updating pvops nomenclature

Author: MichaelHopwood

.coveragerc

@@ -1,3 +1,3 @@
 [run]
 omit =
-     pvOps/tests/conftest.py
+     pvops/tests/conftest.py

.github/workflows/lint-and-test.yml

@@ -30,7 +30,7 @@ jobs:
     - name: Test with pytest
       run: |
         pip install pytest pytest-cov
-        pytest --cov=pvOps --cov-config=.coveragerc --cov-report term-missing pvOps
+        pytest --cov=pvops --cov-config=.coveragerc --cov-report term-missing pvops
 
   lint:
 

.github/workflows/pythonpublish.yml

@@ -9,7 +9,7 @@ jobs:
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v2
       with:
         python-version: '3.x'
     - name: Install dependencies

.gitignore

@@ -1,5 +1,5 @@
 __pycache__/
-pvOps/text/__pycache__/
-pvOps/text2time/__pycache__/
+pvops/text/__pycache__/
+pvops/text2time/__pycache__/
 examples/*.npy
 docs/_build/

README.md

@@ -1,9 +1,9 @@
-# pvOps
-pvOps contains a series of functions to facilitate fusion of text-based data with time series production data collected at photovoltaic sites. The package also contains example datasets and tutorials to help demonstrate how the functions can be used.
+# pvops
+pvops contains a series of functions to facilitate fusion of text-based data with time series production data collected at photovoltaic sites. The package also contains example datasets and tutorials to help demonstrate how the functions can be used.
 
 Installation
 =============
-pvOps can be installed using `pip`. See more information at [readthedocs](https://pvops.readthedocs.io/en/latest/).
+pvops can be installed using `pip`. See more information at [readthedocs](https://pvops.readthedocs.io/en/latest/).
 
 
 Package Layout and Documentation
@@ -16,7 +16,7 @@ The package is delineated into the following directories. Refer to the `examples
 ├───examples            : Functionality examples directory
 │   └─── example_data   : └─── Example data
 |
-└───pvOps               : Source function library
+└───pvops               : Source function library
     ├───tests           : ├─── Library stability tests
     ├───text            : ├─── Text processing functions
     └───text2time       : └─── Text2Timeseries functions
@@ -27,11 +27,11 @@ More information about theses modules is available in the documentation at [read
 Contributing
 ============
 
-The long-term success of pvOps requires community support. Please see the [Contributing page](https://pvops.readthedocs.io/en/latest/) for more on how you can contribute.
+The long-term success of pvops requires community support. Please see the [Contributing page](https://pvops.readthedocs.io/en/latest/) for more on how you can contribute.
 
 
 Copyright and License
 =======
 
-pvOps is copyright through Sandia National Laboratories. The software is distributed under the Revised BSD License. See [copyright and license](https://github.com/tgunda/pvOps/blob/master/LICENSE) for more information.
+pvops is copyright through Sandia National Laboratories. The software is distributed under the Revised BSD License. See [copyright and license](https://github.com/tgunda/pvops/blob/master/LICENSE) for more information.
 

docs/conf.py

@@ -20,14 +20,14 @@
 
 import shlex
 
-sys.path.insert(0, os.path.abspath("../pvOps"))
-sys.path.insert(0, os.path.abspath("../pvOps/text2time"))
-sys.path.insert(0, os.path.abspath("../pvOps/text"))
+sys.path.insert(0, os.path.abspath("../pvops"))
+sys.path.insert(0, os.path.abspath("../pvops/text2time"))
+sys.path.insert(0, os.path.abspath("../pvops/text"))
 
 
 # -- Project information -----------------------------------------------------
 
-project = "pvOps"
+project = "pvops"
 copyright = "2021, Thushara Gunda"
 author = "Thushara Gunda"
 

docs/contributing.rst

@@ -7,11 +7,11 @@ Thank you for wanting to contribute to this library! We will try to make this an
 
 **If you find errors in the codebase:**
 
-1. Submit an issue at the `pvOps issues page <https://github.com/tgunda/pvOps/issues>`_
+1. Submit an issue at the `pvops issues page <https://github.com/tgunda/pvops/issues>`_
 
 **If you would like to contribute:**
 
-2. Create a pull request (PR) or help with other PRs which are in the library by referencing `pvOps PR page <https://github.com/tgunda/pvOps/pulls>`_. Pull requests should contain code that follows the PEP8 python code style.
+2. Create a pull request (PR) or help with other PRs which are in the library by referencing `pvops PR page <https://github.com/tgunda/pvops/pulls>`_. Pull requests should contain code that follows the PEP8 python code style.
 
 **If you are confused:**
 

docs/index.rst

@@ -1,7 +1,7 @@
 .. _index:
 
 ******************************************************
-pvOps: a python package for PV operators & researchers
+pvops: a python package for PV operators & researchers
 ******************************************************
 
 .. toctree::
@@ -12,11 +12,11 @@ pvOps: a python package for PV operators & researchers
 
 Introduction
 ============
-pvOps contains a series of functions to facilitate fusion of text-based data with time series production data collected at photovoltaic sites. The package also contains example datasets and tutorials to help demonstrate how the functions can be used.
+pvops contains a series of functions to facilitate fusion of text-based data with time series production data collected at photovoltaic sites. The package also contains example datasets and tutorials to help demonstrate how the functions can be used.
 
 Installation
 =============
-pvOps can be installed using `pip`.
+pvops can be installed using `pip`.
 
 
 Text Subpackage Layout
@@ -90,12 +90,12 @@ These functions focus on visualizing the processed O&M and production data
 Contributing
 ============
 
-The long-term success of pvOps requires community support. One can make contributions by submitting issues and pull requests. Please follow the PR template
+The long-term success of pvops requires community support. One can make contributions by submitting issues and pull requests. Please follow the PR template
 
 Copyright and License
 =====================
 
-pvOps is copyright through Sandia National Laboratories. The software is distributed under the Revised BSD License.
+pvops is copyright through Sandia National Laboratories. The software is distributed under the Revised BSD License.
 
 Indices and tables
 ==================

docs/modules.rst

@@ -1,4 +1,4 @@
-pvOps
+pvops
 =====
 
 .. toctree::

examples/text_class_example.py

@@ -8,14 +8,14 @@
 import os
 
 sys.path.append("..")
-text_directory = os.path.join("..", "pvOps", "text")
+text_directory = os.path.join("..", "pvops", "text")
 sys.path.append(text_directory)
 
 # Utilities
-from pvOps.text import create_stopwords, summarize_text_data
+from pvops.text import create_stopwords, summarize_text_data
 
 # Visualizations
-from pvOps.text import (
+from pvops.text import (
     visualize_word_frequency_plot,
     visualize_attribute_connectivity,
     visualize_attribute_timeseries,
@@ -24,13 +24,13 @@
 )
 
 # Preprocessing
-from pvOps.text import preprocessor, DataDensifier, Doc2VecModel
+from pvops.text import preprocessor, DataDensifier, Doc2VecModel
 
 # Classification
-from pvOps.text import classification_deployer
+from pvops.text import classification_deployer
 
 # Library example definitions
-from pvOps.text import supervised_classifier_defs, unsupervised_classifier_defs
+from pvops.text import supervised_classifier_defs, unsupervised_classifier_defs
 
 # Embedding
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -271,7 +271,7 @@ def classify_supervised(
                 Thoroughness of supervised classification investigation with following options:
                 'normal': a smaller subset of settings used in grid search
                 'detailed': a comprehensive set of settings used in grid search
-                For specifics, view the `supervised_classifier_defs.py` file located in `/pvOps/text/`.
+                For specifics, view the `supervised_classifier_defs.py` file located in `/pvops/text/`.
                 To specify your own classifier pipeline, use `user_defined_classes` and `user_defined_search_space` parameters.