diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index 55286ec..289144d 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests with a single version of Python
 # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
 
-name: Python application
+name: Build and test
 
 on:
   push:
@@ -26,8 +26,14 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install pytest
+        pip install pytest flake8
         pip install -e .
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
         pytest
diff --git a/README.md b/README.md
index 24fa27e..76a7245 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,20 @@
-# Quantum electron solver
+![example workflow](https://github.com/gkoolstra/quantum_electron/actions/workflows/python-app.yml/badge.svg)
+# Quantum Electron Solver
+![image info](./images/electron_results.png)
+## Main use cases
 This package has two main functions
-1. It can simulate electron positions in a two dimensional plane for electrons confined in an electrostatic potential. The electron-electron interactions are also taken into account.
-2. It can solve the Schrodinger equation for a single electron confined in an electrostatic potential.
+1. It simulates electron positions in a two dimensional plane for electrons confined in an electrostatic potential $\phi$. Electron-electron interactions are also taken into account. Physically, it minimizes the total energy of an $N$-electron system, which is given by: 
 
-In both cases there are methods to calculate couplings to a resonator and resonator frequency shifts due to electrons. This is useful the electrons are detected with a microwave resonator. If your experimental setup does not contain a resonator, you can safely ignore the methods in this library without compromise of the results. 
+$$ -e\sum_i \phi(\mathbf{r}_i) + \sum_{i<j} \frac{e^2}{4\pi \epsilon_0} \frac{1}{|\mathbf{r}_i - \mathbf{r}_j|}$$ 
 
-After installation, it is advised to take a look at the `examples` folder to explore some of the functionalities of this module. 
+2. It calculates properties of in-plane electron modes. This is useful in two cases: (a) the electron motional states can be used for quantum computation, and this package can help to determine eigenfrequencies and eigenvectors of electron clusters (b) in the large $N$ limit the electron motional modes are also known as plasmons. There is an abundance of literature about these charge density waves, and many of the properties can be reproduced with this module.
 
-![image info](./images/electron_results.png)
+### Features
+- Robust operation through the use of the `scipy.optimize.minimize` library. We assure proper and fast convergence because the force (gradient of the energy) is supplied as an argument of the minimizer.
+- Supply arbitrary potential energies $\phi$, as long as they're on a rectangular grid. 
+- Handles problems up to $N \approx 400$ electrons in less than 1 minute on a laptop computer. 
+- Periodic boundary condition support for systems with open boundaries.
+- Seemless integration with finite element modeling software ZeroHeliumKit.
 
 ## Installation
 
@@ -21,21 +28,14 @@ In a terminal window, change into the cloned directory:
 cd quantum_electron
 pip install -e .
 ```
+After installation, it is advised to take a look at the `examples` folder to explore some of the functionalities of this module. 
 
 ### Additional packages
 To generate animations, this module relies on `ffmpeg`. On MacOS this can be easily installed using [homebrew](https://formulae.brew.sh/formula/ffmpeg) from the Terminal. On Windows it can be installed using the following [link](https://www.ffmpeg.org/download.html). 
 
 This module also integrates well with the output of the FEM software [ZeroHeliumKit](https://github.com/eeroqlab/zeroheliumkit). Please refer to any dependencies for ZHK on the linked github page.
 
-## To-do list
-- [ ] Standardize units of the arguments. Sometimes it is unclear whether to use microns or meters.
-- [ ] Figure out how to handle warning messages for convergence issues. Why do problems sometimes have a hard time converging?
-- [ ] Write documentation for example notebook 03
-- [ ] Find a place for the code in example notebook 04.
-- [ ] Split off the Schrodinger solver?
-- [ ] Add some documentation to the library folder
-
-## Test suite
+## Tests
 To test the performance of the minimization, we're building and expanding a suite of tests based on the `pytest` framework. To run these tests, `cd` into the main module directory and run `pytest`. Currently, we have implemented a test in `test_wigner_molecules.py`, which compares the energy per particle of Wigner molecules in a parabolic confinement to known tabulated values.
 
 ## Getting started
@@ -68,10 +68,22 @@ options = {"include_screening" : True, # Include screening of electron-electron
            "max_y_displacement" : 0.1e-6} # Maximum y-displacement of solved electron positions during annealing.
 ```
 
-
 ## Tips for the initial condition
-The initial condition can affect the final minimization result quite strongly. If there are issues with convergence you can first check convergence with `f.plot_convergence()`. A good final value for the cost function is ~1-500 eV/m. If the lowest value of the cost function is signifantly higher than this, or if warnings appear, here are some rules of thumb for successful convergence:
+The initial condition can affect the final minimization result quite strongly. We encourage you to take a look at the example notebook about initial conditions. If there are issues with convergence you can first check convergence with `f.plot_convergence()`. A good final value for the cost function is ~1-500 eV/m. If the lowest value of the cost function is signifantly higher than this, or if warnings appear, here are some rules of thumb for successful convergence:
 1. Don't create an initial condition where too many electrons are placed in a small area.
 2. Don't place electrons in an initial condition where the potential is too flat, such as on a ground plane. 
 3. Be mindful of electron sinks, i.e. channels for electrons to escape. These can appear if an electrode is adjacent to the ground plane, and has an applied voltage that is more positive than the ground plane.
 
+## Contributing
+Contributions to this growing repository are welcome. Please feel free to create a fork and create a pull request with your suggested changes.
+
+## Credit
+If you found this module useful in your research, please consider citing this code in your publication using a hyperlink.
+
+## To-do list
+- [ ] Standardize units of the arguments. Sometimes it is unclear whether to use microns or meters.
+- [ ] Figure out how to handle warning messages for convergence issues. Why do problems sometimes have a hard time converging?
+- [ ] Write documentation for example notebook 03
+- [ ] Find a place for the code in example notebook 04.
+- [ ] Split off the Schrodinger solver?
+- [ ] Performance metrics
\ No newline at end of file
diff --git a/examples/00_harmonic_well.ipynb b/examples/00_harmonic_well.ipynb
index 8fc7bbf..68ae0dd 100644
--- a/examples/00_harmonic_well.ipynb
+++ b/examples/00_harmonic_well.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
     "\n",
     "parabolic_confinement = - (X ** 2 + Y ** 2) / micron ** 2\n",
     "\n",
-    "R = 1.5e-6\n",
+    "R = 2e-6\n",
     "# outside the circle there is a guard ring where the dot \n",
     "parabolic_confinement[np.sqrt(X**2 + Y**2) > R] = -(R / micron) ** 2\n",
     "# parabolic_confinement -= -(R / micron) ** 2\n",
@@ -84,12 +84,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x400 with 2 Axes>"
       ]
@@ -130,12 +130,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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9ihyFIHnsc0UuoO1t1/c4N4JwhIkoBH1vc+SVj48dbb+HkTc/vafZT9wnvJYacouCR7I7IhVUr3y99TdQVrxIkkiHgN6vBibqNMzaYcrXomuRpEOm/iQdUtvliUKwRUGlrgk6BHRfiyQdAtJaJOkQIGuRLZrTpUOAsvon6BCQ1iJZhwBdi2QdAlKakonskQrUoWxINlFZuexskZqSTaTaMC6baNYQRS7ZRFkiNPNEIdiiDwZdh/THQDGbSNchQLaJ9Dbhvcw2kRrx6bKJuq5DgGgT1ds1qEUWdB0Cyt290q25GeC2iczRnEjW5dwsXeaYmwFum6gf5mbqvYZ1bsZIKUIIIYQQQgghhBDScxgpVZBmUEEzqMb7lru1Gmisk+H0hiz5WqSTHmYNXv48q4C2UyBSdTVPu8nD3qu8CSZ8X1oJMOwH1jz3kcfeGLGged+bhlVA10phU3mvatq99P3UtjHoVEynVVkpL1LBM3jem8yfIBLpEICUFvVqNTB8bD7RKku+FkmHwrpmLTGtHKZOyLKc0Kc/N6346VrUy1xSOpIOAWktknQo7EeIqjJELuSKWGi9Vy4dso3BRFuLeqRDgKhF1CE7kk3UrVx2pkhN22nDLpvIdjKxpEPh6zbbRLaIBWuEpsMmGlQdAmSbKBFVJdhEmSIWBB3S25nGIJUBvdQhpR1tokLoOgR0P5ddGXMztcw1NzNd49wsZJjnZsAc2EQ91iFGShFCCCGEEEIIIYSQnkN3e0EardMd2vkTlBW5yKudYzUw7blX9xm36mpe+GjfsWmFL7ViaDnpQT+1wVS3yCpgligE24qhVGbLqdAtbPkS2mX6ykK039i9YmjyortWCv3EikCUSyEcQ0XfH21YPahoHvs4x0LF5KuW/dfRx9MM8nvlfS/QnqcliSfNyDSMp121dEHQIfWx/tccVRXpQeu5pkNAWoskHQLS+mI7UU+va8uNIK0CZolCyKQ7fahDgCnqwKxDgHvFsNOIBZcOJeuG6DoU3lPSot7oUFiW1B3qkB3JJlJ1J08uO8km0nUofGy2iUyR4ZJNZDtRT9Ihtb8s0QhZohCcumPSqh5rUREdAmSbyBTN2UnEgq5DQO9sorQOyXVN0CbqDF2HALdNZLONejU3C/uz20Smupyb7TxzM2DubKJe6RAjpQghhBBCCCGEEEJIz6FTihBCCCGEEEIIIYT0HMaAFqQZVNEMqnFoZ9kh6mqdOHRTCA3NcqSoKZxdT96p18mbKE8PCdXDQIuGf+p1ghxJ9JqWo0l9y/HG6X4yhIiKYaTNVBs9fNSUeM8Vvq4e364fIdqMX1r63vEVPdzeEEaaJnlkKQBU436im2YPFU1vm2l/XiOV1jaQYDTDuHZOIh0CkNKiskLU6/Hvt/Vc0yG1TDpm3XbAgqRDalmeJOaSDpnq6OWJsoa9Tj/oUNhe+x1bw9mTeiDpkFonzzYaWYfS945LTVrq1CLDMe6l6lC7PXUoG5JNlOeAhRl1S7BgE+k6BMg2kfr/lMsmMiURlmyiPNuGVR0pwyYytSlDi7qtQ8k6Zh0CZJsozzaa9JHqvbOJ0jrUvmcnNlGkQwC1yIauQ0C5KVW6NTdTy1xzM6CzbcOcmyV1SC3j3KxVY47mZoyUIoQQQgghhBBCCCE9h5FSBak3g/gfUMZx60kvfL2uRjSZPeuRF952pGieJMK6Bz9vojzJ+25LqpfF06571PMcN2pLvOdXsveje7MDgyff5bE3RyFEKxfpxHuulULT8e3xc0vCzzh5Z1VLNmo5hjQiWp2pqcn2YhVJeuF9z+2VN0coJK/x+GMZVYOKrAamj1tPrwZGWmQ7Ql3XIkmHAHcSYTUCy7UKaEpiblsFlLQoS/RBP+oQkNaiPCuHkg4BnUUsxPexJPyUdEgas4oaxRtrkVOH2mWp+1GHOkayiYoft262iXSNAWSbyFYnSxJhySbKE6GZpa6KyyYyRRiUoUXd1iFTO3MUgtkmKhKxkLiXYBMlkgiXYROldEi5Zwc2kVpOLZLRdQjIt3vFZRN1a24W1kHimi3ivIwITc7N1HvabSLOzdLXuqFDjJQihBBCCCGEEEIIIT2H7vaCNFqe+E5XA+MjQIXVQMDthc9ypGjUr/WI5IbZm54oK7AKGD0v6mnPEtUgYaubbZ9uq25qhS/dr8tjb/KeS955wL1SaDq+PaKij1d9Hv3q68n3Psv7UW35sWcNq4BRv5EXfTauIkcqZPHG13N83jsbkQ4Bna0GSjoEZIt+0rVI0iG1b/GIZIP2FckXZVoFlLQoS/RBP+oQkNaiPCuHkg6Fj4tHLEToOpRoJ+gQ4H5PqoqOzOp5EkQdAvQVw/h+1KGOkWwiu21k1qFEnQKRmqacUi6bSO3PZRPlidA01e3EJsoS1WBDqtttHQJkmyihJSVGLKiINpE6CyrFJtIjpmw2kSFKNEOkFLVIRtchoNjulV7PzUz9STqkPubcrPO5WTgeu03EuVn6Wjd0iJFShBBCCCGEEEIIIaTnMFKqIPVmE/VmM+VNj66pZek9ymkPfuSUzpMvIXpe9PSGWW01Ma4blefYk6zW173vtiiELJ72Tk54sNHI49XPcBqC5LGPy5W3IfLUS975RJkjYgFo70GuI/qctLEkvPXZT4GQMEUq+H4lHIPFw+7a26wOIfrdBEH53vhhIdIhwLCyJ+gQIEdV6ToUltlXA9Uy18l6xjqCDqn9FskXZVoFlFb/skQf9KMOAWktsq0c6lok6VDYLn/EgkuHAFWLzDpkeg029BXCznRILQufUYeyIdlEppM8JZvIdLJVkUjNIqcNzxqiGiSbKE+EpjoGySYy2VGma6bnar+d0G0dAmSbSI1cKCNiQdchtd94LJoOhf3Ej8TXICHpEGDTonTElGQTqb8japGMrkNhmd0mskVV9WpulqjjmJsB5URocm6mlDtsIs7N2jW7aRMxUooQQgghhBBCCCGE9Bw6pQghhBBCCCGEEEJIz+H2vYLMBmEiPVuIenycpyVEvd4wh5JnSeJpC1HPcqSolMSzMdtI1S0Smp5la0y2Y0fNIYK2I0XLIg7D1MdpCKeUwkizhLHrIaPqtSzHt6fa2JLpaeOLfNO11rM60iG9NqJw0fZ31u3rbifKi0paYaZeui2TespEOgTIIeq6DiXrRnpj1iG1zJbEU9q2Z0o4rGuR7YAFXYvyJDHXdUivL90zIp0MXf4edluLJB0C0lpkC2d3hbF3uo1Gv49RdxxHJAPFtKgcHQIkLaIO2ZFsosSWPIdNVG+kdaeM7cNqHckmsh2wIOlQoizDFpksW2NcNtGg6hAg20SmLcadbKPRrxvv2SWbKK1D7f4kkknMo0e0iYqg6xDgtomMhzH0eG5muoekQ6ZrnJuFDPPcDJgLm6i3OsRIKUIIIYQQQgghhBDScxgpVZB6yxOf7dhj92rgrOZhz5LEs514TxmXVifLkaL11vmQesI8U1K9PKuAtgSd0uqf2XNv9wznOYY0C6rnWrq1bzhSVPeaR+990+Ahj8bc9rgHqTqulcIogR4AIFrJbbWp1pLJ9bIckRwntCsteZ3s865kOPY4YpZJPUVMGuT6myjTVgN1HQKyJfHUtUjSIbVMX5HTdShxTUs0bEti7tIhqb1eNx3N4F6h6pYW2W6ta5GkQ0BaiyQdUsuyRCzEWiToECBrUVqHgHK1KL8OAWktog7ZkWyiTLaRpkOAbBPZIjUlHVLrSDZRIjrUYRPlidA01ZXamq4Nug6p7SWbSB2vZBNliViQdAjob5sojw4B1CIbug5FZVn+Am6bqFtzs7C+3SayHQLTj3Mz0706oVtzM/VaP8/NwvpzYxP1SocYKUUIIYQQQgghhBBCeg4jpQrSDMJ/7T3K7WtR2Wx8rfhqoFpWxmqgKfqpURfytiie8Uacf8G9CijtRc4ThZDl+FETnXjlTat2cp1G4nlYFtUxrxiqe5093StvPJLdvFIY+ZLrhiPU1Twv6r1Vz309dQxx8r1X687mzC9lRj3uuNWv9h77XnhBXbEylZEkkQ4BaS2SdAgodzUQcEdImVYD9ZU+XYfUx+06Zh1S62TJE5UnCiHLkeyuNnlxaVFSdxpam6iOvGIYaZGkQ2p/Lh0ClFU/QYfUe6ePazfrkFq3HB1q30vSofBaUneoQ9mQbCL1fXPZRLMGW6aMSE3AbRMl7R27TdRQ7ShHhGYRHVL7bT/PbxN1W4eSdcw6FJbZbSIvoWdmm8gWsaDbRMV0qN2PZBP1gw6pZSSNrkNRGeCem4WP7TZRt+Zman3X3CxZh3OzZJ38czPAbRP1w9xMrT+sczNGShFCCCGEEEIIIYSQnsNIqYI0mk3Um03j6XuNIFlW17zRpvwqeU62yrMaGHveo3tbTm+I9i8XzdeS3tOs15WjEPJEIwQZTnbI45WP9xlb+vUqZk+9+XSppBfd5KXX9zhn83lHH3zrmSGqIc7h0lphsZ0CoZQk+1eIvPLN6ESGHN75eIUgw8ky7d+P6rlP/n5ImkiHAKS0SNIhIK1FRU62ypMvIRGhqWmRpENqWZ58LZIOqfWl1b88q4Dd0iFA1iJJh/T24fN0ZIGuRWXoUNhvsh9dhwD7yVitkvQ9WpShQwAynSyjaxF1KBuSTdRQIzQdNlHRk63y5LKTbCJbvs0i+VrMdtRw2EReRdWUzm0iU/6pTmyilA4Bok1kGm+WKPIybCJdh8JrZpso0qHwGrVIQtchwG0Tqd9hl03UrbkZ4LaJTLmfODfrfG4WlpVnE3FuVhxGShFCCCGEEEIIIYSQnsNIqYJEpztEe5JnE175loe5VVSvt+o00t7z2BsdBMbnatlslH+hpNVA6fQGY26XDlYBbdEHqT3OxtwKZm9sWac6ZNmnDMELr3rwdY99ey9xcq8zoO5TTnrn1bxT8Qk1GXIroFoB0F5RkU6jAQDUWnXrumdd9spH12rx2Ayfu6+t7jTkfvyaeU+yyXMf8KQZEfWUGV2LJB0C0lok6ZCpTNchtZ8iq4GSDgGGnAqWfC1ZVgGlVT9j3pdUbgX5e1iGFhXRIfVapEWSDqmP47wLgg4BbS1y6VDiHoIOAQYtEnUIkLUoqUPh+LTPvQQdAtLRDNQhO5JNZMztIthENt2RdCjszx6pqdaRbCLbacOSDiXKHNEIYZnZJjJFNUT0Wods/RTRIbU/ySZK5rIz20S6DiX6020iTYfU/iQdAsqxiXQdUsvSWpTUIUC2idShUYtkdB0C3DZRIjLcYRN1a26mlrnmZonxcG5mLM8zNwuv2W2i/pibAS6baNDnZoyUIoQQQgghhBBCCCE9h04pQgghhBBCCCGEENJzuH2vIM0AxuOPASXhcBQqGSRD5kzbXuxHsttDQzsNUc9ypGjULktoevu5ZauNIyTduJ3GkIwvVSdDoj0JNdw8Qo9y9FohnaatMdDKovDRZiUdKqo0avWHVp10MnQ9fL1q8iVHyfOEo0nVBJ3u7TPuUFFzOLubSus11Bt6aGhvjx0dFiIdAgyh6YIOhWXm5MG6Dql1JB0Cih2wkD7+OKlDat2mdu8iScwT/UUh7hm2xqTC2LusQ0BaiyQdAgxaJOgQYNOipA6FdezbaPLoEGDQohK28ZmvuZF0CEjrDnUoG5JNlEg47LCJVE2Rj2RP2zKdHLCg6xDgtonUfjtJHizpkHpNeh7e2/7d77YOARabSP2/vASbyLaNJqVFKR0CRJuopNQGvdIhtYyk0XUoKgPcczPAbRN1a26WqOOYm6l1ODdr1elgbpaqH15t9Rdd74e5Wbv9sM7NGClFCCGEEEIIIYQQQnoOI6UKUm8dO6ofMQqkve+zmoc8kchXK4s8ow1DMr2o326vBtqPH5VXA4usAmaJQtDbB13wzrr69Vpe5KARvq+R51711kue+sjza4tYaHvp1def9BlHz6LPUk342T76uNW+nmylHhdaJFLB1xLlNdShRePxovckWtlLf8az2j2i1Yfo92M6mr3JpJ4ikQ4B6eOOJR0C0r8/SYfUfiUdUu9R9mqgfCS7vBpYZBUwy+pfr3TI1reuQ0Bai2wrhroWyToEtLXIrkNAW4tkHWq3dEVM6X2rbXUdAhQtcuiQ2k7SobCbpBZRh7Ih2USmCE3JJlI1RbKJTAmHyzhgoZHQR7tNlCeJsClS0xaN4LKJTPZUr20iT9EJySayRXNKOpSok7KJ0mvokk2U0iFAtImKRpGLNpGmQ4BsE+k6FPZrtolMcwySRtehqAxwz83Ux72emwFum0hN3O+yiTg3U+vY52ZAFpuIc7N2t92ziRgpRQghhBBCCCGEEEJ6DiOlClJvhseMGnNK1TVPe5D0iM9a9i/Hq4zK8e3S3mZj5FWB1UB9FdC0GtiuU3wVMBl5lT8KQdqTXLZ33jPkOYjuHXnho3tmWTFsRjkRckQshI9bXm3tiNLI866+g/Fxo9He5uh8UM0rHxbZIxUSkSri/mTFn91SEfMxo1orP+mxj77ntqPZmT9BJtKh8HFSiyQdCq8ltUjSIbUfW96pVMRDgdVAU4SmrkWSDpnGkGUVsEgUgi03Qre1SNch9Z5ZVgx1LcoWsWDXIaCtRaIOAe5IBUNul/j7Y82T0Gpfgg4BshZRh+xINpH63rpsImOeKM0mMkV+SjZR0Vx2LpsoGbFgj9AsokOmdv1kE6n3lWwiWzSnpEOALWIhqUNhmdkm0nUorBNdjFu3nrojFXqlQwBtok7RdQhw20TJPFF2m6hbczPAbROZdq/089xMql+Ubs3NALdN1A9zMyCLFg323IyRUoQQQgghhBBCCCGk5zBSqiDNIPoXrd6pXvnkil7sEQ8MHmw9T0LkwbVEAKT2OJe8GtgwePDz5GvJswqYJQoh5WnP4PXthKBhKKz4ibG0VwPdK4aR3zxLxIL9lUWrJ+EzXz2Ropr0L0efbdor376bNVIh6rZ1rantQW4m8rR4yTraHufoOZDeG62f+JA8MKPVL/MniEQ6FD5OapHt1M+U7gg6lCyTf/u6FpW1GqhrUZ7VwDyrgFlW/3qtQ+EYtAJNh4C0FtlWDHUtskUsyK8uqUOAokWCDgHuSAVjbpeoraBDyTK7Dqllkg4BaS2iDmVDsolsp35KOpQoa+rP5d++pEOJezoiNU33kHTINi5dhxLXLNEILpvIGHnQa5uo0v6tSjaRLZpT0iGgJJuoml5vl22iDJEKLVSNkmwiXYcA2SYy5YqRbKJIh8LH1CIJXYcAt01k1J0ez83COnabKFHXYRNxbpZ9bga4bSLOzdrXumkTMVKKEEIIIYQQQgghhPQcRkoVpBkEaAZK/gTFYyydLGPKxdLOqZD0oqt1xP3KhpW4IquBDX28ltXAqH/bKmBE5IU3ed6LRCFk2Zts29vsQl3ZS9Fs7UmOvfCt8iwrhi1vtSlioQntVAglX0LQqh/taW6v0kXvTTquIWoTeedTXnnAGangqRFy0YqeY50gOR65bnQKhP6biMpVZ310QoTx4AkCoK1DAFJaZDv1U9cUSYfUOpIOAWktKrIaqOtQst/kNV2HpNcJJFcDdS0qEoXQbR0CLFqk6VB4r9aDLCuGKS0y6xDQ1iK3DgH6b1/XIcAdqWDK7RJpUa90SL0WFVGHsiHZRLZTPyUdAmSbyJrTTtAh9VqWXHYum8hkc9kiNCMkm6hoFIJLi7qtQ4BsE1mjOQUdCh+ZbSJdhwCbTaTpECDbRBkiFXQdUuvIuONOdR0CZJtIPUWUWiSj6xDgtolM+el6PTcD3DaRbfcK52bF52bqtX6emwG9t4l6PTdjpBQhhBBCCCGEEEII6TkD75S67LLLsPfee2NsbAyrV6/GzTffLNa94oor4Hle4t/Y2FgPR0sIGVaoRYSQuYY6RAiZa6hDhJC8DPT2vW984xs4//zzcfnll2P16tX4zGc+gzVr1uD+++/HbrvtZmwzMTGB+++/P37ueZawQAv1ZhgeGkVezqqh5FqCvFnbMaFCok/1iFi9vyjsr1FPJ/MsM0S9bgi7z5JEOEuivPhahq0xvTr22NSf80j2DGHstnt5frK/psFPHJVEYZrx0aJVU62ocut91UJFAXn7TByaXjck1asl66jfT19LntdoDcU3JN6LfgvRa4j6a5e3jz6tR8n5yv2Yu8JcaVGkQwBSWiTpkFoWf2aWhMP68cm6DgFpLSo7RL2u1cmSRDhLEnNJh0ztenXssam/LEeyZwljl+6j6xCQ1iJJhwBViwQdAjJsn1GSCAtapOsQ0P5+unQIaGuRpEPJa6EWUYeyIdlEpiTCkk3UsCQclnQIkG2iPAcsNAz2jmQT5UkinCWJeaLcYRP1kw6p45F0KKzTeiBoUXKrn90mUp/JNpHhPoJNZNs+U8Qm0nUIkG0iXYfU/iQdAvpfi/pJhwC3TWSyI3o9N0vcO0NKFZdNxLnZEM7NAKdNNOhzs4GOlPr0pz+Nc845B2effTae//zn4/LLL8e8efPwpS99SWzjeR6WL18e/1u2bFkPR0wIGUaoRYSQuYY6RAiZa6hDhJAiDGyk1MzMDG699VZceOGFcZnv+zjppJNw4403iu22bNmCvfbaC81mE0cffTT+9m//FocccohYf3p6GtPT0/HzqakpAEATrWR6Fk97KvmdYYUvcnxnSuKprcCZIhbadc0rkcbjTEtYDVTr6Mk7bcmDCx07ajjGtFsEcd66/P5bL0pwBznxZ5p0ws8IPVWdGskmrRB6hve+/bk2W3VaiezilUPleOb4e5RMgmdLrhd51PVjSBPtda+8lywPr7VWAPr8+ONeaJFLhwCD3gg6BKS1SNIhW7/qb1/XIkmHTP10ezXQFqkg6ZDezlhnwHQISGuRrEMAtISfEaaUmdHnZItU0LVI0iEgrUWSDgGyFuk6BCB1JLKuQ2r99jXqkEpemyjxWTlsIvUn5TpgwfTbl3QofGy3iWwHLEg6ZGqfJYmwLXmwyyYy2lM90qJOdAiQbaIiOgTINlExHWr3JNlEfuL/vM5tIl2Hwrpmm8hX3vN+1qJ+0yHAbROZojl7PTczlXWye4VzMxldh8L+XFo093MzwG0TDfrcbGAjpdavX49Go5Hypi9btgxr1641tjnwwAPxpS99Cd/5znfw5S9/Gc1mEyeccAJ+//vfi/e59NJLMTk5Gf9btWpVqa+DEDLY9EKLqEOEEBu0iQghcw11iBBSlIGNlCrC8ccfj+OPPz5+fsIJJ+Dggw/GP/7jP+KjH/2osc2FF16I888/P34+NTWFVatWodkMncJ6ngOg7SVt6F7veB9zuq5t1S7ub7bhrKvvZbbmTehgNbDZGkuefC2Z6rZvKNa10cleZttqnad5n01eetHTbsqxIPiDTbkVUKsk6uSLVEjuXwYMEVHRHuVqy4tuWLnBrLaPWekv9rpH37n4O9by6Kve+FbdhlY38son9utHK1Xd2Lg8x+TVIpcOAWktknRIfawfd2xbvZN0yNRO0qFkndY9O1gNbCrfyzz5WjLVbQ/CWNdGpzkVJC3SdSi8V+uBnyH6QM87ZVmX0nMrSDoEZI1USGqRqENASoskHQrv1Vq1c+gQoORYEHQorJPUIupQkrw2kfreumwi028/FeWg6ZCtbqOeriPmtOswl51kE9nytXTbJuoHHTL2U6IOAVkipgy1jDld7DZRIqpKsIl0HVLHo9tEug6pdSUdAoZPi7qpQ4DbJlI/K5dN1K25WVjHbhNl2b3CuVnrQY65mXqtn+dmgNsmGvS52cA6pZYsWYJKpYJ169YlytetW4fly5dn6qNWq+Goo47CAw88INYZHR3F6OhoR2MlhAwvvdAi6hAhxAZtIkLIXEMdIoQUZWCdUiMjIzjmmGNw3XXX4fTTTwcQepWvu+46vPOd78zUR6PRwF133YVTTjkl9/2bzSD+Fz2PrwmnNhhPeshwskyzYfZ2Ng2e7PRe5GSdoquBkZc/U74W4dQGa26XDNEIkqc9i5c+C6nTGwz31j3txhXDDPkS4j3Njah/X6wbr3xE42p5zfNEKiQ84tFf7X3zDd+RaH9y5H33ou+G2p/j9JlZU76EZrKf+Lma/yUqQ3+vCs6lFpk0SMzXokRzpk6/EnQoUUfQIfVaoI/BoCW6FuVZDZR0yFhmOVGvk1VA24pft7Uoi6bEdTPkspN0yFRX0iEgX6RCrA9RG8N7pmuRpEOAoiEZTsFKnTCj6VCiTFtlpA7ZkWwio5YINlGWUz9NmiLZRDY7StKhRH+CTaRGYDnztRTRofBmyTZ9ZBNl0ZREfYdNlMh757CJEtGxgk2UJbeLrkOA2yZS87VINlGWU7AkHUr0I+hQOOb+1aJ+06GoDHDPzUzXejU3S17Lvnuln+dmUv28cG6W/o4M69xsYJ1SAHD++efjrLPOwrHHHovjjjsOn/nMZ7B161acffbZAIAzzzwTu+++Oy699FIAwMUXX4wXvvCF2H///bFx40Z88pOfxKOPPoq3ve1tc/kyCCEDDrWIEDLXUIcIIXMNdYgQUoSBdkq98Y1vxDPPPIOLLroIa9euxZFHHolrrrkmTrD32GOPJU6seO6553DOOedg7dq12GWXXXDMMcfgl7/8JZ7//Ofnvne90cRso5kpb0Lb241EufrYthdZP9Eh8pTG+4/VE0G0/dCRR7i9f1nO2dDRamD4wo112q9DeS5434t43DvNm9AJphVDKWIhUaZ5/k2nQUg0kN5nrK8Qtr8/rRWX2Xb7aL9y7LlvtW760UZh9W7RtZanXTvxQb1XKrIgfq6cqhXtU/aS+5T1/cuAuoqOvmeutCjSIQDOvAnJfC1IXLPpT3tFz6xDQFqLJB0C0lok6ZBaVmQ1UNKhRF3LKmCRKIR+0CJJh4C0vkg6ZKqrE+sQIEYqJHKwaFok6RBg0iKzDoX30Fb4BB1S60o6lLjWeh+pQ9mQbKKGQUskmyiLbaTrECDbRImoX4dNZNQ8R6Sm2l+WfC2iTWSJ0BxUm0iNLHDZROrrKMMmMuV2kWwi9bRht02kapTZJrJGFmg2ka5DgGwTJaN50Nf0kw4BbptIfT9dNlG35mZqfdfcLLyH3Sbqh7mZq103Gcq5GeC0iQZ9buYFQR+fLdqHTE1NYXJyEv904/0YX7AwPiZxZqZtoEcfWPRhzrRC/EzJPKXjRhNOrlb7qCwSMZPwRdfyOKXiunRKAXAk1TOEj+rtUnV8OfwzFr7oWiW9fUavEz2vqMKnXYtCOqNy1QCIhC/qL6obHzWq1K1o16K6FSXBX9y+Nfaa9lxNvFdrjW+k1T66VmvVHRlR+m2VNaa34S3H7o9NmzZhYmICJK1DAFJaJOlQeC3527cde6wn89R1CEhrkaRD6jXdANN1SL22szqlxETDRXQIELUopUNArEUuHQLaWiTpkHot0hdJh9RrUV1Jh4C2Frl0SK0j6VB4LalF1CE7Lpto1qAlkk2k6o5kE+k6BMg2kapReZxSLptoZ3NKFbGJEk6pEmwik+5INpGuQ4BsE6lOKZdNVDFolG4T6ToEyDaRrkPJa2YdAqhFJiQdAtw2kenwqV7PzdT6rrlZeI+dzynVrbmZqe9+nJup9Yd1buZ2/RFCCCGEEEIIIYQQUjIDvX1vLkkl9bQk82xYEg67knkmylJ/LWHnGepKXnjTNposSTzzJBGW6uRKomdIIFoGgbIrRfWOq2OxeeylbTRZwj/zbJ9RE9pFRC30JHhqIHsUshlfSYUlt+t6ekh69NoKJhputG6a+r4b6poSfJIktsTCkg4ZrwltAaQSc5q32ghh5xnq2lYDdS0qshpoSyJsq5MnwXnXtUjQIUDWIts2mjxalKWurkW6DgFpLZJ0KPk4rCvpEOBONOwbkphLOpToRwh9J2Ykmyjx3jpsItMx7lKCYPNWP7MOAW6bSI2AcNlEWQ5YyJNE2Fann3VIHU+3baJOdAiQbSJ1C4vLJvJMW2M0m0jXIUC2iXQdSvSbIdEwSaPrEOC2iYwa1eO5mVrmmpup1zg3S46liA4Bbn3pj7lZu/6wzs0YKUUIIYQQQgghhBBCeg4jpQrSDILwXzPpgQRMXnNzublu5KVOe89dx64b+8vgaU+1iY65zLEaaKxjWAWM6xpWEfX+YgSvey8S6JmSBQPZVu2y9GXrx7VCqB6NDGUfMaB42g0ee/24Uc/wPYr70b9HJSUa1q/pCSjVezWZ9k4k0iEAKS2SdMh8zaITQjLPLMcdZ9Eo22qgrkWFVgMNq4BSG9O1GMvqX7e1SNIhoBwt6kSHAEWLBB0C0lok6ZDpmqRD4bVkxIMtwklP8GnSqNTvhzqUCckmMts7+nO3bWQ7aEGyiYz9ZYgMd9lEeXLZmfK12Owep000xDpk66cTHQJkm8hk90g2kelodlfCc0C2iWx1JR0CqEU2dB0C3DZRljlZt+dmiXs45mbqvTg30+5dMNamTJuIc7PiMFKKEEIIIYQQQgghhPQcRkoVJJU/QfUi6nsym0mvYp68CcZ+tOOOjXkTGlrdHCuRRU+2cnnhrZ77CIPnvcgpWN0mb9STq59OIxXiVWStjmd479p72KNTINLfjbiu352cLmLeBMv3k6QxaZCuNzaNKiNvglo/lTehYaibYyWyV6uBWVYBi54+023yrPC5+uiWDgFpLZJ0KLym1RV0SH3s0iH1HpIOGa9RhzIh2UTGfDmCTWSsmyennaBDyWtmm8gYsSDYRLly2eWM1HTZRMOsQ2o/rogpW51iOgS4bCL1aHZXTpeEpjhyuhijEDJ8P0kaXYeiMsA9NwPcNlG35mbm/sw6BLhtIs7N2vSjTcS5mRlGShFCCCGEEEIIIYSQnsNIqYLE3njT6Q2tx6kTZjJ4HPW8CQDk/crW/oTTsAwREEEG73mRfAm20xtMOWFMbUzjstWdSzpZKSzilfcq7VMm2u9R0ivvR55ypZ/29yWZA8HsuW951PVVng5zukS/DV+va1lVJ2kSkQmC3hhPmHFoSZ68Cfb+LKfQpKIa3JFNRVYDbZoi6ZCpnW31r5+0qAwdsrUzaVWkRZIOAWktknQIMEUzmHUIgDOni+nUKkmH1Gu2SEOSRrKJ1PfNZRPZopUkHcren90mSkY1OGyiHLnsspysZ7K1XG1M9LMOAb2ziXQdCh+ZbaLk79puE6l1XTldmqaT+oScLurJuJJNxEipbOg6FJWpfyUdMpX1am4GlGsTcW7WhnOzwZmbMVKKEEIIIYQQQgghhPQcOqUIIYQQQgghhBBCSM/h9r2CRCGi1gSd0XGjHSTzTJblSebpDlGP0EM643DDgsetZzpSVAgNLRKaXnZSvUTopXZPz9fDww3tW3XisNpubZ/xPeP1cAzh3zjEU7kWh5m32kffmTipnlo3DgktO9Fwq61nqWsIGyVJ1O17YoJOTYesdQsk81SvpZN5ukPU9XLj8egdhKhnCX1PlQvtUvfUr3VJiyQdst1T1yGg3LD1hFYJWuQpTXUtknQoLEtqkaRDar8uHQJMiYZbfahb/PRkoNShTEg2UVJLkLhmretIcJ7noAVT+05sojwHLNg0Jcv24X6yiUz365VNlGn7TAGbKLHdxWETJbcN2xMNe4m6ZjtH1yFAtol8VaOoRSK6DgFumyjP4VPdmpvpfavXjFpQ4ICFXs/NXO3ysrPPzQC3TTToczNGShFCCCGEEEIIIYSQnsNIqQ4xrbKZEgurz/Mk8wyvyZ76dH9N8Zr+PNNxx1HdHEk88yQH1fuXrpvqxDTTq4ydkMt7niViwZQYOItXX1ghjNvaVie0Y0h9X44sgJZUr2Kp22miYb3fRuulVbXycCCGMiKiv0+SDqllZSTzNPdr1yHTPbMcj97JamCinSHiwdnGUiemD7Qoj6aUoUOAJWJDjWbRtEjSobAsqUWSDgFwJho2JfNs3yepQ4BBi6hDuZB0yHTNlhjYleDclvhZ0iFbXfW76rKJ8hywUESHTO0GzSYqokO2draIqU5sIvP3yGwTmeq6Ep6r/Ug2kdqvaBNVzPWJmSJzM/Vxr+dmpnsO/NxMGU8ZdGtupvbdz3MzwG0TDfrcjJFShBBCCCGEEEIIIaTnMFKqIM0gPHLUHP2ERJnVe+7ImxA+TudwARQvfY68CYEhskDvz7Z6l4c8q4FlRSN0sn853q9s6t/3c/ev52GweeXzYO1POppd8bSbcikAMO5fhra3uUhOF9MKX/tIUsvx7ZZIHxIS6RBg0hAYyxPt9VVeSxSCpENAWouy5E1wHrsOlKJFWfrLshKZoks6BFi0qAMdAtLaUYYOGftL6RCga5GkQ4BBiwQdAtw5XRL9ayt8ug4Zr1GHMiHZRIl8OQ6byGbLdKJD6uMyjl0vSq9tom7rUN57SDaRKYo8D6Ku2Y5m13QIyGATqVENjpwu6ndPsomMdo90fLsl4pC00XUIKNcm6tbcLGznsIksEU156Ie52fT29diw8RbUg03wUMF4bRV2WXwM/Eot1bbbczOgXJuoW3MzwG0TDfrcjE4pQgghhBBCCCGEdIWtU4/gqWe/h+puAZb9yQEY2XUPBPUGpu59DA9c+1PMq+yLFStei0pldK6HSuYAOqUK0gi00x1MHnfLqXupuppn3eYFl1YOTXVs43PtM86TN8FYVpYXXnt9ZZ8sk6Vf1fMNwOidF0/KyuCVt5Hp9JmWx1t/DdG91c9fz6XQfp723HtanQjjqSIt73sFSUynvvn6SkDUn9K40QUv/LCh6xBg+O1bTpjRseVNkOom2uXIm6D3b4tUKJI3IU++hEwRUj3SIVvfKR0CUlpkPSkrQ6SCRCc6pN47rTtpjdG1SNIh9bW4dAhI/xZ0HVLHE3VAHcrGoNtEWU7JK3LqZ55ITdc19X7WOiVRhg4Bbpsoy8lWpnuLp2AV0CFTmaRDejv1teg6BMg2EXWofIrYRP2kQ+o9bFHkRU79nOu52cbn7sT62R/igItfhpFF8xLX5u+zFMtPOQTP3fooHvzS57Dv3v8b1dp8Z79lzM3Ua/08Nwsf222iQZ+bMacUIYQQQgghhBBCSmXb5sewfuYaHHzxySmHVITneVh87N7Y+7xj8fCj/4TA4iwkwwkjpQrSbIZ7lmOPe4cnzLjupf417VfW62bJm2CLKEiUhx0Z2xhX+DpZDcyxCthpfocsSCcxmFYKU3uHu+yVV/uL33OtnyD2eqc97fr+ZV/boxyWmb+7vmW4gba6mIiocZx+ZcqbwPwJMpEOAUhpkTWXXUpTiusQkNaiPHkTXDqUuNbL1cAcq4Dd1iLbiTC6Frl0yHSt09XBWIsEHQLSWmTLLaVrkaRD4TXzOHUdSrTPcPqVlKuNmJFsImvelhJ0KGxntomaBn2QbCKbLZMqz3DqZ2mRmgOuQ0Dvosg70SHAbROpUQRl2ERZTr8y5W+hFsnoOgR0diK6617JNsXnZuq1MmyifpubrX32auz/wRPh10wxzEkWPm8ZFr5gEht/dwcWLT7SWKfMuZnpWj/OzQC3TTToczNGShFCCCGEEEIIIaQ0Zqc3ARM7MLp0YeY2K15zKJ6Zug715lQXR0b6DTqlCCGEEEIIIYQQUhobN96BJWv2zdVmZPECYGwWOxpPoRFs69LISL/B7XsdkiU0PXU9x7HHan2xP0tYry2ZZ4wW0pklPNyWxDPVpqQQ9Syh6Z0k/EyEXAr31ENGE22a5hBOW6hojCXcXGpjDaH3K4l7q22lRMO28M/UMbk5jmQ3qUw6fD0dThofm8x95ZnIEpqealPg2GNrf8I9bck8Ywyh5a7w8CxbbhLtXNv2MoSod1uHAMNvXdAh9V76scnGY9GF7TMx6lYjMYmwQSelEHq/Haqva5GkQ8lryfuYEvG7jmT31LFpWmTeRqONhzqUiyzbhlNtDNoi2USd6JCprGs2UQEdStwrh00UBE3MTm9Eo7EDleo4aiOL4Hle13UIsNhEzbSWZEltEFOCTZTYyiPYRLZEw0VsIl2H1MeSTWTeRmPWIYBalIUiczPAbRN1a24GuG2iQZ2b1YPNGN11vrWOidrkOBozOzDrb0ClksxDtTPOzcJryX6HbW5GpxQhhBBCCCEDxOzMZqzfcAM2b78T43tNojJvBI3N09j+8GYsmn8MFi86AZXq+FwPkxCyE+N5NTRn6rnbNWfq8EeqaATb0Qym4XujXRgd6SfolCpI0Aw0L3i6jn7caFlJwVIrh1lWA3N4t23JPLNg9djr/XXghS/7GGTb8cH6WExJ9lyRCrbVBM9w1Lk0PmuiYX2cFcM4o8R1rTqViu5NTyfeq2QYn0Tid9JKzhf9NqqRxz26ZcXcjpjRdQhIa5Hp2OMy3ltjNFWW1cAckUdSMs8sWKOp9LF0GCHVbS2yRSy4IhWyJBzW+8oyNtPR7NZxalok6ZBaph/J3okOJfoVdCis03pQSbYhdgbdJuq2DqntOjlgIWq7edP9eGrTVdj9Tw7D3seeBq/S/p036w08+4uH8NC3PovdF78J8xbslXm8tnvbosnLilSI79WBTWQdp6BD4TDtNpGaFL0Mm0jXIUC2iahD2ShiE/WTDgEZ5mZhR2J7ibmam41XV2HTbbdg0RF7Zh9ro4nZZ7fD270GNIE6tqLmjcTXy5yb2V5LP83Nwsfl2UT9ODdjTilCCCGEEEIGgC1TD+Lpme/ikL87Bbuu3jfhkAIAv1rB0hMPwMGXnownN38dO7atnaOREkJ2dhZOHoRNv16LwLKlWWfDzQ9jfHQfeF7LeRNkb0sGF0ZKdYjREy7sszQdAarvB42PFDXsRbYdNxqRXlXLkZegw7wJrn6KrAa6xm7qq2OUTbvSSqHulVfrZvHKi/0bog90MvVT0cakjjN6v6K9zVFbg8c905HsqT3NrTaG1cVU/9pvxbRvucH8CZlIrQ5a3jddiyQdSlwrUYfMdTrQISBf/oUOVgOtK5Nd0iJbxIIzUsGS2yUut+RAKEOHEuOKxinpEJDSIkmH1McuHQrrmrXI9FvRc7lQh7JRhg4Bsk2k65BaV8f8e+7cJrJGP5WgQ6Z27fxRAZ5c/x94/ifXoDJWE/sGgNrCMRx40Um4/4Nfxf6rzrPWtSLoECDbRHkjFSIkm6jjnC6CTZT4PjhsIvV7Jh7Jbvj/sRSbSNFSapGbInMzwG0TdWtuZq4z4HOzVn8egMmxo7D2+/dixWmHynWjJrMNPPHNO7Bs0auBljNK/8WUOTdLXJP659ys/aSLNhEjpQghhBBCCOlzNm+8D5MvWI7qvGz5VUZ2mY+xfUYxteVuBHRmEELmgKVLXoYN1zyN9T9/0FqvOdvA/R//ESZqR6Jaa+fDq2CepRUZFjqKlJqdncXatWuxbds2LF26FIsXLy5rXH1PMwicOVqynPTQ0Rgs/ec5YcbQOGzTYZ6UXCuFWhtbndKjEXRM/WsrhaacCNacCjC/D1n2K+uYcrqop1wBaH+22kkP6riKnPTQKebTrtTVQCWvDA1oJ7oOAYZIhS7rkO0eeU6YEToO23SgRZlO8ysSIdVtHTLdwxCx4IpUyLR614EOAebT9gAkP1vH6Ve+MoYsp191gqRD6rVIi6hD2Rh4m6jLOqTesxObaP3Uz7Dfacfkuu/K1x2Oxz7zS4zN3w0j2C1XWwCiDgGyTWSNVNDKVUqxiXQdAkSbqNOTiTuBOlQ+/WATdXtuBpRjE/VybuZ5Pvbe/W14/BtfwYZfPoLd33A45u+ztN10toH1P38AT111FybHj8bCRfvH13yMhUnOuzQ3M72WfpybJa/lHp6Vfpmb5XZKbd68GV/+8pfx9a9/HTfffDNmZmYQBAE8z8Mee+yBV77ylTj33HPxghe8oBvjJYQQQgghZKej6W3D6K4LcrWZt9cSzNY3oR5MoYKFqHg8kY8Q0lt8v4q9dj8LWzc/hkf/7zWoexvgj1bC5PTbm5g/tj9WLD0dlcqI0spDzdt5Al52dnI5pT796U/jkksuwX777YfTTjsNH/jAB7By5UqMj49jw4YNuPvuu3HDDTfgla98JVavXo3Pf/7zOOCAA7o19r7AtKdS8r7H+zot+VrizPoNdx3jPRyr+zZvt+0klFR7LW+C6V6GwWUel7GvDJEJnawemLznqXsLXnnAEqmAZFsTWXK62Manv3/tfAmGcWqnzphOv4rQT3rwjPkSWnu+m+bVRU99bZri2PK2VPU6xIquRbb3Tdcim8bIdYvrECD/5ovokFqWSQOEyIeyIjU7jagQf+vNtJa4IhXMEU1mLeqWDiXG5TiFD0hrkaRDgJovwa5DgKJFkg4BKS2iDuWjDB1KljUz1DX/Jo2Rmj2yiTrRIdO9O9IUL+oTmMVG+N5YtmYuHQKKRSoUsImynGwVtzV8bpJNlOX0qwjTycS6TaTrkNpfyiYyzMAkm6hqqkNEiszN1Dq9npsB5dpE/Tw3mze2CqtG34xprI0TmHuLVFuh3W4ES1CpWrbulTE3U9rbxh3BuZlWp0RyOaVuueUW/OxnP8MhhxxivH7cccfhz/7sz/CFL3wBV1xxBW644Yahd0oRQgghhBDSbbxmBY3tM6iMj7grt5jZsBW+HzqiGtiKIGjC85hSlhAyN1S8eRgL9sCs9xwa2JK67mMcNezCqM6djFxOqa997WuZ6o2NjeHtb397oQERQgghhBBCkkyOH4enr/stVrzafYpVxNrv3Y2FYwfHzwPU4SG7U4sQQsrG90YwimUIgl3RwDYEaMKD38ohRX3aGeko0fmOHTtw55134umnn06FLb7mNa/paGD9TrMZGEPNpbrdGoNEnmOP5T5yhH8a2mVp00loaKdbZLL0lwrLFEJF1bqmRJ+ue2VJqmc7Fj5VNw4NTbeVjmSPSCY6z5/sz4bpeOPkPdMh70RG16GoTKrbzXGYyHPssY1cW2IKtOl02163tUjUIcC5fabbOmQcX1Q3kcQz2d6lQ0A64WdZSDqUuIZ0klEiM+g2Ubd1KGs7l020y6Kj8NCP/hvLTzkk0++1OdvAczf/HruvOF4pzfZ7ymRzFNg+47qPrW7u8eljyHEkewR1aHDoB5uo23Mztc4wzM08r4oqJuSxcG4W02st6vXcrLBT6pprrsGZZ56J9evXp655nodGo9HRwAghhBBCCCEhvl/DLuMn4OEv/AL7/O8XwfPsE6DffvLHWLTgGKWeB6+z9WhCCCGkdAr/z/Sud70Lb3jDG3DRRRdh2bJlZY5poLDld8tybGKUlCzynpqSeDojmmzHeupte3DscSn9lhSVYHvv8njAXV75TH1kSKqXh1xHsivlpgR7if5sqwiWhLS+nmjPoi7SbyPxsUcrCVwhzIT0k+lEh9THmVbvhN/mXBx7bOu7DC3qCx0COtOiks4VLutIdr0/SYsCw/ezTB0K+2s9oA7lohObSE3SKtlEneiQsf0A2kS77vIirHt4K373yeux9zkvxMgu81PVdzw9hYcuuwFj2/bFgsn94vJKsDC8t/ZZ7Ow6BJRjE+k6BLi1KI8O6WMmZgZubgY4baJ+0yGdvrCJCugQUK4WdW1uBjhtokGfmxV2Sq1btw7nn3/+Tu2QIoQQQgghpJcsW/JKbN54P377oWtQWQIsfsneqM4bQX3zDqz/6cMINlUwueBYjE+uSLSzbZMhhBBC5orCTqnXv/71+OlPf4r99tvPXXkno+HIr1J0P2Yzgxe6SN+5vJ2WY4/z9FPkuOOiuWGy1s2yz9i6V7hAThdpLIlx2Vb9hH5s49TJskc5+u7l2cfc9tKrLvbkKkT0W6m6XxrJiaRDQGda1I86BLR/f4OuQ4A790oRHbL1axtL3LZHOgTIOtOJDgGqFpl1CKAWlc2g6RBQjk3UqQZk0ZkF85+H+fP2w+bpe7H1e4+iGWyH741g14mXo7p8PoIgSEREjWApfCHBeREdAiz55ArmdLGNB9Ajzu0/VlsUlI3e2UTpqAzaRN3DNTcD+kuLODcL4dxs55mbFXZK/cM//APe8IY34IYbbsBhhx2GWq2WuP6Xf/mXHQ+OEEIIIYQQksbzKlg4ejBGRndFE9vNdeCjhiWoYmGPR0cIIYRko7BT6mtf+xp+9KMfYWxsDD/96U8TyRY9zxt6p1Szqe3dtO4JL3ffZdc97lGbop72qJ1tU7dEgTZl7WvN4z2PMZyCladd0PJOZ/Ge51k9sLUt5qnP3CRTf6lyw++Hp8240XUIsO0Jn3sdAjrTop7pUMF2faFFPdYhoJgW9aMOAenfD3UoGzuTTVRIh4Cu2USeV8UYVqIZTKOOKTSCHQh/1RVUsAAVLISX8cQ9lV7rENB7m6h4NGfmZpn6S12jDhVi0GyiojZDz22ignZUGTYR52ZphnVuVtgp9dd//df4yEc+ggsuuAB+We8KIYQQQgghJBe+N4oRLE0lMSeEEEL6ncJOqZmZGbzxjW+kQ2oOsXkrO9kHXHZGfesJFD0aZ9BsoNGcRqUyBs+zf2dtOViK7F/Wy13ts5Ll1JhMe7G1tyNLbhdCVCQt6jQfQa+0KMs4m/Ummo0d8PwKfD+Zl6VbpyGZVgiDoInG9DQ8vwrfrzlzuthOtytDhxLjtGmeK0+WQZaz5FQgJKITm6gfdMh1zdm2hzqUGo/fxKbn7sTm2XvRaO5AxR/HgtGDsWiXI4FGmJhEtFO6rEOJe3VgE1GHSBY4N+uuDgHZ52ZBEGDLxvuxYesNqDe2AACq1UnsOvEHWLjogHiXV5la1K25GTD8WlTYKXXWWWfhG9/4Bj7wgQ+UOR5CSqPZmMFzU7/Gxh03ozLhozJWQ2PzNIJtI1g89iJMTB7mdFARQnZutm5+FOs3X4/ZyrOoTYyhORugvnEHFlQPxq6Tf4DaSPdPswqCAFumfotnt/03mqNbUF04huaOBuobZzA5dhR23eUEVKrjXR8HIYSoBEGAZzb8GFOz/4PFf7AnVv3BAajOH0Vj2wye/fkDeOC/f4yFo4dj2dI18MDs3YT0M0HQxJZND2J6+3oAwMjorlgwsV+Bzb9zz+ap+7F20/cwcfQy7PPaIzC6JMypt2PdJjz17Ruw9q5vY+XSP8KCif3neKQkorBTqtFo4BOf+AR++MMf4vDDD08lOv/0pz/d8eAIKcrWLY/gqc3fwrLXHIhDXnYyKqPtr/rs1Has/f59eOin12PVkrdgZHSXORwpIaQfaTZn8diT/46R5wF7/cWhGF/Z1omgGeC52x/D41//Z0xuXY3Fkyd0bRz1+jY8/vSXsODYXbDf6S/A6K4L2mOsN/DsjQ/hoW99HrvNfxUmJw/r2jgIIUQlCJp4fO2XsfCl83DYH70msfpfmxjH7q8/Aitfdzie+u7dePTH/4q99/ozLgQS0oc0GtN45umfYtO22zFx9DLM228R4AHbH/gt1t52FSZGD8eSxSeiUhmd66FmYuPG/8FztZ/ikL9/FSpjSf/E2LJJ7PPnx6O+bQb3f+wqLN34KkwuOnSORkpUCjul7rrrLhx11FEAgLvvvjtxTU16vjPTq8SETICYZNvWx7Bu9koc8olXobpgLHW9NjGOVWccjSUn7ovfXvwv2GvpuajVuh/t0O9E36NKSaH0We7FhdPu00t9GBYtCoIGHvn9F7HirP2xePXeqeue72HxMXthl6P2xIOf/Rme/X0Tuy56cenjaDR24NF1/4h9zj8OC5+3LHXdr1aw9A8OwK6r98FvPnYtvE0+JnYZfMdUr7SIOtRbaBMNF+ue/T4mX7EQK14rT+g8z8PK1x4Gv3oPnvrJt7FyxR/1cISd0XMdAqhFPYA6lGR2ZjMefvRyLH/jAdjzxa+GV1Ecxy8BgrOaWP+LB/HQ1y7D3ivPQa3W36d47tj+NDY0r8PBF70Kfk3+QVXnjeCgD6/BvR/4L4yNrcDo2K49HGV21O/RsNtEhZcsfvKTn4j/rr/++jLHSEhmgqCJJzd9Cwf9zSuMDimV8RWLsP8FL8aTz36jR6MjhAwCz6z/CRafutzokFLxfA/7/dVLMFW9BTMzz2Xq++CDluLyy16Dgw9a6qy7dsP3sOrcw40OKRV/pIoD//oVWLv5e2g2ZjKNgxCy85JHh0w0GtPY5v3W6pBSWX7qIdiK32F2dlOh+xFCyqfZmMXDj/4j9vk/x2Hpic9LOqRaeBUfS19yAPZ7/wvxyBNfLNXG6FSHTDyz6cfY6+3HWR1SEZXRKvb8s2Owdv33eUBEH1B6HO0zzzyDr33ta2V3K3LZZZdh7733xtjYGFavXo2bb77ZWv9b3/oWDjroIIyNjeGwww7D97///R6N1E6z2Qz/NYLwX7P9jyQJmoGYSG/z1P1YtHoFahPZ8qvM32sJsHg7ts08lvteOzPq97P9nQ2/wzsrw6BFKR1q7Hw6FAQBNm27DcvWHJypvud72OP/dxSenvoBAjSc9V99yoE47tg9cOopB1rrNRszmK78HrscvVemcVRGq1j26gPx9IZrEQQ7z++QOpRkGHQIkG0ikqaInZJVh6T7PLfpJiw95YBc91z+6gOx7tkfoRHsyNVuEJB0aGfVomHXoWHRomfX34ilp+2FBfu6nULz916CZX+4D9Y/9wvj9V7pkO1ejcY0ZvynsGDf3TL3NfH8ldhefwTb648NvGNq0OdmhZ1SF198sfHf//k//wfnnntumWMU+cY3voHzzz8fH/7wh3HbbbfhiCOOwJo1a/D0008b6//yl7/Em970Jrz1rW/F7bffjtNPPx2nn356avshGVye2/FzrHjN83O1WfG6Q7Fh8w2YRbZIB0J0qEXDw5ZNv8XEMcvgV7PHL08eujt2NB/DjuD3RsfUiuULcPBBS3HQgUvwyleESTXXvGJ/HHTgEhx80FKsWL4g1ea5qVuxdM1+uca+9GUHYmrbXZgOnhp444rkhzpEbBTRIYmpHXdh6Yn5EgTv+uL9sW37Q5huPIkgqOdqSwYH6tBgEAQBntvyK+z28uwOoaUvOxAbt9+E2WbxiMcydUhn25ZHMXnsylxtPM/D5GErsG3Lo5hpri98b9I5hXNKXXXVVYnnjUYDjz/+OKampvCxj32s44Fl4dOf/jTOOeccnH322QCAyy+/HFdffTW+9KUv4YILLkjV/+xnP4uTTz4Z733vewEAH/3oR3HttdfiH/7hH3D55Zcb7zE9PY3p6en4+dTUVOL6sHjLh4VmZTtGFucTtImDVuD39bsxiw2oYD58jLgbka6QzK0wOLnpuq1FLh0CqEVlsX3mCUwcnn2VDQiNmtHdFmJ2djMqtQ0YQXLV8ervnBk/jpxFi3cZx1f//Y/j8qNX/79Em+nmE1hyyJ65xlEZrcIf99AMtqPubUQNPMShKO3cCtQhFdpEg0sRHRLxG/Br+aYQfrUC+AECNDAbbMKI1585XPoJ6hB1qFvs2LYW4/tNwB/J/jv2R6qY/7zF2PzMfVg4dgiqXn4HUqk6pNFo7EB1Iv8crrpwFM3GDOrNTRjxF8MrfyPZQNOruVnhd/32229P/LvzzjvxzDPP4B3veAfuueeeMsdoZGZmBrfeeitOOumkuMz3fZx00km48cYbjW1uvPHGRH0AWLNmjVgfAC699FJMTk7G/1atWlXOCyDdwcv/H5FX9REEYXRDHenJPiE2eqFF1KHeEaCeKReBjlerIAjqaGALAiRDpf/6omtRr4dl0UEg0d96vYm/vuja9DiCOvxagf+iW/ZCPdjEaKmdCNpExEURHRJpesX0pdWk3pyiPg0h1KHBYXZ6I8b3yH/I0/gek6jPbMEsni30Gy5VhzQqlTHUp/LnvKpvmYFfGQEQoB5sLnx/0hmlugKr1SrOO+88XHnllWV2a2T9+vVoNBpYtiyZAHbZsmVYu3atsc3atWtz1QeACy+8EJs2bYr/Pf7444nrvu/B9wdnBWPYCWaBoJFv7+zMs1tQqYQ5qBrYjAA0lOaK6Pc0SL+pXmiRS4cAalFZVL0JTD+9NXe72ee2oVIZQ4AmGtiSuPaDH/4OZ/7Zfxjbnfln/4Ef/PB3qfKKtxDTz24xtLATzIQO9iCoo4ntuduTkEH7PdEmIi6K6JDEWG0PTN37ZK77b/7tWoxUFwMInf8BeCiDi0H7PfWbDg3Se9dzPB9BI/98J2gE8DwfAepoYlvu9mXqkM68+auw6bancrUJggBTdz2F0XlLAGAoc951Sq9+T6XHpz366KPYZ599yu52zhgdHcXExETiH+lfFtSej2d/9XCuNmu/fx/mjz4PAFoRDu5kxYT0EupQ75jc5XCsvzafhsw8txXBFh9+pQYACDAr1o3CoF3bCxbNOxbrvpfPONvywDrUsCR+HoB5W0i5UIuGg6w6JLHrxEvw1H/em6vNE9/6H0xOHhk/1yNKCckKdahzRseXYOsDG3K32/rAetRGJwEAdeRfOFPpVId0KtVx1GaXYuuj2XNDbfndOox4u8X2G3aig2L6jcI5pT73uc+lytatW4cvfelLOO200xLX//Iv/7LobUSWLFmCSqWCdevWpcawfPlyY5vly5fnqk8Gj8WTL8Lvv/tFLHlRtgTBzXoDG2/5PVYuf2GXR0aGFWrRcFGtzkN1ejG2Pb4B81YtztTmqe/cjYXjh1nrbHhuO9av34q1T2/Bd757H177moOxfLcF2PCcOZppbHwZZh6fRn3LDlQXjGUaxxPfuhOTk6sz1SXDBXWIZCGvDkmMju6K5lNVbH7waSzcz52Db8vD6zH7VB0jq9p57jzk3yZN+hvq0OAwOrYr6g81MTu1PfOJ5fUtOzD9xA6M7D7Z8t0Uc+CUpUMmlk6ehEf/6Rs4+OKT4VXssTfN2QYe+edfYekur4jLPI+6NFcUdkr93//7f43lY2NjuPbaa3HtteGeUM/zuuKUGhkZwTHHHIPrrrsOp59+OoDw6M7rrrsO73znO41tjj/+eFx33XU477zz4rJrr70Wxx9/fOnjy4vvhz8cvxJ6i/1mO0SuwYR9CbxW+KDpONBqdR7GpvfFk1feiZV/dLi1nyAI8MBnforJ+Ue09zXDBxRDyWPorxE1hNNvJb2LvsM7G8OkRboOAW0t2pl0aLfFp+DBT/8rnv+3r0Jl3J40c+q+p7DxprVYsaLtDPJQS9V7+umtOPX0/w+zs6ER959X3YtazY+fm1i64FX47cevxsEfcRtX6294APUnPYyubDvSvOL/xQ8MkRZRh4ZHhwDZJtqZdCgrNptIp4gO6feJ2GPJn+DhT12Ofd/3IizYZ4nQCtj22LN44BM/xYrdXxOX+RiB7w3PoTLUoZCdRYeA4dCiXSdfgqeu+h/sedZxmeo/edX/YOE89YTz9vvRKx1S72VifN4KTD53Au7/2x/jeRf8L/FAhsaOWdx/6Y8wUT0SI+OL4vIiydv7hUGfmxW2WB9+ON/2hm5w/vnn46yzzsKxxx6L4447Dp/5zGewdevW+MSHM888E7vvvjsuvfRSAMBf/dVf4cQTT8Tf//3f49RTT8XXv/51/PrXv8Y//dM/zeXLICWzbPGr8cRPvo7HttyCPc44yniyRH3LDjz4Dz+D/8wyLFj0vLi8goXwQEcUyQe1aLgYG98Ny2f+EPd+4Crs/74TMb5iUapOEAR49hcP4okv34Vly05NOLYrMBs1usHlMsAWLNgP9akX476LrsH+/+dEjOwyPz2ORhNPff9uPPuD32PFylfF5Z5XQ8Wb53qpZIigDpEs5NUhiWptAfZa9ud45JP/jvGDx7DydYdhfGU7Emr7Uxvx5JV3Yus9z2HF8tNQrbX1qOpzu9WwQh0aHBYtPhKP3PprPLPP77D0JQdY667/+QOY+tVzWL6yvbPER7YobhNl6ZCJxbsch8qmMdx13vew+EWrsPzUQ1CbDKPBZp7biqf+6x5s/NXj2GWXF2LBLnvH7TyvhoqftrNIbxjoZdQ3vvGNeOaZZ3DRRRdh7dq1OPLII3HNNdfECfMee+yxhIfwhBNOwFe/+lV88IMfxAc+8AEccMAB+Pa3v41DDz20K+PrVYI99T48BjWMztt96RnY8D+/wl2/+i4WHrYUi1+0NypjNdS37MDTP/wdpn+/DRPzj8T8RXsn2lax8xpKvUwIOWzJJ/tZi+bicx0GHVqwcH/sUT0TD1/6HQTzN2O3Vx2IsWUL0aw3sfnedXj2vx/CWHUPrFj5Gnhe+7/S0LFd3srUoomjMbJtV/z2Q/+FypImdnvV8zCyy3w0Z+rYeOsT2Hjz7zFvbF+s2P0UeF77vlVvsrQx9Jq5+L9zGOhnHQJ6/7kOgw71O7XaQuy7+19g89rf4KGPX4+GvxVeBUAD8JvzMLngKCxatSLRxkNtIPSJOlQM6lD6Pv2qRZ7nYa+9zsaj/3EFttz3NFa+7nCMLlmYqDP97BY8+Z93YOudW7F8xZp4AQ4Aqliod9k3TE4ejoULD8Gz/3MDfnPjT+Jcnz5GsHDBIdhjrxMSrwUARnw54nOu2JnmZl6Q4zzHxx57DHvuuWfmzp944gnsvvvuhQbWr0xNTWFychIXf+82jM1fgNnWyQUzs+3k2LOtoy7rrVPgZlqnITVadaKjMAGg3vIMN6I29UaiPHFtVqvTKldPT4jKmq17RWGUzag/5WS6qCwOtWw2k+VKv3GdRrKNqU5c1kz3I9aNB5X2lEunQ2QJEW0EM9i05RZsm/k9AszCxwjmz9sHI+PpXDE17IoaFiXKpBBRr2Io10IkozqmPuL2rTamuqmy1vad6Lk6hvjoeD9ZJy5Xtv5EZe064XbFajU5FrWsWm3VabWtVJXJby1ZJ7rWLm/XrbTuNTLS6q81rlqrzkitvXWy1hpHc2YbLjj5SGzatInJLFvoOgQgpUWSDgFpLZJ0yHht1lBH0yJJh8KypBZJOpS41upX0iFTHV2HEmXRc0mHtHZbZx7CxuduRwPb4MFHrbIYCyb2heeH39fov1EfIxjF7qU6pYDwtxoEAbZM/wZTW+5BE9vhoYbR6lLMX7RX0rDyfPjeOEa9lfA8L/F71rVI0iG1rkuH1DqSDiWutdpLOgSktUjSIcCkN2YdUvuRdAhIaxF1yI7LJppV7R2HTWS0ezSbSNehRB1BhwC3TdRU+nPZRAnbQ7CJjNoi2EQmHdPbiNeFfrqBbcuMV/EwG2zEbLA+qpyu5AO+V8NYdQ/4Xi3Rtl3HbBMZ7SnBJtJ1SK0j6VCyjtkmUm0ZySbSdch8zaxDgGwT1ZT3iFqURtIhwG0TNWbTtkzP52aA0yZKaJTDJso7NwuCAFPP3YenN14Db2EdYysnAM/Djic3oTnlY3LeYZg3sWfC1qg0JzDiLYVOt3UIyDc3C7wA08FTaEan6vmmPjyM+stQq7V+T304N1P7Hta5Wa5IqRe84AU4/fTT8ba3vQ0veMELjHU2bdqEb37zm/jsZz+Lc889tyv5pEhI5NFsGIyU6ItrEwepTrwvuMBRodb72MZpuZdUJ8v+5Yo3gkULX4BxrLKciOW1HFKTiX5tY7Eh1cnSNg/dGudce8rJ4CFpUSc6BPROi1w6NK+2D2q7LUQ9mDKP0/PgYxwjwW6lOqQShpDnYcHogRgZ3QVNf6vYpuItwIi3DH5VHkevtKgTHQKoRSQfndhE6vewn22ixDgL2ERFyGpr1LxF8DGKerARDSgJi33A86qoehNhnVZkKW0iMox0e24GlKNFqtZ4nofJxc/H/Mm9sW36Icw+MwUAWDi2AJUFY6kT6XzMw0hlKTzPE3XI9Bo6Gm/B37fnVTCK3dGsbMFssKntnAIA+Kh6CzFSWwTfG53TcWapM+xalMspde+99+KSSy7BK17xCoyNjeGYY47BypUrMTY2hueeew733nsv7rnnHhx99NH4xCc+gVNOOaVb4yYkMz5GMIZVaGIb6phCE9MAwpNfKliIKhbyFBhCiIjneRjBUlSxCLPYiAa2AWgA8OFjDFVMouKNIUfgccFx+BjFcgTeDOrYhEawHQGaYR4rbx6qmITvF8/xQAghnVDxxlHxxtH0Gi1bK4BfqcLHeGqrDCGkv6h445g/uj9mauvRCLYZDterhM5lLB6o37Pneaj6E6hiAs1gBgEa8HwfPkbgef5AvZZhJpdTatddd8WnP/1pXHLJJbj66qvx85//HI8++ii2b9+OJUuW4M1vfjPWrFnTtX3A/YTvtzyWLQ+xb/lCx17znozMTHulL0cbUxhkhvax9z2KGDBsyRPx87fJ4pX34KGC+ahATmBX6KS9oqcaGLbLuDCFhuZtm5eyvfJSf6bfT5wXpNQRDBexDgFOLVLf+0HXokI6BHSsRb5Xw4gn5xwoK1LB9Xv1vVGMYDckzmQookU91iG1fR56pUNA+vdDHcoGbSJ3m17bRN3WISPRaWXw4bdOIXVO+Pz09pQ84+uVTdQPOgRQi2zoOgS4baJ+0CFgDmwiQVN8bxRjld3RDGYwG0yFDhzPg++NotKcl8hd6bxnCVpU9twsOvXTqEucmyXLezw3K5TofHx8HK9//evx+te/vuzxEEIIIYQQQgghZA7wvRGM+LsmCz26REn3GOjT9/qVSsuLWNc81160guQX8xj7URLHZstLafA8x57/HLrR9mRnqBwldLP4SLN4xHXveSe5pUz9ZhlLHg91kX3Aufo3eNr1JHp5+slDFo+7X2AFIOrXs6xYVEr29pM2kg4BnWmRrkNAWosGXYcAd06XYdYhtZ9h0SFA1iLqUPdQ39sybSL1uyDZRIno0DnSojw6pNbPaxNN71iPrVseRAPTqGAcCycORG0knXxWjCbvsg7lvofjoIU8feSFNtFw4pqbAeXYRJybyf1mGUs/2kScm/UGOqUIIYQQQggZMKY23Yf1m69HdbmPXV6+OyrzaqhPPYcnfvIrYMM4dlvwCsxbsNdcD5MQQgixQqcUIYQQQgghA8Tap69GY48n8LwLXoza5Hji2rKTno8dT0/h4c9/Bws3rcbiydVzNEpCCCHETWGn1OOPP45Vq1aVOZaBxJZb1pboM65TicI9W+GffvIvADSjxxmOMtbrpI4yVsMNm1oMa/RiGiWn/fPTYaXO40zVN1ZLxpclVDSu22EIohgamiOpcNxH0aToWZD6NoSXdnR8u+H72Q4FbV3L0I+ceFK+J7EjfQU60SH1sUuHAOU7lOUo4+i7KekQ0HUtynSsspBoOMtWv7hut3QI6C8tsvWraVEnxyZ7hu9nmToU9iffk8h0YhOpn5lkE3WiQ4DFJtJ1KLxp+LcPdeiZDT8FDlqP/c45UUwkPrbbBA76mzW4/5Ifozo1HxMTh3b0PaYOGer45u9pQqMc/eTRIb1vYmbg5maA2ybi3Cx1rxRFdChnu1xwbpaLwp/CQQcdhIsuugjbtm0rczyEEEIIIYQQA43GNDbN/hp7n/NC58l2XsXH8y74X1i3+Qdo5jn1jxBCCOkhhZ1S1157LX74wx/igAMOwBVXXFHikAYD3/dSHkm9TKrbrTGoeL6fSGLmVTxjsjeTpzOqG133fM/YXiJPG30MxjbhGa/yOCv5xieOJUt/2lhMdaX31XivDHWlNrZxSu9rcpzJ70j7Jcrf5U7J8xvp1hiGCdNn5XqPe/m5mr5jeX4v0nc+C1l+J7YxpNoIOpQYZwk6lKm/aCwWje+ZDlk+F7WOPE6zDoUvs/f/d2b9PZEked63frSJTN9VvW4eTTH1W4pN5Pt4btMt2O1VBzgdUnGTkSomjlyK5zb/CkGOc+cz6ZpDh0yvyXqfEmwiY90MtqbLJiqbIjpELbKTx5bsJx3K8n+36XfCuZl5LMM4Nwtf5nDPzQo7pU444QTcdNNNuPTSS/GhD30IxxxzDG644YYyx0YIIYQQQghpsWnbbVj60gNytVnxmkMwteN/MI21CIJiJ0ATQggh3aLjROdnnnkmXv/61+PjH/84XvWqV+Hkk0/GJz/5Seyzzz5ljK/vqbRWqupo/ycvrnxE+22bXqpu9DeIjkiuKP01o2tR3bBO05ADIc67oB/nGT1XxhYtmOneXNvxo/E+aMjHj4r7iVUvtpZLIdPR7EJul8T4SohSMGKI9kjd2xBpYWprIqqb6CM64jWDR9p0bKnUVq9j83hH3zX9e5o4jluqY6ib7j95rWJY+eXKYDZ0LbJ+rpoWSToU1m31J+gQkNYiSYfCe2q/eUGHwrrm8es6BKS1yJrXQMjpkulo9j7SIdO9RB0S2kt19WOPO9Eh57hQTIeA9nfZpUNq3XT/6XJdi6hD2ShDh9THuk2k61BY12wTJfK/OGwiNYCoTJsojw6p9a1Hs1eb8Efyme+jSyfQaGxD4O9A09uGqrcgV3vTuFVy2R5FbKIe6VA4PEkn/FQd3c7RdchW13wP2kRloL5vLi3yE/aJ3Sbq1twMcNtEwzY3azR24LmNt2BH/UkEqKPqTWKXhcdhbHw3sR/b2KX7cG42OHOz0k7fe+UrX4mpqSl8/vOfx9VXX413vetduOiii7BgQQf/8RFCCCGEEEIKEzQDeGg5DINNnTmlCCGkII36djz5zFWYqa7F0lftj10P2gt+tYLp9Zux7ntXYub3s1i+6NWYv2CfuR4q6TGFnVKXX345brnlFtxyyy2477774Ps+Dj30ULz97W/HEUccga9//et4/vOfjyuvvBLHHntsmWPuC3zPS2SpT3gMG8kyP+jOqoZpJbjRSK4INFqO6yInPSRW7ywn3UjonmCTl16PQtC98qY6Ke922ck7Ld7zIt5uU3kpJ+B0eMKMaZ+wqbxMpHsY9zK3fl8lnzcyVOg6BCjvZY90KHGPSHcEHQIMWpTh9Kt49a4EHUrcO6qjawzkFUJRh4CeaVHRCAD9WmkncXVwwoyUx0C6Vga2/lPaRB3KhGgTKW9cr22ihvJ7dtpEGU4m7kSH1HtGFLGJvIoHPxjHzMZtGFk0L/O9tz78DGq1RQCAJrYjCBrwvEq2xl22iUo7iWvAbKIiOgRQi2z0g01UZG4G5DuZuEybqGtzMyBlE83ObsYjT/4j9vzfR2PRYUnfwNjySUweugdmp7bjtx//NhZv+l9YNHlEu0KXdEi91s9zM9O1suiXuVnhnFKXXHIJNm3ahDPPPBM/+clPsHHjRtx666247LLLcO655+L666/H29/+drzlLW8pcbiEEEIIIYTsnOy64A+w7ur7crV58j/uxOTCw+PngWF7DyGEdIsgaOLRJ/8F+77vBCw6bA+xXm1iHAdffDI24Hps2/p4D0dI5prCkVKPP+7+orz1rW/Fhz70oaK3GAjaHnhlRU44ESXeU5zYl97ynrc8rE1LboWmtg/UtCgfe2rjfAnhvYJGI3kd5jxTanmiX8E7myenS+LeUbvWi5C88olx6vurbd75krCd5OKsG62u5vHOK150fS+y6/QaUx1zRIV7D3aEr/Vr8qZ72vcyuuYZciuk+s+Qt8W0l5mk0bVI0iG1bjP+jMw6pNaVdCisn+xf0iEgrUWSDqnX4n4t36U8OV3iews6pLaz5VRI3aNLWmTVENdvXjuRxta/LW9CJzpkuncZOqQ+dumQ3i7Rf4Z8CdShbJShQ4BsE+k6lKyb7D/xnXLYRKbcLhFl6FB4TyFCU10fdthEQTPAxC6H4He/vBor/vAwVOeNiGOJmH5mM6af2IGRPXZVXkMF8PLpVUc6BHRkE5lys0ifQyc6ZBufb7i3/r3UdSh5jTZRr0juXrFrUSL3nMMm6tbcTK0zNHMzIGF/TG24G4tevAwL9lliHLOKX61gv3f/AR782H9hnwX/O3XSKOdmwzk3695sHsBuu+2G66+/vpu3IIQQQgghZKfA83ys2PV1uP9j16KxY9Zad2bjNvzmoz/C0iUvU9rX4HmlpZQlhBAnz279byx/9fMz1x/bbQLexA5smf4tAluWdzI0dNUp5XkeTjzxxG7eghBCCCGEkJ2GhZMHYGnlVNzz/qvx7E0PIWgkJ23N2Tqevu43uPcD38eSiZdhZGyX+FrVm+j1cAkhOzHN5iwwfxa1yfFc7Za+cn9s3ngPZoKnuzQy0k9wqaQgnu+F4Wxx4rx0nUoU2mkI++yEdpheOuQ9Om60yPGjqXJD+GeWpHpiOKkpQZ4jVDQxHj2E3raNpgBZjnHvJERdbVskqZ6eRM8aMpphnHJST/d2lzwkwp1b4Z6V1L3ldlwfkYl1CBC1SNehsE7Jn6umRZIOAXKIuq5DiWvalphOdChxb0GHEtcsiT71unH/HeiQqb/U9Q5D1POEn4v38dPh7EXGaU907g4zz0Pcr6BDYR1zG+qQnX60iVTdcdlEyYTDyf5tx627tCiPDqn3yLJ9ZmLRwRiftwee/tY1+P3/dxXGV02iMm8E9c07sOOJzVgwdiB2X/lH8CvKFj+vgiqyOaV6ZRMVPQSm1zZRP+gQQC2yoesQ4LaJ+mFuBrhtooRGlWgT9WJu1pjZltshBQAju8xHw1+HhrcFgT8L3zNvV+50+3CZNlG3dCh8XJ4W9ePcjE4pQgghhBBCBozayEKsXP567Gg8genpp9HcNgu/OoJdV82Hp58u5vkY9VZkP3WPEEJKwPdH0Jyu527XnKnDb7kq6sEmjHhLyx4a6SPolOqQIsce+wnPeNJTH/0N1GiVSsvD3qrb0LzSpsR7RY4fTSXDy5FUD1A861oYuTGiyRGpYEs4bB1fhpW9PLgSaSbIsRqYopL2sLs89sbVxS4dexzXiRPmpaOpUn9tSfqEe9qOHyV2ihx77MfRAubPEGhrkUuHTO11HQrbhX+lI9mNCYILJBrWdQgwRDRJOgRkilQQx9cjHTLeK8dqYIqyknlW0strevsixx6btEVfOSxDh4zjow5lQtIhwG0TBZYDFiQdAmSbKM+R7InoxhJsoiI6pF7LE6ngwcO4vwcq/jjqwSYEUYhFdBvPQwXzMOLvCt8bNY43C0V0yHStiE3UKx1Sy4rYRKa2Li2yaR9tomIkE50nyzqxibo1NwN6bxP1cm5WGRnHzJPbETSa8Ay/S4mNtz6B0dH9wuEH29pjL3FuJrYDODcTxgZ0R4e6mlOKEEIIIYQQ0j08z8OIvyvG/X0w6i9Dzd8FNX9Rq2wvjFVWduSQIoSQonieh4nxw7Dh149kbtOcrWPqf9ZifOHuAICAG1eHHkZKFcTX9i2rRyM2I1ef4J03HT9q250pHYVsOn40rhN5TS338ZpJL2fbI946Ktmyf9mGtNc5l1deQTrONM+e306xrQJKdWyrgXmOO860X1laVYw9+mnvuVdJfx8BZDr22IZ+3GiWVUDT0aJx3R5+zoNGrENASoskHQLSWtSJDgFpLZJ0yHQvUYeAlBZ1okOA5QhjW26XFrZjlXulRbZVQKmObTVQ0iG1bhk6lOwvqQ+SDgHIdOyxhOnY4yyrgLoWUYeyIdlETfXr6bCJmonvsvk3bvoMJZsoUcdhE+k6BMg2ke24dbEPpW4nNpExakKxiTx48DFpHVOnFNEhQLaJElriiJBKtJW0yKZngg6p1ySbyMugJSYkm8gWpSXqEEAtsqDrEOC2idTIqTJsomJzs/a9XHMzoBybqNdzsyVLXoKHv/EP2OXoveDX3FuIn/zOnVgw/yD4rd+zj4o7J9MQz82Acm2ifpybMVKKEEIIIYQQQgghpVMbWYjd5p+M3/7tj9GcteeXevq6+7Hpv5/BoiVHxGUVf363h0jmGEZKFaTieaj4XrynMrl/ubVnWMtm3zSsxEXoez0TdZvmukHs7Wz7FpstT3qe/csRplNodNp7k9PRVFJd0+kQztwuan4HfQXOsiLQtdP3TEcQCG3KWA003TtLXelappwplsiC1H0MUTJiHhjF0x79FvS9yL6hPP7dGHslQFuHAKS1SNAhQNYi057zuK6gQ0BaiyQdSpQZ8twB5hNCdXQdylQX7hVCY26XaOw90iHTuGIEHTK1KWs1UL93lro2jcqUq8Cyoie9FpcOAenfgikngq5F1KFsiDaR+tsvwyYyfBCSTdRUIgtcNpEtV1y3baJMkQqCDqn9pfroAx0CMthEeXLZGU79lOraNKqITdQPOgRQi2zoOgS4baJ+mJsBbptoGOZmixYfBWzwcfd7vodlrz4QS1/6PPgjoSsiCAJsvn8tnvyPOxGsH8PyVafA87z4tdSqi+Dpv5US5mbqtX6em6nXhnVuRqcUIYQQQgghhBBCusaixUdg4eRBWHfdj3D3d/8L3pgHeEAw3cBIdRkWLXoxRnZflGhT9Sfge7W5GTDpGXRKFcT3Qk+8eU9y9Dd9TS0HUOj0q6bmNU+cRKR5/LPsX9a98MZcLNqKm9FrrmHtz7JCKCGuFCobt0s59apANEKmOobVQBvSa8nSX568CfpKkDG6QThhxtaPbQWg/ddcDhgif0iKSIeAtN64dChxTdAh9bGkQ4BhlVjQIcCkRWYdAgwreoIOma7pYzP2V6YOAcYTajqiwCpgpjqGnCsSTh2y9JeMVLDnkjL9v5haVSxwwkweHTJeow5lQrKJjO+tYBPlOZk40iFAtol0HQKy5dt02UQmrenEJrJFKthw2UTd1iHTPYrYRJ3oUNb+XDnt1LJObCKbnklRosn/b4WxmCJ/SApdh4BybaJuzc0At01kjPru57kZIGpRtTKGlStfjR2No9EMtlv7r3jzMeLv1j0dAsq1ibo0NzP1N2xzMzqlCCGEEEIIIYQQ0nU8z8dYZXfMNp9DPdiEIKhr12uo+YtQ9SbT2/bIUEKnFCGEEEIIIYQQQnqC53kYqSxGLdgFzWA7mpgFAPgYQcUfn+PRkV5Dp1SHtBPotcs6OX7U91vJ7wyJhlPHjzbTifJciYbVus0odNM3h3kGymtKJb/LcAypfjxolmOUXUeKGtuUfIhkpjBxS7ssxxNLbWzHHWc56j2uawjlbHerf5+y1zWGJQv3tCXki8qsxx5bykgaXYskHVLrSkey+4om6Ak+TZ+rrkWSDgFpLXLpENDWIkmHALemhPeQw9aldv2oRYV0CHBuszNpikuHTNd0HQq7sW9dMW3Jk+raknmWfuyx8JyYkXQIcNtECU0RbCJdh9THkg6F1+w2UVPVEodNZExM3oFNNKg6BMjjsh6aUMQmMhy77jrcIVFXsHPM212y15USnJu0T7KJ1Oe0icoh8R45bCLzVj+zTdStuVmifQk2UT/MzfS+bXgAKliAiq1Ol+Zmpmucm82NDpX7PxchhBBCCCGEEEIIIRlgpFRBfD9MpFeJvOqGJIR+4D5+1JVUT32sJ9WLEuepfm1XouFEUj1HQrugmcF7nsHDbm3XOgPVenyp4KEvkpAvL1lWAcW6luPW9Tb6aqDpWiqZp2ElUr+HLZln/Fzr35yg0x19oK8Umo4ybifIg7tu9L3nXnKRSIcApLRI0iEgrUWSDiWuCToEpLUoV6LhDL/jSIusq38ZVgilNp3oUDi+7mpRllVAsa7luHVrueu4Y7Vf26pi3F3ymqRDaj9pbcmfzNN27LGuQ+q1iv69pw5ZkWyiZBSC3SYy/d/jSnge1jXbRLkOgcmQbLzrOgQ4tcgU+dkeXx/rENCRTWRqI9lEnegQINtEySgEe/SBKWIhpS2aDlnrKuOkFsnoOgS4bSLV7nTZRN2amwHl2kT9MDcDeq9FReZmpnb9ODdT6wzr3IyRUoQQQgghhBBCCCGk5zBSqiCRN943ec9bHvqK7nE35E0oK6dLROAnvaeRr769V1l5DbE3P//R7CY6XiHM2bYX5MrpUvGNdWw5XrLkSyjjuOMseRNsnnYpb4Lpnln6q+grV4aohvYqFFcFJdRIKV2LJB1Sr0na1Ezkw8ie0yVC0qHEPaLngg4B7qPZTZShQ3nbd5tcuRQEHVL7kfIn5FoNzKFD6rUseRMkDek0b4JeputQoo52jTpkR7KJ1ByaLpsoT75NY8S5oEPhNbtNpEYwuGyiTDlYCuiQ3neR9t2kEx1S6xSxiYxtnDny0nmdJB0KH2e3ZVy5pLLoma5DxjoGjaIWyeg6FJUB7rmZqaxXc7N2iXtuBvTeJuonHQLKnZuZ2vXj3MxUNmxzM0ZKEUIIIYQQQgghhJCew0ipgsTeeJPHUfCaR+V59i+b+jHldIn7S60kp737EbqXM9OJDwW88nlObei3iAVxzJZVwPh5hvwG0mpgoj8hD4PtZBm7Rzz6/miedmMUQoboA8d+ZWO+NWEV0OS556qgjEmDxMgpg0a5cksB+XK6REg6FLZP1pV0CJC1qOjqYCda1Jc6BFgjo4Bs+Q2KrAYa+82xwifpUKJO1/ImuFcB09936pANySYyRiwINlGefJvJyBGzTdRUP3uHTWT6fMuwiUxRVTujDgGd2UTGXHZCnXzRSur/Y3abyHRCaOqvKQJCzCUl17XZXNQiGV2HojLAPTcDys+3GcG5WbmUOTdTy2L6cG6mXhvWuRkjpQghhBBCCCGEEEJIz6FTihBCCCGEEEIIIYT0HG7fK4jveeE/P71FIA7TjBPltdpo5aYyPaleWOZONBzXbZrD6rIczR5Tq4TXZxupNhG5QkXb55yKdW2YwkZd9y4NQygo4EjQqdUxJRGW6tiSeOp1/NbnFHad/E5Yk3lKCYeNifLM10wJRLOFp5rbmI49jsJHefyxTKRDQPq9lHRIretKeB72kz3RcPxc0CH1Wty/pEOAU4vyJBwGOtMiSYds9y4NQYcA+3Y9/bqkRZ2GqEdaJOmQ6ZqkQ6ZrZSfzlHQofLnmMHbqkB3JJkpsoXPYRL6xbtIm0nUIkG0iVWtcNpHpaPaYPrKJBlWH1DqFbCLLMe66TWT6XYsHLeTQqITmORKcZ9s6CLGupEMAtciGrkOA2yay6U4/zs0At03EuZlS5pqbAU6bqB/mZol+hnRuxkgpQgghhBBCCCGEENJzGClVkDiZXuRFNyUMi5J4epGHHYlyoKxEw4bj1qO6cXm6brUaltWjo0m11xhYjv+N62SIVLDVjbzGUcI9E2IdP/RGq8ekloEt6V+WI5GzJBHW+8uVxNNylGh0Lfpsjck8dU976m/2ZJ6JMkcyT0A5blRPomc5mp1JPWXUhJ66Fkk6BKS1SNIhALkSDaeOW9d0KLyWrOvSISCfFmVZpdPrdqRDQM+1qJAOAaIWFVkNtEVB6TqUrKMdjWyNLOhOMk9Jh0xl1KFsSDZR4r112ER5DoEx/19h1qHwXnabSP2ulmETFdEhwK1F6u+un22iPEmETf3lOmBBiDBQ60o2kTEyXLCJrNFPgg6FL8H8m9B1yNTe/P8ttUhC1yHAbRM1lR96uYfA5JmbteuXYRP1w9wMKFeLujU3M/Xdj3Mztf6wzs0YKUUIIYQQQgghhBBCeg4jpQpSrfioVXw0W15gdZWtGYRlkUc8utYIwj3AefYvh/1IuRRa96y3xxUdRVptXavXW573Wuv5rPoqtKiGZK9Ate3tbkDOpRCRij6IvKkGT7l0zHEW73y3yeJxt9bNk68lIkO+hOh5pZrMm6Bek1b4os9f7S/22GtHjFbU6AZH3gTzPfW/7ZeprxJU9LEoK4a11nsS0HUuEukQgJQWSToEpLVI0iG1zJbTRdciSYcAkxaZdUjpNdaiTnQISGuR7bj1ftaiLHkTdB1S2xkjo4Bcq4GVajpvgjVfgqZFkg6pdSspjUrqkOleWfImSDqUuNbSIupQNiSbKNIhwG0T5cm3qR6lLtlE6tHsbpvIENWQ7LUjHQJkm8h23Ho/6xBQjk1kjIDIcty6YBPZIhYkHQLcNpE5t0uGaE7BJtJ1KHzZZpuopmgztUhG1yHAbRNFOgS4baJuzc0At02U+NhLsIk4N5PnWzF9NDcL72G3iQZ9bkZpI4QQQgghhBBCCCE9Z2CdUhs2bMCb3/xmTExMYNGiRXjrW9+KLVu2WNu89KUvhed5iX9vf/vbC90/3rds+qec/qA+r7U8+Ka6FT/8l2rre/ArrX+t59Wqj2rVj8s9v/1PahNfr6jXfPi+3+4vqlsJ/6ljqFQrqFQr8Gvhv/ieFaV+zYdf8+HVKvBqldDDXPHtdVvX4PuJf2rd+J9v/hf15deEdhn/6WMy3UNvo48bvi+3UfqL35vWe2XsP+4nfM/jz0D7nNTPqv1Ztv51+B3Rvxt6myzf4eh7X6v44m/D+nuCvDrSD8ylFhXRIaMWSZ+h2l7QIZMW2b4vqe+ZoEMmLRJ1SK0r6JBJi1w6ZNQiQYdsOlGWFtn6d+mQSYskHTLVlXTI9DmldMigRXm+I6IOqe0y/F/q1CGbFlGHrDh1KItNpNQVP0fD98WpQ4Z2kg5lsYmi34DNJtJ1yGYTJX53ghYVsYm6rUNl2UQJjZZsIk2HbDaR8f8olw5l+I4k+xO+j9r312YTGTVnwG2ivtUh03ur6VAmm8jyfelkbpbFJjLZ/P08NytLi7o+N8tgE/XD3CyTTWT4vg/S3Gxgt++9+c1vxlNPPYVrr70Ws7OzOPvss3Huuefiq1/9qrXdOeecg4svvjh+Pm/evG4PlRAyxFCLCCFzDXWIEDLXUIcIIUUZSKfUfffdh2uuuQa33HILjj32WADA5z//eZxyyin41Kc+hZUrV4pt582bh+XLl2e+1/T0NKanp+PnU1NTANrOV/2EBkDevxztSa5V0rlYorq+VlftL2ZWy8PgK/1FeT7qYZ123oTWruREX8mdytXWtyHa62zM7RJRi055adeS9hrH+5krSmEjeRqEV9H2KxtOb/CiQTTlkyS8SpdWkCx7kaV7x3UqqXcvvha10Z+HZeZcBVEd9XNv7wdO7jeO9x+rdWva3mFtH7RaN67T+rxNORD0dlGdmmGccX9aXf10iLBua7yG97pf6JUWuXQISGuRpEOASYvMOqTWlXRIrR+3E3QIMGmRWYeAtBZJOgS0tciW8yClRYIOJfrRTrTqJx0Kx2PPpZC4XjHrmK5DyTK7Dqllkg4BaS2SdMh0L0mHbHV1HTL256e/26mTaqhDCfLaRLZ8m5IOqXV1myilQ4BsEynfKbdNlLZ4JJvIuMVAs4k60aHwmtkmUvPAuLSon3QoUUfQIbVdWptk20O3iXQdAmSbSP2/yWUTqfeWbCJTXckm0nUo0Z+gQ0D/alE/6hDgtonU+ZbLJura3Axw2kRqbk6XTcS5mXzvQZ2bme45bHMz4/+t/c6NN96IRYsWxaIHACeddBJ838dNN91kbfuVr3wFS5YswaGHHooLL7wQ27Zts9a/9NJLMTk5Gf9btWpVKa+BEDL49EqLqEOEEAnaRISQuYY6RAjphIGMlFq7di122223RFm1WsXixYuxdu1asd2f/MmfYK+99sLKlStx55134v3vfz/uv/9+XHnllWKbCy+8EOeff378fGpqiuJHCAHQOy2iDhFCJGgTEULmGuoQIaQT+sopdcEFF+Dv/u7vrHXuu+++wv2fe+658ePDDjsMK1aswMtf/nI8+OCD2G+//YxtRkdHMTo6mir3ESYBq7bC/xpKSHUcZq5d85uGkLlWaGjVEEYYUdePB9WOITVtjQn8ZBiobwizC1Jl+UNFm4kSewi5GkJqOi45M4Yw9m5hCgUFHKHpEcKRxqY2eog64A4NTW5hSYaG2kLJ22X27TTqeFJhoMr3tdYaR1QWvYS4jZe+d7uu3G/UTm3fK/pNi1w6BBj0RtAhIK1FnehQWJbUIkmH1PYRkg4B+bbPtLXIHUIeaVEZOgR0X4skHQIc2/WARIi6pEXdClG3hZJLOgSktUgaC9D+zrp0CFA0RdAh4zXqUIK8NlFiu4vDJop0SK2ro+sQINtE6tYYl02U1qHwVQH5Uht0okNAf9tEHekQ0JFNZPrtu7btqduHZf1R69htIpt9JulQWEdrI+iQrd+EHdVjLRpkHQLcNpHfNHxP+nhuBrhtIs7NLHU1HTK168e5WeKeQzo36yun1Hve8x685S1vsdbZd999sXz5cjz99NOJ8nq9jg0bNuTak7x69WoAwAMPPCAKHyFk54NaRAiZa6hDhJC5hjpECOkFfeWUWrp0KZYuXeqsd/zxx2Pjxo249dZbccwxxwAArr/+ejSbzVjMsnDHHXcAAFasWJF7rFXfQ9X34iR4NcWL2GwmE+NF15qGlcNKM/Kwh88jj70pSZ/JawogTpwHqCtDrTr11nhrraempHp13SOe9spHY49XBpAm8sxHSe+yJPxMJRE2JP6MPNa6993kEbfey0GWRHy2KASpTpYkwhG2JJ7phJqK51rzwtuTefrJOqm27QgQfaXQmPROW/WraM/V11TTvfCep5W334soiV7T/bGUzqBoUaRDAFJaJOlQ4lpLiyQdAtKJi0UdAmItknQobNcq0hMNp3So3T7Sok50CLDogymJsKZFkg6FdbWoiw50yNRf6rolCsFWx5VEWEVaDTQn1JS0JL3C19YUsw4l21e0NukVuopWJumQ+ljSoeS18Dl1KBuSTdRUIzQdNlFFeZMlm0jXIUC2ifzE78hhE6lJix02kfqaJC3SdQgoxyZSf9cum6jbOqSPB0Ahm8gUoRkh6RAg20SmSE35oAU/XUewiUwRC6mE5AbdkWwiXYfU9pIOAb3XokHWIcBtEzUNO116PTcDsthE7f7LsIk4N/NS1/p5bhZes9tEgz43k+MS+5iDDz4YJ598Ms455xzcfPPN+MUvfoF3vvOdOOOMM+LTHZ544gkcdNBBuPnmmwEADz74ID760Y/i1ltvxSOPPILvfve7OPPMM/GSl7wEhx9++Fy+HELIgEItIoTMNdQhQshcQx0ihHRCX0VK5eErX/kK3vnOd+LlL385fN/H6173Onzuc5+Lr8/OzuL++++PT3AYGRnBj3/8Y3zmM5/B1q1bsWrVKrzuda/DBz/4wUL3973WvuV4L3H7Wk07djT2ypuOMo6xHjacIPaIR/uBFXdl5LGt6/1l8Mp7sbfbNJZmov9ma8+0p64wtDzhkedez62QOG691b7thW+9Ftuxo5bjkyNsOQ86IkcUgl7HvMfZvG/XdMy6mIPFctx6ntXAlJffdNS7JV+CfsxoVatTM0RL6L+J+D4GL7/1Z9MHzKUWRToEIKVFkg6p19Lk1yEgrUWiDoWFYR0hUsFLrLrp7c06pLaTdajdPtYbQYcA+Sj2ftIh071suV3S18y6odZ16RAA8Zhjc54o82pg1aQTeq4Gg6ZEZS4dUttJOpS4l7ZiSB2yI9lE6nvba5tI/U65bCJTFLlsE7XrSjaRrkNhK7NNFCg65rKJbNGcOv2kQ2odSYcA2SbKlIPFcty6K1JTbS/ZRFly2ek6pJbpbYwRC4JNpEZz9rMW9ZsOAW6bqB/mZkAWm8iUzY5zM9d9hmVupl4b1rnZwDqlFi9ejK9+9avi9b333huBEr65atUq/Pd//3cvhkYI2YmgFhFC5hrqECFkrqEOEUKKMrBOqbnG96J/kbeyfS3aZ6yf2qDvZwYgnj6TqBPoddKrgDGzjcS1XF75+ELkIVXyJszGryJ9T+1KNKpovNH70TREQOheeNMeZZunXq9bBp2eMOOKRtAfq23Mq4BJL3zkYfcsdYusBlZqlUS5eQymcWp19X3L6gqffhpE/PtJ141uMRenXg0KkQ6Fj5NaJOlQeC2pM7ZTsHTd0Z8DBi0SdQjIFKkQd5zUok50CDBpkVmHgLQWuXRIrVsWnZwwk2cV0NTGtRqo5kLwhLpZ8iWYVgN1LbLrT1SmPTfllPKS9zSdbKVrEXUoG5JN1FSjBRw2ke0ULEmH1DJJh9RruaLI48aSDgGSFpniK2SbSMnT4rCJbNGccfmA2US6Dqlt7L99s01UJFITcNtEdvvMrEOAbBOZojklm0h9i6hFMroOAW6byJT3rudzs7AwrOOYm4X37Nwm6vbcTK/fKd3SIcBtE/XD3Mx0bdjmZn0cBEoIIYQQQgghhBBChhVGShWk6gM1dQVeiSyoVlve5yDpjTZ6z7X28ekziU/G3C7ev6x6diOPagGvfHyKQ8s7r/rdI+9u7FmP9yi3a8X7lf3k/uUsEQv6qRCm1cA8Jz2UTZ4ohIiyVgH1PcT6aiBgOImhwGqgcXVRzJvQrlOJPep6ToXkc6DtWY9OiIj6iW6p7luutd3xIGZ0HQLaWiLpUHjNrCkpHQIULZL1K6VFgg6pdaVIhcTJVpoWSToUljUT7XUdUl+BtFJoOikry4phRLe1KO/qH2Be4dPb5snXouuQWibpEOCOkFJXA1Ori9a8Ca32Dh1K3FPQofBaq27rAXUoG5JNpL635dhEBXQIKBRFLtlEagSgZBPpOqSWSTqk1pFsIls0Z1zehzoEuKMRTHVs+Vokm0jXIbV9llx2kk1kyk+n20S6Dqn30G0i04lZkk2U+H+eWiRSxCbqj7mZ0p9jbga4baJ+mJvp9bvBzjQ3Ux8P69yMkVKEEEIIIYQQQgghpOfQKUUIIYQQQgghhBBCeg637xWk6vuo+j6aQTN+HhGX6cn0DInyKoFnrIu6EnoZJStr6qG+aZ9iPWpXIFQ0Plo0CgdVwsSjfg0B2crjZKK9ODQ90zaaZKI8ta4tfFRHDW3PS5YwU9vWGKnMlihPf246Zl0PDa0awj5doaFZQtRNSTej75qeIK9iSJAXX7OEveqhpu2jRf10v62yBpN6ikQ6BCClRZIOhdeSWiTqEBBrkaxDgK4Mkg4BWbbPKMeta1ok65Baatah8DXYw9cTCT81LeqVDrn6Dscib42Rnqv9SuHreULUE5qS6jepQ2qdLCHquhZJOgQoIeoOHVIfSzoEpLWIOpQNySaKngNum6iiHp4g2ES6DgGyTVRX7KgiqQ0km0jtV7aJ0omHJZvIN9URbCLbFmOdbutQOJ7ObSJbG0mHwnubbaIi24cBt02k/p8n2UTGfgWbyLQdWbKJ1DkGtUhG16GoDHDPzYAMNlGX5maA2yZKbBt22kScm+nk2TacLp/7uRngtokGfW7GSClCCCGEEEIIIYQQ0nMYKVUQ3wv/Rcm/Gmqicz3JnuVo9myYDhe2lRfzynvRymO9HdUQUUWUTK+1Whcf5awm8Wx51g2J9sK6csRCRUvimUy8Zz5utGlKqleym9XkbZfKdW9+WauAqYR7hrqVasVYp9PVwHSCPNlzn064Z1kFjPqJvf1IPJfKSJJIh4C0Fkk6BBTVIllvpGspHQKckQqemmRd0yJJh4C2Frl0KKxrXimsKL9hXYskHUpca9ErHTJdk3TIVLdIEmFdY9QySYcAd4SUbTVQ0iG1zKVDapmkQ+E1JMqoQ9mQbCLT+9aZTZRfh4BiUeSSTVRV+pdsItthDPFdNB1S+5NsIlM0p942op90SK3TSYSmSc/0uroOqXWyHLAg2UTm5OXmNjWD7riOXQdk3VHfMmqRjK5DgNsm6oe5GZDBJuLcLGZnnJsBbpto0OdmjJQihBBCCCGEEEIIIT2HkVIFqfoeqr6HyCGsH0EKmI5ETvsA45wCTc3jqkQLxPvP2yV6L8pjR6SCwSsfecbj40Yjz67i7W762jHHjaQ3HXCvFKreeX3Vr+1xT68GVjQvt8ljr2Py1EvY+omQ9jTbPe26d969JznLKmDUT7Yj2ctdDYz79Qzj1PY2x156w/Htuqe9Zug3ulZn/gSRSIcAiFpkOppd1wlJh0xlsg4BzogpQIxUSOkQkNIiSYfUMkmH1PqRFok6BKS0SNIhtR+dXumQqb19NdAejWBqb8vXku1I9qQWdbIamOhX0x1Jh8L2UX/yKqCuRdShbJRhEyVymwg2UVqH0v10GkXusokiHVLHrNtEeSIWTNEHkk2k6xDQe5sojw6pZZJN1GmEpm4T6Tpk6idPLjtT1KVkE9lsJN0m0nUIkG0iVaOoRTK6DgFZbKI+mJsBbpuoqtjxDpuIc7PsOhSWdR6h2e25mVo2rHMzRkoRQgghhBBCCCGEkJ7DSKmCVFInPCirbKkT+ZKnxlhPmLEQ7TP2Yy9/9kgF4x7faKW/tU85PmHGEIVgOhlLIqrRzvQvRyxEb5G+x9k0Xt1jb7y3ZTXRRRavvC0XQrss+d7b9iTr/ZhyIOheeP30BlP7eB+z5YSHLKuB0UvR2yRXDM37lE2nX0UedulkmWqibrqMJIl0CEBKiyQdAtJa1IkOASYtkiMVUnlPBB1S68Za1IEOAfJKoaRDpvF2W4fC8djb2VYB28/T770tMkrvw7UaaDpZT9IhtY6eSyHLaqCkQ2odlw4l2gs6pJbpdahDdiSbyHQKlmQTFdEhwGYTyZEKog4Bbpuorra0a5F6VbKJmomoy2h8ndtEtqiGLPTKJtJ1SL1my9ci2UQme0qyiXyD7kg2kfoyJJtI1yFTv5IOAbJNpNahFsmkdQhw2URN5XMtwybq9twMKMcm4twsbRv089xMrT+sczNGShFCCCGEEEIIIYSQnsNIqYL4HhInPDRtp+/Fnve4dSljmG35vbN45euGuuqJLwDgN5N7k22n0eTJrRB5z00nZbXbmiMXwmvpvAuJ/tVRluC5zeaVd0chRJj2JOv3sq0CpnIrWDzsXuTBFvYvA/lWA2NPu7AaqJaJJz2oqwYt73st9sq3xteqwtP38hHpEJDWIkmHgF5qUXp1UNciSYcAkxaZdSjsx51bQdciSYfU9vqKoel3rGtRGTqUtZ8skZkRkhYVyddiO1lP16Fkf/lXAyUdSlxz6FD4ONIbsw6F15Aoow5lQ7KJjKfv9aFNpOsQYLOJ2v1LNpEt751Lh8K6ZpvIlP8l1b9wvSid6JCpfRGbyJavRYp+8tToIsEmypLLTtchUx1JhwDZJtJ1CJB1R32rqEUyug4Bbpuomjg8rnMtKqJDan3X3CzZjnOzZJ38czPTtX6cm4X1w7/DOjdjpBQhhBBCCCGEEEII6Tl0ShFCCCGEEEIIIYSQnsPtewVRj2IHkltjdBqWa2X4BZueGiqphVDroZheOvyznUSvNU7LEcl6GKinHo/qCF+3HXusJ95LjLlpfv8Sidi7hC8k5bNtjdHrGEPUC4SmpxJ/KnX144310HS1bhTCGR81bDluPU8STyk0NJkgz8v0V30c8PhjEV2HAFmLuq1DgKovdh1S64o6BIhHJJu2xri20aj1pWOPTUnR47aCDunj6AaSDgH2kHT9eipEXdAhtR+XDqmPJR0C0lok6RCQ1qKyQtTz6I7+lzpkpxybqLs6BMg2UWIbjcMmUrfXSDZRlm00ug6pjyWbaFB1SK0j6VCyjnvbsGQT6TqkPpZ0CHDbRHkOWKgZbCTdJsqjP2odapFMP9lEeeZman3X3Axw20Scm7UZlrmZqf2wzc0YKUUIIYQQQgghhBBCeg4jpQpS87xEcsJmoPr3Wt7olrO4ZvDGpgnbN1qNqjn8heoKX03rTzzOXC3TPO0R6hHJeqI9U5ssK4X6OHSPuvF4VK0saKa9+93GFn0gldkSDneyChglzLMlGo6T1lmOPY5XCjtYDTS1k1YDgfZvIV4JiOv4Yt2gh5/zoKHrEKBqUec6BBTTIpcOmcokHQLaWiTpUKJM0KGwvvl4Y9PKXkoPBR0CeqdFtugD8bkl4bCkQ2o7lw6pdSUdAtJaJOmQqU6W1UCXDqmPJR0KH/vGutQhO7JNpEQK9dgmqiVKy7OJTIcxFIlYMI3BZRPpOgT03iYqokOAbBN1GqGp20S6DgGyTWSyZVyRmmEds01ktKMEm0jXoWQdsw4B1CIb3baJujU3M5V1YhNxbiaXDercTK0zrHMzRkoRQgghhBBCCCGEkJ7DSKmCVH0fVd9HI2jt/U2491qe8EA/PtjglQ6Sx2xGXs9ZNb9BDt9hO3IgbN+Im7bG5KW93dEKgO2I5GjvsOSdV+tLK4XJyIKojbxiGJdl8NhL2Orm8ebbog/EOtrRpHn2JKuPde+7ra50pGjC014NH1e0fcvm3C721UC1TvRyo0umFT7d664/T64Ytn4LzJ8gEukQAIMWSToE6Fok6RCgHG9cig4p49K0SNchwJQ3waxD4TV7xAKQ1iJJh9T28fM+1CEgrUUuHQLc0QiJaw4dMtXVdUgti+sIOgSktSjLaqBLhwD1uGOzDqll7RVD6lAWZJuo/d66bKJIhwDZJiqmQ+17lWETqblUOolY0HUobGe3ibJEddqQ6nZbh8Iys03UaYSmVFfVCckminQIcNtEtlx2kg6F/bTaaDaRKQpBsolUjaIWyeg6FJZFj/p3bga4bSJznl/OzRLPC8zNgHIiNLs9N1PLhnVuxkgpQgghhBBCCCGEENJzGClVkIofnvBgO2EmfS3tlU/lXajqdfOtEKa8z/Vkv6bTEqTTaIz5WgTvPOBeKTSt8HnaeHyTNz6Dx17CdgqE7SSZVN0cnnaxTY49yWp9qa7qudZPktFX+NTh6l54aaUve53Is570sOurgck65r9Jz33yGkkT6RBg0htYypO6I+tQu24pOqT0rWuR7TSalJYUiFhIlGm6oOtQ2I+e36H/dCgskyMyxTYZTo1xrQKa8rVIOgSktUjSoUQ/OVYDXTqklmU5WaZmqUPSlGETmXJz6jZRRzoElGITmaMQikcsqLhsoixRnTYkLeq2DpnalR2hKemQek3SIfWaZO/YctlJOhQ+NttEWU7f03VIf0ySdKZDgNsm6s7cLKxjt4nUSCqXTcS5WZsybKJ+mJsBbpto0OdmjJQihBBCCCGEEEIIIT2HTilCCCGEEEIIIYQQ0nO4fa8g1UIhohEZEn8qn4zvVwAA9UY6bDt1T8MWGKAdeplMpNdK+icckaweC+/aRhOW2cPXTWPLksQzSxiphCnUPe7HEOYpkSf8U6qTJ1Ge+lgKTa8Y6uY5UtQVVqrWraVCRduvyxUaagtNzxKi7nt1EDORDgF5t+9FZEj82dKiMnQIMGmRWYfUsobWpsg2GrW+Pr4sSTz7UYfC9uYtMdJ1tcx2lLErNF3tt6LVMYed2+vYttpIOhS2D/+WEaKebJf8Sx2yU4ZN1AyaqTLdJuq2DgFum6jsbTSme8XPC2w1tiFpUbd1yFin5G3Dkg6pZZIOJeoINpFaV7KJTIcnuLbt5d0+TC2S6UyHAKdN1KW5GeC2idTnLpuIczOlrASbqB/mZoDbJhr0uRkjpQghhBBCCCGEEEJIz2GkVEF8rw7fq2OkEr2FpiNFbd74uCetfTpSoe2Mzp9ML/K41ltJ9WxHJMde/1Zd35AgWPLOq3WklcKKcufAkAQ0PT7za7IlyNOplOR3zeNp1597Bq9/GauAeZIIm+qKRyRbVgOrFdOqXdKjPlIxrwaaynQv/Ii6atnywle9aRAzkQ4BELWoMx1ql5WhQ4BJi8w6pNb1tVXAIhELQFqLJB1Kjs/8mvpBhwB7RKb+XNeiLEcZu3RIrZMlibBeV9chtW9pNbBaSWuKS4fUx1mSeEbtqUPZKMcmMiQ615Ohd6BDgM0mao/XZROVFbGg65BpzEWiOm2UoUVFdEgtk3RIrVMkQjNPEmFTZLjLJqoZosh1m0jXIUC2ibJEauo6BFCLbKR1CCjXJurO3Axw20RqXZdNxLmZXDaoczO1bFjnZoyUIoQQQgghhBBCCCE9h5FSBal4s6h4s/Hzql9J1cnmjY+IvKatFTmv7S+sp1bGtFUmNQpB29vse+5Vtiw5XqJrknderWNbKWyPI+mpb48lw3HHVaG8C5i87lK5KwrB1L7IKqDJc6/vT7bndtFWATWPe8Xglde98OaVvc7zJairgdHvq8L8CSK6DgFpLepEh8LHYZlLhwBl9c+hQ4Bh5TBD3jtJh4B8eafaYzDrUDgex3HHVcu1kpF0yHQtSxSC/tx2lLFLh9THkg4BppwKZh1S6+orhqbVwLSmFF8NVB9HWkQdykavbKK0DrXrxs8EHQr76dwmUjWqk4iFdnn7fXHZREat6ZFN1IkOAbJN1GmEpq43pnwtcm4XQ4SmYBOp0ZySTVT2ceu6DoWPqUUS3baJujU3A7LYRH6qLudm7vJhmZup9x7WuRkjpQghhBBCCCGEEEJIz2GkVEF8r5HwHAaK9xyx9zG9BzlDzznaRB582SudaV+w5kWPPO6zDTXPgZfoT/fOq9dS+V5aixTNIF1X96w3DSfLmKIYALPnvmxMq30ArDkVxGgET26TZxUwGpJphU9vYzu9IZUvwbh/OcvKXv58Cfre5krru6KucEW/L540I6PrEKBoUSk6lLWdXYsy5WwyRBboWiTpkFrXlndK1yJJh9R7RUg6BHRfiyQdAtJaJOlQ4ppnbmM6NcalQ0BaO0yri9KJVsZcdqmcLt1ZDTTlndK1iDqUDdEmSnwPh8MmUrWhSMSCqEOA0yYaNB3SH6vP89hEtghN3SayRX7aTht22USmXHauSE1TXUmH1GuSDumPSZLu20Td0SHAbROp8y2XTcS5mVLmmJsBbpuoH+ZmyWvDOTdjpBQhhBBCCCGEEEII6TmMlCpI1ZtG1RtB5Cesqu695mj4t8uRClH39cTl5B742fjUmPB54vSGloe27XHXPe1tL2rULjoNQvfOJ9uZczYYVwyj57YTZsR9yuncV2WQJ2+CMRohRxSC3sa2Cmg7tSGdU0HO1xKfyJBaMUx7z+Nrwmqgei1LvgTJC1/1p1v3S58w42n5AUibSIcApLVI1CGg7BXCtBaZdQhIa5GkQ0BaiyQdUttJOqTWMUZRQcqxIEdTtemtFmU6YSZnZKbeRloF1HVIbSfpkFqm65d5xTCpIZIOhWXlrQaGZUktog5lQ7SJIh0Cuh6pIOkQ4LaJ1O+fyyZS7SjJJupEhwCLTWTNZTeYNpGtjS1CU7KJzLldzDaROjaXTZTQKEeEVJZcdrZITUmHAGqRDV2HgCw20dzPzQC3TZSMDLfbRP0wNzNfK063dAhw20T9MDcD3DbRoM/NGClFCCGEEEIIIYQQQnoOnVKEEEIIIYQQQgghpOdw+15BPG8WvjeLisGvV26oaLudHyccayXdjMMila0xtVZIp5aArmEIoYzCKvXjk+OQcqVNFCqoJ+ZMHMkeX0v2nyeMPS43JQg0JOWTyBMyagsJjesYtsJIbcvYGmOsYwlN148JlY4fNdWNEuZF3anhn/rxoHrIqPq4SBJPPTQ0mUyPR7G7iHQIQEqLZB0COglb13UISGuRpEOA4bcu6JBaN64j6JD6WNIhIF8Yu94mpg91yNS+rK0xWY5I1rfjmI471rVI0qHwHuHfSIskHVLLyghRD/tOahF1KBuSTdSt1AZqEmHJJop0CHDbRAl7x2ETqd8/ySbSdShRZ8Btojw6BJSzNcZk90g6pmuLqY6uQ2qZZBMltw2bbaIiByzYtg/rOhSWUYskOpubAWXYRN2emyXqcG6WuW0nNlE/zM0At0006HMzRkoRQgghhBBCCCGEkJ7DSKmCVLy600uYxSsfedilv+Hj0L1dFxz4vtfur655of2WV7Veb3nyFS9qlGivfdxoyyuvlQPplULj8e3aNVMy4gi9n6rF0y551k0rDDEV+VIeKoKnPk8SPXNS4lb/HawC2o4SzZJEWFrhq2SKQpCT6eke/CxJPCux531aqdu65s+AmCmkQ0BKi+z6E5XZdSis09IbQYeAtBZJOhTWTV6TdCjRXtChsF3ytyjpEJDWItsKn6hFXdYhQI4+MF1PJwNt9W+LWHDokFpH0iFATt5pW+GrZIpCMCcY1nUo2a+cxFPXIupQNsqwicy6o/8trkOAbBOphzG4bCJTxELq+HaDRkk2kSmaU7KJCukQUIoWdaJDah1Jh4DOIjQlHVKv2ZIIu2wiexSCnOhcsomyRGrqOhS+XmqRRGc6BLhsom7NzQC3TVQ3aBTnZkmGeW6mjmtY52aMlCKEEEIIIYQQQgghPYeRUgWp+DOo+NOZth/bvfJZMOddiLyp9WZ6EPo1037m1LGj2rHHqhfcFbGgto/qRF8u4x7n2B0a3Ss5/oQHvmIog92D3wl58iXYPO0RtigE/Z6drgKKx45a8rVEz01HirbrmlcD1ceu1cCwzO6FT64GtsrA/AkSsQ4BTi2y53bJgpz/RdIiU7muRZIOAWktKhKxoP4nl8r74tAhta6kQ0DvtcisO3L0QYRt9U+/n2sVMHGUsRDNUDPkdJHytahD0bVI0qFkWeergeFjPUKKOpSF3tlE+XXIdE3SIcBtExWNWJBsokbit+qwiSqGsqj/AbWJ8uR4yROhqfYr2UTqrV02UbKu2SYy5bJzRUhli9RUIhWoRSId6RBQihYV0SHAbROp32eXTdQPczOgXC3qlg6p9ft5bma6NmxzM0ZKEUIIIYQQQgghhJCew0ipglRQRwV1BF52v17klfeCWlzme7HPWmwXOT6j/cnRnub2HuV0jqromp5bQXUCR4t9vh+6tVMnzVgiFkwe9sgjXmTFMOVxV7zHjdjTLkczuDCdrJMHm4dd6teUVyV1zbIXOX0KhLwKmD7hISy3nVal703Oc3pDIleMdi060cF8aoPdC584YQbpMpIk0iEAHWlRJzoEpLVI0qFkWfS81VLTISCtRZIOJcoEHVL7kVYMjVEILS0qQ4fUfsvQISCtRbYVQ1duBVMUgkuHTO11HQJknTHla3GdaGWq69Kh8F6ziTJbvoT2qZbUoSyUYRO1dQiQtEjXofCx2SYyaZRkE6l5YVw2kSliIfVX06HEtQzRnJJN1EhEHnRuE/VKh9SyLHnvdJvImNtFqGM+fS/8a7N3XDaRzTaSdAiQbaIskZq6DqllJE0nOgSUYxMVmZup/bnmZoDbJuLcTK0zHHMztWxY52aMlCKEEEIIIYQQQgghPWdgI6UuueQSXH311bjjjjswMjKCjRs3OtsEQYAPf/jD+OIXv4iNGzfiRS96Eb7whS/ggAMOyH1/35uF783Gb2DDU1zEmrfYaxXYdl+OVKqtfoPEX8C8LzmsE3lGm0rd5DW9bTNQV5hbnvvY4x76KKMmqgdb39scedZNkQrx2LX8BqYVw7QXPtkGsEcz6GMQqWT3wNu89XlOmNGfm6IQ9DpZVgH1Pcnh42S/utdcHVKefC36NVPeqbSn3rwaCABVL9q3HHnj5bwtUZ2q37+rgv2iQwDSWiToECBrka5D6mNJh8I6SS1y6RDQ1iJJh4C0Fkk6pNaRdCi8pxBFZcqNoGmRS4dS7SVK0KI8J8yYykyrf3ob1yqgKV+UpEOJ9q2iPPlaJB1S67h0SC2TdAhIa9Eg6BDQP1rk0iGgM5uoiA6p1ySbKGFzOWyiWSWaU7KJUjoEiDaRNU+LoEOmOqlyG31kE2WJWLBFaOo2ka5DgGwT5cnXYtIoXcfM0Zxmm0jXobCO2SZS6/SzFvWbDgFum6gf5maA2yZSm7hson6Ym+njMNIHOgS4baJ+mJvZrg3L3GxgI6VmZmbwhje8Ae94xzsyt/nEJz6Bz33uc7j88stx0003Yf78+VizZg127NjRxZESQoYV6hAhpB+gFhFC5hrqECGkKAMbKfWRj3wEAHDFFVdkqh8EAT7zmc/ggx/8IF772tcCAP793/8dy5Ytw7e//W2cccYZ3RoqIWRIoQ4RQvoBahEhZK6hDhFCijKwTqm8PPzww1i7di1OOumkuGxychKrV6/GjTfeKArf9PQ0pqfbSVA3bdoEANi2ZSOq/izqzTApXlNJXt5ova2N5kj4NwifB0FUt/22R9fqwWjimpr8LgiSCfJmtee2uk3heViGVhnMdaGGdEZ/k/3YEg2bEu5JdSMagVw3fo1ZQtM7wBSQ67XCHxut5772HGgnp4vax2GgfrJcbR/9DVp1AyVMMxCuBVE4KOTQ9GYc2qmNRRlPQwsnrVsSnetjmDWGxSdD0tsJ8tphn14qNHQm/GsJUd869Vx475KPuZ4LuqVDAFJaJOkQkNYiSYfCfoNWG7MOqWVS3aahbltv0HoOuS4iLWnVMfQrJRo2aVSqjeG7pWtRr3UISGuRrkNAWotEHQqfJK8JOgSoemPXIaCtRZIOhde08Qg6BKS1SNIhoK1FLh1Sr0k6BMjb9oZJh4De2UQNxcx02USqRkk2ka4tgGwTBVbdMetQWAZzXU2Hwsd2u8emUXq5Xh9w6xDQe5vIU35/kk2kJhx22USmrTFpvbFpVKutoEOAxSZSX4vDJlI1SrKJdB0CZJtI1yFAtonUrTLDpEXd1iHAbRMFgVrXbhN1a26WLIue6+VKXYdNxLlZG9fcLHGtj+dmatmwzs12GqfU2rVrAQDLli1LlC9btiy+ZuLSSy+NPf8qe+/5h+UOkBBi5dlnn8Xk5ORcD6MjqEOEDDbDoEMAtYiQQWcYtIg6RMhgU6YO9ZVT6oILLsDf/d3fWevcd999OOigg3o0IuDCCy/E+eefHz/fuHEj9tprLzz22GMD/5+BztTUFFatWoXHH38cExMTcz2cUhnm1wYM9+vbtGkT9txzTyxevLgn96MOzT3D/H3maxtMeq1DALVorhnm7zNf2+BCm4g6NCwM82sDhvv1dUOH+sop9Z73vAdvectbrHX23XffQn0vX74cALBu3TqsWLEiLl+3bh2OPPJIsd3o6ChGR0dT5ZOTk0P3BYuYmJjgaxtQhvn1qaeydRPqUP8wzN9nvrbBpFc6BFCL+oVh/j7ztQ0utImoQ8PCML82YLhfX5k61FdOqaVLl2Lp0qVd6XufffbB8uXLcd1118VCNzU1hZtuuinXKRGEkOGGOkQI6QeoRYSQuYY6RAjpBb1b8iuZxx57DHfccQcee+wxNBoN3HHHHbjjjjuwZcuWuM5BBx2Eq666CgDgeR7OO+88fOxjH8N3v/td3HXXXTjzzDOxcuVKnH766XP0Kgghgwx1iBDSD1CLCCFzDXWIEFKUvoqUysNFF12Ef/u3f4ufH3XUUQCAn/zkJ3jpS18KALj//vvjExkA4H3vex+2bt2Kc889Fxs3bsSLX/xiXHPNNRgbG8t839HRUXz4wx82ho0OOnxtg8swv75+fm3Uoe4wzK+Pr20w6ffXRi0qH762wWSYXxvQ36+POlQ+fG2DyzC/vm68Ni8YhjNFCSGEEEIIIYQQQshAMbDb9wghhBBCCCGEEELI4EKnFCGEEEIIIYQQQgjpOXRKEUIIIYQQQgghhJCeQ6cUIYQQQgghhBBCCOk5dEpl4JJLLsEJJ5yAefPmYdGiRZnaBEGAiy66CCtWrMD4+DhOOukk/O53v+vuQAuwYcMGvPnNb8bExAQWLVqEt771rYmjW0289KUvhed5iX9vf/vbezRimcsuuwx77703xsbGsHr1atx8883W+t/61rdw0EEHYWxsDIcddhi+//3v92ikxcjz+q644orUZ5TnJJNe8bOf/QynnXYaVq5cCc/z8O1vf9vZ5qc//SmOPvpojI6OYv/998cVV1zR9XH2A9ShJP2qQ8Bwa9Ew6hBALcrKMOsQMFxaRB0KoQ4NJ8OsRdShwdAhgFqk0qkW0SmVgZmZGbzhDW/AO97xjsxtPvGJT+Bzn/scLr/8ctx0002YP38+1qxZgx07dnRxpPl585vfjHvuuQfXXnst/uu//gs/+9nPcO655zrbnXPOOXjqqafif5/4xCd6MFqZb3zjGzj//PPx4Q9/GLfddhuOOOIIrFmzBk8//bSx/i9/+Uu86U1vwlvf+lbcfvvtOP3003H66afj7rvv7vHIs5H39QHAxMRE4jN69NFHezjibGzduhVHHHEELrvsskz1H374YZx66ql42ctehjvuuAPnnXce3va2t+GHP/xhl0c691CH0vSbDgHDrUXDqkMAtSgrw6xDwPBoEXUoCXVo+BhmLaIO9b8OAdQilVK0KCCZ+dd//ddgcnLSWa/ZbAbLly8PPvnJT8ZlGzduDEZHR4Ovfe1rXRxhPu69994AQHDLLbfEZT/4wQ8Cz/OCJ554Qmx34oknBn/1V3/VgxFm57jjjgv+4i/+In7eaDSClStXBpdeeqmx/h//8R8Hp556aqJs9erVwZ//+Z93dZxFyfv6sn5X+wkAwVVXXWWt8773vS845JBDEmVvfOMbgzVr1nRxZP0FdSikH3UoCIZbi3YGHQoCalEWhk2HgmC4tIg61IY6NNwMmxZRhwZDh4KAWqRShhYxUqoLPPzww1i7di1OOumkuGxychKrV6/GjTfeOIcjS3LjjTdi0aJFOPbYY+Oyk046Cb7v46abbrK2/cpXvoIlS5bg0EMPxYUXXoht27Z1e7giMzMzuPXWWxPvt+/7OOmkk8T3+8Ybb0zUB4A1a9b01ecTUeT1AcCWLVuw1157YdWqVXjta1+Le+65pxfD7SqD9LnNNdSh3jPMWkQdSjIon9tcMyg6BAyPFlGH0lCHyKBoEXVoML7P1KIkZXx21bIHRYC1a9cCAJYtW5YoX7ZsWXytH1i7di122223RFm1WsXixYut4/yTP/kT7LXXXli5ciXuvPNOvP/978f999+PK6+8sttDNrJ+/Xo0Gg3j+/2b3/zG2Gbt2rV9//lEFHl9Bx54IL70pS/h8MMPx6ZNm/CpT30KJ5xwAu655x7ssccevRh2V5A+t6mpKWzfvh3j4+NzNLL+gzrUe4ZZi6hDSahF2RgUHQKGR4uoQ0moQwQYHC2iDvX35xNBLUpShhbttJFSF1xwQSrZmP5P+lL1O91+beeeey7WrFmDww47DG9+85vx7//+77jqqqvw4IMPlvgqSCccf/zxOPPMM3HkkUfixBNPxJVXXomlS5fiH//xH+d6aESBOkQdGmaoQ4PBMOsQQC3a2aEODQ7DrEXUIUItsrPTRkq95z3vwVve8hZrnX333bdQ38uXLwcArFu3DitWrIjL161bhyOPPLJQn3nI+tqWL1+eSsZWr9exYcOG+DVkYfXq1QCABx54APvtt1/u8XbKkiVLUKlUsG7dukT5unXrxNexfPnyXPXnkiKvT6dWq+Goo47CAw880I0h9gzpc5uYmBjIFUHq0PDoEDDcWkQdSjJMWjTMOgTsfFpEHbJDHepfhlmLqEMhw6BDALVIpwwt2mmdUkuXLsXSpUu70vc+++yD5cuX47rrrouFbmpqCjfddFOuUyKKkvW1HX/88di4cSNuvfVWHHPMMQCA66+/Hs1mMxazLNxxxx0AkBD5XjIyMoJjjjkG1113HU4//XQAQLPZxHXXXYd3vvOdxjbHH388rrvuOpx33nlx2bXXXovjjz++ByPOR5HXp9NoNHDXXXfhlFNO6eJIu8/xxx+fOh62Xz+3LFCHhkeHgOHWIupQkmHSomHWIWDn0yLqkB3qUP8yzFpEHRoeHQKoRTqlaFGRLOw7G48++mhw++23Bx/5yEeCBQsWBLfffntw++23B5s3b47rHHjggcGVV14ZP//4xz8eLFq0KPjOd74T3HnnncFrX/vaYJ999gm2b98+Fy9B5OSTTw6OOuqo4Kabbgp+/vOfBwcccEDwpje9Kb7++9//PjjwwAODm266KQiCIHjggQeCiy++OPj1r38dPPzww8F3vvOdYN999w1e8pKXzNVLCIIgCL7+9a8Ho6OjwRVXXBHce++9wbnnnhssWrQoWLt2bRAEQfCnf/qnwQUXXBDX/8UvfhFUq9XgU5/6VHDfffcFH/7wh4NarRbcddddc/USrOR9fR/5yEeCH/7wh8GDDz4Y3HrrrcEZZ5wRjI2NBffcc89cvQQjmzdvjn9PAIJPf/rTwe233x48+uijQRAEwQUXXBD86Z/+aVz/oYceCubNmxe8973vDe67777gsssuCyqVSnDNNdfM1UvoGdSh/tehIBhuLRpWHQoCalFWhlmHgmB4tIg6RB0adoZZi6hD/a9DQUAtKluL6JTKwFlnnRUASP37yU9+EtcBEPzrv/5r/LzZbAYf+tCHgmXLlgWjo6PBy1/+8uD+++/v/eAdPPvss8Gb3vSmYMGCBcHExERw9tlnJwT94YcfTrzWxx57LHjJS14SLF68OBgdHQ3233//4L3vfW+wadOmOXoFbT7/+c8He+65ZzAyMhIcd9xxwa9+9av42oknnhicddZZifrf/OY3g+c973nByMhIcMghhwRXX311j0ecjzyv77zzzovrLlu2LDjllFOC2267bQ5GbecnP/mJ8bcVvZazzjorOPHEE1NtjjzyyGBkZCTYd999E7+7YYY6NBg6FATDrUXDqENBQC3KyjDrUBAMlxZRh0KoQ8PJMGsRdWgwdCgIqEV6m060yAuCIMgeV0UIIYQQQgghhBBCSOfstKfvEUIIIYQQQgghhJC5g04pQgghhBBCCCGEENJz6JQihBBCCCGEEEIIIT2HTilCCCGEEEIIIYQQ0nPolCKEEEIIIYQQQgghPYdOKUIIIYQQQgghhBDSc+iUIoQQQgghhBBCCCE9h04pQgghhBBCCCGEENJz6JQihBBCCCGEEEIIIT2HTilCCCGEEEIIIYQQ0nPolCJDw7PPPovddtsNjzzySNfvdcYZZ+Dv//7vu34fQshgQR0ihPQD1CJCyFxDHSJZ8YIgCOZ6EISUwfnnn4/Nmzfji1/8Ytfvdffdd+MlL3kJHn74YUxOTnb9foSQwYA6RAjpB6hFhJC5hjpEssJIKTIUbNu2Df/yL/+Ct771rT2536GHHor99tsPX/7yl3tyP0JI/0MdIoT0A9QiQshcQx0ieaBTivQlX/va1zA+Po6nnnoqLjv77LNx+OGHY9OmTan63//+9zE6OooXvvCFifK9994bn/nMZxJlRx55JP7mb/4mfv7Sl74U73rXu3Deeedhl112wbJly/DFL34RW7duxdlnn42FCxdi//33xw9+8INEP6eddhq+/vWvd/5iCSF9CXWIENIPUIsIIXMNdYh0EzqlSF9yxhln4HnPex7+9m//FgDw4Q9/GD/+8Y/xgx/8wBiSecMNN+CYY44pfL9/+7d/w5IlS3DzzTfjXe96F97xjnfgDW94A0444QTcdttteOUrX4k//dM/xbZt2+I2xx13HG6++WZMT08Xvi8hpH+hDhFC+gFqESFkrqEOkW5CpxTpSzzPwyWXXIIvfvGLuOSSS/D5z38e11xzDXbffXdj/UcffRQrV64sfL8jjjgCH/zgB3HAAQfgwgsvxNjYGJYsWYJzzjkHBxxwAC666CI8++yzuPPOO+M2K1euxMzMDNauXVv4voSQ/oU6RAjpB6hFhJC5hjpEukl1rgdAiMSrX/1qPP/5z8fFF1+MH/3oRzjkkEPEutu3b8fY2Fjhex1++OHx40qlgl133RWHHXZYXLZs2TIAwNNPPx2XjY+PA0DCQ08IGS6oQ4SQfoBaRAiZa6hDpFswUor0Lddccw1+85vfoNFoxMIjsWTJEjz33HOZ+m00GqmyWq2WeO55XqLM8zwAQLPZjMs2bNgAAFi6dGmm+xJCBg/qECGkH6AWEULmGuoQ6RZ0SpG+5LbbbsMf//Ef41/+5V/w8pe/HB/60Ies9Y866ijce++9xmvr1q2LH8/OzuLxxx8vZYx333039thjDyxZsqSU/ggh/QV1iBDSD1CLCCFzDXWIdBM6pUjf8cgjj+DUU0/FBz7wAbzpTW/CxRdfjP/8z//EbbfdJrZZs2YN7rnnHqNH/ktf+hJ+/OMf43e/+x3e/e53Y9OmTXjwwQcTgliEG264Aa985Ss76oMQ0p9Qhwgh/QC1iBAy11CHSLehU4r0FRs2bMDJJ5+M1772tbjgggsAAKtXr8arXvUqfOADHxDbHXbYYTj66KPxzW9+M3XttNNOw1/+5V/isMMOw4YNG/Cxj30MV155JX784x8XHueOHTvw7W9/G+ecc07hPggh/Ql1iBDSD1CLCCFzDXWI9AIvCIJgrgdBSBlcffXVeO9734u7774bvh/6W/fee2+cd955OO+880q91xe+8AVcddVV+NGPflRqv4SQwYY6RAjpB6hFhJC5hjpEssLT98jQcOqpp+J3v/sdnnjiCaxataqr96rVavj85z/f1XsQQgYP6hAhpB+gFhFC5hrqEMkKnVJkqCjb6y7xtre9rSf3IYQMHtQhQkg/QC0ihMw11CGSBW7fI4QQQgghhBBCCCE9h4nOCSGEEEIIIYQQQkjPoVOKEEIIIYQQQgghhPQcOqUIIYQQQgghhBBCSM+hU4oQQgghhBBCCCGE9Bw6pQghhBBCCCGEEEJIz6FTihBCCCGEEEIIIYT0HDqlCCGEEEIIIYQQQkjPoVOKEEIIIYQQQgghhPQcOqUIIYQQQgghhBBCSM+hU4oQQgghhBBCCCGE9Jz/P8UYJs3iLLzuAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1200x600 with 4 Axes>"
       ]
@@ -153,7 +153,12 @@
     "    res = fm.get_electron_positions(n_electrons=k+1, electron_initial_positions=None)\n",
     "\n",
     "    fm.plot_potential_energy(ax=ax[k], dxdy=(2, 2), print_voltages=False, plot_contours=False)\n",
-    "    fm.plot_electron_positions(res, ax=ax[k])"
+    "    fm.plot_electron_positions(res, ax=ax[k])\n",
+    "\n",
+    "    if k > 0:\n",
+    "        ax[k].set_ylabel(\"\")\n",
+    "\n",
+    "fig.tight_layout()"
    ]
   },
   {
@@ -168,12 +173,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 900x600 with 3 Axes>"
       ]
@@ -208,7 +213,9 @@
     "    ax[k].set_title(fr\"$E/n$ = {res['fun'] * qe / (n * E0):.5f}\")\n",
     "\n",
     "    if k >= 0:\n",
-    "        ax[k].set_ylabel(\"\")"
+    "        ax[k].set_ylabel(\"\")\n",
+    "\n",
+    "fig.tight_layout()"
    ]
   },
   {
@@ -254,12 +261,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x600 with 5 Axes>"
       ]
@@ -283,7 +290,9 @@
     "    ax[k].set_title(fr\"$E/n$ = {np.round(res['fun'] * qe / (n * E0), 5):.5f}\")\n",
     "\n",
     "    if k >= 1: \n",
-    "        ax[k].set_ylabel(\"\")"
+    "        ax[k].set_ylabel(\"\")\n",
+    "\n",
+    "fig.tight_layout()"
    ]
   },
   {
@@ -314,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,22 +331,39 @@
     "natural_frequency = trap_curvature/(2 * np.pi * np.sqrt(2))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the equations of motion consider electrons coupled to a resonator, we must supply the resonance frequency of the resonator and the impedance to `setup_eom`. If not interested in the cavity, or if you simply want to study the plasmons in the absence of the resonator, you can set the resonance frequency far off-resonant."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resonator_dict = {\"f0\" : 100e6, \n",
+    "                  \"Z0\" : 50}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gkoolstra/Documents/Code/quantum_electron/quantum_electron/eom_solver.py:260: RuntimeWarning: invalid value encountered in sqrt\n",
+      "/Users/gkoolstra/Documents/Code/quantum_electron/quantum_electron/eom_solver.py:300: RuntimeWarning: invalid value encountered in sqrt\n",
       "  return np.sqrt(EVals) / (2 * np.pi), EVecs\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x600 with 4 Axes>"
       ]
@@ -359,8 +385,8 @@
     "\n",
     "    fm.plot_potential_energy(ax=ax[k], dxdy=(2, 2), print_voltages=False, plot_contours=True)\n",
     "\n",
-    "    K, M = fm.setup_eom(res['x'])\n",
-    "    efreqs, evecs = fm.solve_eom(K, M)\n",
+    "    K, M = fm.setup_eom(res['x'], resonator_dict=resonator_dict)\n",
+    "    efreqs, evecs = fm.solve_eom(K, M, sort_by_cavity_participation=False)\n",
     "\n",
     "    ax[k].set_title(f\"{efreqs[m]/natural_frequency:.3f} \"+r\"$\\omega / (\\omega_0/\\sqrt{2})$\")\n",
     "\n",
@@ -370,14 +396,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<video width=\"1200\" height=\"600\" controls autoplay loop>\n",
-       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQAAhOttZGF0AAACrwYF//+r\n",
+       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQAAfXNtZGF0AAACrwYF//+r\n",
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        "b3JnL3gyNjQuaHRtbCAtIG9wdGlvbnM6IGNhYmFjPTEgcmVmPTMgZGVibG9jaz0xOjA6MCBhbmFs\n",
@@ -390,612 +416,578 @@
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        "\">\n",
        "  Your browser does not support the video tag.\n",
        "</video>"
@@ -1014,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1023,13 +1015,13 @@
        "Text(0, 0.5, '$\\\\sqrt{2}\\\\omega / \\\\omega_0$')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1044,6 +1036,13 @@
     "plt.xlabel(\"Mode index\")\n",
     "plt.ylabel(r\"$\\sqrt{2}\\omega / \\omega_0$\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1062,7 +1061,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/quantum_electron/__init__.py b/quantum_electron/__init__.py
index 0280500..2600f47 100644
--- a/quantum_electron/__init__.py
+++ b/quantum_electron/__init__.py
@@ -1,4 +1,4 @@
 from .electron_counter import FullModel
 from .schrodinger_solver import QuantumAnalysis
 from .utils import PotentialVisualization, package_versions
-from ._version import __version__
\ No newline at end of file
+from ._version import __version__
diff --git a/quantum_electron/_version.py b/quantum_electron/_version.py
index 0a0f457..52535bb 100644
--- a/quantum_electron/_version.py
+++ b/quantum_electron/_version.py
@@ -2,4 +2,4 @@
 # 1) we don't load dependencies by storing it in __init__.py
 # 2) we can import it in setup.py for the same reason
 # 3) we can import it into your module module
-__version__ = '0.2.0'
\ No newline at end of file
+__version__ = '0.2.1'
diff --git a/quantum_electron/electron_counter.py b/quantum_electron/electron_counter.py
index 14c2ae4..dac2706 100644
--- a/quantum_electron/electron_counter.py
+++ b/quantum_electron/electron_counter.py
@@ -20,13 +20,13 @@
 
 
 class FullModel(EOMSolver, PositionSolver, PotentialVisualization):
-    def __init__(self, potential_dict: Dict[str, ArrayLike], voltage_dict: Dict[str, float], 
-                 include_screening : bool = False, screening_length : float = np.inf, 
-                 potential_smoothing: float = 5e-4, remove_unbound_electrons : bool = False, remove_bounds : Optional[tuple] = None, 
+    def __init__(self, potential_dict: Dict[str, ArrayLike], voltage_dict: Dict[str, float],
+                 include_screening: bool = False, screening_length: float = np.inf,
+                 potential_smoothing: float = 5e-4, remove_unbound_electrons: bool = False, remove_bounds: Optional[tuple] = None,
                  trap_annealing_steps: list = [0.1] * 5, max_x_displacement: float = 0.2e-6, max_y_displacement: float = 0.2e-6) -> None:
         """This class can be used to determine the coordinates of electrons in an electrostatic potential and solve for the in-plane equations of motion.
         Typical usage:
-        
+
         voltage_dict = {"trap" : 0.5, "res_plus" : 0.4, "res_min" : 0.4}
         fm = FullModel(potential_dict, voltage_dict)
         fm.set_rf_interpolator(rf_electrode_labels=["res_plus", "res_minus"])
@@ -65,18 +65,19 @@ def __init__(self, potential_dict: Dict[str, ArrayLike], voltage_dict: Dict[str,
                                 spline_order_x=self.spline_order, spline_order_y=self.spline_order,
                                 smoothing=self.potential_smoothing, include_screening=self.include_screening, screening_length=self.screening_length)
 
-        EOMSolver.__init__(self, Ex=self.Ex, Ey=self.Ey, 
-                           Ex_up=self.Ex_up, Ex_down=self.Ex_down, Ey_up=self.Ey_up, Ey_down=self.Ey_down, 
+        EOMSolver.__init__(self, Ex=self.Ex, Ey=self.Ey,
+                           Ex_up=self.Ex_up, Ex_down=self.Ex_down, Ey_up=self.Ey_up, Ey_down=self.Ey_down,
                            curv_xx=self.ddVdx, curv_xy=self.ddVdxdy, curv_yy=self.ddVdy)
 
-        PotentialVisualization.__init__(self, potential_dict=potential_dict, voltages=voltage_dict)
+        PotentialVisualization.__init__(
+            self, potential_dict=potential_dict, voltages=voltage_dict)
 
         self.ConvergenceMonitor = ConvergenceMonitor
 
     def set_rf_interpolator(self, rf_electrode_labels: List[str]) -> None:
         """Sets the rf_interpolator object, which allows evaluation of the electric field Ex and Ey at arbitrary coordinates. 
         This must be done before any calls to EOMSolver, such as setup_eom or solve_eom.
-        
+
         The RF field Ex and Ey are determined from the same data as the DC fields, and are evaluated by setting +/- 0.5V on the 
         electrodes that couple to the RF-mode. These electrodes should be specified in the argument rf_electrode_labels.
 
@@ -96,7 +97,8 @@ def set_rf_interpolator(self, rf_electrode_labels: List[str]) -> None:
         elif len(rf_electrode_labels) == 1:
             rf_voltage_dict[rf_electrode_labels[0]] = +1.0
         else:
-            raise ValueError("More than 2 electrodes are not supported for the RF interpolator.")
+            raise ValueError(
+                "More than 2 electrodes are not supported for the RF interpolator.")
 
         potential = make_potential(self.potential_dict, rf_voltage_dict)
 
@@ -105,19 +107,19 @@ def set_rf_interpolator(self, rf_electrode_labels: List[str]) -> None:
         self.rf_interpolator = scipy.interpolate.RectBivariateSpline(self.potential_dict['xlist']*1e-6,
                                                                      self.potential_dict['ylist']*1e-6,
                                                                      potential)
-        
+
         # The code below is only for setting up the coupled LC circuit.
         # For the coupled LC circuit, we must consider the electric field generated by each electrode individually
         # In this case, rf_electrode_labels must contain at least 2 items
         if len(rf_electrode_labels) == 1:
             rf_electrode_labels *= 2
-        
+
         assert len(rf_electrode_labels) == 2
-        
+
         # We assume the first electrode is associated with the 'up' electrode
         rf_voltage_dict[rf_electrode_labels[0]] = 1.0
         rf_voltage_dict[rf_electrode_labels[1]] = 0.0
-        
+
         potential = make_potential(self.potential_dict, rf_voltage_dict)
 
         # By using the interpolator we create a function that can evaluate the potential energy for an electron at arbitrary x,y
@@ -125,11 +127,11 @@ def set_rf_interpolator(self, rf_electrode_labels: List[str]) -> None:
         self.rf_interpolator_up = scipy.interpolate.RectBivariateSpline(self.potential_dict['xlist']*1e-6,
                                                                         self.potential_dict['ylist']*1e-6,
                                                                         potential)
-        
+
         # Repeat for the 'down' electrode
         rf_voltage_dict[rf_electrode_labels[0]] = 0.0
         rf_voltage_dict[rf_electrode_labels[1]] = 1.0
-        
+
         potential = make_potential(self.potential_dict, rf_voltage_dict)
 
         # By using the interpolator we create a function that can evaluate the potential energy for an electron at arbitrary x,y
@@ -137,7 +139,7 @@ def set_rf_interpolator(self, rf_electrode_labels: List[str]) -> None:
         self.rf_interpolator_down = scipy.interpolate.RectBivariateSpline(self.potential_dict['xlist']*1e-6,
                                                                           self.potential_dict['ylist']*1e-6,
                                                                           potential)
-        
+
     def Ex_up(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
         """This function evaluates the electric field in the x-direction due to only the `up` electrode in the differential pair. 
         `setup_rf_interpolator` must be run prior to calling this function.
@@ -151,7 +153,7 @@ def Ex_up(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
             ArrayLike: RF electric field 
         """
         return self.rf_interpolator_up.ev(xe, ye, dx=1)
-    
+
     def Ex_down(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
         """This function evaluates the electric field in the x-direction due to only the `down` electrode in the differential pair. 
         `setup_rf_interpolator` must be run prior to calling this function.
@@ -165,7 +167,7 @@ def Ex_down(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
             ArrayLike: RF electric field 
         """
         return self.rf_interpolator_down.ev(xe, ye, dx=1)
-    
+
     def Ey_up(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
         """This function evaluates the electric field in the y-direction due to only the `up` electrode in the differential pair. 
         `setup_rf_interpolator` must be run prior to calling this function.
@@ -179,7 +181,7 @@ def Ey_up(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
             ArrayLike: RF electric field 
         """
         return self.rf_interpolator_up.ev(xe, ye, dy=1)
-    
+
     def Ey_down(self, xe: ArrayLike, ye: ArrayLike) -> ArrayLike:
         """This function evaluates the electric field in the y-direction due to only the `down` electrode in the differential pair. 
         `setup_rf_interpolator` must be run prior to calling this function.
@@ -231,7 +233,8 @@ def generate_initial_condition(self, n_electrons: int, radius: float = 0.18E-6,
             ArrayLike: One-dimensional array (length = 2 * n_electrons) of x and y coordinates: [x0, y0, x1, y0, ...]
         """
         if center is None:
-            coor = find_minimum_location(self.potential_dict, self.voltage_dict)
+            coor = find_minimum_location(
+                self.potential_dict, self.voltage_dict)
         else:
             coor = center
 
@@ -261,8 +264,8 @@ def count_electrons_in_dot(self, r: ArrayLike, trap_bounds_x: tuple = (-1e-6, 1e
         y_ok = np.logical_and(ey < trap_bounds_y[1], ey > trap_bounds_y[0])
         x_and_y_ok = np.logical_and(x_ok, y_ok)
         return np.sum(x_and_y_ok)
-    
-    def get_dot_area(self, plot: bool=True, barrier_location: tuple=(-1, 0), barrier_offset: float=-0.01, **kwargs) -> float:
+
+    def get_dot_area(self, plot: bool = True, barrier_location: tuple = (-1, 0), barrier_offset: float = -0.01, **kwargs) -> float:
         """Finds the area of the dot spanned by the points that lie on a equipotential that is determined by the 
         `barrier_location` and `barrier_offset`. The resulting area has the same units as self.potential_dict['xlist'] ** 2
 
@@ -280,27 +283,30 @@ def get_dot_area(self, plot: bool=True, barrier_location: tuple=(-1, 0), barrier
         idx = find_nearest(self.potential_dict['ylist'], barrier_location[1])
         idy = find_nearest(self.potential_dict['xlist'], barrier_location[0])
         barrier_height = -potential[idy, idx]
-        
+
         # Contour can return non-integer indices (it interpolates to find the contour)
         # Thus we need to create a mappable for x and y.
-        fx = interp1d(np.arange(len(self.potential_dict['xlist'])), self.potential_dict['xlist'])
-        fy = interp1d(np.arange(len(self.potential_dict['ylist'])), self.potential_dict['ylist'])
+        fx = interp1d(
+            np.arange(len(self.potential_dict['xlist'])), self.potential_dict['xlist'])
+        fy = interp1d(
+            np.arange(len(self.potential_dict['ylist'])), self.potential_dict['ylist'])
 
         # Use sci-kit image function measure to find the contours.
-        contours = measure.find_contours(-potential.T, barrier_height + barrier_offset)
-        
+        contours = measure.find_contours(-potential.T,
+                                         barrier_height + barrier_offset)
+
         # There may be multiple contours, but hopefully just one.
         if len(contours) > 0:
             for contour in contours:
                 xs = fx(contour[:, 1])
                 ys = fy(contour[:, 0])
-                
+
             p = Polygon(np.c_[xs, ys])
-        
+
             if plot:
                 shapely.plotting.plot_polygon(p, **kwargs)
                 plt.grid(None)
-            
+
             return p.area
         else:
             # If there are no contours, the situation is easy
@@ -310,7 +316,7 @@ def get_electron_positions(self, n_electrons: int, electron_initial_positions: O
                                suppress_warnings: bool = False) -> dict:
         """This is the main method to calculate the electron positions in an electrostatic potential. This function can be called with a specific initial condition, 
         which can be useful during voltage sweeps, or with the default initial condition as specified in generate_initial_condition.
-        
+
         Upon running this function, useful feedback about the convergence can be found in the attribute CM
 
         Args:
@@ -327,54 +333,61 @@ def get_electron_positions(self, n_electrons: int, electron_initial_positions: O
         if electron_initial_positions is None:
             electron_initial_positions = self.generate_initial_condition(
                 n_electrons)
-            
+
         if (len(electron_initial_positions) // 2 != n_electrons) and (not suppress_warnings):
-            print("WARNING: The initial condition does not match n_electrons. n_electrons is ignored.")
+            print(
+                "WARNING: The initial condition does not match n_electrons. n_electrons is ignored.")
 
-        self.CM = self.ConvergenceMonitor(self.Vtotal, self.grad_total, call_every=1, verbose=verbose)
+        self.CM = self.ConvergenceMonitor(
+            self.Vtotal, self.grad_total, call_every=1, verbose=verbose)
 
         # Convergence can happen one of two ways
         # (a) if the gradient self.grad_total(res['x']) < gradient_tolerance
         # (b) if res['fun'] changes less than the floating point precision from one iteration to the next.
-        gradient_tolerance = 1e-1 # Units are eV/m
-        
-        # For improved performance we use maxls=100. Default is 20, but if starting close to the final solution, sometimes more 
+        gradient_tolerance = 1e-1  # Units are eV/m
+
+        # For improved performance we use maxls=100. Default is 20, but if starting close to the final solution, sometimes more
         # line searches are needed to converge. This is also helpful if the function landscape is very flat.
         trap_minimizer_options = {'method': 'L-BFGS-B',
                                   'jac': self.grad_total,
-                                  'options': {'disp': False, 'gtol': gradient_tolerance, 'maxls' : 100},
+                                  'options': {'disp': False, 'gtol': gradient_tolerance, 'maxls': 100},
                                   'callback': self.CM.monitor_convergence}
 
         # initial_jacobian = self.grad_total(electron_initial_positions)
-        res = scipy.optimize.minimize(self.Vtotal, electron_initial_positions, **trap_minimizer_options)
+        res = scipy.optimize.minimize(
+            self.Vtotal, electron_initial_positions, **trap_minimizer_options)
 
         while res['status'] > 0:
             no_electrons_left = False
-            
+
             # Try removing unbounded electrons and restart the minimization
             if self.remove_unbound_electrons:
                 # Remove any electrons that are to the left of the trap
                 best_x, best_y = r2xy(res['x'])
-                idcs_x = np.where(np.logical_or(best_x < self.remove_bounds[0], best_x > self.remove_bounds[1]))[0]
-                idcs_y = np.where(np.logical_or(best_y < self.remove_bounds[0], best_y > self.remove_bounds[1]))[0]
+                idcs_x = np.where(np.logical_or(
+                    best_x < self.remove_bounds[0], best_x > self.remove_bounds[1]))[0]
+                idcs_y = np.where(np.logical_or(
+                    best_y < self.remove_bounds[0], best_y > self.remove_bounds[1]))[0]
 
                 all_idcs_to_remove = np.union1d(idcs_x, idcs_y)
                 best_x = np.delete(best_x, all_idcs_to_remove)
                 best_y = np.delete(best_y, all_idcs_to_remove)
-                    
+
                 # Use the solution from the current time step as the initial condition for the next timestep!
                 electron_initial_positions = xy2r(best_x, best_y)
                 if len(best_x) < len(res['x'][::2]) and (not suppress_warnings):
                     print("%d/%d unbounded electrons removed. %d electrons remain." % (
                         int(len(res['x'][::2]) - len(best_x)), len(res['x'][::2]), len(best_x)))
-                else: # sometimes the simulation doesn't converge for other reasons...
+                else:  # sometimes the simulation doesn't converge for other reasons...
                     break
-                
+
                 if len(electron_initial_positions) > 0:
                     print("Restart minimization!")
-                    self.CM = self.ConvergenceMonitor(self.Vtotal, self.grad_total, call_every=1, verbose=verbose)
+                    self.CM = self.ConvergenceMonitor(
+                        self.Vtotal, self.grad_total, call_every=1, verbose=verbose)
                     trap_minimizer_options['callback'] = self.CM.monitor_convergence
-                    res = scipy.optimize.minimize(self.Vtotal, electron_initial_positions, **trap_minimizer_options)
+                    res = scipy.optimize.minimize(
+                        self.Vtotal, electron_initial_positions, **trap_minimizer_options)
                 else:
                     no_electrons_left = True
                     break
@@ -389,19 +402,21 @@ def get_electron_positions(self, n_electrons: int, electron_initial_positions: O
                               (best_x[i] * 1E6, best_y[i] * 1E6))
                 # To skip the infinite while loop.
                 break
-        
-        if res['status'] > 0 and not(no_electrons_left) and not(suppress_warnings):
+
+        if res['status'] > 0 and not (no_electrons_left) and not (suppress_warnings):
             print("WARNING: Initial minimization did not converge!")
-            print(f"Final L-inf norm of gradient = {np.amax(res['jac']):.2f} eV/m")
+            print(
+                f"Final L-inf norm of gradient = {np.amax(res['jac']):.2f} eV/m")
             best_res = res
-            print("Please check your initial condition, are all electrons confined in the simulation area?")
+            print(
+                "Please check your initial condition, are all electrons confined in the simulation area?")
 
         if len(self.trap_annealing_steps) > 0:
             if verbose:
                 print("SUCCESS: Initial minimization for Trap converged!")
                 # This maps the electron positions within the simulation domain
                 print("Perturbing solution %d times at %.2f K. (dx,dy) ~ (%.3f, %.3f) µm..."
-                       % (len(self.trap_annealing_steps), self.trap_annealing_steps[0],
+                      % (len(self.trap_annealing_steps), self.trap_annealing_steps[0],
                           np.mean(self.thermal_kick_x(res['x'][::2], res['x'][1::2], self.trap_annealing_steps[0],
                                                       maximum_dx=self.max_x_displacement)) * 1E6,
                           np.mean(self.thermal_kick_y(res['x'][::2], res['x'][1::2], self.trap_annealing_steps[0],
@@ -409,27 +424,27 @@ def get_electron_positions(self, n_electrons: int, electron_initial_positions: O
 
             best_res = self.perturb_and_solve(self.Vtotal, len(self.trap_annealing_steps), self.trap_annealing_steps[0],
                                               res, maximum_dx=self.max_x_displacement, maximum_dy=self.max_y_displacement,
-                                              do_print=verbose, 
+                                              do_print=verbose,
                                               **trap_minimizer_options)
         else:
             best_res = res
 
         if self.remove_unbound_electrons:
             best_x, best_y = r2xy(best_res['x'])
-            idcs_x = np.where(np.logical_or(best_x < self.remove_bounds[0], 
+            idcs_x = np.where(np.logical_or(best_x < self.remove_bounds[0],
                                             best_x > self.remove_bounds[1]))[0]
-            idcs_y = np.where(np.logical_or(best_y < self.remove_bounds[0], 
+            idcs_y = np.where(np.logical_or(best_y < self.remove_bounds[0],
                                             best_y > self.remove_bounds[1]))[0]
 
             all_idcs_to_remove = np.union1d(idcs_x, idcs_y)
             best_x = np.delete(best_x, all_idcs_to_remove)
             best_y = np.delete(best_y, all_idcs_to_remove)
-            
+
             best_res['x'] = xy2r(best_x, best_y)
-        
+
         return best_res
-    
-    def plot_electron_positions(self, res: dict, ax=None, color: str='mediumseagreen', marker_size: float=10.0) -> None:
+
+    def plot_electron_positions(self, res: dict, ax=None, color: str = 'mediumseagreen', marker_size: float = 10.0) -> None:
         """Plot electron positions obtained from get_electron_positions
 
         Args:
@@ -438,16 +453,15 @@ def plot_electron_positions(self, res: dict, ax=None, color: str='mediumseagreen
             color (str, optional): Color of the markers representing the electrons. Defaults to 'mediumseagreen'.
         """
         x, y = r2xy(res['x'])
-        
-        if ax is None: 
-            plt.plot(x*1e6, y*1e6, 'ok', mfc=color, mew=0.5, ms=marker_size, 
+
+        if ax is None:
+            plt.plot(x*1e6, y*1e6, 'ok', mfc=color, mew=0.5, ms=marker_size,
                      path_effects=[pe.SimplePatchShadow(), pe.Normal()])
         else:
-            ax.plot(x*1e6, y*1e6, 'ok', mfc=color, mew=0.5, ms=marker_size, 
+            ax.plot(x*1e6, y*1e6, 'ok', mfc=color, mew=0.5, ms=marker_size,
                     path_effects=[pe.SimplePatchShadow(), pe.Normal()])
 
-    
-    def animate_voltage_sweep(self, list_of_voltages: list, list_of_electron_positions: list, coor: tuple=(0, 0), dxdy: tuple=(2, 2), frame_interval_ms: int=10) -> matplotlib.animation.FuncAnimation:
+    def animate_voltage_sweep(self, list_of_voltages: list, list_of_electron_positions: list, coor: tuple = (0, 0), dxdy: tuple = (2, 2), frame_interval_ms: int = 10) -> matplotlib.animation.FuncAnimation:
         """
         Animates a voltage sweep by updating the voltage and electron positions over time. 
         This function only animates the sweep, it does not calculate the electron positions. This needs to be done beforehand.
@@ -465,18 +479,19 @@ def animate_voltage_sweep(self, list_of_voltages: list, list_of_electron_positio
         Raises:
             AssertionError: If the length of the voltage list is not the same as the list of electron positions.
         """
-        assert len(list_of_voltages) == len(list_of_electron_positions), "The length of the voltage list must be the same as the list of electron positions."
-        
+        assert len(list_of_voltages) == len(
+            list_of_electron_positions), "The length of the voltage list must be the same as the list of electron positions."
+
         potential = make_potential(self.potential_dict, list_of_voltages[0])
         zdata = -potential.T
 
-        fig = plt.figure(figsize=(7,4))
+        fig = plt.figure(figsize=(7, 4))
         ax = fig.add_subplot(111)
-        img_data = ax.imshow(zdata, cmap=plt.cm.RdYlBu_r, extent=[coor[0] - dxdy[0]/2, coor[0] + dxdy[0]/2, 
+        img_data = ax.imshow(zdata, cmap=plt.cm.RdYlBu_r, extent=[coor[0] - dxdy[0]/2, coor[0] + dxdy[0]/2,
                                                                   coor[1] - dxdy[1]/2, coor[1] + dxdy[1]/2])
-        
+
         final_x, final_y = r2xy(list_of_electron_positions[0])
-        pts_data = ax.plot(final_x*1e6, final_y*1e6, 'ok', mfc='mediumseagreen', mew=0.5, ms=10, 
+        pts_data = ax.plot(final_x*1e6, final_y*1e6, 'ok', mfc='mediumseagreen', mew=0.5, ms=10,
                            path_effects=[pe.SimplePatchShadow(), pe.Normal()])
 
         cbar = plt.colorbar(img_data)
@@ -487,15 +502,15 @@ def animate_voltage_sweep(self, list_of_voltages: list, list_of_electron_positio
 
         xmin, xmax = (coor[0] - dxdy[0]/2, coor[0] + dxdy[0]/2)
         ymin, ymax = (coor[1] - dxdy[1]/2, coor[1] + dxdy[1]/2)
-        
+
         ax.set_xlim(xmin, xmax)
         ax.set_ylim(ymin, ymax)
 
         text_boxes = list()
         initial_voltages = list_of_voltages[0]
         for k, electrode in enumerate(initial_voltages.keys()):
-            text_boxes.append(ax.text(xmin - 0.75, 
-                                      ymax - k * 0.075 * (ymax - ymin), 
+            text_boxes.append(ax.text(xmin - 0.75,
+                                      ymax - k * 0.075 * (ymax - ymin),
                                       f"{electrode} = {initial_voltages[electrode]:.2f} V", ha='right', va='top'))
 
         ax.set_aspect('equal')
@@ -504,31 +519,32 @@ def animate_voltage_sweep(self, list_of_voltages: list, list_of_electron_positio
         plt.locator_params(axis='both', nbins=4)
 
         fig.tight_layout()
-                
+
         def update(frame):
             # Update the voltages and electron positions
             voltages = list_of_voltages[frame]
             final_x, final_y = r2xy(list_of_electron_positions[frame])
-            
+
             potential = make_potential(self.potential_dict, voltages)
             zdata = -potential.T
 
             # Update the color plot
             img_data.set_data(zdata)
-            
+
             # Update the electron positions (green dots)
             pts_data[0].set_xdata(final_x * 1e6)
             pts_data[0].set_ydata(final_y * 1e6)
-            
+
             # Update the voltages to the left of the image
             for k, electrode in enumerate(voltages.keys()):
-                text_boxes[k].set_text(f"{electrode} = {voltages[electrode]:.2f} V")
-                
+                text_boxes[k].set_text(
+                    f"{electrode} = {voltages[electrode]:.2f} V")
+
             return (img_data, pts_data, text_boxes)
 
         return animation.FuncAnimation(fig=fig, func=update, frames=np.arange(len(list_of_voltages)), interval=frame_interval_ms, repeat=True)
-    
-    def animate_convergence(self, coor: tuple=(0, 0), dxdy: tuple=(2, 2), frame_interval_ms: int=10) -> matplotlib.animation.FuncAnimation:
+
+    def animate_convergence(self, coor: tuple = (0, 0), dxdy: tuple = (2, 2), frame_interval_ms: int = 10) -> matplotlib.animation.FuncAnimation:
         """Animate the convergence data stored in the convergence helper class. 
 
         Args:
@@ -541,12 +557,14 @@ def animate_convergence(self, coor: tuple=(0, 0), dxdy: tuple=(2, 2), frame_inte
         """
         # The position data is stored in the coordinates of the helper class
         r = self.CM.curr_xk
-        
+
         fig, ax = plt.subplots(1, 1, figsize=(4, 4))
-        self.plot_potential_energy(ax=ax, coor=coor, dxdy=dxdy, print_voltages=False, plot_contours=False)
-        
+        self.plot_potential_energy(
+            ax=ax, coor=coor, dxdy=dxdy, print_voltages=False, plot_contours=False)
+
         rx, ry = r2xy(r[0, :])
-        pts_data = ax.plot(rx*1e6, ry*1e6, 'ok', mfc='mediumseagreen', mew=0.5, ms=10, path_effects=[pe.SimplePatchShadow(), pe.Normal()])
+        pts_data = ax.plot(rx*1e6, ry*1e6, 'ok', mfc='mediumseagreen', mew=0.5,
+                           ms=10, path_effects=[pe.SimplePatchShadow(), pe.Normal()])
 
         # Only things in the update function will get updated.
         def update(frame):
@@ -555,10 +573,10 @@ def update(frame):
             pts_data[0].set_xdata(rx * 1e6)
             pts_data[0].set_ydata(ry * 1e6)
 
-            return pts_data, 
+            return pts_data,
 
         fig.tight_layout()
-        # The interval is in milliseconds 
+        # The interval is in milliseconds
         return animation.FuncAnimation(fig=fig, func=update, frames=np.arange(self.CM.curr_xk.shape[0]), interval=frame_interval_ms, repeat=True)
 
     def plot_convergence(self, ax=None) -> None:
@@ -567,11 +585,11 @@ def plot_convergence(self, ax=None) -> None:
         Args:
             ax (optional): Matplotlib axes object. Defaults to None.
         """
-        if ax is None: 
-            fig, ax = plt.subplots(1, 1, figsize=(5.,3.5))
+        if ax is None:
+            fig, ax = plt.subplots(1, 1, figsize=(5., 3.5))
         ax.plot(self.CM.curr_grad_norm)
         ax.set_yscale('log')
         ax.set_xlim(-1, len(self.CM.curr_grad_norm) + 1)
         ax.locator_params(axis='x', nbins=4)
         ax.set_xlabel("Iteration")
-        ax.set_ylabel("Cost function")
\ No newline at end of file
+        ax.set_ylabel("Cost function")
diff --git a/quantum_electron/eom_solver.py b/quantum_electron/eom_solver.py
index 89c7f28..ee349cc 100644
--- a/quantum_electron/eom_solver.py
+++ b/quantum_electron/eom_solver.py
@@ -7,10 +7,11 @@
 from matplotlib import pyplot as plt
 import matplotlib.animation as animation
 from matplotlib import patheffects as pe
-from IPython import display 
+from IPython import display
+
 
 class EOMSolver:
-    def __init__(self, Ex: callable, Ey: callable, Ex_up: callable, Ex_down: callable, Ey_up: callable, Ey_down: callable, 
+    def __init__(self, Ex: callable, Ey: callable, Ex_up: callable, Ex_down: callable, Ey_up: callable, Ey_down: callable,
                  curv_xx: callable, curv_xy: callable, curv_yy: callable) -> None:
         """Class that sets up the equations of motion in matrix form and solves them.
 
@@ -21,24 +22,25 @@ def __init__(self, Ex: callable, Ey: callable, Ex_up: callable, Ex_down: callabl
             curv_xy (callable): Second derivative of the electrostatic potential:  d^2 / dx dy V. This function is inherited from the PositionSolver class.
             curv_yy (callable): Second derivative of the electrostatic potential:  d^2 / dy^2 V. This function is inherited from the PositionSolver class.
         """
-        # Electric field functions for the simple single-mode LC circuit    
+        # Electric field functions for the simple single-mode LC circuit
         self.Ex = Ex
         self.Ey = Ey
-        
+
         # Electric field functions (callables) for the coupled LC approach
         self.Ex_up = Ex_up
         self.Ex_down = Ex_down
         self.Ey_up = Ey_up
         self.Ey_down = Ey_down
-        
-        self.curv_xx = curv_xx 
+
+        self.curv_xx = curv_xx
         self.curv_xy = curv_xy
         self.curv_yy = curv_yy
-    
-    def setup_eom_coupled_lc(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[ArrayLike]:
+
+    def setup_eom_coupled_lc(self, ri: ArrayLike,
+                             resonator_dict: Dict) -> tuple[ArrayLike]:
         """
         Set up the Matrix used for determining the electron motional frequencies and cavity frequency.
-        This function is used for the coupled LC resonator model. The electrons are located in between the plates of the 
+        This function is used for the coupled LC resonator model. The electrons are located in between the plates of the
         capacitor Cdot.
 
         Args:
@@ -55,35 +57,39 @@ def setup_eom_coupled_lc(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[Arr
         Cdot = resonator_dict['Cdot']
         L1 = resonator_dict['L1']
         L2 = resonator_dict['L2']
-        
+
         self.num_cavity_modes = 2
-        
-        # We first solve the cavity equations without electrons to identify the common and differential modes
+
+        # We first solve the cavity equations without electrons to identify the
+        # common and differential modes
         D = C1 * C2 + C1 * Cdot + C2 * Cdot
 
         # Mass matrix of the cavity only
-        M = np.array([[L1, 0], 
+        M = np.array([[L1, 0],
                       [0, L2]])
 
         # Kinetic matrix of the cavity only
-        K = np.array([[(C2 + Cdot) / D, Cdot / D], 
+        K = np.array([[(C2 + Cdot) / D, Cdot / D],
                       [Cdot / D, (C1 + Cdot) / D]])
 
         eigenvalues, _ = scipy.linalg.eigh(K, b=M)
         f0, f1 = np.sqrt(eigenvalues) / (2 * np.pi)
-        
-        # The differential mode is the smaller, because the coupling capacitance adds to the resonance
+
+        # The differential mode is the smaller, because the coupling
+        # capacitance adds to the resonance
         self.f0_diff = np.min([f0, f1])
-        # The common mode is higher, because the coupling capacitance doesn't participate in the resonance.
+        # The common mode is higher, because the coupling capacitance doesn't
+        # participate in the resonance.
         self.f0_comm = np.max([f0, f1])
-        
+
         if resonator_dict['mode'] == 'comm':
             self.f0 = self.f0_comm
         elif resonator_dict['mode'] == 'diff':
             self.f0 = self.f0_diff
         else:
-            print("'mode' key was not understood. Please specify either 'comm' or 'diff'.")
-            
+            print(
+                "'mode' key was not understood. Please specify either 'comm' or 'diff'.")
+
         num_electrons = int(len(ri) / 2)
         xe, ye = r2xy(ri)
 
@@ -91,27 +97,33 @@ def setup_eom_coupled_lc(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[Arr
         M = np.diag(np.array([L1] + [L2] + [m_e] * (2 * num_electrons)))
 
         # Set up the kinetic matrix next
-        Kij_plus, Kij_minus, Lij = np.zeros(np.shape(M)), np.zeros(np.shape(M)), np.zeros(np.shape(M))
+        Kij_plus, Kij_minus, Lij = np.zeros(np.shape(M)), np.zeros(
+            np.shape(M)), np.zeros(np.shape(M))
         K = np.zeros((2 * num_electrons + 2, 2 * num_electrons + 2))
-        
-        # Row 1 and column 1 only have bare cavity information, and cavity-electron terms
-        K[:2, :2] = np.array([[(C2 + Cdot) / D, Cdot / D], 
+
+        # Row 1 and column 1 only have bare cavity information, and
+        # cavity-electron terms
+        K[:2, :2] = np.array([[(C2 + Cdot) / D, Cdot / D],
                               [Cdot / D, (C1 + Cdot) / D]])
-        
-        K[2:num_electrons+2, 0] = K[0, 2:num_electrons+2] = q_e / D * ( (C2 + Cdot) * self.Ex_up(xe, ye) - Cdot * self.Ex_down(xe, ye) )
-        K[2:num_electrons+2, 1] = K[1, 2:num_electrons+2] = q_e / D * ( (C1 + Cdot) * self.Ex_down(xe, ye) - Cdot * self.Ex_up(xe, ye) )
-        
-        K[num_electrons+2:2*num_electrons+2, 0] = K[0, num_electrons+2:2*num_electrons+2] = q_e / D * ( (C2 + Cdot) * self.Ey_up(xe, ye) - Cdot * self.Ey_down(xe, ye) )
-        K[num_electrons+2:2*num_electrons+2, 1] = K[1, num_electrons+2:2*num_electrons+2] = q_e / D * ( (C1 + Cdot) * self.Ey_down(xe, ye) - Cdot * self.Ey_up(xe, ye) )
+
+        K[2:num_electrons + 2, 0] = K[0, 2:num_electrons + 2] = q_e / D * \
+            ((C2 + Cdot) * self.Ex_up(xe, ye) - Cdot * self.Ex_down(xe, ye))
+        K[2:num_electrons + 2, 1] = K[1, 2:num_electrons + 2] = q_e / D * \
+            ((C1 + Cdot) * self.Ex_down(xe, ye) - Cdot * self.Ex_up(xe, ye))
+
+        K[num_electrons + 2:2 * num_electrons + 2, 0] = K[0, num_electrons + 2:2 * num_electrons +
+                                                          2] = q_e / D * ((C2 + Cdot) * self.Ey_up(xe, ye) - Cdot * self.Ey_down(xe, ye))
+        K[num_electrons + 2:2 * num_electrons + 2, 1] = K[1, num_electrons + 2:2 * num_electrons +
+                                                          2] = q_e / D * ((C1 + Cdot) * self.Ey_down(xe, ye) - Cdot * self.Ey_up(xe, ye))
 
         kij_plus = np.zeros((num_electrons, num_electrons))
         kij_minus = np.zeros((num_electrons, num_electrons))
         lij = np.zeros((num_electrons, num_electrons))
-        
+
         # Use calculate metrics from eom_solver to take into account periodic boundary conditions
         # This method is inherited from the PositionSolver class
-        XiXj, YiYj, rij = self.calculate_metrics(xe, ye) 
-        
+        XiXj, YiYj, rij = self.calculate_metrics(xe, ye)
+
         np.fill_diagonal(XiXj, 1E-15)
         tij = np.arctan(YiYj / XiXj)
 
@@ -124,40 +136,47 @@ def setup_eom_coupled_lc(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[Arr
             # print("Coulomb!")
             # Note that an infinite screening length corresponds to the Coulomb case. Usually it should be twice the
             # helium depth
-            kij_plus = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * (1 + 3 * np.cos(2 * tij)) / rij ** 3
-            kij_minus = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * (1 - 3 * np.cos(2 * tij)) / rij ** 3
-            lij = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * 3 * np.sin(2 * tij) / rij ** 3
+            kij_plus = 1 / 4. * q_e ** 2 / \
+                (4 * np.pi * eps0) * (1 + 3 * np.cos(2 * tij)) / rij ** 3
+            kij_minus = 1 / 4. * q_e ** 2 / \
+                (4 * np.pi * eps0) * (1 - 3 * np.cos(2 * tij)) / rij ** 3
+            lij = 1 / 4. * q_e ** 2 / \
+                (4 * np.pi * eps0) * 3 * np.sin(2 * tij) / rij ** 3
         else:
             # print("Yukawa!")
             rij_scaled = rij / self.screening_length
             kij_plus = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-rij_scaled) / rij ** 3 * \
-                              (1 + rij_scaled + rij_scaled ** 2 + (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
-                                  2 * tij))
+                (1 + rij_scaled + rij_scaled ** 2 + (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
+                    2 * tij))
             kij_minus = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-rij_scaled) / rij ** 3 * \
-                              (1 + rij_scaled + rij_scaled ** 2 - (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
-                                  2 * tij))
+                (1 + rij_scaled + rij_scaled ** 2 - (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
+                    2 * tij))
             lij = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-rij_scaled) / rij ** 3 * \
-                              (3 + 3 * rij_scaled + rij_scaled ** 2) * np.sin(2 * tij)
+                (3 + 3 * rij_scaled + rij_scaled ** 2) * np.sin(2 * tij)
 
         np.fill_diagonal(kij_plus, 0)
         np.fill_diagonal(kij_minus, 0)
         np.fill_diagonal(lij, 0)
 
-        Kij_plus = -kij_plus + np.diag(q_e*self.curv_xx(xe, ye) + np.sum(kij_plus, axis=1))
-        Kij_minus = -kij_minus + np.diag(q_e*self.curv_yy(xe, ye) + np.sum(kij_minus, axis=1))
-        Lij = -lij + np.diag(q_e*self.curv_xy(xe, ye) + np.sum(lij, axis=1))
+        Kij_plus = -kij_plus + \
+            np.diag(q_e * self.curv_xx(xe, ye) + np.sum(kij_plus, axis=1))
+        Kij_minus = -kij_minus + \
+            np.diag(q_e * self.curv_yy(xe, ye) + np.sum(kij_minus, axis=1))
+        Lij = -lij + np.diag(q_e * self.curv_xy(xe, ye) + np.sum(lij, axis=1))
 
-        K[2:num_electrons+2, 2:num_electrons+2] = Kij_plus
-        K[num_electrons+2:2*num_electrons+2, num_electrons+2:2*num_electrons+2] = Kij_minus
-        K[2:num_electrons+2, num_electrons+2:2*num_electrons+2] = Lij
-        K[num_electrons+2:2*num_electrons+2, 2:num_electrons+2] = Lij
+        K[2:num_electrons + 2, 2:num_electrons + 2] = Kij_plus
+        K[num_electrons + 2:2 * num_electrons + 2,
+            num_electrons + 2:2 * num_electrons + 2] = Kij_minus
+        K[2:num_electrons + 2, num_electrons + 2:2 * num_electrons + 2] = Lij
+        K[num_electrons + 2:2 * num_electrons + 2, 2:num_electrons + 2] = Lij
 
         return K, M
-    
-    def setup_eom(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[ArrayLike]:
+
+    def setup_eom(self, ri: ArrayLike,
+                  resonator_dict: Dict) -> tuple[ArrayLike]:
         """Set up the Matrix used for determining the electron motional frequencies and cavity frequency.
-        This function is used for a simple LC resonator model. The electrons are located in between the 
-        plates of the capacitor C. 
+        This function is used for a simple LC resonator model. The electrons are located in between the
+        plates of the capacitor C.
 
         Args:
             ri (ArrayLike): Electron positions, in the form [x0, y0, x1, y1, ...]
@@ -185,13 +204,17 @@ def setup_eom(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[ArrayLike]:
         M = np.diag(np.array([L] + [m_e] * (2 * num_electrons)))
 
         # Set up the kinetic matrix next
-        Kij_plus, Kij_minus, Lij = np.zeros(np.shape(invM)), np.zeros(np.shape(invM)), np.zeros(np.shape(invM))
+        Kij_plus, Kij_minus, Lij = np.zeros(np.shape(invM)), np.zeros(
+            np.shape(invM)), np.zeros(np.shape(invM))
         K = np.zeros((2 * num_electrons + 1, 2 * num_electrons + 1))
-        
-        # Row 1 and column 1 only have bare cavity information, and cavity-electron terms
+
+        # Row 1 and column 1 only have bare cavity information, and
+        # cavity-electron terms
         K[0, 0] = 1 / C
-        K[1:num_electrons+1, 0] = K[0, 1:num_electrons+1] = q_e / C * self.Ex(xe, ye)
-        K[num_electrons+1:2*num_electrons+1, 0] = K[0, num_electrons+1:2*num_electrons+1] = q_e / C * self.Ey(xe, ye)
+        K[1:num_electrons + 1, 0] = K[0, 1:num_electrons +
+                                      1] = q_e / C * self.Ex(xe, ye)
+        K[num_electrons + 1:2 * num_electrons + 1, 0] = K[0, num_electrons +
+                                                          1:2 * num_electrons + 1] = q_e / C * self.Ey(xe, ye)
 
         kij_plus = np.zeros((num_electrons, num_electrons))
         kij_minus = np.zeros((num_electrons, num_electrons))
@@ -199,8 +222,8 @@ def setup_eom(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[ArrayLike]:
 
         # Use calculate metrics from eom_solver to take into account periodic boundary conditions
         # This method is inherited from the PositionSolver class
-        XiXj, YiYj, rij = self.calculate_metrics(xe, ye)        
-              
+        XiXj, YiYj, rij = self.calculate_metrics(xe, ye)
+
         # Set Xi - Xi to a finite value to avoid dividing by zero.
         np.fill_diagonal(XiXj, 1E-15)
         tij = np.arctan(YiYj / XiXj)
@@ -214,39 +237,46 @@ def setup_eom(self, ri: ArrayLike, resonator_dict: Dict) -> tuple[ArrayLike]:
             # print("Coulomb!")
             # Note that an infinite screening length corresponds to the Coulomb case. Usually it should be twice the
             # helium depth
-            kij_plus = 1 / 2. * q_e ** 2 / (4 * np.pi * eps0) * (1 + 3 * np.cos(2 * tij)) / rij ** 3
-            kij_minus = 1 / 2. * q_e ** 2 / (4 * np.pi * eps0) * (1 - 3 * np.cos(2 * tij)) / rij ** 3
-            lij = 1 / 2. * q_e ** 2 / (4 * np.pi * eps0) * 3 * np.sin(2 * tij) / rij ** 3
+            kij_plus = 1 / 2. * q_e ** 2 / \
+                (4 * np.pi * eps0) * (1 + 3 * np.cos(2 * tij)) / rij ** 3
+            kij_minus = 1 / 2. * q_e ** 2 / \
+                (4 * np.pi * eps0) * (1 - 3 * np.cos(2 * tij)) / rij ** 3
+            lij = 1 / 2. * q_e ** 2 / \
+                (4 * np.pi * eps0) * 3 * np.sin(2 * tij) / rij ** 3
         else:
             # print("Yukawa!")
             rij_scaled = rij / self.screening_length
             kij_plus = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-rij_scaled) / rij ** 3 * \
-                              (1 + rij_scaled + rij_scaled ** 2 + (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
-                                  2 * tij))
+                (1 + rij_scaled + rij_scaled ** 2 + (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
+                    2 * tij))
             kij_minus = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-rij_scaled) / rij ** 3 * \
-                              (1 + rij_scaled + rij_scaled ** 2 - (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
-                                  2 * tij))
+                (1 + rij_scaled + rij_scaled ** 2 - (3 + 3 * rij_scaled + rij_scaled ** 2) * np.cos(
+                    2 * tij))
             lij = 1 / 4. * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-rij_scaled) / rij ** 3 * \
-                              (3 + 3 * rij_scaled + rij_scaled ** 2) * np.sin(2 * tij)
+                (3 + 3 * rij_scaled + rij_scaled ** 2) * np.sin(2 * tij)
 
         np.fill_diagonal(kij_plus, 0)
         np.fill_diagonal(kij_minus, 0)
         np.fill_diagonal(lij, 0)
 
-        Kij_plus = -kij_plus + np.diag(q_e * self.curv_xx(xe, ye) + np.sum(kij_plus, axis=1))
-        Kij_minus = -kij_minus + np.diag(q_e * self.curv_yy(xe, ye) + np.sum(kij_minus, axis=1))
+        Kij_plus = -kij_plus + \
+            np.diag(q_e * self.curv_xx(xe, ye) + np.sum(kij_plus, axis=1))
+        Kij_minus = -kij_minus + \
+            np.diag(q_e * self.curv_yy(xe, ye) + np.sum(kij_minus, axis=1))
         Lij = -lij + np.diag(q_e * self.curv_xy(xe, ye) + np.sum(lij, axis=1))
 
-        K[1:num_electrons+1,1:num_electrons+1] = Kij_plus
-        K[num_electrons+1:2*num_electrons+1, num_electrons+1:2*num_electrons+1] = Kij_minus
-        K[1:num_electrons+1, num_electrons+1:2*num_electrons+1] = Lij
-        K[num_electrons+1:2*num_electrons+1, 1:num_electrons+1] = Lij
+        K[1:num_electrons + 1, 1:num_electrons + 1] = Kij_plus
+        K[num_electrons + 1:2 * num_electrons + 1,
+            num_electrons + 1:2 * num_electrons + 1] = Kij_minus
+        K[1:num_electrons + 1, num_electrons + 1:2 * num_electrons + 1] = Lij
+        K[num_electrons + 1:2 * num_electrons + 1, 1:num_electrons + 1] = Lij
 
         return K, M
 
-    def solve_eom(self, LHS: ArrayLike, RHS: ArrayLike, filter_nan: bool=False, sort_by_cavity_participation: bool=True, cavity_mode_index: int=0) -> tuple[ArrayLike]:
+    def solve_eom(self, LHS: ArrayLike, RHS: ArrayLike, filter_nan: bool = False,
+                  sort_by_cavity_participation: bool = True, cavity_mode_index: int = 0) -> tuple[ArrayLike]:
         """Solves the eigenvalues and eigenvectors for the system of equations constructed with setup_eom()
-        The order of eigenvalues, and order of the columns of EVecs is coupled. By default scipy sorts this from low eigenvalue to high eigenvalue, however, 
+        The order of eigenvalues, and order of the columns of EVecs is coupled. By default scipy sorts this from low eigenvalue to high eigenvalue, however,
         by flagging sort_by_cavity_participation, this function will return the eigenvalues and vectors sorted by largest cavity contribution first.
 
         Args:
@@ -260,13 +290,16 @@ def solve_eom(self, LHS: ArrayLike, RHS: ArrayLike, filter_nan: bool=False, sort
 
         # EVals, EVecs = np.linalg.eig(np.dot(np.linalg.inv(RHS), LHS))
         EVals, EVecs = scipy.linalg.eigh(LHS, b=RHS)
-        
+
         if sort_by_cavity_participation:
-            # The cavity participation is the first element of each eigenvector, because that's how the matrix was constructed.
+            # The cavity participation is the first element of each
+            # eigenvector, because that's how the matrix was constructed.
             cavity_participation = EVecs[cavity_mode_index, :]
-            # Sort by largest cavity participation (argsort will normally put the smallest first, so invert it)
+            # Sort by largest cavity participation (argsort will normally put
+            # the smallest first, so invert it)
             sorted_order = np.argsort(np.abs(cavity_participation))[::-1]
-            # Only the columns are ordered, the rows (electrons) are not shuffled. Keep the Evals and Evecs order consistent.
+            # Only the columns are ordered, the rows (electrons) are not
+            # shuffled. Keep the Evals and Evecs order consistent.
             EVecs = EVecs[:, sorted_order]
             EVals = EVals[sorted_order]
 
@@ -274,10 +307,11 @@ def solve_eom(self, LHS: ArrayLike, RHS: ArrayLike, filter_nan: bool=False, sort
             # Filter out NaNs
             EVecs = EVecs[:, EVals > 0]
             EVals = EVals[EVals > 0]
-        
+
         return np.sqrt(EVals) / (2 * np.pi), EVecs
-    
-    def get_cavity_frequency_shift(self, LHS: ArrayLike, RHS: ArrayLike, cavity_mode_index: int=0) -> float:
+
+    def get_cavity_frequency_shift(
+            self, LHS: ArrayLike, RHS: ArrayLike, cavity_mode_index: int = 0) -> float:
         """Solves the equations of motion and calculates how to resonator frequency is affected.
 
         Args:
@@ -287,11 +321,13 @@ def get_cavity_frequency_shift(self, LHS: ArrayLike, RHS: ArrayLike, cavity_mode
         Returns:
             float: Resonance frequency shift
         """
-        
-        eigenfrequencies, _ = self.solve_eom(LHS, RHS, sort_by_cavity_participation=True, cavity_mode_index=cavity_mode_index)
+
+        eigenfrequencies, _ = self.solve_eom(
+            LHS, RHS, sort_by_cavity_participation=True, cavity_mode_index=cavity_mode_index)
         return eigenfrequencies[0] - self.f0
-    
-    def plot_eigenvector(self, electron_positions: ArrayLike, eigenvector: ArrayLike, length: float=0.5, color: str='k') -> None:
+
+    def plot_eigenvector(self, electron_positions: ArrayLike,
+                         eigenvector: ArrayLike, length: float = 0.5, color: str = 'k') -> None:
         """Plots the eigenvector at the electron positions.
 
         Args:
@@ -305,35 +341,38 @@ def plot_eigenvector(self, electron_positions: ArrayLike, eigenvector: ArrayLike
 
         # The first index of the eigenvector contains the charge displacement, thus we look at the second index and beyond.
         # Normalize the vector to 'length'
-        evec_norm = eigenvector[self.num_cavity_modes:] / np.linalg.norm(eigenvector[self.num_cavity_modes:])
-        # The x and y components are ordered differently than electron positions. This depends on the ordering of the K and M matrix, see setup_eom.
+        evec_norm = eigenvector[self.num_cavity_modes:] / \
+            np.linalg.norm(eigenvector[self.num_cavity_modes:])
+        # The x and y components are ordered differently than electron
+        # positions. This depends on the ordering of the K and M matrix, see
+        # setup_eom.
         dxs = (evec_norm * length)[:N_e]
         dys = (evec_norm * length)[N_e:]
 
         for e_idx in range(len(e_x)):
-            width=0.025
-            plt.arrow(e_x[e_idx] * 1e6, e_y[e_idx] * 1e6, dx=dxs[e_idx], dy=dys[e_idx], width=width, head_length=1.5*3 *width, head_width=3.5*width, 
-                    edgecolor='k', lw=0.4, facecolor=color)
-    
-    def animate_eigenvectors(self, fig, axs_list: list, eigenvector_list: List[ArrayLike], electron_positions: ArrayLike, marker_size: float=10, 
-                             amplitude: float=0.5e-6, time_points: int=31, frame_interval_ms: int=10):
+            width = 0.025
+            plt.arrow(e_x[e_idx] * 1e6, e_y[e_idx] * 1e6, dx=dxs[e_idx], dy=dys[e_idx], width=width, head_length=1.5 * 3 * width, head_width=3.5 * width,
+                      edgecolor='k', lw=0.4, facecolor=color)
+
+    def animate_eigenvectors(self, fig, axs_list: list, eigenvector_list: List[ArrayLike], electron_positions: ArrayLike, marker_size: float = 10,
+                             amplitude: float = 0.5e-6, time_points: int = 31, frame_interval_ms: int = 10):
         """Make a matplotlib animation object for saving as a gif, or for displaying in a notebook.
         For use in displaying only:
 
-        from IPython import display 
+        from IPython import display
         ani = animate_eigenvectors(fig, axs, evecs.T, res['x'], amplitude=0.10e-6, time_points=21, frame_interval_ms=25)
         # Display animation
-        video = ani.to_html5_video() 
-        html = display.HTML(video) 
+        video = ani.to_html5_video()
+        html = display.HTML(video)
         display.display(html)
-        
+
         # Save animation
         writer = animation.PillowWriter(fps=40, bitrate=1800)
         ani.save(savepath, writer=writer)
 
         Args:
             fig (matplotlib.pyplot.figure): Matplotlib figure handle.
-            axs_list (matplotlib.pyplot.axes): List of axes, e.g. for subplots. 
+            axs_list (matplotlib.pyplot.axes): List of axes, e.g. for subplots.
             eigenvector_list (List[ArrayLike]): Eigenvector array. eigenvector_list[0] will be plot on axs_list[0] etc.
             electron_positions (ArrayLike): Electron coordinates in the format [x0, y0, x1, y1, ...]
             amplitude (float, optional): Amplitude of the motion in units of meters. Defaults to 0.5e-6.
@@ -348,27 +387,32 @@ def animate_eigenvectors(self, fig, axs_list: list, eigenvector_list: List[Array
 
         all_points = list()
         for ax in axs_list:
-            pts_data = ax.plot(e_x*1e6, e_y*1e6, 'ok', mfc='mediumseagreen', mew=0.5, ms=marker_size, path_effects=[pe.SimplePatchShadow(), pe.Normal()])
+            pts_data = ax.plot(e_x * 1e6, e_y * 1e6, 'ok', mfc='mediumseagreen', mew=0.5,
+                               ms=marker_size, path_effects=[pe.SimplePatchShadow(), pe.Normal()])
             all_points.append(pts_data)
 
         # Only things in the update function will get updated.
         def update(frame):
             # Update the electron positions (green dots)
             for points, eigenvector in zip(all_points, eigenvector_list):
-                evec_norm = eigenvector[self.num_cavity_modes:] / np.linalg.norm(eigenvector[self.num_cavity_modes:])
+                evec_norm = eigenvector[self.num_cavity_modes:] / \
+                    np.linalg.norm(eigenvector[self.num_cavity_modes:])
                 dxs = (evec_norm * amplitude)[:N_e]
                 dys = (evec_norm * amplitude)[N_e:]
-                
-                points[0].set_xdata((e_x + dxs * np.sin(2 * np.pi * frame / time_points)) * 1e6)
-                points[0].set_ydata((e_y + dys * np.sin(2 * np.pi * frame / time_points)) * 1e6)
 
-            return all_points, 
+                points[0].set_xdata(
+                    (e_x + dxs * np.sin(2 * np.pi * frame / time_points)) * 1e6)
+                points[0].set_ydata(
+                    (e_y + dys * np.sin(2 * np.pi * frame / time_points)) * 1e6)
+
+            return all_points,
 
         # The interval is in milliseconds
-        return animation.FuncAnimation(fig=fig, func=update, frames=time_points, interval=frame_interval_ms, repeat=True)
-    
+        return animation.FuncAnimation(
+            fig=fig, func=update, frames=time_points, interval=frame_interval_ms, repeat=True)
+
     def show_animation(self, matplotlib_animation) -> display.display:
-        """Display an animation in a jupyter notebook. 
+        """Display an animation in a jupyter notebook.
 
         Args:
             matplotlib_animation (matplotlib.animation.FuncAnimation): animation object, for example from `animate_eigenvectors`
@@ -376,15 +420,15 @@ def show_animation(self, matplotlib_animation) -> display.display:
         Returns:
             display.display: looped animation in html format.
         """
-        # converting to an html5 video 
-        video = matplotlib_animation.to_html5_video() 
-        
-        # embedding for the video 
-        html = display.HTML(video) 
-        
-        # draw the animation 
+        # converting to an html5 video
+        video = matplotlib_animation.to_html5_video()
+
+        # embedding for the video
+        html = display.HTML(video)
+
+        # draw the animation
         return display.display(html)
-        
+
     def save_animation(self, matplotlib_animation, filepath) -> None:
         """Save a matplotlib animation to a gif format
 
@@ -394,4 +438,4 @@ def save_animation(self, matplotlib_animation, filepath) -> None:
         """
         writer = animation.PillowWriter(fps=40, bitrate=1800)
 
-        matplotlib_animation.save(filepath, writer=writer)
\ No newline at end of file
+        matplotlib_animation.save(filepath, writer=writer)
diff --git a/quantum_electron/initial_condition.py b/quantum_electron/initial_condition.py
index d69f97e..8714414 100644
--- a/quantum_electron/initial_condition.py
+++ b/quantum_electron/initial_condition.py
@@ -5,6 +5,7 @@
 
 micron = 1e-6
 
+
 class InitialCondition:
     """
     Class to generate initial conditions for a given potential energy landscape.
@@ -29,7 +30,7 @@ def __init__(self, potential_dict: Dict[str, ArrayLike], voltage_dict: Dict[str,
         self.potential_dict = potential_dict
         self.voltage_dict = voltage_dict
 
-    def make_by_chemical_potential(self, max_electrons: int, chemical_potential: float, min_spacing: float=0.1) -> ArrayLike:
+    def make_by_chemical_potential(self, max_electrons: int, chemical_potential: float, min_spacing: float = 0.1) -> ArrayLike:
         """Makes an initial condition for a given chemical potential. The initial condition is a set of random points with a minimum 
         spacing. The number of points is determined by the chemical potential and the potential energy landscape.
         The algorithm will try to fill the dot with electrons until it reaches the desired number of electrons: max_electrons.
@@ -46,12 +47,13 @@ def make_by_chemical_potential(self, max_electrons: int, chemical_potential: flo
         z = -make_potential(self.potential_dict, self.voltage_dict)
         dot = (z < chemical_potential) * z
         bounds, dot_min, dot_max = self._dot_area(dot)
-        points = self._generate_points(max_electrons, bounds, dot, dot_min, dot_max, epsilon=min_spacing) * micron
+        points = self._generate_points(
+            max_electrons, bounds, dot, dot_min, dot_max, epsilon=min_spacing) * micron
         init_condition = xy2r(points[:, 0], points[:, 1])
 
         return init_condition
 
-    def make_circular(self, n_electrons: int, coor: Optional[tuple]=None, min_spacing: float=0.1) -> ArrayLike:
+    def make_circular(self, n_electrons: int, coor: Optional[tuple] = None, min_spacing: float = 0.1) -> ArrayLike:
         """Generates an array with electron coordinates in a circular pattern.
 
         Args:
@@ -61,22 +63,23 @@ def make_circular(self, n_electrons: int, coor: Optional[tuple]=None, min_spacin
 
         Returns:
             ArrayLike: array of electron positions in the order np.array([x0, y0, x1, y1, x2, y2, ... , xN, yN])
-        """        
+        """
         if coor is None:
-            coor = find_minimum_location(self.potential_dict, self.voltage_dict)
+            coor = find_minimum_location(
+                self.potential_dict, self.voltage_dict)
 
         radius = min_spacing * micron * n_electrons / (2 * np.pi)
         # Generate initial guess positions for the electrons in a circle with certain radius.
         init_trap_x = np.array([coor[0] * 1e-6 + radius * np.cos(2 *
-                            np.pi * n / float(n_electrons)) for n in range(n_electrons)])
+                                                                 np.pi * n / float(n_electrons)) for n in range(n_electrons)])
         init_trap_y = np.array([coor[1] * 1e-6 + radius * np.sin(2 *
-                            np.pi * n / float(n_electrons)) for n in range(n_electrons)])
+                                                                 np.pi * n / float(n_electrons)) for n in range(n_electrons)])
 
         init_condition = xy2r(np.array(init_trap_x), np.array(init_trap_y))
 
         return init_condition
 
-    def make_rectangular(self, n_electrons: int, coor: tuple=(0, 0), dxdy: tuple=(2, 2), n_rows: int=2) -> ArrayLike:
+    def make_rectangular(self, n_electrons: int, coor: tuple = (0, 0), dxdy: tuple = (2, 2), n_rows: int = 2) -> ArrayLike:
         """Generates an array with electron coordinates in a rectangular pattern.
 
         Args:
@@ -94,8 +97,10 @@ def make_rectangular(self, n_electrons: int, coor: tuple=(0, 0), dxdy: tuple=(2,
         ymin = coor[1] - dxdy[1] / 2
         ymax = coor[1] + dxdy[1] / 2
 
-        init_x = np.tile(np.linspace(xmin, xmax, n_electrons // n_rows), n_rows) * micron
-        init_y = np.repeat(np.linspace(ymin, ymax, n_rows), n_electrons // n_rows) * micron
+        init_x = np.tile(np.linspace(
+            xmin, xmax, n_electrons // n_rows), n_rows) * micron
+        init_y = np.repeat(np.linspace(ymin, ymax, n_rows),
+                           n_electrons // n_rows) * micron
         init_condition = xy2r(init_x, init_y)
 
         return init_condition
@@ -116,7 +121,7 @@ def _no_overlap(self, existing_points: list, additional_point: tuple, epsilon: f
         trial_points.append(additional_point)
         x = [p[0] for p in trial_points]
         y = [p[1] for p in trial_points]
-        X, Y = np.meshgrid(x,y)
+        X, Y = np.meshgrid(x, y)
 
         R = np.sqrt((X - X.T)**2 + (Y - Y.T)**2)
         np.fill_diagonal(R, 100)
@@ -141,8 +146,8 @@ def _dot_area(self, dot: ArrayLike) -> tuple:
         for yi in range(len(dot[0, :])):
             empty_row = True
             for xi in dot[:, yi]:
-                if xi>0:
-                    empty_row=False
+                if xi > 0:
+                    empty_row = False
 
             if not empty_row and not found1:
                 found1 = True
@@ -160,8 +165,8 @@ def _dot_area(self, dot: ArrayLike) -> tuple:
         for xi in range(len(dot[:, 0])):
             empty_column = True
             for yi in dot[xi, :]:
-                if yi>0:
-                    empty_column=False
+                if yi > 0:
+                    empty_column = False
 
             if not empty_column and not found1:
                 found1 = True
@@ -192,28 +197,30 @@ def _density_function(self, x: ArrayLike, y: ArrayLike, dot: ArrayLike, dot_min:
             float: 
         """
         # Find the minimum and maximum x indices
-        xFloor = np.argmax(self.potential_dict['xlist']>x)-1
-        xCeil = np.argmax(self.potential_dict['xlist']>x)
+        xFloor = np.argmax(self.potential_dict['xlist'] > x)-1
+        xCeil = np.argmax(self.potential_dict['xlist'] > x)
 
         # Find the minimum and maximum y indices
-        yFloor = np.argmax(self.potential_dict['ylist']>y)-1
-        yCeil = np.argmax(self.potential_dict['ylist']>y)
-        
-        dx = self.potential_dict['xlist'][xCeil]-self.potential_dict['xlist'][xFloor]
-        dy = self.potential_dict['ylist'][yCeil]-self.potential_dict['ylist'][yFloor]
+        yFloor = np.argmax(self.potential_dict['ylist'] > y)-1
+        yCeil = np.argmax(self.potential_dict['ylist'] > y)
+
+        dx = self.potential_dict['xlist'][xCeil] - \
+            self.potential_dict['xlist'][xFloor]
+        dy = self.potential_dict['ylist'][yCeil] - \
+            self.potential_dict['ylist'][yFloor]
 
         value_floor_left = (self.potential_dict['xlist'][xCeil] - x)/dx * dot[xFloor, yFloor] + \
             (x - self.potential_dict['xlist'][xFloor])/dx * dot[xCeil, yFloor]
-        
+
         value_ceil_left = (self.potential_dict['xlist'][xCeil] - x)/dx * dot[xFloor, yCeil] + \
             (x - self.potential_dict['xlist'][xFloor])/dx * dot[xCeil, yCeil]
 
         interpolated_value = (self.potential_dict['ylist'][yCeil] - y)/dy * value_floor_left + \
             (y - self.potential_dict['ylist'][yFloor])/dy * value_ceil_left
-        
+
         return (interpolated_value-dot_min)/(dot_max-dot_min)
 
-    def _generate_points(self, max_electrons: int, bounds: list, dot: ArrayLike, dot_min: float, dot_max: float, epsilon: float, verbose: bool=True) -> ArrayLike:
+    def _generate_points(self, max_electrons: int, bounds: list, dot: ArrayLike, dot_min: float, dot_max: float, epsilon: float, verbose: bool = True) -> ArrayLike:
         """Fills the dot with electrons until it reaches the desired number of electrons.
         The points are generated randomly and checked for overlap with the existing points.
         It will retry up to 100 times to add additional points that do not overlap.
@@ -238,7 +245,7 @@ def _generate_points(self, max_electrons: int, bounds: list, dot: ArrayLike, dot
         while len(points) < max_electrons and failures < max_failures:
             x = np.random.uniform(bounds[0], bounds[1])
             y = np.random.uniform(bounds[2], bounds[3])
-            
+
             # Add a random point if it is below the chemical potential and does not overlap with any other point
             if np.random.rand() < self._density_function(x, y, dot, dot_min, dot_max) and self._no_overlap(points, (x, y), epsilon=epsilon):
                 points.append((x, y))
@@ -247,6 +254,7 @@ def _generate_points(self, max_electrons: int, bounds: list, dot: ArrayLike, dot
                 failures += 1
 
         if (failures == max_failures) and verbose:
-            print(f'WARNING in creating initial condition: could not fit more than {len(points)} electrons.')
-                
-        return np.array(points)
\ No newline at end of file
+            print(
+                f'WARNING in creating initial condition: could not fit more than {len(points)} electrons.')
+
+        return np.array(points)
diff --git a/quantum_electron/position_solver.py b/quantum_electron/position_solver.py
index 9eeb705..f61b667 100644
--- a/quantum_electron/position_solver.py
+++ b/quantum_electron/position_solver.py
@@ -2,16 +2,19 @@
 from matplotlib import pyplot as plt
 from scipy.optimize import minimize
 from scipy.interpolate import RectBivariateSpline
-import os, time, multiprocessing
+import os
+import time
+import multiprocessing
 from .utils import xy2r, r2xy
 from scipy.constants import elementary_charge as q_e, epsilon_0 as eps0, electron_mass as m_e, Boltzmann as kB
 from typing import Optional
 from numpy.typing import ArrayLike
 
+
 class ConvergenceMonitor:
-    def __init__(self, Uopt: callable, grad_Uopt: callable, call_every: int, Uext: Optional[callable]=None, 
-                 xext: Optional[ArrayLike]=None, yext: Optional[ArrayLike]=None, verbose: bool=True, eps: float=1E-12, save_path: Optional[str]=None,
-                 figsize: tuple=(6.5,3.), coordinate_transformation: Optional[callable]=None, clim: tuple=(-0.75, 0)) -> None:
+    def __init__(self, Uopt: callable, grad_Uopt: callable, call_every: int, Uext: Optional[callable] = None,
+                 xext: Optional[ArrayLike] = None, yext: Optional[ArrayLike] = None, verbose: bool = True, eps: float = 1E-12, save_path: Optional[str] = None,
+                 figsize: tuple = (6.5, 3.), coordinate_transformation: Optional[callable] = None, clim: tuple = (-0.75, 0)) -> None:
         """
         To be used with scipy.optimize.minimize as a call back function. One has two choices for call-back functions:
         - monitor_convergence: print the status of convergence (value of Uopt and norm of grad_Uopt)
@@ -60,14 +63,14 @@ def monitor_convergence(self, xk: ArrayLike) -> None:
             if self.call_counter == 0:
                 self.curr_xk = xk
                 self.jac = self.grad_Uopt(xk)
-                #self.approx_fprime = approx_fprime(xk, self.Uopt, self.epsilon)
+                # self.approx_fprime = approx_fprime(xk, self.Uopt, self.epsilon)
             else:
                 self.curr_xk = np.vstack((self.curr_xk, xk))
                 self.jac = np.vstack((self.jac, self.grad_Uopt(xk)))
-                #self.approx_fprime = np.vstack((self.approx_fprime, approx_fprime(xk, self.Uopt, self.epsilon)))
+                # self.approx_fprime = np.vstack((self.approx_fprime, approx_fprime(xk, self.Uopt, self.epsilon)))
 
             if self.verbose:
-                print("%d\tUopt: %.8f eV\tNorm of gradient: %.2e eV/m" \
+                print("%d\tUopt: %.8f eV\tNorm of gradient: %.2e eV/m"
                       % (self.call_counter, self.curr_fun[-1], self.curr_grad_norm[-1]))
 
         self.call_counter += 1
@@ -85,7 +88,8 @@ def save_pictures(self, xk: ArrayLike) -> None:
 
         if (Uext is not None) and (xext is not None) and (yext is not None):
             Xext, Yext = np.meshgrid(xext, yext)
-            plt.pcolormesh(xext * 1E6, yext * 1E6, Uext(Xext, Yext), cmap=plt.cm.RdYlBu, vmax=self.clim[1], vmin=self.clim[0])
+            plt.pcolormesh(xext * 1E6, yext * 1E6, Uext(Xext, Yext),
+                           cmap=plt.cm.RdYlBu, vmax=self.clim[1], vmin=self.clim[0])
             plt.xlim(np.min(xext) * 1E6, np.max(xext) * 1E6)
             plt.ylim(np.min(yext) * 1E6, np.max(yext) * 1E6)
 
@@ -96,14 +100,14 @@ def save_pictures(self, xk: ArrayLike) -> None:
             electrons_x, electrons_y = r2xy(r_new)
 
         plt.plot(electrons_x*1E6, electrons_y*1E6, 'o', color='deepskyblue')
-        plt.xlabel("$x$ ($\mu$m)")
-        plt.ylabel("$y$ ($\mu$m)")
+        plt.xlabel("$x$"+f" ({chr(956)}m)")
+        plt.ylabel("$y$"+f" ({chr(956)}m)")
         plt.colorbar()
         plt.close(fig)
 
         self.monitor_convergence(xk)
 
-    def create_movie(self, fps: int, filenames_in: str="%05d.png", filename_out: str="movie.mp4") -> None:
+    def create_movie(self, fps: int, filenames_in: str = "%05d.png", filename_out: str = "movie.mp4") -> None:
         """
         Generate a movie from the pictures generated by save_pictures. Movie gets saved in self.save_path
         For filenames of the type 00000.png etc use filenames_in="%05d.png".
@@ -115,13 +119,15 @@ def create_movie(self, fps: int, filenames_in: str="%05d.png", filename_out: str
         """
         curr_dir = os.getcwd()
         os.chdir(self.save_path)
-        os.system(r"ffmpeg -r {} -b 1800 -i {} {}".format(int(fps), filenames_in, filename_out))
+        os.system(r"ffmpeg -r {} -b 1800 -i {} {}".format(int(fps),
+                  filenames_in, filename_out))
         os.chdir(curr_dir)
 
+
 class PositionSolver:
 
-    def __init__(self, grid_data_x: ArrayLike, grid_data_y: ArrayLike, potential_data: ArrayLike, spline_order_x: int=3, spline_order_y: int=3, 
-                 smoothing: float=0, include_screening: bool=True, screening_length: float=np.inf) -> None:
+    def __init__(self, grid_data_x: ArrayLike, grid_data_y: ArrayLike, potential_data: ArrayLike, spline_order_x: int = 3, spline_order_y: int = 3,
+                 smoothing: float = 0, include_screening: bool = True, screening_length: float = np.inf) -> None:
         """
         This class is used for constructing the functional forms required for scipy.optimize.minimize.
         It deals with the Maxwell input data, as well as constructs the cost function used in the optimizer.
@@ -138,17 +144,17 @@ def __init__(self, grid_data_x: ArrayLike, grid_data_y: ArrayLike, potential_dat
 
         self.include_screening = include_screening
         self.screening_length = screening_length
-        
+
         self.x_max = np.max(grid_data_x)
         self.x_min = np.min(grid_data_x)
         self.x_center = (self.x_max + self.x_min) / 2
         self.y_max = np.max(grid_data_y)
         self.y_min = np.min(grid_data_y)
         self.y_center = (self.y_max + self.y_min) / 2
-        
+
         self.periodic_boundaries = []
 
-    def map_y_into_domain(self, y: ArrayLike, ybounds: Optional[tuple]=None) -> ArrayLike:
+    def map_y_into_domain(self, y: ArrayLike, ybounds: Optional[tuple] = None) -> ArrayLike:
         """Map the y-coordinates back into the solution domain set by (self.y_min, self.y_max), unless otherwise specified.
         This function is called in the case of periodic boundary conditions in the y direction.
 
@@ -162,8 +168,8 @@ def map_y_into_domain(self, y: ArrayLike, ybounds: Optional[tuple]=None) -> Arra
         if ybounds is None:
             ybounds = (self.y_min, self.y_max)
         return ybounds[0] + (y - ybounds[0]) % (ybounds[1] - ybounds[0])
-    
-    def map_x_into_domain(self, x: ArrayLike, xbounds: Optional[tuple]=None) -> ArrayLike:
+
+    def map_x_into_domain(self, x: ArrayLike, xbounds: Optional[tuple] = None) -> ArrayLike:
         """Map the x-coordinates back into the solution domain set by (self.x_min, self.x_max), unless otherwise specified.
         This function is called in the case of periodic boundary conditions in the x-domain.
 
@@ -183,7 +189,7 @@ def calculate_metrics(self, xi: ArrayLike, yi: ArrayLike) -> tuple:
         To deal with this, all electrons should first be mapped into the domain (self.x_min, self.x_max) and (self.y_min, self.y_max). 
         To calculate the xi-xj, yi-yj and ri-rj we artificially move the electron positions and re-calculate 
         the arrays. Finally we return the smallest ri-rj which can then be used to evaluate the electron-electron energy.
-        
+
         Args:
             xi (ArrayLike): 1D array of electron positions (x-coordinate)
             yi (ArrayLike): 1D array of electron positions (y-coordinate)
@@ -193,20 +199,21 @@ def calculate_metrics(self, xi: ArrayLike, yi: ArrayLike) -> tuple:
         """
         Xi, Yi = np.meshgrid(xi, yi)
         Xj, Yj = Xi.T, Yi.T
-        
+
         XiXj = Xi - Xj
         YiYj = Yi - Yj
-        
+
         Rij_standard = np.sqrt((XiXj) ** 2 + (YiYj) ** 2)
 
         if 'y' in self.periodic_boundaries:
             Yi_shifted = Yi.copy()
-            Yi_shifted[Yi_shifted > self.y_center] -= np.abs(self.y_max - self.y_min)
+            Yi_shifted[Yi_shifted >
+                       self.y_center] -= np.abs(self.y_max - self.y_min)
             Yj_shifted = Yi_shifted.T
             YiYj_shifted = Yi_shifted - Yj_shifted
-            
+
             Rij_shifted = np.sqrt((XiXj) ** 2 + (YiYj_shifted) ** 2)
-            
+
             # Calculate the pairwise minimum of the shifted and standard expression.
             Rij = np.minimum(Rij_standard, Rij_shifted)
 
@@ -216,12 +223,13 @@ def calculate_metrics(self, xi: ArrayLike, yi: ArrayLike) -> tuple:
         if 'x' in self.periodic_boundaries:
             # For periodic boundary conditions in the x-direction, if electrons move out of the simulation domain (x_min, x_max), they'll come back around.
             Xi_shifted = Xi.copy()
-            Xi_shifted[Xi_shifted > self.x_center] -= np.abs(self.x_max - self.x_min)
+            Xi_shifted[Xi_shifted >
+                       self.x_center] -= np.abs(self.x_max - self.x_min)
             Xj_shifted = Xi_shifted.T
             XiXj_shifted = Xi_shifted - Xj_shifted
-            
+
             Rij_shifted = np.sqrt((XiXj_shifted) ** 2 + (YiYj) ** 2)
-            
+
             # Calculate the pairwise minimum of the shifted and standard expression.
             Rij = np.minimum(Rij_standard, Rij_shifted)
 
@@ -268,7 +276,7 @@ def Velectrostatic(self, xi: ArrayLike, yi: ArrayLike) -> float:
             yi = self.map_y_into_domain(yi)
         return q_e * np.sum(self.V(xi, yi))
 
-    def Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float=1E-15) -> ArrayLike:
+    def Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float = 1E-15) -> ArrayLike:
         """Returns the repulsive potential between two electrons separated by a distance sqrt(|xi-xj|**2 + |yi-yj|**2)
         Note the factor 1/2. in front of the potential energy to avoid overcounting. This is chosen such that taking the sum
         np.sum(Vee(xi, yi)) gives the total interaction energy of the system (without double counting).
@@ -281,20 +289,20 @@ def Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float=1E-15) -> ArrayLike:
         Returns:
             ArrayLike: 2D array containing the pairwise electron-electron interaction energies in units of Joules.
         """
-        
+
         if len(self.periodic_boundaries) == 0:
             Xi, Yi = np.meshgrid(xi, yi)
             Xj, Yj = Xi.T, Yi.T
 
             Rij = np.sqrt((Xi - Xj) ** 2 + (Yi - Yj) ** 2)
-        else: 
+        else:
             if 'x' in self.periodic_boundaries:
                 xi = self.map_x_into_domain(xi)
             if 'y' in self.periodic_boundaries:
                 yi = self.map_y_into_domain(yi)
-            
+
             XiXj, YiYj, Rij = self.calculate_metrics(xi, yi)
-            
+
         np.fill_diagonal(Rij, eps)
 
         if self.include_screening:
@@ -406,10 +414,10 @@ def ddVdxdy(self, xi: ArrayLike, yi: ArrayLike) -> ArrayLike:
             xi = self.map_x_into_domain(xi)
         if 'y' in self.periodic_boundaries:
             yi = self.map_y_into_domain(yi)
-        
+
         return self.interpolator.ev(xi, yi, dx=1, dy=1)
 
-    def grad_Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float=1E-15) -> ArrayLike:
+    def grad_Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float = 1E-15) -> ArrayLike:
         """Derivative of the electron-electron interaction term
 
         Args:
@@ -423,7 +431,7 @@ def grad_Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float=1E-15) -> ArrayLike:
         if len(self.periodic_boundaries) == 0:
             Xi, Yi = np.meshgrid(xi, yi)
             Xj, Yj = Xi.T, Yi.T
-            XiXj = Xi - Xj 
+            XiXj = Xi - Xj
             YiYj = Yi - Yj
 
             Rij = np.sqrt((Xi - Xj) ** 2 + (Yi - Yj) ** 2)
@@ -432,9 +440,9 @@ def grad_Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float=1E-15) -> ArrayLike:
                 xi = self.map_x_into_domain(xi)
             if 'y' in self.periodic_boundaries:
                 yi = self.map_y_into_domain(yi)
-                
+
             XiXj, YiYj, Rij = self.calculate_metrics(xi, yi)
-            
+
         np.fill_diagonal(Rij, eps)
 
         gradx_matrix = np.zeros(np.shape(Rij))
@@ -443,14 +451,15 @@ def grad_Vee(self, xi: ArrayLike, yi: ArrayLike, eps: float=1E-15) -> ArrayLike:
 
         if self.include_screening:
             gradx_matrix = -1 * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-Rij/self.screening_length) * \
-                           XiXj * (Rij + self.screening_length) / (self.screening_length * Rij ** 3)
+                XiXj * (Rij + self.screening_length) / \
+                (self.screening_length * Rij ** 3)
             grady_matrix = +1 * q_e ** 2 / (4 * np.pi * eps0) * np.exp(-Rij/self.screening_length) * \
-                           YiYj * (Rij + self.screening_length) / (self.screening_length * Rij ** 3)
+                YiYj * (Rij + self.screening_length) / \
+                (self.screening_length * Rij ** 3)
         else:
             gradx_matrix = -1 * q_e ** 2 / (4 * np.pi * eps0) * XiXj / Rij ** 3
             grady_matrix = +1 * q_e ** 2 / (4 * np.pi * eps0) * YiYj / Rij ** 3
 
-
         np.fill_diagonal(gradx_matrix, 0)
         np.fill_diagonal(grady_matrix, 0)
 
@@ -476,7 +485,7 @@ def grad_total(self, r: ArrayLike) -> float:
         gradient += self.grad_Vee(xi, yi) / q_e
         return gradient
 
-    def thermal_kick_x(self, x: ArrayLike, y: ArrayLike, T: float, maximum_dx: Optional[float]=None) -> float:
+    def thermal_kick_x(self, x: ArrayLike, y: ArrayLike, T: float, maximum_dx: Optional[float] = None) -> float:
         ktrapx = np.abs(q_e * self.ddVdx(x, y))
         ret = np.sqrt(2 * kB * T / ktrapx)
         if maximum_dx is not None:
@@ -485,7 +494,7 @@ def thermal_kick_x(self, x: ArrayLike, y: ArrayLike, T: float, maximum_dx: Optio
         else:
             return ret
 
-    def thermal_kick_y(self, x: ArrayLike, y: ArrayLike, T: float, maximum_dy: Optional[float]=None) -> float:
+    def thermal_kick_y(self, x: ArrayLike, y: ArrayLike, T: float, maximum_dy: Optional[float] = None) -> float:
         ktrapy = np.abs(q_e * self.ddVdy(x, y))
         ret = np.sqrt(2 * kB * T / ktrapy)
         if maximum_dy is not None:
@@ -497,14 +506,18 @@ def thermal_kick_y(self, x: ArrayLike, y: ArrayLike, T: float, maximum_dy: Optio
     def single_thread(self, iteration, electron_initial_positions, T, cost_function, minimizer_dict, maximum_dx, maximum_dy):
         xi, yi = r2xy(electron_initial_positions)
         np.random.seed(np.int(time.time()) + iteration)
-        xi_prime = xi + self.thermal_kick_x(xi, yi, T, maximum_dx=maximum_dx) * np.random.randn(len(xi))
-        yi_prime = yi + self.thermal_kick_y(xi, yi, T, maximum_dy=maximum_dy) * np.random.randn(len(yi))
+        xi_prime = xi + \
+            self.thermal_kick_x(
+                xi, yi, T, maximum_dx=maximum_dx) * np.random.randn(len(xi))
+        yi_prime = yi + \
+            self.thermal_kick_y(
+                xi, yi, T, maximum_dy=maximum_dy) * np.random.randn(len(yi))
         electron_perturbed_positions = xy2r(xi_prime, yi_prime)
         return minimize(cost_function, electron_perturbed_positions, **minimizer_dict)
 
-    def parallel_perturb_and_solve(self, cost_function: callable, N_perturbations: int, T: float, 
+    def parallel_perturb_and_solve(self, cost_function: callable, N_perturbations: int, T: float,
                                    solution_data_reference: dict, minimizer_dict: dict,
-                                   maximum_dx: Optional[float]=None, maximum_dy: Optional[float]=None) -> dict:
+                                   maximum_dx: Optional[float] = None, maximum_dy: Optional[float] = None) -> dict:
         """
         This function is to be run after a minimization by scipy.optimize.minimize has already occured.
         It takes the output of that function in solution_data_reference and tries to find a lower energy state
@@ -525,14 +538,15 @@ def parallel_perturb_and_solve(self, cost_function: callable, N_perturbations: i
         iteration = 0
         while iteration < N_perturbations:
             iteration += 1
-            tasks.append((iteration, electron_initial_positions, T, cost_function, minimizer_dict, maximum_dx, maximum_dy,))
+            tasks.append((iteration, electron_initial_positions, T,
+                         cost_function, minimizer_dict, maximum_dx, maximum_dy,))
 
         results = [pool.apply_async(self.single_thread, t) for t in tasks]
         for result in results:
             res = result.get()
 
             if res['status'] == 0 and res['fun'] < best_result['fun']:
-                #cprint("\tNew minimum was found after perturbing!", "green")
+                # cprint("\tNew minimum was found after perturbing!", "green")
                 best_result = res
 
         # Nothing has changed by perturbing the reference solution
@@ -540,14 +554,13 @@ def parallel_perturb_and_solve(self, cost_function: callable, N_perturbations: i
             print("Solution data unchanged after perturbing")
         # Or there is a new minimum
         else:
-            print("Better solution found (%.3f%% difference)" \
-                   % (100 * (best_result['fun'] - solution_data_reference['fun']) / solution_data_reference['fun']))
-
+            print("Better solution found (%.3f%% difference)"
+                  % (100 * (best_result['fun'] - solution_data_reference['fun']) / solution_data_reference['fun']))
 
         return best_result
 
     def perturb_and_solve(self, cost_function: callable, N_perturbations: int, T: float, solution_data_reference: dict,
-                          maximum_dx: Optional[float]=None, maximum_dy: Optional[float]=None, do_print: bool=True, 
+                          maximum_dx: Optional[float] = None, maximum_dy: Optional[float] = None, do_print: bool = True,
                           **minimizer_options) -> dict:
         """This function should only be called after scipy.optimize.minimize has been called.
         It takes the output of scipy.optimize.minimize in solution_data_reference and tries to find a lower energy state
@@ -570,19 +583,24 @@ def perturb_and_solve(self, cost_function: callable, N_perturbations: int, T: fl
 
         for n in range(N_perturbations):
             xi, yi = r2xy(electron_initial_positions)
-            xi_prime = xi + self.thermal_kick_x(xi, yi, T, maximum_dx=maximum_dx) * np.random.randn(len(xi))
-            yi_prime = yi + self.thermal_kick_y(xi, yi, T, maximum_dy=maximum_dy) * np.random.randn(len(yi))
+            xi_prime = xi + \
+                self.thermal_kick_x(
+                    xi, yi, T, maximum_dx=maximum_dx) * np.random.randn(len(xi))
+            yi_prime = yi + \
+                self.thermal_kick_y(
+                    xi, yi, T, maximum_dy=maximum_dy) * np.random.randn(len(yi))
             electron_perturbed_positions = xy2r(xi_prime, yi_prime)
 
-            res = minimize(cost_function, electron_perturbed_positions, **minimizer_options)
-            
+            res = minimize(
+                cost_function, electron_perturbed_positions, **minimizer_options)
+
             xf, yf = r2xy(res['x'])
             if 'x' in self.periodic_boundaries:
                 xf = self.map_x_into_domain(xf)
             if 'y' in self.periodic_boundaries:
                 yf = self.map_y_into_domain(yf)
             res['x'] = xy2r(xf, yf)
-                
+
             if res['status'] == 0 and res['fun'] < best_result['fun']:
                 if do_print:
                     print("\tNew minimum was found after perturbing!")
@@ -598,4 +616,4 @@ def perturb_and_solve(self, cost_function: callable, N_perturbations: int, T: fl
                 if do_print:
                     print("\tSimulation didn't converge after perturbation.")
 
-        return best_result
\ No newline at end of file
+        return best_result
diff --git a/quantum_electron/schrodinger_solver.py b/quantum_electron/schrodinger_solver.py
index b203ee3..29a23c1 100644
--- a/quantum_electron/schrodinger_solver.py
+++ b/quantum_electron/schrodinger_solver.py
@@ -11,6 +11,7 @@
 from numpy.typing import ArrayLike
 from itertools import product
 
+
 class Schrodinger:
     """Abstract class for solving the 1D and 2D Schrodinger equation 
     using finite differences and sparse matrices"""
@@ -22,7 +23,8 @@ def __init__(self, sparse_args=None, solve=True):
         self.solved = False
         self.sparse_args = sparse_args
         self.solved = False
-        if solve: self.solve()
+        if solve:
+            self.solve()
 
     @staticmethod
     def uv(vec):
@@ -40,7 +42,7 @@ def Dmat(numpts, delta=1):
         a = 0.5 / delta * np.ones(numpts)
         a[0] = 0
         a[-2] = 0
-        #b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0
+        # b=-2./delta**2*ones(numpts); b[0]=0;b[-1]=0
         c = -0.5 / delta * np.ones(numpts)
         c[1] = 0
         c[-1] = 0
@@ -58,7 +60,7 @@ def D2mat(numpts, delta=1, periodic=True, q=0):
         a = 1. / delta ** 2 * np.ones(numpts)
         b = -2. / delta ** 2 * np.ones(numpts)
         c = 1. / delta ** 2 * np.ones(numpts)
-        #print "delta = %f" % (delta)
+        # print "delta = %f" % (delta)
         if periodic:
             if q == 0:
                 return sparse.spdiags([c, a, b, c, c], [-numpts + 1, -1, 0, 1, numpts - 1], numpts, numpts)
@@ -76,7 +78,8 @@ def solve(self, sparse_args=None):
         """Constructs and solves for eigenvalues and eigenvectors of Hamiltonian
             @param sparse_args if present used in eigsh sparse solver"""
         Hmat = self.Hamiltonian()
-        if sparse_args is not None: self.sparse_args = sparse_args
+        if sparse_args is not None:
+            self.sparse_args = sparse_args
         if self.sparse_args is None:
             en, ev = eig(Hmat.todense())
         else:
@@ -90,12 +93,14 @@ def solve(self, sparse_args=None):
 
     def energies(self, num_levels=-1):
         """returns eigenvalues of Hamiltonian (solves if not already solved)"""
-        if not self.solved: self.solve()
+        if not self.solved:
+            self.solve()
         return self.en[:num_levels]
 
     def psis(self, num_levels=-1):
         """returns eigenvectors of Hamiltonian (solves if not already solved)"""
-        if not self.solved: self.solve()
+        if not self.solved:
+            self.solve()
         return self.ev[:num_levels]
 
     def reduced_operator(self, operator, num_levels=-1):
@@ -103,14 +108,16 @@ def reduced_operator(self, operator, num_levels=-1):
         @param operator a (sparse) matrix representing an operator in the x basis
         @num_levels number of levels to truncate Hilbert space
         """
-        if not self.solved: self.solve()
+        if not self.solved:
+            self.solve()
         if sparse.issparse(operator):
             return np.array([np.array([np.dot(psi1, operator.dot(psi2)) for psi2 in self.psis(num_levels)]) for psi1 in
-                          self.psis(num_levels)])
+                             self.psis(num_levels)])
         else:
             return np.array([np.array([np.dot(psi1, np.dot(operator, psi2)) for psi2 in self.psis(num_levels)]) for psi1 in
-                          self.psis(num_levels)])
-            
+                             self.psis(num_levels)])
+
+
 class Schrodinger2D(Schrodinger):
     def __init__(self, x, y, U, KEx=1, KEy=1, periodic_x=False, periodic_y=False, qx=0, qy=0, sparse_args=None,
                  solve=True):
@@ -142,8 +149,8 @@ def Hamiltonian(self):
         Vmat = sparse.spdiags([U], [0], len(U), len(U))
         Kmat = sparse.kron(-self.KEy * Schrodinger.D2mat(len(self.y), self.y[1] - self.y[0], self.periodic_y, self.qy),
                            sparse.identity(len(self.x))) + \
-               sparse.kron(sparse.identity(len(self.y)),
-                           -self.KEx * Schrodinger.D2mat(len(self.x), self.x[1] - self.x[0], self.periodic_x, self.qx))
+            sparse.kron(sparse.identity(len(self.y)),
+                        -self.KEx * Schrodinger.D2mat(len(self.x), self.x[1] - self.x[0], self.periodic_x, self.qx))
         return Kmat + Vmat
 
     def get_2Dpsis(self, num_levels=-1):
@@ -161,19 +168,21 @@ def plot(self, num_levels=10):
         plt.figure(figsize=(20, 5))
         plt.subplot(1, num_levels + 1, 1)
         self.plot_potential()
-        #xlabel('$\phi$')
+        # xlabel('$\phi$')
         for ii, psi2D in enumerate(self.get_2Dpsis(num_levels)):
             plt.subplot(1, num_levels + 1, ii + 2)
-            #imshow(psi2D.real,extent=(self.x[0],self.x[-1],self.y[0],self.y[-1]),interpolation="None",aspect='auto')
+            # imshow(psi2D.real,extent=(self.x[0],self.x[-1],self.y[0],self.y[-1]),interpolation="None",aspect='auto')
             plt.imshow(psi2D.real, interpolation="None", aspect='auto')
             plt.xlabel(ii)
 
     def plot_potential(self):
         """Plots potential energy landscape"""
-        plt.imshow(self.U, extent=(self.x[0], self.x[-1], self.y[0], self.y[-1]), aspect='auto', interpolation='None')
+        plt.imshow(self.U, extent=(
+            self.x[0], self.x[-1], self.y[0], self.y[-1]), aspect='auto', interpolation='None')
         plt.xlabel('x')
         plt.ylabel('y')
 
+
 class SingleElectron(Schrodinger2D):
     def __init__(self, x, y, potential_function, sparse_args=None, solve=True):
         """
@@ -201,17 +210,20 @@ def evaluate_potential(self, x, y):
     def sparsify(self, num_levels=10):
         self.U = self.evaluate_potential(self.x, self.y)
         self.sparse_args = {'k': num_levels,  # Find k eigenvalues and eigenvectors
-                            'which': 'LM',  # ‘LM’ : Largest (in magnitude) eigenvalues
-                            'sigma': np.min(self.U),  # 'sigma' : Find eigenvalues near sigma using shift-invert mode.
+                            # ‘LM’ : Largest (in magnitude) eigenvalues
+                            'which': 'LM',
+                            # 'sigma' : Find eigenvalues near sigma using shift-invert mode.
+                            'sigma': np.min(self.U),
                             'maxiter': None}  # Maximum number of Arnoldi update iterations allowed Default: n*10
 
-class QuantumAnalysis(PotentialVisualization): 
+
+class QuantumAnalysis(PotentialVisualization):
     """This class solves the Schrodinger equation for a single electron on helium. Typical workflow: 
-    
+
     qa = QuantumAnalysis(potential_dict=potential_dict, voltage_dict=voltage_dict)
     qa.get_quantum_spectrum(coor=None, dxdy=[.8, .8])
     """
-    
+
     def __init__(self, potential_dict: Dict[str, ArrayLike], voltage_dict: Dict[str, float]):
         """Class for solving quantum properties of a single electron trapped in a dot
 
@@ -225,8 +237,9 @@ def __init__(self, potential_dict: Dict[str, ArrayLike], voltage_dict: Dict[str,
         self.voltage_dict = voltage_dict
         self.solved = False
 
-        PotentialVisualization.__init__(self, potential_dict=potential_dict, voltages=voltage_dict)
-        
+        PotentialVisualization.__init__(
+            self, potential_dict=potential_dict, voltages=voltage_dict)
+
     def update_voltages(self, voltage_dict: Dict[str, float]):
         """Update the voltage dictionary
 
@@ -236,8 +249,8 @@ def update_voltages(self, voltage_dict: Dict[str, float]):
         """
         self.voltage_dict = voltage_dict
         self.solved = False
-        
-    def solve_system(self, coor: List[float]=[0,0], dxdy: List[float]=[1, 2], N_evals: float=10, n_x: int=150, n_y: int=100) -> None:
+
+    def solve_system(self, coor: List[float] = [0, 0], dxdy: List[float] = [1, 2], N_evals: float = 10, n_x: int = 150, n_y: int = 100) -> None:
         """Solve the Schrodinger equation for a given set of voltages.
 
         Args:
@@ -247,59 +260,66 @@ def solve_system(self, coor: List[float]=[0,0], dxdy: List[float]=[1, 2], N_eval
         """
         # If not specified as a function argument, coor will be the minimum of the potential
         if coor is None:
-            coor = find_minimum_location(self.potential_dict, self.voltage_dict)
-        
+            coor = find_minimum_location(
+                self.potential_dict, self.voltage_dict)
+
         # Note that xsol and ysol determine the x and y points for which you want to solve the Schrodinger equation
-        self.xsol = np.linspace(coor[0]-dxdy[0]/2, coor[0]+dxdy[0]/2, n_x) * 1e-6
+        self.xsol = np.linspace(
+            coor[0]-dxdy[0]/2, coor[0]+dxdy[0]/2, n_x) * 1e-6
         y_symmetric = construct_symmetric_y(coor[1]-dxdy[1]/2, n_y) * 1e-6
         self.ysol = np.zeros(2 * len(y_symmetric))
         self.ysol[:len(y_symmetric)] = y_symmetric
         self.ysol[len(y_symmetric):] = -y_symmetric[::-1]
-        
+
         potential = make_potential(self.potential_dict, self.voltage_dict)
 
         # By using the interpolator we create a function that can evaluate the potential energy for an electron at arbitrary x,y
         # This is useful if the original potential data is sparsely sampled (e.g. due to FEM time constraints)
-        potential_function = scipy.interpolate.RegularGridInterpolator((self.potential_dict['xlist']*1e-6, 
-                                                                        self.potential_dict['ylist']*1e-6), 
+        potential_function = scipy.interpolate.RegularGridInterpolator((self.potential_dict['xlist']*1e-6,
+                                                                        self.potential_dict['ylist']*1e-6),
                                                                        -potential)
 
         # Note that the solution is sampled over the arrays xsol, ysol which can be set indepently from the FEM x and y points.
-        se = SingleElectron(self.xsol, self.ysol, potential_function=potential_function, solve=False)
+        se = SingleElectron(self.xsol, self.ysol,
+                            potential_function=potential_function, solve=False)
         se.sparsify(num_levels=N_evals)
         Evals, Evecs = se.solve(sparse_args=se.sparse_args)
 
         self.Psis = se.get_2Dpsis(N_evals)
-        self.mode_frequencies = (Evals - Evals[0]) * hbar**2 / (2 * q_e * m_e) * q_e / (2 * np.pi * hbar)
-        
+        self.mode_frequencies = (
+            Evals - Evals[0]) * hbar**2 / (2 * q_e * m_e) * q_e / (2 * np.pi * hbar)
+
         self.solved = True
-        
+
     def classify_wavefunction_by_well(self) -> ArrayLike:
         """This function classifies the wavefunctions by well. If the potential has a double well, the wave function will be marked with +1 or -1. 
         If there is a well it's assumed to be in the y-direction, and +1 is associated with positive y and -1 with negative. 0 is a single well.
-        
+
         Returns:
             ArrayLike: array with the same length as Psis.
         """
-        assert self.solved is True, print("You must solve the Schrodinger equation first!")
-        
+        assert self.solved is True, print(
+            "You must solve the Schrodinger equation first!")
+
         # classify by finding the center of mass of the wave function
         X, Y = np.meshgrid(self.xsol, self.ysol)
-        
+
         well_classification = list()
         for k in range(len(self.Psis)):
-            y_com = np.mean(np.abs(self.Psis[k]) * Y) / np.mean(np.abs(self.Psis[k]))
-            x_com = np.mean(np.abs(self.Psis[k]) * X) / np.mean(np.abs(self.Psis[k]))
-            
+            y_com = np.mean(np.abs(self.Psis[k])
+                            * Y) / np.mean(np.abs(self.Psis[k]))
+            x_com = np.mean(np.abs(self.Psis[k])
+                            * X) / np.mean(np.abs(self.Psis[k]))
+
             if y_com > 0.1e-6:
                 well_classification.append(+1)
             elif y_com < -0.1e-6:
                 well_classification.append(-1)
-            else: 
+            else:
                 well_classification.append(0)
-            
+
         return np.array(well_classification)
-    
+
     def classify_wavefunction_by_xy(self) -> List:
         """Classifies the wave function by labeling it with a number nx and ny. These numbers capture the number of crests of the wave function in 
         the x and y direction, respectively. 
@@ -307,22 +327,23 @@ def classify_wavefunction_by_xy(self) -> List:
         Returns:
             List: List of dictionaries. The length of this list is equal to the length of Psis.
         """
-        assert self.solved is True, print("You must solve the Schrodinger equation first!")
-        
+        assert self.solved is True, print(
+            "You must solve the Schrodinger equation first!")
+
         classification = list()
         for k in range(len(self.Psis)):
-            
+
             sig = np.sum(self.Psis[k] ** 2, axis=0)
             n_x = len(scipy.signal.find_peaks(sig, height=np.max(sig)/2)[0])
-            
+
             sig = np.sum(self.Psis[k] ** 2, axis=1)
             n_y = len(scipy.signal.find_peaks(sig, height=np.max(sig)/2)[0])
-            
-            classification.append({"nx" : n_x - 1, 
-                                   "ny" : n_y - 1})
-            
+
+            classification.append({"nx": n_x - 1,
+                                   "ny": n_y - 1})
+
         return classification
-    
+
     def classification_to_latex(self, classification: dict) -> str:
         """This function takes the classification dictionary and transforms it into a string for plotting.
 
@@ -334,8 +355,8 @@ def classification_to_latex(self, classification: dict) -> str:
         """
         return fr"$|{classification['nx']:d}_x {classification['ny']:d}_y \rangle$"
 
-    def get_quantum_spectrum(self, coor: Optional[List[float]]=[0,0], dxdy: List[float]=[1, 2], plot_wavefunctions: bool=False, 
-                             axes_zoom: Optional[float]=None, **solve_kwargs) -> tuple[ArrayLike, ArrayLike]:
+    def get_quantum_spectrum(self, coor: Optional[List[float]] = [0, 0], dxdy: List[float] = [1, 2], plot_wavefunctions: bool = False,
+                             axes_zoom: Optional[float] = None, **solve_kwargs) -> tuple[ArrayLike, ArrayLike]:
         """Returns the frequencies of the first N eigenmodes for a single electron trapped in a potential. 
 
         Args:
@@ -351,84 +372,93 @@ def get_quantum_spectrum(self, coor: Optional[List[float]]=[0,0], dxdy: List[flo
         Returns:
             tuple[ArrayLike, ArrayLike]: Eigenfrequencies of the first N motional modes in Hz, and a classification of the mode.
         """
-        
-        if not self.solved: 
+
+        if not self.solved:
             self.solve_system(coor=coor, dxdy=dxdy, **solve_kwargs)
 
         if plot_wavefunctions:
-            fig = plt.figure(figsize=(12.,6.))
+            fig = plt.figure(figsize=(12., 6.))
 
         well_classification = self.classify_wavefunction_by_well()
         xy_classification = self.classify_wavefunction_by_xy()
-        
+
         for k in range(6):
             if plot_wavefunctions:
                 plt.subplot(2, 3, k+1)
-                plt.pcolormesh(self.xsol/1e-6, self.ysol/1e-6, self.Psis[k], cmap=plt.cm.RdBu_r, 
-                               vmin=-np.max(np.abs(self.Psis[k])), 
+                plt.pcolormesh(self.xsol/1e-6, self.ysol/1e-6, self.Psis[k], cmap=plt.cm.RdBu_r,
+                               vmin=-np.max(np.abs(self.Psis[k])),
                                vmax=np.max(np.abs(self.Psis[k])))
                 cbar = plt.colorbar()
                 tick_locator = matplotlib.ticker.MaxNLocator(nbins=4)
                 cbar.locator = tick_locator
                 cbar.update_ticks()
-                
+
             if plot_wavefunctions:
-                zdata = -make_potential(self.potential_dict, self.voltage_dict).T
-                contours = [np.round(np.min(zdata), 3) +k*1e-3 for k in range(5)]
-                CS = plt.contour(self.potential_dict['xlist'], self.potential_dict['ylist'], zdata, levels=contours)
+                zdata = -make_potential(self.potential_dict,
+                                        self.voltage_dict).T
+                contours = [np.round(np.min(zdata), 3) +
+                            k*1e-3 for k in range(5)]
+                CS = plt.contour(
+                    self.potential_dict['xlist'], self.potential_dict['ylist'], zdata, levels=contours)
                 plt.gca().clabel(CS, CS.levels, inline=True, fontsize=10)
-                plt.title(rf"{self.classification_to_latex(xy_classification[k])} "+f"({well_classification[k]} well) - {self.mode_frequencies[k]/1e9:.2f} GHz", size=10)
-                
+                plt.title(rf"{self.classification_to_latex(xy_classification[k])} " +
+                          f"({well_classification[k]} well) - {self.mode_frequencies[k]/1e9:.2f} GHz", size=10)
+
                 if axes_zoom is not None:
                     # classify by finding the center of mass of the wave function
                     X, Y = np.meshgrid(self.xsol, self.ysol)
-                    y_com = np.mean(np.abs(self.Psis[k]) * Y) / np.mean(np.abs(self.Psis[k]))
-                    x_com = np.mean(np.abs(self.Psis[k]) * X) / np.mean(np.abs(self.Psis[k]))
-                    
-                    plt.xlim((x_com/1e-6 - axes_zoom/2), (x_com/1e-6 + axes_zoom/2))
-                    plt.ylim((y_com/1e-6 - axes_zoom/2), (y_com/1e-6 + axes_zoom/2))
+                    y_com = np.mean(
+                        np.abs(self.Psis[k]) * Y) / np.mean(np.abs(self.Psis[k]))
+                    x_com = np.mean(
+                        np.abs(self.Psis[k]) * X) / np.mean(np.abs(self.Psis[k]))
+
+                    plt.xlim((x_com/1e-6 - axes_zoom/2),
+                             (x_com/1e-6 + axes_zoom/2))
+                    plt.ylim((y_com/1e-6 - axes_zoom/2),
+                             (y_com/1e-6 + axes_zoom/2))
                 else:
                     plt.xlim(np.min(self.xsol/1e-6), np.max(self.xsol/1e-6))
                     plt.ylim(np.min(self.ysol/1e-6), np.max(self.ysol/1e-6))
-                
+
                 plt.locator_params(axis='both', nbins=4)
-            
+
                 if k >= 3:
                     plt.xlabel("$x$"+f" ({chr(956)}m)")
-                    
-                if not k%3:
+
+                if not k % 3:
                     plt.ylabel("$y$"+f" ({chr(956)}m)")
-                            
+
         if plot_wavefunctions:
             fig.tight_layout()
-                    
+
         return self.mode_frequencies
-        
+
     def get_anharmonicity(self) -> float:
         """Calculate the anharmonicity. The anharmonicity here is defined as (f|0x2y> - f|0x1y>) - (f|0x1y> - f|0x0y>) 
 
         Returns:
             float: Anharmonicity in Hz.
         """
-        assert self.solved is True, print("You must solve the Schrodinger equation first!")
-        
+        assert self.solved is True, print(
+            "You must solve the Schrodinger equation first!")
+
         frequencies = self.mode_frequencies
         classifications = self.classify_wavefunction_by_xy()
-        
-        f_2y = frequencies[classifications.index({'nx':0, 'ny':2})]
-        f_1y = frequencies[classifications.index({'nx':0, 'ny':1})]
+
+        f_2y = frequencies[classifications.index({'nx': 0, 'ny': 2})]
+        f_1y = frequencies[classifications.index({'nx': 0, 'ny': 1})]
         try:
-            f_0y = frequencies[classifications.index({'nx':0, 'ny':0})]
+            f_0y = frequencies[classifications.index({'nx': 0, 'ny': 0})]
         except:
             # In some pathological cases the ground state is spread out over two wells, and it's not recognized. Then we can assume it's the first index.
             f_0y = frequencies[0]
-        
+
         anharmonicity = (f_2y - f_1y) - (f_1y - f_0y)
-        
+
         return anharmonicity
 
-    def get_resonator_coupling(self, coor: Optional[List[float]]=[0,0], dxdy: List[float]=[1, 2], Ex: float=0, Ey: float=1e6, resonator_impedance: float=50, 
-                               resonator_frequency: float=4e9, plot_result: bool=True, **solve_kwargs) -> ArrayLike:
+    def get_resonator_coupling(self, coor: Optional[List[float]] = [0, 0], dxdy: List[float] = [1, 2], Ex: float = 0, Ey: float = 1e6, resonator_impedance: float = 50,
+                               resonator_frequency: float = 4e9, plot_result: bool = True, **solve_kwargs) -> ArrayLike:
         """Calculate the coupling strength in Hz for mode |i> to mode |j>
 
         Args:
@@ -443,24 +473,27 @@ def get_resonator_coupling(self, coor: Optional[List[float]]=[0,0], dxdy: List[f
         Returns:
             ArrayLike: The g_ij matrix
         """
-        
-        if not self.solved: 
+
+        if not self.solved:
             self.solve_system(coor=coor, dxdy=dxdy, **solve_kwargs)
-            
+
         N_evals = len(self.Psis)
-        
-        # The resonator coupling is a symmetric matrix 
+
+        # The resonator coupling is a symmetric matrix
         g_ij = np.zeros((N_evals, N_evals))
         X, Y = np.meshgrid(self.xsol, self.ysol)
-        
-        prefactor = q_e * np.sqrt(hbar * (2 * np.pi * resonator_frequency) ** 2 * resonator_impedance / 2) * 1 / (2 * np.pi * hbar)
-        
+
+        prefactor = q_e * np.sqrt(hbar * (2 * np.pi * resonator_frequency)
+                                  ** 2 * resonator_impedance / 2) * 1 / (2 * np.pi * hbar)
+
         for i in range(N_evals):
             for j in range(N_evals):
-                g_ij[i, j] = prefactor * np.sum(self.Psis[i] * ( X * Ex + Y * Ey ) * np.conjugate(self.Psis[j]))
-                
+                g_ij[i, j] = prefactor * \
+                    np.sum(self.Psis[i] * (X * Ex + Y * Ey)
+                           * np.conjugate(self.Psis[j]))
+
         if plot_result:
-            fig = plt.figure(figsize=(7.,4.))
+            fig = plt.figure(figsize=(7., 4.))
             plt.imshow(np.abs(g_ij)/1e6, cmap=plt.cm.Blues)
             cbar = plt.colorbar()
             cbar.ax.set_ylabel(r"Coupling strength $g_{ij} / 2\pi$ (MHz)")
@@ -468,9 +501,11 @@ def get_resonator_coupling(self, coor: Optional[List[float]]=[0,0], dxdy: List[f
             plt.ylabel("Mode index $i$")
 
             for (i, j) in product(range(N_evals), range(N_evals)):
-                g_value = np.abs(g_ij[i, j]/ 1e6)
+                g_value = np.abs(g_ij[i, j] / 1e6)
                 if g_value > 0.2:
-                    col = 'white' if g_value > np.max(np.abs(g_ij)) / 1e6 / 2 else 'black'
-                    plt.text(i, j, f"{g_ij[i, j]/ 1e6:.1f}", size=9, ha='center', va='center', color=col)            
-        
-        return g_ij
\ No newline at end of file
+                    col = 'white' if g_value > np.max(
+                        np.abs(g_ij)) / 1e6 / 2 else 'black'
+                    plt.text(i, j, f"{g_ij[i, j]/ 1e6:.1f}",
+                             size=9, ha='center', va='center', color=col)
+
+        return g_ij
diff --git a/quantum_electron/utils.py b/quantum_electron/utils.py
index de411d3..b802b63 100644
--- a/quantum_electron/utils.py
+++ b/quantum_electron/utils.py
@@ -10,12 +10,14 @@
 import matplotlib
 import importlib
 
+
 def package_versions():
     for module in ['quantum_electron', 'numpy', 'scipy', 'matplotlib']:
         globals()[module] = importlib.import_module(module)
         print(globals()[module].__name__, globals()[module].__version__)
 
-def select_outer_electrons(xi: ArrayLike, yi: ArrayLike, plot: bool=True, **kwargs) -> tuple:
+
+def select_outer_electrons(xi: ArrayLike, yi: ArrayLike, plot: bool = True, **kwargs) -> tuple:
     """Select the outermost electrons from a small ensemble of electrons. This is 
     useful for calculating the area of an ensemble.
 
@@ -28,12 +30,13 @@ def select_outer_electrons(xi: ArrayLike, yi: ArrayLike, plot: bool=True, **kwar
         tuple: Polygon points (x and y), polygon area
     """
     # There must be at least 2 electrons to define a surface
-    if len(xi) > 2: 
-        points = np.c_[xi.reshape(-1), yi.reshape(-1), np.zeros(len(yi)).reshape(-1)]
+    if len(xi) > 2:
+        points = np.c_[xi.reshape(-1), yi.reshape(-1),
+                       np.zeros(len(yi)).reshape(-1)]
         cloud = pyvista.PolyData(points)
         surf = cloud.delaunay_2d()
-        boundary = surf.extract_feature_edges(boundary_edges=True, 
-                                              non_manifold_edges=False, 
+        boundary = surf.extract_feature_edges(boundary_edges=True,
+                                              non_manifold_edges=False,
                                               manifold_edges=False)
 
         boundary_x = boundary.points[:, 0] * 1e6
@@ -54,11 +57,12 @@ def select_outer_electrons(xi: ArrayLike, yi: ArrayLike, plot: bool=True, **kwar
         if plot:
             shapely.plotting.plot_polygon(polygon, **kwargs)
             plt.grid(None)
-    
+
         return polygon.exterior.xy, polygon.area
     else:
         return None, None
-        
+
+
 def density_from_positions(xi: ArrayLike, yi: ArrayLike) -> float:
     """Electron density estimate calculated from the nearest neighbor distance
 
@@ -82,6 +86,7 @@ def density_from_positions(xi: ArrayLike, yi: ArrayLike) -> float:
     area = np.pi * np.mean(nearest_neighbor_distance) ** 2 / 4
     return 1 / area
 
+
 def mean_electron_spacing(xi: ArrayLike, yi: ArrayLike) -> float:
     """Mean electron spacing calculated from the nearest neighbor distance
 
@@ -104,6 +109,7 @@ def mean_electron_spacing(xi: ArrayLike, yi: ArrayLike) -> float:
     nearest_neighbor_distance = np.min(Rij_standard, axis=1)
     return np.mean(nearest_neighbor_distance)
 
+
 def gamma_parameter(xi: ArrayLike, yi: ArrayLike, T: float) -> float:
     """Ratio of the Coulomb energy to kinetic energy. For bulk electrons on helium 
     the critical value is 137. If the value exceeds the critical value, we have a Wigner solid. 
@@ -117,9 +123,11 @@ def gamma_parameter(xi: ArrayLike, yi: ArrayLike, T: float) -> float:
     Returns:
         float: Ratio of the Coulomb energy to the Kinetic energy
     """
-    nearest_neighbor_distance = 1 / np.sqrt(np.pi * density_from_positions(xi, yi))    
-    return qe ** 2 / (4 * np.pi * epsilon_0 * nearest_neighbor_distance)  / (kB * T)
-    
+    nearest_neighbor_distance = 1 / \
+        np.sqrt(np.pi * density_from_positions(xi, yi))
+    return qe ** 2 / (4 * np.pi * epsilon_0 * nearest_neighbor_distance) / (kB * T)
+
+
 def construct_symmetric_y(ymin: float, N: int) -> ArrayLike:
     """
     This helper function constructs a one-sided array from ymin to -dy/2 with N points.
@@ -136,13 +144,15 @@ def construct_symmetric_y(ymin: float, N: int) -> ArrayLike:
     dy = 2 * np.abs(ymin) / float(2 * N + 1)
     return np.linspace(ymin, -dy / 2., int((np.abs(ymin) - 0.5 * dy) / dy + 1))
 
+
 def find_nearest(array: ArrayLike, value: float) -> int:
     """
     Finds the nearest value in array. Returns index of array for which this is true.
     """
-    idx=(np.abs(array-value)).argmin()
+    idx = (np.abs(array-value)).argmin()
     return int(idx)
 
+
 def r2xy(r: ArrayLike) -> tuple:
     """
     Reformat electron position array.
@@ -151,6 +161,7 @@ def r2xy(r: ArrayLike) -> tuple:
     """
     return r[::2], r[1::2]
 
+
 def xy2r(x: ArrayLike, y: ArrayLike) -> ArrayLike:
     """
     Reformat electron position array.
@@ -165,7 +176,8 @@ def xy2r(x: ArrayLike, y: ArrayLike) -> ArrayLike:
         return r
     else:
         raise ValueError("x and y must have the same length!")
-    
+
+
 def make_potential(potential_dict: Dict[str, ArrayLike], voltages: Dict[str, float]) -> ArrayLike:
     """Creates a numpy array potential based on an array of coupling coefficient arrays stored in potential_dict. 
     The returned potential values are positive for a positive voltage applied to the gate. Therefore, to transform
@@ -182,14 +194,15 @@ def make_potential(potential_dict: Dict[str, ArrayLike], voltages: Dict[str, flo
     """
 
     for k, key in enumerate(list(voltages.keys())):
-        if k == 0: 
-            potential = potential_dict[key] * voltages[key] 
+        if k == 0:
+            potential = potential_dict[key] * voltages[key]
         else:
             potential += potential_dict[key] * voltages[key]
-    
+
     return potential
 
-def find_minimum_location(potential_dict: Dict[str, ArrayLike], voltages: Dict[str, float], return_potential_value: bool=False) -> tuple[float, float]:
+
+def find_minimum_location(potential_dict: Dict[str, ArrayLike], voltages: Dict[str, float], return_potential_value: bool = False) -> tuple[float, float]:
     """Find the coordinates of the minimum energy point for a single electron.
 
     Args:
@@ -200,17 +213,18 @@ def find_minimum_location(potential_dict: Dict[str, ArrayLike], voltages: Dict[s
     Returns:
         tuple[float, float]: (x_min, y_min, V_min) where the potential energy for a single electron is minimized. Units are in micron, eV.
     """
-    
+
     potential = make_potential(potential_dict, voltages)
     zdata = -potential.T
-    
+
     xidx, yidx = np.unravel_index(zdata.argmin(), zdata.shape)
-    
+
     if return_potential_value:
         return potential_dict['xlist'][yidx], potential_dict['ylist'][xidx], zdata[xidx, yidx]
     else:
         return potential_dict['xlist'][yidx], potential_dict['ylist'][xidx]
 
+
 def crop_potential(x: ArrayLike, y: ArrayLike, U: ArrayLike, xrange: tuple, yrange: tuple) -> tuple:
     """Crops the potential to the boundaries specified by xrange and yrange. 
 
@@ -229,13 +243,14 @@ def crop_potential(x: ArrayLike, y: ArrayLike, U: ArrayLike, xrange: tuple, yran
 
     return x[xmin_idx:xmax_idx], y[ymin_idx:ymax_idx], U[xmin_idx:xmax_idx, ymin_idx:ymax_idx]
 
+
 class PotentialVisualization:
     def __init__(self, potential_dict: Dict[str, ArrayLike], voltages: Dict[str, float]):
         self.potential_dict = potential_dict
-        self.voltage_dict = voltages    
+        self.voltage_dict = voltages
 
-    def plot_potential_energy(self, ax=None, coor: Optional[List[float]]=[0,0], dxdy: List[float]=[1, 2], figsize: tuple[float, float]=(7, 4), 
-                              print_voltages: bool=True,  plot_contours: bool=True) -> None:
+    def plot_potential_energy(self, ax=None, coor: Optional[List[float]] = [0, 0], dxdy: List[float] = [1, 2], figsize: tuple[float, float] = (7, 4),
+                              print_voltages: bool = True,  plot_contours: bool = True) -> None:
         """Plot the potential energy as function of (x,y)
 
         Args:
@@ -253,40 +268,43 @@ def plot_potential_energy(self, ax=None, coor: Optional[List[float]]=[0,0], dxdy
             make_colorbar = True
         else:
             make_colorbar = False
-            
-        pcm = ax.pcolormesh(self.potential_dict['xlist'], self.potential_dict['ylist'], zdata, cmap=plt.cm.RdYlBu_r)
-        
+
+        pcm = ax.pcolormesh(
+            self.potential_dict['xlist'], self.potential_dict['ylist'], zdata, cmap=plt.cm.RdYlBu_r)
+
         if make_colorbar:
             cbar = plt.colorbar(pcm)
             tick_locator = matplotlib.ticker.MaxNLocator(nbins=4)
             cbar.locator = tick_locator
             cbar.update_ticks()
             cbar.ax.set_ylabel(r"Potential energy $-eV(x,y)$")
-        
+
         xidx, yidx = np.unravel_index(zdata.argmin(), zdata.shape)
-        ax.plot(self.potential_dict['xlist'][yidx], self.potential_dict['ylist'][xidx], '*', color='white')
+        ax.plot(self.potential_dict['xlist'][yidx],
+                self.potential_dict['ylist'][xidx], '*', color='white')
 
         ax.set_xlim(coor[0] - dxdy[0]/2, coor[0] + dxdy[0]/2)
         ax.set_ylim(coor[1] - dxdy[1]/2, coor[1] + dxdy[1]/2)
 
         ax.set_aspect('equal')
-        
+
         if print_voltages:
             for k, electrode in enumerate(self.voltage_dict.keys()):
                 xmin, xmax = ax.get_xlim()
                 ymin, ymax = ax.get_ylim()
 
-                ax.text(coor[0] - dxdy[0]/2 - 0.3 * (xmax - xmin), coor[1] + dxdy[1]/2 - k * 0.1 * (ymax - ymin), 
+                ax.text(coor[0] - dxdy[0]/2 - 0.3 * (xmax - xmin), coor[1] + dxdy[1]/2 - k * 0.1 * (ymax - ymin),
                         f"{electrode} = {self.voltage_dict[electrode]:.2f} V", ha='right', va='top')
 
         if plot_contours:
-            contours = [np.round(np.min(zdata), 3) +k*1e-3 for k in range(5)]
-            CS = ax.contour(self.potential_dict['xlist'], self.potential_dict['ylist'], zdata, levels=contours)
+            contours = [np.round(np.min(zdata), 3) + k*1e-3 for k in range(5)]
+            CS = ax.contour(
+                self.potential_dict['xlist'], self.potential_dict['ylist'], zdata, levels=contours)
             ax.clabel(CS, CS.levels, inline=True, fontsize=10)
 
         ax.set_xlabel("$x$"+f" ({chr(956)}m)")
         ax.set_ylabel("$y$"+f" ({chr(956)}m)")
         ax.locator_params(axis='both', nbins=4)
-        
+
         if ax is None:
-            plt.tight_layout()
\ No newline at end of file
+            plt.tight_layout()
diff --git a/setup.py b/setup.py
index 1335b68..57876bf 100644
--- a/setup.py
+++ b/setup.py
@@ -5,6 +5,6 @@
 setup(
     name='quantum_electron',
     version=__version__,
-    packages=find_packages(include=['quantum_electron']), 
+    packages=find_packages(include=['quantum_electron']),
     install_requires=['shapely', 'scikit-image', 'pyvista', 'IPython']
-)
\ No newline at end of file
+)