Loading .idea/pythermogis.iml +2 −1 Original line number Diff line number Diff line Loading @@ -2,6 +2,7 @@ <module type="PYTHON_MODULE" version="4"> <component name="NewModuleRootManager"> <content url="file://$MODULE_DIR$"> <sourceFolder url="file://$MODULE_DIR$/tests" isTestSource="true" /> <sourceFolder url="file://$MODULE_DIR$/tests/tests" isTestSource="true" /> <excludeFolder url="file://$MODULE_DIR$/.venv" /> </content> Loading @@ -11,7 +12,7 @@ <component name="PyNamespacePackagesService"> <option name="namespacePackageFolders"> <list> <option value="$MODULE_DIR$/pythermogis/resources" /> <option value="$MODULE_DIR$/src/pythermogis/resources" /> </list> </option> </component> Loading README.md +21 −20 Original line number Diff line number Diff line Loading @@ -42,7 +42,7 @@ This will fetch the package from the specified GitLab package repository. Here’s a simple example demonstrating how to use `pyThermoGIS` for a single set of input values: ```python from pythermogis import calculate_doublet_performance from src.pythermogis import calculate_doublet_performance import xarray as xr # Initialize an input Dataset with the required input variables: Loading @@ -67,7 +67,7 @@ The API utilizes xarray's ability to efficiently calculate over multiple dimensi dimensions: ```python from pythermogis import calculate_doublet_performance from src.pythermogis import calculate_doublet_performance import xarray as xr import numpy as np Loading @@ -94,8 +94,9 @@ print(doublet_simulation_results) ``` Consider utilizing the pypi package pygridsio to read in and write out 2D raster grids (with either .asc, .zmap, .nc, .tif file formats) to a xarray dataset, before applying the calculate_doublet_performance code: ```python from pythermogis import calculate_doublet_performance from src.pythermogis import calculate_doublet_performance from pygridsio.pygridsio import read_grid # Initialize an input Dataset with the required input variables: Loading pythermogis/physics/__init__.py→src/__init__.py +0 −0 File moved. pythermogis/__init__.py→src/pythermogis/__init__.py +0 −0 File moved. View file pythermogis/statistics/__init__.py→src/pythermogis/physics/__init__.py +0 −0 File moved. Loading
.idea/pythermogis.iml +2 −1 Original line number Diff line number Diff line Loading @@ -2,6 +2,7 @@ <module type="PYTHON_MODULE" version="4"> <component name="NewModuleRootManager"> <content url="file://$MODULE_DIR$"> <sourceFolder url="file://$MODULE_DIR$/tests" isTestSource="true" /> <sourceFolder url="file://$MODULE_DIR$/tests/tests" isTestSource="true" /> <excludeFolder url="file://$MODULE_DIR$/.venv" /> </content> Loading @@ -11,7 +12,7 @@ <component name="PyNamespacePackagesService"> <option name="namespacePackageFolders"> <list> <option value="$MODULE_DIR$/pythermogis/resources" /> <option value="$MODULE_DIR$/src/pythermogis/resources" /> </list> </option> </component> Loading
README.md +21 −20 Original line number Diff line number Diff line Loading @@ -42,7 +42,7 @@ This will fetch the package from the specified GitLab package repository. Here’s a simple example demonstrating how to use `pyThermoGIS` for a single set of input values: ```python from pythermogis import calculate_doublet_performance from src.pythermogis import calculate_doublet_performance import xarray as xr # Initialize an input Dataset with the required input variables: Loading @@ -67,7 +67,7 @@ The API utilizes xarray's ability to efficiently calculate over multiple dimensi dimensions: ```python from pythermogis import calculate_doublet_performance from src.pythermogis import calculate_doublet_performance import xarray as xr import numpy as np Loading @@ -94,8 +94,9 @@ print(doublet_simulation_results) ``` Consider utilizing the pypi package pygridsio to read in and write out 2D raster grids (with either .asc, .zmap, .nc, .tif file formats) to a xarray dataset, before applying the calculate_doublet_performance code: ```python from pythermogis import calculate_doublet_performance from src.pythermogis import calculate_doublet_performance from pygridsio.pygridsio import read_grid # Initialize an input Dataset with the required input variables: Loading