Welcome to cannabis data science. You're in the right place. You're in for one of the
biggest treats I think the group has to offer yet to date. So the cannabis data science
team has been working on many different tasks and you know my background from the laboratory.
So we've got a little bit of know how and we've got some awesome programmers such as
Candice who's got a long career in IT. So we've got a fantastic team here and essentially
we'll go ahead and let the cat out of the bag. The task that's been on everybody's mind
for a long time now and in fact this was one of the reasons that Canelytics was started
was wanted to get lab results in consumers hands. So in the cannabis industry we've got
mandated quality control testing. So that's good for the consumers right. So products
get screened for contaminants. They get tested for cannabinoids so you know what you're consuming.
But the data I mean how often do you actually get your hands on the data. And so we're some
of the most avid pursuers of this data and it's still hard to come by. So your average
consumer may have a really really tough time with this. So that's one of the reasons Canelytics
was started. So we were trying to work with labs, producers, processors, retailers trying
to figure out how can we get this data to the consumers. And then finally there's been
some awesome fine folks. And so I want to really commemorate John. So John and the CESC.
So check out and want to say it's dcesc.org has been moving the ball forward for the consumers
right. So they're trying to get the consumers into the picture right. At the end of the
day they're the ones driving the industry. So we want to make sure that people can make
healthy, wise, educated decisions. And then you know they know what they're in store for
and you know we need to get that data and then get that into consumers hands. So really
want to say that work that John's doing is noble and that the task that John sent me
on was the labs and the producers. One can almost say you know they've taken the ball
as far as they can and so there's just somebody who needs to go and take it you know the last
10 yards. And that's sort of the Cantlydix philosophy is we don't like impose upon anyone
or we try not to. We try to meet you where you are and take you one step further. And
so where people are is they've got COAs. And in fact I've got this is the first day I've
actually had props. So instead of a presentation I brought props today. So at the cannabis
dispensary you get these items. And I was saying the last week that the QR codes were
20th century technology and I may want to walk that back. Technically they are. They
were I think invented in like 1994 1995 or so. But that's late 20th century technology
and I think they're just getting put to good use today. So essentially you know with these
QR codes at the dispensary you can get the COA and we can show you that today. But then
it's like well now we actually need that data. And so that is the task that we've finally
solved or at least begun to solve as you'll see there's almost infinite room for improvement
as always. But I think we've done a lot here. So before we jump into it any thoughts or
I guess I'm still kind of building up suspense here. Well without further ado let's just
go ahead and start the presentation because you're both going to be in for quite the treat
here. We're going to need some tools for the job here. So basically where do we even begin
with this? Well let's look at one of these COAs. So if you were to scan one of these
QR codes say you got a QR code reader on your phone which I do but just for the sake of
the presentation let's just pretend okay I just scanned this QR code on my phone and
then this is going to shoot you over to a URL. And in this case we've got a Confident
Cannabis URL and Confident Cannabis is a common laboratory information management systems
or LIMS provider. And so a lot of laboratories will use Confident Cannabis and they'll have
their online portal here where you can see a picture, you can see the cannabinoids, you
can see the screenings. So no heavy metals in this flower. Awesome. And even better no
pesticides detected. And so this is really what you want to see. So especially I would
say if you're consuming concentrates you probably want to check your solvent cell to
make sure you've got a nice clean concentrate. So long story short you can get the results
but we want to actually get these physical data points. And so this is quality control
tested mandated by the state, the laboratory provided that data and now the data is here
publicly available on the web. So these are now facts. So this is now essentially a fact
about this classic JAK sample. So readily collectible at this point. And in fact I think
as a consumer you should collect your cannabis data because I heard a saying once that if
you're not measuring it you're not managing it. So if you want to manage your cannabis
consumption then you'll need to measure it. And these days with the cannabinoid concentrations
getting quite potent you really want to be keeping track of the milligrams because just
one gram of cannabis is not the same across the board. Cool. So that's the work that's
going to be had and just to kind of knock out two birds with one stone let's say you
also have this PDF. So sometimes the laboratory may only have this PDF. So we'll also save
a copy of this. That's one of the ones I sent you I think right. I think this is actually
one from a recent purchase of mine at a cannabis dispensary. It's at Cali Green. So did that
mean meanwhile you were down here. Exactly. So basically I've been saving some of these
packaging packages because as I was saying like and this is why I think this is novel
right. You know I'm a cannabis consumer and I'm interested in keeping track of this data.
And if you know myself who I think is you know I try to be a a practitioner data scientist
you know that's up for you to debate if I am or not but I try to do data science. And
so if I can't even get a hold of my own lab results you know then how could you know how
could like any you know it's going to be it's going to be tough for consumers to get their
their lab results. So long story short is let's solve this problem. Let's solve it today.
And then we'll basically do a proof of concept and then I'll show you how this can be iterated
upon to you know further get refined and incorporated into many many applications. So just so I've
downloaded some C.A. ways. So here's this same C.A. way the Cali green. It was to see
here you see it was tested at the Cali green laboratory by confident cannabis. And so basically
to to just show you how the magic is going to happen instead of just pretending things
are magic. Right. So we've got PDF plumber. And so my first go of this was OK. Let's find
all the tables on here and just start parsing them. And it's a mess. And I think it can't
be done and probably should be done. But one thing I learned work Tory was there was always
this stress that OK you need to design things to be compatible with being offline. But any
time we were offline the number one goal was getting back online. So it's basically like
if you're not online you know maybe not. Don't be worrying about parsing C.A. ways like you
know you should first worry about getting online because you need to check your emails
and other things. And then once you're online then you can you know parse C.A. ways to your
heart's content. So we can worry about offline in the future. But instead of parsing all
of this let's just be smart about this. Right. So we're going going and going. And so here
I'm parsing I'm like I'm getting the images that that that that that. And then I realize
oh wait a second. This PDF has a QR code on it. So what can we do with that is I was thinking
no way would it be possible to essentially read the QR code programmatically and typically
with Python. If it's on a computer where there's a will there's a way. That's what I've that's
why I love Python. That's kind of why I fell in love with it is I started realizing that
anything on the computer you could essentially automate with Python. And I just want to give
a quick shout out to the Selenium authors because this is a package that I've kind of
shied away from because it's got a bad reputation in the Python community. We're not a bad reputation
but it's basically people will throw a lot of grief at it and say oh it's real heavy.
It's slow. Just use requests. And I agree that if you're consuming from an API then
yeah you know go with requests or if it's a simple web page go with requests. But often
you know we're not dealing with simple web pages here. Right. As you can see we've got
the loading sign. If you actually inspect the source code that they shoot over at you
you'll see that you're working with an I frame. So there's just a lot of JavaScript that's
making this page happen. So you can't just use requests. But that's OK because with the
Selenium authors have said is there's so many tasks that businesses are doing on a day to
day basis that can and should be automated. So if you find yourself you know clicking
through Chrome and you do the same clicks over and over and over again that can be automated.
Right. As I was saying last week you know I'm half me and half Python. So you know if
I want to use Chrome with my Python appendages then you know so be it. And in fact I didn't
really realize it but you know Selenium has been one of my like best used tools in the
past. Right. So out of all the code that I've written you know some of the scripts that
ran the most I mean you know I have written scripts that have run like in the millions
of times and some of those leverage Selenium. It's a great great tool. So. So anywho let's
just go ahead and get down to the brass tacks of this. So we'll basically be using Selenium
plus PDF plumber which I've realized and that's why I said call a plumber. But PDF plumber
is actually just a wrapper around PDF miner. But anywho the code is only so interesting
to so many people but the data is interesting to many. So what we can do here is we can
first read in the PDF. So we've now got this PDF file in memory. And as I was saying you
know you can do a whole bunch of parsing right. So I was originally just trying to parse out
the tables but basically what you can cleverly do here is just iterate over all the images
in the sheet and you basically just stop if one of them gets decoded as a QR code. I saw
originally I was trying to like specify what index the image was but it's basically all
the laboratories like to have their own unique certificate of analysis. So SC labs may have
a different COA I imagine they would. I don't actually know all the different California
laboratories but they all love their COAs and they're real particular about them. There's
a certain compliance requirement as well in California what you can and cannot put on
there. Exactly and so long story short is for any that put the QR code on them we can
utilize that. I wonder how common that is. And that's sort of a downside. So I do know
what so here see they're using Confident Cannabis. I wonder you know what essentially Confident
Cannabis is you know market share is of laboratory tests. That was something Canalytics was always
wondering about. But I think Canalytics can almost instead of just stepping on other people's
toes I find it's more interesting to just solve different pieces of the puzzle. And
so you know they're good at providing certificates right and they've got them online. And so
the piece of the puzzle that I saw that we could fix was okay we can now actually just
wrangle all this data and get it to the consumer and not just Confident Cannabis but also wrote
a thanks to John for sharing the PDFs. I wrote a routine to parse tag leaf limbs COAs. And
so basically the task now is to have this COA parser out there and we basically just
need to identify you know all the COAs that have QR codes on them and then make sure that
we can then parse those results from the web. And then if the lab doesn't have a QR code
at that point we'll have to use the PDF plumber to plumb the PDF. And so that's going to be
a time intensive task. And so this is where I was telling you that I was going to share
with you how I intend to make money with Canlytics and that way you can make money in the exact
same way. And so I've written a and I'll we'll get into the guts of it today because that's
kind of what you would you come here today to see. Right I get to show you all the special
things right we get up we get to pull up the hood and play with the engine and do all the
fun stuff. But that's the point of the cannabis data science to actually get our hands on
this fun stuff. So long story short is we've now got this tool COA doc. And so the idea
is input any arbitrary any arbitrary COA and then parse it. And just to to actually just
show you your maybe just a demonstration of the tool will be better than than showing
you the code. So I'll demonstrate the code I mean I'll demonstrate the tool and then
we can show you the code behind it. So first things first let's just make sure that we
can in fact get the QR code because that's a pretty important part of the whole whole
process. So yes we can get the QR code and then to sprinkle in data science right and
that's actually sort of one of the lessons of the day is a little bit can go a long way
and a little bit of clever you know machine learning clever you know these clever algorithms
can go a long way. And so we only need one line of magic to make a lot happen right.
And so want to give another shout out to Pi Z bar and they're working on cameras and computer
vision to real cool applications. But basically what you can do is you can decode this object.
So decoding and then all of a sudden we've got the decoded QR code data and as you'll
see we've got the URL nice nicely in there. So you know the QR code of course was specifically
designed for computers right. So if you're going to do any computer vision task right
QR code is of course the simplest but that's the way can let it likes to do it right. You
start with the absolute simplest possible use case and then we can take it from there
right before we start like you know applying computer vision to the sample images themselves
which I think we could and maybe should do eventually we can just start simple. So long
story short we can get that QR code just going to fire up the tool real quick and then we
can basically see it in use. So basically I just put this behind a super simple API
just to just show you the actual guts of it. So there's nothing super fancy about the API
itself. So if you make a request it basically tries to find out if you posted any URLs.
So the idea is you can use this programmatically so if you have your own QR code reader in
your app then all you have to do is implement a QR code reader in your app and you can just
scan one of these packages and then post that URL to this endpoint or you can use the Camletics
website and you can upload the files directly. So still working on this so I'm still going
to have to add all the bells and whistles for you so I'm just going to show you under
the hood today so you know so bear with me. But this is the tool COADOC and the idea is
to just be able to upload any arbitrary PDF or scan a QR code and then get that data.
And then under the hood literally three lines of code. I mean of course you have to do all
the rigmarole like check if. I'm doing things like making sure that people are only posting
PDFs and Zips and there's still a bit more safety checks I want to do before I publish
this but essentially you can get it down to just fire up your COA parser you know tell
it to parse your URLs and then you've got your data and keep in mind that the magic
happening is we've basically got two different algorithms here. So I've written a custom
algorithm for confident cannabis and I've written a custom algorithm for tag leaf limbs.
I don't know how prevalent tag leaf limbs is but I see confident cannabis around so
for me I thought it was worthwhile and so the idea is if you know other limbs or you
know other labs you could basically go and make them a value proposition and say hey
I'll write you a parsing algorithm for your PDF that way it can be parsed along with any
other COA by COA doc and just to just put you know just to you know actually put this
to good use here let's just go ahead and drop this classic Jack COA and hopefully
hopefully going to show you things so in production it won't actually open a browser but just
for demonstration hopefully we yep so we just open up this browser look I always say this
is my joke like look mom no hands so just opened up Chrome just got all the heavy metal
results got all the other results hopefully we don't run into errors we may have hit an
error oh no look no hands so so bada bing bada boom and I'll zoom in on this but basically
there we have it so now all you all you have to do is get you know the URL or the actual
COA and as we've seen you know the producers and processors are making an effort to get
those on their packaging so I applaud them for that right so you can have the glass half
full or the glass half empty mentality the glass half empty mentality which I am susceptible
to would be hey you know why aren't why aren't these producers giving us all this data you
know why why don't they have an API you know why can't I just ping this and get all this
data and it's like okay okay you know just you know quit being so demanding right they've
done a pretty good job right they've grown some fantastic cannabis they've got it packaged
up and they've even you know got it nice and labeled and put a QR code on it so it's like
you know they've you know they've taken the ball as far as they can they you know they've
taken as far as they can and it's basically just like okay like thank you you know like
cantlydx will take it the last the last the last yard you know that like I mean think
about all the work that's been done to get the data this far I mean think about this
this is seven analyses here this is an analyst who looked at this under a microscope for
foreign matter they tested it on an ICP for heavy metals not an easy feat they performed
a microbial test a micro toxin test those two tests may take up to 48 hours or so so
those can be timely a pesticide test expensive if you want to go get your sample tested for
pesticides that may cost you north of 300 bucks in California I think good pesticide
tests may cost you almost $800 or so water activity fairly and moisture are fairly simple
tests I think you could if you're clever you could conduct these tests at home probably
with your microwave and a small sample of your flowers so these are pretty easy tests
well maybe not you could do moisture I don't know about water activity but fairly easy
tests but they're still informative so you've got you know your tests here you know you
now know the laboratory if you want to give them a call or you know you want to shoot
them an email we actually didn't get their email in this case but you could go you know
do a search for the laboratory so that way you actually know what laboratory tested your
product just in case you wanted to give them a call you know that of course yeah I mean
you were expecting it to have passed pesticides but check this out we now have 78 compounds
so yes you know they're reporting the THCA and Delta 9 but check this out you know this
wasn't on the label you know the CBGA and once again you can't fault them they only
have so many square inches here right but I think this is an important one like whoa
like look at look at all that CBGA that's a in my opinion an enormous amount of CBGA
and so you know now I know when I consume this cannabis you know in this package there's
almost a hundred gram a hundred milligrams right because this package is 3.5 grams so
now we can do 27.52 times 3.5 whoops there's just shy of a hundred milligrams of CBGA in
this package which is if you're not measuring it you're not managing it so if you know I
like Jack and someone else doesn't like Jack maybe it's got something to do with the CBGA
or maybe maybe it's got to do with one of these many many terpenes in your product right
and so these are what consumers are kind of realizing that they want to be measuring right
so check this out right you've got the terpenolene and then John want to applaud you right so
once again now that I've got my date my great this is my data right because it's a product
that I bought and you know I went and wrangled the data so you know as a consumer right I
think you should be collecting your data but check this out you know let's say I want to
keep track of my beta pionene to d-lemonene ratio beta pionene 0.06 limonene 0.04 which
is a shockingly small amount of limonene you normally see I don't know what the distribution
is off the top of my head but I want to say you normally see more limonene than that in
your products so I don't know if I just you know I kind of like this strains sometimes
I think sometimes I may prefer something else but so am I just maybe adverse to limonene
or maybe I just like the beta pionene or maybe there's something else going on here but you
know it's got terpenelein doesn't it 0.06 over 0.04 it's a high beta pionene strain by
those even even with the small amounts exactly and in fact I this is what I started to figure
out that I may like about Jack is I think the terpenelein at least gives the Jack its
distinctive smell it's just to be frank you know it's a kind of a pungent that people
call it like a almost like a chemically type it's a turpentine smell I got to get when
I first realized this what I got to give him a shout out because they were the ones who
helped me connect the dots it was planet 13 in Las Vegas they were actually labeling the
percentages of terpenes on the products and that was when I mentally connected the dots
between oh you know the reason I like the Jack type strains is the terpenelein and it
was sort of a revolutionary point because it's like oh I don't have to get Jack Harrier
every single time as long as you know it's got some terpenelein in it you know it may
have a you know a similar effect and in fact you can kind of branch out because maybe maybe
some of these you can get something with terpenelein plus something else so in that was sort of
no Keegan I probably should again chime in here on trippin lean is part of a cluster
so if it's going to have terpenelein it's going to have a series of others as well caring
phalaenadrine alpha terpene and gamma trip they run off the same enzyme so while the
terpenelein is the dominant it's the lead of that enzyme the enzyme makes these others
so if you're in a high terpenelein strain you've got these others come along for the
ride as it were.
I love it and now we can finally you know collect data on it and well I guess I guess
the data has already been out there so I guess you already you already know this right so
that's what yeah I've been following this for years it's just what is the utility or
what is the effect I mean is it the terpenelein that's driving the effects in the high terpenelein
strains or is it one of the other terpenes or is it another compound that is also comes
with it we don't know yet but we can only look at what is reported right and this is
this is why I was thinking this was sort of the missing piece of the puzzle right you've
got the lab result data you just need to get this in the hands of the consumer or in your
case you know your research participants that way you know one you know they can act upon
their data and then two you could actually you know match that and study this because
now if you just tell your consumers you know if you and this is why I was saying you know
economic forces now right so if I do this if you do this a bunch of people do this then
it'll put the economic incentive to put your QR code on your package because what you could
say is okay make sure if you're participating in my study make sure to go and get something
with your QR code and scan it you could either have your own app or they maybe they could
just send you the URLs or right and that's the cool thing about this tool is you can
package it up and use it however you want but basically now the consumer doesn't even
have to do anything they just have to buy their package well they do have to they scan
it just beep and then then they it's connected that was that was the only thing that was
missing we've got the lab results the consumer bought it there's just no no match there and
so now if you've got you know your consumer app or your your study app you can now finally
make this connection the consumer you know like myself you know they can get their data
and um thought of being about a boom like I I could see so where are you so what this
is you sucked it up from a QR code that was on the package or was in the PDF or I mean
what you just ran was off of QR code input right and so that's what's so cool about the
algorithm is basically what it all funnels to the URL so it's um and so that's that's
kind of why I wanted to show you under the the hood today so if you look at the parsing
routine okay now I understand basically it just says oh you know so long story short
is the PDF routine subsequently calls the parse URL routine so basically if you have
the the PDF say you're a producer one would hope the producer actually has the raw data
but if for whatever reason they don't then they can just they can just upload this PDF
and it will find this URL from the QR code or if you're just a consumer you can just
scan the QR code and it would just just use this URL directly and I still need to to wire
up this functionality but the idea is you could just however you get that URL that's
sort of up to you so whether you just copy and paste it um
so I'm wondering um of the five PDFs I sent you the other day I wonder I didn't look at
them for URLs for QR codes um I wonder how prevalent that is so with the tag leaf limbs
I was able to get five of six so five so five oh wait a minute tag leaf is a lab um yes
so so here and because I think I sent you several different labs PDFs um here I'll
show you um we've got a little time here um so there's a little glare on my screen okay
so so here um if you're okay I with me using this one then I'll I can demo the tag leaf
limb limbs one um basically let's just talk was that one of the ones I sent you I don't
remember the lab names um yes okay um no you whatever I send you you can use don't worry
about it cool so here let's just just demo this um this sunbeam one real quick then okay
so the idea is okay so we've got the PDF so hopefully we can just get this URL straight
from the QR code so basically you know we're just looking at all the images on the first
page of that one and we should hopefully well first let's just see if we've got an object
so let's see we've found the QR code and so this one is tag leaf limbs and this is where
I was saying like economic forces are just so remarkable because it's basically like
you know once one player introduces the QR code you know everyone else kind of has to
to follow suit because that's now the standard to now tag leaf limbs right they've got a
QR code linking to their certificate online and this isn't actually that hard right when
I worked at the laboratory I implemented this exact same thing you know I uh I'm not bragging
I just think uh you know this is a standard that can be implemented right so if you see
your laboratory and they don't have this that's why I was saying you know you should reach
out to them and don't blame them be the glass half full guy say hey I can meet you or girl
you know say I can be the missing piece of your puzzle right I can meet you where you
are maybe you need help implementing a QR code maybe you need help with an API right
so some labs need more help than others right some limbs need more help than others but
so once again different right so different web page but I find with here I use beautiful
soup to in request so I didn't need selenium to parse this one but I find scraping or parsing
web pages to be easier than parsing PDFs because people use like CSS and things right so just
the the syntax that people use makes it real easy to parse these they're not easy but it's
about a data it's and so actually I shouldn't talk about prices right because technically
you get in trouble you know allowed to just talk about prices really nilly but but long
story short is you know I assess how much time it would take me to parse one of these
and charge accordingly so basically you know if there was a new lens that wanted to a parsing
routine and it doesn't have to be this one and this is what's cool right coa doc is open
source so you can clone this and right you could have your own private and sorry there's
just such a clear here you could have your own private coa doc code right you could just
clone the repository make your own additions or if you wanted to you know benefit the whole
cannabis community you know the whole cannabis industry you could you know make a pull request
you know with your addition right so if you added new limbs you can make a pull request
and then I can review your code if it's valid I could you know incorporate your code here
on the main can lytics website you could get paid for your work coding I would like that
right because it adds to the can lytics code base the laboratory wins because they get
to get they get to get incorporated so long story short it's just it's simply a tool that
can be used to go from URL to data and just yes for one last demo we can just parse this
other strain I got some gmo pie so there's still some work to be done so essentially
if and John this is why I wouldn't mind getting this tool in your hands because essentially
if you've got like a big body of coas that need to be parsed then it'd be cool to just
run it through this see which ones you can parse and then which ones you can't and the
ones you can't maybe they're they're they're on a different lens or a different lab that
has implemented a QR code in which case we can try to write you know like web scraping
routine and then like I said worse comes to worse and they just have a PDF you can use
PDF plumber to parse the tables it's just I would just do that at a at a last case was
like a last resort I would do that at last resort but but as I said it wouldn't be like
I think it would still be worthwhile well worthwhile depends on the costs but it would
still add value to create a parsing routine for confident cannabis PDFs because then you
could parse them offline but I think the marginal benefit of that small the marginal cost is
high I think you're almost always going to have an internet connection and if you don't
then you've got bigger problems than parsing than parsing COAs and so long so long story
short is this tool works pretty well if you've got an internet connection and then you can
get a you can get all these results and in there's still work to be done so for example
like a minor thing that needs to be done is these results need to be cleaned up a little
bit but that that's minor so there's still some polishing that needs to be done so stay
tuned before I you know formally publish everything huh okay um I think um there are from my perspective
two folks two constituencies to be talking to and one of them I'm about to have a conversation
with um is the lab that I work very closely with they the scientific so I'll check what
their COA publication status is if they've got QR codes on there or not so that you know
I mean would be good great to have make sure VEDA can have their um their sets their their
data sets read by uh folks like us um the second one is is a retail store like floor
and they're the ones who have corpuses of COAs so it might be useful uh what I sent
you was the five that they would say sent me for a couple strings we were evaluating
but they have about 70 or so um I'll be having a conversation with them and we can see what
their corpus of COAs are and how readily they could be sucked up into a data set because
that's where the real utility or a real utility of this is is because then in a store can
decide how their inventory parses or you know how to display it how to categorize it or
all that right now they just you know we're teaching them to read COAs but there's no
automation to it you know you it's tough to run the algorithms that we want to run on
so um I would say you know you we go lab by lab or you know labs that are affiliated with
the efforts we're doing and then with the retail as well and um yeah it's a good start
I love it John and let's bring let's bring VEDA on board because that's the whole idea
is just to keep incorporating labs and limbs until we can parse you know the majority of
COAs right so like if we can parse 80% of COAs that would be phenomenal yeah and like
you said right the producer has access to their their data and their client portal but
the retailer the retailers just getting sometimes I mean probably hundreds if not thousands
of COAs from all sorts of different vendors and so if now we just have this nice standardized
tool where they could just take a folder of COAs from five different labs right or and
they they don't even have to think about what limbs it comes from they just right right
they just drag and drop it parses and then they get their data in in in my opinion it's
their data right the the lab testing in my opinion is two products one it's the it's
the signature essentially the the certificate right that yes this was certified tested is
the data in this is mission critical right this needs right because this is for menu
boards this is for labels this is for getting in consumers hands and we're just meeting
the industry where it is right as I said it would be phenomenal right if every single
company had has implemented an API and QR codes and this and this and this but right
they've got a group cannabis and they've got to sell cannabis and test cannabis right people's
got so many things to do then that's why you know the campus data science team is here
to fill in this last piece of the puzzle right so we'll meet you where you are thank you
right it's like thank you Tackleaf Limbs for publishing your results and you know thank
you Confident Cannabis and thank you Cali Green Laboratory and everyone else you know
thank you for doing your part and then we're basically we'll do our part and so basically
our parts just just get everything get all the data and standardize it and then you know
put it nicely in your hands for you to use so so you've got to see the first unveiling
of COA doc you basically got to see the complete raw under the hood unfinished version so today
I'll put all the bells and whistles on it but now you know how the magic happens right
so now when you simply drag your COAs here and get your nice crispy clean data you know
how it's working under the hood and it's all open source and I think that's what's
going to make this tool awesome right because I don't think I'm the first one who's written
in a COA parsing routine in fact I know people who've done this right this is done by a lot
of software companies but if we do it in an open source manner then right like Candace
right you can contribute and get paid for it right you can go to MCR labs in Massachusetts
or who have you and you know see if they want a parsing routine and you know if we kind
of get this tool used by enough people then it would be a no-brainer for the laboratory
or the limbs and they would say yeah like of course we want to be incorporated like
of course we want to get our data to the consumers so got to start somewhere so it's not it's
far from finished but we can at least get the the data for for some samples tested here
in California by Coffin and cannabis so I'll start collecting my results I was kind of
thinking about if I wanted to publish my own personal consumption data that'd be a little
wild but I'm considering it but but anyways I think this would be real interesting data
I'm going to find value out of it so I'm going to start recording my own consumption data
Candace if you're and you don't necessarily have to buy the products right you could potentially
just go to the store if you want to if you want to start recording your consumption data
just use our dosing project app and because the goal is going to be to marry CoA's with
it in the version 2 but if you're building or you're starting to build you know you're
starting to create the tunnel that we're going to be using to feed into the app but the app
will record your use now or you know it can and it asks you dose or how much you're using
and all and it ultimately will calculate dose based or amounts based on CoA's at the moment
it calculates it based on the canonical cannabis class one two or three but at least it's a
recording of your use and so that's the beginnings of it for you so I would recommend you know
trying that out go to the CESC website if you haven't and register you know sign up
for dosing project.
I definitely will because this is something that this is where this is going this is why
I'm asking you to you know kind of help do this is because this is a way to get that
CoA data into dosing project so then we can really get those response curves and all that.
I definitely will because as I would that was sort of my main point today was you know
if you're not measuring it you're not managing it and I'm thrilled I'm just tickled so just
so you know I just pulled this off last night so last night was the first time it you know
it successfully you know I dropped PDF there and it you know came back with the results
so it just happened like late last night and there's still some errors this morning so
luckily the crew demo worked today but I'm like I said I'm tickled with this because
I'm just thrilled because this is something that I've been wanting to keep track of myself
through the longest time and it's just not been possible and as I said there's still
some improvement right now I'm just going to be saving these JSON files but I mean we've
proved that we can work with JSON files so I mean the step one right episode one right
get data right get the data so first things first right before we calculate all the cool
statistics you know we just need to get the actual data but then once we do we can find
all these cool things right so for example here I just calculated a statistic right in
this one eighth of classic Jack there's almost 1.5 grams of THCA which is quite extraordinary
when you think about it so long story short is you know I'll be collecting my data and
you know maybe before long it may be embarrassing but I think I'm going to publish it anyways
just to you know walk the walk and that's what we did today at the cannabis data science
right we walk the walk because this is what can litig set out to do was to get these results
in the consumers hands and we've at least done it we've at least done it once right
if you start on you know start collecting your COAs the way you're doing it and parsing
them and if you start entering your use for those strains in the version of dosing project
which is running I can run charts for you which you probably can't do yet but I can
do pretty easily to start to show you what it would look like in version 2 so that's
a good opportunity I would you know we can talk more about that but why don't you you
know don't publish what you're using do it and I'll show you and then you can make this
exactly because you know I wouldn't encourage anyone to necessarily publish right because
this is great if you're using cannabis medicinally right this that'd be like publishing your
prescription that's sort of an odd thing to do but the idea is right I'm a time series
you know I know a lot of time series statistics so you know I can start to trend to my consumption
over time and like you said John you've got a whole suite of tools that for this this
type of analysis so yeah I mean the version of dosing project which is running now you
know I mean it was designed for a couple of high-level indications for pain and disordered
sleep but that doesn't matter at the moment you know we're gonna make it broader for version
2 but it we're clujing it so you know for the moment just say you know pick one you're
dosing for pain it doesn't matter but what I would recommend well there's a just a four
point scale for how well it worked for you again just don't don't use it in terms of
pain use it in terms of I don't know you know how enjoyable or how you did with it or whatever
there will be mood scales and all that that we're going to incorporate into this but that's
coming in version 2 but you know even we can you know just by you recording your no response
all partial response almost complete or complete and we start mapping that against strains
and we start to get time series which we haven't had and that would be a help this is where
we're going and so you can help prototype it based on coa so yeah go forth and prosper
I think it's great I mean I love your problems figuring out where the app is but it should
be pretty clear from the the CESC website I'll find it today all right because essentially
you're just adding more and more data points right because yeah I mean yeah yeah and you're
adding critical data points right so right you know post consumption surveys so so that
way you could actually you know start to make inference and then if you got like a need
of a diverse panel of participants like your statistics would just be phenomenal and that's
the point that's what is you know that's our initiative and CESC is to do this that's that's
the dosing project in fact we just wrote up a description on will be in the cannabinoid
playbook in August describing our approach with observational studies so if you're not
registered for that I'll send you the link so that you can sign up for the cannabinoid
playbook that comes out each month and just want to thank Candice real quick so Candice
was mentioning that contributing Massachusetts and Florida data and Candice has already helped
with the code for psi labs I just realized that we've written collection routines for
MCR labs and SC labs so I bet you that those two routines could get incorporated so I bet
you with a little bit of work we could read COAs from anyone who does confident cannabis
tag leaf limbs SC labs or MCR labs in Massachusetts so we've still got a long way to go but but
exactly with with the fine folks here of the cannabis data science team we can just get
more and more people on board it's open source so labs could potentially write their own
parsing routines or create their own API so the labs can still do this themselves we're
just we're just filling in that piece of the puzzle for them just to help too cool well
I'll finish this up so that way I can actually get the tool in your hand so thanks for the
demo and your feedback all right but hopefully you found something of value here um I'm
just thrilled I think this is going to be I think it's going to be a groundbreaking
tool so we just need to get it into to more people's hands so that's what I'll be working
on direction that's really great definitely and I want to say couldn't have done it without
both of you couldn't have done it without one you pointing me down this trail John and
also you know providing the initial coas to actually start parsing because as I said I
was originally just going to start parsing the PDF like many people have done in the
past and then I stumbled upon the QR code no that's a good way all righty well cool
cool thanks too cool well thank you both thank you both for advancing cannabis science and
until next week have fun thank you tons of fun very impressed ah I'm impressed with both
of you too so thank you Candice thank you John bye bye until next week see you next
week bye bye now