].values
Main.X = X
if axis is not None:
# in julia convention axis + 1
axis_jl = axis + 1
dim = ds[var].dims[axis]
if isinstance(dim, str):
try:
# in julia convention axis + 1
axis_jl = ds[var].get_axis_num(dim) + 1
except ValueError:
logging.info(f"Variable {var} does not have dimension {dim}. Skipping.")
return
assert isinstance(axis_jl, int)
Main.dim = axis_jl
kwargs_str = _get_bitinformation_kwargs_handler(ds[var], kwargs)
logging.debug(f"get_bitinformation(X, dim={dim}, {kwargs_str})")
info_per_bit = {}
info_per_bit["bitinfo"] = jl.eval(
f"get_bitinformation(X, dim={axis_jl}, {kwargs_str})"
)
info_per_bit["dim"] = dim
info_per_bit["axis"] = axis_jl - 1
info_per_bit["dtype"] = str(ds[var].dtype)
return info_per_bit
def _py_get_bitinformation(ds, var, axis, dim, kwargs={}):
if "set_zero_insignificant" in kwargs.keys():
if kwargs["set_zero_insignificant"]:
raise NotImplementedError(
"set_zero_insignificant is not implemented in the python implementation"
)
else:
assert (
kwargs == {}
), "This implementation only supports the plain bitinfo implementation"
itemsize = ds[var].dtype.itemsize
astype = f"u{itemsize}"
X = da.array(ds[var])
# signed exponent conversion only for floats
if X.dtype in (np.float16, np.float32, np.float64):
X = pb.signed_exponent(X)
X = X.astype(astype)
if axis is not None:
dim = ds[var].dims[axis]
if isinstance(dim, str):
try:
axis = ds[var].get_axis_num(dim)
except ValueError:
logging.info(f"Variable {var} does not have dimension {dim}. Skipping.")
return
info_per_bit = {}
logging.info("Calling python implementation now")
info_per_bit["bitinfo"] = pb.bitinformation(X, axis=axis).compute()
info_per_bit["dim"] = dim
info_per_bit["axis"] = axis
info_per_bit["dtype"] = str(ds[var].dtype)
return info_per_bit
def _get_bitinformation_along_dims(
ds,
dim=None,
label=None,
overwrite=False,
implementation="julia",
**kwargs,
):
"""Helper function for :py:func:`xbitinfo.xbitinfo.get_bitinformation` to handle multi-dimensional analysis for each dim specified.
Simple wrapper around :py:func:`xbitinfo.xbitinfo.get_bitinformation`, which calls :py:func:`xbitinfo.xbitinfo.get_bitinformation`
for each dimension found in the provided :py:func:`xarray.Dataset`. The retrieved bitinformation
is gathered in a joint :py:func:`xarray.Dataset` and is returned.
"""
info_per_bit_per_dim = {}
if dim is None:
dim = ds.dims
for d in dim:
logging.info(f"Get bitinformation along dimension {d}")
if label is not None:
label = "_".join([label, d])
info_per_bit_per_dim[d] = get_bitinformation(
ds,
dim=d,
axis=None,
label=label,
overwrite=overwrite,
implementation=implementation,
**kwargs,
).expand_dims("dim", axis=0)
info_per_bit = xr.merge(info_per_bit_per_dim.values()).squeeze()
return info_per_bit
def _get_bitinformation_kwargs_handler(da, kwargs):
"""Helper function to preprocess kwargs args of :py:func:`xbitinfo.xbitinfo.get_bitinformation`."""
kwargs_var = kwargs.copy()
if "masked_value" not in kwargs_var:
kwargs_var["masked_value"] = f"convert({str(da.dtype).capitalize()},NaN)"
elif kwargs_var["masked_value"] is None:
kwargs_var["masked_value"] = "nothing"
if "set_zero_insignificant" not in kwargs_var:
kwargs_var["set_zero_insignificant"] = True
kwargs_str = ", ".join([f"{k}={v}" for k, v in kwargs_var.items()])
# convert python to julia bool
kwargs_str = kwargs_str.replace("True", "true").replace("False", "false")
return kwargs_str
def load_bitinformation(label):
"""Load bitinformation from JSON file"""
label_file = label + ".json"
if os.path.exists(label_file):
with open(label_file) as f:
logging.debug(f"Load bitinformation from {label+'.json'}")
info_per_bit = json.load(f)
return dict_to_dataset(info_per_bit)
else:
raise FileNotFoundError(f"No bitinformation could be found at {label+'.json'}")
def get_cdf_without_artificial_information(
info_per_bit, bitdim, threshold, tolerance, bit_vars
):
"""
Calculate a Cumulative Distribution Function (CDF) with artificial information removal.
This function calculates a modified CDF for a given set of bit information and variable dimensions,
removing artificial information while preserving the desired threshold of information content.
1.)The function's aim is to return the cdf in a way that artificial information gets removed.
2.)This function calculates the CDF using the provided information content per bit dataset.
3.)It then computes the gradient of the CDF values to identify points where the gradient becomes close to the given tolerance,
indicating a drop in information.
4.)Simultaneously, it keeps track of the minimum cumulative sum of information content which is threshold here, which signifies atleast
this much fraction of total information needs to be passed.
5.)So the bit where the intersection of the gradient reaching the tolerance and the cumulative sum exceeding the threshold. All bits beyond this
index are assumed to contain artificial information and are set to zero in the resulting CDF.
Parameters:
-----------
info_per_bit : :py:class: 'xarray.Dataset'
Information content of each bit. This is the output from :py:func:`xbitinfo.xbitinfo.get_bitinformation`.
bitdim : str
The dimension representing the bit information.
threshold : float
Minimum cumulative sum of information content before artificial information filter is applied.
tolerance : float
The tolerance is the value below which gradient starts becoming constant
bit_vars : list
List of variable names of the dataset.
Returns:
--------
xarray.Dataset
A modified CDF dataset with artificial information removed.
Example:
--------
>>> ds = xr.tutorial.load_dataset("air_temperature")
>>> info = xb.get_bitinformation(ds)
>>> get_keepbits(
... info,
... inflevel=[0.99],
... information_filter="Gradient",
... **{"threshold": 0.7, "tolerance": 0.001}
... )
<xarray.Dataset> Size: 80B
Dimensions: (dim: 3, inflevel: 1)
Coordinates:
* dim (dim) <U4 48B 'lat' 'lon' 'time'
* inflevel (inflevel) float64 8B 0.99
Data variables:
air (dim, inflevel) int64 24B 5 6 6
"""
# Extract coordinates from the 'info_per_bit' dataset.
coordinates = info_per_bit.coords
# Extract the 'dim' values from the coordinates and store them in 'coordinates_array'.
coordinates_array = coordinates["dim"].values
# Initialize a flag to identify if 'coordinates_array' is a scalar value.
flag_scalar_value = False
# Check if 'coordinates_array' is a scalar (has zero dimensions).
if coordinates_array.ndim == 0:
# If it's a scalar, extract the scalar value and set the flag to True.
value = coordinates_array.item()
flag_scalar_value = True
# Convert the scalar value into a 1D numpy array so that we can iterate over it for determining dimensions.
coordinates_array = np.array([value])
cdf = _cdf_from_info_per_bit(info_per_bit, bitdim)
for var_name in bit_vars:
for dimension in coordinates_array:
if flag_scalar_value:
# If it's a scalar, extract the information array directly.
infoArray = info_per_bit[var_name]
else:
# If it's not a scalar, select the information array using the specified dimension.
infoArray = info_per_bit[var_name].sel(dim=dimension)
# total sum of information along a dimension
infSum = sum(infoArray).item()
data_type = np.dtype(bitdim.replace("bit", ""))
_, n_sign, n_exponent, _ = bit_partitioning(data_type)
sign_and_exponent = n_sign + n_exponent
# sum of sign and exponent bits
SignExpSum = sum(infoArray[:sign_and_exponent]).item()
if flag_scalar_value:
cdf_array = cdf[var_name]
else:
cdf_array = cdf[var_name].sel(dim=dimension)
gradient_array = np.diff(cdf_array.values)
# Initialize 'CurrentBit_Sum' with the value of 'SignExpSum'.
CurrentBit_Sum = SignExpSum
for i in range(sign_and_exponent, len(gradient_array) - 1):
# Update 'CurrentBit_Sum' by adding the information content of the current bit.
CurrentBit_Sum = CurrentBit_Sum + infoArray[i].item()
if (
gradient_array[i]
) < tolerance and CurrentBit_Sum >= threshold * infSum:
infbits = i
break
for i in range(0, infbits + 1):
# Normalize CDF values for elements up to 'infbits'.
cdf_array[i] = cdf_array[i] / cdf_array[infbits]
cdf_array[(infbits + 1) :] = 1
return cdf
[docs]
def get_keepbits(info_per_bit, inflevel=0.99, information_filter=None, **kwargs):
"""Get the number of mantissa bits to keep. To be used in :py:func:`xbitinfo.bitround.xr_bitround` and :py:func:`xbitinfo.bitround.jl_bitround`.
Parameters
----------
info_per_bit : :py:class:`xarray.Dataset`
Information content of each bit. This is the output from :py:func:`xbitinfo.xbitinfo.get_bitinformation`.
inflevel : float or list
Level of information that shall be preserved.
Kwargs
threshold(` `float ``) : defaults to ``0.7``
Minimum cumulative sum of information content before artificial information filter is applied.
tolerance(` `float ``) : defaults to ``0.001``
The tolerance is the value below which gradient starts becoming constant
Returns
-------
keepbits : dict
Number of mantissa bits to keep per variable
Example
-------
>>> ds = xr.tutorial.load_dataset("air_temperature")
>>> info_per_bit = xb.get_bitinformation(ds, dim="lon")
>>> xb.get_keepbits(info_per_bit)
<xarray.Dataset> Size: 28B
Dimensions: (inflevel: 1)
Coordinates:
dim <U3 12B 'lon'
* inflevel (inflevel) float64 8B 0.99
Data variables:
air (inflevel) int64 8B 6
>>> xb.get_keepbits(info_per_bit, inflevel=0.99999999)
<xarray.Dataset> Size: 28B
Dimensions: (inflevel: 1)
Coordinates:
dim <U3 12B 'lon'
* inflevel (inflevel) float64 8B 1.0
Data variables:
air (inflevel) int64 8B 7
>>> xb.get_keepbits(info_per_bit, inflevel=1.0)
<xarray.Dataset> Size: 28B
Dimensions: (inflevel: 1)
Coordinates:
dim <U3 12B 'lon'
* inflevel (inflevel) float64 8B 1.0
Data variables:
air (inflevel) int64 8B 52
>>> info_per_bit = xb.get_bitinformation(ds)
>>> xb.get_keepbits(info_per_bit)
<xarray.Dataset> Size: 80B
Dimensions: (dim: 3, inflevel: 1)
Coordinates:
* dim (dim) <U4 48B 'lat' 'lon' 'time'
* inflevel (inflevel) float64 8B 0.99
Data variables:
air (dim, inflevel) int64 24B 5 6 6
"""
if not isinstance(inflevel, list):
inflevel = [inflevel]
keepmantissabits = []
inflevel = xr.DataArray(inflevel, dims="inflevel", coords={"inflevel": inflevel})
if (inflevel < 0).any() or (inflevel > 1.0).any():
raise ValueError("Please provide `inflevel` from interval [0.,1.]")
for bitdim in [
"bitfloat16",
"bitfloat32",
"bitfloat64",
"bitint16",
"bitint32",
"bitint64",
"bituint16",
"bituint32",
"bituint64",
]:
# get only variables of bitdim
bit_vars = [v for v in info_per_bit.data_vars if bitdim in info_per_bit[v].dims]
if bit_vars != []:
if information_filter == "Gradient":
cdf = get_cdf_without_artificial_information(
info_per_bit[bit_vars],
bitdim,
kwargs["threshold"],
kwargs["tolerance"],
bit_vars,
)
else:
cdf = _cdf_from_info_per_bit(info_per_bit[bit_vars], bitdim)
data_type = np.dtype(bitdim.replace("bit", ""))
n_bits, _, _, n_mant = bit_partitioning(data_type)
bitdim_non_mantissa_bits = n_bits - n_mant
keepmantissabits_bitdim = (
(cdf > inflevel).argmax(bitdim) + 1 - bitdim_non_mantissa_bits
)
# keep all mantissa bits for 100% information
if 1.0 in inflevel:
bitdim_all_mantissa_bits = n_bits - bitdim_non_mantissa_bits
keepall = xr.ones_like(keepmantissabits_bitdim.sel(inflevel=1.0)) * (
bitdim_all_mantissa_bits
)
keepmantissabits_bitdim = xr.concat(
[keepmantissabits_bitdim.drop_sel(inflevel=1.0), keepall],
"inflevel",
)
keepmantissabits.append(keepmantissabits_bitdim)
keepmantissabits = xr.merge(keepmantissabits)
if inflevel.inflevel.size > 1: # restore original ordering
keepmantissabits = keepmantissabits.sel(inflevel=inflevel.inflevel)
return keepmantissabits
def _cdf_from_info_per_bit(info_per_bit, bitdim):
"""Convert info_per_bit to cumulative distribution function on dimension bitdim."""
# set below rounding error from last digit to zero
info_per_bit_cleaned = info_per_bit.where(
info_per_bit > info_per_bit.isel({bitdim: slice(-4, None)}).max(bitdim) * 1.5
)
# make cumulative distribution function
cdf = info_per_bit_cleaned.cumsum(bitdim) / info_per_bit_cleaned.cumsum(
bitdim
).isel({bitdim: -1})
return cdf
def _jl_bitround(X, keepbits):
"""Wrap `BitInformation.jl.round <https://github.com/milankl/BitInformation.jl/blob/main/src/round_nearest.jl>`__. Used in :py:func:`xbitinfo.bitround.jl_bitround`."""
if not julia_installed:
raise ImportError("Please install julia or use xr_bitround")
Main.X = X
Main.keepbits = keepbits
return jl.eval("round!(X, keepbits)")
[docs]
def get_prefect_flow(paths=[]):
"""
Create `prefect.Flow <https://docs.prefect.io/core/concepts/flows.html#overview>`__ for paths to be:
1. Analyse bitwise real information content with :py:func:`xbitinfo.xbitinfo.get_bitinformation`
2. Retrieve keepbits with :py:func:`xbitinfo.xbitinfo.get_keepbits`
3. Apply bitrounding with :py:func:`xbitinfo.bitround.xr_bitround`
4. Save as compressed netcdf with :py:class:`xbitinfo.save_compressed.ToCompressed_Netcdf`
Many parameters can be changed when running the flow ``flow.run(parameters=dict(chunk="auto"))``:
- paths: list of paths
Paths to be bitrounded
- analyse_paths: str or int
Which paths to be passed to :py:func:`xbitinfo.xbitinfo.get_bitinformation`. Choose from ``["first_last", "all", int]``, where int is interpreted as stride, i.e. paths[::stride]. Defaults to ``"first"``.
- enforce_dtype : str or None
Enforce dtype for all variables. Currently, :py:func:`xbitinfo.xbitinfo.get_bitinformation` fails for different dtypes in variables. Do nothing if ``None``. Defaults to ``None``.
- label : see :py:func:`xbitinfo.xbitinfo.get_bitinformation`
- dim/axis : see :py:func:`xbitinfo.xbitinfo.get_bitinformation`
- inflevel : see :py:func:`xbitinfo.xbitinfo.get_keepbits`
- non_negative_keepbits : bool
Set negative keepbits from :py:func:`xbitinfo.xbitinfo.get_keepbits` to ``0``. Required when using :py:func:`xbitinfo.bitround.xr_bitround`. Defaults to True.
- chunks : see :py:meth:`xarray.open_mfdataset`. Note that with ``chunks=None``, ``dask`` is not used for I/O and the flow is still parallelized when using ``DaskExecutor``.
- bitround_in_julia : bool
Use :py:func:`xbitinfo.bitround.jl_bitround` instead of :py:func:`xbitinfo.bitround.xr_bitround`. Both should yield identical results. Defaults to ``False``.
- overwrite : bool
Whether to overwrite bitrounded netcdf files. ``False`` (default) skips existing files.
- complevel : see to_compressed_netcdf, defaults to ``7``.
- rename : list
Replace mapping for paths towards new_path of bitrounded file, i.e. ``replace=[".nc", "_bitrounded_compressed.nc"]``
Parameters
------
paths : list
List of paths of files to be processed by :py:func:`xbitinfo.xbitinfo.get_bitinformation`, :py:func:`xbitinfo.xbitinfo.get_keepbits`, :py:func:`xbitinfo.bitround.xr_bitround` and ``to_compressed_netcdf``.
Returns
-------
prefect.Flow
See https://docs.prefect.io/core/concepts/flows.html#overview
Example
-------
Imagine n files of identical structure, i.e. 1-year per file climate model output:
>>> ds = xr.tutorial.load_dataset("rasm")
>>> year, datasets = zip(*ds.groupby("time.year"))
>>> paths = [f"{y}.nc" for y in year]
>>> xr.save_mfdataset(datasets, paths)
Create prefect.Flow and run sequentially
>>> flow = xb.get_prefect_flow(paths=paths)
>>> import prefect
>>> logger = prefect.context.get("logger")
>>> logger.setLevel("ERROR")
>>> st = flow.run()
Inspect flow state
>>> # flow.visualize(st) # requires graphviz
Run in parallel with dask:
>>> import os # https://docs.xarray.dev/en/stable/user-guide/dask.html
>>> os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
>>> from prefect.executors import DaskExecutor, LocalDaskExecutor
>>> from dask.distributed import Client
>>> client = Client(n_workers=2, threads_per_worker=1, processes=True)
>>> executor = DaskExecutor(
... address=client.scheduler.address
... ) # take your own client
>>> executor = DaskExecutor() # use dask from prefect
>>> executor = LocalDaskExecutor() # use dask local from prefect
>>> # flow.run(executor=executor, parameters=dict(overwrite=True))
Modify parameters of a flow:
>>> flow.run(parameters=dict(inflevel=0.9999, overwrite=True))
<Success: "All reference tasks succeeded.">
See also
--------
- https://examples.dask.org/applications/prefect-etl.html
- https://docs.prefect.io/core/getting_started/basic-core-flow.html
"""
from prefect import Flow, Parameter, task, unmapped
from prefect.engine.signals import SKIP
from .bitround import jl_bitround, xr_bitround
@task
def get_bitinformation_keepbits(
paths,
analyse_paths="first",
label=None,
inflevel=0.99,
enforce_dtype=None,
non_negative_keepbits=True,
**get_bitinformation_kwargs,
):
# take subset only for analysis in bitinformation
if analyse_paths == "first_last":
p = [paths[0], paths[-1]]
elif analyse_paths == "all":
p = paths
elif analyse_paths == "first":
p = paths[0]
elif isinstance(analyse_paths, int): # interpret as stride
p = paths[::analyse_paths]
else:
raise ValueError(
"Please provide analyse_paths as int interpreted as stride or from ['first_last','all','first','last']."
)
ds = xr.open_mfdataset(p)
if enforce_dtype:
ds = ds.astype(enforce_dtype)
info_per_bit = get_bitinformation(ds, label=label, **get_bitinformation_kwargs)
keepbits = get_keepbits(info_per_bit, inflevel=inflevel)
if non_negative_keepbits:
keepbits = {v: max(0, k) for v, k in keepbits.items()} # ensure no negative
return keepbits
@task
def bitround_and_save(
path,
keepbits,
chunks=None,
complevel=4,
rename=[".nc", "_bitrounded_compressed.nc"],
overwrite=False,
enforce_dtype=None,
bitround_in_julia=False,
):
new_path = path.replace(rename[0], rename[1])
if not overwrite:
if os.path.exists(new_path):
try:
ds_new = xr.open_dataset(new_path, chunks=chunks)
ds = xr.open_dataset(path, chunks=chunks)
if (
ds.nbytes == ds_new.nbytes
): # bitrounded and original have same number of bytes in memory
raise SKIP(f"{new_path} already exists.")
except Exception as e:
print(
f"{type(e)} when xr.open_dataset({new_path}), therefore delete and recalculate."
)
os.remove(new_path)
ds = xr.open_dataset(path, chunks=chunks)
if enforce_dtype:
ds = ds.astype(enforce_dtype)
bitround_func = jl_bitround if bitround_in_julia else xr_bitround
ds_bitround = bitround_func(ds, keepbits)
ds_bitround.to_compressed_netcdf(new_path, complevel=complevel)
return
with Flow("xbitinfo_pipeline") as flow:
if paths == []:
raise ValueError("Please provide paths of files to bitround, found [].")
paths = Parameter("paths", default=paths)
analyse_paths = Parameter("analyse_paths", default="first")
dim = Parameter("dim", default=None)
axis = Parameter("axis", default=0)
inflevel = Parameter("inflevel", default=0.99)
label = Parameter("label", default=None)
rename = Parameter("rename", default=[".nc", "_bitrounded_compressed.nc"])
overwrite = Parameter("overwrite", default=False)
bitround_in_julia = Parameter("bitround_in_julia", default=False)
complevel = Parameter("complevel", default=7)
chunks = Parameter("chunks", default=None)
enforce_dtype = Parameter("enforce_dtype", default=None)
non_negative_keepbits = Parameter("non_negative_keepbits", default=True)
keepbits = get_bitinformation_keepbits(
paths,
analyse_paths=analyse_paths,
dim=dim,
axis=axis,
inflevel=inflevel,
label=label,
enforce_dtype=enforce_dtype,
non_negative_keepbits=non_negative_keepbits,
) # once
bitround_and_save.map(
paths,
keepbits=unmapped(keepbits),
rename=unmapped(rename),
chunks=unmapped(chunks),
complevel=unmapped(complevel),
overwrite=unmapped(overwrite),
enforce_dtype=unmapped(enforce_dtype),
bitround_in_julia=unmapped(bitround_in_julia),
) # parallel map
return flow
class JsonCustomEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, (np.ndarray, np.number)):
return obj.tolist()
elif isinstance(obj, (complex, np.complex)):
return [obj.real, obj.imag]
elif isinstance(obj, set):
return list(obj)
elif isinstance(obj, bytes): # pragma: py3
return obj.decode()
return json.JSONEncoder.default(self, obj)
def get_julia_package_version(package):
"""Get version information of julia package"""
if julia_installed:
version = jl.eval(
f'Pkg.TOML.parsefile(joinpath(pkgdir({package}), "Project.toml"))["version"]'
)
else:
version = "implementation='python'"
return version
