# Odoo Connect
A simple library to use Odoo RPC.
[](https://pypi.org/project/odoo-connect/)
## Usage
```python
import odoo_connect
odoo = env = odoo_connect.connect(url='http://localhost:8069', username='admin', password='admin')
so = env['sale.order']
so.search_read([('create_uid', '=', 1)], [])
```
## Rationale
[OdooRPC](https://pypi.org/project/OdooRPC/)
or [Odoo RPC Client](https://pypi.org/project/odoo-rpc-client/)
are both more complete and mimic internal Odoo API.
Then [aio-odoorpc](https://pypi.org/project/aio-odoorpc/) provides
an asynchronous API.
This library provides only a simple API for connecting to the server
and call methods, so the maintenance should be minimal.
Note that each RPC call is executed in a transaction.
So the following code on the server, will add one to every line ordered
quantity or fail and do nothing.
However, RPC client libraries will perform multiple steps, on a failure,
already executed code was committed. You can end with race conditions
where some other code sets product_uom_qty to 0 before you increment it.
A better way of doing this is to implement a function on Odoo side and call it.
```python
lines = env['sale.order.line'].search([
('order_id.name', '=', 'S00001')
])
# this is fine on the server, but not in RPC (multiple transactions)
for line in lines:
if line.product_uom_qty > 1:
line.product_uom_qty += 1
# single transaction
lines.increment_qty([('product_uom_qty', '>', 1)])
```
## Export and import data
A separate package provides utilities to more easily extract data from Odoo.
It also contains utility to get binary data (attachments) and reports;
however this requires administrative permissions.
Since Odoo doesn't accept all kind of values, the `format` package will help
with converting between python values and values returned by Odoo.
The provided function will return a table-like (list of lists) structure
with the requested data.
You can also pass `ir.filters` names or `ir.exports` names instead of,
respectively, domains and fields. Note that this doesn't support groupping.
```python
import odoo_connect.data as odoo_data
so = env['sale.order']
# Read data as usual
data = so.search_read_dict([('state', '=', 'sale')], ['name', 'partner_id.name'])
so.read_group([], ['amount_untaxed'], ['partner_id', 'create_date:month'])
odoo_data.add_url(so, data)
# Exporting flattened data
all_data = odoo_data.export_data(so, [('state', '=', 'sale')], ['name', 'partner_id.name'])
with io.StringIO(newline='') as f:
w = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
w.writerows(all_data.to_csv())
all_data.to_pandas() # as a data frame
all_data.to_dbapi(con, 'table_name') # create a table
# Import data using Odoo's load() function
odoo_data.load_data(so, data)
# Import data using writes and creates (or another custom method)
for batch in odoo_data.make_batches(data):
# add ids by querying the model using the 'name' field
# if you remove 'id' from the data, only create() is called
odoo_data.add_fields(so, batch, 'name', ['id'])
odoo_data.load_data(so, batch, method='write')
```
## Explore
Provides a simple abstraction for querying data with a local cache.
It may be easier than executing and parsing a `read()`.
Also, auto-completion for fields is provided in jupyter.
```python
from odoo_connect.explore import explore
sale_order = explore(env['sale.order'])
sale_order = sale_order.search([], limit=1)
sale_order.read()
```
## Development
You can use a vscode container and open this repository inside it.
Alternatively, clone and setup the repository manually.
```bash
git clone $url
cd odoo-connect
# Install dev libraries
pip install -r requirements.txt
./pre-commit install
# Run some tests
pytest
```
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