update readme

README.md

@@ -24,74 +24,14 @@ The package is delineated into the following directories. Refer to the `examples
 
 More information about theses modules is available in the documentation at [readthedocs](https://pvops.readthedocs.io/en/latest/).
 
-Text Subpackage Layout
-======================
-
-The text package can be broken down into three main components: `text processing`, `text classification`, and `visualizations`.
-
-An example implementation of all capabilities can be found in `text_class_example.py` (for specifics) and `tutorial_textmodule.ipynb` (for basics).
-
-##### **Text processing:** 
-Process the documents into concise, machine learning-ready documents. Additionally, extract dates from the text.
-
-* `preprocessor.py` acts as a wrapper function, utilizing the other preprocessing functions, which preps the data for machine learning. 
-* If all you want to do is extract dates (and not continue to preprocess all text for machine learning), then call preprocessor as `preprocessor.preprocessor(..., extract_dates_only = True)`
-  * see `text_class_example.extract_dates()` module for an example
-* To prep documents for machine learning, utilize the `preprocessor.preprocessor()`
-  * see `text_class_example.prep_data_for_ML()` module for an example
-
-##### **Text classification:** 
-The written tickets are used to make an inference on the specified event descriptor.
-
-* Conduct supervised or unsupervised classification of text documents utilizing `classification_deployer.classification_deployer()`. This function conducts a grid search across the passed classifiers and hyperparameters.
-  * See `text_class_example.classify_supervised()` or `text_class_example.classify_unsupervised()` modules for an example
-* Once the model is built and selected, one can conduct classification (for supervised ML) or clustering (for unsupervised ML) by conducting a prediction on the returned pipeline object. 
-  * See `text_class_example.predict_best_model` module for an example
-
-##### **Visualizations:**
-Create visualizations to get a better understanding about your documents.
-
-*  Observe brief description about the passed documents by calling `summarize_text_data.summarize_text_data()`
-*  Observe attribute ticket densities across time using `visualize_ticket_publication_timeseries`
-*  Observe the performance of different text embeddings by calling `visualize_cluster_entropy`
-*  Observe word frequencies in the passed attribute's documents by calling `visualize_freqPlot`
-*  Observe graph which indicates the connectivity of two attributes by calling `visualize_attribute_connectivity`
-*  After clustering, utilize `visualize_document_clusters` to observe popular words in each cluster
-
-
-Text2Time Subpackage Layout
-===========================
-
-The text2time package can be broken down into two main components: `data pre-processing` and `visualizations`.
-
-#### **Data pre-processing:** 
-These functions focus on pre-processing user O&M and production data to create visualizations of the merged data.
-* `om_date_convert` and `prod_date_convert` convert dates in string format to date-time objects
-* `data_site_na` is used to handle missing site IDs in the user data.  This function can be used for both O&M and production data.
-* `om_datelogic_check` is used to detect/correct issues with the logic of the O&M date, specifically when the conclusion of an event occurs before it begins.
-* `prod_anomalies` is used to detect/correct issues when the production data is input in cumulative format and unexpected dips show up in the data.
-* `prod_nadate_process` is used to detect/correct any missing time-stamps in the production data.
-* `om_nadate_process` is used to detect/correct any missing time-stamps in the O&M data.
-* `summarize_overlaps` is used to summarize the overlapping production and O&M data.
-* `overlapping_data` is used to trim the production and O&M data frames and only retain the data where both datasets overlap in time.
-* `iec_calc` is used to calculate a comparison dataset for the production data based on an irradiance as calculated by IEC calculation
-* `prod_quant` is used to calculate a comparison between the actual production data and a baseline (e.g. the IEC calculation)
-
-#### **Visualizations:**
-These functions focus on visualizing the processed O&M and production data
-* `visualize_om_prod_overlap` creates a visualization that overlays the O&M data on top of the coinciding production data.
-* `visualize_categorical_scatter` generates categorical scatter plots of chosen variable based on specified category (e.g. site ID) for the O&M data.
-* `visualize_counts` generates a count plot of categories based on a chosen categorical variable column for the O&M data.  If that variable is the user's site ID for every ticket, a plot for total count of events can be generated.
-
-
 Contributing
 ============
 
-The long-term success of pvOps requires community support. Please see the [Contributing page](http://pvlib-python.readthedocs.io/en/stable/contributing.html) 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] (http://pvlib-python.readthedocs.io/en/stable/) 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.