Remove unlicensed dependency and unused code

Pipfile

@@ -8,21 +8,17 @@ black = "*"
 
 [packages]
 bokeh = "==2.0.2"
-h5py = "==2.10.0"
 h5pyd = "==0.3.3"
 matplotlib = "==3.2.1"
 numpy = "==1.18.5"
 pandas = "~=1.1.2"
 py3samsdk = {editable = true, git = "https://github.com/sjschneider/py3samsdk.git"}
 pyproj = "==1.9.6"
-pywtk = {editable = true, git = "https://github.com/NREL/pywtk.git"}
 pytest = "==5.4.3"
 python-dateutil = "==2.7.5"
 requests = "==2.21.0"
 scipy = "==1.4.1"
 tqdm = "==4.29.1"
 xlrd = "==1.2.0"
-Flask = "==1.1.2"
 netCDF4 = "==1.5.3"
-Shapely = "==1.7.0"
 powersimdata = {editable = true, path = "../PowerSimData"}

README.md

@@ -99,25 +99,6 @@ tracks with the wind-farm level data shown in NREL's validation report.
 
 Check out the **[rap_demo.ipynb][RAP_notebook]** notebook for demo.
 
-#### ii. Techno-Economic Wind Integration National Dataset Toolkit
-The [Techno-Economic WIND (Wind Integration National Dataset) Toolkit][WIND_web]
-provides 5-min resolution data for 7 years, ranging from 2007 to 2013, at
-120,000 points within the continental U.S. selected for their wind resource.
-This set contains power estimates and forecasts along with a subset of
-atmospheric variables. Data can be accessed via an [API][WIND_api].
-
-The closest site to the wind farm in the network is found in the NREL dataset
-and the associated power estimate is simply scaled to the plant capacity to
-obtain a wind power output profile. The procedure is illustrated in the
-**[te_wind_demo.ipynb][TE_WIND_notebook]** notebook.
-
-Also, a test can be run as follows:
-```python
-from prereise.gather.winddata.te_wind.test import te_wind_test
-
-te_wind_test.test()
-```
-
 
 ### B. Solar data
 

prereise/gather/winddata/__init__.py

@@ -1 +1 @@
-__all__ = ["rap", "te_wind"]
+__all__ = ["rap"]