Merge develop into main for new release 0.12.14. (#617)

* Fix assets for pyabc visserver.

* Update R.

* Fix for logo path in abc-server-dash

* Preparing release notes for next patch release.

* Updating about.rst.

* Updating changelog.

* Create plotly versions of selected matplotlib visualizations (#610)

* Create ploty versions of selected matplotlib visualizations

* update r install instructions

* fix pandas float deprecation warnings

* fixarray ndim > 0 float extraction deprecationwarning

---------

Co-authored-by: Stephan Grein <[email protected]>

* add the functionality to evaluate the model using boundry values of p… (#355)

* add the functionality to evaluate the model using boundry values of parameter

* fix flake8 issues

* fix additional flake8 issues

* add test case

* flake8 fixes

* flake8 fixes

* flake8 fixes

* flake8 fixes

* flake8 fixes

* black fixes

* Update test_external.py

Use correct comparison operator.

---------

Co-authored-by: Yannik Schälte <[email protected]>
Co-authored-by: Stephan Grein <[email protected]>
Co-authored-by: Stephan Grein <[email protected]>

---------

Co-authored-by: Yannik Schälte <[email protected]>
Co-authored-by: Emad Alamoudi <[email protected]>

Author: 3 people

.pre-commit-config.yaml

@@ -11,7 +11,7 @@
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 23.1.0
+  rev: 23.7.0
   hooks:
   - id: black
     description: The uncompromising code formatter
@@ -27,7 +27,7 @@ repos:
     name: isort (pyi)
     types: [pyi]
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.6.1
+  rev: 1.7.0
   hooks:
     - id: nbqa-black
     - id: nbqa-pyupgrade

pyabc/external/base.py

@@ -1,13 +1,16 @@
+import copy
 import logging
 import os
 import subprocess  # noqa: S404
 import tempfile
 from typing import List
 
 import numpy as np
+import pandas as pd
 
 from ..model import Model
 from ..parameters import Parameter
+from .utils import timethis
 
 logger = logging.getLogger("ABC.External")
 
@@ -242,6 +245,84 @@ def __call__(self, pars: Parameter):
     def sample(self, pars):
         return self(pars)
 
+    @timethis
+    def sample_timing(self, pars):
+        return self(pars)
+
+    def eval_param_limits(self, limits):
+        """
+        evaluate single parameter's boundary value on computation time.
+
+        Parameters
+        ----------
+        limits: dict
+            the lower and upper boundary values of parameters. The key would
+            be the parameter name and the value would be a list of the lower
+            and upper limit of parameter value, e.g., [lower, upper].
+
+        Returns
+        -------
+        time_eval_dict: dict
+            a dictionary that contains the parameter names as key and a list
+            as a value. The list contains the computation time when using
+            lower and upper limits, e.g., [lower, upper].
+        """
+        time_eval_dict = {}
+        for key, val in limits.items():
+            lower_bound = self.sample_timing({key: val[0]})
+            upper_bound = self.sample_timing({key: val[1]})
+            time_eval_dict[key] = [lower_bound, upper_bound]
+        return time_eval_dict
+
+    def eval_param_limits_matrix(self, limits):
+        """
+        evaluate two paramters' boundary values on computation time.
+
+        Parameters
+        ----------
+        limits: dict
+            the lower and upper boundary values of parameters. The key would
+            be the parameter name and the value would be a list of the lower
+            and upper limit of parameter value, e.g., [lower, upper].
+
+        Returns
+        -------
+        time_eval_mat_df_lower: df
+            a dataframe for the computation time measured when using the lower
+             limit value of parameters.
+        time_eval_mat_df_upper: df
+            a dataframe for the computation time measured when using the upper
+             limit value of parameters.
+        """
+        time_eval_mat = np.zeros(shape=(len(limits), len(limits)))
+        time_eval_mat_df_lower = pd.DataFrame(
+            time_eval_mat,
+            columns=[list(limits.keys())],
+            index=[list(limits.keys())],
+        )
+        time_eval_mat_df_upper = copy.deepcopy(time_eval_mat_df_lower)
+        for i, (key_col, val_col) in enumerate(limits.items(), 0):
+            for j, (key_row, val_row) in enumerate(limits.items(), 0):
+                if i < j:
+                    time_eval_mat_df_lower.loc[[key_col], [key_row]] = 0
+                    time_eval_mat_df_upper.loc[[key_col], [key_row]] = 0
+
+                if key_col == key_row:
+                    lower_bound = self.sample_timing({key_col: val_col[0]})
+                    upper_bound = self.sample_timing({key_col: val_col[1]})
+
+                else:
+                    lower_bound = self.sample_timing(
+                        {key_col: val_col[0], key_row: val_row[0]}
+                    )
+                    lower_bound = self.sample_timing(
+                        {key_col: val_col[1], key_row: val_row[1]}
+                    )
+                time_eval_mat_df_lower.loc[[key_col], [key_row]] = lower_bound
+                time_eval_mat_df_upper.loc[[key_col], [key_row]] = upper_bound
+
+        return time_eval_mat_df_lower, time_eval_mat_df_upper
+
 
 class ExternalSumStat:
     """

pyabc/external/utils.py

@@ -0,0 +1,13 @@
+import time
+from functools import wraps
+
+
+def timethis(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        start = time.time()
+        func(*args, **kwargs)
+        end = time.time()
+        return end - start
+
+    return wrapper

pyabc/transition/jump.py

@@ -142,4 +142,4 @@ def pdf(
             ]
         )
 
-        return pds if pds.size != 1 else float(pds)
+        return pds if pds.size != 1 else float(pds[0])

pyabc/visualization/__init__.py

@@ -13,11 +13,18 @@
     plot_contour_matrix,
     plot_contour_matrix_lowlevel,
 )
-from .credible import plot_credible_intervals, plot_credible_intervals_for_time
+from .credible import (
+    plot_credible_intervals,
+    plot_credible_intervals_for_time,
+    plot_credible_intervals_plotly,
+)
 from .data import plot_data_callback, plot_data_default
 from .distance import plot_distance_weights
-from .effective_sample_size import plot_effective_sample_sizes
-from .epsilon import plot_epsilons
+from .effective_sample_size import (
+    plot_effective_sample_sizes,
+    plot_effective_sample_sizes_plotly,
+)
+from .epsilon import plot_epsilons, plot_epsilons_plotly
 from .histogram import (
     plot_histogram_1d,
     plot_histogram_1d_lowlevel,
@@ -29,26 +36,44 @@
 from .kde import (
     plot_kde_1d,
     plot_kde_1d_highlevel,
+    plot_kde_1d_highlevel_plotly,
+    plot_kde_1d_plotly,
     plot_kde_2d,
     plot_kde_2d_highlevel,
+    plot_kde_2d_highlevel_plotly,
+    plot_kde_2d_plotly,
     plot_kde_matrix,
     plot_kde_matrix_highlevel,
+    plot_kde_matrix_highlevel_plotly,
+    plot_kde_matrix_plotly,
+)
+from .model_probabilities import (
+    plot_model_probabilities,
+    plot_model_probabilities_plotly,
 )
-from .model_probabilities import plot_model_probabilities
 from .sample import (
     plot_acceptance_rates_trajectory,
+    plot_acceptance_rates_trajectory_plotly,
     plot_lookahead_acceptance_rates,
     plot_lookahead_evaluations,
     plot_lookahead_final_acceptance_fractions,
     plot_sample_numbers,
+    plot_sample_numbers_plotly,
     plot_sample_numbers_trajectory,
+    plot_sample_numbers_trajectory_plotly,
     plot_total_sample_numbers,
+    plot_total_sample_numbers_plotly,
 )
 from .sankey import plot_sensitivity_sankey
 from .walltime import (
     plot_eps_walltime,
     plot_eps_walltime_lowlevel,
+    plot_eps_walltime_lowlevel_plotly,
+    plot_eps_walltime_plotly,
     plot_total_walltime,
+    plot_total_walltime_plotly,
     plot_walltime,
     plot_walltime_lowlevel,
+    plot_walltime_lowlevel_plotly,
+    plot_walltime_plotly,
 )