The easiest method by far to download and run the application is to use pip to install it
pip install dao-scripts
Then, you can use this script using the command daoa-cache-scripts or dao-scripts
Enter in your terminal (git must be installed) and write down:
git clone https://github.com/Grasia/dao-scripts
After that, move to the repository root directory with:
cd dao-scripts
All code has been tested on Linux, but it should work on Windows and macOS, 'cause it just uses the Python environment.
So, you must install the following dependencies to run the tool:
- python3 (3.10 or later)
- python3-pip
Now, install the Python dependencies:
pip3 install -r requirements.txt
If you don't want to share Python dependencies among other projects, you should use a virtual environment, such as virtualenv.
If you want all the data used in the app, you can just use:
dao-scripts
this will create a folder called datawarehouse with a lot of files in Apache's arrow format.
You can import those files to pandas with read_feather. For example:
pd.read_feather('datawarehouse/aragon/apps.arr')If you don't want all the data (and it can take a lot of time), you have a lot of options available to select whichever data you want. The full --help output is
usage: daoa-cache-scripts [-h] [-V] [-p [{aragon,daohaus,daostack} ...]]
[--ignore-errors | --no-ignore-errors] [-d] [-f] [-F] [--skip-daohaus-names]
[-n {mainnet,arbitrum,xdai,polygon} [{mainnet,arbitrum,xdai,polygon} ...]]
[-c COLLECTORS [COLLECTORS ...]] [--block-datetime BLOCK_DATETIME]
[-D DATAWAREHOUSE] [--cc-api-key CC_API_KEY]
Main script to populate dao-analyzer cache
options:
-h, --help show this help message and exit
-V, --version Displays the version and exits
-p [{aragon,daohaus,daostack} ...], --platforms [{aragon,daohaus,daostack} ...]
The platforms to update. Every platform is updated by default.
--ignore-errors, --no-ignore-errors
Whether to ignore errors and continue (default: True)
-d, --debug Shows debug info
-f, --force Removes the cache before updating
-F, --delete-force Removes the datawarehouse folder before doing anything
--skip-daohaus-names Skips the step of getting Daohaus Moloch's names, which takes some time
-n {mainnet,arbitrum,xdai,polygon} [{mainnet,arbitrum,xdai,polygon} ...], --networks {mainnet,arbitrum,xdai,polygon} [{mainnet,arbitrum,xdai,polygon} ...]
Networks to update. Every network is updated by default
-c COLLECTORS [COLLECTORS ...], --collectors COLLECTORS [COLLECTORS ...]
Collectors to run. For example: aragon/casts
--block-datetime BLOCK_DATETIME
Get data up to a block datetime (input in ISO format)
-D DATAWAREHOUSE, --datawarehouse DATAWAREHOUSE
Specifies the destination folder of the datawarehouse
--cc-api-key CC_API_KEY
Set the CryptoCompare API key (overrides environment variable)
You can select the platform to download data about with the --platform selector. Let's download only data for daostack and aragon:
daoa-cache-scripts --platforms daostack aragon
You can select the chain to get data from with the --networks switch. For example, to get data only for xdai network, you can do:
daoa-cache-scripts --networks xdai
DAO-Analyzer is created under the umbrella of two research projects: Chain Community, funded by the Spanish Ministry of Science and Innovation (RTI2018‐096820‐A‐I00) and led by Javier Arroyo and Samer Hassan; and P2P Models, funded by the European Research Council (ERC-2017-STG 625 grant no.: 75920), led by Samer Hassan.
You can just cite one of our publications:
Javier Arroyo, David Davó, Elena Martínez-Vicente, Youssef Faqir-Rhazoui, and Samer Hassan (2022). "DAO-Analyzer: Exploring Activity and Participation in Blockchain Organizations.". Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (CSCW'22 Companion). ACM, 193–196.
Or, if you want to explicitly cite the application:
Arroyo, Javier, Davó, David, Faqir-Rhazoui, Youssef, & Martínez Vicente, Elena. (2023). DAO Analyzer. Zenodo. https://doi.org/10.5281/zenodo.7669689