MAINT: np.ediff1d-->np.diff (refnx#224)

.gitattributes

@@ -6,6 +6,7 @@
 *.rst text=auto eol=lf
 *.txt text=auto eol=lf
 *.yml text=auto eol=lf
+*.md text=auto eol=lf
 
 
 # Force git to look at some files as binary

CHANGELOG.txt

@@ -1,2 +1,6 @@
 Details of changes made to refnx
 ================================
+
+0.1.1
+-----
+- removal of Python 2.7 support

INSTALL_CONTRIBUTE.md

@@ -1,153 +1,153 @@
-# refnx - Installation and Development Instructions
-
-refnx is a python package for analysis of neutron and X-ray reflectometry data.
-It can also be used as a generalised curvefitting tool. It uses Markov Chain
-Monte Carlo to obtain posterior distributions for curvefitting problems.
-
---------------
-# Installation
-
-*refnx* has been tested on Python 3.5, 3.6 and 3.7. It requires the *numpy,
-scipy, cython, pandas, emcee* packages to work. Additional features require the
-*pytest, h5py, xlrd, uncertainties, ptemcee, tqdm, matplotlib, pymc3* packages.
-To build the bleeding edge code you will need to have access to a C-compiler to
-build a couple of Python extensions. C-compilers should be installed on Linux.
-On OSX you will need to install Xcode and the command line tools. On Windows you
-will need to install the correct [Visual Studio compiler][Visual-studio-compiler]
-for your Python version.
-
-In the current version of *refnx* the *emcee* package is vendored by *refnx*. That
-is, *refnx* possesses it's own private copy of the package, and there is no need to
-install the *emcee* package separately.
-
-## Installation into a *conda* environment
-
-Perhaps the easiest way to create a scientific computing environment is to use the
-[miniconda][miniconda] package manager. Once *conda* has been installed the first
-step is to create a *conda* environment.
-
-### Creating a conda environment
-
-  1) In a shell window create a conda environment and install the dependencies. The **-n** flag indicates that the environment is called *refnx*.
-
-  ```conda create -n refnx python=3.7 numpy scipy cython pandas h5py xlrd pytest tqdm```
-  2) Activate the environment that we're going to be working in:
-
-  ```
-  # on OSX
-  conda activate refnx
-
-  # on windows  
-  conda activate refnx
-  ```
-  3) Install the remaining dependencies:
-  ```pip install uncertainties ptemcee```
-
-### Installing into a conda environment from source
-
- The latest source code can be obtained from either [PyPi][PyPi] or [Github][github-refnx]. You can also build the package from within the refnx git repository (see later in this document).
-  1) In a shell window navigate into the source directory and build the package. If you are on Windows you'll need to start a Visual Studio command window.
-  ```
-  python setup.py build
-  python setup.py install
-  ```
-  2) Run the tests, they should all work.
-  ```
-  python setup.py test
-  ```
-
-### Installing into a conda environment from a released version
-
-  1) There are pre-built versions on *conda-forge*, but they're not necessarily at the bleeding edge:
-
-  ```conda install -c conda-forge refnx```
-  2) Start up a Python interpreter and make sure the tests run:
-  ```
-  >>> import refnx
-  >>> refnx.test()
-  ```
-
------------------------
-## Development Workflow
-
-These instructions outline the workflow for contributing to refnx development.
-The refnx community welcomes all contributions that will improve the package.
-The following instructions are based on use of a command line *git* client.
-*Git* is a distributed version control program. An example of [how to contribute to the numpy project][numpy-contrib]
-is a useful reference.
-
-### Setting up a local git repository 
-  1) Create an account on [github](https://github.com/).
-  2) On the [refnx github][github-refnx] page fork the *refnx* repository to your own github account. Forking means that now you have your own personal repository of the *refnx* code.
-  3) Now we will make a local copy of your personal repository on your local machine:
-  ```
-  # <username> is your github username
-  git clone https://github.com/<username>/refnx.git
-  ```
-  4) Add the *refnx* remote repository, we're going to refer to the remote with the *upstream* name:
-  ```
-  git remote add upstream https://github.com/refnx/refnx.git
-  ```
-  5) List the remote repositories that your local repository knows about:
-  ```
-  git remote -v
-  ```
-
-### Keeping your local and remote repositories up to date
-The main *refnx* repository may be a lot more advanced than your fork, or your local copy, of the git repository. 
-  1) To update your repositories you need to fetch the changes from the main *refnx* repository:
-  ```
-  git fetch upstream
-  ```
-  2) Now update the local branch you're on by rebasing against the *refnx* master branch:
-  ```
-  git rebase upstream/master
-  ```
-  3) Push your updated local branch to the remote fork on github. You have to specify the remote branch you're pushing to. Here we push to the *master* branch:
-  ```
-  git push origin master
-  ```
-
-### Adding a feature
-The git repository is automatically on the master branch to start with. However,
-when developing features that you'd like to contribute to the *refnx* project
-you'll need to do it on a feature branch.
-
-  1) Create a feature branch and check it out:
-  ```
-  git branch my_feature_name
-  git checkout my_feature_name
-  ```
-  2) Once you're happy with the changes you've made you should check that the tests still work:
-  ```
-  python setup.py test
-  ```
-  3) If the performance of what you've added/changed may be critical, then consider writing a benchmark. The benchmarks use
-  the *asv* package and are run as:
-  ```
-  cd benchmarks
-  pip install asv
-  asv run
-  asv publish
-  asv preview
-  ```
-  For an example benchmark look at one of the files in the *benchmarks* directory.
-  4) Now commit the changes. You'll have to supply a commit message that outlines the changes you made. The commit message should follow the [numpy guidelines][numpy-contib]
-  ```
-  git commit -a
-  ```
-  5) Now you need to push those changes on the *my_feature_branch* branch to *your* fork of the refnx repository on github:
-  ```
-  git push origin my_feature_branch
-  ```
-  6) On the main [refnx][github-refnx] repository you should be able to create a pull request (PR). The PR says that you'd like the *refnx* project to include the changes you made.
-  7) Once the automated tests have passed, and the *refnx* maintainers are happy with the changes you've made then the PR is merged. You can then delete the feature branch on github, and delete your local feature branch:
-  ```
-  git branch -D my_feature_branch
-  ```
-
-   [PyPi]: <https://pypi.python.org/pypi/refnx>
-   [github-refnx]: <https://github.com/refnx/refnx>
-   [Visual-studio-compiler]: <https://wiki.python.org/moin/WindowsCompilers>
-   [miniconda]: <https://conda.io/docs/install/quick.html>
+# refnx - Installation and Development Instructions
+
+refnx is a python package for analysis of neutron and X-ray reflectometry data.
+It can also be used as a generalised curvefitting tool. It uses Markov Chain
+Monte Carlo to obtain posterior distributions for curvefitting problems.
+
+--------------
+# Installation
+
+*refnx* has been tested on Python 3.5, 3.6 and 3.7. It requires the *numpy,
+scipy, cython, pandas, emcee* packages to work. Additional features require the
+*pytest, h5py, xlrd, uncertainties, ptemcee, tqdm, matplotlib, pymc3* packages.
+To build the bleeding edge code you will need to have access to a C-compiler to
+build a couple of Python extensions. C-compilers should be installed on Linux.
+On OSX you will need to install Xcode and the command line tools. On Windows you
+will need to install the correct [Visual Studio compiler][Visual-studio-compiler]
+for your Python version.
+
+In the current version of *refnx* the *emcee* package is vendored by *refnx*. That
+is, *refnx* possesses it's own private copy of the package, and there is no need to
+install the *emcee* package separately.
+
+## Installation into a *conda* environment
+
+Perhaps the easiest way to create a scientific computing environment is to use the
+[miniconda][miniconda] package manager. Once *conda* has been installed the first
+step is to create a *conda* environment.
+
+### Creating a conda environment
+
+  1) In a shell window create a conda environment and install the dependencies. The **-n** flag indicates that the environment is called *refnx*.
+
+  ```conda create -n refnx python=3.7 numpy scipy cython pandas h5py xlrd pytest tqdm```
+  2) Activate the environment that we're going to be working in:
+
+  ```
+  # on OSX
+  conda activate refnx
+
+  # on windows  
+  conda activate refnx
+  ```
+  3) Install the remaining dependencies:
+  ```pip install uncertainties ptemcee```
+
+### Installing into a conda environment from source
+
+ The latest source code can be obtained from either [PyPi][PyPi] or [Github][github-refnx]. You can also build the package from within the refnx git repository (see later in this document).
+  1) In a shell window navigate into the source directory and build the package. If you are on Windows you'll need to start a Visual Studio command window.
+  ```
+  python setup.py build
+  python setup.py install
+  ```
+  2) Run the tests, they should all work.
+  ```
+  python setup.py test
+  ```
+
+### Installing into a conda environment from a released version
+
+  1) There are pre-built versions on *conda-forge*, but they're not necessarily at the bleeding edge:
+
+  ```conda install -c conda-forge refnx```
+  2) Start up a Python interpreter and make sure the tests run:
+  ```
+  >>> import refnx
+  >>> refnx.test()
+  ```
+
+-----------------------
+## Development Workflow
+
+These instructions outline the workflow for contributing to refnx development.
+The refnx community welcomes all contributions that will improve the package.
+The following instructions are based on use of a command line *git* client.
+*Git* is a distributed version control program. An example of [how to contribute to the numpy project][numpy-contrib]
+is a useful reference.
+
+### Setting up a local git repository 
+  1) Create an account on [github](https://github.com/).
+  2) On the [refnx github][github-refnx] page fork the *refnx* repository to your own github account. Forking means that now you have your own personal repository of the *refnx* code.
+  3) Now we will make a local copy of your personal repository on your local machine:
+  ```
+  # <username> is your github username
+  git clone https://github.com/<username>/refnx.git
+  ```
+  4) Add the *refnx* remote repository, we're going to refer to the remote with the *upstream* name:
+  ```
+  git remote add upstream https://github.com/refnx/refnx.git
+  ```
+  5) List the remote repositories that your local repository knows about:
+  ```
+  git remote -v
+  ```
+
+### Keeping your local and remote repositories up to date
+The main *refnx* repository may be a lot more advanced than your fork, or your local copy, of the git repository. 
+  1) To update your repositories you need to fetch the changes from the main *refnx* repository:
+  ```
+  git fetch upstream
+  ```
+  2) Now update the local branch you're on by rebasing against the *refnx* master branch:
+  ```
+  git rebase upstream/master
+  ```
+  3) Push your updated local branch to the remote fork on github. You have to specify the remote branch you're pushing to. Here we push to the *master* branch:
+  ```
+  git push origin master
+  ```
+
+### Adding a feature
+The git repository is automatically on the master branch to start with. However,
+when developing features that you'd like to contribute to the *refnx* project
+you'll need to do it on a feature branch.
+
+  1) Create a feature branch and check it out:
+  ```
+  git branch my_feature_name
+  git checkout my_feature_name
+  ```
+  2) Once you're happy with the changes you've made you should check that the tests still work:
+  ```
+  python setup.py test
+  ```
+  3) If the performance of what you've added/changed may be critical, then consider writing a benchmark. The benchmarks use
+  the *asv* package and are run as:
+  ```
+  cd benchmarks
+  pip install asv
+  asv run
+  asv publish
+  asv preview
+  ```
+  For an example benchmark look at one of the files in the *benchmarks* directory.
+  4) Now commit the changes. You'll have to supply a commit message that outlines the changes you made. The commit message should follow the [numpy guidelines][numpy-contib]
+  ```
+  git commit -a
+  ```
+  5) Now you need to push those changes on the *my_feature_branch* branch to *your* fork of the refnx repository on github:
+  ```
+  git push origin my_feature_branch
+  ```
+  6) On the main [refnx][github-refnx] repository you should be able to create a pull request (PR). The PR says that you'd like the *refnx* project to include the changes you made.
+  7) Once the automated tests have passed, and the *refnx* maintainers are happy with the changes you've made then the PR is merged. You can then delete the feature branch on github, and delete your local feature branch:
+  ```
+  git branch -D my_feature_branch
+  ```
+
+   [PyPi]: <https://pypi.python.org/pypi/refnx>
+   [github-refnx]: <https://github.com/refnx/refnx>
+   [Visual-studio-compiler]: <https://wiki.python.org/moin/WindowsCompilers>
+   [miniconda]: <https://conda.io/docs/install/quick.html>
    [numpy-contrib]: <https://docs.scipy.org/doc/numpy/dev/>

refnx/reduce/platypusnexus.py

@@ -974,7 +974,7 @@ def process(self, h5norm=None, lo_wavelength=2.5, hi_wavelength=19.,
         be compared.
         """
         if normalise_bins:
-            div = 1 / np.ediff1d(m_lambda_hist[0])[:, np.newaxis]
+            div = 1 / np.diff(m_lambda_hist[0])[:, np.newaxis]
             detector, detector_sd = EP.EPmulk(detector,
                                               detector_sd,
                                               div)

refnx/reduce/rebin.py

@@ -193,7 +193,7 @@ def rebin(x1, y1, x2, y1_sd=None):
 
     # Divide y1 by bin widths.
     # This converts y-values from bin total to bin average over bin width.
-    x1_bin_widths = np.ediff1d(x1)
+    x1_bin_widths = np.diff(x1)
     y1_ave = y1 / x1_bin_widths
     y1_ave_sd = y1_sd_temp / x1_bin_widths
 
@@ -226,7 +226,7 @@ def rebin(x1, y1, x2, y1_sd=None):
             i_place[start_pos] == 0 and
             i_place[start_pos + 1] == x1.size):
         sub_edges = x1
-        sub_dx = np.ediff1d(sub_edges)
+        sub_dx = np.diff(sub_edges)
         sub_y_ave = y1_ave
         sub_y_ave_var = y1_ave_var
 
@@ -241,7 +241,7 @@ def rebin(x1, y1, x2, y1_sd=None):
         i_lo, i_hi = i_place[start_pos], i_place[start_pos + 1]
 
         sub_edges = np.hstack([x1[i_lo:i_hi], x2_hi])
-        sub_dx = np.ediff1d(sub_edges)
+        sub_dx = np.diff(sub_edges)
         sub_y_ave = y1_ave[i_lo: i_hi]
         sub_y_ave_var = y1_ave_var[i_lo:i_hi]
 
@@ -256,7 +256,7 @@ def rebin(x1, y1, x2, y1_sd=None):
         i_lo, i_hi = i_place[end_pos - 1], i_place[end_pos]
 
         sub_edges = np.hstack([x2_lo, x1[i_lo:i_hi]])
-        sub_dx = np.ediff1d(sub_edges)
+        sub_dx = np.diff(sub_edges)
         sub_y_ave = y1_ave[i_lo - 1:i_hi]
         sub_y_ave_var = y1_ave_var[i_lo - 1:i_hi]