clarity

CONTRIBUTING.md

@@ -1,6 +1,6 @@
 ###Contributing
 
-Thanks to all those who contributed so far, including: [Will Usher](https://github.com/willu47), [Chris Mutel](https://github.com/cmutel), [Matt Woodruff](https://github.com/matthewjwoodruff), [Fernando Rios](https://github.com/zoidy), and [Dan Hyams](https://github.com/dhyams), [xantares](https://github.com/xantares). The library would be far less usable without these efforts.
+Thanks to all those who contributed so far, including: [Will Usher](https://github.com/willu47), [Chris Mutel](https://github.com/cmutel), [Matt Woodruff](https://github.com/matthewjwoodruff), [Fernando Rios](https://github.com/zoidy), and [Dan Hyams](https://github.com/dhyams), [xantares](https://github.com/xantares). The library would be in much worse shape without these efforts.
 
 In my view, development should follow a few objectives:
 

README-advanced.md

@@ -14,17 +14,17 @@ Parameter files can also be comma-delimited if your parameter names or group nam
 
 #### Command-line interface
 
-First, generate samples ... (the `-p` flag is the parameter file)
+**Generate samples** (the `-p` flag is the parameter file)
 ```
 python -m SALib.sample.saltelli \
      -n 1000 \
      -p ./SALib/test_functions/params/Ishigami.txt \
      -o model_input.txt \
 ```
 
-Then, run the model ... this will usually be a user-defined model, maybe even in another language. Just save the outputs.
+**Run the model** this will usually be a user-defined model, maybe even in another language. Just save the outputs.
 
-Finally, perform the analysis ...
+**Run the analysis**
 ```
 python -m SALib.analyze.sobol \
      -p ./SALib/test_functions/params/Ishigami.txt \
@@ -34,4 +34,4 @@ python -m SALib.analyze.sobol \
 
 This will print indices and confidence intervals to the command line. You can redirect to a file using the `>` operator.
 
-Other methods include `SALib.sample.morris` and `SALib.sample.fast_sampler`. For an explanation of all command line options for each method, [see the examples here](https://github.com/jdherman/SALib/tree/master/examples).
+Other methods include Morris, FAST, Delta-MIM, and DGSM. For an explanation of all command line options for each method, [see the examples here](https://github.com/jdherman/SALib/tree/master/examples).

README.md

@@ -21,6 +21,7 @@ Python implementations of commonly used sensitivity analysis methods. Useful in
 ```python
 from SALib.sample import saltelli
 from SALib.analyze import sobol
+from SALib.test_functions import Ishigami
 import numpy as np
 
 problem = {
@@ -36,25 +37,29 @@ param_values = saltelli.sample(problem, 1000, calc_second_order=True)
 
 # Run model (example)
 Y = Ishigami.evaluate(param_values)
-# for offline models, save param_values to a file:
-# np.savetxt('model_input.txt', param_values, delimiter=' ')
-# then load the model outputs with np.loadtxt()
 
 # Perform analysis
 Si = sobol.analyze(problem, Y, print_to_console=False)
 # Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'
 # (first and total-order indices with bootstrap confidence intervals)
 ```
 
-It's also possible to specify the parameter bounds in a file. Parameter files should be created with 3 columns:
+It's also possible to specify the parameter bounds in a file with 3 columns:
 ```
 # name lower_bound upper_bound
 P1 0.0 1.0
 P2 0.0 5.0
 ...etc.
 ```
 
-Lots of other options are included for parameter files, as well as a command-line interface--see the [Advanced Readme](README-advanced.md).
+Then the `problem` dictionary above can be created from the `read_param_file` function:
+```python
+from SALib.util import read_param_file
+problem = read_param_file('/path/to/file.txt')
+# ... same as above
+```
+
+Lots of other options are included for parameter files, as well as a command-line interface. See the [advanced readme](README-advanced.md).
 
 Also check out the [examples](https://github.com/jdherman/SALib/tree/master/examples) for a full description of options for each method.