docs(README): add a more detailed README

README.md

@@ -2,36 +2,41 @@
 
 Causal discovery made easy.
 
-## Dev usage
-
-### Setup
-We recommend using poetry to manage the dependencies. To install poetry follow the instructions on https://python-poetry.org/docs/#installation.
-
-Install dependencies
+## Installation
+Currently we only support python 3.11. To install causy run
 ```bash
-poetry install
+pip install causy
 ```
 
-Execute tests
-```bash
-poetry run python -m unittest discover -s tests
-```
+## Usage
+Causy can be used via CLI or via code. 
 
 ### Usage via CLI
 
 Run causy with one of the default algorithms
 ```bash
-poetry run causy execute --help
-poetry run causy execute tests/fixtures/toy_data_larger.json --algorithm PC
+causy execute --help
+causy execute your_data.json --algorithm PC --output-file output.json
 ```
 
-Customize your causy pipeline by ejecting and modifying the pipeline file.
+The data you can use is a json file with a list of dictionaries. Each dictionary represents a data point. The keys of the dictionary are the variable names and the values are the values of the variables. The values can be either numeric or categorical. 
+
+```json
+[
+    {"a": 1, "b": 0.3},
+    {"a": 0.5, "b": 0.2}
+]
+```
+
+You can customize your causy pipeline by ejecting and modifying the pipeline file.
 ```bash
-poetry run causy eject PC pc.json
+causy eject PC pc.json
 # edit pc.json
-poetry run causy execute tests/fixtures/toy_data_larger.json --pipeline pc.json
+causy execute tests/fixtures/toy_data_larger.json --pipeline pc.json
 ```
 
+This might be useful if you want to use a custom algorithm or if you want to customize the pipeline of a default algorithm.
+
 
 ### Usage via Code
 
@@ -58,3 +63,84 @@ for edge in edges:
     )
 
 ```
+
+Use a custom algorithm
+
+```python
+from causy.exit_conditions import ExitOnNoActions
+from causy.graph import graph_model_factory, Loop
+from causy.independence_tests import (
+    CalculateCorrelations,
+    CorrelationCoefficientTest,
+    PartialCorrelationTest,
+    ExtendedPartialCorrelationTestMatrix,
+)
+from causy.orientation_tests import (
+    ColliderTest,
+    NonColliderTest,
+    FurtherOrientTripleTest,
+    OrientQuadrupleTest,
+    FurtherOrientQuadrupleTest,
+)
+from causy.utils import retrieve_edges
+
+CustomPC = graph_model_factory(
+    pipeline_steps=[
+        CalculateCorrelations(),
+        CorrelationCoefficientTest(threshold=0.1),
+        PartialCorrelationTest(threshold=0.01),
+        ExtendedPartialCorrelationTestMatrix(threshold=0.01),
+        ColliderTest(),
+        Loop(
+            pipeline_steps=[
+                NonColliderTest(),
+                FurtherOrientTripleTest(),
+                OrientQuadrupleTest(),
+                FurtherOrientQuadrupleTest(),
+            ],
+            exit_condition=ExitOnNoActions(),
+        ),
+    ]
+)
+
+model = CustomPC()
+
+model.create_graph_from_data(
+    [
+        {"a": 1, "b": 0.3},
+        {"a": 0.5, "b": 0.2}
+    ]
+)
+model.create_all_possible_edges()
+model.execute_pipeline_steps()
+edges = retrieve_edges(model.graph)
+
+for edge in edges:
+    print(
+        f"{edge[0].name} -> {edge[1].name}: {model.graph.edges[edge[0]][edge[1]]}"
+    )
+```
+
+### Supported algorithms
+Currently causy supports the following algorithms:
+- PC (Peter-Clark)
+  - PC - the original PC algorithm without any modifications ```causy.algorithms.PC```
+  - ParallelPC - a parallelized version of the PC algorithm ```causy.algorithms.ParallelPC```
+
+### Supported pipeline steps
+Detailed information about the pipeline steps can be found in the [API Documentation](https://causy-dev.github.io/causy/causy.html).
+
+## Dev usage
+
+### Setup
+We recommend using poetry to manage the dependencies. To install poetry follow the instructions on https://python-poetry.org/docs/#installation.
+
+Install dependencies
+```bash
+poetry install
+```
+
+Execute tests
+```bash
+poetry run python -m unittest discover -s tests
+```