Visualization walkthrough
=========================

Activate the environment
-------------------------------

**Either use docker**

.. code::

    cd env && python launch_docker.py (--use_cuda optional)

**Or activate your conda environment**

.. code::

    source activate <conda_env_name>


Exploring the dataset
----------------------
**Using command line:**
.. code::

	python run.py --data --dump_dir tests/dump  # build the database
	python run.py --explore_lightcurves --dump_dir tests/dump


Outputs: ``.png`` files in the ``tests/dump/explore`` folder.
You should obtain something that looks like this:

.. image:: sample_lightcurves_from_hdf5.png


Predictions as a function of time
-------------------------------------------

Assuming you have a trained model stored under ``tests/dump/models/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean``
and that you have already created the database as above:

**Using command line:**
.. code::

	python run.py --plot_lcs --dump_dir tests/dump --model_files tests/dump/models/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.pt

**Using Yaml:**
.. code::

    python run_yaml.py <yaml_file_with_config> --mode plot_lcs 

an example ``<yaml_file_with_config>`` is at ``configs_yml``.

Outputs: a figure folder under ``tests/dump/lightcurves/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean``.

This folder contains the plot of several random lightcurves and the predictions made by the neural network referred to by the ``model_files`` argument.

If you want to plot a selection of lightcurves you can add ``--plot_file <filename.csv>`` which contains a column ``SNID`` with the ids requested to be plotted.

Below is a sample plot:

.. image:: preds.png



Predictions + uncertainty for bayesian models
-------------------------------------------------------

Assuming you have a variational RNN model stored under ``tests/dump/models/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07``
and that you have already created the database as above:

.. code::

	python run.py --plot_lcs --dump_dir tests/dump --model_files tests/dump/models/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07.pt

Outputs: a figure folder under ``tests/dump/lightcurves/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07``.

This folder contains the plot of several lightcurves and the predictions made by the neural network referred to by the ``model_files`` argument.
Several predictions are sampled at each timestep and the prediction contours at 68% and 94% are shown.

Below is a sample plot:

.. image:: preds_variational.png



Predictions from multiple models
-------------------------------------------

To compare the predictions from multiple models, simply call the above, while providing multiple ``model_files``


.. code::

	python run.py --plot_lcs --dump_dir tests/dump --model_files tests/dump/models/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07/variational_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean_WD_1e-07.pt tests/dump/models/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean/vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean.pt

Outputs: a figure folder under ``tests/dump/figures/multimodel_early_prediction``.

This folder contains the plot of several lightcurves and the predictions made by the neural networks referred to by the ``model_files`` argument.

Below is a sample plot:

.. image:: preds_multi.png


Science plots
-------------------------------------------

The plots of the paper can be reproduced by running in the ``paper branch``:

.. code::

	python run_paper.py