From ae3fea7d9a464a96750a62ea44ac814e362da9da Mon Sep 17 00:00:00 2001
From: Robert Lanzafame <R.C.Lanzafame@tudelft.nl>
Date: Thu, 5 Dec 2024 17:08:38 +0100
Subject: [PATCH] GA 2.4 ready for review

---
 .../{ga_draft.ipynb => GA_2_4_solution.ipynb} |  429 +-
 content/GA_2_4/ga_draft_tasks.ipynb           |  993 --
 src/teachers/GA_2_4/GA_2_4_solution.html      | 8974 +++++++++++++++++
 src/teachers/GA_2_4/README.zip                |  Bin 0 -> 766105 bytes
 4 files changed, 9371 insertions(+), 1025 deletions(-)
 rename content/GA_2_4/{ga_draft.ipynb => GA_2_4_solution.ipynb} (98%)
 delete mode 100644 content/GA_2_4/ga_draft_tasks.ipynb
 create mode 100644 src/teachers/GA_2_4/GA_2_4_solution.html
 create mode 100644 src/teachers/GA_2_4/README.zip

diff --git a/content/GA_2_4/ga_draft.ipynb b/content/GA_2_4/GA_2_4_solution.ipynb
similarity index 98%
rename from content/GA_2_4/ga_draft.ipynb
rename to content/GA_2_4/GA_2_4_solution.ipynb
index e2498d6b..d97c3942 100644
--- a/content/GA_2_4/ga_draft.ipynb
+++ b/content/GA_2_4/GA_2_4_solution.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# GA 2.4: Icy Times\n",
+    "# GA 2.4: Beary Icy\n",
     "\n",
     "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n",
     "    <style>\n",
@@ -17,27 +17,28 @@
     "<h2 style=\"height: 10px\">\n",
     "</h2>\n",
     "\n",
-    "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4, Time serie analysis. For: December 6, 2024*"
+    "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4, Time Series Analysis. For: December 6, 2024*"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Winter is coming and it is time to start getting our models ready for the ice classic. Our first goal is to improve the temperature model, as that seems to be an important driver of breakup day. Temperature is notoriously hard to predict, but we can analyze historical data to get a better understanding of the patterns. Today we will analyze some time series data from the first 152 days of the year, up to the first of June. We have hourly data available. This is the period of interest for the ice classic, as the ice forms in this period, reaching its maximum thickness around January, and then starts melting, with breakup day typically happening in April or May.\n",
+    "Winter is coming and it is time to start getting our models ready for the ice classic. Our first goal is to improve the temperature model, as that seems to be an important factor in determining breakup day. Temperature is notoriously hard to predict, but we can analyze historical data to get a better understanding of the patterns.\n",
     "\n",
-    "In this assignment we will analyze some time series data. We will first fit a functional model to the data in order to stationarize the data. Then we will fit an AR model to the residuals.\n",
+    "In this assignment we will analyze a time series from a **single year**; in fact, only the **first 152 days of the year**, from January 1 until June 1. This is the period of interest for the ice classic, as the ice forms in this period, reaching its maximum thickness between January-March, and then starts melting, with breakup day typically happening in April or May.\n",
     "\n",
-    "We will start by loading the data and plotting it."
+    "Remember that we have until April 5 to place a bet. Why, then do we want to fit a model several months beyond this point? This gives us confidence in assessing the ability of the model to predict temperature, so that when we use it on April 5 to make **predictions** about the future, we can understand the uncertainty associated with it.\n",
+    "\n",
+    "Let's start by loading the data and plotting it, then we will determine which components should be used to detrend it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Importing the required libraries\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from statsmodels.graphics.tsaplots import plot_acf\n",
@@ -49,11 +50,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Task 1: Load the data and plot it\n",
+    "### Part 1: Load the data and plot it\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 1.1:</b>   \n",
+    "\n",
+    "Do the following:\n",
+    "\n",
     "- load the data\n",
     "- create time vector\n",
     "- plot the data\n",
-    "- describe the data\n"
+    "\n",
+    "</p>\n",
+    "</div>"
    ]
   },
   {
@@ -97,17 +112,65 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Finding the frequency of the seasonal pattern\n",
-    "We clearly see that the data contains a seasonal pattern. We will start by fitting a functional model to the data in order to stationarize the data. To find the frequency of the seasonal pattern we will use the power spectrum of the data.\n",
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 1.2:</b>   \n",
+    "\n",
+    "Use the Markdown cell below to describe the data (you can use a few bullet points). For example, confirm relevant characteristics like number of points, units, describe the values (qualitatively), etc.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Your answer here._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "- There are 24*152 points (hourly)\n",
+    "- Looks like there is a bit of variation around a central line, but the general increase in temperature throughout the spring is larger than the variation\n",
+    "- the general trend looks linear in the middle, but flattens out at the ends, which is to be expected with a periodic annual cycle\n",
+    "- looks like there is something \"funny\" happening around day 70 and 120\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 2: Extract the Dominant Patterns\n",
+    "\n",
+    "We clearly see that the data contains a strong pattern (the general increase in temperature from winter to summer). We will start by fitting a functional model to the data in order to stationarize it. To find the frequency of the seasonal pattern we will use the power spectrum of the data.\n",
     "\n",
     "We will reuse the function `find_seasonal_pattern` from the workshop.\n",
     "\n",
-    "Remember that for running this function we need to predefine the A-matrix to detrend the data. Since the data only contains the first 5 months of the year, we see that the temperature is increasing over time. Based on what you know of temperature data, what would be the best model to remove the trend? \n",
+    "Remember that for running this function we need to predefine the A-matrix to detrend the data. Since the data only contains the first 5 months of the year, we see that the temperature is increasing over time. What type of model would be most appropriate to remove the seasonal trend? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 2.1:</b>   \n",
+    "\n",
+    "Define functions to help carry out this analysis, for example, <code>fit_model</code> and <code>find_frequency</code>.\n",
     "\n",
-    "### task 1\n",
-    "Provide a A-matrix that removes the trend from the data. There are multiple answers that will work, but some are better than others.\n",
-    "- Provide the A-matrix that you will use to detrend the data.\n",
-    "- Explain why you chose this A-matrix."
+    "</p>\n",
+    "</div>"
    ]
   },
   {
@@ -219,10 +282,51 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### task\n",
-    "- create the A-matrix to detrend\n",
-    "- find the seasonal pattern\n",
-    "- continue till you are happy and have found all the frequencies"
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 2.2:</b>   \n",
+    "\n",
+    "Now provide an A-matrix that removes the trend from the data. There are multiple answers that will work, but some are better than others.\n",
+    "\n",
+    "First, use the Markdown cell below to define your A-matrix and include a brief explanation justifying your choice.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Your answer here._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "- The best model (chosen a priori) would be a periodic signal, as we know the temperature behaves this way.\n",
+    "- A linear model may approximate the increasing temperature well, but it is not going to fit the ends well.\n",
+    "- It turns out a power law model gives the best quantitative fit, but this would also be problematic if we need to extrapolate beyond the data range.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 2.3:</b>   \n",
+    "\n",
+    "Now define the A-matrix in code and extract the seasonal pattern. Continue extracting components until the time series is stationary (you will then summarize your findings in the next task).\n",
+    "</p>\n",
+    "</div>"
    ]
   },
   {
@@ -263,17 +367,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Task 2\n",
-    "Now that you have found the frequency of the seasonal pattern, We can continue to make our data (more) stationary. Copy the A-matrix you found in the previous task and expand it to include the seasonal pattern. When reporting on seasonal components we want you to report $A_i \\cos(2\\pi f_i t + \\theta_i)$, where $A_i$ is the amplitude, $f_i$ is the frequency and $\\theta_i$ is the phase."
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 2.4:</b>   \n",
+    "\n",
+    "Describe your results for making the time series stationary. Include at least: a) the number and types of components used (and their parameters), b) how you decided to stop extracting components.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Your answer here._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "- annual signal is obvious, then of course daily (definitley periodic)\n",
+    "- PSD after taking out the daily signal indicates this is enough\n",
+    "\n",
+    "</p>\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Part 2: Fitting the functional model\n",
+    "## Fitting the Functional Model\n",
     "\n",
-    "In the next cell we will fit the model to generate stationary residuals. Above we have used for each dominant frequency $f_i$ ($i=1,2$) the model:\n",
+    "In the next cell we will fit the model to generate stationary residuals. Above, you may have a periodic signal, where for each dominant frequency $f_i$ ($i=1,2$) the model is:\n",
     "\n",
     "$$a_i  \\cos(2\\pi f_i  t) + b_i  \\sin(2\\pi f_i t)$$ \n",
     "\n",
@@ -284,6 +415,20 @@
     "Note: in Section 4.1 book this was shown where the angular frequency $\\omega = 2\\pi f$ was used.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 2.5:</b>   \n",
+    "\n",
+    "Complete the code cell below to create the functional model.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -395,6 +540,41 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 2.6:</b>   \n",
+    "\n",
+    "Are the residuals stationary? State yes or no and describe why in the cell below.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Your answer here._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "No! There is an offset...\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -403,7 +583,7 @@
     "\n",
     "When we look at the residuals after removing the periodic pattern(s), we see that there is still a pattern in the data. From researchers in the Nenana area we have heard that there is a grizzly bear that likes to take a nap (hibernate) in the area. We suspect that the grizzly bear has slept too close to the temperature sensor and has influenced the data. \n",
     "\n",
-    "In the next cell we will write an offset detection algorithm to find the offset in the data. The offset detection algorithm is based on the likelihood ratio test framework. However, due to the presence of autocorrelation in the residuals, the traditional critical values for the likelihood ratio test are not valid. Therefore, we will use a bootstrap approach to estimate the critical values. Luckily, this is not the first time we had to remove a grizzly bear from our data, so we know that the estimated critical values is approximately 100.\n",
+    "In the next cell we will write an offset detection algorithm to find the offset in the data. The offset detection algorithm is based on the likelihood ratio test framework. However, due to the presence of autocorrelation in the residuals, the traditional critical values for the likelihood ratio test are not valid. Therefore, we will use a bootstrap approach to estimate the critical values. Luckily, this is **not** the first time we had to remove a grizzly bear from our data, so we know that the estimated critical values is approximately 100.\n",
     "\n",
     "## The offset detection algorithm\n",
     "The offset detection algorithm is based on the likelihood ratio test framework. The likelihood ratio test has a test statistic that is given by:\n",
@@ -414,7 +594,8 @@
     "\n",
     "where $S_0$ is the sum of the squared residuals for the model without an offset, $S_1$ is the sum of the squared residuals for the model with an offset, and $n$ is the number of data points. The likelihood ratio test statistic is compared to a critical value to determine if an offset is present in the data.\n",
     "\n",
-    "To find the jump location we will use the following algorithm:\n",
+    "The cell below defines several functions which roughly accomplish the following:\n",
+    " \n",
     "1. Calculate the sum of the squared residuals for the model without an offset, $S_0$.\n",
     "2. Calculate the sum of the squared residuals for the model with an offset at each data point, $S_1$.\n",
     "   1. For each data point we will calculate the sum of the squared residuals for the model with an offset at that data point.\n",
@@ -423,6 +604,19 @@
     "4. We will include the offset in the model and repeat the process until the likelihood ratio test statistic is below the critical value.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 3.1:</b>   \n",
+    "\n",
+    "Using the description above and the comments and docstring in the code, fill in the code below to complete the offset detection algorithm.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -550,9 +744,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we will implement the offset detection algorithm.\n",
-    "run the function `find_offset` to find the offset in the data.\n",
-    "once you have found the offset, include it in the model and repeat the process until the likelihood ratio test statistic is below the critical value."
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 3.2:</b>   \n",
+    "\n",
+    "Now we will implement the offset detection algorithm by running the function to find the offset in the data. The function will provide figures from which you will be able to determine the offset.\n",
+    "</p>\n",
+    "</div>"
    ]
   },
   {
@@ -575,6 +773,40 @@
     "# END SOLUTION BLOCK"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 3.3:</b>   \n",
+    "\n",
+    "Write your chosen offset in the cell below (report both the size and location of the offset). \n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "My offset is: ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 3.4:</b>   \n",
+    "\n",
+    "Once you have found the offset, identify the offset location and update your A-matrix to include it in the model. Then repeat the process until the likelihood ratio test statistic is below the critical value.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -619,8 +851,13 @@
     }
    ],
    "source": [
+    "# A2 = YOUR_CODE_HERE\n",
+    "# YOUR_CODE_HERE = fit_model(YOUR_CODE_HERE)\n",
+    "\n",
+    "# SOLUTION\n",
     "A2 = A_offset\n",
     "x_hat, y_hat, e_hat0 = fit_model(data, time_days, A2)\n",
+    "# END SOLUTION (PART 1 of 2)\n",
     "\n",
     "# Plotting the data and the estimated trend\n",
     "plt.figure(figsize=(10, 3))\n",
@@ -665,7 +902,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analyzing the residuals\n",
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 3.5:</b>   \n",
+    "\n",
+    "Use the Markdown cell below to summarize the number and value of all offsets that you found.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Your answer here._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "Should be 2, at 70 and 120, with a value of around 5 (true values were around 6 and 4).\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 4: Analyzing the residuals\n",
     "Now that we have our residuals we can fit an AR model to the residuals. We will start by plotting the ACF of the residuals. We will then fit an AR model to the residuals and report the parameters of the AR model. Using the likelihood ratio test framework we will determine the order of the AR model."
    ]
   },
@@ -692,6 +963,20 @@
     "ax.grid()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 4.1:</b>   \n",
+    "\n",
+    "Begin by completing the functions below to define AR(1) (hint: you did this on Wednesday).\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -764,6 +1049,55 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 4.2:</b>   \n",
+    "\n",
+    "As you can see, the next task asks you to implement AR(2). State why this is necessary, using the results from the cell above.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Your answer here._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "ACF at lag 1 not zero (or not within the confidence interval).\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 4.3:</b>   \n",
+    "\n",
+    "Now complete the functions to set up AR(2).\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -838,7 +1172,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Part 4: Report the results\n",
+    "## Part 5: Report the Results\n",
+    "\n",
+    "_Note: you did this on Wednesday! It was optional then, so you are not expected to know this for the exam; however, you should implement the code using the WS as a template, and your interpretation at the end will be part of the grade for this assignment._\n",
     "\n",
     "Now that we have found the periodic signals in the data and fitted an AR model to the residuals, we can report the results. By combining including the AR (noise) process, we get residuals that are white noise. When the model hat white noise residuals, we can also report the confidence intervals of the model.\n",
     "\n",
@@ -863,7 +1199,7 @@
     "\n",
     "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p> <b>Task 4.1:</b>   \n",
     "<p>\n",
-    "Complete the missing parts of the code cell below.\n",
+    "Complete the missing parts of the code cell below. Note that you will need to add one additional term, compared to Wednesday.\n",
     "</p>\n",
     "</div>"
    ]
@@ -978,6 +1314,35 @@
     "    print(f'A_{i} = {A_i:.3f}, theta_{i} = {theta_i:.3f}, f_{i} = {f:.3f}')\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Task 4.2:</b>   \n",
+    "\n",
+    "Now we have the complete functional model. Reflect on it's suitability for capturing the time dependent variation of temperature throughout the spring. Comment specifically on the time series components that were included and which ones have the most significant influence on the result.\n",
+    "\n",
+    "Comment also on the suitability of this model for predicting the temperature **beyond the betting deadline of April 5**, assuming that you have data up **until** that date. Remember that the ice typically breaks apart 2 to 6 weeks after the betting deadline.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
+    "<p>\n",
+    "<b>Solution:</b>   \n",
+    "\n",
+    "The model only includes \"memory\" for 2 points, which is 2 hours. However, from the ACF prior to removing the residuals, we see that the \"effect\" of the memory lasts around 20 points. This is 20 hours, so the  inclusion of AR(2) is useless and the functional model without these terms would be a fine best estimate for temperature on a given day several weeks in the future.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/content/GA_2_4/ga_draft_tasks.ipynb b/content/GA_2_4/ga_draft_tasks.ipynb
deleted file mode 100644
index 7881a146..00000000
--- a/content/GA_2_4/ga_draft_tasks.ipynb
+++ /dev/null
@@ -1,993 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# GA 2.4: Beary Icy\n",
-    "\n",
-    "<h1 style=\"position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0\">\n",
-    "    <style>\n",
-    "        .markdown {width:100%; position: relative}\n",
-    "        article { position: relative }\n",
-    "    </style>\n",
-    "    <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png\" style=\"width:100px\" />\n",
-    "    <img src=\"https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png\" style=\"width:100px\" />\n",
-    "</h1>\n",
-    "<h2 style=\"height: 10px\">\n",
-    "</h2>\n",
-    "\n",
-    "*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.4, Time Series Analysis. For: December 6, 2024*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Winter is coming and it is time to start getting our models ready for the ice classic. Our first goal is to improve the temperature model, as that seems to be an important factor in determining breakup day. Temperature is notoriously hard to predict, but we can analyze historical data to get a better understanding of the patterns.\n",
-    "\n",
-    "In this assignment we will analyze a time series from a **single year**; in fact, only the **first 152 days of the year**, from January 1 until June 1. This is the period of interest for the ice classic, as the ice forms in this period, reaching its maximum thickness between January-March, and then starts melting, with breakup day typically happening in April or May.\n",
-    "\n",
-    "Remember that we have until April 5 to place a bet. Why, then do we want to fit a model several months beyond this point? This gives us confidence in assessing the ability of the model to predict temperature, so that when we use it on April 5 to make **predictions** about the future, we can understand the uncertainty associated with it.\n",
-    "\n",
-    "Let's start by loading the data and plotting it, then we will determine which components should be used to detrend it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy.stats import chi2\n",
-    "from scipy.signal import periodogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Part 1: Load the data and plot it\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 1.1:</b>   \n",
-    "\n",
-    "Do the following:\n",
-    "\n",
-    "- load the data\n",
-    "- create time vector\n",
-    "- plot the data\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# YOUR_CODE_HERE\n",
-    "\n",
-    "# SOLUTION\n",
-    "# Reading the data from the file\n",
-    "data = np.loadtxt('temperature.csv')\n",
-    "time_hours = np.arange(0, len(data))\n",
-    "time_days = time_hours / 24\n",
-    "dt = time_days[1] - time_days[0]\n",
-    "fs = 1 / dt\n",
-    "\n",
-    "# Plotting the data\n",
-    "plt.figure(figsize=(10, 3))\n",
-    "plt.plot(time_days, data)\n",
-    "plt.xlabel('Time [days]')\n",
-    "plt.ylabel('Temperature [°C]')\n",
-    "plt.title('Temperature data Nenana, Alaska')\n",
-    "plt.grid(True)\n",
-    "# END SOLUTION BLOCK"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 1.2:</b>   \n",
-    "\n",
-    "Use the Markdown cell below to describe the data (you can use a few bullet points). For example, confirm relevant characteristics like number of points, units, describe the values (qualitatively), etc.\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Your answer here._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Solution:</b>   \n",
-    "\n",
-    "- There are 24*152 points (hourly)\n",
-    "- Looks like there is a bit of variation around a central line, but the general increase in temperature throughout the spring is larger than the variation\n",
-    "- the general trend looks linear in the middle, but flattens out at the ends, which is to be expected with a periodic annual cycle\n",
-    "- looks like there is something \"funny\" happening around day 70 and 120\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Part 2: Extract the Dominant Patterns\n",
-    "\n",
-    "We clearly see that the data contains a strong pattern (the general increase in temperature from winter to summer). We will start by fitting a functional model to the data in order to stationarize it. To find the frequency of the seasonal pattern we will use the power spectrum of the data.\n",
-    "\n",
-    "We will reuse the function `find_seasonal_pattern` from the workshop.\n",
-    "\n",
-    "Remember that for running this function we need to predefine the A-matrix to detrend the data. Since the data only contains the first 5 months of the year, we see that the temperature is increasing over time. What type of model would be most appropriate to remove the seasonal trend? "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 2.1:</b>   \n",
-    "\n",
-    "Define functions to help carry out this analysis, for example, <code>fit_model</code> and <code>find_frequency</code>.\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fit_model(data, time, A, plot=False):\n",
-    "    '''\n",
-    "    Function to find the least squares solution of the data\n",
-    "    data: input data\n",
-    "    time: time vector\n",
-    "    A: A-matrix to fit the data\n",
-    "    plot: boolean to plot the results or not\n",
-    "    '''\n",
-    "\n",
-    "    # x_hat = YOUR_CODE_HERE # least squares solution\n",
-    "    # y_hat = YOUR_CODE_HERE # model prediction\n",
-    "    # e_hat = YOUR_CODE_HERE # residuals\n",
-    "\n",
-    "    # SOLUTION\n",
-    "    x_hat = np.linalg.solve(A.T @ A, A.T @ data)\n",
-    "    y_hat = A @ x_hat\n",
-    "    e_hat = data - y_hat\n",
-    "    # END SOLUTION BLOCK\n",
-    "\n",
-    "    if plot:\n",
-    "        plt.figure(figsize=(10, 5))\n",
-    "        plt.subplot(211)\n",
-    "        plt.plot(time, data, label='Data')\n",
-    "        plt.plot(time, y_hat, label='Estimated data')\n",
-    "        plt.xlabel('Time [days]')\n",
-    "        plt.ylabel('Temperature [°C]')\n",
-    "        plt.title('Data vs Estimated data')\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.subplot(212)\n",
-    "        plt.plot(time, e_hat, label='Residuals')\n",
-    "        plt.xlabel('Time [days]')\n",
-    "        plt.ylabel('Temperature [°C]')\n",
-    "        plt.title('Residuals')\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.tight_layout()\n",
-    "\n",
-    "    return x_hat, y_hat, e_hat\n",
-    "\n",
-    "def find_frequency(data, time, A, fs, plot=True):\n",
-    "    '''\n",
-    "    Function to find the dominant frequency of the signal\n",
-    "    data: input data\n",
-    "    time: time vector\n",
-    "    A: A-matrix to detrend the data (prior to spectral analysis)\n",
-    "    fs: sampling frequency\n",
-    "    plot: boolean to plot the psd or not\n",
-    "    '''\n",
-    "    # Detrending the data\n",
-    "    _, _, e_hat= fit_model(data, time, A)\n",
-    "\n",
-    "    N = len(data)\n",
-    "\n",
-    "    # Finding the dominant frequency in e_hat\n",
-    "    # freqs, pxx = periodogram(YOUR_CODE_HERE, fs=YOUR_CODE_HERE, window='boxcar',\n",
-    "    #                          nfft=N, return_onesided=False,\n",
-    "    #                          scaling='density')\n",
-    "    \n",
-    "    # SOLUTION\n",
-    "    # Finding the dominant frequency in e_hat\n",
-    "    freqs, pxx = periodogram(e_hat, fs=fs, window='boxcar',\n",
-    "                                nfft=N, return_onesided=False,\n",
-    "                                scaling='density')\n",
-    "    # END SOLUTION BLOCK\n",
-    "\n",
-    "    # finding the dominant frequency and amplitude\n",
-    "    # Note: there are many ways to do this\n",
-    "    # amplitude = YOUR_CODE_HERE # Amplitude of the dominant frequency\n",
-    "    # dominant_frequency = YOUR_CODE_HERE # Dominant frequency\n",
-    "\n",
-    "    # SOLUTION\n",
-    "    # finding the dominant frequency and amplitude\n",
-    "    dominant_frequency, amplitude = freqs[np.argmax(pxx)], np.max(pxx)\n",
-    "    # END SOLUTION BLOCK\n",
-    "\n",
-    "    # Plotting the PSD\n",
-    "    if plot:\n",
-    "        plt.figure(figsize=(10, 5))\n",
-    "        plt.subplot(211)\n",
-    "        plt.plot(time, e_hat)\n",
-    "        plt.title('Residuals')\n",
-    "        plt.ylabel('Atmospheric Pressure [hPa]')\n",
-    "        plt.grid(True)\n",
-    "        plt.subplot(212)\n",
-    "        plt.plot(freqs[freqs>0], pxx[freqs>0], label='PSD of residuals')\n",
-    "        plt.xlabel('Frequency')\n",
-    "        plt.ylabel('PSD')\n",
-    "        plt.title('Power Spectral Density')\n",
-    "        plt.grid(True)\n",
-    "        plt.plot(dominant_frequency, amplitude, 'ro', label='Dominant Frequency')\n",
-    "        plt.yscale('log')\n",
-    "        plt.xscale('log')\n",
-    "        plt.legend()\n",
-    "        plt.tight_layout()\n",
-    "\n",
-    "    return dominant_frequency\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 2.2:</b>   \n",
-    "\n",
-    "Now provide an A-matrix that removes the trend from the data. There are multiple answers that will work, but some are better than others.\n",
-    "\n",
-    "First, use the Markdown cell below to define your A-matrix and include a brief explanation justifying your choice.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Your answer here._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Solution:</b>   \n",
-    "\n",
-    "- The best model (chosen a priort) would be a periodic signal, as we know the temperature behaves this way.\n",
-    "- A linear model may approximate the increasing temperature well, but it is not going to fit the ends well.\n",
-    "- It turns out a power law model gives the best quantitative fit, but this would also be problematic if we need to extrapolate beyond the data range.\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 2.3:</b>   \n",
-    "\n",
-    "Now define the A-matrix in code and extract the seasonal pattern. Continue extracting components until the time series is stationary (you will then summarize your findings in the next task).\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dominant Frequency: 1.00\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# YOUR_CODE_HERE\n",
-    "\n",
-    "\n",
-    "# SOLUTION\n",
-    "A = np.column_stack((np.ones(len(data)), np.cos(2*np.pi*time_days/365), np.sin(2*np.pi*time_days/365)))\n",
-    "dom_f = find_frequency(data, time_days, A, fs=fs)\n",
-    "print(f'Dominant Frequency: {dom_f:.2f}')\n",
-    "# END SOLUTION BLOCK"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 2.4:</b>   \n",
-    "\n",
-    "Describe your results for making the time series stationary. Include at least: a) the number and types of components used (and their parameters), b) how you decided to stop extracting components.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Your answer here._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Solution:</b>   \n",
-    "\n",
-    "- annual signal is obvious, then of course daily (definitley periodic)\n",
-    "- PSD after taking out the daily signal indicates this is enough\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fitting the Functional Model\n",
-    "\n",
-    "In the next cell we will fit the model to generate stationary residuals. Above, you may have a periodic signal, where for each dominant frequency $f_i$ ($i=1,2$) the model is:\n",
-    "\n",
-    "$$a_i  \\cos(2\\pi f_i  t) + b_i  \\sin(2\\pi f_i t)$$ \n",
-    "\n",
-    "However, to report the periodic signals we would like to have the amplitude, phase shift and the frequency of those signals, which can be recovered from:\n",
-    "$$A_i  \\cos(2\\pi f_i  t + \\theta_i)$$\n",
-    "Where the amplitude $A_i = \\sqrt{a_i^2 + b_i^2}$ and $\\theta_i = \\arctan(-b_i/a_i)$\n",
-    "\n",
-    "Note: in Section 4.1 book this was shown where the angular frequency $\\omega = 2\\pi f$ was used.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 2.5:</b>   \n",
-    "\n",
-    "Complete the code cell below to create the functional model.\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Estimated Parameters (standard deviation):\n",
-      "x0 = -5.702\n",
-      "x1 = 0.227\n",
-      "x2 = -2.989\n",
-      "x3 = -17.784\n",
-      "x4 = 10.409\n",
-      "\n",
-      "The seasonal component is rewritten as:\n",
-      "Ak = 2.998, theta_k = 1.495, f_k = 1.000\n",
-      "Ak = 20.606, theta_k = -2.612, f_k = 0.003\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def rewrite_seasonal_comp(ak, bk):\n",
-    "    '''\n",
-    "    Function to rewrite the seasonal component in terms of sin and cos\n",
-    "    ak: seasonal component coefficient for cos\n",
-    "    bk: seasonal component coefficient for sin\n",
-    "    '''\n",
-    "    # YOUR_CODE_HERE\n",
-    "\n",
-    "    # SOLUTION\n",
-    "    Ak = np.sqrt(ak**2 + bk**2)\n",
-    "    theta_k = np.arctan2(-bk, ak)\n",
-    "    return Ak, theta_k\n",
-    "    # END SOLUTION BLOCK\n",
-    "\n",
-    "# creating the A matrix of the functional model\n",
-    "# A = YOUR_CODE_HERE\n",
-    "# x_hat, y_hat, e_hat = YOUR_CODE_HERE\n",
-    "\n",
-    "\n",
-    "# SOLUTION\n",
-    "A = np.column_stack((np.ones(len(data)),\n",
-    "                        np.cos(2*np.pi*1*time_days), np.sin(2*np.pi*1*time_days),\n",
-    "                        np.cos(2*np.pi*time_days/365), np.sin(2*np.pi*time_days/365)))\n",
-    "\n",
-    "x_hat, y_hat, e_hat = fit_model(data, time_days, A)\n",
-    "# END SOLUTION BLOCK\n",
-    "\n",
-    "# Plotting the data and the estimated trend\n",
-    "plt.figure(figsize=(10, 3))\n",
-    "plt.plot(time_days, data, label='Original data')\n",
-    "plt.plot(time_days, y_hat, label='Estimated trend')\n",
-    "plt.xlabel('Time [days]')\n",
-    "plt.ylabel('Temperature [°C]')\n",
-    "plt.title('Temperature data Nenana, Alaska')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "\n",
-    "# Plotting the residuals\n",
-    "plt.figure(figsize=(10, 3))\n",
-    "plt.plot(time_days, e_hat)\n",
-    "plt.xlabel('Time [days]')\n",
-    "plt.ylabel('Temperature [°C]')\n",
-    "plt.title('Residuals')\n",
-    "plt.grid(True)\n",
-    "\n",
-    "# Extracting the seasonal component coefficients from the estimated parameters\n",
-    "\n",
-    "# SOLUTION\n",
-    "ak = np.array([x_hat[1], x_hat[3]])\n",
-    "bk = np.array([x_hat[2], x_hat[4]])\n",
-    "freqs = np.array([1, 1/365])\n",
-    "# END SOLUTION BLOCK\n",
-    "\n",
-    "print(f'Estimated Parameters:')\n",
-    "for i in range(len(x_hat)):\n",
-    "    print(f'x{i} = {x_hat[i]:.3f}')\n",
-    "\n",
-    "print('\\nThe seasonal component is rewritten as:')\n",
-    "for i, j, k in zip(ak, bk, freqs):\n",
-    "    Ak, theta_k = rewrite_seasonal_comp(i, j)\n",
-    "    print(f'Ak = {Ak:.3f}, theta_k = {theta_k:.3f}, f_k = {k:.3f}')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n",
-    "<p>\n",
-    "<b>Task 2.6:</b>   \n",
-    "\n",
-    "Are the residuals stationary? State yes or now and describe why in the cell below.\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Part 3: Finding the grizzly\n",
-    "\n",
-    "When we look at the residuals after removing the periodic pattern(s), we see that there is still a pattern in the data. From researchers in the Nenana area we have heard that there is a grizzly bear that likes to take a nap (hibernate) in the area. We suspect that the grizzly bear has slept too close to the temperature sensor and has influenced the data. \n",
-    "\n",
-    "In the next cell we will write an offset detection algorithm to find the offset in the data. The offset detection algorithm is based on the likelihood ratio test framework. However, due to the presence of autocorrelation in the residuals, the traditional critical values for the likelihood ratio test are not valid. Therefore, we will use a bootstrap approach to estimate the critical values. Luckily, this is not the first time we had to remove a grizzly bear from our data, so we know that the estimated critical values is approximately 100.\n",
-    "\n",
-    "## The offset detection algorithm\n",
-    "The offset detection algorithm is based on the likelihood ratio test framework. The likelihood ratio test has a test statistic that is given by:\n",
-    "\n",
-    "$$\\Lambda = n \\log \\left( \\frac{S_0}{S_1} \\right)$$\n",
-    "\n",
-    "where $S_0$ is the sum of the squared residuals for the model without an offset, $S_1$ is the sum of the squared residuals for the model with an offset, and $n$ is the number of data points. The likelihood ratio test statistic is compared to a critical value to determine if an offset is present in the data.\n",
-    "\n",
-    "To find the jump location we will use the following algorithm:\n",
-    "1. Calculate the sum of the squared residuals for the model without an offset, $S_0$.\n",
-    "2. Calculate the sum of the squared residuals for the model with an offset at each data point, $S_1$.\n",
-    "   1. For each data point we will calculate the sum of the squared residuals for the model with an offset at that data point.\n",
-    "   2. The A-matrix for the model with an offset is the same as the A-matrix for the model without an offset, but with an additional column that is 0 till the data point and 1 after the data point.\n",
-    "3. We will find the offset location that maximizes the likelihood ratio test statistic.\n",
-    "4. We will include the offset in the model and repeat the process until the likelihood ratio test statistic is below the critical value.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Break Point: 2871 with : 1305.83\n",
-      "Break Point: 1410 with : 754.18\n",
-      "Break Point: 1540 with : 77.73\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def A1_matrix(A0, break_point):\n",
-    "    '''\n",
-    "    Function to create the A1 matrix\n",
-    "    A0: A matrix under H0\n",
-    "    break_point: break point location\n",
-    "    '''\n",
-    "    A1 = np.zeros((A0.shape[0], A0.shape[1]+1))\n",
-    "    A1[:, :-1] = A0\n",
-    "    new_col = np.zeros(A0.shape[0])\n",
-    "    new_col[break_point:] = np.ones(A0.shape[0]-break_point)\n",
-    "    A1[:, -1] = new_col\n",
-    "    return A1\n",
-    "\n",
-    "\n",
-    "def LR(e0, e1, cv=100, verbose=True):\n",
-    "    '''\n",
-    "    Function to perform the LR test\n",
-    "    e0: residuals under H0\n",
-    "    e1: residuals under H1\n",
-    "    cv: critical value\n",
-    "    '''\n",
-    "    n = len(e0)\n",
-    "    SSR0 = e0.T @ e0\n",
-    "    SSR1 = e1.T @ e1\n",
-    "    test_stat = n*np.log(SSR0 / SSR1)\n",
-    "\n",
-    "    if test_stat > cv:\n",
-    "        if verbose:\n",
-    "            print(f'Test Statistic: {test_stat:.3f} > Critical Value: {cv:.3f}')\n",
-    "            print('Reject the null hypothesis')\n",
-    "    else:\n",
-    "        if verbose:\n",
-    "            print(f'Test Statistic: {test_stat:.3f} < Critical Value: {cv:.3f}')\n",
-    "            print('Fail to reject the null hypothesis')\n",
-    "    return test_stat\n",
-    "\n",
-    "def jump_detection(data, time, A, cv=100, plot=True):\n",
-    "\n",
-    "    _, _, e_hat = fit_model(data, time, A)\n",
-    "    results = np.zeros(len(data))\n",
-    "    for i in range(1, len(data)):\n",
-    "        A1 = A1_matrix(A, i)\n",
-    "\n",
-    "        # We need this statement to avoid singular matrices\n",
-    "        if np.linalg.matrix_rank(A1) < A1.shape[1]:\n",
-    "            pass\n",
-    "        else:\n",
-    "            _, _, e_hat1 = fit_model(data, time, A1)\n",
-    "            test_stat = LR(e_hat, e_hat1, verbose=False)\n",
-    "            results[i] = test_stat\n",
-    "\n",
-    "    results = np.array(results)\n",
-    "    # finding the offset location\n",
-    "    location = np.argmax(results)\n",
-    "    value = results[location]\n",
-    "\n",
-    "    if plot:\n",
-    "        plt.figure(figsize=(10, 3))\n",
-    "        plt.plot(time, results)\n",
-    "        plt.plot(time[location], value, 'ro', label='offset')\n",
-    "        plt.plot([0, max(time)], [cv, cv], 'k--', label='Critical Value')\n",
-    "        plt.xlabel('Time [days]')\n",
-    "        plt.ylabel('Test Statistic')\n",
-    "        plt.title('LR Test')\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "\n",
-    "    return location, value\n",
-    "\n",
-    "\n",
-    "# jump_detection(data, time_days, A)\n",
-    "# # print(A.shape, data.shape, time_days.shape)\n",
-    "\n",
-    "A_offset = A.copy()\n",
-    "\n",
-    "while True:\n",
-    "    break_point, test_stat = jump_detection(data, time_days, A_offset)\n",
-    "    print(f'Break Point: {break_point} with : {test_stat:.2f}')\n",
-    "    if test_stat < 100:\n",
-    "        break\n",
-    "    A_offset = A1_matrix(A_offset, break_point) \n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Estimated Parameters (standard deviation):\n",
-      "x0 = -1.022 (0.198)\n",
-      "x1 = 0.225 (0.046)\n",
-      "x2 = -3.002 (0.046)\n",
-      "x3 = -19.707 (0.162)\n",
-      "x4 = 2.315 (0.208)\n",
-      "x5 = -6.076 (0.166)\n",
-      "x6 = 4.487 (0.155)\n",
-      "The seasonal component is rewritten as:\n",
-      "Ak = 3.011, theta_k = 1.496, f_k = 1.000\n",
-      "Ak = 19.842, theta_k = -3.025, f_k = 0.003\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "A2 = A_offset\n",
-    "x_hat, y_hat, e_hat = fit_model(data, time_days, A2)\n",
-    "\n",
-    "# Plotting the data and the estimated trend\n",
-    "plt.figure(figsize=(10, 3))\n",
-    "plt.plot(time_days, data, label='Original data')\n",
-    "plt.plot(time_days, y_hat, label='Estimated trend')\n",
-    "plt.xlabel('Time [days]')\n",
-    "plt.ylabel('Temperature [°C]')\n",
-    "plt.title('Temperature data Nenana, Alaska')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "\n",
-    "# Plotting the residuals\n",
-    "plt.figure(figsize=(10, 3))\n",
-    "plt.plot(time_days, e_hat)\n",
-    "plt.xlabel('Time [days]')\n",
-    "plt.ylabel('Temperature [°C]')\n",
-    "plt.title('Residuals')\n",
-    "plt.grid(True)\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Extracting the seasonal component coefficients from the estimated parameters\n",
-    "ak = np.array([x_hat[1], x_hat[3]])\n",
-    "bk = np.array([x_hat[2], x_hat[4]])\n",
-    "freqs = np.array([1, 1/365])\n",
-    "sigma_e = np.sqrt((e_hat.T @ e_hat)/(len(data)-A2.shape[1]))\n",
-    "\n",
-    "sigma_x = sigma_e * np.sqrt(np.diag(np.linalg.inv(A2.T @ A2)))\n",
-    "\n",
-    "print(f'Estimated Parameters (standard deviation):')\n",
-    "for i in range(len(x_hat)):\n",
-    "    print(f'x{i} = {x_hat[i]:.3f} ({sigma_x[i]:.3f})')\n",
-    "\n",
-    "print('The seasonal component is rewritten as:')\n",
-    "for i, j, k in zip(ak, bk, freqs):\n",
-    "    Ak, theta_k = rewrite_seasonal_comp(i, j)\n",
-    "    print(f'Ak = {Ak:.3f}, theta_k = {theta_k:.3f}, f_k = {k:.3f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analyzing the residuals\n",
-    "Now that we have our residuals we can fit an AR model to the residuals. We will start by plotting the ACF of the residuals. We will then fit an AR model to the residuals and report the parameters of the AR model. Using the likelihood ratio test framework we will determine the order of the AR model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Lets start with the ACF plot\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(10, 3))\n",
-    "plot_acf(e_hat, ax=ax, lags=20);\n",
-    "ax.grid()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "phi: [0.8846601]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def AR(s, p=1):\n",
-    "    '''\n",
-    "    Function to estimate the AR(p) model\n",
-    "    s: data\n",
-    "    p: order of the AR model\n",
-    "    '''\n",
-    "    y = s[p:]\n",
-    "    # Creating the A matrix for the AR model\n",
-    "    A = np.zeros((len(s)-p, p))\n",
-    "    for i in range(p):\n",
-    "        A[:, i] = s[p-i-1:-i-1]\n",
-    "    \n",
-    "    # Estimating the AR(P) model\n",
-    "    if p == 1:\n",
-    "        phi_hat = (A.T @ y / (A.T @ A))[0]\n",
-    "    else:   \n",
-    "        phi_hat = np.linalg.inv(A.T @ A) @ A.T @ y\n",
-    "    y_hat = np.dot(A, phi_hat)\n",
-    "    e_hat = y - y_hat\n",
-    "    return phi_hat, e_hat, y, A\n",
-    "\n",
-    "# Estimating the AR(1) model\n",
-    "phi_hat, s_e_hat, s_y, s_A = AR(e_hat, p=1)\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(f'phi: {phi_hat}')\n",
-    "\n",
-    "# plotting the AR(1) model  \n",
-    "plt.figure(figsize=(10, 3))\n",
-    "plt.plot(s_y, label='Actual')\n",
-    "plt.plot(np.dot(s_A, phi_hat), label='AR(1)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('Value')\n",
-    "plt.title('AR(1) Model')\n",
-    "plt.legend();\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Testing for number of lags in the AR model\n",
-    "\n",
-    "We will use the likelihood ratio test to determine the number of lags in the AR model. We will start by fitting an AR model with 1 lag and then test if we can reject the null hypothesis that the AR model with 1 lag is better than the AR model with 0 lags. We will continue this process until we can no longer reject the null hypothesis.\n",
-    "\n",
-    "The test is based on nested models, where the null hypothesis is that the model with the least number of lags is better than the model with the most number of lags. The test statistic is given by:\n",
-    "$$LR = -2 \\log \\left( \\frac{L(\\theta_0)}{L(\\theta_1)} \\right)$$\n",
-    "Where $L(\\theta_0)$ is the likelihood of the model with the least number of lags and $L(\\theta_1)$ is the likelihood of the model with the most number of lags. The test statistic is asymptotically $\\chi^2$ distributed with the degrees of freedom equal to the difference in the number of parameters between the two models. To make this test computationally easier we will rewrite the test statistic as:\n",
-    "$$LR = n  \\log(\\frac{\\text{SSR}_0}{\\text{SSR}_1})$$\n",
-    "Where $\\text{SSR}_0$ is the sum of squared residuals of the model under the null hypothesis and $\\text{SSR}_1$ is the residual sum of squares of the model under the alternative hypothesis.\n",
-    "$$\\text{SSR}_i = \\epsilon_i^T\\epsilon_i$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performing LR test for AR(0) and AR(1)\n",
-      "Test Statistic: 5564.603 > Critical Value: 3.841\n",
-      "Reject the null hypothesis\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Performing LR test for AR(1) and AR(2)\n",
-      "Test Statistic: 147.115 > Critical Value: 3.841\n",
-      "Reject the null hypothesis\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Performing LR test for AR(2) and AR(3)\n",
-      "Test Statistic: 0.247 < Critical Value: 3.841\n",
-      "Fail to reject the null hypothesis\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Optimal AR order: 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "def LR(e0, e1, dof, alpha=0.05):\n",
-    "    '''\n",
-    "    Function to perform the LR test\n",
-    "    e0: residuals under H0\n",
-    "    e1: residuals under H1\n",
-    "    dof: degrees of freedom\n",
-    "    alpha: significance level\n",
-    "    '''\n",
-    "    n = len(e0)\n",
-    "    SSR0 = e0.T @ e0\n",
-    "    SSR1 = e1.T @ e1\n",
-    "    test_stat = n*np.log(SSR0 / SSR1)\n",
-    "\n",
-    "    # critical value for the test\n",
-    "    cv = chi2.ppf(1-alpha, dof)\n",
-    "    if test_stat > cv:\n",
-    "        print(f'Test Statistic: {test_stat:.3f} > Critical Value: {cv:.3f}')\n",
-    "        print('Reject the null hypothesis')\n",
-    "        return True\n",
-    "    else:\n",
-    "        print(f'Test Statistic: {test_stat:.3f} < Critical Value: {cv:.3f}')\n",
-    "        print('Fail to reject the null hypothesis')\n",
-    "        return False\n",
-    "\n",
-    "# Performing the LR test\n",
-    "p=0\n",
-    "test = True\n",
-    "while test:\n",
-    "    print(f'Performing LR test for AR({p}) and AR({p+1})')\n",
-    "    _, e0, _, _ = AR(e_hat, p)\n",
-    "    _, e1, _, _ = AR(e_hat, p+1)\n",
-    "    test = LR(e0, e1, 1)\n",
-    "    p += 1\n",
-    "    print('-'*50+'\\n') \n",
-    "print(f'Optimal AR order: {p-1}')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude-base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/teachers/GA_2_4/GA_2_4_solution.html b/src/teachers/GA_2_4/GA_2_4_solution.html
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+++ b/src/teachers/GA_2_4/GA_2_4_solution.html
@@ -0,0 +1,8974 @@
+<!DOCTYPE html>
+
+<html lang="en">
+<head><meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
+<title>GA_2_4_solution</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<style type="text/css">
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
+.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+<style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+ * Mozilla scrollbar styling
+ */
+
+/* use standard opaque scrollbars for most nodes */
+[data-jp-theme-scrollbars='true'] {
+  scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
+    var(--jp-scrollbar-background-color);
+}
+
+/* for code nodes, use a transparent style of scrollbar. These selectors
+ * will match lower in the tree, and so will override the above */
+[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
+[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
+}
+
+/* tiny scrollbar */
+
+.jp-scrollbar-tiny {
+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
+  scrollbar-width: thin;
+}
+
+/* tiny scrollbar */
+
+.jp-scrollbar-tiny::-webkit-scrollbar,
+.jp-scrollbar-tiny::-webkit-scrollbar-corner {
+  background-color: transparent;
+  height: 4px;
+  width: 4px;
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-thumb {
+  background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal {
+  border-left: 0 solid transparent;
+  border-right: 0 solid transparent;
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
+  border-top: 0 solid transparent;
+  border-bottom: 0 solid transparent;
+}
+
+/*
+ * Lumino
+ */
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+  min-height: 16px;
+  max-height: 16px;
+  min-width: 45px;
+  border-top: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] {
+  min-width: 16px;
+  max-width: 16px;
+  min-height: 45px;
+  border-left: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar-button {
+  background-color: #f0f0f0;
+  background-position: center center;
+  min-height: 15px;
+  max-height: 15px;
+  min-width: 15px;
+  max-width: 15px;
+}
+
+.lm-ScrollBar-button:hover {
+  background-color: #dadada;
+}
+
+.lm-ScrollBar-button.lm-mod-active {
+  background-color: #cdcdcd;
+}
+
+.lm-ScrollBar-track {
+  background: #f0f0f0;
+}
+
+.lm-ScrollBar-thumb {
+  background: #cdcdcd;
+}
+
+.lm-ScrollBar-thumb:hover {
+  background: #bababa;
+}
+
+.lm-ScrollBar-thumb.lm-mod-active {
+  background: #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
+  height: 100%;
+  min-width: 15px;
+  border-left: 1px solid #a0a0a0;
+  border-right: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
+  width: 100%;
+  min-height: 15px;
+  border-top: 1px solid #a0a0a0;
+  border-bottom: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-left);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-right);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-up);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-down);
+  background-size: 17px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-Widget {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+}
+
+.lm-Widget.lm-mod-hidden {
+  display: none !important;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.lm-AccordionPanel[data-orientation='horizontal'] > .lm-AccordionPanel-title {
+  /* Title is rotated for horizontal accordion panel using CSS */
+  display: block;
+  transform-origin: top left;
+  transform: rotate(-90deg) translate(-100%);
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  display: flex;
+  flex-direction: column;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-CommandPalette-search {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  min-height: 0;
+  overflow: auto;
+  list-style-type: none;
+}
+
+.lm-CommandPalette-header {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.lm-CommandPalette-item {
+  display: flex;
+  flex-direction: row;
+}
+
+.lm-CommandPalette-itemIcon {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemContent {
+  flex: 1 1 auto;
+  overflow: hidden;
+}
+
+.lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemLabel {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.lm-close-icon {
+  border: 1px solid transparent;
+  background-color: transparent;
+  position: absolute;
+  z-index: 1;
+  right: 3%;
+  top: 0;
+  bottom: 0;
+  margin: auto;
+  padding: 7px 0;
+  display: none;
+  vertical-align: middle;
+  outline: 0;
+  cursor: pointer;
+}
+.lm-close-icon:after {
+  content: 'X';
+  display: block;
+  width: 15px;
+  height: 15px;
+  text-align: center;
+  color: #000;
+  font-weight: normal;
+  font-size: 12px;
+  cursor: pointer;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-DockPanel {
+  z-index: 0;
+}
+
+.lm-DockPanel-widget {
+  z-index: 0;
+}
+
+.lm-DockPanel-tabBar {
+  z-index: 1;
+}
+
+.lm-DockPanel-handle {
+  z-index: 2;
+}
+
+.lm-DockPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-DockPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+.lm-DockPanel-handle[data-orientation='horizontal'] {
+  cursor: ew-resize;
+}
+
+.lm-DockPanel-handle[data-orientation='vertical'] {
+  cursor: ns-resize;
+}
+
+.lm-DockPanel-handle[data-orientation='horizontal']:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+.lm-DockPanel-handle[data-orientation='vertical']:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+.lm-DockPanel-overlay {
+  z-index: 3;
+  box-sizing: border-box;
+  pointer-events: none;
+}
+
+.lm-DockPanel-overlay.lm-mod-hidden {
+  display: none !important;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-Menu {
+  z-index: 10000;
+  position: absolute;
+  white-space: nowrap;
+  overflow-x: hidden;
+  overflow-y: auto;
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-Menu-content {
+  margin: 0;
+  padding: 0;
+  display: table;
+  list-style-type: none;
+}
+
+.lm-Menu-item {
+  display: table-row;
+}
+
+.lm-Menu-item.lm-mod-hidden,
+.lm-Menu-item.lm-mod-collapsed {
+  display: none !important;
+}
+
+.lm-Menu-itemIcon,
+.lm-Menu-itemSubmenuIcon {
+  display: table-cell;
+  text-align: center;
+}
+
+.lm-Menu-itemLabel {
+  display: table-cell;
+  text-align: left;
+}
+
+.lm-Menu-itemShortcut {
+  display: table-cell;
+  text-align: right;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-MenuBar {
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-MenuBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: row;
+  list-style-type: none;
+}
+
+.lm-MenuBar-item {
+  box-sizing: border-box;
+}
+
+.lm-MenuBar-itemIcon,
+.lm-MenuBar-itemLabel {
+  display: inline-block;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-ScrollBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+  flex-direction: row;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] {
+  flex-direction: column;
+}
+
+.lm-ScrollBar-button {
+  box-sizing: border-box;
+  flex: 0 0 auto;
+}
+
+.lm-ScrollBar-track {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+  flex: 1 1 auto;
+}
+
+.lm-ScrollBar-thumb {
+  box-sizing: border-box;
+  position: absolute;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-SplitPanel-child {
+  z-index: 0;
+}
+
+.lm-SplitPanel-handle {
+  z-index: 1;
+}
+
+.lm-SplitPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-SplitPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
+  cursor: ew-resize;
+}
+
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
+  cursor: ns-resize;
+}
+
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-TabBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.lm-TabBar[data-orientation='horizontal'] {
+  flex-direction: row;
+  align-items: flex-end;
+}
+
+.lm-TabBar[data-orientation='vertical'] {
+  flex-direction: column;
+  align-items: flex-end;
+}
+
+.lm-TabBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex: 1 1 auto;
+  list-style-type: none;
+}
+
+.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
+  flex-direction: row;
+}
+
+.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
+  flex-direction: column;
+}
+
+.lm-TabBar-tab {
+  display: flex;
+  flex-direction: row;
+  box-sizing: border-box;
+  overflow: hidden;
+  touch-action: none; /* Disable native Drag/Drop */
+}
+
+.lm-TabBar-tabIcon,
+.lm-TabBar-tabCloseIcon {
+  flex: 0 0 auto;
+}
+
+.lm-TabBar-tabLabel {
+  flex: 1 1 auto;
+  overflow: hidden;
+  white-space: nowrap;
+}
+
+.lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.lm-TabBar-tab.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-TabBar-addButton.lm-mod-hidden {
+  display: none !important;
+}
+
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
+  position: relative;
+}
+
+.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
+  left: 0;
+  transition: left 150ms ease;
+}
+
+.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
+  top: 0;
+  transition: top 150ms ease;
+}
+
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
+  transition: none;
+}
+
+.lm-TabBar-tabLabel .lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing: border-box;
+  background: inherit;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-TabPanel-tabBar {
+  z-index: 1;
+}
+
+.lm-TabPanel-stackedPanel {
+  z-index: 0;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapse {
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+
+.jp-Collapse-header {
+  padding: 1px 12px;
+  background-color: var(--jp-layout-color1);
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  text-transform: uppercase;
+  user-select: none;
+}
+
+.jp-Collapser-icon {
+  height: 16px;
+}
+
+.jp-Collapse-header-collapsed .jp-Collapser-icon {
+  transform: rotate(-90deg);
+  margin: auto 0;
+}
+
+.jp-Collapser-title {
+  line-height: 25px;
+}
+
+.jp-Collapse-contents {
+  padding: 0 12px;
+  background-color: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+/* Icons urls */
+
+:root {
+  --jp-icon-add-above: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTQiIGhlaWdodD0iMTQiIHZpZXdCb3g9IjAgMCAxNCAxNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzEzN18xOTQ5MikiPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGQ9Ik00Ljc1IDQuOTMwNjZINi42MjVWNi44MDU2NkM2LjYyNSA3LjAxMTkxIDYuNzkzNzUgNy4xODA2NiA3IDcuMTgwNjZDNy4yMDYyNSA3LjE4MDY2IDcuMzc1IDcuMDExOTEgNy4zNzUgNi44MDU2NlY0LjkzMDY2SDkuMjVDOS40NTYyNSA0LjkzMDY2IDkuNjI1IDQuNzYxOTEgOS42MjUgNC41NTU2NkM5LjYyNSA0LjM0OTQxIDkuNDU2MjUgNC4xODA2NiA5LjI1IDQuMTgwNjZINy4zNzVWMi4zMDU2NkM3LjM3NSAyLjA5OTQxIDcuMjA2MjUgMS45MzA2NiA3IDEuOTMwNjZDNi43OTM3NSAxLjkzMDY2IDYuNjI1IDIuMDk5NDEgNi42MjUgMi4zMDU2NlY0LjE4MDY2SDQuNzVDNC41NDM3NSA0LjE4MDY2IDQuMzc1IDQuMzQ5NDEgNC4zNzUgNC41NTU2NkM0LjM3NSA0Ljc2MTkxIDQuNTQzNzUgNC45MzA2NiA0Ljc1IDQuOTMwNjZaIiBmaWxsPSIjNjE2MTYxIiBzdHJva2U9IiM2MTYxNjEiIHN0cm9rZS13aWR0aD0iMC43Ii8+CjwvZz4KPHBhdGggY2xhc3M9ImpwLWljb24zIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiIGNsaXAtcnVsZT0iZXZlbm9kZCIgZD0iTTExLjUgOS41VjExLjVMMi41IDExLjVWOS41TDExLjUgOS41Wk0xMiA4QzEyLjU1MjMgOCAxMyA4LjQ0NzcyIDEzIDlWMTJDMTMgMTIuNTUyMyAxMi41NTIzIDEzIDEyIDEzTDIgMTNDMS40NDc3MiAxMyAxIDEyLjU1MjMgMSAxMlY5QzEgOC40NDc3MiAxLjQ0NzcxIDggMiA4TDEyIDhaIiBmaWxsPSIjNjE2MTYxIi8+CjxkZWZzPgo8Y2xpcFBhdGggaWQ9ImNsaXAwXzEzN18xOTQ5MiI+CjxyZWN0IGNsYXNzPSJqcC1pY29uMyIgd2lkdGg9IjYiIGhlaWdodD0iNiIgZmlsbD0id2hpdGUiIHRyYW5zZm9ybT0ibWF0cml4KC0xIDAgMCAxIDEwIDEuNTU1NjYpIi8+CjwvY2xpcFBhdGg+CjwvZGVmcz4KPC9zdmc+Cg==);
+  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bell: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug: url(data:image/svg+xml;base64,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);
+  --jp-icon-build: url(data:image/svg+xml;base64,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);
+  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-case-sensitive: url(data:image/svg+xml;base64,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);
+  --jp-icon-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-clear: url(data:image/svg+xml;base64,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);
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+  --jp-icon-code-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBzaGFwZS1yZW5kZXJpbmc9Imdlb21ldHJpY1ByZWNpc2lvbiI+CiAgICA8cGF0aCBkPSJNNi41OSwzLjQxTDIsOEw2LjU5LDEyLjZMOCwxMS4xOEw0LjgyLDhMOCw0LjgyTDYuNTksMy40MU0xMi40MSwzLjQxTDExLDQuODJMMTQuMTgsOEwxMSwxMS4xOEwxMi40MSwxMi42TDE3LDhMMTIuNDEsMy40MU0yMS41OSwxMS41OUwxMy41LDE5LjY4TDkuODMsMTZMOC40MiwxNy40MUwxMy41LDIyLjVMMjMsMTNMMjEuNTksMTEuNTlaIiAvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-code: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTExLjQgMTguNkw2LjggMTRMMTEuNCA5LjRMMTAgOEw0IDE0TDEwIDIwTDExLjQgMTguNlpNMTYuNiAxOC42TDIxLjIgMTRMMTYuNiA5LjRMMTggOEwyNCAxNEwxOCAyMEwxNi42IDE4LjZWMTguNloiLz4KCTwvZz4KPC9zdmc+Cg==);
+  --jp-icon-collapse-all: url(data:image/svg+xml;base64,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);
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+  --jp-icon-copyright: url(data:image/svg+xml;base64,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);
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+  --jp-icon-ellipses: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iNSIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxOSIgY3k9IjEyIiByPSIyIi8+CiAgPC9nPgo8L3N2Zz4K);
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+  --jp-icon-extension: url(data:image/svg+xml;base64,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);
+  --jp-icon-fast-forward: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTQgMThsOC41LTZMNCA2djEyem05LTEydjEybDguNS02TDEzIDZ6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-file: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkuMyA4LjJsLTUuNS01LjVjLS4zLS4zLS43LS41LTEuMi0uNUgzLjljLS44LjEtMS42LjktMS42IDEuOHYxNC4xYzAgLjkuNyAxLjYgMS42IDEuNmgxNC4yYy45IDAgMS42LS43IDEuNi0xLjZWOS40Yy4xLS41LS4xLS45LS40LTEuMnptLTUuOC0zLjNsMy40IDMuNmgtMy40VjQuOXptMy45IDEyLjdINC43Yy0uMSAwLS4yIDAtLjItLjJWNC43YzAtLjIuMS0uMy4yLS4zaDcuMnY0LjRzMCAuOC4zIDEuMWMuMy4zIDEuMS4zIDEuMS4zaDQuM3Y3LjJzLS4xLjItLjIuMnoiLz4KPC9zdmc+Cg==);
+  --jp-icon-filter-dot: url(data:image/svg+xml;base64,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);
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+  --jp-icon-filter: url(data:image/svg+xml;base64,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);
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+  --jp-icon-home: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjRweCIgdmlld0JveD0iMCAwIDI0IDI0IiB3aWR0aD0iMjRweCIgZmlsbD0iIzAwMDAwMCI+CiAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPjxwYXRoIGNsYXNzPSJqcC1pY29uMyBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xMCAyMHYtNmg0djZoNXYtOGgzTDEyIDMgMiAxMmgzdjh6Ii8+Cjwvc3ZnPgo=);
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+  --jp-icon-info: url(data:image/svg+xml;base64,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);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaW5zcGVjdG9yLWljb24tY29sb3IganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
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+  --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+  --jp-icon-keyboard: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNUg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMTdjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY3YzAtMS4xLS45LTItMi0yem0tOSAzaDJ2MmgtMlY4em0wIDNoMnYyaC0ydi0yek04IDhoMnYySDhWOHptMCAzaDJ2Mkg4di0yem0tMSAySDV2LTJoMnYyem0wLTNINVY4aDJ2MnptOSA3SDh2LTJoOHYyem0wLTRoLTJ2LTJoMnYyem0wLTNoLTJWOGgydjJ6bTMgM2gtMnYtMmgydjJ6bTAtM2gtMlY4aDJ2MnoiLz4KPC9zdmc+Cg==);
+  --jp-icon-launch: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMzIgMzIiIHdpZHRoPSIzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yNiwyOEg2YTIuMDAyNywyLjAwMjcsMCwwLDEtMi0yVjZBMi4wMDI3LDIuMDAyNywwLDAsMSw2LDRIMTZWNkg2VjI2SDI2VjE2aDJWMjZBMi4wMDI3LDIuMDAyNywwLDAsMSwyNiwyOFoiLz4KICAgIDxwb2x5Z29uIHBvaW50cz0iMjAgMiAyMCA0IDI2LjU4NiA0IDE4IDEyLjU4NiAxOS40MTQgMTQgMjggNS40MTQgMjggMTIgMzAgMTIgMzAgMiAyMCAyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4K);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,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);
+  --jp-icon-move-down: url(data:image/svg+xml;base64,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);
+  --jp-icon-move-up: url(data:image/svg+xml;base64,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);
+  --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-not-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtbm90ZWJvb2staWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-offline-bolt: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-palette: url(data:image/svg+xml;base64,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);
+  --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-pdf: url(data:image/svg+xml;base64,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);
+  --jp-icon-python: url(data:image/svg+xml;base64,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);
+  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+  --jp-icon-react: url(data:image/svg+xml;base64,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);
+  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-regex: url(data:image/svg+xml;base64,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);
+  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-search: url(data:image/svg+xml;base64,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);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-share: url(data:image/svg+xml;base64,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);
+  --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-tag: url(data:image/svg+xml;base64,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);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0IiA+CiAgICA8cmVjdCBjbGFzcz0ianAtdGVybWluYWwtaWNvbi1iYWNrZ3JvdW5kLWNvbG9yIGpwLWljb24tc2VsZWN0YWJsZSIgd2lkdGg9IjIwIiBoZWlnaHQ9IjIwIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgyIDIpIiBmaWxsPSIjMzMzMzMzIi8+CiAgICA8cGF0aCBjbGFzcz0ianAtdGVybWluYWwtaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUtaW52ZXJzZSIgZD0iTTUuMDU2NjQgOC43NjE3MkM1LjA1NjY0IDguNTk3NjYgNS4wMzEyNSA4LjQ1MzEyIDQuOTgwNDcgOC4zMjgxMkM0LjkzMzU5IDguMTk5MjIgNC44NTU0NyA4LjA4MjAzIDQuNzQ2MDkgNy45NzY1NkM0LjY0MDYyIDcuODcxMDkgNC41IDcuNzc1MzkgNC4zMjQyMiA3LjY4OTQ1QzQuMTUyMzQgNy41OTk2MSAzLjk0MzM2IDcuNTExNzIgMy42OTcyNyA3LjQyNTc4QzMuMzAyNzMgNy4yODUxNiAyLjk0MzM2IDcuMTM2NzIgMi42MTkxNCA2Ljk4MDQ3QzIuMjk0OTIgNi44MjQyMiAyLjAxNzU4IDYuNjQyNTggMS43ODcxMSA2LjQzNTU1QzEuNTYwNTUgNi4yMjg1MiAxLjM4NDc3IDUuOTg4MjggMS4yNTk3NyA1LjcxNDg0QzEuMTM0NzcgNS40Mzc1IDEuMDcyMjcgNS4xMDkzOCAxLjA3MjI3IDQuNzMwNDdDMS4wNzIyNyA0LjM5ODQ0IDEuMTI4OTEgNC4wOTU3IDEuMjQyMTkgMy44MjIyN0MxLjM1NTQ3IDMuNTQ0OTIgMS41MTU2MiAzLjMwNDY5IDEuNzIyNjYgMy4xMDE1NkMxLjkyOTY5IDIuODk4NDQgMi4xNzk2OSAyLjczNDM3IDIuNDcyNjYgMi42MDkzOEMyLjc2NTYyIDIuNDg0MzggMy4wOTE4IDIuNDA0MyAzLjQ1MTE3IDIuMzY5MTRWMS4xMDkzOEg0LjM4ODY3VjIuMzgwODZDNC43NDAyMyAyLjQyNzczIDUuMDU2NjQgMi41MjM0NCA1LjMzNzg5IDIuNjY3OTdDNS42MTkxNCAyLjgxMjUgNS44NTc0MiAzLjAwMTk1IDYuMDUyNzMgMy4yMzYzM0M2LjI1MTk1IDMuNDY2OCA2LjQwNDMgMy43NDAyMyA2LjUwOTc3IDQuMDU2NjRDNi42MTkxNCA0LjM2OTE0IDYuNjczODMgNC43MjA3IDYuNjczODMgNS4xMTEzM0g1LjA0NDkyQzUuMDQ0OTIgNC42Mzg2NyA0LjkzNzUgNC4yODEyNSA0LjcyMjY2IDQuMDM5MDZDNC41MDc4MSAzLjc5Mjk3IDQuMjE2OCAzLjY2OTkyIDMuODQ5NjEgMy42Njk5MkMzLjY1MDM5IDMuNjY5OTIgMy40NzY1NiAzLjY5NzI3IDMuMzI4MTIgMy43NTE5NUMzLjE4MzU5IDMuODAyNzMgMy4wNjQ0NSAzLjg3Njk1IDIuOTcwNyAzLjk3NDYxQzIuODc2OTUgNC4wNjgzNiAyLjgwNjY0IDQuMTc5NjkgMi43NTk3NyA0LjMwODU5QzIuNzE2OCA0LjQzNzUgMi42OTUzMSA0LjU3ODEyIDIuNjk1MzEgNC43MzA0N0MyLjY5NTMxIDQuODgyODEgMi43MTY4IDUuMDE5NTMgMi43NTk3NyA1LjE0MDYyQzIuODA2NjQgNS4yNTc4MSAyLjg4MjgxIDUuMzY3MTkgMi45ODgyOCA1LjQ2ODc1QzMuMDk3NjYgNS41NzAzMSAzLjI0MDIzIDUuNjY3OTcgMy40MTYwMiA1Ljc2MTcyQzMuNTkxOCA1Ljg1MTU2IDMuODEwNTUgNS45NDMzNiA0LjA3MjI3IDYuMDM3MTFDNC40NjY4IDYuMTg1NTUgNC44MjQyMiA2LjMzOTg0IDUuMTQ0NTMgNi41QzUuNDY0ODQgNi42NTYyNSA1LjczODI4IDYuODM5ODQgNS45NjQ4NCA3LjA1MDc4QzYuMTk1MzEgNy4yNTc4MSA2LjM3MTA5IDcuNSA2LjQ5MjE5IDcuNzc3MzRDNi42MTcxOSA4LjA1MDc4IDYuNjc5NjkgOC4zNzUgNi42Nzk2OSA4Ljc1QzYuNjc5NjkgOS4wOTM3NSA2LjYyMzA1IDkuNDA0MyA2LjUwOTc3IDkuNjgxNjRDNi4zOTY0OCA5Ljk1NTA4IDYuMjM0MzggMTAuMTkxNCA2LjAyMzQ0IDEwLjM5MDZDNS44MTI1IDEwLjU4OTggNS41NTg1OSAxMC43NSA1LjI2MTcyIDEwLjg3MTFDNC45NjQ4NCAxMC45ODgzIDQuNjMyODEgMTEuMDY0NSA0LjI2NTYyIDExLjA5OTZWMTIuMjQ4SDMuMzMzOThWMTEuMDk5NkMzLjAwMTk1IDExLjA2ODQgMi42Nzk2OSAxMC45OTYxIDIuMzY3MTkgMTAuODgyOEMyLjA1NDY5IDEwLjc2NTYgMS43NzczNCAxMC41OTc3IDEuNTM1MTYgMTAuMzc4OUMxLjI5Njg4IDEwLjE2MDIgMS4xMDU0NyA5Ljg4NDc3IDAuOTYwOTM4IDkuNTUyNzNDMC44MTY0MDYgOS4yMTY4IDAuNzQ0MTQxIDguODE0NDUgMC43NDQxNDEgOC4zNDU3SDIuMzc4OTFDMi4zNzg5MSA4LjYyNjk1IDIuNDE5OTIgOC44NjMyOCAyLjUwMTk1IDkuMDU0NjlDMi41ODM5OCA5LjI0MjE5IDIuNjg5NDUgOS4zOTI1OCAyLjgxODM2IDkuNTA1ODZDMi45NTExNyA5LjYxNTIzIDMuMTAxNTYgOS42OTMzNiAzLjI2OTUzIDkuNzQwMjNDMy40Mzc1IDkuNzg3MTEgMy42MDkzOCA5LjgxMDU1IDMuNzg1MTYgOS44MTA1NUM0LjIwMzEyIDkuODEwNTUgNC41MTk1MyA5LjcxMjg5IDQuNzM0MzggOS41MTc1OEM0Ljk0OTIyIDkuMzIyMjcgNS4wNTY2NCA5LjA3MDMxIDUuMDU2NjQgOC43NjE3MlpNMTMuNDE4IDEyLjI3MTVIOC4wNzQyMlYxMUgxMy40MThWMTIuMjcxNVoiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDMuOTUyNjQgNikiIGZpbGw9IndoaXRlIi8+Cjwvc3ZnPgo=);
+  --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-toc: url(data:image/svg+xml;base64,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);
+  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-users: url(data:image/svg+xml;base64,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);
+  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-word: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KIDxnIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzQxNDE0MSI+CiAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiA8L2c+CiA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSguNDMgLjA0MDEpIiBmaWxsPSIjZmZmIj4KICA8cGF0aCBkPSJtNC4xNCA4Ljc2cTAuMDY4Mi0xLjg5IDIuNDItMS44OSAxLjE2IDAgMS42OCAwLjQyIDAuNTY3IDAuNDEgMC41NjcgMS4xNnYzLjQ3cTAgMC40NjIgMC41MTQgMC40NjIgMC4xMDMgMCAwLjItMC4wMjMxdjAuNzE0cS0wLjM5OSAwLjEwMy0wLjY1MSAwLjEwMy0wLjQ1MiAwLTAuNjkzLTAuMjItMC4yMzEtMC4yLTAuMjg0LTAuNjYyLTAuOTU2IDAuODcyLTIgMC44NzItMC45MDMgMC0xLjQ3LTAuNDcyLTAuNTI1LTAuNDcyLTAuNTI1LTEuMjYgMC0wLjI2MiAwLjA0NTItMC40NzIgMC4wNTY3LTAuMjIgMC4xMTYtMC4zNzggMC4wNjgyLTAuMTY4IDAuMjMxLTAuMzA0IDAuMTU4LTAuMTQ3IDAuMjYyLTAuMjQyIDAuMTE2LTAuMDkxNCAwLjM2OC0wLjE2OCAwLjI2Mi0wLjA5MTQgMC4zOTktMC4xMjYgMC4xMzYtMC4wNDUyIDAuNDcyLTAuMTAzIDAuMzM2LTAuMDU3OCAwLjUwNC0wLjA3OTggMC4xNTgtMC4wMjMxIDAuNTY3LTAuMDc5OCAwLjU1Ni0wLjA2ODIgMC43NzctMC4yMjEgMC4yMi0wLjE1MiAwLjIyLTAuNDQxdi0wLjI1MnEwLTAuNDMtMC4zNTctMC42NjItMC4zMzYtMC4yMzEtMC45NzYtMC4yMzEtMC42NjIgMC0wLjk5OCAwLjI2Mi0wLjMzNiAwLjI1Mi0wLjM5OSAwLjc5OHptMS44OSAzLjY4cTAuNzg4IDAgMS4yNi0wLjQxIDAuNTA0LTAuNDIgMC41MDQtMC45MDN2LTEuMDVxLTAuMjg0IDAuMTM2LTAuODYxIDAuMjMxLTAuNTY3IDAuMDkxNC0wLjk4NyAwLjE1OC0wLjQyIDAuMDY4Mi0wLjc2NiAwLjMyNi0wLjMzNiAwLjI1Mi0wLjMzNiAwLjcwNHQwLjMwNCAwLjcwNCAwLjg2MSAwLjI1MnoiIHN0cm9rZS13aWR0aD0iMS4wNSIvPgogIDxwYXRoIGQ9Im0xMCA0LjU2aDAuOTQ1djMuMTVxMC42NTEtMC45NzYgMS44OS0wLjk3NiAxLjE2IDAgMS44OSAwLjg0IDAuNjgyIDAuODQgMC42ODIgMi4zMSAwIDEuNDctMC43MDQgMi40Mi0wLjcwNCAwLjg4Mi0xLjg5IDAuODgyLTEuMjYgMC0xLjg5LTEuMDJ2MC43NjZoLTAuODV6bTIuNjIgMy4wNHEtMC43NDYgMC0xLjE2IDAuNjQtMC40NTIgMC42My0wLjQ1MiAxLjY4IDAgMS4wNSAwLjQ1MiAxLjY4dDEuMTYgMC42M3EwLjc3NyAwIDEuMjYtMC42MyAwLjQ5NC0wLjY0IDAuNDk0LTEuNjggMC0xLjA1LTAuNDcyLTEuNjgtMC40NjItMC42NC0xLjI2LTAuNjR6IiBzdHJva2Utd2lkdGg9IjEuMDUiLz4KICA8cGF0aCBkPSJtMi43MyAxNS44IDEzLjYgMC4wMDgxYzAuMDA2OSAwIDAtMi42IDAtMi42IDAtMC4wMDc4LTEuMTUgMC0xLjE1IDAtMC4wMDY5IDAtMC4wMDgzIDEuNS0wLjAwODMgMS41LTJlLTMgLTAuMDAxNC0xMS4zLTAuMDAxNC0xMS4zLTAuMDAxNGwtMC4wMDU5Mi0xLjVjMC0wLjAwNzgtMS4xNyAwLjAwMTMtMS4xNyAwLjAwMTN6IiBzdHJva2Utd2lkdGg9Ii45NzUiLz4KIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
+
+/* Icon CSS class declarations */
+
+.jp-AddAboveIcon {
+  background-image: var(--jp-icon-add-above);
+}
+
+.jp-AddBelowIcon {
+  background-image: var(--jp-icon-add-below);
+}
+
+.jp-AddIcon {
+  background-image: var(--jp-icon-add);
+}
+
+.jp-BellIcon {
+  background-image: var(--jp-icon-bell);
+}
+
+.jp-BugDotIcon {
+  background-image: var(--jp-icon-bug-dot);
+}
+
+.jp-BugIcon {
+  background-image: var(--jp-icon-bug);
+}
+
+.jp-BuildIcon {
+  background-image: var(--jp-icon-build);
+}
+
+.jp-CaretDownEmptyIcon {
+  background-image: var(--jp-icon-caret-down-empty);
+}
+
+.jp-CaretDownEmptyThinIcon {
+  background-image: var(--jp-icon-caret-down-empty-thin);
+}
+
+.jp-CaretDownIcon {
+  background-image: var(--jp-icon-caret-down);
+}
+
+.jp-CaretLeftIcon {
+  background-image: var(--jp-icon-caret-left);
+}
+
+.jp-CaretRightIcon {
+  background-image: var(--jp-icon-caret-right);
+}
+
+.jp-CaretUpEmptyThinIcon {
+  background-image: var(--jp-icon-caret-up-empty-thin);
+}
+
+.jp-CaretUpIcon {
+  background-image: var(--jp-icon-caret-up);
+}
+
+.jp-CaseSensitiveIcon {
+  background-image: var(--jp-icon-case-sensitive);
+}
+
+.jp-CheckIcon {
+  background-image: var(--jp-icon-check);
+}
+
+.jp-CircleEmptyIcon {
+  background-image: var(--jp-icon-circle-empty);
+}
+
+.jp-CircleIcon {
+  background-image: var(--jp-icon-circle);
+}
+
+.jp-ClearIcon {
+  background-image: var(--jp-icon-clear);
+}
+
+.jp-CloseIcon {
+  background-image: var(--jp-icon-close);
+}
+
+.jp-CodeCheckIcon {
+  background-image: var(--jp-icon-code-check);
+}
+
+.jp-CodeIcon {
+  background-image: var(--jp-icon-code);
+}
+
+.jp-CollapseAllIcon {
+  background-image: var(--jp-icon-collapse-all);
+}
+
+.jp-ConsoleIcon {
+  background-image: var(--jp-icon-console);
+}
+
+.jp-CopyIcon {
+  background-image: var(--jp-icon-copy);
+}
+
+.jp-CopyrightIcon {
+  background-image: var(--jp-icon-copyright);
+}
+
+.jp-CutIcon {
+  background-image: var(--jp-icon-cut);
+}
+
+.jp-DeleteIcon {
+  background-image: var(--jp-icon-delete);
+}
+
+.jp-DownloadIcon {
+  background-image: var(--jp-icon-download);
+}
+
+.jp-DuplicateIcon {
+  background-image: var(--jp-icon-duplicate);
+}
+
+.jp-EditIcon {
+  background-image: var(--jp-icon-edit);
+}
+
+.jp-EllipsesIcon {
+  background-image: var(--jp-icon-ellipses);
+}
+
+.jp-ErrorIcon {
+  background-image: var(--jp-icon-error);
+}
+
+.jp-ExpandAllIcon {
+  background-image: var(--jp-icon-expand-all);
+}
+
+.jp-ExtensionIcon {
+  background-image: var(--jp-icon-extension);
+}
+
+.jp-FastForwardIcon {
+  background-image: var(--jp-icon-fast-forward);
+}
+
+.jp-FileIcon {
+  background-image: var(--jp-icon-file);
+}
+
+.jp-FileUploadIcon {
+  background-image: var(--jp-icon-file-upload);
+}
+
+.jp-FilterDotIcon {
+  background-image: var(--jp-icon-filter-dot);
+}
+
+.jp-FilterIcon {
+  background-image: var(--jp-icon-filter);
+}
+
+.jp-FilterListIcon {
+  background-image: var(--jp-icon-filter-list);
+}
+
+.jp-FolderFavoriteIcon {
+  background-image: var(--jp-icon-folder-favorite);
+}
+
+.jp-FolderIcon {
+  background-image: var(--jp-icon-folder);
+}
+
+.jp-HomeIcon {
+  background-image: var(--jp-icon-home);
+}
+
+.jp-Html5Icon {
+  background-image: var(--jp-icon-html5);
+}
+
+.jp-ImageIcon {
+  background-image: var(--jp-icon-image);
+}
+
+.jp-InfoIcon {
+  background-image: var(--jp-icon-info);
+}
+
+.jp-InspectorIcon {
+  background-image: var(--jp-icon-inspector);
+}
+
+.jp-JsonIcon {
+  background-image: var(--jp-icon-json);
+}
+
+.jp-JuliaIcon {
+  background-image: var(--jp-icon-julia);
+}
+
+.jp-JupyterFaviconIcon {
+  background-image: var(--jp-icon-jupyter-favicon);
+}
+
+.jp-JupyterIcon {
+  background-image: var(--jp-icon-jupyter);
+}
+
+.jp-JupyterlabWordmarkIcon {
+  background-image: var(--jp-icon-jupyterlab-wordmark);
+}
+
+.jp-KernelIcon {
+  background-image: var(--jp-icon-kernel);
+}
+
+.jp-KeyboardIcon {
+  background-image: var(--jp-icon-keyboard);
+}
+
+.jp-LaunchIcon {
+  background-image: var(--jp-icon-launch);
+}
+
+.jp-LauncherIcon {
+  background-image: var(--jp-icon-launcher);
+}
+
+.jp-LineFormIcon {
+  background-image: var(--jp-icon-line-form);
+}
+
+.jp-LinkIcon {
+  background-image: var(--jp-icon-link);
+}
+
+.jp-ListIcon {
+  background-image: var(--jp-icon-list);
+}
+
+.jp-MarkdownIcon {
+  background-image: var(--jp-icon-markdown);
+}
+
+.jp-MoveDownIcon {
+  background-image: var(--jp-icon-move-down);
+}
+
+.jp-MoveUpIcon {
+  background-image: var(--jp-icon-move-up);
+}
+
+.jp-NewFolderIcon {
+  background-image: var(--jp-icon-new-folder);
+}
+
+.jp-NotTrustedIcon {
+  background-image: var(--jp-icon-not-trusted);
+}
+
+.jp-NotebookIcon {
+  background-image: var(--jp-icon-notebook);
+}
+
+.jp-NumberingIcon {
+  background-image: var(--jp-icon-numbering);
+}
+
+.jp-OfflineBoltIcon {
+  background-image: var(--jp-icon-offline-bolt);
+}
+
+.jp-PaletteIcon {
+  background-image: var(--jp-icon-palette);
+}
+
+.jp-PasteIcon {
+  background-image: var(--jp-icon-paste);
+}
+
+.jp-PdfIcon {
+  background-image: var(--jp-icon-pdf);
+}
+
+.jp-PythonIcon {
+  background-image: var(--jp-icon-python);
+}
+
+.jp-RKernelIcon {
+  background-image: var(--jp-icon-r-kernel);
+}
+
+.jp-ReactIcon {
+  background-image: var(--jp-icon-react);
+}
+
+.jp-RedoIcon {
+  background-image: var(--jp-icon-redo);
+}
+
+.jp-RefreshIcon {
+  background-image: var(--jp-icon-refresh);
+}
+
+.jp-RegexIcon {
+  background-image: var(--jp-icon-regex);
+}
+
+.jp-RunIcon {
+  background-image: var(--jp-icon-run);
+}
+
+.jp-RunningIcon {
+  background-image: var(--jp-icon-running);
+}
+
+.jp-SaveIcon {
+  background-image: var(--jp-icon-save);
+}
+
+.jp-SearchIcon {
+  background-image: var(--jp-icon-search);
+}
+
+.jp-SettingsIcon {
+  background-image: var(--jp-icon-settings);
+}
+
+.jp-ShareIcon {
+  background-image: var(--jp-icon-share);
+}
+
+.jp-SpreadsheetIcon {
+  background-image: var(--jp-icon-spreadsheet);
+}
+
+.jp-StopIcon {
+  background-image: var(--jp-icon-stop);
+}
+
+.jp-TabIcon {
+  background-image: var(--jp-icon-tab);
+}
+
+.jp-TableRowsIcon {
+  background-image: var(--jp-icon-table-rows);
+}
+
+.jp-TagIcon {
+  background-image: var(--jp-icon-tag);
+}
+
+.jp-TerminalIcon {
+  background-image: var(--jp-icon-terminal);
+}
+
+.jp-TextEditorIcon {
+  background-image: var(--jp-icon-text-editor);
+}
+
+.jp-TocIcon {
+  background-image: var(--jp-icon-toc);
+}
+
+.jp-TreeViewIcon {
+  background-image: var(--jp-icon-tree-view);
+}
+
+.jp-TrustedIcon {
+  background-image: var(--jp-icon-trusted);
+}
+
+.jp-UndoIcon {
+  background-image: var(--jp-icon-undo);
+}
+
+.jp-UserIcon {
+  background-image: var(--jp-icon-user);
+}
+
+.jp-UsersIcon {
+  background-image: var(--jp-icon-users);
+}
+
+.jp-VegaIcon {
+  background-image: var(--jp-icon-vega);
+}
+
+.jp-WordIcon {
+  background-image: var(--jp-icon-word);
+}
+
+.jp-YamlIcon {
+  background-image: var(--jp-icon-yaml);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+.jp-Icon,
+.jp-MaterialIcon {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-cover {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: cover;
+}
+
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
+
+.jp-Icon-16 {
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-18 {
+  background-size: 18px;
+  min-width: 18px;
+  min-height: 18px;
+}
+
+.jp-Icon-20 {
+  background-size: 20px;
+  min-width: 20px;
+  min-height: 20px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.lm-TabBar .lm-TabBar-addButton {
+  align-items: center;
+  display: flex;
+  padding: 4px;
+  padding-bottom: 5px;
+  margin-right: 1px;
+  background-color: var(--jp-layout-color2);
+}
+
+.lm-TabBar .lm-TabBar-addButton:hover {
+  background-color: var(--jp-layout-color1);
+}
+
+.lm-DockPanel-tabBar .lm-TabBar-tab {
+  width: var(--jp-private-horizontal-tab-width);
+}
+
+.lm-DockPanel-tabBar .lm-TabBar-content {
+  flex: unset;
+}
+
+.lm-DockPanel-tabBar[data-orientation='horizontal'] {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-accent1[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-accent2[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-accent3[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-accent4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-accent0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-accent1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-accent2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-accent3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-accent4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+  fill: none;
+}
+
+.jp-icon-none[stroke] {
+  stroke: none;
+}
+
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+  fill: var(--jp-brand-color0);
+}
+
+.jp-icon-brand1[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+.jp-icon-brand2[fill] {
+  fill: var(--jp-brand-color2);
+}
+
+.jp-icon-brand3[fill] {
+  fill: var(--jp-brand-color3);
+}
+
+.jp-icon-brand4[fill] {
+  fill: var(--jp-brand-color4);
+}
+
+.jp-icon-brand0[stroke] {
+  stroke: var(--jp-brand-color0);
+}
+
+.jp-icon-brand1[stroke] {
+  stroke: var(--jp-brand-color1);
+}
+
+.jp-icon-brand2[stroke] {
+  stroke: var(--jp-brand-color2);
+}
+
+.jp-icon-brand3[stroke] {
+  stroke: var(--jp-brand-color3);
+}
+
+.jp-icon-brand4[stroke] {
+  stroke: var(--jp-brand-color4);
+}
+
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+  fill: var(--jp-warn-color0);
+}
+
+.jp-icon-warn1[fill] {
+  fill: var(--jp-warn-color1);
+}
+
+.jp-icon-warn2[fill] {
+  fill: var(--jp-warn-color2);
+}
+
+.jp-icon-warn3[fill] {
+  fill: var(--jp-warn-color3);
+}
+
+.jp-icon-warn0[stroke] {
+  stroke: var(--jp-warn-color0);
+}
+
+.jp-icon-warn1[stroke] {
+  stroke: var(--jp-warn-color1);
+}
+
+.jp-icon-warn2[stroke] {
+  stroke: var(--jp-warn-color2);
+}
+
+.jp-icon-warn3[stroke] {
+  stroke: var(--jp-warn-color3);
+}
+
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+  fill: var(--jp-icon-contrast-color0);
+}
+
+.jp-icon-contrast1[fill] {
+  fill: var(--jp-icon-contrast-color1);
+}
+
+.jp-icon-contrast2[fill] {
+  fill: var(--jp-icon-contrast-color2);
+}
+
+.jp-icon-contrast3[fill] {
+  fill: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-contrast0[stroke] {
+  stroke: var(--jp-icon-contrast-color0);
+}
+
+.jp-icon-contrast1[stroke] {
+  stroke: var(--jp-icon-contrast-color1);
+}
+
+.jp-icon-contrast2[stroke] {
+  stroke: var(--jp-icon-contrast-color2);
+}
+
+.jp-icon-contrast3[stroke] {
+  stroke: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-dot[fill] {
+  fill: var(--jp-warn-color0);
+}
+
+.jp-jupyter-icon-color[fill] {
+  fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
+}
+
+.jp-notebook-icon-color[fill] {
+  fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
+}
+
+.jp-json-icon-color[fill] {
+  fill: var(--jp-json-icon-color, var(--jp-warn-color1));
+}
+
+.jp-console-icon-color[fill] {
+  fill: var(--jp-console-icon-color, white);
+}
+
+.jp-console-icon-background-color[fill] {
+  fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
+}
+
+.jp-terminal-icon-color[fill] {
+  fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
+}
+
+.jp-terminal-icon-background-color[fill] {
+  fill: var(
+    --jp-terminal-icon-background-color,
+    var(--jp-inverse-layout-color2)
+  );
+}
+
+.jp-text-editor-icon-color[fill] {
+  fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
+}
+
+.jp-inspector-icon-color[fill] {
+  fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
+}
+
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* stylelint-disable selector-max-class, selector-max-compound-selectors */
+
+/**
+* TODO: come up with non css-hack solution for showing the busy icon on top
+*  of the close icon
+* CSS for complex behavior of close icon of tabs in the main area tabbar
+*/
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
+}
+
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+/* stylelint-enable selector-max-class, selector-max-compound-selectors */
+
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* special handling for splash icon CSS. While the theme CSS reloads during
+   splash, the splash icon can loose theming. To prevent that, we set a
+   default for its color variable */
+:root {
+  --jp-warn-color0: var(--md-orange-700);
+}
+
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
+  margin-right: 4px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
+
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-alt .jp-icon1[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-alt .jp-icon2[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-alt .jp-icon3[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-alt .jp-icon4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-alt .jp-icon0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-alt .jp-icon1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-alt .jp-icon2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-alt .jp-icon3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-alt .jp-icon4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-alt .jp-icon-accent1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-alt .jp-icon-accent2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-alt .jp-icon-accent3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-alt .jp-icon-accent4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-alt .jp-icon-accent0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-alt .jp-icon-accent1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-alt .jp-icon-accent2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-alt .jp-icon-accent3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-alt .jp-icon-accent4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
+  display: none !important;
+}
+
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
+
+/**
+ * regular colors
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+  fill: none;
+}
+
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+  stroke: none;
+}
+
+/**
+ * inverse colors
+ */
+
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* inverse recolor the accent elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-IFrame {
+  width: 100%;
+  height: 100%;
+}
+
+.jp-IFrame > iframe {
+  border: none;
+}
+
+/*
+When drag events occur, `lm-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-IFrame {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-IFrame::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-HoverBox {
+  position: fixed;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FormGroup-content fieldset {
+  border: none;
+  padding: 0;
+  min-width: 0;
+  width: 100%;
+}
+
+/* stylelint-disable selector-max-type */
+
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
+  font-size: var(--jp-content-font-size2);
+  border-color: var(--jp-input-border-color);
+  border-style: solid;
+  border-radius: var(--jp-border-radius);
+  border-width: 1px;
+  padding: 6px 8px;
+  background: none;
+  color: var(--jp-ui-font-color0);
+  height: inherit;
+}
+
+.jp-FormGroup-content fieldset input[type='checkbox'] {
+  position: relative;
+  top: 2px;
+  margin-left: 0;
+}
+
+.jp-FormGroup-content button.jp-mod-styled {
+  cursor: pointer;
+}
+
+.jp-FormGroup-content .checkbox label {
+  cursor: pointer;
+  font-size: var(--jp-content-font-size1);
+}
+
+.jp-FormGroup-content .jp-root > fieldset > legend {
+  display: none;
+}
+
+.jp-FormGroup-content .jp-root > fieldset > p {
+  display: none;
+}
+
+/** copy of `input.jp-mod-styled:focus` style */
+.jp-FormGroup-content fieldset input:focus,
+.jp-FormGroup-content fieldset select:focus {
+  -moz-outline-radius: unset;
+  outline: var(--jp-border-width) solid var(--md-blue-500);
+  outline-offset: -1px;
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-FormGroup-content fieldset input:hover:not(:focus),
+.jp-FormGroup-content fieldset select:hover:not(:focus) {
+  background-color: var(--jp-border-color2);
+}
+
+/* stylelint-enable selector-max-type */
+
+.jp-FormGroup-content .checkbox .field-description {
+  /* Disable default description field for checkbox:
+   because other widgets do not have description fields,
+   we add descriptions to each widget on the field level.
+  */
+  display: none;
+}
+
+.jp-FormGroup-content #root__description {
+  display: none;
+}
+
+.jp-FormGroup-content .jp-modifiedIndicator {
+  width: 5px;
+  background-color: var(--jp-brand-color2);
+  margin-top: 0;
+  margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
+  flex-shrink: 0;
+}
+
+.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
+  background-color: var(--jp-error-color0);
+  margin-right: 0.5em;
+}
+
+/* RJSF ARRAY style */
+
+.jp-arrayFieldWrapper legend {
+  font-size: var(--jp-content-font-size2);
+  color: var(--jp-ui-font-color0);
+  flex-basis: 100%;
+  padding: 4px 0;
+  font-weight: var(--jp-content-heading-font-weight);
+  border-bottom: 1px solid var(--jp-border-color2);
+}
+
+.jp-arrayFieldWrapper .field-description {
+  padding: 4px 0;
+  white-space: pre-wrap;
+}
+
+.jp-arrayFieldWrapper .array-item {
+  width: 100%;
+  border: 1px solid var(--jp-border-color2);
+  border-radius: 4px;
+  margin: 4px;
+}
+
+.jp-ArrayOperations {
+  display: flex;
+  margin-left: 8px;
+}
+
+.jp-ArrayOperationsButton {
+  margin: 2px;
+}
+
+.jp-ArrayOperationsButton .jp-icon3[fill] {
+  fill: var(--jp-ui-font-color0);
+}
+
+button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
+  cursor: not-allowed;
+  opacity: 0.5;
+}
+
+/* RJSF form validation error */
+
+.jp-FormGroup-content .validationErrors {
+  color: var(--jp-error-color0);
+}
+
+/* Hide panel level error as duplicated the field level error */
+.jp-FormGroup-content .panel.errors {
+  display: none;
+}
+
+/* RJSF normal content (settings-editor) */
+
+.jp-FormGroup-contentNormal {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
+  margin-left: 7px;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-description {
+  flex-basis: 100%;
+  padding: 4px 7px;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-default {
+  flex-basis: 100%;
+  padding: 4px 7px;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
+  font-size: var(--jp-content-font-size1);
+  font-weight: normal;
+  min-width: 120px;
+}
+
+.jp-FormGroup-contentNormal fieldset:not(:first-child) {
+  margin-left: 7px;
+}
+
+.jp-FormGroup-contentNormal .field-array-of-string .array-item {
+  /* Display `jp-ArrayOperations` buttons side-by-side with content except
+    for small screens where flex-wrap will place them one below the other.
+  */
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+}
+
+.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
+  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+  margin-top: 2px;
+}
+
+/* RJSF compact content (metadata-form) */
+
+.jp-FormGroup-content.jp-FormGroup-contentCompact {
+  width: 100%;
+}
+
+.jp-FormGroup-contentCompact .form-group {
+  display: flex;
+  padding: 0.5em 0.2em 0.5em 0;
+}
+
+.jp-FormGroup-contentCompact
+  .jp-FormGroup-compactTitle
+  .jp-FormGroup-description {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
+  padding-bottom: 0.3em;
+}
+
+.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
+  padding-bottom: 7px;
+}
+
+.jp-FormGroup-contentCompact
+  .jp-objectFieldWrapper
+  .jp-objectFieldWrapper
+  .form-group {
+  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+  margin-top: 2px;
+}
+
+.jp-FormGroup-contentCompact ul.error-detail {
+  margin-block-start: 0.5em;
+  margin-block-end: 0.5em;
+  padding-inline-start: 1em;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-SidePanel {
+  display: flex;
+  flex-direction: column;
+  min-width: var(--jp-sidebar-min-width);
+  overflow-y: auto;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  font-size: var(--jp-ui-font-size1);
+}
+
+.jp-SidePanel-header {
+  flex: 0 0 auto;
+  display: flex;
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin: 0;
+  padding: 2px;
+  text-transform: uppercase;
+}
+
+.jp-SidePanel-toolbar {
+  flex: 0 0 auto;
+}
+
+.jp-SidePanel-content {
+  flex: 1 1 auto;
+}
+
+.jp-SidePanel-toolbar,
+.jp-AccordionPanel-toolbar {
+  height: var(--jp-private-toolbar-height);
+}
+
+.jp-SidePanel-toolbar.jp-Toolbar-micro {
+  display: none;
+}
+
+.lm-AccordionPanel .jp-AccordionPanel-title {
+  box-sizing: border-box;
+  line-height: 25px;
+  margin: 0;
+  display: flex;
+  align-items: center;
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  font-size: var(--jp-ui-font-size0);
+}
+
+.jp-AccordionPanel-title {
+  cursor: pointer;
+  user-select: none;
+  -moz-user-select: none;
+  -webkit-user-select: none;
+  text-transform: uppercase;
+}
+
+.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
+  /* Title is rotated for horizontal accordion panel using CSS */
+  display: block;
+  transform-origin: top left;
+  transform: rotate(-90deg) translate(-100%);
+}
+
+.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
+  user-select: none;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  overflow: hidden;
+}
+
+.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
+  transform: rotate(-90deg);
+  margin: auto 0;
+  height: 16px;
+}
+
+.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
+  transform: rotate(0deg);
+}
+
+.lm-AccordionPanel .jp-AccordionPanel-toolbar {
+  background: none;
+  box-shadow: none;
+  border: none;
+  margin-left: auto;
+}
+
+.lm-AccordionPanel .lm-SplitPanel-handle:hover {
+  background: var(--jp-layout-color3);
+}
+
+.jp-text-truncated {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Spinner {
+  position: absolute;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  z-index: 10;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-layout-color0);
+  outline: none;
+}
+
+.jp-SpinnerContent {
+  font-size: 10px;
+  margin: 50px auto;
+  text-indent: -9999em;
+  width: 3em;
+  height: 3em;
+  border-radius: 50%;
+  background: var(--jp-brand-color3);
+  background: linear-gradient(
+    to right,
+    #f37626 10%,
+    rgba(255, 255, 255, 0) 42%
+  );
+  position: relative;
+  animation: load3 1s infinite linear, fadeIn 1s;
+}
+
+.jp-SpinnerContent::before {
+  width: 50%;
+  height: 50%;
+  background: #f37626;
+  border-radius: 100% 0 0;
+  position: absolute;
+  top: 0;
+  left: 0;
+  content: '';
+}
+
+.jp-SpinnerContent::after {
+  background: var(--jp-layout-color0);
+  width: 75%;
+  height: 75%;
+  border-radius: 50%;
+  content: '';
+  margin: auto;
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
+}
+
+@keyframes fadeIn {
+  0% {
+    opacity: 0;
+  }
+
+  100% {
+    opacity: 1;
+  }
+}
+
+@keyframes load3 {
+  0% {
+    transform: rotate(0deg);
+  }
+
+  100% {
+    transform: rotate(360deg);
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+button.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: none;
+  box-sizing: border-box;
+  text-align: center;
+  line-height: 32px;
+  height: 32px;
+  padding: 0 12px;
+  letter-spacing: 0.8px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input.jp-mod-styled {
+  background: var(--jp-input-background);
+  height: 28px;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+  padding-left: 7px;
+  padding-right: 7px;
+  font-size: var(--jp-ui-font-size2);
+  color: var(--jp-ui-font-color0);
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input[type='checkbox'].jp-mod-styled {
+  appearance: checkbox;
+  -webkit-appearance: checkbox;
+  -moz-appearance: checkbox;
+  height: auto;
+}
+
+input.jp-mod-styled:focus {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-select-wrapper {
+  display: flex;
+  position: relative;
+  flex-direction: column;
+  padding: 1px;
+  background-color: var(--jp-layout-color1);
+  box-sizing: border-box;
+  margin-bottom: 12px;
+}
+
+.jp-select-wrapper:not(.multiple) {
+  height: 28px;
+}
+
+.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-input-active-background);
+}
+
+select.jp-mod-styled:hover {
+  cursor: pointer;
+  color: var(--jp-ui-font-color0);
+  background-color: var(--jp-input-hover-background);
+  box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
+}
+
+select.jp-mod-styled {
+  flex: 1 1 auto;
+  width: 100%;
+  font-size: var(--jp-ui-font-size2);
+  background: var(--jp-input-background);
+  color: var(--jp-ui-font-color0);
+  padding: 0 25px 0 8px;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+select.jp-mod-styled:not([multiple]) {
+  height: 32px;
+}
+
+select.jp-mod-styled[multiple] {
+  max-height: 200px;
+  overflow-y: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-switch {
+  display: flex;
+  align-items: center;
+  padding-left: 4px;
+  padding-right: 4px;
+  font-size: var(--jp-ui-font-size1);
+  background-color: transparent;
+  color: var(--jp-ui-font-color1);
+  border: none;
+  height: 20px;
+}
+
+.jp-switch:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-switch-label {
+  margin-right: 5px;
+  font-family: var(--jp-ui-font-family);
+}
+
+.jp-switch-track {
+  cursor: pointer;
+  background-color: var(--jp-switch-color, var(--jp-border-color1));
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 34px;
+  height: 16px;
+  width: 35px;
+  position: relative;
+}
+
+.jp-switch-track::before {
+  content: '';
+  position: absolute;
+  height: 10px;
+  width: 10px;
+  margin: 3px;
+  left: 0;
+  background-color: var(--jp-ui-inverse-font-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 50%;
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track {
+  background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track::before {
+  /* track width (35) - margins (3 + 3) - thumb width (10) */
+  left: 19px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toolbar-height: calc(
+    28px + var(--jp-border-width)
+  ); /* leave 28px for content */
+}
+
+.jp-Toolbar {
+  color: var(--jp-ui-font-color1);
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: 2px;
+  z-index: 8;
+  overflow-x: hidden;
+}
+
+/* Toolbar items */
+
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
+
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+  display: inline-block;
+  width: 32px;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: 16px;
+}
+
+.jp-Toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  display: flex;
+  padding-left: 1px;
+  padding-right: 1px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: var(--jp-private-toolbar-height);
+  height: 100%;
+}
+
+/* Toolbar buttons */
+
+/* This is the div we use to wrap the react component into a Widget */
+div.jp-ToolbarButton {
+  color: transparent;
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0;
+  margin: 0;
+}
+
+button.jp-ToolbarButtonComponent {
+  background: var(--jp-layout-color1);
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0 6px;
+  margin: 0;
+  height: 24px;
+  border-radius: var(--jp-border-radius);
+  display: flex;
+  align-items: center;
+  text-align: center;
+  font-size: 14px;
+  min-width: unset;
+  min-height: unset;
+}
+
+button.jp-ToolbarButtonComponent:disabled {
+  opacity: 0.4;
+}
+
+button.jp-ToolbarButtonComponent > span {
+  padding: 0;
+  flex: 0 0 auto;
+}
+
+button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
+  font-size: var(--jp-ui-font-size1);
+  line-height: 100%;
+  padding-left: 2px;
+  color: var(--jp-ui-font-color1);
+  font-family: var(--jp-ui-font-family);
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar.jp-Toolbar-micro {
+  padding: 0;
+  min-height: 0;
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar {
+  border: none;
+  box-shadow: none;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-WindowedPanel-outer {
+  position: relative;
+  overflow-y: auto;
+}
+
+.jp-WindowedPanel-inner {
+  position: relative;
+}
+
+.jp-WindowedPanel-window {
+  position: absolute;
+  left: 0;
+  right: 0;
+  overflow: visible;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+body {
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+}
+
+/* Disable native link decoration styles everywhere outside of dialog boxes */
+a {
+  text-decoration: unset;
+  color: unset;
+}
+
+a:hover {
+  text-decoration: unset;
+  color: unset;
+}
+
+/* Accessibility for links inside dialog box text */
+.jp-Dialog-content a {
+  text-decoration: revert;
+  color: var(--jp-content-link-color);
+}
+
+.jp-Dialog-content a:hover {
+  text-decoration: revert;
+}
+
+/* Styles for ui-components */
+.jp-Button {
+  color: var(--jp-ui-font-color2);
+  border-radius: var(--jp-border-radius);
+  padding: 0 12px;
+  font-size: var(--jp-ui-font-size1);
+
+  /* Copy from blueprint 3 */
+  display: inline-flex;
+  flex-direction: row;
+  border: none;
+  cursor: pointer;
+  align-items: center;
+  justify-content: center;
+  text-align: left;
+  vertical-align: middle;
+  min-height: 30px;
+  min-width: 30px;
+}
+
+.jp-Button:disabled {
+  cursor: not-allowed;
+}
+
+.jp-Button:empty {
+  padding: 0 !important;
+}
+
+.jp-Button.jp-mod-small {
+  min-height: 24px;
+  min-width: 24px;
+  font-size: 12px;
+  padding: 0 7px;
+}
+
+/* Use our own theme for hover styles */
+.jp-Button.jp-mod-minimal:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-Button.jp-mod-minimal {
+  background: none;
+}
+
+.jp-InputGroup {
+  display: block;
+  position: relative;
+}
+
+.jp-InputGroup input {
+  box-sizing: border-box;
+  border: none;
+  border-radius: 0;
+  background-color: transparent;
+  color: var(--jp-ui-font-color0);
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+  padding-bottom: 0;
+  padding-top: 0;
+  padding-left: 10px;
+  padding-right: 28px;
+  position: relative;
+  width: 100%;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+  font-size: 14px;
+  font-weight: 400;
+  height: 30px;
+  line-height: 30px;
+  outline: none;
+  vertical-align: middle;
+}
+
+.jp-InputGroup input:focus {
+  box-shadow: inset 0 0 0 var(--jp-border-width)
+      var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-InputGroup input:disabled {
+  cursor: not-allowed;
+  resize: block;
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroup input:disabled ~ span {
+  cursor: not-allowed;
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroupAction {
+  position: absolute;
+  bottom: 1px;
+  right: 0;
+  padding: 6px;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select {
+  background-color: initial;
+  border: none;
+  border-radius: 0;
+  box-shadow: none;
+  color: var(--jp-ui-font-color0);
+  display: block;
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  height: 24px;
+  line-height: 14px;
+  padding: 0 25px 0 10px;
+  text-align: left;
+  -moz-appearance: none;
+  -webkit-appearance: none;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color2);
+  cursor: not-allowed;
+  resize: block;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
+  cursor: not-allowed;
+}
+
+/* Use our own theme for hover and option styles */
+/* stylelint-disable-next-line selector-max-type */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color0);
+}
+
+select {
+  box-sizing: border-box;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-StatusBar-Widget {
+  display: flex;
+  align-items: center;
+  background: var(--jp-layout-color2);
+  min-height: var(--jp-statusbar-height);
+  justify-content: space-between;
+  padding: 0 10px;
+}
+
+.jp-StatusBar-Left {
+  display: flex;
+  align-items: center;
+  flex-direction: row;
+}
+
+.jp-StatusBar-Middle {
+  display: flex;
+  align-items: center;
+}
+
+.jp-StatusBar-Right {
+  display: flex;
+  align-items: center;
+  flex-direction: row-reverse;
+}
+
+.jp-StatusBar-Item {
+  max-height: var(--jp-statusbar-height);
+  margin: 0 2px;
+  height: var(--jp-statusbar-height);
+  white-space: nowrap;
+  text-overflow: ellipsis;
+  color: var(--jp-ui-font-color1);
+  padding: 0 6px;
+}
+
+.jp-mod-highlighted:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-mod-clicked {
+  background-color: var(--jp-brand-color1);
+}
+
+.jp-mod-clicked:hover {
+  background-color: var(--jp-brand-color0);
+}
+
+.jp-mod-clicked .jp-StatusBar-TextItem {
+  color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-StatusBar-HoverItem {
+  box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
+}
+
+.jp-StatusBar-TextItem {
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  line-height: 24px;
+  color: var(--jp-ui-font-color1);
+}
+
+.jp-StatusBar-GroupItem {
+  display: flex;
+  align-items: center;
+  flex-direction: row;
+}
+
+.jp-Statusbar-ProgressCircle svg {
+  display: block;
+  margin: 0 auto;
+  width: 16px;
+  height: 24px;
+  align-self: normal;
+}
+
+.jp-Statusbar-ProgressCircle path {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-Statusbar-ProgressBar-progress-bar {
+  height: 10px;
+  width: 100px;
+  border: solid 0.25px var(--jp-brand-color2);
+  border-radius: 3px;
+  overflow: hidden;
+  align-self: center;
+}
+
+.jp-Statusbar-ProgressBar-progress-bar > div {
+  background-color: var(--jp-brand-color2);
+  background-image: linear-gradient(
+    -45deg,
+    rgba(255, 255, 255, 0.2) 25%,
+    transparent 25%,
+    transparent 50%,
+    rgba(255, 255, 255, 0.2) 50%,
+    rgba(255, 255, 255, 0.2) 75%,
+    transparent 75%,
+    transparent
+  );
+  background-size: 40px 40px;
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 14px;
+  color: #fff;
+  text-align: center;
+  animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
+}
+
+.jp-Statusbar-ProgressBar-progress-bar p {
+  color: var(--jp-ui-font-color1);
+  font-family: var(--jp-ui-font-family);
+  font-size: var(--jp-ui-font-size1);
+  line-height: 10px;
+  width: 100px;
+}
+
+@keyframes jp-Statusbar-ExecutionTime-progress-bar {
+  0% {
+    background-position: 0 0;
+  }
+
+  100% {
+    background-position: 40px 40px;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-commandpalette-search-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  padding-bottom: 0;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Modal variant
+|----------------------------------------------------------------------------*/
+
+.jp-ModalCommandPalette {
+  position: absolute;
+  z-index: 10000;
+  top: 38px;
+  left: 30%;
+  margin: 0;
+  padding: 4px;
+  width: 40%;
+  box-shadow: var(--jp-elevation-z4);
+  border-radius: 4px;
+  background: var(--jp-layout-color0);
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette {
+  max-height: 40vh;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
+  margin-left: 4px;
+  margin-right: 4px;
+}
+
+.jp-ModalCommandPalette
+  .lm-CommandPalette
+  .lm-CommandPalette-item.lm-mod-disabled {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+  padding: 4px;
+  background-color: var(--jp-layout-color1);
+  z-index: 2;
+}
+
+.lm-CommandPalette-wrapper {
+  overflow: overlay;
+  padding: 0 9px;
+  background-color: var(--jp-input-active-background);
+  height: 30px;
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-SearchIconGroup {
+  color: white;
+  background-color: var(--jp-brand-color1);
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  padding: 5px 5px 1px;
+}
+
+.jp-SearchIconGroup svg {
+  height: 20px;
+  width: 20px;
+}
+
+.jp-SearchIconGroup .jp-icon3[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-input {
+  background: transparent;
+  width: calc(100% - 18px);
+  float: left;
+  border: none;
+  outline: none;
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  line-height: var(--jp-private-commandpalette-search-height);
+}
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+  color: var(--jp-ui-font-color2);
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+  margin-top: 0;
+}
+
+.lm-CommandPalette-header {
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin-top: 8px;
+  padding: 8px 0 8px 12px;
+  text-transform: uppercase;
+}
+
+.lm-CommandPalette-header.lm-mod-active {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-header > mark {
+  background-color: transparent;
+  font-weight: bold;
+  color: var(--jp-ui-font-color1);
+}
+
+.lm-CommandPalette-item {
+  padding: 4px 12px 4px 4px;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  font-weight: 400;
+  display: flex;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item.lm-mod-active {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-itemContent {
+  overflow: hidden;
+}
+
+.lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled mark {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+  margin: 0 4px 0 0;
+  position: relative;
+  width: 16px;
+  top: 2px;
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+  opacity: 0.6;
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemCaption {
+  display: none;
+}
+
+.lm-CommandPalette-content {
+  background-color: var(--jp-layout-color1);
+}
+
+.lm-CommandPalette-content:empty::after {
+  content: 'No results';
+  margin: auto;
+  margin-top: 20px;
+  width: 100px;
+  display: block;
+  font-size: var(--jp-ui-font-size2);
+  font-family: var(--jp-ui-font-family);
+  font-weight: lighter;
+}
+
+.lm-CommandPalette-emptyMessage {
+  text-align: center;
+  margin-top: 24px;
+  line-height: 1.32;
+  padding: 0 8px;
+  color: var(--jp-content-font-color3);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Dialog {
+  position: absolute;
+  z-index: 10000;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  top: 0;
+  left: 0;
+  margin: 0;
+  padding: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-dialog-background);
+}
+
+.jp-Dialog-content {
+  display: flex;
+  flex-direction: column;
+  margin-left: auto;
+  margin-right: auto;
+  background: var(--jp-layout-color1);
+  padding: 24px 24px 12px;
+  min-width: 300px;
+  min-height: 150px;
+  max-width: 1000px;
+  max-height: 500px;
+  box-sizing: border-box;
+  box-shadow: var(--jp-elevation-z20);
+  word-wrap: break-word;
+  border-radius: var(--jp-border-radius);
+
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color1);
+  resize: both;
+}
+
+.jp-Dialog-content.jp-Dialog-content-small {
+  max-width: 500px;
+}
+
+.jp-Dialog-button {
+  overflow: visible;
+}
+
+button.jp-Dialog-button:focus {
+  outline: 1px solid var(--jp-brand-color1);
+  outline-offset: 4px;
+  -moz-outline-radius: 0;
+}
+
+button.jp-Dialog-button:focus::-moz-focus-inner {
+  border: 0;
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+  outline-offset: 4px;
+  -moz-outline-radius: 0;
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
+  outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
+  outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+  outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
+}
+
+button.jp-Dialog-close-button {
+  padding: 0;
+  height: 100%;
+  min-width: unset;
+  min-height: unset;
+}
+
+.jp-Dialog-header {
+  display: flex;
+  justify-content: space-between;
+  flex: 0 0 auto;
+  padding-bottom: 12px;
+  font-size: var(--jp-ui-font-size3);
+  font-weight: 400;
+  color: var(--jp-ui-font-color1);
+}
+
+.jp-Dialog-body {
+  display: flex;
+  flex-direction: column;
+  flex: 1 1 auto;
+  font-size: var(--jp-ui-font-size1);
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
+
+.jp-Dialog-footer {
+  display: flex;
+  flex-direction: row;
+  justify-content: flex-end;
+  align-items: center;
+  flex: 0 0 auto;
+  margin-left: -12px;
+  margin-right: -12px;
+  padding: 12px;
+}
+
+.jp-Dialog-checkbox {
+  padding-right: 5px;
+}
+
+.jp-Dialog-checkbox > input:focus-visible {
+  outline: 1px solid var(--jp-input-active-border-color);
+  outline-offset: 1px;
+}
+
+.jp-Dialog-spacer {
+  flex: 1 1 auto;
+}
+
+.jp-Dialog-title {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.jp-Dialog-body > .jp-select-wrapper {
+  width: 100%;
+}
+
+.jp-Dialog-body > button {
+  padding: 0 16px;
+}
+
+.jp-Dialog-body > label {
+  line-height: 1.4;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+  margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Input-Boolean-Dialog {
+  flex-direction: row-reverse;
+  align-items: end;
+  width: 100%;
+}
+
+.jp-Input-Boolean-Dialog > label {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MainAreaWidget > :focus {
+  outline: none;
+}
+
+.jp-MainAreaWidget .jp-MainAreaWidget-error {
+  padding: 6px;
+}
+
+.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
+  width: auto;
+  padding: 10px;
+  background: var(--jp-error-color3);
+  border: var(--jp-border-width) solid var(--jp-error-color1);
+  border-radius: var(--jp-border-radius);
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  white-space: pre-wrap;
+  word-wrap: break-word;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/**
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
+ */
+:root {
+  --md-red-50: #ffebee;
+  --md-red-100: #ffcdd2;
+  --md-red-200: #ef9a9a;
+  --md-red-300: #e57373;
+  --md-red-400: #ef5350;
+  --md-red-500: #f44336;
+  --md-red-600: #e53935;
+  --md-red-700: #d32f2f;
+  --md-red-800: #c62828;
+  --md-red-900: #b71c1c;
+  --md-red-A100: #ff8a80;
+  --md-red-A200: #ff5252;
+  --md-red-A400: #ff1744;
+  --md-red-A700: #d50000;
+  --md-pink-50: #fce4ec;
+  --md-pink-100: #f8bbd0;
+  --md-pink-200: #f48fb1;
+  --md-pink-300: #f06292;
+  --md-pink-400: #ec407a;
+  --md-pink-500: #e91e63;
+  --md-pink-600: #d81b60;
+  --md-pink-700: #c2185b;
+  --md-pink-800: #ad1457;
+  --md-pink-900: #880e4f;
+  --md-pink-A100: #ff80ab;
+  --md-pink-A200: #ff4081;
+  --md-pink-A400: #f50057;
+  --md-pink-A700: #c51162;
+  --md-purple-50: #f3e5f5;
+  --md-purple-100: #e1bee7;
+  --md-purple-200: #ce93d8;
+  --md-purple-300: #ba68c8;
+  --md-purple-400: #ab47bc;
+  --md-purple-500: #9c27b0;
+  --md-purple-600: #8e24aa;
+  --md-purple-700: #7b1fa2;
+  --md-purple-800: #6a1b9a;
+  --md-purple-900: #4a148c;
+  --md-purple-A100: #ea80fc;
+  --md-purple-A200: #e040fb;
+  --md-purple-A400: #d500f9;
+  --md-purple-A700: #a0f;
+  --md-deep-purple-50: #ede7f6;
+  --md-deep-purple-100: #d1c4e9;
+  --md-deep-purple-200: #b39ddb;
+  --md-deep-purple-300: #9575cd;
+  --md-deep-purple-400: #7e57c2;
+  --md-deep-purple-500: #673ab7;
+  --md-deep-purple-600: #5e35b1;
+  --md-deep-purple-700: #512da8;
+  --md-deep-purple-800: #4527a0;
+  --md-deep-purple-900: #311b92;
+  --md-deep-purple-A100: #b388ff;
+  --md-deep-purple-A200: #7c4dff;
+  --md-deep-purple-A400: #651fff;
+  --md-deep-purple-A700: #6200ea;
+  --md-indigo-50: #e8eaf6;
+  --md-indigo-100: #c5cae9;
+  --md-indigo-200: #9fa8da;
+  --md-indigo-300: #7986cb;
+  --md-indigo-400: #5c6bc0;
+  --md-indigo-500: #3f51b5;
+  --md-indigo-600: #3949ab;
+  --md-indigo-700: #303f9f;
+  --md-indigo-800: #283593;
+  --md-indigo-900: #1a237e;
+  --md-indigo-A100: #8c9eff;
+  --md-indigo-A200: #536dfe;
+  --md-indigo-A400: #3d5afe;
+  --md-indigo-A700: #304ffe;
+  --md-blue-50: #e3f2fd;
+  --md-blue-100: #bbdefb;
+  --md-blue-200: #90caf9;
+  --md-blue-300: #64b5f6;
+  --md-blue-400: #42a5f5;
+  --md-blue-500: #2196f3;
+  --md-blue-600: #1e88e5;
+  --md-blue-700: #1976d2;
+  --md-blue-800: #1565c0;
+  --md-blue-900: #0d47a1;
+  --md-blue-A100: #82b1ff;
+  --md-blue-A200: #448aff;
+  --md-blue-A400: #2979ff;
+  --md-blue-A700: #2962ff;
+  --md-light-blue-50: #e1f5fe;
+  --md-light-blue-100: #b3e5fc;
+  --md-light-blue-200: #81d4fa;
+  --md-light-blue-300: #4fc3f7;
+  --md-light-blue-400: #29b6f6;
+  --md-light-blue-500: #03a9f4;
+  --md-light-blue-600: #039be5;
+  --md-light-blue-700: #0288d1;
+  --md-light-blue-800: #0277bd;
+  --md-light-blue-900: #01579b;
+  --md-light-blue-A100: #80d8ff;
+  --md-light-blue-A200: #40c4ff;
+  --md-light-blue-A400: #00b0ff;
+  --md-light-blue-A700: #0091ea;
+  --md-cyan-50: #e0f7fa;
+  --md-cyan-100: #b2ebf2;
+  --md-cyan-200: #80deea;
+  --md-cyan-300: #4dd0e1;
+  --md-cyan-400: #26c6da;
+  --md-cyan-500: #00bcd4;
+  --md-cyan-600: #00acc1;
+  --md-cyan-700: #0097a7;
+  --md-cyan-800: #00838f;
+  --md-cyan-900: #006064;
+  --md-cyan-A100: #84ffff;
+  --md-cyan-A200: #18ffff;
+  --md-cyan-A400: #00e5ff;
+  --md-cyan-A700: #00b8d4;
+  --md-teal-50: #e0f2f1;
+  --md-teal-100: #b2dfdb;
+  --md-teal-200: #80cbc4;
+  --md-teal-300: #4db6ac;
+  --md-teal-400: #26a69a;
+  --md-teal-500: #009688;
+  --md-teal-600: #00897b;
+  --md-teal-700: #00796b;
+  --md-teal-800: #00695c;
+  --md-teal-900: #004d40;
+  --md-teal-A100: #a7ffeb;
+  --md-teal-A200: #64ffda;
+  --md-teal-A400: #1de9b6;
+  --md-teal-A700: #00bfa5;
+  --md-green-50: #e8f5e9;
+  --md-green-100: #c8e6c9;
+  --md-green-200: #a5d6a7;
+  --md-green-300: #81c784;
+  --md-green-400: #66bb6a;
+  --md-green-500: #4caf50;
+  --md-green-600: #43a047;
+  --md-green-700: #388e3c;
+  --md-green-800: #2e7d32;
+  --md-green-900: #1b5e20;
+  --md-green-A100: #b9f6ca;
+  --md-green-A200: #69f0ae;
+  --md-green-A400: #00e676;
+  --md-green-A700: #00c853;
+  --md-light-green-50: #f1f8e9;
+  --md-light-green-100: #dcedc8;
+  --md-light-green-200: #c5e1a5;
+  --md-light-green-300: #aed581;
+  --md-light-green-400: #9ccc65;
+  --md-light-green-500: #8bc34a;
+  --md-light-green-600: #7cb342;
+  --md-light-green-700: #689f38;
+  --md-light-green-800: #558b2f;
+  --md-light-green-900: #33691e;
+  --md-light-green-A100: #ccff90;
+  --md-light-green-A200: #b2ff59;
+  --md-light-green-A400: #76ff03;
+  --md-light-green-A700: #64dd17;
+  --md-lime-50: #f9fbe7;
+  --md-lime-100: #f0f4c3;
+  --md-lime-200: #e6ee9c;
+  --md-lime-300: #dce775;
+  --md-lime-400: #d4e157;
+  --md-lime-500: #cddc39;
+  --md-lime-600: #c0ca33;
+  --md-lime-700: #afb42b;
+  --md-lime-800: #9e9d24;
+  --md-lime-900: #827717;
+  --md-lime-A100: #f4ff81;
+  --md-lime-A200: #eeff41;
+  --md-lime-A400: #c6ff00;
+  --md-lime-A700: #aeea00;
+  --md-yellow-50: #fffde7;
+  --md-yellow-100: #fff9c4;
+  --md-yellow-200: #fff59d;
+  --md-yellow-300: #fff176;
+  --md-yellow-400: #ffee58;
+  --md-yellow-500: #ffeb3b;
+  --md-yellow-600: #fdd835;
+  --md-yellow-700: #fbc02d;
+  --md-yellow-800: #f9a825;
+  --md-yellow-900: #f57f17;
+  --md-yellow-A100: #ffff8d;
+  --md-yellow-A200: #ff0;
+  --md-yellow-A400: #ffea00;
+  --md-yellow-A700: #ffd600;
+  --md-amber-50: #fff8e1;
+  --md-amber-100: #ffecb3;
+  --md-amber-200: #ffe082;
+  --md-amber-300: #ffd54f;
+  --md-amber-400: #ffca28;
+  --md-amber-500: #ffc107;
+  --md-amber-600: #ffb300;
+  --md-amber-700: #ffa000;
+  --md-amber-800: #ff8f00;
+  --md-amber-900: #ff6f00;
+  --md-amber-A100: #ffe57f;
+  --md-amber-A200: #ffd740;
+  --md-amber-A400: #ffc400;
+  --md-amber-A700: #ffab00;
+  --md-orange-50: #fff3e0;
+  --md-orange-100: #ffe0b2;
+  --md-orange-200: #ffcc80;
+  --md-orange-300: #ffb74d;
+  --md-orange-400: #ffa726;
+  --md-orange-500: #ff9800;
+  --md-orange-600: #fb8c00;
+  --md-orange-700: #f57c00;
+  --md-orange-800: #ef6c00;
+  --md-orange-900: #e65100;
+  --md-orange-A100: #ffd180;
+  --md-orange-A200: #ffab40;
+  --md-orange-A400: #ff9100;
+  --md-orange-A700: #ff6d00;
+  --md-deep-orange-50: #fbe9e7;
+  --md-deep-orange-100: #ffccbc;
+  --md-deep-orange-200: #ffab91;
+  --md-deep-orange-300: #ff8a65;
+  --md-deep-orange-400: #ff7043;
+  --md-deep-orange-500: #ff5722;
+  --md-deep-orange-600: #f4511e;
+  --md-deep-orange-700: #e64a19;
+  --md-deep-orange-800: #d84315;
+  --md-deep-orange-900: #bf360c;
+  --md-deep-orange-A100: #ff9e80;
+  --md-deep-orange-A200: #ff6e40;
+  --md-deep-orange-A400: #ff3d00;
+  --md-deep-orange-A700: #dd2c00;
+  --md-brown-50: #efebe9;
+  --md-brown-100: #d7ccc8;
+  --md-brown-200: #bcaaa4;
+  --md-brown-300: #a1887f;
+  --md-brown-400: #8d6e63;
+  --md-brown-500: #795548;
+  --md-brown-600: #6d4c41;
+  --md-brown-700: #5d4037;
+  --md-brown-800: #4e342e;
+  --md-brown-900: #3e2723;
+  --md-grey-50: #fafafa;
+  --md-grey-100: #f5f5f5;
+  --md-grey-200: #eee;
+  --md-grey-300: #e0e0e0;
+  --md-grey-400: #bdbdbd;
+  --md-grey-500: #9e9e9e;
+  --md-grey-600: #757575;
+  --md-grey-700: #616161;
+  --md-grey-800: #424242;
+  --md-grey-900: #212121;
+  --md-blue-grey-50: #eceff1;
+  --md-blue-grey-100: #cfd8dc;
+  --md-blue-grey-200: #b0bec5;
+  --md-blue-grey-300: #90a4ae;
+  --md-blue-grey-400: #78909c;
+  --md-blue-grey-500: #607d8b;
+  --md-blue-grey-600: #546e7a;
+  --md-blue-grey-700: #455a64;
+  --md-blue-grey-800: #37474f;
+  --md-blue-grey-900: #263238;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| RenderedText
+|----------------------------------------------------------------------------*/
+
+:root {
+  /* This is the padding value to fill the gaps between lines containing spans with background color. */
+  --jp-private-code-span-padding: calc(
+    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
+  );
+}
+
+.jp-RenderedText {
+  text-align: left;
+  padding-left: var(--jp-code-padding);
+  line-height: var(--jp-code-line-height);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-RenderedText pre,
+.jp-RenderedJavaScript pre,
+.jp-RenderedHTMLCommon pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+  border: none;
+  margin: 0;
+  padding: 0;
+}
+
+.jp-RenderedText pre a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedText pre a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedText pre a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+  color: #3e424d;
+}
+
+.jp-RenderedText pre .ansi-red-fg {
+  color: #e75c58;
+}
+
+.jp-RenderedText pre .ansi-green-fg {
+  color: #00a250;
+}
+
+.jp-RenderedText pre .ansi-yellow-fg {
+  color: #ddb62b;
+}
+
+.jp-RenderedText pre .ansi-blue-fg {
+  color: #208ffb;
+}
+
+.jp-RenderedText pre .ansi-magenta-fg {
+  color: #d160c4;
+}
+
+.jp-RenderedText pre .ansi-cyan-fg {
+  color: #60c6c8;
+}
+
+.jp-RenderedText pre .ansi-white-fg {
+  color: #c5c1b4;
+}
+
+.jp-RenderedText pre .ansi-black-bg {
+  background-color: #3e424d;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-red-bg {
+  background-color: #e75c58;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-green-bg {
+  background-color: #00a250;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-yellow-bg {
+  background-color: #ddb62b;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-blue-bg {
+  background-color: #208ffb;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-magenta-bg {
+  background-color: #d160c4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-cyan-bg {
+  background-color: #60c6c8;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-white-bg {
+  background-color: #c5c1b4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+  color: #282c36;
+}
+
+.jp-RenderedText pre .ansi-red-intense-fg {
+  color: #b22b31;
+}
+
+.jp-RenderedText pre .ansi-green-intense-fg {
+  color: #007427;
+}
+
+.jp-RenderedText pre .ansi-yellow-intense-fg {
+  color: #b27d12;
+}
+
+.jp-RenderedText pre .ansi-blue-intense-fg {
+  color: #0065ca;
+}
+
+.jp-RenderedText pre .ansi-magenta-intense-fg {
+  color: #a03196;
+}
+
+.jp-RenderedText pre .ansi-cyan-intense-fg {
+  color: #258f8f;
+}
+
+.jp-RenderedText pre .ansi-white-intense-fg {
+  color: #a1a6b2;
+}
+
+.jp-RenderedText pre .ansi-black-intense-bg {
+  background-color: #282c36;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-red-intense-bg {
+  background-color: #b22b31;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-green-intense-bg {
+  background-color: #007427;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-yellow-intense-bg {
+  background-color: #b27d12;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-blue-intense-bg {
+  background-color: #0065ca;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-magenta-intense-bg {
+  background-color: #a03196;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-cyan-intense-bg {
+  background-color: #258f8f;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-white-intense-bg {
+  background-color: #a1a6b2;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-default-inverse-fg {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.jp-RenderedText pre .ansi-default-inverse-bg {
+  background-color: var(--jp-inverse-layout-color0);
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-bold {
+  font-weight: bold;
+}
+
+.jp-RenderedText pre .ansi-underline {
+  text-decoration: underline;
+}
+
+.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
+  background: var(--jp-rendermime-error-background);
+  padding-top: var(--jp-code-padding);
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedLatex
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedLatex {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+}
+
+/* Left-justify outputs.*/
+.jp-OutputArea-output.jp-RenderedLatex {
+  padding: var(--jp-code-padding);
+  text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedHTML
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedHTMLCommon {
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-content-font-family);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+
+  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
+  padding-right: 20px;
+}
+
+.jp-RenderedHTMLCommon em {
+  font-style: italic;
+}
+
+.jp-RenderedHTMLCommon strong {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon u {
+  text-decoration: underline;
+}
+
+.jp-RenderedHTMLCommon a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* Headings */
+
+.jp-RenderedHTMLCommon h1,
+.jp-RenderedHTMLCommon h2,
+.jp-RenderedHTMLCommon h3,
+.jp-RenderedHTMLCommon h4,
+.jp-RenderedHTMLCommon h5,
+.jp-RenderedHTMLCommon h6 {
+  line-height: var(--jp-content-heading-line-height);
+  font-weight: var(--jp-content-heading-font-weight);
+  font-style: normal;
+  margin: var(--jp-content-heading-margin-top) 0
+    var(--jp-content-heading-margin-bottom) 0;
+}
+
+.jp-RenderedHTMLCommon h1:first-child,
+.jp-RenderedHTMLCommon h2:first-child,
+.jp-RenderedHTMLCommon h3:first-child,
+.jp-RenderedHTMLCommon h4:first-child,
+.jp-RenderedHTMLCommon h5:first-child,
+.jp-RenderedHTMLCommon h6:first-child {
+  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
+}
+
+.jp-RenderedHTMLCommon h1:last-child,
+.jp-RenderedHTMLCommon h2:last-child,
+.jp-RenderedHTMLCommon h3:last-child,
+.jp-RenderedHTMLCommon h4:last-child,
+.jp-RenderedHTMLCommon h5:last-child,
+.jp-RenderedHTMLCommon h6:last-child {
+  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
+}
+
+.jp-RenderedHTMLCommon h1 {
+  font-size: var(--jp-content-font-size5);
+}
+
+.jp-RenderedHTMLCommon h2 {
+  font-size: var(--jp-content-font-size4);
+}
+
+.jp-RenderedHTMLCommon h3 {
+  font-size: var(--jp-content-font-size3);
+}
+
+.jp-RenderedHTMLCommon h4 {
+  font-size: var(--jp-content-font-size2);
+}
+
+.jp-RenderedHTMLCommon h5 {
+  font-size: var(--jp-content-font-size1);
+}
+
+.jp-RenderedHTMLCommon h6 {
+  font-size: var(--jp-content-font-size0);
+}
+
+/* Lists */
+
+/* stylelint-disable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon ul:not(.list-inline),
+.jp-RenderedHTMLCommon ol:not(.list-inline) {
+  padding-left: 2em;
+}
+
+.jp-RenderedHTMLCommon ul {
+  list-style: disc;
+}
+
+.jp-RenderedHTMLCommon ul ul {
+  list-style: square;
+}
+
+.jp-RenderedHTMLCommon ul ul ul {
+  list-style: circle;
+}
+
+.jp-RenderedHTMLCommon ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol ol {
+  list-style: upper-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol {
+  list-style: lower-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol {
+  list-style: lower-roman;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol,
+.jp-RenderedHTMLCommon ul {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon ul ul,
+.jp-RenderedHTMLCommon ul ol,
+.jp-RenderedHTMLCommon ol ul,
+.jp-RenderedHTMLCommon ol ol {
+  margin-bottom: 0;
+}
+
+/* stylelint-enable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon hr {
+  color: var(--jp-border-color2);
+  background-color: var(--jp-border-color1);
+  margin-top: 1em;
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon > pre {
+  margin: 1.5em 2em;
+}
+
+.jp-RenderedHTMLCommon pre,
+.jp-RenderedHTMLCommon code {
+  border: 0;
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  line-height: var(--jp-code-line-height);
+  padding: 0;
+  white-space: pre-wrap;
+}
+
+.jp-RenderedHTMLCommon :not(pre) > code {
+  background-color: var(--jp-layout-color2);
+  padding: 1px 5px;
+}
+
+/* Tables */
+
+.jp-RenderedHTMLCommon table {
+  border-collapse: collapse;
+  border-spacing: 0;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  table-layout: fixed;
+  margin-left: auto;
+  margin-bottom: 1em;
+  margin-right: auto;
+}
+
+.jp-RenderedHTMLCommon thead {
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  vertical-align: bottom;
+}
+
+.jp-RenderedHTMLCommon td,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon tr {
+  vertical-align: middle;
+  padding: 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
+  max-width: none;
+}
+
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
+  text-align: right;
+}
+
+.jp-RenderedHTMLCommon th {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
+  background: var(--jp-layout-color0);
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
+  background: var(--jp-rendermime-table-row-background);
+}
+
+.jp-RenderedHTMLCommon tbody tr:hover {
+  background: var(--jp-rendermime-table-row-hover-background);
+}
+
+.jp-RenderedHTMLCommon p {
+  text-align: left;
+  margin: 0;
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon img {
+  -moz-force-broken-image-icon: 1;
+}
+
+/* Restrict to direct children as other images could be nested in other content. */
+.jp-RenderedHTMLCommon > img {
+  display: block;
+  margin-left: 0;
+  margin-right: 0;
+  margin-bottom: 1em;
+}
+
+/* Change color behind transparent images if they need it... */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+  max-width: 100%;
+  height: auto;
+}
+
+.jp-RenderedHTMLCommon img.jp-mod-unconfined,
+.jp-RenderedImage img.jp-mod-unconfined,
+.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
+.jp-RenderedSVG svg.jp-mod-unconfined {
+  max-width: none;
+}
+
+.jp-RenderedHTMLCommon .alert {
+  padding: var(--jp-notebook-padding);
+  border: var(--jp-border-width) solid transparent;
+  border-radius: var(--jp-border-radius);
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon .alert-info {
+  color: var(--jp-info-color0);
+  background-color: var(--jp-info-color3);
+  border-color: var(--jp-info-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-info hr {
+  border-color: var(--jp-info-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-warning {
+  color: var(--jp-warn-color0);
+  background-color: var(--jp-warn-color3);
+  border-color: var(--jp-warn-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-warning hr {
+  border-color: var(--jp-warn-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-success {
+  color: var(--jp-success-color0);
+  background-color: var(--jp-success-color3);
+  border-color: var(--jp-success-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-success hr {
+  border-color: var(--jp-success-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-danger {
+  color: var(--jp-error-color0);
+  background-color: var(--jp-error-color3);
+  border-color: var(--jp-error-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-danger hr {
+  border-color: var(--jp-error-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon blockquote {
+  margin: 1em 2em;
+  padding: 0 1em;
+  border-left: 5px solid var(--jp-border-color2);
+}
+
+a.jp-InternalAnchorLink {
+  visibility: hidden;
+  margin-left: 8px;
+  color: var(--md-blue-800);
+}
+
+h1:hover .jp-InternalAnchorLink,
+h2:hover .jp-InternalAnchorLink,
+h3:hover .jp-InternalAnchorLink,
+h4:hover .jp-InternalAnchorLink,
+h5:hover .jp-InternalAnchorLink,
+h6:hover .jp-InternalAnchorLink {
+  visibility: visible;
+}
+
+.jp-RenderedHTMLCommon kbd {
+  background-color: var(--jp-rendermime-table-row-background);
+  border: 1px solid var(--jp-border-color0);
+  border-bottom-color: var(--jp-border-color2);
+  border-radius: 3px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+  display: inline-block;
+  font-size: var(--jp-ui-font-size0);
+  line-height: 1em;
+  padding: 0.2em 0.5em;
+}
+
+/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
+ * At the bottom of cells this is a bit too much as there is also spacing
+ * between cells. Going all the way to 0 gets too tight between markdown and
+ * code cells.
+ */
+.jp-RenderedHTMLCommon > *:last-child {
+  margin-bottom: 0.5em;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-cursor-backdrop {
+  position: fixed;
+  width: 200px;
+  height: 200px;
+  margin-top: -100px;
+  margin-left: -100px;
+  will-change: transform;
+  z-index: 100;
+}
+
+.lm-mod-drag-image {
+  will-change: transform;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-lineFormSearch {
+  padding: 4px 12px;
+  background-color: var(--jp-layout-color2);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+  font-size: var(--jp-ui-font-size1);
+}
+
+.jp-lineFormCaption {
+  font-size: var(--jp-ui-font-size0);
+  line-height: var(--jp-ui-font-size1);
+  margin-top: 4px;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-baseLineForm {
+  border: none;
+  border-radius: 0;
+  position: absolute;
+  background-size: 16px;
+  background-repeat: no-repeat;
+  background-position: center;
+  outline: none;
+}
+
+.jp-lineFormButtonContainer {
+  top: 4px;
+  right: 8px;
+  height: 24px;
+  padding: 0 12px;
+  width: 12px;
+}
+
+.jp-lineFormButtonIcon {
+  top: 0;
+  right: 0;
+  background-color: var(--jp-brand-color1);
+  height: 100%;
+  width: 100%;
+  box-sizing: border-box;
+  padding: 4px 6px;
+}
+
+.jp-lineFormButton {
+  top: 0;
+  right: 0;
+  background-color: transparent;
+  height: 100%;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.jp-lineFormWrapper {
+  overflow: hidden;
+  padding: 0 8px;
+  border: 1px solid var(--jp-border-color0);
+  background-color: var(--jp-input-active-background);
+  height: 22px;
+}
+
+.jp-lineFormWrapperFocusWithin {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-lineFormInput {
+  background: transparent;
+  width: 200px;
+  height: 100%;
+  border: none;
+  outline: none;
+  color: var(--jp-ui-font-color0);
+  line-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-JSONEditor {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
+}
+
+.jp-JSONEditor-host {
+  flex: 1 1 auto;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0;
+  background: var(--jp-layout-color0);
+  min-height: 50px;
+  padding: 1px;
+}
+
+.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
+  border-color: red;
+  outline-color: red;
+}
+
+.jp-JSONEditor-header {
+  display: flex;
+  flex: 1 0 auto;
+  padding: 0 0 0 12px;
+}
+
+.jp-JSONEditor-header label {
+  flex: 0 0 auto;
+}
+
+.jp-JSONEditor-commitButton {
+  height: 16px;
+  width: 16px;
+  background-size: 18px;
+  background-repeat: no-repeat;
+  background-position: center;
+}
+
+.jp-JSONEditor-host.jp-mod-focused {
+  background-color: var(--jp-input-active-background);
+  border: 1px solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+.jp-Editor.jp-mod-dropTarget {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+.jp-DocumentSearch-input {
+  border: none;
+  outline: none;
+  color: var(--jp-ui-font-color0);
+  font-size: var(--jp-ui-font-size1);
+  background-color: var(--jp-layout-color0);
+  font-family: var(--jp-ui-font-family);
+  padding: 2px 1px;
+  resize: none;
+}
+
+.jp-DocumentSearch-overlay {
+  position: absolute;
+  background-color: var(--jp-toolbar-background);
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  top: 0;
+  right: 0;
+  z-index: 7;
+  min-width: 405px;
+  padding: 2px;
+  font-size: var(--jp-ui-font-size1);
+
+  --jp-private-document-search-button-height: 20px;
+}
+
+.jp-DocumentSearch-overlay button {
+  background-color: var(--jp-toolbar-background);
+  outline: 0;
+}
+
+.jp-DocumentSearch-overlay button:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-overlay button:active {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-overlay-row {
+  display: flex;
+  align-items: center;
+  margin-bottom: 2px;
+}
+
+.jp-DocumentSearch-button-content {
+  display: inline-block;
+  cursor: pointer;
+  box-sizing: border-box;
+  width: 100%;
+  height: 100%;
+}
+
+.jp-DocumentSearch-button-content svg {
+  width: 100%;
+  height: 100%;
+}
+
+.jp-DocumentSearch-input-wrapper {
+  border: var(--jp-border-width) solid var(--jp-border-color0);
+  display: flex;
+  background-color: var(--jp-layout-color0);
+  margin: 2px;
+}
+
+.jp-DocumentSearch-input-wrapper:focus-within {
+  border-color: var(--jp-cell-editor-active-border-color);
+}
+
+.jp-DocumentSearch-toggle-wrapper,
+.jp-DocumentSearch-button-wrapper {
+  all: initial;
+  overflow: hidden;
+  display: inline-block;
+  border: none;
+  box-sizing: border-box;
+}
+
+.jp-DocumentSearch-toggle-wrapper {
+  width: 14px;
+  height: 14px;
+}
+
+.jp-DocumentSearch-button-wrapper {
+  width: var(--jp-private-document-search-button-height);
+  height: var(--jp-private-document-search-button-height);
+}
+
+.jp-DocumentSearch-toggle-wrapper:focus,
+.jp-DocumentSearch-button-wrapper:focus {
+  outline: var(--jp-border-width) solid
+    var(--jp-cell-editor-active-border-color);
+  outline-offset: -1px;
+}
+
+.jp-DocumentSearch-toggle-wrapper,
+.jp-DocumentSearch-button-wrapper,
+.jp-DocumentSearch-button-content:focus {
+  outline: none;
+}
+
+.jp-DocumentSearch-toggle-placeholder {
+  width: 5px;
+}
+
+.jp-DocumentSearch-input-button::before {
+  display: block;
+  padding-top: 100%;
+}
+
+.jp-DocumentSearch-input-button-off {
+  opacity: var(--jp-search-toggle-off-opacity);
+}
+
+.jp-DocumentSearch-input-button-off:hover {
+  opacity: var(--jp-search-toggle-hover-opacity);
+}
+
+.jp-DocumentSearch-input-button-on {
+  opacity: var(--jp-search-toggle-on-opacity);
+}
+
+.jp-DocumentSearch-index-counter {
+  padding-left: 10px;
+  padding-right: 10px;
+  user-select: none;
+  min-width: 35px;
+  display: inline-block;
+}
+
+.jp-DocumentSearch-up-down-wrapper {
+  display: inline-block;
+  padding-right: 2px;
+  margin-left: auto;
+  white-space: nowrap;
+}
+
+.jp-DocumentSearch-spacer {
+  margin-left: auto;
+}
+
+.jp-DocumentSearch-up-down-wrapper button {
+  outline: 0;
+  border: none;
+  width: var(--jp-private-document-search-button-height);
+  height: var(--jp-private-document-search-button-height);
+  vertical-align: middle;
+  margin: 1px 5px 2px;
+}
+
+.jp-DocumentSearch-up-down-button:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-up-down-button:active {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-filter-button {
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-DocumentSearch-filter-button:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-filter-button-enabled {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-filter-button-enabled:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-search-options {
+  padding: 0 8px;
+  margin-left: 3px;
+  width: 100%;
+  display: grid;
+  justify-content: start;
+  grid-template-columns: 1fr 1fr;
+  align-items: center;
+  justify-items: stretch;
+}
+
+.jp-DocumentSearch-search-filter-disabled {
+  color: var(--jp-ui-font-color2);
+}
+
+.jp-DocumentSearch-search-filter {
+  display: flex;
+  align-items: center;
+  user-select: none;
+}
+
+.jp-DocumentSearch-regex-error {
+  color: var(--jp-error-color0);
+}
+
+.jp-DocumentSearch-replace-button-wrapper {
+  overflow: hidden;
+  display: inline-block;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color0);
+  margin: auto 2px;
+  padding: 1px 4px;
+  height: calc(var(--jp-private-document-search-button-height) + 2px);
+}
+
+.jp-DocumentSearch-replace-button-wrapper:focus {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+}
+
+.jp-DocumentSearch-replace-button {
+  display: inline-block;
+  text-align: center;
+  cursor: pointer;
+  box-sizing: border-box;
+  color: var(--jp-ui-font-color1);
+
+  /* height - 2 * (padding of wrapper) */
+  line-height: calc(var(--jp-private-document-search-button-height) - 2px);
+  width: 100%;
+  height: 100%;
+}
+
+.jp-DocumentSearch-replace-button:focus {
+  outline: none;
+}
+
+.jp-DocumentSearch-replace-wrapper-class {
+  margin-left: 14px;
+  display: flex;
+}
+
+.jp-DocumentSearch-replace-toggle {
+  border: none;
+  background-color: var(--jp-toolbar-background);
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-DocumentSearch-replace-toggle:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.cm-editor {
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  border: 0;
+  border-radius: 0;
+  height: auto;
+
+  /* Changed to auto to autogrow */
+}
+
+.cm-editor pre {
+  padding: 0 var(--jp-code-padding);
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+}
+
+.jp-CodeMirrorEditor {
+  cursor: text;
+}
+
+/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
+    border-left: var(--jp-code-cursor-width1) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+/* When zoomed out less than 33% */
+@media screen and (min-width: 4320px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
+    border-left: var(--jp-code-cursor-width2) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+.cm-editor.jp-mod-readOnly .cm-cursor {
+  display: none;
+}
+
+.jp-CollaboratorCursor {
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: none;
+  border-bottom: 3px solid;
+  background-clip: content-box;
+  margin-left: -5px;
+  margin-right: -5px;
+}
+
+.cm-searching,
+.cm-searching span {
+  /* `.cm-searching span`: we need to override syntax highlighting */
+  background-color: var(--jp-search-unselected-match-background-color);
+  color: var(--jp-search-unselected-match-color);
+}
+
+.cm-searching::selection,
+.cm-searching span::selection {
+  background-color: var(--jp-search-unselected-match-background-color);
+  color: var(--jp-search-unselected-match-color);
+}
+
+.jp-current-match > .cm-searching,
+.jp-current-match > .cm-searching span,
+.cm-searching > .jp-current-match,
+.cm-searching > .jp-current-match span {
+  background-color: var(--jp-search-selected-match-background-color);
+  color: var(--jp-search-selected-match-color);
+}
+
+.jp-current-match > .cm-searching::selection,
+.cm-searching > .jp-current-match::selection,
+.jp-current-match > .cm-searching span::selection {
+  background-color: var(--jp-search-selected-match-background-color);
+  color: var(--jp-search-selected-match-color);
+}
+
+.cm-trailingspace {
+  background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
+  background-position: center left;
+  background-repeat: repeat-x;
+}
+
+.jp-CollaboratorCursor-hover {
+  position: absolute;
+  z-index: 1;
+  transform: translateX(-50%);
+  color: white;
+  border-radius: 3px;
+  padding-left: 4px;
+  padding-right: 4px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+  text-align: center;
+  font-size: var(--jp-ui-font-size1);
+  white-space: nowrap;
+}
+
+.jp-CodeMirror-ruler {
+  border-left: 1px dashed var(--jp-border-color2);
+}
+
+/* Styles for shared cursors (remote cursor locations and selected ranges) */
+.jp-CodeMirrorEditor .cm-ySelectionCaret {
+  position: relative;
+  border-left: 1px solid black;
+  margin-left: -1px;
+  margin-right: -1px;
+  box-sizing: border-box;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
+  white-space: nowrap;
+  position: absolute;
+  top: -1.15em;
+  padding-bottom: 0.05em;
+  left: -1px;
+  font-size: 0.95em;
+  font-family: var(--jp-ui-font-family);
+  font-weight: bold;
+  line-height: normal;
+  user-select: none;
+  color: white;
+  padding-left: 2px;
+  padding-right: 2px;
+  z-index: 101;
+  transition: opacity 0.3s ease-in-out;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionInfo {
+  transition-delay: 0.7s;
+  opacity: 0;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
+  opacity: 1;
+  transition-delay: 0s;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MimeDocument {
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-filebrowser-button-height: 28px;
+  --jp-private-filebrowser-button-width: 48px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser .jp-SidePanel-content {
+  display: flex;
+  flex-direction: column;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  flex-wrap: wrap;
+  row-gap: 12px;
+  border-bottom: none;
+  height: auto;
+  margin: 8px 12px 0;
+  box-shadow: none;
+  padding: 0;
+  justify-content: flex-start;
+}
+
+.jp-FileBrowser-Panel {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+}
+
+.jp-BreadCrumbs {
+  flex: 0 0 auto;
+  margin: 8px 12px;
+}
+
+.jp-BreadCrumbs-item {
+  margin: 0 2px;
+  padding: 0 2px;
+  border-radius: var(--jp-border-radius);
+  cursor: pointer;
+}
+
+.jp-BreadCrumbs-item:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-BreadCrumbs-item:first-child {
+  margin-left: 0;
+}
+
+.jp-BreadCrumbs-item.jp-mod-dropTarget {
+  background-color: var(--jp-brand-color2);
+  opacity: 0.7;
+}
+
+/*-----------------------------------------------------------------------------
+| Buttons
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser-toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  padding-left: 0;
+  padding-right: 2px;
+  align-items: center;
+  height: unset;
+}
+
+.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
+  width: 40px;
+}
+
+/*-----------------------------------------------------------------------------
+| Other styles
+|----------------------------------------------------------------------------*/
+
+.jp-FileDialog.jp-mod-conflict input {
+  color: var(--jp-error-color1);
+}
+
+.jp-FileDialog .jp-new-name-title {
+  margin-top: 12px;
+}
+
+.jp-LastModified-hidden {
+  display: none;
+}
+
+.jp-FileSize-hidden {
+  display: none;
+}
+
+.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| DirListing
+|----------------------------------------------------------------------------*/
+
+.jp-DirListing {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+  outline: 0;
+}
+
+.jp-DirListing-header {
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  overflow: hidden;
+  border-top: var(--jp-border-width) solid var(--jp-border-color2);
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+}
+
+.jp-DirListing-headerItem {
+  padding: 4px 12px 2px;
+  font-weight: 500;
+}
+
+.jp-DirListing-headerItem:hover {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-headerItem.jp-id-name {
+  flex: 1 0 84px;
+}
+
+.jp-DirListing-headerItem.jp-id-modified {
+  flex: 0 0 112px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
+.jp-DirListing-headerItem.jp-id-filesize {
+  flex: 0 0 75px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
+.jp-id-narrow {
+  display: none;
+  flex: 0 0 5px;
+  padding: 4px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+  color: var(--jp-border-color2);
+}
+
+.jp-DirListing-narrow .jp-id-narrow {
+  display: block;
+}
+
+.jp-DirListing-narrow .jp-id-modified,
+.jp-DirListing-narrow .jp-DirListing-itemModified {
+  display: none;
+}
+
+.jp-DirListing-headerItem.jp-mod-selected {
+  font-weight: 600;
+}
+
+/* increase specificity to override bundled default */
+.jp-DirListing-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-content mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+/* Style the directory listing content when a user drops a file to upload */
+.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
+  outline: 5px dashed rgba(128, 128, 128, 0.5);
+  outline-offset: -10px;
+  cursor: copy;
+}
+
+.jp-DirListing-item {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  padding: 4px 12px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-DirListing-checkboxWrapper {
+  /* Increases hit area of checkbox. */
+  padding: 4px;
+}
+
+.jp-DirListing-header
+  .jp-DirListing-checkboxWrapper
+  + .jp-DirListing-headerItem {
+  padding-left: 4px;
+}
+
+.jp-DirListing-content .jp-DirListing-checkboxWrapper {
+  position: relative;
+  left: -4px;
+  margin: -4px 0 -4px -8px;
+}
+
+.jp-DirListing-checkboxWrapper.jp-mod-visible {
+  visibility: visible;
+}
+
+/* For devices that support hovering, hide checkboxes until hovered, selected...
+*/
+@media (hover: hover) {
+  .jp-DirListing-checkboxWrapper {
+    visibility: hidden;
+  }
+
+  .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
+  .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
+    visibility: visible;
+  }
+}
+
+.jp-DirListing-item[data-is-dot] {
+  opacity: 75%;
+}
+
+.jp-DirListing-item.jp-mod-selected {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.jp-DirListing-item.jp-mod-dropTarget {
+  background: var(--jp-brand-color3);
+}
+
+.jp-DirListing-item:hover:not(.jp-mod-selected) {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-itemIcon {
+  flex: 0 0 20px;
+  margin-right: 4px;
+}
+
+.jp-DirListing-itemText {
+  flex: 1 0 64px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  user-select: none;
+}
+
+.jp-DirListing-itemText:focus {
+  outline-width: 2px;
+  outline-color: var(--jp-inverse-layout-color1);
+  outline-style: solid;
+  outline-offset: 1px;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
+  outline-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-itemModified {
+  flex: 0 0 125px;
+  text-align: right;
+}
+
+.jp-DirListing-itemFileSize {
+  flex: 0 0 90px;
+  text-align: right;
+}
+
+.jp-DirListing-editor {
+  flex: 1 0 64px;
+  outline: none;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
+  color: var(--jp-success-color1);
+  content: '\25CF';
+  font-size: 8px;
+  position: absolute;
+  left: -8px;
+}
+
+.jp-DirListing-item.jp-mod-running.jp-mod-selected
+  .jp-DirListing-itemIcon::before {
+  color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-DirListing-item.lm-mod-drag-image,
+.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
+  font-size: var(--jp-ui-font-size1);
+  padding-left: 4px;
+  margin-left: 4px;
+  width: 160px;
+  background-color: var(--jp-ui-inverse-font-color2);
+  box-shadow: var(--jp-elevation-z2);
+  border-radius: 0;
+  color: var(--jp-ui-font-color1);
+  transform: translateX(-40%) translateY(-58%);
+}
+
+.jp-Document {
+  min-width: 120px;
+  min-height: 120px;
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Main OutputArea
+| OutputArea has a list of Outputs
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea {
+  overflow-y: auto;
+}
+
+.jp-OutputArea-child {
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+  overflow: hidden;
+}
+
+.jp-OutputPrompt {
+  width: var(--jp-cell-prompt-width);
+  color: var(--jp-cell-outprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-OutputArea-prompt {
+  display: table-cell;
+  vertical-align: top;
+}
+
+.jp-OutputArea-output {
+  display: table-cell;
+  width: 100%;
+  height: auto;
+  overflow: auto;
+  user-select: text;
+  -moz-user-select: text;
+  -webkit-user-select: text;
+  -ms-user-select: text;
+}
+
+.jp-OutputArea .jp-RenderedText {
+  padding-left: 1ch;
+}
+
+/**
+ * Prompt overlay.
+ */
+
+.jp-OutputArea-promptOverlay {
+  position: absolute;
+  top: 0;
+  width: var(--jp-cell-prompt-width);
+  height: 100%;
+  opacity: 0.5;
+}
+
+.jp-OutputArea-promptOverlay:hover {
+  background: var(--jp-layout-color2);
+  box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
+  cursor: zoom-out;
+}
+
+.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
+  cursor: zoom-in;
+}
+
+/**
+ * Isolated output.
+ */
+.jp-OutputArea-output.jp-mod-isolated {
+  width: 100%;
+  display: block;
+}
+
+/*
+When drag events occur, `lm-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+/* pre */
+
+.jp-OutputArea-output pre {
+  border: none;
+  margin: 0;
+  padding: 0;
+  overflow-x: auto;
+  overflow-y: auto;
+  word-break: break-all;
+  word-wrap: break-word;
+  white-space: pre-wrap;
+}
+
+/* tables */
+
+.jp-OutputArea-output.jp-RenderedHTMLCommon table {
+  margin-left: 0;
+  margin-right: 0;
+}
+
+/* description lists */
+
+.jp-OutputArea-output dl,
+.jp-OutputArea-output dt,
+.jp-OutputArea-output dd {
+  display: block;
+}
+
+.jp-OutputArea-output dl {
+  width: 100%;
+  overflow: hidden;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dt {
+  font-weight: bold;
+  float: left;
+  width: 20%;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dd {
+  float: left;
+  width: 80%;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-TrimmedOutputs pre {
+  background: var(--jp-layout-color3);
+  font-size: calc(var(--jp-code-font-size) * 1.4);
+  text-align: center;
+  text-transform: uppercase;
+}
+
+/* Hide the gutter in case of
+ *  - nested output areas (e.g. in the case of output widgets)
+ *  - mirrored output areas
+ */
+.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
+  display: none;
+}
+
+/* Hide empty lines in the output area, for instance due to cleared widgets */
+.jp-OutputArea-prompt:empty {
+  padding: 0;
+  border: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| executeResult is added to any Output-result for the display of the object
+| returned by a cell
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+  margin-left: 0;
+  width: 100%;
+}
+
+/* Text output with the Out[] prompt needs a top padding to match the
+ * alignment of the Out[] prompt itself.
+ */
+.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
+  padding-top: var(--jp-code-padding);
+  border-top: var(--jp-border-width) solid transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| The Stdin output
+|----------------------------------------------------------------------------*/
+
+.jp-Stdin-prompt {
+  color: var(--jp-content-font-color0);
+  padding-right: var(--jp-code-padding);
+  vertical-align: baseline;
+  flex: 0 0 auto;
+}
+
+.jp-Stdin-input {
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  color: inherit;
+  background-color: inherit;
+  width: 42%;
+  min-width: 200px;
+
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0 0.25em;
+  margin: 0 0.25em;
+  flex: 0 0 70%;
+}
+
+.jp-Stdin-input::placeholder {
+  opacity: 0;
+}
+
+.jp-Stdin-input:focus {
+  box-shadow: none;
+}
+
+.jp-Stdin-input:focus::placeholder {
+  opacity: 1;
+}
+
+/*-----------------------------------------------------------------------------
+| Output Area View
+|----------------------------------------------------------------------------*/
+
+.jp-LinkedOutputView .jp-OutputArea {
+  height: 100%;
+  display: block;
+}
+
+.jp-LinkedOutputView .jp-OutputArea-output:only-child {
+  height: 100%;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+@media print {
+  .jp-OutputArea-child {
+    break-inside: avoid-page;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+  .jp-OutputPrompt {
+    display: table-row;
+    text-align: left;
+  }
+
+  .jp-OutputArea-child .jp-OutputArea-output {
+    display: table-row;
+    margin-left: var(--jp-notebook-padding);
+  }
+}
+
+/* Trimmed outputs warning */
+.jp-TrimmedOutputs > a {
+  margin: 10px;
+  text-decoration: none;
+  cursor: pointer;
+}
+
+.jp-TrimmedOutputs > a:hover {
+  text-decoration: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Table of Contents
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toc-active-width: 4px;
+}
+
+.jp-TableOfContents {
+  display: flex;
+  flex-direction: column;
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  height: 100%;
+}
+
+.jp-TableOfContents-placeholder {
+  text-align: center;
+}
+
+.jp-TableOfContents-placeholderContent {
+  color: var(--jp-content-font-color2);
+  padding: 8px;
+}
+
+.jp-TableOfContents-placeholderContent > h3 {
+  margin-bottom: var(--jp-content-heading-margin-bottom);
+}
+
+.jp-TableOfContents .jp-SidePanel-content {
+  overflow-y: auto;
+}
+
+.jp-TableOfContents-tree {
+  margin: 4px;
+}
+
+.jp-TableOfContents ol {
+  list-style-type: none;
+}
+
+/* stylelint-disable-next-line selector-max-type */
+.jp-TableOfContents li > ol {
+  /* Align left border with triangle icon center */
+  padding-left: 11px;
+}
+
+.jp-TableOfContents-content {
+  /* left margin for the active heading indicator */
+  margin: 0 0 0 var(--jp-private-toc-active-width);
+  padding: 0;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-tocItem {
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-tocItem-heading {
+  display: flex;
+  cursor: pointer;
+}
+
+.jp-tocItem-heading:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-tocItem-content {
+  display: block;
+  padding: 4px 0;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+  overflow-x: hidden;
+}
+
+.jp-tocItem-collapser {
+  height: 20px;
+  margin: 2px 2px 0;
+  padding: 0;
+  background: none;
+  border: none;
+  cursor: pointer;
+}
+
+.jp-tocItem-collapser:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+/* Active heading indicator */
+
+.jp-tocItem-heading::before {
+  content: ' ';
+  background: transparent;
+  width: var(--jp-private-toc-active-width);
+  height: 24px;
+  position: absolute;
+  left: 0;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-tocItem-heading.jp-tocItem-active::before {
+  background-color: var(--jp-brand-color1);
+}
+
+.jp-tocItem-heading:hover.jp-tocItem-active::before {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapser {
+  flex: 0 0 var(--jp-cell-collapser-width);
+  padding: 0;
+  margin: 0;
+  border: none;
+  outline: none;
+  background: transparent;
+  border-radius: var(--jp-border-radius);
+  opacity: 1;
+}
+
+.jp-Collapser-child {
+  display: block;
+  width: 100%;
+  box-sizing: border-box;
+
+  /* height: 100% doesn't work because the height of its parent is computed from content */
+  position: absolute;
+  top: 0;
+  bottom: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Hiding collapsers in print mode.
+
+Note: input and output wrappers have "display: block" propery in print mode.
+*/
+
+@media print {
+  .jp-Collapser {
+    display: none;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Header/Footer
+|----------------------------------------------------------------------------*/
+
+/* Hidden by zero height by default */
+.jp-CellHeader,
+.jp-CellFooter {
+  height: 0;
+  width: 100%;
+  padding: 0;
+  margin: 0;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Input
+|----------------------------------------------------------------------------*/
+
+/* All input areas */
+.jp-InputArea {
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  display: table-cell;
+  overflow: hidden;
+  vertical-align: top;
+
+  /* This is the non-active, default styling */
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0;
+  background: var(--jp-cell-editor-background);
+}
+
+.jp-InputPrompt {
+  display: table-cell;
+  vertical-align: top;
+  width: var(--jp-cell-prompt-width);
+  color: var(--jp-cell-inprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  opacity: var(--jp-cell-prompt-opacity);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+  .jp-InputArea-editor {
+    display: table-row;
+    margin-left: var(--jp-notebook-padding);
+  }
+
+  .jp-InputPrompt {
+    display: table-row;
+    text-align: left;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Placeholder {
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+}
+
+.jp-Placeholder-prompt {
+  display: table-cell;
+  box-sizing: border-box;
+}
+
+.jp-Placeholder-content {
+  display: table-cell;
+  padding: 4px 6px;
+  border: 1px solid transparent;
+  border-radius: 0;
+  background: none;
+  box-sizing: border-box;
+  cursor: pointer;
+}
+
+.jp-Placeholder-contentContainer {
+  display: flex;
+}
+
+.jp-Placeholder-content:hover,
+.jp-InputPlaceholder > .jp-Placeholder-content:hover {
+  border-color: var(--jp-layout-color3);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon {
+  width: 32px;
+  height: 16px;
+  border: 1px solid transparent;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon:hover {
+  border: 1px solid var(--jp-border-color1);
+  box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
+  background-color: var(--jp-layout-color0);
+}
+
+.jp-PlaceholderText {
+  white-space: nowrap;
+  overflow-x: hidden;
+  color: var(--jp-inverse-layout-color3);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-InputPlaceholder > .jp-Placeholder-content {
+  border-color: var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-cell-scrolling-output-offset: 5px;
+}
+
+/*-----------------------------------------------------------------------------
+| Cell
+|----------------------------------------------------------------------------*/
+
+.jp-Cell {
+  padding: var(--jp-cell-padding);
+  margin: 0;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Common input/output
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-inputWrapper,
+.jp-Cell-outputWrapper {
+  display: flex;
+  flex-direction: row;
+  padding: 0;
+  margin: 0;
+
+  /* Added to reveal the box-shadow on the input and output collapsers. */
+  overflow: visible;
+}
+
+/* Only input/output areas inside cells */
+.jp-Cell-inputArea,
+.jp-Cell-outputArea {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Collapser
+|----------------------------------------------------------------------------*/
+
+/* Make the output collapser disappear when there is not output, but do so
+ * in a manner that leaves it in the layout and preserves its width.
+ */
+.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
+  border: none !important;
+  background: transparent !important;
+}
+
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
+  min-height: var(--jp-cell-collapser-min-height);
+}
+
+/*-----------------------------------------------------------------------------
+| Output
+|----------------------------------------------------------------------------*/
+
+/* Put a space between input and output when there IS output */
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
+  margin-top: 5px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
+  overflow-y: auto;
+  max-height: 24em;
+  margin-left: var(--jp-private-cell-scrolling-output-offset);
+  resize: vertical;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
+  max-height: unset;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
+  content: ' ';
+  box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
+  width: 100%;
+  height: 100%;
+  position: sticky;
+  bottom: 0;
+  top: 0;
+  margin-top: -50%;
+  float: left;
+  display: block;
+  pointer-events: none;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
+  padding-top: 6px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  width: calc(
+    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
+  );
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
+  left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
+}
+
+/*-----------------------------------------------------------------------------
+| CodeCell
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| MarkdownCell
+|----------------------------------------------------------------------------*/
+
+.jp-MarkdownOutput {
+  display: table-cell;
+  width: 100%;
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-left: var(--jp-code-padding);
+}
+
+.jp-MarkdownOutput.jp-RenderedHTMLCommon {
+  overflow: auto;
+}
+
+/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
+.jp-collapseHeadingButton {
+  display: flex;
+  min-height: var(--jp-cell-collapser-min-height);
+  font-size: var(--jp-code-font-size);
+  position: absolute;
+  background-color: transparent;
+  background-size: 25px;
+  background-repeat: no-repeat;
+  background-position-x: center;
+  background-position-y: top;
+  background-image: var(--jp-icon-caret-down);
+  right: 0;
+  top: 0;
+  bottom: 0;
+}
+
+.jp-collapseHeadingButton.jp-mod-collapsed {
+  background-image: var(--jp-icon-caret-right);
+}
+
+/*
+ set the container font size to match that of content
+ so that the nested collapse buttons have the right size
+*/
+.jp-MarkdownCell .jp-InputPrompt {
+  font-size: var(--jp-content-font-size1);
+}
+
+/*
+  Align collapseHeadingButton with cell top header
+  The font sizes are identical to the ones in packages/rendermime/style/base.css
+*/
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
+  font-size: var(--jp-content-font-size5);
+  background-position-y: calc(0.3 * var(--jp-content-font-size5));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
+  font-size: var(--jp-content-font-size4);
+  background-position-y: calc(0.3 * var(--jp-content-font-size4));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
+  font-size: var(--jp-content-font-size3);
+  background-position-y: calc(0.3 * var(--jp-content-font-size3));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
+  font-size: var(--jp-content-font-size2);
+  background-position-y: calc(0.3 * var(--jp-content-font-size2));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
+  font-size: var(--jp-content-font-size1);
+  background-position-y: top;
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
+  font-size: var(--jp-content-font-size0);
+  background-position-y: top;
+}
+
+/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
+  display: none;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton
+  :is(.jp-MarkdownCell:hover, .jp-mod-active)
+  .jp-collapseHeadingButton {
+  display: flex;
+}
+
+/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
+is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
+  margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
+  margin-top: var(--jp-code-padding);
+  border: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-border-color3) !important;
+  color: var(--jp-content-font-color0) !important;
+  display: flex;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
+  background-color: var(--jp-border-color2) !important;
+}
+
+.jp-showHiddenCellsButton {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Using block instead of flex to allow the use of the break-inside CSS property for
+cell outputs.
+*/
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-notebook-toolbar-padding: 2px 5px 2px 2px;
+}
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel-toolbar {
+  padding: var(--jp-notebook-toolbar-padding);
+
+  /* disable paint containment from lumino 2.0 default strict CSS containment */
+  contain: style size !important;
+}
+
+.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
+  border: none;
+  box-shadow: none;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown select {
+  height: 24px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: 14px;
+  border-radius: 0;
+  display: block;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown span {
+  top: 5px !important;
+}
+
+.jp-Toolbar-responsive-popup {
+  position: absolute;
+  height: fit-content;
+  display: flex;
+  flex-direction: row;
+  flex-wrap: wrap;
+  justify-content: flex-end;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: var(--jp-notebook-toolbar-padding);
+  z-index: 1;
+  right: 0;
+  top: 0;
+}
+
+.jp-Toolbar > .jp-Toolbar-responsive-opener {
+  margin-left: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-ExecutionIndicator {
+  position: relative;
+  display: inline-block;
+  height: 100%;
+  z-index: 9997;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip {
+  visibility: hidden;
+  height: auto;
+  width: max-content;
+  width: -moz-max-content;
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color1);
+  text-align: justify;
+  border-radius: 6px;
+  padding: 0 5px;
+  position: fixed;
+  display: table;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.up {
+  transform: translateX(-50%) translateY(-100%) translateY(-32px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.down {
+  transform: translateX(calc(-100% + 16px)) translateY(5px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.hidden {
+  display: none;
+}
+
+.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
+  visibility: visible;
+}
+
+.jp-Notebook-ExecutionIndicator span {
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  color: var(--jp-ui-font-color1);
+  line-height: 24px;
+  display: block;
+}
+
+.jp-Notebook-ExecutionIndicator-progress-bar {
+  display: flex;
+  justify-content: center;
+  height: 100%;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*
+ * Execution indicator
+ */
+.jp-tocItem-content::after {
+  content: '';
+
+  /* Must be identical to form a circle */
+  width: 12px;
+  height: 12px;
+  background: none;
+  border: none;
+  position: absolute;
+  right: 0;
+}
+
+.jp-tocItem-content[data-running='0']::after {
+  border-radius: 50%;
+  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+  background: none;
+}
+
+.jp-tocItem-content[data-running='1']::after {
+  border-radius: 50%;
+  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+  background-color: var(--jp-inverse-layout-color3);
+}
+
+.jp-tocItem-content[data-running='0'],
+.jp-tocItem-content[data-running='1'] {
+  margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Notebook-footer {
+  height: 27px;
+  margin-left: calc(
+    var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+      var(--jp-cell-padding)
+  );
+  width: calc(
+    100% -
+      (
+        var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+          var(--jp-cell-padding) + var(--jp-cell-padding)
+      )
+  );
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  color: var(--jp-ui-font-color3);
+  margin-top: 6px;
+  background: none;
+  cursor: pointer;
+}
+
+.jp-Notebook-footer:focus {
+  border-color: var(--jp-cell-editor-active-border-color);
+}
+
+/* For devices that support hovering, hide footer until hover */
+@media (hover: hover) {
+  .jp-Notebook-footer {
+    opacity: 0;
+  }
+
+  .jp-Notebook-footer:focus,
+  .jp-Notebook-footer:hover {
+    opacity: 1;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Imports
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-side-by-side-output-size: 1fr;
+  --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
+  --jp-private-notebook-dragImage-width: 304px;
+  --jp-private-notebook-dragImage-height: 36px;
+  --jp-private-notebook-selected-color: var(--md-blue-400);
+  --jp-private-notebook-active-color: var(--md-green-400);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook
+|----------------------------------------------------------------------------*/
+
+/* stylelint-disable selector-max-class */
+
+.jp-NotebookPanel {
+  display: block;
+  height: 100%;
+}
+
+.jp-NotebookPanel.jp-Document {
+  min-width: 240px;
+  min-height: 120px;
+}
+
+.jp-Notebook {
+  padding: var(--jp-notebook-padding);
+  outline: none;
+  overflow: auto;
+  background: var(--jp-layout-color0);
+}
+
+.jp-Notebook.jp-mod-scrollPastEnd::after {
+  display: block;
+  content: '';
+  min-height: var(--jp-notebook-scroll-padding);
+}
+
+.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
+  contain: strict;
+}
+
+.jp-Notebook .jp-Cell {
+  overflow: visible;
+}
+
+.jp-Notebook .jp-Cell .jp-InputPrompt {
+  cursor: move;
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook state related styling
+|
+| The notebook and cells each have states, here are the possibilities:
+|
+| - Notebook
+|   - Command
+|   - Edit
+| - Cell
+|   - None
+|   - Active (only one can be active)
+|   - Selected (the cells actions are applied to)
+|   - Multiselected (when multiple selected, the cursor)
+|   - No outputs
+|----------------------------------------------------------------------------*/
+
+/* Command or edit modes */
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+/* cell is active */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
+  background: var(--jp-brand-color1);
+}
+
+/* cell is dirty */
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
+  color: var(--jp-warn-color1);
+}
+
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
+  color: var(--jp-warn-color1);
+  content: '•';
+}
+
+.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
+  background: var(--jp-warn-color1);
+}
+
+/* collapser is hovered */
+.jp-Notebook .jp-Cell .jp-Collapser:hover {
+  box-shadow: var(--jp-elevation-z2);
+  background: var(--jp-brand-color1);
+  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
+}
+
+/* cell is active and collapser is hovered */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
+/* Command mode */
+
+.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
+  background: var(--jp-notebook-multiselected-color);
+}
+
+.jp-Notebook.jp-mod-commandMode
+  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
+  background: transparent;
+}
+
+/* Edit mode */
+
+.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-cell-editor-active-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook drag and drop
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-cell.jp-mod-dropSource {
+  opacity: 0.5;
+}
+
+.jp-Notebook-cell.jp-mod-dropTarget,
+.jp-Notebook.jp-mod-commandMode
+  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
+  border-top-color: var(--jp-private-notebook-selected-color);
+  border-top-style: solid;
+  border-top-width: 2px;
+}
+
+.jp-dragImage {
+  display: block;
+  flex-direction: row;
+  width: var(--jp-private-notebook-dragImage-width);
+  height: var(--jp-private-notebook-dragImage-height);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+  overflow: visible;
+}
+
+.jp-dragImage-singlePrompt {
+  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
+}
+
+.jp-dragImage .jp-dragImage-content {
+  flex: 1 1 auto;
+  z-index: 2;
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  line-height: var(--jp-code-line-height);
+  padding: var(--jp-code-padding);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background-color);
+  color: var(--jp-content-font-color3);
+  text-align: left;
+  margin: 4px 4px 4px 0;
+}
+
+.jp-dragImage .jp-dragImage-prompt {
+  flex: 0 0 auto;
+  min-width: 36px;
+  color: var(--jp-cell-inprompt-font-color);
+  padding: var(--jp-code-padding);
+  padding-left: 12px;
+  font-family: var(--jp-cell-prompt-font-family);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: 1.9;
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+}
+
+.jp-dragImage-multipleBack {
+  z-index: -1;
+  position: absolute;
+  height: 32px;
+  width: 300px;
+  top: 8px;
+  left: 8px;
+  background: var(--jp-layout-color2);
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
+}
+
+/*-----------------------------------------------------------------------------
+| Cell toolbar
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookTools {
+  display: block;
+  min-width: var(--jp-sidebar-min-width);
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+
+  /* This is needed so that all font sizing of children done in ems is
+    * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  overflow: auto;
+}
+
+.jp-ActiveCellTool {
+  padding: 12px 0;
+  display: flex;
+}
+
+.jp-ActiveCellTool-Content {
+  flex: 1 1 auto;
+}
+
+.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
+  background: var(--jp-cell-editor-background);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0;
+  min-height: 29px;
+}
+
+.jp-ActiveCellTool .jp-InputPrompt {
+  min-width: calc(var(--jp-cell-prompt-width) * 0.75);
+}
+
+.jp-ActiveCellTool-CellContent > pre {
+  padding: 5px 4px;
+  margin: 0;
+  white-space: normal;
+}
+
+.jp-MetadataEditorTool {
+  flex-direction: column;
+  padding: 12px 0;
+}
+
+.jp-RankedPanel > :not(:first-child) {
+  margin-top: 12px;
+}
+
+.jp-KeySelector select.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+}
+
+.jp-KeySelector label,
+.jp-MetadataEditorTool label,
+.jp-NumberSetter label {
+  line-height: 1.4;
+}
+
+.jp-NotebookTools .jp-select-wrapper {
+  margin-top: 4px;
+  margin-bottom: 0;
+}
+
+.jp-NumberSetter input {
+  width: 100%;
+  margin-top: 4px;
+}
+
+.jp-NotebookTools .jp-Collapse {
+  margin-top: 16px;
+}
+
+/*-----------------------------------------------------------------------------
+| Presentation Mode (.jp-mod-presentationMode)
+|----------------------------------------------------------------------------*/
+
+.jp-mod-presentationMode .jp-Notebook {
+  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
+  --jp-code-font-size: var(--jp-code-presentation-font-size);
+}
+
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
+  flex: 0 0 110px;
+}
+
+/*-----------------------------------------------------------------------------
+| Side-by-side Mode (.jp-mod-sideBySide)
+|----------------------------------------------------------------------------*/
+.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
+  margin-top: 3em;
+  margin-bottom: 3em;
+  margin-left: 5%;
+  margin-right: 5%;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) min-content minmax(
+      0,
+      var(--jp-side-by-side-output-size)
+    );
+  grid-template-rows: auto minmax(0, 1fr) auto;
+  grid-template-areas:
+    'header header header'
+    'input handle output'
+    'footer footer footer';
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
+  grid-template-columns: minmax(0, 1fr) min-content minmax(
+      0,
+      var(--jp-side-by-side-resized-cell)
+    );
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
+  grid-area: header;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
+  grid-area: input;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
+  /* overwrite the default margin (no vertical separation needed in side by side move */
+  margin-top: 0;
+  grid-area: output;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
+  grid-area: footer;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
+  grid-area: handle;
+  user-select: none;
+  display: block;
+  height: 100%;
+  cursor: ew-resize;
+  padding: 0 var(--jp-cell-padding);
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
+  content: '';
+  display: block;
+  background: var(--jp-border-color2);
+  height: 100%;
+  width: 5px;
+}
+
+.jp-mod-sideBySide.jp-Notebook
+  .jp-CodeCell.jp-mod-resizedCell
+  .jp-CellResizeHandle::after {
+  background: var(--jp-border-color0);
+}
+
+.jp-CellResizeHandle {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-Placeholder {
+  padding-left: 55px;
+}
+
+.jp-Cell-Placeholder-wrapper {
+  background: #fff;
+  border: 1px solid;
+  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
+  border-radius: 4px;
+  -webkit-border-radius: 4px;
+  margin: 10px 15px;
+}
+
+.jp-Cell-Placeholder-wrapper-inner {
+  padding: 15px;
+  position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body {
+  background-repeat: repeat;
+  background-size: 50% auto;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+  background: #f6f7f8;
+  background-image: -webkit-linear-gradient(
+    left,
+    #f6f7f8 0%,
+    #edeef1 20%,
+    #f6f7f8 40%,
+    #f6f7f8 100%
+  );
+  background-repeat: no-repeat;
+  background-size: 800px 104px;
+  height: 104px;
+  position: absolute;
+  right: 15px;
+  left: 15px;
+  top: 15px;
+}
+
+div.jp-Cell-Placeholder-h1 {
+  top: 20px;
+  height: 20px;
+  left: 15px;
+  width: 150px;
+}
+
+div.jp-Cell-Placeholder-h2 {
+  left: 15px;
+  top: 50px;
+  height: 10px;
+  width: 100px;
+}
+
+div.jp-Cell-Placeholder-content-1,
+div.jp-Cell-Placeholder-content-2,
+div.jp-Cell-Placeholder-content-3 {
+  left: 15px;
+  right: 15px;
+  height: 10px;
+}
+
+div.jp-Cell-Placeholder-content-1 {
+  top: 100px;
+}
+
+div.jp-Cell-Placeholder-content-2 {
+  top: 120px;
+}
+
+div.jp-Cell-Placeholder-content-3 {
+  top: 140px;
+}
+
+</style>
+<style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
+    0 1px 1px 0 var(--jp-shadow-penumbra-color),
+    0 1px 3px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
+    0 2px 2px 0 var(--jp-shadow-penumbra-color),
+    0 1px 5px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
+    0 4px 5px 0 var(--jp-shadow-penumbra-color),
+    0 1px 10px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
+    0 6px 10px 0 var(--jp-shadow-penumbra-color),
+    0 1px 18px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
+    0 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
+    0 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
+    0 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
+    0 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
+    0 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-inverse-border-color: var(--md-grey-600);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+  --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
+    helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+  --jp-content-link-color: var(--md-blue-900);
+  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
+    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
+    'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
+  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+  --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
+  --jp-code-font-family: var(--jp-code-font-family-default);
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-code-presentation-font-size: 16px;
+
+  /* may need to tweak cursor width if you change font size */
+  --jp-code-cursor-width0: 1.4px;
+  --jp-code-cursor-width1: 2px;
+  --jp-code-cursor-width2: 4px;
+
+  /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+  --jp-layout-color0: white;
+  --jp-layout-color1: white;
+  --jp-layout-color2: var(--md-grey-200);
+  --jp-layout-color3: var(--md-grey-400);
+  --jp-layout-color4: var(--md-grey-600);
+
+  /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+  --jp-inverse-layout-color0: #111;
+  --jp-inverse-layout-color1: var(--md-grey-900);
+  --jp-inverse-layout-color2: var(--md-grey-800);
+  --jp-inverse-layout-color3: var(--md-grey-700);
+  --jp-inverse-layout-color4: var(--md-grey-600);
+
+  /* Brand/accent */
+
+  --jp-brand-color0: var(--md-blue-900);
+  --jp-brand-color1: var(--md-blue-700);
+  --jp-brand-color2: var(--md-blue-300);
+  --jp-brand-color3: var(--md-blue-100);
+  --jp-brand-color4: var(--md-blue-50);
+  --jp-accent-color0: var(--md-green-900);
+  --jp-accent-color1: var(--md-green-700);
+  --jp-accent-color2: var(--md-green-300);
+  --jp-accent-color3: var(--md-green-100);
+
+  /* State colors (warn, error, success, info) */
+
+  --jp-warn-color0: var(--md-orange-900);
+  --jp-warn-color1: var(--md-orange-700);
+  --jp-warn-color2: var(--md-orange-300);
+  --jp-warn-color3: var(--md-orange-100);
+  --jp-error-color0: var(--md-red-900);
+  --jp-error-color1: var(--md-red-700);
+  --jp-error-color2: var(--md-red-300);
+  --jp-error-color3: var(--md-red-100);
+  --jp-success-color0: var(--md-green-900);
+  --jp-success-color1: var(--md-green-700);
+  --jp-success-color2: var(--md-green-300);
+  --jp-success-color3: var(--md-green-100);
+  --jp-info-color0: var(--md-cyan-900);
+  --jp-info-color1: var(--md-cyan-700);
+  --jp-info-color2: var(--md-cyan-300);
+  --jp-info-color3: var(--md-cyan-100);
+
+  /* Cell specific styles */
+
+  --jp-cell-padding: 5px;
+  --jp-cell-collapser-width: 8px;
+  --jp-cell-collapser-min-height: 20px;
+  --jp-cell-collapser-not-active-hover-opacity: 0.6;
+  --jp-cell-editor-background: var(--md-grey-100);
+  --jp-cell-editor-border-color: var(--md-grey-300);
+  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-cell-editor-active-background: var(--jp-layout-color0);
+  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+  --jp-cell-prompt-width: 64px;
+  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+  --jp-cell-prompt-letter-spacing: 0;
+  --jp-cell-prompt-opacity: 1;
+  --jp-cell-prompt-not-active-opacity: 0.5;
+  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+
+  /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+  --jp-cell-inprompt-font-color: #307fc1;
+
+  /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+  --jp-cell-outprompt-font-color: #bf5b3d;
+
+  /* Notebook specific styles */
+
+  --jp-notebook-padding: 10px;
+  --jp-notebook-select-background: var(--jp-layout-color1);
+  --jp-notebook-multiselected-color: var(--md-blue-50);
+
+  /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+  --jp-notebook-scroll-padding: calc(
+    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+  );
+
+  /* Rendermime styles */
+
+  --jp-rendermime-error-background: #fdd;
+  --jp-rendermime-table-row-background: var(--md-grey-100);
+  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+  /* Dialog specific styles */
+
+  --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+  /* Console specific styles */
+
+  --jp-console-padding: 10px;
+
+  /* Toolbar specific styles */
+
+  --jp-toolbar-border-color: var(--jp-border-color1);
+  --jp-toolbar-micro-height: 8px;
+  --jp-toolbar-background: var(--jp-layout-color1);
+  --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
+  --jp-toolbar-header-margin: 4px 4px 0 4px;
+  --jp-toolbar-active-background: var(--md-grey-300);
+
+  /* Statusbar specific styles */
+
+  --jp-statusbar-height: 24px;
+
+  /* Input field styles */
+
+  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-input-active-background: var(--jp-layout-color1);
+  --jp-input-hover-background: var(--jp-layout-color1);
+  --jp-input-background: var(--md-grey-100);
+  --jp-input-border-color: var(--jp-inverse-border-color);
+  --jp-input-active-border-color: var(--jp-brand-color1);
+  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+  /* General editor styles */
+
+  --jp-editor-selected-background: #d9d9d9;
+  --jp-editor-selected-focused-background: #d7d4f0;
+  --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+  /* Code mirror specific styles */
+
+  --jp-mirror-editor-keyword-color: #008000;
+  --jp-mirror-editor-atom-color: #88f;
+  --jp-mirror-editor-number-color: #080;
+  --jp-mirror-editor-def-color: #00f;
+  --jp-mirror-editor-variable-color: var(--md-grey-900);
+  --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
+  --jp-mirror-editor-variable-3-color: #085;
+  --jp-mirror-editor-punctuation-color: #05a;
+  --jp-mirror-editor-property-color: #05a;
+  --jp-mirror-editor-operator-color: #a2f;
+  --jp-mirror-editor-comment-color: #408080;
+  --jp-mirror-editor-string-color: #ba2121;
+  --jp-mirror-editor-string-2-color: #708;
+  --jp-mirror-editor-meta-color: #a2f;
+  --jp-mirror-editor-qualifier-color: #555;
+  --jp-mirror-editor-builtin-color: #008000;
+  --jp-mirror-editor-bracket-color: #997;
+  --jp-mirror-editor-tag-color: #170;
+  --jp-mirror-editor-attribute-color: #00c;
+  --jp-mirror-editor-header-color: blue;
+  --jp-mirror-editor-quote-color: #090;
+  --jp-mirror-editor-link-color: #00c;
+  --jp-mirror-editor-error-color: #f00;
+  --jp-mirror-editor-hr-color: #999;
+
+  /*
+    RTC user specific colors.
+    These colors are used for the cursor, username in the editor,
+    and the icon of the user.
+  */
+
+  --jp-collaborator-color1: #ffad8e;
+  --jp-collaborator-color2: #dac83d;
+  --jp-collaborator-color3: #72dd76;
+  --jp-collaborator-color4: #00e4d0;
+  --jp-collaborator-color5: #45d4ff;
+  --jp-collaborator-color6: #e2b1ff;
+  --jp-collaborator-color7: #ff9de6;
+
+  /* Vega extension styles */
+
+  --jp-vega-background: white;
+
+  /* Sidebar-related styles */
+
+  --jp-sidebar-min-width: 250px;
+
+  /* Search-related styles */
+
+  --jp-search-toggle-off-opacity: 0.5;
+  --jp-search-toggle-hover-opacity: 0.8;
+  --jp-search-toggle-on-opacity: 1;
+  --jp-search-selected-match-background-color: rgb(245, 200, 0);
+  --jp-search-selected-match-color: black;
+  --jp-search-unselected-match-background-color: var(
+    --jp-inverse-layout-color0
+  );
+  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+  /* Icon colors that work well with light or dark backgrounds */
+  --jp-icon-contrast-color0: var(--md-purple-600);
+  --jp-icon-contrast-color1: var(--md-green-600);
+  --jp-icon-contrast-color2: var(--md-pink-600);
+  --jp-icon-contrast-color3: var(--md-blue-600);
+
+  /* Button colors */
+  --jp-accept-color-normal: var(--md-blue-700);
+  --jp-accept-color-hover: var(--md-blue-800);
+  --jp-accept-color-active: var(--md-blue-900);
+  --jp-warn-color-normal: var(--md-red-700);
+  --jp-warn-color-hover: var(--md-red-800);
+  --jp-warn-color-active: var(--md-red-900);
+  --jp-reject-color-normal: var(--md-grey-600);
+  --jp-reject-color-hover: var(--md-grey-700);
+  --jp-reject-color-active: var(--md-grey-800);
+
+  /* File or activity icons and switch semantic variables */
+  --jp-jupyter-icon-color: #f37626;
+  --jp-notebook-icon-color: #f37626;
+  --jp-json-icon-color: var(--md-orange-700);
+  --jp-console-icon-background-color: var(--md-blue-700);
+  --jp-console-icon-color: white;
+  --jp-terminal-icon-background-color: var(--md-grey-800);
+  --jp-terminal-icon-color: var(--md-grey-200);
+  --jp-text-editor-icon-color: var(--md-grey-700);
+  --jp-inspector-icon-color: var(--md-grey-700);
+  --jp-switch-color: var(--md-grey-400);
+  --jp-switch-true-position-color: var(--md-orange-900);
+}
+</style>
+<style type="text/css">
+/* Force rendering true colors when outputing to pdf */
+* {
+  -webkit-print-color-adjust: exact;
+}
+
+/* Misc */
+a.anchor-link {
+  display: none;
+}
+
+/* Input area styling */
+.jp-InputArea {
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  overflow: hidden;
+}
+
+.cm-editor.cm-s-jupyter .highlight pre {
+/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
+  padding: var(--jp-code-padding) 4px;
+  margin: 0;
+
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+  color: inherit;
+
+}
+
+.jp-OutputArea-output pre {
+  line-height: inherit;
+  font-family: inherit;
+}
+
+.jp-RenderedText pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+}
+
+/* Hiding the collapser by default */
+.jp-Collapser {
+  display: none;
+}
+
+@page {
+    margin: 0.5in; /* Margin for each printed piece of paper */
+}
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
+}
+</style>
+<!-- Load mathjax -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+<!-- MathJax configuration -->
+<script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+<!-- End of mathjax configuration --><script type="module">
+  document.addEventListener("DOMContentLoaded", async () => {
+    const diagrams = document.querySelectorAll(".jp-Mermaid > pre.mermaid");
+    // do not load mermaidjs if not needed
+    if (!diagrams.length) {
+      return;
+    }
+    const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.6.0/mermaid.esm.min.mjs")).default;
+    const parser = new DOMParser();
+
+    mermaid.initialize({
+      maxTextSize: 100000,
+      startOnLoad: false,
+      fontFamily: window
+        .getComputedStyle(document.body)
+        .getPropertyValue("--jp-ui-font-family"),
+      theme: document.querySelector("body[data-jp-theme-light='true']")
+        ? "default"
+        : "dark",
+    });
+
+    let _nextMermaidId = 0;
+
+    function makeMermaidImage(svg) {
+      const img = document.createElement("img");
+      const doc = parser.parseFromString(svg, "image/svg+xml");
+      const svgEl = doc.querySelector("svg");
+      const { maxWidth } = svgEl?.style || {};
+      const firstTitle = doc.querySelector("title");
+      const firstDesc = doc.querySelector("desc");
+
+      img.setAttribute("src", `data:image/svg+xml,${encodeURIComponent(svg)}`);
+      if (maxWidth) {
+        img.width = parseInt(maxWidth);
+      }
+      if (firstTitle) {
+        img.setAttribute("alt", firstTitle.textContent);
+      }
+      if (firstDesc) {
+        const caption = document.createElement("figcaption");
+        caption.className = "sr-only";
+        caption.textContent = firstDesc.textContent;
+        return [img, caption];
+      }
+      return [img];
+    }
+
+    async function makeMermaidError(text) {
+      let errorMessage = "";
+      try {
+        await mermaid.parse(text);
+      } catch (err) {
+        errorMessage = `${err}`;
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+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+<main>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="GA-2.4:-Beary-Icy">GA 2.4: Beary Icy<a class="anchor-link" href="#GA-2.4:-Beary-Icy">¶</a></h1><h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
+<style>
+        .markdown {width:100%; position: relative}
+        article { position: relative }
+    </style>
+<img alt="No description has been provided for this image" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="width:100px"/>
+<img alt="No description has been provided for this image" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="width:100px"/>
+</h1>
+<h2 style="height: 10px">
+</h2>
+<p><em><a href="http://mude.citg.tudelft.nl/">CEGM1000 MUDE</a>: Week 2.4, Time Series Analysis. For: December 6, 2024</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Winter is coming and it is time to start getting our models ready for the ice classic. Our first goal is to improve the temperature model, as that seems to be an important factor in determining breakup day. Temperature is notoriously hard to predict, but we can analyze historical data to get a better understanding of the patterns.</p>
+<p>In this assignment we will analyze a time series from a <strong>single year</strong>; in fact, only the <strong>first 152 days of the year</strong>, from January 1 until June 1. This is the period of interest for the ice classic, as the ice forms in this period, reaching its maximum thickness between January-March, and then starts melting, with breakup day typically happening in April or May.</p>
+<p>Remember that we have until April 5 to place a bet. Why, then do we want to fit a model several months beyond this point? This gives us confidence in assessing the ability of the model to predict temperature, so that when we use it on April 5 to make <strong>predictions</strong> about the future, we can understand the uncertainty associated with it.</p>
+<p>Let's start by loading the data and plotting it, then we will determine which components should be used to detrend it.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">statsmodels.graphics.tsaplots</span> <span class="kn">import</span> <span class="n">plot_acf</span>
+<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">chi2</span>
+<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">periodogram</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Part-1:-Load-the-data-and-plot-it">Part 1: Load the data and plot it<a class="anchor-link" href="#Part-1:-Load-the-data-and-plot-it">¶</a></h3>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 1.1:</b>
+<p>Do the following:</p>
+<ul>
+<li>load the data</li>
+<li>create time vector</li>
+<li>plot the data</li>
+</ul>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="c1"># Reading the data from the file</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">'temperature.csv'</span><span class="p">)</span>
+<span class="n">time_hours</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+<span class="n">time_days</span> <span class="o">=</span> <span class="n">time_hours</span> <span class="o">/</span> <span class="mi">24</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">time_days</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">time_days</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">fs</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">dt</span>
+
+<span class="c1"># Plotting the data</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Temperature data Nenana, Alaska'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="c1"># END SOLUTION BLOCK</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 1.2:</b>
+<p>Use the Markdown cell below to describe the data (you can use a few bullet points). For example, confirm relevant characteristics like number of points, units, describe the values (qualitatively), etc.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>Your answer here.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ul>
+<li>There are 24*152 points (hourly)</li>
+<li>Looks like there is a bit of variation around a central line, but the general increase in temperature throughout the spring is larger than the variation</li>
+<li>the general trend looks linear in the middle, but flattens out at the ends, which is to be expected with a periodic annual cycle</li>
+<li>looks like there is something "funny" happening around day 70 and 120</li>
+</ul>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Part-2:-Extract-the-Dominant-Patterns">Part 2: Extract the Dominant Patterns<a class="anchor-link" href="#Part-2:-Extract-the-Dominant-Patterns">¶</a></h2><p>We clearly see that the data contains a strong pattern (the general increase in temperature from winter to summer). We will start by fitting a functional model to the data in order to stationarize it. To find the frequency of the seasonal pattern we will use the power spectrum of the data.</p>
+<p>We will reuse the function <code>find_seasonal_pattern</code> from the workshop.</p>
+<p>Remember that for running this function we need to predefine the A-matrix to detrend the data. Since the data only contains the first 5 months of the year, we see that the temperature is increasing over time. What type of model would be most appropriate to remove the seasonal trend?</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 2.1:</b>
+<p>Define functions to help carry out this analysis, for example, <code>fit_model</code> and <code>find_frequency</code>.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to find the least squares solution of the data</span>
+<span class="sd">    data: input data</span>
+<span class="sd">    time: time vector</span>
+<span class="sd">    A: A-matrix to fit the data</span>
+<span class="sd">    plot: boolean to plot the results or not</span>
+<span class="sd">    '''</span>
+
+    <span class="c1"># x_hat = YOUR_CODE_HERE # least squares solution</span>
+    <span class="c1"># y_hat = YOUR_CODE_HERE # model prediction</span>
+    <span class="c1"># e_hat = YOUR_CODE_HERE # residuals</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="n">x_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">A</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">data</span><span class="p">)</span>
+    <span class="n">y_hat</span> <span class="o">=</span> <span class="n">A</span> <span class="o">@</span> <span class="n">x_hat</span>
+    <span class="n">e_hat</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="n">y_hat</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Data'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Estimated data'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Data vs Estimated data'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">e_hat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Residuals'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Residuals'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">x_hat</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">e_hat</span>
+
+<span class="k">def</span> <span class="nf">find_frequency</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">fs</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to find the dominant frequency of the signal</span>
+<span class="sd">    data: input data</span>
+<span class="sd">    time: time vector</span>
+<span class="sd">    A: A-matrix to detrend the data (prior to spectral analysis)</span>
+<span class="sd">    fs: sampling frequency</span>
+<span class="sd">    plot: boolean to plot the psd or not</span>
+<span class="sd">    '''</span>
+    <span class="c1"># Detrending the data</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">e_hat</span><span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
+
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="c1"># Finding the dominant frequency in e_hat</span>
+    <span class="c1"># freqs, pxx = periodogram(YOUR_CODE_HERE, fs=YOUR_CODE_HERE, window='boxcar',</span>
+    <span class="c1">#                          nfft=N, return_onesided=False,</span>
+    <span class="c1">#                          scaling='density')</span>
+    
+    <span class="c1"># SOLUTION</span>
+    <span class="c1"># Finding the dominant frequency in e_hat</span>
+    <span class="n">freqs</span><span class="p">,</span> <span class="n">pxx</span> <span class="o">=</span> <span class="n">periodogram</span><span class="p">(</span><span class="n">e_hat</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="s1">'boxcar'</span><span class="p">,</span>
+                                <span class="n">nfft</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">return_onesided</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                <span class="n">scaling</span><span class="o">=</span><span class="s1">'density'</span><span class="p">)</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+
+    <span class="c1"># finding the dominant frequency and amplitude</span>
+    <span class="c1"># Note: there are many ways to do this</span>
+    <span class="c1"># amplitude = YOUR_CODE_HERE # Amplitude of the dominant frequency</span>
+    <span class="c1"># dominant_frequency = YOUR_CODE_HERE # Dominant frequency</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="c1"># finding the dominant frequency and amplitude</span>
+    <span class="n">dominant_frequency</span><span class="p">,</span> <span class="n">amplitude</span> <span class="o">=</span> <span class="n">freqs</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pxx</span><span class="p">)],</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">pxx</span><span class="p">)</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+
+    <span class="c1"># Plotting the PSD</span>
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">e_hat</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Residuals'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Atmospheric Pressure [hPa]'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freqs</span><span class="p">[</span><span class="n">freqs</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">],</span> <span class="n">pxx</span><span class="p">[</span><span class="n">freqs</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'PSD of residuals'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Frequency'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'PSD'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Power Spectral Density'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">dominant_frequency</span><span class="p">,</span> <span class="n">amplitude</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Dominant Frequency'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s1">'log'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xscale</span><span class="p">(</span><span class="s1">'log'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">dominant_frequency</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 2.2:</b>
+<p>Now provide an A-matrix that removes the trend from the data. There are multiple answers that will work, but some are better than others.</p>
+<p>First, use the Markdown cell below to define your A-matrix and include a brief explanation justifying your choice.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>Your answer here.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ul>
+<li>The best model (chosen a priori) would be a periodic signal, as we know the temperature behaves this way.</li>
+<li>A linear model may approximate the increasing temperature well, but it is not going to fit the ends well.</li>
+<li>It turns out a power law model gives the best quantitative fit, but this would also be problematic if we need to extrapolate beyond the data range.</li>
+</ul>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 2.3:</b>
+<p>Now define the A-matrix in code and extract the seasonal pattern. Continue extracting components until the time series is stationary (you will then summarize your findings in the next task).</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+
+
+<span class="c1"># SOLUTION</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">time_days</span><span class="o">/</span><span class="mi">365</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">time_days</span><span class="o">/</span><span class="mi">365</span><span class="p">)))</span>
+<span class="n">dom_f</span> <span class="o">=</span> <span class="n">find_frequency</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time_days</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="n">fs</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Dominant Frequency: </span><span class="si">{</span><span class="n">dom_f</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+<span class="c1"># END SOLUTION BLOCK</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Dominant Frequency: 1.00
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 2.4:</b>
+<p>Describe your results for making the time series stationary. Include at least: a) the number and types of components used (and their parameters), b) how you decided to stop extracting components.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>Your answer here.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ul>
+<li>annual signal is obvious, then of course daily (definitley periodic)</li>
+<li>PSD after taking out the daily signal indicates this is enough</li>
+</ul>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Fitting-the-Functional-Model">Fitting the Functional Model<a class="anchor-link" href="#Fitting-the-Functional-Model">¶</a></h2><p>In the next cell we will fit the model to generate stationary residuals. Above, you may have a periodic signal, where for each dominant frequency $f_i$ ($i=1,2$) the model is:</p>
+$$a_i  \cos(2\pi f_i  t) + b_i  \sin(2\pi f_i t)$$<p>However, to report the periodic signals we would like to have the amplitude, phase shift and the frequency of those signals, which can be recovered from:
+$$A_i  \cos(2\pi f_i  t + \theta_i)$$
+Where the amplitude $A_i = \sqrt{a_i^2 + b_i^2}$ and $\theta_i = \arctan(-b_i/a_i)$</p>
+<p>Note: in Section 4.1 book this was shown where the angular frequency $\omega = 2\pi f$ was used.</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 2.5:</b>
+<p>Complete the code cell below to create the functional model.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">rewrite_seasonal_comp</span><span class="p">(</span><span class="n">ak</span><span class="p">,</span> <span class="n">bk</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to rewrite the seasonal component in terms of sin and cos</span>
+<span class="sd">    ak: seasonal component coefficient for cos</span>
+<span class="sd">    bk: seasonal component coefficient for sin</span>
+<span class="sd">    '''</span>
+    <span class="c1"># YOUR_CODE_HERE</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="n">Ak</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ak</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">bk</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">theta_k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="o">-</span><span class="n">bk</span><span class="p">,</span> <span class="n">ak</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">Ak</span><span class="p">,</span> <span class="n">theta_k</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+
+<span class="c1"># creating the A matrix of the functional model</span>
+<span class="c1"># A = YOUR_CODE_HERE</span>
+<span class="c1"># x_hat, y_hat, e_hat = YOUR_CODE_HERE</span>
+
+
+<span class="c1"># SOLUTION</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)),</span>
+                        <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="mi">1</span><span class="o">*</span><span class="n">time_days</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="mi">1</span><span class="o">*</span><span class="n">time_days</span><span class="p">),</span>
+                        <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">time_days</span><span class="o">/</span><span class="mi">365</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">time_days</span><span class="o">/</span><span class="mi">365</span><span class="p">)))</span>
+
+<span class="n">x_hat</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">e_hat0</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time_days</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
+<span class="c1"># END SOLUTION BLOCK</span>
+
+<span class="c1"># Plotting the data and the estimated trend</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Estimated trend'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Temperature data Nenana, Alaska'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="c1"># Plotting the residuals</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">e_hat0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Residuals'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Extracting the seasonal component coefficients from the estimated parameters</span>
+<span class="c1"># a_i = YOUR_CODE_HERE</span>
+<span class="c1"># b_i = YOUR_CODE_HERE</span>
+<span class="c1"># freqs = YOUR_CODE_HERE</span>
+
+
+<span class="c1"># SOLUTION</span>
+<span class="n">a_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_hat</span><span class="p">[</span><span class="mi">3</span><span class="p">]])</span>
+<span class="n">b_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x_hat</span><span class="p">[</span><span class="mi">4</span><span class="p">]])</span>
+<span class="n">freqs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">365</span><span class="p">])</span>
+<span class="c1"># END SOLUTION BLOCK</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Estimated Parameters:'</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x_hat</span><span class="p">)):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'x</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">x_hat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">The seasonal component is rewritten as:'</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">a_i</span><span class="p">,</span> <span class="n">b_i</span><span class="p">,</span> <span class="n">freqs</span><span class="p">):</span>
+    <span class="n">Ak</span><span class="p">,</span> <span class="n">theta_k</span> <span class="o">=</span> <span class="n">rewrite_seasonal_comp</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Ak = </span><span class="si">{</span><span class="n">Ak</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, theta_k = </span><span class="si">{</span><span class="n">theta_k</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, f_k = </span><span class="si">{</span><span class="n">k</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Estimated Parameters:
+x0 = -5.702
+x1 = 0.227
+x2 = -2.989
+x3 = -17.784
+x4 = 10.409
+
+The seasonal component is rewritten as:
+Ak = 2.998, theta_k = 1.495, f_k = 1.000
+Ak = 20.606, theta_k = -2.612, f_k = 0.003
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 2.6:</b>
+<p>Are the residuals stationary? State yes or no and describe why in the cell below.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>Your answer here.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>No! There is an offset...</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="Part-3:-Finding-the-grizzly">Part 3: Finding the grizzly<a class="anchor-link" href="#Part-3:-Finding-the-grizzly">¶</a></h1><p>When we look at the residuals after removing the periodic pattern(s), we see that there is still a pattern in the data. From researchers in the Nenana area we have heard that there is a grizzly bear that likes to take a nap (hibernate) in the area. We suspect that the grizzly bear has slept too close to the temperature sensor and has influenced the data.</p>
+<p>In the next cell we will write an offset detection algorithm to find the offset in the data. The offset detection algorithm is based on the likelihood ratio test framework. However, due to the presence of autocorrelation in the residuals, the traditional critical values for the likelihood ratio test are not valid. Therefore, we will use a bootstrap approach to estimate the critical values. Luckily, this is <strong>not</strong> the first time we had to remove a grizzly bear from our data, so we know that the estimated critical values is approximately 100.</p>
+<h2 id="The-offset-detection-algorithm">The offset detection algorithm<a class="anchor-link" href="#The-offset-detection-algorithm">¶</a></h2><p>The offset detection algorithm is based on the likelihood ratio test framework. The likelihood ratio test has a test statistic that is given by:</p>
+$$\Lambda = n \log \left( \frac{S_0}{S_1} \right)$$$$S_i = \sum_{i=1}^n (\hat{e}_i)^2$$<p>where $S_0$ is the sum of the squared residuals for the model without an offset, $S_1$ is the sum of the squared residuals for the model with an offset, and $n$ is the number of data points. The likelihood ratio test statistic is compared to a critical value to determine if an offset is present in the data.</p>
+<p>The cell below defines several functions which roughly accomplish the following:</p>
+<ol>
+<li>Calculate the sum of the squared residuals for the model without an offset, $S_0$.</li>
+<li>Calculate the sum of the squared residuals for the model with an offset at each data point, $S_1$.<ol>
+<li>For each data point we will calculate the sum of the squared residuals for the model with an offset at that data point.</li>
+<li>The A-matrix for the model with an offset is the same as the A-matrix for the model without an offset, but with an additional column that is 0 till the data point and 1 after the data point.</li>
+</ol>
+</li>
+<li>We will find the offset location that maximizes the likelihood ratio test statistic.</li>
+<li>We will include the offset in the model and repeat the process until the likelihood ratio test statistic is below the critical value.</li>
+</ol>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 3.1:</b>
+<p>Using the description above and the comments and docstring in the code, fill in the code below to complete the offset detection algorithm.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">A1_matrix</span><span class="p">(</span><span class="n">A0</span><span class="p">,</span> <span class="n">break_point</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to create the A1 matrix</span>
+<span class="sd">    A0: A matrix under H0</span>
+<span class="sd">    break_point: break point location</span>
+<span class="sd">    return: A1 matrix</span>
+<span class="sd">    A</span>
+<span class="sd">    '''</span>
+    <span class="c1"># create the new column and stack it to the A0 matrix</span>
+    <span class="c1"># YOUR_CODE_HERE</span>
+    
+    <span class="c1"># SOLUTION</span>
+    <span class="n">new_col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">A0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">new_col</span><span class="p">[</span><span class="n">break_point</span><span class="p">:]</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">A1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">A0</span><span class="p">,</span> <span class="n">new_col</span><span class="p">))</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+    <span class="k">return</span> <span class="n">A1</span>
+
+
+<span class="k">def</span> <span class="nf">LR</span><span class="p">(</span><span class="n">e0</span><span class="p">,</span> <span class="n">e1</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to perform the LR test</span>
+<span class="sd">    e0: residuals under H0</span>
+<span class="sd">    e1: residuals under H1</span>
+<span class="sd">    cv: critical value</span>
+<span class="sd">    '''</span>
+    <span class="c1"># n = YOUR_CODE_HERE</span>
+    <span class="c1"># SSR0 = YOUR_CODE_HERE</span>
+    <span class="c1"># SSR1 = YOUR_CODE_HERE</span>
+    <span class="c1"># test_stat = YOUR_CODE_HERE</span>
+    
+    <span class="c1"># SOLUTION</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">e0</span><span class="p">)</span>
+    <span class="n">SSR0</span> <span class="o">=</span> <span class="n">e0</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">e0</span>
+    <span class="n">SSR1</span> <span class="o">=</span> <span class="n">e1</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">e1</span>
+    <span class="n">test_stat</span> <span class="o">=</span> <span class="n">n</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">SSR0</span> <span class="o">/</span> <span class="n">SSR1</span><span class="p">)</span>
+    <span class="c1"># END SOLUTION</span>
+
+    <span class="k">if</span> <span class="n">test_stat</span> <span class="o">&gt;</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Test Statistic: </span><span class="si">{</span><span class="n">test_stat</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> &gt; Critical Value: </span><span class="si">{</span><span class="n">cv</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">'Reject the null hypothesis'</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Test Statistic: </span><span class="si">{</span><span class="n">test_stat</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> &lt; Critical Value: </span><span class="si">{</span><span class="n">cv</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">'Fail to reject the null hypothesis'</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">test_stat</span>
+
+<span class="k">def</span> <span class="nf">jump_detection</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to detect the jump in the data</span>
+<span class="sd">    data: input data</span>
+<span class="sd">    time: time vector</span>
+<span class="sd">    A: A matrix under H0</span>
+<span class="sd">    cv: critical value</span>
+<span class="sd">    plot: boolean to plot the results or not</span>
+<span class="sd">    '''</span>
+    <span class="c1"># initialize the results vector</span>
+    <span class="c1"># results = YOUR_CODE_HERE</span>
+    <span class="c1"># find the residuals under H0</span>
+    <span class="c1"># YOUR_CODE_HERE</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">e_hat0</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+
+    <span class="c1"># loop over the data points</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+        <span class="c1"># create the A1 matrix</span>
+        <span class="c1"># A1 = YOUR_CODE_HERE</span>
+
+        <span class="c1"># SOLUTION</span>
+        <span class="n">A1</span> <span class="o">=</span> <span class="n">A1_matrix</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+        <span class="c1"># END SOLUTION BLOCK</span>
+
+        <span class="c1"># We need this statement to avoid singular matrices</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">matrix_rank</span><span class="p">(</span><span class="n">A1</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">A1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># find the residuals under H1</span>
+            <span class="c1"># _, _, e_hat1 = YOUR_CODE_HERE</span>
+            <span class="c1"># test_stat = YOUR_CODE_HERE</span>
+            <span class="c1"># results[i] = YOUR_CODE_HERE</span>
+
+            <span class="c1"># SOLUTION</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">e_hat1</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A1</span><span class="p">)</span>
+            <span class="n">test_stat</span> <span class="o">=</span> <span class="n">LR</span><span class="p">(</span><span class="n">e_hat0</span><span class="p">,</span> <span class="n">e_hat1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_stat</span>
+            <span class="c1"># END SOLUTION BLOCK</span>
+
+    <span class="n">results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+    <span class="c1"># finding the offset location. </span>
+    <span class="c1"># Offset is the location where the test statistic is maximum</span>
+
+    <span class="c1"># location = YOUR_CODE_HERE</span>
+    <span class="c1"># value = YOUR_CODE_HERE</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="n">location</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+    <span class="n">value</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">location</span><span class="p">]</span>
+    <span class="c1"># END SOLUTION BLOCK</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">results</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">[</span><span class="n">location</span><span class="p">],</span> <span class="n">value</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'offset'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">time</span><span class="p">)],</span> <span class="p">[</span><span class="n">cv</span><span class="p">,</span> <span class="n">cv</span><span class="p">],</span> <span class="s1">'k--'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Critical Value'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Test Statistic'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'LR Test'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">location</span><span class="p">,</span> <span class="n">value</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 3.2:</b>
+<p>Now we will implement the offset detection algorithm by running the function to find the offset in the data. The function will provide figures from which you will be able to determine the offset.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># YOUR_CODE_HERE</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">A_offset</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+    <span class="n">break_point</span><span class="p">,</span> <span class="n">test_stat</span> <span class="o">=</span> <span class="n">jump_detection</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time_days</span><span class="p">,</span> <span class="n">A_offset</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Break Point: </span><span class="si">{</span><span class="n">break_point</span><span class="si">}</span><span class="s1"> with : </span><span class="si">{</span><span class="n">test_stat</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">test_stat</span> <span class="o">&lt;</span> <span class="mi">100</span><span class="p">:</span>
+        <span class="k">break</span>
+    <span class="n">A_offset</span> <span class="o">=</span> <span class="n">A1_matrix</span><span class="p">(</span><span class="n">A_offset</span><span class="p">,</span> <span class="n">break_point</span><span class="p">)</span> 
+<span class="c1"># END SOLUTION BLOCK</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 3.3:</b>
+<p>Write your chosen offset in the cell below (report both the size and location of the offset).</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>My offset is: ...</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 3.4:</b>
+<p>Once you have found the offset, identify the offset location and update your A-matrix to include it in the model. Then repeat the process until the likelihood ratio test statistic is below the critical value.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># A2 = YOUR_CODE_HERE</span>
+<span class="c1"># YOUR_CODE_HERE = fit_model(YOUR_CODE_HERE)</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">A2</span> <span class="o">=</span> <span class="n">A_offset</span>
+<span class="n">x_hat</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">e_hat0</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">time_days</span><span class="p">,</span> <span class="n">A2</span><span class="p">)</span>
+<span class="c1"># END SOLUTION (PART 1 of 2)</span>
+
+<span class="c1"># Plotting the data and the estimated trend</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Original data'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Estimated trend'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Temperature data Nenana, Alaska'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="c1"># Plotting the residuals</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time_days</span><span class="p">,</span> <span class="n">e_hat0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Residuals'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Extracting the seasonal component coefficients from the estimated parameters</span>
+<span class="c1"># a_i = YOUR_CODE_HERE</span>
+<span class="c1"># b_i = YOUR_CODE_HERE</span>
+<span class="c1"># freqs = YOUR_CODE_HERE</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">a_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_hat</span><span class="p">[</span><span class="mi">3</span><span class="p">]])</span>
+<span class="n">b_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x_hat</span><span class="p">[</span><span class="mi">4</span><span class="p">]])</span>
+<span class="n">freqs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">365</span><span class="p">])</span>
+<span class="c1"># END SOLUTION BLOCK</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Estimated Parameters:'</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x_hat</span><span class="p">)):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'x</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">x_hat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">'The seasonal component is rewritten as:'</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">a_i</span><span class="p">,</span> <span class="n">b_i</span><span class="p">,</span> <span class="n">freqs</span><span class="p">):</span>
+    <span class="n">Ak</span><span class="p">,</span> <span class="n">theta_k</span> <span class="o">=</span> <span class="n">rewrite_seasonal_comp</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Ak = </span><span class="si">{</span><span class="n">Ak</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, theta_k = </span><span class="si">{</span><span class="n">theta_k</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, f_k = </span><span class="si">{</span><span class="n">k</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Estimated Parameters:
+x0 = -1.022
+x1 = 0.225
+x2 = -3.002
+x3 = -19.707
+x4 = 2.315
+x5 = -6.076
+x6 = 4.487
+The seasonal component is rewritten as:
+Ak = 3.011, theta_k = 1.496, f_k = 1.000
+Ak = 19.842, theta_k = -3.025, f_k = 0.003
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 3.5:</b>
+<p>Use the Markdown cell below to summarize the number and value of all offsets that you found.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>Your answer here.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>Should be 2, at 70 and 120, with a value of around 5 (true values were around 6 and 4).</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Part-4:-Analyzing-the-residuals">Part 4: Analyzing the residuals<a class="anchor-link" href="#Part-4:-Analyzing-the-residuals">¶</a></h2><p>Now that we have our residuals we can fit an AR model to the residuals. We will start by plotting the ACF of the residuals. We will then fit an AR model to the residuals and report the parameters of the AR model. Using the likelihood ratio test framework we will determine the order of the AR model.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Lets start with the ACF plot</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plot_acf</span><span class="p">(</span><span class="n">e_hat0</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 4.1:</b>
+<p>Begin by completing the functions below to define AR(1) (hint: you did this on Wednesday).</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">AR1</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to find the AR(1) model of the given data</span>
+<span class="sd">    s: input data</span>
+<span class="sd">    return: x_hat, e_hat</span>
+<span class="sd">    '''</span>
+    <span class="c1"># y = YOUR_CODE_HERE</span>
+    <span class="c1"># y_lag_1 = YOUR_CODE_HERE</span>
+    <span class="c1"># A = np.atleast_2d(y_lag_1).T</span>
+    <span class="c1"># x_hat, y_hat, e_hat = fit_model(YOUR_CODE_HERE)</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+    <span class="n">y_lag_1</span> <span class="o">=</span> <span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">y_lag_1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">x_hat</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">e_hat</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
+    <span class="c1"># END SOLUTION</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Original Residuals'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Estimated Residuals'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Original Data vs Estimated Data'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plot_acf</span><span class="p">(</span><span class="n">e_hat</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">lags</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+        
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Estimated Parameters:'</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'phi = </span><span class="si">{</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">x_hat</span><span class="p">,</span> <span class="n">e_hat</span>
+
+<span class="c1"># Estimating the AR(1) model</span>
+<span class="c1"># phi_hat_ar1, e_hat_ar1 = AR1(YOUR_CODE_HERE)</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">phi_hat_ar1</span><span class="p">,</span> <span class="n">e_hat_ar1</span> <span class="o">=</span> <span class="n">AR1</span><span class="p">(</span><span class="n">e_hat0</span><span class="p">,</span> <span class="n">time_days</span><span class="p">)</span>
+<span class="c1"># END SOLUTION</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Estimated Parameters:
+phi = 0.8847
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 4.2:</b>
+<p>As you can see, the next task asks you to implement AR(2). State why this is necessary, using the results from the cell above.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>Your answer here.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>ACF at lag 1 not zero (or not within the confidence interval).</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 4.3:</b>
+<p>Now complete the functions to set up AR(2).</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">AR2</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">'''</span>
+<span class="sd">    Function to find the AR(2) model of the given data</span>
+<span class="sd">    s: input data</span>
+<span class="sd">    return: x_hat, e_hat</span>
+<span class="sd">    '''</span>
+    <span class="c1"># y = YOUR_CODE_HERE</span>
+    <span class="c1"># y_lag_1 = YOUR_CODE_HERE</span>
+    <span class="c1"># A = YOUR_CODE_HERE</span>
+    <span class="c1"># x_hat, y_hat, e_hat = fit_model(YOUR_CODE_HERE)</span>
+
+    <span class="c1"># SOLUTION</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>
+    <span class="n">y_lag_1</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">y_lag_2</span> <span class="o">=</span> <span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">y_lag_1</span><span class="p">,</span> <span class="n">y_lag_2</span><span class="p">))</span>
+    <span class="n">x_hat</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">e_hat</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">A</span><span class="p">)</span>
+    <span class="c1"># END SOLUTION</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Original Residuals'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Estimated Residuals'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Time [days]'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Temperature [°C]'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Original Data vs Estimated Data'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plot_acf</span><span class="p">(</span><span class="n">e_hat</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">lags</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Estimated Parameters:'</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'phi_1 = </span><span class="si">{</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">, phi_2 = </span><span class="si">{</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">x_hat</span><span class="p">,</span> <span class="n">e_hat</span>
+
+<span class="c1"># Estimating the AR(2) model</span>
+<span class="c1"># phi_hat_ar2, e_hat_ar2 = AR2(YOUR_CODE_HERE)</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">phi_hat_ar2</span><span class="p">,</span> <span class="n">e_hat_ar2</span> <span class="o">=</span> <span class="n">AR2</span><span class="p">(</span><span class="n">e_hat0</span><span class="p">,</span> <span class="n">time_days</span><span class="p">)</span>
+<span class="c1"># END SOLUTION</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Estimated Parameters:
+phi_1 = 0.7088, phi_2 = 0.1989
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Part-5:-Report-the-Results">Part 5: Report the Results<a class="anchor-link" href="#Part-5:-Report-the-Results">¶</a></h2><p><em>Note: you did this on Wednesday! It was optional then, so you are not expected to know this for the exam; however, you should implement the code using the WS as a template, and your interpretation at the end will be part of the grade for this assignment.</em></p>
+<p>Now that we have found the periodic signals in the data and fitted an AR model to the residuals, we can report the results. By combining including the AR (noise) process, we get residuals that are white noise. When the model hat white noise residuals, we can also report the confidence intervals of the model.</p>
+<p>We will use the unbiased estimate of the variance of the residuals to calculate the confidence intervals. The unbiased estimate of the variance is given by:</p>
+$$\hat{\sigma}^2 = \frac{1}{n-p} \sum_{t=1}^{n} \hat{e}_t^2$$<p>Where $n$ is the number of observations and $p$ is the number of parameters in the model.</p>
+<p>The covariance matrix of the parameters is given by:</p>
+$$\hat{\Sigma} = \hat{\sigma}^2 (\mathbf{A}^T \mathbf{A})^{-1}$$<p>Where $\mathbf{A}$ is the design matrix of the model.</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p> <b>Task 4.1:</b>
+<p>
+Complete the missing parts of the code cell below. Note that you will need to add one additional term, compared to Wednesday.
+</p>
+</p></div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># combine ar2 and functional model</span>
+
+<span class="c1"># A_final = YOUR_CODE_HERE</span>
+<span class="c1"># x_hat, y_hat, e_hat_final = fit_model(YOUR_CODE_HERE)</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">A_final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">A2</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">e_hat0</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">e_hat0</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
+<span class="n">x_hat</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">e_hat_final</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">time_days</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">A_final</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="c1"># END SOLUTION</span>
+
+<span class="c1"># Plottint the acf of the residuals</span>
+
+<span class="c1"># fig, ax = plt.subplots(1, 1, figsize=(10, 3))</span>
+<span class="c1"># plot_acf(YOUR_CODE_HERE, ax=ax, lags=20);</span>
+<span class="c1"># ax.grid()</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plot_acf</span><span class="p">(</span><span class="n">e_hat_final</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="c1"># END SOLUTION</span>
+
+<span class="c1"># # compute the standard errors</span>
+<span class="c1"># N = YOUR_CODE_HERE</span>
+<span class="c1"># p = YOUR_CODE_HERE</span>
+<span class="c1"># sigma2 = YOUR_CODE_HERE</span>
+<span class="c1"># Cov = YOUR_CODE_HERE</span>
+<span class="c1"># se = YOUR_CODE_HERE</span>
+
+<span class="c1"># # Extracting the seasonal component coefficients from the estimated parameters</span>
+<span class="c1"># a_i = YOUR_CODE_HERE</span>
+<span class="c1"># b_i = YOUR_CODE_HERE</span>
+<span class="c1"># freqs = YOUR_CODE_HERE</span>
+
+<span class="c1"># SOLUTION</span>
+<span class="c1"># compute the standard errors</span>
+<span class="n">N</span> <span class="o">=</span> <span class="n">A_final</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">p</span> <span class="o">=</span> <span class="n">A_final</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">sigma2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">e_hat_final</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="n">p</span><span class="p">)</span>
+<span class="n">Cov</span> <span class="o">=</span> <span class="n">sigma2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">A_final</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">A_final</span><span class="p">)</span>
+<span class="n">se</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">Cov</span><span class="p">))</span>
+
+<span class="c1"># Extracting the seasonal component coefficients from the estimated parameters</span>
+<span class="n">a_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_hat</span><span class="p">[</span><span class="mi">3</span><span class="p">]])</span>
+<span class="n">b_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_hat</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x_hat</span><span class="p">[</span><span class="mi">4</span><span class="p">]])</span>
+<span class="n">freqs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">365</span><span class="p">])</span>
+<span class="c1"># END SOLUTION</span>
+
+<span class="c1"># Check if the number of coefficients match the number of frequencies</span>
+<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">a_i</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">b_i</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">freqs</span><span class="p">),</span> <span class="s1">'The number of coefficients do not match'</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Estimated Parameters (standard deviation):'</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x_hat</span><span class="p">)):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'x</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">x_hat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="se">\t\t</span><span class="s1"> (</span><span class="si">{</span><span class="n">se</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">The seasonal component is rewritten as:'</span><span class="p">)</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">f</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">a_i</span><span class="p">,</span> <span class="n">b_i</span><span class="p">,</span> <span class="n">freqs</span><span class="p">):</span>
+    <span class="n">A_i</span><span class="p">,</span> <span class="n">theta_i</span> <span class="o">=</span> <span class="n">rewrite_seasonal_comp</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+    <span class="n">i</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'A_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">A_i</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, theta_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">theta_i</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">, f_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">f</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Estimated Parameters (standard deviation):
+x0 = -1.019		 (0.091)
+x1 = 0.225		 (0.021)
+x2 = -3.002		 (0.021)
+x3 = -19.714		 (0.074)
+x4 = 2.323		 (0.095)
+x5 = -6.074		 (0.076)
+x6 = 4.475		 (0.071)
+x7 = 0.709		 (0.016)
+x8 = 0.199		 (0.016)
+
+The seasonal component is rewritten as:
+A_1 = 3.011, theta_1 = 1.496, f_1 = 1.000
+A_2 = 19.851, theta_2 = -3.024, f_2 = 0.003
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Task 4.2:</b>
+<p>Now we have the complete functional model. Reflect on it's suitability for capturing the time dependent variation of temperature throughout the spring. Comment specifically on the time series components that were included and which ones have the most significant influence on the result.</p>
+<p>Comment also on the suitability of this model for predicting the temperature <strong>beyond the betting deadline of April 5</strong>, assuming that you have data up <strong>until</strong> that date. Remember that the ice typically breaks apart 2 to 6 weeks after the betting deadline.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>The model only includes "memory" for 2 points, which is 2 hours. However, from the ACF prior to removing the residuals, we see that the "effect" of the memory lasts around 20 points. This is 20 hours, so the  inclusion of AR(2) is useless and the functional model without these terms would be a fine best estimate for temperature on a given day several weeks in the future.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><em>End of the assignment.</em></p>
+</div>
+</div>
+</div>
+</div>
+</main>
+</body>
+</html>
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-- 
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