Starting analysis for OFR Flow Rates
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@@ -1,17 +1,93 @@
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# Overall figure
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# AXES AND BOX
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axes.facecolor: white # axes background color
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axes.edgecolor: black # axes edge color
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axes.linewidth: 0.8 # edge line width
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axes.formatter.use_mathtext: True
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axes.formatter.limits: -3, 4
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axes.grid: True # display grid or not
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# GRID
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axes.grid: False # display grid or not
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axes.grid.axis: both # which axis the grid should apply to
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axes.grid.which: both # grid lines at {major, minor, both} ticks
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axes.grid.which: major # grid lines at {major, minor, both} ticks
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grid.linewidth: 0.2
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grid.color: black
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xtick.top:True
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ytick.right:True
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# TICKS
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xtick.top: True # draw ticks on the top side
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xtick.bottom: True # draw ticks on the bottom side
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xtick.labeltop: False # draw label on the top
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xtick.labelbottom: True # draw label on the bottom
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xtick.major.size: 7 # major tick size in points
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xtick.minor.size: 3.5 # minor tick size in points
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xtick.major.width: 0.8 # major tick width in points
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xtick.minor.width: 0.4 # minor tick width in points
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xtick.major.pad: 3.5 # distance to major tick label in points
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xtick.minor.pad: 3.5 # distance to the minor tick label in points
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xtick.color: black # color of the ticks
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xtick.labelsize: small # font size of the tick labels
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xtick.direction: in # direction: {in, out, inout}
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xtick.minor.visible: True # visibility of minor ticks on x-axis
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xtick.major.top: True # draw x axis top major ticks
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xtick.major.bottom: True # draw x axis bottom major ticks
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xtick.minor.top: True # draw x axis top minor ticks
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xtick.minor.bottom: True # draw x axis bottom minor ticks
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xtick.minor.ndivs: auto # number of minor ticks between the major ticks on x-axis
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xtick.alignment: center # alignment of xticks
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ytick.left: True # draw ticks on the left side
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ytick.right: True # draw ticks on the right side
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ytick.labelleft: True # draw tick labels on the left side
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ytick.labelright: False # draw tick labels on the right side
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ytick.major.size: 7 # major tick size in points
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ytick.minor.size: 3.5 # minor tick size in points
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ytick.major.width: 0.8 # major tick width in points
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ytick.minor.width: 0.4 # minor tick width in points
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ytick.major.pad: 3.5 # distance to major tick label in points
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ytick.minor.pad: 3.5 # distance to the minor tick label in points
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ytick.color: black # color of the ticks
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ytick.labelsize: small # font size of the tick labels
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ytick.direction: in # direction: {in, out, inout}
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ytick.minor.visible: True # visibility of minor ticks on y-axis
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ytick.major.left: True # draw y axis left major ticks
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ytick.major.right: True # draw y axis right major ticks
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ytick.minor.left: True # draw y axis left minor ticks
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ytick.minor.right: True # draw y axis right minor ticks
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ytick.minor.ndivs: auto # number of minor ticks between the major ticks on y-axis
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ytick.alignment: center_baseline # alignment of yticks
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# TEXT
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text.parse_math: True
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# TITLE
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axes.titlelocation: left
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axes.titlesize: large # font size of the axes title
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axes.titleweight: normal # font weight of title
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# LEGEND
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legend.loc: best
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legend.frameon: True # if True, draw the legend on a background patch
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legend.framealpha: 0.8 # legend patch transparency
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legend.facecolor: inherit # inherit from axes.facecolor; or color spec
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legend.edgecolor: black # background patch boundary color
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patch.linewidth: 0.6 # line width of the legend frame, None means inherit from patch.linewidth
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legend.fancybox: False # if True, use a rounded box for the
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# legend background, else a rectangle
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legend.shadow: False # if True, give background a shadow effect
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legend.numpoints: 1 # the number of marker points in the legend line
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legend.scatterpoints: 1 # number of scatter points
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legend.markerscale: 1.0 # the relative size of legend markers vs. original
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legend.fontsize: small
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legend.labelcolor: None
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legend.title_fontsize: None # None sets to the same as the default axes.
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## Dimensions as fraction of font size:
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legend.borderpad: 0.4 # border whitespace
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legend.labelspacing: 0.4 # the vertical space between the legend entries
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legend.handlelength: 2.0 # the length of the legend lines
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legend.handleheight: 0.7 # the height of the legend handle
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legend.handletextpad: 0.8 # the space between the legend line and legend text
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legend.borderaxespad: 1.4 # the border between the axes and legend edge
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legend.columnspacing: 1.0 # column separation
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#### MATPLOTLIBRC FORMAT
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@@ -5,106 +5,265 @@
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"id": "36f6dc0f",
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"metadata": {},
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"source": [
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"### OFR Analysis"
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"# OFR Analysis: File `20260723_OFR_Analysis_01.ipynb`"
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]
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},
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" <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
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],
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"outputs": [],
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"source": [
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"# Imports\n",
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"\n",
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"from defs.computing import OFRSample, OFRAnalyser\n",
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"import numpy as np\n",
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"from pathlib import Path\n",
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"import matplotlib.pyplot as plt\n",
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"plt.style.use('../.mplstyle')\n",
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"from matplotlib.ticker import MultipleLocator\n",
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"import pandas as pd\n",
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"\n",
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"from bokeh.plotting import figure, show\n",
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"from bokeh.io import output_notebook\n",
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"output_notebook()"
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"# Specify the source file for this analysis\n",
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"source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")"
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]
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},
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"cell_type": "code",
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"execution_count": 45,
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"id": "3922e80c",
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"cell_type": "markdown",
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"id": "ee2e9af5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Path to the source file\n",
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"source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full.txt\")"
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"### First we plot a sample spectrum to get a feeling of what the data looks like:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 114,
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"id": "76a9b026",
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"metadata": {},
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"outputs": [
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{
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"ename": "AttributeError",
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"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",
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||||
"output_type": "error",
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||||
"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
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"\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",
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"\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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 truncated
|
||||
"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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truncated
|
||||
"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": {
|
||||
|
||||
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Reference in new issue
Block a user