qMerge branch 'master' of https://github.com/OpenSourceEconomics/ose-…

…course-scientific-computing

.envrc

@@ -14,3 +14,6 @@ export JUPYTER_CONFIG_DIR=$PWD/configurations/jupyter
 
 # Location for startup scripts
 export IPYTHONDIR=$PWD/configurations/ipython
+
+# We need to be able to import our package 
+export PYTHONPATH=$PWD:$PYTHONPATH

conf.py

@@ -12,6 +12,7 @@
 import sys
 
 sys.path.insert(0, os.path.abspath("."))
+print(sys.executable)
 
 # -- Project information -----------------------------------------------------
 

lectures/approximation/approximation_algorithms.py

@@ -1,9 +1,10 @@
 """Algorithms for approximation lecture."""
 import numpy as np
-from approximation_auxiliary import get_chebyshev_nodes
-from approximation_auxiliary import get_uniform_nodes
 from scipy.interpolate import interp1d
 
+from lectures.approximation.approximation_auxiliary import get_chebyshev_nodes
+from lectures.approximation.approximation_auxiliary import get_uniform_nodes
+
 
 def get_interpolator_runge_baseline(func):
     """Return interpolator runge function (baseline)."""

lectures/approximation/approximation_plots.py

@@ -3,15 +3,16 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from approximation_algorithms import get_interpolator_monomial_flexible_nodes
-from approximation_auxiliary import get_chebyshev_nodes
-from approximation_auxiliary import get_uniform_nodes
-from approximation_auxiliary import spline_basis
-from approximation_problems import problem_reciprocal_exponential
-from approximation_problems import problem_two_dimensions
 from scipy.interpolate import interp1d
 from temfpy.interpolation import runge
 
+from lectures.approximation.approximation_algorithms import get_interpolator_monomial_flexible_nodes
+from lectures.approximation.approximation_auxiliary import get_chebyshev_nodes
+from lectures.approximation.approximation_auxiliary import get_uniform_nodes
+from lectures.approximation.approximation_auxiliary import spline_basis
+from lectures.approximation.approximation_problems import problem_reciprocal_exponential
+from lectures.approximation.approximation_problems import problem_two_dimensions
+
 
 def plot_runge():
     """Plot runge function."""

lectures/approximation/approximation_solutions_exercises.py

@@ -3,13 +3,14 @@
 
 import numpy as np
 import pandas as pd
-from approximation_algorithms import get_interpolator
-from approximation_auxiliary import compute_interpolation_error_df
-from approximation_auxiliary import get_uniform_nodes
-from approximation_problems import problem_kinked
-from approximation_problems import problem_reciprocal_exponential
 from temfpy.interpolation import runge
 
+from lectures.approximation.approximation_algorithms import get_interpolator
+from lectures.approximation.approximation_auxiliary import compute_interpolation_error_df
+from lectures.approximation.approximation_auxiliary import get_uniform_nodes
+from lectures.approximation.approximation_problems import problem_kinked
+from lectures.approximation.approximation_problems import problem_reciprocal_exponential
+
 
 def test_exercise_1():
     """Run test exercise 1."""

lectures/integration/integration_algorithms_tests.py

@@ -3,15 +3,16 @@
 
 import chaospy as cp
 import numpy as np
-from integration_algorithms import monte_carlo_naive_one
-from integration_algorithms import monte_carlo_naive_two_dimensions
-from integration_algorithms import monte_carlo_quasi_two_dimensions
-from integration_algorithms import quadrature_gauss_legendre_one
-from integration_algorithms import quadrature_gauss_legendre_two
-from integration_algorithms import quadrature_newton_simpson_one
-from integration_algorithms import quadrature_newton_trapezoid_one
 from scipy.stats import uniform
 
+from lectures.integration.integration_algorithms import monte_carlo_naive_one
+from lectures.integration.integration_algorithms import monte_carlo_naive_two_dimensions
+from lectures.integration.integration_algorithms import monte_carlo_quasi_two_dimensions
+from lectures.integration.integration_algorithms import quadrature_gauss_legendre_one
+from lectures.integration.integration_algorithms import quadrature_gauss_legendre_two
+from lectures.integration.integration_algorithms import quadrature_newton_simpson_one
+from lectures.integration.integration_algorithms import quadrature_newton_trapezoid_one
+
 
 def test_1():
     """Return test 1."""

lectures/integration/integration_plots.py

@@ -5,11 +5,12 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from integration_algorithms import monte_carlo_naive_one
-from integration_algorithms import quadrature_gauss_legendre_one
-from integration_algorithms import quadrature_newton_trapezoid_one
-from integration_problems import problem_kinked
-from integration_problems import problem_smooth
+
+from lectures.integration.integration_algorithms import monte_carlo_naive_one
+from lectures.integration.integration_algorithms import quadrature_gauss_legendre_one
+from lectures.integration.integration_algorithms import quadrature_newton_trapezoid_one
+from lectures.integration.integration_problems import problem_kinked
+from lectures.integration.integration_problems import problem_smooth
 
 
 def plot_gauss_legendre_weights(deg):

lectures/integration/integration_solutions_exercises.py

@@ -4,16 +4,19 @@
 
 import numpy as np
 import pandas as pd
-from integration_algorithms import monte_carlo_naive_two_dimensions as mc_naive
-from integration_algorithms import monte_carlo_quasi_two_dimensions as mc_quasi
-from integration_algorithms import quadrature_gauss_legendre_one
-from integration_algorithms import quadrature_gauss_legendre_two as gc_legendre_two
-from integration_algorithms import quadrature_newton_simpson_one
-from integration_algorithms import quadrature_newton_trapezoid_one
-from integration_problems import problem_kinked
-from integration_problems import problem_smooth
 from temfpy.integration import discontinuous
 
+from lectures.integration.integration_algorithms import monte_carlo_naive_two_dimensions as mc_naive
+from lectures.integration.integration_algorithms import monte_carlo_quasi_two_dimensions as mc_quasi
+from lectures.integration.integration_algorithms import quadrature_gauss_legendre_one
+from lectures.integration.integration_algorithms import (
+    quadrature_gauss_legendre_two as gc_legendre_two,
+)
+from lectures.integration.integration_algorithms import quadrature_newton_simpson_one
+from lectures.integration.integration_algorithms import quadrature_newton_trapezoid_one
+from lectures.integration.integration_problems import problem_kinked
+from lectures.integration.integration_problems import problem_smooth
+
 
 def test_exercise_1():
     """Get solution for exercise 1."""

lectures/linear_equations/linear_algorithms_tests.py

@@ -1,12 +1,13 @@
 """This module contains some tests for our functions."""
 import numpy as np
 import pytest
-from linear_algorithms import backward_substitution
-from linear_algorithms import forward_substitution
-from linear_algorithms import gauss_seidel
-from linear_algorithms import solve
-from linear_problems import get_random_problem
-from linear_solutions_tests import gauss_jacobi
+
+from lectures.linear_equations.linear_algorithms import backward_substitution
+from lectures.linear_equations.linear_algorithms import forward_substitution
+from lectures.linear_equations.linear_algorithms import gauss_seidel
+from lectures.linear_equations.linear_algorithms import solve
+from lectures.linear_equations.linear_problems import get_random_problem
+from lectures.linear_equations.linear_solutions_tests import gauss_jacobi
 
 
 @pytest.mark.repeat(5)

lectures/linear_equations/linear_solutions_tests.py

@@ -4,9 +4,10 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from linear_algorithms import backward_substitution
-from linear_algorithms import eps
-from linear_problems import get_random_problem
+
+from lectures.linear_equations.linear_algorithms import backward_substitution
+from lectures.linear_equations.linear_algorithms import eps
+from lectures.linear_equations.linear_problems import get_random_problem
 
 
 def test_1():

lectures/nonlinear_equations/nonlinear_algorithms_tests.py

@@ -1,10 +1,11 @@
 """Tests for nonlinear equations lecture."""
 import numpy as np
-from nonlinear_algorithms import bisect
-from nonlinear_algorithms import fixpoint
-from nonlinear_algorithms import newton_method
 from scipy.optimize import bisect as sp_bisect
 
+from lectures.nonlinear_equations.nonlinear_algorithms import bisect
+from lectures.nonlinear_equations.nonlinear_algorithms import fixpoint
+from lectures.nonlinear_equations.nonlinear_algorithms import newton_method
+
 
 def test_1():
     """Bisection method is working."""

lectures/nonlinear_equations/nonlinear_plots.py

@@ -1,10 +1,11 @@
 """Plots for nonlinear equations lecture."""
 import matplotlib.pyplot as plt
 import numpy as np
-from nonlinear_algorithms import funcit
-from nonlinear_algorithms import newton_method
 from scipy.optimize import root
 
+from lectures.nonlinear_equations.nonlinear_algorithms import funcit
+from lectures.nonlinear_equations.nonlinear_algorithms import newton_method
+
 
 def plot_bisection_test_function(f):
     """Plot bisect example."""

lectures/nonlinear_equations/nonlinear_solutions_exercises.py

@@ -3,14 +3,15 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from nonlinear_algorithms import bisect
-from nonlinear_algorithms import fixpoint
-from nonlinear_algorithms import newton_method
-from nonlinear_problems import bisection_test_function
-from nonlinear_problems import get_cournot_problem
-from nonlinear_problems import newton_pathological_example
 from scipy import optimize
 
+from lectures.nonlinear_equations.nonlinear_algorithms import bisect
+from lectures.nonlinear_equations.nonlinear_algorithms import fixpoint
+from lectures.nonlinear_equations.nonlinear_algorithms import newton_method
+from lectures.nonlinear_equations.nonlinear_problems import bisection_test_function
+from lectures.nonlinear_equations.nonlinear_problems import get_cournot_problem
+from lectures.nonlinear_equations.nonlinear_problems import newton_pathological_example
+
 
 def test_exercise_1():
     """Test for exercise 1."""

lectures/nonlinear_equations/notebook.ipynb

@@ -70,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#from temfpy.nonlinear_equations import exponential  # noqa: F401\n",
+    "# from temfpy.nonlinear_equations import exponential  # noqa: F401\n",
     "from scipy import optimize\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -1290,7 +1290,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1304,7 +1304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,

lectures/optimization/optimization_solutions_exercises.py

@@ -4,11 +4,12 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from optimization_problems import get_test_function
 from scipy.optimize import minimize
 from scipy.stats import logistic
 from scipy.stats import norm
 
+from lectures.optimization.optimization_problems import get_test_function
+
 
 def test_exercise_1():
     """Solution for exercise 1."""

rtd_environment.yml

@@ -14,6 +14,12 @@ dependencies:
 - jupyter_nbextensions_configurator
 - nbsphinx
 - nb_black
+- numpy
+- scipy
+- pandas
+- chaospy
+- matplotlib
+- pytest
 - pip
 - restructuredtext_lint
 - pip:

tox.ini

@@ -3,7 +3,7 @@ addopts = --black --flake8 --doctest-modules --ignore-glob="configurations/*"
 python_files = *_tests.py *_solutions_*
 
 [flake8]
-ignore = D301, W503, E203, N806, E741, T001, B007, D004
+ignore = D301, W503, E203, N806, E741, T001, B007, D004, D104
 max-line-length = 100
 exclude = configurations/*, conf.py
 
@@ -15,4 +15,4 @@ ignore = W293, W291, W503, D301, W503, E203, N806, E741, T001, B007, F401, D004
 [doc8]
 max-line-length = 100
 ignore-path = iteration/lecture_plan_2021.rst, README.rst
-ignore = D004
+ignore = D004, D104