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QNPy Documentation

Introduction

In exploring the diverse features of quasar light curves, a significant challenge arises from recurring gaps in observations, which pose a primary limitation. This obstacle, compounded by the inherent irregularities in data collection cadences, presents a formidable barrier. This complexity will be particularly pronounced when dealing with data that is going to come from the Legacy Survey of Space and Time (LSST), featuring seasonal gaps. Existing strategies, while effective, entail substantial computational costs. To address the complex nature of quasar light curve modeling, our package QNPy has been developed to efficiently model quasar light curves using Conditional Neural Processes.

Conditional Neural Processes

Conditional Neural Processes (CNPs) are a type of neural network architecture designed for flexible and probabilistic function learning. They are particularly well-suited for tasks involving conditional predictions and have applications in areas like regression, classification, and generative modeling.

The core idea behind CNPs is to learn a distribution over functions conditioned on input-output pairs. They are capable of making predictions not only for specific inputs seen during training but also for new inputs that were not present in the training data.

The CNP is a model designed for analyzing continuous-time light curves, characterized by time instances (x) and corresponding fluxes (or magnitudes) (y). In the CNP framework, we consider a scenario where we have target inputs representing time instances with unknown magnitudes. In the training process, we leverage a set of context points derived from observations, consisting of time instances (x) and observed magnitudes (y). Each pair in the context set is locally encoded using a multilayer perceptron (MLP). The resulting local encodings (Rc) are then aggregated through mean pooling to form a global representation (R). The global representation (R) is a condensed feature representation of the entire context set. This representation, along with the target input (xt), is fed into a decoder MLP. The decoder produces the mean and variance of the predictive distribution for the target output.

.. image:: _static/CNP.png :alt: descriptive text for image :align: left

Key features of Conditional Neural Processes include:

  1. Conditional Predictions: CNPs can provide predictions for a target output given a set of input-output pairs, allowing for context-aware predictions.

  2. Flexibility: CNPs are versatile and can adapt to various types of data and tasks. They are not limited to a specific functional form, making them suitable for a wide range of applications.

  3. Probabilistic Outputs: CNPs provide uncertainty estimates in the form of predictive distributions. This makes them valuable in situations where uncertainty quantification is crucial.

  4. Scalability: CNPs can handle different input and output dimensions, making them scalable to various data types and problem complexities.

In summary, Conditional Neural Processes are a powerful framework for conditional function learning that offers flexibility, probabilistic predictions, and scalability across different tasks. They have shown effectiveness in tasks such as few-shot learning, meta-learning, and regression with uncertainty estimation, making them a great tool for modeling the light curves of quasars.

Self Organizing Maps

Conditional Neural Processes excel at learning complex patterns in data with recurring gaps. However, application to larger datasets requires novel methods to prioritize efficiency and effectively capture subtle trends in the data. Self Organizing Maps (SOMs) provide both these advantages. SOMs provide an unsupervised clustering algorithm that can be trained quickly and include new data points without the need to train over every data point again. Thus, we present QNPy as an ensemble model of SOMs and CNPs.

SOMs comprise a network of nodes mapped onto a (usually) two-dimensional grid. Each node has an input weight associated with it. As the SOM trains on the input data, each input point is assigned a Best Matching Unit (BMU) where the node is at the minimum Euclidean distance from the input. Then, the BMU is updated to match the input data point (the amount that the node moves is dependent on the learning rate). Furthermore, each node can affect neighboring nodes via a neighborhood function (usually Gaussian).

Once the training is ended, each input data point is assigned to a cluster depending on the final BMU. Thus at the end, each node provides a cluster. These can be the final cluster or the distance matrix (a matrix containing the distance of each node with each of the other nodes) of the SOM can be used to group different nodes into more hierarchical clusters. This is done by calculating gradients between the nodes until the lowest node is reached. (For more info, refer to Hamel and Brown).

In QNPy, we treat each light curve as a data point and the magnitudes are the features. Thus, the SOM can effectively parse topological differences in the light curves. These differences allow the CNP to train on similar light curves and effectively capture subtle differences in the modeled light curves. In addition, the clusters now allow for CNPs to be trained in parallel on smaller batches of data, which allows for a massive speed-up in the training time of the QNPy package.

The SOM is based on the minisom package which uses NumPy packages to handle the input data. Thus, every input data point must have the same length. We handle this similarly with the CNP by padding all the light curves to the same length. We also scale the light curves to treat different magnitude ranges differently.

Thus, SOMs provide a useful companion to the CNPs to form an ensemble model with improved speed and accuracy in modeling large amounts of light curve data.

Installation

To install QNPy, use the following command:

.. code-block:: bash

pip install QNPy

Requirements

This package contains a requirements.txt file with all the dependencies that need to be satisfied before you can use it as a standalone package. To install all requirements at once, navigate to the directory where you downloaded your package (where the requirements.txt file is) and run:

.. code-block:: bash

pip install -r requirements.txt

You are now ready to use the QNPy package.

Special note: If you have python >3.9 on your local machine you will encounter some requirements conflicts with torch and numpy versions. In this case, we recomend creating a virtual enviroment using conda:

.. code-block:: bash

conda create -n myenv python=3.9

then you have to activate the virtuel enviroment:

.. code-block:: bash

conda activate "The name of your virtuel enviroment"

After virtual enviroment is activated you can install QNPy and the requirements.txt file in your newly created enviroment.

.. code-block:: virtual enviroment

pip install QNPy

.. code-block:: virtual enviroment

pip install -r requirements.txt

Examples

Check out the Tutorial folder here for notebooks that guide you through the process of using this package. There will be teo tutorial folders. The "QNPy without clustering: Tutorial" folder includes examples for using each of the modules separately. Additionally, you'll find an example of how your light curves should look in the Light_curves folder. The "QNPy with clustering: Tutorial" folder includes examples for using the Clustering_with_SOM module for single band and multiband clustering. You will also find example of how your light curves should look like in the folders Light_curves and Light_curves_Multiband.

Folder Structure

The QNPy automatically creates folders for saving plots, data and saves your trained SOM and CNP. The only requirement for the file structure in SOM module is to save light curves before the module and choose directories to save plots and models during the module's runtime. The files can be saved under any folder as desired and the file name can be given as an input into the loading function.

In the case of multi-band light curves only, the light curves should be saved under a directory (can be named anything) with the filters saved as subfolders. Then, each light curve should be saved as a CSV file with the id as the file name. For example, if you have a light curve in the g filter with ID 10422 and you want to save it in a folder known as Light_Curves, it should be saved under the directory Light_Curves/g/10422.csv. This is the standard recommendation for multi-band data. Then, once the clusters are created, it is easy to either point QNPy to one of the filters of a cluster or to manually flatten the file and provide all the light curves to QNPy. However, QNPy does not yet support explicit multi-band clustering.

For all QNPy modules, your data must contain: mjd - MJD or time, mag - magnitude, and magerr - magnitude error.

Before running the script, you can manually create the following folders in the directory where your Python notebook is located:

  1. ./preproc/ - for saving the transformed data
  2. ./padded_lc/ - for saving the padded light curves
  3. ./dataset/train/, ./dataset/test/, ./dataset/val/ - for organizing your dataset
  4. ./output/prediction/test/data/, ./output/prediction/test/plots/ - for organizing prediction results
  5. ./output/prediction/train/data/, ./output/prediction/train/plots/ - for organizing training results
  6. ./output/prediction/val/data/, ./output/prediction/val/plots/ - for organizing validation results

Modules and Their Functions

Clustering with SOM

Clustering_with_SOM.py

In the clustering module, we first load the light curves from the directory. This also creates the ids from the file names. Thus, it is recommended to have the same light curves saved across all the different bands. Then, we pad the light curves to make them all the same length. In QNPy, we have seen that we require at least 100 data points for accurate modeling. Thus, we recommend that the light curves be padded to at least 100 points (even if the longest curve is under 100 points, which can be controlled through a keyword in the padding function). Finally, we scale the light curves. We have provided many different scalers including minmax, standard and robust scalers. Our default scaler is an adapted version of a minmax scaler that scales all the data to the range [-2,2].

Then, a SOM is trained on the scaled data. The SOM contains different tunable hyperparameters to better adapt to different data sets. These hyperparameters can be tested with different metrics including quantization error, topographical error, silhouette score, davies-bouldin index, or calinski-harabasz score. The trained SOM can be saved as well.

The trained SOM is then used to assign the IDs to different clusters. Then they can be saved into different folders.

We also provide different plots for visualization of the data. These will be described in the plotting functions.

Described below are the functions used in the module:

.. code-block:: python

    def Plot_Lc(Light_Curve,header = 'Light Curve',save_fig = False,filename = 'Figure',x_axis = 'mjd',return_fig = False):
        '''
        Plots light curves interactively. Adapted from https://github.com/DamirBogdan39/time-series-analysis/tree/main

        Parameters
     ----------
     Light_Curve: Dataframe 
     The light curve to plot. Should be in a dataframe with mjd (or any x axis), mag and magerr

        header: str
        The header of the file

        save_fig: bool
        Whether to save the figure

        filename: str
        What to name the saved html file

        x_axis: str
        What to label the x axis 

        return_fig: bool
        Whether the figure is returned

        Returns
        --------
        Figure:
        The interactive plot of the light curve
        '''

.. code-block:: python

    def Load_Light_Curves(folder,one_filter = True,filters = 'a',id_list = None):
        '''
        Loads light curves from a specified folder. Can be used to load either multiple filters or just one filter

        Parameters
        ----------
        folder: str 
        The folder where the light curves are stored

        one_filter: bool
        If set to true, the light curves are only stored in one folder without filters

        filters: list or str(if each filter is a single letter)
        The filters that are to be loaded. Each filter should have a subfolder named after it if there are more than one filters

        id_list: list of str or None
        The subset of IDs to load. If None, retrieves all files in the given folder. NOTE: make sure the ids are strings

        Returns
        --------
        light_curves: list of lists of dataframes
        The list of light curves arranged by filter

        ids: list
        The ids of the light curves (Ensure that they are the same in all filters)
        '''

.. code-block:: python

    def Pad_Light_Curves(light_curves,minimum_length = 100,save_padded_lcs = False,padded_lcs_save_path = './',ids = None):
        '''
        Pads the light curves with the mean value at the end of the curve

        Parameters
        ----------
        light_curves: lists of dataframes 
        The light curves stored in a list

        minimum_length: int
        The minimum length to pad to

        save_padded_lcs: bool
        If True, will save the light curves into a folder known as Padded_Lc in the specified directory

        padded_lcs_save_path: str
      The directory to save the light curves in

        ids: list of str
        A list of the ids. Must provided in order to save the light curves

        Returns
        --------
        light_curves: list of lists
        The new padded light curves
        '''

.. code-block:: python

    def scale_curves(light_curves,what_scaler = 'default',scale_times = True):
        '''
        Scaling the curves (from a single filter) from the choice of minmax, standard and robust. By default, it scales to a range of [-2,2]
        Parameters
        ----------
        light_curves: list of dataframes 
        The light curves stored in a list.

      what_scaler: string
        The type of scaler to use. There are default (see above), standard scaler, min-max scaler and robust scalers available

        scale_times: bool
        Whether to scale the time axis as well (These are always scaled to the default scaler)

        Returns
        --------
        scaled_curves: np.ndarray 
        The scaled light curves

        scaled_times:np.ndarray
        The scaled time steps. It is an empty list if the keyword scale_times is False
        '''

.. code-block:: python

    def SOM_1D(scaled_curves,som_x = None,som_y = None,learning_rate = 0.1,sigma = 1.0,topology = 'rectangular',pca_init = True,\
    neighborhood_function='gaussian',train_mode = 'random',batch_size = 5,epochs = 50000,save_som = True,\
    model_save_path = './',random_seed = 21,stat = 'q',plot_frequency = 100,early_stopping_no = None):
        '''
        Training a SOM on ONE dimensional data (The magnitude of the light curves)
     Parameters
      ----------
     scaled_curves: list of dataframes 
     The scaled light curves stored in a list.

     som_x: int
     The x size of the SOM. If None is given, make sure the som_y is None as well. Then, it chooses the recommended SOM 
     size of sqrt(sqrt(length))

     som_y: int
     The y size of the SOM. If None is given, make sure the som_x is None as well. Then, it chooses the recommended SOM 
     size of sqrt(sqrt(length))

     learning_rate: float
     How much the SOM learns from the new data that it sees

     sigma: float
     The effect each node has on its neighboring nodes

        topology: 'rectangular' or 'hexagonal':
     The topology of the SOM. Note that visualizations are mainly built for the rectangular at the moment.

     pca_init: bool
      Whether to initialize the SOM weights randomly or to initialize by PCA of the input data

      neighborhood_function: str
      Choose from 'gaussian','mexican hat','bubble', or 'triangle'. These affect the influence of a node on its neighbors

      train_mode:'random' or 'all'
      When chosen random, it chooses a random curve each epoch. When trained on all, it batches the data and trains on every
       light curve for a certain number of epochs.

       batch_size: int
       How big the batch is for the 'all' train mode. The smaller the batch size, the finer the progress bar displayed

       epochs: int
      This is defined in two ways. If the train_mode is random, then it is the number of iterations that the SOM runs on.
      If it is all, then it is the number of times that the SOM trains on each input datapoint. Note that the lr and sigma
       decay in each epoch.

       save_som: bool
       Whether to save the trained SOM 

       model_save_path:str
       The file to save the SOM in

       random_seed:int
       The starting state of the random weights of the SOM. Use for reproducibility

       stat: 'q','t', or 'qt'
        Whether to record the quantization error,   topographical error or both. Note that calculating them is expensive

     plot_frequency: int
      The number of epochs

      early_stopping_no: int or None
      The number of batches to process before stopping. Use None if you should train on all

     Returns
     --------
     som_model:
     The trained SOM that can be saved or used for analysis

     q_error: list
      The quantization errors recorded

       t_error: list
       The topographic errors recorded

       indices_to_plot: list
       The indices to plot for the quantization or/and topographic errors
        '''

.. code-block:: python

    def plot_training(training_metric_results,metric,plotting_frequency,indices_to_plot,figsize = (10,10),save_figs = True,fig_save_path = './'):
        '''
     Plots the metric given (quantization error or topographic error)

      Parameters
     ----------
     training_metric_results: list 
     The result obtained from the SOM training

     metric: str
     Name of the metric

     plotting_frequency: int
      How much was the plotting frequency set during the SOM training

      indices_to_plot: list
      The indices to plot obtained from the SOM training

      figsize: tuple
      The size of the figure

      save_figs: bool
      Whether to save the figure or not

      fig_save_path:str
      Where to save the figure. Note that it creates a directory called Plots in the location given.

      Returns
      --------
      Plot:
      The plot of the metric
      '''

.. code-block:: python

    def Plot_SOM_Scaled_Average(som_model,scaled_curves,dba = True,figsize = (10,10),save_figs = True,figs_save_path = './',\
    plot_weights = True,plot_avg = True,plot_background = True,one_fig = True,show_fig = True):
        '''
     Plotting the SOM Clusters with the average light curve and the SOM weights of each cluster. The average can be either simple mean
     or using a dba averaging method (https://github.com/fpetitjean/DBA)

     Parameters
      ----------
     som_model:  
      The trained SOM

     scaled_curves: np.ndarray
     The scaled curves that were the input for training

     dba: bool
     Whether to use Dynamic Barymetric Time Averaging

     figsize: tuple
     The size of the figure

     save_figs: bool
     Whether to save the figure or not

     fig_save_path: str
     Where to save the figure. Note that it creates a directory called Plots in the location given.

     plot_avg: bool
      Whether to plot the mean light curve of the cluster

      plot_weights: bool
      Whether to plot the SOM weight of the cluster

      plot_background: bool
      Whether to plot the light curves that make up the cluster

     one_fig: bool
      Whether to plot all the clusters into one figure or seperate figures

      show_fig: bool
      Whether to show each of the plots in the seperate figures case

     Returns
      --------
       Plot:
       The plots of the clusters
        '''

.. code-block:: python

    def SOM_Distance_Map(som_model,figsize = (5,5),cmap = 'YlOrRd',save_figs = False,figs_save_path = './'):
        '''
        Plots a heatmap of the SOM Nodes. The brighter, the further away they are from their neighbors

        Parameters
        ----------
     som_model:  
     The trained SOM

        cmap: str
        The matplotlib based color scale to use for the plots

        figsize: tuple
     The size of the figure

        save_figs: bool
        Whether to save the figure or not

        fig_save_path: str
        Where to save the figure. Note that it creates a directory called Plots in the location given.

        Returns
        --------
        Plot:
        The heatmap plot
        '''

.. code-block:: python

    def SOM_Activation_Map(som_model,figsize = (5,5),cmap = 'YlOrRd',save_figs = False,figs_save_path = './'):
        '''
        Plots a heatmap of the SOM Nodes. The brighter, the more light curves activate the SOM

        Parameters
        ----------
        som_model:  
        The trained SOM

        cmap: str
        The matplotlib based color scale to use for the plots

        figsize: tuple
        The size of the figure

        save_figs: bool
        Whether to save the figure or not

        fig_save_path: str
      Where to save the figure. Note that it creates a directory called Plots in the location given.

     Returns
     --------
        Plot:
     The heatmap plot
        '''

.. code-block:: python

    def Assign_Cluster_Labels(som_model,scaled_curves,ids):
        '''
     Assigns Cluster labels to each of the curves, making a dataframe with their ids

        Parameters
        ----------
        som_model:  
     The trained SOM

        scaled_curves: np.ndarray
        The scaled curves that were used to train the SOM

        ids: list
     The ids of the curves

        Returns
        --------
     cluster_df: Dataframe
     A map matching each of the cluster ids with the cluster they belong to
        '''

.. code-block:: python

    def SOM_Clusters_Histogram(cluster_map,color = 'tab:blue',save_figs = True,figs_save_path = './',figsize = (5,5)):
     '''
        Plots a heatmap of the SOM Nodes. The brighter, the further away they are from their neighbors

        Parameters
        ----------
        cluster_map:  
        The dataframe with each id and the cluster that it belongs to

        color: str
        The color to plot the histogram

     save_figs: bool
     Whether to save the figure or not

     fig_save_path: str
     Where to save the figure. Note that it creates a directory called Plots in the location given.

        figsize: tuple
     The size of the figure

     Returns
     --------
     Plot:
        The Histogram of how many curves are in each cluster
     '''

.. code-block:: python

    def findMin(x, y, umat):
        '''
        Finds the minimum node in the unified matrix when given the x and y coordinate

        Parameters
        ----------
        x: int  
        The x position of the given input node

        y: int  
        The y position of the given input node

        umat: np.ndarry
        The unified distance matrix of the nodes of the SOM

        Returns
     --------
     minx, miny:
     The minumum x node and minimum y node
        '''

.. code-block:: python

    def findInternalNode(x, y, umat):
        '''
     Finds the minimum node in the unified matrix when given the x and y coordinate, taking into account if the current node is min

        Parameters
        ----------
     x: int  
        The x position of the given input node

        y: int  
     The y position of the given input node

        umat: np.ndarry
        The unified distance matrix of the nodes of the SOM

        Returns
        --------
     minx, miny:
     The minumum x node and minimum y node
        '''

.. code-block:: python

    def Get_Gradient_Cluster(som):
        '''
        Finds the center of the gradient for each node of the SOM

        Parameters
        ----------
        som: int  
        The trained SOM

        Returns
        --------
        cluster_centers:
        The center nodes that become the new gradient clusters

        cluster_pos:
        The original SOM cluster centers
        '''

.. code-block:: python

    def Normal_Cluster_to_Grad(cluster_map,gradient_cluster_map):
        '''
        Maps the normal cluster map to the gradient clusters

        Parameters
        ----------
        cluster_map: pd.DataFrame  
        The map of the ids to the original SOM node clusters

        gradient_cluster_map: pd.DataFrame  
        The map of the ids to the gradient SOM node clusters

        Returns
        --------
        joint_map:
        Mapping of each SOM node cluster to the gradient cluster
        '''

.. code-block:: python

    def Gradient_Cluster_Map(som,scaled_curves,ids,dimension = '1D',fill = 'mean',interpolation_kind = 'cubic',clusters = None,som_x = None,som_y = None):
        '''
        Translates the SOM nodes into larger clusters based on their gradients. Implementation of 
        https://homepage.cs.uri.edu/faculty/hamel/pubs/improved-umat-dmin11.pdf

        Parameters
        ----------
        som_model:  
        The trained SOM

        scaled_curves: np.ndarray
        The scaled curves used to train the SOM

        ids: list
        The ids of the curves

        dimension: str
        If 1D, does 1D clustering, else multivariate

        fill: str
        'mean' or 'interpolate'. Either the empty values are filled with the mean or they are interpolated with a function

        interpolation_kind: 
        Any of the scipy.interp1d interpolation kinds. Recommended to use cubic

        clusters:
        The clusters that the ids are in (only for multi-variate)

        som_x: int
        The x-dimensions of the SOM

        som_y: int
        The y-dimensions of the SOM

        Returns
        --------
        cluster_map:
        The new clusters that the ids are in
        '''

.. code-block:: python

    def matplotlib_cmap_to_plotly(cmap, entries):
        '''
        Creates a colorscale used to create an interactive plot

        Parameters
        ----------
        cmap:  
        The colormap

        entries: 
        The colormap entries

        Returns
        --------
        colorscale:
        The colorscale for the interactive plot
        '''

.. code-block:: python

    def plotStarburstMap(som):
        '''
        Interactive plot of the distance map and gradients of the SOM

        Parameters
        ----------
        som:  
        The trained SOM

        Returns
        --------
        Plot of the distance map and gradients
        '''

.. code-block:: python

    def outliers_detection(clusters_df,som,scaled_curves,ids,outlier_percentage = 0.2):
        '''
        Gives the percentage of the clusters that have high quanitization errors (defined by percentile) for each cluster

        Parameters
        ----------
        clusters_df:
        A map of each of the ids to the clusters

        som:  
        The trained SOM

        scaled_curves: np.ndarray
        The scaled curves used to train the SOM

        ids: list
        The ids of the curves

        outlier_percentage: float
        This top percentile that defines an outlier

        Returns
        --------
        Plots:
        Distribution of Outliers per cluster and distribution of quantization error
        '''

.. code-block:: python

    def Cluster_Metrics(scaled_curves,cluster_map,metric = 'Silhoutte'):
        '''
        Measures metrics related to the clustering

        Parameters
        ----------
        scaled_curves: np.ndarray
        The scaled curves used to train the SOM

        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        metric: str
        The metric to be measured. It can be Silhoutte, DBI or CH. This is for silhoutte score, Davies-Bouldin index and calinski-harabasz score

        Returns
        --------
        score:
        The metric that is calculated
        '''

.. code-block:: python

    def save_chosen_cluster(chosen_cluster,cluster_map,one_filter = True,filters = 'a',overwrite = True,save_path = './',source_path = './Light_Curves'):
        '''
     Saves the chosen cluster into a folder

        Parameters
        ----------
     chosen_cluster: int
        The cluster to save

        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        one_filter: bool
        Whether to save just one filter or all the filters

        filters: str
     The filters to save

        overwrite: bool
        Whether to overwrite the current folder

        save_path: str
        The path to save to. This creates a folder for the cluster in that directory

        source_path: str
     The path that the light curves are saved in. If multifilter, provide the entire larger folder.
        '''

.. code-block:: python

   def scale_to_range(series, min_val=-2, max_val=2):
        '''
        Scales a series to a range

        Parameters
        ----------
        series: pd.Series 
        The series to scale

        min_val: int
        The minimum value to scale to

        max_val: int
        The maximum value to scale to

        Returns
        --------
        scaled_series:
        The scaled series between the max and min values
        '''

.. code-block:: python

    def masked_euclidean_distance(data1, data2, mask):
        '''
        Calculates the masked euclidean distance between two arrays using a common mask

        Parameters
        ----------
        data1: np.ndarray
        The first array

        data2: np.ndarray
        The second array

        mask: np.ma.mask
        The mask used to get the distance

        Returns
        --------
        masked_distance:
     The masked distance measure
        '''

.. code-block: python

    def multi_band_processing(light_curves,ids,filter_names = 'ugriz',return_wide = False):
        '''
        Processes the light curves into a wide table

        Parameters
        ----------
        light_curves: 
        The light curves to be used

        ids: list,array
        The ids of the quasars

        filter_names: list or str(if the filters are one letter)
        The filters that are used

        return_wide: bool
        Whether to return the wide table or a flat table

        Returns
        --------
        light_curves_wide:
     The pivot table of the light curves with time steps

        light_curves_wide.isna():
        The mask used with the wide light curves

     OR 

        light_curves_flat:
     The flattened pivot table of the light curves

        mask_flat:
        The mask used
        '''

.. code-block:: python

    def multi_band_clustering(light_curves,ids,filter_names = 'ugriz',som_x = None,som_y = None,sigma = 1.0,learning_rate = 0.5,\
    num_iterations = 2,batch_size = 5,early_stopping_no = None):
        '''
        Multiband light curve clustering

        Parameters
        ----------
        light_curves: 
        The light curves to be used

        ids: list,array
        The ids of the quasars

        filter_names: list or str(if the filters are one letter)
        The filters that are used

        som_x: int
        The x size of the SOM. If None is given, make sure the som_y is None as well. Then, it chooses the recommended SOM 
        size of sqrt(sqrt(length))

        som_y: int
        The y size of the SOM. If None is given, make sure the som_x is None as well. Then, it chooses the recommended SOM 
        size of sqrt(sqrt(length))

        sigma: float
        The effect each node has on its neighboring nodes

        learning_rate: float
        How much the SOM learns from the new data that it sees

        num_iterations: int
        The number of iterations that the som is trained on each batch

     batch_size: int
        The size of each batch

        early_stopping_no: int or None
        The number of batches to process before stopping. Use None if you should train on all

        Returns
        --------
        som:
        The trained SOM

        processed_light_curve:
        The flat light curves used for the SOM

        processed_mask:
        The mask used for the SOM
        '''

.. code-block:: python

    def find_cluster_and_quantization_errors(som,data,masks):
        '''
        Finding the clusters and the quantization errors from the trained 2D SOM

        Parameters
     ----------
        som: 
        The trained SOM

        data: 
        The processed light curves from the trained SOM

        masks: 
        The masks used from the trained SOM

        Returns
        --------
        min_clusters:
        The clusters for each of the data points

        quantization_error:
        The quantization error of each of the data points
        '''

.. code-block:: python

    def Get_Gradient_Cluster_2D(som,fill = 'mean',interpolation_kind = 'cubic'):
        '''
        Finding the gradient clusters from the 2D SOM

        Parameters
        ----------
        som: 
        The trained SOM

        fill: str
        'mean' or 'interpolate'. Either the empty values are filled with the mean or they are interpolated with a function

        interpolation_kind: 
        Any of the scipy.interp1d interpolation kinds. Recommended to use cubic

        Returns
        --------
        cluster_centers:
        The cluster centers

        cluster_pos:
        The cluster positions
        '''

.. code-block:: python

    def create_dir_save_plot(path,plot_name):
        '''
        If there is no folder named plots in the path, it creates one and saves an figure

        Parameters
        ----------
        path: str  
        The path to create the Plots folder in

        plot_name: 
        The name to save the plot under
        ''' 

.. code-block:: python

    def tolerant_mean(arrs):
        '''
        Calculates the mean of arrays without them having to be the same length

        Parameters
        ----------
        arrs:  
        The arrays to calculate the mean for 

        Returns
        --------
        mean:
        The tolerant mean of the arrays

        std:
        The tolerant std of the arrays
        '''

.. code-block:: python

    def get_best_grid(number_of_objects):
        '''
        Creates a grid that is optimal for the number of objects

        Parameters
        ----------
        number_of_objects: int  
        The number of objects to make the grid for

        Returns
        --------
        rows:
        The number of rows for the grid

        cols:
        The number of cols for the grid
        '''

.. code-block:: python

    def Averaging_Clusters(chosen_cluster,cluster_map,lcs,plot = True,dba = True):
        '''
        Creating a representation of the chosen cluster with the light curves and the average light curve

        Parameters
        ----------
        chosen_cluster: int 
        The cluster of interest

        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        lcs: list of list of pd.Dataframes
        The light curves (provide the input from just one filter)

        plot: bool
        Whether to plot or just return the average value 

        dba: bool
        Whether to use Dynamic Barymetric Time Averaging or to use a simple mean of the light curves

        Returns
        --------
        average_x:
        The x_axis (i.e timesteps) of the average light curve

        average_y:
        The y_axis (i.e magnitudes) of the average light curve

        x:
        The timesteps of all the light curves concatenated into one array

        y: 
        The magnitudes of all the light curves concatenated into one array

        len(x):
        The length of all the light curves
        '''

.. code-block:: python

    def Plot_All_Clusters(cluster_map,lcs,color = 'tab:blue',dba = True,figsize = (10,10),save_figs = True,figs_save_path = './'):
        '''
        Plots all of the clusters on a magnitude plot with the average representation included

        Parameters
        ----------
        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        lcs: list of list of pd.Dataframes
        The light curves (provide the input from just one filter)

        color: str
        The color to plot the averaged curve in

        dba: bool
        Whether to use Dynamic Barymetric Time Averaging or to use a simple mean of the light curves

        figsize: tuple
        The figure size

        save_figs: bool
        Whether to save the figure or not

        figs_save_path: str
        Where to save the figure. Note that it is saved under a directory called Plots in that directory.
        '''

.. code-block:: python

    def get_redshifts(redshifts_map):
        '''
        Gets all the redshifts from a redshifts map  

        Parameters
        ----------
        redshifts_map:  pd.Dataframe
        The mapping of ids to redshifts 

        Returns
        --------
        redshifts:
        The list of redshifts
        '''

.. code-block:: python

    def get_fvars(lcs):
        '''
        Calculates the variability function of the light curves

        Parameters
        ----------
        lcs:  List of pd.Dataframes
        The light curves of interest

        Returns
        --------
        fvars:
        The list of variability functions
        '''

.. code-block:: python

    def get_luminosities_and_masses(lcs, redshifts_map, H0 = 67.4, Om0 = 0.315):
        '''
        Randomly samples the luminosity and masses of the quasar black holes assuming a given Hubble Constant, Omega_0, and redshift

        Parameters
        ----------
        lcs:  List of pd.Dataframes
        The light curves of interest

        redshifts_map: pd.DataFrame
        The map from the ids to their redshifts

        H0: float
        The hubble constant at z=0

        Om0: float
        Omega matter: density of non-relativistic matter in units of the critical density at z=0. 

        Returns
        --------
        Log_lum:
        The logarithm of the luminosities

        Log_Mass:
        The logarithm of the masses
        '''

.. code-block:: python

    def Cluster_Properties(cluster_map,selected_cluster,lcs,redshifts_map = None,plot = True,return_values = False,\
    the_property = 'all',save_figs = True,figs_save_path = './'):
        '''
        Getting the selected property of a chosen cluster

        Parameters
        ----------
        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        chosen_cluster: int 
        The cluster of interest

        lcs: list of list of pd.Dataframes
        The light curves (provide the input from just one filter)

        redshifts_map: pd.Dataframe
        The redshift associated with each source id

        plot: bool
        Whether to plot or just return the average value 

        return_values: bool
        Whether to return the values for the property

        the_property: str
        The property to plot. Choice from z (redshift), Fvar (the variability function), Lum (luminosity), Mass, or all

        save_figs: bool
        Whether to save the figure or not

        figs_save_path: str
        Where to save the figure. Note that it is saved under a directory called Plots in that directory.

        Returns
        --------
        return_list: 
        The list of the property of interest
        '''

.. code-block:: python

    def Cluster_Properties_Comparison(cluster_map,lcs,redshifts_map,the_property = 'Fvar',color = '#1f77b4',\
    figsize = (15,15),save_figs = True,figs_save_path = './'):
        '''
        Plotting the property of interest for all the clusters onto one figure

        Parameters
        ----------
        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        lcs: list of list of pd.Dataframes
        The light curves (provide the input from just one filter)

        redshifts_map: pd.Dataframe
        The redshift associated with each source id

        the_property: str
        The property to plot. Choice from z (redshift), Fvar (the variability function), Lum (luminosity), Mass, or all

        color: str
        The color to make the histogram

        figsize: tuple
        The figure size

        save_figs: bool
        Whether to save the figure or not

        figs_save_path: str
        Where to save the figure. Note that it is saved under a directory called Plots in that directory.

        Returns
        --------
        return_list: 
        The list of the property of interest
        '''

.. code-block:: python

    def SFplus(magnitudes):
        '''
        Calculates the S+ function of given light curves. S+ is the variance of magnitudes where the brightness increases

        Parameters
        ----------
        lcs:  List of pd.Dataframes
        The light curves of interest

        Returns
        --------
        sfplus:
        The list of S+
        '''

.. code-block:: python

    def SFminus(magnitudes):
        '''
        Calculates the S- function of given light curves. S- is the variance of magnitudes where the brightness decreases

        Parameters
        ----------
        lcs:  List of pd.Dataframes
        The light curves of interest

        Returns
        --------
        sfminus:
        The list of S-
        '''

.. code-block:: python

    def SF(magnitudes):
        '''
        Calculates the S function of given light curves. S is the variance of all magnitudes

        Parameters
        ----------
        lcs:  List of pd.Dataframes
        The light curves of interest

        Returns
        --------
        sf:
        The list of SFs
        '''

.. code-block:: python

    def Structure_Function(cluster_map,selected_cluster,lcs,bins,save_figs = True,figs_save_path = './'):
        '''
        Create the structure function for a given cluster

        Parameters
        ----------
        cluster_map: pd.Dataframe
        A map of each of the ids to the clusters

        selected_cluster: int
        The cluster of interest

        lcs: list of list of pd.Dataframes
        The light curves (provide the input from just one filter)

        bins:int or list
        The bins to use for the structure function

        save_figs: bool
        Whether to save the figure or not

        figs_save_path: str
        Where to save the figure. Note that it is saved under a directory called Plots in that directory.

        Returns
        --------
        S+ and S- Plot: 
        A plot of the S+ and S- functions for the cluster

        Difference Plot:
        The evolution of the normalized S+ - S- throughout the observation time of the cluster

        S Plot:
        The evolution of the (regular) structure function through the observation time of the cluster
        '''

Preprocessing the Data

Preprocess.py

In this module, we transform data in the range [-2,2]x[-2,2] to make training faster. This module also contains two functions for cleaning the sample from outliers. If your sample does not require outlier cleaning, the functions can be skipped. Also, you can use your designed outlier cleaning function according to your data requirements. This module also contains a function for padding light curves. This function does not need to be used if your light curves have 100 or more points. It is important to emphasize that all light curves must have the same number of points in order to train the model correctly. By testing the package, we found that it is best to do backward padding with the last measured value up to 100 points. You use the padded light curves to train the model, and later, for prediction and plotting, these points are subtracted. According to the needs of the user, another method for padding can be done. The data transformation function in this module also creates subsets of the data named your_curve_name plus and your_curve_name_minus. These subsets are made with respect to errors in magnitudes and serve to augment the model's training set. The original curves are saved as the name_of_your_curve_original.

Before running this script, you must create the following folders in the directory where your Python notebook is located:

  1. ./preproc/ - It is going to be the folder for saving the transformed data
  2. ./padded_lc/ - It is going to be the folder for saving the padded light curves
  3. ./light_curves_clean/ - It is going to be the folder for saving the light curves that are cleaned from outliers (this can be scipped)

Your data must contain: mjd - MJD or time, mag - magnitude, and magerr - magnitude error

.. code-block:: python

   def clean_and_save_outliers(input_folder, output_folder, threshold=3.0):
        """
        Clean data in CSV files within the input folder, remove outliers, and save cleaned files in the output folder.

        Parameters:
      input_folder (str): Path to the folder containing input CSV files.
      output_folder (str): Path to the folder where cleaned CSV files will be saved.
      threshold (float, optional): Threshold for outlier detection in terms of standard deviations. Default is 3.0.
     """

.. code-block:: python

   def clean_save_aggregate_data(input_folder, output_folder, threshold_aggregation=5, threshold_outliers=3.0):
        """
        Clean data in CSV files within the input folder, remove outliers, aggregate time and fluxes,
        and save cleaned files in the output folder.

        Parameters:
        input_folder (str): Path to the folder containing input CSV files.
        output_folder (str): Path to the folder where cleaned CSV files will be saved.
        threshold_aggregation (float, optional): Threshold for time aggregation. Default is 5.
        threshold_outliers (float, optional): Threshold for outlier detection in terms of standard deviations. Default is 3.0.
        """

.. code-block:: python

   def backward_pad_curves(folder_path, output_folder, desired_observations=100):
       """
       Backward padding the light curves with the last observed value for mag and magerr.
       If your data contains 'time' values it'll add +1 for padded values,
       and if your data contains 'MJD' values it will add +0.2

       :param str folder_path: The path to a folder containing the .csv files.
       :param str output_path: The path to a folder for saving the padded lc.
       :param int desired_observations: The number of points that our package is demanding is 100 but it can be more.

       :return: The padded light curves.
       :rtype: object

       How to use:
       padding = backward_pad_curves('./light_curves', './padded_lc', desired_observations=100)
       """

.. code-block:: python

   def transform(data):
       """
       Transforming data into [-2,2]x[-2,2] range. This function needs to be uploaded before using it.

       :param data: Your data must contain: MJD or time, mag-magnitude, and magerr-magnitude error.
       :type data: object
       """

.. code-block:: python

   def transform_and_save(files, DATA_SRC, DATA_DST, transform):
       """
       Transforms and saves a list of CSV files. The function also saves tr coefficients as a pickle file named trcoeff.pickle.

       :param list files: A list of CSV or TXT file names.
       :param str DATA_SRC: The path to the folder containing the CSV or TXT files.
       :param str DATA_DST: The path to the folder where the transformed CSV or TXT files will be saved.
       :param function transform: The transformation function defined previously.

       :return: A list of transformation coefficients for each file, where each element is a list containing the file name and the transformation coefficients Ax, Bx, Ay, and By.
       :rtype: list

       How to use:
       number_of_points, trcoeff = transform_and_save(files, DATA_SRC, DATA_DST, transform)
       """
  1. SPLITTING AND TRAINING THE DATA

SPLITTING_AND_TRAINING.py

We use this module to split the data into three subsamples that will serve as a test sample, a sample for model training, and a validation sample. This module also contains functions for training and saving models. It contains the following functions that must be executed in order.

Before running this script, you must create the following folders in the directory where your Python notebook is located:

  1. ./dataset/train - folder for saving data for training after splitting your original dataset
  2. ./dataset/test - folder for test data
  3. ./dataset/val - folder for validation data
  4. ./output - folder where you are going to save your trained model

.. code-block:: python

   def create_split_folders(train_folder='./dataset/train/', test_folder='./dataset/test/', val_folder='./dataset/val/'):
        """
        Creates a TRAIN, TEST, and VAL folders in the directory.

        :param str train_folder: Path for saving the train data.
        :param str test_folder: Path for test data.
        :param str val_folder: Path for validation data.

        How to use: create_split_folders(train_folder='./dataset/train/', test_folder='./dataset/test/', val_folder='./dataset/val/')
        """

.. code-block:: python

   def split_data(files, DATA_SRC, TRAIN_FOLDER, TEST_FOLDER, VAL_FOLDER):
        """
        Splits the data into TRAIN, TEST, and VAL folders.

        :param list files: A list of CSV file names.
        :param str DATA_SRC: Path to preprocessed data.
        :param str TRAIN_FOLDER: Path for saving the train data.
        :param str TEST_FOLDER: Path for saving the test data.
        :param str VAL_FOLDER: Path for saving the validation data.

        How to use: split_data(files, DATA_SRC, TRAIN_FOLDER, TEST_FOLDER, VAL_FOLDER)
        """

TRAINING

Special note for mac os users:

When creating folders with mac operating systems, hidden .DS_Store files may be created. The user must delete these files before starting training from each folder. The best way is to go into each folder individually and run the command:

.. code-block:: bash

     !rm -f .DS_Store

Important note: Deleting files using the delete directly in the folders does not remove hidden files.

Before running the training function you must define:

.. code-block:: python

     DATA_PATH_TRAIN = "./dataset/train" - path to train folder
     DATA_PATH_VAL = "./dataset/val" - path to val folder

     MODEL_PATH = "./output/cnp_model.pth" - folder for saving model

     BATCH_SIZE = 32 - training batch size MUST REMAIN 32
     EPOCHS = 6000 - This is optional
     EARLY_STOPPING_LIMIT = 3000 - This is optional

.. code-block:: python

     def get_data_loader(data_path_train, data_path_val, batch_size):
      """
      
      --- Defining train and validation loader for training process and validation


        Args:
        :param str data_path_train: path to train folder
        :param str data_path_val: path to val folder
        :param batch_size: it is recommended to be 32

        How to use: trainLoader, valLoader = get_data_loader(DATA_PATH_TRAIN,BATCH SIZE)
      """

.. code-block:: python

     def create_model_and_optimizer():
      """
        --Defines the model as Deterministic Model, optimizer as torch optimizer, criterion as LogProbLoss, mseMetric as MSELoss and maeMetric as MAELoss

        How to use: model, optimizer, criterion, mseMetric, maeMetric = create_model_and_optimizer(device)
         Device has to be defined before and it can be cuda or cpu
      """

.. code-block:: python

     def train_model(model, train_loader, val_loader,criterion, optimizer, num_runs, epochs, early_stopping_limit, mse_metric, maeMetric, device):
      """
      -- Trains the model


      Args:
      model: Deterministic model
      train_loader: train loader
      val_loader: validation loader
      criterion: criterion
      optimizer: torch optimizer
      num_runs: The number of trainings 
      epochs: epochs for training. This is optional, but minimum of 3000 is recomended
      early_stopping_limit: limits the epochs for stopping the training. This is optional but minimum of 1500 is recomended
      mse_metric: mse metric
      mae_metric: mae metric
      device: torch device cpu or cuda

       How to use: If you want to save history_loss_train, history_loss_val, history_mse_train and history_mse_val for plotting you train your model like:

      history_loss_train, history_loss_val, history_mse_train, history_mse_val, history_mae_train, history_mae_val, epoch_counter_train_loss, epoch_counter_train_mse, epoch_counter_train_mae, epoch_counter_val_loss, epoch_counter_val_mse, epoch_counter_val_mae = st.train_model(model, trainLoader, valLoader, criterion, optimizer, 1, 3000, 1500, mseMetric, maeMetric, device)

      """

.. code-block:: python

     def save_lists_to_csv(file_names,lists):
      """

      --saving the histories to lists


      args:
      :param list file_names: A list of file names to be used for saving the data. Each file name corresponds to a specific data list that will be saved in CSV format.
      :param list lists: A list of lists containing the data to be saved. Each inner list represents a set of rows to be written to a CSV file.

      How to use: 
      # Define the file names for saving the lists
      file_names = ["history_loss_train.csv", "history_loss_val.csv", "history_mse_train.csv", "history_mse_val.csv","history_mae_train.csv", "history_mae_val.csv", "epoch_counter_train_loss.csv", "epoch_counter_train_mse.csv", "epoch_counter_train_mae.csv", "epoch_counter_val_loss.csv","epoch_counter_val_mse.csv", "epoch_counter_val_mae.csv"]

      # Define the lists
      lists = [history_loss_train, history_loss_val, history_mse_train, history_mse_val, history_mae_train,
      history_mae_val, epoch_counter_train_loss, epoch_counter_train_mse, epoch_counter_train_mae,
      epoch_counter_val_loss, epoch_counter_val_mse, epoch_counter_val_mae]

      save_list= save_lists_to_csv(file_names, lists)
      """

.. code-block:: python

   def plot_loss(history_loss_train_file, history_loss_val_file, epoch_counter_train_loss_file):
      """

      -- plotting the history losses


      Args:
      returned data from test_model
      How to use: 

      history_loss_train_file = './history_loss_train.csv'  # Replace with the path to your history_loss_train CSV file
      history_loss_val_file = './history_loss_val.csv'  # Replace with the path to your history_loss_val CSV file
      epoch_counter_train_loss_file = './epoch_counter_train_loss.csv'  # Replace with the path to your epoch_counter_train_loss CSV file

      logprobloss=plot_loss(history_loss_train_file, history_loss_val_file, epoch_counter_train_loss_file)

      """

.. code-block:: python

   def plot_mse_metric(history_mse_train_file, history_mse_val_file, epoch_counter_train_mse_file):
      """

      -- plotting the mse metric


       args:
       returned data from test_model
       How to use: 

      history_mse_train_file = './history_mse_train.csv'  # Replace with the path to your history_mse_train CSV file
      history_mse_val_file = './history_mse_val.csv'  # Replace with the path to your history_mse_val CSV file
      epoch_counter_train_mse_file = './epoch_counter_train_mse.csv'  # Replace with the path to your epoch_counter_train_mse CSV file

      msemetric=plot_mse(history_mse_train_file, history_mse_val_file, epoch_counter_train_mse_file)

      """

.. code-block:: python

   def plot_mae_metric(history_mae_train_file, history_mae_val_file, epoch_counter_train_mae_file):
      """

      -- plotting the mae metric


        args:
      returned data from test_model
      How to use: 

      history_mae_train_file = './history_mae_train.csv'  # Replace with the path to your history_mae_train CSV file
      history_mae_val_file = './history_mae_val.csv'  # Replace with the path to your history_mae_val CSV file
      epoch_counter_train_mae_file = './epoch_counter_train_mae.csv'  # Replace with the path to your epoch_counter_train_mae CSV file

      maemetric=plot_mae(history_mae_train_file, history_mae_val_file, epoch_counter_train_mae_file)
      """

.. code-block:: python

   def save_model(model, MODEL_PATH):
      """

      -- saving the model


      Args:
      model: Deterministic model
      :param str MODEL_PATH: output path for saving the model

      How to use: save_model(model, MODEL_PATH)
      """
  1. PREDICTION AND PLOTTING THE TRANSFORMED DATA, EACH CURVE INDIVIDUALLY

PREDICTION.py

We use this module for prediction and plotting of models of transformed data. Each curve will be plotted separately. It contains the following functions that must be executed in order.

Before running this script, you must create the following folders in the directory where your Python notebook is located:

  1. ./output/predictions/train/plots - folder for saving training plots
  2. ./output/predictions/test/plots - folder for saving test plots
  3. ./output/predictions/val/plots - folder for saving validation plots
  4. ./output/predictions/train/data - folder for sving train data
  5. ./output/predictions/test/data - folder for saving test data
  6. ./output/predictions/val/data - folder for saving val data

.. code-block:: python

   def prepare_output_dir(OUTPUT_PATH):
      """ 

      -- the function prepare_output_dir takes the `OUTPUT_PATH` as an argument and removes all files in the output directory using os.walk method.


      Args:
      :param str OUTPUT_PATH: path to output folder

      How to use: prepare_output_dir(OUTPUT_PATH)
      """

.. code-block:: python

   def load_trained_model(MODEL_PATH, device):
      """ 

      --Uploading trained model


      agrs:
      :param str MODEL_PATH = path to model directorium
      :param device = torch device CPU or CUDA
      How to use: model=load_trained_model(MODEL_PATH, device)
      """

.. code-block:: python

   def get_criteria():
      """

      -- Gives the criterion and mse_metric

      How to use: criterion, mseMetric=get_criteria()
      """

.. code-block:: python

   def remove_padded_values_and_filter(folder_path):
      """

      -- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves


      Args:
      :param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'

      How to use: 
      if __name__ == "__main__":
      folder_path = "./dataset/test"  # Change this to your dataset folder

      remove_padded_values_and_filter(folder_path)
      """

.. code-block:: python

   def plot_function(target_x, target_y, context_x, context_y, pred_y, var, target_test_x, lcName, save = False, flagval=0, isTrainData = None, notTrainData = None):
      """

      -- Defines the plots of the light curve data and predicted mean and variance, and it should be imported separately
     

      Args:
      :param context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
      :param context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
      :param target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
      :param target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
      :param target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
      :param pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
      :param var: An array of shape BATCH_SIZE x 400  that contains the variance of predictions at target_test_x points.
      """

.. code-block:: python

   def load_test_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to Test data

      How to use: testLoader=load_test_data(DATA_PATH_TEST)

      """

.. code-block:: python

   def load_train_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to train data

      How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)

      """

.. code-block:: python

   def load_val_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to VAL data

      How to use: valLoader=load_val_data(DATA_PATH_VAL)

      """

.. code-block:: python

   def plot_light_curves_from_test_set(model, testLoader, criterion, mseMetric, plot_function, device):
      """

      -- Ploting the transformed light curves from test set


      Args:
      :param model: Deterministic model
      :param testLoader: Uploaded test data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA

      How to use: testMetrics = plot_light_curves_from_test_set(model, testLoader, criterion, mseMetric, plot_function, device)
      """

.. code-block:: python

   def save_test_metrics(OUTPUT_PATH, testMetrics):
      """

      -- saving the test metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param testMetrics: returned data from ploting function

       How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
      """

.. code-block:: python

   def plot_light_curves_from_train_set(model, trainLoader, criterion, mseMetric, plot_function, device):
      """

      -- Ploting the transformed light curves from train set


      Args:
      :param model: Deterministic model
      :param trainLoader: Uploaded trained data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA

      How to use: trainMetrics = plot_light_curves_from_train_set(model, trainLoader, criterion, mseMetric, plot_function, device) 
      """

.. code-block:: python

   def save_train_metrics(OUTPUT_PATH, testMetrics)
      """

      -- saving the train metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param trainMetrics: returned data from ploting function

      How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
      """

.. code-block:: python

   def plot_light_curves_from_val_set(model, valLoader, criterion, mseMetric, plot_function, device):
      """

      -- Ploting the transformed light curves from validation set


      Args:
      :param model: Deterministic model
      :param valLoader: Uploaded val data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA

      How to use: valMetrics = plot_light_curves_from_val_set(model, valLoader, criterion, mseMetric, plot_function, device)
      """

.. code-block:: python

   def save_val_metrics(OUTPUT_PATH, valMetrics):
      """

      -- saving the validation metrics as json file

      Args:
      :param str OUTPUT_PATH: path to output folder
      :param valMetrics: returned data from ploting function

      How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
      """
  1. PREDICTION AND PLOTTING THE TRANSFORMED DATA, IN ONE PDF FILE

PREDICTION_onePDF.py

We use this module for prediction and plotting of models of transformed data. All curves will be plotted in one PDF file. This module contains the following functions that must be executed in order.

Before running this script, you must create the following folders in the directory where your Python notebook is located:

  1. ./output/predictions/train/plots -- folder for saving training plots
  2. ./output/predictions/test/plots -- folder for saving test plots
  3. ./output/predictions/val/plots -- folder for saving validation plots
  4. ./output/predictions/train/data -- folder for sving train data
  5. ./output/predictions/test/data -- folder for saving test data
  6. ./output/predictions/val/data -- folder for saving val data

.. code-block:: python

   def clear_output_dir(output_path):
      """

      -- Removes all files in the specified output directory.


      Args:
      :param str output_path: The path to the output directory.

      How to use: clear_output_dir(OUTPUT_PATH)

      """

.. code-block:: python

   def load_model(model_path, device):
      """
      --Loads a trained model from disk and moves it to the specified device.

      Args:
      :param str model_path: The path to the saved model.
      :param str or torch.device device: The device to load the model onto, CPU or CUDA

      How to use: model = load_model(MODEL_PATH, device)
      """

.. code-block:: python

   def get_criteria():
      """

      -- Gives the criterion and mse_metric


      How to use: criterion, mseMetric=get_criteria()
      """

.. code-block:: python

   def remove_padded_values_and_filter(folder_path):
      """

      -- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves


        Args:
      :param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'

      How to use: 
      if __name__ == "__main__":
        folder_path = "./dataset/test"  # Change this to your dataset folder

      remove_padded_values_and_filter(folder_path)
      """

.. code-block:: python

   dev plot_function(target_x, target_y, context_x, context_y, pred_y, var, target_test_x, lcName, save = False, flagval=0, isTrainData = None, notTrainData = None):
      """

      -- Defines the plots of the light curve data and predicted mean and variance


        Args:
      context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
      context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
      target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
      target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
      target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
      pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
      var: An array of shape BATCH_SIZE x 400  that contains the variance of predictions at target_test_x points.
      """

.. code-block:: python

   def load_test_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to Test data

      How to use: testLoader=load_test_data(DATA_PATH_TEST)

      """

.. code-block:: python

   def load_train_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to train data

      How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)

      """

.. code-block:: python

   def load_val_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to VAL data

      How to use: valLoader=load_val_data(DATA_PATH_VAL)

      """

.. code-block:: python

   def plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function, device):
      """
      -- ploting the test set in range [-2,2]


      Args:
      :param model: model
      :param testLoader: Test set
      :param criterion: criterion
      :param mseMetric: mse Metric
      :param plot_function: defined above
      :param device: Torch device, CPU or CUDA


      how to use: testMetrics=plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function, out_pdf_test, device)
      """

.. code-block:: python

   def save_test_metrics(OUTPUT_PATH, testMetrics):
      """

      -- saving the test metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param testMetrics: returned data from ploting function

      How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
      """

.. code-block:: python

   def plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function, device):
      """

      -- Ploting the transformed light curves from train set


      Args:
      :param model: Deterministic model
      :param trainLoader: Uploaded trained data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA
  

      How to use: trainMetrics=plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function, device)
      """

.. code-block:: python

   save_train_metrics(OUTPUT_PATH, testMetrics)
      """

      -- saving the train metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param trainMetrics: returned data from ploting function

      How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
      """

.. code-block:: python

   def plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function, device):
      """

      -- Ploting the transformed light curves from val set


      Args:
      :param model: Deterministic model
      :param valLoader: Uploaded val data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA

      How to use: valMetrics = plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function, device)
      """

.. code-block:: python

   def save_val_metrics(OUTPUT_PATH, valMetrics):
      """

      -- saving the val metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param valMetrics: returned data from ploting function

      How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
      """
  1. PREDICTION AND PLOTTING THE DATA IN ORIGINAL DATA RANGE, EACH CURVE INDIVIDUALLY

PREDICTION_Original_mjd.py

We use this module to predict and plot the model in the original range of data. All curves are plotted individually. This module contains the following functions that must be executed in order.

Before running this script, you must create the following folders in the directory where your Python notebook is located:

  1. ./output/predictions/train/plots -- folder for saving training plots
  2. ./output/predictions/test/plots -- folder for saving test plots
  3. ./output/predictions/val/plots -- folder for saving validation plots
  4. ./output/predictions/train/data -- folder for sving train data
  5. ./output/predictions/test/data -- folder for saving test data
  6. ./output/predictions/val/data -- folder for saving val data

.. code-block:: python

   def prepare_output_dir(OUTPUT_PATH):
      """

      -- the function prepare_output_dir takes the OUTPUT_PATH       as an argument and removes all files in the output    directory using os.walk method.


      Args:
      :param str OUTPUT_PATH: path to output folder

      How to use: prepare_output_dir(OUTPUT_PATH)
      """

.. code-block:: python

   def load_trained_model(MODEL_PATH, device):
      """

      --Uploading trained model


        agrs:
      :param str MODEL_PATH = path to model directorium
      :param str device = torch device CPU or CUDA

      How to use: model=load_trained_model(MODEL_PATH, device)
      """

.. code-block:: python

   def get_criteria():
      """

      -- Gives the criterion and mse_metric


      How to use: criterion, mseMetric=get_criteria()
      """

.. code-block:: python

   def remove_padded_values_and_filter(folder_path):
      """

      -- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves


      Args:
      :param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'

      How to use: 
      if __name__ == "__main__":
      folder_path = "./dataset/test"  # Change this to your dataset folder

      remove_padded_values_and_filter(folder_path)
      """

.. code-block:: python

   def load_trcoeff():
      """ 

      -- loading the original coefficients from pickle file


      How to use: tr=load_trcoeff()

      """

.. code-block:: python

   def plot_function2(tr,target_x, target_y, context_x, context_y, yerr1, pred_y, var, target_test_x, lcName, save = False, isTrainData = None, flagval = 0, notTrainData = None):
      """

      -- function for ploting the light curves


      context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
      context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
      target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
      target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
      target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
      yerr1: Array of shape BATCH_SIZE x NUM_measurement_error that contains the measurement errors.
      pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
      var: An array of shape BATCH_SIZE x 400  that contains the variance of predictions at target_test_x points.
      tr: array of data in pickle format needed to backtransform data from [-2,2] x [-2,2] to MJD x Mag
      """

.. code-block:: python

   def load_test_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to Test data

      How to use: testLoader=load_test_data(DATA_PATH_TEST)

      """

.. code-block:: python

   def load_train_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to train data

       How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)

      """

.. code-block:: python

   def load_val_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to VAL data

       How to use: valLoader=load_val_data(DATA_PATH_VAL)

      """

.. code-block:: python

   def plot_test_data(model, testLoader, criterion, mseMetric, plot_function, device, tr):
      """

      -- Ploting the light curves from test set in original mjd range


        Args:
      :param model: Deterministic model
      :param testLoader: Uploaded test data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA
      :param tr: trcoeff from pickle file

      How  to use: testMetrics=plot_test_data(model, testLoader, criterion, mseMetric, plot_function2, device, tr)
      """

.. code-block:: python

   def save_test_metrics(OUTPUT_PATH, testMetrics):
      """

      -- saving the test metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param testMetrics: returned data from ploting function

      How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
      """

.. code-block:: python

   def plot_train_light_curves(trainLoader, model, criterion, mseMetric, plot_function, device, tr):
      """

      -- Ploting the light curves from train set in original mjd range


      Args:
      :param model: Deterministic model
      :param trainLoader: Uploaded trained data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA
      :param tr: trcoeff from pickle file

      How to use: trainMetrics=plot_train_light_curves(trainLoader, model, criterion, mseMetric, plot_function2, device,tr)
      """

.. code-block:: python

   def save_train_metrics(OUTPUT_PATH, testMetrics):
      """

      -- saving the train metrics as json file


       Args:
      :param str OUTPUT_PATH: path to output folder
      :param trainMetrics: returned data from ploting function

      How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
      """

.. code-block:: python

   def plot_val_curves(model, valLoader, criterion, mseMetric, plot_function, device, tr):
      """

      -- Ploting the light curves from val set in original mjd range


      Args:
      :param model: Deterministic model
      :param valLoader: Uploaded val data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA
      :param tr: trcoeff from pikle file

      How to use: valMetrics=plot_val_curves(model, valLoader, criterion, mseMetric, plot_function2, device,tr)
      """

.. code-block:: python

   def save_val_metrics(OUTPUT_PATH, valMetrics):
      """

      -- saving the val metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      valMetrics: returned data from ploting function

      How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
      """   
  1. PREDICTION AND PLOTTING THE DATA IN ORIGINAL DATA RANGE, IN ONE PDF FILE

PREDICTION_onePDF_original_mjd.py

We use this module to predict and plot the model in the original range of data. All curves are plotted in one PDF file. This module contains the following functions that must be executed in order.

Before running this script, you must create the following folders in the directory where your Python notebook is located:

  1. ./output/predictions/train/plots -- folder for saving training plots
  2. ./output/predictions/test/plots -- folder for saving test plots
  3. ./output/predictions/val/plots -- folder for saving validation plots
  4. ./output/predictions/train/data -- folder for sving train data
  5. ./output/predictions/test/data -- folder for saving test data
  6. ./output/predictions/val/data -- folder for saving val data

.. code-block:: python

   def clear_output_dir(output_path):
      """

      -- Removes all files in the specified output directory.


      Args:
      :param str output_path: The path to the output directory.

      How to use: clear_output_dir(OUTPUT_PATH)

      """

.. code-block:: python

   def load_trained_model(MODEL_PATH, device):
      """

      --Uploading trained model


        agrs:
      :param str MODEL_PATH = path to model directorium
      :param str device = torch device CPU or CUDA

      How to use: model=load_trained_model(MODEL_PATH, device)
      """

.. code-block:: python

   def get_criteria():
      """

      -- Gives the criterion and mse_metric


      How to use: criterion, mseMetric=get_criteria()
      """

.. code-block:: python

   def remove_padded_values_and_filter(folder_path):
      """

      -- Preparing data for plotting. It'll remove the padded values from lc and it'll delete artifitially added lc with plus and minus errors. If your lc are not padded it'll only delete additional curves


      Args:
      :param str folder_path: Path to folder where the curves are. In this case it'll be './dataset/test' or './dataset/train' or './dataset/val'

      How to use: 
      if __name__ == "__main__":
      folder_path = "./dataset/test"  # Change this to your dataset folder

      remove_padded_values_and_filter(folder_path)
      """

.. code-block:: python

   def load_trcoeff():
      """ 

      -- loading the original coefficients from pickle file


      How to use: tr=load_trcoeff()

      """

.. code-block:: python

   def plot_function2(tr,target_x, target_y, context_x, context_y, yerr1, pred_y, var, target_test_x, lcName, save = False, isTrainData = None, flagval = 0, notTrainData = None):
      """

      -- function for ploting the light curves. It needs to be uploaded separately


      context_x: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the x values of the context points.
      context_y: Array of shape BATCH_SIZE x NUM_CONTEXT that contains the y values of the context points.
      target_x: Array of shape BATCH_SIZE x NUM_TARGET that contains the x values of the target points.
      target_y: Array of shape BATCH_SIZE x NUM_TARGET that contains the ground truth y values of the target points.
      target_test_x: Array of shape BATCH_SIZE x 400 that contains uniformly spread points across in [-2, 2] range.
      yerr1: Array of shape BATCH_SIZE x NUM_measurement_error that contains the measurement errors.
      pred_y: Array of shape BATCH_SIZE x 400 that contains predictions across [-2, 2] range.
      var: An array of shape BATCH_SIZE x 400  that contains the variance of predictions at target_test_x points.
      tr: array of data in pickle format needed to backtransform data from [-2,2] x [-2,2] to MJD x Mag
      """

.. code-block:: python

   def load_test_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to Test data

      How to use: testLoader=load_test_data(DATA_PATH_TEST)

      """

.. code-block:: python

   def load_train_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to train data

       How to use: trainLoader=load_train_data(DATA_PATH_TRAIN)

      """

.. code-block:: python

   def load_val_data(data_path):
      """

      -- takes data_path as an argument, creates a LighCurvesDataset and returns a PyTorch DataLoader for the test set. The DataLoader is used to load and preprocess the test data in batches

      Args:
      :param str data_path: path to VAL data

       How to use: valLoader=load_val_data(DATA_PATH_VAL)

      """

.. code-block:: python

   def plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function2, device,tr):
      """

      -- ploting the test set in original range


      Args:
      :param model: model
      :param testLoader: Test set
      :param criterion: criterion
      :param mseMetric: mse Metric
      :param plot_function2: defined above
      :param device: Torch device, CPU or CUDA
      :param tr: trcoeff from pickle file

      how to use: testMetrics=plot_test_light_curves(model, testLoader, criterion, mseMetric, plot_function2, device,tr)
      """

.. code-block:: python

   def save_test_metrics(OUTPUT_PATH, testMetrics):
      """

      -- saving the test metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
       testMetrics: returned data from ploting function

      How to use: save_test_metrics(OUTPUT_PATH, testMetrics)
      """

.. code-block:: python

   def plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function2, device,tr):
      """

      -- Ploting the light curves from train set in original mjd range


      Args:
      :param model: Deterministic model
      :param trainLoader: Uploaded trained data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA
      :param tr: trcoeff from pickle file

      How to use: trainMetrics=plot_train_light_curves(model, trainLoader, criterion, mseMetric, plot_function2, device,tr)
      """

.. code-block:: python

   def save_train_metrics(OUTPUT_PATH, testMetrics):
      """

      -- saving the train metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param trainMetrics: returned data from ploting function

      How to use: save_train_metrics(OUTPUT_PATH, trainMetrics)
      """

.. code-block:: python

   def plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function2, device,tr):
      """

      -- Ploting the light curves from val set in original mjd range


      Args:
      :param model: Deterministic model
      :param valLoader: Uploaded val data
      :param criterion: criterion
      :param mseMetric: Mse Metric
      :param plot_function: plot function defined above
      :param device: torch device CPU or CUDA
      :param tr: trcoeff from pickle file

      How to use: valMetrics=plot_val_light_curves(model, valLoader, criterion, mseMetric, plot_function2, device,tr)
      """

.. code-block:: python

   def save_val_metrics(OUTPUT_PATH, valMetrics):
      """

      -- saving the val metrics as json file


      Args:
      :param str OUTPUT_PATH: path to output folder
      :param valMetrics: returned data from ploting function

      How to use: save_val_metrics(OUTPUT_PATH, valMetrics)
      """

Frequently Asked Questions

Q What should my input data look like?

A: The input data should have three columns: mjd - Modified Julian Date or time, mag - magnitude, and magerr - magnitude error

Q Do I need to run all four prediction modules?

A: No, it is enough to run only one prediction module, depending on what you want on the final plots. There are four options for prediction and plotting namely:

  1. all curves are plotted separately and plots contain transformed data.
  2. all curves are plotted in one pdf document and contain transformed data
  3. all curves are plotted separately and the plots contain the original data.
  4. all curves are plotted in one pdf document and contain original data

Q Can the package be used for other uses outside of astronomy?

A: Yes, the package can be used for different types of time series analysis.

Licence

MIT License

Copyright (c) 2024 Andjelka Kovacevic, Marina Pavlovic, Aman Raju, Nikola Mitrovic - Andric, Iva Cvorovic - Hajdinjak, Dragana Ilic, SER-SAG in-kind team from Serbia

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Modeling Quasar time series with Neural processes in Python

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