docs: update workflow docs

docs/cli/basic-usage/.gitignore

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+*.pkl

docs/cli/basic-usage/README.md

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+# Basic usage
+
+The command line interface allows us to load and save `States` and run arbitrary functions on them.
+
+## Setup
+
+To use the command line, we first define a package `example` containing the functions we want to run on the State:
+
+```python title="example/__init__.py"
+--8<-- "https://raw.githubusercontent.com/AutoResearch/autora-core/main/docs/cli/basic-usage/example/__init__.py"
+```
+
+
+```python title="example/lib.py"
+--8<-- "https://raw.githubusercontent.com/AutoResearch/autora-core/main/docs/cli/basic-usage/example/lib.py"
+```
+
+We can run the pipeline of initialization, condition generation, experiment and theory building as follows.
+
+First we create an initial state file:
+
+```shell
+python -m autora.workflow example.lib.initial_state --out-path initial.pkl
+```
+
+Next we run the condition generation:
+
+
+```shell
+python -m autora.workflow example.lib.experimentalist --in-path initial.pkl --out-path conditions.pkl
+```
+
+We run the experiment:
+
+
+```shell
+python -m autora.workflow example.lib.experiment_runner --in-path conditions.pkl --out-path experiment_data.pkl
+```
+
+And then the theorist:
+
+
+```shell
+python -m autora.workflow example.lib.theorist --in-path experiment_data.pkl --out-path model.pkl
+```
+
+We can interrogate the results by loading them into the current session.
+
+
+```python
+#!/usr/bin/env python
+from autora.workflow.__main__ import load_state
+state = load_state("model.pkl")
+print(state)
+# state = 
+# StandardState(
+#     variables=VariableCollection(
+#         independent_variables=[
+#             Variable(name='x',
+#                      value_range=None, 
+#                      allowed_values=array([-10.  ,  -9.98,  -9.96, ...,   9.96,   9.98,  10.  ]), 
+#                      units='', 
+#                      type=<ValueType.REAL: 'real'>, 
+#                      variable_label='', 
+#                      rescale=1, 
+#                      is_covariate=False)
+#             ], 
+#         dependent_variables=[
+#             Variable(name='y', 
+#                      value_range=None, 
+#                      allowed_values=None, 
+#                      units='', 
+#                      type=<ValueType.REAL: 'real'>, 
+#                      variable_label='', 
+#                      rescale=1, 
+#                      is_covariate=False)
+#         ], 
+#         covariates=[]
+#     ), 
+#     conditions=        x
+#                342 -3.16
+#                869  7.38
+#                732  4.64
+#                387 -2.26
+#                919  8.38
+#                949  8.98
+#                539  0.78
+#                563  1.26
+#                855  7.10
+#                772  5.44, 
+#     experiment_data=      x           y
+#                     0 -3.16    1.257587
+#                     1  7.38  153.259915
+#                     2  4.64   54.291348
+#                     3 -2.26   10.374509
+#                     4  8.38  155.483778
+#                     5  8.98  183.774472
+#                     6  0.78    3.154024
+#                     7  1.26   14.033608
+#                     8  7.10  103.032008
+#                     9  5.44   94.629911, 
+#     models=[
+#         GridSearchCV(
+#             estimator=Pipeline(steps=[
+#                 ('polynomialfeatures', PolynomialFeatures()),
+#                 ('linearregression', LinearRegression())]),
+#             param_grid={'polynomialfeatures__degree': [0, 1, 2, 3, 4]}, 
+#             scoring='r2'
+#         )
+#     ]
+# )
+```
+
+`state` is the following object which, once loaded, can be treated like any other `State` object.
+For instance, we can plot the results:
+
+```python title="example/plot.py"
+--8<-- "https://raw.githubusercontent.com/AutoResearch/autora-core/main/docs/cli/basic-usage/example/plot.py"
+```
+
+```shell
+python -m example.plot model.pkl
+```
+
+
+![png](img/after-one-cycle.png)
+
+If we instead run the experiment for 4 cycles, we can get results closer to the ground truth.
+
+```shell
+set -x  # echo each command 
+
+python -m autora.workflow example.lib.initial_state --out-path "result.pkl"
+
+for i in {1..4}
+do
+    python -m autora.workflow example.lib.experimentalist --in-path "result.pkl" --out-path "result.pkl"
+    python -m autora.workflow example.lib.experiment_runner --in-path "result.pkl" --out-path "result.pkl"
+    python -m autora.workflow example.lib.theorist --in-path "result.pkl" --out-path "result.pkl"
+done
+
+python example.plot result.pkl
+```
+
+![png](img/after-four-cycles.png)