Update code by removing old notebooks and scripts (#70)

Remove old notebooks, unused scripts.
Update README.md and Makefile.
Remove unimplemented tests

Author: santisoler

code/Makefile

@@ -1,4 +1,4 @@
-# Manages building, testing, and cleaning the code as well as running the code
+# Manages building and cleaning the code as well as running the code
 # to generate the results and figures for the paper.
 
 # CONFIGURATION
@@ -7,7 +7,6 @@
 # Set the package name
 PACKAGE = tesseroid_density
 
-TESTDIR = tests
 SCRIPTSDIR = scripts
 RESDIR = $(SCRIPTSDIR)/results
 
@@ -26,13 +25,6 @@ RESULTS = $(RESDIR)/linear           \
 		  $(RESDIR)/computation_time \
 		  $(RESDIR)/neuquen          \
 
-PYTEST_ARGS = --doctest-modules -v
-PYTEST_COV_ARGS = --cov-report=term-missing
-
-# Execute a notebook on the command line. Modifies the notebook inplace so the
-# final execution order will be linear (from top to bottom). Running notebooks
-# like this helps ensure that the final results are not a product of a
-# non-linear execution order that can't be reproduced.
 RUN = python
 
 
@@ -42,10 +34,8 @@ RUN = python
 help:
 	@echo "Commands:"
 	@echo ""
-	@echo "  all            runs 'build', 'test', and 'figures'"
+	@echo "  all            runs 'build' and 'figures'"
 	@echo "  build          build and install the package"
-	@echo "  test           run the test suite (including doctests)"
-	@echo "  coverage       calculate test coverage"
 	@echo "  check          run all code quality checks (pep8, linter)"
 	@echo "  pep8           check for PEP8 style compliance"
 	@echo "  lint           run static analysis using pylint"
@@ -59,12 +49,12 @@ help:
 # the results of long running computations.
 clean:
 	find . -name "*.pyc" -exec rm -v {} \;
-	rm -rvf build dist MANIFEST *.egg-info __pycache__ .coverage .cache
+	rm -rvf build dist MANIFEST *.egg-info __pycache__ .cache
 
 
 # Run all steps to generate the final figures. Results files are generated
 # based on the figure requirements (see below).
-all: build test figures
+all: build figures
 
 
 # Generating figures
@@ -82,22 +72,16 @@ results:
 	$(foreach script, $(SCRIPTS), $(RUN) $(script);)
 
 
-# Building and testing the code
+# Building and linting the code
 #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 build:
 	pip install --no-deps -e .
 
-test:
-	pytest $(PYTEST_ARGS) $(PACKAGE) $(TESTDIR)
-
-coverage:
-	pytest $(PYTEST_COV_ARGS) --cov=$(PACKAGE) $(PYTEST_ARGS) $(PACKAGE) $(TESTDIR)
-
 check: pep8 lint
 
 pep8:
-	flake8 $(PACKAGE) $(TESTDIR) setup.py
+	flake8 $(PACKAGE) setup.py
 
 lint:
 	pylint $(PACKAGE) setup.py

code/README.md

@@ -3,7 +3,6 @@
 The code is divided between Python modules in `tesseroid-density`, Jupyter
 notebooks in `notebooks`, and analysis scripts in `scripts`.
 The modules implement the methodology and code that is reused in different applications.
-This code is tested using `pytest` with the test code in `tests`.
 
 The `Makefile` automates all processes related to executing code.
 Run the following to perform all actions from building the software to
@@ -24,9 +23,9 @@ function to every tesseroid by defining a regular Python function with a single
 `height` argument (float or numpy 1d array) that returns a float.
 
 
-## Building, testing, and linting
+## Building and linting
 
-Use the `Makefile` to build, test, and lint the software:
+Use the `Makefile` to build and lint the software:
 
 * Build and install:
 
@@ -36,45 +35,62 @@ Use the `Makefile` to build, test, and lint the software:
 
         make check
 
-* Run the tests in `tests` and doctests in docstrings:
 
-        make test
-
-* Calculate the test coverage of the main Python code (not including the
-  notebooks):
-
-        make coverage
+## Scripts
+
+Inside `scripts` we can find the Python scripts that create the results and figures
+shown on the manuscript.
+
+- `density-based-discretization.py`: Create example figure for how the
+  discretization-based algorithm works.
+- `linear_density.py`: Compares the numerical results of a spherical shell with its
+  analytical solution in case of a linear density in depth. Only the adaptive
+  discretization algorithm is performed.
+- `grid_search.py`: Performs a grid search by comparing the numerical results of
+  a spherical shell with its analytical solution in case of an exponential density. The
+  grid search is done by exploring values of distance-size ratio (D) and delta ratio.
+- `exponential_density.py`: Compares the numerical results of a spherical shell with its
+  analytical solution in case of an exponential density in depth. It fixes the
+  distance-size ratio (D) for the values obtained on the linear density case.
+- `sine_density.py`: Compares the numerical results of a spherical shell with its
+  analytical solution in case of a sinusoidal density in depth. The purpose of this
+  comparison is only to test the behaviour of the numerical approximation even on
+  a highly varying density function. These kind of density functions are not intended to
+  be applied on an actual geophysical case.
+- `number-of-tesseroids.py`: Compute the number of discretization the density-based
+  algorithm performs for different density functions.
+- `neuquen_basin.py`: Apply the new methodology to forward model the Neuquén Basin with
+  an exponential increasing density in depth and compare it with the results for an
+  homogeneous and a linear density.
+
+Extra files:
+- `tesseroid_model.py`: File containing useful classes for the Neuquén Basin forward
+  modelling.
+
+Directories:
+- `results`: Directory where all resulting files generated by the scripts will be saved.
+
+
+You can generate the results by running all scripts with:
+```
+make results
+```
+This could take a lot of time, so please be patient.
+
+Or you can just recreate the figures with:
+```
+make figures
+```
 
 ## Notebooks
 
-Inside `notebooks` we can find notebooks that show examples on how to use the
-package, solve the analytical solutions of the gravity fields generated by a
-spherical shell with variable density, determine the optimal parameters needed
-to achieve a good numerical approximation of the gravity fields and perform a
-forward modelling of the Neuquén Basin using an increasing exponential density
-function with depth.
+Inside `notebooks` we can find a single notebook showing how `tesseroid_density` could
+be used on forward modelling a mesh of tesseroids or a single one.
 
-* [01-simple-example.ipynb](): Shows examples on how to use the variable
-  density tesseroid code to model a single Tesseroid or Tesseroids meshes and
+* [tesseroid_density_example.ipynb](): Shows examples on how to use the variable
+  density tesseroid code to model a single tesseroid or tesseroids meshes and
   compute the gravitational fields they generate.
-* [02-spherical-shell-analytical-solutions.ipynb](): Obtains the analytical
-  solution for the gravitational potential generated by a spherical shell with
-  variable density in the radial direction. Gives specific solutions for
-  linear and exponential density functions.
-* [03-linear-density-test.ipynb](): Obtains the density-size ratio for the
-  for a linear density Tesseroids by comparing the numerical results with the
-  analytical solution of the gravitational fields generated by a spherical
-  shell with the same linear density.
-* [04-density-based-discretization-algorithm.ipynb](): Illustrates how the new
-  density-based discretization algorithm works and how a lower value of delta
-  produces more subdivisions to the original tesseroids.
-* [05-exponential-density-grid-search.ipynb](): Performs a grid search on the
-  D-delta space to find the optimal distance-size ratio and delta ratio pair by
-  comparing the numerical result with the analytical solution for the spherical
-  shell.
-* [06-exponential-density-delta-determination.ipynb](): Determines the optimal
-  value of delta ratio for several exponential density functions while keeping
-  D equal to the values determined in the linear density case.
-* [07-neuquen-basin.ipynb](): Forward model of the Neuquén Basin using an
-  increasing exponential density.
-* [08-plot-figures.ipynb](): Produces ready to publish figures.
+
+In order to open the notebook, run `jupyter-notebook`. It will open a new tab on your
+web browser in which you can navigate and open the notebook.
+If you want to obtain the same results, run the cells in order.