Merge pull request #26 from KxSystems/2-4-update

2.4 release update

README.md

@@ -93,8 +93,9 @@ KX only officially supports versions of PyKX built by KX, i.e. versions of PyKX
 PyKX depends on the following third-party Python packages:
 
 - `pandas>=1.2, < 2.2.0`
-- `numpy~=1.22; python_version<'3.11'`
-- `numpy~=1.23.2; python_version=='3.11'`
+- `numpy~=1.22, <2.0; python_version<'3.11'`
+- `numpy~=1.23, <2.0; python_version=='3.11'`
+- `numpy~=1.26, <2.0; python_version=='3.12'`
 - `pytz>=2022.1`
 - `toml~=0.10.2`
 
@@ -142,6 +143,13 @@ Windows:
 * [dlfcn-win32](https://github.com/dlfcn-win32/dlfcn-win32). Can be installed using [Vcpkg](https://github.com/microsoft/vcpkg).
 * `msvcr100.dll`. Available in [Microsoft Visual C++ 2010 Redistributable](https://www.microsoft.com/en-ca/download/details.aspx?id=26999).
 
+To install the above dependencies, you can run the `w64_install.ps1` script as an administrator:
+
+```PowerShell
+cd pykx
+.\w64_install.ps1
+```
+
 ### Building
 
 Using a Python virtual environment is recommended:

conda-recipe/conda_build_config.yaml

@@ -3,3 +3,4 @@ python:
     - 3.9
     - 3.10
     - 3.11
+    - 3.12

conda-recipe/meta.yaml

@@ -16,17 +16,20 @@ requirements:
   build:
     - git
     - python
-    - setuptools==60.9.3
-    - setuptools_scm[toml]>=6.0.1
+    - setuptools>=68.0
+    - setuptools_scm[toml]>=7.1.0  # [py==37]
+    - setuptools_scm[toml]>=8.0.0  # [py!=37]
     - cython==3.0.0
-    - numpy==1.22.*
+    - numpy==1.26  # [py==312]
+    - numpy==1.22.*  # [py!=37 and py<312]
+    - numpy==1.20  # [py==37]
     - tomli>=2.0.1
-    - wheel>=0.36.2
+    - wheel>=0.36
     - sysroot_linux-64 # [linux64]
 
   run:
     - python
-    - numpy>=1.22
+    - numpy>=1.22,<2.0
     - pandas>=1.2, <2.2.0
     - pytz>=2022.1
     - toml>=0.10.2

docs/api/compress.md

@@ -0,0 +1,12 @@
+# Compression and Encryption APIs
+
+::: pykx.compress_encrypt
+    rendering:
+      show_root_heading: false
+    options:
+      show_root_heading: false
+      members_order: source
+      members:
+        - CompressionAlgorithm
+        - Compress
+        - Encrypt

docs/api/pykx-execution/q.md

@@ -168,8 +168,9 @@ pykx.List(q('
 Write global tables to disk as splayed, enumerated, indexed q tables.
 
 ```python
->>> pykx.q('t: ([] x: 1 2 3; y: 10 20 30)')
->>> pykx.q.dsave(':v', 't')
+>>> from pathlib import Path
+>>> pykx.q['t'] = kx.Table(data={'x': [1, 2, 3], 'y': [10, 20, 30]})
+>>> pykx.q.dsave(Path('v'), 't')
 pykx.SymbolAtom(q('`t'))
 ```
 
@@ -178,7 +179,7 @@ pykx.SymbolAtom(q('`t'))
 Read or memory-map a variable or q data file.
 
 ```python
->>> pykx.q('a: 10')
+>>> pykx.q['a'] = 10
 >>> pykx.q.get('a')
 pykx.LongAtom(q('10'))
 ```

docs/api/pykx-q-data/type_conversions.md

@@ -771,11 +771,11 @@ True
 	Converting these types to python will return a float object or a `float64` object in numpy's case.
 
 	```Python
-	>>> kx.q('0001.02.03T04:05:06.007, 0001.02.03T04:05:06.007').py(raw=True)
+	>>> kx.q('0001.02.03T04:05:06.007 0001.02.03T04:05:06.007').py(raw=True)
 	[-730085.8297915857, -730085.8297915857]
-	>>> kx.q('0001.02.03T04:05:06.007, 0001.02.03T04:05:06.007').np(raw=True)
+	>>> kx.q('0001.02.03T04:05:06.007 0001.02.03T04:05:06.007').np(raw=True)
 	array([-730085.82979159, -730085.82979159])
-	>>> kx.q('0001.02.03T04:05:06.007, 0001.02.03T04:05:06.007').np(raw=True).dtype
+	>>> kx.q('0001.02.03T04:05:06.007 0001.02.03T04:05:06.007').np(raw=True).dtype
 	dtype('float64')
 	```
 
@@ -1081,15 +1081,19 @@ True
 	Calling `.py()` on a `pykx.Table` will return a python `dict` object.
 
 	```Python
-	>>> kx.q('([] a: 10?10; b: 10?10)').py()
+	>>> kx.Table(data={
+	...     'a': kx.random.random(10, 10),
+	...     'b': kx.random.random(10, 10)}).py()
 	{'a': [5, 6, 4, 1, 3, 3, 7, 8, 2, 1], 'b': [8, 1, 7, 2, 4, 5, 4, 2, 7, 8]}
 	```
 
 === "Numpy"
 	Calling `.np()` on a `pykx.Table` will return a numpy `record` array of the rows of the table with each type converted to it closest analogous numpy type.
 
 	```Python
-	>>> kx.q('([] a: 10?10; b: 10?10)').np()
+	>>> kx.Table(data={
+	...     'a': kx.random.random(10, 10),
+	...     'b': kx.random.random(10, 10)}).np()
 	rec.array([(9, 9), (9, 7), (2, 6), (5, 6), (4, 4), (2, 7), (5, 8), (8, 4),
         	   (7, 4), (9, 6)],
 	           dtype=[('a', '<i8'), ('b', '<i8')])
@@ -1099,7 +1103,9 @@ True
 	Calling `.pd()` on a `pykx.Table` will return a pandas `DataFrame` with each column being converted to its closest pandas `dtype`.
 
 	```Python
-	>>> kx.q('([] a: 10?10; b: 10?10)').pd()
+	>>> kx.Table(data={
+	...     'a': kx.random.random(10, 10),
+	...     'b': kx.random.random(10, 10)}).pd()
 	   a  b
 	0  1  9
 	1  0  7
@@ -1137,7 +1143,9 @@ True
 	Calling `.pa()` on a `pykx.Table` will return a pyarrow `Table`.
 
 	```Python
-	>>> kx.q('([] a: 10?10; b: 10?10)').pa()
+	>>> kx.Table(data={
+	...     'a': kx.random.random(10, 10),
+	...     'b': kx.random.random(10, 10)}).pa()
 	pyarrow.Table
 	a: int64
 	b: int64