Debugged the bit added to views.py a bit; added the appropriate infor…

…mation to the view template. Stumbled across #57 en route to #56.

dataview/templates/dataview/halflife.html

@@ -38,7 +38,23 @@ <h1>{{ sub }}</h1>
 
     <h2>Elimination Extrapolation</h2>
 
-    <h3>Lipid Insoluble Metabolite Breakdown Information</h3>
+    {% if not sub.lipid_solubility %}
+
+      <h3>Lipid Insoluble Metabolite Breakdown Information</h3>
+
+    {% else %}
+
+      <h3>Lipid Soluble Metabolite Breakdown Information</h3>
+
+      <p>Please note that results here should not be used for anything vital, important, or even tangentally
+         useful.  The algorithm implemented is <i>very</i> rough around the edges.  Please feel free to see
+         the <a href="https://www.mayocliniclabs.com/test-info/drug-book/marijuana.html">Mayo Clinic labs'
+         book on THC metabolite testing</a> for more information on the algorithm's basis.</p>
+
+      <p>Based on the last usage date of <i>{{ last_usage }}</i>, your estimated time of return to a negative
+         test result baseline from the most common method of testing is <i><b>{{ undetectable_target }}</b></i></p>
+
+    {% endif %}
 
     <p>Based on the last usage date of <i>{{ last_usage }}</i>, your estimated time of return to pristine bodily
        fluids is <i><b>{{ elimination_target }}</b></i></p>

dataview/views.py

@@ -113,44 +113,44 @@ def get_context_data(self, **kwargs):
 def extrapolate_halflife_data(request, sub_id):
     substance = Substance.objects.filter(id=sub_id).first()
     context = {}
-    elimination_datetime = None
+    full_elimination_datetime = None
 
     if substance.lipid_solubility and ('marijuana' in substance.common_name or 'weed' in substance.common_name):
         # we're working with weed, let's give this a shot based on the information available at
         # https://www.mayocliniI sure hope auto-save was keeping upclabs.com/test-info/drug-book/marijuana.html
         # FWIW we're just going to base our projection on the average of the last 2 weeks of usage
         weeks_averaged = 2
-        relevant_since_date = datetime.now() - datetime.timedelta(weeks=weeks_averaged)
+        relevant_since_date = datetime.datetime.now() - datetime.timedelta(weeks=weeks_averaged)
         relevant_usages = len(Usage.objects.filter(timestamp__gte=relevant_since_date))
-        most_recent_usage = Usage.objects.filter(sub=sub_id).order_by('-timestamp').first()
+        last_usage = Usage.objects.filter(sub=sub_id).order_by('-timestamp').first()
         full_elimination_duration = int(float(substance.active_half_life) * 5.7)
 
         # note that half-life durations here (not flat day count) are calculated @ 5.7 * half-life, as in the
         # standard non-lipid-soluble substances; detectable metabolites will be out of the system sooner (hence
         # the less precise flat day count)
         if relevant_usages <= weeks_averaged:
             # single use: detectable for a standard half-life duration
-            full_elimination_datetime =  most_recent_usage.timestamp + \
-                                         datetime.timedelta(hours=full_elimination_duration)
-            detectable_elimination_datetime = most_recent_usage.timestamp + datetime.timedelta(days=3)
+            full_elimination_datetime = last_usage.timestamp + \
+                                        datetime.timedelta(hours=full_elimination_duration)
+            detectable_elimination_datetime = last_usage.timestamp + datetime.timedelta(days=3)
 
         elif relevant_usages <= (weeks_averaged * 4):
             # moderate use: detectable for standard half-life * 5/3, _or_ 5 days
-            full_elimination_datetime = most_recent_usage.timestamp + \
+            full_elimination_datetime = last_usage.timestamp + \
                                         datetime.timedelta(hours=int(full_elimination_duration * (5/3)))
-            detectable_elimination_datetime = most_recent_usage.timestamp + datetime.timedelta(days=5)
+            detectable_elimination_datetime = last_usage.timestamp + datetime.timedelta(days=5)
 
         elif relevant_usages <= (weeks_averaged * 7):
             # heavy use: detectable for standard half-life * 10/3, _or_ 10 days
-            full_elimination_datetime = most_recent_usage.timestamp + \
+            full_elimination_datetime = last_usage.timestamp + \
                                         datetime.timedelta(hours=int(full_elimination_duration * (10/3)))
-            detectable_elimination_datetime = most_recent_usage.timestamp + datetime.timedelta(days=10)
+            detectable_elimination_datetime = last_usage.timestamp + datetime.timedelta(days=10)
 
         else:
             # chronic heavy use: detectable for standard half-life * 10, _or_ 30 days
-            full_elimination_datetime = most_recent_usage.timestamp + \
+            full_elimination_datetime = last_usage.timestamp + \
                                         datetime.timedelta(hours=(full_elimination_duration * 10))
-            detectable_elimination_datetime = most_recent_usage.timestamp + datetime.timedelta(days=30)
+            detectable_elimination_datetime = last_usage.timestamp + datetime.timedelta(days=30)
 
     elif substance.lipid_solubility:
         # we can't process this yet
@@ -163,8 +163,8 @@ def extrapolate_halflife_data(request, sub_id):
                                     datetime.timedelta(hours=int(float(substance.half_life) * 5.7))
         detectable_elimination_datetime = full_elimination_datetime
 
-        context = {'error_message': None, 'sub': substance, 'elimination_target': full_elimination_datetime,
-                   'undetectable_target': detectable_elimination_datetime, 'last_usage': last_usage.timestamp}
+    context = {'error_message': None, 'sub': substance, 'elimination_target': full_elimination_datetime,
+               'undetectable_target': detectable_elimination_datetime, 'last_usage': last_usage.timestamp}
 
     return render(request, 'dataview/halflife.html', add_header_info(context))