From 22436b10c22ca9ad288cae69e3a21172fe66777b Mon Sep 17 00:00:00 2001
From: josiewood <josiew0101@gmail.com>
Date: Tue, 14 Oct 2025 15:53:50 -0500
Subject: [PATCH 1/2] Analysis

This is the code I used to analyze the data we took with the Keysight MSO.
---
 tests/residual plot.py | 100 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 tests/residual plot.py

diff --git a/tests/residual plot.py b/tests/residual plot.py
new file mode 100644
index 0000000..c3dfc6f
--- /dev/null
+++ b/tests/residual plot.py	
@@ -0,0 +1,100 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import matplotlib as mpl
+
+fontsize = 12
+mpl.rcParams['axes.linewidth'] = 1.5
+mpl.rcParams['xtick.major.width'] = 1.5
+mpl.rcParams['ytick.major.width'] = 1.5
+mpl.rcParams['xtick.major.size'] = 7
+mpl.rcParams['ytick.major.size'] = 7
+mpl.rcParams['xtick.minor.width'] = 1.5
+mpl.rcParams['ytick.minor.width'] = 1.5
+mpl.rcParams['xtick.minor.size'] = 5
+mpl.rcParams['ytick.minor.size'] = 5
+mpl.rcParams["ytick.right"] = True
+mpl.rcParams["xtick.top"] = True
+mpl.rcParams["ytick.labelsize"] = fontsize
+mpl.rcParams["xtick.labelsize"] = fontsize
+mpl.rcParams['xtick.direction'] = 'in'
+mpl.rcParams['ytick.direction'] = 'in'
+mpl.rcParams['figure.autolayout'] = True
+
+run_number_list = (np.arange(22,26))
+#note: this is for the initial runs we did with the original firmware prior to Jason's PR. I used the same analysis
+#for all files, but just note the frequency range is limited to 100-200 MHz here, although we did test to 220 MHz when
+#we updated the firmware. I just edited the code in a separate, duplicate file to account for those higher frequencies.
+#note2: I added comments above some functions to explain what I am doing; hope that's helpful!
+
+#save the data in arrays
+def get_data(run_name):
+    datafile = r"C:\Users\josie\Prawnblaster_project\Data\trace_" + run_name + ".csv"
+    data = pd.read_csv(datafile)
+    xvalues = data['x-axis'].tolist()
+    yvalues = data['D0-D7'].tolist()
+    x_length, y_length = len(xvalues), len(yvalues)
+    x = np.array(xvalues[2:x_length]).astype(float)
+    y = np.array(yvalues[2:y_length]).astype(float)
+    return x,y
+
+#determine the frequency
+def square_wave_frequency(x,y):
+    rising_edges = np.where((y[:-1] < 1) & (y[1:] >= 1))[0]
+    if len(rising_edges) < 2:
+        raise ValueError("Not enough rising edges detected to estimate frequency")
+    else:
+        times = x[rising_edges]
+        periods = np.diff(times)
+        avg_period = np.mean(periods)
+        freq_std = np.std(1/periods) / np.sqrt(len(periods))
+        freq = 1.0 / avg_period
+    return freq, freq_std, periods
+
+#create residuals based on expected frequency values and errors
+residual_list = []
+error_list = []
+for run_number in run_number_list:
+    run_name = str(run_number)
+    x, y = get_data(run_name)
+    if 3 <= run_number <=7:
+        expected = 1.667e6
+    elif 8 <= run_number <= 12:
+        expected = 2.5e6
+    else:
+        expected = 3.33e6
+    freq_meas, freq_meas_unc, p = square_wave_frequency(x,y)
+    # plt.hist(p,bins=50)
+    # plt.show()
+    residuals = freq_meas - expected
+    residual_list.append(residuals)
+    error_list.append(freq_meas_unc)
+    # x_values = np.linspace(1, 100, 100)
+print(error_list)
+
+Freq_array = np.array([100,100,100,100,100,150,150,150,150,150,200,200,200,200,200])
+
+residual_list = np.array(residual_list)
+error_list = np.array(error_list)
+
+#Markers for each frequency
+numbers = np.array([100, 150, 200])
+unique = np.unique(numbers)
+markers = ['.', 'x', '*']
+
+run_number_list = run_number_list-3
+for i, num in enumerate(unique):
+    indices = np.where(Freq_array == num)
+    plt.scatter(run_number_list[indices], residual_list[indices], marker=markers[i], label=f'Frequency {num}', s=100,alpha=0.6)
+
+# plt.scatter(run_number_list,residual_list, s=12)
+plt.errorbar(run_number_list, residual_list,yerr= error_list,linestyle='none',capsize=5,color="black")
+plt.axhline(0, color='gray', linestyle='--')
+plt.xlabel("Run Number", fontsize=fontsize)
+plt.ylabel("Residuals (Hz)", fontsize=fontsize)
+# plt.ylim(-400,3500)
+plt.legend()
+plt.savefig("Residuals.png", dpi=150)
+plt.show()
+
+print(residual_list)
\ No newline at end of file

From 093a5f101edc666e2219549e980697ec52b421aa Mon Sep 17 00:00:00 2001
From: thomasleishner <181062267+thomasleishner@users.noreply.github.com>
Date: Tue, 14 Oct 2025 17:51:36 -0500
Subject: [PATCH 2/2] Added testing and analysis notebooks for measurements
 made on MSO

---
 .gitignore                                    |     1 +
 .../PrawnBlaster_Testing.ipynb                |   428 +
 .../absolute_edge_comparison.ipynb            |   605 +
 tests/{ => Testing with MSO}/residual plot.py |     0
 tests/Testing with MSO/scope_13.csv           |  2002 +
 tests/Testing with MSO/scope_19.csv           | 25002 +++++++
 tests/Testing with MSO/scope_20.csv           | 62502 ++++++++++++++++
 tests/Testing with MSO/scope_21.csv           | 20002 +++++
 8 files changed, 110542 insertions(+)
 create mode 100644 tests/Testing with MSO/PrawnBlaster_Testing.ipynb
 create mode 100644 tests/Testing with MSO/absolute_edge_comparison.ipynb
 rename tests/{ => Testing with MSO}/residual plot.py (100%)
 create mode 100755 tests/Testing with MSO/scope_13.csv
 create mode 100755 tests/Testing with MSO/scope_19.csv
 create mode 100755 tests/Testing with MSO/scope_20.csv
 create mode 100755 tests/Testing with MSO/scope_21.csv

diff --git a/.gitignore b/.gitignore
index 76a2601..0b8371f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 build*/*
 pico_sdk_import.cmake
+.DS_Store
\ No newline at end of file
diff --git a/tests/Testing with MSO/PrawnBlaster_Testing.ipynb b/tests/Testing with MSO/PrawnBlaster_Testing.ipynb
new file mode 100644
index 0000000..87037fd
--- /dev/null
+++ b/tests/Testing with MSO/PrawnBlaster_Testing.ipynb	
@@ -0,0 +1,428 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a94192ca",
+   "metadata": {},
+   "source": [
+    "# Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80fc506d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import serial\n",
+    "import numpy as np\n",
+    "\n",
+    "# To communicate with the pico, you will need to determine the port the pico\n",
+    "# is plugged into. On Windows, you can open device manager and look at the\n",
+    "# 'Ports (COM & LPT)' dropdown, where the pico will show up as 'USB Serial Device'\n",
+    "PICO_PORT = \"/dev/tty.usbmodem1101\"\n",
+    "\n",
+    "MHZ = 1000000\n",
+    "\n",
+    "\n",
+    "class PrawnBlasterClient:\n",
+    "    \"\"\"\n",
+    "    Helper class to setup functions for pico interface\n",
+    "\n",
+    "    Attributes:\n",
+    "        port (str): port pico is assigned to (e.g., 'COM4')\n",
+    "        baudrate (int): speed of data transmission to pico (default=115200 bps)\n",
+    "        timout (float): read timeout in seconds\n",
+    "        debug (bool): activate extra debug messages over serial (only 'on' or 'off')\n",
+    "        ser (): TODO\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, port=PICO_PORT, baudrate=115200, timeout=1, debug=True):\n",
+    "        \"\"\"Setup and initialize PrawnBlaster\"\"\"\n",
+    "        self.port = port\n",
+    "        self.baudrate = baudrate\n",
+    "        self.timeout = timeout\n",
+    "        self.debug = debug\n",
+    "        self.ser = None\n",
+    "\n",
+    "    def open(self, ):\n",
+    "        \"\"\"Open serial port for pico communication\"\"\"\n",
+    "        if self.ser and self.ser.is_open:\n",
+    "            return\n",
+    "        self.ser = serial.Serial(\n",
+    "            port=self.port, baudrate=self.baudrate, timeout=self.timeout)\n",
+    "        if self.debug:\n",
+    "            print(f\"Opened serial connection on {self.port}\")\n",
+    "\n",
+    "    def close(self):\n",
+    "        \"\"\"Close serial port for pico communication\"\"\"\n",
+    "        if self.ser and self.ser.is_open:\n",
+    "            self.ser.close()\n",
+    "            if self.debug:\n",
+    "                print(f\"Closed serial connection on {self.port}\")\n",
+    "\n",
+    "    def response(self):  # TODO: This does the same as end of send_command\n",
+    "        \"\"\"Retrieve the response from the pico\"\"\"\n",
+    "        if self.ser and self.ser.is_open:\n",
+    "            self.ser.readline()\n",
+    "\n",
+    "    def send_command(self, cmd: str, read_bytes=200) -> str:\n",
+    "        \"\"\"Send a command string and return the response\"\"\"\n",
+    "        if not self.ser or not self.ser.is_open:\n",
+    "            self.open()\n",
+    "        if self.debug:\n",
+    "            print(f\"> {cmd.strip()}\")\n",
+    "        if cmd[-2:] != '\\r\\n':\n",
+    "            cmd += '\\r\\n'\n",
+    "\n",
+    "        response = ''\n",
+    "        try:\n",
+    "            self.ser.write(cmd.encode())\n",
+    "            response = self.ser.read(read_bytes).decode(errors=\"ignore\")\n",
+    "            if self.debug:\n",
+    "                print(f\"< {response.strip()}\\n\")\n",
+    "        except Exception as e:\n",
+    "            print(\"Encountered Error: \", e)\n",
+    "        return response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13ea4373",
+   "metadata": {},
+   "source": [
+    "### Test Serial Communication with the Pico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "597db896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pb = PrawnBlasterClient()\n",
+    "\n",
+    "# debug mode within serial communication on or off:\n",
+    "debugmode = \"on\"\n",
+    "\n",
+    "# set number of psuedoclocks\n",
+    "numofclocks = 4\n",
+    "\n",
+    "pb.open()\n",
+    "pb.send_command('status\\r\\n')\n",
+    "pb.send_command(f'debug {debugmode}\\r\\n')\n",
+    "pb.send_command(f'setnumpseudoclocks {numofclocks}\\r\\n')\n",
+    "pb.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fcd3960",
+   "metadata": {},
+   "source": [
+    "### Set Frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1320539b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 0 - internal reference frequency; 1 - external reference frequency\n",
+    "mode = 0\n",
+    "\n",
+    "# set frequency in MHZ\n",
+    "frequencyMHZ = 200\n",
+    "freqHZ = frequencyMHZ * MHZ\n",
+    "\n",
+    "# change frequency, and check frequency/voltage\n",
+    "pb.open()\n",
+    "pb.send_command(f'setclock {mode} {freqHZ}\\r\\n')\n",
+    "pb.send_command('getfreqs\\r\\n')\n",
+    "pb.send_command('getvolts\\r\\n')\n",
+    "pb.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a2d4958",
+   "metadata": {},
+   "source": [
+    "### Basic Testing: Go High and Go Low"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51452166",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Testing Pico Logic\n",
+    "pb.open()\n",
+    "pb.send_command(cmd=\"go high 0/r/n\", read_bytes=10000)\n",
+    "pb.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "898c6fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pb.open()\n",
+    "pb.send_command(cmd=\"go low 0/r/n\", read_bytes=10000)\n",
+    "pb.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61fb4198",
+   "metadata": {},
+   "source": [
+    "# Tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cce51a5c",
+   "metadata": {},
+   "source": [
+    "### Test 1: Single Frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db6fa302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the parameters below as needed\n",
+    "# Set Psuedoclock (0 - 3)\n",
+    "psuedoclock = 0\n",
+    "\n",
+    "# Beginning Address (0 indexed)\n",
+    "baddr = 0\n",
+    "\n",
+    "# Ending Address\n",
+    "eaddr = 1\n",
+    "\n",
+    "# Stop Address (Set Equal to the last Ending Address)\n",
+    "saddr = 1\n",
+    "\n",
+    "# Set Half-period (5 - 2^32)\n",
+    "halfperiod = 30\n",
+    "\n",
+    "# Set Repetitions (0 - 2^32)\n",
+    "reps = 10000\n",
+    "\n",
+    "pb.open()\n",
+    "for i in range(baddr, eaddr):\n",
+    "    pb.send_command(f\"set {psuedoclock} {i} {halfperiod} {reps}\\n\\r\")\n",
+    "pb.send_command(f\"set {psuedoclock} {saddr} 0 0\\r\\n\")\n",
+    "pb.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "066a7464",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run instructions above\n",
+    "pb.open()\n",
+    "pb.send_command(cmd='start\\r\\n', read_bytes=10000)\n",
+    "pb.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c462b8c",
+   "metadata": {},
+   "source": [
+    "### Test 2: Multiple Frequencies without Waits\n",
+    "\n",
+    "Only run either 1, 2, or 3, as they will overwrite each other. Each one tests the pico in a different way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc887ef9",
+   "metadata": {},
+   "source": [
+    "#### 1. Full pulse sequency, no Waits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d131250c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instructions_noWaits = np.array([\n",
+    "    [100, 1],\n",
+    "    [6, 1],\n",
+    "    [101, 3],\n",
+    "    [6, 5],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [100, 5],\n",
+    "    [12, 3],\n",
+    "    [52, 4],\n",
+    "    [53, 4],\n",
+    "    [100000000, 1],\n",
+    "    [6, 1],\n",
+    "    [0, 0]\n",
+    "])\n",
+    "\n",
+    "instructions_noWaits_Lengths = np.prod(instructions_noWaits, axis=1)*10*2\n",
+    "instructions_noWaits_TotalLength = np.sum(instructions_noWaits_Lengths)\n",
+    "[_, instructions_noWaits_pulses] = np.sum(instructions_noWaits, axis=0)\n",
+    "print(f'Total length: {instructions_noWaits_TotalLength:d} ns')\n",
+    "print(\n",
+    "    f'Total number of pulses: {instructions_noWaits_pulses:d}, Edges: {instructions_noWaits_pulses*2:d}')\n",
+    "print(instructions_noWaits_Lengths)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37c5fcc1",
+   "metadata": {},
+   "source": [
+    "#### 2. Mid Length pulses only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5a1a127",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base = 90\n",
+    "instructions_noWaits = np.array([\n",
+    "    [base, 1],\n",
+    "    [base+1, 3],\n",
+    "    [base, 5],\n",
+    "    [base+20, 3],\n",
+    "    [base+21, 1],\n",
+    "    [base+20, 1],\n",
+    "    [base+21, 1],\n",
+    "    [base+20, 1],\n",
+    "    [base+21, 1],\n",
+    "    [base+25, 4],\n",
+    "    [base+28, 4],\n",
+    "    [0, 0]\n",
+    "])\n",
+    "\n",
+    "instructions_noWaits_Lengths = np.prod(instructions_noWaits, axis=1)*10*2\n",
+    "instructions_noWaits_TotalLength = np.sum(instructions_noWaits_Lengths)\n",
+    "[_, instructions_noWaits_pulses] = np.sum(instructions_noWaits, axis=0)\n",
+    "print(f'Total length: {instructions_noWaits_TotalLength:d} ns')\n",
+    "print(\n",
+    "    f'Total number of pulses: {instructions_noWaits_pulses:d}, Edges: {instructions_noWaits_pulses*2:d}')\n",
+    "print(instructions_noWaits_Lengths)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7da43321",
+   "metadata": {},
+   "source": [
+    "#### 3. Increasing Length Pulses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ebce32d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base = 1000000\n",
+    "instructions_noWaits = np.array([\n",
+    "    [base, 1],\n",
+    "    [base*2, 1],\n",
+    "    [base*5, 1],\n",
+    "    [base*10, 1],\n",
+    "    [base*20, 1],\n",
+    "    [base*50, 1],\n",
+    "    [base*100, 1],\n",
+    "    [0, 0]\n",
+    "])\n",
+    "\n",
+    "instructions_noWaits_Lengths = np.prod(instructions_noWaits, axis=1)*10*2\n",
+    "instructions_noWaits_TotalLength = np.sum(\n",
+    "    instructions_noWaits_Lengths, dtype=float)\n",
+    "[_, instructions_noWaits_pulses] = np.sum(instructions_noWaits, axis=0)\n",
+    "print(f'Total length: {instructions_noWaits_TotalLength:e} ns')\n",
+    "print(\n",
+    "    f'Total number of pulses: {instructions_noWaits_pulses:d}, Edges: {instructions_noWaits_pulses*2:d}')\n",
+    "print(instructions_noWaits_Lengths)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a256855c",
+   "metadata": {},
+   "source": [
+    "#### Send command sequence to pico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "874fc159",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# double check that you are sending the correct instructions\n",
+    "for i, line in enumerate(instructions_noWaits):\n",
+    "    print(f'set 0 {i:d} {line[0]:d} {line[1]:d}\\r\\n'.encode())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "982809bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pb.open()\n",
+    "\n",
+    "# do I need this full section? user could look at whether there is an ok after every command\n",
+    "for i, line in enumerate(instructions_noWaits):\n",
+    "    pb.send_command(f'set 0 {i:d} {line[0]:d} {line[1]:d}\\r\\n')\n",
+    "    # if pb.response() == b'ok\\r\\n':\n",
+    "    #     continue\n",
+    "    # else:\n",
+    "    #     print(f'Error on line {i:d}!')\n",
+    "    #     break\n",
+    "\n",
+    "pb.send_command(cmd='start\\r\\n', read_bytes=10000)\n",
+    "pb.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/Testing with MSO/absolute_edge_comparison.ipynb b/tests/Testing with MSO/absolute_edge_comparison.ipynb
new file mode 100644
index 0000000..183769e
--- /dev/null
+++ b/tests/Testing with MSO/absolute_edge_comparison.ipynb	
@@ -0,0 +1,605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b763b51a",
+   "metadata": {},
+   "source": [
+    "# Display MSO Data (Sanity Check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "afef648a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load the CSV file, skipping metadata lines\n",
+    "# Update path if needed\n",
+    "file_path = \"trace_17.csv\"\n",
+    "df = pd.read_csv(file_path, skiprows=2, header=None)\n",
+    "df.columns = [\"Time [s]\", \"Voltage [V]\"]\n",
+    "\n",
+    "# Convert columns to numeric, dropping rows with invalid data\n",
+    "df[\"Voltage [V]\"] = pd.to_numeric(df[\"Voltage [V]\"], errors='coerce')\n",
+    "df[\"Time [s]\"] = pd.to_numeric(df[\"Time [s]\"], errors='coerce')\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(df[\"Time [s]\"], df[\"Voltage [V]\"],\n",
+    "         label=\"Signal\", color=\"blue\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")  # TODO: this should be high/low, not voltage\n",
+    "plt.title(\"Scope Capture using digital input\")\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1abc5a56",
+   "metadata": {},
+   "source": [
+    "# Absolute Edge Comparison Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4df394c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyze_waveform_from_instructions(\n",
+    "    df, instructions, system_freq, start_time=None, edge=\"rising\"\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Compare observed digital waveform (from MSO) to expected waveform defined by instructions.\n",
+    "    Computes edge jitter and uncertainties.\n",
+    "    \"\"\"\n",
+    "    # extract waveform\n",
+    "    time = df.iloc[:, 0].values\n",
+    "    voltage = df.iloc[:, 1].values.astype(int)\n",
+    "\n",
+    "    # detect edges\n",
+    "    if edge == \"rising\":\n",
+    "        crossings = np.where(np.diff(voltage) == 1)[0]\n",
+    "    elif edge == \"falling\":\n",
+    "        crossings = np.where(np.diff(voltage) == -1)[0]\n",
+    "    else:\n",
+    "        raise ValueError(\"edge must be 'rising' or 'falling'\")\n",
+    "\n",
+    "    t_measured = time[crossings]\n",
+    "    if start_time is None:\n",
+    "        start_time = t_measured[0]\n",
+    "\n",
+    "    # construct expected edge times from instructions\n",
+    "    t_expected = [start_time]\n",
+    "    for halfperiod, reps in instructions:\n",
+    "        freq = system_freq / (halfperiod * 2)\n",
+    "        T = 1 / freq\n",
+    "        for _ in range(reps):\n",
+    "            t_expected.append(t_expected[-1] + T)\n",
+    "\n",
+    "    # NOTE: t_expected[0] - t_measured[0] = 0 always, so you can exclude it by uncommenting below\n",
+    "    # t_expected = np.array(t_expected[1:])\n",
+    "    # t_measured = np.array(t_measured[1:])\n",
+    "    t_expected = np.array(t_expected)\n",
+    "    t_measured = np.array(t_measured)\n",
+    "    min_len = min(len(t_measured), len(t_expected))\n",
+    "    t_measured = t_measured[:min_len]\n",
+    "    t_expected = t_expected[:min_len]\n",
+    "\n",
+    "    # edge jitter\n",
+    "    errors = t_measured - t_expected\n",
+    "    edge_jitter_mean = np.mean(errors)\n",
+    "    edge_jitter_std = np.std(errors, ddof=1)\n",
+    "\n",
+    "    # print summary\n",
+    "    print(f\"Detected {len(t_measured)} {edge} edges\")\n",
+    "    print(f\"Mean edge offset: {edge_jitter_mean*1e12:.2f} ps\")\n",
+    "    print(f\"Standard deviation of edge offset: {edge_jitter_std*1e12:.2f} ps\")\n",
+    "\n",
+    "    # determine sampling interval (s)\n",
+    "    delta_t = np.median(np.diff(time))\n",
+    "    sigma_sample = delta_t / np.sqrt(12)  # one-sigma uncertainty per edge (s)\n",
+    "\n",
+    "    # convert to picoseconds for plotting\n",
+    "    sigma_sample_ps = sigma_sample * 1e12\n",
+    "    errors_ps = errors * 1e12\n",
+    "\n",
+    "    # errorbar plot\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    x = np.arange(len(errors_ps))\n",
+    "    plt.errorbar(x, errors_ps, yerr=sigma_sample_ps, fmt='o', markersize=4,\n",
+    "                 ecolor='gray', elinewidth=1, capsize=2, label='Measured error ± sampling unc.')\n",
+    "\n",
+    "    edges_per_inst = [int(reps) for (_, reps) in instructions]\n",
+    "    boundaries = np.cumsum(edges_per_inst[:-1])\n",
+    "    for b in boundaries:\n",
+    "        plt.axvline(b, color='k', linestyle=':', alpha=0.4)\n",
+    "\n",
+    "    # frequency labels\n",
+    "    for i, b in enumerate(boundaries):\n",
+    "        freq = system_freq / (instructions[i+1][0] * 2)\n",
+    "        plt.text(b, plt.ylim()[1]*0.9, f\"{freq/1e6:.1f} MHz\",\n",
+    "                 rotation=90, va='top', ha='right', fontsize=8, color='gray')\n",
+    "    plt.title(\"Timing Error per Edge (with sampling uncertainty)\")\n",
+    "    plt.xlabel(\"Edge index\")\n",
+    "    plt.ylabel(\"Error [ps]\")\n",
+    "    plt.legend(bbox_to_anchor=(0.5, -0.2), loc='upper center', ncol=2)\n",
+    "    plt.grid(True)\n",
+    "\n",
+    "    # return everything else\n",
+    "    return {\n",
+    "        \"t_expected\": t_expected,\n",
+    "        \"t_measured\": t_measured,\n",
+    "        \"errors\": errors\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac82be83",
+   "metadata": {},
+   "source": [
+    "### Single Frequency Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37d9db16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected 33 rising edges\n",
+      "Mean edge offset: 0.00 ps\n",
+      "Standard deviation of edge offset: 0.00 ps\n",
+      "\n",
+      "t_expected:\n",
+      "[-5.000e-09  2.950e-07  5.950e-07  8.950e-07  1.195e-06  1.495e-06\n",
+      "  1.795e-06  2.095e-06  2.395e-06  2.695e-06  2.995e-06  3.295e-06\n",
+      "  3.595e-06  3.895e-06  4.195e-06  4.495e-06  4.795e-06  5.095e-06\n",
+      "  5.395e-06  5.695e-06  5.995e-06  6.295e-06  6.595e-06  6.895e-06\n",
+      "  7.195e-06  7.495e-06  7.795e-06  8.095e-06  8.395e-06  8.695e-06\n",
+      "  8.995e-06  9.295e-06  9.595e-06]\n",
+      "\n",
+      "t_measured:\n",
+      "[-5.000e-09  2.950e-07  5.950e-07  8.950e-07  1.195e-06  1.495e-06\n",
+      "  1.795e-06  2.095e-06  2.395e-06  2.695e-06  2.995e-06  3.295e-06\n",
+      "  3.595e-06  3.895e-06  4.195e-06  4.495e-06  4.795e-06  5.095e-06\n",
+      "  5.395e-06  5.695e-06  5.995e-06  6.295e-06  6.595e-06  6.895e-06\n",
+      "  7.195e-06  7.495e-06  7.795e-06  8.095e-06  8.395e-06  8.695e-06\n",
+      "  8.995e-06  9.295e-06  9.595e-06]\n",
+      "\n",
+      "errors:\n",
+      "[ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
+      "  0.00000000e+00  0.00000000e+00 -2.11758237e-22 -4.23516474e-22\n",
+      " -4.23516474e-22 -4.23516474e-22 -4.23516474e-22 -8.47032947e-22\n",
+      " -8.47032947e-22 -8.47032947e-22 -8.47032947e-22  0.00000000e+00\n",
+      "  0.00000000e+00  8.47032947e-22  8.47032947e-22  8.47032947e-22\n",
+      "  1.69406589e-21  1.69406589e-21  2.54109884e-21  2.54109884e-21\n",
+      "  2.54109884e-21  3.38813179e-21  3.38813179e-21  1.69406589e-21\n",
+      "  1.69406589e-21  1.69406589e-21  0.00000000e+00  0.00000000e+00\n",
+      "  0.00000000e+00]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the CSV file, skipping metadata lines\n",
+    "file_path = \"scope_13.csv\"  # Update path if needed\n",
+    "df = pd.read_csv(file_path, skiprows=2, header=None)\n",
+    "df.columns = [\"Time [s]\", \"Voltage [V]\"]\n",
+    "\n",
+    "# Convert columns to numeric, dropping rows with invalid data\n",
+    "df[\"Voltage [V]\"] = pd.to_numeric(df[\"Voltage [V]\"], errors='coerce')\n",
+    "df[\"Time [s]\"] = pd.to_numeric(df[\"Time [s]\"], errors='coerce')\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# change to the system clock of the analyzed run\n",
+    "sys_freq = 200e6\n",
+    "\n",
+    "# change to instructions for the analyzed run\n",
+    "instructions = np.array([\n",
+    "    [30, 10000]\n",
+    "])\n",
+    "\n",
+    "results = analyze_waveform_from_instructions(\n",
+    "    df=df, instructions=instructions, system_freq=sys_freq)\n",
+    "\n",
+    "print()\n",
+    "for key, value in results.items():\n",
+    "    print(f\"{key}:\")\n",
+    "    print(value)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6b2fe02",
+   "metadata": {},
+   "source": [
+    "### Multiple Frequency Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bd55291",
+   "metadata": {},
+   "source": [
+    "##### Full Pulse Length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d98a2151",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected 35 rising edges\n",
+      "Mean edge offset: -742.86 ps\n",
+      "Standard deviation of edge offset: 364.84 ps\n",
+      "\n",
+      "t_expected:\n",
+      "[-1.60000e-09  9.98400e-07  1.05840e-06  2.06840e-06  3.07840e-06\n",
+      "  4.08840e-06  4.14840e-06  4.20840e-06  4.26840e-06  4.32840e-06\n",
+      "  4.38840e-06  4.45840e-06  4.51840e-06  4.58840e-06  4.64840e-06\n",
+      "  4.71840e-06  4.77840e-06  4.84840e-06  4.90840e-06  5.90840e-06\n",
+      "  6.90840e-06  7.90840e-06  8.90840e-06  9.90840e-06  1.00284e-05\n",
+      "  1.01484e-05  1.02684e-05  1.07884e-05  1.13084e-05  1.18284e-05\n",
+      "  1.23484e-05  1.28784e-05  1.34084e-05  1.39384e-05  1.44684e-05]\n",
+      "\n",
+      "t_measured:\n",
+      "[-1.60000e-09  9.97600e-07  1.05760e-06  2.06800e-06  3.07760e-06\n",
+      "  4.08720e-06  4.14800e-06  4.20800e-06  4.26800e-06  4.32720e-06\n",
+      "  4.38800e-06  4.45760e-06  4.51760e-06  4.58800e-06  4.64720e-06\n",
+      "  4.71760e-06  4.77760e-06  4.84720e-06  4.90800e-06  5.90720e-06\n",
+      "  6.90720e-06  7.90800e-06  8.90800e-06  9.90720e-06  1.00272e-05\n",
+      "  1.01472e-05  1.02672e-05  1.07880e-05  1.13080e-05  1.18280e-05\n",
+      "  1.23480e-05  1.28776e-05  1.34072e-05  1.39376e-05  1.44680e-05]\n",
+      "\n",
+      "errors:\n",
+      "[ 0.0e+00 -8.0e-10 -8.0e-10 -4.0e-10 -8.0e-10 -1.2e-09 -4.0e-10 -4.0e-10\n",
+      " -4.0e-10 -1.2e-09 -4.0e-10 -8.0e-10 -8.0e-10 -4.0e-10 -1.2e-09 -8.0e-10\n",
+      " -8.0e-10 -1.2e-09 -4.0e-10 -1.2e-09 -1.2e-09 -4.0e-10 -4.0e-10 -1.2e-09\n",
+      " -1.2e-09 -1.2e-09 -1.2e-09 -4.0e-10 -4.0e-10 -4.0e-10 -4.0e-10 -8.0e-10\n",
+      " -1.2e-09 -8.0e-10 -4.0e-10]\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/kl/9ttltr511zj0xmdlptrtrjdw0000gp/T/ipykernel_46054/3542829079.py:27: RuntimeWarning: divide by zero encountered in divide\n",
+      "  freq = system_freq / (halfperiod * 2)\n",
+      "/var/folders/kl/9ttltr511zj0xmdlptrtrjdw0000gp/T/ipykernel_46054/3542829079.py:72: RuntimeWarning: divide by zero encountered in divide\n",
+      "  freq = system_freq / (instructions[i+1][0] * 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the CSV file, skipping metadata lines\n",
+    "# Update path if needed\n",
+    "file_path = \"scope_19.csv\"\n",
+    "df = pd.read_csv(file_path, skiprows=2, header=None)\n",
+    "df.columns = [\"Time [s]\", \"Voltage [V]\"]\n",
+    "\n",
+    "# Convert columns to numeric, dropping rows with invalid data\n",
+    "df[\"Voltage [V]\"] = pd.to_numeric(df[\"Voltage [V]\"], errors='coerce')\n",
+    "df[\"Time [s]\"] = pd.to_numeric(df[\"Time [s]\"], errors='coerce')\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# change to the system clock of the analyzed run\n",
+    "sys_freq = 200e6\n",
+    "\n",
+    "# change to instructions for the analyzed run\n",
+    "instructions = np.array([\n",
+    "    [100, 1],\n",
+    "    [6, 1],\n",
+    "    [101, 3],\n",
+    "    [6, 5],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [7, 1],\n",
+    "    [6, 1],\n",
+    "    [100, 5],\n",
+    "    [12, 3],\n",
+    "    [52, 4],\n",
+    "    [53, 4],\n",
+    "    [100000000, 1],\n",
+    "    [6, 1],\n",
+    "    [0, 0]\n",
+    "])\n",
+    "\n",
+    "results = analyze_waveform_from_instructions(\n",
+    "    df=df, instructions=instructions, system_freq=sys_freq)\n",
+    "\n",
+    "print()\n",
+    "for key, value in results.items():\n",
+    "    print(f\"{key}:\")\n",
+    "    print(value)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8d8f355",
+   "metadata": {},
+   "source": [
+    "##### Mid Length Pulses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7854b291",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected 25 rising edges\n",
+      "Mean edge offset: -224.00 ps\n",
+      "Standard deviation of edge offset: 260.26 ps\n",
+      "\n",
+      "t_expected:\n",
+      "[-1.60000e-09  8.98400e-07  1.80840e-06  2.71840e-06  3.62840e-06\n",
+      "  4.52840e-06  5.42840e-06  6.32840e-06  7.22840e-06  8.12840e-06\n",
+      "  9.22840e-06  1.03284e-05  1.14284e-05  1.25384e-05  1.36384e-05\n",
+      "  1.47484e-05  1.58484e-05  1.69584e-05  1.81084e-05  1.92584e-05\n",
+      "  2.04084e-05  2.15584e-05  2.27384e-05  2.39184e-05  2.50984e-05]\n",
+      "\n",
+      "t_measured:\n",
+      "[-1.60000e-09  8.98400e-07  1.80800e-06  2.71840e-06  3.62800e-06\n",
+      "  4.52800e-06  5.42800e-06  6.32800e-06  7.22800e-06  8.12800e-06\n",
+      "  9.22800e-06  1.03280e-05  1.14288e-05  1.25376e-05  1.36384e-05\n",
+      "  1.47480e-05  1.58480e-05  1.69584e-05  1.81080e-05  1.92584e-05\n",
+      "  2.04080e-05  2.15584e-05  2.27384e-05  2.39184e-05  2.50984e-05]\n",
+      "\n",
+      "errors:\n",
+      "[ 0.00000000e+00  0.00000000e+00 -4.00000000e-10  0.00000000e+00\n",
+      " -4.00000000e-10 -4.00000000e-10 -4.00000000e-10 -4.00000000e-10\n",
+      " -4.00000000e-10 -4.00000000e-10 -4.00000000e-10 -4.00000000e-10\n",
+      "  4.00000000e-10 -8.00000000e-10  1.69406589e-21 -4.00000000e-10\n",
+      " -4.00000000e-10  0.00000000e+00 -4.00000000e-10 -3.38813179e-21\n",
+      " -4.00000000e-10 -3.38813179e-21 -3.38813179e-21  0.00000000e+00\n",
+      "  0.00000000e+00]\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/kl/9ttltr511zj0xmdlptrtrjdw0000gp/T/ipykernel_46054/3542829079.py:27: RuntimeWarning: divide by zero encountered in divide\n",
+      "  freq = system_freq / (halfperiod * 2)\n",
+      "/var/folders/kl/9ttltr511zj0xmdlptrtrjdw0000gp/T/ipykernel_46054/3542829079.py:72: RuntimeWarning: divide by zero encountered in divide\n",
+      "  freq = system_freq / (instructions[i+1][0] * 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the CSV file, skipping metadata lines\n",
+    "# Update path if needed\n",
+    "file_path = \"scope_20.csv\"\n",
+    "df = pd.read_csv(file_path, skiprows=2, header=None)\n",
+    "df.columns = [\"Time [s]\", \"Voltage [V]\"]\n",
+    "\n",
+    "# Convert columns to numeric, dropping rows with invalid data\n",
+    "df[\"Voltage [V]\"] = pd.to_numeric(df[\"Voltage [V]\"], errors='coerce')\n",
+    "df[\"Time [s]\"] = pd.to_numeric(df[\"Time [s]\"], errors='coerce')\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# change to the system clock of the analyzed run\n",
+    "sys_freq = 200e6\n",
+    "\n",
+    "# change to instructions for the analyzed run\n",
+    "base = 90\n",
+    "instructions = np.array([\n",
+    "    [base, 1],\n",
+    "    [base+1, 3],\n",
+    "    [base, 5],\n",
+    "    [base+20, 3],\n",
+    "    [base+21, 1],\n",
+    "    [base+20, 1],\n",
+    "    [base+21, 1],\n",
+    "    [base+20, 1],\n",
+    "    [base+21, 1],\n",
+    "    [base+25, 4],\n",
+    "    [base+28, 4],\n",
+    "    [0, 0]\n",
+    "])\n",
+    "\n",
+    "\n",
+    "results = analyze_waveform_from_instructions(\n",
+    "    df=df, instructions=instructions, system_freq=sys_freq)\n",
+    "\n",
+    "print()\n",
+    "for key, value in results.items():\n",
+    "    print(f\"{key}:\")\n",
+    "    print(value)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f49c6e4c",
+   "metadata": {},
+   "source": [
+    "##### Increasing Pulse Length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "15f4fcff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected 7 rising edges\n",
+      "Mean edge offset: -0.00 ps\n",
+      "Standard deviation of edge offset: 0.00 ps\n",
+      "\n",
+      "t_expected:\n",
+      "[-1.000e-04  9.900e-03  2.990e-02  7.990e-02  1.799e-01  3.799e-01\n",
+      "  8.799e-01]\n",
+      "\n",
+      "t_measured:\n",
+      "[-1.000e-04  9.900e-03  2.990e-02  7.990e-02  1.799e-01  3.799e-01\n",
+      "  8.799e-01]\n",
+      "\n",
+      "errors:\n",
+      "[ 0.00000000e+00  0.00000000e+00 -3.46944695e-18  0.00000000e+00\n",
+      "  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/kl/9ttltr511zj0xmdlptrtrjdw0000gp/T/ipykernel_46054/3542829079.py:27: RuntimeWarning: divide by zero encountered in divide\n",
+      "  freq = system_freq / (halfperiod * 2)\n",
+      "/var/folders/kl/9ttltr511zj0xmdlptrtrjdw0000gp/T/ipykernel_46054/3542829079.py:72: RuntimeWarning: divide by zero encountered in divide\n",
+      "  freq = system_freq / (instructions[i+1][0] * 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the CSV file, skipping metadata lines\n",
+    "# Update path if needed\n",
+    "file_path = \"scope_21.csv\"\n",
+    "df = pd.read_csv(file_path, skiprows=2, header=None)\n",
+    "df.columns = [\"Time [s]\", \"Voltage [V]\"]\n",
+    "\n",
+    "# Convert columns to numeric, dropping rows with invalid data\n",
+    "df[\"Voltage [V]\"] = pd.to_numeric(df[\"Voltage [V]\"], errors='coerce')\n",
+    "df[\"Time [s]\"] = pd.to_numeric(df[\"Time [s]\"], errors='coerce')\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# change to the system clock of the analyzed run\n",
+    "sys_freq = 200e6\n",
+    "\n",
+    "# change to instructions for the analyzed run\n",
+    "base = 1000000\n",
+    "instructions = np.array([\n",
+    "    [base, 1],\n",
+    "    [base*2, 1],\n",
+    "    [base*5, 1],\n",
+    "    [base*10, 1],\n",
+    "    [base*20, 1],\n",
+    "    [base*50, 1],\n",
+    "    [base*100, 1],\n",
+    "    [0, 0]\n",
+    "])\n",
+    "\n",
+    "results = analyze_waveform_from_instructions(\n",
+    "    df=df, instructions=instructions, system_freq=sys_freq)\n",
+    "\n",
+    "print()\n",
+    "for key, value in results.items():\n",
+    "    print(f\"{key}:\")\n",
+    "    print(value)\n",
+    "    print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cphy",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/residual plot.py b/tests/Testing with MSO/residual plot.py
similarity index 100%
rename from tests/residual plot.py
rename to tests/Testing with MSO/residual plot.py
diff --git a/tests/Testing with MSO/scope_13.csv b/tests/Testing with MSO/scope_13.csv
new file mode 100755
index 0000000..69d679e
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+++ b/tests/Testing with MSO/scope_13.csv	
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diff --git a/tests/Testing with MSO/scope_19.csv b/tests/Testing with MSO/scope_19.csv
new file mode 100755
index 0000000..3e258b8
--- /dev/null
+++ b/tests/Testing with MSO/scope_19.csv	
@@ -0,0 +1,25002 @@
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diff --git a/tests/Testing with MSO/scope_20.csv b/tests/Testing with MSO/scope_20.csv
new file mode 100755
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