diff --git a/.mplstyle b/.mplstyle
index 59e4d9f..126b62f 100644
--- a/.mplstyle
+++ b/.mplstyle
@@ -1,17 +1,93 @@
-# Overall figure
+# AXES AND BOX
 axes.facecolor:     white   # axes background color
 axes.edgecolor:     black   # axes edge color
 axes.linewidth:     0.8     # edge line width
+axes.formatter.use_mathtext: True
+axes.formatter.limits: -3, 4
 
-axes.grid:          True   # display grid or not
+# GRID
+axes.grid:          False   # display grid or not
 axes.grid.axis:     both    # which axis the grid should apply to
-axes.grid.which:    both   # grid lines at {major, minor, both} ticks
-
+axes.grid.which:    major   # grid lines at {major, minor, both} ticks
 grid.linewidth: 0.2
 grid.color:         black
 
-xtick.top:True
-ytick.right:True
+# TICKS
+xtick.top:           True   # draw ticks on the top side
+xtick.bottom:        True    # draw ticks on the bottom side
+xtick.labeltop:      False   # draw label on the top
+xtick.labelbottom:   True    # draw label on the bottom
+xtick.major.size:    7     # major tick size in points
+xtick.minor.size:    3.5       # minor tick size in points
+xtick.major.width:   0.8     # major tick width in points
+xtick.minor.width:   0.4     # minor tick width in points
+xtick.major.pad:     3.5     # distance to major tick label in points
+xtick.minor.pad:     3.5     # distance to the minor tick label in points
+xtick.color:         black   # color of the ticks
+xtick.labelsize:     small  # font size of the tick labels
+xtick.direction:     in     # direction: {in, out, inout}
+xtick.minor.visible: True   # visibility of minor ticks on x-axis
+xtick.major.top:     True    # draw x axis top major ticks
+xtick.major.bottom:  True    # draw x axis bottom major ticks
+xtick.minor.top:     True    # draw x axis top minor ticks
+xtick.minor.bottom:  True    # draw x axis bottom minor ticks
+xtick.minor.ndivs:   auto    # number of minor ticks between the major ticks on x-axis
+xtick.alignment:     center  # alignment of xticks
+
+ytick.left:          True    # draw ticks on the left side
+ytick.right:         True   # draw ticks on the right side
+ytick.labelleft:     True    # draw tick labels on the left side
+ytick.labelright:    False   # draw tick labels on the right side
+ytick.major.size:    7     # major tick size in points
+ytick.minor.size:    3.5       # minor tick size in points
+ytick.major.width:   0.8     # major tick width in points
+ytick.minor.width:   0.4     # minor tick width in points
+ytick.major.pad:     3.5     # distance to major tick label in points
+ytick.minor.pad:     3.5     # distance to the minor tick label in points
+ytick.color:         black   # color of the ticks
+ytick.labelsize:     small  # font size of the tick labels
+ytick.direction:     in     # direction: {in, out, inout}
+ytick.minor.visible: True   # visibility of minor ticks on y-axis
+ytick.major.left:    True    # draw y axis left major ticks
+ytick.major.right:   True    # draw y axis right major ticks
+ytick.minor.left:    True    # draw y axis left minor ticks
+ytick.minor.right:   True    # draw y axis right minor ticks
+ytick.minor.ndivs:   auto    # number of minor ticks between the major ticks on y-axis
+ytick.alignment:     center_baseline  # alignment of yticks
+
+# TEXT
+text.parse_math: True
+
+# TITLE
+axes.titlelocation: left
+axes.titlesize:     large   # font size of the axes title
+axes.titleweight:   normal  # font weight of title
+
+# LEGEND
+legend.loc:           best
+legend.frameon:       True     # if True, draw the legend on a background patch
+legend.framealpha:    0.8      # legend patch transparency
+legend.facecolor:     inherit  # inherit from axes.facecolor; or color spec
+legend.edgecolor:     black      # background patch boundary color
+patch.linewidth:     0.6    # line width of the legend frame, None means inherit from patch.linewidth
+legend.fancybox:      False     # if True, use a rounded box for the
+                                # legend background, else a rectangle
+legend.shadow:        False    # if True, give background a shadow effect
+legend.numpoints:     1        # the number of marker points in the legend line
+legend.scatterpoints: 1        # number of scatter points
+legend.markerscale:   1.0      # the relative size of legend markers vs. original
+legend.fontsize:      small
+legend.labelcolor:    None
+legend.title_fontsize: None    # None sets to the same as the default axes.
+
+## Dimensions as fraction of font size:
+legend.borderpad:     0.4  # border whitespace
+legend.labelspacing:  0.4  # the vertical space between the legend entries
+legend.handlelength:  2.0  # the length of the legend lines
+legend.handleheight:  0.7  # the height of the legend handle
+legend.handletextpad: 0.8  # the space between the legend line and legend text
+legend.borderaxespad: 1.4  # the border between the axes and legend edge
+legend.columnspacing: 1.0  # column separation
 
 #### MATPLOTLIBRC FORMAT
 
diff --git a/run/202607023_OFR_Analysis_01.ipynb b/run/202607023_OFR_Analysis_01.ipynb
index cf2ba75..16c0bf8 100644
--- a/run/202607023_OFR_Analysis_01.ipynb
+++ b/run/202607023_OFR_Analysis_01.ipynb
@@ -5,106 +5,265 @@
    "id": "36f6dc0f",
    "metadata": {},
    "source": [
-    "### OFR Analysis"
+    "# OFR Analysis: File `20260723_OFR_Analysis_01.ipynb`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 137,
    "id": "8769766b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "    <style>\n",
-       "        .bk-notebook-logo {\n",
-       "            display: block;\n",
-       "            width: 20px;\n",
-       "            height: 20px;\n",
-       "            background-image: url(data:image/png;base64,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);\n",
-       "        }\n",
-       "    </style>\n",
-       "    <div>\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
-       "        <span id=\"ba7b56ac-409d-4c6f-803b-da270f9806de\">Loading BokehJS ...</span>\n",
-       "    </div>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "'use strict';\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    function drop(id) {\n      const view = Bokeh.index.get_by_id(id)\n      if (view != null) {\n        view.model.document.clear()\n        Bokeh.index.delete(view)\n      }\n    }\n\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n\n    // Clean up Bokeh references\n    if (id != null) {\n      drop(id)\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim()\n            drop(id)\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(\"ba7b56ac-409d-4c6f-803b-da270f9806de\");\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? error.toString();\n        wrapper.append(content);\n        el.append(wrapper);\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(() => display_loaded(error), 100);\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.9.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.9.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.9.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.9.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.9.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n      try {\n            for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n\n      } catch (error) {display_loaded(error);throw error;\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ba7b56ac-409d-4c6f-803b-da270f9806de\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
-      "application/vnd.bokehjs_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Imports\n",
     "\n",
     "from defs.computing import OFRSample, OFRAnalyser\n",
     "import numpy as np\n",
     "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('../.mplstyle')\n",
+    "from matplotlib.ticker import MultipleLocator\n",
+    "import pandas as pd\n",
     "\n",
-    "from bokeh.plotting import figure, show\n",
-    "from bokeh.io import output_notebook\n",
-    "output_notebook()"
+    "# Specify the source file for this analysis\n",
+    "source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "3922e80c",
+   "cell_type": "markdown",
+   "id": "ee2e9af5",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Path to the source file\n",
-    "source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full.txt\")"
+    "### First we plot a sample spectrum to get a feeling of what the data looks like:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "id": "76a9b026",
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "unexpected attribute 'show' to figure, possible attributes are above, align, aspect_ratio, aspect_scale, attribution, background_fill_alpha, background_fill_color, background_hatch_alpha, background_hatch_color, background_hatch_extra, background_hatch_pattern, background_hatch_scale, background_hatch_weight, below, border_fill_alpha, border_fill_color, border_hatch_alpha, border_hatch_color, border_hatch_extra, border_hatch_pattern, border_hatch_scale, border_hatch_weight, border_line_alpha, border_line_cap, border_line_color, border_line_dash, border_line_dash_offset, border_line_join, border_line_width, center, context_menu, css_classes, css_variables, disabled, elements, extra_x_ranges, extra_x_scales, extra_y_ranges, extra_y_scales, flow_mode, frame_align, frame_height, frame_width, height, height_policy, hidpi, hold_render, html_attributes, html_id, inner_height, inner_width, js_event_callbacks, js_property_callbacks, left, lod_factor, lod_interval, lod_threshold, lod_timeout, margin, match_aspect, max_height, max_width, min_border, min_border_bottom, min_border_left, min_border_right, min_border_top, min_height, min_width, name, outer_height, outer_width, outline_line_alpha, outline_line_cap, outline_line_color, outline_line_dash, outline_line_dash_offset, outline_line_join, outline_line_width, output_backend, renderers, reset_policy, resizable, right, sizing_mode, styles, stylesheets, subscribed_events, syncable, tags, title, title_location, toolbar, toolbar_inner, toolbar_location, toolbar_sticky, visible, width, width_policy, window_axis, x_range, x_scale, y_range or y_scale",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[47]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m      5\u001b[39m \n\u001b[32m      6\u001b[39m p = figure(width=\u001b[32m600\u001b[39m, height=\u001b[32m400\u001b[39m, x_axis_type=\u001b[33m'log'\u001b[39m)\n\u001b[32m      7\u001b[39m p.line(np.power(\u001b[32m10\u001b[39m,sample.Count.var), sample.Count.fct)\n\u001b[32m      8\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m p.show()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\tschan_d\\AppData\\Local\\miniforge3\\envs\\MA\\Lib\\site-packages\\bokeh\\core\\has_props.py:373\u001b[39m, in \u001b[36mHasProps.__getattr__\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m    370\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(descriptor, \u001b[38;5;28mproperty\u001b[39m): \u001b[38;5;66;03m# Python property\u001b[39;00m\n\u001b[32m    371\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getattribute__\u001b[39m(name)\n\u001b[32m--> \u001b[39m\u001b[32m373\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_raise_attribute_error_with_matches\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mname\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mproperties\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\tschan_d\\AppData\\Local\\miniforge3\\envs\\MA\\Lib\\site-packages\\bokeh\\core\\has_props.py:386\u001b[39m, in \u001b[36mHasProps._raise_attribute_error_with_matches\u001b[39m\u001b[34m(self, name, properties)\u001b[39m\n\u001b[32m    382\u001b[39m     matches, text = \u001b[38;5;28msorted\u001b[39m(properties), \u001b[33m\"\u001b[39m\u001b[33mpossible\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    384\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutil\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrings\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m nice_join\n\u001b[32m--> \u001b[39m\u001b[32m386\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33munexpected attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m attributes are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnice_join(matches)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[31mAttributeError\u001b[39m: unexpected attribute 'show' to figure, possible attributes are above, align, aspect_ratio, aspect_scale, attribution, background_fill_alpha, background_fill_color, background_hatch_alpha, background_hatch_color, background_hatch_extra, background_hatch_pattern, background_hatch_scale, background_hatch_weight, below, border_fill_alpha, border_fill_color, border_hatch_alpha, border_hatch_color, border_hatch_extra, border_hatch_pattern, border_hatch_scale, border_hatch_weight, border_line_alpha, border_line_cap, border_line_color, border_line_dash, border_line_dash_offset, border_line_join, border_line_width, center, context_menu, css_classes, css_variables, disabled, elements, extra_x_ranges, extra_x_scales, extra_y_ranges, extra_y_scales, flow_mode, frame_align, frame_height, frame_width, height, height_policy, hidpi, hold_render, html_attributes, html_id, inner_height, inner_width, js_event_callbacks, js_property_callbacks, left, lod_factor, lod_interval, lod_threshold, lod_timeout, margin, match_aspect, max_height, max_width, min_border, min_border_bottom, min_border_left, min_border_right, min_border_top, min_height, min_width, name, outer_height, outer_width, outline_line_alpha, outline_line_cap, outline_line_color, outline_line_dash, outline_line_dash_offset, outline_line_join, outline_line_width, output_backend, renderers, reset_policy, resizable, right, sizing_mode, styles, stylesheets, subscribed_events, syncable, tags, title, title_location, toolbar, toolbar_inner, toolbar_location, toolbar_sticky, visible, width, width_policy, window_axis, x_range, x_scale, y_range or y_scale"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# First load and get the data of sample 33 (arbitrary) and create a sample object\n",
+    "sample_number = 33\n",
+    "sample = OFRSample(source_file = source_file, sample_number = sample_number)\n",
+    "\n",
+    "#Plotting and saying the data\n",
+    "fig = plt.figure(figsize=(10,6), constrained_layout = True)\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.set_xscale('log')\n",
+    "ax_2 = plt.twinx(ax)\n",
+    "\n",
+    "ax.plot(np.power(10, sample.Count.var), sample.Count.fct, color='red', label='$dN/dlogD$')\n",
+    "ax_2.plot(np.power(10, sample.Mass.var), sample.Mass.fct / 1E12, color='blue', label='$dm/dlogD$')\n",
+    "\n",
+    "ax.set_title('2026-06-12 SET02/EXP01: Sample Nr. ' + str(sample_number))\n",
+    "ax.set_xlabel('Particle Diameter $D$ $(nm)$')\n",
+    "ax.set_ylabel('Number Denisty $dN/dlogD$ $(cm^{-3})$')\n",
+    "ax_2.set_ylabel('Mass Denisty $dm/dlogD$ $(mg cm^{-3})$')\n",
+    "\n",
+    "ax.set_xlim([20, 1100])\n",
+    "ax.set_ylim([0, 1200000])\n",
+    "ax_2.set_ylim([0, 18])\n",
+    "\n",
+    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
+    "lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
+    "\n",
+    "ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
+    "\n",
+    "plt.savefig(\"20260723_OFR_Analysis_01_Spectrum_Sample_\" + str(sample_number) + \".pdf\", format='pdf', dpi=400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "041fc7b6",
+   "metadata": {},
+   "source": [
+    "## Now let's load the data for the entire sampling experiment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "8db19b57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Sample  Number (#/cm^3)  Length (mm/cm^3)  Surface (mm^2/cm^3)  \\\n",
+      "0         1     3.090062e+00          0.001888         1.241420e-05   \n",
+      "1         2     3.454977e+00          0.000126         3.560755e-08   \n",
+      "2         3     9.325444e+00          0.001036         8.623624e-07   \n",
+      "3         4     4.171921e+02          0.402549         2.812996e-03   \n",
+      "4         5     1.052317e+05          6.641894         4.353249e-03   \n",
+      "..      ...              ...               ...                  ...   \n",
+      "151     152     9.529171e+05        177.681731         3.199811e-01   \n",
+      "152     153     1.017064e+06        181.659088         3.188865e-01   \n",
+      "153     154     1.041903e+06        186.626996         3.273445e-01   \n",
+      "154     155     1.008587e+06        181.815120         3.214411e-01   \n",
+      "155     156     9.957403e+05        182.039508         3.234802e-01   \n",
+      "\n",
+      "     Volume (μm^3/cm^3)  Density (pg/μm^3)  Mass (mg/cm^3)  Time (s)  \n",
+      "0              0.836666                1.2    1.003999e-03     180.0  \n",
+      "1              0.000109                1.2    1.302891e-07     360.0  \n",
+      "2              0.007271                1.2    8.725170e-06     540.0  \n",
+      "3            196.905653                1.2    2.362868e-01     720.0  \n",
+      "4             38.618096                1.2    4.634172e-02     900.0  \n",
+      "..                  ...                ...             ...       ...  \n",
+      "151         7645.085680                1.2    9.174103e+00   27360.0  \n",
+      "152         7524.532677                1.2    9.029439e+00   27540.0  \n",
+      "153         7732.212330                1.2    9.278655e+00   27720.0  \n",
+      "154         7646.483862                1.2    9.175781e+00   27900.0  \n",
+      "155         7694.993582                1.2    9.233992e+00   28080.0  \n",
+      "\n",
+      "[156 rows x 8 columns]\n"
      ]
     }
    ],
    "source": [
-    "# Plotting one of the spectra to show properties\n",
+    "sample_number_list = [int(i) for i in range(1, 157)]\n",
     "\n",
-    "# first load and get the data of sample 57 (arbitrary)\n",
-    "sample = OFRSample(source_file = source_file, sample_number = 33)\n",
+    "analyser = OFRAnalyser(source_file = source_file, sample_number_list=sample_number_list)\n",
+    "analyser.Analyse()\n",
     "\n",
-    "p = figure(width=600, height=400, x_axis_type='log')\n",
-    "p.line(sample.Count.var, sample.Count.fct)\n",
+    "print(analyser.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a2ae595",
+   "metadata": {},
+   "source": [
+    "We first plot the overall timeseries of total aerosol count and mass:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "a4477003",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax_2 = plt.twinx(ax)\n",
     "\n",
-    "p.show()"
+    "ax.plot(analyser.data['Time (s)'], analyser.data['Number (#/cm^3)'], color='red', label='Number')\n",
+    "ax_2.plot(analyser.data['Time (s)'], analyser.data['Mass (mg/cm^3)'], color='blue', label='Mass')\n",
+    "\n",
+    "ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview ' + str(sample_number))\n",
+    "ax.set_xlabel('Time $(s)$')\n",
+    "ax.set_ylabel('Number Density $(cm^{-3})$')\n",
+    "ax_2.set_ylabel('Mass Denisty $(mg cm^{-3})$')\n",
+    "ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n",
+    "ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n",
+    "\n",
+    "ax.set_xlim([0, 30000])\n",
+    "ax.set_ylim([0, 1.2E6])\n",
+    "ax_2.set_ylim([0, 15])\n",
+    "\n",
+    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
+    "lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
+    "\n",
+    "ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
+    "\n",
+    "plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview.pdf\", format='pdf', dpi=400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9555beb6",
+   "metadata": {},
+   "source": [
+    "Clearly, something is happening at around 5000 s, 12000s, and after 17000s. This corresponds to shifts in the flow rates... These are read from disk and depicted below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "dec710d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reading from disk\n",
+    "\n",
+    "source_file_for_flows = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full_flows.txt\")\n",
+    "\n",
+    "dry_air_flows = []\n",
+    "species_flows = []\n",
+    "ozone_flows = []\n",
+    "numbers_of_samples = []\n",
+    "\n",
+    "with open(source_file_for_flows, 'r', errors='ignore') as file:\n",
+    "    for i,line in enumerate(file.readlines()):\n",
+    "        if i >= 2:\n",
+    "            items = line.split()\n",
+    "            dry_air_flows.append(float(items[0]))\n",
+    "            species_flows.append(float(items[1]))\n",
+    "            ozone_flows.append(float(items[2]))\n",
+    "            numbers_of_samples.append(float(items[-1]))\n",
+    "\n",
+    "flow_data = pd.DataFrame({'Dry Air (L/min)': dry_air_flows, 'Species (L/min)': species_flows, 'Ozone (L/min)': ozone_flows, 'Samples': numbers_of_samples})"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2363080b",
+   "id": "75347e52",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax_2 = plt.twinx(ax)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax.plot([], analyser.data['Number (#/cm^3)'], color='red', label='Number')\n",
+    "ax_2.plot(analyser.data['Time (s)'], analyser.data['Mass (mg/cm^3)'], color='blue', label='Mass')\n",
+    "\n",
+    "ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview ' + str(sample_number))\n",
+    "ax.set_xlabel('Time $(s)$')\n",
+    "ax.set_ylabel('Number Density $(cm^{-3})$')\n",
+    "ax_2.set_ylabel('Mass Denisty $(mg cm^{-3})$')\n",
+    "ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n",
+    "ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n",
+    "\n",
+    "ax.set_xlim([0, 30000])\n",
+    "ax.set_ylim([0, 1.2E6])\n",
+    "ax_2.set_ylim([0, 15])\n",
+    "\n",
+    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
+    "lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
+    "\n",
+    "ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
+    "\n",
+    "plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview.pdf\", format='pdf', dpi=400)"
+   ]
   }
  ],
  "metadata": {
diff --git a/run/20260723_OFR_Analysis_01_Experiment_Overview.pdf b/run/20260723_OFR_Analysis_01_Experiment_Overview.pdf
new file mode 100644
index 0000000..1eea08c
Binary files /dev/null and b/run/20260723_OFR_Analysis_01_Experiment_Overview.pdf differ
diff --git a/run/20260723_OFR_Analysis_01_Spectrum_Sample_33.pdf b/run/20260723_OFR_Analysis_01_Spectrum_Sample_33.pdf
new file mode 100644
index 0000000..945e231
Binary files /dev/null and b/run/20260723_OFR_Analysis_01_Spectrum_Sample_33.pdf differ