Merge pull request #296 from DimitriPapadopoulos/ruff

Apply Ruff rules to please pre-commit

examples/btune.py

@@ -11,10 +11,11 @@
 # https://github.com/Blosc/blosc2_btune/blob/main/README.md
 #######################################################################
 
-import blosc2
 import blosc2_btune
 import numpy as np
 
+import blosc2
+
 nchunks = 10
 # Set the compression and decompression parameters, use BTUNE tuner
 cparams = blosc2.CParams(codec=blosc2.Codec.LZ4HC, typesize=4, tuner=blosc2.Tuner.BTUNE)

examples/compress2_decompress2.py

@@ -6,9 +6,10 @@
 # LICENSE file in the root directory of this source tree)
 #######################################################################
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 a = np.linspace(0, 1, 1_000_000, dtype=np.float64)
 typesize = a.dtype.itemsize
 c_bytesobj = blosc2.compress2(

examples/filler.py

@@ -8,9 +8,10 @@
 
 # Fill an SChunk with a filler decorator
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 nchunks = 3
 chunk_len = 200 * 1000
 schunk_dtype = np.dtype(np.float64)

examples/mmap-rw.py

@@ -9,9 +9,10 @@
 
 # Example for writing and reading memory-mapped files
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 urlpath = "array.b2nd"
 blosc2.remove_urlpath(urlpath)
 a = np.arange(1_000_000, dtype=np.int64)

examples/ndarray/asarray_.py

@@ -8,9 +8,10 @@
 
 # Import structured arrays using the array interface
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (2, 2)
 dtype = np.float64
 

examples/ndarray/broadcast_expr.py

@@ -9,9 +9,10 @@
 # This shows how to evaluate expressions with NDArray instances having different shapes as operands.
 # The broadcast is done internally and tries to mimic NumPy behavior.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 # Two operands with the next shapes should be supported
 # shape1, shape2 = (2, 1, 3, 2), (3, 3, 2)
 # shape1, shape2 = (2, 1, 3, 2), (3, 1, 2)

examples/ndarray/buffer.py

@@ -8,9 +8,10 @@
 
 # Creating/dumping an NDArray from/to a buffer
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 chunks = (49, 49)
 dtype = np.dtype("|S8")

examples/ndarray/bytedelta_filter.py

@@ -11,9 +11,10 @@
 
 import math
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (1000, 1000)
 
 # Create a buffer

examples/ndarray/c2array_expr.py

@@ -1,8 +1,9 @@
 import pathlib
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 host = "https://demo.caterva2.net/"
 root = "b2tests"
 dir = "expr/"

examples/ndarray/compute_expr.py

@@ -8,9 +8,10 @@
 
 # This shows how to evaluate expressions with NDArray instances as operands.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 
 # Create a NDArray from a NumPy array

examples/ndarray/compute_fields.py

@@ -8,9 +8,10 @@
 
 # This shows how to evaluate expressions with NDField instances as operands.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 
 # Create a structured NumPy array

examples/ndarray/compute_udf_numba.py

@@ -8,10 +8,11 @@
 
 # This shows how to evaluate expressions with NDArray instances as operands.
 
-import blosc2
 import numba as nb
 import numpy as np
 
+import blosc2
+
 
 # The UDF to be evaluated
 @nb.jit(nopython=True, parallel=True)

examples/ndarray/compute_where.py

@@ -8,9 +8,10 @@
 
 # This shows how to evaluate expressions in combination with the where() functionality.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 chunks = (10, 10)
 blocks = (5, 5)

examples/ndarray/copy_.py

@@ -8,9 +8,10 @@
 
 # Copying NDArrays
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (10, 10)
 blocks = (10, 10)
 dtype = np.float64

examples/ndarray/formats.py

@@ -10,9 +10,10 @@
 
 from time import time
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 urlpath_sparse = "ex_formats_sparse.b2nd"
 urlpath_contiguous = "ex_formats_contiguous.b2nd"
 

examples/ndarray/general_expressions.py

@@ -8,9 +8,10 @@
 
 # This shows how to build expressions with a general mix of NDArray and NumPy operands.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 
 # Create a NDArray from a NumPy array

examples/ndarray/getitem.py

@@ -8,9 +8,10 @@
 
 # Show how getitem / setitem works for an NDArray
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (10, 10)
 slices = (slice(2, 7), slice(4, 8))
 

examples/ndarray/meta.py

@@ -8,9 +8,10 @@
 
 # Store metadata in persistent arrays
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (128, 128)
 urlpath = "ex_meta.b2nd"
 dtype = np.complex128

examples/ndarray/ndarray_copy.py

@@ -8,9 +8,10 @@
 
 # Copying NDArrays
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (10, 10)
 blocks = (10, 10)
 dtype = np.float64

examples/ndarray/ndmean.py

@@ -7,9 +7,10 @@
 #######################################################################
 
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 chunks = (49, 49)
 dtype = np.float64

examples/ndarray/persistency.py

@@ -8,9 +8,10 @@
 
 # Shows how you can persist an array on disk
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (128, 128)
 urlpath = "ex_persistency.b2nd"
 dtype = np.complex128

examples/ndarray/reduce_expr.py

@@ -8,9 +8,10 @@
 
 # This shows how to evaluate expressions with NDArray instances as operands.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (10, 10, 2)
 
 # Create a NDArray from a NumPy array

examples/ndarray/reduce_expr_save.py

@@ -8,9 +8,10 @@
 
 # This shows how to evaluate expressions with NDArray instances as operands.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (10, 1, 2)
 
 # Create a NDArray from a NumPy array

examples/ndarray/reduce_string_expr.py

@@ -8,9 +8,10 @@
 
 # This shows how to evaluate expressions with NDArray instances as operands.
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (10, 10, 2)
 
 # Create a NDArray from a NumPy array

examples/ndarray/work_with_numpy.py

@@ -8,9 +8,10 @@
 
 # Shows how you can easily convert from/to NumPy arrays
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (1234, 23)
 chunks = (253, 23)
 dtype = bool

examples/ndarray/zfp_codec.py

@@ -7,9 +7,10 @@
 #######################################################################
 
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 shape = (50, 50)
 chunks = (49, 49)
 dtype = np.float64

examples/pack_array.py

@@ -8,9 +8,10 @@
 
 
 # A simple example using the pack and unpack functions
-import blosc2
 import numpy as np
 
+import blosc2
+
 a = np.array(["å", "ç", "ø"])
 parray = blosc2.pack(a, 9)
 a2 = blosc2.unpack(parray)

examples/pack_tensor.py

@@ -9,9 +9,10 @@
 
 # A simple example using the pack_tensor and unpack_tensor functions
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 a = np.arange(1_000_000)
 
 cparams = blosc2.CParams(

examples/postfilter1.py

@@ -6,9 +6,10 @@
 # LICENSE file in the root directory of this source tree)
 #######################################################################
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 nchunks = 5
 input_dtype = np.dtype(np.int32)
 output_dtype = np.dtype(np.float32)

examples/postfilter2.py

@@ -6,9 +6,10 @@
 # LICENSE file in the root directory of this source tree)
 #######################################################################
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 nchunks = 10
 input_dtype = np.dtype("M8[D]")
 output_dtype = np.int64  # output dtype has to be of the same size as input

examples/postfilter3.py

@@ -6,9 +6,10 @@
 # LICENSE file in the root directory of this source tree)
 #######################################################################
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 nchunks = 10
 input_dtype = np.dtype(np.int64)
 

examples/prefilter.py

@@ -8,9 +8,10 @@
 
 # Example of prefiltering data before compression
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 nchunks = 3
 input_dtype = np.dtype(np.int32)
 output_dtype = np.dtype(np.float32)

examples/save_tensor.py

@@ -9,9 +9,10 @@
 
 # A simple example using the save_tensor and load_tensor functions
 
-import blosc2
 import numpy as np
 
+import blosc2
+
 a = np.arange(1_000_000)
 
 file_size = blosc2.save_tensor(a, "save_tensor.bl2", mode="w")