Release v2.7.0

.gitignore

@@ -19,4 +19,9 @@ log/
 tests/temp/*
 temp/*
 brightwind.egg-info/
-**/.ipynb_checkpoints
+**/.ipynb_checkpoints
+
+### Virtual Environments ###
+.venv/
+venv/
+env/

CHANGELOG.md

@@ -9,6 +9,22 @@ Given a version number MAJOR.MINOR.PATCH, increment the:
 
 Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.
 
+---
+## [2.7.0]
+14-May-2026
+
+### New Features and Enhancements
+1. Updated `apply_cleaning_rules()` to support nested boolean conditions (`and`/`or`/`not`) inside the `conditions` block and a new optional `time_range_conditions` block on cleaning rules, in line with the updated BrightHub `/measurement-locations/{uuid}/cleaning-rules` API response. Existing flat-condition cleaning rule files continue to work unchanged. The new `measurement_point_uuid` and `statistic_type_id` fields on `conditions` and `clean_out` items are accepted but currently ignored — column resolution still relies on `assembled_column_name`. The `cleaning_rule.schema.json` was extended with a `time_condition` definition. ([#609](https://github.com/brightwind-dev/brightwind/issues/609))
+1. Rewrote `README.md` and `contributing.md` to align with the [docs site](https://brightwind-dev.github.io/brightwind-docs/), with clearer install options (venv / conda), a quick-start example, and an expanded contributing guide covering the fork workflow, editable dev install and running the test suite. ([#602](https://github.com/brightwind-dev/brightwind/issues/602))
+2. `test_load_brighthub` is now skipped automatically when no BrightHub credentials are available — either `BRIGHTHUB_CLIENT_ID` and `BRIGHTHUB_CLIENT_SECRET`, or the deprecated `BRIGHTHUB_EMAIL` and `BRIGHTHUB_PASSWORD` — so the test suite runs cleanly without BrightHub credentials. ([#602](https://github.com/brightwind-dev/brightwind/issues/602))
+1. Added support for reading Parquet files from BrightHub in `LoadBrightHub.get_data()` via a new `file_extension` argument (one of `'.csv'` or `'.parquet'`). Default remains `'.csv'` for backwards compatibility; Parquet is expected to become the default in the next major release. Reading Parquet requires a parquet engine — install with `pip install brightwind[parquet]` (pyarrow) or `pip install brightwind[parquet-fastparquet]` (fastparquet). ([#601](https://github.com/brightwind-dev/brightwind/issues/601))
+
+### Bug Fixes
+1. 
+
+### Deprecated
+1. The implicit default of `file_extension='.csv'` in `LoadBrightHub.get_data()` is deprecated and will change to `'.parquet'` in the next major release. Calls that do not pass `file_extension` explicitly will emit a `DeprecationWarning`. Pass `file_extension='.csv'` to keep the current behaviour, or `file_extension='.parquet'` to opt in early. ([#601](https://github.com/brightwind-dev/brightwind/issues/601))
+
 ---
 ## [2.6.0]
 02-Apr-2026

README.md

@@ -13,106 +13,139 @@
 
 <br>
 
-Brightwind is a Python library specifically built for wind analysis. It can load in wind speed, wind direction and 
-other metrological timeseries data. There are various plots you can use to understand this data and to find any 
-potential issues. You can perform many common functions to the data such as shear and long-term adjustments. The 
-resulting adjusted data is then outputted as a frequency distribution tab file which can be used in wind analysis 
-software such as WAsP.
+Brightwind is an open-source Python library for wind (and solar) resource analysis. It loads meteorological
+timeseries data, runs common analyses — shear, long-term adjustments, correlations, distributions — and exports
+results to formats used by wind analysis software such as WAsP.
 
-This library can also be used for solar resource analysis.
+📚 **Full documentation, tutorials and API reference:** https://brightwind-dev.github.io/brightwind-docs/
 
 <br>
 
 ---
 ### Installation
 
-You can use pip from the command line to install the library.
+Install brightwind into its own environment to avoid dependency clashes. Pick whichever option suits you.
 
+#### Option 1 — venv (quick way to try brightwind out)
+
+Check Python is installed (3.9+ recommended):
+
+```bash
+python --version
 ```
-C:\Users\Stephen> pip install brightwind
-```
-It is advisable to use a separate environment to avoid any dependency clashes with other libraries such as Pandas, Numpy 
-or Matplotlib you may already have installed.
 
-<br>
+If not, install it from [python.org/downloads](https://www.python.org/downloads/) — on Windows, tick **"Add
+Python to PATH"** in the installer.
 
-For those that do not have Python installed and are just getting started, we recommend installing Anaconda. Anaconda is 
-a Python distribution for scientific computing and so provides everything you need, Python, pip and Jupyter Notebook 
-along with libraries such as Pandas, Numpy and Matplotlib. Datacamp provide a good tutorial for [installing 
-Anaconda on Windows](https://www.datacamp.com/tutorial/installing-anaconda-windows) to get started.
+Then create an environment and install brightwind:
 
-Once Anaconda is installed, you can use the **Anaconda Prompt** to run the above command line `pip install brightwind`. 
-Or first use **Anaconda Navigator** to create an environment.
+```bash
+python -m venv brightwind_env
+# Windows
+brightwind_env\Scripts\activate
+# macOS / Linux
+source brightwind_env/bin/activate
 
----
-### Documentation
+pip install brightwind
+```
 
-Documentation on how to get setup and use the library can be found at https://brightwind-dev.github.io/brightwind-docs/
+#### Option 2 — conda
 
-<br>
+Common if you already use Anaconda or work primarily in Jupyter.
+[Anaconda](https://www.datacamp.com/tutorial/installing-anaconda-windows) bundles Python, pip and Jupyter in one
+installer. From the **Anaconda Prompt**:
 
-Example usage of the brightwind library is shown below using Jupyter Notebook. Jupyter Notebook is a powerful way to 
-immediately see the results of code you have written.
-<br>
+```bash
+conda create --name brightwind_env python=3.11
+conda activate brightwind_env
+pip install brightwind
+```
 
-<p>
+A step-by-step Windows install walkthrough is also available in the
+[tutorials](https://brightwind-dev.github.io/brightwind-docs/).
 
-![demo_image_1](read_me_1.png)
-![demo_image_2](read_me_2.png)
-</p>
+**Optional — parquet support**
 
+Reading `.parquet` files from BrightHub needs a parquet engine. Install the default (pyarrow):
 
+```bash
+pip install brightwind[parquet]
+```
 
+Or use fastparquet instead: `pip install brightwind[parquet-fastparquet]`.
 
 <br>
 
-##### Features
-The library provides wind analysts with easy to use tools for working with
-meteorological data. It supports loading of meteorological data, averaging,
-filtering, plotting, correlations, shear analysis, long term adjustments, etc.
-The library can then export a resulting long term adjusted tab file to be used in
-other wind analysis software.
+---
+### Quick start
+
+Most analysts use brightwind from a Jupyter Notebook:
+
+```bash
+pip install jupyter
+jupyter notebook
+```
+
+```python
+import brightwind as bw
+
+data = bw.load_csv(bw.demo_datasets.demo_data)
+bw.basic_stats(data)
+```
+
+For full examples — loading, plotting, shear, correlations, exporting — see the
+[tutorials and API reference](https://brightwind-dev.github.io/brightwind-docs/).
 
 <br>
 
-##### Benefits
-The key benefits to an open-source library is that it provides complete transparency
-and traceability. Anyone in the industry can review any part of the code and suggest changes,
-thus creating a standardised, validated toolkit for the industry.
+<p>
 
-By default, during an assessment every manipulation or adjustment made to the wind data is
-contained in a single file. This can easily be reviewed and checked by internal reviewers or,
-as the underlying code is open-sourced, there is no reason why this file cannot be sent to
-3rd parties for review thus increasing the effectiveness of a banks due diligence.
+![demo_image_1](read_me_1.png)
+![demo_image_2](read_me_2.png)
+</p>
 
 <br>
 
-##### License
-The library is licensed under the MIT license.
+---
+### Why open-source?
+
+The brightwind library is open-source, making every step of an assessment transparent, auditable and reproducible. The full record of
+adjustments to a dataset lives in a single file that internal reviewers, third parties and banks can inspect
+directly — sharpening due diligence and removing the "black box" problem of proprietary tools.
+
+The intent is a shared, validated toolkit that the wind and solar industry builds on together, rather than each
+consultancy reinventing the same calculations behind closed doors.
 
 <br>
 
 ---
 ### Test datasets
-A test dataset is included in this repository and is used to demonstrate function and test functions in the code. 
-Other files and datasets are also included to complement this demo dataset. These are outlined below:
 
-<br>
+Demo datasets are bundled with the library to demonstrate functions and exercise the test suite:
 
-| Dataset               | Source           | Notes  |
-|:--------------------- |:-------------|:-----|
-| demo_data.csv         | BrightWind | A modified 2 year met mast dataset in csv and Campbell Scientific format. |
-| MERRA-2_XX_2000-01-01_2017-06-30.csv | NASA [GES DISC](https://disc.gsfc.nasa.gov/) | 4 x MERRA-2 18-yr datasets to complement the demo data for long term analyses. |
-| demo_cleaning_file.csv | BrightWind | A file containing information on what periods to clean out from the demo data. |
-| windographer_flagging_log.txt | BrightWind | The same cleaning info as found in 'demo_cleaning_file.csv' formatted as a Windographer flagging file. |
-| demo_data_iea43_wra_data_model.json | BrightWind | A JSON file formatted according to the IEA Wind Task 43 [WRA Data Model](https://github.com/IEA-Task-43/digital_wra_data_standard) standard which describes the mast configuration for the demo data. |
+| Dataset | Source | Notes |
+|:--- |:--- |:--- |
+| `demo_data.csv` | BrightWind | A modified 2-year met mast dataset in CSV and Campbell Scientific format. |
+| `MERRA-2_XX_2000-01-01_2017-06-30.csv` | NASA [GES DISC](https://disc.gsfc.nasa.gov/) | 4 × MERRA-2 18-year datasets to complement the demo data for long-term analyses. |
+| `demo_cleaning_file.csv` | BrightWind | Periods to clean out from the demo data. |
+| `windographer_flagging_log.txt` | BrightWind | Same cleaning info as `demo_cleaning_file.csv` formatted as a Windographer flagging file. |
+| `demo_data_iea43_wra_data_model.json` | BrightWind | A JSON file formatted to the IEA Wind Task 43 [WRA Data Model](https://github.com/IEA-Task-43/digital_wra_data_standard) standard, describing the mast configuration for the demo data. |
 
 <br>
 
 ---
 ### Contributing
-If you wish to be involved or find out more please contact stephen@brightwindanalysis.com.
 
-More information can be found in the [contributing.md](https://github.com/brightwind-dev/brightwind/blob/master/contributing.md) section of the website.
+Brightwind welcomes contributions from across the wind and solar industry — analysts, engineers, researchers and
+developers.
+
+- **Issues, bugs and feature requests:** [GitHub issue tracker](https://github.com/brightwind-dev/brightwind/issues)
+- **Code contributions and development setup:** see [contributing.md](contributing.md)
+- **General enquiries:** stephen@brightwindanalysis.com
 
 <br>
+
+---
+### License
+
+MIT — see [LICENSE.txt](LICENSE.txt).

brightwind/__init__.py

@@ -34,4 +34,4 @@
 
 __all__ = ['analyse', 'transform', 'export', 'load', 'demo_datasets']
 
-__version__ = '2.6.0'
+__version__ = '2.7.0'

brightwind/load/cleaning_rule.schema.json

@@ -1,6 +1,6 @@
 {
   "$schema": "http://json-schema.org/draft-07/schema#",
-  "$version": "1.0.0",
+  "$version": "1.1.0",
   "definitions": {
     "condition": {
       "type": "object",
@@ -47,6 +47,44 @@
         { "required": ["or"], "maxProperties": 1 },
         { "required": ["not"], "maxProperties": 1 }
       ]
+    },
+    "time_condition": {
+      "type": "object",
+      "properties": {
+        "value": {
+          "type": "string",
+          "format": "date-time",
+          "description": "An ISO 8601 timestamp compared against the timeseries datetime index."
+        },
+        "comparison_operator_id": {
+          "type": "integer",
+          "oneOf": [
+            { "const": 1, "description": "is less than" },
+            { "const": 2, "description": "is less than or equal" },
+            { "const": 3, "description": "is greater than" },
+            { "const": 4, "description": "is greater than or equal" },
+            { "const": 5, "description": "equals" },
+            { "const": 6, "description": "not equals" }
+          ]
+        },
+        "and": {
+          "type": "array",
+          "items": { "$ref": "#/definitions/time_condition" },
+          "minItems": 2
+        },
+        "or": {
+          "type": "array",
+          "items": { "$ref": "#/definitions/time_condition" },
+          "minItems": 2
+        },
+        "not": { "$ref": "#/definitions/time_condition" }
+      },
+      "oneOf": [
+        { "required": ["value", "comparison_operator_id"] },
+        { "required": ["and"], "maxProperties": 1 },
+        { "required": ["or"], "maxProperties": 1 },
+        { "required": ["not"], "maxProperties": 1 }
+      ]
     }
   },
   "type": "object",
@@ -55,6 +93,7 @@
       "type": "object",
       "properties": {
         "conditions": { "$ref": "#/definitions/condition" },
+        "time_range_conditions": { "$ref": "#/definitions/time_condition" },
         "clean_out": {
           "type": "array",
           "items": {