diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 0000000000..d1dc84fa5f --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,4 @@ +Contributing +============ + +We welcome your contributions! Please see the [contributing](http://pvlib-python.readthedocs.io/en/latest/contributing.html) page for information about how to contribute. diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000000..071d9e4d2c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,29 @@ +--- +name: Bug report +about: Create a report to help us improve + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Versions:** + - ``pvlib.__version__``: + - ``pandas.__version__``: + - python: + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000000..066b2d920a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,17 @@ +--- +name: Feature request +about: Suggest an idea for this project + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/support-question.md b/.github/ISSUE_TEMPLATE/support-question.md new file mode 100644 index 0000000000..19b9117bc7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/support-question.md @@ -0,0 +1,10 @@ +--- +name: Support question +about: If you have a support or usage question, please see the pvlib stack overflow + tag or the pvlib-python google group + +--- + +pvlib usage questions can be asked on [Stack Overflow](http://stackoverflow.com) and tagged with the [pvlib](http://stackoverflow.com/questions/tagged/pvlib) tag. + +The [pvlib-python google group](https://groups.google.com/forum/#!forum/pvlib-python) is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group. diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 7e3cb863b9..eb39df06bc 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,17 +1,19 @@ pvlib python pull request guidelines ==================================== -Thank you for your contribution to pvlib python! +Thank you for your contribution to pvlib python! You may delete all of these instructions except for the list below. -You may submit a pull request with your code at any stage of completion, however, before the code can be merged the following items must be addressed: +You may submit a pull request with your code at any stage of completion. + +The following items must be addressed before the code can be merged. Please don't hesitate to ask for help if you're unsure of how to accomplish any of the items below: - [ ] Closes issue #xxxx - - [ ] Fully tested. Added and/or modified tests to ensure correct behavior for all reasonable inputs. Tests must pass on the TravisCI and Appveyor testing services. - - [ ] Code quality and style is sufficient. Passes ``git diff upstream/master -u -- "*.py" | flake8 --diff`` and/or landscape.io linting service. - - [ ] New code is fully documented. Includes sphinx/numpydoc compliant docstrings and comments in the code where necessary. + - [ ] I am familiar with the [contributing guidelines](http://pvlib-python.readthedocs.io/en/latest/contributing.html). + - [ ] Fully tested. Added and/or modified tests to ensure correct behavior for all reasonable inputs. Tests (usually) must pass on the TravisCI and Appveyor testing services. - [ ] Updates entries to `docs/sphinx/source/api.rst` for API changes. - [ ] Adds description and name entries in the appropriate `docs/sphinx/source/whatsnew` file for all changes. - -Please don't hesitate to ask for help if you're unsure of how to accomplish any of the above. You may delete all of these instructions except for the list above. + - [ ] Code quality and style is sufficient. Passes ``git diff upstream/master -u -- "*.py" | flake8 --diff`` + - [ ] New code is fully documented. Includes sphinx/numpydoc compliant docstrings and comments in the code where necessary. + - [ ] Pull request is nearly complete and ready for detailed review. Brief description of the problem and proposed solution (if not already fully described in the issue linked to above): diff --git a/.gitignore b/.gitignore index f18eba0007..cbbc8257e6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ # Byte-compiled / optimized / DLL files +.pytest_cache/ __pycache__/ *.py[cod] diff --git a/appveyor.yml b/appveyor.yml index 9925436c69..b653af1331 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -28,7 +28,7 @@ install: - cmd: conda info -a # install depenencies - - cmd: conda create -n test_env --yes --quiet python=%PYTHON_VERSION% pip numpy scipy pytables pandas nose pytest pytz ephem numba siphon -c conda-forge + - cmd: conda create -n test_env --yes --quiet python=%PYTHON_VERSION% pip numpy scipy pytables pandas nose pytest pytz ephem numba siphon pytest-mock -c conda-forge - cmd: activate test_env - cmd: python --version - cmd: conda list diff --git a/ci/requirements-py27-min.yml b/ci/requirements-py27-min.yml index d132091fdc..ecac0a4857 100644 --- a/ci/requirements-py27-min.yml +++ b/ci/requirements-py27-min.yml @@ -4,6 +4,7 @@ dependencies: - pytz - pytest - pytest-cov + - pytest-mock - nose - pip: - coveralls diff --git a/ci/requirements-py27.yml b/ci/requirements-py27.yml index e6189d073b..81df9d068c 100644 --- a/ci/requirements-py27.yml +++ b/ci/requirements-py27.yml @@ -14,6 +14,7 @@ dependencies: - siphon - pytest - pytest-cov + - pytest-mock - nose - pip: - coveralls diff --git a/ci/requirements-py34.yml b/ci/requirements-py34.yml index 3ad4dd7e65..870ddebd39 100644 --- a/ci/requirements-py34.yml +++ b/ci/requirements-py34.yml @@ -14,6 +14,7 @@ dependencies: - siphon - pytest - pytest-cov + - pytest-mock - nose - pip: - coveralls diff --git a/ci/requirements-py35.yml b/ci/requirements-py35.yml index 8e7678fab0..8f250f1a44 100644 --- a/ci/requirements-py35.yml +++ b/ci/requirements-py35.yml @@ -14,6 +14,7 @@ dependencies: - siphon - pytest - pytest-cov + - pytest-mock - nose - pip: - coveralls diff --git a/ci/requirements-py36.yml b/ci/requirements-py36.yml index ba8e9cdda5..0f17adf978 100644 --- a/ci/requirements-py36.yml +++ b/ci/requirements-py36.yml @@ -14,6 +14,7 @@ dependencies: #- siphon - pytest - pytest-cov + - pytest-mock - nose - pip: - coveralls diff --git a/docs/sphinx/source/api.rst b/docs/sphinx/source/api.rst index 4070ed56c3..31cfabdb02 100644 --- a/docs/sphinx/source/api.rst +++ b/docs/sphinx/source/api.rst @@ -65,7 +65,6 @@ algorithm. spa - Correlations and analytical expressions for low precision solar position calculations. @@ -80,6 +79,7 @@ calculations. solarposition.equation_of_time_pvcdrom solarposition.hour_angle + Clear sky ========= @@ -202,9 +202,22 @@ Functions relevant for the single diode model. :toctree: generated/ pvsystem.calcparams_desoto + pvsystem.calcparams_pvsyst pvsystem.i_from_v pvsystem.singlediode pvsystem.v_from_i + pvsystem.max_power_point + +Low-level functions for solving the single diode equation. + +.. autosummary:: + :toctree: generated/ + + singlediode_methods.estimate_voc + singlediode_methods.bishop88 + singlediode_methods.bishop88_i_from_v + singlediode_methods.bishop88_v_from_i + singlediode_methods.bishop88_mpp SAPM model ---------- @@ -231,7 +244,6 @@ PVWatts model pvsystem.pvwatts_ac pvsystem.pvwatts_losses - Other ----- diff --git a/docs/sphinx/source/contributing.rst b/docs/sphinx/source/contributing.rst index 88eb7aca9e..2819a20248 100644 --- a/docs/sphinx/source/contributing.rst +++ b/docs/sphinx/source/contributing.rst @@ -7,17 +7,27 @@ Encouraging more people to help develop pvlib-python is essential to our success. Therefore, we want to make it easy and rewarding for you to contribute. +There is a lot of material in this section, aimed at a variety of +contributors from novice to expert. Don't worry if you don't (yet) +understand parts of it. + Easy ways to contribute ------------------------ +~~~~~~~~~~~~~~~~~~~~~~~ -Here are a few ideas for you can contribute, even if you are new to +Here are a few ideas for how you can contribute, even if you are new to pvlib-python, git, or Python: +* Ask and answer `pvlib questions on StackOverflow `_ + and participate in discussions in the `pvlib-python google group `_. * Make `GitHub issues `_ - and contribute to the conversation about how to resolve them. + and contribute to the conversations about how to resolve them. * Read issues and pull requests that other people created and contribute to the conversation about how to resolve them. + Look for issues tagged with + `good first issue `_, + `easy `_, + or `help wanted `_. * Improve the documentation and the unit tests. * Improve the IPython/Jupyter Notebook tutorials or write new ones that demonstrate how to use pvlib-python in your area of expertise. @@ -33,7 +43,10 @@ pvlib-python, git, or Python: How to contribute new code --------------------------- +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The basics +---------- Contributors to pvlib-python use GitHub's pull requests to add/modify its source code. The GitHub pull request process can be intimidating for @@ -53,16 +66,11 @@ process. Here's an outline of the process: request. The Pandas project maintains an excellent `contributing page -`_ that goes into -detail on each of these steps. Also see GitHub's `Set Up Git +`_ that goes +into detail on each of these steps. Also see GitHub's `Set Up Git `_ and `Using Pull Requests `_. -Note that you do not need to make all of your changes before creating a -pull request. Your pull requests will automatically be updated when you -commit new changes and push them to GitHub. This gives everybody an easy -way to comment on the code and can make the process more efficient. - We strongly recommend using virtual environments for development. Virtual environments make it trivial to switch between different versions of software. This `astropy guide @@ -73,39 +81,333 @@ environment. You must include documentation and unit tests for any new or improved code. We can provide help and advice on this after you start the pull -request. +request. See the Testing section below. + + +.. _pull-request-scope: + +Pull request scope +------------------ + +This section can be summed up as "less is more". + +A pull request can quickly become unmanageable if too many lines are +added or changed. "Too many" is hard to define, but as a rule of thumb, +we encourage contributions that contain less than 50 lines of primary code. +50 lines of primary code will typically need at least 250 lines +of documentation and testing. This is about the limit of what the +maintainers can review on a regular basis. + +A pull request can also quickly become unmanageable if it proposes +changes to the API in order to implement another feature. Consider +clearly and concisely documenting all proposed API changes before +implementing any code. Modifying +`api.rst `_ +and/or the latest `whatsnew file `_ +can help formalize this process. + +Questions about related issues frequently come up in the process of +addressing implementing code for a pull request. Please try to avoid +expanding the scope of your pull request (this also applies to +reviewers!). We'd rather see small, well-documented additions to the +project's technical debt than see a pull request languish because its +scope expanded beyond what the reviewer community is capable of +processing. + +Of course, sometimes it is necessary to make a large pull request. We +only ask that you take a few minutes to consider how to break it into +smaller chunks before proceeding. + +pvlib-python contains :ref:`3 "layers" of code `: +functions, PVSystem/Location, and ModelChain. We recommend that +contributors focus their work on only one or two of those layers in a +single pull request. New models are *not* required to be available to +the higher-level API! + + +When should I submit a pull request? +------------------------------------ + +The short answer: anytime. + +The long answer: it depends. If in doubt, go ahead and submit. You do +not need to make all of your changes before creating a pull request. +Your pull requests will automatically be updated when you commit new +changes and push them to GitHub. + +There are pros and cons to submitting incomplete pull-requests. On the +plus side, it gives everybody an easy way to comment on the code and can +make the process more efficient. On the minus side, it's easy for an +incomplete pull request to grow into a multi-month saga that leaves +everyone unhappy. If you submit an incomplete pull request, please be +very clear about what you would like feedback on and what we should +ignore. Alternatives to incomplete pull requests include creating a +`gist `_ or experimental branch and linking to +it in the corresponding issue. + +The best way to ensure that a pull request will be reviewed and merged in +a timely manner is to: + +#. Start by creating an issue. The issue should be well-defined and + actionable. +#. Ask the maintainers to tag the issue with the appropriate milestone. +#. Make a limited-scope pull request. It can be a lot of work to check all of + the boxes in `pull request guidelines + `_, + especially for pull requests with a lot of new primary code. + See :ref:`pull-request-scope`. +#. Tag pvlib community members or ``@pvlib/maintainer`` when the pull + request is ready for review. (see :ref:`pull-request-reviews`) + + +.. _pull-request-reviews: + +Pull request reviews +-------------------- + +The pvlib community and maintainers will review your pull request in a +timely fashion. Please "ping" ``@pvlib/maintainer`` if it seems that +your pull request has been forgotten at any point in the pull request +process. + +Keep in mind that the PV modeling community is diverse and each pvlib +community member brings a different perspective when reviewing code. +Some reviewers bring years of expertise in the sub-field that your code +contributes to and will focus on the details of the algorithm. Other +reviewers will be more focused on integrating your code with the rest of +pvlib, ensuring that it is feasible to maintain, that it meets the +:ref:`code style ` guidelines, and that it is +:ref:`comprehensively tested `. Limiting the scope of the pull +request makes it much more likely that all of these reviews can be +conducted and any issues can be resolved in a timely fashion. + +Sometimes it's hard for reviewers to be immediately available, so the +right amount of patience is to be expected. That said, interested +reviewers should do their best to not wait until the last minute to put +in their two cents. + + +.. _code-style: + +Code style +~~~~~~~~~~ + +pvlib python generally follows the `PEP 8 -- Style Guide for Python Code +`_. Maximum line length for code +is 79 characters. + +Code must be compatible with python 2.7 and 3.4+. + +pvlib python uses a mix of full and abbreviated variable names. See +:ref:`variables_style_rules`. We could be better about consistency. +Prefer full names for new contributions. This is especially important +for the API. Abbreviations can be used within a function to improve the +readability of formulae. + +Set your editor to strip extra whitespace from line endings. This +prevents the git commit history from becoming cluttered with whitespace +changes. + +Please see :ref:`Documentation` for information specific to documentation +style. -The maintainers will follow same procedures, rather than making direct -commits to the main repo. Exceptions may be made for extremely minor -changes, such as fixing documentation typos. +Remove any ``logging`` calls and ``print`` statements that you added +during development. ``warning`` is ok. +We typically use GitHub's +"`squash and merge` _" +feature to merge your pull request into pvlib. GitHub will condense the +commit history of your branch into a single commit when merging into +pvlib-python/master (the commit history on your branch remains +unchanged). Therefore, you are free to make commits that are as big or +small as you'd like while developing your pull request. + + +.. _documentation: + +Documentation +~~~~~~~~~~~~~ + +Documentation must be written in +`numpydoc format `_. + +The numpydoc format includes a specification for the allowable input +types. Python's `duck typing `_ +allows for multiple input types to work for many parameters. pvlib uses +the following generic descriptors as short-hand to indicate which +specific types may be used: + +* dict-like : dict, OrderedDict, pd.Series +* numeric : scalar, np.array, pd.Series. Typically int or float dtype. +* array-like : np.array, pd.Series. Typically int or float dtype. + +Parameters that specify a specific type require that specific input type. + +A relatively easy way to test your documentation is to build it on +`readthedocs.org ` by following their +`Import Your Docs `_ +instructions and enabling your branch on the readthedocs +`versions admin page `_. + +Another option is to install the required dependencies in your virtual/conda +environment. See +`docs/environment.yml `_ +for the latest dependences for building the complete documentation. Some +doc files can be compiled with fewer dependencies, but this is beyond +the scope of this guide. + +.. _testing: Testing -------- +~~~~~~~ -pvlib's unit tests can easily be run by executing ``py.test`` on the +pvlib's unit tests can easily be run by executing ``pytest`` on the pvlib directory: -``py.test pvlib`` +``pytest pvlib`` or, for a single module: -``py.test pvlib/test/test_clearsky.py`` +``pytest pvlib/test/test_clearsky.py`` + +or, for a single test: + +``pytest pvlib/test/test_clearsky.py::test_ineichen_nans`` + +We suggest using pytest's ``--pdb`` flag to debug test failures rather +than using ``print`` or ``logging`` calls. For example: -While copy/paste coding should generally be avoided, it's a great way -to learn how to write unit tests! +``pytest pvlib --pdb`` -Unit test code should be placed in the corresponding test module in the -pvlib/test directory. +will drop you into the +`pdb debugger `_ at the +location of a test failure. As described in :ref:`code-style`, pvlib +code does not use ``print`` or ``logging`` calls, and this also applies +to the test suite (with rare exceptions). + +New unit test code should be placed in the corresponding test module in +the pvlib/test directory. Developers **must** include comprehensive tests for any additions or modifications to pvlib. +pvlib-python contains 3 "layers" of code: functions, PVSystem/Location, +and ModelChain. Contributors will need to add tests that correspond to +the layers that they modify. + +Functions +--------- +Tests of core pvlib functions should ensure that the function returns +the desired output for a variety of function inputs. The tests should be +independent of other pvlib functions (see :issue:`394`). The tests +should ensure that all reasonable combinations of input types (floats, +nans, arrays, series, scalars, etc) work as expected. Remember that your +use case is likely not the only way that this function will be used, and +your input data may not be generic enough to fully test the function. +Write tests that cover the full range of validity of the algorithm. +It is also important to write tests that assert the return value of the +function or that the function throws an exception when input data is +beyond the range of algorithm validity. + +PVSystem/Location +----------------- +The PVSystem and Location classes provide convenience wrappers around +the core pvlib functions. The tests in test_pvsystem.py and +test_location.py should ensure that the method calls correctly wrap the +function calls. Many PVSystem/Location methods pass one or more of their +object's attributes (e.g. PVSystem.module_parameters, Location.latitude) +to a function. Tests should ensure that attributes are passed correctly. +These tests should also ensure that the method returns some reasonable +data, though the precise values of the data should be covered by +function-specific tests discussed above. + +We prefer to use the ``pytest-mock`` framework to write these tests. The +test below shows an example of testing the ``PVSystem.ashraeiam`` +method. ``mocker`` is a ``pytest-mock`` object. ``mocker.spy`` adds +features to the ``pvsystem.ashraeiam`` *function* that keep track of how +it was called. Then a ``PVSystem`` object is created and the +``PVSystem.ashraeiam`` *method* is called in the usual way. The +``PVSystem.ashraeiam`` method is supposed to call the +``pvsystem.ashraeiam`` function with the angles supplied to the method +call and the value of ``b`` that we defined in ``module_parameters``. +The ``pvsystem.ashraeiam.assert_called_once_with`` tests that this does, +in fact, happen. Finally, we check that the output of the method call is +reasonable. + +.. code-block:: python + + def test_PVSystem_ashraeiam(mocker): + # mocker is a pytest-mock object. + # mocker.spy adds code to a function to keep track of how it is called + mocker.spy(pvsystem, 'ashraeiam') + + # set up inputs + module_parameters = {'b': 0.05} + system = pvsystem.PVSystem(module_parameters=module_parameters) + thetas = 1 + + # call the method + iam = system.ashraeiam(thetas) + + # did the method call the function as we expected? + # mocker.spy added assert_called_once_with to the function + pvsystem.ashraeiam.assert_called_once_with(thetas, b=module_parameters['b']) + + # check that the output is reasonable, but no need to duplicate + # the rigorous tests of the function + assert iam < 1. + +Avoid writing PVSystem/Location tests that depend sensitively on the +return value of a statement as a substitute for using mock. These tests +are sensitive to changes in the functions, which is *not* what we want +to test here, and are difficult to maintain. + +ModelChain +---------- +The tests in test_modelchain.py should ensure that +``ModelChain.__init__`` correctly configures the ModelChain object to +eventually run the selected models. A test should ensure that the +appropriate method is actually called in the course of +``ModelChain.run_model``. A test should ensure that the model selection +does have a reasonable effect on the subsequent calculations, though the +precise values of the data should be covered by the function tests +discussed above. ``pytest-mock`` can also be used for testing ``ModelChain``. + +The example below shows how mock can be used to assert that the correct +PVSystem method is called through ``ModelChain.run_model``. + +.. code-block:: python + + def test_modelchain_dc_model(mocker): + # set up location and system for model chain + location = location.Location(32, -111) + system = pvsystem.PVSystem(module_parameters=some_sandia_mod_params, + inverter_parameters=some_cecinverter_params) + + # mocker.spy adds code to the system.sapm method to keep track of how + # it is called. use returned mock object m to make assertion later, + # but see example above for alternative + m = mocker.spy(system, 'sapm') + + # make and run the model chain + mc = ModelChain(system, location, + aoi_model='no_loss', spectral_model='no_loss') + times = pd.date_range('20160101 1200-0700', periods=2, freq='6H') + mc.run_model(times) + + # assertion fails if PVSystem.sapm is not called once + # if using returned m, prefer this over m.assert_called_once() + # for compatibility with python < 3.6 + assert m.call_count == 1 + + # ensure that dc attribute now exists and is correct type + assert isinstance(mc.dc, (pd.Series, pd.DataFrame)) + This documentation ------------------- +~~~~~~~~~~~~~~~~~~ If this documentation is unclear, help us improve it! Consider looking at the `pandas -documentation `_ for inspiration. diff --git a/docs/sphinx/source/modelchain.ipynb b/docs/sphinx/source/modelchain.ipynb index 8dff7dc2fc..b3e991679a 100644 --- a/docs/sphinx/source/modelchain.ipynb +++ b/docs/sphinx/source/modelchain.ipynb @@ -730,7 +730,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/docs/sphinx/source/package_overview.rst b/docs/sphinx/source/package_overview.rst index 3a245e368d..f8dd897749 100644 --- a/docs/sphinx/source/package_overview.rst +++ b/docs/sphinx/source/package_overview.rst @@ -282,10 +282,8 @@ change something about pvlib, then please make an issue on our How do I contribute? -------------------- -We're so glad you asked! Please see our -`wiki `_ -for information and instructions on how to contribute. -We really appreciate it! +We're so glad you asked! Please see :ref:`Contributing` for information and +instructions on how to contribute. We really appreciate it! Credits diff --git a/docs/sphinx/source/whatsnew/v0.6.0.rst b/docs/sphinx/source/whatsnew/v0.6.0.rst index 5ca7f3493e..009f015e62 100644 --- a/docs/sphinx/source/whatsnew/v0.6.0.rst +++ b/docs/sphinx/source/whatsnew/v0.6.0.rst @@ -5,13 +5,49 @@ v0.6.0 (___, 2018) API Changes ~~~~~~~~~~~ - +* pvsystem.calcparams_desoto now requires arguments for each module model parameter. +* removed irradiance parameter from ModelChain.run_model and ModelChain.prepare_inputs Enhancements ~~~~~~~~~~~~ -* Add sea surface albedo in irradiance.py (:issue:`458`) -* Implement first_solar_spectral_loss in modelchain.py (:issue:'359') +* Add sea surface albedo in ``irradiance.py`` (:issue:`458`) +* Implement :meth:`~pvlib.modelchain.ModelChain.first_solar_spectral_loss` + in ``modelchain.py`` (:issue:`359`) +* Clarify arguments ``Egref`` and ``dEgdT`` for + :func:`~pvlib.pvsystem.calcparams_desoto` (:issue:`462`) +* Add pvsystem.calcparams_pvsyst to compute values for the single diode equation + using the PVsyst v6 model (:issue:'470') +* Extend :func:`~pvlib.pvsystem.singlediode` with an additional keyword argument + ``method`` in ``('lambertw', 'newton', 'brentq')``, default is ``'lambertw'``, + to select a method to solve the single diode equation for points on the IV + curve. Selecting either ``'brentq'`` or ``'newton'`` as the method uses + :func:`~pvlib.singlediode_methods.bishop88` with the corresponding method. + (:issue:`410`) +* Implement new methods ``'brentq'`` and ``'newton'`` for solving the single + diode equation for points on the IV curve. ``'brentq'`` uses a bisection + method (Brent, 1973) that may be slow but guarantees a solution. ``'newton'`` + uses the Newton-Raphson method and may be faster but is not guaranteed to + converge. However, ``'newton'`` should be safe for well-behaved IV curves. + (:issue:`408`) +* Implement :func:`~pvlib.singlediode_methods.bishop88` for explicit calculation + of arbitrary IV curve points using diode voltage instead of cell voltage. If + ``method`` is either ``'newton'`` or ``'brentq'`` and ``ivcurve_pnts`` in + :func:`~pvlib.pvsystem.singlediode` is provided, the IV curve points will be + log spaced instead of linear. +* Implement :func:`~pvlib.singlediode_methods.estimate_voc` to estimate open + circuit voltage by assuming :math:`R_{sh} \to \infty` and :math:`R_s=0` as an + upper bound in bisection method for :func:`~pvlib.pvsystem.singlediode` when + method is either ``'newton'`` or ``'brentq'``. +* Add :func:`~pvlib.pvsystem.max_power_point` method to compute the max power + point using the new ``'brentq'`` method. +* Add new module ``pvlib.singlediode_methods`` with low-level functions for + solving the single diode equation such as: + :func:`~pvlib.singlediode_methods.bishop88`, + :func:`~pvlib.singlediode_methods.estimate_voc`, + :func:`~pvlib.singlediode_methods.bishop88_i_from_v`, + :func:`~pvlib.singlediode_methods.bishop88_v_from_i`, and + :func:`~pvlib.singlediode_methods.bishop88_mpp`. Bug fixes @@ -19,14 +55,27 @@ Bug fixes * Unset executable bits of irradiance.py and test_irradiance.py (:issue:`460`) * Fix failing tests due to column order on Python 3.6+ and Pandas 0.23+ (:issue:`464`) +* ModelChain.prepare_inputs failed to pass solar_position and airmass to + Location.get_clearsky. Fixed. (:issue:`481`) +* Add User-Agent specification to TMY3 remote requests to avoid rejection. + (:issue:`493`) Documentation ~~~~~~~~~~~~~ +* Expand testing section with guidelines for functions, PVSystem/Location + objects, and ModelChain. +* Updated several incorrect statements in ModelChain documentation regarding + implementation status and default values. (:issue:`480`) +* Expanded general contributing and pull request guidelines. Testing ~~~~~~~ +* Add pytest-mock dependency +* Use pytest-mock to ensure that PVSystem methods call corresponding functions + correctly. Removes implicit dependence on precise return values of functions +* Use pytest-mock to ensure that ModelChain DC model is set up correctly. Contributors @@ -34,4 +83,6 @@ Contributors * Will Holmgren * Yu Cao * Cliff Hansen +* Mark Mikofski +* Alan Mathew diff --git a/docs/tutorials/pvsystem.ipynb b/docs/tutorials/pvsystem.ipynb index 0339305f80..d75c024fd5 100644 --- a/docs/tutorials/pvsystem.ipynb +++ b/docs/tutorials/pvsystem.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -65,20 +65,20 @@ ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "import pvlib\n", - "from pvlib import pvsystem" + "### systemdef" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 4, "metadata": {}, + "outputs": [], "source": [ - "### systemdef" + "import pvlib\n", + "from pvlib import pvsystem" ] }, { @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -120,7 +120,7 @@ " 'tz': -9.0}" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -149,7 +149,7 @@ " 'tz': -5}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -167,24 +167,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5,0,'input angle (deg)')" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAFyCAYAAADlDFy/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -202,36 +202,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:820: RuntimeWarning: invalid value encountered in true_divide\n", - " ((tools.sind(thetar_deg + aoi)) ** 2) +\n", - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:822: RuntimeWarning: invalid value encountered in true_divide\n", - " ((tools.tand(thetar_deg + aoi)) ** 2))))))\n", - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:836: RuntimeWarning: invalid value encountered in less\n", - " iam = np.where((np.abs(aoi) >= 90) | (iam < 0), np.nan, iam)\n" - ] - }, { "data": { "text/plain": [ - "" + "Text(0.5,0,'input index')" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAFyCAYAAADlDFy/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt4lPWd///XPaecJkcSwkGCGEUUQURqVUQriNYjK4ig\nFf1VrbZb2mtda9XaqkWLVr9129Wu2+1uXXV7FexBa61VKx6oWBSpiEFOggTCIQTIaSYzmczc9++P\nZAajxAQzM/fMPc/HdXklc8jkzcfknlc+9/vzuQ3LsiwBAAAA+EwuuwsAAAAAsgHBGQAAABgAgjMA\nAAAwAARnAAAAYAAIzgAAAMAAEJwBAACAAfDYXcBANTW1211CSpWXF6q5ucPuMnIKY55+jHn6Mebp\nx5inH2Oefk4f86qq4kPez4xzhvB43HaXkHMY8/RjzNOPMU8/xjz9GPP0y9UxJzgDAAAAA0BwBgAA\nAAaA4AwAAAAMAMEZAAAAGACCMwAAADAABGcAAABgAAjOAAAAwAAQnAEAAIABIDgDAAAAA0BwBgAA\nAAYgpcH5vffe04IFCz51/yuvvKI5c+Zo3rx5euqpp1JZAgAAAJAUnlS98C9/+Us9++yzKigo6HV/\nV1eX7rvvPv3ud79TQUGBrrjiCk2fPl2VlZWpKgUABsyyLMVMS7GYpahpKho1FY1/HrO6b5tm9+Mx\nU6ZpybS6v860LFmWZJqWrJ7XMs2e+z7+eM9Hq+dj/PtKktWrGPW+z7I++dDH7/rYY9137moKqmpI\nkbwuyZAhSTIM9XzWfcOQpJ77DCPxyMHnxZ9ziOf1vt9Q/MsTH3u+wJDkchlyuwy5XS653YZcLkMe\nl9Fzv6vnMSPxWPy5Hrchn9ctr8cl18fqAwA7pCw419TU6OGHH9Z3v/vdXvdv2bJFNTU1Ki0tlSSd\nfPLJWrVqlc4///zPfL3y8kJ5PO5UlZsRqqqK7S4h5zDm6ZfKMY/FTLUGI2rviCjQ0aX2jojagxG1\nd3QpEOr+GApHFY5E1RmJKRT/2Nn9MRyJqrMrdsgwCvv5vG7led3K833iY8/nRQVe+Qu98hf45E98\n7lVRgVfFhT6Vl+SrKN/T6w+EVOHYkn6Mefrl4pinLDifd955amho+NT9gUBAxcUHB7qoqEiBQKDf\n12tu7khqfZmmqqpYTU3tdpeRUxjz9BvMmFuWpfZQl/Y2h7S3uUP7WsNqCUTU0t6p5kCnWgKdagtG\nDiv0ul1GInTl+9wqLfLJ53XJ43bJ6+meBfW4u2c9uz92z5bGP4/PmLpcRs8M7cHPXYYhl9E9O+ty\nGYmZWper56PR8zUfn9Ht0TvXGb3uO8RDidnkjz9oSOoIRzVkSJE6gp2S1TMT/bFZ7O6xsnqNWXym\nXD2PWx/7Aivx8RNf87HX/vjwW5aV+BrT6p7Fj5mWYqaZmNU/5P1m/L7umf1I1FSkK6ZINKZIl6nO\nrpjaOyLa39p92zyM/+lej0ulRT6V+fNU6u/+WFGSp6FlBaoqK9DQ8gLl+wb31sixJf0Y8/Rz+pj3\n9UdByoJzX/x+v4LBYOJ2MBjsFaQB5DbLstTc3qkdewNqaApox96A9hzoUFNLSKHO2CG/xutxqczv\n0zEjS1Xiz1NxgVdFBR4V5Xu7/4t/XuBVgc8tn7c7KHvczl8f7fQ3t3hrTWdXTB3hqDrCUQXDXZ/4\nGFUg1KW2YEQtPX9kbd3V1mfgLin0qqq8QCOGFGnUUH/iv8J8b5r/dQAyTdqDc21trerr69XS0qLC\nwkK98847uu6669JdBoAMEeqMauuuNm1uaNGWna3atqddwXC013N8Hpeqygs0tGdGcGh5oapK81VW\nnKfy4jwV5qXn9Dsyj2EYiTMCRYcRbE2z+wxGS3un9reFtbc5pKaWkPa2hNTUHNJHu9q1ZWdbr68Z\nUpKnMcNLdPQRZTrmiFKNGurPiT++AByUtuD8pz/9SR0dHZo3b55uu+02XXfddbIsS3PmzFF1dXW6\nygBgs5hpasvONr2/db/qth7Q9r3tvU77Dy0r0LjR5RpVdXCmb0hpPsEYSeVyGSot8qm0yKfRwz59\n1jMaM7Vnf4d29Jz1aNgb0Pa9Ab2zsUnvbGySJPm8Lh09slQTjhqiibVDNKyikJ9TwOEMy8qOZTBO\nPtUoOf90aiZizNOnKxrT2i379d7WA1q9Ya9Cnd0zyh630TODV6pjRpapdmSJigt9NlfrLPycJ49l\nWdrXGtbmhhZ92NCqzTtbtbPpYOthZWm+JtYO0bmnjVGl38suIGnEz3n6OX3MM6bHGUBuMC1L6+ub\n9fe6PfrHpiaFI939yZWl+Tr1+GpNOGqIxo0uG/RCLCBdDMNQVc8iwtNPGC5Jag10qu6jA1q7Zb/q\nPjqgV/6xU6/8Y6eGlOTplOOrdfr4YRpZ5be5cgDJwjsWgKQKhru0Yu1uvbpmlxoPdO+GM6QkT2ef\nNFLnnT5GxT4Xp7PhGKX+PE2dMFxTJwxXzDS1ob5F7209oBVrd+ovK7frLyu369hRZZpx8hGadEwl\nPdFAliM4A0iKlkCnXnx7u159d6ciXaY8bpdOGz9MZ00aoaOPKJXLMBx/ag+5ze1yafyYCn3plNGa\ne9YYvffhfr22Zqc+2NasjTtaVF6cpy+fUqOzJo2Qz+vs6xIATkVwBjAorcGInluxTa+/t0vRmKny\n4jzNmnqEzpg4nH5l5Cyvx60p44Zqyrih2rUvqFf/sVNvvL9bv1m2Wc+vrNf5p47W2SeNkNfhF/YC\nnIbgDOBz6YrG9NKqHfrz3+sVjsRUWZqvC04braknDJfXw+loIG5EZZG+cu5YXXzGkXrp7R1a9o8G\nLVm2WS+/s0Pzph+tyWOraF8CsgTBGcBhq9u6X0+8uFH7WsPyF3h11bm1OvPEEfRvAp+hpNCny75U\nqy9/sUbPvblNy1Y36OdP12lcTZkWnHeshg8psrtEAP0gOAMYsGC4S0uWbdaK9/fI7TJ07hdG6ZKp\nR3JFNeAw+Au8mj/jGJ01aYSWvvKh1m7Zr7sfW6VLpx2lc78wSi4Xs89ApiI4AxiQjdub9Ytn16kl\nEFFNtV/XXnCcaqoPvc8lgP4NH1Kkf5l7olZv3KsnXtyop179UP/Y1KQbLjlelaUFdpcH4BAIzgA+\nk2lZev7v9Xr6b1tlyNClZx6l879YQ1sGkCQnHztUY0eV6f9e2qRVG/bqh4+t0tcuPl4TayvtLg3A\nJ/DOB6BPoc6o/v13a/WH5VtV5s/TrV85SReffiShGUiy4kKfvj5rvP6/88eps8vUT3+7Vk8v36os\nubgvkDOYcQZwSAfawvrZ79Zqx96Axo+p0A0XH8/2ckAKGYahM08codHVxfr50+/rT29uU1NLSF+9\n4Dh2qgEyBL+JAD6lYW9A9z7xjnbsDehLJ43Uv8ydSGgG0mT0sGJ9/5opqh1ZopUfNOqhpWvUEe6y\nuywAIjgD+ITtje164DfvqiUQ0eVnH60F546V28WhAkinkkKfbpl/kk4+tkobd7TowSVrFAgRngG7\n8W4IIKF+T7se/M27Coa69NXzx+nLX6zhwgyATXxet77xTydo2sThqt/Trv+35F3CM2AzgjMASVJD\nU0AP/uZddYSjuvbC4zTtxBF2lwTkPJdh6Jrzx+nME0doe2NAP1myRqHOqN1lATmL4AxAB9rC+ren\n3lNHZ3donjphuN0lAejhMgxd/eVju2eeG9v1H0+/r2jMtLssICcRnIEcFwx36aGn3lNze6fmnl1L\naAYyUDw8Tzq6Uuu2NetXz6+XyVZ1QNoRnIEcZpqW/vOZOu3aF9TMKaP05VNq7C4JQB/cLpdunDVe\ntSNKtHJdo/7893q7SwJyDsEZyGF/WL5V67Y1a2LtEM2bcTQLAYEMl+d161uXTdSQkjw9s3yr1m7Z\nb3dJQE4hOAM56p0Ne/X8ynpVlxfohouPl4vQDGSFkkKfvjl7gtxul/7r2XXa29xhd0lAziA4Azlo\nX0tIv3p+vfK8bi2cPUGF+V67SwJwGI4cVqKrzztWHZ1RPfrMOhYLAmlCcAZyjGla+uVzHygciemq\nc8dqZJXf7pIAfA5nTByuqROGqb6xXX984yO7ywFyAsEZyDF/eatemxtaNWXcUJ1+wjC7ywEwCFee\nM1ZVZfl6/u/12rSjxe5yAMcjOAM5ZHtju57520cq8/t09XnHshgQyHIFeR597aLxkiH993MfqDMS\ns7skwNEIzkCOME1Lj7+wQTHT0rUXHid/AX3NgBMcfUSpzv/iaO1rDdOyAaQYwRnIEa++u1Mf7W7X\naeOrdcKYIXaXAyCJLpl6pKrK8vXSqh2q39NudzmAYxGcgRzQ3N6p37++RUX5Hs2bfozd5QBIMp/X\nravPGyfT6j6zZJpcVRBIBYIzkAOeevVDhSMxzT37aJUU+ewuB0AKjB9TodPGV2vbnna98f5uu8sB\nHIngDDjc1l1teuuDRo0eVqwzJg63uxwAKXTZl46Wz+vS08u3KhyJ2l0O4DgEZ8DBLMvS0lc2S5Lm\nTz+aqwMCDldenKfzvzharcGI/rJyu93lAI5DcAYc7B+b9mlzQ6tOOqZSx9aU210OgDT48ik1KvP7\n9OLb23WgLWx3OYCjEJwBhzJNS79/fYvcLkNzzz7a7nIApEmez61LzzxKkaip597cZnc5gKMQnAGH\nent9o/Yc6NDUCcM0rKLQ7nIApNHpJwxTdXmB/rZ2t/a1huwuB3AMgjPgQKZp6U9vbpPbZejC0460\nuxwAaeZ2uXTx1CMVMy09//d6u8sBHIPgDDjQqg17tXt/h04/YZiqygrsLgeADb54fLWqKwqZdQaS\niOAMOIxpdc82uwxDF55+pN3lALCJ2+XSJaf3zDqzwwaQFARnwGHe37Jfu/YFddr4ag1lthnIaV88\nvlqVpfl68/3dau+I2F0OkPUIzoDDvLRqhyTpvFNqbK4EgN1cLkMzp4xSJGrqtXd32l0OkPUIzoCD\nbG9s1/r6Zh03ulxHDPXbXQ6ADHDGxOEqyPNo2T92qisas7scIKsRnAEHOTjbPMrmSgBkioI8j86a\nNEJtwYhWrmu0uxwgqxGcAYdoCXTqrQ8aNayiUCccNcTucgBkkHNOPkJul6G/vrNDlmXZXQ6QtQjO\ngEP8be1uxUxL50w5Qi7DsLscABmkoiRfJx1TqYamoLbsarO7HCBrEZwBBzAtS397b5d8XpdOGz/M\n7nIAZKCzJo2UJL2+hkWCwOdFcAYcYP22Zu1rDeuUcdUqyPPYXQ6ADHTckeWqKsvXqvV71RHusrsc\nICsRnAEHeP29XZKkMyeNsLkSAJnKZRg6a9JIRaKm3qzbY3c5QFYiOANZrq0jonc3NWlkZZFqR5TY\nXQ6ADDZ1wnC5XYaW9/yxDeDwEJyBLPf3uj2KmZbOPHGEDBYFAvgMpUU+nXh09yLB7Y3tdpcDZB2C\nM5DlVq5rlNtl6NTx1XaXAiALnNZzrGBPZ+DwEZyBLLZ7f1D1je0aP6ZCxYU+u8sBkAUm1laqMM+j\nlR/skWmypzNwOAjOQBZ764PuGaNTj2e2GcDAeD0ufeG4oWoJRLRhe7Pd5QBZheAMZCnLsrTyg0b5\nvC5NOqbS7nIAZJH4fu9/Z3cN4LCkLDibpqk777xT8+bN04IFC1RfX9/r8V/96leaPXu25syZo7/+\n9a+pKgNwrG172rW3OaSTjqlSvo+9mwEM3NFHlGpISb5Wb2pSVzRmdzlA1khZcH755ZcViUS0dOlS\n3Xzzzbr//vsTj7W1temJJ57QkiVL9Ktf/UqLFy9OVRmAY8UX9nyRNg0Ah8llGJoyrkrhSEzrttGu\nAQxUyoLz6tWrNW3aNEnSpEmTVFdXl3isoKBAI0aMUCgUUigUYgst4DBZlqXVm/aqMM+jE8ZU2F0O\ngCx08rFDJUmrN+61uRIge6Ts/G4gEJDf70/cdrvdikaj8ni6v+Xw4cN14YUXKhaL6cYbb+z39crL\nC+XxuFNVbkaoqiq2u4Sck61j/uGOFh1o69SXTj5Cw4eV2l3OYcnWMc9mjHn6ZcOYDxniV0XJOr33\n4X6VVxTJ487uZU/ZMOZOk4tjnrLg7Pf7FQwGE7dN00yE5uXLl2vv3r1atmyZJOm6667T5MmTNXHi\nxD5fr7m5I1WlZoSqqmI1NbEZfTpl85gve7t7zcDxo8qy6t+QzWOerRjz9MumMT/p6Eot+0eD3li9\nQ+Oz+OxVNo25Uzh9zPv6oyBlf15OnjxZy5cvlyStWbNGY8eOTTxWWlqq/Px8+Xw+5eXlqbi4WG1t\nbakqBXCcdzc1yetxacJRQ+wuBUAWO/nYKkm0awADlbIZ55kzZ2rFihWaP3++LMvS4sWL9dhjj6mm\npkYzZszQm2++qcsvv1wul0uTJ0/W1KlTU1UK4CiNBzq0c19Qk46uVJ7P2e1LAFJr7KgyFRd69Y/N\n+3TVeZZcrDkCPlPKgrPL5dKiRYt63VdbW5v4/Nvf/ra+/e1vp+rbA4717uZ9kqST2LsZwCC5XIZO\nrK3UG+/vVv2edo0ZXmJ3SUBGy+6VAEAO+sfmJhmGdCLBGUASTKztbvlau2W/zZUAmY/gDGSRQKhL\nWxpaVTuyVCWFPrvLAeAAxx9ZIbfL0Not++wuBch4BGcgi3yw7YAsiUWBAJKmMN+jY44o1Ue729Ua\njNhdDpDRCM5AFnl/a/ep1AlHZe+2UQAyz8Ta7tavuq20awCfheAMZAnLslT30QEVF3pVU517m84D\nSB36nIGBITgDWaKhKajWQETjx1SwZRSApBo+pFCVpfmq++iAYqZpdzlAxiI4A1kifgp1whj6mwEk\nl2EYOmFMhUKdUW3b49yrwQGDRXAGskTdRwckKasviwsgcx13ZPexZf22ZpsrATIXwRnIAp1dMW3a\n0aLR1cUqKWIbOgDJN66mTJK0vp7gDPSF4AxkgQ93tipmWjruyHK7SwHgUMWFPo0a6tfmhlZFumJ2\nlwNkJIIzkAU2bu+eAYrPCAFAKhw3ulzRmKkPd7baXQqQkQjOQBbYUN8il2HomCMIzgBS5/ies1q0\nawCHRnAGMlxnJKaPdrdp9LBiFeR57C4HgIONHVUmt8vQBywQBA6J4AxkuM07WxQzLdo0AKRcvs+j\nMSNKtG1PmzrCUbvLATIOwRnIcBu3t0iSjq1hYSCA1Dt2VJksS9q6iz5n4JMIzkCG27C9uae/udTu\nUgDkgPixZlMDwRn4JIIzkME6IzFt292uI4fT3wwgPWpHlsqQ9GFDi92lABmH4AxksI92tylmWsw2\nA0ibonyvRlYVaeuuNkVjpt3lABmF4AxksPheqkePJDgDSJ9jjihTJGqqvrHd7lKAjEJwBjLYlp7g\nXEtwBpBG8bNcm3fQ5wx8HMEZyFCWZenDna2qLM1XmT/P7nIA5JD4xZY20+cM9EJwBjLUngMdCoaj\ntGkASLshpfmqKMnT5oZWWZZldzlAxiA4Axlqy842SbRpALDH0SNLFQh1aW9zyO5SgIxBcAYyFAsD\nAdjpqBHdx56PdrfZXAmQOQjOQIbasrNVPq9LRwwtsrsUADnoqOElkqStBGcggeAMZKCOcFQ79wU1\nZliJ3C5+TQGk36hqv1yGwYwz8DG8IwMZKL536lEjSmyuBECuyvO6dURVkbY3BrgQCtCD4AxkoG17\numd4jhxOcAZgnzEjStQVNbWzKWh3KUBGIDgDGWjb7u4Z5yOHFdtcCYBcNqbnj3faNYBuBGcgA9Xv\naVdRvkeVpfl2lwIghx1FcAZ6ITgDGSYY7tLelpCOHFYswzDsLgdADhteWSif10VwBnoQnIEMU7+n\np02D/mYANnO7XDqyulg79wUVjkTtLgewHcEZyDDbeoLz6Gr6mwHY78jhJbIsqWEvCwQBgjOQYbbt\nju+oQXAGYL+aar8kafvedpsrAexHcAYyzLY97fIXeDWkhIWBAOxXM7T7j/jtjQGbKwHsR3AGMkgg\n1KV9rWEWBgLIGMOGFMrjNrS9kRlngOAMZJAde7tndGrobwaQITxul0ZW+tXQFFTM5AqCyG0EZyCD\nNPQE51FD/TZXAgAHjar2KxoztWd/h92lALYiOAMZJD7jfATBGUAGqRkaXyBInzNyG8EZyCA7mgLy\nuF0aVlFgdykAkBBvH9vBAkHkOIIzkCFipqld+4IaWVkkt4tfTQCZI94+Vs8CQeQ43p2BDLG3OaSu\nqKkjhhbZXQoA9FKQ59HQsgLt2BuQZVl2lwPYhuAMZIh4f/OoKvqbAWSeUdV+BUJdam7vtLsUwDb9\nBucNGzakow4g57EwEEAmi/9R39DEpbeRu/oNzjfddFM66gByXgPBGUAGG1HZ3Ua2ax/BGbnL098T\njj76aD3yyCM68cQTlZ9/8BLAX/jCF1JaGJBrdjQFVOr3qaTQZ3cpAPApI6u6g/POfeysgdzVb3Bu\naWnRW2+9pbfeeitxn2EYeuKJJ1JaGJBLguEuHWjr1AljKuwuBQAOaWh5gTxugxln5LR+g/OTTz6Z\njjqAnLazp2cwPqMDAJnG7XJpWEWhdu3rkGlZchmG3SUBaddvj/POnTv11a9+Veeee66ampp09dVX\nq6GhIR21ATlj9/7u4DxiCMEZQOYaUVmkzq6YDrSG7S4FsEW/wfnOO+/Uddddp8LCQlVWVuqiiy7S\nrbfemo7agJyxa1+HpIOLbwAgE42sjPc5066B3NRvcG5ubtYZZ5whqbu3+fLLL1cgwMIAIJniM87D\nmXEGkMFGVHbv+kOfM3JVv8E5Pz9fe/bskdHTy/TOO+/I52PVP5BMu/cHVer3qTC/32UHAGCbgztr\nEJyRm/p9l77tttt04403avv27Zo1a5ZaW1v105/+tN8XNk1Td999tzZu3Cifz6d7771Xo0ePTjz+\n+uuv6+c//7ksy9L48eN11113JcI5kEvCkaj2t3XquNHldpcCAJ9paFmBPG5XYkEzkGv6Dc4TJ07U\n7373O23btk2xWExHHXXUgGacX375ZUUiES1dulRr1qzR/fffr0cffVSSFAgE9OCDD+qJJ55QRUWF\nfvnLX6q5uVkVFWzFhdyze39PfzNtGgAynMtlaPiQQu3eH2RnDeSkPoPzww8/rG9961u6/fbbD/n4\nfffd95kvvHr1ak2bNk2SNGnSJNXV1SUee/fddzV27Fj9+Mc/1o4dOzR37lxCM3JWor+5stDmSgCg\nfyMri7Rjb0D7WkIaWs5xC7mlz+BcXt592viUU075XC8cCATk9x+8dLDb7VY0GpXH41Fzc7Peeust\nPfPMMyosLNRXvvIVTZo0SWPGjOnz9crLC+XxuD9XLdmiqqrY7hJyTiaMeWtohyTp+NqqjKgn1XLh\n35hpGPP0c/KYHz26XCs/aFRH1Mqof2cm1ZIrcnHM+wzOv//973XVVVfpr3/9q/7jP/7jsF/Y7/cr\nGDzYA2Wapjye7m9XVlamCRMmqKqqSpI0ZcoUrV+//jODc3Nzx2HXkE2qqorV1NRudxk5JVPG/MPt\nzZKkAo+REfWkUqaMeS5hzNPP6WNenNf9Xr7xo/06MkMu2uT0Mc9ETh/zvv4o6DM4u1wuXXHFFdq4\ncaOuvvrqTz3e3yW3J0+erFdffVUXXHCB1qxZo7FjxyYeGz9+vDZt2qQDBw6opKRE7733ni6//PKB\n/lsAR9m1v0NF+R6VFHrtLgUA+jWsors9o/GAsye0gEPpMzg//vjjWr9+ve644w4tXLjwsF945syZ\nWrFihebPny/LsrR48WI99thjqqmp0YwZM3TzzTfr+uuvlyR9+ctf7hWsgVzRFTXV1BzSUSNL2FUG\nQFYYWl4gSdpDcEYO6jM4+/1+feELX9CSJUs+18I9l8ulRYsW9bqvtrY28fmFF16oCy+88LBfF3CS\nvc0dMi1LI4awwAZAdsjzujWkJI/gjJzUZ3C+9NJL9fTTT+v000+XYRiyLKvXx/Xr16ezTsCR4m88\nwyoyo08QAAaiuqJQH2xrVjgSVb6PCzchd/T50/70009LkjZs2JC2YoBc09gckiRV95z6BIBsMKwn\nODceCGn0sNzbWQG5q8/g/Mgjj3zmF36evmcAvcUX1wytoFUDQPao7jlm7TnQQXBGTnH194S1a9fq\npZdeksvlks/n0+uvv64PP/wwHbUBjre3OSRD0tCyfLtLAYABG/6x4Azkkj5nnOMzyvPnz9fSpUtV\nUNB9Kvmaa6455PZ0AA5fY3OHKkry5XX4xX0AOEs1W9IhR/U749zc3Nxrm6yuri61tLSktCggF3RG\nYmoJRFRdQX8zgOwypCRfHreLGWfknH6Xws6dO1dz5szRmWeeKdM09dprr+maa65JR22AozX2XA2z\nupz+ZgDZxeUyVF1eoD0HOhK7bQG5oN/gfP311+vUU0/V22+/LZfLpZ/97GcaN25cOmoDHG0vO2oA\nyGLVFYXauS+otmBEpf48u8sB0qLf4GxZltauXat3331XsVhMhmFo7Nixcrn67fIA8BniM85DmXEG\nkIWGfWyBIMEZuaLf4PzAAw+ovr5ec+bMkWVZ+sMf/qCGhgbdcccd6agPcKzGAz0zzvQ4A8hC8Utv\n720J6diacpurAdKj3+C8YsUKPfPMM4kZ5i996Uu6+OKLU14Y4HSNzR0yDKmqjOAMIPvEj11NLSGb\nKwHSp99+i1gspmg02uu2283WWcBgNTaHEivTASDbVPXsP9/UEra5EiB9+p1xvvjii3X11Vfrwgsv\nlCT9+c9/TnwO4PMJdUbVFoxo/JgKu0sBgM+lojhfbpfBjDNySr/B+etf/7qOO+44rVy5UpZl6etf\n/7q+9KUvpaE0wLnYUQNAtnO5DFWW5hOckVMGdI542LBhmj59umbMmKGioiKtWrUq1XUBjra3542G\nHTUAZLOqsgK1d3Qp1Bnt/8mAA/Q743zTTTfpgw8+UHV1tSzLkiQZhqEnnngi5cUBTrWvtTs4V5Xm\n21wJAHx+VeUF0kfdCwRrqovtLgdIuX6D84YNG/T888+zIBBIon09i2kq2VEDQBarKo3vrBEmOCMn\n9NuqceKJJ6q+vj4dtQA5o6lnxrmSGWcAWYwt6ZBr+p1xPvXUU3XRRRdp6NChcrvdiWvSL1u2LB31\nAY60ryXr8PYkAAAfW0lEQVSsonyPCvL6/RUEgIwVvwgKwRm5ot937Z/97Gd6/PHHNWLEiHTUAzie\naVna1xrWyKoiu0sBgEGJnzUjOCNX9Bucy8vLNWXKFBmGkY56AMdrDUQUjZksDASQ9QryPCou9BKc\nkTP6Dc7jxo3T5ZdfrtNPP11erzdx/8KFC1NaGOBU8R01WBgIwAmGlhVo2552maYll4tJNjhbv8F5\nxIgRtGkASRTfUYMZZwBOUFVWoC272nSgPazKUiYE4Gz9BmdmloHkYsYZgJPEj2VNzSGCMxxvQFcO\nBJA8Ta09ezgz4wzAAeJnz/b1HNsAJyM4A2m2r4U9nAE4x5CeY9n+NoIznI/gDKTZvtawSv0+eT1c\njRNA9htSQnBG7uizx3ncuHGJLegsy+r1mGEYWr9+fWorAxwoZpo60Napo0aU2F0KACRFRUmeJGk/\nrRrIAX0G5w0bNqSzDiAnHGjrlGlZqiyjTQOAM3g9bpUW+ZhxRk7od1eN/fv3609/+pOCwaAsy5Jp\nmmpoaNADDzyQjvoARznY38zKcwDOUVGSr+2N7TItSy4umAYH67fHeeHChVq/fr2effZZhUIhvfLK\nK3K5aI0GPo/9bZ2SWBgIwFmGlOYrZlpqDUTsLgVIqX4TcHNzs3784x9r+vTpOvfcc/Xkk09q8+bN\n6agNcJwDPacy4z2BAOAElSwQRI7oNziXlpZKksaMGaMNGzaouLhY0Wg05YUBTnSgvSc4FzPjDMA5\nWCCIXNFvj/Opp56qb3/727r11lt17bXXat26dcrLY7YM+DwO9LRqMOMMwEniezkfYMYZDtdvcL7p\nppu0fft2jRw5Ug899JBWrVqlb37zm+moDXCcA+2dKszzKN/X768eAGSN+F7O+wjOcLh+WzU2bdqk\nf/u3f5Mk5efn66WXXlIwGEx5YYDTWJal/W1hVZTQpgHAWRJXD6RVAw7Xb3D+/ve/r0svvVSSVFtb\nq29+85u64447Ul4Y4DShzqg6IzHaNAA4TveZNDeLA+F4/QbnUCikM888M3F76tSpCoVCKS0KcKKD\n/c3MOANwFsMwNKQ0nx5nOF6/wbmiokK/+c1vFAwGFQwG9dvf/lZDhgxJR22Ao8RnYoYw4wzAgYaU\n5CvUGVNHuMvuUoCU6Tc433fffXrttdd0xhln6Oyzz9Zrr72mH/3oR+moDXCUA+09M85sRQfAgRIL\nBOlzhoP1u7R/xIgR+sUvfpGOWgBH4+InAJysvLj72NYS6FRNdbHN1QCp0WdwvvHGG/WLX/xC06dP\nl3GI684vW7YspYUBThPvcS6nxxmAA8WDc3PP2TXAifoMzvfcc48k6cknn0xbMYCTHWgLy5BU7mfG\nGYDzEJyRC/rscR46dKgkqaioSPX19Ro5cqSee+45/fjHP2ZXDeBzONAeVkmRT15Pv0sLACDrxIPz\nAYIzHKzfd/Cbb75ZW7du1ZtvvqkXXnhB06dP11133ZWO2gDHMC1Lze2d9DcDcKxEjzPBGQ7Wb3Bu\nbW3VVVddpWXLlunSSy/VP/3TPzHjDBym9mBE0ZjFjhoAHCvf51FBnodWDThav8HZNE3V1dXp5Zdf\n1tlnn63169crFoulozbAMRJb0bEwEICDVRTnEZzhaP1uR3fLLbfogQce0Fe/+lWNGjVKl19+uW6/\n/fZ01AY4BlvRAcgFZcV52rkvqHAkqnxfvxEDyDr9/lSvW7dOP/nJT1RVVSVJeuqpp1JeFOA08RmY\neA8gADjRx3fWGD6E4Azn6bdVIxwO66qrrtINN9ygv/zlL+rq4lKawOFqCUQkSWVsRQfAwSpYIAiH\n6zc4L1y4UC+++KJuuOEGvfXWW5o1a5YWLVqk9evXp6M+wBFaAt1vImXMOANwsDK2pIPDDWhD2VAo\npIaGBu3YsUMul0ulpaX60Y9+pJ/85Ceprg9whERwLvLZXAkApE7Fxy67DThRvw1IN998s1auXKmz\nzjpL3/jGNzRlyhRJUiQS0RlnnKGbb775kF9nmqbuvvtubdy4UT6fT/fee69Gjx79qefccMMNmjFj\nhq644ook/HOAzNQSiKgo3yOf1213KQCQMuU9W24y4wyn6jc4n3baabrnnntUWFjY636fz6c///nP\nfX7dyy+/rEgkoqVLl2rNmjW6//779eijj/Z6zk9/+lO1tbV9ztKB7NHS3snCQACOl1gc2EZwhjP1\nG5xnzpypZ555Rs3NzbIsK3H/woULEzttHMrq1as1bdo0SdKkSZNUV1fX6/EXXnhBhmEkngM4VWdX\nTB2dUY0ZXmx3KQCQUkX5Hnk9LjXTqgGH6jc4f/Ob31RFRYWOOeYYGYYx4BcOBALy+/2J2263W9Fo\nVB6PR5s2bdJzzz2nf//3f9fPf/7zAb1eeXmhPB5nn+auqiJYpVs6xnz3vqAkqbrSz/9j8XNuB8Y8\n/XJ5zCtLC9QajKR9DHJ5zO2Si2Peb3BubW3V//3f/x32C/v9fgWDwcRt0zTl8XR/u2eeeUaNjY26\n5pprtHPnTnm9Xo0cOVJnnnlmn6/X3Nxx2DVkk6qqYjU1tdtdRk5J15hv3dEiSSrwunL+/zE/5+nH\nmKdfro95SaFXe/YHtXtPqzzuAe1BMGi5PuZ2cPqY9/VHQb/BeezYsaqrq9MJJ5xwWN9w8uTJevXV\nV3XBBRdozZo1Gjt2bOKx7373u4nPH374YVVWVn5maAayWWJHDfZwBpADyorzZElqC0ZUUZJvdzlA\nUvUZnKdPny7DMBQOh/X888+rurpabrdblmXJMAwtW7bsM1945syZWrFihebPny/LsrR48WI99thj\nqqmp0YwZM5L+DwEyVfxCAGV+tqID4HylPdtuthKc4UB9Bucnn3xyUC/scrm0aNGiXvfV1tZ+6nnf\n+ta3BvV9gEzHVQMB5JLSnkkC9nKGE/UZnEeOHClJ6urq0q9//WutXLlSHo9HZ511li677LK0FQhk\nO1o1AOSSsqLuY11rz6QB4CT99jh///vfVzgc1uWXXy7TNPXHP/5RmzZt0h133JGO+oCsFw/OpbRq\nAMgBzDjDyfoNzu+9955eeOGFxO3p06froosuSmlRgJM0ByIqLvSmbXU5ANiptOfsWmuQGWc4T7/v\n5MOHD1d9fX3i9r59+1RdXZ3SogAnaQl0qpw2DQA5IrE4kFYNOFC/M87RaFSzZs3SlClT5PF4tHr1\nalVVVenqq6+WJD3xxBMpLxLIVqHOqDojMZVxuW0AOaIo3yOP26XWIK0acJ5+g/Mnd7249tprU1YM\n4DQHFwbS3wwgNxiGodIiX2JHIcBJ+g3Op5xySjrqAByJregA5KIyv0/b9rTLtCy5DMPucoCkYbUS\nkEJsRQcgF5UU+RQzLQVDXXaXAiQVwRlIofjimPhiGQDIBfHJAhYIwmkIzkAKtfVsx1RCcAaQQxJ7\nObNAEA5DcAZSqJXgDCAHMeMMpyI4AynU1kFwBpB74u1pXD0QTkNwBlKoLRhRns+tPK/b7lIAIG3i\nrRpcPRBOQ3AGUqgtGFFpIbPNAHJLaRGtGnAmgjOQIqZpqb2jSyVc/ARAjikp8sqQ1EqrBhyG4Ayk\nSCDUJdOymHEGkHPcLpeKi3xqoVUDDkNwBlKEregA5LLSIl/iOAg4BcEZSJFWdtQAkMNKCr0KR2KK\ndMXsLgVIGoIzkCLMOAPIZcU9x772Di67DecgOAMpkgjO9DgDyEHxY198P3vACQjOQIrEg3MpM84A\nclD8bBt9znASgjOQIgdbNbw2VwIA6Vdc2H3sY8YZTkJwBlKExYEAclm8VYMeZzgJwRlIkbZgRD6v\nS/k+j92lAEDa0aoBJyI4AynSFoywMBBAzqJVA05EcAZSwLS6L7fNwkAAuSrRqsGMMxyE4AykQDDU\npZhp0d8MIGf5vG7l+9xqo8cZDkJwBlKAi58AQPesM60acBKCM5ACXPwEAKTiIq8CHV0yLcvuUoCk\nIDgDKcBWdADQPXkQMy11hKN2lwIkBcEZSIG2YHdPH4sDAeQytqSD0xCcgRQIhLrfJOLbMQFALipO\nXASF4AxnIDgDKRC/UpafHmcAOawksZczO2vAGQjOQAoEet4kmHEGkMto1YDTEJyBFGjviMiQ5M8n\nOAPIXfGdhQjOcAqCM5AC7aEuFRV45XIZdpcCALYpLqLHGc5CcAZSoL2jS/4CZpsB5DZ6nOE0BGcg\nyUzTUjDcRX8zgJxXVOCVYdCqAecgOANJFgx3ybLEjDOAnOcyDPkLvAqEmHGGMxCcgSSLv0EUsxUd\nABCc4SgEZyDJ2tmKDgASigu8Coa6ZJqW3aUAg0ZwBpIsEZxp1QAA+Qt9stTdxgZkO4IzkGTxy237\nmXEGgMR6D9o14AQEZyDJEpfbLqDHGQDibWsEZzgBwRlIsoOLA5lxBoCiniuoBtjLGQ5AcAaSLH6F\nLHqcAeDgJEI7M85wAIIzkGTtbEcHAAn0OMNJCM5AkrV3dMnrccnn5dcLAOILpWnVgBPwzg4kWaCj\n+3LbhmHYXQoA2C7ettYe4rLbyH4EZyDJAqEuLrcNAD3iOwwx4wwnIDgDSRTpiqmzK0Z/MwD0KMhz\ny+0y6HGGIxCcgSRKbEXHjDMASJIMw5C/wEtwhiN4UvXCpmnq7rvv1saNG+Xz+XTvvfdq9OjRicf/\n93//V3/+858lSWeddZYWLlyYqlKAtElc/IQ9nAEgwV/oVUt7p91lAIOWshnnl19+WZFIREuXLtXN\nN9+s+++/P/HYjh079Oyzz2rJkiV66qmn9MYbb2jDhg2pKgVIm/jiF2acAeAgf75XwXBUMdO0uxRg\nUFIWnFevXq1p06ZJkiZNmqS6urrEY8OGDdN///d/y+12yzAMRaNR5eXlpaoUIG3iM870OAPAQfGz\ncMFQ1OZKgMFJWatGIBCQ3+9P3Ha73YpGo/J4PPJ6vaqoqJBlWXrggQd0/PHHa8yYMZ/5euXlhfJ4\n3KkqNyNUVRXbXULOSfqYu/ZKkkYMK+H/Zx8Yl/RjzNOPMe+tqqJIUpO8+d6UjQ1jnn65OOYpC85+\nv1/BYDBx2zRNeTwHv11nZ6e+973vqaioSHfddVe/r9fc3JGSOjNFVVWxmpra7S4jp6RizHc3BSRJ\nZiTK/89D4Oc8/Rjz9GPMP81jWJKk7TtbVOBO/h73jHn6OX3M+/qjIGWtGpMnT9by5cslSWvWrNHY\nsWMTj1mWpX/+53/Wscceq0WLFsntdvZMMnJHMNyzOJAeZwBISOzlzM4ayHIpm3GeOXOmVqxYofnz\n58uyLC1evFiPPfaYampqZJqm3n77bUUiEf3tb3+TJP3rv/6rTjrppFSVA6RFsOdNoYjgDAAJB68e\nSHBGdktZcHa5XFq0aFGv+2praxOfv//++6n61oBtguHuhS9F+Sn71QKArHNwcSDBGdmNC6AASRQI\ndcnnccnnpf0IAOLi7WvtXHYbWY7gDCRRMNRFmwYAfEI8ONPjjGxHcAaSKBiO0qYBAJ9QlN8dnDvC\n7OOM7EZwBpIkZpoKdUYTbxAAgG4FeW65DEOBMDPOyG4EZyBJ4jMpbEUHAL0ZhqHCfA+LA5H1CM5A\nkiR21CigVQMAPqko35M4TgLZiuAMJEliD2daNQDgU4oKvOoId8myLLtLAT43gjOQJAEufgIAfSrK\n9yoasxTpMu0uBfjcCM5AksQvt82uGgDwafFjY5AFgshiBGcgSYKh+FUDmXEGgE+KHxvZyxnZjOAM\nJEl8FoVdNQDg0+ILp9nLGdmM4AwkSWLGmeAMAJ8Sn3GmVQPZjOAMJAk9zgDQt8JEjzMzzsheBGcg\nSdhVAwD6Fj82chEUZDOCM5AkwXCXPG6XfB5+rQDgk/yJVg1mnJG9eIcHkiQYiqqowCPDMOwuBQAy\nTnxxID3OyGYEZyBJguEudtQAgD4kFgfSqoEsRnAGksA0LXWEo+zhDAB9YHEgnIDgDCRBR2dUlthR\nAwD64nG7lOdz06qBrEZwBpIgsRUdrRoA0Cd/viex5z2QjQjOQBLEt6Lz06oBAH0qyvcy44ysRnAG\nkuDgVQNp1QCAvhTmexSOxBSNmXaXAnwuBGcgCQ5eNZAZZwDoS7ydraOTdg1kJ4IzkAQdPavEC1kc\nCAB9Yks6ZDuCM5AE8dkTgjMA9O3gRVCYcUZ2IjgDSdDR06pRmEerBgD0hRlnZDuCM5AE8VYN9nEG\ngL7Fj5EdzDgjSxGcgSSIt2oUEJwBoE/xGecAW9IhSxGcgSRILA7MIzgDQF/iM860aiBbEZyBJOjo\njMrndcnj5lcKAPpS2DPjHOqM2VwJ8PnwLg8kQSgcZbYZAPoRb2fr6GTGGdmJ4AwkQTDcxcVPAKAf\n8QkGFgciWxGcgUGyLEsdnVEWBgJAPwry3JKkEFcORJYiOAODFI7EZFksDASA/rhdLuX73Mw4I2sR\nnIFBCnHVQAAYsMJ8T2ILTyDbEJyBQWIrOgAYuMI8DzPOyFoEZ2CQgvHLbbM4EAD6VZjnUagzKtOy\n7C4FOGwEZ2CQ4qccmXEGgP4V5ntlSQqzlzOyEMEZGKREqwY9zgDQr4I89nJG9iI4A4PEjDMADFx8\nkoE+Z2QjgjMwSCFmnAFgwOKTDOzljGxEcAYGKdgTnLlyIAD0r4gZZ2QxgjMwSPE+Pa4cCAD9ix8r\n2csZ2YjgDAwS+zgDwMAV5nWfnWPGGdmI4AwMUrxPryDPbXMlAJD5CplxRhYjOAOD1BGOKt/nltvF\nrxMA9Cd+do4ZZ2Qj3umBQQqGo4nFLgCAz3Zwxpl9nJF9CM7AIHV0RlWQx44aADAQ7OOMbEZwBgbB\ntCyFO6Ps4QwAA1TgIzgjexGcgUEId0ZliR01AGCgXC5DBXluFgciKxGcgUHo4KqBAHDYCvM8zDgj\nKxGcgUGIz5gw4wwAA1eQ52XGGVkpZe/2pmnq7rvv1saNG+Xz+XTvvfdq9OjRicefeuopLVmyRB6P\nR9/4xjd09tlnp6oUIGWqywt10jGVmjJuqN2lAEDWOPukEWpsDtldBnDYUhacX375ZUUiES1dulRr\n1qzR/fffr0cffVSS1NTUpCeffFK///3v1dnZqSuvvFJTp06Vz+dLVTlASuT53PrWnIl2lwEAWeXs\nyUfYXQLwuaSsVWP16tWaNm2aJGnSpEmqq6tLPLZ27VqddNJJ8vl8Ki4uVk1NjTZs2JCqUgAAAIBB\nS9mMcyAQkN/vT9x2u92KRqPyeDwKBAIqLi5OPFZUVKRAIPCZr1deXiiPx9mXNK6qKu7/SUgqxjz9\nGPP0Y8zTjzFPP8Y8/XJxzFMWnP1+v4LBYOK2aZryeDyHfCwYDPYK0ofS3NyRmkIzRFVVsZqa2u0u\nI6cw5unHmKcfY55+jHn6Mebp5/Qx7+uPgpS1akyePFnLly+XJK1Zs0Zjx45NPDZx4kStXr1anZ2d\nam9v15YtW3o9DgAAAGSalM04z5w5UytWrND8+fNlWZYWL16sxx57TDU1NZoxY4YWLFigK6+8UpZl\n6aabblJeXl6qSgEAAAAGzbAsy7K7iIFw8ukAyfmnPDIRY55+jHn6Mebpx5inH2Oefk4f87S3agAA\nAABOQnAGAAAABoDgDAAAAAwAwRkAAAAYAIIzAAAAMAAEZwAAAGAACM4AAADAAGTNPs4AAACAnZhx\nBgAAAAaA4AwAAAAMAMEZAAAAGACCMwAAADAABGcAAABgAAjOAAAAwAAQnG3y17/+VTfffHOv2+ec\nc44WLFigBQsW6O2335Zpmrrzzjs1b948LViwQPX19TZWnP0+OeZr1qzR3LlzNX/+fD3yyCOSxJin\ngGVZmjZtWuJn+yc/+Ykk6ZVXXtGcOXM0b948PfXUUzZX6Sz8HKfXpZdemvj5vv322w95bEFyvPfe\ne1qwYIEkqb6+XldccYWuvPJK3XXXXTJNU5L0yCOP6LLLLtP8+fO1du1aO8t1hI+P+QcffNDreP78\n889LyrExt5B299xzj3XeeedZ//Iv/5K476GHHrJeeOGFXs978cUXrVtvvdWyLMt69913ra9//etp\nrdNJDjXml1xyiVVfX2+Zpmldf/311rp16xjzFNi2bZt144039rovEolY55xzjtXS0mJ1dnZas2fP\ntpqammyq0Hn4OU6fcDhszZo1q9d9hzq2YPD+67/+y7rooousuXPnWpZlWTfeeKO1cuVKy7Is6wc/\n+IH10ksvWXV1ddaCBQss0zStnTt3WrNnz7az5Kz3yTF/6qmnrP/5n//p9ZxcG3NmnG0wefJk3X33\n3b3uW7dunX7/+9/ryiuv1P33369oNKrVq1dr2rRpkqRJkyaprq7Ohmqd4ZNjHggEFIlEVFNTI8Mw\ndMYZZ+jNN99kzFNg3bp1amxs1IIFC/S1r31NW7du1ZYtW1RTU6PS0lL5fD6dfPLJWrVqld2lOgY/\nx+mzYcMGhUIhXXvttbr66qu1atWqQx5bMHg1NTV6+OGHE7fXrVunU045RZJ05plnJo7hZ5xxhgzD\n0IgRIxSLxXTgwAG7Ss56nxzzuro6vfbaa/rKV76i733vewoEAjk35h67C3Cy3/72t3r88cd73bd4\n8WJdcMEFeuutt3rdP3XqVJ1zzjk64ogjdNddd2nJkiUKBALy+/2J57jdbkWjUXk8/G/ry0DH/JNj\nW1RUpB07djDmg3So8b/zzjt1ww036Pzzz9c777yjW265RbfffruKi4sTzykqKlIgEEh3uY7Fz3H6\n5Ofn67rrrtPcuXO1bds2fe1rX1NJSUni8fixBYN33nnnqaGhIXHbsiwZhiGpe5zb29sVCARUVlaW\neE78/oqKirTX6wSfHPOJEydq7ty5OuGEE/Too4/q5z//uYqLi3NqzDmKptDcuXM1d+7cAT13zpw5\niYPtjBkz9OKLL6q4uFjBYDDxHNM0eePrx0DH3O/39xrbYDCokpIShcNhxnwQDjX+oVBIbrdbkjRl\nyhTt3bv3kOP/8SCNwfnk+PJznDpjxozR6NGjZRiGxowZo+LiYrW0tCQejx9bkHwu18GT5vFx5tiS\nWjNnzkz8PM+cOVP33HOPZsyYkVNjTqtGBrAsS5dccon27NkjSfr73/+u8ePHa/LkyVq+fLmk7oVs\nY8eOtbNMR/H7/fJ6vdq+fbssy9Ibb7yhKVOmMOYp8MgjjyRmoTds2KDhw4ertrZW9fX1amlpUSQS\n0TvvvKOTTjrJ5kqdg5/j9Pnd736n+++/X5LU2NioUCikwsLCTx1bkHzHH3984kzi8uXLE8fwN954\nQ6ZpateuXTJN07Ezn3a47rrrEov/Pp5VcmnMmYLIAIZh6N5779XChQuVn5+v2tpaXX755XK73Vqx\nYoXmz58vy7K0ePFiu0t1lB/+8If6zne+o1gspjPOOEMnnniiJkyYwJgn2Q033KBbbrlFr7/+utxu\nt+677z55vV7ddtttuu6662RZlubMmaPq6mq7S3WMmTNn8nOcJpdddpluv/12XXHFFTIMQ4sXL5bL\n5frUsQXJd+utt+oHP/iBHnroIR111FE677zz5Ha7NWXKFM2bNy+xuwyS5+6779Y999wjr9eryspK\n3XPPPfL7/Tk15oZlWZbdRQAAAACZjlYNAAAAYAAIzgAAAMAAEJwBAACAASA4AwAAAANAcAYAAAAG\ngOAMAGn2/vvv64477kjqa+7YsUPf+973kvK9jj322GSVBQCOwj7OAJBmEyZM0IQJE5L6mrt27Trk\npZ1T8b0AIFcx4wwAafbWW29pwYIFkqQFCxbogQce0Lx58zRz5ky9/vrrkqTbbrtNt99+u+bMmaPz\nzjtPzzzzjCTp4Ycf1sMPP5x4renTp6uhoUH33nuv6urq9MMf/vCwv1dDQ4OuuOIKzZo1q9fFC4LB\noG699VbNnj1bs2bN0nPPPSdJuu+++3TLLbdIkv70pz9p3rx5isViqRgqAMgoBGcAsFlXV5eWLl2q\n22+/XT/72c8S9zc2NmrJkiV6/PHH9cADD6ipqanP1/j+97+vE044QXfddddhf6977rlHs2fP1h//\n+EdNnjw58dxHH31U48eP1x/+8Af9+te/1n/+539qx44duummm1RXV6fnnntODz30kB588EG53e5B\njgIAZD6CMwDYbNq0aZKkY445Ri0tLYn7Z8+eLa/Xq2HDhmny5MlavXp1Sr7X22+/rfPPP1+SdMkl\nl8jr9UqS3nzzTS1ZskSzZs3SV77yFXV0dGjz5s3Kz8/Xfffdp+985zu6/vrrVVNTM+i6ACAb0OMM\nADbLy8uTJBmG0ev+j8/imqYpj8cjwzBkmmbi/q6urqR8L8uyEvfHHzNNUw8++KDGjx8vSdq3b59K\nS0slSR999JEqKipUV1d3WN8fALIZM84AkKH+8pe/yLIs7dy5U2vXrtXJJ5+s8vJyffjhh5KktWvX\nJto33G63otHo5/o+p59+up599llJ0ksvvaRIJCJJOvXUU/Wb3/xGkrR3715dcskl2r17txobG/XT\nn/5US5cu1fr16xO90gDgdARnAMhQ4XBYc+bM0Y033qhFixapvLxcF1xwgVpaWnTBBRfoySef1PHH\nHy9Jqq2tVXt7e2LR3uG488479eKLL+riiy/W66+/rqKiIknSwoULFQ6HddFFF+maa67RLbfcopqa\nGv3gBz/QV7/6VY0aNUqLFi3SXXfdpba2tqT+2wEgExlW/PwcACBj3HbbbTrllFM0e/Zsu0sBAPRg\nxhkAAAAYAGacAQAAgAFgxhkAAAAYAIIzAAAAMAAEZwAAAGAACM4AAADAABCcAQAAgAEgOAMAAAAD\n8P8DD6u4CT/nj1AAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -249,24 +237,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAFyCAYAAADlDFy/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -311,25 +299,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -354,7 +342,7 @@ "0 43.509191 40.809191" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -366,24 +354,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAF5CAYAAABk/Bd/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRGrw/wGAXvDq27SZwgAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -410,24 +398,24 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'temperature (deg C)')" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFyCAYAAAA6QiagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJymqhRBCCCGE8JMU1UIIIYQQQvjp/wc8u0KYWt9z7wAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -453,24 +441,24 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'temperature (deg C)')" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERGRIVKIFhEREREZIoVoEREREZEh+v8BomIBLOGRs1wAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -496,24 +484,24 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'temperature (deg C)')" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAFyCAYAAAA+t/adAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbs33yEDAXAAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -539,24 +527,24 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'temperature (deg C)')" ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAH0CAYAAAApEHtHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOVgwExERERE9x38AVMGhotYBz2gAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -590,393 +578,393 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_ABB__MICRO_0_25_I_OUTD_US_208__208V__208V__CEC_2018_ABB__MICRO_0_25_I_OUTD_US_240_240V__CEC_2014_ABB__MICRO_0_3HV_I_OUTD_US_208_208V__CEC_2014_ABB__MICRO_0_3HV_I_OUTD_US_240_240V__CEC_2014_ABB__MICRO_0_25_I_OUTD_US_240__240V__240V__CEC_2018_ABB__MICRO_0_3_I_OUTD_US_208_208V__CEC_2014_ABB__MICRO_0_3_I_OUTD_US_208__208V__208V__CEC_2018_ABB__MICRO_0_3_I_OUTD_US_240_240V__CEC_2014_ABB__PVI_3_0_OUTD_S_US_Z_M_A__208_V__208V__CEC_2014_ABB__PVI_3_0_OUTD_S_US_Z_M_A__240_V__240V__CEC_2014_ABB__PVI_3_0_OUTD_S_US_Z_M_A__277_V__277V__CEC_2014_ABB__PVI_3_6_OUTD_S_US_Z_M__208_V__208V__CEC_2014_ABB__MICRO_0_3_I_OUTD_US_240__240V__240V__CEC_2018_ABB__MICRO_0_3HV_I_OUTD_US_208_208V__CEC_2014_ABB__MICRO_0_3HV_I_OUTD_US_208__208V__208V__CEC_2018_...Yes!_Solar_Inc___ES5000__240V__240V__CEC_2009_Yes!_Solar_Inc___ES5300__208V__208V__CEC_2009_Yes!_Solar_Inc___ES5300__240V__240V__CEC_2009_Zhejiang_Yuhui_Solar_Energy_Source__Replus_250A_240V__CEC_2012_Zhejiang_Yuhui_Solar_Energy_Source__Replus_250B_208V__CEC_2012_Zigor__Sunzet_2_TL_US_240V__CEC_2011_Zigor__Sunzet_3_TL_US_240V__CEC_2011_Zigor__Sunzet_4_TL_US_240V__CEC_2011_Zigor__Sunzet_5_TL_US_240V__CEC_2011_Zigor__SUNZET4_USA_240V__CEC_2011_i_Energy_Corporation__GT260_240V__CEC_2013_i_Energy__GT260__240V__240V__CEC_2018_iPower__SHO_1_1__120V__120V__CEC_2018_iPower__SHO_2_0__240V__240V__CEC_2018_iPower__SHO_2_5__240V__240V__CEC_2018_iPower__SHO_3_0__240V__240V__CEC_2018_iPower__SHO_3_5__240V__240V__CEC_2018_iPower__SHO_4_6__208V__208V__CEC_2018_iPower__SHO_4_8__240V__240V__CEC_2018_iPower__SHO_5_2__240V__240V__CEC_2018_
Vac208.000000240.000000208.000000240.000000208.000000240.000000208.000000208.000000240.000000240.000000277.000000208.000000208.000000...240.000000208.000000240.0000002.400000e+02208.000000120.000000240.000000240.000000240.000000240.000000208.000000240.000000240.000000
Paco250.000000250.000000250.000000250.000000300.000000300.000000300.000000300.000000300.000000300.0000003000.0000003000.0000003000.0000003600.000000...4900.000000230.000000230.0000001100.0000002000.0000002500.0000003000.0000003500.0000004600.0000005300.0000002.251900e+02213.8300002110.0000003180.0000004160.0000005240.0000004030.0000004800.0000005200.000000
Pdco259.522050259.589000259.552697312.523347312.022059259.492000311.714554311.669000311.5049613147.0095283125.7582223110.3429423759.288140311.581000312.523347312.423000...5135.5841324829.4224095571.1809562.348419e+02225.5630552191.8251293313.6758054342.4093145495.8299264267.477069245.790658245.6300001194.0900002161.8800002632.8400003205.9300003641.8300004797.8100004968.0300005382.860000
Vdco40.24260340.00000039.98224645.25942945.49500940.00000040.22711140.00000040.136095313.429286340.842937389.986270309.94825440.00000045.25942945.000000...275.000000275.000000274.9000002.846843e+0128.632617399.207333389.513254388.562050386.082539302.85170740.70952440.000000182.000000199.000000218.000000222.500000263.000000254.000000263.000000280.000000
Pso1.7716142.0896101.9311941.8826201.9285912.2404101.9710531.8465101.99134218.10412219.86611222.72013524.2022121.9505401.8826201.762100...29.35894326.07150628.5190331.646711e+001.84502930.84370331.26504631.60170432.45080837.3727662.5116752.53010022.09540024.46580042.77650031.68200064.76810054.57010085.14570062.486700
C0-0.000025-0.000041-0.000027-0.000049-0.000035-0.000039-0.000036-0.000033-0.000031-0.000009-0.000007-0.000006-0.000005-0.000034-0.000049-0.000045...0.0000480.000062-0.000021-0.000013-0.000014-0.000008-0.000009-0.000006-0.000006-0.000006-3.860000e-07-0.000121-0.000004-0.000006-0.000004-0.000005-0.000009
C1-0.000090-0.000091-0.000158-0.000241-0.000228-0.000132-0.000256-0.000192-0.000289-0.000012-0.000025-0.0000440.000002-0.000256-0.000241-0.000198...0.0000200.0000240.000019-3.580000e-04-0.000533-0.000077-0.000095-0.000079-0.000097-0.000029-0.000086-0.0000980.0000570.0000550.0000610.0000360.0000350.0000280.0000340.000044
C20.0006690.0004940.0014800.000975-0.0002240.002418-0.0008330.000907-0.0021100.0016200.0010500.0000360.0017300.0024530.0009750.002208...0.0018700.0026200.001630-1.350000e-020.0259000.0005020.0002610.000213-0.0002510.002150-0.0025900.0002310.0020010.0017030.0020530.0017080.0014170.0013810.0005860.001260
C3-0.018900-0.013171-0.034600-0.027600-0.039600-0.014926-0.039100-0.031742-0.049500-0.000217-0.000471-0.0015500.001140-0.028223-0.027600-0.023681...-0.0002760.000468-0.000371-3.350684e+01-0.066800-0.003260-0.001960-0.001870-0.002340-0.0019000.1576760.1210320.0006230.0003150.0015300.0008600.0012180.0008890.0001950.000367
Pnt0.0200000.0200000.0500000.0600000.0600000.0500000.0200000.0200000.0500000.1000000.1000000.2000000.1000000.0500000.0600000.060000...0.5000000.5000000.5000001.700000e-010.1700000.2500000.2500000.2000000.2000000.1900000.1500000.1500003.6000003.6000003.9000003.6300003.8600004.0000004.1000004.000000
Vdcmax65.00000050.00000065.00000079.00000079.00000050.00000065.00000050.00000065.000000600.000000600.000000600.000000600.00000050.00000079.00000060.000000...600.000000600.000000600.0000005.500000e+0155.000000500.000000500.000000500.000000500.000000600.00000059.00000049.000000380.000000380.000000400.000000380.000000400.000000400.000000400.000000400.000000
Idcmax10.0000006.48971010.00000010.50000010.5000006.48730010.0000007.79173010.00000020.00000020.00000020.00000032.0000007.78952010.5000006.942740...25.00000025.00000025.0000001.400000e+0114.00000014.60000022.00000028.00000035.30000020.00000010.0000006.1407606.56096010.86370012.07720014.40870013.84730018.88900018.88980019.224500
Mppt_low20.00000030.00000020.00000030.00000030.00000030.00000030.000000160.000000160.000000160.000000120.00000030.00000030.00000030.000000...200.000000200.000000200.0000002.200000e+0122.000000150.000000150.000000150.000000150.00000030.00000030.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000240.000000
Mppt_high50.00000050.00000075.00000075.00000050.00000050.000000530.000000530.000000530.000000530.00000050.00000050.00000050.00000050.00000075.00000060.000000...550.000000550.000000550.0000004.500000e+0145.000000450.000000450.000000450.000000450.000000480.00000050.00000049.000000380.000000380.000000400.000000380.000000400.000000400.000000400.000000400.000000
\n", - "

14 rows × 1799 columns

\n", + "

14 rows × 5100 columns

\n", "
" ], "text/plain": [ @@ -996,6 +984,22 @@ "Mppt_low 20.000000 \n", "Mppt_high 50.000000 \n", "\n", + " ABB__MICRO_0_25_I_OUTD_US_208__208V__208V__CEC_2018_ \\\n", + "Vac 208.000000 \n", + "Paco 250.000000 \n", + "Pdco 259.589000 \n", + "Vdco 40.000000 \n", + "Pso 2.089610 \n", + "C0 -0.000041 \n", + "C1 -0.000091 \n", + "C2 0.000494 \n", + "C3 -0.013171 \n", + "Pnt 0.020000 \n", + "Vdcmax 50.000000 \n", + "Idcmax 6.489710 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 50.000000 \n", + "\n", " ABB__MICRO_0_25_I_OUTD_US_240_240V__CEC_2014_ \\\n", "Vac 240.000000 \n", "Paco 250.000000 \n", @@ -1012,37 +1016,21 @@ "Mppt_low 20.000000 \n", "Mppt_high 50.000000 \n", "\n", - " ABB__MICRO_0_3HV_I_OUTD_US_208_208V__CEC_2014_ \\\n", - "Vac 208.000000 \n", - "Paco 300.000000 \n", - "Pdco 312.523347 \n", - "Vdco 45.259429 \n", - "Pso 1.882620 \n", - "C0 -0.000049 \n", - "C1 -0.000241 \n", - "C2 0.000975 \n", - "C3 -0.027600 \n", - "Pnt 0.060000 \n", - "Vdcmax 79.000000 \n", - "Idcmax 10.500000 \n", - "Mppt_low 30.000000 \n", - "Mppt_high 75.000000 \n", - "\n", - " ABB__MICRO_0_3HV_I_OUTD_US_240_240V__CEC_2014_ \\\n", - "Vac 240.000000 \n", - "Paco 300.000000 \n", - "Pdco 312.022059 \n", - "Vdco 45.495009 \n", - "Pso 1.928591 \n", - "C0 -0.000035 \n", - "C1 -0.000228 \n", - "C2 -0.000224 \n", - "C3 -0.039600 \n", - "Pnt 0.060000 \n", - "Vdcmax 79.000000 \n", - "Idcmax 10.500000 \n", - "Mppt_low 30.000000 \n", - "Mppt_high 75.000000 \n", + " ABB__MICRO_0_25_I_OUTD_US_240__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 250.000000 \n", + "Pdco 259.492000 \n", + "Vdco 40.000000 \n", + "Pso 2.240410 \n", + "C0 -0.000039 \n", + "C1 -0.000132 \n", + "C2 0.002418 \n", + "C3 -0.014926 \n", + "Pnt 0.050000 \n", + "Vdcmax 50.000000 \n", + "Idcmax 6.487300 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 50.000000 \n", "\n", " ABB__MICRO_0_3_I_OUTD_US_208_208V__CEC_2014_ \\\n", "Vac 208.000000 \n", @@ -1060,6 +1048,22 @@ "Mppt_low 30.000000 \n", "Mppt_high 50.000000 \n", "\n", + " ABB__MICRO_0_3_I_OUTD_US_208__208V__208V__CEC_2018_ \\\n", + "Vac 208.000000 \n", + "Paco 300.000000 \n", + "Pdco 311.669000 \n", + "Vdco 40.000000 \n", + "Pso 1.846510 \n", + "C0 -0.000033 \n", + "C1 -0.000192 \n", + "C2 0.000907 \n", + "C3 -0.031742 \n", + "Pnt 0.020000 \n", + "Vdcmax 50.000000 \n", + "Idcmax 7.791730 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 50.000000 \n", + "\n", " ABB__MICRO_0_3_I_OUTD_US_240_240V__CEC_2014_ \\\n", "Vac 240.000000 \n", "Paco 300.000000 \n", @@ -1076,250 +1080,234 @@ "Mppt_low 30.000000 \n", "Mppt_high 50.000000 \n", "\n", - " ABB__PVI_3_0_OUTD_S_US_Z_M_A__208_V__208V__CEC_2014_ \\\n", - "Vac 208.000000 \n", - "Paco 3000.000000 \n", - "Pdco 3147.009528 \n", - "Vdco 313.429286 \n", - "Pso 18.104122 \n", - "C0 -0.000009 \n", - "C1 -0.000012 \n", - "C2 0.001620 \n", - "C3 -0.000217 \n", - "Pnt 0.100000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 20.000000 \n", - "Mppt_low 160.000000 \n", - "Mppt_high 530.000000 \n", - "\n", - " ABB__PVI_3_0_OUTD_S_US_Z_M_A__240_V__240V__CEC_2014_ \\\n", - "Vac 240.000000 \n", - "Paco 3000.000000 \n", - "Pdco 3125.758222 \n", - "Vdco 340.842937 \n", - "Pso 19.866112 \n", - "C0 -0.000007 \n", - "C1 -0.000025 \n", - "C2 0.001050 \n", - "C3 -0.000471 \n", - "Pnt 0.100000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 20.000000 \n", - "Mppt_low 160.000000 \n", - "Mppt_high 530.000000 \n", + " ABB__MICRO_0_3_I_OUTD_US_240__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 300.000000 \n", + "Pdco 311.581000 \n", + "Vdco 40.000000 \n", + "Pso 1.950540 \n", + "C0 -0.000034 \n", + "C1 -0.000256 \n", + "C2 0.002453 \n", + "C3 -0.028223 \n", + "Pnt 0.050000 \n", + "Vdcmax 50.000000 \n", + "Idcmax 7.789520 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 50.000000 \n", "\n", - " ABB__PVI_3_0_OUTD_S_US_Z_M_A__277_V__277V__CEC_2014_ \\\n", - "Vac 277.000000 \n", - "Paco 3000.000000 \n", - "Pdco 3110.342942 \n", - "Vdco 389.986270 \n", - "Pso 22.720135 \n", - "C0 -0.000006 \n", - "C1 -0.000044 \n", - "C2 0.000036 \n", - "C3 -0.001550 \n", - "Pnt 0.200000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 20.000000 \n", - "Mppt_low 160.000000 \n", - "Mppt_high 530.000000 \n", + " ABB__MICRO_0_3HV_I_OUTD_US_208_208V__CEC_2014_ \\\n", + "Vac 208.000000 \n", + "Paco 300.000000 \n", + "Pdco 312.523347 \n", + "Vdco 45.259429 \n", + "Pso 1.882620 \n", + "C0 -0.000049 \n", + "C1 -0.000241 \n", + "C2 0.000975 \n", + "C3 -0.027600 \n", + "Pnt 0.060000 \n", + "Vdcmax 79.000000 \n", + "Idcmax 10.500000 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 75.000000 \n", "\n", - " ABB__PVI_3_6_OUTD_S_US_Z_M__208_V__208V__CEC_2014_ \\\n", - "Vac 208.000000 \n", - "Paco 3600.000000 \n", - "Pdco 3759.288140 \n", - "Vdco 309.948254 \n", - "Pso 24.202212 \n", - "C0 -0.000005 \n", - "C1 0.000002 \n", - "C2 0.001730 \n", - "C3 0.001140 \n", - "Pnt 0.100000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 32.000000 \n", - "Mppt_low 120.000000 \n", - "Mppt_high 530.000000 \n", + " ABB__MICRO_0_3HV_I_OUTD_US_208__208V__208V__CEC_2018_ \\\n", + "Vac 208.000000 \n", + "Paco 300.000000 \n", + "Pdco 312.423000 \n", + "Vdco 45.000000 \n", + "Pso 1.762100 \n", + "C0 -0.000045 \n", + "C1 -0.000198 \n", + "C2 0.002208 \n", + "C3 -0.023681 \n", + "Pnt 0.060000 \n", + "Vdcmax 60.000000 \n", + "Idcmax 6.942740 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 60.000000 \n", "\n", - " ... \\\n", - "Vac ... \n", - "Paco ... \n", - "Pdco ... \n", - "Vdco ... \n", - "Pso ... \n", - "C0 ... \n", - "C1 ... \n", - "C2 ... \n", - "C3 ... \n", - "Pnt ... \n", - "Vdcmax ... \n", - "Idcmax ... \n", - "Mppt_low ... \n", - "Mppt_high ... \n", + " ... \\\n", + "Vac ... \n", + "Paco ... \n", + "Pdco ... \n", + "Vdco ... \n", + "Pso ... \n", + "C0 ... \n", + "C1 ... \n", + "C2 ... \n", + "C3 ... \n", + "Pnt ... \n", + "Vdcmax ... \n", + "Idcmax ... \n", + "Mppt_low ... \n", + "Mppt_high ... \n", "\n", - " Yes!_Solar_Inc___ES5000__240V__240V__CEC_2009_ \\\n", - "Vac 240.000000 \n", - "Paco 4900.000000 \n", - "Pdco 5135.584132 \n", - "Vdco 275.000000 \n", - "Pso 29.358943 \n", - "C0 -0.000006 \n", - "C1 0.000020 \n", - "C2 0.001870 \n", - "C3 -0.000276 \n", - "Pnt 0.500000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 25.000000 \n", - "Mppt_low 200.000000 \n", - "Mppt_high 550.000000 \n", + " i_Energy_Corporation__GT260_240V__CEC_2013_ \\\n", + "Vac 240.000000 \n", + "Paco 230.000000 \n", + "Pdco 245.790658 \n", + "Vdco 40.709524 \n", + "Pso 2.511675 \n", + "C0 0.000048 \n", + "C1 -0.000086 \n", + "C2 -0.002590 \n", + "C3 0.157676 \n", + "Pnt 0.150000 \n", + "Vdcmax 59.000000 \n", + "Idcmax 10.000000 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 50.000000 \n", "\n", - " Yes!_Solar_Inc___ES5300__208V__208V__CEC_2009_ \\\n", - "Vac 208.000000 \n", - "Paco 4600.000000 \n", - "Pdco 4829.422409 \n", - "Vdco 275.000000 \n", - "Pso 26.071506 \n", - "C0 -0.000006 \n", - "C1 0.000024 \n", - "C2 0.002620 \n", - "C3 0.000468 \n", - "Pnt 0.500000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 25.000000 \n", - "Mppt_low 200.000000 \n", - "Mppt_high 550.000000 \n", + " i_Energy__GT260__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 230.000000 \n", + "Pdco 245.630000 \n", + "Vdco 40.000000 \n", + "Pso 2.530100 \n", + "C0 0.000062 \n", + "C1 -0.000098 \n", + "C2 0.000231 \n", + "C3 0.121032 \n", + "Pnt 0.150000 \n", + "Vdcmax 49.000000 \n", + "Idcmax 6.140760 \n", + "Mppt_low 30.000000 \n", + "Mppt_high 49.000000 \n", "\n", - " Yes!_Solar_Inc___ES5300__240V__240V__CEC_2009_ \\\n", - "Vac 240.000000 \n", - "Paco 5300.000000 \n", - "Pdco 5571.180956 \n", - "Vdco 274.900000 \n", - "Pso 28.519033 \n", - "C0 -0.000006 \n", - "C1 0.000019 \n", - "C2 0.001630 \n", - "C3 -0.000371 \n", - "Pnt 0.500000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 25.000000 \n", - "Mppt_low 200.000000 \n", - "Mppt_high 550.000000 \n", + " iPower__SHO_1_1__120V__120V__CEC_2018_ \\\n", + "Vac 120.000000 \n", + "Paco 1100.000000 \n", + "Pdco 1194.090000 \n", + "Vdco 182.000000 \n", + "Pso 22.095400 \n", + "C0 -0.000021 \n", + "C1 0.000057 \n", + "C2 0.002001 \n", + "C3 0.000623 \n", + "Pnt 3.600000 \n", + "Vdcmax 380.000000 \n", + "Idcmax 6.560960 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 380.000000 \n", "\n", - " Zhejiang_Yuhui_Solar_Energy_Source__Replus_250A_240V__CEC_2012_ \\\n", - "Vac 2.400000e+02 \n", - "Paco 2.251900e+02 \n", - "Pdco 2.348419e+02 \n", - "Vdco 2.846843e+01 \n", - "Pso 1.646711e+00 \n", - "C0 -3.860000e-07 \n", - "C1 -3.580000e-04 \n", - "C2 -1.350000e-02 \n", - "C3 -3.350684e+01 \n", - "Pnt 1.700000e-01 \n", - "Vdcmax 5.500000e+01 \n", - "Idcmax 1.400000e+01 \n", - "Mppt_low 2.200000e+01 \n", - "Mppt_high 4.500000e+01 \n", + " iPower__SHO_2_0__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 2000.000000 \n", + "Pdco 2161.880000 \n", + "Vdco 199.000000 \n", + "Pso 24.465800 \n", + "C0 -0.000013 \n", + "C1 0.000055 \n", + "C2 0.001703 \n", + "C3 0.000315 \n", + "Pnt 3.600000 \n", + "Vdcmax 380.000000 \n", + "Idcmax 10.863700 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 380.000000 \n", "\n", - " Zhejiang_Yuhui_Solar_Energy_Source__Replus_250B_208V__CEC_2012_ \\\n", - "Vac 208.000000 \n", - "Paco 213.830000 \n", - "Pdco 225.563055 \n", - "Vdco 28.632617 \n", - "Pso 1.845029 \n", - "C0 -0.000121 \n", - "C1 -0.000533 \n", - "C2 0.025900 \n", - "C3 -0.066800 \n", - "Pnt 0.170000 \n", - "Vdcmax 55.000000 \n", - "Idcmax 14.000000 \n", - "Mppt_low 22.000000 \n", - "Mppt_high 45.000000 \n", + " iPower__SHO_2_5__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 2500.000000 \n", + "Pdco 2632.840000 \n", + "Vdco 218.000000 \n", + "Pso 42.776500 \n", + "C0 -0.000014 \n", + "C1 0.000061 \n", + "C2 0.002053 \n", + "C3 0.001530 \n", + "Pnt 3.900000 \n", + "Vdcmax 400.000000 \n", + "Idcmax 12.077200 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 400.000000 \n", "\n", - " Zigor__Sunzet_2_TL_US_240V__CEC_2011_ \\\n", - "Vac 240.000000 \n", - "Paco 2110.000000 \n", - "Pdco 2191.825129 \n", - "Vdco 399.207333 \n", - "Pso 30.843703 \n", - "C0 -0.000004 \n", - "C1 -0.000077 \n", - "C2 0.000502 \n", - "C3 -0.003260 \n", - "Pnt 0.250000 \n", - "Vdcmax 500.000000 \n", - "Idcmax 14.600000 \n", - "Mppt_low 150.000000 \n", - "Mppt_high 450.000000 \n", + " iPower__SHO_3_0__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 3000.000000 \n", + "Pdco 3205.930000 \n", + "Vdco 222.500000 \n", + "Pso 31.682000 \n", + "C0 -0.000008 \n", + "C1 0.000036 \n", + "C2 0.001708 \n", + "C3 0.000860 \n", + "Pnt 3.630000 \n", + "Vdcmax 380.000000 \n", + "Idcmax 14.408700 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 380.000000 \n", "\n", - " Zigor__Sunzet_3_TL_US_240V__CEC_2011_ \\\n", - "Vac 240.000000 \n", - "Paco 3180.000000 \n", - "Pdco 3313.675805 \n", - "Vdco 389.513254 \n", - "Pso 31.265046 \n", - "C0 -0.000006 \n", - "C1 -0.000095 \n", - "C2 0.000261 \n", - "C3 -0.001960 \n", - "Pnt 0.250000 \n", - "Vdcmax 500.000000 \n", - "Idcmax 22.000000 \n", - "Mppt_low 150.000000 \n", - "Mppt_high 450.000000 \n", + " iPower__SHO_3_5__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 3500.000000 \n", + "Pdco 3641.830000 \n", + "Vdco 263.000000 \n", + "Pso 64.768100 \n", + "C0 -0.000009 \n", + "C1 0.000035 \n", + "C2 0.001417 \n", + "C3 0.001218 \n", + "Pnt 3.860000 \n", + "Vdcmax 400.000000 \n", + "Idcmax 13.847300 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 400.000000 \n", "\n", - " Zigor__Sunzet_4_TL_US_240V__CEC_2011_ \\\n", - "Vac 240.000000 \n", - "Paco 4160.000000 \n", - "Pdco 4342.409314 \n", - "Vdco 388.562050 \n", - "Pso 31.601704 \n", - "C0 -0.000004 \n", - "C1 -0.000079 \n", - "C2 0.000213 \n", - "C3 -0.001870 \n", - "Pnt 0.200000 \n", - "Vdcmax 500.000000 \n", - "Idcmax 28.000000 \n", - "Mppt_low 150.000000 \n", - "Mppt_high 450.000000 \n", + " iPower__SHO_4_6__208V__208V__CEC_2018_ \\\n", + "Vac 208.000000 \n", + "Paco 4600.000000 \n", + "Pdco 4797.810000 \n", + "Vdco 254.000000 \n", + "Pso 54.570100 \n", + "C0 -0.000006 \n", + "C1 0.000028 \n", + "C2 0.001381 \n", + "C3 0.000889 \n", + "Pnt 4.000000 \n", + "Vdcmax 400.000000 \n", + "Idcmax 18.889000 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 400.000000 \n", "\n", - " Zigor__Sunzet_5_TL_US_240V__CEC_2011_ \\\n", - "Vac 240.000000 \n", - "Paco 5240.000000 \n", - "Pdco 5495.829926 \n", - "Vdco 386.082539 \n", - "Pso 32.450808 \n", - "C0 -0.000005 \n", - "C1 -0.000097 \n", - "C2 -0.000251 \n", - "C3 -0.002340 \n", - "Pnt 0.200000 \n", - "Vdcmax 500.000000 \n", - "Idcmax 35.300000 \n", - "Mppt_low 150.000000 \n", - "Mppt_high 450.000000 \n", + " iPower__SHO_4_8__240V__240V__CEC_2018_ \\\n", + "Vac 240.000000 \n", + "Paco 4800.000000 \n", + "Pdco 4968.030000 \n", + "Vdco 263.000000 \n", + "Pso 85.145700 \n", + "C0 -0.000006 \n", + "C1 0.000034 \n", + "C2 0.000586 \n", + "C3 0.000195 \n", + "Pnt 4.100000 \n", + "Vdcmax 400.000000 \n", + "Idcmax 18.889800 \n", + "Mppt_low 100.000000 \n", + "Mppt_high 400.000000 \n", "\n", - " Zigor__SUNZET4_USA_240V__CEC_2011_ \n", - "Vac 240.000000 \n", - "Paco 4030.000000 \n", - "Pdco 4267.477069 \n", - "Vdco 302.851707 \n", - "Pso 37.372766 \n", - "C0 -0.000009 \n", - "C1 -0.000029 \n", - "C2 0.002150 \n", - "C3 -0.001900 \n", - "Pnt 0.190000 \n", - "Vdcmax 600.000000 \n", - "Idcmax 20.000000 \n", - "Mppt_low 240.000000 \n", - "Mppt_high 480.000000 \n", + " iPower__SHO_5_2__240V__240V__CEC_2018_ \n", + "Vac 240.000000 \n", + "Paco 5200.000000 \n", + "Pdco 5382.860000 \n", + "Vdco 280.000000 \n", + "Pso 62.486700 \n", + "C0 -0.000005 \n", + "C1 0.000044 \n", + "C2 0.001260 \n", + "C3 0.000367 \n", + "Pnt 4.000000 \n", + "Vdcmax 400.000000 \n", + "Idcmax 19.224500 \n", + "Mppt_low 240.000000 \n", + "Mppt_high 400.000000 \n", "\n", - "[14 rows x 1799 columns]" + "[14 rows x 5100 columns]" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1331,24 +1319,32 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\cliff\\Anaconda3\\lib\\site-packages\\pvlib\\pvsystem.py:2200: RuntimeWarning: invalid value encountered in minimum\n", + " ac_power = np.minimum(Paco, ac_power)\n" + ] + }, { "data": { "text/plain": [ - "" + "Text(0.5,0,'dc power')" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREJEwaokVEREREwqQhWkREREQkTP8B+JvaZcq79WYAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1378,70 +1374,72 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### SAPM" + "### DC model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The CEC module database." + "This example shows use of the Desoto module performance model and the Sandia Array Performance Model (SAPM). Both models reuire a set of parameter values which can be read from SAM databases for modules.\n", + "\n", + "Foe the Desoto model, the database content is returned by supplying the keyword `cecmod` to `pvsystem.retrievesam`." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1464,80 +1462,80 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1547,339 +1545,339 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -1896,27 +1894,27 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1924,218 +1922,172 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", "
BEoptCA_Default_ModuleExample_Module1Soltech_1STH_215_P1Soltech_1STH_220_P1Soltech_1STH_225_P1Soltech_1STH_230_P1Soltech_1STH_235_WH1Soltech_1STH_240_P1Soltech_1STH_240_WH1Soltech_1STH_245_P1Soltech_1STH_245_WH1Soltech_1STH_FRL_4H_245_M60_BLK1Soltech_1STH_250_P...Zytech_Solar_ZT275PZytech_Solar_ZT280PZytech_Solar_ZT285PZytech_Solar_ZT290PZytech_Solar_ZT295PZytech_Solar_ZT300PZytech_Solar_ZT305PZytech_Solar_ZT310PZytech_Solar_ZT315PZytech_Solar_ZT320PiTek_iT_240iTek_iT_260_HEiTek_iT_265_HEiTek_iT_270_HEiTek_iT_275_HEiTek_iT_280_HEiTek_iT_285_HEiTek_iT_290_HEiTek_iT_295_HEiTek_iT_300_HE
BIPVYYNNNNN
Date12/17/20084/28/200810/7/201010/4/201010/4/201010/4/20103/4/20104/2/20143/4/20104/2/20143/4/20101/14/20134/2/2014...12/23/201412/23/201412/23/201412/23/201412/23/201412/23/201412/23/201412/23/201412/23/201412/23/201410/26/20112/2/20152/2/20152/2/20152/2/20152/2/20155/29/20155/29/20155/29/20155/29/2015
T_NOCT656547.447.447.447.449.94349.94349.948.343...46.446.446.446.446.446.446.446.446.446.448.244.544.544.544.544.548.848.848.748.8
A_c0.670.671.5671.5671.5671.5671.6351.5611.6351.5611.6351.6681.561...1.9311.9311.9311.9311.9311.9311.9311.9311.9311.9311.6371.6341.6341.6341.6341.6341.621.621.621.62
N_s181860606060606060...7272727272727272727260606060606060606060
I_sc_ref7.57.57.847.978.098.188.548.628.588.658.628.818.69...8.318.48.488.558.648.718.878.78.99.019.1299.19.29.310.0510.089.910.08
V_oc_ref10.410.436.336.636.937.13736.937.137.137.238.337.3...45.145.2545.4345.5945.7545.9646.1246.2846.4446.63838.638.738.93939.240.941.241.241.2
I_mp_ref6.66.67.357.477.587.658.028.088.078.148.18.068.25...7.767.877.978.078.168.268.368.468.568.668.28.28.38.48.58.69.539.579.439.57
V_mp_ref8.48.42929.329.629.929.329.729.730.130.230.230.3...35.4435.6235.835.9436.1636.3236.4936.6636.81373031.731.83232.132.333.63434.534
alpha_sc0.0030.0030.0079970.0081290.0082520.0083440.007430.0100850.0074650.0101210.0074990.0061670.010167...0.0040140.0040570.0040960.004130.0041730.0042070.0042840.0042990.0043520.0044050.003480.003560.00360.003640.003680.003720.003760.00380.003840.00388
beta_oc-0.04-0.04-0.13104-0.13213-0.13321-0.13393-0.13653-0.13432-0.1369-0.13504-0.13727-0.13635-0.13577...-0.14428-0.14476-0.14533-0.14584-0.14635-0.14703-0.14754-0.14805-0.14856-0.14907-0.1292-0.10808-0.10836-0.10892-0.1092-0.10976-0.11397-0.11455-0.11513-0.11542
a_ref0.4730.4731.64131.65721.67321.68881.62921.60681.64251.61951.66171.63511.629...1.81021.81471.821.82271.83111.84431.8491.85731.86491.87371.62041.5271.53061.53861.54221.55031.59041.59861.60691.6107
I_L_ref7.5457.5457.8437.9748.0948.1858.5438.6268.5828.6538.6238.8448.781...8.3248.418.4878.5528.6428.8058.8748.9959.1079.2188.78.9239.0229.1219.2219.329.4189.5179.6179.716
I_o_ref1.943e-091.943e-091.936e-092.03e-092.126e-092.332e-091.166e-099.08e-101.325e-099.68e-101.623e-095.7e-109.92e-10...1.24e-101.23e-101.22e-101.17e-101.22e-101.31e-101.3e-101.35e-101.38e-101.44e-105.68e-109e-119.06e-119.19e-119.25e-119.39e-111.69e-101.71e-101.74e-101.75e-10
R_s0.0940.0940.3590.3460.3340.3110.3830.3160.3350.2820.2720.4210.275...0.5670.5530.5440.5390.5210.5160.5070.4960.4880.4760.4110.2760.2710.2650.260.2540.2410.2350.2290.225
R_sh_ref15.7215.72839.4751.03670.65462.561257.84452.561463.82790.96724.06109.31600.38...341.66457.29687.162344.162910.76552.21118.01767.45681.89603.9125925.1109.16111.38113.85116.21118.83125.87128.9132.03135.1
Adjust10.610.616.516.817.117.98.77.7369.88.25911.66.5028.361...5.5545.4065.1974.7925.0335.5485.3735.5785.7115.97111.8817.1917.1417.1717.1217.1518.4418.4718.518.47
gamma_r-0.5-0.5-0.495-0.495-0.495-0.495-0.482-0.43-0.482-0.43-0.482-0.453-0.43...-0.431-0.431-0.431-0.431-0.431-0.431-0.431-0.431-0.431-0.431-0.5-0.4-0.4-0.4-0.4-0.4-0.42-0.42-0.42-0.42
VersionMM106MM105MM107MM107MM107MM107MM107NRELv1MM107NRELv1MM107NRELv1...
PTC48.948.9189.4194198.5203.1205.1220.6209.6225.3214.1217.7230...248252.6257.3261.9266.5271.2275.8280.5285.1289.8210.6239243.7248.4253.1257.8292.1296.8296.9296.8
TechnologyMulti-c-SiMulti-c-SiMulti-c-SiMono-c-SiMulti-c-SiMono-c-SiMulti-c-SiMono-c-SiMulti-c-Si...Mono-c-SiMono-c-SiMono-c-SiMono-c-SiMono-c-SiMono-c-SiMono-c-SiMono-c-SiMono-c-SiMono-c-Si...Multi-c-SiMulti-c-SiMulti-c-SiMulti-c-SiMulti-c-SiMulti-c-SiMulti-c-SiMulti-c-SiMulti-c-SiMulti-c-Si
\n", - "

21 rows × 13953 columns

\n", + "

21 rows × 19254 columns

\n", "
" ], "text/plain": [ - " BEoptCA_Default_Module Example_Module 1Soltech_1STH_215_P \\\n", - "BIPV Y Y N \n", - "Date 12/17/2008 4/28/2008 10/7/2010 \n", - "T_NOCT 65 65 47.4 \n", - "A_c 0.67 0.67 1.567 \n", - "N_s 18 18 60 \n", - "I_sc_ref 7.5 7.5 7.84 \n", - "V_oc_ref 10.4 10.4 36.3 \n", - "I_mp_ref 6.6 6.6 7.35 \n", - "V_mp_ref 8.4 8.4 29 \n", - "alpha_sc 0.003 0.003 0.007997 \n", - "beta_oc -0.04 -0.04 -0.13104 \n", - "a_ref 0.473 0.473 1.6413 \n", - "I_L_ref 7.545 7.545 7.843 \n", - "I_o_ref 1.943e-09 1.943e-09 1.936e-09 \n", - "R_s 0.094 0.094 0.359 \n", - "R_sh_ref 15.72 15.72 839.4 \n", - "Adjust 10.6 10.6 16.5 \n", - "gamma_r -0.5 -0.5 -0.495 \n", - "Version MM106 MM105 MM107 \n", - "PTC 48.9 48.9 189.4 \n", - "Technology Multi-c-Si Multi-c-Si Multi-c-Si \n", - "\n", - " 1Soltech_1STH_220_P 1Soltech_1STH_225_P 1Soltech_1STH_230_P \\\n", + " 1Soltech_1STH_215_P 1Soltech_1STH_220_P 1Soltech_1STH_225_P \\\n", "BIPV N N N \n", - "Date 10/4/2010 10/4/2010 10/4/2010 \n", + "Date 10/7/2010 10/4/2010 10/4/2010 \n", "T_NOCT 47.4 47.4 47.4 \n", "A_c 1.567 1.567 1.567 \n", "N_s 60 60 60 \n", - "I_sc_ref 7.97 8.09 8.18 \n", - "V_oc_ref 36.6 36.9 37.1 \n", - "I_mp_ref 7.47 7.58 7.65 \n", - "V_mp_ref 29.3 29.6 29.9 \n", - "alpha_sc 0.008129 0.008252 0.008344 \n", - "beta_oc -0.13213 -0.13321 -0.13393 \n", - "a_ref 1.6572 1.6732 1.6888 \n", - "I_L_ref 7.974 8.094 8.185 \n", - "I_o_ref 2.03e-09 2.126e-09 2.332e-09 \n", - "R_s 0.346 0.334 0.311 \n", - "R_sh_ref 751.03 670.65 462.56 \n", - "Adjust 16.8 17.1 17.9 \n", + "I_sc_ref 7.84 7.97 8.09 \n", + "V_oc_ref 36.3 36.6 36.9 \n", + "I_mp_ref 7.35 7.47 7.58 \n", + "V_mp_ref 29 29.3 29.6 \n", + "alpha_sc 0.007997 0.008129 0.008252 \n", + "beta_oc -0.13104 -0.13213 -0.13321 \n", + "a_ref 1.6413 1.6572 1.6732 \n", + "I_L_ref 7.843 7.974 8.094 \n", + "I_o_ref 1.936e-09 2.03e-09 2.126e-09 \n", + "R_s 0.359 0.346 0.334 \n", + "R_sh_ref 839.4 751.03 670.65 \n", + "Adjust 16.5 16.8 17.1 \n", "gamma_r -0.495 -0.495 -0.495 \n", "Version MM107 MM107 MM107 \n", - "PTC 194 198.5 203.1 \n", + "PTC 189.4 194 198.5 \n", "Technology Multi-c-Si Multi-c-Si Multi-c-Si \n", "\n", - " 1Soltech_1STH_235_WH 1Soltech_1STH_240_WH 1Soltech_1STH_245_WH \\\n", - "BIPV N N N \n", - "Date 3/4/2010 3/4/2010 3/4/2010 \n", - "T_NOCT 49.9 49.9 49.9 \n", - "A_c 1.635 1.635 1.635 \n", - "N_s 60 60 60 \n", - "I_sc_ref 8.54 8.58 8.62 \n", - "V_oc_ref 37 37.1 37.2 \n", - "I_mp_ref 8.02 8.07 8.1 \n", - "V_mp_ref 29.3 29.7 30.2 \n", - "alpha_sc 0.00743 0.007465 0.007499 \n", - "beta_oc -0.13653 -0.1369 -0.13727 \n", - "a_ref 1.6292 1.6425 1.6617 \n", - "I_L_ref 8.543 8.582 8.623 \n", - "I_o_ref 1.166e-09 1.325e-09 1.623e-09 \n", - "R_s 0.383 0.335 0.272 \n", - "R_sh_ref 1257.84 1463.82 724.06 \n", - "Adjust 8.7 9.8 11.6 \n", - "gamma_r -0.482 -0.482 -0.482 \n", - "Version MM107 MM107 MM107 \n", - "PTC 205.1 209.6 214.1 \n", - "Technology Mono-c-Si Mono-c-Si Mono-c-Si \n", - "\n", - " 1Soltech_1STH_FRL_4H_245_M60_BLK ... \\\n", - "BIPV N ... \n", - "Date 1/14/2013 ... \n", - "T_NOCT 48.3 ... \n", - "A_c 1.668 ... \n", - "N_s 60 ... \n", - "I_sc_ref 8.81 ... \n", - "V_oc_ref 38.3 ... \n", - "I_mp_ref 8.06 ... \n", - "V_mp_ref 30.2 ... \n", - "alpha_sc 0.006167 ... \n", - "beta_oc -0.13635 ... \n", - "a_ref 1.6351 ... \n", - "I_L_ref 8.844 ... \n", - "I_o_ref 5.7e-10 ... \n", - "R_s 0.421 ... \n", - "R_sh_ref 109.31 ... \n", - "Adjust 6.502 ... \n", - "gamma_r -0.453 ... \n", - "Version NRELv1 ... \n", - "PTC 217.7 ... \n", - "Technology Mono-c-Si ... \n", + " 1Soltech_1STH_230_P 1Soltech_1STH_235_WH 1Soltech_1STH_240_P \\\n", + "BIPV N N N \n", + "Date 10/4/2010 3/4/2010 4/2/2014 \n", + "T_NOCT 47.4 49.9 43 \n", + "A_c 1.567 1.635 1.561 \n", + "N_s 60 60 60 \n", + "I_sc_ref 8.18 8.54 8.62 \n", + "V_oc_ref 37.1 37 36.9 \n", + "I_mp_ref 7.65 8.02 8.08 \n", + "V_mp_ref 29.9 29.3 29.7 \n", + "alpha_sc 0.008344 0.00743 0.010085 \n", + "beta_oc -0.13393 -0.13653 -0.13432 \n", + "a_ref 1.6888 1.6292 1.6068 \n", + "I_L_ref 8.185 8.543 8.626 \n", + "I_o_ref 2.332e-09 1.166e-09 9.08e-10 \n", + "R_s 0.311 0.383 0.316 \n", + "R_sh_ref 462.56 1257.84 452.56 \n", + "Adjust 17.9 8.7 7.736 \n", + "gamma_r -0.495 -0.482 -0.43 \n", + "Version MM107 MM107 NRELv1 \n", + "PTC 203.1 205.1 220.6 \n", + "Technology Multi-c-Si Mono-c-Si Multi-c-Si \n", "\n", - " Zytech_Solar_ZT275P Zytech_Solar_ZT280P Zytech_Solar_ZT285P \\\n", - "BIPV N N N \n", - "Date 12/23/2014 12/23/2014 12/23/2014 \n", - "T_NOCT 46.4 46.4 46.4 \n", - "A_c 1.931 1.931 1.931 \n", - "N_s 72 72 72 \n", - "I_sc_ref 8.31 8.4 8.48 \n", - "V_oc_ref 45.1 45.25 45.43 \n", - "I_mp_ref 7.76 7.87 7.97 \n", - "V_mp_ref 35.44 35.62 35.8 \n", - "alpha_sc 0.004014 0.004057 0.004096 \n", - "beta_oc -0.14428 -0.14476 -0.14533 \n", - "a_ref 1.8102 1.8147 1.82 \n", - "I_L_ref 8.324 8.41 8.487 \n", - "I_o_ref 1.24e-10 1.23e-10 1.22e-10 \n", - "R_s 0.567 0.553 0.544 \n", - "R_sh_ref 341.66 457.29 687.16 \n", - "Adjust 5.554 5.406 5.197 \n", - "gamma_r -0.431 -0.431 -0.431 \n", - "Version NRELv1 NRELv1 NRELv1 \n", - "PTC 248 252.6 257.3 \n", - "Technology Multi-c-Si Multi-c-Si Multi-c-Si \n", + " 1Soltech_1STH_240_WH 1Soltech_1STH_245_P 1Soltech_1STH_245_WH \\\n", + "BIPV N N N \n", + "Date 3/4/2010 4/2/2014 3/4/2010 \n", + "T_NOCT 49.9 43 49.9 \n", + "A_c 1.635 1.561 1.635 \n", + "N_s 60 60 60 \n", + "I_sc_ref 8.58 8.65 8.62 \n", + "V_oc_ref 37.1 37.1 37.2 \n", + "I_mp_ref 8.07 8.14 8.1 \n", + "V_mp_ref 29.7 30.1 30.2 \n", + "alpha_sc 0.007465 0.010121 0.007499 \n", + "beta_oc -0.1369 -0.13504 -0.13727 \n", + "a_ref 1.6425 1.6195 1.6617 \n", + "I_L_ref 8.582 8.653 8.623 \n", + "I_o_ref 1.325e-09 9.68e-10 1.623e-09 \n", + "R_s 0.335 0.282 0.272 \n", + "R_sh_ref 1463.82 790.96 724.06 \n", + "Adjust 9.8 8.259 11.6 \n", + "gamma_r -0.482 -0.43 -0.482 \n", + "Version MM107 NRELv1 MM107 \n", + "PTC 209.6 225.3 214.1 \n", + "Technology Mono-c-Si Multi-c-Si Mono-c-Si \n", "\n", - " Zytech_Solar_ZT290P Zytech_Solar_ZT295P Zytech_Solar_ZT300P \\\n", - "BIPV N N N \n", - "Date 12/23/2014 12/23/2014 12/23/2014 \n", - "T_NOCT 46.4 46.4 46.4 \n", - "A_c 1.931 1.931 1.931 \n", - "N_s 72 72 72 \n", - "I_sc_ref 8.55 8.64 8.71 \n", - "V_oc_ref 45.59 45.75 45.96 \n", - "I_mp_ref 8.07 8.16 8.26 \n", - "V_mp_ref 35.94 36.16 36.32 \n", - "alpha_sc 0.00413 0.004173 0.004207 \n", - "beta_oc -0.14584 -0.14635 -0.14703 \n", - "a_ref 1.8227 1.8311 1.8443 \n", - "I_L_ref 8.552 8.642 8.805 \n", - "I_o_ref 1.17e-10 1.22e-10 1.31e-10 \n", - "R_s 0.539 0.521 0.516 \n", - "R_sh_ref 2344.16 2910.76 552.2 \n", - "Adjust 4.792 5.033 5.548 \n", - "gamma_r -0.431 -0.431 -0.431 \n", - "Version NRELv1 NRELv1 NRELv1 \n", - "PTC 261.9 266.5 271.2 \n", - "Technology Multi-c-Si Multi-c-Si Multi-c-Si \n", + " 1Soltech_1STH_250_P ... iTek_iT_240 iTek_iT_260_HE \\\n", + "BIPV N ... N N \n", + "Date 4/2/2014 ... 10/26/2011 2/2/2015 \n", + "T_NOCT 43 ... 48.2 44.5 \n", + "A_c 1.561 ... 1.637 1.634 \n", + "N_s 60 ... 60 60 \n", + "I_sc_ref 8.69 ... 8.7 8.9 \n", + "V_oc_ref 37.3 ... 38 38.6 \n", + "I_mp_ref 8.25 ... 8.2 8.2 \n", + "V_mp_ref 30.3 ... 30 31.7 \n", + "alpha_sc 0.010167 ... 0.00348 0.00356 \n", + "beta_oc -0.13577 ... -0.1292 -0.10808 \n", + "a_ref 1.629 ... 1.6204 1.527 \n", + "I_L_ref 8.781 ... 8.7 8.923 \n", + "I_o_ref 9.92e-10 ... 5.68e-10 9e-11 \n", + "R_s 0.275 ... 0.411 0.276 \n", + "R_sh_ref 600.38 ... 25925.1 109.16 \n", + "Adjust 8.361 ... 11.88 17.19 \n", + "gamma_r -0.43 ... -0.5 -0.4 \n", + "Version NRELv1 ... NRELv1 NRELv1 \n", + "PTC 230 ... 210.6 239 \n", + "Technology Multi-c-Si ... Mono-c-Si Mono-c-Si \n", "\n", - " Zytech_Solar_ZT305P Zytech_Solar_ZT310P Zytech_Solar_ZT315P \\\n", - "BIPV N N N \n", - "Date 12/23/2014 12/23/2014 12/23/2014 \n", - "T_NOCT 46.4 46.4 46.4 \n", - "A_c 1.931 1.931 1.931 \n", - "N_s 72 72 72 \n", - "I_sc_ref 8.87 8.9 9.01 \n", - "V_oc_ref 46.12 46.28 46.44 \n", - "I_mp_ref 8.36 8.46 8.56 \n", - "V_mp_ref 36.49 36.66 36.81 \n", - "alpha_sc 0.004284 0.004299 0.004352 \n", - "beta_oc -0.14754 -0.14805 -0.14856 \n", - "a_ref 1.849 1.8573 1.8649 \n", - "I_L_ref 8.874 8.995 9.107 \n", - "I_o_ref 1.3e-10 1.35e-10 1.38e-10 \n", - "R_s 0.507 0.496 0.488 \n", - "R_sh_ref 1118.01 767.45 681.89 \n", - "Adjust 5.373 5.578 5.711 \n", - "gamma_r -0.431 -0.431 -0.431 \n", - "Version NRELv1 NRELv1 NRELv1 \n", - "PTC 275.8 280.5 285.1 \n", - "Technology Multi-c-Si Multi-c-Si Multi-c-Si \n", + " iTek_iT_265_HE iTek_iT_270_HE iTek_iT_275_HE iTek_iT_280_HE \\\n", + "BIPV N N N N \n", + "Date 2/2/2015 2/2/2015 2/2/2015 2/2/2015 \n", + "T_NOCT 44.5 44.5 44.5 44.5 \n", + "A_c 1.634 1.634 1.634 1.634 \n", + "N_s 60 60 60 60 \n", + "I_sc_ref 9 9.1 9.2 9.3 \n", + "V_oc_ref 38.7 38.9 39 39.2 \n", + "I_mp_ref 8.3 8.4 8.5 8.6 \n", + "V_mp_ref 31.8 32 32.1 32.3 \n", + "alpha_sc 0.0036 0.00364 0.00368 0.00372 \n", + "beta_oc -0.10836 -0.10892 -0.1092 -0.10976 \n", + "a_ref 1.5306 1.5386 1.5422 1.5503 \n", + "I_L_ref 9.022 9.121 9.221 9.32 \n", + "I_o_ref 9.06e-11 9.19e-11 9.25e-11 9.39e-11 \n", + "R_s 0.271 0.265 0.26 0.254 \n", + "R_sh_ref 111.38 113.85 116.21 118.83 \n", + "Adjust 17.14 17.17 17.12 17.15 \n", + "gamma_r -0.4 -0.4 -0.4 -0.4 \n", + "Version NRELv1 NRELv1 NRELv1 NRELv1 \n", + "PTC 243.7 248.4 253.1 257.8 \n", + "Technology Mono-c-Si Mono-c-Si Mono-c-Si Mono-c-Si \n", "\n", - " Zytech_Solar_ZT320P \n", - "BIPV N \n", - "Date 12/23/2014 \n", - "T_NOCT 46.4 \n", - "A_c 1.931 \n", - "N_s 72 \n", - "I_sc_ref 9.12 \n", - "V_oc_ref 46.6 \n", - "I_mp_ref 8.66 \n", - "V_mp_ref 37 \n", - "alpha_sc 0.004405 \n", - "beta_oc -0.14907 \n", - "a_ref 1.8737 \n", - "I_L_ref 9.218 \n", - "I_o_ref 1.44e-10 \n", - "R_s 0.476 \n", - "R_sh_ref 603.91 \n", - "Adjust 5.971 \n", - "gamma_r -0.431 \n", - "Version NRELv1 \n", - "PTC 289.8 \n", - "Technology Multi-c-Si \n", + " iTek_iT_285_HE iTek_iT_290_HE iTek_iT_295_HE iTek_iT_300_HE \n", + "BIPV N N N N \n", + "Date 5/29/2015 5/29/2015 5/29/2015 5/29/2015 \n", + "T_NOCT 48.8 48.8 48.7 48.8 \n", + "A_c 1.62 1.62 1.62 1.62 \n", + "N_s 60 60 60 60 \n", + "I_sc_ref 10.05 10.08 9.9 10.08 \n", + "V_oc_ref 40.9 41.2 41.2 41.2 \n", + "I_mp_ref 9.53 9.57 9.43 9.57 \n", + "V_mp_ref 33.6 34 34.5 34 \n", + "alpha_sc 0.00376 0.0038 0.00384 0.00388 \n", + "beta_oc -0.11397 -0.11455 -0.11513 -0.11542 \n", + "a_ref 1.5904 1.5986 1.6069 1.6107 \n", + "I_L_ref 9.418 9.517 9.617 9.716 \n", + "I_o_ref 1.69e-10 1.71e-10 1.74e-10 1.75e-10 \n", + "R_s 0.241 0.235 0.229 0.225 \n", + "R_sh_ref 125.87 128.9 132.03 135.1 \n", + "Adjust 18.44 18.47 18.5 18.47 \n", + "gamma_r -0.42 -0.42 -0.42 -0.42 \n", + "Version NRELv1 NRELv1 NRELv1 NRELv1 \n", + "PTC 292.1 296.8 296.9 296.8 \n", + "Technology Mono-c-Si Mono-c-Si Mono-c-Si Mono-c-Si \n", "\n", - "[21 rows x 13953 columns]" + "[21 rows x 19254 columns]" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2147,7 +2099,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2166,7 +2118,7 @@ "beta_oc -0.04\n", "a_ref 0.473\n", "I_L_ref 7.545\n", - "I_o_ref 1.943e-09\n", + "I_o_ref 1.94e-09\n", "R_s 0.094\n", "R_sh_ref 15.72\n", "Adjust 10.6\n", @@ -2177,7 +2129,7 @@ "Name: Example_Module, dtype: object" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2191,30 +2143,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Sandia module database." + "The Sandia module database is read by the same function with the keyword `SandiaMod`." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -4185,7 +4137,7 @@ "[42 rows x 523 columns]" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -4197,7 +4149,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -4248,7 +4200,7 @@ "Name: Canadian_Solar_CS5P_220M___2009_, dtype: object" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -4267,7 +4219,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -4309,34 +4261,32 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:1303: RuntimeWarning: divide by zero encountered in log\n", - " module['Voco'] + module['Cells_in_Series']*delta*np.log(Ee) +\n", - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:1309: RuntimeWarning: divide by zero encountered in log\n", - " module['C3']*module['Cells_in_Series']*((delta*np.log(Ee)) ** 2) +\n" + "C:\\Users\\cliff\\Anaconda3\\lib\\site-packages\\pvlib\\pvsystem.py:1479: RuntimeWarning: invalid value encountered in maximum\n", + " spectral_loss = np.maximum(0, np.polyval(am_coeff, airmass_absolute))\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAFZCAYAAAC19cgsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4W+WdNv77LNoXS7ZlO87q7HtICEkIYacNpaWULRBe\nygzQKZMpKZR3aCEdlv5YCgNXmF5JoQOdt1ylQ5lSaAl7O7QsISEhhJDE2RPHiZN4l6x9O+f8/pDX\nrHZypCNZ9+e6jC3pSLr9tUS+es5zniNomqaBiIiIiIgGRDQ6ABERERFRIWIjTURERER0GthIExER\nERGdBjbSRERERESngY00EREREdFpYCNNRERERHQa5Gw9cCqVwrJly3Do0CEkk0ksWbIEY8eOxX33\n3QdBEDBu3Dg89NBDEEURK1euxIcffghZlrFs2TJMnz49W7GIiIiIiHSRtUZ61apV8Hg8eOqppxAI\nBPCd73wHEydOxN133425c+fiwQcfxAcffIDq6mqsX78er776Ko4cOYKlS5fitddey1YsIiIiIiJd\nZK2Rvvzyy7Fw4UIAgKZpkCQJtbW1mDNnDgDgggsuwKeffoqamhosWLAAgiCguroaiqKgvb0dpaWl\nJ338dFqB3x/NVvyi4vXaWUsdsZ76Yj31xXrqh7XUF+upL9ZTPz6f64S3Za2RdjgcAIBwOIwf/vCH\nuPvuu/Hkk09CEITu20OhEMLhMDweT5/7hUKhUzbSsiyd9BejgWEt9cV66ov11BfrqR/WUl+sp75Y\nz+zLWiMNAEeOHMEPfvAD3HTTTbjyyivx1FNPdd8WiUTgdrvhdDoRiUT6XO9y9e8P39IS0j1zMfL5\nXKyljlhPfbGe+mI99cNa6ov11BfrqZ+TfSDJ2qodra2tuO2223DvvffiuuuuAwBMnjwZ69atAwB8\n/PHHmD17NmbNmoXVq1dDVVUcPnwYqqqecjSaiIiIiMhoWRuR/tWvfoVgMIhnn30Wzz77LADgpz/9\nKR599FEsX74co0ePxsKFCyFJEmbPno0bbrgBqqriwQcfzFYkIiIiIiLdCJqmaUaHOF3cZaEP7v7R\nF+upL9ZTX6ynflhLfbGe+mI99WPI1A4iIiIiosGMjTQRERER0WlgI01EREREdBrYSBMRERERnQY2\n0kREREREp4GNNBERERHlpc8+W4M33njd6BgnlNUzGxIRUWHS1DRUNQUBACAAogRBkCEIgsHJiMgI\nf/jbHny+o1nXxzxnYgUWXTL2pNvMmzdf1+fUGxtpIqIio6kKUok2pOOtSCcDSCc7kE4GoCQ7oKSj\n0JQ4NC197B0FCZJshyjZIJlckC1eyGZP5stSCpO1HILIf1aISD/vvPMm6uv3Y8mSpcfctnnzJqxc\n+R+QZRlWqxWPPvokJEnC44//DI2NjUilUrjnnh9j6tTpWcvH/+MREQ1iqpJAMnIIieghpGLNaN7V\ninikBYB6zLaCaIYkOyCa3RAlKwTR1HmLBk1VoCoxqOkY0qkOpOLNwDHnehAgW8tgtlbAZKuA2V4N\ni30YRNma5d+SiLJt0SVjTzl6nGuffPIRLrnkMixadBNWr/4YwWAIH330AaqqqvGzn/0cBw8ewNq1\nq9lIExFR/6jpGOKhOsRD+5CINGQa3l5EyQKzoxomawVM1vKeEWWzZ0ANr6okMqPZiQDSST/S8TYk\n401IxVoQjbcCgW3d25qsFTA7hsHqGgWrazQk2a7b70tExeu7370Vv/3t/8Nddy2Bz1eByZOn4sCB\n+u7pIMOHj8Dw4TdlNQMbaSKiApeKtyIa2IZYcA+SkUMANACAIMiwOEfAYh8Gs2MYzPYhqKoeitbW\n8Bk/pyhZYLZVwmyr7HO9pmlQUkEkY42ZkfDIQSSjh5GKNyPSthEAYLYPhc09FjbPJJhtFWechYiK\n01/+8g6uuOJbuPPOu/HSS7/BqlWvY+TIGmzfvg3nn38RDh1qwAsvPIeHH34saxnYSBMRFaB0Moio\nfwsi/lqkYo2d1wqwOIbB6h4Dq2sMzPYqCILU537ZPlhQEATI5hLI5hLYSyYAADRNRTLWiHhwH+LB\nPZ3N9SF0NH4Ek7UCdu9UOEqnQTaXZDUbEQ0ukyZNxRNPPAqbzQZBEPDjH/8UZWXl+PnP/z/ceef3\noSgK7rrr/2Y1g6BpmpbVZ8iilpZjJujRafD5XKyljlhPfbGePTRNQyJch1DLBsQ6dgLQAEGE1TUG\nDu8U2NzjTzk9Ix/qqSpxxIJ7EPVvRSy4B9BUAAJsJePgLD8HVtfoglgdJB9qOZiwnvpiPfXj87lO\neBtHpImI8pymqYj6axFsWo1UvAUAYLJVwVl+NuyeyZBkm8EJB0aUrHB4p8LhnQo1HUM0sB3hto2I\ndexCrGMXZEsZ3JUL4CidBkHg6Q6Iit2yZfciGOzoc53T6cQTTyw3KFEPNtJERHlK01RE2rcg2PQJ\n0ol2AALs3mlw+c6B2T60IEZtT0WUbXCWz4KzfBYSkUMIt25AxL8F7QfeQLDxY7irFsBROoMNNVER\ne/zxp4yOcEJspImI8lA8VAf/ob8gFWsCBBGOslkoqTwPssVrdLSssTiGwuIYipIhFyHY9CnCbV+i\n/cCbCDV/Bs/Qr8PmHmN0RCKiPthIExHlkXQiAP+h9zvnQAOO0hkoGXJRUR2IJ5tLUDr8CrgrF6Cj\n8WNE2jaiZe9/w+oeB++whTBZSo2OSEQEgI00EVFe0DQN4dbPETj8ATQ1BYtjBLzDvg6zvdroaIaR\nzW6UjfgWXOWz4T/0PuLB3WjcXoeSIRfDVTGX0z2IyHBspImIDJaKt6LtwCokIw0QJRu8I66Ao3T6\noJgDrQezvQoVY29BNLAN/oZ3ETj8V0QD21A24tsw2XxGxyOiLPrsszVoamrEVVddY3SU42IjTURk\noHDbV/A3vANNTcHumQzvsMshmZxGx8o7giDA4Z0Cq6sG/ob3EPVvRePOF+AddjkcZTP5oYNokOo6\nS2G+YiNNRGQAVUmg/eC7iPo3QxAtKBt1LRzeKUbHynuSbEf5qGsQ9UxG+4FVaD/4FuKhOpSO+BZE\nyWJ0PKJB6/U9b+HL5i26PubMimm4Zuy3TrrNO++8ifr6/ViyZOkxt/3Xf/0nDh1qQCAQQDDYgWuu\nuR4ffvg3HDxYj5/+9GcoKyvDAw/ch7KyMrS0NGPu3Pm4444f6Po7cIIZEVGOpeJtaNz5X4j6N8Ns\nr8aQid9nEz1Ads9EVE28A2bHMEQDtWjc8TySsWajYxFRjlksFixfvgIXXngJ1q79FP/+78/g5pv/\nER988BcAQGPjYfz0pw/jhRd+i40bN2Dnzh26Pj9HpImIcigW3IvW/a9BU+Jw+ebCU30ZBFE69R3p\nGLK5BJXj/gEdh/+OYPMaNO36fygfdQ1sJeONjkY06Fwz9lunHD02wvjxEwEALpcTo0bVdP7sRjKZ\nAACMGTMebndm1aPJk6fiwIH9mDBhom7PzxFpIqIcCbWsR8vel6GpKZSOuAreYQvZRJ8hQZDgGXoZ\nykZdA2gqWva9gmDTWmiaZnQ0IsqBUx0eUV9fh3g8DkVRsG3bVowaNVrX5+eINBFRlmmahsDh/0Wo\neS1E2QHf6BtgcQwzOtag4vBOhWz2onXf/yBw+K9IJ/3wDvsGD0IkKnImkwkPPPATtLe346KLLsW4\ncfrusRK0Av7Y3tISMjrCoODzuVhLHbGe+ir0emqagvYDbyLSvhmypRwVY/+PoSdXKfR6nko6GUTL\n3t8jFW+C3TMFZSO/k7VR/8Fey1xjPfXFegJHjhzGQw8tw/PPv3hGj+PzuU54G0ekiYiyRFVTaK37\nI+LB3TDbh8I3ZjEk2W50rEFNNrtROe4f0LLv94gGaqEqMZTXLIIomY2ORkSnadmyexEMdvS5zul0\n4oknlhuUqEfWR6S/+uorPP3003jppZfwox/9CK2trQCAQ4cOYcaMGXjmmWewZMkS+P1+mEwmWCwW\n/PrXv+7XYxf7Jy298FOrvlhPfRVqPVU1hZa9v0civB9W1xiU11yfF81codZzoHp/iLE4hsM35ibd\nl8crllrmCuupL9ZTP4aNSL/wwgtYtWoVbDYbAOCZZ54BAHR0dOCWW27B/fffDwCor6/H22+/zbls\nRDQo9G6ibSUTUD7qOh5UmGOiaIJv9CK07f8zooFatOz9fWczbfyHGSIaPLK6aseIESOwYsWKY65f\nsWIFbr75ZlRUVKC1tRXBYBD//M//jMWLF+Pvf/97NiMREWVVpol+hU10HhAECWWjrobdMxmJyAG0\n7HsZqpI0OhYRDSJZn9rR0NCAe+65B3/4wx8AAG1tbbjllluwatUqSJKEI0eO4N1338Utt9yCjo4O\nLF68GL///e9RVlaWzVhERLpT1TT2fvkbBNt2ocQ3BaNn3AxR5KEoRtNUBfu2vIxA02Y4vWMwbtbt\nECWT0bGIaBDI+f/h33vvPXzrW9+CJGVGaMrLy3HjjTdClmWUlZVh0qRJqKur61cjzbk/+uA8Kn2x\nnvoqlHpqmoa2/a8jGtgFq3sc3NXfQVtbzOhYxyiUeurNNeRKJOJJhP07sGPDiyivWQRBOLOdssVa\ny2xhPfXFeurnZHOkc35ClrVr1+KCCy7ovrxmzRrcddddAIBIJILdu3dj9Gh9F8smIsomTdPgb3gP\n0UAtLI7hKK/hdI58IwgSykddA4uzBrGOXWg/+DZP2kJEZyznI9J1dXUYPnx49+ULL7wQq1evxqJF\niyCKIu655x6UlpbmOhYR0WkLNn2CcOvnMFkr4Bt9I0SR0wbykSDK8I1ehKbdv0Wk7UtIshOe6ouN\njkVEBYwnZCHu/tEZ66mvfK9nuG0T2g+sgmT2oHL8rZBNJ94FmA/yvZ65oKTCaNr1m86zH14Bl2/2\naT0Oa6kv1lNfetez5dVXENrwuW6PBwCu2efAd/2Nuj5mNuTV1A4iosEiHq5H+8G3IEpWVIz5P3nf\nRFOGZHKiYuzNEGUH/A3vIhbca3QkIjqBZcvuxZdffgEA2LFjG+67757jbrdr1w4sWXI77rzz+7jn\nnjvR2NgIAHjxxV/j9tu/i3/8x5vw5z+/pns+Hk5ORHQa0gk/Wvf9AdCA8prrYbJypaFCIlu88NUs\nQtOe36J1/x9RNf52mKzlRsciylu+6280ZPT4yiu/g3fffQszZ56Nt99+E1deefVxt3vyycdw333/\nhnHjJuCTTz7EypXLccstt2HdujV4/vkXoaoqfvWrldA0TdfzlnBEmohogFQljuZ9v4eqxFA6/ApY\nXTVGR6LTYHEOR9mIK6EpCbTsewVKOmp0JCI6yty552L79loEgx3YvPlLzJs3/7jbtba2YNy4CQCA\nGTNmoa5uHw4cqMekSVMgSRJMJhOWLv2R7if/YyNNRDQAmqaite41pOOtcPnmwlk+y+hIdAYcpdPh\nrjwP6UQ7WutehaYpRkciol5EUcTFF1+Gp59+Aueff1H38slHKy/3Yc+e3QCATZs2YvjwERg5chR2\n7doJVVWRTqdx993/gmRS35MycWoHEdEAdBz5EPHQXlhdY+AZ+jWj45AOSoZcglS8FbGOnQgc/gDe\noV83OhIR9fLNb34bixZdhVde+dMJt/nJT36KZ575d2iaBkmScN99D2Do0GGYO/dcLFlyO1RVxdVX\nXwez2axrNq7aQTxSWmesp77yqZ6xjl1o2fcKZLMXVRO+B1G2GR1pwPKpnvlEVRJo3PlrpBNtKB91\nHezeyae8D2upL9ZTX6ynfk62agdHpImI+iGd8KO1/s8QBBnlNdcXZBNNJyZKFpTXXI+mXf+FtgOr\nYLJV8OBDojzT2NiIRx998JjrZ848G7fffocBidhIExGdkqqm0FL3KjQljtIR34bZXmV0JMoCs60C\npSOuRNv+19Fa9yoqx98OUdJ3NzARnb6qqiqsXPm80TH64MGGRESn4G94D6lYIxxls+AsO8voOJRF\nDu9UOH1zkIq38DTiRHRKbKSJiE4i4q9FpO1LmGxVKB12udFxKAe81V+D2T4UUf8WRNo3Gx2HiPIY\nG2kiohNIJwNoP/gWBNGE8lHXQhA5G64YCKKE8lHXQBAt8De8i1Si3ehIRJSn2EgTER2Hpqlo2/8n\naEoC3mGX88yFRUa2eFE6/JvQ1CTa9r8OTeX60kR0LDbSRETHEWz8BInIQdg9k+Eo5bzoYuQonQpH\n6Qwko4cROPJ3o+MQUR7ifkoioqMkwgfQ0fgxJFMJSod/U/dTylLh8A67HInIQYSa18DmGg2re7TR\nkYgMseZve7FvR7Oujzl6YgXmXzLmpNssW3Yvrr/+RsyceTZ27NiGF1/8NZ54Yvkx29155/cxdux4\n1NXthc1mw/TpM7F+/VqEw2EsX74Sq1d/hE8++RDRaBSBQAC33vo9XHTRpWf8O3BEmoioF1VJorX+\nzwCAslHf4XrRRU6ULCgbdQ0AEW0HVkFV4kZHIioqV175Hbz77lsAgLfffhNXXnn1CbedPHkKfvGL\n55BMpmC1WvEf//EsRo2qwaZNGwEAsVgMzzzzSzzzzEqsWPEM0un0GefjiDQRUS+Bw/8LJRmAu/I8\nWJ0jjY5DecBir0ZJ1fnoaPwI/oa/oGzkt42ORJRz8y8Zc8rR42yYO/dcPPvsLxAMdmDz5i9x993/\nesJtx4+fCABwuZwYNaqm82c3kskEAOCss2ZBFEWUlpbB5XIjEAigvPzMTrzEEWkiok7x4D6EWzfA\nZPWhpOpCo+NQHnFXLYDJVoVI+ybEOnYZHYeoaIiiiIsvvgxPP/0Ezj//IkiSdMJtTzUNb+fOHQCA\n9vY2RCIReL3eM893xo9ARDQIqEoCbQfeBCCgbORVXOqO+hAECWUjrwIEEe0H3oKSjhkdiahofPOb\n38ZHH/0N3/zmme0Nam9vw113LcG9996N//t/f3LSpry/BK2AT9vU0hIyOsKg4PO5WEsdsZ76ylU9\n2w68hUjbRrirzodnyMVZfz6j8PV5ZjoaV6PjyN9g907DpHNuYS11xNemvljPvt55503U1+/HkiVL\nB3xfn891wts45EJERS8W3INI20aYrJUoqbzA6DiUx9yV8xHr2IGofwsCzVsBgfPoiXKlsbERjz76\n4DHXz5x5Nm6//Q4DErGRJqIipypJtB94G4DYOaXjzHf10eAlCJnXyZEdz+PA9j+hcsISiJLF6FhE\nRaGqqgorVz5/Wve94oordU6TwTnSRFTUOo58CCXVAXflfJjtVUbHoQJgsvpQUrkAqUQQgcN/MzoO\nERmIjTQRFa1k9AhCLesgW0rhrjrf6DhUQNyV58HqqEC49XMkIgeNjkNEBmEjTURFSdNUtB94C4CG\n0mFXQBRNRkeiAiKIMkZOvg4A0H7gbWiqYnAiIjICG2kiKkqhlvVIxo7A7p3G0z7TaXF6a+AsOxup\neDOCzWuMjkNEBmAjTURFJ53sQMeRv0OUbPAO/brRcaiAeaovhSg70dH4MVLxNqPjEFGOsZEmoqLj\nb3gXmpqCZ+jXIJkcRsehAibKVpQOuxzQFPgb3kMBn5qBiE5D1pe/++qrr/D000/jpZdewrZt23DH\nHXdg1KhRAIDFixfjiiuuwMqVK/Hhhx9ClmUsW7YM06dPz3YsIipSsY7diHXsgsU5Eo7SGUbHoUHA\n5pkEq2s04qG9iHXsgt0zwehIRLrzH/orooFtuj6m3TMZ3qFfO+k277zzJj755ENEo1EEAgHceuv3\ncNFFlx6z3caNG/C7370Ik8mE5uYmXHXVtdi4cQP27NmF669fjKuvvg4333w9pk8/C3V1++B2u/Hw\nw4/DZrOd0e+Q1Ub6hRdewKpVq7pD1tbW4tZbb8Vtt93WvU1tbS3Wr1+PV199FUeOHMHSpUvx2muv\nZTMWERUpTU3Df+h9AAK8wy6HIAhGR6JBQBAyr6cj238F/6H3YXWP5sGrRDqKxWJ45plfIhDw45/+\n6R+wYMGFkOVjW9jm5ma8+OLL2LFjOx588D78z//8GS0tzVi27F5cffV1iMfj+PrXv4GzzpqFZ5/9\nBd544zXceOPNZ5Qtq430iBEjsGLFCvz4xz8GAGzduhV1dXX44IMPMHLkSCxbtgxffPEFFixYAEEQ\nUF1dDUVR0N7ejtLS0mxGI6IiFGpZh3SiHU7fHJhtlUbHoUHEZC2Hq2IuQs1rEWpag5IhFxodiUhX\n3qFfO+XocbacddYsiKKI0tIyuFxuBAIBlJeXH7Pd6NFjIMsyXC4XqquHwmQyweVyI5lMAABkWcZZ\nZ80CAEydOgOfffbpGWfLaiO9cOFCNDQ0dF+ePn06rr/+ekydOhXPPfccfvnLX8LlcsHj8XRv43A4\nEAqF+tVIn+zc5zQwrKW+WE996VHPZLwDDZs/gWxyYMzUb0I22XVIVpj4+tRP71qWeq9A7ae1CDZ/\niuFj58Ni54DQQPG1qa/BUE+Xy4r16/fA53OhtbUV8XgU48ePgCT1PQutx2OH1WqCz+dCMGiH2SzD\n53PBYtEgSSJ8Phc0TUVb2yFMnDgRe/Zsw7Rpk8+4Rjk9RfjXvvY1uN3u7p8feeQRXHrppYhEIt3b\nRCIRuFz9+6VaWkJZyVlsfD4Xa6kj1lNfetWzdf+foSpJeKq/Dn9AAVCcfyO+PvVzvFq6qy5FW/2f\nsHfL6/CNvsGgZIWJr019DZZ6hkJxHDnShJtuuhnhcBh33/1jtLdHj9kuEIgikUihpSUEvz+KZDKN\nlpYQQqEwFEVFS0sIiqJixYpn0dTUiMrKKtx88/f6VaOTNds5baRvv/12PPDAA5g+fTrWrl2LKVOm\nYNasWXjqqadw++23o7GxEaqqcloHEekqHq5H1L8VZns1HGUzjY5Dg5jdOxXh1i8Q69iJWHAPbO6x\nRkciKnhnnTULS5YsPek2s2bNxqxZswEAI0eOwsqVzwMAXC4XXn6559i7++9/EBaLRbdsOW2kH374\nYTzyyCMwmUwoLy/HI488AqfTidmzZ+OGG26Aqqp48MEHcxmJiAY5TVPhb3gPAHiAIWWdIAjwDv8G\nGnc8D/+hv8DqGg1B4EqzRHr5zW9ewBdffH7M9cuWPYTq6qE5zyNoBbzo5WDYZZEPBsvun3zBeurr\nTOsZbvsS7QfehKN0BspGXqVjssLE16d+TlbL9gNvIdy2Ed5hV8Dlm53jZIWJr019sZ76OdnUDn5M\nJqJBS1WS6Dj8dwiCjJIhFxsdh4pIyZCLIIhmdDR+CFWJGx2HiLKEjTQRDVqh5rVQ0mG4Ks+FbHYb\nHYeKiGRywl15HtR0FMHG1UbHIaIsYSNNRIOSkgoh2LwGouyAu2K+0XGoCLkq5kEyuRFsWYd0ImB0\nHCLKAjbSRDQoBY58CE1NwTPkIoiSfkdoE/WXKJrgqb4E0BQEjvzN6DhElAVspIlo0EnGmhBp2wST\n1cfl7shQdu80mO3ViPq3IhE5ZHQcItIZG2kiGnQCh/4XgAZP9WVceowMJQgCPJ2nVQ4c+gsKeKEs\nIkN89tkavPHG60bHOKGcriNNRJRt8dA+xEN7YXXVwMqTYVAesDpHwlYyHrGOXYgH98BWMs7oSEQD\n9u7BFmxpD+v6mNNKnfjGcN9Jt5k3L7+PceFQDRENGpqmIXA4Mxc1MxrNk69QfigZcgkAIHDkbxyV\nJhqAd955E889t+K4t/3yl7/Ar361Eqqq4q67lmDNmtyvkMMRaSIaNGIdu5CMHobNMwlm+xCj4xB1\nM9sqYPdOQ9S/BdFALRzeqUZHIhqQbwz3nXL0ONfuuOMH+Jd/+R4ee+whTJo0BfPnL8h5Bo5IE9Gg\noGkqOo78HYAAz5CLjI5DdIzM61JEx5EPoWmK0XGICp4sy1i0aDE++OCvWLRosSEZ2EgT0aAQ9dci\nFW+Go3Q6TNb8GjUhAgDZ4oWzfBbSiXZE2jYZHYeo4AWDQbz00m+wdOmP8OSTjxqSgY00ERU8TVPQ\nceRDQBBRUnWh0XGITqik6nwIgoyOxo+hqimj4xAVtCeeeAQ33XQLrr32BrjdJXj11VdynoFzpImo\n4EXaNiGd9MNZfg5ki8foOEQnJJlccFXMRbDpU4RbNsBdea7RkYjy2hVXXHnC2x5//Knun3/604dz\nkOZYbKSJqKCpagodjR9DEGSUVOX+QBOigXJXzEeo9QsEm1bDWT6LZ94kOoVly+5FMNjR5zqn04kn\nnlhuUKIebKSJqKCFWzdASYXgrpgPyeQyOg7RKYmyDe6Keeg48iFCLZ/zAyDRKfQeec43nCNNRAVL\nVVMINq2BIJrhqjzP6DhE/ebyzYEgWRFqXgtVSRodh4hOExtpIipY4dYvoKYjcPnmQJJtRsch6jdR\nssJdMQ+qEkO49XOj4xDRaWIjTUQFqWc02gRXxTyj4xANWNeodLBpDUeliQoUG2kiKkiRti+hpsNw\nlZ8DSbYbHYdowETJCrdvLkeliQoYG2kiKjiamkaw6dPO0WguH0aFy+WbC0GyIMi50kTH9dlna/DG\nG68bHeOEuGoHERWccPsmKKkQXBXzIJkcRschOm2ibIXLNxfBxo8Rbt0Ad+V8oyMRHdcf/rYHn+9o\n1vUxz5lYgUWXjD3pNvPm5fd7giPSRFRQNFVBsPFTCIIMd0V+/w+WqD/cvnmdo9KcK010tHfeeRPP\nPbfiuLf98pe/wK9+tRKqquKuu5ZgzZrVx70umzgiTUQFJdL+FZRUB1y+uZBMTqPjEJ2xPqPSbV/C\nXTHX6EhEx1h0ydhTjh7n2h13/AD/8i/fw2OPPYRJk6Zg/vwFmDNn3jHXZRNHpImoYGiaio6m1YAg\nwcVd4DSIuHxzIIgmhJrXQlMVo+MQFQRZlrFo0WJ88MFfsWjR4hNel01spImoYET9tVCSATjLZkLm\nWQxpEJFkO5xls6Ckgoj4txgdh6ggBINBvPTSb7B06Y/w5JOPnvC6bGIjTUQFQdM0BJvWABDg5kod\nNAi5KuYBgohg0xpommZ0HKK898QTj+Cmm27BtdfeALe7BK+++spxr8smQSvgd2tLS8joCIOCz+di\nLXXEeuqrq56x4B607H0Zds8UlNdca3SsgsXXp36yUcu2+lWItG9Cec0i2D0TdX3sfMfXpr5YT/34\nfCfeA5r/GjvUAAAgAElEQVT1gw2/+uorPP3003jppZewfft2PPLII5AkCWazGU8++STKy8vx6KOP\nYuPGjXA4MstYPfvss3C5uNuWiHoEmz4FAC4PRoOau3I+Iu2bEGxaDVvJBAiCYHQkIsMtW3YvgsGO\nPtc5nU488cRygxL1yGoj/cILL2DVqlWw2WwAgMceewwPPPAAJk2ahFdeeQUvvPAC7r//ftTW1uLX\nv/41SktLsxmHiApUInIIiXA9rK7RMNuHGB2HKGtM1nLYSiYi1rEDifB+WF01RkciMtzjjz9ldIQT\nyuoc6REjRmDFip61/5YvX45JkyYBABRFgcVigaqqqK+vx4MPPogbb7wRf/zjH7MZiYgKULB5DQCO\nRlNxcFeeB6BnLwwR5a+sjkgvXLgQDQ0N3ZcrKioAABs3bsTvfvc7/Pd//zei0Shuvvlm3HrrrVAU\nBbfccgumTp2KiRNPPTfsZHNWaGBYS32xnvqJR1oQC+yA3T0Mw2qmc1e3Dvj61E9WaumbiGjrWITa\n98Bu6YDDPUz/58hTfG3qi/XMvpyfkOWdd97Bc889h+effx6lpaXdzXPX9I958+Zhx44d/WqkOYle\nHzwgQV+sp76izR8B0GAvnYfW1rDRcQoeX5/6yWYtrd65CLXvwYGdH6B81DVZeY58w9emvlhP/Zzs\nA0lOl79744038Lvf/Q4vvfQShg8fDgDYv38/Fi9eDEVRkEqlsHHjRkyZMiWXsYgoTympENoOb4Bs\n9sJWZCsYUHGzukbDZK1A1F+LdLLj1HcgIkPkbERaURQ89thjGDJkCJYuXQoAOOecc/DDH/4QV111\nFRYtWgSTyYSrrroK48aNy1UsIspjoZb10DQFrspzIQhc9p6KhyAIcFXMQ/uBVQi1rId36NeMjkRE\nx8F1pIm7f3TGeupDVVM4vPU/IIoiqib/EKJoMjrSoMDXp36yXUtNTeNQ7S+gaWkMnXI3RMmStefK\nB3xt6ov11E/eTO0gIuqvaPtmqEoM5cPnsYmmoiSIMly+c6ApCUTaNhkdh4iOg400EeUdTdMQbFkH\nCCJ8w3g6cCpezvLZEAQZwZZ10DTV6DhEdBQ20kSUd+KhvUjHW2H3TIHZWmJ0HCLDSLIdjtIZUJIB\nxDp2Gh2HiI7CRpqI8k6oeR0AwF0x1+AkRMZzdb4PQs2fGZyEiI7GRpqI8koq3oJ4aC8sjuEw26uN\njkNkOJO1HFb3OCQiB5GINJz6DkSUM2ykiSivhFrWAwBcFfMMTkKUP9yd74euvTVElB/YSBNR3lDS\nUUTavoJk9sBWMsHoOER5w+IclTlBS2A70ikuaUaUL9hIE1HeCLduhKal4fKdwxOwEPUiCAJcvnMA\nqAi3fmF0HCLqxH+piCgvaJqKcOsGCKIZzrKZRschyjt27zQIkiXzgVNVjI5DRGAjTUR5ItaxC0oq\nCEfpdIiS1eg4RHlHlMxwlp4FNR1GtGO70XGICGykiShPhFs/B5A5AQURHV/X+yPc8rnBSYgIYCNN\nRHkgFW9FPFQHi3MkzLYKo+MQ5S2TtQxW1xgkIgeRjDYaHYeo6LGRJiLDdR08xdFoolPLHHQIhFo5\nKk1kNDbSRGQoVUki3L4JouyEvWSi0XGI8p7VPRaS2YNo+xao6ZjRcYiKGhtpIjJU1L8VmpKAs3wW\nBFEyOg5R3hMEEa7y2dC0NMLtm4yOQ1TU2EgTkWE0TUOodQMAAc6yWUbHISoYjrKZEAQZ4ZYN0DTN\n6DhERYuNNBEZJhk9hFSsEbaSCZDNbqPjEBUMSbbB7p2KdNKPeGiv0XGIihYbaSIyTKhlAwDA5eNB\nhkQD5Sw/G0DmjKBEZAw20kRkCCUdRTRQC9lSBouzxug4RAXHbK+GyVbVeTKjkNFxiIoSG2kiMkSk\nbROgKXCWnw1BEIyOQ1RwBKHr2AIV4bavjI5DVJTYSBNRzmmahnDbl4AgwVE6w+g4RAXLUToVgmhC\nuG0jDzokMgAbaSLKuUTkANKJNtg9kyDJNqPjEBUsUbLC7pkCJRlAPLTP6DhERYeNNBHlXKTtSwDg\nkndEOnCWZ95H4TYedEiUa2ykiSin1HQcUf82yJZSWJwjjY5DVPDM9qEwWSsRC+yEkgobHYeoqLCR\nJqKcivi3QNPScJbN5EGGRDoQBKFzVFpFpJ0HHRLlEhtpIsqpcNuXAEQeZEikI0fptMyZDlt50CFR\nLrGRJqKcSUYPd57JcDwkk9PoOESDhihZu890mAjXGR2HqGhkvZH+6quv8N3vfhcAUF9fj8WLF+Om\nm27CQw89BFVVAQArV67EddddhxtvvBGbN2/OdiQiMki4+yDDmQYnIRp8ug86bP3S4CRExSOrjfQL\nL7yAf/u3f0MikQAA/PznP8fdd9+Nl19+GZqm4YMPPkBtbS3Wr1+PV199FcuXL8fPfvazbEYiIoOo\nShKR9i2QTG5Y3WOMjkM06JjtQyFbyxHt2AE1HTM6DlFRyGojPWLECKxYsaL7cm1tLebMmQMAuOCC\nC7BmzRp88cUXWLBgAQRBQHV1NRRFQXt7ezZjEZEBooFt0NQkHGVnQRA4q4xIb4IgwFl6FqApiPi3\nGh2HqCjI2XzwhQsXoqGhofuypmndR+k7HA6EQiGEw2F4PJ7ubbquLy0tPeXj+3wu/UMXKdZSX6zn\nsdrqNgMQMGLcAlhsA6sP66kv1lM/+VZLj/tcBI78DYngFoyefInRcQYs3+pZ6FjP7MtqI300UewZ\nhYpEInC73XA6nYhEIn2ud7n694dvaQnpnrEY+Xwu1lJHrOexUvFWRAL7YXWNRjAsA+H+14f11Bfr\nqZ/8rKUAq2sMosHdOHRgL8y2CqMD9Vt+1rNwsZ76OdkHkpzuX508eTLWrVsHAPj4448xe/ZszJo1\nC6tXr4aqqjh8+DBUVe3XaDQRFY5Ie+YgYkfpWQYnIRr8nGWZ91mkbZPBSYgGv5yOSP/kJz/BAw88\ngOXLl2P06NFYuHAhJEnC7NmzccMNN0BVVTz44IO5jEREWaZpKiLtmyGIFtg8E4yOQzTo2dzjIUo2\nRPxb4Bl6KQRBMjoS0aAlaAW8cjt3WeiDu3/0xXr2FQ/uQ/Pe38FRNgtlI7414PuznvpiPfWTz7Vs\nb3gP4Zb1KB99A+wlhfEBNp/rWYhYT/3kzdQOIio+4c5TFjtLpxuchKh4OEs5vYMoF9hIE1HWqEoC\nsY4dkC2lMDuGGx2HqGiY7VUw2aoQ69gNJRU59R2I6LSwkSairIkGtkNTU3CUTu9e+pKIcsNZOgOA\nioh/i9FRiAYtNtJElDWRzmkdDi+ndRDlmr10GiCIiLRtQgEfDkWU19hIE1FWpBN+JML1sDhHQrZ4\nTn0HItKVJNthc49HKt6MVOyI0XGIBiU20kSUFT1rR88wOAlR8XKUZd5/kXZO7yDKBjbSRKQ7TdM6\n1442we6ZbHQcoqJlc43tXFN6KzRNNToO0aDDRpqIdJeIHEA66YfdMwmiZDY6DlHREkQJdu8UqOkI\n4qF9RschGnTYSBOR7jitgyh/OEqnAeD0DqJsYCNNRLpS1RSi/lpIJjcszlFGxyEqemb7MMhmL2Id\nO6AqSaPjEA0qbKSJSFfxjt3Q1CQcpdO4djRRHhAEAfbSadDUFGIdO4yOQzSosJEmIl11nfzB7p1q\ncBIi6uLwcnoHUTawkSYi3ajpGGLBPTBZK2C2VRodh4g6maxlMNuHIh7aByUVNjoO0aDBRpqIdBMN\nbAc0haPRRHkoc9Chhoh/q9FRiAYNNtJEpJuuf6AdpWykifKN3TMFgIgop3cQ6YaNNBHpIp0MIhHe\nD4tjOGQzTwlOlG8kkwNW9xgkY0eQircYHYdoUGAjTUS6iPprAQD2zjVriSj/OEqnA+BBh0R6YSNN\nRLrIrNYh8pTgRHnMVjIegmhGxL8FmqYZHYeo4LGRJqIzloq3IBVrhNU9BpJsNzoOEZ2AKJpg90yC\nkuxAInLQ6DhEBY+NNBGdsUh750GGXk7rIMp3XavqdE3HIqLTx0aaiM6IpmmI+rdCEE2wlYw3Og4R\nnYLVVQNRtiMa2AZNU42OQ1TQ2EgT0RlJRg8hnfTDVjIRomQ2Og4RnYIgZI5lUNMRJEL7jY5DVNDY\nSBPRGeleO5onYSEqGHbvFABAJMDpHURngo00EZ02TVMR9ddClO2wukcbHYeI+sniGAHJ5EI0sB2a\nmjY6DlHBYiNNRKctHqqDmo7A7pkMQZCMjkNE/SQIAuyeKdCUOGKhvUbHISpYbKSJ6LRFA9sA9Owm\nJqLC0fW+5eodRKePjTQRnRZNUxAL7IAkO2FxjDA6DhENkNleDdnsRaxjJ1Q1ZXQcooIk5/oJX3/9\ndfzpT38CACQSCWzfvh3Lly/Hk08+iSFDhgAAli5dijlz5uQ6GhENQDxUB1WJwembA0EQjI5DRAMk\nCALs3ikINq1GrGMXHNyzRDRgOW+kr7nmGlxzzTUAgJ/97Ge49tprsXXrVtx7771YuHBhruMQ0WmK\n+jundfCU4EQFy+6dimDTakT9tWykiU6DYVM7tmzZgj179uCGG25AbW0tXnvtNdx000144oknkE7z\nCGKifKZpCmIdOyCZXLA4hhsdh4hOk9lWAZPVh1hwN1QlbnQcooKT8xHpLv/5n/+JH/zgBwCA8847\nD5dddhmGDRuGhx56CK+88gpuvvnmUz6Gz+fKdsyiwVrqa7DXs6N1B1Qljoqhs1FR4c768w32euYa\n66mfwVDLdGgWDu95H7Jaj7Kq2YZmGQz1zCesZ/YZ0kgHg0HU1dVh3rx5AIBrr70WbnfmH+NLL70U\n77//fr8ep6UllLWMxcTnc7GWOiqGerbVfwEAECxjs/67FkM9c4n11M9gqaVmHgfgfTTWb4BqmmBY\njsFSz3zBeurnZB9IDJna8fnnn+Pcc88FAGiahm9/+9tobGwEAKxduxZTpnCeFlG+0tSeaR1mTusg\nKngmSynM9mrEQ/ugpKNGxyEqKIaMSNfV1WHYsGEAMkcNP/roo7jzzjthtVoxZswYLFq0yIhYRNQP\n8dA+qEocrtIZXK2DaJCwe6YgGT2MaGA7XOVnGx2HqGAY0kh/73vf63N5wYIFWLBggRFRiGiAuk/C\nwtU6iAYNu3cyAof/iqi/lo000QDwhCxE1G+aqiDasROSyQ2zY5jRcYhIJ7K5BGb7UCTC9VBSEaPj\nEBUMNtJE1G/x0D5oShx2zyRO6yAaZDKnDNcQ7dhhdBSigsFGmoj6rXtah5fTOogGG7tnEgAg6q81\nOAlR4WAjTUT9kpnWsSMzrcPOaR1Egw2ndxANHBtpIuqXeGgvNCXBaR1Eg1hmbxOndxD1FxtpIuqX\nnmkdXOedaLDqWo0n6t9mcBKiwsBGmohOSVPTnat1ZHb9EtHg1DO9Yz+ndxD1AxtpIjqlzGodnNZB\nVAw4vYOo/9hIE9EpRfxcrYOoWHB6B1H/sZEmopPS1DRinNZBVDQ4vYOo/9hIE9FJxUJ7oakJ2L2c\n1kFULDi9g6h/2EgT0Ul17d61e7haB1Gx4PQOov5hI01EJ9Q9rcNcArO92ug4RJQjnN5B1D9spIno\nhDLTOpKweyZzWgdRkcmMSmuIcXoH0QmxkSaiE+qZ1sHVOoiKjd07CUDPyZiI6FhspInouHqmdXg4\nrYOoCMlmD8z2oYiHOL2D6ETYSBPRccWCXdM6uFoHUbHi9A6ik2MjTUTHFQ3UAgDsXq7WQVSsOL2D\n6OTYSBPRMVQ1hVjHrsy0DtsQo+MQkUHkzqldnN5BdHxspInoGPEgV+sgoozMGvKc3kF0PGykiegY\nXbtxHV6u1kFU7Di9g+jE2EgTUR+ZaR07IZu9MHFaB1HR4/QOohNjI01EfWSmdaS4WgcRdeP0DqLj\nYyNNRH1E/Vytg4j66preEfFzegdRb2ykiaibqqYQC+7qnNZRZXQcIsoTXSdnSYQ5vYOoNzbSRNQt\nHtyTmdbh5WodRNRXZi+VhiindxB1k40OQET5I9q52zZzNrMemqJAjUahRKPQUkmoySS0ZOf3VOfP\nqRSgqICmQlM1QFWhaSqgap3XqYCmAYIAQRQBQQREAYIgAmLvn3t9lyTA60IomoIgSxAkGYJ81Jck\nZ26TZQiyCYIsAZIMQZIyz0OUhzSt8z2STvd8KWloaQVaOtV5nQIo6WO3SXVt23d7AD2veUEAIACi\n0Hmx57IgSRBMZggmEwSTCWLn9+7LNhskmx2i3d7nPWT3TELg0F8Q9dfCVX52jitGlJ8MaaSvvvpq\nOJ1OAMCwYcNwww034LHHHoMkSViwYAHuvPNOI2IRFRVN06B0BJBqa0Pa70cq0IqodxuQlND03G+h\nRqJQImGo0SjUWMywnI1ncmdJyjQNnQ22IJsgmORjG3KTqbO5MPXc1rVd93dTT+Pe+7bej310k9/Z\n3EOWIcoy0Pt6jvhnjaZpgKIcp0Ht24AOpEnVej9eOg0oaQRkAbFwrNf9FWip1DHbdj0ejno8aJrR\npTol0WaDaLdDstshOpwQ5pqQ0Paj7YO3YC6pgFzihVxeDtnj4WuailLOG+lEIgFN0/DSSy91X3fV\nVVdhxYoVGD58OL7//e9j27ZtmDyZ69cS6UHTNKRaWpA4sB+JhgakmhqRbGpCsqkRWiLRvZ04xgHz\n5ZVI17Yivd0PwWKF5LDDVF4O0e6AZHdkRqjMZohmc893kxmC2QTRZAYksWc0ufeoc9fPgpBpHrpG\nqLtHqzPfoWrdo9iZ2xU4bDJCgUjfxqRXY4Njmp+jGpo+jU0KajwBLR3u1RApxvxhBtzg9zTr3TUV\nO0fdRRGCKBx1+fjf0247wtFk92VdaDjOngg10yz23iOh9uO70qvhVRSgq6FV+v7dNaWzMVWOvd2w\nv2kXSTpmb4lotkCwH7tXBd3bmLq37fNhrXvPS882ON42ktTz/oKW+VtA6/zbaH2vVxSoqVSmqU8m\noaVSmcudX2osBiUa6fwQHYUSiSLV2gr14EFIZjdM55cjsPl9KLWh7l9ZsFhgrqiAqbIK5soqWIYO\ng2XkSJh8FdwzRINazhvpHTt2IBaL4bbbbkM6ncbSpUuRTCYxYsQIAMCCBQuwZs0aNtJEp0mNxxDb\nsxvRHTsQ31+HRP3+Y0aUBbMZpopKmCsrYSovh+zxIl5ajxQaUfXN78P2j+My/8jnAZ/PBbkldOoN\nT5PWu3lLp3pGIVOZJl3t/K6l053TV3o17qmj7tPVuB/d9Hc/9vGu7/lSk9G+o56qqvvv26L7I+bQ\n0Q1qZxMpWqw9o/3deyF6pv5k7icdu7ehv03qcacSmVBWUQJ/MN69/WCfUqSl00i0NaD58G9hu3AC\nbNMmIB3wI9XSjFRzE5JNTUgcPNjnPqLVCsuIkbDW1MA2fiJs48ZDstsN+g2I9JfzfymtVituv/12\nXH/99di/fz/+6Z/+CW63u/t2h8OBg0e9EU/E53NlK2bRYS31lct6aqqK0K7d8H++AYHNWxDes7dP\nA2atroZz9iw4x4yBo2YUbEOHwlxW2ucfeyWdxFcfPgyLtRwjpp2Td7toi/X1qSkK1HSmYc98T0Ht\nbLC7PwD0GtE99eXeI8AKNEUFdPpTd811F0QRgiRCEKXMZanzuq6R8c7beo+Ud90HogixVyMrykc1\nu3n2ugSAIZVGJ8ixIV5EoqMQCdSj5prLYLL0/PutaRqS7e2INRxCZP9+RPbWIbx3H2K7dyG2ayf8\n778HiCIcNTUomTYFpefMhnvSxMxoeqdifa9nC+uZfTlvpGtqajBy5EgIgoCamhq4XC4EAoHu2yOR\nSJ/G+mRasjhKVUx8PhdrqaNc1FNNpRDdVovwpo2IfLUJSjCYuUGSYK0ZDfuEibBNmAjr6DGQbLbu\n+6U6v9DWd/mqiL8WmpqCxTUJra3hrGYfKL4+u4gALIBsOaNH6apn56FnWdE5qeDMpACkNPR61ead\nYn1tmh0TEAnsx8G9G+DynXP0rUB1DczVNTDPvxheAGo8jnjdPkR37kBs5w5E9u1FZO9eHP7zKohO\nJ5zTZsBx1kyMvHg+2oNJI36lQalYX5/ZcLIPJDlvpP/4xz9i165dePjhh9HU1IRYLAa73Y4DBw5g\n+PDhWL16NQ82JDoOTVUR27Mboc/WILThc6jRKABAcrngXnABnGfNhH3iJIhW64AfOxroXK3DyylV\nRHRyNs8k+A+9j2hg23Ea6WOJVivskybDPinz/xc1kUBs106EN32J8FdfIrj2UwTXformF/8Ljpmz\n4Jp7LuwTJ/UZqSbKVzlvpK+77jrcf//9WLx4MQRBwOOPPw5RFPGv//qvUBQFCxYswIwZM3Idiyhv\nJY8cRnDtGgTXrUW6rQ0AIHk88C64AM6ZZ8M6ZswZzclUlSTiHbshW8phslboFZuIBinZ7IbFMQKJ\ncD2UVAiSaWDTB0SLBY5p0+GYNh0VN9+CRP1+hDd+gciGdQiu+RTBNZ9CcrvhmjMX7nnzYRk5Ki+n\n9RABgKBpBbD+zglwl4U+uPtHX3rUUwmHEfxsDYJr1yBRvx8AIFiscJ09G+5z58M2YaJuBzRF2rei\nrf51uKsugGfIRbo8pp74+tQX66mfYq5lqGU9/A3vwTvscrh8c3R5zPIyBw5+9iWC6z5DaMN6qOHM\nNDPzsOEoWXAB3PPOhdS5dC6dWjG/PvWWV1M7iOjEkk2NaH/7LYQ+XwctlcocmDN9BlzzzoVzxkyI\nljObH3s80UAtgGNPwkJEdCJ2zyT4G95D1L9Nt0ZaEEXYxo2Hbdx4VNx4EyK1WxH89BOEv9qEllf+\nG61//B84Z50NzyWXwTZ2nC7PSXSm2EgT5QFN0+B/7120/vk1QFFgqqhEyYUXwX3ueZD7efDt6VCV\nBGLBPTBZfTDbOK2DiPpHMrlgcY5EIlyPdDII2azv/6cEWYZzxllwzjgL6WAwM4969ScIrV+H0Pp1\ncM8/DxU3/wNEs1nX5yUaKDbSRHnA/+7baH39j5C9XvhuWAznrNk5WYs21rEL0BSORhPRgNk9k5EI\n1yMW2A5XxdysPY/sdqN04Tfg/frliO3ehZZXXkZwzadQwmFUL72b86fJUINz1XiiApJsakTrG3+C\n5PFg+LIH4Zo9J2cndOie1uGdkpPnI6LBw+6ZBKBn1Z9sEwQB9vETMPz+f4N90hRENn+F0No1OXlu\nohNhI01ksMCHfwcUBb5FN8Lk9ebsedV0HLHgXpislTBZy3P2vEQ0OEgmZ2Z6R+Qg0slgzp5XNJlQ\n+Y+3AZIE///+JWfPS3Q8nNpBZLDo1i0QLBa4Zs3O7fN27MxM6/BORjqlIBJOIBJKIhpJIhFPI5lI\nI5lMIxlXkEopUFUVmqpB7fzSVA0aAFEQIIgCJCnzXRQEiJIIURQgm0TIJgkmkwTZJHZ+77ksmySY\nzRLMFhlmqwxZFrmblqifFEXNvE8TaSQTSs/7NpFGIpFGOqVCUVSoigolrUFR1J6vtAZV6TwDqgAI\nmf9kLgqAKIk971tz5j1rNsuwOUyw2c2wO82wO8ywe6YgEa5HNLAd7ixO7ziaqawM9omTEK3dinRH\nAHKJJ2fPTdQbG2kiAynhMJJHDsM+ZSoEOftvx1g0ieYjIbS3RGDV1sNhBd55I4aA/5OsP3d/iKIA\ns6Wzse78crmtALTuZtvSfZsEi7Vnu66fJYk72ij/aZqGZELp0/gmE2kk451NcSLdq0lOdzbJfa9P\np1Sjfw24XCrOPxc4sm8Ddu+uRHmlCxXVLtjs2T8IsKuRju3aBdc5+qwcQjRQbKSJDJRqbQEAmKuH\nZuXxE/E0Dta14+C+djQe6kCgPQYAkOUUvnZxIzqCTkD0YNgoCxwuCxwuMxxOS6YpNcswWSRYLDJM\nZgmiKEAUO0edRQGCIEAQAFXNNAWqonaPVquqBlXRkE4rSCUVpNMq0qnOn1MqUqnMKHfXdT0jaT2N\nRSASPa1GQTaJmcb66Ma7++eeBrz7e+ftZgtHxenUNE1DOq0iFIzD3xY57mjwMU3xMbcrA37ezAfN\nzGvY4bT3fIjs9Ro3d75nzRYZskmCJAmQZBGS1PklC5AkEaIkQpJ6XuddZ5ToOrWEomid79ee92oy\noSAaSSIWyey5ikaSCPpjaGv3oLysDWs+2oF4PHNmVbfHiqqhJRgxphTDa0phtZnOvPBHsQwfASBz\n0ioio7CRJjJQOhAAAMge/XZLppJp7Nnegt3bmnDkYAdUNfMPo8ksYXiNF5XVbpSXNkBMahg+/hxM\nuzB/R3IURYXbZcORw4GjGhWluzFJ9Bmx6/k5Fk0i0B7FQE85JYpCnwa8Z9Q704D3brqPbtYt1kzz\nIopsxPOVpmUaxK7XTqrPKK+C5HF/7ryc7Pm56301EF17W5xu61GvL+k4e1x6NcV5/iEv2CIi0PAu\nLvm6Cc1tI9F0JITmw0Hsqm3CrtomCAJQObQEE6ZWYsxEHyxWfZpqky+zZGeqtVWXxyM6HWykiQyU\nDvgB6NNIt7dGsPnzBuzZ3oxUMjPa5atyYdTYMowcW4ayCmd3g9e8Zw3iScBZOvWMnzebJEmE3WGG\n22M7rftrmoZ0Su0zIpg4TtN93N3n8TQioQTS6dMYFZdFyJ3zSk3mnvnhpl7Xdc8XN3dt03Vb18ih\nCFkWu0cTu3/uvJyPDdWZ0rTOebzpzFc6rXZfTndepyhqZq9GUunes9E9ctq1x+MEt2VGVk9vOoTJ\nLMFskWBzmFFS2jntyGUFBO2okWGp7wetXk3xYPybAYDDMxmBhvdgkfbjnPMvBZD5W7Y1R3BgXxvq\n97ahsaEDjQ0dWP3X3RgzsQIz5gxHeeWZnaVQLsmsXa2EefY+Mg4baSIDdY9In8GBMi2NIXzxaT3q\ndmdGZVwlVpw1ZzgmTKuCq8R6zPZKKoJ4aB/M9mrIltytEmIEQRC6G1W4Tu+skEpa7TMvNdF7JDze\n9V3pvq33lJVUSkEklMgcrKkMfATzZLp32csiZKlXg9059ab3NJzunwUBVpsJqZRy3Gk6A6VpmYap\n9+xljYAAACAASURBVEGoXQeiqkddf/Q2aleD3KtxVnSuUeaA18zf32KV4XRbOg9wlWG2dn639BoR\n7mx+Tb2mR5gtEkxm+bh7GXgK5gzJ5IDVNQrxUB3SiQBkiweCIKC80onySidmnTsS4WAcu2qbsHNL\nY/dI9bBRXpxz/ihUDS05recVLFYIJhPSwdytGEJ0NDbSRAZSQpl/hCXXwM8KFg4lsO6jfdi1tQkA\nUFHtwqxzR2LU2LKTjnxl1nzVYPfm92h0vpBkEXY5s0LBmciMpPY02MeMnvb6rvRpLnuNxvYeoe29\nTWezr6TVPk3rQKe1ZNPRjbsoCZDlzHx22d4z2i7LPSPyx7ssSWLfkf6jRvm7R/fNEg88zSG7dyri\noTpE/FtQUnX+Mbc73VbMOnckZs4bgQP72rFp3UE07PejYb8foyf4MO+iGvh8rgE9pyAIEK1WaPG4\nXr8G0YCxkSYykKZkpmAIstTv+6iqhi0bGrD+kzqkUyrKKhyYf8kYDB3p7deu44h/C/7/9u48So7q\nvPv4996q6mW6Z9FopNGCNBLaNxASFlrAmBgEke0gg5BZvARCgjk4seGQxAc7HEJsYoJ1EjvYchz8\nvjYOxNjY5zXCNraFBMIgC5AQIECA0A5aZjQazd7dVXXfP6p7piWNxCw1UzPm+Rya3qprrp6p6f71\nrVu3QJGSk7AMqMLBXmGND+0OY4IwXdwbXFmZora26aRe4t7SOh9oCr3f+WEnxT3hSvEnO6xBBEoq\nZlC/79e0Ht1GWfX5p/x9K6WomTScmknDObCvgefWv8PON2vZs6OOCy+dxuRZI3v0BUjZDsZ1w/pn\nCNFjEqSFiFIhSFvdC9IN9a2s//V2Du5vJJF0OP/iKUybM6rbB7e5maNkW/aTKJ2I5fSs90cMPR1D\nNrSisIWVpGKUtPb/1GTig0VbCZLlU2lreINc2yFiJaPe9zWjx1VwxWfm8c72Wp5du4N1v97OKy/u\n56KPTaOqunvvT8q2JEiLSEmQFiJCxs9PgaVPH6SNMWx/5SB/+P3buK7PpOkjOP+SKT0ebtBydBsA\nJcPm9Kq9QghxKqlhc2hreIOWo692K0hD8GVv8oyRjJs4jC3P7WXr8/v4+YNbWHTRJObMH/u+ezKU\n7eC3ydAOER0ZQCZEhEz+zGKn65HOZT3WPb6dp37zJtrSXHL5TJYun9XjEG2MCYZ1KIuSiul9arcQ\nQpwoWTYZZSVoPboNY3o2O0o84fAXn5rLx1bOIRa3eXbtDn7z8220t+VO+zpl2xj39MsI0Z8kSAsR\npfcZ2lFf18LPH9zMW68dYuToUq66fj6TZ4zs1Y/KtR3Cba8jWT4VbZ08m4cQQvSF0jYlFTPwck1k\nmvf0ah3jzxzOyhvOZWxNBXt2HOGn/+dFDh849awcQZCWoR0iOhKkhYhQ59COk/8Ud71dxy8e3MLR\nulbmnDuW5Z8+p9fzKUPhIENIyWwdQoh+ksoPGysMI+vVOtJxPv6ps1nw4Ym0NGX4fw9t5a1tB7tc\nthCkzWCaokZ8oEiQFiJCposeaWMMWzbu4Ymfb8MYw9LlMzn/4il9msrLGEPr0ddQVpxk2ZQ+t1sI\nIboST9dgOaW0NryO8XvfU6y1Yv7iGpZdNQfLUjz5+Haee3IHvn/8kBFl52fB8Xp+ynUhwiBBWogo\nnTBG2hjDxvXvsOnpXaRK4yy/7hwmTe/dUI5imeY9eLlGSspnoLQcYyyE6B9KKUqGzcZ4Gdoa3+7z\n+momDefKz82nYngJL7+wn9//8nU8rzNMKyd4P5Nx0iIqEqSFiNCJQzte/MNuXn5+PxXDS7jyc/MY\nMSqcKeo6hnUM8lOCCyGGvo7hHfWvhrK+isoSrvjMPMaMr2Dnm3WsfeyNzqEc+fdO4/fu1O9C9JUE\naSEiZDwPlEJpzf7d9bz47B5KyxNcfs3ZpNK9O6X1iXwvS+vR17CcMuLpCaGsUwghTsVJVuMkRtDW\n+Dae2xbKOuMJm2VXzWH0uHJ2vlnLy8/vO34BGSItIiJBWogo+R7KsvB9wx9+vwOlYOnymZSEFKIB\n2o5tx/hZUpVno5T8yQsh+pdSilTl2WA8Wvtw0OGJHMdi6fJZJFMOLzyzm5amTOd7mhxsKCIin6pC\nRMi4HlgWe3Yc4eiRVqbNGcXI0WWh/ozmI1sBSA0/O9T1CiHEqaQqzwJUx/tPWEpSMRZcMBHX9Xn5\nhX1QOF+LBGkREQnSQkTI+D5Ka3a8cRiA2fPGhrp+N9tApnk38dR4nHhlqOsWQohTsZw0ybIp5NoO\nkG3teuq63po2exSJpM1b2w5h8knayNgOEREJ0kJEyRh8bbF7Rx1lFQmqqtOhrr7lyMuA9EYLIQZe\navhcAFrqXw51vZatmTC5irbWHMf8/Nz6kqNFRCRICxEl49PilOPmfMbWDEMp9f6v6e6qjaG5/mWU\ndiipmBnaeoUQojuS5VPQdoqW+lf6NKd0V8ZOGAZAnZsKHujhKcmFCMuATyiby+W44447ePfdd8lm\ns9x8882MHj2am266iQkTJgBwzTXXsGzZsoFumhADz0CjEwy5GDk6nKnuCjLNe/CyDaQqz0Zb4R28\nKIQQ3aGURapyDk2H/0jbsbcoGRbeF/oRo4K9d01+glEgPdIiMgMepB977DEqKiq47777aGhoYPny\n5dxyyy1cf/313HDDDQPdHCEiZYxPqx18IAwbXhLqujsOMqyUYR1CiGikKufSdPiPNNdvDTVIlw9L\nYlmKRq/QSSBJWkRjwIP0ZZddxqWXXgoEu54ty2Lbtm3s2rWLJ598kpqaGu644w7S6XDHigoxKBlo\ns4Ndk6UVydBW67mttDa8hh0fTjxdE9p6hRCiJ2LJkcRKxtLe+A5uthE7Fs6sRFpryoYlaT4SDBkx\nvgRpEY0BD9KpVBAampub+bu/+zu+9KUvkc1mueqqq5g9ezarV6/mO9/5Dv/4j//4vusaMSLcXeEf\nZFLLcHW3nnt1EKQtWzNhwnCUDmeM9KHdW8B4jKpZxMiR4U6nFwXZPsMl9QyP1LIbMovY+/qjmLbX\nGDF26WkX7Uk9K4aVcLSuFU9ZDK9MEZffxUlk++x/Ax6kAQ4cOMAtt9zCtddeyyc+8QkaGxspKws+\n7C+55BL+5V/+pVvrqa1t6s9mfmCMGFEqtQxRT+rpuT7tySSpdIy6I82h/HxjDAf3PAfKwsSnD/nf\nrWyf4ZJ6hkdq2T2+PRllxTm8dyN22QKUsrpcrqf1dGLBerJWkiNHmnGQY0GKyfYZntN9IRnwWTvq\n6uq44YYb+Pu//3tWrFgBwF/91V/xyiuvALBx40ZmzZo10M0SIhrG4CqHRNIJbZWZ5t24mXpKKmZh\n2eGOuxZCiJ7SVox05Vw8t5nWhu2hrbckHQMgayeRMdIiKgPeI/29732PxsZGvvvd7/Ld734XgC9/\n+cvcc889OI5DVVVVt3ukhRjqPBS+sojFw/tTbK7bDEDpiPmhrVMIIfoiXTWfptpNNNe9QGpYOJ1l\nJakgSGesJMgYaRGRAQ/SX/3qV/nqV7960uM/+clPBropQkQuZ4Jdk2EFaTfXRGvDdpxENbGSM0JZ\npxBC9JWTqCJROpH2pl1k2w4RS1b3eZ2JkmBPXs6Ky5kNRWTkhCxCRMhVQYCOxbseM9hTzbUvAD6l\nI84N9eQuQgjRV+mqDwHQXPdiKOuL5cdIe8qRkR0iMhKkhYiQm98pFE/0vUfa97I0121G2yWUVJ7V\n5/UJIUSYkuVTsZwyWupfwXfb+7w+Jxa8b7rakTMbishIkBYiQrl8j3Q8hKEdLfWv4HttpKvORevw\nDl4UQogwKKUpHbEA4+doCqFXurAnz9PSIy2iI0FaiAjlKAzt6FuQNsbQVPtHUBalVeeG0TQhhAhd\numoeSsdpqt2E8d0+rasw/Z2nHSRJi6hIkBYiQn5+PlXb6dsY6bbGt3Az9aSGzcFy5KygQojBSVsJ\nSqvm47sttNS/3Kd1WVYQYXyl5cyGIjISpIWIkMm/9/fljIbGGBoPPgNA6ciFYTRLCCH6TenI80BZ\nNB7eiOnD2ObOIG0hPdIiKhKkhYiQn/8T1H0I0u2NO8i2vkeyYgax5MiwmiaEEP3CckpJVZ6Fm6mn\nrQ8naLHsoiAtOVpERIK0EBEqvPf3NkgbYzh2cAMA5aM+HFKrhBCif5VVLwEUDQef6nWvtGUF75u+\n0jJrh4iMBGkhItTXHun2pp1kW98lWT49lBMcCCHEQHDilaSGz8Vtr6Ol/tVerUN6pMVgIEFaiAh1\n9EhbPQ/Sxhga3lsHSG+0EGLoKR/1YVAWxw4+jfG9Hr9eaw0YGSMtIiVBWogImT70SLcefZVc2wFK\nhs0hVjIq7KYJIUS/smPlpKvm42UbaD6ypVfrsFQQpI2RIC2iIUFaiAj5BAE66Fnpwev8XNAbrSwq\nxlzUH00TQoh+V159PkrHOXbgKdxsS49frxUYpTunQBJigEmQFiJCpiNI96xHuvHQs3i5RspGnIcd\nq+iPpgkhRL+znDTloz+M77Xx3o7f9vj1CpM/2LAfGidEN0iQFiJCvQnSufZaGg/9AcsppWzUBf3V\nNCGEGBClVQuw41XU7v8j2dYDPXqtUgBKZu0QkZEgLUSEOoJ0Nw82NMZQv/dxMD7DzliGtuL92Twh\nhOh3SltUjrsMMBzZ88senTo8ONxQycgOERkJ0kJEyA+6U1Cqe0G6qXYTmZZ9JMunU1IxrT+bJoQQ\nAyZReiZVZywk136YYwef7vbrVGGMtIztEBGRIC1EhDrmke5Gj3S29T0a3luLtlNUjlvW300TQogB\ndcbUj2PFKmg89BztTbu69RpFfs+edEmLiEiQFiJC3R0j7bmt1O36ORif4TXLsZz0QDRPCCEGjGXH\nqar5JChF3a5HcTMN7/sapQxGSZAW0ZEgLUSETDemvzO+S93OR3CzRymrvoBk2aSBap4QQgyoeHoc\nw874c3yvjdpdj+B7mdMur1ShR3qAGijECSRICxGh9+uRNr5H3e6fk2nZR0nFLMpHf2QAWyeEEAOv\ntGo+6ar55NoOUfvOw/he9pTLKgh6pCVJi4hIkBYiIsaY/EEyXQdp389Ru+untB17k3h6IpU1f9Ht\ngxKFEGIoG3bGn1NSMYtMyz5q33kIz23tcrngLVHLmQ1FZOyoGyDEB5bJn0iAkw82dLON1O58hFzb\ngeBo9jM/hdZOFK0UPWSMIef6ZF2fbM6jPeuScX1asx6tOY+M65F1fXK+j+sbsl5w7fqm4zEvf981\nBt8Et738bR/wCfrfjDHBNcEQUYMJpgLL984Vniu+rVTwegC6+72sGxlFnXhtgjvKFB5TKAwKRf6/\noovquK1VMIuNpUArha0UVv5i66KL0jiWwtEKW2titsLRmpjWxGyLuKNJOTbJmCbu2MQcTcyxcGyN\nli+kg55SmuETlsNuQ2vD6xx66/8yYtI1OPHK45cDGSMtIiVBWogIGU7ukc607KN258/w3WZSlXOp\nHPcxlLaiauKfLGMM2ZxPe9alLePSmMnR3O7SnPVozbq05jza3ODS7hmynke2EGoxeICHwcdglMEv\npEANSmuUVqB1cLs7wU0BVv5y3IPhhL7iNQ3k1lQc5sNdmxfc9PKX3Gle5XkY38f4BuP7wTcJ36AM\naKPQBiw0FmDnw7ujFTHLIm4pErZF0rYocSxSMYt0zKY0YdNuoL0lQyJmEXcs2WMUMqUshk+4Euu9\nCpoOP8ehNx+gasIKEmVnFi0js3aIaEmQFiIqvp8f2xcEaWMMzUc2c3T/E2AMFWOXUjriPPlw7oJv\nDO0Zj5a2LA3tOY615WjM5GjKeLTkXFqyLu2eT7vn0e57uPi4GPyiwGu0RlkarS2UdYpoqYFY4U7X\ny3T0qhof8DDGBTL5aw/fczG4YPLXeBhTSH8+Jv+64tsGH0z+sUIftPEKOT3otSXouQ16cPM9vyrf\ns6uCr2hKKVT+NUoFPb+2rfE9H13oBVZBf3BhM+vMI53BpMtbJyxnivqXgx7zoBfdmOD3ZTD4JvgC\n4xV60juey/ey5+8HJ37O11xpFFbwy1AWCg1YKKXzvxONUsE3EJVfBjQKO7+8DcpGaRtl2SgslLIB\nO7+O4F/g5i8nH9qWf9Z3gyeLF3i3FuN7GM/D93zwfZRv0Aa0r7BROMoirjVxyyJpa0ocm7RjkY7b\nlMUdKpIO5QmHdNIhEbelt/wESimGjb0YJzGc+n2/5vA7D1Ex9pKO90atpEdaREuCtBARKoyRVkpx\n7MB6Gg/9AW2XUDXhShKlEyNuXf8yxtCWcTnakqG+NUd9a4Zj7Tma8kMg2lyfdt8lp3wynoubD8FG\nK7CC8KutU7yFxSAIdTbFb3OFTl9jcsGFDL7JYlwXQw7fz4Kfw/g5fN/FeC4YH+0HYdTK91zaSqON\nzkc3jWWCsKex0cEjKGOhlUaZfJgz8SC6Gg1GYUww04DJ3zYmOMuxMXTc9otudw7hMGCC5ygKp8WP\nGQO5fLDw84m1EFK1pfFc77ggW6xjaEYXga7w0PHPqOOe63r5zmW0Vuh8ANJKBWFIF74UqI6hHYW9\nNMFzhfUVxol0XisVhHRUcFvpztvBtwsfVA5UBqM8fPIX4+IpD894eMrHMz4uPp4xuHjB1xcVnDRJ\naSsI4boQyB20dlA4KOWAFUNbwW2lgiFYhqCTPAd0ju7NP5rLBU80d9bK99zgjH6eB76PNgbLVzgo\nYtoioR1SlkMqZlMasyhLOFQkYwwviVFREiOVdP6kQ3h6+Dk4iSpqd/6Mhnd/R67tULC3rtAjLURE\nJEgLERFj/I6hHS31L9J46A/YsWGMnPIZ7FhFxK3rGc/3aWzLcaQ5Q31rlmNtORoyOZozLk25LK1+\njnbjkcPgKTBaQz4IdzVsxdgGtMH4CuNpDA7GMxjXD0Ku147xcvheLr/b3g2uvWC3vTYK5SuUyV98\nhTIaEyQjjK/xfTC+It+RiOcpfD8OhHna9UJfJ5x27EE3dPQeq0Kw7AydqKCX+rjHyD9W6JUunEVT\nm47ltO4cr1xo7fE36Bxv3UWHX3GvdD7Ld/Fc4fn8MiYI/76f76X2C4913u6/vkWdv/T9eAOtwbEN\nWmfQVgatQWuD0j7oIMAHQ36Kr4Mfb5QCS6G0je74UugEAd12UFYM31J4tianFW2WotFSwZcDcpDN\nQbYdGoO2GONjvBzGd1G+i/YNloGYUiS0RcpyKI8lqIgnqEjGGJaIMTwVpyIVI+Z0c+jRIBBPjWPU\ntBup2/VTWupfDv69lIHS+L7//isQoh9IkBYiKib4QE2nmzl24Fm0nWLklM9ix8ojbVY251LfkuNI\nSxCK69vbOZpppymXodV1aTc+OePjKRUcLKksjG+hsPA9g/H8IPSe6rZrgt43z8V3/fzjheWCcIvX\nk7HBx42/OC3b0jh2cIlbCjumcSyNZSkcS2NbGttSwbXdeT9YJljWtoOD24qft63O9Vg66E3tuFaF\n+/q4x7tepovrot7ZMIwYUUptbVNo6+sPhWBtCoE733N+/GNFgTw/XMT3Da5n8Hwfz8sftOn5eL7B\n84pu5593fYPX8Vj++RNe53o+ruuT83xy+WvXDW4bpWhrz3U+nskvl/MxPRqJboBs/nIayqAsQ2FU\nS/Dnp1CWRuUDudIW2tIoW+WfM2jbRVktKKst/7hG2yrfWx8MN1K+hzY+toGY0iS1RcoOAnhlIsnw\nZJJhKYfhqThlyVio22RP2LEyRk75HId3/JjWhtcYMWoStTvHYnwZ2iGiMWiCtO/73HXXXbz55pvE\nYjG+9rWvUVNTE3WzhOg/+Vk7pk3eDcajctzHQw3RrufR0JrjSGsrtS0tHGltoyHbTmMmS3PGpc31\nyHrguuD5QW+t8S3wdTCUwA3Cre/6nUHYPeG+d0I3ZC8oRX5WBYtk0iYes0g4VscMC6XpOL7nE7ct\nHEcTywfhWNH9mG0RczSObXU+7wS3C+uR2RqGDpWfpQMY2CMje+hUX0qMCcJ4zvVxiwJ48f1sPoxn\ncx6ZnEc2V3Tb9fOPnfz4Scu3evnhOYVR3r2gCcK1pVC27gzndgZttQb3Ox4PwjnKQ2kfrXwsCxwN\nMUuTcjTpuENZLE5lMkFVSYrqdIoR6QSJWDiRQ2uHqgkrOPDGdzljwh7e2lstQVpEZtAE6bVr15LN\nZnnkkUfYunUr3/jGN1i9evUpl39n7z4ajraf9Lg5xSHifn7Cp67+1ArzT5743IljB497zSkf6PIn\nAOB1tWs0+EFd/wxTNE3VSc+d4jX5/3fdiq7Xlj5QQmNT13N0nqoEJ5W5aMGu2nbaI/dNcEjVqX7+\nqV5nTrMrr+t/f1cHSp28vDnxmVM8eeLPKNwvScZoae36kKXjfq7rkhmpaUvBO+2TeGm3T1tuG+05\nl/b8B2XG9cm6XtDb5fm4PvketKAnzvfBNyoIwYb8dWH4Qr75xSHYM6fc3rpDKYNtK5yYIlGiScQs\nEjGLZMyhJO6QisdIxBwSjkU8lr84Vuf9/HXxfcc+/a7lodCDKkQxpVTHnor+VgjtQcAuTLkYhO32\nrEt71iu6uMfdbsvkaM1fgud8Mpkcub6NQgIoCt353nGt8seBBr3qWtN5rcHSBssKhhzZmvweoqCG\nMcvCye/1cSyFY1nEbEWpezap7E7SZxzhhf2GpLOdhKWxLQtb549W0FZ+z44uGhqlPhCjqlsyTRw9\n0vVnu+iZESNKT/ncoAnSmzdv5oILLgBg7ty5bNu27bTLf+lbWwaiWUL0s0r+8GxhXtQDvVzH6YOx\nsoKLtsFJgG1DzFEkYxapuE1ZiUN5SYKyRJzSZIJUPE5J3CYRszuCciIe3B6IYCCE6L7i0F6SCGed\nxpigV/yk8O11TBfZnGmnuT1DU1s7DW1ZmttztGU9MlmfnBsMpfE98D0fP5ufhKZHuvOCODCj8+6r\n7/X0hwjRLWtWTT7lc4MmSDc3N5NOpzvuW5aF67rYdtdNLB126u+Tp3wm5K+gYf4c9T5hqGfr6l0b\nTru+EF8U5q/htOvq8sn3CZ1hNcB0f/vwjKJN56ci0wS9J1ZwbVlgWSoYrmDpoHc3ZpF0bEpiDqmk\nQ2UqSVVZCdWlKcqTKRJxO+i5sYNxvX8KTtcbIHpO6hkeqWX3GGOCXvKMR1smx9HmFg41NlN7rIXG\ntixN7VlaMjna8j3qWbewF84P9sIFk5l0nnwof3E8g60KUygWzYYD+Wtz3H2QmfJEuAZNkE6n07S0\ntHTc933/lCEa4OGv/oXs7g2J7DoPV6T1dD3aXY+TBz0NXbJ9hkvqGR6pZe/YwIhUCSNSJTC683Gp\nZ7ikngNj0HRXzZs3jw0bNgCwdetWpk6dGnGLhBBCCCGEOLVB0yN9ySWX8Oyzz3L11VdjjOGee+6J\nuklCCCGEEEKc0qAJ0lpr7r777qibIYQQQgghRLcMmqEdQgghhBBCDCUSpIUQQgghhOgFCdJCCCGE\nEEL0ggRpIYQQQgghekGCtBBCCCGEEL0gQVoIIYQQQohekCAthBBCCCFEL0iQFkIIIYQQoheUMcZE\n3QghhBBCCCGGGumRFkIIIYQQohckSAshhBBCCNELEqSFEEIIIYToBQnSQgghhBBC9IIEaSGEEEII\nIXpBgrQQQgghhBC9IEFaCCGEEEKIXpAgLUQvZbPZqJsgRJfk9ADh2r17d9RNEEIMUtZdd911V9SN\n6Mr+/ftZvXo1sVgM27ZJp9NRN2nI2r9/P9/+9rcBcByH0tJSjDEopSJu2dC0d+9e/vmf/5nDhw9T\nUVFBRUVF1E0a0vbt28d//dd/Yds2SinKysqibtKQtW/fPr72ta/x1ltvobVmzJgx+L4vf+u9tG/f\nPu699142btzIwoULicfjUTdpSNu3bx/f/OY3aW9vR2tNZWWlbJ+9UPii/B//8R+MHj1aPoNC0JfM\nOSh7pP/4xz9y++23U1ZWxpYtW7j77rujbtKQtXHjRm6//XaGDx/O1q1b+fGPfwwgb1y99MYbb3D3\n3Xdz2WWXMXv2bOmV7qNnnnmGW2+9laqqKt5++23uvPPOqJs0ZG3dupU77riDhQsXMmHCBP72b/8W\nAK0H5dv8oLd27Vr+5m/+hiuuuIJ/+7d/ky94fbR582b+4R/+gZqaGg4cOMB9990HyPbZG0opGhsb\nWbduHT/5yU+ibs6Q19fMOai24Pb29o7rRYsWcfPNN/P5z38ez/P4zne+E3HrhpZCLevq6li4cCE3\n33wzkydPPu5blu/7UTVvyCnUs6WlhZqaGiorK1m9ejUbNmzg8ccfB6SePVGo59GjR/nIRz7CDTfc\nwKc//Wmy2Sw/+MEPIm7d0FLonTp69ChTpkzhyiuv5OMf/zjz5s1j7969Ebdu6CnUc+LEiSSTSdrb\n27nxxhv5p3/6J370ox9F3Lqhp1DPbDbLmDFjuPHGG1m6dCkTJkzo6IiQ987uOXLkCACe5/HTn/6U\ns846izfeeIOnn3464pYNTWFlzkExtOP555/nm9/8Jq+99hrjx4/n7bffprW1lRkzZhCPx5k5cyb/\n/u//zic+8QkSiUTUzR3UimtZU1OD7/ssWbIEy7K49dZbaWpq4re//S2LFy+mpKQk6uYOesX1POOM\nM3j33Xc5dOgQ+/fv54YbbiCZTHLXXXexfPlyqWc3nLh9vvTSS2itmTZtGvF4nD179rBu3TqWLVtG\nLBaLurmDVmF3+J133smYMWOoqqqioaGB+fPnM3z4cHbt2sW6detYsWIFjuNE3dxBr6t6VlZWsnXr\nVtavX8/dd9/NtGnTuP/++zn33HMZPnx41E0e1LqqZ1tbGzt27OCZZ57h+9//Po2Njaxfv565c+dS\nXl4edZMHtRdeeIF77723IzBPmjQJrTVLly6lqqqK//3f/+Xyyy+PuJVDR9iZM/IgXVdXx6pVq7j2\n2mvJ5XI89dRTVFZWsnHjRmbNmkV5eTlVVVXs3LkTgMmTJ0fZ3EGtuJau6/Kb3/yGqVOnMmPGFNqO\nAwAAC7pJREFUDBzHYfLkyXzhC1/gmWee4c0332TJkiVRN3lQO3HbXL9+PWVlZWzatAmlFCtWrGDC\nhAns2rWLvXv38qEPfSjqJg9qJ9Zzw4YNjBkzhldffZVt27bx2GOPUV1dTTqdxnEcJkyYEHWTBy2l\nFNlslq985SsdX5bHjBnTEfAeeughqqurOf/88zl27Jh0QLyP4noCnHfeeWitSafTzJgxg3nz5jFi\nxAj27NnD9u3bOf/88yNu8eBWXE9jDIsXL2bEiBFMnz6dhx9+mE9+8pPcddddvPDCC2zevJmLLroo\n6iYPWgcPHmTVqlVcf/31TJo0iSeeeIJZs2Yxbdo00uk048aNY8OGDRw9epQ5c+ZE3dxBrz8yZ+RD\nO/bv3099fT2LFy/ms5/9LGPHjsUYw8iRI3n88cfZs2cPAE1NTcyYMSPi1g5uxbX8zGc+w9SpU9my\nZQsHDhwAYN68eQCMGjWKRYsWRdnUIeHEbXPcuHHkcjkmTZpEMplk06ZNANi2zbnnnhtxawe/E+s5\nevRoMpkM1113HR/96Ec577zzuPHGG0kkEsyaNSvq5g5qxhjWr1/PsmXLePPNN3n++ec7HgdobGxk\n2bJlPPTQQ/z1X/81hw4dirK5g15xPd944w1efPFFlFIsWLCACy64gLfeeguAeDwuIbobiuu5ffv2\nju2zvb2d6upqpk2bBkBlZaX8rb+Pt99+m8bGRj70oQ9x0UUXUV9fz7FjxzqOc4rH41x33XX88Ic/\n5NixYxG3dvDrj8wZSY908VG6o0aNYt26daRSKSZOnEhJSQlbtmxh6dKlHDt2jHXr1vGjH/2Iqqoq\nLr300o4j+0Xg/Wr50ksvMXr0aJ588kkee+wxHnjgAWKxGFdddZXsOu9Cd+r5Z3/2Z7iuy+9+9zse\nfvhhYrEYK1askHp24XT1TKVSbNq0ienTp+N5Hu+++y6rVq1i2LBhXHTRRViWJX/rRYprqZSivb2d\nlStX4rouv/3tb1m0aBGJRAJjDF/84hfZuHEjFRUV3HHHHVRXV0fc+sHndPX83e9+x+LFi0kkEvzq\nV7/iwQcf5JFHHsG2bXnvPIXT1fOJJ57gwgsvpKysjB07drBt2zYeeOABlFL85V/+pewxOUFxLWtq\najjnnHOorKykrq6OTZs2sXLlyuOGbI0dO5bS0lImT54s2+YJCjOUFa77I3MOSJA2xpDL5fjGN77B\nvHnziMfjHRtKLpfD9302bNjAhRdeSHV1Nb/85S8pLS3l05/+NFOmTGHx4sVcddVVOI7zgf9g7Ukt\nR44cya9//WvS6TQrV65k1KhRLFmyhKuvvlr+2PJ6s22mUimuueYa5s6dy5IlS/jUpz4l9czrzfaZ\nSCS4+OKLaWtr44ILLuDqq6+WL8x0XcviaSsrKyvRWjNr1iwee+wxtNZMnz6dnTt30tzczBe/+EWu\nuOIKmTo0r6f1BJgxYwbTp09nwYIFLF68mJUrV8rfel5P6rlmzRo8z2P27NksWLCAiRMnsmjRIq6+\n+moJ0Zz+fROCWgKsWbOGxsZGli5dyu7du2lra6O0tBSA6dOny7ZZ5PXXX+eee+5h9+7dDBs2jMrK\nSjzPw/O80DPngAztUEpx8OBBnnzySR555JGOxyCY13jhwoVYlsV//ud/Bo3SuuPArZqamo7dQKLn\ntVRKdbxRTZ8+XXajnaA322YhmIwePZpJkyZF0/BBqjfbZ6GeixYt6hh+JLquZTHbtvE8D4Brr72W\nBx98kMOHDzNp0iS+/vWvy3jJE/S0nv/zP//TMSSmurpajs85QU/r+dBDD3Ho0CGUUowfP57p06cP\ndJMHrdO9b0LnrCa1tbXMnj2b733ve3z961+ntbU1kvYOdmvXruVf//Vf+djHPoZlWXzpS18CwLKs\nfsmc/doj3dLSQiwWo7W1lR/+8IeMHTuW559/njlz5lBVVYXruh3BZNasWaxdu5aHH36Y0aNH87nP\nfe4D3yNVTGoZLqlnuKSe4Xm/Whb3VBXm4B0/fjwVFRXMmTNHanmCvtTzrLPOknqeQOoZnu7WUilF\nJpPhtttu45133mHmzJnccccdVFVVRf1PGFSampqIx+O8+OKLlJWVce211zJ//nyeffZZFi1aRDKZ\nBAj9c0iZfjiX7O9//3vWrFlDRUUF1113HdOmTWPjxo3MmTOHRx99lFdffZVVq1Z1LO/7Plprcrkc\nmUxGdkUWkVqGS+oZLqlneHpaywI5S2nXpJ7hknqGp6e1NMbg+z4PPvggF198MePGjYuw9YNPcT0/\n+9nPcvDgQaZMmUJ1dTXPPfccjz76KKtWrerYDj3Pw7Ks0D6HQu+RPnLkCN/+9rf5/Oc/j+/7PPfc\nc7S1tfGRj3yEWCzG+PHjWbNmDWVlZUycOLHjHwRBt7uM8ekktQyX1DNcUs/w9KaWhd4+CSknk3qG\nS+oZnp7W0nVdLMtCa80555wjc26foLienuexadMmysvLOeeccwBYvXo1CxYsYNasWdTX15NMJjt6\n+cP6HAp9jPT27dvRWnPWWWexYsUKzjrrLLZu3doxJ19lZSVXXnkl3/rWtwA6PljFyaSW4ZJ6hkvq\nGR6pZbiknuGSeoanp7W0bTvK5g56xfW86qqrmDlzJtu2beOdd94Bgvqdd9553H///dx22200NzeH\n/uUulB7p4jFR48eP54EHHuDMM89k/PjxWJbFnj17KCkpoaamBggGcyeTSaZMmdLxzUAEpJbhknqG\nS+oZHqlluKSe4ZJ6hkdqGa7u1LOsrIxUKsVtt93GSy+9xIQJE/jKV77SL8MJe90jvW/fPlavXh2s\nRGt83yebzQJw3XXX8YMf/AAIzgpTmEAcgrE+sViMK664Qqa4ypNahkvqGS6pZ3ikluGSeoZL6hke\nqWW4elrPhoYGDhw4wMqVK7nvvvv4whe+QCqV6pe29TpIP/nkk6xZs4annnoqWJHWxGIx3nvvPRYv\nXozv+/z3f/83jY2NHD16tGNXj2wUJ5NahkvqGS6pZ3ikluGSeoZL6hkeqWW4elLPI0eOYNs2s2bN\n4u6772bixIn92rY+jZG+8MILWbNmTccchz/72c+4/vrrqa2t5ctf/jINDQ3ccsstzJw5k2XLloXS\n4D9VUstwST3DJfUMj9QyXFLPcEk9wyO1DFd36zl79myWLl06YO3q1vR3v/jFL9i5cydLlixh0aJF\nANx+++3cdNNN/OpXv6K+vp6zzz6bdDrNwoULjzuqNJvNytH5RaSW4ZJ6hkvqGR6pZbiknuGSeoZH\nahmuoVbP0/ZIG2O4//77eeqpp5g7dy4PPvgg3//+9wGoqqpCKcXmzZt5+umnGTNmDJdeeinl5eUd\nZzMCZAPJk1qGS+oZLqlneKSW4ZJ6hkvqGR6pZbiGaj1PO6+KUoqWlhYuv/xyPvrRj1JTU8NNN93E\n5Zdfzosvvsgrr7zCypUrOXLkCGvXru345iBT35xMahkuqWe4pJ7hkVqGS+oZLqlneKSW4Rqq9Txt\nkPZ9n3Q6TXNzM83NzUyZMoWLLrqIO++8k3vvvZczzzwTpRSvv/46e/fuHag2D0lSy3BJPcMl9QyP\n1DJcUs9wST3DI7UM11Ct52mHdmitWbhwIdu3b+fgwYMA3HrrrTQ2NjJy5MiOo0tnzJjBZZdd1v+t\nHcKkluGSeoZL6hkeqWW4pJ7hknqGR2oZrqFaz/edtWPevHlorVm/fj319fXs3r2badOmUVpa2rGM\nTNfSPVLLcEk9wyX1DI/UMlxSz3BJPcMjtQzXUKxnt2btqK+v59FHH2Xz5s00NTWxcuVKli9fPhDt\n+5MjtQyX1DNcUs/wSC3DJfUMl9QzPFLLcA21enYrSBe89tprTJ06Fcdx+rNNHwhSy3BJPcMl9QyP\n1DJcUs9wST3DI7UM11CpZ4+CtBBCCCGEECLQpzMbCiGEEEII8UElQVoIIYQQQohekCAthBBCCCFE\nL0iQFkIIIYQQohckSAshhBBCCNELEqSFEEIIIYToBQnSQgghhBBC9ML/B1o2scIgi6u5AAAAAElF\nTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4360,10 +4310,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "def plot_sapm(sapm_data, effective_irradiance):\n", @@ -4418,14 +4366,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAIZCAYAAABwG7huAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxBIREREREZHZ+H8nMmnEs8h7MQAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4445,24 +4393,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:1303: RuntimeWarning: divide by zero encountered in log\n", - " module['Voco'] + module['Cells_in_Series']*delta*np.log(Ee) +\n", - "/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/pvsystem.py:1309: RuntimeWarning: divide by zero encountered in log\n", - " module['C3']*module['Cells_in_Series']*((delta*np.log(Ee)) ** 2) +\n" - ] - }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAIWCAYAAACBTQq7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+n6WowAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4479,24 +4417,24 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Comparison of a hot, calm day and a cold, windy day')" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAFkCAYAAAANEdxfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4527,10 +4465,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "import warnings\n", @@ -4539,10 +4475,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "def sapm_to_ivframe(sapm_row):\n", @@ -4568,24 +4502,24 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAHtCAYAAAAa67jdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqRGdZ/B2nAEAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4625,48 +4559,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The same data run through the desoto model." + "The same weather data run through the Desoto model." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "calcparams_desoto() got an unexpected keyword argument 'alpha_sc'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mI_o_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcecmodule\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'I_o_ref'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mR_sh_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcecmodule\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'R_sh_ref'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m R_s=cecmodule['R_s']) )\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: calcparams_desoto() got an unexpected keyword argument 'alpha_sc'" + ] + } + ], "source": [ "photocurrent, saturation_current, resistance_series, resistance_shunt, nNsVth = (\n", " pvsystem.calcparams_desoto(total_irrad['poa_global'],\n", - " temp_cell=temps['temp_cell'],\n", - " alpha_isc=cecmodule['alpha_sc'],\n", - " module_parameters=cecmodule,\n", - " EgRef=1.121,\n", - " dEgdT=-0.0002677) )" + " temp_cell=temps['temp_cell'],\n", + " alpha_sc=cecmodule['alpha_sc'],\n", + " a_ref=cecmodule['a_ref'],\n", + " I_L_ref=cecmodule['I_L_ref'],\n", + " I_o_ref=cecmodule['I_o_ref'],\n", + " R_sh_ref=cecmodule['R_sh_ref'],\n", + " R_s=cecmodule['R_s']) )" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAFWCAYAAABEo3r5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'photocurrent' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mphotocurrent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Light current I_L (A)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'photocurrent' is not defined" + ] } ], "source": [ @@ -4676,28 +4615,19 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAFgCAYAAACWgJ5JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRHygRVpERERExAdapEVEREREfPB/LawVLQv64gkAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'saturation_current' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msaturation_current\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Saturation current I_0 (A)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'saturation_current' is not defined" + ] } ], "source": [ @@ -4930,7 +4860,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/pvlib/__init__.py b/pvlib/__init__.py index b756147eba..3a231da728 100644 --- a/pvlib/__init__.py +++ b/pvlib/__init__.py @@ -11,3 +11,4 @@ from pvlib import pvsystem from pvlib import spa from pvlib import modelchain +from pvlib import singlediode_methods diff --git a/pvlib/data/bishop88_numerical_precision.csv b/pvlib/data/bishop88_numerical_precision.csv new file mode 100644 index 0000000000..94cf44adc9 --- /dev/null +++ b/pvlib/data/bishop88_numerical_precision.csv @@ -0,0 +1,101 @@ +,grad,grad2p,grad_i,grad_p,grad_v,i,p,v +0.0,-0.0029752937017359805,-0.005957140772172728,-0.0029785726893041607,5.870505570369785,1.0011020718950425,5.86405008,-12.723220836076763,-2.1696985296 +0.5845170900989292,-0.00297529507385398,-0.005957144029576585,-0.002978574064448173,5.8670235188728626,1.0011020724038457,5.862309052970334,-9.289047121136205,-1.5845372595000944 +1.1690341801978583,-0.002975296745849822,-0.005957148329744609,-0.0029785757401313675,5.863541465180093,1.0011020730238487,5.860568025051929,-5.856910966556231,-0.9993759890713553 +1.7535512702967875,-0.002975298783262126,-0.0059571539728827455,-0.002978577782036902,5.860059408597745,1.0011020737793537,5.85882699605055,-2.426812373397211,-0.41421471824191614 +2.3380683603957166,-0.002975301265953046,-0.005957161340610417,-0.0029785802702030567,5.856577348233737,1.001102074699975,5.857085965729514,1.0012486569100834,0.17094655307579656 +2.922585450494646,-0.0029753042912386935,-0.005957170917207319,-0.002978583302160564,5.8530952829440785,1.0011020758217994,5.855344933800406,4.427272122453232,0.7561078249884956 +3.507102540593575,-0.0029753079777037196,-0.005957183316258555,-0.0029785869967556004,5.849613211265367,1.0011020771887995,5.853603899911782,7.851258020695973,1.3412690976262158 +4.091619630692504,-0.0029753124698495737,-0.005957199314042896,-0.0029785914988083007,5.846131131329814,1.001102078854559,5.851862863635386,11.27320634829687,1.9264303711474113 +4.676136720791433,-0.002975317943758649,-0.005957219891338891,-0.002978596984789394,5.84264904075839,1.001102080884372,5.85012182444937,14.693117100878508,2.5115916457451664 +5.260653810890362,-0.0029753246139963283,-0.0059572462857335464,-0.002978603669737475,5.839166936526567,1.0011020833578028,5.8483807817178315,18.110990272734274,3.096752921654765 +5.845170900989292,-0.002975332742021483,-0.005957280057027362,-0.002978611815688049,5.835684814795811,1.0011020863718045,5.846639734665894,21.52682585645643,3.681914199162911 +6.429687991088221,-0.0029753426464350914,-0.005957323168961724,-0.002978621741944762,5.8322026707022365,1.0011020900445196,5.844898682349329,24.940623842465143,4.267075478618969 +7.01420508118715,-0.002975354715468701,-0.005957378091279118,-0.0029786338375954085,5.8287204980917755,1.0011020945199103,5.843157623617547,28.352384218412883,4.852236760448657 +7.598722171286079,-0.0029753694222022617,-0.005957447927099995,-0.0029786485767633332,5.825238289188552,1.0011020999734024,5.841416557068495,31.762106968432207,5.4373980451707355 +8.183239261385008,-0.0029753873431078255,-0.0059575365718074124,-0.002978666537192042,5.821756034179936,1.001102106618761,5.8396754809937015,35.16979207218701,6.022559333417338 +8.767756351483937,-0.002975409180645987,-0.005957648911127437,-0.0029786884228914967,5.818273720697683,1.0011021147164698,5.837934393311305,38.57543950367713,6.607720625958756 +9.352273441582867,-0.0029754357908008,-0.005957791067948803,-0.002978715091733781,5.81479133316957,1.0011021245839415,5.836193291484451,41.979049229733924,7.192881923733619 +9.936790531681796,-0.0029754682166324635,-0.0059579707097246176,-0.0029787475890798343,5.811308852009686,1.0011021366079595,5.834452172421854,45.38062120812886,7.778043227885708 +10.521307621780725,-0.0029755077291629788,-0.005958197431147377,-0.002978787188755338,5.8078262526078515,1.0011021512598395,5.832711032356624,48.78015538519764,8.363204539808773 +11.105824711879654,-0.0029755558771973863,-0.005958483230316493,-0.0029788354429819267,5.804343504069003,1.0011021691139033,5.830969866698612,52.177651692858575,8.948365861201166 +11.690341801978583,-0.0029756145480334634,-0.005958843100985721,-0.00297889424322092,5.800860567641557,1.0011021908699917,5.829228669854491,55.57311004487415,9.533527194132422 +12.274858892077512,-0.0029756860414395505,-0.005959295768885225,-0.0029789658943145158,5.797377394759002,1.0011022173808963,5.827487435008501,58.966530332166734,10.118688541124365 +12.859375982176442,-0.0029757731598002535,-0.00595986460680504,-0.002979053204830618,5.793893924600721,1.0011022496857873,5.825746153855294,62.35791241695444,10.703849905249983 +13.44389307227537,-0.0029758793179634957,-0.005960578771406571,-0.002979159597152625,5.790410081055457,1.0011022890509464,5.82400481627438,65.74725612541428,11.289011290253852 +14.0284101623743,-0.002976008677094632,-0.005961474614971044,-0.002979289241629458,5.786925768942823,1.001102337019403,5.822263409933423,69.13456123850973,11.874172700698933 +14.612927252473229,-0.0029761663077843346,-0.005962597437960581,-0.002979447220044218,5.783440869313562,1.0011023954714164,5.820521919804842,72.5198274805304,12.459334142145439 +15.197444342572158,-0.002976358388803591,-0.005964003663929506,-0.0029796397248090935,5.779955233606391,1.0011024666981794,5.818780327576764,75.90305450478175,13.044495621368755 +15.781961432671087,-0.002976592449296402,-0.005965763537684524,-0.002979874301694524,5.776468676386092,1.001102553491627,5.817038610935235,79.28424187572728,13.629657146625048 +16.366478522770016,-0.002976877663903328,-0.0059679644715214695,-0.0029801601456070836,5.7729809663218195,1.0011026592538745,5.815296742689563,82.66338904671491,14.214818727974878 +16.950995612868944,-0.0029772252123836693,-0.005970715193935012,-0.0029805084610099305,5.769491814983297,1.0011027881305736,5.8135546897065025,86.04049533221146,14.799980377677539 +17.535512702967875,-0.002977648717832082,-0.005974150891728205,-0.0029809329011135314,5.766000862931994,1.001102945173412,5.8118124116115,89.4155598732081,15.385142110671623 +18.120029793066806,-0.00297816478066586,-0.005978439581571335,-0.002981450103051982,5.762507662460098,1.0011031365381293,5.810069859206104,92.78858159413763,15.970303945160547 +18.704546883165733,-0.0029787936293126675,-0.005983790002784571,-0.002982080340022682,5.759011656176353,1.0011033697258085,5.808326972539485,96.15955914924689,16.555465903326123 +19.28906397326466,-0.0029795599131022697,-0.005990461391925088,-0.0029828483159518274,5.755512150447955,1.0011036538769023,5.806583678558498,99.5284908558716,17.140628012198015 +19.87358106336359,-0.0029804936684389004,-0.005998775584701608,-0.0029837841338348874,5.752008282472884,1.0011040001295188,5.804839888244158,102.89537461145103,17.72579030471325 +20.458098153462522,-0.002981631496121704,-0.006009131995583255,-0.0029849244757089214,5.748498979467168,1.0011044220560124,5.8030954931222825,106.26020779036207,18.310952821007277 +21.04261524356145,-0.002983017995955065,-0.006022026154854778,-0.0029863140405090977,5.744982908093305,1.0011049361949884,5.8013503610115436,109.62298711571498,18.896115609987177 +21.627132333660377,-0.0029847075148726693,-0.006038072642512993,-0.0029880072961702775,5.741458411813756,1.001105562699583,5.799604330842244,112.98370850009512,19.481278731248747 +22.211649423759308,-0.0029867662770834236,-0.006058033455340477,-0.002990070614652262,5.73792343330725,1.0011063261274213,5.797857206342731,116.34236684779933,20.066442257412493 +22.79616651385824,-0.002989274979714781,-0.00608285308640337,-0.002992584873577066,5.734375418411309,1.0011072564032235,5.7961087483459774,119.69895580934383,20.651606276970227 +23.380683603957166,-0.0029923319556650167,-0.00611370189577642,-0.0029956486264567862,5.730811197222261,1.001108389991789,5.794358665414746,123.05346747682691,21.23677089775371 +23.965200694056094,-0.00299605702759383,-0.006152029720641873,-0.0029993819657781226,5.727226836956887,1.0011097713273378,5.792606602417884,126.40589200602012,21.82193625116148 +24.549717784155025,-0.003000596204048713,-0.0061996321282111674,-0.003003931230368128,5.723617459912789,1.0011114545552362,5.790852126609946,129.75621714771086,22.40710249730935 +25.134234874253956,-0.0030061274017001574,-0.00625873227607497,-0.0030094747415597284,5.719977018301338,1.0011135056543772,5.789094710668513,133.1044276666803,22.992269831306608 +25.718751964352883,-0.0030128674178291435,-0.006332082036065065,-0.003016229793002312,5.716298015799175,1.001116005023411,5.7873337120242905,136.45050462158724,23.577438490903894 +26.30326905445181,-0.003021080426142027,-0.006423086889588923,-0.0030244611681028225,5.712571163286861,1.001119050632198,5.785568347673785,139.79442447271,24.16260876581251 +26.88778614455074,-0.003031088328586912,-0.006535960151633435,-0.0030344915189626518,5.708784953312269,1.0011227618620162,5.783797663487256,143.13615797669814,24.747781009060454 +27.472303234649672,-0.0030432833684303404,-0.006675913372617289,-0.0030467140136421905,5.704925134203544,1.0011272841850476,5.782020496808899,146.4756688178521,25.332955650830378 +28.0568203247486,-0.003058143498242408,-0.006849391357709309,-0.0030616077474984646,5.700974060304231,1.0011327948665745,5.780235430883248,149.81291191355504,25.9181332153218 +28.641337414847527,-0.003076251104057797,-0.007064362200442791,-0.003079756522686961,5.696909889317819,1.0011395099133942,5.77844073932143,153.14783131680173,26.503314341298598 +29.225854504946458,-0.003098315817990082,-0.007330675135155671,-0.0031018717319434906,5.692705590992404,1.0011476925408191,5.776634318430448,156.4803576206475,27.088499807127196 +29.81037159504539,-0.003125202311026061,-0.007660501973435053,-0.003128820243640859,5.688327723056219,1.0011576634901471,5.774813604752972,159.81040474704287,27.67369056128679 +30.394888685144316,-0.0031579641517379847,-0.008068881528586546,-0.0031616583811542435,5.683734920072716,1.001169813601027,5.772975474585356,163.13786597492938,28.258887759547736 +30.979405775243244,-0.0031978850526391375,-0.00857439090203128,-0.00320167332845292,5.678876028280287,1.0011846191315277,5.7711161215352025,166.46260902843693,28.844092810275217 +31.563922865342175,-0.003246529112840734,-0.009199972992622521,-0.003250433584927979,5.673687803977977,1.0012026604264233,5.769230907319024,169.78447000403867,29.42930742963414 +32.148439955441106,-0.0033058020143741994,-0.009973956320365261,-0.0033098504471761533,5.668092073956306,1.0012246446654551,5.767314179951555,173.10324586373662,30.01453370885903 +32.73295704554003,-0.003378025553070552,-0.010931311503920758,-0.0033822529276927283,5.661992233047112,1.0012514335832463,5.765359052200152,176.41868515749206,30.599774196225976 +33.31747413563896,-0.0034660283989182195,-0.012115198827249804,-0.0034704790471242375,5.655268925097611,1.001284077247436,5.763357131620177,179.73047655936463,31.185031996939493 +33.90199122573789,-0.0035732566041354074,-0.013578873671946769,-0.003577987078539832,5.647774718368853,1.0013238552190598,5.761298191589311,183.03823470472858,31.770310894849842 +34.48650831583682,-0.0037039081322002257,-0.015388031652369478,-0.0037089911042561515,5.6393275430704275,1.0013723267085748,5.75916977044608,186.34148269622136,32.35561550077176 +35.07102540593575,-0.0038630965943735403,-0.017623693632213337,-0.00386862619875006,5.629702605722588,1.0014313916935376,5.756956683019679,189.63963049849377,32.94095143321847 +35.65554249603468,-0.004057050483175599,-0.02038575308226625,-0.004063149712472153,5.61862243017849,1.0015033653936147,5.754640425404113,192.93194825988823,33.52632553863516 +36.24005958613361,-0.004293355521479788,-0.023797335221513505,-0.00430018654645125,5.605744595922764,1.001591069022187,5.752198449645108,196.21753337488724,34.11174615976492 +36.82457667623254,-0.004581249343709803,-0.02801014993255118,-0.004589028031923824,5.5906466476921715,1.0016979403718118,5.7496032799090635,199.4952698246832,34.69722346266619 +37.409093766331466,-0.004931979640265873,-0.033211059514087614,-0.004940996130423489,5.572807533010409,1.0018281685682566,5.746821435489804,202.7637779923818,35.28276983520024 +37.993610856430394,-0.005359239181470032,-0.039630128945234155,-0.005369887230186346,5.551584781783767,1.001986858275169,5.7438121184373045,206.02135272915035,35.86840037260859 +38.57812794652932,-0.005879693851065687,-0.04755048150237668,-0.005892512934731463,5.526186469961425,1.0021802297858506,5.740525614366289,209.26588692954923,36.454133469213794 +39.162645036628255,-0.006513623021513256,-0.057320347186224224,-0.00652935904135924,5.495636802151911,1.0024158628453028,5.736901343759845,212.49477723545002,37.0399915394371 +39.74716212672718,-0.007285695351029681,-0.06936776606936956,-0.007305388540099587,5.458733900288981,1.0027029937598368,5.732865487383382,215.7048077000665,37.62600189639534 +40.33167921682611,-0.008225907421738,-0.08421849332264997,-0.008251020108931858,5.413998091061136,1.0030528774403047,5.72832809273029,218.89200627168503,38.2121978225159 +40.916196306925045,-0.009370717591804082,-0.10251774615968948,-0.009403320460173364,5.359608638285938,1.003479228570264,5.723179548078225,222.05146775731706,38.7986198741361 +41.50071339702397,-0.010764412980085559,-0.12505653220817234,-0.010807457275435202,5.293326463334625,1.003998759191911,5.717286285946656,225.17713544588256,39.38531747122371 +42.0852304871229,-0.012460753541850314,-0.15280339779969054,-0.01251846968105451,5.212399935303023,1.0046318337819902,5.710485547540512,228.2615317418191,39.97235083453291 +42.66974757722183,-0.014524943507684662,-0.18694252257421318,-0.014603425662809928,5.113450295921546,1.0054032674952396,5.702579002957414,231.29542589991897,40.559793346127584 +43.254264667320754,-0.01703598663125314,-0.22891914566857377,-0.0171440509859117,4.992332723500172,1.0063432988647874,5.693324977084448,234.2674241568419,41.147734425799506 +43.83878175741969,-0.02008948701834557,-0.28049331005226186,-0.020239932668244195,4.84396846020808,1.0074887750872503,5.682428976456426,237.16346409409655,41.73628303613082 +44.423298847518616,-0.02380096064790911,-0.34380281144062375,-0.02401242257608301,4.662142873281297,1.0088845963531508,5.669532145748974,239.96619077809814,42.32557195359149 +45.00781593761754,-0.02830972227370975,-0.4214359713240549,-0.028609394154798077,4.439263870493397,1.0105854758372752,5.65419720142593,242.65418689950377,42.915762973089954 +45.59233302771648,-0.03378340555144074,-0.5165143255015552,-0.034211038748095036,4.166074866878158,1.0126580843367952,5.635891291170475,245.2010225160716,43.5070532499834 +46.176850117815405,-0.04042315708918318,-0.6327843977180755,-0.04103692870897761,3.8313166925969986,1.0151836636223217,5.613965107226749,247.57408175974606,44.099683028141506 +46.76136720791433,-0.04846951217513105,-0.7747162340807431,-0.04935462416110098,3.4213337208458725,1.0182612109396074,5.587627434940117,249.73311357612394,44.69394505698649 +47.34588429801326,-0.05820890367283452,-0.9476040789303638,-0.05949016077694885,2.9196214786872496,1.0220113594874711,5.555914138853929,251.6284406780968,45.29019606663731 +47.93040138811219,-0.06998066609906431,-1.1576612037329261,-0.07184082967105557,2.306316628998847,1.0265811069782906,5.517650370684814,253.19874470933536,45.88887075095881 +48.51491847821112,-0.08418426188957046,-1.4120961692589842,-0.0868907503524476,1.5576361908975433,1.0321495776304057,5.471404517810895,254.36832520582863,46.49049880662108 +49.09943556831005,-0.1012862621448542,-1.7191514760865074,-0.10522984716235959,0.6452820683996454,1.038935043450073,5.415432087153262,255.04370410222978,47.09572569606333 +49.68395265840898,-0.12182634556357387,-2.088077606107995,-0.12757697303254226,-0.46415966128739167,1.0472034800220407,5.347607324820204,255.10941464495394,47.705337948225505 +50.26846974850791,-0.14642122666854582,-2.5290062924988645,-0.15480808696547316,-1.8097768276699933,1.057278992177225,5.265339891152171,254.42277149133224,48.32029398878161 +50.85298683860684,-0.17576498907130353,-3.052677727102656,-0.18799058973276161,-3.436774886789651,1.0695565182011217,5.165473325009417,252.8073645834666,48.941761708353354 +51.437503928705766,-0.21062380604954392,-3.669969935228677,-0.22842516367685428,-5.396705965486928,1.084517310560436,5.044161317322018,250.04494912056487,49.57116424129662 +52.02202101880469,-0.25182254424459144,-4.391179275391126,-0.2776967566457863,-7.74735236946439,1.102747799958941,4.8967169440929945,245.8653121614329,50.21023574949029 +52.60653810890362,-0.3002203929971172,-5.225015825085368,-0.33773670852095095,-10.552107298134041,1.1249625821527518,4.717428949114363,239.93357550835128,50.861089397731305 +53.191055199002555,-0.3566726468607908,-6.177315083784205,-0.4108984555634116,-13.878683994931766,1.1520324285584622,4.4993378750758986,231.8342339859107,51.526300185224464 +53.77557228910148,-0.42197637789647496,-7.249536657710529,-0.5000497800254029,-17.797003926920357,1.185018418609399,4.2339632678902515,221.05101314891033,52.209005879982094 +54.36008937920041,-0.4967992967974098,-8.437225397893114,-0.6086852210122606,-22.376184840418,1.2252135317745365,3.910971261234011,206.94133972387158,52.91303001254382 +54.944606469299345,-0.5815938882013996,-9.728742691201678,-0.7410630528575037,-27.6806898991169,1.2741933295572765,3.5177695113374905,188.7038215862265,53.64303175010447 +55.52912355939827,-0.6765029438074703,-11.104706308706483,-0.9023722002670144,-33.76591834028289,1.3338777140987954,3.0390136043433786,165.33658856994361,54.40468852579122 +56.1136406494972,-0.7812674544568233,-12.538652143710078,-1.0989356329451032,-40.67380062540061,1.4066061841896882,2.4560055884698335,135.58358854783296,55.20491858176336 +56.69815773959613,-0.8951523346832339,-13.99937902743073,-1.3384582123317916,-48.42925380576363,1.495229538562763,1.7459610547399986,97.86487468711262,56.05215214934233 +57.282674829695054,-1.016907883547717,-15.45519893702795,-1.6303287055074418,-57.03856246502108,1.6032216210377535,0.8811160374274739,50.185428234765006,56.95666189584689 +57.86719191979398,-1.1447833032697259,-16.879887399800843,-1.9859878045565882,-66.49076342414816,1.7348154876859376,-0.17236127335316223,-9.985054995831723,57.930965590934655 diff --git a/pvlib/modelchain.py b/pvlib/modelchain.py index 29f4cfa674..a71f72b0fe 100644 --- a/pvlib/modelchain.py +++ b/pvlib/modelchain.py @@ -216,12 +216,9 @@ def get_orientation(strategy, **kwargs): class ModelChain(object): """ - An experimental class that represents all of the modeling steps - necessary for calculating power or energy for a PV system at a given - location using the SAPM. - - CEC module specifications and the single diode model are not yet - supported. + The ModelChain class to provides a standardized, high-level + interface for all of the modeling steps necessary for calculating PV + power from a time series of weather inputs. Parameters ---------- @@ -233,7 +230,7 @@ class ModelChain(object): A :py:class:`~pvlib.location.Location` object that represents the physical location at which to evaluate the model. - orientation_strategy : None or str, default 'south_at_latitude_tilt' + orientation_strategy : None or str, default None The strategy for aligning the modules. If not None, sets the ``surface_azimuth`` and ``surface_tilt`` properties of the ``system``. Allowed strategies include 'flat', @@ -260,9 +257,9 @@ class ModelChain(object): ac_model: None, str, or function, default None If None, the model will be inferred from the contents of system.inverter_parameters and system.module_parameters. Valid - strings are 'snlinverter', 'adrinverter' (not implemented), - 'pvwatts'. The ModelChain instance will be passed as the first - argument to a user-defined function. + strings are 'snlinverter', 'adrinverter', 'pvwatts'. The + ModelChain instance will be passed as the first argument to a + user-defined function. aoi_model: None, str, or function, default None If None, the model will be inferred from the contents of @@ -273,9 +270,8 @@ class ModelChain(object): spectral_model: None, str, or function, default None If None, the model will be inferred from the contents of system.module_parameters. Valid strings are 'sapm', - 'first_solar', 'no_loss'. The ModelChain instance will be passed - as the first argument to a user-defined - function. + 'first_solar', 'no_loss'. The ModelChain instance will be passed + as the first argument to a user-defined function. temp_model: str or function, default 'sapm' Valid strings are 'sapm'. The ModelChain instance will be passed @@ -301,9 +297,7 @@ def __init__(self, system, location, airmass_model='kastenyoung1989', dc_model=None, ac_model=None, aoi_model=None, spectral_model=None, temp_model='sapm', - losses_model='no_loss', - name=None, - **kwargs): + losses_model='no_loss', name=None, **kwargs): self.name = name self.system = system @@ -713,7 +707,7 @@ def complete_irradiance(self, times=None, weather=None): return self - def prepare_inputs(self, times=None, irradiance=None, weather=None): + def prepare_inputs(self, times=None, weather=None): """ Prepare the solar position, irradiance, and weather inputs to the model. @@ -723,8 +717,6 @@ def prepare_inputs(self, times=None, irradiance=None, weather=None): times : None or DatetimeIndex, default None Times at which to evaluate the model. Can be None if attribute `times` is already set. - irradiance : None or DataFrame - This parameter is deprecated. Please use `weather` instead. weather : None or DataFrame, default None If None, the weather attribute is used. If the weather attribute is also None assumes air temperature is 20 C, wind @@ -747,19 +739,6 @@ def prepare_inputs(self, times=None, irradiance=None, weather=None): if self.weather is None: self.weather = pd.DataFrame() - # The following part could be removed together with the irradiance - # parameter at version v0.5 or v0.6. - # **** Begin **** - wrn_txt = ("The irradiance parameter will be removed soon.\n" + - "Please use the weather parameter to pass a DataFrame " + - "with irradiance (ghi, dni, dhi), wind speed and " + - "temp_air.\n") - if irradiance is not None: - warnings.warn(wrn_txt, FutureWarning) - for column in irradiance.columns: - self.weather[column] = irradiance[column] - # **** End **** - if times is not None: self.times = times @@ -772,8 +751,8 @@ def prepare_inputs(self, times=None, irradiance=None, weather=None): if not any([x in ['ghi', 'dni', 'dhi'] for x in self.weather.columns]): self.weather[['ghi', 'dni', 'dhi']] = self.location.get_clearsky( self.solar_position.index, self.clearsky_model, - zenith_data=self.solar_position['apparent_zenith'], - airmass_data=self.airmass['airmass_absolute']) + solar_position=self.solar_position, + airmass_absolute=self.airmass['airmass_absolute']) if not {'ghi', 'dni', 'dhi'} <= set(self.weather.columns): raise ValueError( @@ -824,7 +803,7 @@ def prepare_inputs(self, times=None, irradiance=None, weather=None): self.weather['temp_air'] = 20 return self - def run_model(self, times=None, irradiance=None, weather=None): + def run_model(self, times=None, weather=None): """ Run the model. @@ -833,8 +812,6 @@ def run_model(self, times=None, irradiance=None, weather=None): times : None or DatetimeIndex, default None Times at which to evaluate the model. Can be None if attribute `times` is already set. - irradiance : None or DataFrame - This parameter is deprecated. Please use `weather` instead. weather : None or DataFrame, default None If None, assumes air temperature is 20 C, wind speed is 0 m/s and irradiation calculated from clear sky data. Column @@ -852,7 +829,7 @@ def run_model(self, times=None, irradiance=None, weather=None): aoi_modifier, spectral_modifier, dc, ac, losses. """ - self.prepare_inputs(times, irradiance, weather) + self.prepare_inputs(times, weather) self.aoi_model() self.spectral_model() self.effective_irradiance_model() diff --git a/pvlib/pvsystem.py b/pvlib/pvsystem.py index 71fb07ecd5..43c07deda1 100644 --- a/pvlib/pvsystem.py +++ b/pvlib/pvsystem.py @@ -20,6 +20,7 @@ from pvlib.tools import _build_kwargs from pvlib.location import Location from pvlib import irradiance, atmosphere +from pvlib import singlediode_methods # not sure if this belongs in the pvsystem module. @@ -279,7 +280,7 @@ def physicaliam(self, aoi): return physicaliam(aoi, **kwargs) - def calcparams_desoto(self, poa_global, temp_cell, **kwargs): + def calcparams_desoto(self, effective_irradiance, temp_cell, **kwargs): """ Use the :py:func:`calcparams_desoto` function, the input parameters and ``self.module_parameters`` to calculate the @@ -287,8 +288,8 @@ def calcparams_desoto(self, poa_global, temp_cell, **kwargs): Parameters ---------- - poa_global : float or Series - The irradiance (in W/m^2) absorbed by the module. + effective_irradiance : numeric + The irradiance (W/m2) that is converted to photocurrent. temp_cell : float or Series The average cell temperature of cells within a module in C. @@ -302,10 +303,39 @@ def calcparams_desoto(self, poa_global, temp_cell, **kwargs): """ kwargs = _build_kwargs(['a_ref', 'I_L_ref', 'I_o_ref', 'R_sh_ref', - 'R_s', 'alpha_isc', 'EgRef', 'dEgdT'], + 'R_s', 'alpha_sc', 'EgRef', 'dEgdT', + 'irrad_ref', 'temp_ref'], self.module_parameters) - - return calcparams_desoto(poa_global, temp_cell, **kwargs) + + return calcparams_desoto(effective_irradiance, temp_cell, **kwargs) + + def calcparams_pvsyst(self, effective_irradiance, temp_cell): + """ + Use the :py:func:`calcparams_pvsyst` function, the input + parameters and ``self.module_parameters`` to calculate the + module currents and resistances. + + Parameters + ---------- + effective_irradiance : numeric + The irradiance (W/m2) that is converted to photocurrent. + + temp_cell : float or Series + The average cell temperature of cells within a module in C. + + Returns + ------- + See pvsystem.calcparams_pvsyst for details + """ + + kwargs = _build_kwargs(['gamma_ref', 'mu_gamma', 'I_L_ref', 'I_o_ref', + 'R_sh_ref', 'R_sh_0', 'R_sh_exp', + 'R_s', 'alpha_sc', 'EgRef', + 'irrad_ref', 'temp_ref', + 'cells_in_series'], + self.module_parameters) + + return calcparams_pvsyst(effective_irradiance, temp_cell, **kwargs) def sapm(self, effective_irradiance, temp_cell, **kwargs): """ @@ -462,7 +492,7 @@ def first_solar_spectral_loss(self, pw, airmass_absolute): coefficients = None return atmosphere.first_solar_spectral_correction(pw, - airmass_absolute, + airmass_absolute, module_type, coefficients) @@ -946,72 +976,59 @@ def physicaliam(aoi, n=1.526, K=4., L=0.002): def calcparams_desoto(effective_irradiance, temp_cell, - alpha_isc, a_ref, I_L_ref, I_o_ref, R_sh_ref, R_s, + alpha_sc, a_ref, I_L_ref, I_o_ref, R_sh_ref, R_s, EgRef=1.121, dEgdT=-0.0002677, irrad_ref=1000, temp_ref=25): ''' - Calculates five parameter values for the single diode equation at - effective irradiance and cell temperature using the De Soto et al. + Calculates five parameter values for the single diode equation at + effective irradiance and cell temperature using the De Soto et al. model described in [1]. The five values returned by calcparams_desoto can be used by singlediode to calculate an IV curve. Parameters ---------- effective_irradiance : numeric - Effective irradiance (suns). + The irradiance (W/m2) that is converted to photocurrent. temp_cell : numeric The average cell temperature of cells within a module in C. - alpha_isc : float + alpha_sc : float The short-circuit current temperature coefficient of the module in units of A/C. a_ref : float - The product of the usual diode ideality factor (n, unitless), + The product of the usual diode ideality factor (n, unitless), number of cells in series (Ns), and cell thermal voltage at reference conditions, in units of V. I_L_ref : float The light-generated current (or photocurrent) at reference conditions, in amperes. - + I_o_ref : float The dark or diode reverse saturation current at reference conditions, in amperes. - + R_sh_ref : float The shunt resistance at reference conditions, in ohms. - + R_s : float The series resistance at reference conditions, in ohms. EgRef : float The energy bandgap at reference temperature in units of eV. - 1.121 eV for silicon. EgRef must be >0. For parameters read from - the SAM CEC module database, EgRef=1.121 is imposed by the - parameter estimation algorithm used by NREL. + 1.121 eV for crystalline silicon. EgRef must be >0. For parameters + from the SAM CEC module database, EgRef=1.121 is implicit for all + cell types in the parameter estimation algorithm used by NREL. dEgdT : float The temperature dependence of the energy bandgap at reference - conditions in units of 1/C. May be either a scalar value (e.g. -0.0002677 as in [1]) - or a DataFrame of dEgdT values corresponding to each input - condition (this may be useful if dEgdT is a function of - temperature). For parameters read from - the SAM CEC module database, dEgdT=-0.0002677 is imposed by the - parameter estimation algorithm used by NREL. - - M : numeric (optional, default=1) - An optional airmass modifier, if omitted, M is given a value of - 1, which assumes absolute (pressure corrected) airmass = 1.5. In - this code, M is equal to M/Mref as described in [1] (i.e. Mref - is assumed to be 1). Source [1] suggests that an appropriate - value for M as a function absolute airmass (AMa) may be: - - >>> M = np.polyval([-0.000126, 0.002816, -0.024459, 0.086257, 0.918093], - ... AMa) # doctest: +SKIP - - M may be a Series. + conditions in units of 1/K. May be either a scalar value + (e.g. -0.0002677 as in [1]) or a DataFrame (this may be useful if + dEgdT is a modeled as a function of temperature). For parameters from + the SAM CEC module database, dEgdT=-0.0002677 is implicit for all cell + types in the parameter estimation algorithm used by NREL. irrad_ref : float (optional, default=1000) Reference irradiance in W/m^2. @@ -1024,28 +1041,21 @@ def calcparams_desoto(effective_irradiance, temp_cell, Tuple of the following results: photocurrent : numeric - Light-generated current in amperes at irradiance=S and - cell temperature=Tcell. + Light-generated current in amperes saturation_current : numeric - Diode saturation curent in amperes at irradiance - S and cell temperature Tcell. + Diode saturation curent in amperes resistance_series : float - Series resistance in ohms at irradiance S and cell temperature - Tcell. + Series resistance in ohms resistance_shunt : numeric - Shunt resistance in ohms at irradiance S and cell temperature - Tcell. + Shunt resistance in ohms nNsVth : numeric - Modified diode ideality factor at irradiance S and cell - temperature Tcell. Note that in source [1] nNsVth = a (equation - 2). nNsVth is the product of the usual diode ideality factor - (n), the number of series-connected cells in the module (Ns), - and the thermal voltage of a cell in the module (Vth) at a cell - temperature of Tcell. + The product of the usual diode ideality factor (n, unitless), + number of cells in series (Ns), and cell thermal voltage at + specified effective irradiance and cell temperature. References ---------- @@ -1064,8 +1074,6 @@ def calcparams_desoto(effective_irradiance, temp_cell, See Also -------- - sapm - sapm_celltemp singlediode retrieve_sam @@ -1089,7 +1097,7 @@ def calcparams_desoto(effective_irradiance, temp_cell, and modifying the reference parameters (for irradiance, temperature, and airmass) per DeSoto's equations. - Silicon (Si): + Crystalline Silicon (Si): * EgRef = 1.121 * dEgdT = -0.0002677 @@ -1133,9 +1141,30 @@ def calcparams_desoto(effective_irradiance, temp_cell, Source: [4] ''' + # test for use of function pre-v0.6.0 API change + if isinstance(a_ref, dict) or \ + (isinstance(a_ref, pd.Series) and ('a_ref' in a_ref.keys())): + import warnings + warnings.warn('module_parameters detected as fourth positional' + + ' argument of calcparams_desoto. calcparams_desoto' + + ' will require one argument for each module model' + + ' parameter in v0.7.0 and later', DeprecationWarning) + try: + module_parameters = a_ref + a_ref = module_parameters['a_ref'] + I_L_ref = module_parameters['I_L_ref'] + I_o_ref = module_parameters['I_o_ref'] + R_sh_ref = module_parameters['R_sh_ref'] + R_s = module_parameters['R_s'] + except Exception as e: + raise e('Module parameters could not be extracted from fourth' + + ' positional argument of calcparams_desoto. Check that' + + ' parameters are from the CEC database and/or update' + + ' your code for the new API for calcparams_desoto') + # Boltzmann constant in eV/K k = 8.617332478e-05 - + # reference temperature Tref_K = temp_ref + 273.15 Tcell_K = temp_cell + 273.15 @@ -1144,16 +1173,158 @@ def calcparams_desoto(effective_irradiance, temp_cell, nNsVth = a_ref * (Tcell_K / Tref_K) - IL = effective_irradiance * (I_L_ref + alpha_isc * (Tcell_K - Tref_K)) + # In the equation for IL, the single factor effective_irradiance is + # used, in place of the product S*M in [1]. effective_irradiance is + # equivalent to the product of S (irradiance reaching a module's cells) * + # M (spectral adjustment factor) as described in [1]. + IL = effective_irradiance / irrad_ref * \ + (I_L_ref + alpha_sc * (Tcell_K - Tref_K)) I0 = (I_o_ref * ((Tcell_K / Tref_K) ** 3) * (np.exp(EgRef / (k*(Tref_K)) - (E_g / (k*(Tcell_K)))))) # Note that the equation for Rsh differs from [1]. In [1] Rsh is given as # Rsh = Rsh_ref * (S_ref / S) where S is broadband irradiance reaching # the module's cells. If desired this model behavior can be duplicated - # by applying reflection and soiing losses to broadband plane of array - # irradiance and not applying a spectral loss modifier, i.e., + # by applying reflection and soiling losses to broadband plane of array + # irradiance and not applying a spectral loss modifier, i.e., # spectral_modifier = 1.0. - Rsh = R_sh_ref * (1.0 / effective_irradiance) + Rsh = R_sh_ref * (irrad_ref / effective_irradiance) + Rs = R_s + + return IL, I0, Rs, Rsh, nNsVth + + +def calcparams_pvsyst(effective_irradiance, temp_cell, + alpha_sc, gamma_ref, mu_gamma, + I_L_ref, I_o_ref, + R_sh_ref, R_sh_0, R_s, + cells_in_series, + R_sh_exp=5.5, + EgRef=1.121, + irrad_ref=1000, temp_ref=25): + ''' + Calculates five parameter values for the single diode equation at + effective irradiance and cell temperature using the PVsyst v6 + model described in [1,2,3]. The five values returned by calcparams_pvsyst + can be used by singlediode to calculate an IV curve. + + Parameters + ---------- + effective_irradiance : numeric + The irradiance (W/m2) that is converted to photocurrent. + + temp_cell : numeric + The average cell temperature of cells within a module in C. + + alpha_sc : float + The short-circuit current temperature coefficient of the + module in units of A/C. + + gamma_ref : float + The diode ideality factor + + mu_gamma : float + The temperature coefficient for the diode ideality factor, 1/K + + I_L_ref : float + The light-generated current (or photocurrent) at reference conditions, + in amperes. + + I_o_ref : float + The dark or diode reverse saturation current at reference conditions, + in amperes. + + R_sh_ref : float + The shunt resistance at reference conditions, in ohms. + + R_sh_0 : float + The shunt resistance at zero irradiance conditions, in ohms. + + R_s : float + The series resistance at reference conditions, in ohms. + + cells_in_series : integer + The number of cells connected in series. + + R_sh_exp : float + The exponent in the equation for shunt resistance, unitless. Defaults + to 5.5. + + EgRef : float + The energy bandgap at reference temperature in units of eV. + 1.121 eV for crystalline silicon. EgRef must be >0. + + irrad_ref : float (optional, default=1000) + Reference irradiance in W/m^2. + + temp_ref : float (optional, default=25) + Reference cell temperature in C. + + Returns + ------- + Tuple of the following results: + + photocurrent : numeric + Light-generated current in amperes + + saturation_current : numeric + Diode saturation current in amperes + + resistance_series : float + Series resistance in ohms + + resistance_shunt : numeric + Shunt resistance in ohms + + nNsVth : numeric + The product of the usual diode ideality factor (n, unitless), + number of cells in series (Ns), and cell thermal voltage at + specified effective irradiance and cell temperature. + + References + ---------- + [1] K. Sauer, T. Roessler, C. W. Hansen, Modeling the Irradiance and + Temperature Dependence of Photovoltaic Modules in PVsyst, + IEEE Journal of Photovoltaics v5(1), January 2015. + + [2] A. Mermoud, PV modules modelling, Presentation at the 2nd PV + Performance Modeling Workshop, Santa Clara, CA, May 2013 + + [3] A. Mermoud, T. Lejeune, Performance Assessment of a Simulation Model + for PV modules of any available technology, 25th European Photovoltaic + Solar Energy Conference, Valencia, Spain, Sept. 2010 + + See Also + -------- + calcparams_desoto + singlediode + + ''' + + # Boltzmann constant in J/K + k = 1.38064852e-23 + + # elementary charge in coulomb + q = 1.6021766e-19 + + # reference temperature + Tref_K = temp_ref + 273.15 + Tcell_K = temp_cell + 273.15 + + gamma = gamma_ref + mu_gamma * (Tcell_K - Tref_K) + nNsVth = gamma * k / q * cells_in_series * Tcell_K + + IL = effective_irradiance / irrad_ref * \ + (I_L_ref + alpha_sc * (Tcell_K - Tref_K)) + + I0 = I_o_ref * ((Tcell_K / Tref_K) ** 3) * \ + (np.exp((q * EgRef) / (k * gamma) * (1 / Tref_K - 1 / Tcell_K))) + + Rsh_tmp = (R_sh_ref - R_sh_0 * np.exp(-R_sh_exp)) / (1.0 - np.exp(-R_sh_exp)) + Rsh_base = np.maximum(0.0, Rsh_tmp) + + Rsh = Rsh_base + (R_sh_0 - Rsh_base) * \ + np.exp(-R_sh_exp * effective_irradiance / irrad_ref) + Rs = R_s return IL, I0, Rs, Rsh, nNsVth @@ -1681,8 +1852,9 @@ def sapm_effective_irradiance(poa_direct, poa_diffuse, airmass_absolute, aoi, def singlediode(photocurrent, saturation_current, resistance_series, - resistance_shunt, nNsVth, ivcurve_pnts=None): - r''' + resistance_shunt, nNsVth, ivcurve_pnts=None, + method='lambertw'): + """ Solve the single-diode model to obtain a photovoltaic IV curve. Singlediode solves the single diode equation [1] @@ -1737,6 +1909,10 @@ def singlediode(photocurrent, saturation_current, resistance_series, Number of points in the desired IV curve. If None or 0, no IV curves will be produced. + method : str, default 'lambertw' + Determines the method used to calculate points on the IV curve. The + options are ``'lambertw'``, ``'newton'``, or ``'brentq'``. + Returns ------- OrderedDict or DataFrame @@ -1765,9 +1941,64 @@ def singlediode(photocurrent, saturation_current, resistance_series, Notes ----- - The solution employed to solve the implicit diode equation utilizes - the Lambert W function to obtain an explicit function of V=f(i) and - I=f(V) as shown in [2]. + If the method is ``'lambertw'`` then the solution employed to solve the + implicit diode equation utilizes the Lambert W function to obtain an + explicit function of :math:`V=f(I)` and :math:`I=f(V)` as shown in [2]. + + If the method is ``'newton'`` then the root-finding Newton-Raphson method + is used. It should be safe for well behaved IV-curves, but the ``'brentq'`` + method is recommended for reliability. + + If the method is ``'brentq'`` then Brent's bisection search method is used + that guarantees convergence by bounding the voltage between zero and + open-circuit. + + If the method is either ``'newton'`` or ``'brentq'`` and ``ivcurve_pnts`` + are indicated, then :func:`pvlib.singlediode_methods.bishop88` is used to + calculate the points on the IV curve points at diode voltages from zero to + open-circuit voltage with a log spacing that gets closer as voltage + increases. If the method is ``'lambertw'`` then the calculated points on + the IV curve are linearly spaced. + + The ``bishop88`` method uses an explicit solution from [4] that finds + points on the IV curve by first solving for pairs :math:`(V_d, I)` where + :math:`V_d` is the diode voltage :math:`V_d = V + I*Rs`. Then the voltage + is backed out from :math:`V_d`. Points with specific voltage, such as open + circuit, are located using the bisection search method, ``brentq``, bounded + by a zero diode voltage and an estimate of open circuit voltage given by + + .. math:: + + V_{oc, est} = n Ns V_{th} \\log \\left( \\frac{I_L}{I_0} + 1 \\right) + + We know that :math:`V_d = 0` corresponds to a voltage less than zero, and + we can also show that when :math:`V_d = V_{oc, est}`, the resulting + current is also negative, meaning that the corresponding voltage must be + in the 4th quadrant and therefore greater than the open circuit voltage + (see proof below). Therefore the entire forward-bias 1st quadrant IV-curve + is bounded, and a bisection search within these points will always find + desired condition. + + .. math:: + + I = I_L - I_0 \\left(\\exp \\left(\\frac{V_{oc, est}}{n Ns V_{th}} \\right) - 1 \\right) + - \\frac{V_{oc, est}}{R_{sh}} \\newline + + I = I_L - I_0 \\left(\\exp \\left(\\frac{n Ns V_{th} \\log \\left(\\frac{I_L}{I_0} + 1 \\right)}{n Ns V_{th}} \\right) - 1 \\right) + - \\frac{n Ns V_{th} \\log \\left(\\frac{I_L}{I_0} + 1 \\right)}{R_{sh}} \\newline + + I = I_L - I_0 \\left(\\exp \\left(\\log \\left(\\frac{I_L}{I_0} + 1 \\right) \\right) - 1 \\right) + - \\frac{n Ns V_{th} \\log \\left(\\frac{I_L}{I_0} + 1 \\right)}{R_{sh}} \\newline + + I = I_L - I_0 \\left(\\frac{I_L}{I_0} + 1 - 1 \\right) + - \\frac{n Ns V_{th} \\log \\left(\\frac{I_L}{I_0} + 1 \\right)}{R_{sh}} \\newline + + I = I_L - I_0 \\left(\\frac{I_L}{I_0} \\right) + - \\frac{n Ns V_{th} \\log \\left(\\frac{I_L}{I_0} + 1 \\right)}{R_{sh}} \\newline + + I = I_L - I_L - \\frac{n Ns V_{th} \log \\left( \\frac{I_L}{I_0} + 1 \\right)}{R_{sh}} \\newline + + I = - \\frac{n Ns V_{th} \\log \\left( \\frac{I_L}{I_0} + 1 \\right)}{R_{sh}} References ----------- @@ -1781,39 +2012,55 @@ def singlediode(photocurrent, saturation_current, resistance_series, [3] D. King et al, "Sandia Photovoltaic Array Performance Model", SAND2004-3535, Sandia National Laboratories, Albuquerque, NM + [4] "Computer simulation of the effects of electrical mismatches in + photovoltaic cell interconnection circuits" JW Bishop, Solar Cell (1988) + https://doi.org/10.1016/0379-6787(88)90059-2 + See also -------- sapm calcparams_desoto - ''' - - # Compute short circuit current - i_sc = i_from_v(resistance_shunt, resistance_series, nNsVth, 0., - saturation_current, photocurrent) - - # Compute open circuit voltage - v_oc = v_from_i(resistance_shunt, resistance_series, nNsVth, 0., - saturation_current, photocurrent) - - params = {'r_sh': resistance_shunt, - 'r_s': resistance_series, - 'nNsVth': nNsVth, - 'i_0': saturation_current, - 'i_l': photocurrent} - - p_mp, v_mp = _golden_sect_DataFrame(params, 0., v_oc * 1.14, _pwr_optfcn) - - # Invert the Power-Current curve. Find the current where the inverted power - # is minimized. This is i_mp. Start the optimization at v_oc/2 - i_mp = i_from_v(resistance_shunt, resistance_series, nNsVth, v_mp, - saturation_current, photocurrent) - - # Find Ix and Ixx using Lambert W - i_x = i_from_v(resistance_shunt, resistance_series, nNsVth, 0.5 * v_oc, - saturation_current, photocurrent) - - i_xx = i_from_v(resistance_shunt, resistance_series, nNsVth, - 0.5 * (v_oc + v_mp), saturation_current, photocurrent) + pvlib.singlediode_methods.bishop88 + """ + # Calculate points on the IV curve using the LambertW solution to the + # single diode equation + if method.lower() == 'lambertw': + out = singlediode_methods._lambertw( + photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth, ivcurve_pnts + ) + i_sc, v_oc, i_mp, v_mp, p_mp, i_x, i_xx = out[:7] + if ivcurve_pnts: + ivcurve_i, ivcurve_v = out[7:] + else: + # Calculate points on the IV curve using either 'newton' or 'brentq' + # methods. Voltages are determined by first solving the single diode + # equation for the diode voltage V_d then backing out voltage + args = (photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth) # collect args + v_oc = singlediode_methods.bishop88_v_from_i( + 0.0, *args, method=method.lower() + ) + i_mp, v_mp, p_mp = singlediode_methods.bishop88_mpp( + *args, method=method.lower() + ) + i_sc = singlediode_methods.bishop88_i_from_v( + 0.0, *args, method=method.lower() + ) + i_x = singlediode_methods.bishop88_i_from_v( + v_oc / 2.0, *args, method=method.lower() + ) + i_xx = singlediode_methods.bishop88_i_from_v( + (v_oc + v_mp) / 2.0, *args, method=method.lower() + ) + + # calculate the IV curve if requested using bishop88 + if ivcurve_pnts: + vd = v_oc * ( + (11.0 - np.logspace(np.log10(11.0), 0.0, + ivcurve_pnts)) / 10.0 + ) + ivcurve_i, ivcurve_v, _ = singlediode_methods.bishop88(vd, *args) out = OrderedDict() out['i_sc'] = i_sc @@ -1824,13 +2071,7 @@ def singlediode(photocurrent, saturation_current, resistance_series, out['i_x'] = i_x out['i_xx'] = i_xx - # create ivcurve if ivcurve_pnts: - ivcurve_v = (np.asarray(v_oc)[..., np.newaxis] * - np.linspace(0, 1, ivcurve_pnts)) - - ivcurve_i = i_from_v(resistance_shunt, resistance_series, nNsVth, - ivcurve_v.T, saturation_current, photocurrent).T out['v'] = ivcurve_v out['i'] = ivcurve_i @@ -1841,91 +2082,57 @@ def singlediode(photocurrent, saturation_current, resistance_series, return out -# Created April,2014 -# Author: Rob Andrews, Calama Consulting - -def _golden_sect_DataFrame(params, VL, VH, func): - ''' - Vectorized golden section search for finding MPPT - from a dataframe timeseries. +def max_power_point(photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth, method='brentq'): + """ + Given the single diode equation coefficients, calculates the maximum power + point (MPP). Parameters ---------- - params : dict - Dictionary containing scalars or arrays - of inputs to the function to be optimized. - Each row should represent an independent optimization. - - VL: float - Lower bound of the optimization - - VH: float - Upper bound of the optimization - - func: function - Function to be optimized must be in the form f(array-like, x) + photocurrent : numeric + photo-generated current [A] + saturation_current : numeric + diode reverse saturation current [A] + resistance_series : numeric + series resitance [ohms] + resistance_shunt : numeric + shunt resitance [ohms] + nNsVth : numeric + product of thermal voltage ``Vth`` [V], diode ideality factor ``n``, + and number of serices cells ``Ns`` + method : str + either ``'newton'`` or ``'brentq'`` Returns ------- - func(df,'V1') : DataFrame - function evaluated at the optimal point - - df['V1']: Dataframe - Dataframe of optimal points + OrderedDict or pandas.Datafrane + ``(i_mp, v_mp, p_mp)`` Notes ----- - This funtion will find the MAXIMUM of a function - ''' - - df = params - df['VH'] = VH - df['VL'] = VL - - err = df['VH'] - df['VL'] - errflag = True - iterations = 0 - - while errflag: - - phi = (np.sqrt(5)-1)/2*(df['VH']-df['VL']) - df['V1'] = df['VL'] + phi - df['V2'] = df['VH'] - phi - - df['f1'] = func(df, 'V1') - df['f2'] = func(df, 'V2') - df['SW_Flag'] = df['f1'] > df['f2'] - - df['VL'] = df['V2']*df['SW_Flag'] + df['VL']*(~df['SW_Flag']) - df['VH'] = df['V1']*~df['SW_Flag'] + df['VH']*(df['SW_Flag']) - - err = df['V1'] - df['V2'] - try: - errflag = (abs(err) > .01).any() - except ValueError: - errflag = (abs(err) > .01) - - iterations += 1 - - if iterations > 50: - raise Exception("EXCEPTION:iterations exeeded maximum (50)") - - return func(df, 'V1'), df['V1'] - - -def _pwr_optfcn(df, loc): - ''' - Function to find power from ``i_from_v``. - ''' - - I = i_from_v(df['r_sh'], df['r_s'], df['nNsVth'], df[loc], df['i_0'], - df['i_l']) - - return I * df[loc] + Use this function when you only want to find the maximum power point. Use + :func:`singlediode` when you need to find additional points on the IV + curve. This function uses Brent's method by default because it is + guaranteed to converge. + """ + i_mp, v_mp, p_mp = singlediode_methods.bishop88_mpp( + photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth, method=method.lower() + ) + if isinstance(photocurrent, pd.Series): + ivp = {'i_mp': i_mp, 'v_mp': v_mp, 'p_mp': p_mp} + out = pd.DataFrame(ivp, index=photocurrent.index) + else: + out = OrderedDict() + out['i_mp'] = i_mp + out['v_mp'] = v_mp + out['p_mp'] = p_mp + return out def v_from_i(resistance_shunt, resistance_series, nNsVth, current, - saturation_current, photocurrent): + saturation_current, photocurrent, method='lambertw'): ''' Device voltage at the given device current for the single diode model. @@ -1974,6 +2181,10 @@ def v_from_i(resistance_shunt, resistance_series, nNsVth, current, IV curve conditions. Often abbreviated ``I_L``. 0 <= photocurrent + method : str + Method to use: ``'lambertw'``, ``'newton'``, or ``'brentq'``. *Note*: + ``'brentq'`` is limited to 1st quadrant only. + Returns ------- current : np.ndarray or scalar @@ -1984,88 +2195,32 @@ def v_from_i(resistance_shunt, resistance_series, nNsVth, current, parameters of real solar cells using Lambert W-function", Solar Energy Materials and Solar Cells, 81 (2004) 269-277. ''' - try: - from scipy.special import lambertw - except ImportError: - raise ImportError('This function requires scipy') - - # Record if inputs were all scalar - output_is_scalar = all(map(np.isscalar, - [resistance_shunt, resistance_series, nNsVth, - current, saturation_current, photocurrent])) - - # This transforms Gsh=1/Rsh, including ideal Rsh=np.inf into Gsh=0., which - # is generally more numerically stable - conductance_shunt = 1./resistance_shunt - - # Ensure that we are working with read-only views of numpy arrays - # Turns Series into arrays so that we don't have to worry about - # multidimensional broadcasting failing - Gsh, Rs, a, I, I0, IL = \ - np.broadcast_arrays(conductance_shunt, resistance_series, nNsVth, - current, saturation_current, photocurrent) - - # Intitalize output V (I might not be float64) - V = np.full_like(I, np.nan, dtype=np.float64) - - # Determine indices where 0 < Gsh requires implicit model solution - idx_p = 0. < Gsh - - # Determine indices where 0 = Gsh allows explicit model solution - idx_z = 0. == Gsh - - # Explicit solutions where Gsh=0 - if np.any(idx_z): - V[idx_z] = a[idx_z]*np.log1p((IL[idx_z] - I[idx_z])/I0[idx_z]) - \ - I[idx_z]*Rs[idx_z] - - # Only compute using LambertW if there are cases with Gsh>0 - if np.any(idx_p): - # LambertW argument, cannot be float128, may overflow to np.inf - # overflow is explicitly handled below, so ignore warnings here - with np.errstate(over='ignore'): - argW = (I0[idx_p] / (Gsh[idx_p]*a[idx_p]) * - np.exp((-I[idx_p] + IL[idx_p] + I0[idx_p]) / - (Gsh[idx_p]*a[idx_p]))) - - # lambertw typically returns complex value with zero imaginary part - # may overflow to np.inf - lambertwterm = lambertw(argW).real - - # Record indices where lambertw input overflowed output - idx_inf = np.logical_not(np.isfinite(lambertwterm)) - - # Only re-compute LambertW if it overflowed - if np.any(idx_inf): - # Calculate using log(argW) in case argW is really big - logargW = (np.log(I0[idx_p]) - np.log(Gsh[idx_p]) - - np.log(a[idx_p]) + - (-I[idx_p] + IL[idx_p] + I0[idx_p]) / - (Gsh[idx_p] * a[idx_p]))[idx_inf] - - # Three iterations of Newton-Raphson method to solve - # w+log(w)=logargW. The initial guess is w=logargW. Where direct - # evaluation (above) results in NaN from overflow, 3 iterations - # of Newton's method gives approximately 8 digits of precision. - w = logargW - for _ in range(0, 3): - w = w * (1. - np.log(w) + logargW) / (1. + w) - lambertwterm[idx_inf] = w - - # Eqn. 3 in Jain and Kapoor, 2004 - # V = -I*(Rs + Rsh) + IL*Rsh - a*lambertwterm + I0*Rsh - # Recast in terms of Gsh=1/Rsh for better numerical stability. - V[idx_p] = (IL[idx_p] + I0[idx_p] - I[idx_p])/Gsh[idx_p] - \ - I[idx_p]*Rs[idx_p] - a[idx_p]*lambertwterm - - if output_is_scalar: - return np.asscalar(V) + if method.lower() == 'lambertw': + return singlediode_methods._lambertw_v_from_i( + resistance_shunt, resistance_series, nNsVth, current, + saturation_current, photocurrent + ) else: + # Calculate points on the IV curve using either 'newton' or 'brentq' + # methods. Voltages are determined by first solving the single diode + # equation for the diode voltage V_d then backing out voltage + args = (current, photocurrent, saturation_current, + resistance_series, resistance_shunt, nNsVth) + V = singlediode_methods.bishop88_v_from_i(*args, method=method.lower()) + # find the right size and shape for returns + size, shape = singlediode_methods._get_size_and_shape(args) + if size <= 1: + if shape is not None: + V = np.tile(V, shape) + if np.isnan(V).any() and size <= 1: + V = np.repeat(V, size) + if shape is not None: + V = V.reshape(shape) return V def i_from_v(resistance_shunt, resistance_series, nNsVth, voltage, - saturation_current, photocurrent): + saturation_current, photocurrent, method='lambertw'): ''' Device current at the given device voltage for the single diode model. @@ -2114,6 +2269,10 @@ def i_from_v(resistance_shunt, resistance_series, nNsVth, voltage, IV curve conditions. Often abbreviated ``I_L``. 0 <= photocurrent + method : str + Method to use: ``'lambertw'``, ``'newton'``, or ``'brentq'``. *Note*: + ``'brentq'`` is limited to 1st quadrant only. + Returns ------- current : np.ndarray or scalar @@ -2124,62 +2283,27 @@ def i_from_v(resistance_shunt, resistance_series, nNsVth, voltage, parameters of real solar cells using Lambert W-function", Solar Energy Materials and Solar Cells, 81 (2004) 269-277. ''' - try: - from scipy.special import lambertw - except ImportError: - raise ImportError('This function requires scipy') - - # Record if inputs were all scalar - output_is_scalar = all(map(np.isscalar, - [resistance_shunt, resistance_series, nNsVth, - voltage, saturation_current, photocurrent])) - - # This transforms Gsh=1/Rsh, including ideal Rsh=np.inf into Gsh=0., which - # is generally more numerically stable - conductance_shunt = 1./resistance_shunt - - # Ensure that we are working with read-only views of numpy arrays - # Turns Series into arrays so that we don't have to worry about - # multidimensional broadcasting failing - Gsh, Rs, a, V, I0, IL = \ - np.broadcast_arrays(conductance_shunt, resistance_series, nNsVth, - voltage, saturation_current, photocurrent) - - # Intitalize output I (V might not be float64) - I = np.full_like(V, np.nan, dtype=np.float64) - - # Determine indices where 0 < Rs requires implicit model solution - idx_p = 0. < Rs - - # Determine indices where 0 = Rs allows explicit model solution - idx_z = 0. == Rs - - # Explicit solutions where Rs=0 - if np.any(idx_z): - I[idx_z] = IL[idx_z] - I0[idx_z]*np.expm1(V[idx_z]/a[idx_z]) - \ - Gsh[idx_z]*V[idx_z] - - # Only compute using LambertW if there are cases with Rs>0 - # Does NOT handle possibility of overflow, github issue 298 - if np.any(idx_p): - # LambertW argument, cannot be float128, may overflow to np.inf - argW = Rs[idx_p]*I0[idx_p]/(a[idx_p]*(Rs[idx_p]*Gsh[idx_p] + 1.)) * \ - np.exp((Rs[idx_p]*(IL[idx_p] + I0[idx_p]) + V[idx_p]) / - (a[idx_p]*(Rs[idx_p]*Gsh[idx_p] + 1.))) - - # lambertw typically returns complex value with zero imaginary part - # may overflow to np.inf - lambertwterm = lambertw(argW).real - - # Eqn. 2 in Jain and Kapoor, 2004 - # I = -V/(Rs + Rsh) - (a/Rs)*lambertwterm + Rsh*(IL + I0)/(Rs + Rsh) - # Recast in terms of Gsh=1/Rsh for better numerical stability. - I[idx_p] = (IL[idx_p] + I0[idx_p] - V[idx_p]*Gsh[idx_p]) / \ - (Rs[idx_p]*Gsh[idx_p] + 1.) - (a[idx_p]/Rs[idx_p])*lambertwterm - - if output_is_scalar: - return np.asscalar(I) + if method.lower() == 'lambertw': + return singlediode_methods._lambertw_i_from_v( + resistance_shunt, resistance_series, nNsVth, voltage, + saturation_current, photocurrent + ) else: + # Calculate points on the IV curve using either 'newton' or 'brentq' + # methods. Voltages are determined by first solving the single diode + # equation for the diode voltage V_d then backing out voltage + args = (voltage, photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth) + I = singlediode_methods.bishop88_i_from_v(*args, method=method.lower()) + # find the right size and shape for returns + size, shape = singlediode_methods._get_size_and_shape(args) + if size <= 1: + if shape is not None: + I = np.tile(I, shape) + if np.isnan(I).any() and size <= 1: + I = np.repeat(I, size) + if shape is not None: + I = I.reshape(shape) return I diff --git a/pvlib/singlediode_methods.py b/pvlib/singlediode_methods.py new file mode 100644 index 0000000000..048870d13a --- /dev/null +++ b/pvlib/singlediode_methods.py @@ -0,0 +1,550 @@ +""" +Low-level functions for solving the single diode equation. +""" + +from functools import partial +import numpy as np +from pvlib.tools import _golden_sect_DataFrame + +# Try to import brentq from scipy to use when specified in bishop88_i_from_v, +# bishop88_v_from_i, and bishop88_mpp methods below. If not imported, raises +# ImportError when 'brentq' method is specified for those methods. +try: + from scipy.optimize import brentq +except ImportError: + brentq = NotImplemented + +# FIXME: change this to newton when scipy-1.2 is released +try: + from scipy.optimize import _array_newton +except ImportError: + from pvlib.tools import _array_newton +# rename newton and set keyword arguments +newton = partial(_array_newton, tol=1e-6, maxiter=100, fprime2=None) + + +def estimate_voc(photocurrent, saturation_current, nNsVth): + """ + Rough estimate of open circuit voltage useful for bounding searches for + ``i`` of ``v`` when using :func:`~pvlib.pvsystem.singlediode`. + + Parameters + ---------- + photocurrent : numeric + photo-generated current [A] + saturation_current : numeric + diode reverse saturation current [A] + nNsVth : numeric + product of thermal voltage ``Vth`` [V], diode ideality factor ``n``, + and number of series cells ``Ns`` + + Returns + ------- + numeric + rough estimate of open circuit voltage [V] + + Notes + ----- + Calculating the open circuit voltage, :math:`V_{oc}`, of an ideal device + with infinite shunt resistance, :math:`R_{sh} \\to \\infty`, and zero + series resistance, :math:`R_s = 0`, yields the following equation [1]. As + an estimate of :math:`V_{oc}` it is useful as an upper bound for the + bisection method. + + .. math:: + + V_{oc, est}=n Ns V_{th} \\log \\left( \\frac{I_L}{I_0} + 1 \\right) + + [1] http://www.pveducation.org/pvcdrom/open-circuit-voltage + """ + + return nNsVth * np.log(np.asarray(photocurrent) / saturation_current + 1.0) + + +def bishop88(diode_voltage, photocurrent, saturation_current, + resistance_series, resistance_shunt, nNsVth, gradients=False): + """ + Explicit calculation of points on the IV curve described by the single + diode equation [1]. + + [1] "Computer simulation of the effects of electrical mismatches in + photovoltaic cell interconnection circuits" JW Bishop, Solar Cell (1988) + https://doi.org/10.1016/0379-6787(88)90059-2 + + Parameters + ---------- + diode_voltage : numeric + diode voltages [V] + photocurrent : numeric + photo-generated current [A] + saturation_current : numeric + diode reverse saturation current [A] + resistance_series : numeric + series resistance [ohms] + resistance_shunt: numeric + shunt resistance [ohms] + nNsVth : numeric + product of thermal voltage ``Vth`` [V], diode ideality factor ``n``, + and number of series cells ``Ns`` + gradients : bool + False returns only I, V, and P. True also returns gradients + + Returns + ------- + tuple + currents [A], voltages [V], power [W], and optionally + :math:`\\frac{dI}{dV_d}`, :math:`\\frac{dV}{dV_d}`, + :math:`\\frac{dI}{dV}`, :math:`\\frac{dP}{dV}`, and + :math:`\\frac{d^2 P}{dV dV_d}` + """ + # calculate temporary values to simplify calculations + v_star = diode_voltage / nNsVth # non-dimensional diode voltage + g_sh = 1.0 / resistance_shunt # conductance + i = (photocurrent - saturation_current * np.expm1(v_star) + - diode_voltage * g_sh) + v = diode_voltage - i * resistance_series + retval = (i, v, i*v) + if gradients: + g_diode = saturation_current * np.exp(v_star) / nNsVth # conductance + grad_i = -g_diode - g_sh # di/dvd + grad_v = 1.0 - grad_i * resistance_series # dv/dvd + # dp/dv = d(iv)/dv = v * di/dv + i + grad = grad_i / grad_v # di/dv + grad_p = v * grad + i # dp/dv + grad2i = -g_diode / nNsVth # d2i/dvd + grad2v = -grad2i * resistance_series # d2v/dvd + grad2p = ( + grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + + grad_i + ) # d2p/dv/dvd + retval += (grad_i, grad_v, grad, grad_p, grad2p) + return retval + + +def bishop88_i_from_v(voltage, photocurrent, saturation_current, + resistance_series, resistance_shunt, nNsVth, + method='newton'): + """ + Find current given any voltage. + + Parameters + ---------- + voltage : numeric + voltage (V) in volts [V] + photocurrent : numeric + photogenerated current (Iph or IL) in amperes [A] + saturation_current : numeric + diode dark or saturation current (Io or Isat) in amperes [A] + resistance_series : numeric + series resistance (Rs) in ohms + resistance_shunt : numeric + shunt resistance (Rsh) in ohms + nNsVth : numeric + product of diode ideality factor (n), number of series cells (Ns), and + thermal voltage (Vth = k_b * T / q_e) in volts [V] + method : str + one of two optional search methods: either ``'brentq'``, a reliable and + bounded method or ``'newton'`` which is the default. + + Returns + ------- + current : numeric + current (I) at the specified voltage (V) in amperes [A] + """ + # collect args + args = (photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth) + + def fv(x, v, *a): + # calculate voltage residual given diode voltage "x" + return bishop88(x, *a)[1] - v + + if method.lower() == 'brentq': + if brentq is NotImplemented: + raise ImportError('This function requires scipy') + # first bound the search using voc + voc_est = estimate_voc(photocurrent, saturation_current, nNsVth) + + # brentq only works with scalar inputs, so we need a set up function + # and np.vectorize to repeatedly call the optimizer with the right + # arguments for possible array input + def vd_from_brent(voc, v, iph, isat, rs, rsh, gamma): + return brentq(fv, 0.0, voc, args=(v, iph, isat, rs, rsh, gamma)) + + vd_from_brent_vectorized = np.vectorize(vd_from_brent) + vd = vd_from_brent_vectorized(voc_est, voltage, *args) + elif method.lower() == 'newton': + # make sure all args are numpy arrays if max size > 1 + # if voltage is an array, then make a copy to use for initial guess, v0 + args, v0 = _prepare_newton_inputs((voltage,), args, voltage) + vd = newton(func=lambda x, *a: fv(x, voltage, *a), x0=v0, + fprime=lambda x, *a: bishop88(x, *a, gradients=True)[4], + args=args) + else: + raise NotImplementedError("Method '%s' isn't implemented" % method) + return bishop88(vd, *args)[0] + + +def bishop88_v_from_i(current, photocurrent, saturation_current, + resistance_series, resistance_shunt, nNsVth, + method='newton'): + """ + Find voltage given any current. + + Parameters + ---------- + current : numeric + current (I) in amperes [A] + photocurrent : numeric + photogenerated current (Iph or IL) in amperes [A] + saturation_current : numeric + diode dark or saturation current (Io or Isat) in amperes [A] + resistance_series : numeric + series resistance (Rs) in ohms + resistance_shunt : numeric + shunt resistance (Rsh) in ohms + nNsVth : numeric + product of diode ideality factor (n), number of series cells (Ns), and + thermal voltage (Vth = k_b * T / q_e) in volts [V] + method : str + one of two optional search methods: either ``'brentq'``, a reliable and + bounded method or ``'newton'`` which is the default. + + Returns + ------- + voltage : numeric + voltage (V) at the specified current (I) in volts [V] + """ + # collect args + args = (photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth) + # first bound the search using voc + voc_est = estimate_voc(photocurrent, saturation_current, nNsVth) + + def fi(x, i, *a): + # calculate current residual given diode voltage "x" + return bishop88(x, *a)[0] - i + + if method.lower() == 'brentq': + if brentq is NotImplemented: + raise ImportError('This function requires scipy') + + # brentq only works with scalar inputs, so we need a set up function + # and np.vectorize to repeatedly call the optimizer with the right + # arguments for possible array input + def vd_from_brent(voc, i, iph, isat, rs, rsh, gamma): + return brentq(fi, 0.0, voc, args=(i, iph, isat, rs, rsh, gamma)) + + vd_from_brent_vectorized = np.vectorize(vd_from_brent) + vd = vd_from_brent_vectorized(voc_est, current, *args) + elif method.lower() == 'newton': + # make sure all args are numpy arrays if max size > 1 + # if voc_est is an array, then make a copy to use for initial guess, v0 + args, v0 = _prepare_newton_inputs((current,), args, voc_est) + vd = newton(func=lambda x, *a: fi(x, current, *a), x0=v0, + fprime=lambda x, *a: bishop88(x, *a, gradients=True)[3], + args=args) + else: + raise NotImplementedError("Method '%s' isn't implemented" % method) + return bishop88(vd, *args)[1] + + +def bishop88_mpp(photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth, method='newton'): + """ + Find max power point. + + Parameters + ---------- + photocurrent : numeric + photogenerated current (Iph or IL) in amperes [A] + saturation_current : numeric + diode dark or saturation current (Io or Isat) in amperes [A] + resistance_series : numeric + series resistance (Rs) in ohms + resistance_shunt : numeric + shunt resistance (Rsh) in ohms + nNsVth : numeric + product of diode ideality factor (n), number of series cells (Ns), and + thermal voltage (Vth = k_b * T / q_e) in volts [V] + method : str + one of two optional search methods: either ``'brentq'``, a reliable and + bounded method or ``'newton'`` which is the default. + + Returns + ------- + OrderedDict or pandas.DataFrame + max power current ``i_mp`` [A], max power voltage ``v_mp`` [V], and + max power ``p_mp`` [W] + """ + # collect args + args = (photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth) + # first bound the search using voc + voc_est = estimate_voc(photocurrent, saturation_current, nNsVth) + + def fmpp(x, *a): + return bishop88(x, *a, gradients=True)[6] + + if method.lower() == 'brentq': + if brentq is NotImplemented: + raise ImportError('This function requires scipy') + # break out arguments for numpy.vectorize to handle broadcasting + vec_fun = np.vectorize( + lambda voc, iph, isat, rs, rsh, gamma: + brentq(fmpp, 0.0, voc, args=(iph, isat, rs, rsh, gamma)) + ) + vd = vec_fun(voc_est, *args) + elif method.lower() == 'newton': + # make sure all args are numpy arrays if max size > 1 + # if voc_est is an array, then make a copy to use for initial guess, v0 + args, v0 = _prepare_newton_inputs((), args, voc_est) + vd = newton( + func=fmpp, x0=v0, + fprime=lambda x, *a: bishop88(x, *a, gradients=True)[7], args=args + ) + else: + raise NotImplementedError("Method '%s' isn't implemented" % method) + return bishop88(vd, *args) + + +def _get_size_and_shape(args): + # find the right size and shape for returns + size, shape = 0, None # 0 or None both mean scalar + for arg in args: + try: + this_shape = arg.shape # try to get shape + except AttributeError: + this_shape = None + try: + this_size = len(arg) # try to get the size + except TypeError: + this_size = 0 + else: + this_size = arg.size # if it has shape then it also has size + if shape is None: + shape = this_shape # set the shape if None + # update size and shape + if this_size > size: + size = this_size + if this_shape is not None: + shape = this_shape + return size, shape + + +def _prepare_newton_inputs(i_or_v_tup, args, v0): + # broadcast arguments for newton method + # the first argument should be a tuple, eg: (i,), (v,) or () + size, shape = _get_size_and_shape(i_or_v_tup + args) + if size > 1: + args = [np.asarray(arg) for arg in args] + # newton uses initial guess for the output shape + # copy v0 to a new array and broadcast it to the shape of max size + if shape is not None: + v0 = np.broadcast_to(v0, shape).copy() + return args, v0 + + +def _lambertw_v_from_i(resistance_shunt, resistance_series, nNsVth, current, + saturation_current, photocurrent): + try: + from scipy.special import lambertw + except ImportError: + raise ImportError('This function requires scipy') + + # Record if inputs were all scalar + output_is_scalar = all(map(np.isscalar, + [resistance_shunt, resistance_series, nNsVth, + current, saturation_current, photocurrent])) + + # This transforms Gsh=1/Rsh, including ideal Rsh=np.inf into Gsh=0., which + # is generally more numerically stable + conductance_shunt = 1. / resistance_shunt + + # Ensure that we are working with read-only views of numpy arrays + # Turns Series into arrays so that we don't have to worry about + # multidimensional broadcasting failing + Gsh, Rs, a, I, I0, IL = \ + np.broadcast_arrays(conductance_shunt, resistance_series, nNsVth, + current, saturation_current, photocurrent) + + # Intitalize output V (I might not be float64) + V = np.full_like(I, np.nan, dtype=np.float64) + + # Determine indices where 0 < Gsh requires implicit model solution + idx_p = 0. < Gsh + + # Determine indices where 0 = Gsh allows explicit model solution + idx_z = 0. == Gsh + + # Explicit solutions where Gsh=0 + if np.any(idx_z): + V[idx_z] = a[idx_z] * np.log1p((IL[idx_z] - I[idx_z]) / I0[idx_z]) - \ + I[idx_z] * Rs[idx_z] + + # Only compute using LambertW if there are cases with Gsh>0 + if np.any(idx_p): + # LambertW argument, cannot be float128, may overflow to np.inf + # overflow is explicitly handled below, so ignore warnings here + with np.errstate(over='ignore'): + argW = (I0[idx_p] / (Gsh[idx_p] * a[idx_p]) * + np.exp((-I[idx_p] + IL[idx_p] + I0[idx_p]) / + (Gsh[idx_p] * a[idx_p]))) + + # lambertw typically returns complex value with zero imaginary part + # may overflow to np.inf + lambertwterm = lambertw(argW).real + + # Record indices where lambertw input overflowed output + idx_inf = np.logical_not(np.isfinite(lambertwterm)) + + # Only re-compute LambertW if it overflowed + if np.any(idx_inf): + # Calculate using log(argW) in case argW is really big + logargW = (np.log(I0[idx_p]) - np.log(Gsh[idx_p]) - + np.log(a[idx_p]) + + (-I[idx_p] + IL[idx_p] + I0[idx_p]) / + (Gsh[idx_p] * a[idx_p]))[idx_inf] + + # Three iterations of Newton-Raphson method to solve + # w+log(w)=logargW. The initial guess is w=logargW. Where direct + # evaluation (above) results in NaN from overflow, 3 iterations + # of Newton's method gives approximately 8 digits of precision. + w = logargW + for _ in range(0, 3): + w = w * (1. - np.log(w) + logargW) / (1. + w) + lambertwterm[idx_inf] = w + + # Eqn. 3 in Jain and Kapoor, 2004 + # V = -I*(Rs + Rsh) + IL*Rsh - a*lambertwterm + I0*Rsh + # Recast in terms of Gsh=1/Rsh for better numerical stability. + V[idx_p] = (IL[idx_p] + I0[idx_p] - I[idx_p]) / Gsh[idx_p] - \ + I[idx_p] * Rs[idx_p] - a[idx_p] * lambertwterm + + if output_is_scalar: + return np.asscalar(V) + else: + return V + + +def _lambertw_i_from_v(resistance_shunt, resistance_series, nNsVth, voltage, + saturation_current, photocurrent): + try: + from scipy.special import lambertw + except ImportError: + raise ImportError('This function requires scipy') + + # Record if inputs were all scalar + output_is_scalar = all(map(np.isscalar, + [resistance_shunt, resistance_series, nNsVth, + voltage, saturation_current, photocurrent])) + + # This transforms Gsh=1/Rsh, including ideal Rsh=np.inf into Gsh=0., which + # is generally more numerically stable + conductance_shunt = 1. / resistance_shunt + + # Ensure that we are working with read-only views of numpy arrays + # Turns Series into arrays so that we don't have to worry about + # multidimensional broadcasting failing + Gsh, Rs, a, V, I0, IL = \ + np.broadcast_arrays(conductance_shunt, resistance_series, nNsVth, + voltage, saturation_current, photocurrent) + + # Intitalize output I (V might not be float64) + I = np.full_like(V, np.nan, dtype=np.float64) + + # Determine indices where 0 < Rs requires implicit model solution + idx_p = 0. < Rs + + # Determine indices where 0 = Rs allows explicit model solution + idx_z = 0. == Rs + + # Explicit solutions where Rs=0 + if np.any(idx_z): + I[idx_z] = IL[idx_z] - I0[idx_z] * np.expm1(V[idx_z] / a[idx_z]) - \ + Gsh[idx_z] * V[idx_z] + + # Only compute using LambertW if there are cases with Rs>0 + # Does NOT handle possibility of overflow, github issue 298 + if np.any(idx_p): + # LambertW argument, cannot be float128, may overflow to np.inf + argW = Rs[idx_p] * I0[idx_p] / ( + a[idx_p] * (Rs[idx_p] * Gsh[idx_p] + 1.)) * \ + np.exp((Rs[idx_p] * (IL[idx_p] + I0[idx_p]) + V[idx_p]) / + (a[idx_p] * (Rs[idx_p] * Gsh[idx_p] + 1.))) + + # lambertw typically returns complex value with zero imaginary part + # may overflow to np.inf + lambertwterm = lambertw(argW).real + + # Eqn. 2 in Jain and Kapoor, 2004 + # I = -V/(Rs + Rsh) - (a/Rs)*lambertwterm + Rsh*(IL + I0)/(Rs + Rsh) + # Recast in terms of Gsh=1/Rsh for better numerical stability. + I[idx_p] = (IL[idx_p] + I0[idx_p] - V[idx_p] * Gsh[idx_p]) / \ + (Rs[idx_p] * Gsh[idx_p] + 1.) - ( + a[idx_p] / Rs[idx_p]) * lambertwterm + + if output_is_scalar: + return np.asscalar(I) + else: + return I + + +def _lambertw(photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth, ivcurve_pnts=None): + # Compute short circuit current + i_sc = _lambertw_i_from_v(resistance_shunt, resistance_series, nNsVth, 0., + saturation_current, photocurrent) + + # Compute open circuit voltage + v_oc = _lambertw_v_from_i(resistance_shunt, resistance_series, nNsVth, 0., + saturation_current, photocurrent) + + params = {'r_sh': resistance_shunt, + 'r_s': resistance_series, + 'nNsVth': nNsVth, + 'i_0': saturation_current, + 'i_l': photocurrent} + + # Find the voltage, v_mp, where the power is maximized. + # Start the golden section search at v_oc * 1.14 + p_mp, v_mp = _golden_sect_DataFrame(params, 0., v_oc * 1.14, + _pwr_optfcn) + + # Find Imp using Lambert W + i_mp = _lambertw_i_from_v(resistance_shunt, resistance_series, nNsVth, + v_mp, saturation_current, photocurrent) + + # Find Ix and Ixx using Lambert W + i_x = _lambertw_i_from_v(resistance_shunt, resistance_series, nNsVth, + 0.5 * v_oc, saturation_current, photocurrent) + + i_xx = _lambertw_i_from_v(resistance_shunt, resistance_series, nNsVth, + 0.5 * (v_oc + v_mp), saturation_current, + photocurrent) + + out = (i_sc, v_oc, i_mp, v_mp, p_mp, i_x, i_xx) + + # create ivcurve + if ivcurve_pnts: + ivcurve_v = (np.asarray(v_oc)[..., np.newaxis] * + np.linspace(0, 1, ivcurve_pnts)) + + ivcurve_i = _lambertw_i_from_v(resistance_shunt, resistance_series, + nNsVth, ivcurve_v.T, saturation_current, + photocurrent).T + + out += (ivcurve_i, ivcurve_v) + + return out + + +def _pwr_optfcn(df, loc): + ''' + Function to find power from ``i_from_v``. + ''' + + I = _lambertw_i_from_v(df['r_sh'], df['r_s'], df['nNsVth'], df[loc], + df['i_0'], df['i_l']) + + return I * df[loc] diff --git a/pvlib/test/test_modelchain.py b/pvlib/test/test_modelchain.py index 405db3e8c5..225063d37b 100644 --- a/pvlib/test/test_modelchain.py +++ b/pvlib/test/test_modelchain.py @@ -211,6 +211,23 @@ def test_dc_models(system, cec_dc_snl_ac_system, pvwatts_dc_pvwatts_ac_system, assert_series_equal(ac, expected, check_less_precise=2) +@requires_scipy +@pytest.mark.parametrize('dc_model', ['sapm', 'singlediode', 'pvwatts_dc']) +def test_infer_dc_model(system, cec_dc_snl_ac_system, + pvwatts_dc_pvwatts_ac_system, location, dc_model, + mocker): + dc_systems = {'sapm': system, 'singlediode': cec_dc_snl_ac_system, + 'pvwatts_dc': pvwatts_dc_pvwatts_ac_system} + system = dc_systems[dc_model] + m = mocker.spy(system, dc_model) + mc = ModelChain(system, location, + aoi_model='no_loss', spectral_model='no_loss') + times = pd.date_range('20160101 1200-0700', periods=2, freq='6H') + mc.run_model(times) + assert m.call_count == 1 + assert isinstance(mc.dc, (pd.Series, pd.DataFrame)) + + def acdc(mc): mc.ac = mc.dc @@ -446,21 +463,6 @@ def test_ModelChain___repr__(system, location, strategy, strategy_str): assert mc.__repr__() == expected -@requires_scipy -def test_weather_irradiance_input(system, location): - """Test will raise a warning and should be removed in future versions.""" - mc = ModelChain(system, location) - times = pd.date_range('2012-06-01 12:00:00', periods=2, freq='H') - i = pd.DataFrame({'dni': [2, 3], 'dhi': [4, 6], 'ghi': [9, 5]}, index=times) - w = pd.DataFrame({'wind_speed': [11, 5], 'temp_air': [30, 32]}, index=times) - mc.run_model(times, irradiance=i, weather=w) - - assert_series_equal(mc.weather['dni'], - pd.Series([2, 3], index=times, name='dni')) - assert_series_equal(mc.weather['wind_speed'], - pd.Series([11, 5], index=times, name='wind_speed')) - - @requires_scipy def test_complete_irradiance_clean_run(system, location): """The DataFrame should not change if all columns are passed""" diff --git a/pvlib/test/test_numerical_precision.py b/pvlib/test/test_numerical_precision.py new file mode 100644 index 0000000000..8d4ebd3d7d --- /dev/null +++ b/pvlib/test/test_numerical_precision.py @@ -0,0 +1,122 @@ +""" +Test numerical precision of explicit single diode calculation using symbolic +mathematics. SymPy is a computer algebra system, that uses infinite precision +symbols instead of standard floating point and integer computer number types. +http://docs.sympy.org/latest/modules/evalf.html#accuracy-and-error-handling + +This module can be executed from the command line to generate a high precision +dataset of I-V curve points to test the explicit single diode calculations +:func:`pvlib.singlediode_methods.bishop88`:: + + $ python test_numeric_precision.py + +This generates a file in the pvlib data folder, which is specified by the +constant ``DATA_PATH``. When the test is run using ``pytest`` it will compare +the values calculated by :func:`pvlib.singlediode_methods.bishop88` with the +high-precision values generated with SymPy. +""" + +import logging +import os +import numpy as np +import pandas as pd +from pvlib import pvsystem +from pvlib.singlediode_methods import bishop88, estimate_voc + +logging.basicConfig() +LOGGER = logging.getLogger(__name__) +LOGGER.setLevel(logging.DEBUG) +TEST_DATA = 'bishop88_numerical_precision.csv' +TEST_PATH = os.path.dirname(os.path.abspath(__file__)) +PVLIB_PATH = os.path.dirname(TEST_PATH) +DATA_PATH = os.path.join(PVLIB_PATH, 'data', TEST_DATA) +POA = 888 +TCELL = 55 +CECMOD = pvsystem.retrieve_sam('cecmod') +# get module from cecmod and apply temp/irrad desoto corrections +SPR_E20_327 = CECMOD.SunPower_SPR_E20_327 +ARGS = pvsystem.calcparams_desoto( + effective_irradiance=POA, temp_cell=TCELL, + alpha_sc=SPR_E20_327.alpha_sc, a_ref=SPR_E20_327.a_ref, + I_L_ref=SPR_E20_327.I_L_ref, I_o_ref=SPR_E20_327.I_o_ref, + R_sh_ref=SPR_E20_327.R_sh_ref, R_s=SPR_E20_327.R_s, + EgRef=1.121, dEgdT=-0.0002677 +) +IL, I0, RS, RSH, NNSVTH = ARGS +IVCURVE_NPTS = 100 + +try: + from sympy import symbols, exp as sy_exp +except ImportError as exc: + LOGGER.exception(exc) + symbols = NotImplemented + sy_exp = NotImplemented + + +def generate_numerical_precision(): + """ + Generate expected data with infinite numerical precision using SymPy. + :return: dataframe of expected values + """ + if symbols is NotImplemented: + LOGGER.critical("SymPy is required to generate expected data.") + raise ImportError("could not import sympy") + il, io, rs, rsh, nnsvt, vd = symbols('il, io, rs, rsh, nnsvt, vd') + a = sy_exp(vd / nnsvt) + b = 1.0 / rsh + i = il - io * (a - 1.0) - vd * b + v = vd - i * rs + c = io * a / nnsvt + grad_i = - c - b # di/dvd + grad_v = 1.0 - grad_i * rs # dv/dvd + # dp/dv = d(iv)/dv = v * di/dv + i + grad = grad_i / grad_v # di/dv + p = i * v + grad_p = v * grad + i # dp/dv + grad2i = -c / nnsvt + grad2v = -grad2i * rs + grad2p = ( + grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + grad_i + ) + # generate exact values + data = dict(zip((il, io, rs, rsh, nnsvt), ARGS)) + vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS) + expected = [] + for test in vdtest: + data[vd] = test + test_data = { + 'i': np.float64(i.evalf(subs=data)), + 'v': np.float64(v.evalf(subs=data)), + 'p': np.float64(p.evalf(subs=data)), + 'grad_i': np.float64(grad_i.evalf(subs=data)), + 'grad_v': np.float64(grad_v.evalf(subs=data)), + 'grad': np.float64(grad.evalf(subs=data)), + 'grad_p': np.float64(grad_p.evalf(subs=data)), + 'grad2p': np.float64(grad2p.evalf(subs=data)) + } + LOGGER.debug(test_data) + expected.append(test_data) + return pd.DataFrame(expected, index=vdtest) + + +def test_numerical_precision(): + """ + Test that there are no numerical errors due to floating point arithmetic. + """ + expected = pd.read_csv(DATA_PATH) + vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS) + results = bishop88(vdtest, *ARGS, gradients=True) + assert np.allclose(expected['i'], results[0]) + assert np.allclose(expected['v'], results[1]) + assert np.allclose(expected['p'], results[2]) + assert np.allclose(expected['grad_i'], results[3]) + assert np.allclose(expected['grad_v'], results[4]) + assert np.allclose(expected['grad'], results[5]) + assert np.allclose(expected['grad_p'], results[6]) + assert np.allclose(expected['grad2p'], results[7]) + + +if __name__ == '__main__': + expected = generate_numerical_precision() + expected.to_csv(DATA_PATH) + test_numerical_precision() diff --git a/pvlib/test/test_pvsystem.py b/pvlib/test/test_pvsystem.py index 90d379370b..729a654ccc 100644 --- a/pvlib/test/test_pvsystem.py +++ b/pvlib/test/test_pvsystem.py @@ -21,38 +21,11 @@ from conftest import needs_numpy_1_10, requires_scipy -latitude = 32.2 -longitude = -111 -tus = Location(latitude, longitude, 'US/Arizona', 700, 'Tucson') -times = pd.date_range(start=datetime.datetime(2014,1,1), - end=datetime.datetime(2014,1,2), freq='1Min') -ephem_data = solarposition.get_solarposition(times, - latitude=latitude, - longitude=longitude, - method='nrel_numpy') -am = atmosphere.relativeairmass(ephem_data.apparent_zenith) -irrad_data = clearsky.ineichen(ephem_data['apparent_zenith'], am, - linke_turbidity=3) -aoi = irradiance.aoi(0, 0, ephem_data['apparent_zenith'], - ephem_data['azimuth']) - - -meta = {'latitude': 37.8, - 'longitude': -122.3, - 'altitude': 10, - 'Name': 'Oakland', - 'State': 'CA', - 'TZ': -8} - -pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(tmy))) - -tmy3_testfile = os.path.join(pvlib_abspath, 'data', '703165TY.csv') -tmy2_testfile = os.path.join(pvlib_abspath, 'data', '12839.tm2') - -tmy3_data, tmy3_metadata = tmy.readtmy3(tmy3_testfile) -tmy2_data, tmy2_metadata = tmy.readtmy2(tmy2_testfile) def test_systemdef_tmy3(): + pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(tmy))) + tmy3_testfile = os.path.join(pvlib_abspath, 'data', '703165TY.csv') + tmy3_data, tmy3_metadata = tmy.readtmy3(tmy3_testfile) expected = {'tz': -9.0, 'albedo': 0.1, 'altitude': 7.0, @@ -65,7 +38,12 @@ def test_systemdef_tmy3(): 'surface_tilt': 0} assert expected == pvsystem.systemdef(tmy3_metadata, 0, 0, .1, 5, 5) + def test_systemdef_tmy2(): + pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(tmy))) + tmy2_testfile = os.path.join(pvlib_abspath, 'data', '12839.tm2') + tmy2_data, tmy2_metadata = tmy.readtmy2(tmy2_testfile) + expected = {'tz': -5, 'albedo': 0.1, 'altitude': 2.0, @@ -78,8 +56,17 @@ def test_systemdef_tmy2(): 'surface_tilt': 0} assert expected == pvsystem.systemdef(tmy2_metadata, 0, 0, .1, 5, 5) + def test_systemdef_dict(): - expected = {'tz': -8, ## Note that TZ is float, but Location sets tz as string + meta = {'latitude': 37.8, + 'longitude': -122.3, + 'altitude': 10, + 'Name': 'Oakland', + 'State': 'CA', + 'TZ': -8} + + # Note that TZ is float, but Location sets tz as string + expected = {'tz': -8, 'albedo': 0.1, 'altitude': 10, 'latitude': 37.8, @@ -113,15 +100,14 @@ def test_ashraeiam_scalar(): assert_allclose(iam, expected, equal_nan=True) -@needs_numpy_1_10 -def test_PVSystem_ashraeiam(): +def test_PVSystem_ashraeiam(mocker): + mocker.spy(pvsystem, 'ashraeiam') module_parameters = pd.Series({'b': 0.05}) system = pvsystem.PVSystem(module_parameters=module_parameters) - thetas = np.array([-90. , -67.5, -45. , -22.5, 0. , 22.5, 45. , 67.5, 89., 90. , np.nan]) + thetas = 1 iam = system.ashraeiam(thetas) - expected = np.array([ 0, 0.9193437 , 0.97928932, 0.99588039, 1. , - 0.99588039, 0.97928932, 0.9193437 , 0, 0, np.nan]) - assert_allclose(iam, expected, equal_nan=True) + pvsystem.ashraeiam.assert_called_once_with(thetas, b=0.05) + assert iam < 1. @needs_numpy_1_10 @@ -151,15 +137,14 @@ def test_physicaliam_scalar(): assert_allclose(iam, expected, equal_nan=True) -@needs_numpy_1_10 -def test_PVSystem_physicaliam(): +def test_PVSystem_physicaliam(mocker): module_parameters = pd.Series({'K': 4, 'L': 0.002, 'n': 1.526}) system = pvsystem.PVSystem(module_parameters=module_parameters) - thetas = np.array([-90. , -67.5, -45. , -22.5, 0. , 22.5, 45. , 67.5, 90. , np.nan]) + mocker.spy(pvsystem, 'physicaliam') + thetas = 1 iam = system.physicaliam(thetas) - expected = np.array([ 0, 0.8893998 , 0.98797788, 0.99926198, 1, - 0.99926198, 0.98797788, 0.8893998 , 0, np.nan]) - assert_allclose(iam, expected, equal_nan=True) + pvsystem.physicaliam.assert_called_once_with(thetas, **module_parameters) + assert iam < 1. # if this completes successfully we'll be able to do more tests below. @@ -189,6 +174,23 @@ def cec_module_params(sam_data): return module_parameters +@pytest.fixture() +def pvsyst_module_params(): + module_parameters = {} + module_parameters['gamma_ref'] = 1.05 + module_parameters['mu_gamma'] = 0.001 + module_parameters['I_L_ref'] = 6.0 + module_parameters['I_o_ref'] = 5e-9 + module_parameters['EgRef'] = 1.121 + module_parameters['R_sh_ref'] = 300 + module_parameters['R_sh_0'] = 1000 + module_parameters['R_s'] = 0.5 + module_parameters['R_sh_exp'] = 5.5 + module_parameters['cells_in_series'] = 60 + module_parameters['alpha_sc'] = 0.001 + return module_parameters + + def test_sapm(sapm_module_params): times = pd.DatetimeIndex(start='2015-01-01', periods=5, freq='12H') @@ -232,14 +234,15 @@ def test_sapm(sapm_module_params): sapm_module_params.to_dict()) -def test_PVSystem_sapm(sapm_module_params): +def test_PVSystem_sapm(sapm_module_params, mocker): + mocker.spy(pvsystem, 'sapm') system = pvsystem.PVSystem(module_parameters=sapm_module_params) - - times = pd.DatetimeIndex(start='2015-01-01', periods=5, freq='12H') - effective_irradiance = pd.Series([-1, 0.5, 1.1, np.nan, 1], index=times) - temp_cell = pd.Series([10, 25, 50, 25, np.nan], index=times) - + effective_irradiance = 0.5 + temp_cell = 25 out = system.sapm(effective_irradiance, temp_cell) + pvsystem.sapm.assert_called_once_with(effective_irradiance, temp_cell, + sapm_module_params) + assert_allclose(out['p_mp'], 100, atol=100) @pytest.mark.parametrize('airmass,expected', [ @@ -257,23 +260,37 @@ def test_sapm_spectral_loss(sapm_module_params, airmass, expected): assert_allclose(out, expected, atol=1e-4) -def test_PVSystem_sapm_spectral_loss(sapm_module_params): +def test_PVSystem_sapm_spectral_loss(sapm_module_params, mocker): + mocker.spy(pvsystem, 'sapm_spectral_loss') system = pvsystem.PVSystem(module_parameters=sapm_module_params) - - times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H') - airmass = pd.Series([1, 10], index=times) - + airmass = 2 out = system.sapm_spectral_loss(airmass) - - -@pytest.mark.parametrize("expected", [1.03173953]) -def test_PVSystem_first_solar_spectral_loss(sapm_module_params, expected): - system = pvsystem.PVSystem(module_parameters=sapm_module_params) + pvsystem.sapm_spectral_loss.assert_called_once_with(airmass, + sapm_module_params) + assert_allclose(out, 1, atol=0.5) + + +# this test could be improved to cover all cell types. +# could remove the need for specifying spectral coefficients if we don't +# care about the return value at all +@pytest.mark.parametrize('module_parameters,module_type,coefficients', [ + ({'Technology': 'mc-Si'}, 'multisi', None), + ({'Material': 'Multi-c-Si'}, 'multisi', None), + ({'first_solar_spectral_coefficients': ( + 0.84, -0.03, -0.008, 0.14, 0.04, -0.002)}, + None, + (0.84, -0.03, -0.008, 0.14, 0.04, -0.002)) + ]) +def test_PVSystem_first_solar_spectral_loss(module_parameters, module_type, + coefficients, mocker): + mocker.spy(atmosphere, 'first_solar_spectral_correction') + system = pvsystem.PVSystem(module_parameters=module_parameters) pw = 3 airmass_absolute = 3 out = system.first_solar_spectral_loss(pw, airmass_absolute) - - assert_allclose(out, expected, atol=1e-4) + atmosphere.first_solar_spectral_correction.assert_called_once_with( + pw, airmass_absolute, module_type, coefficients) + assert_allclose(out, 1, atol=0.5) @pytest.mark.parametrize('aoi,expected', [ @@ -303,13 +320,13 @@ def test_sapm_aoi_loss_limits(): assert pvsystem.sapm_aoi_loss(1, module_parameters) == 0 -def test_PVSystem_sapm_aoi_loss(sapm_module_params): +def test_PVSystem_sapm_aoi_loss(sapm_module_params, mocker): system = pvsystem.PVSystem(module_parameters=sapm_module_params) - - times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H') - aoi = pd.Series([45, 10], index=times) - + mocker.spy(pvsystem, 'sapm_aoi_loss') + aoi = 0 out = system.sapm_aoi_loss(aoi) + pvsystem.sapm_aoi_loss.assert_called_once_with(aoi, sapm_module_params) + assert_allclose(out, 1.0, atol=0.01) @pytest.mark.parametrize('test_input,expected', [ @@ -342,34 +359,39 @@ def test_sapm_effective_irradiance(sapm_module_params, test_input, expected): assert_allclose(out, expected, atol=1e-4) -def test_PVSystem_sapm_effective_irradiance(sapm_module_params): +def test_PVSystem_sapm_effective_irradiance(sapm_module_params, mocker): system = pvsystem.PVSystem(module_parameters=sapm_module_params) + mocker.spy(pvsystem, 'sapm_effective_irradiance') - poa_direct = np.array([np.nan, 1000, 1000]) - poa_diffuse = np.array([100, np.nan, 100]) - airmass_absolute = np.array([1.1, 1.1, 1.1]) - aoi = np.array([10, 10, 10]) + poa_direct = 900 + poa_diffuse = 100 + airmass_absolute = 1.5 + aoi = 0 reference_irradiance = 1000 out = system.sapm_effective_irradiance( poa_direct, poa_diffuse, airmass_absolute, aoi, reference_irradiance=reference_irradiance) + pvsystem.sapm_effective_irradiance.assert_called_once_with( + poa_direct, poa_diffuse, airmass_absolute, aoi, sapm_module_params, + reference_irradiance=reference_irradiance) + assert_allclose(out, 1, atol=0.1) def test_calcparams_desoto(cec_module_params): times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H') - effective_irradiance = pd.Series([0, 800], index=times) + effective_irradiance = pd.Series([0.0, 800.0], index=times) temp_cell = pd.Series([25, 25], index=times) IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_desoto( effective_irradiance, temp_cell, - alpha_isc=cec_module_params['alpha_sc'], + alpha_sc=cec_module_params['alpha_sc'], a_ref=cec_module_params['a_ref'], I_L_ref=cec_module_params['I_L_ref'], I_o_ref=cec_module_params['I_o_ref'], R_sh_ref=cec_module_params['R_sh_ref'], - Rs=cec_module_params['R_s'], + R_s=cec_module_params['R_s'], EgRef=1.121, dEgdT=-0.0002677) @@ -381,24 +403,91 @@ def test_calcparams_desoto(cec_module_params): assert_allclose(nNsVth, 0.473) -def test_PVSystem_calcparams_desoto(cec_module_params): +def test_calcparams_pvsyst(pvsyst_module_params): + times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H') + effective_irradiance = pd.Series([0.0, 800.0], index=times) + temp_cell = pd.Series([25, 50], index=times) + + IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_pvsyst( + effective_irradiance, + temp_cell, + alpha_sc=pvsyst_module_params['alpha_sc'], + gamma_ref=pvsyst_module_params['gamma_ref'], + mu_gamma=pvsyst_module_params['mu_gamma'], + I_L_ref=pvsyst_module_params['I_L_ref'], + I_o_ref=pvsyst_module_params['I_o_ref'], + R_sh_ref=pvsyst_module_params['R_sh_ref'], + R_sh_0=pvsyst_module_params['R_sh_0'], + R_s=pvsyst_module_params['R_s'], + cells_in_series=pvsyst_module_params['cells_in_series'], + EgRef=pvsyst_module_params['EgRef']) + + assert_series_equal(np.round(IL, 3), pd.Series([0.0, 4.8200], index=times)) + assert_series_equal(np.round(I0, 3), + pd.Series([0.0, 1.47e-7], index=times)) + assert_allclose(Rs, 0.500) + assert_series_equal(np.round(Rsh, 3), + pd.Series([1000.0, 305.757], index=times)) + assert_series_equal(np.round(nNsVth, 4), + pd.Series([1.6186, 1.7961], index=times)) + + +def test_PVSystem_calcparams_desoto(cec_module_params, mocker): + mocker.spy(pvsystem, 'calcparams_desoto') module_parameters = cec_module_params.copy() module_parameters['EgRef'] = 1.121 module_parameters['dEgdT'] = -0.0002677 system = pvsystem.PVSystem(module_parameters=module_parameters) - times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H') - effective_irradiance = pd.Series([0, 800], index=times) + effective_irradiance = np.array([0, 800]) temp_cell = 25 - IL, I0, Rs, Rsh, nNsVth = system.calcparams_desoto(effective_irradiance, temp_cell) - - assert_series_equal(np.round(IL, 3), pd.Series([0.0, 6.036], index=times)) - # changed value in GH 444 for 2017-6-5 module file - assert_allclose(I0, 1.94e-9) - assert_allclose(Rs, 0.094) - assert_series_equal(np.round(Rsh, 3), pd.Series([np.inf, 19.65], index=times)) - assert_allclose(nNsVth, 0.473) + pvsystem.calcparams_desoto.assert_called_once_with( + effective_irradiance, + temp_cell, + alpha_sc=cec_module_params['alpha_sc'], + a_ref=cec_module_params['a_ref'], + I_L_ref=cec_module_params['I_L_ref'], + I_o_ref=cec_module_params['I_o_ref'], + R_sh_ref=cec_module_params['R_sh_ref'], + R_s=cec_module_params['R_s'], + EgRef=module_parameters['EgRef'], + dEgdT=module_parameters['dEgdT']) + assert_allclose(IL, np.array([0.0, 6.036]), atol=1) + assert_allclose(I0, 2.0e-9, atol=1.0e-9) + assert_allclose(Rs, 0.1, atol=0.1) + assert_allclose(Rsh, np.array([np.inf, 20]), atol=1) + assert_allclose(nNsVth, 0.5, atol=0.1) + + +def test_PVSystem_calcparams_pvsyst(pvsyst_module_params, mocker): + mocker.spy(pvsystem, 'calcparams_pvsyst') + module_parameters = pvsyst_module_params.copy() + system = pvsystem.PVSystem(module_parameters=module_parameters) + effective_irradiance = np.array([0, 800]) + temp_cell = np.array([25, 50]) + IL, I0, Rs, Rsh, nNsVth = system.calcparams_pvsyst(effective_irradiance, + temp_cell) + pvsystem.calcparams_pvsyst.assert_called_once_with( + effective_irradiance, + temp_cell, + alpha_sc=pvsyst_module_params['alpha_sc'], + gamma_ref=pvsyst_module_params['gamma_ref'], + mu_gamma=pvsyst_module_params['mu_gamma'], + I_L_ref=pvsyst_module_params['I_L_ref'], + I_o_ref=pvsyst_module_params['I_o_ref'], + R_sh_ref=pvsyst_module_params['R_sh_ref'], + R_sh_0=pvsyst_module_params['R_sh_0'], + R_s=pvsyst_module_params['R_s'], + cells_in_series=pvsyst_module_params['cells_in_series'], + EgRef=pvsyst_module_params['EgRef'], + R_sh_exp=pvsyst_module_params['R_sh_exp']) + + assert_allclose(IL, np.array([0.0, 4.8200]), atol=1) + assert_allclose(I0, np.array([0.0, 1.47e-7]), atol=1.0e-5) + assert_allclose(Rs, 0.5, atol=0.1) + assert_allclose(Rsh, np.array([1000, 305.757]), atol=50) + assert_allclose(nNsVth, np.array([1.6186, 1.7961]), atol=0.1) @pytest.fixture(params=[ @@ -519,7 +608,10 @@ def fixture_v_from_i(request): @requires_scipy -def test_v_from_i(fixture_v_from_i): +@pytest.mark.parametrize( + 'method, atol', [('lambertw', 1e-11), ('brentq', 1e-11), ('newton', 1e-8)] +) +def test_v_from_i(fixture_v_from_i, method, atol): # Solution set loaded from fixture Rsh = fixture_v_from_i['Rsh'] Rs = fixture_v_from_i['Rs'] @@ -529,10 +621,7 @@ def test_v_from_i(fixture_v_from_i): IL = fixture_v_from_i['IL'] V_expected = fixture_v_from_i['V_expected'] - # Convergence criteria - atol = 1.e-11 - - V = pvsystem.v_from_i(Rsh, Rs, nNsVth, I, I0, IL) + V = pvsystem.v_from_i(Rsh, Rs, nNsVth, I, I0, IL, method=method) assert(isinstance(V, type(V_expected))) if isinstance(V, type(np.ndarray)): assert(isinstance(V.dtype, type(V_expected.dtype))) @@ -540,43 +629,68 @@ def test_v_from_i(fixture_v_from_i): assert_allclose(V, V_expected, atol=atol) +@requires_scipy +def test_i_from_v_from_i(fixture_v_from_i): + # Solution set loaded from fixture + Rsh = fixture_v_from_i['Rsh'] + Rs = fixture_v_from_i['Rs'] + nNsVth = fixture_v_from_i['nNsVth'] + I = fixture_v_from_i['I'] + I0 = fixture_v_from_i['I0'] + IL = fixture_v_from_i['IL'] + V = fixture_v_from_i['V_expected'] + + # Convergence criteria + atol = 1.e-11 + + I_expected = pvsystem.i_from_v(Rsh, Rs, nNsVth, V, I0, IL, + method='lambertw') + assert_allclose(I, I_expected, atol=atol) + I = pvsystem.i_from_v(Rsh, Rs, nNsVth, V, I0, IL) + assert(isinstance(I, type(I_expected))) + if isinstance(I, type(np.ndarray)): + assert(isinstance(I.dtype, type(I_expected.dtype))) + assert(I.shape == I_expected.shape) + assert_allclose(I, I_expected, atol=atol) + + @pytest.fixture(params=[ { # Can handle all python scalar inputs 'Rsh': 20., 'Rs': 0.1, 'nNsVth': 0.5, - 'V': 40., + 'V': 7.5049875193450521, 'I0': 6.e-7, 'IL': 7., - 'I_expected': -299.746389916 + 'I_expected': 3. }, { # Can handle all rank-0 array inputs 'Rsh': np.array(20.), 'Rs': np.array(0.1), 'nNsVth': np.array(0.5), - 'V': np.array(40.), + 'V': np.array(7.5049875193450521), 'I0': np.array(6.e-7), 'IL': np.array(7.), - 'I_expected': np.array(-299.746389916) + 'I_expected': np.array(3.) }, { # Can handle all rank-1 singleton array inputs 'Rsh': np.array([20.]), 'Rs': np.array([0.1]), 'nNsVth': np.array([0.5]), - 'V': np.array([40.]), + 'V': np.array([7.5049875193450521]), 'I0': np.array([6.e-7]), 'IL': np.array([7.]), - 'I_expected': np.array([-299.746389916]) + 'I_expected': np.array([3.]) }, { # Can handle all rank-1 non-singleton array inputs with a zero # series resistance, Rs=0 gives I=IL=Isc at V=0 'Rsh': np.array([20., 20.]), 'Rs': np.array([0., 0.1]), 'nNsVth': np.array([0.5, 0.5]), - 'V': np.array([0., 40.]), + 'V': np.array([0., 7.5049875193450521]), 'I0': np.array([6.e-7, 6.e-7]), 'IL': np.array([7., 7.]), - 'I_expected': np.array([7., -299.746389916]) + 'I_expected': np.array([7., 3.]) }, { # Can handle mixed inputs with a rank-2 array with zero series # resistance, Rs=0 gives I=IL=Isc at V=0 @@ -604,17 +718,20 @@ def test_v_from_i(fixture_v_from_i): 'Rsh': np.inf, 'Rs': 0.1, 'nNsVth': 0.5, - 'V': 40., + 'V': 7.5049875193450521, 'I0': 6.e-7, 'IL': 7., - 'I_expected': -299.7383436645412 + 'I_expected': 3.2244873645510923 }]) def fixture_i_from_v(request): return request.param @requires_scipy -def test_i_from_v(fixture_i_from_v): +@pytest.mark.parametrize( + 'method, atol', [('lambertw', 1e-11), ('brentq', 1e-11), ('newton', 1e-11)] +) +def test_i_from_v(fixture_i_from_v, method, atol): # Solution set loaded from fixture Rsh = fixture_i_from_v['Rsh'] Rs = fixture_i_from_v['Rs'] @@ -624,10 +741,7 @@ def test_i_from_v(fixture_i_from_v): IL = fixture_i_from_v['IL'] I_expected = fixture_i_from_v['I_expected'] - # Convergence criteria - atol = 1.e-11 - - I = pvsystem.i_from_v(Rsh, Rs, nNsVth, V, I0, IL) + I = pvsystem.i_from_v(Rsh, Rs, nNsVth, V, I0, IL, method=method) assert(isinstance(I, type(I_expected))) if isinstance(I, type(np.ndarray)): assert(isinstance(I.dtype, type(I_expected.dtype))) @@ -636,23 +750,105 @@ def test_i_from_v(fixture_i_from_v): @requires_scipy -def test_PVSystem_i_from_v(): +def test_PVSystem_i_from_v(mocker): system = pvsystem.PVSystem() - output = system.i_from_v(20, .1, .5, 40, 6e-7, 7) - assert_allclose(output, -299.746389916, atol=1e-5) + m = mocker.patch('pvlib.pvsystem.i_from_v', autospec=True) + args = (20, 0.1, 0.5, 7.5049875193450521, 6e-7, 7) + system.i_from_v(*args) + m.assert_called_once_with(*args) + + +@requires_scipy +def test_i_from_v_size(): + with pytest.raises(ValueError): + pvsystem.i_from_v(20, [0.1] * 2, 0.5, [7.5] * 3, 6.0e-7, 7.0) + with pytest.raises(ValueError): + pvsystem.i_from_v(20, [0.1] * 2, 0.5, [7.5] * 3, 6.0e-7, 7.0, + method='brentq') + with pytest.raises(ValueError): + pvsystem.i_from_v(20, 0.1, 0.5, [7.5] * 3, 6.0e-7, np.array([7., 7.]), + method='newton') + + +@requires_scipy +def test_v_from_i_size(): + with pytest.raises(ValueError): + pvsystem.v_from_i(20, [0.1] * 2, 0.5, [3.0] * 3, 6.0e-7, 7.0) + with pytest.raises(ValueError): + pvsystem.v_from_i(20, [0.1] * 2, 0.5, [3.0] * 3, 6.0e-7, 7.0, + method='brentq') + with pytest.raises(ValueError): + pvsystem.v_from_i(20, [0.1], 0.5, [3.0] * 3, 6.0e-7, np.array([7., 7.]), + method='newton') + + +@requires_scipy +def test_mpp_floats(): + """test max_power_point""" + IL, I0, Rs, Rsh, nNsVth = (7, 6e-7, .1, 20, .5) + out = pvsystem.max_power_point(IL, I0, Rs, Rsh, nNsVth, method='brentq') + expected = {'i_mp': 6.1362673597376753, # 6.1390251797935704, lambertw + 'v_mp': 6.2243393757884284, # 6.221535886625464, lambertw + 'p_mp': 38.194210547580511} # 38.194165464983037} lambertw + assert isinstance(out, dict) + for k, v in out.items(): + assert np.isclose(v, expected[k]) + out = pvsystem.max_power_point(IL, I0, Rs, Rsh, nNsVth, method='newton') + for k, v in out.items(): + assert np.isclose(v, expected[k]) + + +@requires_scipy +def test_mpp_array(): + """test max_power_point""" + IL, I0, Rs, Rsh, nNsVth = (np.array([7, 7]), 6e-7, .1, 20, .5) + out = pvsystem.max_power_point(IL, I0, Rs, Rsh, nNsVth, method='brentq') + expected = {'i_mp': [6.1362673597376753] * 2, + 'v_mp': [6.2243393757884284] * 2, + 'p_mp': [38.194210547580511] * 2} + assert isinstance(out, dict) + for k, v in out.items(): + assert np.allclose(v, expected[k]) + out = pvsystem.max_power_point(IL, I0, Rs, Rsh, nNsVth, method='newton') + for k, v in out.items(): + assert np.allclose(v, expected[k]) + + +@requires_scipy +def test_mpp_series(): + """test max_power_point""" + idx = ['2008-02-17T11:30:00-0800', '2008-02-17T12:30:00-0800'] + IL, I0, Rs, Rsh, nNsVth = (np.array([7, 7]), 6e-7, .1, 20, .5) + IL = pd.Series(IL, index=idx) + out = pvsystem.max_power_point(IL, I0, Rs, Rsh, nNsVth, method='brentq') + expected = pd.DataFrame({'i_mp': [6.1362673597376753] * 2, + 'v_mp': [6.2243393757884284] * 2, + 'p_mp': [38.194210547580511] * 2}, + index=idx) + assert isinstance(out, pd.DataFrame) + for k, v in out.items(): + assert np.allclose(v, expected[k]) + out = pvsystem.max_power_point(IL, I0, Rs, Rsh, nNsVth, method='newton') + for k, v in out.items(): + assert np.allclose(v, expected[k]) @requires_scipy def test_singlediode_series(cec_module_params): times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H') - poa_data = pd.Series([0, 800], index=times) + effective_irradiance = pd.Series([0.0, 800.0], index=times) IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_desoto( - poa_data, - temp_cell=25, - alpha_isc=cec_module_params['alpha_sc'], - module_parameters=cec_module_params, - EgRef=1.121, - dEgdT=-0.0002677) + effective_irradiance, + temp_cell=25, + alpha_sc=cec_module_params['alpha_sc'], + a_ref=cec_module_params['a_ref'], + I_L_ref=cec_module_params['I_L_ref'], + I_o_ref=cec_module_params['I_o_ref'], + R_sh_ref=cec_module_params['R_sh_ref'], + R_s=cec_module_params['R_s'], + EgRef=1.121, + dEgdT=-0.0002677 + ) out = pvsystem.singlediode(IL, I0, Rs, Rsh, nNsVth) assert isinstance(out, pd.DataFrame) @@ -667,7 +863,8 @@ def test_singlediode_array(): saturation_current = 1.943e-09 sd = pvsystem.singlediode(photocurrent, saturation_current, - resistance_series, resistance_shunt, nNsVth) + resistance_series, resistance_shunt, nNsVth, + method='lambertw') expected = np.array([ 0. , 0.54538398, 1.43273966, 2.36328163, 3.29255606, @@ -676,12 +873,21 @@ def test_singlediode_array(): assert_allclose(sd['i_mp'], expected, atol=0.01) + sd = pvsystem.singlediode(photocurrent, saturation_current, + resistance_series, resistance_shunt, nNsVth) + + expected = pvsystem.i_from_v(resistance_shunt, resistance_series, nNsVth, + sd['v_mp'], saturation_current, photocurrent, + method='lambertw') + + assert_allclose(sd['i_mp'], expected, atol=0.01) + @requires_scipy def test_singlediode_floats(sam_data): module = 'Example_Module' module_parameters = sam_data['cecmod'][module] - out = pvsystem.singlediode(7, 6e-7, .1, 20, .5) + out = pvsystem.singlediode(7, 6e-7, .1, 20, .5, method='lambertw') expected = {'i_xx': 4.2498, 'i_mp': 6.1275, 'v_oc': 8.1063, @@ -701,7 +907,7 @@ def test_singlediode_floats(sam_data): @requires_scipy def test_singlediode_floats_ivcurve(): - out = pvsystem.singlediode(7, 6e-7, .1, 20, .5, ivcurve_pnts=3) + out = pvsystem.singlediode(7, 6e-7, .1, 20, .5, ivcurve_pnts=3, method='lambertw') expected = {'i_xx': 4.2498, 'i_mp': 6.1275, 'v_oc': 8.1063, @@ -719,14 +925,21 @@ def test_singlediode_floats_ivcurve(): @requires_scipy def test_singlediode_series_ivcurve(cec_module_params): times = pd.DatetimeIndex(start='2015-06-01', periods=3, freq='6H') - poa_data = pd.Series([0, 400, 800], index=times) + effective_irradiance = pd.Series([0.0, 400.0, 800.0], index=times) IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_desoto( - poa_data, temp_cell=25, - alpha_isc=cec_module_params['alpha_sc'], - module_parameters=cec_module_params, - EgRef=1.121, dEgdT=-0.0002677) + effective_irradiance, + temp_cell=25, + alpha_sc=cec_module_params['alpha_sc'], + a_ref=cec_module_params['a_ref'], + I_L_ref=cec_module_params['I_L_ref'], + I_o_ref=cec_module_params['I_o_ref'], + R_sh_ref=cec_module_params['R_sh_ref'], + R_s=cec_module_params['R_s'], + EgRef=1.121, + dEgdT=-0.0002677) - out = pvsystem.singlediode(IL, I0, Rs, Rsh, nNsVth, ivcurve_pnts=3) + out = pvsystem.singlediode(IL, I0, Rs, Rsh, nNsVth, ivcurve_pnts=3, + method='lambertw') expected = OrderedDict([('i_sc', array([0., 3.01054475, 6.00675648])), ('v_oc', array([0., 9.96886962, 10.29530483])), @@ -747,6 +960,20 @@ def test_singlediode_series_ivcurve(cec_module_params): for k, v in out.items(): assert_allclose(v, expected[k], atol=1e-2) + out = pvsystem.singlediode(IL, I0, Rs, Rsh, nNsVth, ivcurve_pnts=3) + + expected['i_mp'] = pvsystem.i_from_v(Rsh, Rs, nNsVth, out['v_mp'], I0, IL, + method='lambertw') + expected['v_mp'] = pvsystem.v_from_i(Rsh, Rs, nNsVth, out['i_mp'], I0, IL, + method='lambertw') + expected['i'] = pvsystem.i_from_v(Rsh, Rs, nNsVth, out['v'].T, I0, IL, + method='lambertw').T + expected['v'] = pvsystem.v_from_i(Rsh, Rs, nNsVth, out['i'].T, I0, IL, + method='lambertw').T + + for k, v in out.items(): + assert_allclose(v, expected[k], atol=1e-2) + def test_scale_voltage_current_power(sam_data): data = pd.DataFrame( @@ -758,19 +985,16 @@ def test_scale_voltage_current_power(sam_data): columns=['i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x', 'i_xx'], index=[0]) out = pvsystem.scale_voltage_current_power(data, voltage=2, current=3) + assert_frame_equal(out, expected, check_less_precise=5) -def test_PVSystem_scale_voltage_current_power(): - data = pd.DataFrame( - np.array([[2, 1.5, 10, 8, 12, 0.5, 1.5]]), - columns=['i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x', 'i_xx'], - index=[0]) - expected = pd.DataFrame( - np.array([[6, 4.5, 20, 16, 72, 1.5, 4.5]]), - columns=['i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x', 'i_xx'], - index=[0]) +def test_PVSystem_scale_voltage_current_power(mocker): + data = None system = pvsystem.PVSystem(modules_per_string=2, strings_per_inverter=3) - out = system.scale_voltage_current_power(data) + m = mocker.patch( + 'pvlib.pvsystem.scale_voltage_current_power', autospec=True) + system.scale_voltage_current_power(data) + m.assert_called_once_with(data, voltage=2, current=3) def test_sapm_celltemp(): @@ -806,20 +1030,19 @@ def test_sapm_celltemp_with_index(): assert_frame_equal(expected, pvtemps) -def test_PVSystem_sapm_celltemp(): - system = pvsystem.PVSystem(racking_model='roof_mount_cell_glassback') - times = pd.DatetimeIndex(start='2015-01-01', end='2015-01-02', freq='12H') - temps = pd.Series([0, 10, 5], index=times) - irrads = pd.Series([0, 500, 0], index=times) - winds = pd.Series([10, 5, 0], index=times) - - pvtemps = system.sapm_celltemp(irrads, winds, temps) +def test_PVSystem_sapm_celltemp(mocker): + racking_model = 'roof_mount_cell_glassback' - expected = pd.DataFrame({'temp_cell':[0., 30.56763059, 5.], - 'temp_module':[0., 30.06763059, 5.]}, - index=times) - - assert_frame_equal(expected, pvtemps) + system = pvsystem.PVSystem(racking_model=racking_model) + mocker.spy(pvsystem, 'sapm_celltemp') + temps = 25 + irrads = 1000 + winds = 1 + out = system.sapm_celltemp(irrads, winds, temps) + pvsystem.sapm_celltemp.assert_called_once_with( + irrads, winds, temps, model=racking_model) + assert isinstance(out, pd.DataFrame) + assert out.shape == (1, 2) def test_adrinverter(sam_data): @@ -1108,36 +1331,57 @@ def make_pvwatts_system_kwargs(): return system -def test_PVSystem_pvwatts_dc(): +def test_PVSystem_pvwatts_dc(mocker): + mocker.spy(pvsystem, 'pvwatts_dc') system = make_pvwatts_system_defaults() - irrad_trans = pd.Series([np.nan, 900, 900]) - temp_cell = pd.Series([30, np.nan, 30]) - expected = pd.Series(np.array([ nan, nan, 88.65])) - out = system.pvwatts_dc(irrad_trans, temp_cell) - assert_series_equal(expected, out) + irrad = 900 + temp_cell = 30 + expected = 90 + out = system.pvwatts_dc(irrad, temp_cell) + pvsystem.pvwatts_dc.assert_called_once_with(irrad, temp_cell, + **system.module_parameters) + assert_allclose(expected, out, atol=10) + +def test_PVSystem_pvwatts_dc_kwargs(mocker): + mocker.spy(pvsystem, 'pvwatts_dc') system = make_pvwatts_system_kwargs() - expected = pd.Series(np.array([ nan, nan, 87.3])) - out = system.pvwatts_dc(irrad_trans, temp_cell) - assert_series_equal(expected, out) + irrad = 900 + temp_cell = 30 + expected = 90 + out = system.pvwatts_dc(irrad, temp_cell) + pvsystem.pvwatts_dc.assert_called_once_with(irrad, temp_cell, + **system.module_parameters) + assert_allclose(expected, out, atol=10) -def test_PVSystem_pvwatts_losses(): +def test_PVSystem_pvwatts_losses(mocker): + mocker.spy(pvsystem, 'pvwatts_losses') system = make_pvwatts_system_defaults() - expected = pd.Series([nan, 14.934904]) - age = pd.Series([nan, 1]) + expected = 15 + age = 1 out = system.pvwatts_losses(age=age) - assert_series_equal(expected, out) + pvsystem.pvwatts_losses.assert_called_once_with(age=age) + assert out < expected -def test_PVSystem_pvwatts_ac(): +def test_PVSystem_pvwatts_ac(mocker): + mocker.spy(pvsystem, 'pvwatts_ac') system = make_pvwatts_system_defaults() - pdc = pd.Series([np.nan, 50, 100]) - expected = pd.Series(np.array([ nan, 48.1095776694, 96.0])) + pdc = 50 + pdc0 = system.module_parameters['pdc0'] out = system.pvwatts_ac(pdc) - assert_series_equal(expected, out) + pvsystem.pvwatts_ac.assert_called_once_with(pdc, pdc0, + **system.inverter_parameters) + assert out < pdc + +def test_PVSystem_pvwatts_ac_kwargs(mocker): + mocker.spy(pvsystem, 'pvwatts_ac') system = make_pvwatts_system_kwargs() - expected = pd.Series(np.array([ nan, 45.88025, 91.5515])) + pdc = 50 + pdc0 = system.module_parameters['pdc0'] out = system.pvwatts_ac(pdc) - assert_series_equal(expected, out) + pvsystem.pvwatts_ac.assert_called_once_with(pdc, pdc0, + **system.inverter_parameters) + assert out < pdc diff --git a/pvlib/test/test_singlediode_methods.py b/pvlib/test/test_singlediode_methods.py new file mode 100644 index 0000000000..93ad8020ed --- /dev/null +++ b/pvlib/test/test_singlediode_methods.py @@ -0,0 +1,127 @@ +""" +testing single-diode methods using JW Bishop 1988 +""" + +import numpy as np +from pvlib import pvsystem +from conftest import requires_scipy + +POA = 888 +TCELL = 55 +CECMOD = pvsystem.retrieve_sam('cecmod') + + +@requires_scipy +def test_newton_spr_e20_327(): + """test pvsystem.singlediode with Newton method on SPR-E20-327""" + spr_e20_327 = CECMOD.SunPower_SPR_E20_327 + x = pvsystem.calcparams_desoto( + effective_irradiance=POA, temp_cell=TCELL, + alpha_sc=spr_e20_327.alpha_sc, a_ref=spr_e20_327.a_ref, + I_L_ref=spr_e20_327.I_L_ref, I_o_ref=spr_e20_327.I_o_ref, + R_sh_ref=spr_e20_327.R_sh_ref, R_s=spr_e20_327.R_s, + EgRef=1.121, dEgdT=-0.0002677) + il, io, rs, rsh, nnsvt = x + pvs = pvsystem.singlediode(*x, method='lambertw') + out = pvsystem.singlediode(*x, method='newton') + isc, voc, imp, vmp, pmp, ix, ixx = out.values() + assert np.isclose(pvs['i_sc'], isc) + assert np.isclose(pvs['v_oc'], voc) + # the singlediode method doesn't actually get the MPP correct + pvs_imp = pvsystem.i_from_v(rsh, rs, nnsvt, vmp, io, il, method='lambertw') + pvs_vmp = pvsystem.v_from_i(rsh, rs, nnsvt, imp, io, il, method='lambertw') + assert np.isclose(pvs_imp, imp) + assert np.isclose(pvs_vmp, vmp) + assert np.isclose(pvs['p_mp'], pmp) + assert np.isclose(pvs['i_x'], ix) + pvs_ixx = pvsystem.i_from_v(rsh, rs, nnsvt, (voc + vmp)/2, io, il, + method='lambertw') + assert np.isclose(pvs_ixx, ixx) + return isc, voc, imp, vmp, pmp, pvs + + +@requires_scipy +def test_newton_fs_495(): + """test pvsystem.singlediode with Newton method on FS495""" + fs_495 = CECMOD.First_Solar_FS_495 + x = pvsystem.calcparams_desoto( + effective_irradiance=POA, temp_cell=TCELL, + alpha_sc=fs_495.alpha_sc, a_ref=fs_495.a_ref, I_L_ref=fs_495.I_L_ref, + I_o_ref=fs_495.I_o_ref, R_sh_ref=fs_495.R_sh_ref, R_s=fs_495.R_s, + EgRef=1.475, dEgdT=-0.0003) + il, io, rs, rsh, nnsvt = x + x += (101, ) + pvs = pvsystem.singlediode(*x, method='lambertw') + out = pvsystem.singlediode(*x, method='newton') + isc, voc, imp, vmp, pmp, ix, ixx, i, v = out.values() + assert np.isclose(pvs['i_sc'], isc) + assert np.isclose(pvs['v_oc'], voc) + # the singlediode method doesn't actually get the MPP correct + pvs_imp = pvsystem.i_from_v(rsh, rs, nnsvt, vmp, io, il, method='lambertw') + pvs_vmp = pvsystem.v_from_i(rsh, rs, nnsvt, imp, io, il, method='lambertw') + assert np.isclose(pvs_imp, imp) + assert np.isclose(pvs_vmp, vmp) + assert np.isclose(pvs['p_mp'], pmp) + assert np.isclose(pvs['i_x'], ix) + pvs_ixx = pvsystem.i_from_v(rsh, rs, nnsvt, (voc + vmp)/2, io, il, + method='lambertw') + assert np.isclose(pvs_ixx, ixx) + return isc, voc, imp, vmp, pmp, i, v, pvs + + +@requires_scipy +def test_brentq_spr_e20_327(): + """test pvsystem.singlediode with Brent method on SPR-E20-327""" + spr_e20_327 = CECMOD.SunPower_SPR_E20_327 + x = pvsystem.calcparams_desoto( + effective_irradiance=POA, temp_cell=TCELL, + alpha_sc=spr_e20_327.alpha_sc, a_ref=spr_e20_327.a_ref, + I_L_ref=spr_e20_327.I_L_ref, I_o_ref=spr_e20_327.I_o_ref, + R_sh_ref=spr_e20_327.R_sh_ref, R_s=spr_e20_327.R_s, + EgRef=1.121, dEgdT=-0.0002677) + il, io, rs, rsh, nnsvt = x + pvs = pvsystem.singlediode(*x, method='lambertw') + out = pvsystem.singlediode(*x, method='brentq') + isc, voc, imp, vmp, pmp, ix, ixx = out.values() + assert np.isclose(pvs['i_sc'], isc) + assert np.isclose(pvs['v_oc'], voc) + # the singlediode method doesn't actually get the MPP correct + pvs_imp = pvsystem.i_from_v(rsh, rs, nnsvt, vmp, io, il, method='lambertw') + pvs_vmp = pvsystem.v_from_i(rsh, rs, nnsvt, imp, io, il, method='lambertw') + assert np.isclose(pvs_imp, imp) + assert np.isclose(pvs_vmp, vmp) + assert np.isclose(pvs['p_mp'], pmp) + assert np.isclose(pvs['i_x'], ix) + pvs_ixx = pvsystem.i_from_v(rsh, rs, nnsvt, (voc + vmp)/2, io, il, + method='lambertw') + assert np.isclose(pvs_ixx, ixx) + return isc, voc, imp, vmp, pmp, pvs + + +@requires_scipy +def test_brentq_fs_495(): + """test pvsystem.singlediode with Brent method on SPR-E20-327""" + fs_495 = CECMOD.First_Solar_FS_495 + x = pvsystem.calcparams_desoto( + effective_irradiance=POA, temp_cell=TCELL, + alpha_sc=fs_495.alpha_sc, a_ref=fs_495.a_ref, I_L_ref=fs_495.I_L_ref, + I_o_ref=fs_495.I_o_ref, R_sh_ref=fs_495.R_sh_ref, R_s=fs_495.R_s, + EgRef=1.475, dEgdT=-0.0003) + il, io, rs, rsh, nnsvt = x + x += (101, ) + pvs = pvsystem.singlediode(*x, method='lambertw') + out = pvsystem.singlediode(*x, method='brentq') + isc, voc, imp, vmp, pmp, ix, ixx, i, v = out.values() + assert np.isclose(pvs['i_sc'], isc) + assert np.isclose(pvs['v_oc'], voc) + # the singlediode method doesn't actually get the MPP correct + pvs_imp = pvsystem.i_from_v(rsh, rs, nnsvt, vmp, io, il, method='lambertw') + pvs_vmp = pvsystem.v_from_i(rsh, rs, nnsvt, imp, io, il, method='lambertw') + assert np.isclose(pvs_imp, imp) + assert np.isclose(pvs_vmp, vmp) + assert np.isclose(pvs['p_mp'], pmp) + assert np.isclose(pvs['i_x'], ix) + pvs_ixx = pvsystem.i_from_v(rsh, rs, nnsvt, (voc + vmp)/2, io, il, + method='lambertw') + assert np.isclose(pvs_ixx, ixx) + return isc, voc, imp, vmp, pmp, i, v, pvs diff --git a/pvlib/tmy.py b/pvlib/tmy.py index 111f2bab91..371bd933f1 100644 --- a/pvlib/tmy.py +++ b/pvlib/tmy.py @@ -7,9 +7,9 @@ import dateutil import io try: - from urllib2 import urlopen + from urllib2 import urlopen, Request except ImportError: - from urllib.request import urlopen + from urllib.request import urlopen, Request import pandas as pd @@ -164,14 +164,23 @@ def readtmy3(filename=None, coerce_year=None, recolumn=True): head = ['USAF', 'Name', 'State', 'TZ', 'latitude', 'longitude', 'altitude'] - try: - csvdata = open(filename, 'r') - except IOError: - response = urlopen(filename) + if filename.startswith('http'): + request = Request(filename, headers={'User-Agent': + 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_5) ' + 'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 ' + 'Safari/537.36'}) + response = urlopen(request) csvdata = io.StringIO(response.read().decode(errors='ignore')) + else: + # assume it's accessible via the file system + csvdata = open(filename, 'r') + + # read in file metadata, advance buffer to second line + firstline = csvdata.readline() + if 'Request Rejected' in firstline: + raise IOError('Remote server rejected TMY file request') - # read in file metadata - meta = dict(zip(head, csvdata.readline().rstrip('\n').split(","))) + meta = dict(zip(head, firstline.rstrip('\n').split(","))) # convert metadata strings to numeric types meta['altitude'] = float(meta['altitude']) @@ -180,8 +189,12 @@ def readtmy3(filename=None, coerce_year=None, recolumn=True): meta['TZ'] = float(meta['TZ']) meta['USAF'] = int(meta['USAF']) + # use pandas to read the csv file/stringio buffer + # header is actually the second line in file, but tell pandas to look for + # header information on the 1st line (0 indexing) because we've already + # advanced past the true first line with the readline call above. data = pd.read_csv( - filename, header=1, + csvdata, header=0, parse_dates={'datetime': ['Date (MM/DD/YYYY)', 'Time (HH:MM)']}, date_parser=lambda *x: _parsedate(*x, year=coerce_year), index_col='datetime') diff --git a/pvlib/tools.py b/pvlib/tools.py index a722500f2a..16596af885 100644 --- a/pvlib/tools.py +++ b/pvlib/tools.py @@ -2,8 +2,9 @@ Collection of functions used in pvlib_python """ +from collections import namedtuple import datetime as dt - +import warnings import numpy as np import pandas as pd import pytz @@ -251,3 +252,179 @@ def _build_kwargs(keys, input_dict): pass return kwargs + + +# FIXME: remove _array_newton when SciPy-1.2.0 is released +# pvlib.singlediode_methods.bishop88_i_from_v(..., method='newton') and other +# functions in singlediode_methods call scipy.optimize.newton with a vector +# unfortunately wrapping the functions with np.vectorize() was too slow +# a vectorized newton method was merged into SciPy but isn't released yet, so +# in the meantime, we just copied the relevant code: "_array_newton" for more +# info see: https://github.com/scipy/scipy/pull/8357 + +def _array_newton(func, x0, fprime, args, tol, maxiter, fprime2, + converged=False): + """ + A vectorized version of Newton, Halley, and secant methods for arrays. Do + not use this method directly. This method is called from :func:`newton` + when ``np.isscalar(x0)`` is true. For docstring, see :func:`newton`. + """ + try: + p = np.asarray(x0, dtype=float) + except TypeError: # can't convert complex to float + p = np.asarray(x0) + failures = np.ones_like(p, dtype=bool) # at start, nothing converged + nz_der = np.copy(failures) + if fprime is not None: + # Newton-Raphson method + for iteration in range(maxiter): + # first evaluate fval + fval = np.asarray(func(p, *args)) + # If all fval are 0, all roots have been found, then terminate + if not fval.any(): + failures = fval.astype(bool) + break + fder = np.asarray(fprime(p, *args)) + nz_der = (fder != 0) + # stop iterating if all derivatives are zero + if not nz_der.any(): + break + # Newton step + dp = fval[nz_der] / fder[nz_der] + if fprime2 is not None: + fder2 = np.asarray(fprime2(p, *args)) + dp = dp / (1.0 - 0.5 * dp * fder2[nz_der] / fder[nz_der]) + # only update nonzero derivatives + p[nz_der] -= dp + failures[nz_der] = np.abs(dp) >= tol # items not yet converged + # stop iterating if there aren't any failures, not incl zero der + if not failures[nz_der].any(): + break + else: + # Secant method + dx = np.finfo(float).eps**0.33 + p1 = p * (1 + dx) + np.where(p >= 0, dx, -dx) + q0 = np.asarray(func(p, *args)) + q1 = np.asarray(func(p1, *args)) + active = np.ones_like(p, dtype=bool) + for iteration in range(maxiter): + nz_der = (q1 != q0) + # stop iterating if all derivatives are zero + if not nz_der.any(): + p = (p1 + p) / 2.0 + break + # Secant Step + dp = (q1 * (p1 - p))[nz_der] / (q1 - q0)[nz_der] + # only update nonzero derivatives + p[nz_der] = p1[nz_der] - dp + active_zero_der = ~nz_der & active + p[active_zero_der] = (p1 + p)[active_zero_der] / 2.0 + active &= nz_der # don't assign zero derivatives again + failures[nz_der] = np.abs(dp) >= tol # not yet converged + # stop iterating if there aren't any failures, not incl zero der + if not failures[nz_der].any(): + break + p1, p = p, p1 + q0 = q1 + q1 = np.asarray(func(p1, *args)) + zero_der = ~nz_der & failures # don't include converged with zero-ders + if zero_der.any(): + # secant warnings + if fprime is None: + nonzero_dp = (p1 != p) + # non-zero dp, but infinite newton step + zero_der_nz_dp = (zero_der & nonzero_dp) + if zero_der_nz_dp.any(): + rms = np.sqrt( + sum((p1[zero_der_nz_dp] - p[zero_der_nz_dp]) ** 2) + ) + warnings.warn('RMS of {:g} reached'.format(rms), RuntimeWarning) + # newton or halley warnings + else: + all_or_some = 'all' if zero_der.all() else 'some' + msg = '{:s} derivatives were zero'.format(all_or_some) + warnings.warn(msg, RuntimeWarning) + elif failures.any(): + all_or_some = 'all' if failures.all() else 'some' + msg = '{0:s} failed to converge after {1:d} iterations'.format( + all_or_some, maxiter + ) + if failures.all(): + raise RuntimeError(msg) + warnings.warn(msg, RuntimeWarning) + if converged: + result = namedtuple('result', ('root', 'converged', 'zero_der')) + p = result(p, ~failures, zero_der) + return p + + +# Created April,2014 +# Author: Rob Andrews, Calama Consulting + +def _golden_sect_DataFrame(params, VL, VH, func): + """ + Vectorized golden section search for finding MPP from a dataframe + timeseries. + + Parameters + ---------- + params : dict + Dictionary containing scalars or arrays + of inputs to the function to be optimized. + Each row should represent an independent optimization. + + VL: float + Lower bound of the optimization + + VH: float + Upper bound of the optimization + + func: function + Function to be optimized must be in the form f(array-like, x) + + Returns + ------- + func(df,'V1') : DataFrame + function evaluated at the optimal point + + df['V1']: Dataframe + Dataframe of optimal points + + Notes + ----- + This function will find the MAXIMUM of a function + """ + + df = params + df['VH'] = VH + df['VL'] = VL + + err = df['VH'] - df['VL'] + errflag = True + iterations = 0 + + while errflag: + + phi = (np.sqrt(5)-1)/2*(df['VH']-df['VL']) + df['V1'] = df['VL'] + phi + df['V2'] = df['VH'] - phi + + df['f1'] = func(df, 'V1') + df['f2'] = func(df, 'V2') + df['SW_Flag'] = df['f1'] > df['f2'] + + df['VL'] = df['V2']*df['SW_Flag'] + df['VL']*(~df['SW_Flag']) + df['VH'] = df['V1']*~df['SW_Flag'] + df['VH']*(df['SW_Flag']) + + err = df['V1'] - df['V2'] + try: + errflag = (abs(err) > .01).any() + except ValueError: + errflag = (abs(err) > .01) + + iterations += 1 + + if iterations > 50: + raise Exception("EXCEPTION:iterations exceeded maximum (50)") + + return func(df, 'V1'), df['V1']