Modifying CI to make automatic releases.

Modified the CI pipeline to make automatic releases to pypi.

enstools/encoding/dataset_encoding.py

 import os
 from copy import deepcopy
 from pathlib import Path
-from typing import Union, Dict
+from typing import Hashable, Union, Dict
 
 import xarray
 import yaml
+import numpy as np
 
 from . import rules
 from .errors import InvalidCompressionSpecification
@@ -12,7 +13,50 @@ from .variable_encoding import _Mapping, parse_variable_specification, Encoding,
     NullEncoding
 
 
-def compression_dictionary_to_string(compression_dictionary: dict) -> str:
+def convert_size(size_bytes):
+    import math
+    if size_bytes < 0:
+        prefix = "-"
+        size_bytes = -size_bytes
+    else:
+        prefix = ""
+
+    if size_bytes == 0:
+        return "0B"
+    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
+    i = int(math.floor(math.log(size_bytes, 1024)))
+    p = math.pow(1024, i)
+    s = round(size_bytes / p, 2)
+    return f"{prefix}{s}{size_name[i]}"
+
+
+def convert_to_bytes(size_string):
+    """
+    This function converts a given size string (e.g. '5MB') to the number of bytes.
+    
+    Args:
+    size_string (str): The size string to be converted (e.g. '5MB')
+    
+    Returns:
+    int: The number of bytes.
+    """
+    import re
+    size_string = size_string.upper()
+    digits = re.match(r'\d+(?:\.\d+)?', size_string)  # matches digits and optionally a dot followed by more digits
+    if digits:
+        digits = digits.group()  # get the matched digits
+    else:
+        raise ValueError(f"Invalid size string: {size_string}")
+    unit = size_string.replace(digits, "")
+    size_name_dict = {'B': 0, 'KB': 1, 'MB': 2, 'GB': 3, 'TB': 4, 'PB': 5, 'EB': 6, 'ZB': 7, 'YB': 8}
+    if unit in size_name_dict:
+        size_bytes = float(digits) * np.power(1024, size_name_dict[unit])
+    else:
+        raise ValueError(f"Invalid size string: {size_string}")
+    return int(size_bytes)
+
+
+def compression_dictionary_to_string(compression_dictionary: Dict[str, str]) -> str:
     """
     Convert a dictionary containing multiple entries to a single line specification
     """
@@ -20,7 +64,7 @@ def compression_dictionary_to_string(compression_dictionary: dict) -> str:
         [f"{key}{rules.VARIABLE_NAME_SEPARATOR}{value}" for key, value in compression_dictionary.items()])
 
 
-def parse_full_specification(spec: str) -> Dict[str, Encoding]:
+def parse_full_specification(spec: Union[str, None]) -> Dict[str, Encoding]:
     from enstools.encoding.rules import VARIABLE_SEPARATOR, VARIABLE_NAME_SEPARATOR, \
         DATA_DEFAULT_LABEL, DATA_DEFAULT_VALUE, COORD_LABEL, COORD_DEFAULT_VALUE
     result = {}
@@ -58,8 +102,8 @@ def parse_full_specification(spec: str) -> Dict[str, Encoding]:
             result[COORD_LABEL] = parse_variable_specification(COORD_DEFAULT_VALUE)
 
     # For each specification, check that the specifications are valid.
-    for key, spec in result.items():
-        spec.check_validity()
+    for _, _spec in result.items():
+        _spec.check_validity()
 
     return result
 
@@ -70,7 +114,7 @@ class DatasetEncoding(_Mapping):
     The kind of encoding that xarray expects is a mapping between the variables and their corresponding h5py encoding.
     """
 
-    def __init__(self, dataset: xarray.Dataset, compression: Union[str, dict, None]):
+    def __init__(self, dataset: xarray.Dataset, compression: Union[str, Dict[str, str], Path, None]):
         self.dataset = dataset
 
         # Process the compression argument to get a single string with per-variable specifications
@@ -80,7 +124,7 @@ class DatasetEncoding(_Mapping):
         self.variable_encodings = parse_full_specification(compression)
 
     @staticmethod
-    def get_a_single_compression_string(compression: Union[str, dict, Path]) -> str:
+    def get_a_single_compression_string(compression: Union[str, Dict[str, str], Path, None]) -> Union[str, None]:
         # The compression parameter can be a string or a dictionary.
 
         # In case it is a string, it can be directly a compression specification or a yaml file.
@@ -120,18 +164,35 @@ class DatasetEncoding(_Mapping):
             var
             in self.dataset.data_vars}
 
-        # Add chunking?
-        for variable in self.dataset.data_vars:
-            chunks = {k: v if k != "time" else 1 for k, v in self.dataset[variable].sizes.items()}
-            chunk_sizes = tuple(chunks.values())
-            # Ugly python magic to add chunk sizes into the encoding mapping object.
-            data_variable_encodings[variable]._kwargs._kwargs["chunksizes"] = chunk_sizes  # noqa
-
         # Merge
         all_encodings = {**coordinate_encodings, **data_variable_encodings}
 
+        # Need to specify chunk size, otherwise it breaks down.
+        self.chunk(encodings=all_encodings)
+
         return all_encodings
 
+    def chunk(self, encodings: Dict[Union[Hashable, str], Encoding], chunk_memory_size="10MB"):
+        """
+        Add a variable "chunksizes" to each variable encoding with the corresponding 
+
+        Args:
+            encodings (dict): Dictionary with the corresponding encoding for each variable.
+            chunk_memory_size (str): Desired chunk size in memory.
+        """
+
+        chunk_memory_size = convert_to_bytes(chunk_memory_size)
+
+        # Loop over all the variables
+        for variable in self.dataset.data_vars:
+            da = self.dataset[variable]
+            type_size = da.dtype.itemsize
+
+            optimal_chunk_size = chunk_memory_size / type_size
+            chunk_sizes = find_chunk_sizes(data_array=da, chunk_size=optimal_chunk_size)
+            chunk_sizes = tuple(chunk_sizes[d] for d in da.dims)
+            encodings[variable].set_chunk_sizes(chunk_sizes)
+
     @property
     def _kwargs(self):
         return self.encoding()
@@ -153,3 +214,21 @@ def is_a_valid_dataset_compression_specification(specification):
         return True
     except InvalidCompressionSpecification:
         return False
+
+
+def find_chunk_sizes(data_array, chunk_size):
+    import math
+    total_points = np.prod(data_array.shape)
+    num_chunks = max(1, int(total_points // chunk_size))
+    chunk_sizes = {}
+    chunk_number = {}
+
+    # Sort dimensions by size
+    dims = sorted(data_array.dims, key=lambda x: data_array[x].shape)
+    pending_num_chunks = num_chunks
+    for dim in dims:
+        chunk_sizes[dim] = max(1, int(data_array[dim].size // pending_num_chunks))
+        chunk_number[dim] = data_array[dim].size // chunk_sizes[dim]
+
+        pending_num_chunks = math.ceil(pending_num_chunks / chunk_number[dim])
+    return chunk_sizes