diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed
new file mode 100644
index 00000000..59f35d35
--- /dev/null
+++ b/.pytest_cache/v/cache/lastfailed
@@ -0,0 +1,3 @@
+{
+  "exoctk/tests/test_limb_darkening.py": true
+}
\ No newline at end of file
diff --git a/.pytest_cache/v/cache/nodeids b/.pytest_cache/v/cache/nodeids
new file mode 100644
index 00000000..0637a088
--- /dev/null
+++ b/.pytest_cache/v/cache/nodeids
@@ -0,0 +1 @@
+[]
\ No newline at end of file
diff --git a/ci/setup_conda_env.sh b/ci/setup_conda_env.sh
index 7877cc7e..996479ff 100755
--- a/ci/setup_conda_env.sh
+++ b/ci/setup_conda_env.sh
@@ -8,5 +8,5 @@ conda env list
 echo "Installing packages..."
 pip install numpy astropy
 conda install numpy astropy scipy cython matplotlib numba mock bokeh h5py sphinx pandas flask
-pip install bibtexparser astroquery svo_filters batman-package lmfit
+pip install bibtexparser astroquery svo_filters==0.2.16 batman-package lmfit
 echo $PATH
\ No newline at end of file
diff --git a/exoctk/data/images/lightcurve_fitting.png b/exoctk/data/images/lightcurve_fitting.png
new file mode 100644
index 00000000..5a94e040
Binary files /dev/null and b/exoctk/data/images/lightcurve_fitting.png differ
diff --git a/exoctk/exoctk_app/app_exoctk.py b/exoctk/exoctk_app/app_exoctk.py
index b6fcc7ba..ac391b9d 100644
--- a/exoctk/exoctk_app/app_exoctk.py
+++ b/exoctk/exoctk_app/app_exoctk.py
@@ -1,63 +1,38 @@
-## -- IMPORTS
 import datetime
-import glob
 import os
 import json
-import shutil
 
 import astropy.constants as constants
 from astropy.extern.six.moves import StringIO
 import astropy.table as at
-import astropy.units as u 
-import bokeh
-from bokeh import mpl
+import astropy.units as u
 from bokeh.resources import INLINE
 from bokeh.util.string import encode_utf8
-from bokeh.core.properties import Override
 from bokeh.embed import components
-from bokeh.models import ColumnDataSource
-from bokeh.models import FuncTickFormatter
-from bokeh.models import HoverTool
-from bokeh.models import Label
 from bokeh.models import Range1d
-from bokeh.models.widgets import Panel
-from bokeh.models.widgets import Tabs
-from bokeh.mpl import to_bokeh
+from bokeh.models.widgets import Panel, Tabs
 from bokeh.plotting import figure
-from bokeh.plotting import output_file
-from bokeh.plotting import show
-from bokeh.plotting import save
 import flask
-from flask import current_app
-from flask import Flask
-from flask import make_response
-from flask import redirect
-from flask import render_template
-from flask import request
-from flask import send_file
-from flask import send_from_directory
-from flask import Response
+from flask import Flask, Response
+from flask import request, send_file, make_response, render_template
 from functools import wraps
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from sqlalchemy import *
-import sqlite3
 
-import exoctk
 from exoctk.modelgrid import ModelGrid
 from exoctk.contam_visibility import resolve
 from exoctk.contam_visibility import visibilityPA as vpa
 from exoctk.contam_visibility import sossFieldSim as fs
 from exoctk.contam_visibility import sossContamFig as cf
-from exoctk.integrations_groups.integrations_groups import perform_calculation
+from exoctk.groups_integrations.groups_integrations import perform_calculation
 from exoctk.limb_darkening import limb_darkening_fit as lf
 from exoctk.utils import find_closest, filter_table
 import log_exoctk
 from svo_filters import svo
+from sqlalchemy import create_engine
 
 
-## -- FLASK SET UP (?)
+# FLASK SET UP
 app_exoctk = Flask(__name__)
 
 # define the cache config keys, remember that it can be done in a settings file
@@ -70,57 +45,62 @@
 EXOCTKLOG_DIR = os.environ.get('EXOCTKLOG_DIR')
 
 # Load the database to log all form submissions
-dbpath = os.path.realpath(os.path.join(EXOCTKLOG_DIR,'exoctk_log.db'))
-if not os.path.isfile(dbpath):
-    log_exoctk.create_db(dbpath)
+if EXOCTKLOG_DIR is None:
+    dbpath = ':memory:'
+else:
+    dbpath = os.path.realpath(os.path.join(EXOCTKLOG_DIR, 'exoctk_log.db'))
+    if not os.path.isfile(dbpath):
+        log_exoctk.create_db(dbpath)
 DB = log_exoctk.load_db(dbpath)
 
-# register the cache instance and binds it on to your app
-# cache = Cache(app_exoctk)
-
 # Nice colors for plotting
-COLORS = ['blue', 'red', 'green', 'orange', 
+COLORS = ['blue', 'red', 'green', 'orange',
           'cyan', 'magenta', 'pink', 'purple']
 
 # Supported profiles
-PROFILES = ['uniform', 'linear', 'quadratic', 
-            'square-root', 'logarithmic', 'exponential', 
+PROFILES = ['uniform', 'linear', 'quadratic',
+            'square-root', 'logarithmic', 'exponential',
             '3-parameter', '4-parameter']
 
-# Redirect to the index
+# Set the version
 VERSION = '0.2'
+
+
+# Redirect to the index
 @app_exoctk.route('/')
 @app_exoctk.route('/index')
-
-# Load the Index page
 def index():
+    """The Index page"""
+
     return render_template('index.html')
 
-# Load the LDC page
+
 @app_exoctk.route('/limb_darkening', methods=['GET', 'POST'])
 def limb_darkening():
+    """The limb darkening form page"""
+
     # Get all the available filters
     filters = svo.filters()['Band']
-    
+
     # Make HTML for filters
-    filt_list = '\n'.join(['<option value="{0}"{1}> {0}</option>'.format(b,\
-                ' selected' if b=='Kepler.K' else '') for b in filters])
-    
+    filt_list = '\n'.join(['<option value="{0}"{1}> {0}</option>'.format(b, ' selected' if b == 'Kepler.K' else '') for b in filters])
+
     return render_template('limb_darkening.html', filters=filt_list)
-    
-# Load the LDC results page
+
+
 @app_exoctk.route('/limb_darkening_results', methods=['GET', 'POST'])
 def limb_darkening_results():
-    
+    """The limb darkening results page"""
+
     # Log the form inputs
     try:
-        log_exoctk.log_form_input(request.form, 'ldc', DB)
+        log_exoctk.log_form_input(request.form, 'limb_darkening', DB)
     except:
         pass
-        
+
     # Get the input from the form
     modeldir = request.form['modeldir']
-    profiles = list(filter(None,[request.form.get(pf) for pf in PROFILES]))
+    profiles = list(filter(None, [request.form.get(pf) for pf in PROFILES]))
     bandpass = request.form['bandpass']
 
     # protect against injection attempts
@@ -128,219 +108,230 @@ def limb_darkening_results():
     profiles = [str(p).replace('<', '&lt') for p in profiles]
 
     # Get models from local directory if necessary
-    if modeldir=='default':
+    if modeldir == 'default':
         modeldir = MODELGRID_DIR
-    
+
     # Throw error if input params are invalid
     try:
         teff = int(request.form['teff'])
         logg = float(request.form['logg'])
         feh = float(request.form['feh'])
-        mu_min = float(request.form['mu_min'])
     except:
         teff = str(request.form['teff']).replace('<', '&lt')
         logg = str(request.form['logg']).replace('<', '&lt')
         feh = str(request.form['feh']).replace('<', '&lt')
-        message = 'Could not calculate limb darkening with the above input parameters.'
-        
-        return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                    band=bandpass or 'None', profile=', '.join(profiles), models=modeldir, \
-                    message=message)
-                    
+        message = 'Could not calculate limb darkening for those parameters.'
+
+        return render_template('limb_darkening_error.html', teff=teff,
+                               logg=logg, feh=feh, band=bandpass or 'None',
+                               profile=', '.join(profiles), models=modeldir,
+                               message=message)
+
     n_bins = request.form.get('n_bins')
     pixels_per_bin = request.form.get('pixels_per_bin')
     wl_min = request.form.get('wave_min')
     wl_max = request.form.get('wave_max')
-    
+
     model_grid = ModelGrid(modeldir, resolution=500)
-    
+
     # No data, redirect to the error page
     if not hasattr(model_grid, 'data'):
-        message = 'Could not find a model grid to load. Please check your path.'
-    
-        return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                    band=bandpass or 'None', profile=', '.join(profiles), models=model_grid.path, \
-                    message=message)
-        
+        message = 'Could not find a model grid to load. Please check the path.'
+
+        return render_template('limb_darkening_error.html', teff=teff,
+                               logg=logg, feh=feh, band=bandpass or 'None',
+                               profile=', '.join(profiles),
+                               models=model_grid.path, message=message)
+
     else:
-        
-        if len(model_grid.data)==0:
-        
-            message = 'Could not calculate limb darkening with the above input parameters.'
-        
-            return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                        band=bandpass or 'None', profile=', '.join(profiles), models=model_grid.path, \
-                        message=message)
-                    
+
+        if len(model_grid.data) == 0:
+
+            message = 'Could not calculate limb darkening with those parameters.'
+
+            return render_template('limb_darkening_error.html', teff=teff,
+                                   logg=logg, feh=feh,
+                                   band=bandpass or 'None',
+                                   profile=', '.join(profiles),
+                                   models=model_grid.path, message=message)
+
     # Trim the grid to the correct wavelength
     # to speed up calculations, if a bandpass is given
     min_max = model_grid.wave_rng
-        
+
     try:
-        
-        kwargs = {'n_bins':int(n_bins)} if n_bins else \
-                 {'pixels_per_bin':int(pixels_per_bin)} if pixels_per_bin else {}
-             
+
+        kwargs = {'n_bins': int(n_bins)} if n_bins else \
+                 {'pixels_per_bin': int(pixels_per_bin)} if pixels_per_bin else\
+                 {}
+
         if wl_min and wl_max:
-            kwargs['wl_min'] = float(wl_min)*u.um
-            kwargs['wl_max'] = float(wl_max)*u.um
-        
+            kwargs['wl_min'] = float(wl_min) * u.um
+            kwargs['wl_max'] = float(wl_max) * u.um
+
         # Make filter object
         bandpass = svo.Filter(bandpass, **kwargs)
-        min_max = (bandpass.WavelengthMin,bandpass.WavelengthMax)
+        min_max = (bandpass.WavelengthMin, bandpass.WavelengthMax)
         n_bins = bandpass.n_bins
         bp_name = bandpass.filterID
-        
+
         # Get the filter plot
-        TOOLS = 'box_zoom,resize,reset'
-        bk_plot = figure(tools=TOOLS, title=bp_name, plot_width=400, plot_height=300,
-                         x_range=Range1d(bandpass.WavelengthMin,bandpass.WavelengthMax))
-                     
+        TOOLS = 'box_zoom, resize, reset'
+        bk_plot = figure(tools=TOOLS, title=bp_name, plot_width=400,
+                         plot_height=300,
+                         x_range=Range1d(bandpass.WavelengthMin,
+                                         bandpass.WavelengthMax))
+
         bk_plot.line(*bandpass.raw, line_width=5, color='black', alpha=0.1)
         try:
-            for i,(x,y) in enumerate(bandpass.rsr):
-                bk_plot.line(x, y, color=(COLORS*5)[i])
+            for i, (x, y) in enumerate(bandpass.rsr):
+                bk_plot.line(x, y, color=(COLORS * 5)[i])
         except:
             bk_plot.line(*bandpass.raw)
-        
+
         bk_plot.xaxis.axis_label = 'Wavelength [um]'
         bk_plot.yaxis.axis_label = 'Throughput'
-        
+
         js_resources = INLINE.render_js()
         css_resources = INLINE.render_css()
         filt_script, filt_plot = components(bk_plot)
 
     except:
         message = 'Insufficient filter information. Please complete the form and try again!'
-    
-        return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                    band=bandpass or 'None', profile=', '.join(profiles), models=model_grid.path, \
-                    message=message)
-                    
+
+        return render_template('limb_darkening_error.html', teff=teff,
+                               logg=logg, feh=feh, band=bandpass or 'None',
+                               profile=', '.join(profiles),
+                               models=model_grid.path, message=message)
+
     # Trim the grid to nearby grid points to speed up calculation
-    full_rng = [model_grid.Teff_vals,model_grid.logg_vals,model_grid.FeH_vals]
-    trim_rng = find_closest(full_rng, [teff,logg,feh], n=1, values=True)
+    full_rng = [model_grid.Teff_vals, model_grid.logg_vals, model_grid.FeH_vals]
+    trim_rng = find_closest(full_rng, [teff, logg, feh], n=1, values=True)
 
     if not trim_rng:
 
         message = 'Insufficient models grid points to calculate coefficients.'
 
-        return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                band=bp_name, profile=', '.join(profiles), models=model_grid.path,\
-                message=message)
+        return render_template('limb_darkening_error.html', teff=teff,
+                               logg=logg, feh=feh, band=bp_name,
+                               profile=', '.join(profiles),
+                               models=model_grid.path, message=message)
 
     elif not profiles:
 
         message = 'No limb darkening profiles have been selected. Please select at least one.'
 
-        return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                band=bp_name, profile=', '.join(profiles), models=model_grid.path,\
-                message=message)
+        return render_template('limb_darkening_error.html', teff=teff,
+                               logg=logg, feh=feh, band=bp_name,
+                               profile=', '.join(profiles),
+                               models=model_grid.path, message=message)
 
     else:
 
         try:
             model_grid.customize(Teff_rng=trim_rng[0], logg_rng=trim_rng[1],
-                         FeH_rng=trim_rng[2], wave_rng=min_max)
+                                 FeH_rng=trim_rng[2], wave_rng=min_max)
 
         except:
 
             message = 'Insufficient wavelength coverage to calculate coefficients.'
 
-            return render_template('limb_darkening_error.html', teff=teff, logg=logg, feh=feh, \
-                    band=bp_name, profile=', '.join(profiles), models=model_grid.path,\
-                    message=message)
-                         
+            return render_template('limb_darkening_error.html', teff=teff,
+                                   logg=logg, feh=feh, band=bp_name,
+                                   profile=', '.join(profiles),
+                                   models=model_grid.path, message=message)
+
     # Calculate the coefficients for each profile
     ld = lf.LDC(model_grid)
     for prof in profiles:
         ld.calculate(teff, logg, feh, prof, bandpass=bandpass)
-                    
+
     # Draw a figure for each wavelength bin
     tabs = []
     for wav in np.unique(ld.results['wave_eff']):
-        dat = ld.results[ld.results['wave_eff'] == wav]
-        
+
         # Plot it
-        TOOLS = 'box_zoom,box_select,crosshair,resize,reset,hover'
+        TOOLS = 'box_zoom, box_select, crosshair, resize, reset, hover'
         fig = figure(tools=TOOLS, x_range=Range1d(0, 1), y_range=Range1d(0, 1),
                      plot_width=800, plot_height=400)
         ld.plot(wave_eff=wav, fig=fig)
-                                    
+
         # Plot formatting
         fig.legend.location = 'bottom_right'
         fig.xaxis.axis_label = 'mu'
         fig.yaxis.axis_label = 'Intensity'
-        
+
         tabs.append(Panel(child=fig, title=str(wav)))
-                
+
     final = Tabs(tabs=tabs)
-    
+
     # Get HTML
     script, div = components(final)
-    
+
     # Store the tables as a string
     file_as_string = str(ld.results[[c for c in ld.results.dtype.names if
                                      ld.results.dtype[c] != object]])
-    
-    # # Format mu and r_eff vals
-    # r_eff = '{:.4f} R_\odot'.format(grid_point['r_eff']*1.438e-11)
-    # mu_eff = '{:.4f}'.format(0)
     r_eff = mu_eff = ''
-    
+
     # Make a table for each profile with a row for each wavelength bin
     profile_tables = []
     for profile in profiles:
-        
+
         # Make LaTeX for polynomials
         latex = lf.ld_profile(profile, latex=True)
-        poly = '\({}\)'.format(latex).replace('*','\cdot').replace('\e','e')
-        
+        poly = '\({}\)'.format(latex).replace('*', '\cdot').replace('\e', 'e')
+
         # Make the table into LaTeX
         table = filter_table(ld.results, profile=profile)
         co_cols = [c for c in ld.results.colnames if (c.startswith('c') or
                    c.startswith('e')) and len(c) == 2 and not
                    np.all([np.isnan(i) for i in table[c]])]
-        table = table[['wave_min','wave_max']+co_cols]
+        table = table[['wave_min', 'wave_max'] + co_cols]
         table.rename_column('wave_min', '\(\lambda_\mbox{min}\hspace{5px}(\mu m)\)')
         table.rename_column('wave_max', '\(\lambda_\mbox{max}\hspace{5px}(\mu m)\)')
-        
+
         # Add the results to the lists
         html_table = '\n'.join(table.pformat(max_width=500, html=True))\
-                     .replace('<table','<table id="myTable" class="table table-striped table-hover"')
-        
+                     .replace('<table', '<table id="myTable" class="table table-striped table-hover"')
+
         # Add the table title
-        header = '<br></br><strong>{}</strong><br><p>\(I(\mu)/I(\mu=1)\) = {}</p>'.format(profile,poly)
-        html_table = header+html_table
+        header = '<br></br><strong>{}</strong><br><p>\(I(\mu)/I(\mu=1)\) = {}</p>'.format(profile, poly)
+        html_table = header + html_table
 
         profile_tables.append(html_table)
-        
-    return render_template('limb_darkening_results.html', teff=teff, logg=logg, feh=feh, \
-                band=bp_name, mu=mu_eff, profile=', '.join(profiles), \
-                r=r_eff, models=model_grid.path, table=profile_tables, \
-                script=script, plot=div, file_as_string=repr(file_as_string), \
-                filt_plot=filt_plot, filt_script=filt_script, js=js_resources, css=css_resources)
-
-# Load the LDC error page
+
+    return render_template('limb_darkening_results.html', teff=teff,
+                           logg=logg, feh=feh, band=bp_name, mu=mu_eff,
+                           profile=', '.join(profiles), r=r_eff,
+                           models=model_grid.path, table=profile_tables,
+                           script=script, plot=div,
+                           file_as_string=repr(file_as_string),
+                           filt_plot=filt_plot, filt_script=filt_script,
+                           js=js_resources, css=css_resources)
+
+
 @app_exoctk.route('/limb_darkening_error', methods=['GET', 'POST'])
 def limb_darkening_error():
+    """The limb darkening error page"""
+
     return render_template('limb_darkening_error.html')
 
 
-# Load the integrations and groups page
-@app_exoctk.route('/integrations_groups', methods=['GET', 'POST'])
-def integrations_groups():
+@app_exoctk.route('/groups_integrations', methods=['GET', 'POST'])
+def groups_integrations():
+    """The groups and integrations calculator form page"""
 
     # Print out pandeia sat values
     with open(INTEGRATIONS_DIR) as f:
         sat_data = json.load(f)['fullwell']
-    
-    return render_template('integrations_groups.html', sat_data=sat_data)
 
-# Load the integrations and groups results
-@app_exoctk.route('/integrations_groups_results', methods=['GET', 'POST'])
-def integrations_groups_results():
-    
+    return render_template('groups_integrations.html', sat_data=sat_data)
+
+
+@app_exoctk.route('/groups_integrations_results', methods=['GET', 'POST'])
+def groups_integrations_results():
+    """The groups and integrations calculator results page"""
+
     # Read in parameters from form
     params = {}
     for key in dict(request.form).keys():
@@ -348,53 +339,57 @@ def integrations_groups_results():
     print(params)
     try:
         err = 0
-    
+
         # Specific error catching
         if params['n_group'].isdigit():
             params['n_group'] = int(params['n_group'])
             if params['n_group'] <= 1:
-               err = 'Please try again with at least one group.'
-        else:
-            if params['n_group'] != 'optimize':
-             err = "You need to double check your group input. Please put the number of groups per integration or 'optimize' and we can calculate it for you."
+                err = 'Please try again with at least one group.'
+            else:
+                if params['n_group'] != 'optimize':
+                    err = "You need to double check your group input. Please put the number of groups per integration or 'optimize' and we can calculate it for you."
+
         if (False in [params['mag'].isdigit(), params['obs_time'].isdigit()]) and ('.' not in params['mag']) and ('.' not in params['obs_time']):
             err = 'Your magnitude or observation time is not a number, or you left the field blank.'
-    
+
         else:
             if (4.5 > float(params['mag'])) or (12.5 < float(params['mag'])):
                 err = 'Looks like we do not have useful approximations for your magnitude. Could you give us a number between 5.5-12.5?'
             if float(params['obs_time']) <= 0:
                 err = 'You have a negative transit time -- I doubt that will be of much use to anyone.'
-    
+
         if float(params['sat_max']) <= 0:
             err = 'You put in an underwhelming saturation level. There is something to be said for being too careful...'
         if (params['sat_mode'] == 'well') and float(params['sat_max']) > 1:
             err = 'You are saturating past the full well. Is that a good idea?'
 
         if type(err) == str:
-            return render_template('integrations_groups_error.html', err=err)
-    
+            return render_template('groups_integrations_error.html', err=err)
+
         # Only create the dict if the form input looks okay
         # Make sure everything is the right type
         ins = params['ins']
         float_params = ['obs_time', 'mag', 'sat_max']
-        str_params = ['mod', 'band', '{}_filt'.format(ins), '{}_ta_filt'.format(ins), 'ins', '{}_subarray'.format(ins), '{}_subarray_ta'.format(ins), 'sat_mode']
+        str_params = ['mod', 'band', '{}_filt'.format(ins),
+                      '{}_ta_filt'.format(ins), 'ins',
+                      '{}_subarray'.format(ins), '{}_subarray_ta'.format(ins),
+                      'sat_mode']
         for key in params:
             if key in float_params:
                 params[key] = float(params[key])
             if key in str_params:
                 params[key] = str(params[key])
-    
+
         # Also get the data path in there
-        params['infile'] = INTEGRATIONS_DIR 
-    
+        params['infile'] = INTEGRATIONS_DIR
+
         # Rename the ins-mode params to more general counterparts
         params['filt'] = params['{}_filt'.format(ins)]
         params['filt_ta'] = params['{}_filt_ta'.format(ins)]
         params['subarray'] = params['{}_subarray'.format(ins)]
         params['subarray_ta'] = params['{}_subarray_ta'.format(ins)]
         results = perform_calculation(params)
-    
+
         if type(results) == dict:
             results_dict = results
             one_group_error = ""
@@ -402,7 +397,7 @@ def integrations_groups_results():
             if results_dict['n_group'] == 1:
                 one_group_error = 'Be careful! This only predicts one group, and you may be in danger of oversaturating!'
             if results_dict['max_ta_groups'] == 0:
-                zero_group_error ='Be careful! This oversaturated the TA in the minimum groups. Consider a different TA setup.'
+                zero_group_error = 'Be careful! This oversaturated the TA in the minimum groups. Consider a different TA setup.'
             if results_dict['max_ta_groups'] == -1:
                 zero_group_error = 'This object is too faint to reach the required TA SNR in this filter. Consider a different TA setup.'
                 results_dict['min_sat_ta'] = 0
@@ -423,29 +418,35 @@ def integrations_groups_results():
                     results_dict['subarray_ta'] = 'SUBTASOSS -- FAINT'
             results_dict['subarray'] = results_dict['subarray'].upper()
             results_dict['subarray_ta'] = results_dict['subarray_ta'].upper()
-     
+
             form_dict = {'miri': 'MIRI', 'nircam': 'NIRCam', 'nirspec': 'NIRSpec', 'niriss': 'NIRISS'}
             results_dict['ins'] = form_dict[results_dict['ins']]
-      
-            return render_template('integrations_groups_results.html',
-                    results_dict=results_dict, one_group_error=one_group_error,
+
+            return render_template('groups_integrations_results.html',
+                                   results_dict=results_dict,
+                                   one_group_error=one_group_error,
                                    zero_group_error=zero_group_error)
-        
+
         else:
-            err = results 
-            return render_template('integrations_groups_error.html', err=err)
-    
+            err = results
+            return render_template('groups_integrations_error.html', err=err)
+
     except IOError:
         err = 'One of you numbers is NOT a number! Please try again!'
     except Exception as e:
         err = 'This is not an error we anticipated, but the error caught was : ' + str(e)
-        return render_template('integrations_groups_error.html', err=err)
-
+        return render_template('groups_integrations_error.html', err=err)
 
 
-# Load the contam and visibility page
-@app_exoctk.route('/contam_visibility', methods = ['GET', 'POST'])
+@app_exoctk.route('/contam_visibility', methods=['GET', 'POST'])
 def contam_visibility():
+    """The contamination and visibility form page"""
+
+    # Log the form inputs
+    try:
+        log_exoctk.log_form_input(request.form, 'contam_visibility', DB)
+    except:
+        pass
 
     contamVars = {}
     if request.method == 'POST':
@@ -455,190 +456,217 @@ def contam_visibility():
         contamVars['PAmax'] = request.form['pamax']
         contamVars['PAmin'] = request.form['pamin']
 
-        if request.form['bininfo']!='':
-            contamVars['binComp'] = list(map(float, request.form['bininfo'].split(',')))
+        if request.form['bininfo'] != '':
+            contamVars['binComp'] = list(map(float, request.form['bininfo'].split(', ')))
         else:
             contamVars['binComp'] = request.form['bininfo']
-        
+
         radec = ', '.join([contamVars['ra'], contamVars['dec']])
-        
-        if contamVars['PAmax']=='':
+
+        if contamVars['PAmax'] == '':
             contamVars['PAmax'] = 359
-        if contamVars['PAmin']=='':
+        if contamVars['PAmin'] == '':
             contamVars['PAmin'] = 0
 
         if request.form['submit'] == 'Resolve Target':
             contamVars['ra'], contamVars['dec'] = resolve.resolve_target(tname)
-            
-            return render_template('contam_visibility.html', contamVars = contamVars)
-            
+
+            return render_template('contam_visibility.html', contamVars=contamVars)
+
         else:
-            
+
             try:
 
                 contamVars['visPA'] = True
-    
+
                 # Make plot
-                TOOLS = 'crosshair,resize,reset,hover,save'
-                fig = figure(tools=TOOLS, plot_width=800, plot_height=400, x_axis_type='datetime', title=contamVars['tname'] or radec)
-                pG, pB, dates, vis_plot = vpa.checkVisPA(contamVars['ra'], contamVars['dec'], tname, fig=fig)
-    
+                TOOLS = 'crosshair, resize, reset, hover, save'
+                fig = figure(tools=TOOLS, plot_width=800, plot_height=400,
+                             x_axis_type='datetime',
+                             title=contamVars['tname'] or radec)
+                pG, pB, dates, vis_plot = vpa.checkVisPA(contamVars['ra'],
+                                                         contamVars['dec'],
+                                                         tname, fig=fig)
+
                 # Format x axis
                 day0 = datetime.date(2019, 6, 1)
-                vis_plot.x_range = Range1d(day0,day0+datetime.timedelta(days=367))
-    
+                dtm = datetime.timedelta(days=367)
+                vis_plot.x_range = Range1d(day0, day0 + dtm)
+
                 # Get scripts
                 vis_js = INLINE.render_js()
                 vis_css = INLINE.render_css()
                 vis_script, vis_div = components(vis_plot)
 
                 if request.form['submit'] == 'Calculate Visibility and Contamination':
-        
+
                     contamVars['contam'] = True
-                    
+
                     # Make field simulation
-                    contam_cube = fs.sossFieldSim(contamVars['ra'], contamVars['dec'], binComp=contamVars['binComp'])
-                    contam_plot = cf.contam(contam_cube, contamVars['tname'] or radec, paRange=[int(contamVars['PAmin']),int(contamVars['PAmax'])], badPA=pB, fig='bokeh')
-                    
-                    
+                    contam_cube = fs.sossFieldSim(contamVars['ra'],
+                                                  contamVars['dec'],
+                                                  binComp=contamVars['binComp'])
+                    contam_plot = cf.contam(contam_cube,
+                                            contamVars['tname'] or radec,
+                                            paRange=[int(contamVars['PAmin']),
+                                                     int(contamVars['PAmax'])],
+                                            badPA=pB, fig='bokeh')
+
                     # Get scripts
                     contam_js = INLINE.render_js()
                     contam_css = INLINE.render_css()
                     contam_script, contam_div = components(contam_plot)
-                
+
                 else:
-                
+
                     contamVars['contam'] = False
                     contam_script = contam_div = contam_js = contam_css = ''
-            
-                return render_template('contam_visibility_results.html', contamVars=contamVars, \
-                        vis_plot=vis_div, vis_script=vis_script, vis_js=vis_js, vis_css=vis_css,\
-                        contam_plot=contam_div, contam_script=contam_script, contam_js=contam_js, contam_css=contam_css)
 
-            except IOError:#Exception as e:
+                return render_template('contam_visibility_results.html',
+                                       contamVars=contamVars, vis_plot=vis_div,
+                                       vis_script=vis_script, vis_js=vis_js,
+                                       vis_css=vis_css, contam_plot=contam_div,
+                                       contam_script=contam_script,
+                                       contam_js=contam_js,
+                                       contam_css=contam_css)
+
+            except Exception as e:
                 err = 'The following error occurred: ' + str(e)
-                return render_template('integrations_groups_error.html', err=err)
+                return render_template('groups_integrations_error.html', err=err)
+
+    return render_template('contam_visibility.html', contamVars=contamVars)
 
-    return render_template('contam_visibility.html', contamVars = contamVars)
 
-    
-# Save the results to file
 @app_exoctk.route('/download', methods=['POST'])
 def exoctk_savefile():
+    """Save results to file"""
+
     file_as_string = eval(request.form['file_as_string'])
-    
+
     response = make_response(file_as_string)
     response.headers["Content-type"] = 'text; charset=utf-8'
     response.headers["Content-Disposition"] = "attachment; filename=ExoXTK_results.txt"
     return response
 
+
 def _param_fort_validation(args):
-    temp = args.get('ptemp',1000)
-    chem = args.get('pchem','noTiO')
-    cloud = args.get('cloud','0')
-    pmass = args.get('pmass','1.5')
-    m_unit = args.get('m_unit','M_jup')
-    reference_radius = args.get('refrad',1)
-    r_unit = args.get('r_unit','R_jup')
-    rstar = args.get('rstar',1)
-    rstar_unit = args.get('rstar_unit','R_sun')
-    return temp,chem,cloud,pmass,m_unit,reference_radius,r_unit,rstar,rstar_unit
-
-@app_exoctk.route('/fortney', methods=['GET','POST'])
+    """Validates the input parameters for the forward models"""
+
+    temp = args.get('ptemp', 1000)
+    chem = args.get('pchem', 'noTiO')
+    cloud = args.get('cloud', '0')
+    pmass = args.get('pmass', '1.5')
+    m_unit = args.get('m_unit', 'M_jup')
+    reference_radius = args.get('refrad', 1)
+    r_unit = args.get('r_unit', 'R_jup')
+    rstar = args.get('rstar', 1)
+    rstar_unit = args.get('rstar_unit', 'R_sun')
+
+    return temp, chem, cloud, pmass, m_unit, reference_radius, r_unit, rstar, rstar_unit
+
+
+@app_exoctk.route('/fortney', methods=['GET', 'POST'])
 def fortney():
     """
     Pull up Forntey Grid plot the results and download
     """
-    
+
     # Grab the inputs arguments from the URL
     args = flask.request.args
-    
-    temp,chem,cloud,pmass,m_unit,reference_radius,r_unit,rstar,rstar_unit = _param_fort_validation(args)
-    #get sqlite database
 
+    temp, chem, cloud, pmass, m_unit, reference_radius, r_unit, rstar, rstar_unit = _param_fort_validation(args)
+
+    # get sqlite database
     try:
-        db = create_engine('sqlite:///'+FORTGRID_DIR)
-        header= pd.read_sql_table('header',db)
+        db = create_engine('sqlite:///' + FORTGRID_DIR)
+        header = pd.read_sql_table('header', db)
     except:
         raise Exception('Fortney Grid File Path is incorrect, or not initialized')
 
     if args:
-        rstar=float(rstar)
-        rstar = (rstar*u.Unit(rstar_unit)).to(u.km)
+        rstar = float(rstar)
+        rstar = (rstar * u.Unit(rstar_unit)).to(u.km)
         reference_radius = float(reference_radius)
-        rplan = (reference_radius*u.Unit(r_unit)).to(u.km)
+        rplan = (reference_radius * u.Unit(r_unit)).to(u.km)
 
-        #clouds 
-        if cloud.find('flat') != -1: 
+        # clouds
+        if cloud.find('flat') != -1:
             flat = int(cloud[4:])
-            ray = 0 
-        elif cloud.find('ray') != -1: 
+            ray = 0
+        elif cloud.find('ray') != -1:
             ray = int(cloud[3:])
-            flat = 0 
-        elif int(cloud) == 0: 
-            flat = 0 
-            ray = 0     
+            flat = 0
+        elif int(cloud) == 0:
+            flat = 0
+            ray = 0
         else:
-            flat = 0 
-            ray = 0 
+            flat = 0
+            ray = 0
             print('No cloud parameter not specified, default no clouds added')
-        
-        #chemistry 
-        if chem == 'noTiO': 
+
+        # chemistry
+        if chem == 'noTiO':
             noTiO = True
-        if chem == 'eqchem': 
+        if chem == 'eqchem':
             noTiO = False
-            #grid does not allow clouds for cases with TiO
-            flat = 0 
-            ray = 0 
+            # grid does not allow clouds for cases with TiO
+            flat = 0
+            ray = 0
 
-        fort_grav = 25.0*u.m/u.s/u.s
+        fort_grav = 25.0 * u.m / u.s**2
 
         temp = float(temp)
-        df = header.loc[(header.gravity==fort_grav) & (header.temp==temp)
-                           & (header.noTiO==noTiO) & (header.ray==ray) &
-                           (header.flat==flat)]
-
-        wave_planet=np.array(pd.read_sql_table(df['name'].values[0],db)['wavelength'])[::-1]
-        r_lambda=np.array(pd.read_sql_table(df['name'].values[0],db)['radius'])*u.km
-        z_lambda = r_lambda- (1.25*u.R_jup).to(u.km) #all fortney models have fixed 1.25 radii
-        
-        #scale with planetary mass 
-        pmass=float(pmass)
-        mass = (pmass*u.Unit(m_unit)).to(u.kg)
-        gravity = constants.G*(mass)/(rplan.to(u.m))**2.0 #convert radius to m for gravity units
-        #scale lambbda (this technically ignores the fact that scaleheight is altitude dependent)
-        #therefore, it will not be valide for very very low gravities
-        z_lambda = z_lambda*fort_grav/gravity
-        
-        #create new wavelength dependent R based on scaled ravity
+        df = header.loc[(header.gravity == fort_grav) & (header.temp == temp) &
+                        (header.noTiO == noTiO) & (header.ray == ray) &
+                        (header.flat == flat)]
+
+        wave_planet = np.array(pd.read_sql_table(df['name'].values[0], db)['wavelength'])[::-1]
+        r_lambda = np.array(pd.read_sql_table(df['name'].values[0], db)['radius']) * u.km
+
+        # All fortney models have fixed 1.25 radii
+        z_lambda = r_lambda - (1.25 * u.R_jup).to(u.km)
+
+        # Scale with planetary mass
+        pmass = float(pmass)
+        mass = (pmass * u.Unit(m_unit)).to(u.kg)
+
+        # Convert radius to m for gravity units
+        gravity = constants.G * (mass) / (rplan.to(u.m))**2.0
+
+        # Scale lambbda (this technically ignores the fact that scaleheight
+        # is altitude dependent) therefore, it will not be valide for very
+        # very low gravities
+        z_lambda = z_lambda * fort_grav / gravity
+
+        # Create new wavelength dependent R based on scaled ravity
         r_lambda = z_lambda + rplan
-        #finally compute (rp/r*)^2
-        flux_planet = np.array(r_lambda**2/rstar**2)
-
-        x=wave_planet
-        y=flux_planet[::-1]
-    else:   
-        df= pd.read_sql_table('t1000g25_noTiO',db)
-        x, y = df['wavelength'], df['radius']**2.0/7e5**2.0
-    
+
+        # Finally compute (rp/r*)^2
+        flux_planet = np.array(r_lambda**2 / rstar**2)
+
+        x = wave_planet
+        y = flux_planet[::-1]
+
+    else:
+        df = pd.read_sql_table('t1000g25_noTiO', db)
+        x, y = df['wavelength'], df['radius']**2.0 / 7e5**2.0
+
     tab = at.Table(data=[x, y])
     fh = StringIO()
     tab.write(fh, format='ascii.no_header')
     table_string = fh.getvalue()
 
     fig = figure(plot_width=1100, plot_height=400, responsive=False)
-    fig.line(x, 1e6*(y-np.mean(y)), color='Black', line_width=0.5)
+    fig.line(x, 1e6 * (y - np.mean(y)), color='Black', line_width=0.5)
     fig.xaxis.axis_label = 'Wavelength (um)'
     fig.yaxis.axis_label = 'Rel. Transit Depth (ppm)'
-    
+
     js_resources = INLINE.render_js()
     css_resources = INLINE.render_css()
-    
+
     script, div = components(fig)
-    
-    html = flask.render_template(
-                                 'fortney.html',
+
+    html = flask.render_template('fortney.html',
                                  plot_script=script,
                                  plot_div=div,
                                  js_resources=js_resources,
@@ -648,75 +676,109 @@ def fortney():
                                  )
     return encode_utf8(html)
 
+
 @app_exoctk.route('/fortney_result', methods=['POST'])
 def save_fortney_result():
+    """SAve the results of the Fortney grid"""
+
     table_string = flask.request.form['data_file']
     return flask.Response(table_string, mimetype="text/dat",
-                          headers={"Content-disposition":
-                          "attachment; filename=fortney.dat"})
+                          headers={"Content-disposition": "attachment; filename=fortney.dat"})
+
 
-@app_exoctk.route('/integrations_groups_download')
-def integrations_groups_download():
-    return send_file(INTEGRATIONS_DIR, mimetype="text/json", attachment_filename='integrations_groups_input_data.json', as_attachment=True)
+@app_exoctk.route('/groups_integrations_download')
+def groups_integrations_download():
+    """Download the groups and integrations calculator data"""
+
+    return send_file(INTEGRATIONS_DIR, mimetype="text/json",
+                     attachment_filename='groups_integrations_input_data.json',
+                     as_attachment=True)
 
 
 @app_exoctk.route('/fortney_download')
 def fortney_download():
+    """Download the fortney grid data"""
+
     fortney_data = FORTGRID_DIR
-    return send_file(fortney_data, attachment_filename='fortney_grid.db', as_attachment=True)
+    return send_file(fortney_data, attachment_filename='fortney_grid.db',
+                     as_attachment=True)
 
 
 def check_auth(username, password):
-    """This function is called to check if a username /
-    password combination is valid.
+    """This function is called to check if a username password combination is
+    valid
+
+    Parameters
+    ----------
+    username: str
+        The username
+    password: str
+        The password
     """
+
     return username == 'admin' and password == 'secret'
 
+
 def authenticate():
     """Sends a 401 response that enables basic auth"""
-    return Response(
-    'Could not verify your access level for that URL.\n'
-    'You have to login with proper credentials', 401,
-    {'WWW-Authenticate': 'Basic realm="Login Required"'})
 
-def requires_auth(f):
-    @wraps(f)
+    return Response('Could not verify your access level for that URL.\n'
+                    'You have to login with proper credentials', 401,
+                    {'WWW-Authenticate': 'Basic realm="Login Required"'})
+
+
+def requires_auth(page):
+    """Requires authentication for a page before loading
+
+    Parameters
+    ----------
+    page: function
+        The function that sets a route
+
+    Returns
+    -------
+    function
+        The decorated route
+    """
+
+    @wraps(page)
     def decorated(*args, **kwargs):
         auth = request.authorization
         if not auth or not check_auth(auth.username, auth.password):
             return authenticate()
-        return f(*args, **kwargs)
+        return page(*args, **kwargs)
     return decorated
-    
+
+
 @app_exoctk.route('/admin')
 @requires_auth
 def secret_page():
-    
+    """Shhhhh! This is a secret page of admin stuff"""
+
     tables = [i[0] for i in DB.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()]
     print(tables)
-    
+
     log_tables = []
     for table in tables:
-        
+
         try:
             data = log_exoctk.view_log(DB, table)
-        
+
             # Add the results to the lists
-            html_table = '\n'.join(data.pformat(max_width=500, html=True)).replace('<table','<table id="myTable" class="table table-striped table-hover"')
-        
+            html_table = '\n'.join(data.pformat(max_width=500, html=True)).replace('<table', '<table id="myTable" class="table table-striped table-hover"')
+
         except:
             html_table = '<p>No data to display</p>'
 
         # Add the table title
         header = '<h3>{}</h3>'.format(table)
-        html_table = header+html_table
+        html_table = header + html_table
 
         log_tables.append(html_table)
-    
+
     return render_template('admin_page.html', tables=log_tables)
 
 
-## -- RUN
 if __name__ == '__main__':
     port = int(os.environ.get('PORT', 5000))
     app_exoctk.run(host='0.0.0.0', port=port, debug=True)
diff --git a/exoctk/exoctk_app/log_exoctk.py b/exoctk/exoctk_app/log_exoctk.py
index cc11522a..65807a21 100644
--- a/exoctk/exoctk_app/log_exoctk.py
+++ b/exoctk/exoctk_app/log_exoctk.py
@@ -1,11 +1,14 @@
 """
-This module creates and manages a SQL database as a log for all jobs submitted via the ExoCTKWeb app
+This module creates and manages a SQL database as a log for all jobs submitted
+via the exoctk web app
 """
 import os
-import numpy as np
 import sqlite3
 import datetime
+
 import astropy.table as at
+import numpy as np
+
 
 def create_db(dbpath, overwrite=True):
     """
@@ -14,61 +17,75 @@ def create_db(dbpath, overwrite=True):
     Parameters
     ----------
     dbpath: str
-        The full path for the new database, including the filename and .db file extension.
+        The full path for the new database, including the filename and .db
+        file extension.
     schema: str
         The path to the .sql schema for the database
     overwrite: bool
         Overwrite dbpath if it already exists
     """
-    # Make sure the path is valid
-    if not os.path.exists(os.path.dirname(dbpath)):
-        raise IOError('Not a valid path:',dbpath)
-        
-    # Make sure the file is a .db
-    if not dbpath.endswith('.db'):
-        raise IOError('Please provide a path and file name with a .db file extension')
-        
-    # Remove existing file if overwriting
-    if os.path.isfile(dbpath) and overwrite:
-        os.system('rm {}'.format(dbpath))
-    
-    # Make the new file
-    os.system('touch {}'.format(dbpath))
-    
+    if dbpath != ':memory:':
+
+        # Make sure the path is valid
+        if not os.path.exists(os.path.dirname(dbpath)):
+            raise IOError('Not a valid path:', dbpath)
+
+        # Make sure the file is a .db
+        if not dbpath.endswith('.db'):
+            raise IOError('Please provide a path with a .db file extension')
+
+        # Remove existing file if overwriting
+        if os.path.isfile(dbpath) and overwrite:
+            os.system('rm {}'.format(dbpath))
+
+        # Make the new file
+        os.system('touch {}'.format(dbpath))
+
     # Generate the tables
     conn = sqlite3.connect(dbpath)
     cur = conn.cursor()
-    cur.execute("CREATE TABLE 'exotransmit' ( 'id' INTEGER NOT NULL UNIQUE, 'date' TEXT NOT NULL, 'eos' TEXT, 'tp' TEXT, 'g' REAL, 'R_p' REAL, 'R_s' REAL, 'P' REAL, 'Rayleigh' REAL, PRIMARY KEY(id));")
-    cur.execute("CREATE TABLE 'tor' ( 'id' INTEGER NOT NULL UNIQUE, 'date' TEXT NOT NULL, 'ins' TEXT, 'mag' REAL, 'groups' TEXT, 'amps' INTEGER, 'subarray' TEXT, 'sat_lvl' REAL, 'sat' TEXT, 'T' REAL, 'n_reset' INTEGER, 'band' TEXT, 'filt' TEXT, PRIMARY KEY(id));")
-    cur.execute("CREATE TABLE 'ldc' ( 'id' INTEGER NOT NULL UNIQUE, 'date' TEXT NOT NULL, 'n_bins' INTEGER, 'teff' REAL, 'logg' REAL, 'feh' REAL, 'bandpass' TEXT, 'modeldir' TEXT, 'wave_min' REAL, 'mu_min' REAL, 'wave_max' REAL, 'local_files' TEXT, 'pixels_per_bin' INTEGER, 'uniform' TEXT, 'linear' TEXT, 'quadratic' TEXT, 'squareroot' TEXT, 'logarithmic' TEXT, 'exponential' TEXT, 'three_parameter' TEXT, 'four_parameter' TEXT, PRIMARY KEY(id));")
+
+    # Table for groups_integrations
+    cur.execute("CREATE TABLE 'groups_integrations' ('id' INTEGER NOT NULL UNIQUE, 'date' TEXT NOT NULL, 'ins' TEXT, 'mag' REAL, 'groups' TEXT, 'amps' INTEGER, 'subarray' TEXT, 'sat_lvl' REAL, 'sat' TEXT, 'T' REAL, 'n_reset' INTEGER, 'band' TEXT, 'filt' TEXT, PRIMARY KEY(id));")
+
+    # Table for limb_darkening
+    cur.execute("CREATE TABLE 'limb_darkening' ('id' INTEGER NOT NULL UNIQUE, 'date' TEXT NOT NULL, 'n_bins' INTEGER, 'teff' REAL, 'logg' REAL, 'feh' REAL, 'bandpass' TEXT, 'modeldir' TEXT, 'wave_min' REAL, 'mu_min' REAL, 'wave_max' REAL, 'local_files' TEXT, 'pixels_per_bin' INTEGER, 'uniform' TEXT, 'linear' TEXT, 'quadratic' TEXT, 'squareroot' TEXT, 'logarithmic' TEXT, 'exponential' TEXT, 'three_parameter' TEXT, 'four_parameter' TEXT, PRIMARY KEY(id));")
+
+    # Tale for contam_visibility
+    cur.execute("CREATE TABLE 'contam_visibility' ('id' INTEGER NOT NULL UNIQUE, 'date' TEXT NOT NULL, 'targetname' TEXT, 'ra' REAL, 'dec' REAL, 'pamax' REAL, 'pamin' REAL, 'bininfo' TEXT, PRIMARY KEY(id));")
+
+    # Commit and close the connection
+    conn.commit()
     conn.close()
-    
+
     if os.path.isfile(dbpath):
         print("ExoCTK database created at {}".format(dbpath))
 
+
 def load_db(dbpath):
     """
     Load a database
-    
+
     Parameters
     ----------
     dbpath: str
         The path to the .db database file
     """
     if os.path.isfile(dbpath):
-    
+
         con = sqlite3.connect(dbpath, isolation_level=None, detect_types=sqlite3.PARSE_DECLTYPES, check_same_thread=False)
         cur = con.cursor()
-        
+
         return cur
-        
+
     else:
         print("Sorry, could not find the file '{}'".format(dbpath))
 
+
 def log_form_input(form_dict, table, database):
     """
     A function to store the form inputs of any page GET requests in a database
-    
+
     Parameters
     ----------
     form_dict: dict
@@ -79,24 +96,27 @@ def log_form_input(form_dict, table, database):
         The database cursor object
     """
     try:
-        # Convert hyphens to underscores and leading numerics to letters for db column names
-        inpt = {k.replace('-','_').replace('3','three').replace('4','four'):v[0] for k,v in dict(form_dict).items()}
+        # Convert hyphens to underscores and leading numerics to letters for
+        # db column names
+        inpt = {k.replace('-', '_').replace('3', 'three').replace('4', 'four'): v[0] for k, v in dict(form_dict).items()}
     except TypeError:
         inpt = form_dict
-    
+
     # Add a timestamp
     inpt['date'] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
-    
+
     # Insert the form valsues
     qmarks = ', '.join('?' * len(inpt))
-    qry = "Insert Into {} ({}) Values ({})".format(table, ', '.join(inpt.keys()), qmarks)
-    
+    cols = ', '.join(inpt.keys())
+    qry = "Insert Into {} ({}) Values ({})".format(table, cols, qmarks)
+
     database.execute(qry, list(inpt.values()))
-    
+
+
 def view_log(database, table):
     """
     Visually inspect the job log
-    
+
     Parameters
     ----------
     database: str, sqlite3.connection.cursor
@@ -110,20 +130,20 @@ def view_log(database, table):
         DB = database
     else:
         print("Please enter the path to a .db file or a sqlite.Cursor object.")
-        
+
     # Query the database
     table = scrub(table)
-    colnames = np.array(database.execute("PRAGMA table_info('{}')".format(table)).fetchall()).T[1]
-    results = database.execute("SELECT * FROM {}".format(table)).fetchall()
-    
+    colnames = np.array(DB.execute("PRAGMA table_info('{}')".format(table)).fetchall()).T[1]
+    results = DB.execute("SELECT * FROM {}".format(table)).fetchall()
+
     results = at.Table(list(np.array(results).T), names=colnames)
-    
+
     # Print them
     return results
-    
+
+
 def scrub(table_name):
     """
-    Snippet to prevent SQL injection attcks!
+    Snippet to prevent SQL injection attcks! PEW PEW PEW!
     """
-    return ''.join( chr for chr in table_name if chr.isalnum() )
-    
\ No newline at end of file
+    return ''.join(chr for chr in table_name if chr.isalnum())
diff --git a/exoctk/exoctk_app/templates/base.html b/exoctk/exoctk_app/templates/base.html
index f7113708..a196b117 100644
--- a/exoctk/exoctk_app/templates/base.html
+++ b/exoctk/exoctk_app/templates/base.html
@@ -73,7 +73,7 @@
                         <li class="dropdown">
                         <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Observation Planning <span class="caret"></span></a>
                          <ul class="dropdown-menu">
-                                <li><a href="integrations_groups">Groups and Integrations Calculator</a></li>
+                                <li><a href="groups_integrations">Groups and Integrations Calculator</a></li>
                                 <li><a href="contam_visibility">Contamination Overlap</a></li>
                         </ul>
                         </li>
@@ -111,7 +111,7 @@
                         <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button"
                             aria-haspopup="true" aria-expanded="false">ExoCTK Data <span class="caret"></span></a>
                      <ul class="dropdown-menu">
-                         <li><a href="{{ url_for('integrations_groups_download') }}">Integrations Calculator</a></li>
+                         <li><a href="{{ url_for('groups_integrations_download') }}">Integrations Calculator</a></li>
                          <!-- <li><a href="https://github.com/elizakempton/Exo_Transmit">Exo-Transmit</a></li> -->
                          <li><a href="{{ url_for('fortney_download') }}">Fortney Grid</a></li>
                      </ul>
diff --git a/exoctk/exoctk_app/templates/integrations_groups.html b/exoctk/exoctk_app/templates/groups_integrations.html
similarity index 100%
rename from exoctk/exoctk_app/templates/integrations_groups.html
rename to exoctk/exoctk_app/templates/groups_integrations.html
diff --git a/exoctk/exoctk_app/templates/integrations_groups_error.html b/exoctk/exoctk_app/templates/groups_integrations_error.html
similarity index 100%
rename from exoctk/exoctk_app/templates/integrations_groups_error.html
rename to exoctk/exoctk_app/templates/groups_integrations_error.html
diff --git a/exoctk/exoctk_app/templates/integrations_groups_results.html b/exoctk/exoctk_app/templates/groups_integrations_results.html
similarity index 100%
rename from exoctk/exoctk_app/templates/integrations_groups_results.html
rename to exoctk/exoctk_app/templates/groups_integrations_results.html
diff --git a/exoctk/exoctk_app/templates/index.html b/exoctk/exoctk_app/templates/index.html
index 57ce8ad2..6225fe26 100644
--- a/exoctk/exoctk_app/templates/index.html
+++ b/exoctk/exoctk_app/templates/index.html
@@ -19,7 +19,7 @@
                     <div class="col-sm-6">
                         <h3>Observation Planning</h3>
                         <span id="helpBlock" class="help-block">Tools for observation planning with JWST.</span>
-                        <a class="btn btn-primary" href="./integrations_groups">Integrations and Groups</a>
+                        <a class="btn btn-primary" href="./groups_integrations">Groups and Integrations</a>
                         <a class="btn btn-primary" href="./contam_visibility">Contamination Overlap</a>
                     </div>
                     
@@ -81,7 +81,7 @@ <h3> GitHub Source Code</h3>
                     <div class="col-sm-6">
                         <h3>ExoCTK Data</h3>
                         <ul>
-                            <li><a href="{{url_for('integrations_groups_download')}}">This</a> is the JSON data file required to run the Integrations and Groups Calculator through ExoCTK.</li>
+                            <li><a href="{{url_for('groups_integrations_download')}}">This</a> is the JSON data file required to run the Groups and Integrations Calculator through ExoCTK.</li>
                             <li><a
                                    href="{{url_for('fortney_download')}}">This</a> is an SQLite database of the Fortney Grid of stellar models.
                 </div>
diff --git a/exoctk/limb_darkening/limb_darkening_fit.py b/exoctk/limb_darkening/limb_darkening_fit.py
index c8460d7a..9d2f1b64 100755
--- a/exoctk/limb_darkening/limb_darkening_fit.py
+++ b/exoctk/limb_darkening/limb_darkening_fit.py
@@ -239,16 +239,16 @@ def calculate(self, Teff, logg, FeH, profile, mu_min=0.05, ld_min=0.01,
         # Use tophat oif no bandpass
         if bandpass is None:
             units = self.model_grid.wl_units
-            bandpass = svo.Filter('tophat', wl_min=np.min(wave)*units,
-                                  wl_max=np.max(wave)*units)
+            bandpass = svo.Filter('tophat', wave_min=np.min(wave)*units,
+                                  wave_max=np.max(wave)*units)
 
         # Check if a bandpass is provided
         if not isinstance(bandpass, svo.Filter):
             raise TypeError("Invalid bandpass of type", type(bandpass))
 
         # Make sure the bandpass has coverage
-        bp_min = bandpass.WavelengthMin*q.Unit(bandpass.WavelengthUnit)
-        bp_max = bandpass.WavelengthMax*q.Unit(bandpass.WavelengthUnit)
+        bp_min = bandpass.wave_min
+        bp_max = bandpass.wave_max
         mg_min = self.model_grid.wave_rng[0]*self.model_grid.wl_units
         mg_max = self.model_grid.wave_rng[-1]*self.model_grid.wl_units
         if bp_min < mg_min or bp_max > mg_max:
@@ -263,13 +263,13 @@ def calculate(self, Teff, logg, FeH, profile, mu_min=0.05, ld_min=0.01,
         # Make rsr curve 3 dimensions if there is only one
         # wavelength bin, then get wavelength only
         bp = bandpass.rsr
-        if len(bp.shape) == 2:
+        if bp.ndim == 2:
             bp = bp[None, :]
         wave = bp[:, 0, :]
 
         # Calculate mean intensity vs. mu
-        wave = wave[None, :] if len(wave.shape) == 1 else wave
-        flux = flux[None, :] if len(flux.shape) == 2 else flux
+        wave = wave[None, :] if wave.ndim == 1 else wave
+        flux = flux[None, :] if flux.ndim == 2 else flux
         mean_i = np.nanmean(flux, axis=-1)
         mean_i[mean_i == 0] = np.nan
 
@@ -294,6 +294,7 @@ def calculate(self, Teff, logg, FeH, profile, mu_min=0.05, ld_min=0.01,
 
             # Calculate errors from covariance matrix diagonal
             errs = np.sqrt(np.diag(cov))
+            wave_eff = bandpass.centers[0, n].round(5)
 
             # Make a dictionary or the results
             result = {}
@@ -316,7 +317,7 @@ def calculate(self, Teff, logg, FeH, profile, mu_min=0.05, ld_min=0.01,
             result['n_bins'] = bandpass.n_bins
             result['pixels_per_bin'] = bandpass.pixels_per_bin
             result['wave_min'] = wave[n, 0].round(5)
-            result['wave_eff'] = bandpass.centers[0, n].round(5)
+            result['wave_eff'] = wave_eff
             result['wave_max'] = wave[n, -1].round(5)
 
             # Add the coeffs
diff --git a/exoctk/notebooks/lightcurve_fitting_demo.ipynb b/exoctk/notebooks/lightcurve_fitting_demo.ipynb
index 553d015c..0310ed34 100644
--- a/exoctk/notebooks/lightcurve_fitting_demo.ipynb
+++ b/exoctk/notebooks/lightcurve_fitting_demo.ipynb
@@ -12,11 +12,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jfilippazzo/miniconda3/envs/astroconda/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
    "source": [
     "# Imports\n",
     "import numpy as np\n",
@@ -30,9 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Toy data\n",
@@ -57,15 +62,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Kn/xkN65eTTr8W8Tj4BJXK1aYp8uBI6io2IPPfKaULgCcnVAff1yV7OKH5tli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\nMVNIG4kofAw+iChoRvajHcABk8drMX9UTAOcFaR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MTk5avifd8LD2O4aHs1974IFHbMuevS8x3VdQtDKcl49/fKXF8dQeV1+9UlKplG9lt/+b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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x106e34978>"
+       "<matplotlib.figure.Figure at 0x1088253c8>"
       ]
      },
      "metadata": {},
@@ -91,15 +94,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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p/nv17GQLICUQWoAU9pm5n9HS/7WUnWwBpARCC5DCcrNyGV0BkDJYPQQAAHyB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\nWgAAgC8QWgAAgC8QWgAAgC/8f2bhPikvaOrHAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11532fd30>"
+       "<matplotlib.figure.Figure at 0x1270e5438>"
       ]
      },
      "metadata": {},
@@ -123,16 +124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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9AKDECB8AIi0t9HwAKAvCB4BIS0vU8+EeuhIACUf4ABBpbZWeeUb6yU9CVwIg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\nAMqK8AEAAMrq/wGDNb0CGoAjkwAAAABJRU5ErkJggg==\n",
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mtiDetTZu/UgMCcgyVrYFkGIhx3y0Shou2DesqNuk0FnLd8YtIco7d4+kPjPbaWYbJd1T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\nDwAAUFX/H8D+WWUqOmK4AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1155a2160>"
+       "<matplotlib.figure.Figure at 0x12d76a1d0>"
       ]
      },
      "metadata": {},
@@ -144,7 +143,7 @@
     "params = Parameters()\n",
     "params.rp = 0.22, 'free', 0.0, 0.4  # rprs\n",
     "params.per = 10.721490, 'fixed'\n",
-    "params.t0 = 0.5, 'free', 0, 1\n",
+    "params.t0 = 0.48, 'free', 0, 1\n",
     "params.inc = 89.7, 'free', 80., 90.\n",
     "params.a = 18.2, 'free', 15., 20.    # aprs\n",
     "params.ecc = 0., 'fixed'\n",
@@ -172,16 +171,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BtbZxmDWIFAS/H7q7LBw/4dx7K6nr6eH48ht54kfv4MNq+RBJSybndtnBmcsg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\noEEAgO/x5wMAaCU+AIBW4gMAaCU+AIBW4gMAaCU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mqOVj/J59ljk//CnTWEUgoCVtRTIt5fAxUteKMWY1UINz98tYhIDwkOfCQCXO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\niIiISFYpfIiIiEhWKXyIiIhIVv1/PD6XrGRNn00AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11559c8d0>"
+       "<matplotlib.figure.Figure at 0x12d47ed68>"
       ]
      },
      "metadata": {},
@@ -207,10 +204,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -222,14 +217,14 @@
       "    # function evals   = 13\n",
       "    # data points      = 100\n",
       "    # variables        = 10\n",
-      "    chi-square         = 1334.093\n",
-      "    reduced chi-square = 14.823\n",
-      "    Akaike info crit   = 279.084\n",
-      "    Bayesian info crit = 305.135\n",
+      "    chi-square         = 1218.389\n",
+      "    reduced chi-square = 13.538\n",
+      "    Akaike info crit   = 270.011\n",
+      "    Bayesian info crit = 296.063\n",
       "[[Variables]]\n",
       "    rp:    0.22000000 +/- 0        (0.00%) (init= 0.22)\n",
       "    per:   10.72149 (fixed)\n",
-      "    t0:    0.50000000 +/- 0        (0.00%) (init= 0.5)\n",
+      "    t0:    0.48000000 +/- 0        (0.00%) (init= 0.48)\n",
       "    inc:   89.7000000 +/- 0        (0.00%) (init= 89.7)\n",
       "    a:     18.2000000 +/- 0        (0.00%) (init= 18.2)\n",
       "    ecc:   0 (fixed)\n",
@@ -245,9 +240,9 @@
     },
     {
      "data": {
-      "image/png": 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ADTF1elY6aEGIhPi+DFntw9x861vmdaBKS8u8hSi1ieOoL5y/LS2DoEG0SlEorF47q32R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\nRU5ErkJggg==\n",
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Cp079IvBX1q9fYzp9eLD3cuON1/P888GOLZJRsEka33zTfByMQDO+ek5SZ3X6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\nOJ1OPWjQVD+Bh/HTrdtUPXy4U0+YoPWUKVovGTN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u+7JTSo3z9X2LSK2IbPJ+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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115891208>"
+       "<matplotlib.figure.Figure at 0x12d71bb70>"
       ]
      },
      "metadata": {},
diff --git a/exoctk/tests/test_limb_darkening.py b/exoctk/tests/test_limb_darkening.py
index 12433891..89ac2d85 100755
--- a/exoctk/tests/test_limb_darkening.py
+++ b/exoctk/tests/test_limb_darkening.py
@@ -73,6 +73,27 @@ def test_ldc_calculation_filter():
     assert len(ld_session.results) == 2
 
 
+def test_ldc_calculation_grism():
+    """Test to see if a calculation can be performed with a grism and
+    that they are appended to the results table"""
+    print('Testing LDC calculation using a filter grism...')
+
+    # Make the session
+    ld_session = ldf.LDC(MODELGRID)
+
+    # Make a filter
+    filt = Filter('2MASS.H', n_bins=10)
+
+    # Run the calculations
+    ld_session.calculate(Teff=4000, logg=4.5, FeH=0, profile='quadratic',
+                         bandpass=filt)
+    ld_session.calculate(Teff=4000, logg=4.5, FeH=0, profile='4-parameter',
+                         bandpass=filt)
+
+    # Two profiles split into 10 measurements = 20 calculations
+    assert len(ld_session.results) == 20
+
+
 def test_ldc_calculation_interpolation():
     """Test to see if a calculation can be performed with no filter and
     an interpolated grid point and that they are appended to the results
diff --git a/setup.py b/setup.py
index 601cc97f..02a5b545 100755
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
       description='Observation reduction and planning tools for exoplanet science',
       install_requires=['numpy', 'astropy', 'scipy', 'cython', 'matplotlib', 'numba', 'pysynphot', 'sphinx_automodapi', 'sphinx_rtd_theme', 'bibtexparser', 'bokeh', 'pandas', 'svo_filters', 'sphinx_astropy', 'batman-package', 'lmfit', 'flask'],
       author='The ExoCTK Group',
-      author_email='jfilippazzo@stsci.edu',
+      author_email='exoctk@gmail.com',
       license='MIT',
       url='https://github.com/ExoCTK/exoctk',
       long_description='',