From 2e70179b15367ce42bda243cfccad7ab88ea210c Mon Sep 17 00:00:00 2001
From: Morten Engen <morten.engen@multiconsult.no>
Date: Tue, 13 Dec 2022 21:01:29 +0100
Subject: [PATCH 01/33] Draft initial structure for the concrete class

Co-authored-by: talledodiego <38036285+talledodiego@users.noreply.github.com>
---
 structuralcodes/__init__.py                   |  10 +-
 structuralcodes/{code => codes}/__init__.py   |   7 +
 .../{code => codes}/mc2010/__init__.py        |   0
 .../mc2010/_concrete_material_properties.py   |  18 +-
 structuralcodes/core/__init__.py              |   0
 structuralcodes/core/base.py                  |  26 +++
 structuralcodes/material/__init__.py          |   0
 structuralcodes/material/concrete/__init__.py |  58 ++++++
 .../material/concrete/_concrete.py            |  45 +++++
 .../material/concrete/_concreteMC2010.py      | 189 ++++++++++++++++++
 tests/test_get_set_design_code.py             |  12 +-
 tests/test_mc2010_material_properties.py      |  12 +-
 12 files changed, 355 insertions(+), 22 deletions(-)
 rename structuralcodes/{code => codes}/__init__.py (94%)
 rename structuralcodes/{code => codes}/mc2010/__init__.py (100%)
 rename structuralcodes/{code => codes}/mc2010/_concrete_material_properties.py (82%)
 create mode 100644 structuralcodes/core/__init__.py
 create mode 100644 structuralcodes/core/base.py
 create mode 100644 structuralcodes/material/__init__.py
 create mode 100644 structuralcodes/material/concrete/__init__.py
 create mode 100644 structuralcodes/material/concrete/_concrete.py
 create mode 100644 structuralcodes/material/concrete/_concreteMC2010.py

diff --git a/structuralcodes/__init__.py b/structuralcodes/__init__.py
index 440bb17c..8f0abf85 100644
--- a/structuralcodes/__init__.py
+++ b/structuralcodes/__init__.py
@@ -1,7 +1,9 @@
 """A Python package that contains models from structural design codes"""
-from .code import set_design_code, get_design_codes, set_national_annex
+from .codes import set_design_code, get_design_codes, set_national_annex
 
-from .code import mc2010
+from . import material
+from . import core
+from . import codes
 
 __version__ = ''
 
@@ -9,5 +11,7 @@
     'set_design_code',
     'get_design_codes',
     'set_national_annex',
-    'mc2010',
+    'codes',
+    'core',
+    'material',
 ]
diff --git a/structuralcodes/code/__init__.py b/structuralcodes/codes/__init__.py
similarity index 94%
rename from structuralcodes/code/__init__.py
rename to structuralcodes/codes/__init__.py
index 47fcddd1..497e5086 100644
--- a/structuralcodes/code/__init__.py
+++ b/structuralcodes/codes/__init__.py
@@ -4,6 +4,13 @@
 
 from . import mc2010
 
+__all__ = [
+    'mc2010',
+    'set_design_code',
+    'get_design_codes',
+    'set_national_annex',
+]
+
 # Global code object used by material classes
 _CODE: t.Optional[types.ModuleType] = None
 
diff --git a/structuralcodes/code/mc2010/__init__.py b/structuralcodes/codes/mc2010/__init__.py
similarity index 100%
rename from structuralcodes/code/mc2010/__init__.py
rename to structuralcodes/codes/mc2010/__init__.py
diff --git a/structuralcodes/code/mc2010/_concrete_material_properties.py b/structuralcodes/codes/mc2010/_concrete_material_properties.py
similarity index 82%
rename from structuralcodes/code/mc2010/_concrete_material_properties.py
rename to structuralcodes/codes/mc2010/_concrete_material_properties.py
index 65c022c2..6be8b025 100644
--- a/structuralcodes/code/mc2010/_concrete_material_properties.py
+++ b/structuralcodes/codes/mc2010/_concrete_material_properties.py
@@ -11,7 +11,7 @@ def fcm(fck: float, delta_f: float = 8.0) -> float:
     Args:
         fck (float): The characteristic compressive strength in MPa.
 
-    Kwargs:
+    Keyword Args:
         delta_f (float): The difference between the mean and the
         characteristic strength.
 
@@ -38,34 +38,34 @@ def fctm(fck: float) -> float:
     return 2.12 * math.log(1 + 0.1 * fcm(fck))
 
 
-def fctkmin(fck: float) -> float:
+def fctkmin(_fctm: float) -> float:
     """Compute the lower bound value of the characteristic tensile strength
-    from the characteristic compressive strength.
+    from the mean tensile strength.
 
     fib Model Code 2010, Eq. (5.1-4)
 
     Args:
-        fck (float): The characteristic compressive strength in MPa.
+        _fctm (float): The mean tensile strength in MPa.
 
     Returns:
         float: Lower bound of the characteristic tensile strength in MPa.
     """
-    return 0.7 * fctm(fck)
+    return 0.7 * _fctm
 
 
-def fctkmax(fck: float) -> float:
+def fctkmax(_fctm: float) -> float:
     """Compute the upper bound value of the characteristic tensile strength
-    from the characteristic compressive strength.
+    from the mean tensile strength.
 
     fib Model Code 2010, Eq. (5.1-5)
 
     Args:
-        fck (float): The characteristic compressive strength in MPa.
+        _fctm (float): The mean tensile strength in MPa.
 
     Returns:
         float: Upper bound of the characteristic tensile strength in MPa.
     """
-    return 1.3 * fctm(fck)
+    return 1.3 * _fctm
 
 
 def Gf(fck: float) -> float:
diff --git a/structuralcodes/core/__init__.py b/structuralcodes/core/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/structuralcodes/core/base.py b/structuralcodes/core/base.py
new file mode 100644
index 00000000..2a9fc080
--- /dev/null
+++ b/structuralcodes/core/base.py
@@ -0,0 +1,26 @@
+"""Abstract base classes"""
+import abc
+import typing as t
+
+
+class Material(abc.ABC):
+    """Abstract base class for materials."""
+
+    def __init__(self, density: float, name: t.Optional[str] = None) -> None:
+        """
+        Initializes an instance of a new material
+        :param float density: density of the material in kg/m3
+        :param Optional[str] name: descriptive name of the material
+        """
+        self._density = abs(density)
+        self._name = name if name is not None else "Material"
+
+    @property
+    def name(self):
+        """Returns the name of the material"""
+        return self._name
+
+    @property
+    def density(self):
+        """Returns the density of the material in kg/m3"""
+        return self._density
diff --git a/structuralcodes/material/__init__.py b/structuralcodes/material/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/structuralcodes/material/concrete/__init__.py b/structuralcodes/material/concrete/__init__.py
new file mode 100644
index 00000000..ca314baf
--- /dev/null
+++ b/structuralcodes/material/concrete/__init__.py
@@ -0,0 +1,58 @@
+"""Concrete material"""
+import typing as t
+from structuralcodes.codes import _use_design_code
+from ._concrete import Concrete
+from ._concreteMC2010 import ConcreteMC2010
+
+__all__ = [
+    'create_concrete',
+    'Concrete',
+    'ConcreteMC2010',
+]
+
+
+def create_concrete(
+    fck: float,
+    name: t.Optional[str] = None,
+    density: float = 2400.0,
+    existing: bool = False,
+    design_code: t.Optional[str] = None,
+) -> t.Optional[Concrete]:
+    """
+    A factory function to create the correct type of concrete based on the
+    desired design code.
+
+    Args:
+        fck (float): Characteristic strength of concrete in MPa.
+            (if existing it is intended as the mean strength)
+
+    Keyword Args:
+        density (float): Density of Concrete in kg/m3 (default: 2400)
+        existing (bool): Boolean indicating if the concrete is of an
+            existing structure (default: False)
+        deisgn_code (str): Optional string (default: None) indicating the
+            desired standard. If None (default) the globally used design
+            standard will be adopted. Otherwise the design standard specified
+            will be used for the instance of the material.
+            Currently available codes: 'mc2010'
+
+    Raises:
+        ValueError: if the design code is not valid or does not cover
+            concrete as a material.
+    """
+    # Get the code from the global variable
+    _code = _use_design_code(design_code)
+
+    # Check if the code is a proper concrete code
+    code = _code if 'concrete' in _code.__materials__ else None
+    if code is None:
+        raise ValueError(
+            'The design code is not set, either use '
+            'structuralcodes.code.set_designcode, or provide a valid '
+            'string in the function.'
+        )
+
+    # Create the proper concrete object
+    if code.__title__ == 'fib Model Code 2010':
+        return ConcreteMC2010(fck, name, density, existing)
+    return None
diff --git a/structuralcodes/material/concrete/_concrete.py b/structuralcodes/material/concrete/_concrete.py
new file mode 100644
index 00000000..19ac2048
--- /dev/null
+++ b/structuralcodes/material/concrete/_concrete.py
@@ -0,0 +1,45 @@
+"""Core implementation of the concrete material"""
+import abc
+import typing as t
+from structuralcodes.core.base import Material
+
+
+class Concrete(Material):
+    """The abstract concrete material."""
+
+    _fck: float
+    _existing: bool
+
+    def __init__(
+        self,
+        fck: float,
+        name: t.Optional[str] = None,
+        density: float = 2400,
+        existing: t.Optional[bool] = False,
+    ) -> None:
+        """Initializes an abstract concrete material"""
+        name = name if name is not None else "Concrete"
+        super().__init__(density=density, name=name)
+
+        self._fck = abs(fck)
+        if existing:
+            raise NotImplementedError(
+                'Existing concrete feature not implemented yet'
+            )
+        self._existing = existing
+
+    @property
+    def fck(self) -> float:
+        """Returns fck in MPa"""
+        return self._fck
+
+    @fck.setter
+    def fck(self, fck: float) -> None:
+        """Setter for fck (in MPa)"""
+        self._fck = abs(fck)
+        self._reset_attributes()
+
+    @abc.abstractmethod
+    def _reset_attributes(self):
+        """Each concrete should define its own _reset_attributes method
+        This is because fck setting, reset the object arguments"""
diff --git a/structuralcodes/material/concrete/_concreteMC2010.py b/structuralcodes/material/concrete/_concreteMC2010.py
new file mode 100644
index 00000000..faf0ad97
--- /dev/null
+++ b/structuralcodes/material/concrete/_concreteMC2010.py
@@ -0,0 +1,189 @@
+"""The concrete class for Model Code 2020 Concrete Material"""
+import typing as t
+import warnings
+
+from structuralcodes.codes import mc2010
+from ._concrete import Concrete
+
+
+class ConcreteMC2010(Concrete):
+    """Concrete implementation for MC 2010"""
+
+    _fcm: t.Optional[float] = None
+    _fctm: t.Optional[float] = None
+    _fctkmin: t.Optional[float] = None
+    _fctkmax: t.Optional[float] = None
+    _Gf: t.Optional[float] = None
+
+    def __init__(
+        self,
+        fck: float,
+        name: t.Optional[str] = None,
+        density: float = 2400.0,
+        existing: bool = False,
+    ):
+        """Initializes a new instance of Concrete for MC 2010
+
+        Args:
+            fck (float): Characteristic strength in MPa if concrete is not
+                existing.
+
+        Keyword Args:
+            name (str): A descriptive name for concrete
+            density (float): Density of material in kg/m3 (default: 2400)
+            existing (bool): The material is of an existing structure
+                (default: False)
+        """
+
+        if name is None:
+            name = f'C{round(fck):d}'
+        super().__init__(
+            fck=fck, name=name, density=density, existing=existing
+        )
+
+    def _reset_attributes(self):
+        self._fcm = None
+        self._fctm = None
+        self._fctkmin = None
+        self._fctkmax = None
+        self._Gf = None
+
+    def update_attributes(self, updated_attributes: dict) -> None:
+        """Function for updating the attributes specified in the input
+        dictionary
+
+        Args:
+            updated_attributes (dict): the dictionary of parameters to be
+                updated (not found parameters are skipped with a warning)
+        """
+        for key, value in updated_attributes.items():
+            if not hasattr(self, '_' + key):
+                str_list_keys = ''
+                for k in updated_attributes.keys():
+                    str_list_keys += k + ', '
+                str_warn = (
+                    f'WARNING: attribute {key} not found. Ignoring the entry.'
+                )
+                str_warn += '\nAvailable keys: ' + str_list_keys
+                warnings.warn(str_warn)
+                continue
+            setattr(self, '_' + key, value)
+
+    @property
+    def fcm(self) -> float:
+        """Returns fcm in MPa.
+
+        Returns:
+            float: The mean compressive strength in MPa.
+        """
+        if self._fcm is not None:
+            return self._fcm
+        return mc2010.fcm(self._fck)
+
+    @fcm.setter
+    def fcm(self, value: float):
+        """Sets a user defined value for fcm
+
+        Args:
+            value (float): the value of fcm in MPa
+
+        Raises:
+            ValueError: if value is lower than fck
+        """
+        if abs(value) <= self._fck:
+            raise ValueError(
+                (
+                    'Mean compressive strength cannot be lower than',
+                    'characteristic strength.\n',
+                    'Current characteristing strength: ',
+                    f'fck = {self._fck}.',
+                    f'Current value: value = {value}',
+                )
+            )
+        self._fcm = abs(value)
+
+    @property
+    def fctm(self) -> float:
+        """Returns fctm in MPa
+
+        Returns:
+            float: The mean tensile strength in MPa
+        """
+        if self._fctm is not None:
+            return self._fctm
+        return mc2010.fctm(self._fck)
+
+    @fctm.setter
+    def fctm(self, value: float):
+        """Sets a user defined value for fctm
+
+        Args:
+            value (float): the value of fctm in MPa
+        """
+        if value > 0.5 * self._fck:
+            warnings.warn(
+                'A suspect value of fctm has been input. Please check.'
+            )
+        self._fctm = abs(value)
+
+    @property
+    def fctkmin(self) -> float:
+        """Returns fctkmin in MPa
+
+        Returns:
+            float: The lower bound tensile strength in MPa
+        """
+        if self._fctkmin is not None:
+            return self._fctkmin
+
+        return mc2010.fctkmin(self.fctm)
+
+    @fctkmin.setter
+    def fctkmin(self, value: float):
+        """Sets a user defined value for fctkmin
+
+        Args:
+            value (float): the value of fctkmin in MPa
+        """
+        self._fctkmin = abs(value)
+
+    @property
+    def fctkmax(self) -> float:
+        """Returns fctkmax in MPa
+
+        Returns:
+            float: The upper bound tensile strength in MPa
+        """
+        if self._fctkmax is not None:
+            return self._fctkmax
+
+        return mc2010.fctkmax(self.fctm)
+
+    @fctkmax.setter
+    def fctkmax(self, value: float):
+        """Sets a user defined value for fctkmax
+
+        Args:
+            value (float): the value of fctkmax in MPa
+        """
+        self._fctkmax = abs(value)
+
+    @property
+    def Gf(self) -> float:
+        """Fracture energy of concrete
+
+        Returns:
+            float: The fracture energy in N/m
+        """
+        if self._Gf is not None:
+            return self._Gf
+        return mc2010.Gf(self._fck)
+
+    @Gf.setter
+    def Gf(self, value: float):
+        """Sets a user defined value for fracture energy Gf
+
+        Args:
+            value (float): the value of Gf in N/m
+        """
+        self._Gf = abs(value)
diff --git a/tests/test_get_set_design_code.py b/tests/test_get_set_design_code.py
index 65686dcf..1711be83 100644
--- a/tests/test_get_set_design_code.py
+++ b/tests/test_get_set_design_code.py
@@ -19,14 +19,14 @@ def test_set_design_code(design_code_to_set):
     structuralcodes.set_design_code(design_code_to_set)
 
     # Assert
-    assert isinstance(structuralcodes.code._CODE, types.ModuleType)
-    assert structuralcodes.code._CODE.__title__ == expected_design_code_title
+    assert isinstance(structuralcodes.codes._CODE, types.ModuleType)
+    assert structuralcodes.codes._CODE.__title__ == expected_design_code_title
 
 
 def test_get_design_codes():
     """Test get a list of implemented design codes."""
     # Arrange
-    expected_list_of_codes = list(structuralcodes.code._DESIGN_CODES.keys())
+    expected_list_of_codes = list(structuralcodes.codes._DESIGN_CODES.keys())
 
     # Act
     available_codes = structuralcodes.get_design_codes()
@@ -48,7 +48,7 @@ def test_set_national_annex(na_to_set):
     structuralcodes.set_national_annex(na_to_set)
 
     # Assert
-    assert structuralcodes.code._NATIONAL_ANNEX == expected_na
+    assert structuralcodes.codes._NATIONAL_ANNEX == expected_na
 
 
 @pytest.mark.parametrize(
@@ -61,7 +61,7 @@ def test_use_design_code(design_code_to_user):
     expected_design_code_title = 'fib Model Code 2010'
 
     # Act
-    code_to_use = structuralcodes.code._use_design_code(design_code_to_user)
+    code_to_use = structuralcodes.codes._use_design_code(design_code_to_user)
 
     # Assert
     assert isinstance(code_to_use, types.ModuleType)
@@ -76,7 +76,7 @@ def test_use_design_code_none():
     structuralcodes.set_design_code(design_code_to_set)
 
     # Act
-    code_to_use = structuralcodes.code._use_design_code()
+    code_to_use = structuralcodes.codes._use_design_code()
 
     # Assert
     assert isinstance(code_to_use, types.ModuleType)
diff --git a/tests/test_mc2010_material_properties.py b/tests/test_mc2010_material_properties.py
index 13e339f7..e43bd26e 100644
--- a/tests/test_mc2010_material_properties.py
+++ b/tests/test_mc2010_material_properties.py
@@ -3,7 +3,7 @@
 
 import pytest
 
-from structuralcodes.code.mc2010 import _concrete_material_properties
+from structuralcodes.codes.mc2010 import _concrete_material_properties
 
 
 @pytest.mark.parametrize(
@@ -71,7 +71,9 @@ def test_fctm(test_input, expected):
 def test_fctkmin(test_input, expected):
     """Test the fctkmin function."""
     assert math.isclose(
-        _concrete_material_properties.fctkmin(test_input),
+        _concrete_material_properties.fctkmin(
+            _concrete_material_properties.fctm(test_input)
+        ),
         expected,
         rel_tol=0.031,
     )
@@ -102,7 +104,9 @@ def test_fctkmin(test_input, expected):
 def test_fctkmax(test_input, expected):
     """Test the fctkmax function."""
     assert math.isclose(
-        _concrete_material_properties.fctkmax(test_input),
+        _concrete_material_properties.fctkmax(
+            _concrete_material_properties.fctm(test_input)
+        ),
         expected,
         rel_tol=0.028,
     )
@@ -115,7 +119,7 @@ def test_fctkmax(test_input, expected):
         (35, 143.664),
         (55, 153.888),
         (90, 166.626),
-        (120, 174.832)
+        (120, 174.832),
     ],
 )
 def test_Gf(test_input, expected):

From b8f67bbfa78de0b09896870de8210be8c76e8e6f Mon Sep 17 00:00:00 2001
From: Morten Engen <morten.engen@multiconsult.no>
Date: Tue, 13 Dec 2022 21:05:57 +0100
Subject: [PATCH 02/33] Update docstring of base material

---
 structuralcodes/core/base.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/structuralcodes/core/base.py b/structuralcodes/core/base.py
index 2a9fc080..e0fa6d91 100644
--- a/structuralcodes/core/base.py
+++ b/structuralcodes/core/base.py
@@ -7,10 +7,13 @@ class Material(abc.ABC):
     """Abstract base class for materials."""
 
     def __init__(self, density: float, name: t.Optional[str] = None) -> None:
-        """
-        Initializes an instance of a new material
-        :param float density: density of the material in kg/m3
-        :param Optional[str] name: descriptive name of the material
+        """Initializes an instance of a new material
+
+        Args:
+            density (float): density of the material in kg/m3
+
+        Keyword Args:
+            name (Optional[str]): descriptive name of the material
         """
         self._density = abs(density)
         self._name = name if name is not None else "Material"

From c299dcc894f019ade6a4da8fe306b484e942a804 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 15 Dec 2022 13:17:10 +0100
Subject: [PATCH 03/33] minimum reinforcement areas functions

---
 .vscode/settings.json                         |  11 +-
 structuralcodes/codes/ec2_2004/__init__.py    |  10 +
 .../codes/ec2_2004/_crack_control.py          | 302 ++++++++++++++++++
 tests/test_ec2_2004_crack_control.py          | 150 +++++++++
 4 files changed, 471 insertions(+), 2 deletions(-)
 create mode 100644 structuralcodes/codes/ec2_2004/__init__.py
 create mode 100644 structuralcodes/codes/ec2_2004/_crack_control.py
 create mode 100644 tests/test_ec2_2004_crack_control.py

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 72069360..2676da93 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,10 +1,17 @@
 {
-    "python.formatting.provider": "black",
     "python.testing.pytestArgs": [
         "tests"
     ],
+    "python.formatting.provider": "black",
     "python.testing.unittestEnabled": false,
     "python.testing.pytestEnabled": true,
     "python.linting.pylintEnabled": true,
-    "python.linting.flake8Enabled": true
+    "python.linting.flake8Enabled": true,
+    "[python]": {
+        "editor.formatOnSave": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": true
+        },
+        "editor.defaultFormatter": "ms-python.python",
+    },
 }
\ No newline at end of file
diff --git a/structuralcodes/codes/ec2_2004/__init__.py b/structuralcodes/codes/ec2_2004/__init__.py
new file mode 100644
index 00000000..96694004
--- /dev/null
+++ b/structuralcodes/codes/ec2_2004/__init__.py
@@ -0,0 +1,10 @@
+"""EUROCODE 2 1992-1-1:2004"""
+import typing as t
+
+from ._crack_control import w_max
+
+__all__ = ['w_max']
+
+__title__: str = 'EUROCODE 2 1992-1-1'
+__year__: str = '2004'
+__materials__: t.Tuple[str] = ('concrete',)
diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
new file mode 100644
index 00000000..76b1e988
--- /dev/null
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -0,0 +1,302 @@
+"""Collection of functions from EUROCODE 1992-1-1:2004
+Chapter 7.3 - Crack control"""
+import scipy.interpolate
+
+
+def w_max(exposure_class: str, load_combination: str) -> float:
+    """Computes the recomended value of the maximum crack width.
+
+    EUROCODE 2 1992-1-1:2004, Table (7.1N)
+
+    Args:
+        exposure_class (str): The exposure class.
+            Possible values: X0, XC1, XC2, XC3, XC4, XD1, XD2, XS1, XS2, XS3
+        load_combination (str):
+            - f: for frequent load combination
+            - qp: for quasi-permanent load combination
+
+    Returns:
+        float: The maximum recommended value for the crack width wmax in mm.
+
+    Raises:
+        ValueError: if not valid exposure_class or load_combination values.
+    """
+    _load_combination = load_combination.lower()
+    _exposure_class = exposure_class.upper()
+    if _load_combination == 'f':
+        if _exposure_class in ('X0', 'XC1'):
+            return 0.2
+        if _exposure_class in ('XC2', 'XC3', 'XC4'):
+            return 0.2
+    if _load_combination == 'qp':
+        if _exposure_class in ('X0', 'XC1'):
+            return 0.4
+        if _exposure_class in (
+            'XC2',
+            'XC3',
+            'XC4',
+            'XD1',
+            'XD2',
+            'XS1',
+            'XS2',
+            'XS3',
+        ):
+            return 0.3
+        raise ValueError(
+            f'{exposure_class} is not a valid value for exposure_class.'
+            + ' Please enter one of the following: X0, XC1, XC2, XC3, XC4, XD1'
+            + ',XD2, XS1, XS2, XS3'
+        )
+    raise ValueError(
+        f'{load_combination} is not a valid value for load_combination.'
+        + 'Please enter "f" for frequent load combination or "qp" for'
+        + 'quasi-permanent load combination.'
+    )
+
+
+def crack_min_steel_area(
+    a_ct: float, s_steel: float, fct_eff: float, k: float, kc: float
+) -> float:
+    """Computes the minimum area of reinforcing steel within the tensile zone
+    for control of cracking areas
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Args:
+        a_ct (float): is the area of concrete within the tensile zone in mm2.
+            The tensile zone is that parg of the section which is calculated
+            to be in tension just before the formation of the first crack.
+        s_steel (float): is the absolute value of the maximum stress in MPa
+            permitted in the reinforcement immediately after the formation
+            of the crack. This may be taken as theyield strength of the
+            reinforcement, fyk. A lower value may, however, be needed to
+            satisfy the crack width limits according to the maximum
+            bar size of spacing (see 7.3.3 (2)).
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        k (float): is the coefficient which allow for the effect of
+            non-uniform self-equilibrating stresses, which lead to a
+            reduction of restraint forces. Use 'k_crack_min_steel_area'
+            to compute it
+            k=1 for webs w<=300mm or flanges widths less than 300mm
+            k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
+            Intermediate values may be interpolated.
+        kc (float): is a coefficient which takes account of the stress
+            distribution within the section immediately prior to cracking and
+            the change of the lever arm.
+
+    Returns:
+        float: the minimm area of reinforcing steel within the tensile
+            zone in mm2.
+
+    Raises:
+        ValueError: if k value is not between 0.65 and 1 or kc is not
+            larger than 0 and lower than 1.
+    """
+    s_steel = abs(s_steel)
+    fct_eff = abs(fct_eff)
+
+    if k < 0.65 or k > 1.0:
+        raise ValueError(f'k={k} must be between 0.65 and 1')
+    if kc > 1 or kc < 0:
+        raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
+
+    return kc * k * fct_eff * a_ct / s_steel
+
+
+def k_crack_min_steel_area(h: float) -> float:
+    """Is the coefficient which allow for the effect of
+    non-uniform self-equilibrating stresses, which lead to a
+    reduction of restraint forces. Use 'k_crack_min_steel_area'
+    to compute it
+    k=1 for webs w<=300mm or flanges widths less than 300mm
+    k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Args:
+        h (float): flange length or flange width in mm
+
+    Returns:
+        float: k coefficient value
+
+    Raises:
+        ValueError: if h is less than 0
+    """
+    if h < 0:
+        raise ValueError(f'h={h} cannot be less than 0mm')
+    if h <= 300:
+        return 1
+    if h < 800:
+        interpol = scipy.interpolate.interp1d((300, 800), (1, 0.65))
+        return (float)(interpol(h))
+    return 0.65
+
+
+def kc_crack_min_steel_area_pure_tension() -> float:
+    """Computes the coefficient which takes account of the stress
+    distribution within the section immediately prior to cracking and
+    the change of the lever arm in pure dtension.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Returns:
+        float: value of the kc coefficient in pure tension
+    """
+    return 1
+
+
+def kc_crack_min_steel_area_rectangular(
+    h: float, b: float, fct_eff: float, n_ed: float
+) -> float:
+    """Computes the coefficient which takes account of the stress
+    distribution within the section immediately prior to cracking and
+    the change of the lever arm for bending+axial combination
+    in rectangular sections and webs of box sections and T-sections.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.2)
+
+    Args:
+        h (float): heigth of the element in mm
+        b (float): width of the element in mm
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        n_ed (str): axial force at the serviceability limit state acting on
+            the part of the cross-section under consideration (compressive
+            force positive). n_ed should be determined considering the
+            characteristic values of prestress and axial forces under the
+            relevant combination of actions
+
+    Returns:
+        float: value of the kc coefficient
+
+    Raises:
+        ValueError: is h or b are less than 0
+    """
+    if h < 0:
+        raise ValueError(f'h={h} should be larger than 0mm')
+    if b < 0:
+        raise ValueError(f'b={b} should be larger than 0mm')
+
+    h_s = min(h, 1000)
+    k1 = 1.5 if n_ed >= 0 else 2 * h_s / 3 / h
+    s_concrete = n_ed * 1000 / b / h
+    h_ratio = h / h_s
+    return min(max(0.4 * (1 - s_concrete / k1 / h_ratio / fct_eff), 0), 1)
+
+
+def kc_crack_min_steel_area_flanges(
+    f_cr: float, a_ct: float, fct_eff: float
+) -> float:
+    """Computes the coefficient which takes account of the stress
+    distribution within the section immediately prior to cracking and
+    the change of the lever arm for bending+axial combination
+    in rectangular sections for flanges of box sections and T-sections.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.3)
+
+    Args:
+        f_cr: is the absolute value in kN of the tensile force within the
+            flange immediately prior to cracking due to cracking moment
+            calculated with fct,eff
+        a_ct (float): is the area of concrete within the tensile zone in mm2.
+            The tensile zone is that part of the section which is calculated
+            to be in tension just before the formation of the first crack.
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+
+    Returns:
+        float: value of the kc coefficient
+
+    Raises:
+        ValueError: is a_ct is less than 0mm2
+    """
+    f_cr = abs(f_cr)
+    return max(0.9 * f_cr * 1000 / a_ct / fct_eff, 0.5)
+
+
+def crack_min_steel_area_with_prestresed_tendons(
+    a_ct: float,
+    s_steel: float,
+    fct_eff: float,
+    k: float,
+    kc: float,
+    ap: float,
+    d_steel: float,
+    d_press: float,
+    e: float,
+    incr_stress: float,
+) -> float:
+    """Computes the minimum area of reinforcing steel within the tensile zone
+    for control of cracking areas in addition with bonded tendons
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Args:
+        a_ct (float): is the area of concrete within the tensile zone in mm2.
+            The tensile zone is that part of the section which is calculated
+            to be in tension just before the formation of the first crack.
+        s_steel (float): is the absolute value of the maximum stress in MPa
+            permitted in the reinforcement immediately after the formation
+            of the crack. This may be taken as theyield strength of the
+            reinforcement, fyk. A lower value may, however, be needed to
+            satisfy the crack width limits according to the maximum
+            bar size of spacing (see 7.3.3 (2)).
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        k (float): is the coefficient which allow for the effect of
+            non-uniform self-equilibrating stresses, which lead to a
+            reduction of restraint forces. Use 'k_crack_min_steel_area'
+            to compute it
+            k=1 for webs w<=300mm or flanges widths less than 300mm
+            k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
+            Intermediate values may be interpolated.
+        kc (float): is a coefficient which takes account of the stress
+            distribution within the section immediately prior to cracking and
+            the change of the lever arm.
+        ac_eff (float): is the effective area in mm2 of concrete in tension
+            surrounding or prestressing tendons if depth hc,ef
+        ap (float): is the area in mm2 of pre or post-tensioned tendons
+            within ac_eff
+        d_steel (float): largest bar diameter in mm of reinforcing steel.
+            Equal to zero if only prestressing is used in control cracking
+        d_press (float): equivalent diameter in mm of tendon acoording
+            to 6.8.2
+        e (float): ratio of bond strength of prestressing and reinforcing
+            steel, according to Table 6.2 in 6.8.2
+        incr_stress (float): stress variation in MPa in prestressing tendons
+            from the state of zero strain of the concrete at the same level
+
+    Returns:
+        float: the minimm area of reinforcing steel within the tensile
+            zone in mm2.
+
+    Raises:
+        ValueError: if k value is not between 0.65 and 1 or kc is not
+            larger than 0 and lower than 1. If diameters d_steel or
+            d_press are lower than 0. If ratio of bond strength e
+            is less than 0 or larger than 1. If area of tendons ac_eff
+            is less than 0. Is stress variation incr_stress is less than 0
+    """
+    as_min = crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
+
+    if d_press < 0:
+        raise ValueError(f'd_press={d_press} cannot be less than 0')
+    if d_steel < 0:
+        raise ValueError(f'd_steel={d_steel} cannot be less than 0')
+    if ap < 0:
+        raise ValueError(f'ap={ap} cannot be less than 0')
+    if incr_stress < 0:
+        raise ValueError(f'incr_stress={incr_stress} cannot be less than 0')
+
+    e1 = d_steel > 0 if (e * d_steel / d_press) ** 0.5 else e**0.5
+    f = e1 * ap * incr_stress
+    return as_min * f
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
new file mode 100644
index 00000000..2b49e3d9
--- /dev/null
+++ b/tests/test_ec2_2004_crack_control.py
@@ -0,0 +1,150 @@
+"""Tests for EUROCODE 2-1-1:2004 Chapter 7.3 Crack Control"""
+import math
+
+import pytest
+from structuralcodes.codes.ec2_2004 import _crack_control
+
+
+@pytest.mark.parametrize(
+    'test_exposure_class, test_load_combination, expected',
+    [
+        ('X0', 'f', 0.2),
+        ('x0', 'F', 0.2),
+        ('X0', 'qp', 0.4),
+        ('x0', 'QP', 0.4),
+        ('XC2', 'f', 0.2),
+        ('xc2', 'F', 0.2),
+        ('XC3', 'f', 0.2),
+        ('xc3', 'F', 0.2),
+        ('XC4', 'f', 0.2),
+        ('xc4', 'F', 0.2),
+        ('XC2', 'qp', 0.3),
+        ('xc2', 'QP', 0.3),
+        ('XC3', 'qp', 0.3),
+        ('xc3', 'QP', 0.3),
+        ('XC4', 'qp', 0.3),
+        ('xc4', 'QP', 0.3),
+        ('XD1', 'qp', 0.3),
+        ('xd1', 'QP', 0.3),
+        ('XD2', 'qp', 0.3),
+        ('xd2', 'QP', 0.3),
+        ('XS1', 'qp', 0.3),
+        ('xs1', 'QP', 0.3),
+        ('XS2', 'qp', 0.3),
+        ('xs2', 'QP', 0.3),
+        ('XS3', 'qp', 0.3),
+        ('xs3', 'QP', 0.3),
+    ],
+)
+def test_w_max_returns_expected_values(
+    test_exposure_class, test_load_combination, expected
+):
+    """Test that the w_max function returns expected values"""
+    w_max = _crack_control.w_max(test_exposure_class, test_load_combination)
+    assert w_max == expected
+
+
+@pytest.mark.parametrize(
+    'test_exposure_class, test_load_combination',
+    [('dummy1', 'f'), ('dummy2', 'qp'), ('XD1', 'dummy3'), ('XS1', 'dummy4')],
+)
+def test_w_max_not_valid_input_raises_valueerror(
+    test_exposure_class, test_load_combination
+):
+    """Test that not valid input returns ValueError"""
+    with pytest.raises(ValueError):
+        _crack_control.w_max(test_exposure_class, test_load_combination)
+
+
+@pytest.mark.parametrize(
+    'h, expected',
+    [
+        (200, 1),
+        (300, 1),
+        (800, 0.65),
+        (1000, 0.65),
+        (400, 0.93),
+        (500, 0.86),
+        (600, 0.79),
+        (700, 0.72),
+    ],
+)
+def test_k_crack_min_steel_area_returns_expected_values(h, expected):
+    """Test the k_crack_min_steel_area function"""
+    k = _crack_control.k_crack_min_steel_area(h)
+    assert math.isclose(k, expected)
+
+
+def test_k_crack_min_steel_area_raises_valueerror():
+    """Test that not valid input returns ValueError exeption"""
+    with pytest.raises(ValueError):
+        h = -100
+        _crack_control.k_crack_min_steel_area(h)
+
+
+def test_kc_crack_min_steel_area_pure_tension_returns_expected_values():
+    """Test the kc_crack_min_steel_area_pure_tension function"""
+    assert 1 == _crack_control.kc_crack_min_steel_area_pure_tension()
+
+
+@pytest.mark.parametrize(
+    'h, b, fct_eff, n_ed, expected',
+    [
+        (600, 400, 3, 20, 0.3925926),
+        (600, 400, 3, -20, 0.4166667),
+        (400, 200, 4, 3, 0.397500),
+        (200, 50, 5, -80, 1),
+        (200, 50, 5, 80, 0),
+    ],
+)
+def test_kc_crack_min_steel_area_rectangular_returns_expected_values(
+    h, b, fct_eff, n_ed, expected
+):
+    """Test the kc_crack_min_steel_area_rectangular"""
+    kc = _crack_control.kc_crack_min_steel_area_rectangular(
+        h, b, fct_eff, n_ed
+    )
+    assert math.isclose(kc, expected, rel_tol=0.000001)
+
+
+def test_kc_crack_min_steel_area_rectangular_rasies_valueerror():
+    """Test the kc_crack_min_steel_area_rectangular raises Value
+    Error for not correct input values for b and h"""
+    with pytest.raises(ValueError):
+        _crack_control.kc_crack_min_steel_area_rectangular(
+            h=-100, b=100, fct_eff=100, n_ed=10
+        )
+        _crack_control.kc_crack_min_steel_area_rectangular(
+            h=100, b=-100, fct_eff=100, n_ed=10
+        )
+
+
+@pytest.mark.parametrize(
+    'f_cr, a_ct, fct_eff, expected',
+    [
+        (30, 10000, 5, 0.54),
+        (20, 5000, 3, 1.2),
+        (55, 7500, 4, 1.65),
+        (55, 50000, 4, 0.5),
+    ],
+)
+def test_kc_crack_min_steel_area_flanges(f_cr, a_ct, fct_eff, expected):
+    """Test the kc_crack_min_steel_area_flanges function"""
+    kc = _crack_control.kc_crack_min_steel_area_flanges(f_cr, a_ct, fct_eff)
+    assert math.isclose(kc, expected, rel_tol=0.000001)
+
+
+@pytest.mark.parametrize(
+    'a_ct, s_steel, fct_eff, k, kc, expected',
+    [
+        (10000, 500, 3, 1, 1, 60),
+        (80000, 500, 5, 0.65, 0.5, 260),
+        (80000, 400, 4, 0.9, 0.75, 540),
+    ],
+)
+def test_crack_min_steel_area_returns_expected_values(
+    a_ct, s_steel, fct_eff, k, kc, expected
+):
+    """Test the crack_min_steel_area returns expected values"""
+    as_min = _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
+    assert math.isclose(as_min, expected, rel_tol=0.000001)

From 59a04f5e92b7684fdfb6d1c58a186f3c6b2c55c0 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Tue, 27 Dec 2022 11:11:12 +0100
Subject: [PATCH 04/33] raise ValueError test functions for min area

---
 .../codes/ec2_2004/_crack_control.py          |  5 +++-
 tests/test_ec2_2004_crack_control.py          | 24 ++++++++++++++++++-
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index 76b1e988..251c78c2 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -95,9 +95,12 @@ def crack_min_steel_area(
         ValueError: if k value is not between 0.65 and 1 or kc is not
             larger than 0 and lower than 1.
     """
-    s_steel = abs(s_steel)
     fct_eff = abs(fct_eff)
 
+    if a_ct <= 0:
+        raise ValueError(f'a_ct={a_ct} must be larger than 0')
+    if s_steel < 0:
+        raise ValueError(f's_steel={s_steel} must be equal or larger than 0')
     if k < 0.65 or k > 1.0:
         raise ValueError(f'k={k} must be between 0.65 and 1')
     if kc > 1 or kc < 0:
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 2b49e3d9..802f7bfd 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -107,7 +107,7 @@ def test_kc_crack_min_steel_area_rectangular_returns_expected_values(
     assert math.isclose(kc, expected, rel_tol=0.000001)
 
 
-def test_kc_crack_min_steel_area_rectangular_rasies_valueerror():
+def test_kc_crack_min_steel_area_rectangular_raises_valueerror():
     """Test the kc_crack_min_steel_area_rectangular raises Value
     Error for not correct input values for b and h"""
     with pytest.raises(ValueError):
@@ -148,3 +148,25 @@ def test_crack_min_steel_area_returns_expected_values(
     """Test the crack_min_steel_area returns expected values"""
     as_min = _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
     assert math.isclose(as_min, expected, rel_tol=0.000001)
+
+
+@pytest.mark.parametrize(
+    'a_ct, s_steel, fct_eff, k, kc',
+    [
+        (-10000, 100, 3, 0.7, 0.67),
+        (10000, -100, 3, 0.7, 0.65),
+        (10000, 100, 3, 0.5, 0.65),
+        (10000, 100, 3, 1.1, 0.65),
+        (10000, 100, 3, 0.7, -0.1),
+        (10000, 100, 3, 0.7, 1.1),
+    ],
+)
+def test_crack_min_steel_area_raises_valueerror(a_ct, s_steel, fct_eff, k, kc):
+    """Test the crack_min_steel_area raises value error"""
+    with pytest.raises(ValueError):
+        _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
+
+
+def test_crack_min_steel_area_with_prestressed_tendons_returns_expected_values():
+    """Test the crack_min_steel_area returns expected values"""
+    pass

From b7167aa4b462253002c3581c579180d1488ce4ef Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 09:57:48 +0100
Subject: [PATCH 05/33] crack_min_steel_without_direct_calculation

---
 requirements.txt                              |   2 +
 .../codes/ec2_2004/_crack_control.py          | 156 +++++++++++++++++-
 tests/test_ec2_2004_crack_control.py          |  84 +++++++++-
 3 files changed, 232 insertions(+), 10 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index e69de29b..80ba09bb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+numpy==1.23.5
+scipy==1.9.3
diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index 251c78c2..b3a2ba53 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -1,5 +1,6 @@
 """Collection of functions from EUROCODE 1992-1-1:2004
 Chapter 7.3 - Crack control"""
+import numpy as np
 import scipy.interpolate
 
 
@@ -270,7 +271,7 @@ def crack_min_steel_area_with_prestresed_tendons(
         ap (float): is the area in mm2 of pre or post-tensioned tendons
             within ac_eff
         d_steel (float): largest bar diameter in mm of reinforcing steel.
-            Equal to zero if only prestressing is used in control cracking
+            Equal to 0 if only prestressing is used in control cracking
         d_press (float): equivalent diameter in mm of tendon acoording
             to 6.8.2
         e (float): ratio of bond strength of prestressing and reinforcing
@@ -289,9 +290,9 @@ def crack_min_steel_area_with_prestresed_tendons(
             is less than 0 or larger than 1. If area of tendons ac_eff
             is less than 0. Is stress variation incr_stress is less than 0
     """
-    as_min = crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
+    fct_eff = abs(fct_eff)
 
-    if d_press < 0:
+    if d_press <= 0:
         raise ValueError(f'd_press={d_press} cannot be less than 0')
     if d_steel < 0:
         raise ValueError(f'd_steel={d_steel} cannot be less than 0')
@@ -299,7 +300,150 @@ def crack_min_steel_area_with_prestresed_tendons(
         raise ValueError(f'ap={ap} cannot be less than 0')
     if incr_stress < 0:
         raise ValueError(f'incr_stress={incr_stress} cannot be less than 0')
+    if e < 0.15:
+        raise ValueError(f'The minimum value for e={e} is 0.15')
+    if e > 0.8:
+        raise ValueError(f'The maximum value for e={e} is 0.8')
+    if a_ct <= 0:
+        raise ValueError(f'a_ct={a_ct} must be larger than 0')
+    if s_steel < 0:
+        raise ValueError(f's_steel={s_steel} must be equal or larger than 0')
+    if k < 0.65 or k > 1.0:
+        raise ValueError(f'k={k} must be between 0.65 and 1')
+    if kc > 1 or kc < 0:
+        raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
+
+    a1 = kc * k * fct_eff * a_ct
+    e1 = ((e * d_steel / d_press) ** 0.5) if d_steel > 0 else e**0.5
+    a2 = e1 * ap * incr_stress
+    a = a1 - a2
+
+    return a / s_steel
+
+
+def crack_min_steel_without_direct_calculation(
+    wk: float,
+    s_steel: float,
+    fct_eff: float,
+    kc: float,
+    h_cr: float,
+    h: float,
+    d: float,
+    incr_stress: float = 0,
+) -> tuple(float, float):
+    """Computes the minimum area of reinforcing steel within the tensile zone
+    for control of cracking areas
+
+    EUROCODE 2 1992-1-1:2004, Table (7.2N), Table (7.3N)
+
+    Args:
+        wk (float): the characteristic crack width value in mm.
+        s_steel (float): the steel stress value in MPa under the relevant
+            combination of actions.
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        kc (float): is a coefficient which takes account of the stress
+            distribution within the section immediately prior to cracking and
+            the change of the lever arm.
+        h_cr (float): is the depth of the tensile zone immediately prior to
+            cracking, considering the characteristic values of prestress and
+            axial forces under the quasi-permanent combination of actions.
+        h (float): the overall depth of the section in mm.
+        d (float): is the effective depth to the centroid of the outer layer
+            of the reinforcement.
+        incr_stress (float, optional): value of prestressed stress in MPa if
+            applicable
+
+    Returns:
+        tuple(float, float): with the value of the maximum bar diameters in mm
+        in the first position and the maximum bar spacing in mm in the
+        second position
+    Raises:
+        ValueError: if wk, fct_eff, h_cr, h or d are less than 0
+        ValueError: if kc is not between 0 and 1
+    """
+    if wk < 0:
+        raise ValueError(f'wd={wk} cannot be less than 0')
+    if fct_eff < 0:
+        raise ValueError(f'fct_eff={fct_eff} is less than 0')
+    if h_cr < 0:
+        raise ValueError(f'h_cr={h_cr} is less than 0')
+    if h < 0:
+        raise ValueError(f'h={h} is less than 0')
+    if d < 0:
+        raise ValueError(f'd={d} is less than 0')
+    if kc < 0 or kc > 1:
+        raise ValueError(f'kc={kc} is not between 0 and 1')
+
+    s = s_steel - incr_stress
+    if s <= 0:
+        return (0, 0)
+
+    x = (0.4, 0.3, 0.2)
+    y_phi = (160, 200, 240, 280, 320, 360, 400, 450)
+    y_spa = (160, 200, 240, 280, 320, 360)
+    phi_s_v = (
+        40,
+        32,
+        25,
+        32,
+        25,
+        16,
+        20,
+        16,
+        12,
+        16,
+        12,
+        8,
+        12,
+        10,
+        6,
+        10,
+        8,
+        5,
+        8,
+        6,
+        4,
+        6,
+        5,
+        None,
+    )
+    spa_v = (
+        300,
+        300,
+        200,
+        300,
+        250,
+        150,
+        250,
+        200,
+        100,
+        200,
+        150,
+        50,
+        150,
+        100,
+        None,
+        100,
+        50,
+        None,
+    )
+
+    points_phi = np.meshgrid(x, y_phi)
+    points_spa = np.meshgrid(x, y_spa)
+    xi = (wk, s)
+
+    phi_grid = scipy.interpolate.griddata(
+        points_phi, phi_s_v, xi, method='linear'
+    )
+    phi_star = phi_grid[0]
+    phi = phi_star * (fct_eff / 2.9) * kc * h_cr / (2 * (h - d))
+
+    spa_grid = scipy.interpolate.griddata(
+        points_spa, spa_v, xi, method='linear'
+    )
+    spa = spa_grid[0]
 
-    e1 = d_steel > 0 if (e * d_steel / d_press) ** 0.5 else e**0.5
-    f = e1 * ap * incr_stress
-    return as_min * f
+    return (phi, spa)
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 802f7bfd..39fa0f98 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -102,7 +102,10 @@ def test_kc_crack_min_steel_area_rectangular_returns_expected_values(
 ):
     """Test the kc_crack_min_steel_area_rectangular"""
     kc = _crack_control.kc_crack_min_steel_area_rectangular(
-        h, b, fct_eff, n_ed
+        h,
+        b,
+        fct_eff,
+        n_ed,
     )
     assert math.isclose(kc, expected, rel_tol=0.000001)
 
@@ -147,7 +150,7 @@ def test_crack_min_steel_area_returns_expected_values(
 ):
     """Test the crack_min_steel_area returns expected values"""
     as_min = _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
-    assert math.isclose(as_min, expected, rel_tol=0.000001)
+    assert math.isclose(as_min, expected, rel_tol=10e-6)
 
 
 @pytest.mark.parametrize(
@@ -167,6 +170,79 @@ def test_crack_min_steel_area_raises_valueerror(a_ct, s_steel, fct_eff, k, kc):
         _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
 
 
-def test_crack_min_steel_area_with_prestressed_tendons_returns_expected_values():
+@pytest.mark.parametrize(
+    (
+        'a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e,    '
+        ' incr_stress, expected'
+    ),
+    [
+        (80000, 400, 4, 0.9, 0.75, 500, 10, 10, 0.5, 10, 531.161),
+        (50000, 500, 3, 0.7, 0.4, 700, 10, 30, 0.8, 20, 69.541),
+        (50000, 500, 4, 1, 1, 1000, 0, 20, 0.8, 20, 364.223),
+    ],
+)
+def test_crack_min_steel_area_with_press_tendons_returns_expected_values(
+    a_ct,
+    s_steel,
+    fct_eff,
+    k,
+    kc,
+    ap,
+    d_steel,
+    d_press,
+    e,
+    incr_stress,
+    expected,
+):
     """Test the crack_min_steel_area returns expected values"""
-    pass
+    as_min = _crack_control.crack_min_steel_area_with_prestresed_tendons(
+        a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
+    )
+    assert math.isclose(as_min, expected, rel_tol=10e-6)
+
+
+@pytest.mark.parametrize(
+    'a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e,     incr_stress',
+    [
+        (-80000, 400, 4, 0.9, 0.75, 500, 10, 10, 0.5, 10),
+        (80000, -400, 4, 0.9, 0.75, 500, 10, 10, 0.5, 10),
+        (80000, 400, 4, 0.5, 0.75, 500, 10, 10, 0.5, 10),
+        (80000, 400, 4, 1.1, 0.75, 500, 10, 10, 0.5, 10),
+        (80000, 400, 4, 0.9, -0.1, 500, 10, 10, 0.5, 10),
+        (80000, 400, 4, 0.9, 1.1, 500, 10, 10, 0.5, 10),
+        (80000, 400, 4, 0.9, 0.75, -500, 10, 10, 0.5, 10),
+        (80000, 400, 4, 0.9, 0.75, 500, -10, 10, 0.5, 10),
+        (80000, 400, 4, 0.9, 0.75, 500, 10, 0, 0.5, 10),
+        (80000, 400, 4, 0.9, 0.75, 500, 10, 10, 0.1, 10),
+        (80000, 400, 4, 0.9, 0.75, 500, 10, 10, 0.9, 10),
+    ],
+)
+def test_crack_min_steel_area_with_press_tendons_raise_valueerror(
+    a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
+):
+    """Test the crack_min_steel_area raise ValueError for non valid values"""
+    with pytest.raises(ValueError):
+        _crack_control.crack_min_steel_area_with_prestresed_tendons(
+            a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
+        )
+
+
+@pytest.mark.parametrize(
+    'wk, s_steel, fct_eff, kc, h_cr, h, d, incr_stress',
+    [
+        (-0.1, 200, 3, 0.7, 250, 300, 280, 0),
+        (0.2, 200, -3, 0.7, 250, 300, 280, 0),
+        (0.2, 200, 3, 1.1, 250, 300, 280, 0),
+        (0.2, 200, 3, 0.7, -250, 300, 280, 0),
+        (0.2, 200, 3, 0.7, -250, -300, 280, 0),
+        (0.2, 200, 3, 0.7, -250, -300, -280, 0),
+    ],
+)
+def test_crack_min_steel_without_direct_calculation_raise_valueerror(
+    wk, s_steel, fct_eff, kc, h_cr, h, d, incr_stress
+):
+    """Test the crack_min_steel_area raise ValueError for non valid values"""
+    with pytest.raises(ValueError):
+        _crack_control.crack_min_steel_without_direct_calculation(
+            wk, s_steel, fct_eff, kc, h_cr, h, d, incr_stress
+        )

From 7189d31c244aafbb3f599def76d85d94fb1e4744 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 11:08:41 +0100
Subject: [PATCH 06/33] Commit

---
 .../codes/ec2_2004/_crack_control.py          | 42 ++++++++++-----
 tests/test_ec2_2004_crack_control.py          | 54 +++++++++++++++----
 2 files changed, 75 insertions(+), 21 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index b3a2ba53..d96da5d3 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -1,5 +1,8 @@
 """Collection of functions from EUROCODE 1992-1-1:2004
 Chapter 7.3 - Crack control"""
+import math
+import typing as t
+
 import numpy as np
 import scipy.interpolate
 
@@ -329,8 +332,9 @@ def crack_min_steel_without_direct_calculation(
     h_cr: float,
     h: float,
     d: float,
+    load_type: str,
     incr_stress: float = 0,
-) -> tuple(float, float):
+) -> t.Tuple[float, float]:
     """Computes the minimum area of reinforcing steel within the tensile zone
     for control of cracking areas
 
@@ -353,6 +357,9 @@ def crack_min_steel_without_direct_calculation(
         h (float): the overall depth of the section in mm.
         d (float): is the effective depth to the centroid of the outer layer
             of the reinforcement.
+        load_type (str): load combination type:
+            - bending: for at least part of section in compression
+            - tension: uniform axial tension
         incr_stress (float, optional): value of prestressed stress in MPa if
             applicable
 
@@ -363,6 +370,7 @@ def crack_min_steel_without_direct_calculation(
     Raises:
         ValueError: if wk, fct_eff, h_cr, h or d are less than 0
         ValueError: if kc is not between 0 and 1
+        ValueError: if combination of wk and stress values are out of scope
     """
     if wk < 0:
         raise ValueError(f'wd={wk} cannot be less than 0')
@@ -376,6 +384,12 @@ def crack_min_steel_without_direct_calculation(
         raise ValueError(f'd={d} is less than 0')
     if kc < 0 or kc > 1:
         raise ValueError(f'kc={kc} is not between 0 and 1')
+    load_type = load_type.lower()
+    if load_type != 'bending' and load_type != 'tension':
+        raise ValueError(
+            f'load_type={load_type} can only have as values "bending" or'
+            ' "tension"'
+        )
 
     s = s_steel - incr_stress
     if s <= 0:
@@ -431,19 +445,23 @@ def crack_min_steel_without_direct_calculation(
         None,
     )
 
-    points_phi = np.meshgrid(x, y_phi)
-    points_spa = np.meshgrid(x, y_spa)
-    xi = (wk, s)
+    points_phi = np.array(np.meshgrid(y_phi, x)).T.reshape(-1, 2)
+    points_spa = np.array(np.meshgrid(y_spa, x)).T.reshape(-1, 2)
+    xi = (s, wk)
 
-    phi_grid = scipy.interpolate.griddata(
-        points_phi, phi_s_v, xi, method='linear'
+    phi_star = float(
+        scipy.interpolate.griddata(points_phi, phi_s_v, xi, method='linear')
     )
-    phi_star = phi_grid[0]
-    phi = phi_star * (fct_eff / 2.9) * kc * h_cr / (2 * (h - d))
+    if load_type == 'bending':
+        phi = phi_star * (fct_eff / 2.9) * kc * h_cr / (2 * (h - d))
+    else:
+        phi = phi_star * (fct_eff / 2.9) * h_cr / (8 * (h - d))
 
-    spa_grid = scipy.interpolate.griddata(
-        points_spa, spa_v, xi, method='linear'
+    spa = float(
+        scipy.interpolate.griddata(points_spa, spa_v, xi, method='linear')
     )
-    spa = spa_grid[0]
 
-    return (phi, spa)
+    if math.isnan(phi) or math.isnan(spa):
+        raise ValueError('Combination of wk or stress values out of scope')
+
+    return phi, spa
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 39fa0f98..894b0a32 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -228,21 +228,57 @@ def test_crack_min_steel_area_with_press_tendons_raise_valueerror(
 
 
 @pytest.mark.parametrize(
-    'wk, s_steel, fct_eff, kc, h_cr, h, d, incr_stress',
+    (
+        'wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress, exp_phi,'
+        ' exp_sep'
+    ),
+    [
+        (0.3, 240, 2.9, 0.4, 200, 400, 360, 'bending', 40, 25, 250),
+        (0.2, 260, 2.9, 0.4, 200, 400, 360, 'axial', 40, 14, 125),
+        (0.35, 360, 2.9, 0.4, 200, 400, 360, 'bending', 40, 11, 125),
+        (0.35, 360, 2.9, 0.4, 200, 400, 360, 'axial', 40, 11, 125),
+    ],
+)
+def test_crack_min_steel_without_direct_calculation_returns_expected_values(
+    wk,
+    s_steel,
+    fct_eff,
+    kc,
+    h_cr,
+    h,
+    d,
+    load_type,
+    incr_stress,
+    exp_phi,
+    exp_sep,
+):
+    """Test the crack_min_steel_area raise ValueError for non valid values"""
+    phi, sep = _crack_control.crack_min_steel_without_direct_calculation(
+        wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress
+    )
+    assert math.isclose(phi, exp_phi, rel_tol=10e-6)
+    assert math.isclose(sep, exp_sep, rel_tol=10e-6)
+
+
+@pytest.mark.parametrize(
+    'wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress',
     [
-        (-0.1, 200, 3, 0.7, 250, 300, 280, 0),
-        (0.2, 200, -3, 0.7, 250, 300, 280, 0),
-        (0.2, 200, 3, 1.1, 250, 300, 280, 0),
-        (0.2, 200, 3, 0.7, -250, 300, 280, 0),
-        (0.2, 200, 3, 0.7, -250, -300, 280, 0),
-        (0.2, 200, 3, 0.7, -250, -300, -280, 0),
+        (-0.1, 200, 3, 0.7, 250, 300, 280, 'bending', 0),
+        (0.2, 200, -3, 0.7, 250, 300, 280, 'bending', 0),
+        (0.2, 200, 3, 0.7, 250, 300, 280, 'bending', 0),
+        (0.2, 200, 3, 1.1, 250, 300, 280, 'bending', 0),
+        (0.2, 200, 3, 0.7, -250, 300, 280, 'bending', 0),
+        (0.2, 200, 3, 0.7, -250, -300, 280, 'bending', 0),
+        (0.2, 200, 3, 0.7, -250, -300, -280, 'bending', 0),
+        (0.2, 360, 2.9, 0.4, 200, 400, 360, 'bending', 0),
+        (0.5, 200, 2.9, 0.4, 200, 400, 360, 'bending', 0),
     ],
 )
 def test_crack_min_steel_without_direct_calculation_raise_valueerror(
-    wk, s_steel, fct_eff, kc, h_cr, h, d, incr_stress
+    wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress
 ):
     """Test the crack_min_steel_area raise ValueError for non valid values"""
     with pytest.raises(ValueError):
         _crack_control.crack_min_steel_without_direct_calculation(
-            wk, s_steel, fct_eff, kc, h_cr, h, d, incr_stress
+            wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress
         )

From 4a0fcfbb446369218e3ef578c214fd4ad70b064c Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 11:28:57 +0100
Subject: [PATCH 07/33] crack without direct calculation tests

---
 requirements.txt                              |  2 -
 .../codes/ec2_2004/_crack_control.py          | 23 ++++-------
 tests/test_ec2_2004_crack_control.py          | 41 ++++++++-----------
 3 files changed, 25 insertions(+), 41 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 80ba09bb..e69de29b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +0,0 @@
-numpy==1.23.5
-scipy==1.9.3
diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index d96da5d3..a78f572e 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -328,12 +328,11 @@ def crack_min_steel_without_direct_calculation(
     wk: float,
     s_steel: float,
     fct_eff: float,
-    kc: float,
     h_cr: float,
     h: float,
     d: float,
-    load_type: str,
     incr_stress: float = 0,
+    kc: t.Optional[float] = None,
 ) -> t.Tuple[float, float]:
     """Computes the minimum area of reinforcing steel within the tensile zone
     for control of cracking areas
@@ -348,20 +347,18 @@ def crack_min_steel_without_direct_calculation(
             the concrete effective at the time when the cracks may first be
             expected to occur: fct,eff=fct or lower (fct(t)), is cracking
             is expected earlier than 28 days.
-        kc (float): is a coefficient which takes account of the stress
-            distribution within the section immediately prior to cracking and
-            the change of the lever arm.
         h_cr (float): is the depth of the tensile zone immediately prior to
             cracking, considering the characteristic values of prestress and
             axial forces under the quasi-permanent combination of actions.
         h (float): the overall depth of the section in mm.
         d (float): is the effective depth to the centroid of the outer layer
             of the reinforcement.
-        load_type (str): load combination type:
-            - bending: for at least part of section in compression
-            - tension: uniform axial tension
         incr_stress (float, optional): value of prestressed stress in MPa if
             applicable
+        kc (float, optional): is a coefficient which takes account of the
+            stress distribution within the section immediately prior to
+            cracking and the change of the lever arm in a bending section.
+            'None' for pure tensile uniform axial section.
 
     Returns:
         tuple(float, float): with the value of the maximum bar diameters in mm
@@ -382,14 +379,8 @@ def crack_min_steel_without_direct_calculation(
         raise ValueError(f'h={h} is less than 0')
     if d < 0:
         raise ValueError(f'd={d} is less than 0')
-    if kc < 0 or kc > 1:
+    if kc is not None and (kc < 0 or kc > 1):
         raise ValueError(f'kc={kc} is not between 0 and 1')
-    load_type = load_type.lower()
-    if load_type != 'bending' and load_type != 'tension':
-        raise ValueError(
-            f'load_type={load_type} can only have as values "bending" or'
-            ' "tension"'
-        )
 
     s = s_steel - incr_stress
     if s <= 0:
@@ -452,7 +443,7 @@ def crack_min_steel_without_direct_calculation(
     phi_star = float(
         scipy.interpolate.griddata(points_phi, phi_s_v, xi, method='linear')
     )
-    if load_type == 'bending':
+    if kc is not None:
         phi = phi_star * (fct_eff / 2.9) * kc * h_cr / (2 * (h - d))
     else:
         phi = phi_star * (fct_eff / 2.9) * h_cr / (8 * (h - d))
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 894b0a32..06c23820 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -228,57 +228,52 @@ def test_crack_min_steel_area_with_press_tendons_raise_valueerror(
 
 
 @pytest.mark.parametrize(
-    (
-        'wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress, exp_phi,'
-        ' exp_sep'
-    ),
+    'wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc, exp_phi,  exp_sep',
     [
-        (0.3, 240, 2.9, 0.4, 200, 400, 360, 'bending', 40, 25, 250),
-        (0.2, 260, 2.9, 0.4, 200, 400, 360, 'axial', 40, 14, 125),
-        (0.35, 360, 2.9, 0.4, 200, 400, 360, 'bending', 40, 11, 125),
-        (0.35, 360, 2.9, 0.4, 200, 400, 360, 'axial', 40, 11, 125),
+        (0.3, 240, 2.9, 200, 400, 360, 40, 0.4, 25, 250),
+        (0.2, 260, 2.9, 200, 400, 360, 40, None, 8.75, 125),
+        (0.35, 360, 2.9, 200, 400, 360, 40, 0.4, 11, 125),
+        (0.35, 360, 2.9, 200, 400, 360, 40, None, 6.875, 125),
     ],
 )
 def test_crack_min_steel_without_direct_calculation_returns_expected_values(
     wk,
     s_steel,
     fct_eff,
-    kc,
     h_cr,
     h,
     d,
-    load_type,
     incr_stress,
+    kc,
     exp_phi,
     exp_sep,
 ):
     """Test the crack_min_steel_area raise ValueError for non valid values"""
     phi, sep = _crack_control.crack_min_steel_without_direct_calculation(
-        wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress
+        wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
     )
     assert math.isclose(phi, exp_phi, rel_tol=10e-6)
     assert math.isclose(sep, exp_sep, rel_tol=10e-6)
 
 
 @pytest.mark.parametrize(
-    'wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress',
+    'wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc',
     [
-        (-0.1, 200, 3, 0.7, 250, 300, 280, 'bending', 0),
-        (0.2, 200, -3, 0.7, 250, 300, 280, 'bending', 0),
-        (0.2, 200, 3, 0.7, 250, 300, 280, 'bending', 0),
-        (0.2, 200, 3, 1.1, 250, 300, 280, 'bending', 0),
-        (0.2, 200, 3, 0.7, -250, 300, 280, 'bending', 0),
-        (0.2, 200, 3, 0.7, -250, -300, 280, 'bending', 0),
-        (0.2, 200, 3, 0.7, -250, -300, -280, 'bending', 0),
-        (0.2, 360, 2.9, 0.4, 200, 400, 360, 'bending', 0),
-        (0.5, 200, 2.9, 0.4, 200, 400, 360, 'bending', 0),
+        (-0.1, 200, 3, 250, 300, 280, 0, 0.7),
+        (0.2, 200, -3, 250, 300, 280, 0, 0.7),
+        (0.2, 200, 3, 250, 300, 280, 0, 1.1),
+        (0.2, 200, 3, -250, 300, 280, 0, 0.7),
+        (0.2, 200, 3, -250, -300, 280, 0, 0.7),
+        (0.2, 200, 3, -250, -300, -280, 0, 0.7),
+        (0.2, 360, 2.9, 200, 400, 360, 0, 0.4),
+        (0.5, 200, 2.9, 200, 400, 360, 0, 0.4),
     ],
 )
 def test_crack_min_steel_without_direct_calculation_raise_valueerror(
-    wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress
+    wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
 ):
     """Test the crack_min_steel_area raise ValueError for non valid values"""
     with pytest.raises(ValueError):
         _crack_control.crack_min_steel_without_direct_calculation(
-            wk, s_steel, fct_eff, kc, h_cr, h, d, load_type, incr_stress
+            wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
         )

From 84c0140cec46e29754ada8f328c1bf427e7b1c97 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 12:39:12 +0100
Subject: [PATCH 08/33] adjusted bond strength

---
 .../codes/ec2_2004/_crack_control.py          | 46 +++++++++++++++----
 tests/test_ec2_2004_crack_control.py          | 35 ++++++++++++++
 2 files changed, 71 insertions(+), 10 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index a78f572e..903ced7d 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -228,6 +228,40 @@ def kc_crack_min_steel_area_flanges(
     return max(0.9 * f_cr * 1000 / a_ct / fct_eff, 0.5)
 
 
+def adjusted_bond_strength(e: float, d_press: float, d_steel: float) -> float:
+    """Computes the adjusted ratio of bond strength taking into account
+    the different diameters of prestressing and reinforcing steel.
+
+    Args:
+        e (float): ratio of bond strength of prestressing and reinforcing
+            steel, according to Table 6.2 in 6.8.2
+        d_steel (float): largest bar diameter in mm of reinforcing steel.
+            Equal to 0 if only prestressing is used in control cracking
+        d_press (float): equivalent diameter in mm of tendon acoording
+            to 6.8.2
+
+    Returns:
+        float: with the value of the ratio
+
+    Raises:
+        ValueError: if diameters d_steel or d_press are lower than 0.
+            If ratio of bond strength e is less than 0.15 or larger than 0.8.
+            If area of tendons ac_eff is less than 0. Is stress variation
+            incr_stress is less than 0.
+    """
+
+    if d_press <= 0:
+        raise ValueError(f'd_press={d_press} cannot be less than 0')
+    if d_steel < 0:
+        raise ValueError(f'd_steel={d_steel} cannot be less than 0')
+    if e < 0.15:
+        raise ValueError(f'The minimum value for e={e} is 0.15')
+    if e > 0.8:
+        raise ValueError(f'The maximum value for e={e} is 0.8')
+
+    return ((e * d_steel / d_press) ** 0.5) if d_steel > 0 else e**0.5
+
+
 def crack_min_steel_area_with_prestresed_tendons(
     a_ct: float,
     s_steel: float,
@@ -290,23 +324,15 @@ def crack_min_steel_area_with_prestresed_tendons(
         ValueError: if k value is not between 0.65 and 1 or kc is not
             larger than 0 and lower than 1. If diameters d_steel or
             d_press are lower than 0. If ratio of bond strength e
-            is less than 0 or larger than 1. If area of tendons ac_eff
+            is less than 0.15 or larger than 0.8. If area of tendons ac_eff
             is less than 0. Is stress variation incr_stress is less than 0
     """
     fct_eff = abs(fct_eff)
 
-    if d_press <= 0:
-        raise ValueError(f'd_press={d_press} cannot be less than 0')
-    if d_steel < 0:
-        raise ValueError(f'd_steel={d_steel} cannot be less than 0')
     if ap < 0:
         raise ValueError(f'ap={ap} cannot be less than 0')
     if incr_stress < 0:
         raise ValueError(f'incr_stress={incr_stress} cannot be less than 0')
-    if e < 0.15:
-        raise ValueError(f'The minimum value for e={e} is 0.15')
-    if e > 0.8:
-        raise ValueError(f'The maximum value for e={e} is 0.8')
     if a_ct <= 0:
         raise ValueError(f'a_ct={a_ct} must be larger than 0')
     if s_steel < 0:
@@ -317,7 +343,7 @@ def crack_min_steel_area_with_prestresed_tendons(
         raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
 
     a1 = kc * k * fct_eff * a_ct
-    e1 = ((e * d_steel / d_press) ** 0.5) if d_steel > 0 else e**0.5
+    e1 = adjusted_bond_strength(e, d_press, d_steel)
     a2 = e1 * ap * incr_stress
     a = a1 - a2
 
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 06c23820..12c07ce3 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -277,3 +277,38 @@ def test_crack_min_steel_without_direct_calculation_raise_valueerror(
         _crack_control.crack_min_steel_without_direct_calculation(
             wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
         )
+
+
+@pytest.mark.parametrize(
+    'e, d_press, d_steel, expected',
+    [
+        (0.8, 20, 0, 0.894427),
+        (0.6, 25, 10, 0.489898),
+        (0.5, 10, 10, 0.707107),
+    ],
+)
+def test_adjusted_bond_length_return_expected_values(
+    e, d_press, d_steel, expected
+):
+    """Test the adjusted_bond_length_function returns expected values"""
+    assert math.isclose(
+        _crack_control.adjusted_bond_strength(e, d_press, d_steel),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'e, d_press, d_steel',
+    [
+        (0.1, 20, 0),
+        (-2, 25, 10),
+        (1.15, 10, 10),
+        (0.6, -10, 10),
+        (0.6, 10, -10),
+    ],
+)
+def test_adjusted_bond_length_raise_valuerror(e, d_press, d_steel):
+    """Test the adjusted_bond_length_function raises exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.adjusted_bond_strength(e, d_press, d_steel)

From e0f1baac8887cfe4a6386924c648a25453bcfa57 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 13:01:54 +0100
Subject: [PATCH 09/33] hc_eff_concrete_tension formulation and testing

---
 .../codes/ec2_2004/_crack_control.py          | 31 +++++++++++++++++
 tests/test_ec2_2004_crack_control.py          | 33 +++++++++++++++++++
 2 files changed, 64 insertions(+)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index 903ced7d..56d9f79d 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -262,6 +262,37 @@ def adjusted_bond_strength(e: float, d_press: float, d_steel: float) -> float:
     return ((e * d_steel / d_press) ** 0.5) if d_steel > 0 else e**0.5
 
 
+def hc_eff_concrete_tension(h: float, d: float, x: float):
+    """Returns the effective height of concrete in tension surrounding
+    the reinforcement or prestressing tendons.
+
+    Args:
+        h (float): total depth of the element in mm
+        d (float): distance in mm to the level of the steel centroid
+        x (float): distance in mm to the zero tensile stress line
+
+    Returns:
+        float: the effective height in mm
+
+    Raises:
+        ValueError: if any of h, d or x is lower than zero.
+        ValueError: if d is greater than h
+        ValueError: if x is greater than h
+    """
+    if h < 0:
+        raise ValueError(f'h={h} cannot be less than 0')
+    if d < 0:
+        raise ValueError(f'd={d} cannot be less than 0')
+    if x < 0:
+        raise ValueError(f'x={x} cannot be less than zero')
+    if d > h:
+        raise ValueError(f'd={d} cannot be larger than h={h}')
+    if x > h:
+        raise ValueError(f'x={x} cannot be larger than h={h}')
+
+    return min(2.5 * (h - d), (h - x) / 3, h / 2)
+
+
 def crack_min_steel_area_with_prestresed_tendons(
     a_ct: float,
     s_steel: float,
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 12c07ce3..289d8dfd 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -312,3 +312,36 @@ def test_adjusted_bond_length_raise_valuerror(e, d_press, d_steel):
     """Test the adjusted_bond_length_function raises exceptions"""
     with pytest.raises(ValueError):
         _crack_control.adjusted_bond_strength(e, d_press, d_steel)
+
+
+@pytest.mark.parametrize(
+    'h, d, x, expected',
+    [
+        (400, 200, 100, 100),
+        (400, 200, 150, 83.333333),
+        (550, 150, 150, 133.33333),
+    ],
+)
+def test_hc_eff_concrete_tension_returns_expected_values(h, d, x, expected):
+    """Test the hc_eff_concrete_tension returns expected results"""
+    assert math.isclose(
+        _crack_control.hc_eff_concrete_tension(h, d, x),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'h, d, x',
+    [
+        (-50, 200, 100),
+        (50, -200, 100),
+        (50, 200, -100),
+        (400, 450, 100),
+        (400, 200, 450)
+    ],
+)
+def test_hc_eff_concrete_tension_raise_exceptions(h, d, x):
+    """Test hc_eff_concrete tension raises expected exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.hc_eff_concrete_tension(h, d, x)

From f2cbb4989a7233e685b8dc55e343ac692866b61d Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 13:03:10 +0100
Subject: [PATCH 10/33] requiremets.txt updated

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index e69de29b..1de8c504 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+numpy==1.23.5
+scipy==1.9.3
\ No newline at end of file

From 333dcbe11c4b284fee41d8761817fea107885c82 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 14:04:38 +0100
Subject: [PATCH 11/33] rho_p_eff

---
 .../codes/ec2_2004/_crack_control.py          | 61 ++++++++++++++++++-
 tests/test_ec2_2004_crack_control.py          | 59 +++++++++++++++++-
 2 files changed, 118 insertions(+), 2 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index 56d9f79d..e80c6c66 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -232,6 +232,8 @@ def adjusted_bond_strength(e: float, d_press: float, d_steel: float) -> float:
     """Computes the adjusted ratio of bond strength taking into account
     the different diameters of prestressing and reinforcing steel.
 
+    EUROCODE 2 1992-1-1:2004, Eq. (7.5)
+
     Args:
         e (float): ratio of bond strength of prestressing and reinforcing
             steel, according to Table 6.2 in 6.8.2
@@ -262,10 +264,12 @@ def adjusted_bond_strength(e: float, d_press: float, d_steel: float) -> float:
     return ((e * d_steel / d_press) ** 0.5) if d_steel > 0 else e**0.5
 
 
-def hc_eff_concrete_tension(h: float, d: float, x: float):
+def hc_eff_concrete_tension(h: float, d: float, x: float) -> float:
     """Returns the effective height of concrete in tension surrounding
     the reinforcement or prestressing tendons.
 
+    EUROCODE 2 1992-1-1:2004, Section (7.3.2-3)
+
     Args:
         h (float): total depth of the element in mm
         d (float): distance in mm to the level of the steel centroid
@@ -513,3 +517,58 @@ def crack_min_steel_without_direct_calculation(
         raise ValueError('Combination of wk or stress values out of scope')
 
     return phi, spa
+
+
+def alpha_e(es: float, ecm: float) -> float:
+    """Compute the ratio between the steel and mean concrete
+    modules.
+
+    EUROCODE 2 1992-1-1:2004, Section 7.3.4-2
+
+    Args:
+        es (float): steel elastic modulus in MPa
+        ecm (float): ecm concrete mean elastic modulus in MPa
+
+    Returns:
+        float: ratio between modules
+    Raise:
+        ValueError: if any of es or ecm is lower than 0.
+    """
+    if es < 0:
+        raise ValueError(f'es={es} cannot be less than 0')
+    if ecm < 0:
+        raise ValueError(f'ecm={ecm} cannot be less than 0')
+
+    return es / ecm
+
+
+def rho_p_eff(a_s: float, e1: float, a_p, ac_eff: float) -> float:
+    """Effective bond ratio between areas
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.10)
+
+    Args:
+        a_s (float): steel area in mm2
+        e1 (float): the adjusted ratio of bond according
+            to expression (7.5)
+        a_p (float): the area in mm2 of post-tensioned tendons in ac_eff
+        ac_eff (float): effective area of concrete in tension surrounding
+        the reinforcement or prestressing tendons of depth hc_eff.
+
+    Returns:
+        float: with the retio between areas
+
+
+    Raise:
+        ValueError: if any of a_s, e1, a_p or ac_eff is less than 0
+    """
+    if a_s < 0:
+        raise ValueError(f'a_s={a_s} cannot be less than 0')
+    if e1 < 0:
+        raise ValueError(f'e1={e1} cannot be less than 0')
+    if a_p < 0:
+        raise ValueError(f'a_p={a_p} cannot be less than 0')
+    if ac_eff < 0:
+        raise ValueError(f'ac_eff={ac_eff} cannot be less than 0')
+
+    return (a_s + e1**2 * a_p) / ac_eff
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index 289d8dfd..a935db15 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -338,10 +338,67 @@ def test_hc_eff_concrete_tension_returns_expected_values(h, d, x, expected):
         (50, -200, 100),
         (50, 200, -100),
         (400, 450, 100),
-        (400, 200, 450)
+        (400, 200, 450),
     ],
 )
 def test_hc_eff_concrete_tension_raise_exceptions(h, d, x):
     """Test hc_eff_concrete tension raises expected exceptions"""
     with pytest.raises(ValueError):
         _crack_control.hc_eff_concrete_tension(h, d, x)
+
+
+@pytest.mark.parametrize(
+    'es, ecm, expected',
+    [
+        (10e9, 10e5, 1e4),
+    ],
+)
+def test_alpha_e_returns_expected_values(es, ecm, expected):
+    """Test alpha_e returns expected values"""
+    assert math.isclose(
+        _crack_control.alpha_e(es, ecm),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'es, ecm',
+    [
+        (-10e9, 10e5),
+        (100e9, -10e-5),
+    ],
+)
+def test_alpha_e_raise_exceptions(es, ecm):
+    """Test alpha_e raises exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.alpha_e(es, ecm)
+
+
+@pytest.mark.parametrize(
+    'a_s, e1, a_p, ac_eff, expected',
+    [
+        (200, 0.8, 125, 600, 0.46666667),
+        (125, 1.5, 125, 1200, 0.33854),
+    ],
+)
+def test_rho_p_eff_returns_expected_values(a_s, e1, a_p, ac_eff, expected):
+    """Test rho_p_eff returns expeceted values"""
+    assert math.isclose(
+        _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff), expected, rel_tol=10e-5
+    )
+
+
+@pytest.mark.parametrize(
+    'a_s, e1, a_p, ac_eff',
+    [
+        (-200, 0.8, 125, 600),
+        (200, -0.8, 125, 600),
+        (200, 0.8, -125, 600),
+        (200, 0.8, 125, -600),
+    ],
+)
+def test_rho_p_eff_raise_value_error(a_s, e1, a_p, ac_eff):
+    """Test rho_p_eff raise exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff)

From 59f1198a1aabf6d331d0b499a19e0a5dbaf83c72 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 16:06:32 +0100
Subject: [PATCH 12/33] kt load duration

---
 .../codes/ec2_2004/_crack_control.py          | 27 +++++++++++++++++++
 tests/test_ec2_2004_crack_control.py          | 21 +++++++++++++++
 2 files changed, 48 insertions(+)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index e80c6c66..76e30cb4 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -572,3 +572,30 @@ def rho_p_eff(a_s: float, e1: float, a_p, ac_eff: float) -> float:
         raise ValueError(f'ac_eff={ac_eff} cannot be less than 0')
 
     return (a_s + e1**2 * a_p) / ac_eff
+
+
+def kt_load_duration(load_type: str):
+    """Returns the kt factor dependent on the load duration for
+    the crack width calculation
+
+    Args:
+        load_type (str): the load type:
+            - 'short' for term loading
+            - 'long' for long term loading
+
+    Returns:
+        float: with the kt factor
+
+    Raises:
+        ValueError: if load_type is not 'short' and not 'long'
+    """
+    if not isinstance(load_type, str):
+        raise TypeError
+
+    load_type = load_type.lower()
+    if load_type != 'short' and load_type != 'long':
+        raise ValueError(
+            f'load_type={load_type} can only have "short" or "long" as a value'
+        )
+
+    return 0.6 if load_type == 'short' else 0.4
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index a935db15..c7bf42d8 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -402,3 +402,24 @@ def test_rho_p_eff_raise_value_error(a_s, e1, a_p, ac_eff):
     """Test rho_p_eff raise exceptions"""
     with pytest.raises(ValueError):
         _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff)
+
+
+@pytest.mark.parametrize(
+    'load_type, expected',
+    [
+        ('short', 0.6),
+        ('long', 0.4),
+    ],
+)
+def test_kt_load_duration_returns_expected_values(load_type, expected):
+    """Test kt_load_duration returns expected values"""
+    assert _crack_control.kt_load_duration(load_type) == expected
+
+
+def test_kt_load_duration_raise_value_errors():
+    """Test kt_load_duration raise value errors"""
+    with pytest.raises(TypeError):
+        _crack_control.kt_load_duration(load_type=123)
+
+    with pytest.raises(ValueError):
+        _crack_control.kt_load_duration(load_type='asdf')

From 34d85d24609279f074f61cf8620a2dab05d4fba1 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Thu, 12 Jan 2023 18:10:44 +0100
Subject: [PATCH 13/33] strain diff formula

---
 .../codes/ec2_2004/_crack_control.py          | 65 ++++++++++++++++++-
 1 file changed, 64 insertions(+), 1 deletion(-)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index 76e30cb4..91ab738a 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -574,7 +574,7 @@ def rho_p_eff(a_s: float, e1: float, a_p, ac_eff: float) -> float:
     return (a_s + e1**2 * a_p) / ac_eff
 
 
-def kt_load_duration(load_type: str):
+def kt_load_duration(load_type: str) -> float:
     """Returns the kt factor dependent on the load duration for
     the crack width calculation
 
@@ -599,3 +599,66 @@ def kt_load_duration(load_type: str):
         )
 
     return 0.6 if load_type == 'short' else 0.4
+
+
+def steel_stress_strain(
+    s_steel: float,
+    alpha_e: float,
+    rho_p_eff: float,
+    kt: float,
+    fct_eff: float,
+    es: float,
+) -> float:
+    """Returns the strain difference (esm - ecm) needed to compute the crack
+    width. esm is the mean strain in the reinforcement under the relevant
+    combination of loads of imposed deformations and taking into account the
+    effects of tension stiffening. Only the additional tensile strain beyond
+    the state of zero strain of the concrete is considered. ecm is the mean
+    strain in the concrete between the cracks.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.9)
+
+    Args:
+        s_steel (float): is the stress in MPa in the tension reinforcement
+            assuming a cracked section. FOr pretensioned members, s_steel may
+            be replaced by increment of s_steel stress variation in
+            prestressing tendons from the state of zero strain of the
+            concrete at the same level.
+        alpha_e (float): is the ratio Es/Ecm
+        rho_p_eff (float): effective bond ratio between areas given by the
+            Eq. (7.10)
+        kt (float): is a factor dependent on the load duration
+        fct_eff (float): is the mean value of the tensile strength in MPa
+            of the concrete effectvie at the time when the cracks may
+            first be expected to occur: fct_eff=fctm or fctm(t) if
+            crack is expected earlier than 28 days.
+        es: steel elastic mudulus in MPa
+
+    Returns:
+        float: the strain difference between concrete and steel
+
+    Raises:
+        ValueError: if any s_steel, alpha_e, rho_p_eff, fct_Eff is less
+            than 0.
+        ValueError: if kt is not 0.6 and not 0.4
+    """
+    if s_steel < 0:
+        raise ValueError(f's_steel={s_steel} cannot be less than 0')
+    if alpha_e < 0:
+        raise ValueError(f'alpha_e={alpha_e} cannot be less than 0')
+    if rho_p_eff < 0:
+        raise ValueError(f'rho_p_eff={rho_p_eff} cannot be less than 0')
+    if fct_eff < 0:
+        raise ValueError(f'fct_eff={fct_eff} cannot be less than 0')
+    if es < 0:
+        raise ValueError(f'es={es} cannot be less than 0')
+    if kt != 0.6 and kt != 0.4:
+        raise ValueError(f'kt={kt} can only take as values 0.4 and 0.6')
+
+    min_val = 0.6 * s_steel / es
+
+    a = 1 + alpha_e * rho_p_eff
+    b = kt * fct_eff / rho_p_eff * a
+    c = (s_steel - b) / es
+
+    return max(c, min_val)

From a8ab1298037c223f97ded415ce6d1662f9fcc029 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Fri, 13 Jan 2023 13:32:14 +0100
Subject: [PATCH 14/33] chapter completed

---
 .../codes/ec2_2004/_crack_control.py          | 288 +++++++++++++++++-
 tests/test_ec2_2004_crack_control.py          | 278 ++++++++++++++++-
 2 files changed, 558 insertions(+), 8 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_crack_control.py
index 91ab738a..e0561fe3 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_crack_control.py
@@ -25,8 +25,8 @@ def w_max(exposure_class: str, load_combination: str) -> float:
     Raises:
         ValueError: if not valid exposure_class or load_combination values.
     """
-    _load_combination = load_combination.lower()
-    _exposure_class = exposure_class.upper()
+    _load_combination = load_combination.lower().strip()
+    _exposure_class = exposure_class.upper().strip()
     if _load_combination == 'f':
         if _exposure_class in ('X0', 'XC1'):
             return 0.2
@@ -519,7 +519,7 @@ def crack_min_steel_without_direct_calculation(
     return phi, spa
 
 
-def alpha_e(es: float, ecm: float) -> float:
+def get_alpha_e(es: float, ecm: float) -> float:
     """Compute the ratio between the steel and mean concrete
     modules.
 
@@ -592,7 +592,7 @@ def kt_load_duration(load_type: str) -> float:
     if not isinstance(load_type, str):
         raise TypeError
 
-    load_type = load_type.lower()
+    load_type = load_type.lower().strip()
     if load_type != 'short' and load_type != 'long':
         raise ValueError(
             f'load_type={load_type} can only have "short" or "long" as a value'
@@ -601,7 +601,7 @@ def kt_load_duration(load_type: str) -> float:
     return 0.6 if load_type == 'short' else 0.4
 
 
-def steel_stress_strain(
+def esm_ecm(
     s_steel: float,
     alpha_e: float,
     rho_p_eff: float,
@@ -662,3 +662,281 @@ def steel_stress_strain(
     c = (s_steel - b) / es
 
     return max(c, min_val)
+
+
+def s_threshold(c: float, phi: float) -> float:
+    """Computes the distance threshold from which the
+    maximum crack spacing is constant.
+
+    EUROCODE 2 1992-1-1:2004, Sect. (7.3.4-3)
+
+    Args:
+        c (float): cover of  the longitudinal reinforcement in mm
+        phi (float): is the bar diameter in mm. Where mixed bar diameters
+            used, then it should be replaced for an equivalente bar diameter.
+
+    Returns:
+        float: threshold distance in mm
+
+    Raises:
+        ValueError: if any of c or phi is less than 0.
+    """
+    if c < 0:
+        raise ValueError(f'c={c} cannot be less than 0')
+    if phi < 0:
+        raise ValueError(f'phi={phi} cannot be less than 0')
+
+    return 5 * (c + phi / 2)
+
+
+def phi_eq(n1: int, n2: int, phi1: float, phi2: float) -> float:
+    """Computes the equivalent diameter. For a section with n1 bars of
+    diameter phi1 and n2 bars of diameter phi2
+
+    EUROCODE 2 1992-1-1:2004, Sect. (7.12)
+
+    Args:
+        n1 (int): number of bars with diameter phi1
+        n2 (int): number of bars with diameter phi2
+        phi1 (float): diameter of n1 bars in mm
+        phi2 (float): diamater of n2 bars in mm
+
+    Returns:
+        float: the equivalent diameter in mm
+
+    Raises:
+        ValueError: if any of n1 or n2 is less than 0
+        ValueError: if any of phi1 or phi2 is less than 0
+        TypeError: if any of n1 or n2 is not an integer
+    """
+    if n1 < 0:
+        raise ValueError(f'n1={n1} cannot be less than 0')
+    if not isinstance(n1, int):
+        raise TypeError(f'n1={n1} needs to be an integer value')
+    if n2 < 0:
+        raise ValueError(f'n2={n2} cannot be less than 0')
+    if not isinstance(n2, int):
+        raise TypeError(f'n2={n2} needs to be an integer value')
+    if phi1 < 0:
+        raise ValueError(f'phi1={phi1} cannot be less than 0')
+    if phi2 < 0:
+        raise ValueError(f'phi2={phi2} cannot be less than 0')
+
+    a = n1 * phi1**2 + n2 * phi2**2
+    b = n1 * phi1 + n2 * phi2
+    return a / b
+
+
+def k1(bond_type: str) -> float:
+    """Get the k1 coefficient which takes account of the bond properties
+    of the bounded reinforcement
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.11-k1)
+
+    Args:
+        bond_type (str): the bond property of the reinforcement.
+        Possible values:
+            - 'bond': for high bond bars
+            - 'plane': for bars with an effectively plain surface (e.g.
+            prestressing tendons)
+
+    Returns:
+        (float): value of the k1 coefficient
+
+    Raises:
+        ValueError: if bond_type is neither 'bond' nor 'plane'
+        TypeError: if bond_type is not an str
+    """
+    if not isinstance(bond_type, str):
+        raise TypeError(f'bond_type={bond_type} is not an str')
+
+    bond_type = bond_type.lower().strip()
+    if bond_type != 'bond' and bond_type != 'plane':
+        raise ValueError(
+            f'bond_type={bond_type} can only have "bond" or "plane" as values'
+        )
+
+    return 0.8 if bond_type == 'bond' else 1.6
+
+
+def k2(epsilon_r: float) -> float:
+    """Computes a coefficient which takes into account of the
+    distribution of strain:
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.13)
+
+    Args:
+        epsilon_r (float): ratio epsilon_2/epsilon_1 where epsilon_1 is
+            thre greater and epsilon_2 is the lesser strain at the boundaries
+            of the section considererd, assessed on the basis of a cracked
+            section. epsilon_r=0 for bending and epsilon_r=1 for pure tension.
+
+    Returns:
+        float: the k2 coefficient value.
+
+    Raises:
+        ValueError: if epsilon_r is not between 0 and 1.
+    """
+    if epsilon_r < 0 or epsilon_r > 1:
+        raise ValueError(f'epsilon_r={epsilon_r} must be between 0 and 1')
+
+    return (1 + epsilon_r) / 2
+
+
+def k3():
+    """Returns the k3 coefficient for computing sr_max
+
+    Returns:
+        float: value for the coefficient
+    """
+    return 3.4
+
+
+def k4():
+    """Returns the k4 coefficient for computing sr_max
+
+    Returns:
+        float: value for the coefficient
+    """
+    return 0.425
+
+
+def sr_max_close(
+    c: float,
+    phi: float,
+    rho_p_eff: float,
+    k1: float,
+    k2: float,
+    k3: float,
+    k4: float,
+) -> float:
+    """Computes the maximum crack spacing in cases where bonded reinforcement
+    is fixed at reasonably close centres within the tension zone
+    (spacing<=5(c+phi/2)).
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.11)
+
+    Args:
+        c (float): is the cover in mm of the longitudinal reinforcement
+        phi (float): is the bar diameter in mm. Where mixed bar diameters
+            used, then it should be replaced for an equivalente bar diameter.
+        rho_p_eff (float): effective bond ratio between areas given by the
+            Eq. (7.10)
+        k1 (float): coefficient that takes into account the bound properties
+            of the bonded reinforcement
+        k2 (float): coefficient that takes into account the distribution of
+            of the strain
+        k3 (float): coefficient from the National Annex
+        k4 (float): coefficient from the National Annex
+
+    Returns:
+        float: the maximum crack spaing in mm.
+
+    Raises:
+        ValueError: if one or more of c, phi, rho_p_eff, k3 or k4
+            is lower than zero.
+        ValueError: if k1 is not 0.8 or 1.6
+        ValueError: if k2 is not between 0.5 and 1.0
+    """
+    if c < 0:
+        raise ValueError(f'c={c} cannot be less than zero')
+    if phi < 0:
+        raise ValueError(f'phi={phi} cannot be less than zero')
+    if rho_p_eff < 0:
+        raise ValueError(f'rho_p_eff={rho_p_eff} cannot be less than zero')
+    if k3 < 0:
+        raise ValueError(f'k3={k3} cannot be less than zero')
+    if k4 < 0:
+        raise ValueError(f'k4={k4} cannot be less than zero')
+    if k1 != 0.8 and k1 != 1.6:
+        raise ValueError(f'k1={k1} can only take as values 0.8 and 1.6')
+    if k2 < 0.5 or k2 > 1:
+        raise ValueError(f'k2={k2} is not between 0.5 and 1.0')
+
+    return k3 * c + k1 * k2 * k4 * phi / rho_p_eff
+
+
+def sr_max_far(h: float, x: float) -> float:
+    """Computes the maximum crack spacing in cases where bonded reinforcement
+    is fixed at reasonably close centres within the tension zone
+    (spacing>5(c+phi/2)).
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.14)
+
+    Args:
+        h (float): total depth of the beam in mm
+        x (float): distance to non tension area of the element mm
+
+    Returns:
+        float: maximum crack spacing in mm
+
+    Raises:
+        ValueError: if one of h or x is less than zero.
+        ValueError: x is greater than h.
+    """
+    if x < 0:
+        raise ValueError(f'x={x} cannot be less than zero')
+    if h < 0:
+        raise ValueError(f'h={h} cannot be less than zero')
+    if x > h:
+        raise ValueError(f'x={x} cannot be larger than h={h}')
+
+    return 1.3 * (h - x)
+
+
+def sr_max_theta(sr_max_y: float, sr_max_z: float, theta: float) -> float:
+    """Computes the crack spacing sr_max when there is an angle
+    between the angle of  principal stress and the direction
+    of the reinforcement, for members in two orthogonal directions,
+    that is significant (> 15º).
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.15)
+
+    Args:
+        sr_max_y (float): crack spacing in mm in the y-direction.
+        sr_max_z (float): crack spacing in mm in the z-direction.
+        theta (float): angle in radians between the reinforcement in the
+            y-direction and the direction of the principal tensile stress.
+
+    Returns:
+        float: the crack spacing in mm.
+
+    Raises:
+        ValueError: if sr_max_y or sr_max_z is negative.
+        ValueError: if theta is not between 0 and pi/2
+    """
+    if sr_max_y < 0:
+        raise ValueError(f'sr_max_y={sr_max_y} cannot be less than zero')
+    if sr_max_z < 0:
+        raise ValueError(f'sr_max_z={sr_max_z} cannot be less than zero')
+
+    a = math.cos(theta) / sr_max_y
+    b = math.sin(theta) / sr_max_z
+    return 1 / (a + b)
+
+
+def wk(sr_max: float, esm_ecm: float) -> float:
+    """Computes the crack width
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.8)
+
+    Args:
+        sr_max (float): the maximum crack length spacing in mm.
+        esm_ecm (float): the difference between the mean strain in the
+            reinforcement under relevant combination of loads, including
+            the effect of imposed deformations and taking into account
+            tension stiffening and the mean strain in the concrete
+            between cracks.
+
+    Returns:
+        float: crack width in mm.
+
+    Raises:
+        ValueError: if any of sr_max or esm_ecm is less than zero.
+    """
+    if sr_max < 0:
+        raise ValueError(f'sr_max={sr_max} cannot be less than zero')
+    if esm_ecm < 0:
+        raise ValueError(f'esm_scm={esm_ecm} cannot be less than zero')
+
+    return sr_max * esm_ecm
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_crack_control.py
index c7bf42d8..66e8adeb 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_crack_control.py
@@ -356,7 +356,7 @@ def test_hc_eff_concrete_tension_raise_exceptions(h, d, x):
 def test_alpha_e_returns_expected_values(es, ecm, expected):
     """Test alpha_e returns expected values"""
     assert math.isclose(
-        _crack_control.alpha_e(es, ecm),
+        _crack_control.get_alpha_e(es, ecm),
         expected,
         rel_tol=10e-5,
     )
@@ -372,7 +372,7 @@ def test_alpha_e_returns_expected_values(es, ecm, expected):
 def test_alpha_e_raise_exceptions(es, ecm):
     """Test alpha_e raises exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.alpha_e(es, ecm)
+        _crack_control.get_alpha_e(es, ecm)
 
 
 @pytest.mark.parametrize(
@@ -385,7 +385,9 @@ def test_alpha_e_raise_exceptions(es, ecm):
 def test_rho_p_eff_returns_expected_values(a_s, e1, a_p, ac_eff, expected):
     """Test rho_p_eff returns expeceted values"""
     assert math.isclose(
-        _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff), expected, rel_tol=10e-5
+        _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff),
+        expected,
+        rel_tol=10e-5,
     )
 
 
@@ -423,3 +425,273 @@ def test_kt_load_duration_raise_value_errors():
 
     with pytest.raises(ValueError):
         _crack_control.kt_load_duration(load_type='asdf')
+
+
+@pytest.mark.parametrize(
+    's_steel, alpha_e, rho_p_eff, kt, fct_eff, es, expected',
+    [
+        (250, 5.25, 0.34, 0.4, 2.9, 210000, 0.00114523),
+        (200, 5.25, 0.4, 0.6, 3.1, 210000, 0.00088374),
+        (250, 5.25, 0.2, 0.6, 2.5, 210000, 0.00111726),
+    ],
+)
+def test_esm_ecm_returns_expected_values(
+    s_steel, alpha_e, rho_p_eff, kt, fct_eff, es, expected
+):
+    """Test esm_ecm returns the expected values"""
+    assert math.isclose(
+        _crack_control.esm_ecm(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es),
+        expected,
+        abs_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    's_steel, alpha_e, rho_p_eff, kt, fct_eff, es',
+    [
+        (-250, 5.25, 0.34, 0.4, 2.9, 210000),
+        (250, -5.25, 0.34, 0.4, 2.9, 210000),
+        (250, 5.25, -0.34, 0.4, 2.9, 210000),
+        (250, 5.25, 0.34, 0.4, -2.9, 210000),
+        (250, 5.25, 0.34, 0.4, 2.9, -210000),
+        (250, 5.25, 0.34, 0.2, 2.9, 210000),
+    ],
+)
+def test_esm_ecm_raises_exception(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es):
+    """Test esm_ecm raise expected exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.esm_ecm(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es)
+
+
+@pytest.mark.parametrize(
+    'c, phi, expected',
+    [
+        (30, 16, 190),
+        (25, 8, 145),
+    ],
+)
+def test_s_returns_expected_returns(c, phi, expected):
+    """Test s returns expected results"""
+    assert math.isclose(
+        _crack_control.s_threshold(c, phi),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'c, phi',
+    [
+        (-20, 18),
+        (20, -18),
+    ],
+)
+def test_s_raise_expected_exceptions(c, phi):
+    """Test s raise expected exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.s_threshold(c, phi)
+
+
+@pytest.mark.parametrize(
+    'n1, n2, phi1, phi2, expected',
+    [(3, 5, 20, 12, 16), (5, 5, 20, 12, 17), (6, 2, 24, 10, 22.29268)],
+)
+def test_phi_eq_returns_expected_results(n1, n2, phi1, phi2, expected):
+    """Test phi_eq returns expected results"""
+    assert math.isclose(
+        _crack_control.phi_eq(n1, n2, phi1, phi2),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'n1, n2, phi1, phi2, exception_type',
+    [
+        (-2, 2, 20, 18, ValueError),
+        (2, -2, 20, 18, ValueError),
+        (2, 2, -20, 18, ValueError),
+        (2, 2, 20, -18, ValueError),
+        (4.5, 2, 20, 18, TypeError),
+        (3, 4.5, 20, 20, TypeError),
+    ],
+)
+def test_phi_eq_raises_expected_values(n1, n2, phi1, phi2, exception_type):
+    """Test phi_eq raises expected exception"""
+    with pytest.raises(exception_type):
+        _crack_control.phi_eq(n1, n2, phi1, phi2)
+
+
+@pytest.mark.parametrize(
+    'bond_type, expected',
+    [('bond', 0.8), ('plane', 1.6), ('BOND ', 0.8), (' PLANE ', 1.6)],
+)
+def test_k1_returns_expected_values(bond_type, expected):
+    """Test k1 returns expected values"""
+    assert _crack_control.k1(bond_type) == expected
+
+
+@pytest.mark.parametrize(
+    'bond_type, exception_type',
+    [('asdf ad', ValueError), (123, TypeError), (14.2, TypeError)],
+)
+def test_k1_raise_expected_exceptions(bond_type, exception_type):
+    """Test k1 raises expected exceptions"""
+    with pytest.raises(exception_type):
+        _crack_control.k1(bond_type)
+
+
+@pytest.mark.parametrize(
+    'epsilon_r, expected',
+    [(0, 0.5), (1, 1), (0.75, 0.875), (0.67, 0.835)],
+)
+def test_k2_returns_expected_values(epsilon_r, expected):
+    """Test k2 returns expected values"""
+    assert math.isclose(
+        _crack_control.k2(epsilon_r),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize('epsilon_r', [(-0.1), (-2), (1.1), (2)])
+def test_k2_raises_value_exceptions(epsilon_r):
+    """Test k2 raises expected exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.k2(epsilon_r)
+
+
+def test_k3_returns_expected_values():
+    """Test k3 returns the expected values"""
+    assert _crack_control.k3() == 3.4
+
+
+def test_k4_returns_expected_values():
+    """Test k4 returns the expected values"""
+    assert _crack_control.k4() == 0.425
+
+
+@pytest.mark.parametrize(
+    'c, phi, rho_p_eff, k1, k2, k3, k4, expected',
+    [
+        (20, 8, 5, 0.8, 0.5, 3.4, 0.425, 68.272),
+        (30, 15, 0.2, 1.6, 0.5, 3.4, 0.425, 127.5),
+        (45, 20, 0.4, 0.8, 1, 3.4, 0.425, 170),
+    ],
+)
+def test_sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4, expected):
+    """Test sr_max_close returns the expected values"""
+    assert math.isclose(
+        _crack_control.sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'c, phi, rho_p_eff, k1, k2, k3, k4',
+    [
+        (-20, 8, 5, 0.8, 0.5, 3.4, 0.425),
+        (20, -8, 5, 0.8, 0.5, 3.4, 0.425),
+        (20, 8, -5, 0.8, 0.5, 3.4, 0.425),
+        (20, 8, 5, -0.8, 0.5, 3.4, 0.425),
+        (20, 8, 5, 0.8, -0.5, 3.4, 0.425),
+        (20, 8, 5, 0.8, 0.5, -3.4, 0.425),
+        (20, 8, 5, 0.8, 0.5, 3.4, -0.425),
+        (20, 8, 5, 0.9, 0.5, 3.4, 0.425),
+        (20, 8, 5, 0.8, 0.2, 3.4, 0.425),
+        (20, 8, 5, 0.8, 1.1, 3.4, 0.425),
+    ],
+)
+def test_sr_max_close_raises_exceptions(c, phi, rho_p_eff, k1, k2, k3, k4):
+    """Test sr_max_close raises the expected value errors"""
+    with pytest.raises(ValueError):
+        _crack_control.sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4)
+
+
+@pytest.mark.parametrize(
+    'h, x, expected',
+    [
+        (300, 100, 260),
+        (200, 75, 162.5),
+        (400, 100, 390),
+    ],
+)
+def test_sr_max_far_returns_expected_values(h, x, expected):
+    """Test sr_max_far returns the expected values"""
+    assert math.isclose(
+        _crack_control.sr_max_far(h, x), expected, rel_tol=10e-5
+    )
+
+
+@pytest.mark.parametrize(
+    'h, x',
+    [
+        (-100, 100),
+        (200, -100),
+        (100, 200),
+    ],
+)
+def test_sr_max_far_raises_exceptions(h, x):
+    """Test sr_max_far raises exceptions"""
+    with pytest.raises(ValueError):
+        _crack_control.sr_max_far(h, x)
+
+
+@pytest.mark.parametrize(
+    'sr_max_y, sr_max_z, theta, expected',
+    [
+        (200, 160, 0.2618, 155.10493),
+        (265, 50, 0.7854, 59.4868),
+        (140, 10, 1.39626, 10.028),
+    ],
+)
+def test_sr_max_theta_returns_expected_values(
+    sr_max_y, sr_max_z, theta, expected
+):
+    """Test sr_max_theta returns expeceted values"""
+    assert math.isclose(
+        _crack_control.sr_max_theta(sr_max_y, sr_max_z, theta),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'sr_max_y, sr_max_z, theta',
+    [
+        (-100, 200, 0),
+        (100, -200, -0.5),
+        (100, -200, 150),
+    ],
+)
+def test_sr_max_theta_raises_exceptions(sr_max_y, sr_max_z, theta):
+    """Test sr_max_theta raises value errors"""
+    with pytest.raises(ValueError):
+        _crack_control.sr_max_theta(sr_max_y, sr_max_z, theta)
+
+
+@pytest.mark.parametrize(
+    'sr_max, esm_ecm, expected',
+    [
+        (200, 0.00112, 0.224),
+        (260, 0.0007, 0.182),
+    ],
+)
+def test_wk_returns_expected_values(sr_max, esm_ecm, expected):
+    """Test wk returns expected values"""
+    assert math.isclose(
+        _crack_control.wk(sr_max, esm_ecm),
+        expected,
+        rel_tol=10e-5,
+    )
+
+
+@pytest.mark.parametrize(
+    'sr_max, esm_ecm',
+    [(-200, 0.0001), (200, -0.0001)],
+)
+def test_wk_raises_exceptions(sr_max, esm_ecm: float):
+    """Test wk raises value errors"""
+    with pytest.raises(ValueError):
+        _crack_control.wk(sr_max, esm_ecm)

From ce4e432ba2a0059149e58582d2cb45080757cbcf Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Fri, 13 Jan 2023 14:31:24 +0100
Subject: [PATCH 15/33] imports and renamed functions

---
 prueba.py                                     |   0
 structuralcodes/codes/ec2_2004/__init__.py    |  54 +-
 .../{_crack_control.py => _section_7.3.py}    | 347 ++++---
 .../ec2_2004/_section_7_3_crack_control.py    | 933 ++++++++++++++++++
 ...est_ec2_2004_section_7_3_crack_control.py} | 127 ++-
 5 files changed, 1228 insertions(+), 233 deletions(-)
 create mode 100644 prueba.py
 rename structuralcodes/codes/ec2_2004/{_crack_control.py => _section_7.3.py} (74%)
 create mode 100644 structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
 rename tests/{test_ec2_2004_crack_control.py => test_ec2_2004_section_7_3_crack_control.py} (83%)

diff --git a/prueba.py b/prueba.py
new file mode 100644
index 00000000..e69de29b
diff --git a/structuralcodes/codes/ec2_2004/__init__.py b/structuralcodes/codes/ec2_2004/__init__.py
index 96694004..d322788b 100644
--- a/structuralcodes/codes/ec2_2004/__init__.py
+++ b/structuralcodes/codes/ec2_2004/__init__.py
@@ -1,9 +1,59 @@
 """EUROCODE 2 1992-1-1:2004"""
 import typing as t
 
-from ._crack_control import w_max
+from ._section_7_3_crack_control import (
+    As_min,
+    As_min_2,
+    As_min_p,
+    alpha_e,
+    esm_ecm,
+    hc_eff,
+    k,
+    k1,
+    k2,
+    k3,
+    k4,
+    kc_flanges_area,
+    kc_rect_area,
+    kc_tension,
+    kt,
+    phi_eq,
+    rho_p_eff,
+    sr_max_close,
+    sr_max_far,
+    sr_max_theta,
+    w_max,
+    w_spacing,
+    wk,
+    xi1,
+)
 
-__all__ = ['w_max']
+__all__ = [
+    'As_min',
+    'As_min_2',
+    'As_min_p',
+    'alpha_e',
+    'esm_ecm',
+    'hc_eff',
+    'k',
+    'k1',
+    'k2',
+    'k3',
+    'k4',
+    'kc_flanges_area',
+    'kc_rect_area',
+    'kc_tension',
+    'kt',
+    'phi_eq',
+    'rho_p_eff',
+    'sr_max_close',
+    'sr_max_far',
+    'sr_max_theta',
+    'w_max',
+    'w_spacing',
+    'wk',
+    'xi1',
+]
 
 __title__: str = 'EUROCODE 2 1992-1-1'
 __year__: str = '2004'
diff --git a/structuralcodes/codes/ec2_2004/_crack_control.py b/structuralcodes/codes/ec2_2004/_section_7.3.py
similarity index 74%
rename from structuralcodes/codes/ec2_2004/_crack_control.py
rename to structuralcodes/codes/ec2_2004/_section_7.3.py
index e0561fe3..3ad05170 100644
--- a/structuralcodes/codes/ec2_2004/_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_section_7.3.py
@@ -58,8 +58,8 @@ def w_max(exposure_class: str, load_combination: str) -> float:
     )
 
 
-def crack_min_steel_area(
-    a_ct: float, s_steel: float, fct_eff: float, k: float, kc: float
+def As_min(
+    A_ct: float, sigma_s: float, fct_eff: float, _k: float, kc: float
 ) -> float:
     """Computes the minimum area of reinforcing steel within the tensile zone
     for control of cracking areas
@@ -67,10 +67,10 @@ def crack_min_steel_area(
     EUROCODE 2 1992-1-1:2004, Eq. (7.1)
 
     Args:
-        a_ct (float): is the area of concrete within the tensile zone in mm2.
+        A_ct (float): is the area of concrete within the tensile zone in mm2.
             The tensile zone is that parg of the section which is calculated
             to be in tension just before the formation of the first crack.
-        s_steel (float): is the absolute value of the maximum stress in MPa
+        sigma_s (float): is the absolute value of the maximum stress in MPa
             permitted in the reinforcement immediately after the formation
             of the crack. This may be taken as theyield strength of the
             reinforcement, fyk. A lower value may, however, be needed to
@@ -80,7 +80,7 @@ def crack_min_steel_area(
             the concrete effective at the time when the cracks may first be
             expected to occur: fct,eff=fct or lower (fct(t)), is cracking
             is expected earlier than 28 days.
-        k (float): is the coefficient which allow for the effect of
+        _k (float): is the coefficient which allow for the effect of
             non-uniform self-equilibrating stresses, which lead to a
             reduction of restraint forces. Use 'k_crack_min_steel_area'
             to compute it
@@ -96,28 +96,27 @@ def crack_min_steel_area(
             zone in mm2.
 
     Raises:
-        ValueError: if k value is not between 0.65 and 1 or kc is not
+        ValueError: if _k value is not between 0.65 and 1 or kc is not
             larger than 0 and lower than 1.
     """
     fct_eff = abs(fct_eff)
 
-    if a_ct <= 0:
-        raise ValueError(f'a_ct={a_ct} must be larger than 0')
-    if s_steel < 0:
-        raise ValueError(f's_steel={s_steel} must be equal or larger than 0')
-    if k < 0.65 or k > 1.0:
-        raise ValueError(f'k={k} must be between 0.65 and 1')
+    if A_ct <= 0:
+        raise ValueError(f'A_ct={A_ct} must be larger than 0')
+    if sigma_s < 0:
+        raise ValueError(f'sigma_s={sigma_s} must be equal or larger than 0')
+    if _k < 0.65 or _k > 1.0:
+        raise ValueError(f'_k={_k} must be between 0.65 and 1')
     if kc > 1 or kc < 0:
         raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
 
-    return kc * k * fct_eff * a_ct / s_steel
+    return kc * _k * fct_eff * A_ct / sigma_s
 
 
-def k_crack_min_steel_area(h: float) -> float:
+def k(h: float) -> float:
     """Is the coefficient which allow for the effect of
     non-uniform self-equilibrating stresses, which lead to a
-    reduction of restraint forces. Use 'k_crack_min_steel_area'
-    to compute it
+    reduction of restraint forces.
     k=1 for webs w<=300mm or flanges widths less than 300mm
     k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
 
@@ -142,7 +141,7 @@ def k_crack_min_steel_area(h: float) -> float:
     return 0.65
 
 
-def kc_crack_min_steel_area_pure_tension() -> float:
+def kc_pure_tension() -> float:
     """Computes the coefficient which takes account of the stress
     distribution within the section immediately prior to cracking and
     the change of the lever arm in pure dtension.
@@ -155,8 +154,8 @@ def kc_crack_min_steel_area_pure_tension() -> float:
     return 1
 
 
-def kc_crack_min_steel_area_rectangular(
-    h: float, b: float, fct_eff: float, n_ed: float
+def kc_rectangular_area(
+    h: float, b: float, fct_eff: float, N_ed: float
 ) -> float:
     """Computes the coefficient which takes account of the stress
     distribution within the section immediately prior to cracking and
@@ -172,7 +171,7 @@ def kc_crack_min_steel_area_rectangular(
             the concrete effective at the time when the cracks may first be
             expected to occur: fct,eff=fct or lower (fct(t)), is cracking
             is expected earlier than 28 days.
-        n_ed (str): axial force at the serviceability limit state acting on
+        N_ed (str): axial force at the serviceability limit state acting on
             the part of the cross-section under consideration (compressive
             force positive). n_ed should be determined considering the
             characteristic values of prestress and axial forces under the
@@ -190,15 +189,13 @@ def kc_crack_min_steel_area_rectangular(
         raise ValueError(f'b={b} should be larger than 0mm')
 
     h_s = min(h, 1000)
-    k1 = 1.5 if n_ed >= 0 else 2 * h_s / 3 / h
-    s_concrete = n_ed * 1000 / b / h
+    _k1 = 1.5 if N_ed >= 0 else 2 * h_s / 3 / h
+    s_concrete = N_ed * 1000 / b / h
     h_ratio = h / h_s
-    return min(max(0.4 * (1 - s_concrete / k1 / h_ratio / fct_eff), 0), 1)
+    return min(max(0.4 * (1 - s_concrete / _k1 / h_ratio / fct_eff), 0), 1)
 
 
-def kc_crack_min_steel_area_flanges(
-    f_cr: float, a_ct: float, fct_eff: float
-) -> float:
+def kc_flanges_area(f_cr: float, A_ct: float, fct_eff: float) -> float:
     """Computes the coefficient which takes account of the stress
     distribution within the section immediately prior to cracking and
     the change of the lever arm for bending+axial combination
@@ -210,7 +207,7 @@ def kc_crack_min_steel_area_flanges(
         f_cr: is the absolute value in kN of the tensile force within the
             flange immediately prior to cracking due to cracking moment
             calculated with fct,eff
-        a_ct (float): is the area of concrete within the tensile zone in mm2.
+        A_ct (float): is the area of concrete within the tensile zone in mm2.
             The tensile zone is that part of the section which is calculated
             to be in tension just before the formation of the first crack.
         fct_eff (float): is the mean value of the tensile strength in MPa of
@@ -222,49 +219,47 @@ def kc_crack_min_steel_area_flanges(
         float: value of the kc coefficient
 
     Raises:
-        ValueError: is a_ct is less than 0mm2
+        ValueError: is A_ct is less than 0mm2
     """
     f_cr = abs(f_cr)
-    return max(0.9 * f_cr * 1000 / a_ct / fct_eff, 0.5)
+    return max(0.9 * f_cr * 1000 / A_ct / fct_eff, 0.5)
 
 
-def adjusted_bond_strength(e: float, d_press: float, d_steel: float) -> float:
+def xi_1(xi: float, phi_p: float, phi_s: float) -> float:
     """Computes the adjusted ratio of bond strength taking into account
     the different diameters of prestressing and reinforcing steel.
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.5)
 
     Args:
-        e (float): ratio of bond strength of prestressing and reinforcing
+        xi (float): ratio of bond strength of prestressing and reinforcing
             steel, according to Table 6.2 in 6.8.2
-        d_steel (float): largest bar diameter in mm of reinforcing steel.
+        phi_p (float): largest bar diameter in mm of reinforcing steel.
             Equal to 0 if only prestressing is used in control cracking
-        d_press (float): equivalent diameter in mm of tendon acoording
+        phi_s (float): equivalent diameter in mm of tendon acoording
             to 6.8.2
 
     Returns:
         float: with the value of the ratio
 
     Raises:
-        ValueError: if diameters d_steel or d_press are lower than 0.
-            If ratio of bond strength e is less than 0.15 or larger than 0.8.
-            If area of tendons ac_eff is less than 0. Is stress variation
-            incr_stress is less than 0.
+        ValueError: if diameters phi_s or phi_p are lower than 0.
+            If ratio of bond strength xi is less than 0.15 or larger than 0.8.
     """
 
-    if d_press <= 0:
-        raise ValueError(f'd_press={d_press} cannot be less than 0')
-    if d_steel < 0:
-        raise ValueError(f'd_steel={d_steel} cannot be less than 0')
-    if e < 0.15:
-        raise ValueError(f'The minimum value for e={e} is 0.15')
-    if e > 0.8:
-        raise ValueError(f'The maximum value for e={e} is 0.8')
+    if phi_p <= 0:
+        raise ValueError(f'phi_p={phi_p} cannot be less than 0')
+    if phi_s < 0:
+        raise ValueError(f'phi_s={phi_s} cannot be less than 0')
+    if xi < 0.15:
+        raise ValueError(f'The minimum value for xi={xi} is 0.15')
+    if xi > 0.8:
+        raise ValueError(f'The maximum value for xi={xi} is 0.8')
 
-    return ((e * d_steel / d_press) ** 0.5) if d_steel > 0 else e**0.5
+    return ((xi * phi_s / phi_p) ** 0.5) if phi_s > 0 else xi**0.5
 
 
-def hc_eff_concrete_tension(h: float, d: float, x: float) -> float:
+def hc_eff(h: float, d: float, x: float) -> float:
     """Returns the effective height of concrete in tension surrounding
     the reinforcement or prestressing tendons.
 
@@ -297,17 +292,17 @@ def hc_eff_concrete_tension(h: float, d: float, x: float) -> float:
     return min(2.5 * (h - d), (h - x) / 3, h / 2)
 
 
-def crack_min_steel_area_with_prestresed_tendons(
-    a_ct: float,
-    s_steel: float,
+def As_min_p(
+    A_ct: float,
+    sigma_s: float,
     fct_eff: float,
-    k: float,
+    _k: float,
     kc: float,
-    ap: float,
-    d_steel: float,
-    d_press: float,
-    e: float,
-    incr_stress: float,
+    Ap: float,
+    phi_s: float,
+    phi_p: float,
+    xi: float,
+    delta_s: float,
 ) -> float:
     """Computes the minimum area of reinforcing steel within the tensile zone
     for control of cracking areas in addition with bonded tendons
@@ -315,10 +310,10 @@ def crack_min_steel_area_with_prestresed_tendons(
     EUROCODE 2 1992-1-1:2004, Eq. (7.1)
 
     Args:
-        a_ct (float): is the area of concrete within the tensile zone in mm2.
+        A_ct (float): is the area of concrete within the tensile zone in mm2.
             The tensile zone is that part of the section which is calculated
             to be in tension just before the formation of the first crack.
-        s_steel (float): is the absolute value of the maximum stress in MPa
+        sigma_s (float): is the absolute value of the maximum stress in MPa
             permitted in the reinforcement immediately after the formation
             of the crack. This may be taken as theyield strength of the
             reinforcement, fyk. A lower value may, however, be needed to
@@ -328,7 +323,7 @@ def crack_min_steel_area_with_prestresed_tendons(
             the concrete effective at the time when the cracks may first be
             expected to occur: fct,eff=fct or lower (fct(t)), is cracking
             is expected earlier than 28 days.
-        k (float): is the coefficient which allow for the effect of
+        _k (float): is the coefficient which allow for the effect of
             non-uniform self-equilibrating stresses, which lead to a
             reduction of restraint forces. Use 'k_crack_min_steel_area'
             to compute it
@@ -338,17 +333,15 @@ def crack_min_steel_area_with_prestresed_tendons(
         kc (float): is a coefficient which takes account of the stress
             distribution within the section immediately prior to cracking and
             the change of the lever arm.
-        ac_eff (float): is the effective area in mm2 of concrete in tension
-            surrounding or prestressing tendons if depth hc,ef
-        ap (float): is the area in mm2 of pre or post-tensioned tendons
+        Ap (float): is the area in mm2 of pre or post-tensioned tendons
             within ac_eff
-        d_steel (float): largest bar diameter in mm of reinforcing steel.
+        phi_s (float): largest bar diameter in mm of reinforcing steel.
             Equal to 0 if only prestressing is used in control cracking
-        d_press (float): equivalent diameter in mm of tendon acoording
+        phi_p (float): equivalent diameter in mm of tendon acoording
             to 6.8.2
-        e (float): ratio of bond strength of prestressing and reinforcing
+        chi (float): ratio of bond strength of prestressing and reinforcing
             steel, according to Table 6.2 in 6.8.2
-        incr_stress (float): stress variation in MPa in prestressing tendons
+        delta_s (float): stress variation in MPa in prestressing tendons
             from the state of zero strain of the concrete at the same level
 
     Returns:
@@ -356,43 +349,43 @@ def crack_min_steel_area_with_prestresed_tendons(
             zone in mm2.
 
     Raises:
-        ValueError: if k value is not between 0.65 and 1 or kc is not
-            larger than 0 and lower than 1. If diameters d_steel or
-            d_press are lower than 0. If ratio of bond strength e
-            is less than 0.15 or larger than 0.8. If area of tendons ac_eff
-            is less than 0. Is stress variation incr_stress is less than 0
+        ValueError: if _k value is not between 0.65 and 1 or kc is not
+            larger than 0 and lower than 1. If diameters phi_s or
+            phi_p are lower than 0. If ratio of bond xi strength e
+            is less than 0.15 or larger than 0.8.
+            Is stress variation incr_stress is less than 0.
     """
     fct_eff = abs(fct_eff)
 
-    if ap < 0:
-        raise ValueError(f'ap={ap} cannot be less than 0')
-    if incr_stress < 0:
-        raise ValueError(f'incr_stress={incr_stress} cannot be less than 0')
-    if a_ct <= 0:
-        raise ValueError(f'a_ct={a_ct} must be larger than 0')
-    if s_steel < 0:
-        raise ValueError(f's_steel={s_steel} must be equal or larger than 0')
-    if k < 0.65 or k > 1.0:
-        raise ValueError(f'k={k} must be between 0.65 and 1')
+    if Ap < 0:
+        raise ValueError(f'Ap={Ap} cannot be less than 0')
+    if delta_s < 0:
+        raise ValueError(f'delta_s={delta_s} cannot be less than 0')
+    if A_ct <= 0:
+        raise ValueError(f'A_ct={A_ct} must be larger than 0')
+    if sigma_s < 0:
+        raise ValueError(f'sigma_s={sigma_s} must be equal or larger than 0')
+    if _k < 0.65 or _k > 1.0:
+        raise ValueError(f'_k={_k} must be between 0.65 and 1')
     if kc > 1 or kc < 0:
         raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
 
-    a1 = kc * k * fct_eff * a_ct
-    e1 = adjusted_bond_strength(e, d_press, d_steel)
-    a2 = e1 * ap * incr_stress
+    a1 = kc * _k * fct_eff * A_ct
+    e1 = xi_1(xi, phi_p, phi_s)
+    a2 = e1 * Ap * delta_s
     a = a1 - a2
 
-    return a / s_steel
+    return a / sigma_s
 
 
-def crack_min_steel_without_direct_calculation(
-    wk: float,
-    s_steel: float,
+def As_min_2(
+    _wk: float,
+    sigma_s: float,
     fct_eff: float,
     h_cr: float,
     h: float,
     d: float,
-    incr_stress: float = 0,
+    delta_s: float = 0,
     kc: t.Optional[float] = None,
 ) -> t.Tuple[float, float]:
     """Computes the minimum area of reinforcing steel within the tensile zone
@@ -401,8 +394,8 @@ def crack_min_steel_without_direct_calculation(
     EUROCODE 2 1992-1-1:2004, Table (7.2N), Table (7.3N)
 
     Args:
-        wk (float): the characteristic crack width value in mm.
-        s_steel (float): the steel stress value in MPa under the relevant
+        _wk (float): the characteristic crack width value in mm.
+        sigma_s (float): the steel stress value in MPa under the relevant
             combination of actions.
         fct_eff (float): is the mean value of the tensile strength in MPa of
             the concrete effective at the time when the cracks may first be
@@ -414,7 +407,7 @@ def crack_min_steel_without_direct_calculation(
         h (float): the overall depth of the section in mm.
         d (float): is the effective depth to the centroid of the outer layer
             of the reinforcement.
-        incr_stress (float, optional): value of prestressed stress in MPa if
+        delta_s (float, optional): value of prestressed stress in MPa if
             applicable
         kc (float, optional): is a coefficient which takes account of the
             stress distribution within the section immediately prior to
@@ -426,12 +419,12 @@ def crack_min_steel_without_direct_calculation(
         in the first position and the maximum bar spacing in mm in the
         second position
     Raises:
-        ValueError: if wk, fct_eff, h_cr, h or d are less than 0
+        ValueError: if _wk, fct_eff, h_cr, h or d are less than 0
         ValueError: if kc is not between 0 and 1
         ValueError: if combination of wk and stress values are out of scope
     """
-    if wk < 0:
-        raise ValueError(f'wd={wk} cannot be less than 0')
+    if _wk < 0:
+        raise ValueError(f'_wk={_wk} cannot be less than 0')
     if fct_eff < 0:
         raise ValueError(f'fct_eff={fct_eff} is less than 0')
     if h_cr < 0:
@@ -443,7 +436,7 @@ def crack_min_steel_without_direct_calculation(
     if kc is not None and (kc < 0 or kc > 1):
         raise ValueError(f'kc={kc} is not between 0 and 1')
 
-    s = s_steel - incr_stress
+    s = sigma_s - delta_s
     if s <= 0:
         return (0, 0)
 
@@ -499,7 +492,7 @@ def crack_min_steel_without_direct_calculation(
 
     points_phi = np.array(np.meshgrid(y_phi, x)).T.reshape(-1, 2)
     points_spa = np.array(np.meshgrid(y_spa, x)).T.reshape(-1, 2)
-    xi = (s, wk)
+    xi = (s, _wk)
 
     phi_star = float(
         scipy.interpolate.griddata(points_phi, phi_s_v, xi, method='linear')
@@ -519,40 +512,40 @@ def crack_min_steel_without_direct_calculation(
     return phi, spa
 
 
-def get_alpha_e(es: float, ecm: float) -> float:
+def alpha_e(Es: float, Ecm: float) -> float:
     """Compute the ratio between the steel and mean concrete
-    modules.
+    elastic modules.
 
     EUROCODE 2 1992-1-1:2004, Section 7.3.4-2
 
     Args:
-        es (float): steel elastic modulus in MPa
-        ecm (float): ecm concrete mean elastic modulus in MPa
+        Es (float): steel elastic modulus in MPa
+        Ecm (float): concrete mean elastic modulus in MPa
 
     Returns:
         float: ratio between modules
     Raise:
         ValueError: if any of es or ecm is lower than 0.
     """
-    if es < 0:
-        raise ValueError(f'es={es} cannot be less than 0')
-    if ecm < 0:
-        raise ValueError(f'ecm={ecm} cannot be less than 0')
+    if Es < 0:
+        raise ValueError(f'Es={Es} cannot be less than 0')
+    if Ecm < 0:
+        raise ValueError(f'Ecm={Ecm} cannot be less than 0')
 
-    return es / ecm
+    return Es / Ecm
 
 
-def rho_p_eff(a_s: float, e1: float, a_p, ac_eff: float) -> float:
+def rho_p_eff(As: float, xi1: float, Ap: float, Ac_eff: float) -> float:
     """Effective bond ratio between areas
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.10)
 
     Args:
-        a_s (float): steel area in mm2
-        e1 (float): the adjusted ratio of bond according
+        As (float): steel area in mm2
+        xi1 (float): the adjusted ratio of bond according
             to expression (7.5)
-        a_p (float): the area in mm2 of post-tensioned tendons in ac_eff
-        ac_eff (float): effective area of concrete in tension surrounding
+        Ap (float): the area in mm2 of post-tensioned tendons in ac_eff
+        Ac_eff (float): effective area of concrete in tension surrounding
         the reinforcement or prestressing tendons of depth hc_eff.
 
     Returns:
@@ -560,21 +553,21 @@ def rho_p_eff(a_s: float, e1: float, a_p, ac_eff: float) -> float:
 
 
     Raise:
-        ValueError: if any of a_s, e1, a_p or ac_eff is less than 0
+        ValueError: if any of As, xi1, Ap or Ac_eff is less than 0
     """
-    if a_s < 0:
-        raise ValueError(f'a_s={a_s} cannot be less than 0')
-    if e1 < 0:
-        raise ValueError(f'e1={e1} cannot be less than 0')
-    if a_p < 0:
-        raise ValueError(f'a_p={a_p} cannot be less than 0')
-    if ac_eff < 0:
-        raise ValueError(f'ac_eff={ac_eff} cannot be less than 0')
+    if As < 0:
+        raise ValueError(f'As={As} cannot be less than 0')
+    if xi1 < 0:
+        raise ValueError(f'xi1={xi1} cannot be less than 0')
+    if Ap < 0:
+        raise ValueError(f'Ap={Ap} cannot be less than 0')
+    if Ac_eff < 0:
+        raise ValueError(f'Ac_eff={Ac_eff} cannot be less than 0')
 
-    return (a_s + e1**2 * a_p) / ac_eff
+    return (As + xi1**2 * Ap) / Ac_eff
 
 
-def kt_load_duration(load_type: str) -> float:
+def kt(load_type: str) -> float:
     """Returns the kt factor dependent on the load duration for
     the crack width calculation
 
@@ -602,12 +595,12 @@ def kt_load_duration(load_type: str) -> float:
 
 
 def esm_ecm(
-    s_steel: float,
-    alpha_e: float,
-    rho_p_eff: float,
-    kt: float,
+    sigma_s: float,
+    _alpha_e: float,
+    _rho_p_eff: float,
+    _kt: float,
     fct_eff: float,
-    es: float,
+    Es: float,
 ) -> float:
     """Returns the strain difference (esm - ecm) needed to compute the crack
     width. esm is the mean strain in the reinforcement under the relevant
@@ -619,52 +612,52 @@ def esm_ecm(
     EUROCODE 2 1992-1-1:2004, Eq. (7.9)
 
     Args:
-        s_steel (float): is the stress in MPa in the tension reinforcement
+        sigma_s (float): is the stress in MPa in the tension reinforcement
             assuming a cracked section. FOr pretensioned members, s_steel may
             be replaced by increment of s_steel stress variation in
             prestressing tendons from the state of zero strain of the
             concrete at the same level.
-        alpha_e (float): is the ratio Es/Ecm
-        rho_p_eff (float): effective bond ratio between areas given by the
+        _alpha_e (float): is the ratio Es/Ecm
+        _rho_p_eff (float): effective bond ratio between areas given by the
             Eq. (7.10)
-        kt (float): is a factor dependent on the load duration
+        _kt (float): is a factor dependent on the load duration
         fct_eff (float): is the mean value of the tensile strength in MPa
             of the concrete effectvie at the time when the cracks may
             first be expected to occur: fct_eff=fctm or fctm(t) if
             crack is expected earlier than 28 days.
-        es: steel elastic mudulus in MPa
+        Es: steel elastic mudulus in MPa
 
     Returns:
         float: the strain difference between concrete and steel
 
     Raises:
-        ValueError: if any s_steel, alpha_e, rho_p_eff, fct_Eff is less
+        ValueError: if any sigma_s, alpha_e, rho_p_eff, fct_eff or Es is less
             than 0.
         ValueError: if kt is not 0.6 and not 0.4
     """
-    if s_steel < 0:
-        raise ValueError(f's_steel={s_steel} cannot be less than 0')
-    if alpha_e < 0:
-        raise ValueError(f'alpha_e={alpha_e} cannot be less than 0')
-    if rho_p_eff < 0:
-        raise ValueError(f'rho_p_eff={rho_p_eff} cannot be less than 0')
+    if sigma_s < 0:
+        raise ValueError(f'sigma_s={sigma_s} cannot be less than 0')
+    if _alpha_e < 0:
+        raise ValueError(f'_alpha_e={_alpha_e} cannot be less than 0')
+    if _rho_p_eff < 0:
+        raise ValueError(f'_rho_p_eff={_rho_p_eff} cannot be less than 0')
     if fct_eff < 0:
         raise ValueError(f'fct_eff={fct_eff} cannot be less than 0')
-    if es < 0:
-        raise ValueError(f'es={es} cannot be less than 0')
-    if kt != 0.6 and kt != 0.4:
-        raise ValueError(f'kt={kt} can only take as values 0.4 and 0.6')
+    if Es < 0:
+        raise ValueError(f'Es={Es} cannot be less than 0')
+    if _kt != 0.6 and _kt != 0.4:
+        raise ValueError(f'_kt={_kt} can only take as values 0.4 and 0.6')
 
-    min_val = 0.6 * s_steel / es
+    min_val = 0.6 * sigma_s / Es
 
-    a = 1 + alpha_e * rho_p_eff
-    b = kt * fct_eff / rho_p_eff * a
-    c = (s_steel - b) / es
+    a = 1 + _alpha_e * _rho_p_eff
+    b = _kt * fct_eff / _rho_p_eff * a
+    c = (sigma_s - b) / Es
 
     return max(c, min_val)
 
 
-def s_threshold(c: float, phi: float) -> float:
+def w_spacing(c: float, phi: float) -> float:
     """Computes the distance threshold from which the
     maximum crack spacing is constant.
 
@@ -804,15 +797,15 @@ def k4():
 def sr_max_close(
     c: float,
     phi: float,
-    rho_p_eff: float,
-    k1: float,
-    k2: float,
-    k3: float,
-    k4: float,
+    _rho_p_eff: float,
+    _k1: float,
+    _k2: float,
+    _k3: float,
+    _k4: float,
 ) -> float:
     """Computes the maximum crack spacing in cases where bonded reinforcement
     is fixed at reasonably close centres within the tension zone
-    (spacing<=5(c+phi/2)).
+    (w_spacing<=5(c+phi/2)).
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.11)
 
@@ -820,14 +813,14 @@ def sr_max_close(
         c (float): is the cover in mm of the longitudinal reinforcement
         phi (float): is the bar diameter in mm. Where mixed bar diameters
             used, then it should be replaced for an equivalente bar diameter.
-        rho_p_eff (float): effective bond ratio between areas given by the
+        _rho_p_eff (float): effective bond ratio between areas given by the
             Eq. (7.10)
-        k1 (float): coefficient that takes into account the bound properties
+        _k1 (float): coefficient that takes into account the bound properties
             of the bonded reinforcement
-        k2 (float): coefficient that takes into account the distribution of
+        _k2 (float): coefficient that takes into account the distribution of
             of the strain
-        k3 (float): coefficient from the National Annex
-        k4 (float): coefficient from the National Annex
+        _k3 (float): coefficient from the National Annex
+        _k4 (float): coefficient from the National Annex
 
     Returns:
         float: the maximum crack spaing in mm.
@@ -842,24 +835,24 @@ def sr_max_close(
         raise ValueError(f'c={c} cannot be less than zero')
     if phi < 0:
         raise ValueError(f'phi={phi} cannot be less than zero')
-    if rho_p_eff < 0:
-        raise ValueError(f'rho_p_eff={rho_p_eff} cannot be less than zero')
-    if k3 < 0:
-        raise ValueError(f'k3={k3} cannot be less than zero')
-    if k4 < 0:
-        raise ValueError(f'k4={k4} cannot be less than zero')
-    if k1 != 0.8 and k1 != 1.6:
-        raise ValueError(f'k1={k1} can only take as values 0.8 and 1.6')
-    if k2 < 0.5 or k2 > 1:
-        raise ValueError(f'k2={k2} is not between 0.5 and 1.0')
+    if _rho_p_eff < 0:
+        raise ValueError(f'_rho_p_eff={_rho_p_eff} cannot be less than zero')
+    if _k3 < 0:
+        raise ValueError(f'_k3={_k3} cannot be less than zero')
+    if _k4 < 0:
+        raise ValueError(f'_k4={_k4} cannot be less than zero')
+    if _k1 != 0.8 and _k1 != 1.6:
+        raise ValueError(f'_k1={_k1} can only take as values 0.8 and 1.6')
+    if _k2 < 0.5 or _k2 > 1:
+        raise ValueError(f'_k2={_k2} is not between 0.5 and 1.0')
 
-    return k3 * c + k1 * k2 * k4 * phi / rho_p_eff
+    return _k3 * c + _k1 * _k2 * _k4 * phi / _rho_p_eff
 
 
 def sr_max_far(h: float, x: float) -> float:
     """Computes the maximum crack spacing in cases where bonded reinforcement
     is fixed at reasonably close centres within the tension zone
-    (spacing>5(c+phi/2)).
+    (w_spacing>5(c+phi/2)).
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.14)
 
@@ -915,14 +908,14 @@ def sr_max_theta(sr_max_y: float, sr_max_z: float, theta: float) -> float:
     return 1 / (a + b)
 
 
-def wk(sr_max: float, esm_ecm: float) -> float:
+def wk(sr_max: float, _esm_ecm: float) -> float:
     """Computes the crack width
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.8)
 
     Args:
         sr_max (float): the maximum crack length spacing in mm.
-        esm_ecm (float): the difference between the mean strain in the
+        _esm_ecm (float): the difference between the mean strain in the
             reinforcement under relevant combination of loads, including
             the effect of imposed deformations and taking into account
             tension stiffening and the mean strain in the concrete
@@ -932,11 +925,11 @@ def wk(sr_max: float, esm_ecm: float) -> float:
         float: crack width in mm.
 
     Raises:
-        ValueError: if any of sr_max or esm_ecm is less than zero.
+        ValueError: if any of sr_max or _esm_ecm is less than zero.
     """
     if sr_max < 0:
         raise ValueError(f'sr_max={sr_max} cannot be less than zero')
-    if esm_ecm < 0:
-        raise ValueError(f'esm_scm={esm_ecm} cannot be less than zero')
+    if _esm_ecm < 0:
+        raise ValueError(f'_esm_scm={_esm_ecm} cannot be less than zero')
 
-    return sr_max * esm_ecm
+    return sr_max * _esm_ecm
diff --git a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
new file mode 100644
index 00000000..1de528de
--- /dev/null
+++ b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
@@ -0,0 +1,933 @@
+"""Collection of functions from EUROCODE 1992-1-1:2004
+Chapter 7.3 - Crack control"""
+import math
+import typing as t
+
+import numpy as np
+import scipy.interpolate
+
+
+def w_max(exposure_class: str, load_combination: str) -> float:
+    """Computes the recomended value of the maximum crack width.
+
+    EUROCODE 2 1992-1-1:2004, Table (7.1N)
+
+    Args:
+        exposure_class (str): The exposure class.
+            Possible values: X0, XC1, XC2, XC3, XC4, XD1, XD2, XS1, XS2, XS3
+        load_combination (str):
+            - f: for frequent load combination
+            - qp: for quasi-permanent load combination
+
+    Returns:
+        float: The maximum recommended value for the crack width wmax in mm.
+
+    Raises:
+        ValueError: if not valid exposure_class or load_combination values.
+    """
+    _load_combination = load_combination.lower().strip()
+    _exposure_class = exposure_class.upper().strip()
+    if _load_combination == 'f':
+        if _exposure_class in ('X0', 'XC1'):
+            return 0.2
+        if _exposure_class in ('XC2', 'XC3', 'XC4'):
+            return 0.2
+    if _load_combination == 'qp':
+        if _exposure_class in ('X0', 'XC1'):
+            return 0.4
+        if _exposure_class in (
+            'XC2',
+            'XC3',
+            'XC4',
+            'XD1',
+            'XD2',
+            'XS1',
+            'XS2',
+            'XS3',
+        ):
+            return 0.3
+        raise ValueError(
+            f'{exposure_class} is not a valid value for exposure_class.'
+            + ' Please enter one of the following: X0, XC1, XC2, XC3, XC4, XD1'
+            + ',XD2, XS1, XS2, XS3'
+        )
+    raise ValueError(
+        f'{load_combination} is not a valid value for load_combination.'
+        + 'Please enter "f" for frequent load combination or "qp" for'
+        + 'quasi-permanent load combination.'
+    )
+
+
+def As_min(
+    A_ct: float, sigma_s: float, fct_eff: float, _k: float, kc: float
+) -> float:
+    """Computes the minimum area of reinforcing steel within the tensile zone
+    for control of cracking areas
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Args:
+        A_ct (float): is the area of concrete within the tensile zone in mm2.
+            The tensile zone is that parg of the section which is calculated
+            to be in tension just before the formation of the first crack.
+        sigma_s (float): is the absolute value of the maximum stress in MPa
+            permitted in the reinforcement immediately after the formation
+            of the crack. This may be taken as theyield strength of the
+            reinforcement, fyk. A lower value may, however, be needed to
+            satisfy the crack width limits according to the maximum
+            bar size of spacing (see 7.3.3 (2)).
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        _k (float): is the coefficient which allow for the effect of
+            non-uniform self-equilibrating stresses, which lead to a
+            reduction of restraint forces. Use 'k_crack_min_steel_area'
+            to compute it
+            k=1 for webs w<=300mm or flanges widths less than 300mm
+            k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
+            Intermediate values may be interpolated.
+        kc (float): is a coefficient which takes account of the stress
+            distribution within the section immediately prior to cracking and
+            the change of the lever arm.
+
+    Returns:
+        float: the minimm area of reinforcing steel within the tensile
+            zone in mm2.
+
+    Raises:
+        ValueError: if _k value is not between 0.65 and 1 or kc is not
+            larger than 0 and lower than 1.
+    """
+    fct_eff = abs(fct_eff)
+
+    if A_ct <= 0:
+        raise ValueError(f'A_ct={A_ct} must be larger than 0')
+    if sigma_s < 0:
+        raise ValueError(f'sigma_s={sigma_s} must be equal or larger than 0')
+    if _k < 0.65 or _k > 1.0:
+        raise ValueError(f'_k={_k} must be between 0.65 and 1')
+    if kc > 1 or kc < 0:
+        raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
+
+    return kc * _k * fct_eff * A_ct / sigma_s
+
+
+def k(h: float) -> float:
+    """Is the coefficient which allow for the effect of
+    non-uniform self-equilibrating stresses, which lead to a
+    reduction of restraint forces.
+    k=1 for webs w<=300mm or flanges widths less than 300mm
+    k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Args:
+        h (float): flange length or flange width in mm
+
+    Returns:
+        float: k coefficient value
+
+    Raises:
+        ValueError: if h is less than 0
+    """
+    if h < 0:
+        raise ValueError(f'h={h} cannot be less than 0mm')
+    if h <= 300:
+        return 1
+    if h < 800:
+        interpol = scipy.interpolate.interp1d((300, 800), (1, 0.65))
+        return (float)(interpol(h))
+    return 0.65
+
+
+def kc_tension() -> float:
+    """Computes the coefficient which takes account of the stress
+    distribution within the section immediately prior to cracking and
+    the change of the lever arm in pure dtension.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Returns:
+        float: value of the kc coefficient in pure tension
+    """
+    return 1
+
+
+def kc_rect_area(h: float, b: float, fct_eff: float, N_ed: float) -> float:
+    """Computes the coefficient which takes account of the stress
+    distribution within the section immediately prior to cracking and
+    the change of the lever arm for bending+axial combination
+    in rectangular sections and webs of box sections and T-sections.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.2)
+
+    Args:
+        h (float): heigth of the element in mm
+        b (float): width of the element in mm
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        N_ed (str): axial force at the serviceability limit state acting on
+            the part of the cross-section under consideration (compressive
+            force positive). n_ed should be determined considering the
+            characteristic values of prestress and axial forces under the
+            relevant combination of actions
+
+    Returns:
+        float: value of the kc coefficient
+
+    Raises:
+        ValueError: is h or b are less than 0
+    """
+    if h < 0:
+        raise ValueError(f'h={h} should be larger than 0mm')
+    if b < 0:
+        raise ValueError(f'b={b} should be larger than 0mm')
+
+    h_s = min(h, 1000)
+    _k1 = 1.5 if N_ed >= 0 else 2 * h_s / 3 / h
+    s_concrete = N_ed * 1000 / b / h
+    h_ratio = h / h_s
+    return min(max(0.4 * (1 - s_concrete / _k1 / h_ratio / fct_eff), 0), 1)
+
+
+def kc_flanges_area(f_cr: float, A_ct: float, fct_eff: float) -> float:
+    """Computes the coefficient which takes account of the stress
+    distribution within the section immediately prior to cracking and
+    the change of the lever arm for bending+axial combination
+    in rectangular sections for flanges of box sections and T-sections.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.3)
+
+    Args:
+        f_cr: is the absolute value in kN of the tensile force within the
+            flange immediately prior to cracking due to cracking moment
+            calculated with fct,eff
+        A_ct (float): is the area of concrete within the tensile zone in mm2.
+            The tensile zone is that part of the section which is calculated
+            to be in tension just before the formation of the first crack.
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+
+    Returns:
+        float: value of the kc coefficient
+
+    Raises:
+        ValueError: is A_ct is less than 0mm2
+    """
+    f_cr = abs(f_cr)
+    return max(0.9 * f_cr * 1000 / A_ct / fct_eff, 0.5)
+
+
+def xi1(xi: float, phi_p: float, phi_s: float) -> float:
+    """Computes the adjusted ratio of bond strength taking into account
+    the different diameters of prestressing and reinforcing steel.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.5)
+
+    Args:
+        xi (float): ratio of bond strength of prestressing and reinforcing
+            steel, according to Table 6.2 in 6.8.2
+        phi_p (float): largest bar diameter in mm of reinforcing steel.
+            Equal to 0 if only prestressing is used in control cracking
+        phi_s (float): equivalent diameter in mm of tendon acoording
+            to 6.8.2
+
+    Returns:
+        float: with the value of the ratio
+
+    Raises:
+        ValueError: if diameters phi_s or phi_p are lower than 0.
+            If ratio of bond strength xi is less than 0.15 or larger than 0.8.
+    """
+
+    if phi_p <= 0:
+        raise ValueError(f'phi_p={phi_p} cannot be less than 0')
+    if phi_s < 0:
+        raise ValueError(f'phi_s={phi_s} cannot be less than 0')
+    if xi < 0.15:
+        raise ValueError(f'The minimum value for xi={xi} is 0.15')
+    if xi > 0.8:
+        raise ValueError(f'The maximum value for xi={xi} is 0.8')
+
+    return ((xi * phi_s / phi_p) ** 0.5) if phi_s > 0 else xi**0.5
+
+
+def hc_eff(h: float, d: float, x: float) -> float:
+    """Returns the effective height of concrete in tension surrounding
+    the reinforcement or prestressing tendons.
+
+    EUROCODE 2 1992-1-1:2004, Section (7.3.2-3)
+
+    Args:
+        h (float): total depth of the element in mm
+        d (float): distance in mm to the level of the steel centroid
+        x (float): distance in mm to the zero tensile stress line
+
+    Returns:
+        float: the effective height in mm
+
+    Raises:
+        ValueError: if any of h, d or x is lower than zero.
+        ValueError: if d is greater than h
+        ValueError: if x is greater than h
+    """
+    if h < 0:
+        raise ValueError(f'h={h} cannot be less than 0')
+    if d < 0:
+        raise ValueError(f'd={d} cannot be less than 0')
+    if x < 0:
+        raise ValueError(f'x={x} cannot be less than zero')
+    if d > h:
+        raise ValueError(f'd={d} cannot be larger than h={h}')
+    if x > h:
+        raise ValueError(f'x={x} cannot be larger than h={h}')
+
+    return min(2.5 * (h - d), (h - x) / 3, h / 2)
+
+
+def As_min_p(
+    A_ct: float,
+    sigma_s: float,
+    fct_eff: float,
+    _k: float,
+    kc: float,
+    Ap: float,
+    phi_s: float,
+    phi_p: float,
+    xi: float,
+    delta_s: float,
+) -> float:
+    """Computes the minimum area of reinforcing steel within the tensile zone
+    for control of cracking areas in addition with bonded tendons
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
+
+    Args:
+        A_ct (float): is the area of concrete within the tensile zone in mm2.
+            The tensile zone is that part of the section which is calculated
+            to be in tension just before the formation of the first crack.
+        sigma_s (float): is the absolute value of the maximum stress in MPa
+            permitted in the reinforcement immediately after the formation
+            of the crack. This may be taken as theyield strength of the
+            reinforcement, fyk. A lower value may, however, be needed to
+            satisfy the crack width limits according to the maximum
+            bar size of spacing (see 7.3.3 (2)).
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        _k (float): is the coefficient which allow for the effect of
+            non-uniform self-equilibrating stresses, which lead to a
+            reduction of restraint forces. Use 'k_crack_min_steel_area'
+            to compute it
+            k=1 for webs w<=300mm or flanges widths less than 300mm
+            k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
+            Intermediate values may be interpolated.
+        kc (float): is a coefficient which takes account of the stress
+            distribution within the section immediately prior to cracking and
+            the change of the lever arm.
+        Ap (float): is the area in mm2 of pre or post-tensioned tendons
+            within ac_eff
+        phi_s (float): largest bar diameter in mm of reinforcing steel.
+            Equal to 0 if only prestressing is used in control cracking
+        phi_p (float): equivalent diameter in mm of tendon acoording
+            to 6.8.2
+        chi (float): ratio of bond strength of prestressing and reinforcing
+            steel, according to Table 6.2 in 6.8.2
+        delta_s (float): stress variation in MPa in prestressing tendons
+            from the state of zero strain of the concrete at the same level
+
+    Returns:
+        float: the minimm area of reinforcing steel within the tensile
+            zone in mm2.
+
+    Raises:
+        ValueError: if _k value is not between 0.65 and 1 or kc is not
+            larger than 0 and lower than 1. If diameters phi_s or
+            phi_p are lower than 0. If ratio of bond xi strength e
+            is less than 0.15 or larger than 0.8.
+            Is stress variation incr_stress is less than 0.
+    """
+    fct_eff = abs(fct_eff)
+
+    if Ap < 0:
+        raise ValueError(f'Ap={Ap} cannot be less than 0')
+    if delta_s < 0:
+        raise ValueError(f'delta_s={delta_s} cannot be less than 0')
+    if A_ct <= 0:
+        raise ValueError(f'A_ct={A_ct} must be larger than 0')
+    if sigma_s < 0:
+        raise ValueError(f'sigma_s={sigma_s} must be equal or larger than 0')
+    if _k < 0.65 or _k > 1.0:
+        raise ValueError(f'_k={_k} must be between 0.65 and 1')
+    if kc > 1 or kc < 0:
+        raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
+
+    a1 = kc * _k * fct_eff * A_ct
+    e1 = xi1(xi, phi_p, phi_s)
+    a2 = e1 * Ap * delta_s
+    a = a1 - a2
+
+    return a / sigma_s
+
+
+def As_min_2(
+    _wk: float,
+    sigma_s: float,
+    fct_eff: float,
+    h_cr: float,
+    h: float,
+    d: float,
+    delta_s: float = 0,
+    kc: t.Optional[float] = None,
+) -> t.Tuple[float, float]:
+    """Computes the minimum area of reinforcing steel within the tensile zone
+    for control of cracking areas
+
+    EUROCODE 2 1992-1-1:2004, Table (7.2N), Table (7.3N)
+
+    Args:
+        wk (float): the characteristic crack width value in mm.
+        sigma_s (float): the steel stress value in MPa under the relevant
+            combination of actions.
+        fct_eff (float): is the mean value of the tensile strength in MPa of
+            the concrete effective at the time when the cracks may first be
+            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
+            is expected earlier than 28 days.
+        h_cr (float): is the depth of the tensile zone immediately prior to
+            cracking, considering the characteristic values of prestress and
+            axial forces under the quasi-permanent combination of actions.
+        h (float): the overall depth of the section in mm.
+        d (float): is the effective depth to the centroid of the outer layer
+            of the reinforcement.
+        delta_s (float, optional): value of prestressed stress in MPa if
+            applicable
+        kc (float, optional): is a coefficient which takes account of the
+            stress distribution within the section immediately prior to
+            cracking and the change of the lever arm in a bending section.
+            'None' for pure tensile uniform axial section.
+
+    Returns:
+        tuple(float, float): with the value of the maximum bar diameters in mm
+        in the first position and the maximum bar spacing in mm in the
+        second position
+    Raises:
+        ValueError: if wk, fct_eff, h_cr, h or d are less than 0
+        ValueError: if kc is not between 0 and 1
+        ValueError: if combination of wk and stress values are out of scope
+    """
+    if _wk < 0:
+        raise ValueError(f'_wk={_wk} cannot be less than 0')
+    if fct_eff < 0:
+        raise ValueError(f'fct_eff={fct_eff} is less than 0')
+    if h_cr < 0:
+        raise ValueError(f'h_cr={h_cr} is less than 0')
+    if h < 0:
+        raise ValueError(f'h={h} is less than 0')
+    if d < 0:
+        raise ValueError(f'd={d} is less than 0')
+    if kc is not None and (kc < 0 or kc > 1):
+        raise ValueError(f'kc={kc} is not between 0 and 1')
+
+    s = sigma_s - delta_s
+    if s <= 0:
+        return (0, 0)
+
+    x = (0.4, 0.3, 0.2)
+    y_phi = (160, 200, 240, 280, 320, 360, 400, 450)
+    y_spa = (160, 200, 240, 280, 320, 360)
+    phi_s_v = (
+        40,
+        32,
+        25,
+        32,
+        25,
+        16,
+        20,
+        16,
+        12,
+        16,
+        12,
+        8,
+        12,
+        10,
+        6,
+        10,
+        8,
+        5,
+        8,
+        6,
+        4,
+        6,
+        5,
+        None,
+    )
+    spa_v = (
+        300,
+        300,
+        200,
+        300,
+        250,
+        150,
+        250,
+        200,
+        100,
+        200,
+        150,
+        50,
+        150,
+        100,
+        None,
+        100,
+        50,
+        None,
+    )
+
+    points_phi = np.array(np.meshgrid(y_phi, x)).T.reshape(-1, 2)
+    points_spa = np.array(np.meshgrid(y_spa, x)).T.reshape(-1, 2)
+    xi = (s, _wk)
+
+    phi_star = float(
+        scipy.interpolate.griddata(points_phi, phi_s_v, xi, method='linear')
+    )
+    if kc is not None:
+        phi = phi_star * (fct_eff / 2.9) * kc * h_cr / (2 * (h - d))
+    else:
+        phi = phi_star * (fct_eff / 2.9) * h_cr / (8 * (h - d))
+
+    spa = float(
+        scipy.interpolate.griddata(points_spa, spa_v, xi, method='linear')
+    )
+
+    if math.isnan(phi) or math.isnan(spa):
+        raise ValueError('Combination of _wk or stress values out of scope')
+
+    return phi, spa
+
+
+def alpha_e(Es: float, Ecm: float) -> float:
+    """Compute the ratio between the steel and mean concrete
+    elastic modules.
+
+    EUROCODE 2 1992-1-1:2004, Section 7.3.4-2
+
+    Args:
+        Es (float): steel elastic modulus in MPa
+        Ecm (float): concrete mean elastic modulus in MPa
+
+    Returns:
+        float: ratio between modules
+    Raise:
+        ValueError: if any of es or ecm is lower than 0.
+    """
+    if Es < 0:
+        raise ValueError(f'Es={Es} cannot be less than 0')
+    if Ecm < 0:
+        raise ValueError(f'Ecm={Ecm} cannot be less than 0')
+
+    return Es / Ecm
+
+
+def rho_p_eff(As: float, _xi1: float, Ap: float, Ac_eff: float) -> float:
+    """Effective bond ratio between areas
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.10)
+
+    Args:
+        As (float): steel area in mm2
+        _xi1 (float): the adjusted ratio of bond according
+            to expression (7.5)
+        Ap (float): the area in mm2 of post-tensioned tendons in ac_eff
+        Ac_eff (float): effective area of concrete in tension surrounding
+        the reinforcement or prestressing tendons of depth hc_eff.
+
+    Returns:
+        float: with the retio between areas
+
+
+    Raise:
+        ValueError: if any of As, xi1, Ap or Ac_eff is less than 0
+    """
+    if As < 0:
+        raise ValueError(f'As={As} cannot be less than 0')
+    if _xi1 < 0:
+        raise ValueError(f'_xi1={_xi1} cannot be less than 0')
+    if Ap < 0:
+        raise ValueError(f'Ap={Ap} cannot be less than 0')
+    if Ac_eff < 0:
+        raise ValueError(f'Ac_eff={Ac_eff} cannot be less than 0')
+
+    return (As + _xi1**2 * Ap) / Ac_eff
+
+
+def kt(load_type: str) -> float:
+    """Returns the kt factor dependent on the load duration for
+    the crack width calculation
+
+    Args:
+        load_type (str): the load type:
+            - 'short' for term loading
+            - 'long' for long term loading
+
+    Returns:
+        float: with the kt factor
+
+    Raises:
+        ValueError: if load_type is not 'short' and not 'long'
+    """
+    if not isinstance(load_type, str):
+        raise TypeError
+
+    load_type = load_type.lower().strip()
+    if load_type != 'short' and load_type != 'long':
+        raise ValueError(
+            f'load_type={load_type} can only have "short" or "long" as a value'
+        )
+
+    return 0.6 if load_type == 'short' else 0.4
+
+
+def esm_ecm(
+    sigma_s: float,
+    _alpha_e: float,
+    _rho_p_eff: float,
+    _kt: float,
+    fct_eff: float,
+    Es: float,
+) -> float:
+    """Returns the strain difference (esm - ecm) needed to compute the crack
+    width. esm is the mean strain in the reinforcement under the relevant
+    combination of loads of imposed deformations and taking into account the
+    effects of tension stiffening. Only the additional tensile strain beyond
+    the state of zero strain of the concrete is considered. ecm is the mean
+    strain in the concrete between the cracks.
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.9)
+
+    Args:
+        sigma_s (float): is the stress in MPa in the tension reinforcement
+            assuming a cracked section. FOr pretensioned members, s_steel may
+            be replaced by increment of s_steel stress variation in
+            prestressing tendons from the state of zero strain of the
+            concrete at the same level.
+        _alpha_e (float): is the ratio Es/Ecm
+        _rho_p_eff (float): effective bond ratio between areas given by the
+            Eq. (7.10)
+        _kt (float): is a factor dependent on the load duration
+        fct_eff (float): is the mean value of the tensile strength in MPa
+            of the concrete effectvie at the time when the cracks may
+            first be expected to occur: fct_eff=fctm or fctm(t) if
+            crack is expected earlier than 28 days.
+        Es: steel elastic mudulus in MPa
+
+    Returns:
+        float: the strain difference between concrete and steel
+
+    Raises:
+        ValueError: if any sigma_s, _alpha_e, _rho_p_eff, fct_eff or Es is less
+            than 0.
+        ValueError: if _kt is not 0.6 and not 0.4
+    """
+    if sigma_s < 0:
+        raise ValueError(f'sigma_s={sigma_s} cannot be less than 0')
+    if _alpha_e < 0:
+        raise ValueError(f'_alpha_e={_alpha_e} cannot be less than 0')
+    if _rho_p_eff < 0:
+        raise ValueError(f'_rho_p_eff={_rho_p_eff} cannot be less than 0')
+    if fct_eff < 0:
+        raise ValueError(f'fct_eff={fct_eff} cannot be less than 0')
+    if Es < 0:
+        raise ValueError(f'Es={Es} cannot be less than 0')
+    if _kt != 0.6 and _kt != 0.4:
+        raise ValueError(f'_kt={_kt} can only take as values 0.4 and 0.6')
+
+    min_val = 0.6 * sigma_s / Es
+
+    a = 1 + _alpha_e * _rho_p_eff
+    b = _kt * fct_eff / _rho_p_eff * a
+    c = (sigma_s - b) / Es
+
+    return max(c, min_val)
+
+
+def w_spacing(c: float, phi: float) -> float:
+    """Computes the distance threshold from which the
+    maximum crack spacing is constant.
+
+    EUROCODE 2 1992-1-1:2004, Sect. (7.3.4-3)
+
+    Args:
+        c (float): cover of  the longitudinal reinforcement in mm
+        phi (float): is the bar diameter in mm. Where mixed bar diameters
+            used, then it should be replaced for an equivalente bar diameter.
+
+    Returns:
+        float: threshold distance in mm
+
+    Raises:
+        ValueError: if any of c or phi is less than 0.
+    """
+    if c < 0:
+        raise ValueError(f'c={c} cannot be less than 0')
+    if phi < 0:
+        raise ValueError(f'phi={phi} cannot be less than 0')
+
+    return 5 * (c + phi / 2)
+
+
+def phi_eq(n1: int, n2: int, phi1: float, phi2: float) -> float:
+    """Computes the equivalent diameter. For a section with n1 bars of
+    diameter phi1 and n2 bars of diameter phi2
+
+    EUROCODE 2 1992-1-1:2004, Sect. (7.12)
+
+    Args:
+        n1 (int): number of bars with diameter phi1
+        n2 (int): number of bars with diameter phi2
+        phi1 (float): diameter of n1 bars in mm
+        phi2 (float): diamater of n2 bars in mm
+
+    Returns:
+        float: the equivalent diameter in mm
+
+    Raises:
+        ValueError: if any of n1 or n2 is less than 0
+        ValueError: if any of phi1 or phi2 is less than 0
+        TypeError: if any of n1 or n2 is not an integer
+    """
+    if n1 < 0:
+        raise ValueError(f'n1={n1} cannot be less than 0')
+    if not isinstance(n1, int):
+        raise TypeError(f'n1={n1} needs to be an integer value')
+    if n2 < 0:
+        raise ValueError(f'n2={n2} cannot be less than 0')
+    if not isinstance(n2, int):
+        raise TypeError(f'n2={n2} needs to be an integer value')
+    if phi1 < 0:
+        raise ValueError(f'phi1={phi1} cannot be less than 0')
+    if phi2 < 0:
+        raise ValueError(f'phi2={phi2} cannot be less than 0')
+
+    a = n1 * phi1**2 + n2 * phi2**2
+    b = n1 * phi1 + n2 * phi2
+    return a / b
+
+
+def k1(bond_type: str) -> float:
+    """Get the k1 coefficient which takes account of the bond properties
+    of the bounded reinforcement
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.11-k1)
+
+    Args:
+        bond_type (str): the bond property of the reinforcement.
+        Possible values:
+            - 'bond': for high bond bars
+            - 'plane': for bars with an effectively plain surface (e.g.
+            prestressing tendons)
+
+    Returns:
+        (float): value of the k1 coefficient
+
+    Raises:
+        ValueError: if bond_type is neither 'bond' nor 'plane'
+        TypeError: if bond_type is not an str
+    """
+    if not isinstance(bond_type, str):
+        raise TypeError(f'bond_type={bond_type} is not an str')
+
+    bond_type = bond_type.lower().strip()
+    if bond_type != 'bond' and bond_type != 'plane':
+        raise ValueError(
+            f'bond_type={bond_type} can only have "bond" or "plane" as values'
+        )
+
+    return 0.8 if bond_type == 'bond' else 1.6
+
+
+def k2(epsilon_r: float) -> float:
+    """Computes a coefficient which takes into account of the
+    distribution of strain:
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.13)
+
+    Args:
+        epsilon_r (float): ratio epsilon_2/epsilon_1 where epsilon_1 is
+            thre greater and epsilon_2 is the lesser strain at the boundaries
+            of the section considererd, assessed on the basis of a cracked
+            section. epsilon_r=0 for bending and epsilon_r=1 for pure tension.
+
+    Returns:
+        float: the k2 coefficient value.
+
+    Raises:
+        ValueError: if epsilon_r is not between 0 and 1.
+    """
+    if epsilon_r < 0 or epsilon_r > 1:
+        raise ValueError(f'epsilon_r={epsilon_r} must be between 0 and 1')
+
+    return (1 + epsilon_r) / 2
+
+
+def k3():
+    """Returns the k3 coefficient for computing sr_max
+
+    Returns:
+        float: value for the coefficient
+    """
+    return 3.4
+
+
+def k4():
+    """Returns the k4 coefficient for computing sr_max
+
+    Returns:
+        float: value for the coefficient
+    """
+    return 0.425
+
+
+def sr_max_close(
+    c: float,
+    phi: float,
+    _rho_p_eff: float,
+    _k1: float,
+    _k2: float,
+    _k3: float,
+    _k4: float,
+) -> float:
+    """Computes the maximum crack spacing in cases where bonded reinforcement
+    is fixed at reasonably close centres within the tension zone
+    (w_spacing<=5(c+phi/2)).
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.11)
+
+    Args:
+        c (float): is the cover in mm of the longitudinal reinforcement
+        phi (float): is the bar diameter in mm. Where mixed bar diameters
+            used, then it should be replaced for an equivalente bar diameter.
+        _rho_p_eff (float): effective bond ratio between areas given by the
+            Eq. (7.10)
+        _k1 (float): coefficient that takes into account the bound properties
+            of the bonded reinforcement
+        _k2 (float): coefficient that takes into account the distribution of
+            of the strain
+        _k3 (float): coefficient from the National Annex
+        _k4 (float): coefficient from the National Annex
+
+    Returns:
+        float: the maximum crack spaing in mm.
+
+    Raises:
+        ValueError: if one or more of c, phi, _rho_p_eff, _k3 or _k4
+            is lower than zero.
+        ValueError: if _k1 is not 0.8 or 1.6
+        ValueError: if _k2 is not between 0.5 and 1.0
+    """
+    if c < 0:
+        raise ValueError(f'c={c} cannot be less than zero')
+    if phi < 0:
+        raise ValueError(f'phi={phi} cannot be less than zero')
+    if _rho_p_eff < 0:
+        raise ValueError(f'_rho_p_eff={_rho_p_eff} cannot be less than zero')
+    if _k3 < 0:
+        raise ValueError(f'_k3={_k3} cannot be less than zero')
+    if _k4 < 0:
+        raise ValueError(f'_k4={_k4} cannot be less than zero')
+    if _k1 != 0.8 and _k1 != 1.6:
+        raise ValueError(f'_k1={_k1} can only take as values 0.8 and 1.6')
+    if _k2 < 0.5 or _k2 > 1:
+        raise ValueError(f'_k2={_k2} is not between 0.5 and 1.0')
+
+    return _k3 * c + _k1 * _k2 * _k4 * phi / _rho_p_eff
+
+
+def sr_max_far(h: float, x: float) -> float:
+    """Computes the maximum crack spacing in cases where bonded reinforcement
+    is fixed at reasonably close centres within the tension zone
+    (w_spacing>5(c+phi/2)).
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.14)
+
+    Args:
+        h (float): total depth of the beam in mm
+        x (float): distance to non tension area of the element mm
+
+    Returns:
+        float: maximum crack spacing in mm
+
+    Raises:
+        ValueError: if one of h or x is less than zero.
+        ValueError: x is greater than h.
+    """
+    if x < 0:
+        raise ValueError(f'x={x} cannot be less than zero')
+    if h < 0:
+        raise ValueError(f'h={h} cannot be less than zero')
+    if x > h:
+        raise ValueError(f'x={x} cannot be larger than h={h}')
+
+    return 1.3 * (h - x)
+
+
+def sr_max_theta(sr_max_y: float, sr_max_z: float, theta: float) -> float:
+    """Computes the crack spacing sr_max when there is an angle
+    between the angle of  principal stress and the direction
+    of the reinforcement, for members in two orthogonal directions,
+    that is significant (> 15º).
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.15)
+
+    Args:
+        sr_max_y (float): crack spacing in mm in the y-direction.
+        sr_max_z (float): crack spacing in mm in the z-direction.
+        theta (float): angle in radians between the reinforcement in the
+            y-direction and the direction of the principal tensile stress.
+
+    Returns:
+        float: the crack spacing in mm.
+
+    Raises:
+        ValueError: if sr_max_y or sr_max_z is negative.
+        ValueError: if theta is not between 0 and pi/2
+    """
+    if sr_max_y < 0:
+        raise ValueError(f'sr_max_y={sr_max_y} cannot be less than zero')
+    if sr_max_z < 0:
+        raise ValueError(f'sr_max_z={sr_max_z} cannot be less than zero')
+
+    a = math.cos(theta) / sr_max_y
+    b = math.sin(theta) / sr_max_z
+    return 1 / (a + b)
+
+
+def wk(sr_max: float, _esm_ecm: float) -> float:
+    """Computes the crack width
+
+    EUROCODE 2 1992-1-1:2004, Eq. (7.8)
+
+    Args:
+        sr_max (float): the maximum crack length spacing in mm.
+        _esm_ecm (float): the difference between the mean strain in the
+            reinforcement under relevant combination of loads, including
+            the effect of imposed deformations and taking into account
+            tension stiffening and the mean strain in the concrete
+            between cracks.
+
+    Returns:
+        float: crack width in mm.
+
+    Raises:
+        ValueError: if any of sr_max or esm_ecm is less than zero.
+    """
+    if sr_max < 0:
+        raise ValueError(f'sr_max={sr_max} cannot be less than zero')
+    if _esm_ecm < 0:
+        raise ValueError(f'_esm_scm={_esm_ecm} cannot be less than zero')
+
+    return sr_max * _esm_ecm
diff --git a/tests/test_ec2_2004_crack_control.py b/tests/test_ec2_2004_section_7_3_crack_control.py
similarity index 83%
rename from tests/test_ec2_2004_crack_control.py
rename to tests/test_ec2_2004_section_7_3_crack_control.py
index 66e8adeb..54b14fc7 100644
--- a/tests/test_ec2_2004_crack_control.py
+++ b/tests/test_ec2_2004_section_7_3_crack_control.py
@@ -2,7 +2,7 @@
 import math
 
 import pytest
-from structuralcodes.codes.ec2_2004 import _crack_control
+from structuralcodes.codes.ec2_2004 import _section_7_3_crack_control
 
 
 @pytest.mark.parametrize(
@@ -40,7 +40,9 @@ def test_w_max_returns_expected_values(
     test_exposure_class, test_load_combination, expected
 ):
     """Test that the w_max function returns expected values"""
-    w_max = _crack_control.w_max(test_exposure_class, test_load_combination)
+    w_max = _section_7_3_crack_control.w_max(
+        test_exposure_class, test_load_combination
+    )
     assert w_max == expected
 
 
@@ -53,7 +55,9 @@ def test_w_max_not_valid_input_raises_valueerror(
 ):
     """Test that not valid input returns ValueError"""
     with pytest.raises(ValueError):
-        _crack_control.w_max(test_exposure_class, test_load_combination)
+        _section_7_3_crack_control.w_max(
+            test_exposure_class, test_load_combination
+        )
 
 
 @pytest.mark.parametrize(
@@ -71,7 +75,7 @@ def test_w_max_not_valid_input_raises_valueerror(
 )
 def test_k_crack_min_steel_area_returns_expected_values(h, expected):
     """Test the k_crack_min_steel_area function"""
-    k = _crack_control.k_crack_min_steel_area(h)
+    k = _section_7_3_crack_control.k(h)
     assert math.isclose(k, expected)
 
 
@@ -79,12 +83,12 @@ def test_k_crack_min_steel_area_raises_valueerror():
     """Test that not valid input returns ValueError exeption"""
     with pytest.raises(ValueError):
         h = -100
-        _crack_control.k_crack_min_steel_area(h)
+        _section_7_3_crack_control.k(h)
 
 
 def test_kc_crack_min_steel_area_pure_tension_returns_expected_values():
     """Test the kc_crack_min_steel_area_pure_tension function"""
-    assert 1 == _crack_control.kc_crack_min_steel_area_pure_tension()
+    assert 1 == _section_7_3_crack_control.kc_tension()
 
 
 @pytest.mark.parametrize(
@@ -101,7 +105,7 @@ def test_kc_crack_min_steel_area_rectangular_returns_expected_values(
     h, b, fct_eff, n_ed, expected
 ):
     """Test the kc_crack_min_steel_area_rectangular"""
-    kc = _crack_control.kc_crack_min_steel_area_rectangular(
+    kc = _section_7_3_crack_control.kc_rect_area(
         h,
         b,
         fct_eff,
@@ -114,11 +118,11 @@ def test_kc_crack_min_steel_area_rectangular_raises_valueerror():
     """Test the kc_crack_min_steel_area_rectangular raises Value
     Error for not correct input values for b and h"""
     with pytest.raises(ValueError):
-        _crack_control.kc_crack_min_steel_area_rectangular(
-            h=-100, b=100, fct_eff=100, n_ed=10
+        _section_7_3_crack_control.kc_rect_area(
+            h=-100, b=100, fct_eff=100, N_ed=10
         )
-        _crack_control.kc_crack_min_steel_area_rectangular(
-            h=100, b=-100, fct_eff=100, n_ed=10
+        _section_7_3_crack_control.kc_rect_area(
+            h=100, b=-100, fct_eff=100, N_ed=10
         )
 
 
@@ -133,7 +137,7 @@ def test_kc_crack_min_steel_area_rectangular_raises_valueerror():
 )
 def test_kc_crack_min_steel_area_flanges(f_cr, a_ct, fct_eff, expected):
     """Test the kc_crack_min_steel_area_flanges function"""
-    kc = _crack_control.kc_crack_min_steel_area_flanges(f_cr, a_ct, fct_eff)
+    kc = _section_7_3_crack_control.kc_flanges_area(f_cr, a_ct, fct_eff)
     assert math.isclose(kc, expected, rel_tol=0.000001)
 
 
@@ -145,11 +149,11 @@ def test_kc_crack_min_steel_area_flanges(f_cr, a_ct, fct_eff, expected):
         (80000, 400, 4, 0.9, 0.75, 540),
     ],
 )
-def test_crack_min_steel_area_returns_expected_values(
+def test_As_min_returns_expected_values(
     a_ct, s_steel, fct_eff, k, kc, expected
 ):
-    """Test the crack_min_steel_area returns expected values"""
-    as_min = _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
+    """Test the As_min returns expected values"""
+    as_min = _section_7_3_crack_control.As_min(a_ct, s_steel, fct_eff, k, kc)
     assert math.isclose(as_min, expected, rel_tol=10e-6)
 
 
@@ -164,10 +168,10 @@ def test_crack_min_steel_area_returns_expected_values(
         (10000, 100, 3, 0.7, 1.1),
     ],
 )
-def test_crack_min_steel_area_raises_valueerror(a_ct, s_steel, fct_eff, k, kc):
-    """Test the crack_min_steel_area raises value error"""
+def test_crack_As_min_raises_valueerror(a_ct, s_steel, fct_eff, k, kc):
+    """Test the As_min raises value error"""
     with pytest.raises(ValueError):
-        _crack_control.crack_min_steel_area(a_ct, s_steel, fct_eff, k, kc)
+        _section_7_3_crack_control.As_min(a_ct, s_steel, fct_eff, k, kc)
 
 
 @pytest.mark.parametrize(
@@ -195,7 +199,7 @@ def test_crack_min_steel_area_with_press_tendons_returns_expected_values(
     expected,
 ):
     """Test the crack_min_steel_area returns expected values"""
-    as_min = _crack_control.crack_min_steel_area_with_prestresed_tendons(
+    as_min = _section_7_3_crack_control.As_min_p(
         a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
     )
     assert math.isclose(as_min, expected, rel_tol=10e-6)
@@ -222,7 +226,7 @@ def test_crack_min_steel_area_with_press_tendons_raise_valueerror(
 ):
     """Test the crack_min_steel_area raise ValueError for non valid values"""
     with pytest.raises(ValueError):
-        _crack_control.crack_min_steel_area_with_prestresed_tendons(
+        _section_7_3_crack_control.As_min_p(
             a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
         )
 
@@ -249,7 +253,7 @@ def test_crack_min_steel_without_direct_calculation_returns_expected_values(
     exp_sep,
 ):
     """Test the crack_min_steel_area raise ValueError for non valid values"""
-    phi, sep = _crack_control.crack_min_steel_without_direct_calculation(
+    phi, sep = _section_7_3_crack_control.As_min_2(
         wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
     )
     assert math.isclose(phi, exp_phi, rel_tol=10e-6)
@@ -274,7 +278,7 @@ def test_crack_min_steel_without_direct_calculation_raise_valueerror(
 ):
     """Test the crack_min_steel_area raise ValueError for non valid values"""
     with pytest.raises(ValueError):
-        _crack_control.crack_min_steel_without_direct_calculation(
+        _section_7_3_crack_control.As_min_2(
             wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
         )
 
@@ -292,7 +296,7 @@ def test_adjusted_bond_length_return_expected_values(
 ):
     """Test the adjusted_bond_length_function returns expected values"""
     assert math.isclose(
-        _crack_control.adjusted_bond_strength(e, d_press, d_steel),
+        _section_7_3_crack_control.xi1(e, d_press, d_steel),
         expected,
         rel_tol=10e-5,
     )
@@ -311,7 +315,7 @@ def test_adjusted_bond_length_return_expected_values(
 def test_adjusted_bond_length_raise_valuerror(e, d_press, d_steel):
     """Test the adjusted_bond_length_function raises exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.adjusted_bond_strength(e, d_press, d_steel)
+        _section_7_3_crack_control.xi1(e, d_press, d_steel)
 
 
 @pytest.mark.parametrize(
@@ -325,7 +329,7 @@ def test_adjusted_bond_length_raise_valuerror(e, d_press, d_steel):
 def test_hc_eff_concrete_tension_returns_expected_values(h, d, x, expected):
     """Test the hc_eff_concrete_tension returns expected results"""
     assert math.isclose(
-        _crack_control.hc_eff_concrete_tension(h, d, x),
+        _section_7_3_crack_control.hc_eff(h, d, x),
         expected,
         rel_tol=10e-5,
     )
@@ -344,7 +348,7 @@ def test_hc_eff_concrete_tension_returns_expected_values(h, d, x, expected):
 def test_hc_eff_concrete_tension_raise_exceptions(h, d, x):
     """Test hc_eff_concrete tension raises expected exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.hc_eff_concrete_tension(h, d, x)
+        _section_7_3_crack_control.hc_eff(h, d, x)
 
 
 @pytest.mark.parametrize(
@@ -356,7 +360,7 @@ def test_hc_eff_concrete_tension_raise_exceptions(h, d, x):
 def test_alpha_e_returns_expected_values(es, ecm, expected):
     """Test alpha_e returns expected values"""
     assert math.isclose(
-        _crack_control.get_alpha_e(es, ecm),
+        _section_7_3_crack_control.alpha_e(es, ecm),
         expected,
         rel_tol=10e-5,
     )
@@ -372,7 +376,7 @@ def test_alpha_e_returns_expected_values(es, ecm, expected):
 def test_alpha_e_raise_exceptions(es, ecm):
     """Test alpha_e raises exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.get_alpha_e(es, ecm)
+        _section_7_3_crack_control.alpha_e(es, ecm)
 
 
 @pytest.mark.parametrize(
@@ -385,7 +389,7 @@ def test_alpha_e_raise_exceptions(es, ecm):
 def test_rho_p_eff_returns_expected_values(a_s, e1, a_p, ac_eff, expected):
     """Test rho_p_eff returns expeceted values"""
     assert math.isclose(
-        _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff),
+        _section_7_3_crack_control.rho_p_eff(a_s, e1, a_p, ac_eff),
         expected,
         rel_tol=10e-5,
     )
@@ -403,7 +407,7 @@ def test_rho_p_eff_returns_expected_values(a_s, e1, a_p, ac_eff, expected):
 def test_rho_p_eff_raise_value_error(a_s, e1, a_p, ac_eff):
     """Test rho_p_eff raise exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.rho_p_eff(a_s, e1, a_p, ac_eff)
+        _section_7_3_crack_control.rho_p_eff(a_s, e1, a_p, ac_eff)
 
 
 @pytest.mark.parametrize(
@@ -415,16 +419,16 @@ def test_rho_p_eff_raise_value_error(a_s, e1, a_p, ac_eff):
 )
 def test_kt_load_duration_returns_expected_values(load_type, expected):
     """Test kt_load_duration returns expected values"""
-    assert _crack_control.kt_load_duration(load_type) == expected
+    assert _section_7_3_crack_control.kt(load_type) == expected
 
 
 def test_kt_load_duration_raise_value_errors():
     """Test kt_load_duration raise value errors"""
     with pytest.raises(TypeError):
-        _crack_control.kt_load_duration(load_type=123)
+        _section_7_3_crack_control.kt(load_type=123)
 
     with pytest.raises(ValueError):
-        _crack_control.kt_load_duration(load_type='asdf')
+        _section_7_3_crack_control.kt(load_type='asdf')
 
 
 @pytest.mark.parametrize(
@@ -440,7 +444,9 @@ def test_esm_ecm_returns_expected_values(
 ):
     """Test esm_ecm returns the expected values"""
     assert math.isclose(
-        _crack_control.esm_ecm(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es),
+        _section_7_3_crack_control.esm_ecm(
+            s_steel, alpha_e, rho_p_eff, kt, fct_eff, es
+        ),
         expected,
         abs_tol=10e-5,
     )
@@ -457,10 +463,19 @@ def test_esm_ecm_returns_expected_values(
         (250, 5.25, 0.34, 0.2, 2.9, 210000),
     ],
 )
-def test_esm_ecm_raises_exception(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es):
+def test_esm_ecm_raises_exception(
+    s_steel,
+    alpha_e,
+    rho_p_eff,
+    kt,
+    fct_eff,
+    es,
+):
     """Test esm_ecm raise expected exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.esm_ecm(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es)
+        _section_7_3_crack_control.esm_ecm(
+            s_steel, alpha_e, rho_p_eff, kt, fct_eff, es
+        )
 
 
 @pytest.mark.parametrize(
@@ -473,7 +488,7 @@ def test_esm_ecm_raises_exception(s_steel, alpha_e, rho_p_eff, kt, fct_eff, es):
 def test_s_returns_expected_returns(c, phi, expected):
     """Test s returns expected results"""
     assert math.isclose(
-        _crack_control.s_threshold(c, phi),
+        _section_7_3_crack_control.w_spacing(c, phi),
         expected,
         rel_tol=10e-5,
     )
@@ -489,7 +504,7 @@ def test_s_returns_expected_returns(c, phi, expected):
 def test_s_raise_expected_exceptions(c, phi):
     """Test s raise expected exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.s_threshold(c, phi)
+        _section_7_3_crack_control.w_spacing(c, phi)
 
 
 @pytest.mark.parametrize(
@@ -499,7 +514,7 @@ def test_s_raise_expected_exceptions(c, phi):
 def test_phi_eq_returns_expected_results(n1, n2, phi1, phi2, expected):
     """Test phi_eq returns expected results"""
     assert math.isclose(
-        _crack_control.phi_eq(n1, n2, phi1, phi2),
+        _section_7_3_crack_control.phi_eq(n1, n2, phi1, phi2),
         expected,
         rel_tol=10e-5,
     )
@@ -519,7 +534,7 @@ def test_phi_eq_returns_expected_results(n1, n2, phi1, phi2, expected):
 def test_phi_eq_raises_expected_values(n1, n2, phi1, phi2, exception_type):
     """Test phi_eq raises expected exception"""
     with pytest.raises(exception_type):
-        _crack_control.phi_eq(n1, n2, phi1, phi2)
+        _section_7_3_crack_control.phi_eq(n1, n2, phi1, phi2)
 
 
 @pytest.mark.parametrize(
@@ -528,7 +543,7 @@ def test_phi_eq_raises_expected_values(n1, n2, phi1, phi2, exception_type):
 )
 def test_k1_returns_expected_values(bond_type, expected):
     """Test k1 returns expected values"""
-    assert _crack_control.k1(bond_type) == expected
+    assert _section_7_3_crack_control.k1(bond_type) == expected
 
 
 @pytest.mark.parametrize(
@@ -538,7 +553,7 @@ def test_k1_returns_expected_values(bond_type, expected):
 def test_k1_raise_expected_exceptions(bond_type, exception_type):
     """Test k1 raises expected exceptions"""
     with pytest.raises(exception_type):
-        _crack_control.k1(bond_type)
+        _section_7_3_crack_control.k1(bond_type)
 
 
 @pytest.mark.parametrize(
@@ -548,7 +563,7 @@ def test_k1_raise_expected_exceptions(bond_type, exception_type):
 def test_k2_returns_expected_values(epsilon_r, expected):
     """Test k2 returns expected values"""
     assert math.isclose(
-        _crack_control.k2(epsilon_r),
+        _section_7_3_crack_control.k2(epsilon_r),
         expected,
         rel_tol=10e-5,
     )
@@ -558,17 +573,17 @@ def test_k2_returns_expected_values(epsilon_r, expected):
 def test_k2_raises_value_exceptions(epsilon_r):
     """Test k2 raises expected exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.k2(epsilon_r)
+        _section_7_3_crack_control.k2(epsilon_r)
 
 
 def test_k3_returns_expected_values():
     """Test k3 returns the expected values"""
-    assert _crack_control.k3() == 3.4
+    assert _section_7_3_crack_control.k3() == 3.4
 
 
 def test_k4_returns_expected_values():
     """Test k4 returns the expected values"""
-    assert _crack_control.k4() == 0.425
+    assert _section_7_3_crack_control.k4() == 0.425
 
 
 @pytest.mark.parametrize(
@@ -582,7 +597,9 @@ def test_k4_returns_expected_values():
 def test_sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4, expected):
     """Test sr_max_close returns the expected values"""
     assert math.isclose(
-        _crack_control.sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4),
+        _section_7_3_crack_control.sr_max_close(
+            c, phi, rho_p_eff, k1, k2, k3, k4
+        ),
         expected,
         rel_tol=10e-5,
     )
@@ -606,7 +623,9 @@ def test_sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4, expected):
 def test_sr_max_close_raises_exceptions(c, phi, rho_p_eff, k1, k2, k3, k4):
     """Test sr_max_close raises the expected value errors"""
     with pytest.raises(ValueError):
-        _crack_control.sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4)
+        _section_7_3_crack_control.sr_max_close(
+            c, phi, rho_p_eff, k1, k2, k3, k4
+        )
 
 
 @pytest.mark.parametrize(
@@ -620,7 +639,7 @@ def test_sr_max_close_raises_exceptions(c, phi, rho_p_eff, k1, k2, k3, k4):
 def test_sr_max_far_returns_expected_values(h, x, expected):
     """Test sr_max_far returns the expected values"""
     assert math.isclose(
-        _crack_control.sr_max_far(h, x), expected, rel_tol=10e-5
+        _section_7_3_crack_control.sr_max_far(h, x), expected, rel_tol=10e-5
     )
 
 
@@ -635,7 +654,7 @@ def test_sr_max_far_returns_expected_values(h, x, expected):
 def test_sr_max_far_raises_exceptions(h, x):
     """Test sr_max_far raises exceptions"""
     with pytest.raises(ValueError):
-        _crack_control.sr_max_far(h, x)
+        _section_7_3_crack_control.sr_max_far(h, x)
 
 
 @pytest.mark.parametrize(
@@ -651,7 +670,7 @@ def test_sr_max_theta_returns_expected_values(
 ):
     """Test sr_max_theta returns expeceted values"""
     assert math.isclose(
-        _crack_control.sr_max_theta(sr_max_y, sr_max_z, theta),
+        _section_7_3_crack_control.sr_max_theta(sr_max_y, sr_max_z, theta),
         expected,
         rel_tol=10e-5,
     )
@@ -668,7 +687,7 @@ def test_sr_max_theta_returns_expected_values(
 def test_sr_max_theta_raises_exceptions(sr_max_y, sr_max_z, theta):
     """Test sr_max_theta raises value errors"""
     with pytest.raises(ValueError):
-        _crack_control.sr_max_theta(sr_max_y, sr_max_z, theta)
+        _section_7_3_crack_control.sr_max_theta(sr_max_y, sr_max_z, theta)
 
 
 @pytest.mark.parametrize(
@@ -681,7 +700,7 @@ def test_sr_max_theta_raises_exceptions(sr_max_y, sr_max_z, theta):
 def test_wk_returns_expected_values(sr_max, esm_ecm, expected):
     """Test wk returns expected values"""
     assert math.isclose(
-        _crack_control.wk(sr_max, esm_ecm),
+        _section_7_3_crack_control.wk(sr_max, esm_ecm),
         expected,
         rel_tol=10e-5,
     )
@@ -694,4 +713,4 @@ def test_wk_returns_expected_values(sr_max, esm_ecm, expected):
 def test_wk_raises_exceptions(sr_max, esm_ecm: float):
     """Test wk raises value errors"""
     with pytest.raises(ValueError):
-        _crack_control.wk(sr_max, esm_ecm)
+        _section_7_3_crack_control.wk(sr_max, esm_ecm)

From 938c0f5807cf6a12a11a0ddc8f004422172a8d4c Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Fri, 13 Jan 2023 14:33:29 +0100
Subject: [PATCH 16/33] removed duplicate file

---
 .../codes/ec2_2004/_section_7.3.py            | 935 ------------------
 1 file changed, 935 deletions(-)
 delete mode 100644 structuralcodes/codes/ec2_2004/_section_7.3.py

diff --git a/structuralcodes/codes/ec2_2004/_section_7.3.py b/structuralcodes/codes/ec2_2004/_section_7.3.py
deleted file mode 100644
index 3ad05170..00000000
--- a/structuralcodes/codes/ec2_2004/_section_7.3.py
+++ /dev/null
@@ -1,935 +0,0 @@
-"""Collection of functions from EUROCODE 1992-1-1:2004
-Chapter 7.3 - Crack control"""
-import math
-import typing as t
-
-import numpy as np
-import scipy.interpolate
-
-
-def w_max(exposure_class: str, load_combination: str) -> float:
-    """Computes the recomended value of the maximum crack width.
-
-    EUROCODE 2 1992-1-1:2004, Table (7.1N)
-
-    Args:
-        exposure_class (str): The exposure class.
-            Possible values: X0, XC1, XC2, XC3, XC4, XD1, XD2, XS1, XS2, XS3
-        load_combination (str):
-            - f: for frequent load combination
-            - qp: for quasi-permanent load combination
-
-    Returns:
-        float: The maximum recommended value for the crack width wmax in mm.
-
-    Raises:
-        ValueError: if not valid exposure_class or load_combination values.
-    """
-    _load_combination = load_combination.lower().strip()
-    _exposure_class = exposure_class.upper().strip()
-    if _load_combination == 'f':
-        if _exposure_class in ('X0', 'XC1'):
-            return 0.2
-        if _exposure_class in ('XC2', 'XC3', 'XC4'):
-            return 0.2
-    if _load_combination == 'qp':
-        if _exposure_class in ('X0', 'XC1'):
-            return 0.4
-        if _exposure_class in (
-            'XC2',
-            'XC3',
-            'XC4',
-            'XD1',
-            'XD2',
-            'XS1',
-            'XS2',
-            'XS3',
-        ):
-            return 0.3
-        raise ValueError(
-            f'{exposure_class} is not a valid value for exposure_class.'
-            + ' Please enter one of the following: X0, XC1, XC2, XC3, XC4, XD1'
-            + ',XD2, XS1, XS2, XS3'
-        )
-    raise ValueError(
-        f'{load_combination} is not a valid value for load_combination.'
-        + 'Please enter "f" for frequent load combination or "qp" for'
-        + 'quasi-permanent load combination.'
-    )
-
-
-def As_min(
-    A_ct: float, sigma_s: float, fct_eff: float, _k: float, kc: float
-) -> float:
-    """Computes the minimum area of reinforcing steel within the tensile zone
-    for control of cracking areas
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
-
-    Args:
-        A_ct (float): is the area of concrete within the tensile zone in mm2.
-            The tensile zone is that parg of the section which is calculated
-            to be in tension just before the formation of the first crack.
-        sigma_s (float): is the absolute value of the maximum stress in MPa
-            permitted in the reinforcement immediately after the formation
-            of the crack. This may be taken as theyield strength of the
-            reinforcement, fyk. A lower value may, however, be needed to
-            satisfy the crack width limits according to the maximum
-            bar size of spacing (see 7.3.3 (2)).
-        fct_eff (float): is the mean value of the tensile strength in MPa of
-            the concrete effective at the time when the cracks may first be
-            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
-            is expected earlier than 28 days.
-        _k (float): is the coefficient which allow for the effect of
-            non-uniform self-equilibrating stresses, which lead to a
-            reduction of restraint forces. Use 'k_crack_min_steel_area'
-            to compute it
-            k=1 for webs w<=300mm or flanges widths less than 300mm
-            k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
-            Intermediate values may be interpolated.
-        kc (float): is a coefficient which takes account of the stress
-            distribution within the section immediately prior to cracking and
-            the change of the lever arm.
-
-    Returns:
-        float: the minimm area of reinforcing steel within the tensile
-            zone in mm2.
-
-    Raises:
-        ValueError: if _k value is not between 0.65 and 1 or kc is not
-            larger than 0 and lower than 1.
-    """
-    fct_eff = abs(fct_eff)
-
-    if A_ct <= 0:
-        raise ValueError(f'A_ct={A_ct} must be larger than 0')
-    if sigma_s < 0:
-        raise ValueError(f'sigma_s={sigma_s} must be equal or larger than 0')
-    if _k < 0.65 or _k > 1.0:
-        raise ValueError(f'_k={_k} must be between 0.65 and 1')
-    if kc > 1 or kc < 0:
-        raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
-
-    return kc * _k * fct_eff * A_ct / sigma_s
-
-
-def k(h: float) -> float:
-    """Is the coefficient which allow for the effect of
-    non-uniform self-equilibrating stresses, which lead to a
-    reduction of restraint forces.
-    k=1 for webs w<=300mm or flanges widths less than 300mm
-    k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
-
-    Args:
-        h (float): flange length or flange width in mm
-
-    Returns:
-        float: k coefficient value
-
-    Raises:
-        ValueError: if h is less than 0
-    """
-    if h < 0:
-        raise ValueError(f'h={h} cannot be less than 0mm')
-    if h <= 300:
-        return 1
-    if h < 800:
-        interpol = scipy.interpolate.interp1d((300, 800), (1, 0.65))
-        return (float)(interpol(h))
-    return 0.65
-
-
-def kc_pure_tension() -> float:
-    """Computes the coefficient which takes account of the stress
-    distribution within the section immediately prior to cracking and
-    the change of the lever arm in pure dtension.
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
-
-    Returns:
-        float: value of the kc coefficient in pure tension
-    """
-    return 1
-
-
-def kc_rectangular_area(
-    h: float, b: float, fct_eff: float, N_ed: float
-) -> float:
-    """Computes the coefficient which takes account of the stress
-    distribution within the section immediately prior to cracking and
-    the change of the lever arm for bending+axial combination
-    in rectangular sections and webs of box sections and T-sections.
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.2)
-
-    Args:
-        h (float): heigth of the element in mm
-        b (float): width of the element in mm
-        fct_eff (float): is the mean value of the tensile strength in MPa of
-            the concrete effective at the time when the cracks may first be
-            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
-            is expected earlier than 28 days.
-        N_ed (str): axial force at the serviceability limit state acting on
-            the part of the cross-section under consideration (compressive
-            force positive). n_ed should be determined considering the
-            characteristic values of prestress and axial forces under the
-            relevant combination of actions
-
-    Returns:
-        float: value of the kc coefficient
-
-    Raises:
-        ValueError: is h or b are less than 0
-    """
-    if h < 0:
-        raise ValueError(f'h={h} should be larger than 0mm')
-    if b < 0:
-        raise ValueError(f'b={b} should be larger than 0mm')
-
-    h_s = min(h, 1000)
-    _k1 = 1.5 if N_ed >= 0 else 2 * h_s / 3 / h
-    s_concrete = N_ed * 1000 / b / h
-    h_ratio = h / h_s
-    return min(max(0.4 * (1 - s_concrete / _k1 / h_ratio / fct_eff), 0), 1)
-
-
-def kc_flanges_area(f_cr: float, A_ct: float, fct_eff: float) -> float:
-    """Computes the coefficient which takes account of the stress
-    distribution within the section immediately prior to cracking and
-    the change of the lever arm for bending+axial combination
-    in rectangular sections for flanges of box sections and T-sections.
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.3)
-
-    Args:
-        f_cr: is the absolute value in kN of the tensile force within the
-            flange immediately prior to cracking due to cracking moment
-            calculated with fct,eff
-        A_ct (float): is the area of concrete within the tensile zone in mm2.
-            The tensile zone is that part of the section which is calculated
-            to be in tension just before the formation of the first crack.
-        fct_eff (float): is the mean value of the tensile strength in MPa of
-            the concrete effective at the time when the cracks may first be
-            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
-            is expected earlier than 28 days.
-
-    Returns:
-        float: value of the kc coefficient
-
-    Raises:
-        ValueError: is A_ct is less than 0mm2
-    """
-    f_cr = abs(f_cr)
-    return max(0.9 * f_cr * 1000 / A_ct / fct_eff, 0.5)
-
-
-def xi_1(xi: float, phi_p: float, phi_s: float) -> float:
-    """Computes the adjusted ratio of bond strength taking into account
-    the different diameters of prestressing and reinforcing steel.
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.5)
-
-    Args:
-        xi (float): ratio of bond strength of prestressing and reinforcing
-            steel, according to Table 6.2 in 6.8.2
-        phi_p (float): largest bar diameter in mm of reinforcing steel.
-            Equal to 0 if only prestressing is used in control cracking
-        phi_s (float): equivalent diameter in mm of tendon acoording
-            to 6.8.2
-
-    Returns:
-        float: with the value of the ratio
-
-    Raises:
-        ValueError: if diameters phi_s or phi_p are lower than 0.
-            If ratio of bond strength xi is less than 0.15 or larger than 0.8.
-    """
-
-    if phi_p <= 0:
-        raise ValueError(f'phi_p={phi_p} cannot be less than 0')
-    if phi_s < 0:
-        raise ValueError(f'phi_s={phi_s} cannot be less than 0')
-    if xi < 0.15:
-        raise ValueError(f'The minimum value for xi={xi} is 0.15')
-    if xi > 0.8:
-        raise ValueError(f'The maximum value for xi={xi} is 0.8')
-
-    return ((xi * phi_s / phi_p) ** 0.5) if phi_s > 0 else xi**0.5
-
-
-def hc_eff(h: float, d: float, x: float) -> float:
-    """Returns the effective height of concrete in tension surrounding
-    the reinforcement or prestressing tendons.
-
-    EUROCODE 2 1992-1-1:2004, Section (7.3.2-3)
-
-    Args:
-        h (float): total depth of the element in mm
-        d (float): distance in mm to the level of the steel centroid
-        x (float): distance in mm to the zero tensile stress line
-
-    Returns:
-        float: the effective height in mm
-
-    Raises:
-        ValueError: if any of h, d or x is lower than zero.
-        ValueError: if d is greater than h
-        ValueError: if x is greater than h
-    """
-    if h < 0:
-        raise ValueError(f'h={h} cannot be less than 0')
-    if d < 0:
-        raise ValueError(f'd={d} cannot be less than 0')
-    if x < 0:
-        raise ValueError(f'x={x} cannot be less than zero')
-    if d > h:
-        raise ValueError(f'd={d} cannot be larger than h={h}')
-    if x > h:
-        raise ValueError(f'x={x} cannot be larger than h={h}')
-
-    return min(2.5 * (h - d), (h - x) / 3, h / 2)
-
-
-def As_min_p(
-    A_ct: float,
-    sigma_s: float,
-    fct_eff: float,
-    _k: float,
-    kc: float,
-    Ap: float,
-    phi_s: float,
-    phi_p: float,
-    xi: float,
-    delta_s: float,
-) -> float:
-    """Computes the minimum area of reinforcing steel within the tensile zone
-    for control of cracking areas in addition with bonded tendons
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.1)
-
-    Args:
-        A_ct (float): is the area of concrete within the tensile zone in mm2.
-            The tensile zone is that part of the section which is calculated
-            to be in tension just before the formation of the first crack.
-        sigma_s (float): is the absolute value of the maximum stress in MPa
-            permitted in the reinforcement immediately after the formation
-            of the crack. This may be taken as theyield strength of the
-            reinforcement, fyk. A lower value may, however, be needed to
-            satisfy the crack width limits according to the maximum
-            bar size of spacing (see 7.3.3 (2)).
-        fct_eff (float): is the mean value of the tensile strength in MPa of
-            the concrete effective at the time when the cracks may first be
-            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
-            is expected earlier than 28 days.
-        _k (float): is the coefficient which allow for the effect of
-            non-uniform self-equilibrating stresses, which lead to a
-            reduction of restraint forces. Use 'k_crack_min_steel_area'
-            to compute it
-            k=1 for webs w<=300mm or flanges widths less than 300mm
-            k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
-            Intermediate values may be interpolated.
-        kc (float): is a coefficient which takes account of the stress
-            distribution within the section immediately prior to cracking and
-            the change of the lever arm.
-        Ap (float): is the area in mm2 of pre or post-tensioned tendons
-            within ac_eff
-        phi_s (float): largest bar diameter in mm of reinforcing steel.
-            Equal to 0 if only prestressing is used in control cracking
-        phi_p (float): equivalent diameter in mm of tendon acoording
-            to 6.8.2
-        chi (float): ratio of bond strength of prestressing and reinforcing
-            steel, according to Table 6.2 in 6.8.2
-        delta_s (float): stress variation in MPa in prestressing tendons
-            from the state of zero strain of the concrete at the same level
-
-    Returns:
-        float: the minimm area of reinforcing steel within the tensile
-            zone in mm2.
-
-    Raises:
-        ValueError: if _k value is not between 0.65 and 1 or kc is not
-            larger than 0 and lower than 1. If diameters phi_s or
-            phi_p are lower than 0. If ratio of bond xi strength e
-            is less than 0.15 or larger than 0.8.
-            Is stress variation incr_stress is less than 0.
-    """
-    fct_eff = abs(fct_eff)
-
-    if Ap < 0:
-        raise ValueError(f'Ap={Ap} cannot be less than 0')
-    if delta_s < 0:
-        raise ValueError(f'delta_s={delta_s} cannot be less than 0')
-    if A_ct <= 0:
-        raise ValueError(f'A_ct={A_ct} must be larger than 0')
-    if sigma_s < 0:
-        raise ValueError(f'sigma_s={sigma_s} must be equal or larger than 0')
-    if _k < 0.65 or _k > 1.0:
-        raise ValueError(f'_k={_k} must be between 0.65 and 1')
-    if kc > 1 or kc < 0:
-        raise ValueError(f'kc={kc} must be lower than 1 and larger than 0')
-
-    a1 = kc * _k * fct_eff * A_ct
-    e1 = xi_1(xi, phi_p, phi_s)
-    a2 = e1 * Ap * delta_s
-    a = a1 - a2
-
-    return a / sigma_s
-
-
-def As_min_2(
-    _wk: float,
-    sigma_s: float,
-    fct_eff: float,
-    h_cr: float,
-    h: float,
-    d: float,
-    delta_s: float = 0,
-    kc: t.Optional[float] = None,
-) -> t.Tuple[float, float]:
-    """Computes the minimum area of reinforcing steel within the tensile zone
-    for control of cracking areas
-
-    EUROCODE 2 1992-1-1:2004, Table (7.2N), Table (7.3N)
-
-    Args:
-        _wk (float): the characteristic crack width value in mm.
-        sigma_s (float): the steel stress value in MPa under the relevant
-            combination of actions.
-        fct_eff (float): is the mean value of the tensile strength in MPa of
-            the concrete effective at the time when the cracks may first be
-            expected to occur: fct,eff=fct or lower (fct(t)), is cracking
-            is expected earlier than 28 days.
-        h_cr (float): is the depth of the tensile zone immediately prior to
-            cracking, considering the characteristic values of prestress and
-            axial forces under the quasi-permanent combination of actions.
-        h (float): the overall depth of the section in mm.
-        d (float): is the effective depth to the centroid of the outer layer
-            of the reinforcement.
-        delta_s (float, optional): value of prestressed stress in MPa if
-            applicable
-        kc (float, optional): is a coefficient which takes account of the
-            stress distribution within the section immediately prior to
-            cracking and the change of the lever arm in a bending section.
-            'None' for pure tensile uniform axial section.
-
-    Returns:
-        tuple(float, float): with the value of the maximum bar diameters in mm
-        in the first position and the maximum bar spacing in mm in the
-        second position
-    Raises:
-        ValueError: if _wk, fct_eff, h_cr, h or d are less than 0
-        ValueError: if kc is not between 0 and 1
-        ValueError: if combination of wk and stress values are out of scope
-    """
-    if _wk < 0:
-        raise ValueError(f'_wk={_wk} cannot be less than 0')
-    if fct_eff < 0:
-        raise ValueError(f'fct_eff={fct_eff} is less than 0')
-    if h_cr < 0:
-        raise ValueError(f'h_cr={h_cr} is less than 0')
-    if h < 0:
-        raise ValueError(f'h={h} is less than 0')
-    if d < 0:
-        raise ValueError(f'd={d} is less than 0')
-    if kc is not None and (kc < 0 or kc > 1):
-        raise ValueError(f'kc={kc} is not between 0 and 1')
-
-    s = sigma_s - delta_s
-    if s <= 0:
-        return (0, 0)
-
-    x = (0.4, 0.3, 0.2)
-    y_phi = (160, 200, 240, 280, 320, 360, 400, 450)
-    y_spa = (160, 200, 240, 280, 320, 360)
-    phi_s_v = (
-        40,
-        32,
-        25,
-        32,
-        25,
-        16,
-        20,
-        16,
-        12,
-        16,
-        12,
-        8,
-        12,
-        10,
-        6,
-        10,
-        8,
-        5,
-        8,
-        6,
-        4,
-        6,
-        5,
-        None,
-    )
-    spa_v = (
-        300,
-        300,
-        200,
-        300,
-        250,
-        150,
-        250,
-        200,
-        100,
-        200,
-        150,
-        50,
-        150,
-        100,
-        None,
-        100,
-        50,
-        None,
-    )
-
-    points_phi = np.array(np.meshgrid(y_phi, x)).T.reshape(-1, 2)
-    points_spa = np.array(np.meshgrid(y_spa, x)).T.reshape(-1, 2)
-    xi = (s, _wk)
-
-    phi_star = float(
-        scipy.interpolate.griddata(points_phi, phi_s_v, xi, method='linear')
-    )
-    if kc is not None:
-        phi = phi_star * (fct_eff / 2.9) * kc * h_cr / (2 * (h - d))
-    else:
-        phi = phi_star * (fct_eff / 2.9) * h_cr / (8 * (h - d))
-
-    spa = float(
-        scipy.interpolate.griddata(points_spa, spa_v, xi, method='linear')
-    )
-
-    if math.isnan(phi) or math.isnan(spa):
-        raise ValueError('Combination of wk or stress values out of scope')
-
-    return phi, spa
-
-
-def alpha_e(Es: float, Ecm: float) -> float:
-    """Compute the ratio between the steel and mean concrete
-    elastic modules.
-
-    EUROCODE 2 1992-1-1:2004, Section 7.3.4-2
-
-    Args:
-        Es (float): steel elastic modulus in MPa
-        Ecm (float): concrete mean elastic modulus in MPa
-
-    Returns:
-        float: ratio between modules
-    Raise:
-        ValueError: if any of es or ecm is lower than 0.
-    """
-    if Es < 0:
-        raise ValueError(f'Es={Es} cannot be less than 0')
-    if Ecm < 0:
-        raise ValueError(f'Ecm={Ecm} cannot be less than 0')
-
-    return Es / Ecm
-
-
-def rho_p_eff(As: float, xi1: float, Ap: float, Ac_eff: float) -> float:
-    """Effective bond ratio between areas
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.10)
-
-    Args:
-        As (float): steel area in mm2
-        xi1 (float): the adjusted ratio of bond according
-            to expression (7.5)
-        Ap (float): the area in mm2 of post-tensioned tendons in ac_eff
-        Ac_eff (float): effective area of concrete in tension surrounding
-        the reinforcement or prestressing tendons of depth hc_eff.
-
-    Returns:
-        float: with the retio between areas
-
-
-    Raise:
-        ValueError: if any of As, xi1, Ap or Ac_eff is less than 0
-    """
-    if As < 0:
-        raise ValueError(f'As={As} cannot be less than 0')
-    if xi1 < 0:
-        raise ValueError(f'xi1={xi1} cannot be less than 0')
-    if Ap < 0:
-        raise ValueError(f'Ap={Ap} cannot be less than 0')
-    if Ac_eff < 0:
-        raise ValueError(f'Ac_eff={Ac_eff} cannot be less than 0')
-
-    return (As + xi1**2 * Ap) / Ac_eff
-
-
-def kt(load_type: str) -> float:
-    """Returns the kt factor dependent on the load duration for
-    the crack width calculation
-
-    Args:
-        load_type (str): the load type:
-            - 'short' for term loading
-            - 'long' for long term loading
-
-    Returns:
-        float: with the kt factor
-
-    Raises:
-        ValueError: if load_type is not 'short' and not 'long'
-    """
-    if not isinstance(load_type, str):
-        raise TypeError
-
-    load_type = load_type.lower().strip()
-    if load_type != 'short' and load_type != 'long':
-        raise ValueError(
-            f'load_type={load_type} can only have "short" or "long" as a value'
-        )
-
-    return 0.6 if load_type == 'short' else 0.4
-
-
-def esm_ecm(
-    sigma_s: float,
-    _alpha_e: float,
-    _rho_p_eff: float,
-    _kt: float,
-    fct_eff: float,
-    Es: float,
-) -> float:
-    """Returns the strain difference (esm - ecm) needed to compute the crack
-    width. esm is the mean strain in the reinforcement under the relevant
-    combination of loads of imposed deformations and taking into account the
-    effects of tension stiffening. Only the additional tensile strain beyond
-    the state of zero strain of the concrete is considered. ecm is the mean
-    strain in the concrete between the cracks.
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.9)
-
-    Args:
-        sigma_s (float): is the stress in MPa in the tension reinforcement
-            assuming a cracked section. FOr pretensioned members, s_steel may
-            be replaced by increment of s_steel stress variation in
-            prestressing tendons from the state of zero strain of the
-            concrete at the same level.
-        _alpha_e (float): is the ratio Es/Ecm
-        _rho_p_eff (float): effective bond ratio between areas given by the
-            Eq. (7.10)
-        _kt (float): is a factor dependent on the load duration
-        fct_eff (float): is the mean value of the tensile strength in MPa
-            of the concrete effectvie at the time when the cracks may
-            first be expected to occur: fct_eff=fctm or fctm(t) if
-            crack is expected earlier than 28 days.
-        Es: steel elastic mudulus in MPa
-
-    Returns:
-        float: the strain difference between concrete and steel
-
-    Raises:
-        ValueError: if any sigma_s, alpha_e, rho_p_eff, fct_eff or Es is less
-            than 0.
-        ValueError: if kt is not 0.6 and not 0.4
-    """
-    if sigma_s < 0:
-        raise ValueError(f'sigma_s={sigma_s} cannot be less than 0')
-    if _alpha_e < 0:
-        raise ValueError(f'_alpha_e={_alpha_e} cannot be less than 0')
-    if _rho_p_eff < 0:
-        raise ValueError(f'_rho_p_eff={_rho_p_eff} cannot be less than 0')
-    if fct_eff < 0:
-        raise ValueError(f'fct_eff={fct_eff} cannot be less than 0')
-    if Es < 0:
-        raise ValueError(f'Es={Es} cannot be less than 0')
-    if _kt != 0.6 and _kt != 0.4:
-        raise ValueError(f'_kt={_kt} can only take as values 0.4 and 0.6')
-
-    min_val = 0.6 * sigma_s / Es
-
-    a = 1 + _alpha_e * _rho_p_eff
-    b = _kt * fct_eff / _rho_p_eff * a
-    c = (sigma_s - b) / Es
-
-    return max(c, min_val)
-
-
-def w_spacing(c: float, phi: float) -> float:
-    """Computes the distance threshold from which the
-    maximum crack spacing is constant.
-
-    EUROCODE 2 1992-1-1:2004, Sect. (7.3.4-3)
-
-    Args:
-        c (float): cover of  the longitudinal reinforcement in mm
-        phi (float): is the bar diameter in mm. Where mixed bar diameters
-            used, then it should be replaced for an equivalente bar diameter.
-
-    Returns:
-        float: threshold distance in mm
-
-    Raises:
-        ValueError: if any of c or phi is less than 0.
-    """
-    if c < 0:
-        raise ValueError(f'c={c} cannot be less than 0')
-    if phi < 0:
-        raise ValueError(f'phi={phi} cannot be less than 0')
-
-    return 5 * (c + phi / 2)
-
-
-def phi_eq(n1: int, n2: int, phi1: float, phi2: float) -> float:
-    """Computes the equivalent diameter. For a section with n1 bars of
-    diameter phi1 and n2 bars of diameter phi2
-
-    EUROCODE 2 1992-1-1:2004, Sect. (7.12)
-
-    Args:
-        n1 (int): number of bars with diameter phi1
-        n2 (int): number of bars with diameter phi2
-        phi1 (float): diameter of n1 bars in mm
-        phi2 (float): diamater of n2 bars in mm
-
-    Returns:
-        float: the equivalent diameter in mm
-
-    Raises:
-        ValueError: if any of n1 or n2 is less than 0
-        ValueError: if any of phi1 or phi2 is less than 0
-        TypeError: if any of n1 or n2 is not an integer
-    """
-    if n1 < 0:
-        raise ValueError(f'n1={n1} cannot be less than 0')
-    if not isinstance(n1, int):
-        raise TypeError(f'n1={n1} needs to be an integer value')
-    if n2 < 0:
-        raise ValueError(f'n2={n2} cannot be less than 0')
-    if not isinstance(n2, int):
-        raise TypeError(f'n2={n2} needs to be an integer value')
-    if phi1 < 0:
-        raise ValueError(f'phi1={phi1} cannot be less than 0')
-    if phi2 < 0:
-        raise ValueError(f'phi2={phi2} cannot be less than 0')
-
-    a = n1 * phi1**2 + n2 * phi2**2
-    b = n1 * phi1 + n2 * phi2
-    return a / b
-
-
-def k1(bond_type: str) -> float:
-    """Get the k1 coefficient which takes account of the bond properties
-    of the bounded reinforcement
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.11-k1)
-
-    Args:
-        bond_type (str): the bond property of the reinforcement.
-        Possible values:
-            - 'bond': for high bond bars
-            - 'plane': for bars with an effectively plain surface (e.g.
-            prestressing tendons)
-
-    Returns:
-        (float): value of the k1 coefficient
-
-    Raises:
-        ValueError: if bond_type is neither 'bond' nor 'plane'
-        TypeError: if bond_type is not an str
-    """
-    if not isinstance(bond_type, str):
-        raise TypeError(f'bond_type={bond_type} is not an str')
-
-    bond_type = bond_type.lower().strip()
-    if bond_type != 'bond' and bond_type != 'plane':
-        raise ValueError(
-            f'bond_type={bond_type} can only have "bond" or "plane" as values'
-        )
-
-    return 0.8 if bond_type == 'bond' else 1.6
-
-
-def k2(epsilon_r: float) -> float:
-    """Computes a coefficient which takes into account of the
-    distribution of strain:
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.13)
-
-    Args:
-        epsilon_r (float): ratio epsilon_2/epsilon_1 where epsilon_1 is
-            thre greater and epsilon_2 is the lesser strain at the boundaries
-            of the section considererd, assessed on the basis of a cracked
-            section. epsilon_r=0 for bending and epsilon_r=1 for pure tension.
-
-    Returns:
-        float: the k2 coefficient value.
-
-    Raises:
-        ValueError: if epsilon_r is not between 0 and 1.
-    """
-    if epsilon_r < 0 or epsilon_r > 1:
-        raise ValueError(f'epsilon_r={epsilon_r} must be between 0 and 1')
-
-    return (1 + epsilon_r) / 2
-
-
-def k3():
-    """Returns the k3 coefficient for computing sr_max
-
-    Returns:
-        float: value for the coefficient
-    """
-    return 3.4
-
-
-def k4():
-    """Returns the k4 coefficient for computing sr_max
-
-    Returns:
-        float: value for the coefficient
-    """
-    return 0.425
-
-
-def sr_max_close(
-    c: float,
-    phi: float,
-    _rho_p_eff: float,
-    _k1: float,
-    _k2: float,
-    _k3: float,
-    _k4: float,
-) -> float:
-    """Computes the maximum crack spacing in cases where bonded reinforcement
-    is fixed at reasonably close centres within the tension zone
-    (w_spacing<=5(c+phi/2)).
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.11)
-
-    Args:
-        c (float): is the cover in mm of the longitudinal reinforcement
-        phi (float): is the bar diameter in mm. Where mixed bar diameters
-            used, then it should be replaced for an equivalente bar diameter.
-        _rho_p_eff (float): effective bond ratio between areas given by the
-            Eq. (7.10)
-        _k1 (float): coefficient that takes into account the bound properties
-            of the bonded reinforcement
-        _k2 (float): coefficient that takes into account the distribution of
-            of the strain
-        _k3 (float): coefficient from the National Annex
-        _k4 (float): coefficient from the National Annex
-
-    Returns:
-        float: the maximum crack spaing in mm.
-
-    Raises:
-        ValueError: if one or more of c, phi, rho_p_eff, k3 or k4
-            is lower than zero.
-        ValueError: if k1 is not 0.8 or 1.6
-        ValueError: if k2 is not between 0.5 and 1.0
-    """
-    if c < 0:
-        raise ValueError(f'c={c} cannot be less than zero')
-    if phi < 0:
-        raise ValueError(f'phi={phi} cannot be less than zero')
-    if _rho_p_eff < 0:
-        raise ValueError(f'_rho_p_eff={_rho_p_eff} cannot be less than zero')
-    if _k3 < 0:
-        raise ValueError(f'_k3={_k3} cannot be less than zero')
-    if _k4 < 0:
-        raise ValueError(f'_k4={_k4} cannot be less than zero')
-    if _k1 != 0.8 and _k1 != 1.6:
-        raise ValueError(f'_k1={_k1} can only take as values 0.8 and 1.6')
-    if _k2 < 0.5 or _k2 > 1:
-        raise ValueError(f'_k2={_k2} is not between 0.5 and 1.0')
-
-    return _k3 * c + _k1 * _k2 * _k4 * phi / _rho_p_eff
-
-
-def sr_max_far(h: float, x: float) -> float:
-    """Computes the maximum crack spacing in cases where bonded reinforcement
-    is fixed at reasonably close centres within the tension zone
-    (w_spacing>5(c+phi/2)).
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.14)
-
-    Args:
-        h (float): total depth of the beam in mm
-        x (float): distance to non tension area of the element mm
-
-    Returns:
-        float: maximum crack spacing in mm
-
-    Raises:
-        ValueError: if one of h or x is less than zero.
-        ValueError: x is greater than h.
-    """
-    if x < 0:
-        raise ValueError(f'x={x} cannot be less than zero')
-    if h < 0:
-        raise ValueError(f'h={h} cannot be less than zero')
-    if x > h:
-        raise ValueError(f'x={x} cannot be larger than h={h}')
-
-    return 1.3 * (h - x)
-
-
-def sr_max_theta(sr_max_y: float, sr_max_z: float, theta: float) -> float:
-    """Computes the crack spacing sr_max when there is an angle
-    between the angle of  principal stress and the direction
-    of the reinforcement, for members in two orthogonal directions,
-    that is significant (> 15º).
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.15)
-
-    Args:
-        sr_max_y (float): crack spacing in mm in the y-direction.
-        sr_max_z (float): crack spacing in mm in the z-direction.
-        theta (float): angle in radians between the reinforcement in the
-            y-direction and the direction of the principal tensile stress.
-
-    Returns:
-        float: the crack spacing in mm.
-
-    Raises:
-        ValueError: if sr_max_y or sr_max_z is negative.
-        ValueError: if theta is not between 0 and pi/2
-    """
-    if sr_max_y < 0:
-        raise ValueError(f'sr_max_y={sr_max_y} cannot be less than zero')
-    if sr_max_z < 0:
-        raise ValueError(f'sr_max_z={sr_max_z} cannot be less than zero')
-
-    a = math.cos(theta) / sr_max_y
-    b = math.sin(theta) / sr_max_z
-    return 1 / (a + b)
-
-
-def wk(sr_max: float, _esm_ecm: float) -> float:
-    """Computes the crack width
-
-    EUROCODE 2 1992-1-1:2004, Eq. (7.8)
-
-    Args:
-        sr_max (float): the maximum crack length spacing in mm.
-        _esm_ecm (float): the difference between the mean strain in the
-            reinforcement under relevant combination of loads, including
-            the effect of imposed deformations and taking into account
-            tension stiffening and the mean strain in the concrete
-            between cracks.
-
-    Returns:
-        float: crack width in mm.
-
-    Raises:
-        ValueError: if any of sr_max or _esm_ecm is less than zero.
-    """
-    if sr_max < 0:
-        raise ValueError(f'sr_max={sr_max} cannot be less than zero')
-    if _esm_ecm < 0:
-        raise ValueError(f'_esm_scm={_esm_ecm} cannot be less than zero')
-
-    return sr_max * _esm_ecm

From 6ba6dc905ffbe76be7c2d9b0e948e3c4c742c285 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Fri, 13 Jan 2023 14:36:25 +0100
Subject: [PATCH 17/33] removed testing file

---
 prueba.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 prueba.py

diff --git a/prueba.py b/prueba.py
deleted file mode 100644
index e69de29b..00000000

From a9c926338152606c8e0a57e88127da84d7915e2a Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Mon, 16 Jan 2023 08:29:41 +0100
Subject: [PATCH 18/33] test renaming and docstring corrections

---
 .../ec2_2004/_section_7_3_crack_control.py    |  4 +-
 ...test_ec2_2004_section_7_3_crack_control.py | 60 +++++++++----------
 2 files changed, 30 insertions(+), 34 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
index 1de528de..529ef679 100644
--- a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
@@ -392,7 +392,7 @@ def As_min_2(
     EUROCODE 2 1992-1-1:2004, Table (7.2N), Table (7.3N)
 
     Args:
-        wk (float): the characteristic crack width value in mm.
+        _wk (float): the characteristic crack width value in mm.
         sigma_s (float): the steel stress value in MPa under the relevant
             combination of actions.
         fct_eff (float): is the mean value of the tensile strength in MPa of
@@ -417,7 +417,7 @@ def As_min_2(
         in the first position and the maximum bar spacing in mm in the
         second position
     Raises:
-        ValueError: if wk, fct_eff, h_cr, h or d are less than 0
+        ValueError: if _wk, fct_eff, h_cr, h or d are less than 0
         ValueError: if kc is not between 0 and 1
         ValueError: if combination of wk and stress values are out of scope
     """
diff --git a/tests/test_ec2_2004_section_7_3_crack_control.py b/tests/test_ec2_2004_section_7_3_crack_control.py
index 54b14fc7..8dc120f8 100644
--- a/tests/test_ec2_2004_section_7_3_crack_control.py
+++ b/tests/test_ec2_2004_section_7_3_crack_control.py
@@ -73,21 +73,21 @@ def test_w_max_not_valid_input_raises_valueerror(
         (700, 0.72),
     ],
 )
-def test_k_crack_min_steel_area_returns_expected_values(h, expected):
-    """Test the k_crack_min_steel_area function"""
+def test_k_returns_expected_values(h, expected):
+    """Test the k function"""
     k = _section_7_3_crack_control.k(h)
     assert math.isclose(k, expected)
 
 
-def test_k_crack_min_steel_area_raises_valueerror():
+def test_k_raises_valueerror():
     """Test that not valid input returns ValueError exeption"""
     with pytest.raises(ValueError):
         h = -100
         _section_7_3_crack_control.k(h)
 
 
-def test_kc_crack_min_steel_area_pure_tension_returns_expected_values():
-    """Test the kc_crack_min_steel_area_pure_tension function"""
+def test_kc_tension_returns_expected_values():
+    """Test the kc_tension function"""
     assert 1 == _section_7_3_crack_control.kc_tension()
 
 
@@ -101,10 +101,8 @@ def test_kc_crack_min_steel_area_pure_tension_returns_expected_values():
         (200, 50, 5, 80, 0),
     ],
 )
-def test_kc_crack_min_steel_area_rectangular_returns_expected_values(
-    h, b, fct_eff, n_ed, expected
-):
-    """Test the kc_crack_min_steel_area_rectangular"""
+def test_kc_rect_area_returns_expected_values(h, b, fct_eff, n_ed, expected):
+    """Test the kc_rect_area"""
     kc = _section_7_3_crack_control.kc_rect_area(
         h,
         b,
@@ -114,8 +112,8 @@ def test_kc_crack_min_steel_area_rectangular_returns_expected_values(
     assert math.isclose(kc, expected, rel_tol=0.000001)
 
 
-def test_kc_crack_min_steel_area_rectangular_raises_valueerror():
-    """Test the kc_crack_min_steel_area_rectangular raises Value
+def test_kc_rect_area_raises_valueerror():
+    """Test the kc_rect_area raises Value
     Error for not correct input values for b and h"""
     with pytest.raises(ValueError):
         _section_7_3_crack_control.kc_rect_area(
@@ -135,8 +133,8 @@ def test_kc_crack_min_steel_area_rectangular_raises_valueerror():
         (55, 50000, 4, 0.5),
     ],
 )
-def test_kc_crack_min_steel_area_flanges(f_cr, a_ct, fct_eff, expected):
-    """Test the kc_crack_min_steel_area_flanges function"""
+def test_kc_flanges_area(f_cr, a_ct, fct_eff, expected):
+    """Test the kc_flanges function"""
     kc = _section_7_3_crack_control.kc_flanges_area(f_cr, a_ct, fct_eff)
     assert math.isclose(kc, expected, rel_tol=0.000001)
 
@@ -185,7 +183,7 @@ def test_crack_As_min_raises_valueerror(a_ct, s_steel, fct_eff, k, kc):
         (50000, 500, 4, 1, 1, 1000, 0, 20, 0.8, 20, 364.223),
     ],
 )
-def test_crack_min_steel_area_with_press_tendons_returns_expected_values(
+def test_As_min_p_returns_expected_values(
     a_ct,
     s_steel,
     fct_eff,
@@ -198,7 +196,7 @@ def test_crack_min_steel_area_with_press_tendons_returns_expected_values(
     incr_stress,
     expected,
 ):
-    """Test the crack_min_steel_area returns expected values"""
+    """Test the As_min_p returns expected values"""
     as_min = _section_7_3_crack_control.As_min_p(
         a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
     )
@@ -221,10 +219,10 @@ def test_crack_min_steel_area_with_press_tendons_returns_expected_values(
         (80000, 400, 4, 0.9, 0.75, 500, 10, 10, 0.9, 10),
     ],
 )
-def test_crack_min_steel_area_with_press_tendons_raise_valueerror(
+def test_As_min_p_raise_valueerror(
     a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
 ):
-    """Test the crack_min_steel_area raise ValueError for non valid values"""
+    """Test the As_min_p raise ValueError for non valid values"""
     with pytest.raises(ValueError):
         _section_7_3_crack_control.As_min_p(
             a_ct, s_steel, fct_eff, k, kc, ap, d_steel, d_press, e, incr_stress
@@ -240,7 +238,7 @@ def test_crack_min_steel_area_with_press_tendons_raise_valueerror(
         (0.35, 360, 2.9, 200, 400, 360, 40, None, 6.875, 125),
     ],
 )
-def test_crack_min_steel_without_direct_calculation_returns_expected_values(
+def test_As_min_2_returns_expected_values(
     wk,
     s_steel,
     fct_eff,
@@ -252,7 +250,7 @@ def test_crack_min_steel_without_direct_calculation_returns_expected_values(
     exp_phi,
     exp_sep,
 ):
-    """Test the crack_min_steel_area raise ValueError for non valid values"""
+    """Test the As_min_2 raise ValueError for non valid values"""
     phi, sep = _section_7_3_crack_control.As_min_2(
         wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
     )
@@ -273,10 +271,10 @@ def test_crack_min_steel_without_direct_calculation_returns_expected_values(
         (0.5, 200, 2.9, 200, 400, 360, 0, 0.4),
     ],
 )
-def test_crack_min_steel_without_direct_calculation_raise_valueerror(
+def test_As_min_2_raise_valueerror(
     wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
 ):
-    """Test the crack_min_steel_area raise ValueError for non valid values"""
+    """Test the As_min_2 raise ValueError for non valid values"""
     with pytest.raises(ValueError):
         _section_7_3_crack_control.As_min_2(
             wk, s_steel, fct_eff, h_cr, h, d, incr_stress, kc
@@ -291,10 +289,8 @@ def test_crack_min_steel_without_direct_calculation_raise_valueerror(
         (0.5, 10, 10, 0.707107),
     ],
 )
-def test_adjusted_bond_length_return_expected_values(
-    e, d_press, d_steel, expected
-):
-    """Test the adjusted_bond_length_function returns expected values"""
+def test_xi1_values(e, d_press, d_steel, expected):
+    """Test xi1 returns expected values"""
     assert math.isclose(
         _section_7_3_crack_control.xi1(e, d_press, d_steel),
         expected,
@@ -312,7 +308,7 @@ def test_adjusted_bond_length_return_expected_values(
         (0.6, 10, -10),
     ],
 )
-def test_adjusted_bond_length_raise_valuerror(e, d_press, d_steel):
+def test_xi1_raise_valuerror(e, d_press, d_steel):
     """Test the adjusted_bond_length_function raises exceptions"""
     with pytest.raises(ValueError):
         _section_7_3_crack_control.xi1(e, d_press, d_steel)
@@ -326,7 +322,7 @@ def test_adjusted_bond_length_raise_valuerror(e, d_press, d_steel):
         (550, 150, 150, 133.33333),
     ],
 )
-def test_hc_eff_concrete_tension_returns_expected_values(h, d, x, expected):
+def test_hc_eff_returns_expected_values(h, d, x, expected):
     """Test the hc_eff_concrete_tension returns expected results"""
     assert math.isclose(
         _section_7_3_crack_control.hc_eff(h, d, x),
@@ -345,7 +341,7 @@ def test_hc_eff_concrete_tension_returns_expected_values(h, d, x, expected):
         (400, 200, 450),
     ],
 )
-def test_hc_eff_concrete_tension_raise_exceptions(h, d, x):
+def test_hc_eff_raise_exceptions(h, d, x):
     """Test hc_eff_concrete tension raises expected exceptions"""
     with pytest.raises(ValueError):
         _section_7_3_crack_control.hc_eff(h, d, x)
@@ -417,13 +413,13 @@ def test_rho_p_eff_raise_value_error(a_s, e1, a_p, ac_eff):
         ('long', 0.4),
     ],
 )
-def test_kt_load_duration_returns_expected_values(load_type, expected):
-    """Test kt_load_duration returns expected values"""
+def test_kt_returns_expected_values(load_type, expected):
+    """Test kt returns expected values"""
     assert _section_7_3_crack_control.kt(load_type) == expected
 
 
-def test_kt_load_duration_raise_value_errors():
-    """Test kt_load_duration raise value errors"""
+def test_kt_raise_value_errors():
+    """Test kt raise value errors"""
     with pytest.raises(TypeError):
         _section_7_3_crack_control.kt(load_type=123)
 

From 4fd8b7e9ac5dc274cb86f8fcb658679ac3df4312 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Daniel=20Gonz=C3=A1lez=20de=20la=20Morena?= <dgm@fhecor.es>
Date: Thu, 9 Mar 2023 09:53:18 +0100
Subject: [PATCH 19/33] 230309 requested changes applied

---
 requirements.txt                              |   4 +-
 .../ec2_2004/_section_7_3_crack_control.py    |  61 +++---
 structuralcodes/material/__init__.py          |   0
 structuralcodes/material/concrete/__init__.py |  58 ------
 .../material/concrete/_concrete.py            |  45 -----
 .../material/concrete/_concreteMC2010.py      | 189 ------------------
 ...test_ec2_2004_section_7_3_crack_control.py |   5 +-
 7 files changed, 39 insertions(+), 323 deletions(-)
 delete mode 100644 structuralcodes/material/__init__.py
 delete mode 100644 structuralcodes/material/concrete/__init__.py
 delete mode 100644 structuralcodes/material/concrete/_concrete.py
 delete mode 100644 structuralcodes/material/concrete/_concreteMC2010.py

diff --git a/requirements.txt b/requirements.txt
index 1de8c504..d68dafa2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,2 @@
-numpy==1.23.5
-scipy==1.9.3
\ No newline at end of file
+numpy>=1.20.0
+scipy>=1.6.0
\ No newline at end of file
diff --git a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
index 529ef679..e530a885 100644
--- a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
@@ -8,7 +8,7 @@
 
 
 def w_max(exposure_class: str, load_combination: str) -> float:
-    """Computes the recomended value of the maximum crack width.
+    """Computes the recommended value of the maximum crack width.
 
     EUROCODE 2 1992-1-1:2004, Table (7.1N)
 
@@ -68,7 +68,7 @@ def As_min(
 
     Args:
         A_ct (float): is the area of concrete within the tensile zone in mm2.
-            The tensile zone is that parg of the section which is calculated
+            The tensile zone is that part of the section which is calculated
             to be in tension just before the formation of the first crack.
         sigma_s (float): is the absolute value of the maximum stress in MPa
             permitted in the reinforcement immediately after the formation
@@ -82,8 +82,7 @@ def As_min(
             is expected earlier than 28 days.
         _k (float): is the coefficient which allow for the effect of
             non-uniform self-equilibrating stresses, which lead to a
-            reduction of restraint forces. Use 'k_crack_min_steel_area'
-            to compute it
+            reduction of restraint forces.
             k=1 for webs w<=300mm or flanges widths less than 300mm
             k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
             Intermediate values may be interpolated.
@@ -137,14 +136,14 @@ def k(h: float) -> float:
         return 1
     if h < 800:
         interpol = scipy.interpolate.interp1d((300, 800), (1, 0.65))
-        return (float)(interpol(h))
+        return interpol(h)
     return 0.65
 
 
 def kc_tension() -> float:
     """Computes the coefficient which takes account of the stress
     distribution within the section immediately prior to cracking and
-    the change of the lever arm in pure dtension.
+    the change of the lever arm in pure tension.
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.1)
 
@@ -313,7 +312,7 @@ def As_min_p(
             to be in tension just before the formation of the first crack.
         sigma_s (float): is the absolute value of the maximum stress in MPa
             permitted in the reinforcement immediately after the formation
-            of the crack. This may be taken as theyield strength of the
+            of the crack. This may be taken as the yield strength of the
             reinforcement, fyk. A lower value may, however, be needed to
             satisfy the crack width limits according to the maximum
             bar size of spacing (see 7.3.3 (2)).
@@ -323,8 +322,7 @@ def As_min_p(
             is expected earlier than 28 days.
         _k (float): is the coefficient which allow for the effect of
             non-uniform self-equilibrating stresses, which lead to a
-            reduction of restraint forces. Use 'k_crack_min_steel_area'
-            to compute it
+            reduction of restraint forces.
             k=1 for webs w<=300mm or flanges widths less than 300mm
             k=0.65 for webs w>=800mm or flanges with widths greater than 800mm
             Intermediate values may be interpolated.
@@ -337,7 +335,7 @@ def As_min_p(
             Equal to 0 if only prestressing is used in control cracking
         phi_p (float): equivalent diameter in mm of tendon acoording
             to 6.8.2
-        chi (float): ratio of bond strength of prestressing and reinforcing
+        xi (float): ratio of bond strength of prestressing and reinforcing
             steel, according to Table 6.2 in 6.8.2
         delta_s (float): stress variation in MPa in prestressing tendons
             from the state of zero strain of the concrete at the same level
@@ -611,7 +609,7 @@ def esm_ecm(
 
     Args:
         sigma_s (float): is the stress in MPa in the tension reinforcement
-            assuming a cracked section. FOr pretensioned members, s_steel may
+            assuming a cracked section. For pretensioned members, s_steel may
             be replaced by increment of s_steel stress variation in
             prestressing tendons from the state of zero strain of the
             concrete at the same level.
@@ -620,10 +618,10 @@ def esm_ecm(
             Eq. (7.10)
         _kt (float): is a factor dependent on the load duration
         fct_eff (float): is the mean value of the tensile strength in MPa
-            of the concrete effectvie at the time when the cracks may
+            of the concrete effective at the time when the cracks may
             first be expected to occur: fct_eff=fctm or fctm(t) if
             crack is expected earlier than 28 days.
-        Es: steel elastic mudulus in MPa
+        Es: steel elastic modulus in MPa
 
     Returns:
         float: the strain difference between concrete and steel
@@ -664,7 +662,7 @@ def w_spacing(c: float, phi: float) -> float:
     Args:
         c (float): cover of  the longitudinal reinforcement in mm
         phi (float): is the bar diameter in mm. Where mixed bar diameters
-            used, then it should be replaced for an equivalente bar diameter.
+            used, then it should be replaced for an equivalent bar diameter.
 
     Returns:
         float: threshold distance in mm
@@ -728,23 +726,23 @@ def k1(bond_type: str) -> float:
         bond_type (str): the bond property of the reinforcement.
         Possible values:
             - 'bond': for high bond bars
-            - 'plane': for bars with an effectively plain surface (e.g.
+            - 'plain': for bars with an effectively plain surface (e.g.
             prestressing tendons)
 
     Returns:
         (float): value of the k1 coefficient
 
     Raises:
-        ValueError: if bond_type is neither 'bond' nor 'plane'
+        ValueError: if bond_type is neither 'bond' nor 'plain'
         TypeError: if bond_type is not an str
     """
     if not isinstance(bond_type, str):
         raise TypeError(f'bond_type={bond_type} is not an str')
 
     bond_type = bond_type.lower().strip()
-    if bond_type != 'bond' and bond_type != 'plane':
+    if bond_type not in ('bond', 'plain'):
         raise ValueError(
-            f'bond_type={bond_type} can only have "bond" or "plane" as values'
+            f'bond_type={bond_type} can only have "bond" or "plain" as values'
         )
 
     return 0.8 if bond_type == 'bond' else 1.6
@@ -758,8 +756,8 @@ def k2(epsilon_r: float) -> float:
 
     Args:
         epsilon_r (float): ratio epsilon_2/epsilon_1 where epsilon_1 is
-            thre greater and epsilon_2 is the lesser strain at the boundaries
-            of the section considererd, assessed on the basis of a cracked
+            the greater and epsilon_2 is the lesser strain at the boundaries
+            of the section considered, assessed on the basis of a cracked
             section. epsilon_r=0 for bending and epsilon_r=1 for pure tension.
 
     Returns:
@@ -798,8 +796,8 @@ def sr_max_close(
     _rho_p_eff: float,
     _k1: float,
     _k2: float,
-    _k3: float,
-    _k4: float,
+    _k3: t.Optional[float] = None,
+    _k4: t.Optional[float] = None,
 ) -> float:
     """Computes the maximum crack spacing in cases where bonded reinforcement
     is fixed at reasonably close centres within the tension zone
@@ -810,15 +808,17 @@ def sr_max_close(
     Args:
         c (float): is the cover in mm of the longitudinal reinforcement
         phi (float): is the bar diameter in mm. Where mixed bar diameters
-            used, then it should be replaced for an equivalente bar diameter.
+            used, then it should be replaced for an equivalent bar diameter.
         _rho_p_eff (float): effective bond ratio between areas given by the
             Eq. (7.10)
         _k1 (float): coefficient that takes into account the bound properties
             of the bonded reinforcement
         _k2 (float): coefficient that takes into account the distribution of
             of the strain
-        _k3 (float): coefficient from the National Annex
-        _k4 (float): coefficient from the National Annex
+        _k3 (float, optional): coefficient from the National Annex.
+            If not specified then _k3=3.4
+        _k4 (float): coefficient from the National Annex.
+            If not specified then _k4=0.425
 
     Returns:
         float: the maximum crack spaing in mm.
@@ -829,6 +829,11 @@ def sr_max_close(
         ValueError: if _k1 is not 0.8 or 1.6
         ValueError: if _k2 is not between 0.5 and 1.0
     """
+    if _k3 is None:
+        _k3 = k3()
+    if _k4 is None:
+        _k4 = k4()
+
     if c < 0:
         raise ValueError(f'c={c} cannot be less than zero')
     if phi < 0:
@@ -839,7 +844,7 @@ def sr_max_close(
         raise ValueError(f'_k3={_k3} cannot be less than zero')
     if _k4 < 0:
         raise ValueError(f'_k4={_k4} cannot be less than zero')
-    if _k1 != 0.8 and _k1 != 1.6:
+    if _k1 not in (0.8, 1.6):
         raise ValueError(f'_k1={_k1} can only take as values 0.8 and 1.6')
     if _k2 < 0.5 or _k2 > 1:
         raise ValueError(f'_k2={_k2} is not between 0.5 and 1.0')
@@ -849,8 +854,8 @@ def sr_max_close(
 
 def sr_max_far(h: float, x: float) -> float:
     """Computes the maximum crack spacing in cases where bonded reinforcement
-    is fixed at reasonably close centres within the tension zone
-    (w_spacing>5(c+phi/2)).
+    exceeds (w_spacing>5(c+phi/2)) or where there is no bonded reinforcement
+    at all.
 
     EUROCODE 2 1992-1-1:2004, Eq. (7.14)
 
diff --git a/structuralcodes/material/__init__.py b/structuralcodes/material/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/structuralcodes/material/concrete/__init__.py b/structuralcodes/material/concrete/__init__.py
deleted file mode 100644
index ca314baf..00000000
--- a/structuralcodes/material/concrete/__init__.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Concrete material"""
-import typing as t
-from structuralcodes.codes import _use_design_code
-from ._concrete import Concrete
-from ._concreteMC2010 import ConcreteMC2010
-
-__all__ = [
-    'create_concrete',
-    'Concrete',
-    'ConcreteMC2010',
-]
-
-
-def create_concrete(
-    fck: float,
-    name: t.Optional[str] = None,
-    density: float = 2400.0,
-    existing: bool = False,
-    design_code: t.Optional[str] = None,
-) -> t.Optional[Concrete]:
-    """
-    A factory function to create the correct type of concrete based on the
-    desired design code.
-
-    Args:
-        fck (float): Characteristic strength of concrete in MPa.
-            (if existing it is intended as the mean strength)
-
-    Keyword Args:
-        density (float): Density of Concrete in kg/m3 (default: 2400)
-        existing (bool): Boolean indicating if the concrete is of an
-            existing structure (default: False)
-        deisgn_code (str): Optional string (default: None) indicating the
-            desired standard. If None (default) the globally used design
-            standard will be adopted. Otherwise the design standard specified
-            will be used for the instance of the material.
-            Currently available codes: 'mc2010'
-
-    Raises:
-        ValueError: if the design code is not valid or does not cover
-            concrete as a material.
-    """
-    # Get the code from the global variable
-    _code = _use_design_code(design_code)
-
-    # Check if the code is a proper concrete code
-    code = _code if 'concrete' in _code.__materials__ else None
-    if code is None:
-        raise ValueError(
-            'The design code is not set, either use '
-            'structuralcodes.code.set_designcode, or provide a valid '
-            'string in the function.'
-        )
-
-    # Create the proper concrete object
-    if code.__title__ == 'fib Model Code 2010':
-        return ConcreteMC2010(fck, name, density, existing)
-    return None
diff --git a/structuralcodes/material/concrete/_concrete.py b/structuralcodes/material/concrete/_concrete.py
deleted file mode 100644
index 19ac2048..00000000
--- a/structuralcodes/material/concrete/_concrete.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Core implementation of the concrete material"""
-import abc
-import typing as t
-from structuralcodes.core.base import Material
-
-
-class Concrete(Material):
-    """The abstract concrete material."""
-
-    _fck: float
-    _existing: bool
-
-    def __init__(
-        self,
-        fck: float,
-        name: t.Optional[str] = None,
-        density: float = 2400,
-        existing: t.Optional[bool] = False,
-    ) -> None:
-        """Initializes an abstract concrete material"""
-        name = name if name is not None else "Concrete"
-        super().__init__(density=density, name=name)
-
-        self._fck = abs(fck)
-        if existing:
-            raise NotImplementedError(
-                'Existing concrete feature not implemented yet'
-            )
-        self._existing = existing
-
-    @property
-    def fck(self) -> float:
-        """Returns fck in MPa"""
-        return self._fck
-
-    @fck.setter
-    def fck(self, fck: float) -> None:
-        """Setter for fck (in MPa)"""
-        self._fck = abs(fck)
-        self._reset_attributes()
-
-    @abc.abstractmethod
-    def _reset_attributes(self):
-        """Each concrete should define its own _reset_attributes method
-        This is because fck setting, reset the object arguments"""
diff --git a/structuralcodes/material/concrete/_concreteMC2010.py b/structuralcodes/material/concrete/_concreteMC2010.py
deleted file mode 100644
index faf0ad97..00000000
--- a/structuralcodes/material/concrete/_concreteMC2010.py
+++ /dev/null
@@ -1,189 +0,0 @@
-"""The concrete class for Model Code 2020 Concrete Material"""
-import typing as t
-import warnings
-
-from structuralcodes.codes import mc2010
-from ._concrete import Concrete
-
-
-class ConcreteMC2010(Concrete):
-    """Concrete implementation for MC 2010"""
-
-    _fcm: t.Optional[float] = None
-    _fctm: t.Optional[float] = None
-    _fctkmin: t.Optional[float] = None
-    _fctkmax: t.Optional[float] = None
-    _Gf: t.Optional[float] = None
-
-    def __init__(
-        self,
-        fck: float,
-        name: t.Optional[str] = None,
-        density: float = 2400.0,
-        existing: bool = False,
-    ):
-        """Initializes a new instance of Concrete for MC 2010
-
-        Args:
-            fck (float): Characteristic strength in MPa if concrete is not
-                existing.
-
-        Keyword Args:
-            name (str): A descriptive name for concrete
-            density (float): Density of material in kg/m3 (default: 2400)
-            existing (bool): The material is of an existing structure
-                (default: False)
-        """
-
-        if name is None:
-            name = f'C{round(fck):d}'
-        super().__init__(
-            fck=fck, name=name, density=density, existing=existing
-        )
-
-    def _reset_attributes(self):
-        self._fcm = None
-        self._fctm = None
-        self._fctkmin = None
-        self._fctkmax = None
-        self._Gf = None
-
-    def update_attributes(self, updated_attributes: dict) -> None:
-        """Function for updating the attributes specified in the input
-        dictionary
-
-        Args:
-            updated_attributes (dict): the dictionary of parameters to be
-                updated (not found parameters are skipped with a warning)
-        """
-        for key, value in updated_attributes.items():
-            if not hasattr(self, '_' + key):
-                str_list_keys = ''
-                for k in updated_attributes.keys():
-                    str_list_keys += k + ', '
-                str_warn = (
-                    f'WARNING: attribute {key} not found. Ignoring the entry.'
-                )
-                str_warn += '\nAvailable keys: ' + str_list_keys
-                warnings.warn(str_warn)
-                continue
-            setattr(self, '_' + key, value)
-
-    @property
-    def fcm(self) -> float:
-        """Returns fcm in MPa.
-
-        Returns:
-            float: The mean compressive strength in MPa.
-        """
-        if self._fcm is not None:
-            return self._fcm
-        return mc2010.fcm(self._fck)
-
-    @fcm.setter
-    def fcm(self, value: float):
-        """Sets a user defined value for fcm
-
-        Args:
-            value (float): the value of fcm in MPa
-
-        Raises:
-            ValueError: if value is lower than fck
-        """
-        if abs(value) <= self._fck:
-            raise ValueError(
-                (
-                    'Mean compressive strength cannot be lower than',
-                    'characteristic strength.\n',
-                    'Current characteristing strength: ',
-                    f'fck = {self._fck}.',
-                    f'Current value: value = {value}',
-                )
-            )
-        self._fcm = abs(value)
-
-    @property
-    def fctm(self) -> float:
-        """Returns fctm in MPa
-
-        Returns:
-            float: The mean tensile strength in MPa
-        """
-        if self._fctm is not None:
-            return self._fctm
-        return mc2010.fctm(self._fck)
-
-    @fctm.setter
-    def fctm(self, value: float):
-        """Sets a user defined value for fctm
-
-        Args:
-            value (float): the value of fctm in MPa
-        """
-        if value > 0.5 * self._fck:
-            warnings.warn(
-                'A suspect value of fctm has been input. Please check.'
-            )
-        self._fctm = abs(value)
-
-    @property
-    def fctkmin(self) -> float:
-        """Returns fctkmin in MPa
-
-        Returns:
-            float: The lower bound tensile strength in MPa
-        """
-        if self._fctkmin is not None:
-            return self._fctkmin
-
-        return mc2010.fctkmin(self.fctm)
-
-    @fctkmin.setter
-    def fctkmin(self, value: float):
-        """Sets a user defined value for fctkmin
-
-        Args:
-            value (float): the value of fctkmin in MPa
-        """
-        self._fctkmin = abs(value)
-
-    @property
-    def fctkmax(self) -> float:
-        """Returns fctkmax in MPa
-
-        Returns:
-            float: The upper bound tensile strength in MPa
-        """
-        if self._fctkmax is not None:
-            return self._fctkmax
-
-        return mc2010.fctkmax(self.fctm)
-
-    @fctkmax.setter
-    def fctkmax(self, value: float):
-        """Sets a user defined value for fctkmax
-
-        Args:
-            value (float): the value of fctkmax in MPa
-        """
-        self._fctkmax = abs(value)
-
-    @property
-    def Gf(self) -> float:
-        """Fracture energy of concrete
-
-        Returns:
-            float: The fracture energy in N/m
-        """
-        if self._Gf is not None:
-            return self._Gf
-        return mc2010.Gf(self._fck)
-
-    @Gf.setter
-    def Gf(self, value: float):
-        """Sets a user defined value for fracture energy Gf
-
-        Args:
-            value (float): the value of Gf in N/m
-        """
-        self._Gf = abs(value)
diff --git a/tests/test_ec2_2004_section_7_3_crack_control.py b/tests/test_ec2_2004_section_7_3_crack_control.py
index 8dc120f8..d4e8543b 100644
--- a/tests/test_ec2_2004_section_7_3_crack_control.py
+++ b/tests/test_ec2_2004_section_7_3_crack_control.py
@@ -535,7 +535,7 @@ def test_phi_eq_raises_expected_values(n1, n2, phi1, phi2, exception_type):
 
 @pytest.mark.parametrize(
     'bond_type, expected',
-    [('bond', 0.8), ('plane', 1.6), ('BOND ', 0.8), (' PLANE ', 1.6)],
+    [('bond', 0.8), ('PLAIN', 1.6), ('BOND ', 0.8), (' PLAIN ', 1.6)],
 )
 def test_k1_returns_expected_values(bond_type, expected):
     """Test k1 returns expected values"""
@@ -588,6 +588,9 @@ def test_k4_returns_expected_values():
         (20, 8, 5, 0.8, 0.5, 3.4, 0.425, 68.272),
         (30, 15, 0.2, 1.6, 0.5, 3.4, 0.425, 127.5),
         (45, 20, 0.4, 0.8, 1, 3.4, 0.425, 170),
+        (45, 20, 0.4, 0.8, 1, 3.4, None, 170),
+        (45, 20, 0.4, 0.8, 1, None, 0.425, 170),
+        (45, 20, 0.4, 0.8, 1, None, None, 170),
     ],
 )
 def test_sr_max_close(c, phi, rho_p_eff, k1, k2, k3, k4, expected):

From 1cffa61f8583e680ad31950de8c4e7288c416ed2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Daniel=20Gonz=C3=A1lez=20de=20la=20Morena?= <dgm@fhecor.es>
Date: Thu, 9 Mar 2023 10:01:50 +0100
Subject: [PATCH 20/33] small lint fixes

---
 structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
index e530a885..205bb410 100644
--- a/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
+++ b/structuralcodes/codes/ec2_2004/_section_7_3_crack_control.py
@@ -582,7 +582,7 @@ def kt(load_type: str) -> float:
         raise TypeError
 
     load_type = load_type.lower().strip()
-    if load_type != 'short' and load_type != 'long':
+    if load_type not in ('short', 'long'):
         raise ValueError(
             f'load_type={load_type} can only have "short" or "long" as a value'
         )
@@ -641,7 +641,7 @@ def esm_ecm(
         raise ValueError(f'fct_eff={fct_eff} cannot be less than 0')
     if Es < 0:
         raise ValueError(f'Es={Es} cannot be less than 0')
-    if _kt != 0.6 and _kt != 0.4:
+    if _kt not in (0.6, 0.4):
         raise ValueError(f'_kt={_kt} can only take as values 0.4 and 0.6')
 
     min_val = 0.6 * sigma_s / Es

From b483d4047f4cdecd897cab958fba87afd0a4bc3d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Daniel=20Gonz=C3=A1lez=20de=20la=20Morena?= <dgm@fhecor.es>
Date: Thu, 9 Mar 2023 10:05:28 +0100
Subject: [PATCH 21/33] vscode config updated

---
 .vscode/settings.json | 11 ++---------
 1 file changed, 2 insertions(+), 9 deletions(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 2676da93..72069360 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,17 +1,10 @@
 {
+    "python.formatting.provider": "black",
     "python.testing.pytestArgs": [
         "tests"
     ],
-    "python.formatting.provider": "black",
     "python.testing.unittestEnabled": false,
     "python.testing.pytestEnabled": true,
     "python.linting.pylintEnabled": true,
-    "python.linting.flake8Enabled": true,
-    "[python]": {
-        "editor.formatOnSave": true,
-        "editor.codeActionsOnSave": {
-            "source.organizeImports": true
-        },
-        "editor.defaultFormatter": "ms-python.python",
-    },
+    "python.linting.flake8Enabled": true
 }
\ No newline at end of file

From fed3b479b5fec9e199d5c42323b8d60fde2d5e3d Mon Sep 17 00:00:00 2001
From: Carlos Mestre <cmg@fhecor.es>
Date: Mon, 22 Jul 2024 12:57:15 +0200
Subject: [PATCH 22/33] Examples 1 to 3. Section plots. SLS functios and issues
 to comment

---
 EX1&2Beam.png                          | Bin 0 -> 87799 bytes
 EX1_part1.ipynb                        | 479 ++++++++++++++++
 EX2_part1.ipynb                        | 276 ++++++++++
 EX3.ipynb                              | 194 +++++++
 EX3.png                                | Bin 0 -> 191779 bytes
 EX3_beam.png                           | Bin 0 -> 38706 bytes
 EX3_beamM.png                          | Bin 0 -> 33901 bytes
 EX3_ved_neg.png                        | Bin 0 -> 5697 bytes
 EX3_ved_pos.png                        | Bin 0 -> 6881 bytes
 Issues.ipynb                           | 725 +++++++++++++++++++++++++
 Moment-Curvature.png                   | Bin 0 -> 22797 bytes
 My_Mz_N_-500kN.png                     | Bin 0 -> 66051 bytes
 My_Mz_N_0kN.png                        | Bin 0 -> 77907 bytes
 N2000_my_min.png                       | Bin 0 -> 26484 bytes
 N_My.png                               | Bin 0 -> 53077 bytes
 SLS_section_response.py                | 228 ++++++++
 error_N_M.png                          | Bin 0 -> 43955 bytes
 structuralcodes/plots/__init__.py      |   0
 structuralcodes/plots/section_plots.py | 377 +++++++++++++
 structuralcodes/sections/_generic.py   |   2 +-
 20 files changed, 2280 insertions(+), 1 deletion(-)
 create mode 100644 EX1&2Beam.png
 create mode 100644 EX1_part1.ipynb
 create mode 100644 EX2_part1.ipynb
 create mode 100644 EX3.ipynb
 create mode 100644 EX3.png
 create mode 100644 EX3_beam.png
 create mode 100644 EX3_beamM.png
 create mode 100644 EX3_ved_neg.png
 create mode 100644 EX3_ved_pos.png
 create mode 100644 Issues.ipynb
 create mode 100644 Moment-Curvature.png
 create mode 100644 My_Mz_N_-500kN.png
 create mode 100644 My_Mz_N_0kN.png
 create mode 100644 N2000_my_min.png
 create mode 100644 N_My.png
 create mode 100644 SLS_section_response.py
 create mode 100644 error_N_M.png
 create mode 100644 structuralcodes/plots/__init__.py
 create mode 100644 structuralcodes/plots/section_plots.py

diff --git a/EX1&2Beam.png b/EX1&2Beam.png
new file mode 100644
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diff --git a/EX1_part1.ipynb b/EX1_part1.ipynb
new file mode 100644
index 00000000..fbd92eaa
--- /dev/null
+++ b/EX1_part1.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instantaneous and delayed deflections on a concrete and composite beam. Obtaining camber of the beam\n",
+    "\n",
+    "A concrete beam with a span of L=6.75 m is considered, simply supported and subjected to the following uniformly distributed loads:\n",
+    "\n",
+    "- self weight: qpp=12.2 kN/m\n",
+    "- Dead load: qcm=5.2 kN/m\n",
+    "- Live load: qsc=11.7 kN/m\n",
+    "\n",
+    "It is considered that 30% of the live load is quasi-permanent (ψ2=0.3)\n",
+    "\n",
+    "![beam](EX1&2Beam.png)\n",
+    "\n",
+    "The section, 0.35 m wide and 0.50 m heigth, is reinforced with 6Φ20 on the lower face and 3Φ12 on the upper face. For calculation purposes, the cover is 5 cm. The concrete is class C25/30.\n",
+    "\n",
+    "The load history is as follows:\n",
+    "- After 14 days, a load equal to the characteristic load is removed and acts on this element.\n",
+    "- After a short time, 70% of the overload is removed, leaving the quasi-permanent charge that is supposed to be maintained indefinitely over time.\n",
+    "\n",
+    "As rheological parameters, φ(t,14)=2.5 and εc=0.4 mm/m can be adopted.\n",
+    "\n",
+    "It is requested:\n",
+    "1. Determine the total deflection at infinite time due to the quasi-permanent load. To do this, the method of EN 1992-1-1 7.4.3 will be applied and a discretization of the span into 10 parts will be considered.\n",
+    "2. Compare with the results if the equivalent stiffness of the center of the span is considered for the entire span."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from structuralcodes.codes.ec2_2004 import __materials__\n",
+    "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n",
+    "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023, ReinforcementEC2_2004\n",
+    "from shapely import Polygon\n",
+    "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry\n",
+    "from structuralcodes.sections._reinforcement import (\n",
+    "    add_reinforcement,\n",
+    "    add_reinforcement_line,\n",
+    ")\n",
+    "from structuralcodes.sections._generic import GenericSection\n",
+    "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle,UserDefined\n",
+    "from structuralcodes.plots.section_plots import draw_section\n",
+    "import math\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "long_beam = 6.75\n",
+    "\n",
+    "# caracteriscic load\n",
+    "qk=12.2 + 5.2 + 11.7 # kN/m\n",
+    "# cuasipermant load\n",
+    "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n",
+    "\n",
+    "eps_cs = 0.4\n",
+    "fi = 2.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Long-term mechanical properties of the uncracked section:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ecm = 31476  MPa\n",
+      "Ec_eff = 8993 MPa\n",
+      "Ec = 16667 MPa\n",
+      "Reinf. area = 495 cm2\n",
+      "Sect. area = 1750 cm2\n",
+      "I11_1 = 541390 cm4\n",
+      "cz_1 = 28.06 cm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "Ec_eff=concrete.Ecm/(1+fi)\n",
+    "Ec = concrete.constitutive_law.get_tangent(0)[0]\n",
+    "print(f\"Ecm = {round(concrete.Ecm)}  MPa\")\n",
+    "print(f\"Ec_eff = {round(Ec_eff)} MPa\")\n",
+    "print(f\"Ec = {round(Ec)} MPa\")\n",
+    "\n",
+    "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforfocement\" with reinforcement areas multiplied by Es/Ec is used.\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)   \n",
+    "n=200000/Ec_eff\n",
+    "\n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "i11_1 = sec.gross_properties.i11\n",
+    "cz_1=sec.gross_properties.cz\n",
+    "print(f\"Reinf. area = {round(sec.gross_properties.area_reinforcement/(10**2))} cm2\")\n",
+    "print(f\"Sect. area = {round(sec.gross_properties.area/(10**2))} cm2\")\n",
+    "print(f\"I11_1 = {round(i11_1/(10**4))} cm4\") \n",
+    "print(f\"cz_1 = {round(cz_1/(10),2)} cm\") \n",
+    "\n",
+    "draw_section(sec,\"gross 'homogeneus' section\",math.sqrt(n))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mechanical properties of the cracked section at long term:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eps_z0 -2.1936412900686266e-05\n",
+      "x (mm) from z=0: 219.4\n",
+      "I11_2 = 367781 cm4\n",
+      "cz_2 = 21.91 cm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the cracked cross section\n",
+    "curv = 1e-7 # Curvature which produces elastic stresses in the most compressed fibre of the concrete section\n",
+    "eps =  sec.section_calculator.find_equilibrium_fixed_curvature(sec.geometry,0,curv,0)[0]\n",
+    "x=-eps/curv # Distance from the bottom to the neutral fibre\n",
+    "print(\"eps_z0\",eps)\n",
+    "print(\"x (mm) from z=0:\",round(x,1))\n",
+    "\n",
+    "# 2) Create the cracked section\n",
+    "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
+    "geo2 = SurfaceGeometry(poly2, concrete)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "sec2 = GenericSection(geo2)\n",
+    "i11_2 = sec2.gross_properties.i11\n",
+    "cz_2 = sec2.gross_properties.cz\n",
+    "print(f\"I11_2 = {round(i11_2/(10**4))} cm4\") \n",
+    "print(f\"cz_2 = {round(cz_2/(10),2)} cm\") \n",
+    "draw_section(sec2,\"cracked section\",math.sqrt(n))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the cracking moment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OO8oyCoUCEokE+/btK3SbvE475XI5unTpgidPnmDChAmoW7cuLC0t8eDBAwwfPlzlbyD/iOPEiRO4ffs2mjVrBktLS7Rr1w6//PILMjIyEBkZqbxmrK6yqvdVZfm3mr+d3n//fQwbNqzQMq9eu1YnnpL2mdqgje2idwkReHH66bPPPsNnn32G5ORkNGvWDDNmzFAmJE1PxeVfLLa2tkbnzp1fe/0A8MYbb+CNN97A999/j9OnT6NNmzZYtmwZpk+fjvnz56v8KnFzc1P+38zMDL1790bv3r2hUCjw2Wef4ffff8fkyZNRs2bNUp1m3L17N7Kzs7Fr1y6VX0mvewqqJE5OTrC2tsbVq1eLLZf/Sy0lJUWl08Srv/BK4u3tjRYtWmDTpk0YPXo0tm3bhr59+6p01/bx8cGhQ4fQpk0bjW/VyD8FWlx7nJycUKVKFURHRxeY999//8HIyAgeHh4arTdfy5Yt0bJlS8yYMQMbNmzAe++9h40bN+Kjjz7S+HtRVHk7OzukpKQUmF7YZ6HJOi0tLdGrVy+EhoZiwYIF2LRpE9q1a6fy3ffx8YEQAt7e3qhdu7badavjypUruHHjBtauXYuhQ4cqpxd2ecLT0xOenp4IDw/H7du3lafW2rdvj3HjxiE0NBRyuRzt27fXKAZt1Vteow4Vth4nJyfIZDLI5XK19pWaKG6fWRgvL68i/87y52ubXp0ylcvlBQ6jnZ2d4ebmpnL6yNLSUq3TgPl8fX3h4+ODn376CRkZGQXmP3z4UO31p6WlIS8vT6XMG2+8ASMjI2UZX19fdO7cWfmqX78+AODx48cqyxkZGSl/beUva2lpCQCF7rSKkv/L6OVfQqmpqQgJCVG7jtIwMjJC3759sXv3bly8eLHA/Px48n+QnDhxQjkvMzNT7aPilwUFBeHs2bNYvXo1Hj16pHJKDnhxhC+XyzFt2rQCy+bl5RW7XZ2cnNC+fXusXr0ad+/eLbQtxsbGCAgIwM6dO1VGEklKSsKGDRvQtm1bWFtba9Smp0+fFvgVm38mI/97UaVKFQDqfy8sLS0LLevj44PU1FRcvnxZOS0hIQHbt29Xu46iBAUFIT4+HitXrsQ///xT4LPp378/jI2N8cMPPxRorxCiwN+HJgr7GxBC4Oeffy60fLt27XDkyBGcP39embiaNGkCmUyG2bNnw8LCAr6+vhrHoY16S7MPKI3CPl9jY2MMGDAAW7duLfSHYf6+UhPq7DML06NHD5w/f155Gh54sd9Yvnw5qlevrtyvapNeHSGmp6fD3d0db7/9Nho3bgwrKyscOnQIFy5cwPz585XlfH19sWnTJowbNw7NmzeHlZUVevfuXWS9RkZGWLlyJbp3744GDRpgxIgRqFatGh48eICjR4/C2toau3fvVmv9R44cwejRo/HOO++gdu3ayMvLw7p165RfpOJ89NFHePLkCTp27Ah3d3fcuXMHv/76K5o0aaI8L96kSRMYGxtjzpw5SE1NhVQqRceOHeHs7FxkvQEBAcojz5EjRyIjIwMrVqyAs7MzEhISNPkINDZz5kwcPHgQ/v7+CA4ORr169ZCQkIDQ0FCcPHkStra2CAgIgKenJz788EN8/fXXMDY2xurVq+Hk5FQg8ZRk4MCBGD9+PMaPHw97e/sCv2L9/f0xcuRIzJo1C1FRUQgICICpqSlu3ryJ0NBQ/Pzzz3j77beLrP+XX35B27Zt0axZMwQHB8Pb2xtxcXH4+++/ERUVBQCYPn06wsLC0LZtW3z22WcwMTHB77//juzsbMydO1fjbbh27VosWbIE/fr1g4+PD9LT07FixQpYW1ujR48eAF6cRq5fvz42bdqE2rVrw97eHg0bNizyeqevry8OHTqEBQsWwM3NDd7e3vDz88OgQYMwYcIE9OvXD2PGjEFWVhaWLl2K2rVrF+gsUVQdRenRowdkMhnGjx9f6N+Dj48Ppk+fjokTJyIuLg59+/aFTCZDbGwstm/fjuDgYIwfP17j7QcAdevWhY+PD8aPH48HDx7A2toaW7duLfL6Ubt27bB+/XpIJBLlqU5jY2O0bt0aBw4cQIcOHUp1y5A26i3NPqA0ivp8Z8+ejaNHj8LPzw8ff/wx6tevjydPniAiIgKHDh3CkydPNFpPafeZ3377Lf766y90794dY8aMgb29PdauXYvY2Fhs3bq1bAZbUbs/ajnIzs4WX3/9tWjcuLHyhufGjRsXuME8IyNDvPvuu8LW1rbQG/OL6t4bGRkp+vfvLxwcHIRUKhVeXl5i4MCB4vDhw2qv//bt2+KDDz4QPj4+wtzcXNjb24u33npLHDp0qMT2bdmyRQQEBChvdvX09BQjR44UCQkJKuVWrFghatSoIYyNjQu9Kbcwu3btEo0aNVLe1D1nzhyxevXqArc1FHXbxavbLP9m8JCQkBLbdefOHTF06FDh5OQkpFKpqFGjhhg1apRKF+hLly4JPz8/ZbsXLFhQ7I35xWnTpo0AID766KMiyyxfvlz4+voKCwsLIZPJxBtvvCG++eYbER8fX2J7rl69Kvr16ydsbW2Fubm5qFOnjpg8ebJKmYiICNG1a1dhZWUlqlSpIt566y1x+vRplTL57Xv1dopXbxmIiIgQgwcPFp6ensqBBHr16iUuXryostzp06eFr6+vMDMzK/bGfCGE+O+//0T79u2FhYVFgZuuDx48KBo2bCjMzMxEnTp1xJ9//qlRHYV9bvnee+895a0mRdm6dato27atsLS0FJaWlqJu3bpi1KhRIjo6ushlXlXYbRf//vuv6Ny5s7CyshKOjo7i448/Fv/880+h3+Nr164pu+q/bPr06QJAgc9bXaWpt7Ab0jXdB7z6d62O4r4jSUlJYtSoUcLDw0OYmpoKFxcX0alTJ7F8+XJlGXX3HeruM4u7MT//b7FFixZF3pj/OvuwfBIhtHAlloiIyMDp1TVEIiIiXdGra4hEVLk9e/asxA5z9vb25TpIfWpqKp49e1ZsmZeHE9S1xMTEYudbWFgUO8RkZcZTpkSkN/LHoC3O0aNHy/Uh18OHDy+xR7Q+7UZLum0j/75oKogJkYj0RkJCAq5du1ZsGV9fX+W9reXh33//LXEoSW3fs/c6Dh06VOx8Nze3MrlloSJgQiQiIgI71RAREQFgpxqtUigUiI+Ph0wmK7fhl4iIypIQAunp6XBzcyubm+H1CBOiFsXHx5d6HEsiIn127949rQ3Era+YELVIJpMBePHF0XQ8y+IkJiYiJCQEI0aM0Kvu3URU8aWlpcHDw0O5f6vImBC1KP80qbW1tVYTYmZmJszNzSGTybRaLxGRuirDZaCKfUKYiIhITUyIREREYEIkIiICwIRIREQEgAmRiIgIABMiERERACZEIiIiAAaWEHNzczFhwgS88cYbsLS0hJubG4YOHVpgJPrq1atDIpGovGbPnl1s3c+fP8eoUaPg4OAAKysrDBgwAElJSWXZHCIi0iMGlRCzsrIQERGByZMnIyIiAtu2bUN0dDQCAwMLlP3xxx+RkJCgfH3++efF1j127Fjs3r0boaGhOH78OOLj49G/f/+yagoREekZgxqpxsbGBmFhYSrTFi9ejBYtWuDu3bvw9PRUTpfJZGoPc5aamopVq1Zhw4YN6NixIwAgJCQE9erVw9mzZ9GyZUvtNYJIzymEAsmZyUjKSEJadhrSstOQLc/WdVikBRJI0K9eP12HobcMKiEWJjU1FRKJBLa2tirTZ8+ejWnTpsHT0xPvvvsuxo4dCxOTwpt76dIl5Obmqjzks27duvD09MSZM2eKTIjZ2dnIzv6/HUVaWtrrN4ioDMkVciRlJuFe6j3cT7v/f6/0//v/g7QHyFXk6jpUKgMmRibInczPtigGnRCfP3+OCRMmYPDgwSpjfI4ZMwbNmjWDvb09Tp8+jYkTJyIhIQELFiwotJ7ExESYmZkVSKpVq1ZFYmJikeufNWsWfvjhB620hags3Eu9h7DbYThx5wRO3TuF2KexkAt5ictJIIGTpRNspDawllpDaiKFBBV/LMuKztjIWNch6DW9Tojr16/HyJEjle/37duHdu3aAXjRwWbgwIEQQmDp0qUqy40bN075/0aNGsHMzAwjR47ErFmzIJVKtRbfxIkTVdaVPyo8kS7JFXLsvbkXyy4tw76b+yAgVOYbS4zhKnOFh7UH3K3dUU1WDR42HvCw9kA162rwsPaAi5ULTI1NddQCIt3Q64QYGBgIPz8/5ftq1aoB+L9keOfOHRw5cqTEJ0D4+fkhLy8PcXFxqFOnToH5Li4uyMnJQUpKispRYlJSUrHXIaVSqVYTLNHriE+Px6qIVVgRsQL30u4pp7f2aA1/L3+092qPhs4N4WrlyiMFokLodUKUyWQFnsGVnwxv3ryJo0ePwsHBocR6oqKiYGRkBGdn50Ln+/r6wtTUFIcPH8aAAQMAANHR0bh79y5atWr1+g0hKiMKocCh24ew7OIy7IrepTwd6mDhgA+afoBg32DUtK+p4yiJDINeJ8RX5ebm4u2330ZERAT27NkDuVyuvMZnb28PMzMznDlzBufOncNbb70FmUyGM2fOYOzYsXj//fdhZ2cHAHjw4AE6deqEP/74Ay1atICNjQ0+/PBDjBs3Dvb29rC2tsbnn3+OVq1asYcp6aXkzGSERIZgecRy3H56Wzm9nWc7fPLmJ+hfrz/MTcx1GCGR4TGohPjgwQPs2rULANCkSROVeUePHkWHDh0glUqxceNGTJ06FdnZ2fD29sbYsWNVrvXl5uYiOjoaWVlZymkLFy6EkZERBgwYgOzsbHTt2hVLliwpl3YRqetK0hXMOjkLW/7douwJaiO1wdDGQzHSdyQaODfQcYREhksihBAlFyN1pKWlwcbGBqmpqVp9sn1CQgKWL1+O4OBguLq6aq1eMhw3Ht/AlGNTsOnqJmUnmeZuzTHSdyQGNRwESzNLHUdIFVVZ7df0kUEdIRJVNnKFHHNOzcGUY1OQp8gDALxT/x1MaDMBvm6+Oo6OqGJhQiTSU7FPYzFk+xCcuncKANC9ZnfM6DgDTV2b6jgyooqJCZFIzwgh8OflPzFq7yik56RDZibD4h6LMaTREEgkvDmeqKwwIRLpkSfPnuDTvz/F5mubAQBtPNpgXb918Lbz1nFkRBUfEyKRnjgSewRDtw/Fg/QHMDEywVT/qZjQdgJMjPhnSlQe+JdGpGPZedmYdGQS5p+ZDwCoZV8L6/uvR/NqzXUcGVHlwoRIpEPXkq/h3W3v4nLSZQDASN+RmB8wn7dREOkAEyKRDiiEAovPL8Y3Yd8gW54NxyqOWBW4CoF1Cj7smojKBxMiUTlLfZ6KIduHYPeN3QCAHrV6YFXgKrhYqfdAayIqG0yIROXo+sPr6LupL248vgGpsRQLui7Ap29+ytspiPQAEyJROQmLCcOAzQOQnpMOD2sPbA/aztFmiPQIEyJROTgYcxCBfwUiW54Nfy9/bH5nM5wtC38cGRHpBhMiURk7dPsQ+mzsg2x5NgLrBCL0nVCYGZvpOiwieoWRrgMgqsgO3z6M3n/1xvO850yGRHqOCZGojBy+fRi9/uqF53nP0bt2byZDIj3HhEhUBl4+MuxVuxeTIZEBYEIk0rIjsUfQ+6/eeJb3DD1r9cSWd7ZAaiLVdVhEVAImRCItOhJ7BL029MKzvGfoUasHtg7cymRIZCCYEIm05GjsUSZDIgPGhEikBcfijqHXXy+SYfea3bF14FaYm5jrOiwi0gATItFrOh53HD039ERWbha61eyGbUHbmAyJDBATItFrOHHnBHps6KFMhtuDtjMZEhkoJkSiUrrw4ILyyLCrT1cmQyIDx4RIVArXH15H9/XdkZGTgY7eHbFj0A4mQyIDx4RIpKE7KXcQ8GcAHj97jOZuzbEjiMmQqCJgQiTSQFZuFnr/1Rv30+6jnmM97H1vL2RSma7DIiItYEIkUpMQAp/+/SmuJF9BVcuqOPD+AThWcdR1WESkJUyIRGpaGbESf/zzB4wkRtj49kZ42HjoOiQi0iImRCI1RCRE4PN9nwMAZnScgQ7VO+g2ICLSOiZEohI8ffYUb29+G9nybPSu3RvftPlG1yERURlgQiQqhkIoMGzHMMSmxMLb1htr+66FkYR/NkQVEf+yiYrx0+mfsPvGbkiNpdgycAvsLOx0HRIRlREmRKIiHI87ju8OfwcA+KX7L2jm2kzHERFRWWJCJCpEQnoCgrYEQS7kGNJoCD5u9rGuQyKiMsaESPSKPEUeBm0dhKTMJDR0boilPZdCIpHoOiwiKmNMiESvmHR4Ek7cOQGZmQxb3tkCSzNLXYdEROWACZHoJbuid2Hu6bkAgNV9VqOOYx0dR0RE5YUJkej/i3kSg6HbhwIAvvD7Am/Xf1vHERFReWJCJALwLPcZ3g59G6nZqWjl3gpzu8zVdUhEVM6YEIkAjNk3BlGJUXCs4ojN72yGmbGZrkMionLGhEiV3vrL67EyciUkkGBD/w1wt3bXdUhEpANMiFSp3Xx8E5/8/QkA4H/+/0MXny46joiIdIUJkSqt7LxsDNo6CBk5GfD38sfk9pN1HRIR6RATIlVa3x76FhEJEXCwcMCf/f+EsZGxrkMiIh1iQqRK6fS901h0bhEAYE3fNbxuSERMiFT55MpzMXLPSADAiCYj0Kt2Lx1HRET6gAmRKp35Z+bjavJVOFZxxLwu83QdDhHpCSZEqlRuP72NH47/AABYELAADlUcdBwREekLJkSqNIQQ+Ozvz/A87zk6enfE+43e13VIRKRHmBCp0th0bRMOxByA1FjKRzoRUQFMiFQppDxPwZf7vwQAfNfuO9R2qK3bgIhI7zAhUqUw8dBEJGUmoY5DHUxoM0HX4RCRHmJCpArvzL0zWHZpGQDg916/Q2oi1XFERKSPmBCpQsuV5yJ4TzCAF/cc+lf313FERKSvmBCpQltwZgGuJl+Fg4UDn3FIRMViQqQK6+V7DucHzIdjFUcdR0RE+owJkSokIQRG7R2FZ3nP0KF6BwxtPFTXIRGRnmNCpAop9N9Q7L+1H2bGZljWcxnvOSSiEjEhUoWT8jwFX+z/AgAwse1E1HGso+OIiMgQ6FVC3LZtGwICAuDg4ACJRIKoqKgCZRITEzFkyBC4uLjA0tISzZo1w9atW4ut98SJE+jduzfc3NwgkUiwY8eOUq2bDMN3h79DYkYiajvUxsS2E3UdDhEZCL1KiJmZmWjbti3mzJlTZJmhQ4ciOjoau3btwpUrV9C/f38MHDgQkZGRxdbbuHFj/Pbbb6+1btJ/Z+6dwbKLvOeQiDRnousAXjZkyBAAQFxcXJFlTp8+jaVLl6JFixYAgO+//x4LFy7EpUuX0LRp00KX6d69O7p37/7a6yb9lv+cQwGB4U2Go0P1DroOiYgMiF4dIaqjdevW2LRpE548eQKFQoGNGzfi+fPn6NChQ7nHkp2djbS0NJUX6c7P537GleQrcLBw4HMOiUhjBpcQN2/ejNzcXDg4OEAqlWLkyJHYvn07atasWe6xzJo1CzY2NsqXh4dHucdAL9xLvYepx6YCAOZ2mct7DolIYzpLiOvXr4eVlZXyFR4ertZykydPRkpKCg4dOoSLFy9i3LhxGDhwIK5cuVLGERc0ceJEpKamKl/37t0r9xjohbEHxiIzNxNtPNpgeJPhug6HiAyQzq4hBgYGws/PT/m+WrVqJS4TExODxYsX4+rVq2jQoAEAoHHjxggPD8dvv/2GZcuWlVm8hZFKpZBK2WlD1/bd3Iet17fCWGKMJT2XwEhicCc+iEgP6CwhymQyyGQyjZbJysoCABgZqe7wjI2NoVAotBYbGY7nec/x+b7PAQBf+H2BRlUb6TgiIjJUetXL9MmTJ7h79y7i4+MBANHR0QAAFxcXuLi4oG7duqhZsyZGjhyJn376CQ4ODtixYwfCwsKwZ88eZT2dOnVCv379MHr0aABARkYGbt26pZwfGxuLqKgo2Nvbw9PTU611k35acmEJYp7GwE3mhqkdpuo6HCIyZEKPhISECAAFXlOmTFGWuXHjhujfv79wdnYWVapUEY0aNRJ//PGHSj1eXl4qyxw9erTQeocNG6bRukuSmpoqAIjU1NRSboHCxcfHi6lTp4r4+Hit1mvoUp+nCoc5DgJTIVZeWqnrcIgqpLLar+kjiRBClHsWrqDS0tJgY2OD1NRUWFtba63ehIQELF++HMHBwXB1ddVavYbuh2M/YOrxqajjUAdXP7sKEyO9OuFBVCGU1X5NH7H3ARmkR1mPMP/MfADAtLemMRkS0WtjQiSDNCt8FtJz0tHUpSkG1B+g63CIqAJgQiSDcz/tPn678GJc2pmdZvI2CyLSCu5JyOD8ePxHZMuz0d6rPbr6dNV1OERUQTAhkkG5+fgmVkeuBgDM7DiTD/4lIq1hQiSD8r9j/4NcyNGzVk+08Wyj63CIqAJhQiSD8U/iP9h4dSMAYEbHGTqOhogqGiZEMhiTjkwCAAxqOAiNXRrrOBoiqmiYEMkgnLp7Cn/f/BvGEmP82OFHXYdDRBUQEyLpPSEEJh6eCAD4sOmHqOVQS8cREVFFxIRIem//rf0IvxsOqbEUk/0n6zocIqqgmBBJrymEQnntcHSL0XC3dtdxRERUUTEhkl7b8u8WRCZGQmYmw7dtv9V1OERUgTEhkt7KU+Rh8tEXp0i/avUVHKs46jgiIqrImBBJb62NWosbj2/AwcIBY1uN1XU4RFTBMSGSXnqe9xxTj08FAHzX7jtYSyv2c9iISPeYEEkvLb2wFPfT7sPd2h2fNf9M1+EQUSXAhEh6Jz07HTNPzgQATPGfAnMTcx1HRESVARMi6Z2FZxfiUdYj1HaojeFNhus6HCKqJJgQSa88efYE88/MBwD82OFHmBiZ6DgiIqosmBBJr8w9NRdp2WloXLUx3mnwjq7DIaJKRK2f32lpaRpXbG3NXoGkmYT0BPxy7hcAwPSO02Ek4e81Iio/aiVEW1tbjZ5MLpFIcOPGDdSoUaPUgVHlMzN8Jp7lPUNL95boWaunrsMhokpG7Qs0W7Zsgb29fYnlhBDo0aPHawVFlc+dlDv4/dLvAF48/FeTH2BERNqgVkL08vJC+/bt4eDgoFalNWrUgKmp6WsFRpXLvNPzkKvIRUfvjujo3VHX4RBRJaRWQoyNjdWo0qtXr5YqGKqcHmU9wurI1QCA79t9r+NoiKiyYq8F0rmlF5biWd4zNHNthg7VO+g6HCKqpEp1k1dmZiaOHz+Ou3fvIicnR2XemDFjtBIYVQ7P857j1/O/AgC+bv01rx0Skc5onBAjIyPRo0cPZGVlITMzE/b29nj06BGqVKkCZ2dnJkTSyB///IGHWQ/hZeOFt+u/retwiKgS0/iU6dixY9G7d288ffoUFhYWOHv2LO7cuQNfX1/89NNPZREjVVAKoVCOSjO25ViOSkNEOqVxQoyKisJXX30FIyMjGBsbIzs7Gx4eHpg7dy6+++67soiRKqjd0btx4/EN2Jrb4sNmH+o6HCKq5DROiKampjAyerGYs7Mz7t69CwCwsbHBvXv3tBsdVWg/nXlxRuET309gZWal42iIqLLT+BxV06ZNceHCBdSqVQv+/v743//+h0ePHmHdunVo2LBhWcRIFdDZ+2dx8u5JmBqZYowfrzsTke5pfIQ4c+ZMuLq6AgBmzJgBOzs7fPrpp3j48CGWL1+u9QCpYvrp9Iujw/cbvQ9XmauOoyEi0vAIUQgBGxsbWFhYIC8vD87Ozti/f39ZxUYVVMyTGGy7vg0A8FWrr3QcDRHRC2ofIcbGxqJRo0aoW7cuGjVqBB8fH1y8eLEsY6MKasGZBRAQ6FGrBxo4N9B1OEREADRIiF9//TXy8vLw559/YsuWLXB3d8fIkSPLMjaqgB5lPUJIVAgAYHyr8TqOhojo/6h9yvTkyZPYsmUL2rZtCwBo2bIl3N3dkZmZCUtLyzILkCqWJReWcJg2ItJLah8hJicno1atWsr3rq6usLCwQHJycpkERhXPs9xnWHx+MQAO00ZE+kftI0SJRIKMjAxYWFgopxkZGSE9PR1paWnKadbW1tqNkCqMdZfX4WHWQ3jaeHKYNiLSO2onRCEEateuXWBa06ZNlf+XSCSQy+XajZAqBA7TRkT6Tu290tGjR8syDqrgVIZpa8ph2ohI/6idEP39/csyDqrg5p2eB+DFMG0yqUzH0RARFcQHBFOZO3PvDE7dOwVTI1N87ve5rsMhIiqU2keIxsbGapXjNUR6Vf61w/cbvQ83mZuOoyEiKpxGnWq8vLwwbNgwZUcaopJwmDYiMhRqJ8Tz589j1apV+Pnnn+Ht7Y0PPvgA7733Huzs7MoyPjJwHKaNiAyF2tcQ33zzTSxduhQJCQkYN24ctm/fDnd3dwwaNAhhYWFlGSMZKA7TRkSGRONONebm5nj//fdx+PBhXL16FcnJyejWrRuePHlSFvGRAVt6YSme5T2Dr6svh2kjIr1Xqruj79+/jzVr1mDNmjXIysrC119/zRFqSMWz3Gf49fyvAIDxrcdzmDYi0ntqJ8ScnBxs374dq1atQnh4OLp3745Fixahe/fuavdApcojf5g2LxsvDtNGRAZB7YTo6uoKmUyGYcOGYcmSJXB2dgYAZGZmqpTjkSJxmDYiMkRq76mePn2Kp0+fYtq0aZg+fXqB+RzLlPK9PEzbB00/0HU4RERq4VimpHU/nfkJAIdpIyLDwrFMSavO3j+Lk3dPwtTIFGP8xug6HCIital128XLzztUR3p6eqmCIcO38OxCAC+GaXOVueo4GiIi9amVEO3s7JCcnKx2pdWqVcPt27dLHRQZpqSMJOUwbV/4faHjaIiINKPWKVMhBFauXAkrKyu1Ks3NzX2toMgwrYlagzxFHvyq+aGxS2Ndh0NEpBG1EqKnpydWrFihdqUuLi4wNTUtdVBkeBRCgRURL74jwb7BOo6GiEhzap0yjYuLQ2xsrEYvDw8PjYPZtm0bAgIC4ODgAIlEgqioqAJlli9fjg4dOsDa2hoSiQQpKSkl1jtr1iw0b94cMpkMzs7O6Nu3L6Kjo5Xznzx5gs8//xx16tSBhYUFPD09MWbMGKSmpmrchsrqSOwRxDyNgbXUGkENgnQdDhGRxvTqAcGZmZlo27Yt5syZU2SZrKwsdOvWDd99953a9R4/fhyjRo3C2bNnERYWhtzcXAQEBCgHFYiPj0d8fDx++uknXL16FWvWrMH+/fvx4YcfvnabKovll5YDAN5/431YmlnqOBoiIs3p1RAiQ4YMAfDiiLQoX375JQDg2LFjate7f/9+lfdr1qyBs7MzLl26hPbt26Nhw4bYunWrcr6Pjw9mzJiB999/H3l5eTAx0avNpHeSMpKw/b/tAHi6lIgMV6Xc0+efCrW3ty+2jLW1dbHJMDs7G9nZ2cr3mt6eUlHkd6ZpUa0FO9MQkcHSq1Om5UGhUODLL79EmzZt0LBhw0LLPHr0CNOmTUNwcPFHO7NmzYKNjY3yVZrrpoZOIRRYHvHidGlwMx4dEpHh0llCXL9+PaysrJSv8PDwclnvqFGjcPXqVWzcuLHQ+WlpaejZsyfq16+PqVOnFlvXxIkTkZqaqnzdu3evDCLWb0dij+D209uwllpjUMNBug6HiKjUND5lun//flhZWaFt27YAgN9++w0rVqxA/fr18dtvv8HOzk6tegIDA+Hn56d8X61aNU1D0djo0aOxZ88enDhxAu7u7gXmp6eno1u3bpDJZNi+fXuJt45IpVJIpdKyCtcgsDMNEVUUGh8hfv3118prZVeuXMFXX32FHj16IDY2FuPGjVO7HplMhpo1aypfFhYWmoaiNiEERo8eje3bt+PIkSPw9vYuUCYtLQ0BAQEwMzPDrl27YG5uXmbxVBTsTENEFYnGR4ixsbGoX78+AGDr1q3o1asXZs6ciYiICPTo0eO1gnny5Anu3r2L+Ph4AFDeK+ji4gIXFxcAQGJiIhITE3Hr1i0AL5KyTCaDp6enspNMp06d0K9fP4wePRrAi9OkGzZswM6dOyGTyZCYmAgAsLGxgYWFhTIZZmVl4c8//0RaWpoy6Ts5OfEByEXgyDREVJFofIRoZmaGrKwsAMChQ4cQEBAA4EWPzdftZblr1y40bdoUPXv2BAAMGjQITZs2xbJly5Rlli1bhqZNm+Ljjz8GALRv3x5NmzbFrl27lGViYmLw6NEj5fulS5ciNTUVHTp0gKurq/K1adMmAEBERATOnTuHK1euoGbNmiplKuN1QXVwZBoiqmgkQgihyQKBgYHIyclBmzZtMG3aNMTGxqJatWo4ePAgRo8ejRs3bpRVrHovLS0NNjY2yls2tCUhIQHLly9HcHAwXF314wkSh24fQpd1XWAttUb8uHhePySqoMpqv6aPND5CXLx4MUxMTLBlyxYsXbpU2Rlm37596Natm9YDJP3EzjREVNFofA3R09MTe/bsKTB94cKFWgmI9B870xBRRaTxEWJERASuXLmifL9z50707dsX3333HXJycrQaHOkndqYhoopI44Q4cuRI5XXC27dvY9CgQahSpQpCQ0PxzTffaD1A0i8vj0wz0nekjqMhItIejRPijRs30KRJEwBAaGgo2rdvjw0bNmDNmjUqA2RTxfTyyDQDGwzUdThERFqjcUIUQkChUAB4cdtF/r2HHh4eKrc6UMX0+6XfAbAzDRFVPBonxDfffBPTp0/HunXrcPz4ceU9g7GxsahatarWAyT9kZSRhB3/7QDAzjREVPFonBAXLVqEiIgIjB49GpMmTULNmjUBAFu2bEHr1q21HiDpj5CoEHamIaIKS+PbLho1aqTSyzTfvHnzOMRZBfbyyDTsTENEFVGpHv+UkpKClStXYuLEiXjy5AkA4N9//0VycrJWgyP9cfj2Ydx+ehs2UhsENQzSdThERFqn8RHi5cuX0alTJ9ja2iIuLg4ff/wx7O3tsW3bNty9exd//PFHWcRJOpZ/q8X7jd5HFdMqOo6GiEj7ND5CHDduHEaMGIGbN2+qPCKpR48eOHHihFaDI/2QmJGo7EzD06VEVFFpnBAvXLiAkSML7hSrVaumfKwSVSz5I9O0dG+JN6q+oetwiIjKhMYJUSqVFvqYpxs3bsDJyUkrQZH+YGcaIqosNE6IgYGB+PHHH5GbmwsAkEgkuHv3LiZMmIABAwZoPUDSrZc703BkGiKqyDROiPPnz0dGRgacnZ3x7Nkz+Pv7o2bNmpDJZJgxY0ZZxEg6lN+ZZkijIexMQ0QVmsa9TG1sbBAWFoZTp07hn3/+QUZGBpo1a4bOnTuXRXykQy93puHINERU0WmUEHNzc2FhYYGoqCi0adMGbdq0Kau4SA/kd6Zp5d6KnWmIqMLT6JSpqakpPD09IZfLyyoe0hMvd6bh0SERVQYaX0OcNGkSvvvuO+UINVQxsTMNEVU2Gl9DXLx4MW7dugU3Nzd4eXnB0lL1EUARERFaC450h51piKiy0Tgh9unTBxKJpCxiIT3xOOsxdv63EwDwse/HOo6GiKh8aJwQp06dWgZhkD4J/TcUuYpcNHVpikZVG+k6HCKicqHxNcQaNWrg8ePHBaanpKSgRo0aWgmKdOvPy38CeDGQNxFRZaFxQoyLiyu0l2l2djbu37+vlaBId24/vY1T907BSGKEQQ0H6TocIqJyo/Yp0127din/f+DAAdjY2Cjfy+VyHD58GN7e3tqNjsrdhisbAACdvDvBTeam42iIiMqP2gmxb9++AF6MXTps2DCVeaampqhevTrmz5+v1eCofAkheLqUiCottROiQqEAAHh7e+PChQtwdHQss6BINy4lXEL042hYmFigX91+ug6HiKhcadzLNDY2tiziID2w7p91AIC+dftCJpXpOBoiovKldqeaM2fOYM+ePSrT/vjjD3h7e8PZ2RnBwcHIzs7WeoBUPvIUedh4bSMA4L033tNxNERE5U/thPjjjz/i2rVryvdXrlzBhx9+iM6dO+Pbb7/F7t27MWvWrDIJksreoduHkJyZDMcqjgjwCdB1OERE5U7thBgVFYVOnTop32/cuBF+fn5YsWIFxo0bh19++QWbN28ukyCp7OV3pglqEARTY1MdR0NEVP7UTohPnz5F1apVle+PHz+O7t27K983b94c9+7d0250VC4ycjKw/b/tANi7lIgqL7UTYtWqVZUdanJychAREYGWLVsq56enp8PUlEcWhmjnfzuRlZsFHzsf+FXz03U4REQ6oXZC7NGjB7799luEh4dj4sSJqFKlCtq1a6ecf/nyZfj4+JRJkFS21l9ZD+BFZxoO3E5ElZXat11MmzYN/fv3h7+/P6ysrLB27VqYmZkp569evRoBAeyMYWiSM5NxMOYgAOC9RuxdSkSVl9oJ0dHRESdOnEBqaiqsrKxgbGysMj80NBRWVlZaD5DK1qarmyAXcrzp9iZqO9TWdThERDqj8Y35L49h+jJ7e/vXDobK359XXvQu5b2HRFTZafy0C6o4bj6+ifMPzvPJFkREYEKs1PI703Sp0QUuVi46joaISLeYECspIYQyIfLeQyIiJsRK60L8Bdx6cgtVTKugb92+ug6HiEjnmBArqfyh2vrW7QsrM/YOJiJiQqyEcuW52HiVT7YgInoZE2IldOj2ITzMeginKk7oUqOLrsMhItILTIiVUH5nGj7Zgojo/zAhVjJ8sgURUeGYECuZ/Cdb1LSviRbVWug6HCIivcGEWMm8PFQbn2xBRPR/mBArkaSMJITFhAFg71IiolcxIVYim669eLJFi2otUMuhlq7DISLSK0yIlYhyqLY32JmGiOhVTIiVRP6TLYwlxghqGKTrcIiI9A4TYiWhfLKFTxc4WzrrOBoiIv3DhFgJCCGUY5fydCkRUeGYECuB8w/OI+ZpDKqYVkGfun10HQ4RkV5iQqwE8o8O+9XtxydbEBEVgQmxgsuV52LTtU0AeO8hEVFxmBAruLDbYf/3ZAsfPtmCiKgoBp0QP/nkE0gkEixatKjYckuXLkWjRo1gbW0Na2trtGrVCvv27VMpM3LkSPj4+MDCwgJOTk7o06cP/vvvvzKMvnzk9y4d1HAQTIxMdBwNEZH+MtiEuH37dpw9exZubm4llnV3d8fs2bNx6dIlXLx4ER07dkSfPn1w7do1ZRlfX1+EhITg+vXrOHDgAIQQCAgIgFwuL8tmlKmMnAzs+G8HAD7ZgoioJAZ5yPDgwQN8/vnnOHDgAHr27Fli+d69e6u8nzFjBpYuXYqzZ8+iQYMGAIDg4GDl/OrVq2P69Olo3Lgx4uLi4OPjo90GlJO9N/ciKzcLPnY+aO7WXNfhEBHpNYNLiAqFAkOGDMHXX3+tTGaakMvlCA0NRWZmJlq1alVomczMTISEhMDb2xseHh5F1pWdnY3s7Gzl+7S0NI3jKUtb/t0CAHi7/tt8sgURUQkM7pTpnDlzYGJigjFjxmi03JUrV2BlZQWpVIpPPvkE27dvR/369VXKLFmyBFZWVrCyssK+ffsQFhYGMzOzIuucNWsWbGxslK/ikmd5y8rNwt83/wbwIiESEVHx9Dohrl+/XpmgrKyscPz4cfz8889Ys2aNxkc8derUQVRUFM6dO4dPP/0Uw4YNw7///qtS5r333kNkZCSOHz+O2rVrY+DAgXj+/HmRdU6cOBGpqanK171790rVzrJw4NYBZOVmwcvGC76uvroOh4hI7+n1KdPAwED4+fkp34eGhiI5ORmenp7KaXK5HF999RUWLVqEuLi4IusyMzNDzZo1AbzoQHPhwgX8/PPP+P3335Vl8o/0atWqhZYtW8LOzg7bt2/H4MGDC61TKpVCKpW+ZivLxpbrL06XDqg3gKdLiYjUoNcJUSaTQSaTKd8HBwcX6CDTtWtXDBkyBCNGjNCoboVCoXL971VCCAghii2jr7LzsrE7ejcAni4lIlKXXifEVzk4OMDBwUFlmqmpKVxcXFCnTh3ltE6dOqFfv34YPXo0gBenNrt37w5PT0+kp6djw4YNOHbsGA4cOAAAuH37NjZt2oSAgAA4OTnh/v37mD17NiwsLNCjR4/ya6CWhN0OQ3pOOqrJqsHP3a/kBYiIyLASorpiYmLw6NEj5fvk5GQMHToUCQkJsLGxQaNGjXDgwAF06fJi5BZzc3OEh4dj0aJFePr0KapWrYr27dvj9OnTcHY2vEcl5fcu7V+vP4wken2ZmIhIbxh8QizsuuGr01atWlVsHW5ubti7d68Wo9KdXHkudkXvAsDTpUREmuDhQwVzNO4onj5/CmdLZ7TxaKPrcIiIDAYTYgWz9d+tAF486snYyFjH0RARGQ4mxApErpBjR/QOAC+uHxIRkfqYECuQU/dOITkzGXbmdnir+lu6DoeIyKAwIVYg+adLA+sEwtTYVMfREBEZFibECkIhFNj23zYAPF1KRFQaTIgVxMX4i7ifdh+WppYI8AnQdThERAaHCbGCyD9d2rN2T5ibmOs4GiIiw8OEWAEIIbD1+ouEOKDeAB1HQ0RkmJgQK4AryVcQ8zQGUmMpetQyvLFXiYj0ARNiBZB/urRrza6wMrPScTRERIaJCbEC4OlSIqLXx4Ro4KIfRePaw2swMTJB79q9S16AiIgKxYRo4LZdf3HvYUfvjrCzsNNxNEREhosJ0cDxdCkRkXYwIRqwOyl3cCnhEowkRuhbt6+uwyEiMmhMiAYs/3RpO892cLZ01nE0RESGjQnRgOWfLuXYpUREr48J0UAlpCfg9L3TAJgQiYi0gQnRQO34bwcEBPyq+cHd2l3X4RARGTwmRAPFRz0REWkXE6IBepz1GEdjjwLg7RZERNrChGiAdkXvglzI0bhqY/jY++g6HCKiCoEJ0QDxdCkRkfYxIRqYtOw0HIw5CICnS4mItIkJ0cDsvbkXOfIc1HGog/pO9XUdDhFRhcGEaGBevhlfIpHoOBoiooqDCdGAPM97jr039wLg6VIiIm1jQjQgp++dRlZuFrxsvNDMtZmuwyEiqlCYEA1I/r2HPF1KRKR9TIgG5MSdEwB4upSIqCwwIRqQjNwMuFi5oJVHK12HQkRU4TAhGph+dfvBSMKPjYhI27hnNQB5ijzl/3m6lIiobDAhGoDIhEgAgLXUGv7V/XUcDRFRxcSEaACOxR0DAPh7+cPEyES3wRARVVDcu5aBxMREZGZmaqUuIQQib0eiAzqgpV1LJCQkaKVeIiJ1pKen6zqEcsOEWAZCQkJgbm6utfo6oAMA4O65u1h+brnW6iUiKsnz5891HUK5kQghhK6DqCjS0tJgY2OD6OhoyGQyrdT5+8XfsS1iGwZgAPr37w9HR0et1EtEpI709HTUqVMHqampsLa21nU4ZYpHiGXAxcVFa1+cnfE78QiPAACOjo5wdXXVSr1EROqwtLTUdQjlhp1q9FhcShyiEqMgAYdpIyIqa0yIemxX9C4AQBOXJroNhIioEmBC1GM7/tsBAHir+lu6DYSIqBJgQtRTT549UQ7mzZvxiYjKHhOinvr7xt+QCzkaOjeEu7W7rsMhIqrwmBD11K4bL64f9qnTR8eREBFVDkyIeig7Lxv7b+0HwIRIRFRemBD10JHYI8jIyYCbzA2+br66DoeIqFJgQtRDO6N3AgACawfy2YdEROWEe1s9oxAK5f2HferydCkRUXlhQtQzF+MvIiEjATIzGe8/JCIqR0yIembnfy9Ol3ar2Q1SE6mOoyEiqjyYEPWM8vphnUAdR0JEVLkwIeqR209v49rDazCWGKNHrR66DoeIqFJhQtQj+adL23u1h72FvY6jISKqXJgQ9Uj+6VLejE9EVP6YEPXE46zHCL8bDoC3WxAR6QITop74++bfUAgFGlVthOq21XUdDhFRpcOEqCd4upSISLcMMiHOmjULzZs3h0wmg7OzM/r27Yvo6Ohil1mzZg0kEonKy9zcvEC569evIzAwEDY2NrC0tETz5s1x9+7dsmoKAOB53nMcuHUAAG+3ICLSFYNMiMePH8eoUaNw9uxZhIWFITc3FwEBAcjMzCx2OWtrayQkJChfd+7cUZkfExODtm3bom7dujh27BguX76MyZMnF5o4telI7BFk5maimqwafF05mDcRkS6Y6DqA0ti/f7/K+zVr1sDZ2RmXLl1C+/bti1xOIpHAxcWlyPmTJk1Cjx49MHfuXOU0Hx+f1w+4BPm3WwTWCYREIinz9RERUUEGeYT4qtTUVACAvX3x9+5lZGTAy8sLHh4e6NOnD65du6acp1Ao8Pfff6N27dro2rUrnJ2d4efnhx07dhRZX3Z2NtLS0lRemlIIBR8GTESkBww+ISoUCnz55Zdo06YNGjZsWGS5OnXqYPXq1di5cyf+/PNPKBQKtG7dGvfv3wcAJCcnIyMjA7Nnz0a3bt1w8OBB9OvXD/3798fx48cLrXPWrFmwsbFRvjw8PDSOPyMnA91qdkMNuxroUL2DxssTEZF2SIQQQtdBvI5PP/0U+/btw8mTJ+Hu7q72crm5uahXrx4GDx6MadOmIT4+HtWqVcPgwYOxYcMGZbnAwEBYWlrir7/+KlBHdnY2srOzle/T0tLg4eGB1NRUWFtba9QOIUSRp0sTEhKwfPlyBAcHw9XVVaN6iYheR1paGmxsbEq1XzM0BnkNMd/o0aOxZ88enDhxQqNkCACmpqZo2rQpbt26BQBwdHSEiYkJ6tevr1KuXr16OHnyZKF1SKVSSKXaeSIFrx0SEemWQZ4yFUJg9OjR2L59O44cOQJvb2+N65DL5bhy5YryiMvMzAzNmzcvcPvGjRs34OXlpZW4iYhIfxnkEeKoUaOwYcMG7Ny5EzKZDImJiQAAGxsbWFhYAACGDh2KatWqYdasWQCAH3/8ES1btkTNmjWRkpKCefPm4c6dO/joo4+U9X799dcICgpC+/bt8dZbb2H//v3YvXs3jh07Vu5tJCKi8mWQCXHp0qUAgA4dOqhMDwkJwfDhwwEAd+/ehZHR/x0AP336FB9//DESExNhZ2cHX19fnD59WuUUab9+/bBs2TLMmjULY8aMQZ06dbB161a0bdu2zNtERES6ZfCdavRJWV18ZqcaItKVytSpxiCvIRIREWkbEyIRERGYEImIiAAwIRIREQFgQiQiIgLAhEhERATAQO9D1Ff5d7CU5qkXxUlPT8fz58+Rnp4OS0tLrdZNRFSc/P1ZZbhDj/chatH9+/dL9cQLIiJ9d+/ePY3HjDY0TIhapFAoEB8fD5lMptFg3flPybh3716Fv/FVU9w2heN2KRy3S9FKu22EEEhPT4ebm5vK6F8VEU+ZapGRkdFr/YKytrbmH3ERuG0Kx+1SOG6XopVm29jY2JRRNPqlYqd7IiIiNTEhEhERgQlRL0ilUkyZMkVrDxuuSLhtCsftUjhul6Jx25SMnWqIiIjAI0QiIiIATIhEREQAmBCJiIgAMCESEREBYELUit9++w3Vq1eHubk5/Pz8cP78+SLLXrt2DQMGDED16tUhkUiwaNGiUtU5cuRI+Pj4wMLCAk5OTujTpw/+++8/bTZLK4QQ+N///gdXV1dYWFigc+fOuHnzZrHL5G+bV1+jRo1SlomJiUG/fv3g5OQEa2trDBw4EElJSSr13LhxA3369IGjoyOsra3Rtm1bHD16tEzaqalt27YhICAADg4OkEgkiIqKUmu5lJQUjBo1Cq6urpBKpahduzb27t2rnJ+eno4vv/wSXl5esLCwQOvWrXHhwgWVOpKSkjB8+HC4ubmhSpUq6NatW4mfSXnJzc3FhAkT8MYbb8DS0hJubm4YOnQo4uPji11u6tSpBb4vdevWVSmjzncmIiICXbp0ga2tLRwcHBAcHIyMjAytt7OsTJ06FXXr1oWlpSXs7OzQuXNnnDt3rsRlStp2lQUT4mvatGkTxo0bhylTpiAiIgKNGzdG165dkZycXGj5rKws1KhRA7Nnz4aLi0up6/T19UVISAiuX7+OAwcOQAiBgIAAyOXyMmlnac2dOxe//PILli1bhnPnzsHS0hJdu3bF8+fPi1zmwoULSEhIUL7CwsIAAO+88w4AIDMzEwEBAZBIJDhy5AhOnTqFnJwc9O7dGwqFQllPr169kJeXhyNHjuDSpUto3LgxevXqhcTExLJttBoyMzPRtm1bzJkzR+1lcnJy0KVLF8TFxWHLli2Ijo7GihUrUK1aNWWZjz76CGFhYVi3bh2uXLmCgIAAdO7cGQ8ePADw4gdK3759cfv2bezcuRORkZHw8vJC586dkZmZqfV2aiorKwsRERGYPHkyIiIisG3bNkRHRyMwMLDEZRs0aKDyvTl58qRynjrfmfj4eHTu3Bk1a9bEuXPnsH//fly7dg3Dhw8vq+ZqXe3atbF48WJcuXIFJ0+eRPXq1REQEICHDx8Wu1xx265SEfRaWrRoIUaNGqV8L5fLhZubm5g1a1aJy3p5eYmFCxdqpc5//vlHABC3bt3SrAFlSKFQCBcXFzFv3jzltJSUFCGVSsVff/2ldj1ffPGF8PHxEQqFQgghxIEDB4SRkZFITU1VqVcikYiwsDAhhBAPHz4UAMSJEyeUZdLS0gQAZRl9EBsbKwCIyMjIEssuXbpU1KhRQ+Tk5BQ6PysrSxgbG4s9e/aoTG/WrJmYNGmSEEKI6OhoAUBcvXpVOV8ulwsnJyexYsWK0jekDJ0/f14AEHfu3CmyzJQpU0Tjxo2LnK/Od+b3338Xzs7OQi6XK8tcvnxZABA3b958/YboQGpqqgAgDh06VGSZkrZdZcIjxNeQk5ODS5cuoXPnzsppRkZG6Ny5M86cOVNudWZmZiIkJATe3t569bSN2NhYJCYmqrTFxsYGfn5+am+fnJwc/Pnnn/jggw+UA6ZnZ2dDIpGo3GBsbm4OIyMj5S9bBwcH1KlTB3/88QcyMzORl5eH33//Hc7OzvD19dViK8vPrl270KpVK4waNQpVq1ZFw4YNMXPmTOVZgby8PMjlcpibm6ssZ2Fhodwu2dnZAKBSxsjICFKpVG+PClJTUyGRSGBra1tsuZs3b8LNzQ01atTAe++9h7t37yrnqfOdyc7OhpmZmcoA1hYWFgCgt9umODk5OVi+fDlsbGzQuHHjYssWt+0qEybE1/Do0SPI5XJUrVpVZXrVqlVLfVpOkzqXLFkCKysrWFlZYd++fQgLC4OZmVmp1lsW8uN9ne2zY8cOpKSkqJy2atmyJSwtLTFhwgRkZWUhMzMT48ePh1wuR0JCAgBAIpHg0KFDiIyMhEwmg7m5ORYsWID9+/fDzs5OOw0sZ7dv38aWLVsgl8uxd+9eTJ48GfPnz8f06dMBADKZDK1atcK0adMQHx8PuVyOP//8E2fOnFFul7p168LT0xMTJ07E06dPkZOTgzlz5uD+/fvKMvrk+fPnmDBhAgYPHlzsgNR+fn5Ys2YN9u/fj6VLlyI2Nhbt2rVDeno6APW+Mx07dkRiYiLmzZuHnJwcPH36FN9++y0A6OW2KcqePXtgZWUFc3NzLFy4EGFhYXB0dCyyfEnbrjJhQjRg7733HiIjI3H8+HHUrl0bAwcOLPbaXFlbv369MkFbWVkhNzf3tetctWoVunfvDjc3N+U0JycnhIaGYvfu3bCysoKNjQ1SUlLQrFkz5a97IQRGjRoFZ2dnhIeH4/z58+jbty969+5d7ju3V7dLeHh4qepRKBRwdnbG8uXL4evri6CgIEyaNAnLli1Tllm3bh2EEKhWrRqkUil++eUXDB48WLldTE1NsW3bNty4cQP29vaoUqUKjh49iu7du+vk0T7FbZvc3FwMHDgQQggsXbq02Hq6d++Od955B40aNULXrl2xd+9epKSkYPPmzQDU+840aNAAa9euxfz581GlShW4uLjA29sbVatW1cvHHhW17d566y1ERUXh9OnT6NatGwYOHFhknwag5G1XmfDxT6/B0dERxsbGBXqqJSUlFdlhRpt12tjYwMbGBrVq1ULLli1hZ2eH7du3Y/DgwaVa9+sKDAyEn5+f8n3+6bmkpCS4uroqpyclJaFJkyYl1nfnzh0cOnQI27ZtKzAvICAAMTExePToEUxMTGBrawsXFxfUqFEDAHDkyBHs2bMHT58+VR5ZLFmyBGFhYVi7dq3yl395eHW7vNwJRhOurq4wNTWFsbGxclq9evWQmJiInJwcmJmZwcfHB8ePH0dmZibS0tLg6uqKoKAg5XYBXnTIioqKQmpqKnJycuDk5AQ/Pz+8+eabpW9kKRW1bfKT4Z07d3DkyBGNH1dka2uL2rVr49atW8ppJX1nAODdd9/Fu+++i6SkJFhaWkIikWDBggUqZfRFUdvO0tISNWvWRM2aNdGyZUvUqlULq1atwsSJE9Wqt7BtV1no388eA2JmZgZfX18cPnxYOU2hUODw4cNo1apVudYphIAQQpmEdEEmkyn/EGvWrIn69evDxcVFpS1paWk4d+6cWtsnJCQEzs7O6NmzZ5FlHB0dYWtriyNHjiA5OVnZGzErKwsACvyyNzIyUumJWh5e3S7516U01aZNG9y6dUsl/hs3bsDV1bXAqXJLS0u4urri6dOnOHDgAPr06VOgPhsbGzg5OeHmzZu4ePFioWXKWmHbJj8Z3rx5E4cOHYKDg4PG9WZkZCAmJkblh1i+or4zL6tatSqsrKywadMmmJubo0uXLqVqX1lS93ulUCg02i8Ut+0qPJ126akANm7cKKRSqVizZo34999/RXBwsLC1tRWJiYlCCCGGDBkivv32W2X57OxsERkZKSIjI4Wrq6sYP368iIyMVOnFVlKdMTExYubMmeLixYvizp074tSpU6J3797C3t5eJCUlle8GKMHs2bOFra2t2Llzp7h8+bLo06eP8Pb2Fs+ePVOW6dixo/j1119VlpPL5cLT01NMmDCh0HpXr14tzpw5I27duiXWrVsn7O3txbhx45TzHz58KBwcHET//v1FVFSUiI6OFuPHjxempqYiKiqqbBqrgcePH4vIyEjx999/CwBi48aNIjIyUiQkJCjLvPrduXv3rpDJZGL06NEiOjpa7NmzRzg7O4vp06cry+zfv1/s27dP3L59Wxw8eFA0btxY+Pn5qfRM3bx5szh69KiIiYkRO3bsEF5eXqJ///7l0/AS5OTkiMDAQOHu7i6ioqJEQkKC8pWdna0s9+p35quvvhLHjh0TsbGx4tSpU6Jz587C0dFRJCcnK8uU9J0RQohff/1VXLp0SURHR4vFixcLCwsL8fPPP5d9w7UgIyNDTJw4UZw5c0bExcWJixcvihEjRgipVKrSq7g0266yYELUgl9//VV4enoKMzMz0aJFC3H27FnlPH9/fzFs2DDl+/xu9q++/P391a7zwYMHonv37sLZ2VmYmpoKd3d38e6774r//vuvrJuqMYVCISZPniyqVq0qpFKp6NSpk4iOjlYp4+XlJaZMmaIy7cCBAwJAgbL5JkyYIKpWrSpMTU1FrVq1xPz585W3ZeS7cOGCCAgIEPb29kImk4mWLVuKvXv3arV9pRUSElLo9+Dl7fDqd0cIIU6fPi38/PyEVCoVNWrUEDNmzBB5eXnK+Zs2bRI1atQQZmZmwsXFRYwaNUqkpKSo1PHzzz8Ld3d3YWpqKjw9PcX333+vkmx0qai/DwDi6NGjynKvfmeCgoKEq6urMDMzE9WqVRNBQUEFbkFS5zszZMgQYW9vL8zMzESjRo3EH3/8UZbN1apnz56Jfv36CTc3N2FmZiZcXV1FYGCgOH/+vEq50my7yoKPfyIiIgKvIRIREQFgQiQiIgLAhEhERASACZGIiAgAEyIREREAJkQiIiIATIhEREQAmBCJiIgAMCESlbm4uDhIJBJERUW9dh0SiUStgdH12Zo1a5Rt+fLLL3UdDpESEyJVOg8fPsSnn34KT09PSKVSuLi4oGvXrjh16pSyjEQiwY4dO7SyPg8PDyQkJKBhw4avXdehQ4dUBkvXhR9++AHvv/9+qZcPCgpCQkJCqQfAJyorfPwTVToDBgxATk4O1q5dixo1aiApKQmHDx/G48ePNaon/5FLJTE2Ni7148Be5eDgUKqnP2jTzp07X+vxWRYWFrCwsNCrh1kTATxCpEomJSUF4eHhmDNnDt566y14eXmhRYsWmDhxovIxQNWrVwcA9OvXDxKJRPl+6tSpaNKkCVauXAlvb2+Ym5sDAPbv34+2bdvC1tYWDg4O6NWrF2JiYpTrfPWU6bFjxyCRSHD48GG8+eabqFKlClq3bo3o6GiN2zN8+HD07dsXM2fORNWqVWFra4sff/wReXl5+Prrr2Fvbw93d3eEhIQUiGfz5s1o164dLCws0Lx5c9y4cQMXLlzAm2++CSsrK3Tv3h0PHz5UWd+9e/dw7do1dOvWDcCLI+nff/8dvXr1QpUqVVCvXj2cOXMGt27dQocOHWBpaYnWrVurbA8ifcWESJVK/tPFd+zYUeQz4i5cuADgxfMYExISlO8B4NatW9i6dSu2bdumTHCZmZkYN24cLl68iMOHD8PIyAj9+vUr8bmLkyZNwvz583Hx4kWYmJjggw8+KFWbjhw5gvj4eJw4cQILFizAlClT0KtXL9jZ2eHcuXP45JNPMHLkSNy/f19luSlTpuD7779HREQETExM8O677+Kbb77Bzz//jPDwcNy6dQv/+9//VJbZtWsXOnTooPLA3mnTpmHo0KGIiopC3bp18e6772LkyJGYOHEiLl68CCEERo8eXaq2EZUrHT9tg6jcbdmyRdjZ2Qlzc3PRunVrMXHiRPHPP/+olAEgtm/frjJtypQpwtTUtMTnxD18+FAAEFeuXBFC/N8jjSIjI4UQQhw9elQAEIcOHVIuk/9cxJefE/myV+vIN2zYMOHl5SXkcrlyWp06dUS7du2U7/Py8oSlpaX466+/VOpauXKlssxff/0lAIjDhw8rp82aNUvUqVNHZX1dunQRixcvVr4HIL7//nvl+zNnzggAYtWqVSp1m5ubF2iTv7+/+OKLLwptL5Eu8AiRKp0BAwYgPj4eu3btQrdu3XDs2DE0a9YMa9asKXFZLy8vODk5qUy7efMmBg8ejBo1asDa2lp5ivXu3bvF1tWoUSPl//OfTp6cnKxZYwA0aNAARkb/96dctWpVvPHGG8r3xsbGcHBwKFD3y+uvWrUqAKgsV7VqVZVl0tLScPz48QJPmFennufPnyMtLU3jthGVJyZEqpTMzc3RpUsXTJ48GadPn8bw4cMxZcqUEpeztLQsMK1379548uQJVqxYgXPnzuHcuXMAXnS6KY6pqany/xKJBABKPM1aUj35dRU27dW6C1v/q9NeXmbfvn2oX78+PDw8NK4HKF3biMoTEyIRgPr16yMzM1P53tTUFHK5vMTlHj9+jOjoaHz//ffo1KkT6tWrh6dPn5ZlqDqzc+dO9OnTR9dhEJUZJkSqVB4/foyOHTvizz//xOXLlxEbG4vQ0FDMnTtXZWdfvXp1HD58GImJicUmODs7Ozg4OGD58uW4desWjhw5gnHjxpVHU8pVXl4e9u3bV+B0KVFFwoRIlYqVlRX8/PywcOFCtG/fHg0bNsTkyZPx8ccfY/Hixcpy8+fPR1hYGDw8PNC0adMi6zMyMsLGjRtx6dIlNGzYEGPHjsW8efPKoynl6vjx47CyskKzZs10HQpRmZEIIYSugyCi4sXFxcHb2xuRkZE6GbptzJgxyMvLw5IlS7RWZ4cOHdCkSRMsWrRIa3USvQ4eIRIZkNatW6N169blvt6GDRvi008/1Upd69evh5WVFcLDw7VSH5G28AiRyADk5eUhLi4OACCVSgv09DQk6enpSEpKAgDY2trC0dFRxxERvcCESEREBJ4yJSIiAsCESEREBIAJkYiICAATIhEREQAmRCIiIgBMiERERACYEImIiAAwIRIREQEA/h+Ceahp+XD4AgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z neutral axis = 265.78\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z neutral axis = 260.43\n",
+      "m_cracking (mkN) =  46.29\n",
+      "fctm (MPa) =  2.56\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A concrete material failing at fctm is used for the cracking moment calculation.\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
+    "\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "\n",
+    "from SLS_section_response import calculate_cracking_moment\n",
+    "m_cracking,_ = calculate_cracking_moment(sec,plot=True)\n",
+    "m_cracking = m_cracking/1e6 #mkN\n",
+    "print('m_cracking (mkN) = ', round(m_cracking,2))\n",
+    "print('fctm (MPa) = ',round(concrete.fctm,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Checking deflections by calculation according to EN 1992 1-1 section 7.4.3\n",
+    "\n",
+    "Expresion 7.18:\n",
+    "$$\\alpha = \\zeta\\alpha_{II} + (1-\\zeta)\\alpha_{I}$$\n",
+    "$$(1/r) = \\zeta(1/r)_{II} + (1-\\zeta)(1/r)_{I}$$\n",
+    "where  $(1/r)_{II}$ is the curvature of the fully cracked seciont and $(1/r)_{I}$ is the curvature of the uncracked section \n",
+    "Expresion 7.19:\n",
+    "$$\n",
+    "\\zeta = 1 - \\beta(M_{cr}/M) ^2  \n",
+    "$$\n",
+    "where  $\\beta=0.5$ for sustained loads \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\cmg\\AppData\\Local\\Temp\\ipykernel_29892\\3607814390.py:11: RuntimeWarning: divide by zero encountered in divide\n",
+      "  zeta = 1-0.5*(m_cracking/m_x)**2\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum deflection [mm] = 16.89\n"
+     ]
+    }
+   ],
+   "source": [
+    "def bending_momment_x_beam(q,L,n_sections=11): # n_sections -> number of sections in the beam\n",
+    "    \"\"\"Return M for a simply supported beam under a uniform load q.\"\"\"\n",
+    "    x = np.linspace(0,L,n_sections)\n",
+    "    # Maximum deflection at the centre of the span. Only the first half of the sections are required\n",
+    "    x = x[:math.ceil(n_sections/2)] \n",
+    "    m_x=q*x/2*(L-x)\n",
+    "    return x,m_x\n",
+    "\n",
+    "def compute_zeta_expresion_7_19(m_cracking,m_x):\n",
+    "    \"\"\"Expresion 7.19 EN 1992 1-1.\"\"\"\n",
+    "    zeta = 1-0.5*(m_cracking/m_x)**2\n",
+    "    zeta[m_x == 0] = 0\n",
+    "    return zeta\n",
+    "\n",
+    "def compute_curvature(m_x,E,I):\n",
+    "    \"\"\"Return the curvature in n sections given the moments in each section.\"\"\"\n",
+    "    return m_x/(E*1000*I/1000**4)\n",
+    "\n",
+    "x_beam,m_x_characteristic = bending_momment_x_beam(qk,long_beam)\n",
+    "x_beam,m_x_cuaisp = bending_momment_x_beam(q_cuasip,long_beam)\n",
+    "\n",
+    "zeta =compute_zeta_expresion_7_19(m_cracking,m_x_characteristic)\n",
+    "curv_1 = compute_curvature(m_x_cuaisp,Ec_eff,i11_1)\n",
+    "curv_2 = compute_curvature(m_x_cuaisp,Ec_eff,i11_2)\n",
+    "\n",
+    "# Expresion 7.18\n",
+    "curv = zeta*curv_2 + (1-zeta)*curv_1\n",
+    "\n",
+    "# curvature integration\n",
+    "deflection = np.trapz(curv*x_beam*1000,x_beam)\n",
+    "\n",
+    "\n",
+    "print('Maximum deflection [mm] =', round(deflection,2))\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Shrinkage deflections :\n",
+    "\n",
+    "Expresion 7.21:  \n",
+    "$\n",
+    "1/r_{cs} = \\epsilon_{cs}\\alpha_e  S/I   \n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "S_I (mm3)=  241009\n",
+      "S_II (mm3)=  377817\n",
+      "I_I (mm4)=  5413898904\n",
+      "I_II (mm4)=  3677810527\n",
+      "Shrinkage deflection [mm] = 4.9\n",
+      "Total deflection [mm] = 21.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "a_fi20 = math.pi * 20**2/4\n",
+    "a_fi12 = math.pi * 12**2/4\n",
+    "s_1 = (6*a_fi20 * (450-cz_1) + 3*a_fi12 * (50-cz_1))\n",
+    "s_2 = (6*a_fi20 * (450-cz_2) + 3*a_fi12 * (50-cz_2))\n",
+    "s_= zeta*s_2 + (1-zeta)*s_1\n",
+    "i_= zeta*i11_2 + (1-zeta)*i11_1\n",
+    "print('S_I (mm3)= ',round(s_1))\n",
+    "print('S_II (mm3)= ',round(s_2))\n",
+    "#print(\"S = \",s_)\n",
+    "print('I_I (mm4)= ',round(i11_1))\n",
+    "print('I_II (mm4)= ',round(i11_2))\n",
+    "#print(\"I = \",i_)\n",
+    "curv_cs = eps_cs * n * s_/i_ \n",
+    "\n",
+    "# curvature integration\n",
+    "deflection_cs = np.trapz(curv_cs*x_beam*1000,x_beam)\n",
+    "print('Shrinkage deflection [mm] =', round(deflection_cs,2))\n",
+    "print('Total deflection [mm] =', round(deflection+deflection_cs,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Compare with the results if the equivalent stiffness of the center of the span is considered for the entire span.\n",
+    "\n",
+    "$$\n",
+    "f = 5/384  qL^4/EI\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zeta =  0.96\n",
+      "\n",
+      "CUSIPERMANTENT LOAD DEFLECTION:\n",
+      "deflection I (mm) =  11.61\n",
+      "deflection II (mm) =  17.09\n",
+      "deflection cuasip (mm) =  16.88\n",
+      "\n",
+      "SHRINKAGE DEFLECTION:\n",
+      "deflection_cs (mm) =  5.04\n",
+      "\n",
+      "TOTAL DEFLECTION:\n",
+      "deflection (mm) =  21.91\n"
+     ]
+    }
+   ],
+   "source": [
+    "def_1 = 5/384*q_cuasip*long_beam**4 / (Ec_eff*1000) / (i11_1/1000**4) *1000\n",
+    "def_2 = 5/384*q_cuasip*long_beam**4 / (Ec_eff*1000) / (i11_2/1000**4) *1000\n",
+    "zeta_ = 1-0.5*(m_cracking/(qk*long_beam**2/8))**2\n",
+    "def_ =  zeta_*def_2 + (1-zeta_)*def_1\n",
+    "deflection_cs = eps_cs * n * s_[-1]/i_[-1] * long_beam**2 /8 *1000\n",
+    "\n",
+    "print ('zeta = ', round(zeta_,2))\n",
+    "print()\n",
+    "print ('CUSIPERMANTENT LOAD DEFLECTION:') \n",
+    "print ('deflection I (mm) = ', round(def_1,2))\n",
+    "print ('deflection II (mm) = ', round(def_2,2))\n",
+    "print ('deflection cuasip (mm) = ', round(def_,2))\n",
+    "print()\n",
+    "print ('SHRINKAGE DEFLECTION:') \n",
+    "print ('deflection_cs (mm) = ', round(deflection_cs,2))\n",
+    "print()\n",
+    "print ('TOTAL DEFLECTION:') \n",
+    "print ('deflection (mm) = ', round(def_ + deflection_cs,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb
new file mode 100644
index 00000000..a4c94cbe
--- /dev/null
+++ b/EX2_part1.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A concrete beam with a span of L=6.75 m is considered, simply supported and subjected to the following uniformly distributed loads:\n",
+    "- Self weight: $q_{sw}$=12.2 kN/m\n",
+    "- Dead load: $q_{dl}$=5.2 kN/m\n",
+    "- Live load: $q_{ll}$=11.7 kN/m\n",
+    "It is considered that 30% of the live load is quasi-permanent (ψ2=0.3)\n",
+    "\n",
+    "![beam](EX1&2Beam.png)\n",
+    "\n",
+    "The section, 0.35 m wide and 0.50 m height, is reinforced with 6φ20 on the lower face and 3φ12 on the upper face. For calculation purposes, the cover is 5 cm. The concrete is class C25/30. The structure is located 2 km from the coast in\n",
+    "the open. A creep coefficient of 2.5 will be considered.\n",
+    "\n",
+    "It is requested:\n",
+    "1. Determine the characteristic crack opening in the critical section.\n",
+    "2. Evaluate the admissibility of the crack opening applying EN 1992-1-1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from structuralcodes.codes.ec2_2004 import __materials__\n",
+    "from structuralcodes.codes.ec2_2004 import _section_7_3_crack_control\n",
+    "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n",
+    "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023, ReinforcementEC2_2004\n",
+    "from shapely import Polygon\n",
+    "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry\n",
+    "from structuralcodes.sections._reinforcement import (\n",
+    "    add_reinforcement,\n",
+    "    add_reinforcement_line,\n",
+    ")\n",
+    "from structuralcodes.sections._generic import GenericSection\n",
+    "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle,UserDefined\n",
+    "from structuralcodes.plots.section_plots import draw_section\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculation of the inertia of the cracked cross-section in order to obtain $\\sigma_s$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x (mm) from z=0: 219.4\n",
+      "I of cracked section (mm4):  3677810527\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "fi =2.5\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "Ec_eff=concrete.Ecm/(1+fi)\n",
+    "Ec = concrete.constitutive_law.get_tangent(0)[0]\n",
+    "\n",
+    "\n",
+    "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforfocement\" with reinforcement areas multiplied by Es/Ec is used.\n",
+    "#reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)\n",
+    "n=200000/Ec_eff\n",
+    "\n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "\n",
+    "# Get the neutral axe of the cracked section\n",
+    "curv = 1e-7 # Curvature which produces elastic stresses in the most compressed fibre of the concrete section\n",
+    "eps =  sec.section_calculator.find_equilibrium_fixed_curvature(sec.geometry,0,curv,0)[0]\n",
+    "x=-eps/curv # Distance from z=0 to the neutral axe\n",
+    "print(\"x (mm) from z=0:\",round(x,1))\n",
+    "\n",
+    "# 2) Create the cracked section\n",
+    "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
+    "geo2 = SurfaceGeometry(poly2, concrete)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "sec2 = GenericSection(geo2)\n",
+    "Icr = sec2.gross_properties.i11\n",
+    "print(\"I of cracked section (mm4): \",round(Icr))\n",
+    "draw_section(sec2,\"cracked section\",math.sqrt(n))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Section 7.3.4. Calculation of crack widths\n",
+    "\n",
+    "1.) Calculation of $\\epsilon_{sm} - \\epsilon_{cm}$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eps_sm_eps_cm = 0.00063\n",
+      " alfa_e = 22.239\n",
+      " hc_eff (mm) = 89\n",
+      " rho_p = 0.0602\n",
+      " kt = 0.4\n",
+      " sigma_s (MPa) = 166\n",
+      " fct_eff (MPa) = 2.56\n"
+     ]
+    }
+   ],
+   "source": [
+    "long_beam = 6.75\n",
+    "\n",
+    "# caracteriscic load\n",
+    "qk=12.2 + 5.2 + 11.7 # kN/m\n",
+    "# cuasipermant load\n",
+    "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n",
+    "\n",
+    "fi = 2.5\n",
+    "\n",
+    "# Mcuasip at midspan\n",
+    "m_cuasip = q_cuasip * long_beam**2/8\n",
+    "\n",
+    "# Create section to get hc_eff\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "hc_eff = _section_7_3_crack_control.hc_eff(500,450,500-sec.gross_properties.cz)\n",
+    "\n",
+    "Ec_eff = concrete.Ecm/(1+fi)\n",
+    "alfa_e = _section_7_3_crack_control.alpha_e(200000,Ec_eff)\n",
+    "rho_p = _section_7_3_crack_control.rho_p_eff(6* math.pi*100, 0 ,0,hc_eff*350)  # b=350mm, fi=20mm\n",
+    "kt = _section_7_3_crack_control.kt('long')\n",
+    "fct_eff = concrete.fctm\n",
+    "sigma_s = (n * m_cuasip / (Icr/1000**4) * (450-x)/1000) / 1000 # MPa\n",
+    "eps_sm_eps_cm =_section_7_3_crack_control.eps_sm_eps_cm(sigma_s,alfa_e,rho_p,kt,fct_eff,200000)\n",
+    "print('eps_sm_eps_cm =',round(eps_sm_eps_cm, 5))\n",
+    "print(' alfa_e =',round(alfa_e,3))\n",
+    "print(' hc_eff (mm) =',round(hc_eff))\n",
+    "print(' rho_p =',round(rho_p,5))\n",
+    "print(' kt =',kt)\n",
+    "print(' sigma_s (MPa) =',round(sigma_s))\n",
+    "print(' fct_eff (MPa) =',round(fct_eff,2))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2.) Crack spacing $S_{r,max}$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sr_max (mm) 192\n",
+      " c (mm) 40.0\n",
+      " fi (mm) 20\n",
+      " k1 0.8\n",
+      " k2 0.5\n",
+      " k3 3.4\n",
+      " k4 0.425\n"
+     ]
+    }
+   ],
+   "source": [
+    "# crack spacing\n",
+    "k1 =_section_7_3_crack_control.k1(bond_type='bond')\n",
+    "k2 =_section_7_3_crack_control.k2(0)\n",
+    "k3 =_section_7_3_crack_control.k3()\n",
+    "k4 =_section_7_3_crack_control.k4()\n",
+    "c=50-20/2\n",
+    "fi_rebars=20\n",
+    "sr_max = _section_7_3_crack_control.sr_max_close(c,fi_rebars,rho_p,k1,k2,k3,k4)\n",
+    "print('sr_max (mm)',round(sr_max))\n",
+    "print(' c (mm)',c)\n",
+    "print(' fi (mm)',fi_rebars)\n",
+    "print(' k1',k1)\n",
+    "print(' k2',k2)\n",
+    "print(' k3',k3)\n",
+    "print(' k4',k4)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3) Crack width $w_k$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wk (mm) 0.12\n",
+      "wmax (mm) 0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "wk = _section_7_3_crack_control.wk(sr_max,eps_sm_eps_cm)\n",
+    "print('wk (mm)',round(wk,2))\n",
+    "wmax = _section_7_3_crack_control.w_max('XS1','qp')\n",
+    "print('wmax (mm)',wmax)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/EX3.ipynb b/EX3.ipynb
new file mode 100644
index 00000000..1a558dd1
--- /dev/null
+++ b/EX3.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is considered a beam of 13.00 m central span with two cantilevers of 6.00 meters. The cross section is formed by a flange 1.00 meter wide and 0.10 m thick. The web is 0.17 meters wide.\n",
+    "The total height of the beam is 0.45 m. The beam in the supports is reinforced in the upper face with 8fi16 bars, two of which are within 17 cm of the web.\n",
+    "\n",
+    "![beam](EX3.png)\n",
+    "\n",
+    "The structure is subject to the following actions:\n",
+    "- Self weight of the beam\n",
+    "- Dead load consisting of two railings of 0.5 kN/m weight each, which will be assumed to be located at the edge of the section\n",
+    "- Live load of 4.0 kN/m²\n",
+    "Geometric cover of the reinforcements is 30 mm.\n",
+    "\n",
+    "It is requested:\n",
+    "1. Shear reinforcement in critical sections\n",
+    "2. Shear reinforcement between web and flanges\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n",
+    "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023, ReinforcementEC2_2004\n",
+    "from shapely import Polygon\n",
+    "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry\n",
+    "from structuralcodes.sections._reinforcement import (\n",
+    "    add_reinforcement,\n",
+    "    add_reinforcement_line,\n",
+    ")\n",
+    "from structuralcodes.sections._generic import GenericSection\n",
+    "from structuralcodes.plots.section_plots import draw_section\n",
+    "import math\n",
+    "from structuralcodes.codes.ec2_2004 import shear"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cross section"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "fi =2.5\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (1000, 0), (1000, 100), (500+170/2, 100),(500+170/2, 450),(500-170/2, 450),(500-170/2, 100),(0,100)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 30+16/2), (950, 30+16/2), 16, reinforcemnet,n=8)\n",
+    "sec = GenericSection(CompoundGeometry([geo]))\n",
+    "draw_section(sec,\"Section\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$V_{ED}$:\n",
+    "\n",
+    "\n",
+    "\n",
+    "![Beam](EX3_beamV.png)\n",
+    "![Beam](EX3_beamM.png)\n",
+    "\n",
+    "![Ved_pos](EX3_ved_pos.png)\n",
+    "![Ved_neg](EX3_ved_neg.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ved left section = 70.9 kN\n",
+      "Ved right section = 84.0 kN\n"
+     ]
+    }
+   ],
+   "source": [
+    "sw = sec.gross_properties.mass  * 10 # kN/m2\n",
+    "dead_load = 2*0.5 # kN/m2\n",
+    "live_load = 4 # kN/m2\n",
+    "cover = 30 # mm\n",
+    "\n",
+    "\n",
+    "q = (1.35*(sw+dead_load)+1.5*(4)) # kN/m\n",
+    "d = (450-30-16/2)/1000 # m\n",
+    "ved_left = q*(6-d)\n",
+    "ved_right = 89.2 - q*d\n",
+    "print(f'Ved left section = {round(ved_left,1)} kN')\n",
+    "print(f'Ved right section = {round(ved_right,1)} kN')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "370.8\n",
+      "Vrd_max = 196 kN\n",
+      "Required transversal reinf. left section = 1.76 cm2/m\n",
+      "Required transversal reinf. right section = 2.08 cm2/m\n"
+     ]
+    }
+   ],
+   "source": [
+    "bw=170 #mm\n",
+    "z = 0.9*(450-30-16/2) #mm\n",
+    "print(z)\n",
+    "theta = 21.8 #º \n",
+    "ac=z*bw\n",
+    "\n",
+    "\n",
+    "# compression strut\n",
+    "vrd_max= shear.VRdmax(bw,z,concrete.fck,theta,0,ac,concrete.fcd())/1000 # kN\n",
+    "print(f'Vrd_max = {round(vrd_max)} kN')\n",
+    "\n",
+    "# Required transversar reinforcement strut\n",
+    "# asw_max_s = shear.Asw_max(concrete.fcd(),concrete.fck,bw,1,500/1.15,0,ac) *10  # cm2/m\n",
+    "asw_left = ved_left /z/500*1e6*1.15/(1/math.tan(math.radians(theta))) /100  # cm2/m\n",
+    "asw_right = ved_right /z/500*1e6*1.15/(1/math.tan(math.radians(theta))) /100  # cm2/m\n",
+    "print(f'Required transversal reinf. left section = {round(asw_left,2)} cm2/m')\n",
+    "print(f'Required transversal reinf. right section = {round(asw_right,2)} cm2/m')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/Issues.ipynb b/Issues.ipynb
new file mode 100644
index 00000000..863c3484
--- /dev/null
+++ b/Issues.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from structuralcodes.codes.ec2_2004 import __materials__\n",
+    "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n",
+    "from structuralcodes.materials.reinforcement import ReinforcementEC2_2023,ReinforcementEC2_2004\n",
+    "from shapely import Polygon\n",
+    "from structuralcodes.geometry import SurfaceGeometry,CompoundGeometry,Geometry\n",
+    "from structuralcodes.sections._reinforcement import (\n",
+    "    add_reinforcement,\n",
+    "    add_reinforcement_line,\n",
+    ")\n",
+    "from structuralcodes.sections._generic import GenericSection\n",
+    "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle\n",
+    "from structuralcodes.plots.section_plots import draw_section,draw_constitutive_law,draw_My_Mz_diagram,draw_section_response,draw_N_M_diagram\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) I can not compute gross properties of a secion from a SurfaceGeometry. It must be a CompoundGeometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area = 1750.0 cm2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "#sec = GenericSection(geo)\n",
+    "#print(f\"Area = {sec.gross_properties.area/(10**2)} cm2\") # -> Error\n",
+    "\n",
+    "sec = GenericSection(CompoundGeometry([geo]))\n",
+    "print(f\"Area = {sec.gross_properties.area/(10**2)} cm2\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2) some properties are not working if coordinates are defined negative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I11 = 364583 cm2\n",
+      "I11_neg = 6823345 cm4\n",
+      "Sy = 43750 cm3\n",
+      "Sy_neg = 43750 cm3\n",
+      "I22 = 178646 cm2\n",
+      "I22_neg = 238634 cm4\n",
+      "Sz = 30625 cm3\n",
+      "Sz_neg = 30625 cm3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "poly_neg = Polygon(((0, 0), (-350, 0), (-350, -500), (0, -500)))  # -500 instead of 500\n",
+    "geo_neg = SurfaceGeometry(poly_neg, concrete)\n",
+    "geo_neg = add_reinforcement_line(geo_neg, (50, -50), (300, -50), 0.01, concrete, n=2)\n",
+    "sec_neg = GenericSection(geo_neg)\n",
+    "print(f'I11 = {round(sec.gross_properties.i11/(10**4))} cm2') \n",
+    "print(f'I11_neg = {round(sec_neg.gross_properties.i11/(10**4))} cm4')\n",
+    "print(f'Sy = {round(sec.gross_properties.sy/(10**3))} cm3') \n",
+    "print(f'Sy_neg = {round(sec_neg.gross_properties.sy/(10**3))} cm3')\n",
+    "\n",
+    "print(f'I22 = {round(sec.gross_properties.i22/(10**4))} cm2') \n",
+    "print(f'I22_neg = {round(sec_neg.gross_properties.i22/(10**4))} cm4')\n",
+    "print(f'Sz = {round(sec.gross_properties.sz/(10**3))} cm3') \n",
+    "print(f'Sz_neg = {round(sec_neg.gross_properties.sz/(10**3))} cm3')\n",
+    "draw_section(sec)\n",
+    "draw_section(sec_neg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3)  should be \"area\" the total surface area (reinf + concrete)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "area = 1750 cm2\n",
+      "area_reinforcement = 22 cm2\n",
+      "area_surface = 1750 cm2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo, integrator='marin')\n",
+    "print(f\"area = {round(sec.gross_properties.area/(10**2))} cm2\")\n",
+    "print(f\"area_reinforcement = {round(sec.gross_properties.area_reinforcement/(10**2))} cm2\")\n",
+    "print(f\"area_surface = {round(sec.gross_properties.area_surface/(10**2))} cm2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. I dont undestand why:\n",
+    "\n",
+    "- calculate_bending_strength(theta=0).m_z)\n",
+    "\n",
+    "is not the same as:\n",
+    "- calculate_bending_strength(theta=math.pi/2).m_y\n",
+    "\n",
+    "or:\n",
+    "\n",
+    "- sec.geometry= sec.geometry.rotate(math.pi/2)\n",
+    "- calculate_bending_strength(theta=0).m_y)\n",
+    "\n",
+    "which is the purpose of the m_z result?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mz_max [mkN]=0\n",
+      "Mz_max [mkN]=0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My_max_rotated [mkN]=-255\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=10)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcemnet, n=10)\n",
+    "sec = GenericSection(geo, integrator='marin')\n",
+    "\n",
+    "\n",
+    "draw_section(sec)\n",
+    "print(f\"Mz_max [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_z/1000**2)}\")\n",
+    "print(f\"Mz_max [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=math.pi/2).m_y/1000**2)}\")\n",
+    "sec.geometry= sec.geometry.rotate(math.pi/2)\n",
+    "draw_section(sec)\n",
+    "print(f\"My_max_rotated [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_y/1000**2)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5. By default (theta=0) \"calculate_bending_strength\" gets negative value with \"theta=0\" correspondig with My_min and positive if theta = pi/2 (My_max). It seem strange to me. At least it should be documented. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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66y1s2rQJy5Yt67lREVGvUwkhhOzGp0+fRlhYGPLz83HjjTfCbrcjNDQUmzdvxuzZswEAx48fx+jRo2G1WjFlyhTs3LkTt99+O6qrq2EymQAAGzZswJNPPonTp09Dp9P9bLsOhwNGoxF2ux0Gg6HTeq01NWh8/XXZ4RH1S4FpadCEh3da3tX3V7euedjtdgBASEgIAKC4uBjnz59HQkKCUicmJgZRUVGwWq0AAKvVirFjxyrBAQCJiYlwOBwoKyvrsB2n0wmHw+G2EJFvSYeHy+XCokWLcP3112PMmDEAAJvNBp1Oh+DgYLe6JpMJNptNqfPT4GgrbyvrSHZ2NoxGo7JERkbKdpuIeoh0eKSnp+Po0aPYsmVLT/anQ1lZWbDb7cpSVVXV620S0cVpZTbKyMhATk4O9u/fj6FDhyrrzWYzmpubUV9f73b0UVtbC7PZrNQ5ePCg2+u13Y1pq3MhvV4PvV4v01Ui6iUeHXkIIZCRkYFt27Zh7969iI6OdiuPi4uDn58f8vLylHXl5eWorKyExWIBAFgsFpSWlqKurk6pk5ubC4PBgNjY2O6MhYi8yKMjj/T0dGzevBnvv/8+goKClGsURqMRAwcOhNFoxPz585GZmYmQkBAYDAYsXLgQFosFU6ZMAQDMmDEDsbGxuP/++7Fy5UrYbDYsXboU6enpPLog6kM8Co/169cDAG666Sa39Rs3bsSDDz4IAFi1ahXUajVSUlLgdDqRmJiIdevWKXU1Gg1ycnKwYMECWCwWBAQEIDU1FStWrOjeSIjIq7r1nIev8DkPInmXxHMeRHT5YngQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJYXgQkRSGBxFJ8Sg81q9fj3HjxsFgMMBgMMBisWDnzp1KeVNTE9LT0zF48GAEBgYiJSUFtbW1bq9RWVmJ5ORk+Pv7IywsDEuWLEFLS0vPjIaIvMaj8Bg6dChefPFFFBcX4/Dhw7jllltw1113oaysDACwePFifPjhh9i6dSvy8/NRXV2NWbNmKdu3trYiOTkZzc3NKCgowFtvvYVNmzZh2bJlPTsqIup1KiGE6M4LhISE4KWXXsLs2bMRGhqKzZs3Y/bs2QCA48ePY/To0bBarZgyZQp27tyJ22+/HdXV1TCZTACADRs24Mknn8Tp06eh0+m61KbD4YDRaITdbofBYOi0XmtNDRpff707wyPqdwLT0qAJD++0vKvvL+lrHq2trdiyZQvOnj0Li8WC4uJinD9/HgkJCUqdmJgYREVFwWq1AgCsVivGjh2rBAcAJCYmwuFwKEcvHXE6nXA4HG4LEfmWx+FRWlqKwMBA6PV6PPLII9i2bRtiY2Nhs9mg0+kQHBzsVt9kMsFmswEAbDabW3C0lbeVdSY7OxtGo1FZIiMjPe02EfUwj8Nj1KhRKCkpQVFRERYsWIDU1FQcO3asN/qmyMrKgt1uV5aqqqpebY+Ifp7W0w10Oh2GDx8OAIiLi8OhQ4fwyiuv4N5770VzczPq6+vdjj5qa2thNpsBAGazGQcPHnR7vba7MW11OqLX66HX6z3tKhH1om4/5+FyueB0OhEXFwc/Pz/k5eUpZeXl5aisrITFYgEAWCwWlJaWoq6uTqmTm5sLg8GA2NjY7naFiLzIoyOPrKwsJCUlISoqCg0NDdi8eTM++eQT7N69G0ajEfPnz0dmZiZCQkJgMBiwcOFCWCwWTJkyBQAwY8YMxMbG4v7778fKlSths9mwdOlSpKen88iCqI/xKDzq6urwwAMPoKamBkajEePGjcPu3bsxffp0AMCqVaugVquRkpICp9OJxMRErFu3Ttleo9EgJycHCxYsgMViQUBAAFJTU7FixYqeHRUR9bpuP+fhC3zOg0iez5/zIKLLG8ODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhISrfC48UXX4RKpcKiRYuUdU1NTUhPT8fgwYMRGBiIlJQU1NbWum1XWVmJ5ORk+Pv7IywsDEuWLEFLS0t3ukJEXiYdHocOHcJrr72GcePGua1fvHgxPvzwQ2zduhX5+fmorq7GrFmzlPLW1lYkJyejubkZBQUFeOutt7Bp0yYsW7ZMfhRE5HVS4dHY2Ii5c+fiT3/6EwYNGqSst9vteOONN/Dyyy/jlltuQVxcHDZu3IiCggIUFhYCAPbs2YNjx47h7bffxoQJE5CUlITnnnsOa9euRXNzc8+Mioh6nVR4pKenIzk5GQkJCW7ri4uLcf78ebf1MTExiIqKgtVqBQBYrVaMHTsWJpNJqZOYmAiHw4GysrIO23M6nXA4HG4LEfmW1tMNtmzZgs8++wyHDh1qV2az2aDT6RAcHOy23mQywWazKXV+Ghxt5W1lHcnOzsazzz7raVeJqBd5dORRVVWFxx57DO+88w4GDBjQW31qJysrC3a7XVmqqqq81jYRdcyj8CguLkZdXR2uvfZaaLVaaLVa5Ofn49VXX4VWq4XJZEJzczPq6+vdtqutrYXZbAYAmM3mdndf2n5uq3MhvV4Pg8HgthCRb3kUHtOmTUNpaSlKSkqUZeLEiZg7d67ybz8/P+Tl5SnblJeXo7KyEhaLBQBgsVhQWlqKuro6pU5ubi4MBgNiY2N7aFhE1Ns8uuYRFBSEMWPGuK0LCAjA4MGDlfXz589HZmYmQkJCYDAYsHDhQlgsFkyZMgUAMGPGDMTGxuL+++/HypUrYbPZsHTpUqSnp0Ov1/fQsIiot3l8wfTnrFq1Cmq1GikpKXA6nUhMTMS6deuUco1Gg5ycHCxYsAAWiwUBAQFITU3FihUrerorRNSLVEII4etOeMrhcMBoNMJut1/0+kdrTQ0aX3/diz0juvQFpqVBEx7eaXlX31/8bAsRSWF4EJEUhgcRSWF4EJEUhgcRSWF4EJEUhgcRSWF4EJEUhgcRSWF4EJEUhgcRSWF4EJEUhgcRSWF4EJEUhkcPEi4XvPkNB2yP7flSj38Z0OXmh8ZGlFqtOFlWhsb6eqjUagwKDcXICRMweuJEaP382B7b81l7vYlfBtQN1RUV2LNlC1qamzv8C2IICUHS/ffD8JOJsdge2/NWe53hlwH52Jm6Oux65x2c7+QXAQAa6uvx0aZNaG5qYntsz6vteQPDQ9KnH30EV2srcJEDN+Fy4azDgSP/+hfbY3tebc8bGB4SHGfOwPb111262CWEwPHiYgiXi+2xPa+05y0MDwmnq6s9qt/c1ISGCybCYntsr7fa8xaGh4Tzzc1e2Ybtsb1LGcNDQqDR6JVt2B7bu5QxPCSYIyO7fD9epVJhSEQE9AMHsj2255X2vIXhIUGr02Hc9dd3qa4QAnE33cT22J7X2vMWhoekX9xwAyKuvBJQqS5ab9x11yFq5Ei2x/a82p43MDwkqTUaJM6dizGTJ0P1f78QarUaavWPu1Tr54frkpIwefp0tsf2vN6eN/Dx9B5w1uHA1+XlcJw5A7VajRCTCVEjR0I3YADbY3s+b+9CPfV4OsOD6DLDz7YQkU8xPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhICsODiKQwPIhIikfh8cwzz0ClUrktMTExSnlTUxPS09MxePBgBAYGIiUlBbW1tW6vUVlZieTkZPj7+yMsLAxLlixBS0tLz4yGiLxG6+kG11xzDT7++OP/fwHt/7/E4sWL8dFHH2Hr1q0wGo3IyMjArFmz8OmnnwIAWltbkZycDLPZjIKCAtTU1OCBBx6An58f/vCHP/TAcIjIWzwOD61WC7PZ3G693W7HG2+8gc2bN+OWW24BAGzcuBGjR49GYWEhpkyZgj179uDYsWP4+OOPYTKZMGHCBDz33HN48skn8cwzz0Cn03V/RETkFR5f8zhx4gQiIiJw1VVXYe7cuaisrAQAFBcX4/z580hISFDqxsTEICoqClarFQBgtVoxduxYmEwmpU5iYiIcDgfKyso6bdPpdMLhcLgtRORbHoVHfHw8Nm3ahF27dmH9+vWoqKjADTfcgIaGBthsNuh0OgQHB7ttYzKZYLPZAAA2m80tONrK28o6k52dDaPRqCyRkZFd6q/K3x/QenxwRdR/abU/vi964qU8qZyUlKT8e9y4cYiPj8ewYcPw97//HQN7cXq8rKwsZGZmKj87HI4uBYjaaERQRgbEuXO91jeivkTl7w91D82D260/y8HBwRg5ciS++uorTJ8+Hc3Nzaivr3c7+qitrVWukZjNZhw8eNDtNdruxnR0HaWNXq+HXq+X6qPaaAT6wKTBRH1Nt57zaGxsxMmTJxEeHo64uDj4+fkhLy9PKS8vL0dlZSUsFgsAwGKxoLS0FHV1dUqd3NxcGAwGxMbGdqcrRORtwgOPP/64+OSTT0RFRYX49NNPRUJCghgyZIioq6sTQgjxyCOPiKioKLF3715x+PBhYbFYhMViUbZvaWkRY8aMETNmzBAlJSVi165dIjQ0VGRlZXnSDWG32wUAYbfbPdqOiH5eV99fHp22/Oc//8F9992H7777DqGhoZg6dSoKCwsRGhoKAFi1ahXUajVSUlLgdDqRmJiIdevWKdtrNBrk5ORgwYIFsFgsCAgIQGpqKlasWNGTeUhEXtCvp5skIs9xukki6lUMDyKSwvAgIikMDyKSwvAgIikMDyKS0ic/NdZ2d5mfriXqeW3vq597iqNPhkdDQwMAdPnTtUTkuYaGBhgv8rmwPvmQmMvlQnV1NYKCgqBSqTqt1/bp26qqqn75MBnH17ddquMTQqChoQERERFQqzu/stEnjzzUajWGDh3a5foGg+GS+s/paRxf33Ypju9iRxxteMGUiKQwPIhISr8OD71ej+XLl0t/kdCljuPr2/r6+PrkBVMi8r1+feRBRL2H4UFEUhgeRCSF4UFEUvpteKxduxZXXnklBgwYgPj4+HZTPlyq9u/fjzvuuAMRERFQqVTYvn27W7kQAsuWLUN4eDgGDhyIhIQEnDhxwq3OmTNnMHfuXBgMBgQHB2P+/PlobGz04ig6l52djUmTJiEoKAhhYWGYOXMmysvL3er05QnT169fj3HjxikPflksFuzcuVMp78tja6dXv4bZR7Zs2SJ0Op148803RVlZmXj44YdFcHCwqK2t9XXXftaOHTvE73//e/Hee+8JAGLbtm1u5S+++KIwGo1i+/bt4vPPPxd33nmniI6OFj/88INS59ZbbxXjx48XhYWF4l//+pcYPny4uO+++7w8ko4lJiaKjRs3iqNHj4qSkhJx2223iaioKNHY2KjUeeSRR0RkZKTIy8sThw8fFlOmTBHXXXedUt72LfwJCQniyJEjYseOHWLIkCEefwt/b/jggw/ERx99JP7973+L8vJy8bvf/U74+fmJo0ePCiH69tgu1C/DY/LkySI9PV35ubW1VURERIjs7Gwf9spzF4aHy+USZrNZvPTSS8q6+vp6odfrxd/+9jchhBDHjh0TAMShQ4eUOjt37hQqlUp88803Xut7V9XV1QkAIj8/Xwjx43j8/PzE1q1blTpffvmlACCsVqsQ4seAVavVwmazKXXWr18vDAaDcDqd3h1AFwwaNEj8+c9/7ndj63enLc3NzSguLnabcFutViMhIUGZcLuvqqiogM1mcxub0WhEfHy822TiwcHBmDhxolInISEBarUaRUVFXu/zz7Hb7QCAkJAQAL07Ybq3tba2YsuWLTh79iwsFku/GhvQRz8YdzHffvstWltbO5xQ+/jx4z7qVc9omwy8o7H9dDLxsLAwt3KtVouQkJCLTibuCy6XC4sWLcL111+PMWPGAECvTpjuLaWlpbBYLGhqakJgYCC2bduG2NhYlJSU9Pmx/VS/Cw/qO9LT03H06FEcOHDA113pUaNGjUJJSQnsdjv+8Y9/IDU1Ffn5+b7uVo/rd6ctQ4YMgUajaXcF+6cTbvdVbf2/2NjMZrPbXMAA0NLSgjNnzlxS48/IyEBOTg727dvn9vUKZrNZmTD9py4cY0f7oK3M13Q6HYYPH464uDhkZ2dj/PjxeOWVV/rF2H6q34WHTqdDXFyc24TbLpcLeXl5yoTbfVV0dDTMZrPb2BwOB4qKitwmE6+vr0dxcbFSZ+/evXC5XIiPj/d6ny8khEBGRga2bduGvXv3Ijo62q28P06Y7nK54HQ6+9/YfH3Ftjds2bJF6PV6sWnTJnHs2DGRlpYmgoOD3a5gX6oaGhrEkSNHxJEjRwQA8fLLL4sjR46Ir7/+Wgjx463a4OBg8f7774svvvhC3HXXXR3eqv3FL34hioqKxIEDB8SIESMumVu1CxYsEEajUXzyySeipqZGWc6dO6fU8daE6b3hqaeeEvn5+aKiokJ88cUX4qmnnhIqlUrs2bNHCNG3x3ahfhkeQgixZs0aERUVJXQ6nZg8ebIoLCz0dZe6ZN++fQJAuyU1NVUI8ePt2qefflqYTCah1+vFtGnTRHl5udtrfPfdd+K+++4TgYGBwmAwiHnz5omGhgYfjKa9jsYGQGzcuFGp88MPP4hHH31UDBo0SPj7+4u7775b1NTUuL3OqVOnRFJSkhg4cKAYMmSIePzxx8X58+e9PJr2HnroITFs2DCh0+lEaGiomDZtmhIcQvTtsV2IH8knIin97poHEXkHw4OIpDA8iEgKw4OIpDA8iEgKw4OIpDA8iEgKw4OIpDA8iEgKw4OIpDA8iEgKw4OIpPwvHIFY/KKBo+UAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My_meg [mkN]=-69\n",
+      "My_pos [mkN]=324\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
+    "sec = GenericSection(geo)\n",
+    "draw_section(sec)\n",
+    "\n",
+    "print(f\"My_meg [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_y/1000**2)}\")\n",
+    "print(f\"My_pos [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=math.pi).m_y/1000**2)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6. Error when computing bending capacity with external axial load n<>0  (#1) or the section response for a given N-M (#2)\n",
+    "\n",
+    "\n",
+    "![N-M](error_N_M.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-3500 -1082.3516216091132\n",
+      "-3000 -1039.8614525599924\n",
+      "-2500 -968.2633649200853\n",
+      "-2000 -869.2276407658625\n",
+      "-1500 -715.0189200393919\n",
+      "-1000 -512.7545687674593\n",
+      "-500 -291.01410396661913\n",
+      "0 -65.77991375540094\n",
+      "500 160.05431990676033\n",
+      "900 343.05573912271814\n",
+      "-0.0007827106434851892\n",
+      "2.589796122664981e-06\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z neutral axis = 302.23\n"
+     ]
+    }
+   ],
+   "source": [
+    "from  SLS_section_response import calculate_strain_profile\n",
+    "from  structuralcodes.plots.section_plots import draw_section_response\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "draw_section(sec)\n",
+    "\n",
+    "# 1\n",
+    "n_ = [-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,900] # kN\n",
+    "for n_ed in n_:\n",
+    "    print(n_ed,sec.section_calculator.calculate_bending_strength(0,n=n_ed*1e3).m_y/1e6)\n",
+    "\n",
+    "\n",
+    "# 2\n",
+    "my_ed =0 # mkN\n",
+    "n_ed=-500  # kN\n",
+    "#eps,chi= calculate_strain_profile(sec,n_ed,my_ed)\n",
+    "#draw_section_response(sec,eps,chi)\n",
+    "eps,chi= calculate_strain_profile(sec,n_ed*1e3,my_ed*1e6)\n",
+    "print(eps)\n",
+    "print(chi)\n",
+    "draw_section_response(sec,eps,chi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Error copmputing bending stress with axial load comes from \"integrate_strain_response_on_geometry\" since the strain profile is validated\n",
+    "\n",
+    "\n",
+    "![N-M](N2000_my_min.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsa= 0.002741093057504882 similar to 2.703e-3 (validation result)\n",
+      "chi= -1.248218611500977e-05 similar to 12.3e-6 (validation result)\n",
+      "my = -869 <> 348.85 (validation result)\n"
+     ]
+    }
+   ],
+   "source": [
+    "strain = sec.section_calculator.find_equilibrium_fixed_pivot(sec.geometry, n=-2000*1e3)\n",
+    "print(f'epsa= {strain[0]} similar to 2.703e-3 (validation result)' )\n",
+    "print(f'chi= {strain[1]} similar to 12.3e-6 (validation result)' )\n",
+    "print(f'my = {round(sec.section_calculator.calculate_bending_strength(0, n=-2000*1e3).m_y/1e6)} <> 348.85 (validation result)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "7. How can we obtain the material of a part of a section? We only have access to the constitutive law of each geometry but not to the material. It could be useful to get for example if the constitutive law of concrete was created with gamma=1 or gamma=1.5 (maybe constitutive_law should have this attribute). It could also be important to store other attributes of the original material\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "8. I miss information about the units in the docstrings within the functions of the section classes.\n",
+    "- Force: N\n",
+    "- Moment: N*mm\n",
+    "- Dimensions: mm\n",
+    "- curvature: 1/mm\n",
+    "\n",
+    "Also the sign criteria of internal moments in cross section, eps and curvature\n",
+    "- section_calculator.calculate_bending_strength(0).m_y -> returns a negative value. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "9. My-Mz diagram:\n",
+    "Small discrepancies in N=0. If axial load, something goes wrong.\n",
+    "\n",
+    "N= 0 kN\n",
+    "\n",
+    "![my-Mz](My_Mz_N_0kN.png)\n",
+    "\n",
+    "\n",
+    "N= -500 kN\n",
+    "\n",
+    "![my-Mz](My_Mz_N_-500kN.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "draw_section(sec)\n",
+    "\n",
+    "\n",
+    "n= 0 *1e3\n",
+    "res = sec.section_calculator.calculate_mm_interaction_domain(n)\n",
+    "draw_My_Mz_diagram(res,n,(5,5))\n",
+    "n= -500 *1e3\n",
+    "res = sec.section_calculator.calculate_mm_interaction_domain(n)\n",
+    "draw_My_Mz_diagram(res,n,(5,5))\n",
+    "#print(res.m_y)\n",
+    "#print('---------------')\n",
+    "#print(res.m_z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "10. N-M diagram:\n",
+    "\n",
+    "Not working properly. It should be interesting to get the whole diagram  and not just the minimum range of M\n",
+    "\n",
+    "![N-M](N_My.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "draw_section(sec)\n",
+    "\n",
+    "\n",
+    "theta = 0\n",
+    "res = sec.section_calculator.calculate_nm_interaction_domain(theta)\n",
+    "draw_N_M_diagram(res)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "11. Moment curvature results (fib package vs FH-PIEM)\n",
+    "- Something fails with axial load\n",
+    "- Why only return the negative domain of curvature with theta=0?  Wouldn't it be better to have the whole domain instead of use theta parameter for that?\n",
+    "- The moment-curvature results should start at (0,0). I miss that point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "sec = GenericSection(geo)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.set_title(f'M-X   N={n/1e3} kN')\n",
+    "ax.set_xlabel('X (1/km)')\n",
+    "ax.set_ylabel('My (mkN)')\n",
+    "ax.axhline(0, color='gray', linewidth=1)\n",
+    "ax.axvline(0, color='gray', linewidth=1)\n",
+    "xmin,xmax,ymin,ymax = 0,0,0,0\n",
+    "\n",
+    "n=-1500 * 1e3\n",
+    "res = sec.section_calculator.calculate_moment_curvature(theta=0, n=n)\n",
+    "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='red',  label='N=-1500 fib')\n",
+    "xmin = min(xmin, np.min(res.chi_y*1e6))\n",
+    "ymin= min(ymin,np.min(res.m_y/1e6))\n",
+    "xmax = max(xmax, np.max(res.chi_y*1e6))\n",
+    "ymax= max(ymax, np.max(res.m_y/1e6))\n",
+    "res = sec.section_calculator.calculate_moment_curvature(theta=math.pi, n=n)\n",
+    "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='red')\n",
+    "xmin = min(xmin, np.min(res.chi_y*1e6))\n",
+    "ymin= min(ymin,np.min(res.m_y/1e6))\n",
+    "xmax = max(xmax, np.max(res.chi_y*1e6))\n",
+    "ymax= max(ymax, np.max(res.m_y/1e6))\n",
+    "\n",
+    "#FH results\n",
+    "m1=[-324.42,-324.36,-324.3,-324.24,-324.18,-324.11,-324.03,-323.96,-323.87,-323.79,-323.69,-323.44,-322.81,-322.15,-321.47,-320.77,-320.03,-319.27,-318.47,-317.64,-316.78,-315.88,-314.93,-313.95,-312.91,-311.83,-310.69,-309.49,-308.23,-306.9,-305.35,-301.59,-297.76,-293.86,-289.88,-285.81,-281.64,-277.34,-272.91,-268.33,-263.57,-258.61,-253.43,-247.98,-242.22,-236.1,-229.57,-222.51,-214.83,-206.39,-196.94,-186.25,-173.91,-159.25,-141.39,-120.32,-99.1,-78.07,-56.94,-36.38,-15.46,-5.13,4.79,14.53,24.24,34.05,43.77,53.33,62.79,72.2,81.47,90.62,99.68,108.67,117.47,126.07,134.19,141.85,149.03,155.84,162.33,168.53,174.44,180.1,185.56,190.79,195.81,200.63,205.29,209.77,214.05,218.17,222.14,225.95,229.61,233.15,236.56,239.86,243.06,246.18,249.21,252.16,255.04,257.28,259.23,261.12,262.94,264.7,266.41,268.06,269.66,271.21,272.72,274.18,275.6,276.98,278.33,279.64,280.91,282.15,283.37\n",
+    "]\n",
+    "x1=[-19.92,-19.58,-19.25,-18.92,-18.59,-18.26,-17.92,-17.59,-17.26,-16.93,-16.6,-16.26,-15.93,-15.6,-15.27,-14.94,-14.6,-14.27,-13.94,-13.61,-13.28,-12.94,-12.61,-12.28,-11.95,-11.62,-11.29,-10.95,-10.62,-10.29,-9.96,-9.63,-9.29,-8.96,-8.63,-8.3,-7.97,-7.63,-7.3,-6.97,-6.64,-6.31,-5.97,-5.64,-5.31,-4.98,-4.65,-4.31,-3.98,-3.65,-3.32,-2.99,-2.66,-2.32,-1.99,-1.66,-1.33,-1,-0.66,-0.33,0,0.16,0.33,0.49,0.65,0.81,0.98,1.14,1.3,1.46,1.63,1.79,1.95,2.11,2.28,2.44,2.6,2.76,2.93,3.09,3.25,3.41,3.58,3.74,3.9,4.06,4.23,4.39,4.55,4.71,4.88,5.04,5.2,5.37,5.53,5.69,5.85,6.02,6.18,6.34,6.5,6.67,6.83,6.99,7.15,7.32,7.48,7.64,7.8,7.97,8.13,8.29,8.45,8.62,8.78,8.94,9.1,9.27,9.43,9.59,9.75\n",
+    "]\n",
+    "ax.plot(x1, m1, color='blue',  label='N= 1500 FH', linestyle='--')\n",
+    "\n",
+    "n=0\n",
+    "res = sec.section_calculator.calculate_moment_curvature(theta=0, n=n)\n",
+    "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='green',  label='N= 0 fib')\n",
+    "xmin = min(xmin, np.min(res.chi_y*1e6))\n",
+    "ymin= min(ymin,np.min(res.m_y/1e6))\n",
+    "xmax = max(xmax, np.max(res.chi_y*1e6))\n",
+    "ymax= max(ymax, np.max(res.m_y/1e6))\n",
+    "res = sec.section_calculator.calculate_moment_curvature(theta=math.pi, n=n)\n",
+    "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='green')\n",
+    "xmin = min(xmin, np.min(res.chi_y*1e6))\n",
+    "ymin= min(ymin,np.min(res.m_y/1e6))\n",
+    "xmax = max(xmax, np.max(res.chi_y*1e6))\n",
+    "ymax= max(ymax, np.max(res.m_y/1e6))\n",
+    "\n",
+    "#FH results\n",
+    "m1=[-65.77,-65.76,-65.74,-65.73,-65.71,-65.69,-65.67,-65.65,-65.63,-65.61,-65.58,-65.56,-65.53,-65.5,-65.47,-65.44,-65.41,-65.38,-65.34,-65.3,-65.26,-65.21,-65.17,-65.12,-65.06,-65.01,-64.94,-64.88,-64.81,-64.73,-64.65,-64.57,-64.48,-64.39,-64.3,-64.2,-64.09,-63.98,-63.87,-63.76,-63.64,-63.52,-63.39,-63.26,-63.12,-62.99,-62.84,-62.7,-62.55,-62.39,-62.23,-62.06,-61.88,-61.7,-61.49,-61.26,-51.88,-38.96,-26,-13,0,16.12,32.23,48.29,64.12,79.85,95.42,110.84,126.11,141.19,156.11,170.82,185.35,199.67,213.78,227.66,241.3,254.69,267.82,280.66,293.21,305.43,310.76,311.55,312.26,312.92,313.52,314.07,314.58,315.05,315.48,315.88,316.26,316.61,316.94,317.25,317.55,317.82,318.09,318.34,318.58,318.81,319.03,319.24,319.44,319.64,319.83,320.01,320.19,320.36,320.52,320.68,320.84,320.99,321.14,321.28,321.42,321.51,321.55,321.59,321.62\n",
+    "]\n",
+    "x1=[-73.87,-72.64,-71.4,-70.17,-68.94,-67.71,-66.48,-65.25,-64.02,-62.79,-61.56,-60.32,-59.09,-57.86,-56.63,-55.4,-54.17,-52.94,-51.71,-50.48,-49.24,-48.01,-46.78,-45.55,-44.32,-43.09,-41.86,-40.63,-39.4,-38.16,-36.93,-35.7,-34.47,-33.24,-32.01,-30.78,-29.55,-28.32,-27.08,-25.85,-24.62,-23.39,-22.16,-20.93,-19.7,-18.47,-17.24,-16,-14.77,-13.54,-12.31,-11.08,-9.85,-8.62,-7.39,-6.16,-4.92,-3.69,-2.46,-1.23,0,0.41,0.82,1.23,1.63,2.04,2.45,2.86,3.27,3.68,4.09,4.5,4.9,5.31,5.72,6.13,6.54,6.95,7.36,7.76,8.17,8.58,8.99,9.4,9.81,10.22,10.63,11.03,11.44,11.85,12.26,12.67,13.08,13.49,13.9,14.3,14.71,15.12,15.53,15.94,16.35,16.76,17.16,17.57,17.98,18.39,18.8,19.21,19.62,20.03,20.43,20.84,21.25,21.66,22.07,22.48,22.89,23.29,23.7,24.11,24.52\n",
+    "]\n",
+    "ax.plot(x1, m1, color='orange',  label='N= 0 FH', linestyle='--')\n",
+    "\n",
+    "x_legend = np.linspace(xmin, xmax, 10)\n",
+    "y_legend = np.linspace(ymin, ymax, 10)\n",
+    "ax.set_xticks(x_legend)\n",
+    "ax.set_yticks(y_legend)\n",
+    "ax.set_xticklabels(np.around(x_legend, decimals=1))\n",
+    "ax.set_yticklabels(np.around(y_legend, decimals=1))\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "12. When calculating the geometric properties EA, EI uses the tangent modulus at eps=0. With concrete it does not use Ecm. \n",
+    "In order to homogenise a section it is now necessary to adapt Es =Ec,tang.\n",
+    "Would be usefull to get section properties in homogenised section using Ecm. Instead of using E=constitutive_law.get_tangent(0)[0], a parameter of E could be entered for each material just for gross properties calculation. The default should be Ecm for concrete and constitutive_law.get_tangent(0)[0] for others.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Should be SurfaceGeometry class inherited from Geometry class??"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Moment-Curvature.png b/Moment-Curvature.png
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diff --git a/SLS_section_response.py b/SLS_section_response.py
new file mode 100644
index 00000000..1ac8e61a
--- /dev/null
+++ b/SLS_section_response.py
@@ -0,0 +1,228 @@
+import math
+
+import numpy as np
+
+from structuralcodes.codes import _CODE, get_design_codes, set_design_code
+from structuralcodes.geometry import CompoundGeometry, SurfaceGeometry
+from structuralcodes.materials.concrete import create_concrete
+from structuralcodes.materials.constitutive_laws import (
+    ParabolaRectangle,
+    Sargin,
+    UserDefined,
+)
+from structuralcodes.sections._generic import GenericSection
+
+
+def calculate_strain_profile_batch(sec: GenericSection, n_ed, my_ed):
+    """'Get the moment curvature diagram. Then interpolate to get curvature for my_d.
+
+    Args:
+        n_ed [N]: Axial load
+        my_ed [N*m]: List of bending moment My
+
+    Returns:
+        eps_a: strain at (0,0)
+        chi: curvature of the section
+    """
+    my_ed = np.array(my_ed, dtype=float)
+    result_neg = sec.section_calculator.calculate_moment_curvature(0, n=n_ed)
+    result_pos = sec.section_calculator.calculate_moment_curvature(
+        math.pi, n=n_ed
+    )
+    chi_y = np.vstack((result_neg.chi_y, result_pos.chi_y)).reshape(-1)
+    #  results.m_y in (Nmm)
+    m_y = (np.vstack((result_neg.m_y, result_pos.m_y))).reshape(-1)
+    eps_axial = np.vstack(
+        (result_neg.eps_axial, result_pos.eps_axial)
+    ).reshape(-1)
+    # sorts results M-chi
+    index = np.argsort(chi_y)
+    chi_y = chi_y[index]
+    m_y = m_y[index]
+    eps_axial = eps_axial[index]
+
+    chi_y_ = np.interp(my_ed, m_y, chi_y)  #  results.m_y in (Nmm)
+    epsa_ = np.interp(my_ed, m_y, eps_axial)
+    return epsa_, chi_y_
+
+
+def calculate_strain_profile(section: GenericSection, n_ed=0, m_ed=0):
+    """'Get the stress in a given point (y,z) inside the cross section.
+
+    Args:
+        n_ed [N]: Axial load
+        m_ed [N*m]: Bending moment My
+
+    Returns:
+        eps_a: strain at (0,0)
+        chi: curvature of the section
+    """
+    # Rotate the section of angle theta
+    sec_geom = section.geometry
+
+    def interpolate(x1, x2, y1, y2, x):
+        if x2 - x1 == 0:
+            return y1
+        y = y1 + (y2 - y1) * (x - x1) / (x2 - x1)
+        return y
+
+    # Check if the section can carry the axial load
+    section.section_calculator.check_axial_load(n=n_ed)
+
+    if m_ed < 0:
+        r = section.section_calculator.calculate_bending_strength(
+            theta=0, n=n_ed
+        )
+        m1, m2 = r.m_y, 0
+        chi1, chi2 = r.chi_y, 0
+    else:
+        r = section.section_calculator.calculate_bending_strength(
+            theta=math.pi, n=n_ed
+        )
+        m1, m2 = 0, r.m_y
+        chi1, chi2 = 0, -r.chi_y
+
+    chi = interpolate(m1, m2, chi1, chi2, m_ed)
+
+    # Previous position of strain at (0,0)
+    strain = [0, 0, 0]
+
+    ITMAX = 200
+    tolerance = 1000  # (1e-3 mkN)
+    iter = 0
+    My = 1e11
+
+    while abs(m_ed - My) > tolerance and iter < ITMAX:
+        strain = section.section_calculator.find_equilibrium_fixed_curvature(
+            sec_geom, n_ed, chi, strain[0]
+        )
+        (
+            _,
+            My,
+            Mz,
+            _,
+        ) = section.section_calculator.integrator.integrate_strain_response_on_geometry(
+            geo=sec_geom,
+            strain=strain,
+            tri=section.section_calculator.triangulated_data,
+        )
+
+        if My > m_ed:
+            m2 = My
+            chi2 = chi
+        else:
+            m1 = My
+            chi1 = chi
+
+        chi = interpolate(m1, m2, chi1, chi2, m_ed)
+        eps_a = strain[0]
+        iter += 1
+
+    if iter == ITMAX:
+        return None, None
+    else:
+        return eps_a, chi
+
+
+def calculate_cracking_moment(section: GenericSection, n=0, plot=False):
+    """Calculate the cracking moment of a R.C section in N*mm.
+
+    Args:
+        n [N]: Axial external load
+
+    Returns:
+        m_cracking_pos: positive My_cracking
+        m_cracking_neg: negative My_cracking
+    """
+
+    def modify_consitutive_law(geom: SurfaceGeometry):
+        mat = geom.material
+        gamma_c = 1.5  # !!!! Temporary. gamma_c should be readable from ConstitutiveLaw class (TODO)
+        if (
+            _CODE == None
+        ):  # should be the code of the material used to create the section
+            set_design_code(get_design_codes()[1])
+        if isinstance(mat, ParabolaRectangle):
+            strains = np.linspace(mat._eps_u, 0, 20)
+            aux_concrete = create_concrete(abs(mat._fc * gamma_c), gamma_c=1)
+        elif isinstance(mat, Sargin):
+            strains = np.linspace(mat._eps_cu1, 0, 20)
+            aux_concrete = create_concrete(abs(mat._fc * gamma_c), gamma_c=1)
+        else:  # UserDefined
+            strains = np.linspace(-0.0035, 0, 20)
+            eps_max, eps_min = mat.get_ultimate_strain()
+            s1 = np.linspace(eps_min, eps_max, 100)
+            s2 = mat.get_stress(s1)
+            aux_concrete = create_concrete(abs(s2.min() * gamma_c), gamma_c=1)
+
+        stress = aux_concrete.constitutive_law.get_stress((strains))
+        # stress = mat.get_stress((strains))
+        strains = strains.tolist()
+        stress = stress.tolist()
+
+        fctm = aux_concrete.fctm
+        Ec = aux_concrete.constitutive_law.get_tangent(0)[0]
+        tensile_strain_limit = fctm / Ec
+        strains.append(tensile_strain_limit)
+        stress.append(fctm)
+        ec_const = UserDefined(
+            strains, stress, 'constitutive_law_with_tension'
+        )
+        geom.material = ec_const
+        if plot:
+            from structuralcodes.plots.section_plots import (
+                draw_constitutive_law,
+            )
+
+            draw_constitutive_law(ec_const)
+
+    import copy
+
+    sec = copy.deepcopy(section)
+    mat = None
+    m_cracking_pos, m_cracking_neg = 0, 0
+    if sec.geometry.reinforced_concrete:
+        if isinstance(sec.geometry, SurfaceGeometry):
+            modify_consitutive_law(sec.geometry)
+            # mat = sec.geometry.material
+            # print(f'SurfaceGeometry  = {mat}')
+        elif isinstance(sec.geometry, CompoundGeometry):
+            for i in range(len(sec.geometry.geometries)):
+                modify_consitutive_law(sec.geometry.geometries[i])
+                # mat = sec.geometry.geometries[i].material
+                # print(f'CompoundGeometry-geometries {i+1} = {mat}')
+        else:
+            raise ValueError(
+                'geometry must be SurfaceGeometry or CompoundGeometry'
+            )
+
+        m_cracking_pos = sec.section_calculator.calculate_bending_strength(
+            theta=math.pi, n=n
+        ).m_y
+        m_cracking_neg = sec.section_calculator.calculate_bending_strength(
+            theta=0, n=n
+        ).m_y
+
+        # PLOT STRESS DIAGRAM
+        if plot:
+            from structuralcodes.plots.section_plots import (
+                draw_section_response,
+            )
+
+            eps, chi = calculate_strain_profile(sec, 0, m_cracking_pos)
+            draw_section_response(
+                sec,
+                eps,
+                chi,
+                title=f'M cracking pos {round(m_cracking_pos/1e6,2)} mkN',
+            )
+
+            eps, chi = calculate_strain_profile(sec, 0, m_cracking_neg)
+            draw_section_response(
+                sec,
+                eps,
+                chi,
+                title=f'M cracking neg {round(m_cracking_neg/1e6,2)} mkN',
+            )
+
+    return m_cracking_pos, m_cracking_neg
diff --git a/error_N_M.png b/error_N_M.png
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HcmV?d00001

diff --git a/structuralcodes/plots/__init__.py b/structuralcodes/plots/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/structuralcodes/plots/section_plots.py b/structuralcodes/plots/section_plots.py
new file mode 100644
index 00000000..06eaf200
--- /dev/null
+++ b/structuralcodes/plots/section_plots.py
@@ -0,0 +1,377 @@
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+from shapely import Point
+
+sys.path.append('../')
+from structuralcodes.materials.constitutive_laws import (
+    ParabolaRectangle,
+    Sargin,
+)
+
+
+def get_stress_point(section, y, z, eps_a, chi_y, chi_z):
+    """Get the stress in a given point (y,z) inside the cross section.
+
+    Args:
+        y,z : coordinates of point inside the c.s.
+        eps_a :  strain at (0,0)
+        chi_y,chi_z :  curvatures of the cross secction (1/mm)
+
+    Returns:
+        stress in y,z
+    """
+    pt = Point(y, z)
+    strain = []
+    strain.append(eps_a + z * chi_y + y * chi_z)
+    for i, g in enumerate(section.geometry.point_geometries):
+        center = g._point
+        r = g._diameter / 2
+        poly = center.buffer(r)
+        if poly.contains(pt) or poly.touches(pt):
+            return g.material.get_stress(strain)[0]
+    for i, g in enumerate(section.geometry.geometries):
+        poly = g.polygon
+        if poly.contains(pt) or poly.touches(pt):
+            return g.material.get_stress(strain)[0]
+    return None
+
+
+def draw_section(section, title='', reduction_reinf=None):
+    """Draw the cross section in matplotlib.
+
+    Args:
+        reduction_reinf : float to reduce the diameter in homogenised
+        section
+    """
+    fig, ax = plt.subplots(figsize=(4, 4))
+    for i, g in enumerate(section.geometry.geometries):
+        poly = g.polygon
+        x, y = poly.exterior.xy
+        ax.fill(x, y, color='lightcoral')
+    for i, g in enumerate(section.geometry.point_geometries):
+        center = g._point
+        if reduction_reinf is not None:
+            r = g._diameter / 2 / reduction_reinf
+            color = 'navy'
+        else:
+            r = g._diameter / 2
+            color = 'darkred'
+        poly = center.buffer(r)
+        x, y = poly.exterior.xy
+        ax.fill(x, y, color=color)
+    ax.invert_yaxis()  # Invert Y axis
+
+    # same scale in X and Y
+    ax.set_aspect('equal', 'box')
+    ax.set_title(title if title else 'Section')
+
+    # plt.grid(True)
+    plt.show()
+
+
+def draw_section_response(
+    section, eps_a, chi_y, lim_Sneg=None, lim_Spos=None, title=None
+):
+    """Draw the stres and strains of a cross section.
+
+    Args:
+        eps_a : strain at (0,0)
+        chi_y : curvature in Y axis
+        lim_Sneg, lim_Spos: limits of stress in the plot
+    """
+    fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 5))
+    if title is not None:
+        fig.suptitle(title)
+    ax.set_title('Section response - stress')
+    ax.set_xlabel('Stress [MPa]')
+    ax.set_ylabel('z')
+    ax.invert_yaxis()  # Invert axis
+    if (lim_Sneg is not None) and (lim_Spos is not None):
+        ax.set_xlim(lim_Sneg, lim_Spos)
+    else:
+        lim_Sneg, lim_Spos = -1e11, 1e11
+    ax.invert_xaxis()
+
+    labels_stress = {}
+    labels_strain = {}
+    # Asumiendo que puedes obtener estos valores correctamente de tus objetos section y geometries
+    for i, g in enumerate(section.geometry.geometries):
+        poly = g.polygon
+        x_min, y_min, x_max, y_max = poly.bounds
+        z_range = np.linspace(y_min, y_max, 500)
+        stress = [
+            get_stress_point(
+                section, 0.5 * (x_min + x_max), z, eps_a, chi_y, 0
+            )
+            for z in z_range
+        ]
+        ax.plot(stress, z_range, color='gray')
+        ax.fill_betweenx(
+            z_range,
+            0,
+            stress,
+            where=(np.array(stress) >= 0),
+            facecolor='lightblue',
+            interpolate=True,
+        )
+        ax.fill_betweenx(
+            z_range,
+            0,
+            stress,
+            where=(np.array(stress) < 0),
+            facecolor='lightcoral',
+            interpolate=True,
+        )
+        labels_stress[(stress[0], z_range[0])] = round(stress[0], 2)
+        labels_stress[(stress[-1], z_range[-1])] = round(stress[-1], 2)
+
+        # strain
+        eps = [(eps_a + chi_y * z) * 1000 for z in z_range]
+        ax2.plot(eps, z_range, color='gray')
+        ax2.fill_betweenx(
+            z_range,
+            0,
+            eps,
+            where=(np.array(stress) >= 0),
+            facecolor='lightblue',
+            interpolate=True,
+        )
+        ax2.fill_betweenx(
+            z_range,
+            0,
+            eps,
+            where=(np.array(stress) < 0),
+            facecolor='lightcoral',
+            interpolate=True,
+        )
+        labels_strain[(eps[0], z_range[0])] = round(eps[0], 2)
+        labels_strain[(eps[-1], z_range[-1])] = round(eps[-1], 2)
+
+    for i, g in enumerate(section.geometry.point_geometries):
+        center = g._point
+        r = g._diameter / 2
+        poly = center.buffer(r)
+        x_min, y_min, x_max, y_max = poly.bounds
+        z_range = [y_min, y_max]
+
+        # stress
+        stress = [
+            get_stress_point(
+                section, 0.5 * (x_min + x_max), z, eps_a, chi_y, 0
+            )
+            for z in z_range
+        ]
+        ax.plot(stress, z_range, color='gray')
+        ax.fill_betweenx(
+            z_range,
+            0,
+            stress,
+            where=(np.array(stress) >= 0),
+            facecolor='lightblue',
+            interpolate=True,
+        )
+        ax.fill_betweenx(
+            z_range,
+            0,
+            stress,
+            where=(np.array(stress) < 0),
+            facecolor='lightcoral',
+            interpolate=True,
+        )
+        labels_stress[
+            (
+                min(max(stress[0], lim_Sneg), lim_Spos),
+                0.5 * (z_range[0] + z_range[1]),
+            )
+        ] = round(0.5 * (stress[0] + stress[1]), 1)
+
+        # strain
+        eps = [(eps_a + chi_y * z) * 1000 for z in z_range]
+        ax2.plot(eps, z_range, color='gray')
+        ax2.fill_betweenx(
+            z_range,
+            0,
+            eps,
+            where=(np.array(stress) >= 0),
+            facecolor='lightblue',
+            interpolate=True,
+        )
+        ax2.fill_betweenx(
+            z_range,
+            0,
+            eps,
+            where=(np.array(stress) < 0),
+            facecolor='lightcoral',
+            interpolate=True,
+        )
+
+        labels_strain[(eps[0], 0.5 * (z_range[0] + z_range[1]))] = round(
+            0.5 * (eps[0] + eps[1]), 2
+        )
+
+    for coords, text in labels_stress.items():
+        ax.text(
+            *coords, text, fontsize=10, color='black', ha='left', va='center'
+        )
+    for coords, text in labels_strain.items():
+        ax2.text(
+            *coords, text, fontsize=10, color='black', ha='left', va='center'
+        )
+
+    # strains
+    ax2.set_title('Section response - strain')
+    ax2.set_xlabel('strain [mm/m]')
+    ax2.set_ylabel('z')
+    ax2.invert_yaxis()  # Invert axis
+    ax2.invert_xaxis()
+    plt.show()
+    if abs(chi_y) > 0:
+        print(f'z neutral axis = {round(-eps_a/chi_y,2)}')
+
+
+def draw_constitutive_law(ec_const, lim_strain_neg=None, lim_strain_pos=None):
+    if isinstance(ec_const, ParabolaRectangle):
+        strain_p = 0
+        strain_n = ec_const._eps_u
+    elif isinstance(ec_const, Sargin):
+        strain_p = 0
+        strain_n = ec_const._eps_cu1
+    else:
+        strain_p, strain_n = ec_const.get_ultimate_strain()
+    strain = np.linspace(strain_n, strain_p, 100)
+    stress = ec_const.get_stress(strain)
+
+    fig, ax = plt.subplots(figsize=(4, 4))
+    if (lim_strain_neg is not None) and (lim_strain_pos is not None):
+        ax.set_xlim(lim_strain_neg, lim_strain_pos)
+    else:
+        lim_strain_neg, lim_strain_pos = -1e11, 1e11
+
+    ax.plot(strain * 1000, stress, color='green')
+    ax.invert_yaxis()
+    ax.invert_xaxis()
+    ax.set_title(f'stress-strain curve {ec_const.name}')
+    ax.set_xlabel('Strain [mm/m]')
+    ax.set_ylabel('Stress [MPa]')
+    ax.axhline(0, color='gray', linewidth=1)
+    ax.axvline(0, color='gray', linewidth=1)
+
+    # ax.set_xticks([strain[0], strain[-1]])
+    # ax.set_xticklabels([round(strain[0], 3), round(strain[-1], 3)])
+    # ax.set_yticks([stress[0], stress[-1]])
+    # ax.set_yticklabels([round(stress[0], 2), round(stress[-1], 2)])
+
+    x_legend = (
+        np.linspace(
+            max(strain[0], lim_strain_neg), min(lim_strain_pos, strain[-1]), 5
+        )
+        * 1000
+    )
+    y_legend = np.linspace(stress[0], stress[-1], 5)
+    ax.set_xticks(x_legend)
+    ax.set_yticks(y_legend)
+    ax.set_xticklabels(np.around(x_legend, decimals=3))
+    ax.set_yticklabels(np.around(y_legend, decimals=2))
+
+    plt.show()
+
+
+def draw_My_Mz_diagram(res, n=0, figsize=(10, 10), nticks_x=10, nticks_y=10):
+    """Draw the My-Mz diagram of a cross section.
+
+    Args:
+        res : MMInteractionDomain results
+        figsize : Size of the plot
+        nticks_x,nticks_y : number of ticks in the axes
+    """
+    m_y = res.m_y / 1e6
+    m_z = res.m_z / 1e6
+    fig, ax = plt.subplots(figsize=figsize)
+    ax.plot(m_y, m_z, color='green')
+    ax.set_title(f'My-Mz   N={n/1e3} kN')
+    ax.set_xlabel('My (mkN)')
+    ax.set_ylabel('Mz (mkN)')
+    ax.axhline(0, color='gray', linewidth=1)
+    ax.axvline(0, color='gray', linewidth=1)
+
+    x_legend = np.linspace(np.min(m_y), np.max(m_y), nticks_x)
+    y_legend = np.linspace(np.min(m_z), np.max(m_z), nticks_y)
+    ax.set_xticks(x_legend)
+    ax.set_yticks(y_legend)
+    ax.set_xticklabels(np.around(x_legend, decimals=1))
+    ax.set_yticklabels(np.around(y_legend, decimals=1))
+    plt.show()
+
+
+def draw_N_M_diagram(res, figsize=(10, 10), nticks_x=10, nticks_y=10):
+    """Draw the N-M diagram of a cross section.
+
+    Args:
+        res : NMInteractionDomain results
+        figsize : Size of the plot
+        nticks_x,nticks_y : number of ticks in the axes
+    """
+    n = res.n / 1e3
+    m_y = res.m_y / 1e6
+    m_z = res.m_z / 1e6
+    fig, ax = plt.subplots(figsize=figsize)
+    ax.plot(n, m_y, color='red')
+    ax.set_title('N-My')
+    ax.set_xlabel('N (kN)')
+    ax.set_ylabel('My (mkN)')
+    ax.axhline(0, color='gray', linewidth=1)
+    ax.axvline(0, color='gray', linewidth=1)
+    x_legend = np.linspace(np.min(n), np.max(n), nticks_x)
+    y_legend = np.linspace(np.min(m_y), np.max(m_y), nticks_y)
+    ax.set_xticks(x_legend)
+    ax.set_yticks(y_legend)
+    ax.set_xticklabels(np.around(x_legend, decimals=1))
+    ax.set_yticklabels(np.around(y_legend, decimals=1))
+    plt.show()
+
+    fig, ax = plt.subplots(figsize=figsize)
+    ax.plot(n, m_z, color='red')
+    ax.set_title('N-MZ')
+    ax.set_xlabel('N (kN)')
+    ax.set_ylabel('Mz (mkN)')
+    ax.axhline(0, color='gray', linewidth=1)
+    ax.axvline(0, color='gray', linewidth=1)
+    x_legend = np.linspace(np.min(n), np.max(n), nticks_x)
+    y_legend = np.linspace(np.min(m_z), np.max(m_z), nticks_y)
+    ax.set_xticks(x_legend)
+    ax.set_yticks(y_legend)
+    ax.set_xticklabels(np.around(x_legend, decimals=1))
+    ax.set_yticklabels(np.around(y_legend, decimals=1))
+    plt.show()
+
+
+def draw_M_curvature_diagram(res, figsize=(10, 10), nticks_x=10, nticks_y=10):
+    """Draw the N-M diagram of a cross section.
+
+    Args:
+        res : MomentCurvatureResults results
+        figsize : Size of the plot
+        nticks_x,nticks_y : number of ticks in the axes
+    """
+    fig, ax = plt.subplots(figsize=figsize)
+    ax.set_title(f'M-X   N={res.n/1e3} kN')
+    ax.set_xlabel('X (1/km)')
+    ax.set_ylabel('My (mkN)')
+    ax.axhline(0, color='gray', linewidth=1)
+    ax.axvline(0, color='gray', linewidth=1)
+    xmin, xmax, ymin, ymax = 0, 0, 0, 0
+    ax.plot(res.chi_y * 1e6, res.m_y / 1e6, color='red')
+    xmin = min(xmin, np.min(res.chi_y * 1e6))
+    ymin = min(ymin, np.min(res.m_y / 1e6))
+    xmax = max(xmax, np.max(res.chi_y * 1e6))
+    ymax = max(ymax, np.max(res.m_y / 1e6))
+    x_legend = np.linspace(xmin, xmax, nticks_x)
+    y_legend = np.linspace(ymin, ymax, nticks_y)
+    ax.set_xticks(x_legend)
+    ax.set_yticks(y_legend)
+    ax.set_xticklabels(np.around(x_legend, decimals=1))
+    ax.set_yticklabels(np.around(y_legend, decimals=1))
+    # plt.legend()
+    plt.show()
diff --git a/structuralcodes/sections/_generic.py b/structuralcodes/sections/_generic.py
index 8bdfdca5..33aa76ea 100644
--- a/structuralcodes/sections/_generic.py
+++ b/structuralcodes/sections/_generic.py
@@ -402,7 +402,7 @@ def _prefind_range_curvature_equilibrium(
         existence of at least one zero in the function dn vs. curv in order
         to apply the bisection algorithm.
         """
-        ITMAX = 20
+        ITMAX = 50
         sign = -1 if dn_a > 0 else 1
         found = False
         it = 0

From 1c37827b9624e30bd972327cee067f62e5dd8cc6 Mon Sep 17 00:00:00 2001
From: Carlos Mestre <cmg@fhecor.es>
Date: Mon, 22 Jul 2024 14:09:46 +0200
Subject: [PATCH 23/33] External axial load error may be solved if origin in cg

---
 Issues.ipynb | 33 +++++++++++++++++++--------------
 1 file changed, 19 insertions(+), 14 deletions(-)

diff --git a/Issues.ipynb b/Issues.ipynb
index 863c3484..43fef163 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -291,13 +291,14 @@
    "source": [
     "6. Error when computing bending capacity with external axial load n<>0  (#1) or the section response for a given N-M (#2)\n",
     "\n",
+    "(if center of gravity is in the origin it works more or less ok.)\n",
     "\n",
     "![N-M](error_N_M.png)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +349,7 @@
    ],
    "source": [
     "from  SLS_section_response import calculate_strain_profile\n",
-    "from  structuralcodes.plots.section_plots import draw_section_response\n",
+    "from  structuralcodes.plots.section_plots import draw_section_response, draw_N_M_diagram\n",
     "concrete = ConcreteEC2_2004(25)\n",
     "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
     "# Create section\n",
@@ -357,6 +358,7 @@
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
+    "#sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
     "draw_section(sec)\n",
     "\n",
     "# 1\n",
@@ -388,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -513,13 +515,14 @@
     "10. N-M diagram:\n",
     "\n",
     "Not working properly. It should be interesting to get the whole diagram  and not just the minimum range of M\n",
+    "(if center of gravity is in the origin it works more or less ok.)\n",
     "\n",
     "![N-M](N_My.png)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -562,6 +565,7 @@
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
+    "# sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
     "draw_section(sec)\n",
     "\n",
     "\n",
@@ -577,19 +581,19 @@
    "metadata": {},
    "source": [
     "11. Moment curvature results (fib package vs FH-PIEM)\n",
-    "- Something fails with axial load\n",
+    "- Something fails with axial load. if center of gravity is in the origin it works ok.\n",
     "- Why only return the negative domain of curvature with theta=0?  Wouldn't it be better to have the whole domain instead of use theta parameter for that?\n",
     "- The moment-curvature results should start at (0,0). I miss that point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -607,6 +611,7 @@
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
+    "#sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.set_title(f'M-X   N={n/1e3} kN')\n",

From a388da7b0049ae608179d8bf54a09226132ebdef Mon Sep 17 00:00:00 2001
From: Tu Nombre <tuemail@example.com>
Date: Wed, 24 Jul 2024 12:15:50 +0200
Subject: [PATCH 24/33] tests, effective depth and fibre width

---
 SLS_section_response.py                     |  90 ++++++++-
 tests/test_core/test_generic_section_SLS.py | 203 ++++++++++++++++++++
 2 files changed, 287 insertions(+), 6 deletions(-)
 create mode 100644 tests/test_core/test_generic_section_SLS.py

diff --git a/SLS_section_response.py b/SLS_section_response.py
index 1ac8e61a..d3b6ca49 100644
--- a/SLS_section_response.py
+++ b/SLS_section_response.py
@@ -1,6 +1,7 @@
 import math
 
 import numpy as np
+from shapely.geometry import LineString, Polygon
 
 from structuralcodes.codes import _CODE, get_design_codes, set_design_code
 from structuralcodes.geometry import CompoundGeometry, SurfaceGeometry
@@ -139,7 +140,7 @@ def modify_consitutive_law(geom: SurfaceGeometry):
         mat = geom.material
         gamma_c = 1.5  # !!!! Temporary. gamma_c should be readable from ConstitutiveLaw class (TODO)
         if (
-            _CODE == None
+            _CODE is None
         ):  # should be the code of the material used to create the section
             set_design_code(get_design_codes()[1])
         if isinstance(mat, ParabolaRectangle):
@@ -179,18 +180,13 @@ def modify_consitutive_law(geom: SurfaceGeometry):
     import copy
 
     sec = copy.deepcopy(section)
-    mat = None
     m_cracking_pos, m_cracking_neg = 0, 0
     if sec.geometry.reinforced_concrete:
         if isinstance(sec.geometry, SurfaceGeometry):
             modify_consitutive_law(sec.geometry)
-            # mat = sec.geometry.material
-            # print(f'SurfaceGeometry  = {mat}')
         elif isinstance(sec.geometry, CompoundGeometry):
             for i in range(len(sec.geometry.geometries)):
                 modify_consitutive_law(sec.geometry.geometries[i])
-                # mat = sec.geometry.geometries[i].material
-                # print(f'CompoundGeometry-geometries {i+1} = {mat}')
         else:
             raise ValueError(
                 'geometry must be SurfaceGeometry or CompoundGeometry'
@@ -226,3 +222,85 @@ def modify_consitutive_law(geom: SurfaceGeometry):
             )
 
     return m_cracking_pos, m_cracking_neg
+
+
+def calculate_width_at_z(geometry: CompoundGeometry | GenericSection, z):
+    """Calculate the width of a section or a section part at a certain level of z.
+
+    Args:
+           geometry: the CompoundGeometry (part) or the whole section (GenericSection)
+           z: z value to evaluate the width in mm
+
+    Returns:
+           Returns the width at z value
+    """
+
+    def width_fibre(geometry, z):
+        # Create a horizontal line at the specified y-coordinate
+        min_y, min_z, max_y, max_z = geometry.bounds
+        horizontal_line = LineString([(min_y, z), (max_y, z)])
+
+        # Find the intersection of the horizontal line with the geometry
+        intersection = geometry.intersection(horizontal_line)
+
+        # If the intersection is a MultiLineString, get the total length
+        if intersection.is_empty:
+            return 0
+        elif intersection.geom_type == 'LineString':
+            return intersection.length
+        elif intersection.geom_type == 'MultiLineString':
+            return sum(segment.length for segment in intersection)
+        else:
+            return 0
+
+    width = 0
+    if isinstance(geometry, Polygon):
+        width = width_fibre(geometry, z)
+    elif isinstance(geometry, CompoundGeometry):
+        for g in geometry.geometries:
+            width += width_fibre(g.polygon, z)
+    elif isinstance(geometry, GenericSection):
+        for g in geometry.geometry.geometries:
+            width += width_fibre(g.polygon, z)
+
+    return width
+
+
+def effective_depth(section: GenericSection, neg_bending: bool = False):
+    """Calculate the width of a section or a section part at a certain level of z.
+
+    Args:
+           section (GenericSection).
+           neg_bending: If negative=True the function return 'd' for negative bending moments
+           z: z value to evaluate the width in mm
+
+    Returns:
+           Returns the efective depth 'd' in mm
+    """
+    if not section.geometry.reinforced_concrete:
+        raise TypeError('Not a reinforced concrete section')
+    min_y, max_y, min_z, max_z = section.geometry.calculate_extents()
+    res = section.section_calculator.calculate_bending_strength()
+    neutral_axe = -res.eps_a / res.chi_y
+    print('na. ', neutral_axe)
+    sum_a = 0
+    sum_az = 0
+    mid_z = 0.5 * (min_z + max_z)
+
+    for pg in section.geometry.point_geometries:
+        pg_z = pg._point.y
+        if (neg_bending and pg_z < mid_z) or (
+            not neg_bending and pg_z > mid_z
+        ):
+            sum_a += pg._area
+            sum_az += pg._area * pg_z
+
+    if (
+        sum_a == 0
+    ):  # To handle the case where sum_a is zero to avoid division by zero
+        return 0
+
+    if neg_bending:
+        return max_z - sum_az / sum_a
+    else:
+        return sum_az / sum_a
diff --git a/tests/test_core/test_generic_section_SLS.py b/tests/test_core/test_generic_section_SLS.py
new file mode 100644
index 00000000..f7e0a636
--- /dev/null
+++ b/tests/test_core/test_generic_section_SLS.py
@@ -0,0 +1,203 @@
+import os
+import sys
+
+import numpy as np
+import pytest
+from shapely import Polygon
+
+from structuralcodes.geometry import CompoundGeometry, SurfaceGeometry
+from structuralcodes.materials.concrete import ConcreteEC2_2004
+from structuralcodes.materials.reinforcement import ReinforcementEC2_2004
+from structuralcodes.sections import GenericSection
+from structuralcodes.sections._reinforcement import add_reinforcement_line
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(os.path.dirname(current_dir))
+sys.path.append(parent_dir)
+
+from SLS_section_response import (
+    calculate_cracking_moment,
+    calculate_strain_profile,
+    calculate_strain_profile_batch,
+    calculate_width_at_z,
+    effective_depth,
+)
+
+
+def test_calculate_width_at_z():
+    """Test width_at_z in Polygon, CompoundGeometry and GenericSection."""
+    # Test with a simple polygon
+    polygon = Polygon([(0, 0), (4, 0), (4, 4), (0, 4)])
+    assert calculate_width_at_z(polygon, 2) == 4
+
+    # Test with a polygon where z is outside the bounds
+    assert calculate_width_at_z(polygon, 5) == 0
+
+    # Test with a compound geometry
+    compound_geometry = CompoundGeometry(
+        [
+            SurfaceGeometry(
+                Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ConcreteEC2_2004(25)
+            ),
+            SurfaceGeometry(
+                Polygon([(3, 0), (5, 0), (5, 2), (3, 2)]), ConcreteEC2_2004(25)
+            ),
+        ]
+    )
+    assert calculate_width_at_z(compound_geometry, 1) == 4
+
+    # Test with a generic section
+    generic_section = GenericSection(compound_geometry)
+    assert calculate_width_at_z(generic_section, 1) == 4
+
+    # Test with an empty polygon
+    empty_polygon = Polygon()
+    assert calculate_width_at_z(empty_polygon, 1) == 0
+
+    # Test with an reinforced concrete section
+    concrete = ConcreteEC2_2004(25)
+    reinforcemnet = ReinforcementEC2_2004(
+        fyk=500,
+        Es=200000,
+        density=7850,
+        ftk=500,
+        epsuk=0.07,
+    )
+    poly = Polygon(((0, 0), (0, 500), (350, 500), (350, 0)))
+    geo = SurfaceGeometry(poly, concrete)
+    geo = add_reinforcement_line(
+        geo, (50, 50), (300, 50), 12, reinforcemnet, n=3
+    )
+    geo = add_reinforcement_line(
+        geo, (50, 450), (300, 450), 20, reinforcemnet, n=6
+    )
+    generic_section = GenericSection(geo)
+    assert calculate_width_at_z(generic_section, 1) == 350
+
+
+def test_effective_depth():
+    """Test effective_depth 'd' in GenericSection."""
+    concrete = ConcreteEC2_2004(25)
+    reinforcemnet = ReinforcementEC2_2004(
+        fyk=500,
+        Es=200000,
+        density=7850,
+        ftk=500,
+        epsuk=0.07,
+    )
+    poly = Polygon(((0, 0), (0, 500), (350, 500), (350, 0)))
+    geo = SurfaceGeometry(poly, concrete)
+    geo = add_reinforcement_line(
+        geo, (50, 60), (300, 60), 12, reinforcemnet, n=3
+    )
+    geo = add_reinforcement_line(
+        geo, (50, 450), (300, 450), 20, reinforcemnet, n=6
+    )
+    section = GenericSection(geo)
+
+    # Test for positive bending (neg_bending=False)
+    effective_d = effective_depth(section, neg_bending=False)
+    assert effective_d == 450, f'Expected 450, got {effective_d}'
+
+    # Test for negative bending (neg_bending=True)
+    effective_d_neg = effective_depth(section, neg_bending=True)
+    assert effective_d_neg == 440, f'Expected 440, got {effective_d_neg}'
+
+
+def test_calculate_cracking_moment():
+    """Test cracking moments in reinforced concrete section."""
+    concrete = ConcreteEC2_2004(25)
+    reinforcemnet = ReinforcementEC2_2004(
+        fyk=500,
+        Es=200000,
+        density=7850,
+        ftk=500,
+        epsuk=0.07,
+    )
+    # Create section
+    poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))
+    geo = SurfaceGeometry(poly, concrete)
+    geo = add_reinforcement_line(
+        geo, (50, 50), (300, 50), 12, reinforcemnet, n=3
+    )
+    geo = add_reinforcement_line(
+        geo, (50, 450), (300, 450), 20, reinforcemnet, n=6
+    )
+    sec = GenericSection(geo)
+    mcr_pos, mcr_neg = calculate_cracking_moment(sec, plot=False)
+    mcr_pos = mcr_pos * 1e-6
+    mcr_neg = mcr_neg * 1e-6
+    assert mcr_pos == pytest.approx(
+        46.286, abs=1
+    ), f'Expected approximately 46.286, got {mcr_pos}'
+    assert mcr_neg == pytest.approx(
+        -41.889, abs=1
+    ), f'Expected approximately -41.889, got {mcr_neg}'
+
+
+def test_calculate_strain_profile():
+    """Test strain profile given N and M."""
+    concrete = ConcreteEC2_2004(25)
+    reinforcemnet = ReinforcementEC2_2004(
+        fyk=500,
+        Es=200000,
+        density=7850,
+        ftk=500,
+        epsuk=0.07,
+    )
+    # Create section
+    poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))
+    geo = SurfaceGeometry(poly, concrete)
+    geo = add_reinforcement_line(
+        geo, (50, 50), (300, 50), 12, reinforcemnet, n=3
+    )
+    geo = add_reinforcement_line(
+        geo, (50, 450), (300, 450), 20, reinforcemnet, n=6
+    )
+    sec = GenericSection(geo)
+    eps, chi = calculate_strain_profile(sec, 0, 200 * 1e6)  # m = 200 mkN
+    eps = eps * 1e3
+    chi = chi * 1e6
+    assert eps == pytest.approx(
+        -1.017, abs=1e-2
+    ), f'Expected approximately -1.017 mm/m, got {eps}'
+    assert chi == pytest.approx(
+        5.322, abs=1e-2
+    ), f'Expected approximately 5.322 1/km, got {chi}'
+
+
+def test_calculate_strain_profile_batch():
+    """Test strain profile given N and np.array of M."""
+    concrete = ConcreteEC2_2004(25)
+    reinforcemnet = ReinforcementEC2_2004(
+        fyk=500,
+        Es=200000,
+        density=7850,
+        ftk=500,
+        epsuk=0.07,
+    )
+    # Create section
+    poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))
+    geo = SurfaceGeometry(poly, concrete)
+    geo = add_reinforcement_line(
+        geo, (50, 50), (300, 50), 12, reinforcemnet, n=3
+    )
+    geo = add_reinforcement_line(
+        geo, (50, 450), (300, 450), 20, reinforcemnet, n=6
+    )
+    sec = GenericSection(geo)
+
+    my_ed = np.array([-50, 50, 100]) * 1e6  # mkN
+    n_ed = 0 * 1e3  # kN
+    eps_batch, chi_batch = calculate_strain_profile_batch(sec, n_ed, my_ed)
+    for i, eps1 in enumerate(eps_batch):
+        chi1 = chi_batch[i]
+        eps2, chi2 = calculate_strain_profile(sec, n_ed, my_ed[i])
+        print(eps1, eps2)
+        print(chi1, chi2)
+        assert eps1 == pytest.approx(
+            eps2, rel=1e-2, abs=1e-8
+        ), f'Expected approximately {eps2} , got {eps1}'
+        assert chi1 == pytest.approx(
+            chi2, rel=1e-2, abs=1e-8
+        ), f'Expected approximately {chi2} , got {chi1}'

From 32220426440102366c0e62d1f62b5f6d89bdcbee Mon Sep 17 00:00:00 2001
From: Tu Nombre <tuemail@example.com>
Date: Wed, 24 Jul 2024 15:07:41 +0200
Subject: [PATCH 25/33] changes in EX2

---
 EX2_part1.ipynb | 91 ++++++++++++++-----------------------------------
 Issues.ipynb    | 27 ++++++++-------
 2 files changed, 40 insertions(+), 78 deletions(-)

diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb
index a4c94cbe..945991f7 100644
--- a/EX2_part1.ipynb
+++ b/EX2_part1.ipynb
@@ -26,6 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import math\n",
     "from structuralcodes.codes.ec2_2004 import __materials__\n",
     "from structuralcodes.codes.ec2_2004 import _section_7_3_crack_control\n",
     "from structuralcodes.materials.concrete import ConcreteEC2_2004,ConcreteEC2_2023\n",
@@ -38,15 +39,15 @@
     ")\n",
     "from structuralcodes.sections._generic import GenericSection\n",
     "from structuralcodes.materials.constitutive_laws import Elastic, ElasticPlastic,ParabolaRectangle,UserDefined\n",
-    "from structuralcodes.plots.section_plots import draw_section\n",
-    "import math"
+    "from structuralcodes.plots.section_plots import draw_section,get_stress_point,draw_section_response\n",
+    "from SLS_section_response import calculate_strain_profile"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calculation of the inertia of the cracked cross-section in order to obtain $\\sigma_s$"
+    "Calculation of  $\\sigma_s$ for $\\epsilon_{sm} - \\epsilon_{cm}$"
    ]
   },
   {
@@ -58,56 +59,38 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "x (mm) from z=0: 219.4\n",
-      "I of cracked section (mm4):  3677810527\n"
+      " sigma_s (MPa) = 163\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Materials\n",
-    "fi =2.5\n",
-    "concrete = ConcreteEC2_2004(25)\n",
-    "Ec_eff=concrete.Ecm/(1+fi)\n",
-    "Ec = concrete.constitutive_law.get_tangent(0)[0]\n",
+    "long_beam = 6.75\n",
     "\n",
+    "# caracteriscic load\n",
+    "qk=12.2 + 5.2 + 11.7 # kN/m\n",
+    "# cuasipermant load\n",
+    "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n",
+    "\n",
+    "fi = 2.5\n",
     "\n",
-    "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforfocement\" with reinforcement areas multiplied by Es/Ec is used.\n",
-    "#reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)\n",
-    "n=200000/Ec_eff\n",
+    "# Mcuasip at midspan\n",
+    "m_cuasip = q_cuasip * long_beam**2/8\n",
     "\n",
-    "# Create section\n",
+    "# Define the cross section\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
     "\n",
-    "# Get the neutral axe of the cracked section\n",
-    "curv = 1e-7 # Curvature which produces elastic stresses in the most compressed fibre of the concrete section\n",
-    "eps =  sec.section_calculator.find_equilibrium_fixed_curvature(sec.geometry,0,curv,0)[0]\n",
-    "x=-eps/curv # Distance from z=0 to the neutral axe\n",
-    "print(\"x (mm) from z=0:\",round(x,1))\n",
     "\n",
-    "# 2) Create the cracked section\n",
-    "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
-    "geo2 = SurfaceGeometry(poly2, concrete)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
-    "sec2 = GenericSection(geo2)\n",
-    "Icr = sec2.gross_properties.i11\n",
-    "print(\"I of cracked section (mm4): \",round(Icr))\n",
-    "draw_section(sec2,\"cracked section\",math.sqrt(n))\n"
+    "eps1,chiy = calculate_strain_profile(sec,0,m_cuasip*1e6)\n",
+    "#draw_section_response(sec,eps1,chiy)\n",
+    "# get stress in a reinforced bar\n",
+    "sigma_s = get_stress_point (sec,50,450,eps1,chiy,0)\n",
+    "print(' sigma_s (MPa) =',round(sigma_s))\n"
    ]
   },
   {
@@ -128,45 +111,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "eps_sm_eps_cm = 0.00063\n",
+      "eps_sm_eps_cm = 0.00062\n",
       " alfa_e = 22.239\n",
       " hc_eff (mm) = 89\n",
       " rho_p = 0.0602\n",
       " kt = 0.4\n",
-      " sigma_s (MPa) = 166\n",
+      " sigma_s (MPa) = 163\n",
       " fct_eff (MPa) = 2.56\n"
      ]
     }
    ],
    "source": [
-    "long_beam = 6.75\n",
-    "\n",
-    "# caracteriscic load\n",
-    "qk=12.2 + 5.2 + 11.7 # kN/m\n",
-    "# cuasipermant load\n",
-    "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n",
-    "\n",
-    "fi = 2.5\n",
-    "\n",
-    "# Mcuasip at midspan\n",
-    "m_cuasip = q_cuasip * long_beam**2/8\n",
-    "\n",
-    "# Create section to get hc_eff\n",
-    "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
-    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
-    "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
-    "sec = GenericSection(geo)\n",
     "hc_eff = _section_7_3_crack_control.hc_eff(500,450,500-sec.gross_properties.cz)\n",
-    "\n",
     "Ec_eff = concrete.Ecm/(1+fi)\n",
     "alfa_e = _section_7_3_crack_control.alpha_e(200000,Ec_eff)\n",
     "rho_p = _section_7_3_crack_control.rho_p_eff(6* math.pi*100, 0 ,0,hc_eff*350)  # b=350mm, fi=20mm\n",
     "kt = _section_7_3_crack_control.kt('long')\n",
     "fct_eff = concrete.fctm\n",
-    "sigma_s = (n * m_cuasip / (Icr/1000**4) * (450-x)/1000) / 1000 # MPa\n",
     "eps_sm_eps_cm =_section_7_3_crack_control.eps_sm_eps_cm(sigma_s,alfa_e,rho_p,kt,fct_eff,200000)\n",
     "print('eps_sm_eps_cm =',round(eps_sm_eps_cm, 5))\n",
     "print(' alfa_e =',round(alfa_e,3))\n",
diff --git a/Issues.ipynb b/Issues.ipynb
index 43fef163..3c27d1b6 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -588,12 +588,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -613,6 +613,8 @@
     "sec = GenericSection(geo)\n",
     "#sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
     "\n",
+    "n=-1500 * 1e3\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "ax.set_title(f'M-X   N={n/1e3} kN')\n",
     "ax.set_xlabel('X (1/km)')\n",
@@ -621,7 +623,6 @@
     "ax.axvline(0, color='gray', linewidth=1)\n",
     "xmin,xmax,ymin,ymax = 0,0,0,0\n",
     "\n",
-    "n=-1500 * 1e3\n",
     "res = sec.section_calculator.calculate_moment_curvature(theta=0, n=n)\n",
     "ax.plot(res.chi_y*1e6, res.m_y/1e6, color='red',  label='N=-1500 fib')\n",
     "xmin = min(xmin, np.min(res.chi_y*1e6))\n",

From 9cc4fbfa1ab2a76930c5580b41025a28dfdf55a6 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Mon, 29 Jul 2024 10:26:32 +0200
Subject: [PATCH 26/33] comments and suggestions added

---
 EX1_part1.ipynb | 201 +++++++++++++++---------------------------------
 EX2_part1.ipynb |  34 +++++---
 Issues.ipynb    |  18 ++++-
 3 files changed, 101 insertions(+), 152 deletions(-)

diff --git a/EX1_part1.ipynb b/EX1_part1.ipynb
index fbd92eaa..46b2dacd 100644
--- a/EX1_part1.ipynb
+++ b/EX1_part1.ipynb
@@ -95,7 +95,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -107,20 +107,20 @@
    "source": [
     "# Materials\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "Ec_eff=concrete.Ecm/(1+fi)\n",
-    "Ec = concrete.constitutive_law.get_tangent(0)[0]\n",
+    "Ec_eff=concrete.Ecm/(1+fi) # DGM: cannot we implement this in a formula in the concrete or the Eurocode?\n",
+    "Ec = concrete.constitutive_law.get_tangent(0)[0] # DGM: we should clarify the return type in the abstract class (all implementations return ArrayLike) # DGM: if scalar -> return scalar, if vector -> return vector\n",
     "print(f\"Ecm = {round(concrete.Ecm)}  MPa\")\n",
     "print(f\"Ec_eff = {round(Ec_eff)} MPa\")\n",
     "print(f\"Ec = {round(Ec)} MPa\")\n",
     "\n",
-    "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforfocement\" with reinforcement areas multiplied by Es/Ec is used.\n",
+    "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforcement\" with reinforcement areas multiplied by Es/Ec is used.\n",
     "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)   \n",
     "n=200000/Ec_eff\n",
     "\n",
     "# Create section\n",
-    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500))) # DGM: here it would be useful to create a wrapper function for creating rectangular cross-sections\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3) # DGM: why 12 * sqrt(2E5/Ec_eff)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
     "i11_1 = sec.gross_properties.i11\n",
@@ -130,45 +130,58 @@
     "print(f\"I11_1 = {round(i11_1/(10**4))} cm4\") \n",
     "print(f\"cz_1 = {round(cz_1/(10),2)} cm\") \n",
     "\n",
+    "\n",
     "draw_section(sec,\"gross 'homogeneus' section\",math.sqrt(n))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mechanical properties of the cracked section at long term:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "eps_z0 -2.1936412900686266e-05\n",
-      "x (mm) from z=0: 219.4\n",
-      "I11_2 = 367781 cm4\n",
-      "cz_2 = 21.91 cm\n"
+      "['mc2010', 'ec2_2004', 'ec2_2023']\n",
+      "fck=25 MPa\n",
+      "fcd=17 MPa\n",
+      "Ecm=16667 MPa\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
+   "source": [
+    "# DGM: just for the sake of the example, I will show here how we can instantiate concrete classes setting a global design code\n",
+    "import structuralcodes.codes as codes\n",
+    "import structuralcodes.materials as materials\n",
+    "\n",
+    "print(codes.get_design_codes())\n",
+    "codes.set_design_code(design_code='ec2_2004') \n",
+    "# codes.set_design_code(design_code='ec2_2080') # DGM: it would be nice to raise error or print warning if the passed design code does not exist\n",
+    "\n",
+    "sample_concrete = materials.concrete.create_concrete(fck=25)\n",
+    "print(f\"fck={sample_concrete.fck} MPa\")\n",
+    "sample_fcd = sample_concrete.fcd()\n",
+    "print(f\"fcd={round(sample_fcd)} MPa\")\n",
+    "sample_Ecm = sample_concrete.constitutive_law.get_tangent(0)[0]\n",
+    "print(f\"Ecm={round(sample_Ecm)} MPa\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mechanical properties of the cracked section at long term:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Get the cracked cross section\n",
-    "curv = 1e-7 # Curvature which produces elastic stresses in the most compressed fibre of the concrete section\n",
+    "curv = 1e-7 # Curvature which produces elastic stresses in the most compressed fibre of the concrete section.\n",
     "eps =  sec.section_calculator.find_equilibrium_fixed_curvature(sec.geometry,0,curv,0)[0]\n",
     "x=-eps/curv # Distance from the bottom to the neutral fibre\n",
     "print(\"eps_z0\",eps)\n",
@@ -177,14 +190,15 @@
     "# 2) Create the cracked section\n",
     "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
     "geo2 = SurfaceGeometry(poly2, concrete)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50), 12*math.sqrt(n), reinforcemnet, n=3)\n",
     "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
     "sec2 = GenericSection(geo2)\n",
     "i11_2 = sec2.gross_properties.i11\n",
     "cz_2 = sec2.gross_properties.cz\n",
     "print(f\"I11_2 = {round(i11_2/(10**4))} cm4\") \n",
     "print(f\"cz_2 = {round(cz_2/(10),2)} cm\") \n",
-    "draw_section(sec2,\"cracked section\",math.sqrt(n))"
+    "draw_section(sec2,\"cracked section\",math.sqrt(n))\n",
+    "\n"
    ]
   },
   {
@@ -196,56 +210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z neutral axis = 265.78\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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TTxg8eDBPPvkk4eHhTJ482TYYy7XEdHGL1MVGjx5NaGhoke54K1euZOXKlUBB0piens4bb7wBQIcOHWytoRs3buTOO+/klVde4dVXXwXgwQcf5N///jePPPIIW7dupUmTJmzZsoX/+7//o0mTJvTt29fumuzcuZPIyEjc3NyYPXs2ixYt4o033qB169ZXfZ7Xo3r16ixevJh27doRHR3N6tWrqVmz5iXLF7aaXenfhIhIRaQWMxGRcjBixAh++OEHAgIC+Oc//8lzzz3Hli1bbM8FXU61atWYO3cu4eHhvPjii/z73/+ma9euxbYqxMTEsGzZMm666Sbee+89xowZw5IlSy7bchMQEMDChQsJCwuja9euJeredyWPP/44H330EQkJCTzzzDO292IFBQUVGZnyeoSGhrJx40aGDx/OzJkzefzxx/nwww85e/Ysb7/9tq1c4bvfOnfuzIcffsgbb7xB+/bteemllwBKNaZLWbp0KS+99BIvvfQSJ0+eJD4+3jZ/ufeAQcHvwC+//EJsbCw//vgjTzzxBD/88AMPPfQQy5cvtxswplmzZuzbt49//OMfPPPMM6SmpjJ9+nT+8Y9/lPUpFqtmzZr8/PPPZGdn07VrV7uWwOK8+OKLl3yWTUSkIrMYpf20toiISDGsVivBwcH069evRF0Iy8P48eN56qmnOHr06GVbckRERMqaWsxERKTUZWVlFelq+b///Y+zZ8+W2iAjV+vP7+/Kysriiy++oEGDBkrKRETEdHrGTERESt369et56qmnGDBgANWqVWPLli3897//pWnTpgwYMMCUmPr160edOnVo2bIlKSkpxMXFsWfPHiZPnmxKPCIiIhdTYiYiIqWubt261K5dm48++oizZ89StWpVhg4dyltvvVXsC7TLQ0xMDP/3f//H5MmTyc/Pp3HjxkydOpX77rvPlHhEREQupmfMRERERERETKZnzEREREREREymxExERERERMRkSsxERERERERMpsRMRERERETEZErMRERERERETKbETERERERExGRKzEREREREREymxExERERERMRkSsxERERERERMpsRMnJrFYuHVV181OwwREREb1U1SHpYvX47FYmH58uVmhyKlRImZXJUdO3Zw7733EhERgZeXFzVr1qRr1658/PHHZXbMefPmqYKTK/rss8+YOHGi2WGIiAlUN4mjUt0kV8NiGIZhdhDiHNauXcudd95JnTp1GDZsGGFhYRw5coT169dz4MAB9u/fXybHffzxx/n0008p7lc1KysLNzc33NzcyuTY4jyaNm1K9erV9c2hSCWjukkcWVnWTVarlZycHDw8PHBxUVtLRaA7hpTYm2++SWBgIJs2bSIoKMhu3cmTJ02JycvLy5TjFic9PR1fX1+zw5AS0M9KpOJQ3XR5ut85j6v9Wbm4uDjU75pcP6XXUmIHDhygSZMmRSo+gJCQkCLL4uLiaNWqFd7e3lStWpVBgwZx5MiRIuU2bNjA3XffTZUqVfD19aV58+Z8+OGHADz44IN8+umnQEGf/cJPoeL68W/dupXu3bsTEBCAn58fXbp0Yf369XZlJk6ciMViYc2aNYwZM4bg4GB8fX3p27cvp06duuK1ePDBB/Hz8+PAgQPcfffd+Pv7M2TIEKDgG6zx48fTpEkTvLy8CA0N5ZFHHuHcuXN2+/jll1+IiYmhevXqeHt7U69ePR566CHb+vj4eCwWC//+97/54IMPiIiIwNvbm44dO7Jz584iMS1dupT27dvj6+tLUFAQvXv35rfffrMr8+qrr2KxWNi/fz8PPvggQUFBBAYGMnz4cDIyMuzKLl68mHbt2hEUFISfnx8NGzbkhRdesCuTnZ3NK6+8wo033oinpye1a9fm2WefJTs7+4rX8GokJiYyfPhwatWqhaenJ+Hh4fTu3Zv4+HgA6taty65du1ixYoXtd6RTp07AHz/rFStW8NhjjxESEkKtWrVs+54/f77tuvn7+9OjRw927dp1VceHK/88RaRsqG76g+qmAs5eNx0+fJjHHnuMhg0b4u3tTbVq1RgwYIBdnQPFP2PWqVMnmjZtyu7du7nzzjvx8fGhZs2avPPOO6V67lI21GImJRYREcG6devYuXMnTZs2vWzZN998k5deeomBAwfyl7/8hVOnTvHxxx/ToUMHtm7daqtAFy9eTM+ePQkPD+fJJ58kLCyM3377jblz5/Lkk0/yyCOPcPz4cRYvXsykSZOuGOOuXbto3749AQEBPPvss7i7u/PFF1/QqVMnVqxYQWRkpF35J554gipVqvDKK68QHx/P+PHjefzxx5k2bdoVj5WXl0dMTAzt2rXj3//+Nz4+PgA88sgjTJw4keHDh/O3v/2NQ4cO8cknn7B161bWrFmDu7s7J0+epFu3bgQHB/P8888TFBREfHw8M2fOLHKc//3vf6SlpTFq1CiysrL48MMP6dy5Mzt27CA0NBSAn3/+me7du3PDDTfw6quvkpmZyccff0zbtm3ZsmULdevWtdvnwIEDqVevHuPGjWPLli383//9HyEhIbz99tu269izZ0+aN2/O66+/jqenJ/v372fNmjW2fVitVu655x5Wr17Nww8/zM0338yOHTv44IMP+P3335k9e/YVr2FJ9e/fn127dvHEE09Qt25dTp48yeLFi0lISKBu3bqMHz+eJ554Aj8/P/7xj38A2K5Noccee4zg4GBefvll0tPTAZg0aRLDhg0jJiaGt99+m4yMDCZMmEC7du3YunWr7bpd6fhX8/MUkdKlusme6ibnr5s2bdrE2rVrGTRoELVq1SI+Pp4JEybQqVMndu/ebfuZXsq5c+e466676NevHwMHDuS7777jueeeo1mzZnTv3r3Uzl/KgCFSQosWLTJcXV0NV1dXIyoqynj22WeNhQsXGjk5OXbl4uPjDVdXV+PNN9+0W75jxw7Dzc3NtjwvL8+oV6+eERERYZw7d86urNVqtU2PGjXKuNSvKmC88sortvk+ffoYHh4exoEDB2zLjh8/bvj7+xsdOnSwLfv6668NwIiOjrY71lNPPWW4uroaycnJl70Ww4YNMwDj+eeft1u+atUqAzAmT55st3zBggV2y2fNmmUAxqZNmy55jEOHDhmA4e3tbRw9etS2fMOGDQZgPPXUU7ZlLVu2NEJCQowzZ87Ylm3fvt1wcXExhg4dalv2yiuvGIDx0EMP2R2rb9++RrVq1WzzH3zwgQEYp06dumR8kyZNMlxcXIxVq1bZLf/8888NwFizZs0lt70a586dMwDj3XffvWy5Jk2aGB07diyyvPBn3a5dOyMvL8+2PC0tzQgKCjJGjhxpVz4xMdEIDAy0LS/J8Uvy8xSRsqG66Q+qm5y/bjIMw8jIyChSft26dQZg/O9//7MtW7ZsmQEYy5Ytsy3r2LFjkXLZ2dlGWFiY0b9//xKenZhFXRmlxLp27cq6deu455572L59O++88w4xMTHUrFmTH374wVZu5syZWK1WBg4cyOnTp22fsLAwGjRowLJly4CCbh2HDh1i9OjRRbqgXNwlpKTy8/NZtGgRffr04YYbbrAtDw8P5/7772f16tWkpqbabfPwww/bHat9+/bk5+dz+PDhEh3z0UcftZufMWMGgYGBdO3a1e7cW7VqhZ+fn+3cC8937ty55ObmXvYYffr0oWbNmrb522+/ncjISObNmwfAiRMn2LZtGw8++CBVq1a1lWvevDldu3a1lbvYX//6V7v59u3bc+bMGdv1KYxvzpw5WK3WYuOaMWMGN998M40aNbI7186dOwPYzvV6eXt74+HhwfLly4t0ubkaI0eOxNXV1Ta/ePFikpOTGTx4sF38rq6uREZG2uIvyfGv5ucpIqVLdVNRqpuct24q3Heh3Nxczpw5w4033khQUBBbtmy54j79/PyIjY21zXt4eHD77bdz8ODBa45TyocSM7kqrVu3ZubMmZw7d46NGzcyduxY0tLSuPfee9m9ezcA+/btwzAMGjRoQHBwsN3nt99+sz2MfeDAAYArdj0pqVOnTpGRkUHDhg2LrLv55puxWq1FniOoU6eO3XyVKlUASnSTdXNzs3tWCQrOPSUlhZCQkCLnfv78edu5d+zYkf79+/Paa69RvXp1evfuzddff11s//cGDRoUWXbTTTfZ+poXVtSXOu/Tp0/bukeU9Lzvu+8+2rZty1/+8hdCQ0MZNGgQ06dPt6sI9+3bx65du4qc50033QRc/qH78+fPk5iYaPtc7tkJT09P3n77bebPn09oaCgdOnTgnXfeITEx8ZLbFKdevXp28/v27QOgc+fORc5h0aJFtvhLcvyr+XmKSOlT3fQH1U3OXTcBZGZm8vLLL1O7dm08PT2pXr06wcHBJCcnk5KScsV91qpVq8iXCFWqVLmuBFLKh54xk2vi4eFB69atad26NTfddBPDhw9nxowZvPLKK1itViwWC/Pnzy/yLRAUfJPjKIqLDyh2+OM/8/T0LDI8rdVqJSQkhMmTJxe7TXBwMFDwret3333H+vXr+fHHH1m4cCEPPfQQ7733HuvXry/za3Sl8/b29mblypUsW7aMn376iQULFjBt2jQ6d+7MokWLcHV1xWq10qxZM95///1i91W7du1LHv/f//43r732mm0+IiKiyEPNFxs9ejS9evVi9uzZLFy4kJdeeolx48axdOlSbrnllhKcsf03kICtIp80aRJhYWFFyl88zPWVjm/2z1NECqhuUt3k7HUTFDxj+PXXXzN69GiioqIIDAzEYrEwaNCgS7YUXux6fn/EXErM5LrddtttQEG3BYD69etjGAb16tWzfUNVnPr16wOwc+dOoqOjL1mupF1HgoOD8fHxYe/evUXW7dmzBxcXl8vekEtD/fr1+fnnn2nbtm2xN9s/a9OmDW3atOHNN99kypQpDBkyhKlTp/KXv/zFVqawZediv//+u+2h6YiICIBLnnf16tWvaahkFxcXunTpQpcuXXj//ff517/+xT/+8Q+WLVtGdHQ09evXZ/v27XTp0uWqu/cMHTqUdu3a2eZLcq3q16/P008/zdNPP82+ffto2bIl7733HnFxccDVdzEq/P0LCQm57O9fSY8PJft5ikj5UN30B9VNJeMIdRPAd999x7Bhw3jvvfdsy7KyskhOTr7qfYlzUVdGKbFly5YV+21LYT/xwu4K/fr1w9XVlddee61IecMwOHPmDAC33nor9erVY/z48UVuNhdvV3jjvtINydXVlW7dujFnzhy7b7iSkpKYMmUK7dq1IyAgoETneq0GDhxIfn4+//znP4usy8vLs53DuXPnilybli1bAhTpMjJ79myOHTtmm9+4cSMbNmywjawUHh5Oy5Yt+eabb+yu0c6dO1m0aBF33333VZ/H2bNniyz7c3wDBw7k2LFjfPnll0XKZmZmFumicrEbbriB6Oho26dt27aXLJuRkUFWVpbdsvr16+Pv7293rXx9fa+q0oqJiSEgIIB//etfxT5LUdiFpSTHv5qfp4iULtVNV6a6qYAz1E1Q8Dvz55/Dxx9/TH5+/lXtR5yPWsykxJ544gkyMjLo27cvjRo1Iicnh7Vr1zJt2jTq1q3L8OHDgYIb0xtvvMHYsWOJj4+nT58++Pv7c+jQIWbNmsXDDz/MM888g4uLCxMmTKBXr160bNmS4cOHEx4ezp49e9i1axcLFy4EoFWrVgD87W9/IyYmBldXVwYNGlRsjG+88YbtHSePPfYYbm5ufPHFF2RnZ5fLOzw6duzII488wrhx49i2bRvdunXD3d2dffv2MWPGDD788EPuvfdevvnmGz777DP69u1L/fr1SUtL48svvyQgIKBIZXXjjTfSrl07Hn30UbKzsxk/fjzVqlXj2WeftZV599136d69O1FRUYwYMcI2JHFgYGCRd+mUxOuvv87KlSvp0aMHERERnDx5ks8++4xatWrZvk184IEHmD59On/9619ZtmwZbdu2JT8/nz179jB9+nQWLlxo+8b6evz+++906dKFgQMH0rhxY9zc3Jg1axZJSUl2vwetWrViwoQJvPHGG9x4442EhITYHvYuTkBAABMmTOCBBx7g1ltvZdCgQQQHB5OQkMBPP/1E27Zt+eSTT0p0/Kv5eYpI6VLddGWqm5ynbgLo2bMnkyZNIjAwkMaNG7Nu3Tp+/vlnqlWrdt1xi4Mr1zEgxanNnz/feOihh4xGjRoZfn5+hoeHh3HjjTcaTzzxhJGUlFSk/Pfff2+0a9fO8PX1NXx9fY1GjRoZo0aNMvbu3WtXbvXq1UbXrl0Nf39/w9fX12jevLnx8ccf29bn5eUZTzzxhBEcHGxYLBa74Yn505DEhmEYW7ZsMWJiYgw/Pz/Dx8fHuPPOO421a9falSkcpvbPQwIXN/RscYYNG2b4+vpecv1//vMfo1WrVoa3t7fh7+9vNGvWzHj22WeN48eP22IcPHiwUadOHcPT09MICQkxevbsafzyyy+2fRQOSfzuu+8a7733nlG7dm3D09PTaN++vbF9+/Yix/z555+Ntm3bGt7e3kZAQIDRq1cvY/fu3XZlCock/vNQw4XX49ChQ4ZhGMaSJUuM3r17GzVq1DA8PDyMGjVqGIMHDzZ+//13u+1ycnKMt99+22jSpInh6elpVKlSxWjVqpXx2muvGSkpKZe9hiV1+vRpY9SoUUajRo0MX19fIzAw0IiMjDSmT59uVy4xMdHo0aOH4e/vbwC24Ykv9bMutGzZMiMmJsYIDAw0vLy8jPr16xsPPvig7WdRkuOX5OcpImVDddMfVDcVcPa66dy5c8bw4cON6tWrG35+fkZMTIyxZ88eIyIiwhg2bJit3KWGy2/SpEmRfQ4bNsyIiIgojVOXMmQxDD0JKOKI4uPjqVevHu+++y7PPPOM2eGIiIiobhIpQ3rGTERERERExGRKzEREREREREymxExERERERMRkesZMRERERETEZGoxExERERERMZkSMxEREREREZPpBdOA1Wrl+PHj+Pv7Y7FYzA5HRKTSMAyDtLQ0atSogYuLvisspHpJRMQ8ZtVNSsyA48ePU7t2bbPDEBGptI4cOUKtWrXMDsNhqF4SETFfeddNSswAf39/oODiBwQEmByNiEjlkZqaSu3atW33YSmgeklExDxm1U1KzMDWTSQgIEAVoIiICdRdz57qJRER85V33aQO/SIiIiIiIiZTYiYiIiIiImIyJWYiIiIiIiImU2ImIiIiIiJiMiVmIiIiIiIiJlNiJiIiIiIiYjIlZiIiIiIiIiZTYiYiIiIiImIyJWYiIiIiIiImU2ImYqJx48bRunVr/P39CQkJoU+fPuzdu/ey20ycOBGLxWL38fLyKlLut99+45577iEwMBBfX19at25NQkJCWZ2KiDiwTz/9lLp16+Ll5UVkZCQbN268ZNldu3bRv39/6tati8ViYfz48eUXqIhIJVZhErOrqXREHMWKFSsYNWoU69evZ/HixeTm5tKtWzfS09Mvu11AQAAnTpywfQ4fPmy3/sCBA7Rr145GjRqxfPlyfv31V1566aViEzgRKTuOUDdNmzaNMWPG8Morr7BlyxZatGhBTEwMJ0+eLLZ8RkYGN9xwA2+99RZhYWHlHK2ISOVlMQzDMDuI6zVt2jSGDh3K559/TmRkJOPHj2fGjBns3buXkJCQK26fmppKYGAgKSkpBAQElEPEIsU7deoUISEhrFixgg4dOhRbZuLEiYwePZrk5ORL7mfQoEG4u7szadKkMopUpHRU5Pvv9dRNpXldIiMjad26NZ988gkAVquV2rVr88QTT/D8889fdtu6desyevRoRo8efV0xiIg4E7PqpgrRYvb+++8zcuRIhg8fTuPGjfn888/x8fHhq6++Mjs0kauSkpICQNWqVS9b7vz580RERFC7dm169+7Nrl27bOusVis//fQTN910EzExMYSEhBAZGcns2bPLMnQR+ROz66asrCxycnLYvHkz0dHRtuUuLi5ER0ezbt26colDRERKxukTs2updLKzs0lNTbX7iJSnadOmMWHCBI4ePWpbZrVaGT16NG3btqVp06aX3LZhw4Z89dVXzJkzh7i4OKxWK3fccYdtXydPnuT8+fO89dZb3HXXXSxatIi+ffvSr18/VqxYUebnJiJXXzeVdr20detWPvzwQ3bu3El+fj6hoaF260NDQ0lMTLyuY4iISOlyMzuA63X69OlLVjp79uwpdptx48bx2muvlUd4IsU6e/YsJ0+eZNPR0+zPL3ju64N//J1ftv3Kx9/NZfnh05feuEYD6tRoQDJAlVo8+cF/2BJ9By+88wEPPT2W00kF27aJvothfx1FVW8PWrZsydq1a/n888/p2LFjmZ+fSGV3tXVTadZLhmGwY8cOsrKymP79TAC2nEgm+6L7SkJqJqnZeZe/1wBZeVb2nz1/xXIiV6NVeBD+Hk7/J6hIqauU/yrGjh3LmDFjbPOpqanUrl3bxIiksrFYLACcSzuPUSWXL19/gU1LF/LPuFm4VgnmbFbuVe0volFTDh44wNmsXPK9/XF1cyO03o3kWv94hPTmm29m9erVpXoeIlI6SrNeslgs3HfffXzzzTccOXIEFxcXDh2OJ7xJS1uZpMQk/KtVv+K9xmoYZORZr/qeJHI5eVanH95ApEw4fVfG6tWr4+rqSlJSkt3ypKSkS44m5enpSUBAgN1HpDzVqVMHgPNJx/ny9RfY+PMCXp04g9Bada56X/n5+Rz+/TeqBBd8M+/u4cGNTVtw7NABu3K///47ERER1x+8iFzR1dZNpV0veXp6MmTIEEJCQggPD2ftzG/Jz8kGCrpN/7p+NTe1bHVdxxARkdLl9ImZh4cHrVq1YsmSJbZlVquVJUuWEBUVZWJkIpdW+IfZ1C8nsPLHmYz+96d4+/px7tRJzp06SXZWpq3sR8/9jbj3/mWbn/7p+2xbvZzEI4c5uOtXPvz745w+fozoAffbyvQe8Rhr5/9A3Nf/Zf/+/XzyySf8+OOPPPbYY+V3kiKVmCPUTb6+vjzwwAPceeedbFy3lu/eeYWEvb/xn1efJzszg879BgFF7zG5OTkc+m0nh37bSV5uLmeSTnDot52cOHyoXOIWEamsKkRXxjFjxjBs2DBuu+02br/9dsaPH096ejrDhw83OzSRYhWOurhu5XIAXh7a3279qH99QOd+9wFw+vgxLJY/vkNJT01hwst/J/nUKfwCA7mhSXPe/HYOtW+8yVYmsmt3Hn71LT4d/z4v/v1pGjZsyPfff0+7du3K+MxEpJAj1E1BQUH8+9//JjU1lYU/zuH7qVOod3NTXvxyMkHVg4Gi95hzJ5N4pm832/wPX33OD199TpPWUbw+6ftyi11EpLKpEO8xA/jkk0949913SUxMpGXLlnz00UdERkaWaNuK/B4dcUwpKSmMHz8eLBaaDhiBxaVsGq/b1qpKqK9nmexbpDRU9PvvtdZNpX1dEhISmDRpEnl5eQTVbUCtyE62Z11FytudEdWp4uVudhgil6T3mF2nxx9/nMOHD5Odnc2GDRtKnJSJmCEgIABXV1cwDHIzzpsdjoiUEUepm+rUqcOAAQOwWCwkx+8jcdt6Ksj3siIiFUaFScxEnInFYqFKlSoAZJ/Xe/REpOzddNNN9O7dG4DTe3dwes92kyMSEZGLKTETMUnhc2Y5SsxEpJy0aNGCbt0Knh9L3L6RsweLf9+niIiUPyVmIiYpbDFTYiYi5SkqKoq2bdsCcGzTKlKPxpsbkIiIAErMREyjxExEzNKlSxdatmwJhkHC2iWknzxhdkgiIpWeEjMRk9i6MqYpMROR8mWxWOjVqxcNGzbEsOYTv2oBmefOmB2WiEilpsRMxCS2FrP0VI2OJiLlzsXFhf79+xMREYE1N5f4FfPUgi8iYiIlZiImCQoKwmKxYM3LIy8r0+xwRKQScnd3Z9CgQYSGhpKXlcmhZT+Rm5lhdlgiIpWSEjMRk7i5udleWqhvqUXELF5eXsTGxlKlShVy0tOIXzGf/Jwcs8MSEal0lJiJmEhD5ouII/Dz8yM2NhZfX1+yks9weNVCrPl5ZoclIlKpKDETMZFGZhQRR1G1alViY2Px9PQk/dQJjqxdimG1mh2WiEilocRMxESFiVm2EjMRcQBhYWEMGjQIV1dXUo/Fc+yXVRqcSESknCgxEzGRujKKiKOpW7cu9957LxaLhXMH95L06yazQxIRqRSUmImYSImZiDiiRo0a0bNnTwBO/baNU3t+NTkiEZGKT4mZiIkKuzLmZ2eRn6tR0ETEcdx666106dIFgMRt6zl36HeTIxIRqdiUmImYyNPTEx8fH0CtZiLieNq2bUubNm0AOLpxBanHE0yOSESk4lJiJmIyW3fGNCVmIuJYLBYL3bp1o3nz5mAYJKxZTPqpRLPDEhGpkJSYiZhMIzOKiCOzWCzcc889NGjQACM/n/iVC8hKPmt2WCIiFY4SMxGTaQAQEXF0rq6uDBgwgNq1a2PNzeHQinnknE8zOywRkQpFiZmIyfSSaRFxBu7u7gwePJiQkBDyMjM4tGIeeVmZZoclIlJhKDETMZlazETEWXh7ezNkyBACAwPJSUvh0Ir5GlFWRKSUKDETMVlhi1luxnms+fkmRyMicnkBAQE88MAD+Pj4kHXuNIdXLdK9S0SkFCgxEzGZr68v7u7uAOSk65kNEXF81apVY8iQIXh4eJB+8jhH1i/FsFrNDktExKm5mR1ARbB8+XLat2/PmjVrePfdd9m8eTMnTpxg1qxZ9OnTBwDDMADYvXs3zz//PCtXriQvL4+bb76ZadOmUbt2bbtyhmHYps+dO8drr73GnDlzOHfuHLVr1+att96iW7dul9zmUsuutN6Rt3GUOK5nm0uVKZRzPhWvgCBERBxdjRo1GDRoEJMnTyb1yCH2p82iXnRv3NzcmD/5a+b8dwLJp09Rt1FjRrz4Bg2a33LJfa1d8CPffvgOp44dJTyiHrHP/INWHbvY1mempxP33ptsXLKQ88nnCKlVm7sfGEHMoKHlcaoiIuXCYvz5L8NKKDU1lcDAQFJSUggICLiqbadNm8aePXsA2LdvH0ePHiU8PJypU6cyaNAgGjVqZCt79uxZvvzyS2655RaaNWuGp6cnJ0+epFatWvj5+RW7/7y8PL766it8fX1p3749/v7+pKSk4OXlRVhY2LWftDikiPbdCKhZt9T217ZWVUJ9PUttfyKl7XruvxWZM12X3bt3M2PGDAAsbu6k+FTlo+ee5JFX36JBi1uZ+82XrFs4l4/nryKwWvUi2+/ZsomXHujHkDFjua1TV1bNncXs//uUd79fSJ2bCurQCS/9nZ0b1vDoP/9NSM3abFuzgi9fH8uzH/8frTvHlOv5yvW7M6I6VbzczQ5D5JLMugerxew6pab+MWBDgwYNaNCggW3+zznvkiVLaNCgga2lC/4Y+OFStm7dSmZmJiNGjMDV1RW48EySxQIUvF+mgAUsBf+3XPj/hQJ/KlewzGIrX1jGwh+bWf7YrnD9n+cv2t5yUXlbDBcd+1KxWi6K+Y+47Lezm7ft46LYsfyx34ti/2O15aJrconrdNH/LdjH/Me+LrPdxed20Tn+udyVtnPz8sY/vA6l6fez55WYiUiZaty4se0PGCMvl+kf/IvoAffTuf8gAB557W22rFjCku+/pd/DTxTZ/qdJ/8ct7e6kz4jHABj85LNsX7uS+ZO/5pHX3gZg77Zf6NRnAE0j7wCg232xLJ42iX2/blNi5oTiU9Kp4hVkdhgiDkeJ2XUaOXIkX331FUeOHLEtq9nmTuBVakV2otGdXcEChtXgwDvvcs+DDzNz4RLi9+wmpGYt+vzlMW7vEkNxCYbFYmHOkhU0bduJdXsOsmnpQgKqVqN9j770GTnKlqiJXFqlbxAXkXIwevRo3n77bc6fP8+xo0fp5PVH/eTi4kLzqPb8vm1zsdv+vm0zvR58xG5Zy7Yd2bhkoW2+Ycvb2LR0EZ37D6JqSBg7N6zlePxBHhz7WtmckJQp9dUSKZ4G/ygFDz30EE2aNLHNH1u/DABXDw/cvX1w9/IhPT2drIx05nz9Obd27MLLX31LZLe7ee+pv7Jn62Zc3d1xcXMr+Li64eLqisXFhaSjCaxfNA+rNZ9/fBHHgEdH88PXX/D9hPEmna2IiEhRTz/9NNnZ2RiGgacFDi6ba1sXWL06yadPFbtd8ulTRbo4BlUPJvn0Sdv8X156g9r1b+Lhjq24r1kEb4wcwsiX/0WT1m3K5mREREygFrNScu+99xIYGMjatWtty5Lj99umC0erat05hl4PPgxAvZubsnfrLyyc+j+a3B5V7H4Nq0FgtWr89fV3cXV1pX7T5pxJSmTOVxMY+PjTZXhGIiIiJefm5sbjjz/Ou+++C0B60nGOrF9G7TZ3Xve+5036it+3b+b5zyYSXLMWuzet58vXX6BKSCgt7uhw3fsXEXEESsxKUdeuXQkKCmLevHkApBw5yJH1S6ndpjP+Vari6uZG7RtvstumVv0G/LZ54yX3WSU4BFd3N7tui7XqNyD51Elyc3Jw9/Aom5MRERG5SmFhYbi6unL+/HkAkuP34e7jR8rp0wRVDy52m6DqwaScOW23LPn0KYKqhwCQnZXJlPFv8ezH/6VVp2gA6jZsTPyeXfzw1edKzESkwlBXxlLWunVr7r//ftt8cvx+Diz9EXcPD25s2oJjhw7YlT8ef5DgGrUuub9Gt7Ym8XA81oveD3M8/iBVgkOVlImIiEPx8PCgVatW+Pr62pYl7dzMttXLuKllq2K3uallK35dt8pu2a9rV9LwQvn8vDzycnOxuNj/yeLi4qp3p4lIhaIWs1J0/vx59u//o/ticnIyJ06cwDs5mdyMdGLuHcSnr46lfsObadzqNnZsWMcvyxbzwsf/R8aZk1gsFj5//R9UCQ7lvsdGgwU69biHeXFf8eUrz9Jt4BASjyTw/ecfctd9sWSnpfxppMPCkf5s//ljld36y4zkeJmRBu1GdhQRESnGmDFjGDZsGK+99hpHjhxh/fr1ZKWfp/Ud7QH46Lm/UTUkjNinXwCgxwN/4eWh/fnhq8+5tVMX1vw0hwO7fuWvrxd0ifTx86dJ6yj+9+4/8fD0IrhmLXZtXMeKOd8x7PlXTDtPEZHSpveYUXrvKli+fDl33lm0L32LFi3o27cvAFu2bGH16tWkpqZSrVo17rzzTrt3nX399dcEBQXZygMcOXKEBQsWkJiYSEBAALfccgvt2rXDxcWkBs8/J2p2Q8QXHVny4u3shsi/xPD+lx4y/6Ih+i+TeP4xtH8xQ+FfKvEs8jqAYmK53ND8VzNM/iWH5bdgcXWl6g2N8PDzpzQE+7jTvnbR9waJOApnel9XeXL26/LJJ5/w7rvvcuLECUJCQujevTu1atXixph+jPvbIwTXrM0Tb423lV+74Ee+Hf82J48dJbxuPR545kW7F0yfO3WSye//i+1rVnI+JZnqNWrSdWAsvR58WF8aOqG6gd7cGhZkdhgil2TWPViJGWV78fPy8nj77bfJy8sDwM3TCzcPT8C4MFysUTCiuWFgFA5tblyYuvCjsf2I/rTcfpvCckW3EecSGHEjdaI6l8q+Glb1pUmw8/1RJ5WHsycgZaUiXZelS5eyalVBV0WLqyuN+z2Ii173Uqm1DAnghiq+Vy4oYhK9YLqCcnNz4/nnn+ezzz6jSpUqxMbGmhKHcVGSd7XT17qds+7PrFiTk5PZtWsX2annivkJXpvqPnq5tIiYq3PnzqSmprJ9+3aM/HyOblhO7ajOaumqxKp46xl5keIoMSsHrq6uPPHEE6bGYNFzYg7v1KlT7Nq1i5zzqRiGoZ+ViFQYffr0oVmzZkyZMoWUhAO4eXoRfusdus+JiFxEozKKOIigoCAArLm55OdkmxuMiEgpq1+/Pn369AHgzL5dnNy11dyAREQcjBIzEQfh7u6Ov3/BoB8551NNjkZEpPQ1a9aMu+66C4CTO3/hzP7dJkckIuI4lJiJOJCqVasCkJOmxExEKqbIyEg6dCh4KfTxX1aTcuSgyRGJiDgGJWYiDqRKlSoAZKvFTEQqsE6dOtGqVcELpI+sW8r5xGMmRyQiYj4lZiIOpDAxU1dGEanILBYLd999N40bN8awWjm8ehEZZ0+ZHZaIiKmUmIk4EFtXRiVmIlLBubi40LdvX+rVq4c1L5f4FfPJTk02OywREdMoMRNxIGoxE5HKxM3Njfvuu4/w8HDys7M4tHweuRnpZoclImIKJWYiDqSwxSwvKwNrXp7J0YiIlD1PT0+GDBlCtWrVyM04z6EV88jLzjI7LBGRcqfETMSBeHt74+XlBajVTEQqD19fX2JjY/H39yc75RyHVy3Ul1MiUukoMRNxMOrOKCKVUVBQELGxsXh5eZFxOomENYsxrFazwxIRKTdKzEQcTGF3Rg2ZLyKVTUhICIMHD8bNzY20E0c4umE5hmGYHZaISLlQYibiYNRiJiKVWZ06dRgwYAAWi4Xkw/s5sW29kjMRqRSUmIk4GCVmIlLZ3XTTTfTu3RuAM3t3cHrPdpMjEhEpe0rMRByM3mUmIgItWrSgW7duACRu38jZA3tMjkhEpGwpMRNxMLYWs/Q0PfguIpVaVFQUbdu2BeDYL6tIORpvbkAiImVIiZmIgwkICMDV1RUMg9yM82aHIyJiqi5dunDLLbeAYXBk7RLOnzxudkgiImVCiZmIg7FYLHrOTETkAovFQs+ePWnUqBGGNZ/DqxaSee602WGJiJQ6JWYiDqgwMdOQ+SIi4OLiQv/+/YmIiMCam0v8ivlkp+n+KCIVixIzEQekFjMREXtubm4MGjSI0NBQ8rIyiV/+E7mZGWaHJSJSapSYiTggjcwoIlKUl5cXsbGxVKlShZz0NOJXzCc/J9vssERESoUSMxEH9EeLWZrJkYiIOBY/Pz8eeOAB/Pz8yEo+w+FVi7Dm5ZkdlojIdVNiJuKALm4xMwzD5GhERBxLlSpVGDJkCJ6enqSfOkHCuiV6vYiIOD0lZiIOKCgoCABrXi752VnmBiMi4oDCwsIYPHgwrq6upB07zLFNK/VFlog4NSVmIg7Izc2NgIAAQCMziohcSkREBPfeey8Wi4Vzh34n6deNZockInLNTE3MVq5cSa9evahRowYWi4XZs2fbrTcMg5dffpnw8HC8vb2Jjo5m3759dmXOnj3LkCFDCAgIICgoiBEjRnD+vF7KK85PA4CImEN1k3Np1KgRvXr1AuDUb9s5tedXkyMSEbk2piZm6enptGjRgk8//bTY9e+88w4fffQRn3/+ORs2bMDX15eYmBiysv7o2jVkyBB27drF4sWLmTt3LitXruThhx8ur1MQKTMaMl/EHKqbnM8tt9xCdHQ0AInb1nPu0O8mRyQicvUshoN0yLZYLMyaNYs+ffoABd9I1qhRg6effppnnnkGgJSUFEJDQ5k4cSKDBg3it99+o3HjxmzatInbbrsNgAULFnD33Xdz9OhRatSoUaJjp6amEhgYSEpKiq37mIjZVq1axdKlSwmq24Dabe68pn20rVWVUF/PUo5MpPQ4+v3XrLrJ0a+LIzIMg8WLF7Nu3TqwWIho142AmhFmhyXFuDOiOlW83M0OQ+SSzLoHO+wzZocOHSIxMdH2DRhAYGAgkZGRBTddYN26dQQFBdkqPoDo6GhcXFzYsGHDJfednZ1Namqq3UfE0agro4jjKau6SfXS9bNYLHTt2pUWLVqAYZCw9mfSTyWaHZaISIk5bGKWmFhwMw0NDbVbHhoaaluXmJhISEiI3Xo3NzeqVq1qK1OccePGERgYaPvUrl27lKMXuX7qyijieMqqblK9VDosFgu9evWiQYMGGPn5xK9cQFbyWbPDEhEpEYdNzMrS2LFjSUlJsX2OHDlidkgiRRS2mOVlZZKfm2tyNCJSllQvlR5XV1cGDBhA7dq1sebmcGj5PHLOp5kdlojIFTlsYhYWFgZAUlKS3fKkpCTburCwME6ePGm3Pi8vj7Nnz9rKFMfT05OAgAC7j4ij8fLywtvbG4CcdLWaiTiCsqqbVC+VLnd3dwYPHkxISAh5WRkcWv4TeVmZZoclInJZDpuY1atXj7CwMJYsWWJblpqayoYNG4iKigIgKiqK5ORkNm/ebCuzdOlSrFYrkZGR5R6zSGlTd0YRx6K6yXl4e3sTGxtLYGAgOedTObRiPvm5OWaHJSJySaYmZufPn2fbtm1s27YNKHioetu2bSQkJGCxWBg9ejRvvPEGP/zwAzt27GDo0KHUqFHDNjrWzTffzF133cXIkSPZuHEja9as4fHHH2fQoEElHpFRxJFpABCR8qe6qeLw9/fngQcewMfHh6xzpzm8ahHW/DyzwxIRKZabmQf/5ZdfuPPOP4YBHzNmDADDhg1j4sSJPPvss6Snp/Pwww+TnJxMu3btWLBgAV5eXrZtJk+ezOOPP06XLl1wcXGhf//+fPTRR+V+LiJlQS1mIuVPdVPFUq1aNWJjY5k4cSLpJ49zZN0y6tzRBYuLw3YaEpFKymHeY2YmvS9GHNXWrVv54Ycf8AutSb07e1z19nqPmTg63X+Lp+tS+g4dOsTkyZPJz8+nav1G1LitPRaLxeywKiW9x0wcnd5jJiJFqCujiEjpqFevHv369QPg7IE9JO34xeSIRETsKTETcWC2rowZ5zGsVpOjERFxbo0bN6Znz54AnNq9ldO/7zQ5IhGRPygxE3Fg/v7+uLm5gWGQk37e7HBERJxeq1atbM8QntiyluT4/SZHJCJSQImZiAOzWCwaAEREpJS1b9+e22+/HYAjG5aTdkIv9BYR8ykxE3FwSsxEREqXxWLhrrvuomnTpmBYObx6MRmnk668oYhIGVJiJuLglJiJiJQ+i8VCnz59qF+/PkZ+HvErF5CVcs7ssESkElNiJuLgNDKjiEjZcHV1ZeDAgdSsWZP8nGzil8/T87wiYholZiIOrrDFLPt8ismRiIhUPB4eHtx///1Ur16d3Mx04pf/RF52ltlhiUglpMRMxMH90WKWht4HLyJS+nx8fIiNjSUgIIDstBTiV8wnPzfX7LBEpJJRYibi4IKCgrBYLBj5eeRlZZodjohIhRQYGMgDDzyAt7c3mWdPkbB6Edb8fLPDEpFKRImZiINzdXUlICAA0HNmIiJlqXr16gwZMgR3d3fOJx3j6IZl6qkgIuVGiZmIE9AAICIi5aNmzZrcd999uLi4kJJwkONb1ig5E5FyocRMxAloyHwRkfJTv359+vbtC8DZfbs5uWuLyRGJSGWgxEzECfwxMqMSMxGR8tC0aVO6d+8OwMmdmzmzb7fJEYlIRafETMQJqCujiEj5u/322+nQoQMAxzevJiXhoMkRiUhFpsRMxAkoMRMRMUenTp247bbbADiyfinnE4+aHJGIVFRKzEScQGFXxvzsLPJzc0yORkSk8rBYLHTv3p3GjRtjWK0cXr2YjLOnzA5LRCogJWYiTsDT0xMfHx9ArWYiIuXNxcWFvn37Uq9ePax5ucSvmE92arLZYYlIBaPETMRJqDujiIh53NzcuO+++6hRowb52VkcWj6P3Ix0s8MSkQpEiZmIk9CQ+SIi5vL09OT++++nWrVq5Gac59CKeeRlZ5kdlohUEErMRJyEbcj8NCVmIiJm8fX1JTY2Fn9/f7JTznF45UKseblmhyUiFYASMxEnoa6MIiKOISgoiNjYWLy8vMg4k8ThNT9jWK1mhyUiTk6JmYiTUFdGERHHERISwv3334+bmxvnTxzh6IblGIZhdlgi4sSUmIk4icIWs9zMdKz5+SZHIyIitWvXZuDAgbi4uJB8eD8ntq5TciYi10yJmYiT8PX1xd3dHQyD3PQ0s8MRERGgQYMG9O7dG4Azv+/k1G/bzA1IRJyWEjMRJ2GxWNSdUUTEATVv3pyYmBgAkn7dxNkDe0yOSESckRIzESdS2J0xW4mZiIhDadOmDe3atQPg2C+rSDl6yOSIRMTZKDETcSJqMRMRcVydO3fmlltuAcPgyNqlnE86bnZIIuJElJiJOBElZiIijstisdCzZ08aNWqEYc3n8KqFZJ47bXZYIuIklJiJOJE/3mWmwT9ERByRi4sL/fv3JyIiAmteLvHL55OdlmJ2WCLiBJSYiTgRW4tZeqqGZBYRcVBubm4MGjSIsLAw8rIzObR8HrmZGWaHJSIOTomZiBMJDAzEYrFg5OeTp0peRMRheXl5MWTIEKpUqUJuehrxK+aRn5Ntdlgi4sCUmIk4EVdXV4KCggA9ZyYi4uj8/Px44IEH8PPzIyv5LPGrFmLNyzM7LBFxUErMRJxMYXdGDZkvIuL4qlSpQmxsLJ6enmScSiRh7c8YVqvZYYmIA1JiJuJkNDKjiIhzCQ0NZfDgwbi5uZF2PIFjm1bqOWERKUKJmYiT+WNkRiVmIiLOIiIignvvvReLxcK5Q7+TuH2j2SGJiINRYibiZNRiJiLinBo2bMg999wDwOk92zm1Z7vJEYmII1FiJuJk1GImIuK8WrZsSXR0NACJ2zZw7tDvJkckIo5CiZmIkylsMcvPydbQyyIiTqht27ZERUUBcHTjClKPHTY5IhFxBErMRJyMh4cHvr6+gFrNREScVdeuXWnRogUYBglrfyb9VKLZIYmIyZSYiTihwu6MGjJfRMQ5WSwWevXqxU033YSRn0/8ygVkJp8xOywRMZESMxEnpAFAREScn6urK/feey916tTBmptD/PL5uq+LVGJKzESckBIzEZGKwd3dnUGDBhESEkJeVgaHls8jNyvD7LBExARKzESckEZmFBGpOLy9vYmNjSUoKIic86nEr5hPfm6O2WGJSDlTYibihNRiJiJSsfj7+xMbG4uvry9Z585weNUirPl5ZoclIuVIiZmIEypsMcvNSMean29yNCIiUhqqVavGkCFD8PDwIP3kcY6sW4phtZodloiUEyVmIk7Ix8cHDw8PAHLS00yORkRESkt4eDiDBg3C1dWV1KPxHPtlNYZhmB2WiJQDJWYiTshisag7o4hIBVWvXj369++PxWLh3ME9JO34xeyQRKQcKDETcVIaAEREpOK6+eab6dGjBwCndm/l9N4dJkckImVNiZmIk1KLmYhIxdaqVSs6d+4MwImt6zgXv8/kiESkLCkxE3FSSsxERCq+du3aERkZCcDRDStIO55gckQiUlaUmIk4KXVlFBGp+CwWCzExMTRr1gwMK4fXLCb9dJLZYYlIGVBiJuKk/mgxS9OIXSIiFZjFYqF3797ceOONGPn5HF65gKyUs2aHJSKlTImZiJMKDAzExcUFw5pPbka62eGIiEgZcnV1ZcCAAdSqVYv8nGwOLZ+v16WIVDBKzESclIuLC0FBQYC6M4qIVAYeHh7cf//9BAcHk5eZzqHl88jLyjQ7LBEpJUrMRJyYBgAREalcvL29iY2NJSAggJy0FOJXLiA/N8fssESkFCgxE3FiSsxERCqfgIAAHnjgAby9vck8e4qE1Yux5uebHZaIXCclZiJOTCMziohUTtWrV2fIkCG4u7tzPukYR9cvw7BazQ5LRK6DEjMRJ6YWMxGRyqtmzZrcd999uLi4kHLkIMe3rNUovSJOzNTEbNy4cbRu3Rp/f39CQkLo06cPe/futSuTlZXFqFGjqFatGn5+fvTv35+kJPv3dyQkJNCjRw98fHwICQnh73//O3l5eeV5KiKmKGwxy1ZiJlIqVC+Js6lfvz79+vUD4Oz+3ZzctcXkiETkWpmamK1YsYJRo0axfv16Fi9eTG5uLt26dSM9/Y+hv5966il+/PFHZsyYwYoVKzh+/LjtBgSQn59Pjx49yMnJYe3atXzzzTdMnDiRl19+2YxTEilXhS1m1twc8rKzTI5GxPmpXhJn1KRJE+6++24ATu7czJl9u0yOSESuhcVwoDbvU6dOERISwooVK+jQoQMpKSkEBwczZcoU7r33XgD27NnDzTffzLp162jTpg3z58+nZ8+eHD9+nNDQUAA+//xznnvuOU6dOoWHh8cVj5uamkpgYCApKSkEBASU6TmKlLb33nuP8+fPU79rH3yqhdita1urKqG+niZFJnJljn7/Vb0kzmT58uWsWLECgNp3dCGoTn2TIyrenRHVqeLlbnYYIpdk1j3YoZ4xS0lJAf7onrV582Zyc3OJjo62lWnUqBF16tRh3bp1AKxbt45mzZrZKj+AmJgYUlNT2bWr+G+MsrOzSU1NtfuIOCsNACJSdlQviTPp2LEjt912GwBH1y8jLfGoyRGJyNVwmMTMarUyevRo2rZtS9OmTQFITEzEw8PD9hLdQqGhoSQmJtrKXFz5Fa4vXFeccePGERgYaPvUrl27lM9GpPxoABCRsqF6SZyNxWKhe/fuNGnSBMNqJWH1IjLOnDQ7LBEpIYdJzEaNGsXOnTuZOnVqmR9r7NixpKSk2D5Hjhwp82OKlBUlZiJlQ/WSOCMXFxf69OnDDTfcgDUvj/gV88lKTTY7LBEpAYdIzB5//HHmzp3LsmXLqFWrlm15WFgYOTk5JCcn25VPSkoiLCzMVubPo2EVzheW+TNPT08CAgLsPiLO6o+ujGkmRyJScaheEmfm5ubGwIEDqVGjBvk52cQvn0duxnmzwxKRKzA1MTMMg8cff5xZs2axdOlS6tWrZ7e+VatWuLu7s2TJEtuyvXv3kpCQQFRUFABRUVHs2LGDkyf/aKpfvHgxAQEBNG7cuHxORMREhS1mGjJf5PqpXpKKwtPTk/vvv59q1aqRm3GeQ8vna/ReEQdnamI2atQo4uLimDJlCv7+/iQmJpKYmEhmZiYAgYGBjBgxgjFjxrBs2TI2b97M8OHDiYqKok2bNgB069aNxo0b88ADD7B9+3YWLlzIiy++yKhRo/D01Gh0UvEVtpjlZaZj1XuSRK6L6iWpSHx9fYmNjcXf35/s1HPEr1yANS/X7LBE5BJMTcwmTJhASkoKnTp1Ijw83PaZNm2arcwHH3xAz5496d+/Px06dCAsLIyZM2fa1ru6ujJ37lxcXV2JiooiNjaWoUOH8vrrr5txSiLlztvb2/bHXk66ujOKXA/VS1LRBAUFERsbi5eXF5lnTnJ4zc8YVqvZYYlIMRzqPWZm0ftixNl98cUXJCYmEtE+hoCaEbbleo+ZODrdf4un6yKl7ciRI0yaNInc3FyCIm6kVps7sVgspsSi95iJo9N7zETkmuldZiIicjm1a9dmwIABuLi4kHx4Pye2rkPfzYs4FiVmIhWAhswXEZEradCgAb179wbgzO87ObV7m7kBiYgdJWYiFYBGZhQRkZJo3rw5MTExACTt2MTZA7+ZHJGIFFJiJlIBqCujiIiUVJs2bWjXrh0Ax35ZTcqRQyZHJCKgxEykQihMzHLT0zTaloiIXFHnzp259dZbwTA4sm4J55OOmx2SSKWnxEykAvD398fV1RXDaiU3M93scERExMFZLBZ69OjBzTffjGG1cnjVQjLPnjY7LJFKTYmZSAXg4uJCUFAQoO6MIiJSMi4uLvTr14+6detizcvl0Ip5ZKelmB2WSKWlxEykgtBzZiIicrXc3NwYNGgQYWFh5GdncWj5PPW8EDGJEjORCkJD5ouIyLXw9PRkyJAhVK1aldz0NOKXzyc/J9vssEQqHSVmIhWEhswXEZFr5efnR2xsLH5+fmSlnCV+5QKseXlmhyVSqSgxE6kg1JVRRESuR5UqVYiNjcXT05OM00kkrP1ZI/2KlCMlZiIVxMVdGQ3DMDkaERFxRqGhoQwePBg3NzfSjidwdONK1Ski5USJmUgFUZiYWXNzyc/OMjkaERFxVhEREdx7771YLBaS438ncfsGs0MSqRSUmIlUEG5ubgQEBADqzigiItenYcOG3HPPPQCc3vMrp37bbnJEIhWfEjORCkQDgIiISGlp2bIlXbt2BSBx+wbOHtxrckQiFZsSM5EKREPmi4hIabrjjju44447ADi2aSWpx+LNDUikAlNiJlKBaGRGEREpbdHR0bRs2RIMg4S1S0g/ecLskEQqJCVmIhWIWsxERKS0WSwWevXqRcOGDTHy84lftZDMc2fMDkukwlFiJlKBqMVMRETKgouLC/3796dOnTpYc3OIXzFPdY1IKVNiJlKBFLaY5WVlYs3LNTkaERGpSNzd3Rk8eDAhISHkZWVyaPk8crMyzA5LpMJQYiZSgXh7e+Pl5QVAzvk0k6MREZGKxsvLi9jYWIKCgsg5n0r88vnk5+SYHZZIhaDETKSCKezOqCHzRUSkLPj7+/PAAw/g6+tLVvIZDq9aiDU/z+ywRJyeEjORCkYDgIiISFmrWrUqQ4YMwcPDg/RTJziydimG1Wp2WCJOTYmZSAWjxExERMpDeHg4gwcPxtXVldRj8Rz7ZTWGYZgdlojTUmImUsFoZEYRESkvdevWpX///lgsFs4d3EPSjk1mhyTitJSYiVQwajETEZHydPPNN9OjRw8ATu3exum9O0yOSMQ5XVNi1rlzZ1577bUiy8+dO0fnzp2vOygRuXa2FrP0NKzq7y+VhOolEXO1atXK9m/txNZ1nIvfZ3JEIs7nmhKz5cuX88knn9CnTx/S09Nty3NyclixYkWpBSciV8/f3x9XV1cwDM6nqtVMKgfVSyLma9euHZGRkQAc3bCC1OMJJkck4lyuuSvjzz//TGJiIm3atCE+Pr4UQxKR62GxWGzdGVOSz5kcjUj5Ub0kYi6LxUJMTAzNmjUDw0rCmsWkn04yOywRp3HNiVl4eDgrVqygWbNmtG7dmuXLl5diWCJyPQq7M6YmJ5sbiEg5Ur0kYj6LxULv3r258cYbMfLzObxyAVkpZ80OS8QpXFNiZrFYAPD09GTKlCk8+eST3HXXXXz22WelGpyIXJvCFrNUtZhJJaF6ScRxuLq6MmDAAGrVqkV+TjaHls8jJz3N7LBEHN41JWZ/fkfFiy++yOTJk3nvvfdKJSgRuT62xCwl2dxARMqJ6iURx+Lh4cH9999PcHAweZkZHFo+j7ysTLPDEnFo15SYHTp0iODgYLtl/fv3Z8OGDXz11VelEpiIXLs/ujKqxUwqB9VLIo7H29ub2NhYAgMDyUlLIX7lAvJzc8wOS8RhXVNiFhERYes2crEmTZowbNiw6w5KRK7PH4N/JBdpSRCpiFQviTimgIAAYmNj8fHxIfPsKQ6vXkxeXp7ZYYk4JL1gWqQCCgoKAiAvN9du6HAREZHyVr16dYYMGYKHhwfpScdYNPcHvWdTpBhKzEQqIDc3NwIDA4GCF+yKiIiYqUaNGtx33324uLiwf+8e5s2bpx4dIn+ixEykgirsznj2rIYpFhER891www3069cPgM2bN+vl7yJ/osRMpIIqTMzUYiYiIo6iSZMmdAkIAGDFihVs3LjR5IhEHIcSM5EKqnBkRiVmIiLiSFr4+NDmwvT8+fPZuXOnqfGIOAolZiIVlLoyioiIo4oEWlyYnjVrFgcOHDAzHBGHoMRMpIJSi5mIiDgqC9AJuAmwWq1MmzaNY8eOmRuUiMmUmIlUUIUtZunp6WRnZ5scjYiIiD0LEAPUAXJzc5k8eTKnT582OSoR8ygxE6mgvLy88HYp+CeuVjMREXFErkBPIBTIzMxk0qRJpKSkmByViDmUmIlUYIHu7oASMxERcVweQB+gCpCamkpcXBwZGRnmBiViAiVmIhVY0IXETAOAiIiII/MG+gJ+wOnTp5kyZQo5OTkmRyVSvpSYiVRgQWoxExERJxFAQXLmCRw7dozp06eTn59vclQi5UeJmUgFFuTmBigxExER51CNgm6NbsCBAweYPXs2hmGYG5RIOVFiJlKBqSujiIg4m3AKBgRxAXbu3MmCBQuUnEmloMRMpAIrTMxSUlLUHURERJxGXaDbhemNGzeyatUqE6MRKR9KzEQqMF9XV9wAwzA0/LCIiDiVRkDHC9PLli1j8+bNZoYjUuaUmIlUYBaLhcAL0+rOKCIizuYW4PYL0z/99BO7d+82MxyRMqXETKSCK0zMNACIiIg4oyigKQW9P2bOnMmhQ4fMDkmkTCgxE6ng1GImIiLOzAJ0Bm4E8vPzmTp1KsePHzc5KpHSp8RMpIILuvD/5ORkE6MQERG5di7AXUAtICcnh8mTJ3PmzBmToxIpXUrMRCq4gAv/V1dGERFxZm5ALyAEyMjIIC4ujrS0NJOjEik9SsxEKriLnzHTe2BERMSZeVLwAuogCnqCxMXFkZmZaWpMIqVFiZlIBVfYYpaTk6PKS0REnJ4P0BfwBU6ePMm3335Lbm6uyVGJXD8lZiIVnBvgd2Fa3RlFRKQiCKQgOfMEjhw5wowZM8jPzzc5KpHro8RMpBLQc2YiIlLRVAfuAVyBffv28eOPP6rLvjg1UxOzCRMm0Lx5cwICAggICCAqKor58+fb1mdlZTFq1CiqVauGn58f/fv3JykpyW4fCQkJ9OjRAx8fH0JCQvj73/9OXl5eeZ+KiEMrfM5MIzOKXJ7qJRHnUhPoQcGQ+tu3b2fx4sVKzsRpmZqY1apVi7feeovNmzfzyy+/0LlzZ3r37s2uXbsAeOqpp/jxxx+ZMWMGK1as4Pjx4/Tr18+2fX5+Pj169CAnJ4e1a9fyzTffMHHiRF5++WWzTknEIanFTKRkVC+JOJ8bgK4XptetW8fatWvNDEfkmlkMB/taoWrVqrz77rvce++9BAcHM2XKFO69914A9uzZw80338y6deto06YN8+fPp2fPnhw/fpzQ0FAAPv/8c5577jlOnTqFh4dHiY6ZmppKYGAgKSkpBAQEXHkDESeRHhdH3oED7AYWAfXq1WPo0KFmhyVi4wz3X9VLIqUr7YsvsCYmlvp+NwOrLkzfc8893HLLLaV+DKkczLoHO8wzZoVvck9PTycqKorNmzeTm5tLdHS0rUyjRo2oU6cO69atAwq+FWnWrJmt8gOIiYkhNTXV9u1mcbKzs0lNTbX7iFRk6soocvVUL4k4l1YXPgA//vgje/fuNTMckatmemK2Y8cO/Pz88PT05K9//SuzZs2icePGJCYm4uHhQVBQkF350NBQEi98y5KYmGhX+RWuL1x3KePGjSMwMND2qV27dumelIiDuTgxs1qtpsYi4uhUL4k4r3ZAY8AwDGbMmMHhw4fNDkmkxExPzBo2bMi2bdvYsGEDjz76KMOGDWP37t1lesyxY8eSkpJi+xw5cqRMjydiNl8KRq0yDEPfxItcgeolEedlAaIpeO4sPz+fb7/99rJfiog4EtMTMw8PD2688UZatWrFuHHjaNGiBR9++CFhYWHk5OQU6XqVlJREWFgYAGFhYUVGwyqcLyxTHE9PT9uIW4UfkYrMggYAESkp1Usizs0FuJuCERuzs7OJi4tT3SdOwfTE7M+sVivZ2dm0atUKd3d3lixZYlu3d+9eEhISiIqKAiAqKoodO3Zw8uRJW5nFixcTEBBA48aNyz12EUdW2J1RlZPI1VG9JOJ83IBeFLzrLD09nUmTJnH+/HmToxK5PDczDz527Fi6d+9OnTp1SEtLY8qUKSxfvpyFCxcSGBjIiBEjGDNmDFWrViUgIIAnnniCqKgo2rRpA0C3bt1o3LgxDzzwAO+88w6JiYm8+OKLjBo1Ck9PTzNPTcThqMVM5MpUL4lUHF5AX2A6BXVfXFwcDz74IF5eXiZHJlI8UxOzkydPMnToUE6cOEFgYCDNmzdn4cKFdO1a8DaKDz74ABcXF/r37092djYxMTF89tlntu1dXV2ZO3cujz76KFFRUfj6+jJs2DBef/11s05JxGFpZEaRK1O9JFKx+PJHcpaUlMTUqVOJjY3Fzc3UP4FFiuVw7zEzg94XIxVV4XvMAPYDc4GaNWvyl7/8xdS4RArp/ls8XRepyMrqPWaXcxL4Dsih4DUXAwYMwMXF4Z7oEQdR6d9jJiJlS10ZRUSksgqh4JkzVwpeDD937lzUNiGORomZSCVR2JUxIyODnJwcU2MREREpb7WB7hSMVLx161a7gXxEHIESM5FKwpOCB6FBrWYiIlI53Qh0uTC9Zs0a1q1bZ2Y4InaUmIlUIhoAREREKrumQNsL04sWLWL79u1mhiNio8RMpBLRc2YiIiJwG3DLhek5c+bw+++/mxmOCKDETKRS0UumRURECp4z6wA0AgzDYMaMGRw5csTkqKSyU2ImUokUtpipK6OIiFR2FqArUBfIy8tjypQpnDx50tygpFJTYiZSiajFTERE5A+uQA8gHMjKyiIuLk5fXopplJiJVCIXJ2Z6f4uIiAi4A72BakBaWhqTJk0iPT3d5KikMlJiJlKJ+FPQdSMvL0+VjoiIyAVeQF8K6smzZ88yefJksrOzTY5KKhslZiKViCvgd2Fa3RlFRET+4Af0A7yBEydOMG3aNPLy8kyOSioTJWYilYyeMxMRESleFaAPBd0bDx06xKxZs7BareYGJZWGEjORSkaJmYiIyKWFAr0o6GWye/du5s2bp+eypVwoMROpZDRkvoiIyOXVAWIuTG/evJnly5ebGI1UFkrMRCoZtZiJiIhc2U1A5wvTK1euZMOGDWaGI5WAEjORSkYtZiIiIiXTHIi6ML1gwQJ27NhhZjhSwSkxE6lkClvMUlJSyM/PNzUWERERR3c70OLC9OzZs9m/f7+Z4UgFpsRMpJLxAdwuTKekpJgZioiIiMOzAJ0o6NpotVqZPn06R48eNTcoqZCUmIlUMhb0nJmIiMjVsFAwGEgdIDc3lylTpnDq1CmTo5KKRomZSCWkxExEROTquAI9KRhOPzMzk7i4OPU8kVKlxEykEiocAESJmYiISMl5UPAC6ipAamoqcXFxZGRkmBuUVBhKzEQqocIWM43MKCIicnW8gb6AH3D69GmmTJlCTk6OyVFJRaDETKQSUouZiIjItQugIDnzAo4dO8b06dM10rFcNyVmIpWQnjETERG5PtWA3hSMdHzgwAFmz56NYRgmRyXOTImZSCVUmJhlZWWRlZVlaiwiIiLOKpyCAUFcgJ07dzJ//nwlZ3LNlJiJVELuFLzPDNRqJiIicj3qAt0uTG/atIlVq1aZGI04MyVmIpWUnjMTEREpHY2Ajhemly1bxi+//GJmOOKklJiJVFIamVFERKT03ALcfmH6p59+Yvfu3WaGI05IiZlIJaUBQEREREpXFNDswvTMmTM5ePCgmeGIk1FiJlJJFXZlVIuZiIhI6bAAdwI3Avn5+UybNo3jx4+bHJU4CyVmIpWUWsxERERKnwtwF1AbyMnJYfLkyZw5c8bkqMQZKDETqaQufsZMQ/uKiIiUHjcKhtEPATIyMpg0aRKpqakmRyWOTomZSCXlR8ENID8/n7S0NLPDERERqVA8gT5AEJCSkkJcXByZmZmmxiSOTYmZSCXlAvhfmFZ3RhERkdLnA/QFfIFTp07x7bffkpuba3JU4qiUmIlUYnrOTEREpGwFUpCceQJHjhxhxowZ5OfnmxyVOCIlZiKVmF4yLSIiUvaqA70pePZs3759/PDDD3q+W4pQYiZSiekl0yIiIuWjBtCDgiH1f/31VxYtWqTkTOwoMROpxNSVUUREpPzUA7pemF6/fj1r1qwxMxxxMErMRCoxvWRaRESkfDUGOlyYXrJkCVu2bDEzHHEgSsxEKrHCFrO0tDSNEiUiIlJObgVuuzA9d+5c9uzZY2Y44iCUmIlUYl6Ax4VptZqJiIiUn7ZAE8AwDL777jvi4+NNjkjMpsRMpBKzoO6MIiIiZrAAXYAbgPz8fKZOnUpiYqLJUYmZlJiJVHIaAERERMQcLsDdQE0gOzubuLg4zp49a3JUYhYlZiKVnBIzERER87gB91DwrrP09HTi4uJIS0szOSoxgxIzkUpO7zITERExlyfQl4I6+dy5c0yePJmsrCyTo5LypsRMpJIrfMZMLWYiIiLm8aUgOfMBkpKSmDp1qkZMrmSUmIlUchd3ZTQMw9RYREREKrMgoA8FIyYfPnyY77//HqvVampMUn6UmIlUcoUtZjk5OWRmZpoai4iISGUXQsEzZ67A3r17+fHHH/XFaSWhxEykknOjoPsEqDujiIiII6gFdKdgSP1t27axZMkSkyOS8qDETEQ0MqOIiIiDuRGIvjC9Zs0a1q5da2Y4Ug6UmImIRmYUERFxQE2AthemFy9ezPbt280MR8qYEjMR0ciMIiIiDuo24NYL03PmzOH33383MxwpQ0rMRERdGUVERByUBWgP3AwYhsGMGTNISEgwOSopC0rMRERdGUVERByYhYLnzeoBeXl5fPvttyQlJZkclZQ2JWYiYpeY6X0pIiIijscVuBsIB7KysoiLi9MXqhWMEjMRwZeCG75hGKSmppodjoiIiBTDHegNVAPOnz/PpEmTSE9PNzkqKS1KzEQECxoARERExBl4AX0Bf+Ds2bNMnjyZ7Oxsk6OS0qDETEQAJWYiIiLOwg/oB3gDJ06cYOrUqeTl5ZkclVwvJWYiAmhkRhEREWdSBehDQffG+Ph4Zs6cqefEnZzDJGZvvfUWFouF0aNH25ZlZWUxatQoqlWrhp+fH/379y8yAk1CQgI9evTAx8eHkJAQ/v73v+sbA5FroJEZRYpS3SQijiwUuIeC58R/++035s2bh2EYJkcl18ohErNNmzbxxRdf0Lx5c7vlTz31FD/++CMzZsxgxYoVHD9+nH79+tnW5+fn06NHD3Jycli7di3ffPMNEydO5OWXXy7vUxBxemoxE7GnuklEnEFt4K4L05s3b2bZsmVmhiPXwfTE7Pz58wwZMoQvv/ySKlWq2JanpKTw3//+l/fff5/OnTvTqlUrvv76a9auXcv69esBWLRoEbt37yYuLo6WLVvSvXt3/vnPf/Lpp5+Sk5Nj1imJOCU9YybyB9VNIuJMGgCdL0yvWrWKDRs2mBmOXCPTE7NRo0bRo0cPoqOj7ZZv3ryZ3Nxcu+WNGjWiTp06rFu3DoB169bRrFkzQkNDbWViYmJITU1l165dlzxmdnY2qampdh+Ryq6wxSwjI0N/PEqlV951k+olEblezYGoC9MLFixgx44dZoYj18DUxGzq1Kls2bKFcePGFVmXmJiIh4cHQUFBdstDQ0NJTEy0lbm44itcX7juUsaNG0dgYKDtU7t27es8ExHn50nBELygVjOp3Myom1QviUhpuB1oeWF69uzZ7N+/38Ro5GqZlpgdOXKEJ598ksmTJ+Pl5XXlDUrR2LFjSUlJsX2OHDlSrscXcVSF3Rk1AIhUVmbVTaqXRKQ0WICOQEPAarUyffp0jh49anJUUlKmJWabN2/m5MmT3Hrrrbi5ueHm5saKFSv46KOPcHNzIzQ0lJycnCJ/ICYlJREWFgZAWFhYkZGwCucLyxTH09OTgIAAu4+IaAAQEbPqJtVLIlJaLEA3IALIzc1lypQpnDp1yuSopCRMS8y6dOnCjh072LZtm+1z2223MWTIENu0u7s7S5YssW2zd+9eEhISiIoq6EEbFRXFjh07OHnypK3M4sWLCQgIoHHjxuV+TiLOTgOASGWnuklEKgJXoCcQBmRmZhIXF0dKSorJUcmVuJl1YH9/f5o2bWq3zNfXl2rVqtmWjxgxgjFjxlC1alUCAgJ44okniIqKok2bNgB069aNxo0b88ADD/DOO++QmJjIiy++yKhRo/D09Cz3cxJxdnqXmVR2qptEpKJwB3oDM4CzqanExcUxfPhwfHx8TI5MLsX0URkv54MPPqBnz57079+fDh06EBYWxsyZM23rXV1dmTt3Lq6urkRFRREbG8vQoUN5/fXXTYxaxHmpK6PIlaluEhFn4Q30BfyA06dPM2XKFI287MAshl4PTmpqKoGBgaSkpKhfv1Qo6XFx5B04UOLyycBEwM3NjRdeeAGLxVJGkYkU0P23eLouUpGlffEF1suMni2l7ywwHcgCbrjhBu6//35cXV1NjspxmXUPdugWMxEpX/4UPDScl5dHenq62eGIiIhIKagK9KGge+PBgweZNWsWaptxPErMRMTGlYLuDqDujCIiIhVJGAUDgrgAu3btYv78+UrOHIwSMxGxo+fMREREKqYICobSB9i0aRMrV640Mxz5EyVmImJHQ+aLiIhUXI2AThemly9fzqZNm0yMRi6mxExE7GjIfBERkYqtJRB5YXrevHns2rXLxGikkBIzEbGjrowiIiIVXxug2YXpmTNncvDgQTPDEZSYicifFHZlVIuZiIhIxWUB7gQaAFarlWnTpnH8+HGTo6rclJiJiJ3CFrOUlBTy8/NNjUVERETKjgsQA9QGcnJymDx5MqdPnzY5qspLiZmI2PEB3C5Mp6SkmBmKiIiIlDE3oBcQAmRkZBAXF0dqaqrJUVVOSsxExI4FPWcmIiJSmXhQ8ALqKhR8KRsXF0dmZqa5QVVCSsxEpAgNmS8iIlK5+FCQnPkCp06dYsqUKeTm5pobVCWjxExEilCLmYiISOUTCPQFPIGjR48yffp0PW9ejpSYiUgRGplRRESkcqoO9Kbg2bP9+/fzww8/YBiGyVFVDkrMRKQItZiJiIhUXjWAHhQ8d/7rr7+ycOFCJWflQImZiBShxExERKRyqwd0uzC9YcMG1qxZY2Y4lYISMxEporArY1ZWFllZWabGIiIiIua4GehwYXrJkiVs2bLFzHAqPCVmIlKEBwWjM4FazURERCqzW4HbLkzPnTuX3377zcxwKjQlZiJSLA2ZLyIiIgBtgSaAYRh8//33xMfHmxxRxaTETESKVficmUZmFBERqdwsQBegPpCfn8+3337LiRMnTI6q4lFiJiLFUotZ5bFjxw6zQxAREQfnAnQHagI5OTlMnjyZdevWmRxVxaLETESKpRazyuHjjz9m5syZvP/++2aHIiIiDs4NuIeCL2/T09NZtGgRixcvNjmqisPN7ABEpOzkHThwzdsWJmZn9u8n5bXXSicgcThnL/w/LS2NcePGMXbsWFPjEZGKz5qYaHYIch08gVbAsgvza9eupXr16txyyy0mRlUxqMVMRIpV2JUxFdArJSuuv1w0nZOTwz//+U9ycnIAmDlzJt26daNatWpYLBa2bdt2yf0YhkH37t2xWCzMnj37ssdMSkriwQcfpEaNGoSFhQFw4Dq+RBARkfLVAmh40fwPP/zA4cOHbfOGYfDyyy8THh6Ot7c30dHR7Nu374r7/fTTT6lbty5eXl5ERkayceNG27r4+HgsFkuxnxkzZpTi2ZlHiZmIFMufghtEPnDe5Fik7PgBj100b7VaGTduHDk5OaSnp9OuXTvefvvtK+5n/PjxWCyWK5YzDIM+ffpw8OBB5syZw6pVqwDo3bs36enp13gWIiJS3roD4RfNT5w4kdTUVADeeecdPvroIz7//HM2bNiAr68vMTExl3036rRp0xgzZgyvvPIKW7ZsoUWLFsTExHDy5EkAateuzYkTJ+w+r732Gn5+fnTv3r0Mz7T8KDETkWK5UJCcAaSYGYiUOQ9gNPYVwrhx4+jRowcvv/wy0dHRl91+27ZtvPfee3z11VdXPNa+fftYv349EyZMoHXr1jRo0ACAzMxMvv3222s+BxERKX/38UcPG4APPviArKwsxo8fz4svvkjv3r1p3rw5//vf/zh+/Phle1S8//77jBw5kuHDh9O4cWM+//xzfHx8bHWLq6srYWFhdp9Zs2YxcOBA/Pz8yvQ8y4sSMxG5pMLnzFJNjULKy98oeHag0Mcff8zBgwcvu01GRgb3338/n376qa1b4uVkZ2cD4OXlZbfc09OT1atXX23IIiJisoewrzuee+45EhMT7b7UCwwMJDIy8pKjOObk5LB582a7bVxcXIiOjr7kNps3b2bbtm2MGDGiNE7DISgxE5FLKvwWTC1mlcej2H/7OWnSJLZv337J8k899RR33HEHvXv3LtH+GzVqRJ06dRg7diznzp2zPc927NgxvRNHRMRJPQq4Xpg+f77gAYjQ0FC7MqGhoSReYuCX06dPk5+ff1Xb/Pe//+Xmm2/mjjvuuK7YHYlGZRSRS1KLWeUx/ddfeerHH23zj8XG4hsRAcCyZcuK3eaHH35g6dKlbN26tcTHcXd3Z+bMmYwYMYKqVavi6lpQlXft2hUXF31XKCLibC6uP3KB+4cMAQqePS7JM8rXIjMzkylTpvDSSy+Vyf7NosRMRC6pMDFTi1nF171hQ26rWdM2Hx4QwDLg94vKzJs3j5YtW9rmly5dyoEDBwgKCrLbV//+/Wnfvj3Lly8v9litWrVi27ZtpKSkcObMGerXr8/Zs2eJjIwstfMREZHycXH9YQD/zc8HICUlhXfeeYdnn30WKBiR9+I65GLVq1fH1dWVpKQku+VJSUnFdpP/7rvvyMjIYOjQoaV3Ig5AX0+KyCWpK2Pl4e/pyQ3Vqtk+3u7u3A1cnColJCTwxRdf2Oaff/55fv31V7Zt22b7QMHD319//fUVjxkYGEj16tUB2Lp1a4m7Q4qIiOO4uP6oX60aLwcH4+fnx6FDh8jMzOTjjz8mNTWVDRs2EBUVVew+PDw8aNWqFUuWLLEts1qtLFmypNht/vvf/3LPPfcQHBxcZudlBiVmInJJhS1m6UCemYGIKc5lZOB74gQRp04BcObMGbZu3cqrr74KQFhYGE2bNrX7ANSpU4d69erZ9tOoUSNmzZplm58xYwbLly/n4MGD/PTTTwD06NGDbt26ldOZiYhIWfGyWHisTRtWrlzJnj17+O233+jYsSM1atSgT58+tnJdunThk08+sc2PGTOGL7/8km+++YbffvuNRx99lPT0dIYPH263//3797Ny5Ur+8pe/UNGoK6OIXJIXBUOp51DwnFlVc8ORcjZv715GzZljm//uu+8A6NixI+PGjWPs2LEl2s/evXtJSfmj3fXEiROMGTPGrotKSYbaFxER5zC2bVtScnKI+/FHsrKyqFOnDk8//bTdiLwHDhzg9OnTtvn77ruPU6dO8fLLL5OYmEjLli1ZsGBBkQFBvvrqK2rVqlUhv8yzGIZhmB2E2VJTUwkMDCQlJYWAgIArbyDiJFJee+269xEHnAZ6A/WuUFYqtvPA/1007+LiwnPPPYeHh8c171P33+LpukhFVhp1kziHHcCSi+Y7depEx44dzQqnxMy6B6sro4hclgYAkUJ+wGMXzVutVsaNG2cb8l5ERORizYBbLppfvnw5O3fuNCsch6fETEQuS0Pmy8U8gNHYVx7jxo3j7Nmz5gQkIiIOrSNQ/6L577//nqNHj5oVjkPTM2YiFZmPD2RkXNcubC1mXl64VNVTZlJgNPDp8eNkX5hfv349d999t4kRiYjTcHeH3Fyzo5By1AuYApy8MB8XF8ezzz6r91f+iRIzkQrMLTycvAMHrmsfhT2rzwcG4j9y5PUHJRXG88CHH36Iv7+/kjIRKTGXatWwJiaaHYaUs/speE75PJCdnc1PP/1Ez549sVgsJkfmOJSYichlFbaYnTt3DsMwdAMVO08++aTZIYiIiJP4C7APmAds2bIFX19fOnfubHJUjkPthyJyWYUtZjk5OWRmZpoai4iIiDi3BkBhKrZq1SrWr19vZjgORYmZiFyWG+B7YfrcuXNmhiIiIiIVQDPgjgvTCxcu5NdffzUzHIehxExEruji7owiIiIi16s10PLC9Jw5c9i3b5+J0TgGJWYickWFiVlycrKZYYiIiEgFYaFgKP2GFLwXc/r06Rw5csTkqMylxExErqjwOTO1mImIiEhpsQDdgAggLy+PKVOmcPLkyStsVXEpMRORK1KLmYiIiJQFV6AnEAZkZWURFxdHSkqKyVGZQ4mZiFyRnjETERGRsuIO9AaqAmlpaUyaNImMjAyToyp/SsxE5IoKuzImJydjtVpNjUVEREQqHm+gL+APnDlzhsmTJ5OdnW1yVOVLiZmIXJEfBV0NDMMgNTXV7HBERESkAvKnIDnzAo4fP8706dPJy8szOaryo8RMRK7IggYAERERkbJXFehDQffGgwcPMnv27ErTW0eJmYiUiBIzERERKQ9hFAwI4gLs2rWLBQsWYBiGyVGVPSVmIlIiGgBEREREyksEEHNhetOmTaxYscLMcMqFEjMRKRENmS8iIiLlqSFw54XpFStWsGnTJjPDKXNKzESkRNRiJiIiIuWtBRB5YXrevHns2rXLzHDKlBIzESkRPWMmIiIiZmgDNL8wPXPmTA4ePGhmOGVGiZmIlEhhi1lGRgY5OTmmxiIiIiKVhwXoBDQArFYrU6dO5dixY+YGVQaUmIlIiXhe+IBazURERKR8uVAwGEgdIDc3l8mTJ3P69GmToypdSsxEpMQ0AIiIiIiYxY2CYfRDgczMTOLi4khNTTU5qtKjxExESkwDgIiIiIiZPIDeQBUgJSWFuLg4MjMzTY6qdCgxE5ES0wAgIiIiYjYfoC/gC5w6dYopU6ZUiOfflZiJSIldritjbm4uzz33HM2aNcPX15caNWowdOhQjh8/ftl9jhs3jtatW+Pv709ISAh9+vRh7969dmU6deqExWKx+/z1r38tpbMSERERZxMA9KPg+fejR48yY8YM8vPzL7uNYRi8/PLLhIeH4+3tTXR0NPv27bvisT799FPq1q2Ll5cXkZGRbNy40W79gQMH6Nu3L8HBwQQEBDBw4ECSkpKu+pxMTcxeffXVIn9sNWrUyLY+KyuLUaNGUa1aNfz8/Ojfv3+Rk0xISKBHjx74+PgQEhLC3//+d/Ly8sr7VEQqhct1ZczIyGDLli289NJLbNmyhZkzZ7J3717uueeey+5zxYoVjBo1ivXr17N48WJyc3Pp1q0b6enpduVGjhzJiRMnbJ933nmntE5LxEb1koiI86hGQbdGN2D//v3MmTMHwzAuWf6dd97ho48+4vPPP2fDhg34+voSExNDVlbWJbeZNm0aY8aM4ZVXXmHLli20aNGCmJgYTp48CUB6ejrdunXDYrGwdOlS1qxZQ05ODr169cJqtV7V+bhdVeky0KRJE37++WfbvJvbHyE99dRT/PTTT8yYMYPAwEAef/xx+vXrx5o1awDIz8+nR48ehIWFsXbtWk6cOMHQoUNxd3fnX//6V7mfi0hFd3FiZhgGFovlj3WBgSxevNiu/CeffMLtt99OQkICderUKXafCxYssJufOHEiISEhbN68mQ4dOtiW+/j4EBYWVjonInIZqpdERJxHDaAH8COwY8cOfHx8iImJsfsbBQpay8aPH8+LL75I7969Afjf//5HaGgos2fPZtCgQcXu//3332fkyJEMHz4cgM8//5yffvqJr776iueff541a9YQHx/P1q1bCQgoeOjjm2++oUqVKixdupTo6OgSn4vpXRnd3NwICwuzfapXrw4UPMz33//+l/fff5/OnTvTqlUrvv76a9auXcv69esBWLRoEbt37yYuLo6WLVvSvXt3/vnPf/Lpp59WiH6mIo7G/8L/8/LyirRoFSclJQWLxUJQUFCJj5GSkgJA1apV7ZZPnjyZ6tWr07RpU8aOHUtGRkaJ9ylyNVQviYg4l3pA1wvTGzZsYPXq1UXKHDp0iMTERLtEKTAwkMjISNatW1fsfnNycti8ebPdNi4uLkRHR9u2yc7OxmKx4OnpaSvj5eWFi4tLsXFcjumJ2b59+6hRowY33HADQ4YMISEhAYDNmzeTm5trdyEaNWpEnTp1bBdi3bp1NGvWjNDQUFuZmJgYUlNT2bVr1yWPmZ2dTWpqqt1HRK7MlT+SsysNAJKVlcVzzz3H4MGDbd8gXYnVamX06NG0bduWpk2b2pbff//9xMXFsWzZMsaOHcukSZOIjY29xrMQuTzVSyIizudmoLCfzdKlS9m8ebPd+sTERAC7+3PhfOG6Pztz5gz5+fmX3aZNmzb4+vry3HPPkZGRQXp6Os888wz5+fmcOHHiqs7B1K6MkZGRTJw4kYYNG3LixAlee+012rdvz86dO0lMTMTDw6PIN+0XX4jExMRiL1ThuksZN24cr732WumejIgD8oqJwbhMv+lrUWXePNISE4mLi+PNN9+0LZ8/fz7t27cHCgYCGThwIIZhMGHChBLve9SoUezcubPIN0wPP/ywbbpZs2aEh4fTpUsXDhw4QP369a/zjET+oHpJpOz59OmDoRZkKQPtgbxffmHtr78yYcIEpk6dalv3008/lckxg4ODmTFjBo8++igfffQRLi4uDB48mFtvvRUXl6trAzM1Mevevbttunnz5kRGRhIREcH06dPx9vYus+OOHTuWMWPG2OZTU1OpXbt2mR1PxCyuwcGlvs8u3btjGAbe3t4MGDDAtrxmzZrAH0nZ4cOHWbp0aYlbyx5//HHmzp3LypUrqVWr1mXLRkZGAgUP+ioxk9Kkekmk7Ln+6csLkdIUXasW/uHh3HjjjTz//PO25dnZ2QAkJSURHh5uW56UlETLli2L3Ve1atVwdXUtMshTUlKS3XPv3bp148CBA5w+fRo3NzeCgoIICwvjhhtuuKrYTR/842JBQUHcdNNN7N+/n65du5KTk0NycrLdt5MXX4iwsLAiw1UWXrjLDRLg6elp1w9UREru4kE8QkJC7NYVJmX79u1j2bJlVKtW7Yr7MwyDJ554glmzZrF8+XLq1at3xW22bdsGYHdjFSkLqpdERJyLxWKhTZs2ALZnhKHg742wsDCWLFliS8RSU1PZsGEDjz76aLH78vDwoFWrVixZsoQ+ffoABY9dLFmyhMcff7xI+cLjLV26lJMnT15xZOo/M/0Zs4udP3+eAwcOEB4eTqtWrXB3d2fJkiW29Xv37iUhIYGoqCgAoqKi2LFjh224SoDFixcTEBBA48aNyz1+kcosNzeXe++9l19++YXJkyeTn59PYmIiiYmJdoMedOnShU8++cQ2P2rUKOLi4pgyZQr+/v62bTIzM4GCd4P885//ZPPmzcTHx/PDDz8wdOhQOnToQPPmzcv9PKVyUb0kIlIxWCwWRo8ezRtvvMEPP/zAjh07GDp0KDVq1LAlXVDwd8p//vMf2/yYMWP48ssv+eabb/jtt9949NFHSU9Pt43SCPD111+zfv16Dhw4QFxcHAMGDOCpp56iYcOGVxekYaKnn37aWL58uXHo0CFjzZo1RnR0tFG9enXj5MmThmEYxl//+lejTp06xtKlS41ffvnFiIqKMqKiomzb5+XlGU2bNjW6detmbNu2zViwYIERHBxsjB079qriSElJMQAjJSWlVM9PpDI5dOiQART7WbZsma1cRESE8corr9jmL7XN119/bRiGYSQkJBgdOnQwqlatanh6eho33nij8fe//13/XisIR7v/ql4SEam4rFar8dJLLxmhoaGGp6en0aVLF2Pv3r12ZSIiIoznn3/e7h788ccfG3Xq1DE8PDyM22+/3Vi/fr3dNs8995wRGhpquLu7Gw0aNDDee+89w2q1XnV8FsO4zFvYytigQYNYuXIlZ86cITg4mHbt2vHmm2/anhnJysri6aef5ttvvyU7O5uYmBg+++wzu+4ghw8f5tFHH2X58uX4+voybNgw3nrrLbv3zlxJamoqgYGBpKSklPh5GBERuX6Odv9VvSQiImbdg01NzByFKkAREXPo/ls8XRcREfOYdQ92qGfMREREREREKiMlZiIiIiIiIiZTYiYiIiIiImIyJWYiIiIiIiImU2ImIiIiIiJiMiVmIiIiIiIiJlNiJiIiIiIiYjIlZiIiIiIiIiZTYiYiIiIiImIyJWYiIiIiIiImczM7AEdgGAYAqampJkciIlK5FN53C+/DUkD1koiIecyqm5SYAWlpaQDUrl3b5EhERCqntLQ0AgMDzQ7DYaheEhExX3nXTRZDX1NitVo5fvw4/v7+WCwWs8MhNTWV2rVrc+TIEQICAswO55o4+zk4e/ygc3AUOofLMwyDtLQ0atSogYuLetcXKot6qSL8LppN1/D66RpeP13D0nG562hW3aQWM8DFxYVatWqZHUYRAQEBTv8PztnPwdnjB52Do9A5XJpayooqy3qpIvwumk3X8PrpGl4/XcPScanraEbdpK8nRURERERETKbETERERERExGRKzByQp6cnr7zyCp6enmaHcs2c/RycPX7QOTgKnYM4Cv0cr5+u4fXTNbx+uoalwxGvowb/EBERERERMZlazEREREREREymxExERERERMRkSsxERERERERMpsRMRERERETEZErMTLJy5Up69epFjRo1sFgszJ492279gw8+iMVisfvcdddd5gR7CVc6B8MwePnllwkPD8fb25vo6Gj27dtnTrAl9Oqrrxa57o0aNTI7rGvy6aefUrduXby8vIiMjGTjxo1mh3Rd6tatW+Rn89Zbb5kd1mW9+eab3HHHHfj4+BAUFFRsmYSEBHr06IGPjw8hISH8/e9/Jy8vr3wDvYySnMOffy4Wi4WpU6eWb6CV0NX8G9+1axf9+/e3/TsaP378de+zIrja850xYwaNGjXCy8uLZs2aMW/ePLv1zlB3l4XSvo7O+PdDaTt79ixDhgwhICCAoKAgRowYwfnz5y+7TVZWFqNGjaJatWr4+fnRv39/kpKS7MpUpvv1tVzD//znP3Tq1ImAgAAsFgvJycmlst+SUmJmkvT0dFq0aMGnn356yTJ33XUXJ06csH2+/fbbcozwyq50Du+88w4fffQRn3/+ORs2bMDX15eYmBiysrLKOdKr06RJE7vrvnr1arNDumrTpk1jzJgxvPLKK2zZsoUWLVoQExPDyZMnzQ7turz++ut2P5snnnjC7JAuKycnhwEDBvDoo48Wuz4/P58ePXqQk5PD2rVr+eabb5g4cSIvv/xyOUd6aVc6h0Jff/213c+mT58+5RNgJXW1/8YzMjK44YYbeOuttwgLCyuVfTq7qz3ftWvXMnjwYEaMGMHWrVvp06cPffr0YefOnXblHL3uLm1lcR2d9e+H0jRkyBB27drF4sWLmTt3LitXruThhx++7DZPPfUUP/74IzNmzGDFihUcP36cfv36FSlXWe7X13INMzIyuOuuu3jhhRdKdb8lZojpAGPWrFl2y4YNG2b07t3blHiuxZ/PwWq1GmFhYca7775rW5acnGx4enoa3377rQkRlswrr7xitGjRwuwwrtvtt99ujBo1yjafn59v1KhRwxg3bpyJUV2fiIgI44MPPjA7jGvy9ddfG4GBgUWWz5s3z3BxcTESExNtyyZMmGAEBAQY2dnZ5RjhlV3qHAyj+HuYlK3r+Td+qX9LFfG+cTlXe74DBw40evToYbcsMjLSeOSRR2zzzlZ3l4bSvo7O+vdDadq9e7cBGJs2bbItmz9/vmGxWIxjx44Vu01ycrLh7u5uzJgxw7bst99+MwBj3bp1tmWV5X59LdfwYsuWLTMA49y5c6W63ytRi5kDW758OSEhITRs2JBHH32UM2fOmB1SiR06dIjExESio6NtywIDA4mMjGTdunUmRnZl+/bto0aNGtxwww0MGTKEhIQEs0O6Kjk5OWzevNnu2ru4uBAdHe3w1/5K3nrrLapVq8Ytt9zCu+++61Bd/q7FunXraNasGaGhobZlMTExpKamsmvXLhMju3qjRo2ievXq3H777Xz11VcYekVmmSmLf+MV+b5RnGs533Xr1tmVh4J/r38u78x199Uqi+vozH8/lJZ169YRFBTEbbfdZlsWHR2Ni4sLGzZsKHabzZs3k5uba3fdGjVqRJ06dYpct8pwv76Wa2jmfgu5XfcepEzcdddd9OvXj3r16nHgwAFeeOEFunfvzrp163B1dTU7vCtKTEwEsPuDs3C+cJ0jioyMZOLEiTRs2JATJ07w2muv0b59e3bu3Im/v7/Z4ZXI6dOnyc/PL/ba79mzx6Sort/f/vY3bv3/9u49KKryjQP4dxOW23JnZUnjZkCKoFxGBi0RUBHLtBhvGaKkoZZKYqNOGV7KS+ZtbOxCJU6adkHTxkwJ2EFBEXFXqBgKBcxcLiIUEGLg8/vD4fxc7iyLR/D5zOwM5933vOd5z3Les8+es+/6+sLGxgaZmZlYs2YNNBoNduzYIXZoOistLW3zdWp+rq/YsGEDQkJCYGpqitOnT2PJkiWora3FsmXLxA6tX+qNY7y/jhvt0aW/7R2v9x+rff3c3V29sR/76vsHfSotLcXAgQO1ygwMDGBjY9PuPigtLYVUKm31XeCW++1RGa912Yditiu01eMWWK+YNWuW8LeXlxe8vb0xZMgQKJVKhIaGihhZ/xYeHi787e3tjYCAADg5OeGbb77BK6+8ImJk/dPq1auxdevWDuvk5+fjqaeewooVK4Qyb29vSKVSxMTEYPPmzTAyMurtUAXdiflhpe8+rF27Vvjbx8cHdXV12LZtW7870TPWGT53s450deztTX19vH4Y9mFv4sSsj3B1dYWdnR0KCwv7xODe/OXysrIyODg4COVlZWUYOXKkSFF1n5WVFdzd3VFYWCh2KF1mZ2eHAQMGtJqJqaysrN0v/YslLi4O8+bN67COq6trm+UBAQFobGxEcXExPDw8eiG6tvUk5pYUCkWr2cuaX7fefK302Ye2BAQEYOPGjWhoaHigSfOjojeO8b40buiDLv1VKBTd3j997dzdXb2xH/vL+4e2dHXsVSgUrSZPaWxsxK1btzrcr3fu3EF1dbXWVbPO/kf72njdm/uwK3qr3WacmPUR169fR2VlpdYg9TBzcXGBQqFASkqKMJD+888/yMrK6nR2t4dJbW0trly5gsjISLFD6TKpVAo/Pz+kpKQIMy3dvXsXKSkpeP3118UNrgW5XA65XK7Tumq1Go899lirWwp6W09ibikwMBDvvfceysvLhX4kJyfDwsICw4YN08s22qLPPrRFrVbD2tq6T5zk+6LeOMb70rihD7r0NzAwECkpKYiNjRXKkpOTERgY2O52+tq5u7t6Yz/2l/cPbenq2BsYGIjq6mrk5OTAz88PAJCamoq7d+8iICCgzXX8/PxgaGiIlJQUREREAAAKCgpw7dq1Dv9H+9p43Zv7sCt6q11Bj6cPYTqpqakhlUpFKpWKANCOHTtIpVJRSUkJ1dTU0MqVK+ncuXNUVFREP//8M/n6+pKbmxvdvn1b7NAFHfWBiGjLli1kZWVFx44do9zcXJo6dSq5uLhQfX29yJG3Ly4ujpRKJRUVFVFGRgaNHz+e7OzsqLy8XOzQuuXw4cNkZGREiYmJ9Ntvv9Grr75KVlZWWrP/9SWZmZm0c+dOUqvVdOXKFTpw4ADJ5XKaO3eu2KF1qKSkhFQqFa1fv55kMplwvNTU1BARUWNjIw0fPpwmTpxIarWafvrpJ5LL5bRmzRqRI/+/zvpw/PhxSkhIoLy8PPrjjz9o7969ZGpqSu+8847IkfdvnR3jkZGRtHr1aqF+Q0OD8No5ODjQypUrSaVS0R9//NHlNvub7u7DjIwMMjAwoA8++IDy8/MpPj6eDA0NKS8vj4ioz5y79U3f+5Gob75/0LdJkyaRj48PZWVl0dmzZ8nNzY1mz54tPH/9+nXy8PCgrKwsoWzRokXk6OhIqampdPHiRQoMDKTAwEDh+UdtvNZlH2o0GlKpVJSQkEAAKD09nVQqFVVWVna53Z7gxEwkzdNwtnxERUXRv//+SxMnTiS5XE6Ghobk5ORECxcufOhOjh31gejelLdr164le3t7MjIyotDQUCooKBA36E7MnDmTHBwcSCqV0qBBg2jmzJlUWFgodlg62bNnDzk6OpJUKqVRo0bR+fPnxQ5JZzk5ORQQEECWlpZkbGxMQ4cOpU2bNj30b3aioqLaPEbS0tKEOsXFxRQeHk4mJiZkZ2dHcXFx9N9//4kXdAud9eHkyZM0cuRIkslkZGZmRiNGjKCPP/6YmpqaxA38EdDRMR4UFCSMxURERUVFbb6OQUFBXW6zP+rOPiQi+uabb8jd3Z2kUil5enrSiRMnhOf6yrm7N+hzPxL1zfcP+lZZWUmzZ88mmUxGFhYWNH/+fOEDMaL/H9P3n0/q6+tpyZIlZG1tTaampvTCCy+QRqMRnn/Uxmtd9mF8fHybY+W+ffu63G5PSIj64RyZjDHGGGOMMdaH8O+YMcYYY4wxxpjIODFjjDHGGGOMMZFxYsYYY4wxxhhjIuPEjDHGGGOMMcZExokZY4wxxhhjjImMEzPGGGOMMcYYExknZowxxhhjjDEmMk7MGOunnJ2dIZFIIJFIUF1d/cjHwRhjrLVx48YhNja2x200j/NqtVovcYmluR9WVlZih8IeQZyYMdaBiooKLF68GI6OjjAyMoJCoUBYWBgyMjKEOhKJBN9//714QXZgw4YN0Gg0sLS0BAAolUpIJBJYW1vj9u3bWnWzs7OFE1Kz5vrND3t7e0RERODq1atdjiE7OxtJSUn66RBjjD3i5s2bh2nTpumtvSNHjmDjxo09bmfhwoXQaDQYPny4HqLSTUlJCUxMTFBbW6tzGxqNBrt27dJfUIx1AydmjHUgIiICKpUK+/fvx++//47jx49j3LhxqKys7FY7d+7c6aUIO2Zubg6FQqGVbDWXHz16VKvs888/h6OjY5vtFBQU4MaNG/j222/x66+/YsqUKWhqaupSDHK5HDY2Nrp1gDHGmE7++++/LtWzsbGBubl5j7dnamoKhUIBAwODHrelq2PHjiE4OBgymUznNhQKhfBhJmMPGidmjLWjuroaZ86cwdatWxEcHAwnJyeMGjUKa9aswfPPPw/g3m16APDCCy9AIpEIy+vWrcPIkSPx2WefwcXFBcbGxkKbCxYsgFwuh4WFBUJCQnD58mVhm5cvX0ZwcDDMzc1hYWEBPz8/XLx4EcC9TwKnTJkCa2trmJmZwdPTEz/++KNOfYuKisIXX3whLNfX1+Pw4cOIiopqs/7AgQPh4OCAsWPH4p133sFvv/2GwsJCZGdnY8KECbCzs4OlpSWCgoJw6dIlnWJijDF2z3fffQcvLy+YmJjA1tYW48ePR11dHdatW4f9+/fj2LFjwp0MSqUSxcXFkEgk+PrrrxEUFARjY2McPHgQlZWVmD17NgYNGgRTU1N4eXnh0KFDWttqeSujs7MzNm3ahOjoaJibm8PR0RGffvppt/vQfMfFqVOn4OPjAxMTE4SEhKC8vBwnT57E0KFDYWFhgZdeegn//vuvVjxLly5FbGwsrK2tYW9vj4SEBNTV1WH+/PkwNzfHk08+iZMnT7ba5rFjx4Tzc/OVxU2bNsHe3h5WVlbYsGEDGhsb8eabb8LGxgaDBw/Gvn37ut03xnoLJ2aMtUMmk0Emk+H7779HQ0NDm3Wys7MBAPv27YNGoxGWAaCwsBBJSUk4cuSIcM/99OnThZNSTk4OfH19ERoailu3bgEA5syZg8GDByM7Oxs5OTlYvXo1DA0NAQCvvfYaGhoakJ6ejry8PGzdulXnTwUjIyNx5swZXLt2DQCQlJQEZ2dn+Pr6drquiYkJgHtXAWtqahAVFYWzZ8/i/PnzcHNzw+TJk1FTU6NTXIwx9qjTaDSYPXs2oqOjkZ+fD6VSiRdffBFEhJUrV2LGjBmYNGkSNBoNNBoNRo8eLay7evVqLF++HPn5+QgLC8Pt27fh5+eHEydO4JdffsGrr76KyMhIXLhwocMYtm/fDn9/f6hUKixZsgSLFy9GQUGBTv1Zt24dPvzwQ2RmZuLPP//EjBkzsGvXLnz11Vc4ceIETp8+jT179mits3//ftjZ2eHChQtYunQpFi9ejOnTp2P06NG4dOkSJk6ciMjISK2Errq6GmfPnhUSMwBITU3FjRs3kJ6ejh07diA+Ph7PPfccrK2tkZWVhUWLFiEmJgbXr1/XqW+M6R0xxtr13XffkbW1NRkbG9Po0aNpzZo1dPnyZa06AOjo0aNaZfHx8WRoaEjl5eVC2ZkzZ8jCwoJu376tVXfIkCH0ySefEBGRubk5JSYmthmLl5cXrVu3rsuxOzk50c6dO7XK0tLSCABVVVXRtGnTaP369UREFBwcTLt376ajR4/S/cPC/fWJiG7cuEGjR4+mQYMGUUNDQ6ttNjU1kbm5Of3www/tbpcxxlj7cnJyCAAVFxe3+XxUVBRNnTpVq6yoqIgA0K5duzpt/9lnn6W4uDhhOSgoiJYvXy4sOzk50csvvyws3717lwYOHEgfffRRu222bIPo/+P+zz//LJRt3ryZANCVK1eEspiYGAoLC9Nq6+mnnxaWGxsbyczMjCIjI4UyjUZDAOjcuXNC2cGDB8nf319YjoqKIicnJ2pqahLKPDw86JlnnmnV9qFDh7Ri37dvH1laWrbbX8Z6C18xY6wDERERuHHjBo4fP45JkyZBqVTC19cXiYmJna7r5OQEuVwuLF++fBm1tbWwtbUVrsbJZDIUFRXhypUrAIAVK1ZgwYIFGD9+PLZs2SKUA8CyZcvw7rvvYsyYMYiPj0dubm6P+hYdHY3ExERcvXoV586dw5w5c9qtO3jwYJiZmeHxxx9HXV0dkpKSIJVKUVZWhoULF8LNzQ2WlpawsLBAbW2tcCWOMcZY94wYMQKhoaHw8vLC9OnTkZCQgKqqqi6t6+/vr7Xc1NSEjRs3wsvLCzY2NpDJZDh16lSnY7S3t7fwt0QigUKhQHl5efc706Ite3t7mJqawtXVVausZdv3rzNgwADY2trCy8tLax0AWuvdfxtjM09PTzz22GNa693fTnPbuvaNMX3jxIyxThgbG2PChAlYu3YtMjMzMW/ePMTHx3e6npmZmdZybW0tHBwcoFartR4FBQV48803Ady75ePXX3/Fs88+i9TUVAwbNkyYpGPBggW4evUqIiMjkZeXB39//1a3f3RHeHg46uvr8corr2DKlCmwtbVtt+6ZM2eQm5uLf/75B2q1GgEBAQDufVdNrVZj9+7dyMzMhFqthq2trWiTnTDGWF83YMAAJCcn4+TJkxg2bBj27NkDDw8PFBUVdbpuy/POtm3bsHv3bqxatQppaWlQq9UICwvrdIxuvoW+mUQiwd27d7vfmRZtSSSSLrXdVp2W7QAQ1rtz5w5++umnVolZZ+20t33GxMKJGWPdNGzYMNTV1QnLhoaGXZqh0NfXF6WlpTAwMMCTTz6p9bCzsxPqubu744033sDp06fx4osvan0x+YknnsCiRYtw5MgRxMXFISEhQed+GBgYYO7cuVAqlYiOju6wrouLC4YMGdJq5q6MjAwsW7YMkydPhqenJ4yMjHDz5k2dY2KMMXYvWRgzZgzWr18PlUoFqVQqfEgnlUq7PCtuRkYGpk6dipdffhkjRoyAq6srfv/9994MXRRKpRLW1tYYMWKE2KEw1iOcmDHWjsrKSoSEhODAgQPIzc1FUVERvv32W7z//vuYOnWqUM/Z2RkpKSkoLS3t8HaT8ePHIzAwENOmTcPp06dRXFyMzMxMvPXWW7h48SLq6+vx+uuvQ6lUoqSkBBkZGcjOzsbQoUMBALGxsTh16hSKiopw6dIlpKWlCc/pauPGjaioqEBYWJhO67u5ueHLL79Efn4+srKyMGfOHGFyEMYYY92XlZWFTZs24eLFi7h27RqOHDmCiooKYbx3dnZGbm4uCgoKcPPmzQ6nxXdzc0NycjIyMzORn5+PmJgYlJWVPaiuPDDHjx9vdbWMsb6IEzPG2iGTyRAQEICdO3di7NixGD58ONauXYuFCxfiww8/FOpt374dycnJeOKJJ+Dj49NuexKJBD/++CPGjh2L+fPnw93dHbNmzUJJSQns7e0xYMAAVFZWYu7cuXB3d8eMGTMQHh6O9evXA7j3XYHXXnsNQ4cOxaRJk+Du7o69e/f2qI9SqRR2dnatfuesqz7//HNUVVXB19cXkZGRWLZsGQYOHNijmBhj7FFmYWGB9PR0TJ48Ge7u7nj77bexfft2hIeHA7j3Q84eHh7w9/eHXC5HRkZGu229/fbb8PX1RVhYGMaNGweFQqHXH6d+WHBixvoLCRGR2EEwxvTP2dkZsbGxWr9PIxalUong4GBUVVXByspK7HAYY4zp0bhx4zBy5Ejs2rXrgW/70qVLCAkJQUVFRavvj+kqMTERsbGxqK6u1kt7jHUVXzFjrB9btWoVZDIZ/v77b9Fi8PT0FD7pZYwx1j/t3bsXMpkMeXl5D3S7jY2N2LNnj96SMplMhkWLFumlLca6i6+YMdZPlZSUCN89cHV11Zoy+FGMgzHGWO/466+/UF9fDwBwdHSEVCoVOSLdFRYWArg3O6aLi4vI0bBHDSdmjDHGGGOMMSYy/uiaMcYYY4wxxkTGiRljjDHGGGOMiYwTM8YYY4wxxhgTGSdmjDHGGGOMMSYyTswYY4wxxhhjTGScmDHGGGOMMcaYyDgxY4wxxhhjjDGRcWLGGGOMMcYYYyLjxIwxxhhjjDHGRPY/3gQMUEEVSw4AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z neutral axis = 260.43\n",
-      "m_cracking (mkN) =  46.29\n",
-      "fctm (MPa) =  2.56\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# A concrete material failing at fctm is used for the cracking moment calculation.\n",
     "concrete = ConcreteEC2_2004(25)\n",
@@ -258,7 +225,7 @@
     "sec = GenericSection(geo)\n",
     "\n",
     "from SLS_section_response import calculate_cracking_moment\n",
-    "m_cracking,_ = calculate_cracking_moment(sec,plot=True)\n",
+    "m_cracking,_ = calculate_cracking_moment(sec,plot=True) # DGM: in the near future, i would implement plotting functions in another method/module/package,.,....\n",
     "m_cracking = m_cracking/1e6 #mkN\n",
     "print('m_cracking (mkN) = ', round(m_cracking,2))\n",
     "print('fctm (MPa) = ',round(concrete.fctm,2))"
@@ -283,26 +250,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\cmg\\AppData\\Local\\Temp\\ipykernel_29892\\3607814390.py:11: RuntimeWarning: divide by zero encountered in divide\n",
-      "  zeta = 1-0.5*(m_cracking/m_x)**2\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Maximum deflection [mm] = 16.89\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# DGM: I guess that this should be a part of a further development (like a BeamElement/LineElement class) if so...\n",
+    "\n",
     "def bending_momment_x_beam(q,L,n_sections=11): # n_sections -> number of sections in the beam\n",
     "    \"\"\"Return M for a simply supported beam under a uniform load q.\"\"\"\n",
     "    x = np.linspace(0,L,n_sections)\n",
@@ -311,7 +264,7 @@
     "    m_x=q*x/2*(L-x)\n",
     "    return x,m_x\n",
     "\n",
-    "def compute_zeta_expresion_7_19(m_cracking,m_x):\n",
+    "def compute_zeta_expresion_7_19(m_cracking,m_x): # DGM: is this an standard expression?\n",
     "    \"\"\"Expresion 7.19 EN 1992 1-1.\"\"\"\n",
     "    zeta = 1-0.5*(m_cracking/m_x)**2\n",
     "    zeta[m_x == 0] = 0\n",
@@ -354,26 +307,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "S_I (mm3)=  241009\n",
-      "S_II (mm3)=  377817\n",
-      "I_I (mm4)=  5413898904\n",
-      "I_II (mm4)=  3677810527\n",
-      "Shrinkage deflection [mm] = 4.9\n",
-      "Total deflection [mm] = 21.8\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "a_fi20 = math.pi * 20**2/4\n",
+    "a_fi20 = math.pi * 20**2/4 # DGM: wouldnt it be useful to obtain reinf. areas from the generic section?\n",
     "a_fi12 = math.pi * 12**2/4\n",
-    "s_1 = (6*a_fi20 * (450-cz_1) + 3*a_fi12 * (50-cz_1))\n",
+    "s_1 = (6*a_fi20 * (450-cz_1) + 3*a_fi12 * (50-cz_1))  # DGM: wouldnt it be useful to obtain S_1/S_2 from the generic section?\n",
     "s_2 = (6*a_fi20 * (450-cz_2) + 3*a_fi12 * (50-cz_2))\n",
     "s_= zeta*s_2 + (1-zeta)*s_1\n",
     "i_= zeta*i11_2 + (1-zeta)*i11_1\n",
@@ -383,7 +323,7 @@
     "print('I_I (mm4)= ',round(i11_1))\n",
     "print('I_II (mm4)= ',round(i11_2))\n",
     "#print(\"I = \",i_)\n",
-    "curv_cs = eps_cs * n * s_/i_ \n",
+    "curv_cs = eps_cs * n * s_/i_\n",
     "\n",
     "# curvature integration\n",
     "deflection_cs = np.trapz(curv_cs*x_beam*1000,x_beam)\n",
@@ -404,28 +344,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zeta =  0.96\n",
-      "\n",
-      "CUSIPERMANTENT LOAD DEFLECTION:\n",
-      "deflection I (mm) =  11.61\n",
-      "deflection II (mm) =  17.09\n",
-      "deflection cuasip (mm) =  16.88\n",
-      "\n",
-      "SHRINKAGE DEFLECTION:\n",
-      "deflection_cs (mm) =  5.04\n",
-      "\n",
-      "TOTAL DEFLECTION:\n",
-      "deflection (mm) =  21.91\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def_1 = 5/384*q_cuasip*long_beam**4 / (Ec_eff*1000) / (i11_1/1000**4) *1000\n",
     "def_2 = 5/384*q_cuasip*long_beam**4 / (Ec_eff*1000) / (i11_2/1000**4) *1000\n",
@@ -471,7 +392,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb
index a4c94cbe..57a718fd 100644
--- a/EX2_part1.ipynb
+++ b/EX2_part1.ipynb
@@ -83,14 +83,14 @@
     "\n",
     "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforfocement\" with reinforcement areas multiplied by Es/Ec is used.\n",
     "#reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)\n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)\n",
     "n=200000/Ec_eff\n",
     "\n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcement, n=6)\n",
     "sec = GenericSection(geo)\n",
     "\n",
     "# Get the neutral axe of the cracked section\n",
@@ -102,8 +102,8 @@
     "# 2) Create the cracked section\n",
     "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
     "geo2 = SurfaceGeometry(poly2, concrete)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcement, n=3)\n",
+    "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcement, n=6)\n",
     "sec2 = GenericSection(geo2)\n",
     "Icr = sec2.gross_properties.i11\n",
     "print(\"I of cracked section (mm4): \",round(Icr))\n",
@@ -152,16 +152,20 @@
     "m_cuasip = q_cuasip * long_beam**2/8\n",
     "\n",
     "# Create section to get hc_eff\n",
+    "# DGM: as creating rectangular reinforced cross sections would be an ordinary task, \n",
+    "# a wrapper function for creating the rectangular section plus the override of the __add__ operator \n",
+    "# could lead to a significant smoother approach like: 'section.rectangle(b, h, mat) + reinf_line() + reinf_line() + [...]'\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
     "sec = GenericSection(geo)\n",
     "hc_eff = _section_7_3_crack_control.hc_eff(500,450,500-sec.gross_properties.cz)\n",
     "\n",
     "Ec_eff = concrete.Ecm/(1+fi)\n",
+    "\n",
     "alfa_e = _section_7_3_crack_control.alpha_e(200000,Ec_eff)\n",
     "rho_p = _section_7_3_crack_control.rho_p_eff(6* math.pi*100, 0 ,0,hc_eff*350)  # b=350mm, fi=20mm\n",
     "kt = _section_7_3_crack_control.kt('long')\n",
@@ -232,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -245,11 +249,21 @@
     }
    ],
    "source": [
+    "# DGM and CMG: would it be a good idea to wrap this calculations inside the generic_cross_section class?\n",
+    "# section.wk(...). In some codes it may be not as straightforward to implement (american standards)\n",
+    "\n",
     "wk = _section_7_3_crack_control.wk(sr_max,eps_sm_eps_cm)\n",
     "print('wk (mm)',round(wk,2))\n",
     "wmax = _section_7_3_crack_control.w_max('XS1','qp')\n",
-    "print('wmax (mm)',wmax)"
+    "print('wmax (mm)', wmax)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Issues.ipynb b/Issues.ipynb
index 43fef163..0dc5408f 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -690,9 +690,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n"
+    "13. Just for making the end user easier to know which functions or assumptions are being used int each of the calculations we could use the `logging` module to log debug messages. This way the user can set the level to `logging.DEBUG` to be able to visualize the assumptions and then when feeling confortable with the code is just as easy as removing the logger configuration or elevating the logging level. We know that this may lead to a more complicated development structure, but it is just an idea as may be we do not need this or there are smoother approaches.\n",
+    "\n",
+    "```python\n",
+    "import logging\n",
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)\n",
+    "logging.debug(\"k1=1.2 because of...\")\n",
+    "```"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -722,7 +736,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,

From 36cc8d3da0dc4aaf2fc5cd8b21a51479ad4b606d Mon Sep 17 00:00:00 2001
From: Tu Nombre <tuemail@example.com>
Date: Mon, 29 Jul 2024 10:29:40 +0200
Subject: [PATCH 27/33] minor changes

---
 EX1_part1.ipynb         |  51 ++++++++--------
 EX2_part1.ipynb         | 132 ++++++++++++++++++++++++++--------------
 Issues.ipynb            |  10 ++-
 SLS_section_response.py |   3 +
 4 files changed, 124 insertions(+), 72 deletions(-)

diff --git a/EX1_part1.ipynb b/EX1_part1.ipynb
index fbd92eaa..b362c537 100644
--- a/EX1_part1.ipynb
+++ b/EX1_part1.ipynb
@@ -4,15 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Instantaneous and delayed deflections on a concrete and composite beam. Obtaining camber of the beam\n",
+    "Instantaneous and delayed deflections on a concrete and composite beam - Obtaining camber of the beam\n",
     "\n",
     "A concrete beam with a span of L=6.75 m is considered, simply supported and subjected to the following uniformly distributed loads:\n",
     "\n",
-    "- self weight: qpp=12.2 kN/m\n",
-    "- Dead load: qcm=5.2 kN/m\n",
-    "- Live load: qsc=11.7 kN/m\n",
+    "- self weight: $q_{pp}$=12.2 kN/m\n",
+    "- Dead load: $q_{cm}$=5.2 kN/m\n",
+    "- Live load: $q_{sc}$=11.7 kN/m\n",
     "\n",
-    "It is considered that 30% of the live load is quasi-permanent (ψ2=0.3)\n",
+    "It is considered that 30% of the live load is quasi-permanent ($ψ_2$=0.3)\n",
     "\n",
     "![beam](EX1&2Beam.png)\n",
     "\n",
@@ -123,10 +123,11 @@
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
-    "i11_1 = sec.gross_properties.i11\n",
-    "cz_1=sec.gross_properties.cz\n",
-    "print(f\"Reinf. area = {round(sec.gross_properties.area_reinforcement/(10**2))} cm2\")\n",
-    "print(f\"Sect. area = {round(sec.gross_properties.area/(10**2))} cm2\")\n",
+    "gp = sec.gross_properties\n",
+    "i11_1 = gp.i11\n",
+    "cz_1=gp.cz\n",
+    "print(f\"Reinf. area = {round(gp.area_reinforcement/(10**2))} cm2\")\n",
+    "print(f\"Sect. area = {round(gp.area/(10**2))} cm2\")\n",
     "print(f\"I11_1 = {round(i11_1/(10**4))} cm4\") \n",
     "print(f\"cz_1 = {round(cz_1/(10),2)} cm\") \n",
     "\n",
@@ -149,8 +150,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "eps_z0 -2.1936412900686266e-05\n",
-      "x (mm) from z=0: 219.4\n",
+      "eps_z0 =  -2.19e-05\n",
+      "x (mm) from z=0 =  219.4\n",
       "I11_2 = 367781 cm4\n",
       "cz_2 = 21.91 cm\n"
      ]
@@ -168,13 +169,13 @@
    ],
    "source": [
     "# Get the cracked cross section\n",
-    "curv = 1e-7 # Curvature which produces elastic stresses in the most compressed fibre of the concrete section\n",
+    "curv = 1e-7 # Small curvature which produces 'elastic' stresses in the most compressed fibre of the concrete section\n",
     "eps =  sec.section_calculator.find_equilibrium_fixed_curvature(sec.geometry,0,curv,0)[0]\n",
     "x=-eps/curv # Distance from the bottom to the neutral fibre\n",
-    "print(\"eps_z0\",eps)\n",
-    "print(\"x (mm) from z=0:\",round(x,1))\n",
+    "print(\"eps_z0 = \",round(eps,7))\n",
+    "print(\"x (mm) from z=0 = \",round(x,1))\n",
     "\n",
-    "# 2) Create the cracked section\n",
+    "# 2) Create the cracked section (sec2)\n",
     "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
     "geo2 = SurfaceGeometry(poly2, concrete)\n",
     "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcemnet, n=3)\n",
@@ -196,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -247,7 +248,6 @@
     }
    ],
    "source": [
-    "# A concrete material failing at fctm is used for the cracking moment calculation.\n",
     "concrete = ConcreteEC2_2004(25)\n",
     "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
     "\n",
@@ -258,6 +258,7 @@
     "sec = GenericSection(geo)\n",
     "\n",
     "from SLS_section_response import calculate_cracking_moment\n",
+    "# A concrete material failing at fctm is used for the cracking moment calculation.\n",
     "m_cracking,_ = calculate_cracking_moment(sec,plot=True)\n",
     "m_cracking = m_cracking/1e6 #mkN\n",
     "print('m_cracking (mkN) = ', round(m_cracking,2))\n",
@@ -283,22 +284,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\cmg\\AppData\\Local\\Temp\\ipykernel_29892\\3607814390.py:11: RuntimeWarning: divide by zero encountered in divide\n",
-      "  zeta = 1-0.5*(m_cracking/m_x)**2\n"
+      "Maximum deflection [mm] = 16.89\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Maximum deflection [mm] = 16.89\n"
+      "C:\\Users\\cmg\\AppData\\Local\\Temp\\ipykernel_14064\\3294689468.py:11: RuntimeWarning: divide by zero encountered in divide\n",
+      "  zeta = 1-0.5*(m_cracking/m_x)**2\n"
      ]
     }
    ],
@@ -354,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb
index 945991f7..cfa6e5d3 100644
--- a/EX2_part1.ipynb
+++ b/EX2_part1.ipynb
@@ -8,6 +8,7 @@
     "- Self weight: $q_{sw}$=12.2 kN/m\n",
     "- Dead load: $q_{dl}$=5.2 kN/m\n",
     "- Live load: $q_{ll}$=11.7 kN/m\n",
+    "\n",
     "It is considered that 30% of the live load is quasi-permanent (ψ2=0.3)\n",
     "\n",
     "![beam](EX1&2Beam.png)\n",
@@ -47,7 +48,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calculation of  $\\sigma_s$ for $\\epsilon_{sm} - \\epsilon_{cm}$"
+    "Define first the quasipermanent moment and an instance of the beam section"
    ]
   },
   {
@@ -56,11 +57,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " sigma_s (MPa) = 163\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -84,40 +88,78 @@
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
-    "\n",
-    "\n",
-    "eps1,chiy = calculate_strain_profile(sec,0,m_cuasip*1e6)\n",
-    "#draw_section_response(sec,eps1,chiy)\n",
-    "# get stress in a reinforced bar\n",
-    "sigma_s = get_stress_point (sec,50,450,eps1,chiy,0)\n",
-    "print(' sigma_s (MPa) =',round(sigma_s))\n"
+    "draw_section(sec,'Cross section EX2')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Section 7.3.4. Calculation of crack widths"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Section 7.3.4. Calculation of crack widths\n",
-    "\n",
     "1.) Calculation of $\\epsilon_{sm} - \\epsilon_{cm}$:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculation of  $\\sigma_s$"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "eps_sm_eps_cm = 0.00062\n",
-      " alfa_e = 22.239\n",
-      " hc_eff (mm) = 89\n",
-      " rho_p = 0.0602\n",
-      " kt = 0.4\n",
-      " sigma_s (MPa) = 163\n",
-      " fct_eff (MPa) = 2.56\n"
+      "z neutral axis = 185.28\n",
+      " sigma_s (MPa) = 163\n"
+     ]
+    }
+   ],
+   "source": [
+    "eps1,chiy = calculate_strain_profile(sec,0,m_cuasip*1e6)\n",
+    "draw_section_response(sec,eps1,chiy)\n",
+    "# get stress in a reinforced bar\n",
+    "sigma_s = get_stress_point (sec,50,450,eps1,chiy,0)\n",
+    "print(' sigma_s (MPa) =',round(sigma_s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eps_sm_eps_cm = 0.00062 , where:\n",
+      " - alfa_e = 22.239\n",
+      " - hc_eff (mm) = 89\n",
+      " - rho_p = 0.0602\n",
+      " - kt = 0.4\n",
+      " - sigma_s (MPa) = 163\n",
+      " - fct_eff (MPa) = 2.56\n"
      ]
     }
    ],
@@ -129,13 +171,13 @@
     "kt = _section_7_3_crack_control.kt('long')\n",
     "fct_eff = concrete.fctm\n",
     "eps_sm_eps_cm =_section_7_3_crack_control.eps_sm_eps_cm(sigma_s,alfa_e,rho_p,kt,fct_eff,200000)\n",
-    "print('eps_sm_eps_cm =',round(eps_sm_eps_cm, 5))\n",
-    "print(' alfa_e =',round(alfa_e,3))\n",
-    "print(' hc_eff (mm) =',round(hc_eff))\n",
-    "print(' rho_p =',round(rho_p,5))\n",
-    "print(' kt =',kt)\n",
-    "print(' sigma_s (MPa) =',round(sigma_s))\n",
-    "print(' fct_eff (MPa) =',round(fct_eff,2))\n",
+    "print('eps_sm_eps_cm =',round(eps_sm_eps_cm, 5),  ', where:')\n",
+    "print(' - alfa_e =',round(alfa_e,3))\n",
+    "print(' - hc_eff (mm) =',round(hc_eff))\n",
+    "print(' - rho_p =',round(rho_p,5))\n",
+    "print(' - kt =',kt)\n",
+    "print(' - sigma_s (MPa) =',round(sigma_s))\n",
+    "print(' - fct_eff (MPa) =',round(fct_eff,2))\n",
     "\n"
    ]
   },
@@ -148,20 +190,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sr_max (mm) 192\n",
-      " c (mm) 40.0\n",
-      " fi (mm) 20\n",
-      " k1 0.8\n",
-      " k2 0.5\n",
-      " k3 3.4\n",
-      " k4 0.425\n"
+      "sr_max (mm) 192 , where:\n",
+      " - c (mm) 40.0\n",
+      " - fi (mm) 20\n",
+      " - k1 0.8\n",
+      " - k2 0.5\n",
+      " - k3 3.4\n",
+      " - k4 0.425\n"
      ]
     }
    ],
@@ -174,13 +216,13 @@
     "c=50-20/2\n",
     "fi_rebars=20\n",
     "sr_max = _section_7_3_crack_control.sr_max_close(c,fi_rebars,rho_p,k1,k2,k3,k4)\n",
-    "print('sr_max (mm)',round(sr_max))\n",
-    "print(' c (mm)',c)\n",
-    "print(' fi (mm)',fi_rebars)\n",
-    "print(' k1',k1)\n",
-    "print(' k2',k2)\n",
-    "print(' k3',k3)\n",
-    "print(' k4',k4)\n",
+    "print('sr_max (mm)',round(sr_max), ', where:')\n",
+    "print(' - c (mm)',c)\n",
+    "print(' - fi (mm)',fi_rebars)\n",
+    "print(' - k1',k1)\n",
+    "print(' - k2',k2)\n",
+    "print(' - k3',k3)\n",
+    "print(' - k4',k4)\n",
     "\n"
    ]
   },
@@ -193,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
diff --git a/Issues.ipynb b/Issues.ipynb
index 3c27d1b6..d568d1d3 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -242,7 +242,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "5. By default (theta=0) \"calculate_bending_strength\" gets negative value with \"theta=0\" correspondig with My_min and positive if theta = pi/2 (My_max). It seem strange to me. At least it should be documented. "
+    "5. Sign criteria in section class:\n",
+    "\n",
+    "I am assuming z-axis downward. By default (theta=0) \"calculate_bending_strength\" gets negative value if \"theta=0\" correspondig with My_min; and positive if theta = pi/2 (My_max). It seem strange to me. At least it should be documented. "
    ]
   },
   {
@@ -691,7 +693,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n"
+    "13. CompoundGeometry class contains:\n",
+    "- geometries & point_geometries (SurfaceGeometry and Point+diameter)\n",
+    "- geom (Multipoligon)\n",
+    "\n",
+    "What is the purpose of 'geom' since SurfaceGeometry contains attribute 'poly' (Polygon) ?\n"
    ]
   },
   {
diff --git a/SLS_section_response.py b/SLS_section_response.py
index d3b6ca49..e92858cf 100644
--- a/SLS_section_response.py
+++ b/SLS_section_response.py
@@ -127,6 +127,9 @@ def interpolate(x1, x2, y1, y2, x):
 
 def calculate_cracking_moment(section: GenericSection, n=0, plot=False):
     """Calculate the cracking moment of a R.C section in N*mm.
+    A concrete material failing at fctm is used for the cracking moment
+    calculation. This method modify the constitutive law of concrete to reach
+    fctm in tension.
 
     Args:
         n [N]: Axial external load

From c8ce3fc0bd94ba251a6597cb54c8ee2832786628 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Mon, 29 Jul 2024 12:33:33 +0200
Subject: [PATCH 28/33] minor changes

---
 EX1_part1.ipynb | 95 +++++++++++++++++++++++++++++++++++++++++++------
 EX2_part1.ipynb | 63 +++++++-------------------------
 2 files changed, 98 insertions(+), 60 deletions(-)

diff --git a/EX1_part1.ipynb b/EX1_part1.ipynb
index d7a4c087..048d2977 100644
--- a/EX1_part1.ipynb
+++ b/EX1_part1.ipynb
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +192,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -232,10 +232,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z neutral axis = 265.78\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z neutral axis = 260.43\n",
+      "m_cracking (mkN) =  46.29\n",
+      "fctm (MPa) =  2.56\n"
+     ]
+    }
+   ],
    "source": [
     "concrete = ConcreteEC2_2004(25)\n",
     "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
@@ -273,9 +319,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum deflection [mm] = 16.89\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dgm\\AppData\\Local\\Temp\\ipykernel_41380\\3658154747.py:13: RuntimeWarning: divide by zero encountered in divide\n",
+      "  zeta = 1-0.5*(m_cracking/m_x)**2\n"
+     ]
+    }
+   ],
    "source": [
     "# DGM: I guess that this should be a part of a further development (like a BeamElement/LineElement class) if so...\n",
     "\n",
@@ -330,9 +392,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "S_I (mm3)=  241009\n",
+      "S_II (mm3)=  377817\n",
+      "I_I (mm4)=  5413898904\n",
+      "I_II (mm4)=  3677810527\n",
+      "Shrinkage deflection [mm] = 4.9\n",
+      "Total deflection [mm] = 21.8\n"
+     ]
+    }
+   ],
    "source": [
     "a_fi20 = math.pi * 20**2/4 # DGM: wouldnt it be useful to obtain reinf. areas from the generic section?\n",
     "a_fi12 = math.pi * 12**2/4\n",
diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb
index 28ee170a..68980b00 100644
--- a/EX2_part1.ipynb
+++ b/EX2_part1.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,12 +53,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -77,30 +77,18 @@
     "\n",
     "fi = 2.5\n",
     "\n",
-    "# The package does not compute the Inertia of homogeneus cross secction, so a \"concrete reinforfocement\" with reinforcement areas multiplied by Es/Ec is used.\n",
-    "#reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)   \n",
-    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=Ec, density=7850, ftk=550, epsuk=0.07)\n",
-    "n=200000/Ec_eff\n",
+    "# Mcuasip at midspan\n",
+    "m_cuasip = q_cuasip * long_beam**2/8\n",
     "\n",
     "# Define the cross section\n",
     "concrete = ConcreteEC2_2004(25)\n",
     "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12*math.sqrt(n), reinforcement, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcement, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
     "sec = GenericSection(geo)\n",
-    "\n",
-    "\n",
-    "# 2) Create the cracked section\n",
-    "poly2 = Polygon(((0, x), (350, x), (350, 0), (0, 0)))\n",
-    "geo2 = SurfaceGeometry(poly2, concrete)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 50), (300, 50),12*math.sqrt(n), reinforcement, n=3)\n",
-    "geo2 = add_reinforcement_line(geo2, (50, 450), (300, 450), 20*math.sqrt(n) , reinforcement, n=6)\n",
-    "sec2 = GenericSection(geo2)\n",
-    "Icr = sec2.gross_properties.i11\n",
-    "print(\"I of cracked section (mm4): \",round(Icr))\n",
-    "draw_section(sec2,\"cracked section\",math.sqrt(n))\n"
+    "draw_section(sec,'Cross section EX2')\n"
    ]
   },
   {
@@ -119,12 +107,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Ba9aswejRo/Gf//wHcrkcP//8M55//nm0adMGo0aNgqOjI86fP48zZ85g27ZtAID27dsDAN577z0EBwdDoVBg6NCh5cb45Zdfqu9x8u6770JPTw+//vor8vLy6uQeHgEBAXj77bcxffp0xMfHo2/fvtDX18elS5ewcuVKfP/993jppZewePFizJ8/Hy+++CKaNGmCzMxMLFiwACqVqkxj1bRpU3Tr1g1jxoxBXl4e5s6dC2tra3z00UfqMrNmzUJISAj8/f3xxhtvqIckNjc3L3Mvncr44osvsG/fPoSGhsLNzQ0pKSmYP38+nJ2d1b8mjhw5EitWrMA777yDPXv2oGvXrigqKsL58+exYsUKbNu2Tf2L9bO4ePEievfujSFDhsDb2xt6enpYs2YNkpOTNf4P2rdvj59//hlffvklmjZtCjs7O/XF3uVRqVT4+eefMXLkSLRr1w5Dhw6Fra0tbty4gU2bNqFr16748ccfK7X/qryfRFSz2DY9Hdsm3WmbAOC5557DkiVLYG5uDm9vb8TGxmLnzp2wtrZ+5rhJy9XpGJCk07Zs2SJef/114eXlJUxNTYVSqRRNmzYV48aNE8nJyWXK//XXX6Jbt27CxMREmJiYCC8vLxERESEuXLigUe7vv/8Wffr0EWZmZsLExET4+vqKefPmqZcXFhaKcePGCVtbWyGTyTSGJ8ZjQxILIcSxY8dEcHCwMDU1FcbGxqJnz54iJiZGo0zpMLWPDwlc3tCz5QkPDxcmJiYVLv/f//4n2rdvL4yMjISZmZnw8fERH330kUhMTFTHOGzYMOHq6ioMDAyEnZ2deO6558TRo0fV2ygdknjWrFni22+/FS4uLsLAwEB0795dnDhxosw+d+7cKbp27SqMjIyESqUSzz//vDh79qxGmdIhiR8farj09UhISBBCCLFr1y4xYMAA4eTkJJRKpXBychLDhg0TFy9e1FgvPz9ffPPNN6Jly5bCwMBAWFpaivbt24upU6eK9PT0J76GlXX37l0REREhvLy8hImJiTA3Nxd+fn5ixYoVGuWSkpJEaGioMDMzEwDUwxNX9F6X2rNnjwgODhbm5ubC0NBQNGnSRLz22mvq96Iy+6/M+0lEtYNt07/YNpXQ9bbp/v37YtSoUcLGxkaYmpqK4OBgcf78eeHm5ibCw8PV5SoaLr9ly5ZlthkeHi7c3NxqoupUi2RC8EpAIm107do1eHh4YNasWfjPf/4jdThERERsm4hqEa8xIyIiIiIikhgTMyIiIiIiIokxMSMiIiIiIpIYrzEjIiIiIiKSGM+YERERERERSYyJGRERERERkcR4g2kAxcXFSExMhJmZGWQymdThEBE1GEIIZGZmwsnJCXI5fyssxXaJiEg6UrVNTMwAJCYmwsXFReowiIgarJs3b8LZ2VnqMLQG2yUiIunVddvExAyAmZkZgJIXX6VSSRwNEVHDkZGRARcXF/VxmEqwXSIiko5UbRMTM0DdTUSlUrEBJCKSALvraWK7REQkvbpum9ihn4iIiIiISGJMzIiIiIiIiCTGxIyIiIiIiEhiTMyIiIiIiIgkxsSMiIiIiIhIYkzMiIiIiIiIJMbEjIiIiIiISGJMzIiIiIiIiCTGxIyIiIiIiEhiTMyIqF7JysrC2LFj4ezsDCMjI3h7e+OXX3554jpnzpzB4MGD4e7uDplMhrlz55YpM2XKFMhkMo2Hl5dXLdWCiIiIapoQAp9//jkcHR1hZGSEoKAgXLp06YnrPK39v3btWpnlpY+VK1dWKb56k5j99NNPcHd3h6GhIfz8/HD48GGpQyIiCUyYMAFbt25FZGQkzp07h/Hjx2Ps2LFYv359hevk5OSgcePGmDFjBhwcHCos17JlS9y+fVv9+Pvvv2ujClSPsG0iItIeM2fOxA8//IBffvkFhw4dgomJCYKDg5Gbm/vE9Z7U/ru4uGgsu337NqZOnQpTU1OEhIRUKb56kZj9+eefmDBhAiZPnoxjx46hdevWCA4ORkpKitShEVEdi4mJQXh4OAIDA+Hu7o7Ro0ejdevWT/xC3LFjR8yaNQtDhw6FgYFBheX09PTg4OCgftjY2NRGFaieYNtERKQ9hBCYO3cuPv30UwwYMAC+vr74448/kJiYiLVr1z5x3Se1/wqFQmOZg4MD1qxZgyFDhsDU1LRKMdaLxGzOnDl46623MGrUKHW3JWNjY/z+++9Sh0ZEdaxLly5Yv349bt26BSEE9uzZg4sXL6Jv377PvO1Lly7ByckJjRs3RlhYGG7cuFEDEVN9xbaJiEgaDx48KDMvISEBSUlJCAoKUs8zNzeHn58fYmNjn7i9qrT/cXFxiI+PxxtvvFHluHU+McvPz0dcXJzGiyyXyxEUFFThi5yXl4eMjAyNBxHpjs2bN+OXX37B2bNnyyybN28evL294ezsDKVSiX79+uGnn35Cjx49nmmffn5+WLRoEbZu3Yqff/4ZCQkJ6N69OzIzM59pu1Q/VbVtYrtERFQzkpOTMW/ePBw5ckRjflJSEgDA3t5eY769vb16WXmq2v7/9ttvaNGiBbp06VLl2PWqvIaWuXv3LoqKisp9kc+fP1/uOtOnT8fUqVPrIjwiqqKcVatQnJ7+xDIpd+4gOS8Pa9euRadOndTzt2zZgkOHDuHgwYNYv3493NzcsG/fPkRERMDJyUnjS3JVPdpP3NfXF35+fnBzc8OKFSuq9asY1W9VbZvYLhHVPVFUhOxFi6QOg2pQWmEholJS8KC4GAsWLEDPnj3VyzZt2lStbVal/X/w4AGWLVuGzz77rFr70vnErDomTZqECRMmqKczMjLg4uIiYUREVKro9m0U37v3xDKlHRT69euHIUOGqOc3atQIvXv3xpo1axAaGgqg5CAaHx+P2bNnP1Ni9jgLCws0b94cly9frrFtUsPFdolIAsXFKPrnH6mjoBqSDWDVw792dnYYM2YMPvroI/XyvLw8ACVn1BwdHdXzk5OT0aZNm0rv50nt/6pVq5CTk4NXX321WnXQ+cTMxsYGCoUCycnJGvOTk5MrHF3NwMDgiRf4E5F2y3v418bGBq6urur5GRkZKCgogFyu2UtboVCguLi4RmPIysrClStXMHLkyBrdLtUPVW2b2C4REVVfHoC1ANJRkjiNGDECZmZmsLOzU5cRQsDBwQG7du1SJ2IZGRk4dOgQxowZU+l9Pan9/+233/DCCy/A1ta2WvXQ+WvMlEol2rdvj127dqnnFRcXY9euXfD395cwMiKqLaWD2hoZGWnMV6lUCAgIwMSJExEdHY2EhAQsWrQIf/zxB1588UV1uVdffRWTJk1ST+fn5yM+Ph7x8fHIz8/HrVu3EB8fr/Fr2H/+8x/s3bsX165dQ0xMDF588UUoFAoMGzasVutKuoltExFR3SgEsAHAHQAmJibqpOxxMpkM48ePx5dffon169fj1KlTePXVV+Hk5ISBAweqy/Xu3Rv/+9//1NOVbf8vX76Mffv24c0336x2XXT+jBlQct+i8PBwdOjQAZ06dcLcuXORnZ2NUaNGSR0aEdWwYvx7xuzxxAwAoqKiMGnSJISFheHevXtwc3PDV199hXfeeUdd5saNGxpn1RITE9G2bVv19OzZszF79mwEBAQgOjoaAPDPP/9g2LBhSE1Nha2tLbp164aDBw9W+1cxqv/YNhER1a5iAFsA/IOSH8TCwsJgbW1dYfmPPvoI2dnZGD16NNLS0tCtWzds3boVhoaG6jJXrlxBamqqerqy7f/vv/8OZ2fnZxoFWiaEENVeW4v8+OOPmDVrFpKSktCmTRv88MMP8PPzq9S6GRkZMDc3R3p6OlQqVS1HSkRPkjlv3hOvMcsF8MvD559++ikUCkWdxEW1o74ff6vbNtX314VIG4iCAmR8/bXUYVA1CQC7AJxGySULYWFh8PDwqJFtS3UMrhdnzABg7NixGDt2rNRhEFEtK+3GqK+vz6SMtB7bJiKi2hGDkqRMJpNh8ODBNZaUSUnnrzEjooalouvLiIiIqGE4BqD0LmWhoaFo0aKFlOHUGCZmRKRTnnR9GREREdVv5wHse/i8V69eaN++vZTh1CgmZkSkU0rPmD16oS4RERHVfwkAtj987ufnh27dukkZTo1jYkZEOoVdGYmIiBqeRACbUDISo4+PD4KDgyGTySSOqmYxMSMincKujERERA1LKoB1KLlnWdOmTTFgwIB6l5QBTMyISMewKyMREVHDkQFgDUp+mHV2dsbLL79cb0dlZmJGRDqFXRmJiIgahhyUJGVZAGxtbTF8+HAolUqJo6o9TMyISKcwMSMiIqr/8lHSffE+AJVKhREjRtT7tp+JGRHpFF5jRkREVL8VAtgAIBkl7f3IkSOhUqkkjqr2MTEjIp3Ca8yIiIjqr2IA2wDcBKCvr4+wsDDY2NhIHFXdYGJGRDqFZ8yIiIjqJwEgGsAlAHK5HK+88goaNWokbVB1iIkZEekUXmNGRERUPx0EcPLh80GDBqFJkyZShlPnmJgRkc4ofPgAmJgRERHVJycAHHr4vH///mjZsqWU4UiCiRkR6YzSbowymQwGBgaSxkJEREQ14wKAPQ+fBwQEoGPHjlKGIxkmZkSkMx4d+EMmk0kaCxERET276ygZ7AMAOnTogICAACnDkRQTMyLSGby+jIiIqP5IArARJSMxtmzZEiEhIQ36h1cmZkSkM0q7MnKofCIiIt12D8BaAAUAGjdujIEDB0Iub9ipScOuPRHpFJ4xIyIi0n2ZANagpF13cnLCkCFDoKenJ3FU0mNiRkQ6g4kZERGRbnuAkqQsE4C1tTWGDx/OAb0eYmJGRDqDXRmJiIh0VwGAdSjpxmhmZoYRI0bAxMRE4qi0BxMzItIZPGNGRESkm4pQMtBHEkp+YB0xYgQsLCykDUrLMDEjIp3BxIyIiEj3CADbUTI0vr6+PoYPHw47OzuJo9I+vMqOiHRGaVfG06dPIzExETKZTOMBoMy86pThtiou87izZ8/C2NgY7u7uNfxuExFRfVAIYCWAZAByuRwvv/wyXFxcJI5KOzExIyKdoXj4NzExEYmJiZLG0lA9mqwVFxer50dERMDGxkajbFZWFj755BOsXbsWqamp8PDwwHvvvYd33nlHXeb9998HANjb28PU1BRdunTBN998Ay8vr3L3X1BQgE8//RSbN2/G1atXYW5ujqCgIMyYMQNOTk41XV0iInpGC/DvD6sDBgxAs2bN1MuEEJg8eTIWLFiAtLQ0dO3aFT///LNGmSeZMWMGJk2ahPfffx9z584FANy7dw+TJ0/G9u3bcePGDdja2mLgwIGYNm0azM3Na7ZyNYyJGRFpleJ79ypc1hOAK0puRCkezhOPPR6fVx/K1OZ2q0oIofG3VHp6epnEbMKECdi9ezciIyPh7u6O7du3491334WTkxNeeOEFAECbNm0AAIcPH0ZBQQGmTJmCvn37IiEhAQqFAo/LycnBsWPH8Nlnn6F169a4f/8+3n//fbzwwgs4evRoNWtFRA1RUVaW1CHUe7/j36TM1NQUvr6+GstnzpyJH374AYsXL4aHhwc+++wzBAcH4+zZs08d6OvIkSP49ddfy2yz9Mfb2bNnw9vbG9evX8c777yDxMRErFq1qgZrV/Nk4vHWtQHKyMiAubk50tPToVKppA6HqEFLnzpV6hAajOomeOsApDyynZEjR6Jx48Zltt+qVSu88sor+Oyzz9Tz2rdvj5CQEHz55ZcAyh5/T548idatW+Py5cto0qRJpepx5MgRdOrUCdevX4erq2ul1tF2bJeIal9haiqyf/xR6jDqrWX4t61QKpWYNGmSxnIhBJycnPDhhx/iP//5D4CSH/ns7e2xaNEiDB06tMJtZ2VloV27dpg/fz6+/PJLtGnTRn3GrDwrV67EiBEjkJ2dXan7pUl1DObgH0REDZTs4UOOkm6ieg8f+gCUAAwAGD58GAEwRsl1Ao8mZREREeUmZQDQpUsXrF+/Hrdu3YIQAnv27MHFixfRt2/fcstnZ2dj4cKF8PDwqNL1B+np6ZDJZBzdi4hISzz6A55cLsfEiRPLlElISEBSUhKCgoLU88zNzeHn54fY2Ngnbj8iIgKhoaEa6z5JaYKl7Tex1u7oiIhIKxQC+D/8OzKmXC7Hf/7znyeOkDlv3jyMHj0azs7O0NPTg1wux4IFC9CjR48yZZ2cnJCdnQ1PT0/s2LEDSqWyUnHl5ubi448/xrBhw3hmiYhIC+wCkPDwuUwmw8SJE8tNiJKSkgCUXGP8KHt7e/Wy8kRFReHYsWM4cuRIpeK5e/cupk2bhtGjR1eqvJR4xoyIiJ6oEMDP+Dcp09fXx6RJkzSSsqVLl8LU1FT92L9/P+bNm4eDBw9i/fr1iIuLw7fffouIiAjs3LmzzD7279+PvXv3onnz5hgyZAhyc3PLlHlcQUEBhgwZAiEEfv7555qpLBERVdthAKcemX7vvffU14o93k4UFBRUefs3b97E+++/j6VLlz71GjSgpEtiaGgovL29MWXKlCrvr67xjBkREVUoC8Bv+PdaMxMTE/W1AI964YUX4Ofnp55u1KgRevfujTVr1iA0NBQA4Ovri/j4eMyePbtM95MmTZpApVKhc+fOsLS0xJo1azBs2LAK4ypNyq5fv47du3fzbBkRkcTOAoh5ZPrNN9/U6GL+eDuRl1cyLEhycjIcHR3V85OTk9UDQz0uLi4OKSkpaNeunXpeUVER9u3bhx9//BF5eXnqgaMyMzPRr18/mJmZYc2aNdDX13/mOtY2JmZERFrsSYNyVGZZZQf4KG+dTABbHonFxsYGERER5cZpZmYGMzMz9XRGRgYKCgogl2t2zFAoFBrD7JeprxAQQqgb7PKUJmWXLl3Cnj17YG1tXWFZIiKqff+g5AbSpV5++WU0atRIo8zj7YQQAg4ODti1a5c6EcvIyMChQ4cwZsyYcvfTu3dvnDp1SmPeqFGj4OXlhY8//lidlGVkZCA4OBgGBgZYv359pc6uaQMmZkSkE/IBnADwANVPSGpjnapurzrltYG7uzvCw8MrXV6lUiEgIAATJ06EkZER3NzcsHfvXvzxxx+YM2cOAODq1atYvHgxgJLuKenp6ZgxYwaMjIzQv39/9ba8vLwwffp0vPjiiygoKMBLL72EY8eOYePGjSgqKlJfi2BlZVXpa9OIiKhm3APw6CD0wcHB8Pb2fup6MpkM48ePx5dffolmzZqph8t3cnLCwIED1eV69+6NF198EWPHjoWZmRlatWqlsR0TExNYW1ur52dkZKBv377IyclBZGQkMjIykJGRAQCwtbUt91Ys2oKJGRFpFz09oLCwzOwLAA7UfTQ6R/bIAyhp+MrMK2e+7JH5AJBRVKRODFu3bq3RSFZWVFQUJk2ahLCwMNy7dw9ubm746quv1DeYNjQ0VI+81bZtW9jb26NHjx6IiYmBnZ2dejsXLlxAeno6AODWrVtYv349AJTp6rJnzx4EBgZWOU4iaqB05CyKNitCybD4pTp37ozOnTtXev2PPvoI2dnZGD16NNLS0tCtWzds3bpV4wzXlStXcPfu3Upv89ixYzh06BAAoGnTphrLEhIS4O7uXult1TXexwy8XwyRNsmcN6/cm0zHoOSiYkdHRzRu3LgksajDB4A632d1YqgpWVlZmDdvHvr06YMOHTrU2HYfx+Nv+fi6ENU+UVCAjK+/ljoMnSVQ0n3x3MPpFi1aYMiQIRJGVHOkOgbzjBkR6YQHD/82a9YMPXv2lDSWhsDU1LTMzUCJiIhK/Y2SpEwmk+GVV16Bp6en1CHpPA6XT0Q6oXTwdGNjY0njICIiauiOAoh7+PyFF15gUlZDmJgRkU4oPWPGxIyIiEg6Z1BytgwA+vTpU+HQ9lR1TMyISCfwjBkREZG0rgDY+fB5ly5d0KVLFynDqXeYmBGRTuAZMyIiIun8A2AzSgb9aNOmDYKCgiSOqP5hYkZEWk+AZ8yIiIikcgfAepQMj+/p6Ynnn3++RkcCphJMzIhI6xWgpDEAACMjIylDISIialDSAawBkA/A1dUVgwcPhlzOFKI28FUlIq1X2o1RT08P+vr6ksZCRETUUGQDWA0gB4C9vT2GDRvGdrgWMTEjIq1X2o3RyMiIXSeIiIjqQB6AtSg5Y2ZhYYGwsDAYGhpKG1Q9x8SMiLQeB/4gIiKqO4UouabsDgATExOMHDkSZmZmEkdV/zExIyKtx4E/iIiI6kYxgC0AbgFQKpUICwuDlZWVxFE1DEzMiEjr8YwZERFR7RMAdqHkfmUKhQLDhg2Do6OjxFE1HEzMiEjrPXqNGREREdWOGABnAMhkMgwePBju7u4SR9SwMDEjIq3HM2ZERES16xiAIw+fP/fcc2jRooWU4TRITMyISOsxMSMiIqo95wDse/i8V69eaNeunZThNFhMzIhI63HwDyIiotqRAGDHw+d+fn7o1q2blOE0aEzMiEjr8YwZERFRzUsEsAklIzH6+voiODiY9wuVEBMzItJ6pYkZB/8gIiKqGXcBrEPJPcuaNm2KF154gUmZxJiYEZFWE2BXRiIiopqUAWANgDwAzs7OePnll6FQKCSOipiYEZFWKwBQ9PA5EzMiIqJnk4OSpCwbgK2tLYYPHw6lUilxVAQwMSMiLVfajVGhUEBfX1/SWIiIiHRZPoC1AO4DMDc3x4gRI3iZgBZhYkZEWu3Rbozs+05ERFQ9hQA2AEhBSZs6YsQIqFQqiaOiRzExIyKtxhEZiYiInk0xgG0AbgJQKpUICwuDjY2NxFHR45iYEZFW48AfRERE1ScA7AFwCYBcLscrr7wCJycniaOi8jAxIyKtxjNmRERE1XcQwKmHzwcNGoTGjRtLGQ49gaSJ2b59+/D888/DyckJMpkMa9eu1VguhMDnn38OR0dHGBkZISgoCJcuXdIoc+/ePYSFhUGlUsHCwgJvvPEGsrKy6rAWRFSbSs+Y8eJkqitsm4iovogHcOjh8/79+6Nly5YSRkNPI2lilp2djdatW+Onn34qd/nMmTPxww8/4JdffsGhQ4dgYmKC4OBg5ObmqsuEhYXhzJkz2LFjBzZu3Ih9+/Zh9OjRdVUFIqplPGNGdY1tExHVBxcARD98HhgYiI4dO0oYDVWGnpQ7DwkJQUhISLnLhBCYO3cuPv30UwwYMAAA8Mcff8De3h5r167F0KFDce7cOWzduhVHjhxBhw4dAADz5s1D//79MXv2bPafJaoHmJhRXWPbRES67jpKBvsAgI4dO6JHjx5ShkOVpLXXmCUkJCApKQlBQUHqeebm5vDz80NsbCwAIDY2FhYWFuqGDwCCgoIgl8tx6NChMtsslZeXh4yMDI0HEWkndmUkbVJbbRPbJSKqKbcBbETJSIwtW7ZESEgIbzejI7Q2MUtKSgIA2Nvba8y3t7dXL0tKSoKdnZ3Gcj09PVhZWanLlGf69OkwNzdXP1xcXGo4eiKqKTxjRtqkttomtktEVBPuAVgHoABAkyZN8OKLLzIp0yFam5jVpkmTJiE9PV39uHnzptQhEVEFOFw+NQRsl4joWWUCWIOSdrNRo0YYMmQIFAqFxFFRVWhtYubg4AAASE5O1pifnJysXubg4ICUlBSN5YWFhbh37566THkMDAygUqk0HkSkfQR4xoy0S221TWyXiOhZPEBJUpYJwNraGsOHD4dSqZQ4KqoqrU3MPDw84ODggF27dqnnZWRk4NChQ/D39wcA+Pv7Iy0tDXFxceoyu3fvRnFxMfz8/Oo8ZiKqWfkAih4+Z2JG2oBtExFpm3yUdF+8B0ClUmHkyJFsM3WUpKMyZmVl4fLly+rphIQExMfHw8rKCq6urhg/fjy+/PJLNGvWDB4eHvjss8/g5OSEgQMHAgBatGiBfv364a233sIvv/yCgoICjB07FkOHDuWoV0T1QOnZMn19ff7yR3WGbRMR6YoiAJsAJKFkkKwRI0bA3Nxc4qiouiRNzI4ePYqePXuqpydMmAAACA8Px6JFi/DRRx8hOzsbo0ePRlpaGrp164atW7fC0NBQvc7SpUsxduxY9O7dG3K5HIMHD8YPP/xQ53UhoprHbowkBbZNRKQLBIDtKBkaX19fH8OHD4etra3EUdGzkAkhhNRBSC0jIwPm5uZIT09nv34iiWXOm4fie/cAAFcAbADg5OSEt956S9K4qHbw+Fs+vi5EtU8UFCDj66+lDqNaBIC9AOIByOVyDBs2DE2bNpU2qHpEqmOw1l5jRkRUesbMxMRE0jiIiIi0yWGUJGUAMHDgQCZl9QQTMyLSWuzKSEREpOkkgNiHz/v16wcfHx8pw6EaxMSMiLRWzsO/PGNGREQEXAKw++Hz7t27c6TXeoaJGRFpLZ4xIyIiKnETwNaHz9u1a6cxSBHVD0zMiEhr8YwZERERkAxgPUqGx2/RogVCQ0Mhk8kkjopqGhMzItJaHPyDiIgauvsA1gIoAODu7o5BgwZBLudX+PqI7yoRaS12ZSQiooYsC8BqlLSHjo6OGDp0KPT0JL0NMdUiJmZEpJUEeMaMiIgarlwAawBkArCyskJYWBgMDAwkjopqExMzItJK+SjpSw/wjBkRETUsBSi5piwVgKmpKUaMGMEfKRsAJmZEpJVKz5bp6+tDqVRKGgsREVFdKQKwGUAiAENDQ4wYMQKWlpYSR0V1gYkZEWml0hEZebaMiIgaCgFgJ4AEAHp6ehg2bBjs7e0ljorqChMzItJKvL6MiIgaEgFgP4BzAGQyGV5++WW4urpKHBXVJSZmRKSVmJgREVFDchTAsYfPBwwYgObNm0sZDkmAiRkRaSV2ZSQioobiNIADD5/36dMHrVu3ljIckggTMyLSSjxjRkREDcFlALsePu/atSu6dOkiZTgkISZmRKSVeHNpIiKq7/4BsAUl15e1adMGvXv3ljgikhITMyLSSqVdGXnGjIiI6qM7KLlXWREAT09PPP/885DJZBJHRVJiYkZEWoldGYmIqL5KA7AGQD4ANzc3DB48GHI5v5Y3dPwPICKtxK6MRERUH2WjJCnLAWBvb4+hQ4dCX19f4qhIGzAxIyKtI8AzZkREVP/koSQpSwdgaWmJESNGwNDQUOKoSFswMSMirZOPkj73ABMzIiKqHwpRck3ZXZS0bSNGjICpqanEUZE2YWJGRFqn9GyZvr4+u3cQEZHOKwawGcAtAAYGBhgxYgSsrKwkjoq0DRMzItI6HJGRiIjqC4GS+5RdBaBQKDB06FA4ODhIHBVpIyZmRKR1OPAHERHVFwcAnAEgk8nw0ksvwd3dXeKISFsxMSMircOBP4iIqD44BuDow+fPPfccvLy8pAyHtBwTMyLSOuzKSEREuu4sgH0Pn/fu3Rvt2rWTMhzSAUzMiEjrsCsjERHpsgQAOx4+79y5M7p27SplOKQjmJgRkdZhYkZERLoqEcAmlAz64evri759+0Imk0kcFekCJmZEpHXYlZGIiHTRXQDrUHLPsmbNmuGFF15gUkaVxsSMiLQOB/8gIiJdkw5gDYA8AC4uLnj55ZehUCgkjop0CRMzItI6TMyIiEiX5KAkKcsGYGtri2HDhkFfX1/iqEjXMDEjIq0ihFB3ZeQ1ZkREpO3yAKwFkAbA3NwcI0aMgJGRkaQxkW5iYkZEWiVfCBQ/fM4zZkREpM0KAWwEkIKSHxNHjhwJlUolcVSkq5iYEZFWyRECAKBUKtkNhIiItFYxgG0AbqKkzQoLC4O1tbXEUZEuY2JGRFrlQXHJ+TJ2YyQiIm0lAOwBcAmAQqHAK6+8AicnJ4mjIl3HxIyItErpGTN2YyQiIm11EMCph88HDRqExo0bSxkO1RNMzIhIq+Q8PGPGxIyIiLRRPIBDD5+HhobC29tbwmioPmFiRkRa5cHDM2bsykhERNrmPIDoh88DAwPRoUMHCaOh+oaJGRFpFZ4xIyIibXQNwPaHzzt27IgePXpIGA3VR0zMiEir8BozIiLSNrdRMix+MYBWrVohJCQEMplM4qiovmFiRkRahaMyEhGRNrkHYB1K7lnWpEkTDBw4kEkZ1QomZkSkVXjGjIiItEUmgNUAcgE0atQIQ4YMgUKhkDgqqq+YmBGRVnnAa8yIiEgLPEBJUpYFwMbGBsOHD4dSqZQ4KqrPmJgRkdYQQqhHZWRiRkREUskHsBbAfQAqlQojRoxgF3uqdUzMiEhr5ObmovjhczaAREQkhSKUDPSRDMDIyAgjRoyAubm5xFFRQ8DEjIi0RnZ2NgDAwMAAenp6EkdDREQNjQCwDcANAPr6+hg+fDhsbW0ljooaCiZmRKQ1ShMzni0jIqK6JlBy8+iLAORyOYYMGQJnZ2dpg6IGhYkZEWmNnJwcALy+jIiI6t5hACcePh84cCCaNm0qZTjUADExIyKtUXrGjIkZERHVpZMAYh8+79evH3x8fKQMhxooJmZEpDXYlZGIiOraJQC7Hz7v0aMH/Pz8pAyHGjAmZkSkNXjGjIiI6tINAFsfPm/fvj0CAwMljIYaOiZmRKQ1eI0ZERHVlcTbt7EBJcPje3t7o3///pDJZFKHRQ0YEzMi0ho8Y0ZERHUhNTUVy6KiUADAw8MDL774IuRyfi0mafE/kIi0Bq8xIyKi2paRkYElS5Yg58EDODo64pVXXuG9M0krMDEjIq2Rm5sLADAyMpI4EiIiqo8ePHiAyMhIpKenQ2lmjrCwMBgYGEgdFhEAJmZEpIXYx5+IiGpaQUEBli9fjjt37kDPyBhNAvuz6zxpFSZmRERERFSvFRUVYeXKlbh58ybk+kp4BPSH0tRM6rCINDAxIyIiIqJ6SwiB9evX49KlS5ApFHDv0Q+GFlZSh0VUBhMzIiIiIqqXhBDYvn07Tp48CchkcO3aBya2DlKHRVQuSROz6dOno2PHjjAzM4OdnR0GDhyICxcuaJTJzc1FREQErK2tYWpqisGDByM5OVmjzI0bNxAaGgpjY2PY2dlh4sSJKCwsrMuqEBFRPcB2iah+OXDgAA4ePAgAcO4UAJWTq8QREVVM0sRs7969iIiIwMGDB7Fjxw4UFBSgb9++6iGzAeCDDz7Ahg0bsHLlSuzduxeJiYkYNGiQenlRURFCQ0ORn5+PmJgYLF68GIsWLcLnn38uRZWIiEiHsV0iqj+OHTuGXbt2AQAc23aGpUdziSMiejKZEEJIHUSpO3fuwM7ODnv37kWPHj2Qnp4OW1tbLFu2DC+99BIA4Pz582jRogViY2PRuXNnbNmyBc899xwSExNhb28PAPjll1/w8ccf486dO1AqlU/db0ZGBszNzZGeng6VSlWrdSSiis2ZMweZmZkYPXo0HB0dpQ6H6oC2H3/ZLhHppvPnz2PFihUQQsC2RRs4tO5UpoxcBgxszraGypLqGKxV15ilp6cDAKysSi7IjIuLQ0FBAYKCgtRlvLy84OrqitjYWABAbGwsfHx81I0fAAQHByMjIwNnzpwpdz95eXnIyMjQeBARET2O7RKR7rl27RpWrVoFIQQsPTxh79tR6pCIKkVrErPi4mKMHz8eXbt2RatWrQAASUlJUCqVsLCw0Chrb2+PpKQkdZlHG7/S5aXLyjN9+nSYm5urHy4uLjVcGyIi0nVsl4h0z+3btxEVFYWioiKoGrmjUcfuvDcm6QytScwiIiJw+vRpREVF1fq+Jk2ahPT0dPXj5s2btb5PIiLSLWyXiHTLvXv3sHTpUuTl5cHE1hEuXXpBJtear7pET6UndQAAMHbsWGzcuBH79u2Ds7Ozer6DgwPy8/ORlpam8etkcnIyHBwc1GUOHz6ssb3S0bFKyzzOwMAABgYGNVwLIiKqL9guEemWzMxMREZGIjs7G4YW1nDrHgy5Qiu+5hJVmqQ/IwghMHbsWKxZswa7d++Gh4eHxvL27dtDX19fPaIOAFy4cAE3btyAv78/AMDf3x+nTp1CSkqKusyOHTugUqng7e1dNxUhIqJ6ge0Ske7Jzc3F0qVLcf/+fShNzOAeEAJFJQbZIdI2kv6UEBERgWXLlmHdunUwMzNT9703NzeHkZERzM3N8cYbb2DChAmwsrKCSqXCuHHj4O/vj86dOwMA+vbtC29vb4wcORIzZ85EUlISPv30U0RERPDXRyIiqhK2S0S6paCgAFFRUUhOToaeoRHcA0Ohb2QsdVhE1SJpYvbzzz8DAAIDAzXmL1y4EK+99hoA4LvvvoNcLsfgwYORl5eH4OBgzJ8/X11WoVBg48aNGDNmDPz9/WFiYoLw8HB88cUXdVUNIiKqJ9guEemO4uJi/PXXX7h+/Trk+vpwDwiBgRlvL0G6S6vuYyYV3i+GSDvwPmYND4+/5ePrQvRkQgisX78e8fHxkMkV8AjsDxO7qrUbvI8ZVYT3MSMiIiIiqoRdu3YhPj4ekMng2qV3lZMyIm3ExIyIiIiIdEZMTAwOHDgAAGjUsTtUzu7SBkRUQ5iYEREREZFOOHHiBHbs2AEAcGjdCVaNvSSOiKjmMDEjIiIiIq138eJFrFu3DgBg4+kLG6/WEkdEVLOYmBERERGRVrtx4wZWrlwJIQQs3JvBoY0fZDKZ1GER1SgmZkRERESktZKTk7F8+XIUFhbCzMkVzp0CmJRRvcTEjIiIiIi00v379xEZGYnc3FwY29jDtUsQZHJ+faX6if/ZRERERKR1srOzERkZiaysLBiYW8K9Rz/I9fSkDouo1jAxIyIiIiKtkpeXh6VLl+LevXvQNzaFR2B/KJQGUodFVKuYmBERERGR1igsLERUVBRu374NhYEhPAL7Q9/IROqwiGodEzMiIiIi0grFxcVYvXo1rl27BrmePjwCQmCgspA6LKI6wcSMiIiIiCQnhMCmTZtw7tw5yORyuHXvCyMrW6nDIqozTMyIiIiISHJ79uzBsWPHAAAu/r1gat9I4oiI6hYTMyIiIiKS1KFDh7B//34AgFOHbjB3aSxxRER1j4kZEREREUnm1KlT2Lp1KwDA3qcDrJt6SxwRkTSYmBERERGRJC5fvoy1a9cCAKybt4Ktd1tpAyKSEBMzIiIiIqpz//zzD1asWIHi4mKYuzaBY1t/yGQyqcMikgwTMyIiIiKqU3fu3MGyZctQUFAAUwdnOPsFMimjBo+JGRERERHVmfT0dERGRuLBgwcwsraDW7c+kCsUUodFJDkmZkRERERUJ3JycrBkyRJkZGTAQGUB9x79INfTlzosIq3AxIyIiIiIal1+fj6WLl2K1NRU6BubwD2gP/QMDKUOi0hrMDEjIiIiolpVVFSEP//8E4mJiVAoDeAeGAqlianUYRFpFSZmRERERFRrhBBYs2YNrl69CrmeHtwDQmCospA6LCKtw8SMiIiIiGqFEAJbtmzBmTNnIJPL4dq1L4yt7aQOi0grMTEjIiIiolqxb98+HDlyBADg3LknzBydJY6ISHsxMSMiIiKiGnfkyBFER0cDABzbdYGFaxNpAyLSckzMiIiIiKhGnTlzBps3bwYA2LVsB5vmrSSOiEj7VSsx69WrF6ZOnVpm/v3799GrV69nDoqIiKgq2C4RaY+rV69i9erVAACrpi1g16q9xBER6Qa96qwUHR2NU6dO4fjx41i6dClMTEwAlNyfYu/evTUaIBER0dOwXSLSDrdu3UJUVBSKi4th7tIYTu26QiaTSR0WkU6odlfGnTt3IikpCZ07d8a1a9dqMCQiIqKqY7tEJK27d+9i2bJlKCgogIl9Izh37gmZnFfNEFVWtT8tjo6O2Lt3L3x8fNCxY0f1xZ1ERERSYLtEJJ2MjAxERkYiJycHRpY2cOvWB3KFQuqwiHRKtRKz0lPSBgYGWLZsGd5//33069cP8+fPr9HgiIiIKoPtEpF0Hjx4gMjISKSnp0NpZg73gBAo9JVSh0Wkc6p1jZkQQmP6008/RYsWLRAeHl4jQREREVUF2yUiaeTn52PZsmW4c+cO9IyM4RHYH3qGRlKHRaSTqpWYJSQkwNbWVmPe4MGD4eXlhaNHj9ZIYERERJXFdomo7hUVFWHlypX4559/oFAawCOwP5QmZlKHRaSzqpWYubm5lTu/ZcuWaNmy5TMFREREVFVsl4jqlhAC69evx+XLlyFTKODWIxiG5lZSh0Wk0zhUDhERERFVmhAC27Ztw8mTJwGZHK5d+8DExkHqsIh0HhMzIiIiIqq0v//+G4cOHQIAOPsFQOXkKnFERPUDEzMiIiIiqpRjx45h9+7dAADHtv6wdG8mcURE9QcTMyIiIiJ6qnPnzmHjxo0AANsWbWDj6SNxRET1CxMzIiIiInqia9eu4a+//oIQApaNvWDv21HqkIjqHSZmRERERFSh27dvY/ny5SgqKoLK2R2NOnRT39SdiGoOEzMiIiIiKte9e/ewdOlS5Ofnw8TWES7+vSCT8+sjUW3gJ4uIiIiIysjMzMSSJUuQnZ0NQwtruHUPhlxRrVvgElElMDEjIiIiIg25ublYunQp0tLSoDRVwT0wBAqlUuqwiOo1JmZEREREpFZQUIDly5cjOTkZeoZG8AjsD31DY6nDIqr3mJgREREREQCguLgYq1atwo0bNyDXV8I9sD+UpiqpwyJqEJiYERERERGEENiwYQMuXrwImUIB9+7BMLKwljosogaDiRkRERERYefOnYiPjwdkMrh26Q0TO0epQyJqUJiYERERETVwMTExiImJAQA06tgDqkbu0gZE1AAxMSMiIiJqwOLj47Fjxw4AgENrP1g19pQ4IqKGiYkZERERUQN14cIFrF+/HgBg4+UL2xatJY6IqOFiYkZERETUAN24cQOrVq2CEAIW7s3h0NpP6pCIGjQmZkREREQNTHJyMpYtW4bCwkKYObnCuVMPyGQyqcMiatCYmBERERE1IPfv30dkZCTy8vJgbOMA1y5BkMn5lZBIavwUEhERETUQWVlZiIyMRFZWFgzNreDeIxhyPT2pwyIiMDEjIiIiahDy8vKwdOlS3Lt3D/omZnAPDIFCaSB1WET0EBMzIiIionqusLAQUVFRSEpKgp6BETwC+0PfyETqsIjoEUzMiIiIiOqx4uJirF69GteuXYNcTx/uASEwMDOXOiwiegwTMyIiIqJ6SgiBTZs24dy5c5DJ5XDrHgwjKxupwyKickiamP3888/w9fWFSqWCSqWCv78/tmzZol6em5uLiIgIWFtbw9TUFIMHD0ZycrLGNm7cuIHQ0FAYGxvDzs4OEydORGFhYV1XhYiI6gG2S1Tf7NmzB8eOHQNkMrj494apvZPUIRFRBSRNzJydnTFjxgzExcXh6NGj6NWrFwYMGIAzZ84AAD744ANs2LABK1euxN69e5GYmIhBgwap1y8qKkJoaCjy8/MRExODxYsXY9GiRfj888+lqhIREekwtktUnxw8eBD79+8HADTq0A3mLh4SR0RETyITQgipg3iUlZUVZs2ahZdeegm2trZYtmwZXnrpJQDA+fPn0aJFC8TGxqJz587YsmULnnvuOSQmJsLe3h4A8Msvv+Djjz/GnTt3oFQqK7XPjIwMmJubIz09HSqVqtbqRkRPNmfOHGRmZmL06NFwdHSUOhyqA7pw/GW7RLro5MmTWLNmDQDA3qcj7Fq2lTgi7SOXAQObs62hsqQ6BmvNNWZFRUWIiopCdnY2/P39ERcXh4KCAgQFBanLeHl5wdXVFbGxsQCA2NhY+Pj4qBs/AAgODkZGRob6183y5OXlISMjQ+NBRET0KLZLpKsuXbqEdevWAQCsm7eCrXcbaQMiokqRPDE7deoUTE1NYWBggHfeeQdr1qyBt7c3kpKSoFQqYWFhoVHe3t4eSUlJAICkpCSNxq90eemyikyfPh3m5ubqh4uLS81WioiIdBbbJdJlN2/exIoVK1BcXAxzt6ZwbOsPmUwmdVhEVAmSJ2aenp6Ij4/HoUOHMGbMGISHh+Ps2bO1us9JkyYhPT1d/bh582at7o+IiHQH2yXSVSkpKVi2bBkKCwth6ugC504BTMqIdIie1AEolUo0bdoUANC+fXscOXIE33//PV555RXk5+cjLS1N49fJ5ORkODg4AAAcHBxw+PBhje2Vjo5VWqY8BgYGMDDgne6JiKgstkuki9LT0xEZGYnc3FwYWdvBrWsQ5AqF1GERURVIfsbsccXFxcjLy0P79u2hr6+PXbt2qZdduHABN27cgL+/PwDA398fp06dQkpKirrMjh07oFKp4O3tXeexExFR/cN2ibRddnY2lixZgszMTBioLOHeox/kevpSh0VEVSTpGbNJkyYhJCQErq6uyMzMxLJlyxAdHY1t27bB3Nwcb7zxBiZMmAArKyuoVCqMGzcO/v7+6Ny5MwCgb9++8Pb2xsiRIzFz5kwkJSXh008/RUREBH95JCKiKmO7RLomLy8Py5YtQ2pqKvSNTeARGAI9A0OpwyKiapA0MUtJScGrr76K27dvw9zcHL6+vti2bRv69OkDAPjuu+8gl8sxePBg5OXlITg4GPPnz1evr1AosHHjRowZMwb+/v4wMTFBeHg4vvjiC6mqREREOoztEumSwsJCrFixAomJiVAoDeAeGAp9Y1OpwyKiatK6+5hJgfeLIdIOvI9Zw8Pjb/n4utDTFBcXY/Xq1Thz5gzkenrw6PkcjK3tpA5Lp/A+ZlSRBn8fMyIiIiJ6OiEEtmzZgjNnzkAml8O1W18mZUT1ABMzIiIiIh2yd+9eHD16FADg3LknzBycJY6IiGoCEzMiIiIiHXHkyBHs3bsXAODUvissXJtIHBER1RQmZkREREQ64MyZM9i8eTMAwK5lO1g3aylxRERUk5iYEREREWm5K1euYPXq1QAAq6besGvVXuKIiKimMTEjIiIi0mK3bt3Cn3/+ieLiYpi7NIZTuy6QyWRSh0VENYyJGREREZGWunv3LpYuXYqCggKY2jeCc+eekMn59Y2oPuInm4iIiEgLZWRkIDIyEg8ePICRlS1cu/WBXKGQOiwiqiVMzIiIiIi0TE5ODiIjI5Geng4DM3O49+gHhb5S6rCIqBYxMSMiIiLSIvn5+Vi+fDnu3LkDPSMTuAf2h56hkdRhEVEtY2JGREREpCWKioqwcuVK/PPPP1AoDeARGAKliZnUYRFRHWBiRkRERKQFhBBYt24dLl++DLlCD249+sHQ3ErqsIiojjAxIyIiIpKYEALbtm3DqVOnAJkcrl2DYGJjL3VYRFSHmJgRERERSezvv//GoUOHAADOfgEwc3KVOCIiqmtMzIiIiIgkFBcXh927dwMAHNv6w9K9mcQREZEUmJgRERERSeTcuXPYtGkTAMDWuw1sPH0kjoiIpMLEjIiIiEgCCQkJ+OuvvyCEgGVjL9j7dJQ6JCKSEBMzIiIiojp2+/ZtREVFoaioCCpndzTq0A0ymUzqsIhIQkzMiIiIiOpQamoqIiMjkZ+fDxM7R7j494JMzq9kRA0djwJEREREdSQzMxORkZHIycmBoaU13LoHQ67QkzosItICTMyIiIiI6kBubi4iIyORlpYGpakK7gEhUOgrpQ6LiLQEEzMiIiKiWlZQUIDly5cjJSUFeoZG8AjsD31DY6nDIiItwsSMiIiIqBYVFRVh1apVuHHjBuT6SrgH9ofSVCV1WESkZZiYEREREdUSIQQ2bNiAixcvQqZQwL17MIwsrKUOi4i0EBMzIiIiolqyc+dOnDhxApDJ4NolCCZ2jlKHRERaiokZERERUS04cOAAYmJiAADOnQKgauQmcUREpM2YmBERERHVsOPHj2Pnzp0AAIc2frD0aC5xRESk7ZiYEREREdWgCxcuYMOGDQAAGy9f2Hq1ljgiItIFTMyIiIiIasj169exatUqCCFg6dEcDq39pA6JiHQEEzMiIiKiGpCUlITly5ejsLAQZo3c0KhjD8hkMqnDIiIdwcSMiIiI6Bndv38fS5cuRV5eHoxtHODq3xsyOb9mEVHl8YhBRERE9AyysrKwZMkSZGVlwdDcCu49giHX05M6LCLSMUzMiIiIiKopNzcXS5cuxf3796FvYgb3wP5QKA2kDouIdBATMyIiIqJqKCwsxJ9//omkpCToGRjBI7A/9I2MpQ6LiHQUEzMiIiKiKiouLsZff/2Fa9euQa6nD/fAEBiYmUsdFhHpMCZmRERERFUghMDGjRtx/vx5yORyuHUPhpGljdRhEZGOY2JGREREVAW7d+/G8ePHAZkMLv69YWrvJHVIRFQPMDEjIiIiqqSDBw/i77//BgA06tAN5i4eEkdERPUFEzMiIiKiSjh58iS2bdsGALD37QirJi0kjoiI6hMmZkRERERPcenSJaxbtw4AYN28FWxbtJE2ICKqd5iYERERET3BzZs3sWLFChQXF8PCrSkc2/pDJpNJHRYR1TNMzIiIiIgqkJKSgmXLlqGwsBCmji5w9gtkUkZEtYKJGREREVE50tLSEBkZidzcXBhb28OtaxBkcn51IqLawaMLERER0WOys7MRGRmJzMxMGKgs4dYjGHI9fanDIqJ6jIkZERER0SPy8vKwbNkypKamQt/YFB6BIdAzMJQ6LCKq55iYERERET1UWFiIP//8E4mJiVAYGMIjsD/0jU2lDouIGgAmZkREREQAiouLsWbNGiQkJECupw/3gBAYqCykDouIGggmZkRERNTgCSGwefNmnD17FjK5HG7d+sDYylbqsIioAWFiRkRERA3e3r17ERcXBwBw7twTpg7OEkdERA0NEzMiIiJq0A4fPoy9e/cCAJzad4OFaxOJIyKihoiJGRERETVYp0+fxpYtWwAAdq3aw7qZt8QREVFDxcSMiIiIGqQrV65gzZo1AACrZt6wa9lO4oiIqCFjYkZEREQNzq1bt/Dnn3+iuLgY5q6N4dSuK2QymdRhEVEDxsSMiIiIGpS7d+9i6dKlKCgogKl9Izj79WRSRkSSY2JGREREDUZGRgaWLFmCBw8ewMjKFq7d+kKuUEgdFhEREzMiIiJqGHJycrBkyRJkZGTAwMwc7gEhUOjrSx0WEREAJmZERETUAOTn52PZsmW4e/cu9IxM4B4YCj0DQ6nDIiJSY2JGRERE9VpRURFWrFiBW7duQaE0gEdgfyhNTKUOi4hIAxMzIiIiqreEEFi3bh2uXLkCmUIP7j36wdDcUuqwiIjK0JrEbMaMGZDJZBg/frx6Xm5uLiIiImBtbQ1TU1MMHjwYycnJGuvduHEDoaGhMDY2hp2dHSZOnIjCwsI6jp6IiOojtk26TQiBrVu34tSpU4BMDrdufWBsYy91WERE5dKKxOzIkSP49ddf4evrqzH/gw8+wIYNG7By5Urs3bsXiYmJGDRokHp5UVERQkNDkZ+fj5iYGCxevBiLFi3C559/XtdVICKieoZtk+7bv38/Dh8+DABw8QuEmaOLxBEREVVM8sQsKysLYWFhWLBgASwt/+1akJ6ejt9++w1z5sxBr1690L59eyxcuBAxMTE4ePAgAGD79u04e/YsIiMj0aZNG4SEhGDatGn46aefkJ+fL1WViIhIx7Ft0n1xcXHYs2cPAMCxXRdYuDeVOCIioieTPDGLiIhAaGgogoKCNObHxcWhoKBAY76XlxdcXV0RGxsLAIiNjYWPjw/s7f/tlhAcHIyMjAycOXOmwn3m5eUhIyND40FERFSqrtsmtks16+zZs9i0aRMAwNa7LWyat5I4IiKip9OTcudRUVE4duwYjhw5UmZZUlISlEolLCwsNObb29sjKSlJXebRhq90eemyikyfPh1Tp059xuiJiKg+kqJtYrtUcxISErB69WoIIWDVxAv2Ph2kDomIqFIkO2N28+ZNvP/++1i6dCkMDev2PiKTJk1Cenq6+nHz5s063T8REWknqdomtks1IzExEVFRUSgqKoLK2R1O7btBJpNJHRYRUaVIlpjFxcUhJSUF7dq1g56eHvT09LB371788MMP0NPTg729PfLz85GWlqaxXnJyMhwcHAAADg4OZUbCKp0uLVMeAwMDqFQqjQcREZFUbRPbpWeXmpqKpUuXIj8/HyZ2TnDx7wWZXPIrNoiIKk2yI1bv3r1x6tQpxMfHqx8dOnRAWFiY+rm+vj527dqlXufChQu4ceMG/P39AQD+/v44deoUUlJS1GV27NgBlUoFb2/vOq8TERHpNrZNuikzMxORkZHIycmBoaUN3Lr3hVwh6dUaRERVJtlRy8zMDK1aaV6Ma2JiAmtra/X8N954AxMmTICVlRVUKhXGjRsHf39/dO7cGQDQt29feHt7Y+TIkZg5cyaSkpLw6aefIiIiAgYGBnVeJyIi0m1sm3TPgwcPEBkZibS0NChNVfAICIFCXyl1WEREVabVPyd99913kMvlGDx4MPLy8hAcHIz58+erlysUCmzcuBFjxoyBv78/TExMEB4eji+++ELCqImIqD5j26Q9CgoKsHz5cqSkpEDP0Bgegf2hZ2gkdVhERNUiE0IIqYOQWkZGBszNzZGens5+/UQSmjNnDjIzMzF69Gg4OjpKHQ7VAR5/y8fX5emKiorw559/4tKlS5DrK9Gk9wswtLCSOizSIXIZMLA52xoqS6pjMK+KJSIiIp0ihMCGDRtw6dIlyBQKuPfox6SMiHQeEzMiIiLSKTt27MCJEycAmQyuXYJgYlvxSMxERLqCiRkRERHpjAMHDiA2NhYA4NwpAKpGbhJHRERUM5iYERERkU44fvw4du7cCQBwaNMZlh7NJY6IiKjmMDEjIiIirXfhwgVs2LABAGDj1Rq2Xr4SR0REVLOYmBEREZFWu379OlatWgUhBCw9POHQupPUIRER1TgmZkRERKS1kpKSsHz5chQWFsKskRsadewOmUwmdVhERDWOiRkRERFppfv37yMyMhJ5eXkwtnWAq39vyOT86kJE9ROPbkRERKR1srKysGTJEmRnZ8PQwhru3YMh19OTOiwiolrDxIyIiIi0Sm5uLpYuXYr79+9DaWIG94AQKJQGUodFRFSrmJgRERGR1igsLERUVBSSkpKgZ2gE98BQ6BsZSx0WEVGtY2JGREREWqG4uBh//fUXrl+/Drm+PtwDQmBgppI6LCKiOsHEjIiIiCQnhMDGjRtx/vx5yOQKuHULhpGljdRhERHVGSZmREREJLndu3fj+PHjgEwGly69YWrvJHVIRER1iokZERERSSo2NhZ///03AKBRh+4wd3aXNiAiIgkwMSMiIiLJnDhxAtu3bwcA2Pt2glUTL4kjIiKSBhMzIiIiksTFixexbt06AIC1pw9sW7SWOCIiIukwMSMiIqI6d/PmTaxcuRJCCFi4NYVjm86QyWRSh0VEJBkmZkRERFSnUlJSsGzZMhQWFsLM0QXOfoFMyoiowdOTOoCGLisrC7du3YKnp6fUoRAREdW6w4cPY/fu3cjLy4OxjT1cu/aBTM7fiYmImJhJKDk5Gb/88gsAIDw8HO7u7tIGREREVIv27t2L6OhoAIDMyARu3YMh1+NXESIigImZZC5cuICoqCj19PK16+EZ8rKEERFJ70FhkdQhEFEtOXv2rDopAwDbJi2gZ2AoXUDU4BULqSMg0sTETAKxsbHqoYEBQN/MnEkZERHVW4mJiVi5cqV62tTJDfat2kkYERGR9mFiVsc2bNiAY8eOqaeN7RzRpNfzAIAzRw5i3W/zcfXMKdy/k4yPfvwNfkEhGuv/c+USlsz+EmePHERRUSGcmzTHxB8WwNbJGZlp9/HnvNk4cWAv7t5OhMrKCp1698PQ9z+CiZmqwpjS7t7Bktlf4cSBvcjOTId3h85449Mv4eTeuHZeBCIiajAyMjKwYMEC9bSBhTU8egSrp7csXYh1v/2MtLt34O7ljTc+/RLNfNtWuL2Nixdg2/LFuHs7EWaWlvAPfg5hEyZB+fDsW2XaUiIibcSrbevQH3/8oZGUWXh4qpMyAMh7kAN3r5Z46/Ovy10/6cY1/Hf4QDRq3BRT/1iFOet24eV3x6sbo/spybiXkoxXP/oc323YjbHT5+L4/mjM/++HFcYkhMA3Ea8j+Z/r+GT+QsxevR22Ts6Y+voryM3JqZmKExFRg5SXl4fvvvtOPa0wMkHzfoPV0wc2r8OiGVMxJGICZq3eBjdPb0x7czjSU++Wu739G1Yj8tuvMSRiAr7ftBfvfvktDmxej6VzZvy7z6e0pURE2opnzOrITz/9hLt3/21o7Hw7wd67jUaZdj16oV2PXhVuY9ncGWgX0AuvTvxMPc/B1V393LW5Fz6a938ay4Z/8DG+nzgORYWFUJRzgfXta1dx8UQcvtuwB67NSkaGHD1lBt7o1hp/b1qDoJfDqlpVIiIiAMCMGf8mTHI9fXgP0GxTNiz6H4JeHo5eg4cCAN6e+g2O7d2FXX8tx6DR48ps7/zxo/Bq1xHdnx8EALBzdkG30IG4dPLfHz2f1pYSEWkrnjGrA7NmzdJIyly69imTlD1NcXEx4qJ3wcm9Mb54YxhGdfHBJ0NCcWjnlieul5OZAWNT03KTMgAoyM8HACgNDNTz5HI59JVKnIs7UqUYiYiISk2bNu3fCZkMLV8apbG8ID8fV86chG+X7up5crkcvv7dcTE+rtxterXtgCtnTuLSyeMAgKSb13Fs3y6069G75itARFTHmJjVsq+++go5j3QJ9Og7CBYuHlXeTnrqXeTmZGPNgh/RtntPfP7bcnQK6odZ497EmcOx5a6TcT8VK3+ei6AhIyrcbqPGTWHj1AiRc6YjKz0NBfn5WLPgR6Qm3cb9O8lVjpOIiOjrr79GcXGxetrnlbfKlMm8fw/FRUWwsLbVmG9uY4O0u3fK3W735wdh6Lj/4NOwgRjSyhURffzRslMXDH7nvZqtABGRBNiVsZbk5+djxowZEOLfsVg9Bw6H0tC0WtsTDxu4jr2C8fxrowEAHi1a4cLxo9gW9QdadvLXKJ+TlYmv334VLk2a45WxFV9jpqevj49++A3zP52AcD9vyBUK+Pp3R9sevQDBcWSJiKhqZs+ejYKCAvV08xfDa2zbpw/FYPX/5uGtz79GM992SLpxDb9//RlWzv8OL7/7QY3th4hICkzMakFWVha+/fZbjXmeg16DUqms9jbNLK2g0NODS9PmGvOdmzTDubjDGvMeZGXhyzeHw9DEBB/9+Bv09PWfuO0mrXzx7dqdyM7MQGFBAcytrPHJkFA0aeVb7XiJiKjhmT9/PrKzs9XTzV8Mh8EjXeUfZWZpBblCgbRUzbNj6XfvwsLGttx1on6YiR4vDFZf/+zm2QK5D3Lwy+cTMfid9yGXsyMQEekuHsFqWHJyskZSJpMr4DN09DMlZQCgr1SiaavWuJVwRWN+4rWrsHVyVk/nZGXiizeGQU9fiUnzF6lHbKwMEzMVzK2skXjtKq6cPoGOvYKfvhIRERGAyMhI3Lnzb5LVNGRQhUkZUNKuNWnpi1Oxf6vnFRcX4+TBv9G8Tfty18l78KBM8lU6LdjLg4h0HM+Y1aALFy4gKipKPa1QGsJ70KuVXv9BdjaSbiSop1P+uYmEc6dham4BWydnDHjjXcyZ8A68O3RGK78uOL5/D47u2YEv/lgF4N+kLO/BA7w/ax5ysrKQk5UFAFBZWUOhUAAAxoV0x4gJ/w9+fUru6xKzdQNUltawcWqEGxfP4fevPkfH3v3Qplvgs74kRETUAGzevBlXrvz7w6Frt74wMrd56nrPvzYa8z4ZjyatWqOZb1tsXLwAeQ9y0GtQySiNP3z8HqzsHDDiw/8HAOjQsw82LPofPFq0QrPW7ZB0PQFRP8xCh5591G3c09pSIiJtxcSshsTGxmL79u3qaX0zc3iFvlKlbVw5fQKTw19STy+aMQUAEDhwCMbNmAu/PiEYPWUGVv/vR/z+1Wdw8miMiT8sQIv2fgCAq2dO4dKJkiGDI/p20dj2zzsPwc7ZBQCQmHAF2ZkZ6mX3U5KxaMYUpKfehYWtHQIHvIyXxoyvUuxERNQwHTx4EEeO/DuKr31rP5g7u1dq3a79ByD9Xiqi5s1C2p078GjREp8uWKruyng38RZksn/PkL00ZjxkMhmWfz8T95KToLKyQoeefTB8/CfqMk9rS4mItJVM8Nw/MjIyYG5ujvT0dKhUqiqvv2nTJhw9elQ9bWznqHHjaCKqnHPrIlH4IAejR4+Go6Oj1OFQHXjW4299pSuvy+M9RSwae8KlU4CEERFVzSBPtjVUllTHYJ4xe0ZLlizB1atX1dMWHp5w8WOjRERE9dudO3c0kjJj+0ZMyoiIngETs2d07do19XO3tp3g6lv+BctE9HRyyKQOgYgqQQiBRYsWqacNVBZo0jNUuoCIiOoBJmbP6OOPP8asWbMwaNAgtGjRQupwiHRavJ4c+VIHQURPJITA1q1bkZOTAwBQGBihef8hEkdFVHVy/hZIWoaJ2TNSKpX473//K3UYREREdWL//v04fLjk/pku/r1g4dZU4oiIiOoH3seMiIiIKuXo0aPYs2cPAMCxXRcmZURENYiJGRERET3V2bNnsWnTJgCAXct2sGneSuKIiIjqFyZmRERE9EQJCQlYvXo1AMCqSQvYteJAV0RENY2JGREREVUoMTERUVFRKCoqgsrZA07tu0Im46gJREQ1jYkZERERlSs1NRVLly5Ffn4+TOyc4OLfCzI5vzoQEdUGHl2JiIiojMzMTCxZsgQ5OTkwsrSBW/e+kCsUUodFRFRvMTEjIiIiDQ8ePEBkZCTS09OhNDOHe0AIFPpKqcMiIqrXmJgRERGRWkFBAZYvX46UlBToGRrDI6A/9AyNpA6LiKjeY2JGREREAICioiKsXLkSN2/ehFxfCY/A/lCamkkdFhFRg8DEjIiIiCCEwPr163Hp0iXIFAq49+gHQwsrqcMiImowmJgRERE1cEIIbN++HSdPngRkMrh27QMTWwepwyIialCYmBERETVwBw4cwMGDBwEAzp0CoHJylTgiIqKGh4kZERFRA3bs2DHs2rULAODQpjMsPZpLHBERUcPExIyIiKiBOn/+PDZu3AgAsG3RBrZevhJHRETUcDExIyIiaoCuX7+OVatWQQgBSw9P2Pt2lDokIqIGjYkZERFRA5OUlITly5ejqKgIqkZuaNSxO2QymdRhERE1aEzMiIiIGpB79+4hMjISeXl5MLF1hIt/b8jk/DpARCQ1HomJiIgaiKysLERGRiI7OxuGFtZw6x4MuZ6e1GERERGYmBERETUIubm5iIyMxP3796E0MYN7QAgUSqXUYRER0UNMzIiIiOq5goICREVFITk5GXqGRnAPDIW+kbHUYRER0SOYmBGR1vrpp5/g7u4OQ0ND+Pn54fDhw08sv3LlSnh5ecHQ0BA+Pj7YvHlzHUVKpL2Ki4vx9ttvY/z48fjyyy+x8I8luJFwpcLyhQUFWPHTHLzbxx9DfT0wYUAQju/fo1HmnV6dMNjLqcxjwReTars6RET1lqSJ2ZQpUyCTyTQeXl5e6uW5ubmIiIiAtbU1TE1NMXjwYCQnJ2ts48aNGwgNDYWxsTHs7OwwceJEFBYW1nVViKiG/fnnn5gwYQImT56MY8eOoXXr1ggODkZKSkq55WNiYjBs2DC88cYbOH78OAYOHIiBAwfi9OnTdRw56bL61i4JIfDxxx/jjz/+QM+evfDF78vg0bI1pr05HOmpd8tdZ/n332DHn5F449MvMXdTNPoOHYmZY9/A1bOn1GW+WbUF/7c/Xv34/PcoAIB/8PN1Ui8iovpI8jNmLVu2xO3bt9WPv//+W73sgw8+wIYNG7By5Urs3bsXiYmJGDRokHp5UVERQkNDkZ+fj5iYGCxevBiLFi3C559/LkVViKgGzZkzB2+99RZGjRoFb29v/PLLLzA2Nsbvv/9ebvnvv/8e/fr1w8SJE9GiRQtMmzYN7dq1w48//ljHkZOuq0/t0q5du7B8+XK0a98eA8ZOhGenbnh76jcwMDTCrr+Wl7vO3nV/YdDb49A+oDccXNzQb1g42vbohQ0Lf1WXMbeyhqWtnfoRF70TDq7uaNnJv66qRkRU70iemOnp6cHBwUH9sLGxAQCkp6fjt99+w5w5c9CrVy+0b98eCxcuRExMDA4ePAgA2L59O86ePYvIyEi0adMGISEhmDZtGn766Sfk5+dLWS0iegb5+fmIi4tDUFCQep5cLkdQUBBiY2PLXSc2NlajPAAEBwdXWJ6oIvWlXYqNjVUnjx37PgeVszuAks+Sr393XIyPK3e9gvx86BsYaMwzMDTEubjyuxIX5Odj3/q/0GvQUN4LjYjoGUiemF26dAlOTk5o3LgxwsLCcOPGDQBAXFwcCgoKNL5oeXl5wdXVVf1FKzY2Fj4+PrC3t1eXCQ4ORkZGBs6cOVPhPvPy8pCRkaHxICLtce/ePRQVFWl8tgHA3t4eSUlJ5a6TlJRUpfJEFakP7dKJEyewfft25OTkQAgB55ZtNJab29gg7e6dctdt0y0AGxb9D4nXrqK4uBgnDuzFwR2bcf9O+d2ID+/aiuzMDPR8ccgzxUxE1NBJevMSPz8/LFq0CJ6enrh9+zamTp2K7t274/Tp00hKSoJSqYSFhYXGOo9+0aroi1jpsopMnz4dU6dOrdnKEBGRzqsP7ZIQAseOHQMAtGrdFgDQ1sEcHV2t1WX2qIxwU6mHgEfmlWo5/0d8EDEG7/fvAZlMBvfGjTHi1XAs+2NxueV/3LQKQX2D8WLHljUSP1Fd4fld0jaSJmYhISHq576+vvDz84ObmxtWrFgBIyOjWtvvpEmTMGHCBPV0RkYGXFxcam1/RFQ5gwcPRlFREWxtbaFQKMoMqpCcnAwHB4dy13VwcKhSeaLy1Id2SSaTISwsDHFxcWjXrh3eev015Kbdg7XRv/csy0i9C2cnR415paxdG2HzhvXIzc1FamoqnJyc8Mknn6Bx48Zlyl+/fh17d+/G6tWry90WERFVnuRdGR9lYWGB5s2b4/Lly3BwcEB+fj7S0tI0yjz6RauiL2KlyypiYGAAlUql8SAi6bm5uaFx48YwMzND+/btsWvXLvWy4uJi7Nq1C/7+5Q8u4O/vr1EeAHbs2FFheaLK0NV2SalUwt/fHwYGBlX+LJUyNDREo0aNUFhYiL/++gsDBgwoU2bhwoWws7NDaGjoM8VLRERalphlZWXhypUrcHR0RPv27aGvr6/RmFy4cAE3btxQNyb+/v44deqUxvDZO3bsgEqlgre3d53HT0Q1Z8KECViwYAEWL16Mc+fOYcyYMcjOzsaoUaMAAK+++iomTfr3nknvv/8+tm7dim+//Rbnz5/HlClTcPToUYwdO1aqKlA9UB/apap+lg4dOoTVq1fj6tWr2L9/P/r164fi4mJ89NFHGtstLi7GwoULER4eDj09STvgEBHVD0JCH374oYiOjhYJCQniwIEDIigoSNjY2IiUlBQhhBDvvPOOcHV1Fbt37xZHjx4V/v7+wt/fX71+YWGhaNWqlejbt6+Ij48XW7duFba2tmLSpElViiM9PV0AEOnp6TVaPyJ6NvPmzROurq5CqVSKTp06iYMHD6qXBQQEiPDwcI3yK1asEM2bNxdKpVK0bNlSbNq0qY4jpqrStuNvfW2XqvJZio6OFi1atBAGBgbC2tpajBw5Uty6davMNrdt2yYAiAsXLtRIjERE2kKqtkkmhBBSJYVDhw7Fvn37kJqaCltbW3Tr1g1fffUVmjRpAqDkRp4ffvghli9fjry8PAQHB2P+/Pka3UGuX7+OMWPGIDo6GiYmJggPD8eMGTOq9OtdRkYGzM3NkZ6ezm6NRER1SNuOv2yXiIhIqmOwpImZtmADSEQkDR5/y8fXhYhIOlIdg7XqGjMiIiIiIqKGiIkZERERERGRxJiYERERERERSYyJGRERERERkcSYmBEREREREUmMiRkREREREZHEmJgRERERERFJjIkZERERERGRxJiYERERERERSYyJGRERERERkcT0pA5AGwghAAAZGRkSR0JE1LCUHndLj8NUgu0SEZF0pGqbmJgByMzMBAC4uLhIHAkRUcOUmZkJc3NzqcPQGmyXiIikV9dtk0zwZ0oUFxcjMTERZmZmkMlkUoejISMjAy4uLrh58yZUKpXU4dQp1r3h1b2h1htouHUXQiAzMxNOTk6Qy9m7vlRl26X69n/D+mg31ke7sT41R6q2iWfMAMjlcjg7O0sdxhOpVKp68SGrDta94dW9odYbaJh155mysqraLtW3/xvWR7uxPtqN9akZUrRN/HmSiIiIiIhIYkzMiIiIiIiIJMbETMsZGBhg8uTJMDAwkDqUOse6N7y6N9R6Aw277lR99e3/hvXRbqyPdmN9dB8H/yAiIiIiIpIYz5gRERERERFJjIkZERERERGRxJiYERERERERSYyJGRERERERkcSYmElk3759eP755+Hk5ASZTIa1a9dqLH/ttdcgk8k0Hv369dMoc+/ePYSFhUGlUsHCwgJvvPEGsrKy6rAWVfe0egsh8Pnnn8PR0RFGRkYICgrCpUuXNMroYr3LM2XKlDLvsZeXl3p5bm4uIiIiYG1tDVNTUwwePBjJyckSRlz7fvrpJ7i7u8PQ0BB+fn44fPiw1CHVCXd39zL/CzNmzNAoc/LkSXTv3h2GhoZwcXHBzJkzJYqWasvTjo/liY6ORrt27WBgYICmTZti0aJFZcpI+bmqyr4DAwPLfA5kMhlCQ0PVZSrTNtamqtRn0aJFZWI1NDTUKFOZNq82VaU+CxYsQPfu3WFpaQlLS0sEBQWVKS/1+wNU/f995cqV8PLygqGhIXx8fLB582aN5VK/R4+q6vefe/fuYdy4cfD09ISRkRFcXV3x3nvvIT09XaNceZ+7qKio2q5Otb7PlXeceOeddzTK3LhxA6GhoTA2NoadnR0mTpyIwsLC2qxKzREkic2bN4v//ve/YvXq1QKAWLNmjcby8PBw0a9fP3H79m314969expl+vXrJ1q3bi0OHjwo9u/fL5o2bSqGDRtWh7WouqfVe8aMGcLc3FysXbtWnDhxQrzwwgvCw8NDPHjwQF1GF+tdnsmTJ4uWLVtqvMd37txRL3/nnXeEi4uL2LVrlzh69Kjo3Lmz6NKli4QR166oqCihVCrF77//Ls6cOSPeeustYWFhIZKTk6UOrda5ubmJL774QuN/ISsrS708PT1d2Nvbi7CwMHH69GmxfPlyYWRkJH799VcJo6aa9rTj4+OuXr0qjI2NxYQJE8TZs2fFvHnzhEKhEFu3blWXkfJzVdV9p6amanwGTp8+LRQKhVi4cKG6TGXaRm2pz8KFC4VKpdKINSkpSaNMZdo8banP8OHDxU8//SSOHz8uzp07J1577TVhbm4u/vnnH3UZKd8fIapepwMHDgiFQiFmzpwpzp49Kz799FOhr68vTp06pS4j5Xv0uKp+/zl16pQYNGiQWL9+vbh8+bLYtWuXaNasmRg8eLBGOQBi4cKFGu9bXdSvOt/nAgICxFtvvaURa3p6unp5YWGhaNWqlQgKChLHjx8XmzdvFjY2NmLSpEm1XZ0awcRMC1SUmA0YMKDCdc6ePSsAiCNHjqjnbdmyRchkMnHr1q1airRmPV7v4uJi4eDgIGbNmqWel5aWJgwMDMTy5cuFEPWj3qUmT54sWrduXe6ytLQ0oa+vL1auXKmed+7cOQFAxMbG1lGEdatTp04iIiJCPV1UVCScnJzE9OnTJYyqbri5uYnvvvuuwuXz588XlpaWIi8vTz3v448/Fp6ennUQHUmhMonZRx99JFq2bKkx75VXXhHBwcHqaSk/V8+67++++06YmZlp/EjxtLaxNlW1PgsXLhTm5uYVbq8ybV5tetb3p7CwUJiZmYnFixer50n5/ghR9ToNGTJEhIaGaszz8/MTb7/9thBC+vfoUTX1/WfFihVCqVSKgoIC9bzKHG9qWnXrExAQIN5///0Kl2/evFnI5XKNH0F+/vlnoVKpNNpQbcWujFosOjoadnZ28PT0xJgxY5CamqpeFhsbCwsLC3To0EE9LygoCHK5HIcOHZIi3GeWkJCApKQkBAUFqeeZm5vDz88PsbGxAOpfvS9dugQnJyc0btwYYWFhuHHjBgAgLi4OBQUFGq+Fl5cXXF1d1a9FfZKfn4+4uDiN+srlcgQFBdXL+pZnxowZsLa2Rtu2bTFr1iyNbhexsbHo0aMHlEqlel5wcDAuXLiA+/fvSxEuaYHY2FiNzwxQ8n9R+pmR8nNVE/v+7bffMHToUJiYmGjMf1LbWFuqW5+srCy4ubnBxcUFAwYMwJkzZ9TLKtPm1ZaaeH9ycnJQUFAAKysrjflSvD9A9er0tM+QlO9RebHWxPef9PR0qFQq6OnpacyPiIiAjY0NOnXqhN9//x2ilm9z/Cz1Wbp0KWxsbNCqVStMmjQJOTk5Gtv18fGBvb29el5wcDAyMjI0Pn/aSu/pRUgK/fr1w6BBg+Dh4YErV67g//2//4eQkBDExsZCoVAgKSkJdnZ2Guvo6enBysoKSUlJEkX9bErjfvTDVDpduqw+1dvPzw+LFi2Cp6cnbt++jalTp6J79+44ffo0kpKSoFQqYWFhobHOo69FfXL37l0UFRWV+96fP39eoqjqznvvvYd27drBysoKMTExmDRpEm7fvo05c+YAKPm/9/Dw0Fin9LVKSkqCpaVlncdM0ktKSir3M5ORkYEHDx7g/v37kn2unvUzffjwYZw+fRq//fabxvyntY21pTr18fT0xO+//w5fX1+kp6dj9uzZ6NKlC86cOQNnZ+dKtXm1pSaOuR9//DGcnJw0khap3h+genWq6DP06HeO0nkVlakrNfH95+7du5g2bRpGjx6tMf+LL75Ar169YGxsjO3bt+Pdd99FVlYW3nvvvRqL/3HVrc/w4cPh5uYGJycnnDx5Eh9//DEuXLiA1atXq7db3vtVukzbMTHTUkOHDlU/9/Hxga+vL5o0aYLo6Gj07t1bwsiopoSEhKif+/r6ws/PD25ublixYgWMjIwkjIxqwieffIJvvvnmiWXOnTsHLy8vTJgwQT3P19cXSqUSb7/9NqZPnw4DA4PaDpVI6/z222/w8fFBp06dNObrUtvo7+8Pf39/9XSXLl3QokUL/Prrr5g2bZqEkT27GTNmICoqCtHR0RoDmujS+6MtKttWPKuMjAyEhobC29sbU6ZM0Vj22WefqZ+3bdsW2dnZmDVrVrUSs9quz6NJpY+PDxwdHdG7d29cuXIFTZo0qfZ2tQUTMx3RuHFj2NjY4PLly+jduzccHByQkpKiUaawsBD37t2Dg4ODRFE+m9K4k5OT4ejoqJ6fnJyMNm3aqMvUt3qXsrCwQPPmzXH58mX06dMH+fn5SEtL0zhrlpycrPP1LI+NjQ0UCkWZUSd1ub4ffvghXnvttSeWady4cbnz/fz8UFhYiGvXrsHT0xMODg7lvjYAdPb1oWdX0f+FSqWCkZERFAqFZJ+rZ/lMZ2dnIyoqCl988cVT9/N421hbauIYpa+vj7Zt2+Ly5csAKtfm1ZZnqc/s2bMxY8YM7Ny5E76+vk8sW1fvD1C9OlX0GSotXxfvUWXbimf5/pOZmYl+/frBzMwMa9asgb6+/hPL+/n5Ydq0acjLy6vyj4N1UZ/HYwWAy5cvo0mTJnBwcCgzEqcutZe8xkxH/PPPP0hNTVUfGPz9/ZGWloa4uDh1md27d6O4uFj9T6prPDw84ODggF27dqnnZWRk4NChQ+pfHetjvUtlZWXhypUrcHR0RPv27aGvr6/xWly4cAE3btzQ+AW2vlAqlWjfvr1GfYuLi7Fr1y6dra+trS28vLye+Hj0mrFHxcfHQy6Xq7t5+Pv7Y9++fSgoKFCX2bFjBzw9PdmNsQHz9/fX+MwAJf8XpZ8ZKT9Xz7LvlStXIi8vDyNGjHjqfh5vG2tLTbyWRUVFOHXqlDrWyrR5taW69Zk5cyamTZuGrVu3alwbVJG6en+A6tXpaZ+huniPKttWVPf7T0ZGBvr27QulUon169eXuWVDeeLj42FpaVmtHhu1XZ/yYgWg8f341KlTGknfjh07oFKp4O3tXeX61DmpRx9pqDIzM8Xx48fF8ePHBQAxZ84ccfz4cXH9+nWRmZkp/vOf/4jY2FiRkJAgdu7cKdq1ayeaNWsmcnNz1dvo16+faNu2rTh06JD4+++/RbNmzbR+2Pgn1VuIkmFpLSwsxLp168TJkyfFgAEDyh0uX9fqXZ4PP/xQREdHi4SEBHHgwAERFBQkbGxsREpKihCiZLh8V1dXsXv3bnH06FHh7+8v/P39JY669kRFRQkDAwOxaNEicfbsWTF69GhhYWFRZnjp+iYmJkZ89913Ij4+Xly5ckVERkYKW1tb8eqrr6rLpKWlCXt7ezFy5Ehx+vRpERUVJYyNjTlcfj3ztOPjJ598IkaOHKkuXzpc/sSJE8W5c+fETz/9VO5w+VJ9rp6275EjR4pPPvmkzHrdunUTr7zySpn5lW0btaU+U6dOFdu2bRNXrlwRcXFxYujQocLQ0FCcOXNGXaYybZ621GfGjBlCqVSKVatWaQxVnpmZKYSQ/v2pTp0OHDgg9PT0xOzZs8W5c+fE5MmTyx0uX6r36HFP+/7zzz//CE9PT3Ho0CEhRMmtVvz8/ISPj4+4fPmyxvtWWFgohBBi/fr1YsGCBeLUqVPi0qVLYv78+cLY2Fh8/vnnWlefy5cviy+++EIcPXpUJCQkiHXr1onGjRuLHj16qNcpHS6/b9++Ij4+XmzdulXY2tpyuHx6sj179ggAZR7h4eEiJydH9O3bV9ja2gp9fX3h5uYm3nrrrTINaWpqqhg2bJgwNTUVKpVKjBo1Sn2A1FZPqrcQJUPTfvbZZ8Le3l4YGBiI3r17iwsXLmhsQxfrXZ5XXnlFODo6CqVSKRo1aiReeeUVcfnyZfXyBw8eiHfffVdYWloKY2Nj8eKLL4rbt29LGHHtmzdvnnB1dRVKpVJ06tRJHDx4UOqQal1cXJzw8/MT5ubmwtDQULRo0UJ8/fXXZb7InDhxQnTr1k0YGBiIRo0aiRkzZkgUMdWWpx0fw8PDRUBAQJl12rRpI5RKpWjcuLHGPb9KSfm5etK+AwIC1HUrdf78eQFAbN++vcy2Kts21qaq1Gf8+PHqsvb29qJ///7i2LFjGturTJtXm6pSHzc3t3L/PydPniyE0I73p6p1EqJk+PjmzZsLpVIpWrZsKTZt2qSxXOr36FFP+/6TkJAgAIg9e/YIISo+pgAQCQkJQoiSIerbtGkjTE1NhYmJiWjdurX45ZdfRFFRkdbV58aNG6JHjx7CyspKGBgYiKZNm4qJEydq3MdMCCGuXbsmQkJChJGRkbCxsREffvihxu0BtJlMiFoeD5OIiIiIiIieiNeYERERERERSYyJGRERERERkcSYmBEREREREUmMiRkREREREZHEmJgRERERERFJjIkZERERERGRxJiYERERERERSYyJGRERERERkcSYmBHVU+7u7pDJZJDJZEhLS2vwcRARUVmBgYEYP378M2+j9DgfHx9fI3FJpbQeFhYWUodCDRATM6InuHPnDsaMGQNXV1cYGBjAwcEBwcHBOHDggLqMTCbD2rVrpQvyCb744gvcvn0b5ubmAIDo6GjIZDJYWloiNzdXo+yRI0fUDVKp0vKlD3t7ewwePBhXr16tdAxHjhzBX3/9VTMVIiJq4F577TUMHDiwxra3evVqTJs27Zm389Zbb+H27dto1apVDURVPdevX4eRkRGysrKqvY3bt29j7ty5NRcUURUwMSN6gsGDB+P48eNYvHgxLl68iPXr1yMwMBCpqalV2k5+fn4tRfhkZmZmcHBw0Ei2SuevWbNGY95vv/0GV1fXcrdz4cIFJCYmYuXKlThz5gyef/55FBUVVSoGW1tbWFlZVa8CRERULQUFBZUqZ2VlBTMzs2fen7GxMRwcHKCnp/fM26qudevWoWfPnjA1Na32NhwcHNQ/ZhLVNSZmRBVIS0vD/v378c0336Bnz55wc3NDp06dMGnSJLzwwgsASrrpAcCLL74ImUymnp4yZQratGmD//u//4OHhwcMDQ3V23zzzTdha2sLlUqFXr164cSJE+p9njhxAj179oSZmRlUKhXat2+Po0ePAij5JfD555+HpaUlTExM0LJlS2zevLladQsPD8fvv/+unn7w4AGioqIQHh5ebnk7Ozs4OjqiR48e+Pzzz3H27FlcvnwZR44cQZ8+fWBjYwNzc3MEBATg2LFj1YqJiIhKrFq1Cj4+PjAyMoK1tTWCgoKQnZ2NKVOmYPHixVi3bp26J0N0dDSuXbsGmUyGP//8EwEBATA0NMTSpUuRmpqKYcOGoVGjRjA2NoaPjw+WL1+usa/HuzK6u7vj66+/xuuvvw4zMzO4urrif//7X5XrUNrjYtu2bWjbti2MjIzQq1cvpKSkYMuWLWjRogVUKhWGDx+OnJwcjXjGjRuH8ePHw9LSEvb29liwYAGys7MxatQomJmZoWnTptiyZUuZfa5bt07dPpeeWfz6669hb28PCwsLfPHFFygsLMTEiRNhZWUFZ2dnLFy4sMp1I6otTMyIKmBqagpTU1OsXbsWeXl55ZY5cuQIAGDhwoW4ffu2ehoALl++jL/++gurV69W97l/+eWX1Y1SXFwc2rVrh969e+PevXsAgLCwMDg7O+PIkSOIi4vDJ598An19fQBAREQE8vLysG/fPpw6dQrffPNNtX8VHDlyJPbv348bN24AAP766y+4u7ujXbt2T13XyMgIQMlZwMzMTISHh+Pvv//GwYMH0axZM/Tv3x+ZmZnViouIqKG7ffs2hg0bhtdffx3nzp1DdHQ0Bg0aBCEE/vOf/2DIkCHo168fbt++jdu3b6NLly7qdT/55BO8//77OHfuHIKDg5Gbm4v27dtj06ZNOH36NEaPHo2RI0fi8OHDT4zh22+/RYcOHXD8+HG8++67GDNmDC5cuFCt+kyZMgU//vgjYmJicPPmTQwZMgRz587FsmXLsGnTJmzfvh3z5s3TWGfx4sWwsbHB4cOHMW7cOIwZMwYvv/wyunTpgmPHjqFv374YOXKkRkKXlpaGv//+W52YAcDu3buRmJiIffv2Yc6cOZg8eTKee+45WFpa4tChQ3jnnXfw9ttv459//qlW3YhqnCCiCq1atUpYWloKQ0ND0aVLFzFp0iRx4sQJjTIAxJo1azTmTZ48Wejr64uUlBT1vP379wuVSiVyc3M1yjZp0kT8+uuvQgghzMzMxKJFi8qNxcfHR0yZMqXSsbu5uYnvvvtOY96ePXsEAHH//n0xcOBAMXXqVCGEED179hTff/+9WLNmjXj0sPBoeSGESExMFF26dBGNGjUSeXl5ZfZZVFQkzMzMxIYNGyrcLxERVSwuLk4AENeuXSt3eXh4uBgwYIDGvISEBAFAzJ0796nbDw0NFR9++KF6OiAgQLz//vvqaTc3NzFixAj1dHFxsbCzsxM///xzhdt8fBtC/Hvc37lzp3re9OnTBQBx5coV9by3335bBAcHa2yrW7du6unCwkJhYmIiRo4cqZ53+/ZtAUDExsaq5y1dulR06NBBPR0eHi7c3NxEUVGRep6np6fo3r17mW0vX75cI/aFCxcKc3PzCutLVFt4xozoCQYPHozExESsX78e/fr1Q3R0NNq1a4dFixY9dV03NzfY2tqqp0+cOIGsrCxYW1urz8aZmpoiISEBV65cAQBMmDABb775JoKCgjBjxgz1fAB477338OWXX6Jr166YPHkyTp48+Ux1e/3117Fo0SJcvXoVsbGxCAsLq7Css7MzTExM4OTkhOzsbPz1119QKpVITk7GW2+9hWbNmsHc3BwqlQpZWVnqM3FERFQ1rVu3Ru/eveHj44OXX34ZCxYswP379yu1bocOHTSmi4qKMG3aNPj4+MDKygqmpqbYtm3bU4/Rvr6+6ucymQwODg5ISUmpemUe25a9vT2MjY3RuHFjjXmPb/vRdRQKBaytreHj46OxDgCN9R7txliqZcuWkMvlGus9up3SbVe3bkQ1jYkZ0VMYGhqiT58++OyzzxATE4PXXnsNkydPfup6JiYmGtNZWVlwdHREfHy8xuPChQuYOHEigJIuH2fOnEFoaCh2794Nb29v9SAdb775Jq5evYqRI0fi1KlT6NChQ5nuH1UREhKCBw8e4I033sDzzz8Pa2vrCsvu378fJ0+eREZGBuLj4+Hn5weg5Fq1+Ph4fP/994iJiUF8fDysra0lG+yEiEjXKRQK7NixA1u2bIG3tzfmzZsHT09PJCQkPHXdx9udWbNm4fvvv8fHH3+MPXv2ID4+HsHBwU89Rpd2oS8lk8lQXFxc9co8ti2ZTFapbZdX5vHtAFCvl5+fj61bt5ZJzJ62nYr2TyQVJmZEVeTt7Y3s7Gz1tL6+fqVGKGzXrh2SkpKgp6eHpk2bajxsbGzU5Zo3b44PPvgA27dvx6BBgzQuTHZxccE777yD1atX48MPP8SCBQuqXQ89PT28+uqriI6Oxuuvv/7Esh4eHmjSpEmZkbsOHDiA9957D/3790fLli1hYGCAu3fvVjsmIiIqSRa6du2KqVOn4vjx41Aqleof6ZRKZaVHxT1w4AAGDBiAESNGoHXr1mjcuDEuXrxYm6FLIjo6GpaWlmjdurXUoRA9EyZmRBVITU1Fr169EBkZiZMnTyIhIQErV67EzJkzMWDAAHU5d3d37Nq1C0lJSU/sbhIUFAR/f38MHDgQ27dvx7Vr1xATE4P//ve/OHr0KB48eICxY8ciOjoa169fx4EDB3DkyBG0aNECADB+/Hhs27YNCQkJOHbsGPbs2aNeVl3Tpk3DnTt3EBwcXK31mzVrhiVLluDcuXM4dOgQwsLC1IODEBFR1R06dAhff/01jh49ihs3bmD16tW4c+eO+njv7u6OkydP4sKFC7h79+4Th8Vv1qwZduzYgZiYGJw7dw5vv/02kpOT66oqdWb9+vVlzpYR6SImZkQVMDU1hZ+fH7777jv06NEDrVq1wmeffYa33noLP/74o7rct99+ix07dsDFxQVt27atcHsymQybN29Gjx49MGrUKDRv3hxDhw7F9evXYW9vD4VCgdTUVLz66qto3rw5hgwZgpCQEEydOhVAybUCERERaNGiBfr164fmzZtj/vz5z1RHpVIJGxubMvc5q6zffvsN9+/fR7t27TBy5Ei89957sLOze6aYiIgaMpVKhX379qF///5o3rw5Pv30U3z77bcICQkBUHIjZ09PT3To0AG2trY4cOBAhdv69NNP0a5dOwQHByMwMBAODg41enNqbcHEjOoLmRBCSB0EEdU8d3d3jB8/XuP+NFKJjo5Gz549cf/+fVhYWEgdDhER1aDAwEC0adMGc+fOrfN9Hzt2DL169cKdO3fKXD9WXYsWLcL48eORlpZWI9sjqiyeMSOqxz7++GOYmpoiPT1dshhatmyp/qWXiIjqp/nz58PU1BSnTp2q0/0WFhZi3rx5NZaUmZqa4p133qmRbRFVFc+YEdVT169fV1970LhxY40hgxtiHEREVDtu3bqFBw8eAABcXV2hVColjqj6Ll++DKBkdEwPDw+Jo6GGhokZEREREf3/duyYAAAAACBY/9ZieLYWADPrGgAAYCbMAAAAZsIMAABgJswAAABmwgwAAGAmzAAAAGbCDAAAYCbMAAAAZgF/Ou87I5ZOegAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -151,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -169,32 +157,8 @@
     }
    ],
    "source": [
-    "long_beam = 6.75\n",
-    "\n",
-    "# caracteriscic load\n",
-    "qk=12.2 + 5.2 + 11.7 # kN/m\n",
-    "# cuasipermant load\n",
-    "q_cuasip=12.2 + 5.2 + 11.7 * 0.3 # kN/m\n",
-    "\n",
-    "fi = 2.5\n",
-    "\n",
-    "# Mcuasip at midspan\n",
-    "m_cuasip = q_cuasip * long_beam**2/8\n",
-    "\n",
-    "# Create section to get hc_eff\n",
-    "# DGM: as creating rectangular reinforced cross sections would be an ordinary task, \n",
-    "# a wrapper function for creating the rectangular section plus the override of the __add__ operator \n",
-    "# could lead to a significant smoother approach like: 'section.rectangle(b, h, mat) + reinf_line() + reinf_line() + [...]'\n",
-    "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
-    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
-    "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50),12, reinforcement, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
-    "sec = GenericSection(geo)\n",
     "hc_eff = _section_7_3_crack_control.hc_eff(500,450,500-sec.gross_properties.cz)\n",
     "Ec_eff = concrete.Ecm/(1+fi)\n",
-    "\n",
     "alfa_e = _section_7_3_crack_control.alpha_e(200000,Ec_eff)\n",
     "rho_p = _section_7_3_crack_control.rho_p_eff(6* math.pi*100, 0 ,0,hc_eff*350)  # b=350mm, fi=20mm\n",
     "kt = _section_7_3_crack_control.kt('long')\n",
@@ -206,8 +170,7 @@
     "print(' - rho_p =',round(rho_p,5))\n",
     "print(' - kt =',kt)\n",
     "print(' - sigma_s (MPa) =',round(sigma_s))\n",
-    "print(' - fct_eff (MPa) =',round(fct_eff,2))\n",
-    "\n"
+    "print(' - fct_eff (MPa) =',round(fct_eff,2))"
    ]
   },
   {
@@ -264,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {

From a48150c72c59b20f3fa8d481ee5191e1b3cdf67e Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Mon, 29 Jul 2024 12:34:10 +0200
Subject: [PATCH 29/33] minor changes

---
 EX2_part1.ipynb | 12 ++++++------
 EX3.ipynb       | 12 ++++++------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/EX2_part1.ipynb b/EX2_part1.ipynb
index 68980b00..961f664b 100644
--- a/EX2_part1.ipynb
+++ b/EX2_part1.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -227,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
diff --git a/EX3.ipynb b/EX3.ipynb
index 1a558dd1..402d08aa 100644
--- a/EX3.ipynb
+++ b/EX3.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,12 +51,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -186,7 +186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,

From 2a1e7b7b71e81f209d8ece6a3ccefe2e6b9bc29a Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Tue, 20 Aug 2024 08:15:46 +0200
Subject: [PATCH 30/33] changes

---
 Issues.ipynb | 195 ++++++++++++++++++++++++++++-----------------------
 1 file changed, 109 insertions(+), 86 deletions(-)

diff --git a/Issues.ipynb b/Issues.ipynb
index 6d322035..ef9a46e9 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -84,7 +84,7 @@
     },
     {
      "data": {
-      "image/png": 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kIDc3F/v27cPw4cOV/SaTCS0tLWhsbPR69OFwOGAymZQxBw4c8Lq99mdj2secT6fTQafTyUyViHqJT488hBDIyMjAtm3bsGfPHsTGxnodT0xMRGBgIAoKCpR9VVVVqKmpgdlsBgCYzWZUVFSgoaFBGZOfnw+9Xo/4+PjurIWI+pBPjzysVis2b96M999/H6Ghoco1CoPBgMGDB8NgMGDBggXIyspCeHg49Ho9MjMzYTabMWXKFADAjBkzEB8fj/vvvx8rV66E3W7H0qVLYbVa+eiCqB/xKR7r168HANx0001e+zdu3IgHH3wQALBq1Sqo1WqkpqbC7XYjOTkZ69atU8ZqNBrk5uZi4cKFMJvNCA4ORlpaGlasWNG9lRBRn+rW6zz8ha/zIJJ3SbzOg4guX4wHEUlhPIhICuNBRFIYDyKSwngQkRTGg4ikMB5EJIXxICIpjAcRSWE8iEgK40FEUhgPIpLCeBCRFMaDiKQwHkQkhfEgIimMBxFJYTyISArjQURSGA8iksJ4EJEUxoOIpDAeRCSF8SAiKYwHEUlhPIhICuNBRFIYDyKSwngQkRTGg4ikMB5EJIXxICIpjAcRSWE8iEgK40FEUhgPIpLCeBCRFMaDiKQwHkQkhfEgIimMBxFJYTyISArjQURSGA8ikuJTPNavX4+EhATo9Xro9XqYzWbk5eUpx8+ePQur1YqhQ4ciJCQEqampcDgcXrdRU1ODlJQUBAUFITIyEkuWLEFra2vPrIaI+oxP8Rg+fDhefPFFlJWV4eDBg7jllltw1113obKyEgCwePFifPjhh9i6dSsKCwtRV1eH2bNnK+e3tbUhJSUFLS0tKCoqwltvvYVNmzZh2bJlPbsqIup1KiGE6M4NhIeH46WXXsKcOXMQERGBzZs3Y86cOQCAo0ePYuzYsbDZbJgyZQry8vJw++23o66uDkajEQCwYcMGPPnkkzh58iS0Wm2X7tPlcsFgMMDpdEKv119wXFt9PZpff707yyMacELS06GJirrg8a7+fElf82hra8OWLVtw+vRpmM1mlJWV4dy5c7BYLMqYuLg4xMTEwGazAQBsNhvGjx+vhAMAkpOT4XK5lEcvnXG73XC5XF4bEfmXz/GoqKhASEgIdDodHnnkEWzbtg3x8fGw2+3QarUICwvzGm80GmG32wEAdrvdKxztx9uPXUhOTg4MBoOyjRgxwtdpE1EP8zkeY8aMQXl5OUpKSrBw4UKkpaXhyJEjvTE3RXZ2NpxOp7LV1tb26v0R0U8L8PUErVaLUaNGAQASExNRWlqKV155Bffeey9aWlrQ2Njo9ejD4XDAZDIBAEwmEw4cOOB1e+3PxrSP6YxOp4NOp/N1qkTUi7r9Og+PxwO3243ExEQEBgaioKBAOVZVVYWamhqYzWYAgNlsRkVFBRoaGpQx+fn50Ov1iI+P7+5UiKgP+fTIIzs7GzNnzkRMTAyampqwefNmfPLJJ9i1axcMBgMWLFiArKwshIeHQ6/XIzMzE2azGVOmTAEAzJgxA/Hx8bj//vuxcuVK2O12LF26FFarlY8siPoZn+LR0NCABx54APX19TAYDEhISMCuXbswffp0AMCqVaugVquRmpoKt9uN5ORkrFu3Tjlfo9EgNzcXCxcuhNlsRnBwMNLS0rBixYqeXRUR9bpuv87DH/g6DyJ5fn+dBxFd3hgPIpLCeBCRFMaDiKQwHkQkhfEgIimMBxFJYTyISArjQURSGA8iksJ4EJEUxoOIpDAeRCSF8SAiKYwHEUlhPIhICuNBRFIYDyKSwngQkRTGg4ikMB5EJIXxICIpjAcRSWE8iEgK40FEUhgPIpLCeBCRFMaDiKQwHkQkhfEgIimMBxFJYTyISArjQURSGA8iksJ4EJEUxoOIpDAeRCSF8SAiKYwHEUlhPIhICuNBRFIYDyKSwngQkRTGg4ikMB5EJKVb8XjxxRehUqmwaNEiZd/Zs2dhtVoxdOhQhISEIDU1FQ6Hw+u8mpoapKSkICgoCJGRkViyZAlaW1u7MxUi6mPS8SgtLcVrr72GhIQEr/2LFy/Ghx9+iK1bt6KwsBB1dXWYPXu2crytrQ0pKSloaWlBUVER3nrrLWzatAnLli2TXwUR9TmpeDQ3N2PevHn405/+hCFDhij7nU4n3njjDbz88su45ZZbkJiYiI0bN6KoqAjFxcUAgN27d+PIkSN4++23MXHiRMycORPPPfcc1q5di5aWlp5ZFRH1Oql4WK1WpKSkwGKxeO0vKyvDuXPnvPbHxcUhJiYGNpsNAGCz2TB+/HgYjUZlTHJyMlwuFyorKzu9P7fbDZfL5bURkX8F+HrCli1b8Nlnn6G0tLTDMbvdDq1Wi7CwMK/9RqMRdrtdGfPjcLQfbz/WmZycHDz77LO+TpWIepFPjzxqa2vx2GOP4Z133sGgQYN6a04dZGdnw+l0KlttbW2f3TcRdc6neJSVlaGhoQHXXXcdAgICEBAQgMLCQrz66qsICAiA0WhES0sLGhsbvc5zOBwwmUwAAJPJ1OHZl/av28ecT6fTQa/Xe21E5F8+xWPatGmoqKhAeXm5sk2aNAnz5s1T/jkwMBAFBQXKOVVVVaipqYHZbAYAmM1mVFRUoKGhQRmTn58PvV6P+Pj4HloWEfU2n655hIaGYty4cV77goODMXToUGX/ggULkJWVhfDwcOj1emRmZsJsNmPKlCkAgBkzZiA+Ph73338/Vq5cCbvdjqVLl8JqtUKn0/XQsoiot/l8wfSnrFq1Cmq1GqmpqXC73UhOTsa6deuU4xqNBrm5uVi4cCHMZjOCg4ORlpaGFStW9PRUiKgXqYQQwt+T8JXL5YLBYIDT6bzo9Y+2+no0v/56H86M6NIXkp4OTVTUBY939eeL720hIimMBxFJYTyISArjQURSGA8iksJ4EJEUxoOIpDAeRCSF8SAiKYwHEUlhPIhICuNBRFIYDyKSwngQkRTGg4ikMB5EJIXxICIpjAcRSWE8iEgK40FEUhgPIpLCeBCRFMaDiKQwHkQkhfEgIimMBxFJYTyISArjQURSGA8iksJ4EJEUxoOIpDAeRCSF8SAiKYwHEUlhPIhICuNBRFIYDyKSwngQkRTGg4ikMB5EJIXxICIpjAcRSWE8iEgK40FEUhgPIpLiUzyeeeYZqFQqry0uLk45fvbsWVitVgwdOhQhISFITU2Fw+Hwuo2amhqkpKQgKCgIkZGRWLJkCVpbW3tmNUTUZwJ8PeHaa6/Fxx9//L8bCPjfTSxevBgfffQRtm7dCoPBgIyMDMyePRuffvopAKCtrQ0pKSkwmUwoKipCfX09HnjgAQQGBuIPf/hDDyyHiPqKz/EICAiAyWTqsN/pdOKNN97A5s2bccsttwAANm7ciLFjx6K4uBhTpkzB7t27ceTIEXz88ccwGo2YOHEinnvuOTz55JN45plnoNVqu78iIuoTPl/zOHbsGKKjo3HVVVdh3rx5qKmpAQCUlZXh3LlzsFgsyti4uDjExMTAZrMBAGw2G8aPHw+j0aiMSU5OhsvlQmVl5QXv0+12w+VyeW1E5F8+xSMpKQmbNm3Czp07sX79elRXV+OGG25AU1MT7HY7tFotwsLCvM4xGo2w2+0AALvd7hWO9uPtxy4kJycHBoNB2UaMGNGl+aqCgoAAnx9cEQ1cAQE//Fz0xE35MnjmzJnKPyckJCApKQkjR47E3//+dwwePLhHJtSZ7OxsZGVlKV+7XK4uBURtMCA0IwPizJlemxtRf6IKCoLaYOiR2+rW/5bDwsJwzTXX4KuvvsL06dPR0tKCxsZGr0cfDodDuUZiMplw4MABr9tofzams+so7XQ6HXQ6ndQc1QYD0EPfLCL6n269zqO5uRnHjx9HVFQUEhMTERgYiIKCAuV4VVUVampqYDabAQBmsxkVFRVoaGhQxuTn50Ov1yM+Pr47UyGiviZ88Pjjj4tPPvlEVFdXi08//VRYLBYxbNgw0dDQIIQQ4pFHHhExMTFiz5494uDBg8JsNguz2ayc39raKsaNGydmzJghysvLxc6dO0VERITIzs72ZRrC6XQKAMLpdPp0HhH9tK7+fPn0a8t//vMf3Hffffj2228RERGBqVOnori4GBEREQCAVatWQa1WIzU1FW63G8nJyVi3bp1yvkajQW5uLhYuXAiz2Yzg4GCkpaVhxYoVPdlDIuoDKiGE8PckfOVyuWAwGOB0OqHX6/09HaIBpas/X3xvCxFJYTyISArjQURSGA8iksJ4EJEUxoOIpPTLd421P7vMd9cS9bz2n6ufehVHv4xHU1MTAHT53bVE5LumpiYYLvK+sH75IjGPx4O6ujqEhoZCpVJdcFz7u29ra2sH5IvJuL7+7VJdnxACTU1NiI6Ohlp94Ssb/fKRh1qtxvDhw7s8Xq/XX1L/cnoa19e/XYrru9gjjna8YEpEUhgPIpIyoOOh0+mwfPly6T8kdKnj+vq3/r6+fnnBlIj8b0A/8iCi3sN4EJEUxoOIpDAeRCRlwMZj7dq1uPLKKzFo0CAkJSV1+MiHS9W+fftwxx13IDo6GiqVCtu3b/c6LoTAsmXLEBUVhcGDB8NiseDYsWNeY06dOoV58+ZBr9cjLCwMCxYsQHNzcx+u4sJycnIwefJkhIaGIjIyErNmzUJVVZXXmP78genr169HQkKC8sIvs9mMvLw85Xh/XlsHvfpnmP1ky5YtQqvVijfffFNUVlaKhx9+WISFhQmHw+Hvqf2kHTt2iN///vfivffeEwDEtm3bvI6/+OKLwmAwiO3bt4vPP/9c3HnnnSI2NlZ8//33yphbb71VTJgwQRQXF4t//etfYtSoUeK+++7r45V0Ljk5WWzcuFEcPnxYlJeXi9tuu03ExMSI5uZmZcwjjzwiRowYIQoKCsTBgwfFlClTxM9//nPlePtf4bdYLOLQoUNix44dYtiwYT7/Ff7e8MEHH4iPPvpI/Pvf/xZVVVXid7/7nQgMDBSHDx8WQvTvtZ1vQMbj+uuvF1arVfm6ra1NREdHi5ycHD/Oynfnx8Pj8QiTySReeuklZV9jY6PQ6XTib3/7mxBCiCNHjggAorS0VBmTl5cnVCqV+O9//9tnc++qhoYGAUAUFhYKIX5YT2BgoNi6dasy5ssvvxQAhM1mE0L8EFi1Wi3sdrsyZv369UKv1wu32923C+iCIUOGiD//+c8Dbm0D7teWlpYWlJWVeX3gtlqthsViUT5wu7+qrq6G3W73WpvBYEBSUpLXh4mHhYVh0qRJyhiLxQK1Wo2SkpI+n/NPcTqdAIDw8HAAvfuB6X2tra0NW7ZswenTp2E2mwfU2oB++sa4i/nmm2/Q1tbW6QdqHz161E+z6hntHwbe2dp+/GHikZGRXscDAgIQHh5+0Q8T9wePx4NFixbhF7/4BcaNGwcAvfqB6X2loqICZrMZZ8+eRUhICLZt24b4+HiUl5f3+7X92ICLB/UfVqsVhw8fxv79+/09lR41ZswYlJeXw+l04h//+AfS0tJQWFjo72n1uAH3a8uwYcOg0Wg6XMH+8Qdu91ft87/Y2kwmk9dnAQNAa2srTp06dUmtPyMjA7m5udi7d6/Xn1cwmUzKB6b/2Plr7Ox70H7M37RaLUaNGoXExETk5ORgwoQJeOWVVwbE2n5swMVDq9UiMTHR6wO3PR4PCgoKlA/c7q9iY2NhMpm81uZyuVBSUuL1YeKNjY0oKytTxuzZswcejwdJSUl9PufzCSGQkZGBbdu2Yc+ePYiNjfU6PhA/MN3j8cDtdg+8tfn7im1v2LJli9DpdGLTpk3iyJEjIj09XYSFhXldwb5UNTU1iUOHDolDhw4JAOLll18Whw4dEl9//bUQ4oenasPCwsT7778vvvjiC3HXXXd1+lTtz372M1FSUiL2798vRo8efck8Vbtw4UJhMBjEJ598Iurr65XtzJkzypi++sD03vDUU0+JwsJCUV1dLb744gvx1FNPCZVKJXbv3i2E6N9rO9+AjIcQQqxZs0bExMQIrVYrrr/+elFcXOzvKXXJ3r17BYAOW1pamhDih6drn376aWE0GoVOpxPTpk0TVVVVXrfx7bffivvuu0+EhIQIvV4v5s+fL5qamvywmo46WxsAsXHjRmXM999/Lx599FExZMgQERQUJO6++25RX1/vdTsnTpwQM2fOFIMHDxbDhg0Tjz/+uDh37lwfr6ajhx56SIwcOVJotVoREREhpk2bpoRDiP69tvPxLflEJGXAXfMgor7BeBCRFMaDiKQwHkQkhfEgIimMBxFJYTyISArjQURSGA8iksJ4EJEUxoOIpDAeRCTl/wHnOHk/n4D0vwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -94,7 +94,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -146,12 +146,12 @@
    "source": [
     "# Materials\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "reinforcement = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcement, n=6)\n",
     "sec = GenericSection(geo, integrator='marin')\n",
     "print(f\"area = {round(sec.gross_properties.area/(10**2))} cm2\")\n",
     "print(f\"area_reinforcement = {round(sec.gross_properties.area_reinforcement/(10**2))} cm2\")\n",
@@ -179,19 +179,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -202,40 +192,68 @@
     },
     {
      "data": {
-      "image/png": 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+      ],
       "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
+       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x15e8411ae70>"
       ]
      },
+     "execution_count": 51,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My_max_rotated [mkN]=-255\n"
-     ]
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# Materials\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "reinforcement = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)\n",
+    "\n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=10)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcemnet, n=10)\n",
-    "sec = GenericSection(geo, integrator='marin')\n",
-    "\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=10)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcement, n=10)\n",
+    "geo = geo.translate(-175,-200)\n",
+    "sec = GenericSection(geo)\n",
     "\n",
-    "draw_section(sec)\n",
+    "# Draw & print\n",
     "print(f\"Mz_max [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_z/1000**2)}\")\n",
     "print(f\"Mz_max [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=math.pi/2).m_y/1000**2)}\")\n",
-    "sec.geometry= sec.geometry.rotate(math.pi/2)\n",
-    "draw_section(sec)\n",
-    "print(f\"My_max_rotated [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_y/1000**2)}\")\n"
+    "sec.geometry\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mz_max [mkN]=0\n",
+      "Mz_max [mkN]=0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rotated section\n",
+    "new_geo = geo.rotate(math.pi/2)\n",
+    "new_sec = GenericSection(new_geo)\n",
+    "draw_section(new_sec)\n",
+    "print(f\"Mz_max [mkN]={round(new_sec.section_calculator.calculate_bending_strength(theta=0).m_z/1000**2)}\")\n",
+    "print(f\"Mz_max [mkN]={round(new_sec.section_calculator.calculate_bending_strength(theta=math.pi/2).m_y/1000**2)}\")"
    ]
   },
   {
@@ -254,7 +272,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -274,12 +292,12 @@
    "source": [
     "# Materials\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "reinforcement = ReinforcementEC2_2023(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
     "sec = GenericSection(geo)\n",
     "draw_section(sec)\n",
     "\n",
@@ -300,40 +318,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-3500 -1082.3516216091132\n",
-      "-3000 -1039.8614525599924\n",
-      "-2500 -968.2633649200853\n",
-      "-2000 -869.2276407658625\n",
-      "-1500 -715.0189200393919\n",
-      "-1000 -512.7545687674593\n",
-      "-500 -291.01410396661913\n",
-      "0 -65.77991375540094\n",
-      "500 160.05431990676033\n",
-      "900 343.05573912271814\n",
-      "-0.0007827106434851892\n",
-      "2.589796122664981e-06\n"
+      "-3500 -142.97802173067802\n",
+      "-3000 -234.68408069492062\n",
+      "-2500 -297.2822197042793\n",
+      "-2000 -332.44272554576077\n",
+      "-1500 -312.4302326436426\n",
+      "-1000 -244.36211210538195\n",
+      "-500 -156.81787347217988\n",
+      "0 -65.77991345543168\n",
+      "500 25.858090576155707\n",
+      "900 101.50252856107927\n",
+      "-0.00015388452820479877\n",
+      "4.370451609244653e-09\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -345,23 +353,36 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z neutral axis = 302.23\n"
+      "z neutral axis = 35210.21\n"
      ]
+    },
+    {
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+      ],
+      "text/plain": [
+       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x15e83a7e0c0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "from  SLS_section_response import calculate_strain_profile\n",
     "from  structuralcodes.plots.section_plots import draw_section_response, draw_N_M_diagram\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
     "sec = GenericSection(geo)\n",
-    "#sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
-    "draw_section(sec)\n",
+    "sec.geometry = sec.geometry.translate(-sec.gross_properties.cy, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
+    "#draw_section(sec)\n",
     "\n",
     "# 1\n",
     "n_ = [-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,900] # kN\n",
@@ -377,7 +398,9 @@
     "eps,chi= calculate_strain_profile(sec,n_ed*1e3,my_ed*1e6)\n",
     "print(eps)\n",
     "print(chi)\n",
-    "draw_section_response(sec,eps,chi)"
+    "draw_section_response(sec,eps,chi)\n",
+    "\n",
+    "sec.geometry"
    ]
   },
   {
@@ -392,16 +415,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epsa= 0.002741093057504882 similar to 2.703e-3 (validation result)\n",
+      "epsa= -0.0003794534712475606 similar to 2.703e-3 (validation result)\n",
       "chi= -1.248218611500977e-05 similar to 12.3e-6 (validation result)\n",
-      "my = -869 <> 348.85 (validation result)\n"
+      "my = -369 <> 348.85 (validation result)\n"
      ]
     }
    ],
@@ -489,12 +512,12 @@
    ],
    "source": [
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
     "sec = GenericSection(geo)\n",
     "draw_section(sec)\n",
     "\n",
@@ -560,12 +583,12 @@
    ],
    "source": [
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07)  \n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
     "sec = GenericSection(geo)\n",
     "# sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
     "draw_section(sec)\n",
@@ -606,12 +629,12 @@
    ],
    "source": [
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
-    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcemnet, n=3)\n",
-    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcemnet, n=6)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
     "sec = GenericSection(geo)\n",
     "#sec.geometry = sec.geometry.translate(0, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
     "\n",

From 7a44d64391cffda154993c71ea043d26216c3f4c Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Tue, 20 Aug 2024 09:46:15 +0200
Subject: [PATCH 31/33] clarification in rotated section results

---
 Issues.ipynb | 98 ++++++++++++++++++++++++++++++++--------------------
 1 file changed, 60 insertions(+), 38 deletions(-)

diff --git a/Issues.ipynb b/Issues.ipynb
index ef9a46e9..8e6ca27d 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,8 @@
     "from structuralcodes.plots.section_plots import draw_section,draw_constitutive_law,draw_My_Mz_diagram,draw_section_response,draw_N_M_diagram\n",
     "import math\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n"
+    "import numpy as np\n",
+    "from pprint import pprint\n"
    ]
   },
   {
@@ -32,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -73,13 +74,13 @@
      "output_type": "stream",
      "text": [
       "I11 = 364583 cm2\n",
-      "I11_neg = 6823345 cm4\n",
+      "I11_neg = 364583 cm4\n",
       "Sy = 43750 cm3\n",
-      "Sy_neg = 43750 cm3\n",
+      "Sy_neg = -43750 cm3\n",
       "I22 = 178646 cm2\n",
-      "I22_neg = 238634 cm4\n",
+      "I22_neg = 178646 cm4\n",
       "Sz = 30625 cm3\n",
-      "Sz_neg = 30625 cm3\n"
+      "Sz_neg = -30625 cm3\n"
      ]
     },
     {
@@ -130,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -138,8 +139,7 @@
      "output_type": "stream",
      "text": [
       "area = 1750 cm2\n",
-      "area_reinforcement = 22 cm2\n",
-      "area_surface = 1750 cm2\n"
+      "area_reinforcement = 22 cm2\n"
      ]
     }
    ],
@@ -155,7 +155,7 @@
     "sec = GenericSection(geo, integrator='marin')\n",
     "print(f\"area = {round(sec.gross_properties.area/(10**2))} cm2\")\n",
     "print(f\"area_reinforcement = {round(sec.gross_properties.area_reinforcement/(10**2))} cm2\")\n",
-    "print(f\"area_surface = {round(sec.gross_properties.area_surface/(10**2))} cm2\")"
+    "# print(f\"area_surface = {round(sec.gross_properties.area_surface/(10**2))} cm2\") DGM: area_surface is no longer defined in the new update"
    ]
   },
   {
@@ -179,27 +179,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mz_max [mkN]=0\n",
-      "Mz_max [mkN]=0\n"
+      "====== Theta = 0 ======\n",
+      "UltimateBendingMomentResults(theta=0,\n",
+      "                             n=-0.0029919015942141414,\n",
+      "                             m_y=-562853428.6034824,\n",
+      "                             m_z=1.0471996460227893,\n",
+      "                             chi_y=-3.909551620178221e-05,\n",
+      "                             chi_z=0,\n",
+      "                             eps_a=0.025821637151336656)\n",
+      "====== Theta = pi/2 ======\n",
+      "UltimateBendingMomentResults(theta=1.5707963267948966,\n",
+      "                             n=-0.004536690656095743,\n",
+      "                             m_y=-2.268342482223884,\n",
+      "                             m_z=-255115348.5413602,\n",
+      "                             chi_y=-2.689176391228454e-05,\n",
+      "                             chi_z=0,\n",
+      "                             eps_a=-0.008206058684649797)\n"
      ]
     },
     {
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       ],
       "text/plain": [
-       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x15e8411ae70>"
+       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x1def1bdb560>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,46 +228,54 @@
     "geo = SurfaceGeometry(poly, concrete)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=10)\n",
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 20, reinforcement, n=10)\n",
-    "geo = geo.translate(-175,-200)\n",
+    "geo = geo.translate(-175,-250)\n",
     "sec = GenericSection(geo)\n",
     "\n",
     "# Draw & print\n",
-    "print(f\"Mz_max [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=0).m_z/1000**2)}\")\n",
-    "print(f\"Mz_max [mkN]={round(sec.section_calculator.calculate_bending_strength(theta=math.pi/2).m_y/1000**2)}\")\n",
+    "print(\"====== Theta = 0 ======\")\n",
+    "pprint(sec.section_calculator.calculate_bending_strength(theta=0))\n",
+    "print(\"====== Theta = pi/2 ======\")\n",
+    "pprint(sec.section_calculator.calculate_bending_strength(theta=math.pi/2))\n",
     "sec.geometry\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UltimateBendingMomentResults(theta=0,\n",
+      "                             n=-0.004536679247394204,\n",
+      "                             m_y=-255115351.7170453,\n",
+      "                             m_z=-2.268342404117335,\n",
+      "                             chi_y=-2.689176391228454e-05,\n",
+      "                             chi_z=0,\n",
+      "                             eps_a=0.010618176053949381)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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+      ],
       "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
+       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x1def1b48d10>"
       ]
      },
+     "execution_count": 33,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mz_max [mkN]=0\n",
-      "Mz_max [mkN]=0\n"
-     ]
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# Rotated section\n",
-    "new_geo = geo.rotate(math.pi/2)\n",
-    "new_sec = GenericSection(new_geo)\n",
-    "draw_section(new_sec)\n",
-    "print(f\"Mz_max [mkN]={round(new_sec.section_calculator.calculate_bending_strength(theta=0).m_z/1000**2)}\")\n",
-    "print(f\"Mz_max [mkN]={round(new_sec.section_calculator.calculate_bending_strength(theta=math.pi/2).m_y/1000**2)}\")"
+    "rot_sec = GenericSection(geo.rotate(math.pi/2))\n",
+    "pprint(rot_sec.section_calculator.calculate_bending_strength(theta=0))\n",
+    "rot_sec.geometry"
    ]
   },
   {

From 70d4a03f86684ba91a3dbb91b1ea3358742f46a3 Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Tue, 20 Aug 2024 11:23:36 +0200
Subject: [PATCH 32/33] changes

---
 Issues.ipynb | 135 ++++++++++++++++++++++++++-------------------------
 1 file changed, 70 insertions(+), 65 deletions(-)

diff --git a/Issues.ipynb b/Issues.ipynb
index 8e6ca27d..5432271e 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -179,43 +179,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "====== SECTION ======\n",
       "====== Theta = 0 ======\n",
       "UltimateBendingMomentResults(theta=0,\n",
-      "                             n=-0.0029919015942141414,\n",
-      "                             m_y=-562853428.6034824,\n",
-      "                             m_z=1.0471996460227893,\n",
+      "                             n=-0.0029919976950623095,\n",
+      "                             m_y=-562853427.1074414,\n",
+      "                             m_z=4.4796883128622224e-08,\n",
       "                             chi_y=-3.909551620178221e-05,\n",
       "                             chi_z=0,\n",
-      "                             eps_a=0.025821637151336656)\n",
+      "                             eps_a=0.00627387905044555)\n",
       "====== Theta = pi/2 ======\n",
       "UltimateBendingMomentResults(theta=1.5707963267948966,\n",
-      "                             n=-0.004536690656095743,\n",
-      "                             m_y=-2.268342482223884,\n",
-      "                             m_z=-255115348.5413602,\n",
+      "                             n=-0.004536689957603812,\n",
+      "                             m_y=-4.6863929542362246e-08,\n",
+      "                             m_z=-255115350.12920046,\n",
       "                             chi_y=-2.689176391228454e-05,\n",
       "                             chi_z=0,\n",
-      "                             eps_a=-0.008206058684649797)\n"
+      "                             eps_a=0.001206058684649792)\n"
      ]
     },
     {
      "data": {
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       "text/plain": [
-       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x1def1bdb560>"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
-     "execution_count": 43,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -232,50 +230,62 @@
     "sec = GenericSection(geo)\n",
     "\n",
     "# Draw & print\n",
+    "print(\"====== SECTION ======\")\n",
     "print(\"====== Theta = 0 ======\")\n",
     "pprint(sec.section_calculator.calculate_bending_strength(theta=0))\n",
     "print(\"====== Theta = pi/2 ======\")\n",
     "pprint(sec.section_calculator.calculate_bending_strength(theta=math.pi/2))\n",
-    "sec.geometry\n"
+    "draw_section(sec)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "====== ROTATED SECTION ======\n",
+      "====== Theta = 0 ======\n",
       "UltimateBendingMomentResults(theta=0,\n",
-      "                             n=-0.004536679247394204,\n",
-      "                             m_y=-255115351.7170453,\n",
-      "                             m_z=-2.268342404117335,\n",
+      "                             n=-0.004536689957603812,\n",
+      "                             m_y=-255115350.12920046,\n",
+      "                             m_z=2.751732939644625e-08,\n",
       "                             chi_y=-2.689176391228454e-05,\n",
       "                             chi_z=0,\n",
-      "                             eps_a=0.010618176053949381)\n"
+      "                             eps_a=0.001206058684649792)\n",
+      "====== Theta = pi/2 ======\n",
+      "UltimateBendingMomentResults(theta=1.5707963267948966,\n",
+      "                             n=-0.0029919976950623095,\n",
+      "                             m_y=-7.926171552343454e-08,\n",
+      "                             m_z=-562853427.1074414,\n",
+      "                             chi_y=-3.909551620178221e-05,\n",
+      "                             chi_z=0,\n",
+      "                             eps_a=0.00627387905044555)\n"
      ]
     },
     {
      "data": {
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Jycn42c9+hl/84hf4j//4D/N7xMTEYPv27dizZw/GjBmDP/zhD/jf//1fpKamKlW2zUxJTbXqAyaEwBQNbC+RvRkyahQCw8JueTNvSZIQGReH0MGDbVRZ/5KEow7+7IPa2lr4+vqipqYG+j7cRq/tyhXUv/OOYnXlZWbi0M6dkCSpS+B3LrvrvvswcvJkxWogcmb1NTXYtnkz6mtquj/okiQMDAnBvCVL4NbLecD+4L1iBXRhYVa3tzbXOHeNikYlJcE/OBiHd+/GtStXLF4bGBqKKampCIuOVqc4Iifg7euLh595Bsf27UPh8eNoMxrNrw1wdUXClCkYO326w94wBGDIqy48JgYPP/006qqqLCYo83HQmwYTORpXd3dMnTsXk2fORGVZmXmCsoGhodC5OH5EOv4WaISPvz98eHKVSDUurq4IdeA7QPWE88kTEWkYj+TtwPXycpzPyzN31/gHBSFm5EgE3DSlAxEpo7WlBefz8y1v/xcdjegRIxy+y8axq3dwrS0tOLh9O86eONE+mqZj+YWCAvy4fz/ixozBXfPmaeLGBUT26mJhIfZ/9RVampogyTKEyQRJllF4/Dg8fXxw76OPIsxBh08C7K5RTZvRiJ0ffIBzJ08CaB8Pj45H51CusydOYOcHH8DU1qZmqUSadS43F7s//hgtHRMeds5I2flvY309tr/3nsPOQAkw5FWTc/AgyktKer0gSgiB8uJi5Pzwgw0rI3IOjXV1OPDVV7036jjw2vvZZ2jtuM+Fo2HIq6DNaMSJW8yJf6OcH36wGL9LRHeu4Ngxq/YrIQSaGxpw9sQJG1TV/xjyKqi8cgWtLS1Wt29tacG1jjn2iah/lJw9a/XcNZIk4fL58wpXpAyGvAoa6+r6vE7DbaxDRD1r6MMd44QQqK+pUbAa5TDkVeB6G3Pd3846RNQzV3f3PrVXeu4apTDkVRAYHt6nm4BIsoyg8HAFKyJyPmGDB99yBspOkiQhxM5uI2othrwK3Dw8MCQhwer55IcmJPT5qIOIehc/YcItb+LdSZIkDBszRuGKlMGQV8mU2bPh6u7ea9BLkgQ3Dw9MnjXLhpUROYfAsDAkWnmXuUkpKfB20EkDGfIq8fTxwf1Ll8Krl3mgvfR63P/kk/D08bFhZUTOY/KsWRjVcb+Gmw+4OrtyJsyYYfUvA3vEaQ1UFBAcjMdWrkTB8eM4e+IEqq9dAwD4BQZiaGIiRkyYABdOaUCkGFmWMfW++xCbkIC8I0dQWlQEQ0sL3Dw8MCg2FglTpmBgaKjaZd4RhrzKXFxdkZic7NBHCkSOLiQqCiEanGYYYHcNEZGmMeTtiKmtjZOREalICIE2o9HqK2EdAbtrVNbc2IiTGRk4e/Ik6qurAQA+fn4YOno0EpOTHfYCDCJHIYRA8enTyM3MRHlxMdqMRrgMGIDwmBiMnjrV4e+zzJBXUcWlS9j14YcwNDdbHDnUVVcj+/vvUXDsGOYsXoygiAgVqyTSrjajEfu/+grnTp5sv6dDx35obG1FyZkzKD59GglTpmDK7NlWXzhlbxyzag2orarCjvff7xLwnYQQaGlqwvb330ddxxE+EfWvH7Zvx7ncXADosh92Ps89fBg/7t9v89r6C0NeJYfT02G8Rd+fEALG1lZkpqfbsDIi51BeUoJTWVntc8bfQtaBA6jpGOLsaBjyKmhqaMDFwkKrLqkWJhMuFBaiubHRBpUROY/CH3/sUxfMmZwcBatRDkNeBZVXrvSpvRAC1/q4DhH1rry42Oq5a4QQqLh0SeGKlMGQV4HxNm4jZnDQW48R2au+3s7P0Icb/dgThrwKepuvpifevr4KVELkvPoy4ZgkSfDx91euGAUx5FUQGBYGtz5MHezu6YmBISEKVkTkfAYPG2b1fR2EEIiMjVW4ImUw5FUg63QYO3261e3HTp8OWadTsCIi5xNv5QSAkiTBS6/HkIQEG1TV/xjyKklITsagoUOB3o4kJAmRcXEYlZRku8KInIS7pydmPvpo+wibHvZDSZIg63SYtXAhXAYMsHGF/YMhrxJZljF70SIkJCW1/8koSZBkGXLHB06SJCROmYJZCxe2LyOifhcZF4d5v/iF+TyZLMuQOh4A4DtwIB5ctsyhrzrntAYq0rm4IHnOHCROmYKiggJUV1YCAPyDghA9YgRPthLZQFh0NBb9+tcoOXMGZcXFaG5qgoeXF8KjoxExZIjDTmfQiSFvB7z9/DifPJGKZJ0Og+PjMTg+Xu1S+p1j/4oiIqJe8UjeDgiTCdfKyszdNX5BQRgYGmr18C4iunNNDQ2ouHQJLR3dNcGDBmliqm+GvMqKCgqQmZ7eZaZJH39/TElNRbQG/3wksieNdXU4vHs3zufmWkwYKMky4sePx+SUFLj24boWe8PuGhX9uH8/vvnkk26nEq6rqsKejz9G1oEDti+MyEnUXLuGz996q0vAA+1/YRceP44v3nkHjXV1KlV45xjyKrl46hSO79t3y3bHvv0WxadP26AiIufSZjQifetWNDc19TjltxACddXV2PvZZw57S0CGvAqEEMjcvbv3C6E6SRIyd+922A8Ykb06d/IkaiorbzkTpTCZUHbxIi6dO2ejyvoXQ14FNZWV7TcgsCa4hUB1Z3si6jfn8/KsHtwgyTIu5OcrXJEyGPIqqL6NwGbIE/WvqqtXrf4LWZhMqLp6VeGKlMGQV8HtDI3kcEqiftbHfcpR90GGvAr8g4Jssg4R9WxgSEifumsCHHS6b4a8CvQBAVZf7CRJEgLDwx32hgVE9mro6NF96q6J5VTDytq0aROio6Ph7u6OpKQkHDlyRO2S7kjynDlWfcCEEEhOTbVBRUTOJWbECARFRNxyAjJJkjA4Ph6hgwfbqLL+5RAh/8knn2DVqlVYu3YtfvzxR4wZMwapqamoqKhQu7TbFhYdjenz5wPovq+vc9n0Bx5w2A8XkT2TOqb71vv79/pXdWB4OO5ZsMB2hfUzhwj5N954A8uXL8fSpUsxcuRIvPXWW/D09MS7776rdml3JH7CBDzwy192O1d18KBBeGDZMsSPH69CZUTOwdPHBwtWrEBicnKXm4K4urtjwowZmL90qUNPa2D3c9cYDAYcP34cq1evNi+TZRkpKSnIyMhQsbL+ERIVhQefegqNdXX/nKAsMBCePj4qV0bkHFzd3JA0ezYm3nsvrldUmCco8w8K0sRtN+0+5CsrK9HW1oaQm85sh4SEoLCwsNt1Wlpa0NLSYn5eW1uraI39wdPHh8FOpCKdiwuCwsPVLqPfOUR3TV+tX78evr6+5kdkZKTaJRERqcLuj+QDAwOh0+lQXl5usby8vByhoaHdrrN69WqsWrXK/Ly2ttaug/7q5cs4n5eHqqtXIUkS/IKCEDtqFAI1eFRBZI8Mzc04e/IkyoqL0dLYCA9vb4RHR2NIQoLD3sC7k92HvKurKyZMmIC9e/diQccZbpPJhL179yItLa3bddzc3ODm5mbDKm+PobkZB77+GkX5+ZBk2TxRUsnZszjxww8YMmoUpj/wAFwdYFuIHNX5vDwc+PprtLa0QJIkCCEgyTLO5OQg85tvcO/DDyMiNlbtMm+bQ3TXrFq1Cn/961/x3nvvoaCgAM8++ywaGhqwdOlStUu7bcbWVux4/31cKCgAAIuZ8Dr/X5Sfjx3vv482o1GVGom07uyJE9j76ado7TiH13ntSuc+2NzYiJ0ffOCwM1ACDhLyCxcuxOuvv441a9Zg7NixyM7Oxq5du7qcjHUk2d9/j6tXrvR6QZQQAlcvX0b2wYM2rIzIOTTU1uLA11/33kgICADffvYZDDcM5nAkDhHyAJCWloaLFy+ipaUFmZmZSEpKUruk29ZmNOLk4cPWTTUM4GRGBo/mifpZwbFjMLW13bqhEGhpasLZEyeUL0oBDhPyWlJZWgqjwWB1+9aWFlReuaJgRUTO59LZs9bfjEeScPn8eWULUghDXgWN9fU2WYeIetbQl/u2CoEGB7jepjsMeRXcziXSbg58WTWRPerrPuXm4aFQJcpiyKsgKDz8ljPf3UiSZQSGhSlYEZHzCYuOtno/lCQJoVFRClekDIa8Clzd3TE0MdHq+eTjRo926AmSiOzRiIkTb3kT706SLGPY2LHKFqQQhrxKJs+aBXdPz16DXpIkuHt5YfKsWTasjMg5BISEYMy0aVa1nTJ7Nrz0eoUrUgZDXiWe3t64f+lS+Pj59djGx98f85cuhYeXl+0KI3Iik+69F6PvugsAunTdSLIMSZKQNGsWRjnwkG27n9ZAy/wCA/HIr36F01lZOJubi+qOu8H7BwUhNjERw8eNg86FPyIipUiyjKRZszBk5EjkHz2K0qIitDQ3w93TE4NiYzEqKcnh76/MBFGZy4ABGDl5MkZOnqx2KUROKygiAj/p5uY9WsDuGiIiDWPI25FWg6FPV8ISUf8ytbXB0NwMk5WjbhwBu2tU1lRfjxOHDuHMiRNo6riq1dPbG0NHj8bou+7iSVcihQkhcKGgACcPH0bFpUsQJhNknQ5hgwdj9F13YZADTzMMMORVVVZcjPSPPkJrS4vFHBqN9fU4mZGBUz/+iNSf/hQhDnoRBpG9azMa8e0//oELBQXmueSB9iP60qIiXD5/HiMmTsRd993XpwsY7YljVq0BNdeuYeff/gbDTQHfSQgBQ0sLdnzwAWqvX1ehQiLt+/7//g8XOu4VffN+2Pm84NgxHPv2W5vX1l8Y8io5nJ6Otra2XqcbFkKgzWjE4fR0G1ZG5BzKLl7EmZwcq6b8zj54ENWVlTaoqv8x5FXQVF+P4tOnrbqkWphMuHjqFJobGmxQGZHzOJWVZf3cNR23A3REDHkV3M7c8JVlZQpUQuS8yktKrJ67RphMqLh0SeGKlMGQV4GxtbXv63BoJVG/au3jftjqoPsgQ14F3r6+fV+nlzluiKjveps36maSJEHv769cMQpiyKtgYGgo3D09rW7v7uWFAAe+aTmRPRo8bJhV030D7YMgIuPiFK5IGQx5Fcg6HcbdfbfV7cfffTdkBx2jS2Sv4idMgKubG3CLoJckCd5+fhgyapSNKutfTA6VjJw8GYOHD79lu+j4eIycNMkGFRE5FzcPD8x87DFIktTjEb0kSdC5uGDWwoUOOyMsQ14lsiwj5fHHMWbaNPMwLlmWzUfskixj7PTp7R9CHsUTKSIiNhbzly6FT0d/u9SxD3bucwEhIXhw+XKHvv2mY/5q0ghZp8PklBQkJCXhQmEhajoutvALCsLg4cPh6eOjcoVE2hcSGYnH0tJQev48yoqL0dLUBHcvL4RHRyN08GCr++3tFUPeDnj6+LBLhkhFsixj0NChGDR0qNql9Dv2AxARaRiP5O2AyWTC1cuXzbf/8wsKQlBEBEfUENlQQ20tyktKzN01oVFRmpjqmyGvsnMnT+Lw7t1orKuzWO7p44MpqamITUhQqTIi59BQW4tDO3fiQkGBxXJJkhA3ZgySZs/u03Ut9oYhr6Kje/ci+/vvu32tsa4O3372GaoqKjDx3nttXBmRc6i6ehX/9+67MLS0dHlNCIEzOTkoLSrCA8uWwUuvV6HCO8f+AJUU5ef3GPA3yjpwwDzfNRH1nzajEbu3boWhubnHicqEEGioq8M3n35q9WRm9oYhrwIhBI7s2WNdY0nCkT17ur2xCBHdvjM5Oai9fv2W+5YwmVBRUoKSs2dtVFn/YsiroPrqVdRWVVnXWAjUXLtmHkNPRP2jKD//llMadJJkuUufvaNgyKug5to1m6xDRD2runrVqrtCAe1H81Udo98cDUNeBbczTQGnNiDqX30doizrdApVoiwmhwoCgoNtsg4R9WxgWJjVUxZIsoyBDjrdN0NeBT7+/ggMD7fqAyZJEoIHDeJNQ4j62dDERKsHNAiTCbGJiQpXpAyGvEqmzplj9QdsSmqqwtUQOZ/o+HiEREbe8mBLkiTEjByJkMhIG1XWvxjyKgmJisJPFizocS7rzuU/WbDAYT9cRPZMkmXMWrgQfoGBvY6yCYmMxE8efNCGlfUvXvGqomFjx8I/KAhHvvkGpUVFFq+FRUcjadYsBIaHq1QdkfZ5eHtjwfLlyD54EHlHjsDQ3Gx+zd3LC2OmTkXClCkOe9IVYMirLigiAvOWLEFzY6N5mKRfYCDcPDxUrozIObi4umLivfdi/D33oLqyEi1NTfDw8oJvQIAmRrUx5O2Eu6enQ0+CROToZFnW5Cg2x/81RUREPeKRvMqEECgvKcH5vDxUVVQAAPyDgxE7ahRCoqJUro7IOTQ3NuJMTg7KLl5Ec2MjPHx8EB4djaGjR8PVzU3t8u4IQ15FLU1N+O6LL1B8+jQkWTbPcnfl4kXkZWZi8PDh+MmCBeyfJ1LQmZwcHNy2DUajsX2BEJAkCUV5eTj27be456GHEDVsmLpF3gF216jE2NqKbVu2oOTMGQCwmMa08//Fp09j+/vvw9jaqkqNRFp3KisL333xRfs+JoR5LpvOa1hampqQ/tFHKD59Ws0y7whDXiU/7t+PqoqKXi+IEkLgWlkZsg4csGFlRM6hvqYGB7dts6rtvs8/txhe6UgUDfkDBw5g/vz5CO+4hP/LL7+0eF0IgTVr1iAsLAweHh5ISUnBmY4j207Xr1/H4sWLodfr4efnh2XLlqG+vl7JshVnbG1FXmamdVe8CoHczEy0df4pSUT9ouDYMatvBGJobsaZnByFK1KGoiHf0NCAMWPGYNOmTd2+vmHDBvzpT3/CW2+9hczMTHh5eSE1NRXNN/zGXLx4MfLy8rBnzx5s27YNBw4cwIoVK5QsW3GVpaV96oIxGgyovHJFwYqInM+ls2etnlpEkiRcvumCRUeh6InXuXPnYu7cud2+JoTAm2++iZdffhkPdlwy/P777yMkJARffvklFi1ahIKCAuzatQtHjx7FxIkTAQAbN27Efffdh9dffx3hDno1aFNDQ5/XaXTwv16I7E1f9ikhBBrr6hSsRjmq9ckXFRWhrKwMKSkp5mW+vr5ISkpCRkYGACAjIwN+fn7mgAeAlJQUyLKMzMzMHt+7paUFtbW1Fg974uru3ud13G5jHSLqWV9HrTnqxYqqhXxZWRkAIOSmOZpDQkLMr5WVlSH4pivQXFxcEBAQYG7TnfXr18PX19f8iLSzCb6CwsP7dLm0LMucw4aon4VHR1u9H0qShFAHvW5Fk6NrVq9ejZqaGvOjpKRE7ZIsuLq7I27MGKvnkx82bpzDX5BBZG9GTJxo9YlXWafDsLFjlS1IIaqFfGhoKACgvLzcYnl5ebn5tdDQUFR0XAXayWg04vr16+Y23XFzc4Ner7d42JuklBR4eHv3eiQhSRI8vL0xaeZMG1ZG5Bz8g4Mx/ic/sapt8pw58PTxUbgiZagW8jExMQgNDcXevXvNy2pra5GZmYnk5GQAQHJyMqqrq3H8+HFzm2+//RYmkwlJSUk2r7k/uXt5Yf7SpfANCGhfcONRfcf//QIDMf+Xv3TYvkAiezf+nnvMQX/zAZcky5BkGVPnzsWIG84LOhpFR9fU19fj7Nmz5udFRUXIzs5GQEAAoqKi8Pzzz+O1115DXFwcYmJi8MorryA8PBwLFiwAAIwYMQJz5szB8uXL8dZbb6G1tRVpaWlYtGiRw46suZE+IAAPP/MMzp44gXO5uea7wfsHBSE2MRFxo0c79DzWRPZOkiRMmDEDMSNHIv/oUZQWFZmnGo6IjcWoyZOh7zwQc1CKhvyxY8cwY8YM8/NVq1YBAJYsWYItW7bgpZdeQkNDA1asWIHq6mpMmzYNu3btgvsNI0k+/PBDpKWlYebMmZBlGY888gj+9Kc/KVm2TelcXDB8/HgMHz9e7VKInFZASAim3X+/2mUoQhLWXg3gwGpra+Hr64uampo+9c+3XbmC+nfeUbAyIqJ23itWQBcWZnV7a3NNk6NrHJEQAs2NjWhubLT6Kjwi6l/G1lY01tdrahoRTjWssobaWmQfPIhzJ0+ipakJQPtFGkMTEzF2+nSHPaNP5CiEyYRzubk4mZGByrIy81TDwZGRGHPXXYgaNsyq4c72iiGvotKiIuzeuhXG1laLo/eWpibkHz2K0zk5SH3iCYRFR6tXJJGGGVtbsffTT7tMJSyEQEVJCXZv3YphY8di+vz5DjsIgt01KqmurMSuDz9E600B30kIgVaDATs/+MB8g28i6l/7v/zSfE+Hm3Xul6ezs3Hkm29sWVa/YsirJGPnTphMJvNNCrolBEwmEzJ27bJdYUROorSoCOfz8qw6B3YyI8M8xNnRMORV0FhXh0vnzll1SbUwmVBy5gyaOAslUb86nZ1t/dw1ssz55Ml613qZXK0/1yGinpWXlFg9d40wmXD18mWFK1IGQ14Ft3PPVt7nlah/Gfs4TLLVYFCoEmUx5FXg7efX53V8bmMdIuqZvg/7lCRJ/5xnysEw5FUwMDQU7l5eVrf38PaG/03z7hPRnRkcH285MWAvhBCIjItTuCJlMORVIMsyJlg5xSkATLjnHsh9uMkIEd1a/PjxcHN3v+WFTpIkQR8QgJiRI21UWf9icqhkxMSJVn1ohowahfgJE2xQEZFzcXV3x6yFC9unFO4h6CVJgourK2YtXAidi2NeO8qQV4kky7j3kUcw/p57zFfSybJsPmKXdTpMmDEDMx55xKEvqSayZ2HR0Xhg2TL4BgYCaA91+YbQD4qIwILlyxHgwN2ljvmrSSNknQ4T7rkHoyZNQvHp06iqrIQEwC8oCFHDhvFmIUQ2EBQejkeffRZlxcW4cvEiDM3NcPfyQnh0NIIiIhz+IIshbwfcvbwwbNw4tcsgclqSLCMsOlqT80Sxu4aISMMY8kREGsaQJyLSMIY8EZGGMeSJiDSMIU9EpGEMeSIiDWPIExFpGEOeiEjDGPJERBrGkCci0jCGPBGRhjHkiYg0jCFPRKRhDHkiIg1jyBMRaRhDnohIwxjyREQaxpAnItIwhjwRkYYx5HsheXoCLrzXOREpzMWlPW+UeGtF3lUjZF9f+KSlQTQ2ql0KEWmY5OkJ2ddXkfdmyN+C7OsLKPTNJyJSGrtriIg0jCFPRKRhDHkiIg1jyBMRaRhDnohIwxjyREQa5hRDKIUQAIDa2lqVKyEi6h+dedaZbz1xipCvq6sDAERGRqpcCRFR/6qrq4NvL9fySOJWvwY0wGQyobS0FD4+PpAkSe1yelRbW4vIyEiUlJRAr9erXU6/0/r2AdrfRm6f/RBCoK6uDuHh4ZDlnnveneJIXpZlDBo0SO0yrKbX6+3+A3YntL59gPa3kdtnH3o7gu/EE69ERBrGkCci0jCGvB1xc3PD2rVr4ebmpnYpitD69gHa30Zun+NxihOvRETOikfyREQaxpAnItIwhjwRkYYx5ImINIwhr4ILFy5g2bJliImJgYeHB2JjY7F27VoYDAaLdidOnMD06dPh7u6OyMhIbNiwoct7ffrpp4iPj4e7uzsSExOxY8cOW21Gr9atW4epU6fC09MTfn5+3bYpLi7GvHnz4OnpieDgYPz2t7+F0Wi0aPPdd99h/PjxcHNzw9ChQ7Flyxbli79NmzZtQnR0NNzd3ZGUlIQjR46oXZJVDhw4gPnz5yM8PBySJOHLL7+0eF0IgTVr1iAsLAweHh5ISUnBmTNnLNpcv34dixcvhl6vh5+fH5YtW4b6+nobbkXP1q9fj0mTJsHHxwfBwcFYsGABTp06ZdGmubkZK1euxMCBA+Ht7Y1HHnkE5eXlFm2s+bzaJUE2t3PnTvHkk0+K9PR0ce7cOfHVV1+J4OBg8eKLL5rb1NTUiJCQELF48WKRm5srtm7dKjw8PMTbb79tbvPDDz8InU4nNmzYIPLz88XLL78sBgwYIE6ePKnGZllYs2aNeOONN8SqVauEr69vl9eNRqNISEgQKSkpIisrS+zYsUMEBgaK1atXm9ucP39eeHp6ilWrVon8/HyxceNGodPpxK5du2y4Jdb5+OOPhaurq3j33XdFXl6eWL58ufDz8xPl5eVql3ZLO3bsEP/2b/8mPv/8cwFAfPHFFxav//73vxe+vr7iyy+/FDk5OeKBBx4QMTExoqmpydxmzpw5YsyYMeLw4cPi+++/F0OHDhVPPPGEjbeke6mpqWLz5s0iNzdXZGdni/vuu09ERUWJ+vp6c5tnnnlGREZGir1794pjx46JKVOmiKlTp5pft+bzaq8Y8nZiw4YNIiYmxvz8z3/+s/D39xctLS3mZf/6r/8qhg8fbn7++OOPi3nz5lm8T1JSknj66aeVL9hKmzdv7jbkd+zYIWRZFmVlZeZlf/nLX4Rerzdv80svvSRGjRplsd7ChQtFamqqojXfjsmTJ4uVK1ean7e1tYnw8HCxfv16Favqu5tD3mQyidDQUPFf//Vf5mXV1dXCzc1NbN26VQghRH5+vgAgjh49am6zc+dOIUmSuHz5ss1qt1ZFRYUAIPbv3y+EaN+eAQMGiE8//dTcpqCgQAAQGRkZQgjrPq/2it01dqKmpgYBAQHm5xkZGbj77rvh6upqXpaamopTp06hqqrK3CYlJcXifVJTU5GRkWGbou9ARkYGEhMTERISYl6WmpqK2tpa5OXlmds4wvYZDAYcP37colZZlpGSkmJ3tfZVUVERysrKLLbN19cXSUlJ5m3LyMiAn58fJk6caG6TkpICWZaRmZlp85pvpaamBgDM+9vx48fR2tpqsY3x8fGIioqy2MZbfV7tFUPeDpw9exYbN27E008/bV5WVlZm8YECYH5eVlbWa5vO1+3ZnWxfbW0tmpqabFOoFSorK9HW1uawP4vedNbf27aVlZUhODjY4nUXFxcEBATY3fabTCY8//zzuOuuu5CQkACgvX5XV9cu545u3sZbfV7tFUO+H/3ud7+DJEm9PgoLCy3WuXz5MubMmYPHHnsMy5cvV6ly69zO9hHZk5UrVyI3Nxcff/yx2qXYjFNMNWwrL774Ip588sle2wwZMsT8/9LSUsyYMQNTp07FO++8Y9EuNDS0y9n9zuehoaG9tul8vb/1dft6Exoa2mX0ibXbp9fr4eHhYWXVygsMDIROp7Ppz8JWOusvLy9HWFiYeXl5eTnGjh1rblNRUWGxntFoxPXr1+1q+9PS0rBt2zYcOHDAYurx0NBQGAwGVFdXWxzN3/jzs+bzarfUPingrC5duiTi4uLEokWLhNFo7PJ654lXg8FgXrZ69eouJ17vv/9+i/WSk5Md6sTrjaNP3n77baHX60Vzc7MQov3Ea0JCgsV6TzzxhN2eeE1LSzM/b2trExEREZo58fr666+bl9XU1HR74vXYsWPmNunp6XZz4tVkMomVK1eK8PBwcfr06S6vd554/eyzz8zLCgsLuz3x2tvn1V4x5FVw6dIlMXToUDFz5kxx6dIlceXKFfOjU3V1tQgJCRE///nPRW5urvj444+Fp6dnlyGULi4u4vXXXxcFBQVi7dq1djOE8uLFiyIrK0u8+uqrwtvbW2RlZYmsrCxRV1cnhPjnkLTZs2eL7OxssWvXLhEUFNTtEMrf/va3oqCgQGzatMmuh1C6ubmJLVu2iPz8fLFixQrh5+dnMRrDXtXV1Zl/PgDEG2+8IbKyssTFixeFEO1DKP38/MRXX30lTpw4IR588MFuh1COGzdOZGZmioMHD4q4uDi7GUL57LPPCl9fX/Hdd99Z7GuNjY3mNs8884yIiooS3377rTh27JhITk4WycnJ5tet+bzaK4a8CjZv3iwAdPu4UU5Ojpg2bZpwc3MTERER4ve//32X9/r73/8uhg0bJlxdXcWoUaPE9u3bbbUZvVqyZEm327dv3z5zmwsXLoi5c+cKDw8PERgYKF588UXR2tpq8T779u0TY8eOFa6urmLIkCFi8+bNtt2QPti4caOIiooSrq6uYvLkyeLw4cNql2SVffv2dfuzWrJkiRCi/Uj4lVdeESEhIcLNzU3MnDlTnDp1yuI9rl27Jp544gnh7e0t9Hq9WLp0qfkXutp62tdu/Cw1NTWJX/3qV8Lf3194enqKhx56yOKgSwjrPq/2iFMNExFpGEfXEBFpGEOeiEjDGPJERBrGkCci0jCGPBGRhjHkiYg0jCFPRKRhDHkiIg1jyBMRaRhDnohIwxjyREQaxpAnItKw/wc/z+zPRhNlZwAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x1def1b48d10>"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
-     "execution_count": 33,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Rotated section\n",
     "rot_sec = GenericSection(geo.rotate(math.pi/2))\n",
+    "print(\"====== ROTATED SECTION ======\")\n",
+    "print(\"====== Theta = 0 ======\")\n",
     "pprint(rot_sec.section_calculator.calculate_bending_strength(theta=0))\n",
-    "rot_sec.geometry"
+    "print(\"====== Theta = pi/2 ======\")\n",
+    "pprint(rot_sec.section_calculator.calculate_bending_strength(theta=math.pi/2))\n",
+    "draw_section(rot_sec)"
    ]
   },
   {
@@ -340,32 +350,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-3500 -142.97802173067802\n",
-      "-3000 -234.68408069492062\n",
-      "-2500 -297.2822197042793\n",
-      "-2000 -332.44272554576077\n",
-      "-1500 -312.4302326436426\n",
-      "-1000 -244.36211210538195\n",
-      "-500 -156.81787347217988\n",
-      "0 -65.77991345543168\n",
-      "500 25.858090576155707\n",
-      "900 101.50252856107927\n",
-      "-0.00015388452820479877\n",
-      "4.370451609244653e-09\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -375,41 +367,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z neutral axis = 35210.21\n"
+      "N=-3500 M=-57.41500206243937\n",
+      "N=-3000 M=37.50515961013787\n",
+      "N=-2500 M=126.54526434968352\n",
+      "N=-2000 M=202.82744527332497\n",
+      "N=-1500 M=268.0839726049206\n",
+      "N=-1000 M=327.6974724965341\n",
+      "N=-500 M=365.41914469028745\n",
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",
       "text/plain": [
-       "<structuralcodes.geometry._geometry.CompoundGeometry at 0x15e83a7e0c0>"
+       "<Figure size 1000x500 with 2 Axes>"
       ]
      },
-     "execution_count": 22,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z neutral axis = 1193.69\n"
+     ]
     }
    ],
    "source": [
     "from  SLS_section_response import calculate_strain_profile\n",
     "from  structuralcodes.plots.section_plots import draw_section_response, draw_N_M_diagram\n",
     "concrete = ConcreteEC2_2004(25)\n",
-    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07,)  \n",
+    "reinforcement = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=500, epsuk=0.07)  \n",
     "# Create section\n",
     "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
     "geo = SurfaceGeometry(poly, concrete)\n",
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
+    "# geo = geo.translate(-175,-250)\n",
     "sec = GenericSection(geo)\n",
-    "sec.geometry = sec.geometry.translate(-sec.gross_properties.cy, -sec.gross_properties.cz) # -> uncomment this line and results will improve\n",
-    "#draw_section(sec)\n",
+    "draw_section(sec)\n",
     "\n",
     "# 1\n",
     "n_ = [-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,900] # kN\n",
     "for n_ed in n_:\n",
-    "    print(n_ed,sec.section_calculator.calculate_bending_strength(0,n=n_ed*1e3).m_y/1e6)\n",
+    "    print(f\"N={n_ed}\",f\"M={sec.section_calculator.calculate_bending_strength(theta=math.pi,n=n_ed*1e3).m_y/1e6}\")\n",
     "\n",
     "\n",
     "# 2\n",
@@ -418,11 +425,9 @@
     "#eps,chi= calculate_strain_profile(sec,n_ed,my_ed)\n",
     "#draw_section_response(sec,eps,chi)\n",
     "eps,chi= calculate_strain_profile(sec,n_ed*1e3,my_ed*1e6)\n",
-    "print(eps)\n",
-    "print(chi)\n",
-    "draw_section_response(sec,eps,chi)\n",
-    "\n",
-    "sec.geometry"
+    "print(f\"eps={eps}\")\n",
+    "print(f\"chi={chi}\")\n",
+    "draw_section_response(sec,eps,chi)"
    ]
   },
   {

From 961fd22c9f6ea8ee4db4dd82773fdc2ddf64d83c Mon Sep 17 00:00:00 2001
From: DanielGMorenaFhecor <dgm@fhecor.es>
Date: Wed, 21 Aug 2024 09:23:33 +0200
Subject: [PATCH 33/33] point 6 updated

---
 Issues.ipynb | 93 +++++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 73 insertions(+), 20 deletions(-)

diff --git a/Issues.ipynb b/Issues.ipynb
index 5432271e..8602e3b9 100644
--- a/Issues.ipynb
+++ b/Issues.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -350,7 +350,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N=-3500.00 N\t M=-207.35 kNm\n",
+      "N=-3000.00 N\t M=-289.86 kNm\n",
+      "N=-2500.00 N\t M=-343.26 kNm\n",
+      "N=-2000.00 N\t M=-369.23 kNm\n",
+      "N=-1500.00 N\t M=-340.02 kNm\n",
+      "N=-1000.00 N\t M=-262.75 kNm\n",
+      "N=-500.00 N\t M=-166.01 kNm\n",
+      "N=0.00 N\t M=-65.78 kNm\n",
+      "N=500.00 N\t M=35.05 kNm\n",
+      "N=900.00 N\t M=118.06 kNm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Materials\n",
+    "concrete = ConcreteEC2_2004(25)\n",
+    "reinforcemnet = ReinforcementEC2_2004(fyk=500, Es=200000, density=7850, ftk=550, epsuk=0.07)  \n",
+    "\n",
+    "# Create section\n",
+    "poly = Polygon(((0, 0), (350, 0), (350, 500), (0, 500)))\n",
+    "geo = SurfaceGeometry(poly, concrete)\n",
+    "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
+    "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
+    "geo = geo.translate(-175, -250) # Translate the section\n",
+    "sec = GenericSection(geo)\n",
+    "\n",
+    "# Compute bending strength for a list of axial loads\n",
+    "for n_ed in [-3500, -3000, -2500, -2000, -1500, -1000, -500, 0, 500, 900]:\n",
+    "    m_y = sec.section_calculator.calculate_bending_strength(n=n_ed*1e3).m_y/1e6\n",
+    "    print(f\"N={n_ed:.2f} N\\t\", f\"M={m_y:.2f} kNm\")\n",
+    "\n",
+    "draw_section(sec)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -367,16 +420,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N=-3500 M=-57.41500206243937\n",
-      "N=-3000 M=37.50515961013787\n",
-      "N=-2500 M=126.54526434968352\n",
-      "N=-2000 M=202.82744527332497\n",
-      "N=-1500 M=268.0839726049206\n",
-      "N=-1000 M=327.6974724965341\n",
-      "N=-500 M=365.41914469028745\n",
-      "N=0 M=321.6377932991219\n",
-      "N=500 M=234.91308970037807\n",
-      "N=900 M=150.75531817244286\n",
+      "N=-3500 M=-207.35162272030666\n",
+      "N=-3000 M=-289.8614530087984\n",
+      "N=-2500 M=-343.2633634358853\n",
+      "N=-2000 M=-369.22764046576964\n",
+      "N=-1500 M=-340.0189189008534\n",
+      "N=-1000 M=-262.75456950042377\n",
+      "N=-500 M=-166.01410231795526\n",
+      "N=0 M=-65.77991347598572\n",
+      "N=500 M=35.054319342174544\n",
+      "N=900 M=118.0557401868925\n",
       "eps=-0.0001565482951700688\n",
       "chi=1.311466882295137e-07\n"
      ]
@@ -409,14 +462,14 @@
     "geo = SurfaceGeometry(poly, concrete)\n",
     "geo = add_reinforcement_line(geo, (50, 50), (300, 50), 12, reinforcement, n=3)\n",
     "geo = add_reinforcement_line(geo, (50, 450), (300, 450), 20, reinforcement, n=6)\n",
-    "# geo = geo.translate(-175,-250)\n",
+    "geo = geo.translate(-175,-250)\n",
     "sec = GenericSection(geo)\n",
     "draw_section(sec)\n",
     "\n",
     "# 1\n",
     "n_ = [-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,900] # kN\n",
     "for n_ed in n_:\n",
-    "    print(f\"N={n_ed}\",f\"M={sec.section_calculator.calculate_bending_strength(theta=math.pi,n=n_ed*1e3).m_y/1e6}\")\n",
+    "    print(f\"N={n_ed}\",f\"M={sec.section_calculator.calculate_bending_strength(n=n_ed*1e3).m_y/1e6}\")\n",
     "\n",
     "\n",
     "# 2\n",