diff --git a/.github/workflows/chatops-binder.yaml b/.github/workflows/chatops-binder.yaml deleted file mode 100644 index e8ea3d482..000000000 --- a/.github/workflows/chatops-binder.yaml +++ /dev/null @@ -1,34 +0,0 @@ -#./github/workflows/chatops-binder.yaml -name: Chatops Binder -on: [issue_comment] # issues and PRs are equivalent in terms of comments for the GitHub API - -jobs: - trigger-chatops: - # Make sure the comment is on a PR, and contains the command "/binder" - if: (github.event.issue.pull_request != null) && contains(github.event.comment.body, '/binder') - runs-on: ubuntu-latest - steps: - # Use the GitHub API to: - # (1) Get the branch name of the PR that has been commented on with "/binder" - # (2) make a comment on the PR with the binder badge - - name: comment on PR with Binder link - uses: actions/github-script@v6 - with: - github-token: ${{secrets.GITHUB_TOKEN}} - script: | - // Get the branch name - github.pulls.get({ - owner: context.repo.owner, - repo: context.repo.repo, - pull_number: context.payload.issue.number - }).then( (pr) => { - - // use the branch name to make a comment on the PR with a Binder badge - var BRANCH_NAME = pr.data.head.ref - github.issues.createComment({ - issue_number: context.payload.issue.number, - owner: context.repo.owner, - repo: context.repo.repo, - body: `[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/${context.repo.owner}/${context.repo.repo}/${BRANCH_NAME}) :point_left: Launch a binder notebook on this branch` - }) - }) diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index fc9332869..91af9ca34 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -26,73 +26,73 @@ jobs: contents: write steps: - - uses: actions/checkout@v3 - - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: "3.10" # Interpolation.py doesn't support Python 3.11 [2023-07] - cache: 'pip' - cache-dependency-path: | - requirements/base.txt - requirements/doc.txt - - - name: Install Pandoc - run: sudo apt-get install --yes pandoc - - - name: Update pip - run: python -m pip install --upgrade pip - - - name: Install HARK - run: python -m pip install .[doc] - - - name: Run Sphinx - run: > - sphinx-build - -M html Documentation HARK-docs - -T - -W - -j auto - - - name: Set up git for deployment - run: | - git config user.name "${{ github.actor }}" - git config user.email "${{ github.actor }}@users.noreply.github.com" - git config --local --unset-all http.https://github.com/.extraheader - - - name: Commit all rendered HTML files - run: | - git switch --orphan gh-pages - git add --all HARK-docs/html - git commit -qm "Documentation from @ ${{ github.repository }}@${{ github.sha }}" - - - name: Deploy to GitHub Pages - # Only deploy to Pages on pushes to HEAD - if: (github.repository_owner == 'Econ-ARK') && (github.event_name == 'push') && (github.ref_name == 'master') - run: > - git push - --force - https://x-access-token:${{ github.token }}@github.com/${{ github.repository }} - `git subtree split --prefix HARK-docs/html gh-pages`:refs/heads/gh-pages + - uses: actions/checkout@v3 + + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: "3.10" # Interpolation.py doesn't support Python 3.11 [2023-07] + cache: "pip" + cache-dependency-path: | + requirements/base.txt + requirements/doc.txt + + - name: Install Pandoc + run: sudo apt-get install --yes pandoc + + - name: Update pip + run: python -m pip install --upgrade pip + + - name: Install HARK + run: python -m pip install .[doc] + + - name: Run Sphinx + run: > + sphinx-build + -M html Documentation HARK-docs + -T + -W + -j auto + + - name: Set up git for deployment + run: | + git config user.name "${{ github.actor }}" + git config user.email "${{ github.actor }}@users.noreply.github.com" + git config --local --unset-all http.https://github.com/.extraheader + + - name: Commit all rendered HTML files + run: | + git switch --orphan gh-pages + git add --all HARK-docs/html + git commit -qm "Documentation from @ ${{ github.repository }}@${{ github.sha }}" + + - name: Deploy to GitHub Pages + # Only deploy to Pages on pushes to HEAD + if: (github.repository_owner == 'Econ-ARK') && (github.event_name == 'push') && (github.ref_name == 'master') + run: > + git push + --force + https://x-access-token:${{ github.token }}@github.com/${{ github.repository }} + `git subtree split --prefix HARK-docs/html gh-pages`:refs/heads/gh-pages lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: 3 - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install --upgrade sphinx-lint - - name: Lint documentation with sphinx-lint - run: > - sphinx-lint - --ignore Documentation/example_notebooks/GenIncProcessModel.py - --enable all - --max-line-length 85 - README.md - Documentation/ + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3 + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install --upgrade sphinx-lint + - name: Lint documentation with sphinx-lint + run: > + sphinx-lint + --ignore Documentation/example_notebooks/GenIncProcessModel.py + --enable all + --max-line-length 85 + README.md + Documentation/ diff --git a/.github/workflows/execute-notebooks.yml b/.github/workflows/execute-notebooks.yml index fdb5a8c6a..06cb735e7 100644 --- a/.github/workflows/execute-notebooks.yml +++ b/.github/workflows/execute-notebooks.yml @@ -5,7 +5,7 @@ on: workflow_dispatch: # 6.49 am (GMT) every Monday; time chosen at random schedule: - - cron: "49 6 * * MON" + - cron: "49 6 * * MON" # Limit workflow permissions permissions: @@ -29,47 +29,47 @@ jobs: pull-requests: write steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: "3.10" # Numba doesn't support Python 3.11 [2023-05] - cache: 'pip' - cache-dependency-path: | - requirements/base.txt - .github/workflows/execute-notebooks.yml + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: "3.10" # Numba doesn't support Python 3.11 [2023-05] + cache: "pip" + cache-dependency-path: | + requirements/base.txt + .github/workflows/execute-notebooks.yml - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install . - # For LabeledModels.ipynb - python -m pip install estimagic - # For nbstripout - python -m pip install nbstripout - # For nb_exec.py - python -m pip install ipykernel nbclient nbformat + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install . + # For LabeledModels.ipynb + python -m pip install estimagic + # For nbstripout + python -m pip install nbstripout + # For nb_exec.py + python -m pip install ipykernel nbclient nbformat - - name: Strip output - run: nbstripout examples/**/*.ipynb + - name: Strip output + run: nbstripout examples/**/*.ipynb - # This step takes c. 20 minutes - - name: Execute notebooks - run: python tools/nb_exec.py examples/**/*.ipynb - env: - PYTHONUNBUFFERED: "1" + # This step takes c. 20 minutes + - name: Execute notebooks + run: python tools/nb_exec.py examples/**/*.ipynb + env: + PYTHONUNBUFFERED: "1" - - name: Open PR - uses: peter-evans/create-pull-request@v5 - with: - author: "Econ-ARK Bot " - branch: "bot/update-notebooks" - commit-message: "[bot] updated notebooks" - delete-branch: true - title: "[bot] Execute example notebooks" - # language=Markdown - body: > - This PR was [automatically generated] to re-execute - the example notebooks for use in the documentation. - - [automatically generated]: https://github.com/Econ-ARK/HARK/actions/workflows/execute-notebooks.yml + - name: Open PR + uses: peter-evans/create-pull-request@v5 + with: + author: "Econ-ARK Bot " + branch: "bot/update-notebooks" + commit-message: "[bot] updated notebooks" + delete-branch: true + title: "[bot] Execute example notebooks" + # language=Markdown + body: > + This PR was [automatically generated] to re-execute + the example notebooks for use in the documentation. + + [automatically generated]: https://github.com/Econ-ARK/HARK/actions/workflows/execute-notebooks.yml diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 000000000..21fba1c97 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,27 @@ +name: pre-commit + +on: [push, pull_request] + +jobs: + format: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install packages + run: | + python -m pip install --upgrade pip + python -m pip install ".[dev]" + pip list + + - name: Lint + run: pre-commit run --all-files --show-diff-on-failure --color always diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1acc0a54e..30117c837 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,13 +1,14 @@ exclude: Documentation/example_notebooks/ repos: - - repo: https://github.com/mwouts/jupytext - rev: v1.15.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.1.4 hooks: - - id: jupytext - args: [--sync, --set-formats, "ipynb", --pipe, black, --execute] - additional_dependencies: [jupytext, black, nbconvert] - files: ^examples/.*\.ipynb$ + - id: ruff + types_or: [jupyter] + - id: ruff-format + args: [--check] + types_or: [jupyter] - repo: https://github.com/psf/black rev: 23.7.0 diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md index 9ec54333c..d91d204f1 100644 --- a/Documentation/CHANGELOG.md +++ b/Documentation/CHANGELOG.md @@ -16,6 +16,7 @@ Release Date: TBD - Adds `HARK.core.AgentPopulation` class to represent a population of agents with ex-ante heterogeneous parametrizations as distributions. [#1237](https://github.com/econ-ark/HARK/pull/1237) - Adds `HARK.core.Parameters` class to represent a collection of time varying and time invariant parameters in a model. [#1240](https://github.com/econ-ark/HARK/pull/1240) +- Adds `HARK.simulation.monte_carlo` module for generic Monte Carlo simulation functions using Python model configurations. [1296](https://github.com/econ-ark/HARK/pull/1296) ### Minor Changes @@ -26,6 +27,8 @@ Release Date: TBD - Fixes bug that prevented risky-asset consumer types from working with time-varying interest rates `Rfree`. [1343](https://github.com/econ-ark/HARK/pull/1343) - Overhauls and expands condition checking for the ConsIndShock model [#1294](https://github.com/econ-ark/HARK/pull/1294). Condition values and a description of their interpretation is stored in the bilt dictionary of IndShockConsumerType. - Creates a `models/` directory with Python model configurations for perfect foresight and Fisher 2-period models. [1347](https://github.com/econ-ark/HARK/pull/1347) +- Fixes bug in AgentType simulations where 'who_dies' for period t was being recorded in period t-1in the history Carlo simulation functions using Python model configurations.[1296](https://github.com/econ-ark/HARK/pull/1296) +- Removes unused `simulation.py` .[1296](https://github.com/econ-ark/HARK/pull/1296) ### 0.13.0 diff --git a/Documentation/_static/override-nbsphinx-gallery.css b/Documentation/_static/override-nbsphinx-gallery.css index 65ee21fe6..c020c4b0e 100644 --- a/Documentation/_static/override-nbsphinx-gallery.css +++ b/Documentation/_static/override-nbsphinx-gallery.css @@ -1,3 +1,3 @@ .nbsphinx-gallery { - grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); + grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); } diff --git a/Documentation/conf.py b/Documentation/conf.py index 044ca72af..3edbc487e 100644 --- a/Documentation/conf.py +++ b/Documentation/conf.py @@ -1,17 +1,19 @@ -from datetime import date import warnings +from datetime import date try: import numba except ImportError: pass else: - warnings.filterwarnings("ignore", - message="numba.generated_jit.*", - category=numba.NumbaDeprecationWarning) - warnings.filterwarnings("ignore", - message=".* 'nopython' .*", - category=numba.NumbaDeprecationWarning) + warnings.filterwarnings( + "ignore", + message="numba.generated_jit.*", + category=numba.NumbaDeprecationWarning, + ) + warnings.filterwarnings( + "ignore", message=".* 'nopython' .*", category=numba.NumbaDeprecationWarning + ) # Project information project = "HARK" @@ -64,7 +66,7 @@ html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] html_css_files = [ - 'override-nbsphinx-gallery.css', + "override-nbsphinx-gallery.css", ] html_theme_options = { @@ -95,7 +97,7 @@ "type": "local", "attributes": {"target": "_blank"}, }, - ] + ], } # Point to Econ-ARK repo for edit buttons diff --git a/Documentation/overview/ARKitecture.md b/Documentation/overview/ARKitecture.md index b087250df..94aeeaef7 100644 --- a/Documentation/overview/ARKitecture.md +++ b/Documentation/overview/ARKitecture.md @@ -45,7 +45,6 @@ After you [installed](https://docs.econ-ark.org/guides/quick_start.html) and [cl HARK's root directory contains six tool modules, [^1] each containing a variety of functions and classes that can be used in many economic models-- or even for mathematical purposes that have nothing to do with economics. Some of the tool modules are very sparely populated at this time, while others are quite large. We expect that all of these modules will grow considerably in the near future, as new tools are ''low hanging fruit'' for contribution to the project. [^2] [^1]: The ''taxonomy'' of these modules is in flux; the functions described here could be combined into fewer modules or further divided by purpose. - [^2]: That is, as the foundational, building-block elements of HARK, new tools are not difficult to program and do not require extensive integration with many moving parts. #### HARK.core @@ -87,7 +86,6 @@ Methods for optimizing an objective function for the purposes of estimating a mo By default, processes in Python are single-threaded, using only a single CPU core. The **_HARK.parallel_** module provides basic tools for using multiple CPU cores simultaneously, with minimal effort. [^4] In particular, it provides the function **_multiThreadCommands_**, which takes two arguments: a list of **_AgentType_**s and a list of commands as strings; each command should be a method of the **_AgentType_**s. The function simply distributes the **_AgentType_**s across threads on different cores and executes each command in order, returning no output (the **_AgentType_**s themselves are changed by running the commands). Equivalent results would be achieved by simply looping over each type and running each method in the list. Indeed, **_HARK.parallel_** also has a function called **_multiThreadCommandsFake_** that does just that, with identical syntax to **_multiThreadCommands_**; multithreading in HARK can thus be easily turned on and off. [^5] The module also has functions for a parallel implementation of the Nelder-Mead simplex algorithm, as described in Wiswall and Lee (2011). See [here](https://docs.econ-ark.org/reference/tools/parallel.html) for full documentation. [^4]: **_HARK.parallel_** uses two packages that aren't included in the default distribution of Anaconda: **_joblib_** and **_dill_**; see [here](https://docs.econ-ark.org/guides/quick_start.html#using-hark-with-anaconda) for instructions on how to install them. - [^5]: In the future, **_HARK.parallel_** might be absorbed into **_HARK.core_** and **_HARK.estimation_**, particularly if **_joblib_** and **_dill_** become part of the standard Anaconda distribution. ### AgentType Class diff --git a/Documentation/reference/index.rst b/Documentation/reference/index.rst index 1d9cced2f..de65cad62 100644 --- a/Documentation/reference/index.rst +++ b/Documentation/reference/index.rst @@ -13,6 +13,7 @@ API Reference tools/frame tools/helpers tools/interpolation + tools/model tools/numba_tools tools/parallel tools/rewards diff --git a/Documentation/reference/tools/model.rst b/Documentation/reference/tools/model.rst new file mode 100644 index 000000000..141b2ac93 --- /dev/null +++ b/Documentation/reference/tools/model.rst @@ -0,0 +1,7 @@ +Model +------------- + +.. automodule:: HARK.model + :members: + :undoc-members: + :show-inheritance: diff --git a/Documentation/reference/tools/simulation.rst b/Documentation/reference/tools/simulation.rst index 55e17756f..a56040159 100644 --- a/Documentation/reference/tools/simulation.rst +++ b/Documentation/reference/tools/simulation.rst @@ -1,7 +1,7 @@ Simulation ------------ -.. automodule:: HARK.simulation +.. automodule:: HARK.simulation.monte_carlo :members: :undoc-members: :show-inheritance: diff --git a/HARK/Calibration/Income/tests/test_IncomeTools.py b/HARK/Calibration/Income/tests/test_IncomeTools.py index a8f6f5aff..5438be385 100644 --- a/HARK/Calibration/Income/tests/test_IncomeTools.py +++ b/HARK/Calibration/Income/tests/test_IncomeTools.py @@ -203,7 +203,7 @@ def test_Cagetti(self): age_max=age_max, adjust_infl_to=adjust_infl_to, start_year=start_year, - **spec + **spec, ) MeanP = find_profile(params["PermGroFac"], params["P0"]) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 89a4f9ae0..2d14f81c7 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -144,7 +144,7 @@ def __init__(self, **kwds): self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_), pseudo_terminal=False, - **params + **params, ) # Add consumer-type specific objects, copying to create independent versions @@ -2441,7 +2441,7 @@ def __init__( "KtoLnow", "Mrkv", # This one is new ], - **kwds + **kwds, ): agents = agents if agents is not None else list() params = init_mrkv_cobb_douglas.copy() @@ -2453,7 +2453,7 @@ def __init__( tolerance=tolerance, act_T=act_T, sow_vars=sow_vars, - **params + **params, ) self.sow_init["Mrkv"] = params["MrkvNow_init"] @@ -2885,7 +2885,7 @@ def __init__(self, agents=None, tolerance=0.0001, **kwds): reap_vars=["aNow", "EmpNow"], track_vars=["Mrkv", "Aprev", "Mnow", "Urate"], dyn_vars=["AFunc"], - **params + **params, ) self.update() diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 37ceaff43..693089d5b 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -709,7 +709,7 @@ def set_and_update_values(self, solution_next, IncShkDstn, LivPrb, DiscFac): try: self.MPCminNow = 1.0 / (1.0 + self.PatFac / solution_next.MPCmin) except: - self.MPCminNow = 0.0 + self.MPCminNow = 0.0 self.Ex_IncNext = np.dot( self.ShkPrbsNext, self.TranShkValsNext * self.PermShkValsNext ) @@ -1604,7 +1604,7 @@ def __init__(self, verbose=1, quiet=False, **kwds): self, solution_terminal=deepcopy(self.solution_terminal_), pseudo_terminal=False, - **kwds + **kwds, ) # Add consumer-type specific objects, copying to create independent versions @@ -1820,7 +1820,7 @@ def get_shocks(self): PermGroFac = np.array(self.PermGroFac) # Cycle time has already been advanced self.shocks["PermShk"] = PermGroFac[self.t_cycle - 1] - #self.shocks["PermShk"][self.t_cycle == 0] = 1. # Add this at some point + # self.shocks["PermShk"][self.t_cycle == 0] = 1. # Add this at some point self.shocks["TranShk"] = np.ones(self.AgentCount) def get_Rfree(self): @@ -1930,19 +1930,19 @@ def log_condition_result(self, name, result, message, verbose): self.conditions[name] = result set_verbosity_level((4 - verbose) * 10) _log.info(message) - self.bilt['conditions_report'] += message + '\n' + self.bilt["conditions_report"] += message + "\n" def check_AIC(self, verbose=None): """ Evaluate and report on the Absolute Impatience Condition. """ name = "AIC" - APFac = self.bilt['APFac'] - result = APFac < 1. + APFac = self.bilt["APFac"] + result = APFac < 1.0 messages = { True: f"APFac={APFac:.5f} : The Absolute Patience Factor satisfies the Absolute Impatience Condition (AIC) Þ < 1.", - False: f"APFac={APFac:.5f} : The Absolute Patience Factor violates the Absolute Impatience Condition (AIC) Þ < 1." + False: f"APFac={APFac:.5f} : The Absolute Patience Factor violates the Absolute Impatience Condition (AIC) Þ < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) @@ -1952,13 +1952,12 @@ def check_GICRaw(self, verbose=None): Evaluate and report on the Growth Impatience Condition for the Perfect Foresight model. """ name = "GICRaw" - GPFacRaw = self.bilt['GPFacRaw'] - result = GPFacRaw < 1. + GPFacRaw = self.bilt["GPFacRaw"] + result = GPFacRaw < 1.0 messages = { True: f"GPFacRaw={GPFacRaw:.5f} : The Growth Patience Factor satisfies the Growth Impatience Condition (GICRaw) Þ/G < 1.", - False: f"GPFacRaw={GPFacRaw:.5f} : The Growth Patience Factor violates the Growth Impatience Condition (GICRaw) Þ/G < 1." - + False: f"GPFacRaw={GPFacRaw:.5f} : The Growth Patience Factor violates the Growth Impatience Condition (GICRaw) Þ/G < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) @@ -1968,13 +1967,13 @@ def check_RIC(self, verbose=None): Evaluate and report on the Return Impatience Condition. """ name = "RIC" - RPFac = self.bilt['RPFac'] - result = RPFac < 1. + RPFac = self.bilt["RPFac"] + result = RPFac < 1.0 messages = { True: f"RPFac={RPFac:.5f} : The Return Patience Factor satisfies the Return Impatience Condition (RIC) Þ/R < 1.", - False: f"RPFac={RPFac:.5f} : The Return Patience Factor violates the Return Impatience Condition (RIC) Þ/R < 1." - } + False: f"RPFac={RPFac:.5f} : The Return Patience Factor violates the Return Impatience Condition (RIC) Þ/R < 1.", + } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) @@ -1983,33 +1982,33 @@ def check_FHWC(self, verbose=None): Evaluate and report on the Finite Human Wealth Condition. """ name = "FHWC" - FHWFac = self.bilt['FHWFac'] - result = FHWFac < 1. + FHWFac = self.bilt["FHWFac"] + result = FHWFac < 1.0 messages = { True: f"FHWFac={FHWFac:.5f} : The Finite Human Wealth Factor satisfies the Finite Human Wealth Condition (FHWC) G/R < 1.", - False: f"FHWFac={FHWFac:.5f} : The Finite Human Wealth Factor violates the Finite Human Wealth Condition (FHWC) G/R < 1." + False: f"FHWFac={FHWFac:.5f} : The Finite Human Wealth Factor violates the Finite Human Wealth Condition (FHWC) G/R < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - + def check_FVAC(self, verbose=None): """ Evaluate and report on the Finite Value of Autarky Condition under perfect foresight. """ name = "PFFVAC" - PFVAFac = self.bilt['PFVAFac'] - result = PFVAFac < 1. + PFVAFac = self.bilt["PFVAFac"] + result = PFVAFac < 1.0 messages = { True: f"PFVAFac={PFVAFac:.5f} : The Finite Value of Autarky Factor satisfies the Finite Value of Autarky Condition βG^(1-ρ) < 1.", - False: f"PFVAFac={PFVAFac:.5f} : The Finite Value of Autarky Factor violates the Finite Value of Autarky Condition βG^(1-ρ) < 1." + False: f"PFVAFac={PFVAFac:.5f} : The Finite Value of Autarky Factor violates the Finite Value of Autarky Condition βG^(1-ρ) < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - + def describe_parameters(self): - ''' + """ Make a string describing this instance's parameter values, including their representation in code and symbolically. @@ -2017,40 +2016,47 @@ def describe_parameters(self): ------- param_desc : str Description of parameters as a unicode string. - ''' + """ params_to_describe = [ - #[name, description, symbol, time varying] - ['DiscFac', 'intertemporal discount factor', 'β',False], - ['Rfree', 'risk free interest factor', 'R',False], - ['PermGroFac', 'permanent income growth factor', 'G',True], - ['CRRA', 'coefficient of relative risk aversion','ρ',False], - ['LivPrb', 'survival probability','ℒ',True], - ['APFac', 'absolute patience factor', 'Þ=(βℒR)^(1/ρ)',False] + # [name, description, symbol, time varying] + ["DiscFac", "intertemporal discount factor", "β", False], + ["Rfree", "risk free interest factor", "R", False], + ["PermGroFac", "permanent income growth factor", "G", True], + ["CRRA", "coefficient of relative risk aversion", "ρ", False], + ["LivPrb", "survival probability", "ℒ", True], + ["APFac", "absolute patience factor", "Þ=(βℒR)^(1/ρ)", False], ] - - param_desc = '' + + param_desc = "" for j in range(len(params_to_describe)): this_entry = params_to_describe[j] if this_entry[3]: - val = getattr(self,this_entry[0])[0] + val = getattr(self, this_entry[0])[0] else: try: - val = getattr(self,this_entry[0]) + val = getattr(self, this_entry[0]) except: val = self.bilt[this_entry[0]] - this_line = this_entry[2] + f'={val:.5f} : ' + this_entry[1] + ' (' + this_entry[0] + ')\n' + this_line = ( + this_entry[2] + + f"={val:.5f} : " + + this_entry[1] + + " (" + + this_entry[0] + + ")\n" + ) param_desc += this_line - + return param_desc - + def calc_limiting_values(self): - ''' + """ Compute various scalar values that are relevant to characterizing the solution to an infinite horizon problem. This method should only be called when T_cycle=1 and cycles=0, otherwise the values generated are meaningless. This method adds the following values to the instance in the dictionary attribute auxiliary. - + APFac : Absolute Patience Factor GPFacRaw : Growth Patience Factor FHWFac : Finite Human Wealth Factor @@ -2064,26 +2070,34 @@ def calc_limiting_values(self): Returns ------- None - ''' + """ aux_dict = self.bilt - aux_dict['APFac'] = (self.Rfree * self.DiscFac * self.LivPrb[0]) ** (1 / self.CRRA) - aux_dict['GPFacRaw'] = aux_dict['APFac'] / self.PermGroFac[0] - aux_dict['FHWFac'] = self.PermGroFac[0] / self.Rfree - aux_dict['RPFac'] = aux_dict['APFac'] / self.Rfree - aux_dict['PFVAFac'] = (self.DiscFac * self.LivPrb[0]) * self.PermGroFac[0]**(1. - self.CRRA) - aux_dict['cNrmPDV'] = 1. / (1. - aux_dict['RPFac']) - aux_dict['MPCmin'] = np.maximum(1. - aux_dict['RPFac'], 0.) - constrained = hasattr(self, "BoroCnstArt") and (self.BoroCnstArt is not None) and (self.BoroCnstArt > -np.inf) - + aux_dict["APFac"] = (self.Rfree * self.DiscFac * self.LivPrb[0]) ** ( + 1 / self.CRRA + ) + aux_dict["GPFacRaw"] = aux_dict["APFac"] / self.PermGroFac[0] + aux_dict["FHWFac"] = self.PermGroFac[0] / self.Rfree + aux_dict["RPFac"] = aux_dict["APFac"] / self.Rfree + aux_dict["PFVAFac"] = (self.DiscFac * self.LivPrb[0]) * self.PermGroFac[0] ** ( + 1.0 - self.CRRA + ) + aux_dict["cNrmPDV"] = 1.0 / (1.0 - aux_dict["RPFac"]) + aux_dict["MPCmin"] = np.maximum(1.0 - aux_dict["RPFac"], 0.0) + constrained = ( + hasattr(self, "BoroCnstArt") + and (self.BoroCnstArt is not None) + and (self.BoroCnstArt > -np.inf) + ) + if constrained: - aux_dict['MPCmax'] = 1. + aux_dict["MPCmax"] = 1.0 else: - aux_dict['MPCmax'] = aux_dict['MPCmin'] - if aux_dict['FHWFac'] < 1.: - aux_dict['hNrm'] = 1. / (1. - aux_dict['FHWFac']) + aux_dict["MPCmax"] = aux_dict["MPCmin"] + if aux_dict["FHWFac"] < 1.0: + aux_dict["hNrm"] = 1.0 / (1.0 - aux_dict["FHWFac"]) else: - aux_dict['hNrm'] = np.inf - + aux_dict["hNrm"] = np.inf + self.bilt = aux_dict def check_conditions(self, verbose=None): @@ -2111,17 +2125,17 @@ def check_conditions(self, verbose=None): None """ self.conditions = {} - self.bilt['conditions_report'] = '' + self.bilt["conditions_report"] = "" self.degenerate = False verbose = self.verbose if verbose is None else verbose # This method only checks for the conditions for infinite horizon models # with a 1 period cycle. If these conditions are not met, we exit early. if self.cycles != 0 or self.T_cycle > 1: - trivial_message = 'No conditions report was produced because this functionality is only supported for infinite horizon models with a cycle length of 1.' + trivial_message = "No conditions report was produced because this functionality is only supported for infinite horizon models with a cycle length of 1." self.log_condition_result(None, None, trivial_message, verbose) if not self.quiet: - _log.info(self.bilt['conditions_report']) + _log.info(self.bilt["conditions_report"]) return # Calculate some useful quantities that will be used in the condition checks @@ -2135,71 +2149,77 @@ def check_conditions(self, verbose=None): self.check_GICRaw(verbose) self.check_FVAC(verbose) self.check_FHWC(verbose) - constrained = hasattr(self, "BoroCnstArt") and (self.BoroCnstArt is not None) and (self.BoroCnstArt > -np.inf) - + constrained = ( + hasattr(self, "BoroCnstArt") + and (self.BoroCnstArt is not None) + and (self.BoroCnstArt > -np.inf) + ) + # Exit now if verbose output was not requested. if not verbose: if not self.quiet: - _log.info(self.bilt['conditions_report']) + _log.info(self.bilt["conditions_report"]) return - + # Report on the degeneracy of the consumption function solution if not constrained: - if self.conditions['FHWC']: - RIC_message = '\nBecause the FHWC is satisfied, the solution is not c(m)=Infinity.' - if self.conditions['RIC']: + if self.conditions["FHWC"]: + RIC_message = "\nBecause the FHWC is satisfied, the solution is not c(m)=Infinity." + if self.conditions["RIC"]: RIC_message += " Because the RIC is also satisfied, the solution is also not c(m)=0 for all m, so a non-degenerate linear solution exists." degenerate = False else: RIC_message += " However, because the RIC is violated, the solution is degenerate at c(m) = 0 for all m." degenerate = True else: - RIC_message = '\nBecause the FHWC condition is violated and the consumer is not constrained, the solution is degenerate at c(m)=Infinity.' + RIC_message = "\nBecause the FHWC condition is violated and the consumer is not constrained, the solution is degenerate at c(m)=Infinity." degenerate = True else: - if self.conditions['RIC']: + if self.conditions["RIC"]: RIC_message = "\nBecause the RIC is satisfied and the consumer is constrained, the solution is not c(m)=0 for all m." - if self.conditions['GICRaw']: + if self.conditions["GICRaw"]: RIC_message += " Because the GICRaw is also satisfied, the solution is non-degenerate. It is piecewise linear with an infinite number of kinks, approaching the unconstrained solution as m goes to infinity." degenerate = False else: RIC_message += " Because the GICRaw is violated, the solution is non-degenerate. It is piecewise linear with a single kink at some 0 < m < 1; it equals the unconstrained solution above that kink point and has c(m) = m below it." degenerate = False else: - if self.conditions['GICRaw']: + if self.conditions["GICRaw"]: RIC_message = "\nBecause the RIC is violated but the GIC is satisfied, the FHWC is necessarily also violated. In this case, the consumer's pathological patience is offset by his infinite human wealth, against which he cannot borrow arbitrarily; a non-degenerate solution exists." degenerate = False else: - RIC_message = '\nBecause the RIC is violated but the FHWC is satisfied, the solution is degenerate at c(m)=0 for all m.' + RIC_message = "\nBecause the RIC is violated but the FHWC is satisfied, the solution is degenerate at c(m)=0 for all m." degenerate = True self.log_condition_result(None, None, RIC_message, verbose) - - if degenerate: # All of the other checks are meaningless if the solution is degenerate + + if ( + degenerate + ): # All of the other checks are meaningless if the solution is degenerate if not self.quiet: - _log.info(self.bilt['conditions_report']) - return - + _log.info(self.bilt["conditions_report"]) + return + # Report on the consequences of the Absolute Impatience Condition - if self.conditions['AIC']: + if self.conditions["AIC"]: AIC_message = "\nBecause the AIC is satisfied, the absolute amount of consumption is expected to fall over time." else: AIC_message = "\nBecause the AIC is violated, the absolute amount of consumption is expected to grow over time." self.log_condition_result(None, None, AIC_message, verbose) - + # Report on the consequences of the Growth Impatience Condition - if self.conditions['GICRaw']: + if self.conditions["GICRaw"]: GIC_message = "\nBecause the GICRaw is satisfed, the ratio of individual wealth to permanent income is expected to fall indefinitely." - elif self.conditions['FHWC']: + elif self.conditions["FHWC"]: "\nBecause the GICRaw is violated but the FHWC is satisfied, the ratio of individual wealth to permanent income is expected to rise toward infinity." else: pass # This can never be reached! If GICRaw and FHWC both fail, then the RIC also fails, and we would have exited by this point. self.log_condition_result(None, None, GIC_message, verbose) - + if not self.quiet: - _log.info(self.bilt['conditions_report']) - - + _log.info(self.bilt["conditions_report"]) + + # Make a dictionary to specify an idiosyncratic income shocks consumer init_idiosyncratic_shocks = dict( init_perfect_foresight, @@ -2234,7 +2254,7 @@ def check_conditions(self, verbose=None): # Use permanent income neutral measure (see Harmenberg 2021) during simulations when True. # Whether Newborns have transitory shock. The default is False. "NewbornTransShk": False, - } + }, ) @@ -2350,17 +2370,17 @@ def reset_rng(self): if hasattr(self, "IncShkDstn"): for dstn in self.IncShkDstn: dstn.reset() - + def post_solve(self): """ Method that is run automatically at the end of a call to solve. Here, it simply calls calc_stable_points() if appropriate: an infinite horizon problem with a single repeated period in its cycle. - + Parameters ---------- None - + Returns ------- None @@ -2436,7 +2456,6 @@ def get_shocks(self): self.shocks["PermShk"] = PermShkNow self.shocks["TranShk"] = TranShkNow - def define_distribution_grid( self, dist_mGrid=None, @@ -3024,24 +3043,24 @@ def calc_jacobian(self, shk_param, T): ######## # STEP4 # of the algorithm ######## - + # Function to compute jacobian matrix from fake news matrix def J_from_F(F): J = F.copy() for t in range(1, F.shape[0]): - J[1:, t] += J[:-1, t-1] + J[1:, t] += J[:-1, t - 1] return J - + J_A = J_from_F(Curl_F_A) J_C = J_from_F(Curl_F_C) - + ######## # Additional step due to compute Zeroth Column of the Jacobian - ######## - + ######## + params = deepcopy(self.__dict__["parameters"]) - params["T_cycle"] = 2 # Dimension of Jacobian Matrix - + params["T_cycle"] = 2 # Dimension of Jacobian Matrix + params["LivPrb"] = params["T_cycle"] * [self.LivPrb[0]] params["PermGroFac"] = params["T_cycle"] * [self.PermGroFac[0]] params["PermShkStd"] = params["T_cycle"] * [self.PermShkStd[0]] @@ -3049,13 +3068,13 @@ def J_from_F(F): params["Rfree"] = params["T_cycle"] * [self.Rfree] params["UnempPrb"] = params["T_cycle"] * [self.UnempPrb] params["IncUnemp"] = params["T_cycle"] * [self.IncUnemp] - params['IncShkDstn'] = params['T_cycle']* [self.IncShkDstn[0]] - params['cFunc_terminal_'] = deepcopy(self.solution[0].cFunc) - + params["IncShkDstn"] = params["T_cycle"] * [self.IncShkDstn[0]] + params["cFunc_terminal_"] = deepcopy(self.solution[0].cFunc) + # Create instance of a finite horizon agent for calculation of zeroth ZerothColAgent = IndShockConsumerType(**params) ZerothColAgent.cycles = 1 # required - + # If parameter is in time invariant list then add it to time vary list ZerothColAgent.del_from_time_inv(shk_param) ZerothColAgent.add_to_time_vary(shk_param) @@ -3065,20 +3084,22 @@ def J_from_F(F): # Solve ZerothColAgent.solve() - + # this condition is because some attributes are specified as lists while other as floats if type(getattr(self, shk_param)) == list: - peturbed_list = ( - [getattr(self, shk_param)[0] + dx] - + (params["T_cycle"] - 1) * [getattr(self, shk_param)[0]] - ) # Sequence of interest rates the agent faces + peturbed_list = [getattr(self, shk_param)[0] + dx] + ( + params["T_cycle"] - 1 + ) * [ + getattr(self, shk_param)[0] + ] # Sequence of interest rates the agent faces else: - peturbed_list = ( - [getattr(self, shk_param) + dx] - + (params["T_cycle"] - 1) * [getattr(self, shk_param)] - ) # Sequence of interest rates the agent - - setattr(ZerothColAgent, shk_param, peturbed_list) # Set attribute to agent + peturbed_list = [getattr(self, shk_param) + dx] + ( + params["T_cycle"] - 1 + ) * [ + getattr(self, shk_param) + ] # Sequence of interest rates the agent + + setattr(ZerothColAgent, shk_param, peturbed_list) # Set attribute to agent # Use Harmenberg Neutral Measure ZerothColAgent.neutral_measure = True @@ -3087,28 +3108,26 @@ def J_from_F(F): # Calculate Transition Matrices ZerothColAgent.define_distribution_grid() ZerothColAgent.calc_transition_matrix() - + tranmat_t_zeroth_col = ZerothColAgent.tran_matrix dstn_t_zeroth_col = self.vec_erg_dstn.T[0] - + C_t_no_sim = np.zeros(T) A_t_no_sim = np.zeros(T) for i in range(T): - if i ==0: - dstn_t_zeroth_col = np.dot(tranmat_t_zeroth_col[i],dstn_t_zeroth_col) + if i == 0: + dstn_t_zeroth_col = np.dot(tranmat_t_zeroth_col[i], dstn_t_zeroth_col) else: - dstn_t_zeroth_col = np.dot(tranmat_ss,dstn_t_zeroth_col) - - C_t_no_sim[i] = np.dot(self.cPol_Grid ,dstn_t_zeroth_col) - A_t_no_sim[i] = np.dot( self.aPol_Grid ,dstn_t_zeroth_col) - - J_A.T[0] = (A_t_no_sim - self.A_ss)/dx - J_C.T[0] = (C_t_no_sim - self.C_ss)/dx - - return J_C, J_A + dstn_t_zeroth_col = np.dot(tranmat_ss, dstn_t_zeroth_col) + + C_t_no_sim[i] = np.dot(self.cPol_Grid, dstn_t_zeroth_col) + A_t_no_sim[i] = np.dot(self.aPol_Grid, dstn_t_zeroth_col) + J_A.T[0] = (A_t_no_sim - self.A_ss) / dx + J_C.T[0] = (C_t_no_sim - self.C_ss) / dx + return J_C, J_A def make_euler_error_func(self, mMax=100, approx_inc_dstn=True): """ @@ -3221,13 +3240,12 @@ def pre_solve(self): self.update_solution_terminal() if not self.quiet: self.check_conditions(verbose=self.verbose) - - + def describe_parameters(self): - ''' + """ Generate a string describing the primitive model parameters that will be used to calculating limiting values and factors. - + Parameters ---------- None @@ -3236,33 +3254,44 @@ def describe_parameters(self): ------- param_desc : str Description of primitive parameters. - ''' + """ # Get parameter description from the perfect foresight model param_desc = PerfForesightConsumerType.describe_parameters(self) - + # Make a new entry for weierstrass-p (the weird formatting here is to # make it easier to adapt into the style of the superclass if we add more # parameter reports later) - this_entry = ['WorstPrb', 'probability of worst income shock realization', '℘', False] + this_entry = [ + "WorstPrb", + "probability of worst income shock realization", + "℘", + False, + ] try: - val = getattr(self,this_entry[0]) + val = getattr(self, this_entry[0]) except: - val = self.bilt[this_entry[0]] - this_line = this_entry[2] + f'={val:.5f} : ' + this_entry[1] + ' (' + this_entry[0] + ')\n' - + val = self.bilt[this_entry[0]] + this_line = ( + this_entry[2] + + f"={val:.5f} : " + + this_entry[1] + + " (" + + this_entry[0] + + ")\n" + ) + # Add in the new entry and return it param_desc += this_line return param_desc - - + def calc_limiting_values(self): - ''' + """ Compute various scalar values that are relevant to characterizing the solution to an infinite horizon problem. This method should only be called when T_cycle=1 and cycles=0, otherwise the values generated are meaningless. This method adds the following values to this instance in the dictionary attribute auxiliary. - + APFac : Absolute Patience Factor GPFacRaw : Growth Patience Factor GPFacMod : Risk-Modified Growth Patience Factor @@ -3284,39 +3313,43 @@ def calc_limiting_values(self): Returns ------- None - ''' + """ PerfForesightConsumerType.calc_limiting_values(self) aux_dict = self.bilt - + # Calculate the risk-modified growth impatience factor PermShkDstn = self.PermShkDstn[0] - inv_func = lambda x : x**(-1.) - GroCompPermShk = expected(inv_func, PermShkDstn)[0]**(-1.) - aux_dict['GPFacMod'] = aux_dict['APFac'] / (self.PermGroFac[0] * GroCompPermShk) - + inv_func = lambda x: x ** (-1.0) + GroCompPermShk = expected(inv_func, PermShkDstn)[0] ** (-1.0) + aux_dict["GPFacMod"] = aux_dict["APFac"] / (self.PermGroFac[0] * GroCompPermShk) + # Calculate the mortality-adjusted growth impatience factor (and version # with Modigiliani bequests) - aux_dict['GPFacLiv'] = aux_dict['GPFacRaw'] * self.LivPrb[0] - aux_dict['GPFacLivMod'] = aux_dict['GPFacLiv'] * self.LivPrb[0] - + aux_dict["GPFacLiv"] = aux_dict["GPFacRaw"] * self.LivPrb[0] + aux_dict["GPFacLivMod"] = aux_dict["GPFacLiv"] * self.LivPrb[0] + # Calculate the risk-modified value of autarky factor - if self.CRRA == 1.: + if self.CRRA == 1.0: UtilCompPermShk = np.exp(expected(np.log, PermShkDstn)[0]) else: - CRRAfunc = lambda x : x**(1.-self.CRRA) - UtilCompPermShk = expected(CRRAfunc, PermShkDstn)[0]**(1/(1.-self.CRRA)) - aux_dict['VAFac'] = self.DiscFac*(self.PermGroFac[0]*UtilCompPermShk)**(1.-self.CRRA) - + CRRAfunc = lambda x: x ** (1.0 - self.CRRA) + UtilCompPermShk = expected(CRRAfunc, PermShkDstn)[0] ** ( + 1 / (1.0 - self.CRRA) + ) + aux_dict["VAFac"] = self.DiscFac * (self.PermGroFac[0] * UtilCompPermShk) ** ( + 1.0 - self.CRRA + ) + # Calculate the expected log permanent income shock, which will be used # for the Szeidl variation of the Growth Impatience condition - aux_dict['ELogPermShk'] = expected(np.log, PermShkDstn)[0] - + aux_dict["ELogPermShk"] = expected(np.log, PermShkDstn)[0] + # Calculate the Harmenberg permanent income neutral expected log permanent # shock and the Harmenberg Growth Patience Factor - Hrm_func = lambda x : x * np.log(x) + Hrm_func = lambda x: x * np.log(x) PermShk_Hrm = np.exp(expected(Hrm_func, PermShkDstn)[0]) - aux_dict['GPFacHrm'] = aux_dict['GPFacRaw'] / PermShk_Hrm - + aux_dict["GPFacHrm"] = aux_dict["GPFacRaw"] / PermShk_Hrm + # Calculate the probability of the worst income shock realization PermShkValsNext = self.IncShkDstn[0].atoms[0] TranShkValsNext = self.IncShkDstn[0].atoms[1] @@ -3328,17 +3361,17 @@ def calc_limiting_values(self): WorstIncPrb = np.sum( ShkPrbsNext[(PermShkValsNext * TranShkValsNext) == WorstIncNext] ) - aux_dict['WorstPrb'] = WorstIncPrb - + aux_dict["WorstPrb"] = WorstIncPrb + # Calculate the weak return patience factor - aux_dict['WRPFac'] = WorstIncPrb**(1./self.CRRA) * aux_dict['RPFac'] - + aux_dict["WRPFac"] = WorstIncPrb ** (1.0 / self.CRRA) * aux_dict["RPFac"] + # Calculate human wealth and the infinite horizon natural borrowing constraint - if aux_dict['FHWFac'] < 1.: - hNrm = Ex_IncNext / (1. - aux_dict['FHWFac']) + if aux_dict["FHWFac"] < 1.0: + hNrm = Ex_IncNext / (1.0 - aux_dict["FHWFac"]) else: hNrm = np.inf - temp = PermShkMinNext * aux_dict['FHWFac'] + temp = PermShkMinNext * aux_dict["FHWFac"] BoroCnstNat = -TranShkMinNext * temp / (1.0 - temp) # Find the upper bound of the MPC as market resources approach the minimum @@ -3346,111 +3379,104 @@ def calc_limiting_values(self): if BoroCnstNat < BoroCnstArt: MPCmax = 1.0 # if natural borrowing constraint is overridden by artificial one, MPCmax is 1 else: - MPCmax = 1.0 - WorstIncPrb ** (1.0 / self.CRRA) * aux_dict['RPFac'] + MPCmax = 1.0 - WorstIncPrb ** (1.0 / self.CRRA) * aux_dict["RPFac"] MPCmax = np.maximum(MPCmax, 0.0) - + # Store maximum MPC and human wealth - aux_dict['hNrm'] = hNrm - aux_dict['MPCmax'] = MPCmax - - self.bilt = aux_dict + aux_dict["hNrm"] = hNrm + aux_dict["MPCmax"] = MPCmax + self.bilt = aux_dict def check_GICMod(self, verbose=None): """ Evaluate and report on the Risk-Modified Growth Impatience Condition. """ name = "GICMod" - GPFacMod = self.bilt['GPFacMod'] - result = GPFacMod < 1. + GPFacMod = self.bilt["GPFacMod"] + result = GPFacMod < 1.0 messages = { True: f"GPFacMod={GPFacMod:.5f} : The Risk-Modified Growth Patience Factor satisfies the Risk-Modified Growth Impatience Condition (GICMod) Þ/(G‖Ψ‖_(-1)) < 1.", - False: f"GPFacMod={GPFacMod:.5f} : The Risk-Modified Growth Patience Factor violates the Risk-Modified Growth Impatience Condition (GICMod) Þ/(G‖Ψ‖_(-1)) < 1." + False: f"GPFacMod={GPFacMod:.5f} : The Risk-Modified Growth Patience Factor violates the Risk-Modified Growth Impatience Condition (GICMod) Þ/(G‖Ψ‖_(-1)) < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - def check_GICSdl(self, verbose=None): """ Evaluate and report on the Szeidl variation of the Growth Impatience Condition. """ name = "GICSdl" - ELogPermShk = self.bilt['ELogPermShk'] - result = np.log(self.bilt['GPFacRaw']) < ELogPermShk + ELogPermShk = self.bilt["ELogPermShk"] + result = np.log(self.bilt["GPFacRaw"]) < ELogPermShk messages = { True: f"E[log Ψ]={ELogPermShk:.5f} : The expected log permanent income shock satisfies the Szeidl Growth Impatience Condition (GICSdl) log(Þ/G) < E[log Ψ].", - False: f"E[log Ψ]={ELogPermShk:.5f} : The expected log permanent income shock violates the Szeidl Growth Impatience Condition (GICSdl) log(Þ/G) < E[log Ψ]." + False: f"E[log Ψ]={ELogPermShk:.5f} : The expected log permanent income shock violates the Szeidl Growth Impatience Condition (GICSdl) log(Þ/G) < E[log Ψ].", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - - + def check_GICHrm(self, verbose=None): """ Evaluate and report on the Harmenberg variation of the Growth Impatience Condition. """ name = "GICHrm" - GPFacHrm = self.bilt['GPFacHrm'] - result = GPFacHrm < 1. + GPFacHrm = self.bilt["GPFacHrm"] + result = GPFacHrm < 1.0 messages = { True: f"GPFacHrm={GPFacHrm:.5f} : The Harmenberg Expected Growth Patience Factor satisfies the Harmenberg Growth Normalized Impatience Condition (GICHrm) Þ/G < exp(E[Ψlog Ψ]).", - False: f"GPFacHrm={GPFacHrm:.5f} : The Harmenberg Expected Growth Patience Factor violates the Harmenberg Growth Normalized Impatience Condition (GICHrm) Þ/G < exp(E[Ψlog Ψ])." + False: f"GPFacHrm={GPFacHrm:.5f} : The Harmenberg Expected Growth Patience Factor violates the Harmenberg Growth Normalized Impatience Condition (GICHrm) Þ/G < exp(E[Ψlog Ψ]).", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - - + def check_GICLiv(self, verbose=None): """ Evaluate and report on the Mortality-Adjusted Growth Impatience Condition. """ name = "GICLiv" - GPFacLiv = self.bilt['GPFacLiv'] - result = GPFacLiv < 1. + GPFacLiv = self.bilt["GPFacLiv"] + result = GPFacLiv < 1.0 messages = { True: f"GPFacLiv={GPFacLiv:.5f} : The Mortality-Adjusted Growth Patience Factor satisfies the Mortality-Adjusted Growth Impatience Condition (GICLiv) ℒÞ/G < 1.", - False: f"GPFacLiv={GPFacLiv:.5f} : The Mortality-Adjusted Growth Patience Factor violates the Mortality-Adjusted Growth Impatience Condition (GICLiv) ℒÞ/G < 1." + False: f"GPFacLiv={GPFacLiv:.5f} : The Mortality-Adjusted Growth Patience Factor violates the Mortality-Adjusted Growth Impatience Condition (GICLiv) ℒÞ/G < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - - + def check_FVAC(self, verbose=None): """ Evaluate and report on the Finite Value of Autarky condition in the presence of income risk. """ name = "FVAC" - VAFac = self.bilt['VAFac'] - result = VAFac < 1. + VAFac = self.bilt["VAFac"] + result = VAFac < 1.0 messages = { True: f"VAFac={VAFac:.5f} : The Risk-Modified Finite Value of Autarky Factor satisfies the Risk-Modified Finite Value of Autarky Condition β(G‖Ψ‖_(1-ρ))^(1-ρ) < 1.", - False: f"VAFac={VAFac:.5f} : The Risk-Modified Finite Value of Autarky Factor violates the Risk-Modified Finite Value of Autarky Condition β(G‖Ψ‖_(1-ρ))^(1-ρ) < 1." + False: f"VAFac={VAFac:.5f} : The Risk-Modified Finite Value of Autarky Factor violates the Risk-Modified Finite Value of Autarky Condition β(G‖Ψ‖_(1-ρ))^(1-ρ) < 1.", } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - - + def check_WRIC(self, verbose=None): """ Evaluate and report on the Weak Return Impatience Condition. """ name = "WRIC" - WRPFac = self.bilt['WRPFac'] - result = WRPFac < 1. + WRPFac = self.bilt["WRPFac"] + result = WRPFac < 1.0 messages = { True: f"WRPFac={WRPFac:.5f} : The Weak Return Patience Factor satisfies the Weak Return Impatience Condition (WRIC) ℘ Þ/R < 1.", - False: f"WRPFac={WRPFac:.5f} : The Weak Return Patience Factor violates the Weak Return Impatience Condition (WRIC) ℘ Þ/R < 1." - } + False: f"WRPFac={WRPFac:.5f} : The Weak Return Patience Factor violates the Weak Return Impatience Condition (WRIC) ℘ Þ/R < 1.", + } verbose = self.verbose if verbose is None else verbose self.log_condition_result(name, result, messages[result], verbose) - def check_conditions(self, verbose=None): """ @@ -3471,17 +3497,17 @@ def check_conditions(self, verbose=None): None """ self.conditions = {} - self.bilt['conditions_report'] = '' + self.bilt["conditions_report"] = "" self.degenerate = False verbose = self.verbose if verbose is None else verbose # This method only checks for the conditions for infinite horizon models # with a 1 period cycle. If these conditions are not met, we exit early. if self.cycles != 0 or self.T_cycle > 1: - trivial_message = 'No conditions report was produced because this functionality is only supported for infinite horizon models with a cycle length of 1.' + trivial_message = "No conditions report was produced because this functionality is only supported for infinite horizon models with a cycle length of 1." self.log_condition_result(None, None, trivial_message, verbose) if not self.quiet: - _log.info(self.bilt['conditions_report']) + _log.info(self.bilt["conditions_report"]) return # Calculate some useful quantities that will be used in the condition checks @@ -3501,80 +3527,79 @@ def check_conditions(self, verbose=None): PerfForesightConsumerType.check_FVAC(self, verbose) self.check_FVAC(verbose) self.check_FHWC(verbose) - + # Exit now if verbose output was not requested. if not verbose: if not self.quiet: - _log.info(self.bilt['conditions_report']) + _log.info(self.bilt["conditions_report"]) return - + # Report on the degeneracy of the consumption function solution - if self.conditions['WRIC'] and self.conditions['FVAC']: - degen_message = '\nBecause both the WRIC and FVAC are satisfied, the recursive solution to the infinite horizon problem represents a contraction mapping on the consumption function. Thus a non-degenerate solution exists.' + if self.conditions["WRIC"] and self.conditions["FVAC"]: + degen_message = "\nBecause both the WRIC and FVAC are satisfied, the recursive solution to the infinite horizon problem represents a contraction mapping on the consumption function. Thus a non-degenerate solution exists." degenerate = False - elif not self.conditions['WRIC']: - degen_message = '\nBecause the WRIC is violated, the consumer is so pathologically patient that they will never consume at all. Thus the solution will be degenerate at c(m) = 0 for all m.\n' + elif not self.conditions["WRIC"]: + degen_message = "\nBecause the WRIC is violated, the consumer is so pathologically patient that they will never consume at all. Thus the solution will be degenerate at c(m) = 0 for all m.\n" degenerate = True - elif not self.conditions['FVAC']: + elif not self.conditions["FVAC"]: degen_message = "\nBecause the FVAC is violated, the recursive solution to the infinite horizon problem might not be a contraction mapping, so the produced solution might not be valid. Proceed with caution." degenerate = False self.log_condition_result(None, None, degen_message, verbose) self.degenerate = degenerate - + # Stop here if the solution is degenerate if degenerate: if not self.quiet: - _log.info(self.bilt['conditions_report']) + _log.info(self.bilt["conditions_report"]) return - + # Report on the limiting behavior of the consumption function as m goes to infinity - if self.conditions['RIC']: - if self.conditions['FHWC']: - RIC_message = '\nBecause both the RIC and FHWC condition are satisfied, the consumption function will approach the linear perfect foresight solution as m becomes arbitrarily large.' + if self.conditions["RIC"]: + if self.conditions["FHWC"]: + RIC_message = "\nBecause both the RIC and FHWC condition are satisfied, the consumption function will approach the linear perfect foresight solution as m becomes arbitrarily large." else: - RIC_message = '\nBecause the RIC is satisfied but the FHWC is violated, the GIC is satisfied.' + RIC_message = "\nBecause the RIC is satisfied but the FHWC is violated, the GIC is satisfied." else: - RIC_message = '\nBecause the RIC is violated, the FHWC condition is also violated. The consumer is pathologically impatient but has infinite expected future earnings. Thus the consumption function will not approach any linear limit as m becomes arbitrarily large, and the MPC will asymptote to zero.' + RIC_message = "\nBecause the RIC is violated, the FHWC condition is also violated. The consumer is pathologically impatient but has infinite expected future earnings. Thus the consumption function will not approach any linear limit as m becomes arbitrarily large, and the MPC will asymptote to zero." self.log_condition_result(None, None, RIC_message, verbose) - + # Report on whether a pseudo-steady-state exists at the individual level - if self.conditions['GICRaw']: - GIC_message = '\nBecause the GICRaw is satisfied, there exists a pseudo-steady-state wealth ratio at which the level of wealth is expected to grow at the same rate as permanent income.' + if self.conditions["GICRaw"]: + GIC_message = "\nBecause the GICRaw is satisfied, there exists a pseudo-steady-state wealth ratio at which the level of wealth is expected to grow at the same rate as permanent income." else: - GIC_message = '\nBecause the GICRaw is violated, there might not exist a pseudo-steady-state wealth ratio at which the level of wealth is expected to grow at the same rate as permanent income.' + GIC_message = "\nBecause the GICRaw is violated, there might not exist a pseudo-steady-state wealth ratio at which the level of wealth is expected to grow at the same rate as permanent income." self.log_condition_result(None, None, GIC_message, verbose) - + # Report on whether a target wealth ratio exists at the individual level - if self.conditions['GICMod']: - GICMod_message = '\nBecause the GICMod is satisfied, expected growth of the ratio of market resources to permanent income is less than one as market resources become arbitrarily large. Hence the consumer has a target ratio of market resources to permanent income.' + if self.conditions["GICMod"]: + GICMod_message = "\nBecause the GICMod is satisfied, expected growth of the ratio of market resources to permanent income is less than one as market resources become arbitrarily large. Hence the consumer has a target ratio of market resources to permanent income." else: - GICMod_message = '\nBecause the GICMod is violated, expected growth of the ratio of market resources to permanent income exceeds one as market resources go to infinity. Hence the consumer might not have a target ratio of market resources to permanent income.' + GICMod_message = "\nBecause the GICMod is violated, expected growth of the ratio of market resources to permanent income exceeds one as market resources go to infinity. Hence the consumer might not have a target ratio of market resources to permanent income." self.log_condition_result(None, None, GICMod_message, verbose) - + # Report on whether a target level of wealth exists at the aggregate level - if self.conditions['GICLiv']: - GICLiv_message = '\nBecause the GICLiv is satisfied, a target ratio of aggregate market resources to aggregate permanent income exists.' + if self.conditions["GICLiv"]: + GICLiv_message = "\nBecause the GICLiv is satisfied, a target ratio of aggregate market resources to aggregate permanent income exists." else: - GICLiv_message = '\nBecause the GICLiv is violated, a target ratio of aggregate market resources to aggregate permanent income might not exist.' + GICLiv_message = "\nBecause the GICLiv is violated, a target ratio of aggregate market resources to aggregate permanent income might not exist." self.log_condition_result(None, None, GICLiv_message, verbose) - + # Report on whether invariant distributions exist - if self.conditions['GICSdl']: - GICSdl_message = '\nBecause the GICSdl is satisfied, there exist invariant distributions of permanent income-normalized variables.' + if self.conditions["GICSdl"]: + GICSdl_message = "\nBecause the GICSdl is satisfied, there exist invariant distributions of permanent income-normalized variables." else: - GICSdl_message = '\nBecause the GICSdl is violated, there do not exist invariant distributions of permanent income-normalized variables.' + GICSdl_message = "\nBecause the GICSdl is violated, there do not exist invariant distributions of permanent income-normalized variables." self.log_condition_result(None, None, GICSdl_message, verbose) - + # Report on whether blah blah - if self.conditions['GICHrm']: - GICHrm_message = '\nBecause the GICHrm is satisfied, there exists a target ratio of the individual market resources to permanent income, under the permanent-income-neutral measure.' + if self.conditions["GICHrm"]: + GICHrm_message = "\nBecause the GICHrm is satisfied, there exists a target ratio of the individual market resources to permanent income, under the permanent-income-neutral measure." else: - GICHrm_message = '\nBecause the GICHrm is violated, there does not exist a target ratio of the individual market resources to permanent income, under the permanent-income-neutral measure..' + GICHrm_message = "\nBecause the GICHrm is violated, there does not exist a target ratio of the individual market resources to permanent income, under the permanent-income-neutral measure.." self.log_condition_result(None, None, GICHrm_message, verbose) - + if not self.quiet: - _log.info(self.bilt['conditions_report']) - + _log.info(self.bilt["conditions_report"]) def calc_stable_points(self): """ @@ -3597,7 +3622,6 @@ def calc_stable_points(self): ) return - # = Functions for generating discrete income processes and # simulated income shocks = # ======================================================== @@ -3893,7 +3917,7 @@ def __init__( # kinked R is now compatible with linear cFunc and cubic cFunc "aXtraCount": 48, # ...so need lots of extra gridpoints to make up for it - } + }, ) del init_kinked_R["Rfree"] # get rid of constant interest factor @@ -4106,7 +4130,7 @@ def apply_flat_income_tax( age_max=death_age, adjust_infl_to=adjust_infl_to, **income_calib, - SabelhausSong=True + SabelhausSong=True, ) # Initial distribution of wealth and permanent income diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index d7b93909e..4dcb61992 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -37,7 +37,7 @@ "PrefShkStd": [0.30], # Standard deviation of utility shocks "aXtraCount": 48, "CubicBool": False, # pref shocks currently only compatible with linear cFunc - } + }, ) # Make a dictionary to specify a "kinky preference" consumer @@ -49,7 +49,7 @@ "PrefShkStd": [0.30], # Standard deviation of utility shocks "aXtraCount": 48, "CubicBool": False, # pref shocks currently only compatible with linear cFunc - } + }, ) init_kinky_pref["BoroCnstArt"] = None diff --git a/HARK/ConsumptionSaving/ConsRiskyContribModel.py b/HARK/ConsumptionSaving/ConsRiskyContribModel.py index dcf94c064..368aea69d 100644 --- a/HARK/ConsumptionSaving/ConsRiskyContribModel.py +++ b/HARK/ConsumptionSaving/ConsRiskyContribModel.py @@ -1033,7 +1033,7 @@ def solve_RiskyContrib_Cns( AdjustPrb, DiscreteShareBool, joint_dist_solver, - **unused_params + **unused_params, ): """ Solves the consumption stage of the agent's problem @@ -1504,7 +1504,7 @@ def solve_RiskyContrib_Sha( ShareGrid, DiscreteShareBool, vFuncBool, - **unused_params + **unused_params, ): """ Solves the income-contribution-share stag of the agent's problem diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py index 3a61b2166..f1881a275 100644 --- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py @@ -922,4 +922,4 @@ def test_calc_jacobian(self): self.assertAlmostEqual(CJAC_Perm.T[30][29], -0.06120, places=HARK_PRECISION) self.assertAlmostEqual(CJAC_Perm.T[30][30], 0.05307, places=HARK_PRECISION) - self.assertAlmostEqual(CJAC_Perm.T[30][31], 0.04674, places=HARK_PRECISION) \ No newline at end of file + self.assertAlmostEqual(CJAC_Perm.T[30][31], 0.04674, places=HARK_PRECISION) diff --git a/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py b/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py index f27ca7471..aee2d3860 100644 --- a/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py +++ b/HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py @@ -29,7 +29,7 @@ def test_small_open(self): Rfree=1.03, wRte=1.0, KtoLnow=1.0, - **copy.copy(init_cobb_douglas) + **copy.copy(init_cobb_douglas), ) small_economy.act_T = 400 # Short simulation history diff --git a/HARK/core.py b/HARK/core.py index d7db51b30..71a79e2a9 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -6,6 +6,9 @@ model adds an additional layer, endogenizing some of the inputs to the micro problem by finding a general equilibrium dynamic rule. """ +# Set logging and define basic functions +# Set logging and define basic functions +import logging import sys from collections import defaultdict, namedtuple from copy import copy, deepcopy @@ -27,8 +30,6 @@ from HARK.parallel import multi_thread_commands, multi_thread_commands_fake from HARK.utilities import NullFunc, get_arg_names -# Set logging and define basic functions -import logging logging.basicConfig(format="%(message)s") _log = logging.getLogger("HARK") _log.setLevel(logging.ERROR) @@ -1061,7 +1062,14 @@ def simulate(self, sim_periods=None): elif var_name in self.controls: self.history[var_name][self.t_sim, :] = self.controls[var_name] else: - self.history[var_name][self.t_sim, :] = getattr(self, var_name) + if var_name == "who_dies" and self.t_sim > 1: + self.history[var_name][self.t_sim - 1, :] = getattr( + self, var_name + ) + else: + self.history[var_name][self.t_sim, :] = getattr( + self, var_name + ) self.t_sim += 1 return self.history diff --git a/HARK/mat_methods.py b/HARK/mat_methods.py index abe2cb4fd..2e3abbe6a 100644 --- a/HARK/mat_methods.py +++ b/HARK/mat_methods.py @@ -1,6 +1,7 @@ +from typing import List + import numpy as np from numba import njit -from typing import List @njit diff --git a/HARK/model.py b/HARK/model.py index 2ea919ea6..44e47261b 100644 --- a/HARK/model.py +++ b/HARK/model.py @@ -2,9 +2,28 @@ Tools for crafting models. """ +from HARK.distribution import Distribution + + +class Aggregate: + """ + Used to designate a shock as an aggregate shock. + If so designated, draws from the shock will be scalar rather + than array valued. + """ + + def __init__(self, dist: Distribution): + self.dist = dist + + class Control: """ - Should go in different model support module. + Used to designate a variabel that is a control variable. + + Parameters + ---------- + args : list of str + The labels of the variables that are in the information set of this control. """ def __init__(self, args): diff --git a/HARK/models/__init__.py b/HARK/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/HARK/models/fisher.py b/HARK/models/fisher.py index 3f9730531..bc5aa83fd 100644 --- a/HARK/models/fisher.py +++ b/HARK/models/fisher.py @@ -8,23 +8,21 @@ # This way of distributing parameters across the scope is clunky # Can be handled better if parsed from a YAML file, probably # But it would be better to have a more graceful Python version as well. -CRRA = 2.0, +CRRA = (2.0,) model = { - 'shocks' : {}, - 'parameters' : { - 'DiscFac' : 0.96, - 'CRRA' : CRRA, - 'Rfree' : 1.03, - 'y' : [1.0, 1.0], - 'BoroCnstArt' : None, + "shocks": {}, + "parameters": { + "DiscFac": 0.96, + "CRRA": CRRA, + "Rfree": 1.03, + "y": [1.0, 1.0], + "BoroCnstArt": None, }, - 'dynamics' : { - 'm' : lambda Rfree, a, y : Rfree * a + y, - 'c' : Control(['m']), - 'a' : lambda m, c : m - c + "dynamics": { + "m": lambda Rfree, a, y: Rfree * a + y, + "c": Control(["m"]), + "a": lambda m, c: m - c, }, - 'reward' : { - 'u' : lambda c : c ** (1 - CRRA) / (1 - CRRA) - } -} \ No newline at end of file + "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)}, +} diff --git a/HARK/models/perfect_foresight.py b/HARK/models/perfect_foresight.py index cca33e2ed..38bedd97d 100644 --- a/HARK/models/perfect_foresight.py +++ b/HARK/models/perfect_foresight.py @@ -4,29 +4,27 @@ # This way of distributing parameters across the scope is clunky # Can be handled better if parsed from a YAML file, probably # But it would be better to have a more graceful Python version as well. -CRRA = 2.0, +CRRA = (2.0,) LivPrb = 0.98 model = { - 'shocks' : { - 'live' : Bernoulli(p=LivPrb), + "shocks": { + "live": Bernoulli(p=LivPrb), }, - 'parameters' : { - 'DiscFac' : 0.96, - 'CRRA' : CRRA, - 'Rfree' : 1.03, - 'LivPrb' : LivPrb, - 'PermGroFac' : 1.01, - 'BoroCnstArt' : None, + "parameters": { + "DiscFac": 0.96, + "CRRA": CRRA, + "Rfree": 1.03, + "LivPrb": LivPrb, + "PermGroFac": 1.01, + "BoroCnstArt": None, }, - 'dynamics' : { - 'm' : lambda Rfree, a, y : Rfree * a + y, - 'c' : Control(['m']), - 'y' : lambda p : p, - 'p' : lambda PermGroFac, p: PermGroFac * p, - 'a' : lambda m, c : m - c + "dynamics": { + "y": lambda p: p, + "m": lambda Rfree, a, y: Rfree * a + y, + "c": Control(["m"]), + "p": lambda PermGroFac, p: PermGroFac * p, + "a": lambda m, c: m - c, }, - 'reward' : { - 'u' : lambda c : c ** (1 - CRRA) / (1 - CRRA) - } -} \ No newline at end of file + "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)}, +} diff --git a/HARK/models/perfect_foresight_normalized.py b/HARK/models/perfect_foresight_normalized.py new file mode 100644 index 000000000..79fab78dc --- /dev/null +++ b/HARK/models/perfect_foresight_normalized.py @@ -0,0 +1,31 @@ +from HARK.distribution import Bernoulli +from HARK.model import Control + +# This way of distributing parameters across the scope is clunky +# Can be handled better if parsed from a YAML file, probably +# But it would be better to have a more graceful Python version as well. +CRRA = (2.0,) +LivPrb = 0.98 + +model = { + "shocks": { + "live": Bernoulli(p=LivPrb), + }, + "parameters": { + "DiscFac": 0.96, + "CRRA": CRRA, + "Rfree": 1.03, + "LivPrb": LivPrb, + "PermGroFac": 1.01, + "BoroCnstArt": None, + }, + "dynamics": { + "p": lambda PermGroFac, p: PermGroFac * p, + "r_eff": lambda Rfree, PermGroFac: Rfree / PermGroFac, + "b_nrm": lambda r_eff, a_nrm: r_eff * a_nrm, + "m_nrm": lambda b_nrm: b_nrm + 1, + "c_nrm": Control(["m_nrm"]), + "a_nrm": lambda m_nrm, c_nrm: m_nrm - c_nrm, + }, + "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)}, +} diff --git a/HARK/simulation.py b/HARK/simulation.py deleted file mode 100644 index 918e4e377..000000000 --- a/HARK/simulation.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Currently empty. Will be used for future simulation handling code. -""" - -import warnings # A library for runtime warnings - -import numpy as np # Numerical Python diff --git a/HARK/simulation/__init__.py b/HARK/simulation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py new file mode 100644 index 000000000..13a3e34af --- /dev/null +++ b/HARK/simulation/monte_carlo.py @@ -0,0 +1,450 @@ +""" +Functions to support Monte Carlo simulation of models. +""" + +from copy import copy +from inspect import signature +from typing import Any, Callable, Mapping, Sequence, Union + +import numpy as np + +from HARK.distribution import ( + Distribution, + IndexDistribution, + TimeVaryingDiscreteDistribution, +) +from HARK.model import Aggregate, Control + + +def draw_shocks(shocks: Mapping[str, Distribution], conditions: Sequence[int]): + """ + Draw from each shock distribution values, subject to given conditions. + + Parameters + ------------ + shocks Mapping[str, Distribution] + A dictionary-like mapping from shock names to distributions from which to draw + + conditions: Sequence[int] + An array of conditions, one for each agent. + Typically these will be agent ages. + + Parameters + ------------ + draws : Mapping[str, Sequence] + A mapping from shock names to drawn shock values. + """ + draws = {} + + for shock_var in shocks: + shock = shocks[shock_var] + + if isinstance(shock, (int, float)): + draws[shock_var] = np.ones(len(conditions)) * shock + elif isinstance(shock, Aggregate): + draws[shock_var] = shock.dist.draw(1)[0] + elif isinstance(shock, IndexDistribution) or isinstance( + shock, TimeVaryingDiscreteDistribution + ): + ## TODO his type test is awkward. They should share a superclass. + draws[shock_var] = shock.draw(conditions) + else: + draws[shock_var] = shock.draw(len(conditions)) + + return draws + + +def simulate_dynamics( + dynamics: Mapping[str, Union[Callable, Control]], + pre: Mapping[str, Any], + dr: Mapping[str, Callable], +): + """ + From the beginning-of-period state (pre), follow the dynamics, + including any decision rules, to compute the end-of-period state. + + Parameters + ------------ + + dynamics: Mapping[str, Callable] + Maps variable names to functions from variables to values. + Can include Controls + ## TODO: Make collection of equations into a named type + + + pre : Mapping[str, Any] + Bound values for all variables that must be known before beginning the period's dynamics. + + + dr : Mapping[str, Callable] + Decision rules for all the Control variables in the dynamics. + """ + vals = pre.copy() + + for varn in dynamics: + # Using the fact that Python dictionaries are ordered + + feq = dynamics[varn] + + if isinstance(feq, Control): + # This tests if the decision rule is age varying. + # If it is, this will be a vector with the decision rule for each agent. + if isinstance(dr[varn], np.ndarray): + ## Now we have to loop through each agent, and apply the decision rule. + ## This is quite slow. + for i in range(dr[varn].size): + vals_i = { + var: vals[var][i] + if isinstance(vals[var], np.ndarray) + else vals[var] + for var in vals + } + vals[varn][i] = dr[varn][i]( + *[vals_i[var] for var in signature(dr[varn][i]).parameters] + ) + else: + vals[varn] = dr[varn]( + *[vals[var] for var in signature(dr[varn]).parameters] + ) # TODO: test for signature match with Control + else: + vals[varn] = feq(*[vals[var] for var in signature(feq).parameters]) + + return vals + + +def parameters_by_age(ages, parameters): + """ + Returns parameters for this model, but with vectorized + values which map age-varying values to agent ages. + + Parameters + ---------- + ages: np.array + An array of agent ages. + + parameters: dict + A parameters dictionary + + Returns + -------- + aged_parameters: dict + A dictionary of parameter values. + If a parameter is age-varying, the value is a vector + corresponding to the values for each input age. + """ + + def aged_param(ages, p_value): + if isinstance(p_value, (float, int)) or callable(p_value): + return p_value + elif isinstance(p_value, list) and len(p_value) > 1: + pv_array = np.array(p_value) + + return np.apply_along_axis(lambda a: pv_array[a], 0, ages) + else: + return np.empty(ages.size) + + return {p: aged_param(ages, parameters[p]) for p in parameters} + + +class Simulator: + pass + + +class AgentTypeMonteCarloSimulator(Simulator): + """ + A Monte Carlo simulation engine based on the HARK.core.AgentType framework. + + Unlike HARK.core.AgentType, this class does not do any model solving, + and depends on dynamic equations, shocks, and decision rules paased into it. + + The purpose of this class is to provide a way to simulate models without + relying on inheritance from the AgentType class. + + This simulator makes assumptions about population birth and mortality which + are not generic. All agents are replaced with newborns when they expire. + + Parameters + ------------ + parameters: Mapping[str, Any] + + shocks: Mapping[str, Distribution] + + dynamics: Mapping[str, Union[Callable, Control]] + + dr: Mapping[str, Callable] + + initial: dict + + seed : int + A seed for this instance's random number generator. + + Attributes + ---------- + agent_count : int + The number of agents of this type to use in simulation. + + T_sim : int + The number of periods to simulate. + """ + + state_vars = [] + + def __init__( + self, parameters, shocks, dynamics, dr, initial, seed=0, agent_count=1, T_sim=10 + ): + super().__init__() + + self.parameters = parameters + self.shocks = shocks + self.dynamics = dynamics + self.dr = dr + self.initial = initial + + self.seed = seed # NOQA + self.agent_count = agent_count + self.T_sim = T_sim + + # changes here from HARK.core.AgentType + self.vars = list(shocks.keys()) + list(dynamics.keys()) + + self.vars_now = {v: None for v in self.vars} + self.vars_prev = self.vars_now.copy() + + self.read_shocks = False # NOQA + self.shock_history = {} + self.newborn_init_history = {} + self.history = {} + + self.reset_rng() # NOQA + + def reset_rng(self): + """ + Reset the random number generator for this type. + """ + self.RNG = np.random.default_rng(self.seed) + + def initialize_sim(self): + """ + Prepares for a new simulation. Resets the internal random number generator, + makes initial states for all agents (using sim_birth), clears histories of tracked variables. + """ + if self.T_sim <= 0: + raise Exception( + "T_sim represents the largest number of observations " + + "that can be simulated for an agent, and must be a positive number." + ) + + self.reset_rng() + self.t_sim = 0 + all_agents = np.ones(self.agent_count, dtype=bool) + blank_array = np.empty(self.agent_count) + blank_array[:] = np.nan + for var in self.vars: + if self.vars_now[var] is None: + self.vars_now[var] = copy(blank_array) + + self.t_age = np.zeros( + self.agent_count, dtype=int + ) # Number of periods since agent entry + self.t_cycle = np.zeros( + self.agent_count, dtype=int + ) # Which cycle period each agent is on + + # Get recorded newborn conditions or initialize blank history. + if self.read_shocks and bool(self.newborn_init_history): + for init_var_name in self.initial: + self.vars_now[init_var_name] = self.newborn_init_history[init_var_name][ + self.t_sim, : + ] + else: + for var_name in self.initial: + self.newborn_init_history[var_name] = ( + np.zeros((self.T_sim, self.agent_count)) + np.nan + ) + + self.sim_birth(all_agents) + + self.clear_history() + return None + + def sim_one_period(self): + """ + Simulates one period for this type. Calls the methods get_mortality(), get_shocks() or + read_shocks, get_states(), get_controls(), and get_poststates(). These should be defined for + AgentType subclasses, except get_mortality (define its components sim_death and sim_birth + instead) and read_shocks. + """ + # Mortality adjusts the agent population + self.get_mortality() # Replace some agents with "newborns" + + # state_{t-1} + for var in self.vars: + self.vars_prev[var] = self.vars_now[var] + + if isinstance(self.vars_now[var], np.ndarray): + self.vars_now[var] = np.empty(self.agent_count) + self.vars_now[var][:] = np.nan + else: + # Probably an aggregate variable. It may be getting set by the Market. + pass + + shocks_now = {} + + if self.read_shocks: # If shock histories have been pre-specified, use those + for var_name in self.shocks: + shocks_now[var_name] = self.shock_history[var_name][self.t_sim, :] + else: + ### BIG CHANGES HERE from HARK.core.AgentType + shocks_now = draw_shocks(self.shocks, self.t_age) + + pre = parameters_by_age(self.t_age, self.parameters) + + pre.update(self.vars_prev) + pre.update(shocks_now) + # Won't work for 3.8: self.parameters | self.vars_prev | shocks_now + + # Age-varying decision rules captured here + dr = parameters_by_age(self.t_age, self.dr) + + post = simulate_dynamics(self.dynamics, pre, dr) + + self.vars_now = post + ### BIG CHANGES HERE + + # Advance time for all agents + self.t_age = self.t_age + 1 # Age all consumers by one period + + # What will we do with cycles? + # self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + # self.t_cycle[ + # self.t_cycle == self.T_cycle + # ] = 0 # Resetting to zero for those who have reached the end + + def make_shock_history(self): + """ + Makes a pre-specified history of shocks for the simulation. Shock variables should be named + in self.shock, a mapping from shock names to distributions. This method runs a subset + of the standard simulation loop by simulating only mortality and shocks; each variable named + in shocks is stored in a T_sim x agent_count array in history dictionary self.history[X]. + Automatically sets self.read_shocks to True so that these pre-specified shocks are used for + all subsequent calls to simulate(). + + Returns + ------- + shock_history: dict + The subset of simulation history that are the shocks for each agent and time. + """ + # Re-initialize the simulation + self.initialize_sim() + self.simulate() + + for shock_name in self.shocks: + self.shock_history[shock_name] = self.history[shock_name] + + # Flag that shocks can be read rather than simulated + self.read_shocks = True + self.clear_history() + + return self.shock_history + + def get_mortality(self): + """ + Simulates mortality or agent turnover. + Agents die when their states `live` is less than or equal to zero. + """ + who_dies = self.vars_now["live"] <= 0 + + self.sim_birth(who_dies) + + self.who_dies = who_dies + return None + + def sim_birth(self, which_agents): + """ + Makes new agents for the simulation. Takes a boolean array as an input, indicating which + agent indices are to be "born". Does nothing by default, must be overwritten by a subclass. + + Parameters + ---------- + which_agents : np.array(Bool) + Boolean array of size self.agent_count indicating which agents should be "born". + + Returns + ------- + None + """ + if self.read_shocks: + t = self.t_sim + initial_vals = { + init_var: self.newborn_init_history[init_var][t, which_agents] + for init_var in self.initial + } + + else: + initial_vals = draw_shocks(self.initial, np.zeros(which_agents.sum())) + + if np.sum(which_agents) > 0: + for varn in initial_vals: + self.vars_now[varn][which_agents] = initial_vals[varn] + self.newborn_init_history[varn][ + self.t_sim, which_agents + ] = initial_vals[varn] + + self.t_age[which_agents] = 0 + self.t_cycle[which_agents] = 0 + + def simulate(self, sim_periods=None): + """ + Simulates this agent type for a given number of periods. Defaults to + self.T_sim if no input. + Records histories of attributes named in self.track_vars in + self.history[varname]. + + Parameters + ---------- + None + + Returns + ------- + history : dict + The history tracked during the simulation. + """ + if not hasattr(self, "t_sim"): + raise Exception( + "It seems that the simulation variables were not initialize before calling " + + "simulate(). Call initialize_sim() to initialize the variables before calling simulate() again." + ) + if sim_periods is not None and self.T_sim < sim_periods: + raise Exception( + "To simulate, sim_periods has to be larger than the maximum data set size " + + "T_sim. Either increase the attribute T_sim of this agent type instance " + + "and call the initialize_sim() method again, or set sim_periods <= T_sim." + ) + + # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- + # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is + # -np.inf, np.inf/np.inf is np.nan and so on. + with np.errstate( + divide="ignore", over="ignore", under="ignore", invalid="ignore" + ): + if sim_periods is None: + sim_periods = self.T_sim + + for t in range(sim_periods): + self.sim_one_period() + + # track all the vars -- shocks and dynamics + for var_name in self.vars: + self.history[var_name][self.t_sim, :] = self.vars_now[var_name] + + self.t_sim += 1 + + return self.history + + def clear_history(self): + """ + Clears the histories. + """ + for var_name in self.vars: + self.history[var_name] = np.empty((self.T_sim, self.agent_count)) + self.history[var_name].fill(np.nan) diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py new file mode 100644 index 000000000..bb1620c3c --- /dev/null +++ b/HARK/simulation/test_monte_carlo.py @@ -0,0 +1,160 @@ +""" +This file implements unit tests for the Monte Carlo simulation module +""" +import unittest + +from HARK.distribution import Bernoulli, IndexDistribution, MeanOneLogNormal +from HARK.model import Aggregate, Control +from HARK.simulation.monte_carlo import * + +cons_shocks = { + "agg_gro": Aggregate(MeanOneLogNormal(1)), + "psi": IndexDistribution(MeanOneLogNormal, {"sigma": [1.0, 1.1]}), + "theta": MeanOneLogNormal(1), + "live": Bernoulli(p=0.98), +} + +cons_pre = { + "R": 1.05, + "aNrm": 1, + "gamma": 1.1, + "psi": 1.1, # TODO: draw this from a shock, + "theta": 1.1, # TODO: draw this from a shock +} + +cons_dynamics = { + "G": lambda gamma, psi: gamma * psi, + "Rnrm": lambda R, G: R / G, + "bNrm": lambda Rnrm, aNrm: Rnrm * aNrm, + "mNrm": lambda bNrm, theta: bNrm + theta, + "cNrm": Control(["mNrm"]), + "aNrm": lambda mNrm, cNrm: mNrm - cNrm, +} + +cons_dr = {"cNrm": lambda mNrm: mNrm / 2} + + +class test_draw_shocks(unittest.TestCase): + def test_draw_shocks(self): + drawn = draw_shocks(cons_shocks, np.array([0, 1])) + + self.assertEqual(len(drawn["theta"]), 2) + self.assertEqual(len(drawn["psi"]), 2) + self.assertTrue(isinstance(drawn["agg_gro"], float)) + + +class test_simulate_dynamics(unittest.TestCase): + def test_simulate_dynamics(self): + post = simulate_dynamics(cons_dynamics, cons_pre, cons_dr) + + self.assertAlmostEqual(post["cNrm"], 0.98388429) + + +class test_AgentTypeMonteCarloSimulator(unittest.TestCase): + def setUp(self): + self.shocks = { + "theta": MeanOneLogNormal(1), + "agg_R": Aggregate(MeanOneLogNormal(1)), + "live": Bernoulli(p=0.98), + } + + self.initial = {"a": MeanOneLogNormal(1), "live": 1} + + self.parameters = { # TODO + "G": 1.05, + } + + self.dynamics = { + "b": lambda agg_R, G, a: agg_R * G * a, + "m": lambda b, theta: b + theta, + "c": Control(["m"]), + "a": lambda m, c: m - c, + } + + self.dr = {"c": lambda m: m / 2} + + def test_simulate(self): + self.simulator = AgentTypeMonteCarloSimulator( + self.parameters, + self.shocks, + self.dynamics, + self.dr, + self.initial, + agent_count=3, + ) + + self.simulator.initialize_sim() + history = self.simulator.simulate() + + a1 = history["a"][5] + b1 = ( + history["a"][4] * history["agg_R"][5] * self.parameters["G"] + + history["theta"][5] + - history["c"][5] + ) + + self.assertTrue((a1 == b1).all()) + + def test_make_shock_history(self): + self.simulator = AgentTypeMonteCarloSimulator( + self.parameters, + self.shocks, + self.dynamics, + self.dr, + self.initial, + agent_count=3, + ) + + self.simulator.make_shock_history() + + newborn_init_1 = self.simulator.newborn_init_history.copy() + shocks_1 = self.simulator.shock_history.copy() + + self.simulator.initialize_sim() + self.simulator.simulate() + + self.assertEqual(newborn_init_1, self.simulator.newborn_init_history) + self.assertTrue(np.all(self.simulator.history["theta"] == shocks_1["theta"])) + + +class test_AgentTypeMonteCarloSimulatorAgeVariance(unittest.TestCase): + def setUp(self): + self.shocks = { + "theta": MeanOneLogNormal(1), + "agg_R": Aggregate(MeanOneLogNormal(1)), + "live": Bernoulli(p=0.98), + "psi": IndexDistribution(MeanOneLogNormal, {"sigma": [1.0, 1.1]}), + } + + self.initial = {"a": MeanOneLogNormal(1), "live": 1} + + self.parameters = { # TODO + "G": 1.05, + } + + self.dynamics = { + "b": lambda agg_R, G, a: agg_R * G * a, + "m": lambda b, theta: b + theta, + "c": Control(["m"]), + "a": lambda m, c: m - c, + } + + self.dr = {"c": [lambda m: m * 0.5, lambda m: m * 0.9]} + + def test_simulate(self): + self.simulator = AgentTypeMonteCarloSimulator( + self.parameters, + self.shocks, + self.dynamics, + self.dr, + self.initial, + agent_count=3, + ) + + self.simulator.initialize_sim() + history = self.simulator.simulate(sim_periods=2) + + a1 = history["a"][1] + b1 = history["m"][1] - self.dr["c"][1](history["m"][1]) + + self.assertTrue((a1 == b1).all()) diff --git a/HARK/tests/test_mat_methods.py b/HARK/tests/test_mat_methods.py index 7e5b84374..834d78b83 100644 --- a/HARK/tests/test_mat_methods.py +++ b/HARK/tests/test_mat_methods.py @@ -1,7 +1,9 @@ import unittest + import numpy as np -from HARK.utilities import jump_to_grid_1D, jump_to_grid_2D + from HARK.mat_methods import mass_to_grid +from HARK.utilities import jump_to_grid_1D, jump_to_grid_2D # Compare general mass_to_grid with jump_to_grid_1D diff --git a/examples/Calibration/Income_calibrations.ipynb b/examples/Calibration/Income_calibrations.ipynb index 9703b284a..719948309 100644 --- a/examples/Calibration/Income_calibrations.ipynb +++ b/examples/Calibration/Income_calibrations.ipynb @@ -96,7 +96,6 @@ } ], "source": [ - "\n", "age_min = 25\n", "age_max = 91\n", "# Cagetti has a year trend in his specification, so we have to say on what\n", @@ -114,7 +113,7 @@ " age_max=age_max,\n", " adjust_infl_to=adjust_infl_to,\n", " start_year=start_year,\n", - " **spec[1]\n", + " **spec[1],\n", " )\n", " MeanY = find_profile(params[\"PermGroFac\"], params[\"P0\"])\n", "\n", diff --git a/examples/Calibration/Life_Cycle_example.ipynb b/examples/Calibration/Life_Cycle_example.ipynb index 28316463f..8d1e95df9 100644 --- a/examples/Calibration/Life_Cycle_example.ipynb +++ b/examples/Calibration/Life_Cycle_example.ipynb @@ -47,7 +47,7 @@ " age_max=death_age,\n", " adjust_infl_to=adjust_infl_to,\n", " **income_calib[education],\n", - " SabelhausSong=True\n", + " SabelhausSong=True,\n", ")\n", "\n", "# Initial distribution of wealth and permanent income\n", diff --git a/examples/Calibration/SCF_distributions.ipynb b/examples/Calibration/SCF_distributions.ipynb index 16a8142e5..83769032c 100644 --- a/examples/Calibration/SCF_distributions.ipynb +++ b/examples/Calibration/SCF_distributions.ipynb @@ -84,7 +84,6 @@ } ], "source": [ - "\n", "# Formatting\n", "frame = frame.melt(id_vars=[\"base_year\", \"age\", \"education\", \"wave\"])\n", "aux = frame[\"variable\"].str.split(\"(Mean|Std)\", n=1, expand=True)\n", diff --git a/examples/Calibration/Sabelhaus_Song_var_profiles.ipynb b/examples/Calibration/Sabelhaus_Song_var_profiles.ipynb index 103baac3c..f005f7028 100644 --- a/examples/Calibration/Sabelhaus_Song_var_profiles.ipynb +++ b/examples/Calibration/Sabelhaus_Song_var_profiles.ipynb @@ -77,7 +77,6 @@ } ], "source": [ - "\n", "# Plot transitory shock variances\n", "plt.figure()\n", "for i in range(len(cohorts)):\n", diff --git a/examples/Calibration/US_SSA_life_tables.ipynb b/examples/Calibration/US_SSA_life_tables.ipynb index 6b76077c0..3f941ac35 100644 --- a/examples/Calibration/US_SSA_life_tables.ipynb +++ b/examples/Calibration/US_SSA_life_tables.ipynb @@ -59,7 +59,6 @@ } ], "source": [ - "\n", "tables = get_ssa_life_tables()\n", "print(tables.head)" ] @@ -73,7 +72,6 @@ }, "outputs": [], "source": [ - "\n", "# We will find 1-year survival probabilities from ages 21 to 100\n", "min_age = 21\n", "max_age = 100\n", @@ -111,7 +109,6 @@ } ], "source": [ - "\n", "# First, the \"longitudinal method\", which gives us the probabilities\n", "# experienced by agents born in \"year\" throughout their lived\n", "plt.figure()\n", @@ -156,7 +153,6 @@ } ], "source": [ - "\n", "# Second, the \"cross-sectional method\", which gives us the probabilities of\n", "# survivals of individuals of differnet ages that are alive in the given year.\n", "plt.figure()\n", diff --git a/examples/ConsIndShockModel/Finite Cyclical Test.ipynb b/examples/ConsIndShockModel/Finite Cyclical Test.ipynb index 6d05f9056..829647c62 100644 --- a/examples/ConsIndShockModel/Finite Cyclical Test.ipynb +++ b/examples/ConsIndShockModel/Finite Cyclical Test.ipynb @@ -8,9 +8,7 @@ "source": [ "# Initial imports and notebook setup, click arrow to show\n", "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", - "from HARK.utilities import plot_funcs_der, plot_funcs\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", + "from HARK.utilities import plot_funcs\n", "\n", "mystr = lambda number: \"{:.4f}\".format(number)" ] diff --git a/examples/ConsIndShockModel/IndShockConsumerType.ipynb b/examples/ConsIndShockModel/IndShockConsumerType.ipynb index 29f50ebda..d1ae8592f 100644 --- a/examples/ConsIndShockModel/IndShockConsumerType.ipynb +++ b/examples/ConsIndShockModel/IndShockConsumerType.ipynb @@ -57,15 +57,15 @@ "\n", "Specifically, this type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\newcommand{\\tShkEmp}{\\theta}{\\tShkEmp_t}$ and a completely permanent shock $\\newcommand{\\pShk}{\\psi}{\\pShk_t}$. Moreover, the agent is subject to borrowing a borrowing limit: the ratio of end-of-period assets $A_t$ to permanent income $P_t$ must be greater than $\\underline{a}$. As with the perfect foresight problem, this model is stated in terms of *normalized* variables, dividing all real variables by $P_t$:\n", "\n", - "\\begin{eqnarray*}\n", - "v_t(m_t) &=& \\max_{c_t} {~} u(c_t) + \\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n", - "a_t &=& m_t - c_t, \\\\\n", - "a_t &\\geq& \\text{$\\underline{a}$}, \\\\\n", - "m_{t+1} &=& \\Rfree/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", - "(\\psi_{t+1},\\theta_{t+1}) &\\sim& F_{t+1}, \\\\\n", - "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1, \\\\\n", - "u(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", - "\\end{eqnarray*}" + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t} u(c_t) + \\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n", + "a_t &= m_t - c_t, \\\\\n", + "a_t &\\geq \\text{$\\underline{a}$}, \\\\\n", + "m_{t+1} &= \\Rfree/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", + "(\\psi_{t+1},\\theta_{t+1}) &\\sim F_{t+1}, \\\\\n", + "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &= 1, \\\\\n", + "u(c) &= \\frac{c^{1-\\rho}}{1-\\rho}.\n", + "\\end{align*}" ] }, { @@ -78,21 +78,21 @@ "\n", "Briefly, the transition equation for $m_{t+1}$ can be substituted into the problem definition; the second term of the reformulated maximand represents \"end of period value of assets\" $\\mathfrak{v}_t(a_t)$ (\"Gothic v\"):\n", "\n", - "\\begin{eqnarray*}\n", - "v_t(m_t) &=& \\max_{c_t} {~} u(c_t) + \\underbrace{\\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(\\Rfree/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}) \\right]}_{\\equiv \\mathfrak{v}_t(a_t)}.\n", - "\\end{eqnarray*}\n", + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t} {~} u(c_t) + \\underbrace{\\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(\\Rfree/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}) \\right]}_{\\equiv \\mathfrak{v}_t(a_t)}.\n", + "\\end{align*}\n", "\n", "The first order condition with respect to $c_t$ is thus simply:\n", "\n", - "\\begin{eqnarray*}\n", + "\\begin{align*}\n", "u^{\\prime}(c_t) - \\mathfrak{v}'_t(a_t) = 0 \\Longrightarrow c_t^{-\\CRRA} = \\mathfrak{v}'_t(a_t) \\Longrightarrow c_t = \\mathfrak{v}'_t(a_t)^{-1/\\CRRA},\n", - "\\end{eqnarray*}\n", + "\\end{align*}\n", "\n", "and the marginal value of end-of-period assets can be computed as:\n", "\n", - "\\begin{eqnarray*}\n", + "\\begin{align*}\n", "\\mathfrak{v}'_t(a_t) = \\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ \\Rfree (\\PermGroFac_{t+1}\\psi_{t+1})^{-\\CRRA} v'_{t+1}(\\Rfree/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}) \\right].\n", - "\\end{eqnarray*}\n", + "\\end{align*}\n", "\n", "To solve the model, we choose an exogenous grid of $a_t$ values that span the range of values that could plausibly be achieved, compute $\\mathfrak{v}'_t(a_t)$ at each of these points, calculate the value of consumption $c_t$ whose marginal utility is consistent with the marginal value of assets, then find the endogenous $m_t$ gridpoint as $m_t = a_t + c_t$. The set of $(m_t,c_t)$ gridpoints is then interpolated to construct the consumption function." ] @@ -118,9 +118,9 @@ "| $N_\\theta$| Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | $7$ | |\n", "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\texttt{UnempPrb}$ | $0.05$ | |\n", "| $\\underline{\\theta}$| Transitory shock when unemployed | $\\texttt{IncUnemp}$ | $0.3$ | |\n", - "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\texttt{UnempPrb}$ | $0.0005$ | |\n", - "| $\\underline{\\theta}^{Ret}$| Transitory shock when \"unemployed\" and retired | $\\texttt{IncUnemp}$ | $0.0$ | |\n", - "| $(none)$ | Period of the lifecycle model when retirement begins | $\\texttt{T_retire}$ | $0$ | |\n", + "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\texttt{UnempPrbRet}$ | $0.0005$ | |\n", + "| $\\underline{\\theta}^{Ret}$| Transitory shock when \"unemployed\" and retired | $\\texttt{IncUnempRet}$ | $0.0$ | |\n", + "| $(none)$ | Period of the lifecycle model when retirement begins | $\\texttt{T\\_retire}$ | $0$ | |\n", "| $(none)$ | Minimum value in assets-above-minimum grid | $\\texttt{aXtraMin}$ | $0.001$ | |\n", "| $(none)$ | Maximum value in assets-above-minimum grid | $\\texttt{aXtraMax}$ | $20.0$ | |\n", "| $(none)$ | Number of points in base assets-above-minimum grid | $\\texttt{aXtraCount}$ | $48$ | |\n", @@ -129,7 +129,7 @@ "| $\\underline{a}$| Artificial borrowing constraint (normalized) | $\\texttt{BoroCnstArt}$ | $0.0$ | |\n", "| $(none)$|Indicator for whether $\\texttt{vFunc}$ should be computed | $\\texttt{vFuncBool}$ | $True$ | |\n", "| $(none)$ |Indicator for whether $\\texttt{cFunc}$ should use cubic splines | $\\texttt{CubicBool}$ | $False$ | |\n", - "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T_cycle}$| $1$ | |\n", + "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T\\_cycle}$| $1$ | |\n", "|(none)| Number of times the \"cycle\" occurs |$\\texttt{cycles}$| $0$ | |" ] }, @@ -219,69 +219,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPFRaw = 0.984539 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFNrm = 0.993777 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFAggLivPrb = 0.964848 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Thorn = APF = 0.994384 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermGroFacAdj = 1.000611 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "uInvEpShkuInv = 0.990704 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "VAF = 0.932054 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WRPF = 0.213705 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DiscFacGPFNrmMax = 0.972061 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "GPFRaw = 0.984539 \n", + "GPFNrm = 0.993777 \n", + "GPFAggLivPrb = 0.964848 \n", + "Thorn = APF = 0.994384 \n", + "PermGroFacAdj = 1.000611 \n", + "uInvEpShkuInv = 0.990704 \n", + "VAF = 0.932054 \n", + "WRPF = 0.213705 \n", + "DiscFacGPFNrmMax = 0.972061 \n", "DiscFacGPFAggLivPrbMax = 1.010600 \n" ] } @@ -308,7 +254,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': 0.0, 'hNrm': 44.991920196607595, 'MPCmin': 0.044536273404377116, 'MPCmax': 1.0, 'mNrmStE': 1.5488165705077002, 'mNrmTrg': 1.5799173260214086}\n" + "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': 0.0, 'hNrm': 44.991920196607595, 'MPCmin': 0.044536273404377116, 'MPCmax': 1.0, 'mNrmStE': 1.548816570507704, 'mNrmTrg': 1.5799173260214134}\n" ] } ], @@ -341,7 +287,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" ] @@ -454,7 +400,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -478,13 +424,13 @@ "| Description | Code | Example value |\n", "| :---: | --- | --- |\n", "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n", - "| Number of periods to simulate | $\\texttt{T_sim}$ | $120$ |\n", + "| Number of periods to simulate | $\\texttt{T\\_sim}$ | $120$ |\n", "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n", "| Stdev of initial log (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n", "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n", "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n", "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n", - "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n", + "| Age after which consumers are automatically killed | $\\texttt{T\\_age}$ | $None$ |\n", "\n", "Here, we will simulate 10,000 consumers for 120 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\texttt{pLvlInitMean})$, as $\\texttt{pLvlInitStd}$ has been set to zero; they will have essentially zero assets at birth, as $\\texttt{aNrmInitMean}$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", "\n", @@ -578,7 +524,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -661,7 +607,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb b/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb index 0fc2cbcd6..cc0df25a2 100644 --- a/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb +++ b/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb @@ -30,9 +30,7 @@ "\n", "\n", "import time\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from copy import copy, deepcopy" + "import matplotlib.pyplot as plt" ] }, { @@ -73,7 +71,6 @@ }, "outputs": [], "source": [ - "\n", "Agent = IndShockConsumerType(**Dict)" ] }, @@ -118,7 +115,6 @@ } ], "source": [ - "\n", "start = time.time()\n", "Agent.compute_steady_state()\n", "print(\"Seconds to compute steady state\", time.time() - start)" @@ -159,7 +155,6 @@ } ], "source": [ - "\n", "start = time.time()\n", "\n", "CJAC_Perm, AJAC_Perm = Agent.calc_jacobian(\"PermShkStd\", 300)\n", @@ -195,7 +190,6 @@ } ], "source": [ - "\n", "plt.plot(CJAC_Perm.T[0])\n", "plt.plot(CJAC_Perm.T[10])\n", "plt.plot(CJAC_Perm.T[30])\n", @@ -230,7 +224,6 @@ } ], "source": [ - "\n", "plt.plot(AJAC_Perm.T[0])\n", "plt.plot(AJAC_Perm.T[10])\n", "plt.plot(AJAC_Perm.T[30])\n", @@ -286,7 +279,6 @@ } ], "source": [ - "\n", "plt.plot(CJAC_Rfree.T[0])\n", "plt.plot(CJAC_Rfree.T[10])\n", "plt.plot(CJAC_Rfree.T[30])\n", @@ -320,7 +312,6 @@ } ], "source": [ - "\n", "plt.plot(AJAC_Rfree.T[0])\n", "plt.plot(AJAC_Rfree.T[10])\n", "plt.plot(AJAC_Rfree.T[30])\n", diff --git a/examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb b/examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb index d21aaa77d..22c1df453 100644 --- a/examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb +++ b/examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb @@ -47,16 +47,13 @@ }, "outputs": [], "source": [ - "\n", "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", "\n", "\n", "import time\n", - "from copy import copy, deepcopy\n", + "from copy import deepcopy\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import time" + "import matplotlib.pyplot as plt" ] }, { @@ -85,7 +82,6 @@ } ], "source": [ - "\n", "Dict = {\n", " # Parameters shared with the perfect foresight model\n", " \"CRRA\": 2, # Coefficient of relative risk aversion\n", @@ -150,75 +146,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPFRaw = 0.992274 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFNrm = 0.995482 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFAggLivPrb = 0.986072 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Thorn = APF = 0.992274 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermGroFacAdj = 0.996777 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "uInvEpShkuInv = 0.996777 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "VAF = 0.965783 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WRPF = 0.000000 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DiscFacGPFNrmMax = 0.983869 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "GPFRaw = 0.992274 \n", + "GPFNrm = 0.995482 \n", + "GPFAggLivPrb = 0.986072 \n", + "Thorn = APF = 0.992274 \n", + "PermGroFacAdj = 0.996777 \n", + "uInvEpShkuInv = 0.996777 \n", + "VAF = 0.965783 \n", + "WRPF = 0.000000 \n", + "DiscFacGPFNrmMax = 0.983869 \n", "DiscFacGPFAggLivPrbMax = 0.996471 \n" ] } ], "source": [ - "\n", "example1 = IndShockConsumerType(**Dict)\n", "example1.cycles = 0\n", "example1.solve()" @@ -257,7 +198,6 @@ }, "outputs": [], "source": [ - "\n", "# Simulation Parameters\n", "\n", "# Simulate\n", @@ -300,7 +240,6 @@ } ], "source": [ - "\n", "example1.define_distribution_grid(num_pointsP=110, timestonest=3)\n", "p = example1.dist_pGrid # Grid of permanent income levels\n", "\n", @@ -311,9 +250,7 @@ "asset = example1.aPol_Grid # Normalized Asset Policy Grid\n", "\n", "example1.calc_ergodic_dist()\n", - "vecDstn = (\n", - " example1.vec_erg_dstn\n", - ") # Distribution of market resources and permanent income as a vector (m*p)x1 vector where\n", + "vecDstn = example1.vec_erg_dstn # Distribution of market resources and permanent income as a vector (m*p)x1 vector where\n", "# m is the number of market resource gridpoints and p is the number of permanent income gridpoints\n", "erg_dstn = example1.erg_dstn\n", "\n", @@ -332,7 +269,6 @@ }, "outputs": [], "source": [ - "\n", "# Compute Aggregate Consumption and Aggregate Assets\n", "gridc = np.zeros((len(c), len(p)))\n", "grida = np.zeros((len(asset), len(p)))\n", @@ -373,7 +309,6 @@ } ], "source": [ - "\n", "print(\"TranMatrix Assets = \" + str(AggA))\n", "print(\"Simulated Assets = \" + str(Monte_Carlo_Assets))\n", "\n", @@ -400,8 +335,6 @@ }, "outputs": [], "source": [ - "\n", - "\n", "aLvls = [] # Time series of aggregate assets\n", "\n", "for i in range(example1.T_sim):\n", @@ -489,7 +422,6 @@ } ], "source": [ - "\n", "num_pts = len(example1.dist_mGrid)\n", "mdstn = np.zeros(num_pts)\n", "\n", @@ -548,7 +480,6 @@ } ], "source": [ - "\n", "dstn = example1.erg_dstn\n", "\n", "pdstn = np.zeros(len(dstn[0]))\n", @@ -686,7 +617,6 @@ } ], "source": [ - "\n", "mLvl = (\n", " example1.state_now[\"mNrm\"] * example1.state_now[\"pLvl\"]\n", ") # market resources from Monte Carlo Simulations\n", @@ -742,7 +672,6 @@ } ], "source": [ - "\n", "asset_Lvl = example1.state_now[\"aLvl\"] # market resources from Monte Carlo Simulations\n", "pmf = jump_to_grid_fast(\n", " aLvl_vals, vecDstn, example1.aPol_Grid\n", @@ -803,75 +732,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPFRaw = 0.992274 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFNrm = 0.995482 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPFAggLivPrb = 0.986072 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Thorn = APF = 0.992274 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermGroFacAdj = 0.996777 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "uInvEpShkuInv = 0.996777 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "VAF = 0.965783 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WRPF = 0.000000 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DiscFacGPFNrmMax = 0.983869 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "GPFRaw = 0.992274 \n", + "GPFNrm = 0.995482 \n", + "GPFAggLivPrb = 0.986072 \n", + "Thorn = APF = 0.992274 \n", + "PermGroFacAdj = 0.996777 \n", + "uInvEpShkuInv = 0.996777 \n", + "VAF = 0.965783 \n", + "WRPF = 0.000000 \n", + "DiscFacGPFNrmMax = 0.983869 \n", "DiscFacGPFAggLivPrbMax = 0.996471 \n" ] } ], "source": [ - "\n", "ss = IndShockConsumerType(**Dict)\n", "ss.cycles = 0\n", "ss.solve()" @@ -899,7 +773,6 @@ }, "outputs": [], "source": [ - "\n", "# Change the income process to use Neutral Measure\n", "ss.neutral_measure = True\n", "ss.update_income_process()\n", @@ -925,7 +798,6 @@ } ], "source": [ - "\n", "# Set up grid and calculate steady state transition Matrices\n", "\n", "start = time.time()\n", @@ -937,9 +809,7 @@ "a = ss.aPol_Grid # Normalized Asset Policy grid\n", "\n", "ss.calc_ergodic_dist() # Calculate steady state distribution\n", - "vecDstn_fast = (\n", - " ss.vec_erg_dstn\n", - ") # Distribution as a vector (mx1) where m is the number of gridpoint on the market resources grid\n", + "vecDstn_fast = ss.vec_erg_dstn # Distribution as a vector (mx1) where m is the number of gridpoint on the market resources grid\n", "\n", "print(\n", " \"Seconds to calculate both the transition matrix and the steady state distribution with Harmenberg\",\n", @@ -978,8 +848,6 @@ } ], "source": [ - "\n", - "\n", "plt.plot(\n", " aLvls[100:], label=\"Monte Carlo\", linewidth=2.0\n", ") # Plot time series path of aggregate assets using Monte Carlo simulation methods\n", @@ -1024,9 +892,7 @@ " ss.calc_transition_matrix()\n", "\n", " ss.calc_ergodic_dist() # Calculate steady state distribution\n", - " vecDstn_fast = (\n", - " ss.vec_erg_dstn\n", - " ) # Distribution as a vector (mx1) where m is the number of gridpoint on the market resources grid\n", + " vecDstn_fast = ss.vec_erg_dstn # Distribution as a vector (mx1) where m is the number of gridpoint on the market resources grid\n", " Asset_val = np.dot(ss.aPol_Grid, vecDstn_fast)\n", "\n", " Agg_AVals.append(Asset_val)" @@ -1104,7 +970,6 @@ } ], "source": [ - "\n", "ss.AgentCount = 25000\n", "ss.T_sim = 700\n", "ss.initialize_sim()\n", @@ -1130,7 +995,6 @@ }, "outputs": [], "source": [ - "\n", "# We will solve a finite horizon problem that begins at the steady state computed above.\n", "# Therefore parameters must be specified as lists, each item's index indicating the period of the horizon.\n", "\n", @@ -1182,7 +1046,6 @@ }, "outputs": [], "source": [ - "\n", "dx = -0.05 # Change in the Interest Rate\n", "i = 10 # Period in which the change in the interest rate occurs\n", "\n", @@ -1210,7 +1073,6 @@ }, "outputs": [], "source": [ - "\n", "FinHorizonAgent.solve()" ] }, @@ -1239,7 +1101,6 @@ } ], "source": [ - "\n", "# Simulate with Monte Carlo\n", "\n", "FinHorizonAgent.PerfMITShk = True\n", @@ -1309,7 +1170,6 @@ } ], "source": [ - "\n", "# Change Income Process to allow permanent income shocks to be drawn from neutral measure\n", "FinHorizonAgent.mCount = ss.mCount\n", "FinHorizonAgent.mMax = ss.mMax\n", @@ -1394,7 +1254,6 @@ } ], "source": [ - "\n", "# plt.plot(AggC, label = 'without Harmenberg') #Without Neutral Measure\n", "plt.plot(\n", " AggC_fast, label=\" Transition Matrices\", linewidth=3.0\n", diff --git a/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb index 0223b2733..19090cd69 100644 --- a/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb +++ b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb @@ -8,7 +8,7 @@ "source": [ "# Perfect foresight consumption-saving\n", "\n", - "**The `PerfForesightConsumerType` class**" + "**The** `PerfForesightConsumerType` **class**" ] }, { @@ -38,7 +38,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The module `HARK.ConsumptionSaving.ConsIndShockModel` concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks are fully transitory or fully permanent.\n", + "The module `HARK.ConsumptionSaving.ConsIndShockModel` concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks that are either fully transitory or fully permanent.\n", "\n", "`ConsIndShockModel` currently includes three models:\n", "1. A very basic \"perfect foresight\" model with no uncertainty.\n", @@ -59,40 +59,39 @@ "source": [ "## Statement of perfect foresight consumption-saving model\n", "\n", - "The `PerfForesightConsumerType` class the problem of a consumer with Constant Relative Risk Aversion utility\n", - "${\\CRRA}$\n", - "\\begin{equation}\n", + "The `PerfForesightConsumerType` class models the problem of a consumer with Constant Relative Risk Aversion utility specified by\n", + "\\begin{align*}\n", "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho},\n", - "\\end{equation}\n", - "has perfect foresight about everything except whether he will die between the end of period $t$ and the beginning of period $t+1$, which occurs with probability $\\DiePrb_{t+1}$. Permanent labor income $P_t$ grows from period $t$ to period $t+1$ by factor $\\PermGroFac_{t+1}$.\n", + "\\end{align*}\n", + "who has perfect foresight about everything except whether he will die between the end of period $t$ and the beginning of period $t+1$, which occurs with probability $\\DiePrb_{t+1}$. Permanent labor income $P_t$ grows from period $t$ to period $t+1$ by factor $\\PermGroFac_{t+1}$.\n", "\n", - "At the beginning of period $t$, the consumer has an amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$, which will earn return factor $\\Rfree$. The consumer cannot necessarily borrow arbitarily; instead, he might be constrained to have a wealth-to-income ratio at least as great as some \"artificial borrowing constraint\" $\\underline{a} \\leq 0$.\n", + "At the beginning of period $t$, the consumer has an amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$, while retaining the rest in a riskless asset $A_t$, which will earn return factor $\\Rfree$. The consumer cannot necessarily borrow arbitarily; instead, he might be constrained to have a wealth-to-income ratio at least as great as some \"artificial borrowing constraint\" $\\underline{a} \\leq 0$.\n", "\n", - "The agent's flow of future utility $U(C_{t+n})$ from consumption is geometrically discounted by factor $\\DiscFac$ per period. If the consumer dies, he receives zero utility flow for the rest of time.\n", + "The agent's flow of future utility $U(C_{t+n})$ from consumption is geometrically discounted by factor $\\DiscFac^n$. If the consumer dies, he receives zero utility flow for the rest of time.\n", "\n", "The agent's problem can be written in Bellman form as:\n", "\n", - "\\begin{eqnarray*}\n", - "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) ~+ \\DiscFac (1 - \\DiePrb_{t+1}) V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", - "& s.t. & \\\\\n", - "A_t &=& M_t - C_t, \\\\\n", - "A_t/P_t &\\geq& \\underline{a}, \\\\\n", - "M_{t+1} &=& \\Rfree A_t + Y_{t+1}, \\\\\n", - "Y_{t+1} &=& P_{t+1}, \\\\\n", - "P_{t+1} &=& \\PermGroFac_{t+1} P_t.\n", - "\\end{eqnarray*}\n", + "\\begin{align*}\n", + "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\DiscFac (1 - \\DiePrb_{t+1}) V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", + "& \\text{s.t.} \\\\\n", + "A_t &= M_t - C_t, \\\\\n", + "A_t/P_t &\\geq \\underline{a}, \\\\\n", + "M_{t+1} &= \\Rfree A_t + Y_{t+1}, \\\\\n", + "Y_{t+1} &= P_{t+1}, \\\\\n", + "P_{t+1} &= \\PermGroFac_{t+1} P_t.\n", + "\\end{align*}\n", "\n", - "The consumer's problem is characterized by a coefficient of relative risk aversion $\\CRRA$, an intertemporal discount factor $\\DiscFac$, an interest factor $\\Rfree$, and age-varying sequences of the permanent income growth factor $\\PermGroFac_t$ and survival probability $(1 - \\DiePrb_t)$.\n", + "The consumer's problem is characterized by the coefficient of relative risk aversion $\\CRRA$, the intertemporal discount factor $\\DiscFac$, the interest factor $\\Rfree$, and age-varying sequences of the permanent income growth factor $\\PermGroFac_t$ and survival probability $(1 - \\DiePrb_t)$.\n", "\n", "While it does not reduce the computational complexity of the problem (as permanent income is deterministic, given its initial condition $P_0$), HARK represents this problem with *normalized* variables (represented in lower case), dividing all real variables by permanent income $P_t$ and utility levels by $P_t^{1-\\CRRA}$. The Bellman form of the model thus reduces to:\n", "\n", - "\\begin{eqnarray*}\n", - "v_t(m_t) &=& \\max_{c_t}~U(c_t) ~+ \\DiscFac (1 - \\DiePrb_{t+1}) \\PermGroFac_{t+1}^{1-\\CRRA} v_{t+1}(m_{t+1}), \\\\\n", - "& s.t. & \\\\\n", - "a_t &=& m_t - c_t, \\\\\n", - "a_t &\\geq& \\underline{a}, \\\\\n", - "m_{t+1} &=& \\Rfree/\\PermGroFac_{t+1} a_t + 1.\n", - "\\end{eqnarray*}" + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t}u(c_t) + \\DiscFac (1 - \\DiePrb_{t+1}) \\PermGroFac_{t+1}^{1-\\CRRA} v_{t+1}(m_{t+1}), \\\\\n", + "& \\text{s.t.} \\\\\n", + "a_t &= m_t - c_t, \\\\\n", + "a_t &\\geq \\underline{a}, \\\\\n", + "m_{t+1} &= \\Rfree a_t/\\PermGroFac_{t+1} + 1.\n", + "\\end{align*}" ] }, { @@ -101,7 +100,7 @@ "source": [ "## Solution method for PerfForesightConsumerType\n", "\n", - "Because of the assumptions of CRRA utility, no risk other than mortality, and no artificial borrowing constraint, the problem has a closed form solution. In fact, the consumption function is perfectly linear, and the value function composed with the inverse utility function is also linear. The mathematical solution of this model is described in detail in the lecture notes [PerfForesightCRRA](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA).\n", + "Because of the assumptions of CRRA utility and no risk other than mortality, the problem has a closed form solution when there is no artificial borrowing constraint. In fact, the consumption function is perfectly linear, and the value function composed with the inverse utility function is also linear. The mathematical solution of this model is described in detail in the lecture notes [PerfForesightCRRA](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA).\n", "\n", "The one period problem for this model is solved by the function `solveConsPerfForesight`, which creates an instance of the class `ConsPerfForesightSolver`. To construct an instance of the class `PerfForesightConsumerType`, several parameters must be passed to its constructor as shown in the table below." ] @@ -114,15 +113,15 @@ "\n", "| Parameter | Description | Code | Example value | Time-varying? |\n", "| :---: | --- | --- | --- | --- |\n", - "| $\\DiscFac$ |Intertemporal discount factor | $\\texttt{DiscFac}$ | $0.96$ | |\n", - "| $\\CRRA$ |Coefficient of relative risk aversion | $\\texttt{CRRA}$ | $2.0$ | |\n", - "| $\\Rfree$ | Risk free interest factor | $\\texttt{Rfree}$ | $1.03$ | |\n", - "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\texttt{LivPrb}$ | $[0.98]$ | $\\surd$ |\n", - "|$\\PermGroFac_{t+1}$|Permanent income growth factor|$\\texttt{PermGroFac}$| $[1.01]$ | $\\surd$ |\n", - "|$\\underline{a}$|Artificial borrowing constraint|$\\texttt{BoroCnstArt}$| $None$ | |\n", - "|$(none)$|Maximum number of gridpoints in consumption function |$\\texttt{aXtraCount}$| $200$ | |\n", - "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T_cycle}$| $1$ | |\n", - "|(none)| Number of times the \"cycle\" occurs |$\\texttt{cycles}$| $0$ | |\n", + "| $\\DiscFac$ |Intertemporal discount factor | `DiscFac` | $0.96$ | |\n", + "| $\\CRRA$ |Coefficient of relative risk aversion | `CRRA` | $2.0$ | |\n", + "| $\\Rfree$ | Risk free interest factor | `Rfree` | $1.03$ | |\n", + "| $1 - \\DiePrb_{t+1}$ |Survival probability | `LivPrb` | $[0.98]$ | $\\surd$ |\n", + "|$\\PermGroFac_{t+1}$|Permanent income growth factor|`PermGroFac`| $[1.01]$ | $\\surd$ |\n", + "|$\\underline{a}$|Artificial borrowing constraint|`BoroCnstArt`| $None$ | |\n", + "|$(none)$|Maximum number of gridpoints in consumption function |`aXtraCount`| $200$ | |\n", + "|$T$| Number of periods in this type's \"cycle\" |`T_cycle`| $1$ | |\n", + "|(none)| Number of times the \"cycle\" occurs |`cycles`| $0$ | |\n", "\n", "Note that the survival probability and income growth factor have time subscripts; likewise, the example values for these parameters are *lists* rather than simply single floats. This is because those parameters are *time-varying*: their values can depend on which period of the problem the agent is in. All time-varying parameters *must* be specified as lists, even if the same value occurs in each period for this type.\n", "\n", @@ -130,9 +129,9 @@ "\n", "The last two parameters in the table specify the \"nature of time\" for this type: the number of (non-terminal) periods in this type's \"cycle\", and the number of times that the \"cycle\" occurs. *Every* subclass of `AgentType` uses these two code parameters to define the nature of time. Here, `T_cycle` has the value $1$, indicating that there is exactly one period in the cycle, while `cycles` is $0$, indicating that the cycle is repeated in *infinite* number of times-- it is an infinite horizon model, with the same \"kind\" of period repeated over and over.\n", "\n", - "In contrast, we could instead specify a life-cycle model by setting `T_cycle` to $1$, and specifying age-varying sequences of income growth and survival probability. In all cases, the number of elements in each time-varying parameter should exactly equal $\\texttt{T_cycle}$.\n", + "In contrast, we could instead specify a life-cycle model by setting `T_cycle` to $1$, and specifying age-varying sequences of income growth and survival probability. In all cases, the number of elements in each time-varying parameter should exactly equal `T_cycle`.\n", "\n", - "The parameter $\\texttt{AgentCount}$ specifies how many consumers there are of this *type*-- how many individuals have these exact parameter values and are *ex ante* homogeneous. This information is not relevant for solving the model, but is needed in order to simulate a population of agents, introducing *ex post* heterogeneity through idiosyncratic shocks. Of course, simulating a perfect foresight model is quite boring, as there are *no* idiosyncratic shocks other than death!\n", + "The parameter `AgentCount` specifies how many consumers there are of this *type*-- how many individuals have these exact parameter values and are *ex ante* homogeneous. This information is not relevant for solving the model, but is needed in order to simulate a population of agents, introducing *ex post* heterogeneity through idiosyncratic shocks. Of course, simulating a perfect foresight model is quite boring, as there are *no* idiosyncratic shocks other than death!\n", "\n", "The cell below defines a dictionary that can be passed to the constructor method for `PerfForesightConsumerType`, with the values from the table here." ] @@ -184,7 +183,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The $\\texttt{solve}$ method fills in the instance's attribute `solution` as a time-varying list of solutions to each period of the consumer's problem. In this case, `solution` will be a list with exactly one instance of the class `ConsumerSolution`, representing the solution to the infinite horizon model we specified." + "The `solve` method fills in the instance's attribute `solution` as a time-varying list of solutions to each period of the consumer's problem. In this case, `solution` will be a list with exactly one instance of the class `ConsumerSolution`, representing the solution to the infinite horizon model we specified." ] }, { @@ -196,7 +195,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n" + "[]\n" ] } ], @@ -208,9 +207,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Each element of `solution` has a few attributes. To see all of them, we can use the $\\texttt{vars}$ built in function:\n", + "Each element of `solution` has a few attributes. To see all of them, we can use the `vars` built in function:\n", "\n", - "the consumption functions reside in the attribute $\\texttt{cFunc}$ of each element of `ConsumerType.solution`. This method creates a (time varying) attribute $\\texttt{cFunc}$ that contains a list of consumption functions." + "the consumption functions reside in the attribute `cFunc` of each element of `ConsumerType.solution`. This method creates a (time varying) attribute `cFunc` that contains a list of consumption functions." ] }, { @@ -222,7 +221,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': -50.49994992551661, 'hNrm': 50.49994992551661, 'MPCmin': 0.04428139169919579, 'MPCmax': 0.04428139169919579}\n" + "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': -50.49994992551661, 'hNrm': 50.49994992551661, 'MPCmin': 0.04428139169919579, 'MPCmax': 0.04428139169919579}\n" ] } ], @@ -234,7 +233,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The two most important attributes of a single period solution of this model are the (normalized) consumption function $\\texttt{cFunc}$ and the (normalized) value function $\\texttt{vFunc}$. Let's plot those functions near the lower bound of the permissible state space (the attribute $\\texttt{mNrmMin}$ tells us the lower bound of $m_t$ where the consumption function is defined)." + "The two most important attributes of a single period solution of this model are the (normalized) consumption function `cFunc` and the (normalized) value function `vFunc`. Let's plot those functions near the lower bound of the permissible state space (the attribute `mNrmMin` tells us the lower bound of $m_t$ where the consumption function is defined)." ] }, { @@ -251,7 +250,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -298,11 +297,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "An element of `solution` also includes the (normalized) marginal value function $\\texttt{vPfunc}$, and the lower and upper bounds of the marginal propensity to consume (MPC) $\\texttt{MPCmin}$ and $\\texttt{MPCmax}$. Note that with a linear consumption function, the MPC is constant, so its lower and upper bound are identical.\n", + "An element of `solution` also includes the (normalized) marginal value function `vPfunc`, and the lower and upper bounds of the marginal propensity to consume (MPC) `MPCmin` and `MPCmax`. Note that with a linear consumption function, the MPC is constant, so its lower and upper bound are identical.\n", "\n", "### Liquidity constrained perfect foresight example\n", "\n", - "Without an artificial borrowing constraint, a perfect foresight consumer is free to borrow against the PDV of his entire future stream of labor income-- his \"human wealth\" $\\texttt{hNrm}$-- and he will consume a constant proportion of his total wealth (market resources plus human wealth). If we introduce an artificial borrowing constraint, both of these features vanish. In the cell below, we define a parameter dictionary that prevents the consumer from borrowing *at all*, create and solve a new instance of `PerfForesightConsumerType` with it, and then plot its consumption function." + "Without an artificial borrowing constraint, a perfect foresight consumer is free to borrow against the PDV of his entire future stream of labor income -- his \"human wealth\" `hNrm` -- and he will consume a constant proportion of his total wealth (market resources plus human wealth). If we introduce an artificial borrowing constraint, both of these features vanish. In the cell below, we define a parameter dictionary that prevents the consumer from borrowing *at all*, create and solve a new instance of `PerfForesightConsumerType` with it, and then plot its consumption function." ] }, { @@ -314,14 +313,6 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/HARK/metric.py:52: UserWarning: Arrays of different shapes. Returning differences in size.\n", - " warn(\"Arrays of different shapes. Returning differences in size.\")\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -331,7 +322,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -353,14 +344,12 @@ ] }, { - "cell_type": "code", - "execution_count": 9, + "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, - "outputs": [], "source": [ - "# At this time, the value function for a perfect foresight consumer with an artificial borrowing constraint is not computed nor included as part of its $\\texttt{solution}$." + "At this time, the value function for a perfect foresight consumer with an artificial borrowing constraint is not computed nor included as part of its `solution`." ] }, { @@ -369,7 +358,7 @@ "source": [ "## Simulating the perfect foresight consumer model\n", "\n", - "Suppose we wanted to simulate many consumers who share the parameter values that we passed to `PerfForesightConsumerType`-- an *ex ante* homogeneous *type* of consumers. To do this, our instance would have to know *how many* agents there are of this type, as well as their initial levels of assets $a_t$ and permanent income $P_t$.\n", + "Suppose we wanted to simulate many consumers who share the parameter values that we passed to `PerfForesightConsumerType` -- an *ex ante* homogeneous *type* of consumers. To do this, our instance would have to know *how many* agents there are of this type, as well as their initial levels of assets $a_t$ and permanent income $P_t$.\n", "\n", "### Setting simulation parameters\n", "\n", @@ -377,27 +366,27 @@ "\n", "| Description | Code | Example value |\n", "| :---: | --- | --- |\n", - "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n", - "| Number of periods to simulate | $\\texttt{T_sim}$ | $120$ |\n", - "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n", - "| Stdev of initial log (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n", - "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n", - "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n", - "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n", - "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n", + "| Number of consumers of this type | `AgentCount` | $10000$ |\n", + "| Number of periods to simulate | `T_sim` | $120$ |\n", + "| Mean of initial log (normalized) assets | `aNrmInitMean` | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | `aNrmInitStd` | $1.0$ |\n", + "| Mean of initial log permanent income | `pLvlInitMean` | $0.0$ |\n", + "| Stdev of initial log permanent income | `pLvlInitStd` | $0.0$ |\n", + "| Aggregrate productivity growth factor | `PermGroFacAgg` | $1.0$ |\n", + "| Age after which consumers are automatically killed | `T_age` | $None$ |\n", "\n", "We have specified the model so that initial assets and permanent income are both distributed lognormally, with mean and standard deviation of the underlying normal distributions provided by the user.\n", "\n", - "The parameter $\\texttt{PermGroFacAgg}$ exists for compatibility with more advanced models that employ aggregate productivity shocks; it can simply be set to 1.\n", + "The parameter `PermGroFacAgg` exists for compatibility with more advanced models that employ aggregate productivity shocks; it can simply be set to 1.\n", "\n", - "In infinite horizon models, it might be useful to prevent agents from living extraordinarily long lives through a fortuitous sequence of mortality shocks. We have thus provided the option of setting $\\texttt{T_age}$ to specify the maximum number of periods that a consumer can live before they are automatically killed (and replaced with a new consumer with initial state drawn from the specified distributions). This can be turned off by setting it to `None`.\n", + "In infinite horizon models, it might be useful to prevent agents from living extraordinarily long lives through a fortuitous sequence of mortality shocks. We have thus provided the option of setting `T_age` to specify the maximum number of periods that a consumer can live before they are automatically killed (and replaced with a new consumer with initial state drawn from the specified distributions). This can be turned off by setting it to `None`.\n", "\n", "The cell below puts these parameters into a dictionary, then gives them to `PFexample`. Note that all of these parameters *could* have been passed as part of the original dictionary; we omitted them above for simplicity." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" @@ -427,18 +416,18 @@ "source": [ "To generate simulated data, we need to specify which variables we want to track the \"history\" of for this instance. To do so, we set the `track_vars` attribute of our `PerfForesightConsumerType` instance to be a list of strings with the simulation variables we want to track.\n", "\n", - "In this model, valid arguments to `track_vars` include $\\texttt{mNrm}$, $\\texttt{cNrm}$, $\\texttt{aNrm}$, and $\\texttt{pLvl}$. Because this model has no idiosyncratic shocks, our simulated data will be quite boring.\n", + "In this model, valid arguments to `track_vars` include `mNrm`, `cNrm`, `aNrm`, and `pLvl`. Because this model has no idiosyncratic shocks, our simulated data will be quite boring.\n", "\n", "### Generating simulated data\n", "\n", - "Before simulating, the `initialize_sim` method must be invoked. This resets our instance back to its initial state, drawing a set of initial $\\texttt{aNrm}$ and $\\texttt{pLvl}$ values from the specified distributions and storing them in the attributes $\\texttt{aNrmNow_init}$ and $\\texttt{pLvlNow_init}$. It also resets this instance's internal random number generator, so that the same initial states will be set every time `initialize_sim` is called. In models with non-trivial shocks, this also ensures that the same sequence of shocks will be generated on every simulation run.\n", + "Before simulating, the `initialize_sim` method must be invoked. This resets our instance back to its initial state, drawing a set of initial `aNrm` and `pLvl` values from the specified distributions and storing them in the attributes `aNrmNow_init` and `pLvlNow_init`. It also resets this instance's internal random number generator, so that the same initial states will be set every time `initialize_sim` is called. In models with non-trivial shocks, this also ensures that the same sequence of shocks will be generated on every simulation run.\n", "\n", "Finally, the `simulate` method can be called." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" @@ -463,7 +452,7 @@ " -5.20458357, -46.23524203]])}" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -475,19 +464,17 @@ ] }, { - "cell_type": "code", - "execution_count": 12, + "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, - "outputs": [], "source": [ - "# Each simulation variable $\\texttt{X}$ named in $\\texttt{track_vars}$ will have the *history* of that variable for each agent stored in the attribute $\\texttt{X_hist}$ as an array of shape $(\\texttt{T_sim},\\texttt{AgentCount})$. To see that the simulation worked as intended, we can plot the mean of $m_t$ in each simulated period:" + "Each simulation variable `X` named in `track_vars` will have the *history* of that variable for each agent stored in the attribute `X_hist` as an array of shape `(T_sim, AgentCount)`. To see that the simulation worked as intended, we can plot the mean of $m_t$ in each simulated period:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" @@ -496,7 +483,7 @@ "outputs": [ { "data": { - "image/png": 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dCIKAoVE1ZWfr6SyZ0xAREdkuycVn+vTpGDZsGK5cuYINGzZgw4YNSE1NxSOPPIJp06ZZISLVxSMda+b2/HYhF2X6apnTEBER2aZ6jfjMmjULSuX/rpIplUrMnDkTx48ft2g4qrvIQHe09HaGvtqE3Rdy5Y5DRERkkyQXH3d3d6Slpd10PD09HW5ubhYJRdIJgoChv4/6bD2dKXMaIiIi2yS5+IwaNQoTJkzA+vXrkZ6ejvT0dKxbtw4TJ07E008/bY2MVEc35vnEJuWhpLJK5jRERES2R/Kqrvfffx+CIODZZ59FdXXNXBKVSoVJkybhvffes3hAqrt2zdwQ5uuClLwy7E7MxYho7qRNRET0R5JHfNRqNZYuXYrr168jISEBCQkJKCwsxIcffgiNRmONjFRHgiDgkd83MNzCy11EREQ3qfe9upydnREVFYUWLVpg586d5j19SF6PdKq53LX3Yh6Ky3m5i4iI6I8kF5+nnnoKy5cvB1Bzc9Ju3brhqaeeQseOHfHjjz9aPCBJ09rfDW383VBlFLHjfLbccYiIiGyK5OKzb98+9O3bFwDw008/QRRFFBUVYdmyZXjnnXcsHpCke7RTzeWuzad4uYuIiOiPJBef4uJieHl5AQC2b9+OJ554As7Ozhg6dCguXbpk8YAk3Y1bVhy6XICCUr3MaYiIiGyH5OITHByMuLg4lJWVYfv27Rg0aBAA4Pr163B0dLR4QJKupY8LooK0MJpEbDvLy11EREQ3SC4+06ZNw+jRo9G8eXM0a9YM/fv3B1BzCSwqKsrS+aieeLmLiIjoZpKLz4svvoi4uDisXr0aBw8ehEJR8xFhYWGc42NDhv5+uevolUJkF1fKnIaIiMg21Gs5e7du3TB06FBkZGSYNzEcOnQoevfubdFwVH9BHk7o2sIToghsPcM7thMREQH1KD7l5eWYMGECnJ2dERkZab5v15QpU7hzs415tCM3MyQiIvojycXn9ddfx6lTp7Bnz55ak5kHDhyI9evXWzQc3Zu/dGwGhQDEpxUhvbBc7jhERESyk1x8Nm7ciOXLl6NPnz4QBMF8PDIyEpcvX7ZoOLo3fm6O6BnmDQDYxEnORERE0otPXl4e/Pz8bjpeVlZWqwiRbRjeuWaS80/xGRBFUeY0RERE8pJcfLp164atW7eaf75RdlatWoWYmBjLJSOLGBLVDBqlAsm5pTiboZM7DhERkayUUt8wf/58DBkyBOfPn0d1dTWWLl2K8+fP49ChQ9i7d681MtI9cHdU4aH2/thyOgsb4q8hqrlW7khERESykTzi06dPH5w6dQrV1dWIiorCzp074efnh7i4OHTt2tUaGekePd4lCEDNZoZVRpPMaYiIiOQjacSnqqoKL7zwAubMmYPPP//cWpnIwvpG+MLbRY38UgP2X8rDA2395Y5EREQkC0kjPiqVCj/++KO1spCVqBwUeLRTzSTnDSczZE5DREQkH8mXukaMGIGNGzdaIQpZ0xNdmgMAdp3Pga6ySuY0RERE8pA8uTkiIgJvvfUWDh48iK5du8LFxaXW81OnTrVYOLKcDkHuaOXniuTcUmw/k42n7guWOxIREVGDE0SJm7uEhobe/sMEASkpKfccSk46nQ5arRbFxcVwd3eXO45FrYhNxuIdSegZ5oV1f+fWA0RE1HTU9ftb8ohPamrqPQUj+YyIDsLiHUk4nFKIa9fL0dzTWe5IREREDaped2enxinIwwkxv9/CYmM8JzkTEZH9YfGxM092rZnk/MOJa7yFBRER2R0WHzvzcIcAOKsdcKWgHCeuXpc7DhERUYNi8bEzLhol/hLVDADw48lrMqchIiJqWJKLT1pa2i0vkYiiiLS0NIuEIuu6safPllNZqKwyypyGiIio4UguPqGhocjLy7vpeGFh4R2XupPt6BHqheaeTijRV2PHuWy54xARETUYycVHFEUIgnDT8dLSUjg6OlokFFmXQiHg8S7/m+RMRERkL+q8j8+MGTMA1GxSOGfOHDg7/28PGKPRiCNHjqBz584WD0jW8USXICzbfQkHk/ORXVyJAC1LKxERNX11Lj7x8fEAakZ8zpw5A7VabX5OrVajU6dOePXVVy2fkKyihbcLuod64WhqITbEX8OL/VvJHYmIiMjq6lx8YmNjAQDjxo3D0qVLm9ztHOzRk12a42hqIX44cQ2T+oXf8hImERFRUyJ5js+XX34Jd3d3JCcnY8eOHaioqAAAbobXCP2lYzM4qRyQkleGk2nc04eIiJo+ycWnsLAQDz74IFq3bo2//OUvyMrKAgBMmDABr7zyisUDkvW4apR4pGPNnj7rjqbLnIaIiMj6JBefadOmQaVSIS0trdYE51GjRmH79u0WDfdH7777Lnr16gVnZ2d4eHjc8jVpaWkYOnQonJ2d4efnh9deew3V1dVWy9QUjLovGACw5XQWSiqrZE5DRERkXZKLz86dO7Fw4UI0b9681vGIiAhcvXrVYsH+zGAwYOTIkZg0adItnzcajRg6dCgMBgMOHTqEtWvXYs2aNZg7d67VMjUFXVt4ItzXBRVVRmw+lSV3HCIiIquSXHzKyspqjfTcUFhYCI1GY5FQt/Lmm29i+vTpiIqKuuXzO3fuxPnz5/HNN9+gc+fOGDJkCN5++22sWLECBoPBarkaO0EQ8Nf7QgAA649x520iImraJBefvn374quvvjL/LAgCTCYTFi1ahAEDBlg0nBRxcXGIioqCv7+/+djgwYOh0+lw7ty5275Pr9dDp9PVetibx7oEQeUg4NS1YiRm2d/vT0RE9kNy8Vm0aBE+++wzDBkyBAaDATNnzkSHDh2wb98+LFy40BoZ6yQ7O7tW6QFg/jk7+/a3ZViwYAG0Wq35ERwcbNWctsjHVYOB7WrO1fpjnORMRERNl+Ti06FDB1y8eBF9+vTB8OHDUVZWhscffxzx8fEIDw+X9FmzZ8+GIAh3fFy4cEFqRElef/11FBcXmx/p6fb5xX9jkvNP8Rm8cSkRETVZdd7A8IbY2FgMGDAAb7zxxk3PrVixApMnT67zZ73yyit47rnn7viasLCwOn1WQEAAjh49WutYTk6O+bnb0Wg0Vp2b1Fj0jfBFoNYRmcWV2HEuG8M7B8kdiYiIyOIkj/g8/vjjOHHixE3Hly5ditdff13SZ/n6+qJt27Z3fPzx1hh3EhMTgzNnziA3N9d8bNeuXXB3d0f79u0l5bJHDgoBI7vVjPpwTx8iImqqJBefxYsXY8iQIbUuQX3wwQeYO3cutm7datFwf5SWloaEhASkpaXBaDQiISEBCQkJKC0tBQAMGjQI7du3x5gxY3Dq1Cns2LED//d//4fJkydzRKeOnrovGAoBiEspwOW8UrnjEBERWZzkS10TJ05EYWEhBg4ciAMHDmD9+vWYP38+fvnlF/Tu3dsaGQEAc+fOxdq1a80/R0dHA6i59Na/f384ODhgy5YtmDRpEmJiYuDi4oKxY8firbfeslqmpibIwwkD2vhh94VcfHs4DXMf5UgZERE1LYJYz5tszZo1C1988QWMRiO2bduGnj17WjqbLHQ6HbRaLYqLi+3yRqyxF3Ixbs0xuDsqceSfA+GkdpA7EhER0V3V9fu7TiM+y5Ytu+lYUFAQnJ2dcf/99+Po0aPmicVTp06tZ2SyBfe39kVzTydcu16Bzacz8VQ3+1veT0RETVedRnxCQ0Pr9mGCgJSUlHsOJSd7H/EBgI/3JGPR9iR0aq7Fzy/1kTsOERHRXVl0xCc1NdViwcj2PdUtGB/uuohT14px5loxoppr5Y5ERERkEZJWdVVVVSE8PByJiYnWykM2wMdVg4c7NAMAfHvEejeeJSIiamiSio9KpUJlZaW1spANeaZHzY1Lf07IhK6ySuY0REREliF5H5/Jkydj4cKFqK6utkYeshHdQ73Q2t8VFVVG/HjimtxxiIiILELyPj7Hjh3D7t27sXPnTkRFRcHFxaXW8xs2bLBYOJKPIAh4pmcLzP35HL6Ku4qxMS2hUAhyxyIiIronkouPh4cHnnjiCWtkIRvzRJfmWLwjCan5ZYhNysWDv9/BnYiIqLGSXHy+/PJLa+QgG+SiUeKv9wXj8/2pWH0wlcWHiIgaPclzfMi+jO3VEgoBOJhcgAvZOrnjEBER3RPJIz4A8MMPP+D7779HWloaDAZDredOnjxpkWBkG5p7OuPhDgH45Uw2Vh9IxaInO8kdiYiIqN4kj/gsW7YM48aNg7+/P+Lj49G9e3d4e3sjJSUFQ4YMsUZGktn43jU7d29MyER+qV7mNERERPUnufh8/PHH+Oyzz/DRRx9BrVZj5syZ2LVrF6ZOnYri4mJrZCSZdW3hiU7NtTBUm/CfI2lyxyEiIqo3ycUnLS0NvXr1AgA4OTmhpKQEADBmzBh89913lk1HNkEQBIzvUzPq8/Xhq9BXG2VOREREVD+Si09AQAAKCwsBACEhITh8+DCAmvt51eF+p9RIDenQDP7uGuSV6LH5VJbccYiIiOpFcvF54IEHsGnTJgDAuHHjMH36dDz00EMYNWoUHnvsMYsHJNugVirwbExLAMCq/SksuURE1CgJosRvMJPJBJPJBKWyZkHYunXrcOjQIUREROCFF16AWq22StCGUtfb2tujonIDYhb8hooqI76e0B19I3zljkRERASg7t/fkotPU8fic2f/2nQOaw5dwf2tffHV+O5yxyEiIgJQ9+/veu3jU1lZidOnTyM3Nxcmk6nWc8OGDavPR1IjMb53KL6Ku4J9F/OQlF2CNgFuckciIiKqM8nFZ/v27Xj22WeRn59/03OCIMBo5IqfpizE2xmDIwOw7Ww2Vu1PweKR3NCQiIgaD8mTm6dMmYKRI0ciKyvLPN/nxoOlxz5M7BsGAPg5IRO5JZUypyEiIqo7ycUnJycHM2bMgL8/b1hpr7q28ESXEA8YjCZ8deiq3HGIiIjqTHLxefLJJ7Fnzx4rRKHG5O/314z6fHPkKsoN1TKnISIiqhvJc3yWL1+OkSNHYv/+/YiKioJKpar1/NSpUy0WjmzXQ+0D0MLbGVcLyvH9sXQ89/v9vIiIiGyZ5OLz3XffYefOnXB0dMSePXsgCIL5OUEQWHzshINCwMS+YZiz8Sw+35+K0T1bQOUgeQCRiIioQUn+pnrjjTfw5ptvori4GFeuXEFqaqr5kZKSYo2MZKNGdm0OH1c1MooqsPU0b2NBRES2T3LxMRgMGDVqFBQK/te9vXNUOeC5Xi0BACv3XuZtLIiIyOZJbi9jx47F+vXrrZGFGqExPVvCWe2AC9kl2HsxT+44REREdyR5jo/RaMSiRYuwY8cOdOzY8abJzUuWLLFYOLJ9WmcVnu4egi8OpGLl3svo38ZP7khERES3Jbn4nDlzBtHR0QCAs2fP1nrujxOdyX5M6BOKtYeu4HBKIRLSi9A52EPuSERERLckufjExsZaIwc1YoEeThjeOQg/nryGlXsuY+WYrnJHIiIiuiXOUCaL+Ee/mg0Nt5/LxvlMncxpiIiIbo3Fhywiwt8Nj3YKBAB8sDNJ5jRERES3xuJDFjN9YAQcFAJ2X8jFiauFcschIiK6CYsPWUyYrytGdm0OAFi0PYn7+hARkc1h8SGLmvpgBNQOChxJLcT+S/lyxyEiIqqlTqu6Nm3aVOcPHDZsWL3DUOMX6OGEZ3q2wOqDqVi8Iwl9I3y4zQEREdmMOhWfESNG1PpZEIRalzH++MVmNBotk4warRcHhGPdsTScySjGjnPZeLhDM7kjERERAajjpS6TyWR+7Ny5E507d8a2bdtQVFSEoqIi/PLLL+jSpQu2b99u7bzUCPi4ajCxTygAYPGOJFQbTTInIiIiqiF5A8Np06Zh5cqV6NOnj/nY4MGD4ezsjL///e9ITEy0aEBqnCbeH4avD1/F5bwy/HDiGv7aPUTuSERERNInN1++fBkeHh43Hddqtbhy5YoFIlFT4O6owksPRAAAPvz1IioMvARKRETyk1x87rvvPsyYMQM5OTnmYzk5OXjttdfQvXt3i4ajxu2ZniEI8nBCjk6P1QdT5Y5DREQkvfisXr0aWVlZCAkJQatWrdCqVSuEhIQgIyMDX3zxhTUyUiOlUTrg1cGtAQAr91zG9TKDzImIiMjeCWI9dpkTRRG7du3ChQsXAADt2rXDwIEDm8SyZZ1OB61Wi+LiYri7u8sdp9EzmUQM/egAErN0mNgnFP/3SHu5IxERURNU1+/vehWfGyorK6HRaJpE4bmBxcfy9iTl4rkvj0HtoMBvr/ZDc09nuSMREVETU9fvb8mXukwmE95++20EBQXB1dUVqak1czfmzJnDS110S/1a+6JXuDcMRhM+3HVJ7jhERGTHJBefd955B2vWrMGiRYugVqvNxzt06IBVq1ZZNBw1DYIgYObDbQEAP8Vfw+W8UpkTERGRvZJcfL766it89tlnGD16NBwcHMzHO3XqZJ7zQ/RnnYM9MLCdP0wi8OGui3LHISIiOyW5+GRkZKBVq1Y3HTeZTKiqqrJIqFt599130atXLzg7O99yHyGgZmThz49169ZZLRNJM+OhmhVeW05nITFLJ3MaIiKyR5KLT/v27bF///6bjv/www+Ijo62SKhbMRgMGDlyJCZNmnTH13355ZfIysoyP/58nzGST/tAdzzSsea+XUs46kNERDKQfMuKuXPnYuzYscjIyIDJZMKGDRuQlJSEr776Clu2bLFGRgDAm2++CQBYs2bNHV/n4eGBgIAAq+WgezNtYGv8ciYLu87nICG9CJ2DPeSOREREdkTyiM/w4cOxefNm/Prrr3BxccHcuXORmJiIzZs346GHHrJGRkkmT54MHx8fdO/eHatXr8bdVuvr9XrodLpaD7KeVn6ueCy6OQDgg51JMqchIiJ7I3nEBwD69u2LXbt2WTrLPXvrrbfwwAMPwNnZGTt37sSLL76I0tJSTJ069bbvWbBggXk0iRrGtIER+DkhA/sv5SPucgFiwr3ljkRERHZC8ojP+PHjsXbt2puO63Q6jB8/XtJnzZ49+5YTkv/4kLJSbM6cOejduzeio6Mxa9YszJw5E4sXL77je15//XUUFxebH+np6ZJ+B5Iu2MsZT/9+t/YF2xJhMtV7D00iIiJJJO/crFAo4OTkhAkTJuDf//43FIqa7pSTk4PAwEAYjXW/C3deXh4KCgru+JqwsLBa+wWtWbMG06ZNQ1FR0V0/f+vWrXjkkUfMO0zXBXdubhj5pXr0X7wHpfpqLP1rZwzvHCR3JCIiasTq+v1dr0tdW7duxcSJE5GYmIjvv/8enp6e9Qrp6+sLX1/fer23LhISEuDp6Vnn0kMNx8dVg0n9w7F4RxIWbU/C4MgAOKoc7v5GIiKieyD5UhdQs6T9yJEjqKqqQvfu3ZGYmGjpXDdJS0tDQkIC0tLSYDQakZCQgISEBJSW1uwCvHnzZqxatQpnz55FcnIyPvnkE8yfPx9Tpkyxejaqn/G9QxHg7oiMogqsPXRF7jhERGQHJBefGzck9fb2xq+//op+/fohJiYGmzZtsni4P5o7dy6io6Mxb948lJaWIjo6GtHR0Th+/DgAQKVSYcWKFYiJiUHnzp3x6aefYsmSJZg3b55Vc1H9Oakd8OrgNgCA5bHJKCwzyJyIiIiaunrN8cnOzoafn5/52JIlSzBr1iyYTCZJc3xsEef4NCyjScSjHx3A+SwdnuvVEv8aFil3JCIiaoSsdnf22NhYeHl51To2Y8YMbNu2DXPnzpWelOyag0LAP//SDgDwzeGrSOENTImIyIokj/g0dRzxkce4L48iNikPA9v5Y9XYbnLHISKiRsaiq7pmzJiBt99+Gy4uLpgxY8YdX7tkyRJpSYkAvDG0PfZd2odfE3Nw4FI++kT4yB2JiIiaoDoVn/j4ePOd1+Pj42/7uhsTn4mkauXnijE9W2DNoSt4e8t5bJ3aB0qHei06JCIiui1e6voTXuqST1G5Af0W70FxRRXefawDRvdoIXckIiJqJKw2uZnIWjyc1Zg2MAIA8MHOi9BVVsmciIiImpo6Xep6/PHH6/yBGzZsqHcYomd6tsDXh68iJa8MH+2+hDeGtpc7EhERNSF1Kj5ardbaOYgAACoHBeYMbY9xa45h9cErGN45CB2C+P8/IiKyDM7x+RPO8bENk745gW1ns9GumTt+ntwbaiWvyhIR0e1xjg81am8N7wBPZxUSs3T4ZM9lueMQEVETUa+7s//www/4/vvvkZaWBoOh9v2VTp48aZFgZN983TT417BIvLwuActjL2FwB3+0DeAIHBER3RvJIz7Lli3DuHHj4O/vj/j4eHTv3h3e3t5ISUnBkCFDrJGR7NSwToF4qL0/qowiXvvvaVQbTXJHIiKiRk5y8fn444/x2Wef4aOPPoJarcbMmTOxa9cuTJ06FcXFxdbISHZKEAS8O6IDtE4qnMkoxmf7U+SOREREjZzk4pOWloZevXoBAJycnFBSUgIAGDNmDL777jvLpiO75+fuiLmP1CxpX7b7EtILy2VOREREjZnk4hMQEIDCwkIAQEhICA4fPgwASE1NBReIkTU83iUIMWHeqKwy4c3N5+WOQ0REjZjk4vPAAw9g06ZNAIBx48Zh+vTpeOihhzBq1Cg89thjFg9IJAgC3h4RCaVCwK+JOdh1PkfuSERE1EhJ3sfHZDLBZDJBqaxZELZu3TocOnQIEREReOGFF6BWq60StKFwHx/b9d62C1i59zKCPJzw64x+cFI7yB2JiIhsRF2/v7mB4Z+w+NiuckM1Bn6wF5nFlXhpQCu8OriN3JGIiMhG1PX7u177+FRWVuL06dPIzc2FyVR7ifGwYcPq85FEd+WsVmLuo5H4xzcn8Om+yxgRHYRWfq5yxyIiokZEcvHZvn07nn32WeTn59/0nCAIMBqNFglGdCuDI/0xoI0vYpPy8NoPp/DfF2KgdOAG5EREVDeSvzGmTJmCkSNHIisryzzf58aDpYesTRAEvPNYFNw0SsSnFWHlXt7OgoiI6k5y8cnJycGMGTPg7+9vjTxEdxXk4YQ3h0cCAP796yWczeDGmUREVDeSi8+TTz6JPXv2WCEKUd09Fh2EhyMDUG0SMX19AiqrONpIRER3J3lVV3l5OUaOHAlfX19ERUVBpVLVen7q1KkWDdjQuKqr8Sgo1WPwv/cjv1SP5/uG4o2h7eWOREREMrHacvYvvvgC//jHP+Do6Ahvb28IgvC/DxMEpKQ07vspsfg0LrsTczBh7XEIArB2XHfc39pX7khERCQDqxWfgIAATJ06FbNnz4ZC0fRW07D4ND7//OkM/nMkDZ7OKmye0gfNPZ3ljkRERA2srt/fkpuLwWDAqFGjmmTpocZp7iPt0bG5FtfLqzDpm5Oc70NERLclub2MHTsW69evt0YWonpxVDng49Fd4OmswpmMYvxr0zm5IxERkY2SvIGh0WjEokWLsGPHDnTs2PGmyc1LliyxWDiiumru6YxlT0fj2dVHse5YOjoHe+Cv3UPkjkVERDZGcvE5c+YMoqOjAQBnz56t9dwfJzoTNbS+Eb54dVAbLN6RhLk/n0OEvxu6tvCUOxYREdkQSZObjUYjDh48iKioKHh6Ns0vFE5ubtxMJhGTvj2BHedy4OOqxsbJvTnZmYjIDlhlcrODgwMGDRqEoqKie81HZBUKhYAlT3VGu2buyC81YOLa4yjTV8sdi4iIbITkyc0dOnRo9Hv1UNPmolFi1dhu8HHV4EJ2CaavT4DJJGnXBiIiaqIkF5933nkHr776KrZs2YKsrCzodLpaDyJbEOThhE/HdIVaqcDO8zlYtCNJ7khERGQDJG9g+Mf9e/44mVkURQiC0Ojv0M45Pk3LxvgMTFufAAB4Z0QHPNOzhbyBiIjIKur6/S15VVdsbOw9BSNqSCOig3C1oBwf/noRc38+iwB3Rwxs7y93LCIikonkEZ+mjiM+TY8oipj94xmsP54OR5UC3z3fE9EhTXNVIhGRvbLaiA8AFBUV4YsvvkBiYiIAIDIyEuPHj4dWq61fWiIrEgQB7zzWATklldiTlIcJa49jw6ReaOnjInc0IiJqYJInNx8/fhzh4eH48MMPUVhYiMLCQixZsgTh4eE4efKkNTIS3TOVgwIr/tYFHYLcUVhmwPNfcZk7EZE9knypq2/fvmjVqhU+//xzKJU1A0bV1dWYOHEiUlJSsG/fPqsEbSi81NW05eoq8ejyA8jR6TE0qhmW/y2aO44TETUBVrs7+/HjxzFr1ixz6QEApVKJmTNn4vjx4/VLS9RA/Nwd8fHoLlA5CNh6Jgur9qfKHYmIiBqQ5OLj7u6OtLS0m46np6fDzc3NIqGIrKlrCy/MeaQ9AGDBtkQcupwvcyIiImookovPqFGjMGHCBKxfvx7p6elIT0/HunXrMHHiRDz99NPWyEhkcWN6tsDjXYJgEoEp/4lHZlGF3JGIiKgBSF7V9f7770MQBDz77LOorq6ZHKpSqTBp0iS89957Fg9IZA2CIGD+Y1G4kFWC81k6TPrmBNa/EANHlYPc0YiIyIrqvY9PeXk5Ll++DAAIDw+Hs3PTuAM2Jzfbl/TCcjy6/ACKyqswsmtzLHqyIyc7ExE1Qlab3HyDs7MzoqKiEBUV1WRKD9mfYC9nfPR0NBQC8N8T1/DNkZvnrxERUdMh+VJXWVkZ3nvvPezevRu5ubkwmUy1nued26mx6Rvhi5kPt8V72y7gzU3n0C7ADd1aeskdi4iIrEBy8Zk4cSL27t2LMWPGoFmzZrwsQE3CC/eH4cy1Ymw9k4VJ357Ez5N7I9DDSe5YRERkYZLn+Hh4eGDr1q3o3bu3tTLd5MqVK3j77bfx22+/ITs7G4GBgXjmmWfwxhtvQK1Wm193+vRpTJ48GceOHYOvry+mTJmCmTNnSvqzOMfHfpXpq/H4x4eQlFOC1v6u+O8/ekHrpJI7FhER1YHV5vh4enrCy6thLwNcuHABJpMJn376Kc6dO4cPP/wQK1euxD//+U/za3Q6HQYNGoQWLVrgxIkTWLx4Mf71r3/hs88+a9Cs1Hi5aJRYPe4++LlpcDGnFC98fRz6aqPcsYiIyIIkj/h88803+Pnnn7F27VpZJzUvXrwYn3zyiXlO0SeffII33ngD2dnZ5lGg2bNnY+PGjbhw4UKdP5cjPnQusxhPrYxDmcGI4Z0D8eFTnaFQ8JIuEZEts9rd2T/44ANcvnwZ/v7+aNmyJVSq2pcCGupGpcXFxbVGnuLi4nD//ffXuvQ1ePBgLFy4ENevX4enp+ctP0ev10Ov15t/1ul01gtNjUJkoBafPNMV49ccw88JmWimdcLsIW3ljkVERBYgufiMGDHCCjGkSU5OxkcffYT333/ffCw7OxuhoaG1Xufv729+7nbFZ8GCBXjzzTetF5Yapftb+2LB41F47YfTWLn3MnzdNJjQJ/TubyQiIpsmufjMmzfPYn/47NmzsXDhwju+JjExEW3b/u+/tjMyMvDwww9j5MiReP755+85w+uvv44ZM2aYf9bpdAgODr7nz6XGb2S3YOSW6LF4RxLe3nIens4qPN6ludyxiIjoHkguPpb0yiuv4Lnnnrvja8LCwsz/nJmZiQEDBqBXr143TVoOCAhATk5OrWM3fg4ICLjt52s0Gmg0GonJyV682D8cBaUGrD6Yitd+OA2tkwoPtvOXOxYREdWTrMXH19cXvr6+dXptRkYGBgwYgK5du+LLL7+EQlF7QVpMTAzeeOMNVFVVmecd7dq1C23atLntZS6iuxEEAf83tB2Kyg3YEJ+BF789iW8m9sB93OCQiKhRqvctKxpSRkYG+vfvj5CQELz//vvIy8tDdnY2srOzza/529/+BrVajQkTJuDcuXNYv349li5dWusyFlF9KBQCFj7ZEQ+29YO+2oQJa47hYk6J3LGIiKge6n2T0oa0Zs0ajBs37pbP/TH+Hzcw9PHxwZQpUzBr1ixJfxaXs9PtVFYZMXrVEZy4eh3NtI7Y8GIvNNNyd2ciIltQ1+/vRlF8GhKLD93J9TIDnlx5CJfzytDG3w3f/yOGuzsTEdkAqxUfo9GINWvW3PYmpb/99lv9EtsIFh+6m2vXy/H4x4eQW6JHj1AvfDWhOzRKB7ljERHZNavdsuLll1/Gyy+/DKPRiA4dOqBTp061HkRNXXNPZ6wZ1x2uGiWOpBZizBdHkVeiv/sbiYhIdpJHfHx8fPDVV1/hL3/5i7UyyYojPlRXhy7n4+9fnUCpvhrNtI5Y+UxXdAr2kDsWEZFdstqIj1qtRqtWre4pHFFT0CvcBxsn90KYrwuyiisx8tM4fH88Xe5YRER0B5KLzyuvvIKlS5eCc6KJgFZ+bvh5cm881N4fhmoTZv5wGv/adA7VRtPd30xERA1O8qWuxx57DLGxsfDy8kJkZORNNyndsGGDRQM2NF7qovowmUQsj03Gkl0XAQB9Wvlg+d+i4eGsvss7iYjIEqx2d3YPDw889thj9xSOqKlRKARMfTACbQLcMH19Ag4k52PEioNYNfY+tPJzlTseERH9jvv4/AlHfOheJWbpMHHtcWQUVcBVo8T8x6MwrFOg3LGIiJo0q01uJqI7a9fMHZte6o3uoV4o1Vdj6nfxmPnDKZQbquWORkRk9+o14vPDDz/g+++/R1paGgwGQ63nTp48abFwcuCID1lKtdGEZbsv4aPYZIgiEO7rgo+e7oL2gfz/FRGRpVltxGfZsmUYN24c/P39ER8fj+7du8Pb2xspKSkYMmTIPYUmakqUDgrMGNQG307sAX93DS7nlWHEioP4bN9lmEy8wkxEJAfJxefjjz/GZ599ho8++ghqtRozZ87Erl27MHXqVBQXF1sjI1Gj1ivcB9tevh8D2/nBYDRh/i8XMHrVEWQWVcgdjYjI7kguPmlpaejVqxcAwMnJCSUlJQCAMWPG4LvvvrNsOqImwstFjc+f7YYFj0fBSeWAuJQCDP73Pmw+lSl3NCIiuyK5+AQEBKCwsBAAEBISgsOHDwMAUlNTuakh0R0IgoCnu4fgl5f7onOwB0oqqzHlu3jM/vE0KgxGueMREdkFycXngQcewKZNmwAA48aNw/Tp0/HQQw9h1KhR3N+HqA5CfVzwwz9iMOWBVhAEYN2xdAxfcQAXc0rkjkZE1ORJXtVlMplgMpmgVNbsfbhu3TocOnQIEREReOGFF6BWN+6darmqixrSweR8TFufgLwSPRxVCrw5LBJPdQuGIAhyRyMialTq+v3NDQz/hMWHGlpeiR4zvk/A/kv5AIBhnQLx7mMd4Oaouss7iYjoBqtuYLh//34888wziImJQUZGBgDg66+/xoEDB+qXlsiO+bppsHZcd8x8uA0cFAI2ncrEox8dwNkMrpIkIrI0ycXnxx9/xODBg+Hk5IT4+Hjo9XoAQHFxMebPn2/xgET2QKEQ8GL/Vvj+hZ4I8nDClYJyPPbxQSzecYETn4mILEhy8XnnnXewcuVKfP7557XuzN67d+9Gv2szkdy6tvDC1ql9MKi9P6qMIlbEXsbAJXux81w2V00SEVmA5OKTlJSE+++//6bjWq0WRUVFlshEZNc8nNX4dExXrHymKwK1jsgoqsDfvz6B5786joJSvdzxiIgatXrt45OcnHzT8QMHDiAsLMwioYjsnSAIeLhDAH59pR9e7B8OlYOAXxNz8Zdl+3EkpUDueEREjZbk4vP888/j5ZdfxpEjRyAIAjIzM/Htt9/i1VdfxaRJk6yRkchuOauVmPlwW2ye0gfhvi7I0enx9OeHsSI2mff7IiKqB8nL2UVRxPz587FgwQKUl5cDADQaDV599VW8/fbbVgnZkLicnWxVmb4aczaexYb4mpWUA9r44t9/jYbWicveiYisvo+PwWBAcnIySktL0b59e7i6utY7rC1h8SFbJooi/nviGub+fBaVVSaE+rjg82e7opWfm9zRiIhkxQ0M64nFhxqDsxnFeOHrE8goqoCrRol/j+qMge395Y5FRCQbixef8ePH1+kPXr16dd0S2igWH2os8kv1ePHbkziaWghBACb2CcXUByO44zMR2SWLFx+FQoEWLVogOjr6jvuJ/PTTT9LT2hAWH2pMqowmvLX5PL4+fBVAzS7Qsx9ui8eig6BQ8H5fRGQ/LF58Jk+ejO+++w4tWrTAuHHj8Mwzz8DLy8tigW0Fiw81RrFJuXhr83mk5pcBAKJDPDBtYGvcH+HDG54SkV2wyhwfvV6PDRs2YPXq1Th06BCGDh2KCRMmYNCgQU3mL1cWH2qsDNUmfHkwFct2X0LZ77e5aBvghgl9QjGscyA0SgeZExIRWY/VJzdfvXoVa9aswVdffYXq6mqcO3euSazsYvGhxi5XV4mVe1Ow/liauQA10zrig6c6oVe4j8zpiIisw6p3Zwdq5vwIggBRFGE08iaKRLbCz90Rcx9tj0OvP4jZQ9rC312DrOJKPLPqCDc+JCK7J6n46PV6fPfdd3jooYfQunVrnDlzBsuXL0daWlqTGO0hakq0Tir8o1849rw6AE92bQ6TCCzekYQJa4/heplB7nhERLKo86WuF198EevWrUNwcDDGjx+P0aNHw8en6Q2b81IXNVXfH0vHnJ/PQl9tgo+rBpP6h2N0jxA4qjj3h4gaP6ssZw8JCUF0dPQdJzJv2LBBelobwuJDTdn5TB1e+s9JpPy++svHVYN/9AvD6B4t4KRmASKixsvixee5556r08qtL7/8su4pbRCLDzV1hmoTNpy8huWxybh2vQIAEOThhLdHROKBttz9mYgaJ96yop5YfMheVBlrCtCy3cnIKKopQEOjmmHeo+3h5+4oczoiImlYfOqJxYfsTbmhGkt/vYRVB1JhNIlw0yjxQr8wjOnZElpn3v6CiBoHFp96YvEhe3Uusxj/3HAGp64VAwCc1Q54unsIxvcJRZCHk8zpiIjujMWnnlh8yJ4ZTSI2n8rEyr2XcSG7BACgVAgY26slXh4YAXfeAJWIbBSLTz2x+BABoihi36V8rNxzGXEpBQAAH1c1Zg5uiye7NucNUInI5rD41BOLD1Fte5Jy8daW80jJq1kC376ZO0b3DMEjHQOhdeIIEBHZBhafemLxIbqZodqEtYeuYOnuSyjVVwMA1EoFBrX3x9heLXFfSy+ZExKRvWPxqScWH6LbKyjV48eT1/DjiQwk5ZSYjz/XqyVmPdyWmyASkWxYfOqJxYfo7kRRxLlMHdYeuoL/nrgGAAjzccGSUZ3ROdhD3nBEZJdYfOqJxYdImr0X8zDzh1PI0enhoBDwt+4hGNurJVr58cbFRNRwWHzqicWHSLqicgPm/HwOm09lmo/1jfDBmJ4tMKCtH1QOChnTEZE9YPGpJxYfovo7mJyPLw9ewe4LObjxN4uboxID2vhhYHt/9G/jy72AiMgq6vr93Sj+M+zKlSuYMGECQkND4eTkhPDwcMybNw8Gg6HWawRBuOlx+PBhGZMT2ZferXywamw37HttAF7oFwYfVzVKKqux6VQmpn4Xj25v/4qX/nMSh5LzYTLxv7mIqOEp5Q5QFxcuXIDJZMKnn36KVq1a4ezZs3j++edRVlaG999/v9Zrf/31V0RGRpp/9vb2bui4RHYv2MsZrw9ph5mD2yIh/Tp2nc/FrvPZuJxXhi2ns7DldBZaejtj1H0heLJrc/i6aeSOTER2otFe6lq8eDE++eQTpKSkAKgZ8QkNDUV8fDw6d+5c58/R6/XQ6/Xmn3U6HYKDg3mpi8gKzmYU47ujafg5IdO8H5BSIWBgO3+M6h6M+yN84cBdoYmoHprUpa5bKS4uhpfXzZumDRs2DH5+fujTpw82bdp0189ZsGABtFqt+REcHGyNuEQEoEOQFu8+FoWjbzyIRU90RHSIB6pNIrafy8a4L4+h78LfsGTXRaQXlssdlYiaqEY54pOcnIyuXbvi/fffx/PPPw8AyM/Px1dffYXevXtDoVDgxx9/xKJFi7Bx40YMGzbstp/FER8ieSVll2DdsTRsOJmB4ooqAIAgAL3DffDX7sEY1D4AamWj/W80ImogjWJV1+zZs7Fw4cI7viYxMRFt27Y1/5yRkYF+/fqhf//+WLVq1R3f++yzzyI1NRX79++vcyau6iKSR2WVETvP5+D7Y+k4kJxvPu7tosaTXZtj1H3BCPPl3kBEdGuNovjk5eWhoKDgjq8JCwuDWq0GAGRmZqJ///7o2bMn1qxZA4Xizv8VuGLFCrzzzjvIysqqcyYWHyL5pReW47/H0/H98WvI1lWaj3dqrsWQqGb4S4dmCPF2ljEhEdmaRlF8pMjIyMCAAQPQtWtXfPPNN3BwuPs9gZ5//nmcOHECJ0+erPOfw+JDZDuqjSbsScrDd0fTEJuUiz+ugI8K0uKZniEY3jkIjireI4zI3jWp4pORkYH+/fujRYsWWLt2ba3SExAQAABYu3Yt1Go1oqOjAQAbNmzAnDlzsGrVKowbN67OfxaLD5FtyivRY+f5bGw7k424lAIYf29BHs4q/PW+EIyJaYEgDyeZUxKRXJpU8VmzZs1ty8uN+GvXrsXChQtx9epVKJVKtG3bFq+99hqefPJJSX8Wiw+R7SssM+DHE9ewNu4Krl2vAACoHASMui8YLw2IQIDWUeaERNTQmlTxaUgsPkSNh9EkYndiDlYfTMXhlEIAgFqpwJieLfBCvzD4ubEAEdkLFp96YvEhapwOpxTgg51JOHblOgDAQSGgf2tfPNG1OR5s5weNkvOAiJoyFp96YvEharxEUcT+S/lYuvsSTly9bj6udVKhX2tfxIR7IybMGy28nSEI3CGaqClh8aknFh+ipiE5txQ/nryGn05m1FoSDwAB7o7o2FyLDkFaRAa6o0OQFn5uGpYhokaMxaeeWHyImhajScSxK4U4dLkAhy8XID79OqqMN/+15+WiRvtm7mgf6I5gL2e4ahzgolbC1VGJVr6u8HPnfCEiW8biU08sPkRNW4XBiFPXinA2oxjnM3U4m1mM5NzSWnsE3UqwlxO6tfBClxAPuDmqzMfVSgVaeDsjzMcVTmrOIyKSC4tPPbH4ENmfyiojLuaU4HymDolZOuTo9CjVV6NUXw1dRRWuFJTdtRgBQJCHE9o1c0P/Nn54sJ0fmmm5rxBRQ2HxqScWHyL6s5LKKsSnFeH41es4m1GMKqMJN/7mLDdUIyW/DEXlVTe9r30zdwyK9MfwzkEI9XFp4NRE9oXFp55YfIioPgrLDEjOLcWxK4X47UIuTqZdxx//du3YXIthnQLRv40fwnxcoFBwIjWRJbH41BOLDxFZQkGpHr9dyMXWM1nYfynffIsNoGZ5fedgD0QFaWEURegqqqCrrIYA4IG2fniovT9cNEr5whM1Qiw+9cTiQ0SWVlCqxy9nsrD1TBYS0otQWWW64+sdVQo82NYfgyL9ERmoRaiPCxw4QkR0Ryw+9cTiQ0TWVGU04UJWCU6mXceF7BI4qhRwd1RB66TC9XIDtpzOQmp+Wa33OKoUaBvgjq4tPPFgOz/c19ILKgeFTL8BkW1i8aknFh8ikpMoijiXqcPm05k4mlqIC1klqKgy1nqNu6MS/dv4oU+ED3qGeiPYy4mbL5LdY/GpJxYfIrIlRpOIKwVlOJtRjH0X8xGblIvCMkOt1wS4O6JrS0/4uWng5qiCu6MS7k4q+Lpq4OOqgY+bGlonFTRKh9teMtNXG1FcUYXi8pr5RoBonpzt5qhChJ8rJ2STTWPxqScWHyKyZUaTiPi06/jtQi6OpBbi9LWiW+5EfTsqBwFqBwUUggARgEkUYTSJ0Fffed6Rj6safSN8cX9rH0QFecDdSQl3RxUcVdy0kWwDi089sfgQUWNSYTAiPu06zmQUo6iiCiWVVSiprMb18ioUlOqRV6JHQZmh1qqy2xEEwN1RBTdHJRwUAgQAgiAgR1eJcoPxlu9RKxXwcFLBy0UNb1c1PJ1vPFTwcFbD102Dds3cuYSfrK6u399cL0lE1Ig5qR3Qq5UPerXyue1rTCYRFVVG6KtN0FcbUVllgiiKUAgCBAFQCALcHJVwc1Td8lKYodqEE1evY9+lPOy/lIerBeUo1VdDFGueyy3RI7dEf8ecLmoHRAZqEeTphJLKaugqq6CrqIK/uyMGtvPDg+38Eehx+52uKwxG6Cqr4OFcc8kOqJkofuLqdcQm5WL/xXwoFDWbRrb7/RHi5Qw/Nw2UnAhOf8ARnz/hiA8R0d2ZTCJKDTW39Cgqr0JBmQHXywwoKDOgqNyA6+UGXC+vQmZRBRKzdHddwg8AHYLcEah1wo0vpWqjCTk6PTKLK2rtjO3mqISPqwb5JXqU6Kvv+JmCAPi4atBM64iW3i4I83VBqI8LWnq7wNetZg6UWsli1BTwUlc9sfgQEVlWtdGEy3llOJNRjPxSvXn5vqujEolZOuw6n3PTTte3Igi46TVeLmr0b+2Lfm18oVEqcD5Th/NZJbiQrUN2cSWq63CJT+ukQktvZ3QP9UL3UG90b+kFrbPqru8j28LiU08sPkREDS+/VI+Dyfko09fMJaq5BAf4umkQ6OGEZlonuDsqoauoRn6ZHgWlBjipHBAZ6H7buUMmk4iCMgNydJXIKKpAan4ZUvJKkZJXhvTr5SgoNdyyGAkC0NLbBa38XBHh54oIf1c00zrB100DXzcN3DTKJrV9QGZRBc5kFMNJ5QB3p5pVgRqVAyoM1SjTG1Gmr0aZwYhyQ82Ne8v1Rvi4qdHG3x3hfi7mS49/JooiygxG6KuMcFI7wFHpYNV5Xiw+9cTiQ0RkH0wmEcUVVcgr1SMxS4fDKYU4mlqAy3lld3yfs9oBXUI80TPMCz3DvNGxuUedL5eJoohqkwhDtQn6ahMM1SZUVhlRWW2EvsqErOKaEnI2Q4dzmTq4OyrRr40vHmjrh+6hXrctGXdjqDbhernh99ujVCGvRI/DKYXYfynvrr/vnSgVAlr6uMBRpUC1UYRJFFFlrDmvuoqqm4qlk8oBzmoH7J81AM5qy04zZvGpJxYfIiL7ll+qR1J2CS7llOBSbiku55UiV1ezQu5Wc4rUDgqE+bqgtb8bWvu7wtdNg2pTzTYBhmoTsoorcSW/DKn5NSNNUrYf+CMXtQNa+bshUOuIAK0j/NwcoRAAk1izLYGDQoCPa82olK+rBkXlBhxOLcSRlALEpxfBcJstCxQC0DbAHSJQU4wqqlBZbYSzWglXjRLOagc4a5Rw1TjAWa2Ek8oBWcUVuJBdgpLKO8+xup3L8/9i8duwsPjUE4sPERHdToXBiKuFZTiaWojDKQU4nFJ404aSUigVAhxVDnBUKaBROsDTRYXIZlp0aK5Fh0B35Oj0iL2Qi9+ScpF3l5Vzd6MQ8PulrJo5Vh2CtOjX2gcx4T7QOkmf0ySKIrKKK5GcWwqjKEKpEOAgCFA6KODupISH042NMxWoqDKi3GBEhcGIiioj2gS43dPvcissPvXE4kNERHUliiKuXa/AxZwSXMwpxcWcEhRXVEGpEKB0EOCgUMDPTYOWPi4I83FBiJcz3B1VUCsVUCsVdR71MJlEJOWUIL2wHNm6SmQVVyJXp4eImm0JFAJQZRSR//veTbklemiUCnQP9UKPUG/0CPNCmI9Lk5qb9GcsPvXE4kNERNT41PX7m5sXEBERkd1g8SEiIiK7weJDREREdoPFh4iIiOwGiw8RERHZDRYfIiIishssPkRERGQ3WHyIiIjIbrD4EBERkd1g8SEiIiK7weJDREREdoPFh4iIiOwGiw8RERHZDRYfIiIishtKuQPYGlEUAdTc3p6IiIgahxvf2ze+x2+HxedPSkpKAADBwcEyJyEiIiKpSkpKoNVqb/u8IN6tGtkZk8mEzMxMuLm5QRAEi32uTqdDcHAw0tPT4e7ubrHPbap4vqTh+ao7nitpeL6k4fmSxpLnSxRFlJSUIDAwEArF7WfycMTnTxQKBZo3b261z3d3d+e/DBLwfEnD81V3PFfS8HxJw/MljaXO151Gem7g5GYiIiKyGyw+REREZDdYfBqIRqPBvHnzoNFo5I7SKPB8ScPzVXc8V9LwfEnD8yWNHOeLk5uJiIjIbnDEh4iIiOwGiw8RERHZDRYfIiIishssPkRERGQ3WHwayIoVK9CyZUs4OjqiR48eOHr0qNyRZLdgwQLcd999cHNzg5+fH0aMGIGkpKRar6msrMTkyZPh7e0NV1dXPPHEE8jJyZEpsW157733IAgCpk2bZj7G81VbRkYGnnnmGXh7e8PJyQlRUVE4fvy4+XlRFDF37lw0a9YMTk5OGDhwIC5duiRjYnkYjUbMmTMHoaGhcHJyQnh4ON5+++1a9zyy53O1b98+PProowgMDIQgCNi4cWOt5+tybgoLCzF69Gi4u7vDw8MDEyZMQGlpaQP+Fg3nTuerqqoKs2bNQlRUFFxcXBAYGIhnn30WmZmZtT7DmueLxacBrF+/HjNmzMC8efNw8uRJdOrUCYMHD0Zubq7c0WS1d+9eTJ48GYcPH8auXbtQVVWFQYMGoayszPya6dOnY/Pmzfjvf/+LvXv3IjMzE48//riMqW3DsWPH8Omnn6Jjx461jvN8/c/169fRu3dvqFQqbNu2DefPn8cHH3wAT09P82sWLVqEZcuWYeXKlThy5AhcXFwwePBgVFZWypi84S1cuBCffPIJli9fjsTERCxcuBCLFi3CRx99ZH6NPZ+rsrIydOrUCStWrLjl83U5N6NHj8a5c+ewa9cubNmyBfv27cPf//73hvoVGtSdzld5eTlOnjyJOXPm4OTJk9iwYQOSkpIwbNiwWq+z6vkSyeq6d+8uTp482fyz0WgUAwMDxQULFsiYyvbk5uaKAMS9e/eKoiiKRUVFokqlEv/73/+aX5OYmCgCEOPi4uSKKbuSkhIxIiJC3LVrl9ivXz/x5ZdfFkWR5+vPZs2aJfbp0+e2z5tMJjEgIEBcvHix+VhRUZGo0WjE7777riEi2oyhQ4eK48ePr3Xs8ccfF0ePHi2KIs/VHwEQf/rpJ/PPdTk358+fFwGIx44dM79m27ZtoiAIYkZGRoNll8Ofz9etHD16VAQgXr16VRRF658vjvhYmcFgwIkTJzBw4EDzMYVCgYEDByIuLk7GZLanuLgYAODl5QUAOHHiBKqqqmqdu7Zt2yIkJMSuz93kyZMxdOjQWucF4Pn6s02bNqFbt24YOXIk/Pz8EB0djc8//9z8fGpqKrKzs2udL61Wix49etjd+erVqxd2796NixcvAgBOnTqFAwcOYMiQIQB4ru6kLucmLi4OHh4e6Natm/k1AwcOhEKhwJEjRxo8s60pLi6GIAjw8PAAYP3zxZuUWll+fj6MRiP8/f1rHff398eFCxdkSmV7TCYTpk2bht69e6NDhw4AgOzsbKjVavO/DDf4+/sjOztbhpTyW7duHU6ePIljx47d9BzPV20pKSn45JNPMGPGDPzzn//EsWPHMHXqVKjVaowdO9Z8Tm7176a9na/Zs2dDp9Ohbdu2cHBwgNFoxLvvvovRo0cDAM/VHdTl3GRnZ8PPz6/W80qlEl5eXnZ//iorKzFr1iw8/fTT5puUWvt8sfiQTZg8eTLOnj2LAwcOyB3FZqWnp+Pll1/Grl274OjoKHccm2cymdCtWzfMnz8fABAdHY2zZ89i5cqVGDt2rMzpbMv333+Pb7/9Fv/5z38QGRmJhIQETJs2DYGBgTxXZDVVVVV46qmnIIoiPvnkkwb7c3mpy8p8fHzg4OBw08qanJwcBAQEyJTKtrz00kvYsmULYmNj0bx5c/PxgIAAGAwGFBUV1Xq9vZ67EydOIDc3F126dIFSqYRSqcTevXuxbNkyKJVK+Pv783z9QbNmzdC+fftax9q1a4e0tDQAMJ8T/rsJvPbaa5g9ezb++te/IioqCmPGjMH06dOxYMECADxXd1KXcxMQEHDTYpbq6moUFhba7fm7UXquXr2KXbt2mUd7AOufLxYfK1Or1ejatSt2795tPmYymbB7927ExMTImEx+oijipZdewk8//YTffvsNoaGhtZ7v2rUrVCpVrXOXlJSEtLQ0uzx3Dz74IM6cOYOEhATzo1u3bhg9erT5n3m+/qd37943bY9w8eJFtGjRAgAQGhqKgICAWudLp9PhyJEjdne+ysvLoVDU/jpwcHCAyWQCwHN1J3U5NzExMSgqKsKJEyfMr/ntt99gMpnQo0ePBs8stxul59KlS/j111/h7e1d63mrn697nh5Nd7Vu3TpRo9GIa9asEc+fPy/+/e9/Fz08PMTs7Gy5o8lq0qRJolarFffs2SNmZWWZH+Xl5ebX/OMf/xBDQkLE3377TTx+/LgYExMjxsTEyJjatvxxVZco8nz90dGjR0WlUim+++674qVLl8Rvv/1WdHZ2Fr/55hvza9577z3Rw8ND/Pnnn8XTp0+Lw4cPF0NDQ8WKigoZkze8sWPHikFBQeKWLVvE1NRUccOGDaKPj484c+ZM82vs+VyVlJSI8fHxYnx8vAhAXLJkiRgfH29ehVSXc/Pwww+L0dHR4pEjR8QDBw6IERER4tNPPy3Xr2RVdzpfBoNBHDZsmNi8eXMxISGh1t/9er3e/BnWPF8sPg3ko48+EkNCQkS1Wi12795dPHz4sNyRZAfglo8vv/zS/JqKigrxxRdfFD09PUVnZ2fxscceE7OysuQLbWP+XHx4vmrbvHmz2KFDB1Gj0Yht27YVP/vss1rPm0wmcc6cOaK/v7+o0WjEBx98UExKSpIprXx0Op348ssviyEhIaKjo6MYFhYmvvHGG7W+iOz5XMXGxt7y76qxY8eKoli3c1NQUCA+/fTToqurq+ju7i6OGzdOLCkpkeG3sb47na/U1NTb/t0fGxtr/gxrni9BFP+wNScRERFRE8Y5PkRERGQ3WHyIiIjIbrD4EBERkd1g8SEiIiK7weJDREREdoPFh4iIiOwGiw8RERHZDRYfIiIishssPkTUpDz33HMYMWKE3DGIyEYp5Q5ARFRXgiDc8fl58+Zh6dKl4Ib0RHQ7LD5E1GhkZWWZ/3n9+vWYO3durTuwu7q6wtXVVY5oRNRI8FIXETUaAQEB5odWq4UgCLWOubq63nSpq3///pgyZQqmTZsGT09P+Pv74/PPP0dZWRnGjRsHNzc3tGrVCtu2bav1Z509exZDhgyBq6sr/P39MWbMGOTn5zfwb0xElsbiQ0RN3tq1a+Hj44OjR49iypQpmDRpEkaOHIlevXrh5MmTGDRoEMaMGYPy8nIAQFFRER544AFER0fj+PHj2L59O3JycvDUU0/J/JsQ0b1i8SGiJq9Tp074v//7P0REROD111+Ho6MjfHx88PzzzyMiIgJz585FQUEBTp8+DQBYvnw5oqOjMX/+fLRt2xbR0dFYvXo1YmNjcfHiRZl/GyK6F5zjQ0RNXseOHc3/7ODgAG9vb0RFRZmP+fv7AwByc3MBAKdOnUJsbOwt5wtdvnwZrVu3tnJiIrIWFh8iavJUKlWtnwVBqHXsxmoxk8kEACgtLcWjjz6KhQsX3vRZzZo1s2JSIrI2Fh8ioj/p0qULfvzxR7Rs2RJKJf+aJGpKOMeHiOhPJk+ejMLCQjz99NM4duwYLl++jB07dmDcuHEwGo1yxyOie8DiQ0T0J4GBgTh48CCMRiMGDRqEqKgoTJs2DR4eHlAo+NcmUWMmiNzilIiIiOwE/9OFiIiI7AaLDxEREdkNFh8iIiKyGyw+REREZDdYfIiIiMhusPgQERGR3WDxISIiIrvB4kNERER2g8WHiIiI7AaLDxEREdkNFh8iIiKyG/8PmR+tt2ab8/UAAAAASUVORK5CYII=", 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y8/ORm5uLhQsXIiIiwuo3KKXaGxpTdT+yDcezZE5CRETUcEkuPjfuf3Xx4kWsXbsW69atQ2pqKh544AFMnz7dBhGpNoa084NCAE5kFOJSXonccYiIiBqkOo34vPLKK1Cp/neVTKVS4eWXX8ahQ4esGo5qz8tFi7iIqrvYbzjBUR8iIqKbkVx83NzckJaWVuN4eno6XF25c7CceLmLiIjo9iQXn1GjRmHChAmIj49Heno6Ll++jFWrVmHixIl44oknbJGRamlwWz8oFQJOZeqRmsvLXURERH8leVXXggULIAgCnn76aVRWVgIA1Go1nnvuOXzwwQdWD0i15+msQfdIb+w6dxUbjmdiSr8WckciIiJqUARRFMW6fGNpaSkuXLgAURQRGRkJJ6emsXeMXq+HTqdDYWEh3Nzc5I4j2eqD6Xh5zXG09nPFxum95I5DRERUL2r7+V3ne3U5OTkhOjoaoaGh2LRpE5KSkur6VmRFg9r6Qq0UcCa7COevFMkdh4iIqEGRXHxGjhyJxYsXA6i6OWmnTp0wcuRIxMTEYM2aNVYPSNK4O2nQq0UzAMB6TnImIiKqRnLx2bVrF3r27AkAWLduHURRREFBARYtWoR3333X6gFJugfvCQAA/HIsE3W8kklERNQkSS4+hYWF8PT0BABs3LgRjz76KJycnDB06FCcP3/e6gFJugFRvtCqFEjJLcGpTL3ccYiIiBoMycUnKCgICQkJKCkpwcaNGzFo0CAAwLVr1+Dg4GD1gCSdi1aFfq19AAC/8HIXERGRheTiM336dDz11FMIDAyEv78/+vTpA6DqElh0dLS181Ed3bjctZ6Xu4iIiCwk7+MzefJkdOnSBenp6Rg4cCAUiqruFB4ezjk+DUjfVj5w1iiRUVCGxPQCdAz2kDsSERGR7Oq0nL1Tp04YOnQoMjIyLJsYDh06FN27d7dqOKo7R40SA6J8AVSN+hAREVEdik9paSkmTJgAJycntG3b1nLfrqlTp3Ln5gbmwZiqy10bjmfBZOblLiIiIsnFZ/bs2Th27Bh27NhRbTLzgAEDEB8fb9VwdHd6tvSGm4MKOUUGHLyYL3ccIiIi2UkuPj/99BMWL16MHj16QBAEy/GoqChcuHDBquHo7mhVStzXzg8A8N+jvNxFREQkufhcvXoVPj4+NY6XlJRUK0LUMDzUvjkAYMPxTJQbTTKnISIikpfk4tO5c2ds2LDB8vWNsrN06VJ069bNesnIKu4N94K/zgH68kpsP5MjdxwiIiJZSV7OPn/+fNx33304ffo0Kisr8dlnn+HUqVNISEjAzp07bZGR7oJSIeCh9s2xZOcFrE3MwJBof7kjERERyUbyiE9cXBz27duH0tJSREREYNOmTfD19UVCQgJiY2NtkZHu0iMdqy537Tibg/ySCpnTEBERyUfSiI/RaMT//d//4c0338SKFStslYmsrKWvK9oGuOFUph6/HM/E091C5Y5EREQkC0kjPmq1GuvWrbNVFrKhhztUjfqsPZIhcxIiIiL5SL7U9fDDD+Onn36yQRSypWHtA6BUCDiaXoCUq8VyxyEiIpKF5MnNkZGReOedd7Bv3z7ExsbC2dm52vNTp061WjiyHh9XB/Rs4Y0dZ6/ip8QMzBzUSu5IRERE9U4QJd66Oyws7NZvJghISUm561By0uv10Ol0KCwshJubm9xxrOq/RzMwbdVRBHk6YtdLfbnvEhERNRm1/fyWPOKTmpp6V8FIPoOi/OCiVSE9vwwHL15DlzBPuSMRERHVqzrdnZ0aJ0eNEkOu38Ji7ZHLMqchIiKqfyw+duax2EAAwC/Hs1BWwVtYEBGRfWHxsTOdQz0R5OmIYkMlfj+VLXccIiKiesXiY2cUCgGPdqwa9VnDy11ERGRnJBeftLQ03GwhmCiKSEtLs0oosq0bxWdPci4yC8pkTkNERFR/JBefsLAwXL16tcbx/Pz82y51p4YjyNMJXcM8IYrAukTu5ExERPZDcvERRfGm+78UFxfDwcHBKqHI9h69Psl5zeHLNx3BIyIiaopqvY/PzJkzAVRtUvjmm2/CycnJ8pzJZMKBAwfQvn17qwck27g/2h9z/nsKKbklSEwvQMdgD7kjERER2Vyti09iYiKAqhGfEydOQKPRWJ7TaDS455578OKLL1o/IdmEi1aFIdF+WHskAz8evsziQ0REdqHWxWf79u0AgHHjxuGzzz5rcrdzsEePdQzE2iMZWH8sE289EAUHtVLuSERERDYleY7PsmXL4ObmhuTkZPz+++8oK6taFcR5Io3PveFeaO7uiKJy7ulDRET2QXLxyc/PR//+/dGyZUvcf//9yMrKAgBMnDgRs2bNsnpAsh2FQsDITkEAgFV/pMuchoiIyPYkF5/p06dDrVYjLS2t2gTnUaNGYePGjVYN92fvvfce4uLi4OTkBHd395u+Ji0tDQ8++CCcnZ3h7e2NqVOnoqKiwmaZmoLHOgVCEICElDxczC2ROw4REZFNSS4+mzZtwt///ncEBgZWO96iRQtcunTJasH+qqKiAiNGjMBzzz130+dNJhOGDh2KkpIS7NmzB6tWrcKaNWs4CnUHzd0d0atFMwDA6kMc9SEioqZNcvEpKSmpNtJzQ25uLrRarVVC3czbb7+NGTNmIDo6+qbPb9q0CadPn8bKlSvRoUMHDBgwAB9//DGWLl0KvV5vs1xNweOdqy53/Xj4MipNZpnTEBER2Y7k4tOrVy989913lq8FQYDZbMZHH32Evn37WjWcFAkJCWjXrh0CAgIsxwYPHgyDwYDDhw/f8vsMBgP0en21h73p38YXXs4a5BQZsONszV25iYiImopaL2e/4aOPPkKfPn1w6NAhVFRU4OWXX8apU6eQn5+PvXv32iJjrWRnZ8PX17faMQ8PD2g0GmRn33rF0vz58/H222/bOl6DplEp8EjH5li6OxWrDqZjQJTvnb+JiIioEZI84hMVFYXjx4+jS5cuGDhwIEpKSvDII48gMTERERERkt5r7ty5EAThto9Dhw7V+v1udiuNW91i44bZs2ejsLDQ8khPt895LqOuX+7afjYHV/TlMqchIiKyDckjPlu3bkX//v1vOkqyePFiTJkypdbvNWXKFDz++OO3fU1oaGit3svPzw8HDhyoduzatWswGo01RoL+TKvV2nRuUmMR6eOKTiEeOHTpGn48fBnP942UOxIREZHVSR7xefTRR3Hw4MEaxz/99FO89tprkt7L29sbrVu3vu2jtjc+7datG06ePGnZVwiomvCs1WoRGxsrKZe9ujHqE38wHWYzN6QkIqKmR3Lx+eSTT3D//ffj9OnTlmMLFizAnDlzsGHDBquG+7O0tDQcPXoUaWlpMJlMOHr0KI4ePYri4mIAwKBBgxAVFYUxY8YgMTERW7duxYsvvohnn32Wt9eopaEx/nDVqpCWX4o9yblyxyEiIrI6yZe6xo0bh7y8PAwaNAh79uxBfHw83n//ffz222+Ii4uzRUYAwFtvvYUVK1ZYvu7QoQOAqnuI9enTB0qlEhs2bMDkyZPRvXt3ODo64sknn8SCBQtslqmpcdKo8EjH5liRcAkr919Cr5bN5I5ERERkVYJYx5tszZ49G0uXLoXJZMLGjRvRtWtXa2eThV6vh06nQ2FhoV2OFJ27UoRBn+yCQgD2vtoP/jpHuSMRERHdUW0/v2s14rNo0aIax/z9/eHk5IRevXrhwIEDlonFU6dOrWNkagha+rqiS5gn/kjNx/d/pGPmwJZyRyIiIrKaWo34hIWF1e7NBAEpKSl3HUpO9j7iAwA/H8vE1O8T4eOqxd5X+0GtlDwVjIiIqF5ZdcQnNTXVasGo4Rvc9n87OW9NuoL72vnLHYmIiMgqJP2vvNFoRHh4eLUVXdT0aFVKjLy+tH3l/jSZ0xAREVmPpOKjVqthMBhuuxMyNQ1PdgmGIAB7knORmlsidxwiIiKrkDx544UXXsDf//53VFZW2iIPNRBBnk7oc305+8r9l2ROQ0REZB2S9/E5cOAAtm7dik2bNiE6OhrOzs7Vnl+7dq3VwpG8xnQLwfazV7H6UNXqLmet5H9diIiIGhTJn2Tu7u549NFHbZGFGpg+LX0Q6uWEi3ml+PHwZYyNC5U7EhER0V2p8waGTRWXs1e3Yt9FzPn5FEK9nLBtVh8oFJzfRUREDU9tP7+5QQvd1mOxgXB1UOFiXim2n82ROw4REdFdqdOkjR9//BGrV69GWloaKioqqj135MgRqwSjhsFZq8ITXYLx1a4UfLMnFf3b+ModiYiIqM4kj/gsWrQI48aNg4+PDxITE9GlSxd4eXkhJSUFQ4YMsUVGktnT3UKgEIB9F/KQlKWXOw4REVGdSS4+n3/+Ob766issXrwYGo0GL7/8MjZv3oypU6eisLDQFhlJZoEeThhyfffmZXu5izcRETVekotPWloa4uLiAACOjo4oKioCAIwZMwbff/+9ddNRgzG+RygA4KejmcgtNsgbhoiIqI4kFx8/Pz/k5eUBAEJCQrB//34AVffz4gKxpqtjsAfuCdShotLMDQ2JiKjRklx8+vXrh/Xr1wMAJkyYgBkzZmDgwIEYNWoUHn74YasHpIZBEASM7xEGAPhXwiWUG00yJyIiIpJO8j4+ZrMZZrMZKlXVgrDVq1djz549iIyMxKRJk6DRaGwStL5wH59bM5rM6P3hdmQWlmP+I9F4okuw3JGIiIgA1P7zmxsY/gWLz+19vTsF725IQkQzZ2ye0ZsbGhIRUYNQ28/vOu3jU15ejuPHjyMnJwdms7nac8OGDavLW1IjMapzED7bch4XrpZgx7kc9GvNfX2IiKjxkFx8Nm7ciKeffhq5ubk1nhMEASYT5340Za4OajzeJQhLd6di6a5UFh8iImpUJE9unjJlCkaMGIGsrCzLfJ8bD5Ye+/BM9zAoFQISUvJwMoN7NxERUeMhufjk5ORg5syZ8PXl/+nbq+bujhgaXbWh4de7U2ROQ0REVHuSi89jjz2GHTt22CAKNSbP9gwHAPxyPAuZBWUypyEiIqodyau6SktLMWLECDRr1gzR0dFQq9XVnp86dapVA9Y3ruqqvce/SsD+lHxM7BGGNx6IkjsOERHZMZut6vrPf/6D33//HY6OjtixYwcE4X/LmQVBaPTFh2rvb70jsD8lH9//kYYX+rWAzkl9528iIiKSkeRLXW+88QbmzZuHwsJCXLx4EampqZZHSgrne9iTPi2bobWfK0oqTPjX/otyxyEiIrojycWnoqICo0aNgkIh+VupiREEAZN6RwAAlu+7yNtYEBFRgye5vYwdOxbx8fG2yEKN0NAYfzR3d0RucQV+PHxZ7jhERES3JXmOj8lkwocffojff/8dMTExNSY3L1y40GrhqOFTKxWY2DMMb68/jaW7U/BEl2AoeRsLIiJqoCQXnxMnTqBDhw4AgJMnT1Z77s8Tncl+jOochEVbz+NSXil+O5mFB2IC5I5ERER0U5KLz/bt222RgxoxJ40KT3cLxWdbz2PJzgsYGu3PEkxERA0SZyiTVYyNC4WjWomTGXpsTcqROw4REdFNsfiQVXg6a/BM91AAwIJNZ2E2S9oXk4iIqF6w+JDV/K1XOFwdVDiTXYT1xzPljkNERFQDiw9ZjbuTBn/rVXUPr082n4PRZJY5ERERUXUsPmRV47qHwctZg4t5pfjhEPf1ISKihqVWq7p+/vnnWr/hsGHD6hyGGj9nrQrP943EvF9OY9HW83ikY3M4qJVyxyIiIgJQy+IzfPjwal8LgoA/39T9z0uXTSbetsDePdk1GF/vTkFmYTlW7r+EiT3D5Y5EREQEoJaXusxms+WxadMmtG/fHr/99hsKCgpQWFiIX3/9FR07dsTGjRttnZcaAQe1EtMHtAQA/HN7MvTlRpkTERERVZE8x2f69On47LPPMHjwYLi5ucHV1RWDBw/GwoULMXXqVFtkpEbokY7NEdHMGddKjfhqZ4rccYiIiADUofhcuHABOp2uxnGdToeLFy9aIxM1ASqlAi/f1xoA8PWeFOToy2VOREREVIfi07lzZ0yfPh1ZWVmWY9nZ2Zg1axa6dOli1XDUuA2K8kXHYHeUG834ZMt5ueMQERFJLz7ffvstcnJyEBISgsjISERGRiI4OBhZWVn45ptvbJGRGilBEPDqkDYAgNWH0pGcUyxzIiIisneSb1IaGRmJ48ePY/PmzThz5gxEUURUVBQGDBjAG1NSDV3CPDGgjQ+2JOVgwe9nsWRMrNyRiIjIjgnin9elS1ReXg6tVtukCo9er4dOp0NhYSHc3NzkjtMknLtShPs+3QWzCKydHIeOwR5yRyIioiamtp/fki91mc1mvPPOO2jevDlcXFyQmpoKAHjzzTd5qYtuqqWvKx6LDQQA/P23M7iLrk1ERHRXJBefd999F8uXL8eHH34IjUZjOR4dHY2vv/7aquGo6Zg+oCU0SgUOpOZj34U8ueMQEZGdklx8vvvuO3z11Vd46qmnoFT+71YEMTExOHPmjFXDUdMR4O6IJ7sGAwA++v0sR32IiEgWkotPRkYGIiMjaxw3m80wGm23Q+97772HuLg4ODk5wd3d/aavEQShxmPJkiU2y0TSTO4bAQe1AkfTC7DtTI7ccYiIyA5JLj5t27bF7t27axz/4Ycf0KFDB6uEupmKigqMGDECzz333G1ft2zZMmRlZVkeY8eOtVkmksbH1QHPxIUBAD7edA5mM0d9iIiofklezj5nzhyMGTMGGRkZMJvNWLt2Lc6ePYvvvvsOv/zyiy0yAgDefvttAMDy5ctv+zp3d3f4+fnZLAfdnb/1CsfK/ZdwOkuP305mY2iMv9yRiIjIjkge8XnwwQcRHx+PX3/9FYIg4K233kJSUhLWr1+PgQMH2iKjJFOmTIG3tzc6d+6MJUuWwGw23/b1BoMBer2+2oNsx8NZgwk9qkZ9Fm4+CxNHfYiIqB5JHvEBgMGDB2Pw4MHWznLX3nnnHfTv3x+Ojo7YunUrZs2ahdzcXLzxxhu3/J758+dbRpOofkzoGYYVCRdx4WoJ1h65jBGdguSOREREdkLyiM/48eOxYsWKGsf1ej3Gjx8v6b3mzp170wnJf34cOnSo1u/3xhtvoFu3bmjfvj1mzZqFefPm4aOPPrrt98yePRuFhYWWR3p6uqSfgaRzc1Djud4RAICFm8+hrMIkcyIiIrIXknduVigUcHR0xIQJE/Dpp59CoajqTleuXEFAQABMptp/iOXm5iI3N/e2rwkNDYWDg4Pl6+XLl2P69OkoKCi44/vv3bsXPXr0QHZ2Nnx9fWuViTs3149yown9P96JjIIyvDS4FZ7vW3OlIBERUW3V9vO7Tpe6NmzYgGeffRZJSUlYvXo1PDzqdgsCb29veHt71+l7ayMxMREODg63XP5O8nFQK/Hyfa0wbdVRfLHjAkZ1DoK3i1buWERE1MRJvtQFAFFRUdi/fz+MRiM6d+6MpKQka+eqIS0tDUePHkVaWhpMJhOOHj2Ko0ePori46o7f69evx9KlS3Hy5ElcuHABX3/9NV5//XX83//9H7RafqA2RA/GBCC6uQ7Fhkp8tuW83HGIiMgOSC4+N25I6uXlhS1btqBPnz6499578fPPP1s93J+99dZb6NChA+bMmYPi4mJ06NABHTp0sMwBUqvV+Pzzz9GtWzfExMTgs88+w7x58/Dxxx/bNBfVnUIh4LX72wAA/vNHGpJzimVORERETV2d5vhkZ2fDx8fHcmzhwoV45ZVXYDabJc3xaYg4x6f+TVxxCFuSrmBAG198PbaT3HGIiKgRstkcn+3bt8PT07PasZkzZyImJgZ79+6VnpTs3qtDWmP72RxsSbqChAt56BbhJXckIiJqoiSP+DR1HPGRx5s/ncS/9l9ClL8b1r/QA0qFIHckIiJqRKw64jNz5ky88847cHZ2xsyZM2/72oULF0pLSgRgxsCW+OloBk5n6fHj4XSM6hwsdyQiImqCalV8EhMTLXdeT0xMvOXrbkx8JpLK01mDaf1b4N0NSfjo93O4P9ofrg5quWMREVETw0tdf8FLXfKpqDRj8Ke7kJpbguf6ROCV+1rLHYmIiBqJ2n5+12kfHyJb0KgUluXt3+xORXp+qcyJiIioqanVpa5HHnmk1m+4du3aOochGtDGB90jvbA3OQ/v/5qEL0bHyh2JiIiakFoVH51OZ+scRACq5om9MTQKQxftxm8ns7E16Qr6t6ndfdaIiIjuhHN8/oJzfBqG9zacxtLdqfB102LTjN7QOXKiMxER3Rrn+FCjNmtQK4R7O+OK3oB3fzktdxwiImoi6nR39h9//BGrV69GWloaKioqqj135MgRqwQj++agVuLDx2Iw4ssE/HD4MobG+KNPK587fyMREdFtSB7xWbRoEcaNGwcfHx8kJiaiS5cu8PLyQkpKCoYMGWKLjGSnOoV64pm4UADA7LUnUFRulDcQERE1epKLz+eff46vvvoKixcvhkajwcsvv4zNmzdj6tSpKCwstEVGsmMvDW6FYE8nZBWWY/5vZ+SOQ0REjZzk4pOWloa4uDgAgKOjI4qKigAAY8aMwffff2/ddGT3nDQqfPhYDADgPwfScCTtmsyJiIioMZNcfPz8/JCXlwcACAkJwf79+wEAqamp4AIxsoV7w73wWGwggKqbmVaazDInIiKixkpy8enXrx/Wr18PAJgwYQJmzJiBgQMHYtSoUXj44YetHpAIAGYPaQ2doxqnMvVYuf+S3HGIiKiRkryPj9lshtlshkpVtSBs9erV2LNnDyIjIzFp0iRoNBqbBK0v3Men4Vq5/xLe+OkkXLUqbJ3VGz5uDnJHIiKiBqK2n9/cwPAvWHwaLpNZxCOf78Wxy4UY3j4Anz7eQe5IRETUQNT287tO+/iUl5fj+PHjyMnJgdlcfb7FsGHD6vKWRHekVAh4Z3g7PPTPvfjpaCZGdgpCXKS33LGIiKgRkVx8Nm7ciKeffhq5ubk1nhMEASaTySrBiG4mJtAdo7uG4F/7L+GVtcfx27RecNHWqb8TEZEdkjy5ecqUKRgxYgSysrIs831uPFh6qD68fF8rNHd3RHp+GW9nQUREkkguPjk5OZg5cyZ8fXnHbJKHq4MaH4+8B4IArDqYjq1JV+SOREREjYTk4vPYY49hx44dNohCVHv3hnthQvcwAMAra04gr9ggcyIiImoMJK/qKi0txYgRI9CsWTNER0dDrVZXe37q1KlWDVjfuKqr8Sg3mvDgP/bgfE4x7mvrhy9Gd4QgCHLHIiIiGdhsOfvXX3+NSZMmwdHREV5eXtU+aARBQEpKSt1TNwAsPo3LyYxCDP/nXlSaRXzwSDQe7xIsdyQiIpKBzYqPn58fpk6dildffRUKheQrZQ0ei0/j8/mOZHy48Sw0KgXWTIpDdKBO7khERFTPavv5Lbm5VFRUYNSoUU2y9FDjNKlXBAa08UFFpRmTVh7GtZIKuSMREVEDJbm9jB07FvHx8bbIQlQnCoWAj0e2R6iXEzIKyjB1VSJMZm5ITkRENUne+c1kMuHDDz/E77//jpiYmBqTmxcuXGi1cES1pXNUY8mYWAz/517sPp+LT7ecw6xBreSORUREDYzk4nPixAl06FB1j6STJ09We44rakhOrf3c8MEjMZgefxT/2JaMKH83DIn2lzsWERE1IJKKj8lkwty5cxEdHQ1PT09bZSKqs+EdmuP45UJ8uzcVM1cfQ5CnE9o152RnIiKqImmOj1KpxODBg1FYWGirPER37bX7W6NXy2YoM5owccUh5OjL5Y5EREQNhOTJzdHR0Y1+rx5q2lRKBRY/2QERzZyRrS/Hs/86jHIj7yNHRER1KD7vvfceXnzxRfzyyy/IysqCXq+v9iBqCNwc1PhmbGe4O6lxLL0AL/14HGau9CIisnuSNzD88/49f57MLIoiBEFo9Hdo5waGTUvChTyM+eYAKs0inusTgVfuay13JCIisoHafn5LXtW1ffv2uwpGVJ+6RXjhg0dj8OIPx/DFjgsI0DlgTLdQuWMREZFMJBef3r172yIHkc08FhuIrIIyfLz5HOb8fAq+bg4Y1NZP7lhERCQDycUHAAoKCvDNN98gKSkJgiAgKioK48ePh07HZcPUME3pF4nMwjJ8/0c6Xvg+Ef959l7EhnjIHYuIiOqZ5MnNhw4dQkREBD755BPk5+cjNzcXCxcuREREBI4cOWKLjER3TRAEvPNQO/Rt1QyGSjP+9q/DuMJl7kREdkfy5OaePXsiMjISS5cuhUpVNWBUWVmJiRMnIiUlBbt27bJJ0PrCyc1NW4mhEo9+sQ9nsosQG+KB75+9FxoVb7hLRNTY2ezu7IcOHcIrr7xiKT0AoFKp8PLLL+PQoUN1S0tUT5y1KiwZHQtXBxUOX7qG9zacljsSERHVI8nFx83NDWlpaTWOp6enw9XV1SqhiGwp1NsZn45qDwBYkXAJa49cljcQERHVG8nFZ9SoUZgwYQLi4+ORnp6Oy5cvY9WqVZg4cSKeeOIJW2Qksrr+bXwxtX8LAMDstSdwKpO3YSEisgeSV3UtWLAAgiDg6aefRmVlJQBArVbjueeewwcffGD1gES2Mr1/Cxy/XIAdZ6/ib/86jPVTesDDWSN3LCIisiHJk5tvKC0txYULFyCKIiIjI+Hk5GTtbLLg5Gb7UlhqxLB/7sGlvFL0iPTG8nGdoVJysjMRUWNjs8nNNzg5OSE6OhoxMTFNpvSQ/dE5qfHlmFg4qpXYk5yLjzadlTsSERHZkOTiU1JSgjfffBNxcXGIjIxEeHh4tQdRY9Pazw0fjYgBAHy5MwW/HM+UOREREdmK5Dk+EydOxM6dOzFmzBj4+/tXu1EpUWP1QEwATlwuxJe7UvDSD8cR5u2MtgHciZyIqKmRPMfH3d0dGzZsQPfu3W2VqYaLFy/inXfewbZt25CdnY2AgACMHj0ar7/+OjSa/01GTUtLw/PPP49t27bB0dERTz75JBYsWFDtNXfCOT72q9JkxjPLDmJPci58XLVY93x3NHd3lDsWERHVgs3m+Hh4eMDT0/Ouwkl15swZmM1mfPnllzh16hQ++eQTLFmyBK+99prlNSaTCUOHDkVJSQn27NmDVatWYc2aNZg1a1a9ZqXGS6VU4J9PdURLXxfkFBnwzLd/oLDUKHcsIiKyIskjPitXrsR///tfrFixQtZJzR999BG++OILpKSkAAB+++03PPDAA0hPT0dAQAAAYNWqVXjmmWeQk5NT69EbjvhQZkEZHv58L67oDega5onvJnSBVqWUOxYREd1GbT+/Jc/x+fjjj3HhwgX4+voiNDQUarW62vP1daPSwsLCaiNPCQkJaNeunaX0AMDgwYNhMBhw+PBh9O3b96bvYzAYYDAYLF/r9XrbhaZGIcDdEcue6YKRXybgQGo+XvrhOD4d1R4KBeezERE1dpKLz/Dhw20QQ5oLFy7gH//4Bz7++GPLsezsbPj6+lZ7nYeHBzQaDbKzs2/5XvPnz8fbb79ts6zUOEUFuOGL0R0xbtlB/HwsE14uGrz1QBQn8xMRNXKSi8+cOXOs9ofPnTv3jqXj4MGD6NSpk+XrzMxM3HfffRgxYgQmTpxY7bU3+1ASRfG2H1azZ8/GzJkzLV/r9XoEBQXV9kegJqxni2b4aEQMZsQfw7K9F+HlrMGUfi3kjkVERHdBcvGxpilTpuDxxx+/7WtCQ0Mtf5+ZmYm+ffuiW7du+Oqrr6q9zs/PDwcOHKh27Nq1azAajTVGgv5Mq9VCq9VKD0924eEOgbhWYsS8X05jwaZzcHfSYPS9IXLHIiKiOpK1+Hh7e8Pb27tWr83IyEDfvn0RGxuLZcuWQaGoviCtW7dueO+995CVlQV/f38AwKZNm6DVahEbG2v17GQ/xvcIw7XSCvxjWzLe/O9JeDhpMDTGX+5YRERUB43ipkSZmZno06cPgoKCsGDBAly9ehXZ2dnV5u4MGjQIUVFRGDNmDBITE7F161a8+OKLePbZZ7k6i+7azIEt8VTXYIgiMCP+KPZdyJU7EhER1UGjKD6bNm1CcnIytm3bhsDAQPj7+1seNyiVSmzYsAEODg7o3r07Ro4cieHDh2PBggUyJqemQhAEzHuoHe6P9kOFyYy/fXcYZ7K5ApCIqLGp893Zmyru40O3U2404elv/sAfF/Ph66bF2snc3ZmIqCGo7ee35OJjMpmwfPlybN26FTk5OTCbzdWe37ZtW90SNxAsPnQnhaVGjPhyH85dKUakjwt+nNQN7k61vy0KERFZn81uWTFt2jRMmzYNJpMJ7dq1wz333FPtQdTU6ZzUWD6uC/zcHJCcU4yRXybgYm6J3LGIiKgWJI/4eHt747vvvsP9999vq0yy4ogP1dbZ7CKM/uYArhYZ4OagwmdPdEDfVj5yxyIisks2G/HRaDSIjIy8q3BETUErP1f88kIPdAx2h768EuOXH8TibefBaXNERA2X5OIza9YsfPbZZ/zlTgTA180B3//fvXjy+lL3BZvOYfK/j6C0olLuaEREdBOSL3U9/PDD2L59Ozw9PdG2bdsaNyldu3atVQPWN17qorpa9Uca3vzvSRhNIqL83bB0bCeu+CIiqic2uzu7u7s7Hn744bsKR9QUPd4lGJE+Lpi08jBOZ+nx0OI9+HJMLGJDPOWORkRE13Efn7/giA/drYyCMkxccQhJWXqolQJmD2mDcd1DeWd3IiIbstnkZiK6vebujljzXDfcH+0Ho0nEvF9OY+KKQ8gvqZA7GhGR3avTiM+PP/6I1atXIy0tDRUV1X+ZHzlyxGrh5MARH7IWURTxr/2X8O6GJFRUmuHrpsUno9ojLqJ2N+YlIqLas9mIz6JFizBu3Dj4+PggMTERXbp0gZeXF1JSUjBkyJC7Ck3UlAiCgKe7heKnyd0R0cwZV/QGPPX1Acz/NQmGSpPc8YiI7JLk4vP555/jq6++wuLFi6HRaPDyyy9j8+bNmDp1KgoLC22RkahRiwpww/oXeuDxzkEQReDLXSkY/s99OJtdJHc0IiK7I7n4pKWlIS4uDgDg6OiIoqKqX95jxozB999/b910RE2Ek0aFDx6NwZdjYuHprEFSlh4PLt6Db/ekck8sIqJ6JLn4+Pn5IS8vDwAQEhKC/fv3AwBSU/kLnOhOBrf1w8bpPdG3VTNUVJox75fTePa7w7jGic9ERPVCcvHp168f1q9fDwCYMGECZsyYgYEDB2LUqFHc34eoFnxcHfDtM50x76G20CgV2JJ0Bfcv2o2DF/PljkZE1ORJXtVlNpthNpuhUlXtfbh69Wrs2bMHkZGRmDRpEjQajU2C1heu6qL6dDKjEC98n4jU3BIoFQJmDGiB5/pEQqngnj9ERFLU9vObGxj+BYsP1bdiQyXe/Okk1iVmAAB6RHpj4ah74OPqIHMyIqLGw6YbGO7evRujR49Gt27dkJFR9cv6X//6F/bs2VO3tER2zEWrwsKR9+Cjx2LgqFZiT3Iu7v9sN3afvyp3NCKiJkdy8VmzZg0GDx4MR0dHJCYmwmAwAACKiorw/vvvWz0gkT0QBAEjOgVh/Qvd0crXFbnFFXj62z/w2roTKCjlxGciImuRXHzeffddLFmyBEuXLq12Z/a4uLhGv2szkdwifVzx3ynd8WTXYIgi8J8Daei7YAfiD6bBbOZVaSKiuyW5+Jw9exa9evWqcdzNzQ0FBQXWyERk1xzUSrz/cDRW/d+9aOnrgmulRryy5gQeW7IPaXmlcscjImrUJBcff39/JCcn1zi+Z88ehIeHWyUUEQH3hnthw9SeeP3+NnDWKHEkrQBDF+3Gbyey5I5GRNRoSS4+f/vb3zBt2jQcOHAAgiAgMzMT//73v/Hiiy9i8uTJtshIZLfUSgWe7RWOTTN7o2OwO4oMlXju30cw9+dTvN8XEVEd1Gk5++uvv45PPvkE5eXlAACtVosXX3wR77zzjtUD1jcuZ6eGymgyY8Gms/hyZwoA4J5AHZaMiYW/zlHmZERE8rP5Pj6lpaU4ffo0zGYzoqKi4OLiUuewDQmLDzV0285cwczVx1BQaoS3ixZfjumI2BBPuWMREcmKGxjWEYsPNQbp+aV49rtDOJNdBLVSwLyH2uGJLsFyxyIiko3Vi8/48eNr9Qd/++23tUvYQLH4UGNRYqjESz8ew68nsgEAj3RsjtlD2qCZq1bmZERE9c/qxUehUCAkJAQdOnS47V3Y161bJz1tA8LiQ42JKIr45/ZkfLz5HEQRcNWqMG1AC4yNC4VaWaeN2YmIGiWrF5/Jkydj1apVCA4Oxvjx4zF69Gh4eja9eQUsPtQYHUm7hrk/n8Lxy4UAgEgfF8wY0BKD2/pCxQJERHbAJnN8DAYD1q5di2+//Rb79u3D0KFDMWHCBAwaNAiC0DTuJs3iQ42V2Szih8Pp+HDjWeSVVN3mItDDERN6hGFkpyA4a1UyJyQish2bT26+dOkSli9fju+++w5GoxGnT59uEiu7WHyosSssM+KbPalYuf8S8q8XIJ2jGu893A4PxATInI6IyDZsend2oOqmioIgQBRFmM3mur4NEVmZzlGNmQNbYu8r/fDu8HYI83ZGYZkRU/6TiLk/n0JFJf97JSL7Jan4GAwGfP/99xg4cCBatWqFEydOYPHixUhLS2sSoz1ETYmjRonR94Zg84xeeK5PBABg+b6LGPFlAi5f4z2/iMg+1Wly87hx4zB69Gh4eXnZOl+946Uuaqq2JlVtfFhYZoSrVoXxPcIwvkcYdI5quaMREd01myxnDw4ORocOHW47kXnt2rXS0zYgLD7UlKXnl+KF7xNxNL0AQNXy93HdQzGhRzh0TixARNR4Wb34PPPMM7VaubVs2bLap2yAWHyoqTObRWw8lY1FW8/jTHYRAMDDSY3Xh0bh0Y7Nm8wKTSKyL7xlRR2x+JC9MJtF/H4qG59sOYdzV4oBAHERXnjv4WiEeTvLnI6ISBoWnzpi8SF7YzSZ8c2eVHy65RzKjWZoVAqMiwvF+B5h8HVzkDseEVGtsPjUEYsP2au0vFK88d+T2HXuKgBArRTwcIfm+L9e4Yj0cZU5HRHR7bH41BGLD9kzURSx7UwOluy8gIMXr1mOP9KxOV69rzV8OAJERA0Ui08dsfgQVTl8KR9f7kzBptNXAADOGiWm9m+Bcd3DoFHx/l9E1LCw+NQRiw9RdYlp1zB3/Wkcu74EPtTLCU91DcFDHQLg48oRICJqGFh86ojFh6gms1nEmiOX8feNZ5FbbAAAKBUCerdshqe6BqN/G1+ZExKRvWPxqSMWH6JbKyo34r9HM7HmyGUkphVYjj8Q4493HmoHD2eNfOGIyK6x+NQRiw9R7Vy4WozvD6Rh2b6LMJlF+Lhq8ffHYtC3lY/c0YjIDrH41BGLD5E0xy8XYEb8UVy4WgIAeKh9ACb0CENMoLu8wYjIrrD41BGLD5F05UYTPvjtDJbvu2g51j7IHWPjQjCknT8c1Er5whGRXWDxqSMWH6K6O5ZegOX7LuKX45kwmqp+tTioFejZohkGRvmiX2sfeLtoZU5JRE1RbT+/G8VmHBcvXsSECRMQFhYGR0dHREREYM6cOaioqKj2OkEQajyWLFkiU2oi+3NPkDs+GdUe+17tjxcHtURzd0eUG83YfPoKXv7xOLq8twXjlx/EplPZqDSZ5Y5LRHZIJXeA2jhz5gzMZjO+/PJLREZG4uTJk3j22WdRUlKCBQsWVHvtsmXLcN9991m+1ul09R2XyO41c9ViSr8WeL5vJE5n6bHldA42J2XjZIYe287kYNuZHPi6aTEiNgijOgchyNNJ7shEZCca7aWujz76CF988QVSUlIsxwRBwLp16zB8+PBav4/BYIDBYLB8rdfrERQUxEtdRDaQcrUY8QfT8ePhy8grqRqxFQSgR6Q3Hu8cjIFRvtwVmojqpEld6rqZwsJCeHp61jg+ZcoUeHt7o3PnzliyZAnM5tsPp8+fPx86nc7yCAoKslVkIrsX3swFs+9vg4TZ/fHPJzuiZwtviCKw+3wunv/PEdw7fyve+eU0zl0pkjsqETVRjXLE58KFC+jYsSM+/vhjTJw40XL83XffRf/+/eHo6IitW7firbfewuzZs/HGG2/c8r044kMkr/T8UsQfTMcPh9NxRf+//xY7BLvj8c5BeCAmAM7aRnFVnohk1ChWdc2dOxdvv/32bV9z8OBBdOrUyfJ1ZmYmevfujd69e+Prr7++7fd+/PHHmDdvHgoLC2udiau6iORRaTJj57mriD+Yjm1nclBprvrV5KJV4aH2AXiiSzDaNeecPSK6uUZRfHJzc5Gbm3vb14SGhsLBoepGiJmZmejbty+6du2K5cuXQ6G4/ZW6vXv3okePHsjOzoavb+3uJcTiQyS/q0UGrD1yGasOpiM1t8RyvIWPC4ZE+2NotD9a+rpAEAQZUxJRQ9Ioio8UGRkZ6Nu3L2JjY7Fy5UoolXfeEG3x4sV46aWXUFBQAK22dnuHsPgQNRyiKGJ/Sj6+/yMNG09mo+JPS+AjmjnjiS7BGNEpCDpHtYwpiaghaFLF58blreDgYHz33XfVSo+fnx8AYP369cjOzka3bt3g6OiI7du3Y9asWXjmmWfw2Wef1frPYvEhapgKy4zYduYKNhzPxq7zV1FRWVWCnDRKPNKxOZ6JC0Wkj6vMKYlILk2q+Cxfvhzjxo276XM34m/cuBGzZ89GcnIyzGYzwsPDMXHiRDz//PNQqWo/MZLFh6jhKyo3Yv2xLKzYdxFnr68AEwTggZgAzBjQAuHNXGROSET1rUkVn/rE4kPUeIiiiISUPCzbexGbT18BACgE4NGOgZjSLxIhXs4yJySi+sLiU0csPkSN06nMQnyy+Ry2JOVYjt0b7onHYoMwpJ0fl8QTNXEsPnXE4kPUuCWmXcOnW85j1/mruPHbzUmjRM8W3ugW7oVuEd5cEUbUBLH41BGLD1HTkFFQhnVHLmPNkYxqS+IBwNNZg3sCdWgboEO75m5oG6BDoIcjyxBRI8biU0csPkRNiyiKOH65EHuSc7E/JQ8HL+aj3FjzVjauDiq08XdDlL8bwryd4eqggrNWBRetCsGeTixGRA0ci08dsfgQNW0VlWaczCzEqYxCnMzQ42RmIc5dKYLRdPtfhT6uWnQO9UTHEA94OWssx5UKAcGeTghv5gxXB+4nRCQXFp86YvEhsj8VlWZcuFqM05l6JGXpkVFQhmJDJYoNlSgqr8TF3BLLLTRux9dNi5a+rujdshn6t/FFmDdXlRHVFxafOmLxIaK/Kqsw4djlAhy+dA1H0wtQbjRZJk4bKk24mFeKq0WGGt8X3swZA6N8MeyeAET5u/FSGZENsfjUEYsPEdVFYZkRKVeLcSStANvOXMGBlPxqo0SRPi546J4A9G/ji1Z+rlAqWIKIrInFp45YfIjIGvTlRuw+l4sNJzKxJSnHcosNAHDWKBET6I6YIB1UCgH6skroy42oNInoHumNIe384PGneUREdGcsPnXE4kNE1lZUbsTvp67gl+OZOHTxGooNlbd9vUohoGcLbwxp54/oQB0ifVygVirqKS1R48TiU0csPkRkSyaziOScYiSmXcPJzEKoFAq4Oajg5qhGWYUJG09l41Smvtr3aJQKtPB1Qfsgd/Rv44O4CG84qJW3+BOI7BOLTx2x+BCR3C5cLcb6Y5nYl5yHpCw9iv4yQuSoVqJHC2/0atkM94Z5ItKHO1ETsfjUEYsPETUkoiji8rUynMqs2oRxy+kcZOvLq73G01mDTiEeCHB3hKuDCq4OKugc1fBy1sLbVQtvFw08nDRwUCtvOanaaDKjsMyIglIjisqNMIuiZeWag1qJ1n6uUPFyGzVgLD51xOJDRA2ZKIo4lanHtjM52J+ShyNp1266E/WtqBQCtCoFFIIAEYBZFGEWxTu+h6uDCj0iq0aZOgZ7wN1JDTcHNRzUCo42UYPA4lNHLD5E1JhUVJpxIqMAiWkFuFZagaLyqk0XC0orkFtcgdxiA3KLDXfcmfoGVwcV3BzUUCkFCAAEQUBesQH68ptPyFYrBegcNfB0VsPTWQMvZy3cndTwcNLA3UkNbxctWvm5ooWPC0eMyKZq+/mtqsdMRERkZRqVArEhnogN8bzla0RRRJnRBIPRDEOlGeVGE8yiCIUgQBAAhSDAWauCm4PqpuXEZBZx/HIBdp3Lxa7zV5GcU3z9chhgNImWcnU7WpUCba7fB63EULV8X19WCTdHFfq19sHAKL/b7nRtqDShoNQInaPaMrHbbBZxPKMQ28/kYOe5qyg3mtDG3w1t/F0R5a9DiJcTfN0coFGxcNH/cMTnLzjiQ0R0Z6IooqTCBH2ZEddKK3CtxIi8EgPySypwrdSIgtKqv17RlyMps+YE7ZuJaOaMiGYuuPGhZDaLyCkyIKuwDLnFFZbXOWuU8HLRothQifySipu/2Z94OWvgp3NAiJcTwr1dEObtjFBvZ/i6aeHtouUKuSaCl7rqiMWHiMi6zGYRF/NKcCKjEJkF5ZbJ164OKlzKK8Xm01ewPyXvjvdDEwTgr59YrloVerb0Rp9WPvB00iApS4+kbD2SsoqQca0MFaY7z39y1aoQ6OmEzqEe6BLmiS5hnvBxdbibH5lkwOJTRyw+RET1T19uxJ7zuSgoNQKoKjkCAC8XLfx1Dghwd4SHkxpFhkrkFVcgr9gAQRAQE6i75eaOoijiWqkR2YXlyCosQ2puCVJyS5BytRhpeaXILa64ZTFq7u6Ilr4uaOHrikgfFwS6O6KZqxbNXLXQOaqb1ITu3GIDjqYVQKUU4OZYNWndSaNEmdGEEkMlSgwmlFZU3bS3tKLqmJuDGq39XdHCxxWOmpuPmN24xFpaYYKDWgkntRIKG96qhcWnjlh8iIjsgyiK0JdX4mqRAck5Rdifko8Dqfk4k62vMbL0ZxqVAvcE6nBvuBfuDfdCx2CPW37430ylyYwKkxkGY9Vfy40mlBvNMFSacLXIgJMZepzIKMSpzEIoBAG9WjZDv9Y+6B7pBSdN3abmVprMyC+tsNweJb+4AocuXcPu81drbJgphSAAoV7OcNYqYTIDJrMZlaaq81pYVlFjUr1WpYCTRokfJsUh0selzn/uzbD41BGLDxGRfSssM+JsdhHO5xTh/JViJOcUI1tfjqtFBhSWGWu8XqkQEOrldH31miv8dQ4wiSLMZhFGk4grReVIvVqCi3kluJRXCkNl7bcf+DONSoFWvq7w0znAX+cAXzcHqJUCzGLVBHSFIMDLWWMZmSo3mnAgNR/7U/Jw+NI1lFaYbvneLX2rbotSWGaEvsyIMqMJjmolnLUqOGmq/uqsUcFZq4STRoXcYgPOZBfVao7Vzex6qS+CvZzq9L23wuJTRyw+RER0K4ZKEy5fK8Ohi/nYn5KPhAt5NTaUlEKpEOCgUkCrVsJBpYCboxpR/m5o11yHds11KK2oxPYzOdh6JgeXr5XdVXZBqJrPdONyVms/V/Rq2QzdI73RzFUr+f1EUcTVYgOSrxTDYDJDKQhQKQQoFQJcHdTQOanh7qiGo1qJ8sqqS15lFSaUGU0I9XK2+mo7Fp86YvEhIqLaEkURV/QGnLtShHNXinA2uwh5JRVQKQSolIJlFObGSrJQL2e4O6mhUSmgUSpqvbeRKIpIyS3BxdwSZBWWI7uwHFf05TBd35ZAIQCVZhH5JRXI0RtwtdgAUQQ6hXiga7gnuoZ5oZWf6y137m4KWHzqiMWHiIio8ant5zd3dSIiIiK7weJDREREdoPFh4iIiOwGiw8RERHZDRYfIiIishssPkRERGQ3WHyIiIjIbrD4EBERkd1g8SEiIiK7weJDREREdoPFh4iIiOwGiw8RERHZDRYfIiIishssPkRERGQ3VHIHaGhEUQRQdXt7IiIiahxufG7f+By/FRafvygqKgIABAUFyZyEiIiIpCoqKoJOp7vl84J4p2pkZ8xmMzIzM+Hq6gpBEKz2vnq9HkFBQUhPT4ebm5vV3rep4vmShudLGp6v2uO5kobnSxprni9RFFFUVISAgAAoFLeeycMRn79QKBQIDAy02fu7ubnxPwYJeL6k4fmShuer9niupOH5ksZa5+t2Iz03cHIzERER2Q0WHyIiIrIbLD71RKvVYs6cOdBqtXJHaRR4vqTh+ZKG56v2eK6k4fmSRo7zxcnNREREZDc44kNERER2g8WHiIiI7AaLDxEREdkNFh8iIiKyGyw+9eTzzz9HWFgYHBwcEBsbi927d8sdSXbz589H586d4erqCh8fHwwfPhxnz56t9hpRFDF37lwEBATA0dERffr0walTp2RK3LDMnz8fgiBg+vTplmM8X9VlZGRg9OjR8PLygpOTE9q3b4/Dhw9bnuf5qlJZWYk33ngDYWFhcHR0RHh4OObNmwez2Wx5jT2fq127duHBBx9EQEAABEHATz/9VO352pwbg8GAF154Ad7e3nB2dsawYcNw+fLlevwp6s/tzpfRaMQrr7yC6OhoODs7IyAgAE8//TQyMzOrvYdNz5dINrdq1SpRrVaLS5cuFU+fPi1OmzZNdHZ2Fi9duiR3NFkNHjxYXLZsmXjy5Enx6NGj4tChQ8Xg4GCxuLjY8poPPvhAdHV1FdesWSOeOHFCHDVqlOjv7y/q9XoZk8vvjz/+EENDQ8WYmBhx2rRpluM8X/+Tn58vhoSEiM8884x44MABMTU1VdyyZYuYnJxseQ3PV5V3331X9PLyEn/55RcxNTVV/OGHH0QXFxfx008/tbzGns/Vr7/+Kr7++uvimjVrRADiunXrqj1fm3MzadIksXnz5uLmzZvFI0eOiH379hXvuecesbKysp5/Gtu73fkqKCgQBwwYIMbHx4tnzpwRExISxK5du4qxsbHV3sOW54vFpx506dJFnDRpUrVjrVu3Fl999VWZEjVMOTk5IgBx586doiiKotlsFv38/MQPPvjA8pry8nJRp9OJS5YskSum7IqKisQWLVqImzdvFnv37m0pPjxf1b3yyitijx49bvk8z9f/DB06VBw/fny1Y4888og4evRoURR5rv7srx/ktTk3BQUFolqtFletWmV5TUZGhqhQKMSNGzfWW3Y53Kwo/tUff/whArAMBtj6fPFSl41VVFTg8OHDGDRoULXjgwYNwr59+2RK1TAVFhYCADw9PQEAqampyM7OrnbutFotevfubdfn7vnnn8fQoUMxYMCAasd5vqr7+eef0alTJ4wYMQI+Pj7o0KEDli5danme5+t/evToga1bt+LcuXMAgGPHjmHPnj24//77AfBc3U5tzs3hw4dhNBqrvSYgIADt2rWz+/MHVP3uFwQB7u7uAGx/vniTUhvLzc2FyWSCr69vteO+vr7Izs6WKVXDI4oiZs6ciR49eqBdu3YAYDk/Nzt3ly5dqveMDcGqVatw5MgRHDx4sMZzPF/VpaSk4IsvvsDMmTPx2muv4Y8//sDUqVOh1Wrx9NNP83z9ySuvvILCwkK0bt0aSqUSJpMJ7733Hp544gkA/HfrdmpzbrKzs6HRaODh4VHjNfb+OVBeXo5XX30VTz75pOUmpbY+Xyw+9UQQhGpfi6JY45g9mzJlCo4fP449e/bUeI7nrkp6ejqmTZuGTZs2wcHB4Zav4/mqYjab0alTJ7z//vsAgA4dOuDUqVP44osv8PTTT1tex/MFxMfHY+XKlfjPf/6Dtm3b4ujRo5g+fToCAgIwduxYy+t4rm6tLufG3s+f0WjE448/DrPZjM8///yOr7fW+eKlLhvz9vaGUqms0VJzcnJq/B+CvXrhhRfw888/Y/v27QgMDLQc9/PzAwCeu+sOHz6MnJwcxMbGQqVSQaVSYefOnVi0aBFUKpXlnPB8VfH390dUVFS1Y23atEFaWhoA/vv1Zy+99BJeffVVPP7444iOjsaYMWMwY8YMzJ8/HwDP1e3U5tz4+fmhoqIC165du+Vr7I3RaMTIkSORmpqKzZs3W0Z7ANufLxYfG9NoNIiNjcXmzZurHd+8eTPi4uJkStUwiKKIKVOmYO3atdi2bRvCwsKqPR8WFgY/P79q566iogI7d+60y3PXv39/nDhxAkePHrU8OnXqhKeeegpHjx5FeHg4z9efdO/evcb2COfOnUNISAgA/vv1Z6WlpVAoqn8cKJVKy3J2nqtbq825iY2NhVqtrvaarKwsnDx50i7P343Sc/78eWzZsgVeXl7Vnrf5+brr6dF0RzeWs3/zzTfi6dOnxenTp4vOzs7ixYsX5Y4mq+eee07U6XTijh07xKysLMujtLTU8poPPvhA1Ol04tq1a8UTJ06ITzzxhN0soa2NP6/qEkWerz/7448/RJVKJb733nvi+fPnxX//+9+ik5OTuHLlSstreL6qjB07VmzevLllOfvatWtFb29v8eWXX7a8xp7PVVFRkZiYmCgmJiaKAMSFCxeKiYmJllVItTk3kyZNEgMDA8UtW7aIR44cEfv169dkl7Pf7nwZjUZx2LBhYmBgoHj06NFqv/sNBoPlPWx5vlh86sk///lPMSQkRNRoNGLHjh0tS7btGYCbPpYtW2Z5jdlsFufMmSP6+fmJWq1W7NWrl3jixAn5Qjcwfy0+PF/VrV+/XmzXrp2o1WrF1q1bi1999VW153m+quj1enHatGlicHCw6ODgIIaHh4uvv/56tQ8iez5X27dvv+nvqrFjx4qiWLtzU1ZWJk6ZMkX09PQUHR0dxQceeEBMS0uT4aexvdudr9TU1Fv+7t++fbvlPWx5vgRRFMW7HzciIiIiavg4x4eIiIjsBosPERER2Q0WHyIiIrIbLD5ERERkN1h8iIiIyG6w+BAREZHdYPEhIiIiu8HiQ0RERHaDxYeImpS5c+eiffv2cscgogaKOzcTUaMhCMJtnx87diwWL14Mg8FQ48aHREQAiw8RNSLZ2dmWv4+Pj8dbb71V7Q7sjo6O0Ol0ckQjokaCl7qIqNHw8/OzPHQ6HQRBqHHsr5e6nnnmGQwfPhzvv/8+fH194e7ujrfffhuVlZV46aWX4OnpicDAQHz77bfV/qyMjAyMGjUKHh4e8PLywkMPPYSLFy/W7w9MRFbH4kNETd62bduQmZmJXbt2YeHChZg7dy4eeOABeHh44MCBA5g0aRImTZqE9PR0AEBpaSn69u0LFxcX7Nq1C3v27IGLiwvuu+8+VFRUyPzTENHdYPEhoibP09MTixYtQqtWrTB+/Hi0atUKpaWleO2119CiRQvMnj0bGo0Ge/fuBQCsWrUKCoUCX3/9NaKjo9GmTRssW7YMaWlp2LFjh7w/DBHdFZXcAYiIbK1t27ZQKP73/3m+vr5o166d5WulUgkvLy/k5OQAAA4fPozk5GS4urpWe5/y8nJcuHChfkITkU2w+BBRk6dWq6t9LQjCTY+ZzWYAgNlsRmxsLP7973/XeK9mzZrZLigR2RyLDxHRX3Ts2BHx8fHw8fGBm5ub3HGIyIo4x4eI6C+eeuopeHt746GHHsLu3buRmpqKnTt3Ytq0abh8+bLc8YjoLrD4EBH9hZOTE3bt2oXg4GA88sgjaNOmDcaPH4+ysjKOABE1ctzAkIiIiOwGR3yIiIjIbrD4EBERkd1g8SEiIiK7weJDREREdoPFh4iIiOwGiw8RERHZDRYfIiIishssPkRERGQ3WHyIiIjIbrD4EBERkd1g8SEiIiK78f/0cXe3jWyoTAAAAABJRU5ErkJggg==", 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" ] @@ -518,14 +505,14 @@ "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, "source": [ - "A perfect foresight consumer can borrow against the PDV of his future income-- his human wealth-- and thus as time goes on, our simulated agents approach the (very negative) steady state level of $m_t$ while being steadily replaced with consumers with roughly $m_t=1$.\n", + "A perfect foresight consumer can borrow against the PDV of his future income -- his human wealth -- and thus as time goes on, our simulated agents approach the (very negative) steady state level of $m_t$ while being steadily replaced with consumers with roughly $m_t=1$.\n", "\n", - "The slight wiggles in the plotted curve are due to consumers randomly dying and being replaced; their replacement will have an initial state drawn from the distributions specified by the user. To see the current distribution of ages, we can look at the attribute $\\texttt{t_age}$." + "The slight wiggles in the plotted curve are due to consumers randomly dying and being replaced; their replacement will have an initial state drawn from the distributions specified by the user. To see the current distribution of ages, we can look at the attribute `t_age`." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" @@ -534,7 +521,7 @@ "outputs": [ { "data": { - "image/png": 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", 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m4v8O5uGlr7NRqTdAbzRb5tPc0jYI3SN9oVHJMTA+CF0itNIWStRAmsVzboiI6Or0RhO2/FqAixV66I1m/P3bYzZ9ogM88M6DCXB3411ORFdiuCEiaiKEEDCYBHRGE9bsysHybSds+sy+tT3uToiARiWHp5sScnn9j84gaskYboiImoDiSj3GvrW73nk0ExIjoFEp0Errjr8kxUCj4pkaomthuCEikojOYELupWroDCbsy7lkE2x8PVT4x93dMLwjl0IgcgTDDRGRBExmgeQ3fkDupWqr9o6tfLDxif5QK+VXfVo7EV0bww0RUSPRGUw4dK4MNQYTSqpqLcEm1EcDdzcF3FUKPHZLLC87Ed0khhsiokby148zkXH8olWbXAbsmj2EyyAQORHDDRFRAzl+oQJvZ5xCeY0ReqMJO08UWbb1jPKFRqXA0PbBDDZETsZwQ0TUQP618zQ2Hsizad/57BBE+ntIUBFRy8BwQ0TkJDlFVZi27gAulOuhM5hQpTcCAEZ3bYXhHYKhVirQOsiLwYaogTHcEBE5ScaxQvyaV27V5qaUY0q/GPSO9ZeoKqKWh+GGiOgG6Y0mPP5RJk4WVkJnMKHif2dqBrUNwvwxHaFRKaB1V8FTzV+1RI2J/+KIiBxQqTfiWEE5dAYzsvPLbe5+AurCTVwQV+QmkgrDDRGRnYQQuH3FTvxebP3gvXBfd6x5qBc0Kjm81EoEeKklqpCIgBsIN1VVVVi6dCm2b9+OwsJCmM1mq+2nT592WnFERFLbc7IIa3/KRVWtEdW1JkuwCfJWI0yrgUalwP19otAu1FviSonoMofDzaOPPoqMjAxMmjQJrVq14uPBicilvZZ+HJlnSqzavDVK7Jo9BGolnyRM1BQ5HG7++9//4ptvvkFSUlJD1ENEJKm9p4oxb9NhlFTXQmcwo8ZgAgA8MiAW3SJ9oVHK0Slcy2BD1IQ5HG78/Pzg789bGonINeiNJmz4+SzySmugN5jx4Z7fbfr4aJR4dGAsWmndG79AInKYw+HmpZdewosvvoh///vf8PDgg6iIqHn7/rdCvPh/R2za7+8ThZRbWkOjkkProeKZGqJmxOFw89prr+HUqVMICQlBTEwMVCqV1fYDBw44rTgiImcTQuDlb7Lx6/ky6AxmFJbrAACR/u4Y2y0MGqUCAV5qjO8RDnc3Bhqi5sjhcDNu3LgGKIOIqOEIIXCxUg+9wYzTRVX4164cmz7D2odg1sj2ElRHRM4mE0IIqYtoTOXl5dBqtSgrK4OPj4/U5RBRI5iyZp/Nw/a81Uq8NqEbNCoFvDRKdIvwhULOuz+JmipHPr9v+CF+mZmZyM7OhkwmQ8eOHdGjR48bPRQRkVPlldbg61/Oo0pvhM5oxq6TRQAAtVIOdzcFNEoFJvaKRHKnUIkrJaKG4HC4KSwsxL333osdO3bA19cXQgiUlZVhyJAhWL9+PYKCghqiTiIiuy366gi+PXLBqk0mA/bOHQZ/TzeJqiKixuJwuJk2bRrKy8tx5MgRdOjQAQBw9OhRTJkyBdOnT8e6deucXiQR0bVU6Y34v4PncalKjxqDCYfOlQEABrYJRPtQb2hUCnSN8GWwIWohHA43W7ZswbZt2yzBBgA6duyIlStXIjk52anFERHZ46O9Z/Dqlt9s2h8eEIsh7YIlqIiIpORwuDGbzTa3fwOASqWyWWeKiKghGE1mfJZ5DmcvVaPGYMJPpy8BANqHeqNvXAA0KgXC/dwxID5Q4kqJSAoOh5uhQ4fiqaeewrp16xAWFgYAyMvLw8yZMzFs2DCnF0hE9Gc/nr6EuRsP27SP7tIK04a1kaAiImpKHA43b731Fu644w7ExMQgMjISMpkMubm56NKlCz755JOGqJGICCVVtTh5sRI6gwl7ThUDAEJ81LirZwQ0KgX8PFQY1yNc4iqJqClwONxERkbiwIEDSE9Px2+//QYhBDp27Ijhw4c3RH1ERNAZTBj8jx0oqzFYtccGeuLZW/ngPSKydsPPuRkxYgRGjBjhzFqIiKyYzAI6gwlniqstweby3U+eagUeHRgncYVE1BTZFW5WrFiBxx9/HBqNBitWrLhm3+nTpzulMCJq2Z7/8jA++THXqs1NIceWGbdIVBERNRd2Lb8QGxuL/fv3IyAgALGxsVc/mEyG06dPO7VAZ+PyC0RNV1m1ARV6A3QGM+55ew9Kqv+4DOWmkGNs9zD8455uElZIRFJx+vILOTk59f6ZiMhZ3sk4hSX/tX1WzZdPJqFLuJbrPhGR3eSO7rBo0SJUV1fbtNfU1GDRokVOKYqIWoaaWhMKy3XILa7GjmN1C1uqFDJo3VUI8VFjcLsgdArzYbAhIoc4vCq4QqFAfn4+goOtn/pZXFyM4OBgmEwmpxbobLwsRdQ07D5ZhL98+DNqjdYP/1x6Zxfc2ztKoqqIqKlq0FXBhRCQyWz/F/XLL7/A39/f0cMRUQuVlVtiCTYebgpoVAqE+miQxKcKE9FNsjvc+Pn5QSaTQSaToW3btlYBx2QyobKyEikpKQ1SJBE1f7VGMxZ+dQQ5RVXQGUzIK60BANzbKxJL7+oqcXVE5ErsDjfLly+HEAIPP/wwFi5cCK1Wa9nm5uaGmJgY9OvXr0GKJKLmL/NMCdb+lGvTHunvIUE1ROTK7A43U6ZMAVB3W3j//v3rXTyTiOhKG37Oxe6TxdAZTLhQrgMAhPu648UxHaFRKaB1V6FbhPY6RyEicozDc25iY2ORn59/1e1RUZwISER1d0LN3XgY5j/dshAf7IWRnUKlKYqIWgSHw01MTEy9E4ova+p3SxFRw7lQrkPGsYuoMZhQWm2wBJuFYzvBU62Eh5sCSa05YZiIGpbD4SYrK8vqtcFgQFZWFl5//XW8/PLLTiuMiJqfaZ9mYd/vl6za3JRyPNAnCkqFw4/VIiK6IQ6Hm27dbB99npiYiLCwMPz973/HnXfe6ZTCiKjpE0LgRGElymsMqDGYcOZSFQCgX1wAWvlqoFEpMDA+kMGGiBrVDa8K/mdt27bFzz//7KzDEVEzsGrHKfz922M27c+MbIeEaD8JKiIiuoFwU15ebvVaCIH8/HwsWLAAbdq0cVphRNR0mc0COqMJR/Prfh/4e7oh2FsNtUqB+CAvdOUdUEQkIYfDja+vr82EYiEEIiMjsX79eqcVRkRNT3Z+OSav2YeLFXqr9r8Nao3HbomTqCoiImsOh5vvv//e6rVcLkdQUBDi4+OhVDrtKhcRNUE/nS62CTaBXm7oGxcgUUVERLYcTiODBg1qiDqIqInKOH4RJy5UQGcwYd/vJQCA5I4hWH5vd6iVCq7YTURNzg2dajl27BjefPNNZGdnQyaToX379pg6dSrat2/v7PqISEK/F1Vhypp9Nu1+Hm7wcOOZWiJqmhz+7fT555/jvvvuQ2JiomUtqR9//BFdunTBp59+invuucfpRRJR46nSG3Hsf2dqsvMrANSt2j22Wxg0KgW8NUpM7BUpcZVERFcnE0KI63f7Q1xcHB588EEsWrTIqn3+/Pn4+OOPcfr0aacW6Gzl5eXQarUoKyuDj4+P1OUQNSlCCAx7LQOni6qs2mMCPLBj1hCJqiIicuzz2+EnaxUUFGDy5Mk27Q8++CAKCgocPRwRNTGXg01ckCe6hGvRO8Yf04fxMQ9E1Hw4fFlq8ODB2LlzJ+Lj463ad+3ahYEDBzqtMCJqHF9m5eHtjFOorjVBZ/hjbbjPU/rD39NNwsqIiG6MXeFm8+bNlj+PHTsWs2fPRmZmJvr27Qugbs7NZ599hoULFzZMlUTUYD7Y8zt+K6iwaovwc4ePhhOGiah5smvOjVxu39UrmUzW5FcF55wbaul+OVuKl74+ipLqWugMZlwo18FoFph3Wwf0ax0AdzcFwn3doVEppC6ViMjC6XNuzGazXV83EmxWrVqF2NhYaDQaJCQkYOfOndfsr9frMW/ePERHR0OtVqN169ZYs2aNw+9L1FJtysrD/jMlOHWxCnmlNTCaBdyUciR3CkHncC1aB3kx2BBRsybpeecNGzZgxowZWLVqFZKSkvDOO+9g1KhROHr0KKKiourdZ8KECbhw4QLef/99xMfHo7CwEEajsZErJ2q+TOa6k7V3J0TggT5R0KgUCPXRwI/za4jIRdh1WWrFihV4/PHHodFosGLFimv2nT59ut1v3qdPH/Ts2ROrV6+2tHXo0AHjxo3DkiVLbPpv2bIF9957L06fPg1/f3+73kOv10Ov/+Nx8eXl5YiMjORlKWoxKvVGLE8/jrzSGsuzawrKdXhqWBvMHNFW6vKIiOziyGUpu87cvPHGG3jggQeg0WjwxhtvXLWfTCazO9zU1tYiMzMTc+bMsWpPTk7Gnj176t1n8+bNSExMxLJly/Dxxx/D09MTY8eOxUsvvQR3d/d691myZAknOlOL9v1vhfjXrhyb9lCtRoJqiIganl3hJicnp94/34yioiKYTCaEhIRYtYeEhFz1eTmnT5/Grl27oNFosGnTJhQVFeGJJ57ApUuXrjrvZu7cuUhNTbW8vnzmhqil0BvNAIC2IV54dGAcNCoFAj3d0IeLXRKRi3Jozo3BYEC7du3w9ddfo2PHjk4pQCazXnRPCGHTdpnZbIZMJsPatWuh1WoBAK+//jruvvturFy5st6zN2q1Gmq12im1EjUXn+0/i1/zyqAzmHGisO427zBfd0xIZLAnItfnULhRqVTQ6/VXDR+OCAwMhEKhsDlLU1hYaHM257JWrVohPDzcEmyAujk6QgicO3cObdrwKapE50trMOvzQzbtfCAfEbUUDi+/MG3aNLz66qs3fYeSm5sbEhISkJ6ebtWenp6O/v3717tPUlISzp8/j8rKSkvb8ePHIZfLERERcVP1EDV3JrNAld6I/LIaAIBGJcczyW3x/OgOWHJnFzw/2jlnW4mImjqHbwX/6aefsH37dmzduhVdunSBp6en1faNGzfafazU1FRMmjTJssL4u+++i9zcXKSkpAComy+Tl5eHjz76CABw//3346WXXsJf/vIXLFy4EEVFRZg1axYefvjhq04oJmoJZm44iE1ZeVZt7ioFpg7l2UwiankcDje+vr646667nPLmEydORHFxMRYtWoT8/Hx07twZaWlpiI6OBgDk5+cjNzfX0t/Lywvp6emYNm0aEhMTERAQgAkTJmDx4sVOqYeoudp6xPryrptSjrHdwiSqhohIWnY958aVcPkFcgVniqvwxYE8VOmN0BlM2PDzWRjNAltn3oL4IC/I5Tc/L46IqClx+vILVxo6dChKS0vrfdOhQ4c6ejgiugEvf5ONFdtP4P1dOVj7Uy6MZgG5DAj0UjPYEFGL5/BlqR07dqC2ttamXafTXXddKCJyjkp93YT+4R1C0CnMBxqVAl3CtbwjiogIDoSbQ4f+uLX06NGjVrdwm0wmbNmyBeHh4c6tjogAADqDCelHL+BSVS10BhPOldTdETW2exjn1hAR/Ynd4aZ79+6QyWSQyWT1Xn5yd3fHm2++6dTiiKjOJz+eweJvsm3aPbh6NxGRDbvDTU5ODoQQiIuLw759+xAUFGTZ5ubmhuDgYCgU/EVL1BCKq+ouBccFeqJ7pC/UKgUi/NwxsG2gxJURETU9doeby7dnm83mBiuGiP5QazSjXGeAzmBCabUBADCkfTBeuJ0P4yMiuhaHJxT/+9//RmBgIEaPHg0AePbZZ/Huu++iY8eOWLdunSUEEdGNO3upGre/uQtlNQardt4HRUR0fQ7fCv7KK69Ynga8d+9evPXWW1i2bBkCAwMxc+ZMpxdI1BIdOV9uCTZqpRxadxViAjwwsnOoxJURETV9Dp+5OXv2LOLj4wEAX375Je6++248/vjjSEpKwuDBg51dH1GLlhjth8//Vv9aa0REVD+Hw42XlxeKi4sRFRWFrVu3Ws7WaDQa1NTUOL1Aopbil7OlOHSuFDUGE37NK5e6HCKiZsvhcDNixAg8+uij6NGjB44fP26Ze3PkyBHExMQ4uz6iFqFCZ8A9b+9Frcl6wr6H2uF/okRELZ7DvzlXrlyJ559/HmfPnsUXX3yBgIAAAEBmZibuu+8+pxdI1BJU6U2oNZkhkwHjuodDo5LDw02JexIjpC6NiKjZ4cKZRBLJPFOCLb/mo8ZgQkm1Ad8cyodKIcOJl2+TujQioibHkc9vu87cHDp0CJ07d4ZcLrdahqE+Xbt2tb9SohZs1ue/4PTFKqs2Pw+uDUVEdLPsCjfdu3dHQUEBgoODLcswXHnC5/JrmUwGk8nUYMUSuZKq/y1+eX+fKET6eUCjkqN/az5xmIjoZtkVbnJycizLLeTk5DRoQUSu6lxJNT7bfw4VOiN0RpPlOTYP9IlCpzCtxNUREbkOu8LNlU8d5hOIiW7M6+nHsfFAnk27Ly9FERE5lV3hZvPmzXYfcOzYsTdcDJErq9TVXYYa1DYIPaJ8oVEp0D7UG+G+7hJXRkTkWuwKN+PGjbN6Xd+cm8s454boDwVlOhRV6qE3mlBSXbey98hOobi/T5TElRERuS671pYym82Wr61bt6J79+7473//i9LSUpSVlSEtLQ09e/bEli1bGrpeomZje/YF9Fu6Hbe/uQt3rd6Ln38vAQAoHF7RjYiIHOHwQ/xmzJiBt99+GwMGDLC0jRw5Eh4eHnj88ceRnZ3t1AKJmqvfCiogBOCuUiDIWw2NSo5QrTsGtwuWujQiIpfmcLg5deoUtFrbOzu0Wi1+//13Z9RE1GydvliJ34uroDOYcTS/bn2osd3C8OrdfP4TEVFjcTjc9OrVCzNmzMAnn3yCVq1aAQAKCgrw9NNPo3fv3k4vkKi5yCmqwtDXMmzaVUpZPb2JiKihOBxu1qxZg/HjxyM6OhpRUXWTInNzc9G2bVt8+eWXzq6PqNk4X1oDANCo5OgSroVGpYCPuwqT+8VIWxgRUQvjcLiJj4/HoUOHkJ6ejt9++w1CCHTs2BHDhw+3umuKqCURQsBorruDMCbAE5+l9Je4IiKilsvhcAPU3fqdnJyM5ORkZ9dD1GwIIfC3Tw4g4/hF6IwmtKwlaImImq4bCjdEBFTVmrDlSIFN+5D2vBuKiEhKDDdETrDz2SHQeqigUSrgpuSDbIiIpMRwQ+SAKr0R+8+UoKbWiNJqg6W97jk2CgkrIyKiyxhuiBzwt7UH8MPxi1ZtCrkMck6mJyJqMm4o3Jw6dQoffPABTp06hX/+858IDg7Gli1bEBkZiU6dOjm7RqImI6+kGgDQPtQbgV51Tx0e1DaIl6KIiJoQh38jZ2RkoEuXLvjpp5+wceNGVFZWAgAOHTqE+fPnO71AoqbAYDKjXGeA6X+3ey8c2wmfPNoH/5rSC5P4HBsioibF4TM3c+bMweLFi5Gamgpvb29L+5AhQ/DPf/7TqcURSe2rX85j9heHUF3L1e6JiJoLh8/cHD58GOPHj7dpDwoKQnFxsVOKImoqvj9WaBNsWgd5okOYj0QVERHR9Th85sbX1xf5+fmIjY21as/KykJ4eLjTCiNqSp4e0RaP3RIHtVLOJ3ETETVxDp+5uf/++zF79mwUFBRAJpPBbDZj9+7deOaZZzB58uSGqJGoUVXpjcgrrcGpi5Uor6m73VutkkOjUjDYEBE1Aw6fuXn55Zfx0EMPITw83LKulMlkwv3334/nn3++IWokajQHcktw37s/Qm80W7XLwFBDRNRcOBxuVCoV1q5di0WLFiErKwtmsxk9evRAmzZtGqI+okb1a14Z9EYz5DLAS62EWqVAsLeaSyoQETUjDoebjIwMDBo0CK1bt0br1q0boiYiyY3q3AorH+gpdRlERHQDHJ5zM2LECERFRWHOnDn49ddfG6ImokZVWK7DT6eLseNYIbLzK6Quh4iIbpLDZ27Onz+P9evXY926dVi2bBk6d+6MBx98EPfffz8iIiIaokaiBnOxQo8Br36PWpP1HBulgnNsiIiaK4fP3AQGBmLq1KnYvXs3Tp06hYkTJ+Kjjz5CTEwMhg4d2hA1EjWY86U1qDWZoZTL0LGVD3pG+WJQ2yBM6R8jdWlERHSDbmrhzNjYWMyZMwfdunXDCy+8gIyMDGfVRdSoQnw0SHtqoNRlEBGRE9xwuNm9ezfWrl2Lzz//HDqdDmPHjsUrr7zizNqIGsSWXwuw41ghdAYTCiv0UpdDRERO5nC4ee6557Bu3TqcP38ew4cPx/LlyzFu3Dh4eHg0RH1ETjdzw0HUGKyXVAjyVktUDREROZvD4WbHjh145plnMHHiRAQGBjZETUQN6nKwmTm8Lfy93OCuUmBgG/4sExG5CofDzZ49exqiDqJG90DfKAR68YwNEZGrsSvcbN68GaNGjYJKpcLmzZuv2Xfs2LFOKYzIWcp1Buw+UYTqWhN0RtP1dyAiombNrnAzbtw4FBQUIDg4GOPGjbtqP5lMBpOJHx7UtDzzn1+w9egFqzaZDFApHH4SAhERNQN2hRuz2Vzvn4magwv/uyOqc7gPwrTu0KgU6B3rD627SuLKiIioITg85+ajjz7CxIkToVZbz1Wora3F+vXrMXnyZKcVR+RMM4e3xbAOIVKXQUREDczh8/J/+ctfUFZWZtNeUVGBv/zlL04piuhmlNUY8O89v2P5tuNY+t/fkFdSI3VJRETUiBw+cyOEgExmu+7OuXPnoNVqnVIU0c14f+dprPjupE27t4aXoYiIWgK7w02PHj0gk8kgk8kwbNgwKJV/7GoymZCTk4Nbb721QYokckRZjQEA0CVci14x/nB3kyPa3xOJ0X4SV0ZERI3B7nBz+S6pgwcPYuTIkfDy8rJsc3NzQ0xMDO666y6nF0h0o4a0C0JqcjupyyAiokZmd7iZP38+ACAmJgYTJ06ERqNpsKKIHGE2C+w+VYS8khroDCYcOV8udUlERCQhhycUT5kyxanBZtWqVYiNjYVGo0FCQgJ27txp1367d++GUqlE9+7dnVYLNU+7TxVh0vv7MGfjYSz46ij2nykBAGjcFBJXRkREUnB4QrHJZMIbb7yB//znP8jNzUVtba3V9kuXLtl9rA0bNmDGjBlYtWoVkpKS8M4772DUqFE4evQooqKirrpfWVkZJk+ejGHDhuHChQtX7Uctw8X/PccmwNMNfeMCoFbJEeilxoTESIkrIyIiKTh85mbhwoV4/fXXMWHCBJSVlSE1NRV33nkn5HI5FixY4NCxXn/9dTzyyCN49NFH0aFDByxfvhyRkZFYvXr1Nff761//ivvvvx/9+vVztHxyYZ3CtVj5QE+8PqE7nrutA9eNIiJqoRw+c7N27Vq89957GD16NBYuXIj77rsPrVu3RteuXfHjjz9i+vTpdh2ntrYWmZmZmDNnjlV7cnLyNRfn/OCDD3Dq1Cl88sknWLx48XXfR6/XQ6/XW16Xl3M+hit474fTOJRXBp3BhLOXqqUuh4iImhCHw01BQQG6dOkCAPDy8rI80O/222/HCy+8YPdxioqKYDKZEBJi/cTYkJAQFBQU1LvPiRMnMGfOHOzcudPqVvRrWbJkCRYuXGh3XdT0nSupxstp2TbtId48U0NERDcQbiIiIpCfn4+oqCjEx8dj69at6NmzJ37++WebJRns8ecHAl7tIYEmkwn3338/Fi5ciLZt29p9/Llz5yI1NdXyury8HJGRnIvRnOmNdeubaVRyvHB7R2iUCniqlbilbaDElRERUVPgcLgZP348tm/fjj59+uCpp57Cfffdh/fffx+5ubmYOXOm3ccJDAyEQqGwOUtTWFhoczYHqFveYf/+/cjKysLUqVMB1C3iKYSAUqnE1q1bMXToUJv91Gr1DYUuavrUSgUe6BMtdRlERNTEOBxuli5davnz3XffjYiICOzZswfx8fEYO3as3cdxc3NDQkIC0tPTMX78eEt7eno67rjjDpv+Pj4+OHz4sFXbqlWr8N133+Hzzz9HbGyso98KNSOfZ57DN4fOo8ZgQmm1QepyiIioCXM43PxZ37590bdv3xvaNzU1FZMmTUJiYiL69euHd999F7m5uUhJSQFQd0kpLy8PH330EeRyOTp37my1f3BwMDQajU07uZ5X0rJxqcr6sQMRfu4SVUNERE2ZXeFm8+bNdh/QkbM3EydORHFxMRYtWoT8/Hx07twZaWlpiI6uu9SQn5+P3Nxcu49Hrsvwv3k288d0RISfBzQqObpH+kpbFBERNUkyIYS4Xie53L7H4chkMphMppsuqiGVl5dDq9WirKwMPj4+UpdDduoy/1tU6I3Y8cxgxAR6Sl0OERE1Mkc+v+06c2M2m51SGJG9vsg8h79/ewxVeiNqDCYYzdfN4ERERACcMOeGqCF8eTAPBeU6q7bYQE+08uWCrUREdG0Oh5tFixZdc/uLL754w8UQXXb5YunzoztgdNdW0CgV8HFXQSG3fQYSERHRlRwON5s2bbJ6bTAYkJOTA6VSidatWzPckFMFeavRSsu7ooiIyH4Oh5usrCybtvLycjz00ENWz6shcsTR8+WYu+kwiiv10BnMKK2uvf5ORERE9XB4VfD6+Pj4YNGiRQ6tLUV0pf/+mo9fzpbiXEkNiir1MJoF3JRytA3xlro0IiJqZpw2obi0tNSyiCaRo8z/m2Rze9dWeGJwPDQqOQK91fDRqCSujIiImhuHw82KFSusXgshkJ+fj48//hi33nqr0wqjlkMIAdP/njYQ5K1GxzA+f4iIiG6cw+HmjTfesHotl8sRFBSEKVOmYO7cuU4rjFzbxQo9HvjXjzhTXG1Z5ZuIiMgZHA43OTk5DVEHtTBZuSU4fqHSqk2tlKN/60CJKiIiIlfBh/iRpDqH++DDv/SGRqWARimHUuGUOe5ERNSCORxudDod3nzzTXz//fcoLCy0WZrhwIEDTiuOXJ+bQo5AL7XUZRARkQtxONw8/PDDSE9Px913343evXtDJuMTY+n6hBD468eZ+Pn3S6gxmDjPhoiIGozD4eabb75BWloakpKSGqIeclEXK/TYevSCTXtSPOfYEBGRczkcbsLDw+HtzQer0Y2Ry4CMWUOgVsnh4aaEl5rTvoiIyLkcnr352muvYfbs2Thz5kxD1EMuTiaTIdLfA8HeGgYbIiJqEA5/uiQmJkKn0yEuLg4eHh5QqayfIHvp0iWnFUfN28UKPfaeLoau1oSLlXqpyyEiohbC4XBz3333IS8vD6+88gpCQkI4oZiu6pF//4xD56yX5NAoeas3ERE1LIfDzZ49e7B3715069atIeohF1JYXne2pleMH4K81dAoFRjWIUTiqoiIyNU5HG7at2+PmpqahqiFXNT8MZ3QOVwrdRlERNRCOHyNYOnSpXj66aexY8cOFBcXo7y83OqLiIiISEoOn7m5vPL3sGHDrNqFEJDJZDCZTM6pjJqdk4WV+Gjv76jQGaEzmHCpulbqkoiIqAVyONx8//33DVEHuYC3vjuBLw+et2qTyYAALzeJKiIiopbI4XAzaNCghqiDXECNoe6s3ajOoejfOgBqlQJtQ7zRSusucWVERNSSOBxufvjhh2tuv+WWW264GHINSfGBeLBvtNRlEBFRC+VwuBk8eLBN25XPuuGcGyIiIpKSw+GmpKTE6rXBYEBWVhZeeOEFvPzyy04rjJq+C+U6LNtyDIUVOugNZvxWwLvliIhIeg6HG63W9nklI0aMgFqtxsyZM5GZmemUwqjp++qX8/jiwDmb9gg/zrEhIiLpOG3lwqCgIBw7dsxZh6NmoNZkBgD0jvXHQ/1joFHJEeKjQacwPrCPiIik43C4OXTokNVrIQTy8/OxdOlSLsnQQkX7e+C2Lq2kLoOIiAjADYSb7t27QyaTQQhh1d63b1+sWbPGaYURERER3QiHw01OTo7Va7lcjqCgIGg0GqcVRU1X5pkSnL5YCZ3RjMzfS66/AxERUSNzONxER/P5JS3ViQsVuGv1Hpt2dzeFBNUQERHVz+6FM7/77jt07Nix3sUxy8rK0KlTJ+zcudOpxVHTcrFCDwDwdFNgZKcQ3NE9DJP7ReORAbESV0ZERPQHu8/cLF++HI899hh8fHxstmm1Wvz1r3/F66+/joEDBzq1QGp6wv3c8c6kRKnLICIiqpfdZ25++eUXy4rg9UlOTuYzboiIiEhydp+5uXDhAlQq1dUPpFTi4sWLTimKmg6zWaC4qhY6gwnny3RSl0NERHRddoeb8PBwHD58GPHx8fVuP3ToEFq14rNOXInZLHDHyt04nFdm1S6D7Cp7EBERSc/uy1K33XYbXnzxReh0tv97r6mpwfz583H77bc7tTiSVo3BZAk2aqUcvh4qtNJqcG/vSIkrIyIiujqZ+PPT+K7iwoUL6NmzJxQKBaZOnYp27dpBJpMhOzsbK1euhMlkwoEDBxASEtLQNd+U8vJyaLValJWV1Ts5mv5QpTei0/xvAQDZi27lLd9ERCQZRz6/7b4sFRISgj179uBvf/sb5s6da3lCsUwmw8iRI7Fq1aomH2yIiIjI9Tn0EL/o6GikpaWhpKQEJ0+ehBACbdq0gZ+fX0PVR0REROSQG1oV3M/PD7169XJ2LdQE7P/9Er44kIeaWiOqak1Sl0NEROSwGwo35LoWfnXU5u4oH40SKgXvkCIiouaB4Yas1BjqztY82DcK7UK8oVYp0DPKD0qF3TfWERERSYrhhuo1uksY+rUOkLoMIiIihzHctHBCCJwsrERZjQE6gxnVeqPUJREREd0UhpsW7u2M03h1y2827UrOsSEiomaK4aaFO1lYCQDw81Ah2FsDjUqO1kFe6BbhK21hREREN4jhhgAAfx3UGimDWktdBhER0U3jLTBERETkUnjmpoWpNZrxfwfzUFCmg85owuG8UqlLIiIiciqGmxbmv7/mY9bnh2zavTX8USAiItfAT7QWprzGAACI9HfHsPYhUKvkCPbWYHyPcIkrIyIicg6Gmxaqc5gWC8Z2kroMIiIip+OEYiIiInIpkoebVatWITY2FhqNBgkJCdi5c+dV+27cuBEjRoxAUFAQfHx80K9fP3z77beNWC0RERE1dZKGmw0bNmDGjBmYN28esrKyMHDgQIwaNQq5ubn19v/hhx8wYsQIpKWlITMzE0OGDMGYMWOQlZXVyJU3H0II/HvP75i78TBSNxzEhv1npS6JiIioQcmEEEKqN+/Tpw969uyJ1atXW9o6dOiAcePGYcmSJXYdo1OnTpg4cSJefPFFu/qXl5dDq9WirKwMPj4+N1R3c3KysALDX//Bpv3BvlFYPK6LBBURERE5zpHPb8kmFNfW1iIzMxNz5syxak9OTsaePXvsOobZbEZFRQX8/f2v2kev10Ov11tel5eX31jBzVR1rQkA4K1WYtqweGhUCniplRjeMUTiyoiIiBqGZOGmqKgIJpMJISHWH7IhISEoKCiw6xivvfYaqqqqMGHChKv2WbJkCRYuXHhTtboCb40Sj9/C5RWIiMj1ST6hWCazXn1aCGHTVp9169ZhwYIF2LBhA4KDg6/ab+7cuSgrK7N8nT3r+nNOTGaBvNIanCysRE5RldTlEBERNSrJztwEBgZCoVDYnKUpLCy0OZvzZxs2bMAjjzyCzz77DMOHD79mX7VaDbVafdP1NicT3tmLzDMlVm32BEYiIiJXINmZGzc3NyQkJCA9Pd2qPT09Hf3797/qfuvWrcNDDz2ETz/9FKNHj27oMpulA7l1wcZbo0SIjxrRAR54qH+MtEURERE1EkmfUJyamopJkyYhMTER/fr1w7vvvovc3FykpKQAqLuklJeXh48++ghAXbCZPHky/vnPf6Jv376Wsz7u7u7QarWSfR9N1fanByHYWyN1GURERI1K0nAzceJEFBcXY9GiRcjPz0fnzp2RlpaG6OhoAEB+fr7VM2/eeecdGI1GPPnkk3jyySct7VOmTMGHH37Y2OUTERFREyTpc26k0BKecxM79xsIAeybN4xnboiIyCU0i+fckPMJIaA3mtGy4ioREZE1hhsX8K+dp/F6+nHLA/uIiIhaMsmfc0M37+tD+TbBpmeULwI9W9Yt8ERERADP3LiU1yd0w4iOIdCoFFApmFuJiKhlYrhxId4aFbw1KqnLICIikhT/e09EREQuheGGiIiIXAovSzVDJwsr8Xr6MZRUGaAzmnCsoELqkoiIiJoMhptmaP2+XKQdLrBpj/Bzl6AaIiKipoXhphkymuue0jeyUwju7BkBd5UCkf4eiA30lLgyIiIi6THcNGNtgr0xslOo1GUQERE1KZxQTERERC6F4YaIiIhcCi9LNRPFlXpcKNdDZzShoEwndTlERERNFsNNM/BrXhnGrdxtmUh8mVwuk6giIiKipovhphk4UVgBo1nATSFHsI8aGpUC/p5uuL1rK6lLIyIianIYbpqRPnH++PiRPlKXQURE1KRxQjERERG5FIYbIiIicim8LNVEmc0ClbVG6AwmXKoySF0OERFRs8Fw0wTpjSaM+udOnL5YJXUpREREzQ7DTROUV1JjFWw0Kjm81EqM7RYmYVVERETNA8NNE+atVuLQgmTIZHyeDRERkb04obgpk4HBhoiIyEEMN0RERORSGG6IiIjIpXDOTRNxvrQGP+UUo6bWjPOlNVKXQ0RE1Gwx3DQRk97/Caf+dOu3u0ohUTVERETNF8NNE1FYoQcAJMUHwN9TDY1SjlFdQiWuioiIqPlhuGliXrqjM+KCvKQug4iIqNnihGIiIiJyKQw3RERE5FIYbpoAs1kAQuoqiIiIXAPn3EjkPz+fxZL/ZqNKb0KtySx1OURERC6D4UYiXx06j5Jqg1Vbm2AvRPh5SFQRERGRa2C4kdiCMR0xtns4NCo53FUKriVFRER0kxhuJObr4QZ/TzepyyAiInIZnFBMRERELoXhRgIGkxkmM2+PIiIiagi8LNVIfv79Ep5cewDFVbUMNkRERA2I4aaRZBy7aFk/6rJALzd0j/SVpiAiIiIXxXDTyCYmRmLube2hUSmgVsp5dxQREZGTMdw0Mnc3BXw9eHcUERFRQ+GEYiIiInIpDDdERETkUnhZqoHojSb8c9sJnLlUDb3BhGMXKqQuiYiIqEVguGkgP52+hFU7Ttm0h/hoJKiGiIio5WC4aSB6Y91K3xF+7nhySDw0Kjl8PdwwID5Q4sqIiIhcG8NNAwvyVuO+3lFSl0FERNRicEIxERERuRSGGyIiInIpDDdERETkUjjnxone/eEU9uVcgs5gxoVyndTlEBERtUgMN05SWl2LV9J+s2kP83WXoBoiIqKWi+HGSWpNZsufl0/sDo1KDnc3JfrE+ktYFRERUcvDcONkchkwrke41GUQERG1WJJPKF61ahViY2Oh0WiQkJCAnTt3XrN/RkYGEhISoNFoEBcXh7fffruRKiUiIqLmQNJws2HDBsyYMQPz5s1DVlYWBg4ciFGjRiE3N7fe/jk5ObjtttswcOBAZGVl4bnnnsP06dPxxRdfNHLlRERE1FTJhBBCqjfv06cPevbsidWrV1vaOnTogHHjxmHJkiU2/WfPno3NmzcjOzvb0paSkoJffvkFe/futes9y8vLodVqUVZWBh8fn5v/Jv6nsEKH3i9vh1wGnF4y2mnHJSIiIsc+vyU7c1NbW4vMzEwkJydbtScnJ2PPnj317rN3716b/iNHjsT+/fthMBjq3Uev16O8vNzqi4iIiFyXZOGmqKgIJpMJISEhVu0hISEoKCiod5+CgoJ6+xuNRhQVFdW7z5IlS6DVai1fkZGRzvkG6qFWyqFWKhrs+ERERHR9kk8olslkVq+FEDZt1+tfX/tlc+fORVlZmeXr7NmzN1lx/YK9NTi2eBSyX7q1QY5PRERE9pHsVvDAwEAoFAqbszSFhYU2Z2cuCw0Nrbe/UqlEQEBAvfuo1Wqo1WrnFE1ERERNnmRnbtzc3JCQkID09HSr9vT0dPTv37/effr162fTf+vWrUhMTIRKpWqwWomIiKj5kPSyVGpqKv71r39hzZo1yM7OxsyZM5Gbm4uUlBQAdZeUJk+ebOmfkpKCM2fOIDU1FdnZ2VizZg3ef/99PPPMM1J9C0RERNTESPqE4okTJ6K4uBiLFi1Cfn4+OnfujLS0NERHRwMA8vPzrZ55Exsbi7S0NMycORMrV65EWFgYVqxYgbvuukuqb4GIiIiaGEmfcyOFhnrODRERETWcZvGcGyIiIqKGwHBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXIunyC1K4/EDm8vJyiSshIiIie13+3LZnYYUWF24qKioAAJGRkRJXQkRERI6qqKiAVqu9Zp8Wt7aU2WzG+fPn4e3tDZlMdlPHKi8vR2RkJM6ePct1quzA8XIMx8sxHC/7cawcw/FyTEONlxACFRUVCAsLg1x+7Vk1Le7MjVwuR0REhFOP6ePjwx94B3C8HMPxcgzHy34cK8dwvBzTEON1vTM2l3FCMREREbkUhhsiIiJyKQw3N0GtVmP+/PlQq9VSl9IscLwcw/FyDMfLfhwrx3C8HNMUxqvFTSgmIiIi18YzN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBzE1atWoXY2FhoNBokJCRg586dUpckuSVLlqBXr17w9vZGcHAwxo0bh2PHjln1EUJgwYIFCAsLg7u7OwYPHowjR45IVHHTsmTJEshkMsyYMcPSxvGylpeXhwcffBABAQHw8PBA9+7dkZmZadnO8apjNBrx/PPPIzY2Fu7u7oiLi8OiRYtgNpstfVryWP3www8YM2YMwsLCIJPJ8OWXX1ptt2ds9Ho9pk2bhsDAQHh6emLs2LE4d+5cI34Xjeda42UwGDB79mx06dIFnp6eCAsLw+TJk3H+/HmrYzTqeAm6IevXrxcqlUq899574ujRo+Kpp54Snp6e4syZM1KXJqmRI0eKDz74QPz666/i4MGDYvTo0SIqKkpUVlZa+ixdulR4e3uLL774Qhw+fFhMnDhRtGrVSpSXl0tYufT27dsnYmJiRNeuXcVTTz1laed4/eHSpUsiOjpaPPTQQ+Knn34SOTk5Ytu2beLkyZOWPhyvOosXLxYBAQHi66+/Fjk5OeKzzz4TXl5eYvny5ZY+LXms0tLSxLx588QXX3whAIhNmzZZbbdnbFJSUkR4eLhIT08XBw4cEEOGDBHdunUTRqOxkb+bhnet8SotLRXDhw8XGzZsEL/99pvYu3ev6NOnj0hISLA6RmOOF8PNDerdu7dISUmxamvfvr2YM2eORBU1TYWFhQKAyMjIEEIIYTabRWhoqFi6dKmlj06nE1qtVrz99ttSlSm5iooK0aZNG5Geni4GDRpkCTccL2uzZ88WAwYMuOp2jtcfRo8eLR5++GGrtjvvvFM8+OCDQgiO1ZX+/GFtz9iUlpYKlUol1q9fb+mTl5cn5HK52LJlS6PVLoX6wuCf7du3TwCw/Ie/sceLl6VuQG1tLTIzM5GcnGzVnpycjD179khUVdNUVlYGAPD39wcA5OTkoKCgwGrs1Go1Bg0a1KLH7sknn8To0aMxfPhwq3aOl7XNmzcjMTER99xzD4KDg9GjRw+89957lu0crz8MGDAA27dvx/HjxwEAv/zyC3bt2oXbbrsNAMfqWuwZm8zMTBgMBqs+YWFh6Ny5c4sfP6Dud79MJoOvry+Axh+vFrdwpjMUFRXBZDIhJCTEqj0kJAQFBQUSVdX0CCGQmpqKAQMGoHPnzgBgGZ/6xu7MmTONXmNTsH79ehw4cAA///yzzTaOl7XTp09j9erVSE1NxXPPPYd9+/Zh+vTpUKvVmDx5MsfrCrNnz0ZZWRnat28PhUIBk8mEl19+Gffddx8A/mxdiz1jU1BQADc3N/j5+dn0aemfAzqdDnPmzMH9999vWTizsceL4eYmyGQyq9dCCJu2lmzq1Kk4dOgQdu3aZbONY1fn7NmzeOqpp7B161ZoNJqr9uN41TGbzUhMTMQrr7wCAOjRoweOHDmC1atXY/LkyZZ+HC9gw4YN+OSTT/Dpp5+iU6dOOHjwIGbMmIGwsDBMmTLF0o9jdXU3MjYtffwMBgPuvfdemM1mrFq16rr9G2q8eFnqBgQGBkKhUNikzcLCQpuk31JNmzYNmzdvxvfff4+IiAhLe2hoKABw7P4nMzMThYWFSEhIgFKphFKpREZGBlasWAGlUmkZE45XnVatWqFjx45WbR06dEBubi4A/nxdadasWZgzZw7uvfdedOnSBZMmTcLMmTOxZMkSAByra7FnbEJDQ1FbW4uSkpKr9mlpDAYDJkyYgJycHKSnp1vO2gCNP14MNzfAzc0NCQkJSE9Pt2pPT09H//79JaqqaRBCYOrUqdi4cSO+++47xMbGWm2PjY1FaGio1djV1tYiIyOjRY7dsGHDcPjwYRw8eNDylZiYiAceeAAHDx5EXFwcx+sKSUlJNo8WOH78OKKjowHw5+tK1dXVkMutf8UrFArLreAcq6uzZ2wSEhKgUqms+uTn5+PXX39tkeN3OdicOHEC27ZtQ0BAgNX2Rh8vp09RbiEu3wr+/vvvi6NHj4oZM2YIT09P8fvvv0tdmqT+9re/Ca1WK3bs2CHy8/MtX9XV1ZY+S5cuFVqtVmzcuFEcPnxY3HfffS3m9lN7XHm3lBAcryvt27dPKJVK8fLLL4sTJ06ItWvXCg8PD/HJJ59Y+nC86kyZMkWEh4dbbgXfuHGjCAwMFM8++6ylT0seq4qKCpGVlSWysrIEAPH666+LrKwsy9099oxNSkqKiIiIENu2bRMHDhwQQ4cOddlbwa81XgaDQYwdO1ZERESIgwcPWv3u1+v1lmM05ngx3NyElStXiujoaOHm5iZ69uxpud25JQNQ79cHH3xg6WM2m8X8+fNFaGioUKvV4pZbbhGHDx+Wrugm5s/hhuNl7auvvhKdO3cWarVatG/fXrz77rtW2zledcrLy8VTTz0loqKihEajEXFxcWLevHlWHzYteay+//77en9XTZkyRQhh39jU1NSIqVOnCn9/f+Hu7i5uv/12kZubK8F30/CuNV45OTlX/d3//fffW47RmOMlE0II558PIiIiIpIG59wQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQUYuxe/dudOnSBSqVCuPGjZO6HCJqIAw3RI2soKAA06ZNQ1xcHNRqNSIjIzFmzBhs375d6tKuSSaT4csvv5S6jJuSmpqK7t27IycnBx9++KHU5RBRA1FKXQBRS/L7778jKSkJvr6+WLZsGbp27QqDwYBvv/0WTz75JH777bcbOq4QAiaTCUql9T/p2tpauLm5OaN0l3Dq1CmkpKQgIiJC6lKaBYPBAJVKJXUZRA7jmRuiRvTEE09AJpNh3759uPvuu9G2bVt06tQJqamp+PHHHwHUBSCZTIaDBw9a9istLYVMJsOOHTsAADt27IBMJsO3336LxMREqNVq7Ny5E4MHD8bUqVORmpqKwMBAjBgxAgBw9OhR3HbbbfDy8kJISAgmTZqEoqIiy/EHDx6M6dOn49lnn4W/vz9CQ0OxYMECy/aYmBgAwPjx4yGTySyv6zN79my0bdsWHh4eiIuLwwsvvACDwWDVZ/HixQgODoa3tzceffRRzJkzB927d7fq88EHH6BDhw7QaDRo3749Vq1adc2x1ev1mD59OoKDg6HRaDBgwAD8/PPPVmNaXFyMhx9+GDKZ7KpnbvR6PZ599llERkZCrVajTZs2eP/99y3bMzIy0Lt3b6jVarRq1Qpz5syB0Wi0eywBYMGCBYiKioJarUZYWBimT59u2VbfGTJfX19LvZe/l//85z8YOHAg3N3d0atXLxw/fhw///wzEhMT4eXlhVtvvRUXL160e0yvPO7gwYOh0WjwySef4MyZMxgzZgz8/Pzg6emJTp06IS0t7Zp/F0SSa5DlOInIRnFxsZDJZOKVV165Zr/LK+xmZWVZ2kpKSqxW2L28Qm/Xrl3F1q1bxcmTJ0VRUZEYNGiQ8PLyErNmzRK//fabyM7OFufPnxeBgYFi7ty5Ijs7Wxw4cECMGDFCDBkyxHL8QYMGCR8fH7FgwQJx/Phx8e9//1vIZDKxdetWIYQQhYWFltXd8/PzRWFh4VXrf+mll8Tu3btFTk6O2Lx5swgJCRGvvvqqZfsnn3wiNBqNWLNmjTh27JhYuHCh8PHxEd26dbP0effdd0WrVq3EF198IU6fPi2++OIL4e/vLz788MOrvu/06dNFWFiYSEtLE0eOHBFTpkwRfn5+ori4WBiNRpGfny98fHzE8uXLRX5+vqiurq73OBMmTBCRkZFi48aN4tSpU2Lbtm1i/fr1Qgghzp07Jzw8PMQTTzwhsrOzxaZNm0RgYKCYP3++3WP52WefCR8fH5GWlibOnDkjfvrpJ6uVzQGITZs2WdWk1WrFBx98IIT44+ejffv2YsuWLeLo0aOib9++omfPnmLw4MFi165d4sCBAyI+Pl6kpKTYPaaXjxsTE2Ppk5eXJ0aPHi1GjBghDh06JE6dOiW++uorkZGRcdW/B6KmgOGGqJH89NNPAoDYuHHjNfs5Em6+/PJLq30HDRokunfvbtX2wgsviOTkZKu2s2fPCgDi2LFjlv0GDBhg1adXr15i9uzZltf1fejaY9myZSIhIcHyuk+fPuLJJ5+06pOUlGQVbiIjI8Wnn35q1eell14S/fr1q/c9KisrhUqlEmvXrrW01dbWirCwMLFs2TJL25UhoT7Hjh0TAER6enq925977jnRrl07YTabLW0rV64UXl5ewmQyCSGuP5avvfaaaNu2raitra33PewNN//6178s29etWycAiO3bt1valixZItq1a2d5fb0xvXzc5cuXW/Xp0qWLWLBgQb21EjVVvCxF1EiEEADqLjs4S2Ji4nXbMjMz8f3338PLy8vy1b59ewB1c1Au69q1q9V+rVq1QmFhocM1ff755xgwYABCQ0Ph5eWFF154Abm5uZbtx44dQ+/eva32ufL1xYsXcfbsWTzyyCNWNS9evNiq3iudOnUKBoMBSUlJljaVSoXevXsjOzvb7toPHjwIhUKBQYMG1bs9Ozsb/fr1s/o7TEpKQmVlJc6dO2dpu9ZY3nPPPaipqUFcXBwee+wxbNq0yeqylr2ufI+QkBAAQJcuXazaLr+nI2P655+f6dOnY/HixUhKSsL8+fNx6NAhh2slamwMN0SNpE2bNpDJZNf9sJXL6/5ZXg5DAGzmrFzm6el53Taz2YwxY8bg4MGDVl8nTpzALbfcYun354mjMpkMZrP52t/Un/z444+49957MWrUKHz99dfIysrCvHnzUFtba3PsK135vV5+z/fee8+q3l9//dUyL+nPrhYchRAOhUl3d/drbq/vePW997XGMjIyEseOHcPKlSvh7u6OJ554Arfccovl71gmk1mNB1D/3/+V73H5vf/cdvk9HRnTP//8PProozh9+jQmTZqEw4cPIzExEW+++Wa940PUVDDcEDUSf39/jBw5EitXrkRVVZXN9tLSUgBAUFAQACA/P9+y7crJxY7q2bMnjhw5gpiYGMTHx1t91ReOrkalUsFkMl2zz+7duxEdHY158+YhMTERbdq0wZkzZ6z6tGvXDvv27bNq279/v+XPISEhCA8Px+nTp23qjY2Nrfd94+Pj4ebmhl27dlnaDAYD9u/fjw4dOtj9PXbp0gVmsxkZGRn1bu/YsSP27NljFT727NkDb29vhIeH2/0+7u7uGDt2LFasWIEdO3Zg7969OHz4MIC6v/8r/+5PnDiB6upqu49dnxsZ0ytFRkYiJSUFGzduxNNPP4333nvvpuohami8FZyoEa1atQr9+/dH7969sWjRInTt2hVGoxHp6elYvXo1srOz4e7ujr59+2Lp0qWIiYlBUVERnn/++Rt+zyeffBLvvfce7rvvPsyaNQuBgYE4efIk1q9fj/feew8KhcKu48TExGD79u1ISkqCWq2Gn5+fTZ/4+Hjk5uZi/fr16NWrF7755hts2rTJqs+0adPw2GOPITExEf3798eGDRtw6NAhxMXFWfosWLAA06dPh4+PD0aNGgW9Xo/9+/ejpKQEqampNu/r6emJv/3tb5g1axb8/f0RFRWFZcuWobq6Go888ojdYxUTE4MpU6bg4YcfxooVK9CtWzecOXMGhYWFmDBhAp544gksX74c06ZNw9SpU3Hs2DHMnz8fqampljNu1/Phhx/CZDKhT58+8PDwwMcffwx3d3dER0cDAIYOHYq33noLffv2hdlsxuzZs51yO7ajY3rZjBkzMGrUKLRt2xYlJSX47rvvHAqMRJKQbLYPUQt1/vx58eSTT4ro6Gjh5uYmwsPDxdixYy2ThYUQljtg3N3dRffu3cXWrVvrnVBcUlJidexBgwaJp556yuY9jx8/LsaPHy98fX2Fu7u7aN++vZgxY4ZlYmx9+91xxx1iypQpltebN28W8fHxQqlUiujo6Kt+f7NmzRIBAQHCy8tLTJw4UbzxxhtCq9Va9Vm0aJEIDAwUXl5e4uGHHxbTp08Xffv2teqzdu1a0b17d+Hm5ib8/PzELbfccs3J2DU1NWLatGkiMDBQqNVqkZSUJPbt22fV53oTii8fZ+bMmaJVq1bCzc1NxMfHizVr1li279ixQ/Tq1Uu4ubmJ0NBQMXv2bGEwGCzbrzeWmzZtEn369BE+Pj7C09NT9O3bV2zbts3SNy8vTyQnJwtPT0/Rpk0bkZaWVu+E4isnnNf38/DBBx/YjPu1xrS+4wohxNSpU0Xr1q2FWq0WQUFBYtKkSaKoqOiaY0gkNZkQf7q4S0TUyEaMGIHQ0FB8/PHHUpdCRC6Al6WIqFFVV1fj7bffxsiRI6FQKLBu3Tps27YN6enpUpdGRC6CZ26IqFHV1NRgzJgxOHDgAPR6Pdq1a4fnn38ed955p9SlEZGLYLghIiIil8JbwYmIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FL+HyUoC+A4wqKqAAAAAElFTkSuQmCC", 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" ] @@ -562,16 +549,16 @@ "\n", "One might wonder why HARK requires users to call `initialize_sim` before calling `simulate`: Why doesn't `simulate` just call `initialize_sim` as its first step? We have broken up these two steps so that users can simulate some number of periods, change something in the environment, and then resume the simulation.\n", "\n", - "When called with no argument, `simulate` will simulate the model for $\\texttt{T_sim}$ periods. The user can optionally pass an integer specifying the number of periods to simulate (which should not exceed $\\texttt{T_sim}$).\n", + "When called with no argument, `simulate` will simulate the model for `T_sim` periods. The user can optionally pass an integer specifying the number of periods to simulate (which should not exceed `T_sim`).\n", "\n", "In the cell below, we simulate our perfect foresight consumers for 80 periods, then seize a bunch of their assets (dragging their wealth even more negative), then simulate for the remaining 40 periods.\n", "\n", - "The `state_prev` attribute of an AgenType stores the values of the model's state variables in the _previous_ period of the simulation." + "The `state_prev` attribute of an `AgentType` stores the values of the model's state variables in the _previous_ period of the simulation." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" @@ -580,7 +567,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -602,13 +589,6 @@ "plt.ylabel(\"Mean normalized market resources\")\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -632,7 +612,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/examples/ConsPortfolioModel/example_ConsPortfolioModel.ipynb b/examples/ConsPortfolioModel/example_ConsPortfolioModel.ipynb index bd7f6f4de..96b599de9 100644 --- a/examples/ConsPortfolioModel/example_ConsPortfolioModel.ipynb +++ b/examples/ConsPortfolioModel/example_ConsPortfolioModel.ipynb @@ -21,7 +21,7 @@ }, "outputs": [], "source": [ - "from copy import copy, deepcopy\n", + "from copy import copy\n", "from time import time\n", "\n", "import matplotlib.pyplot as plt\n", @@ -36,7 +36,6 @@ ")\n", "from HARK.ConsumptionSaving.ConsPortfolioModel import (\n", " PortfolioConsumerType,\n", - " init_portfolio,\n", ")\n", "from HARK.utilities import plot_funcs" ] diff --git a/examples/ConsPortfolioModel/example_ConsSequentialPortfolioModel.ipynb b/examples/ConsPortfolioModel/example_ConsSequentialPortfolioModel.ipynb index a54dd20a3..b2608a17c 100644 --- a/examples/ConsPortfolioModel/example_ConsSequentialPortfolioModel.ipynb +++ b/examples/ConsPortfolioModel/example_ConsSequentialPortfolioModel.ipynb @@ -9,13 +9,11 @@ "\"\"\"\n", "Example implementations of SequentialPortfolioConsumerType\n", "\"\"\"\n", - "from copy import copy\n", "from time import time\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", - "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle\n", "from HARK.ConsumptionSaving.ConsPortfolioModel import (\n", " SequentialPortfolioConsumerType,\n", " init_portfolio,\n", diff --git a/examples/ConsumptionSaving/example_ConsRiskyContribModel.ipynb b/examples/ConsumptionSaving/example_ConsRiskyContribModel.ipynb index 84fa55e92..a5b8d42ba 100644 --- a/examples/ConsumptionSaving/example_ConsRiskyContribModel.ipynb +++ b/examples/ConsumptionSaving/example_ConsRiskyContribModel.ipynb @@ -27,12 +27,9 @@ }, "outputs": [], "source": [ - "\n", - "\n", "def plot_slices_3d(\n", " functions, bot_x, top_x, y_slices, N=300, y_name=None, titles=None, ax_labs=None\n", "):\n", - "\n", " import matplotlib.pyplot as plt\n", "\n", " if type(functions) == list:\n", @@ -52,7 +49,6 @@ " ax = fig.add_subplot(1, nfunc, k + 1)\n", "\n", " for y in y_slices:\n", - "\n", " if y_name is None:\n", " lab = \"\"\n", " else:\n", @@ -89,7 +85,6 @@ " titles=None,\n", " ax_labs=None,\n", "):\n", - "\n", " import matplotlib.pyplot as plt\n", "\n", " if type(functions) == list:\n", @@ -113,7 +108,6 @@ " ax = fig.add_subplot(nws, nfunc, j * nfunc + k + 1)\n", "\n", " for y in y_slices:\n", - "\n", " if slice_names is None:\n", " lab = \"\"\n", " else:\n", @@ -244,9 +238,9 @@ "\n", "# Adjust discounting and returns distribution so that they make sense in a\n", "# 4-period model\n", - "par_finite[\"DiscFac\"] = 0.95 ** 15\n", - "par_finite[\"Rfree\"] = 1.03 ** 15\n", - "par_finite[\"RiskyAvg\"] = 1.08 ** 15 # Average return of the risky asset\n", + "par_finite[\"DiscFac\"] = 0.95**15\n", + "par_finite[\"Rfree\"] = 1.03**15\n", + "par_finite[\"RiskyAvg\"] = 1.08**15 # Average return of the risky asset\n", "par_finite[\"RiskyStd\"] = 0.20 * np.sqrt(15) # Standard deviation of (log) risky returns\n", "\n", "\n", @@ -335,7 +329,6 @@ }, "outputs": [], "source": [ - "\n", "import pandas as pd\n", "\n", "df = contrib_agent.history\n", diff --git a/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb b/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb index 68ee63b8e..7fbbdcfd0 100644 --- a/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb +++ b/examples/ConsumptionSaving/example_TractableBufferStockModel.ipynb @@ -21,7 +21,6 @@ "from time import process_time # timing utility\n", "from HARK.distribution import DiscreteDistributionLabeled\n", "from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType\n", - "import numpy as np\n", "\n", "do_simulation = True" ] diff --git a/examples/Distributions/ExpectedValue.ipynb b/examples/Distributions/ExpectedValue.ipynb index 62a519f08..6597ade76 100644 --- a/examples/Distributions/ExpectedValue.ipynb +++ b/examples/Distributions/ExpectedValue.ipynb @@ -17,7 +17,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "from time import time\n", @@ -41,7 +43,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dd_0_1_20 = Normal().discretize(20)\n", @@ -66,13 +70,15 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "4.33 µs ± 18.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" + "1.63 µs ± 2.66 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n" ] } ], @@ -93,13 +99,15 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "202 µs ± 1.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + "81.4 µs ± 525 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" ] } ], @@ -127,13 +135,15 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "9.86 µs ± 82.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" + "3.69 µs ± 51 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" ] } ], @@ -153,13 +163,15 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "208 µs ± 10.2 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + "88.2 µs ± 1.16 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" ] } ], @@ -186,13 +198,15 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "28 µs ± 858 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" + "9.78 µs ± 60.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" ] } ], @@ -205,13 +219,15 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "515 µs ± 4.25 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + "214 µs ± 2.88 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], @@ -238,7 +254,9 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "PermShkDstn = MeanOneLogNormal().discretize(200)\n", @@ -255,13 +273,15 @@ { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "81.3 ms ± 7.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "16.7 ms ± 499 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -274,13 +294,15 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "582 ms ± 29.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "246 ms ± 7.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -307,7 +329,9 @@ { "cell_type": "code", "execution_count": 12, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "size = np.arange(1, 11) * 100\n", @@ -320,7 +344,6 @@ " TranShkDstn = MeanOneLogNormal().discretize(n)\n", " IncShkDstn = combine_indep_dstns(PermShkDstn, TranShkDstn)\n", "\n", - " m_next = lambda X, a, r: r * a / X[0] + X[1]\n", " a_grid = np.linspace(0, 20, 100).reshape((10, 10))\n", " R = 1.05\n", "\n", @@ -339,11 +362,13 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -383,7 +408,9 @@ { "cell_type": "code", "execution_count": 14, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "from HARK.distribution import expected" @@ -392,7 +419,9 @@ { "cell_type": "code", "execution_count": 15, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -431,12 +460,14 @@ { "cell_type": "code", "execution_count": 16, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "3.7147215033526995" + "3.7147215033526537" ] }, "execution_count": 16, @@ -447,13 +478,6 @@ "source": [ "expected(func=lambda x: 1 / x[0] + x[1], dist=IncShkDstn)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/FrameAgentType/FrameAgentType Demo.ipynb b/examples/FrameAgentType/FrameAgentType Demo.ipynb index 5bb5d6fc3..0a74a4f16 100644 --- a/examples/FrameAgentType/FrameAgentType Demo.ipynb +++ b/examples/FrameAgentType/FrameAgentType Demo.ipynb @@ -10,7 +10,6 @@ "import HARK.ConsumptionSaving.ConsPortfolioModel as cpm\n", "\n", "from HARK.frame import Frame, draw_frame_model\n", - "import numpy as np\n", "\n", "from HARK.rewards import (\n", " CRRAutility,\n", diff --git a/examples/FrameAgentType/FrameModels.ipynb b/examples/FrameAgentType/FrameModels.ipynb index 379053901..40e1fc54a 100644 --- a/examples/FrameAgentType/FrameModels.ipynb +++ b/examples/FrameAgentType/FrameModels.ipynb @@ -9,19 +9,16 @@ "source": [ "from HARK.frame import (\n", " BackwardFrameReference,\n", - " ForwardFrameReference,\n", " Frame,\n", " FrameAgentType,\n", " FrameModel,\n", " draw_frame_model,\n", ")\n", "\n", - "from HARK.distribution import combine_indep_dstns, add_discrete_outcome_constant_mean\n", "from HARK.distribution import (\n", " IndexDistribution,\n", " Lognormal,\n", - " MeanOneLogNormal,\n", - " Bernoulli, # Random draws for simulating agents\n", + " MeanOneLogNormal, # Random draws for simulating agents\n", ")\n", "\n", "from HARK.rewards import (\n", @@ -874,7 +871,7 @@ " {\n", " \"mean\": init_parameters[\"RiskyAvg\"],\n", " \"std\": init_parameters[\"RiskyStd\"],\n", - " }\n", + " },\n", " # seed=self.RNG.integers(0, 2 ** 31 - 1) : TODO: Seed logic\n", " ).discretize(init_parameters[\"RiskyCount\"], method=\"equiprobable\"),\n", " aggregate=True,\n", diff --git a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb index 8d460eed8..18b34a54a 100644 --- a/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb +++ b/examples/Gentle-Intro/Gentle-Intro-To-HARK.ipynb @@ -28,9 +28,6 @@ "# The most common problem beginners have is to execute a cell before all its predecessors\n", "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import HARK\n", "from copy import deepcopy\n", "\n", "mystr = lambda number: \"{:.4f}\".format(number)\n", diff --git a/examples/HowWeSolveIndShockConsumerType/HowWeSolveIndShockConsumerType.ipynb b/examples/HowWeSolveIndShockConsumerType/HowWeSolveIndShockConsumerType.ipynb index efdb7eddc..1b52f63ae 100644 --- a/examples/HowWeSolveIndShockConsumerType/HowWeSolveIndShockConsumerType.ipynb +++ b/examples/HowWeSolveIndShockConsumerType/HowWeSolveIndShockConsumerType.ipynb @@ -109,7 +109,6 @@ " init_lifecycle,\n", ")\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "\n", "LifecycleExample = IndShockConsumerType(**init_lifecycle)\n", "LifecycleExample.cycles = (\n", diff --git a/examples/Journeys/AzureMachineLearning.ipynb b/examples/Journeys/AzureMachineLearning.ipynb index 66e35805a..efa7d2b5e 100644 --- a/examples/Journeys/AzureMachineLearning.ipynb +++ b/examples/Journeys/AzureMachineLearning.ipynb @@ -68,9 +68,6 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", "# Initial imports and notebook setup, click arrow to show\n", "from HARK.ConsumptionSaving.ConsIndShockModelFast import IndShockConsumerTypeFast\n", "from HARK.utilities import plot_funcs_der, plot_funcs\n", diff --git a/examples/Journeys/Journey-Policymaker.ipynb b/examples/Journeys/Journey-Policymaker.ipynb index a8cfb18a6..df7c10eba 100644 --- a/examples/Journeys/Journey-Policymaker.ipynb +++ b/examples/Journeys/Journey-Policymaker.ipynb @@ -404,7 +404,7 @@ } ], "source": [ - "from HARK.utilities import get_lorenz_shares, get_percentiles\n", + "from HARK.utilities import get_lorenz_shares\n", "\n", "pctiles = np.linspace(0.001, 0.999, 200)\n", "sim_Lorenz_points = get_lorenz_shares(\n", diff --git a/examples/Journeys/Quickstart_tutorial/Quick_start_with_solution.ipynb b/examples/Journeys/Quickstart_tutorial/Quick_start_with_solution.ipynb index e917b0cd9..3d86c7612 100644 --- a/examples/Journeys/Quickstart_tutorial/Quick_start_with_solution.ipynb +++ b/examples/Journeys/Quickstart_tutorial/Quick_start_with_solution.ipynb @@ -99,17 +99,15 @@ }, "outputs": [], "source": [ - "\n", "# import sys\n", "# import os\n", "# sys.path.insert(0, os.path.abspath('../../../.'))\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import HARK\n", "\n", "from copy import deepcopy\n", "from HARK.ConsumptionSaving.ConsIndShockModel import *\n", - "from HARK.utilities import plot_funcs_der, plot_funcs" + "from HARK.utilities import plot_funcs" ] }, { @@ -3284,8 +3282,7 @@ }, "outputs": [], "source": [ - "\n", - "from HARK.utilities import get_lorenz_shares, get_percentiles" + "from HARK.utilities import get_lorenz_shares" ] }, { diff --git a/examples/LabeledModels/LabeledModels.ipynb b/examples/LabeledModels/LabeledModels.ipynb index 5e3f333d1..da73a7fff 100644 --- a/examples/LabeledModels/LabeledModels.ipynb +++ b/examples/LabeledModels/LabeledModels.ipynb @@ -23,7 +23,6 @@ "from types import SimpleNamespace\n", "\n", "import estimagic as em\n", - "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n", diff --git a/examples/LifecycleModel/Cycles_tutorial.ipynb b/examples/LifecycleModel/Cycles_tutorial.ipynb index a3952818c..b362dec6b 100644 --- a/examples/LifecycleModel/Cycles_tutorial.ipynb +++ b/examples/LifecycleModel/Cycles_tutorial.ipynb @@ -18,13 +18,10 @@ "outputs": [], "source": [ "# Attempt at combining the imports from both notebooks -- it works!\n", - "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import HARK\n", "\n", - "from copy import deepcopy\n", "from HARK.ConsumptionSaving.ConsIndShockModel import *\n", - "from HARK.utilities import plot_funcs_der, plot_funcs\n", + "from HARK.utilities import plot_funcs\n", "\n", "mystr = lambda number: \"{:.4f}\".format(number)" ] diff --git a/examples/LifecycleModel/LifecycleModel.ipynb b/examples/LifecycleModel/LifecycleModel.ipynb index b071ab50d..e57fab58b 100644 --- a/examples/LifecycleModel/LifecycleModel.ipynb +++ b/examples/LifecycleModel/LifecycleModel.ipynb @@ -42,7 +42,7 @@ "\n", "import HARK.ConsumptionSaving.ConsIndShockModel as Model # The consumption-saving micro model\n", "import EstimationParameters as Params # Parameters for the consumer type and the estimation\n", - "from HARK.utilities import plot_funcs_der, plot_funcs # Some tools\n", + "from HARK.utilities import plot_funcs # Some tools\n", "\n", "import numpy as np" ] diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb new file mode 100644 index 000000000..2699e7125 --- /dev/null +++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb @@ -0,0 +1,1933 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "be704ca8", + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n", + "from HARK.distribution import Bernoulli\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "d0698156", + "metadata": {}, + "source": [ + "## Original Perfect Foresight Example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "83e6f76e", + "metadata": {}, + "outputs": [], + "source": [ + "PFexample = PerfForesightConsumerType()\n", + "PFexample.cycles = 0\n", + "\n", + "SimulationParams = {\n", + " \"AgentCount\": 3, # Number of agents of this type\n", + " \"T_sim\": 120, # Number of periods to simulate\n", + " \"aNrmInitMean\": -6.0, # Mean of log initial assets\n", + " \"aNrmInitStd\": 0, # 1.0, # Standard deviation of log initial assets\n", + " \"pLvlInitMean\": 0.0, # Mean of log initial permanent income\n", + " \"pLvlInitStd\": 0.0, # Standard deviation of log initial permanent income\n", + " \"PermGroFacAgg\": 1.0, # Aggregate permanent income growth factor\n", + " \"T_age\": None, # Age after which simulated agents are automatically killed,\n", + " \"LivPrb\": [0.98],\n", + "}\n", + "\n", + "PFexample.assign_parameters(**SimulationParams)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0f219ec", + "metadata": {}, + "outputs": [], + "source": [ + "PFexample.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c3981c6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "66cc08fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'who_dies': array([[ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 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-34.60064474],\n", + " [-18.69179151, -4.92818269, -35.00379088],\n", + " [-19.49832683, -6.08371181, -35.39671474],\n", + " [-20.28441144, -7.20994102, -35.77967551],\n", + " [-21.05056386, -8.30761326, -36.15292583],\n", + " [-21.79728952, -9.37745262, -36.51671191],\n", + " [-22.525081 , -10.42016485, -36.87127373],\n", + " [-23.2344184 , -11.43643779, -37.21684518],\n", + " [-23.92576966, -12.42694183, -1.27807355],\n", + " [-24.59959082, -13.39233038, -2.52615583]])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample.track_vars = [\"who_dies\", \"mNrm\", \"pLvl\", \"aNrm\"]\n", + "PFexample.make_shock_history()\n", + "\n", + "PFexample.initialize_sim()\n", + "PFexample.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3b126cc4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DRfbDyKmZq3fx7oItvDLb3R5XjY2DiHQUkaFAoogMrrKM9RighatRNTPRUcLlY7vx44Y9zFqzK9ThGFNv+4rLSYqLJipK6vW4E/p2ILtdEo/MWGU/jBwqKvMMpjz+1RrKKipdO09tPYeT8CxZ7QLcz8FlrL8B/uhaRM3U2UO70DYpjke/XB3qUIypt8I68irVJDpK+MXYbizctJdvrVKcI0WlnsZh054ipi/c4tp5amwcVPVpVT0WuOSwZayTVPUN1yJqphJio7lodFc+W7ad5VsLQh2OaaYqKpXPl2+vd2K8fTUU+nHizCHptGsZzyMzVjXo8c1NcVkF8TFR9GjfkkdmuDcU7XSH9CHFfkTkMleiaeYuGp1Fi7hoHrU/EhMis9fu4tKps3l1bv3Gsz0ZWWMbdM6E2GimjOnKVz/tYNEmS/hcl6KyClrERXPFUd1YuiWfL3/a4cp5nDQOU7FiP0HROimO80Zk8vaPm62cogmJ/CJP6rRHZ6yu1woiT/3ohqdBmzwyi5bxMTas6kBRaQWJsdGcPiidDinxTHUpu7MV+wkzl4/NJkrgia/sj8QEX0m5Z4Jz9Y7Cei2trqvQT11SE2OZPDKT9xZsZu2OQsePu+mNBfzprYUNPm8kKiqrICEumriYKB6aPIT7zxnkynms2E+Y6ZSayBmD03lp9gZ27CsJdTimmfE1DikJMTz0xUrH49meOYeGDSv5XDYmm5joKB790vmw6ozleTw/cz2r8vY16tyRpLjM03MAT+XJNklxrpzHiv2EoSuO6k5pRSX/+2ZtqEMxzUyxd5nk5WO7sWhTPl85HM8uKC5r1LASQPuUBM4Z1oXX525i6966E/KVlleyJb8YVXj4i+YzT1dUpXFwk5PGYTeHFfsBGrVD2tSuR/uWjO/Xkae/W0t+cSDKZxjjjK/ncN6ITDqkxPPQFyvrfIyqUlha0aClrIf75VHdqVB1NKy6ZW8RqtAhJZ635m1i4+7mMU9XVFpBYlx4NA6vAR18xX7wpLx4yt2wzFXH9qCguJxnLZ23CSJfzyE5IYYrjurO96t3MWdt7Rszi8sqqajURg8rAWS0acGkgZ15fuZ6dheW1nrfDbuKAPjDhN4APN5MJrOLyipJCJOew5XAW94d0ycD/wFOdjcsk5ueyrG90njiq9WWztsEja/nEB8TxXkjMmibFMeDn9feeygo8fRuA9FzAPjVMd0pKquos8a6r6cwvGsbzhzimafbXtD060MUh8uwkqrOBq4BPgJuA8apqqVRDIKrj8th9/4yXpi5PtShmGaipNyzwUpEaBEXw2Vjs/lieR4LN9a8BqWwxNPbaOycg09Oh2TG9+vI/75dy96imodVN+zeT0yU0DElgV8d04OyikqebAZld31LWd1WW26ld0RkmohMA27Ck0+pBHjSe51x2dCs1hzRvS2Pfbn6QHffGDeVlFUSH3Pwa+HCUVmkJMTw4Oc/1fiYgxlZGz+s5HP1cZ5h1ae/XVvjfTbuLqJTqwRioqPIbpfEqQM78+z369hVx3BUpCsqC/2cw70czKd0H3AZ8Kcql00QXH1sD7YXlPCK5bw3QVBSXkF8lV+lyQmxXHpkNh8u3sayrfl+H3NgWKkR+xwOl5ueyrg+7XnqmzXsq6EY0IZd+8lofTAH6NXH9qCorIInv27acw9FZRWhnXNQ1RnA18Btqjrj8MP1yAwAo7u3ZVhWax7+YhUl5dZ7MO4qLqskIfbQr4VLj+xKy/gY/vOZ/7mHg8NKgWscAH59XA579pfVuChj4+4iurROPHA5p0MyJ+d24ulv17Fnf9PsPVRUKqXllaGfc/AW2qkUkVTXIzF+iQjXjsthy95iXp1jpUSNuzxzDod+8bRqEcfFR2QxfeEWVmyrnhRyX4AnpH0GZrTiqJ7+F2UUl1WwvaDkkJ4DeIaj9pWUM7WJ7hHyDS8nxrlfxNPJGfYBC73J9/7tO9wOzBw0pkc7hmS24uEvVlFa7l7+dmP89RwALh/TjRax0fz70+pzDw2pAufUtcf3YGdhKc99f2jvYdMezzLWLm0SD7m+T6cUTuzbgae+WVPrZHak8tVyCHnPwesN4BbgS2BulcMEiYhwzfE5bNpTxGtzrfdg3OOv5wCepJAXH9GV9xZu4afDeg879nmGcJID3HMAT3qIsTnteHTGob0HX2LKw3sOANeOy6GguJynvm56K5d8tRzCYp+Dt65DtcP1yMwhju6ZxsCMVvz385XWezCuKamh5wCelBqJsdGHzD2oKu8t3MLgzFaufWFdNy6HnYWlh8w9bNzt7Tn4aRz6dU7lpH5Ns/dwcFgpDBoHEckRkddEZImIrPYdjTmpiPxMRBaLSKWIDKtyfVcRKRKR+d7jkcacpykREX4zztN7sJVLxi3FNfQcANp4ew/vLNh8oPcwd91uVm7fx3nDM12L6UDv4cuDvYcNu/cTFx1F+2T/mXyuPb4nBcXlPNnEeg/hNqw0FXgYKAeOxZN477lGnncRcCaeoarDrVLVQd7jykaep0k5umcaQzI9vQfb92DccPg+h8NdMdYz9/DPTzxzDy/MWk/L+BgmDuzkalzXjevJrsJSnvH2HjbuLiK9dWKNNav7dk5hfL+OTP16DXv3N53eg29YKVwah0RV/RQQVV2nqrcBpzTmpKq6VFWXN+Y5miMR4foTerFlbzEvz7begwm84vLa19C3Topjyphs3lu4hZmrdzJ94RZOG9SZFnGBn2+oamhWa47umcajM1ZRUFzGxl37D1nG6s+143IoKCnn8SZUG8XXc0gIh2EloEREooCfRORqETkDaOliTNkiMk9EZojI2JruJCJXiMgcEZmTl5fnYjjh5cgebRmR3cZ6D8YVdfUcwLNyKTkhhl88M4fiskpXh5SquuHEnuzeX8bUb9Z69zhUn2+oqk+nFE4Z0Imp36xhZxOpjVIcZsNK1+JJnXENMBS4ELi4rgeJyCcissjPMamWh20BMlV1MJ46Ei+ISIq/O6rqY6o6TFWHpaWlOXgZTYOn99CT7QUl1Zb3GdNYJeV1Z/xMbRHL5WO6kV9cTm56Cv27BGcb1IAurTihbwce/3I1OwtL6+w5APxmXA5FZRVNpvxoWM05qOpsVd2nqhtV9VJVPVNVv3fwuHGqmuvneLuWx5So6k7vv+cCq4Ce9XlBzcGobm0Z06MdD32xqsbUAsY0RHFZRZ09B4ApY7rSu2MyVx7dPQhRHXT9CT0p8H7mM9rU3nMA6NE+mdMHp/P0t2vZnu9+xtbrXprHB4ucl1etr6JSz0rFcFmtNExE3hSRH0Rkge9wIxgRSRORaO+/uwE5QNNo8gPstyf1YldhKVOb2GoMEzqqSkl55SG5lWqSnBDLB9cdxcQBnYMQ2UF9OqUwcYBn8ttJzwHg2uNzqKhU/ltH6vHGKi2v5K35m7l12iLXhnwPzDmEQ88BeB7PiqWzgFOrHA0mImeIyEY8hYPeE5EPvTcdBSwQkfl4igxdqaq1VxpppgZleLrYj321usnmkTHBVbWWQzi76eQ+TDkym9zOzoazstom8bNhGbwwa/2BzXNuKPBWbdyWX8Iz36115RzhNueQp6rTVHWNd7XSOlVt1GC3qr6pql1UNV5VO6jqSd7rX1fVft5lrENU9Z3GnKepu+HEnuwrKW8y46kmtCKlcUhvlcifT+1LXD3ivOb4HkSJ8MAnK1yLK9+bRiQhNoqHvljlSonfotIKoqOE2Gj/S3gDycn/3VtF5AkROU9EzvQdrkdm6tS7YwqnDezM1G/WBGU81TRtJUEcsgi2TqmJXHJEV96ct4nlW6snDwyEfO9u7CuP7s6e/WU84ULhoSJvFTiR8GgcLgUGAeM5OKQ00cWYTD1cf0JPyiuUf/lJiGZMfURKz6Ghrjy6Oy3jYrj3I3e2WPl6Ckf2aMfJ/Tvy5Fer2RHgJbTBquUAzhqH4d4loxd7VytdqqpTXI/MOJLVNonzR2by0uwNrNlRGOpwTATz1Qtpij0H8Gzg++XR3fh4yTZ+WL874M+fX+QZVkpJiOWGE3tRXF7JgzXUwGio4tKKoKTrBmeNw7ci0tf1SEyD/fq4HOJjolz7RWSah+Kypt1zALj0yGzatYzn7veXoaoBfW5fzyElMYbuaS05Z1gGz89cx7qdgfvR5htWCgYnn4JRwHwRWe5dxrrQraWspmHSkuO5fEw27y3YwoKNe0IdjolQTb3nAJAUH8O143KYtWYXny3bHtDn9s05pCZ6amlfNy6H6Cjhvo8CNwkebo3DeDz7DU7k4HxDo5aymsD7xVHdaJMU58ovItM8NIeeA8C5wzPIbpfE3e8vo7wicOnv84vLiImSA1/eHVISuGxMNtN+3MyiTXsDco6i0jCac6i6fDVQS1lN4CUnxHLNcT34dtVOvljRfHJNmcDx9RycbIKLZLHRUfzupF78tH0fr/8QuOJZ+UXlpCTGHrKS6JdHd6d1i9iA/WgrLqsIyu5ocNZzMBHi/JFZdG3bgrunL6Oi0noPpn5KvD2Hmor9NCUTcjsyKKMV93+84kAa7MbKLy4j5bBqeCkJsfz6uBy+XrmDGQH40RZuw0omQsTFRHHj+N4s31bA61ZO1NRTsa/nUEOxn6ZERPjjyX3Yll/CEwFK6Z1fVEaKd76hqgtGZZHVtgV3TV/a6GEsaxxMg03I7cjgzFbc9/HyQ2ruGlOX5tRzABiR3YYT+3bg4Rmr2F7Q+E2k+cXlpCRUbxziYqL4/fjerNi2r9E14ItKK4NSywFqaRxEpEBE8ms6ghKdqTcR4WbvL6LHLK2GqQdf3p7m0HPw+cOE3pSWV/LAx43fROrpOfgvejQhtyNDMltx38crKGxEJuXicOg5qGqyqqYA/wL+AKQDXYDfA/8MSnSmQYZ1bcMp/Tvx6IzVbN1raTWMM019h7Q/3dJacsGoLF6evb7RaTU8cw7Vew7g/dF2Sh/yCkp4dMaqBj2/qobdsNJpqvqQqhaoar6qPgzUVrDHhIHfj+9NRaXaxrgIt6+knM+WbQvK8uTmspT1cNcen0NSfAx3Tl/aqOfxrVaqydCsNkwc0InHvlrN5j1F9X7+sgqlolLDarVSoYhMFpFoEYkSkcmA5WkIc5ltW3DpkV15/YeNAVtjbYLvvQWbmfK/OXy4eJvr5yopryAmSoiJbl6NQ+ukOK49PocvV+TxeQM3xpWWV1JUVlFttdLh/jChN5UK93ywrN7nCGYtB3DWOJwPnANs8x4/815nwtxVx/WgdYs47nhniW2Mi1AF3jTQd01femAfgluclAhtqi4a3ZXsdkn85b0llDVgRVHBgdQZNfccALq0bsHlY7J5a/5m5m/YU69zBLOWAzjbBLdWVSepajtVTVPV01V1bRBiM42UkhDLb0/sxay1u5i+0L3ShcY9vi+E9bv289TXa10/V3MbUvKJi4ni5pP7sDqvsEG12X21HGqac6jq/47tQbuW8dzxzuJ6/Wjz7ccIm8R7ItJTRD4VkUXeywNE5E/uh2YC4efDM+jTKYW7pi91rXShcU9Rmae4y7g+7Xnws58CsuSyJs255wBwfJ/2jM1pxwMfr2B3Yf2qK/ryKtW0WqmqlvEx3HhSL35Yv4e35292fI6icOs5AI8DNwFlAKq6ADjXzaBM4ERHCX+e2JdNe4psaWsEKi6rJDE2mptP6UtpRSX/+MC9BQbNuecAnhVFfzqlL4WlFdz3cf3+Px/IyOqg5wBw9tAuDOiSyt/eX+p4aWs4zjm0UNVZh13XqN1VIvIPEVnmzfL6poi0qnLbTSKy0psF9qTGnMd4jO7elpP7d+ThL1Y1aJWECR1PcZcostslcemR2bw6d2O9x6qdKimvrFfpzaaoV8dkLhyVxQsz17N4s/OFHAdqOdQx5+ATFSXcemo/tuWX8N/PndV8KC4Nv57DDhHpDiiAiJwNbGnkeT8GclV1ALACT88Eb92Ic4F+eLLBPiQizbefG0A3TehDpSp3NXK5ngkuz695z5/Ar4/rQVpyPLe+vYhKF3JnNfdhJZ/fjOtJqxZx3D7N+UKO+vYcAIZmteaMwek88dUaRzUfDgwrhdFS1quAR4HeIrIJuA74VWNOqqofqaqv9/E9ns114Nk/8ZKqlqjqGmAlMKIx5zIeGW1a8H/H9ODdBVv4dtWOUIdjHCopqzzwZZCcEMtNE3rz48a9jU7D4E9zH1bySW0Ry+9O8izkmPajszmB+sw5VPWHCb2JjRbueGdJnfcNuzkHVV2tquOANKC3qo4J8GqlKcD73n+nAxuq3LbRe50JgF8e3Y2MNoncNm1xg5brmeDzDSv5nDE4nSGZrfj7B8vY6/1CChTrORx0zrAM+qenctf0pexzMCdweC0HpzqkJHDN8Tl8umw7ny6tfS+Lb7VS2Mw5iEiFiNwN7FfVAu91Pzh43CcissjPManKfW7GM3/xfH0DF5ErRGSOiMzJy7P6BU4kxEbz54n9WLFtH09/uzbU4RgHissqSKiS60hEuGNSLrv2l3J/gHe/l1jP4YDoKOGOSZ45gX9/WnfeJX+1HJy69Mhsuqclcfs7S2pdUVgchsNKi733+0hE2nivq/P/gKqOU9VcP8fbACJyCZ6qcpP14MDeJiCjytN08V7n7/kfU9VhqjosLS3NwcswAOP6tOeYXmn885Of2JZveZfCXZGf4i656alcMDKLZ79fF9Dd79ZzONTgzNacOzyDp75ew4pttedd8lfLwam4mCjumJTL+l37eXRGzSsKw25YCShX1RuBJ4CvRGQo3snphhKR8cCNePI27a9y0zTgXBGJF5FsPOVJD18pZRpBRLj9tH6UVlRy53s2OR3uissq/WZJ/e2JvWjdIo5bAjg5bXMO1d04vjdJ8TH8+e1FtU5O762hloNTR/Zoxyn9O/HQFytZv3O/3/sUlfpSqodP4yAAqvoy8HNgKtCtked9EEgGPhaR+SLyiPcci4FXgCXAB8BVqmo7twIsq20S/3dMd6b9uJlvVtrkdDgrqaEsZGqLWG46uQ/z1u/h1bkb/DyyAecqryS+mdRycKpNUhw3ju/F96t31bphLb+o5oysTt0ysS8xUcKt0/w3REVlFcTFRBEdVf+hq4Zw8km43PcPVV0EjAWuacxJVbWHqmao6iDvcWWV2+5U1e6q2ktV36/teUzDXXl0d7LatuCWtxe5nrPHNFxRWQUJNfyaP2tIOsO7tubu95exq547ev0pOWx+w3icOzyTgRmt+Ot7S9i73/8igPzi8nqvVDpcx9QEfnNCTz5fnseHi6unuwlmLQeovdjPcd5/ZonImb4DOB7YF5TojGsSYqO5/bR+rM4r5LFaxjlNaBWXVdQ4jCAi/PX0/hQUlwdk/0qx9Rz8io4S7jw9l12FpfzjI//ZVAPRcwC45Iiu9OmUwu3vLKm2c7qoNEwaB+Bo739P9XNMdDkuEwTH9GrPKQM68Z/PV7Jmh2VhD0f+JqSr6tUxmcvHduO1uRv5fvXOBp+nvKKSikq1nkMNctNTueSIbJ6fuZ5563dXuz2/uHFzDj4x0VH89fRctuwt5oGPVxxyW12fhUCrrRLcrd7/XurnmBK0CI2rbp3Yl/joKG55q/YJNxN8qkpxWWWNw0o+1x6fQ5fWifzprUWUljds/0qxrwqc9RxqdP2JPemQnMDNby6ivMo+oZLyCorLKhu8WulwQ7Nac/7ITJ76Zs0hq9GKaulFuqG2YaXrazuCFqFxVfuUBG6c0JuvV+6oV4ZI4z5f2c66CsonxkXzl0m5rNy+r8ElKEuCnNQtErWMj+G20/qxZEs+T3695sD1vpobgeg5+Px+fG/atoznpjcWHmiIPHMOwWu8aztTch2HaSImj8hkUEYr7nh3SUAmNk1g+DY9ORnqOba3d4jws5Wsyqv/lGBzrB/dEONzO3Ji3w488MmKA0tOD6TOCMCcg09qYiy3ntqXhZv28vR3nvoSRaXhM6x0e21H0CI0rouKEu4+qz/5RWX89b26c7yY4PDVdHb6a/7WU/uSEBvFTW8srPfeB19D5G9PhTnU7ZP6ERMVxc1vLURVDxb6aeRqpcOd0r8Tx/ZK476PlrNx937PnEM4DCv5iEiCiFwlIg+JyFO+IxjBmeDp3TGFXx3TnTd+2MSXKywdSTg4mC7B2a/59skJ3HxKH2at2cXLc+q39+HAEJbNOdSpU2oiN47vxVc/7eDNeZtc6TmAZzXaX07PBeDmNxdRVBomcw5VPAt0BE4CZuBJaVH7XnITka46tgfd0pL445sL2V/aqJIdJgCK6jGs5HPOsAxGdWvDXdOXsnWv8/Qo1nOon8kjsxia1Zo73l3Cau8wXiDnHHy6tG7BjSf1YsaKPFbvKAyvngPQQ1VvAQpV9WngFGCku2GZUEiIjebuMwewcXcR9364ou4HGFcVN2CSWES4+8wBlFVU8qd6rEArsdVK9RIdJfz9rP7sL6ngvo88fyuB7jn4XDi6K0MyWwHBS7oHzhoH35bAPSKSC6QC7d0LyYTSiOw2XDgqi6nfrmHuul2hDidofli/mx/8rF8PpYaWhezaLokbTujFJ0u38c4CZ3W5rOdQfz3aJ3PN8T0oKHFnzsHH0xANIC46ilYt4lw5hz9OGofHRKQ1cAuexHhLgHtcjcqE1O8n9KZzaiK/e21BrSmEm5K73lvKxU/NYsve8CmjWlLW8HmAKWOyGZjRitumLWbnvpK6z2VzDg3yy6O706dTCnHRUa4O+eR0SOaD68ZyxVGNTWvnnJNiP0+o6m5VnaGq3VS1vao+EozgTGi0jI/h7rP6szqvkH9+Uncu+6ZgX0k5BcXl/PGNhWGzGbAx+fujo4R/nD2AguIybp22uM77H1zKaj2H+oiNjuLxi4by0OQhDarlUB/d0lrSMt6d3ok/TlYrtRKRa0TkfhH5t+8IRnAmdMbmpPHzYRk89uUq1wrah5Pisgpaxsfw+fI8V0pwNkRDJqSr6tkhmWuPz+HdBVuYvrD24aWDw0rWc6ivLq1bMK5vh1CHEXBOPgnTga7AQmBulcM0cTdP7EPHlARueGV+kx9eKiqrYEJuR0Z0bcMd7y6p10oft9R3n4M/Vx7dnf7pqfzprUXsqGV46eCwkvUcjIeTxiFBVa9X1amq+rTvcD0yE3IpCbH8/ewBrMor5P6Pm/bqpaLSCpLiY7jn7AGUVyi/f31ByIeXAlH5KyY6ivvOGci+4vJaC9b40mfYaiXj42ifg4j8QkQ6iUgb3+F6ZCYsjM1J4/yRmTz+1WrmrG26q5eKvSUyu7ZL4o8n92bGijxemLU+tDEF6Au7Z4dkrjshh+kLtzLtR//5sw70HGzOwXg5+dSVAv8AvuPgkNIcN4My4eWPJ/ehS+tErn/lx2o55puCikqltLzywC/0ySOzGNOjHXe+t7TGko3BUFJWgUhg5gGuGNuNIZmtuOWtRX6HzHznio0OTpUxE/6cfOpuwLMRrquqZnuP4K2nMiHXMj6G+342iA279/PXJlh3+uBmM8+fQ1SUcM/ZA4gW4YZX51MRoBrN9VXkrcwWiFUwnuGlQZRVKDf6GTIrLq8M2LlM0+CkcVgJhO7nkwkLI7LbcMXYbrw4az2fLdsW6nACqsjPktHOrRK57bR+zF67m0e/bFga7MYqLqsM6L6D7HZJ/PGUPny5Io/nvl93yG0lZRU232AO4eTTUAjMF5FHA7WUVUT+ISLLRGSBiLwpIq2813cVkSIRme89bD9FGLn+xJ707pjMja8tdLSxKlIUlfrfiXzmkHRO7t+RBz5ecUjRlaDF5UIWzgtGZnJUzzTunL6UldsPpvYuLqu0ZazmEE4+DW8BdwLfErilrB8Duao6AFgB3FTltlWqOsh7XNnI85gAio+J5oGfDyK/qIzfvx4+m8Uaq6Tc/6ogEeHO0/vTukUc170c/OW8tdWPbigR4d6zB5AYG811L887UDmupDy4GT9N+Ku1cRCRaOCSqktYA7GUVVU/UlXfzOb3eDK9mgjQp1MKN4735O15cVb90kKHq6JSzxekv1/prZPiuPdnA1m5fR9/mx7c+ZbiskriXfjCbp+SwN1nDWDRpnwe+GTFwXNZz8FUUeunQVUrgEoRSXUxhinA+1UuZ4vIPBGZISJja3qQiFwhInNEZE5entUfCKYpR2YzNqcdd7y7+JChiUjlb86hqqN6pjHlyGye/m5dUOdb3CwLeVK/jpw7PINHZqzi21U7rOdgqnHyydsHLBSRJ+sz5yAin4jIIj/HpCr3uRkoB573XrUFyFTVwcD1wAsikuLv+VX1MVUdpqrD0tLSHLwMEyhRUcK9PxtIYmw0174078CwTKQqOmy1kj+/n9CLPp1S+O2rC9heEJzd024MK1V1y8S+ZLdN4jcvz2dbfon1HMwhnHwa3sCTkfVL6jHnoKrjVDXXz/E2gIhcAkwEJqt38FpVS1R1p/ffc4FVQM8GvC7jsg4pCdxz9kAWb87nng+WhzqcRqlpQrqq+Jho/n3uIPaXlnPDKz/Wuwxng+JyuSxkUnwM/z5vMLsKS1myJd+S7plDOMnK+jTwIgcbhRcaO+cgIuOBG4HTVHV/levTvPMciEg3IAdY3ZhzGfec0LcDlxzRlSe/XsPny7aHOpwGq2lC+nA5HZK5ZWJfvvppB4995f7H0u2eA0Bueiq/H98bsHTd5lBOsrIeA/wE/Bd4CFghIkc18rwPAsnAx4ctWT0KWCAi84HXgCtVtenmbGgC/jChN707JnPDqz+yLT/0yeoawtdzcJIa+/wRmZzcvyP3frjc9eJAnglp97+wpxyZzbnDMziqpw3PmoOcfPLuA05U1aNV9Sg8taQfaMxJVbWHqmYcvmRVVV9X1X7e64ao6juNOY9xX0JsNA+eP5ii0gqufWleyHYTN0Z9EtyJCH87cwAdUxO45sV57C0qq/MxDVXs8rCST1SUcPdZA7hodFfXz2Uih5PGIVZVDwwqq+oKwJ1iqSYi9WifzF9Pz+X71bv416eRVxyovuU4UxNj+c95g9m6t5gbX/vRtf0ewRhWMqYmThqHOSLyhIgc4z0exxLvmcOcNbQLZw/twn8++4lvVu4IdTj1Ulxa/wR3gzNb8/vxvflw8TamfrM24DGpqjdTrM0DmNBw8sn7FZ660dd4jyXe64w5xB2T+tEjrSXXvjQvLIrlONXQBHeXj83mhL4duGv6UuYFeP6hrEKpqNSgDCsZ44+T1Uolqnq/qp7pPR5Q1aaTWMcETIu4GB6+YAj7Syv49Ys/UFZRGeqQHCkuq2xQnWZPKoqBdExN4OoX5rG7sDRwMZXXb6jLmEBzslrpSBH5WERWiMhq3xGM4Ezk6dE+mb+d2Z/Za3dzzwfLQh2OI43ZT5DaIpb/nj+EvIISrns5cOm9ix3svTDGTU6GlZ4E7gfGAMOrHMb4NWlQOheOyuLxr9bwfh2F7cNBUVlFo8b2B2a04tbT+jJjRR7/+SwwE/KBqB9tTGM4+YvYq6rvq+p2Vd3pO1yPzES0P03sw8CMVvz21R9Zub0g1OHUqri08auCzh+RyVlDuvCvT3/i8+WN3xB4cFjJJqRNaDj55H3urb8wWkSG+A7XIzMRLT4mmkcuGEJCbDS/fHYu+8K4vGgg0lSICH89PZfeHVO49sV5rNtZ2LiYSp3vvTDGDU4ah5HAMOAuPBvi7gPudTMo0zR0Sk3kP+cPZs2OQn77inv7ARqruKyiQRPSh0uMi+bRC4YiIvzy2bnsL214g1hcz70XxgSak9VKx/o5jgtGcCbyHdG9HX88uQ8fLN7Kfz9fGepw/CoqqwzYl3Bm2xb857zBrNhWwO9eq16r2XlM1jiY0LIBTeO6y8Zkc8bgdO77eAWfLg2/+tOBTlNxVM80fndSb95bsIVHZjRsYd/BCWn7EzWhYZ884zpPPqL+9OucwrUvzQ+7AkFFpYHPYXTl0d2YOKAT93y4rEEFgkpsn4MJMWscTFAkxEbz6IXDSIiN4vKnZ7Nnf+A2jDVWY5ey+iMi/OPsgfTtlMK1L86v94otm5A2oeboL0JEjhCR80XkIt/hdmCm6UlvlcijFw5l855irnohfHZQF5VVkBCACenDJcZF89hFw4iPjeLyp+fUq0G0CWkTak52SD+LZ3VS1U1ww1yOyzRRQ7PacOcZuXyzcid/eXdJqMOhslIpLa907Rd61QbxV885bxCLbM7BhFiMg/sMA/pquK5DNBHnZ8MyWLGtgMe/WkP3tJZcfETXkMVS7LAKXGMMzWrD387szw2v/sif317MXWfk1pnk70DPwUp3mhBx0jgsAjoC4Z8HwUSMP0zow5od+7n9ncVktm3Bsb3ahySO+lSBa4yzhnZhZd4+Hv5iFd3Tkrh8bLda719cXkFcTBRRUfXLFGtMoDjps7YDlojIhyIyzXe4HZhp2qKjhH+dO4jeHVP49QvzWLY1PyRxBHM/we9O7MWE3I7cOX0pHy7eWut9i11YQWVMfThpHG4DTufQHdL3NfbEIvIXEVngrSH9kYh09l4vIvJvEVnpvd1SdTRRSfExPHnJMJLio5kydXZIalAHc+I3Kkq4/5xBDOjSiutems+CjXtqicsK/ZjQcrJDeoa/IwDn/oeqDlDVQcC7wJ+9108AcrzHFcDDATiXCVOdUhN56pLh7C0q49Kps4Oeg8m32SxYv9IT46J54qJhtG0Zx5T/zWHDrv3+4yq3EqEmtJysVholIrNFZJ+IlIpIhYg0egxAVas+RxLgm/CeBDyjHt8DrUSkU2PPZ8JXv86p/HfyEJZvK+Cq54O7xNU3rBTMIZy05Hj+d+lwyioquWTqLL9LXN3YmGdMfTjptz4InAf8BCQClwP/DcTJReROEdkATOZgzyEd2FDlbhu91x3+2CtEZI6IzMnLywtEOCaEjunVnr+ensuMFXnc/ObCoCXpOzghHdwhnB7tk3n8omFs2FXEL56Zc2B4y6e4vJJ4axxMCDn6i1DVlUC0qlao6lRgvJPHicgnIrLIzzHJ+7w3q2oG8DxwdX0CV9XHVHWYqg5LS0urz0NNmDpvRCbXHJ/DK3M2cv/HK4JyzlAmuBuR3Yb7fz6Q2Wt3c91Lh1aR80xI25yDCR0nS1n3i0gcMF9E7sGzpNVpozLOYRzPA9OBW4FNQEaV27p4rzPNwG/G5bBtbzH/+Wwl7VMSuHBUlqvnC/VO5IkDOrMtv4S/vLuEP7+9iL+e7tkDUVxeQZukuJDEZAw4+5K/0Hu/q4FCPF/cZzX2xCKSU+XiJMBXcHgacJF31dIoPJXobI9FMyEi3HlGLsf3bs+f317Euws2u3q+cMhhdNmYbK48ujvPz1zPvz71lBktLquwDXAmpOrsOajqOhFJBDqp6u0BPPfdItILqATWAVd6r58OnAysBPYDlwbwnCYCxERH8eD5Q7joqZn85uX5tEqMY0xOO1fOVRyCCWl/fj++F3kFJfzzk59okxTnqU7n8sY8Y2rjZLXSqcB84APv5UGB2ASnqmepaq53OeupqrrJe72q6lWq2l1V+6vqnMaey0SexLhonrh4ON3TWnLFs3OYt363K+fx5TAK9RexiPD3s/ozrk8H/vz2YrbsKbZ9DiaknG6CGwHsAVDV+UC2axEZ45WaGMszU0bQrmU8l0ydzdItgd9F7ZuQjo8J/Rexp8c0mFHd2lBeqcTbsJIJISd/EWWquvew6ywJnwmK9ikJPH/5SBJjo7nwyVms2VEY0Of3VYGrKxFesCTERvP4RcOYkNuRMT3cGUozxgknjcNiETkfiBaRHBH5D/Cty3EZc0BGmxY8d/kIKlW54ImZbNztf1dxQxSVBr7QT2MlJ8Ty8AVDGde3Q6hDMc2Yk7+KXwP9gBLgRSAfuM7FmIyppkf7ZJ6ZMoKC4jImPzGTrXsDk4cp0PWjjWkqnORW2u/drDbcu+nsZlUNfoY00+zlpqfy9JQR7NxXyuQnvievoKTRz+lWFThjIl2NS1nrWpGkqqcFPhxjajc4szVPXTKci5+axfmPf8+LV4yiXcv4Bj+f9RyM8a+2fQ6j8eQ4ehGYCYTHjJ1p9kZkt+GpS4Zz6f9mMfnxmbzwi5G0bWADUWSNgzF+1Tas1BH4I5AL/As4AdgRwJTdxjTY6O5teeri4azdWcj5j89kx76GDTEVldpmM2P8qbFx8CbZ+0BVLwZG4dmx/IWI1CtBnjFuOaJHO566ZDjrdhVy3mMNm4MoLqu0/QTG+FHrhLSIxIvImcBzwFXAv4E3gxGYMU4c2aMdUy8ZwcbdRZz72Hdsr2c1uWJLU2GMXzU2DiLyDPAdMAS43bta6S++NBfGhIvR3dvy9JQRbN1bzDmPfsemPUWOH+uZcwivfQ7GhIPa/iouwFOq81rgWxHJ9x4FgagEZ0wgjchuw7OXj2RnYSnnPPIdax3upLYJaWP8q23OIUpVk71HSpUjWVVTghmkMU4MyWzNi78Yxf7Scs559DuWby2o8zFFpbbPwRh/rD9tmpTc9FRe/uVoAM559Ltas7lWViol5ZVWN8EYP6xxME1Ozw7JvP6rI0hNjGXyEzP5ZuUOv/crKQ+PdN3GhCNrHEyTlNGmBa9dOZrMNi24dOps3ltQvZhgUZgU+jEmHFnjYJqs9ikJvHzFaAZmpHL1iz/w7HdrD7ndGgdjamaNg2nSUlvE8uxlIzm+d3tueXsx9364HFVPORJf/WibkDamOmscTJOXEBvNIxcM5bwRGTz4+Up+++oCyioqw6Z+tDHhqLbEe64Rkb8Ak4BKYDtwiapuFpFjgLeBNd67vqGqd4QiRtO0xERHcdcZ/emYksgDn6xge0ExU470VLsNt2I/xoSDkDQOwD9U9RYAEbkG+DNwpfe2r1R1YojiMk2YiHDtuBw6t0rgpjcWMn/DHsB6Dsb4E5KfTKpadYd1ElaT2gTRz4Zl8PSUEQc+dQnWOBhTTah6DojIncBFwF7g2Co3jRaRH4HNwG9VdXENj78CuAIgMzPT5WhNU3Nkj3a8/n9H8OKs9eR0aBnqcIwJO+JbuRHwJxb5BE9NiMPdrKpvV7nfTUCCqt4qIilAparuE5GTgX+pak5d5xo2bJjOmTMnYLEbY0xzICJzVXWYv9tc6zmo6jiHd30emA7cWnW4SVWni8hDItJOVf1vcTXGGOOKkMw5iEjV3sAkYJn3+o4iIt5/j8AT387gR2iMMc1bqOYc7haRXniWsq7j4Eqls4FfiUg5UAScq26NexljjKlRSBoHVT2rhusfBB4McjjGGGMOY7t/jDHGVGONgzHGmGqscTDGGFONNQ7GGGOqcW0TXDCJSB6eVU8N1Q5oKnsp7LWEJ3st4am5v5YsVU3zd0OTaBwaS0Tm1LRLMNLYawlP9lrCk72WmtmwkjHGmGqscTDGGFONNQ4ej4U6gACy1xKe7LWEJ3stNbA5B2OMMdVYz8EYY0w11jgYY4ypplk3DiIyXkSWi8hKEflDqOOpDxHJEJHPRWSJiCwWkWu917cRkY9F5Cfvf1uHOlanRCRaROaJyLvey9kiMtP7/rwsInGhjtEJEWklIq+JyDIRWSoioyP8ffmN9zO2SEReFJGESHlvROQpEdkuIouqXOf3vRCPf3tf0wIRGRK6yKur4bX8w/s5WyAib4pIqyq33eR9LctF5KT6nq/ZNg4iEg38F5gA9AXOE5G+oY2qXsqBG1S1LzAKuMob/x+AT70V9D71Xo4U1wJLq1z+O/CAqvYAdgOXhSSq+vsX8IGq9gYG4nlNEfm+iEg6cA0wTFVzgWjgXCLnvfkfMP6w62p6LyYAOd7jCuDhIMXo1P+o/lo+BnJVdQCwArgJwPtdcC7Qz/uYh7zfeY4128YBGAGsVNXVqloKvISn8FBEUNUtqvqD998FeL6A0vG8hqe9d3saOD0kAdaTiHQBTgGe8F4W4DjgNe9dIuK1iEgqcBTwJICqlqrqHiL0ffGKARJFJAZoAWwhQt4bVf0S2HXY1TW9F5OAZ9Tje6CViHQKSqAO+HstqvqRqpZ7L34PdPH+exLwkqqWqOoaYCWe7zzHmnPjkA5sqHJ5o/e6iCMiXYHBwEygg6pu8d60FegQqrjq6Z/AjXgKQAG0BfZU+eBHyvuTDeQBU71DZE+ISBIR+r6o6ibgXmA9nkZhLzCXyHxvfGp6LyL9O2EK8L73341+Lc25cWgSRKQl8DpwXdUa3ADeKnphv1ZZRCYC21V1bqhjCYAYYAjwsKoOBgo5bAgpUt4XAO94/CQ8jV5nIInqQxsRK5Lei9qIyM14hpqfD9RzNufGYROQUeVyF+91EUNEYvE0DM+r6hveq7f5usLe/24PVXz1cCRwmoisxTO8dxyecftW3qEMiJz3ZyOwUVVnei+/hqexiMT3BWAcsEZV81S1DHgDz/sVie+NT03vRUR+J4jIJcBEYHKVssqNfi3NuXGYDeR4V13E4Zm8mRbimBzzjsk/CSxV1fur3DQNuNj774uBt4MdW32p6k2q2kVVu+J5Hz5T1cnA53jqikPkvJatwAZvjXSA44ElROD74rUeGCUiLbyfOd/ribj3poqa3otpwEXeVUujgL1Vhp/CkoiMxzMce5qq7q9y0zTgXBGJF5FsPJPss+r15KrabA/gZDwz/KuAm0MdTz1jH4OnO7wAmO89TsYzVv8p8BPwCdAm1LHW83UdA7zr/Xc37wd6JfAqEB/q+By+hkHAHO978xbQOpLfF+B2YBmwCHgWiI+U9wZ4Ec9cSRmeXt1lNb0XgOBZwbgKWIhnhVbIX0Mdr2UlnrkF33fAI1Xuf7P3tSwHJtT3fJY+wxhjTDXNeVjJGGNMDaxxMMYYU401DsYYY6qxxsEYY0w11jgYY4ypxhoHY+pBRNqKyHzvsVVENnn/vU9EHgp1fMYEii1lNaaBROQ2YJ+q3hvqWIwJNOs5GBMAInJMlToUt4nI0yLylYisE5EzReQeEVkoIh94054gIkNFZIaIzBWRD8MpA6gx1jgY447ueHJEnQY8B3yuqv2BIuAUbwPxH+BsVR0KPAXcGapgjTlcTN13McY0wPuqWiYiC/EUyPnAe/1CoCvQC8gFPvakLCIaT2oEY8KCNQ7GuKMEQFUrRaRMD07uVeL5uxNgsaqODlWAxtTGhpWMCY3lQJqIjAZP+nUR6RfimIw5wBoHY0JAPaVpzwb+LiI/4smoeURIgzKmClvKaowxphrrORhjjKnGGgdjjDHVWONgjDGmGmscjDHGVGONgzHGmGqscTDGGFONNQ7GGGOq+X9Zj8YvSXvG2QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1))\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Mean normalized market resources\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "633034d3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.mean(PFexample.history[\"mNrm\"] * PFexample.history[\"pLvl\"], axis=1))\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Mean normalized market resources\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bb741c54", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_14820/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n", + " plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " np.log(\n", + " np.mean(PFexample.history[\"mNrm\"], axis=1)\n", + " - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))\n", + " )\n", + ")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Mean normalized market resources\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "603ae6e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0,)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample.newborn_init_history[\"pLvl\"][1, PFexample.history[\"who_dies\"][1, :] > 0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "567440dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0,)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PFexample.newborn_init_history[\"aNrm\"][2, PFexample.history[\"who_dies\"][2, :] > 0].shape" + ] + }, + { + "cell_type": "markdown", + "id": "0ead3ec8", + "metadata": {}, + "source": [ + "## Using the Generic Monte Carlo Simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "adfbe431", + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.distribution import Lognormal\n", + "import HARK.models.perfect_foresight_normalized as pfn\n", + "from HARK.simulation.monte_carlo import AgentTypeMonteCarloSimulator" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5a0c394b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'shocks': {'live': },\n", + " 'parameters': {'DiscFac': 0.96,\n", + " 'CRRA': (2.0,),\n", + " 'Rfree': 1.03,\n", + " 'LivPrb': 0.98,\n", + " 'PermGroFac': 1.01,\n", + " 'BoroCnstArt': None},\n", + " 'dynamics': {'p': (PermGroFac, p)>,\n", + " 'r_eff': (Rfree, PermGroFac)>,\n", + " 'b_nrm': (r_eff, a_nrm)>,\n", + " 'm_nrm': (b_nrm)>,\n", + " 'c_nrm': ,\n", + " 'a_nrm': (m_nrm, c_nrm)>},\n", + " 'reward': {'u': (c)>}}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pfn.model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e9d068bd", + "metadata": {}, + "outputs": [], + "source": [ + "pfn_simulator = AgentTypeMonteCarloSimulator(\n", + " pfn.model[\"parameters\"],\n", + " pfn.model[\"shocks\"],\n", + " pfn.model[\"dynamics\"],\n", + " {\"c_nrm\": lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)},\n", + " { # initial states\n", + " \"a_nrm\": Lognormal(-6, 0),\n", + " #'live' : 1,\n", + " \"p\": 1.0,\n", + " },\n", + " agent_count=3,\n", + " T_sim=120,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "36ba1dda", + "metadata": {}, + "outputs": [], + "source": [ + "pfn_simulator.read_shocks = True\n", + "# pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n", + "\n", + "pfn_simulator.shock_history[\"live\"] = 1 - PFexample.history[\"who_dies\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bc84d3e5", + "metadata": {}, + "outputs": [], + "source": [ + "pfn_simulator.newborn_init_history[\"a_nrm\"] = PFexample.newborn_init_history[\"aNrm\"]\n", + "pfn_simulator.newborn_init_history[\"p\"] = PFexample.newborn_init_history[\"pLvl\"]\n", + "# pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "65df3a7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'live': array([[ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 0.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 0., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + 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-31.46916387],\n", + " [-12.42694183, -21.79728952, -31.95171268],\n", + " [-13.39233038, -22.525081 , -32.42202586],\n", + " [-14.33324028, -23.2344184 , -32.88041365],\n", + " [-15.25029222, -23.92576966, -33.32717843],\n", + " [-16.14409113, -1.27807355, -33.76261493],\n", + " [-17.01522664, -2.52615583, -34.18701038],\n", + " [-17.8642734 , -3.74259139, -34.60064474],\n", + " [-18.69179151, -4.92818269, -35.00379088],\n", + " [-19.49832683, -6.08371181, -35.39671474],\n", + " [-20.28441144, -7.20994102, -35.77967551],\n", + " [-21.05056386, -8.30761326, -36.15292583],\n", + " [-21.79728952, -9.37745262, -36.51671191],\n", + " [-22.525081 , -10.42016485, -36.87127373],\n", + " [-23.2344184 , -11.43643779, -37.21684518],\n", + " [-23.92576966, -12.42694183, -1.27807355],\n", + " [-24.59959082, -13.39233038, -2.52615583]])}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pf_simulator.track_vars = [\"mNrm\"]\n", + "pfn_simulator.initialize_sim()\n", + "pfn_simulator.simulate(sim_periods=120)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9e2c7ad0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Mean normalized market resources\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2b471cf1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_14820/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n", + " plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " np.log(\n", + " np.mean(pfn_simulator.history[\"m_nrm\"], axis=1)\n", + " - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n", + " )\n", + ")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Mean normalized market resources\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "464f19e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pfn_simulator.history[\"live\"].sum(axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1cc1dc83", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.mean(pfn_simulator.history[\"a_nrm\"], axis=1), label=\"Generic monte carlo\")\n", + "plt.plot(\n", + " np.mean(PFexample.history[\"aNrm\"], axis=1),\n", + " label=\"HARK 0.13 PerfForesightConsumerType\",\n", + ")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Mean normalized market resources\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "dcff94ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(pfn_simulator.history[\"a_nrm\"], axis=1) - np.mean(\n", + " PFexample.history[\"aNrm\"], axis=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "70de1058", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8, 50, 65, 76, 81, 82, 93, 107, 117]),\n", + " array([2, 1, 2, 0, 0, 1, 0, 1, 2]))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(pfn_simulator.history[\"live\"] < 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0e37528d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8, 50, 65, 76, 81, 82, 93, 107, 117]),\n", + " array([2, 1, 2, 0, 0, 1, 0, 1, 2]))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(1 - PFexample.history[\"who_dies\"] < 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e5cf6a1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-env", + "language": "python", + "name": "hark-env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements/dev.txt b/requirements/dev.txt index c1c6b0c4e..9e8c1d67f 100644 --- a/requirements/dev.txt +++ b/requirements/dev.txt @@ -2,4 +2,5 @@ estimagic nbval pre-commit pytest -pytest-xdist \ No newline at end of file +pytest-xdist +ruff diff --git a/requirements/doc.txt b/requirements/doc.txt index 9aa79df64..a2717f479 100644 --- a/requirements/doc.txt +++ b/requirements/doc.txt @@ -1,11 +1,11 @@ -sphinx>=6.1 - -# theme requirements -pydata-sphinx-theme # extension requirements ipython # for the Pygments lexer myst-parser>=2 nbsphinx>=0.8 + +# theme requirements +pydata-sphinx-theme +sphinx>=6.1 sphinx-copybutton sphinx-design diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 000000000..84d7322cf --- /dev/null +++ b/ruff.toml @@ -0,0 +1,3 @@ +include = ["*.ipynb"] +# ignore F401 for now: https://github.com/astral-sh/ruff/issues/8354 +ignore = ["E731", "E721", "E402", "F841", "F821", "F405", "F403", "F401"] diff --git a/tools/nb_exec.py b/tools/nb_exec.py index e8b5868e2..8c2e299f3 100644 --- a/tools/nb_exec.py +++ b/tools/nb_exec.py @@ -24,29 +24,31 @@ def run_notebook(notebook_file: Path): rel_file_name = notebook_file.relative_to(ROOT_DIR).as_posix() - print(f'{rel_file_name}: Loading notebook') + print(f"{rel_file_name}: Loading notebook") try: # Journey-PhD and LifecycleModel expect execution from their own directory os.chdir(notebook_file.parent) nb = nbformat.read(notebook_file, as_version=4) - client = NotebookClient(nb, timeout=600, kernel_name='python3', record_timing=False) - print(f'{rel_file_name}: Executing') + client = NotebookClient( + nb, timeout=600, kernel_name="python3", record_timing=False + ) + print(f"{rel_file_name}: Executing") start = time.perf_counter() client.execute() elapsed = time.perf_counter() - start - print(f'{rel_file_name}: Writing') + print(f"{rel_file_name}: Writing") nbformat.write(nb, notebook_file) - print(f'{rel_file_name}: Finished (executed in {elapsed:.2f}s)') + print(f"{rel_file_name}: Finished (executed in {elapsed:.2f}s)") del nb, client, start, elapsed except Exception as err: - print(f'{rel_file_name}: Failed to execute\n {err}', file=sys.stderr) + print(f"{rel_file_name}: Failed to execute\n {err}", file=sys.stderr) -if __name__ == '__main__': +if __name__ == "__main__": if len(sys.argv) > 1: notebooks = (Path(p).resolve() for p in sys.argv[1:]) else: - notebooks = ROOT_DIR.joinpath('examples').rglob('*.ipynb') + notebooks = ROOT_DIR.joinpath("examples").rglob("*.ipynb") with multiprocessing.Pool() as pool: pool.map(run_notebook, notebooks)