Improve Solvers (#2)

README.md

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+# River Route
+
+`river-route` is a Python package for routing runoff through a river network.
+The routing calculations are vectorized and use numpy and scipy which keeps the array computation times on par with faster compiled languages. 
+
+The implemented routing methods are:
+- Muskingum Cunge - Analytical solution
+- Muskingum Cunge - Numerical solution
+
+## Quick Start Guide
+You will need to prepare a configuration file for the routing.
+
+```python
+import river_route as rr
+
+rm = rr.RouteMuskingum('/path/to/config.yml')
+rm.route()
+
+river_id_to_inspect = 12345
+rm.plot(river_id_to_inspect)
+```
+
+## Tips for efficient computations
+Results will vary based on your system specifications, the size of the river network, and the length of the simulation. 
+These tips may help you achieve faster results.
+
+1. **Use fewer inflow files**: File IO operations can be relatively slow and are a probable bottleneck on HPC systems 
+when I/O operations depend on networked drives. You may achieve faster results by doing a single computation 
+covering 2 weeks instead of 14 computations covering 1 day each.
+2. **Cache routing Matrices**: The adjacency matrix and inverted I-C2@A matrix can be time consuming to compute. Provide
+paths to store them in the config file to cache them between simulations
+3. **Adjust the time step**: Using a longer time step will reduce the number of computations which takes less time to 
+compute. It also requires storing fewer intermediate results in memory yielding a modest reduction in memory usage. A 
+longer time step can increase performance if it does not induce numerical instability in the outflows.
+

environment.yml

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+name: rr
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - dask
+  - fastparquet
+  - matplotlib
+  - netcdf4
+  - networkx
+  - numpy
+  - pandas
+  - petsc4py
+  - pyyaml
+  - scipy
+  - tqdm
+  - xarray
+  - zarr

requirements.txt

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+dask
 fastparquet
+matplotlib
+netcdf4
 networkx
 numpy
 pandas
+petsc4py
 pyyaml
 scipy
 tqdm

river_route/__init__.py

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 from river_route._route_muskingum import RouteMuskingum
 
+__version__ = '0.1.0'
+__author__ = 'Riley Hales PhD'
+__url__ = 'https://github.com/rileyhales/river_route'
+
 __all__ = [
     'RouteMuskingum',
+
+    __version__,
+    __author__,
+    __url__
 ]