From 6a3ab9672e817af3ce136b94c0bc1f6382116b1e Mon Sep 17 00:00:00 2001
From: Andrzej <novak5andrzej@gmail.com>
Date: Thu, 9 Jun 2022 12:35:44 +0200
Subject: [PATCH] fix: satisfy precommit

---
 src/cabinetry/fit/__init__.py            |   9 +--
 src/cabinetry/fit/results_containers.py  |   4 +-
 src/cabinetry/model_utils.py             |  15 ++++-
 src/cabinetry/visualize/__init__.py      |   6 +-
 src/cabinetry/visualize/plot_result.py   |   2 +-
 tests/cli/test_cli.py                    |  34 ++++++++--
 tests/fit/test_fit.py                    |  81 ++++++++++++++++++-----
 tests/fit/test_fit_results_containers.py |  16 ++++-
 tests/test_model_utils.py                |   3 +
 tests/visualize/reference/pulls.png      | Bin 7114 -> 5376 bytes
 tests/visualize/test_visualize.py        |  10 ++-
 11 files changed, 139 insertions(+), 41 deletions(-)

diff --git a/src/cabinetry/fit/__init__.py b/src/cabinetry/fit/__init__.py
index 3283c2b0..d6e0536c 100644
--- a/src/cabinetry/fit/__init__.py
+++ b/src/cabinetry/fit/__init__.py
@@ -8,7 +8,6 @@
 import pyhf
 import scipy.optimize
 import scipy.stats
-import re
 
 from cabinetry import model_utils
 from cabinetry.fit.results_containers import (
@@ -87,10 +86,7 @@ def _fit_model_pyhf(
 
     bestfit = pyhf.tensorlib.to_numpy(result[:, 0])
     uncertainty = pyhf.tensorlib.to_numpy(result[:, 1])
-    labels = model.config.par_names()
-    _mod_dict = dict(model.config.modifiers)
-    _clean_labels = [re.sub(r"\[.*\]", "", label) for label in labels]
-    types = [_mod_dict[n] if n in _mod_dict else None for n in _clean_labels]
+    labels, types = model_utils._labels_modifiers(model)
     corr_mat = pyhf.tensorlib.to_numpy(corr_mat)
     best_twice_nll = float(best_twice_nll)  # convert 0-dim np.ndarray to float
 
@@ -150,7 +146,7 @@ def _fit_model_custom(
     fix_pars = fix_pars or model.config.suggested_fixed()
     par_bounds = par_bounds or model.config.suggested_bounds()
 
-    labels = model.config.par_names()
+    labels, types = model_utils._labels_modifiers(model)
 
     # set initial step size to 0 for fixed parameters
     # this will cause the associated parameter uncertainties to be 0 post-fit
@@ -195,6 +191,7 @@ def twice_nll_func(pars: np.ndarray) -> Any:
         bestfit,
         uncertainty,
         labels,
+        types,
         corr_mat,
         best_twice_nll,
         minos_uncertainty=minos_results,
diff --git a/src/cabinetry/fit/results_containers.py b/src/cabinetry/fit/results_containers.py
index 2c8a3857..8f04af17 100644
--- a/src/cabinetry/fit/results_containers.py
+++ b/src/cabinetry/fit/results_containers.py
@@ -1,6 +1,6 @@
 """Provides containers for inference results."""
 
-from typing import Dict, List, NamedTuple, Tuple
+from typing import Dict, List, NamedTuple, Optional, Tuple
 
 import numpy as np
 
@@ -24,7 +24,7 @@ class FitResults(NamedTuple):
     bestfit: np.ndarray
     uncertainty: np.ndarray
     labels: List[str]
-    types: List[str]
+    types: List[Optional[str]]
     corr_mat: np.ndarray
     best_twice_nll: float
     goodness_of_fit: float = -1
diff --git a/src/cabinetry/model_utils.py b/src/cabinetry/model_utils.py
index 827f4e8a..37ab38b0 100644
--- a/src/cabinetry/model_utils.py
+++ b/src/cabinetry/model_utils.py
@@ -2,6 +2,7 @@
 
 import json
 import logging
+import re
 from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
 
 import awkward as ak
@@ -516,6 +517,17 @@ def _filter_channels(
     return filtered_channels
 
 
+def _labels_modifiers(
+    model: pyhf.pdf.Model,
+) -> Tuple[List[str], List[Optional[str]]]:
+    """ """
+    labels = model.config.par_names()
+    _mod_dict = dict(model.config.modifiers)
+    _clean_labels = [re.sub(r"\[.*\]", "", label) for label in labels]
+    types = [_mod_dict[n] if n in _mod_dict else None for n in _clean_labels]
+    return labels, types
+
+
 def match_fit_results(model: pyhf.pdf.Model, fit_results: FitResults) -> FitResults:
     """Matches results from a fit to a model by adding or removing parameters as needed.
 
@@ -543,7 +555,7 @@ def match_fit_results(model: pyhf.pdf.Model, fit_results: FitResults) -> FitResu
 
     bestfit = asimov_parameters(model)  # Asimov parameter values for target model
     uncertainty = prefit_uncertainties(model)  # pre-fit uncertainties for target model
-    labels = model.config.par_names()  # labels for target model
+    labels, types = _labels_modifiers(model)
 
     # indices of parameters in current fit results, or None if they are missing
     indices_for_corr: List[Optional[int]] = [None] * len(labels)
@@ -577,6 +589,7 @@ def match_fit_results(model: pyhf.pdf.Model, fit_results: FitResults) -> FitResu
         np.asarray(bestfit),
         np.asarray(uncertainty),
         labels,
+        types,
         corr_mat,
         fit_results.best_twice_nll,
         fit_results.goodness_of_fit,
diff --git a/src/cabinetry/visualize/__init__.py b/src/cabinetry/visualize/__init__.py
index a339d20f..6387282e 100644
--- a/src/cabinetry/visualize/__init__.py
+++ b/src/cabinetry/visualize/__init__.py
@@ -1,9 +1,9 @@
 """High-level entry point for visualizing fit models and inference results."""
 
+import fnmatch
 import glob
 import logging
 import pathlib
-import fnmatch
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import matplotlib as mpl
@@ -442,7 +442,7 @@ def pulls(
     *,
     figure_folder: Union[str, pathlib.Path] = "figures",
     exclude: Optional[Union[str, List[str], Tuple[str, ...]]] = None,
-    exclude_by_type: Optional[Union[str, List[str]]] = ["staterror"],
+    exclude_by_type: Optional[List[str]] = None,
     close_figure: bool = True,
     save_figure: bool = True,
 ) -> mpl.figure.Figure:
@@ -489,6 +489,8 @@ def pulls(
     )
 
     # exclude by type
+    if exclude_by_type is None:
+        exclude_by_type = ["staterror"]
     exclude_set.update(
         [
             label
diff --git a/src/cabinetry/visualize/plot_result.py b/src/cabinetry/visualize/plot_result.py
index 8f3708a4..755bcfe0 100644
--- a/src/cabinetry/visualize/plot_result.py
+++ b/src/cabinetry/visualize/plot_result.py
@@ -91,7 +91,7 @@ def pulls(
         matplotlib.figure.Figure: the pull figure
     """
     num_pars = len(bestfit)
-    numeric = np.zeros(num_pars, dtype=bool) if numeric is None else numeric
+    numeric = np.zeros(num_pars, dtype=bool) if numeric is None else np.asarray(numeric)
     y_positions = np.arange(num_pars)[::-1]
 
     fig, ax = plt.subplots(figsize=(6, 1 + num_pars / 4), dpi=100)
diff --git a/tests/cli/test_cli.py b/tests/cli/test_cli.py
index afb5acd4..74a4c9db 100644
--- a/tests/cli/test_cli.py
+++ b/tests/cli/test_cli.py
@@ -95,7 +95,12 @@ def test_workspace(mock_validate, mock_build, cli_helpers, tmp_path):
 @mock.patch(
     "cabinetry.fit.fit",
     return_value=fit.FitResults(
-        np.asarray([1.0]), np.asarray([0.1]), ["label"], np.asarray([[1.0]]), 1.0
+        np.asarray([1.0]),
+        np.asarray([0.1]),
+        ["label"],
+        ["type"],
+        np.asarray([[1.0]]),
+        1.0,
     ),
     autospec=True,
 )
@@ -109,8 +114,9 @@ def test_fit(mock_utils, mock_fit, mock_pulls, mock_corrmat, tmp_path):
     bestfit = np.asarray([1.0])
     uncertainty = np.asarray([0.1])
     labels = ["label"]
+    types = ["type"]
     corr_mat = np.asarray([[1.0]])
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, corr_mat, 1.0)
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, corr_mat, 1.0)
 
     workspace_path = str(tmp_path / "workspace.json")
 
@@ -204,7 +210,12 @@ def test_fit(mock_utils, mock_fit, mock_pulls, mock_corrmat, tmp_path):
 @mock.patch(
     "cabinetry.fit.fit",
     return_value=fit.FitResults(
-        np.asarray([1.0]), np.asarray([0.1]), ["label"], np.asarray([[1.0]]), 1.0
+        np.asarray([1.0]),
+        np.asarray([0.1]),
+        ["label"],
+        ["type"],
+        np.asarray([[1.0]]),
+        1.0,
     ),
     autospec=True,
 )
@@ -218,8 +229,9 @@ def test_ranking(mock_utils, mock_fit, mock_rank, mock_vis, tmp_path):
     bestfit = np.asarray([1.0])
     uncertainty = np.asarray([0.1])
     labels = ["label"]
+    types = ["type"]
     corr_mat = np.asarray([[1.0]])
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, corr_mat, 1.0)
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, corr_mat, 1.0)
 
     workspace_path = str(tmp_path / "workspace.json")
 
@@ -467,7 +479,12 @@ def test_significance(mock_utils, mock_sig, tmp_path):
 @mock.patch(
     "cabinetry.fit.fit",
     return_value=fit.FitResults(
-        np.asarray([1.0]), np.asarray([0.1]), ["label"], np.asarray([[1.0]]), 1.0
+        np.asarray([1.0]),
+        np.asarray([0.1]),
+        ["label"],
+        ["type"],
+        np.asarray([[1.0]]),
+        1.0,
     ),
     autospec=True,
 )
@@ -508,7 +525,12 @@ def test_data_mc(
     config_path = str(tmp_path / "config.yml")
     cli_helpers.write_config(config_path, config)
     fit_results = fit.FitResults(
-        np.asarray([1.0]), np.asarray([0.1]), ["label"], np.asarray([[1.0]]), 1.0
+        np.asarray([1.0]),
+        np.asarray([0.1]),
+        ["label"],
+        ["type"],
+        np.asarray([[1.0]]),
+        1.0,
     )
 
     result = runner.invoke(
diff --git a/tests/fit/test_fit.py b/tests/fit/test_fit.py
index d2c901b1..63d15e57 100644
--- a/tests/fit/test_fit.py
+++ b/tests/fit/test_fit.py
@@ -16,7 +16,8 @@ def test_print_results(caplog):
     bestfit = np.asarray([1.0, 2.0])
     uncertainty = np.asarray([0.1, 0.3])
     labels = ["param_A", "param_B"]
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 0.0)
+    types = ["normsys", "shapesys"]
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 0.0)
 
     fit.print_results(fit_results)
     assert "param_A =  1.0000 +/- 0.1000" in [rec.message for rec in caplog.records]
@@ -143,13 +144,13 @@ def test__fit_model_custom(mock_minos, example_spec, example_spec_multibin):
 @mock.patch(
     "cabinetry.fit._fit_model_custom",
     return_value=fit.FitResults(
-        np.asarray([1.2]), np.asarray([0.2]), ["par"], np.empty(0), 2.0
+        np.asarray([1.2]), np.asarray([0.2]), ["par"], ["normsys"], np.empty(0), 2.0
     ),
 )
 @mock.patch(
     "cabinetry.fit._fit_model_pyhf",
     return_value=fit.FitResults(
-        np.asarray([1.1]), np.asarray([0.2]), ["par"], np.empty(0), 2.0
+        np.asarray([1.1]), np.asarray([0.2]), ["par"], ["normsys"], np.empty(0), 2.0
     ),
 )
 def test__fit_model(mock_pyhf, mock_custom, example_spec):
@@ -295,7 +296,7 @@ def test__goodness_of_fit(
 @mock.patch(
     "cabinetry.fit._fit_model",
     return_value=fit.FitResults(
-        np.asarray([1.0]), np.asarray([0.1]), ["par"], np.empty(0), 2.0
+        np.asarray([1.0]), np.asarray([0.1]), ["par"], ["normsys"], np.empty(0), 2.0
     ),
 )
 def test_fit(mock_fit, mock_print, mock_gof):
@@ -382,33 +383,78 @@ def test_fit(mock_fit, mock_print, mock_gof):
     "cabinetry.fit._fit_model",
     side_effect=[
         fit.FitResults(
-            np.asarray([0.9, 1.3]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 1.3]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         fit.FitResults(
-            np.asarray([0.9, 0.7]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 0.7]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         fit.FitResults(
-            np.asarray([0.9, 1.2]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 1.2]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         fit.FitResults(
-            np.asarray([0.9, 0.8]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 0.8]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         # for second ranking call with fixed parameter
         fit.FitResults(
-            np.asarray([0.9, 1.2]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 1.2]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         fit.FitResults(
-            np.asarray([0.9, 0.8]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 0.8]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         # for third ranking call without reference results
         fit.FitResults(
-            np.asarray([0.9, 1.0]), np.asarray([0.3, 0.3]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 1.0]),
+            np.asarray([0.3, 0.3]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         fit.FitResults(
-            np.asarray([0.9, 1.3]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 1.3]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
         fit.FitResults(
-            np.asarray([0.9, 0.7]), np.asarray([0.1, 0.1]), ["a", "b"], np.empty(0), 0.0
+            np.asarray([0.9, 0.7]),
+            np.asarray([0.1, 0.1]),
+            ["a", "b"],
+            ["normsys", "normsys"],
+            np.empty(0),
+            0.0,
         ),
     ],
 )
@@ -417,7 +463,8 @@ def test_ranking(mock_fit, example_spec):
     bestfit = np.asarray([0.9, 1.0])
     uncertainty = np.asarray([0.02, 0.1])
     labels = ["staterror", "mu"]
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 0.0)
+    types = ["staterror", "normfactor"]
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 0.0)
     model, data = model_utils.model_and_data(example_spec)
     ranking_results = fit.ranking(model, data, fit_results=fit_results)
 
@@ -500,16 +547,16 @@ def test_ranking(mock_fit, example_spec):
     "cabinetry.fit._fit_model",
     side_effect=[
         fit.FitResults(
-            np.asarray([0.9, 1.3]), np.asarray([0.1, 0.1]), [], np.empty(0), 8.0
+            np.asarray([0.9, 1.3]), np.asarray([0.1, 0.1]), [], [], np.empty(0), 8.0
         )
     ]  # nominal fit
     + [
-        fit.FitResults(np.empty(0), np.empty(0), [], np.empty(0), abs(i) + 8)
+        fit.FitResults(np.empty(0), np.empty(0), [], [], np.empty(0), abs(i) + 8)
         for i in np.linspace(-5, 5, 11)
     ]  # fits in scan
     + [
         fit.FitResults(
-            np.asarray([0.9, 1.3]), np.asarray([0.1, 0.1]), [], np.empty(0), 2.0
+            np.asarray([0.9, 1.3]), np.asarray([0.1, 0.1]), [], [], np.empty(0), 2.0
         )
     ]
     * 6,  # fits for custom parameter range
diff --git a/tests/fit/test_fit_results_containers.py b/tests/fit/test_fit_results_containers.py
index 0739663b..c9746849 100644
--- a/tests/fit/test_fit_results_containers.py
+++ b/tests/fit/test_fit_results_containers.py
@@ -9,9 +9,12 @@ def test_FitResults():
     bestfit = np.asarray([1.0])
     uncertainty = np.asarray([0.1])
     labels = ["par_a"]
+    types = [None]
     corr_mat = np.asarray([[1.0]])
     best_twice_nll = 2.0
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, corr_mat, best_twice_nll)
+    fit_results = fit.FitResults(
+        bestfit, uncertainty, labels, types, corr_mat, best_twice_nll
+    )
     assert np.allclose(fit_results.bestfit, bestfit)
     assert np.allclose(fit_results.uncertainty, uncertainty)
     assert fit_results.labels == labels
@@ -30,7 +33,13 @@ def test_RankingResults():
     postfit_up = np.asarray([0.2])
     postfit_down = np.asarray([-0.2])
     ranking_results = fit.RankingResults(
-        bestfit, uncertainty, labels, prefit_up, prefit_down, postfit_up, postfit_down
+        bestfit,
+        uncertainty,
+        labels,
+        prefit_up,
+        prefit_down,
+        postfit_up,
+        postfit_down,
     )
     assert np.allclose(ranking_results.bestfit, bestfit)
     assert np.allclose(ranking_results.uncertainty, uncertainty)
@@ -100,7 +109,8 @@ def test_print_results(caplog):
     bestfit = np.asarray([1.0, 2.0])
     uncertainty = np.asarray([0.1, 0.3])
     labels = ["param_A", "param_B"]
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 0.0)
+    types = [None, None]
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 0.0)
 
     fit.print_results(fit_results)
     assert "param_A =  1.0000 +/- 0.1000" in [rec.message for rec in caplog.records]
diff --git a/tests/test_model_utils.py b/tests/test_model_utils.py
index ca5e40bb..9624f2f2 100644
--- a/tests/test_model_utils.py
+++ b/tests/test_model_utils.py
@@ -265,6 +265,7 @@ def test_prediction(
         np.asarray([1.01, 1.1]),
         np.asarray([0.03, 0.1]),
         ["staterror_Signal-Region[0]", "Signal strength"],
+        ["staterror", "normfactor"],
         np.asarray([[1.0, 0.2], [0.2, 1.0]]),
         0.0,
     )
@@ -298,6 +299,7 @@ def test_prediction(
         np.asarray([1.01, 1.1]),
         np.asarray([0.03, 0.1]),
         ["a", "b"],
+        ["staterror", "normfactor"],
         np.asarray([[1.0, 0.2], [0.2, 1.0]]),
         0.0,
     )
@@ -394,6 +396,7 @@ def test_match_fit_results(mock_pars, mock_uncs):
         np.asarray([1.0, 2.0, 3.0]),
         np.asarray([0.1, 0.2, 0.3]),
         ["par_a", "par_b", "par_c"],
+        [None, None, None],
         np.asarray([[1.0, 0.2, 0.5], [0.2, 1.0, 0.1], [0.5, 0.1, 1.0]]),
         5.0,
         0.1,
diff --git a/tests/visualize/reference/pulls.png b/tests/visualize/reference/pulls.png
index 8fad063113bf8d15be32dcf14b2f3d32bb73344a..9759e84396d8cb9d86d9f3c1adbe970300a03758 100644
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diff --git a/tests/visualize/test_visualize.py b/tests/visualize/test_visualize.py
index 303eb54c..714fef87 100644
--- a/tests/visualize/test_visualize.py
+++ b/tests/visualize/test_visualize.py
@@ -360,10 +360,11 @@ def test_correlation_matrix(mock_draw):
     corr_mat = np.asarray([[1.0, 0.2, 0.1], [0.2, 1.0, 0.1], [0.1, 0.1, 1.0]])
     corr_mat_pruned = np.asarray([[1.0, 0.2], [0.2, 1.0]])
     labels = ["a", "b", "c"]
+    types = [None, None, None]
     labels_pruned = ["a", "b"]
     folder_path = "tmp"
     figure_path = pathlib.Path(folder_path) / "correlation_matrix.pdf"
-    fit_results = fit.FitResults(np.empty(0), np.empty(0), labels, corr_mat, 1.0)
+    fit_results = fit.FitResults(np.empty(0), np.empty(0), labels, types, corr_mat, 1.0)
 
     # pruning with threshold
     fig = visualize.correlation_matrix(
@@ -382,7 +383,7 @@ def test_correlation_matrix(mock_draw):
     # close figure, do not save
     corr_mat_fixed = np.asarray([[1.0, 0.2, 0.0], [0.2, 1.0, 0.0], [0.0, 0.0, 0.0]])
     fit_results_fixed = fit.FitResults(
-        np.empty(0), np.empty(0), labels, corr_mat_fixed, 1.0
+        np.empty(0), np.empty(0), labels, types, corr_mat_fixed, 1.0
     )
     _ = visualize.correlation_matrix(
         fit_results_fixed,
@@ -407,9 +408,10 @@ def test_pulls(mock_draw):
     bestfit = np.asarray([0.8, 1.0, 1.05, 1.1])
     uncertainty = np.asarray([0.9, 1.0, 0.03, 0.7])
     labels = ["a", "b", "staterror_region[0]", "c"]
+    types = [None, None, "staterror", None]
     exclude = ["a"]
     folder_path = "tmp"
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 1.0)
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 1.0)
 
     filtered_bestfit = np.asarray([1.0, 1.1])
     filtered_uncertainty = np.asarray([1.0, 0.7])
@@ -429,6 +431,7 @@ def test_pulls(mock_draw):
             for i in range(len(filtered_labels))
         ]
     )
+    assert np.allclose(mock_draw.call_args[1].pop("numeric"), np.array([False, False]))
     assert mock_draw.call_args[1] == {"figure_path": figure_path, "close_figure": True}
 
     # filtering single parameter instead of list
@@ -462,6 +465,7 @@ def test_pulls(mock_draw):
             for i in range(len(labels_expected))
         ]
     )
+    assert np.allclose(mock_draw.call_args[1].pop("numeric"), np.array([False, False]))
     assert mock_draw.call_args[1] == {"figure_path": None, "close_figure": False}