From be216165faeb2bf2d3bfa607d5b955e4fc222099 Mon Sep 17 00:00:00 2001 From: TchilDill Date: Mon, 23 Oct 2023 21:59:45 +0200 Subject: [PATCH 01/13] change to doctest setup in sphinx --- docs/.DS_Store | Bin 6148 -> 6148 bytes docs/source/conf.py | 6 ++- docs/source/usage.rst | 66 +++++++++++++++++---------- requirements.txt | 101 +++++++++++++++++++++++++++--------------- 4 files changed, 113 insertions(+), 60 deletions(-) diff --git a/docs/.DS_Store b/docs/.DS_Store index 010a211e0fb36d4254c0ce8bdb364f6b6b5454f1..ab8780a9bdfd7a5934a15774d36c2d06997ef439 100644 GIT binary patch delta 50 zcmZoMXfc@JFUrKgz`)4BAi%(o%#fN?UR;orlb^KNka;=dWCIbF&6-U2SSL0}ZD!~A G%MSoGhYhy? delta 69 zcmZoMXfc@JFU-uqz`)4BAi%(oQWjj4my@5DzFClYC8IP*f|VhOp_Cz$AqOD|Qofmo M=^^W8c8>> |\.\.\. " +copybutton_prompt_is_regexp = True + # -- Options for LaTeX output ------------------------------------------------ latex_engine = "pdflatex" numfig = True diff --git a/docs/source/usage.rst b/docs/source/usage.rst index efe0a32..290c062 100644 --- a/docs/source/usage.rst +++ b/docs/source/usage.rst @@ -17,39 +17,57 @@ Example 1 - Create a pile A pile can be created in the simple following way in openpile. -.. code-block:: python - - from openpile.construct import Pile +.. doctest:: - # Create a pile instance with two sections of respectively 10m and 30m length. - pile = Pile(name = "", - kind='Circular', - material='Steel', - top_elevation = 0, - pile_sections={ - 'length':[10,30], - 'diameter':[7.5,7.5], - 'wall thickness':[0.07, 0.08], - } - ) + >>> # import the Pile object from the construct module + >>> from openpile.construct import Pile + + >>> # Create a Pile + >>> pile = Pile(name = "", + ... kind='Circular', + ... material='Steel', + ... top_elevation = 0, + ... pile_sections={ + ... 'length':[10,30], + ... 'diameter':[7.5,7.5], + ... 'wall thickness':[0.07, 0.08], + ... } + ... ) + + >>> # Print the pile data + >>> print(pile) # doctest: +NORMALIZE_WHITESPACE + Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] + 0 0.0 7.5 0.07 1.633942 11.276204 + 1 -10.0 7.5 0.07 1.633942 11.276204 + 2 -10.0 7.5 0.08 1.864849 12.835479 + 3 -40.0 7.5 0.08 1.864849 12.835479 + >>> Additional methods can be used to create a Pile, these methods can shorten the lines of codes needed to create the pile. For instance: -* :py:meth:`openpile.construct.Pile.create_tubular` which creates a circular pile of constant diameter. + +.. doctest:: + + >>> # Import Pile object from constuct module + >>> from openpile.construct import Pile + + >>> # create pile + >>> p = Pile.create_tubular( + ... name="", top_elevation=0, bottom_elevation=-40, diameter=10, wt=0.050 + ... ) + + >>> print(p) + Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] + 0 0.0 10.0 0.05 1.562942 19.342388 + 1 -40.0 10.0 0.05 1.562942 19.342388 + >>> + + Once the pile (object) is created, the user can use its properties and methods to interact with it. A simple view of the pile can be extracted by printing the object as below: -.. code-block:: python - - # Print the pile data - print(pile) - Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] - 0 0.0 7.5 0.07 1.633942 11.276204 - 1 -10.0 7.5 0.07 1.633942 11.276204 - 2 -10.0 7.5 0.08 1.864849 12.835479 - 3 -40.0 7.5 0.08 1.864849 12.835479 The user can also extract easily the pile length, elevations and other properties. Please see the :py:class:`openpile.construct.Pile` diff --git a/requirements.txt b/requirements.txt index acb3205..065626e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,91 +1,122 @@ -alabaster==0.7.12 +alabaster==0.7.13 +anyio==3.6.2 +appnope==0.1.3 +argon2-cffi==21.3.0 +argon2-cffi-bindings==21.2.0 attrs==22.2.0 -Babel==2.11.0 +autodoc-pydantic==1.9.0 +Babel==2.12.1 backcall==0.2.0 +beautifulsoup4==4.12.2 black==22.12.0 +bleach==6.0.0 cachetools==5.3.0 certifi==2022.12.7 +cffi==1.15.1 chardet==5.1.0 -charset-normalizer==2.1.1 +charset-normalizer==3.1.0 click==8.1.3 colorama==0.4.6 -coverage==7.2.1 +coverage==7.2.2 cycler==0.11.0 -debugpy==1.6.4 +debugpy==1.6.6 decorator==5.1.1 defusedxml==0.7.1 distlib==0.3.6 -docutils==0.17.1 +docutils==0.18.1 entrypoints==0.4 -exceptiongroup==1.1.0 -filelock==3.10.6 -flake8==5.0.4 -flake8-black==0.3.6 +exceptiongroup==1.1.1 +fastjsonschema==2.16.3 +filelock==3.10.7 fonttools==4.38.0 -fpdf2==2.6.0 +fpdf2==2.7.1 idna==3.4 imagesize==1.4.1 -importlib-metadata==6.0.0 +importlib-metadata==6.1.0 +importlib-resources==5.12.0 iniconfig==2.0.0 ipykernel==6.16.2 ipython==7.34.0 +ipython-genutils==0.2.0 jedi==0.18.2 Jinja2==3.1.2 -jupyter_client==7.4.8 -jupyter_core==4.12.0 +jsonschema==4.17.3 +jupyter-client==7.4.9 +jupyter-core==4.12.0 +jupyter-server==1.24.0 +jupyterlab-pygments==0.2.2 kiwisolver==1.4.4 -line-profiler==4.0.3 llvmlite==0.39.1 -MarkupSafe==2.1.1 +MarkupSafe==2.1.2 matplotlib==3.5.3 matplotlib-inline==0.1.6 -mccabe==0.7.0 +mistune==2.0.5 mypy-extensions==1.0.0 +nbclassic==0.5.5 +nbclient==0.7.3 +nbconvert==7.3.1 +nbformat==5.8.0 nest-asyncio==1.5.6 +notebook==6.5.4 +notebook-shim==0.2.2 numba==0.56.4 numpy==1.21.6 --e git+https://github.com/TchilDill/openpile.git +-e git+https://github.com/TchilDill/openpile.git@e8aac1b7152a3ccbf6955d7df6f435d7f1ea753a#egg=openpile packaging==23.0 pandas==1.3.5 +pandocfilters==1.5.0 parso==0.8.3 pathspec==0.11.1 +pexpect==4.8.0 pickleshare==0.7.5 -Pillow==9.4.0 +Pillow==9.5.0 +pkgutil-resolve-name==1.3.10 platformdirs==3.2.0 pluggy==1.0.0 -prompt-toolkit==3.0.36 +prometheus-client==0.16.0 +prompt-toolkit==3.0.38 psutil==5.9.4 -pycodestyle==2.9.1 -pydantic==1.10.4 -pyflakes==2.5.0 +ptyprocess==0.7.0 +pycparser==2.21 +pydantic==1.10.7 Pygments==2.14.0 pyparsing==3.0.9 -pyproject_api==1.5.1 -pytest==7.2.0 +pyproject-api==1.5.1 +pyrsistent==0.19.3 +pytest==7.2.2 pytest-cov==4.0.0 +pytest-sphinx==0.5.0 python-dateutil==2.8.2 -pytz==2022.7 -pywin32==305 +pytz==2023.3 pyzmq==24.0.1 -requests==2.28.1 +requests==2.28.2 scipy==1.7.3 +Send2Trash==1.8.0 six==1.16.0 +sniffio==1.3.0 snowballstemmer==2.2.0 -Sphinx==5.3.0 -sphinx-rtd-theme==1.1.1 +soupsieve==2.4 +sphinx==5.3.0 +sphinx-copybutton==0.5.2 +sphinx-rtd-theme==1.3.0 sphinxcontrib-applehelp==1.0.2 sphinxcontrib-devhelp==1.0.2 sphinxcontrib-htmlhelp==2.0.0 +sphinxcontrib-jquery==4.1 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 +terminado==0.17.1 +tinycss2==1.2.1 tomli==2.0.1 tornado==6.2 tox==4.4.8 -traitlets==5.8.0 +traitlets==5.9.0 typed-ast==1.5.4 -typing_extensions==4.5.0 -urllib3==1.26.13 +typing-extensions==4.5.0 +urllib3==1.26.15 virtualenv==20.21.0 -wcwidth==0.2.5 -zipp==3.11.0 +wcwidth==0.2.6 +webencodings==0.5.1 +websocket-client==1.5.1 +zipp==3.15.0 From 8eee29893e44e9853f572ab82bcd277c18237aed Mon Sep 17 00:00:00 2001 From: TchilDill Date: Tue, 24 Oct 2023 21:51:21 +0200 Subject: [PATCH 02/13] finalize doctest and plot codes --- docs/.DS_Store | Bin 6148 -> 6148 bytes docs/source/conf.py | 3 + docs/source/usage.rst | 319 +++++++++++++++++++++++------------------- 3 files changed, 175 insertions(+), 147 deletions(-) diff --git a/docs/.DS_Store b/docs/.DS_Store index ab8780a9bdfd7a5934a15774d36c2d06997ef439..010a211e0fb36d4254c0ce8bdb364f6b6b5454f1 100644 GIT binary patch delta 69 zcmZoMXfc@JFU-uqz`)4BAi%(oQWjj4my@5DzFClYC8IP*f|VhOp_Cz$AqOD|Qofmo M=^^W8c8>> |\.\.\. " copybutton_prompt_is_regexp = True +#option for matplotlib extension +plot_include_source = True + # -- Options for LaTeX output ------------------------------------------------ latex_engine = "pdflatex" numfig = True diff --git a/docs/source/usage.rst b/docs/source/usage.rst index 290c062..d6ba2d8 100644 --- a/docs/source/usage.rst +++ b/docs/source/usage.rst @@ -35,13 +35,12 @@ A pile can be created in the simple following way in openpile. ... ) >>> # Print the pile data - >>> print(pile) # doctest: +NORMALIZE_WHITESPACE - Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] + >>> print(pile) + Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] 0 0.0 7.5 0.07 1.633942 11.276204 1 -10.0 7.5 0.07 1.633942 11.276204 2 -10.0 7.5 0.08 1.864849 12.835479 3 -40.0 7.5 0.08 1.864849 12.835479 - >>> Additional methods can be used to create a Pile, these methods can shorten the lines of codes needed to create the pile. For instance: @@ -61,8 +60,6 @@ For instance: Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] 0 0.0 10.0 0.05 1.562942 19.342388 1 -40.0 10.0 0.05 1.562942 19.342388 - >>> - Once the pile (object) is created, the user can use its properties and methods to interact with it. @@ -77,36 +74,39 @@ As of now, only a circular pile can be modelled in openpile, however the user ca the construcutor by updating the pile's properties governing the pile's behaviour under axial or lateral loading. -.. code-block:: python +.. doctest:: - # Override young's modulus - pile.E = 250e6 - # Check young's modulus (value in kPa) - print(pile.E) + >>> # Override young's modulus + >>> pile.E = 250e6 + >>> # Check young's modulus (value in kPa) + >>> print(pile.E) 250000000.0 - # Override second moment of area across first section [in meters^4] - pile.set_I(value=1.11, section=1) - # Check updated second moment of area - print(pile) - Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] + + >>> # Override second moment of area across first section [in meters^4] + >>> pile.set_I(value=1.11, section=1) + >>> # Check updated second moment of area + >>> print(pile) + Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] 0 0.0 7.5 0.07 1.633942 1.110000 1 -10.0 7.5 0.07 1.633942 1.110000 2 -10.0 7.5 0.08 1.864849 12.835479 3 -40.0 7.5 0.08 1.864849 12.835479 - # Override pile's width or pile's diameter [in meters] - pile.width = 2.22 - # Check updated width or diameter - print(pile) - Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] + + >>> # Override pile's width or pile's diameter [in meters] + >>> pile.width = 2.22 + >>> # Check updated width or diameter + >>> print(pile) + Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] 0 0.0 2.22 0.07 1.633942 1.110000 1 -10.0 2.22 0.07 1.633942 1.110000 2 -10.0 2.22 0.08 1.864849 12.835479 3 -40.0 2.22 0.08 1.864849 12.835479 - # Override pile's area [in meters^2] - pile.area = 1.0 - # Check updated width or diameter - print(pile) - Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] + + >>> # Override pile's area [in meters^2] + >>> pile.area = 1.0 + >>> # Check updated width or diameter + >>> print(pile) + Elevation [m] Diameter [m] Wall thickness [m] Area [m2] I [m4] 0 0.0 2.22 0.07 1.0 1.110000 1 -10.0 2.22 0.07 1.0 1.110000 2 -10.0 2.22 0.08 1.0 12.835479 @@ -131,10 +131,11 @@ The different curves available can be found in the below modules. * :py:mod:`openpile.utils.Hb_curves` (base shear curves) * :py:mod:`openpile.utils.Mb_curves` (base moment curves) -Here below is an example of a quick check of how a static curve for the -API sand model looks like. +Here below is an example of how a static curve for the +API sand model looks like. The `matplotlib` library can be used +easily with OpenPile. -.. code-block:: python +.. plot:: # import p-y curve for api_sand from openpile.utils from openpile.utils.py_curves import api_sand @@ -150,12 +151,11 @@ API sand model looks like. # create a plot of the results with Matplotlib import matplotlib.pyplot as plt + # use matplotlib to visual the soil curve plt.plot(y,p) plt.ylabel('p [kN/m]') plt.xlabel('y [m]') -.. image:: _static/usage/pycurves/api_sand_example_build.png - :width: 65% .. _Ex3-create_a_layer: @@ -166,31 +166,26 @@ Example 3 - Create a soil profile's layer The creation of a layer can be done with the below lines of code. A Lateral and/or Axial soil model can be assigned to a layer. -.. code-block:: python +.. doctest:: - from openpile.construct import Layer - from openpile.soilmodels import API_clay + >>> from openpile.construct import Layer + >>> from openpile.soilmodels import API_clay - # Create a layer - layer1 = Layer(name='Soft Clay', - top=0, - bottom=-10, - weight=18, - lateral_model=API_clay(Su=[30,35], eps50=[0.01, 0.02], kind="static"), ) - - print(layer1) - -Printing the layer would give the following output: - -.. code-block:: pycon - + >>> # Create a layer + >>> layer1 = Layer(name='Soft Clay', + ... top=0, + ... bottom=-10, + ... weight=18, + ... lateral_model=API_clay(Su=[30,35], eps50=[0.01, 0.02], kind="static"), ) + + >>> print(layer1) # doctest: +NORMALIZE_WHITESPACE Name: Soft Clay Elevation: (0.0) - (-10.0) m Weight: 18.0 kN/m3 - Lateral model: API clay - Su = 30.0-35.0 kPa - eps50 = 0.01-0.02 - static curves + Lateral model: API clay + Su = 30.0-35.0 kPa + eps50 = 0.01-0.02 + static curves Axial model: None @@ -199,40 +194,35 @@ Printing the layer would give the following output: Example 4 - Create a soil profile ================================= -.. code-block:: python - - from openpile.construct import SoilProfile, Layer - from openpile.soilmodels import API_sand, API_clay - - # Create a 40m deep offshore Soil Profile with a 15m water column - sp = SoilProfile( - name="Offshore Soil Profile", - top_elevation=0, - water_line=15, - layers=[ - Layer( - name='medium dense sand', - top=0, - bottom=-20, - weight=18, - lateral_model= API_sand(phi=33, kind="cyclic") - ), - Layer( - name='firm clay', - top=-20, - bottom=-40, - weight=18, - lateral_model= API_clay(Su=[50, 70], eps50=0.015, kind="cyclic") - ), - ] - ) - - print(sp) - -The output of the print out will yield the following: - -.. code-block:: pycon +.. doctest:: + + >>> from openpile.construct import SoilProfile, Layer + >>> from openpile.soilmodels import API_sand, API_clay + + >>> # Create a 40m deep offshore Soil Profile with a 15m water column + >>> sp = SoilProfile( + ... name="Offshore Soil Profile", + ... top_elevation=0, + ... water_line=15, + ... layers=[ + ... Layer( + ... name='medium dense sand', + ... top=0, + ... bottom=-20, + ... weight=18, + ... lateral_model= API_sand(phi=33, kind="cyclic") + ... ), + ... Layer( + ... name='firm clay', + ... top=-20, + ... bottom=-40, + ... weight=18, + ... lateral_model= API_clay(Su=[50, 70], eps50=0.015, kind="cyclic") + ... ), + ... ] + ... ) + >>> print(sp) # doctest: +NORMALIZE_WHITESPACE Layer 1 ------------------------------ Name: medium dense sand @@ -258,67 +248,102 @@ The output of the print out will yield the following: .. _Ex5-run_winkler: -Example 5 - Create a Model and run an analysis -============================================== - -.. code-block:: python - - from openpile.construct import Pile, SoilProfile, Layer, Model - from openpile.soilmodels import API_clay, API_sand - - - p = Pile.create_tubular( - name="", top_elevation=0, bottom_elevation=-40, diameter=7.5, wt=0.075 - ) - - # Create a 40m deep offshore Soil Profile with a 15m water column - sp = SoilProfile( - name="Offshore Soil Profile", - top_elevation=0, - water_line=15, - layers=[ - Layer( - name="medium dense sand", - top=0, - bottom=-20, - weight=18, - lateral_model=API_sand(phi=33, kind="cyclic"), - ), - Layer( - name="firm clay", - top=-20, - bottom=-40, - weight=18, - lateral_model=API_clay(Su=[50, 70], eps50=0.015, kind="cyclic"), - ), - ], - ) - - # Create Model - M = Model(name="", pile=p, soil=sp) - - # Apply bottom fixity along x-axis - M.set_support(elevation=-40, Tx=True) - # Apply axial and lateral loads - M.set_pointload(elevation=0, Px=-20e3, Py=5e3) - - # Run analysis - from openpile.analyze import winkler - Result = winkler(M) - - # plot the results - Result.plot() - -.. image:: _static/usage/analyses_plots/main_results_plot.png - :width: 65% - -Finally, if one would like to check the input of the model, a quick visual on this -can be provided by simply plotting the model. - -.. code-block:: python - - # plot the model (mesh, boundary conditions and soil profile) - M.plot() - -.. image:: _static/usage/analyses_plots/model_plot_with_soil.png - :width: 65% +Example 5 - Run a winkler analysis +================================== + +.. plot:: + + >>> from openpile.construct import Pile, SoilProfile, Layer, Model + >>> from openpile.soilmodels import API_clay, API_sand + >>> + >>> p = Pile.create_tubular( + ... name="", top_elevation=0, bottom_elevation=-40, diameter=7.5, wt=0.075 + ... ) + >>> + >>> # Create a 40m deep offshore Soil Profile with a 15m water column + >>> sp = SoilProfile( + ... name="Offshore Soil Profile", + ... top_elevation=0, + ... water_line=15, + ... layers=[ + ... Layer( + ... name="medium dense sand", + ... top=0, + ... bottom=-20, + ... weight=18, + ... lateral_model=API_sand(phi=33, kind="cyclic"), + ... ), + ... Layer( + ... name="firm clay", + ... top=-20, + ... bottom=-40, + ... weight=18, + ... lateral_model=API_clay(Su=[50, 70], eps50=0.015, kind="cyclic"), + ... ), + ... ], + ... ) + >>> + >>> # Create Model + >>> M = Model(name="", pile=p, soil=sp) + >>> + >>> # Apply bottom fixity along x-axis + >>> M.set_support(elevation=-40, Tx=True) + >>> # Apply axial and lateral loads + >>> M.set_pointload(elevation=0, Px=-20e3, Py=5e3) + >>> + >>> # Run analysis + >>> from openpile.analyze import winkler + >>> Result = winkler(M) + Converged at iteration no. 2 + >>> + >>> # plot the results + >>> Result.plot() + +.. _Ex6-check_model: + +Example 6 - Visualize a model +============================= + +If one would like to check the input of the model, a quick visual on this +can be provided by plotting the model with the method: :meth:`openpile.construct.Model.plot`. + +.. plot:: + + >>> from openpile.construct import Pile, SoilProfile, Layer, Model + >>> from openpile.soilmodels import API_clay, API_sand + >>> + >>> p = Pile.create_tubular( + ... name="", top_elevation=0, bottom_elevation=-40, diameter=7.5, wt=0.075 + ... ) + >>> + >>> # Create a 40m deep offshore Soil Profile with a 15m water column + >>> sp = SoilProfile( + ... name="Offshore Soil Profile", + ... top_elevation=0, + ... water_line=15, + ... layers=[ + ... Layer( + ... name="medium dense sand", + ... top=0, + ... bottom=-20, + ... weight=18, + ... lateral_model=API_sand(phi=33, kind="cyclic"), + ... ), + ... Layer( + ... name="firm clay", + ... top=-20, + ... bottom=-40, + ... weight=18, + ... lateral_model=API_clay(Su=[50, 70], eps50=0.015, kind="cyclic"), + ... ), + ... ], + ... ) + >>> + >>> # Create Model + >>> M = Model(name="", pile=p, soil=sp) + >>> # Apply bottom fixity along x-axis + >>> M.set_support(elevation=-40, Tx=True) + >>> # Apply axial and lateral loads + >>> M.set_pointload(elevation=0, Px=-20e3, Py=5e3) + >>> # Plot the Model + >>> M.plot() From ba984ff6ef6aa44ceef86a50a6aefa8988026dc3 Mon Sep 17 00:00:00 2001 From: TchilDill Date: Thu, 26 Oct 2023 21:48:02 +0200 Subject: [PATCH 03/13] add options to matplotlib ext --- docs/.DS_Store | Bin 6148 -> 6148 bytes docs/source/conf.py | 2 ++ docs/source/usage.rst | 32 ++------------------------------ 3 files changed, 4 insertions(+), 30 deletions(-) diff --git a/docs/.DS_Store b/docs/.DS_Store index 010a211e0fb36d4254c0ce8bdb364f6b6b5454f1..ab8780a9bdfd7a5934a15774d36c2d06997ef439 100644 GIT binary patch delta 50 zcmZoMXfc@JFUrKgz`)4BAi%(o%#fN?UR;orlb^KNka;=dWCIbF&6-U2SSL0}ZD!~A G%MSoGhYhy? delta 69 zcmZoMXfc@JFU-uqz`)4BAi%(oQWjj4my@5DzFClYC8IP*f|VhOp_Cz$AqOD|Qofmo M=^^W8c8>> from openpile.construct import Pile, SoilProfile, Layer, Model >>> from openpile.soilmodels import API_clay, API_sand @@ -308,37 +309,8 @@ If one would like to check the input of the model, a quick visual on this can be provided by plotting the model with the method: :meth:`openpile.construct.Model.plot`. .. plot:: + :context: close-figs - >>> from openpile.construct import Pile, SoilProfile, Layer, Model - >>> from openpile.soilmodels import API_clay, API_sand - >>> - >>> p = Pile.create_tubular( - ... name="", top_elevation=0, bottom_elevation=-40, diameter=7.5, wt=0.075 - ... ) - >>> - >>> # Create a 40m deep offshore Soil Profile with a 15m water column - >>> sp = SoilProfile( - ... name="Offshore Soil Profile", - ... top_elevation=0, - ... water_line=15, - ... layers=[ - ... Layer( - ... name="medium dense sand", - ... top=0, - ... bottom=-20, - ... weight=18, - ... lateral_model=API_sand(phi=33, kind="cyclic"), - ... ), - ... Layer( - ... name="firm clay", - ... top=-20, - ... bottom=-40, - ... weight=18, - ... lateral_model=API_clay(Su=[50, 70], eps50=0.015, kind="cyclic"), - ... ), - ... ], - ... ) - >>> >>> # Create Model >>> M = Model(name="", pile=p, soil=sp) >>> # Apply bottom fixity along x-axis From e6a5a961ee5a4e637cbac9fe43d2f25632e13cff Mon Sep 17 00:00:00 2001 From: TchilDill Date: Tue, 31 Oct 2023 22:31:59 +0100 Subject: [PATCH 04/13] added bothkennar clay --- CHANGELOG.md | 1 + docs/source/introsoilmodels.rst | 5 + src/openpile/soilmodels.py | 202 ++++++++++++++++++++++++++++++++ src/openpile/utils/Hb_curves.py | 64 +++++++++- src/openpile/utils/Mb_curves.py | 61 ++++++++++ src/openpile/utils/mt_curves.py | 56 +++++++++ src/openpile/utils/py_curves.py | 57 +++++++++ 7 files changed, 445 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 73ef4a0..8e3860e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,6 +19,7 @@ and [PEP 440](https://www.python.org/dev/peps/pep-0440/). - `openpile.utils.Hb_curves.custom_pisa_sand` and `openpile.utils.Hb_curves.custom_pisa_clay` - `openpile.utils.Mb_curves.custom_pisa_sand` and `openpile.utils.Mb_curves.custom_pisa_clay` - added soil models: + - `openpile.soilmodels.Bothkennar_clay` from the PISA joint-industry project - `openpile.soilmodels.Custom_pisa_sand` and `openpile.soilmodels.Custom_pisa_clay`, these models can be used to customise PISA formulations based on external sources, such as an FE model. - new functions to calculate Dunkirk Sand and Cowden Clay normalized parameters, these functions are in the module: `openpile.utils.multipliers` and are the following: `get_cowden_clay_(..)_norm_param()` and `get_dunkirk_sand_(..)_norm_param()`. diff --git a/docs/source/introsoilmodels.rst b/docs/source/introsoilmodels.rst index 7d5f452..63967f3 100644 --- a/docs/source/introsoilmodels.rst +++ b/docs/source/introsoilmodels.rst @@ -47,5 +47,10 @@ Please refer to the :ref:`ApplicationProgrammingInterface` for more details and Houlsby, G. T., Gavin, K. G., Igoe, D. J. P., Jardine, R. J., Martin, C. M., McAdam, R. A., Pedro, A. M. G. & Potts, D. M. (2020). PISA design model for monopiles for offshore wind turbines: application to a marine sand. Géotechnique, https://doi.org/10.1680/jgeot.18.P.277. +.. [BABH20] Burd, H. J., Abadie, C. N., Byrne, B. W., Houlsby, G. T., Martin, C. M., McAdam, R. A., + Jardine, R.J., Pedro, A.M., Potts, D.M., Taborda, D.M., Zdravković, L., and Andrade, M.P. + (2020). Application of the PISA Design Model to Monopiles Embedded in Layered Soils. + Géotechnique 70(11): 1-55. +https://doi.org/10.1680/jgeot.20.PISA.009 .. [Rees97] Reese, L.C. (1997), Analysis of Laterally Loaded Piles in Weak Rock, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, vol. 123 (11) Nov., ASCE, pp. 1010-1017. diff --git a/src/openpile/soilmodels.py b/src/openpile/soilmodels.py index 522f48e..e8fdddb 100644 --- a/src/openpile/soilmodels.py +++ b/src/openpile/soilmodels.py @@ -109,6 +109,208 @@ def Qz_spring_fct(): pass +@dataclass(config=PydanticConfigFrozen) +class Bothkennar_clay(LateralModel): + """A class to establish the PISA Bothkennar clay model as per Burd et al 2020 (see [BABH20]_). + + Parameters + ---------- + Su: float or list[top_value, bottom_value] + Undrained shear strength. Value to range from 0 to 100 [unit: kPa] + G0: float or list[top_value, bottom_value] + Small-strain shear modulus [unit: kPa] + p_multiplier: float or function taking the depth as argument and returns the multiplier + multiplier for p-values + y_multiplier: float or function taking the depth as argument and returns the multiplier + multiplier for y-values + m_multiplier: float or function taking the depth as argument and returns the multiplier + multiplier for m-values + t_multiplier: float or function taking the depth as argument and returns the multiplier + multiplier for t-values + + See also + -------- + :py:func:`openpile.utils.py_curves.bothkennar_clay`, :py:func:`openpile.utils.mt_curves.bothkennar_clay`, + :py:func:`openpile.utils.Hb_curves.bothkennar_clay`, :py:func:`openpile.utils.Mb_curves.bothkennar_clay` + + + """ + + #: Undrained shear strength [kPa], if a variation in values, two values can be given. + Su: Union[PositiveFloat, conlist(PositiveFloat, min_items=1, max_items=2)] + #: small-strain shear stiffness modulus [kPa] + G0: Union[PositiveFloat, conlist(PositiveFloat, min_items=1, max_items=2)] + #: p-multiplier + p_multiplier: Union[Callable[[float], float], confloat(ge=0.0)] = 1.0 + #: y-multiplier + y_multiplier: Union[Callable[[float], float], confloat(gt=0.0)] = 1.0 + #: m-multiplier + m_multiplier: Union[Callable[[float], float], confloat(ge=0.0)] = 1.0 + #: t-multiplier + t_multiplier: Union[Callable[[float], float], confloat(gt=0.0)] = 1.0 + + # spring signature which tells that API sand only has p-y curves + # signature if of the form [p-y:True, Hb:False, m-t:False, Mb:False] + spring_signature = np.array([True, True, True, True], dtype=bool) + + def __str__(self): + return f"\tCowden clay (PISA)\n\tSu = {var_to_str(self.Su)} kPa.\n\tG0 = {round(self.G0/1000,1)} MPa" + + def py_spring_fct( + self, + sig: float, + X: float, + layer_height: float, + depth_from_top_of_layer: float, + D: float, + L: float = None, + below_water_table: bool = True, + ymax: float = 0.0, + output_length: int = 15, + ): + # validation + if depth_from_top_of_layer > layer_height: + raise ValueError("Spring elevation outside layer") + + # define Su + Su_t, Su_b = from_list2x_parse_top_bottom(self.Su) + Su = Su_t + (Su_b - Su_t) * depth_from_top_of_layer / layer_height + # define G0 + G0_t, G0_b = from_list2x_parse_top_bottom(self.G0) + Gmax = G0_t + (G0_b - G0_t) * depth_from_top_of_layer / layer_height + + y, p = py_curves.bothkennar_clay( + X=X, + Su=Su, + G0=Gmax, + D=D, + output_length=output_length, + ) + + # parse multipliers and apply results + y_mult = self.y_multiplier if isinstance(self.y_multiplier, float) else self.y_multiplier(X) + p_mult = self.p_multiplier if isinstance(self.p_multiplier, float) else self.p_multiplier(X) + + return y * y_mult, p * p_mult + + def Hb_spring_fct( + self, + sig: float, + X: float, + layer_height: float, + depth_from_top_of_layer: float, + D: float, + L: float = None, + below_water_table: bool = True, + ymax: float = 0.0, + output_length: int = 15, + ): + # validation + if depth_from_top_of_layer > layer_height: + raise ValueError("Spring elevation outside layer") + + # define Dr + Su_t, Su_b = from_list2x_parse_top_bottom(self.Su) + Su = Su_t + (Su_b - Su_t) * depth_from_top_of_layer / layer_height + # define G0 + G0_t, G0_b = from_list2x_parse_top_bottom(self.G0) + Gmax = G0_t + (G0_b - G0_t) * depth_from_top_of_layer / layer_height + + y, Hb = Hb_curves.bothkennar_clay( + X=X, + Su=Su, + G0=Gmax, + D=D, + L=L, + output_length=output_length, + ) + + return y, Hb + + def mt_spring_fct( + self, + sig: float, + X: float, + layer_height: float, + depth_from_top_of_layer: float, + D: float, + L: float = None, + below_water_table: bool = True, + ymax: float = 0.0, + output_length: int = 15, + ): + # validation + if depth_from_top_of_layer > layer_height: + raise ValueError("Spring elevation outside layer") + + # define Dr + Su_t, Su_b = from_list2x_parse_top_bottom(self.Su) + Su = Su_t + (Su_b - Su_t) * depth_from_top_of_layer / layer_height + # define G0 + G0_t, G0_b = from_list2x_parse_top_bottom(self.G0) + Gmax = G0_t + (G0_b - G0_t) * depth_from_top_of_layer / layer_height + + _, p_array = py_curves.bothkennar_clay( + X=X, + Su=Su, + G0=Gmax, + D=D, + output_length=output_length, + ) + + m = np.zeros((output_length, output_length), dtype=np.float32) + t = np.zeros((output_length, output_length), dtype=np.float32) + + for count, _ in enumerate(p_array): + t[count, :], m[count, :] = mt_curves.bothkennar_clay( + X=X, + Su=Su, + G0=Gmax, + D=D, + output_length=output_length, + ) + + # parse multipliers and apply results + t_mult = self.t_multiplier if isinstance(self.t_multiplier, float) else self.t_multiplier(X) + m_mult = self.m_multiplier if isinstance(self.m_multiplier, float) else self.m_multiplier(X) + + return t * t_mult, m * m_mult + + def Mb_spring_fct( + self, + sig: float, + X: float, + layer_height: float, + depth_from_top_of_layer: float, + D: float, + L: float = None, + below_water_table: bool = True, + ymax: float = 0.0, + output_length: int = 15, + ): + # validation + if depth_from_top_of_layer > layer_height: + raise ValueError("Spring elevation outside layer") + + # define Dr + Su_t, Su_b = from_list2x_parse_top_bottom(self.Su) + Su = Su_t + (Su_b - Su_t) * depth_from_top_of_layer / layer_height + # define G0 + G0_t, G0_b = from_list2x_parse_top_bottom(self.G0) + Gmax = G0_t + (G0_b - G0_t) * depth_from_top_of_layer / layer_height + + y, Mb = Mb_curves.bothkennar_clay( + X=X, + Su=Su, + G0=Gmax, + D=D, + L=L, + output_length=output_length, + ) + + return y, Mb + + @dataclass(config=PydanticConfigFrozen) class Cowden_clay(LateralModel): """A class to establish the PISA Cowden clay model as per Byrne et al 2020 (see [BHBG20]_). diff --git a/src/openpile/utils/Hb_curves.py b/src/openpile/utils/Hb_curves.py index 919772d..f2237ff 100644 --- a/src/openpile/utils/Hb_curves.py +++ b/src/openpile/utils/Hb_curves.py @@ -13,6 +13,68 @@ from openpile.core.misc import conic +@njit(cache=True) +def bothkennar_clay( + X: float, + Su: float, + G0: float, + D: float, + L: float, + output_length: int = 20, +): + """ + Creates the base shear spring from the PISA clay formulation + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + response (a normally consolidated soft clay). + + Parameters + ---------- + X : float + Depth below ground level [unit: m] + Su : float + Undrained shear strength [unit: kPa] + G0 : float + Small-strain shear modulus [unit: kPa] + D : float + Pile diameter [unit: m] + L : float + Embedded pile length [unit: m] + output_length : int, optional + Number of datapoints in the curve, by default 20 + + Returns + ------- + 1darray + y vector [unit: m] + 1darray + Hb vector [unit: kN] + + """ + + # Generalised Bothkennar clay Model parameters + v_hu1 = 291.5 + v_hu2 = 0.00 + k_h1 = 3.008 + k_h2 = -0.2701 + n_h1 = 0.3113 + n_h2 = 0.04263 + p_u1 = 0.5279 + p_u2 = 0.06864 + + # Depth variation parameters + v_max = v_hu1 + v_hu2 * L / D + k = k_h1 + k_h2 * L / D + n = n_h1 + n_h2 * L / D + p_max = p_u1 + p_u2 * L / D + + # calculate normsalised conic function + y, p = conic(v_max, n, k, p_max, output_length) + + # return non-normalised curve + return y * (Su * D / G0), p * (Su * D**2) + + + @njit(cache=True) def dunkirk_sand( sig: float, @@ -116,7 +178,7 @@ def cowden_clay( """ - # Generalised Dunkirk Sand Model parameters + # Generalised Cowden clay Model parameters v_hu1 = 235.7 v_hu2 = 0.00 k_h1 = 2.717 diff --git a/src/openpile/utils/Mb_curves.py b/src/openpile/utils/Mb_curves.py index cc0c673..460bf1d 100644 --- a/src/openpile/utils/Mb_curves.py +++ b/src/openpile/utils/Mb_curves.py @@ -13,6 +13,67 @@ from openpile.core.misc import conic # SPRING FUNCTIONS -------------------------------------------- +@njit(cache=True) +def bothkennar_clay( + X: float, + Su: float, + G0: float, + D: float, + L: float, + output_length: int = 20, +): + """ + Create the base moment springs from the PISA clay formulation + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + response (a normally consolidated soft clay). + + Parameters + ---------- + X : float + Depth below ground level [unit: m] + Su : float + Undrained shear strength [unit: kPa] + G0 : float + Small-strain shear modulus [unit: kPa] + D : float + Pile diameter [unit: m] + L : float + Pile length [unit: m] + output_length : int, optional + Number of datapoints in the curve, by default 20 + + Returns + ------- + 1darray + t vector of length [unit: rad] + 1darray + Mb vector [unit: kN] + + """ + + # Bothkennar clay parameters + k_m1 = 0.3409 + k_m2 = -0.01995 + n_m1 = 0.6990 + n_m2 = -0.1155 + m_m1 = 0.8756 + m_m2 = -0.09195 + psi_u = 187.0 + + # Depth variation parameters + k = k_m1 + k_m2 * L / D + n = n_m1 + n_m2 * L / D + m_max = m_m1 + m_m2 * L / D + psi_max = psi_u + + # calculate normsalised conic function + t, m = conic(psi_max, n, k, m_max, output_length) + + # return non-normalised curve + return t * (Su / G0), m * (Su * D**3) + + + @njit(cache=True) def cowden_clay( X: float, diff --git a/src/openpile/utils/mt_curves.py b/src/openpile/utils/mt_curves.py index efcfcad..23e76d3 100644 --- a/src/openpile/utils/mt_curves.py +++ b/src/openpile/utils/mt_curves.py @@ -13,6 +13,62 @@ from openpile.core.misc import conic # SPRING FUNCTIONS -------------------------------------------- +@njit(cache=True) +def bothkennar_clay( + X: float, + Su: float, + G0: float, + D: float, + output_length: int = 20, +): + """ + Create the rotational springs from the PISA clay formulation + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + response (a normally consolidated soft clay). + + Parameters + ---------- + X : float + Depth below ground level [unit: m] + Su : float + Undrained shear strength [unit: kPa] + G0 : float + Small-strain shear modulus [unit: kPa] + D : float + Pile diameter [unit: m] + output_length : int, optional + Number of datapoints in the curve, by default 20 + + Returns + ------- + 1darray + t vector of length [unit: rad] + 1darray + m vector [unit: kN] + + """ + + # Bothkennar clay parameters + k_m1 = 1.698 + k_m2 = -0.1576 + n_m1 = 0.00 + n_m2 = 0.00 + m_m1 = 0.4862 + m_m2 = -0.05674 + + # Depth variation parameters + k = k_m1 + k_m2 * X / D + n = n_m1 + n_m2 * X / D + m_max = m_m1 + m_m2 * X / D + psi_max = m_max / k + + # calculate normsalised conic function + t, m = conic(psi_max, n, k, m_max, output_length) + + # return non-normalised curve + return t * (Su / G0), m * (Su * D**2) + + @njit(cache=True) def cowden_clay( X: float, diff --git a/src/openpile/utils/py_curves.py b/src/openpile/utils/py_curves.py index 42c155f..4aa8348 100644 --- a/src/openpile/utils/py_curves.py +++ b/src/openpile/utils/py_curves.py @@ -13,6 +13,63 @@ from openpile.core.misc import conic # SPRING FUNCTIONS -------------------------------------------- +@njit(cache=True) +def bothkennar_clay( + X: float, + Su: float, + G0: float, + D: float, + output_length: int = 20, +): + """ + Creates a spring from the PISA clay formulation + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + response (a normally consolidated soft clay). + + Parameters + ---------- + X : float + Depth below ground level [unit: m] + Su : float + Undrained shear strength [unit: kPa] + G0 : float + Small-strain shear modulus [unit: kPa] + D : float + Pile diameter [unit: m] + output_length : int, optional + Number of datapoints in the curve, by default 20 + + Returns + ------- + 1darray + y vector [unit: m] + 1darray + p vector [unit: kN/m] + + """ + + # # Bothkennar clay parameters + v_pu = 173.8 + k_p1 = 12.05 + k_p2 = -1.547 + n_p1 = 0.7204 + n_p2 = -0.002679 + p_u1 = 7.743 + p_u2 = -3.945 + + # Depth variation parameters + v_max = v_pu + k = k_p1 + k_p2 * X / D + n = n_p1 + n_p2 * X / D + p_max = p_u1 + p_u2 * m.exp(-0.8456 * X / D) + + # calculate normsalised conic function + y, p = conic(v_max, n, k, p_max, output_length) + + # return non-normalised curve + return y * (Su * D / G0), p * (Su * D) + + @njit(cache=True) def cowden_clay( X: float, From 564d22f7ca8f611711e2cf5d20d399accdd817d9 Mon Sep 17 00:00:00 2001 From: TchilDill Date: Tue, 31 Oct 2023 22:32:21 +0100 Subject: [PATCH 05/13] ditto --- docs/source/_static/validation/GDSM_D2t.png | Bin 36999 -> 38176 bytes samples/Burd_et_al_G0_profile.png | Bin 0 -> 86407 bytes samples/GDSM_Burd2020_D2t.ipynb | 65 +++++++++++++++++--- 3 files changed, 55 insertions(+), 10 deletions(-) create mode 100644 samples/Burd_et_al_G0_profile.png diff --git a/docs/source/_static/validation/GDSM_D2t.png b/docs/source/_static/validation/GDSM_D2t.png index 541485185cfcfd27e44f51795343540962a3f4ae..61eb9c8693aedffe1c13a813065fcc99a843ade8 100644 GIT binary patch literal 38176 zcmcG$byQbv*Dd^`k?w8~ltw@rq>*ls4n^sbkd|&lQc^&akdl^e6%{E#X%HzX2@wR& 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"name": "stdout", "output_type": "stream", "text": [ + "Converged at iteration no. 3\n", + "Converged at iteration no. 4\n", "Converged at iteration no. 4\n", "Converged at iteration no. 4\n", "Converged at iteration no. 5\n", + "Converged at iteration no. 4\n", + "Converged at iteration no. 4\n", + "Converged at iteration no. 4\n", + "Converged at iteration no. 4\n", + "Converged at iteration no. 4\n", "Converged at iteration no. 5\n", "Converged at iteration no. 5\n", "Converged at iteration no. 5\n", "Converged at iteration no. 5\n", - "Converged at iteration no. 5\n", - "Converged at iteration no. 5\n", - "Converged at iteration no. 5\n", - "Converged at iteration no. 5\n", - "Converged at iteration no. 5\n", - "Converged at iteration no. 6\n", - "Converged at iteration no. 5\n", - "Converged at iteration no. 6\n" + "Converged at iteration no. 5\n" ] }, { "data": { - "image/png": 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", + "image/png": 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iaoBKpUJqaipOnDiBsNBQxMbGIjs7G5WVlfcVlGroGxjAsnt3TJo0Cc/Nnw+ZTNYCVTdOJDT20rANVFVVISUlBYWFhdi1axd+/PFHBAUF1Yanvzt16hTGjh2LGzduoEePHmrvp67HycHBAZMnT8ahQ4da7TmIiIjof1QqFY4cOYIPP/wQaampLRKUjIyMYGlpiV69e8PXxwdeXl5w9/CAubn5fc2oKyoqglwuR2FhIUxMTBpt2+49TlKpFG5ubgAAHx8fhIWF4euvv8b3339fr62/vz8ANBqcZDJZmyROIiIialhZWRk+/+wzpNy+3ex7mJiYwM7ODr1794afvz/69OkDF2dnmMjlEIvFEIlELVixZto9OP2TSqWq02P0d5GRkQAAGxubNqyIiIiItFVWVoacnByN24vFYhgZGcHFxQV9+vSBv78/PPv0gZ2dXZ1eoJYMSzUv3bR5+dauwWnVqlWYNGkSHB0dUVxcjG3btuHMmTM4duwYbt68iW3btmHy5MkwNzdHdHQ0li9fjhEjRqBfv37tWTYREVGTCsurcTQ2E5GpBVj7aNf7uWVqaoqRo0Zh/759al/T6ejowMjICG5ubvDq2xeDBg1Cnz59YGVlBWNj41bpTRIEASqVCtXV1cjKzMS169dx7do1FBQUaHyPdg1OOTk5mDNnDjIzMyGXy9GvXz8cO3YM48ePR2pqKk6ePImvvvoKpaWlcHBwwMyZM/HOO++0Z8lEREQNqqhWIvBqDvZFpuNMYi6qlPcCw3NDXeBhZdzO1bUtXV1dvPvuu7C0tMTx48eRn5cHqUwGNzc39O/XDz6+vujTpw8sLCxgaGjYKgtX1szmKy8vR0pKCuLj4hAdHY3IqCikp6WhsLAQVVVVWo2/avfB4a2tZsAXB4cTEVFrUChVuHgzH/siM3AsLgsllYp6bZaMdsOrE3q2Q3XtT6FQoKioCHfv3oWhoSGMjY2hr6/fakGpoqICRUVFuHnzJqKiohATE4PY2FjkZGejrKwMSqWy3nUqlQpZ2dmdY3A4ERFRZyMIAq6kFmB/ZAYORmcgr6Sq0fb7otKx8iGPdhnMXKMmVOTl5SEhIQHJSUkwMTFBb09P9OzZs9UmVuno6MDMzAxmZmYtfm9BEFBaWoq8vDxcu3YNlyMiEBsbi8TEROTn56O6urpFZvP9HYMTERGRhm7kFGPvlQzsj8pAyp0yja9LvVOOK6kFGOjYrRWrq0+pVCI3Nxe3bt5EcEgIwsPCkJCQgPz8fKhUKohEIujr6+O5+fPxyiuvwNDQsE3r05YgCCguKkJmVhbi4+MRHhaGuLg43Lp1CwUFBWp7k1oagxMREVEjMgrKcSAqA/siMxCf2bzdKLzsTFBR1bo/1AVBgCAIKCkpwa1bt3D58mWcP38eCVevIj09HQpF/VeINT02P3z/Pby9vTFhwoR27RX7e101/ywsLERKSgpiY2MRFhqK+Ph4pKSkoKSkRKvZcOqIRCIYGhrC2toaWdnZGl3D4ERERPQPd0urcDg2E/siMxCadKdZ93AyN8C0AXZ4pL8t3CyNWrjC/wWl6upqpKamIjYmBhcvXsTly5eRmpqKkpISje9VVVWF28nJLV6jpmoCkFKpREFBwb3962JjERoaioSrV5GRmYlSLZ5HHZFIBLFYDFNTU7i6usLT0xM+Pj7o7ekJQ0NDODs7a3QfBiciIiIAZVUKnLyag/2R6Qi6lotqpfa9GRZGMkztb4PpA+zQz17e4r03NUGpsKAAcXFxCAsLw8XgYNy6dQt379xp9qsqMzMzDBw4sEVrbczfQ19BQQESExMREx2N8PBwJCYmIi8vD6Wlpff1PcRiMXR1ddG9e3f07NULfb284D1wINzd3WFubg5DQ8Pa3x9t9rVlcCIioi6rWqnC+et52BeZjuPx2Shrxus0Y5kOJnpZY9oAOwT0MIdE3LILNKpUKpSWliI5ORnhYWEIuXQJUZGRyMvLQ0VFRbPvXTO+ycPDAy+++CIGeHu36ms6lUqFiooK3L17F1evXq3d6DcxIQEFBQUoLy+/r/tLJBIYGBjAxsYGfby80L9fP/QfMADOzs4wMTGBTCZrkedjcCIioi5FpRIQkXIX+yLTcSg6E3fLqrW+h1RHjLG9LDFtgC1G9bSEnm7z90lriCAISEtLw/Zt23AyMBC3k5NRVlZ2X7PEZDIZzMzM4OnpCT9/f/j4+MDT0xMmJiat0jtWWlqK/Px8xMfF4fLly4iKikJiYiKKi4sb3CVEU7q6ujAxMYG9vT0GeHujf//+8PLygr29PQwMDKCjo9MqQZDBiYiIuoSErCLsi8zA/sgMpBdo37shFgFDeljgkQG2mNDHGnJ93Vao8n8yMjLw/IIFiI+Pb3ZYEovFMDMzg4urKwYPHgzfvxadNDc3h66ubotvX1JaWoqcnBxcjY9HaFgYYqKjcf36dRQVFakdnK4NXakU5ubmcHJygq+PD/oPGIBevXrB1tYWUqm0zfauY3AiIqIHVuqdMuyPuheWErOLm3WP/g6mmNbfFg/3s4GliV4LV9iw06dPNys0GRoawtHREf0HDMCwoUPh1bcvHB0da4PS/YaLv89kq6ioQEZGBhKuXsWlS5cQFRWFmzdvorCw8L7XT9LV1YWVtTVcXVzg6+sL74ED4ebmBmtr69reJG7yS0REdJ/ySypxKObejLiI23ebdQ/X7oaY1t8O0wbYwtmifdY2EmsYCiQSCWxtbdHb0xNDhw6Fr68vXFxcWnRj3JqwVFVVhYz0dCQkJiIkJARXLl9GcnIy7ty5c99LA0ilUtjZ2cHd3R2+vr4YOHAgnJydYWlpCYlE0iLP0RIYnIiIqNMrqVTgRHwW9kVm4Nz1PChV2v8QtzKR4ZH+tpg2wA59bFt+zI+2xowdCz8/P4SGhtbpvZFIJDAyMkKvXr3gO2gQhg4dCnd3d1haWkJHp+V+rAuCAIVCgdzcXMTHxyMsNBSXQkNxOzkZ+fn597XYpFgshkwmg72DA3r36gUfHx94DxwIBwcHmJmZtehztLSOWxkREVEjqhQqBF3Lxb7IdJy8mo2Kau1fDZno6WBKPxs80t8Ofi5mLToj7n5ZWVlh/bffYvv27YiJjkZZeTkcHBzgN2gQBvr4wMrKqs6U+vslCAKUSiVKSkpw/fp1hIeHI/jiRVy9ehX5+fn3NZhbIpFAT18fjg4O8PprWQBvb2/Y2dlBLpdDIpG0e1DVFIMTERF1GiqVgEtJd7A/Kh2HY7JQWK79jDiZjhjjPK0wfYAdRnhYQKbT8jPiWoJIJIKVlRWWLl2K6upqKJVKSKXSFu2NUalUKCsrQ05ODi5HRCDk0iWEh4UhIyMDZWVlzX79pqOjA0NDQ9jZ2WHAgAEY4O2NAQMGwM7ODkZGRp0qKP0TgxMREXVogiAgLqOodpB3VpH2axdJxCIMc7PAtAG2eKiPNYxkzf/xJwgCysvLUVZWBl1dXRgaGrZaEKgZAN1SG/AKgoDKykrcuXMHV69exaWQEERERCAhIQElJSXNnvkmFoshl8tha2eHgQMHYuDAgejbty8cHBygp6fXqYPSPzE4ERFRh5ScV4r9URnYF5mOm7nNW0Xax6kbpg2wxeS+NrAwan74UKlUKCgowI0bN3Du7FmEhoUhMzMTRoaG6N27N1586SW4ubl1uHDw93FKyUlJCA0NRcTly7gaH4+8vDwoFIpm9yrJ5XLY2dnB29sbfv7+8PT0hLOzM2QyWZstDdAeGJyIiKjDyCmuwKHoTOyNzEBUakGz7uFuaYTp3vf2iHMwM9D6+pogoVKpkJ2djZiYGJw+fRphYWFIunWr3lifqKgoREVFYdPmzXBwcGhWzS2hpm6FQoGsrCwk3bqF8IgIRERE4OaNG8jMzLyvtZT09PTg6OgIr759MXToUHh5ecHZ2RkGBgbttjRAe2BwIiKidlVUUY1jsVnYH5WBCzfy0IwJcbAz1cfU/raYNsAWvayNtf4hXhM6qqurkZKSgojwcJw6dQqRkZHIzMxscgZZQkICzp8/jyeffFL74pupZr83hUKB9PR0XL9+HREREYgID0fy7dvIzcm5r6AkkUhgaWkJzz59EDB4MPz8/eHi4gJTU9PaX9+uEpb+jsGJiIjaXEW1EmcSc7AvMgOBCTmoUmg/I66bgS6m9LPBtAF28HHsBrGWM+Jqgkd5eTmuX7+O4OBgnDlzBgkJCbiTn6/VAo4ikQjGxsbaPoJW/r4xbnZ2NuLi4hAREYHwsDDcTknBnfz8+w5K+vr6cPfwgI+PD4YOHQpPT09YWlpCKpW24JN0bgxORETUJpQqASG38rH3SjqOxmahuFL7H/L6uhI81McK0wbYYrh7d+hKxFpdXzPlvri4GHFxcTh//jzOnTuHWzdvori4uFnjfaRSKcaMGYMhQ4Zofa0m9VZXV6OwsBDx8fEIDwtDaFgYEhMTUVhQgKqqqmbfWywWQ09PD9bW1vAeOBB+gwbBd9Ag2NrawsjIqEu9ftMGgxMREbUaQRAQnVaIfZEZOBCdgdxi7dcC0hGLMNKjOx4ZYIvxnlYwkGr3o6vmddadO3cQGRmJM2fOIPjiRaSlpaG8XPs964B724GYm5uj/4ABmDxpEh6aMKHFepxUKhVKSkqQlpaG0EuXEHLpEiKvXEFubi4qKrSfUVhDJBLBwMAA5ubm6Nu3b+2ik+7u7jAyMmq1TXEfNAxORETU4m7mlvy1oW46kvPLmnUPP2czTPO2xWQvG3QzbN6rosrKSgRfvIg9e/YgNDQUmZmZqK7Wfu0nANDX14ednR38/f0xfPhwDPTxQffu3e97s9yaXqXcnBzEX72KC+fPIzw8HDdu3EBpael97flmbGyM7paW8OzdG76+vujbrx/c3d1hbGzc4pv8dhUMTkRE1CKyCitwMDoD+yIzEJNe2Kx79LYxwbQBtpja3xZ2pvr3VU9FRQX+7+uvsXHjRpSVNS+8GRsbo4ebG4YPG4bhw4fDs08fmJiY3Nd0+5rXgUVFRUhOSsKl0FBcvHABcXFxyM7Ovq+tTIyNjWFrZwevPn0wyM8Pffr0gaurK4yNjSEW33utybB0fxiciIio2QrLqnEk9t6GuiFJ+WjOkkAOZvqY1t8OjwywhYdVyw2wvn37NrZs2aJVaBKJRDAzM0MfLy+MGjkSQ4YMQQ83N+jr69ee11ZNUFIqlUhPT0dcbCzOnj2Ly5cvIykpqdmhDgAMDAzg7OyMPn36YHBAALy8vODo6Fg7Rqm5NVPDGJyIiEgrgnBv25MtIbdxPC4L1Urt05KFkRQP97PFIwNs4e1g2io/3KurqzUaw6SjowNLS0sM9PHB6FGjMMjPD/b29ve1WrcgCFCpVCgtLUViYiLCw8Jw7tw5JCYmIjc3t1m9SiKRCLq6urC1tYWXlxcChgyBj48PHOztITdtnV9Dqo/BiYiINFJcUY09V9KxJfg2rueUaH29oVSCCV7WmDbADkN7mENHyxlx2nKwt8e4ceNw6NCheuOEZDIZ7OztMdjfH6NGjYL3wIG145Wao2apgKqqKuTk5ODK5cu4GByMsNBQpKeno6RE+18v4F6oMzY2hqenJwb5+WHIkCFwc3ODmZkZB3O3EwYnIiJqVEJWEX4LuY09l9NRWqVdT4lUIsaont0xbYAdxva2hJ5u222oayKX498ffojenp4IvngRt2/fhqWlJXx9fTFixAh49e0LuVze7E1z/760wY3r1xEcEoKQ4GDExcWhsLCwWYPQa2a+2drawtfXF4MDAuDj4wNLS8vaFbqpfTE4ERFRPVUKFY7GZeG34NsITb6j1bUiETDYxRzTBthikpcN5AbN68W5XyKRCBYWFnj55ZexaNEiVFdXQ0dHBzKZDBJJ8wKcIAioqKhAbm4urly5ggsXLiA8LAypqakoLy9v1jpQurq6MDMzQ29PTwQEBMBv0CB49OwJQ0ND9ip1QAxORERUK6OgHNtDU7A9NBV5JdqtudTXTo5pA2zxcD9bWMv1WqlC7dQs4qivr187wFtbgiCgoKAAycnJuHjhAoKDgxEbG4u7d+82e6VuY2NjODg4wM/PDwFDhqB///6wsrSEzl9LBDAsdVwMTkREXZxKJeDCzTxsCb6Nk1eztdorzsxQitm+Dpjla48e3Y00uubvs8wqKiqgp6dX2wPU3oHh77VlZWUhLjYWZ86cubdR7s2bqKysbFavkkQigZWVFTw9PTF8+HD4DhoENze3LrdB7oOAwYmIqIsqLKvGzohUbL2UgqS8Uq2u9XHqhmcHO2FSX2vIdDR77SUIAiorK5GYmIigM2dw9uxZFBYWwtzcHJMmTcKjM2fWmUbfVmoGdldWVuLmzZsIDw9H0JkziI2LQ1ZmZrMWoKzp5XJzc4Ovry+GjxgBT09PWFtbd5iQSM3D4ERE1MXEphdiS/Bt7ItKR0W15qFAX1eC6d62eGawE/rYyjW6piaQXL92DWfPncOJ48eRmJiIoqKiOu2Cg4Nx7do1vP3OOzAwMNDqeZqjZmB3UVERYmNjcfHiRZw/dw5JSUkoKCho1j11dHRg0b07+vXtiyFDh8Lf3x9OTk4wMTFhSHqAMDgREXUBFdVKHIrOxJaQ24hMLdDqWtfuhnh2sBMeHWgPuX7TA71rBlDfvn0bZ8+exYnjxxEfH4/CwsIGX3MpFArs3bsXz82fDzc3N63q01RNiMvJyUFERATOnzt3bxuWrCyUN2MRSrFYDP2/FqD0GzQIw4YNg1ffvujevTukUinD0gOKwYmI6AGWkl+GrZdu44/wVNwt03x6vEQswkOeVnh2sBMCepg3GQJqQklaWhrOBgXh5MmTiIqORlFhocavulpjBlnNIpS3k5Pv9SqdP4/o6GgUFBQ0a7kAXV1dmJmbo6+XV22vkqurKwwNDZs9U486FwYnIqIHjFIlIOhaDrYE38aZa7labYPS3ViGJ/0c8ZSfY5Mz42o2p83IyMDFixdx4vhxhIeHo7CwUOuVsQ0MDPDEk0/Czs5Oq+vUUalUuHPnzr2xVEFBCAkORmJiIsrKypo1XsnIyOje5r6DB2PIkCHw9vZmr1IXxuBERPSAyC+pxB/hadh66TbS7ja91cjfDXY1w7ODnfFQHyvoNrGit0qlQlZWFsLCwnDk8GGEhoYiNzdX61BSs86St7c3Zs+ejdFjxjR7m5OaWXAx0dE4deoUwsLCkJycjOrqaq1nwYlEIpibm8PDwwMjRo6Ev78/evXqBUNDw/va3JceDAxORESdmCAIuJJagN+Cb+NgdCaqlJqHFyOZDh4daIdnBjs1urluTfC4e/curly5gsOHDuHixYtIS0trVg+Oubk5+vfvjwkTJyIgIACOjo5av6arGdydkpKCiPBwnAwMROSVK8jMzGzWPnA6Ojqwt7dH//79MXrMGHh7e8PR0bF2CxaGJarB4ERE1AmVVSmwPzIDW0JuIy6jqOkL/qaXtTGeGeyE6d52MJI1/mOgZvHHrb/9hj179uDmzZtajw0SiUQwMTFB//79Mf6hhzB8+HA4OTlB96/FHjVVsxfcrVu3EBISgsCTJxEXF4fc3Nxm9Srp6enBw8MDfn5+GDV6NHr36gWL7t3Zq0SNYnAiIupEbuWW4LeQFOyMSEVxhearVutKRJjkZYNnA5zg69RN42BQWlqKNatX488//9SqJ0ckEsHIyAh9+/bFmLFjMXr0aDg6OkJfX1+rUKJSqVBRUYFriYm4cPEiTp06hWuJibh7967WYUkikcDExAT9+vXDkKFDMXz4cDg7O8PY2BhicetuOEwPDgYnIqIOTqFU4eTVHPwWchvnb+Rpda2tXA9P+Tti9iAHWBprvw1Kbm4ujhw5olFoEolEMDQ0RM+ePTF27FiMGTsWLi4uMDQ01CosKZVKlJaWIuHqVZw9exZngoJw88YNFBcXax2WpDIZultYYNCgQRg2fDj8/f1hbW2tdYAjqsHgRETUQeUUV2BHaCq2XUpBVlGFVtcOd7fAs4OdMKaXJXSaGOzdGB0dHRgaGqK4uLjBNgYGBnBxccHYsWMxduxY9PzbQGpNqVQqFBYUIP7qVZw+fRrnzp1D0q1bKCsr0zosGRgYwN7eHkOGDMGw4cMxcOBAdOvWTetXg0TqMDgREXUggiAgNOkOtoTcxtHYLCi02DjORE8Hs3wd8LS/I1w13DeuKVZWVli4aBG+/uqrOqt9S6VSODg4YNTo0Rg/fjz69+9fu12KpuGkZtmA+Lg4nAwMxMULF5CUlNSs/eBMTEzg6uqKESNHYviwYejj5QUjIyOOV6IW167B6bvvvsN3332H5ORkAECfPn3w3nvvYdKkSQCAiooKrFy5Ejt27EBlZSUmTJiAb7/9FlZWVu1YNRFRyyuuqMbeK+nYEnIb17JLtLrWy84EcwY7Y2p/W+hLW3YRRl1dXTz//PPo17cvLgYH49bNm7C1tYWfnx8G+vjAzMxM47BUE4by8/MRGxuLEydO4OLFi0hOSkJVVZXWtZl264bevXtjzOjRGDJ0KDw8PKCnp8dNc6lVtWtwsre3xyeffAJ3d3cIgoBffvkF06ZNw5UrV9CnTx8sX74chw4dws6dOyGXy7FkyRI8+uijuHDhQnuWTUTUYhKzirElJBl7LqejtErzwddSHTEe7meDOQHO6G8vb7WgIBKJIJFIEDBkCAKGDKl3rik1G+jevXsX0dHROHH8+L2w9NcaS9rWYmZmhv79+2PM2LEICAiAi4sLpFKpxvUQ3S+RoG1/aCszMzPDf/7zHzz22GPo3r07tm3bhsceewwAkJCQgN69eyM4OBiDBw/W6H5FRUWQy+WYPHkyDh061JqlExFppEqhwrG4LGwJuY3QpDtaXetgpo9n/J0wy9cBZobSVqrw/giCcG/MUmEhoiIjcTIwEOfPn0dqSgoqKyu1updEIoGFhQV8fHwweswYDB48GHZ2ds1eKJNInZqsUFhYCBMTk0bbdpgxTkqlEjt37kRpaSkCAgIQERGB6upqjBs3rrZNr1694OjoqFVwIiLqKDILy7H9Ugq2h6Uit1jzACESAWN6WuKZACeMdO8Osbjj9azUCUtRUTh16hTOBgUhLS0NFRXaDWzX1dVF9+7d4efvj9GjR8Pfzw9W1ta1PUtE7andg1NMTAwCAgJQUVEBIyMj7NmzB56enoiMjIRUKoWpqWmd9lZWVsjKymrwfpWVlXX+j+bvgxmJiNqaSiXg4s18bAlJxsmrOVBqMdjbzFCK2X8N9nYwM6h3XqlUoqCgABkZGVAoFLCxsYGFhQV0dNrur3alUonCwkLExsQgMDAQZ8+eRWpqKsrLtdvyRSqVwsrKCoMDAjB61Cj4+fnB3MKCYYk6nHYPTj179kRkZCQKCwuxa9cuzJ07F0FBQc2+39q1a7F69eoWrJCISHuFZdXYdTkNW0Nu41ZeqVbXDnQ0xbMBTpjkZQM93bqDvQVBQFlZGRITEnDo0CEcP3ECWZmZEAQBpqameOqpp/D8woUwNjZutTE/KpUKBQUFuHr1Kk6cOIFzZ88iOTlZ654lqVQKGxsbBAQEYMzYsfD19YWZmZnW268QtaV2D05SqRRubm4AAB8fH4SFheHrr7/G448/jqqqKhQUFNTpdcrOzoa1tXWD91u1ahVWrFhR+7moqAgODg6tVj8R0d/Fphfit5Db2BuZjopqzfdx09MVY/qAe/vGednJ651XqVRIT0tD0NmzOLB/P65cuVJvjaPy8nJ8/fXXMDIywsJFi1rkeWoIgoCioiIkXL2K48eP4+y5c7h544bWY5Z0dXVhb2+PwQEBGDduHHz+mpnHZQOos2j34PRPKpUKlZWV8PHxga6uLgIDAzFz5kwAQGJiIlJSUhAQENDg9TKZjIMGiahNCYKAM4m5+Ob0DUTcvqvVta4WhnhmsBNm+thDrq9b554AUFZWhujoaOzduxdnTp9Genp6o2scKRQKnAkKuu/gVPM9ysvKkJCYiJMnTuDMmTNITEzUumdJIpHAwcEB/oMHY8KECfD29oaFhQWXDaBOqV2D06pVqzBp0iQ4OjqiuLgY27Ztw5kzZ3Ds2DHI5XIsWLAAK1asgJmZGUxMTPDyyy8jICCAA8OJqEMQBAGBV3Pwf6euIzqtUOPrJGIRxve2wrMBThjSw7xOeKgZZJ2WloZTgYHYt38/YmNitBoz5O7urtVz/P17A0BVVRWuX7+OM2fO4MTx47h69SpKS7V73SiRSGBvbw8/f39MnDgRAwYMgKWlJcMSdXrtGpxycnIwZ84cZGZmQi6Xo1+/fjh27BjGjx8PAPjyyy8hFosxc+bMOgtgEhG1J0EQcCI+G/936jpi0zWfgGJhJMNTfg540t8RNnL9OudUKhXKysoQHh6OA/v3IygoCFlZWVqtoC2TyTBu/Hg8//zzGl8D3HueqqoqpKam4mxQEI4dO4bY2FgUFBRodR8dHR3Y2Njc61l66CF4DxwIS0tLSCQtuygnUXvqcOs4tTSu40RELUWlEnA8Phv/F3gd8ZmaByY/FzPMCXDCQ57WkOr8b/82QRCgUCiQkZGBEydOYP++fbh69SrKyso0vnfNOkfDhg3DI488gsEBARptqisIAqqrq5GTk4Pz58/jxPHjCA8Px507d6BSaT42S1dXF90tLREweDDGjR+PQYMG1c7sY88SdRadch0nIqKOSqUScCwuC18HXkdCVsOb3f6doVSCRwfa45nBTuhpbVznnCAIKCkpwZXLl7F7926cO3cOubm5UCo1Xznc0NAQnp6emPLwwxg3bhzs7Ow02sRWqVQiPz8fYWFhOH7sGC5cuIDc3FwoFAqNv7eOjg66d++OQX5+GD9uHAYHBMDCwoKb6FKXwOBERNQAlUrAkdgs/PeU5oHJykSGF0b0wOxBDjCS1f0rVhAEZGdn4+jRo9i3dy9itBy7JJFIYGNjgzFjx2Lq1Kno16+fxr1LhYWFiImJwdGjRxEUFIS01FSttjwRi8Xo3r07vL29MWHiRAQEBMDa2po9S9TlMDgREf2DUiXgcEwm/nvqusYb7lqb6OGlUT3w+CCHemsvAffCy8WLF7Fm9WrEx8dr9TrM0NAQ/fv3x/Tp0zFy1CjY2Ng0On2/ZgRGRUUFrl27huPHjyPw5Elcu3ZN6+UDunXrhn79+mHS5MkYOmQIHJ2cIJFIGJaoy2JwIiL6i1Il4GB0Bv576gZu5GgWmGzlenhptBtm+9pDptPwIOiKigp8+OGHiI2N1ei+YrEYdnZ2GDduHB6ZNg19+/aFnp5eo4FFEAQolUrcvn0bZ8+exeFDhxATE4PiYs16y2oYGhqiT58+mDBhAkaOGoUePXpAV/feUgkMTNTVMTgRUZenVAk4EJWB/566jpu5mk27tzPVx79G98BjPo0HphpFRUXIyc5usp2BgQG8vb0xbdo0jBo9GtbW1o3OSqtZviA/Px8hISE4ePAgwsLCkJuTo/WMPA8PD4weMwbjx49Hz549YWBgwKBE9A8MTkTUZSmUKuyPysA3p25ovC2KfTd9LB7thpkD7evMkGuKuZkZfH19cejQoXqBpmbs0tixYzF9+nR49ukDIyOjBu8lCAIEQUBpaSmioqJw9MgRnDlzBmlpaVqNW5JKpbC3t8fIUaMwceJEePXpA7mpKcMSUSMYnIioy1EoVdgbmYH1p28gScPA5GCmjyWj3fDoQHvoSjQPTDUkOjp4+513YGBoiJMnTqCqqgq6urpwd3fH9BkzMGbMGNjY2DQ6fkgQBFRUVCA5ORknjh/HsWPHcO3aNa2WL6iZETd06FBMmDABg/z8YGZmxrWWiDTE4EREXUa1UoU9V9Kx/vQN3M7XLGw4mRtg8Wg3zPC2a1ZgqiESieDg4IBPPvkEuStWICc3F3K5HNbW1k2+ElMoFLXrLR0+fBhhoaEoKirSeIC5SCSCmZkZBg4ciIkTJ2LY8OGwsrLijDiiZmBwIqIHXrVShd2X0/DN6RtIvaPZ9H9ncwMsGeOO6QNsoXMfgenvRCIRZDIZ7B0cYN/E5uOCIKC4uBjR0dE4eOAAzpw5g8zMTK3WWzIyMkKvXr0wafJkjB49Gs7OzpBKpQxLRPeBwYmIHlhVChX+vJyG9advIO2uZoHJ1cIQS8a44ZH+LReYNFUzKy45KQmBgYE4ePAg4uLitFpCQEdHB66urhg9ZgwmTZyIPl5e0Ne/t70LAxPR/WNwIqIHTpVChZ0Rqfj29E2kF2gYmLob4pUx7pja3xYScdsFjJqB3gUFBbh06RL27d2LkJAQ5ObmanwPkUgEKysrBAwZgqlTp8LHxwfm5ua154io5TA4EdEDo1KhxB/hafju9A1kFFZodI2bpRFeHuOGh/vZQiyqCTL3zrVm6BAEAZWVlUhMTMTRI0dw7Phx3Lp1C9VVVRpdLxKJYGhoCG9vb0yeMgUjRoyAvb09dHT41zpRa+J/YUTU6VVUK/FHeCq+O3MTmRoGJg8rI7w8xh2T+9pABAEFBXcRHhaGuLg4iMRieHp6YsiQIRptaaINhUKB3NxcnDlzBocOHsTly5dRWFio8fUymQwuLi4Y/9BDmDhxItzd3bneElEbYnAiok6rolqJHaEp+C7oJrKLNBsH1NPKGK+MdcfEPlYABGRmpOPQwYPYvXs3rl+/XjueSE9PD6NGj8batWthaWl5X3XWrLkUExODgwcPIvDkSa0GetfsEzds+HBMmTIFgwYNgqlcDjGXECBqcwxORNTpVFQrse1SCjYE3UROsWaBqZe1MZaOdceEPtZQKhW4di0Ru//8EwcOHEBGRgaUSmXd71FRgePHjmGgtzcWL1nSrDoVCgXS09Nx+vRpHNi/H9HR0VqtuWRkZATPPn3w8JQpGDtuHOzs7LiEAFE7Y3Aiok6jvEqJrZdu4/uzt5CrYWDytDHBK2PdMb63JaqrqxAWFood27fj5MmTuHv3bqPbkqhUKiTfvq1VjTW9S9HR0di7Zw9Onz6NrKwsjddckkgkcHR0xOjRozHl4YfRr18/zooj6kAYnIiowyurUmBrSAq+P3sLeSWaBSYvOxO8MsYd43pboqysDKdOBWLrb7/h4sWLKC3VbLVwQ0NDjBkzpsl2NTPjMjIycCowEHv37kVUVBQqKjQbbwUAcrkcvr6+mDZ9OoYOHQorKysADEtEHQ2DExF1WKWVCmwJuY2NZ28hv1Sz2Wb97OVYOtYdo3t2R0FBAf7ctQvbt29HZGSkxush6ejooGevXli8eDHGjRvXYLuamXHRUVHYt28fTp48ifT0dI03163ZcmXixImYOGkS3NzcuEAlUQfH4EREHU5JpQK/Bifjx3NJuKNhYOrvYIplY90x3M0Mubm52PTzz/j9999x7do1jTe+1dPTw8CBA/H4E09g1MiRMLewqBdiBEGASqVCXl4eTp8+jT27dyMqKgrFxcUafQ+xWIxu3bph2LBheGTaNPj5+aFbt24MS0SdBIMTEXUYxRXV+DX4Nn48dwt3yzQLO96Oplg6xh1De3RDWloavvh8Ew4cOIDbt2/XG/DdELlcjhEjRmDW7Nnw8/ODkZGR2sBUWVmJ69eu4cCBAzh85AjSUlO1CmUeHh6YPHkyJk6aBEdHR8hkMo2uJaKOg8GJiNpdcUU1Nl9Ixk8XklCgYWDyceqGpWPdMdhZjls3b2Ltx9/h4MGDyMjI0OhVWc1q2w9NmIDHZ89Gr969IZPJ6gUmlUqFgoIChISE4M8//0RIcDAKCws1/h7du3fHkKFDMX36dAwaNAgmJiYQiUTsYSLqpBiciKjdVCqU+Pl8MjYE3URhuWaBaZBzNywd64EhPcyQmpqKD97/AgcPHsSdO3c0CjNisRhOTk6YPmMGpk+fDmdnZ0gkknpBRqFQICUlBYcPHcKBAweQmJioce+STCaDu7s7HnnkETw0YQKcnZ25jADRA4LBiYjanCAIOHk1Bx8eisftfM3WNfJzMcOyse4I6HFvD7aMjAwsW7YMoZcuaXS9RCJBr9698fjs2Zg8ZQqsrKzq9fwIgoCKigpEXrmCP//8E4GBgcjNzdV4sLeZmRkCAgIwc+ZMDA4IgImJCQDOjCN6kDA4EVGbup5djDUH43Huep5G7Qe7mmHpWI/awAQASqUSu3fvRnhYWJPXS6VSDBgwAE89/TTGjh1bbyB2TSjKy8tD0Jkz2LlzJy5fvqzxQpUSiQSurq6YMmUKpjz8MDw8PNT2YBHRg4HBiYjaRGFZNb4KvIZfg29DqWq6B2dID3MsHesOf1fzJtuqo29gAH9/fzzzzDMYOnQojI2N6wUmhUKB5KQkHDhwAAcOHMCtW7c03gbFyMgIgwYNwqMzZ2L4sGEwt7CAWCxuVq1E1HkwOBFRq1KqBOwIS8Hnx69ptLTAMDcLLB3njkHOZg22EYvFmD59Ok4FBiI8PLzOqtzGxsYYNWoUnnzqKQwaNAj6+vr1AlN5eTmuXL6MP//8E6dPn9b4dZxEIoGNjQ3GP/QQZkyfjt6envXuT0QPNgYnImo1l27lY/WBeMRnFjXZtp+9HO8+7NloYKohEolgb2+Pb9avr12lu6qyEj169MCkSZPg1bdvvRlyKpUKd+/eRVBQEHb+8QciIiI0XkFcX18fvT09MWP6dIx/6CHY2NjwdRxRF8XgREQtLr2gHB8fvopD0ZlNtrUwkuH1iT3x2EB7iMWaBxGRSAQ7Ozu89NJLUCgUEAQBOjo6EIvFdQKNUqlEWloaDh86hN179uDG9euoqmq650skEqGbmRlGDB+OGY8+Cn9/f7XrOxFR18LgREQtprxKie/P3sSGoJuoqG58U1tdiQjzh7pgyRg3GOvpNvt7isViSKXSeserqqqQmJiIP3ftwpEjR5CRkaHRRrtisRguLi6YPHkypk2fjh49ekBXV5eBiYgAMDgRUQsQBAGHYjKx9nAC0gvKm2w/ppcl3pnSG67djVq8jvLycoSFheH3HTtwJigIhQUFGl0rk8nQv39/zJo9G2PGjOEmu0SkFoMTEd2X+IwirD4Qh0tJd5ps69rdEO8+7InRPS1b7PvXDOq+c+cOzpw+jR07duDKlSsoL286wAGAqakpRo4cidmPPw5fX18YGhoCYGAiIvUYnIioWe6UVuGz44nYEZqCplYXMJbpYOk4d8wJcIZUp2Wm7NdstpuZmYlDBw9i165duH79ukare4vFYtja2mLylCl49NFH0bNnT7Wv+4iI/onBiYi0Uq1UYUvwbXx18hqKKhpf80gkAmb7OmDF2B7obtIy0/YFQUB1dTVu3bqFP3ftwsGDB5GWlqbR+CWpVAoPDw/MePRRTJkyBba2tpBIJPddExF1HQxORKSxc9dzseZAPK7nlDTZdqCDHLN6AClRR/DphWx0t7DA2HHj4O3tDV1d7QeDC4KAyspKREdH44/ff8fxEydwJz9fo/WXjIyMMNDHB4/Pno0RI0fC1NSUi1USUbMwOBFRk27nl+LDQ1dxIj67ybZWJjLMctPBtZO/4oOvz6Ck5H8ha8uWLVj56quYN3cuJDqa//VTUlKC0NBQbNu6FefPn0dxcXGT14hEIpiZmWHMmDF4bNYs+Pj4QE9Pj2OXiOi+MDgRUYNKKhVYf/oGfjqXhCpl46/CpDpiTPMwRFn4Hmz4ab/awdmFhYX4ceNGDBs2DD179mz0foIgoLCwEGeDgrB161aEh4ejoqKiyZpFIhEcHB3x8JQpmDlzJtzc3blYJRG1GAYnIqpHpRKwNzIdnxxJQE5xZZPthzkZwjT5NI58ugt3795ttG1FRQUqGpjxVjtDLj8fx44dw7Zt2xATE6PR/nESiQQeHh6YNXs2pkyeDFs7O4hEIgYmImpRDE5EVEdkagFWH4jDlZSCJtu6mMnQpyIeF37YhJzspl/jicViTJw4ER7/6G0SBAGCICA7OxsHDx7E7zt24Nq1a1AqlU3eUyaTYcCAAXjyyScxeswYmJubMywRUavRKDgVFTW9z9Q/mZiYaH0NEbWfnKIKrDuWiF0RaU22NdGTYJAsG3F7NmBvSkqTA7RFIhEsunfHU08+iYWLFkFfX7/2nFKpRHp6Ovbs2YM///wTSbduaTRDztDICEOHDsUTTzyBgIAAGBsbMzARUavTKDiZmppq9ReSSCTCtWvX4Orq2uzCiKhtVCqU2HQhGf8NvI7SqsZ7eMQiwFdejuzAn3AiIVajHiEzc3M8MnUqnn32Wbi5u0Pnr0HhCoUCycnJ+HPXLuzeswcZ6elNBqa/D/h+8qmn0LdvX+jrt8wyB0REmtD4Vd2uXbtgZtb0ruWCIGDy5Mn3VRQRtT5BEHAqIQf/PhiP5PyyJtu7m6igCv8DoRFnNVpk0tTUFA899BCemz8fvXv3hq6ubu0aTDdv3sSOHTtwYP9+5OTkaBSYrKysMOXhhzF71ix49OzJ/eOIqF1oFJycnJwwYsQImJuba3RTV1fXZq3TQkRt40ZOCdYcjMfZa7lNtrXQF8EiJQiJv/+BqsqmB4obGhpi9OjRmL9gAQYOHAgdHR2IRCIoFAokJCRg29atOHT4MPLz8jR6xefo6Ijp06dj5mOPwcnJiTPkiKhdaRSckpKStLppbGysRu3Wrl2L3bt3IyEhAfr6+hgyZAg+/fTTOtOUR40ahaCgoDrXvfDCC9iwYYNWNRERUFhejf8LvI5fLiZD0cQ+KTKJCM5libi1ZyMyiwuavLdUKsWQIUPw/MKFGDJkSO0WJkqlEnFxcdi2dSsOHz6MO3ea3tNOLBbD1dUVj82ahenTp8POzo4LVhJRh9Cus+qCgoKwePFiDBo0CAqFAm+99RYeeughxMfH1260CQALFy7EmjVraj8bGBi0R7lEnZZSJeCP8FR8diwR+aVVTbbvoXMHOce/R2zarSbb6ujooH///lj0wgsYPXp07X+fCoUC8fHx+PWXX3Ds2LEmlykA/rekwFNPP43JkyfDysoKADfcJaKOQ+Pg9Ouvv2rUbs6cORp/86NHj9b5vHnzZlhaWiIiIgIjRoyoPW5gYABra2uN70tE/xOWfAcf7I9DXEbTs2N7WxvBMvUMTv22sclxR2KxGB4eHpj33HOYOnUq5HI5gL8CU1wcfv31V40Dk46ODry8vPDkk09i4qRJXFKAiDosjYPT0qVLGzwnEolQWloKhUKhVXD6p8LCQgCoNwh969at+O2332BtbY2pU6fi3XffbbDXqbKyEpV/G4fRnKUUiB4EGQXlWHskAQeiMppsa2EkxasP9YS7Tj4en72l0dAkEolgb2+PZ555Bo/NmgVLS8vaMUxxcXHYsmULjh09qlFgkkql6Ne/P5555hmMHTsW3bp1Y2Aiog5N4+DU0F+CmZmZWL16NX7++WeMHz++2YWoVCosW7YMQ4cOhZeXV+3xp556Ck5OTrC1tUV0dDTeeOMNJCYmYvfu3Wrvs3btWqxevbrZdRB1dtVKFX44ewv/PXUdFdWN9xrpiEV4bqgzXh7rDmOZDuLji6Enk6G8TP0sO3Nzczw2axaeeeaZ2oHa1dXVSEhIwK+//IKjR49qNIZJT08PA7y9MWfOHIwcORJyuZyBiYg6BZGgydbiahQXF+PTTz/F119/jT59+mDt2rUYPXp0swt56aWXcOTIEZw/fx729vYNtjt16hTGjh2LGzduoEePHvXOq+txcnBwwOTJk3Ho0KFm10fUGSRmFWPlzkjEpjfd0zqqZ3e8+7AnenQ3qj1WWVmJ9evX4/sNG+pszmtkZIQJEybg+eefR29PT+jq6kKhUCAxMRG//fYbDh44oHFg8vX1xbNz5mDUqFEwNDRkYCKidldUVAS5XI7CwsImF/DWenB4dXU1/vvf/+Ljjz+Gubk5Nm3ahMcee6zZxQLAkiVLcPDgQZw9e7bR0AQA/v7+ANBgcJLJZJDJZPdVD1Fno1Cq8P3ZW/jq5DVUKxv/fyEXC0O8+3BvjOllVe+cTCbDkiVL4OvjgwsXLiAjIwNOzs4YEhAAH19fSKVSqFQqXL9+Hb9t2YK9e/ciLy+vyfpkMhn8/Pwwd948jBgxAgYGBgxMRNQpaRycBEHAr7/+ivfeew8KhQIff/wxFixYAIlE0uxvLggCXn75ZezZswdnzpyBi4tLk9dERkYCAGxsbJr9fYkeJNezi7FyZxSi0wobbWck08ErY90wb4gLpDoNT+2XSqUYPmIEhv9tggZw77/X1JQUbN26Fbt27UJWVlaTtUmlUvj5+eG5+fMxfPhwBiYi6vQ0Dk79+vXDrVu38PLLL2PZsmUwMDBAaWlpvXba7FG3ePFibNu2Dfv27YOxsXHtX8RyuRz6+vq4efMmtm3bhsmTJ8Pc3BzR0dFYvnw5RowYgX79+mn8fYgeRAqlChvPJeHLE9dQpWx8LNMsH3u8NrEnLI31NLp3Tbip2Xw3KysLO3fuxLatW5Gent7kwpW6uroY5OeH5+bNw4iRI/lKjogeGBqPcfr74nPq/gIUBAEikUijvasauw8AbNq0CfPmzUNqaiqeeeYZxMbGorS0FA4ODpgxYwbeeecdjQNazXtLjnGiB8mNnBK8ujMKkakFjbZzszTCfx7rB2/Hblp/D5VKhfz8fOzbuxdbtmzBLQ0235XJZBgwYADmPfccRo0axc2+iahTaJUxTqdPn77vwv6pqczm4OBQb9Vwoq5MqRLw0/lb+Oz4NVQpGg4xYhGwaEQPLBvnDj1d7V6nC4KAwsJCHD58GJs3b0ZiQgIUCkWj10ilUvTt2xdz583D+HHjYGxiwh4mInogaRycRo4c2Zp1EFETbuXe62W6nFLQaDvX7ob4bFZ/DNSyl0kQBJSUlODM6dP48ccfER0djaqqxlcZ19HRgaenJ+bOm4eJEydyWQEieuC165YrRNQ0pUrApgtJ+M+xRFQ20sskEgELh7tixXgPrXuZKioqEBISgo0bNyL44sU6S3qoIxaL4e7ujrnz5uHhhx+GmZkZAxMRdQkaBydNZ89pM8aJiBqXlFeK13ZGIfx246twu1gY4rNZ/eDjZNZou78TBAFKpRJRUVH48ccfcfLECZQ1sPBlne/l4oInn3oKs2bNQvfu3RmYiKhL0Wo5AicnJ8ydOxfe3t6tWRNRl6dSCdh8MRnrjiU0uvq3CMA4RwleHmUNTzuT2kkaTVEqlUi6dQs//fwz9u/bh4KCgiavsbW1xezZs/HEE0/A3sHh3vdnaCKiLkbj4BQaGoqffvoJX3/9NVxcXDB//nw8/fTT6NZN+9k6RNSw2/mleG1XNEKTGl+JW1yWD9WFzTiafQ1h35tjxowZWLZ8ee1mu+qoVCpkZWVhx/bt2LZtGzIzM5usx8zMDDNmzMAzzz4LNze3OjNsiYi6Gq23XKmoqMCuXbuwadMmhISEYOrUqViwYMF97VPXmrgcAXUWKpWA3y7dxtrDCSivbvyVtyohEMrLuwHF/wZvSyQSvPX223jxxRfrtRcEAXfv3sX+/fux6eefNVpaQC6X46EJE7Bg/nz06t0burq6zXswIqIOrlW3XNHT08MzzzyDZ555BklJSViwYAEmTpyI3NxcmJlpPr6CiP4n9U4ZXtsVhZBbTez3VpILxYXNELKv1TulVCoReulSneAkCALKy8sRGBiIHzduRFRUFKqrqxv9FgYGBhg+fDieX7gQPj4+kEqlfCVHRPSXZs2qS0tLw+bNm7F582aUlZXhtdde40J3RM2gUgnYGpqCtYevoqyq8V4mZcIpqC7vBhTqZ7zp6upixF/LhgiCgOrqakRERGDDhg04f+4cKioqGr2/VCqFj68vnn/+eYwcORJ6enoMTERE/6BxcKqqqsKePXvw008/4dy5c5g0aRK++uorTJo06b72qyPqqtLuluGNP6Nx4UZ+o+2EkjwoL2yGkJ3YYBtjY2PMmTMHjz32WO0mvD9u3IiDBw+iqKio0ftLJBL07NkTCxcuxMRJk2BsbMzARETUAI2Dk42NDYyNjTF37lx8++23sLS0BIB6+9Wx54mocYIgYEdYKj48GI/SpnqZEs9AFbGrwV4msVgMX19fvPzyyxg+YgTy8/Pxww8/4LfffkO2BpvwOjg4YM7cuZg1axYsLCwYmIiImtCue9W1BQ4Op44ko6Acb/wZjXPX8xptJ5TkQxn8C4TMqw22sbKywvz58/HkU09BJpPh0MGD+OGHH5CYmNjkdkbm5uaY+dhjmDt3LhwdHTlTjoi6tE6zVx1RVyEIAv4IT8WHB6+iuLLxfd+U14Lu9TJVqx+TJJPJMH78eCx5+WV4eHjgUkgIvtuwASHBwU1ukWJgYIAJEyfi+eefh5eXF3R0uHkAEZE2uFcdUSvLLCzHm3/GIOhabqPthNI7UAb/CiEjTu15kUgEd3d3LHn5ZYwfPx5ZmZl4+623cPjwYRQWFjZ6b6lUCn9/f7zw4osYPHgw9PX1m/08RERdmUbBqaioSKuxS8XFxTA2Nm52UUQPAkEQsCsiDWsOxqO4ovFeJtX181CG/wFUl6s9b2xsjJkzZ+KFF1+Enp4efvnlF/z666/IzMho9LXc3wd+T5g4ESYmJhzHRER0HzQKTt26dUNmZmbtgPCm2NnZITIyEq6urvdVHFFnlV1UgVW7Y3AqIafRdkLZXSgv/gohI1btebFYjAHe3li5ciUGDBiA8+fPY/369YiLjW1yPKGdnR2eevppPPXUU9xTjoiohWgUnARBwI8//ggjIyONbtrUAntEDypBELDnSjo+2B+HoqZ6mW5cgDLs9wZ7mczMzTF37lw8++yzyMrKwptvvIETJ040uR6TsbExpk6dioULF8LN3R0ikYihiYiohWgUnBwdHbFx40aNb2ptbc3tGajLySmuwFu7Y3Hyanaj7YSygntjmdJj1J7X0dFBQEAAXn31VdjZ2eG3LVuwZcsW5OY2PkZKR0cHQ4cOxUv/+hcGDx4MHR0dBiYiohamUXBKTk5u5TKIOi9BELA/KgPv749DQVnjva0uyMG1/R8BVWVqz1tZW2PhwoV47LHHEBISgnfffRcxMTGNjmMSiURwc3fHCy+8gClTpnAtNSKiVsS5yET34W5pFd7cHY1jcY33MlkYybD20b6Q5SXipf1SFPwjOEmlUowYORLLly+Hro4OVn/wAY4dO4ayMvUBq4a5uTmeePJJzJkzB3Z2duxhIiJqZQxORM0Ul1GIF7ZEIO2u+jFKNaYNsMUHU/ugm6EUCoU51q1bhw3ff4+kW7cgCAIcHR3x5FNPYczo0Thw4AA2bd6M9LS0Ru+pr6+PUaNGYfGSJejbty/XYyIiaiP825aoGfZFpuONP6NRUa1qsI2FkRQfTu+LiV7Wtcd0dHQwecoUjBw1Cvn5+VCpVJCbmCA6JgavvPIKIiIioFA0PKhcLBbD09MTL770EiZMmAB9fX32MhERtSEGJyItKJQqfHIkAT+eT2q03cP9bLBmmhfMDKX1zolEIhgZGcHAwABpaWn47PPPsWf37iY34+3evTueevppzJ07F5aWlgxMRETtgMGJSEN3SquwZNtlXLyZ32AbM0MpPpzuhcl9bRpsIwgCysvLceDAAfz3v/9FclJSo4O/dXV1MXr0aLz8yisYMGAAlxcgImpHGgWn6OhojW/Yr1+/ZhdD1FHFpt8bz5Re0PB4Jj8XM6x/aiC6G8sabKNUKpGQkIAvv/gCgYGBTe4t18PNDS+99BIeeeQRGBgYMDAREbUzjYJTzf/lCoLQ5F/cTa1mTNTZ7L1ybzxTpaLh8Uzzhjjj7Sm9oSsRqz0vCAIKCgqwY/t2bNy4EdnZjc/CMzY2xmOPPYYFzz8PZ2dnBiYiog5Co+CUlPS/8RxXrlzBq6++itdeew0BAQEAgODgYHz++edYt25d61RJ1A4UShU+PpyAny80PJ5JqiPG2hl9MdPHvsE21dXVCA8Px5dffIFLly41OvhbR0cHvr6+WLp0KQYHBEAqrT9GioiI2o9GwcnJyan232fNmoX/+7//w+TJk2uP9evXDw4ODnj33Xcxffr0Fi+SqK3ll1Ri8bbLCLl1p8E2tnI9fP+sL/ray9WeFwQB2dnZ2LRpE7b+9hvu3r3b6Pe0sbHB8wsXYtasWTAzM2MvExFRB6T14PCYmBi4uLjUO+7i4oL4+PgWKYqoPcWkFeKFLeHIKGx4T7jBrvfGM5kbqR/PVFVVhaCgIHz15ZeIjo6GStXwaz49PT089NBDeGXpUnh4eEAikdz3MxARUevQOjj17t0ba9euxY8//lj7GqGqqgpr165F7969W7xAora0+3IaVu2OaXQ80/yhLlg1uZfa8UyCICAjPR3ff/89fv/9d5SUlDR4H5FIhB49emDpsmWYOHEi12QiIuoEtA5OGzZswNSpU2Fvb187gy46OhoikQgHDhxo8QKJ2kK1UoWPDl3F5ovJDbbRFQOrH+6JJwN61As4giBAoVAgMDAQX37xBWJjYxv9foaGhnj00UexeMkS2NvbMzAREXUSWgcnPz8/3Lp1C1u3bkVCQgIA4PHHH8dTTz0FQ0PDFi+QqLXllVRi8dbLuJTU8HgmoSQf1Rc24sAtOwy2/hguLi61YUcQBGRkZODbb7/Frp07G+1lqln5e+Wrr2LUqFHQ1dVlaCIi6kSatQCmoaEhFi1a1NK1ELW56LQCvLAlApmNjGdSZSZAefZ7oLIE53Ju4csvvsCn69ZBX18fCoUCZ86cweeffYbY2NhGF7I0NTXFk08+iYWLFnHlbyKiTqrZK4fHx8cjJSWl3gJ+jzzyyH0XRdQWdkWk4a09MahqZDyTMv4EVBG7AOFeG0EQkJCQgNLSUhQVFmLDhg34/fffG90uRSKRwHvgQKxcuRIBAQHQ0dFhaCIi6qS0Dk63bt3CjBkzEBMTU7soJoDaHwRcAJM6umqlCh8ejMcvwbcbbCMoKqEM3gIh6VKd4xKJBH5+fggLC8M333yDmCZmzJmbm+PZZ5/Fc/Pnw9zcnIGJiKiT0zo4LV26FC4uLggMDISLiwtCQ0ORn5+PlStX4rPPPmuNGolaTG7xvfFMocmNjWfKg+LMt8Cd1DrHpVIp/P39UV5ejpUrVjTayyQWi+Hn74+VK1fC398fYrGYoYmI6AGgdXAKDg7GqVOnYGFhAbFYDLFYjGHDhmHt2rV45ZVXcOXKldaok+i+RaYW4MUtEcgqamw8UzyUZzcClXUHeHfr1g3+/v5ISUnB+fPnGx3L1M3MDPPmzcP8+fPRrVs3BiYiogeI1sFJqVTC2NgYAGBhYYGMjAz07NkTTk5OSExMbPECiVrCH+GpeGdvbOPjmeKOQXV5d+14phouLi5wcXFBSEgICgoKGrxeJBJhkJ8fXn31VQwePJi9TEREDyCtg5OXlxeioqLg4uICf39/rFu3DlKpFD/88ANcXV1bo0aiZqtSqPDvg/HYEtLEeKaLv0JIDq1zXFdXF71790Z1dTWCgoIaHb8nNzXF3LlzMX/+fFhYWDAwERE9oLQOTu+88w5KS0sBAGvWrMHDDz+M4cOHw9zcHL///nuLF0jUXDnFFVi89TLCkhveI04o/ms8092645mMjY3h4uKC9PR05OfnN3i9WCyGl5cX3njjDQwdNgy6urotVj8REXU8WgenCRMm1P67m5sbEhIScOfOHY7loA7lSspdvPhbBLKLKhtso8qIh/LcD0Blae0xkUgECwsLGBoaIiEhod5yG39nZGSExx57DIuXLIGNjQ3//BMRdQHNXscJANLS0gAA9vb2LVIMUUv4PSwF7+6NQ5WykfFMsUehurKnzngmHR0dmJubo7KyErdv3250AHjPnj2xfPlyTJg4kat/ExF1IfV3KW2CSqXCmjVrIJfL4eTkBCcnJ5iamuLf//53o+vZELW2KoUKb++JwRt/xjQYmoTqSijOfg/V5T/rhCY9PT2Ymprizp07KCgoaDA0yWQyzHzsMfz88894eOpUSKVShiYioi5E6+D09ttv45tvvsEnn3yCK1eu4MqVK/j444/x3//+F++++65W91q7di0GDRoEY2NjWFpaYvr06fVm5lVUVGDx4sUwNzeHkZERZs6ciezsbG3LpgdcTlEFntwYgq2XUhpsIxTnQnH0EwjJ4XWOGxkZQUdHB3l5eaiurm7wejs7O/z73//GunXr4OTszMBERNQFiYTG3keoYWtriw0bNtTbWmXfvn3417/+hfT0dI3vNXHiRDzxxBMYNGgQFAoF3nrrLcTGxiI+Pr52w+CXXnoJhw4dwubNmyGXy7FkyRKIxWJcuHBBo+9RVFQEuVyOyZMn49ChQ5o/KHUal1Pu4sUtEcgpbmw8UxyUZ38Aqspqj4lEIhgYGqKqsrLRwCSRSDB8xAisevNNePbpA7FY6//fICKiDqwmKxQWFsLExKTRtlqPcbpz5w569epV73ivXr1w507DqzGrc/To0TqfN2/eDEtLS0RERGDEiBEoLCzETz/9hG3btmHMmDEAgE2bNqF3794ICQnB4MGDtS2fHjBnEnPw4m8RqKhubDzTkb/GM/3v/xHEYjFkMhnKy8oafcXcrVs3zF+wAPPnz4dcLmcvExFRF6f1/zr3798f33zzTb3j33zzDfr3739fxRQWFgIAzMzMAAARERGorq7GuHHjatv06tULjo6OCA4OVnuPyspKFBUV1fmiB9ORmEws/DW8wdAkERRQnv3+r0Ut/xeaJBIJJBIJysvLGwxNYrEY/fr1w/pvv8Urr7wCU1NThiYiItK+x2ndunWYMmUKTp48iYCAAAD3tmFJTU3F4cOHm12ISqXCsmXLMHToUHh5eQEAsrKyIJVKYWpqWqetlZUVsrKy1N5n7dq1WL16dbProM5hZ3gq3vgzGqoGXjQ7mhlgzUN22FfVA0HlqbUhqaqqCgqFotFXc/r6+pg+fTqWr1gBW1tbBiYiIqqldXAaOXIkrl27hvXr1yMhIQEA8Oijj+Jf//oXbG1tm13I4sWLERsbi/Pnzzf7HgCwatUqrFixovZzUVERHBwc7uue1LFsvpCEDw7EN3h+hEd3/N8TAyDX18WQTz9FdnY2IiMj8dOPP+Ly5cuNvpqztbPDiuXLMePRRyGTyRiaiIiojmat42Rra4uPPvqozrG0tDQsWrQIP/zwg9b3W7JkCQ4ePIizZ8/WWRPK2toaVVVVKCgoqNPrlJ2dDWtra7X3kslkkMlkWtdAHZ8gCFh/+gY+O36twTazfe2x9tF+kIjvBR6JRIK0tDT897//RXxcXIPLDEgkEvj7++Pd996Dl5cXB4ATEZFaLfbTIT8/Hz/99JNW1wiCgCVLlmDPnj04deoUXFxc6pz38fGBrq4uAgMDa48lJiYiJSWl9jUhdQ2CIOCTIwmNhqb5Q13wyV+hSRAEVFZWYuvWrXjpxRcRFxvbYGgyNDTE888/j+9/+AF9+/ZlaCIiogbd18rh92vx4sXYtm0b9u3bB2Nj49pxS3K5HPr6+pDL5ViwYAFWrFgBMzMzmJiY4OWXX0ZAQABn1HUhKpWAd/fFNrpG09Kx7lg2zh0i0b3QlJ+Xhy+/+gq/79iB8vLyBq9zcHTEm2++icmTJ3MFcCIialK7BqfvvvsOADBq1Kg6xzdt2oR58+YBAL788kuIxWLMnDkTlZWVmDBhAr799ts2rpTaS7VShVd3RmFfZEaDbd6e3BsLR7gCuDfJIC42Fh9++CEuXrzY6Ky5YcOG4Z1330Xv3r3Zy0RERBpp1+Ckydqbenp6WL9+PdavX98GFVFHUlGtxJJtV3DyqvqV4kUi4OMZffGknyOAe0tRHDlyBOs+/RS3b99u8L56enp45tlnsXjxYnTv3p29TEREpDGNg9Ojjz7a6PmCgoL7rYWoVmmlAou2hOPCjXy153XEInzx+AA80t8WgiAgNzcXGzZswJZff0VZWZnaa4B7Ew5efe01zJgxA3p6eq1VPhERPaA0Dk5yubzJ83PmzLnvgogKy6rx3OZQXE4pUHteqiPGd08PxNjeVlCpVIiKisLajz9GcHBwo0sNeA8ciHffeQeD/Pz4ao6IiJpF4+C0adOm1qyDCACQV1KJZ38KxdVM9Su+G0ol2DjXFwGu5qioqMCePXvw+WefITMzs8F76urq4pFp0/D666/Dzs6Or+aIiKjZ2nWME9HfZRSU45kfL+FWXqna83J9XWx+bhAGOJgiLy8PX331FXZs346KiooG72lubo7FS5bg2Wefhb6+PkMTERHdFwYn6hCS8krxzI+XkF6gfukACyMZtizwQ08rI8THx+OD999HyKVLUCmVatuLRCL06dMH7777LgYHBEBHh3/UiYjo/vGnCbW7hKwiPPNjKPJKKtWetzGRYevCwXDspocjR47go48+wu3k5Abvp6enh0ceeQSvvvYa95ojIqIWxeBE7SoytQBzfw5FYbn6TXeFomz0q7wGudgb3323CRs2bEDB3bsN3s/S0hLLli3DrNmz+WqOiIhaHIMTtZvgm/l4/pcwlFapf90m3EmF4uSXOCVW4vnkREReuYKqqiq1bcViMfr164f3P/gAPj4+kEgkrVk6ERF1URoFp/3792t8w0ceeaTZxVDXcSohGy/9dhmVCvXLB6hyb0IZ+H9AVRnKAIReutTgvXR0dPDItGlYtWoVbGxs2MtEREStRqPgNH36dI1uJhKJoGxgsC5RjQNRGVj+eyQUKvUrx6syr0J5ej2gUD/m6e+MjY3x4ksv4fnnn4ehoSFDExERtSqNglNjiwoSaWNHaApW7YlBQ7vtqFIjoQz6HlApmryXvb093n7nHUyePJmz5oiIqE3wpw21mR/P3cKHh642eF516xKUFzYBQuO9liKRCAMGDMDqNWswcOBA9jIREVGbaVZwKi0tRVBQEFJSUuoN1n3llVdapDB6cAiCgK8Dr+Ork9cbbKO8FgTVpa1osCvqLzo6Ohj/0EN477334ODgwNBERERtSuvgdOXKFUyePBllZWUoLS2FmZkZ8vLyYGBgAEtLSwYnqkMQBHx06Cp+PJ/UYBtl7FGoLv/Z5L309PQwZ+5cLF26FHK5nKGJiIjanNY7nS5fvhxTp07F3bt3oa+vj5CQENy+fRs+Pj747LPPWqNG6qSUKgGrdsc0Hpqu7NEoNMnlcrz19tt4/fXXGZqIiKjdaB2cIiMjsXLlSojFYkgkElRWVsLBwQHr1q3DW2+91Ro1UidUpVBh6Y4r2BGW2mAbZeh2qGION3kvOzs7/OezzzB37lwuaklERO1K6+Ckq6sLsfjeZZaWlkhJSQFwr0cgNbXhH5LUdahUAlbujMLB6Ey15wWVCooLm6BKONXkvXr37o2v/+//OHOOiIg6BK1/Enl7eyMsLAzu7u4YOXIk3nvvPeTl5WHLli3w8vJqjRqpk/no8FUciMpQe05QKqA8txFCyuVG7yEWi+E/eDA+/vhjuLu7s5eJiIg6BK17nD7++GPY2NgAAD766CN069YNL730EnJzc/HDDz+0eIHUufxw9iZ+amBMk6CohPL0N02GJh0dHUyfPh3r169naCIiog5Fqx4nQRBgaWlZ27NkaWmJo0ePtkph1Pnsi0zHx4cT1J4TqsqhPPVfCDkNL0kAADKZDM8//zwWL1kCExMThiYiIupQtOpxEgQBbm5uHMtE9Zy/nodXd0apPScoq+/1NDURmkxMTPDmm29i+YoVnDlHREQdklbBSSwWw93dHfn5+a1VD3VCsemFeGFLOKqV9RevFAQVlOd/hpB9rdF7WFpaYs2//435CxZAX1+/tUolIiK6L1qPcfrkk0/w2muvITY2tjXqoU4mJb8M8zaFobRK/TYpqrDfIdwOb/Qedvb2+OyzzzBz5kxIJJLWKJOIiKhFaD2rbs6cOSgrK0P//v0hlUrr9Q7cuXOnxYqjji2/pBJzN4Uir6RS7Xnj1GDcaWLJgZ49e2LdunUY6ONTu8wFERFRR6V1cPrqq69aoQzqbMqqFJj/SziS8krVnh/hqIekC0FoLEb7+Phg7SefwNPTk+OZiIioU9A6OM2dO7c16qBOpFqpwuKtlxGVWqD2fF8LCZJ2rMGtmzfVnheJRBg6dCg+XrsWrq6uDE1ERNRpNOvdyM2bN/HOO+/gySefRE5ODgDgyJEjiIuLa9HiqOMRBAFv7Y7B6cRcteedjEXI+vND3LqhfgadWCzG2HHj8MWXXzI0ERFRp6N1cAoKCkLfvn1x6dIl7N69GyUlJQCAqKgovP/++y1eIHUsnx+/hp0RaWrPWegJuLv3I2TcvqX2vFgsxsMPP4z//Oc/sLW1ZWgiIqJOR+vg9Oabb+LDDz/EiRMnIJVKa4+PGTMGISEhLVocdSxbQm7jm9M31J4z0hFQcfRz5KerXzVcIpFg1qxZ+PCjj9C9e3eGJiIi6pS0HuMUExODbdu21TtuaWmJvLy8FimKOp6jsZl4b5/6JSikYgGq0/9FYUqi2vO6urp48qmn8Oabb3I1cCIi6tS07nEyNTVFZmb9Xe+vXLkCOzu7FimKOpbQpDt4ZUckhPrrW0IsEiC68BMKk2LUXiuVSjF/wQK88847XA2ciIg6Pa2D0xNPPIE33ngDWVlZEIlEUKlUuHDhAl599VXMmTOnNWqkdnQtuxjP/xKGKoVK7Xnp5T9Qcv2S2nMymQyLlyzBypUrYWBg0JplEhERtQmtg9PHH3+MXr16wcHBASUlJfD09MSIESMwZMgQvPPOO61RI7WTjIJyzP05FEUVCrXnpVcPozjmpNpzBgYGWLJkCf71r3/B0NCQPU1ERPRA0HqMk1QqxcaNG/Hee+8hJiYGJSUl8Pb2hru7e2vUR+2ksKwa8zaFIrOwQu15yc1zKA3bo/acvr4+Fi9ejJf+9a86EwiIiIg6O617nNasWYOysjI4ODhg8uTJmD17Ntzd3VFeXo41a9a0Ro3UxiqqlVj4aziuZZeoPS9Oj0LFxS1qz+np6WHpsmV44cUXIZVK2dNEREQPFK2D0+rVq2vXbvq7srIyrF69ukWKovajVAlYtuMKQpPVb5YizruJytMboG6kuL6+PpYvX45FixZBX1+foYmIiB44Wr+qEwRB7Q/EqKgomJmZtUhR1D5UKhXe3RONo3HZas+LijJReeJrQFV/zJOhoSGWLl2KhYsWQVdXt7VLJSIiahcaB6du3bpBJBJBJBLBw8OjTnhSKpUoKSnBiy++2CpFUttYdzAK28Iy1J4TlReg6tgXQHV5vXP6+vpYuXIl5j33HMc0ERHRA03j4PTVV19BEATMnz8fq1evhlwurz0nlUrh7OyMgICAVimSWpcgCPj13DVsuKg+NAmVpag+/gVQXlDvnIGBAZYvX4658+ZBJpO1cqVERETtS+PgNHfuXACAi4sLhgwZwtcxD5DjMen44PB1APVfwQqKKihP/RcorL/oqb6+PhYvWYLnFy5kTxMREXUJWo9xGjlyZO2/V1RUoKqqqs55ExOT+6+K2kzYzRws3n4Fgpp5AoJKBeW5jRByb9Y7J5VK8a9//QsvvvgiQzQREXUZWs+qKysrw5IlS2BpaQlDQ0N069atzpc2zp49i6lTp8LW1hYikQh79+6tc37evHm146pqviZOnKhtydSAjLulmLfpEhSC+j8GqktbIaRG1juuq6uLRYsW4cWXXoKenh5nzxERUZehdXB67bXXcOrUKXz33XeQyWT48ccfsXr1atja2uLXX3/V6l6lpaXo378/1q9f32CbiRMnIjMzs/Zr+/bt2pZMaiiVKiz4/gxKFer/CCijDkB1/Wy947q6unh2zhwsXbYM+vr6rV0mERFRh6L1q7oDBw7g119/xahRo/Dcc89h+PDhcHNzg5OTE7Zu3Yqnn35a43tNmjQJkyZNarSNTCaDtbW1tmVSIwRBwOodZ3G1QP151fWzUEXtr3dcLBZj9uOP4/XXX+fec0RE1CVp3eN0584duLq6Arg3nunOnXsLJQ4bNgxnz9bvobhfZ86cgaWlJXr27ImXXnoJ+fn5Lf49uhJBEHAiNA6/RhWqPa9Ki4YyZGu94xKJBNOmT8cbb7wBY2Pj1i6TiIioQ9I6OLm6uiIpKQkA0KtXL/zxxx8A7vVEmZqatmhxEydOxK+//orAwEB8+umnCAoKwqRJk6BUKhu8prKyEkVFRXW+6B5BEJCcloFlv0cB4vqdjULpXSjP/wQIqjrHxWIxxo4bhw8++ICLnBIRUZem9au65557DlFRURg5ciTefPNNTJ06Fd988w2qq6vxxRdftGhxTzzxRO2/9+3bF/369UOPHj1w5swZjB07Vu01a9eu5dYvDSgtLcX8r/ehTOpU75wgqO6FpqqyeueGDRuGDz/8EObm5hwITkREXZrWwWn58uW1/z5u3DgkJCQgIiICbm5u6NevX4sW90+urq6wsLDAjRs3GgxOq1atwooVK2o/FxUVwcHBoVXr6gwqKirw6uebkKTjqva8Ku44hOzEese9Bw7Ehx99VDvzkYiIqCvT+lXdPzk5OeHRRx+FmZkZFi1a1BI1NSgtLQ35+fmwsbFpsI1MJoOJiUmdr65OqVTil9/34OhdC7XnhfzbUEXurXfc3d0d69atg6urK0MTERERWiA41cjPz8dPP/2k1TUlJSWIjIxEZGQkACApKQmRkZFISUlBSUkJXnvtNYSEhCA5ORmBgYGYNm0a3NzcMGHChJYq+4EnCALOBgXhP0EZgF79ECkoKqE4txFQ1R035uDggE/XrUPv3r0ZmoiIiP7SYsGpOcLDw+Ht7Q1vb28AwIoVK+Dt7Y333nsPEokE0dHReOSRR+Dh4YEFCxbAx8cH586d455oGhIEAQkJCVi5YT8Ulr3UtlGG/Q4UZdc5ZmFhgQ8++AB+fn4MTURERH+j9RinljRq1CgIgtDg+WPHjrVhNQ+e3NxcvPbvz5HvOEXNLnSAKuUKhOvn6hwzNDLCW2+/jXHjxzM0ERER/UO79jhR66mursa3332PGGNfiHTqb8ArlBVAGVx3pXd9fX0sXboUM2bMgI5Ou2ZqIiKiDknjn46PPvpoo+cLCgrutxZqQUVFRTiUrguRmfoZhcoLm4DKktrPOjo6eOaZZ7BgwQJIpfWDFhEREWkRnORyeZPn58yZc98FUcuIyqlCrllfteeU8ScgZMbXfhaJRHh46lQsXbaM48eIiIgaoXFw2rRpU2vWQS3oTmkV3tgdp/accCcVqsu76xwbPHgw3nnnHZiamnJcExERUSM4xukBIwgC3vgzGrnFlfXPKaqgOPcjoFLUHvPo2RMfffwxrK2tGZqIiIiawOD0gNkemooT8dlqz6kidgKFGbWfbWxssHr1anh4eDA0ERERaYDB6QFyI6cEaw6qf0WnSouGKvFM7WdDQ0O8uWoVhg4dytBERESkIQanB0SVQoWlO66golpV75xQXgTlxc21n2UyGRYvWYKpU6dCIpG0YZVERESdG4PTA+LzE4mIyyhSe055YRNQUQwAEIvFmDFjBhYuXMgZdERERFpicHoAXLyRhx/O3lJ7Tnk1EEJGbO3nIUOG4LXXX4e+vn5blUdERPTAYHDq5ArKqrDijyio27lGuJsO1eU/az+7uLjggw8+gJWVFcc1ERERNQODUycmCAJW7Y5BVlFF/XPKaijObQSU1QCAbmZmWL1mDXr26sXQRERE1EwMTp3YzvA0HInNUntOdflPoCAdwL096F55+WWMGjUKYjF/y4mIiJqLP0U7qaS8UnxwoIGlBzLioLp6CsC97VRmPPoonnn2Wc6gIyIiuk8MTp2QUiVg2e+RKKtS1jsnVBTfm0WHe4OefAcNwvLly2FgYNDGVRIRET14GJw6oe2hKYhKLVB7TnnxF6C8EMC9lcHff/992NjYtGF1REREDy4Gp07mTmkV/nMsUe05ZeIZCGlRAO6tDP7a66+jf//+HAxORETUQnTauwDSjCAIEAQBnx65isLy6vrni7KhCt8J4N64pqeffhozZsxgaCIiImpBDE4dnCAIyM3NReDJkwiKS8VRZR8A9cOQMnQ7oKwCAAwfMQIv/etfkEqlbVwtERHRg43BqQMTBAHJycl4deVKhIWHAw+9AbFF/dCkuh0BIePeDDsnJye8++676N69e1uXS0RE9MDjGKcOrLSkBP9Ztw6XLl2C4DIEYgvnem0ERSWU4X8AAORyOVa99RZ6cZFLIiKiVsHg1EEplUrs3r0bhw8fhiA1hHjgo2rbqaIPA6V3IJFI8OycOZg0aRIXuSQiImol/AnbAQmCgNjYWKxfvx7V1dWQeM+ASGZYv11RNlTxxwEAI0aMwMKFC7nIJRERUSticOpgBEFAaWkpvvj8c6SlpUFk7gyR+zC1bZWh2wGVAnZ2dnjr7bdhbm7OV3REREStiMGpg1EqldiyZQvOnj0LiEQQ+z8Nkaj+b5Pq9mUIGXEwNDTEq6++ip49ezI0ERERtTIGpw5EEATExcXhh++/R1VVFURuwxoZEP77vX3oZszAtOnT+YqOiIioDTA4dRCCIODu3bv4/LPPkJOTA8gMIWloQHjMvQHhAwYMwJKXX+Z6TURERG2EwamDEAQBO//4A0FBQQAAsfcMiGRG9dsVZUMVdxxyuRyvv/EG7O3t+YqOiIiojTA4dQCCICAmOho//vgjFAoFRObOELsPV9tWGboduhIRFixYgMGDBzM0ERERtSEGpw6gtLQUX375JTIyMjQaEO7n748Fzz8PXV3ddqiWiIio62JwamdKpRJ//vknzpw5AwBNDgi3trbGa6++Crlczt4mIiKiNsbg1M6uX7+ODRs2oLq6uokB4UcgqSjEwoULMdDHh6GJiIioHTA4taPKykps+O47pNy+DaCpAeHHMHLkSDzx5JNceoCIiKidMDi1E5VKhWPHjuHI0aMAAJG5UyMDwnfAxqo7Xnv9dcjl8rYsk4iIiP6Gwamd5Obk4OuvvkJJcTGARgaEp1yBbt41vPjii/Dy8uIrOiIionbE4NQOVCoV/ti5E9evXwcAiNyHQWzhUq+doKiCMux3jBw1CrNmz2ZoIiIiamcMTu1AoVDgxvXrUCqV9waEe89Q204VcxiWhhKsWLECJiYmDE5ERETtjMGpHejq6sLH1xdSqRTiATMg0jOu10YoyoHk+mm88MIL8PT0ZGgiIiLqAHTau4CuSCQSYebMmQi/mYP9FT3VtlGGbcdwP1889fTT0NHhbxMREVFHwB6ndqKjo4OLVQ5AAwPCLRW5WLZsGYyN6/dGERERUftgcGoHgiDg2/0XkCeoWbNJUQVV+B94+umn4TtoEF/RERERdSAMTu0gLz8fGy+mqz2nijkMbw9HPPPss1zokoiIqINp1+B09uxZTJ06Fba2thCJRNi7d2+d84Ig4L333oONjQ309fUxbty42in8nZUgCPhkyxGUyczqnyvJh/7tC3hl6VJYWlq2Q3VERETUmHYNTqWlpejfvz/Wr1+v9vy6devwf//3f9iwYQMuXboEQ0NDTJgwARUVFW1cacsQBAFXr17FvpvVas+rYg9j6pRJGDlyJF/RERERdUDtOl1r0qRJmDRpktpzgiDgq6++wjvvvINp06YBAH799VdYWVlh7969eOKJJ9qy1BZRVVWF9zfugcJoQL1zQkkeHJUZeOGFNdDV1W374oiIiKhJHXaMU1JSErKysjBu3LjaY3K5HP7+/ggODm7wusrKShQVFdX56ggEQUBgYCDCK9S/ghNij2Dh/Ofg7u7O3iYiIqIOqsMGp6ysLACAlZVVneNWVla159RZu3Yt5HJ57ZeDg0Or1qmp3NxcfLL1GAS5bb1zQnEehtpK8Mi0aQxNREREHViHDU7NtWrVKhQWFtZ+paamtndJUKlU2LXrT9w26qP2vOzmKSx95WV069atjSsjIiIibXTY4GRtbQ0AyM7OrnM8Ozu79pw6MpkMJiYmdb7akyAIuHbtGjYeDYeom13988W5eMLfFT4+PuxtIiIi6uA6bHBycXGBtbU1AgMDa48VFRXh0qVLCAgIaMfKtKNUKvHTzz/jju1gtectssMwf94cbqtCRETUCbTrT+uSkhLcuHGj9nNSUhIiIyNhZmYGR0dHLFu2DB9++CHc3d3h4uKCd999F7a2tpg+fXr7Fa0FQRAQHByMfREpEA0aWf98cS7+NdkXTs7O7G0iIiLqBNo1OIWHh2P06NG1n1esWAEAmDt3LjZv3ozXX38dpaWlWLRoEQoKCjBs2DAcPXoUenp67VWyVgoLC7F+/beodB8LdbHItTwR06e9ytBERETUSbRrcBo1ahQEQWjwvEgkwpo1a7BmzZo2rKplCIKAo0ePIji9EiLn+jPpUJKLd555CGZm9VcQJyIioo6pw45x6swEQUB6Whp+/nkT0Ef9Ap++erkYOiSAvU1ERESdCEcktxBBEFBZWYm4uDhERUbi/IULSCgzgNi0fm+TpCwfH7wwBTKZrB0qJSIiouZicGohFRUV+PTTT7Ft61aUlZVBAKAzdbXatmOsKtHHszd7m4iIiDoZvqprAYIg4NixY9j6228oLS2FIAgQOQ+CyNSmXlvdirt465kJ7VAlERER3S8GpxZQVFSEX3/5BWVlZfcOiESQ9HtYbdtHXHXg5GDP3iYiIqJOiMHpPgmCgJMnT+Ly5cu1x0TOfhDJ6/c2SSsL8ObTD0Es5i87ERFRZ8Sf4PdBEATk5eVhy5YtqK6uvndQJG6wt+kJLxNYmHE/OiIios6Kwek+nTt3DpFXrtR+Frn4QSSvv5eefnURXn9iLF/RERERdWIMTvehrKwM27Zt06i3aY5Pdxga6LdhdURERNTSGJyaSRAEBJ05g+ioqNpjIhd/iEys6rU1VJbg5WlD2NtERETUyTE4NVN5eTm2b9+O0tLSewdEYkj6TVHbdmGALXubiIiIHgAMTs0gCALCw8MRHBxce6yh3iZjoRQLJ/qyt4mIiOgBwODUDGVlZdi6dSvKy8trj0n6qF/UctFQBxjo67VVaURERNSKGJy0JAgC4uPjcTYoqPaYyMoDom529dqaCKV4YdIg9jYRERE9IBictKRSqfDHH3+gqKio9pi452i1bef520FXR9JWpREREVErY3DSUlxsLAJPnvzfAX05RI4D6rWTCVVYNNGHvU1EREQPEAYnLSgUChw7fhw5OTm1x8QeIyAS69Rr+4iXBYwMOLaJiIjoQcLgpIX8/Hzs2b0bgiDcOyCSQOw+on5DQcBL4/u2bXFERETU6hicNCQIAg4fPoz09PTaYyJHb4gMTOu19bPXh4uVvA2rIyIiorbA4KShgoICHD58GAqFovaYuOcotW3/Nb4PxzYRERE9gBicNFRWWorsrKz/HTC1g9i6Z712NoZiDHe3bMPKiIiIqK0wOGmom5kZBvr8b5ZcQ0sQPDe8ByQS/rISERE9iPgTXkP6+vqYMWMGZDIZoKsPsat/vTZSCfC4n3PbF0dERERtgsFJQwqFAoEnT6KyshLiHgEQ6dZfamBqX2vIDaTtUB0RERG1BQYnDd2+fRtHjhyBIAgNDgpfMMKtbYsiIiKiNsXgpAFBEHDo0CFkZGRAZNMbIrlNvTYDHUzgacslCIiIiB5kDE4ayMnOxoH9+wE0si/dUNe2LImIiIjaAYNTE1QqFc4EBeHmzZuAoRlE9v3rtTE30MGEPtbtUB0RERG1JQanJlRWVGDnH3+gqqoKYo+REInr/5I9PdgZMl1JO1RHREREbYnBqRGCICA8PBxRUVGAWAdit2H12ohFwFP+Tu1QHREREbU1BqdGVFZWYveePSgrK4PIyQcifZN6bcb3toS1vP7SBERERPTgYXBqREpKCk6dOgUAEPfioHAiIqKujsGpAYIg4PDhw8jLzQXMHCHu3qNeG7fuhhjsatYO1REREVF7YHBqQF5eHo4dPQoAkDSwBMGcAKfaveuIiIjowcfgpIYgCAgLC8P169cBqQFELn712hjoivGoj0M7VEdERETthcFJDaVSiQMHDqC8vBxit6EQ6dTff+4xXwcYyXTaoToiIiJqLwxOatxOTsbFCxcAiBrcl25OAJcgICIi6moYnP5BpVLh6NGjKCgogMi6J0TGlvXa+DnJ4WZp3A7VERERUXticPqHgoICHDt+HAqFAiInX7VtnhtWf4YdERERPfg4SOcvgiCgoqICJ44fv7dSuEgMsdPAeu3kehKM87RqhwqJiIiovTE4/aWyshJr167F9m3boKiuhsi6F0R69V/HTfSyga6EHXVERERdERMA7vU2nT9/Hr/v2IGysjIAgMhZ/Wu6qf1t27I0IiIi6kA6dHD64IMPIBKJ6nz16tWrxb9PdXU1zpw+jZKSknsHRGKIHeu/pjMz0MVgV/MW//5ERETUOXT4V3V9+vTByZMnaz/r6LR8yUWFhTh3/nztZ5F1T7Wv6Sb1tYEOX9MRERF1WR0+OOno6MDa2rpVv0dUdDSSk5JqP4sbeE03pa9Nq9ZBREREHVuH7z65fv06bG1t4erqiqeffhopKSmNtq+srERRUVGdr8aoVCqcOHECCoXi3gGRBCJ1r+kMdeHP13RERERdWocOTv7+/ti8eTOOHj2K7777DklJSRg+fDiKi4sbvGbt2rWQy+W1Xw4Oje8nl5eXd2/5gb+IbHpBJDOq126ylw0kYm7oS0RE1JV16OA0adIkzJo1C/369cOECRNw+PBhFBQU4I8//mjwmlWrVqGwsLD2KzU1tcG2giDg+vXruHnjRu0xcQOLXk7px9l0REREXV2HH+P0d6ampvDw8MCNvwWdf5LJZJDJZBrf89zZsygtLb33QSSByNG7XhsLQyn8XMy0rpeIiIgeLB26x+mfSkpKcPPmTdjYtMwg7aqqKly4cKH2873XdIb12k3qy9d0RERE1MGD06uvvoqgoCAkJyfj4sWLmDFjBiQSCZ588skWuX9iQgKSb9+u/dzQbLqH+3E2HREREXXwV3VpaWl48sknkZ+fj+7du2PYsGEICQlB9+7d7/vegiAgIiICRYWF9w6IJRA51H9NZ2ksg68zX9MRERFRBw9OO3bsaLV7V1RUICQkpHYZApFNb/Wv6bys+ZqOiIiIAHTwV3WtqbCwEGFhYbWfxU6D1LabzEUviYiI6C9dNjiFhYUhPz//3gexDkSOA+q16W4kxSC+piMiIqK/dMngpFKpcObMmb+9pvOESGpQr92UfrYQ8zUdERER/aVLBqe7d+/ixvXrtZ/Fzj5q23E2HREREf1dlwtOgiAgIyMDt27dundArAORw4B67SyNpRjo2K1tiyMiIqIOrcsFJwC4ceMG7ty5AwAQ2Tbwmq6vDV/TERERUR1dMjgZGBhALL736A3tTfdwf7u2LImIiIg6gQ69jlNrMTY2hkQigQpita/prE1k8HYwbfO6iIiIqGPrkj1OUZGRqK6uhsiuD0RS/XrnJ/M1HREREanR5YKTSqXCuXPnAABix4Fq23A2HREREanT5YJTWloabty4AeDe+k3/1N1QAm/OpiMiIiI1ulRwEgQBMTEx92bUyW0gMjCt12ZMLyuIRHxNR0RERPV1qeCkVCpx5fJllJeXQ2zTW22bUb2s27gqIiIi6iy6VHCqKC9H6F8b+4rUBCcRgCE9LNq4KiIiIuosulRwysnNRWJiIiCSQGTVs9753lYGkBvotkNlRERE1Bl0qeAUHh6OyooKiCyc1S5DMNSNvU1ERETUsC4TnFRKJcLDwqBQKNS+pgOAUb05vomIiIga1mWCU0FBwb3XdFA/vklXDPg4mbV1WURERNSJdJktV/Ly8lBcXAzoyCDq7lrvvI+jKfR0Je1QGREREXUWXSY4lZWVoaysDCI7L4jE9R97RE/LdqiKiIiIOpMu86pOpVIBUL9aOAAMd+/eluUQERFRJ9RlglMNdQtfGumK4Glj0g7VEBERUWfStYKTnglE3ezrHfZ1NIZE0rV+KYiIiEh7XSotiGx6qT0+po9dG1dCREREnVGXCk4N7k/Xk+s3ERERUdO6VHBSt36ThR7gaG7QDtUQERFRZ9N1gpORJUSG5vUO+zubtn0tRERE1Cl1meAktqm/qS8ADOMyBERERKShLhOcRFYeao8P92BwIiIiIs106eBkIS6DnYVp2xdDREREnVLXCU7S+gPA3eUqiESidqiGiIiIOqMuE5zUGefl0N4lEBERUSfSdYOTSoFpQ73auwoiIiLqRLpscDIozYSJgV57l0FERESdSJcNTq6G1RCLu+zjExERUTN02eQw3MMSEomkvcsgIiKiTqRrBqeqMvSzl7d3FURERNTJdJngJBSk1/67JP8mzLp141IEREREpBWd9i6grSiOfAKVgSlENr1gYyGHs/Nz7V0SERERdTJdpscJAFBRBCEpFHY6JbCwsGjvaoiIiKiT6VrB6S/m5uYcGE5ERERa6xTBaf369XB2doaenh78/f0RGhp6X/dz7dGjhSojIiKirqTDB6fff/8dK1aswPvvv4/Lly+jf//+mDBhAnJycpp9T3d39xaskIiIiLqKDh+cvvjiCyxcuBDPPfccPD09sWHDBhgYGODnn39u9j17uLq2YIVERETUVXToWXVVVVWIiIjAqlWrao+JxWKMGzcOwcHBzbqnWCzGpdBQpKSmtlSZRERE1In17t1b47YdOjjl5eVBqVTCysqqznErKyskJCSovaayshKVlZW1nwsLCwEAKpWq9p9rVq/mGk5EREQEAPhg9WoAgCAITbbt0MGpOdauXYvVf/0C/F1Obm47VENEREQd3fPPPw8AKC4uhlze+M4iHTo4WVhYQCKRIDs7u87x7OxsWFtbq71m1apVWLFiRe3ngoICODk5ISUlpclfjAdBUVERHBwckJqaChMTk/Yup1V1pWcF+LwPOj7vg6srPSvQOZ9XEAQUFxfD1ta2ybYdOjhJpVL4+PggMDAQ06dPB3DvVVtgYCCWLFmi9hqZTAaZTFbvuFwu7zS/gS3BxMSkyzxvV3pWgM/7oOPzPri60rMCne95Ne1c6dDBCQBWrFiBuXPnwtfXF35+fvjqq69QWlqK557jlilERETUtjp8cHr88ceRm5uL9957D1lZWRgwYACOHj1ab8A4ERERUWvr8MEJAJYsWdLgq7mmyGQyvP/++2pf3z2IutLzdqVnBfi8Dzo+74OrKz0r8OA/r0jQZO4dEREREXX8lcOJiIiIOgoGJyIiIiINMTgRERERaeiBCE7r16+Hs7Mz9PT04O/vj9DQ0Ebb79y5E7169YKenh769u2Lw4cPt1GlLUOb542Li8PMmTPh7OwMkUiEr776qu0KbQHaPOvGjRsxfPhwdOvWDd26dcO4ceOa/LPQ0WjzvLt374avry9MTU1haGiIAQMGYMuWLW1Y7f3T9r/dGjt27IBIJKpd362z0OZ5N2/eDJFIVOdLT0+vDau9P9r+3hYUFGDx4sWwsbGBTCaDh4dHp/q7WZvnHTVqVL3fW5FIhClTprRhxfdH29/fr776Cj179oS+vj4cHBywfPlyVFRUtFG1LUzo5Hbs2CFIpVLh559/FuLi4oSFCxcKpqamQnZ2ttr2Fy5cECQSibBu3TohPj5eeOeddwRdXV0hJiamjStvHm2fNzQ0VHj11VeF7du3C9bW1sKXX37ZtgXfB22f9amnnhLWr18vXLlyRbh69aowb948QS6XC2lpaW1cefNo+7ynT58Wdu/eLcTHxws3btwQvvrqK0EikQhHjx5t48qbR9vnrZGUlCTY2dkJw4cPF6ZNm9Y2xbYAbZ9306ZNgomJiZCZmVn7lZWV1cZVN4+2z1pZWSn4+voKkydPFs6fPy8kJSUJZ86cESIjI9u48ubR9nnz8/Pr/L7GxsYKEolE2LRpU9sW3kzaPu/WrVsFmUwmbN26VUhKShKOHTsm2NjYCMuXL2/jyltGpw9Ofn5+wuLFi2s/K5VKwdbWVli7dq3a9rNnzxamTJlS55i/v7/wwgsvtGqdLUXb5/07JyenThWc7udZBUEQFAqFYGxsLPzyyy+tVWKLut/nFQRB8Pb2Ft55553WKK/FNed5FQqFMGTIEOHHH38U5s6d26mCk7bPu2nTJkEul7dRdS1L22f97rvvBFdXV6GqqqqtSmxR9/vf7pdffikYGxsLJSUlrVVii9L2eRcvXiyMGTOmzrEVK1YIQ4cObdU6W0unflVXVVWFiIgIjBs3rvaYWCzGuHHjEBwcrPaa4ODgOu0BYMKECQ2270ia87ydVUs8a1lZGaqrq2FmZtZaZbaY+31eQRAQGBiIxMREjBgxojVLbRHNfd41a9bA0tISCxYsaIsyW0xzn7ekpAROTk5wcHDAtGnTEBcX1xbl3pfmPOv+/fsREBCAxYsXw8rKCl5eXvj444+hVCrbquxma4m/q3766Sc88cQTMDQ0bK0yW0xznnfIkCGIiIiofZ1369YtHD58GJMnT26Tmltap1gAsyF5eXlQKpX1VhG3srJCQkKC2muysrLUts/Kymq1OltKc563s2qJZ33jjTdga2tbLyh3RM193sLCQtjZ2aGyshISiQTffvstxo8f39rl3rfmPO/58+fx008/ITIysg0qbFnNed6ePXvi559/Rr9+/VBYWIjPPvsMQ4YMQVxcHP6/vTsPaup6+wD+DQkBhCiOlMWytGwVKbiAYogWmdFa6biPu8G6K0Wg7tbSYFGaabEda0eqjpJWp0VbrW3FBaXi1CggkliUGBZxaQdpdaCDaFHM+f3Rl/saDTQbS+zzmckMOefcc5/nXmAe7j2XeHt7d0bYZjEn12vXruHnn3/G7NmzcfToUVRVVSEhIQGPHj2CTCbrjLDNZunvquLiYly+fBm7d+/uqBCtypx8Z82ahTt37mD48OFgjKGlpQVLly7Fu+++2xkhW51NF06EtEUulyMnJwcFBQU2taDWVCKRCGq1Gvfu3UN+fj5WrFgBf39/jBw5sqtDs6rGxkZIpVLs2rULbm5uXR1OpxCLxRCLxdz76OhohISEYMeOHUhPT+/CyKxPp9PB3d0dO3fuBJ/PR0REBH7//Xd8/PHH3b5wstTu3bsRFhaGoUOHdnUoHaagoAAZGRnYvn07oqKiUFVVheTkZKSnpyM1NbWrwzOZTRdObm5u4PP5qKur02uvq6uDp6enwW08PT1NGt+dmJOvrbIk18zMTMjlcpw6dQrh4eEdGabVmJuvnZ0dAgMDAQADBw6ERqPBhx9+2O0LJ1Pzra6uxvXr1zFu3DiuTafTAQAEAgG0Wi0CAgI6NmgLWONn197eHoMGDUJVVVVHhGg15uTq5eUFe3t78Pl8ri0kJAS3b9/Gw4cPIRQKOzRmS1hybpuampCTk4MPPvigI0O0KnPyTU1NhVQqxcKFCwEAYWFhaGpqwuLFi7FhwwbY2dnWqiHbivYpQqEQERERyM/P59p0Oh3y8/P1/lJ7klgs1hsPACdPnmxzfHdiTr62ytxcP/roI6Snp+P48eOIjIzsjFCtwlrnVqfTobm5uSNCtCpT8+3Xrx/KysqgVqu51/jx4xEbGwu1Wg0fH5/ODN9k1ji/jx8/RllZGby8vDoqTKswJ1eJRIKqqiquGAaAiooKeHl5deuiCbDs3H777bdobm7GnDlzOjpMqzEn3/v37z9THLUWycwWP/WtixenWywnJ4c5ODgwhULBysvL2eLFi5mrqyv32K5UKmXr1q3jxiuVSiYQCFhmZibTaDRMJpPZ3L8jMCXf5uZmplKpmEqlYl5eXmzVqlVMpVKxysrKrkrBaKbmKpfLmVAoZN99953eo76NjY1dlYJJTM03IyOD5eXlserqalZeXs4yMzOZQCBgu3bt6qoUTGJqvk+ztafqTM1348aN7MSJE6y6uppdvHiRzZgxgzk6OrIrV650VQpGMzXXmzdvMpFIxBITE5lWq2VHjhxh7u7ubNOmTV2VgknM/V4ePnw4mz59emeHazFT85XJZEwkErFvvvmGXbt2jeXl5bGAgAA2bdq0rkrBIjZfODHG2LZt25ivry8TCoVs6NChrLCwkOuLiYlhc+fO1Rt/4MABFhwczIRCIQsNDWW5ubmdHLFlTMm3pqaGAXjmFRMT0/mBm8GUXP38/AzmKpPJOj9wM5mS74YNG1hgYCBzdHRkvXv3ZmKxmOXk5HRB1OYz9Wf3SbZWODFmWr4pKSncWA8PDxYXF8dKS0u7IGrzmHpuz507x6KiopiDgwPz9/dnmzdvZi0tLZ0ctflMzffq1asMAMvLy+vkSK3DlHwfPXrE0tLSWEBAAHN0dGQ+Pj4sISGB1dfXd37gVsBjzBavkxFCCCGEdD6bXuNECCGEENKZqHAihBBCCDESFU6EEEIIIUaiwokQQgghxEhUOBFCCCGEGIkKJ0IIIYQQI1HhRAghhBBiJCqcCCGEEEKMRIUTIcSq0tLSMHDgQJO2KSgoAI/HQ0NDAwBAoVDA1dW1S2N63rUecx6Ph4kTJ1o01/Xr17m56DiT5x0VToR0U7dv30ZycjICAwPh6OgIDw8PSCQSZGVl4f79+10dXoeaPn06KioqujoMm9RaxKjVaqPGa7VaKBQKi/bp4+OD2tparFy50qJ5CLEFgq4OgBDyrGvXrkEikcDV1RUZGRkICwuDg4MDysrKsHPnTrz44osYP368wW0fPXoEe3v7To7YupycnODk5NTVYfwnuLu7W3x1j8/nw9PTEy4uLtYJipBujK44EdINJSQkQCAQoKSkBNOmTUNISAj8/f0xYcIE5ObmYty4cdxYHo+HrKwsjB8/Hs7Ozti8eTMAICsrCwEBARAKhXjllVewd+9ebhtDVyUaGhrA4/FQUFAA4P9v5eTn5yMyMhI9evRAdHQ0tFqtXqxyuRweHh4QiURYsGAB/v7773/N7+jRowgODoaTkxNiY2Nx/fp1vf6nb9VdunQJsbGxEIlE6NmzJyIiIlBSUqI39vDhwwgKCoKjoyPGjBmDW7dutbn/CxcuYPTo0XBzc0OvXr0QExOD0tJSvTENDQ1YsmQJPDw84OjoiFdffRVHjhzh+s+ePYsRI0bAyckJPj4+SEpKQlNTE9f/0ksvYdOmTYiPj4eLiwv8/Pzw448/4s8//8SECRPg4uKC8PBwLg9T5s3IyMD8+fMhEong6+uLnTt3cv0vv/wyAGDQoEHg8XgYOXJk+yfjKSNHjsTy5cuRkpKC3r17w8PDA7t27UJTUxPmzZsHkUiEwMBAHDt2zKR5CXleUOFESDdz9+5d5OXl4e2334azs7PBMTweT+99WloaJk2ahLKyMsyfPx/ff/89kpOTsXLlSly+fBlLlizBvHnzcPr0aZPj2bBhA7Zs2YKSkhIIBALMnz+f6ztw4ADS0tKQkZGBkpISeHl5Yfv27e3Od+vWLUyePBnjxo2DWq3GwoULsW7duna3mT17Nry9vXHhwgVcvHgR69at07uqdv/+fWzevBlfffUVlEolGhoaMGPGjDbna2xsxNy5c3H27FkUFhYiKCgIcXFxaGxsBADodDqMHTsWSqUS+/btQ3l5OeRyOfh8PgCguroab7zxBqZMmYJff/0V+/fvx9mzZ5GYmKi3n08//RQSiQQqlQpvvvkmpFIp4uPjMWfOHJSWliIgIADx8fFo/ax1Y+fdsmULIiMjoVKpkJCQgGXLlnEFbXFxMQDg1KlTqK2txaFDh9o9toZ8+eWXcHNzQ3FxMZYvX45ly5Zh6tSpiI6ORmlpKV5//XVIpdLn/pYxIQYxQki3UlhYyACwQ4cO6bX36dOHOTs7M2dnZ7ZmzRquHQBLSUnRGxsdHc0WLVqk1zZ16lQWFxfHGGOspqaGAWAqlYrrr6+vZwDY6dOnGWOMnT59mgFgp06d4sbk5uYyAOzBgweMMcbEYjFLSEjQ209UVBQbMGBAm/mtX7+e9e/fX69t7dq1DACrr69njDGWnZ3NevXqxfWLRCKmUCgMzpednc0AsMLCQq5No9EwAKyoqIgxxphMJms3psePHzORSMR++uknxhhjJ06cYHZ2dkyr1Rocv2DBArZ48WK9tl9++YXZ2dlxx8bPz4/NmTOH66+trWUAWGpqKtd2/vx5BoDV1taaPa9Op2Pu7u4sKyuLMWb43BrSen5bj3mrmJgYNnz4cO59S0sLc3Z2ZlKp9Jlczp8/r7ftvx1nQp4HdMWJEBtRXFwMtVqN0NBQNDc36/VFRkbqvddoNJBIJHptEokEGo3G5P2Gh4dzX3t5eQEA/vjjD24/UVFReuPFYnG785mzzYoVK7Bw4UKMGjUKcrkc1dXVev0CgQBDhgzh3vfr1w+urq5t5ltXV4dFixYhKCgIvXr1Qs+ePXHv3j3cvHkTAKBWq+Ht7Y3g4GCD21+6dAkKhQIuLi7ca8yYMdDpdKipqeHGPXnsPDw8AABhYWHPtLUeT3Pm5fF48PT05Oawhifn5/P56NOnT7txE/JfQovDCelmAgMDwePxnllL5O/vDwAGF023dUuvLXZ2//zNxP7vFhHwz6JyQ568JdZ6i1Cn05m0P0ulpaVh1qxZyM3NxbFjxyCTyZCTk4NJkyaZNd/cuXNx9+5dbN26FX5+fnBwcIBYLMbDhw8BGD7GT7p37x6WLFmCpKSkZ/p8fX25rw0du/aOpznzts5jzXNiaP7u8H1ASHdAV5wI6Wb69OmD0aNH4/PPP9dbFGyKkJAQKJVKvTalUon+/fsDAF544QUAQG1tLddv7OPrT++nqKhIr62wsPBft2ldh2PsNgAQHByMd955B3l5eZg8eTKys7O5vpaWFr1F1lqtFg0NDQgJCTE4l1KpRFJSEuLi4hAaGgoHBwfcuXOH6w8PD8dvv/3W5r9EGDx4MMrLyxEYGPjMSygU/msubbHGvK3jHj9+bHYchJC2UeFESDe0fft2tLS0IDIyEvv374dGo4FWq8W+fftw9epVbpFyW1avXg2FQoGsrCxUVlbik08+waFDh7Bq1SoA/1xRGTZsGORyOTQaDc6cOYP33nvP5DiTk5OxZ88eZGdno6KiAjKZDFeuXGl3m6VLl6KyshKrV6+GVqvF119/3e7/EXrw4AESExNRUFCAGzduQKlU4sKFC3pFkb29PZYvX46ioiJcvHgRb731FoYNG4ahQ4canDMoKAh79+6FRqNBUVERZs+erXeVKSYmBq+99hqmTJmCkydPoqamBseOHcPx48cBAGvXrsW5c+eQmJgItVqNyspK/PDDD88s4jaVNeZ1d3eHk5MTjh8/jrq6Ovz1118WxUQI0UeFEyHdUEBAAFQqFUaNGoX169djwIABiIyMxLZt27Bq1Sqkp6e3u/3EiROxdetWZGZmIjQ0FDt27EB2drbeo+l79uxBS0sLIiIikJKSgk2bNpkc5/Tp05Gamoo1a9YgIiICN27cwLJly9rdxtfXFwcPHsThw4cxYMAAfPHFF8jIyGhzPJ/Px927dxEfH4/g4GBMmzYNY8eOxcaNG7kxPXr0wNq1azFr1ixIJBK4uLhg//79bc65e/du1NfXY/DgwZBKpUhKSoK7u7vemIMHD2LIkCGYOXMm+vfvjzVr1nBXccLDw3HmzBlUVFRgxIgRGDRoEN5//3307dvXmMPWJmvMKxAI8Nlnn2HHjh3o27cvJkyYYFFMhBB9PPbkIgdCCLExCoUCKSkp3Me1EOMUFBQgNjYW9fX1Vvt4m7S0NBw+fNis276E2Aq64kQIIf9h3t7emDlzpkVz3Lx5Ey4uLu1eOSTkeUFP1RFCyH9QVFQUKisrAcDij0rp27cvd5XJwcHB0tAI6dboVh0hhBBCiJHoVh0hhBBCiJGocCKEEEIIMRIVToQQQgghRqLCiRBCCCHESFQ4EUIIIYQYiQonQgghhBAjUeFECCGEEGIkKpwIIYQQQoxEhRMhhBBCiJH+By6SUcJeW+v4AAAAAElFTkSuQmCC", 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" ] @@ -164,6 +164,51 @@ "result.plot()\n" ] }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1000.0)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# G0 modulus\n", + "G0_func = (\n", + " lambda x: (875 * 101.3) / (0.3 + 0.7 * 0.629**2) * ((x * 10.09 * 1.8 / 3) / 101.3) ** 0.5\n", + ")\n", + "\n", + "y = np.linspace(0, 100, 50)\n", + "x = G0_func(y) / 1000\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 5))\n", + "ax.invert_yaxis()\n", + "im = plt.imread(\"Burd_et_al_G0_profile.png\")\n", + "im = ax.imshow(im, extent=[0, 1000, 100, 0], aspect=\"auto\")\n", + "ax.plot(x, y)\n", + "ax.set_xlim([None, 1000])\n" + ] + }, { "cell_type": "code", "execution_count": null, From 88c391256daf4eca15ba2925fd97c5f8c880d08f Mon Sep 17 00:00:00 2001 From: TchilDill Date: Tue, 31 Oct 2023 22:34:22 +0100 Subject: [PATCH 06/13] new bothkennar model --- CHANGELOG.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8e3860e..b7ec5fa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,13 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/) and this project adheres to [Semantic Versioning](http://semver.org/), and [PEP 440](https://www.python.org/dev/peps/pep-0440/). +## [0.7.0] - 2023-xx-xx + +### Added + +- Added soil models: + - `openpile.soilmodels.Bothkennar_clay` from the PISA joint-industry project + ## [0.6.0] - 2023-10-23 ### Added From 007d83101783ce025da3d80289b6b241a2582594 Mon Sep 17 00:00:00 2001 From: TchilDill Date: Thu, 2 Nov 2023 21:22:20 +0100 Subject: [PATCH 07/13] added entrapped soil weight testing --- src/openpile/calculate.py | 26 ++++++++--------- src/openpile/utils/misc.py | 2 +- test/test_calculate.py | 58 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 72 insertions(+), 14 deletions(-) create mode 100644 test/test_calculate.py diff --git a/src/openpile/calculate.py b/src/openpile/calculate.py index ab66f59..16e63ff 100644 --- a/src/openpile/calculate.py +++ b/src/openpile/calculate.py @@ -128,6 +128,8 @@ def unit_end_bearing( ) * layer.axial_model.Q_multiplier ) + + return 0.0 def entrapped_soil_weight(model) -> float: @@ -143,6 +145,9 @@ def entrapped_soil_weight(model) -> float: float value of entrapped total weight of soil inside the pile in unit:kN """ + #weight water in kN/m3 + uw_water = 10 + # soil volume Vi = _pile_inside_volume(model) # element mid-point elevation @@ -156,25 +161,20 @@ def entrapped_soil_weight(model) -> float: & (model.soil_properties["x_bottom [m]"] >= layer.bottom) ].index - if layer.axial_model is None: - pass - else: - # Set local layer parameters for each element of the layer - for i in elements_for_layer: - # Calculate inner soil weight - element_sw[i] = ( - layer.weight * Vi[i] - if elevation[i] <= model.soil.water_line - else (layer.weight - 10) * Vi[i] - ) + # Set local layer parameters for each element of the layer + for i in elements_for_layer: + # Calculate inner soil weight + element_sw[i] = ( + layer.weight * Vi[i] + if elevation[i] <= model.soil.water_line + else (layer.weight - uw_water) * Vi[i] + ) return element_sw.sum() def shaft_resistance( model, - outer_shaft: bool = True, - inner_shaft: bool = True, ) -> float: """Calculates shaft resistance of the pile based on the axial models assigned to the SoilProfile layers. (Unit: kN) diff --git a/src/openpile/utils/misc.py b/src/openpile/utils/misc.py index 5bf7261..bd8b54a 100644 --- a/src/openpile/utils/misc.py +++ b/src/openpile/utils/misc.py @@ -6,7 +6,7 @@ # maximum resistance values -@njit(cahce=True) +@njit(cache=True) def _Qmax_api_clay( Su: float, ) -> float: diff --git a/test/test_calculate.py b/test/test_calculate.py new file mode 100644 index 0000000..dcced67 --- /dev/null +++ b/test/test_calculate.py @@ -0,0 +1,58 @@ +from openpile import construct +from openpile.soilmodels import API_clay +import pytest +import numpy as np +import math as m + +from pydantic import ValidationError + +from openpile.construct import Pile, SoilProfile, Layer, Model +from openpile.soilmodels import API_clay, API_sand, API_clay_axial +from openpile.calculate import entrapped_soil_weight + + +def test_entrapped_soil_weight(): + """calculate the weight of the soil inside the pile + """ + + # the special diameter and wall thickness is calculated and applied such that + # a metre long of pile with this diameter ie quivalent + # to one cubic metre + special_diameter = (4/m.pi)**0.5 + special_wallthickness = 0.01 + soil_weight = 18 + + # a pile with the special diameter and an unreasonably thin wall thickness + p = Pile.create_tubular( + name="", + top_elevation=0, + bottom_elevation=-10, + diameter=special_diameter+(2*special_wallthickness), + wt=special_wallthickness + ) + + # Create a 40m deep offshore Soil Profile with a 15m water column + sp = SoilProfile( + name="Offshore Soil Profile", + top_elevation=0, + water_line=15, + layers=[ + Layer( + name="medium dense sand", + top=0, + bottom=-20, + weight=soil_weight, + lateral_model=API_sand( + phi=33, + kind="cyclic", + extension="mt_curves", + ), + ), + ], + ) + + # Create Model + M = Model(name="", pile=p, soil=sp) + + # check + assert m.isclose(entrapped_soil_weight(M), soil_weight*10) From 973c9db45e55526aed2ab8d2ab9f45897a4d8eda Mon Sep 17 00:00:00 2001 From: TchilDill Date: Thu, 2 Nov 2023 22:01:11 +0100 Subject: [PATCH 08/13] add test for effective pile weight --- src/openpile/calculate.py | 49 ++++++++++++++----- test/test_calculate.py | 100 +++++++++++++++++++++++++++++++++++++- 2 files changed, 134 insertions(+), 15 deletions(-) diff --git a/src/openpile/calculate.py b/src/openpile/calculate.py index 16e63ff..a11baf8 100644 --- a/src/openpile/calculate.py +++ b/src/openpile/calculate.py @@ -62,22 +62,45 @@ def _pile_inside_volume(model): return area_inside * L -def _embedded_pile_effective_weight(model): +def effective_pile_weight(model): + """Calculates the pile weight in the model with consideration of buoyancy - embedded_element = model.element_properties["x_bottom [m]"].values < model.soil.top_elevation - submerged_element = model.element_properties["x_bottom [m]"].values < model.soil.water_line + Parameters + ---------- + model : openpile.construct.Model + OpenPile Model object - L = ( - model.element_properties["x_top [m]"].values - - model.element_properties["x_bottom [m]"].values - ) - V = L * model.element_properties["Area [m2]"].values - W = np.zeros(shape=V.shape) - W[submerged_element] = V[submerged_element] * (model.pile._uw - 10) - W[~submerged_element] = V[~submerged_element] * (model.pile._uw) - W[~embedded_element] = 0 + Returns + ------- + float + pile weight in kN - return W.sum() + Raises + ------ + Exception + if soil profile does not exist + + See also + -------- + `openpile.construct.Pile.weight` + """ + + if model.soil is not None: + submerged_element = model.element_properties["x_bottom [m]"].values < model.soil.water_line + + L = ( + model.element_properties["x_top [m]"].values + - model.element_properties["x_bottom [m]"].values + ) + V = L * model.element_properties["Area [m2]"].values + W = np.zeros(shape=V.shape) + W[submerged_element] = V[submerged_element] * (model.pile._uw - 10) + W[~submerged_element] = V[~submerged_element] * (model.pile._uw) + + return W.sum() + + else: + raise Exception("Model must be linked to a soil profile, use `openpile.construct.Pile.weight instead.`") def bearingcapacity(model, kind): diff --git a/test/test_calculate.py b/test/test_calculate.py index dcced67..2f8bbd8 100644 --- a/test/test_calculate.py +++ b/test/test_calculate.py @@ -8,7 +8,7 @@ from openpile.construct import Pile, SoilProfile, Layer, Model from openpile.soilmodels import API_clay, API_sand, API_clay_axial -from openpile.calculate import entrapped_soil_weight +from openpile import calculate def test_entrapped_soil_weight(): @@ -55,4 +55,100 @@ def test_entrapped_soil_weight(): M = Model(name="", pile=p, soil=sp) # check - assert m.isclose(entrapped_soil_weight(M), soil_weight*10) + assert m.isclose(calculate.entrapped_soil_weight(M), 18*10) + +def test_submerged_effective_pile_weight(): + + # the special diameter and wall thickness is calculated and applied such that + # a metre long of pile with this diameter ie quivalent + # to one cubic metre + special_diameter = (10/m.pi) + special_wallthickness = 0.001 + steel_weight = 78 + + # a pile with the special diameter and an unreasonably thin wall thickness + p = Pile.create_tubular( + name="", + top_elevation=0, + bottom_elevation=-100, + diameter=special_diameter, + wt=special_wallthickness, + ) + + # Create a 40m deep offshore Soil Profile with a 15m water column + sp = SoilProfile( + name="Offshore Soil Profile", + top_elevation=0, + water_line=15, + layers=[ + Layer( + name="medium dense sand", + top=0, + bottom=-100, + weight=18, + lateral_model=API_sand( + phi=33, + kind="cyclic", + extension="mt_curves", + ), + ), + ], + ) + + # Create Model + M = Model(name="", pile=p, soil=sp) + + print(calculate.effective_pile_weight(M)) + print((steel_weight-10)/10) + + # check + assert m.isclose(calculate.effective_pile_weight(M), (steel_weight-10), abs_tol=0.1) + + +def test_half_submerged_effective_pile_weight(): + + # the special diameter and wall thickness is calculated and applied such that + # a metre long of pile with this diameter ie quivalent + # to one cubic metre + special_diameter = (10/m.pi) + special_wallthickness = 0.001 + steel_weight = 78 + + # a pile with the special diameter and an unreasonably thin wall thickness + p = Pile.create_tubular( + name="", + top_elevation=0, + bottom_elevation=-100, + diameter=special_diameter, + wt=special_wallthickness, + ) + + # Create a 40m deep offshore Soil Profile with a 15m water column + sp = SoilProfile( + name="Offshore Soil Profile", + top_elevation=0, + water_line=-50, + layers=[ + Layer( + name="medium dense sand", + top=0, + bottom=-100, + weight=18, + lateral_model=API_sand( + phi=33, + kind="cyclic", + extension="mt_curves", + ), + ), + ], + ) + + # Create Model + M = Model(name="", pile=p, soil=sp) + + print(calculate.effective_pile_weight(M)) + print((steel_weight-10)/10) + + # check + target_weight = 0.5 * ( (steel_weight-10) + steel_weight) + assert m.isclose(calculate.effective_pile_weight(M), target_weight, abs_tol=0.1) \ No newline at end of file From b9dcd6fda73117e9bea7e7a56ea56ffa282382db Mon Sep 17 00:00:00 2001 From: TchilDill Date: Thu, 2 Nov 2023 22:01:15 +0100 Subject: [PATCH 09/13] trivial --- samples/usage3.ipynb | 55 ++++++++++++++++++++++++++++++++++++++------ 1 file changed, 48 insertions(+), 7 deletions(-) diff --git a/samples/usage3.ipynb b/samples/usage3.ipynb index 6e53f58..b39643b 100644 --- a/samples/usage3.ipynb +++ b/samples/usage3.ipynb @@ -2,16 +2,43 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/guillaumemelin/Desktop/svc/openpile/.venv/lib/python3.7/site-packages/ipykernel_launcher.py:48: DeprecationWarning: \n", + "The method Analyze.simple_winkler_analysis() will be removed in version 1.0.0.\n", + "Please use the Analyze.winkler() instead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged at iteration no. 6\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from openpile.construct import Pile, SoilProfile, Layer, Model\n", - "from openpile.soilmodels import API_clay, API_sand\n", + "from openpile.soilmodels import API_clay, API_sand, API_clay_axial\n", "\n", "\n", "p = Pile.create_tubular(\n", - " name=\"\", top_elevation=0, bottom_elevation=-40, diameter=10, wt=0.050\n", + " name=\"\", top_elevation=0, bottom_elevation=-40, diameter=10.0, wt=0.10\n", ")\n", "\n", "# Create a 40m deep offshore Soil Profile with a 15m water column\n", @@ -37,6 +64,7 @@ " bottom=-40,\n", " weight=18,\n", " lateral_model=API_clay(Su=[50, 70], eps50=0.015, kind=\"cyclic\", extension=\"mt_curves\"),\n", + " axial_model=API_clay_axial(Su=70),\n", " ),\n", " ],\n", ")\n", @@ -60,11 +88,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "8459.680697586553" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Result.deflection\n" + "from openpile.calculate import effective_pile_weight\n", + "\n", + "effective_pile_weight(M)\n" ] }, { From 8e5c683b692e52961a31b2f9abfdf08043a7541a Mon Sep 17 00:00:00 2001 From: TchilDill Date: Sun, 5 Nov 2023 11:41:41 +0100 Subject: [PATCH 10/13] D as width and not diameter --- src/openpile/utils/tz_curves.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/openpile/utils/tz_curves.py b/src/openpile/utils/tz_curves.py index e381cd6..07f792c 100644 --- a/src/openpile/utils/tz_curves.py +++ b/src/openpile/utils/tz_curves.py @@ -96,7 +96,7 @@ def api_clay( Su : float Undrained shear strength [unit: kPa] D: float - Pile diameter [unit: m] + Pile width [unit: m] residual: float residual strength after peak strength, according to API-RP-2A, this value is between 0.7 and 0.9, default to 0.9 @@ -298,7 +298,7 @@ def api_sand_kraft( delta: float interface friction angle [unit: degrees] D: float - Pile diameter [unit: m] + Pile width [unit: m] G0: float small-strain stiffness [unit: kPa] K: float From 3da90620e2de37bef84871f27ce0e030b78a841b Mon Sep 17 00:00:00 2001 From: TchilDill Date: Sun, 12 Nov 2023 20:33:32 +0100 Subject: [PATCH 11/13] add setting for inlinesuggest --- .vscode/settings.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 9fb326d..7ae7e2d 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -4,5 +4,6 @@ ], "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, - "python.formatting.provider": "black" + "python.formatting.provider": "black", + "editor.inlineSuggest.showToolbar": "onHover" } \ No newline at end of file From e58736321eb7e39684bf15d439ba1d27b6c08f59 Mon Sep 17 00:00:00 2001 From: TchilDill Date: Sun, 12 Nov 2023 21:36:23 +0100 Subject: [PATCH 12/13] quick fix --- docs/source/introsoilmodels.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/introsoilmodels.rst b/docs/source/introsoilmodels.rst index 63967f3..a9640d6 100644 --- a/docs/source/introsoilmodels.rst +++ b/docs/source/introsoilmodels.rst @@ -50,7 +50,6 @@ Please refer to the :ref:`ApplicationProgrammingInterface` for more details and .. [BABH20] Burd, H. J., Abadie, C. N., Byrne, B. W., Houlsby, G. T., Martin, C. M., McAdam, R. A., Jardine, R.J., Pedro, A.M., Potts, D.M., Taborda, D.M., Zdravković, L., and Andrade, M.P. (2020). Application of the PISA Design Model to Monopiles Embedded in Layered Soils. - Géotechnique 70(11): 1-55. -https://doi.org/10.1680/jgeot.20.PISA.009 + Géotechnique 70(11): 1-55. https://doi.org/10.1680/jgeot.20.PISA.009 .. [Rees97] Reese, L.C. (1997), Analysis of Laterally Loaded Piles in Weak Rock, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, vol. 123 (11) Nov., ASCE, pp. 1010-1017. From 0b031b43a11ece5387bcd61fd34f1f82ac10219a Mon Sep 17 00:00:00 2001 From: TchilDill Date: Sun, 12 Nov 2023 21:44:25 +0100 Subject: [PATCH 13/13] version + black + pytests --- CHANGELOG.md | 2 +- docs/source/conf.py | 4 +-- src/openpile/calculate.py | 10 +++--- src/openpile/globals.py | 2 +- src/openpile/utils/Hb_curves.py | 3 +- src/openpile/utils/Mb_curves.py | 3 +- src/openpile/utils/mt_curves.py | 2 +- src/openpile/utils/py_curves.py | 2 +- test/test_calculate.py | 64 ++++++++++++++++----------------- 9 files changed, 46 insertions(+), 46 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b7ec5fa..b5ec38a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/) and this project adheres to [Semantic Versioning](http://semver.org/), and [PEP 440](https://www.python.org/dev/peps/pep-0440/). -## [0.7.0] - 2023-xx-xx +## [0.7.0] - 2023-11-12 ### Added diff --git a/docs/source/conf.py b/docs/source/conf.py index 0b39274..998304b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -55,11 +55,11 @@ templates_path = ["_templates"] exclude_patterns = [] -#option for the copy button extension +# option for the copy button extension copybutton_prompt_text = r">>> |\.\.\. " copybutton_prompt_is_regexp = True -#option for matplotlib extension +# option for matplotlib extension plot_include_source = True plot_html_show_source_link = False plot_html_show_formats = False diff --git a/src/openpile/calculate.py b/src/openpile/calculate.py index a11baf8..63ff320 100644 --- a/src/openpile/calculate.py +++ b/src/openpile/calculate.py @@ -98,9 +98,11 @@ def effective_pile_weight(model): W[~submerged_element] = V[~submerged_element] * (model.pile._uw) return W.sum() - + else: - raise Exception("Model must be linked to a soil profile, use `openpile.construct.Pile.weight instead.`") + raise Exception( + "Model must be linked to a soil profile, use `openpile.construct.Pile.weight instead.`" + ) def bearingcapacity(model, kind): @@ -151,7 +153,7 @@ def unit_end_bearing( ) * layer.axial_model.Q_multiplier ) - + return 0.0 @@ -168,7 +170,7 @@ def entrapped_soil_weight(model) -> float: float value of entrapped total weight of soil inside the pile in unit:kN """ - #weight water in kN/m3 + # weight water in kN/m3 uw_water = 10 # soil volume diff --git a/src/openpile/globals.py b/src/openpile/globals.py index ab15756..48ec9b2 100644 --- a/src/openpile/globals.py +++ b/src/openpile/globals.py @@ -1,2 +1,2 @@ # version of the package -VERSION = "0.6.0" +VERSION = "0.7.0" diff --git a/src/openpile/utils/Hb_curves.py b/src/openpile/utils/Hb_curves.py index f2237ff..b5e68be 100644 --- a/src/openpile/utils/Hb_curves.py +++ b/src/openpile/utils/Hb_curves.py @@ -24,7 +24,7 @@ def bothkennar_clay( ): """ Creates the base shear spring from the PISA clay formulation - published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay response (a normally consolidated soft clay). Parameters @@ -74,7 +74,6 @@ def bothkennar_clay( return y * (Su * D / G0), p * (Su * D**2) - @njit(cache=True) def dunkirk_sand( sig: float, diff --git a/src/openpile/utils/Mb_curves.py b/src/openpile/utils/Mb_curves.py index 460bf1d..f34ad42 100644 --- a/src/openpile/utils/Mb_curves.py +++ b/src/openpile/utils/Mb_curves.py @@ -24,7 +24,7 @@ def bothkennar_clay( ): """ Create the base moment springs from the PISA clay formulation - published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay response (a normally consolidated soft clay). Parameters @@ -73,7 +73,6 @@ def bothkennar_clay( return t * (Su / G0), m * (Su * D**3) - @njit(cache=True) def cowden_clay( X: float, diff --git a/src/openpile/utils/mt_curves.py b/src/openpile/utils/mt_curves.py index 23e76d3..d7aa14b 100644 --- a/src/openpile/utils/mt_curves.py +++ b/src/openpile/utils/mt_curves.py @@ -23,7 +23,7 @@ def bothkennar_clay( ): """ Create the rotational springs from the PISA clay formulation - published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay response (a normally consolidated soft clay). Parameters diff --git a/src/openpile/utils/py_curves.py b/src/openpile/utils/py_curves.py index 4aa8348..a3c0073 100644 --- a/src/openpile/utils/py_curves.py +++ b/src/openpile/utils/py_curves.py @@ -23,7 +23,7 @@ def bothkennar_clay( ): """ Creates a spring from the PISA clay formulation - published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay + published by Burd et al 2020 (see [BABH20]_) and calibrated based on Bothkennar clay response (a normally consolidated soft clay). Parameters diff --git a/test/test_calculate.py b/test/test_calculate.py index 2f8bbd8..473d75b 100644 --- a/test/test_calculate.py +++ b/test/test_calculate.py @@ -12,23 +12,22 @@ def test_entrapped_soil_weight(): - """calculate the weight of the soil inside the pile - """ + """calculate the weight of the soil inside the pile""" - # the special diameter and wall thickness is calculated and applied such that - # a metre long of pile with this diameter ie quivalent + # the special diameter and wall thickness is calculated and applied such that + # a metre long of pile with this diameter ie quivalent # to one cubic metre - special_diameter = (4/m.pi)**0.5 + special_diameter = (4 / m.pi) ** 0.5 special_wallthickness = 0.01 soil_weight = 18 # a pile with the special diameter and an unreasonably thin wall thickness p = Pile.create_tubular( - name="", - top_elevation=0, - bottom_elevation=-10, - diameter=special_diameter+(2*special_wallthickness), - wt=special_wallthickness + name="", + top_elevation=0, + bottom_elevation=-10, + diameter=special_diameter + (2 * special_wallthickness), + wt=special_wallthickness, ) # Create a 40m deep offshore Soil Profile with a 15m water column @@ -55,23 +54,24 @@ def test_entrapped_soil_weight(): M = Model(name="", pile=p, soil=sp) # check - assert m.isclose(calculate.entrapped_soil_weight(M), 18*10) + assert m.isclose(calculate.entrapped_soil_weight(M), 18 * 10) + def test_submerged_effective_pile_weight(): - - # the special diameter and wall thickness is calculated and applied such that - # a metre long of pile with this diameter ie quivalent + + # the special diameter and wall thickness is calculated and applied such that + # a metre long of pile with this diameter ie quivalent # to one cubic metre - special_diameter = (10/m.pi) + special_diameter = 10 / m.pi special_wallthickness = 0.001 steel_weight = 78 # a pile with the special diameter and an unreasonably thin wall thickness p = Pile.create_tubular( - name="", - top_elevation=0, - bottom_elevation=-100, - diameter=special_diameter, + name="", + top_elevation=0, + bottom_elevation=-100, + diameter=special_diameter, wt=special_wallthickness, ) @@ -99,27 +99,27 @@ def test_submerged_effective_pile_weight(): M = Model(name="", pile=p, soil=sp) print(calculate.effective_pile_weight(M)) - print((steel_weight-10)/10) + print((steel_weight - 10) / 10) # check - assert m.isclose(calculate.effective_pile_weight(M), (steel_weight-10), abs_tol=0.1) + assert m.isclose(calculate.effective_pile_weight(M), (steel_weight - 10), abs_tol=0.1) def test_half_submerged_effective_pile_weight(): - - # the special diameter and wall thickness is calculated and applied such that - # a metre long of pile with this diameter ie quivalent + + # the special diameter and wall thickness is calculated and applied such that + # a metre long of pile with this diameter ie quivalent # to one cubic metre - special_diameter = (10/m.pi) + special_diameter = 10 / m.pi special_wallthickness = 0.001 steel_weight = 78 # a pile with the special diameter and an unreasonably thin wall thickness p = Pile.create_tubular( - name="", - top_elevation=0, - bottom_elevation=-100, - diameter=special_diameter, + name="", + top_elevation=0, + bottom_elevation=-100, + diameter=special_diameter, wt=special_wallthickness, ) @@ -147,8 +147,8 @@ def test_half_submerged_effective_pile_weight(): M = Model(name="", pile=p, soil=sp) print(calculate.effective_pile_weight(M)) - print((steel_weight-10)/10) + print((steel_weight - 10) / 10) # check - target_weight = 0.5 * ( (steel_weight-10) + steel_weight) - assert m.isclose(calculate.effective_pile_weight(M), target_weight, abs_tol=0.1) \ No newline at end of file + target_weight = 0.5 * ((steel_weight - 10) + steel_weight) + assert m.isclose(calculate.effective_pile_weight(M), target_weight, abs_tol=0.1)