added env, updated params, cleanup #1

config.yml

@@ -1,4 +1,5 @@
 
-davbin : "/nfs/software/davinci_install/share/davinci/library/bin/"
-themisroot: "/home/krodriguez/jay/day/I01210009_I00849001/"
-outpath : "/data/out"
+davinci_bin : "/nfs/software/davinci_install/share/davinci/library/bin/"
+themis_root: "/home/krodriguez/jay/day/I01210009_I00849001/"
+inpath : "/data/themis"
+outpath : "/data/themis"

environment.yml

@@ -0,0 +1,164 @@
+name: themis
+channels:
+- menpo
+- jlaura
+- defaults
+- conda-forge
+- krodriguez
+- usgs-astrogeology
+dependencies:
+- appnope=0.1.0=py36_0
+- backports=1.0=py36_1
+- backports.functools_lru_cache=1.4=py36_1
+- blas=1.1=openblas
+- bleach=2.0.0=py36_0
+- boost=1.65.1=py36_0
+- boost-cpp=1.65.1=1
+- bzip2=1.0.6=1
+- ca-certificates=2017.11.5=0
+- cairo=1.14.6=5
+- certifi=2017.11.5=py36_0
+- click=6.7=py_1
+- click-plugins=1.0.3=py36_0
+- cligj=0.4.0=py36_0
+- curl=7.55.1=0
+- cycler=0.10.0=py36_0
+- cython=0.27.3=py36_0
+- descartes=1.1.0=py36_0
+- dill=0.2.7.1=py36_0
+- entrypoints=0.2.3=py36_1
+- expat=2.1.0=3
+- ffmpeg=3.2.4=3
+- fontconfig=2.12.1=6
+- freetype=2.7=2
+- freexl=1.0.4=0
+- gdal=2.2.3=py36_0
+- geopandas=0.3.0=py36_0
+- geos=3.6.2=1
+- geotiff=1.4.2=0
+- gettext=0.19.8.1=0
+- giflib=5.1.4=0
+- glib=2.51.4=0
+- harfbuzz=1.3.4=2
+- hdf4=4.2.13=0
+- hdf5=1.10.1=1
+- html5lib=1.0.1=py_0
+- icu=58.2=0
+- ipykernel=4.7.0=py36_0
+- ipython=6.2.1=py36_1
+- ipython_genutils=0.2.0=py36_0
+- jasper=1.900.1=4
+- jedi=0.10.2=py36_0
+- jinja2=2.10=py36_0
+- jpeg=9b=2
+- json-c=0.12.1=0
+- jsonschema=2.6.0=py36_0
+- jupyter_client=5.2.1=py36_0
+- jupyter_core=4.4.0=py_0
+- kealib=1.4.7=4
+- krb5=1.14.2=0
+- libdap4=3.18.3=2
+- libffi=3.2.1=3
+- libgdal=2.2.3=1
+- libgfortran=3.0.0=0
+- libiconv=1.15=0
+- libnetcdf=4.4.1.1=10
+- libpng=1.6.28=2
+- libpq=9.6.3=0
+- libsodium=1.0.15=1
+- libspatialindex=1.8.5=1
+- libspatialite=4.3.0a=18
+- libssh2=1.8.0=2
+- libtiff=4.0.7=1
+- libwebp=0.5.2=7
+- libxml2=2.9.7=0
+- markupsafe=1.0=py36_0
+- matplotlib=2.1.1=py36_0
+- mistune=0.8.3=py_0
+- munch=2.2.0=py36_0
+- nbconvert=5.3.1=py_1
+- nbformat=4.4.0=py36_0
+- ncurses=5.9=10
+- networkx=2.0=py36_1
+- numexpr=2.6.4=py36_0
+- numpy=1.14.0=py36_blas_openblas_200
+- openblas=0.2.20=7
+- opencv=3.3.0=py36_blas_openblas_203
+- openjpeg=2.3.0=1
+- openssl=1.0.2n=0
+- pandas=0.22.0=py36_0
+- pandoc=2.1=0
+- pandocfilters=1.4.1=py36_0
+- pcre=8.39=0
+- pexpect=4.3.1=py36_0
+- pickleshare=0.7.4=py36_0
+- pillow=4.3.0=py36_1
+- pip=9.0.1=py36_1
+- pixman=0.34.0=1
+- poppler=0.61.1=2
+- poppler-data=0.4.8=0
+- proj4=4.9.3=5
+- prompt_toolkit=1.0.15=py36_0
+- psycopg2=2.7.3.2=py36_0
+- ptyprocess=0.5.2=py36_0
+- pvl=0.3.0=py36_0
+- pygments=2.2.0=py36_0
+- pyparsing=2.2.0=py36_0
+- pyproj=1.9.5.1=py36_0
+- pyqt=5.6.0=py36_4
+- python=3.6.3=1
+- python-dateutil=2.6.1=py36_0
+- pytz=2017.3=py_2
+- pyyaml=3.12=py36_1
+- pyzmq=16.0.2=py36_3
+- qt=5.6.2=h9e3eb04_4
+- readline=6.2=0
+- rtree=0.8.3=py36_0
+- runipy=0.1.5=py36_1
+- scikit-learn=0.19.1=py36_blas_openblas_201
+- scipy=1.0.0=py36_blas_openblas_201
+- setuptools=38.4.0=py36_0
+- shapely=1.6.3=py36_0
+- simplegeneric=0.8.1=py36_0
+- sip=4.18=py36_1
+- six=1.11.0=py36_1
+- sqlite=3.13.0=1
+- testpath=0.3.1=py36_0
+- tk=8.5.19=2
+- tornado=4.5.3=py36_0
+- traitlets=4.3.2=py36_0
+- vlfeat=0.9.20=1
+- wcwidth=0.1.7=py36_0
+- webencodings=0.5=py36_0
+- wheel=0.30.0=py36_2
+- x264=20131217=3
+- xerces-c=3.2.0=0
+- xz=5.2.3=0
+- zeromq=4.2.1=1
+- zlib=1.2.11=0
+- affine=2.1.0=pyh128a3a6_1
+- decorator=4.1.2=py36h69a1b52_0
+- fiona=1.7.10=py36h42caba7_0
+- h5py=2.7.1=py36h39cdac5_0
+- libboost=1.65.1=hcc95346_4
+- libcxx=4.0.1=h579ed51_0
+- libcxxabi=4.0.1=hebd6815_0
+- libkml=1.3.0=h694af70_2
+- libprotobuf=3.4.1=h326466f_0
+- olefile=0.44=py36ha08bf50_0
+- protobuf=3.4.1=py36h314970b_0
+- pysal=1.14.2=py36h48a81df_1
+- sqlalchemy=1.2.0=py36h1de35cc_0
+- yaml=0.1.7=hc338f04_2
+- cyvlfeat=0.4.6=py36_0
+- plio=0.1.0=py36_0
+- pip:
+  - autocnet==0.1.0
+  - backports.functools-lru-cache==1.4
+  - ipython-genutils==0.2.0
+  - jupyter-client==5.2.1
+  - jupyter-core==4.4.0
+  - prompt-toolkit==1.0.15
+  - pysis==0.6.0
+prefix: /Users/krodriguez/anaconda3/envs/themis
+

matching.py

@@ -1,7 +1,13 @@
+# Deal with annoying numpy warning
+import warnings
+warnings.filterwarnings("ignore")
+
 from shapely import wkt
 import glob
 import os
 
+import argparse
+
 import autocnet
 from autocnet import CandidateGraph
 from autocnet.graph.edge import Edge
@@ -23,7 +29,6 @@
 import numpy as np
 from plio.io.io_gdal import GeoDataset
 
-# Hack to get ISIS setup in a Jupyterhub environment running as a root service
 import subprocess
 from subprocess import Popen, PIPE
 
@@ -33,6 +38,7 @@
 from plio.date import marstime
 from collections import OrderedDict
 
+import yaml
 
 record = OrderedDict({
     'file_id1' : '',
@@ -62,38 +68,54 @@
     'diff_stddev' : 0,
 })
 
+def run_davinci(script, infile, outfile, root_dir, args=[]):
+    command = ['davinci', '-f', '{}{}'.format(root_dir, script), 'from={}'.format(infile), 'to={}'.format(outfile)]
+    if args:
+        command.extend(args)
+    print(' '.join(command))
+    p = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
+    output, err = p.communicate(b"input data that is passed to subprocess' stdin")
+    rc = p.returncode
+
+    if rc != 0:
+        raise Exception('Davinci returned non-zero error code {} : {}'.format(rc, err.decode('utf-8')))
+    return output.decode('utf-8'), err.decode('utf-8')
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('config', action='store', help='path to config file', default='config.yaml')
+    parser.add_argument('id1', action='store', help='Image ID for image one')
+    parser.add_argument('id2', action='store', help='Image ID for image two')
+    parser.add_argument('-c', '--config', action='store', help='path to config file', default='./config.yml')
+    parser.add_argument('-v', '--verbose', action='store_true', help='Activates verbose output', default=False)
+    parser.add_argument('-g', '--graph', action='store_true', help='Display graphs of the data as is goes through.', default=False)
     args = parser.parse_args().__dict__
 
-    with open(arg['config'], 'r') as ymlfile:
+    with open(args['config'], 'r') as ymlfile:
         cfg = yaml.load(ymlfile)
 
-    # Glob the dir for a file list of the lvl 1
-    root_dir = cfg['dpath']
+    if args['verbose']:
+        def verboseprint(*args):
+            # Print each argument separately so caller doesn't need to
+            # stuff everything to be printed into a single string
+            import pprint
+            pp = pprint.PrettyPrinter(indent=2)
+            for arg in args:
+               pp.pprint(arg),
+            print()
+    else:
+        verboseprint = lambda *a: None      # do-nothing function
 
-    def run_davinci(script, infile, outfile, args=[]):
-        command = ['davinci', '-f', '{}{}'.format(root_dir, script), 'from={}'.format(infile), 'to={}'.format(outfile)]
-        if args:
-            command.extend(args)
-        print(' '.join(command))
-        p = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
-        output, err = p.communicate(b"input data that is passed to subprocess' stdin")
-        rc = p.returncode
+    verboseprint('ARGS:', args)
+    verboseprint('CONFIG:', cfg)
 
-        if rc != 0:
-            raise Exception('Davinci returned non-zero error code {} : {}'.format(rc, err.decode('utf-8')))
-        return output.decode('utf-8'), err.decode('utf-8')
+    # patch in davinci_bin onto run_davinci to prevent contstantly having to pass it in
+    run_davinci = lambda x, y, z=[]: run_davinci(x,y,cfg['davinci_bin'], z)
 
-    files = glob.glob(os.path.join(root_dir, '*lev1.cub'))
-
-    filid1 = os.path.basename(files[0].split('.')[0])
-    filid2 = os.path.basename(files[1].split('.')[0])
+    # Glob the dir for a file list of the lvl 1
+    root_dir = cfg['inpath']
 
-    record['file_id1'] = filid1
-    record['file_id2'] = filid2
+    record['id1'] = args['id1']
+    record['id2'] = args['id2']
 
     out, err = run_davinci('thm_pre_process.dv', filid1, '{}.lev1.cub'.format(filid1))
     out, err = run_davinci('thm_pre_process.dv', filid2, '{}.lev1.cub'.format(filid2))
@@ -124,8 +146,10 @@ def run_davinci(script, infile, outfile, args=[]):
             print("STDOUT:", e.stdout.decode('utf-8'))
             print("STDERR:", e.stderr.decode('utf-8'))
 
+    #############################################################
 
     cg = CandidateGraph.from_filelist(files)
+
     # The range of DN values over the data is small, so the threshold for differentiating interesting features must be small.
     cg.extract_features(extractor_parameters={'contrastThreshold':0.0000000001})
     cg.match()