example arrays release 0.5.5

darr/readcodearray.py

@@ -148,7 +148,7 @@ def readcodescilab_complex(numtype, shape, endianness,
                          filepath=filepath, varname=varname)
     dimstr = ",:" * ndim
     ct += f'{varname} = complex(squeeze({varname}(1{dimstr})),squeeze' \
-          f'({varname}(2{dimstr})));'
+          f'({varname}(2{dimstr})));\n'
     return ct
 
 

examplearrays/arrays/array_complex128_1D.darr/README.txt

@@ -57,6 +57,14 @@ fileid <- file("arrayvalues.bin", "rb")
 a <- readBin(con=fileid, what=complex(), n=7, size=16, signed=TRUE, endian="little")
 close(fileid)
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(14, "dl", fileid);
+a = matrix(a, [2, 7]);
+mclose(fileid);
+a = complex(squeeze(a(1,:)),squeeze(a(2,:)));
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');

examplearrays/arrays/array_complex128_1D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "complex128",
     "shape": [
         7

examplearrays/arrays/array_complex128_1D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_complex128_2D.darr/README.txt

@@ -26,7 +26,7 @@ Code for reading the numeric data
 
 Note that the array is 2-dimensional and stored with a row-major memory
 layout. In column-major languages (see Note below), the code provided here
-will lead to an array that has its dimensions inversed (2, 8) with respect to
+will lead to an array that has its dimensions reversed (2, 8) with respect to
 the format description above (8, 2).
 
 Python with Darr:
@@ -53,6 +53,14 @@ a <- readBin(con=fileid, what=complex(), n=16, size=16, signed=TRUE, endian="lit
 a <- array(data=a, dim=c(2, 8), dimnames=NULL)
 close(fileid)
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(32, "dl", fileid);
+a = matrix(a, [2, 2, 8]);
+mclose(fileid);
+a = complex(squeeze(a(1,:,:)),squeeze(a(2,:,:)));
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');
@@ -94,4 +102,4 @@ major memory layout. To keep things efficient, the code examples above do not
 change the memory layout when reading the array in a different language. This
 means that in column-major languages, the dimension axes will be *reversed*.
 Row-major languages are: Python and Mathematica. Columns-major languages are:
-Julia, Matlab/Octave, R, Maple, and IDL/GDL.
+Julia, Scilab, Matlab/Octave, R, Maple, and IDL/GDL.

examplearrays/arrays/array_complex128_2D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "complex128",
     "shape": [
         8,

examplearrays/arrays/array_complex128_2D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_complex64_1D.darr/README.txt

@@ -51,6 +51,14 @@ Python with Numpy (memmap):
 import numpy as np
 a = np.memmap('arrayvalues.bin', dtype='<c8', shape=(7,), order='C')
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(14, "fl", fileid);
+a = matrix(a, [2, 7]);
+mclose(fileid);
+a = complex(squeeze(a(1,:)),squeeze(a(2,:)));
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');

examplearrays/arrays/array_complex64_1D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "complex64",
     "shape": [
         7

examplearrays/arrays/array_complex64_1D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_complex64_2D.darr/README.txt

@@ -26,7 +26,7 @@ Code for reading the numeric data
 
 Note that the array is 2-dimensional and stored with a row-major memory
 layout. In column-major languages (see Note below), the code provided here
-will lead to an array that has its dimensions inversed (2, 8) with respect to
+will lead to an array that has its dimensions reversed (2, 8) with respect to
 the format description above (8, 2).
 
 Python with Darr:
@@ -46,6 +46,14 @@ Python with Numpy (memmap):
 import numpy as np
 a = np.memmap('arrayvalues.bin', dtype='<c8', shape=(8, 2), order='C')
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(32, "fl", fileid);
+a = matrix(a, [2, 2, 8]);
+mclose(fileid);
+a = complex(squeeze(a(1,:,:)),squeeze(a(2,:,:)));
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');
@@ -87,4 +95,4 @@ major memory layout. To keep things efficient, the code examples above do not
 change the memory layout when reading the array in a different language. This
 means that in column-major languages, the dimension axes will be *reversed*.
 Row-major languages are: Python and Mathematica. Columns-major languages are:
-Julia, Matlab/Octave, R, Maple, and IDL/GDL.
+Julia, Scilab, Matlab/Octave, R, Maple, and IDL/GDL.

examplearrays/arrays/array_complex64_2D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "complex64",
     "shape": [
         8,

examplearrays/arrays/array_complex64_2D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_float16_1D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "float16",
     "shape": [
         7

examplearrays/arrays/array_float16_1D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_float16_2D.darr/README.txt

@@ -26,7 +26,7 @@ Code for reading the numeric data
 
 Note that the array is 2-dimensional and stored with a row-major memory
 layout. In column-major languages (see Note below), the code provided here
-will lead to an array that has its dimensions inversed (2, 8) with respect to
+will lead to an array that has its dimensions reversed (2, 8) with respect to
 the format description above (8, 2).
 
 Python with Darr:
@@ -76,4 +76,4 @@ major memory layout. To keep things efficient, the code examples above do not
 change the memory layout when reading the array in a different language. This
 means that in column-major languages, the dimension axes will be *reversed*.
 Row-major languages are: Python and Mathematica. Columns-major languages are:
-Julia, Matlab/Octave, R, Maple, and IDL/GDL.
+Julia, Scilab, Matlab/Octave, R, Maple, and IDL/GDL.

examplearrays/arrays/array_float16_2D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "float16",
     "shape": [
         8,

examplearrays/arrays/array_float16_2D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_float32_1D.darr/README.txt

@@ -52,6 +52,12 @@ fileid <- file("arrayvalues.bin", "rb")
 a <- readBin(con=fileid, what=numeric(), n=7, size=4, signed=TRUE, endian="little")
 close(fileid)
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(7, "fl", fileid);
+mclose(fileid);
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');

examplearrays/arrays/array_float32_1D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "float32",
     "shape": [
         7

examplearrays/arrays/array_float32_1D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_float32_2D.darr/README.txt

@@ -26,7 +26,7 @@ Code for reading the numeric data
 
 Note that the array is 2-dimensional and stored with a row-major memory
 layout. In column-major languages (see Note below), the code provided here
-will lead to an array that has its dimensions inversed (2, 8) with respect to
+will lead to an array that has its dimensions reversed (2, 8) with respect to
 the format description above (8, 2).
 
 Python with Darr:
@@ -53,6 +53,13 @@ a <- readBin(con=fileid, what=numeric(), n=16, size=4, signed=TRUE, endian="litt
 a <- array(data=a, dim=c(2, 8), dimnames=NULL)
 close(fileid)
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(16, "fl", fileid);
+a = matrix(a, [2, 8]);
+mclose(fileid);
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');
@@ -97,4 +104,4 @@ major memory layout. To keep things efficient, the code examples above do not
 change the memory layout when reading the array in a different language. This
 means that in column-major languages, the dimension axes will be *reversed*.
 Row-major languages are: Python and Mathematica. Columns-major languages are:
-Julia, Matlab/Octave, R, Maple, and IDL/GDL.
+Julia, Scilab, Matlab/Octave, R, Maple, and IDL/GDL.

examplearrays/arrays/array_float32_2D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "float32",
     "shape": [
         8,

examplearrays/arrays/array_float32_2D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_float64_1D.darr/README.txt

@@ -52,6 +52,12 @@ fileid <- file("arrayvalues.bin", "rb")
 a <- readBin(con=fileid, what=numeric(), n=7, size=8, signed=TRUE, endian="little")
 close(fileid)
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(7, "dl", fileid);
+mclose(fileid);
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');

examplearrays/arrays/array_float64_1D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "float64",
     "shape": [
         7

examplearrays/arrays/array_float64_1D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }

examplearrays/arrays/array_float64_2D.darr/README.txt

@@ -26,7 +26,7 @@ Code for reading the numeric data
 
 Note that the array is 2-dimensional and stored with a row-major memory
 layout. In column-major languages (see Note below), the code provided here
-will lead to an array that has its dimensions inversed (2, 8) with respect to
+will lead to an array that has its dimensions reversed (2, 8) with respect to
 the format description above (8, 2).
 
 Python with Darr:
@@ -53,6 +53,13 @@ a <- readBin(con=fileid, what=numeric(), n=16, size=8, signed=TRUE, endian="litt
 a <- array(data=a, dim=c(2, 8), dimnames=NULL)
 close(fileid)
 
+Scilab:
+-------
+fileid = mopen("arrayvalues.bin", "rb");
+a = mget(16, "dl", fileid);
+a = matrix(a, [2, 8]);
+mclose(fileid);
+
 Matlab/Octave:
 --------------
 fileid = fopen('arrayvalues.bin');
@@ -97,4 +104,4 @@ major memory layout. To keep things efficient, the code examples above do not
 change the memory layout when reading the array in a different language. This
 means that in column-major languages, the dimension axes will be *reversed*.
 Row-major languages are: Python and Mathematica. Columns-major languages are:
-Julia, Matlab/Octave, R, Maple, and IDL/GDL.
+Julia, Scilab, Matlab/Octave, R, Maple, and IDL/GDL.

examplearrays/arrays/array_float64_2D.darr/arraydescription.json

@@ -2,7 +2,7 @@
     "arrayorder": "C",
     "byteorder": "little",
     "darrobject": "Array",
-    "darrversion": "0.5.1",
+    "darrversion": "0.5.5+0.gbf45bc1.dirty",
     "numtype": "float64",
     "shape": [
         8,

examplearrays/arrays/array_float64_2D.darr/metadata.json

@@ -4,6 +4,6 @@
         9
     ],
     "comments": "This example array has metadata, which is stored in a separate JSON file. Metadata in Darr is a dictionary that can contain anything that is JSON serializable.",
-    "datetime": "20220516084023",
+    "datetime": "20230920144041",
     "samplingrate": 25000.0
 }