Please note that results here should not be used for anything vital, important, or even tangentally + useful. The algorithm implemented is very rough around the edges. Please feel free to see + the Mayo Clinic labs' + book on THC metabolite testing for more information on the algorithm's basis.
+ +Based on the last usage date of {{ last_usage }}, your estimated time of return to a negative + test result baseline from the most common method of testing is {{ undetectable_target }}
+ + {% endif %}Based on the last usage date of {{ last_usage }}, your estimated time of return to pristine bodily fluids is {{ elimination_target }}
diff --git a/dataview/views.py b/dataview/views.py index 1f2af0c..25a8b02 100644 --- a/dataview/views.py +++ b/dataview/views.py @@ -113,16 +113,16 @@ 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 @@ -130,27 +130,27 @@ def extrapolate_halflife_data(request, sub_id): # 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))