Add usage section in README (#9)

Signed-off-by: Sylvain Leclerc <[email protected]>

README.md

@@ -1,2 +1,56 @@
 # antares-timeseries-generation
-Timeseries generation tools for Antares simulator
+
+Timeseries generation library aiming at creating input data
+for Antares simulator studies.
+
+## Install
+
+The library is not yet released on PyPI, for now you may install it 
+in your dev environment from sources:
+```bash
+git clone https://github.com/AntaresSimulatorTeam/antares-timeseries-generation
+pip install .
+```
+
+## Usage
+
+The generation requires to define a few input data in a `ThermalCluster` object: 
+
+```python
+import numpy as np
+
+days = 365
+cluster = ThermalCluster(
+    unit_count=10,
+    nominal_power=100,
+    modulation=np.ones(dtype=float, shape=24),
+    fo_law=ProbabilityLaw.UNIFORM,
+    fo_volatility=0,
+    po_law=ProbabilityLaw.UNIFORM,
+    po_volatility=0,
+    fo_duration=10 * np.ones(dtype=int, shape=days),
+    fo_rate=0.2 * np.ones(dtype=float, shape=days),
+    po_duration=10 * np.ones(dtype=int, shape=days),
+    po_rate=np.zeros(dtype=float, shape=days),
+    npo_min=np.zeros(dtype=int, shape=days),
+    npo_max=10 * np.ones(dtype=int, shape=days),
+)
+```
+
+You then need to provide a random number generator: we provide `MersernneTwisterRNG` 
+to ensure the same generation as in `antares-solver` tool.
+```python
+rng = MersernneTwisterRNG()
+```
+
+Then perform the timeseries generation:
+```python
+generator = ThermalDataGenerator(rng=rng, days=days)
+results = generator.generate_time_series(cluster, 1)
+```
+
+The actual timeseries for the total available power of the cluster are available in
+the results object as a numpy 2D-array:
+```python
+print(results.available_power)
+```