Repository: jakobrunge/tigramite (language: python)

Issue:
fixed max_combinations bug



Discussion / hints:

Fix patch:
diff --git a/tigramite/jpcmciplus.py b/tigramite/jpcmciplus.py
new file mode 100644
index 00000000..0c8d07e6
--- /dev/null
+++ b/tigramite/jpcmciplus.py
@@ -0,0 +1,960 @@
+"""Tigramite causal discovery for time series."""
+
+# Authors: Wiebke Günther <wiebke.guenther@dlr.de>, Urmi Ninad, Jakob Runge <jakob@jakob-runge.com>
+#
+# License: GNU General Public License v3.0
+
+from __future__ import print_function
+import numpy as np
+from tigramite.pcmci import PCMCI
+from copy import deepcopy
+import itertools
+
+from tigramite.toymodels.context_model import _group_links
+
+
+class JPCMCIplus(PCMCI):
+    r"""J-PCMCI+ causal discovery for time series datasets from multiple contexts.
+        This class is based on the PCMCI framework as described in [1]_.
+        JPCMCIplus enables causal discovery for time series data from different contexts,
+        i.e. datasets, where some of the variables describing the context might be unobserved.
+        The method is described in detail in [10]_.
+        See the tutorial for guidance in applying the method.
+
+        References
+        ----------
+        .. [1] J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic,
+               Detecting and quantifying causal associations in large nonlinear time
+               series datasets. Sci. Adv. 5, eaau4996 (2019)
+               https://advances.sciencemag.org/content/5/11/eaau4996
+        .. [10] W. Günther, U. Ninad, J. Runge,
+               Causal discovery for time series from multiple datasets with latent contexts. UAI 2023
+        Parameters
+        ----------
+        node_classification : dictionary
+            Classification of nodes into system, context, or dummy nodes.
+            Keys of the dictionary are from {0, ..., N-1} where N is the number of nodes.
+            Options for the values are "system", "time_context", "space_context", "time_dummy", or "space_dummy".
+        Attributes
+        ----------
+        all_parents : dictionary
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+            the conditioning-parents estimated with PC algorithm.
+        val_min : dictionary
+            Dictionary of form val_min[j][(i, -tau)] = float
+            containing the minimum test statistic value for each link estimated in
+            the PC algorithm.
+        pval_max : dictionary
+            Dictionary of form pval_max[j][(i, -tau)] = float containing the maximum
+            p-value for each link estimated in the PC algorithm.
+        iterations : dictionary
+            Dictionary containing further information on algorithm steps.
+        N : int
+            Number of variables.
+        T : dict
+            Time series sample length of dataset(s).
+        dummy_parents : dictionary or None
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+            the dependence of the system nodes on the dummy nodes.
+        observed_context_parents : dictionary or None
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+            the dependence of the system nodes on the observed context nodes.
+        dummy_ci_test : conditional independence test object
+            Conditional independence test used to test dependence between system nodes and dummy nodes.
+            Currently, ParCorr is used with one-hot encoded dummies.
+        mode : "system_search" or "context_search" or "dummy_search" (default: "system_search")
+        time_context_nodes : list
+            List with entries from {0, ..., N-1} where N is the number of nodes.
+            This is the list of the temporal context nodes which are assumed to be constant over the different datasets.
+        space_context_nodes :
+            List with entries from {0, ..., N-1} where N is the number of nodes.
+            This is the list of the spatial context nodes which are assumed to be constant over time.
+        time_dummy : int or None (default: None)
+            Node corresponding to the temporal dummy variable.
+        space_dummy : int or None (default: None)
+            Node corresponding to the spatial dummy variable.
+        system_nodes : list
+            List with entries from {0, ..., N-1} where N is the number of nodes.
+            This is the list of the system nodes.
+        """
+
+    def __init__(self, node_classification, **kwargs):
+        # Init base class
+        PCMCI.__init__(self, **kwargs)
+
+        self.system_nodes = self.group_nodes(node_classification, "system")
+        self.time_context_nodes = self.group_nodes(node_classification, "time_context")
+        self.space_context_nodes = self.group_nodes(node_classification, "space_context")
+        self.time_dummy = self.group_nodes(node_classification, "time_dummy")
+        self.space_dummy = self.group_nodes(node_classification, "space_dummy")
+
+        self.dummy_parents = {i: [] for i in range(self.N)}
+        self.observed_context_parents = {i: [] for i in range(self.N)}
+        self.mode = "system_search"
+
+    def group_nodes(self, node_types, node_type):
+        nodes = range(self.N)
+        return [node for node in nodes if node_types[node] == node_type]
+
+    def run_jpcmciplus(self,
+                       contemp_collider_rule='majority',
+                       link_assumptions=None,
+                       tau_min=0,
+                       tau_max=2,
+                       pc_alpha=0.01,
+                       conflict_resolution=True,
+                       reset_lagged_links=False,
+                       max_conds_dim=None,
+                       max_combinations=1,
+                       max_conds_py=None,
+                       max_conds_px=None,
+                       max_conds_px_lagged=None,
+                       fdr_method='none'):
+        """
+        Runs JPCMCIplus time-lagged and contemporaneous causal discovery for time series from multiple contexts.
+        Method described in [10]_:
+            W. Günther, U. Ninad, J. Runge,
+            Causal discovery for time series from multiple datasets with latent contexts. UAI 2023
+        Notes
+        -----
+        The JPCMCIplus causal discovery method is described in [10]_, where
+        also analytical and numerical results are presented. JPCMCIplus can identify the joint causal graph
+        over multiple datasets containing time series data from different contexts under the standard assumptions
+        of Causal Sufficiency, Faithfulness and the Markov condition, as well as some background knowledge assumptions.
+        JPCMCIplus estimates time-lagged and contemporaneous causal links from context to system
+        variables and in between system variables by a four-step procedure:
+
+        1.  **Discovery of supersets of the lagged parents of the system and observed temporal context nodes** by
+        running the :math:`PC_1` lagged phase on this subset of nodes to obtain :math:`\\hat{\\mathcal{B}}^-_t(X_t^j)`.
+
+        2.  Next, the **MCI test is run on pairs of system and context nodes conditional on subsets of system
+        and context**, i.e. perform MCI tests for pairs :math:`((C^j_{t-\\tau}, X^i_t))_{\\tau > 0}`,
+        :math:`(C_t^j, X_t^i)`, :math:`(X_t^i, C_t^j)` for all :math:`i,j`,
+
+        .. math:: C_{t-\\tau}^i \\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^-_t(X_t^j)
+                    \\setminus \\{ C_{t-\\tau}^i \\}, \\hat{\\mathcal{B}}^-_{t-\\tau}(C_{t-\\tau}^i)
+
+        with :math:`\\mathbf{S}` being a subset of the contemporaneous adjacencies :math:`\\mathcal{A}_t(X_t^j)` and
+        :math:`\\hat{\\mathcal{B}}^-_t(X_t^j)` are the lagged adjacencies from step one. If :math:`C` is a
+        spatial context variable, we only have to test the contemporaneous pairs
+        :math:`(C_t^j, X_t^i)`, :math:`(X_t^i, C_t^j)` for all :math:`i,j`.
+        If :math:`C_t^j` and :math:`X_t^i` are conditionally independent, all lagged links between :math:`C_t^j` and
+        :math:`X^j_{t-\\tau}` are also removed for all :math:`\\tau`.
+
+        3.  **Perform MCI tests on all system-dummy pairs conditional on the superset of lagged links, the discovered
+        contemporaneous context adjacencies, as well as on subsets of contemporaneous system links**, i.e. test
+        for :math:`(D, X_t^i)`, :math:`(X_t^i, D)` for all :math:`i`, i.e.
+
+        .. math:: D \\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^C_t(X_t^j),
+
+        where :math:`\\mathbf{S} \\subset \\mathcal{A}_t(X_t^i)` and :math:`\\hat{\\mathcal{B}}^C_t(X_t^j)`
+        are the lagged and contextual adjacencies found in the previous step.
+        If :math:`D` and :math:`X_t^j` are found to be conditionally independence, links between :math:`D` and
+        :math:`X^j_{t-\\tau}` are removed for all :math:`\\tau`.
+        By assumption context node is the parent in all system-context links.
+
+        4.  Finally, we **perform  MCI tests on all system pairs conditional on discovered lagged, context and dummy
+        adjacencies, as well as on subsets of contemporaneous system links** and **orientation phase**. In more detail,
+        we perform MCI test for pairs :math:`((X^j_{t-\\tau}, X_t^i))_{\\tau > 0}`, :math:`(X_t^i, X_t^j)` for all
+        :math:`i, j`, i.e.
+
+        .. math:: X^i_{t-\\tau} \\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^{CD}_t(X_t^j)
+                    \\setminus \\{ X_{t-\\tau}^i \\},\\hat{\\mathcal{B}}^{CD}_t(X_{t-\\tau}^i)
+
+        where :math:`\\mathbf{S} \\subset \\mathcal{A}_t(X_t^i)` and :math:`\\hat{\\mathcal{B}}^{CD}_t(X_t^j)`
+        are the lagged, contextual, and dummy adjacencies found in the previous steps.
+        Finally, all remaining edges (without expert knowledge) are oriented using the PCMCI+ orientation phase while
+        making use of all triples involving one context or dummy variable and two system variables as in the non-time
+        series case.
+
+        JPCMCIplus can be flexibly combined with any kind of conditional
+        independence test statistic adapted to the kind of data (continuous
+        or discrete) and its assumed dependency types. These are available in
+        ``tigramite.independence_tests``.
+        See PCMCIplus for a description of the parameters of JPCMCIplus. Also, guidance on best practices for
+        setting these parameters is given there.
+
+        Parameters
+        ----------
+        link_assumptions : dict
+            Dictionary of form {j:{(i, -tau): link_type, ...}, ...} specifying
+            assumptions about links. This initializes the graph with entries
+            graph[i,j,tau] = link_type. For example, graph[i,j,0] = '-->'
+            implies that a directed link from i to j at lag 0 must exist.
+            Valid link types are 'o-o', '-->', '<--'. In addition, the middle
+            mark can be '?' instead of '-'. Then '-?>' implies that this link
+            may not exist, but if it exists, its orientation is '-->'. Link
+            assumptions need to be consistent, i.e., graph[i,j,0] = '-->'
+            requires graph[j,i,0] = '<--' and acyclicity must hold. If a link
+            does not appear in the dictionary, it is assumed absent. That is,
+            if link_assumptions is not None, then all links have to be specified
+            or the links are assumed absent.
+        tau_min : int, optional (default: 0)
+            Minimum time lag to test.
+        tau_max : int, optional (default: 1)
+            Maximum time lag. Must be larger or equal to tau_min.
+        pc_alpha : float or list of floats, default: 0.01
+            Significance level in algorithm. If a list or None is passed, the
+            pc_alpha level is optimized for every graph across the given
+            pc_alpha values ([0.001, 0.005, 0.01, 0.025, 0.05] for None) using
+            the score computed in cond_ind_test.get_model_selection_criterion().
+        contemp_collider_rule : {'majority', 'conservative', 'none'}
+            Rule for collider phase to use. See the paper for details. Only
+            'majority' and 'conservative' lead to an order-independent
+            algorithm.
+        conflict_resolution : bool, optional (default: True)
+            Whether to mark conflicts in orientation rules. Only for True
+            this leads to an order-independent algorithm.
+        reset_lagged_links : bool, optional (default: False)
+            Restricts the detection of lagged causal links in Step 2 to the
+            significant adjacencies found in the PC1 algorithm in Step 1. For
+            True, *all* lagged links are considered again, which improves
+            detection power for lagged links, but also leads to larger
+            runtimes.
+        max_conds_dim : int, optional (default: None)
+            Maximum number of conditions to test. If None is passed, this number
+            is unrestricted.
+        max_combinations : int, optional (default: 1)
+            Maximum number of combinations of conditions of current cardinality
+            to test. Defaults to 1 for PC_1 algorithm. For original PC algorithm
+            a larger number, such as 10, can be used.
+        max_conds_py : int, optional (default: None)
+            Maximum number of lagged conditions of Y to use in MCI tests. If
+            None is passed, this number is unrestricted.
+        max_conds_px : int, optional (default: None)
+            Maximum number of lagged conditions of X to use in MCI tests. If
+            None is passed, this number is unrestricted.
+        max_conds_px_lagged : int, optional (default: None)
+            Maximum number of lagged conditions of X when X is lagged in MCI
+            tests. If None is passed, this number is equal to max_conds_px.
+        fdr_method : str, optional (default: 'none')
+            Correction method, default is Benjamini-Hochberg False Discovery
+            Rate method.
+        Returns
+        -------
+        graph : array of shape [N, N, tau_max+1]
+            Resulting causal graph, see description above for interpretation.
+        val_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of test statistic values regarding adjacencies.
+        p_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of p-values regarding adjacencies.
+        sepset : dictionary
+            Separating sets. See paper for details.
+        ambiguous_triples : list
+            List of ambiguous triples, only relevant for 'majority' and
+            'conservative' rules, see paper for details.
+        """
+        observed_context_nodes = self.time_context_nodes + self.space_context_nodes
+
+        # initialize / clean link_assumptions
+        if link_assumptions is not None:
+            _link_assumptions = deepcopy(link_assumptions)
+        else:
+            _link_assumptions = self._set_link_assumptions(link_assumptions, tau_min, tau_max,
+                       remove_contemp=False)
+            # {j: {(i, -tau): ("-?>" if tau > 0 else "o?o") for i in range(self.N)
+            #                          for tau in range(tau_max + 1)} for j in range(self.N)}
+
+        _link_assumptions = self.assume_exogenous_context(_link_assumptions, observed_context_nodes)
+        _link_assumptions = self.clean_link_assumptions(_link_assumptions, tau_max)
+
+        # for j in _link_assumptions:
+        #     print(j, _link_assumptions[j])
+        # self._set_link_assumptions(_link_assumptions, tau_min, tau_max,
+        #                        remove_contemp=False)
+
+        # Check if pc_alpha is chosen to optimize over a list
+        if pc_alpha is None or isinstance(pc_alpha, (list, tuple, np.ndarray)):
+            # Call optimizer wrapper around run_pcmciplus()
+            return self._optimize_pcmciplus_alpha(
+                link_assumptions=link_assumptions,
+                tau_min=tau_min,
+                tau_max=tau_max,
+                pc_alpha=pc_alpha,
+                contemp_collider_rule=contemp_collider_rule,
+                conflict_resolution=conflict_resolution,
+                reset_lagged_links=reset_lagged_links,
+                max_conds_dim=max_conds_dim,
+                max_combinations=max_combinations,
+                max_conds_py=max_conds_py,
+                max_conds_px=max_conds_px,
+                max_conds_px_lagged=max_conds_px_lagged,
+                fdr_method=fdr_method)
+
+        elif pc_alpha < 0. or pc_alpha > 1:
+            raise ValueError("Choose 0 <= pc_alpha <= 1")
+
+        # Check the limits on tau
+        self._check_tau_limits(tau_min, tau_max)
+
+        # Step 0 and 1:
+        context_results = self.discover_lagged_context_system_links(
+            _link_assumptions,
+            tau_min=tau_min,
+            tau_max=tau_max,
+            pc_alpha=pc_alpha,
+            reset_lagged_links=reset_lagged_links,
+            max_conds_dim=max_conds_dim,
+            max_combinations=max_combinations,
+            max_conds_py=max_conds_py,
+            max_conds_px=max_conds_px,
+            max_conds_px_lagged=max_conds_px_lagged,
+            fdr_method=fdr_method
+        )
+        ctxt_res = deepcopy(context_results)
+        # Store the parents in the pcmci member
+        self.observed_context_parents = deepcopy(context_results['parents'])
+        self.all_lagged_parents = deepcopy(
+            context_results['lagged_parents'])  # remove context nodes from lagged parents
+        self.all_lagged_parents = {i: [el for el in self.all_lagged_parents[i] if el[0] not in self.time_context_nodes]
+                                   for i in
+                                   range(self.N)}
+
+        # if self.verbosity > 0:
+        #     print("\nDiscovered observed context parents: ", context_results['parents'])
+
+        if len(self.time_dummy) > 0 or len(self.space_dummy) > 0:
+            # step 2:
+            dummy_system_results = self.discover_dummy_system_links(
+                _link_assumptions,
+                ctxt_res,
+                self.all_lagged_parents,
+                tau_min=tau_min,
+                tau_max=tau_max,
+                pc_alpha=pc_alpha,
+                reset_lagged_links=reset_lagged_links,
+                max_conds_dim=max_conds_dim,
+                max_conds_py=max_conds_py,
+                max_conds_px=max_conds_px,
+                max_conds_px_lagged=max_conds_px_lagged,
+                fdr_method=fdr_method
+            )
+            # Store the parents in the pcmci member
+            self.dummy_parents = dummy_system_results['parents']
+        else:
+            dummy_system_results = deepcopy(context_results)
+
+        # if self.verbosity > 0:
+        #     print("Discovered dummy parents: ", self.dummy_parents)
+
+        # step 3:
+        self.mode = "system_search"
+
+        lagged_context_dummy_parents = {
+            i: list(
+                dict.fromkeys(self.all_lagged_parents[i] + self.observed_context_parents[i] + self.dummy_parents[i]))
+            for i in self.system_nodes}
+        # we only care about the parents of system nodes
+        lagged_context_dummy_parents.update(
+            {i: [] for i in observed_context_nodes + self.time_dummy + self.space_dummy})
+
+        dummy_system_results_copy = deepcopy(dummy_system_results)
+
+        # step 4:
+        system_skeleton_results = self.discover_system_system_links(link_assumptions=_link_assumptions,
+                                                                    lagged_context_dummy_parents=lagged_context_dummy_parents,
+                                                                    tau_min=tau_min,
+                                                                    tau_max=tau_max,
+                                                                    pc_alpha=pc_alpha,
+                                                                    reset_lagged_links=reset_lagged_links,
+                                                                    max_conds_dim=max_conds_dim,
+                                                                    max_conds_py=max_conds_py,
+                                                                    max_conds_px=max_conds_px,
+                                                                    max_conds_px_lagged=max_conds_px_lagged,
+                                                                    fdr_method=fdr_method)
+
+        # orientation phase
+        colliders_step_results = self._pcmciplus_collider_phase(
+            system_skeleton_results['graph'], system_skeleton_results['sepsets'],
+            lagged_context_dummy_parents, pc_alpha,
+            tau_min, tau_max, max_conds_py, max_conds_px, max_conds_px_lagged,
+            conflict_resolution, contemp_collider_rule)
+
+        final_graph = self._pcmciplus_rule_orientation_phase(colliders_step_results['graph'],
+                                                             colliders_step_results['ambiguous_triples'],
+                                                             conflict_resolution)
+
+        # add context-system and dummy-system values and pvalues back in (lost because of link_assumption)
+        for c in observed_context_nodes + self.time_dummy + self.space_dummy:
+            for j in range(self.N):
+                for lag in range(tau_max + 1):
+                    # add context-system links to results
+                    system_skeleton_results['val_matrix'][c, j, lag] = dummy_system_results_copy['val_matrix'][
+                        c, j, lag]
+                    system_skeleton_results['val_matrix'][j, c, lag] = dummy_system_results_copy['val_matrix'][
+                        j, c, lag]
+
+                    system_skeleton_results['p_matrix'][c, j, lag] = dummy_system_results_copy['p_matrix'][c, j, lag]
+                    system_skeleton_results['p_matrix'][j, c, lag] = dummy_system_results_copy['p_matrix'][j, c, lag]
+
+        # No confidence interval estimation here
+        return_dict = {'graph': final_graph, 'p_matrix': system_skeleton_results['p_matrix'],
+                       'val_matrix': system_skeleton_results['val_matrix'],
+                       'sepsets': colliders_step_results['sepsets'],
+                       'ambiguous_triples': colliders_step_results['ambiguous_triples'], 
+                       'conf_matrix': None}
+
+        # Print the results
+        if self.verbosity > 0:
+            self.print_results(return_dict, alpha_level=pc_alpha)
+
+        # Return the dictionary
+        self.results = return_dict
+
+        # Return the dictionary
+        return return_dict
+
+    def assume_exogenous_context(self, link_assumptions, observed_context_nodes):
+        """Helper function to amend the link_assumptions to ensure that all context-system links are oriented
+        such that the context variable is the parent."""
+        for j in link_assumptions:
+            if j in self.system_nodes:
+                for link in link_assumptions[j]:
+                    i, lag = link
+                    if i in observed_context_nodes + self.time_dummy + self.space_dummy:  # is context var
+                        link_type = link_assumptions[j][link]
+                        link_assumptions[j][link] = '-' + link_type[1] + '>'
+        return link_assumptions
+
+    def clean_link_assumptions(self, link_assumptions, tau_max):
+        """Helper function to amend the link_assumptions in the following ways
+            * remove any links where dummy is the child
+            * remove any lagged links to dummy, and space_context (not to observed time context)
+            * and system - context links where context is the child
+            * and any links between spatial and temporal context
+        """
+        for node in self.time_dummy + self.space_dummy:
+            link_assumptions[node] = {}
+
+        for j in self.system_nodes + self.time_context_nodes + self.space_context_nodes:
+            for lag in range(1, tau_max + 1):
+                for c in self.time_dummy + self.space_dummy + self.space_context_nodes:
+                    if (c, -lag) in link_assumptions[j]: link_assumptions[j].pop((c, -lag), None)
+        for c in self.space_context_nodes + self.time_context_nodes:
+            for j in self.system_nodes:
+                for lag in range(tau_max + 1):
+                    if (j, -lag) in link_assumptions[c]: link_assumptions[c].pop((j, -lag), None)
+            if (c, 0) in link_assumptions[c]: link_assumptions[c].pop((c, 0), None)  # remove self-links
+
+        for c in self.space_context_nodes:
+            for k in self.time_context_nodes:
+                for lag in range(tau_max + 1):
+                    if (k, -lag) in link_assumptions[c]: link_assumptions[c].pop((k, -lag), None)
+        for c in self.time_context_nodes:
+            for k in self.space_context_nodes:
+                if (k, 0) in link_assumptions[c]: link_assumptions[c].pop((k, 0), None)
+
+        return link_assumptions
+
+    def remove_dummy_link_assumptions(self, link_assumptions):
+        """Helper function to remove any links to dummy from link_assumptions."""
+        link_assumptions_wo_dummy = deepcopy(link_assumptions)
+        for j in self.system_nodes + self.time_context_nodes + self.space_context_nodes:
+            for dummy_node in self.time_dummy + self.space_dummy:
+                if (dummy_node, 0) in link_assumptions_wo_dummy[j]:
+                    link_assumptions_wo_dummy[j].pop((dummy_node, 0), None)
+        return link_assumptions_wo_dummy
+
+    def add_found_context_link_assumptions(self, link_assumptions, tau_max):
+        """Helper function to add discovered links between system and observed context nodes to link_assumptions."""
+        link_assumptions_dummy = deepcopy(link_assumptions)
+
+        for c in self.space_context_nodes + self.time_context_nodes:
+            link_assumptions_dummy[c] = {}
+        for j in self.system_nodes + self.time_context_nodes + self.space_context_nodes:
+            for c in self.space_context_nodes + self.time_context_nodes:
+                for lag in range(tau_max + 1):
+                    if (c, -lag) in link_assumptions_dummy[j]:
+                        link_assumptions_dummy[j].pop((c, -lag), None)
+            link_assumptions_dummy[j].update({parent: '-->' for parent in self.observed_context_parents[j]})
+
+        return link_assumptions_dummy
+
+    def clean_system_link_assumptions(self, link_assumptions, tau_max):
+        """Helper function to remove any links to dummy and observed context nodes from link_assumptions.
+            Add discovered links to contextual parents (from steps 1 and 2) to the link_assumptions.
+        """
+        dummy_vars = self.time_dummy + self.space_dummy
+        observed_context_nodes = self.time_context_nodes + self.space_context_nodes
+        system_links = deepcopy(link_assumptions)
+
+        for j in self.system_nodes:
+            for C in dummy_vars + observed_context_nodes:
+                for lag in range(tau_max + 1):
+                    if (C, -lag) in system_links[j]:
+                        system_links[j].pop((C, -lag), None)
+
+        for j in system_links:
+            system_links[j].update(
+                {parent: '-->' for parent in self.observed_context_parents[j] + self.dummy_parents[j]})
+
+        for C in observed_context_nodes + dummy_vars:
+            # we are not interested in links between context variables (thus system_links[C] = {})
+            system_links[C] = {}
+        return system_links
+
+    def discover_lagged_context_system_links(self, link_assumptions,
+                                             tau_min=0,
+                                             tau_max=1, pc_alpha=0.01,
+                                             reset_lagged_links=False,
+                                             max_conds_dim=None,
+                                             max_combinations=1,
+                                             max_conds_py=None,
+                                             max_conds_px=None,
+                                             max_conds_px_lagged=None,
+                                             fdr_method='none'):
+        """
+        Step 1 of JPCMCIplus, i.e. discovery of links between observed context nodes and system nodes through an
+        application of the skeleton phase of PCMCIplus to this subset of nodes (observed context nodes and system
+        nodes).
+        See run_jpcmciplus for a description of the parameters.
+
+        Returns
+        -------
+        graph : array of shape [N, N, tau_max+1]
+            Resulting causal graph, see description above for interpretation.
+        val_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of test statistic values regarding adjacencies.
+        p_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of p-values regarding adjacencies.
+        parents : dictionary
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+            the estimated context parents of the system nodes.
+        lagged_parents : dictionary
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+            the conditioning-parents estimated with PC algorithm.
+        """
+
+        # Initializing
+        context_parents = {i: [] for i in range(self.N)}
+
+        # find links between expressive context, and between expressive context and system nodes
+        # here, we exclude any links to dummy
+        _link_assumptions_wo_dummy = self.remove_dummy_link_assumptions(link_assumptions)
+        _int_link_assumptions = self._set_link_assumptions(_link_assumptions_wo_dummy, tau_min, tau_max)
+
+        # Step 1: Get a superset of lagged parents from run_pc_stable
+        if self.verbosity > 0:
+            print("\n##\n## J-PCMCI+ Step 1: Selecting lagged conditioning sets\n##")
+
+        lagged_parents = self.run_pc_stable(link_assumptions=link_assumptions,
+                                            tau_min=tau_min,
+                                            tau_max=tau_max,
+                                            pc_alpha=pc_alpha,
+                                            max_conds_dim=max_conds_dim,
+                                            max_combinations=max_combinations)
+
+        self.mode = "context_search"
+
+        p_matrix = self.p_matrix
+        val_matrix = self.val_matrix
+
+        # run PCMCI+ skeleton phase on subset of links to discover context-system links
+        if self.verbosity > 0:
+            print("\n##\n## J-PCMCI+ Step 2: Discovering context-system links\n##")
+            if link_assumptions is not None:
+                print("\nWith link_assumptions = %s" % str(_int_link_assumptions))
+
+        skeleton_results = self._pcmciplus_mci_skeleton_phase(
+            lagged_parents, _int_link_assumptions, pc_alpha,
+            tau_min, tau_max, max_conds_dim, None,
+            max_conds_py, max_conds_px, max_conds_px_lagged,
+            reset_lagged_links, fdr_method,
+            p_matrix, val_matrix
+        )
+
+        skeleton_graph = skeleton_results['graph']
+
+        for j in self.system_nodes + self.time_context_nodes + self.space_context_nodes:
+            for c in self.space_context_nodes + self.time_context_nodes:
+                for k in range(tau_max + 1):
+                    if skeleton_graph[c, j, k] == 'o?o' or skeleton_graph[c, j, k] == '-?>' or skeleton_graph[
+                        c, j, k] == 'o-o' or skeleton_graph[c, j, k] == '-->':
+                        context_parents[j].append((c, -k))
+
+        return_dict = {'graph': skeleton_results['graph'], 'p_matrix': skeleton_results['p_matrix'],
+                       'val_matrix': skeleton_results['val_matrix'],
+                       'parents': context_parents, 'lagged_parents': lagged_parents}
+
+        # Print the results
+        if self.verbosity > 0:
+            self.print_results(return_dict, alpha_level=pc_alpha)
+
+        return return_dict
+
+    def discover_dummy_system_links(self, link_assumptions,
+                                    context_system_results,
+                                    lagged_parents,
+                                    tau_min=0,
+                                    tau_max=1,
+                                    pc_alpha=0.01,
+                                    reset_lagged_links=False,
+                                    max_conds_dim=None,
+                                    max_conds_py=None,
+                                    max_conds_px=None,
+                                    max_conds_px_lagged=None,
+                                    fdr_method='none'):
+        """
+        Step 2 of JPCMCIplus, i.e. discovery of links between observed (time and space) dummy nodes and system nodes
+        through an application of the skeleton phase of PCMCIplus to this subset of nodes (dummy nodes and
+        system nodes).
+        See run_jpcmciplus for a description of the parameters.
+
+        Parameters
+        ----------
+        context_system_results : dictionary
+            Output of discover_lagged_and_context_system_links, i.e. lagged and context parents together with the
+            corresponding estimated test statistic values regarding adjacencies.
+        lagged_parents : dictionary
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing the conditioning-parents
+            estimated with PC algorithm.
+
+        Returns
+        -------
+        graph : array of shape [N, N, tau_max+1]
+            Resulting causal graph, see description above for interpretation.
+        val_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of test statistic values regarding adjacencies.
+        p_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of p-values regarding adjacencies.
+        parents : dictionary
+            Dictionary of form {0:[(0, -1), (3, -2), ...], 1:[], ...} containing
+            the estimated dummy parents of the system nodes.
+        """
+        lagged_context_parents = {i: list(dict.fromkeys(context_system_results['parents'][i] + lagged_parents[i])) for
+                                  i in range(self.N)}
+        dummy_parents = {i: [] for i in range(self.N)}
+        p_matrix = context_system_results['p_matrix']
+
+        # setup link assumptions without the observed context nodes
+        _link_assumptions_dummy = self.add_found_context_link_assumptions(link_assumptions, tau_max)
+        _int_link_assumptions = self._set_link_assumptions(_link_assumptions_dummy, tau_min, tau_max)
+
+        self.mode = "dummy_search"
+        if self.verbosity > 0:
+            print("\n##\n## J-PCMCI+ Step 3: Discovering dummy-system links\n##")
+            if _link_assumptions_dummy is not None:
+                print("\nWith link_assumptions = %s" % str(_int_link_assumptions))
+
+        skeleton_results_dummy = self._pcmciplus_mci_skeleton_phase(
+            lagged_context_parents, _int_link_assumptions, pc_alpha,
+            tau_min, tau_max, max_conds_dim, None,
+            max_conds_py, max_conds_px, max_conds_px_lagged,
+            reset_lagged_links, fdr_method,
+            self.p_matrix, self.val_matrix
+        )
+
+        skeleton_graph_dummy = skeleton_results_dummy['graph']
+
+        for j in self.system_nodes:
+            for k in range(tau_max + 1):
+                for dummy_node in self.time_dummy + self.space_dummy:
+                    if skeleton_graph_dummy[dummy_node, j, k] == 'o?o' or \
+                            skeleton_graph_dummy[dummy_node, j, k] == '-?>' or \
+                            skeleton_graph_dummy[dummy_node, j, k] == 'o-o' or \
+                            skeleton_graph_dummy[dummy_node, j, k] == '-->':
+                        dummy_parents[j].append((dummy_node, k))
+                for context_node in self.time_context_nodes + self.space_context_nodes:
+                    skeleton_results_dummy['val_matrix'][context_node, j, k] = context_system_results['val_matrix'][
+                        context_node, j, k]
+                    skeleton_results_dummy['val_matrix'][j, context_node, k] = context_system_results['val_matrix'][
+                        j, context_node, k]
+
+                    skeleton_results_dummy['p_matrix'][context_node, j, k] = p_matrix[context_node, j, k]
+                    skeleton_results_dummy['p_matrix'][j, context_node, k] = p_matrix[j, context_node, k]
+
+        return_dict = {'graph': skeleton_results_dummy['graph'], 'p_matrix': skeleton_results_dummy['p_matrix'],
+                       'val_matrix': skeleton_results_dummy['val_matrix'], 'parents': dummy_parents}
+
+        # Print the results
+        if self.verbosity > 0:
+            self.print_results(return_dict, alpha_level=pc_alpha)
+        return return_dict
+
+    def discover_system_system_links(self, link_assumptions,
+                                     lagged_context_dummy_parents,
+                                     tau_min=0,
+                                     tau_max=1,
+                                     pc_alpha=0.01,
+                                     reset_lagged_links=False,
+                                     max_conds_dim=None,
+                                     max_conds_py=None,
+                                     max_conds_px=None,
+                                     max_conds_px_lagged=None,
+                                     fdr_method='none'
+                                     ):
+        """
+        Step 4 of JPCMCIplus and orientation phase, i.e. discovery of links between system nodes given the knowledge
+        about their context parents through an application of PCMCIplus to this subset of nodes (system nodes).
+        See run_jpcmciplus for a description of the other parameters.
+
+
+        Parameters
+        ----------
+        lagged_context_dummy_parents : dictionary
+            Dictionary containing lagged and (dummy and observed) context parents of the system nodes estimated during
+            step 1 and step 2 of J-PCMCI+.
+
+        Returns
+        -------
+        graph : array of shape [N, N, tau_max+1]
+            Resulting causal graph, see description above for interpretation.
+        val_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of test statistic values regarding adjacencies.
+        p_matrix : array of shape [N, N, tau_max+1]
+            Estimated matrix of p-values regarding adjacencies.
+        sepset : dictionary
+            Separating sets. See paper for details.
+        ambiguous_triples : list
+            List of ambiguous triples, only relevant for 'majority' and
+            'conservative' rules, see paper for details.
+        """
+        self.mode = "system_search"
+
+        # Get the parents from run_pc_stable only on the system links
+        system_links = self.clean_system_link_assumptions(link_assumptions, tau_max)
+        # Set the selected links
+        _int_link_assumptions = self._set_link_assumptions(system_links, tau_min, tau_max)
+
+        if self.verbosity > 0:
+            print("\n##\n## J-PCMCI+ Step 4: Discovering system-system links \n##")
+            if system_links is not None:
+                print("\nWith link_assumptions = %s" % str(_int_link_assumptions))
+
+        skeleton_results = self._pcmciplus_mci_skeleton_phase(
+            lagged_context_dummy_parents, _int_link_assumptions, pc_alpha,
+            tau_min, tau_max, max_conds_dim, None,
+            max_conds_py, max_conds_px, max_conds_px_lagged,
+            reset_lagged_links, fdr_method,
+            self.p_matrix, self.val_matrix
+        )
+
+        return skeleton_results
+
+    def _remaining_pairs(self, graph, adjt, tau_min, tau_max, p):
+        """Helper function returning the remaining pairs that still need to be
+        tested depending on the JPCMCIplus step, i.e. discovery of context-system links (step 1),
+        dummy-context links (step 2) or system-system links in which case the function of the parent class is called.
+        """
+        all_context_nodes = self.time_context_nodes + self.space_context_nodes
+        if self.mode == "context_search":
+            # during discovery of context-system links we are only
+            # interested in context-context and context-system pairs
+            N = graph.shape[0]
+            pairs = []
+            for (i, j) in itertools.product(range(N), range(N)):
+                for abstau in range(tau_min, tau_max + 1):
+                    if (graph[i, j, abstau] != ""
+                            and len(
+                                [a for a in adjt[j] if a != (i, -abstau)]) >= p
+                            and i in all_context_nodes):
+                        pairs.append((i, j, abstau))
+            return pairs
+        elif self.mode == "dummy_search":
+            # during discovery of dummy-system links we are only
+            # interested in dummy-system pairs
+            N = graph.shape[0]
+            pairs = []
+            for (i, j) in itertools.product(range(N), range(N)):
+                for abstau in range(tau_min, tau_max + 1):
+                    if (graph[i, j, abstau] != ""
+                            and len(
+                                [a for a in adjt[j] if a != (i, -abstau)]) >= p
+                            and i in self.time_dummy + self.space_dummy):
+                        pairs.append((i, j, abstau))
+            return pairs
+        else:
+            return super()._remaining_pairs(graph, adjt, tau_min, tau_max, p)
+
+    def _run_pcalg_test(self, graph, i, abstau, j, S, lagged_parents, max_conds_py,
+                        max_conds_px, max_conds_px_lagged, tau_max, alpha_or_thres=None):
+        """MCI conditional independence tests within PCMCIplus or PC algorithm. Depending on the JPCMCIplus step
+        the setup is adapted slightly. During the discovery of dummy-system links (step 2) we are using
+        the dummy_ci_test and condition on the parents found during step 1; during the discovery of system-system links
+        (step 3) we are conditioning on the found contextual parents.
+        Parameters
+        ----------
+        graph : array
+            ...
+        i : int
+            Variable index.
+        abstau : int
+            Time lag (absolute value).
+        j : int
+            Variable index.
+        S : list
+            List of contemporaneous conditions.
+        lagged_parents : dictionary of lists
+            Dictionary of lagged parents for each node.
+        max_conds_py : int
+            Max number of lagged parents for node j.
+        max_conds_px : int
+            Max number of lagged parents for lagged node i.
+        max_conds_px_lagged : int
+            Maximum number of lagged conditions of X when X is lagged in MCI
+            tests. If None is passed, this number is equal to max_conds_px.
+        tau_max : int
+            Maximum time lag.
+        Returns
+        -------
+        val : float
+            Test statistic value.
+        pval : float
+            Test statistic p-value.
+        Z : list
+            List of conditions.
+        """
+
+        if self.mode == 'dummy_search':
+            # during discovery of dummy-system links we condition on the found contextual parents from step 1.
+            cond = list(S) + self.observed_context_parents[j]
+            cond = list(dict.fromkeys(cond))  # remove overlapps
+            return super()._run_pcalg_test(graph, i, abstau, j, cond, lagged_parents, max_conds_py,
+                                           max_conds_px, max_conds_px_lagged, tau_max, alpha_or_thres)
+
+        elif self.mode == 'system_search':
+            # during discovery of system-system links we are conditioning on the found contextual parents
+            cond = list(S) + self.dummy_parents[j] + self.observed_context_parents[j]
+            cond = list(dict.fromkeys(cond))  # remove overlapps
+            return super()._run_pcalg_test(graph, i, abstau, j, cond, lagged_parents, max_conds_py,
+                                           max_conds_px, max_conds_px_lagged, tau_max, alpha_or_thres)
+        else:
+            return super()._run_pcalg_test(graph, i, abstau, j, S, lagged_parents, max_conds_py,
+                                           max_conds_px, max_conds_px_lagged, tau_max, alpha_or_thres)
+
+if __name__ == '__main__':
+    # Imports
+    from numpy.random import SeedSequence, default_rng
+
+    import tigramite
+    from tigramite.toymodels import structural_causal_processes as toys
+    from tigramite.toymodels.context_model import ContextModel
+    # from tigramite.jpcmciplus import JPCMCIplus
+    from tigramite.independence_tests.parcorr_mult import ParCorrMult
+    import tigramite.data_processing as pp
+    import tigramite.plotting as tp
+
+
+    # Set seeds for reproducibility
+    ss = SeedSequence(12345)
+    child_seeds = ss.spawn(2)
+
+    model_seed = child_seeds[0]
+    context_seed = child_seeds[1]
+
+    random_state = np.random.default_rng(model_seed)
+
+    # Choose the time series length and number of spatial contexts
+    T = 100
+    nb_domains = 50
+
+    transient_fraction=0.2
+    tau_max = 2
+    frac_observed = 0.5
+
+    # Specify the model
+    def lin(x): return x
+
+    links = {0: [((0, -1), 0.3, lin), ((3, -1), 0.7, lin), ((4, 0), 0.9, lin)],
+             1: [((1, -1), 0.4, lin), ((3, -1), 0.8, lin)],
+             2: [((2, -1), 0.3, lin), ((1, 0), -0.5, lin), ((4, 0), 0.5, lin), ((5, 0), 0.6, lin)] ,
+             3: [], 
+             4: [], 
+             5: []
+                }
+
+    # Specify which node is a context node via node_type (can be "system", "time_context", or "space_context")
+    node_classification = {
+        0: "system",
+        1: "system",
+        2: "system",
+        3: "time_context",
+        4: "time_context",
+        5: "space_context"
+    }
+
+    # Specify dynamical noise term distributions, here unit variance Gaussians
+    #random_state = np.random.RandomState(seed)
+    noises = [random_state.standard_normal for j in range(6)]
+
+    contextmodel = ContextModel(links=links, node_classification=node_classification,
+                                noises=noises, 
+                                seed=context_seed)
+
+    data_ens, nonstationary = contextmodel.generate_data(nb_domains, T)
+
+    assert not nonstationary
+
+    system_indices = [0,1,2]
+    # decide which context variables should be latent, and which are observed
+    observed_indices_time = [4]
+    latent_indices_time = [3]
+
+    observed_indices_space = [5]
+    latent_indices_space = []
+
+    # all system variables are also observed, thus we get the following observed data
+    observed_indices = system_indices + observed_indices_time + observed_indices_space
+    data_observed = {key: data_ens[key][:,observed_indices] for key in data_ens}
+
+
+    # Add one-hot-encoding of time-steps and dataset index to the observational data. 
+    # These are the values of the time and space dummy variables.
+    dummy_data_time = np.identity(T)
+
+    data_dict = {}
+    for i in range(nb_domains):
+        dummy_data_space = np.zeros((T, nb_domains))
+        dummy_data_space[:, i] = 1.
+        data_dict[i] = np.hstack((data_observed[i], dummy_data_time, dummy_data_space))
+
+    # Define vector-valued variables including dummy variables as well as observed (system and context) variables
+    nb_observed_context_nodes = len(observed_indices_time) + len(observed_indices_space)
+    N = len(system_indices)
+    process_vars = system_indices
+    observed_temporal_context_nodes = list(range(N, N + len(observed_indices_time)))
+    observed_spatial_context_nodes = list(range(N + len(observed_indices_time), 
+                                                N + len(observed_indices_time) + len(observed_indices_space)))
+    time_dummy_index = N + nb_observed_context_nodes
+    space_dummy_index = N + nb_observed_context_nodes + 1
+    time_dummy = list(range(time_dummy_index, time_dummy_index + T))
+    space_dummy = list(range(time_dummy_index + T, time_dummy_index + T + nb_domains))
+
+    vector_vars = {i: [(i, 0)] for i in process_vars + observed_temporal_context_nodes + observed_spatial_context_nodes}
+    vector_vars[time_dummy_index] = [(i, 0) for i in time_dummy]
+    vector_vars[space_dummy_index] = [(i, 0) for i in space_dummy]
+
+    # Name all the variables and initialize the dataframe object
+    # Be careful to use analysis_mode = 'multiple'
+    sys_var_names = ['X_' + str(i) for i in process_vars]
+    context_var_names = ['t-C_'+str(i) for i in observed_indices_time] + ['s-C_'+str(i) for i in observed_indices_space]
+    var_names = sys_var_names + context_var_names + ['t-dummy', 's-dummy']
+
+    dataframe = pp.DataFrame(
+        data=data_dict,
+        vector_vars = vector_vars,
+        analysis_mode = 'multiple',
+        var_names = var_names
+        )
+
+
+    # Classify all the nodes into system, context, or dummy
+    node_classification_jpcmci = {i: node_classification[var] for i, var in enumerate(observed_indices)}
+    node_classification_jpcmci.update({time_dummy_index : "time_dummy", space_dummy_index : "space_dummy"})
+
+    # Create a J-PCMCI+ object, passing the dataframe and (conditional)
+    # independence test objects, as well as the observed temporal and spatial context nodes 
+    # and the indices of the dummies.
+    JPCMCIplus = JPCMCIplus(dataframe=dataframe,
+                              cond_ind_test=ParCorrMult(significance='analytic'), 
+                              node_classification=node_classification_jpcmci,
+                              verbosity=1,)
+
+    # Define the analysis parameters.
+    tau_max = 2
+    pc_alpha = 0.01
+
+    # Run J-PCMCI+
+    results = JPCMCIplus.run_jpcmciplus(tau_min=0, 
+                                  tau_max=tau_max, 
+                                  pc_alpha=pc_alpha)
\ No newline at end of file
diff --git a/tigramite/toymodels/context_model.py b/tigramite/toymodels/context_model.py
new file mode 100644
index 00000000..e6211da7
--- /dev/null
+++ b/tigramite/toymodels/context_model.py
@@ -0,0 +1,261 @@
+import numpy as np
+
+from tigramite.toymodels import structural_causal_processes as toys
+
+
+def _nb_latent_before(node, observed_context_indices, node_classification):
+    return len(
+        [el for el in range(node) if not (el in observed_context_indices or node_classification[el] == "system")])
+
+def _do_dummy_projection(links, node_classification, observed_context_indices, time_dummy_index, space_dummy_index):
+    """
+    Helper function to augment the true_parents by remove context-context links and adding dummy
+    (i.e. perform dummy projection)
+
+    links : dictionary
+        Ground truth links
+    node_classification : dictionary
+        Corresponds to ground truth links
+    """
+
+    # remove latent links, shift remaining, add dummy
+    augmented_links = {}
+    for node in node_classification.keys():
+        if node_classification[node] == "system":
+            keep_parents = []
+            for parent in links[node]:
+                if node_classification[parent[0][0]] == "system":
+                    keep_parents.append(parent)
+                elif node_classification[parent[0][0]] == "time_context":
+                    if parent[0][0] in observed_context_indices:
+                        keep_parents.append(((parent[0][0] - _nb_latent_before(parent[0][0], observed_context_indices,
+                                                                               node_classification),
+                                              parent[0][1]), parent[1], parent[2]))
+                    else:
+                        keep_parents.append(((time_dummy_index, 0), 1., "dummy"))
+                elif node_classification[parent[0][0]] == "space_context":
+                    if parent[0][0] in observed_context_indices:
+                        keep_parents.append(((parent[0][0] - _nb_latent_before(parent[0][0], observed_context_indices,
+                                                                               node_classification), parent[0][1]),
+                                             parent[1], parent[2]))
+                    else:
+                        keep_parents.append(((space_dummy_index, 0), 1., "dummy"))
+                augmented_links[node] = list(dict.fromkeys(keep_parents))
+
+        # remove all parents of context nodes
+        elif node_classification[node] == "time_context":
+            if node in observed_context_indices:
+                augmented_links[node - _nb_latent_before(node, observed_context_indices, node_classification)] = []
+        elif node_classification[node] == "space_context":
+            if node in observed_context_indices:
+                augmented_links[node - _nb_latent_before(node, observed_context_indices, node_classification)] = []
+
+    augmented_links[time_dummy_index] = []
+    augmented_links[space_dummy_index] = []
+    return augmented_links
+
+def _shift_link_entries(links, const):
+    """
+    Helper Function to shift keys and values of a link dictionary by an integer constant.
+    """
+    shifted_links = {}
+    for key in links.keys():
+        shifted_key = key + const
+        values = links[key]
+        shifted_values = [((item + const, lag), c, f) for ((item, lag), c, f) in values]
+        shifted_links[shifted_key] = shifted_values
+    return shifted_links
+
+def _group_links(links, node_types, node_type):
+    return {i: links[i] for i in links.keys() if node_types[i] == node_type}
+
+
+class ContextModel:
+    """Allows to sample from a joint structural causal model over different spatial
+        and temporal contexts. Restricts temporal and spatial context nodes to be constant over datasets or
+        time, respectively.
+
+        Parameters
+        ----------
+        links : dict
+            Dictionary of format: {0:[((i, -tau), coeff, func),...], 1:[...],
+            ...} for all variables where i must be in [0..N-1] and tau >= 0 with
+            number of variables N. coeff must be a float and func a python
+            callable of one argument.
+        node_classification : dictionary
+            Classification of nodes into system, or context nodes.
+            Keys of the dictionary are from {0, ..., N-1} where N is the number of nodes.
+            Options for the values are "system", "time_context", "space_context". The temporal context variables are
+            assumed exogenous to the system variables. They cannot interact with the spatial context variables due
+            to the assumption that they are constant across datasets.
+        transient_fraction : float
+            Added percentage of T used as a transient. In total a realization of length
+            (transient_fraction + 1)*T will be generated, but then transient_fraction*T will be
+            cut off.
+        noises : list of callables or list of arrays, optional (default: None)
+            Random distribution function that is called with noises[j](T) for system and time-context nodes, or
+            noises[j](M) for space-context nodes where M is the number of datasets. If list of arrays,
+            for noises corresponding to time-context and system variables the array needs to be of
+            shape ((transient_fraction + 1)*T, ), for space-context variables it needs to be of shape (M, )
+        seed : int, optional (default: None)
+            Random seed.
+
+        Attributes
+        -------
+        links_tc : dict
+            Dictionary of format: {0:[((i, -tau), coeff, func),...], 1:[...],
+            ...} for all temporal context variables where i must be in [0..N-1] and tau >= 0 with
+            number of variables N. coeff must be a float and func a python
+            callable of one argument. The temporal context variables are assumed exogenous
+            to the system variables. They cannot interact with the spatial context variables due to the assumption
+            that they are constant across datasets.
+        links_sc : dict
+            Dictionary of format: {0:[((i, -tau), coeff, func),...], 1:[...],
+            ...} for all spatial context variables where i must be in [0..N-1] and tau >= 0 with
+            number of variables N. coeff must be a float and func a python
+            callable of one argument. The spatial context variables are assumed exogenous
+            to the system variables. They cannot interact with the temporal context variables due to the assumption
+            that they are time-independent, i.e. constant across time.
+        links_sys : dict
+            Dictionary of format: {0:[((i, -tau), coeff, func),...], 1:[...],
+            ...} for all system variables where i must be in [0..N-1] and tau >= 0 with
+            number of variables N. coeff must be a float and func a python
+            callable of one argument.
+        transient_fraction : float
+            Added percentage of T used as a transient. In total a realization of length
+            (transient_fraction + 1)*T will be generated, but then transient_fraction*T will be
+            cut off.
+        noises : list of callables or list of arrays, optional (default: None)
+            Random distribution function that is called with noises[j](T) for system and time-context nodes, or
+            noises[j](M) for space-context nodes where M is the number of datasets. If list of arrays,
+            for noises corresponding to time-context and system variables the array needs to be of
+            shape ((transient_fraction + 1)*T, ), for space-context variables it needs to be of shape (M, )
+        seed : int, optional (default: None)
+            Random seed.
+
+        """
+
+    def __init__(self, links={}, node_classification={}, transient_fraction=0.2, noises=None, seed=None):
+        self.links_tc = _group_links(links, node_classification, "time_context")
+        self.links_sc = _group_links(links, node_classification, "space_context")
+        self.links_sys = _group_links(links, node_classification, "system")
+
+        self.N = len(self.links_sys.keys())
+        self.noises = noises
+        self.seed = seed
+        self.transient_fraction = transient_fraction
+
+
+
+    def _constant_over_space(self, data_tc, M):
+        data_tc_list = [data_tc for _ in range(M)]
+        return data_tc_list
+
+    def _constant_over_time(self, data_sc, T, M, shift):
+        data_sc_list = [{i: np.repeat(data_sc[m, i-shift], T) for i in self.links_sc.keys()} for m in
+                        range(M)]
+        return data_sc_list
+
+    def _generate_temporal_context_data(self, links_tc, T, M, seed):
+        """
+        Helper Function to generate data for the temporal context nodes. It essentially is a
+        wrapper around toys.structural_causal_process to generate data that is random across time
+        but constant across datasets.
+        """
+        if self.noises is not None:
+            noises_tc = [self.noises[key] for key in links_tc.keys()]
+            if np.all([isinstance(el, np.ndarray) for el in noises_tc]):
+                noises_tc = np.stack(noises_tc).transpose()
+        else:
+            noises_tc = None
+        shifted_links_tc = _shift_link_entries(links_tc, -self.N)
+        data_tc, nonstat_tc = toys.structural_causal_process(shifted_links_tc, T=T, noises=noises_tc,
+                                                             transient_fraction=self.transient_fraction,
+                                                             seed=seed)
+        data_tc = {i: data_tc[:, i - self.N] for i in links_tc.keys()}
+
+        data_tc_list = self._constant_over_space(data_tc, M)
+        return data_tc_list, np.any(nonstat_tc)
+
+    def _generate_spatial_context_data(self, links_sc, T, M, shift, seed):
+        """
+        Helper Function to generate data for the spatial context nodes. It essentially is a
+        wrapper around toys.structural_causal_process to generate data that is random across datasets
+        but constant across time.
+        """
+        shifted_links_sc = _shift_link_entries(links_sc, -shift)
+        if self.noises is not None:
+            noises_sc = [self.noises[key] for key in links_sc.keys()]
+            if np.all([isinstance(el, np.ndarray) for el in noises_sc]):
+                noises_sc = np.stack(noises_sc).transpose()
+        else:
+            noises_sc = None
+
+        data_sc, nonstat_sc = toys.structural_causal_process(shifted_links_sc, T=M, noises=noises_sc,
+                                                             transient_fraction=0.,
+                                                             seed=seed)
+
+        data_sc_list = self._constant_over_time(data_sc, T, M, shift)
+        return data_sc_list, np.any(nonstat_sc)
+
+    def generate_data(self, M, T):
+        """
+        Generates M datasets of time series generated from a joint structural causal model over different spatial
+        and temporal contexts.
+
+         Parameters
+         ----------
+         M : int
+            Number of datasets.
+         T : int
+            Sample size.
+
+        Returns
+        ----------
+        data : dictionary with array-like values
+            Datasets generated from this process, each dataset has the shape (T, N).
+        nonvalid : bool
+            Indicates whether data has NaNs or infinities.
+        """
+
+        links = {**self.links_tc, **self.links_sc, **self.links_sys}
+
+        K_time = len(self.links_tc.keys())
+
+        data = {}
+        nonstationary = []
+
+        time_seed = [1, self.seed]
+        space_seed = [2, self.seed]
+        system_seed = [3, self.seed]
+
+        # first generate data for temporal context nodes
+        data_tc_list, nonstat_tc = self._generate_temporal_context_data(self.links_tc, T, M, time_seed)
+
+        # generate spatial context data (constant in time)
+        data_sc_list, nonstat_sc = self._generate_spatial_context_data(self.links_sc,
+                                                                       T, M,
+                                                                       K_time + self.N,
+                                                                       space_seed)
+        for m in range(M):
+            data_context = dict(data_tc_list[m])
+            data_context.update(data_sc_list[m])
+
+            if self.noises is not None:
+                noises_filled = self.noises
+                if np.all([isinstance(el, np.ndarray) for el in self.noises]):
+                    noises_filled = np.copy(self.noises)
+                    for key in self.links_sc.keys():
+                        # fill up any space-context noise to have T entries, then convert to numpy array
+                        noises_filled[key] = np.random.standard_normal(len(self.noises[list(self.links_sys.keys())[0]]))
+                    noises_filled = np.stack(noises_filled).transpose()
+            else:
+                noises_filled = None
+
+            # generate system data that varies over space and time
+            data_m, nonstat = toys.structural_causal_process(links, T=T, intervention=data_context,
+                                                             transient_fraction=self.transient_fraction,
+                                                             seed=system_seed, noises=noises_filled)
+            data[m] = data_m
+            nonstationary.append(nonstat or nonstat_tc or nonstat_sc)
+        return data, np.any(nonstationary)
diff --git a/tutorials/causal_discovery/figures/intro_jpcmci.png b/tutorials/causal_discovery/figures/intro_jpcmci.png
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diff --git a/tutorials/causal_discovery/tigramite_tutorial_jpcmciplus.ipynb b/tutorials/causal_discovery/tigramite_tutorial_jpcmciplus.ipynb
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@@ -0,0 +1,968 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4dedf0c6",
+   "metadata": {},
+   "source": [
+    "# J(oint)-PCMCI$^+$: Causal discovery for time series from multiple (possibly latent) contexts\n",
+    "\n",
+    "TIGRAMITE is a time series analysis python module. It allows to reconstruct causal graphical models from discrete or continuously-valued time series based on the PCMCI framework and create high-quality plots of the results.\n",
+    "\n",
+    "This tutorial explains the Joint-PCMCI$^+$ (J-PCMCI$^+$) algorithm, which is implemented as the function ```J_PCMCI.run_jpcmciplus```. J-PCMCI$^+$ is specifically designed to handle time series data from different datasets that include time series observations of the same variables (system-variables), but that were collected in different contexts. While also the PCMCI and PCMCIplus algorithms can handle multiple datasets by simply pooling them, J-PCMCI+ allows to characterize the contexts by specific context-variables and can learn 1) how these contexts cause the system variables and 2) deconfound spurious system-variable links due to observed as well as latent contexts.\n",
+    "\n",
+    "* * * * *\n",
+    "Publication on J-PCMCI$^+$: Günther, Wiebke, and Ninad, Urmi and Runge, Jakob (2023). Causal discovery for time series from multiple datasets with latent contexts. Accepted at 39th Conference on Uncertainty in Artificial Intelligence 2023.\n",
+    "https://arxiv.org/abs/2306.12896\n",
+    "* * * * *\n",
+    "\n",
+    "The tutorial covers the following:\n",
+    "1. Motivation and basic concepts\n",
+    "2. The algorithm\n",
+    "3. Application example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "115a35a2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:19.654759431Z",
+     "start_time": "2023-07-28T08:52:19.594341582Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import numpy as np\n",
+    "from numpy.random import SeedSequence\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import tigramite\n",
+    "from tigramite.toymodels import structural_causal_processes as toys\n",
+    "from tigramite.toymodels.context_model import ContextModel\n",
+    "from tigramite.jpcmciplus import JPCMCIplus\n",
+    "from tigramite.independence_tests.parcorr_mult import ParCorrMult\n",
+    "import tigramite.data_processing as pp\n",
+    "import tigramite.plotting as tp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47ad21d6",
+   "metadata": {},
+   "source": [
+    "## 1. Motivation and basic concepts\n",
+    "\n",
+    "We are interested in discovering the causal graph between variables that we have observed within different contexts, i.e., we have different datasets with observational time series of these variables (see schematic below). For instance, such contexts can be different locations at which we have observed these variables, such as time series of river runoff and local weather variables from different catchments. \n",
+    "\n",
+    "<img style=\"float: left; margin:10px 30px 0 0\" src=\"./figures/intro_jpcmci.png\" alt= “” width=\"30%\" height=\"30%\">\n",
+    "\n",
+    "In this example, the local catchment systems share certain causal influences, such as time-dependent large-scale weather over all catchments, but differ in other catchment-specific but constant-in-time drivers, such as the altitude of the catchment. We call these drivers ***temporal*** and ***spatial contexts*** ($\\tilde{C}_\\text{time}$ and $\\tilde{C}_\\text{space}$ in the schematic), respectively. They differ from the ***system variables*** (runoff, local weather variables such as precipitation) in the sense that some background knowledge on their structure and on their relationship to the system variables is available. In particuar, we assume the context variables to be either constant in time and varying over the datasets, or varying in time and constant over the datasets. Furthermore, we assume them to be exogenous to the system variables.\n",
+    "\n",
+    "These assumptions allow us to learn how contexts affect system variables as well as overcome latent confounding that is due to unobserved context variables by pooling the datasets and considering the joint causal graph among system, context, and certain auxiliary variables as we will see in more detail in the following sections of this tutorial.\n",
+    "\n",
+    "\n",
+    "\n",
+    "***To summarize***, in this tutorial we take a look at J-PCMCI$^+$, which is a consistent causal discovery algorithm that can:\n",
+    "\n",
+    "   1. de-confound those system nodes that are confounded by latent contexts without having any knowledge of the latent contexts themselves;\n",
+    "   2. retain as much information about the causal links between the observed  context and system variables as possible by checking conditional independencies appropriately;\n",
+    "   3. discover the correct induced causal graph between the system nodes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0ac9511",
+   "metadata": {},
+   "source": [
+    "### 1.1 The joint time-dependent  structural causal model\n",
+    "\n",
+    "To formalize the ideas presented above, we assume that the observed data was generated by an underlying mechanism across datasets $d\\in \\mathcal{D}$ with $|\\mathcal{D}|=M$ that can be represented by an acyclic time-dependent structural causal model (SCM) involving the time-dependent system variables $\\mathbf{X}_t=\\{X_t^i\\}_{i\\in \\mathcal{I}}$ at time $t$ as well as context variables $\\tilde{\\mathbf{C}}=\\tilde{\\mathbf{C}}_{time}\\dot{\\cup} \\ \n",
+    "    \\tilde{\\mathbf{C}}_{space}$ with temporal contexts $\\tilde{\\mathbf{C}}_{time,t}=\\{\\tilde{C}_t^k\\}_{k\\in \\mathcal{K}_{\\rm time}}$ and (time-independent) spatial-contexts $\\tilde{\\mathbf{C}}_{space}=\\{\\tilde{C}^l\\}_{l\\in \\mathcal{K}_{\\rm space}}$, for $i\\in \\mathcal{I}$:\n",
+    "    \n",
+    "\\begin{aligned}\n",
+    "            &\\mathbf{X}^{d}_t:= \\mathbf{f}({Pa}_X(\\mathbf{X}^{d}_t),\\operatorname{Pa}_{\\tilde{C}_{\\rm time}}(\\mathbf{X}^{d}_t), \\operatorname{Pa}_{\\tilde{C}_{\\rm space}}(\\mathbf{X}^{d}_t), \\boldsymbol{\\eta}^{d}_t) \\\\ \n",
+    "            &\\mathbf{\\tilde{C}}_{{\\rm time}, t} := \\mathbf{g}({Pa}_{\\tilde{C}_{\\rm time}}(\\mathbf{\\tilde{C}}_{{\\rm time}, t}), \\boldsymbol{\\eta}_{{\\rm time}, t}) \\\\\n",
+    "            &\\mathbf{\\tilde{C}}^{d}_{\\rm space} := \\mathbf{h}({Pa}_{\\tilde{C}_{\\rm space}}(\\mathbf{\\tilde{C}}_{\\rm space}), \\boldsymbol{\\eta}_{\\rm space}^{d})\n",
+    "\\end{aligned}\n",
+    " where the exogenous noise variables $(\\boldsymbol{\\eta}^{d}_t, \\boldsymbol{\\eta}_{{\\rm time}, t}, \\boldsymbol{\\eta}_{\\rm space}^{d})$ are jointly independent and $\\eta^{i,d}_{t}$ are identically distributed across time and space, $\\eta^k_{{\\rm time},t}$ are identically distributed across time, and $\\eta^{l,d}_{\\rm space}$ are identically distributed across space. $\\operatorname{Pa}_X$ denotes the causal parents within $\\mathbf{X}$, and analogously for $\\operatorname{Pa}_{\\tilde{C}_{\\rm time}}$ and $\\operatorname{Pa}_{\\tilde{C}_{\\rm space}}$.\n",
+    " \n",
+    "Note that we assume that the context variables are exogeneous to the system. In other words, system variables cannot be parents of context variables.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3662ec25",
+   "metadata": {},
+   "source": [
+    "### 1.2 Context variables and dummy variables\n",
+    "\n",
+    "Let us take a closer look at the context variables $\\tilde{\\mathbf{C}}_\\text{time}$ and $\\tilde{\\mathbf{C}}_\\text{space}$ in the SCM above. \n",
+    "\n",
+    "A temporal context variable $C^i_\\text{time} \\in \\tilde{\\mathbf{C}}_\\text{time}$ is a time-dependent random variable that remains the same across datasets. A spatial context variable $C^i_\\text{space} \\in \\tilde{\\mathbf{C}}_\\text{space}$ is a random variable that is constant over time and within a dataset but can change across datasets. (A context that various over both time and datasets is rather a system variable.) In our motivational example,$C^i_\\text{time}$ could represent large-scale weather dynamics that affect multiple observational sites at once. Another example could be the economic development in a country over a time that affects all individuals living in that country. An example for $C^i_\\text{space}$ could be the average slope in a river catchment or the height of an individual. \n",
+    "We think of the context variables as corresponding to real-world phenomena. \n",
+    "\n",
+    "However, J-PCMCI+ can also deal with unobserved context variables. This is achieved through auxiliary variables, so-called dummies that mimic the structure of context variables. These dummy variables label each time-step or dataset, respectively (and thus the time dummy has a unique value for each time step, and the space dummy has a unique value within each dataset). Generally, their values can not be interpreted as a physical quantity. In that sense, the dummy variable is not a causal variable itself. They are intended to be a placeholder for the unobserved context variables, in order to capture confounding between system variables due to latent context variables. \n",
+    "\n",
+    "Within J-PCMCI+, the dummy variables are encoded using one-hot vectors, i.e., for the time dummy the first time point has the corresponding dummy-value $(1,0,\\ldots,0)^T \\in \\{ 0,1 \\}^T$, the second time point corresponds to $(0,1,\\ldots,0)^T$, and so on. For the space dummy dataset $1$ corresponds to the dummy-value $(1,0,\\ldots,0)^T \\in \\{ 0,1 \\}^M$, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "765a5d57",
+   "metadata": {},
+   "source": [
+    "## 2. The algorithm\n",
+    "\n",
+    "J-PCMCI+ extends the PCMCI+ time series causal discovery algorithm to the case of datasets from multiple dataset- or time dependent contexts with potentially unobserved context confounders of the system variables. It does so by combining PCMCI+ with the JCI-approach [1], i.e., pooling datasets from multiple contexts, and adding observed context variables to the graph. Furthermore, it uses time- and space-dummy variables to account for latent context variables that confound system variables, similar to [2].\n",
+    "\n",
+    "\n",
+    "The method consists of four main steps: one $\\text{PC}_1$ lagged phase and three MCI phases similar to PCMCI+. \n",
+    "\n",
+    "1. ***$\\text{PC}_1$ lagged discovery phase: Discovery of supersets of the lagged parents of the system and observed temporal context nodes on all pairs $((C^i_{t-\\tau}, X^j_t))_{\\tau > 0}$ and $((X^i_{t-\\tau}, X^j_t))_{\\tau > 0}$, as well as $((C^i_{t-\\tau}, C^i_t))_{\\tau > 0}$.*** (The last set of pairs covers autocorrelation in time-contexts, spatial context nodes do not carry temporal information and can be ignored here.)\n",
+    "\n",
+    "2. ***MCI skeleton phase on context-system pairs conditional on subsets of system and context nodes***, i.e. perform MCI tests for pairs $((C^i_{t-\\tau}, X^j_t))_{\\tau \\geq 0}$,\n",
+    "   $$\n",
+    "         C_{t-\\tau}^i \\perp\\!\\!\\!\\!\\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^-_t(X_t^j)  \\setminus \\{ C_{t-\\tau}^i \\}, \\hat{\\mathcal{B}}^-_{t-\\tau}(C_{t-\\tau}^i)\n",
+    "   $$\n",
+    "with $\\mathbf{S}$ denoting a subset of the contemporaneous adjacencies $\\mathcal{A}_t(X_t^j)$ (among system or time-context variables) and where $\\hat{\\mathcal{B}}^-_t(X_t^j)$ are the lagged adjacencies from step one. If $C$ is a spatial context variable, we only have to test the contemporaneous pairs $(C_t^j, X_t^i)$, $(X_t^i, C_t^j)$ for all $i,j$. Remember that the space-context nodes are constant over time and therefore essentially don't have a time-dimension associated with them (so we could also drop the time index, i.e. $C^j_t = C^j$). That means there is an edge $C^j \\rightarrow X_{t}^i$ in the joint time series graph if and only if there also exist edges $C^j \\rightarrow X_{t-\\tau}^i$ for all $\\tau > 0$. Thus, if $C^j$ and $X_t^i$ are conditionally independent, all these links between $C^j$ and $X^i_{t-\\tau}$ are also removed for all $\\tau$.\n",
+    "\n",
+    "3. ***MCI skeleton phase on all system-dummy pairs conditional on the superset of lagged links, the discovered contemporaneous context adjacencies, as well as on subsets of contemporaneous system links***, i.e. test for $(D, X_t^i)$, $(X_t^i, D)$ for all $i$ where $D$ stands for the time- and space-dummy respectively, i.e. test\n",
+    "    $$\n",
+    "    D \\perp\\!\\!\\!\\!\\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^C_t(X_t^j)\n",
+    "    $$\n",
+    "    where $\\mathbf{S} \\subset \\mathcal{A}_t(X_t^i)$ and $\\hat{\\mathcal{B}}^C_t(X_t^j)$ are the lagged and contextual adjacencies found in the previous step.\n",
+    "If $D$ and $X_t^j$ are found to be conditionally independence, links between $D$ and $X^j_{t-\\tau}$ are removed for all $\\tau$.\n",
+    "Using the exogeneity assumption, the context node is the parent in all system-context links.\n",
+    "\n",
+    "4. ***MCI skeleton phase on all system pairs conditional on discovered lagged, context and dummy adjacencies, as well as on subsets of contemporaneous system links and orientation phase***. In more detail, we perform MCI test for pairs $((X^j_{t-\\tau}, X_t^i))_{\\tau \\geq 0}$, $(X_t^i, X_t^j)$ for all $i, j$, i.e.\n",
+    "    $$\n",
+    "     X^i_{t-\\tau} \\perp\\!\\!\\!\\!\\perp X_t^j | \\mathbf{S}, \\hat{\\mathcal{B}}^{CD}_t(X_t^j)  \\setminus \\{ X_{t-\\tau}^i \\},  \\hat{\\mathcal{B}}^{CD}_t(X_{t-\\tau}^i) \n",
+    "    $$\n",
+    "    where $\\mathbf{S} \\subset \\mathcal{A}_t(X_t^i)$ and $\\hat{\\mathcal{B}}^{CD}_t(X_t^j)$ are the lagged, contextual, and dummy adjacencies found in the previous steps.\n",
+    "Finally, all remaining edges (without expert knowledge) are oriented using the PCMCI$^+$ orientation phase while making use of all triples involving at most one context or dummy variable and two or three system variables as in the non-time series case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb0a30a",
+   "metadata": {},
+   "source": [
+    "## 3. Application Example\n",
+    "This section demonstrates and explains the application of J-PCMCI+ on synthetic data.\n",
+    "\n",
+    "### 3.1 Setup \n",
+    "Here we choose the random seed, how large of a sample we want to generate, and some other hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "53886d51",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:19.696591899Z",
+     "start_time": "2023-07-28T08:52:19.611299475Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set seeds for reproducibility\n",
+    "seed = 12345\n",
+    "ss = SeedSequence(seed)\n",
+    "noise_seed = ss.spawn(1)[0]\n",
+    "\n",
+    "random_state = np.random.default_rng(noise_seed)\n",
+    "\n",
+    "# Choose the time series length and number of spatial contexts\n",
+    "T = 100\n",
+    "nb_domains = 50\n",
+    "\n",
+    "transient_fraction=0.2\n",
+    "tau_max = 2\n",
+    "frac_observed = 0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4e44d49",
+   "metadata": {},
+   "source": [
+    "### 3.2 Data generation\n",
+    "For generating synthetic data, we consider a specific version of the time-dependent SCM (1). Namely, within each dataset $d$ at time point $t$:\n",
+    "\\begin{aligned}\n",
+    "X^{1,d}_t &= 0.3X^{1,d}_{t-1} + 0.6C^4_t + 0.9L_{t-1}^5 + \\eta^{1,d}_t, \\\\\n",
+    "X^{2,d}_t &= 0.4X^{2,d}_{t-1} + 0.4C^4_{t-1} + \\eta^{2,d}_t, \\\\\n",
+    "X^{3,d}_t &= 0.3X^{3,d}_{t-1} - 0.5X^{2,d}_{t-2} + 0.5L_{t-1}^5 + 0.6C^6 + \\eta^{3,d}_t, \\\\\n",
+    "C^4_t &= \\eta^4_t, \\\\\n",
+    "L_t^{5} &= \\eta^{5}_t, \\\\\n",
+    "C^{6,d} &= \\eta^{5,d}, \\\\\n",
+    "\\end{aligned}\n",
+    "where the temporal context variables $C_t^4$ and $L_t^5$ have the same value within each dataset, and the spatial context variable $C^6$ is constant over time, $L_t^5$ is a hidden temporal context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fc59ca73",
+   "metadata": {
+    "scrolled": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:19.792112175Z",
+     "start_time": "2023-07-28T08:52:19.622262903Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify the model\n",
+    "def lin(x): return x\n",
+    "\n",
+    "links = {0: [((0, -1), 0.3, lin), ((3, -1), 0.6, lin), ((4, -1), 0.9, lin)],\n",
+    "         1: [((1, -1), 0.4, lin), ((3, -1), 0.4, lin)],\n",
+    "         2: [((2, -1), 0.3, lin), ((1, -2), -0.5, lin), ((4, -1), 0.5, lin), ((5, 0), 0.6, lin)] ,\n",
+    "         3: [], \n",
+    "         4: [], \n",
+    "         5: []\n",
+    "            }\n",
+    "\n",
+    "# Specify which node is a context node via node_type (can be \"system\", \"time_context\", or \"space_context\")\n",
+    "node_classification = {\n",
+    "    0: \"system\",\n",
+    "    1: \"system\",\n",
+    "    2: \"system\",\n",
+    "    3: \"time_context\",\n",
+    "    4: \"time_context\",\n",
+    "    5: \"space_context\"\n",
+    "}\n",
+    "\n",
+    "# Specify dynamical noise term distributions, here unit variance Gaussians\n",
+    "noises = [random_state.standard_normal for j in range(6)]\n",
+    "\n",
+    "contextmodel = ContextModel(links=links, node_classification=node_classification,\n",
+    "                            noises=noises, \n",
+    "                            seed=seed)\n",
+    "\n",
+    "data_ens, nonstationary = contextmodel.generate_data(nb_domains, T)\n",
+    "\n",
+    "assert not nonstationary\n",
+    "\n",
+    "system_indices = [0,1,2]\n",
+    "# decide which context variables should be latent, and which are observed\n",
+    "observed_indices_time = [4]\n",
+    "latent_indices_time = [3]\n",
+    "\n",
+    "observed_indices_space = [5]\n",
+    "latent_indices_space = []\n",
+    "\n",
+    "# all system variables are also observed, thus we get the following observed data\n",
+    "observed_indices = system_indices + observed_indices_time + observed_indices_space\n",
+    "data_observed = {key: data_ens[key][:,observed_indices] for key in data_ens}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7707cc1e",
+   "metadata": {},
+   "source": [
+    "We have a quick look at the ground truth graph that we now have specified and which corresponds to the SCM (1) above. For that we use the function ```tp.plot_graph```. The observed temporal and spatial context nodes are labelled as t-$C_i$ and s-$C_i$, respectively. Their latent counterparts are labelled as t-$L_i$ and s-$L_i$, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e8869b75",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:20.702378582Z",
+     "start_time": "2023-07-28T08:52:19.835579500Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gt_graph = toys.links_to_graph(links, tau_max=tau_max)\n",
+    "\n",
+    "sys_var_names = ['$X_%s$' % str(i) for i in system_indices]\n",
+    "temp_context_names = ['t-$L_%s$' % str(i) for i in latent_indices_time] + \\\n",
+    "                     ['t-$C_%s$' % str(i) for i in observed_indices_time]\n",
+    "\n",
+    "spatial_context_names = ['s-$L_%s$' % str(i) for i in latent_indices_space] \\\n",
+    "                        + ['s-$C_%s$' % str(i) for i in observed_indices_space]\n",
+    "                            \n",
+    "tp.plot_graph(gt_graph, var_names=sys_var_names+temp_context_names+spatial_context_names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f10ed4c",
+   "metadata": {},
+   "source": [
+    "The corresponding ground truth time series graph looks like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "780b1c2c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:22.274332354Z",
+     "start_time": "2023-07-28T08:52:20.715310747Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp.plot_time_series_graph(gt_graph, \n",
+    "                          var_names=sys_var_names+temp_context_names+spatial_context_names, \n",
+    "                          node_classification=node_classification)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d30eaf83",
+   "metadata": {
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:22.286244295Z",
+     "start_time": "2023-07-28T08:52:22.280263260Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Add one-hot-encoding of time-steps and dataset index to the observational data. \n",
+    "# These are the values of the time and space dummy variables.\n",
+    "dummy_data_time = np.identity(T)\n",
+    "\n",
+    "data_dict = {}\n",
+    "for i in range(nb_domains):\n",
+    "    dummy_data_space = np.zeros((T, nb_domains))\n",
+    "    dummy_data_space[:, i] = 1.\n",
+    "    data_dict[i] = np.hstack((data_observed[i], dummy_data_time, dummy_data_space))\n",
+    "\n",
+    "# Define vector-valued variables including dummy variables as well as observed (system and context) variables\n",
+    "nb_observed_context_nodes = len(observed_indices_time) + len(observed_indices_space)\n",
+    "N = len(system_indices)\n",
+    "process_vars = system_indices\n",
+    "observed_temporal_context_nodes = list(range(N, N + len(observed_indices_time)))\n",
+    "observed_spatial_context_nodes = list(range(N + len(observed_indices_time), \n",
+    "                                            N + len(observed_indices_time) + len(observed_indices_space)))\n",
+    "time_dummy_index = N + nb_observed_context_nodes\n",
+    "space_dummy_index = N + nb_observed_context_nodes + 1\n",
+    "time_dummy = list(range(time_dummy_index, time_dummy_index + T))\n",
+    "space_dummy = list(range(time_dummy_index + T, time_dummy_index + T + nb_domains))\n",
+    "\n",
+    "vector_vars = {i: [(i, 0)] for i in process_vars + observed_temporal_context_nodes + observed_spatial_context_nodes}\n",
+    "vector_vars[time_dummy_index] = [(i, 0) for i in time_dummy]\n",
+    "vector_vars[space_dummy_index] = [(i, 0) for i in space_dummy]\n",
+    "\n",
+    "# Name all the variables and initialize the dataframe object\n",
+    "# Be careful to use analysis_mode = 'multiple'\n",
+    "sys_var_names = ['$X_%s$' % str(i) for i in process_vars]\n",
+    "context_var_names = ['t-$C_%s$' % str(i) for i in observed_indices_time] + ['s-$C_%s$' % str(i) for i in observed_indices_space]\n",
+    "var_names = sys_var_names + context_var_names + ['t-dummy', 's-dummy']\n",
+    "\n",
+    "dataframe = pp.DataFrame(\n",
+    "    data=data_dict,\n",
+    "    vector_vars = vector_vars,\n",
+    "    analysis_mode = 'multiple',\n",
+    "    var_names = var_names\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8462d8e4",
+   "metadata": {},
+   "source": [
+    "Let's look at the first two datasets by plotting the time series with the function ```tp.plot_timeseries```. Note that the observed spatial context variable ```s-C_5``` is constant across time but has a different value within each dataset, and the temporal context variable ```t-C_3``` has the same values in each dataset but it varies over time. \n",
+    "\n",
+    "We are not plotting the values of the dummy variables since these are very high-dimensional and not informative since they are just labelling the time steps and datasets, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "aff51ed0",
+   "metadata": {
+    "scrolled": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:23.478570963Z",
+     "start_time": "2023-07-28T08:52:22.288421417Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 700x300 with 5 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 700x300 with 5 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# only plot system variables and observed context variables\n",
+    "selected_variables=process_vars+observed_temporal_context_nodes+observed_spatial_context_nodes\n",
+    "\n",
+    "for dataset in [0,1]:\n",
+    "    tp.plot_timeseries(selected_dataset = dataset, \n",
+    "                       dataframe = dataframe, \n",
+    "                       figsize=(7, 3), \n",
+    "                       selected_variables=selected_variables)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb5f58e0",
+   "metadata": {},
+   "source": [
+    "### Run J-PCMCI$^+$\n",
+    "We set ``tau_max = 2``. Heuristics for choosing this hyper parameter are described in the PCMCI tutorial. There, it is also described how to choose an appropriate conditional independence test based on density plots. We further choose ``pc_alpha = 0.01`` and leave all other hyperparameters as default settings.\n",
+    "\n",
+    "As for PCMCIplus, the most important initilization parameters of the `JPCMCIplus` class are the `dataframe`, and the conditional independence test (`cond_ind_test`).\n",
+    "Additionally, `JPCMCIplus` also needs to know which of the variables are system variables, which are time-context or space-context variables, and the respective dummy nodes. This information is passed as a dictionary in the `node_classification` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f9082826",
+   "metadata": {
+    "scrolled": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:27.698063665Z",
+     "start_time": "2023-07-28T08:52:23.483827691Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 1: Selecting lagged conditioning sets\n",
+      "##\n",
+      "\n",
+      "##\n",
+      "## Step 1: PC1 algorithm for selecting lagged conditions\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (5, 0): 'o?o', (6, 0): 'o?o'}, 4: {(5, 0): 'o?o', (6, 0): 'o?o'}, 5: {}, 6: {}}\n",
+      "independence test = par_corr_mult\n",
+      "tau_min = 1\n",
+      "tau_max = 2\n",
+      "pc_alpha = [0.01]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Resulting lagged parent (super)sets:\n",
+      "\n",
+      "    Variable $X_0$ has 2 link(s):\n",
+      "        (t-$C_4$ -1): max_pval = 0.00000, min_val =  0.616\n",
+      "        ($X_0$ -1): max_pval = 0.00000, min_val =  0.298\n",
+      "\n",
+      "    Variable $X_1$ has 1 link(s):\n",
+      "        ($X_1$ -1): max_pval = 0.00000, min_val =  0.382\n",
+      "\n",
+      "    Variable $X_2$ has 5 link(s):\n",
+      "        ($X_2$ -1): max_pval = 0.00000, min_val =  0.438\n",
+      "        ($X_1$ -2): max_pval = 0.00000, min_val = -0.392\n",
+      "        (t-$C_4$ -1): max_pval = 0.00000, min_val =  0.352\n",
+      "        ($X_2$ -2): max_pval = 0.00000, min_val =  0.148\n",
+      "        (t-$C_4$ -2): max_pval = 0.00300, min_val = -0.043\n",
+      "\n",
+      "    Variable t-$C_4$ has 2 link(s):\n",
+      "        (t-$C_4$ -2): max_pval = 0.00000, min_val =  0.084\n",
+      "        (t-$C_4$ -1): max_pval = 0.00000, min_val =  0.077\n",
+      "\n",
+      "    Variable s-$C_5$ has 0 link(s):\n",
+      "\n",
+      "    Variable t-dummy has 0 link(s):\n",
+      "\n",
+      "    Variable s-dummy has 0 link(s):\n",
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 2: Discovering context-system links\n",
+      "##\n",
+      "\n",
+      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 4: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 5: {}, 6: {}}\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X_0$ has 4 link(s):\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
+      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable $X_1$ has 3 link(s):\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable $X_2$ has 7 link(s):\n",
+      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable t-$C_4$ has 2 link(s):\n",
+      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.080\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.070\n",
+      "\n",
+      "    Variable s-$C_5$ has 1 link(s):\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.402\n",
+      "\n",
+      "    Variable t-dummy has 0 link(s):\n",
+      "\n",
+      "    Variable s-dummy has 0 link(s):\n",
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 3: Discovering dummy-system links\n",
+      "##\n",
+      "\n",
+      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>', (3, -1): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-?>', (6, 0): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {(3, -1): '-->', (3, -2): '-->'}, 4: {(2, 0): '<--'}, 5: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 6: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}}\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X_0$ has 5 link(s):\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
+      "        (t-dummy  0): pval = 0.00000 | val =  0.119\n",
+      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable $X_1$ has 4 link(s):\n",
+      "        (t-dummy  0): pval = 0.00001 | val =  0.078\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable $X_2$ has 7 link(s):\n",
+      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable t-$C_4$ has 2 link(s):\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.000\n",
+      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable s-$C_5$ has 0 link(s):\n",
+      "\n",
+      "    Variable t-dummy has 2 link(s):\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.119\n",
+      "        ($X_1$  0): pval = 0.00001 | val =  0.078\n",
+      "\n",
+      "    Variable s-dummy has 0 link(s):\n",
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 4: Discovering system-system links \n",
+      "##\n",
+      "\n",
+      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, -1): '-->', (5, 0): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (5, 0): '-->'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {}, 4: {(2, 0): '<--'}, 5: {(0, 0): '<--', (1, 0): '<--'}, 6: {}}\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X_0$ has 3 link(s):\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
+      "        ($X_0$ -1): pval = 0.00000 | val =  0.296\n",
+      "        (t-dummy  0): pval = 0.00000 | val =  0.119\n",
+      "\n",
+      "    Variable $X_1$ has 2 link(s):\n",
+      "        ($X_1$ -1): pval = 0.00000 | val =  0.382\n",
+      "        (t-dummy  0): pval = 0.00001 | val =  0.078\n",
+      "\n",
+      "    Variable $X_2$ has 4 link(s):\n",
+      "        ($X_1$ -2): pval = 0.00000 | val = -0.443\n",
+      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
+      "        ($X_2$ -1): pval = 0.00000 | val =  0.291\n",
+      "\n",
+      "    Variable t-$C_4$ has 0 link(s):\n",
+      "\n",
+      "    Variable s-$C_5$ has 0 link(s):\n",
+      "\n",
+      "    Variable t-dummy has 0 link(s):\n",
+      "\n",
+      "    Variable s-dummy has 0 link(s):\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Categorize all the nodes into system, context, or dummy\n",
+    "node_classification_jpcmci = {i: node_classification[var] for i, var in enumerate(observed_indices)}\n",
+    "node_classification_jpcmci.update({time_dummy_index : \"time_dummy\", space_dummy_index : \"space_dummy\"})\n",
+    "\n",
+    "# Create a J-PCMCI+ object, passing the dataframe and (conditional)\n",
+    "# independence test objects, as well as the observed temporal and spatial context nodes \n",
+    "# and the indices of the dummies.\n",
+    "jpcmciplus = JPCMCIplus(dataframe=dataframe,\n",
+    "                          cond_ind_test=ParCorrMult(significance='analytic'), \n",
+    "                          node_classification=node_classification_jpcmci,\n",
+    "                          verbosity=1,)\n",
+    "\n",
+    "# Define the analysis parameters.\n",
+    "tau_max = 2\n",
+    "pc_alpha = 0.01\n",
+    "\n",
+    "# Run J-PCMCI+\n",
+    "results = jpcmciplus.run_jpcmciplus(tau_min=0, \n",
+    "                              tau_max=tau_max, \n",
+    "                              pc_alpha=pc_alpha)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7ef2707",
+   "metadata": {},
+   "source": [
+    "#### Plotting the results\n",
+    "Next, we use the function ```tp.plot_graph``` to plot the learned summary DAG. In our example the learned graph exactly agrees with the true DAG. \n",
+    "\n",
+    "Note, in particular, that J-PCMCI+ has correctly identified the links between the observed context variables and the system variables. It also managed to deconfound $X^0$ and $X^2$ which are confounded by an unobserved temporal context variable ($L^4_t$) using the time dummy.\n",
+    "\n",
+    "The edges are colored according to the test statistic values returned by ```JPCMCIplus.run_jpcmciplus```, i.e., the maximum absolute value of ```val_matrix[i, j, tau]``` determines the color of the edge between $X^i_{t-\\tau}$ and $X^j_t$ (provided this edge exists) according to the color scale at the bottom. The color of node denotes the maximum of the absolute value of ```val_matrix[i, i, tau]``` across all $1 \\leq \\tau \\leq \\tau_\\text{max}$.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "95daef9f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:30.627280339Z",
+     "start_time": "2023-07-28T08:52:27.743898670Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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sTy0KvSpGlgUwtCSAYcV+FPs1Sg4cZuzemCU9AseREuKTnRCRxh5XCyXuQ8lAlhg+fDgGDRqEm2++GQBw1VVX4brrrgMASNOEvn0NjF3rAVNP/8kZgzJoLNRRp4AXlKb/+Gkik3EkP14OfeuqDju5dRY38wWhDJsMZdhkq8cg2ggZboCM1APJBKQ0ASEAroApCpi/ECxUAhYsBjx+xy6MxX4Nn5s+CE+sPZC2JYepaEoY2PBJEzZ80gQAKPSqGFrix7CSAIaV+FEW8FBykGbm4T3o7fCVEAKn3v0LLJw9Gfddc2Hb7a+t24LP/exJPHHXtVg4e3IvI+3kvNX7wEdMTftxSd+gZCBLeDwebNy4ETt27MDXvva1dvfpO9bAGOXA/0quQBkyAeqoGeAB91a2k8KEvmMdkpuWdrmdMm/pGegK4wpYqBQIuSPZGVTkx5WTBziy5DBVTQkDHx1uxkeHrc2qApqCYSV+q+egxI9+IRpW6A0pRVpKDnPO8ZUr5uPex1/Afy88C0UBP9bvPoTPP/gM7r/uIkcSATAOUVcFUDKQtSgZyCJlZWUoKytDIBBof4eR5vFlRYU6bDLUkdNTmg/QV1pXSiTWv9Vt5URWcPJkwI0m9CvAeWMq8NYOh7eqTVFUN7GlOowt1dYqFZ/KMaTYj6ElfvQv8KFfyIugR6EEoYdkUx1gGmk51ufOnI4f/+Mt/N9rK3HNp2bgPx54DDfMn4k7Lz0zLcfvQAqYNYecOTbpE5QMZBkphbWN8IkJQTpoXqjDp0IdMdX1VcXM2k+QWPcmRPW+Hj2+u54Bt5ozrAT1sSQ+ONhxyeEZw0sxrCSAD6sasbU6nNEhBQCIGwI7aiLYURNpuy2gKagMedGvwIvKkAeVIS8qQ16qc9AJUVeVtmOpioK7F56F7z/7Bv62fANOHTUEP7vpsrQdvzOyqRbS1MEU5yYFE+dQMpBFRFMNkhvfgWiuB8r6pe/A3gDUkdOhDpsEprp7aZkINyC58Z12kwN7grl4rsPJtC45jCbNtk/grU4ZVISSgAejy4NIGAJbq5vxYVVTWjc/6q2obmJvfRR769vHVBrQ2hKDfi1fSwIaeB73IohwHcB42lYSfPbMGbj38RdRiQI8/uVrwE+Y5Pq5n/0ZyzbvxtmTR+Hpr9yQhjNKyHAjWFF5Go5F+holA1lAmgaM7WuQ3PkB1m7bi7rmCI5fwLbj0BFsP3gEYwfbTBBUD9TRp1o9AWnYD8BJ1uTA96Bvfb/D5MBuqR4wrwM9KX2EM4YrpwxAYv2hthoEw0r87WoCeFWOaQOLMG1gEZriOj463Iwt1c041BjPVNgnVRfVURfVsfW4BEfhDIVeFYU+FYU+DYVeFUWt3/tUFPk0+FSe1cMOQkqEEwYa4wYa4zoa4zqa4gYaYjomJTSMTuO57v7T8wCA2uZIp0nWFy8+A5+fPxNPL/0gbeeUTkxgJn3C3VcAAvPoAeiblkBGm3D9T/6E51ZsAAAMm3Jq22N+/9K71r8vX4fPnzen+4MyDmXYJGhjTjtpzXw36MnkwO7wgtKsvoAAgMo5PjNtEP78wQF80hTH9IFdL+Eq9GmYO7wUc4eXojlhYHtLgaE9tVGYLl62ZgqJ+piO+pgOoPN9MzTO2pKDQp+KQq+GAq8Kr8rhUTg8KodX4fCoDB6Fw6tyaAp3pMfBFBIx3URUNxE77p/1szj2c7L9fV2N5pT61LQlA99/9nW8vm4r3vnhHbjs/j/iibfX4D8vnNvuMZ+aNArvfrwrTWdsYTdRJ65ByYBLyWQM+uYVMA9ubbvtzXWb8Z3vfAc333wzgkFrYt+2bdsghMDnP/95vLVuS7fJAO8/Etr4OY6u808HKSXMgy2TA5t7t60yy9L5AifyqBzXzhiMv2w4iAn9elbcqMCr4tTBxTh1cDEShsCu2gi2VTdjR00EccM9hW1MQ8fejaux98M1aDxaBV+oEP1GjMOU+ZfA4zuWsOpCojaaRG3U3qRZjTN4WhKG1sRBUxgkrGX9QkoIaT3v2n1F+59bH5cwTCTN9CZWDSI9Y+2PvfU+fvXSMrzyP7di6vCBuOOSeXjwhaVYdN5saKqzpafd3sNIukb/51zIrN6H5IbFHXYVHFhWjL///e9YvXp1hzZbtmzBKWdO6/KYrLgS2sQzoJQOTHu86SYajyK++pUeTw7sDs/ClQRdCXgU3DRzKJQUdiD0qhwT+xVgYr8CmEJiX30U24+GsfVoGE3x9MxiT0XtoX3489dvRsORT9C/f3+MHj0atYe244OX/oJ3nvgl/uObP8fwqbN6dQ5dSOhJExG495NrYxqSgdfWbcXdjz6PJ+66FrPGDgMA/OeFc/DQC0vxzLvrcOM5p/X6HCdFkwezFiUDLiJNA/rWlTD3fNjp/X+463o88sp7iDYf6XDf586YgruuOq/D7SxQCHX86VAGjM6arnIWKAIvqkhfMpAjPQOtUkkEOjvGyLIgRpYFsWBcJQ43J6z9CqrDOBLu221ztyx/EyIWxrp16zBjxgzU1NSgqKgIBw8exC233IJnv3cn7vrzW/AFe1fq2e0ahNqryYPrdx/E5x98Gj+4/mJcPutYLYGigB9fuOgM/OL5Jbj+7FOhpKlaZgeMg1EFwqxFyYBLiOY6JNe/cdL18rPHj8Ts8SN7dkDNC3XMTKjDpji6EZATmOaBb9bFMAaPRWLVi9ZSyt4cL0tXEvQVxhgGFPowoNCHs0eVI5wwsL8hhn31UeyvjzmeHJiGAVVV8fzzz+Pyyy/HwYMHEQqF8OSTT+Kpp57C8OHDsfGt5zF74fWOxpFpccEQZxp8MrVJeDNGDkb1k/d3et93PnsBvvPZC3oTXrdYUQUNE2Qx2qgow6SUMPd/DP3j5YBIT1etMnAMtEnzsnoGfSuZiCGx9jXbSwmPF1j4X66fI+FmMd3E/voY9jVYyUFVUzyt+z0e2rYJj91jLW2bfsFVGDFtFpY+8zuMqCzBypUrMX36dHiGTsKld/5PGs/qTjdE30O/ZO/myPTUJfc/go/2ViGSSKIk5MdTd1+P2S1DC7YxDnXUdHhmdOydJNmB0rgMksk4kh++A3F4d1qOx/whaJPPgtJveFqO5wbM64cyaEzqyQDnYC4upZwN/JqCcZUhjKsMAQAShsCBhhj2N0Sxrz6GQ42xLmfI98SgcVPwlWeWQvV4oXl9iDbV47Xf/wSj5syEaZo4ePAgpkx1qHKeyzQVDkK/mr5JBl7+zq3pO5gU4KUD0nc80ucoGcgQs+YQkhveBOKR7h/cLQZlxFRo42a5vmiQXSLajMSaV1Juz4IladtRkFi8Ksfo8iBGl1srWnRT4FBjHAcarCGF6nACtZGkrd4Df4E11pyIRfDM/3wRXmbixz/+MR5++GHU1tZi4rzzHfhN3KcpNACo6XzOkKsxBl45NNNRkF6gZKCPSWHC2L4Gxs70FPpgheXwTJ0PXlyZluO5iZQSiVUvAMnUC+fk0koCt9IUjuGlAQwvPTYsZZgCRyNJVIcTbQlCdXMC4ZNsy5yMx/DM/3wBDQd24q233sLOnTtx7733YtbC69BvxLi++FUyrtFfDihq2vYo6BOMQxk8Dtyf2xM8cx0lA31IxJqR/OB1yIaOqwFs4yrUcadBHTENjGfXBMGeMnaug1nVu6Iobt5yOZepCm+blHi8aNLAkXCyLTk4Ek7gaDiBaCyGv3z3i6jZtRmvv/46DMPA5ZdfjsGTZ2LBrV/r4iy5pyEhoI6Yam1Hni3TuaSAOnZmpqMgvUTJQB8xaz9B8oPXOtQOSAUvHwJtylngObyMRzTXI7HujV4fJ1cKDuWKgEfFiFIVI47rRYjH47jksstxZPuHePXVV6GqKs4//3yEw2HMmzgZ2999CWPmXQTN5+5qmXaonKHIp6HIp6LI3/LVp6Es6IGqhGDsXJfpEHuIgZf2h0LzBbIeJQN9wNj3MfSP3u39BiSKBm3ymVAGj8+amgGpkEIgvvI5wOh9nfNcqzGQi5566iksX/YuXn31VQwcOBALFy5EKBRCKBTCh4tfRG1tLb4RSOLLX/smmuIGmuI6Glu+NicMJEyBpCGQNAWShmz7ua9KLyuMwa9xBDwKfKqCgEeBX2v9xxHQjv9ZQdCjIKCdbGtnP5ShE2Ee2JIFvQMS2qR5mQ6CpAElAw6SwoT+8Xsw933U62Oxokp4TjkfPFjc+8BcTt+6CuLoAXuNvH5oo0+FvnlFu6SL5gy435YtWzBixAjMnz8fAPDRR+1fL+eddx62bNnS8klaA9CzHgJTSCRNgURbotDyteU2Q0hwZtVZ4LA2hGLs2NfW2xlj4Kz9/Spn8GvWRV1TWNqTc8+M8xCr3gfEo0BaF3KmE4MychqU/iMyHQhJA0oGHCITUSQ/eB2i7pNeH0sddQrUcbNydm7A8cyGaiQ3vmO7ne+0S6AOmwh16AQkVjwP0VgNgIYJssH06dPx4IMPIhQKdXp/PB7HD3/4Q9vHVTiDn1ufxrMN8/jgnX0ZEkufzXQonWMMLFAEz7T5mY6EpAklAw4QjUeRXPsKZCzc/YNPxhuEZ8a5UMqHpCcwl5PCRGLl87Z3PlOHTYY6bCIAQCkdAP9FtyD54RIY+z6mimhZ4Nprr0VZWRl27NjR6f2DBw/G5Zdf3sdRZZ5SORTquFkwtnXci8QNvHMuB1NpL4JcQRUI08z4ZAf0DW/3upog7zccnmnngHlyZ9JUdxIfLoG+6V1bbZg/hMAlX+h0K2YRrqc5AySrSWEisfzfEIf3wE3DBZ7TLoY6fHL3DyRZg6qxpImUEvrWVdDXvdG7RIAr0CafBc/Mi/MqETBrD0H/aJntdt7TL+80EQBo8iDJfowr8M69Arz/8EyH0sYz80JKBHIQ9QykgTSSSK5/E+LI3l4dhxWUWZMEC8rSE1iWkIaO6KuPQDbV2Gqnjj4VvtmXOBQVIe4hhYnk2tfTMhk5NQzgDJ7TL4c6aGyGYiBOogHVXpLJGBKrX4JsqO7VcZQhE6FNPjMvx7iTG9+2nQiwUAm8p+RHiVpCGFfgOe0iGEXlLcuU0fulynbOHyyCZ/alUMoG9tk5Sd/KvytPGolYM5KrXoCMNKR+EMahTToTyrBJOV07oCvGkb3Qt75vu51vzuVgWm7tw0DIyTDGoI2bBWXQGCQ/XAJxqPMJl2k8IQAGdcLp0MafnpcfVPIJ/d9NkWiuQ+L9F3q30ZDHB8+pF0IpG5S+wLKI1BPW6gGbtAlzoFSmuNUqIVlMGDpM04A65Wxg0FgkN7xtVTVlLH0FilqOxUsHwjPzQvDC/Bq2zFeUDKTArD+M5OqXAD2R8jFYYRk8My8Gz+PtdRMfvA4ZabTVhhdV0NpmknekMGFGmiAS1ocPHvRBGTYJyuBxMD/ZCWP3Bojq/b1IChgACSga1OGToY6YAl7SP62/A3E3SgZsMqv3Ibn2tV6tGFAGjII27dy8XqNrHNwGY9cGe40Yh3fuFdRdSfKGlAIiFoYZbcbxSwsZt14DTFGhDhkPdch4iEgDjL0fQVQfgGg4AhjJlgdb3f3tD3xsvgELFICV9Ic6cAyUwePy+n0pn9G7qg3GwW3QN77dq4k76rjZUEefmpfzA1rJeBSJ91+y3c4z5VO0IQrJC1JKiEQUZrQREJ2833SSEPNgMTyT5gGTrPYy1gzRcASyqRbS0K1iXoxZiYTHB15cCV5cCaZ5++A3Im5HyUAPGbs3Qt/8XuoHUDV4ZpwPpV9+1/GWUiKx5hVIm3MteNlA2hCF5AWRjMOMNEKaXW/U1V3vGGMMLFBoDUMOHJPuEEkOomSgG1JKGFtXwdiV+paiLFAEz2kXgxeUpjGy7GTs+xjG/s32GikqfHOuAONUI4vkLmHoMCMNkN3NReI8r3sWiTMoGTgJKSX0TUtg2r14HYcV94N31qVgHl/6AstSItqExJpXbLfzTD8XvKjcgYgIybwTJwd2p3W+ACHpRM+qLkgpoX/4jrWneIp4xVB4Tr2QJuSgZXhg1YtAMm6rndJvOLRxsxyKipDM6WpyYHdoAi1xAj2rOmElAm/DPLA15WMog8ZCm3ZOXmw73BPGznUwq3bZa6R6rL0HqEuU5JBuJwd2g5IB4oSceVZJKa1/QkC0fN9yhzWDFi2TajgHP8mYm5QC+sZ3YB7sRSIwYhq0iWfQRayFaK5DYt0bttt5Zy4ADxWnPyBCMqQnkwO7RckAcUBWP6uklDANA6ZpIpX9ljjnUFS1LTmwEoG3YR7clnJM6vjToY46hRKBFlIIxFc+Dxj23vyUQWOhjpzuTFCE9LEeTw7sAZozQJyQdc8qKSWEEDANAyKFLrbjCSEgklZhDrUlKYCSar17Bm3q2VCHTuxVTLlG37oK4ugBe428fnhnX0oJVR+IxWKoqqqCz+fDwIG0CU26SSlhRhshYuG0HZOGCYgTsupZJYSAnkym1AvQHcOwKgrycadD4Qzmng973pgr8JxyAZT+I9MeVzYzG6qR3PhOh9tbh3MgJKSwvm/9CiGhTZ2J2N49kLoOaVj/hK5DGob183G3S8M49rOU1hwNzsGUlq/tvj/2tcNtimItXeQKmHLC7R4vFH8A3B8A9/uh+ANZPSm0vr4e3/ve9/DOO+/g448/hmmaAIDbb78dv/vd7zIcXW5hjEHxhVqet73vFUDLUCch6cakE1fWNJNSwtD1tjetvjgf37MOYs/G7h+seuA57eK822xI6DpEJAwzEoYZbfkaCbfdZoSbkNyxASIShtANCMO0vuoGpHD9U65bTNPA/UEofn9LkhAA9wWsnwNBcH8Aiu/YfUogCO73tzwmAB4IQAkWZOSN/b777sODDz6Iz33uc5g5cyZGjRqFF154AX/4wx8QjUb7PJ58IKWEiEdgRhphZ+XAiZiqQSvul77ACGnh+p4B0zSht3Tl9xXGGMSIU8DAIPds6PqBmg/e0y8HL6ros9icIuIx6PW10OtqYdS3/GtqaLnIRyCOu+CbkTBkMg2fcrKY1HWYegPMpobUD8I51KJiqEWlUItLoBYf+6oVHfdzUXFaeyKqqqowZswYfOUrX8HatWsxbtw4jB07ts+S7XzEGIPiD4F7fDCaalOeQEjzBYhTXP3MMg0Dut6LWbe9wBiDHDHD+rp7fccHeHzwnr4QvNDdxXCkEDDDTW0Xeb2+FkZdLfT6mraLvl5XCxGjT4R9TggY9XUw6uu6fagSKjguOegkeWj5yr3dF7eaMmUK/vCHP2DcuHEAgOXLl/f6VyE9xDlkL/Y2oZUExCmufWYZhgEjQ4lAK8YYMGIGGOOQuz44dofHB+/pV2R8n28pBIy6Guh1Ncdd5Fsv8C0X+4Y6SCP1HRaJO5jhZpjhZiQO7jvp43ggCE+/AfBUDrC+tn0/EEpRMRhjuOOOOzBmzBjs27cP//mf/9lHvwEBYA0TiNR7YGjyIHGKK59ZbkgE2hk+zUoIdq4BPP6WHoG+SQSklDAa65E8fAjJw59Y/460fK2ugnTT34lknIhGEN+zE/E9Ozvcx7w+eCr7w9NvAKb3G4iiVBfOkJSIZBzC5gZdJ6JkgDjFdc8sIYS7EoFWw6aAqR5olUPSnghIKWGGm5A8XGVd9Fsv9i0XfBGPpfV8JD/JRByJA3uROLAXAHC0sbn9/aaB/b/4fkuPwsC23gWtrAJMdd1bRVaRQsAI1/f6OFTRlDjFVa9wKWWfTxa0ZdA4SC31iVxmNNL+k/2RT5A4/AmShw9BRHv3iYGQXpNAeMOajrcrCrwDBsM3dAS8Q0bAN2Q4fENHQC0u6fsYs5QZaejV8EAbSgaIQ1yVDOi67kgNgXQydB28paRxV8xYFImD+5E4uBfxA/uQOLQPiUMHejfznJA0ihsmDjS3nzQqpcTuxjCGFQSh8OMKPpkmEgf3tcxXWNJ2s1JYDN/Q4fANGQFv69eBg7O6BoMTRCIKkej9BF2mqFSIizjGNcmAaZoQWbK0SU8m4fF6rTfJw4esN8oDexFv+arXVGc6REK6VBWJ4eqX3kVjsv1wnCElLnthCUYVhfDPSz8FrZsaCGZTAyIfbUDkow3HblRUeAcNgW/IiJaehJZehMIiB34T95PChBFusN2O+wsAYbZPImi+AHGQa55dRhbNeJdSQo+EsfPOzwNm9sRNCAAs/+QomnQD7733HgBrqeHIkSMxbdo07Nq1CzfeeCO21Tdhclmx/YObBhL79yCxfw8aj1uxqBaXtvUetCYJ3oGDc3oMXEppzROwuZSQqR4ogUKr3onH33YMqjFAnOSKZ5cQwipFm000D9SiYhh1NZmOJDcoitUNqmngqgamaWBtX1Xre1UD01Tr/hMfo6gAZ4AQkKYAhNlS3lhAmiakMK3vW7+ax34+6eMNAyIRgxmNQibimf4rpUWRV4OUEn/605863Fdfb01yK/Skt6vfaKiD0VCHyIfr2m7jPj/8o8bBP3ocAmMmwD9qHJRgKK3nzSSRiEIm7T5nGNSC0rbhAO71Q9M8MMINtJKAOMoV5YiTyWTWDBEc78hvf4rIxrWZDiOzGMBVFVxTwDUVXG35qnV2mwLu9SJ4/g1QSvu1v9hnwSdEKUyIWAwiFoUZb/kajbTdJuJRmLEoRDTa8hjrq4jFrMfFre8zvTokaZr4+bot+Ki2scN9DMC5Q/rj5kmj+j4wxuAdOAT+MRMQGDMe/tET4Ok/MCvHyaVpQG84Ym2hboMSLIbi75gQWW/TEozRvgTEGRlPBqSUSMSz8xNX/Qt/Rf3L/8x0GGmlFBRCLSmDVlIGtaQMakEheDAEJRiCEmj5GgyBKUBi2bNgpm7rzdpz6gJ4xs928DdwPylMmE1N0BvqYDTWW5+aG+thNLR833DstnyvI6EUFMI/ejwCo8fDP2YC/CNG96jKYiZJKWE01djemIhpXqiF5VmZ/JDsl/F+J7vbEF+wYAGmTp2K//3Zz3r0+HXr1uHBhx7Ce++9h/r6egwaNAizZs3CV++5BxMn9m67YW3QsF6171OKAq241LrQl7Zc6EvKoJWWH7v4F5eCe7qvRCOlRPydZ8CFAdh441L6DYc2blZvfoucwLjSUkb45EvzpJQQ0QiMhjronSQKxycRuVpO2mxuQnj9aoTXr7ZuUBT4ho1EYPQE+MeMR2DMBGil7ioJLuJh+zsUMgY1VEKJAMmYrEsG7Hj00UfxX1/+Mm668UY8++yz6N+/P3bv2oXf/va3+Otf/4rvfe97vTq+Z/DQNEXaO8zjgVZWaV3QS499qj/+Qq8UFqVthzxjxwcwq3bZa6R64D39cnqzs4Ex1tYT4x108ueaSMSh1xxFsrrKqmNxpArJ6sNIHqmCXnMEyLY5OV0xTcR370B89w7gjRcAAGppuTXnYMx4BMdNhnfI8Ixt8ysNvWVnQnuUYAnNCSAZlfFhgkQi0ePJg7fedhueeuqpdrdt3bIFw4Z1/IS+fMUKXHDBBXjwF7/Abbfd1uH+uro6lJaWphZ0CykE9n75xr7ZwU9RrVKy/QfB038AvP0HWVXi+g+EWlzaZ29+orkO0Vf+DzDsdV97T78c2qjpzgRFTkoaBvTa6uMShJZk4UgV9KOHc27vCqWgCMFJ0xCaMgPBSTOglfZl6fBqSJuvDebxt5s0SEgmZDQZsDtfoLGxEQuvuAKTJk7Ed77zHQBARUUFFKXj5LN5Z56JUCiE1159NW3xduaTB76N+O7t6TkY49Aq+lkX+34DrQt/v4HwDBholYTN8CQ7KQRibz0BcfSArXbKoLHwnfVZerNzISlMGHW1LT0KHZOFXNiq2jNwCEKTZyA4eTqC4yeD+/yOnMeMNsGMNtlrxDi0kn4Zf20TklX9UkVFRfB4PPAHAujfv3+Xj9u6dSs++OADPPPMMz06bjQaxfQZM3DVVVfhJz/+sa2YPEOG204G1NJyePoPbPfp3tN/IDwV/VxdvU3fusp2IgBvAN7Zl1Ii4FKMK9DKK6GVVyI4cVq7+6SU0GuPIrF/D+L79yB+wKofkKw+bHuWfCYlPzmAuk8OoO6NFwBFRWDMeAQnT0do0gz4RoxKy4VYGEn7iQBgzROgRIC4QGaTgTS8ofzl2Wdx5513tv383HPP4cAB64J1yowZPTrGAw88gFmzUpvY5h3c9SRCtbQcvsHD4B08DN4hw+AbPBye/gNdPxu6M2b9ESQ3vmO7nW/WJeCdLJUi7scYg6e8Ep7yShSccmwFiIjHrGqbbUmCtflRppdM9ohpILr1I0S3foSj/3gKPBhCcOI0hCZPR3DydHgquv6Q0RUpJczmOtvtuDcA7nWml4IQuzKaDKTjs8Wll1yCWaed1vbzwIEDsX3bNgBAMBjstv3OnTuxbft2XHLxxfh482bb5/cMHgYeCMI7eNhxF/7h8A0eljMFVKRpIrHyedsbrajDp0AdOsGhqEimcJ8fgZblfq2kENBrqq3kYP8eJA5YSYJefTiDkXZPRMJoXrMczWuscomefgMQnDQdwckzEJwwpUevYTPaCGm3EilXoASLU4iYEGdkNBlIpePY4/HAPK5AUUFBAQoKCto9ZuKkSQCA5cuXY+HChR2OEYvF4PdbGfk3vvEN/OhHP8KqVatSiAbwjRqHcb/7S053gyc/ehei3t6bOvMXwDvzQociIm7DOLcmuFb2R+HMOW23m7GotW/H/j1tX+P7d7u2fkLrXIn6t18FOId/5FiEps1EwamnwztoaIfXudATELGw7fNYwwNUQIi4R2aHCVK4gA4bOhRr1qzBvn37EAwGUVpa2mEHwdNnz8Z5552Hu+66C5FIBLNnz4YQAms/+ACPPPIIHv7NbzBhwgS8+OKLGD1mDMaMGZNyMsA5z+lEwKw5CP3j92y3855+GRh1geY9xR9AYOxEBMYeq+khdB3xfbsQ27EV0Z1bENu+BUZjfQaj7IIQiO3citjOrTj6z6fg6TcABafOQcGpc+AfNRZggJHK8IAvBO7JvqFCktuyajUBAOzYsQO33HorNm3ahFgs1uXSwng8jl//+tf469/+hj179sDn82HkyJG45OKLce+994Jzju/8z//gL3/5CxRFQSQSga7r+PJ//Re++c1v9jgeVdOgqlk1D7PHpKEj+sofIJtrbbVTx8yEb9bFDkVFco2UEnpNNWI7tiC6cytiO7Ygvn+v7Q1++pJaVILglOkITJyMwOixYD19D1BUaMWVVFaYuE7G6wwkEwlHCw/11JNPPomPN2+2v5rA4wHvZGljLkisfQ36ttW22rBQCQKX/CeY2n0lQ0K6IuIxxHZvR3TH1rYkQUQjmQ6rU9znQ2DCZISmTEdgwqSTThBWiyrBNXptEPfJ+EdazrkrkoFU5eq4n3F4j+1EAAB8cxZSIkB6jfv8CE6c1rbcUQqBZNVBRHdsaRle2Ipk1cEMR2kR8TjC69civH4tmKrCP3YCQlOmIzhpCpTQsflM3F9AiQBxrYz3DJimCT2ZzGQIKWOMwevLvbE/mYwj+vL/QUbtlVXVJs6Fd8Z5DkVFSHtGcxNiu7Yhun0zIh9vQHzvLnfVP2AMvhGjEZoyDaFpM+EfNTGn5xeR7JbxZCCbdy1UVBWa5t4iQamKr3wBxu4Nttrwokr4L7qF6quTjDGamxDZvBGRjzYg8tF66LVHMx1SO76hI1Ew83QUnDoH3sHDKDEgrpLxZAAAdF2HmYX10b0+X869oI2D2xBf+ld7jTiHf8EtUErtF2whxAlSSiQPf4LIxxsQ3rQe0S0fuqookmfgEBTNPRtFc8+Gp7wy0+EQ4o5kQAqBRCK7aqBzzuHxejMdRlrJeATRl38PGbc3UcszbT48k890KCpCek8aBmK7tyPc0msQ273dNTs5BsZNQtHc+SicdUbOFCoj2ccVyQDgnlUFPeXxejvUN8hmUkrEl/0D5oEtttrxskHwX3Bzzk6kJLnJjEYQ2fIhIpvWI/LxBiSPVGU6JDBVRWjGLBTNPRsF02a6ep8SkntckwwIIZDMkt6BXOwV0PdsQmLFv+01UlQELr4NvLDcmaAI6SPJo4cR+WiD1XOweSNExH5VwXRSggUonD0PRWfMh3/0+JwbjiTu45pkAAAMXYeRBXMHcm2ugIg2Ifry74GkvYmcnpkXwjMutQ2eCHErKUzEdm5D8wer0PTByozvr6BV9kfRnLNQNHc+vAMGZTQWkrtclQxIKZFMJOCikDrQPB4oOVRkSEqJ+DtPw6zabaud0m84fOfekFNJESEnMqJNiO3cgsimDYh8tBGJQza38E4z38ixKD5jPgpnnwm1sCijsZDc4qpkAHD3cAFXFHg8uVU0RN++Fok1r9hrpHkRuOR28CC9GZHcJQ0desORdrfpdbWIbNqA8EcbEd+9M3N1DThHaMopKDrjHBScMgvck1vDlqTvuS4ZANxZiIhxDo/Hk1OfhEVzHaIv/x9g2ttBznv65dBGTXcmKEJcQEoJo7Ea0uj6tWGGmxH5eBPCmzYgtn0LZIaGOLnPj8LTzkDx2QvgHz0up96jSN9xZTIAuCshyMVEQAqB2FtPQBy11+2pDB4L36c+m1N/C0JOZEabYEabevx4kYgjunULotu2IvzhBxnbR8E7dARKzrkIRXPOguIPZCQGkp1cmwwAgDBNJDOcEHDOoeVYIgAAyY+XI7lhsb1G3oA1POCntdAkdwkjCaOh2nY7taAM3OuHNHREtmxC8wer0PzBqoxsz8x9fhTNOQsl51wE37CRfX5+kn1cnQwA1hwCPZnMyKRCVVWhqGrOJQJm/RHEXvsjIExb7Xxnfhrq0AkORUVI5kkpYTQcgTTtdflzbwBqQWnH4wmB2O7taF6zAo2r3oVRb2878HTwjxqHknMuQuHseTS3gHTJ9ckAYL1ATdOEodsb204V4xyapuVUUaFW0jQRe/2PEPVHun/wcdThU+A740qHoiLEHYxIA0TMZo0BrkAr7tdt4S0pTES3foSG5e+gec2KPi+PzANBFJ95LkrmXwjvwCF9em7iflmRDLSSUkJPJh2tVKhqGhRFybnegFaJDW9D//g9W22YvwCBS78A5sm9HRoJaSX0BIxG+5sbqYXl4DZfGyIRR/P6NWhc/jbCm9b1eWnkwIQpVm/BqadTpUMCIMuSgVZCCJimmbbNjRjnUFUVnPOcTQIAwKw5iNgbj9leDuU75zqoA0Y5FBUhmSelgF5/xPbQGfeFoIaKe3Vuo6kRTe8vQ8PydxDfvb1Xx7JLKSxG8Vnno+TsC+CpoI3G8llWJgOtpJQQQkCYJoQQtuYVMM6hcA6uKDk5HHAiaeiIvvIHyGZ7Y5bamJnwzrrYoagIcQcjXA9hc4MuKKo1PJDGDxCJqkNoXLkEjSuW9G3lQ8YQmnIKSs65CKHpM8F47hRWIz2T1cnAiaSUbQlC66dfCYABAGNgjLVd+HO5B6AzibWvQd+22lYbVlCKwMW3gam5VWiJkOOJZBxGU43tdmpRJbjmzGtDSonYzq1oXP4OGt9f1qd7Jail5Sg5ewGKz74AWnHHSZEkN+VUMkA6Zxzejfjip+w1Ygz+82+CUkETjUjukkJAbzhse8ye+wug9lEFTmnoCG/8AA0r3kF4/eq+K26kqCieezZKL7oCvsHD+uacJGMoGchxMhlH9OXfQ9oooAIA2sS58M44z6GoCHEHo7kWImFvVj9TNKjFlRnpXTQjYTStWY7G5e8guu3jPjtvaOqpKLv4SgQmTM27XtV8QclAjouvfB7G7o222vDiSvgvvAVMUR2KipDME4kojOY62+3U4n7gLpiBn/jkAOrfeR0N7y3us2EE37CRKLvoShTOmgem0vtDLqFkIIcZB7Yh/u5f7TXiHP4Ft0AppZnFJHdJYVqrB6S94QElUAQlUOBQVKkRyQSaVi9H/eJXENu1rU/OqZVVoHTB5Sg+6wIqe5wjKBnIUTIesYYHbM6Q9kybD8/kMx2KipDMk1LCaK6FTMZttWOqB2pRhau7yeP7dqP+ndfQuGJJnxQ14oEgSuZfiNLzL4NWWub4+YhzKBnIQVJKxJf9HeaBrbba8fJB8J9/c7eV1AjJZmY8AjNsd78ABq2kX9YMnZmxKJpWLkXd268isX+P8ydUVBTN+RTKLroSviHDnT8fSTtKBnKQvudDJFY8Z6+RoiJw8X+CF1J2T3KXNA3oDUdsF95SgsVQsnCDLiklYru2of7tV9H0/nuQuvMbvwWnnIKyi65EcNI0V/eikPYoGcgxItqE6Eu/A/SErXaemRfCM26WQ1ERknlSShhNNZA2XxtM80ItLM/6C5sZbkbDe2+j/p1Xkaw65Pj5vENHoOyiK1E0+0yabJgFKBnIIVJKxN9+Gubh3bbaKf1HwHfO9Vn/ZkfIyZixZpiRRnuNGINW3B9MyZ2KfFJKRLdssnoLPlgJmPZKMNullpajbMHlKD57AU02dDFKBnKIvn0NEmtetddI8yJwye3gfVRAhZBMkIZuDQ/YpBSUQvHm7gXMaKhHw7K3UP/Oa9Brqh09F/cHUHr+pShdcDnUgu7fb6QQqHnhryhf+Dn6oNIHKBnIEaK5DtGX/w8w7W3z7J2zENrIaQ5FRUjmSSlhNFZDGvZeG9zjh1JQmhcXIilMNK9fjdpX/o3Yji2Onot5fSg992KUXXQF1KKSLh/XtPo9HPzNAxhw8x0omX+hozERSgZyghQCsTcfh6g5aKudMngcfJ/6TF682ZH8ZUabYNqswAnGrdUDebhhT3THFtS++m80f7DK9kRLO5jHg5L5F6Ls4quglbSfuCyFwO5v3YnEof1gmgcj7vs5rVJwGCUDOSD58XIkNyy218gbQODSL4D7gs4ERYgLCCMJo8F+97daWAbu8TsQUfZIHvkEta89j4Zlb0EmnVuFwDQNxZ86H+WXXA2tvBIA0Pj+Mhx6+Kdtj/EMGIyR338Q3OtzLI58R8lAljPrjyD22h9t78Pu+9RnoA4Z71BUhGSelBJGwxFI097GPtwbgFpAu/W1MpobUb/4VdS9+RLMZpsTMO1QVBSfeS7KL7kaBx66H4lDB9rdXXTmuRh0613OnT/PUTKQxaRpIvb6HyHq7U2MUkdMhW/uFc4ERYhLGJEGiJjNmv1cgVbcjwpvdUIkE2hc/g5qX30OycPOL03szMD//G8Un3FORs6d6ygZyGKJDW9D//g9W21YoBCBS24H81B3G8ldQk/AaDxqu51aWA5Or42TkkIgvGENal/9d5/unAhYkw9Hfv9BeAcM7tPz5gNKBrKUWXMQsTcesz3Bx3fOdVAHjHIoKkIyT0phbUJkc+iM+0JQQ8XOBJWjoru2oe7Vf6NpzUrbmz6lyjt0BEb8z/+Cezx9cr58QclAFpKGjugrf4BsrrXVThszE95ZFzsUFSHuYITrIWxu0AVFtYYHaGVNSpLVh63Jhu++CZm0V+ExFSXnXowBN37B8fPkE0oGslBi7WvQt6221YYVlCJw8W1gKmXTJHeJZBxGU43tdmpRJbhGr43eMpqbUP9Oy2TDxgZHzzX4zq+j8LQz0n5cmYxDRuohww0Q4TrIaBNgGlbPB2MAV8A0H1hBKViwGCxUAuYvzPp5JpQMZBnj8G7EFz9lrxFj8J9/E5SKIc4ERYgLSCGgNxwGhL3uau4vgEoVONNKJJNoeG8xal/8O/Ra+3M3eoIHghj5g1/B07IcsTdkIgpxZA/Mw7uARLTl1tZeoi4ukYwdG6ZlDKxsEJT+o8CK+2dlDxMlA1lEJuOIvvx7K1O1QZt4BrwzznUoKkLcwWiuhUjEbLVhiga1uDIr37yzgTR0NCx/BzUv/h169eG0Hz8wbhKGfeOHKRWHklJC1n0Cs2onZH1V74NpTQ48figDRoH3H51VE7UpGcgi8ZXPw9i90VYbXtwP/gv/X9bsw05IKkQiCqO5znY7tbgfuKo5EBE5njRNNK5cipoX/pb2ZYmVn/48yi/7tL14ok0wdqyBbDoKqwfAgcsgV6GMnA7ef1RWJJuUDGQJ48A2xN/9q71GnMN/4S1QSvo7ExQhLiCFaa0esDmbXQkUQQkUOBQV6YwUJppWL0fN8892KCqUMkXBiO/8FP6RY3t0fnFwK8z9H7Vc/52//LGCMihjZrl+MzhKBrKAiEcQe+l3kG1jWT3jmXYOPJPnORQVIZknpYTRVAupx221Y6oHalFFVnxiy0VSCDR/sBJHn/8rEvv39Pp4nn4DMfL+h8B9XZeQlvEw9I/eBWI296noNes5poycDmXQuD4+d89l9/THPCClRGL1y7YTAV4+CNrEuQ5FRYg7iETUdiIAMKh5shuhWzHOUXjaGSi/+Kq0HC955BMcfvqPXd4vwnXQ178BxJrTcj57JAAJc/d6GLvWwa2fvykZcDljzyaYB7baa6So8M25IuuXuhByMtI0YEYabLdTQkU0h8YFpDBx9Pln03a8hqVvoGnNig63i+ZaGBsXA4aOvhgWOBnxyXZrroILEwK6WriYiDQisfZV2+08M84DLyzr/oGEZCkppTVh0OabKtN84F7aqdMNmlYtQ7IqvZMJqx79DfS6Y8XYRKQBxofvtCw3dccFWB7ZDXPXukyH0QElAy4lpURi1YuAbq+al9J/BLSxpzkUFSHuIOJhSMPmtrqMQQ2V0PCAC6S7V6CVGWnGJ394EFIISNOAsWV5S1lqdyQCrUTVDoiaNE2gTBNKBlzK2LEW5uHd9hppXnhPv5ze7EhOk4YOM2J/K10lVAKm2F+PTtKvafUKJKuPAIpi/eMcYNxaq99Lkc0bUff68zD3ftgyR8BdiUArY/tqSJt1MZxEqwlcSDTVIvrK/1klMG3wzlkIbeQ0h6IiJPOklDAaqyEN3VY77vFDpaGzrCOltIaCpIQ0DUhdhzR0CF1v+17qOqSehDAM6+dkEjJSDx9vcvkHIwZWVAF1ynxXxEmzaFxGCoH4yudtJwLK4HFQR0x1KCpC3EHEmm0nAmAcCu1GmJUYY229BUxRAI+32zZSmNBXvwjomb/AnpyEbKyGqN4Lpd+ITAdDwwRuo29ZAVFz0FYb5g3AO/tSV2SXhDhFGEmYNktxA4BaUJJSuVqSncTR/YDt5aaZYx7Y4orVBZQMuIhZfwTJD5fYbuedfSm4j2ZIk9wlpYSZQrlh7g2Ae7ouRENyi5QS5kGbS7EzLdYE2XAk01FQMuAW0jSQWPGc7R3X1BFToQ4Z70xQhLiEGW2EtDl0Bq5ACRY7Eg9xJ9l0FIjan1yaWQzmoW2ZDoKSAbdIbloKYTM7ZIFCeGde6FBEhLiD0BMQsbDtdmpBKRXeyjNm1U4c23o4W0jI+irIeCSjUdArxQXMowegb+5YOas73tMvz6otMgmxSwqR0m6E3BcC17qfbEZyi2yoRm+XEgohMOX6r+B/Hmm/MdwrK9chdM71eG7p6l4dv8vzNtU4ctyeomQgw6SRtFYP2JxAoo09DeqAkQ5FRYg7mNHGlqIxNigqFJfvEEfSTyZiaZk4yDnHvdddjt//+w00hq09YdZv24Pr7/sVfnj7tbjirFm9PkcHjEGG7Se96UTJQIYl1y+GtPnJhxWUwjPjXIciIsQdRDIGkULXqRqiTYjykQzXdv+gHrrm/HkoKQjhd/96HfuP1ODKr/8Un7/oLHz5Mxen7RztSAmZ4Z4BqjOQQUbVbujb19hrxBh8c68AUz3OBEWIC0ghYITrbbfj/gJwjV4b+Ug011k1CdKwTE9VFdxz7WW4749/w18Xr8Cp40fhF/91Yxqi7JoM10MKkbF5LtQzkCEyGUdi1Qu222kT50IpH+xARIS4hxmpt72yhqkalEChQxER10tE01p5+Jrz5yEci4OB4cnv3gl+wkX65RXrMPm6uzHxmv/Goy+93fsTSgHY3W8jjahnIEMSa1+DtFlAhRf3g2fKWQ5FRIg7mIkoRAo122l4IM+leUOiux56DABQ29gMfsLzyjBM3PubJ/HGL7+DomAAp9/6TSw88zSUFRX07qR258ekEfUMZIBxYCuMPR/aa8Q5vHOvoH3YSU6TwoQZbrDdTgkUgala+gMiWSOdVfzu++Pf8OrK9Vj2+/thmCYee3lJu/vXbNmFiSMGY1BFKUIBHxbMnoa31th8T++MtNcblk6UDPQxEY8g8f5Lttt5pp4NpaSfAxER4g5SShjN9bbfEJnqAfeHHIqKZAur5HTve4b+9OJiPPTXl/Gvn3wVU0cPw52fvgi/+MuL0I1jRa+qausxsLy07eeBFaU4dNT+HJcOeOY+7FEy0IeklEi8/xJkImqrHS8fDG3CXIeiIsQdRCIKaXdpGGNWcSEaHiCK1utc4NWV63HXQ4/jie98CbMnjQEAfOGqBWiMRPH068vSEGQ3MtjzS8lAHzL2fAjzoM2yk4oG35yFVEmN5DRpGjAjDbbbKcEiGjojAAAWKu7VSoJ123bjuvt+iR/dfi0Wfuq0ttuLQgHccfWF+NnTL8A0rV6rAWUl+KTm2JLwT47WYWB5ScrnBgB4Axkd6mLSDdsl5QERaUT05d8DesJWO8/Mi+AZd1r3DyQkS0kpYTQehbQ5k5ppPqiFZdQrQAAAIlwPY/3rfXIuwzAx9YZ78Oavjk0gXPrb7/ViAiEDqxgCbXzmeoAppe4DUkprGaHNREDpPwLa2JkORUWIO4h42HYiAMaghkooESBtWKAIYLxPJuGpqoIH7rgOF3z5fggp8ZVrLuvlSgIJXlCWtvhSQclAH9C3r4V5eI+9RprX2nuA3uxIDpOGDjNif5c5JVQCpigORESyFeMcLFQC2Zy+SoQnc9m8mbhsXvo+rLEMJwM0EO0w0VSL5Po3bbfzzrwInOqrkxwmpYSRQj127vFD8QYciIhkO14xNNMhpMbjByso7f5xDqJkwEFSCMRXPgfY3IddGTIe6ogpzgRFiEuIWDOkodtrxDiUULEj8ZDsx/uNBHj29Rgpg8aBscxejikZcJC+eQVEzSFbbZg3AN+sS2h4gOQ0YSRh2qzACQBqQUnLenJCOmKqBj5gNNJRb6DPcAW8f+Z3oKVkwCFm/WEkNy2x3c47+1IwXzDt8RDiFlJKmDZ36gQA7g2Ce/wORERyiTJwDNK6SYGjGHi/ka7YeI6SAQdI00BixfO2N1pRR06DOmS8Q1ER4g5mtBHS5tAZuAKF5tCQbkgpAU8AfMCYTIfSM4oCZciETEcBgFYTOCL54VKIhiO22rBAIbynLnAoIkLcQegJiFjYdju1oJQKb+UYKaVVJEiK4/5JSNH+Z0jRzW2y/X0AWMUQoL4KiEfg5l4CdcxsMJdMhqVkIM3Mowegb1lhu513zuVgHp8DERHiDlIIGKkMD/hC4JrXgYhIppiNR2zv2tpjqge8sBxswhkw1r/hzDl6jYFVDgOvGJLpQNpQqp1G0kgivvJ52yUxtbGnQXXBBBJCnGRGGu1v0aqoNDyQg3ioFE5N8lOK+4MxDh4qgTJimiPn6B0GeP1QR52a6UDaoWQgjZLrF0Pa/OTDCkrhmXGeQxER4g4iGYNIRGy3U0O0CVEuYooGFuplLf9O8FAp2HG9SHzQuJbVBW7BAM0Dbcp81225TclAmhhVu6BvX2OvEWPwzb3CdU8KQtJJCgEjbH97VyVQCK5lfpY1cQYPlqS3JoDmBQu1L9zDGIMy6lSXTChkgOaFNvVcMH9vShc7g5KBNJDJOBIrX7DdTpt4BpTywQ5ERIh7mOF62ytrmKqBu/ANk6QP4xw8lK4SvAxKUb9Oe5GshOAUKMMzOWTAgEABtBkXgAUKMxhH12gCYRok1r4GGWu21YaX9INnylkORUSIO5iJKEQyZrsdDQ/kNiklZDwMEbE/obQzvKCs3fDAiRhjUIZMAAsWw9ixGkjhOZkaBkCCDxgNZfhUV/cCUzLQS8aBrTD2fGivEefwzrmCNlohOU0KE2a4wXY7JVjk6jdN0jsyGYPZVAPo8fQcUPOBBYt79FBeOgDazEtg7tsEcWgbWi/WjvEXQB07C7yw3LlzpAklA70g4hEk3n/JdjvP1LOhlPRzICJC3EFKCaO53vZ2skz1gPtCDkVFMkkaOkRzDWTcfp2JLjEGpbjz4YEumygq1JEzICqHw9y9HrKxGulLClqOo3qhDB4PPmhc1tTHoGQgRVJKJN5/CTIRtdWOlw+GNmGuQ1ER4g4iEYW0+8mPMau4EA0P5BQpTIhwPWSkAen+FM4LylMu5ctDJeBTz4GMNcM8sgfi8C5ATwCM2VseftzjWUl/KANGgZUMzJokoBUlAyky9nwI8+A2e40UzVo9kGVPEkLskKYBM9Jgu50SLAZT6C0pV0gpIaONEOE6+/UleoB5/GCB3tegYP4CqMOnQg6bDFl/GKKpBjJcB9lcDxiJkzRkYIEisIJSsGAJeNkg11QTTAW98lIgIo1IrH3NdjvvKeeBZ3jPakKcZA0P1NkuvMU0H3gWv5GSY6SUkIkoRHMNYCSdOQnj4F2sHkj5kIyDlQ4ELx3YdptMxiFjTVYyI4TVC8AVaxmjvzCnPthRMmCTlBKJVS9Y3Uk2KP1HQh0z06GoCHEHEQ9D2r0AMG5tTUzDA1lP6gnrk3XS3vCpXbywvE8mmTKPL2/KxFMyYJO+fQ3Mw3vsNdK81t4D9GZHcpg0dKvksE1KqBgsncVnSJ+TpgHRXGt9inYY8wbA/O5cq5/NKBmwQTTVIrn+LdvtvKddBO7SQhOEpIOUEkY4hU2IPH4oNDyQtaQUkOEGq16AzaGhlDgwPEAslAz0kBQC8RXPATb3YVeGjIc6fIozQRHiEiLWDGno9hpxDsWB+vTEeVJKyFgzRHMtIOy9J3ardVy+k/daXlRBk0wdQn/VHtI3L4eoPWSrDfMF4Zt1CWWxJKcJPQkzhe1o1VBJTk3AyhdW0aCjtudN9QQLFFolik0dZu3B9vf5gmA+KlHtFEoGesCsP4zkpqW223lnXQrmCzoQESHukPLwgDcI7vE7EBFxijSS1ryAdBYNasE8AWtSYGtJYUUF84WOnYsr4IWV9MHKQZQMdEOaBhIrnrO90Yo6chrUIeOcCYoQlzAjjbaHzsAVKMHerw8nfcMqGlTXUjQozVQPeEE5eCcfmnhBOcx4BIC0EgEaHnAU/XW7kfxwKURDta02LFAE76kLHIqIEHcQegIihU+JakEpDQ9kgbaiQc21tstKd4sr4KFSq2hPF5/2mapZew6YBrifSlQ7jZKBkzCPHoC+ZYXtdt45l+fN2lSSn6QQVnEhm7g/BH6S3eVI5llFgyIQTTWAaXNSaLcYWLAYPFTSo+WknCaY9hlKBrogjaS1esDmchlt3Cyo/Uc4ExQhLmFGGm2XmGWKCiUN5WOJc6QebykalP4tfpkv1LKXQM+LBVH9ib5DyUAXkuveggzX22rDCsrgmX6uQxER4g4iGYNIRGy3U2gTIteyigbVQMaa039wzQelsByMJoy6GiUDnTCqdkHfsdZeI8bgm7uQ9mEnOU0K09qa2CYlUAie4u5yxDlSCIhIfcsHnzQXDVJUqyfAF6IkMAtQMnACmYghsfIF2+20iWdAKR/sQESEuIcZbrA9mYypGrif1oe7iVU0qKmlaFCadxRk3JocGCwCYzRRNFtQMnCCxNrXbHeV8ZL+8Ew5y6GICHEHMxGFSGEsWQ3R8ICbiETUmhx4su15U8QCRVYiQMsAsw79HzuOsX8LjL2b7DXiCrxzFoIpNNGF5C5pmlavgE1KsIiGzlxCGklrcmAK8z26w7xBa0hAo6GgbEXJQAsRCyO++mXb7TxTz4ZS0s+BiAhxB6vKYH0KwwMecB+tD880KUyrcmDU/o6S3VI94IUV4LTZVNajZADWm11i9ctAwt4e3Lx8MLQJcxyKihB3EIkIpB6314gxq7gQDQ9kjJQCMtIIEa5zpmhQQRmYv5D+H+eInEwGZBe1Abp60hp7PoR5cJu9kygafHOvoEpqJKdJ07BqCtikBItp3DhDpJSQ8TBEc439UtHdYmChEvAgbTKVa7L+1SqlhJQSQghrmYwQXSYDYAycMXDOwTgH5xwwdSQ+eMP2eb2nnA9eUNrL6AlxLymlVWXQZuEtpvmo2zhDZDLesqOgzZ6cHmD+Aqs3QKE5ILmIyS6vnO4mpYRpGDCM3mW+nHOwpmokV70I2VTTozbKgJHwzb+OusdITjNjzfZ7BRiHVtKPKsf1MWnoLTsKOlA0yOO3igZpVGI9l2VdMiBME4ZhQNjcRbA70tSBLcthbl9z8gdqXgQu/QJ4oDCt5yfETYShw2g4YrudUlAKhXoF+oy1o2B9y46C6S4apFnbCnuD9MEnD2RNMiClhK7rEGaaC2SceJ6jByDWvQrZxSci79wroY2Y4mgMhGSSlBJGQ7WVINvAvX6oBWUORUWOZ+0o2AQRdqhoUEEpWKCYkoA84vpkQEoJYZrQ9XTvnnWSc+pJyI/egdjzYbvblSET4DvzP+gFQnKaEWmEsFujnnNoxf1pUlkfEK07ChrJtB/b2lGwlIZ58pCrkwEpJfRkMu1DAj0lqnZBrH8diEfAfEEELrkdzBfMSCyE9AWhJ2E0VttupxaWgdNGNI6SesLaTMjmEuieYN6gNSRA+0fkLdcmA1JKJJNJyAwlAm1xJGLAh4uhjZwKdfC4jMZCiJOklNAbjthejsZ9Qai077xjpGlAhGsho03pP7jmhVJQAealRC7fuTIZkFIimUh0vUQwAzRNg6Jm/UpMQrpkhBsg4mF7jbgCrbgfDQ84wCoa1NBSNCjN74VcbSkaVEDDngSAC+sMtPUIuCgRAGDNWWAMCu1BQHKQ0BP2EwHAqjJIiUBaWUWDmq0dBdNdNIgx8GApWKiYdhQk7bguGTANI+NDA13Rk0lwn48yaZJTpBBWcSGbuD8ErnkdiCh/yWSspWiQAzsK+gtbiga57m2fuICrnhVCiF4XEXKankxC83goISA5w4w02l6exhQVSqDIoYjyjzSSLUWD7PfOdId5/OCFFWCUuJGTcE0y0LpywO2EEDBNEyrNHyA5QCRjEClsaavQJkRpYRUNqmspGpRmimYlAd4A/b8i3XLNFc00DNfNE+iKoetQFIVeYCSrSWHCaK633U4JFILTErResYoGNVrzApzYUTBUChYoovco0mOuSAaklK4fHjiRaRhQNdqwg2QvM9xg+0LEVA3cX+BMQHlASgnZWjTIZoXH7rGWokElVDSI2OaKZMDpEsNOMAwDiqpS5k2ykpmIQiRjttupIRoeSJXU4xBNNZAp/N27w3wh8IJyMJU+oJDUZDwZyMZegVZCCFpqSLKONE2Y4RSGB4JFdLFJgTQNa3JgzImiQT5rR0Gq/kh6yRXJQLbMFTiRaRiUDJCsIqWEEa63XcSGaV5wX8ihqHKTFAIyUg8Rsf/37paiWj0BvhD11JC0yHjVib7ad2DdunW44fOfx4iRI1FcUoJJkyfj5kWLsHnz5pSPKYTI2kSG5CeRiEDqcXuNGIMaKqGLTg9JKSGiTTCP7k1/9UDGwQvKoFQMA6fqgSSNMp4M9EWBoUcffRSfOussFBUW4tlnn8XGjRvxm1//GuHmZvz1r3/t1bEpGSDZQpoGzHDnW3OfjBIspkI1PSQSUZg1ByAaj6R9a2EWKLKSgFApVQ8kaZfxvQkS8bitC+q//v1v/OhHP8KuXbsQCAQwbdo0/P1vf0Mw2PlugstXrMAFF1yAB3/xC9x2220d7q+rq0NpaWnK8dOeBSQbSClhNB6FtLntLdN8UAvL6BNoN6SRtCYHplCzoTvMG7CGBKhoEHFQRpMBKSUS8Z53WVZVVWHsuHH44Q9/iIWXX47m5mYsX74c1113HUKhzscz5515JkKhEF579dV0hd2OoijQPLTmmribGW2GGbXZK8A4tJJ+tEztJKQwIZrrIKMN6T+46gEvLAf30rbpxHlZ9ZH28OHDMAwDCxcuxLChQwEAkydP7vLxW7duxQcffIBnnnnmpMcdN348CgsLwRlDcUkJXn/ttR7HRIMExO2EodtPBACooWJKBLpg7SjY2DInwIGiQQVlYP5C6pEhfSajyYDdTompU6di/vz5OO2003DeeefhvHPPxZVXXomSkhL85dlnceedd7Y99rnnnsOBAwcAAKfMmNHtsd95++0uexdOiuYMEBeTUsJMZRMirx/cG3Agouxm7SgYbtlR0IGiQaES8GAJ7QRJ+lxW9QwoioKXX3oJK1etwuK33sLvfv973Pe97+HdpUtx6SWXYNZpp7U9duDAgdi+bRsAdDmfgJBcZ0abIO1etDiHEixxJqAsJpPxlh0Fba7G6AHmL2jZUZDqOJDMyGj6mUoXGGMMc+fMwXe+8x2sWrkSmqbh+RdeQEFBAUaNGtX2z+/3Y+KkSQCA5cuXd3qsWCzWdswLLrgAZ8ybh788+6zdgGz/DoT0BaEnIWLNttupoVL6ZHocaeow6w/DrD2Q/kTA44NSNgRKcX9KBEhGZVXPwOrVq7FkyRKce+65qKisxJo1a1BTU4Px48Z1+vjTZ8/Geeedh7vuuguRSASzZ8+GEAJrP/gAjzzyCB7+zW8wYcIELH7rLQwaNAhVVVW45NJLMXnSJEyZMqVHMVEqQNzIKi6UwvCALwju8TkQUfaRQhy3o2C6iwZpLUWDgjQvgLhCRpMBxhgYYz2eO1BYWIj33nsPv3n4YTQ1NWHo0KH4yY9/jAULFnTZ5u9/+xt+/etf4xcPPog9e/bA5/Nh5MiRuOTiizGuJYkYNGgQAGDAgAFYsGABNmzY0ONkgNMnKOJCZqQRMG2W+eYKlGCRMwFlESklZKzJmheQ5loBYNyqExAspiSAuErG6wzoySTMDG5UFIlEIIRAQUEBwuEwLrjgAvzqV7/CzJkze9Te4/VSQkBcRSTjMJpqbLdTiyrA83wtu2jdUdBmPYaeYIFi8IJSWqFBXCnjwwSMcyCDyUB1dTU++7nPAQBM08TNN9/c40QASG3eAyFOkUJYew/YxP2hvE4EpJ6AaK6BTETTfmzmDYIXloOpVI+EuFfGewaEEEgmEpkMIWWMc3i9+fsGStzHaK6DsHlBY4oKtbhfXia20jSseQEp1GHoluptKRpESzSJ+2W+Z8DmvAE3UWnHQuIiIhmznQgAgFJQmneJgFU0qAEiXO9Q0aByMNpIiGQRVyQDqqpC19NdwMN5nJIB4hJSmDCa7Q8PKIFC8Dzqvj5WNKjG/gTL7jBmFQyiokEkC2U8GQBaLqpZlgyoqkpZP3EFKSXMcIPtT7hM1cD9Bc4E5UIyGYPZVONQ0aDClqJBrnhLJcQ2VzxzW3sHDCPNmbqDaKdC4hYiGYNIxmy3U0P5MTwgDd2aHBgPp/3YzOMHL6ygHQVJ1nPNFU1RVZimmRVzB1RNy4s3UeJ+0jRhprB6QAkWgam5XfFOChMiXO9c0aDCcjAvFQ0iucE1yQBjDJrH4/qVBYxzKDRXgLhAW5VBmwk007zgvhQ25coSUkrIaMuOgk4UDSooAwsUURJAcoprkgHAqubn9uECj8dDbwLEFUQiAqnbTJ4ZgxoqycnnsJQSsrVokBM7CgaLrOqBVDSI5CBXJQOAu4cLNEoEiEtI04AZtr82XgkW5+QkN6knIJqOQqYwd6I7zBeylgrm+LAKyW+ue1dgjMHj9SKZSLgqIVA1jYYHiCtIKWE018HuODjz+HKuAI40DYjmWshYU/oPrnmhFFaAefzpPzYhLuO6ZABwX0KgaRqtHiCuIWJhSLu18xnPqeEBKQVkuB4iUm97zkS3FLVlR8FQzvy9COmOa69wrQmBnkxCiDRXCLNB83ioR4C4hjB0mCmUzlVDxTkx1m3tKNjcsqNguosGcfBQScuOglQ0iOQX1yYDwLEVBqZpwujjokScc6iaRjsSEteQUsJsrrPdjnv9OTE8IBNRq2iQkf4VRyxQCB6iokEkf7n+md9akEhRlD7rJdA0DVxRqIuQuIoZbYK0O0uecyjBEmcC6iPSSEI01UAmImk/NvMGrCEBKhpE8pzrk4FWrcMGpmnCMAxIB5ICRVWpzDBxJaEnIWLNttupodKsrZMvhQnRXAcZbUj/wVVPy46CwfQfm5AslDXJQCtFUaAoirVvu2nC7GVNAs45FFUF55ySAOJKbcWFbOK+ILjH50BEzrKKBjVANNc5tKNgGZi/kF7vhBwn65KBVoxzaC1FiqSUEEJACmF9PcnsYs45GOfgLf/oDYG4nRlptL/DHlegBIucCcghzhcNKrYmCObAREpC0i1rk4FWjDEwxtpN9OsqGaALP8k2IhmHSGGDHbWgNKtmxMtkHGZzDeBI0aAC8MIyMIWKBhHSlaxPBjpDF32SC6QQMFLYhIj7Q+BZMiFOmnpL0SD78yG6pflaigZl31AJIX0tJ5MBQnKBGWmwvdEOU1QoAfcPD0ghICL1kOF6OLKjYEEZFQ0ixAZKBghxIZGIQSSittspBaWuvgBaRYOaWooGObCjYKgULFiUVUMkhLgBJQOEuIwUZkrDA0qgEFz1OBBReojWyYF2Syn3AAsUWb0BNDmQkJRQMkCIi1jLCBtsL6ljqgbuL3AmqF6SegKiuQYyhZ6O7jBvELywHMzFSVCm0YRq0hOUDBDiIiIRS2kbXjXkvuEBaRoQ4TrIFPZS6JbqbSkalP1lltOpdZn18Uutu9K6CotxDs4YGC21zmuUDBDiEtI0YUZSGB4IFoGp7lk2J6WAjDRAhOsdKhpUDuYvoAtXi9YEwDQMW+XapZQwTRMwj83dUFpKv9OeLPmHkgFCXKCtyqDN7XiZ5gX3hRyKyh4pJWQ8DNFcY79IUncYAwuWgAdLsra8crpJKWEYRq+rsB7PbDkeY6xtozZKuvIDJQOEuICIRyB1m7vxMQY1VOKKN2uZjFk7CurxtB+b+Qus3gDaUbCNaZrQk+mfiNlKSgk9mQTnHJqmUQKWB+jVRUiGSdOwSg7bpASLM36BlIZuTQ5MoUpid5jHb00O1KhoUKvWi3Rf7N4KAEIIJBIJqJoGhXZyzWmUDBCSQVJKGM11sFt4h3l8GZ08J4UJEa6HjDTAkaJBheVg3iBdfI4jTBNJB3sDTsbQdZimCY/HQ/9PchQlA4RkkIiFIe2uu2c8Y8MD1o6CjRDhOmeKBhWUgQWK6IJzAqeHBXpCCoFkIgGP10v/f3IQJQOEZIgwdJgpLLtTQ8V9XlzH2lEwak0OdKJoULDYqh5IRYM6cEMi0EpKSQlBjqJkgJAMkFLCbK6z3Y57A30+PCD1BERTDWTSgaJBvpDVG0BFgzolXJQItKKEIDdRMkBIBpjRJkhTt9eIK1CCxY7E0xlpGi07Cjal/+Cat2VHQX/6j50jpJQZmyPQHSkldF2Hx0NJXK6gZICQPib0JEQKW/aqob5ZYy+lgAw3QETs1z3oFlfBC8vAfFQ0qDtu6xE4kTBNmKYJRaGhnVxAyQAhfaituJBN3BcE9zi7xM7aUbC5ZUfB9BcNsnYULKYdBXvAbjXBTNGTSXCfjxK7HEDJACF9yIw02q/OxxUowSJnAmohEzGYzUcBu4WPeoAFCsFDZRmviZAtWrvgs4Wh69BouCDr0auTkD4iknGIFIrzqAWljn2alkbSmhyYiKT92MwTaCka5E37sXOZaaZ5yabDTNOEKiX1DmQ5SgYI6QNSCBhh+5sQcX8BuAMXU6toUF1L0aA0Uz3gBeXgvmD6j53jpJRp3Wugr5iGAVVzz2ZZxD5KBgjpA2akwXaRHqZoUAKFaY3DKhrUANFc58yOgqEysEAhfUpMkRACMt2TNvuAYRhQVJX+v2cxSgYIcZhIxCAS9tfoKwXpqzJoFQ2KQDTVAHaXNHaLtRQNKqGiQb2UbUMExxNC0MqCLEbTeglxkBRmSsMDSqAQPE2FeKQeh1l3CKK+Ku2JAPOFoFQMg1JYTolAGvRkBcEFCxbgnq9+tQ+isUdmweoH0jXqGSDEIdYywgbb3fFM9YD7C3p/flNvKRpkv6ZBtzRfS9Eg2lEwXaSU6a/r0IeyYSkk6RolA4Q4RCRikMmYzVas15sQSSEgIvWQ4Xqkf0dBFbygHMwXovHhNMv2i2nrfAd6XmQnGiYgxAHSNGFGUhgeCBaBqanNypZSQkQbYR7dCxm2vy3ySTEOXlAOpWIYuJ+qB/bGP/7xD0yZMgV+vx9lZWU477zzEIlEOu1mj0Qi+H+33ILyigqMGDECD/3ylx0e4w8E8MILL7S7rf+AAXjyyScBAPv27YM/EMA//vlPnHveeSgpLcUZ8+Zhx44dWLt2Lc444wyUV1Rg4cKFOHr0aNsxbr3tNnz6M5/BT3/6UwwbPhz9BwzAj370IxiGgW9885sYOGgQRo0ejT//+c9tbc4991x86UtfahfL0aNH4fF4sHjx4l793YizKBkgJM3aqgza7PJlmjfl5XgiEYVZsx+isTrtWwuzQBGUyuHWBEGqHtgrVVVVuOaaa7Bo0SJs2bIFS5YswVVXXWVN8Ozk8d/85jexbNky/P1vf8OLL76IZe++iw0bNqR07h/84Af4+te+hpUrVkBVVdx000341re/jZ/97//irTffxK7du3H//fe3a7N06VJ8UlWFN994Aw/85Ce4/wc/wFVXXYWS4mK8u3Qpbr3lFnzpzjtx8OBBAMCiRYvwzDPPIJE4VrzqqaeewqBBg3DOOeekFDfpG/TKJiTNRDwCabeSH0tteEAaSZh1n0DUHUr71sLMG7QmBxZV0uTANKmqqoJhGLjqqqswfPhwTJkyBV/84hcRCoU6JI/hcBiPP/EEfvzjH2P+/PmYPHkyHnnkERgp1iG468tfxvnnn4/x48fjjjvuwLr16/GNr38dc+fMwfTp03HjjTdi6bvvtmtTUlKCX/z85xg7dixuvPFGjB07FtFYDPfeey9Gjx6Nr371q/B4PFixciUA4KorrwQAPP/8823HePzxx3HTTTdRb5LLUTJASBpJ07BKDtukBIttleuVwoTZWA3z6L70Vw9UPeClg6CUDqSthdNs2rRpOPfcczFlyhR8+tOfxiOPPIL6+nosW7YMJaWlKK+oQHlFBf7y7LPYvXs3kskkTjvttLb2paWlGDtmTErnnjx5ctv3/SorAQCTJk1qd9vxwwQAMHHCBPDjNseqrKzE5OPaKIqC0tLStnY+vx833HADHn30UQDAunXr8NFHH+Gmm25KKWbSd2gCISFpIqWE0Wx/rJ55fODeQA/PISAjjRBhh4oGFZSB+alokFMURcGbb76JFStW4I033sCvf/1rfOtb38LSpUuxZs0aiJY6A5WVldizZ0+PjskY61CoqLO9DbTjKgS2/v898bYTJzGeWFWQMdbpbce3u+WWWzB9+nQcPHgQjz32GM455xwMGzasR78LyRzqGSAkTUQsDGm3q57xHg0PSCkhYs0wj+6DaK5JcyLAwEKlUCqGgweKKBFwGGMMZ5xxBr73ve9h/fr18Hg8ePXVVzFm9GiMGjUKo0aNQkFBAUaOHAlN07BmzZq2tvX19dixc2e741VUVODw4cNtP+/cuRPRqP0iV+nAGMOUKVMwc+ZMPPLII3jmmWewaNGijMRC7KGeAULSQBg6zKj94QG1B1X7ZDIOs+kooMdTDa9LzF9g9QYoVFe+L7z//vtYvHgxLrjgAlRWVuL999/H0aNHMWHCBDDe/rNZKBTCTTfeiG9+85soKy1FRUUF7rvvvnbd9gBw1lln4ff/93+YPXs2TNPEt7/znXaf+DPhlltuwZe+9CUEg0Fc2TKPgLgbJQOE9JKUEmZzne123BsA9/q7Pq6hQzTXQKaw02F3mMffsqMgFQ3qS4WFhXj33Xfx0EMPoampCcOGDcPPf/5zXHTRRZ3uSfCjH/0I4UgEV//Hf6AgFMJ/ffnLaGxqaveYn/z4x/jP22/HeeefjwEDBuB/f/YzrF+/vq9+pTac87ZepWuuuQZ33XUXrrnmGvh89BzLBkxm464YhDhEStlWCa71hcEAgDGwln8nMiKNEHar/HEFWnG/Dp8GgdYdBetbdhRMd9EgzUoCvEEaDnCheDyetVUIVVVtm0+wd+9ejBo1CmvWrMEpp5yS4chIT1AyQPKalBKmaVpV+3q4Yxzn3PoUxDkYA4yGI4DN6nFqYTn4CaV8rR0FmyDCtWmvFWAVDSoDozkBrqYnk1m7WZHm8UAIgdraWtxzzz3Ys2cPli9fnumwSA/RMAHJO1JKCCFgGkZKJWBFS+LQSgmWAYlmyGTPxvS5L9ghERDxiDUxMM21AgC07ChYSrUCsoCiKFmbDHDOsWzZMsyfPx9jx47FP/7xj0yHRGygngGSN1p7AYxOll2l4eDgQgcS4ZN383IVWkllWyU/qScgmmogk+mf/c18QWsfAaoVkDWklEgmEj3qoXITVdOgqvTZMpvR/z2SF4QQ0JNJ595kGYNQPICvCFyPdvkJXy2wSvpK04AI10JGmzp9XK9o3pYdBbuenEjciTEGVVU7rRPgZopCvU7ZjpIBktOklDB0ve+6XrkC4QmBKQmwRBTHTwDk/gIwVYMI17UUDUpzYsLVlqJBtJFQNuOKAmRRMqAoCj3fcgANE5CclfEuV2GCJyOAqQOKCsXrh2yuA0RqteW7xBh4sBQsVEwbCeUI0zShJ9M/f8QJXp+PkoEcQMkAyUlCCCQTNjcLcoKU4CIJLgwghS2Nu8P8hS1Fg6iTL9ckk8m28sRupXk8NESQI+gdhOQc1yQCQMtcAi+Y5gOLNqatjDDzBFqKBnnTcjziPpqmIeHiZEBRVUoEcgj1DJCckvGhgZNQ9SgQsV+psP1BPNYKAW+AumbzgKsS2+MwxuDxeuk5mEOoZ4DkFF3XXZkIAICh+qEqmjWHwC6uWLUCqGhQXuGcQ/N4XDV/gBKB3EQ9AyRnZMOkK6bHoURq7LRoKRrU/YZGJHe55blNiUDuomSA5AQpJRLx9O/ql3ZSQonWgvVgB0LmC7UUDaIdBUnmhwxaeykoEchNlAyQnJBVNd2NJNRwddf3az4oheVUNIh0IKWErut9vspA1TSqJ5DjKBkgWS9regWOo8QawBInbE2sqFZPgC9Eb7rkpFrLajv99k29AfmDJhCSrGcaaS7i0wdMbwHUZMSqQsi4NTkwWERFg0iPKIoCzjlMw7B23UxzUsA4h0pLB/MK9QyQrJaNvQKt1GQYDNJKBKhoEEmRlBJSCBgp7sJ5vNYEgHFKSvMN/R8nWU128+b35JNPov+AAX0UjT2mtwBKUSUlAqRXGGPgigKP1wuvzwfN44GqquDdjPEzxqAoClRNa2uraholAnmK3oVIVjj77LMxffp0PPTQQ+1uF1ncsSWlhJSSxmNJ2rRe4HFc935Xnb/0vCPHo2SAZDW3127vDiUDxGn0/CI9Qf1BxPVuuukmLF26FL/85S/BGANjDHv37gWADmOkTz75JMaMHYvSsjJ85rOfRW1d+/K/t952Gz79mc+0u+2er34VFyxY0PbzBQsW4L/vvhv3fPWrGDBwIIYNH45HH30UkUgEt912GyoqKzFp8mS8/vrrbW3effdd+AMBvPnmmzj99NNRUlqKCy+6CNXV1Xj99dcxfcYMVPbrhxtvugnRaBQA8PTTT6OiogKJE9aOX3HFFbjhhht6/XcjhJCeomSAuN4vf/lLzJkzB7feeiuqqqpQVVWFIUOGdOj+XL16NW7/whdw++234/1Vq3DWWWfhgQceSOmcTz/9NMrLyrDs3Xfxhdtvx399+cu47rrrcPrpp2PlihU479xz8f9uuaXtwt7qBz/8IX7x4IN45+23cfDgQVx/ww34zcMP4/HHHsO//vUvLF68GL/93e8AAFdddRVM08QLL7zQ1r66uhovv/wyFi1alFLchBCSCkoGiOsVFRXB4/EgEAigf//+6N+/vzUuekIy8PBvf4sLzj8fX7n7bowZMwZ3fPGLOO+881I655QpU/D1r38do0ePxle/+lX4fD6UlZdj0aJFGD16NL7xjW+gtrYWmz76qF27+777XcydMwfTp0/HjTfeiGXLluFXv/wlpk+fjnlnnIErr7gC7y5dCgDw+/347Gc/i8cee6yt/VNPPYWhQ4fi7LPPTiluQghJBSUDJCtNmjQJBYWFKK+owMKFCwEA27ZuxWmnndbucbNnz07p+FMmT277XlEUlJaWYtKkSW239evXDwBwtLp9JcHJx7XrV1mJQCCAESNGtN1WWVmJo0ePtv38/xYtwhtvvIFDhw4BAB5//HHcdNNNNM5LCOlTNIGQZKVXXnkFiUQCejIJn8/X43acsQ49CobecRdBVWu/HwBjDNpxt7VerE+cs3DiY7ROjnN8m+kzZmDatGn485//jAsuuAAff/wxXn755R7/PoQQkg6UDJCs4PF42u09MGzYsA4Fh8aNH481a9a0a7d69ep2P5dXVODjzZvb3bbxww87XLT7CgNwyy234KGHHsKhQ4dw3nnnYciQIRmJhRCSv2iYgGSF4cOH4/3338fevXtRU1PTaaW1L37xi3jjzTfx4EMPYefOnfjd736HN998s91jzj7rLKxbtw5PP/00du7cifvvvx+bT0gO+hRjuPbaa3Hw4EE88sgjNHGQEJIRlAyQrHDPPfdAURRMnDgRFRUV2L9/f9syw1azZ83Cbx9+GA8//DBmzZ6NxYsX42tf+1q745x//vn4xte/jm9961uYd+aZaA6Hce211/b1r9OGc46ioiJcffXVCIVCuOKKKzIWCyEkf9HeBCSrZdXWxZ3w+nxgjOHcc8/FpEmT8Ktf/SrTIRFC8hAlAySrmYYBvZMJgNkiFo9jyZIl+I//+A9s3rwZ48aNy3RIhJA8RBMISVbL5k1VOOeYMWMG6uvr8cADD1AiQAjJGOoZIFkvkUh0u3uhG3k8HnDaL54Q4gLZ+7GKkBaqmn0dXIyxrO7VIITkFno3IlmPc551FftUVc26mAkhuYuSAZL1GGNQsqx3gIYHCCFuQskAyQmKomTNJ23N48maWAkh+YGSAZITGGPweDyZDqNbXFGsHRcJIcRFKBkgOYNxnrE9Bnqis42LCCHEDSgZIDlFUVXXjsfT8AAhxK0oGSA5R9M0cJct2/N4PK6LiRBCWtG7E8k5jDFoLiro4/F6XRMLIYR0hioQkpwlpYRpGDAMIyPnb0tKqEeAEOJylAyQnCeEgJ5Moi+f6oqqUmEhQkjWoGSA5AUpJUzThOHwDofUG0AIyUaUDJC8IqWEYRgw0zx0wDiHqqpZWRqZEEIoGSB5SUoJIQRMw4DoxY6HiqpCURTqCSCEZDVKBkjek1K2JQdSCOurdcexBzEGzpj1yZ9zcMYAxqgXgBCSEygZIIQQQvIc9W0SQggheY6SAUIIISTPUTJACCGE5DlKBgghhJA8R8kAIYQQkucoGSCEEELyHCUDhBBCSJ6jZIAQQgjJc5QMEEIIIXmOkgFCCCEkz1EyQAghhOQ5SgYIIYSQPEfJACGEEJLnKBkghBBC8hwlA4QQQkieUzMdwPE8MxaBqx4wroBxBYp27HvG+bH7FAVc9YC33ad0uI9xBZwzMM6gKBzshO85Z+AKa3vMSe9jDIrKoXAGhTN4Wr5X235Wjt2nHHucetxjlc6+ZwycMSgM0BTe9r2qcCgM1s+cQeOsk++t+zXO275XGANjAGcAY2g5PsAAKJyBA9bvwtH2PWeAwo7/3joGkxKQAkwYQLvvhfVPdH0fkwIwzWPfCwMQJqQQgJGENE1ACOs2Q4cUpvW9rgOt37c+tvVxevJYG2FC6AakKSCFgEgaEKbVRpoCQjcgzGPfy5bvTd2APO5xZtI47nsTUkgIU7b83NJeSOs+U0KaEsIUMHXRckwJUzdb2hxrJ6SEKSWSQsKUOOH7E3+2vhewvjclWu479v3v5d6Mvi7ThV7f9Pqm17d7X9/UM0AIIYTkOUoGCCGEkDxHyQAhhBCS5ygZIIQQQvIcJQOEEEJInqNkgBBCCMlzlAwQQggheY6SAUIIISTPUTJACCGE5DlKBgghhJA8R8kAIYQQkucoGSCEEELyHCUDhBBCSJ6jZIAQQgjJc5QMEEIIIXmOkgFCCCEkz1EyQAghhOQ5SgYIIYSQPEfJACGEEJLnKBkghBBC8hwlA4QQQkieo2SAEEIIyXOUDBBCCCF5jpIBQgghJM9RMkAIIYTkO5mj4vG4/O53vyvj8XimQ+nAzbFJSfH1hptjyyVu/ju7OTYpKb7ecHNsvcWklDLTCYkTmpqaUFRUhMbGRhQWFmY6nHbcHBtA8fWGm2PLJW7+O7s5NoDi6w03x9ZbNExACCGE5DlKBgghhJA8R8kAIYQQkudyNhnwer347ne/C6/Xm+lQOnBzbADF1xtuji2XuPnv7ObYAIqvN9wcW2/l7ARCQgghhPRMzvYMEEIIIaRnKBkghBBC8hwlA4QQQkiey7lk4Gtf+xrOPPNM3HDDDdB1vd19sVgMl112Gc466yyce+65OHLkiKvia/WTn/wEM2fOzHhMpmli0aJFOPPMM3HXXXf1WTw9ja9VX/+9jtdVbG54ruUien2nLyZ6fXcvn17fOZUMbNy4EYcOHcKyZcswfvx4/OMf/2h3/6uvvorJkydj6dKluOmmm/CnP/3JVfEBQHNzMzZt2uSKmF566SUMHDgQy5YtQyQSwcqVK/ssrp7EB/T936unsWX6uZaL6PWd3pjo9Z16bJl+rjkhp5KBFStW4IILLgAAXHjhhVi+fHm7+0ePHo1IJAIAqK+vR3l5uaviA4Bf/vKX+NKXvuSKmHoSbybjA/r+73W8k8WW6edaLqLXd3pjotf3yeXb61vNdADpVF9fjwEDBgAAioqKUFdX1+7+MWPGYPPmzZg0aRKklFi9erWr4mtsbMSmTZvw7W9/2xUx1dfXt9Xf7izeTMeXib9XT2PL9HMtF9HrO70x0es79dgy/VxzQlb2DBw+fBhnn312h3+AtZEEYD2RSktL27V74oknMG/ePHz88cf4/ve/j/vvv99V8T300EO48847HYmpK8XFxV3GdLL73BBfJv5exztZbH31XMtF9PpOH3p9py7fXt9ZmQz0798fS5Ys6fDv4osvxltvvQUAeP3113HGGWe0ayelbOvOKS8vR2Njo6vi27lzJ37wgx/gwgsvxI4dO/DDH/7QkfiON3fu3C5jOtl9feVkMWTi79XT2PrquZaL6PWdPvT6dia2nHx9Z2bnZOfcc889ct68efLaa6+ViURCSinlbbfdJqWUsqGhQS5YsECeddZZct68eXLbtm2uiu94p556asZiao1H13V54403ynnz5sk777yzz+LpaXzH68u/1/G6is0Nz7VcRK/v3sdEr++ey6fXN5UjJoQQQvJcVg4TEEIIISR9KBkghBBC8hwlA4QQQkieo2SAEEIIyXOUDOSBxx9/HMXFxWk51t69e8EYg6IoOHToULv7qqqqoKoqGGPYu3dvu/v++c9/4uyzz0ZRURFCoRCmTp2K73//+22FPNIZIyH55qabbgJjDLfffnuH++644w4wxnDTTTe13Xb48GHceeedGDlyJLxeL4YMGYLLLrsMixcvbnvM8OHD8dBDD/VB9MQNKBkgKRk0aBD+/Oc/t7vtiSeewKBBgzo89lvf+hY++9nP4rTTTsOrr76Kjz76CD//+c+xceNGPPnkk30VMiE5bciQIXj22WcRi8XabovH43jmmWcwdOjQttv27t2LU089FW+//TZ+9rOfYdOmTXjttdcwf/583HHHHZkInbgAJQNZ4LXXXsO8efNQXFyMsrIyXHrppdi1axcAYMmSJWCMoaGhoe3xGzZsaPt0vmTJEtx8881obGwEYwyMMdx3330ArHKbn//851FSUoJAIICLLroIO3bs6FFMN954Ix577LF2tz322GO48cYb2922evVq/OhHP8LPf/5z/OxnP8PcuXMxfPhwnH/++fjnP//Z4fGEkNSccsopGDJkCP71r3+13favf/0LQ4cOxYwZM9pu++IXvwjGGFavXo2rr74aY8eOxaRJk3D33Xdj1apVmQiduAAlA1kgEong7rvvxtq1a7F48WJwznHllVdCCNFt27lz5+Khhx5CYWEhqqqqUFVVhXvuuQeA1bW4du1avPDCC1i5ciWklLj44ou73Hr1eJdffjnq6+vx3nvvAQDee+891NfX47LLLmv3uKeffhqhUAhf/OIXOz0ODQ0Qkj6LFi1ql6Q/+uijuPnmm9t+rqurw2uvvYY77rgDwWCwQ3t6PeavnNqoKFddffXV7X5+9NFHUVFRgc2bN3fb1uPxoKioCIwx9O/fv+32HTt24IUXXsDy5csxd+5cANaFe8iQIXjuuefw6U9/+qTH1TQN119/PR599FHMmzcPjz76KK6//npomtbucTt27MDIkSM73E4ISb/rr78e3/jGN7Bv3z4AwPLly/Hss89iyZIlAKwSv1JKjB8/PoNREjeinoEssGPHDlxzzTUYOXIkCgsLMXz4cADA/v37Uz7mli1boKoqZs+e3XZbWVkZxo0bhy1btgAALrroIoRCIYRCIUyaNKnDMRYtWoS///3vOHz4MP7+979j0aJFHR5DBS4J6TsVFRW45JJL8Pjjj+Oxxx7DJZdc0m57XXo9kq5Qz0AWuOyyyzBs2DA88sgjGDhwIIQQmDx5MpLJJEKhEID2L/KedPP3xB//+Me2yUidfbKfMmUKxo8fj2uuuQYTJkzA5MmTsWHDhnaPGTt2LN577z3ouk69A4T0gUWLFuFLX/oSAODhhx9ud9+YMWPAGMPWrVszERpxMeoZcLna2lps27YN3/72t3HuuediwoQJqK+vb7u/oqICgLWsr9WJF2SPxwPTNNvdNmHCBBiGgffff7/DuSZOnAjAWjEwevRojB49GsOGDes0vkWLFmHJkiWd9goAwLXXXotwOIzf/va3nd5//MRHQkjvXXjhhUgmk9B1HQsWLGh3X2lpKRYsWICHH34YkUikQ1t6PeYvSgZcrqSkBGVlZfjDH/6AnTt34u2338bdd9/ddv/o0aMxZMgQ3HfffdixYwdefvll/PznP293jOHDhyMcDmPx4sWoqalBNBrFmDFjsHDhQtx666147733sHHjRlx//fUYNGgQFi5c2OP4br31Vhw9ehS33HJLp/fPnj0b9957L77yla/g3nvvxcqVK7Fv3z4sXrwYn/70p/HEE0+k9ochhHRKURRs2bIFmzdvhqIoHe5/+OGHYZomZs2ahX/+85/YsWMHtmzZgl/96leYM2dOBiImbkDJgMtxzvHss8/igw8+wOTJk/Hf//3f+NnPftZ2v6Zp+Mtf/oKtW7di6tSpeOCBB/CDH/yg3THmzp2L22+/HZ/97GdRUVGBn/70pwCspYCnnnoqLr30UsyZMwdSSrzyyiu2uvNVVUV5eTlUtesRpwceeADPPPMM3n//fSxYsKBtGdPUqVNpaSEhDigsLERhYWGn940cORLr1q3D/Pnz8ZWvfAWTJ0/G+eefj8WLF+N3v/tdH0dK3IK2MCaEEELyHPUMEEIIIXmOkgFCCCEkz1EyQAghhOQ5SgYIIYSQPEfJACGEEJLnKBkghBBC8hwlA4QQQkieo2SAEEIIyXOUDBBCCCF5jpIBQgghJM9RMkAIIYTkuf8PTWXdkw9phF8AAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp.plot_graph(results['graph'], val_matrix=results['val_matrix'], var_names=var_names)\n",
+    "tp.plot_time_series_graph(results['graph'], val_matrix = results['val_matrix'], \n",
+    "              node_classification = node_classification_jpcmci, var_names=var_names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14f1b107",
+   "metadata": {},
+   "source": [
+    "If we do not include the dummy variables, we see that $X^1$ and $X^0$ are confounded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "92babc1a",
+   "metadata": {
+    "scrolled": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:32.047956945Z",
+     "start_time": "2023-07-28T08:52:30.628970850Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 1: Selecting lagged conditioning sets\n",
+      "##\n",
+      "\n",
+      "##\n",
+      "## Step 1: PC1 algorithm for selecting lagged conditions\n",
+      "##\n",
+      "\n",
+      "Parameters:\n",
+      "link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>'}, 4: {}}\n",
+      "independence test = par_corr_mult\n",
+      "tau_min = 1\n",
+      "tau_max = 2\n",
+      "pc_alpha = [0.01]\n",
+      "max_conds_dim = None\n",
+      "max_combinations = 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "## Resulting lagged parent (super)sets:\n",
+      "\n",
+      "    Variable $X_0$ has 2 link(s):\n",
+      "        (t-$C_4$ -1): max_pval = 0.00000, min_val =  0.616\n",
+      "        ($X_0$ -1): max_pval = 0.00000, min_val =  0.298\n",
+      "\n",
+      "    Variable $X_1$ has 1 link(s):\n",
+      "        ($X_1$ -1): max_pval = 0.00000, min_val =  0.382\n",
+      "\n",
+      "    Variable $X_2$ has 5 link(s):\n",
+      "        ($X_2$ -1): max_pval = 0.00000, min_val =  0.438\n",
+      "        ($X_1$ -2): max_pval = 0.00000, min_val = -0.392\n",
+      "        (t-$C_4$ -1): max_pval = 0.00000, min_val =  0.352\n",
+      "        ($X_2$ -2): max_pval = 0.00000, min_val =  0.148\n",
+      "        (t-$C_4$ -2): max_pval = 0.00300, min_val = -0.043\n",
+      "\n",
+      "    Variable t-$C_4$ has 2 link(s):\n",
+      "        (t-$C_4$ -2): max_pval = 0.00000, min_val =  0.084\n",
+      "        (t-$C_4$ -1): max_pval = 0.00000, min_val =  0.077\n",
+      "\n",
+      "    Variable s-$C_5$ has 0 link(s):\n",
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 2: Discovering context-system links\n",
+      "##\n",
+      "\n",
+      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (3, 0): '-?>', (3, -1): '-?>', (3, -2): '-?>', (4, 0): '-?>'}, 3: {(3, -1): '-?>', (3, -2): '-?>', (0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}, 4: {(0, 0): '<?-', (1, 0): '<?-', (2, 0): '<?-'}}\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X_0$ has 4 link(s):\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
+      "        ($X_0$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable $X_1$ has 3 link(s):\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable $X_2$ has 7 link(s):\n",
+      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.000\n",
+      "        ($X_1$ -2): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$ -1): pval = 0.00000 | val =  0.000\n",
+      "        ($X_2$ -2): pval = 0.00000 | val =  0.000\n",
+      "\n",
+      "    Variable t-$C_4$ has 2 link(s):\n",
+      "        (t-$C_4$ -2): pval = 0.00000 | val =  0.080\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.070\n",
+      "\n",
+      "    Variable s-$C_5$ has 1 link(s):\n",
+      "        ($X_2$  0): pval = 0.00000 | val =  0.402\n",
+      "\n",
+      "##\n",
+      "## J-PCMCI+ Step 4: Discovering system-system links \n",
+      "##\n",
+      "\n",
+      "With link_assumptions = {0: {(0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>', (3, -1): '-->'}, 1: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, -1): '-?>', (1, -2): '-?>', (2, 0): 'o?o', (2, -1): '-?>', (2, -2): '-?>'}, 2: {(0, 0): 'o?o', (0, -1): '-?>', (0, -2): '-?>', (1, 0): 'o?o', (1, -1): '-?>', (1, -2): '-?>', (2, -1): '-?>', (2, -2): '-?>', (4, 0): '-->', (3, -1): '-->'}, 3: {}, 4: {(2, 0): '<--'}}\n",
+      "\n",
+      "## Significant links at alpha = 0.01:\n",
+      "\n",
+      "    Variable $X_0$ has 3 link(s):\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.606\n",
+      "        ($X_0$ -1): pval = 0.00000 | val =  0.262\n",
+      "        ($X_1$  0): pval = 0.00000 | val =  0.156 | unclear orientation due to conflict\n",
+      "\n",
+      "    Variable $X_1$ has 2 link(s):\n",
+      "        ($X_1$ -1): pval = 0.00000 | val =  0.382\n",
+      "        ($X_0$  0): pval = 0.00000 | val =  0.156 | unclear orientation due to conflict\n",
+      "\n",
+      "    Variable $X_2$ has 4 link(s):\n",
+      "        ($X_1$ -2): pval = 0.00000 | val = -0.461\n",
+      "        (s-$C_5$  0): pval = 0.00000 | val =  0.402\n",
+      "        (t-$C_4$ -1): pval = 0.00000 | val =  0.351\n",
+      "        ($X_2$ -1): pval = 0.00000 | val =  0.291\n",
+      "\n",
+      "    Variable t-$C_4$ has 0 link(s):\n",
+      "\n",
+      "    Variable s-$C_5$ has 0 link(s):\n",
+      "\n",
+      "## Ambiguous triples (not used for orientation):\n",
+      "\n",
+      "    [($X_0$ -1), $X_0$, $X_1$]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# without dummy\n",
+    "# Classify all the nodes into system, context, or dummy\n",
+    "node_classification_jpcmci_wo_dummy = {i: node_classification[var] for i, var in enumerate(observed_indices)}\n",
+    "\n",
+    "# Name all the variables and initialize the dataframe object\n",
+    "# Be careful to use analysis_mode = 'multiple'\n",
+    "var_names_wo_dummy = sys_var_names + context_var_names \n",
+    "\n",
+    "\n",
+    "dataframe = pp.DataFrame(\n",
+    "    data=data_observed,\n",
+    "    analysis_mode = 'multiple',\n",
+    "    var_names = var_names_wo_dummy\n",
+    "    )\n",
+    "\n",
+    "# Create a J-PCMCI+ object, passing the dataframe and (conditional)\n",
+    "# independence test objects, as well as the observed temporal and spatial context nodes \n",
+    "# and the indices of the dummies.\n",
+    "jpcmciplus = JPCMCIplus(dataframe=dataframe,\n",
+    "                          cond_ind_test=ParCorrMult(significance='analytic'), \n",
+    "                          verbosity=1,\n",
+    "                          node_classification=node_classification_jpcmci_wo_dummy)\n",
+    "\n",
+    "\n",
+    "# Define the analysis parameters.\n",
+    "tau_max = 2\n",
+    "pc_alpha = 0.01\n",
+    "\n",
+    "\n",
+    "# Run J-PCMCI+\n",
+    "results = jpcmciplus.run_jpcmciplus(tau_min=0, \n",
+    "                              tau_max=tau_max, \n",
+    "                              pc_alpha=pc_alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "70e4ef8b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:32.791185754Z",
+     "start_time": "2023-07-28T08:52:32.054028782Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tp.plot_graph(results['graph'], val_matrix=results['val_matrix'], var_names=var_names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc60582",
+   "metadata": {},
+   "source": [
+    "The confounded link ``x-x`` with the `x`-edgemarks indicates that the orientation phase resulted in conflicting orientations when different rules where applied, see the PCMCIplus tutorial for details.\n",
+    "\n",
+    "We suggest to first study toymodels that mimic the datasets of interest, but where causal ground truth is known. This helps to assess whether J-PCMCI$^+$ is applicable given, eg, too small sample sizes, and how to choose hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65be5017",
+   "metadata": {},
+   "source": [
+    "#### References\n",
+    "\n",
+    "- [1] Joris M. Mooij, Sara Magliacane, and Tom Claassen. \"Joint causal inference from multiple contexts.\" The Journal of Machine Learning Research 21.1 (2020): 3919-4026.\n",
+    "- [2] Biwei Huang, et al. \"Causal discovery from heterogeneous/nonstationary data.\" The Journal of Machine Learning Research 21.1 (2020): 3482-3534."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f59150ea",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:32.831924057Z",
+     "start_time": "2023-07-28T08:52:32.787616244Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "278d3762",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-28T08:52:32.832076114Z",
+     "start_time": "2023-07-28T08:52:32.831597516Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}


Test patch:
diff --git a/tests/test_jpcmciplus_calculations.py b/tests/test_jpcmciplus_calculations.py
new file mode 100644
index 00000000..b07e36f5
--- /dev/null
+++ b/tests/test_jpcmciplus_calculations.py
@@ -0,0 +1,382 @@
+from copy import deepcopy
+
+import pytest
+import numpy as np
+from numpy.random import MT19937, SeedSequence
+
+from tigramite.jpcmciplus import JPCMCIplus
+from tigramite.toymodels import structural_causal_processes as toys
+from tigramite.independence_tests.parcorr_mult import ParCorrMult
+from tigramite.independence_tests.oracle_conditional_independence import OracleCI
+
+from tigramite.toymodels.context_model import _shift_link_entries, ContextModel, _do_dummy_projection, _nb_latent_before
+import tigramite.data_processing as pp
+
+
+# ############ Utils for model generation
+def lin_f(x):
+    return x
+
+
+def mergeDictionary(dict_1, dict_2):
+    dict_3 = dict(dict_1)
+    dict_3.update(dict_2)
+    for key, value in dict_3.items():
+        if key in dict_1 and key in dict_2:
+            dict_3[key] = value + dict_1[key]
+    return dict_3
+
+
+def choose_cause_tau(candidates, tau_max, random_state):
+    if type(candidates[0]) is tuple:
+        cause, tau = random_state.choice(candidates)
+    else:
+        cause = random_state.choice(candidates)
+        tau = 0 if tau_max == 0 else int(random_state.integers(1, tau_max))
+    return cause, tau
+
+
+def get_non_parents(links, tau_max):
+    parents = sum(links.values(), [])
+    parents = [i[0] for i in parents]
+    non_parents = [(var, tau) for var in links.keys() for tau in range(tau_max + 1) if (var, -tau) not in parents]
+    return non_parents
+
+
+def populate_links(links, chosen_links, contemp_links, max_lag, dependency_coeffs, dependency_funcs, random_state):
+    for (i, j, tau) in chosen_links:
+        # Choose lag
+        if tau is None:
+            if (i, j) in contemp_links or max_lag == 0:
+                tau = 0
+            else:
+                tau = int(random_state.integers(1, max_lag))
+
+        # Choose dependency
+        c = float(random_state.choice(dependency_coeffs))
+        if c != 0:
+            func = random_state.choice(dependency_funcs)
+            if func == "linear":
+                func = lin_f
+
+            links[j].append(((int(i), -int(tau)), c, func))
+    return links
+
+
+def set_links(effect_candidates, L, cause_candidates, tau_max, dependency_funcs, dependency_coeffs, random_state):
+    chosen_links = []
+    contemp_links = []
+    links = {i: [] for i in effect_candidates}
+    for l in range(L):
+        cause, tau = choose_cause_tau(cause_candidates, tau_max, random_state)
+        effect = random_state.choice(effect_candidates)
+
+        while (cause, effect, tau) in chosen_links:
+            cause, tau = choose_cause_tau(cause_candidates, tau_max, random_state)
+            effect = random_state.choice(effect_candidates)
+        if tau == 0:
+            contemp_links.append((cause, effect, tau))
+            chosen_links.append((cause, effect, tau))
+        else:
+            chosen_links.append((cause, effect, tau))
+
+    links = populate_links(links, chosen_links, contemp_links, tau_max, dependency_coeffs, dependency_funcs,
+                           random_state)
+
+    return links
+
+
+def a_generate_random_context_model(N=3,
+                                    K_space=2,
+                                    K_time=2,
+                                    tau_max=2):
+    # use seedsequence
+    ss = SeedSequence(12347)
+    child_seeds = ss.spawn(7)
+
+    dependency_funcs = ['linear']
+    dependency_coeffs = [-0.5, 0.5]
+    auto_coeffs = [0.5, 0.7]
+    contemp_fraction = 0.
+    noise_dists = ['gaussian']
+    noise_means = [0.]
+    noise_sigmas = [0.5, 2.]
+
+    L = 1 if N == 2 else N
+    L_space = 1 if K_space == 2 else 1  # K_space
+    L_time = 1 if K_time == 2 else 1  # K_time
+
+    nodes_sc = list(range(N + K_time, N + K_space + K_time))
+    nodes_tc = list(range(N, K_time + N))
+    nodes_sys = list(range(N))
+
+    links_tc = {}
+    links_sc = {}
+    noises_tc = []
+    noises_sc = []
+
+    # graph for temporal context vars
+    if K_time != 0:
+        links_tc, noises_tc = toys.generate_structural_causal_process(len(nodes_tc),
+                                                                      L_time,
+                                                                      dependency_funcs,
+                                                                      dependency_coeffs,
+                                                                      auto_coeffs,
+                                                                      contemp_fraction,
+                                                                      tau_max,
+                                                                      noise_dists,
+                                                                      noise_means,
+                                                                      noise_sigmas,
+                                                                      MT19937(child_seeds[0]),
+                                                                      MT19937(child_seeds[1]), )
+        links_tc = _shift_link_entries(links_tc, N)
+
+    if K_space != 0:
+        # graph for spatial context vars
+        links_sc, noises_sc = toys.generate_structural_causal_process(len(nodes_sc),
+                                                                      L_space,
+                                                                      dependency_funcs,
+                                                                      dependency_coeffs,
+                                                                      auto_coeffs,
+                                                                      1.,
+                                                                      0,
+                                                                      noise_dists,
+                                                                      noise_means,
+                                                                      noise_sigmas,
+                                                                      MT19937(child_seeds[2]),
+                                                                      MT19937(child_seeds[3]))
+        links_sc = _shift_link_entries(links_sc, N + K_time)
+
+    # graph for system vars
+    links_sys, noises_sys = toys.generate_structural_causal_process(len(nodes_sys),
+                                                                    L,
+                                                                    dependency_funcs,
+                                                                    dependency_coeffs,
+                                                                    auto_coeffs,
+                                                                    contemp_fraction,
+                                                                    tau_max,
+                                                                    noise_dists,
+                                                                    noise_means,
+                                                                    noise_sigmas,
+                                                                    MT19937(child_seeds[4]),
+                                                                    MT19937(child_seeds[5]))
+
+    links = dict(links_tc)
+    links.update(links_sc)
+    links.update(links_sys)
+    noises = noises_sys + noises_tc + noises_sc
+
+    # set context-system links
+    non_parent_tc = get_non_parents(links_tc, tau_max)
+    non_parent_sc = get_non_parents(links_sc, 0)
+
+    # number of links between system and context
+    L_context_sys = 2 * (len(non_parent_sc) + len(non_parent_tc))
+
+    context_sys_links = set_links(nodes_sys, L_context_sys, non_parent_sc + non_parent_tc,
+                                  tau_max, dependency_funcs, dependency_coeffs,
+                                  np.random.default_rng(child_seeds[6]))
+
+    # join all link-dicts to form graph over context and system nodes
+    links = mergeDictionary(links, context_sys_links)
+
+    node_classification = {}
+    for node in links_sys.keys():
+        node_classification[node] = "system"
+    for node in links_tc.keys():
+        node_classification[node] = "time_context"
+    for node in links_sc.keys():
+        node_classification[node] = "space_context"
+
+    return links, node_classification, noises
+
+
+# J-PCMCIplus TESTING for random context models
+# ################################################################
+
+@pytest.fixture(params=[
+    # Generate a test data sample
+    # value, total time length, and random seed to different numbers
+    # links,               node_classification, noises, time, nb_domains, seed_val
+
+    (a_generate_random_context_model(N=3,
+                                     K_space=2,
+                                     K_time=2,
+                                     tau_max=2), 100, 100, SeedSequence(12346).spawn(1)[0]),
+    (a_generate_random_context_model(N=5,
+                                     K_space=2,
+                                     K_time=2,
+                                     tau_max=2), 100, 100, SeedSequence(12347).spawn(1)[0]),
+    (({0: [((0, -1), 0.3, lin_f), ((3, -1), 0.6, lin_f), ((4, -1), 0.9, lin_f)],
+       1: [((1, -1), 0.4, lin_f), ((3, -1), 0.4, lin_f)],
+       2: [((2, -1), 0.3, lin_f), ((1, -2), -0.5, lin_f), ((4, -1), 0.5, lin_f), ((5, 0), 0.6, lin_f)],
+       3: [], 4: [], 5: []
+       }, {
+          0: "system",
+          1: "system",
+          2: "system",
+          3: "time_context",
+          4: "time_context",
+          5: "space_context"
+      }, None), 100, 50, SeedSequence(12345).spawn(1)[0]),
+])
+def a_jpcmciplus(request):
+    # Set the parameters
+    ct_model, T, nb_domains, seed_val = request.param
+    links, node_classification_gt, noises = ct_model
+
+    percent_observed = 0.5
+
+    # Retrieve lags
+    tau_min, tau_max = toys._get_minmax_lag(links)
+    # Generate the data
+    contextmodel = ContextModel(links=links, node_classification=node_classification_gt, noises=noises,
+                                seed=seed_val)
+
+    data_ens, nonstationary = contextmodel.generate_data(nb_domains, T)
+    assert not nonstationary
+
+    # decide which context variables should be latent, and which are observed
+    time_context_indices = [node for node in links.keys() if node_classification_gt[node] == "time_context"]
+    space_context_indices = [node for node in links.keys() if node_classification_gt[node] == "space_context"]
+    system_indices = [node for node in links.keys() if node_classification_gt[node] == "system"]
+
+    random_state = np.random.default_rng(seed_val.spawn(1)[0])
+    # take care to only select nodes as latent that fulfill the no latent context confounder and mediator assumption
+    observed_indices_time = random_state.choice(time_context_indices,
+                                                size=int(percent_observed * len(time_context_indices)),
+                                                replace=False).tolist()
+    observed_indices_space = random_state.choice(space_context_indices,
+                                                 size=int(percent_observed * len(space_context_indices)),
+                                                 replace=False).tolist()
+    observed_context_indices = observed_indices_time + observed_indices_space
+
+    # all system variables are also observed, thus we get the following observed data
+    observed_indices = system_indices + observed_context_indices
+    data_observed = {key: data_ens[key][:, observed_indices] for key in data_ens}
+
+    dummy_data_time = np.identity(T)
+
+    data_dict = {}
+    for i in range(nb_domains):
+        dummy_data_space = np.zeros((T, nb_domains))
+        dummy_data_space[:, i] = 1.
+        data_dict[i] = np.hstack((data_observed[i], dummy_data_time, dummy_data_space))
+
+    # Define vector-valued variables including dummy variables as well as observed (system and context) variables
+    nb_observed_context_nodes = len(observed_context_indices)
+    N = len(system_indices)
+    observed_temporal_context_nodes = list(range(N, N + len(observed_indices_time)))
+    observed_spatial_context_nodes = list(range(N + len(observed_indices_time),
+                                                N + nb_observed_context_nodes))
+    time_dummy_index = N + nb_observed_context_nodes
+    space_dummy_index = N + nb_observed_context_nodes + 1
+    time_dummy = list(range(time_dummy_index, time_dummy_index + T))
+    space_dummy = list(range(time_dummy_index + T, time_dummy_index + T + nb_domains))
+
+    vector_vars = {i: [(i, 0)] for i in
+                   system_indices + observed_temporal_context_nodes + observed_spatial_context_nodes}
+    vector_vars[time_dummy_index] = [(i, 0) for i in time_dummy]
+    vector_vars[space_dummy_index] = [(i, 0) for i in space_dummy]
+
+    node_classification = {}
+    for node in observed_indices:
+        node_classification[node - _nb_latent_before(node, observed_context_indices, node_classification_gt)] = \
+            node_classification_gt[node]
+    node_classification[time_dummy_index] = "time_dummy"
+    node_classification[space_dummy_index] = "space_dummy"
+
+    dataframe = pp.DataFrame(
+        data=data_dict,
+        vector_vars=vector_vars,
+        analysis_mode='multiple',
+    )
+
+    # Get the true parents
+    # augment the true_parents by remove context-context links and adding dummy (i.e. perform dummy projection)
+    augmented_links = _do_dummy_projection(links, node_classification_gt, observed_context_indices,
+                                           time_dummy_index, space_dummy_index)
+    augmented_true_graph = toys.links_to_graph(augmented_links, tau_max=tau_max)
+
+    return dataframe, augmented_true_graph, augmented_links, tau_min, tau_max, node_classification
+
+
+@pytest.fixture(params=[
+    # Keep parameters for the algorithm here
+    # pc_alpha, contemp_collider_rule, conflict_resolution, reset, cond_ind_test
+    # OracleCI tests
+    (0.01, 'majority', True, False, 'oracle_ci'),
+    (0.01, 'none', True, False, 'oracle_ci'),
+    (0.01, 'conservative', True, False, 'oracle_ci'),
+    (0.01, 'majority', False, False, 'oracle_ci'),
+    (0.01, 'none', False, False, 'oracle_ci'),
+    (0.01, 'conservative', False, False, 'oracle_ci'),
+    (0.01, 'majority', True, True, 'oracle_ci'),
+    (0.01, 'none', True, True, 'oracle_ci'),
+    (0.01, 'conservative', True, True, 'oracle_ci'),
+    (0.01, 'majority', False, True, 'oracle_ci'),
+    (0.01, 'none', False, True, 'oracle_ci'),
+    (0.01, 'conservative', False, True, 'oracle_ci'),
+])
+def a_jpcmciplus_params(request):
+    # Return the parameters for the mci test
+    return request.param
+
+
+@pytest.fixture()
+def a_run_jpcmciplus(a_jpcmciplus, a_jpcmciplus_params):
+    # Unpack the pcmci and the true parents, and common parameters
+    dataframe, true_graph, links_coeffs, tau_min, tau_max, node_classification = a_jpcmciplus
+
+    # Unpack the parameters
+    (pc_alpha,
+     contemp_collider_rule, conflict_resolution,
+     reset_lagged_links, cond_ind_test_class,
+     ) = a_jpcmciplus_params
+
+    cond_ind_test = OracleCI(links=links_coeffs)
+    if cond_ind_test_class == 'par_corr_mult':
+        cond_ind_test = ParCorrMult()
+
+    # Run the PCMCI algorithm with the given parameters
+    jpcmci = JPCMCIplus(dataframe=dataframe, cond_ind_test=cond_ind_test, verbosity=0,
+                         node_classification=node_classification
+                         )
+    results = jpcmci.run_jpcmciplus(
+        link_assumptions=None,
+        tau_min=tau_min,
+        tau_max=tau_max,
+        pc_alpha=pc_alpha,
+        contemp_collider_rule=contemp_collider_rule,
+        conflict_resolution=conflict_resolution,
+        reset_lagged_links=reset_lagged_links,
+        max_conds_dim=None,
+        max_conds_py=None,
+        max_conds_px=None,
+        max_combinations=1,
+    )
+    # Print true links
+    print("************************")
+    print("\nTrue Graph")
+    for lag in range(tau_max + 1):
+        print(f"Lag {lag} = ")
+        print(true_graph[:, :, lag])
+
+    # Print algorithm output
+    print("************************")
+    print("\nJ-PCMCI+ Output")
+    for lag in range(tau_max + 1):
+        print(f"Lag {lag} = ")
+        print(results['graph'][:, :, lag])
+    # Return the results and the expected result
+    return results['graph'], true_graph
+
+
+def test_jpcmciplus(a_run_jpcmciplus):
+    """
+    Test the J-PCMCIplus algorithm and check it calculates the correct graph.
+    """
+    # Unpack the calculated and true graph
+    graph, true_graph = a_run_jpcmciplus
+    # Ensure they are the same
+    np.testing.assert_equal(graph, true_graph)