Good morning.
Morning.
Morning Nick.
Morning. Morning.
Morning.
Right.
Just give everybody five minutes to join and then then we'll kick it off. Got a full plate for today so should be a good day.
You got a lot to explore today.
Let's bring you right back.
So, I looked at like the testing times.
Oh, thanks. And I mean, there's not, you know, all those records, there's only about 3000 records that have like, dates that could be valid, but there's no guarantee that they're valid.
And it may or may not be something that, sorry, sorry to interrupt you Charles.
So Charles was just talking about turnaround time so you're saying there are not many observations with good timestamps or.
Yeah.
There's some that are like 125 years in the past.
And some that are like 125 years in the future.
And there's some that have like, that look like they could be valid. I have that Jupiter notebook, if I, you know, I could share it with you and you can look at it.
Well, welcome Danny.
We're just letting people join several kick it off in about five minutes or so.
And you guys, I'm Alex.
Good to meet you.
Charles you wouldn't mind just dropping the Jupiter notebook in the chat if
it's you know it's on my other machine.
Hey guys, how are you. Nice to meet you all.
Nice to meet you.
David.
Hello, David.
So I don't turn on my camera because I just had surgery in my nose.
It's not the end of the world. My picture looks better I guess. I mean, I don't care but just a case.
And then, you know, for what it's worth you know we're just going to do a little recording just of the presentation, and just to make the presentation available to people afterwards.
And so, I'll share the link. Our good friend Stephanie Powell over at Fortune Creative is helping us actually assemble those videos.
So, I'll just go ahead and just start sharing some of the handouts for today. So, two just interesting, you know, places to get data so as I go about my adventures, I'll just share interesting things with you.
So, over at OpenTHC, there's a strain database API.
If you need to look at strain names.
There's just the spec to the OpenTHC API.
Let me go ahead and show you a few things here.
Sorry, that was the...
All right, so something you can check out
is data.openthc.com
And what you can check, our friend David Busby over at OpenTHC has put together some of the data that we've been looking at.
So, this is the LCB data.
And we've just been looking at the month of December here, but David's gone ahead and compiled everything from 2019.
And he's working to make that available. So, as soon as it is available, I'll share that with everybody.
But there's a few interesting things that you can look at. There's business to consumer sales.
So, you can actually see that this is going to take a second to load.
But you can see month to month sales.
And you can drill down into that. Well, like I said, it's still sort of being hammered out.
I think it's just an interesting tool that David's put together that, you know, it's just a good place here.
I'll put the link in the chat here so everyone can check that out.
And if you just want to get some ideas of, you know, what can you do with this data, you know, this can give you some ideas.
Just wanted to spend a couple minutes and show you that resource.
The main thing I wanted to bring you here today, well, the later half will get to talking about APIs
because David's interested in integrating with the state traceability system.
And then I'll show you some of the work I've been doing just with charting data.
But before we really get too far ahead, I guess let's just spend, I guess, five minutes just since this group's not all about me.
But I guess can we just do a round of introductions, I guess?
I guess if you don't mind, I guess, Danny and Nick and I guess everybody else, would you mind just spending just 30 seconds just to introduce yourself?
So, yeah, I guess, Danny, I guess go ahead and kick it off, please.
That's okay.
So, my name is Danny Hurtado. I'll put my LinkedIn here to connect. I'm always open to connect, collaborate.
I own a marketing agency, a staffing agency, a software development company.
And this is pretty much, and partner up with other businesses.
I'm the CEO for the companies. We are in Colombia, Venezuela, Chile, Peru, Panama, Costa Rica, Mexico, USA, and hopefully opening Spain.
We also register in Nigeria and venturing out to Uganda. I have developers all over the world.
Bangladesh, too, is in there. I apologize, in India. I have developers all over the world, people working all over the world.
And that's pretty much it. I mean, why this meetup and why the cannabis?
You know, we have a platform that's basically an ERP or MRP, if you guys, to software people. And I wanted to see how we could put this service to the cannabis industry and the data and all that stuff, being like a platform that is decentralized, because I own the platform.
We developed a platform decentralized for the cannabis industry people, because I know it's been tough, you know, and I'm from South America, Colombia, and I've always been like, oh, yeah, marked as, you know, drug dealer and all that stuff, which is not.
I'm just a business person. So that platform, I want to see how I could, you know, put it into the industry.
We're also working with blockchain technology and decentralized stuff. So, like I know before Shopify, they know that stuff. They didn't want to do cannabis companies and a lot of things with the cannabis, a lot of beef to the cannabis people, which I think is not right.
And I know cannabis back back from the day on the other good side of things, if you say. And so I just want to do that, the platform to be able to customize it and help the cannabis industry.
And on the other side, the marketing side, we have clients, you know, high end clients like General Motors, Petsys and so forth. So what can we do for the cannabis industry as far as helping the cannabis, you know, entrepreneurs or companies have that edge on marketing.
I love it, Danny. And I think you may want to reach out to I'll share his contact afterwards with you and everybody feel free to share your LinkedIn.
But David Busby works with people in Colombia and exactly I think you're in the perfect place because exactly we want to work with people all around the world.
And yeah, it really helped the cannabis industry take off. So I think you're well positioned and we all have a lot to learn from you because.
You know, so I work in the cannabis industry here in Washington State in the United States. And, you know, we have a lot to learn from other countries. So, you know, definitely feel free to share.
I'm based in Florida. If anyone's coming to Florida, you got a new friend here.
Definitely, Danny. So feel free to share. And then I guess let's go ahead and just keep moving on. So I guess, Nick, just 30 seconds if you don't mind sharing.
Yeah, I am currently an accountant for the number one edible company in the United States. We produce gummies and chocolates and I am just I'm switching my careers to data analytics and I thought this would be a great meetup to like figure out where you guys get data and stuff like that.
And so, yeah, that's why I'm here. Just kind of learn and observe.
Awesome, Nick. We're happy to have you. We'll share some previous links and we're always sharing links to cannabis data.
And then Charles, I guess, would you correct 30 seconds.
Hi, I'm Charles and I have 27 years of software development experience. I've worked with Windows, iOS, Mac OS.
And now I'm trying to transition into like the machine learning data analytics, data science field. So, and this is seems like a good group. This is sort of a data set that hasn't been well explored.
So it's a great opportunity.
Awesome.
And then Alex.
Sure. Hey, Alex, I have been a professional software developer for about five years at this point, and I have a lot of experience developing API's and maintaining them.
And I'm super interested in the cannabis industry is kind of like, I do a lot of side projects and businesses after work. And this is definitely one of the areas I would like to learn more about.
So most are just kind of here to like observe and hopefully meet people. Awesome. Awesome Alex. Well, honestly, we probably have a lot to learn from you because we're diving into API's.
So as you know, a lot of the cannabis industry will essentially every state relies on a traceability system and people who direct through that went through an API.
So, so API's are at the heart of traceability. Plus, companies are learning that different people at different stages of the supply chain can interact with each other through API's.
So there's a lot of opportunities.
And so we'll be talking about that.
And then, yeah, I'm definitely a terminalologist. Awesome.
So we're thrilled to have you out. So, David, this leads well into you. So would you give us a 30 second intro, please?
David, we may not be able to hear you.
Okay. Yes. Awesome. Okay. That's better. Hold on. Let me change from my headset.
Microphones to
Is that better? Yes, we can hear you. Okay, great. Great. Great hearing everybody.
Kind of similar to Danny, except we sell a ERP MRP platform that doesn't have a good option for API integration with the state of Washington.
And we're looking to develop one for it. So we're a reseller. I'm a CPA who's a techie nerd and geek all combined together.
And as a CPA, I understand the accounting side of things. But as a programmer developer, I've kind of got the geek side of things as well.
And we sell an accounting cloud accounting software called Acumatica that is one of the newer cloud based ERP MRP systems and would be perfect for cannabis if it had a built in integration to the API to get the state IDs for all of the different vendors, customers,
batches in production, things of that nature. So we're looking to learn more about the API in terms of building our own customization.
One of the nice things with Acumatica is it's customizable in.NET. So if you're a C sharp developer, Acumatica lets you actually code your development in C sharp right within the application.
And you can even extend it out and publish it into Visual Studio to publish a DLL from it.
So we find it's actually a more rapid development of Visual Studio because we can develop right within the application. We can view their source code right within the application as we make all the references and calls to it.
So, like I said, been in this biz in the accounting ERP product biz for 20 some years.
I've got an engineering background and accounting background and a computer science background.
A little bit of everything Renaissance man and I play guitar when we used to have open mics.
Awesome. So it looks like we've got a wide array of people here from the data geeks to the people.
Well, I guess maybe that's all of us to the people wanting to play with the API.
So just to just to share just real quick, just what I've been working on here in the past week. So, as you know, you can make public records requests. So that way you can get a nice data dump.
So the people who were with us and the people who recently knew this is what we've been looking at. So, you know, this is the state of Washington for December of 2020.
So they're generating about 30 gigabytes a month of traceability data. And this is rich for analysis. There's probably a lot to be discovered.
So, you know, so the rule number one about data analysis, you know, look at the data. So I discovered this interesting library, Boca.
So this is a Python based library and you can make these nice visualizations here.
So an interesting dimension of data is the geographic area. Right. So, you know, knowing where things happened is pretty important.
So we're going to try to make a so this is called a chloropleth. So we're going to try to make a chloropleth for Washington data.
OK. So we're just going to kick it off here in Spider and I'm just getting some of these working in the background.
All right. So the external packages I'm using here, just going to be using pandas as always.
And then you'll need to install Boca if you're using Python or it actually comes packaged with Anaconda because I believe Boca is produced by Anaconda.
And so I'm going to just read in some sample data here. If you are following along, you will need to to download the sample data set.
So you'd import Boca and download your data. And so this is just some sample data that comes with Boca.
But it's nice because, you know, we can get, you know, we can get all the counties in Washington.
Well, I'll need to import the sample data.
All right. And so now you have a nice list of all the counties in Washington. So the place I thought we could start off is we can essentially read in the licensees that we were working with last week.
And if any of you are curious about where you can find that data.
We've got another joinee. Well, welcome, Jake.
And so that's good to have you, Jake.
And so there's I've created just a GitHub here for the cannabis data science meetup group that you can just find all the source files and, you know, I'll just keep track of, you know, everything as we start accumulating material.
So one thing I, you know, we did in this past week is, you know, we.
Well, the first week is we basically just, you know, created.
You know, an Excel spreadsheet of all the active licensees. And so so that's essentially just the file I'm working with here.
So just to give you an example of that data. And so we've got our licensees now.
Okay. And so the next step is you actually need to get the counties that these are in.
So I'll share this in the GitHub repository afterwards, but essentially you can use Google Maps to geo code the locations.
So I'll share this with you afterwards, but essentially I'm just using the street, the city, the state and the zip code to geo code these.
And so they're actually it's running over here. And we'll just work with.
Well, I probably should have kept that running. I realized.
We may have to do an actual count later on, but.
I'll go ahead and show you what what Boko looks like nonetheless.
So we can just do this with just with random values here.
So essentially I just copied the.
I just copied this code here. And created a Boko chloropleth.
And so essentially I'm just taking the state as an argument so I can get the correct counties and then just a list of values for the given counties.
So nothing.
That glamorous, but we're just, you know, just doing it, you know, a quick little mock up just to kind of show you what what you could possibly do.
So let's just see if we can. May have to kind of fiddle with the shape because I just put in random. I just put in.
So I think.
So it's not liking. Hold on one second. I'm going to debug this.
Well, we'll just do it this way.
Alright, just bear with me for a second.
Grab the color palette.
Get the imports.
We'll just get some random values here.
OK. So this will spit out in HTML file.
And so not glamorous needs a lot of touch up needs a lot of work. So apologize that it was in a book more refined product here, but you know this is just a quick mock up just to show viability.
So you're going to have to work on the actual shape of this and, you know, actually put in the values that you actually want to to map.
So here I basically just created random values between zero and 100.
But but it's a proof of concept, so you can, you know, you could embed that you know you could theoretically embed this, you know, in your own website, you know, so you could have a series of Bokeh charts that are.
Moderately interactive in that you can hover over them and get a tool tip.
So so so that was just a tool that I was exploring, but I'll quit boring you to death with that and then we can maybe move back into into API.
But what are your thoughts or does Bokeh look like a tool that any of you would use?
Yeah, it looks interesting. I shared a link. There's a Kaggle class on using geospatial pandas.
Excellent. You can do a sort of a similar kind of thing, but it's a free class and it'll take you through step by step how to build maps and you know how to deal with geospatial data.
Exactly. And so there's a lot of opportunities there, and so it's as far as a data analysis perspective.
It's really, you know. If you did a study, but your spatial data that could be well respected. So, for example.
You could you can even use, you know, essentially distance in regression models and.
There's a lot of opportunities there, so you can calculate distance between places.
You know, to try to find out nearest neighbors. There's a lot you can do there.
So, so that's.
You know, that's kind of what I was working on was trying to plot various things.
But I guess let's jump and just I guess change gears now and look a bit more at API's.
Okay.
So I guess let's just pick up, I guess, where we were last week with just essentially writing calls to the to the state traceability system.
So we can find that in our resources from last time.
And there is.
Yeah. Okay. So essentially, we're just sort of going through here and you're seeing. Okay.
How do we actually go about making calls to the API?
So the first step we did discover that at least for the Washington state API, you do in fact have to create the users through the user interface.
So that's, you know, that's going to be a communication hurdle, you know, for for you to develop your software, because essentially you may have to communicate to the.
To your clients, the the users that they may need to create.
But let's go ahead and just pick up where we were last time.
Okay, so.
We've got our base endpoint.
We were passing, we're passing our API key in the header.
And.
I was reading in the documentation.
So where was it?
That. Okay, so I don't think it's a hundred percent necessary, but you may also have to pass a header that is your email password.
So it says that in the documentation, but for some reason, I don't think it's actually necessary to make a valid API call.
So I would I would test it without it first.
And so.
All you can do at the moment is.
So you can get the MMEs, which you'll need to get your MME code.
And then we're just going to start walking through this. So we'll to create an area.
You pass it a list of areas.
Okay, simple enough.
And then you'll need to populate your cultivator with strains and inventory types.
So this is.
You know, this is where there's, you know, a lot of, you know, I guess, you know, naming convention confusion between when do you use strain name and when do you use inventory name?
See.
And so this is where there's maybe not a lot of consensus in the industry, but let's just go ahead and make.
Make an example post.
So it looks like the only required.
Looks like the only required field to create a strain name is simply the actual name.
We can maybe make this more interesting. So let's say you have a handful of strains.
All right.
So.
We can go ahead and make a bunch of posts.
So if we had like a record that if we were pulling a list of strains from a table and acumatica, we could just do a record set loop through the record set.
Yeah. Okay. Cool. Yeah. And so, you know.
So then you can just make a bunch of strains and then, you know, go ahead and then, you know, post that up to your strains.
So that's what you know, that's what your data would look like, you know, to create, you know, a handful of strains.
So not that exciting yet. So let's jump up to inventories and see what the situation is there.
Because essentially we'll just walk through the whole traceability process and see if there's any curve balls.
So you'll need to create an inventory type here. It looks like.
Okay. And so this is.
This is where.
Things get interesting.
Because so let's go ahead and figure out like what's what's going on here.
So first, it looks like there are sort of general types.
So this is just going to be like a broad spectrum.
Just sort of to categorize things sort of to know, you know, what area things are going to be in.
So these.
These are broad types.
And in fact.
Let me open this twice.
Because I kind of want to keep it the link to that to that manual in the chat box too. Yes, I think.
It should be in the chat box. Is it? Okay. Let me look.
At this.
But I'm going to mark actually, I forgot to do it. But after this call, I'm going to mark up this diagram because I've got a lot of comments that I would like to add on this in the white space here.
So it's not clear here what licensee is doing what right? Because so right here, you know, you've got cultivators clearly who are making the strain types and inventories.
Actually, oh, never mind. This is the production workflow.
So that's.
Still still notice the.
So.
The type essentially designates. Okay. What you know, what stage in the process is it?
So the immature plants is when it's still getting propagated.
Then you get the mature plants.
When, you know, it's moved into the flower room.
Then you get harvest material.
And so this is once you've actually made a harvest on the mature plant. So you've, you know, you've moved it out of your flower room. You've destroyed the plant.
And then you've created the harvest material.
Right. And so then of the harvest material, you essentially have intermediate products which need to get lab tested.
You have waste, which needs to be disposed of. And this disposing it, it typically means mixing it 50 50 with another substance.
So, for example, I have heard of cultivators using dirt. So they'll mix their waste 50 50 50 percent cannabis waste with 50 percent soil or dirt and then dispose of that.
And so I just kind of throwing it out there. If any of you run into cannabis cultivators who need a waste solution,
there's better carbon solutions, which this is a you know, this is a friend of mine.
Rocky Holland.
You know, he is basically trying to tackle the problem of, OK, what, you know, how can we, you know, deal with cannabis waste in, you know, an environmentally friendly way?
Right. Because if you're just mixing it with 50 percent dirt and going and putting it in the landfill, well, that's, you know, a lot of organic material that's just going straight to a landfill.
So, you know, his his idea is there may be, you know, something you could actually do with that waste.
So. So I don't know. I think it's a it's a good story. And I'd like to see where he goes with that.
And so one of the things that I'm trying to help with is just calculate the amount of waste waste weight that's generated.
And so I am still digging into it, but I believe that data should be in inventory adjustments.
So if any of you are feeling ambitious, then essentially just trying to calculate the amount of waste.
But it's actually in batches. Oh, you've discovered it. Yeah, batches. Oh, awesome, Charles.
Do you know what the data looks like by chance? I guess it's probably described here in the.
Yeah, look in the manual for it. Yeah. So, well, I won't get us off track, but that's that's an awesome lead. Thank you, Charles.
Yeah. Yeah. From a manufacturing standpoint, a batch essentially batch processing, right?
So you're moving you're transferring things from one status to another. Right. And what they want to know is that those steps as it's being transferred,
how much of it is becoming, you know, wet harvest, how much of it is becoming waste in order to fully trace that right in any accounting system.
So you've got to have some type of think of it like a transaction, but it's a batch transaction that is saying a certain lot has been created.
And as a result of that, there's some waste or side products, essentially, with the waste. So that makes sense.
Awesome. And that's exactly so, you know, like I said, I'm not really here to preach to the choir because it's so I'm actually here to learn from you as much as as much as anything.
So so that's cool. Charles found that. That's awesome.
Yes. So because, you know, that's that's an important piece to their business model is knowing, OK, well, how much waste is actually generated and where where is it generated?
So make sure this stuff isn't, you know, that everything's traced right.
It's a very controlling state. They want to know every every jot and tittle, as it were. So it's something people may not think much about waste.
But, you know, it's an important part of the whole ecosystem. So, Brian, it would make it would make good fertilizer.
I mean, any kind of organic material, right, as it breaks down, becomes good fertilizer. Exactly. I'll take it off his hands.
We have a garden in the back. Well, essentially. Well, I think he's pretty open about his business plan, but essentially that's the idea is so you take the waste, you make biochar.
And so you can use it for I think you can make, you know, so essentially you can use the char for fertilizer. But but let's just keep.
Keep go. Sorry. Keep turning along. That's interesting. It's just.
It's just interesting there. So here we're going to break it down a bit, a bit more. So this, I think this is where the field that I really use to differentiate between the different types of products.
So you have, you know, which you call your, you know, your intermediate type.
And we'll figure out maybe how to classify this a bit more refined.
Let me just part. Let me just parse this out for once.
Just sort of just cleaning this data up real quick. One second.
So here we're just sort of building an array or a list of all the product.
So not pretty, but just sort of getting a crude list here. Okay. So, you know, so this is essentially how I would break everything down by, you know, different types.
But, you know, you're, you know, you're welcome to sort of refine this a bit, a bit more because so.
So everything here is sort of a finished.
It will is a product that's going to end up on the shelves and, you know, we'll need lab testing, you know, things down here are more of supply chain.
Categories. So, for example, if you, you know, hard, you know, if you harvest any seeds out of your crops, you know, if you make any clones, you make any plant tissue.
So, you know, there's almost, you know, types that you're going to be creating more for you, your cultivation workflow. So, so we'll need to take that into consideration.
Research about the non mandatory plant sample. I'm not a hundred percent certain.
And then.
And I think once again.
When you actually harvest the cannabis, you're essentially going to be denoting it as.
So I actually may need to read up on these fields, so I actually don't actually know too much about those fields.
So this could be a good direction to head in to towards next time is, okay, how do we actually go about creating these types?
So let's let's look at what a type post request would look like.
Because you we've got to create the data. So.
If we're creating a type, we'll give it an external ID.
And so that will be an ID that you can generate for it so you can keep track of it in your system.
The name, which will be the name that the licensee would like to refer to it as.
The type, which is just the broad category.
And then the intermediate type, so that depends on what you're actually going to do with it.
And then the weight. And I believe it can either be each or grams.
So let's see. So the system doesn't enforce the each or grams.
Like there'll be dried flour and they'll have it'll be an each, you know, like there'll be a thousand.
But it's actually should be grams or like seedlings.
What can be will come up in grams or seeds will come up in grams.
But I think they should be each or seedlings.
So the system doesn't really enforce a lot of these things, just like there's a negative amount of dried flour and negative waste.
So negative waste to me tells me that you've actually like gained, you know, you've gained some flour.
So there's a lot of anomalies like this in the data.
So what would be interesting to see is, is the negative waste.
Are they just doing a correction or is maybe that just the licensee just thinks that waste is a negative.
So they just code it as negative. So that would be.
It should be consistent. Yes, everybody.
And you're talking about.
So what you're discovering, Charles, is the intricacies of data, essentially data science is, you know.
It's amazing, you know, because, you know, they've got, you know, fairly well defined fields here.
But you're right. So so that's where we're sort of doing our sort of investigation slash analysis.
We're basically. You know, this is the API.
You know, this is what it. You know, it says it's going in as.
And like you said, you know, we can actually then come and.
You know, read it in the.
The I think they have inventories here somewhere.
You see, interesting to see whether on the data entry, when you send it to them, whether you're required to make it negative on the post.
Or whether it's just being recorded as a negative in their system. So when you do it, get you get the negative, because that's what's stored in their system.
Right. I think it's just going to be testing it, just trial and error to find out.
OK, do I post this as a positive and then they flip the sign because it's a waste type of transaction.
Yeah, but out of all the hundreds of thousands of records that are only like five, five or five records that have negative waste.
So obviously somebody just put it out.
So they don't have any error checking on it. Their valid data validation, maybe on the entry is not good.
They don't. There's there are things that were tested one hundred and twenty five years ago and there are things that were tested one hundred and twenty five years in the future.
There's no data validation at all.
And so exactly. And so this is sort of where the exploration begins, because, you know, as you're building your system out, you just kind of want to keep in mind these data intricacies because, you know, your system.
So say you build it for wholesalers, you know, they're going to be dealing with retailers, they're going to be dealing with other producers, they're going to be dealing with processors.
And, you know, the better your system can interact with other systems, you know, in their data.
If somebody puts in a wonky strain name, someone puts in a weird inventory type, you know, someone codes it as flower, but it should be flower lots.
You know, the better your software system can handle that, you know, the happier your clients are going to be because, you know, at the end of the day, they just want things to just run smoothly.
They want to be able to face nicely with the people they're doing business with.
And, you know, like I said, you just have to take the state traceability system as given and then, you know, make your system run as smoothly as possible around it.
At least that's my two cents.
But I would just like to say that just to sort of round this up that I think
So I think I actually have a lot more exploration to do with the inventory types because I, you know, wasn't actually as ready to go with that as I thought.
So for next week, you know, I'm going to be reading up on the inventory types to actually try to figure out, you know, what's the difference between all these intermediate types.
And that way, you know, we can actually do, you know, you know, a bit better mock, you know, towards the API next time.
But I guess, you know, we're kind of near the end of the hour.
So I guess just, you know, are there any questions, comments from from the from the gang?
All right. All right. All right. Well, well, you know, thank you all for coming in.
You know, I apologize that the chloropleth that was sort of a hiccup.
So I'm going to clean that up and I'll send you all, you know, a link to the code so that way you can see a cleaned up version.
And then, you know, like I said, we'll continue reading up on this traceability system so that way, you know, next week you can have a bit more informative of a discussion.
But, you know, you know, and for Nick, I'm going to keep an eye out for good data sources for you, too.
Yeah, I think I'll be joining every week. This is really interesting stuff.
So you question real quick before we go, do you work in Washington state or Oregon?
Yeah. Well, that's good to know. I'll keep that in mind.
Oregon has an API that you can hit for pesticides, but I'm not sure much else, but I'll I'll keep that in mind in case we come across any good Oregon data.
Yeah, I am in Oregon, but like we are hopefully by the end of the year, we're going to like be producing in Washington. So I work for Grand Wild and we right now we're in California, Colorado, Nevada.
So we're in multiple states, not just in Oregon. Awesome. Awesome. Well, we'll go ahead and let you go here.
And you know, stay tuned because we'll we'll dig up some good data for you. And if you're interested in any in particular data points, I know producers and processors are interested in like, you know, what, you know, what other brands, retailers are carrying and things like that.
So, you know, we can do some good analysis for you. Awesome. Yeah, I look forward to it. Awesome. Well, thanks for joining everyone.
And until next time, keep your nose to the grindstone. Before you disconnect, I would like to definitely connect with you because I'm interested in all this, not, you know, I would like to, you know, set up an actual meeting with my team and see how we could connect all this information.
All this data you guys are acquiring to see how that, you know, we could use it in our platform. So whenever you I put my LinkedIn there, if you want to connect with me there or I can do my phone or my email and we could connect from there.
This is definitely very interesting. All these data points you guys are taking and see how we could integrate it with our platform. Absolutely, Danny. I'll send you a message and that's exactly what we're trying to do is get good data into your platform.
So you can make it make it useful. Thank you. All right, guys. Awesome, Danny. Well, until next week. It's good meeting you all.
Thank you. Bye.