pycdfpp package

pycdfpp

Indices and tables

class pycdfpp.Attribute

Bases: pybind11_object

property name

set_values(entries_values: List[ndarray], entries_types: List[DataType] = None)

Sets the values of the attribute with the given values. If entries_types is not None, the attribute is created with the given data type. Otherwise, the data type is inferred from the values.

Parameters:

entries_valuesList[np.ndarray or List[float or int or datetime] or str]: The values entries to set for the attribute. When a list is passed, the values are converted to a numpy.ndarray with the appropriate data type, with integers, it will choose the smallest data type that can hold all the values.
entries_typesList[DataType] or None, optional: The data type for each entry of the attribute. If None, the data type is inferred from the values. (Default is None)

type(self: pycdfpp._pycdfpp.Attribute, arg0: int) → pycdfpp._pycdfpp.DataType

class pycdfpp.CDF

Bases: pybind11_object

A CDF file object.

Attributes:

attributes: dict: file attributes
variables: dict: file variables
majority: cdf_majority: file majority
distribution_version: int: file distribution version
lazy_loaded: bool: file lazy loading state
compression: CompressionType: file compression type

Methods

`add_attribute`(name, entries_values[, ...])	Adds a new attribute with the given values to the CDF object.
`add_variable`(name[, values, data_type, ...])	Adds a new variable to the CDF.

add_attribute(name: str, entries_values: List[ndarray], entries_types: List[DataType] = None)

Adds a new attribute with the given values to the CDF object. If entries_types is not None, the attribute is created with the given data type. Otherwise, the data type is inferred from the values.

Parameters:

namestr: The name of the attribute to add.
entries_valuesList[np.ndarray or List[float or int or datetime] or str]: The values entries to set for the attribute. When a list is passed, the values are converted to a numpy.ndarray with the appropriate data type, with integers, it will choose the smallest data type that can hold all the values.
entries_typesList[DataType] or None, optional: The data type for each entry of the attribute. If None, the data type is inferred from the values. (Default is None)

Returns:

Attribute or None: Returns the newly created attribute if successful. Otherwise, returns None.

Raises:

ValueError: If the attribute already exists.

Examples

>>> from pycdfpp import CDF, DataType
>>> import numpy as np
>>> from datetime import datetime
>>> cdf = CDF()
>>> cdf.add_attribute("attr1", [np.arange(10, dtype=np.int32)], [DataType.CDF_INT4])
attr1: [ [ [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] ] ]
>>> cdf.add_attribute("multi", [np.arange(2, dtype=np.int32), [1.,2.,3.], "hello", [datetime(2010,1,1), datetime(2020,1,1)]])

add_variable(name: str, values: ~numpy.ndarray = None, data_type: ~pycdfpp._pycdfpp.DataType = None, is_nrv: bool = False, compression: ~pycdfpp._pycdfpp.CompressionType = <CompressionType.no_compression: 0>, attributes: ~typing.Mapping[str, ~typing.List[~typing.Any]] = None)

Adds a new variable to the CDF. If values is None, the variable is created with no values. Otherwise, the variable is created with the given values. If attributes is not None, the variable is created with the given attributes. If data_type is not None, the variable is created with the given data type. Otherwise, the data type is inferred from the values.

Parameters:

namestr: The name of the variable to add.
valuesnumpy.ndarray or list or None, optional: The values to set for the variable. If None, the variable is created with no values. Otherwise, the variable is created with the given values. (Default is None) When a list is passed, the values are converted to a numpy.ndarray with the appropriate data type, with integers, it will choose the smallest data type that can hold all the values.
data_typeDataType or None, optional: The data type of the variable. If None, the data type is inferred from the values. (Default is None)
is_nrvbool, optional: Whether or not the variable is a non-record variable. (Default is False)
compressionCompressionType, optional: The compression type to use for the variable. (Default is CompressionType.no_compression)
attributesMapping[str, List[Any]] or None, optional: The attributes to set for the variable. If None, the variable is created with no attributes. (Default is None)

Returns:

Variable or None: Returns the newly created variable if successful. Otherwise, returns None.

Raises:

ValueError: If the variable already exists.

Examples

>>> from pycdfpp import CDF, DataType, CompressionType
>>> import numpy as np
>>> cdf = CDF()
>>> cdf.add_variable("var1", np.arange(10, dtype=np.int32), DataType.CDF_INT4, compression=CompressionType.gzip_compression)
var1:
  shape: [ 10 ]
  type: CDF_INT1
  record varry: True
  compression: GNU GZIP
  ...

property attributes

property compression

property distribution_version

items(self: pycdfpp._pycdfpp.CDF) → Iterator

property lazy_loaded

property majority

class pycdfpp.CompressionType

Bases: pybind11_object

Members:

no_compression

gzip_compression

rle_compression

ahuff_compression

huff_compression

ahuff_compression = <CompressionType.ahuff_compression: 3>

gzip_compression = <CompressionType.gzip_compression: 5>

huff_compression = <CompressionType.huff_compression: 2>

property name

no_compression = <CompressionType.no_compression: 0>

rle_compression = <CompressionType.rle_compression: 1>

property value

class pycdfpp.DataType

Bases: pybind11_object

Members:

CDF_BYTE

CDF_CHAR

CDF_INT1

CDF_INT2

CDF_INT4

CDF_INT8

CDF_NONE

CDF_EPOCH

CDF_FLOAT

CDF_REAL4

CDF_REAL8

CDF_UCHAR

CDF_UINT1

CDF_UINT2

CDF_UINT4

CDF_DOUBLE

CDF_EPOCH16

CDF_TIME_TT2000

CDF_BYTE = <DataType.CDF_BYTE: 41>

CDF_CHAR = <DataType.CDF_CHAR: 51>

CDF_DOUBLE = <DataType.CDF_DOUBLE: 45>

CDF_EPOCH = <DataType.CDF_EPOCH: 31>

CDF_EPOCH16 = <DataType.CDF_EPOCH16: 32>

CDF_FLOAT = <DataType.CDF_FLOAT: 44>

CDF_INT1 = <DataType.CDF_INT1: 1>

CDF_INT2 = <DataType.CDF_INT2: 2>

CDF_INT4 = <DataType.CDF_INT4: 4>

CDF_INT8 = <DataType.CDF_INT8: 8>

CDF_NONE = <DataType.CDF_NONE: 0>

CDF_REAL4 = <DataType.CDF_REAL4: 21>

CDF_REAL8 = <DataType.CDF_REAL8: 22>

CDF_TIME_TT2000 = <DataType.CDF_TIME_TT2000: 33>

CDF_UCHAR = <DataType.CDF_UCHAR: 52>

CDF_UINT1 = <DataType.CDF_UINT1: 11>

CDF_UINT2 = <DataType.CDF_UINT2: 12>

CDF_UINT4 = <DataType.CDF_UINT4: 14>

property name

property value

class pycdfpp.Majority

Bases: pybind11_object

Members:

row

column

column = <Majority.column: 0>

property name

row = <Majority.row: 1>

property value

class pycdfpp.Variable

Bases: pybind11_object

A CDF Variable (either R or Z variable)

Attributes:

attributes: dict: variable attributes
name: str: variable name
type: DataType: variable data type (ie CDF_DOUBLE, CDF_TIME_TT2000, …)
shape: List[int]: variable shape (records + record shape)
majority: cdf_majority: variable majority as writen in the CDF file, note that pycdfpp will always expose row major data.
values_loaded: bool: True if values are availbale in memory, this is usefull with lazy loading to know if values are already loaded.
compression: CompressionType: variable compression type (supported values are no_compression, rle_compression, gzip_compression)
values: numpy.array: returns variable values as a numpy.array of the corresponding dtype and shape, note that no copies are involved, the returned array is just a view on variable data.
values_encoded: numpy.array: same as values except that string variable are encoded wihch involves a data copy and since numpy uses UTF-32, expect a 4x memory increase for string values

Methods

add_attribute(name, values[, data_type])

Adds a new attribute to the variable.

set_compression_type	Sets the variable compression type
set_values	Sets the variable values

add_attribute(name: str, values: ndarray, data_type=None)

Adds a new attribute to the variable. If values is None, the attribute is created with no values. Otherwise, the attribute is created with the given values. If data_type is not None, the attribute is created with the given data type. Otherwise, the data type is inferred from the values.

Parameters:

namestr: The name of the attribute to add.
valuesnp.ndarray or List[float or int or datetime] or str: The values to set for the attribute. When a list is passed, the values are converted to a numpy.ndarray with the appropriate data type, with integers, it will choose the smallest data type that can hold all the values.
data_typeDataType or None, optional: The data type of the attribute. If None, the data type is inferred from the values. (Default is None)

Returns:

Attribute or None: Returns the newly created attribute if successful. Otherwise, returns None.

Raises:

ValueError: If the attribute already exists.

Examples

>>> from pycdfpp import CDF, DataType
>>> import numpy as np
>>> cdf = CDF()
>>> cdf.add_variable("var1", np.arange(10, dtype=np.int32), DataType.CDF_INT4)
var1:
  shape: [ 10 ]
  type: CDF_INT1
  record varry: True
  compression: None
  ...
>>> cdf["var1"].add_attribute("attr1", np.arange(10, dtype=np.int32), DataType.CDF_INT4)
attr1: [ [ [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] ] ]

property attributes

property compression

property is_nrv

property majority

property name

set_values(values: ndarray, data_type: DataType = None)

property shape

property type

property values

property values_encoded

property values_loaded

class pycdfpp.epoch

Bases: pybind11_object

property value

class pycdfpp.epoch16

Bases: pybind11_object

property picoseconds

property seconds

pycdfpp.load(file_or_buffer: str, iso_8859_1_to_utf8: bool = False, lazy_load: bool = True)[source]

Load and parse a CDF file.

Parameters:

file_or_bufferstr or ByteString: Either a filename to be loaded or an in-memory file implementing the Python buffer protocol.
iso_8859_1_to_utf8bool, optional: Automatically convert Latin-1 characters to their equivalent UTF counterparts when True. For CDF files prior to version 3.8, UTF-8 wasn’t supported and some CDF files might contain “illegal” Latin-1 characters. (Default is False)
lazy_loadbool, optional: Controls whether variable values are loaded immediately or only when accessed by the user. If True, variables’ values are loaded on demand. If False, all variable values are loaded during parsing. (Default is True)

Returns:

CDF or None: Returns a CDF object upon successful read. If there’s an issue with the read, None is returned.

pycdfpp.save(*args, **kwargs)

Overloaded function.

save(cdf: pycdfpp._pycdfpp.CDF, fname: str) -> bool
save(cdf: pycdfpp._pycdfpp.CDF) -> pycdfpp._pycdfpp._cdf_bytes

pycdfpp.to_datetime(values)[source]

to_datetime

Parameters:

values: Variable or epoch or List[epoch] or epoch16 or List[epoch16] or tt2000_t or List[tt2000_t]: input value(s)
to convert to datetime.datetime

Returns:

List[datetime.datetime]

pycdfpp.to_datetime64(values)[source]

to_datetime64

Parameters:

values: Variable or epoch or List[epoch] or numpy.ndarray[epoch] or epoch16 or List[epoch16] or numpy.ndarray[epoch16] or tt2000_t or List[tt2000_t] or numpy.ndarray[tt2000_t]: input value(s)
to convert to numpy.datetime64

Returns:

numpy.ndarray[numpy.datetime64]

class pycdfpp.tt2000_t

Bases: pybind11_object

property value