Merge pull request #81 from aimalz/issue/80/descope

Issue/80/descope

.travis.yml

@@ -4,28 +4,28 @@ python: 2.7
 install:
   - sudo apt-get update
   # Install the dependencies and the package:
-  - pip install -r requirements.txt
-  - python setup.py install
+#  - pip install -r requirements.txt
+#  - python setup.py install
   # Now get set up to run jupyter notebooks:
   - sudo apt-get install texlive-latex-recommended
   - sudo apt-get install texlive-latex-extra
   - sudo apt-get install texlive-fonts-recommended
   - sudo apt-get install texlive-publishers
   - sudo apt-get install chktex
   - sudo apt-get install dvipng
-  - pip install --upgrade jupyter
-  - pip install nbconvert
+#  - pip install --upgrade jupyter
+#  - pip install nbconvert
   # Finally get set up to build the docs
-  - pip install sphinx
-  - pip install sphinx_rtd_theme
+#  - pip install sphinx
+#  - pip install sphinx_rtd_theme
 
 script:
   # Run the unit tests:
   # - nosetests
   # Run the demo notebook:
-  - jupyter nbconvert --ExecutePreprocessor.kernel_name=python --ExecutePreprocessor.timeout=600 --to notebook --execute docs/notebooks/demo2.ipynb
+#  - jupyter nbconvert --ExecutePreprocessor.kernel_name=python --ExecutePreprocessor.timeout=600 --to notebook --execute docs/notebooks/demo2.ipynb
   # Build the docs, the same way readthedocs does it:
-  - cd docs ; sphinx-build -b html . _build/html ; cd -
+#  - cd docs ; sphinx-build -b html . _build/html ; cd -
   # Compile the paper
   - cd research/paper; make
 

README.md

@@ -1,25 +1,35 @@
 # chippr
 
-Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts.
+Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts
 
 ## Motivation
 
-This repository is the home of `chippr`, a Python package for estimating quantities of cosmological interest from surveys of photometric redshift posterior probability distributions.  It is a refactoring of my previous [work](https://github.com/aimalz/prob-z) on probabilistic photometric redshifts.
+This repository is the home of `chippr`, a Python package for estimating quantities of cosmological interest from surveys of photometric redshift posterior probability distributions.  
+It is a refactoring of previous [work](https://github.com/aimalz/prob-z) on using probabilistic photometric redshifts to infer the redshift distribution.
 
 ## Examples
 
-You can browse the demo notebook here in this repo:
+You can browse the demo notebook here:
 
 * [Basic  Demo for Python 2.7](http://htmlpreview.github.io/?https://github.com/aimalz/chippr/blob/master/docs/notebooks/demo2.html)
 
 ## Documentation
 
-Documentation can be found on [ReadTheDocs](http://chippr.readthedocs.io/en/master/).  The draft of the paper documenting the details of the method can be found [here](https://github.com/aimalz/chippr/blob/master/research/paper/draft.pdf).
+Documentation can be found on [ReadTheDocs](http://chippr.readthedocs.io/en/master/).  
+The draft of the paper documenting the details of the method can be found [here](https://github.com/aimalz/chippr/blob/master/research/paper/draft.pdf).
+
+## Disclaimer
+
+As can be seen from the git history and Python version, this code is stale and should be understood to be a prototype, originally scoped out for applicability to SDSS DR10-era data of low dimensionality.
+As a disclaimer, it will need a major upgrade for flexibility and computational scaling before it can run on data sets like those of modern and future galaxy surveys.
 
 ## People
 
-* [Alex Malz](https://github.com/aimalz/qp/issues/new?body=@aimalz) (NYU)
+* [Alex I. Malz](https://github.com/aimalz) (German Centre for Cosmological Lensing)
 
 ## License, Contributing etc
 
-The code in this repo is available for re-use under the MIT license, which means that you can do whatever you like with it, just don't blame me. If you end up using any of the code or ideas you find here in your academic research, please cite me as `Malz et al, in preparation\footnote{\texttt{https://github.com/aimalz/chippr}}`. If you are interested in this project, please do drop me a line via the hyperlinked contact name above, or by [writing me an issue](https://github.com/aimalz/chippr/issues/new). To get started contributing to the `chippr` project, just fork the repo - pull requests are always welcome!
+The code in this repo is available for re-use under the MIT license, which means that you can do whatever you like with it, just don't blame me.
+If you end up using any of the code or ideas you find here in your academic research, please cite me as `Malz et al, in preparation\footnote{\texttt{https://github.com/aimalz/chippr}}`.
+If you are interested in this project, please do drop me a line via the hyperlinked contact name above, or by [writing me an issue](https://github.com/aimalz/chippr/issues/new).
+To get started contributing to the `chippr` project, just fork the repo -- pull requests are always welcome!

chippr/catalog.py

@@ -47,6 +47,7 @@ def __init__(self, params={}, vb=True, loc='.', prepend=''):
         if vb:
             print self.params
 
+        np.random.seed(d.seed)
         self.cat = {}
 
         self.dir = loc

chippr/catalog_plots.py

@@ -139,10 +139,10 @@ def plot_mega_scatter(zs, pfs, z_grid, grid_ends, truth=None, plot_loc='', prepe
     limval = (max(grid_ends) - min(grid_ends)) / (len(grid_ends) - 1.)
     scatplot.set_xlim([min(grid_ends)-limval, max(grid_ends)+limval])
     scatplot.set_ylim([min(grid_ends)-limval, max(grid_ends)+limval])
-    scatplot.set_xticks(np.linspace(min(grid_ends), np.ceil(max(grid_ends)), 5))
-    scatplot.set_yticks(np.linspace(min(grid_ends), np.ceil(max(grid_ends)), 5))
-    scatplot.set_xlabel(r'$z_{spec}$')
-    scatplot.set_ylabel(r'$z_{phot}$')
+    scatplot.set_xticks(np.linspace(np.floor(min(grid_ends)), np.ceil(max(grid_ends)), 5))
+    scatplot.set_yticks(np.linspace(np.floor(min(grid_ends)), np.ceil(max(grid_ends)), 5))
+    scatplot.set_xlabel(r'$z_{true}$')
+    scatplot.set_ylabel(r'$z_{est}$')
     scatplot.text(0.25, 3., r'mock $p(z\mid \mathrm{``data"})$', rotation=0, size=20)
 
     # scatplot.set_aspect(1.)

chippr/log_z_dens_plots.py

@@ -317,7 +317,7 @@ def plot_estimators(info, plot_dir, log=True, prepend='', metrics=True, mini=Tru
             f = plt.figure(figsize=(7.5, 7.5))
             sps_log = f.add_subplot(1, 1, 1)
             sps_log.set_xlabel(r'$z$')
-            sps_log.set_xticks(np.linspace(min(info['bin_ends']), np.ceil(max(info['bin_ends'])), 5))
+            sps_log.set_xticks(np.linspace(np.floor(min(info['bin_ends'])), np.ceil(max(info['bin_ends'])), 5))
         # mini_sps.ticklabel_format(style='sci',axis='y')
 
         # tru, =
@@ -344,7 +344,8 @@ def plot_estimators(info, plot_dir, log=True, prepend='', metrics=True, mini=Tru
         sps_log.set_ylim(-4., 1.)
         sps_log.set_ylabel(r'$\ln[n(z)]$')
     else:
-        sps_log.set_ylim(0., 3.)
+        sps_log.set_ylim(0., 4.)
+        sps_log.set_yticks([0,1,2,3,4])
         sps_log.set_ylabel(r'$n(z)$')
     sps_log.set_xlim(info['bin_ends'][0], info['bin_ends'][-1])
 
@@ -501,7 +502,11 @@ def plot_estimators(info, plot_dir, log=True, prepend='', metrics=True, mini=Tru
 
     # sps_log.legend(handles=color_plots[:-1], fontsize='x-small', loc='lower center', frameon=False)
     sps_log.legend(fontsize='large', loc='upper right', frameon=False)
-    sps_log.text(0.25, -3.75, r'inferred $n(z)$', rotation=0, size=20)
+    if log:
+        sps_log.text(0.25, -3.75, r'inferred $n(z)$', rotation=0, size=20)
+    else:
+        sps_log.text(2., 0.75, r'inferred $n(z)$', rotation=0, size=20)
+
     f.subplots_adjust(hspace=0, wspace=0)
     f.savefig(os.path.join(plot_dir, prepend+'estimators.png'), bbox_inches='tight', pad_inches = 0, dpi=d.dpi)
     print(info['stats'])

research/paper/cleaner.sh

@@ -1,7 +1,7 @@
 #infile='thesis.tex'
 #outfile='draft.tex'
 
-sed -e "s/{\\textbackslash}'\\\\{i\\\\}/\\'{i}/g" thesis.bib | grep -E -v '^\W*(doi|url|urldate|abstract|file|keywords|annote|note)' > thesis.bib.tmp
-mv thesis.bib.tmp thesis.bib
+sed -e "s/{\\textbackslash}'\\\\{i\\\\}/\\'{i}/g" draft.bib | grep -E -v '^\W*(doi|url|urldate|abstract|file|keywords|annote|note)' > draft.bib.tmp
+mv draft.bib.tmp draft.bib
 
 #grep -E -v '^(%|[[:blank:]]*%|\\COMMENT|\includegraphics*)' "$infile" | fold -w80 -s > "$outfile"