From 7864616c58f945b995833ec3292bda3bf93e678d Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Thu, 26 May 2022 19:20:48 -0400 Subject: [PATCH 01/17] try longer url check --- .github/workflows/url_checker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index c4f0109f61..5a67a49589 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -22,10 +22,10 @@ jobs: print_all: false # Timeout in 10 seconds if url is not reached - timeout: 10 + timeout: 20 # How many times to retry a failed request (each is logged, defaults to 1) - retry_count: 5 + retry_count: 10 # A comma separated patterns to exclude during URL checks exclude_patterns: https://www.datacamp.com/community/tutorials/fuzzy-string-python,http://127.0.0.1,https://github.com/pybamm-team/PyBaMM/tree/v From 59d365e8608c7a0964e7006d5df7fc9aa5a4752a Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Fri, 27 May 2022 18:34:37 -0400 Subject: [PATCH 02/17] more retries, less time --- .github/workflows/url_checker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 5a67a49589..dcc844ae2e 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -22,10 +22,10 @@ jobs: print_all: false # Timeout in 10 seconds if url is not reached - timeout: 20 + timeout: 10 # How many times to retry a failed request (each is logged, defaults to 1) - retry_count: 10 + retry_count: 20 # A comma separated patterns to exclude during URL checks exclude_patterns: https://www.datacamp.com/community/tutorials/fuzzy-string-python,http://127.0.0.1,https://github.com/pybamm-team/PyBaMM/tree/v From b74c390484ddef647539e5eff0607eab9f1d2647 Mon Sep 17 00:00:00 2001 From: ndrewwang <56122552+ndrewwang@users.noreply.github.com> Date: Thu, 23 Jun 2022 00:20:56 +0100 Subject: [PATCH 03/17] Change y-axis limits for charge experiment Previous y-max limit used the initial voltage, which assumes every experiment is a discharge one. --- pybamm/plotting/plot_voltage_components.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py index 71cb6f2bb4..ec55e6c4e9 100644 --- a/pybamm/plotting/plot_voltage_components.py +++ b/pybamm/plotting/plot_voltage_components.py @@ -71,7 +71,7 @@ def plot_voltage_components( ax.set_xlim([time[0], time[-1]]) ax.set_xlabel("Time [h]") - y_min, y_max = 0.98 * np.nanmin(V), 1.02 * np.nanmax(initial_ocv) + y_min, y_max = 0.98 * np.nanmin(V), 1.02 * np.nanmax(V) ax.set_ylim([y_min, y_max]) if not testing: # pragma: no cover From 3ba8c56e6bfa291448f9bef3b4a17963123b8633 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Fri, 24 Jun 2022 12:59:09 -0400 Subject: [PATCH 04/17] longer timeout --- .github/workflows/url_checker.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index dcc844ae2e..f2117208fc 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -21,11 +21,11 @@ jobs: # Choose whether to include file with no URLs in the prints. print_all: false - # Timeout in 10 seconds if url is not reached - timeout: 10 + # Timeout in 20 seconds if url is not reached + timeout: 20 # How many times to retry a failed request (each is logged, defaults to 1) - retry_count: 20 + retry_count: 5 # A comma separated patterns to exclude during URL checks exclude_patterns: https://www.datacamp.com/community/tutorials/fuzzy-string-python,http://127.0.0.1,https://github.com/pybamm-team/PyBaMM/tree/v From 1bcc713b35025d5c2034eb50d3c8d7ddb39e5487 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Tue, 28 Jun 2022 17:22:44 -0400 Subject: [PATCH 05/17] longer timeout --- .github/workflows/url_checker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index f2117208fc..a2ab21e689 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -21,8 +21,8 @@ jobs: # Choose whether to include file with no URLs in the prints. print_all: false - # Timeout in 20 seconds if url is not reached - timeout: 20 + # Timeout in 30 seconds if url is not reached + timeout: 30 # How many times to retry a failed request (each is logged, defaults to 1) retry_count: 5 From 12dc4975899fc1c598d9d03156a8b33d4d466eb5 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Tue, 28 Jun 2022 17:38:13 -0400 Subject: [PATCH 06/17] 60 seconds --- .github/workflows/url_checker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index a2ab21e689..626c8dd5dd 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -21,8 +21,8 @@ jobs: # Choose whether to include file with no URLs in the prints. print_all: false - # Timeout in 30 seconds if url is not reached - timeout: 30 + # Timeout in 60 seconds if url is not reached + timeout: 60 # How many times to retry a failed request (each is logged, defaults to 1) retry_count: 5 From ebc86c29b38ff0e1258952a511dec40a184842e8 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Wed, 29 Jun 2022 15:51:57 -0400 Subject: [PATCH 07/17] use older version --- .github/workflows/url_checker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 626c8dd5dd..1996d1d142 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v2 - name: URLs-checker - uses: urlstechie/urlchecker-action@master + uses: urlstechie/urlchecker-action@v0.0.25 with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb From f38dac4f5447574373a621223c2ad00e493bd4a3 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Wed, 29 Jun 2022 15:54:05 -0400 Subject: [PATCH 08/17] remove v --- .github/workflows/url_checker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 1996d1d142..9810687d32 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v2 - name: URLs-checker - uses: urlstechie/urlchecker-action@v0.0.25 + uses: urlstechie/urlchecker-action@0.0.25 with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb From 23dde8cd4675b325f4d61ae8f9297b6080c11df3 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Wed, 29 Jun 2022 16:08:44 -0400 Subject: [PATCH 09/17] more retries, exclude doi and sciencedirect --- .github/workflows/url_checker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 9810687d32..14089989af 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -25,10 +25,10 @@ jobs: timeout: 60 # How many times to retry a failed request (each is logged, defaults to 1) - retry_count: 5 + retry_count: 10 # A comma separated patterns to exclude during URL checks - exclude_patterns: https://www.datacamp.com/community/tutorials/fuzzy-string-python,http://127.0.0.1,https://github.com/pybamm-team/PyBaMM/tree/v + exclude_patterns: https://www.datacamp.com/community/tutorials/fuzzy-string-python,http://127.0.0.1,https://github.com/pybamm-team/PyBaMM/tree/v,https::/doi.org,https::/www.sciencedirect.com # A comma separated list of file patterns (direct paths work as well) to exclude exclude_files: CHANGELOG.md From 8a48b6ee28ca31f7aac6d2999938484c653b10ba Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Wed, 20 Jul 2022 11:50:54 -0400 Subject: [PATCH 10/17] fix labelling in voltage components --- .../Tutorial 3 - Basic plotting.ipynb | 420 ++++++++---------- pybamm/plotting/plot_voltage_components.py | 26 +- 2 files changed, 193 insertions(+), 253 deletions(-) diff --git a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb index 1384c9dfa8..c5e4d4abd0 100644 --- a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb @@ -19,15 +19,6 @@ "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: Invalid requirement: '#'\n", - "WARNING: You are using pip version 21.1.2; however, version 21.2.4 is available.\n", - "You should consider upgrading via the 'c:\\users\\saransh\\saransh_softwares\\python_3.9\\python.exe -m pip install --upgrade pip' command.\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -38,7 +29,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -83,20 +74,10 @@ " 'x_s [m]',\n", " 'x_p',\n", " 'x_p [m]',\n", - " 'Sum of electrolyte reaction source terms',\n", - " 'Sum of negative electrode electrolyte reaction source terms',\n", - " 'Sum of positive electrode electrolyte reaction source terms',\n", - " 'Sum of x-averaged negative electrode electrolyte reaction source terms',\n", - " 'Sum of x-averaged positive electrode electrolyte reaction source terms',\n", - " 'Sum of interfacial current densities',\n", - " 'Sum of negative electrode interfacial current densities',\n", - " 'Sum of positive electrode interfacial current densities',\n", - " 'Sum of x-averaged negative electrode interfacial current densities',\n", - " 'Sum of x-averaged positive electrode interfacial current densities',\n", - " 'r_n',\n", - " 'r_n [m]',\n", " 'r_p',\n", " 'r_p [m]',\n", + " 'r_n',\n", + " 'r_n [m]',\n", " 'Current density variable',\n", " 'Total current density',\n", " 'Total current density [A.m-2]',\n", @@ -104,29 +85,37 @@ " 'C-rate',\n", " 'Discharge capacity [A.h]',\n", " 'Porosity',\n", - " 'Negative electrode porosity',\n", " 'Separator porosity',\n", " 'Positive electrode porosity',\n", - " 'X-averaged negative electrode porosity',\n", " 'X-averaged separator porosity',\n", " 'X-averaged positive electrode porosity',\n", + " 'Negative electrode porosity',\n", + " 'X-averaged negative electrode porosity',\n", " 'Leading-order porosity',\n", - " 'Leading-order negative electrode porosity',\n", " 'Leading-order separator porosity',\n", " 'Leading-order positive electrode porosity',\n", - " 'Leading-order x-averaged negative electrode porosity',\n", " 'Leading-order x-averaged separator porosity',\n", " 'Leading-order x-averaged positive electrode porosity',\n", + " 'Leading-order negative electrode porosity',\n", + " 'Leading-order x-averaged negative electrode porosity',\n", " 'Porosity change',\n", - " 'Negative electrode porosity change',\n", " 'Separator porosity change',\n", " 'Positive electrode porosity change',\n", - " 'X-averaged negative electrode porosity change',\n", " 'X-averaged separator porosity change',\n", " 'X-averaged positive electrode porosity change',\n", - " 'Leading-order x-averaged negative electrode porosity change',\n", + " 'Negative electrode porosity change',\n", + " 'X-averaged negative electrode porosity change',\n", " 'Leading-order x-averaged separator porosity change',\n", " 'Leading-order x-averaged positive electrode porosity change',\n", + " 'Leading-order x-averaged negative electrode porosity change',\n", + " 'Negative electrode interface utilisation variable',\n", + " 'X-averaged negative electrode interface utilisation variable',\n", + " 'Negative electrode interface utilisation',\n", + " 'X-averaged negative electrode interface utilisation',\n", + " 'Positive electrode interface utilisation variable',\n", + " 'X-averaged positive electrode interface utilisation variable',\n", + " 'Positive electrode interface utilisation',\n", + " 'X-averaged positive electrode interface utilisation',\n", " 'Negative electrode active material volume fraction',\n", " 'X-averaged negative electrode active material volume fraction',\n", " 'Negative electrode capacity [A.h]',\n", @@ -151,50 +140,50 @@ " 'X-averaged positive electrode active material volume fraction change',\n", " 'Separator pressure',\n", " 'X-averaged separator pressure',\n", - " 'Negative electrode transverse volume-averaged velocity',\n", " 'Separator transverse volume-averaged velocity',\n", " 'Positive electrode transverse volume-averaged velocity',\n", - " 'Negative electrode transverse volume-averaged velocity [m.s-2]',\n", " 'Separator transverse volume-averaged velocity [m.s-2]',\n", " 'Positive electrode transverse volume-averaged velocity [m.s-2]',\n", - " 'X-averaged negative electrode transverse volume-averaged velocity',\n", " 'X-averaged separator transverse volume-averaged velocity',\n", " 'X-averaged positive electrode transverse volume-averaged velocity',\n", - " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]',\n", " 'X-averaged separator transverse volume-averaged velocity [m.s-2]',\n", " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]',\n", " 'Transverse volume-averaged velocity',\n", " 'Transverse volume-averaged velocity [m.s-2]',\n", - " 'Negative electrode transverse volume-averaged acceleration',\n", + " 'Negative electrode transverse volume-averaged velocity',\n", + " 'Negative electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]',\n", " 'Separator transverse volume-averaged acceleration',\n", " 'Positive electrode transverse volume-averaged acceleration',\n", - " 'Negative electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'Separator transverse volume-averaged acceleration [m.s-2]',\n", " 'Positive electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged negative electrode transverse volume-averaged acceleration',\n", " 'X-averaged separator transverse volume-averaged acceleration',\n", " 'X-averaged positive electrode transverse volume-averaged acceleration',\n", - " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n", " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'Transverse volume-averaged acceleration',\n", " 'Transverse volume-averaged acceleration [m.s-2]',\n", - " 'Negative electrode volume-averaged velocity',\n", + " 'Negative electrode transverse volume-averaged acceleration',\n", + " 'Negative electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'Positive electrode volume-averaged velocity',\n", - " 'Negative electrode volume-averaged velocity [m.s-1]',\n", " 'Positive electrode volume-averaged velocity [m.s-1]',\n", - " 'Negative electrode volume-averaged acceleration',\n", + " 'Negative electrode volume-averaged velocity',\n", + " 'Negative electrode volume-averaged velocity [m.s-1]',\n", " 'Positive electrode volume-averaged acceleration',\n", - " 'Negative electrode volume-averaged acceleration [m.s-1]',\n", " 'Positive electrode volume-averaged acceleration [m.s-1]',\n", - " 'X-averaged negative electrode volume-averaged acceleration',\n", " 'X-averaged positive electrode volume-averaged acceleration',\n", - " 'X-averaged negative electrode volume-averaged acceleration [m.s-1]',\n", " 'X-averaged positive electrode volume-averaged acceleration [m.s-1]',\n", - " 'Negative electrode pressure',\n", + " 'Negative electrode volume-averaged acceleration',\n", + " 'Negative electrode volume-averaged acceleration [m.s-1]',\n", + " 'X-averaged negative electrode volume-averaged acceleration',\n", + " 'X-averaged negative electrode volume-averaged acceleration [m.s-1]',\n", " 'Positive electrode pressure',\n", - " 'X-averaged negative electrode pressure',\n", " 'X-averaged positive electrode pressure',\n", + " 'Negative electrode pressure',\n", + " 'X-averaged negative electrode pressure',\n", " 'Negative particle concentration',\n", " 'Negative particle concentration [mol.m-3]',\n", " 'X-averaged negative particle concentration',\n", @@ -258,9 +247,9 @@ " 'X-averaged positive electrode ohmic losses [V]',\n", " 'Gradient of positive electrode potential',\n", " 'Porosity times concentration',\n", - " 'Negative electrode porosity times concentration',\n", " 'Separator porosity times concentration',\n", " 'Positive electrode porosity times concentration',\n", + " 'Negative electrode porosity times concentration',\n", " 'Negative electrolyte potential',\n", " 'Negative electrolyte potential [V]',\n", " 'Separator electrolyte potential',\n", @@ -279,10 +268,10 @@ " 'X-averaged positive electrolyte potential [V]',\n", " 'X-averaged electrolyte overpotential',\n", " 'X-averaged electrolyte overpotential [V]',\n", - " 'Gradient of negative electrolyte potential',\n", " 'Gradient of separator electrolyte potential',\n", " 'Gradient of positive electrolyte potential',\n", " 'Gradient of electrolyte potential',\n", + " 'Gradient of negative electrolyte potential',\n", " 'Negative current collector temperature',\n", " 'Negative current collector temperature [K]',\n", " 'X-averaged negative electrode temperature',\n", @@ -307,20 +296,22 @@ " 'Volume-averaged cell temperature [K]',\n", " 'Ambient temperature [K]',\n", " 'Ambient temperature',\n", + " 'Negative current collector potential',\n", + " 'Negative current collector potential [V]',\n", " 'Inner SEI thickness',\n", " 'Inner SEI thickness [m]',\n", - " 'X-averaged inner SEI thickness',\n", - " 'X-averaged inner SEI thickness [m]',\n", " 'Outer SEI thickness',\n", " 'Outer SEI thickness [m]',\n", + " 'X-averaged inner SEI thickness',\n", + " 'X-averaged inner SEI thickness [m]',\n", " 'X-averaged outer SEI thickness',\n", " 'X-averaged outer SEI thickness [m]',\n", " 'SEI thickness',\n", " 'SEI thickness [m]',\n", - " 'X-averaged SEI thickness',\n", - " 'X-averaged SEI thickness [m]',\n", " 'Total SEI thickness',\n", " 'Total SEI thickness [m]',\n", + " 'X-averaged SEI thickness',\n", + " 'X-averaged SEI thickness [m]',\n", " 'X-averaged total SEI thickness',\n", " 'X-averaged total SEI thickness [m]',\n", " 'X-averaged negative electrode resistance [Ohm.m2]',\n", @@ -328,7 +319,7 @@ " 'X-averaged inner SEI concentration [mol.m-3]',\n", " 'Outer SEI concentration [mol.m-3]',\n", " 'X-averaged outer SEI concentration [mol.m-3]',\n", - " 'Negative SEI concentration [mol.m-3]',\n", + " 'SEI concentration [mol.m-3]',\n", " 'X-averaged SEI concentration [mol.m-3]',\n", " 'Loss of lithium to SEI [mol]',\n", " 'Loss of capacity to SEI [A.h]',\n", @@ -344,81 +335,42 @@ " 'SEI interfacial current density [A.m-2]',\n", " 'X-averaged SEI interfacial current density',\n", " 'X-averaged SEI interfacial current density [A.m-2]',\n", - " 'Inner positive electrode SEI thickness',\n", - " 'Inner positive electrode SEI thickness [m]',\n", - " 'X-averaged inner positive electrode SEI thickness',\n", - " 'X-averaged inner positive electrode SEI thickness [m]',\n", - " 'Outer positive electrode SEI thickness',\n", - " 'Outer positive electrode SEI thickness [m]',\n", - " 'X-averaged outer positive electrode SEI thickness',\n", - " 'X-averaged outer positive electrode SEI thickness [m]',\n", - " 'Positive electrode SEI thickness',\n", - " 'Positive electrode SEI thickness [m]',\n", - " 'X-averaged positive electrode SEI thickness',\n", - " 'X-averaged positive electrode SEI thickness [m]',\n", - " 'Total positive electrode SEI thickness',\n", - " 'Total positive electrode SEI thickness [m]',\n", - " 'X-averaged total positive electrode SEI thickness',\n", - " 'X-averaged total positive electrode SEI thickness [m]',\n", - " 'X-averaged positive electrode resistance [Ohm.m2]',\n", - " 'Inner positive electrode SEI concentration [mol.m-3]',\n", - " 'X-averaged inner positive electrode SEI concentration [mol.m-3]',\n", - " 'Outer positive electrode SEI concentration [mol.m-3]',\n", - " 'X-averaged outer positive electrode SEI concentration [mol.m-3]',\n", - " 'Positive SEI concentration [mol.m-3]',\n", - " 'X-averaged positive electrode SEI concentration [mol.m-3]',\n", - " 'Loss of lithium to positive electrode SEI [mol]',\n", - " 'Loss of capacity to positive electrode SEI [A.h]',\n", - " 'Inner positive electrode SEI interfacial current density',\n", - " 'Inner positive electrode SEI interfacial current density [A.m-2]',\n", - " 'X-averaged inner positive electrode SEI interfacial current density',\n", - " 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]',\n", - " 'Outer positive electrode SEI interfacial current density',\n", - " 'Outer positive electrode SEI interfacial current density [A.m-2]',\n", - " 'X-averaged outer positive electrode SEI interfacial current density',\n", - " 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI interfacial current density',\n", - " 'Positive electrode SEI interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode SEI interfacial current density',\n", - " 'X-averaged positive electrode SEI interfacial current density [A.m-2]',\n", " 'Lithium plating concentration',\n", " 'Lithium plating concentration [mol.m-3]',\n", " 'X-averaged lithium plating concentration',\n", " 'X-averaged lithium plating concentration [mol.m-3]',\n", + " 'Dead lithium concentration',\n", + " 'Dead lithium concentration [mol.m-3]',\n", + " 'X-averaged dead lithium concentration',\n", + " 'X-averaged dead lithium concentration [mol.m-3]',\n", " 'Lithium plating thickness',\n", " 'Lithium plating thickness [m]',\n", " 'X-averaged lithium plating thickness [m]',\n", + " 'Dead lithium thickness',\n", + " 'Dead lithium thickness [m]',\n", + " 'X-averaged dead lithium thickness [m]',\n", " 'Loss of lithium to lithium plating [mol]',\n", " 'Loss of capacity to lithium plating [A.h]',\n", + " 'Negative electrode lithium plating reaction overpotential',\n", + " 'X-averaged negative electrode lithium plating reaction overpotential',\n", + " 'Negative electrode lithium plating reaction overpotential [V]',\n", + " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n", " 'Lithium plating interfacial current density',\n", " 'Lithium plating interfacial current density [A.m-2]',\n", " 'X-averaged lithium plating interfacial current density',\n", " 'X-averaged lithium plating interfacial current density [A.m-2]',\n", - " 'Positive electrode lithium plating concentration',\n", - " 'Positive electrode lithium plating concentration [mol.m-3]',\n", - " 'X-averaged positive electrode lithium plating concentration',\n", - " 'X-averaged positive electrode lithium plating concentration [mol.m-3]',\n", - " 'Positive electrode lithium plating thickness',\n", - " 'Positive electrode lithium plating thickness [m]',\n", - " 'X-averaged positive electrode lithium plating thickness [m]',\n", - " 'Loss of lithium to positive electrode lithium plating [mol]',\n", - " 'Loss of capacity to positive electrode lithium plating [A.h]',\n", - " 'Positive electrode lithium plating interfacial current density',\n", - " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode lithium plating interfacial current density',\n", - " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Electrolyte tortuosity',\n", - " 'Negative electrolyte tortuosity',\n", - " 'Positive electrolyte tortuosity',\n", - " 'X-averaged negative electrolyte tortuosity',\n", - " 'X-averaged positive electrolyte tortuosity',\n", - " 'Separator tortuosity',\n", - " 'X-averaged separator tortuosity',\n", - " 'Electrode tortuosity',\n", - " 'Negative electrode tortuosity',\n", - " 'Positive electrode tortuosity',\n", - " 'X-averaged negative electrode tortuosity',\n", - " 'X-averaged positive electrode tortuosity',\n", + " 'Electrolyte transport efficiency',\n", + " 'Positive electrolyte transport efficiency',\n", + " 'X-averaged positive electrolyte transport efficiency',\n", + " 'Negative electrolyte transport efficiency',\n", + " 'X-averaged negative electrolyte transport efficiency',\n", + " 'Separator transport efficiency',\n", + " 'X-averaged separator transport efficiency',\n", + " 'Electrode transport efficiency',\n", + " 'Positive electrode transport efficiency',\n", + " 'X-averaged positive electrode transport efficiency',\n", + " 'Negative electrode transport efficiency',\n", + " 'X-averaged negative electrode transport efficiency',\n", " 'Separator volume-averaged velocity',\n", " 'Separator volume-averaged velocity [m.s-1]',\n", " 'Separator volume-averaged acceleration',\n", @@ -432,22 +384,38 @@ " 'Volume-averaged acceleration [m.s-1]',\n", " 'X-averaged volume-averaged acceleration [m.s-1]',\n", " 'Pressure',\n", - " 'Negative electrode surface potential difference',\n", - " 'X-averaged negative electrode surface potential difference',\n", - " 'Negative electrode surface potential difference [V]',\n", - " 'X-averaged negative electrode surface potential difference [V]',\n", - " 'Positive electrode surface potential difference',\n", - " 'X-averaged positive electrode surface potential difference',\n", - " 'Positive electrode surface potential difference [V]',\n", - " 'X-averaged positive electrode surface potential difference [V]',\n", + " 'Negative electrode open circuit potential',\n", + " 'Negative electrode open circuit potential [V]',\n", + " 'X-averaged negative electrode open circuit potential',\n", + " 'X-averaged negative electrode open circuit potential [V]',\n", + " 'Negative electrode entropic change',\n", + " 'Negative electrode entropic change [V.K-1]',\n", + " 'X-averaged negative electrode entropic change',\n", + " 'X-averaged negative electrode entropic change [V.K-1]',\n", + " 'Positive electrode open circuit potential',\n", + " 'Positive electrode open circuit potential [V]',\n", + " 'X-averaged positive electrode open circuit potential',\n", + " 'X-averaged positive electrode open circuit potential [V]',\n", + " 'Positive electrode entropic change',\n", + " 'Positive electrode entropic change [V.K-1]',\n", + " 'X-averaged positive electrode entropic change',\n", + " 'X-averaged positive electrode entropic change [V.K-1]',\n", " 'Negative particle flux',\n", " 'X-averaged negative particle flux',\n", + " 'Negative effective diffusivity',\n", + " 'Negative effective diffusivity [m2.s-1]',\n", + " 'X-averaged negative effective diffusivity',\n", + " 'X-averaged negative effective diffusivity [m2.s-1]',\n", " 'Negative electrode SOC',\n", " 'Negative electrode volume-averaged concentration',\n", " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", " 'Total lithium in negative electrode [mol]',\n", " 'Positive particle flux',\n", " 'X-averaged positive particle flux',\n", + " 'Positive effective diffusivity',\n", + " 'Positive effective diffusivity [m2.s-1]',\n", + " 'X-averaged positive effective diffusivity',\n", + " 'X-averaged positive effective diffusivity [m2.s-1]',\n", " 'Positive electrode SOC',\n", " 'Positive electrode volume-averaged concentration',\n", " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", @@ -459,6 +427,12 @@ " 'Positive electrode current density',\n", " 'Positive electrode current density [A.m-2]',\n", " 'Electrode current density',\n", + " 'Positive current collector potential',\n", + " 'Positive current collector potential [V]',\n", + " 'Local voltage',\n", + " 'Local voltage [V]',\n", + " 'Terminal voltage',\n", + " 'Terminal voltage [V]',\n", " 'Electrolyte concentration',\n", " 'Electrolyte concentration [mol.m-3]',\n", " 'Electrolyte concentration [Molar]',\n", @@ -486,6 +460,14 @@ " 'X-averaged electrolyte ohmic losses',\n", " 'X-averaged concentration overpotential [V]',\n", " 'X-averaged electrolyte ohmic losses [V]',\n", + " 'Negative electrode surface potential difference',\n", + " 'Negative electrode surface potential difference [V]',\n", + " 'X-averaged negative electrode surface potential difference',\n", + " 'X-averaged negative electrode surface potential difference [V]',\n", + " 'Positive electrode surface potential difference',\n", + " 'Positive electrode surface potential difference [V]',\n", + " 'X-averaged positive electrode surface potential difference',\n", + " 'X-averaged positive electrode surface potential difference [V]',\n", " 'Ohmic heating',\n", " 'Ohmic heating [W.m-3]',\n", " 'X-averaged Ohmic heating',\n", @@ -510,50 +492,51 @@ " 'X-averaged total heating [W.m-3]',\n", " 'Volume-averaged total heating',\n", " 'Volume-averaged total heating [W.m-3]',\n", - " 'Negative current collector potential',\n", - " 'Negative current collector potential [V]',\n", " 'Current collector current density',\n", " 'Current collector current density [A.m-2]',\n", " 'Leading-order current collector current density',\n", - " 'SEI interfacial current density',\n", - " 'SEI interfacial current density [A.m-2]',\n", - " 'SEI interfacial current density per volume [A.m-3]',\n", - " 'Lithium plating reaction overpotential',\n", - " 'X-averaged lithium plating reaction overpotential',\n", - " 'Lithium plating reaction overpotential [V]',\n", - " 'X-averaged lithium plating reaction overpotential [V]',\n", - " 'Lithium plating interfacial current density',\n", - " 'Lithium plating interfacial current density [A.m-2]',\n", - " 'Lithium plating interfacial current density per volume [A.m-3]',\n", - " 'Positive electrode lithium plating reaction overpotential',\n", - " 'X-averaged positive electrode lithium plating reaction overpotential',\n", - " 'Positive electrode lithium plating reaction overpotential [V]',\n", - " 'X-averaged positive electrode lithium plating reaction overpotential [V]',\n", + " 'X-averaged negative electrode SEI interfacial current density',\n", + " 'Negative electrode SEI interfacial current density',\n", + " 'Negative electrode SEI interfacial current density [A.m-2]',\n", + " 'Negative electrode SEI volumetric interfacial current density',\n", + " 'X-averaged negative electrode SEI volumetric interfacial current density',\n", + " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode SEI interfacial current density',\n", + " 'Positive electrode SEI interfacial current density',\n", + " 'Positive electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode SEI volumetric interfacial current density',\n", + " 'Positive electrode SEI volumetric interfacial current density',\n", + " 'X-averaged negative electrode lithium plating interfacial current density',\n", + " 'X-averaged positive electrode lithium plating interfacial current density',\n", + " 'X-averaged positive electrode lithium plating volumetric interfacial current density',\n", + " 'Negative electrode lithium plating interfacial current density',\n", + " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n", + " 'Positive electrode lithium plating interfacial current density',\n", + " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'Positive electrode lithium plating volumetric interfacial current density',\n", + " 'Negative electrode lithium plating volumetric interfacial current density',\n", + " 'X-averaged negative electrode lithium plating volumetric interfacial current density',\n", + " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", " 'Negative electrode interfacial current density',\n", " 'X-averaged negative electrode interfacial current density',\n", " 'Negative electrode interfacial current density [A.m-2]',\n", " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", - " 'Negative electrode interfacial current density per volume [A.m-3]',\n", - " 'X-averaged negative electrode interfacial current density per volume [A.m-3]',\n", " 'X-averaged negative electrode total interfacial current density',\n", " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode total interfacial current density per volume [A.m-3]',\n", " 'Negative electrode exchange current density',\n", " 'X-averaged negative electrode exchange current density',\n", " 'Negative electrode exchange current density [A.m-2]',\n", " 'X-averaged negative electrode exchange current density [A.m-2]',\n", - " 'Negative electrode exchange current density per volume [A.m-3]',\n", - " 'X-averaged negative electrode exchange current density per volume [A.m-3]',\n", " 'Negative electrode reaction overpotential',\n", " 'X-averaged negative electrode reaction overpotential',\n", " 'Negative electrode reaction overpotential [V]',\n", " 'X-averaged negative electrode reaction overpotential [V]',\n", - " 'Negative electrode open circuit potential',\n", - " 'Negative electrode open circuit potential [V]',\n", - " 'X-averaged negative electrode open circuit potential',\n", - " 'X-averaged negative electrode open circuit potential [V]',\n", - " 'Negative electrode entropic change',\n", - " 'X-averaged negative electrode entropic change',\n", + " 'Negative electrode volumetric interfacial current density',\n", + " 'X-averaged negative electrode volumetric interfacial current density',\n", + " 'Negative electrode volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n", " 'SEI film overpotential',\n", " 'X-averaged SEI film overpotential',\n", " 'SEI film overpotential [V]',\n", @@ -562,100 +545,49 @@ " 'X-averaged positive electrode interfacial current density',\n", " 'Positive electrode interfacial current density [A.m-2]',\n", " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", - " 'Positive electrode interfacial current density per volume [A.m-3]',\n", - " 'X-averaged positive electrode interfacial current density per volume [A.m-3]',\n", " 'X-averaged positive electrode total interfacial current density',\n", " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode total interfacial current density per volume [A.m-3]',\n", " 'Positive electrode exchange current density',\n", " 'X-averaged positive electrode exchange current density',\n", " 'Positive electrode exchange current density [A.m-2]',\n", " 'X-averaged positive electrode exchange current density [A.m-2]',\n", - " 'Positive electrode exchange current density per volume [A.m-3]',\n", - " 'X-averaged positive electrode exchange current density per volume [A.m-3]',\n", " 'Positive electrode reaction overpotential',\n", " 'X-averaged positive electrode reaction overpotential',\n", " 'Positive electrode reaction overpotential [V]',\n", " 'X-averaged positive electrode reaction overpotential [V]',\n", - " 'Positive electrode open circuit potential',\n", - " 'Positive electrode open circuit potential [V]',\n", - " 'X-averaged positive electrode open circuit potential',\n", - " 'X-averaged positive electrode open circuit potential [V]',\n", - " 'Positive electrode entropic change',\n", - " 'X-averaged positive electrode entropic change',\n", - " 'Positive electrode SEI film overpotential',\n", - " 'X-averaged positive electrode SEI film overpotential',\n", - " 'Positive electrode SEI film overpotential [V]',\n", - " 'X-averaged positive electrode SEI film overpotential [V]',\n", + " 'Positive electrode volumetric interfacial current density',\n", + " 'X-averaged positive electrode volumetric interfacial current density',\n", + " 'Positive electrode volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n", + " 'Electrolyte flux',\n", + " 'Electrolyte flux [mol.m-2.s-1]',\n", + " 'Total lithium in electrolyte',\n", + " 'Total lithium in electrolyte [mol]',\n", + " 'Sum of electrolyte reaction source terms',\n", + " 'Sum of positive electrode electrolyte reaction source terms',\n", + " 'Sum of x-averaged positive electrode electrolyte reaction source terms',\n", + " 'Sum of interfacial current densities',\n", + " 'Sum of volumetric interfacial current densities',\n", + " 'Sum of positive electrode interfacial current densities',\n", + " 'Sum of x-averaged positive electrode interfacial current densities',\n", + " 'Sum of positive electrode volumetric interfacial current densities',\n", + " 'Sum of x-averaged positive electrode volumetric interfacial current densities',\n", + " 'Sum of negative electrode electrolyte reaction source terms',\n", + " 'Sum of x-averaged negative electrode electrolyte reaction source terms',\n", + " 'Sum of negative electrode interfacial current densities',\n", + " 'Sum of x-averaged negative electrode interfacial current densities',\n", + " 'Sum of negative electrode volumetric interfacial current densities',\n", + " 'Sum of x-averaged negative electrode volumetric interfacial current densities',\n", " 'Interfacial current density',\n", " 'Interfacial current density [A.m-2]',\n", - " 'Interfacial current density per volume [A.m-3]',\n", " 'Exchange current density',\n", " 'Exchange current density [A.m-2]',\n", - " 'Exchange current density per volume [A.m-3]',\n", - " 'Negative electrode oxygen interfacial current density',\n", - " 'X-averaged negative electrode oxygen interfacial current density',\n", - " 'Negative electrode oxygen interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode oxygen interfacial current density [A.m-2]',\n", - " 'Negative electrode oxygen interfacial current density per volume [A.m-3]',\n", - " 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]',\n", - " 'Negative electrode oxygen exchange current density',\n", - " 'X-averaged negative electrode oxygen exchange current density',\n", - " 'Negative electrode oxygen exchange current density [A.m-2]',\n", - " 'X-averaged negative electrode oxygen exchange current density [A.m-2]',\n", - " 'Negative electrode oxygen exchange current density per volume [A.m-3]',\n", - " 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]',\n", - " 'Negative electrode oxygen reaction overpotential',\n", - " 'X-averaged negative electrode oxygen reaction overpotential',\n", - " 'Negative electrode oxygen reaction overpotential [V]',\n", - " 'X-averaged negative electrode oxygen reaction overpotential [V]',\n", - " 'Negative electrode oxygen open circuit potential',\n", - " 'Negative electrode oxygen open circuit potential [V]',\n", - " 'X-averaged negative electrode oxygen open circuit potential',\n", - " 'X-averaged negative electrode oxygen open circuit potential [V]',\n", - " 'Positive electrode oxygen interfacial current density',\n", - " 'X-averaged positive electrode oxygen interfacial current density',\n", - " 'Positive electrode oxygen interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode oxygen interfacial current density [A.m-2]',\n", - " 'Positive electrode oxygen interfacial current density per volume [A.m-3]',\n", - " 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]',\n", - " 'Positive electrode oxygen exchange current density',\n", - " 'X-averaged positive electrode oxygen exchange current density',\n", - " 'Positive electrode oxygen exchange current density [A.m-2]',\n", - " 'X-averaged positive electrode oxygen exchange current density [A.m-2]',\n", - " 'Positive electrode oxygen exchange current density per volume [A.m-3]',\n", - " 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]',\n", - " 'Positive electrode oxygen reaction overpotential',\n", - " 'X-averaged positive electrode oxygen reaction overpotential',\n", - " 'Positive electrode oxygen reaction overpotential [V]',\n", - " 'X-averaged positive electrode oxygen reaction overpotential [V]',\n", - " 'Positive electrode oxygen open circuit potential',\n", - " 'Positive electrode oxygen open circuit potential [V]',\n", - " 'X-averaged positive electrode oxygen open circuit potential',\n", - " 'X-averaged positive electrode oxygen open circuit potential [V]',\n", - " 'Oxygen interfacial current density',\n", - " 'Oxygen interfacial current density [A.m-2]',\n", - " 'Oxygen interfacial current density per volume [A.m-3]',\n", - " 'Oxygen exchange current density',\n", - " 'Oxygen exchange current density [A.m-2]',\n", - " 'Oxygen exchange current density per volume [A.m-3]',\n", - " 'Positive current collector potential',\n", - " 'Positive current collector potential [V]',\n", - " 'Local voltage',\n", - " 'Local voltage [V]',\n", - " 'Terminal voltage',\n", - " 'Terminal voltage [V]',\n", - " 'Electrolyte flux',\n", - " 'Electrolyte flux [mol.m-2.s-1]',\n", - " 'Total lithium in electrolyte [mol]',\n", " 'X-averaged open circuit voltage',\n", " 'Measured open circuit voltage',\n", " 'X-averaged open circuit voltage [V]',\n", " 'Measured open circuit voltage [V]',\n", " 'X-averaged reaction overpotential',\n", " 'X-averaged reaction overpotential [V]',\n", - " 'X-averaged SEI film overpotential',\n", - " 'X-averaged SEI film overpotential [V]',\n", " 'X-averaged solid phase ohmic losses',\n", " 'X-averaged solid phase ohmic losses [V]',\n", " 'X-averaged battery open circuit voltage [V]',\n", @@ -670,6 +602,8 @@ " 'Local ECM resistance',\n", " 'Local ECM resistance [Ohm]',\n", " 'Terminal power [W]',\n", + " 'Power [W]',\n", + " 'Resistance [Ohm]',\n", " 'LAM_ne [%]',\n", " 'LAM_pe [%]',\n", " 'LLI [%]',\n", @@ -720,7 +654,7 @@ "Electrolyte flux [mol.m-2.s-1]\n", "Electrolyte potential\n", "Electrolyte potential [V]\n", - "Electrolyte tortuosity\n", + "Electrolyte transport efficiency\n", "Gradient of electrolyte potential\n", "Gradient of negative electrolyte potential\n", "Gradient of positive electrolyte potential\n", @@ -731,13 +665,13 @@ "Negative electrolyte concentration [mol.m-3]\n", "Negative electrolyte potential\n", "Negative electrolyte potential [V]\n", - "Negative electrolyte tortuosity\n", + "Negative electrolyte transport efficiency\n", "Positive electrolyte concentration\n", "Positive electrolyte concentration [Molar]\n", "Positive electrolyte concentration [mol.m-3]\n", "Positive electrolyte potential\n", "Positive electrolyte potential [V]\n", - "Positive electrolyte tortuosity\n", + "Positive electrolyte transport efficiency\n", "Separator electrolyte concentration\n", "Separator electrolyte concentration [Molar]\n", "Separator electrolyte concentration [mol.m-3]\n", @@ -748,6 +682,7 @@ "Sum of positive electrode electrolyte reaction source terms\n", "Sum of x-averaged negative electrode electrolyte reaction source terms\n", "Sum of x-averaged positive electrode electrolyte reaction source terms\n", + "Total lithium in electrolyte\n", "Total lithium in electrolyte [mol]\n", "Total lithium lost from electrolyte [mol]\n", "X-averaged battery electrolyte ohmic losses [V]\n", @@ -764,12 +699,12 @@ "X-averaged negative electrolyte concentration [mol.m-3]\n", "X-averaged negative electrolyte potential\n", "X-averaged negative electrolyte potential [V]\n", - "X-averaged negative electrolyte tortuosity\n", + "X-averaged negative electrolyte transport efficiency\n", "X-averaged positive electrolyte concentration\n", "X-averaged positive electrolyte concentration [mol.m-3]\n", "X-averaged positive electrolyte potential\n", "X-averaged positive electrolyte potential [V]\n", - "X-averaged positive electrolyte tortuosity\n", + "X-averaged positive electrolyte transport efficiency\n", "X-averaged separator electrolyte concentration\n", "X-averaged separator electrolyte concentration [mol.m-3]\n", "X-averaged separator electrolyte potential\n", @@ -803,7 +738,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a38e975fd6e24d2eb807a36bc56402ed", + "model_id": "0b8dbfec708148ed93cf7be8cde2f807", "version_major": 2, "version_minor": 0 }, @@ -817,7 +752,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -845,7 +780,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37a8d21c0d7944b08e7295eb5b6551cd", + "model_id": "c5c8128da10247d3b4e705e6c573a0ef", "version_major": 2, "version_minor": 0 }, @@ -859,7 +794,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -887,7 +822,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "66c9f0f6a3e242098ea54f149f37dbd9", + "model_id": "eaa22d22a76e4d96b120b003db1941da", "version_major": 2, "version_minor": 0 }, @@ -901,7 +836,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -921,7 +856,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fcc6500e55fa4862a85fe56e1ee72b8d", + "model_id": "98417becc6fa483a8b4156baa251b761", "version_major": 2, "version_minor": 0 }, @@ -935,7 +870,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -961,7 +896,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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mIzY2lrVr15KSksJbz72A9dQpRt7/W5LTUukdFsYtzVtwS4sWNPf1RQiBzdNEVqCZZD8NZ3ysnDbm4dS4zrlAEKAx0kzoaWZ30rQghyZ5mSoVRCPiEDrsGiN2jQmbMGDTGLEKA1YMWNFjxUAhOgqknkJ0FMrLPwVOHQVODflST6FTS4HUUuhUF5CbXV328H2BFcBjUsr9Fbw/CfitlPLOquq6kXr4VZk7dy6rV69m44YNZGS6FmS/O6YXc0fdhkByODmZtoGB6DQapE5LXqAHl/z1XPCRJHqU/hagRRCkNdNUagl1OAgtyCU4L0s9E6BUi0TgFDocGgMOocUp9Ng1ehzoXBcXdNiL/mtDi00YsEstNrRY0WGTWqzS9btVaimUOqxSR4FTS6FT476oFDi16kG3elJnuXSklBlCiHXAKKBcwAfuBb6+lobcyB5//HEef/xxnE4n+/fvZ9OmTTRv3pygIcM4v28fQ/v2xsNooluzpsQ0C6NnWBg9wsJobjIRA9g9jOQEWEj11ZDkZeeURz4XtHYQgFmgMfsSqDERKnSE2J2EWPNpkp+N2V5Y3x9daWAEEq20oa2toUIBaF0/TjQ4Nfqii4vrguJAj63oAmMXOmzoseG6wFjRY0NLIXoKir+pOLUUoKfAqSPPqSXfqSPXqVffVGpJlQFfCBEE2IqCvRm4FfhrBeV8gEHAb2u8lTcIjUZDZGQkkZGR7m0BkRF89dVXbN68mS2bN/P+5k04nU7+MW0693bszPkziazZfZzuzZrRPiiIthoNCLB6m8nyN5Hmq+G8p50z5nyStfnEC1zzpYwe+Gr8aSIMNHFCE1shwQV5BORnqecDlDqhwYnGWYjOWYMdj6ILitRqsGsMRcNdRqzC9VMojORjIh8judJEtjSQ5TSRYTdwyWFWD9dVoTo9/FDg86JxfA3wjZTyeyHEIwBSynlF5cYBq6SUubXT1BuTh4cHkydPZvLkyQDk5OSwY8cOOnfuTEhICD//+2Oeef89V1mjicimoXQPDWVar1tokulFINAB3BeBHH8zaT6CCx52zljyOaLL5gi4/k966tB5BhKsNREstQQ5nATbCgkuyMbbml8vn19RroXAid5ZgN55FfmtNODQ6ijUeZOv8SBb40UGPqRK1/oMiYXejf6C0OgevGpopJQcP36c7du3s337drZt20Z8fDz7V35HoNXGZ0sW879dO4kKDSEqtCmRoaEEeV5+QtTuYSTX30yGt5ZkLyfnzYVcNBYgy/TyTUJHoMZIsNQQ6HQSZC0ksCAHn8I89Y1AaTQKtR5k6QNJFkGcdgZyrND/hnvY7qbNh98YCCFo164d7dq147e/dY2GFRYWYjQaATCdO8XZ3btYvWEDxRfn5gEBbJ4xEx1wIvEsXueNtPDyomVR5JZ6LQW+ZrJ9DKR7Ci562kkyFXBWn8vZ4gMbAIMJg/AgQBgIEjoCnJJAm5XAwnz8C7LVbCHlpmN05BLkyCWIU3QBbgMKzV6c1zfnuGzG3vwmpNhN9d3MWqN6+DeI7Oxsdu/eTWxsLCkpKbw++1Ws5y8ycvxYNu3ZTaCXF12Cg+kS3IReLZozon2HcnU4zAby/EzkeOm55AnJFjtJpnxydfZyZQUCX42RAKEjQAoC7A4C7IUEFOTirb4VKDcpiSDD2JTDmjZszW9Ois1c300qR+XSacS2b9/Ojh072LNnD3F79rD/wAEG9+rF4udfxp58gal/fwcfnY5OTYLpHNyEjsHB+FtKf4V1mA3k+5rI8daT7gGpZgcXzIXudQPK0qPFX2PAX+gIcEKAw06AtQD/gly1ophy05AIkk2t2OroxI68kAazFKcK+Iqb1WolPT2dJk2a4HA4GDVqFHt27ybt0uXn4B4eOoyXBw5C2m0s27uX9kFBtAsMxGIonRrAadBR4G0i11tPlqeGSx6SZJOVi4Z87JqK/90Y0RKgNeKPFn8n+Ntt+NsK8C/IUxcD5YaVr/dlty6aVdmt6v3paBXwlSuSUnLhwgX27t3L3r17iYqKYtiQoRzbvYdOvXsBrnsJLfwDaB/gz+9iejGwdWtsDgc2pxOLvvTMBikEdi8T+d5Gsj01ZHgI0kx2LpoLSz1EVpZJ6AjQGEpcDKwEFBYQWJijUkwoN4R8nQ/bdD35NbtlvT14pm7aKlckhCA0NJTQ0FBGjhzp3t6uZ3cOHTrEgQMHOHDgAPv37+fggQPIQUPwjOnFxjWrGf30U4T5+dPO3592gQG0DQxkeLv2BEuJPisfb6BZiWNJvZZCLxN5XgayPTWkWyRpZjsXjQXk6Oycc9g5V1xYC1gEWLzwFHoCNUb8pSDI4STQVkhQQQ4+1ry6O1GKUgWzPZMh9l/pZQlkhRzIgXz/+m7SVVE9fKVSJ06cYOHChRw8eJCDBw9y9OhRCgoK+Hnuh/QICeX7Nb/y759/oI2fP20DA2gbEEibgABa+PmhqeCursOop9DHRK6nzjVEZHGSYrJx0VDgTjNdlhEtQVoTTaSGJnYHIdY8QnIzVaoJpd5JBEcs3fgmK5J8WXd9Z9XDV2pF69ateemll9yvHQ4Hp06domnTpphMJvwMGrT74vjp4EEu7dntLrf7nfcIAX7esZU9587SpuhC0DYwEJ9CG5ZkCALaFO8gwOZZNETkpSXdQ5DsYeO8sYBcnZ2zjqLppBrABBqTL8FF+Yaa22w0z8siQKWeVuqYQNIxbzfPmE+w2HkrRwp867tJVVI9fKVGpKamcuTIEY4cOcLUqVPRaDQ8+/Qz/P29d3E4LvfGQ3x82fn0M2ithWxOPEm+zUabgEBa+Pqi1ZQfE7VbDOT7mcn01pHqKUnysJJkzHdnHC3mKfS01JgItzsIz80isCCr1j+zohRzCD3/MwxjY05YrR9L3bRVGiybzcaJEyfcF4NLly7x1ptvYc3M5o677uDXTZsAMOh0tA4M4pawZvzlttsBSMrKItDDA4O29KwIqdeS5+9Bhp+eJF8niZ4F5aaQegkDrYSRVjY7rXLT1b0ApdZJBHvMfViS1blWj6MCvnJDunTpEocPH+bQoUMcOXKEQ4cO4efjy7/f+gv2lFRixo8l4dxZ2gQG0TkokE5FD5X1at6iXF02LxOZQRaS/OGEdwHJxtJTQP01JsLR09pmpWVOOp42NUVUqR3HzFF8mhVTa/WrgK/clBYvXuzKK7R/P/FxcZw+c4Z7R93OB7/7Pbakc8z65N90DAqmZ1gYkU2blpo+avc0kd7Ewjl/OO6Tz6Uy3wD8NSZaoKel3U6z/BwC8zLV08NKjYk392ZRVkSt1K0CvtIoZGRkkJeXR9OmTUlLS6P3LbdwPCEBAJ1WS0RoUx7v25dRHcqnlbB5mcgIdn0DOOlV/huAES1NtWZCnYIQu42QglwC8rPQUD9/H8qNb5N5CD9ktam64FVSs3SURsHX1xdfX18AAgICOHb8OKmpqWzbto2tW7eyadMmPEfdhkenzmxfs4bHP3iP3mHN6dOyJbe0aEFwtidBCRCF62ZwdpAHyX5Fi85YcjlJDifB9XyAhxadRwCBWhPBaAl0SAJsVgKs+fgX5KhpoUqV+uWvI9vTyIY6uJFbXSrgKze0wMBARo8ezejRo0tt98JBi/VrWLJpM/N37QSgVWAg8yfeQ7vAQKyZufjkFuJ3StAB19PDVl8L2X5GLnkLkj0dJJkKuEAeF4or1QN6LcLDFy+NHj/0+KHBzynxcdjxsVvxsRbgXZiHFpVptLETSIbb1pBguJtzVo/6bg6gAr5yk+rduzf/+9//sNls7N69m40bN7Jp0yYinn8Rw6VL/P2tN/no25V0a9aUyCYhdA0NpWtICC39/AgUgvZF9TiNOgp8LOR468jy0JBudpBqspFiKOSU1sqp4gMWLfuH0YDwMuIhdHhr9HijxVOCl5R4Oxx42G142W142vLxsBao+wY3OZ3TymTjJv5uHdEgkq+pMXylUVq1ahUrVqxg29at7D9wALvdjreHB0ffnYsjJYVF2zaTU1jofnq4mY9PuecEHCY9Vi8j+R568kwacsyQaXKSZXCQrreSqbOWW4imJIHAInR4aHR4osWCwENKLBIsDgceDgcWhx2Lw4bFVojZXqjuKdygNpsH831W2xqpq1bH8IUQJmADrpVUdcBSKeUrFZQbDLyH64tvqpRy0LU0SFHqwogRIxgxYgTgWnBm//79JCUl0eTOO5FS8t9Bg1i/caO7vFGnZ1inTnw8fjzC6WTl/v2Y9HrCfHwI8/GhmcmEKNNdl0LgsBiwmvVYLToKzFry9ZBrhFyDk2y9g2ydjQx9IcmaMkNAmqIfPRQlHULggVFoMQsdFqHBjAYLArMEs5SYnU5MTgdmhwOLw4bJYcdst2K2FapvEvXslsIt7DGE1vvQTpU9fOH6V+whpcwRQuiBTcATUsptJcr4AluAUVLK00KIYCll8pXqVT18pSGTUpKSkuJ+YOzIkSMEBQXx1B+ewp6eSXhEJy6mpLjLW4xGJvaM4a0RoxB2K3M3bcTLaCLEy4smXp4Ee3gS5OmJUVdxH0vqtdiNeuxmHVajDqtRQ6FeUGgQ5Bsk+XpJrs5BntZBjs5OjtZWaYrqsgQCU9GFwlx8oZACM2BxStfFwmHHUnShMDtcFwmDs/zCOMq1SzM25+851z+0U6s9fOm6IhQnKtEX/ZT9l/YbYLmU8nTRPlcM9orS0AkhCA4OJjg4mAEDBpR6TxccwL4DBzh16pT758yZM0RERBAy9UEKMrL4x1/nYLWWThU9bcQo3pxwD/mZ6dz74T8JspgJ9PAg0OJBgIeFnmHN6dykCQank4z8fELMZnQVpJsoJvVaHAYddqMOm1GL3aDFahBY9YICAxToJAV6yNM6yNU5yNXauagrLH+hELgiga74hREwokODWWixCB0W4RpyskhcP04HHk6H60Jht+FhK8TDVqCGnK4goPAM/T3P1Un6hcpU66atEEILxAJtgQ+llNvLFGkP6IUQ6wAv4H0p5RcV1DMdmA7QokX5pyUV5UYRFBREUFAQPXuW72hZAnzJy8sjJSWFpKQkzp8/z/nz5+nYsSPB/ftz6dIldN+u4PDFi6QcPUpGZiYAr856nJ7DR3Ay4QTdHrwfAB+LBT+LBT+TmVn9+nFb+/ZczM5mwe5YfM1m/ExmfMwmfM1m2gYE0sRc9ZJ8Uq/FYdS7LxQ2gwarUUOBXlBgkOTpJXl6J7laB9l6O2naAi5WdJEovlFt0ABmBBaMQouH0OEhtHigwSLBwynxlBIPhw0Pux0PeyFetoJGuQZCPxnHRuov4F/VTduioZsVwGNSyv0ltv8T6AkMA8zAVuAOKeXRyupSQzqK4mK1WklJScFiseDn58elS5f46quvSE1NJS0tjbS0NC5dusSsmbMYMfRWtq5fz+A7RpWr59M/v8CYmF6s3bGDB9//B75mC35mM74mI75GE0/070+nJk04nZ7OrrNn8beY8bdY8Ddb8LdYyq14VpLUa7Gb9NhNl4ecCgyCfAPkGSR5Bkm2zkaW3k52NYebdGjw0hjwFFo80eAlwdPpxNvhwMtuw9tWgLc1/6YbWlquv5OdeU2uef86e/BKSplR1IsfBewv8dZZXDdqc4FcIcQGXM+3VBrwFUVxMRgMNGt2eRkZf39/Zs2aVWn5QbePxG63k56e7v65dOkS0dHR+IeE0KlDWx7MyyY9Pd19wTiYloZhwkR8O3bi+6+/YtY/PyhX7/9emk1kSAg/7djOF5s2ElA05ORvsRDo4cHoTp3xMhpxWq3ohSCozEpoJTmNOuwmPTaznkKjlgKTIN8oyDVIcopuWKfrrWToCkgvO6TtnuLqGkE2CR0+Qo+P0OErwcfhxM9uw89WiN8NuFraIE0cOxlZdcFaUJ1ZOkGArSjYm4Fbgb+WKfYt8E8hhA4wALcA79Z0YxVFcdFqtQQGBhIYGFjuvc6dO/P+++9Xuu9vZj7KwDF3kpaWRkpKCqmpqaSmptJj2jQCAgLwCPQj7+ABTiZfJDXhBLl5uQCMfmQmFrOJuZ98wpyvvsTDaCLIy5NAiwdBZjMfjB2LxWAgPimJC9nZrpvVnl4EeXjgr614HVipETjMBqwWA1azjjyzIM+kIcvoJNPouihc0lu5qLFzsXgnQdHdxMurpflrDASgIdDuJLiBr5YWUHiGLuZL9bJaVnV6+KHA50Xj+BrgGynl90KIRwCklPOklIeEED8DewEn8EnJIR9FURoODw8POlSQb6jYPffcwz333ON+nZeXR3JyMs2bN0er1XKXyYB3RGcuXrxIcnIyFy5c4HRyMk3/+Azk5bH46aeYv3SJe38hBKG+vuz880uQl803u2M5lZ5OqJc3od5eRf/1xt9iwbeC9kghsHsYKPQwkO+pJ8eiIdMsSTPZSDYWkq2zkeOwcRpcFwMDYDBhEp6EaIyESA2hRQvl+DWQhXJu1cVzgCF1flz14JWiKDUqOTmZU6dOceHCBfcN6/z8fObMmYPd7uC3997LN8uXUTL2tAxuws45f8WRmcWby5eSmp1V9IyDL819fQj38yfU27vC47mXzvTSk+4pSPV0cs6cT7reWq6sp9ATVrRQTuu8bILyM2vtPFyJRPAfzUQSCiv+TFeikqcpitJgFE9nrYhOp2XR0iV8abdz4cIFzp07x9mzZ3E6nQRNnAhA2o5trF+7hvNxce79enfuwn//9ByO9HT+/NWX6ISglb8/bQICaBcYSEiBF5ZkQRC402I4zAZy/cxk+Om46C056ZFHht7KYYeNwwLw0OLlGUJbjZGO+fm0zk5FJ+smB5JAMth4mITCXnVyPPdxVQ9fUZSGqKCggDNnzpCYmIhOp2PIENcQSL9+/YiPi3ffWwC4p09f3r1rLCIvhy93x9ImIIAuTULwNplK1Vm8UM7ZADjmk19qpTQjWjpoLUTl59EqM6XWn062aj14rWASDip/1qIiKh++oiiNipSS8+fPu1dKa9WqFaNGjeLc8ZM0b385B33roGCimgRzb3Q0A1q1LldPoZ8HyU1NHAm0ccJyeXzfV2Okm1NPj4xkPOy1tzraEv1d7M6r+NtQZdSQjqIojYoQgqZNm9K0aVN3zx+gWdtWJCUlER8fz+7du9m5cyc7duzgti5dMXfvwf7t23jx88/oHx7O4NZt6Cyb0Dw9l+a4Vkm70NyD+JBCzpnyWEshG3096aZpQt+MVHytuZU36Bp10yWym6sL+NdDBXxFUW4aQghCQ0MJDQ1l1KjLD6c5nU40Gg3SbCB10Ve8+euvvPnrrwR7ezOkVWueHjSIZkDYoQLCDkFekBfHww3s8s9gJ9nEepuI0gYy9NLFGl0PuYU1AS09r3pY51rVzVEURVHqkaYoJ9GAAQPYf+AA586d47PPPmPIbbfx69kzBI4YiTasBetPnGTrqUTMyVlE7kzjgQ1ahp7zw+jQsseRzT99vNgY2By7qJnQaXDkEmVJrZG6qkON4SuK0qg5HA60RQ+GjRg6jF/WrqF1YCCTo6K4N7obARYLToOOUx18WReagU3rxE9jYlxeAc1zLl338Y+bI/lPVvVn61zPGL7q4SuK0qhpSzwFvPL77/j8889p2qEjb/76K70+mMu8rVvRWO202pfKfZu19EzzJd1ZwHyTYF1QS5zXme7YNaxTN9NBVcBXFEUpYrFYuP/++9m4aSP79+9n4r330uL2O9B36Ei+3UHahVS677jElD2eBBYaWS+z+bRJM7IMVWcprYzBkUu0JaXqgjVABXxFUZQKdOnShc8//5zf/eEJAqdMZqHTQf//+xcfbNqE9uwl7tpUSO9UP8458viPrw/JZp9rPlY33amqC9UAFfAVRVGqYfK033HrqFH8Ze0aBs2bx6bDx4jcmcbEgz7YbE4+8zRyyrN8MrvqaG5NqOHWVkwFfEVRlGpo27YtK1euZPXq1ViCArl34Zd8tG0bfqfSmRhrwGzVssAER3xCrrpugyOX5obaT+ymAr6iKMpVGDZsGLv37OGxxx7j1t/9HmG2YErLYew2SXCBkWV6B0kWv6uut5Ox9qdnqgevFEVRrpLFYmHu3LkAWNMzeXryvQwICuJ2W3vW9jazyNPK7+yWq8rJHy4uAOG10+AiqoevKIpyHQq0grVnzzDl66/4b+wehuwowJyv4WtfP6ya6vepg+1JtdhKFxXwFUVRroO3tzcbN22iT9++zFyxnI/XrOe23ZBts7EiqFnVFRTxsF3CX1d7idpABXxFUZTr5uvry/9WrWLixIm8tvoXlq7ZxJj9Zo7acjjgF1rtejqb0mqxlWoMX1EUpUaYTCa++uor7HY7gS1a4pWUyWgPf/7XPo82WgMmR/kVuMpqo7nIJqr/reBqVdnDF0KYhBA7hBDxQogDQohXKygzWAiRKYSIK/p5uXaaqyiK0nDpdDqWL1/O/XPmoA1tit+hZMIyTKwJqF4vP8RRu+P41RnSKQSGSimjgGhglBCidwXlNkopo4t+XqvJRiqKotxINCYjP+m0DPv4I9psTSPOnstZD/8q9/OxJmPR2GuvXVUVkC7FTwToi37qJ8WmoijKDaJzdBRJ2dm8vHg5Q8758r2HucpEawInnWpxHL9aN22FEFohRByQDPwipdxeQbE+RcM+PwkhulRSz3QhxC4hxK6UlLpJFqQoilIfYmJieGX2bH48fIgjK7agyYODvlU/hdtWV3uxsVoBX0rpkFJGA2FALyFERJkiu4GWRcM+HwArK6nnIyllTyllz6CgoGtvtaIoyg3gj3/8IxFduvDizz/Ta4+dbUZtlfs0k+drrT1XNS1TSpkBrANGldmeVTzsI6X8EdALIa4ti5CiKMpNQq/X89HHH3MhO5vd2+Lxv6TjlGfAFffxs56vtfz41ZmlEySE8C363QzcChwuUyZECCGKfu9VVG/tTihVFEW5AfTp04cTJ04wZux4IhKdbPPwuGJ5ndNKe3NWrbSlOj38UGCtEGIvsBPXGP73QohHhBCPFJWZAOwXQsQDc4F7ZX2tnagoitLAtGjRAp/bR3Hx0Fnysp1cMnpeubwuvVbaUeWDV1LKvUC3CrbPK/H7P4F/1mzTFEVRbh4/rF/H2Lnv8ZnHLLaPacVtyZWnQ24i0oFWNd6GBvWkbXJyMk8//TSHDx/G6aybNR4VpaHQaDR07NiRd955h+Dg4PpujlLDhg8fToC/P4tWrOau26aSrzNithdWWDZA1lMPvy49/fTTDBkyhP/85z/o9fr6bo6i1CmbzcaCBQt4+umn+eKLL+q7OUoNs1gsPDpjBm+++SbTduewJyaYvmlnKizrba+dW6ANKnna4cOH+e1vf6uCvdIo6fV67rvvPg4fPlx1YeWGNHPmTPR6Pd9//QsJmsofwjLZs/DQ2Gr8+A0q4DudThXslUZNr9er4cybWEhICFOmTOHHQ4cIOivIMpgrLRtuzK7x4zeogK8oinKze/PNN9nz40+0SZYcucKc/DBdZo0fWwX8EgYPHsz//ve/Utvee+89ZsyYUWH58PBwUlNTycjI4F//+lddNFFRlBtcaGgooX1641Wg4ZSsPAQ3ERk1fmwV8EuYPHkyixYtKrVt0aJFTJ48+Yr7qYCvKMrVuHDhAqPm/R/7fzpEvs5YYZlAWfM3bhvULJ2yBg8eXG7bPffcw4wZM8jLy+P2228v9/7UqVOZOnUqqampTJgwodR769atu+LxJkyYwIsvvkhhYSFGo5HExESSkpI4e/YsXbt2RUrJHXfcwV//+tdS+z333HMkJCQQHR3N8OHDeeWVVxgzZgzp6enYbDbeeOMNxowZA8Drr7/OwoULad68OYGBgfTo0YOnn36ahIQEZs6cSUpKChaLhY8//piOHTte3QlTFOWGEBQUxOmUFOLWxnPstglEppfPg18bM3UadMCvawEBAfTq1Yuff/6ZMWPGsGjRIkaOHMmzzz5LbGwsfn5+jBgxgpUrVzJ27Fj3fnPmzGH//v3ExcUBYLfbWbFiBd7e3qSmptK7d2/uuusuYmNjWbZsGXv27MFut9O9e3d69OgBwPTp05k3bx7t2rVj+/btzJgxgzVr1tTDWVAUpbbp9XpG33kn3y1bxlGpJbKCMkZ7Np5aGzmOmpvI0qAD/pV65BaL5YrvBwYGVtmjr0jxsE5xwB83bhyDBw+mOLvnlClT2LBhQ6mAX5aUkueff54NGzag0Wg4d+4cFy9eZNOmTYwZMwaz2XVn/s477wQgJyeHLVu2MHHiRHcdhYUVP5ChKMrNYfz48SxYsIBjG09iizSil45yZVoZs9iXd+Vka1ejQQf8+jB27Fieeuopdu/eTX5+PlFRUSQkJFxVHQsXLiQlJYXY2Fj0ej3h4eEUFBRQWXohp9OJr6+v+xuCoig3vxEjRmA2mYhfvZcT/W+jQ+bFcmXCdJnso+YCvrppW4anpyeDBw/moYceYvLkydxyyy2sX7+e1NRUHA4HX3/9NYMGDSq1j5eXF9nZl+fMZmZmEhwcjF6vZ+3atZw6dQqA/v37891331FQUEBOTg4//PADAN7e3rRq1YolS5YArm8I8fHxdfSJFUWpDxaLhRdfeonB4W05ajBVWCa4hmfqqIBfgcmTJxMfH8+9995LaGgof/nLXxgyZAhRUVF0797dfQO2WEBAAP369SMiIoJnnnmGKVOmsGvXLnr27MnChQvdN19jYmK46667iIqKYvz48fTs2RMfHx/A9a3gP//5D1FRUXTp0oVvv/22zj+3oih16/nnn2fyxEloMiteGCXAealGjyfqK4txz5495a5du8puo+y2m01OTg6enp7k5eUxcOBAPvroI7p3717fzVIakMbwd6BclrhjBxt+/Jo7+loIKCydQbNQ58ns/HtLbZtzd2SslLLntRxLjeHXsenTp3Pw4EEKCgp44IEHVLBXlEbu9y+8wIm98UQOe6pcwDfac/DS2siuoZk6KuDXsa+++qq+m6AoSgMyfvx4Zqxezb6EDKIruD/bypDJ3vyaWTFWjeEriqLUo+J7gpvW7K/w/WY1mFOnOmvamoQQO4QQ8UKIA0KIV69QNkYI4RBCTKisjKIoinJZ06ZN6RYdTdzuYxRqyw/d1GROner08AuBoVLKKCAaGCWE6F22kBBCC/wV+F/Z9xRFUZTK9e3Xj/3HT3HK6F3uPR9qbkHzKgO+dCm+k6Av+qloas9jwDIgucZapyiK0gj86U9/YsfHH5NaweLmno6aC/jVumlb1HuPBdoCH0opt5d5vxkwDhgKxFyhnunAdHCt4l6VPy/fV53mVdtfxnetsszZs2eZOXMmBw8exOl0Mnr0aN5++20MBkONtqU6/vvf/3Lw4EGee+65665r2rRpPPXUU3Tu3Jm33nqL559/vgZa6HpQLScnh8TERLZs2cJvfvObGqlXURqTFi1akBcVTdzF9eXeM9nrcAwfQErpkFJGA2FALyFERJki7wHPSllBMojS9XwkpewppexZnJumIZFSMn78eMaOHcuxY8c4evQoOTk5vPDCC/XSnrvuuqvCYG+326+6rk8++YTOnTsD8NZbb11328pKTExUM5AU5Tp8vWkjyxeto+yjUTqnFS9tzSx3eFWzdKSUGcA6YFSZt3oCi4QQicAE4F9CiLHX37y6tWbNGkwmEw8++CAAWq2Wd999l08//ZS8vDzmz5/PmDFjGDVqFB06dODVVy/fv/7yyy/p1asX0dHRPPzwwzgcrmufp6cnL7zwAlFRUfTu3ZuLF8vnywD4+eef6d69O1FRUQwbNgyA+fPnM2vWLMCV9vmpp55iyJAhPPvssxw/fpxbb73V/fRvQkIC69atY/To0e46Z82axfz58wFXquldu3bx3HPPkZ+fT3R0NFOmTCnVhv/7v//jT3/6k/v1/PnzeeyxxwD4xz/+QUREBBEREbz33nvl2v/cc8+xceNGoqOjeffdd0lMTGTAgAF0796d7t27s2XLFsCVN2jGjBl06dKF0aNHc/vtt7N06VIAYmNjGTRoED169GDkyJGcP3++ev/jFOUm8PPatSz+bj0pFp9y74Xqc2vkGNWZpRMkhPAt+t0M3AqUWmVZStlKShkupQwHlgIzpJQra6SFdejAgQPudMXFvL29adGiBcePHwdgx44dLFy4kLi4OJYsWcKuXbs4dOgQixcvZvPmzcTFxaHValm4cCEAubm59O7dm/j4eAYOHMjHH39c7rgpKSn8/ve/Z9myZcTHx7tz6pR19OhRVq9ezd///nemTJnCzJkziY+PZ8uWLYSGhlbrM86ZMwez2UxcXJy7jcUmTJjA8uXL3a8XL17MpEmTiI2N5bPPPmP79u1s27aNjz/+mD179pSrd8CAAcTFxfGHP/yB4OBgfvnlF3bv3s3ixYt5/PHHAVi+fDmJiYns27ePTz75hK1btwJgs9l47LHHWLp0KbGxsTz00EP19s1KUepD3759OZuSyp6c8msaB2nzauQY1RnDDwU+LxrH1wDfSCm/F0I8AiClnFcjLWkApJQIUX4l+ZLbhw8fTkCA6+mI8ePHs2nTJnQ6HbGxscTEuG5f5OfnExwcDIDBYHD3unv06MEvv/xSrv5t27YxcOBAWrVqBYC/v3+F7Zs4cSJarZbs7GzOnTvHuHHjADCZKk68dLWCgoJo3bo127Zto127dhw5coR+/foxd+5cxo0bh4eHh/tzb9y4kW7dulVal81mY9asWe4L4NGjRwHYtGkTEydORKPREBISwpAhQwA4cuQI+/fvZ/jw4QA4HI5qX8QU5WbQt29fAHbFJzGyu1ep9/y1NdPDrzLgSyn3AuX+sisL9FLKqdffrPrRpUsXli1bVmpbVlYWZ86coU2bNsTGxpa7IAghkFLywAMP8Je//KVcnXq93r2PVqvFbrfjcDjc3yTuuusuevbsWeGFpqzigFtZ/iOdTofTebl3UFBQUGWdZU2aNIlvvvmGjh07Mm7cOPfnu1rvvvsuTZo0IT4+HqfT6b4oVVaXlJIuXbq4e/yK0th069YNo9HIgT2J0L30BBM/cire6SqpJ21LGDZsGHl5eXzxxReAq5f5xz/+kalTp2KxWAD45ZdfuHTpEvn5+axcuZJ+/foxbNgwli5dSnKya0bqpUuX3CmRK6LVaomLiyMuLo7XXnuNPn36sH79ek6ePOne/0q8vb0JCwtj5cqVgGuxlLy8PFq2bMnBgwcpLCwkMzOTX3/9tcL99Xo9NlvFN4HGjx/PypUr+frrr5k0aRIAAwcOZOXKleTl5ZGbm8uKFSsYMGBAqf0qShEdGhqKRqNhwYIF7nsa/fv3Z9myZTidTi5evOhepKZDhw6kpKSUGuI5cODAFc+DotxMDAYDvW+5hZxLeeTpSn9r9ya7kr2uToPOpVOdaZQ1SQjBihUrmDFjBq+//jpOp5Pbb7+91KyW/v37c99993H8+HF+85vf0LOnK2ndG2+8wYgRI3A6nej1ej788ENatmxZreMGBQXx0UcfMX78eJxOp3v8+0oWLFjAww8/zMsvv4xer2fJkiW0bt2ae+65h8jISNq1a1fpkMv06dOJjIyke/fu5cbx/fz86Ny5MwcPHqRXr14AdO/enalTp7pfT5s2rVzdkZGR6HQ6oqKimDp1KjNmzODuu+9myZIlDBkyxP3t5O677+bXX38lIiKC9u3bc8stt+Dj44PBYGDp0qU8/vjjZGZmYrfbefLJJ+nSpUu1zqGi3Ax+XbOGcysWk2Q5Sdusy9/Qa2ouvkqPfBXmz5/Prl27+Oc//1nfTbmhFaeITktLo1evXmzevJmQkJD6blaD0dD/DpTalfnLak4a44jOPOPeZtV68ErBZEClR1ZuMKNHjyYjIwOr1cpLL72kgr2iFCksLGTcyy/Rp1szom9r5t5ucORiFnby5fWFbBXwr8LUqVOZOnVqfTfjhncti8srSmNgNBo5k5KCiM2DEgEfINiQz6lCr0r2rB5101ZRFKUB6duvH3FHEsvNaGuiu/6pmSrgK4qiNCA9evTgUmYWJ3JLB/wAjQr4iqIoN5XinFfbLpYO8H4q4CuKotxcIiIiGDlgAE6tudR2H3n9c/Eb9k3b756o2frufL/KIlqtlq5du2K322nVqhULFizA19e3xppQNjVx37593YnFbgZxcXEkJSVx++23X3Md13KOitM0K8qNLiQkhP9+s4RjcYvBluDe7uW8/rn4qodfRnFisf379+Pv78+HH35Yo/WXTU3ckIP9taRhjouL48cff7yu495I50hRaoMuMAB7Xul0K2bH9ffwVcC/gj59+nDu3DkAEhISGDVqFD169GDAgAEcPuxKGPrdd99xyy230K1bN2699VZ3+uOcnBwefPBBunbtSmRkJMuWLaswNbGnp2uFGyklzzzzDBEREXTt2pXFixcDrimMgwcPZsKECXTs2JEpU6ZUmI8mLi6O3r17ExkZybhx40hPT+fQoUPup2PBlbM+MjISqDwV8eDBg3n++ecZNGgQ77//PlOnTuWRRx5hwIABtG/fnu+//x5w5ekp/nzdunVj7dq1WK1WXn75ZRYvXkx0dDSLFy8mNzeXhx56iJiYGLp168a3334LuB5iGz9+PKNGjaJdu3butMxXOkc5OTkMGzaM7t2707VrV3ddinKzef7FFxkx/eVS24z2bHTiikuOVKlhD+nUI4fDwa+//srvfvc7wJWOYN68ebRr147t27czY8YM1qxZQ//+/dm2bRtCCD755BP+9re/8fe//53XX38dHx8f9u1zrdqVnp7O3XffzT//+U/i4uLKHW/58uXExcURHx9PamoqMTExDBw4EIA9e/Zw4MABmjZtSr9+/di8eTP9+/cvtf/999/PBx98wKBBg3j55Zd59dVXee+997BarZw4cYLWrVuzePFi7rnnHncq4m+//ZagoCAWL17MCy+8wKeffgpARkYG69e7Vt6ZOnUqiYmJrF+/noSEBIYMGcLx48fd33z27dvH4cOHGTFiBEePHuW1114r9TTy888/z9ChQ/n000/JyMigV69e3HrrrYDrIrVnzx6MRiMdOnTgscceY86cOZWeI5PJxIoVK/D29iY1NZXevXtz1113VSvxnKLcSIKCgkhOy+BMnpPmFle/XCBpor/6hIglqYBfRnHvMjExkR49ejB8+HBycnLYsmULEydOdJcrLCwEXEsiTpo0ifPnz2O1Wt0pjlevXs2iRYvc5f38/K543E2bNjF58mS0Wi1NmjRh0KBB7Ny5E29vb3r16kVYWBiAu20lA35mZiYZGRkMGjQIgAceeMDd1nvuuYdvvvmG5557jsWLF7N48eIqUxEXJ00rds8996DRaGjXrh2tW7fm8OHDbNq0yb04SseOHWnZsqU7BXJJq1at4r///S/vvPMO4PpmcPr0acCVrM7Hx7XYQ+fOnTl16hTNmzev9BxJKXn++efZsGEDGo2Gc+fOcfHiRfWkrnLTKZ6ps/1iHs1bXV7nNliXf131qiGdMorH8E+dOoXVauXDDz/E6XTi6+vrznAZFxfHoUOHAHjssceYNWsW+/bt49///rc7JXFlufUrc6WcRkaj0f17cYrl6ipOd3z06FGEELRr186dirj4s+zbt49Vq1a59ylOdFasspTQ1SGlZNmyZe5jnT59mk6dOl3T51q4cCEpKSnExsYSFxdHkyZNrikFtKI0dMUBf/+pjFLbA7TXNzFBBfxK+Pj4MHfuXN555x3MZjOtWrVyr0QlpSQ+Ph5w9a6bNXM9Av3555+79x8xYkSpJGvp6elA5amJBw4cyOLFi3E4HKSkpLBhw4ZS4+9VtdXPz4+NGzcCrkyaxb39Nm3aoNVqef31190996tNRbxkyRKcTicJCQmcOHGCDh06MHDgQHemzaNHj3L69Gk6dOhQLk3yyJEj+eCDD9wXiLIrZVWksnOUmZlJcHAwer2etWvXXjEFtaLcyJo3b46HxULCidRS2wPE9c3Fb9hDOtWYRlmbunXrRlRUFIsWLWLhwoU8+uijvPHGG9hsNu69916ioqKYPXs2EydOpFmzZvTu3dud0/7FF19k5syZREREoNVqeeWVVxg/fnylqYnHjRvH1q1biYqKQgjB3/72N0JCQtw3h6vy+eef88gjj5CXl0fr1q357LPP3O9NmjSJZ555xt22q01F3KFDBwYNGsTFixeZN28eJpOJGTNm8Mgjj9C1a1d0Oh3z58/HaDQyZMgQ5syZQ3R0NH/+85956aWXePLJJ4mMjERKSXh4uPvGb2UqO0dTpkzhzjvvpGfPnkRHR9OxY8dqnRtFudFoNBqe/dOf8Co4W2q7z3XmxVfpkZUrmjp1KqNHj2bChAn13ZRGQ/0dKMX2fz+fCHn5W3GKqSXBI/54zemRq7OIuUkIsUMIES+EOCCEeLWCMmOEEHuFEHFCiF1CiP4V1aUoiqJUj9Pp5GJ6AVnOy/fQPK5zLn51xvALgaFSyiggGhglhOhdpsyvQJSUMhp4CPjkulqlNBjz589XvXtFqQc//PADt97/KOvOXL5Ra7JnXledVQZ86VJ8RH3RjyxTJkdeHhvyKPu+oiiKcnWKZ7PtPXM5yGvk9T14Va1ZOkIIrRAiDkgGfpFSbq+gzDghxGHgB1y9/IrqmV405LMrJSXlOpqtKIpyc2vVqhVGo5HjCWk1Vme1Ar6U0lE0XBMG9BJCRFRQZoWUsiMwFni9kno+klL2lFL2DAoKuvZWK4qi3OS0Wi0dO3TgZOLFGqvzqubhSykzgHXAqCuU2QC0EUIEXlfLFEVRGrnOXbpw4kxyjdVX5Tx8IUQQYJNSZgghzMCtwF/LlGkLJEgppRCiO2AArvt7yKtby00Iui6v9HmlyjIXLlzgySefZOfOnRiNRsLDw3nvvfdo3759jbblarz33ntMnz4di8VyVfvNnz+fESNG0LRpUwCmTZvGU0895X6K70aXkZHBV199xYwZM665jms5R4MHD+add96hZ89rmhmnKNX2yCOP0K9dM8BaI/VVp4cfCqwVQuwFduIaw/9eCPGIEOKRojJ3A/uLxvk/BCbJ+prgfx2klIwbN47BgweTkJDAwYMHeeutt9wZMOvLe++9R15eXoXvORyV38SZP38+SUlJ7teffPJJgw3215KKOSMjg3/961/Xddwb6Rwpjc/AgQMZfdtt2DQ184xsdWbp7JVSdpNSRkopI6SUrxVtnyelnFf0+1+llF2klNFSyj5Syk010ro6tnbtWvR6PY888oh7W3R0NAMGDLim9MU7d+6kb9++REVF0atXL7Kzs3E4HDzzzDPExMQQGRnJv//97yvWM3fuXJKSkhgyZAhDhgwBXOmCX375ZW655Ra2bt3Ka6+9RkxMDBEREUyfPh0pJUuXLmXXrl1MmTKF6Oho8vPzGTx4sPuBnq+//pquXbsSERHBs88+6/68np6evPDCC0RFRdG7d+8KL3aXLl1i7NixREZG0rt3b/bu3YvT6SQ8PJyMjAx3ubZt23Lx4kVSUlK4++67iYmJISYmhs2bNwMwe/Zspk+fzogRI7j//vuZP38+Y8aMYdSoUXTo0IFXX738De8f//gHERERRERE8N577wGuVMoJCQlER0fzzDPPAPD222+7z+0rr7i+0SUmJtKpUyd+//vf06VLF0aMGEF+fn6V5+jRRx+lZ8+edOnSxV2XotQlm83G3kMJxKfVTM4olUunhP3799OjR48K3yuZvnj16tU888wz7hzye/bs4b333uPgwYOcOHGCzZs3Y7VamTRpEu+//757H7PZzH/+8x98fHzYuXMnO3fu5OOPP3anPKionscff5ymTZuydu1a1q5dC0Bubi4RERFs376d/v37M2vWLHbu3Mn+/fvJz8/n+++/Z8KECfTs2ZOFCxcSFxeH2Xx5ubSkpCSeffZZ1qxZQ1xcHDt37mTlypXuunv37k18fDwDBw7k448/LncuXnnlFbp168bevXt56623uP/++9FoNIwZM4YVK1YAsH37dsLDw2nSpAlPPPEEf/jDH9i5cyfLli1j2rRp7rpiY2P59ttv+eqrrwDYsWOHu81Llixh165dxMbG8tlnn7F9+3a2bdvGxx9/zJ49e5gzZw5t2rQhLi6Ot99+m1WrVnHs2DF27NhBXFwcsbGxbNiwAYBjx44xc+ZMDhw4gK+vL8uWLbviOQJ488032bVrF3v37mX9+vXs3bv3qv9NKcr1sNvt3PXQdL5ed6RG6lMBv5oqS18MuNMXazQad/riI0eOEBoaSkxMDADe3t7odDpWrVrFF198QXR0NLfccgtpaWkcO3as0noqotVqufvuu92v165dyy233ELXrl1Zs2bNFROhgeubx+DBgwkKCkKn0zFlyhR3YDQYDIwePRqAHj16VNiGTZs2cd999wEwdOhQ0tLSyMzMZNKkSe5vPosWLXIna1u9ejWzZs0iOjqau+66i6ysLHeCtbvuuqtUoB0+fDgBAQGYzWbGjx/Ppk2b2LRpE+PGjcPDwwNPT0/Gjx/vThRX0qpVq1i1ahXdunWje/fuHD582H1uW7VqRXR09BU/V1nffPMN3bt3p1u3bhw4cICDBw9WuY+i1CSz2UxQYCBJF2pm+c6GnTytjnXp0oWlS5dW+N7Vpi+uLD2ylJIPPviAkSNHltq+bt26aqcLNplMaLVawJVffsaMGezatYvmzZsze/bsKlMGX+mz6PV6d7sra0NF+wsh6NOnD8ePHyclJYWVK1fy4osvAq5HxLdu3VquBw01n4r5z3/+Mw8//HCp7YmJieXObX7+lfOKnzx5knfeeYedO3fi5+fH1KlTVSpmpV60bNmScxcyaqQu1cMvYejQoRQWFpYaxti5cyfr16+/6vTFHTt2JCkpyf0tIDs7G7vdzsiRI/m///s/d/rfo0ePkpt75ZSnZVMOl1QchAIDA8nJySl1wapsv1tuuYX169eTmpqKw+Hg66+/dqdTro6SqZHXrVtHYGAg3t7eCCEYN24cTz31FJ06dSIgIAAonyq6otWsiv3yyy9cunSJ/Px8Vq5cSb9+/Rg4cCArV64kLy+P3NxcVqxYwYABAypMxfzpp5+6FzM/d+4cyclXntJW2TnKysrCw8MDHx8fLl68yE8//VTt86MoNalFy5YkpV5fSoViDbqHX51plDVJCMGKFSt48sknmTNnDiaTyT0tc+DAgVeVvthgMLB48WIee+wx8vPzMZvNrF69mmnTppGYmEj37t2RUhIUFOQeP6/M9OnTue222wgNDXWP4xfz9fXl97//PV27diU8PNw9hAS416M1m83u3PcAoaGh/OUvf2HIkCFIKbn99tsZM2ZMtc/T7NmzefDBB4mMjMRisZRaB2DSpEnExMQwf/5897a5c+cyc+ZMIiMjsdvtDBw4kHnz5lVYd//+/bnvvvs4fvw4v/nNb9xTH6dOneq+wE6bNo1u3boB0K9fPyIiIrjtttt4++23OXToEH369AFcN6C//PJL97ehilR2jqKioujWrRtdunShdevW9OvXr9rnR1FqUsuWLfnpxx+uelGliqj0yEqDMX/+/FLr4TZW6u9AKenIkSPsXbOSu5ueQ6MRiLvmXnN65Abdw1cURWnsOnTogCErBnE+qerCVVBj+EqDMXXq1Ebfu1eUsnJycvjxl43sTq744curoQK+oihKA1ZQUMCsF2bz3Z6zVReuggr4iqIoDVhAQABmk4mzF69/Lr4K+IqiKA2YEIKWLVpw7kLWddelAr6iKEoD1yI8nKTk65+L36Bn6Zx/uWbn4Ye+VnW65bNnzzJz5kwOHjyI0+lk9OjRvP322xgMhuueNrhr1y6++OIL5s6de037F5s6dSqjR4++6rVmExMT2bJlC7/5zW+uar/qfO6yaYZvJNd6Xkoqm8L69ttv56uvvsLX17fSfcLDw9m1axeBgWrpCOXKWrZsSeyObdddj+rhlyClZPz48YwdO5Zjx45x9OhRcnJyeOGFF2qk/p49e153sL8eiYmJ7iRlZV1LeuKSyqYZri/X8jmudF6qq2wK6x9//PGKwV5RrsbLL7/Myo/+WnXBKqiAX8KaNWswmUw8+OCDgCvnyrvvvsunn37q/mNOSkpi1KhRtGvXjj/96U/ufT09PXn22Wfp0aMHt956Kzt27GDw4MG0bt2a//73v4ArDUFxYrKcnBwefPBBunbtSmRkJMuWLSvXntjYWAYNGkSPHj0YOXKkOztndcocP36cW2+9laioKLp3705CQgLPPfccGzduJDo6mnfffZf58+czceJE7rzzTkaMGFFh2uOSsrOzadWqlTstRFZWFuHh4e6sliXTDFen7adOnWLYsGFERkYybNgwTp8+TWZmJuHh4TidTgDy8vJo3rw5NpuNhIQERo0aRY8ePRgwYID7KeepU6fy1FNPMWTIEJ599llmz57Nfffdx9ChQ2nXrp07VUZlKa7LnpeaSGEdHh5OamoqAGPHjqVHjx506dKFjz766Er/BBWlQmFhYTRt3uq661EBv4QDBw6US4/s7e1NixYtOH78OODKA7N48WL27dvH4sWLOXPmDOBKKzx48GBiY2Px8vLixRdf5JdffmHFihW8/PLL5Y71+uuv4+Pjw759+9i7dy9Dhw4t9b7NZuOxxx5j6dKlxMbG8tBDD5X7pnGlMlOmTGHmzJnEx8ezZcsWQkNDmTNnDgMGDCAuLo4//OEPAGzdupXPP/+cNWvWVJj2uCQvLy8GDx7MDz/8ALgyYt59991MnDixVJphnU5XZdsBZs2axf3338/evXuZMmUKjz/+OD4+PkRFRbF+/XoAvvvuO0aOHIler2f69Ol88MEHxMbG8s4775Ra6ero0aOsXr2av//97wDs3buXH374wb1eQFJSUqUprsuel5pIYV3Sp59+SmxsLLt27WLu3LmkpdXcotRK45CcnMy/Fy5nz7nru3HboMfw69qVMlwWbx82bBg+Pj4AdO7cmVOnTtG8eXMMBgOjRrmW+u3atStGoxG9Xk/Xrl0rTMW7evVqFi1a5H7t5+dX6v0jR46wf/9+hg8fDrhWtgoNDa1WmezsbM6dO8e4ceMAV3bNygwfPhx/f3/Alfa4+JtGybTHJU2bNo2//e1vjB07ls8++6zCfPnVaTu4LjbLly8H4L777nN/YypOszxkyBAWLVrEjBkzyMnJYcuWLUycONG9f2Fhofv3iRMnlsqZM2bMGMxmM2azmSFDhrBjx45KU1x7e3uXateqVavYu3evOxFdZmYmx44dw2AwuFNYA+4U1v3796/0/IIrl1DxOgFnzpzh2LFj7sRyilIdubm5/O2fHxHy1Ojrqqc6a9qagA2Asaj8UinlK2XKTAGKl03KAR6VUsZfV8vqQZcuXcoNrWRlZXHmzBnatGlDbGxspSmMS6YV1mg07nIajabSFMNXSoQkpaRLly6lEnpVt0xWVvV7ASXTE1eW9rikfv36kZiYyPr163E4HERERFxT2ytSfKy77rqLP//5z1y6dInY2FiGDh1Kbm4uvr6+lWbarOk0y9ebwrrkPqtXr2br1q1YLBYGDx6s0iwrVy0sLAwhBIkpV86sW5XqDOkUAkOllFFANDBKCNG7TJmTwCApZSTwOnBDDlQOGzaMvLw8vvjiC8DVM/3jH//I1KlTr3oB8aqUTRmcnp5e6v0OHTqQkpLiDpo2m63cwiaVlfH29iYsLMydhbOwsJC8vLwrplmGytMel3X//fczefJk970OKJ1muDptB+jbt6/7W87ChQvdPWVPT0969erFE088wejRo9FqtXh7e9OqVSuWLFkCuIJyfHzlfYpvv/2WgoIC0tLSWLduHTExMZWmuK4ozXJNpbDOzMzEz88Pi8XC4cOH2bbt+mdaKI2PXq+naUgI587X8pBO0WLkxY946Yt+ZJkyW0q83AaEXVerilRnGmVNKk6PPGPGDF5//XWcTie33347b731Vo0f68UXX2TmzJlERESg1Wp55ZVXGD9+vPt9g8HA0qVLefzxx8nMzMRut/Pkk0/SpUuXapVZsGABDz/8MC+//DJ6vZ4lS5YQGRmJTqcjKiqKqVOnlhtGulLa45KmTJnCiy++yOTJk93byqYZrqrt4BrqeOihh3j77bcJCgris88+c783adIkJk6cyLp169zbFi5cyKOPPsobb7yBzWbj3nvvJSoqqsI29urVizvuuIPTp0/z0ksv0bRpU8aNG1dhiuuAgIBS5+WJJ56osRTWo0aNYt68eURGRtKhQwd69y7bV1KU6mnZqhVJySnXVUe10iMLIbRALNAW+FBK+ewVyj4NdJRSTqvgvenAdIAWLVr0OHXqVKn3VVrYG8PSpUv59ttvWbBgQX03pUKzZ8/G09OTp59+ur6bck3U34FSkcmTJ7N1wxpOJSXXbnpkKaUDiBZC+AIrhBARUsr9ZcsJIYYAvwMqvIslpfyIouGenj171k8ifuW6PPbYY/z000/8+OOP9d0URWlU5s2bx8Wd39Nh+G+vuY6rmqUjpcwQQqwDRgGlAr4QIhL4BLhNSqnmnd2kPvjgg/puQpVmz55d301QlBrn4+NDmqX8PbWrUeVNWyFEUFHPHiGEGbgVOFymTAtgOXCflPLodbVIURRFKSchIYFX3v3PddVRnR5+KPB50Ti+BvhGSvm9EOIRACnlPOBlIAD4V9F0OPu1jjEpiqIo5eXl5fHl0m+vq47qzNLZC3SrYPu8Er9PA8rdpFUURVFqRsuWLa+7DpVaQVEU5Qbg7e2NTwXPxVyNBp1aYe3Cw1UXugpDpnSssoxKj1y1GzWt77p16zAYDPTt2/ea9s/IyOCrr75y5/BJSkri8ccfd6dgqEhiYiKjR49m//5yk9oU5aq1bNmSvfv2XfP+qodfgkqPfONwOBxXvc+6devYsmVL1QUrkZGRwb/+9S/366ZNm14x2CtKTdu+Y8d17a8CfgkqPXLF6ZHT0tIYMWIE3bp14+GHHy6Vk+bLL7+kV69eREdH8/DDD1cYiH/99Ve6detG165deeihhygsLOSnn37innvucZdZt24dd955J+BKXtanTx+6d+/OxIkTyclxPegdHh7Oa6+9Rv/+/VmyZAmDBw/mySefpG/fvkRERLCj6I+hos+RmJjIvHnzePfdd4mOjmbjxo2kpKRw9913ExMTQ0xMDJs3bwZc0zofeugh9/+/4ov0c889R0JCAtHR0TzzzDMkJia6cwklJiYyYMAAunfvTvfu3a/rwqIolblSIsRqkVLWy0+PHj1kWWW3rfnyUI3+VOX999+XTz75ZLnt0dHRMj4+Xn722WeyVatWMiMjQ+bn58sWLVrI06dPy6L0E/LHH3+UUko5duxYOXz4cGm1WmVcXJyMioqSUkq5du1aeccdd0gppfzTn/4kn3jiCfcxLl26VOqYVqtV9unTRyYnJ0sppVy0aJF88MEHpZRSPvDAA3LJkiVXLNOrVy+5fPlyKaWU+fn5Mjc3t9TxpZTys88+k82aNZNpaWlSSilnzZolZ8+eLaWU8tdff3W3+7HHHpOvvvqqlFLK77//XgIyJSVFHjx4UI4ePVparVYppZSPPvqo/Pzzz0t9jvz8fBkWFiaPHDkipZTyvvvuk++++6602WyyefPmMicnR0op5SOPPCIXLFggU1JS5IABA9zb58yZ4z52y5Yt5V//+ld33YMGDZLTpk2TUkq5fv162aVLlyt+jldeeUW+/fbb7v0nT54sN27cKKWU8tSpU7Jjx47ucn369JEFBQUyJSVF+vv7S6vVKk+ePOk+hpSy1Ovc3FyZn58vpZTy6NGj7n/LZfepjor+NhSlGLBLXmPcbdBj+HVNqvTIFaZH3rBhgzuN8R133OFu66+//kpsbCwxMTEA5OfnExwcXK6NrVq1on379gA88MADfPjhhzz55JOMGjWK7777jgkTJvDDDz/wt7/9jfXr13Pw4EH69esHgNVqpU+fPu76Jk2aVKr+4nw+AwcOJCsri4yMjGqleQbX/4ODBw+6X2dlZbkToN1xxx0YjUaMRiPBwcFcvHix0nMIrgRxs2bNIi4uDq1Wy9Gj6nEUpeFRAb8ElR658vTIlV0IH3jgAf7yl79csY2VmTRpEh9++CH+/v7ExMTg5eWFlJLhw4fz9ddfV9neitpVWRrkitrvdDrZunUrZrO53HtXmwb53XffpUmTJsTHx+N0Oq//q7ei1AI1hl+CSo9ccXrkktt/+uknd1uHDRvG0qVLSU5OBlxj52UT4nXs2JHExET3imELFixg0KBBAAwePJjdu3fz8ccfu3vuvXv3ZvPmze7yeXl5V+wtFy9TuGnTJnx8fPDx8an0c5T9/GX/H1SWa7/Ylc5fZmYmoaGhaDQaFixYcE03lRWltjXoHn51plHWJJUeueL0yK+88gqTJ0+me/fuDBo0iBYtWgCuIa033niDESNG4HQ60ev1fPjhh6UeEDGZTHz22WdMnDgRu91OTEwMjzzyCODqOY8ePZr58+e7jxUUFMT8+fOZPHmye0WrN954wz0kVJafnx99+/YlKyuLTz/99Iqf484772TChAl8++23fPDBB8ydO5eZM2cSGRmJ3W5n4MCBzJs3r8LjAAQEBNCvXz8iIiK47bbbmDlzpvu9GTNmcPfdd7NkyRKGDBlS7puIojQE1UqPXBt69uwpy6aAVWlhlasxePBg3nnnHXr2vLmyeKi/A+VKhBDXnB5ZDekoiqI0Eg16SEdRrqTkaliKolStQfXwNRqNex1RRWmMbDYbGk2D+rNUbiIN6l9Wx44dWbBggQr6SqNks9lYsGABHTvW7WQFpfFoUEM677zzDk8//TTz5s3D6XTWd3MUpU5pNBo6duzIO++8U99NUW5SDSrgBwcHu+fAK4qiKDWrQQ3pKIqiKLVHBXxFUZRGQgV8RVGURqLenrQVQmQDR+rl4A1PIJBa341oINS5uEydi8vUubisg5TS61p2rM+btkeu9fHgm40QYpc6Fy7qXFymzsVl6lxcJoS45rwbakhHURSlkVABX1EUpZGoz4D/UT0eu6FR5+IydS4uU+fiMnUuLrvmc1FvN20VRVGUuqWGdBRFURoJFfAVRVEaiVoP+EKIUUKII0KI40KI5yp4Xwgh5ha9v1cI0b2221RfqnEuphSdg71CiC1CiKj6aGddqOpclCgXI4RwCCEm1GX76lJ1zoUQYrAQIk4IcUAIsb6u21hXqvE34iOE+E4IEV90Lh6sj3bWNiHEp0KIZCHE/krev7a4KaWstR9ACyQArQEDEA90LlPmduAnQAC9ge212ab6+qnmuegL+BX9fltjPhclyq0BfgQm1He76/HfhS9wEGhR9Dq4vttdj+fieeCvRb8HAZcAQ323vRbOxUCgO7C/kvevKW7Wdg+/F3BcSnlCSmkFFgFjypQZA3whXbYBvkKI0FpuV32o8lxIKbdIKdOLXm4Dwuq4jXWlOv8uAB4DlgHJddm4Oladc/EbYLmU8jSAlPJmPR/VORcS8BJCCMATV8C3120za5+UcgOuz1aZa4qbtR3wmwFnSrw+W7TtasvcDK72c/4O1xX8ZlTluRBCNAPGAfPqsF31oTr/LtoDfkKIdUKIWCHE/XXWurpVnXPxT6ATkATsA56QUjbGxTOuKW7WdmoFUcG2svNAq1PmZlDtzymEGIIr4Pev1RbVn+qci/eAZ6WUDldn7qZVnXOhA3oAwwAzsFUIsU1KebS2G1fHqnMuRgJxwFCgDfCLEGKjlDKrltvW0FxT3KztgH8WaF7idRiuK/PVlrkZVOtzCiEigU+A26SUaXXUtrpWnXPRE1hUFOwDgduFEHYp5co6aWHdqe7fSKqUMhfIFUJsAKKAmy3gV+dcPAjMka6B7ONCiJNAR2BH3TSxwbimuFnbQzo7gXZCiFZCCANwL/DfMmX+C9xfdNe5N5AppTxfy+2qD1WeCyFEC2A5cN9N2HsrqcpzIaVsJaUMl1KGA0uBGTdhsIfq/Y18CwwQQuiEEBbgFuBQHbezLlTnXJzG9U0HIUQToANwok5b2TBcU9ys1R6+lNIuhJgF/A/XHfhPpZQHhBCPFL0/D9cMjNuB40Aeriv4Taea5+JlIAD4V1HP1i5vwgyB1TwXjUJ1zoWU8pAQ4mdgL+AEPpFSVjhd70ZWzX8XrwPzhRD7cA1rPCulvOnSJgshvgYGA4FCiLPAK4Aeri9uqtQKiqIojYR60lZRFKWRUAFfURSlkVABX1EUpZFQAV9RFKWRUAFfURSlkVABX1EUpZFQAV+54QkhAopSB8cJIS4IIc4V/Z4jhPhXLRxvvhDiZPH88KLX5dI3CyHaFLejptugKNeitlMrKEqtK0pBEQ0ghJgN5Egp36nlwz4jpVxaRbsSgGgV8JWGQvXwlZtW0aIh3xf9PlsI8bkQYpUQIlEIMV4I8TchxD4hxM9CCH1RuR5CiPVFWSn/dxWpugcK16I1Jyrq7StKQ6ACvtKYtAHuwJVL/EtgrZSyK5AP3FEU9D/AtdhKD+BT4M1q1h2KK7vpaGBOTTdcUWqCGtJRGpOfpJS2ojwsWuDnou37gHBcibgicKXcpahMdRP5rSzKy36wKKmXojQ4KuArjUkhgJTSKYSwycuJpJy4/hYEcEBK2eda6y5yUyfwV25cakhHUS47AgQJIfoACCH0Qogu9dwmRakxKuArSpGidVQnAH8VQsTjWlmpb702SlFqkEqPrChXSQgxH/i+qmmZJcrnSCk9a7dVilI11cNXlKuXCbxe/OBVZYofvAIu1kmrFKUKqoevKIrSSKgevqIoSiOhAr6iKEojoQK+oihKI6ECvqIoSiPx/8ZrAg2l4DsUAAAAAElFTkSuQmCC", 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" ] @@ -1000,7 +935,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -1072,7 +1007,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.12 ('conda_jl')", "language": "python", "name": "python3" }, @@ -1086,7 +1021,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.8.12" + }, + "vscode": { + "interpreter": { + "hash": "612adcc456652826e82b485a1edaef831aa6d5abc680d008e93d513dd8724f14" + } } }, "nbformat": 4, diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py index 71cb6f2bb4..5eba1e18ec 100644 --- a/pybamm/plotting/plot_voltage_components.py +++ b/pybamm/plotting/plot_voltage_components.py @@ -40,31 +40,31 @@ def plot_voltage_components( "X-averaged battery electrolyte ohmic losses [V]", "X-averaged battery solid phase ohmic losses [V]", ] + labels = [ + "Reaction overpotential", + "Concentration overpotential", + "Ohmic electrolyte overpotential", + "Ohmic electrode overpotential", + ] # Plot # Initialise time = solution["Time [h]"].entries initial_ocv = solution["X-averaged battery open circuit voltage [V]"](0) ocv = solution["X-averaged battery open circuit voltage [V]"].entries - ax.fill_between(time, ocv, initial_ocv, **kwargs_fill) + ax.fill_between(time, ocv, initial_ocv, **kwargs_fill, label="Open-circuit voltage") top = ocv # Plot components - for overpotential in overpotentials: + for overpotential, label in zip(overpotentials, labels): bottom = top + solution[overpotential].entries - ax.fill_between(time, bottom, top, **kwargs_fill) + ax.fill_between(time, bottom, top, **kwargs_fill, label=label) top = bottom + V = solution["Battery voltage [V]"].entries - ax.plot(time, V, "k--") + ax.plot(time, V, "k--", label="Voltage") + if show_legend: - labels = [ - "Open-circuit voltage", - "Reaction overpotential", - "Concentration overpotential", - "Ohmic electrolyte overpotential", - "Ohmic electrode overpotential", - "Voltage", - ] - leg = ax.legend(labels, loc="lower left", frameon=True) + leg = ax.legend(loc="lower left", frameon=True) leg.get_frame().set_edgecolor("k") # Labels From dbe29bbc2b5366079a4b71cb34f054f5ad0b575f Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Fri, 22 Jul 2022 14:54:13 -0400 Subject: [PATCH 11/17] fix labeling and ylims --- .../Tutorial 3 - Basic plotting.ipynb | 26 +++++++++---------- pybamm/plotting/plot_voltage_components.py | 9 ++++--- .../test_plot_voltage_components.py | 4 +-- 3 files changed, 20 insertions(+), 19 deletions(-) diff --git a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb index c5e4d4abd0..ae6d41bc17 100644 --- a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb @@ -29,7 +29,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -400,22 +400,20 @@ " 'Positive electrode entropic change [V.K-1]',\n", " 'X-averaged positive electrode entropic change',\n", " 'X-averaged positive electrode entropic change [V.K-1]',\n", - " 'Negative particle flux',\n", - " 'X-averaged negative particle flux',\n", " 'Negative effective diffusivity',\n", " 'Negative effective diffusivity [m2.s-1]',\n", " 'X-averaged negative effective diffusivity',\n", " 'X-averaged negative effective diffusivity [m2.s-1]',\n", + " 'Negative particle flux',\n", " 'Negative electrode SOC',\n", " 'Negative electrode volume-averaged concentration',\n", " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", " 'Total lithium in negative electrode [mol]',\n", - " 'Positive particle flux',\n", - " 'X-averaged positive particle flux',\n", " 'Positive effective diffusivity',\n", " 'Positive effective diffusivity [m2.s-1]',\n", " 'X-averaged positive effective diffusivity',\n", " 'X-averaged positive effective diffusivity [m2.s-1]',\n", + " 'Positive particle flux',\n", " 'Positive electrode SOC',\n", " 'Positive electrode volume-averaged concentration',\n", " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", @@ -738,7 +736,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b8dbfec708148ed93cf7be8cde2f807", + "model_id": "4233bb603c614ff0a17469374723ee2b", "version_major": 2, "version_minor": 0 }, @@ -752,7 +750,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -780,7 +778,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c5c8128da10247d3b4e705e6c573a0ef", + "model_id": "605a4bbe33454b718be1315c51793793", "version_major": 2, "version_minor": 0 }, @@ -794,7 +792,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -822,7 +820,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eaa22d22a76e4d96b120b003db1941da", + "model_id": "ae8de6b692eb4776b16994ad762b395a", "version_major": 2, "version_minor": 0 }, @@ -836,7 +834,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -856,7 +854,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "98417becc6fa483a8b4156baa251b761", + "model_id": "393396525a1e41ff8d32b3b30b1ca4ad", "version_major": 2, "version_minor": 0 }, @@ -870,7 +868,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -909,7 +907,7 @@ { "data": { "text/plain": [ - "" + "(
, )" ] }, "execution_count": 8, diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py index 7615c34106..d622e7791f 100644 --- a/pybamm/plotting/plot_voltage_components.py +++ b/pybamm/plotting/plot_voltage_components.py @@ -30,9 +30,10 @@ def plot_voltage_components( kwargs_fill = {"alpha": 0.6, **kwargs_fill} if ax is not None: + fig = None testing = True else: - _, ax = plt.subplots() + fig, ax = plt.subplots() overpotentials = [ "X-averaged battery reaction overpotential [V]", @@ -71,10 +72,12 @@ def plot_voltage_components( ax.set_xlim([time[0], time[-1]]) ax.set_xlabel("Time [h]") - y_min, y_max = 0.98 * np.nanmin(V), 1.02 * np.nanmax(V) + y_min, y_max = 0.98 * min(np.nanmin(V), np.nanmin(ocv)), 1.02 * ( + max(np.nanmax(V), np.nanmax(ocv)) + ) ax.set_ylim([y_min, y_max]) if not testing: # pragma: no cover plt.show() - return ax + return fig, ax diff --git a/tests/unit/test_plotting/test_plot_voltage_components.py b/tests/unit/test_plotting/test_plot_voltage_components.py index 593ddda468..0e22807156 100644 --- a/tests/unit/test_plotting/test_plot_voltage_components.py +++ b/tests/unit/test_plotting/test_plot_voltage_components.py @@ -9,13 +9,13 @@ def test_plot(self): model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model) sol = sim.solve([0, 3600]) - ax = pybamm.plot_voltage_components(sol, show_legend=True, testing=True) + _, ax = pybamm.plot_voltage_components(sol, show_legend=True, testing=True) t, V = ax.get_lines()[0].get_data() np.testing.assert_array_equal(t, sol["Time [h]"].data) np.testing.assert_array_equal(V, sol["Battery voltage [V]"].data) _, ax = plt.subplots() - ax_out = pybamm.plot_voltage_components(sol, ax=ax, show_legend=True) + _, ax_out = pybamm.plot_voltage_components(sol, ax=ax, show_legend=True) self.assertEqual(ax_out, ax) From 33ac95898e485697eef3650b6342463d6026620d Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Fri, 22 Jul 2022 14:55:38 -0400 Subject: [PATCH 12/17] revert notebook --- .../Tutorial 3 - Basic plotting.ipynb | 424 ++++++++++-------- 1 file changed, 243 insertions(+), 181 deletions(-) diff --git a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb index ae6d41bc17..1384c9dfa8 100644 --- a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb @@ -19,6 +19,15 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR: Invalid requirement: '#'\n", + "WARNING: You are using pip version 21.1.2; however, version 21.2.4 is available.\n", + "You should consider upgrading via the 'c:\\users\\saransh\\saransh_softwares\\python_3.9\\python.exe -m pip install --upgrade pip' command.\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -29,7 +38,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -74,10 +83,20 @@ " 'x_s [m]',\n", " 'x_p',\n", " 'x_p [m]',\n", - " 'r_p',\n", - " 'r_p [m]',\n", + " 'Sum of electrolyte reaction source terms',\n", + " 'Sum of negative electrode electrolyte reaction source terms',\n", + " 'Sum of positive electrode electrolyte reaction source terms',\n", + " 'Sum of x-averaged negative electrode electrolyte reaction source terms',\n", + " 'Sum of x-averaged positive electrode electrolyte reaction source terms',\n", + " 'Sum of interfacial current densities',\n", + " 'Sum of negative electrode interfacial current densities',\n", + " 'Sum of positive electrode interfacial current densities',\n", + " 'Sum of x-averaged negative electrode interfacial current densities',\n", + " 'Sum of x-averaged positive electrode interfacial current densities',\n", " 'r_n',\n", " 'r_n [m]',\n", + " 'r_p',\n", + " 'r_p [m]',\n", " 'Current density variable',\n", " 'Total current density',\n", " 'Total current density [A.m-2]',\n", @@ -85,37 +104,29 @@ " 'C-rate',\n", " 'Discharge capacity [A.h]',\n", " 'Porosity',\n", + " 'Negative electrode porosity',\n", " 'Separator porosity',\n", " 'Positive electrode porosity',\n", + " 'X-averaged negative electrode porosity',\n", " 'X-averaged separator porosity',\n", " 'X-averaged positive electrode porosity',\n", - " 'Negative electrode porosity',\n", - " 'X-averaged negative electrode porosity',\n", " 'Leading-order porosity',\n", + " 'Leading-order negative electrode porosity',\n", " 'Leading-order separator porosity',\n", " 'Leading-order positive electrode porosity',\n", + " 'Leading-order x-averaged negative electrode porosity',\n", " 'Leading-order x-averaged separator porosity',\n", " 'Leading-order x-averaged positive electrode porosity',\n", - " 'Leading-order negative electrode porosity',\n", - " 'Leading-order x-averaged negative electrode porosity',\n", " 'Porosity change',\n", + " 'Negative electrode porosity change',\n", " 'Separator porosity change',\n", " 'Positive electrode porosity change',\n", + " 'X-averaged negative electrode porosity change',\n", " 'X-averaged separator porosity change',\n", " 'X-averaged positive electrode porosity change',\n", - " 'Negative electrode porosity change',\n", - " 'X-averaged negative electrode porosity change',\n", + " 'Leading-order x-averaged negative electrode porosity change',\n", " 'Leading-order x-averaged separator porosity change',\n", " 'Leading-order x-averaged positive electrode porosity change',\n", - " 'Leading-order x-averaged negative electrode porosity change',\n", - " 'Negative electrode interface utilisation variable',\n", - " 'X-averaged negative electrode interface utilisation variable',\n", - " 'Negative electrode interface utilisation',\n", - " 'X-averaged negative electrode interface utilisation',\n", - " 'Positive electrode interface utilisation variable',\n", - " 'X-averaged positive electrode interface utilisation variable',\n", - " 'Positive electrode interface utilisation',\n", - " 'X-averaged positive electrode interface utilisation',\n", " 'Negative electrode active material volume fraction',\n", " 'X-averaged negative electrode active material volume fraction',\n", " 'Negative electrode capacity [A.h]',\n", @@ -140,50 +151,50 @@ " 'X-averaged positive electrode active material volume fraction change',\n", " 'Separator pressure',\n", " 'X-averaged separator pressure',\n", + " 'Negative electrode transverse volume-averaged velocity',\n", " 'Separator transverse volume-averaged velocity',\n", " 'Positive electrode transverse volume-averaged velocity',\n", + " 'Negative electrode transverse volume-averaged velocity [m.s-2]',\n", " 'Separator transverse volume-averaged velocity [m.s-2]',\n", " 'Positive electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity',\n", " 'X-averaged separator transverse volume-averaged velocity',\n", " 'X-averaged positive electrode transverse volume-averaged velocity',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]',\n", " 'X-averaged separator transverse volume-averaged velocity [m.s-2]',\n", " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]',\n", " 'Transverse volume-averaged velocity',\n", " 'Transverse volume-averaged velocity [m.s-2]',\n", - " 'Negative electrode transverse volume-averaged velocity',\n", - " 'Negative electrode transverse volume-averaged velocity [m.s-2]',\n", - " 'X-averaged negative electrode transverse volume-averaged velocity',\n", - " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'Negative electrode transverse volume-averaged acceleration',\n", " 'Separator transverse volume-averaged acceleration',\n", " 'Positive electrode transverse volume-averaged acceleration',\n", + " 'Negative electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'Separator transverse volume-averaged acceleration [m.s-2]',\n", " 'Positive electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration',\n", " 'X-averaged separator transverse volume-averaged acceleration',\n", " 'X-averaged positive electrode transverse volume-averaged acceleration',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n", " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n", " 'Transverse volume-averaged acceleration',\n", " 'Transverse volume-averaged acceleration [m.s-2]',\n", - " 'Negative electrode transverse volume-averaged acceleration',\n", - " 'Negative electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged negative electrode transverse volume-averaged acceleration',\n", - " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'Positive electrode volume-averaged velocity',\n", - " 'Positive electrode volume-averaged velocity [m.s-1]',\n", " 'Negative electrode volume-averaged velocity',\n", + " 'Positive electrode volume-averaged velocity',\n", " 'Negative electrode volume-averaged velocity [m.s-1]',\n", - " 'Positive electrode volume-averaged acceleration',\n", - " 'Positive electrode volume-averaged acceleration [m.s-1]',\n", - " 'X-averaged positive electrode volume-averaged acceleration',\n", - " 'X-averaged positive electrode volume-averaged acceleration [m.s-1]',\n", + " 'Positive electrode volume-averaged velocity [m.s-1]',\n", " 'Negative electrode volume-averaged acceleration',\n", + " 'Positive electrode volume-averaged acceleration',\n", " 'Negative electrode volume-averaged acceleration [m.s-1]',\n", + " 'Positive electrode volume-averaged acceleration [m.s-1]',\n", " 'X-averaged negative electrode volume-averaged acceleration',\n", + " 'X-averaged positive electrode volume-averaged acceleration',\n", " 'X-averaged negative electrode volume-averaged acceleration [m.s-1]',\n", - " 'Positive electrode pressure',\n", - " 'X-averaged positive electrode pressure',\n", + " 'X-averaged positive electrode volume-averaged acceleration [m.s-1]',\n", " 'Negative electrode pressure',\n", + " 'Positive electrode pressure',\n", " 'X-averaged negative electrode pressure',\n", + " 'X-averaged positive electrode pressure',\n", " 'Negative particle concentration',\n", " 'Negative particle concentration [mol.m-3]',\n", " 'X-averaged negative particle concentration',\n", @@ -247,9 +258,9 @@ " 'X-averaged positive electrode ohmic losses [V]',\n", " 'Gradient of positive electrode potential',\n", " 'Porosity times concentration',\n", + " 'Negative electrode porosity times concentration',\n", " 'Separator porosity times concentration',\n", " 'Positive electrode porosity times concentration',\n", - " 'Negative electrode porosity times concentration',\n", " 'Negative electrolyte potential',\n", " 'Negative electrolyte potential [V]',\n", " 'Separator electrolyte potential',\n", @@ -268,10 +279,10 @@ " 'X-averaged positive electrolyte potential [V]',\n", " 'X-averaged electrolyte overpotential',\n", " 'X-averaged electrolyte overpotential [V]',\n", + " 'Gradient of negative electrolyte potential',\n", " 'Gradient of separator electrolyte potential',\n", " 'Gradient of positive electrolyte potential',\n", " 'Gradient of electrolyte potential',\n", - " 'Gradient of negative electrolyte potential',\n", " 'Negative current collector temperature',\n", " 'Negative current collector temperature [K]',\n", " 'X-averaged negative electrode temperature',\n", @@ -296,22 +307,20 @@ " 'Volume-averaged cell temperature [K]',\n", " 'Ambient temperature [K]',\n", " 'Ambient temperature',\n", - " 'Negative current collector potential',\n", - " 'Negative current collector potential [V]',\n", " 'Inner SEI thickness',\n", " 'Inner SEI thickness [m]',\n", - " 'Outer SEI thickness',\n", - " 'Outer SEI thickness [m]',\n", " 'X-averaged inner SEI thickness',\n", " 'X-averaged inner SEI thickness [m]',\n", + " 'Outer SEI thickness',\n", + " 'Outer SEI thickness [m]',\n", " 'X-averaged outer SEI thickness',\n", " 'X-averaged outer SEI thickness [m]',\n", " 'SEI thickness',\n", " 'SEI thickness [m]',\n", - " 'Total SEI thickness',\n", - " 'Total SEI thickness [m]',\n", " 'X-averaged SEI thickness',\n", " 'X-averaged SEI thickness [m]',\n", + " 'Total SEI thickness',\n", + " 'Total SEI thickness [m]',\n", " 'X-averaged total SEI thickness',\n", " 'X-averaged total SEI thickness [m]',\n", " 'X-averaged negative electrode resistance [Ohm.m2]',\n", @@ -319,7 +328,7 @@ " 'X-averaged inner SEI concentration [mol.m-3]',\n", " 'Outer SEI concentration [mol.m-3]',\n", " 'X-averaged outer SEI concentration [mol.m-3]',\n", - " 'SEI concentration [mol.m-3]',\n", + " 'Negative SEI concentration [mol.m-3]',\n", " 'X-averaged SEI concentration [mol.m-3]',\n", " 'Loss of lithium to SEI [mol]',\n", " 'Loss of capacity to SEI [A.h]',\n", @@ -335,42 +344,81 @@ " 'SEI interfacial current density [A.m-2]',\n", " 'X-averaged SEI interfacial current density',\n", " 'X-averaged SEI interfacial current density [A.m-2]',\n", + " 'Inner positive electrode SEI thickness',\n", + " 'Inner positive electrode SEI thickness [m]',\n", + " 'X-averaged inner positive electrode SEI thickness',\n", + " 'X-averaged inner positive electrode SEI thickness [m]',\n", + " 'Outer positive electrode SEI thickness',\n", + " 'Outer positive electrode SEI thickness [m]',\n", + " 'X-averaged outer positive electrode SEI thickness',\n", + " 'X-averaged outer positive electrode SEI thickness [m]',\n", + " 'Positive electrode SEI thickness',\n", + " 'Positive electrode SEI thickness [m]',\n", + " 'X-averaged positive electrode SEI thickness',\n", + " 'X-averaged positive electrode SEI thickness [m]',\n", + " 'Total positive electrode SEI thickness',\n", + " 'Total positive electrode SEI thickness [m]',\n", + " 'X-averaged total positive electrode SEI thickness',\n", + " 'X-averaged total positive electrode SEI thickness [m]',\n", + " 'X-averaged positive electrode resistance [Ohm.m2]',\n", + " 'Inner positive electrode SEI concentration [mol.m-3]',\n", + " 'X-averaged inner positive electrode SEI concentration [mol.m-3]',\n", + " 'Outer positive electrode SEI concentration [mol.m-3]',\n", + " 'X-averaged outer positive electrode SEI concentration [mol.m-3]',\n", + " 'Positive SEI concentration [mol.m-3]',\n", + " 'X-averaged positive electrode SEI concentration [mol.m-3]',\n", + " 'Loss of lithium to positive electrode SEI [mol]',\n", + " 'Loss of capacity to positive electrode SEI [A.h]',\n", + " 'Inner positive electrode SEI interfacial current density',\n", + " 'Inner positive electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged inner positive electrode SEI interfacial current density',\n", + " 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]',\n", + " 'Outer positive electrode SEI interfacial current density',\n", + " 'Outer positive electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged outer positive electrode SEI interfacial current density',\n", + " 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI interfacial current density',\n", + " 'Positive electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode SEI interfacial current density',\n", + " 'X-averaged positive electrode SEI interfacial current density [A.m-2]',\n", " 'Lithium plating concentration',\n", " 'Lithium plating concentration [mol.m-3]',\n", " 'X-averaged lithium plating concentration',\n", " 'X-averaged lithium plating concentration [mol.m-3]',\n", - " 'Dead lithium concentration',\n", - " 'Dead lithium concentration [mol.m-3]',\n", - " 'X-averaged dead lithium concentration',\n", - " 'X-averaged dead lithium concentration [mol.m-3]',\n", " 'Lithium plating thickness',\n", " 'Lithium plating thickness [m]',\n", " 'X-averaged lithium plating thickness [m]',\n", - " 'Dead lithium thickness',\n", - " 'Dead lithium thickness [m]',\n", - " 'X-averaged dead lithium thickness [m]',\n", " 'Loss of lithium to lithium plating [mol]',\n", " 'Loss of capacity to lithium plating [A.h]',\n", - " 'Negative electrode lithium plating reaction overpotential',\n", - " 'X-averaged negative electrode lithium plating reaction overpotential',\n", - " 'Negative electrode lithium plating reaction overpotential [V]',\n", - " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n", " 'Lithium plating interfacial current density',\n", " 'Lithium plating interfacial current density [A.m-2]',\n", " 'X-averaged lithium plating interfacial current density',\n", " 'X-averaged lithium plating interfacial current density [A.m-2]',\n", - " 'Electrolyte transport efficiency',\n", - " 'Positive electrolyte transport efficiency',\n", - " 'X-averaged positive electrolyte transport efficiency',\n", - " 'Negative electrolyte transport efficiency',\n", - " 'X-averaged negative electrolyte transport efficiency',\n", - " 'Separator transport efficiency',\n", - " 'X-averaged separator transport efficiency',\n", - " 'Electrode transport efficiency',\n", - " 'Positive electrode transport efficiency',\n", - " 'X-averaged positive electrode transport efficiency',\n", - " 'Negative electrode transport efficiency',\n", - " 'X-averaged negative electrode transport efficiency',\n", + " 'Positive electrode lithium plating concentration',\n", + " 'Positive electrode lithium plating concentration [mol.m-3]',\n", + " 'X-averaged positive electrode lithium plating concentration',\n", + " 'X-averaged positive electrode lithium plating concentration [mol.m-3]',\n", + " 'Positive electrode lithium plating thickness',\n", + " 'Positive electrode lithium plating thickness [m]',\n", + " 'X-averaged positive electrode lithium plating thickness [m]',\n", + " 'Loss of lithium to positive electrode lithium plating [mol]',\n", + " 'Loss of capacity to positive electrode lithium plating [A.h]',\n", + " 'Positive electrode lithium plating interfacial current density',\n", + " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode lithium plating interfacial current density',\n", + " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'Electrolyte tortuosity',\n", + " 'Negative electrolyte tortuosity',\n", + " 'Positive electrolyte tortuosity',\n", + " 'X-averaged negative electrolyte tortuosity',\n", + " 'X-averaged positive electrolyte tortuosity',\n", + " 'Separator tortuosity',\n", + " 'X-averaged separator tortuosity',\n", + " 'Electrode tortuosity',\n", + " 'Negative electrode tortuosity',\n", + " 'Positive electrode tortuosity',\n", + " 'X-averaged negative electrode tortuosity',\n", + " 'X-averaged positive electrode tortuosity',\n", " 'Separator volume-averaged velocity',\n", " 'Separator volume-averaged velocity [m.s-1]',\n", " 'Separator volume-averaged acceleration',\n", @@ -384,36 +432,22 @@ " 'Volume-averaged acceleration [m.s-1]',\n", " 'X-averaged volume-averaged acceleration [m.s-1]',\n", " 'Pressure',\n", - " 'Negative electrode open circuit potential',\n", - " 'Negative electrode open circuit potential [V]',\n", - " 'X-averaged negative electrode open circuit potential',\n", - " 'X-averaged negative electrode open circuit potential [V]',\n", - " 'Negative electrode entropic change',\n", - " 'Negative electrode entropic change [V.K-1]',\n", - " 'X-averaged negative electrode entropic change',\n", - " 'X-averaged negative electrode entropic change [V.K-1]',\n", - " 'Positive electrode open circuit potential',\n", - " 'Positive electrode open circuit potential [V]',\n", - " 'X-averaged positive electrode open circuit potential',\n", - " 'X-averaged positive electrode open circuit potential [V]',\n", - " 'Positive electrode entropic change',\n", - " 'Positive electrode entropic change [V.K-1]',\n", - " 'X-averaged positive electrode entropic change',\n", - " 'X-averaged positive electrode entropic change [V.K-1]',\n", - " 'Negative effective diffusivity',\n", - " 'Negative effective diffusivity [m2.s-1]',\n", - " 'X-averaged negative effective diffusivity',\n", - " 'X-averaged negative effective diffusivity [m2.s-1]',\n", + " 'Negative electrode surface potential difference',\n", + " 'X-averaged negative electrode surface potential difference',\n", + " 'Negative electrode surface potential difference [V]',\n", + " 'X-averaged negative electrode surface potential difference [V]',\n", + " 'Positive electrode surface potential difference',\n", + " 'X-averaged positive electrode surface potential difference',\n", + " 'Positive electrode surface potential difference [V]',\n", + " 'X-averaged positive electrode surface potential difference [V]',\n", " 'Negative particle flux',\n", + " 'X-averaged negative particle flux',\n", " 'Negative electrode SOC',\n", " 'Negative electrode volume-averaged concentration',\n", " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", " 'Total lithium in negative electrode [mol]',\n", - " 'Positive effective diffusivity',\n", - " 'Positive effective diffusivity [m2.s-1]',\n", - " 'X-averaged positive effective diffusivity',\n", - " 'X-averaged positive effective diffusivity [m2.s-1]',\n", " 'Positive particle flux',\n", + " 'X-averaged positive particle flux',\n", " 'Positive electrode SOC',\n", " 'Positive electrode volume-averaged concentration',\n", " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", @@ -425,12 +459,6 @@ " 'Positive electrode current density',\n", " 'Positive electrode current density [A.m-2]',\n", " 'Electrode current density',\n", - " 'Positive current collector potential',\n", - " 'Positive current collector potential [V]',\n", - " 'Local voltage',\n", - " 'Local voltage [V]',\n", - " 'Terminal voltage',\n", - " 'Terminal voltage [V]',\n", " 'Electrolyte concentration',\n", " 'Electrolyte concentration [mol.m-3]',\n", " 'Electrolyte concentration [Molar]',\n", @@ -458,14 +486,6 @@ " 'X-averaged electrolyte ohmic losses',\n", " 'X-averaged concentration overpotential [V]',\n", " 'X-averaged electrolyte ohmic losses [V]',\n", - " 'Negative electrode surface potential difference',\n", - " 'Negative electrode surface potential difference [V]',\n", - " 'X-averaged negative electrode surface potential difference',\n", - " 'X-averaged negative electrode surface potential difference [V]',\n", - " 'Positive electrode surface potential difference',\n", - " 'Positive electrode surface potential difference [V]',\n", - " 'X-averaged positive electrode surface potential difference',\n", - " 'X-averaged positive electrode surface potential difference [V]',\n", " 'Ohmic heating',\n", " 'Ohmic heating [W.m-3]',\n", " 'X-averaged Ohmic heating',\n", @@ -490,51 +510,50 @@ " 'X-averaged total heating [W.m-3]',\n", " 'Volume-averaged total heating',\n", " 'Volume-averaged total heating [W.m-3]',\n", + " 'Negative current collector potential',\n", + " 'Negative current collector potential [V]',\n", " 'Current collector current density',\n", " 'Current collector current density [A.m-2]',\n", " 'Leading-order current collector current density',\n", - " 'X-averaged negative electrode SEI interfacial current density',\n", - " 'Negative electrode SEI interfacial current density',\n", - " 'Negative electrode SEI interfacial current density [A.m-2]',\n", - " 'Negative electrode SEI volumetric interfacial current density',\n", - " 'X-averaged negative electrode SEI volumetric interfacial current density',\n", - " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged positive electrode SEI interfacial current density',\n", - " 'Positive electrode SEI interfacial current density',\n", - " 'Positive electrode SEI interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode SEI volumetric interfacial current density',\n", - " 'Positive electrode SEI volumetric interfacial current density',\n", - " 'X-averaged negative electrode lithium plating interfacial current density',\n", - " 'X-averaged positive electrode lithium plating interfacial current density',\n", - " 'X-averaged positive electrode lithium plating volumetric interfacial current density',\n", - " 'Negative electrode lithium plating interfacial current density',\n", - " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Positive electrode lithium plating interfacial current density',\n", - " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Positive electrode lithium plating volumetric interfacial current density',\n", - " 'Negative electrode lithium plating volumetric interfacial current density',\n", - " 'X-averaged negative electrode lithium plating volumetric interfacial current density',\n", - " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'SEI interfacial current density',\n", + " 'SEI interfacial current density [A.m-2]',\n", + " 'SEI interfacial current density per volume [A.m-3]',\n", + " 'Lithium plating reaction overpotential',\n", + " 'X-averaged lithium plating reaction overpotential',\n", + " 'Lithium plating reaction overpotential [V]',\n", + " 'X-averaged lithium plating reaction overpotential [V]',\n", + " 'Lithium plating interfacial current density',\n", + " 'Lithium plating interfacial current density [A.m-2]',\n", + " 'Lithium plating interfacial current density per volume [A.m-3]',\n", + " 'Positive electrode lithium plating reaction overpotential',\n", + " 'X-averaged positive electrode lithium plating reaction overpotential',\n", + " 'Positive electrode lithium plating reaction overpotential [V]',\n", + " 'X-averaged positive electrode lithium plating reaction overpotential [V]',\n", " 'Negative electrode interfacial current density',\n", " 'X-averaged negative electrode interfacial current density',\n", " 'Negative electrode interfacial current density [A.m-2]',\n", " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", + " 'Negative electrode interfacial current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode interfacial current density per volume [A.m-3]',\n", " 'X-averaged negative electrode total interfacial current density',\n", " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode total interfacial current density per volume [A.m-3]',\n", " 'Negative electrode exchange current density',\n", " 'X-averaged negative electrode exchange current density',\n", " 'Negative electrode exchange current density [A.m-2]',\n", " 'X-averaged negative electrode exchange current density [A.m-2]',\n", + " 'Negative electrode exchange current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode exchange current density per volume [A.m-3]',\n", " 'Negative electrode reaction overpotential',\n", " 'X-averaged negative electrode reaction overpotential',\n", " 'Negative electrode reaction overpotential [V]',\n", " 'X-averaged negative electrode reaction overpotential [V]',\n", - " 'Negative electrode volumetric interfacial current density',\n", - " 'X-averaged negative electrode volumetric interfacial current density',\n", - " 'Negative electrode volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode open circuit potential',\n", + " 'Negative electrode open circuit potential [V]',\n", + " 'X-averaged negative electrode open circuit potential',\n", + " 'X-averaged negative electrode open circuit potential [V]',\n", + " 'Negative electrode entropic change',\n", + " 'X-averaged negative electrode entropic change',\n", " 'SEI film overpotential',\n", " 'X-averaged SEI film overpotential',\n", " 'SEI film overpotential [V]',\n", @@ -543,49 +562,100 @@ " 'X-averaged positive electrode interfacial current density',\n", " 'Positive electrode interfacial current density [A.m-2]',\n", " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", + " 'Positive electrode interfacial current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode interfacial current density per volume [A.m-3]',\n", " 'X-averaged positive electrode total interfacial current density',\n", " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode total interfacial current density per volume [A.m-3]',\n", " 'Positive electrode exchange current density',\n", " 'X-averaged positive electrode exchange current density',\n", " 'Positive electrode exchange current density [A.m-2]',\n", " 'X-averaged positive electrode exchange current density [A.m-2]',\n", + " 'Positive electrode exchange current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode exchange current density per volume [A.m-3]',\n", " 'Positive electrode reaction overpotential',\n", " 'X-averaged positive electrode reaction overpotential',\n", " 'Positive electrode reaction overpotential [V]',\n", " 'X-averaged positive electrode reaction overpotential [V]',\n", - " 'Positive electrode volumetric interfacial current density',\n", - " 'X-averaged positive electrode volumetric interfacial current density',\n", - " 'Positive electrode volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n", - " 'Electrolyte flux',\n", - " 'Electrolyte flux [mol.m-2.s-1]',\n", - " 'Total lithium in electrolyte',\n", - " 'Total lithium in electrolyte [mol]',\n", - " 'Sum of electrolyte reaction source terms',\n", - " 'Sum of positive electrode electrolyte reaction source terms',\n", - " 'Sum of x-averaged positive electrode electrolyte reaction source terms',\n", - " 'Sum of interfacial current densities',\n", - " 'Sum of volumetric interfacial current densities',\n", - " 'Sum of positive electrode interfacial current densities',\n", - " 'Sum of x-averaged positive electrode interfacial current densities',\n", - " 'Sum of positive electrode volumetric interfacial current densities',\n", - " 'Sum of x-averaged positive electrode volumetric interfacial current densities',\n", - " 'Sum of negative electrode electrolyte reaction source terms',\n", - " 'Sum of x-averaged negative electrode electrolyte reaction source terms',\n", - " 'Sum of negative electrode interfacial current densities',\n", - " 'Sum of x-averaged negative electrode interfacial current densities',\n", - " 'Sum of negative electrode volumetric interfacial current densities',\n", - " 'Sum of x-averaged negative electrode volumetric interfacial current densities',\n", + " 'Positive electrode open circuit potential',\n", + " 'Positive electrode open circuit potential [V]',\n", + " 'X-averaged positive electrode open circuit potential',\n", + " 'X-averaged positive electrode open circuit potential [V]',\n", + " 'Positive electrode entropic change',\n", + " 'X-averaged positive electrode entropic change',\n", + " 'Positive electrode SEI film overpotential',\n", + " 'X-averaged positive electrode SEI film overpotential',\n", + " 'Positive electrode SEI film overpotential [V]',\n", + " 'X-averaged positive electrode SEI film overpotential [V]',\n", " 'Interfacial current density',\n", " 'Interfacial current density [A.m-2]',\n", + " 'Interfacial current density per volume [A.m-3]',\n", " 'Exchange current density',\n", " 'Exchange current density [A.m-2]',\n", + " 'Exchange current density per volume [A.m-3]',\n", + " 'Negative electrode oxygen interfacial current density',\n", + " 'X-averaged negative electrode oxygen interfacial current density',\n", + " 'Negative electrode oxygen interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode oxygen interfacial current density [A.m-2]',\n", + " 'Negative electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'Negative electrode oxygen exchange current density',\n", + " 'X-averaged negative electrode oxygen exchange current density',\n", + " 'Negative electrode oxygen exchange current density [A.m-2]',\n", + " 'X-averaged negative electrode oxygen exchange current density [A.m-2]',\n", + " 'Negative electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'Negative electrode oxygen reaction overpotential',\n", + " 'X-averaged negative electrode oxygen reaction overpotential',\n", + " 'Negative electrode oxygen reaction overpotential [V]',\n", + " 'X-averaged negative electrode oxygen reaction overpotential [V]',\n", + " 'Negative electrode oxygen open circuit potential',\n", + " 'Negative electrode oxygen open circuit potential [V]',\n", + " 'X-averaged negative electrode oxygen open circuit potential',\n", + " 'X-averaged negative electrode oxygen open circuit potential [V]',\n", + " 'Positive electrode oxygen interfacial current density',\n", + " 'X-averaged positive electrode oxygen interfacial current density',\n", + " 'Positive electrode oxygen interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode oxygen interfacial current density [A.m-2]',\n", + " 'Positive electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'Positive electrode oxygen exchange current density',\n", + " 'X-averaged positive electrode oxygen exchange current density',\n", + " 'Positive electrode oxygen exchange current density [A.m-2]',\n", + " 'X-averaged positive electrode oxygen exchange current density [A.m-2]',\n", + " 'Positive electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'Positive electrode oxygen reaction overpotential',\n", + " 'X-averaged positive electrode oxygen reaction overpotential',\n", + " 'Positive electrode oxygen reaction overpotential [V]',\n", + " 'X-averaged positive electrode oxygen reaction overpotential [V]',\n", + " 'Positive electrode oxygen open circuit potential',\n", + " 'Positive electrode oxygen open circuit potential [V]',\n", + " 'X-averaged positive electrode oxygen open circuit potential',\n", + " 'X-averaged positive electrode oxygen open circuit potential [V]',\n", + " 'Oxygen interfacial current density',\n", + " 'Oxygen interfacial current density [A.m-2]',\n", + " 'Oxygen interfacial current density per volume [A.m-3]',\n", + " 'Oxygen exchange current density',\n", + " 'Oxygen exchange current density [A.m-2]',\n", + " 'Oxygen exchange current density per volume [A.m-3]',\n", + " 'Positive current collector potential',\n", + " 'Positive current collector potential [V]',\n", + " 'Local voltage',\n", + " 'Local voltage [V]',\n", + " 'Terminal voltage',\n", + " 'Terminal voltage [V]',\n", + " 'Electrolyte flux',\n", + " 'Electrolyte flux [mol.m-2.s-1]',\n", + " 'Total lithium in electrolyte [mol]',\n", " 'X-averaged open circuit voltage',\n", " 'Measured open circuit voltage',\n", " 'X-averaged open circuit voltage [V]',\n", " 'Measured open circuit voltage [V]',\n", " 'X-averaged reaction overpotential',\n", " 'X-averaged reaction overpotential [V]',\n", + " 'X-averaged SEI film overpotential',\n", + " 'X-averaged SEI film overpotential [V]',\n", " 'X-averaged solid phase ohmic losses',\n", " 'X-averaged solid phase ohmic losses [V]',\n", " 'X-averaged battery open circuit voltage [V]',\n", @@ -600,8 +670,6 @@ " 'Local ECM resistance',\n", " 'Local ECM resistance [Ohm]',\n", " 'Terminal power [W]',\n", - " 'Power [W]',\n", - " 'Resistance [Ohm]',\n", " 'LAM_ne [%]',\n", " 'LAM_pe [%]',\n", " 'LLI [%]',\n", @@ -652,7 +720,7 @@ "Electrolyte flux [mol.m-2.s-1]\n", "Electrolyte potential\n", "Electrolyte potential [V]\n", - "Electrolyte transport efficiency\n", + "Electrolyte tortuosity\n", "Gradient of electrolyte potential\n", "Gradient of negative electrolyte potential\n", "Gradient of positive electrolyte potential\n", @@ -663,13 +731,13 @@ "Negative electrolyte concentration [mol.m-3]\n", "Negative electrolyte potential\n", "Negative electrolyte potential [V]\n", - "Negative electrolyte transport efficiency\n", + "Negative electrolyte tortuosity\n", "Positive electrolyte concentration\n", "Positive electrolyte concentration [Molar]\n", "Positive electrolyte concentration [mol.m-3]\n", "Positive electrolyte potential\n", "Positive electrolyte potential [V]\n", - "Positive electrolyte transport efficiency\n", + "Positive electrolyte tortuosity\n", "Separator electrolyte concentration\n", "Separator electrolyte concentration [Molar]\n", "Separator electrolyte concentration [mol.m-3]\n", @@ -680,7 +748,6 @@ "Sum of positive electrode electrolyte reaction source terms\n", "Sum of x-averaged negative electrode electrolyte reaction source terms\n", "Sum of x-averaged positive electrode electrolyte reaction source terms\n", - "Total lithium in electrolyte\n", "Total lithium in electrolyte [mol]\n", "Total lithium lost from electrolyte [mol]\n", "X-averaged battery electrolyte ohmic losses [V]\n", @@ -697,12 +764,12 @@ "X-averaged negative electrolyte concentration [mol.m-3]\n", "X-averaged negative electrolyte potential\n", "X-averaged negative electrolyte potential [V]\n", - "X-averaged negative electrolyte transport efficiency\n", + "X-averaged negative electrolyte tortuosity\n", "X-averaged positive electrolyte concentration\n", "X-averaged positive electrolyte concentration [mol.m-3]\n", "X-averaged positive electrolyte potential\n", "X-averaged positive electrolyte potential [V]\n", - "X-averaged positive electrolyte transport efficiency\n", + "X-averaged positive electrolyte tortuosity\n", "X-averaged separator electrolyte concentration\n", "X-averaged separator electrolyte concentration [mol.m-3]\n", "X-averaged separator electrolyte potential\n", @@ -736,7 +803,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4233bb603c614ff0a17469374723ee2b", + "model_id": "a38e975fd6e24d2eb807a36bc56402ed", "version_major": 2, "version_minor": 0 }, @@ -750,7 +817,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -778,7 +845,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "605a4bbe33454b718be1315c51793793", + "model_id": "37a8d21c0d7944b08e7295eb5b6551cd", "version_major": 2, "version_minor": 0 }, @@ -792,7 +859,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -820,7 +887,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae8de6b692eb4776b16994ad762b395a", + "model_id": "66c9f0f6a3e242098ea54f149f37dbd9", "version_major": 2, "version_minor": 0 }, @@ -834,7 +901,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -854,7 +921,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "393396525a1e41ff8d32b3b30b1ca4ad", + "model_id": "fcc6500e55fa4862a85fe56e1ee72b8d", "version_major": 2, "version_minor": 0 }, @@ -868,7 +935,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -894,7 +961,7 @@ "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -907,7 +974,7 @@ { "data": { "text/plain": [ - "(
, )" + "" ] }, "execution_count": 8, @@ -933,7 +1000,7 @@ "outputs": [ { "data": { - "image/png": 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etUASOCGEEKKZcwIfMkkSOCGEEI1Efc5buncb17sWYFfBaIcihBBCdBkGHeLswIdMdu6PdihCCCG6iKgnbxBJ4G6zzeV77m3SCyeEEELUM+gQZ/k/4IaY+fS1Vkc7HCGEEFEWtQVLmrMGa5gSnMc4+yq+Nk5gXnUfQl0jtxRCCCGiKtOzhWvZxq6Y4bzrGU1+wBHtkIQQQkRBl0neDrAHKpgV+JTJjjhWmMbwRU0/6sJdLkwhhBDimFKE6e9Zxw1qI3ticvjCP4KtXlnJUwghepMumxXZAxVMDsxjosXOJusoPq0bTHHAHu2whBBCiKgy6BD9PBu4mg0UufsxPzya5bUp0Q5LCCHEMdBlk7cDzCEPo+qWMJJl7I8ZxNfB4aysS4p2WEIIIUTUpXh3cRG7OMORyCrjSL6s7Udt2BztsIQQQnSSLp+8HaAIk+nZwiVs4UxnMquNI/iqti/VIWmkhBBC9G7OQCmTA19xvHkRO6zD+co3hB2+mGiHJYQQooN1m+StMZe/mMnMY5LRzB5nDl8HhrDekxDtsIQQQoioMoX9DPGsYgirKHdlslwNZ2FtFr6wMdqhCSGE6ADdMnk7wKgD9Pesoz/rqHUksdY0nAV12ZQFbdEOTQghhIiqeF8es8hjhtnGDmsOC/1DZIETIYTo5rp18taYM1DCpMB8JhqM5LsHsFwPYUlNqmw3IIQQolczhb0M8axmCKupdKax1jiM+bXZMu1ACCG6oR6TvB1g0CEyvVvJZCun25xst+TwtW8Q22XsvxBCiF4u1l/AFAo40Whkv3MwS0ODWF6bIg86hRCim+hxyVtjllAtwzwrGMYKqp0pbDTmsLCuL8UyrFIIIUQvZtAhsjybyGITZ9rdbDfn8I1vgDzoFEKILq5HJ2+Nuf1FTKSICeprStzZrGEQi2ozZUllIYQQvZo1WM3w4DKGs4wqZwqbjDks9GTL3qpCCNEFHTZ5U0rZgPmAtb78XK31nc3KxALPA9n1Ze7VWj/V8eEePUWYZO8uZrKLGSYT+baBrA4PYGldmqzGJYQQoleLOfCgE0WJO5v1DOLruixqZH6cEEJ0CW3pefMBJ2uta5RSZmChUupDrfXiRmV+BmzQWp+tlEoGNiulXtBa+zsj6I5i1EGyPJvJYjOnmS3ssw5iVWgAy+uSCWpJ5IQQQvROCk2ydzcz2M00o5FCR3/W6oF8W5dOXbjXDNoRQogu57CfwFprDdTUvzTXf+nmxQC3UkoBLqAMCHZgnJ3OFPbTz7OBfmzgLIuNvdZBrA71k0ROCCFEr2bQIdK920hnGzNNZgps/VkXHsCSujRJ5IQQ4hhr06euUsoILAcGAY9orb9tVuRh4B1gP+AGLtVah1up5xrgGoDs1Lgjj7qTmcLehv3jzrLY2GcdwLpQX5Z5ZGilEEKIztGkjUx2Rzma1hl1gEzPFjLZwqz6RG59uB9L6tJlDrkQQhwDbUretNYhYIxSKg54Uyk1Umu9rlGR04BVwMnAQOBTpdQCrXVVs3qeAJ4AGD80q3nvXZdkCnsbeuRON1sotPZjY7gfSzzpskeOEEKIDtOkjRyc2uXbyKaJnJFia1820p+lnnTKZFVnIYToFO0a76C1rlBKzQNOBxonb1cB99QPsdymlNoJDAWWdFSgXYEp7G9oqGYaDJQ4+rBV9WOFN4M8vzPa4QkhhBBRYdAhUr07SGUH05WiwpXBNkN/lnsz2e3vmr2IQgjRHbVltclkIFCfuNmBWcDfmhXbA8wEFiilUoEcYEdHB9uVRFat3E0yuzkRqHEms9PUn7WBTDbUxcuGp0IIIXolhSbel8cE8pgA1DoS2GPqz7pgJms8iTKPXAghjkJbet7SgWfq570ZgFe11u8ppa4D0Fo/BvwJeFoptRZQwG1a65LOCrorcvmLGeUvZhQQsNkpsPRlczib5Z5UKkLWaIcnhBBCRIUzUMawQBnDWM75FiuF1r5s1X1Y4U2TveSEEKKd2rLa5BrguFaOP9bo+/3AqR0bWvdlDnno49lEHzYx06CotKWyx5jNuqD0ygkhhOi9TGFfw/SDGUC1M4U9xmw2BtNZ60nCL71yQghxSLLGbydTaOL8BcRRwGggaLVSZMlmO1ms9qXJXDkhhBAtRKaQ93xufxEjKGIEcL7ZTKk1i10qk3X+NLZ5Y9GoaIcohBBdStSSt3AvfbpmCvvI8G4lg61MBTyOOPLNfdgWymC1N1lW6BJCCMHGveUUV9aRHOuIdijHjFEHSPHuJIWdTAT8NidFlj7s1Bms9yXLwidCCEEUk7dQQFFoiyfVWx6tELoEe6CCAYEKBrCWU5Si1plEnjGTbeF01nqSqQxZoh2iEEKIY8wbCHHS797i8z+eR2p870ngGrOEasnybCKLTUwFfHYXReYsdul0NvqS2eV3S8+cEKLXiVrypkKa/LJErHF+4vy10QqjS1FoXP5icigmh1WcaVDU2JLZb8xgWziddZ4kWfxECCF6gcGDB7Nz315O+t2bfPHn80iLlyH21mANfYKR+eRTgYDNQbElkzxS2RxIYas3VubMCSF6vKglb3tKy/Av3s4X5/XnjHAe9qAvWqF0WQqN219EDkXksIrvGaDWmkiBKZOdOpVNviSZMyeEED2Q2+3mgw8+5HvfO5NT7nyHVfdfitEoi101Zg7VkeGJTEOYAITMJios6RQaUtkZTmajL4nSgDzwFEL0LFFL3irravnt+x/y6KCf8Or4FGaX5GMJB6MVTrfhDJQyMFDKQCIb7vnsbkrM6ewhja2BZLZ6Y2QPHSGE6AGmT5/Ohx9+xLuvv4wxbTgUb6K0ykNijCyv3xqjDpLo20siexkOfA/w2mMoNaWxX6WwPZjEVm8cdWFZq00I0X1F7RMsIzOTz7Zu4csvv6Vv9lQeTk7i5JCR3NI8lAxhbzNrsJrMYDWZbOEEIk8eqyyp5BvS2RVOYrMvkSLZR0cIIbqlqVOnMnXqVAC++eBVZl59OTd8bzS3nDe2186Faw9bsIrMYBWZbGECoI2KWlsiJaYU8khhdzCe7d5YSeiEEN1G1D6tUlJSSElO5v8+/YQvhg5h78w43rbV8W1aFqfVeelX3av2+O4wRh0k3pdHPHkMB86kvnfOlEaBSmZnMJHNvgRqQuZohyqEEKIdskZO4pLLvs99zz3HQ++v4YczcvjVeWPJyYqPdmjdhkLjCpTgCpTQjw1MJpLQ1dkSKDWlUEgiu0MJ7PTFyOrPQoguSUVrL5nx48frJ554ggkTJnD1hAncdeYZbB0Rz1fp5WgF/Y0uJtd5GFhdHJX4ejKNos4cT5kphf06md2hRLZ4Y6kNS0InhOh4o7Ni+f7xfZdrrcdHO5buYvz48XrZsmWtntuyZQv33XcfzzzzNC6bhf3/m4PFLPPhOprP5KLSlEyJSmS/jmdvIJZdPrcsiiKE6FD3XDi6Xe1jVJO3ZcuW8dJLLzHW6ydm5w4AalNj+HREkCKrF4B0g4PJgRDDygsw0Ds2LY2GhoTOmEKhSmRXKFGePAohOoQkb+13qOTtgMLCQtauXcusSbmEd8zjgmvv4Myx2Vw+IwenTR7GdQaNgTpzHBXGREpUIgXhWPYFYtjjd0lSJ4Q4Iu1N3qI+yHv27NkEa+rY8sgjLFu5glMYwrllRvYNiWdhRg35pjrmGiEmJZXxYQvjKopwBL3RDrvHUWicgTKcgTL6AOMjB/HZ3ZSbUyhWiewPJbAzEMsen0v21hFCiChLTU0lNTUVgIKkyez2OLn20S+47dlvuPKkoVx3xkiGZiVEOcqeRRFuaCsz2Upu/XFtMuAxx1FpjKdMxVOkY8kLxrLP75L9WoUQHSrqyRuAyeXgmdIS/v7Ky1ycm8sfTz2NPutDXLbFyL7B8XydUUOV2c8X+Jkf52KkIZVxtVVk1fbuDb6PBWuwmrRgNWlsZ1T9sZDFRI05iTJjEgUksC8Yxy6/9NIJIUS0ZGRksGLlSr7++msefvhhHn3jDR58dxXz7r6Y6SPSox1ej6cI4wiU4QiUkQ6MOHDCAAGzgxpTPBWGeEqJpSjkJi/oJi/gwheW3johRPt0ieQN4E9//hNWm5W//vWvLNy1m7tmzeLMYcPos6GUS7cYKewfx/IsP3m2OlaFq1llV6S5shgX0IyuKJRtBo4how4S6y8glgL6HziowG9zUmVKpNSQQKGOZ18ojl2+GKplcRQhhOh0SimmTJnClClTKCoq4oUXXmDyNXMgfzn/vO8+9uUX8JNTRzKsj/TGHUvmUB3xoTriyfuuzSSyUIrP6qbaFE+liqWsPrHbH3ST53fIMEwhRKuiPuetuWXLlnHVnDmsW7+eX846lVtOmPTdSQVVGbGs66tYH1OFrh+5Z8XICIODsbVVZEpvXJfjM7mpMiVQphIo1HHsD8Wy2+emIiSbpwrRG8ict/Zry5y39vj5jTfy78ceIxgMcuKwDH40axgXTx6M2yFD+roijcJrion02KnYSI9d2M1+v4u8gEP2cxWiB+l2C5a0JhQK8corr3D8+AkkFpfx7auvsGnXDs4dMQKrKdJZGHDb2D3AxbLkaqrMgYZrUw12xoYUIyuLcQR9x+S9iCMTMDqoMiVSYYiniDgKQjHsCcRQ6LfLnDohehBJ3tqvo5M3gKKiIp599ln++5//sHnLFmbPHMOLN00DQGuNkk1WuwVNZH5dlTGeMhVHUTiW/aEY9vhjZH6dEN1Qj0jemrvummt5/D9PkOR288PjjuPKceNJdrmAyLCD8ux41mWG2OyqbuiNM2JgqNHJGI+HgZVFsvF3NxI0WKgzxVNhjKeU+PrVvNzsk9W8hOiWJHlrv85I3g7QWrN48WIcDge5/ZLYPP8NvnftnVwxYwg/PGko/dNiO+W+ovP5jU4qzUmUqQTyw/HsCSWww+eWuXVCdGE9MnnTWvPZZ5/x4IMP8v7772Mxmbhy/HjuOuXUJuX8MXb29nOyPKWGCrO/4bhbWRitrORWV5DsqezQ9yGOHY2KPG00JVBGHEU6jv2hGJlXJ0QXJ8lb+3Vm8tbcypUrufXWW/jiiy/RWjN1RCZXzMjh+9Nly4GeQGOg1pJIiTGZPJ3M9mAS270x8jBUiC6iRyZvjW3ZsoWHH36YjKQUrj1xCjXLlvHKwq84a/hwYm2R1Q61UlRlxbIly8Ca2EpC6rv3mGF0MDoIIytLcMqWAz2G3+ikypxYP68unrxQLLt8MoREiK5Akrf2O5bJ2wF79+7lueee47lnn2Xnzp0UvP0H4gKF7CioICPBic3SZdY4E0cprIxUWtLYb8xgWzCV9d5EeQgqRJT0+OStuY8+/IgzzjwDu8XC94YO4wdjj2NCVp+Gsfshu4XCvm7WpvrZ7ahtuM6AYoDRSa4vQE5VMWZZrbJH8hldVJqTKVaJ5IcT2BGIZ5fsUyfEMSXJW/tFI3k7QGvNzp07GTBgANSVMenEyWzctpMLJg1g9rQhnDy6DyajISqxic6hUVRZUtljymZTIJ11ngTpmRPiGOl1yRtEVqj873//y4svvEB1TQ2DU1J5afZsMmJimpTzJjjZnW1jdVJtk2GVFmUkx+BgpMfLwKpijIQ7JC7RNQUNFirNqRQZUtgZTmaTN4li2aNOiE4jyVv7RTN5a+7TTz/lxRdf5I03XqeqqpqUOCe/u2Q8N56Ve/iLRbcUNFgotmSzhWyWejMpDcjq0EJ0ll6ZvB1QU1PDq6++ygcffMD//ng3/nVrePaF54mzWpg5eDAWY+QpklaK6owYdmSaWBNXhdcYaqjDrkwMU3ZGeOvoV1WCgej8fMSx5THHUWjKYLvOYI03laKAPdohCdFjSPLWfl0peTvA6/XywQcf8Morr3DGtAnMmTGIkk3fcNdTH3PR5EFMGZaBUXrkehyNotKSxjZDf5b5+rDb7452SEL0KL06eWtOa03u6FzWrltLgtPFucOHcWluLqPS0huGVWqzkbKsWLakhdkQW91kfpxLmRmmbAzz1NK3ulQSuV6k1pzAPlM2G0NZrPIky0pdQhwFSd7arysmb635+KOPOO/88/B6faTEOTl/Un8uPGEQM0ZlYjbJ52ZPVGNJZrtxIEt82ezwxRz+AiHEIUny1kwwGOSTTz7hmWee4e2338bn8/Hzmadw+4kntCgbspopznazJbXptgMATmVmqLIx3OOhX3WxJHK9SEiZKLb1ZRvZLPVkSK+cEO0kyVv7dZfkDSKjXj788EPmzp3Le++9S12dh10v3kpfl4+9xdUkuG2yamUPVWtOYptpkCRyQhwFSd4Ooby8nFdffZWxY8cxPD6JxW+9ye8eeYgLR4zgzGHDiLd/90d5yG6hKNvNppQg25xNEzm7MjFY2Rnu8zKgqgSzDrVyN9FTVVtS2GXsz+pAJhs88bL4iRCHIclb+3Wn5K2xuro6Fi9ezMknnww1RZx33vl8tGAJs0Zncc7EAZw1oR8Zia5ohyk6Qa05gR3mQSzzZrPFFxftcIToNiR5a4dPPvmEG2+4gS1bt2I2GpkxaBDnD48kcgfmxwEEnVaKs1xsSQ6ypVmPnBkjA412cvwhhlSX4Aj6ovBORLQEjA72W/qxOdyH1d5kymThEyFakOSt/bpCG9kRFixYwBtvvMHbb73Jzl27AfjBScN57uZZQGR6w4FpDKLn8Jjj2GPqz5pgH1bXJRFC5kIKcTCSvLWT1pqVK1fy4osv8vJLL1FVWcnaBx7GkreHbYWFZMbGYjd/N9wjZLdQnOlie0qYTTFN58gZUPQxOhkS0gyprSTJUxWNtySiRKOotiSzz9iHzaF01nmSqAvLvkhCSPLWfl2ljewoWms2bNjA22+/TUpSIj8+bxrePSsZee6NTBmayhnj+nLKmGwS3PIArKcJGmzkW/uxTWex0ptKsUw9EKIJSd6OQjgcZuvWreTk5OCvqiV3zGj27t/PKYMHcfaw4cwYOLBJIhe2mijLjGFnsmZjbE2TVSsBEgw2BmNmiMdDdk0JJi1bEPQmGgNVlhTyTRnsCKWw2Zco8+VEryTJW/t1xTayoxUUFHDzzTfzyccfU1ZejsGgOH5IGnf/8ASmj8yKdniik9RYktln6sOOUArrvUkyYkX0eu1N3g7bLaCUsgHzAWt9+bla6zublbkVuLxRncOAZK11WVsD6QoMBgM5OTkAmN0OHnricV555RXefOMN3lq3DofVyi3Tp3Pd8ZMi5X1BknaUkbQDxhsV1emx7E01siG+lnKzn7Kwl2/x8q0VLLZE+hscDAqGGFRdTpy/9lChiB5AESbWX0Csv4ChwJmA3+akzJxGgUohLxzHbn8s+/xOmTcnhOh10tLSeOmllwiFQixdupQPP/yQjz78ENuYiyHbymcfvc8jc79kVm4fZub2ISczXoZY9gAufzFD/cWRdlFBrSOBYlM6eSSzM5DANl+srPAsxCEctudNRT4pnVrrGqWUGVgI3KS1XnyQ8mcDN2utTz5Uvd3pqWIwGGTevHm89tprzJw6ndNzhrF14QJ+89ijnJGTwylDhpDocDS5xpvoIj/DxrYEP7vsNU3myQEkGWwMwsxAr4e+1aWy6EkvFjRYqDElUmmMp5Q4CsIxFATd5PmdMuxS9AjS89Z+3amN7CyvvPIKt/36Vnbv2QtARqKbmaMzefja6cQ4ZNPonkpjoNaSSIUxkWLi2R+OIy8QQ57fgV9LUid6ng7vedOR7K6m/qW5/utQGd9s4KW2BtAdmEwmZs2axaxZsxqOVddWsqmulk/ffQeDMjCxb19OHzyYS8eMIdZmw1ZaQ//SGvoTmSdXluFiV7JmU0wtHmOQkrCXErwstoAxMZ5so51BIRhQV0NqbTnycLH3MIX9xPnziSOfvo1PGMFvcVBriqfG4KYSFxW4KQk5KQvZKQ7YqAjJHzBCiJ7p0ksv5ZJLLmHHjh18/vnnfP755yxfsxrXxB9A2XZ+d99/2FdYxvSRmUwbkcGAtFjpmesBFGFc/mJcFJMFHFd/XJsUXpObGlM81YYYKrSbsrCLkrCDkmCkTZTkTvQGbZrzppQyAsuBQcAjWuvbDlLOAewDBh1uyGRPeKqotWbFihW8/fbbvPnmm2zYsIENz7yAu7CQZetWoxSMzczC0Kgx0QZFbYqbglQLW+O97LXXtajXocz0N9gYEAjRr7aCBF9NizJCAISVEZ/RhdfowqMc1CoHtTio1jaqw1YqwzYqQ1YqQhaqQhYZnimiQnre2q8ntJGd7aabbuKF55+ntCzy50ZGopvvTxvMP66aAshKlr1RwGjHa3TjMTioUw5qlZNqbacmbKMqbKEyHHnoWRmyyNBM0WV06oIlSqk44E3gRq31ulbOXwr8QGt99kGuvwa4BiA7O3vc7t2723zv7mD//v1kZGSgw5pTTjqJz+d/RaLbzckDBjBr0GCmDxhAjK3pxNygw0J5uou9SbA5ppZqU6BFvXEGK/2w0D8QoG9tBbH+lgmfEIejUQSNNvwGOwGDDZ+y4lV2fJjxYcODmTptxaPNeMJm6rSJurCFmrCJupCJurBJkj9xRCR5a5ue3kZ2hnA4zKZNm5g/fz7z58+nT1oSf/vVlejSHQw+46cMTHVyQk4aJwxN5/ghacS5ZLSCiAgpEwGjo749tOEz2PBiw6useLQFj7ZSpy1Uawt1YTN1IRPVYTM1IbP08IkO1emrTSql7gRqtdb3tnLuTeA1rfWLh6unpz9VLC8v56OPPuK9997jww8+oLyighOGDmPuFT9EeT3sraggK7bZEA8F3gQXxalWdsWH2OqqJmho+e8Tb7DRHzPZgQD96iqJ9cniJ6LzaRQhg5mQwUJQWQgaLAQxE1RmAspCABM+ZSGgzfgw4dVmApjw6siXT5vwhEx4tRFPOJIMesNG2f+nF5Dkrf16ehvZ2erq6rjlllv4euFC1q1fTzgcRinFPVdN59fnjcYfCLF+Tymj+iVhMspnkGifsDISNNgIGqyR9k+ZCSgrfmXBR+S1T5vxUv/f+jbQq014wiZ8YSN12oQ3bMIbVgS0zG/vzTpjtclkIKC1rlBK2YFZwN9aKRcLTAd+0I54e6z4+Hhmz57N7NmzCQaDLF68mGAwSMrkKRSv28iUiWNJccdwUv9+nDRwEFP698dttWIrraFPaQ19gCkmAzUpbgqTzeyM+27hk/Kwl3K8rDACbitxsTFkYyE7GKSvp1r2lxOdQqExhf2Ywn6O+tm1AoyRr5AyETZYCKoDiaGZgDITxIz/wH8PfGkjPg40iPUNnzbhCZvrk0IjdaHIMSFE7+VwOHj00UcBqK6uZsmSJXzzzTdMPukkGDOEFZ+/xwk3/wi71cyY/klMGJzChEGpnDY2m+RYx2FqF72dQYewhGqxhI7y4bkh8qUxEDKYCBkshJWJkDITwkhQmQljJKiMhJSJIEZCjb6CykhIGwhhIIyBAEbCWhFWhsjx+nMhDWEOlIMQBrRWhFCEUYS0isSgIdjo+xAGgloRri8b0EaCYQhgICi9j1HTltUmRwPPEPlTywC8qrX+o1LqOgCt9WP15eYAp2utL2vLjXvzU8WamhpeeuklPvjgAz7/7HOqa6oxGY3cf8FFXDg0h2A4jEGpJnPlILKvXFWKi8IkE9tjveTZ6lqsYgmROXPZBht9QmH6eGtJr62QPeZEr/JdQ2glqMwElYWAwUIAC/4DvYREnpD6tAkvFuq0OdIjqC0yPKaDSc9b+/XmNvJYKCsr4+OPP2bJkiUsW7qUFStXUFfn4asnf8+0gS4WLF7K619vZezAZI4bkMzQrHjMJvksEKKxsDISVqb6hNNEuP7ha0iZCCgLfmXGhxWvsuLVFmq1lSodmYtfGbJSErTJ3ENkk+5ux+/3s2jRIj788EN+8IMr6O9w8+pTT3Lrg/czrW8/pg3oz7T+A0iPiWlxbchuoSrFSWGigR2xPvKsrSdzRgxkGO30CRvI8vvIqqvEHfAcg3cnRPcXUiaCBitBgx2fwYpf2fAoOx6s1GGjRtuo0jYqQjYqglbKQlZpjJqR5K39pI08toLBIBs3bmTQoEHY7XYe//ej3PyrX+HxeAGwWkyMzE7koz+cQ1KMnfyyWuwWk8yhE+Io+Y0OvEY3dQYXFYZYSsIxFIRj2O93kR/oHb3gkrz1AIsWLeLRRx/lk48/pqi4GIDBqam8dcUPibfbD7qCVshqpibFSWGikd2xAXbbagm3MmcOIougZCoLmaEwWd460morZK85ITrIgRXP6gxOapSbClyUhN0Uhlzs9zupDFmiHeIxJclb+0kbGX3BYJAtW7awatUqVq5cyYYNG3j3pScxVO/nJ7/4Df+d+wnZKbGMyo5ndL8kxgxI4pIpQ6IdthA9RtBgpdKcQokhiX06kc2+JPb6XdEOq8NJ8taDaK1Zu3Ytn376KatWreKJ+x7Av3M31/76V2zZuYOp2dlM7t+P8Vl9sJvNLa83G6lJdlKaYGFfXJAdjlq8xtYTNAOKFKOdDG0kKxgk3VNDiqcKwyG39BNCHAm/0Um1KYFyQzwFOoEdgQR2+GN7bI+dJG/tJ21k17Zo0SK++uor1q5dy5rVq9i0eQv9MlPZ+s79UJ3Pz+55hpKKakZkJzAiO5ER2QkMTIuVoZdCHCWfyU2xOZOdOoOl3nSKA/Zoh3TUJHnrBR544AFeffVVlixZQigUwmIycf7oXO7/3vcACIbDmAwtV8/SSuFLcFKRZCU/TrPb6aXI6j3ofUwYSDPayNRG0gMB0r21JElCJ0Sn0BiosSSRb8xgWziNtZ7kHrMJuyRv7SdtZPfi9/spKCggOzsbgGuuuYbPPv2Enbu+2+5h5rjBfPbnCyFQx8PvrSY51s6wrASGZMZhs8giS0K0l0ZRZUllu3EAS3x92O1zRzukIyLJWy9SXV3NwoUL+eKLL0hOTuaGS3+AZ+cOhp13FgPjE5jUJ4tJ2X0Zn5WFw9L6MK2Q3UJ1spOSeCP7Y4Lssh+8dw7AjJE0o400bSA9GCTdW0dyXSVGZEEUITqSRlFjSWaXsT/LfH3Y4ouLdkhHTJK39pM2smeora1l8+bNrF+/HrfbzXnnnUeothx3ckbDfDqDQdE/NY7rzxjBL88bi9aaJVsKGZoVT6yzZzzAEeJYKLNmskINZ2FtVrcaySLJWy9XXV3NXXfdxbwv57Fy1UrC4TAmo5G7zjqHq0aPwh8KUePzkeBofRKoVgpfvIPKBCvFsYp9Lh/7bJ6Dzp2DyIIoyUYbadpIWihEms9LmqcSa6jlhuNCiCPjMcWy0zyY+d6B7PZ3r6eLkry1n7SRPZvX62Xz5s1s3Lix4eu0mSfxo4tPp2DHBtInnAVAeqKb4VlxDMuK5/vTczhhaPpB570LISKCBhubbaN4t2ZYt5hj3uH7vInuxe12c++9kf3Tq6qq+Oabb5g/fz4nnX0Ocf0G8tmbb3DOz65jSGoaEzIymNCnDxP69KFffDxKKZTW2MpqsZXVkgqMBLTJiCfBRUWCmaIY2Ov0UmD1NKxsGSJMQaiOAojs32UDbG7ilJU0g4XUMKT6faR6a4j31iBtjhDtZw9WMjy4jOEso8SVzTKG83Vtuuy1I0Q3ZLPZyM3NJTc3t8W5GFsqb7/99neJ3YYNPDN/IxNmXcAJg8ez/NvFnPmL+xnRJ56R2QmM6pvEyL6JjBmQhMPacv67EL2NKexlRN1SckyrWe8cy7s1OdSGe87/G9Lz1svs3LmTl156iYULF/LN199QWVUJwIe3/5bRNivbCgsoq6tlVHoGjlYWQTkgbDFSl+CkIs5EUSzkObwUWL2tblXQmBUjyUYbKdpAWihMqq+OFE81tpC/I9+mEL2C1xTDUtN4Pqnp26WTOOl5az9pI0VjWmtCoRAmk4kNGzZw3333sX7dOtatX0dtbR0An/3rJmbmuFi2bhsfrdjN2IEpjB+UQkpc71huXYiDCRjtfGs+ng+qB6Lpej0I0vMmDql///7ccccdAITDYTZs2MDXX3/NyVddhSEEd99wPQ8/8wwmo5HhaemMTU9nXGYm540cibHRIigGfwhXQRWuAsgCxhJZ3bIu3kFVvIXiGMh3+NlnryOkvntA4CPEvlAt+6BRL52LGIOFFGUhLaxICQZI8daR5KmSuXRCHIItWMXU4BeMt8XyrWk8n1X3JUTLxYqEEN2bUgqTKfIn2/Dhw3nyySeBSDu+Z88e1q5dy4Tp0yEmhm/W/p3/e+GVhmuzU2KZMCiJx64/maSY7r8ynxDtZQ55mBKaR457F897J1PUzVeolJ430URJSQmLFi1i8eLFLF68mCXfLsFutbDh2RcJ7s/n0ddfpaauljEZGYzJyCDZdej9NrRR4YtzUh1nocytyHcH2Wf3UGcMHjYWI4pEg40UZSIlpEkJ+En1VBPnr+2otytEj1JtSeHN8FQ2euKjHUoT0vPWftJGiqNRVVXFypUrWbZsGcuWLWP9urWs+OBZTNV7ufVPD7JozWamDc/g5NFZTB6Wgd0qz/JF7xA0WJlnmcbn1X2jHUoDWbBEdKhQKMS+ffvo2zfyS37OOefw/vvvEw5HesQy4xM4Z9gw/m/mTADqAoFDDrc8IOCyURdvozzWRLE7TJ7dS7Hl8MMuAWzKRLLBWj/0MkSqz0OKp0oWSBGCyCqVW+xjeLUml7pw1/iDTJK39pM2UnSWhx56iBdfeIFly5cTDAaxmI2cM3EAr912RrRDE+KY2WYfzf+qJnSJYZQybFJ0KKPR2JC4AbzzzjvU1NSwYsUKli5dytKlS4nJ7kfMnB/j35/H8NNPIc5uZ1RKCqPT0xidns7o9AxibbYm9ZprvMTWeIndC/2ACUTm0XnjnFTFmiiNgUJnkDybF0+zXjqvDrI3FGQvNAy9VLYY4gxW0pSZ1LAmze8jzVNNrL+uk39CQnQtCk2OZyW3WrfzljqZ1XVJ0Q5JCNGF3Hjjjdx4441Nthty2Cww4TJ0wXqmX3EbQzPcnHv8AGaN6YPVLH8qip5nkGcNP3N7eKJmCv4uPGe8NdLzJjqMz+fjn//8J8uWLWP5smXs3rMHgJvOv5DbTp5F7f69PLl4EaPS0hmZlnbYIZcAqEgvXW2cjYoYI8XuMPsdPora2EvnUGYyDFbSw4qMgI/M2krcAc9RvlMhuoewMrLINpX3qgZFNQ7peWs/aSNFNNTW1nLllVfyyScfU11dg9th5azxffn5WblMGpoe7fCE6HCl1j78u+7kqK5GKT1vImqsViu/+c1vGl6XlJSwYsUKsrOzSR86lEULvuGeu//acD41NpaRqWn8YvJkxmVlEQyHMSrVdP8aDeZqL3HVXuKI9NJBZHEUT7yDqlgzZTGKfEeAPLunxQbjdTrAtlCAbRD5bY+1E2eII1OZ6RMM09dTQ2pthWxfIHokgw4x2TOPjJgSnq6e0O2eLgohji2n08ncuXPx+Xx8/vnnvPHGG7z91ltccNFQiHWzd9tGVu0s5tTjsqVHTvQIib69/Nz+EY96T+sWe8KB9LyJY6yiooJVq1axcuVKVq5cyYoVK/nXn//K+D59eeP11/j5A/cxPDWNEcnJjEhLZURqGsNSUjAb2/BHpwK/205Ngo3yGCMF7iB7HB6qTYeeC2dXJvoZ7PQLhhhYW0mit7qD3q0QXUeFJZ3HvbOoCFmP+b2l5639pI0UXUUwGERrjdls5m9//D9uv/PPxLlsXHTiQC6fnsO0EZkYDPIEVHRvxba+PFg9MyorNsuCJaLbWrZsGf/73/9YvXo1q1etprYusqrkt/f9iywF85cvYWNhYUNSl+Bo2941AZeNmkQ7pXFG9scE2WWvbdFD11iCwcYgzAz2euhXXYJJy3YFomeotqTwiPf0Y/50UZK39pM2UnRFgUCAzz77jBdffJE333yD2to6BqTHs/Hh72MxS8++6N422sfxbNVxx/y+MmxSdFvjx49n/PjI7244HGb79u2sWrWK8RdeiMFg4JsbbuShF55vKJ8RH8+olFT+e9FFGA0Gqn0+XBZL02GXRBZHia/xEr8bBgFTlcKX4KQiycq+uDDb3HVUmb/rnSsLe1mClyUWsCYmMshoZ6gvwODqElnRUnRrbn8RP7V9wiPe06gORW98vxCiezKbzZxxxhmcccYZ1NY+xjvvvMO2rVuxTL0M8pZz9a/vpn+yk9nThjAoIy7a4QrRLsM8y5npTuhS2wi0RnreRLdSXFzM6tWrWbVqFatWraK8rJzXHv8vwcJCLvzptSzdsI4RaWmMSklhVFo6YzIyGJCYeNh6fXEOylPt7EoMs8VV02rPnBED/Y0OhgdC5FSV4gh6O+MtCtHpKizpPOw59ZhN0Jaet/aTNlJ0N36/n1NPOYWv5s8H4LiBKVx84kAumzqE/mmxUY5OiLYJKRMvGM9joyfumN1Tet5Ej5acnMysWbOYNWtW0xN90rn6l79g4FdfsWL5Cp5euRKfbzEnjhzFGzffQqhgP//96ksyYmIZl5VFmtvd5HJrRR1pFXWkAccbFTWpbvJTzGxM8FBojSRpIcJsC9WwzQCGOCfZxhRygpqh1WWycbjoVuL8+Vzv+Jz7a08hKIuYCCE6gMViYd5XX7F3715effVV5r72Gnc8twh3+kBuyLJRXlHBlrwKJgxOlTlyossy6iCX8An/NJ7fZUeoSM+b6JECgQAbN27E5/MxYcIEfF4fSUmJ1NRGkqyM+HjGpWdw8ehRzBo85JB1+WPtFGY42JocYLujptUtCtKMDgaHjQz2VJNVXSarV4puYYN9HM8dg/H90vPWftJGip5gz549xMTEEOd28dQjf+fqm35LSpyTM8Zmc8qYPszM7UNavDPaYQrRwmb7cTxdNe6Y3Et63oQgMi5/9OjRDa+tNislpaWsWrWKxYsXs2jRIr75+muO79MX6+Tp7F+3jlv+82+m9e/P9AED6J+Q0DB3zlLpoU+lhz4bYbrdQkmmi+0pYTa5qwkaIg8/CkJ1FAALbGC3pzDIYGdAIEj/2gpifdIrJ7qmYZ4VTHKms7g2LdqhCCF6oOzs7Ibvz7vip1iT+vHuu+/yzkcf8cwXGwHIf+EG0twGNu8rx2E10SfZfbDqhDhmhnhWM8A6mB2+mGiH0oL0vIleLRgMYjKZWL58ORdfdBE7d+0CoE9CItP79eX6E06kX0JCq9dqs5HK9Bj2pBrYEFvTZNGTxhIMNvphJjsQILuuinhfTWe9HSHaLWC083DoAooC9k67h/S8tZ+0kaInC4VCrFq1iqVLl3LdNddA1T4uvOwK3vh4PllJMUwaksK4QSkcPySVk0b3iXa4opcqsWVzX/WpnX4f2SpAiKOwfft2PvnkEz755BM+/+xz5j/9HBkeL/M+/4Qt+fs5dcgQ+sTFtbxQgSfRRVGqle2JAXbYawkbWv9/y22wkKWsZITCZPi8pHuqsAd9nfvGhDiEMmsW/6w5tdP2t5Hkrf2kjRS9zZo1a5g3bx5ff/01S5d8y85du5k4Oodv//MrqNrPzQ+9hdGgGNk3kWFZCeRkxhPnOvb7Vore5UPr6cyvyerUe0jyJkQHCQQCmM2Ryao3/uwGHn70EQCGpqUza8AAThkyhPFZWS22JgAIW01Up7goSDSwKzbAHnttq3PlABSKOIOVVGUiNaxI9ftI8dUS763BQHT+/xS9z2r7JF6uGtkpdUvy1n7SRorerry8nKKiInJycgCYNnUKS5YuxefzN5SZc9pxPHXDDNAh/vHGcjITXQxIjaFfagypcY5W22ch2sNriuHv3gvw6M6baSZz3oToIAcSN4CHHnmYG2/6Oe+99x7vvvsu/16wgE/27eOr395JOG833+7axeCkJBKdkYnXBl+Q2L0VxO6FHCLJXE2Sk9IEM3mxQXY56qgzBgHQaMrDXsqBTQBmwGzG6Eok0WAlWZlIDGkSQ0ES/R4SfHXSUyc63EjvMtIsAyjwO6IdihBCEB8fT3x8fMPr+QsWEgwG2b59O5s3b2bLli0MHDgQzjyX2pJ93HZuPxp3SFgtJu68fAq/OX80db4A/3hjBX2SXPRJcpOV5CIr0YXbYYnGWxPdiC1YxfnujbxYNSraoTSQnjchjkB5eTl79uwhNzeXmpJyMvtlU1Vbw4iMTKb2zWZa/wFM7NMHh+UgDYMCv9tGTYKdslgjha4g+21eyi3+1ss3Y1cm4pWFeGUkPgxxoSDxgQCxAQ+xvlpMOtyB71b0Frvsw3m86sQOr1d63tpP2kgh2sfj8bBr1y62b9/O7t272b17NyeffDKnnzKTrWtXMOS4SS2uefim8/jZGcPZtTef37+4iIwEJ5mJLtLjHWQkuBiRnUCsU4Zm9nYhZeLe0GwqQp3zuyA9b0IcA42fCDoSYvnsyy/49NNP+fTTT3ny6695bNEibjr3fG47cTKe8lK+2rGDcZmZJLtckQo0WKq8JFR5SQAG1dcbsprxxtupjjFR6VCUOEIU2n2Umn1Nhl16dBCPDrL/wAEDYAWsZpQrHqcyEWswE6MNxKCICYWIDYeICfhx+724Ah5J8EQLfT0bGWwbwVavbKgrhOhe7HY7w4YNY9iwYS3ODR5zPD6fj7y8PPbu3cu+ffvIy8tjyqmnQm4uxYsX8dW2T9mfv41gMNhw3Rv33sT5k3OYt3glP/3nG6TH2UlPcJIW5yAt3sHsaUPISnJTXefH4w+S6LZhNHbO3GERPUYdZKZzB69XtfzdigZJ3oQ4SgaDgQkTJjBhwgTuuOMOamtrWbBgAf379ydt8BA+fuMtrr7nbgCyEhIZk5bKmPQMzh0xgszYpn8kG30BnAUBnAWQRmTIJYA2GfC77XhcZmpcRiodmkqbpsTio8Tia9iyACLDMGt0gJpQgLwDBxVgrP+yWVHYsCsjbmXGrYy4tMKlwR0O4QoFcQUDOAM+3AEvlnAQ0TsoNGeZl3K/d1a0QxFCiA5lsVjo378//fv3b3FuwqQT2L1nL+FwmJKSEvLz88nPz+e4446D1FTs6ltGTCwjP38/i3bvp2DJbjweLyddcDVZOVm89vJb/OjO/2IwKJJjnaTG2UmNtfHfG2eRnexm1Y5iVu0sJiXWQUqsneRYOymxDuxW+TO8uxgZWMtb5HTawl7tIb81QnQwp9PJ6aef3vB6xllnsGDBApYsWcKSJUv4dvFi3vv8M2ZcOpt+Scm8/+knPPPpxwxJSiQnOYUhyUkMSkwixmZrqEMFw1jLa7GWQxzQZN0jBUGHFb/DgtdlptahqLFCpS1MpTlIqcVHjalpAqbR1OkgdTpIYbO6MNV/2SyABTNGXAYzTmXEiQGnBqcGVziEMxzCEQxEkj2/F3vQJxuUd3Mp3l2McxaxvDYl2qEIIcQxZTAYSElJISUlhdzc3Ibjxx9/PHPnzm14rbWmpqYGm80GZjMnXpLNw4kjKCgooLCwMPJVUID15Nsgzs47C/7OnQ++1OJ+RW/9nmQHPPHON7yzeBtJMZHELjnGTlKMjStPHobRaKCkyoMC4pxW6dmLEluwikmufL6uyYx2KIdP3pRSNmA+kUFZJmCu1vrOVsrNAB4gstxCidZ6ekcGKkR3ZbPZmDJlClOmTGk4VlpaSkxMDGazGZu/jtKvF/DMqlV4vd6GMqv+fh9JwTDz1q5gQ2ER/eLj6ZeQQL/4eFzWRuOuNZhqfZhqfTiKobVd6bTJQNBuwe+04LObqLNCnU1RY9XUWMJUmgJUmP14jKEW1wYIUR4OUd78ROPePKsRnE4MuHAoE05lwqmMuFA4NLjCGmcohDMcxBX04Qz4cPp9GJGhm13RqXzLCs5CI5m4EEI0p5TC7f5uM/GhQ4cydOjQg5a/9a5/8IPrfklhYSHFxcUUFxdTWFhIwlm3gdGIZ8OD7F/4NKs3F1Fcsh2fz4/ZbOKqn/8GAh5uvf1enn7nK5RSxLttJLrtZCe5+OxP5wHw7Bcb2VVURYLLRoLbRrzLSmqcg7EDIw/hwmGNwSCf50freLWBr4l+8nbYBUtUZJ1Vp9a6RillBhYCN2mtFzcqEwd8A5yutd6jlErRWhcdql6ZjC1EU6FQiJ07d7J+/Xq2bNnCLbfcAhp+dt1P+fd/Hm9SNi0ujmW//QOqupwPNmygsKaazJhYMmMjX3E22xEtkaxNRoJ2MwG7mYDViNdmwGs1UGfW1Fo1NeYQNaYgVaYAtcbgQbc/aAuFwqaMuJS5IdFzanCGw7jDYVwHevQCHpwBn2ybcIx9Yj2NL2s6ZnNcWbCk/aSNFKJ30lpTV1dHWVkZffpEPoPnzZvH6tWrKS0tpbS0lLKyMoxGI88//ST4aznv4tm8/cEnTerJ6Z/Jpjfvg0AtM665mxWb9xLviiR28U4LYwckc9+PpgLw1Gcb8PiD9cmflQSXjfT6xVtEU08YLmWnz334gu3Q4QuW6Eh2V1P/0lz/1fyvqO8Db2it99Rfc8jETQjRktFoZNCgQQwaNOi7gwoefeIx7rn372zfvr3hq7q6msxbbiLgDTD3rDP5+PPPmtSVk5nFF7/+DVRX8fi8L6j0ekh1u0lzx5DmdpERE0uKq+WHsgqGMFeHMFdHegAPtWyFVoqQ3UzQZiZgM+GzGvBZDXgtilpLmFpLmBpzmEpzgCqjv8m8PIgM3Tyw8Epx88oNgAWwKMCBwlnfm2dqdY6eOxjA5ffiloVYOswE1vElHZO8CSGEaBulFE6nE2f91kMAM2bMYMaMGa1fYLLy1vsfEwgEKC8vb/gKh8OQG1k9ePa1AY7btKnJ+Yr4/nDy/0Ggjrt/MY2tO3Y3qfb0E0fz4f03gL+W8T/6Bx6vn+QYK8kxdlLi7Ewdnsll04YAsHxbEYluG+kJDqzmnj0j6yTrJnb6JkQ1hjb9hJVSRmA5kUXxHtFaf9usyBDArJSaB7iBB7XWz3ZkoEL0ZjExMRx33HGRydONmG1mPvjkY4qLi9mzZw+7d+9mz549WCwWMm64Hh3WzP/0YxYsWkQo9N2QyBNHjuLNO/6PcE0tVz10Px6PhxSHnWSni2SXkxGpaUypn9RdWltLrN2OydB0nL3SGlOdH1OdHxuR//EPJWwxNfTq+axGfDZDZPimFWpMIWosYSrMfiqNAcKtJHo1OkCNDhxijp4VsGJXpoaFWNxa4dbgDoWICQWICfpw+yI9eTI379DifXn0tVazu4OfMAohhOh4ZrO5Yb5ec9dee+0hr127YTPl5eWUlZVRXl5OaWkpcXFxMHEaACeetoO8vDxKSkpYX1TIl5v2EUgcxmVjf0jYU8mkC04lGIz8jZES56RPkourZw3j+jNHEw5r3lu6kwFpsQxMi+32i7T0923EZRxDTch8+MKdpE0/Qa11CBhTPzzyTaXUSK31umb1jANmAnZgkVJqsdZ6S+N6lFLXANcAZGdnd0D4QgiDwUBqaiqpqalMmND0aZAyKL6c9yWhUIiioiLy8vLYv38/DoeDlFmRFQUTv/qMrVu28m1BPgWbNuH3+7nkzLM49fobCdfWMunsM6n1eoh3uUhyukh0ODh76FCuGj8erTXPrVhOgt1BktNJotNJktPZ6rBNgz+IxR/EUunBySEoCNksBOxm/DYTPrsRj01RZ4VqS5gqS4gqc5Byk69Fbx58t41Ck+5/Q/2X2QB2J0bcxBgsxCgTsShiwhATChETDBAb8BLvr8MaChzJP0ePMs26ned8Y6IdRq8hbaQQIhqsVitpaWmkpaW1ev5f//pXi2Naa1AKHQrx9tvvkJ+fz/79+9m3bx979+7FNOpMmHYeBTs2cu55pwKRXsXslBiGZsTy87PHcOb4fgRDYQLBcLdJ6kxhPyc7d/JO1ZDoxdCewlrrivretdOBxsnbPiKLlNQCtUqp+UAusKXZ9U8AT0BkPP9RxC2EaAej0Uh6ejrp6ektzj3//PMN32utqaysJBAIEJucjNaav993L0VFRQ1fxcXFmGbMIOHH11KWn8/tf/5TizpvPvd8fn3G9ygrLuaGZ58i2WEnxekixeUi2eXiuIwMsuPjOTDntkmip8Ho8WP0RHr0DkpF9sULOCz4HWa8NgN1NgM1Vk2VNUSlJUi52d9ipU2AEGHKw96mi7A0DNU0gtONTZmIM1iIx0ishvhQiPhggHifhzhfTa8YnjnIvxGTGkVQG6MdSq8gbaQQors40G4bjUbOPPPMg5ZLGpHCt99+y44dO9iyZQubN29m08aN1PY/HXLHsuLreZx4xR3kZCYwcXAyJ+Skc+KwdIb3Seiyi6yMDq7nHbpw8qaUSgYC9YmbHZgF/K1ZsbeBh5VSJiJ//hwP3N/RwQohOpdSKjJUotHr66+//qDlU+Nc5OfnNyR2hYWFFBUVcfzxx5N24on49+yh6q25bC0ooHDDBgKBSG/Wfb/6NYMnT2fN+rWc+ZtfkxITQ4rLRarDQarLxWW5YxiRlkaNz0d+VRVpMTG4LJaWSZ43gNEbwFYGMQeJUZsMBBxWfC4zXruJOpui2gZVtjDllgClZh/eVlbZ9OogBaEgBQ0/DOpn/ZowuOKJNVhJVCYStCIpFCbJ7yXZW40r4G1RV3dlCdUyyVnAwi6wNLIQQojux2KxMHHiRCZOnNjq+aRgMr/5TQ0rV67k3UWLePrzjQB8ft+POXmwk30lVdR6gwzJjDuihdg6gzNQSl9LNbv90ZlW0Jaet3Tgmfp5bwbgVa31e0qp6wC01o9prTcqpT4C1gBh4L/NhlUKIXogg8FwyKEW2dnZHFgxT2tNeXk5BQUFJCcnk5CcTL+cAVy/f2/DcIst+/OZv3ETZ1zzU1wTj2fBxx9x6d8jz4ocVitpsbGkOd38YdZMRqalsbeigg2FhaTHxJARE0Oiw9Hiw10Fw1iqPFiqPAedlxeymvG7rPicZmodBqodUG4NUWYLUmT2EjA27WULo5v23Ckim6lYHdhUDKkGGylakRIMkubzkFpXiVm3TBC7g/FqMwu7wNLIQgghep4BAwbwpz9FRvBordm2bRuLFi1i4gUXgCnMY7++mb889Bx9kmM47/j+XHTiICYPS4/6fnfjbHns9h98e4jOdNitAjqLLIMshDgYrTVKKfbv38+8efPYv38/eXl5DV+PPvgwQzKzeOKJx/jFH37fcJ3VbCY9No7nr7yaAQ4rq/bnsa6ggMyYWLJiY8mMi8NhbuckYwVBuwVfjI1al4lqh4EyR4hiW4ACq6fVeXfNGVAkG2xkYCIzGCLbU01SXWW3WDRFY+B+ZlMcsB9xHbJVQPtJGymEELB7924++ugjPvjgAz7++CN8Pj8DM+LZ8ugPojqsstTah3trTuuQujp8qwAhhDjWDvSeZWRk8P3vf/+g5eb84udM/t4Z7N27t2GS9N69exl+x204LXa+vON2/vH++02uSXC5+Oa23+EOePhq2zZ2lZfRJzaO7Ph4+sTFYTM1+1jUNKyq6QRSgIENgULAZaMuzkaV20ipU1PkDLDPWtckqQujKQx7KARWGgCnEbsrhT4GO/1CYQbWVpHiqTzaH1unUISZYd/Ja4Hh0Q5FCCFEL9O3b1+uvfZarr32Wmpqavjggw/Yv38/hmnnw55FXPmrP3PCkBS+P30IMQ7rMYsrwZdHrNFPZchyzO55gPS8CSF6rGAwSH5+fsMWCrt37yYvL4+HHnqIoC/I1VdewfOvvtLkmgEpKXx10y0YvTV8tnULdf4A/RMSGJiYiMPStg9prRT+OAfV8VZKYxT57gC7HHX4DQdf5MStLAwwWMnx+hhUXYI53HKhlWjxmOP4U92FaI7sKaf0vLWftJFCCHFo5eXlzJg+nTVr1+KyW7jhzFH88rzjSI51HJP7f2Y9hc9r+h51PdLzJoQQ9UwmE3369KFPn5abTZttZp556UX+/sD97Nixg507d7Jjxw48Hg+Zt9+Cv8bDE6eczMIl321rmR4Xx+T+/fnXWWcDsKGwkCSnk2Sns8lcO6U11vJarOW1JAE5RBI6X7yDikQr+fGwPcZDmdnXcE219rM65Ge1GcwJ8QwyOhju85NTWRT1+XL2QAVjHKWsrEuKahxCCCHEAfHx8axavZqlS5fyz3/+k7+9+ioPfbCWj+88h8nDMzr9/kMNe/ico0/e2kuSNyFEr2UwGBq2UJg8eXKTc9YYBx9+/hnbt29n69atDUscJyYkEn/TrwiWFPOjk6axO38/8U4XQ1NSGJaUxLQB/Tl1SE6LeymtsZXVklZWSxpwHJEhl+VpDnYnarbE1FJbv61BgBAbQ9VsNIEtKZFRys7YmirS6spb1HusjDDtYyWSvAkhhOg6lFJMnDiRl19+mTvvvJOHHnqIsZdcDXu+ZNumdfRLjcHUSYubpPl2YlEn4j/G2+lI8iaEEAfhcrnIzc0lNze35cnEGP73/HOsW7eOtWvXsnbNWl5etxbVvz/nTDsJ7/58zvn9b8lJiGdsVibjMrMYlJSEoVEPnbnGS8o2LynbYLxSeJLd5GWYWZfoodgS2XLAq4Ms1dUsdSgy3Vmc4AsxvDz/mC920ie0BxhzbG8qhBBCtNGwYcN49NFHAfCkDGPm9wcSb9M8/tMZHJ/T+qrYR8MU9jPKUcry2pQOr/uQ9z2mdxNCiB7k5JNP5uSTT254HQ6Hqaurw+VyUVpaSvzzT/HB0qW8sHIFADF2O3847QwuGzWSUDiMBkyGyBNBpTWOoioGF8FgwJPkYl+WlVVJdZTXD6/MC9Ux1wSJqRmcGFCMrsg/ZpuFu/3FJJi8lAUPuXW6EEIIEXU2h4P7H3qUn994Iyf8+jWuP2MU9149BZulY1OfUcY9LOfYJm/R3SRBCCF6EIPBgMvlAiAxMZFPPvmEktJSNm3axFNPPcVlV1zBuF/cRMyVV7MyIZFh997LD195mWeWLWNfRUWTuuwlNQxeVcpFn3u5ZH0sIytjUPXrS5WGvbxr9PBIcirr4zr+aWJrFJoxtsJjci8hhBDiaCiluOCCC9i4aRM///nPeeSDNUz9zRuUVnk69D7ZgZ0dWl9bSM+bEEJ0IoPBQE5ODjk5OcyZM6fheP9TZ/HDbVv4+KOP+OzDDwDISU3jqYsuol9CQkM5pTVxe8o5cQ9McNnYNsTJ4qRKAsYwFWEfc02wNDWbU6uryejkOXGDDXl8EYXJ2UIIIcSRcLvdPPDAA5x88sm89Oz/iE/NAk9ph9VvD1YyyFrJNl9sh9V5ONLzJoQQUTBy5EgeffRRtm3fzqZNm/jnP//JsEmTGHHzLVhOmMJ/16zlH/Pmsbv8u4TMXONl2IpSfrjAwMn7E7CHIs/fdodq+K/DwAcpfQkYOu+ZXHpgT6fVLYQQQnSWc845h5fmvoVh+q3sI5NH3l/dYXUfZ83rsLraQpI3IYSIIqUUOTk53Hzzzbz51pvEDOxL4hmnsC3GzQMLF3DCww9x2Ysv8P7GjQRCkS0DjB4/g1aX8P0FcGJRHEqDRrM0XM3jSSnkOeM7JVZrsIYB1q65mbgQQghxWGY7Tyyp5obHv+KeuR2zl2b/8O4OqaetJHkTQogu6Jlnn2Hv3r3cdddd7PT5+Mnc17j9yy+blDF6/IxcXsYVS+wMrYkBIvPh/mc38FVyNuEj3FT7UEZbCjq8TiGEEOJYufMPf+D73/8+v3n2G/751oqjri/GX4hJHbv9WCV5E0KILiozM5Pf//737Ny1i3fffZdf/etfuC/7Pnusdv4+70vK6uoAsJXVMm1BBRdsjsMRMhFGM0/X8FJqH3xGc4fG1J+9HVqfEEIIcSwZjUaeeeYZLrroIn71v4U8/N7RDaE06iCDbdUdFN3hSfImhBBdnNFo5KyzzmLc+HG4huewLM7NAwsWcPzDD/PQ1wvxBiObeyftKOOybwwNvXDbQjU8lZhCpcXRYbEk+vZhOYZPGIUQQoiOZjKZePHFFzn33HN5/PPt+ALBo6pvgKmkgyI7PEnehBCim/nZz37G2rVrmXnaqdz9xRec9MTjfLR5MwCmGi/TFlZw5q54TGFFYdjDk7ExFDg6Zh6cUQcZZT92jZQQQgjRGcxmM6+++irzFq3Amj78qOrKUkUdFNXhSfImhBDd0MiRI3n77bf55JNPcKakML+uDsyWyEkNWRtLuWS1E2fQRLX285TTxF5XwqErbaNhpvwOqUcIIYSIJovFQmJyMjWDz+fWZ76losZ3RPUkB4/dfHBJ3oQQohs75ZRTWL1mDQ+99CIJ117LulCYN9etA8BVUMXFS4yk+Gz4dYgX7SaK7Ee/F02fkGwZIIQQoufYuDOPB95ezk8e/hytdbuvdwTKiTX6OyGyliR5E0KIbs5kMuFyubCmJPH83j387M03uP7NN6jweLBUejh7UZCBtS68OsjzLjsVFudR3c/tLybB5O2g6IUQQojomjBhAn/5y1+Y+802Hv9oXbuvV2hybGWdEFlLkrwJIUQP8uT/nuRPf/oT723axOn/e5KtxcUYPX5O/qaGoTUxVGs/z8fFUmuyHfE9FJrB1oqOC1oIIYSIsltuuYXTTj2VXzy5gDU72z+3u7+xtBOiakmSNyGE6EFMJhO/+93vWLBgAV6jkXOeeYbNRUWoYJgp31bTt85JadjLS4lJhI6iCcg2HpsnjEIIIcSxYDAYePa554iPT+S6f89r9/XpurDjg2qFJG9CCNEDTZo0iW+XLOGyH1xOzgmTATD4Q8xa4iPVZyMvVMe85D5HXH8qsuKkEEKIniUlJYW5r7/Oy4/e3e5rEwLHZjEvSd6EEKKH6tevH4898QSpV3yf6pzhPLt8GUaPnzOXhokPWPlG17DXlXhEdScEj92yyEIIIcSxMnnyZLKnXoq2uAiH2754iTnkoY+lphMji5DkTQghejiDwcArO7Zw+wcf8MyyZZirvZy90oghDG86rPiM5nbXaQ9U4DYGOiFaIYQQIrrq/CFO/fMn3PfWinZdN8TS+VMKJHkTQohe4Pbbb+d73/sed3z0Ie9u2ICttIZTd8dSHvbycVLGEdU5yFrZwVEKIYQQ0edwOLC4k/nLa8sprfK0+bo+hs4flSLJmxBC9AJms5lXX32VySeeyA1vvcn8HTvI2lzG0Go3K0PVbIpLa3ed2SZZtEQIIUTP9Le//51qj5+/vraszdekhjp/0RJJ3oQQopdwOBy8+957DB02jD/P/4pwWHPiSi+OkImPLardq0+my6IlQggheqiRI0cyZ84cHv5gDTsL2jbSJCZQhEWFOjUuSd6EEKIXiYuL4+NPPuGjL+dhjInBVOvj9G0uKsI+Via2b/hkYqi4k6IUQgghou+Pf/wjRpOZv85tW++bQYcYbOvcKQWSvAkhRC+Tnp5O5sABWE8/g/c3biJpRxnjyuJYYAwSVG1vFpz+UqyGzn3CKIQQQkRLZmYmH330Mff98vI2XzPQ3LmjUiR5E0KIXuqpTz/mJ3Nf5bOtW8hdV0tdMMDyhLb3vinCDLDIoiVCCCF6rmnTphEz7OQ2l8+kc0elHDZ5U0rZlFJLlFKrlVLrlVJ3tVJmhlKqUim1qv7r950TrhBCiI7ys5/9jNGjRvGr99+nsqiME4viWGgIEFDGNtfR31zReQEKIYQQXcDby/dxyT8+QevD7/sWF+rcxbza0vPmA07WWucCY4DTlVKTWim3QGs9pv7rjx0ZpBBCiI5ntVp5/oUXqPT5uOX99xi0sQpfMMTShPQ215GhZNESIYQQPVtpeSWvLdjE1xvzD1vWGShD0fbNvdvrsMmbjjiwXbi5/qvzIhJCCHHMjBo1irvvvpuPN2/m/eWrmFwUyzeGAH6DqU3XJ4Y6f08bIYQQIpouvfRSYmLcPP7R2sOWNeoAyWZvp8XSpjlvSimjUmoVUAR8qrX+tpViJ9QPrfxQKTWiI4MUQgjReX7xi1/wkx/9iH4ZGQzcXE0oGGZ5G3vfYgLFGAl3coRCCCFE9DidTq644oe89s32Nm3a3cdS3WmxtCl501qHtNZjgCxgolJqZLMiK4C+9UMrHwLeaq0epdQ1SqllSqllxcWyxLQQQnQFBoOBJ/77XybPno3R42dyQSyrDG1LyAw6RD9rzeELisOSNlIIIbqua665Bp8/yHNfbjps2XRD5y3m1a7VJrXWFcA84PRmx6sODK3UWn8AmJVSSa1c/4TWerzWenxycvIRBy2EEKLjlaelcffChWRuKKM6EKDAEd+m6/pZyjs5st5B2kghhOi6Ro8ezbXX/IQBWSmHLZukopi8KaWSlVJx9d/bgVnApmZl0pRSqv77ifX1lnZ4tEIIITrNmg3reejLL3h9yTJOKIxhtSumTddlGeTjXgghRM/32ONPcM5Fsw9bLkFXdFoMbel5Swe+VEqtAZYSmfP2nlLqOqXUdfVlLgLWKaVWA/8CLtNtWUtTCCFEl/G9732P4ydO5IGvF5K6vYr1+AijDntdckiG+AkhhOgd8s39Wbhh/yHLuIOdt11AW1abXKO1Pk5rPVprPfLANgBa68e01o/Vf/+w1nqE1jpXaz1Ja/1Np0UshBCiUyil+OOf/kReRQWvz/uapBoLO2IOP3wvNlB4DKITQgghou8Xv/8bF//9o0Pu+WYNVmNXwU65f7vmvAkhhOjZTjnlFKZMnsy/vvmGgXvCrLXbD3uNKewn0ew7BtEJIYQQ0XXmmWdSUFbDqh0HH3Wi0PTppMW8JHkTQgjRQCnFn//yF06fPgPH1jK2BD1t2vMtwywrTgohhOj5Tj89sm7j+8t2HbJchkmSNyGEEMfA9OnT+e8LL5BqsTGkysnG2MOvrJVilORNCCFEz5eamsr4ceP4YPnuQ5frpO0CJHkTQgjRgsHpYJ3BQNLmGtZYDt/zlqw6b0NSIYQQoiv53lln8e2WAsprvActk0jnbKMjyZsQQogWtm/fzml3/p5PP1hAgd9LleXQc9/iqDpGkQkhhBDRdd1117F7wavEu2wHLRMTruiUe0vyJoQQooVBgwYxccIE3lizltwyF1vciYcsHxPuvA1JhRBCiK4kLS2NrNwZhyzjDHTOdgGSvAkhhGjVFT/8IesL8vF/u4edxkM3F85gxbEJSgghhOgC5i1dx+UPziMUCrd63hT2k2Ku6/D7SvImhBCiVZdddhkmk4n3v1xErU9ziC1tsIRqcRoCxy44IYQQIooKCwt58fM1LNl68L1OszphJWZJ3oQQQrQqKSmJM888k8+3bye71EyhM+6Q5TMttccmMCGEECLKTj31VAwGAx8cYsuAdGPHzweX5E0IIcRBPfLII8x/6mnSS8PstLsOWTa1k/a0EUIIIbqa+Ph4TjzhBN4/xJYByarj54NL8iaEEOKgsrKySDj+eGKKatl9mHlvKQbZLkAIIUTv8b2zzmLl9iL2l7b+8DJed/x2AZK8CSGEOKQvli7hzMf/g680SBh10HLxSPImhBCi9zjzzDM5bsQQ8stbX5jEHZTkTQghxDEWDodZs2cPZUv2sP8Q895itGwXIIQQovcYPXo0KxYvZNyglFbP24JVWA2hDr2nJG9CCCEOaerUqZhMJjZ9u/mQ895csl2AEEKI3saZRNhob/WUQpNt6dj54JK8CSGEOCS3283ECRNYun4LewwHHzZpC1ZhUR37hFEIIYToyp783/+Iu/hBquv8rZ7PMHXslAJJ3oQQQhzWyTNnsnrfPgL5PoKq9aZDoUnvhA1JhRBCiK4qPT2d6jovK3YUtXo+1dCxUwokeRNCCHFYZ5xxBmdPm0bM/gD7nPEHLZdmlr3ehBBC9B4TJkwAYOnW1pO3eCR5E0IIcYydeOKJvPrCC4zSTnYdYt5bslH2ehNCCNF7JCcn0ze7D0u3FrZ63qk79qGmJG9CCCHaxJSeTl1+Jfu0PmiZRFV1DCMSQgghom/i8ZNYsrW41XOOkMx5E0IIEQWPPfYYY/7yZ/x76/AbTK2WiQvLdgFCCCF6l9mzZ3PVOdMIh1s+3LSGZLVJIYQQUTB+/HgA8r/eQYEjttUy7pAkb0IIIXqX888/n9/fegOGVlZkNoX9uI2BDruXJG9CCCHaZOzYscS43axbtpFCq6PVMrZgBYqDD6sUQggheqIKFceuwtanDqSaOm4lZknehBBCtInJZGLGSSfx7ebtVND6sEmjDpJs9h7jyIQQQojomnLuldzw+LxWzyWaPB12H0nehBBCtNnJJ5/MztISSncefHhkpmwXIIQQopcZP2EiS7cVo1tZ1CvBKD1vQgghouC8887jiT/cRRx2wrQc2w+QYuzYlbWEEEKIrm7ixIkUVdSyp7hlGxhLxz3UlORNCCFEm/Xt25c5N9xARtBKmd3daplEgyRvQgghepfvNutuud9bjCRvQgghomV3eRmff72aAquz1fPxWvZ6E0II0buMHj0as9nM0q1FLc65wh23XYAkb0IIIdrlzTff5Jf/eYptta0vfewOVRzbgIQQQogos1qtvPDcM1w1a0SLc45jmbwppWxKqSVKqdVKqfVKqbsOUXaCUiqklLqowyIUQgjRpeTm5gKwfX3LoSEAtg5spIQQQoju4uJLZzN0eMvkzRrquBEpbel58wEna61zgTHA6UqpSc0LKaWMwN+AjzssOiGEEF3OgeRt58b9rZ63BOswEj6WIQkhhBBRV1FRwYvf7CWvtOlDTFPYj9PQMRt1HzZ50xEHIjDXf7W2A+uNwOtAy4GeQggheozU1FTSUlLZuWUftSZbi/OKMAkmfxQiE0IIIaJn//79XH7Hw3y2am+LcykdtNdbm+a8KaWMSqlVRBKzT7XW3zY7nwmcDzx2mHquUUotU0otKy4uPsKQhRBCRNuYscexfed+Cu0xrZ5PMMlG3e0lbaQQQnRvQ4YMwWw2s3FfWYtzSaaO2eutTcmb1jqktR4DZAETlVIjmxV5ALhNax06TD1PaK3Ha63HJycnH0m8QgghuoBHHnmE1++4gwKLtdXzCcaOecLYm0gbKYQQ3ZvJZGLQwAFsyatoca6j2sV2rTapta4A5gGnNzs1HnhZKbULuAh4VCl13tGHJ4QQoisaMGAAWdn98ATNrZ6Pk+RNCCFELzQkZyib91e2OB6njlHPm1IqWSkVV/+9HZgFbGpcRmvdX2vdT2vdD5gLXK+1fqtDIhRCCNHl1NXV8Y9332Ht4u2tno9BkjchhBC9z5AhQ9iWX04o1HThLjcdsxJzW3re0oEvlVJrgKVE5ry9p5S6Til1XYdEIYQQoluxWq3885lnWDx/LUHVsilxURuFqIQQQojo+sUvfsG2t+/DYFBNjrt0xyRvpsMV0FqvAY5r5Xiri5NoreccfVhCCCG6MqPRyMiRI9m5PY8S+3TS6sqbnHfqjhkeIoQQQnQnGRkZUDMMNu9octwROnY9b0IIIUQLuWPGsGXvfvKt9hbn7LJRtxBCiF4oGAzyj+c+5PPVTbcLsIeqO6R+Sd6EEEIckTFjxlBRW8vmkpZDJO1hGTYphBCi9zEajfzlX//jzUVN54Sbwl4chuBR1y/JmxBCiCOSm5uL3WqlcFdVi3OWYC0KHYWohBBCiOhRSjFkyBA255W3OJfcAXu9SfImhBDiiJxwwgkUr17N2P4DW5xThEkw+6MQlRBCCBFdQ3Jy2JLf8sFmkunoV2KW5E0IIcQRMRqNWNMzcdVo6kwtN+tOlL3ehBBC9EJDhgxhT1ElHl/TYZIJBul5E0IIEUX/e/lFfvvgM5RbHC3OxRu9UYhICCGEiK6cnByMRgO7i5v2vsUZpOdNCCFEFBUUFPDRspXsD6kW5zqikRJCCCG6m/POO4+6dR8xNCuhyfGYDtioW5I3IYQQR2zMmDForVm/rbTFuRgle70JIYTofaxWK5aErBbHXfroV2KW5E0IIcQRy83NBWDLpoIW59xI8iaEEKJ3uuvBp7nvzRVNjjnDR7/XmyRvQgghjlh2djZxsbFs3bq/xTmnJG9CCCF6qa8WLWXuN033erN1wEbdkrwJIYQ4YkopvnfWWTit9hbnHKGjH9svhBBCdEdDhgxhS35Fk2PmkAerIXRU9UryJoQQ4qg8//zz/HLO5YSaNSm28NGP7RdCCCG6o5ycHMqqPJRWNV28K+Uo93qT5E0IIcRRc1pdVFibbhdgCdWi0FGKSAghhIieIUOGALA5r6LJ8aSj3ANVkjchhBBH5bPPPuOkq29geUHTYZIGHSLe5ItSVEIIIUT0DBkyhIzUJCrrmraDCcajmw8uyZsQQoij4nQ62VtQyOaClnPcEiV5E0II0QsNHjyYvPWLOWNcvybH4w1HN6VAkjchhBBHZeDAgQDs21Pe4lz8UQ4PEUIIIbotV0rLQ8iwSSGEEFGUnJyM0+EgL69l8hZn8EYhIiGEECL67v7nw1xwz0dNjtk5unZRkjchhBBHRSnFgAED2LevpMW5GCV7vQkhhOidSktL+WDZDsLh7xbvsmvpeRNCCBFlF150EcMHDWhx3C0bdQshhOilcnJy8PmD7C35bnNua1iSNyGEEFF25513cuPll1JnsjY57tSy15sQQoje6bvtAr6bVmCR5E0IIURXYDfaKTHamhxzaOl5E0II0TsNGBAZkbKr8LueN3NItgoQQggRZfPmzWPIOZfw1baiJsdtoZbbBwghhBC9QXp6OpMnjiPWaWk4ZtRBHIbgEdcpyZsQQoijlp6eTiAYZHdeVZPj1lANKkoxCSGEENFkMplY+PmHXDp1SJPj8UexB6okb0IIIY5av379UEqRt7eiyXGDDuEyyEbdQggheil7XItDMUb/EVcnyZsQQoijZrVaycrIaHW7gHij7PUmhBCid/rFr25l6m/eaHIs5igeakryJoQQokMMGDSIffllLY7Hyl5vQggheqlQKMTaXU0fbLoNR/5QU5I3IYQQHeLKK6/ktCmTCDVrWmLU0S2LLIQQQnRXmZmZVNZ6qfF8N1TSrY68583UEUEJIYQQV111FdvT3FQYV5Lo+26VSRey15sQQojeKSsrC4C80lpysiKrTjo48oeah03elFI2YD5grS8/V2t9Z7My5wJ/AsJAEPiF1nphe4MpKirilltuYdOmTYTD4fZeLkS3ZjAYGDp0KPfeey8pKSnRDkeIdtNaU1vlxxNrJLHRcUdYkreOIG2k6M2kjRTd1XfJWw05WfEAODnyYZNt6XnzASdrrWuUUmZgoVLqQ6314kZlPgfe0VprpdRo4FVgaHuDueWWWzjppJN48sknMZvN7b1ciG4tEAjw3HPPccstt/Dss89GOxwh2m3JkiVMuuxyHrj7KkaOcDcct4Zl2GRHkDZS9GbSRoruauDAgVx69ixiHN/t9WbTnTjnTUccGP9irv/SzcrUaK0PHHM2P99WmzZt4gc/+IE0SqJXMpvNXHHFFWzatCnaoQhxRAYMGABA3t7yJsfVkTUJohlpI0VvJm2k6K769OnDy//9F+MHpzYcs+kjX8irTQuWKKWMSqlVQBHwqdb621bKnK+U2gS8D1x9kHquUUotU0otKy4ubnE+HA5LoyR6NbPZLMOhRLeVlJSE2+Vi376WK06Kw5M2UohDkzZSdFu2OIKh7353reFOXm1Sax3SWo8BsoCJSqmRrZR5U2s9FDiPyPy31up5Qms9Xms9Pjk5+YiD7iwzZszg448/bnLsgQce4Prrr2+1fL9+/SgpKaGiooJHH330WIQohBBdllKKAQMGsC+vNNqhdEvSRgohRM805YwLufDu9xteW0Kd3PN2gNa6ApgHnH6IMvOBgUqppCOOKkpmz57Nyy+/3OTYyy+/zOzZsw95nTRMQggRMWDgQPIKpeetJ5I2UgghjkxMbBx5Zd8lbMawDyNH1ot82ORNKZWslIqr/94OzAI2NSszSCml6r8fC1iAbvfo9aKLLuK9997D54vsvbBr1y7279/Pvn37GDVqFCNHjuS2225rcd3tt9/O9u3bGTNmDLfeeis1NTXMnDmTsWPHMmrUKN5+++2Gsn/6058YOnQop5xyCrNnz+bee+8FYPv27Zx++umMGzeOqVOnyphuIUS3dO211/LTS88njIp2KKKDSRsphBBHJjMzk32l3628rNDEmQNHVFdbVptMB55RShmJJHuvaq3fU0pdB6C1fgy4EPihUioAeIBLGy1gcsRmzJjR4tgll1zC9ddfT11dHWeeeWaL83PmzGHOnDmUlJRw0UUXNTk3b968Q94vMTGRiRMn8tFHH3Huuefy8ssvc9ppp3HbbbexfPly4uPjOfXUU3nrrbc477zzGq675557WLduHatWrQIgGAzy5ptvEhMTQ0lJCZMmTeKcc85h+fLlvP7666xcuZJgMMjYsWMZN24cANdccw2PPfYYgwcP5ttvv+X666/niy++aNfPSwghou20005jmPJTG/4ad0BWmexM0kZKGymE6B6ysrIoLK/BHwhhMRsBiDV4KcXa7roOm7xprdcAx7Vy/LFG3/8N+Fu7794FHRgWcqBhOv/885kxYwYH5h9cfvnlzJ8/v0nD1JzWmjvuuIP58+djMBjIy8ujsLCQhQsXcu6552K32wE4++yzAaipqeGbb77h4osvbqjjwJNNIYToTnw+H5t27iU9Kcgoy+HLi+5F2kghhGi/zMxMAPLLa+mbEgNAnNF/RHW1pectag71FNDhcBzyfFJS0mGfIrbmvPPO45e//CUrVqzA4/GQm5vL9u3b21XHCy+8QHFxMcuXL8dsNtOvXz+8Xi8H64wMh8PExcU1PJUUQojuas+ePZx23c/4+20XMmpyZrTD6dGkjRRCiO5h/Pjx/GrOeVhMxoZjbsORPYRq14IlvYHL5WLGjBlcffXVzJ49m+OPP56vvvqKkpISQqEQL730EtOnT29yjdvtprq6uuF1ZWUlKSkpmM1mvvzyS3bv3g3AlClTePfdd/F6vdTU1PD++5FVZ2JiYujfvz+vvfYaEHkquXr16mP0joUQouP07dsXg8HA7ryKaIciOoG0kUII0X5jxozh3t//kvQEZ8Mxlzqy7QIkeWvF7NmzWb16NZdddhnp6encfffdnHTSSeTm5jJ27FjOPffcJuUTExOZPHkyI0eO5NZbb+Xyyy9n2bJljB8/nhdeeIGhQ4cCMGHCBM455xxyc3O54IILGD9+PLGxsUDkSeSTTz5Jbm4uI0aMaDKBWwghuguLxUJaSgoFRdWHLyy6JWkjhRCi/Wq1hcra73rbXOrIet5UB6wrckTGjx+vly1b1vwYzY/1NDU1NbhcLurq6pg2bRpPPPEEY8eOjXZYogvpDf8fiJ5t/LhxOPAw/w+nRA5kHIcaf9VyrfX46EbWfUgbKW2kaF1v+P9A9Dxaa1wuJ9edMpT7fjQVgC32XJ6qmsA9F45uV/vYpee89UTXXHMNGzZswOv1cuWVV0qjJITocdIzMti2flW0wxDdkLSRQoieSClFZkYG+0prGo45OLKeN0nejrEXX3wx2iEIIUSnuuOOO9jx9btA7WHLCtGYtJFCiJ4qq082+wq+26PSqo9sOx2Z8yaEEKJDnXDCCUwY12KHGSGEEKLXyszMJK+sruG1LSzJmxBCiC4gPz+fTxaupLruyPawEUIIIXqarKws8kqqCYcj641YwnWHuaJ1krwJIYToUN9++y03/v5u1hTKsEkhhBAC4Oyzz+bemy4lGAoDYA4dWc+bzHkTQgjRodLT0wHYWeVncpRjEUIIIbqCE088kRNjC2HHPAAMOoTLGGh3PdLz1sy+ffs499xzGTx4MAMHDuSmm27C74/O0J933nmHe+65p0Pq+vGPf8yGDRsA+Otf/9ohdUJkw1aAXbt2yURzIQTwXfK2p+LINiAVXZe0ke0jbaQQ4oBgMMimvEqKK78bLhlvbP+Kk116n7ffvLG2Q+959wWjDnlea83xxx/PT3/6U6666ipCoRDXXHMNCQkJ/OMf/+jQWI5GMBjEZDryTlOXy0VNTc3hC7ajrnnz5nHvvffy3nvvdUi9vZnsYSO6O7/fj9Vq5edXzuLBC4fLPm9HQNrIIydtZM8mbaTorvLz88nIyOCR62Zw/ZmjAXjedAFXnDmjXe2j9Lw18sUXX2Cz2bjqqqsAMBqN3H///fzvf/+jrq6Op59+mnPPPZfTTz+dnJwc7rrrroZrn3/+eSZOnMiYMWO49tprCYVCQOSD+7e//S25ublMmjSJwsLCVu/90UcfMXbsWHJzc5k5cyYATz/9NDfccAMAc+bM4Ze//CUnnXQSt912G9u2bWPWrFnk5uYyduxYtm/fzrx58zjrrLMa6rzhhht4+umnAZgxYwbLli3j9ttvx+PxMGbMGC6//PImMfz73//m17/+dcPrp59+mhtvvBGAf/7zn4wcOZKRI0fywAMPtIj/9ttvZ8GCBYwZM4b777+fXbt2MXXqVMaOHcvYsWP55ptvAAiHw1x//fWMGDGCs846izPPPJO5c+cCsHz5cqZPn864ceM47bTTyM/Pb9s/nBCiS7FYLCQmJFBcInPeehJpI6WNFEIcuZSUFEwmE/tKvns4FHMEPW+SvDWyfv16xo0b1+RYTEwM2dnZbNu2DYAlS5bwwgsvsGrVKl577TWWLVvGxo0beeWVV/j6669ZtWoVRqORF154AYDa2lomTZrE6tWrmTZtGv/5z39a3Le4uJif/OQnvP7666xevZrXXnut1fi2bNnCZ599xn333cfll1/Oz372M1avXs0333zTMEzpcO655x7sdjurVq1qiPGAiy66iDfeeKPh9SuvvMKll17K8uXLeeqpp/j2229ZvHgx//nPf1i5cmWLeqdOncqqVau4+eabSUlJ4dNPP2XFihW88sor/PznPwfgjTfeYNeuXaxdu5b//ve/LFq0CIBAIMCNN97I3LlzWb58OVdffTW//e1v2/SehBBdzztvvMG1F50S7TBEB5I2UtpIIcSRMxqNpKelktdoo263an/yJguWNKK1Ril1yOOnnHIKiYmJAFxwwQUsXLgQk8nE8uXLmTBhAgAej4eUlBQg8gT6wJO+cePG8emnn7aof/HixUybNo3+/fsDkJCQ0Gp8F198MUajkerqavLy8jj//PMBsNlsR/O2GyQnJzNgwAAWL17M4MGD2bx5M5MnT+Zf//oX559/Pk6ns+F9L1iwgOOOO/g+ToFAgBtuuKGhod6yZQsACxcu5OKLL8ZgMJCWlsZJJ50EwObNm1m3bh2nnBL5Yy8UCrW5sRVCdD0nTp/Olqr1QEW0QxEdRNpIaSOFEEcnq08f9pXua3jtVu1fcVKSt0ZGjBjB66+/3uRYVVUVe/fuZeDAgSxfvrxFw6WUQmvNlVdeyd13392iTrPZ3HCN0WgkGAwSCoUanl6ec845jB8/vtUGsbkDDcPB5imaTCbC4XDDa6+3/YsFXHrppbz66qsMHTqU888/v+H9tdf9999Pamoqq1evJhwONzSeB6tLa82IESManjIKIbq3JUuW8OnnS/ntzJhohyI6iLSR0kYKIY5OZmYWaxdvaXjtQIZNHpWZM2dSV1fHs88+C0SebP3qV79izpw5OBwOAD799FPKysrweDy89dZbTJ48mZkzZzJ37lyKiooAKCsrY/fu3Qe9j9FoZNWqVaxatYo//vGPnHDCCXz11Vfs3Lmz4fpDiYmJISsri7feegsAn89HXV0dffv2ZcOGDfh8PiorK/n8889bvd5sNhMItL406QUXXMBbb73FSy+9xKWXXgrAtGnTeOutt6irq6O2tpY333yTqVOnNrnO7XZTXV3d8LqyspL09HQMBgPPPfdcw/yGKVOm8PrrrxMOhyksLGTevHkA5OTkUFxc3GSIyPr16w/5cxBCdF1vvPEGdz3y3BH9YSu6JmkjpY0UQhydG2+8kX9cf07Dazvtf4gkyVsjSinefPNNXnvtNQYPHsyQIUOw2WxNlg2eMmUKV1xxBWPGjOHCCy9k/PjxDB8+nD//+c+ceuqpjB49mlNOOaVdE4mTk5N54oknuOCCC8jNzW1oEA7lueee41//+hejR4/mxBNPpKCggD59+nDJJZcwevRoLr/88oMO2bjmmmsayjQXHx/P8OHD2b17NxMnTgRg7NixzJkzh4kTJ3L88cfz4x//uEXdo0ePxmQykZuby/3338/111/PM888w6RJk9iyZUvDE9ELL7yQrKwsRo4cybXXXsvxxx9PbGwsFouFuXPnctttt5Gbm8uYMWMaJnALIbqftLQ0AsEQpdWyXUBPIW2ktJFCiKMzbdo0zp55YsNru27/sMkuvVVAV/P000+zbNkyHn744WiH0q3V1NTgcrkoLS1l4sSJfP3116SlpUU7rC6jq/9/IERbvPLKK1x22WWsePiHHDfpJNkqoJ2kjey9pI08tK7+/4EQh1JZWcnSV+5lnLuIeJeNCmsG8afd3q72Uea8iWPurLPOoqKiAr/fz//93/9JoyRED3RgMYUdVQEOvmyDEKI5aSOF6LnWrFnDKdf+mY/vOpdTj+uLNVx3+IuakeStHebMmcOcOXOiHUa3d2AMvxCi5zqQvO2pkmGTvYW0kR1D2kgheq4DK+2W1LeN5pAkb0IIIbqAfv36seajj1DhJdEORQghhOgSkpKSACipisx1M4X97a5DFiwRQgjR4cxmMzmjR2Mz2aMdihBCCNElxMfHYzAYKK5s/0IlB0jyJoQQolM8//Y7vP3J6miHIYQQQnQJBoOBxIT4hmGTR1RHB8YjhBBCNHj25Zd4+UPZVFgIIYQ44KVnn+Kmc3KP+HpJ3poxGo2MGTOGkSNHcvbZZ1NRUdGh9TfeDwfgxBNPPEjJ7mnVqlV88MEHR1XHkfyMXC7XUd1TCNHx0tLSKC6vPnxB0W1IG3l0pI0UQsw87XsMzUo84uu79j5v797UsTc9+8HDFnG5XNTU1ABw5ZVXMmTIEH772992WAiN6+/qgsEgJlP71rTpiH1+juRn1J1+rocje9iInuLmm2/micf+Te3Cx2Sft3aSNrLrkzYyOqSNFN3d8uXL2ffO3Zw7PhMAdc6/2tU+Ss/bIZxwwgnk5eUBsH37dk4//XTGjRvH1KlT2bRpEwDvvvsuxx9/PMcddxyzZs2isLAQiGyyedVVVzFq1ChGjx7N66+/zu23347H42HMmDFcfvnlwHdPw7TW3HrrrYwcOZJRo0bxyiuvAJElg2fMmMFFF13E0KFDufzyy2kt4V61ahWTJk1i9OjRnH/++ZSXl7Nx40YmTpzYUGbXrl2MHj0aiPziTJ8+nXHjxnHaaaeRn58PwIwZM7jjjjuYPn06Dz74IHPmzOG6665j6tSpDBkyhPfeew8Ar9fb8P6OO+44vvzyS/x+P7///e955ZVXGDNmDK+88gq1tbVcffXVTJgwgeOOO463334biDRgF1xwAaeffjqDBw/m17/+NcAhf0Y1NTXMnDmTsWPHMmrUqIa6hBBdU3p6OnVeHzV1sl1ATyRtpLSRQoj2e/zxx7n2oY+P+PrDPjJSStmA+YC1vvxcrfWdzcpcDtxW/7IG+KnWulvPUg+FQnz++ef86Ec/AuCaa67hscf+v717j6uqSh8//lkgAl5AEzIca1DHdAQOd8IbAipqmXdizMmwn2NmapfJ0dKUasbxazY6auVkGWaUJN66jDPKKCqKCuQRL6lpYZZmeAMVL4Dr98fBE3fOUfCAPu/Xi9frnL3X3vvZD5zzsPZee+9FtG/fnp07dzJu3Dg2btxIt27d2LFjB0op3n//fWbPns1bb73FG2+8gaurK3v37gXg3LlzDB06lIULF2I0Gsttb9WqVRiNRvbs2cPp06cJDg4mLCwMgN27d7N//35atWpF165d2bZtG926dSu1/MiRI1mwYAE9evRg+vTpvPbaa8ybN49r167x3Xff0bZtWxITE3nssccoKChgwoQJrF27Fnd3dxITE5k6dSpLliwB4Pz582zevBkwPbcnOzubzZs3c/ToUSIiIjhy5Ahvv/02AHv37uXgwYNERUVx+PBhXn/99VJHFV955RUiIyNZsmQJ58+fJyQkhF69egGmYrp7924cHR3p0KEDEyZMYNasWZXmyMnJidWrV+Pi4sLp06cJDQ1lwIABKKVu8bcthKgNN5719tNZGTp5p5EaKTVSCHFz3N3dOZ2Xj9b6pj6flpzvvwpEaq0vKqUcgFSl1Dqt9Y4Sbb4Hemitzyml+gHvAQ9ZHU0dcOOIVnZ2NoGBgfTu3ZuLFy+yfft2oqOjze2uXr0KwI8//khMTAwnT57k2rVrtGnTBoDk5GSWL19ubt+8efMqt5uamsrw4cOxt7enZcuW9OjRg/T0dFxcXAgJCaF169YA5thKFqbc3FzOnz9Pjx49ANNQlhuxPvbYY3z22WdMmTKFxMREEhMTOXToEPv27aN3796AqQjf+CcLICYmplRsjz32GHZ2drRv3562bdty8OBBUlNTmTBhAgAdO3bkt7/9LYcPHy63X+vXr+fzzz9nzpw5gOlo5A8//ABAz549cXV1BaBTp04cO3aM+++/v9Icaa155ZVX2LJlC3Z2dvz000+cOnWK++67r8rcCiFsIzo6ms6eLXF3PWvrUEQNkRopNVIIcWvc3NwoKrpO7qVrNGviaPXy1XbetGn8wY2B0g7FP7pMm+0l3u4AWlsdSR3h7OyM0WgkNzeX/v378/bbbxMbG0uzZs0qPNI1YcIEXnzxRQYMGEBKSgpxcXEAVvemq7r20NHx11+svb09hYWFFq83JiaG6OhohgwZglKK9u3bs3fvXry8vEhLq/gucI0bNy71vux+KKWqjLckrTUrV66kQ4cOpabv3LnT6v1KSEggJyeHzMxMHBwc8PT05MoVGY4lRF3l5OREiyb3cNE+z9ahiBoiNVJqpBDi1pR8UPfNdN4suuZNKWWvlDICvwAbtNY7q2j+/4B1laxnjFIqQymVkZOTY3Wwt5Orqyvz589nzpw5ODs706ZNG1asWAGYvmz37DGNCs3NzeU3vzFdcLh06VLz8lFRUaUuSD537hxgenBtQUFBue2FhYWRmJhIUVEROTk5bNmypdRY/Opibd68OVu3bgVg2bJl5iOM7dq1w97enjfeeMN8tLBDhw7k5OSYC1NBQQH79++vdP0rVqzg+vXrHD16lO+++44OHToQFhZGQkICAIcPH+aHH36gQ4cONG3alAsXfh0i1adPHxYsWGAuZLt37652fyrLUW5uLvfeey8ODg5s2rSJY8eOWZIeIYSNXLt2jRn/XMh/U+v1KPrbRmqk1EipkULc+dzd3QHIybu5B3Vb1HnTWhdprf0wnVELUUp5V9ROKRWBqfM2uaL5Wuv3tNZBWuugG4HXZf7+/vj6+rJ8+XISEhL44IMP8PX1xcvLy3whcFxcHNHR0XTv3t3ckwaYNm0a586dw9vbG19fXzZt2gSYrgswGAzmC41vGDx4MAaDAV9fXyIjI5k9e7ZVQx2WLl3KpEmTMBgMGI1Gpk+fbp4XExPDxx9/zGOPPQZAw4YNSUpKYvLkyfj6+uLn58f27dsrWzUdOnSgR48e9OvXj0WLFuHk5MS4ceMoKirCx8eHmJgY4uPjcXR0JCIiggMHDpgvxn711VcpKCjAYDDg7e3Nq6++Wu2+VJajESNGkJGRQVBQEAkJCXTs2NHi/Aghbj8HBwfeW76cHTsO2DqUekFqpNRIqZFC3Pk6d+5Mxpp3MXi6Vd+4AlY/KkApNQO4pLWeU2a6AVgN9NNalx/YXYZFt0EWNhcbG0v//v0ZNmyYrUO5a8jnQNxJPB94AH/v37Fm3SZ5VIAVpEbWD1Ijbz/5HIg7wvFdYDSdna/xRwUopdyVUs2KXzsDvYCDZdo8AKwCnrCk4yaEEOLucJ+HB7/knLN1GEIIIUSdcP36dRZ/to4dB0/e1PKW3G3SA1iqlLLH1Nn7TGv9pVJqLIDWehEwHWgBvFN84W6hHGG9M8THx9s6BCFEPebRqhUH9hhtHYYQtUJqpBDCWkopJk77OxP6eRHa0aP6Bcqw5G6TWYB/BdMXlXg9Ghht9daFEELc0Vq1asWeTBniJIQQQoCp8+bWogWn827ubrAW3bBECCGEuBkLFy7E+MXntg5DCCGEqDPc3NzJycu/qWWl8yaEEKLWKKVo6Ny4+oZCCCHEXcLN3Z3TeVdvalnpvAkhhKg1Bw4c4I8vvWTrMIQQQog6w93dndMXpPNWI37++Wf+8Ic/0K5dOzp16sTDDz/M4cO2vYHmvHnzyM+3/tRqfHw8J06cML8fPXo0Bw7cOc9bOn/+PO+8884treNmchQeHi63KRbCQleuXGHlF1/YOgxRQ6RG1h9SI4Wou9566y22Lhh3U8tacrdJm3kt7bUaXd+MzjOqnK+1ZvDgwTz55JMsX74cAKPRyKlTp3jwwQdrNBZrzJs3jz/+8Y80atSo3LyioiLs7e0rXC4+Ph5vb29atWoFwPvvv1+rcd6KwsJCGjSw7s/xRmEaN+7m/vihfuVIiPqobdu2zJw5k1deecXWodxxpEaaSI2smNRIIeouDw8P8LgPTudZvayceSth06ZNODg4MHbsWPM0Pz8/unfvjtaaSZMm4e3tjY+PD4mJiQCkpKQQHh7OsGHD6NixIyNGjODGg8/T09Pp0qULvr6+hISEcOHCBYqKipg0aRLBwcEYDAb+9a9/Vbme+fPnc+LECSIiIoiIiACgSZMmTJ8+nYceeoi0tDRef/11goOD8fb2ZsyYMWitSUpKIiMjgxEjRuDn58fly5dLHQ379NNP8fHxwdvbm8mTJ5v3t0mTJkydOhVfX19CQ0M5depUuTydPXuWQYMGYTAYCA0NJSsri+vXr+Pp6cn58+fN7X73u99x6tQpcnJyGDp0KMHBwQQHB7Nt2zYA4uLiGDNmDFFRUYwcOZL4+HgGDhxI37596dChA6+99us/Jv/4xz/w9vbG29ubefPmATBlyhSOHj2Kn58fkyZNAuDNN98053bGDNM/ItnZ2fz+97/nT3/6E15eXkRFRXH58uVqc/TMM88QFBSEl5eXeV1CCOs0a9aMl19+2dZhiBogNVJqpNRIIWrGvn37iPtwA+cvWj90UjpvJezbt4/AwMAK561atQqj0ciePXtITk5m0qRJnDxperje7t27mTdvHgcOHOC7775j27ZtXLt2jZiYGP75z3+al3F2duaDDz7A1dWV9PR00tPTWbx4Md9//32l65k4cSKtWrVi06ZNbNq0CYBLly7h7e3Nzp076datG+PHjyc9PZ19+/Zx+fJlvvzyS4YNG0ZQUBAJCQkYjUacnZ3N+3LixAkmT57Mxo0bMRqNpKens2bNGvO6Q0ND2bNnD2FhYSxevLhcLmbMmIG/vz9ZWVnMnDmTkSNHYmdnx8CBA1m9ejUAO3fuxNPTk5YtW/Lcc8/xwgsvkJ6ezsqVKxk9+tenSmRmZrJ27Vo++eQTAHbt2mWOecWKFWRkZJCZmcmHH37Izp072bFjB4sXL2b37t3MmjWLdu3aYTQaefPNN1m/fj3ffvstu3btwmg0kpmZyZYtWwD49ttvefbZZ9m/fz/NmjVj5cqVVeYI4G9/+xsZGRlkZWWxefNmsrKyrP6bEkKIO4XUSKmRJUmNFOLmffPNN7y2eC3HT1+welnpvFkoNTWV4cOHY29vT8uWLenRowfp6ekAhISE0Lp1a+zs7PDz8yM7O5tDhw7h4eFBcHAwAC4uLjRo0ID169fz0Ucf4efnx0MPPcSZM2f49ttvK11PRezt7Rk6dKj5/aZNm3jooYfw8fFh48aN7N+/v8p9SU9PJzw8HHd3dxo0aMCIESPMX+ANGzakf//+AAQGBlYYQ2pqKk888QQAkZGRnDlzhtzcXGJiYsxHW5cvX05MTAwAycnJjB8/Hj8/PwYMGEBeXh4XLpj+WAcMGFCqIPTu3ZsWLVrg7OzMkCFDSE1NJTU1lcGDB9O4cWOaNGnCkCFD2Lp1a7m41q9fz/r16/H39ycgIICDBw+ac9umTRv8/Pyq3K+yPvvsMwICAvD392f//v131LUQQghRk6RGls6F1EghRFXc3NwAOJ132epl6/Q1b7ebl5cXSUlJFc67McyjIo6OjubX9vb2FBYWorVGKVXhehYsWECfPn1KTU9JSalwPRVxcnIyj+G/cuUK48aNIyMjg/vvv5+4uDiuXKn6oX9V7YuDg4M57spiqGh5pRSdO3fmyJEj5OTksGbNGqZNmwbA9evXSUtLK3fUDqBx48bl1lP2fVXxlo3r5Zdf5umnny41PTs7u1xuL1+u+sPy/fffM2fOHNLT02nevDmxsbHV5lUIIe5kUiOlRt4gNVKIW+Pu7g5wUw/qljNvJURGRnL16tVSwyDS09PZvHkzYWFhJCYmUlRURE5ODlu2bCEkJKTSdXXs2JETJ06YjzxeuHCBwsJC+vTpw7vvvktBQQEAhw8f5tKlS1XG1bRpU/NRuLJufFm6ublx8eLFUoW1suUeeughNm/ezOnTpykqKuLTTz+lR48eVcZQUlhYGAkJCYCpoLq5ueHi4oJSisGDB/Piiy/y+9//nhYtWgAQFRXFwoULzcsbjcZK171hwwbOnj3L5cuXWbNmDV27diUsLIw1a9aQn5/PpUuXWL16Nd27dy+3f3369GHJkiVcvHgRgJ9++olffvmlyn2pLEd5eXk0btwYV1dXTp06xbp16yzOjxBC3ImkRlpGaqQQojpy5q2GKKVYvXo1zz//PLNmzcLJyQlPT0/mzZtHWFgYaWlp+Pr6opRi9uzZ3HfffRw8eLDCdTVs2JDExEQmTJjA5cuXcXZ2Jjk5mdGjR5OdnU1AQABaa9zd3c1j6SszZswY+vXrh4eHh3lM/w3NmjXjT3/6Ez4+Pnh6epqHoADExsYyduxYnJ2dSUtLM0/38PDg73//OxEREWitefjhhxk4cKDFeYqLi2PUqFEYDAYaNWrE0qVLzfNiYmIIDg4mPj7ePG3+/Pk8++yzGAwGCgsLCQsLY9GiRRWuu1u3bjzxxBMcOXKExx9/nKCgIPO+3PhHYPTo0fj7+wPQtWtXvL296devH2+++SbffPMNnTt3BkwXln/88ceV3mmsqhz5+vri7++Pl5cXbdu2pWvXrhbnRwgh7kRSIy0jNVIIUZ0bB29ybqLzpiw93V7TgoKCdNnngAQFBcmzQe5i8fHxZGRklDoCeTeSz4G4EymlMrXWQbaOo76QGinKkhppIp8Dcac4m72PZnvew37QAqvqo5x5E0IIIYQQQojb6J6WrcGu/LW/1ZHOm6gzYmNjiY2NtXUYQgghRJ0jNVKIO8u/4j8hP3O31ctJ500IIYQQQgghbqOv1v2HHw8csno5udukEEIIIYQQQtxGbm5u5MijAoQQQgghhBCibnNzcyMnN9/q5aTzJoQQQgghhBC3kZubG1evFVq9nHTeyvjxxx8ZOHAg7du3p127djz33HNcu3YNMN2md/z48Te97oyMDCZOnHjLMcbGxpZ60KilsrOz+eSTT6xezpL9jo+P58SJE1avuy642byUNG/ePPLzfz168vDDD3P+/Pkql/H09OT06dO3tF0hhLidpEaWJzWyelIjhSjP3d0dx4YOVi9Xp29YcnL6jBpdn8frr1U5X2vNkCFDeOaZZ1i7di1FRUWMGTOGqVOn8uabb97y9oOCgswP1LSFG1/Ajz/+eLl5hYWFNGhw838O8fHxeHt706pVq1sJ8ZbdzH5UlRdLzZs3jz/+8Y80atQIgH//+983vS4hhLCE1MiaJTWyYlIjhagdTz75JLHBrtj5DLVqOTnzVsLGjRtxcnJi1KhRANjb2zN37lyWLFliPmJ04sQJ+vbtS/v27fnLX/5iXrZJkyZMnjyZwMBAevXqxa5duwgPD6dt27Z8/vnnAKSkpNC/f38ALl68yKhRo/Dx8cFgMLBy5cpy8WRmZtKjRw8CAwPp06cPJ0+etLjNkSNH6NWrF76+vgQEBHD06FGmTJnC1q1b8fPzY+7cucTHxxMdHc2jjz5KVFQUZ8+eZdCgQRgMBkJDQ8nKyiq1rQsXLtCmTRsKCgoAyMvLw9PTkxUrVpCRkcGIESPw8/Pj8uXLFsV+7NgxevbsicFgoGfPnvzwww/k5ubi6enJ9evXAcjPz+f++++noKCAo0eP0rdvXwIDA+nevTsHDx4ETEdZX3zxRSIiIpg8eTJxcXE88cQTREZG0r59exYvXgyY/vGYNGkS3t7e+Pj4kJiYCFAuL0VFRUyaNIng4GAMBgP/+te/zL+/8PBwhg0bRseOHRkxYgRaa+bPn8+JEyeIiIggIiICKH3EcNCgQQQGBuLl5cV7771X1Z+gEELUWVIjpUZKjRSi5tjZ2aEcXaxfrhZiqbf2799PYGBgqWkuLi488MADHDlyBACj0UhiYiJ79+4lMTGR48ePA3Dp0iXCw8PJzMykadOmTJs2jQ0bNrB69WqmT59ebltvvPEGrq6u7N27l6ysLCIjI0vNLygoYMKECSQlJZGZmclTTz3F1KlTLW4zYsQInn32Wfbs2cP27dvx8PBg1qxZdO/eHaPRyAsvvABAWloaS5cuZePGjcyYMQN/f3+ysrKYOXMmI0eOLLW9pk2bEh4ezldffQXA8uXLGTp0KNHR0QQFBZGQkIDRaKRBgwbVxg4wfvx4Ro4cSVZWFiNGjGDixIm4urri6+vL5s2bAfjiiy/o06cPDg4OjBkzhgULFpCZmcmcOXMYN26ceV2HDx8mOTmZt956C4CsrCy++uor0tLSeP311zlx4gSrVq3CaDSyZ88ekpOTmTRpEidPniyXlw8++ABXV1fS09NJT09n8eLFfP/99wDs3r2befPmceDAAb777ju2bdvGxIkTadWqFZs2bWLTpk3l9nPJkiVkZmaSkZHB/PnzOXPmTLk2QghR10mNlBopNVKImnPmzBliX55r9XJ1etjk7aa1RqnyTzovOb1nz564uroC0KlTJ44dO8b9999Pw4YN6du3LwA+Pj44Ojri4OCAj48P2dnZ5daZnJzM8uXLze+bN29eav6hQ4fYt28fvXv3BqCoqAgPDw+L2ly4cIGffvqJwYMHA+Dk5FTpPvfu3Zt77rkHgNTUVPPRzcjISM6cOUNubm6p9qNHj2b27NkMGjSIDz/80HzEztrYwVQUV61aBcATTzxhPkobExNDYmIiERERLF++nHHjxnHx4kW2b99OdHS0efmrV6+aX0dHR2Nvb29+P3DgQJydnXF2diYiIoJdu3aRmprK8OHDsbe3p2XLlvTo0YP09HRcXEof9Vi/fj1ZWVnmayZyc3P59ttvadiwISEhIbRu3RoAPz8/srOz6datW6X5BZg/fz6rV68G4Pjx43z77be0aNGiymWEEKKukRopNRKkRgpRU+zs7Fi60vohxNJ5K8HLy6vc0Iy8vDyOHz9Ou3btyMzMxNHR0TzP3t6ewkLTXWIcHBzMxcvOzs7czs7OztympMqKYMn5Xl5epKWlWd0mLy+vmj39VePGjUutr6yyMXbt2pXs7Gw2b95MUVER3t7eNxV7RW5sa8CAAbz88sucPXuWzMxMIiMjuXTpEs2aNcNoNFa7HxXFrZSqcP8qorVmwYIF9OnTp9T0lJSUSn//lUlJSSE5OZm0tDQaNWpEeHg4V65Y/0wPIYSwNamRUiNvxC81Uohb5+rqir29PUVFRVYtJ8MmS+jZsyf5+fl89NFHgOlo2J///GdiY2PNF9nWlKioKBYuXGh+f+7cuVLzO3ToQE5OjvnLvaCggP3791vUxsXFhdatW7NmzRrAdPQtPz+fpk2bcuHChUpjCgsLIyEhATB9obq5uZU74gYwcuRIhg8fbr7uASi1bktiB+jSpYv5yGpCQoL56FyTJk0ICQnhueeeo3///tjb2+Pi4kKbNm1YsWIFYCoee/bsqXRf1q5dy5UrVzhz5gwpKSkEBwcTFhZGYmIiRUVF5OTksGXLFkJCQsrlpU+fPrz77rvm6xYOHz7MpUuXKt1W2f0vKTc3l+bNm9OoUSMOHjzIjh07qlyPEELUVVIjpUaC1EghaoqdnR1uLe6xfrlaiKXeUkqxevVqVqxYQfv27XnwwQdxcnJi5syZNb6tadOmce7cOby9vfH19S03Drxhw4YkJSUxefJkfH198fPzY/v27Ra3WbZsGfPnz8dgMNClSxd+/vlnDAYDDRo0wNfXl7lzy4+xjYuLIyMjA4PBwJQpU1i6dGmFsY8YMYJz584xfPhw87TY2FjGjh2Ln58fRUVF1cYOpqESH374IQaDgWXLlvHPf/7TPC8mJoaPP/6YmJgY87SEhAQ++OADfH198fLyYu3atZXmNyQkhEceeYTQ0FBeffVVWrVqxeDBgzEYDPj6+hIZGcns2bO57777yuVl9OjRdOrUiYCAALy9vXn66aerPXo4ZswY+vXrZ74Y+4a+fftSWFiIwWDg1VdfJTQ0tMr1CCFEXSU1Umqk1Eghapabm5vVyyhLT5PXtKCgIJ2RkVF2GmWnibonKSmJtWvXsmzZMluHUqG4uDiaNGnCSy+9ZOtQbop8DsSdSCmVqbW23X3g6xmpkfWX1MjaJZ8DcSd59NFH+fLLL62qj9Ve86aUcgK2AI7F7ZO01jPKtOkIfAgEAFO11nOsilzUGxMmTGDdunXyjBYhhBCiDKmRQghrfPHFF1Ve31sRS25YchWI1FpfVEo5AKlKqXVa65IDk88CE4FBVm1d1DsLFiywdQjViouLs3UIQggh7kJSI4UQta3aa960ycXitw7FP7pMm1+01ulAQc2HKIQQQgghhBDCohuWKKXslVJG4Bdgg9Z6Z61GJYQQQgghhBCiFIs6b1rrIq21H9AaCFFKlX9wiQWUUmOUUhlKqYycnJybWYUQQghxR5IaKYQQojpWPSpAa30eSAH63szGtNbvaa2DtNZB7u7uN7MKIYQQ4o4kNVIIIUR1qu28KaXclVLNil87A72Ag7Ucl838+OOPDBw4kPbt29OuXTuee+45rl27BkB8fDzjx4+/6XVnZGQwceLEW44xNjaWpKQkq5fLzs7mk08+ueXte3p6cvr06Vtez+2WkpJS4bN0LHX+/Hneeecd8/sTJ04wbNiwKpfJzs7G2/umTlQLIUSdIzWyelIjTaRGClE7LLnbpAewVCllj6mz95nW+kul1FgArfUipdR9QAbgAlxXSj0PdNJa591KcJsSaraPGDGiY5XztdYMGTKEZ555hrVr11JUVMSYMWOYOnUqb7755i1vPygoiKAg2z3m6EZhevzxx8vNKywspEEDS/4c6oaioiLs7e2tWiYlJYUmTZrQpUuXm9rmjcI0btw4AFq1anVT/yAIIURNkBpZs6RGSo0Uoj6w5G6TWVprf621QWvtrbV+vXj6Iq31ouLXP2utW2utXbTWzYpf31LHzRY2btyIk5MTo0aNAsDe3p65c+eyZMkS8vPzAdORpL59+9K+fXv+8pe/mJdt0qQJkydPJjAwkF69erFr1y7Cw8Np27Ytn3/+OWD6Yuzfvz8AFy9eZNSoUfj4+GAwGFi5cmW5eDIzM+nRoweBgYH06dOHkydPWtzmyJEj9OrVC19fXwICAjh69ChTpkxh69at+Pn5MXfuXOLj44mOjubRRx8lKiqKs2fPMmjQIAwGA6GhoWRlZQFw5swZoqKi8Pf35+mnn6bkg90//vhjQkJC8PPz4+mnn6aoqKhcjP/73//w9/fHx8eHp556iqtXr7Ju3Toee+wxc5uUlBQeffRRANavX0/nzp0JCAggOjqaixdNNzv19PTk9ddfp1u3bqxYsYLw8HCef/55unTpgre3N7t27QKocD+ys7NZtGgRc+fOxc/Pj61bt5KTk8PQoUMJDg4mODiYbdu2AabbKD/11FPm39/8+fMBmDJlCkePHsXPz49JkyaVOmKYnZ1N9+7dCQgIICAg4JaOXgohRF0kNVJqJEiNFMLWrLrm7U63f/9+AgMDS01zcXHhgQce4MiRIwAYjUYSExPZu3cviYmJHD9+HIBLly4RHh5OZmYmTZs2Zdq0aWzYsIHVq1czffr0ctt64403cHV1Ze/evWRlZREZGVlqfkFBARMmTCApKYnMzEyeeuoppk6danGbESNG8Oyzz7Jnzx62b9+Oh4cHs2bNonv37hiNRl544QUA0tLSWLp0KRs3bmTGjBn4+/uTlZXFzJkzGTlyJACvvfYa3bp1Y/fu3QwYMIAffvgBgG+++YbExES2bduG0WjE3t6ehISEUjFeuXKF2NhYc84KCwt599136d27Nzt27ODSpUsAJCYmEhMTw+nTp/nrX/9KcnIyX3/9NUFBQfzjH/8wr8/JyYnU1FT+8Ic/mPO+fft23nnnHZ566imACvfD09OTsWPH8sILL2A0GunevTvPPfccL7zwAunp6axcuZLRo0ebt3Pw4EH++9//smvXLl577TUKCgqYNWsW7dq1w2g0ljvKfO+997Jhwwa+/vprEhMTa2TojxBC1CVSI6VG3iA1UgjbqT9jAG4DrXWFTzkvOb1nz564uroC0KlTJ44dO8b9999Pw4YN6dvXdB8XHx8fHB0dcXBwwMfHh+zs7HLrTE5OZvny5eb3zZs3LzX/0KFD7Nu3j969ewOmIRAeHh4Wtblw4QI//fQTgwcPBkxf5pXp3bs399xzDwCpqanmo5uRkZGcOXOG3NxctmzZwqpVqwB45JFHzLH+73//IzMzk+DgYAAuX77MvffeWy7GNm3a8OCDDwLw5JNP8vbbb/P888/Tt29fvvjiC4YNG8ZXX33F7Nmz2bx5MwcOHKBr164AXLt2jc6dO5vXFxMTU2r9w4cPByAsLIy8vDzOnz9f6X6UlZyczIEDB8zv8/LyuHDhgnk/HR0dcXR05N577+XUqVOV5hBM/ySMHz/eXKAPHz5cZXshhKhvpEZKjZQaKYTtSeetBC8vr3JDM/Ly8jh+/Djt2rUjMzMTR0dH8zx7e3sKCwsBcHBwMBcvOzs7czs7Oztzm5IqK4Il53t5eZGWlmZ1m7w8y0esNm7cuNT6yroRY2UF+8knn+Tvf/97lTFWJiYmhrfffpt77rmH4OBgmjZtitaa3r178+mnn1Ybb0VxKaWq3I+Srl+/TlpaGs7OzuXmVfZ7rszcuXNp2bIle/bs4fr161X+MyCEEPWR1EipkTdIjRTCdmTYZAk9e/YkPz+fjz76CDAdpfvzn/9MbGwsjRo1qtFtRUVFsXDhQvP7c+fOlZrfoUMHcnJyzEWnoKCA/fv3W9TGxcWF1q1bs2bNGgCuXr1Kfn4+TZs2NR81q0hYWJh5SEdKSgpubm64uLiUmr5u3TpzrD179iQpKYlffvkFMI2jP3bsWKl1duzYkezsbPOQmmXLltGjRw8AwsPD+frrr1m8eLH5aGFoaCjbtm0zt8/Pz6/yCF1iYiJgOiLq6uqKq6trpftRdv/L/g6MRmOl2wGqzF9ubi4eHh7Y2dmxbNmyCq9rEEKI+kxqpNTIqkiNFOL2kM5bCUopVq9ezYoVK2jfvj0PPvggTk5OzJw5s8a3NW3aNM6dO4e3tze+vr5s2rSp1PyGDRuSlJTE5MmT8fX1xc/Pr9wFvlW1WbZsGfPnz8dgMNClSxd+/vlnDAYDDRo0wNfXl7lz55aLKS4ujoyMDAwGA1OmTGHp0qWAaXz8li1bCAgIYP369TzwwAOAaUjMX//6V6KiojAYDPTu3bvcBeNOTk58+OGHREdH4+Pjg52dHWPHjgVMR+v69+/PunXrzBepu7u7Ex8fz/Dhw80XUx88WPkd1Zo3b06XLl0YO3YsH3zwQZX78eijj7J69Wrzxdjz5883t+vUqROLFi2q8nfWokULunbtire3N5MmTSo1b9y4cSxdupTQ0FAOHz5c7uinEELUd1IjpUZWRWqkELeHquqUfW0KCgrSGRkZZadRdpoQlQkPD2fOnDk2vbV0bZDPgbgTKaUytdZ31oe1FkmNFLdKaqQQ9YO19VHOvAkhhBBCCCFEPSA3LBH1VkpKiq1DEEIIIeokqZFC3JnkzJsQQgghhBBC1AN1qvNmZ2dHQUGBrcMQwmYKCgqws6tTH0shRB0hNVLc7aRGClHHOm8dO3Zk2bJlUpzEXamgoIBly5bRsWNHW4cihKiDpEaKu5nUSCFM6tQ1b3PmzOGll15i0aJFXL9+3dbhCHFb2dnZ0bFjR+bMmWPrUIQQdZDUSHE3kxophEmd6rzde++95od/CiGEEOJXUiOFEELUqWGTQgghhBBCCCEqJp03IYQQQgghhKgHpPMmhBBCCCGEEPWA0lrbZsNKXQAO2WTj9ZMbcNrWQdQjki/rSL6sJzmzTgetdVNbB1FfSI20inwWrSP5so7ky3KSK+vcyNdvtdbuli5kyxuWHNJaB9lw+/WKUipD8mU5yZd1JF/Wk5xZRymVYesY6hmpkRaSz6J1JF/WkXxZTnJlnZvNlwybFEIIIYQQQoh6QDpvQgghhBBCCFEP2LLz9p4Nt10fSb6sI/myjuTLepIz60i+rCP5spzkyjqSL+tIviwnubLOTeXLZjcsEUIIIYQQQghhORk2KYQQQgghhBD1QK133pRSfZVSh5RSR5RSUyqYr5RS84vnZymlAmo7prrMgnyNKM5TllJqu1LK1xZx1hXV5atEu2ClVJFSatjtjK+usSRfSqlwpZRRKbVfKbX5dsdYl1jweXRVSn2hlNpTnK9RtoizrlBKLVFK/aKU2lfJfPm+L0Hqo3WkPlpH6qPlpDZaR2qj5WqlLmqta+0HsAeOAm2BhsAeoFOZNg8D6wAFhAI7azOmuvxjYb66AM2LX/eTfFWdrxLtNgL/BobZOu66nC+gGXAAeKD4/b22jruO5+sV4P+KX7sDZ4GGto7dhjkLAwKAfZXMl+/7X3Mh9bHm8yX10Yp8lWh3V9dHqY21ki+pjb/mosbrYm2feQsBjmitv9NaXwOWAwPLtBkIfKRNdgDNlFIetRxXXVVtvrTW27XW54rf7gBa3+YY6xJL/r4AJgArgV9uZ3B1kCX5ehxYpbX+AUBrfTfnzJJ8aaCpUkoBTTAVqMLbG2bdobXegikHlZHv+19JfbSO1EfrSH20nNRG60httEJt1MXa7rz9Bjhe4v2PxdOsbXO3sDYX/w9Tb/1uVW2+lFK/AQYDi25jXHWVJX9fDwLNlVIpSqlMpdTI2xZd3WNJvhYCvwdOAHuB57TW129PePWSfN//SuqjdaQ+Wkfqo+WkNlpHamPNsvp7vkGthmM6BVhW2dtbWtLmbmFxLpRSEZiKU7dajahusyRf84DJWusi0wGgu5ol+WoABAI9AWcgTSm1Q2t9uLaDq4MsyVcfwAhEAu2ADUqprVrrvFqOrb6S7/tfSX20jtRH60h9tJzURutIbaxZVn/P13bn7Ufg/hLvW2PqhVvb5m5hUS6UUgbgfaCf1vrMbYqtLrIkX0HA8uLC5AY8rJQq1FqvuS0R1i2Wfh5Pa60vAZeUUlsAX+BuLFCW5GsUMEubBq4fUUp9D3QEdt2eEOsd+b7/ldRH60h9tI7UR8tJbbSO1MaaZfX3fG0Pm0wH2iul2iilGgJ/AD4v0+ZzYGTx3VZCgVyt9clajquuqjZfSqkHgFXAE3fpEZ+Sqs2X1rqN1tpTa+0JJAHj7sLCdIMln8e1QHelVAOlVCPgIeCb2xxnXWFJvn7AdCQWpVRLoAPw3W2Nsn6R7/tfSX20jtRH60h9tJzURutIbaxZVn/P1+qZN611oVJqPPBfTHenWaK13q+UGls8fxGmOxw9DBwB8jH11u9KFuZrOtACeKf4aFmh1jrIVjHbkoX5EsUsyZfW+hul1H+ALOA68L7WusLb297pLPz7egOIV0rtxTT0YbLW+rTNgrYxpdSnQDjgppT6EZgBOIB835cl9dE6Uh+tI/XRclIbrSO10Tq1UReV6YymEEIIIYQQQoi6rNYf0i2EEEIIIYQQ4tZJ500IIYQQQggh6gHpvAkhhBBCCCFEPSCdNyGEEEIIIYSoB6TzJoQQQgghhBD1gHTehBBCCCGEEKIekM6bEJVQSrVQShmLf35WSv1U/PqiUuqdWthevFLq+xvPSil+P6yCdu1uxFHTMQghhBCWkBophG3U6kO6hajPtNZnAD8ApVQccFFrPaeWNztJa51UTVxHAT8pTEIIIWxFaqQQtiFn3oSwklIqXCn1ZfHrOKXUUqXUeqVUtlJqiFJqtlJqr1LqP0oph+J2gUqpzUqpTKXUf5VSHhZuLkwptV0p9V1FRxiFEEKIukRqpBC1SzpvQty6dsAjwEDgY2CT1toHuAw8UlycFgDDtNaBwBLgbxau2wPoBvQHZtV04EIIIUQtkxopRA2SYZNC3Lp1WusCpdRewB74T/H0vYAn0AHwBjYopShuc9LCda/RWl8HDiilWtZo1EIIIUTtkxopRA2SzpsQt+4qgNb6ulKqQGuti6dfx/QZU8B+rXXnm113MXVrYQohhBC3ndRIIWqQDJsUovYdAtyVUp0BlFIOSikvG8ckhBBC1AVSI4WwgnTehKhlWutrwDDg/5RSewAj0MWmQQkhhBB1gNRIIayjfj17LYSwJaVUPPBldbdBLtH+ota6Se1GJYQQQtie1EghTOTMmxB1Ry7wxo0HkFbmxgNIgVO3JSohhBDC9qRGCoGceRNCCCGEEEKIekHOvAkhhBBCCCFEPSCdNyGEEEIIIYSoB6TzJoQQQgghhBD1gHTehBBCCCGEEKIekM6bEEIIIYQQQtQD/x9UrD+B+krjeQAAAABJRU5ErkJggg==", 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\n", 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" ] @@ -1005,7 +1072,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.12 ('conda_jl')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1019,12 +1086,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "vscode": { - "interpreter": { - "hash": "612adcc456652826e82b485a1edaef831aa6d5abc680d008e93d513dd8724f14" - } + "version": "3.9.0" } }, "nbformat": 4, From f26a9a6ecb2103eb8c6e03616eaf015b90500c89 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Sun, 24 Jul 2022 19:43:00 -0400 Subject: [PATCH 13/17] new version --- .github/workflows/url_checker.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index d4c4f0201b..4fd8dce73b 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v3 - name: URLs-checker - uses: urlstechie/urlchecker-action@0.0.25 + uses: urlstechie/urlchecker-python@a9bf156 with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb @@ -21,11 +21,11 @@ jobs: # Choose whether to include file with no URLs in the prints. print_all: false - # Timeout in 60 seconds if url is not reached - timeout: 60 + # Timeout in 10 seconds if url is not reached + timeout: 10 # How many times to retry a failed request (each is logged, defaults to 1) - retry_count: 10 + retry_count: 5 # A comma separated patterns to exclude during URL checks exclude_patterns: https://www.datacamp.com/community/tutorials/fuzzy-string-python,http://127.0.0.1,https://github.com/pybamm-team/PyBaMM/tree/v,https::/doi.org,https::/www.sciencedirect.com From b34b22ae6744b2ead587ea46dce05c77aff01264 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Sun, 24 Jul 2022 19:44:07 -0400 Subject: [PATCH 14/17] full commit SHA --- .github/workflows/url_checker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 4fd8dce73b..7804cec968 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v3 - name: URLs-checker - uses: urlstechie/urlchecker-python@a9bf156 + uses: urlstechie/urlchecker-python@a9bf1564962176725805a024573c1377a3edf664 with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb From f92b11bdba304c9667902e654af1332b8edad934 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Sun, 24 Jul 2022 19:48:56 -0400 Subject: [PATCH 15/17] @test-release-31-drivers --- .github/workflows/url_checker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 7804cec968..44cba2177f 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v3 - name: URLs-checker - uses: urlstechie/urlchecker-python@a9bf1564962176725805a024573c1377a3edf664 + uses: urlstechie/urlchecker-python@test-release-31-drivers with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb From f86bbf46a2c62abaf224ead7fd58c55a403ee073 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Sun, 24 Jul 2022 19:50:20 -0400 Subject: [PATCH 16/17] use action instead of python --- .github/workflows/url_checker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 44cba2177f..1fab18f567 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v3 - name: URLs-checker - uses: urlstechie/urlchecker-python@test-release-31-drivers + uses: urlstechie/urlchecker-action@test-release-31-drivers with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb From 6f25e3bcc08fe2dd76dea0f3ce5851975830bd54 Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Sun, 24 Jul 2022 19:58:29 -0400 Subject: [PATCH 17/17] use 0.0.31 --- .github/workflows/url_checker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index 1fab18f567..f10ecd64fd 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v3 - name: URLs-checker - uses: urlstechie/urlchecker-action@test-release-31-drivers + uses: urlstechie/urlchecker-action@0.0.31 with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb