diff --git a/.gitattributes b/.gitattributes
index dab0dfb116dc924f907bae204113473bd6d6b1b1..1ac481ddb4bc2edd523ed7005ea048184f191b47 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -9,3 +9,5 @@ assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:sp
assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:space:]]growth[[:space:]]light[[:space:]]and[[:space:]]day[[:space:]]night[[:space:]]cycle/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:space:]]growth[[:space:]]light/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]homogeneous[[:space:]]growth[[:space:]]light/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
+assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]homogeneous[[:space:]]growth[[:space:]]light/protocols/Growth_Rate.ipynb filter=lfs diff=lfs merge=lfs -text
+assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:space:]]growth[[:space:]]light[[:space:]]and[[:space:]]day[[:space:]]night[[:space:]]cycle/protocols/Growth_Rate_Day_Night.ipynb filter=lfs diff=lfs merge=lfs -text
diff --git a/assays/Growth in Multi-Cultivator/isa.assay.xlsx b/assays/Growth in Multi-Cultivator/isa.assay.xlsx
index 8bb2cec30b88af31377df6b8c2ae716c14c91983..37387e55064831128d7687e4a62bc1e7feb71abf 100644
Binary files a/assays/Growth in Multi-Cultivator/isa.assay.xlsx and b/assays/Growth in Multi-Cultivator/isa.assay.xlsx differ
diff --git a/assays/Microfluidic cultivation with gradient growth light and CO2 control/protocols/Growth_Rate_CO2.ipynb b/assays/Microfluidic cultivation with gradient growth light and CO2 control/protocols/Growth_Rate_CO2.ipynb
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index 0000000000000000000000000000000000000000..986ec531e5fa7108c5ef9db3c3927693327fdefd
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+++ b/assays/Microfluidic cultivation with gradient growth light and CO2 control/protocols/Growth_Rate_CO2.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Welcome to this analysis notebook\n",
+ "\n",
+ "This notebook is designed to perform analyses based on the request: https://jugit.fz-juelich.de/j.seiffarth/analysis-projects/-/issues/1 and has been jointly developed by Markus Leygeber and Johannes Seiffarth 💪\n",
+ "\n",
+ "Therfore, we concentrate on:\n",
+ "\n",
+ "1. Perform segmentation on an omero sequence\n",
+ "2. Extracting individual cell information\n",
+ "3. Filtering cells based on there individual information to reduce the number of artifacts\n",
+ "4. Plot the quantities of interest\n",
+ "\n",
+ "Please make sure that you replace `<your username>` and `<your password>` with your OMERO credentials in the following code snippets 👇. The cell segmentation is performed on an image sequence specified by the `image_id` parameter. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from pathlib import Path\n",
+ "\n",
+ "# your omero credentials\n",
+ "username = \"lwitting\"\n",
+ "password = \"lwitting\"\n",
+ "\n",
+ "# OMERO image that you want to analyze\n",
+ "image_id = 28238 # change the id if you want to apply the analysis to different image data\n",
+ "\n",
+ "image_channels = [1]\n",
+ "\n",
+ "# the address of the segmentation service\n",
+ "segmentation_service = os.environ.get(\"SEGMENTATION_SERVICE\", \"http://main/segService\")\n",
+ "\n",
+ "# use current working directory as default storage folder for outputs\n",
+ "storage_folder = os.getcwd()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create the output directory\n",
+ "output_path = Path(storage_folder) / \"tmp/\"\n",
+ "output_path.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# make path relative (advantage in video embedding)\n",
+ "output_path_rel = output_path.relative_to(Path(os.getcwd()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# do not change the lines below\n",
+ "assert username != \"<your username>\", \"Please replace '<your username>' with your OMERO username\"\n",
+ "assert password != \"<your password>\", \"Please replace '<your password>' with your OMERO username\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "\n",
+ "if not \"OMERO_SERVER\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_SERVER' defined. Fallback to default OMERO_SERVER address 'omero'! This can lead to connection faults!\")\n",
+ "if not \"OMERO_WEB\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_WEB' defined. Links to view OMERO data in web viewer might not work!\")\n",
+ "\n",
+ "credentials = dict(\n",
+ " serverUrl= os.environ.get('OMERO_SERVER', 'omero'),\n",
+ " username= username,\n",
+ " password = password,\n",
+ " port = int(os.environ.get('OMERO_PORT', '4064'))\n",
+ ")\n",
+ "\n",
+ "omero_cred = dict(\n",
+ " host = credentials['serverUrl'],\n",
+ " username = credentials['username'],\n",
+ " passwd = credentials['password'],\n",
+ " port = credentials['port'],\n",
+ " secure = True\n",
+ ")\n",
+ "\n",
+ "omero_web = os.environ.get(\"OMERO_WEB\", \"<Your OMERO_WEB address should be here>\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Information about the image stack"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from acia.segm.omero.utils import getImage\n",
+ "from omero.gateway import BlitzGateway\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "with BlitzGateway(**omero_cred) as conn:\n",
+ " image = getImage(conn, image_id)\n",
+ " dataset = image.getParent()\n",
+ " project = dataset.getParent()\n",
+ " group = image.getDetails().getGroup()\n",
+ " owner = image.getOwner()\n",
+ " \n",
+ " channels = image.getChannels()\n",
+ " \n",
+ " # display markdown\n",
+ " from IPython.display import Video, Markdown, display\n",
+ " display(Markdown(\"# Image information\"))\n",
+ "\n",
+ " dataset_name = dataset.getName()\n",
+ " \n",
+ " table = f\"\"\"\n",
+ "| Value | Content |\n",
+ "| --- | --- |\n",
+ "| Project Name | {project.getName()} |\n",
+ "| Dataset Name | {dataset_name} |\n",
+ "| Image Name | {image.getName()} |\n",
+ "| Data Owner | [{owner.getName()}]({omero_web}/webclient/active_group/?active_group={group.getId()}&url=/webclient/userdata/?experimenter={owner.getId()}) |\n",
+ "| Group | [{group.getName()}]({omero_web}/webclient/active_group/?active_group={group.getId()}&url=/webclient/userdata/?experimenter=-1) |\n",
+ "| Omero Web Link | {omero_web}/webclient/?show=image-{image.getId()} |\n",
+ "| View Image Data | {omero_web}/webclient/img_detail/{image.getId()}/?dataset={dataset.getId()} |\n",
+ "| Open in SegUI | Coming soon! |\n",
+ "| T Size | { image.getSizeT() } |\n",
+ "| Z Size | { image.getSizeZ() } |\n",
+ "| Channels | {','.join([ch.getLabel() for ch in channels])} |\n",
+ " \"\"\"\n",
+ "\n",
+ " display(Markdown(table))\n",
+ " display(Markdown(f\"## Preview of channels\"))\n",
+ "\n",
+ " image.setGreyscaleRenderingModel()\n",
+ " size_c = image.getSizeC()\n",
+ " z = image.getSizeZ() // 2\n",
+ " t = image.getSizeT() // 2\n",
+ " \n",
+ " width = image.getSizeX()\n",
+ " height = image.getSizeY()\n",
+ " \n",
+ " image_size = width * height\n",
+ " \n",
+ " print(image_size)\n",
+ " \n",
+ " fig, ax = plt.subplots(1, size_c, figsize=(15, 15))\n",
+ " for i, c in enumerate(range(1, size_c + 1)): # Channel index starts at 1\n",
+ " channels = [c] # Turn on a single channel at a time\n",
+ " image.setActiveChannels(channels)\n",
+ " rendered_image = image.renderImage(z, t)\n",
+ " \n",
+ " if size_c > 1:\n",
+ " loc_ax = ax[i]\n",
+ " else:\n",
+ " loc_ax = ax\n",
+ " loc_ax.imshow(rendered_image)\n",
+ " loc_ax.set_title(f\"Channel {i}, t: {t} , z: {z}\")\n",
+ " \n",
+ " plt.tight_layout()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Cell Segmentation\n",
+ "\n",
+ "No we specify the segmentation model: [Omnipose](https://doi.org/10.1101/2021.11.03.467199) and the channel we want to select to extract the image data. The channel data can be observed in the [Omero Web Viewer](http://ibt056.ibt.kfa-juelich.de:4080/). Please keep in mind that you have to enter the channel value+1 in `image_channels`. With the model and image sequence we kick off the segmentation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from acia.segm.omero.storer import OmeroRoIStorer, OmeroSequenceSource\n",
+ "from acia.segm.processor.online import FlexibleOnlineModel, ModelDescriptor\n",
+ "from urllib.parse import urljoin\n",
+ "\n",
+ "# the model description\n",
+ "model_desc = ModelDescriptor(\n",
+ " repo=\"https://gitlab+deploy-token-281:TZYmjRQZzLZsBfWsd2XS@jugit.fz-juelich.de/mlflow-executors/omnipose-executor.git\",\n",
+ " entry_point=\"main\",\n",
+ " version=\"main\",\n",
+ " parameters={\n",
+ " # specific model trained on cyanobacteria? http://ibt082:5000/#/experiments/711115886395583850/runs/3e50bc690ed147559dbf0254d7e701bb\n",
+ " \"model\": \"https://fz-juelich.sciebo.de/s/SJHXyT7xQfITHgw/download\"\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# connect to remote machine learning model\n",
+ "model = FlexibleOnlineModel(urljoin(segmentation_service, 'batch-image-prediction/'), model_desc, batch_size=30, timeout=600*30)\n",
+ "\n",
+ "\n",
+ "# create local image data source\n",
+ "source = OmeroSequenceSource(image_id, **credentials, channels=image_channels)\n",
+ "\n",
+ "# perform overlay prediction\n",
+ "print(\"Perform Prediction...\")\n",
+ "result = model.predict(source)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To validate the segmentation result, we create a short video:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import acia\n",
+ "from acia.segm.output import renderVideo\n",
+ "\n",
+ "framerate=2\n",
+ "\n",
+ "# Make a video with\n",
+ "video_file = str(output_path_rel / \"segmented.mp4\")\n",
+ "renderVideo(source, result.timeIterator(), filename=video_file, codec=\"vp09\", framerate=framerate, draw_frame_number=True)\n",
+ "\n",
+ "# display markdown\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "display(Markdown(\"# Your segmentation\"))\n",
+ "Video(video_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Extracting individual cell properties\n",
+ "\n",
+ "Now that we have the cell segmentation, we can move on and extract individual cell properties like Area, Time, Length, ....\n",
+ "and visualize them in a table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from acia.analysis import ExtractorExecutor, AreaEx, IdEx, FrameEx, TimeEx, LengthEx, FluorescenceEx\n",
+ "from acia import ureg\n",
+ "import pint\n",
+ "import numpy as np\n",
+ "\n",
+ "# create local image data source\n",
+ "source = OmeroSequenceSource(image_id, **credentials, channels=image_channels)\n",
+ "\n",
+ "assert source.pixelSize, \"The pixel size is not saved in omero -> we cannot extract meaningful area or length because we do not know the size of the pixels\"\n",
+ "\n",
+ "ex = ExtractorExecutor()\n",
+ "\n",
+ "df = ex.execute(result, source, [\n",
+ " # define the cell properties that you want to extract here\n",
+ " AreaEx(input_unit=(source.pixelSize[0] * ureg.micrometer) ** 2), # pass the correct area of pixels\n",
+ " LengthEx(input_unit=source.pixelSize[0] * ureg.micrometer), # pass the correct size of pixels\n",
+ " IdEx(),\n",
+ " FrameEx(),\n",
+ " TimeEx(input_unit=\"2 * hour\"), # one picture every 2 hour\n",
+ " FluorescenceEx(channels=[1], channel_names=[\"autofluorescence_sum\"], summarize_operator=np.sum, parallel=1), \n",
+ " FluorescenceEx(channels=[1], channel_names=[\"autofluorescence_mean\"], summarize_operator=np.mean, parallel=1),\n",
+ " FluorescenceEx(channels=[1], channel_names=[\"autofluorescence_std\"], summarize_operator=np.std, parallel=1)\n",
+ "])\n",
+ "\n",
+ "print(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_size_um = image_size * (source.pixelSize[0] * ureg.micrometer) ** 2\n",
+ "image_size_um"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# 3. Filtering artifacts in segmentation\n",
+ "\n",
+ "In the segmentation, we can often observe artifacts, that is objects that are mistakenly recoginzed as cells. To reduce the number of artifacts in our analysis we can utilize some simple filtering functionality for the area: We only keep all the objects that have an area between `min_area` and `max_area` as defined below in the code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "min_area = 0.7 # the minimal area in micrometer ** 2. All smaller objects are dropped\n",
+ "max_area = 10 # the maximal area in micrometer ** 2. All larger objects are dropped\n",
+ "\n",
+ "fig, ax = plt.subplots(2, 1, facecolor='white', figsize=(15,10))\n",
+ "\n",
+ "area_unit = ex.units['area']\n",
+ "\n",
+ "# plot the area distribution before filtering\n",
+ "ax[0].hist(df['area'], bins=100)\n",
+ "ax[0].set_title('Area distribution before filtering')\n",
+ "ax[0].set_ylabel('Frequency')\n",
+ "ax[0].set_xlabel(f'Cell area [${area_unit:~L}$]')\n",
+ "\n",
+ "# filter by area\n",
+ "filtered_df = df[(min_area < df['area']) & (df['area'] < max_area)]\n",
+ "\n",
+ "# plot the area distribution after filtering\n",
+ "ax[1].hist(filtered_df['area'], bins=100)\n",
+ "ax[1].set_title('Area distribution after filtering')\n",
+ "ax[1].set_ylabel('Frequency')\n",
+ "ax[1].set_xlabel(f'Cell area [${area_unit:~L}$]')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "\n",
+ "# export with german decimal: ,\n",
+ "filtered_df.to_csv(str(output_path / 'allcells.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "print(\"Done\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now let's look at the new video with filtered content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create local image data source\n",
+ "source = OmeroSequenceSource(image_id, **credentials, channels=image_channels)\n",
+ "\n",
+ "# Make a video with\n",
+ "video_file = str(output_path_rel / \"filter_segmented.mp4\")\n",
+ "renderVideo(source, result.timeIterator(), filename=video_file, codec=\"vp09\", framerate=framerate, draw_frame_number=True, filter_contours=lambda i,c: c.id in filtered_df['id'])\n",
+ "\n",
+ "# display markdown\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "display(Markdown(\"# Your segmentation\"))\n",
+ "Video(video_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# 4. Visualizing interesting properties\n",
+ "\n",
+ "We start with the count of cells per frame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count_df = filtered_df.groupby(['frame', 'time']).size().reset_index(name='counts')\n",
+ "\n",
+ "# export with german decimal: ,\n",
+ "count_df.to_csv(str(output_path / 'counts.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "print(count_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate min_time and max_time from % chamber filling\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#set min and max time for fitting\n",
+ "\n",
+ "min_thresh = 4 # minimal number of cells\n",
+ "max_thresh = 0.6 # maximal area filling of the chamber\n",
+ "\n",
+ "#try getting cell count and sum area from variables, otherwise load .csv files\n",
+ "\n",
+ "try:\n",
+ " sum_df = filtered_df.groupby(['frame', 'time']).sum().reset_index()\n",
+ "except:\n",
+ " filtered_df = pd.read_csv('tmp/allcells.csv', delimiter=';')\n",
+ " sum_df = filtered_df.groupby(['frame', 'time']).sum().reset_index()\n",
+ "\n",
+ "min_time = [6*2, 18*2, 30*2, 42*2, 54*2]\n",
+ "max_time = [12*2, 24*2, 36*2, 48*2, 60*2]\n",
+ " \n",
+ "try:\n",
+ " timed_df = count_df[(count_df['time'] >= min_time + n*interval) & (count_df['time'] <= min_time + (n+1)*interval)]\n",
+ "except:\n",
+ " count_df = pd.read_csv('tmp/counts.csv', delimiter=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a figure\n",
+ "fig, ax = plt.subplots(1,2, figsize=(12, 4), facecolor='white')\n",
+ "\n",
+ "corperate_idendity = ['#023d6b', '#adbde3', '#faeb5a', '#eb5f73', '#b9d25f', '#af82b9', '#fab45a', '#ebebeb'] # Fz Juelich corperate identity\n",
+ "\n",
+ "# plot the cell count\n",
+ "ax[0].plot(count_df['time'], count_df['counts'], label='Cell count', color='#023d6b')\n",
+ "\n",
+ "m_count = []\n",
+ "b_count = []\n",
+ "\n",
+ "# fit a model N=m*t+b; cell count\n",
+ "for n in range(0,len(min_time)):\n",
+ " timed_df = count_df[(count_df['time'] >= min_time[n]) & (count_df['time'] <= max_time[n])]\n",
+ " m, b = np.polyfit(timed_df['time'], np.log(timed_df['counts']), 1)\n",
+ " m_count.append(m)\n",
+ " b_count.append(b)\n",
+ " ax[0].plot(timed_df['time'], np.exp(m * timed_df['time'] + b), label='fit count [h$^{-1}$]', color=corperate_idendity[n+2])\n",
+ "\n",
+ "ax[0].set_xlabel(f'Time [h$^{-1}$]')\n",
+ "ax[0].set_ylabel('Cell count')\n",
+ "ax[0].set_yscale('log')\n",
+ "ax[0].set_title('Cell Count')\n",
+ "\n",
+ "# plot the sum cell area\n",
+ "ax[1].plot(sum_df['time'], sum_df['area'], label='Cell area', color='#adbde3')\n",
+ "\n",
+ "m_area = []\n",
+ "b_area = []\n",
+ "\n",
+ "# fit a model N=m*t+b; cell area\n",
+ "for n in range(0,len(min_time)):\n",
+ " timedsum_df = sum_df[(sum_df['time'] >= min_time[n]) & (sum_df['time'] <= max_time[n])]\n",
+ " m, b = np.polyfit(timedsum_df['time'], np.log(timedsum_df['area']), 1)\n",
+ " m_area.append(m)\n",
+ " b_area.append(b)\n",
+ " ax[1].plot(timedsum_df['time'], np.exp(m * timedsum_df['time'] + b), label='fit area [h$^{-1}$]', color=corperate_idendity[n+2])\n",
+ "\n",
+ "ax[0].set_xlabel(f'Time [h$^{-1}$]')\n",
+ "ax[1].set_xlabel(f'Time [h$^{-1}$]')\n",
+ "ax[1].set_ylabel('Cell area')\n",
+ "ax[1].set_yscale('log')\n",
+ "ax[1].set_title('Cell Area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.25), ncol=4)\n",
+ "\n",
+ "#plt.yscale('log')\n",
+ "\n",
+ "plt.savefig('tmp/Growth_Rate_Count_vs_Are.png', bbox_inches='tight', transparent=1)\n",
+ "\n",
+ "#summerize growth rates for group statistics\n",
+ "\n",
+ "rates = [m_count, m_area]\n",
+ "labels = ['µ_count [1/h]', 'µ_area [1/h]']\n",
+ "\n",
+ "df_results = pd.DataFrame(rates, labels)\n",
+ "df_results.to_csv(str('tmp/results.csv'), decimal='.', sep=';')\n",
+ "print(df_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Plot the growth rate over the intervall for area and time\n",
+ "\n",
+ "CO2 = [100, 50, 15, 0, 400] # define CO2 concentrations; first to last\n",
+ "\n",
+ "# plot; first count then area\n",
+ "fig, ax = plt.subplots(1,2, figsize=(12, 4), facecolor='white', sharey = True)\n",
+ "\n",
+ "ax[0].plot(CO2, m_count, color='#023d6b', marker = 'o', linestyle = 'dotted')\n",
+ "ax[1].plot(CO2, m_area, color='#023d6b', marker = 'o', linestyle = 'dotted')\n",
+ "\n",
+ "ax[0].set_xlabel('CO$_2$-concentration [ppm]')\n",
+ "ax[1].set_xlabel('CO$_2$-concentration [ppm]')\n",
+ "ax[0].set_ylabel('Grwoth rate [1/h]')\n",
+ "# ax[1].set_ylabel('Grwoth rate [1/h]')\n",
+ "ax[0].set_title('Cell Count')\n",
+ "ax[1].set_title('Cell Area')\n",
+ "\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[0].set_xlim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "ax[1].set_xlim(0, )\n",
+ "\n",
+ "plt.savefig('tmp/Growth_Rate_over_CO2.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from scipy.signal import argrelextrema\n",
+ "\n",
+ "mean_df = filtered_df.groupby(['frame', 'time']).mean().reset_index()\n",
+ "\n",
+ "std_df = filtered_df.groupby(['frame', 'time']).std().reset_index()\n",
+ "\n",
+ "std_df.to_csv(str('tmp/std_df.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "# create a figure\n",
+ "plt.figure(facecolor='white')\n",
+ "\n",
+ "# Find local peaks\n",
+ "\n",
+ "n = 2 # number of points to be checked before and after\n",
+ "\n",
+ "mean_df['max'] = mean_df.iloc[argrelextrema(mean_df.area.values, np.greater_equal, order=n)[0]]['area']\n",
+ "\n",
+ "\n",
+ "mean_df.to_csv(str('tmp/ mean_df.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "# calculate doubling time\n",
+ "\n",
+ "extrema_df = mean_df.dropna(subset=['max']).reset_index()\n",
+ "\n",
+ "extrema_df['doubling_time'] = extrema_df['time'].diff(1)\n",
+ "\n",
+ "# plot mean area over time with error\n",
+ "fig, ax1 = plt.subplots(facecolor='white')\n",
+ "ax1.scatter(mean_df['time'], mean_df['max'], c='#b9d25f',zorder=2)\n",
+ "ax1.errorbar(mean_df['time'], mean_df['area'], yerr=std_df['area'], label='Average cell area', color='#adbde3', ecolor='#ebebeb',zorder=1)\n",
+ "\n",
+ "ax2 = ax1.twinx()\n",
+ "ax2.plot(extrema_df['time'], extrema_df['doubling_time'], label='Doubling Time', color='#fab45a',zorder=3)\n",
+ "\n",
+ "ax1.set_xlabel(f'Time [h$^{-1}$]')\n",
+ "ax1.set_ylabel('Average cell area [µm$^2$]', color='#adbde3')\n",
+ "ax2.set_ylabel('Doubling Time [h]', color='#fab45a')\n",
+ "ax2.set_ylim(0, )\n",
+ "ax2.set_xlim(0, )\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.1), ncol=3)\n",
+ "\n",
+ "plt.savefig('tmp/Mean_Area_Over_Time.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "# calculate mean area and lenght again\n",
+ "\n",
+ "mean_df = filtered_df.groupby(['frame', 'time']).mean().reset_index()\n",
+ "\n",
+ "std_df = filtered_df.groupby(['frame', 'time']).std().reset_index()\n",
+ "\n",
+ "# plot mean area over mean lenght\n",
+ "fig, ax = plt.subplots(facecolor='white', figsize=(6, 5))\n",
+ "\n",
+ "ax.errorbar(mean_df['area'], mean_df['length'], yerr=std_df['area'], xerr=std_df['length'], fmt=\"o\",color='#adbde3', ecolor='#ebebeb', markersize = 0,zorder=1)\n",
+ "im = ax.scatter(mean_df['area'], mean_df['length'], s=10, c=mean_df['time'], cmap='rainbow',zorder=2)\n",
+ "\n",
+ "# Add a colorbar\n",
+ "cbar = fig.colorbar(im, ax=ax)\n",
+ "cbar.set_label('Time in [h]',rotation=270)\n",
+ "\n",
+ "ax.set_xlabel('Mean cell lenght [µm]')\n",
+ "ax.set_ylabel('Mean cell area [µm$^2$]')\n",
+ "ax.set_ylim(0, 8)\n",
+ "ax.set_xlim(0, 8)\n",
+ "\n",
+ "plt.savefig('tmp/Mean_Area_Over_Mean_Cell_Lenght.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Calculate Fluorescence per cell area\n",
+ "\n",
+ "mean_df['Fluorescence/Cell_Area'] = mean_df['autofluorescence_sum']/mean_df['area']\n",
+ "\n",
+ "# Plot mean autofluorescence of the cells\n",
+ "\n",
+ "fig, ax = plt.subplots(facecolor='white')\n",
+ "ax.plot(mean_df['time'], mean_df['Fluorescence/Cell_Area'], color='#023d6b')\n",
+ "\n",
+ "ax.set_ylim(0, )\n",
+ "ax.set_xlim(0, )\n",
+ "\n",
+ "ax.set_xlabel('Time [h]')\n",
+ "ax.set_ylabel('Autofluorescence/Cell area [µm$^-$$^2$]')\n",
+ "\n",
+ "plt.savefig('tmp/Mean_Fluorescence_per_Cell_Area.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "43e720662e2b73f3f858656968524fca68eb44fc0b1d15b9eb878c7d185562f9"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assays/Microfluidic cultivation with gradient growth light and CO2 control/protocols/ScalingAnalysis_SequenceNames.ipynb b/assays/Microfluidic cultivation with gradient growth light and CO2 control/protocols/ScalingAnalysis_SequenceNames.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8568ffd757affab760dda1c4a23e27d219d77522
--- /dev/null
+++ b/assays/Microfluidic cultivation with gradient growth light and CO2 control/protocols/ScalingAnalysis_SequenceNames.ipynb
@@ -0,0 +1,1096 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Scaling Analysis\n",
+ "\n",
+ "You have developed your analysis notebook that works perfectly for a single cultivation chamber 💪? And now you you want to apply it for all cultivation chambers in our experiment but it is lots of work to apply the scripts one by one 🤔? That's why this example shows how you can quickly apply your single analysis script to a large amount of image sequences organized in the OMERO `project` or `dataset` structures 🚀! Therefore, your custom developed analyses can scale to large image volumes without you touching or changing the code!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Setup\n",
+ "\n",
+ "Define the `omero_id` and `omero_type` of the image data you would like to process. The `omerod_id` is the number you can find in the top right corner when selecting a OMERO `project`, `dataset` or `image` in the `OMERO Web` application. The `omero_type` must be `project` or `dataset` when the OMERO id points to a project or dataset and `image` if it is just a single image! Please note that if you define the wrong `omero_type` you will get an error lateron 🤯!\n",
+ "\n",
+ "Also provide your credentials for the OMERO server!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "# OMERO resource that you want to analyze\n",
+ "omero_type = \"dataset\" # can be \"image\", \"project\" or \"dataset\"\n",
+ "omero_id = 2989 # change the id if you want to apply the analysis to a different omero resource\n",
+ "\n",
+ "# your omero credentials\n",
+ "username = \"lwitting\"\n",
+ "password = \"lwitting\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# prepare credentials (usually you do not have to change this!)\n",
+ "\n",
+ "import logging\n",
+ "\n",
+ "if not \"OMERO_SERVER\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_SERVER' defined. Fallback to default OMERO_SERVER address 'omero'! This can lead to connection faults!\")\n",
+ "if not \"OMERO_WEB\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_WEB' defined. Links to view OMERO data in web viewer might not work!\")\n",
+ "\n",
+ "credentials = dict(\n",
+ " serverUrl= os.environ.get('OMERO_SERVER', 'omero'),\n",
+ " username= username,\n",
+ " password = password,\n",
+ " port = int(os.environ.get('OMERO_PORT', '4064'))\n",
+ ")\n",
+ "\n",
+ "omero_cred = dict(\n",
+ " host = credentials['serverUrl'],\n",
+ " username = credentials['username'],\n",
+ " passwd = credentials['password'],\n",
+ " port = credentials['port'],\n",
+ " secure = True\n",
+ ")\n",
+ "\n",
+ "omero_web = os.environ.get(\"OMERO_WEB\", \"<Your OMERO_WEB address should be here>\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.2 Specify the analysis script\n",
+ "\n",
+ "Now you have to specify the name of the analysis script you want to apply to the image data. At best copy the script to the same location as this script! Then you only have to specify the name of the script!\n",
+ "\n",
+ "**Note:** If the analysis script is not located in the same folder you need to specify the path to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "analysis_script = \"Growth_Rate_CO2.ipynb\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Information about the underlying data\n",
+ "\n",
+ "We summarize the amount of underlying data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[28516, 28520, 28517, 28519, 28521, 28518, 28522, 28523, 28524, 28525, 28526, 28529, 28530, 28532, 28534, 28542, 28545, 28547, 28548, 28550, 28527, 28531, 28535, 28540, 28549, 28554, 28556, 28557, 28562, 28563, 28567, 28528, 28533, 28536, 28539, 28541, 28543, 28546, 28551, 28552, 28553, 28561, 28565, 28558, 28564, 28566]\n",
+ "{28516: PosixPath('03_1009.tif'), 28520: PosixPath('07_1036.tif'), 28517: PosixPath('04_1013.tif'), 28519: PosixPath('06_1029.tif'), 28521: PosixPath('09_1104.tif'), 28518: PosixPath('05_1018.tif'), 28522: PosixPath('10_1110.tif'), 28523: PosixPath('11_1119.tif'), 28524: PosixPath('12_1127.tif'), 28525: PosixPath('13_1128.tif'), 28526: PosixPath('14_1135.tif'), 28529: PosixPath('18_1227.tif'), 28530: PosixPath('19_1230.tif'), 28532: PosixPath('21_1304.tif'), 28534: PosixPath('23_1315.tif'), 28542: PosixPath('32_2337.tif'), 28545: PosixPath('35_2323.tif'), 28547: PosixPath('37_2304.tif'), 28548: PosixPath('38_2302.tif'), 28550: PosixPath('40_2234.tif'), 28527: PosixPath('15_1136.tif'), 28531: PosixPath('20_1302.tif'), 28535: PosixPath('24_1319.tif'), 28540: PosixPath('29_1331.tif'), 28549: PosixPath('39_2237.tif'), 28554: PosixPath('44_2134.tif'), 28556: PosixPath('46_2104.tif'), 28557: PosixPath('47_2036.tif'), 28562: PosixPath('55_3227.tif'), 28563: PosixPath('56_3228.tif'), 28567: PosixPath('60_3319.tif'), 28528: PosixPath('17_1218.tif'), 28533: PosixPath('22_1310.tif'), 28536: PosixPath('25_1321.tif'), 28539: PosixPath('28_1339.tif'), 28541: PosixPath('30_1327.tif'), 28543: PosixPath('33_2330.tif'), 28546: PosixPath('36_2310.tif'), 28551: PosixPath('41_2224.tif'), 28552: PosixPath('42_2212.tif'), 28553: PosixPath('43_2200.tif'), 28561: PosixPath('53_3128.tif'), 28565: PosixPath('58_3334.tif'), 28558: PosixPath('48_2013.tif'), 28564: PosixPath('57_3338.tif'), 28566: PosixPath('59_3326.tif')}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from acia.segm.omero.utils import list_image_ids_in, getImage\n",
+ "from omero.gateway import BlitzGateway\n",
+ "from pathlib import Path\n",
+ "\n",
+ "image_names = {}\n",
+ "\n",
+ "with BlitzGateway(**omero_cred) as conn:\n",
+ " image_ids = list_image_ids_in(omero_id, omero_type, conn)\n",
+ " \n",
+ " # get all the image names\n",
+ " for image_id in image_ids:\n",
+ " image_names[image_id] = Path(getImage(conn, image_id).getName())\n",
+ "\n",
+ "## TODO: give an overview about the data\n",
+ "print(image_ids)\n",
+ "print(image_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. Scale the analysis script to all image sequences\n",
+ "\n",
+ "Now we apply the analysis script to every image sequence individually 🚀! You can lean back and enjoy the working computer 😎 🥂\n",
+ "\n",
+ "**Note:** For heavy analysis scripts or for larget `datasets` or `projects` this process may take a while (from minutes to hours or days). The top-level progress bar will indicate the total progress and give you an indication how long this will take. For large image data volumes we can recommend execution over night 🌔!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results are stored in: /home/jovyan/work/A4_IntensityGradient+CO2/2024.03.12_CO2_Switching/Growth_Rate/S. elongatus UTEX2973/automated_executions\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ef2d8dee7281407b97d6b3e636197383",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/46 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
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+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e9dc9e0d4b3c4c6b9c6758805311442d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c84e6c584720417e96e68e063393b1cd",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2c491cacdbb847e698402e8eafe7cdf7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "de798a0bca82472894219946e28ca53a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fd0e469019c34440b9ce855717002ab3",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "57890ca797744a20a07990329292120d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3aedf0ab9e96462eb764fda7e1ea5070",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/29 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "from pathlib import Path\n",
+ "from acia.analysis import scale\n",
+ "\n",
+ "# set the base path for all results\n",
+ "stem = Path(analysis_script).stem\n",
+ "output_path = Path(\"./automated_executions\") \n",
+ "\n",
+ "print(f\"Results are stored in: {output_path.absolute()}\")\n",
+ "\n",
+ "# scale your analysis script to many images\n",
+ "result = scale(output_path, analysis_script=analysis_script, image_ids=image_ids, additional_parameters=dict(username=username, password=password), exist_ok=True, execution_naming=lambda image_id: image_names[image_id])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Inspect your analysis results\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib.parse\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "\n",
+ "base_url = os.environ.get(\"JUPYTERHUB_SERVICE_PREFIX\", None)\n",
+ "\n",
+ "if base_url is None:\n",
+ " url = f\"file://{output_path.absolute()}\"\n",
+ "else:\n",
+ " url = f\"{base_url}lab/tree/{urllib.parse.quote(str(output_path))}\"\n",
+ "\n",
+ "output = f\"\"\"# Inspect your analyses\n",
+ "You can find all the individual analysis scripts here: <a href=\"{url}\">{url}</a>\"\"\"\n",
+ "\n",
+ "display(Markdown(output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Generate Summary Statistics\n",
+ "\n",
+ "In this section you can generate your custom summary statistics that combine the results of all experiment analyses. Just design the analysis script that you scaled above such that it outputs the results into a local files. Here, these results can be loaded, merged together and further processed or visualized!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No results.csv found in automated_executions/.ipynb_checkpoints\n",
+ " Unnamed: 0 0 1 2 3 4 \\\n",
+ "1 µ_area [1/h] 0.079862 0.047212 0.051715 0.020789 0.026376 \n",
+ "3 µ_area [1/h] 0.108072 0.077907 -0.000328 -0.134488 0.113329 \n",
+ "5 µ_area [1/h] 0.087625 0.083524 0.019066 -0.086377 0.032671 \n",
+ "7 µ_area [1/h] 0.077550 0.070272 0.068354 -0.001313 -0.010270 \n",
+ "9 µ_area [1/h] 0.086112 0.083645 0.056089 0.010926 0.014759 \n",
+ "11 µ_area [1/h] 0.083803 0.082189 0.069708 -0.028809 -0.295593 \n",
+ "13 µ_area [1/h] 0.039417 0.126847 0.066690 -0.019070 -0.137100 \n",
+ "15 µ_area [1/h] 0.057110 0.054844 0.053895 0.009179 0.037982 \n",
+ "17 µ_area [1/h] 0.095654 0.086943 0.073134 -0.086665 0.034949 \n",
+ "19 µ_area [1/h] 0.092459 0.073397 0.072523 0.009442 0.029857 \n",
+ "21 µ_area [1/h] 0.108463 0.096770 0.052059 -0.106373 -0.138045 \n",
+ "23 µ_area [1/h] 0.076102 0.083489 0.069847 0.004303 -0.006968 \n",
+ "25 µ_area [1/h] 0.057819 0.053844 0.062527 0.008273 0.028342 \n",
+ "27 µ_area [1/h] 0.011330 0.042260 0.036806 0.023821 0.034254 \n",
+ "29 µ_area [1/h] 0.147613 0.097344 0.062025 -0.040978 -0.023378 \n",
+ "31 µ_area [1/h] 0.050359 0.050943 0.049072 0.002273 0.013677 \n",
+ "33 µ_area [1/h] 0.074180 0.085706 0.069612 0.010400 -0.015484 \n",
+ "35 µ_area [1/h] 0.140446 0.102038 0.062976 -0.020080 -0.001646 \n",
+ "37 µ_area [1/h] 0.119779 0.079463 0.067312 -0.037959 -0.009710 \n",
+ "39 µ_area [1/h] 0.094670 0.084169 0.064785 -0.003287 -0.219195 \n",
+ "41 µ_area [1/h] 0.347312 0.109433 0.065413 -0.026367 0.005601 \n",
+ "43 µ_area [1/h] 0.092443 0.080850 0.067124 0.004803 -0.054416 \n",
+ "45 µ_area [1/h] 0.067522 0.087563 0.085981 -0.042873 0.008511 \n",
+ "47 µ_area [1/h] 0.098063 0.087123 0.069175 -0.008179 0.004959 \n",
+ "49 µ_area [1/h] 0.106487 0.078184 0.056619 -0.036344 0.018054 \n",
+ "51 µ_area [1/h] 0.044572 0.052575 0.052792 0.049733 0.044985 \n",
+ "53 µ_area [1/h] 0.097654 0.077329 0.067190 -0.012516 0.015427 \n",
+ "55 µ_area [1/h] 0.072067 0.062944 0.058238 0.049662 0.009971 \n",
+ "57 µ_area [1/h] 0.093737 0.086466 0.047663 -0.053376 0.032851 \n",
+ "59 µ_area [1/h] 0.077530 0.081125 0.068027 0.007929 -0.004233 \n",
+ "61 µ_area [1/h] 0.094309 0.048636 0.049345 0.031103 0.016946 \n",
+ "63 µ_area [1/h] 0.076885 0.081731 0.051362 -0.039638 -0.130166 \n",
+ "65 µ_area [1/h] 0.116995 0.071443 0.068090 0.006866 -0.017926 \n",
+ "67 µ_area [1/h] 0.083780 0.081054 0.070333 0.012219 -0.004118 \n",
+ "69 µ_area [1/h] 0.119925 0.073651 0.063824 0.001694 0.009707 \n",
+ "71 µ_area [1/h] 0.108858 0.087904 0.071393 -0.007649 0.013463 \n",
+ "73 µ_area [1/h] 0.225831 0.067684 0.067977 0.010609 0.018483 \n",
+ "75 µ_area [1/h] 0.067143 0.064114 0.064533 0.008002 0.034693 \n",
+ "77 µ_area [1/h] 0.076511 0.073822 0.065900 0.019183 -0.067047 \n",
+ "79 µ_area [1/h] 0.108975 0.084404 0.040669 -0.029416 0.021226 \n",
+ "81 µ_area [1/h] 0.082809 0.084080 0.068794 -0.043590 0.003955 \n",
+ "83 µ_area [1/h] 0.080017 0.082558 0.056005 0.018447 -0.076570 \n",
+ "85 µ_area [1/h] 0.044837 0.102198 0.089215 0.001387 -0.008489 \n",
+ "87 µ_area [1/h] 0.075606 0.058516 0.057404 0.049793 0.016684 \n",
+ "89 µ_area [1/h] 0.129384 0.090548 0.065060 -0.065956 -0.047552 \n",
+ "91 µ_area [1/h] 0.086947 0.091663 0.074521 -0.041576 0.013966 \n",
+ "\n",
+ " experiment \n",
+ "1 03_1009.tif \n",
+ "3 29_1331.tif \n",
+ "5 28_1339.tif \n",
+ "7 14_1135.tif \n",
+ "9 41_2224.tif \n",
+ "11 42_2212.tif \n",
+ "13 40_2234.tif \n",
+ "15 07_1036.tif \n",
+ "17 33_2330.tif \n",
+ "19 20_1302.tif \n",
+ "21 24_1319.tif \n",
+ "23 21_1304.tif \n",
+ "25 09_1104.tif \n",
+ "27 05_1018.tif \n",
+ "29 30_1327.tif \n",
+ "31 06_1029.tif \n",
+ "33 22_1310.tif \n",
+ "35 57_3338.tif \n",
+ "37 56_3228.tif \n",
+ "39 18_1227.tif \n",
+ "41 36_2310.tif \n",
+ "43 19_1230.tif \n",
+ "45 59_3326.tif \n",
+ "47 32_2337.tif \n",
+ "49 39_2237.tif \n",
+ "51 48_2013.tif \n",
+ "53 43_2200.tif \n",
+ "55 46_2104.tif \n",
+ "57 25_1321.tif \n",
+ "59 44_2134.tif \n",
+ "61 04_1013.tif \n",
+ "63 23_1315.tif \n",
+ "65 15_1136.tif \n",
+ "67 17_1218.tif \n",
+ "69 53_3128.tif \n",
+ "71 37_2304.tif \n",
+ "73 13_1128.tif \n",
+ "75 10_1110.tif \n",
+ "77 12_1127.tif \n",
+ "79 35_2323.tif \n",
+ "81 60_3319.tif \n",
+ "83 11_1119.tif \n",
+ "85 38_2302.tif \n",
+ "87 47_2036.tif \n",
+ "89 58_3334.tif \n",
+ "91 55_3227.tif \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get results.csv from each individual chamber\n",
+ "\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "data_folder = Path(\"./automated_executions\") \n",
+ "dfs = []\n",
+ "for sub_folder in data_folder.glob(\"*\"): # hole dir alle Ordner, die mit UTEX enden\n",
+ " try:\n",
+ " data_file = sub_folder / \"tmp\" / \"results.csv\"\n",
+ " sub_df = pd.read_csv(data_file, delimiter = ';')\n",
+ " sub_df[\"experiment\"] = sub_folder.name\n",
+ " dfs.append(sub_df)\n",
+ " except:\n",
+ " print('No results.csv found in {}'.format(sub_folder))\n",
+ "\n",
+ "joint_df = pd.concat(dfs, ignore_index=True)\n",
+ "\n",
+ "# Group dataframe by category (code by chat gpt) \n",
+ "grouped_df = joint_df.groupby('Unnamed: 0')\n",
+ "\n",
+ "count_df = grouped_df.get_group('µ_count [1/h]')\n",
+ "\n",
+ "area_df = grouped_df.get_group('µ_area [1/h]')\n",
+ "\n",
+ "print(area_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pathlib import Path\n",
+ "\n",
+ "# Grab calibration results from Calibration folder\n",
+ "\n",
+ "Calibration = Path(\"..\") / \"..\" / \"Calibration\" / \"Meta-fit.csv\"\n",
+ "\n",
+ "df_calibration = pd.read_csv(Calibration, sep = ';', encoding = 'utf8', header = 0, index_col=0, decimal=',')\n",
+ "\n",
+ "# Then specify the gradient that was used\n",
+ "\n",
+ "Light_Intensity_Homo = 190 # Specify light-intensity of homogeneous illumination\n",
+ " \n",
+ "slope = Light_Intensity_Homo * df_calibration['Slope'].iloc[0] + df_calibration['Interception'].iloc[0]\n",
+ "intercept = Light_Intensity_Homo * df_calibration['Slope'].iloc[1] + df_calibration['Interception'].iloc[1]\n",
+ "\n",
+ "Total_Number_chambers = 40 # Specify number of chambers present on chip\n",
+ "First_Chamber_Calibration = 38 # First chamber seen in calibration picture\n",
+ "Last_Chamber_Calibration = 40 # Last chamber seen in calibration picture\n",
+ "\n",
+ "step = (23460-60)/(Total_Number_chambers - 1) # From CleWin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " index Chamber Vertical_Position Horizontal_Position Intensity \\\n",
+ "0 0 03_1009.tif 6 3 10.419226 \n",
+ "1 2 29_1331.tif 4 38 76.213848 \n",
+ "2 4 28_1339.tif 4 40 79.973541 \n",
+ "3 6 14_1135.tif 4 19 40.496768 \n",
+ "4 8 41_2224.tif 9 27 55.535539 \n",
+ "5 10 42_2212.tif 9 24 49.896000 \n",
+ "6 12 40_2234.tif 11 29 59.295231 \n",
+ "7 14 07_1036.tif 5 10 23.578151 \n",
+ "8 16 33_2330.tif 11 38 76.213848 \n",
+ "9 18 20_1302.tif 7 31 63.054924 \n",
+ "10 20 24_1319.tif 4 35 70.574309 \n",
+ "11 22 21_1304.tif 5 32 64.934770 \n",
+ "12 24 09_1104.tif 5 12 27.337843 \n",
+ "13 26 05_1018.tif 7 5 14.178919 \n",
+ "14 28 30_1327.tif 4 37 74.334002 \n",
+ "15 30 06_1029.tif 6 8 19.818458 \n",
+ "16 32 22_1310.tif 7 33 66.814617 \n",
+ "17 34 57_3338.tif 15 40 79.973541 \n",
+ "18 36 56_3228.tif 13 28 57.415385 \n",
+ "19 38 18_1227.tif 4 27 55.535539 \n",
+ "20 40 36_2310.tif 11 33 66.814617 \n",
+ "21 42 19_1230.tif 7 28 57.415385 \n",
+ "22 44 59_3326.tif 15 37 74.334002 \n",
+ "23 46 32_2337.tif 10 40 79.973541 \n",
+ "24 48 39_2237.tif 10 30 61.175078 \n",
+ "25 50 48_2013.tif 10 4 12.299073 \n",
+ "26 52 43_2200.tif 9 21 44.256461 \n",
+ "27 54 46_2104.tif 9 12 27.337843 \n",
+ "28 56 25_1321.tif 6 36 72.454156 \n",
+ "29 58 44_2134.tif 11 19 40.496768 \n",
+ "30 60 04_1013.tif 6 4 12.299073 \n",
+ "31 62 23_1315.tif 4 34 68.694463 \n",
+ "32 64 15_1136.tif 5 20 42.376614 \n",
+ "33 66 17_1218.tif 7 25 51.775846 \n",
+ "34 68 53_3128.tif 13 18 38.616921 \n",
+ "35 70 37_2304.tif 9 32 64.934770 \n",
+ "36 72 13_1128.tif 5 18 38.616921 \n",
+ "37 74 10_1110.tif 7 13 29.217690 \n",
+ "38 76 12_1127.tif 4 17 36.737075 \n",
+ "39 78 35_2323.tif 8 36 72.454156 \n",
+ "40 80 60_3319.tif 12 35 70.574309 \n",
+ "41 82 11_1119.tif 4 15 32.977382 \n",
+ "42 84 38_2302.tif 11 31 63.054924 \n",
+ "43 86 47_2036.tif 9 10 23.578151 \n",
+ "44 88 58_3334.tif 15 39 78.093695 \n",
+ "45 90 55_3227.tif 12 27 55.535539 \n",
+ "\n",
+ " Channel \n",
+ "0 2.0 \n",
+ "1 2.0 \n",
+ "2 2.0 \n",
+ "3 2.0 \n",
+ "4 3.0 \n",
+ "5 3.0 \n",
+ "6 3.0 \n",
+ "7 2.0 \n",
+ "8 3.0 \n",
+ "9 2.0 \n",
+ "10 2.0 \n",
+ "11 2.0 \n",
+ "12 2.0 \n",
+ "13 2.0 \n",
+ "14 2.0 \n",
+ "15 2.0 \n",
+ "16 2.0 \n",
+ "17 4.0 \n",
+ "18 4.0 \n",
+ "19 2.0 \n",
+ "20 3.0 \n",
+ "21 2.0 \n",
+ "22 4.0 \n",
+ "23 3.0 \n",
+ "24 3.0 \n",
+ "25 3.0 \n",
+ "26 3.0 \n",
+ "27 3.0 \n",
+ "28 2.0 \n",
+ "29 3.0 \n",
+ "30 2.0 \n",
+ "31 2.0 \n",
+ "32 2.0 \n",
+ "33 2.0 \n",
+ "34 4.0 \n",
+ "35 3.0 \n",
+ "36 2.0 \n",
+ "37 2.0 \n",
+ "38 2.0 \n",
+ "39 3.0 \n",
+ "40 4.0 \n",
+ "41 2.0 \n",
+ "42 3.0 \n",
+ "43 3.0 \n",
+ "44 4.0 \n",
+ "45 4.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract Postion from Naming of Image Sequence\n",
+ "\n",
+ "Channels = []\n",
+ "Horizontal_Positions = []\n",
+ "Vertical_Positions = []\n",
+ "Intensities = []\n",
+ "\n",
+ "for chamber in area_df['experiment']: # Extract Postion from Naming of Image Sequence\n",
+ " Identifier_a = float(chamber[3]) # First number decodes channel\n",
+ " Identifier_b = float(chamber[4]) # The last three numbers decode Position\n",
+ " Identifier_c = float(chamber[5:7])\n",
+ " Channel = Identifier_a +1\n",
+ " Channels.append(Channel)\n",
+ " Horizontal_Position = int(Identifier_b*10 + round(((Identifier_c + 1)/4) + 0.49)) # Calculate Horizontal Position\n",
+ " Vertical_Position = int((((Identifier_c + 1)/4 - round(((Identifier_c + 1)/4) - 0.49))*4) + Identifier_a * 4) # Calculate Vertical Position\n",
+ " Intensity = intercept + ((Horizontal_Position - 1) - First_Chamber_Calibration)*step*slope\n",
+ " Horizontal_Positions.append(Horizontal_Position)\n",
+ " Vertical_Positions.append(Vertical_Position)\n",
+ " Intensities.append(Intensity)\n",
+ "\n",
+ "information_position = pd.DataFrame({'Chamber': count_df['experiment'],\n",
+ " 'Vertical_Position': Vertical_Positions,\n",
+ " 'Horizontal_Position': Horizontal_Positions,\n",
+ " 'Intensity': Intensities,\n",
+ " 'Channel': Channels}).reset_index()\n",
+ "\n",
+ "print(information_position)\n",
+ "information_position.to_csv(str('information_position.csv'), sep=';')\n",
+ "area_df.to_csv(str('µarea.csv'), sep=';')\n",
+ "count_df.to_csv(str('µcount.csv'), sep=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plot growth rate over CO2 concentration\n",
+ "\n",
+ "CO2 = [100, 50, 15, 0, 400] # define CO2 concentrations; first to last\n",
+ "\n",
+ "# calculate mean growth rates\n",
+ "\n",
+ "mu_count_mean = []\n",
+ "mu_area_mean = []\n",
+ "mu_count_std = []\n",
+ "mu_area_std = []\n",
+ "\n",
+ "for n in range(0,len(CO2)):\n",
+ " count_mean = np.mean(count_df.iloc[:,n+1])\n",
+ " area_mean = np.mean(area_df.iloc[:,n+1])\n",
+ " count_std = np.std(count_df.iloc[:,n+1])\n",
+ " area_std = np.std(area_df.iloc[:,n+1])\n",
+ " mu_count_mean.append(count_mean)\n",
+ " mu_area_mean.append(area_mean)\n",
+ " mu_count_std.append(count_std)\n",
+ " mu_area_std.append(area_std)\n",
+ "\n",
+ "# plot; first count then area\n",
+ "fig, ax = plt.subplots(1,2, figsize=(12, 4), facecolor='white', sharey = True)\n",
+ "\n",
+ "for n in range(0,len(count_df['experiment'])):\n",
+ " ax[0].plot(CO2, count_df.iloc[n,range(1,len(CO2)+1)], color='#adbde3', marker = 'o', linestyle = 'dotted')\n",
+ " ax[1].plot(CO2, area_df.iloc[n,range(1,len(CO2)+1)], color='#adbde3', marker = 'o', linestyle = 'dotted')\n",
+ "ax[0].errorbar(CO2, mu_count_mean, color='r', yerr=mu_count_std, marker = 'o', linestyle = 'dotted')\n",
+ "ax[1].errorbar(CO2, mu_area_mean, color='r', yerr=mu_area_std, marker = 'o', linestyle = 'dotted')\n",
+ " \n",
+ "ax[0].set_xlabel('CO$_2$-concentration [ppm]')\n",
+ "ax[1].set_xlabel('CO$_2$-concentration [ppm]')\n",
+ "ax[0].set_ylabel('Grwoth rate [1/h]')\n",
+ "# ax[1].set_ylabel('Grwoth rate [1/h]')\n",
+ "ax[0].set_title('Cell Count')\n",
+ "ax[1].set_title('Cell Area')\n",
+ "\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[0].set_xlim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "ax[1].set_xlim(0, )\n",
+ "\n",
+ "plt.savefig('CO2_dependency_growth_rate.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7f51bd3a4dc0>"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot PI Curve for each CO2 concentration\n",
+ "\n",
+ "# plot; first count then area\n",
+ "fig, ax = plt.subplots(1,2, figsize=(12, 4), facecolor='white', sharey = True)\n",
+ "corperate_idendity = ['#023d6b', '#adbde3', '#faeb5a', '#eb5f73', '#b9d25f', '#af82b9', '#fab45a', '#ebebeb'] # Fz Juelich corperate identity\n",
+ "\n",
+ "for n in range(0,len(CO2)):\n",
+ " ax[0].scatter(information_position['Intensity'], count_df.iloc[:,n+1], color=corperate_idendity[n], label = f'{CO2[n]} ppm')\n",
+ " ax[1].scatter(information_position['Intensity'], area_df.iloc[:,n+1], color=corperate_idendity[n])\n",
+ " \n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, )\n",
+ "ax[1].set_xlim(0, )\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " experiment µcount µarea std_count std_area \\\n",
+ "0 2023.08.15_10uE_AmbientCO2 0.021800 0.019503 0.002515 0.001579 \n",
+ "1 2023.08.01_140uE_AmbientCO2 0.097658 0.086454 0.004956 0.003792 \n",
+ "2 2023.03.01_80uE_AmbientCO2 0.088108 0.083899 0.005103 0.005700 \n",
+ "3 2023.08.08_50uE_AmbientCO2 0.083962 0.074748 0.004330 0.003089 \n",
+ "4 2023.06.27_20uE_AmbientCO2 0.045981 0.037946 0.005256 0.004308 \n",
+ "5 2023.07.18_60uE_AmbientCO2 0.087836 0.074547 0.006631 0.004350 \n",
+ "6 2023.07.25_30uE_AmbientCO2 0.069880 0.064130 0.005927 0.004519 \n",
+ "\n",
+ " Intensity \n",
+ "0 10.0 \n",
+ "1 140.0 \n",
+ "2 80.0 \n",
+ "3 50.0 \n",
+ "4 20.0 \n",
+ "5 60.0 \n",
+ "6 30.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "PI_curve = Path(\"..\") / \"..\" / \"..\" / \"..\"/ \"A2.2_PI_Curve_µFluidic_newSegAI\" / \"PI_curve_UTEX.csv\" # read previous experimentall data to compare\n",
+ "\n",
+ "df_PI_curve = pd.read_csv(PI_curve, sep = ';', encoding = 'utf8', header = 0, index_col=0)\n",
+ "print(df_PI_curve)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fit a PI curve model to data\n",
+ "\n",
+ "import numpy as np\n",
+ "from scipy.optimize import curve_fit\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def tanh_function(x, umax, a):\n",
+ " \"\"\"\n",
+ " Tanh function: a * tanh(b * (x - c)) + d\n",
+ " Parameters:\n",
+ " - umax: amplitude\n",
+ " - a: initial slope\n",
+ " \"\"\"\n",
+ " return umax * np.tanh(a*x/umax)\n",
+ "\n",
+ "def fit_tanh_to_data(x_data, y_data):\n",
+ " \"\"\"\n",
+ " Fit a tanh function to the given data.\n",
+ "\n",
+ " Parameters:\n",
+ " - x_data: Input data (independent variable)\n",
+ " - y_data: Output data (dependent variable)\n",
+ "\n",
+ " Returns:\n",
+ " - popt: Optimal values for the parameters (a, b, c, d)\n",
+ " \"\"\"\n",
+ "\n",
+ " # Initial guess for the parameters (you may need to adjust these)\n",
+ " initial_guess = (0.06, 0.0001)\n",
+ "\n",
+ " # Perform the curve fitting using scipy.optimize.curve_fit\n",
+ " popt, pcov = curve_fit(tanh_function, x_data, y_data, p0=initial_guess)\n",
+ "\n",
+ " return popt\n",
+ "\n",
+ "x_data = np.linspace(0,150,16)\n",
+ "\n",
+ "# Fit model to data from homogeneous experiments\n",
+ "\n",
+ "para_Homo_area = fit_tanh_to_data(df_PI_curve['Intensity'], df_PI_curve['µarea'])\n",
+ "para_Homo_count = fit_tanh_to_data(df_PI_curve['Intensity'], df_PI_curve['µcount'])\n",
+ "fit_Homo_area = tanh_function(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), * para_Homo_area)\n",
+ "fit_Homo_count = tanh_function(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), * para_Homo_count)\n",
+ "fit_Homo_area_extra = tanh_function(x_data, * para_Homo_area)\n",
+ "fit_Homo_count_extra = tanh_function(x_data, * para_Homo_count)\n",
+ "\n",
+ "# Fit model to data from gradient experiments\n",
+ "\n",
+ "fits_Grad_area = []\n",
+ "fits_Grad_count = []\n",
+ "paras_Grad_area = []\n",
+ "paras_Grad_count = []\n",
+ "\n",
+ "for n in range(0,len(CO2)):\n",
+ " para_Grad_area = fit_tanh_to_data(information_position['Intensity'], area_df.iloc[:,n+1])\n",
+ " para_Grad_count = fit_tanh_to_data(information_position['Intensity'], count_df.iloc[:,n+1])\n",
+ " fit_Grad_area = tanh_function(np.linspace(min(information_position['Intensity']), max(information_position['Intensity']), 50), * para_Grad_area)\n",
+ " fit_Grad_count = tanh_function(np.linspace(min(information_position['Intensity']), max(information_position['Intensity']), 50), * para_Grad_count)\n",
+ " fits_Grad_area.append(fit_Grad_area)\n",
+ " fits_Grad_count.append(fit_Grad_count)\n",
+ " paras_Grad_area.append(para_Grad_area)\n",
+ " paras_Grad_count.append(para_Grad_count)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharey=True)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].errorbar(df_PI_curve['Intensity'], df_PI_curve['µcount'], yerr = df_PI_curve['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='Homogeneous', zorder = 1)\n",
+ "ax[1].errorbar(df_PI_curve['Intensity'], df_PI_curve['µarea'], yerr = df_PI_curve['std_area'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', zorder = 1)\n",
+ "\n",
+ "for n in range(0,len(CO2)):\n",
+ " ax[0].scatter(information_position['Intensity'], count_df.iloc[:,n+1], color=corperate_idendity[n], label = f'{CO2[n]} ppm')\n",
+ " ax[1].scatter(information_position['Intensity'], area_df.iloc[:,n+1], color=corperate_idendity[n])\n",
+ " \n",
+ "ax[0].set_ylim(0, 0.15)\n",
+ "ax[1].set_ylim(0, 0.15)\n",
+ "\n",
+ "ax[0].set_xlim(0, 100)\n",
+ "ax[1].set_xlim(0, 100)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.3), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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3wK+5uZnvvvuOY489tr0xus6xxx7LV1991aNjX3nllZx88skexw7EbrdTU1Pj8SWEEEJECumnhBADRWF5cOenYPcT4dNvgV9ZWRkul4shQ4Z4bB8yZAhFRUXdPu7LL7/MmjVruOuuu4J+zl133UViYmLbV25ubrdfXwghhAg36aeEEANFVmpCWPcT4dPvyV3Cae/evVx77bX885//JCoqKujn3XzzzVRXV7d97d27txdbKYQQQoRG+ikhxEAxN28MQ9OT0Pw8rgG5GUnMzRvTl80S9GM5h7S0NEwmE8XFxR7bi4uLu0zc4s93331HSUkJhxxySNs2l8vFF198wSOPPILdbsdkMnk9z2azBVwzKISIXEopyqrsNNkNomw6aUk2NM1fdyPEwCT9lBBioDCZdO6/5iwWLlmGBh5JXlp75/uuPkvq+fWDfvvErVYr06dP55NPPmnbZhgGn3zyCYcffni3jnnMMcfw/fffs27duravGTNmcMEFF7Bu3TqfQZ8QYuAqKGnkg5VFrFhbxupNFaxYW8YHK4soKGns76YJIYQQB6wF86bx6u2XkpOe5LF9aEYSr95+qdTx6yf9WsB98eLFXHjhhcyYMYNDDz2UBx54gPr6ei6++GIAFi1aRE5OTtt6vebmZjZt2tT274KCAtatW0dcXBxjxowhPj6eyZMne7xGbGwsqampXtuFEANbQUkjqzaUe21vtLtYtaGcWZNTycmI7oeWCSGEEGLBvGnMn5PHivztFJbXkJWawNy8MTLS14/6NfA755xzKC0t5ZZbbqGoqIhp06bxwQcftCV82bNnD7re/suxf/9+Dj744Lbv77nnHu655x7mzZvH559/3tfNF0L0E6UU+duqAu6Tv62K7PQomfYphBBC9BOTSeeog8f1dzNEC00p5au+4gGtpqaGxMREqqurSUiQjENCRJrSyiZWrC3rcr+5B6eRnhx8oicx+A2W8/tgeR9CCCE89eb5XcZahRADTpPdCOt+QgghhBCDnQR+QogBJ8oW3Kkr2P2EEEIIIQY7uSoSQgw4aUk2om2Bs/RG20ykJUn6eyGEEEIIkMBPCDEAaZpG3tikgPvkjU2SxC5CCCGEEC0k8BNCDEg5GdHMmpzqNfIXbTNJKQchhBBCiE76tZyDEEL0RE5GNNnpUZRV2WmyG0TZdNKSbDLSJ4QQQgjRiQR+QogBTdM0KdkghBBCCNEFCfyEEH3G5TJYkb+dwvIaslITmJs3BpNJZpwLIYQQQvQ2CfyEEH3izeXruP6h19lXWtW2bWh6EvdfcxYL5k3rt3YJIYQQQhwI5Fa7EKLXvbl8HQuXLPMI+gAKSqtYuGQZby5f1y/tEkIIIYQ4UEjgJ4ToVS6XwfUPvY7y8VjrtsUPv47LZfRls4QQQgghDigS+AkhetWK/O1eI30dKWBvSRUr8rf3WZuEEEIIER4ul8Hna7fy0sff8vnarXIjN4LJGj8hRK8qLK8J635CCCGEiAyyfn9gkRE/IUSvykpNCOt+QgghhOh/sn5/4JHATwjRq+bmjWFoehL+SqprQG5GEnPzxvRls4QQQgjRTbJ+f2CSwE+IMJO57p5MJp37rzkLwCv4a/3+vqvPknp+QgghxAAh6/cHJlnjJ0QYyVx33xbMm8art1/q/dlkJHHf1Qf2ZyOEEGJgUEpRVmWnyW4QZdNJS7Khaf7mswxusn5/YJLAT4gwaZ3r3nnaQ+tc91dvv/SADnAWzJvG/Dl5rMjfTmF5DVmpCczNGyMjfUIIISJeQUkj+duqaLS72rZF20zkjU0iJyO6H1vWP2T9/sAkgZ8QYdDVXHcN91z3+XPyDuhAx2TSOergcf3dDCGEECJoBSWNrNpQ7rW90e5i1YZyZk1OPeCCv9b1+wWlVT6vfTTcs3pk/X5kOXCvQIUII5nrLoQQQgw+Sinyt1UF3Cd/WxVK+Qp/Bi9Zvz8wyU9DiDCQue5CCCHE4FNWZfeY3ulLo91FWZW9j1oUOVrX7+ekJ3lsH5qRdMAvb4lUMtVTiDCQue5CCCHE4NNkDy4zd7D7DTayfn9gkcBPiDCQue5CCCHE4BNlCy6ACXa/wUjW7w8cB+5vqRBhJHPdhRBCiMEnLclGtM0UcJ9om4m0JFsftUiI7pOrUCHCROa6CyGEEIOLpmnkjU0KuE/e2KQDtp6fGFhkqqcQYSRz3YUQQojBJScjmlmTU6WOnxjwJPATIsxkrrsQQggxuORkRJOdHkVZlZ0mu0GUTSctySYjfWJAkcBPCCGEEEKILmiaRnpyVH83Q4huk/lnQgghhBBCCDHISeAnhBBCCCGEEIOcTPUUQogBzOUyJJmQECIkct4Q4sDU73/ljz76KCNGjCAqKopZs2bxzTff+N1348aNnHnmmYwYMQJN03jggQe89rnrrruYOXMm8fHxZGRkcPrpp7Nly5ZefAdCCNE/3ly+jlELb+GYax/iZ398hmOufYhRC2/hzeXr+rtpQogIJeeNwFwug8/XbuWlj7/l87VbcbmM/m6SEGHTr4HfK6+8wuLFi1m6dClr1qxh6tSpHH/88ZSUlPjcv6GhgVGjRvHnP/+ZzMxMn/ssX76cK6+8kq+//pqPPvoIh8PBcccdR319fW++FSGE6FNvLl/HwiXL2Fda5bG9oLSKhUuWyUWcEMKLnDcCk6BYDHaaUkr114vPmjWLmTNn8sgjjwBgGAa5ublcffXV/Pa3vw343BEjRnDddddx3XXXBdyvtLSUjIwMli9fzpFHHhlUu2pqakhMTKS6upqEhISgniOEEH3F5TIYtfAWr4u3VhowNCOJHa/8UaZvdTJYzu+D5X2IviPnjcBag+LOF8WtxRpevf1SFsyb1setEgei3jy/99tfdnNzM9999x3HHntse2N0nWOPPZavvvoqbK9TXV0NQEpKit997HY7NTU1Hl9CCBGpVuRv93vxBqCAvSVVrMjf3mdtEr1L+inRU3Le8M/lMrj+ode9gj6gbdvih1+XaZ9iwOu3wK+srAyXy8WQIUM8tg8ZMoSioqKwvIZhGFx33XUcccQRTJ482e9+d911F4mJiW1fubm5YXl9IYToDYXlwV30B7ufiHzST4mekvOGfxIUiwPFoB7Lv/LKK9mwYQMvv/xywP1uvvlmqqur27727t3bRy0UQojQZaUGN/Uj2P1E5JN+SvSUnDf8k6BYHCj6rZxDWloaJpOJ4uJij+3FxcV+E7eE4qqrruK9997jiy++YOjQoQH3tdls2Gy2Hr+mEEL0hbl5YxiankRBaZXPqUmta3Xm5o3p66aJXiL9lOipcJw3BmsZCAmKxYGi3/5arVYr06dP55NPPmnbZhgGn3zyCYcffni3j6uU4qqrruKtt97i008/ZeTIkeForhBCRAyTSef+a84C2hMPtGr9/r6rzxoUF2RCiPDo6XljMGe8bA2KO38urTQgV26miUGgX68KFi9ezN///neeffZZfvjhB371q19RX1/PxRdfDMCiRYu4+eab2/Zvbm5m3bp1rFu3jubmZgoKCli3bh3bt7fPub7yyit54YUXePHFF4mPj6eoqIiioiIaGxv7/P0JIURvWTBvGq/efik56Uke24dmJEn2OSGET909bwz2MhByM00cKPq1nAPAI488wt13301RURHTpk3joYceYtasWQAcddRRjBgxgmeeeQaAXbt2+RzBmzdvHp9//jkAmub7fs3TTz/NRRddFFSbJE22EGKgGKxTr3rLYDm/D5b3IfpHKOeNA6kMxJvL13H9Q697vNfcjCTuu/osuZkm+kxvnt/7PfCLRNKhCiHE4DRYzu+D5X2IyPf52q0cc+1DXe73yYPXcNTB4/qgRb1LbqaJ/tab5/d+S+4ihBBCCCEi24GW8dJk0gdFACuEL3ILQwghhBBC+CQZL4UYPGTETwgREZRSlFXZabIbRNl00pJsftfsCiGE6BtSPkYAKGVQWb8Fu7MamzmR5NjxaJqMHw00EvgJIfpdQUkj+duqaLS72rZF20zkjU0iJyO6H1smhBAHttaMlwuXLEMDj+BPMl4eGIqrv2Vz4T+xOyrbttksyUzIuoAhiTP6sWUiVPJXKoToVwUljazaUO4R9AE02l2s2lBOQYmUYhFCiP4k5WMOXMXV37J+zyMeQR+A3VHJ+j2PUFz9bT+1THSHjPgJIfqNUor8bVUB98nfVkV2epRM+xRCiH60YN405s/Jk4yXBxClDDYX/jPgPpsLXyQj4RCZ9jlASOAnhOg3ZVV2r5G+zhrtLsqq7KQnR/VRq/qHrJ8QQkQ6yXh5YKms3+I10teZ3VFBZf0WUuIm9lGrRE9I4CeE6DdNdiOs+/WF3qjxJOsnhBBCRBq7szqs+4n+J4GfEKLfRNmCC5iC3a+3vbl8Hdc/9Dr7Sqvatg1NT+L+a87q9hqX1vUTnbWun5g67CoJ/oQQQvQ5mzkxrPuJ/hcZV1NCiANSWpKNaJsp4D7RNhNpSbY+apF/by5fx8IlyzyCPoCC0ioWLlnGm8vXhXzMYNdPKOV/xNPlMvh87VZe+vhbPl+7FZcrckZHhRBCDFzJseOxWZID7mOzpJAcO76PWiR6Skb8hBD9RtM08sYmsWpDud998sYm9XtiF5fL4PqHXvdZw0rhTmm++OHXmT8nL6Rpnz1dP9EbI5BCCCEiQ3/Xt9U0nQlZF/icldJqQtb5sh59AJGflBCiX+VkRDNrcqrXyF+0zcSsyakRUcdvRf52r5G+jhSwt6SKFfnbQzpuT9ZP9MYIpBBCiMhQUNLIByuLWLG2jNWbKlixtowPVhb1uMRRqLNEhiTOYOqwq7xG/myWFFmKMADJiJ8Qot/lZESTnR7Vr3c2Ayksrwnrfq26u36it0YghRBC9C1fCcOKyu0+Z8K01rft7k3R7s4SGZI4g4yEQyTz9CAggZ8QIiJomhaxJRuyUhPCul+r1vUTgaZ7+lo/EcoIpKReF0KIyOQrEMvNSOa+X18K+F//3lV9W1/B5Dtf5rNwyTKvG4ats0Revf3SgMGfYUD+NhOF5WayUk3MzQNT4CX6IgJJ4CeEEF2YmzeGoelJFJRW+Rxl04ChGUnMzRsT0nG7u36it0YghRBC9I3W6fqd+5SEmHgCBX0QuL6tr2AyJy2RpmZnt2eJyHrywUPGaIUQogsmk87915wFuDvIjlq/v+/qs7o1rbI76yd6awRSCCFE7ws0XT8pLjaoY/iqb+t37XdZNeU19X6PFWiduqwnH1xkxE8IIYKwYN40Xr39Uu+7nhlJ3Hd1z+56hrp+ordGIIUQQvS+QNP1K+v8B2gdda5vGyiYDFbnWSKynnzwkcBPCCGCtGDeNObPyfNaOxGODk/TdJ8lG3xpHYFcuGQZGnh0yj0dgRRCCBE6X+vq/J2DA03D37x7L2XVNaQkxKP7WcPnq75tV2u/g9F5loisJx98JPATQogQmEx6RHRwvTkCKYQQInihroELNA3fUIqnP/iYGxeeQfu4midf9W17sqbb3ywRWU8++EjgJ4QY0JQyUI7NoKpAS0KzTDhgUkz35gikEEKIrvlL0hIoW2ZX0/VX/7CVZ/77Ib867USamtvX8kXbTOSNTfJZyqG7a7oDzRKR9eSDjwR+QogBy7CvxlX/HBgV7Rv1FEyxi9BtM/uvYX0oUkYghRDiQNPdNXDBTNe/+OTpnHhEVtD1bYNZ+52SEEOUzUpBgFkiSimamh00NDUzYkgqQ1LiKa6o9fsZJMVF89X3O1m+dhuNdgdNzU6amh3YHU7sjpZ/Nztpdji58KTD+Nlxh/o9luh9EvgJIQYkw74aV+0DPh6oaNl+3QET/AkhhOh7PVkDF+x0/a7q2yqlqGu0U13XyOJzj2Hxw2/4bcv0CcNJS4pjd2E5VXWNGMpAR+M3j73Flfe9Qn2TnYYmB0oFnyKmqq6RPyz7V1D7zp0qCcf6mwR+QogBRynDPdIXgKv+OTTr9LBP+wxlAb8QQojBq6dr4DpO199bXElMlIVROelU1jTw+udrqaipp7KmgYraBve/axuormuksraBqrpGquoaqa5vxDCCC9Q+/OaHoN8bgNViJsZmQdM0auqbcBnt005tVjN5o3MYnZNGlNXS8mUmymrBZjG3fW+1mtu+zxudE9Lri/CTwE8IMeAox2bP6Z2+GBUox2Y066Swva4UsRVCCNEq2LVtReXVvPjRakor6yipqqW4opaSqlpKK2spraqjrLqe2oamHrXFbNJJjIsmMTaauGgrALqukxwfw6jsNBJjo4iPiSIu2kZstI241q8YGzE2C7HRNmKjrMRG2YiJshBjs2I2txeSl5ueg4MEfkKIsFJKBb0mofsvUhXe/YLQnQX8QgghBh+H0+VeS6cgJT6GitqGgPvf+OhbQR3XZNJJTYh1fyXGkhwfQ0pCDCnxsaQkxJAcH0NSfAxJcdEkx8eQGBtNUlw0iXHRRLeMzPUWWU8+OEjgJ4QIm4KSRvK3VdFod7VtC5SFrNu0pPDu1wUpYiuEEAeOZoeTPcWV/Li/jJ2F5ewqLGdXUTl7iyvZU1LJ/rLqoNfBxURZGT4khcyUBDJS4slIiicjOY6MlATSE+PISI4jLTGOtKQ4EmOj0HXpQ0TvkcBPCBEWBSWNrNpQ7rW90e5i1YZyZk1ODVvwp1kmgJ4SeLqnnuLeLwykiK0QQgwuzQ4nOwrK2LavhO37StleUMr2faVs21fC3pKqLgM7q8VMbkYSuRnJuFwGa7fto67R3vZ4VmoC9155JuccO71vZsJEqAP5vUciCfyEED2mlCJ/W1XAffK3VZGdHhWWE76m6ZhiF/nO6tnCFLsobIldpIitEEIMTPWNdjbtKmLjrkI27ypi855ituwpZsf+Mlwuw+/zom0WRmalMiIrlZEtX8OGpLR8JZOeFOcxOudvDVyfzYSJQAfye49UEvgJIXqsrMrucWL3pdHuoqzK3mVq6mC5SzVc1yd1/KSIrRBCRDbDMPhxfznrtu8jf3sBG3buZ8OP+/lxf7nf0bu4aBvjcjMYMzSdMTnpjBmaztihGYzOSSMjOT6kG5W+1sD15UyYSHMgv/dIFlTgt3jx4pAP/Ic//IGUlJSQnyeEGHia7P7vmnZnv2Dptplo1unuLJ+qCrQkNMuEsJdwCKYw7tCMJObmSY0iIX2mEL3NMAy27Cnhmx92sXbrPtZt28u67QV+M2NmJMdz0IgsJo7IZMKwIUxo+X92WmKvTTvs65kwkeRAfu+RLqjA74EHHuDwww/HarUGddAvv/ySq666SjoxIQ4QUbbgAq1g9wuFpulhLdngi8mkc/81Z7FwyTI08Aj+Wrus+64+SxK7CED6TCHCrai8hq827mT1D7tY/cNuVm/e4zPIs1nNTBmVzdQxQ5kyKpuDRmYxeVQ2Gcnxfd7m/pgJEykO5Pce6YKe6vnWW2+RkZER1L7x8cH/gT366KPcfffdFBUVMXXqVB5++GEOPfRQn/tu3LiRW265he+++47du3dz//33c9111/XomEKInktLshFtMwU80UfbTKQl2fqwVeG1YN40Xr39Uu86fhlJ3He11PETnnqrzxRisDMMg817ivnf9z/yv/wdrNzwIzsKyrz2i4mycsi4XKaPH8a0sUM5eOxQJgzPxNKh9lx/6q+ZMJHgQH7vkS6owO/pp58mMTEx6IM+8cQTDBkypMv9XnnlFRYvXszjjz/OrFmzeOCBBzj++OPZsmWLzw6zoaGBUaNGcfbZZ3P99deH5ZgDiRTPFJFK0zTyxib5nM/fKm9sEoahWJG/zefv8ED4/V4wbxrz5+RFfDtF/+qtPlOIwUgpxda9JXy6ZiufrdnK8rXbKKuu89hH0zQmj8xi1kEjmDlxBIdOHM6k4ZkeBcYjTX/OhOlvB/J7j3SaCrYQSS+YNWsWM2fO5JFHHgHcd3lyc3O5+uqr+e1vfxvwuSNGjOC6667zGvHryTFb1dTUkJiYSHV1NQkJkZGs4c3l67xHGtKTuP8aGWkQkSNQBq9VP2zx+zsMyO+36BOReH7vjsHyPsSBqaSylg9X/8CH3/zAp99t8cqIHG2zMGvSCGZPHsUReaM5bNIIkuJj+qm13aOU4oOVRV3OhDlhduagW+d2IL/3cOjN83u3s3o2NzdTUlKCYXgO0w4bNizo53/33XfcfPPNbdt0XefYY4/lq6++6nabunNMu92O3d5ee6WmJrJSsr+5fB0LlyzzSipRUFrFwiXLePX2S+XiWESEnIxostOjvGr2vPXFer+/w2cvWebzWAPx97s/Ri0Hwkip6HmfGen9lBCBOJ0uVv2wiw++3sR/v9nEd1v2ejxus5qZfdAojj5kHEcdMpaZE4ZjtQzsxPPBzoQZjIHPgfzeI13If1Xbtm3jkksuYeXKlR7blVJomobLFXgxZ6uysjJcLpfX9JYhQ4awefPmUJvVo2Pedddd3Hbbbd16zd7mchlc/9DrPjMJKtyJJRY//Drz5+TJxZ6ICIah2LhrT1sgMvugUQF/h/0ZaL/f/TEqLzMBIl+4+sxI7qeEAO+bUIeMzeWTNVt5Z8V6/v31Rsqr6z32P3jsUI47dCI/nTmRww8aSZTN0k8t7z05GdHMmpx6QNayy8mIBr2Gymqd5Pi4tu2VtbUkJypyMob2Y+sOXCEHfhdddBFms5n33nuPrKysQRGt33zzzR7pt2tqasjNze3HFrVbkb/d46KuMwXsLaliRf52r/oxQvQ1X4FIWmIsZZ06/GANlN/vYEfllVJeo6HdPYfKTICBIVx9ZiT3U0L4Ovd3lpIQw3GHTuT4Qydx3MyJZB4gdU/9zYSJxOvnsPdRtyxD0zQmDM8lOS6Wyrp6Nu/ei1JK+qh+EnLgt27dOr777jsmTJjQoxdOS0vDZDJRXFzssb24uJjMzMw+PabNZsNmi8xsg53nvfd0PyF6i79ApLtBX0eR/Psd7Kj8zPFj+X57NU3N7VP9oqw6U8clh3zXV2YCDBzh6jMjuZ8SB7Z/fvgNi/70nN/HTz58MjecdwxHTB4V0clYepOmaX1atqA7AVygNfo96aOUUmzatcfjcemj+k/In/akSZMoK/NOqxsqq9XK9OnT+eSTT9q2GYbBJ598wuGHHx4xx+xvWUHeEQt2PyF6Q6BAJBwi+fc7mFH5rNQMvtlY4bXQvdHuYtWGMgpKGrv9mrqmMWnEMI6YPJFJI4aha5rHSKnoX+HqM4WIJM0OJ++syOfM3z8ZMOjTgPwd+5gzZfQBG/T1tYKSRj5YWcSKtWWs3lTBirVlfLCyKGA/U1DSyKoN5X76qPIe9VG+SB/Vf4Ia8eu4iPwvf/kLv/nNb7jzzjuZMmUKFovnnOxQss8sXryYCy+8kBkzZnDooYfywAMPUF9fz8UXXwzAokWLyMnJ4a677gLci+M3bdrU9u+CggLWrVtHXFwcY8aMCeqYA83cvDEMTU+ioLTK50W1hruO2Ny8MX3dNBFmAzlJR1cn+e4aCL/fXY1G6prGxSccC+B1x1XTNAylWLWhhDOOHhb0lJrW1zx04jguPuFY0hLbz7tl1TU8/cHHfPPD1ogeKR3MeqvPFKK/rdu2j2f/8zUvfvStV8kFXwbKdP3BojWA66w1gJs1OdVr9E4pRf62qoDHzd9WxZAUK19+vyOoaxSZrRa5ggr8kpI8M+8opTjmmGM89gl1oTrAOeecQ2lpKbfccgtFRUVMmzaNDz74oC05y549e9D19l+q/fv3c/DBB7d9f88993DPPfcwb948Pv/886COOdCYTDr3X3MWC5csQ8MzGUbrT+S+q88aMAGC8G2gJ+no7sm74+/0QP39HpIcuPj2hOG5HoFZZ7qmASZKKpoYkhrcdJqs1AQOnTiOGxee4fVYSkI8Ny48g3tefSuiR0oHs97qM4XoD9V1jTz/31X8472vyN9R0LY9MyWB6eOH8f5XG7o8hlzg975gA7js9CiP81NZlT1g2QVwB47HXf8Ey9dvatsW6BpFZqtFrqACv88++6zXGnDVVVdx1VVX+XysNZhrNWLECIIpOxjomAPRgnnTePX2S70Dg4wk7rt6YAQGwr/BkKQj2JN3elIcpVXtd4lbf4fBRx2/AfL7rbqY4JocFxvUcdZt38/xqaOD2nfOlNFcetJxgPcoot4yinjpST9lzpTgjifCqzf7TCH6ypote3n8nRW89PG3NDQ1A2C1mJk/J49FJ8ziuJkT+PL7HUEFfpF2gT+QZ9j4E2wAV1Zl91hv2GQ3AjyjndPl2dcFukaR2WqRK6jAb968eTz11FOceuqppKen93abhA8L5k1j/py8QXeiOtANliQdwZ7kt754Kys3/ujzd3ig/n6XVAae7lRZF1xym4qa2qBfs7LW4ZEeuzNd00iOj6ey1kF6sqyr6WvSZ/o2GC+2B5tmh5OXP/mOv731Bd/8sLtt+0Ejs7j8tDmc99MZpCS038waiBf4A32GjT/BBnCd94uyBfc32LkvC3SNIrPVIlfQWT1feOEFfv3rX3PIIYcwf/58TjvtNCZOnNibbROdmEy6zJEfZAZLuY5gT/JWq9nv++jr3+9wpa3u6k725t17KauuISUhvmVapydDKSpqamlwVAT9mt3t4EXfkT7T02C92B4sKmrqefLdL3nkjeVt0zItZhNnHjWNK+bPZU7eaJ/nx4F2gT8YZtj4E2wAZ7VofL52a9sNmDlTRhNtM/kdLWztozbv3uv1WKBrFJmtFpmCDvw+/fRTKisref/993n33Xe54447GDJkCKeddhrz589nzpw5HuvxhBjMwnXnejAtgB5IJ/lwpq3u6o63oRRPf/AxNy48A0Mpj+DPUAoNePqDj7nqzOAzDwfbwQe7nwg/6TPbDeaL7YFuR0EpD772GU//++u26ZzZaYlcueBILjl5NhldrGGGgXPuHywzbPxJS7IFDODcXMy6/E72llS2bRmansT9V58PeN/EVB36KCPAUit/1ygyWy3yaCqYRXM+NDc38+mnn/Luu+/yr3/9i8bGRk466SROO+00TjzxRGJjg1vXEolqampITEykurpaMq4JL+G8c/352q0cc+1DXe73yYPXRPSIX0eRPp3LX9azVr6ynnWl9cIWvO94t37fVQbOj+6/Gl3XgvrclFJ8sLIoYAcfbTNxwuzMiCwS3J/66/we7j5zoPRTLpfBqIW3+J3Z0DoVcMcrf4yo88Rgt2lXIXc+919e+fQ7DMN9lpo6JofrzzmGc35yCFZLyGWeI/7cPxj7284C92+Ke155i1U/bPXY2tpDvPrHXxNtSfboVzTN4K8vv803nZ7T2UD+zCJRb57fux34dfbtt9/y7rvv8s4773DWWWexZMmScBy2XwyUDlX0PX93rttOnCHeuW69KOrJ2rjBpLCwkMLCQr+PZ2VlkZWV1e3jhyNgcjicFJQ1UNfgQCkXuslJU7OT/67axIOvfe6R4jw5PobT5+Tx5hfrqK5vQtc0JgzPJTkulsq6ejbv3ouhFFazCU0Du6O9XRaziazUBKJtVpodThxOF81OF46Wr6ljRnHtgtNQ4HMKaXcC2MFMKQMMJzVVFSSlZfX7+b2nfeZA6acOhIvtgSR/RwF3PPcBb3y+ri1Z3vGzJnHDucfwk0PGDeobRS99/C0/++MzXe733B8WkZOeFNb+ti+DYt8zWnQeffs//GfVWp/Pab3W2P7ybVTWOtqWQCTHWxh9ztIur1Hkxk14DYjAryOHw+FVq2ggGSgdquhbvXXnOtBoEcAN5x7Dy598d0Csjbn11lu57bbb/D6+dOlSbr311qCP53IZ1DY0UdPQRHVdI2VVdiqrrV0+719ffcn2gv3UN9mpb2ymwd5MfVMzBw0fzs+P+4nfUTt/gV1v8TWK2N0pq71FKQWGA1zNKFcTNJSBswGsCeCyg+FANRRDYzmYY8Aa17KvHSq2gNEM8blguMBwtuxbAZYYNGu8+/kuB9QXgFLu4yoDlBOcTe7X7qCmwUHawtci6vzenT5zoPRTwV5sv3DLRZx37Izeb9ABKn9HAbc99W/eXrG+bdsZR07l94tO4OBxuf3Ysr4T7E0Ir+zTPexv+2N9a+c17Bt27uHY67p3A6araxSZqh1+vXl+D30s34+9e/eydOlSnnrqqQEd9AnhT28lYgm0PuKcn0zn3pc/OWDWxlx++eWcdtppNDY2MmfOHABWrFiBoelU1TViiU7gP19vpLK2gcqaBipqG6ioqaeitoGq2gaq6xqp6vBV29DkcfwjJk/kurPmd9mOHQXlrNzwo8e2QyeO8/nc1Ja6eSvy85k6ZgyJHabs1Tc1sm7HFsprKigsr+HrjT9S19jc9nhibBQOl9G2tsaX9KQ43rjjMqKtVixmHYvZ1P5lMmE26dQ3KlwGJMRau52kRhlOdyDmbEQ5GlCN5eCoQzNHu7e57KiaPe4AzRKLZo5yb3PUQfVOd2AWneY+hssOzbVeQVe3VWzx3taE70IaTf6n8UaSA6XPlHpe/WtXYTm3/OM9Xvzo27bakWcddTC/X3Q8U0bn9Hfz+lTH9dhagJt0HYM+6Fl/21/rWzVN8yjZUPRd9/MJDJQ1nCI4YQv8KioqePbZZ3nqqafCdUghIkpvJmLxtQB69kGjGHverYNyIbrLZVBSWUtheQ1FFTUUlldTWlVLSWWde3tpWdu+x/z+RZyqZ+/PZjWTGBtNTFRwp7zLTz+C68+dQ0yUhdgoGzE2K/uKNJw+Zoi2BllHTp3q9VhsVDRHHDStbdpl5+k+LsPguOsfCdiW0qo6HE4XR0zxvCtfWFhIYYF7WqxhONFczVQ5G9ntbETVFZGVoMjKygTNBM4GlL0KVbkDDCdadKp7m7PRPXpm+A88/fEZdDkCl7YAHWjJNBqVBmYr6FZw1LsDxegUtLgc0K0o3ewO+HQzWnoemiUGdDOqsQKaytFihqAljnA/XzejqneCboakMeit+7rs7lE/azxaVDLoZsy1DcBrIb/fcDtQ+syBmO5/MCirquPO5//L395eQbPDCcDZRx/C0otPYuKIzH5uXf9ozUB6z4tfcFGA9daddbe/jaRkMj29ASNJWgaPoAO/d999N+DjP/74Y8DHhRjoevvOdedyBp+v3TogSz002pvZW1LJnuJK9pZUsr+0moKyKgpKq9hf5v53aVVdW0IBX5SrfaTI4XShmXSirBbSk+JITYwlNSGWpPgYUuJjSEmIITk+hqT4lv/HRbd8xZAYF0VCTBQ2q3tEJdg1fmccPdVj1Ky0soldBWV+n9OV/G1VZKdHef2MX/r426CeX/DDCpzqA2goQ1mi0ZTisSf+w5+e+dLvc/5w3mRuuSDP52Oqocj/i+kW95ezAdAgcTSaJQrM0e6gq7kGLX4oWuJIMNlQmhmqd7inXqZPdY8Qmmzgagkmo5LQrAlgsqLpYbvX6G3IwV6bfI17aqYwjUJ2QfpMt4GW7n+ga7I7uP/VT/nLPz9qm/FwzIzx3HX5fKaPH9bPret/syaO58ZzUuk8A7915sY9r77lN/gLtb+NpHJN4bgBE6jkUrjKI4neF3QvfPrpp6NpGoGWBMoPWQxmfX3nOlJLPdibHewuruDH/eXsLCxn5/4ydhaWs6e4gj3FlZRUBleIXNc1MpLiyUxNYEhKApkp8WQkxZORHE9CtJnLTn8SgE3P/4Fh2UOIiep6bV5XNE0jb2xSwKyeeWOTvM5lPa2H12h3UbJvN6mWYjRjJ8pZDs0G6cXFQT1/SPUXqEJn2/cKuOwn6Zw67QQam10c9ZuPAPj8nhOJjo0Hw0lmkgUShqPF56KZY1CaGVW9A80SjZ59BJijwRID9mqU4XTvF5eFppvbzvNyTu8+6TPbyVSx3qeU4p0v87nxkTfZWeg+vx0yLpc7Lz+Nn848cOtHdqSUIn9bFaDR+U9P0zQMpbj4hGP5dvM2v2uzQ+lvI6kP780bMOEsjyR6X9CBX1ZWFo899hjz5/teH7Nu3TqmT58etoYJEWn6+s51f66NcbkMdhaWs2VvMdv3lrJ1Xwnb9pWwfV8pe4orA17MAsRGWxk+JIWc9CSGpieTk55IdloSQzOSyE5NJCs1kfSkOL+fVX19PZe1/Dt3SEpYgr5WORnRzJqcGlJHFY56eKrgSVTubpQNsAIxcORRcPGWGJ753IryMT6lATmJOnNnTkdzVKEcjWjJ49BTxjHUEkuOKYqG6nJoCfxmXPFaWErpHCgBSW+SPtOTTBXrPT/sKuL6h1/no9WbAchJT+LPV8zn3GOmHzC1IoNRVmUPONtD1zTSEhOYMDyXTbv2+NwnlP420ta39sYNGH/lIxrtLlZtKJfs0hEo6MBv+vTpfPfdd347sa7ubAoxGPTlneuuFqIrpXo8wuh0utheUMr3P+7nh11FbNpVxObdRWzdV4K92en3ebHRVkZmpjIyO42RWamMzEpleGYqw4YkMzwzheT4mIgOHnIyoslOjwo4NUW57NBYjmqqILkknyhtOk0qBt8TCAPLTNhAyvDdXts1Kzx2dQOVjRrvrLL6vJlw/42XYJsxze+xdVstR87OIGtINJprC0pNQ9PkYq+/SZ/pLdBUMRG62oYmbnvq3zz8xuc4XQZWi5kbzv0JN//seGKjbf3dvIgT7MyN5Djvm2fdmdHj0YfrijkTnWQmK4oqNb78wYwytD5f3xrOGzDtI6j+tS5ziOTrgQNN0IHfTTfdRH19vd/Hx4wZw2effRaWRgkRyfrqznVXC9Gf+eBjbjz/yKBft6a+kTVb97J+WwH5Pxbw/Y4CNu4soqnZ95qnKKuFcbkZjM3NYOzQ9Lb/j8lJJyM53u+J3OUyWL5uW8Tf1VfKRYplPcq1B63BiavEjKrdB1XbwNHglZFysr6Lb60/BxQe84SU6hAL+vpMDKbkvOs1tah1dw146nqdadcksqekuu2hnIwk/vqrBRw3czKVtc04nQYOp3L/36VwugyiWEOa7VU+fudY95Oa78VZmYIpdhG6bWb3PxzRY9Jnit70/lcbuPLeV9hbUgnAKbMnc9/VZzI6J72fWxa5gp25UVXn+Xfb3Rk9rX34P997jHt+0cDQtPYbPfvKNG78RwwXnNL361vDdQOmqxFUcI/8lVXZPTKMiv7VK3X8BrqBUh9JDH7uaRRl7tii42iUUmgazJqc5nMaRV2DnW+37Oa7zXv4bute1mzZw7Z9pT5fIybKyuSRWUwamcWk4ZlMGJHJxOGZDB+SEnKHFK56RfX19cTFxbnfS11dt6cvGoZBWWkFTTUVWB1FpBm7oaEIZd4NQyqh401xO7AbqOywzRQF0amAAsNFYexcNjQdTJOz/Z5ZtE1naEYM2/b6zmiZGruD2aOf7LKt6/ZfwfLvLZRU1ZIQE8P43FxMAaZpZSZsYMbw591xaB3QjHsKaTyggSn+Ogn+fBgs5/fB8j5EaIorarj+4Td45ZPvABiZlcrD1y/kxMMO6ueWRb5gknuBi8WPLWsLqAFyezCjx7CvxlnzAArQO9z8M1ruF5oTBu55em9RA6s3VXS538xJKeRmxvRBiwaPAVHHTwgRXl0tRAf3NIqsNBvbC0r5asNOVm3axapNu8jfUeAza+awIckcPC6XqaNzmDI6h7zROYzKTg3LOpDWekWapjFpxDCPaakh1ytSBkdOySArORqtcgsqJvD0RXcB70KMun2oyu2oxnIKGxLZ4JhFk5YIxACjiDJSmBH3Asm5ld4HsQJjgZJR6OYpaMOPQ7Mltn3WSikynYrEJgc79zdQ1+jEbNKIjzHjcClSE6xU1DR7Jf6xWYJLduNyVjMqaxqjsjy3m00aZrOOxaxhMekt38OUjH9BBWh7cAd9Hd/HcHDpz6FZp8u0TyEGAaUUz36wihsfeZPK2gZ0XeP6hT9h6cUnybTOIAWT3GvW5Ax2vHJbmKZCGrjqn0PTvOeCtAaBrvrnwHII5dWOiMiIGUp2zmBHUHft2s4Hy/dSVtNAWkIM00YPabupmZWVRVZWVhdHEOEUVOC3YMECnnnmmaCjzgsuuID777+fjIyMHjVOiANZsNMojr76Ub783rvAdW5GMjMnDmf6+FwOGTeMQ8blkpYU1yttba1XNHPiOC72My012HpFRuFqLBuf5eO7WqYvrr8X55YUTJMWoWXOgMYyjJrdqOK1qNo97jptDUWg2tdvFOoH8a31LK9jN+nxRA2rcddQ6tyXae6EPa6MSnbW/pSmXYqm5nLsdhdNzQb2ZhcBKlD4ZXfEB7WfSyV6fB9l1ckbl8TQDO87pUbzJly7qmC7jwM1A9sAKlBxm9Gsk0Jtsugh6TNFOO0vq+LSv7zIf1dtAmDa2KE8+ZvzpTxDNwSb3CscUyGVYzMYXYyIGRV8s24l+6tG+m1LX/GVnTPKpjN5dCJDUqJwuhQuo+Wr5d9Wi06zw//aybq6Ws75/f1MGDux7WbwD1tfw7V9BZT/yNKlS7n11lv74N2JVkEFfu+88w6lpb6niXWmlOJf//oXt99+u3RiQvRAsAvRldKxWc3MnDCcWZNGMGvSCA47aCQ56Um928AOVuRvJzstgxsXnuH1WEpCPDe01Ef6Yv12Jo8c5vduolG4GteaB7xfoKnCvV23eK29a2OOAUsMqqmSDbYFLRs9o7vU2F1EW6u9n9thb7NWSWlpPuX1owO/6SCV14/EpZLRqfS5zk8paHQkUlw93GN7U7PBNxsq0CZrXhcAyqhwT0sNZDeoYV1PwxHhJ32mCJfXP1/Lr+55iYqaBqKsFm695CSuW/gTLGZTfzdtwMrJiGZIipXP1uykoqaJlIQojj5kJOZwf6aqKsgdPfuknmTEdBkKh9PA6VQ4XEb72nCXe324s2V9uNPl3s/lcj9W3+iktsE7oVuT3eDbTT5myARBKcXH6zby6G9u8roZ/PQHh3DmYUO54ITDu3Vs0X1BBX5KKcaNk0xcQvSVmvpGvvnhRzSSutz31ktO5Ii8X7YVKQ9GYWEhhYWFfh8PdfpFYXkNF5/gHqHrPC1Eb6mPdPkpJ1BRaWNFVXsh9NY7m9mpJlzVu1Dr/+Y+ho/XUAoa7A6+LbQwZ2ImJmctyhSNa8QpNMQdRIMrhvpGB+U1DprK7T7bGey0y9z0ZjIyErBZdaKsJmwWja83VGBv7k49P538glOZlvNcy1pNz/cEsHH/aYDvkVCfWdGqaj2nd/rS3LJfZjeaLHpE+kzRU9V1jVzz4Gu88N9vAHdNvuf+cCETR8gfdE+9uXwd1z30OgUd1qLnpCfxQIhr0bukJQW1m79ZIeu2VmIxg8MJzU4Dh8No+7/D2fLvlsDO0fJvo2clZ0OiaRATZUbXoL7J6fHaJh0++GYd84+Y5fW8lIR4blx4Bs/890OuXzSk7xosgCADv+5kHsvJyQn5OUIcyHYUlPL+yg2899UGvli3HZfL4NHrfkVKQjy6nzn20TYTR08fGfJ6gCeeeILbbrvN7+OhTr9IS0jCpvvP2qVrGgmxMdBpBVyj3cmqDWXMaH6RLFd+wNfQNIi1wB1f2tj8H50r5p/NIWPH4tipcGdm8R3sdRTstMsRQ3PQre13KEsrm7oZ9LntqzgIp/PnTM5+12PEsdGRyMb9p1FUM9nvc31mRXMFudg72P1EWEmfKXpi+bptXHTHc+wprkTXNX57wXEsuehErBZJy9BTby5fx9lLlnltLyit4uwly3gtlLXoXdAsE0BP8Tvds3W2R3n9SJ+P25sNvlznfz1iICaThsWkYTHrmFvXh5s1mh0GlTUOXB3WLZhNGk5X6/cGqbE7sVlqsTviW9rm+6akUlDf6GTW5FSv8kjf/7iHg8e6Z834uxl86uGH88X67Rx9iNwk60tBnUXmzZvX2+0Qol+EspA5WC6XEdTCcKUU67cX8Mbna3lz+To27yn2eHxcbgYl1YWkJfoPVvLGJnWrvZdffjmnnXYajY2NzJkzB4Avv/yS6Gj3tJJQF1uPy83kux+qgtjT18I6xQbzyQxhK7qrqcsjZMYpvthbwx+ffZ0bF57BrEnjibLqxEabiY02o5Rib3Gjz+eW14+ksTmRKEu17/IKAHqKu8PuINhpt4EU1UymqGZS0J1qoNfXopKDes1g9xPhJX2m6A6Xy+CuF/7LbU//G8NQjMpO49nfL2L2lFH93bRBweUyuOLulwLuc8U9LwW1Fh3cfXhTs0Gj3UWT3UVjy1dT61ezQYLtFKblPAeEPtvDF5NJIzM1itREKxazjsWsY20N7lq+t5g1n9cF/oqttwZ9mQkbvG9ONieyoYubk+5ZKZkeNydLKptIS0zx+xxd00hLTKC4QpYj9DW5fSQOWL4WMvd0UXVX5QyUUqzZupfXP1vLG8vXsqOgfdqj2aQzN28MpxwxmZMOP4hxuUN6rZ2tUzk71hmbNm1at8smxET14FSiaTRpSawyL+JwV9dlD4rq3B2aBrz06Wfc/sujsFrb12a4g3l/Kbt1Nuw/zV0GwQ9T7CKvTJjBZi/rmt6ttYOdX19LmQBRKdAUoNOMSnHvJ4SIeCWVtfz89mf5+NvNAFx00mE8eM3ZxMVIxs5wWb5uG+U1/mtrApRX17N83TZ+Mn08LpeioclJfZOLhiYnDY0uGu1OGprag7yuCqLV1Pue7dHsSiJ/36kBAypfXC5FQUkjQzNiQur/uyq23lYeqJMoSzUzhj/Pt7t/7retvmalJMfHEqCMqcd+om9J4CcOSP7ufPVkUXVrOYPO/UBBaRULlyzj7KMPYc3WvWwvaE/6EGW1cOJhk1gwbxonHXYQSfHeGRxzMqK9plH0Z7rnzpSzkRT7D0TpCTS6ut+uMm0kjVoiUaradxl0BQW1Gl/uc5+2FFBQVsXKjTs8MrB5puz2nrZSVDOZGvUrEk2veE7B0f0XPk9LshFtM3WZZbU3RNtMpCV5Xvxpmo5p0iJca9z1oXx9XqZJ3gGsECLyrFi/nfNve5r9ZdXERFl5dPE5LDrBe22U6JnP124Nar9n/r2Oxsb4oKb3axpEWU1E2UxE23T3/1u+j7KZ3I9Zs7CYfwrOLe6EL1oSMebxVO8oAbrXp/hc+x1A4CzhBpOz3217P53fn1JwUPa7FNVMwt/oZOdZKdPGZPO/9V1PU502JrvLfUR4SeAnDjhd3fmC0E+qreUMfN38a9326mdrAHfB9JMOP4gz5x3MSYcdFNQdXU3TPNd4hUuI9fLcT3Giqn7EKF2PKs2Hmt2gXBykH8R31p/TOYOJu9h815/j9EmpxKqLMNY+6BXMtC5HuPGTGAzleazC8hqvY+VkRHPk5F3YXC8SZWm/y9rkSMRuOp+0jDkoNdudbrulI9YsE/y+92DqPwViNes0Ow1CWT/Ryt90Xj1rJhxyHc6Nz4K9Q9a1KHfpCz1rYBYFFuJAYRgG97z0CX9Y9i9cLoOJwzN55Y+/4KCRUtesVaClE8EuqzCMlqyV9e1ZK3VNY8LwXI96s0bL8F2T3dUW9JlNGjFRZmKjTcREmYmOMhFjMxEdZSLa5v4K+mZnp9I6PelTfK797qDzMpbGJv8BZmrszsDZrjWIsVaTGrvT74yVzrNSMlKiABeG0t05CpRBqrETm6rFrsVTqo1A11v3E31JAj9xwAm2Pl6gk2pnK/K3e0zv9Of/fnYcv/vZ8RExfSdQvbzOQYOqL8IoWY8q24Cq2ATOJhxEUakPo8L0EyotY6jWckB5d4CapqHROa2Lp2ibiWGZMWjaoWiadzBTUKtx4ycxvLPV6vXcrFTvBCaGfTWJ+t+8YqooSzVR/A3DbkO3zQypxl1X9Z+AgI/t3rPc7/qJ+MTD2FdST4x5e1tQ2OAcQ97YlIAjz3rWTBzxEzhp9hiykqN56p9vEp3TdfAuhOhf9Y12Lrrzed5cvg6AC46byWOLz42IviFSBFo6Afh87K7Lz2DetEnU1DuoqXdQW++gtsGJUpAUkwbAoX7qzT79wcd888NWzjzqII6ekUFstNnverlwaO1T1m+r9Bgxs5g1HM6ui8b6W3vua3mI1eK/Twg227W//XzPStGYNTmDVRvKyHR+z2THv4juULaikUSaR5+Ppg3vfDjRy7oV+DmdTj7//HN27NjB+eefT3x8PPv37ychIYG4uN4pEC1EuASbqCOUhB7b9pYEtd+UUdkR0bF3VS9PHXINmi0ZVbIGY98KlL2KRi2Zcn0kFfrJVESNoE7rVHNMga5DYqyFaJsJm9VEcqKVYUOi2bijhm176/y2Z2hGdFvn2h7MjCVrxk8ptsfw5T6z10ifBgzNSGLOlNGUVja13dlMTbTgqn8u4Pt31T+HZp0ecoDU1bRbf48Z9tWkB1g/YYquZELqSp9TTyHwyJ1Co1wfhWEaQrlpFEN9TvwU/Un6TNHR7qIKzvjdE6zfXoDFbOKh687mslOPiJjp+5Eg0NIJX1k5AfaVVvHzPz3dlvSrI5NJY3beGI6aOplfn36y13NTW0oMPPGvf3PW0VOCSu4SNl3HeD75WnvubxlLoCLrwWa79refv1kpORnRHJy0g5zCF7wei6Ka6B1/w0i0DfjZKUopMBwoVzM4m8BwoNG6zYGqK3Bvj0pBwwCXA9VYiqovBnM0WnQKGE4wnKjyTSiXHWfn66swCjnw2717NyeccAJ79uzBbrfz05/+lPj4eP7yl79gt9t5/PHHe6OdQoRNsIk6gtkvf0cBD772Gf/88JugjulrdKqvKWXg2tSSZczPPq41D1NPCuWm0ZTrx1NuG0WTnuS1X2y0idREGymJVlISrCTEWtB1z6MqpdhX4jvLZqt9JY1MHpPY3nloOl98Xwz7V2KadKLX/q2vcN9V5/PRqhKPO5vZSTuZ3lXhcqMC5dgc0ohf22sHmHbr6zGlDHeg6ePDbt1mNL3ns42u2geA63yuOwR3J79uazV/vO81AFb/UMeGHxt7lPhHhJf0maKjFeu3c/aSZZRW1ZGRHM/rf7qUI6YETvjUG9mnI1kwSycCefbDT1hw1FSSE2wkxJhJiLUQHeVOAFZbf1LLaoRONxI1DaUUV5x2olcf1lv8BWnBjPb5GmVrX8bib+W3b8Fku3apZBqcY+j4E+gqyVxBcT2pha+Cj9a0zgJybXoOLTP0m7DBaAvInI1ormZw2VEuO6quEBpKwByFZolBuZpRjgYo34hyNaMljADDAUaze9+mCjBHu78MBzjtYK9oWdZiAuVd+D6kdvra1rC9R8cMJOTA79prr2XGjBmsX7+e1NTUtu1nnHEGl112WVgbJ0RvCCZRh6+TaivDMPj31xt58NXP+HRN+2Jxi9mEw+n7mO2jU6MwmjcFta6st6iKzYGzQQIainzrmZSb2i9INA2S462kJVlJaQn2ojpk0/SnR1Nry3/k+T/8nN8+8S/PKT0ZSdx31flgJPg4tv+1Ch5UVZe7+HyaMoJeGwi49/VTxykY/kYneyNBkQg/6TNFq2XvreSq+17B4XRx8NihvHnnLxk2xH/Ke+idrM6R7uPvtrad7wOtxfOntKqGRkcFs4Z51ocrqWgCdL/BjTsY1CittPf62rNgcg0E4muUrb2vDTVwbc923WmJfhtrwoWcMDs76BsQSin2/rCG6QH6Yw2gqQJVsRktdVLb8zAc4GhA2atQTZVgON0jZY5GjOZaqN4OTjtaTDrK2QTOJlTdfmiuBpPVfWSXe3t3qCofQVezdy4B986+gj4dLNGgW9xfrddbcTloLduVox4ay8GWhJY4HE23gGbGqP7R/dmkDwVe61b7uxJy4LdixQpWrlyJ1eq51mbEiBEUFBSErWFC9JZgEnX4Oqnamx08859V3P/KJ2zb587MqesaC+ZN47qzj6awvIaFLVNQOnZLrUd58Xd5qJrrcQWZSbK3qPrirncCoqklLclKWpKNtCR3oGcOsrZRsIvKO/I3tfa0Iyaz8JiZHov450wZ7TXS1yrYaStoSX4f8neH3bCvdk8jDeVn2M0As42P0cneSFAkeof0mcIwDG589C0efO0zAM4++hCeuvlnxER5r1nu6EC4uaOUor7RfeOvvNpOWVUz//1mD9D1WrxAfCX9Kq20B9Wmvgj8grkhCu61eR2naXYM+jv3U/WN3R95KqqZzLe7f870Ee+h0SFZWKf+LZi8B0opSspq3YFYEFxr/4bLUQuqJWhVwV0z+Az/nX5mF2lmdzBmsoHL4d4vKhktLhtMVpRugaqdYLKgpU1Bs8S4g0h7NaqpEi02Cy1xuDuQM1mhdp/7WIkj2/ZVygCl0Ew2NJMlqPfQWeutdHNNDfDLbh2jKyEHfoZh4HJ5/1D27dtHfHyQF1xC9LOuEnV07EwbmppZ9q//cc/Ln1DQchcyMS6aS0+ZzZUL5jE8s/2O7au3X+q94DwjiRd/l8fMYe9A59gmiOl84aBcdlTxGur2raeqvJJgcsYdPHUklozQ5pmHuqi8o0BTa00m3aNkQ2llk99Os7x+JHZnDFZTg/8i7VqcV5H2Vv7usM8cu8OdMKazrn6GAQLMoHUKHnsjQZHoHdJnHtia7A4W3fEcb3y+FoA//uIUfrfo+C5vyPTnzZ3enlra0OSktNJOSYXdvT67U9mE5LhYDp04jhsXnuH13JSWtXj3vPoW327e5nc0MBKWVQQSbA6BvDGJREeZvH4Wvvop9wVG92cQVdunYknxLDvR2k8qe417BK6hBBrK3MOCjlporsWo+hGaKt1BkXJAcx2N2iTspiBLkth9zYjR2qdRWhPR4jLBHI0yWaFyO+hWtKxZaNY4NHMUqqkS1VyLljAMPXEUmKNQutU9UmdLQrMlhHd2VfJYXy0eEEIO/I477jgeeOABnnzSXWhZ0zTq6upYunQpJ510UtgbKERv6SpRR019I397awX3v/oppVXuxCQ56UnccM4xXHLK4cTHeF9QL5g3jflz8jqNTo1C1VzvHfR10N1kI4Eow4lRkk/F7nyKqnWK9QnU6KeA1eDYpj8The96eQBEpWBOnxjS67XfnQ69ZEGgqbW+dNlpdnOxvP877A6szhdRFt/TYMD/z1CzTAA9pUfTPRWJHglsejqKKvqO9JkHrsraBs64+QlW5O/AYjbx9O9+znnHzgjquf11c6c3ppY6nAallXaKK5oorbBT12lkStMgJcFKapKNtEQrJxw+hOy01JbHPE+4uqZhKMXlp5zgczTwmQ8+Zn95CXPzxni1Iz3ZypbdXbc3Lcnicb7tjTWVweYaiI4yef18/fVTSrXnz/bVXtVhiqzn4+7tkxO2ozZ/4g7yanaBvcr9mKORUDtVm15LuT6SRhL9XmsowDDHYZ7yC7SGIrAloKUehGaJBXNUyxo6vdufvQYQHXgq9YEo5MDv3nvv5fjjj2fSpEk0NTVx/vnns23bNtLS0njppZd6o41C9BpfyTga7c089uYX/PmfH1JR0wDAyKxUfnPBT7nwhFnYrIGH8DuPThnNmzynd/rSg2QjXoeq2kXJj+vYX6Eo0sbRpB3b4S9dkRqvUZ1+DlF7nwxb8e/Wu9OZCRuYnP0O0db2aTaNzQls2D+foprJfp/vLyuYP4E6TXfQ2dBFg+tCmj7ZVZ0jwO/PUNN0TLGLWkYFOzWj7T++A0qloNGRyIpvEml2lLVtD8coqugb0mcemPYUV3DyTY+xaVcRCbFRvHnHLzn6kHFdP7FFb2Sf7kq4ppYqpaitd1JU3kRxeRNl1XY6L8tLTrCSkWwjPdlGaqINk6n9BFha2URyvP9st7qmkRAb4xHIgHs08IaFZ6CZan1m5UxPjupQU9U3s67x3eYqj8+1N9ZUdjfXQKB+SmsJijXAUMpdP69F6/Z3/reKMw6bhDK3B8xRRjWTHe+SuWdjoPvTYI4FZz1oOgyZgW5LAEs8qqkCnA1oyePQUyeCJY4McyxR31SxwTiNGc3Pe11rtP7kLHm/wJR1qO/XGyhDaANMyIHf0KFDWb9+Pa+88grr16+nrq6OX/ziF1xwwQVER4f+R/Hoo49y9913U1RUxNSpU3n44Yc59FA/vwTAa6+9xpIlS9i1axdjx47lL3/5i8dd07q6On7729/y9ttvU15ezsiRI7nmmmu44oorQm6bGPw6FoDNSIpjx/4y/vTsB21TOscPG8LNPz+e846ZjtncdSITn4Jd49WDtWAuRwNlO9azr6iaQtdwmrUZbYNsJpxk6HvJzEgga/QkomwWIBcjPSZsxb/Lquwk2tYzw2fJghpmDH+eb3f/nIrGPL/rFUIRqNMMtiZRKNMnu3vMVu4poNd5rQ80VDI7S/MYnb7ca1F96zXNxv2n0ezwPF6g1NytQh1FFb0j3H1mpAu2qPZglr+jgJNveoz9ZdXkpCfx/l9/xZTROSEdI5zZp4PR06mlhqEor7azv7SJwrJGGjrNSoiNNjMk1UZGchTpyTYsZv/tDjaY9TUaCIpoSzJKKZ+ZOw+ekBxwfb/TUDjtngFlb6yp7G6uga5GgvW2GUv1JMbGtm2vqKnlmQ8+omDfFp44+G2qXCOxa/HYNDupzi3uG71Zh6NHp4A1AZoqUc569JRJaOlTwBrvnnppONBMgdemgnut2tRxGqs2TOZb68+Z3PyuVx0/x+jzSfMX9IleE3Lg98UXXzB79mwuuOACLrjggrbtTqeTL774giOPPDLoY73yyissXryYxx9/nFmzZvHAAw9w/PHHs2XLFjJ8rC1auXIl5513HnfddRennHIKL774Iqeffjpr1qxh8mT3aMLixYv59NNPeeGFFxgxYgQffvghv/71r8nOzua0004L9e2KAaI7Fxu+isO2GjYkmaWXnMzPjzu05xctwa7xCnEtmGEYVOzfyd49hRQ0ptGs5QK5oIFFNZCVYiInN4uM5GhMphFez9ezZtIUM45TT5hNVlo81930B2bMPgHdHHp5zya7k7ycN9xvo9M1gaa5g5i8nDeo0I4g2mbxO4WmsLCQwsJCGhvbF2ivW7eu7QI5KyuLrKysgJ1msMldlNZp+mSAzjQcCWPcBeOne2QEraobwQ9FFVQ2DPMu7u5IZOP+0wKOlAYS6iiq6B3h7DMjXaCC2wvmTeu3dvWlld//yMm/eYya+iYOGpnF+3/9NblDkkM+jvumjQtD6R4jN63cIzpG2G7udGdqqculKK5oYn9pI0XlTR43pHQd0pNsDEmNIjM1iriY4JNd9CyY1QJOgW0rmr610mNtoc2ioZQWcDQw3GsqA+UamDIqimxbEcb+YlRDCUZdAVRspd45DMxnd3nsZ//zMeV19R7rH5VSvHzxUKxTZpKZPgVsSe4EJcqFpgfZ7wcR9Hm/v6l8rE8i1diJTdWirAkMmzidnCGxXR9EhJ2mOo+Vd8FkMlFYWOgVmJWXl5ORkeFzEbs/s2bNYubMmTzyyCOA+0I2NzeXq6++mt/+9rde+59zzjnU19fz3nvtNa8OO+wwpk2b1lYLafLkyZxzzjksWbKkbZ/p06dz4okn8qc//SmodtXU1JCYmEh1dTUJCZG9QFh072LDX3HYVi/ecjHnHDvd72uGsvhdKQNn5bUoV4Xf6XyaKQVz8oNBTa+sqa5h747t7K2y0kBS23YLjWTF1JDV9DUZIyZgHv6TgB3Um8vXce2Dr7G/rD3Y6O5FWmX5GuLUvV3uV6fdQHLqIX4fv/XWW7ntttv8Pr506VJuvfXWtu99rUeJssLc0XdiM/uuSaQUNDmT+HLHzTQ1t2/vnD3Nk8GxE/4csM4RevA/w1ZOp4tn399GYlwsuqYCrI30v27SaoF46462xxqcY8gbmzLgs/31lr4+v4ezz+wo0vopf+fU1j+XV2+/dNAHf5+t2cr8mx+nvrGZuXmjeeuuy0mOj+nWsVwug/m/fZqLTzgOBT6n7T3z3w95+66LwzKiureogdWbul6HPGNiMhaLTkFJI/tLG3G62n/iVotOZmoU2enRZKTYgsoC7YtSig9WFgWV9dKfmZNSyM30/9l37sOVgi/Xlfndv9Xcg9PCtqZSuRzQUIyrpoDyyjoaG+qJsheQUr8azeU7A2mZPoqvbJd3eexH3nqH5et/aPs+NyOJ+67unxswSin2FVVz/gUXUVlRzNdffkRcnP+pvKJ3z+8h39r3NXwO7k4sNjb46L25uZnvvvuOm2++uW2brusce+yxfPXVVz6f89VXX7F48WKPbccffzxvv/122/ezZ8/m3Xff5ZJLLiE7O5vPP/+crVu3cv/99/tti91ux25v/yOrqfFTr0NEHH8XGwWlVSxcssznxYbLZXDNA6/5Dfo04P8ef4uzjj7YZ4ca6uJ3TdNZvetoZuS+4Xc63+pdRzM7xX8n6XAa7NtTwK59VVQ6kwH3RaRJNZOp7WDYqBFk5I5uaa//gLVVdz63QOJt21FBlMyJt20H/Ad+l19+uc+ReXewZiEhMYXSyqa2QNtXgh6l4Psffdckav28NxSc6hH0Qcfpk75WPups2H8qM4a/4LftptjQ1kUCfPn9Dpb9+0NuXHgGhtIor2+vm+i+o0/LuslOo4HNiWzY7/6cDhn+HibNO/029F2JEOFfuPrMSO6nuiq4rQGLH36d+XPyBu20zw9WbeLM3/+dpmYHx86YwFt3/rLLcg2BrMjfzn9WraW8pt4riUlFTW1bSYMV+XM91pR3V7CjbGu3VOLqcH8s2mYiO919Hk5NtIWl+Hkw0yC70tX76by+f09RfVDHDRSM+rshrBwNGDV7oWobqr7QPY2yvqWIeAufKUh0K1riCIjJQIvJQFVsJS0mg6hqaHL4eoJbtM3Eh/dfwZff74iIKdeappGSYOHLz95p+170n6ADvwULFgDuH9hFF12EzdY+vcDlcpGfn8/s2bODfuGysjJcLhdDhgzx2D5kyBA2b97s8zlFRUU+9y8qKmr7/uGHH+aXv/wlQ4cOxWw2o+s6f//73wNOp7nrrrsCjjKIyNSdiw2H08X1D71OYbn/RB0K2FtSxYr87V4dancWvzudLn7YOwmMaCZn/8trOt+G/aeyuWAEh+a5PNYRtnYiu3cVUFBpwoUZSEZTLtL1fQyNr2VI9YdYRx6HaYR3amF/XC6Da+5/xe/nBnDtA6+GdpEW7Im8i/1ap3J21DHQLq5XbNtf5hFod+7A9xY1tNUkCn36pEIpUHgvjC+snsy3e89k5ojPQqvjF0BheQ3f/LCVe159y+virra+gfHZO/2sm6xu3975I+2jEiEisHD3mZHcT63I3+5zynyrQOfUweCtL9Zz3q1P4XC6OGX2ZF657Rcta6m7r7UO3Tc/bA1YtsBXvbruCCbZCIDLAJtVZ2hGDEMzoklJtPbKhby/aZBRVh3DIOCUzO6sb7Y3Bze66G+/gpJG1m+r9FifGKXVM1l9RFaj78EMD+lT0ZPHuuvKAaq5Dj1lHFp8rteuU/1ch7TKG5uE2WwK+99ab5f5EH0j6MAvMTERcP/g4+PjPRalW61WDjvsMC677LLwtzBEDz/8MF9//TXvvvsuw4cP54svvuDKK68kOzubY4891udzbr75Zo+RxJqaGnJzvf/YRGQJ9WLj6407ufyvL7JhZ2FQx+/coXZ38ftna3aSHB9HUc0UimoO8jllLzkePv1uJwePG0pdg5OqWjslJdXUO62AuwOLNUoYZt7G8IkHE53ResF4XMijTCvyt1NY4U5WomuazwuK/eU1IV2kaZZJqMa3g9ovFN0JtFvv9BbVTKaoZlKIpSU0NM3fwnh3mvDtL9+Pbmz1qHPU3TIcrbWmfF3cbdu3l6Ln3bd1/a2bdLfYt94oESKCF+4+M5L7qWCDj3AFKZHkpY+/5cI7nsPlMjj76EN4fsmFWLqbCKyDjnXoDKXYtGtPl/v1ROso29cbykB5jsq0rgjKSI5iwsh4vxf84Q4M/JVc2l/aFHJSlK7YLMH9zFr3U44GjOJvUYXfsL8xlW+dx7bccW5/3SYjhm+Zzwy9hix9JzgbIDYTfeRJaHFZaLFZqMZytKgkiEoJ+lwdSh3icOmNMh+ifwQd+D399NMAjBgxghtvvDGkKSq+pKWlYTKZKC4u9theXFxMZmamz+dkZmYG3L+xsZHf/e53vPXWW5x88skA5OXlsW7dOu655x6/gZ/NZvO4GysGhmAvInbuL+fN5a/y2FsrUEqREBtFTX3X8xI7d6jdratUUdOERW/9XveYztdRTZ2FFWs7rjGwoisHOUY+w2OKSGIVeu4M9GQnoFo6idA71dbP7dCJ43zWQWqdQhTKRZpumYihxYGq87+TFoduCb42YHcDbc871/4/70Ce+c/HVHRaGN96h/3L73/kqIMDB7BKOd2FbHF0+H8zqu3fTsDBEeObGZoWQ0FZg9fF3ZEHOYi1+f88u7yuCWOJEBG6cPeZkdxPBRt8RHpR7VC98OE3XHTH8yilWHTCLJb93wVhm043N28MQ9OTKCit8jk7QwOGZiT5rFfXHdV1Dl755Dte+3yNV79Q3jK19Kbzj2Ru8jSfz++twMBXyaXeCHyio4IL/GryX+Cbd7+AJvesD4VG0fg/gwXfd+hQbIy/kKGHpaFpOprJcyRYiwo98Q+4P4MhKVY+W7OTipomUhKiOPqQkd3PPh5AuMp8iMgQ8hq/pUuXhuWFrVYr06dP55NPPuH0008H3MldPvnkE6666iqfzzn88MP55JNPuO6669q2ffTRRxx++OEAOBwOHA4Huu554jWZTBiGFDIebIK9iPjt429TVu2ev7/ohFn85Yr5zLzsryF3qJ4ppv0n2+icijolIYraAPFQ++t2umBQCkMzM2JKAkmWj0DZUfwPV83/ejS9MCs1gUMnjuPGhWd4PZaSEM+NC8/gnlffCukiTdN0THGX+qxV18oUd2lIo0/dDbSDXx/i/2dYVV9HWuyPZCYpTEoDzQzK3akX7HsZ5yhLSyDXDDSDsrcEeM2gmiFwNSQP91xi4by/xuK+RGi/cMhKDsM5qwclQkR4hKvPjGR9HaREgtc+W8PFd7qDvsvnz+GR6xd6XXv0hMmkc/81Z7FwyTLay3K7tZ4l7rv6rICBZlfZrl2GoqC4gR/311Na2cRtz7xNXWOT36mlV9y9z+cSgP4IDPyNBnZ3hLHLqa5KEaWqeeW5x/jTi/ltmw+aehh/vC9QkXCNxmZFea0iPbln03876qsMuj0t8xFspm7Rd0LP2w68/vrrvPrqq+zZs4fmZs8MCWvWrAn6OIsXL+bCCy9kxowZHHrooTzwwAPU19dz8cUXA7Bo0SJycnK46667ALj22muZN28e9957LyeffDIvv/wy3377LU8++SQACQkJzJs3j5tuuono6GiGDx/O8uXLee6557jvvvu681ZFBOvqYqNVWXU9o7LTeOyGc/jpTPeIU3c61NYphIGSbRTVTPZaVH70ISM7ZG70PT0GfCx41jQyEzaQaHoerzfYg3Vcc6aM5tKTjvP5mnpLAdhLT/opR0weidG8KegpjW216uqeBdUh2YiWginOHaQqZXiUMwh0zGBrOTU27MeIrQajAaXqQTWQGdvAjNFxbNgzgSZHe2a3KEsNZTVmRg/ZwZTO6y2bE/l+/6nUNTTy9s3fMzSt/UPfV6Zxwz9ieGeVlSHx21GOULLNWdyBIxbQLG3/1zQLYOaMuRZeNjWz+O+VFJS1H9dpWIEuCtF3JcQSIaJ3hKvPjFThCFIGkndW5POzPz6DYSh+ccrssAd9rRbMm8art1/qfYEfRIbGQIHBSYdPYWdBPT8W1GFvKWmwaddu6hrdM2H8TS0tr2ng83VbOWb6hLZtPQ0MAmkNGvwJR9CgmmtRldtQlds4SNn5Vh3tfsBHVrDJrn8zZdGZzL/kdzSZkpl79PEkpwzxcVRvgfqzUMtRhTs5WyDdvQHb6oknnvBamzxnzpy2f3fO1C16X8iB30MPPcTvf/97LrroIt555x0uvvhiduzYwerVq7nyyitDOtY555xDaWkpt9xyC0VFRUybNo0PPvigLYHLnj17PE6ms2fP5sUXX+QPf/gDv/vd7xg7dixvv/12Ww0/gJdffpmbb76ZCy64gIqKCoYPH84dd9whBdwHoUAXG610XWPxOcew9OKTPDKsdadDTUuyMSx1E3nZ/pNt5O+/kLSkn3o8ZjabSE7QUYZ3hj9/Gf/cDCZnvxvgE+jeOq7KWgfJ8f5TKeuaxsSc3RjVjwLe2SJDH2V0/2QM+2qvAuboKegxP0e3HgSqBmXUgOH+v1UpYILvQ3ZgcTyNq+ZHr+1ZsZA5QaO8fiR2Rzw2Sy0bdu5l4/YRnHvoNq/9oyzVzGzJ2tn5dyk7RfHSb+pZ9qHiyMk2PAIyLR49+mR06zTQrLgDO2vLv81B/WzOOhHOOM6z858zZRSq5nrPzysUegqapevPT/SucPaZkawnQUqkCXQh/sGqTZx761M4XQYXHDeTv91wbq8Efa0WzJvG/Dl5YQsMzl6yjJvOPYNDJ4wHIMpmYlR2LKu3VnofyIfP127zCPx6GhgE4ito6Kg7QYNqKEWVb8So2Ioq2wBN7SOVWcAMfR8bLKfR1OGmWbRNZ8qYBHKG3ISmaeQC9fXuWUSVFZ7Lj/zxl2U01JG7vs6gG+wNWH/7+cvU3UpG+/peyHX8JkyYwNKlSznvvPOIj49n/fr1jBo1iltuuYWKioq2mnwDWaTVRxKB+SvEnpOexFt3/pLp44f5fW4od9qUMrCXXYNOpd/6cAbJ2NIe8rjYV0rx7/8V0mR3hXTHMzV2B7NHP9nlfqaE36OHsI6rq3pNmQkb2jJG+mquKd73KKNhXx1wqmeolIKPN99MkyMR32sZFVGWGo6dtAxNj0HTY0GLBa313zHuf2vRoEWDFsMrn/3IYcNfJjvVXSrB12uC7/dtKHcr/P0I/X0uPdGTz7Q32jMY9PX5vbf6zEjtp0IdvYg0gS7Ek+KiOfX/Hqep2cFZRx/MP5dc1CtrqnrC5TIYtfCWgInPUhPieXHJNYwfnkhORjS6rnHLsn9xx3P/7fL4v190PH+89NS274Ot/9dVXT1f2qYJNtTzu1+fRVZyNL+77a8440eBpgc14qeaKlHlm3AVr4GyjeCo9d7JFIWWNQs9eRxa8lhc9SVUkIOdOL9TSOvr64mLi0PXdd5dvgd7s/9L6WibiRNmZ3odozu1Lz9fu5Vjrn0o4HsG+OTBa8KS1bO0sqlT7gHfwlnfUERYHb89e/a0paCOjo6mttb9R/Tzn/+cww47bFAEfiIy+bugWDBvGlmpCZy79Km2zu6qM+fx5yvmE20LXEfJZNKDPjkqx2bPemmdaBqYqPRIqFFV20z+tirszUY3soz56KB8NqwqpON63nnsvM5teNsoo7/mto4yghOMcpSrFOUqwah/KaR2dEXTbMwY9TVfbjke37X1NKaOH4U11X+Nzs7G5+7zmMLp/Zr+n9tVeareyKLZPn32OVAdLq5aps+2vm64ykuI8DvQ+sxQzqmRpquRMqvFTLPDySmzJ/NCBAZ90HW2a3AnazFZasnNbA+ajjp4XFCBX+efbbD1/4Ldr6OsrCyGsA/nxlf4+K6W5Hz174ArBdOkReg+gj7VXIsq34RRmo+q2Ar1+zvtoaMlj4GU8RBjR9n3oidMQc84te3cbY7PaamUG4AyOHJKBlnJ0UxL3ceq/Vng59zvK8tod0fu+jqDbjBlPrpTPkP0n5ADv8zMzLYplMOGDePrr79m6tSp7Ny5kxAHD4UImr+7sPddfSY7Csr4w7J/4WysJT1GZ+nFJ3HYQSP5YeMGj2P0eD1AsAGWqqKqtplNO2soKguiqrkfdkd8cDuGuI6r9USeaFvvtVbR7ojFZumikK1RgbPiV4EzeIaFnWTbZxw5eQKrt40OS/a2vJGxqODq9Iaul7Jovv21hRseTmBEejOZyYqiSo1dpQnce7XFfTfYcgjVVd/jdFRitiSTmDQFXY+8C9IDlfSZA0NXF+IAzQ4nx84Yzyu3/SIsJRt6Q2FZ+/ncX7kegKIKz8Bg3rSxpCbEUl7j/wSZmhjLvGmeNWN7MzAwClfjWvOA9wNNFe7th1yHljkdVbUDVbIWo2Q91OzqtLOGljgC5bSDcqFPPB8tWbXfMIsBgx8wKj8O+oaZUbgay8Zn24PRHx/iJEsyGyynsUe1n/8D9VPBlqP6fN1WTLredsN7SHJw1wXhLvMR7vIZov+EHPj95Cc/4d133+Xggw/m4osv5vrrr+f111/n22+/bStYK0Q4BVzIfMs/2r6fFFXBxs/e5crPnvJ5nB4vIg4ywNq0S2drQYn7G2WQZuygzBR8gfVW5fUjaWxOJMpS7X8kqhvruDRNY+bYHSRo3msVreYgo6K2oM8GpnTABK7dIbUjWInmVzj+8Acor3b0OHubbkomlLQsIQtzFs2Ov/t7StozwmlUuxfx//HXRFuSabSnAWkARNtKpLZSBJE+c2AIZqQMYPE5x/S4OHtvUEpRXNFEUZl7rVVX5XoyUxIorWzyOKc+ftN5nL1kmd/XePzG87ym7fZWYKCUgWvTc+7X8LOPa+0jLfPzfZ3VdfRpv0bPyEOzxKKUgabp/qfPB5kwzV8wanJUMtXxLGPG/5rauIO9+qnONQ6DHZE7d+lTVNS0rynPSUskNSGWipr6Psug2x91A0XvCTnwe/LJJ9tKI1x55ZWkpqaycuVKTjvtNC6//PKwN1Ac2IK5Cwvw2A3ncOrM0RQVLaWxsbEta9SXX37pkTa4JzTLBNBT/CbbUAoaHYlsLcgGpchxrWWc8xPi4hP5iNE0OXT8lRCwmnV0k+cC6WibhWbz+URrf/PbJlPsoqCnFiplgFGC4dhFoukZlOGn7FAwoi7EHHMYaPFomobRvAlXzR1BPjlERgU4t5Ce3PORtNafoXJV+F2n2aMbl2HMotnV7/6sieNQrngaDc+LHqmtFFmkzxwYgr0Qr6jtYabdXlBWaWfjj9WUVzczND2LYw6ZyuWnnuC1X2u5ns/WraOxIc5j7Va0zcSsieN57fZLue6h1ykIoUxAbwQGqmJzW608/zs53f/XTGiZM9AzDkZLm4KqL0RLHIlm7ljmR3cHk/XPBTxkoCn7wQSjsbtfJvEnh3s831eNwyhTBodOHMc3P2wN2J6OQR/A/rLqtj6hLzPo5mREk5Vmldklg0BIgZ/T6eTOO+/kkksuYejQoQCce+65nHvuub3SOCGCvQs7ftgQsrOzyc7Obsu2BTBt2rQeF05upWk6pthFPu8Wts7Y2rj/NLJcGxjv+Jh4VQzxuZjn3MrUUju79yz3WwZi+LB5fk6qWRh2W8jruJRyoJx7wbUL5dzt/nLtBdU+9bQ7AY5hQEG5xp6mYRx1cPudZEMfR2GFiSFJLnwluAuUNCUoYRpJC/QzdD/eg4PrKWAeF1IJjEAC/e7rmsZFJxwb8PndTaEuwkf6zIFjIBahr6ptZsOOakoq7ADoOoweGs/lp/ov16OU4icHT6Op2TMLY/sNo/HsfPWPISfoCXddPdUUXJZRUiZgGr8QPWV82yYtKsn3MR2bu86SHGDKflDBaFMFqmIzWqr7+e01Dr1v+t648AzuffUtVnUR/Hm0AXeAl5IQQ5TN6hmg92IG3das3HFGBZgAA1xVKSDryQeckAI/s9nMX//6VxYtWtRb7RHCQ18vZO6Kv2QbjY5ECvYdzLiyj0nUytFGnYhmiUUfeTyappOVuIH04f7LQJiiK3FVrfR7UtWs0/3Wv1PKANc+lPPHDl978D39xQJ6EhilXb5XQ3kmNDEMQIMbn4rh7OM91/d9+f2PPPRiFC/9ph7DwCP46/x9twQYSQtUG9BnQqDWn6GfYBp8J0zRrbMxmt7z2w7dOhtX1fVhS7QS6Hd6wvBcjylcvnQ3hboIH+kzB45wFaHvi6ymjXYXm36sZnehezRI02BEdiwTRiRQ1+Bgxz7/U/a7CsTcN4wyu5WgR9O0Hp1vlDIwKrahdv0HVZLf9RMA07gzPYK+wC9Q1bP9moJ8fst+rTUO/df+PZWLTjiW1Zu3ta29DKp5uGsqfnj/JR7r/3org25Pp8eKyBLyVM9jjjmG5cuXM2LEiF5ojhCeIu0urGEodpdNZNPO/yPOsgObpRZLcyM5lWsZa3wO2bMxT7gZLTq17Tmt00t89bet23wGFJ1Oqq13IJVRj3J8j+HYhnJuQzm3e4zktR88Ds08ouVrOJppGJiyUI4tQU3LLK/RSE9s74wKKjRubClifs35np93YXkN76yyct5f4d5fNHhkzqysgx79eAKMpPmrDWiKXcTbX1sC1EcKHEz7e0yzjAktKOxBxxjodzo5LrhR7GBrMIneI33mwNCxLmxnwU6hC7UmW6icLoNte+rYuqcWl8t9jh2aEc1BoxOJjXZfzpVV2nv0Gl3dMOq8Vq0no3ru4xmoyq2owm8wir6BYEf6AKJS0FJCWOMe7FR8f/v5GUn0t19ZlZ1E2/q28kgeu1iqmTH8BeDnzJs6AZcjvy1516Z9CZRVN3b5MiWVdZx37Izg2tRNPZ0eKyJPyIHfiSeeyG9/+1u+//57pk+f7jWNLlChRiFCNTdvDOlJcZRW+c4g2RsLmf0prbSTv62K6joHACZ7CiMcX5PlyndfGOhWzOPPhqhkjyDFvbaum0W4AVfdsyjlAucWDMdmcO3Fq8S4FoVmHuXxhZ7mu0PuYq2ioaC02soH31/P2OzKtqkpW/enUlTzKbkZJV6fd2uQ8s4qK/9abWHORGdbJ6bpiv/e1v1Umv5G0gIFW86aB/jne7HsK/Us51FQWuVOitJSH8lfBk5N030+5mv0FfM4d/sC6E7HGGgEorIuuM+zOynURXhJnzlwLJg3jZ8dfyjP//cbj+3BTKELmISswzmnO5RS7CtuZMOO6rZ1YikJVqaMTSQ10TNbZjj+5v3dMPK1Vq076/iUUlCzC9fe5aiCleDscD4zR4NuQUsYjjZkOsbGZ3wW8wEwTQp+jTt0vU4fCJgwTUuZAFEpgad7dghGm+xOv+WRNM29BCIv5w3eW2LFpLVf49idOovudfHOqsDlqPrihndPp8eKyBNy4PfrX/8agPvuu8/rMU3TcLl6NWeeOMD886PVVPpZTN+bC5k7amhysmF7NftK3HfgLNgZ1/whw11fYYpOwjThKpRhoKeOR+k7cVXe7nmi1Hq4xlBVYtQ97LlNH4JmGYdmHoNuGQemoUF3gF2ucwP2VC0kOSGZ8vqUtu3JCYobFp6BZqr1+rw7BimGofHFxvasd7qu2Fem+S2a7n7ROMACqsPd3i6Cu0DTLhVw9yUNvLvagmFoHtv91UcKVueg0Gje1CsdY8cRiM6L+Dfv3ktZdQ1pifH4SzMgtZUig/SZA8e7X+bzwoerAbhywZEcPnlUUFPouluTLRi19Q7Wba2itGUkLybKxOTR7sLrvm7sBVNeoSu+gsf2tWqeQkkmpZqqMPb/D2PfF1C7r8MjGmQfgSl7FlraFNDNbe9NsyXh3Pgs2Dv0DVGtdfxCm0XRVd8HgROmaZqOadIiXGseCCoYjbNuJ9qo9rFX6/HAZmlA4XmNYzPX8NJv4Py/wts+gr++vOHd4+mxIuKEHPi1ZicTojcZhsHvnniXu1/6GICZE4dTUFrF/g41inpzITOAy1Bs31PL5l21uAwFKIa7VjOh+T9YaUAbcTymCeeimdwnZr/z4MNROE5LQbdNR7NMdE871BN7dDg9wDq3/L2nUlQz0WdiAFBEW5JRSnk8HihIUYZ7iuhLv/H/OZjiLu3WSJrf96dBbrpizkSnRxAK7fWRVuRvD0+h6V7sGBfMm8art1/qNX0sJz2RtGTA8BdJS22lSCF95sCwfvs+fnb7MyiluHz+HB689uyg/36CrckWyjnH6TLYvKuWbXtqUcq9TnrC8ATGDovHZPLfrmDKKwQSbTORmmj1KPWQmmglf1tVwOf5SyalXM2okrW49n4Opd/T1jvoFsg4GKq2o2Ueimn82R5ZOFvpWTNxxE/gpNljyEqO5ql/vkl0zrS2LJ3+puv7E6jvC2Y9tp41Ew65LqhgNCGmHiOIcre+fpoa7puX/1ptwdXhPN9XN7zbXzApvPuJfhdy4CdEb7M3O7jozud59dM1APx+0QnceslJKEVwi+aVwZFTMshKjkar3IKKmRby3POSiibWbamirtGdLjpFK2Jy4yskqv1gsoELtJiMtqBPKQNXnf/6Rz1liv8VepinUfiatlheN4I95YFGrzS/a0D8BSlDM5K44JSzMCc4uuxsQx5J60Jmsv8F82FLCNTLHeOCedOYPyfP5+9+uKZeCXEgKyqvYf5vn6C+sZljpo8PKeiD8CchKyxrZP3WKhqa3H/XmalRTB2X1LaOryuByisMzYhm217/0cjQjGj++1Wxx/OsFp1mR+AbGB37BaUUqvpH1N4vMPavBGeHES1TNPrE89CzD2upr6f8ftaFhYUUFhZg5keyxkRTWNzI2t31RJesIyF6G0PTlmPSOoyoBRu8dZEwrSuBgtGOND05qOP5orXcvDxjto2Syvq2pRO7y9K456qze+2Gt1c7ejg9VkQeCfxERKmua+TM3/+dz9ZuxWI28Y/f/owLjms/ifu7W9p6508Vf4dl85d8fFdLqvv19+LcEvy0EHuzi++3V7OnyN1R2fRmJjW9RY5rDZopCn3ChWgpE6GpHD1jWtvzDMcPHYqah1kvnlQ7T1tsag6uRpW/NSCBghTwnzTFpzBMHSmq9H/xFq71EZplgnuqaqCfvxbXo5+hyaT7/N0Pdwp1IQ40TXYHZ/7hSfaWVDIuN4NX/vgLLObQapOFKwmZvdnF+q1VbcsKom0mpo5LIist9LIsgc4NKYm2kILCroK+Vo2NTbiqV2Ds+hDq97c/EJWCNmQGqvg79GFHow/7SftUzgDv6/OP7mXW1O3k5sTy/JNHALC34F5eeXMXi6+chNZ5rCyEZFr+1nEHTdP54vsSAP6RPN5nP9ZV3dhgvLC4DugwW0azYopzdO9g3dDT6bEQOPO26HsS+ImIsb+sipNv+hv5OwqIi7bxxh2XceyMri+W2zI7llXANh/TJpoqcK15AA65zm/wp5RiT1ED32+vbunkFCPIZ0L9m1hogrhczIfe1J6tMyHX8/mOTUG9x84FwhVR6NapqOZVfp8TSpH2ngo2MUCg/fwFKRBiZ9uDqSOGgoIyjS9/8D7F9en6iD7Q0xTqQhyolFL84i8v8PXGXSTHx/Dun68gOT4m5OP0tBSEUoqCEvcon91hoGkwNjeOCSMTMPdgOp+/c4OvoDA10cp/vyru9msBWDY87E5A1ipmCKYpl6ClTnJPzTxoUdABrGFfzZknFgKea+SHZsdyw1UHAf7rrkZKlslggqaudVoioYILbsMZbPVkemygzNtSAqJ/SOAnIsIPu4o46aZH2VNcyZCUeN7/6685eFxul89rW1engN2B93Vteg4t07szqG90smZzZdvi+QRTLXn1z5Gs9oA5CpyAyQJR/qdtdF7X5s+j70fx7ipz27SNXaVp3Hv1kZx+2OERcXIMJjFAXyUNCWqKib/nAjc9FYPqtAauN9ZHuDvXLkZ7VZ1kPRMiwtz94se8/PF3mE06r91+KWNzM7p1nEBrnLs65zTZXazbWsn+UndJnoRYC9MnJpOcEDijY091DgpLK5s6nPedjEz9ihhbOQ32VHaWH0775aJ3IXKURpSqJtWxBWKz0NKmoMo2YBp1InraZI/XDEYwJZAC6sUsk+7pp4U0NraXW1i3bh3R0e6p9VlZWWRlZbU95jdoIgmoJrgrB2+BgtveCLa6Mz1W6v9FJgn8RL9btWkXp/zmMSpqGhg7NJ3/3HMlI7PTunyeR32ZWqC5iyc0VaAqNqOlttTDU4qdBfV8v6Mal0uha4pxxheMrvsAHQMt9yj0CeeiClehDz0y8FQGU3CjR++vNnskG9Gobkv1fcaRD/b7dIhgEgP0VdKQYO6W6lGnYDSv9OrgzLGLuOAUC9/+6L3eMOwJgSTrmRADzsffbub3f3en2n/wurM5+pCeJXoKtMbZ3zlnX3EDa7dU4nAqNA3GD49nwogEdL/pj8PHMFxUV32P01GJ2ZJMo300ABMz/82o9C/QtfaAZFL2+/xYeiSVDcP8FiIfWrcH89T/Q0s7iNZwt7v9RFAlBLo8SFXPnu/HE088wW233eaxbc6cOW3/Xrp0KbfeeqvH476CJoXCqLmz+w3xE9z2ZrAVyowdqf8XuboV+BmGwfbt2ykpKfHKWHbkkUeGpWHiwLBi/XZO+c3fqGu0c+jE4bz7lytIT4oP6rkenUNXQV+rpioAGhqdfNdhlC/VWs3UmseJVRWgm9Fn/h+m1juVw4/13wbnLoymzygt30e8NQarucHnHUmloNkZQ2ndKGBv+3Y6pfqOgBGhQIkB+jppSDBTTHR1js+AecE8Aq43DBvJeia6IH1mZNlTXMH5tz2NYSguPulwLj9tTtdPCkJXa5xdLsOdAbSkmvoGnZT4dEy6TmKce5QvKb53R/lalZV8ic31InGWajABBphJZMbwHDITvJctaChGpy/3eazWQuQm4xS0tIM8MzN39+ZlOIK2niwVMJyopo9QRjGaPgQt6qfouvty+fLLLw9Ye7PjaJ9HczqXAbKv7Hb72nT6nMIRbLX+jva0z5T6f5Er5MDv66+/5vzzz2f37t3uIpwdSE2i/hOuP9a+9Ol3W5h/8xM0NDVz9MHjePuuy4mLCWEKYceTXpD9pbIlsnN/Pd9vq8LpUph0mKj9jxHV/0JDgWYCdHRLnP9jGA0Y9pUYTZ+BaxcA9uap7Cw9kxnDn/dex9fyZ5JfcCaJsTu8j0eYywuEQSQlDelqikmgu5CB1huGi2Q9E4FInxlZmuwOzl6yjPLqeqaPz+WR6xeG9bzm75zz5vJ1XqOBiTFR/OLYgznnqAn8uK0A8J4qGG5lJV+SoP3N6+rPZq4mM8E9kuev2Li/xwAM8+cYFf/zqsXaremFPb1J1oPzravuJYym92mdgqkAGv6JijoZU9x5ZGYOYUhqZc9n5oTjRmCnY/Q02PL1Ozo0PYn7r+nGLBmZCROxQg78rrjiCmbMmMH7779PVlaWZI+LAGH9Y+0jH6zaxJm//ztNzQ6OO3Qib95xGdG2EO92djzpxeMO/gKM/ClbCl/vS6e4wt0xpUQ1MrXmceJcRWCOwTTlEjDZ0OJy0GKHeD/fuQdX439R9q8Ae8tWE5p1BtGJP6Vobzzf7v45k7PfIdranra70ZHAxv3zKaqZTGVdvt/2ha28QJhEUtKQHmdg60XhyHomBi/pMyPLNQ++xreb95CSEMOrf7yUKJul6yf10JvL17FwyTKv1VxV9Y3c+85K7vnzLVD+I+B7qmC4GIYLm+tFMPsP4Pzp8tfW1zrnbk4v7Mn6buj++dYd9L3n4xGF0fQeylWIcu0My9q5nr5Hn8FtD4Itf7+jBaVVbctRQrqelJkwESvkwG/btm28/vrrjBkzODLiDXRh/2PtA+9+mc85S5+i2eHklNmTeeW2X3Sr8/U4cWrAcGCb730VsF4/heKKZnQNJtjyGVX5onuUL2Ek5hnXoUV7rytUyoVqXoPR9F+U44f2B0zZ6FFHo9vmounxZMQrom1FFNVMpqhmktfid0NpVNTUsHn3Xq/XaBWu8gKi7/W0KLAYvKTPjBzL3lvJP95biaZp/POWixmRldrrr+lyGVz34Ou+s31qGkopMg8/k3eXLsSk62Ed7euc2bG6tsk9vbOPhbqWqyfru7t7vjUMZ8tIn3/K8Z2PJ3YzuO1hxk+fwW03gy2Xy+D6h3z/jnotRwlyJpnMhIlcIQd+s2bNYvv27dKJRYDe+GPtbW98vpbzb3sap8tgwbxp/POWi7Bagvs1VEp5TT30OHGmAGNxZ/fsMPLnMCezTj+FIg4iIcrFwY3PkFC5te1xLSPPK+hTRgNG02cYTR+CUdayVUezzkSPPg7NPN7jzr1nUhSd8vrRHsfTUDzzwccYyvunNdjKCxyoeloUWAxO0mdGhtU/7Obq+18F4PZLT+G4Qyf2yeu+++UmCsqq/D6uaRol1Q3UmxLCOi3dV2bHGBXro95RH+jGWq6erO/uDtX0Ed3NsAndS1TS1XsEcNU96zl9VkvGFHehzyCzu8HWivztHjPGOuvOchSZCRO5grrizs9vn5529dVXc8MNN1BUVMSUKVOwWDxHavLy8sLbQuFXb/yx9qZ//e/7tqDvvGNn8Mzvfo45yEK5BSWNfpKNTCYrscOJMwVIhpIfG3nppV0cfurdFDMWNJ2RMfuYWP43TDghNhN94s/RjGb0rEPbjqmMaozG/2I0fQSqpZi5Foce9RP0qGPRTP7vEAdKitLoqOSbH7aGnOpbDCyRPCVV9B3pMyNLVW0D5yz9B80OJ6fNyeP/Lvhpr7+mUorNu2pZsW5/1zsT3qn+/jI7alq99859pRtruXqyvrvL5nQaDVWuom4dp003E5UEeo+GfXXLAssOTwgw57a7wVawv3uB9vNVN1BmwkSmoAK/adOmtU1JaHXJJZe0/bv1MVmo3rc6/hHqmuKgMS5SEhQVNRobt5swlOa1X3/5+NvNLLzlHzhdBuceO51nf78o6ECnoKTRZ3mBRruLVRvKmTV5Mtnp7aUQmuxRDJs9A8NQ/POnuSTE60xzvcOQcncWLS33aEyTfoZmbl+/plwlGI3vYzQtBxzujaZsTNEno9lmo2nBrT/MyYgmK83Gjn111De6iI02MXpoHLqeFXKqbyHEwCR9ZuRQSnHZX19kd1EFo7LTeOZ3P0fXe/cmm73ZxepNFZRU2EmOay9AruuKOROdbXVcv/zBjNFSazRcU/0DZXbU8J+khS4e67FuruXqjZtpPuvcEYZs1d1MVOLrPXa3LEN3gq1gf/f87ddV3UCZCRNZggr8du7c2dvtEN3Q+kc4e6qTK862k57cfpFRWqnx+Gs2Vq439/vasS/zd3DG756k2eFk/tw8nvld8EGfUor8bVUB98nfVkV2eia6dRIul2LNhn0YhvuzSItzcrj9AaKaC90725IxHXQhmsl91125SnA1vIWyfwm406xr5tHo0aehWQ8J+eTka2Ry+9568sYmdZnqWwgxOEifGTmeePdL3ly+DovZxItLLyYxrnfL0VTWNPP19+U02l2YdI2fnZDHsn//hxmjSrjnFw0MTWvppxWU7oMXP7ayalcyc6aMCsvrd5XZsTWo85d92q6mEaWtp/PcFD3qZDTLGJ8X+Khm38ldOuwTKWu5/AZUNPrYFqIwJSrpaVmGUIOtuXljGJqeREFple+1qPhfjhJsgCozYSJHUIHf8OHD2/79xRdfMHv2bMxmz6c6nU5Wrlzpsa/oXXPzxnDqkTZ+tdD7hJuapPjDZU08/mpqv64d+3bzbk75zd9oaGrm+FmTeGnpxViCnN4JUFZl9wiifGm0uyirshNtM7FqQwXFpe0n8JnltxNl0yF+GHr2bPQRP0UzWVCuMlwNb6Psy2kN+DCNRI85F916ULcy73U9MplKTkZ0REy7FUL0HukzI0P+jgIWP/wGAHddfhozJ/buZ723uIHvfqjAMCAuxsysyakkxll46fd5zMh9p33HCmA3pDfDtZOaYVIxavn1GJMWoWf1cPpbt9PjazSo40jKWBSwjp2vgEI1fzcg1nIFE1B1WxiD23DUwAtlpNRk0rn/mrNYuGRZSMtRpEj7wBTyT+Loo4+mosL7F7K6upqjjz46LI0SwdF1uOIsd1mBznGK3jIt/PKz7PTyrBa/vt9RwIk3PkptQxPzpo3h9dsvxWYNLXtnk93oeidgf2kjn64uobrOQccEoZoy0LJnY569FNOYU0FrxFX3NM7KxSj7Z7QFfQCunRj1T6Cavw2pjRD8yGTnOl5CiMFN+sz+Ud9o57ylT2FvdnLiYQdx7dm991krpdiwo5rVG91BX3KChXHD4mh2uDAMF4fkfgG09NMVuLNPdy491FSBa80DGIWre9aYIEedOt/a1DRFrP5fDPtqdN2MKeZEzHEXYYo5sS3oc++no1snodtmo1snub+3zcQUf5179K8jPQVTfGjZLntTUAEV4OPTQbNMD/iMsAa3/VADb8G8abx6+6XkpCd5bB+akeQ3O3ywAarh+AGjeZO7/nHzJpQK7rpO9I6Qs3q2rkvorLy8nNjYWB/PEL2lsn4Lusn/9ApdA0x1VNZvISWubzKYtdpRUMrxNzxCRU0DsyaN4J0/X0FMVIh1+sA9WhfM6+1zL1pPjdcYX/p023Yt92hM0y4H7LjqX8do/DftNfh86GZq5lBGJiOlNt6BwOUyZGqt6FfSZ/aPax98nc17islOS+Tp3/2s19b1OZwGqzdWUFTeBIDZpNBdmykqcpfzsVpgxrCWrIwKd9bpAFybnkPL7P4ISdD14fxMaunuCM2AWMsVZKCkxV6Opuq8Rjy7WssWNv1UAy/k5ShBfp5G7YOgOiQWkuQu/SrowG/BggWAe1H6RRddhM1ma3vM5XKRn5/P7Nmzw99C4ZfdGVw9nmD3C5fSqlpOuvExiitqmTomh/fv/jXxMd0LdtKSbO6smF0EVQCJahe2VfezpbG2bdv3zZOI+voJjMZPyMxwkZUZDaYxYBSDqvV7rFA7v2BHJoPdT/Tcm8vXeSfTSU/i/mskmY7ofdJn9p8XP1rN0//+Ck3TeH7JhaQnxffK69Q3Olm5vozaBieaBkPiNzA5+12ire19brOzw5rCWrxH+jprqkBVbEZL7eaaKMOJVjsBFeNOZhZy+YZuZqeEAZDVOMhASTelolvnem/vo+C2P2vg6ToceZDTvW5TcxLwrQUbeKpO2WS7eYNdhEfQgV9iYiLgvnsZHx9PdHT7ycxqtXLYYYdx2WWXhb+Fwi+bOTGs+4VDfaOd0/7vcbYXlDI8M4X3//prkuNjun08z/p4vukaTIvfwCsvPcd7a11UVtSi6zqGYTB37ry2/f7wm8O47Y8PorRYjJo7A79wiJ1fsCOTwe4neubN5etYuGSZ10L1gtIqFi5Z5nfqihDhIn1m/9i5v4xf3/sKAH+48IReW1NdUdPMV+vLsDsMbBaNjPgNTM153ms/i6lD0pCugr5WTVUht0cphSpZi2vTC9BQDMnASBNYOtw01eICJ2FpO1jorz8QhCOg6ovgtr9q4IU6ohn06LIfsv6vfwQd+D39tHv63IgRI7jxxhtlikoESI4dj82SjN1R6XcfmyWF5NjxfdIep9PFebc9zTc/7CYlIYZ/3/1rstJ6HnT6q48HYDVrjOEbNlWNZezJf+X6k93bTdSSpJ4nmrWgxaBFHU3OiPnotlwM+8rgXjiEzi+Ykclom4m0JJvfx0V4uFwG1z/0us/sZAr3DfDFD7/O/Dl5Mu1T9BrpM/uey2Vw0Z3PU9vQxJy80fxh0Qm98jqFZY18s6ECl6FIjLMwfkQsCU538pbOs3o1rUOZhGBXO0QlhdQeVbcf58bnoOx79wZbEqbh58OQw1BNa9FMze5adcrAqL2r6wOGeQphpBhIRcX7ugZed8pHBPN5Bn7R7o8ui+4LeY1fbm4uJSUljBw5sjfaI0KgaToTsi5g/Z5H/O4zIev8PjmJKaW48v5XeH/lBqKsFt6+63ImDM8M2/Fb6+Ot3VLF7kJ3YfXUeBha9yHrjWO9prO4iKNc+xUzx2xk6NC5aHqHi65emD8fzMhk3tikbmULFaFZkb/dY3pnZwrYW1LFivztkmFV9DrpM/vOfa98wpf5O4iLtvHM736OOYQM0sH6cV8d67ZWAZCRYmPW5FQqytd7TO/srK2EQlxL8Bdo5C8qBS0luCl8ytGAsf1tjJ0fgGq56ajpmA79DXpCSwbTmBnufZUC5QA9GQz/N4vRElDKidG8HnCBMtz/x9XyGgZgoDr8G1TLfp2+lHI/huq0b8ft3l/K43GC+3fbnT7Vxf8B82hw7qGtZi8AFjDnYthXYNhXeO7fdmh/txN7oovn60NBS8bdVgtosRhNn2E0fRam12/53XBuDriPq/ZRXI0TfF/DmIaDaz+en6cJ9+9NVy9eFUJLRTiEHPjdddddXHbZZeTk5DBv3jzmzZvHUUcdxZgx/Vcy4EA2JHEGU4ddxebCf3qM/NksKUzIOp8hiTP6pB13PPcBy/61El3X+OfSizhiyuiwHt8wFGs2V7GnyB30jUp3MaH4Xj7ll+6gz+tk5P5+w96pDM31nGraW/Pn/Y1MRttM5I1NIiejd+tHCbfC8pqw7idET0if2TfWb9/HkmXvAXD/NWcxMjstrMdXSrFxRzVb97inSg7PiuHg8cnouobNXBPU9bfDiMY6vNGd1dMP0yT3iJNSze61UUYDStWDagCjHqWaQDWgaraiKjYAzTAKiE4AzQkWGy7nY7gqHO7HlMP9RTNBNVLVYNT+pev9Bh0HOH8MQxjV+/qnjQ5wfh/CawcR9MGgHV2OZCEHftu2baOgoIDPP/+cL774gnvuuYfLL7+crKwsjjrqKF544YWQjvfoo49y9913U1RUxNSpU3n44Yc59NBD/e7/2muvsWTJEnbt2sXYsWP5y1/+wkknneSxzw8//MD//d//sXz5cpxOJ5MmTeKNN95g2LBhob7dAWFI4gwyEg6hsn4Ldmc1NnMiybHj+2y6wvP//Yal/3gfgIeuPZvT504N6/GbHQZff19OWZUdTYO8zBqG7foLZQyjyZYU8Lm+Mmn25nSPnIxostP/n737jo+izB84/pnZzW56J4XeewkQQBAUFQVFgVNRgRPsnicCong2LIce6v2kWLGLp4jlFLt3HAqiIFIFpAgIgpAC6XWT7Dy/P0IWluxuNskm2STf9+sVZWeemXl2stlnnvZ9AjmRbaPYZhBo1YmNtEpPXz1KjAn3aTohasPXZaaozFZSyrTH3qK0zM5lZ/fh+kvO8un5DUOxeXcWR9LKGx57dginW/swx/d6WFgshhftSNuP/ZmSUo34yF20zt2K1Tgt6IUlADpEYrf+C/uJF6hyQqAZiDt9Q0UGCj336LmlgRYGegjlvTUmNM108t86aObyNJhAO7mt4kfTnV6Xl53ayden/5+TabXTtnNqv8Zp+zht/+nbTvu3dvp2zkhDFftO/dt9+exqe3XK8tqW+/VzvFG6D2VbVfXZrBegB3Tx6pxKGRiF71QO7HK6OgpQIzzTVC0WFissLGTt2rW8++67vPPOOyilKCsr8/r49957j6lTp7JkyRKGDBnCokWL+OCDD9i7dy9xcXGV0q9bt45zzjmH+fPnc+mll7Js2TKefPJJtmzZQu/evQE4cOAAgwcP5sYbb2TSpEmEh4fzyy+/cNZZZ7k8pyu5ublERESQk5NDeLg8HHqyfudvnD/zGUpKy7hn8oXM/8t4n56/oKiMH34+QX5hGWaTxqC4w8T+Vj609Wh4P7aUTq7yHIN6RtMmoXKAmXoLzSzqld1u0PGqhzh6PNtl66RG+dpEB977u8zxa4Ya8vu9tmXm6aScOuVvL67g/979Hy0iQ9m+9AHionwXxdNuV2z4JYPUE8VoGgzoHkW7ROf5mkoZ2E7MQCer8uATykcIGioAXSslo6AjttIwVFkWN1zzJvGRVl5+4SyC4kwuntM10EJACy7/KS6A/BNQpso7VAzQopPQYvqgaVbQrIAFtEDQAtA0C+XDAy2gBZzcZy7fBv699IKoN0bJLuy5j1eZzhT+AHo15uO5nTdYcT4/WuPR39Tl93u1K37//e9/Wb16NatXr2br1q306NHDMXTlnHPOISoqyutzDRkyhEGDBvHcc+UP8oZh0KZNG+644w7uvffeSumvvvpqCgoK+Pzzzx3bzjrrLJKSkliyZAkA11xzDQEBAfzrX5Wja3lLClTvHDx2guSbniQ7v4iz+3Ri1aI7CAiodieyWxk5NtZvz6Ck1CDIauKssA2EHvmgvMzqZOGEqTXrf7u1yvOM6B/rdu08pQwp/Jqgiqie4DwspuK5SqJ6Nl/1/f3uyzLzdFJOlftu237On7kYpRQf/+MWxg3vW+tzKqU4kW2joKiMA0cKyCkoRddhSO8YEmODyudEGcdRZYfKf+zHUGUHUPbyRsTTK3+OwC6ODqcg0KMoLAohstWjAOSkf0hoWAvQw0ALR9PDyiNwaoFomo6R/Rv2n1+G/CPlp4hLQovtB2X56B0vRTNVf41cISooZVCWNbPK6S/mqMXVfj6SBvaaqcvv92o/pY8ZM4YWLVpw11138eWXXxIZGVmjC5eUlLB582buu+8+xzZd1xk1ahTr1693ecz69euZPXu207bRo0ezYsUKoLzi+MUXX3DPPfcwevRotm7dSocOHbjvvvuYMGGC27zYbDZstlOLeufmytyfqiz770ZumP8vSu3l69L9sOMAna95xGdrpB07XsRPv2RgGBAZamaw9gmWI2s5EdURW2wUVlMW0cFHCLTYKC5xHymzqkiafr/ukKiRy89N4v15N1Vexy8ukgV3yDp+ov74qsyUcqqy3IIirvvHWyiluGHsUJ9U+o6mF1Wap61h0LPtCeICV1OW8zuq7FD5nLsznB7B89S2IPSgi9Ctw8AUi6aVN0KaLQVAecXPFDwGPbBy1Fdlt2H/9d8Yv5VPpcAUhKnP9Wgth8n0AeEzdTn9pb7WPhTeq3bFb8GCBXz33Xc89dRTLF682NFyOXLkSLp29T5C3okTJ7Db7cTHxzttj4+PZ88e19GFUlNTXaZPTU0FID09nfz8fJ544gkee+wxnnzySb7++msuv/xyvv32W84991xXp2X+/Pk8+uijXue9ufv36q1c+9jSStt9tUbawWMFbN1TPkchIdpM//xXOF5oYmfoPYSYsrDm52ErDaOgtD1t4iPYd8T9ukQSSbP5uvzcJMYP78va7ftJycglMSacEX07y/BOUa98VWZKOVXZPS+s4PfUTDokxrDgjitqfb6j6UUuIzMrNHYeakGgOkxixK6TW81o5jZgaodmboNmaomyp2MUrgB12hw7LRDN3AHN3BqAlJQUUlJSKCo6tb7ftm3bHOs8JiYmkpiYiJGxC/v2V8vX5Ks4VUwP9FZn1/p9CnGmulw+QhrY/Uut5vjt2LGDNWvW8M033/D5558TFxfHH3/84dWxx44do1WrVqxbt46hQ4c6tt9zzz2sWbOGDRs2VDrGYrGwdOlSJk2a5Nj2wgsv8Oijj5KWluY456RJk1i2bJkjzbhx4wgJCeHdd991mRdXLalt2rRp9kNoXLHbDWLGziGv0OZyf23mTyml2Pt7Hrt+K2/JbtdCo/fxBaSZYvgjpD+9W37mFDK7qCSCncfGERZxFn+kF0kkTSFElRpyiGRtykwpp5x9s3kvF975bPm/n5nJuUneBZ1wRSkDo/QA//lJp7jEAihiQg5iDShvZMwo6ABoBFlsXDTwKHpAezC1QtNOtZ0bto2U5S5CAfppbY2GKi8XzeHl85keeeQRjxX4hx68n7lXtEYd/b58Q2AUerdroCQPvf2FaLrvplMIcSaZ/uIf/GqoJ5Q/oG/dupXVq1fz7bff8v3332MYBi1atPD6HLGxsZhMJtLS0py2p6WlkZDgev23hIQEj+ljY2Mxm8307OncstCjRw++//57t3mxWq1YrbKwtjf+/uaXbit9UPM10pRS/Lwvm9/+KI8A1TW+lK7H5kN8IenGMJJbVY58FxiQQ3K7f7H9mM7ooaPIyCmRSJpCCL/jizJTyqlTCops3PJUeePuXyaMqFGlr/wBdyeGbR2qZBsn8lpQXHIrCeE76d3yU5eNjKm5vcksHkKLoMBK58rPfBWr2bnSB+WvDQPyMl4jPHEgt956K+PGjXOZJyNzL3HHv0AdPVR+bNvz0btPQguoHJxM+J+mUGmS3rmmr9oVv8suu4wffviB3Nxc+vXrx8iRI7n55ps555xzqjV3wWKxMHDgQFatWuWYf2cYBqtWrWL69Okujxk6dCirVq1i1qxZjm0rV6509BhaLBYGDRrE3r17nY779ddfadeuXbXep6hs52/HeGrZSq/SVmeNNMNQbNqVyR/p5UNf+rQson3K49CtlBNGB7paysMMn1mPq5hP0aXFCjKyh9MiWgpHIYR/8VWZKU6Z++rnHEzJoG18FE9UM5K0KjtycpHuH8DIdmwvLu1BQvhOkttVDgxX0ci46fdrKbadU2m/3baboAD3Uw50HYItedhtu0lM7EViYqJznuwlGLuXYdhWQkXjfnA8es+paKaAar0/0TAkiIloLKpd8evevTu33norI0aMICIiolYXnz17NtOmTSM5OZnBgwezaNEiCgoKuP766wGYOnUqrVq1Yv78+QDMnDmTc889l6effpqxY8eyfPlyNm3axMsvv+w455w5c7j66qs555xzOO+88/j666/57LPPWL16da3y6u8qopDVVY9XbkERVz74CiWl3i3K6WmNtNNbxQwVwYY9saRllqJpMLBVLomZ86GnASZQ+YFOLa9n0jQItuRQXLoX6F/dtyWEEHXKl2WmgHU7fuOZD1cDsGTOJMKCXUdsPp1SJRjF32EUfwv2Q6d2aKHo1rMoIZmdKWGc2/mp8s1uGhl7tfyUYvOISuff8/seusVUnfc9v++hd7deTtuM4zux73gVio4DoHcYA9E90eOS0HTTyfw3/p4kf+PLe+p22QIj8+R2WbZA+I9qV/z++c9/+uziV199NcePH+ehhx4iNTWVpKQkvv76a0cAl8OHD6Prp/4Qhw0bxrJly3jwwQe5//776dKlCytWrHCs4Qfwpz/9iSVLljB//nxmzJhBt27d+Pe//83w4cN9lm9/4yoKmS/nuCmluGH+2+z74zitW0RiNxSpGTke10gb0bezy3O5ahXrExeByRhH+7AWxJQshM4nz5yrYbV2B3ZXmUeLyX3lUAghGoovy8zmrthWyk1Plq9/OO3iIYwe7HlImjJyMIr+h1G8ElTeya0mNEt/dOsINEsSRTb4bstxMjPXetXIeCDtB+JjL3Lal5qle1XxS83SqXhaUcrAvusd1KGvyzdYwjEl3YbewjkyqfQk+Z4v76lSRvm5PLAXvIVmGSiVdeEXajTHb82aNfzf//0fu3eXP5D37NmTOXPmMGJE5ZawqkyfPt3t0E5XvXQTJ05k4sSJHs95ww03cMMNN1Q7L42RuyhkRTY7G3ZmMKR3TK0rfwvf+4aPv/uZALOJ9+fd5IjeqeF6jbQFd1zpMrCLu1axwIAcBrT5F1oxULGklT0ZPfJifk85TJ/4SodUcjCthKjY6r0vIYSoD74sM5uzeUu/Yu/hNBKiw3l6uvsonsqeglH0JUbxWqC0fKPeAj1oNLp1ePk6eUBhcRnfbTlOYbGdAHO2V3koLEqvtM1s7cEfJzRaRit0F8/2hgFHMzTM1h7l+SvOxr79JdTx7SdPEIxp0Bz0yI7Ox0lPks/5+p6q0j2e1787eW5Vukfmzgm/UO3mh7fffptRo0YRHBzMjBkzmDFjBkFBQVxwwQVOkTRF3VNKsX1ftsc02/dlU4vArXy3bT/3vvQJAAvvuIIhPds71khr1SLSKW3ruEi3Szl4ahXTtJOVxiDAMKGH3U1A/J2YoruzNyWS9BxLpbWRTp0X0rIt7E2JdJ1ACCEakJSZvrFl7xH++e7/AHj+rquJCqs8p1sZOdjzX6Msaw5G8TdAKZq5I6awGZijnsYUdLGj0ldcYuf7bScoLLYTEmSiU5s2XuUjODiu0rYRfbvyjw9bwMlALqczDECDJ/7dghF9u2I/up6ytfeVV/r0ALSuEzFduKRSpc/bniSlDI9pxCl1ck9Vtm/TCVHHqt3j9/jjj/PUU09x5513OrbNmDGDBQsWMG/ePCZPnuzTDAr3TmTbnIZ3ulJks3Mi20aLqKrnQZwp5UQO1zzyGna7wZSLBvGXCadap6u7RlqVrWInuwu14BswWU/N1UuMieSOJQEsv6cEpZznXlRUBme8FMCMyZHVfn9CCFHXpMysvbIyOzc/9Q52u8HE8wYwYUQ/p/1K2cp7+Io+B1UMgBaQhB58GZq5W6X57iWlBj9sO0F+YRlBVhMj+rfAYr6Q44c/ICbMVmmOX/k14ESelb5dz6+0z2TSuXjENCY/9QL/d2MhrWNPtVQezdSY81owUy6Zgtq6CJW2uXxHWFvM/W9HC2vt8j1LT5Lv1ck91SJ9m06IOlbtit9vv/3GZZddVmn7uHHjuP/++32SKeGdYpt3rVLepjtdWZmdSY+8TlpmHn06tmTJ3ZMqFZ4mk+79kg1etnZpqsTp9Yi+nZn2WByTnkqvVKD+kVFeoG45GOd2TqEQQjQkKTNr77mP1rBt3x9EhQXzzKxTUz2UMlC277EXvg9G+aLpmrkjesgU9IDuLs9VVmaw7ucT5OSXYrXojOgfS3CgGaUUBzOvICZsmdtGxkOZV5DYweTyvJefmwRqFt/uhU45xx1rAP6W1oIpo3K5TP8AlXZyzcbAKEzDHkEze1ieQ3qSfK8O7qkW0B30aM8VSj26PJ0QfqDaFb82bdqwatUqOnd2ftD+3//+Rxsvh0oI3wi0ejdS19t0p/v7m1+xdvsBwkMC+WDeTQQHWqp9jtOVlIXjurh0plmdWz9NJp2FM67kqrmv8tnGAM7uUUZClCI1S+OH3WYMQ+P9ea7nFAohREOTMrN2jqRl8dBrnwMw/y/jiYsqH6qp7OnY819GlZ4M/qXHYgq5Bs0yxG0QDbtd8eOODDJzSwgwawxPakFocPlyCSeybVBlKWVyO4LmaHoRGOFEhkFGwamo1hGhClQYKYURJFpy0eIHYuo1Fc1URZkqPUm+Vwf3VNN0TCFTXc8bPMkUMlUCuwi/Ue2K31133cWMGTPYtm0bw4YNA+CHH37gzTffZPHixT7PoHAvNtJKkNXkcbhnkNVEbGT1Fv1dvfVX/vGv/wDw0pzJdGlTeU5DdRSX2Fm7I5ohbSMIDMhxOYwGcNsqVjGn8M5nPuS7X7Id29vERbLgjitdzikUormQUO/+TcrMUwzDTk72DspKszAHRBER2Qdd91zZmvnMBxQUlXB2n47cOHYoSimM4m8wCpedHNZpRQ/+E3rQaDTNfWXKMBQ//ZJBepYNk0nj7H6xRISeWiOv2FZG75afAp6Xc8i1VY4Q7nm+fXkYtJ2BV9B6aDx6YKTH9+s4SnqSfK6u7ml5MJhZEn1VNArVrvjddtttJCQk8PTTT/P+++8D0KNHD9577z3Gj6/eQqqidjRNo2+XSJdRPSv07RJZrfX8TmTnc+28pSiluPHSYVx1/oBa5bG4xM7arcfJKzRIy+tPu+jVlYbRVPDUKnb5uUmMO7s323/9hsLCdIKD4+jb9XzM5hoFphWiSZBQ7/5PysxyJ9K/x2pfRmhATnnHmgEFaRHYTJOJjXO93NKn32/nk7XbMZt0XrjrGjSVhT3vZVTpTgA0czdMYbeimTyHflZKsWVPFiknitF1GNY3hugI5wbRUMt+goyql3Mw9P1AkvN7q2q+vaZRrELIKAqkhZfT7aUnyffq8p7q1kFoloF+0wgnDYLCnWo9NZeVlfGPf/yDG264ge+//76u8iSqoVVcEEN6x/hkHT+lFDc+8TbHTuTQvW08C+9wHy7bG7YSO99vPU5eQRld4tfRLno1P/1qom0Lg8ToU3P1jmWaOJp/KcMGuH9QNWwbUQVv0Sfu1AOuyv0MQx5wRTMlod79X3MsM109cGYcX0e49mKlJw6rOQcrL3IinUqVv/xCGzMWfQDA7KsvoGerQ5Rlvw6qCAhAD7kaPXC0Vw+zu37L5XBqIZoGQ3rHuByqGR5cgJFf9fsLDy6otK2u5ttLT5Lv1eU91TTdLwLtSIOg8KRaFT+z2cxTTz3F1KlT6yo/ogZaxQXRskUgJ7JtFNsMAq06sZHWavX0Abzw0Xd8vm4nlgAzyx65npCg6g0RPZ2t1M7arSfILSihT6uvaB/zHSt+DGDhZ/25fswFdG2Z4Zj8/uuxGN74+hvunrzN5bBNecAVwpksGtw4NLcy0+UDpxZFkGEDk/shlFb7uxjGUKdhn4++8QVH0rPomBjNw5PysOeV95Zq5s6YQm9FM7esMj8VQzAP/FFeWUvqGklirOvGUE2Pcrndm3R1Od/e33qSmoKmck9dNbKoks3yvCQ8qvY4uQsuuIA1a9bQvn37OsiOqClN02q0ZEOFn/f/wZwXPwbgn3+dQL/OrkNMe6O0zGDdthPkFxYyqP0HJIT/jN0OS1f1566rLgcM4NTw1KjwMO666k+88cV/GT+8r1OgFnnAFaIyCfXeeDSXMtNdA51SWVg8PGloGgQGZJOTvYOo6CSgvDxa/OFqwoMVa54CU+lXAOhB49CDJ3r1XX80vYite7MoKT3Vy7bnUB6WAJPLkTAV87+UPdPtcg6ayfX8r5hQRaBWSLER5HoeAzWbb+/Im5/0JDUljf2euuvV44zI6GeS5yVR7YrfxRdfzL333suOHTsYOHAgISEhTvvHjRvns8yJ+lFQZGPyI29gKynj0mG9uf3yc2t8LrtdsX77CfIKchnaaSnRwQfBgLX/C2DcOReSGLGT3i0/I8hyai5FUUkEO45dxmVDh/Ldz/s5b8CpJSLkAVcIFyTUe6PRHMpMTw103o47KSstX47BMAxu+7/ldIgr5b/zymgRcgAIwBR2K7p1qFfnOppexIadGSgU2mk5KLLZ2bAzgyG9YypV/qqa/6Vprud/qfxj2DcvondxJJss1+JuEnt159sL4Y6nUVBVHyzPS81dtSt+f/3rXwFYsGBBpX2apmG3e15QXPifOS98zJ7DabSMjeC1e/9c48LJMBQbdmaQm5fJ2Z1eIzzoGJQB++B4cT96dztMcru3Kx0XGJDDoHZvo3EthzPPGL4jD7hCVCah3huN5lBmetVABygD7L9ZMPJ09DADU8cSKupR5oDyIZRvfPkjYZZ9fPpUAREhCvRozOGz0cwdvMuLUmz7NQullJuyrHz4Z8sWgZX2V8z/Kst7C/v+/FP57ByKOazy/CgjdRP2n1+EsmISrQUMij/MjuMJWFPSsRQVUBIUQkmbtvTtFl2t+fZCuOPNKKiqT5Ltk7yIxqnaFT/DqP5i4MJ//eenXbz0SXnQgTcfmEpsZGiVx6SkpJCSkuK0TSnFzgM55OSe4MIB/yU8qAhKgT2gt76CmIDe9G65CPAcKjvPfucZOyO9eyPygCuaEQn13ng0izLTiwfJ0h2BFK0IR+WcmsenRdgJHJ+L0SOQiPg+ZOUV8uPW5ax4IB+zCTRzF0zhs9D0SK+zcux4EbYS42SlziAm5KBjPnlGQQdAp8hmd7seX+mOQAreicPIOjUsU4+KIGRKINbkk29XKYzfvsTYs6z8fUR3w9R/BnE7j3DWp8swsnLOOHYCxPXx+j0I4Y63jSweyfNSsyax8JuxzNwCbnriHQDuuGIkFwzs5tVxL730Eo8++qjb/Q/O6c1DM/vCbtBbX4be+U8kR20jiKpDZSd3cg6rJg+4QlQmod6FX6niQbJ0RyCFSyunUTk6RW9FwrVDCEs08ck3S3j2lgxMJiBgGKbwW9C0gErHuWO3K7bvzwYgIXwnvVt+Wmlawc5j40jN7U1hcVml422bdpD33NJK242snPLt06dhGdAD45elGIe/Kd9pCkRPmk7JziNVHmtNlsqfqKXa9tbJ81Kz53XFr6ioiFWrVnHppZcCcN9992Gz2Rz7TSYT8+bNIzCw5gFGhGdKqVpH7jzdHYve59iJHLq1jecft3o/z+TWW29l3LhxFBUVMXx4eQjuBS++QnKHr7CaC0mICIJdQKmO1nYUmqYRGVroVajsyNBCp9fygCuEaxLq3b81pzLTUwOdMqBoRXhFyjOPBBTa5zv4vZOVa4ZuxqRDSl4SbdrfVq3v9Yq1+nYdPMbQbidIbvevSmkCA3JIbvcvNv1+Lb8e6Uy7xM6n5dOg4J0VHq9R8M7H6KWfQeYv5RsCQiGsNWimqo9d9gmWAb3QdCmrRC3UsrdOnpeE1xW/pUuX8sUXXzgKseeee45evXoRFFQ+bn3Pnj20bNmSO++809NpRA0dTS/yyVp9Fd7/ZgvL/7cZk0nnzfuvJTjQ4vWxiYmJJCYmUlBwaj2jKRetJSo8EAoCYQ9oIR3Q+k9CD44FahcqWx5whXCtqYQlb4qaU5npqYHO/pvFaXini6NRWUXEpq7A1Bn+tyORMSPvqvZneO/veRxJKyQl8wQ9E1eczNeZ+SyfVtAj4RO+3nO9077Svb85DdF0xcjKpWxPBuZEK6b+09HC24E1gtK9h6o+NjOb0r2/YenR2WM6ITzxahSUFgoEgMo6te205yVZ3L1587ri984773DPPfc4bVu2bBkdO3YE4O233+b5559vEoWYv6mIUHYmTxHKPEk5kcPtC5YDcN+fL2Jwz/Y1ytcfaad65yymYsgzw17QEkdg6nuL0xdJbUJlgzzgCuFOYw9L3lQ1tzLTXQOdkRfi/qDT5eq88p8gxl5wb7W/14+mF7Lrt1wAxiSXEmLNc5tW0yA0MJduLbOdtqsc98eczigNxjx0DlpE+2of6206IdzxahRU6E1un5dkcXfhdcVv//799Olzanx6YGAg+mlDFgYPHsztt9/u29wJxwK0nriLUObufDc/tYzM3EIGdG3DA1PH1Chf6ZnFHPh9x6nz5gFHgLghlSp9UPNQ2WeeQx5whRCNQXMsM1NyerN9370Em/c7AqrE2A6TwM9VHrstI4qM0vNplxBbrWtm5ZawaVd5z0an1qH0ahONKqjiIKBvB+cKqRYR5tX19GANQp2jT3t7rLfphPDE21FQZz4veVoGQhZ3bz68rvhlZ2c7zU84fvy4037DMJz2C984kW1zGt7piqcIZWd67Yv1fPXjL1gtZt58YCqWgOrH98nOK2H3/q0MaveGY5u2DwgMwNRzstvKmwzZFEI0F82tzDx9ZEqRrdOpHTGKxIgtqBwd16v6KbRIg/9ktuG+m0ZV65pFNjvrt5/AbijiYwLp0zkCyqLwZoEM3eQ8rSCgW0f0qAiPQza1UBPWsbPRTM5TI7w5Vo+OJKBbRy9yJkTVqjsKyptlIGRx9+bB699u69at2blzp9v927dvp3Xr1j7JlDil2OZdKHBv0h1Jy+Lu5z4C4LGbLqNXh8Rq56egqIwde7cwqN2rBOglnNMnjqvPaYduiUQfPg89yHNrrW4dhDlqMabwBzCF3Y4p/AHMUYul0ieEaFKaU5npaWRKRlFHTGMrImiqM48EQL+kjP69LyTI6v1cc7uh+HFHBsUlBuEhZgb3ikbXtVPTCs68lCOvuIxsqOl6+bILHoRe92f0qMqVN2+ODZk8XgK7CJ/SNB3d0hPdOgzd0tNjhc2rZSBOLu4umjavv4UuueQSHnroIYqLiyvtKyoq4tFHH2Xs2LE+zZyAQKt3v6Kq0iml+OuC5eQVFjO0VwdmTjyv2nmxldrZvnsLA9u8QkBOMUF7zPxv/ij+dc/ZBJGP8dNTGCkbqzxPdb6shBCiMWpOZabnkSk6u6LHEjQ1Gy3CuYFSizQImprN1tBRjB/Ry+vrKaX4eW82WbklBJg1hvaNJcBcXo5UTCtwN/PB07QCa3IfQm+7Bi3kjIXdoyMJq2I5BmtyH8KmT0OPiqj2sULUOW+XgZDF3Zs8TSl37WLO0tLSSEpKwmKxMH36dLp27QrA3r17ee655ygrK2Pr1q3Ex8fXaYbrQ25uLhEREeTk5BAeHl71AXVIKcXX61I9DvcMspoYMyzB4xy/d/67kamPLcUSYGbLa/fSo31CtfJhNxRbdv5Mz7jnsOQWofYBSrm8pmnALPRE6cETQvif+vp+r+sy05/KqSOphWzc5bk3ISF8Jz3jP6EopQibTcNqVQQmBrE7bRymoH4M6dXS4/Gn++1oPtv2ZgNwdr9Y4mMqT3OoSRALZcvFvvEpjOyD2E8EYRQpTJ3PxnrBdV731inDoHTvb6icPLSIMAK6dZSePoHdbrB2+35SMnJJjAlnRN/OmEz197kwSnZhz328ynSm8AfQJZZCg6vL73evJ3jFx8ezbt06brvtNu69914q6ouapnHhhRfywgsvNIlKn7/RNI2+XSJdRvWs0LdLpMdKX3pWHnc++yEAc6eNqXalTynFzl/30r3Fi1hMRahDGhqqcqzsk+y73kJLkHHiQojmqzmVmd6MTDlerLEh04wWBASV34uy4yYOHD7GHZdf7PW1MrJt/PxrNgC9OoW7rPRBDeZAFZ2gbMMTUJCCZg3DOuEeMErRo7t5nTcoH/YpSzaI0320Zht3PvMhfxzPdmxr3SKShTOu5PJzk+olD14tAyGLuzcLXvf4nS4zM5P9+/cD0LlzZ6Kjo32esYbkTy2pFWqzjt+kR17n/W+20K9zKza8fA8BZk9rKlX268GDxFufJtiShT0jFNP+qldiN531AHpM/bUaKWWQVbAXW1kOVnMEUSHdpOIphKikIb7f66LM9KdyqqqRKSbLLwSGvws4txcaqjzcS1K76cRHJFd5nSKbnW82pmErMWgVF8TgXtFeRbOuMv95Ryn78TEoyQVrNOaz7kML9b4HUgh3PlqzjavmvlppdmvFp/b9eTfVW+XPbVTPk0xhEtXTX/hFj9/poqOjGTx4sE8zIjxrFRdEyxaBnMi2UWwzCLTqxEZaqyz0Plm7nfe/2YKua0wcOYAfdhyo1hCDIymptAhYTLAli9KySAJ+y/Yuw8VepvOBtJxN7El5B1vpqcVKrQFRdE+c4tXDhBBC1KWmXmZ6HpliYA394mQ65z26Vl7523rwTUb3G+Cxsc5uKDbsyMB2MpjLwO5RPqn0GdkHsG94EsrK14DQW50tlT7hE3a7wZ3PfFip0gflYY00YPazHzJ+eN96GfYpkdUF1LDiJxqGpmleLdlQITuvkBufeBsAw1A8+OpngPdDDE5kZhJU9jShQccpLQsnYEc2eBdkFAIjvc6nt1zNnUjP28LPh5+rlNZWmsXPh5+jX1vvWpKFEELUXKu4IIb0jqk8MiX4d3RTrtvjdA0w5ZORt4fYcPejRLbvyybzZDCXs/rEYjbX/kHZOLET+6aFYC+G4Di04Dj0zuNqfV4hANZu3+80vPNMCjiSns3a7fsZ2b9rveSpukOgRdMjFb8m7KqHXiMrr7DS9qPHs7lq7qsehxjk5Rdgz3+a6OBjlNpDMP+SDyWAboGAELBluTwOgMBotGjfjhO3bdpBwTsrnNZJ0qLD2X1duuuloU7ak7KMuHDPLcn1SSb+CyGaqjNHplgtOj//+oLLHo8z/fzbXi5Icl3xO5xayMGj5T1yg3pFExpc+0cX+9H1GD+/CMqOFtMLU/KdYAr0SS+iEAApGe4bPGqSzlc0Ta+0uLtoPqTi10R9s2UvqzbvdbmvqiEGJSVl5J14hhahhyizB6L/ClqxAZYw9L63ohll2LcscpznTKaerkNl15Rt0w7ynltaaXtu8AlKNM/zDW2lmWQV7CU6tIfP8lNTriqvelQEIVMmSKhvIUSTcPrIFKN4NZnZx4gKqfq4zByN41nFlaYy5BaUsnVPeUNj9/ZhJMR4ntPuDftvX2Lsfqf8RdwATANmoJkCan1eIU6XGOPd3Cxv0wnhC9LV0ASVlJZxwz/e9pjm9CEGp7PbDdKOvUKL0J0YhgnjUCSm3AK0yE6YzluEKb4/euIgTANmgTXK+aSB0T5fykEZBgXvrHC5rzTUu7hEtrKcqhPVsYrK6+mVPgAjK4e855Zi27SjgXImhBC+p8qOYc9/i+hAxfEsDcPN17WhICcvALORxNqtJ9i4K5O1W0/w9bpUDqcWsGFHBnZD0SLKSo8OtX9Ath/47FSlTzNj6jxeKn2iTozo25nWLSLdDkrSgDZxkYzoK1FgRf2RHr8mQinDMWb7/f/t4+iJTCr643RNo3u7NkSFhpCVX8Ce349gnAzmeuYQg5SjH5IQ+j1KaRQd7UDIif0Q1AJT8t1o5lPzC/XEQZSGdeeSYZ1JjAri9Xc+IqhVks+HVJbu/a1SZalCQL53Q3Ks5oiqE9UhT5XXCgXLPsEyoJcM+xRCNHpKlVKW9xxg45y+vbh6QQa3XZWBoU7O6TupIqqnxbgCu3J+HCmy2dm0q7ynL9CiM6iWETyVUhh738M4UD7XXWs1Ar3TpehhrWt8TiE8MZl0Fs64kqvmvooGTkOeKz7JC+64sl7X8xNCKn5NwJkL1V49BM5eonHXa8Gk5Pbm+jGjiI041VJ6IieXN77+Hz/t/tVpiEFa6jfEB38CQG5GPyKObQNAbzsSzeqipVXT+W5HOgCvRdXN8gkqJ8/tvtCjAQTk6ZSGGm7n+VkDookKqd46TL7mqfJawcjMpnTvb7L+kxCi0TMKloP9d9DCsETeRo/W3/HYK1/xl4k2WkSdevzNyNJQtvEEm3t5PN+gXtEEWqq3DNHplFLYN8xHZfwCgN59MqZOY2t8PiG8dfm5Sbw/76bK6/jFRbLgjvpbx+90p3cUSHCX5kcqfo2cu3VZWsYolt9TwKbfu5CSE+a0Lzo8jLuv+hNv/ue/jiEGWZlbidTfLP937kCiDmwuT2wORG95dl2+BY+0iDD3+5RG629CODjOfeWwe+LkBv9C81R5rUk6IYTwV0bJTozirwEwhd2C3Qjn83U72HXIzM79wbRrWUh0uCIzVyM2tA9/HV83UZdTUlJISUkpr/TtfAOyD5TvaD8ac04iiSkpJCYm1sm1hTjd5ecmMX54X9Zu309KRi6JMeHVWlbLl87sKABkOYdmxi+q+M8//zzt27cnMDCQIUOG8NNPP3lM/8EHH9C9e3cCAwPp06cPX375pdu0f/nLX9A0jUWLFvk41w1PKaP8D9iFiuE0vVp+hqapM/ZpKOD6MaPQdY3Cgt8ILH0WXbeTWdCbyN3llT6t+yTM5zyFFtzCt/k2DEp278f241ZKdu9HGe7XiAjo1hE9yv1Qzaj9Vjp9m4DV7Dzf0BoQ7TdLOXiqvNYknRBC+CstoAt64PnogRehWwaw9OsN7DqUSnR4MPvf/TuLps/m5rE3s2j6bF67d5pX5yy2ebuO0CkvvfQSAwcOJDk5mSHXPc+QWV+X/0y4k4EDB/LSSy9V+5xC1JTJpDOyf1cmjUpmZP+uDVfpy1vkXOkDMDKx5y3CsG2s9zyJ+tfgPX7vvfces2fPZsmSJQwZMoRFixYxevRo9u7dS1xcXKX069atY9KkScyfP59LL72UZcuWMWHCBLZs2ULv3r2d0n788cf8+OOPtGzZNBdjVaV7Kv8Bn0bTINiSQ0zIQTIKOjnt0zUNMJF64gShZQsIDLCRXdSR8D2H0ACt9bmYOo71eWjr6ka21HSdkCkTXEb1rND67Cl07N6LrIK92MpysJojiAqpm6GnNVFRefU03FOPjiSgW8d6zJUQQvieplkxhd6IUgaFxSU88nr54u33XzuamMhQp/XKjmcVe3XOQGv1v8tvueUWxvbUKPz9B0besxKA77//nqCg8qig0tsnasJuN/yi5666PHUUVLAXvIVmGeg3z06ibjR4xW/BggXcfPPNXH/99QAsWbKEL774gtdff5177723UvrFixczZswY5syZA8C8efNYuXIlzz33HEuWLHGkO3r0KHfccQf/+c9/GDu2iY7lV9leJbMGuB9CmJPxObGxWRTYYrEeKMVclg/WSPSe17qt9FUMoSkqKnJs27Ztm1OB6qpQdbcsQ0VkS6ZPc1n5syb3genTKlcYoyMJmTzecYw/LNngijeV15DJ4yWwixCiydA0nWc/XM2xEzm0S4jmr386p1Ka2EgrQVaT04LvZwqymoiNtFbr2kop4g6/TouQXynoGO3YnpSUREiIF2tLCOHCR2u2VZ6r1yKShTMaZq5edVTVUQCAkYkq3SNr/DVxDfqkWVJSwubNmxk1apRjm67rjBo1ivXr17s8Zv369U7pAUaPHu2U3jAMrr32WubMmUOvXp4njQPYbDZyc3OdfhoFLdKrZLZS90MIIwJ/o6QsiJI/OhFUcKR8ozUCTXc/kb5iCM3w4cMd24YPH87AgQPdDqGpiGzpbgEGRXlkS3fDPq3JfYh6+gHC//YXwv4yhfC//YWo/7u/0ax/Z03uQ9j0aZWGrerRkYS5qfAKIUSFxlZOZeTk8+Sy8p62v990KVZL5SUTNE2jb5dIj+fp2yWyWiNPlDKwb1qIytgNgN6xiTb8inr10ZptXDX3VadKH8DR49lcNfdVPlqzrUHy5TUvOwq8TicarQbt8Ttx4gR2u534+Hin7fHx8ezZs8flMampqS7Tp6amOl4/+eSTmM1mZsyY4VU+5s+fz6OPPlrN3Dc8LaA7aNEYRqZTiOwKSkFRaQQZBR1cHK0IDMghKvh30lJG0fLEf8oDuXS/Bj1uAJrJ4va6t956K+PGjXO731VvX0VkS0/r2VQV2VLT9UYd9dKa3AfLgF6U7v0NlZOHFhFGQLeO0tMnhKhSYyun/vGv/5CTX0S/zq2YPMr9XOtWcUEM6hXNpl2ZqNNaBoOsJvp2iaRVnPcLtitlYOx8A5V+cp56dHf0zuM5p08ciVFBaFl7UcFVLzuklOG3UwdE/bPbDe585kOXDdeK8ueX2c9+yPjhff132KeXHQVepxONVoMP9fS1zZs3s3jxYrZs2eJ1K+F9993H7NmzHa9zc3Np06ZNXWXRZzRN57t9Qxje6SsMA1zVH345No7KHbvlX1+9W37K0RMX0eaPrwANU9J09Pj+VV7X3VBOT4zsU63TSlPktyqlNFQRkK8RejQATWmV0jVFjb3yKoRoGI2pnDqUksELH68FYP5fxqNX0biVlVuCUmA2afTqFEF4iJnYSGv1e/p2vIY6shrQ0PveAmYrlg3387/5J0cJ/fw0ZXujMfWcip7oOoJhWs4m9qS8g600y7HNGhBF98QpfhEsTNS/tdv3V+rpO50CjqRns3b7fqc5rP5EC+gOerTn4Z56dHk60aQ1aMUvNjYWk8lEWlqa0/a0tDQSEhJcHpOQkOAx/dq1a0lPT6dt27aO/Xa7nbvuuotFixZx6NChSue0Wq1YrdWbQ+APcvKL+PP8XxjaNYRXZyrCAgtP7dSjMYdMpV3b3uTsy3aaQxEYkEPvlp9SZo+k5cH/lQdz6XqlV5W+01VnLZg8cyAakNXZxh/nF1AadmpIZ0CeTutvQojabyXPHEigyzMIIUTz1ZjKqYde+5yS0jIuGNiNiwZ5nnudmlHE/iP5gMGwXhlEh/1+stehO24XaD2DUgZlm5+BtPKohKZ+fwGTFfuWRZUTF2eWbx8wq1LlLy1nEz8ffq7SIbbSLH4+/JzfRIoW9Sslw7sGaW/TNQRN0zGFTHW5/FcFU8hU6dluBhq04mexWBg4cCCrVq1iwoQJQPn8vFWrVjF9+nSXxwwdOpRVq1Yxa9Ysx7aVK1cydOhQAK699lqXcwCvvfZaRwCZpuLvb35JelYee1LaENpiDrr9W5SRhqbHowVeiK6baRUHLVsEciLbRlFxHlrRq8SH/cLx/O5E7NuPmdLykxml1bp2ddeCKW7VlvxeGkdGVw40UxpqcHBcHqX/CSSsVdtK+4UQQjQO2/b9wbKVm4Dy3j5PvXbFNjubd2WREL6TpDafEUA29ooiwsu1xZQyKNu2xFHp01qejdZqGGXfzCx/7eY4+6630BJORTBUymBPyjser7UnZRlx4QPk4biZSYwJ92m6hlL+tzRL1vFr5hp8qOfs2bOZNm0aycnJDB48mEWLFlFQUOCopE2dOpVWrVoxf/58AGbOnMm5557L008/zdixY1m+fDmbNm3i5ZdfBiAmJoaYmBinawQEBJCQkEC3bt3q983VoV2HUnju32sAePveXmgFczBO/iErgOIv4eQfsqZpxEaaKTj+LNbwA+QVx6EdDiHYvhvC26GFJGDqPMHra7tbNL5iLRiYVekLJMCiceT8gvIXZ5bEWnmm/zivgL4W3y4fIURjUZ0edCH8VVxUKNddfBa20jIGdnPfkKeUYvOeLKKCtpPc7l9Uqh96KE9OP4ex62049kP5BmsEpp5TUJl7oLiKCIbFmajMPWgx5REMswr2Og3vdMVWmklWwV6/jSAt6saIvp1p3SKSo8ezXc7z04DWcZGM6Ov/0zh06yA0y0Apa5qxBq/4XX311Rw/fpyHHnqI1NRUkpKS+Prrrx0BXA4fPuw0P2DYsGEsW7aMBx98kPvvv58uXbqwYsWKSmv4NWVKKWYt/pAyu8Fj1yXSJ+HfcGYwzDMKzaKsN7HqByi1B5JxbCjt8z+BwGjMg/+GZnW/QHrla9dsLZhfjqwDS6H7gzTAWsgvR9aREHuh1/nxR8owJICLqJbq9qAL4a9axkby6r1TMNxEaK5w8GgBaRmFjOr+qccRnZ7WFjN+/RDj0H+A8uGdWuIQNJMFdeIX7zJbnO34p63M/Tqrp/M2nWg6TCadhTOu5Kq5r1a0UztUfHQX3HGl/wZ2OYOm6bJkQzPW4BU/gOnTp7sd2rl69epK2yZOnMjEiRO9Pr+reX2N2UdrtrFq816CrCZmXXbMY1p7wVvYS3MIML5FKY0DqePodvwD0EyYBs6qVqUPar4WTHbBccK8uFR2wfFq5cffVHeBeiFq0oMuhL/zFNAlr7CUHftziAk5SJClioqUm7XFyvZ/gtq/ovxava5Dbz3i1M7ASO8yeVo6q9m7stDbdKJpufzcJN6fd1PldfziIllwh/+v4+cLEu22afCLip/wXmFxCXc//xEAz0zvh1lb5fkAIxOjaCm6BgeOj6LjH5+joUA3owXGeD7WlRquBRMV2oIyLw6LCm1R3Rz5jZouUC+ar5r2oAvRWBmGYtMvmdgNRVxkkWO7UpBdolFigEWHSIs6NfzzjPLEfmglau/75S/CWmNq7zxKRIvuDoHRnod7BkaXpzspKqQb1oAoj8M9rQHRRIXUfMqIPDg3bpefm8T44X1Zu30/KRm5JMaEM6Jv50bT01cbEu226ZCKXyPz5Dv/5XBaFm3jo5h0fmewVVHxA3TNICWnNzF/HMJCIQSEorUahuZtq+jpargWzDm9z+PDH5cTGW53ueagoSA718SVZ51X/Tz5gYoF6j0pWPYJlgG9ZNinjzXmobU17UEXorHacyiXrLxSAswa7Vu1gmI4XqSxL9eEzThVOFh1RZdwOy2ClFN5YvyxFuOXN8tf6GZM3a6pdA1N0zH1nIp9yyLHOmtnMvV0jmCoaTrdE6e4jOpZoXvi5BpX1OTBuWkwmXS/XbKhrki026ZFKn6NSE5+EYve/xaA/7v9cqxWK3Zb+T5lgP03C0aejh5mYOpYQkX5VFgSScnRViSW/gctoiP64L+hmWu2aEJN14Ixm80E6Rej8TmGwqnyZ6jygjlIvxizuXF+JCsWqPekqgXqRfU1+qG1NexBF6Ixysyxsff38rCd/btFERiSSEpOGDuziyqltRmwM9tEbz2IxJPliZG6Efv28kBuevuL0LpcgW4JdXktPXEQDJhF2S9LwXZaL16g+3X84iOS6dd2uosKWjTdEyfX+OFWHpxFYyXRbpuexvmU3UxFhAaxbsndvPPfn06OJ1egR1P6cyFFK8JROSZHWi3CTtCEXMy9izl45FJ65r0NAaGYBsxAc1NQeqM2a8GMH3Yln6yDIuMroiNOrSuYnWsiSL+Y8cOurHG+GprKqbxMRW3Siao1iaG1NexBF6KxKSsz2LgrC6WgTXwQreODUcpgX25FueU63PO+XDOJLcE4vh375mcAA631Oeg9r63yQVNPHERpWHcuGdaZxKggXn/nI4JaJXk8Lj4imbjwAT4bkikPzsLfVGfIsUS7bXqk4tfI9OqQyD9uHX/ylYZ97/kULv2+UjqVo1O4NJL8PyXSzfx++SLtbUaiBdd+Dl1t1oIZP+xKysom8N3Ob8nKP05UaAuuPOu8RtvTV0GLCPNpOuFZUxlaW9MedCEamx37cygoKiPIaqJf1yjg5ENlWQHuw3pq2MryyUj9HxFb3wEMMAWid5/kfSVJ0/luRzoAr0V5V4HTNN1nD7Hy4Cz8SXWHHPsi2q3MbfUvjftpu5lThkHh+ztxXWhqKCD061RMY0tAAz28nc+uXZu1YMxmM+cnNe4lG84U0K0jelSEx+GeenQkAd061mOumq6mMrS2Nj3oQjQWaRnFHDxWvo7rwB5RWALKP8/ePlQW71tOhCoDPaB8yQZL42lAk2UihL+oyZDj2ka7lbmt/kcqfo1YVQ+/GkAR2Mv6YBl2CXpcX59ev77XgvHnViNN1wmZMsHl0MMKIZPH+3XvU2PSlIbW1qYHXQh/V1pmsGVP+UNfp9ahxEWfml/u7UOlpaQELboHer9b0QKj0Sqt9l5ZSkoKKSkpFBWdmj+4bds2goKCAEhMTCQxMbE6b6VGZJkI4Q9qOuS4NtFuZW6rf5KKXyPm7UOtFjsSk48rffWtMbQaWZP7wPRplYONREcSMnm8/883a0Sa2tDa2vSgC+HPtu/LpshmJyTIRM8OoZTs3u+IwBvZtYvnh0oFVrsiKrA9puTZaAHBXl/3pZde4tFHH3XaNnz4cMe/H374YR555JGavKVqqY9lIoSoSk2HHNc02q3MbfVfUvFrxLx9qNVjYus4J3WrMbUaWZP7YBnQq9EuL9BYNMWhtfXdgy5EXUs9UcTvKYUADCxJIe+eVytF4O107Qh2BXxa+WBV/p8uWQamvpOrVekDuPXWWxk3bpzb/fXR2wd1v0yEEN6ozZDjmkS7lbmt/ksqfo2YuWsHbCGhWAryXc7yU0BJSCjmrh3qO2s+0xhbjTRd9+t5ZU2BDK0Vwr+VlJ4a4tm74A/494cYZ6QxsnIIfGYdPWeM40DwWueHSruiS7aiBTFoYa2qff36GsrpjbpaJkIIb9V2yHF1o93K3Fb/JRW/RkQpw2koWEZ+e/Ynj6Tnms8rLVKrTv5/f/JILLmltIgyVT5hIyCtRsIdGVorhP/avi+b4hKD0ECdmM9WOsokV4Lf3sWIf/6TrMJfKdq3HEvmb0TaAzEPuQ/NGolmbfzz33y9TIRoXBo6RoEvhhxXJ9qtzG31X1LxayQM28ZKwR/CVBQBfS5lF5fSeeNqrIX5jn224FAODBpJRrvOFNvObGdtPKTVSHgiQ2uF8D/HjhdxOLV8iOcASw7Kiwi8Zb8eIqJ0PeEpB0A3Yxp8F3pkp/rIbr3x5TIRovHwhxgF9T3kWOa2+i+p+DUChm2jy3DvOlkkt/sXm7iWDW1uICL9KJaiAkqCQsiJawUnH34DrY33IVhajURVZGitEP7DVmpn697yh72ubUMJO3Ecb8KQle35hgDLGgC0thegx0gFSTR+/hSjoD6HHMvcVv8lFT8/p5RR3tPngqaBUtCr5aek5vYkJ6FNpTRBVhOxkda6zmadkVYjIYRoPPILy1AKwoLN9OgQgb3Uywi82eshDkBDb9F4o1A39JA+4T/8MUZBfQ45lrmt/kkqfn5Ole5xXtvrDJoGwZYcYoJ/I6Owcq9H3y6RXq155K+k1UgIIRqPmAgro4bEU1JqYDJp6F5E4NWC7JhalKB1vAw9cVCjHeLpD0P6hP/w1xgF9TnkWOa2+h+58/5OZXuVLMx8nIjUI7Q4uIeI1CMEBWgM6R1Dq7igusuaYVCyez+2H7eWr81k1M1cwopWI2tAlNN2a0C0Xy3lIIQQAgItJsJDAoBTEXjdUwQl5aK3GYGp+9WNutL38+HnKj3oVwzpS8vZ1EA5Ew1FYhSUq6hoJkaeRXRoD6n0NTDp8fN3WmSVSUp3BNLuk+20zd7g2KZHRZQXtnF1E9nQtmlH5WiKJ69ZF9EUpdVINAYyzEuIytxF4NWCDYL65RDQJQy9942NdnSKPw7pEw1PYhQIfyQVPz+nBXQHPdrtcM/S7YEUvhUJFDltN7Jyytc4mz7N5xUx26YdLtdPq8trgkREE/5NhnkJ4b7x4/QIvMaJVNSRf2MKT0fTQIsZjKY33scRfx3SJxqWxCgQ/qjxftM2E5qmYwqZij1vUeW1+uxQ9En4GVudFSz7BMuAXj4Lb68Mg4J3VnhM4+trCuHv/ClymxANparGD03XCejSGnvGm6iIdAiOR+82ET1xSKPt7QMZ0idckxgFwh/Jp60R0K2DMIXNQtOjnbbb9weicjwvzG5kZlO69zef5aV0728eJ+nXxTWF8GfeDvNSqvGupylEVbyZ46aUQdnmRaicg2AJwzz4b5haDm30D74ypE+4IzEKhL+RHr9GQrcOQrMMpGzfIozMLeh2C5RdC3xe5bEqx5tVlLzj7bl8eU0hPLHbDdZu309KRi6JMeGM6NsZk6n+HiQbepiXzCsUDc3bxo/o37fCiR2Ahp50B1pIfP1ksI7JkD7hicQoEP5EKn6NiLH/U9i3GR0wDfwrKivSq+O0iFCMkl3lEUK1SLSA7jX+wtEivFyTyct0QtTGR2u2ceczH/LH8WzHttYtIlk440ouPzepXvLQkMO8ZF6h8AdeN36kryEKwBqBHl553dnGSob0iapIjALhL+RbqJFQhccx9n1U/iKkJXpCMgEn10fyRIsKghbPYM99HHve89hzH6csayaGbWON8uHNNfXoSAK6dazR+YXw1kdrtnHV3FedKn0AR49nc9XcV/lozbZ6yUdDDfOS8PHCX3jbqFFiAq31uZiHPYJmDXfaV1/LA9UVGdInhGgMpMevkTB++7w8mktgNKazHwFOrY/kKsJmhaBxx9AoPuNkmdjzFgGz0K2DqpUPb64ZMnm8x8AuMjRN1JbdbnDnMx+iXOyrCII0+9kPGT+8b50P+2yIYV4SPl74E68bP6L7YOp7c6VALvW9PFBdkSF9Qgh/JxW/RkLveS2YQ9DbjkQLCHFsd7c+kh4dQeC4EwT0LnZ1OgDsBW+hWQZWu1Byf81IQiaP91hQy9A04Qtrt+939PTpwFmBOvEmSLPDj8UGBnAkPZu12/czsn/XOs1LQwzzauh5hUKcrsrGD6WwKjMxve90WelriOWB6ooM6RNC+DOp+Pm5Y79u5Ni+zWgJg9C0zrDnD+APx/7ExEQST1sfSeXkoUWEYepQjJE/3/PJjUxU6R40S89q58vq4poB3Tp67OmTkPfCV1IycgG4JFjnsdgAWplPPUweLVM8eKKULwsNR7q6VjHMq3KjRjTdEyf7/HMt4eOFP/HY+KHK++W7RoxCN1mcd8nyQEIIUa+k4ufHlL2UF+ffyWNv/uA2zcMPP8wjjzyCputYenR2bDds67y8SHaN83fmNT1eRoamCR9KjAnnkmCd1+IDKu8zwWvxAdyYVkpiTLiLo+tGfQ7zkvDxwt+4bfywa3QNHUFi+0mVjqnO8kDeljVCCCHck4qfP9NN3HrrrVw2pBUlXa/jgVnXkRgVxP2PPkVZWEfQdBITE4HKIe2H9/DygU+LrHH2lFKcyLZRbDMItOrERlrdLsIrQ9OELw3v3ZEWcRZAoZ/xmdM1DUMp5sdZ6N67foMM1dcwLwkfL/xRRePHiV+ew3b8JyzKREy/ezHFuP6bkOWBhBCifknFz49pmk7rs64lsU0n7LuX8b/5o8p3FHwC9mhMPaeiJya6DGnfNi6Cn58NJSgg3/0F9Gi0gO41ytvR9CK278umyGZ3bAuymujbJZJWcUGV0svQNOFLxv5DxOtQHsalMl3TSNDK09EEewokfLzwV8bON4g8vAEAU9It6G4qfSDLAwnRECTAXvMmFT8/pOwloJvRNB0jZSPGtucrJyrOxL5lET8GjueqR7+rFN3wSHoO1y9QvHsPuOmEwxQytUZ/7EfTi9iwM6PS9iKbnQ07MxjSO6ZS5a85DU1ThlGtuY+i+qSnoP7nFQpRFfvxHdh//wb7cQuG3pKA3HgCEg23338VywN5Gu4pywMJ4TsSYE9Ixc8P2Xe+CYXp6H1vxr7rLcBdvwa0OvE5mhaGUs4pFPDJBgvTXwrh+dvKQGWe2qlHYwqZWu2lHKB8eOf2fdke02zfl03LFoFOwz6by9C0phKW3N+d3gOgNEV+q1JKQxUB+RqhRwPQKv4ewkLK1wRropVwCR8v/IUy7Ni+eIOiDXGoIhNgo+jbJR6//3yxPJAQwjsSYE+Anyzg/vzzz9O+fXsCAwMZMmQIP/30k8f0H3zwAd27dycwMJA+ffrw5ZdfOvaVlpbyt7/9jT59+hASEkLLli2ZOnUqx44dq+u34ROqKAOV8iMqcw8qfRsUZ3pM3zLUzvDWZa7PBbz231J+OHw7pvAHMIWV/98ctbhGlT6AE9k2p+GdrhTZ7JzItjltqxia5kljH5pWEZb8zNbrirDktk07GihnTU9FT0FWZxs7b85i39W5HBqbx76rc9l5cxZZnW1oocEUvPoeuU8uIW/JO+Q+uYSsux5vcr+HinmFiZFnER3ao1H/DYnGq2TzTmy/lGKOLcHUwgYnx6FU9f1nTe5D2PRp6FHOoz306EjCGtlSDkL4K28D7Cll1FOORENp8B6/9957j9mzZ7NkyRKGDBnCokWLGD16NHv37iUuLq5S+nXr1jFp0iTmz5/PpZdeyrJly5gwYQJbtmyhd+/eFBYWsmXLFubOnUu/fv3Iyspi5syZjBs3jk2bNjXAO6weLSgG84j5qIxfwBTo1TEJoa6WsT4lJSMf3eKbVpxim3dfCq7SNeWhaRKWvH5puk7htb04aD5QaV9pqMHBcXnwKUTttzrta6xrgwnhr44dO0beD0/RTsskdOSpRkGjUKdoWzhlR8uH/Xv6/qvJ8kCiajLtQFRoyAB7Shmo0j3lUeS1SLSA7tJA2YAavOK3YMECbr75Zq6//noAlixZwhdffMHrr7/OvffeWyn94sWLGTNmDHPmzAFg3rx5rFy5kueee44lS5YQERHBypUrnY557rnnGDx4MIcPH6Zt27Z1/6ZqSQuJRwuJx8jY5VX61Hx3A0HL+TKkfaDVuz9Wd+ma6tA0CUtev5QyOBC8FkqoPA5aAxT8cV4BkQcsp4Z9nkYq4UL4xuql93Nln8p/iFqQQfDQbArXQ9nRoCq//6qzPJComkw7EKdrqAB7hm0j9oK3wPDNdCNRew361FNSUsLmzZsZNWqUY5uu64waNYr169e7PGb9+vVO6QFGjx7tNj1ATk4OmqYRGRnpcr/NZiM3N9fpp74ZqRtReUedtmnR3SEw2uNxx/JN/PCH6/q7BrSJi2REX98VprGRVoKsJo9pgqwmYiOtbvc3xaFpEmykfjlaL921eWhQGm6Q36rU5e6Kh1AhGgt/KKfOpJTB5QODAa1SELGK10FJuVQM+5Tvv5pRhkHJ7v3YftxaPmfZ8DzyxptpB0oZZObvJiX7RzLzd8sQvyauIQLsGbaN2PMWOVf6AIxM7HmLMGwbfXYt4b0G7fE7ceIEdrud+Ph4p+3x8fHs2bPH5TGpqaku06emprpMX1xczN/+9jcmTZpEeLjrnq/58+fz6KOP1uAd+IYqSMW+7UVQBuZhj6BFtAfKK0imnlOxb1mEwvkZt2JwZ0r8QAy1r6KTw6Ei7YI7rsRk8l3FStM0+naJdBnVs0LfLpFu1/NrqrwNNiJhyX3D21bJUg/DoOUhVDQmDV1OuaIy92AqzXHbAKNpoAUbmFqUYD9ule+/Gqhuz5030w7++OEd/gixYSuTyI6+5M/LJNR3gD2ljPKePg/sBW+hWQb6zT1qLpr03S4tLeWqq65CKcWLL77oNt19991HTk6O4+fIkSP1mEvAZEWL6ooW1QXCnYeiptGa/VGDsJ3RyWYzwc4YE7kxW/n3EyNp1SLSaX/ruEjen3cTl5+b5PPstooLYkjvmEo9f0FWk8ulHJoDb4KNSFhy3/G2VTLAwzBoeQgVjUmDl1OnUWXFGIdXo4o9zxmqoAca8v1XAzUJGFbVtIOszjYOnJfqVOmDU5Ed03L8PxaCP0rL2cR3e+9i08En2XFkCZsOPsl3e+/ym/tZ3wH2VOmeyj19ZzIyy9OJetWgPX6xsbGYTCbS0tKctqelpZGQkODymISEBK/SV1T6fv/9d7755hu3vX0AVqsVq9X90MS6pgVGYRr8NygrqvRH99JLS+hy9i4OJwQRVQoWO5SYINuqgaahFERGr+PAe0/x/Y7fSMnIJTEmnBF9O/u0p+9MreKCaNkikBPZNoptBoFWndhIa7Pr6avgTbCRoLLziJY5ZT5RZeulgoA8ndCjAS53y0OoaGwaupyqoJTCvuNV1LH1aPHe9Q4ZxTohk8eDBpn5u/2yR+R0/tBzU9OAYZ5GMihN8cf5BR7PuSdlGXHhA/zy9+KvGssyCfUaYE9l+zad8JkGrfhZLBYGDhzIqlWrmDBhAgCGYbBq1SqmT5/u8pihQ4eyatUqZs2a5di2cuVKhg4d6nhdUenbt28f3377LTExMXX5NmpMGWVoevmvQNM0CAiulGbK9RdxMPd3ALJdTK3TtPJITLnF+xjZ33UkproqxDRNo0WUd5FHmzpvgo0cCP6eVmqCFKg+UNF66aqwrdD62xCXgV1A1gYToja0qK6otC1oHcagcn5zu+yQUqBsZoKvuonsLjb27L3L7xeO9pcFrmsaMMzTSIb8VqWUhlUxP7COIjs2Vd4uk+Avlel6C7CnRfo2nfCZBo/qOXv2bKZNm0ZycjKDBw9m0aJFFBQUOKJ8Tp06lVatWjF//nwAZs6cybnnnsvTTz/N2LFjWb58OZs2beLll18Gyit9V155JVu2bOHzzz/Hbrc75v9FR0djsVga5o2eQdlLKPvhYfTEIeidLkPTXQdMCY0wgRdz+N3NefKXQqyp8ybYiBSovlVV62XkGGvluTHRkYRMHi9R7YSoIU3TMLW/CD1xCJo1guzEywg7WL4Au9M89JPTa/O7T6KsdWmj6BHxp56bmgYMq5h24KrS6GnO8+l8HdmxKWvIZRJqqiLAXp1eI6A76NGeh3vq0eXpRL1q8Irf1VdfzfHjx3nooYdITU0lKSmJr7/+2hHA5fDhw+intcwPGzaMZcuW8eCDD3L//ffTpUsXVqxYQe/evQE4evQon376KQBJSUlO1/r2228ZOXJkvbyvqqhj6yHvMEZJLnq7UWAJdZmuNpGY/KkQa+oaKlRyc+ex9TIZWRtMCB9RxdlgCT01SsVaXuY8+/46fv7vdzx9y0DatAhxpP/jRAF3vbyZfhd15rxrPLde+kOPiL/13Hg7B/nMdJquEzJlQvl6pWfwNOf5dDWN7OgPQ2Trm5T9rmmajilkanlUTzdMIVOb/OfDHzV4xQ9g+vTpbod2rl69utK2iRMnMnHiRJfp27dvj1LetWo1JK31OZg0E1jD0dxU+qDmkZj8rRBr6hoiVHJdq+niv/W9aLCn1ktZG0yI2lNlxZT99CSaORDTgJlogZGOfbfeeisp48aRrQxKS45iNvIp00MpSGjF3Od0wmKKOJj7isfz+0OPiL/13Hjquavgbq6yNbkPTJ9WacRDeFELLMqgRMt3e86aRnZsbKOLfFVJbYplv6+Ur9M3S9bx8zN+UfFrjjRNQ2s93It0Vc9lchWJyd8KsaauvkMl17WaLv4riwYL0fSo/KNQdBylB4CyO+1LTEwkMTHx5KvKD/gp2T/WarpCffG3nhtPPXcVPM1Vtib3cTnioUfelmo/T1SlsY0u8mUltamV/b6mWwehWQaWR+9U2aBFogV0lw6HBiR3vp4ZR39A2V0vKu1OxVwma0CU03ZrQLTbL1R/K8SauvoOlVyXahJCvDbHCSH8mx7ZCfPZ8zAlz0YLql6wtMbSI+KP+bQm9yFs+jT0KOdr6tGRhE2fVmVjWsWIB+tZ/bH06Iym6zV6nvDE29FF/rJAfEUl9cyKWk2Xs2hKZX9d0TQd3dIT3ToM3dKzWd8LfyA9fvXIOLoO+7YXIPxLzGc/6pgr4Y3qRmLyx0Kstup7CGF11Wuo5DpS4xDiNTxOCOG/lFKOJXq00ES3sas8acgekeoM5/PXnht3PXe1+R71ZWTHxjS6qK6mwNS27G+OcyNFw5GKX30yB4IlHD1+QLUqfRWqE4nJXwuxmmosQwjrLVRyHalpCPGaHieE8E+qMB371ucx9b0ZLax1jc9T0+kKtVXd4XwNlU9v1MVcZV9FdmxMo4vqspJa07K/sc2NFI1f43gabSL0+AGYz3kCvfP4Or9WUxp+0NiGEFYUqImRZxEd2sPre6wMg5Ld+7H9uJWS3ftRRv0PjalpCPGaHieE8E/2Xf9CZe/H/stbtT6Xr4cXVqWmw/nqO59NQWMaXVRcRaWvuunOVN2y39fDToXwhvT41QOlDMcXQEUI7PrQnIceVuf8/jB81F96NGscQryGxwkh/JOpz83Y9Tcx9fDcgOit+hoNUdvhfI191EZ9a9ChvNUsv0vKvGt49DZdbUjkddFQpOJXx1TeH5RtfQ5TnxvRo7rU7lw1qKQ09kKsLocQ+ktlq6JH80wVPZp4MYnfV2oaQtzb48xd2pf3ZvrpPE0hRDnNGo55wAzfnrMeFo72xXC++shnU9FQQ2RrUn5bzN41PHqbrjYa09xI0bRIxa+O2fe+B3lHMPZ/gj7o7hqfpzaVlMZciNXVEEJ/qWz5W1CUmoYQ9+Y4y5AksufMb/CKthDCNePEL4BCj+1d79e22w3Wbt9PSkYuiTHhjOjbGZOp+t95jWnOWVNR36OLalp+B54xhNcdb9PVhnxORUORil8dM/X7C8ae99C7/KnG5/CXSkpDqIshhP5U2fLHoCjuFv/VoyMJmTze7WfN03GWIUkUf7W60jHN4TMsRGOgCo9j3/IMlBbA4HvQW/Stt2t/tGYbdz7zIX8cz3Zsa90ikoUzruTyc5Oqda7GNOesIfl6mkO9DeWtRfntGJZakoXLELUKrJb6CXonn1PRUKTiV8e0gBBMfW6o8fH+VEnxleoUODUdeuiJP1W2/DUoSk1DiLs6ztylPdlz5ns8rrF9hoVocqwRaHH9If8oWnT3ervsR2u2cdXcV1FnbD96PJur5r7K+/Nuqlblr6EjWjeG0Px1Nc2hPkYX1ab81jSdToUj2GX+FBTOlb+TH8BOhcPr5ffl7ec0MqiLTI8QPiUVvzqgyopR2QfQY3vV+lzefsmV7NlPQVu7Xxc2UP0Cp6ZDDz3xRWXLV62l/hwUpaYhxM88rmT3fr+paAshXNNMFkz9bgV7MZrJUi/XtNsN7nzmw0qVPjj1XD772Q8ZP7yv18M+G3JZhsYQmr9iBJHSFPmtSykNVQTka4QezW4Uoy9qU34rwyD4X7/QISaMP84voDTsVOTsgDyd1t+GEJy5C5U0oc4rV958TjsVDif7bpkeIXxLKn51wL7rbdSRb1HdrsJUy6UbvPmSy+psY2fpM5QczHdsO72w8afIlTUZsloxhDB/2cfkBp9wFFThRS0InVT9L8DaVrZ82VpaFz2a/sZfezWFEKDyjqKFtQIoX6zdHFRv1167fb/T8M4zKeBIejZrt+9nZP+uXp+31gtq16DMrAjNf6aK0Pz+sBxExQiirM421xWfb0LQ/Xz0RW3K74qG9KgsK5EHLOS3Or3iG4CmNAzqphHS1WfK0+e0U+FwAp9Zx5mLOsn0CFFbUvHzMaUMNN2EQkOL8r6gcqeqL7mszjYOjsurNF69orDpWTqO4H/90uAtRrUdsprdxcaem7OwleU6tlnNJrq3tBFfzbzUprLl6/mWddGj6W8aslezMQy7qtCY8iqaBuPETuwbnkBvdyF6r2vr/fOWkpFbdaJqpDtdTeec1aRhr7GE5i/d+xsZMenlzwxn7gs1yrd/CqF+PPqiNuX36Y2LmtII+8N1z7avGyE9fabikyt/TiODupT39Hk4p0yPEDUlFT8f0zQdU+/r0duPQQtNrPX5PH3JKU3xx/kF7g9WsK/oc3pnR6GdVjNsiBaj2ozLd9uSWlazltSaVrbqar5lTYOpNBYN1avZGIZdVWhMeRVNh8r5HVAoezGuo13UrcSYcJ+mO1N155zVtGGvsYTmN3JyTj0znPnr1gAFf5xXQJsc/40kWZvG0oZohPT2M3X656Ihp0dIA2TTJxW/OuKLSh94/pLLb1XqNFSj8sFQGm6Q36rUZctWfbYY1XS4n7ctqS1Ckyj79ZDXQ3NqUtmqy6AwNQ2m0hg0RK9mYxh2VaEx5VU0LaZOY9Ei2qFFdS0f5lnPRvTtTOsWkRw9nu1ynp8GtI6LZETfuu99qk3Dnrch94tLsxo0UEdOWBalRtXPDDl6FrofBxSpaWNpfTdC1vQz1VDTI6QBsnmQip+PGEdWY2TuwdRzKlpAsE/P7e5Lriw+CKh6CExpqKsitX4DatS0pc3bltTD//cQobtLHNu8Gc5a3cpWXX8Z1zSYSmNQn72ajWXYFTSuvIqmQSkFKMfnqSHW7KtgMuksnHElV819taLDyaGiGrrgjitrtJ5fddWmYc/bkPslL39ObjXLKV+ytwyGP6pOl7vqv5i2nKog+mNAkZo0ltZ3I2RNP1MN0TMpDZDNh1T8fECVFmLf9Q6UFWKEd8DUYbTPr+HqS05rXcLBQ09VeWxAvvuW3PoKqFHTljZvW1JLVB5gdbz2djhrdSpb/hyBszGor17NxjLsChpXXkXTYBz8CpWxG1PSbT5vpKyJy89N4v15N1Vexy8ukgV3VH8dv5qqTcNeZFAXAvJNlIbY3a4PF5CnE7LHBg047cLbhcnN6cXAqVFCp+fTn0am1KSxtF4bIWv4mar3nklpgGxWpOLnA1pAMKZBd2Mc/ga9/YV1d50zvuSileF5HZiThU3o0QD356ynSkpNW9q8bUl1V7n15XDW5hCBs67VR69mYxl2Bd7n1dt0QniibLkYv34IdhsqdSNam3MbOktAeeVv/PC+rN2+n5SMXBJjwhnRt3O99PRVqE3DXtmvh2i9Krg8OIqb9eFafxuCpuq+nPLEmwXMPT0z5L/5AVodrP9X3+qrEbKmn6n67pmUBsjmRSp+PqJHd0OPrpsFYd3xZh0YT4VNfVdSatLSVuUip1UUVL4cztocInA2BY1l2BV4n1dv0wnhiWYNx3TWg6iUDWitz2no7DgxmfRqLdnga7WNFhm13wqf4nZ9uKj91krHVaivaRe1fWZQ+YWV5mI21uUF6qMRsjafqfrsmZQGyOZFKn61oPJTICAEzVqziGO+UOU6MPvXuT22ISop1W1p81hQedGSCr4dzlrbL2N/WVOxKfO2saChh115lVfK/5ajQuq3UUk0XXpkR4iUUQln8kW0yKj97teHq0p9Tbtw+8xghNLyM81jBdUTWV6gsto2FluT+xDQvwfH96ympPAEluBYoruPRDf59tFdGiCbF6n41ZCyl1C2eRGU5mEaOBs9quGCcnhar8g2vYvfLRNQ3ZY2twWVFk7LT1SVBZWvh7PWdJiILxd+F+7VtlW7Ph9gvMlr98TJMq9C1JhSCmP/x+itz0ELim3o7Pg1X0SL9LQ+nCf1OTfc1TNDyGETeftfrvE56zNYXF3zZQNtbRqLHVE2jSwIBAyw7vvC51E267oBUpaI8C9S8asp26k/YC04rgEzcjIPbtYrairLBLgqqCKDupD9+nwM6n/OXXUrr75e+F145raxgHBafuq5saC+H2A89dp3T5xcZQEvvcjCE+P3lRi//hvj91WYRz6NZg5s6Cz5tbqKFulJQ8wNP/OZQXU3qhyWWJX66rWsS3XRQFvVZ8rVd3h63pZ6i7JZlw2QskSE/5GKXw1pwS0wD58HBakNOtTTG01lmQBXldvGMOfOFwu/y8N99blqLAjeWUj+/nerPLa+H2A89dp7Ir3Ioip6XH+MI2vQW4+QSp+XfB0t0jIkieKvVrs91h/KqdpWXqHxR7SuywZad58pV9/hWnQ4u69Ldx2A56SqomxWt5ettg2QrsgSEf5JKn61oJksEN62obPRrNXnBOiaqu3C7/Jw77uhIlq4/y7J4a7X3h3pRRbe0IJbYB72COhS3Nc1Tz07AZ3a+XU5Be7LUy0qAkpLUfmFbo9t7BGtfdFAW13uvsNzg09QouV7PtZDlM2a9rLVtAHSFVkiwn9JSVBN9p1vokV1RW81rKGzIk7y9+GstVkfqjE+3Pt6PH9NCzGXx5mjaJ1kIWKb3e1xjeEBpiEeUkTjoew2KEhDO9kwqZncL+kjfMtdz46/l1MV3OWzZMsvfj+6pjZq20BbXZ6+w0tDz4yd6pqrKJu17WWrbgOkO7JEhP+Sil81GOnbMH5fCb//Dy2iPVpoy4bOkjjJn4ez1nQtn8b4cO/r8fw1LcTcHleWxYHzoUN+mNt5frV5gKmvSez1/ZAiGg+lFPbtr6FSf8LU71b0lkMbOkviJH8up07nKp+NYXRNbdSmgbYmPH2Hu1uX+ExnRtn0p142WSLCf0nFrxq02N7oXS4v/7dU+oSXarqWT2N7uPf1eP6aFmJVHqfBsXEQ/WY4KjPXsbm2DzD1OYm9vh9SRCNilEJZISg7WCMbOjcNSuZG+1Zj6bWsiZo20NaUp+/m0KMBBOTplIYabuf5uYqy6U+9bLJEhP+Sil81aLoZU9crGjobws+56vWpSRCaxvRwXxctjTUtxLw6TsuDR28j/A+LTx5g6nsSe30/pIjGQzNZMCXPRmUfQI/q0tDZaTAyN9qzmlaKG0uvZXXVZrH1mvD03awpjdbfhHBwnPuy3VWUTX/qZZM1av2XVPy8oPL+gNBWaJp33e+i+XLb69NlCpHVHCbTmB7u66KlsaaFmLfHldhzsfQ4y6u0njTE8Jr6fkgR/k8ZZWgnA7homo7WzCt9jW1udH2SSnFltV1svSpnVrTNXdp7/A6P2m9F/zaEPy6yYSvzLsqmP/WyyRq1/ksqflVQ+cco+34uWkxPTAOmo5mDGjpLwk9V2evTZTpxTz/gdStrY3q4r4uWxpoWYvVd+DXE8Jq6fkgRjYuy27Cvn4cWPwC984Rm/TDVGOdG1yepFLtXV/MY3VW0LWf197jMR+uzp9Cxey+v5437Wy9bXSwRIWpPKn5VULm/AwpUGZjcL/osmrfq9Pp4O0ymMT3cW0zerWXpbTrwvhALD+zC6q2/kpKRS2JMOMP7dCk/riTL9fwIBVaL7wq/hhpe09SDLQjvqZSfUDkHUUUn0NucD4GRDZ2lBtPY5kbXJ6kUV83X8xg9VbSLv1pN4MUjKflxq8fvcG8bDP2xl82XS0QI3/CLit/zzz/PP//5T1JTU+nXrx/PPvssgwcPdpv+gw8+YO7cuRw6dIguXbrw5JNPcskllzj2K6V4+OGHeeWVV8jOzubss8/mxRdfpEuX6g990VsORQtrAwHB8kEVbtVVr0/jebj3Lvy09+m8K8SyM4fR6epH+ON4tmNb6xaRvDG1K3TYUH650yt/Jy/fqXC4z/6eG3J4TVMOtiC8p7ceAcqAoFi0Zlzpg8Y1N7q+SaXYO76ax+hNRbtkwzYi/3kfZfsO+eQ7vC572ZQyUKV7QGWDFokW0N2rctRXS0QI32jwit97773H7NmzWbJkCUOGDGHRokWMHj2avXv3EhcXVyn9unXrmDRpEvPnz+fSSy9l2bJlTJgwgS1bttC7d28AnnrqKZ555hmWLl1Khw4dmDt3LqNHj2bXrl0EBgZWO49aWOtav0/RtNVlr09jeLgvsXv3EOUpnatgA54KsezMYVxx7+pKVcmU49nEflBEUPcw/ji/gNIww7EvIE+n9bchBGfuQiVN8Mk9bOjhNU012IKoHr3NuQ2dBb/QmOZG1zepFNcvbyvaZfsO+fQ7vC562QzbRuwFb4GReWqjHo0pZCq6dZAPci3qS4NX/BYsWMDNN9/M9ddfD8CSJUv44osveP3117n33nsrpV+8eDFjxoxhzpw5AMybN4+VK1fy3HPPsWTJEpRSLFq0iAcffJDx48cD8NZbbxEfH8+KFSu45pprvM6bKjwB4d4PTRPNV133+vj7w31t37+nYAPxyZULsfDALnS6+hGX/YdDAnVamjXYbyXygIX8VqWUhioC8jVCjwagKQ0D37VqV2d4TX2t8yeaD2Uvaegs+JXGNDe6vnldKQ4PpWT3fr9taGwsGrKi7cteNsO2EXveIhc7Mk9unyWVv0akQSt+JSUlbN68mfvuu8+xTdd1Ro0axfr1610es379embPnu20bfTo0axYsQKAgwcPkpqayqhRoxz7IyIiGDJkCOvXr3dZ8bPZbNhsNsfrnJzyAiPr+6fgwifkwUxUyaQSKbMFYyvNdpvGGhCFyZ5Ibm6u2zSNVW3ev23LL+S/vKzyAWnp5Cx4mdBbJmMd0AszrTDrrcCA/6zfzpGUdJfXiVY6eSWnVQl/gwAAFPmcekhWx1Kwtqo8qqAmgrSudIy8jl/T3ne6B9aAKLrGTyRI68r+I6td7I+ka/xVxEUM8Ek+RNUqPn9KeT/s2B+4Lac2vY425C8NlS2/ZB8/yvV3ykmh4y4gLz+/HnPkH1RiLPnBVlS2+zJICw0m/9k3nCojWmQ4IVddinVAr/rIZpNRYoK8ElvVCU1g89PnAqUMyrJeBVXqPlH+q5ijurh9Vi5v8NxHSVkuFnM4USHu04pydVpOqQZ09OhRBah169Y5bZ8zZ44aPHiwy2MCAgLUsmXLnLY9//zzKi4uTiml1A8//KAAdezYMac0EydOVFdddZXLcz788MOK8tk/8iM/8iM/8tMMfg4cOFDToqtBSDklP/IjP/LTvH7qopxq8KGe/uC+++5z6kXMzs6mXbt2HD58mIiIiAbMmf/Lzc2lTZs2HDlyhHAZFuuR3KvqkfvlPblX3svJyaFt27ZER0c3dFaqRcqpmpO/D+/JvfKe3Kvqkfvlvbospxq04hcbG4vJZCItLc1pe1paGgkJCS6PSUhI8Ji+4v9paWkkJiY6pUlKSnJ5TqvVitVaeamGiIgI+XB6KTw8XO6Vl+ReVY/cL+/JvfKe3sjmLEk5VXvy9+E9uVfek3tVPXK/vFcX5VSDlnwWi4WBAweyatUqxzbDMFi1ahVDhw51eczQoUOd0gOsXLnSkb5Dhw4kJCQ4pcnNzWXDhg1uzymEEEIIIYQQTVmDD/WcPXs206ZNIzk5mcGDB7No0SIKCgocUT6nTp1Kq1atmD9/PgAzZ87k3HPP5emnn2bs2LEsX76cTZs28fLLLwOgaRqzZs3iscceo0uXLo7lHFq2bMmECRMa6m0KIYQQQgghRINp8Irf1VdfzfHjx3nooYdITU0lKSmJr7/+mvj4eAAOHz7s1NU5bNgwli1bxoMPPsj9999Ply5dWLFihWMNP4B77rmHgoICbrnlFrKzsxk+fDhff/2112v4Wa1WHn74YZfDaoQzuVfek3tVPXK/vCf3yntN5V41lfdRH+ReeU/ulffkXlWP3C/v1eW90pRqZDGthRBCCCGEEEJUS+Oa3S6EEEIIIYQQotqk4ieEEEIIIYQQTZxU/IQQQgghhBCiiZOKnxBCCCGEEEI0cVLxc+H555+nffv2BAYGMmTIEH766aeGzlKDmj9/PoMGDSIsLIy4uDgmTJjA3r17ndIUFxdz++23ExMTQ2hoKFdccQVpaWkNlGP/8cQTTziWGKkg98rZ0aNH+fOf/0xMTAxBQUH06dOHTZs2OfYrpXjooYdITEwkKCiIUaNGsW/fvgbMccOw2+3MnTuXDh06EBQURKdOnZg3bx6nx+dqrvfqu+++47LLLqNly5ZomsaKFSuc9ntzXzIzM5kyZQrh4eFERkZy4403kp+fX4/vwntSRlUm5VTNSTlVNSmnvCPllHt+U04p4WT58uXKYrGo119/Xf3yyy/q5ptvVpGRkSotLa2hs9ZgRo8erd544w21c+dOtW3bNnXJJZeotm3bqvz8fEeav/zlL6pNmzZq1apVatOmTeqss85Sw4YNa8BcN7yffvpJtW/fXvXt21fNnDnTsV3u1SmZmZmqXbt26rrrrlMbNmxQv/32m/rPf/6j9u/f70jzxBNPqIiICLVixQr1888/q3HjxqkOHTqooqKiBsx5/Xv88cdVTEyM+vzzz9XBgwfVBx98oEJDQ9XixYsdaZrrvfryyy/VAw88oD766CMFqI8//thpvzf3ZcyYMapfv37qxx9/VGvXrlWdO3dWkyZNqud3UjUpo1yTcqpmpJyqmpRT3pNyyj1/Kaek4neGwYMHq9tvv93x2m63q5YtW6r58+c3YK78S3p6ugLUmjVrlFJKZWdnq4CAAPXBBx840uzevVsBav369Q2VzQaVl5enunTpolauXKnOPfdcR4Eq98rZ3/72NzV8+HC3+w3DUAkJCeqf//ynY1t2drayWq3q3XffrY8s+o2xY8eqG264wWnb5ZdfrqZMmaKUkntV4cwC1Zv7smvXLgWojRs3OtJ89dVXStM0dfTo0XrLuzekjPKOlFNVk3LKO1JOeU/KKe80ZDklQz1PU1JSwubNmxk1apRjm67rjBo1ivXr1zdgzvxLTk4OANHR0QBs3ryZ0tJSp/vWvXt32rZt22zv2+23387YsWOd7gnIvTrTp59+SnJyMhMnTiQuLo7+/fvzyiuvOPYfPHiQ1NRUp/sVERHBkCFDmt39GjZsGKtWreLXX38F4Oeff+b777/n4osvBuReuePNfVm/fj2RkZEkJyc70owaNQpd19mwYUO959kdKaO8J+VU1aSc8o6UU96Tcqpm6rOcMvsu243fiRMnsNvtxMfHO22Pj49nz549DZQr/2IYBrNmzeLss8+md+/eAKSmpmKxWIiMjHRKGx8fT2pqagPksmEtX76cLVu2sHHjxkr75F45++2333jxxReZPXs2999/Pxs3bmTGjBlYLBamTZvmuCeu/iab2/269957yc3NpXv37phMJux2O48//jhTpkwBkHvlhjf3JTU1lbi4OKf9ZrOZ6Ohov7p3UkZ5R8qpqkk55T0pp7wn5VTN1Gc5JRU/US233347O3fu5Pvvv2/orPilI0eOMHPmTFauXElgYGBDZ8fvGYZBcnIy//jHPwDo378/O3fuZMmSJUybNq2Bc+df3n//fd555x2WLVtGr1692LZtG7NmzaJly5Zyr4Q4jZRTnkk5VT1STnlPyin/J0M9TxMbG4vJZKoUuSotLY2EhIQGypX/mD59Op9//jnffvstrVu3dmxPSEigpKSE7Oxsp/TN8b5t3ryZ9PR0BgwYgNlsxmw2s2bNGp555hnMZjPx8fFyr06TmJhIz549nbb16NGDw4cPAzjuifxNwpw5c7j33nu55ppr6NOnD9deey133nkn8+fPB+ReuePNfUlISCA9Pd1pf1lZGZmZmX5176SMqpqUU1WTcqp6pJzynpRTNVOf5ZRU/E5jsVgYOHAgq1atcmwzDINVq1YxdOjQBsxZw1JKMX36dD7++GO++eYbOnTo4LR/4MCBBAQEON23vXv3cvjw4WZ33y644AJ27NjBtm3bHD/JyclMmTLF8W+5V6ecffbZlUKu//rrr7Rr1w6ADh06kJCQ4HS/cnNz2bBhQ7O7X4WFhei681e2yWTCMAxA7pU73tyXoUOHkp2dzebNmx1pvvnmGwzDYMiQIfWeZ3ekjHJPyinvSTlVPVJOeU/KqZqp13KqtpFpmprly5crq9Wq3nzzTbVr1y51yy23qMjISJWamtrQWWswt912m4qIiFCrV69WKSkpjp/CwkJHmr/85S+qbdu26ptvvlGbNm1SQ4cOVUOHDm3AXPuP06OlKSX36nQ//fSTMpvN6vHHH1f79u1T77zzjgoODlZvv/22I80TTzyhIiMj1SeffKK2b9+uxo8f3yxCP59p2rRpqlWrVo4w2R999JGKjY1V99xzjyNNc71XeXl5auvWrWrr1q0KUAsWLFBbt25Vv//+u1LKu/syZswY1b9/f7Vhwwb1/fffqy5duvjtcg5SRlUm5VTtSDnlnpRT3pNyyj1/Kaek4ufCs88+q9q2bassFosaPHiw+vHHHxs6Sw0KcPnzxhtvONIUFRWpv/71ryoqKkoFBwerP/3pTyolJaXhMu1HzixQ5V45++yzz1Tv3r2V1WpV3bt3Vy+//LLTfsMw1Ny5c1V8fLyyWq3qggsuUHv37m2g3Dac3NxcNXPmTNW2bVsVGBioOnbsqB544AFls9kcaZrrvfr2229dfkdNmzZNKeXdfcnIyFCTJk1SoaGhKjw8XF1//fUqLy+vAd5N1aSMqkzKqdqRcsozKae8I+WUe/5STmlKKVXtPkkhhBBCCCGEEI2GzPETQgghhBBCiCZOKn5CCCGEEEII0cRJxU8IIYQQQgghmjip+AkhhBBCCCFEEycVPyGEEEIIIYRo4qTiJ4QQQgghhBBNnFT8hBBCCCGEEKKJk4qfEEIIIYQQQjRxUvETQgghhBBCiCZOKn5CiEbhyJEjjBw5kp49e9K3b18++OCDhs6SEEII4SDllPB3mlJKNXQmhBCiKikpKaSlpZGUlERqaioDBw7k119/JSQkpKGzJoQQQkg5Jfye9PgJ4adGjhzJrFmz6uS8mqahaRrbtm3z+fldycjIIC4ujkOHDtX4HImJiSQlJQGQkJBAbGwsmZmZNTrXNddcw9NPP+207brrrnPclxUrVtQ4n0II0VxIOeVMyinh76TiJ5qd6667jgkTJnidvq4Ktqp89NFHzJs3r07ycfPNN5OSkkLv3r29Pub0Auf0nzFjxlRKe/311/Pggw86Xj/++OOMHz+e9u3b+yL7bN68GbvdTps2bWp0/IMPPsjjjz9OTk6OY9vixYtJSUnxSf6EEKI2pJySckrKKVEXzA2dASGEa9HR0XV27uDgYBISEqp93JgxY3jjjTectlmtVqfXdrudzz//nC+++AKAwsJCXnvtNf7zn//UPMOnm1VvOgAAKD5JREFUyczMZOrUqbzyyis1Pkfv3r3p1KkTb7/9NrfffjsAERERRERE+CSPQgjRHEg55ZqUU8JfSY+faPZGjhzJjBkzuOeee4iOjiYhIYFHHnkEKG89XLNmDYsXL3a0HFYMAzEMg/nz59OhQweCgoLo168fH374oVfnrfDhhx/Sp08fgoKCiImJYdSoURQUFDiOr2g5dZePt956i5iYGGw2m9N5J0yYwLXXXuv1PWjfvj2LFi1y2paUlFQpv1arlYSEBKefqKgopzTr1q0jICCAQYMGAfDll19itVo566yznO7NHXfcwaxZs4iKiiI+Pp5XXnmFgoICrr/+esLCwujcuTNfffWV07ltNhsTJkzg3nvvZdiwYR7fk6d7C3DZZZexfPlyb2+REEI0GCmnpJwSwhekx08IYOnSpcyePZsNGzawfv16rrvuOs4++2wWL17Mr7/+Su/evfn73/8OQIsWLQCYP38+b7/9NkuWLKFLly589913/PnPf6ZFixace+65Hs974YUXkpKSwqRJk3jqqaf405/+RF5eHmvXrsVVvCV3+Zg4cSIzZszg008/ZeLEiQCkp6fzxRdf8N///rc+bl0ln376KZdddhmapgGwdu1aBg4cWCnd0qVLueeee/jpp5947733uO222/j444/505/+xP3338/ChQu59tprOXz4MMHBwSiluO666zj//POrfFjw5t4OHjyYxx9/HJvNhtVqxW63U1paSrt27TCbzRQXF/v2xgghmoWAgABMJpPPzyvllO801nJKiNqSip8QQN++fXn44YcB6NKlC8899xyrVq3iwgsvxGKxVBpyYrPZ+Mc//sH//vc/hg4dCkDHjh35/vvveemllxwFqqfzpqSkUFZWxuWXX067du0A6NOnj8v8RUREuMxHUFAQkydP5o033nAUqG+//TZt27Zl5MiRvr1JwOeff05oaKjTtvvvv5/777/f8fqTTz5h4cKFjte///47LVu2rHSufv36OeZX3HfffTzxxBPExsZy8803A/DQQw/x4osvsn37ds466yx++OEH3nvvPfr27euY1P6vf/2LPn36sHjxYpYvX8769esBvLq3LVu2pKSkhJSUFKxWK9nZ2QAsWbKEFi1acPDgwVrcKSFEcxYZGUlCQoKjYuELUk55pymWU6mpqY40QtSGVPyEoLzgO11iYiLp6elu0+/fv5/CwkIuvPBCp+0lJSX079/fq/P269ePCy64gD59+jB69GguuugirrzyykpDUqpy8803M2jQII4ePUqrVq148803HRPcfe28887jxRdfdNp2+hyP3bt3c+zYMS644ALHtqKiIgIDAyud6/R7YzKZiImJcSr04uPjARz3a/jw4RiG4TJfx48fZ9++fY7X3tzboKAgAHJzcwkICCAuLo7g4GAKCgpo3bq1zKMQQlSbUorCwkLH91ZiYqLPzi3llHeaYjlVWFhY9RsXwgtS8ROC8qE5p9M0ze2XN0B+fj4AX3zxBa1atXLad/pwDE/nNZlMrFy5knXr1vHf//6XZ599lgceeIANGzbQoUMHr/Pev39/+vXrx1tvvcVFF13EL7/84piwXht2u73StpCQEDp37uz2mE8//ZQLL7zQqQCNjY0lKyurUlpX9+b0bRUPBJ5+DxUee+wxHnvsMcdrb+5tZmYmISEh6LpOXFwcMTExjuMtFovLhwAhhKhKxcN6eno6cXFxPhv2KeVUZc2hnIJTQ3eFqC0J7iJEFSwWS6XCpWfPnlitVg4fPkznzp2dfqoTulnTNM4++2weffRRtm7disVi4eOPP/Y6HxVuuukm3nzzTd544w1GjRpVo/DRaWlpjn+XlpZy5MiRap/jk08+Yfz48U7b+vfvz65du6p9rtqq6t7u3LmTXr16YTKZCA4Orvf8CSGarorvlNLS0nq5npRT3mts5VTr1q2JjY2t97yJpkl6/ISoQvv27dmwYQOHDh0iNDSU6OhowsLCuPvuu7nzzjsxDIPhw4eTk5PDDz/8QHh4ONOmTavyvBs2bGDVqlVcdNFFxMXFsWHDBo4fP06PHj28zoeul7fdTJ48mbvvvptXXnmFt956q0bv8/XXX+eCCy6gXbt2LF68mJycHA4cOEBaWppjOIvNZiM1NdXpOLPZTGxsLOnp6WzatIlPP/3Uaf/o0aO57777yMrKqvbwoJry5t6uXbuW4cOHA9TJcCMhRPNV398pUk413XLqoosuqpf8iOZBevyEqMLdd9+NyWSiZ8+etGjRgsOHDwMwb9485s6dy/z58+nRowdjxozhiy++8Hr4S3h4ON999x2XXHIJXbt25cEHH+Tpp5/m4osvrlY+oHxS/RVXXEFoaGi1Fv093WWXXcaMGTPo06cPmZmZPPbYY3z00Uf873//c6T5+uuvSUxMdPqpqDx99tlnDB48uFLLZJ8+fRgwYADvv/9+jfJVE1Xd2+LiYlasWOEINCCEEI2ZlFNNt5yqCCQjhC9oylVMXiFEo3PBBRfQq1cvnnnmGY/pRo4cSVJSktN6SO3bt2fWrFmO9ZhqYty4cQwfPpx77rmn0r4vvviCOXPmsHPnTkfrb0N68cUX+fjjj/n00085ePAgHTp0cMz32LRpE506daq3Vl8hRNNTXFxc6btFSDlVHRXllKslLzRN4+OPP65xBVo0Xw3/yRZC1EpWVhYff/wxq1ev5vbbb/fqmBdeeIHQ0FB27Njhs3wMHz6cSZMmudw3duxYbrnlFo4ePeqz69VGQEAAzz77rNO233//nS1btjRQjqrvu+++47LLLqNly5ZomuYIHX46pRQPPfQQiYmJBAUFMWrUKKeoclAePGDKlCmEh4cTGRnJjTfe6AgKIfzb888/T/v27QkMDGTIkCH89NNPDZ0lIVyScqr6XJVTf/nLXyotVSFEdUiPnxCNXPv27cnKymLu3LncfffdVaY/evQoRUVFALRt2xaLxeKTltTG6PRWeZPJ5AhKUJMFmO12g7Xb95OSkUtiTDgj+nbGZKq7trWvvvqKH374gYEDB3L55Ze7bP198sknmT9/PkuXLqVDhw7MnTuXHTt2sGvXLkcvxMUXX0xKSgovvfQSpaWlXH/99QwaNIhly5bVWd6bImUYlO79DZWThxYRRkC3jmh12Gvw3nvvMXXqVJYsWcKQIUNYtGgRH3zwAXv37iUuLq7Oriu8Iz1+zqSc8o309HRyc3OB8mU3QkJCGjhHotFRQgjRTBUVFaldu3apoqKiWp3n36u3qraXP6D0Ebc7ftpe/oD69+qtvsloFQD18ccfO20zDEMlJCSof/7zn45t2dnZymq1qnfffVcppdSuXbsUoDZu3OhI89VXXylN09TRo0c9Xu+FF15QY8aMUYGBgapDhw7qgw8+cOw/ePCgAtS7776rhg4dqqxWq+rVq5davXq1I823336rAPX111+rpKQkFRgYqM477zyVlpamvvzyS9W9e3cVFhamJk2apAoKCmp7i+pU8cbtKmPW39XxaXc5fjJm/V0Vb9xeZ9ccPHiwuv322x2v7Xa7atmypZo/f77bY6ZNm6bGjx+vHnnkERUbG6vCwsLUrbfeqmw2myPNueeeq26//XZ1++23q/DwcBUTE6MefPBBZRiGI027du3UvHnz1LXXXqtCQkJU27Zt1SeffKLS09PVuHHjVEhIiOrTp4/T56q58dV3ixBC+JIM9RRCiFr4aM02rpr7Kn8cz3bafvR4NlfNfZWP1mxrkHwdPHiQ1NRURo0a5dgWERHBkCFDWL9+PQDr168nMjKS5ORkR5pRo0ah6zobNmzweP65c+dyxRVX8PPPPzNlyhSuueYadu/e7ZRmzpw53HXXXWzdupWhQ4dy2WWXkZGR4ZTmkUce4bnnnmPdunUcOXKEq666ikWLFrFs2TK++OILx/pW/sq2aQd5zy3FyMpx2m5k5ZD33FJsm3w3TK1CSUkJmzdvdvrd6rrOqFGjHL9bd1atWsXu3btZvXo17777Lh999BGPPvqoU5qlS5diNpv56aefWLx4MQsWLODVV191SrNw4ULOPvtstm7dytixY7n22muZOnUqf/7zn9myZQudOnVi6tSpKBlUJIQQfkMqfkIIUUN2u8Gdz3yIq0fbim2zn/0Qu73qxX19rSKceUWI8wrx8fGOfampqZWGBZrNZqKjoyuFQz/TxIkTuemmm+jatSvz5s0jOTm5UgVt+vTpXHHFFfTo0YMXX3yRiIgIXnvtNac0jz32GGeffTb9+/fnxhtvZM2aNbz44ov079+fESNGcOWVV/Ltt9/W6B7UNWUYFLyzwmOagmWfoLxY3Lk6Tpw4gd1u9/i7dcdisfD666/Tq1cvxo4dy9///neeeeYZpwWo27Rpw8KFC+nWrRtTpkzhjjvuYOHChU7nueSSS7j11lvp0qULDz30ELm5uQwaNIiJEyfStWtX/va3v7F7926nddeEEEI0LKn4CSFEDa3dvr9ST9/pFHAkPZu12/fXW57qy9ChQyu9PrPH7/Q0ZrOZ5OTkSmn69u3r+Hd8fDzBwcF07NjRaVt6erovs+4zpXt/q9TTdyYjM5vSvb/VU46q1q9fP8fi4lD+O8rPz3daCPuss85yWodu6NCh7Nu3z2lh7jN/b1AeEv/Mbf76uxNCiOZIKn5CCFFDKRm5Pk3nSwkJCQCVelzS0tIc+xISEio9mJeVlZGZmelIU9cCAgIc/9Y0zel1xTbDxz1mvqJy8nyazluxsbGYTCaPv9u6dubvzd02f/3dCSFEcyQVPyGEqKHEmHCfpvOlDh06kJCQwKpVqxzbcnNz2bBhg6MnbujQoWRnZ7N582ZHmm+++QbDMBgyZIjH8//444+VXvfo0cNtmrKyMjZv3lwpTWOmRYT5NJ23LBYLAwcOdPrdGobBqlWrKvXEnunnn392REuE8t9RaGgobdq0cWw7c37njz/+SJcuXaod6VYIIYR/MTd0BoQQorEa0bczrVtEcvR4tst5fhrQOi6SEX07+/za+fn57N9/agjpwYMH2bZtG9HR0bRt2xZN05g1axaPPfYYXbp0cSzn0LJlS8eyDz169GDMmDHcfPPNLFmyhNLSUqZPn84111xDy5YtPV7/gw8+IDk5meHDh/POO+/w008/VZq/9/zzz9OlSxd69OjBwoULycrK4oYbbvD5vWgoAd06okdFeBzuqUdHEtCto9v9NTV79mymTZtGcnIygwcPZtGiRRQUFHD99dd7PK6kpIQbb7yRBx98kEOHDvHwww8zffp0pwWrDx8+zOzZs7n11lvZsmULzz77LE8//bTP34MQQoj6JRU/IYSoIZNJZ+GMK7lq7qto4FT5q5ghteCOK+tkPb9NmzZx3nnnOV7Pnj0bgGnTpvHmm28CcM8991BQUMAtt9xCdnY2w4cP5+uvv3ZaV+ydd95h+vTpXHDBBei6zhVXXMEzzzxT5fUfffRRli9fzl//+lcSExN599136dmzp1OaJ554gieeeIJt27bRuXNnPv30U2JjY33w7v2DpuuETJlA3nNL3aYJmTy+Ttbzu/rqqzl+/DgPPfQQqampJCUl8fXXX1cK+HKmCy64gC5dunDOOedgs9mYNGkSjzzyiFOaqVOnUlRUxODBgzGZTMycOZNbbrnF5+9BCCFE/ZIF3IUQzZavFln+aM027nzmQ6dAL23iIllwx5Vcfm5S7TPqZzRNc7lgfIVDhw7RoUMHtm7dSlJSUr3mrSHYNu2g4J0VTj1/enQkIZPHY03u4+HI+nXdddeRnZ3NihUr3KYZOXIkSUlJLFq0qN7y1RTJAu5CCH8kPX5CCFFLl5+bxPjhfVm7fT8pGbkkxoQzom/nOunpE/7HmtwHy4BelO79DZWThxYRRkC3jnXS0yeEEELUlFT8hBDCB0wmnZH9uzZ0NkQD0XQdSw/fz+UUQgghfEWGegohmi0ZjiWEqAvy3SKE8EcyDkUIIYQQQgghmjip+AkhhBBCCCFEEycVPyGEEEIIIYRo4qTiJ4QQQgghhBBNnFT8hBBCCCGEEKKJk4qfEEIIIYQQQjRxUvETQgghhBBCiCZOKn5CCNEIPfLII2ia5vTTvXt3pzTFxcXcfvvtxMTEEBoayhVXXEFaWloD5VjUlSeeeAJN05g1a5bTdm9+/4cPH2bs2LEEBwcTFxfHnDlzKCsrq8fcCyGEqC9S8RNCCB9QSnE8q5gjqYUczypGKVXn1+zVqxcpKSmOn++//95p/5133slnn33GBx98wJo1azh27BiXX355neerOVLKIDN/NynZP5KZvxuljHq57saNG3nppZfo27dvpX1V/f7tdjtjx46lpKSEdevWsXTpUt58800eeuihesm7EEKI+iUVPyGEqKWj6UV8vS6VtVtPsHFXJmu3nuDrdakcTS+q0+uazWYSEhIcP7GxsY59OTk5vPbaayxYsIDzzz+fgQMH8sYbb7Bu3Tp+/PFHt+ds37498+bNY9KkSYSEhNCqVSuef/55pzSapvHiiy9y8cUXExQURMeOHfnwww8d+w8dOoSmabz//vuMGDGCoKAgBg0axK+//srGjRtJTk4mNDSUiy++mOPHj/v+xtSztJxNfLf3LjYdfJIdR5aw6eCTfLf3LtJyNtXpdfPz85kyZQqvvPIKUVFRTvu8+f3/97//ZdeuXbz99tskJSVx8cUXM2/ePJ5//nlKSkpcXrPid7t8+XKGDRtGYGAgvXv3Zs2aNY40q1evRtM0vvjiC/r27UtgYCBnnXUWO3fudKR58803iYyM5PPPP6dbt24EBwdz5ZVXUlhYyNKlS2nfvj1RUVHMmDEDu91eB3dPCCGaH6n4CSFELRxNL2LDzgyKbM4Pp0U2Oxt2ZtRp5W/fvn20bNmSjh07MmXKFA4fPuzYt3nzZkpLSxk1apRjW/fu3Wnbti3r16/3eN5//vOf9OvXj61bt3Lvvfcyc+ZMVq5c6ZRm7ty5XHHFFfz8889MmTKFa665ht27dzulefjhh3nwwQfZsmULZrOZyZMnc88997B48WLWrl3L/v37G33vUlrOJn4+/By20iyn7bbSLH4+/FydVv5uv/12xo4d6/Q7ruDN73/9+vX06dOH+Ph4R5rRo0eTm5vLL7/84vHac+bM4a677mLr1q0MHTqUyy67jIyMjEppnn76aTZu3EiLFi247LLLKC0tdewvLCzkmWeeYfny5Xz99desXr2aP/3pT3z55Zd8+eWX/Otf/+Kll15yalQQQghRc1LxE0KIGlJKsX1ftsc02/dl18mwzyFDhvDmm2/y9ddf8+KLL3Lw4EFGjBhBXl4eAKmpqVgsFiIjI52Oi4+PJzU11eO5zz77bO699166du3KHXfcwZVXXsnChQud0kycOJGbbrqJrl27Mm/ePJKTk3n22Wed0tx9992MHj2aHj16MHPmTDZv3szcuXM5++yz6d+/PzfeeCPffvtt7W9GA1HKYE/KOx7T7ElZVifDPpcvX86WLVuYP3++y/3e/P5TU1OdKn0V+yv2eTJ9+nSuuOIKevTowYsvvkhERASvvfaaU5qHH36YCy+8kD59+rB06VLS0tL4+OOPHftLS0t58cUX6d+/P+eccw5XXnkl33//Pa+99ho9e/bk0ksv5bzzzmvUnxEhhPAnUvETQogaOpFtq9TTd6Yim50T2TafX/viiy9m4sSJ9O3bl9GjR/Pll1+SnZ3N+++/X+tzDx06tNLrM3vzvElz+ryzigpFnz59nLalp6fXOr8NJatgb6WevjPZSjPJKtjr0+seOXKEmTNn8s477xAYGOjTc3vr9N+/2WwmOTnZ42ckOjqabt26OaUJDg6mU6dOjtfx8fG0b9+e0NBQp22N+TMihBD+RCp+QghRQ8U273pyvE1XG5GRkXTt2pX9+/cDkJCQQElJCdnZ2U7p0tLSSEhIqPP8AAQEBDj+rWmay22GUT9BUOqCrSzHp+m8tXnzZtLT0xkwYABmsxmz2cyaNWt45plnMJvN2O12r37/CQkJlaJ8Vryuj8/I6Z8FKP88uNrWmD8jQgjhT6TiJ4QQNRRo9e4r1Nt0tZGfn8+BAwdITEwEYODAgQQEBLBq1SpHmr1793L48OFKvXVnOjP4y48//kiPHj2qnaaps5ojfJrOWxdccAE7duxg27Ztjp/k5GSmTJnCtm3bMJlMXv3+hw4dyo4dO5x61FauXEl4eDg9e/b0mIfTf/9lZWVs3rzZ42ckKyuLX3/9tdl9RoQQwp+YGzoDQgjRWMVGWgmymjwO9wyymoiNtPr82nfffTeXXXYZ7dq149ixYzz88MOYTCYmTZoEQEREBDfeeCOzZ88mOjqa8PBw7rjjDoYOHcpZZ53l8dw//PADTz31FBMmTGDlypV88MEHfPHFF05pPvjgA5KTkxk+fDjvvPMOP/30U6U5Xk1dVEg3rAFRHod7WgOiiQrp5tPrhoWF0bt3b6dtISEhxMTEOLZ78/u/6KKL6NmzJ9deey1PPfUUqampPPjgg9x+++1YrZ4/s88//zxdunShR48eLFy4kKysLG644QanNH//+9+JiYkhPj6eBx54gNjYWCZMmOC7GyGEEKJapOInhBA1pGkafbtEsmFnhts0fbtEOoY5+tIff/zBpEmTyMjIoEWLFgwfPpwff/yRFi1aONIsXLgQXde54oorsNlsjB49mhdeeKHKc991111s2rSJRx99lPDwcBYsWMDo0aOd0jz66KMsX76cv/71ryQmJvLuu+9W2UvU1GiaTvfEKfx8+Dm3abonTkbTGmZwTVW/f5PJxOeff85tt93G0KFDCQkJYdq0afz973+v8txPPPEETzzxBNu2baNz5858+umnTsuJVKSZOXMm+/btIykpic8++wyLxeLz9ymEEMI7mqqPVYaFEMIPFRcXc/DgQTp06FCrIBlH04vYvi/bqecvyGqib5dIWsUF+SKr9aZ9+/bMmjWLWbNmuU2jaRoff/yx9N6clJaziT0p7zj1/FkDoumeOJn4iOQGzJnvHTp0iA4dOrB161aSkpJcplm9ejXnnXceWVlZlaKKNhe++m4RQghfkh4/IYSopVZxQbRsEciJbBvFNoNAq05spLVOevqE/4mPSCYufEB5lM+yHKzmCKJCujVYT58QQgjhilT8hBDCBzRNo0WUtOw3V5qmEx0qgUuEEEL4L6n4CSGEcDh06FCVaWSGQPPVvn37Kn//I0eOlM+IEEL4IRmHIoQQQgghhBBNnFT8hBBCCCGEEKKJk4qfEEIIIYQQQjRxUvETQgghhBBCiCZOgrsIIYQQNZSSkkJKSorb/YmJiSQmJtZjjoQQQgjXpOInhBBC1NBLL73Eo48+6nb/ww8/zCOPPFJ/GRJCCCHckIqfEEIIUUO33nor48aNo6ioiOHDhwPw/fffExQUBCC9fUIIIfyGzPETQohG6LvvvuOyyy6jZcuWaJrGihUrKqW57rrr0DTN6WfMmDH1n9kmLDExkQEDBpDUry/n9Inj6nPa0b9dEP37JzFgwIA6rfhdd911TJgwodL21atXo2ka2dnZdXZtIYQQjY/0+AkhhA8oZaBK94DKBi0SLaA7mlZ3bWsFBQX069ePG264gcsvv9xtujFjxvDGG284Xlut1jrLU3NlpGwk4Jel/G/+qPINPz9N2d5oTD2noicOatjMCSGEECdJj58QQtSSYdtIWdZM7LmPY897Hnvu45RlzcSwbayza1588cU89thj/OlPf/KYzmq1kpCQ4PiJiorymH7kyJFMnz6d6dOnExERQWxsLHPnzkUp5UjTvn175s2bx6RJkwgJCaFVq1Y8//zzTufRNI2XXnqJSy+9lODgYHr06MH69evZv38/I0eOJCQkhGHDhnHgwIGa3wQ/YKRsxL5lEdiynHcUZ2Lfsggjpe4+A97697//Ta9evbBarbRv356nn37aaX/79u157LHHmDp1KqGhobRr145PP/2U48ePM378eEJDQ+nbty+bNm2q1nlTUlIYO3YsQUFBdOjQgWXLltG+fXsWLVrkSJOdnc1NN91EixYtCA8P5/zzz+fnn3927H/kkUdISkriX//6F+3btyciIoJrrrmGvLw8RxrDMJg/fz4dOnQgKCiIfv368eGHHzrlZc2aNQwePBir1UpiYiL33nsvZWVlTvfg9HwBJCUlOeZnKqV45JFHaNu2LVarlZYtWzJjxgyvfwdCCOEPpOInhBC1YNg2Ys9bBEbmGTsysectqtPKnzdWr15NXFwc3bp147bbbiMjI6PKY5YuXYrZbOann35i8eLFLFiwgFdffdUpzT//+U/69evH1q1buffee5k5cyYrV650SjNv3jymTp3Ktm3b6N69O5MnT+bWW2/lvvvuY9OmTSilmD59uk/fb31SysC+6y0ANDdp7LveQimj/jJ1hs2bN3PVVVdxzTXXsGPHDh555BHmzp3Lm2++6ZRu4cKFnH322WzdupWxY8dy7bXXMnXqVP785z+zZcsWOnXqxNSpUx0NAN6cd+rUqRw7dozVq1fz73//m5dffpn09HSn606cOJH09HS++uorNm/ezIABA7jgggvIzDz193TgwAFWrFjB559/zueff86aNWt44oknHPvnz5/PW2+9xZIlS/jll1+48847+fOf/8yaNWsAOHr0KJdccgmDBg3i559/5sUXX+S1117jscce8/o+/vvf/2bhwoW89NJL7Nu3jxUrVtCnTx+vjxdCCL+ghBCimSoqKlK7du1SRUVFNTreMOyqJGO6Kjk+2f1PxnRlGHYf59wZoD7++ONK29999131ySefqO3bt6uPP/5Y9ejRQw0aNEiVlZW5Pde5556revTooQzDcGz729/+pnr06OF43a5dOzVmzBin466++mp18cUXO+XpwQcfdLxev369AtRrr73mlL/AwMBqvVd/Yj/xiyr5fHKVP/YTv9TJ9adNm6ZMJpMKCQlx+gkMDFSAysrKUpMnT1YXXnih03Fz5sxRPXv2dLxu166d+vOf/+x4nZKSogA1d+5cx7aK319KSopSSlV53t27dytAbdy40bF/3759ClALFy5USim1du1aFR4eroqLi53O06lTJ/XSSy8ppZR6+OGHVXBwsMrNzXW6zpAhQ5RSShUXF6vg4GC1bt06p3PceOONatKkSUoppe6//37VrVs3p8/0888/r0JDQ5Xdbnfcg4p8VejXr596+OGHlVLq/9u7/5Cm+j0O4O9tLp2mnTsTt8xaskwNK7Z+7VEsywouGUpE/aWFZGBFNkvIwrD9YRhFUBT+czHp/lP09EfpP0rYhVGRlBXhlazxiDQxeh7znsd5W3P3j13P08nSmrPN7f2Cwc7xu+8+25HD+ezzPd+v9/z589709HTvx48fvd9juucWIqKZwIofEZGfvO5/T6z0fWnsd1+7INizZw927NiB7OxsFBUV4e7du3j8+DE6Ojomfd369euhUPxVw7JYLHj16hU8Ho9s3+csFgu6u7tl+1asWCE9T05OBgBZlSQ5ORmjo6MYHh7+4c8WEkaHAtvOD/n5+ejq6pI9Pq/Odnd3IycnR/aanJycCcfze44VAKliN1W/PT09iIqKgslkkv5uNBplQ42fPXsGURSRmJiIuXPnSg+HwyEbAmwwGBAfHy9t6/V6KY7e3l6MjIxgy5Ytsj6am5ulPrq7u2GxWGT/0zk5ORBFEf39/ZN/wf+3a9cuuFwupKWlYf/+/bh9+7ZsqCgR0WzAyV2IiPzlHQpsuxmWlpaG+fPno7e3F5s3b57x91Or1dLz8Yvur+0bGwveUMhpiREC284PcXFxMBqNsn3fm8x8LhjHShRF6PX6r/4QIQjCV2Mbj2U8DlEUAQAtLS1ISUmRtfuRiYyUSqXsPlYAcLvd0vPU1FT09PSgvb0dbW1tqKiowLlz53D//v0J8RERhSomfkRE/lIIgW03w/r7+/H+/fsplxh49OiRbPvhw4dYunQpVCqVbN+XbTIzMwMX7Cyg0GYAMVpgdJKqb4zW1y5IMjMzYbfbZfvsdjvS09NlxzPQ/S5btgyfPn3C06dPYTabAfiqc3/88dckOCaTCQMDA4iKioLBYPArjqysLERHR6Ovrw8bNmz4Zqy3bt2C1+uVEli73Y74+HgsXLgQAJCUlASn0ym9Znh4GA6HQ9aPRqNBYWEhCgsLcfDgQWRkZODFixeyqiYRUShj4kdE5CeFOgNQaicf7qnU+toFmCiK6O3tlbYdDge6urqg1WqxaNEiiKKIuro67Ny5EzqdDq9fv0Z1dTWMRiO2bds2ad99fX2wWq04cOAAnjx5gkuXLk2YsdFut6OhoQFFRUVoa2vDzZs30dLSEvDPGcoUCiVUWSXwPLkIL74+wYsqq2RGl/WYSlVVFdasWQObzYbdu3fjwYMHuHz5Mq5cuTKj/WZkZKCgoADl5eW4evUq1Go1qqqqoNFopOSroKAAFosFRUVFaGhoQHp6Ot6+fYuWlhYUFxdj9erVU8YRHx+PY8eO4ejRoxgbG0Nubi4+fPgAu92OhIQElJaWoqKiAhcvXsThw4dx6NAh9PT04PTp07BarVAqfcdm06ZNaGpqQmFhIQRBQG1trSwxbmpqgsfjwbp16xAbG4vr169Do9Fg8eLF0/oeiYh+JiZ+RER+UiiUUMWV+Gb1/AZV3Mxc+Hd2diI/P1/atlqtAIDS0lI0NTVBpVLh+fPnuHbtGoaGhrBgwQJs3boVNpttyiFwJSUlcLlcWLt2LVQqFY4cOYLy8nJZm6qqKnR2dqKurg4JCQm4cOHClAllOFLq1wCmSnx6eU2+pENMaKzjZzKZcOPGDdTW1sJms0Gv1+PMmTPYu3fvjPfb3NyMsrIy5OXlQafTob6+Hi9fvkRMTAwA35DN1tZWnDx5Evv27cO7d++g0+mQl5cn3VP4PWw2G5KSklBfX483b95AEASYTCbU1NQAAFJSUtDa2orjx49j5cqV0Gq1KCsrw6lTp6Q+Tpw4AYfDge3bt2PevHmw2Wyyip8gCDh79iysVis8Hg+ys7Nx584dJCYmTut7JCL6mRTeLwe1ExFFiNHRUTgcDixZskS6GPXH2H8fw/Nns7zyp9RCFVcCZfTsWsB748aNWLVq1YQ1zT5nMBhQWVmJysrKnxZXqPtT/A/+/osR+r9p8I9//gpNyqqgVvpCUX9/P1JTU9He3v5T7jENpkCdW4iIAokVPyKiaVJGr4Fijtk3e6d3CFAIUKgzeOEfAZxOJ5xOJ1wuF/71wjfT5OHfXNAMdgHwzUA51T2V4erevXsQRRHZ2dlwOp2orq6GwWBAXl5esEMjIopIvCohIgoAhUIJ5ZwsKKN/gXJOFpO+CNHY2Aiz2Yzc3FxpX25uLsxmM8xmMxobG4MYXXC53W7U1NRg+fLlKC4uRlJSEjo6OjgLJhFRkHCoJxFFLA7Houkar/h9SyRX/CIZzy1EFIo41JOIiMhPTOyIiGi24FgkIop4HPhARIHEcwoRhSImfkQUscbvNRoZGQlyJEQUTsbPKbyfkYhCCYd6ElHEUqlUEAQBg4O+2RhjY2OlxaWJiH6U1+vFyMgIBgcHIQiCbBF4IqJg4+QuRBTRvF4vBgYGMDQ0FOxQiChMCIIAnU7HH5KIKKQw8SMiAuDxeOB2u4MdBhHNcmq1mpU+IgpJTPyIiIiIiIjCHCd3ISIiIiIiCnNM/IiIiIiIiMIcEz8iIiIiIqIwx8SPiIiIiIgozDHxIyIiIiIiCnNM/IiIiIiIiMIcEz8iIiIiIqIw9z+D6PhEIKdRzwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharey=True)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].errorbar(df_PI_curve['Intensity'], df_PI_curve['µcount'], yerr = df_PI_curve['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='Homogeneous', zorder = 1)\n",
+ "ax[0].plot(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), fit_Homo_count, color='#fab45a', zorder = 0)\n",
+ "ax[0].plot(x_data, fit_Homo_count_extra, color='#fab45a', zorder = 0, linestyle = 'dotted')\n",
+ "ax[1].errorbar(df_PI_curve['Intensity'], df_PI_curve['µarea'], yerr = df_PI_curve['std_area'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', zorder = 1)\n",
+ "ax[1].plot(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), fit_Homo_area, color='#fab45a', zorder = 0)\n",
+ "ax[1].plot(x_data, fit_Homo_area_extra, color='#fab45a', zorder = 0, linestyle = 'dotted')\n",
+ "\n",
+ "for n in range(0,len(CO2)):\n",
+ " ax[0].scatter(information_position['Intensity'], count_df.iloc[:,n+1], color=corperate_idendity[n], label = f'{CO2[n]} ppm')\n",
+ " ax[0].plot(np.linspace(min(information_position['Intensity']), max(information_position['Intensity']), 50), fits_Grad_count[n], color=corperate_idendity[n], zorder = 0)\n",
+ " ax[1].scatter(information_position['Intensity'], area_df.iloc[:,n+1], color=corperate_idendity[n])\n",
+ " ax[1].plot(np.linspace(min(information_position['Intensity']), max(information_position['Intensity']), 50), fits_Grad_area[n], color=corperate_idendity[n], zorder = 0)\n",
+ " \n",
+ "ax[0].set_ylim(0, 0.15)\n",
+ "ax[1].set_ylim(0, 0.15)\n",
+ "\n",
+ "ax[0].set_xlim(0, 100)\n",
+ "ax[1].set_xlim(0, 100)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve_with_fit.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "46 image sequences were analyzed\n",
+ "containing a total of 3104 images\n",
+ "883203 S. elongatus UTEX2973 cells were segmented\n"
+ ]
+ }
+ ],
+ "source": [
+ "# First all analyzed chambers are collected\n",
+ "\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Create a list with all experiments\n",
+ "\n",
+ "number_cells = 0\n",
+ "number_sequences = 0\n",
+ "number_images = 0\n",
+ "\n",
+ "path = Path('./automated_executions')\n",
+ "\n",
+ "chambers = []\n",
+ "\n",
+ "for sub_folder in path.glob(\"*.tif\"): # grab all folders that end with 'CO2'\n",
+ " if Path.exists(sub_folder/'tmp'/'results.csv') == True:\n",
+ " number_sequences = number_sequences + 1\n",
+ " count_df = pd.read_csv(sub_folder/'tmp'/'counts.csv', delimiter = ';')\n",
+ " number_cells = number_cells + count_df['counts'].sum()\n",
+ " number_images = number_images + len(count_df)\n",
+ " else:\n",
+ " ()\n",
+ "\n",
+ "# Print results\n",
+ "\n",
+ "print('{} image sequences were analyzed'.format(number_sequences))\n",
+ "print('containing a total of {} images'.format(number_images))\n",
+ "print('{} S. elongatus UTEX2973 cells were segmented'.format(number_cells)) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "43e720662e2b73f3f858656968524fca68eb44fc0b1d15b9eb878c7d185562f9"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assays/Microfluidic cultivation with gradient growth light and day night cycle/protocols/Growth_Rate_Day_Night.ipynb b/assays/Microfluidic cultivation with gradient growth light and day night cycle/protocols/Growth_Rate_Day_Night.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..36ba581fffa4abdcb646cc131f8094ce52bf76da
--- /dev/null
+++ b/assays/Microfluidic cultivation with gradient growth light and day night cycle/protocols/Growth_Rate_Day_Night.ipynb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c00f3ca756cf624f44c55a06622e7acb98b1926997911686ae91afd5876fdcbb
+size 1141733
diff --git a/assays/Microfluidic cultivation with gradient growth light and day night cycle/protocols/ScalingAnalysis_SequenceNames.ipynb b/assays/Microfluidic cultivation with gradient growth light and day night cycle/protocols/ScalingAnalysis_SequenceNames.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..89021f86720382c3f1819eb7ff1a8a3f6c300fed
--- /dev/null
+++ b/assays/Microfluidic cultivation with gradient growth light and day night cycle/protocols/ScalingAnalysis_SequenceNames.ipynb
@@ -0,0 +1,1037 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Scaling Analysis\n",
+ "\n",
+ "You have developed your analysis notebook that works perfectly for a single cultivation chamber 💪? And now you you want to apply it for all cultivation chambers in our experiment but it is lots of work to apply the scripts one by one 🤔? That's why this example shows how you can quickly apply your single analysis script to a large amount of image sequences organized in the OMERO `project` or `dataset` structures 🚀! Therefore, your custom developed analyses can scale to large image volumes without you touching or changing the code!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Setup\n",
+ "\n",
+ "Define the `omero_id` and `omero_type` of the image data you would like to process. The `omerod_id` is the number you can find in the top right corner when selecting a OMERO `project`, `dataset` or `image` in the `OMERO Web` application. The `omero_type` must be `project` or `dataset` when the OMERO id points to a project or dataset and `image` if it is just a single image! Please note that if you define the wrong `omero_type` you will get an error lateron 🤯!\n",
+ "\n",
+ "Also provide your credentials for the OMERO server!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "# OMERO resource that you want to analyze\n",
+ "omero_type = \"dataset\" # can be \"image\", \"project\" or \"dataset\"\n",
+ "omero_id = 2978 # change the id if you want to apply the analysis to a different omero resource\n",
+ "\n",
+ "# your omero credentials\n",
+ "username = \"lwitting\"\n",
+ "password = \"lwitting\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# prepare credentials (usually you do not have to change this!)\n",
+ "\n",
+ "import logging\n",
+ "\n",
+ "if not \"OMERO_SERVER\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_SERVER' defined. Fallback to default OMERO_SERVER address 'omero'! This can lead to connection faults!\")\n",
+ "if not \"OMERO_WEB\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_WEB' defined. Links to view OMERO data in web viewer might not work!\")\n",
+ "\n",
+ "credentials = dict(\n",
+ " serverUrl= os.environ.get('OMERO_SERVER', 'omero'),\n",
+ " username= username,\n",
+ " password = password,\n",
+ " port = int(os.environ.get('OMERO_PORT', '4064'))\n",
+ ")\n",
+ "\n",
+ "omero_cred = dict(\n",
+ " host = credentials['serverUrl'],\n",
+ " username = credentials['username'],\n",
+ " passwd = credentials['password'],\n",
+ " port = credentials['port'],\n",
+ " secure = True\n",
+ ")\n",
+ "\n",
+ "omero_web = os.environ.get(\"OMERO_WEB\", \"<Your OMERO_WEB address should be here>\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.2 Specify the analysis script\n",
+ "\n",
+ "Now you have to specify the name of the analysis script you want to apply to the image data. At best copy the script to the same location as this script! Then you only have to specify the name of the script!\n",
+ "\n",
+ "**Note:** If the analysis script is not located in the same folder you need to specify the path to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "analysis_script = \"Growth_Rate_Day_Night.ipynb\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Information about the underlying data\n",
+ "\n",
+ "We summarize the amount of underlying data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[28298, 28308, 28295, 28296, 28305, 28294, 28297, 28300, 28302, 28304, 28307, 28309, 28310, 28311, 28299, 28301, 28303, 28306]\n",
+ "{28298: PosixPath('45_5316.tif'), 28308: PosixPath('56_5032.tif'), 28295: PosixPath('42_5330.tif'), 28296: PosixPath('43_5324.tif'), 28305: PosixPath('53_5120.tif'), 28294: PosixPath('41_5334.tif'), 28297: PosixPath('44_5321.tif'), 28300: PosixPath('47_5235.tif'), 28302: PosixPath('50_5210.tif'), 28304: PosixPath('52_5130.tif'), 28307: PosixPath('55_5107.tif'), 28309: PosixPath('57_5001.tif'), 28310: PosixPath('58_4001.tif'), 28311: PosixPath('59_4000.tif'), 28299: PosixPath('46_5305.tif'), 28301: PosixPath('48_5227.tif'), 28303: PosixPath('51_5203.tif'), 28306: PosixPath('54_5113.tif')}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from acia.segm.omero.utils import list_image_ids_in, getImage\n",
+ "from omero.gateway import BlitzGateway\n",
+ "from pathlib import Path\n",
+ "\n",
+ "image_names = {}\n",
+ "\n",
+ "with BlitzGateway(**omero_cred) as conn:\n",
+ " image_ids = list_image_ids_in(omero_id, omero_type, conn)\n",
+ " \n",
+ " # get all the image names\n",
+ " for image_id in image_ids:\n",
+ " image_names[image_id] = Path(getImage(conn, image_id).getName())\n",
+ "\n",
+ "## TODO: give an overview about the data\n",
+ "print(image_ids)\n",
+ "print(image_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. Scale the analysis script to all image sequences\n",
+ "\n",
+ "Now we apply the analysis script to every image sequence individually 🚀! You can lean back and enjoy the working computer 😎 🥂\n",
+ "\n",
+ "**Note:** For heavy analysis scripts or for larget `datasets` or `projects` this process may take a while (from minutes to hours or days). The top-level progress bar will indicate the total progress and give you an indication how long this will take. For large image data volumes we can recommend execution over night 🌔!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results are stored in: /home/jovyan/work/A5_Day_Night_Rhythm/2024.03.01_Day_Night/Growth_Rate/S. elongatus PCC7942 CscB/automated_executions\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "84547a8b644246d09a19ec3a3b8da87d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/18 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "33fa9d7cfb8d4b4bb00062525c526c78",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/28 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "458f4505e0ca4660a78b419e8d230de1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/28 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "837fc380704945d382998c40de20a73d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/28 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fd59a892c1a6472bb4e4f04ba2343857",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/28 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "from pathlib import Path\n",
+ "from acia.analysis import scale\n",
+ "\n",
+ "# set the base path for all results\n",
+ "stem = Path(analysis_script).stem\n",
+ "output_path = Path(\"./automated_executions\") \n",
+ "\n",
+ "print(f\"Results are stored in: {output_path.absolute()}\")\n",
+ "\n",
+ "# scale your analysis script to many images\n",
+ "result = scale(output_path, analysis_script=analysis_script, image_ids=image_ids, additional_parameters=dict(username=username, password=password), exist_ok=True, execution_naming=lambda image_id: image_names[image_id])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Inspect your analysis results\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib.parse\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "\n",
+ "base_url = os.environ.get(\"JUPYTERHUB_SERVICE_PREFIX\", None)\n",
+ "\n",
+ "if base_url is None:\n",
+ " url = f\"file://{output_path.absolute()}\"\n",
+ "else:\n",
+ " url = f\"{base_url}lab/tree/{urllib.parse.quote(str(output_path))}\"\n",
+ "\n",
+ "output = f\"\"\"# Inspect your analyses\n",
+ "You can find all the individual analysis scripts here: <a href=\"{url}\">{url}</a>\"\"\"\n",
+ "\n",
+ "display(Markdown(output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Generate Summary Statistics\n",
+ "\n",
+ "In this section you can generate your custom summary statistics that combine the results of all experiment analyses. Just design the analysis script that you scaled above such that it outputs the results into a local files. Here, these results can be loaded, merged together and further processed or visualized!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No results.csv found in automated_executions/45_5316.tif\n",
+ "No results.csv found in automated_executions/46_5305.tif\n",
+ "No results.csv found in automated_executions/57_5001.tif\n",
+ " Unnamed: 0 0 1 2 3 4 \\\n",
+ "0 µ_count [1/h] 0.100701 0.010698 0.114939 -1.739383e-17 0.148815 \n",
+ "1 µ_area [1/h] 0.129265 0.009480 0.112700 -2.238848e-03 0.123022 \n",
+ "2 µ_count [1/h] 0.128104 -0.062364 0.156517 4.330330e-03 0.158399 \n",
+ "3 µ_area [1/h] 0.152058 -0.036065 0.134781 2.499251e-03 0.125354 \n",
+ "4 µ_count [1/h] 0.012378 0.024755 0.058854 1.187870e-17 0.055665 \n",
+ "5 µ_area [1/h] 0.010381 0.005854 0.057505 4.430952e-04 0.061508 \n",
+ "6 µ_count [1/h] 0.092002 -0.015345 0.154510 7.849610e-04 0.137872 \n",
+ "7 µ_area [1/h] 0.118296 -0.008336 0.117399 1.519705e-03 0.115605 \n",
+ "8 µ_count [1/h] 0.108365 0.125012 0.056443 3.382983e-02 0.024710 \n",
+ "9 µ_area [1/h] 0.074369 0.081905 0.045918 3.748216e-02 0.030345 \n",
+ "10 µ_count [1/h] 0.049511 0.000000 0.136154 9.531972e-19 0.121498 \n",
+ "11 µ_area [1/h] 0.080907 -0.003458 0.079641 -1.145110e-04 0.074639 \n",
+ "12 µ_count [1/h] 0.219562 0.007240 0.173374 3.001271e-04 0.151958 \n",
+ "13 µ_area [1/h] 0.155119 0.003509 0.144660 1.403857e-03 0.134981 \n",
+ "14 µ_count [1/h] 0.090934 0.006511 0.105533 1.906394e-18 0.088331 \n",
+ "15 µ_area [1/h] 0.084497 0.004604 0.071008 8.657898e-04 0.071903 \n",
+ "16 µ_count [1/h] 0.188718 -0.046477 0.137327 2.896458e-03 0.166012 \n",
+ "17 µ_area [1/h] 0.152296 -0.008920 0.129064 1.259942e-04 0.126655 \n",
+ "18 µ_count [1/h] 0.052271 -0.002753 0.057942 -7.519992e-03 0.050229 \n",
+ "19 µ_area [1/h] 0.048936 0.000254 0.059125 -8.805851e-03 0.054480 \n",
+ "20 µ_count [1/h] 0.102784 0.076369 0.120376 2.612434e-03 0.155768 \n",
+ "21 µ_area [1/h] 0.122145 0.039932 0.115164 1.015411e-03 0.128411 \n",
+ "22 µ_count [1/h] 0.143700 0.011649 0.116758 3.666039e-03 0.102159 \n",
+ "23 µ_area [1/h] 0.102269 0.012013 0.092196 2.105679e-03 0.090636 \n",
+ "24 µ_count [1/h] 0.197992 0.034047 0.068696 1.825613e-02 0.112741 \n",
+ "25 µ_area [1/h] 0.144262 0.025769 0.088671 1.163690e-02 0.095024 \n",
+ "26 µ_count [1/h] 0.124957 -0.021522 0.153691 3.526309e-03 0.137462 \n",
+ "27 µ_area [1/h] 0.138243 -0.008744 0.127842 5.557098e-03 0.127487 \n",
+ "28 µ_count [1/h] 0.125745 -0.035259 0.068368 -3.403935e-03 0.099571 \n",
+ "29 µ_area [1/h] 0.104908 -0.015500 0.079463 -1.724957e-03 0.085206 \n",
+ "\n",
+ " 5 6 7 experiment \n",
+ "0 -4.409395e-04 0.082334 -3.824704e-03 51_5203.tif \n",
+ "1 -3.592910e-03 0.076686 -1.663911e-03 51_5203.tif \n",
+ "2 8.113262e-04 0.102980 5.404383e-03 43_5324.tif \n",
+ "3 7.519656e-04 0.092683 2.722408e-04 43_5324.tif \n",
+ "4 2.667522e-17 0.052413 -9.310342e-17 59_4000.tif \n",
+ "5 -2.853142e-04 0.057463 -3.397245e-05 59_4000.tif \n",
+ "6 -2.658594e-04 0.087300 4.419670e-04 48_5227.tif \n",
+ "7 5.716313e-04 0.067311 -2.749019e-03 48_5227.tif \n",
+ "8 -2.254065e-03 0.035680 -2.276390e-02 58_4001.tif \n",
+ "9 8.566200e-03 0.030963 -1.769957e-02 58_4001.tif \n",
+ "10 1.394029e-02 0.056363 4.225632e-03 55_5107.tif \n",
+ "11 8.725189e-03 0.062874 3.233073e-03 55_5107.tif \n",
+ "12 -2.130457e-04 0.082042 -4.332051e-04 41_5334.tif \n",
+ "13 9.136787e-04 0.063991 -1.008418e-03 41_5334.tif \n",
+ "14 6.616783e-03 0.092401 4.558139e-03 54_5113.tif \n",
+ "15 2.939241e-03 0.069770 4.255892e-03 54_5113.tif \n",
+ "16 6.948340e-04 0.124395 5.061760e-04 47_5235.tif \n",
+ "17 1.379779e-03 0.106675 -1.228519e-04 47_5235.tif \n",
+ "18 4.893961e-03 0.080856 3.529575e-03 56_5032.tif \n",
+ "19 1.258844e-03 0.068029 -3.385268e-03 56_5032.tif \n",
+ "20 -3.118983e-04 0.051743 7.866420e-05 42_5330.tif \n",
+ "21 9.365134e-04 0.042901 1.786528e-03 42_5330.tif \n",
+ "22 5.300310e-03 0.080112 6.211006e-03 52_5130.tif \n",
+ "23 5.248020e-03 0.070107 1.271497e-02 52_5130.tif \n",
+ "24 7.144402e-03 0.091824 -6.494240e-03 50_5210.tif \n",
+ "25 4.702919e-03 0.078819 -2.602840e-03 50_5210.tif \n",
+ "26 -3.626717e-03 0.075662 2.061624e-03 44_5321.tif \n",
+ "27 -3.530166e-03 0.064174 2.566887e-03 44_5321.tif \n",
+ "28 -1.098988e-03 0.117211 1.629357e-03 53_5120.tif \n",
+ "29 -6.738559e-04 0.077540 -4.342006e-04 53_5120.tif \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get results.csv from each individual chamber\n",
+ "\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "data_folder = Path(\"./automated_executions\") \n",
+ "dfs = []\n",
+ "for sub_folder in data_folder.glob(\"*\"): # hole dir alle Ordner, die mit UTEX enden\n",
+ " try:\n",
+ " data_file = sub_folder / \"tmp\" / \"results.csv\"\n",
+ " sub_df = pd.read_csv(data_file, delimiter = ';')\n",
+ " sub_df[\"experiment\"] = sub_folder.name\n",
+ " dfs.append(sub_df)\n",
+ " except:\n",
+ " print('No results.csv found in {}'.format(sub_folder))\n",
+ "\n",
+ "joint_df = pd.concat(dfs, ignore_index=True)\n",
+ "\n",
+ "# Group dataframe by category (code by chat gpt) \n",
+ "grouped_df = joint_df.groupby('Unnamed: 0')\n",
+ "\n",
+ "count_df = grouped_df.get_group('µ_count [1/h]')\n",
+ "\n",
+ "area_df = grouped_df.get_group('µ_area [1/h]')\n",
+ "\n",
+ "print(joint_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now let's plot the growth rates\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "data = [count_df['0'], area_df['0']]\n",
+ "\n",
+ "fig, ax1 = plt.subplots(facecolor='white')\n",
+ "ax1.boxplot(data,labels=['Cell count','Cell area'])\n",
+ "ax1.set_ylabel('Growth rate [h$^{-1}$]')\n",
+ "ax1.set_ylim(0, )\n",
+ "\n",
+ "plt.savefig('Boxplot_growth_rates.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Calculate Mean, Median and Standard deviation\n",
+ "\n",
+ "mean = [np.mean(count_df['0']), np.mean(area_df['0'])]\n",
+ "median = [np.median(count_df['0']), np.median(area_df['0'])]\n",
+ "std = [np.std(count_df['0']), np.std(area_df['0'])]\n",
+ "\n",
+ "statistics_df = pd.DataFrame({'Chamber': ['Mean','Median','STD'],\n",
+ " 'µcount': [mean[0], median [0], std[0]],\n",
+ " 'µarea': [mean[1], median [1], std[1]]})\n",
+ "# print(statistics_df)\n",
+ "\n",
+ "# Rearrange Growth rates for setting up results.csv\n",
+ "\n",
+ "results_df_1 = pd.DataFrame({'Chamber': count_df['experiment'],\n",
+ " 'µcount': count_df['2']}).reset_index()\n",
+ "\n",
+ "results_df_2 = pd.DataFrame({'µarea': area_df['2']}).reset_index()\n",
+ "\n",
+ "rates_df = pd.concat([results_df_1, results_df_2], axis=1)\n",
+ "\n",
+ "del rates_df['index']\n",
+ "\n",
+ "result_df = pd.concat([rates_df, statistics_df])\n",
+ "\n",
+ "# print(result_df)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pathlib import Path\n",
+ "\n",
+ "# Grab calibration results from Calibration folder\n",
+ "\n",
+ "Calibration = Path(\"..\") / \"..\" / \"Calibration\" / \"Meta-fit.csv\"\n",
+ "\n",
+ "df_calibration = pd.read_csv(Calibration, sep = ';', encoding = 'utf8', header = 0, index_col=0, decimal=',')\n",
+ "\n",
+ "# Then specify the gradient that was used\n",
+ "\n",
+ "Light_Intensity_Homo = 240 # Specify light-intensity of homogeneous illumination\n",
+ " \n",
+ "slope = Light_Intensity_Homo * df_calibration['Slope'].iloc[0] + df_calibration['Interception'].iloc[0]\n",
+ "intercept = Light_Intensity_Homo * df_calibration['Slope'].iloc[1] + df_calibration['Interception'].iloc[1]\n",
+ "\n",
+ "Total_Number_chambers = 40 # Specify number of chambers present on chip\n",
+ "First_Chamber_Calibration = 40 # First chamber seen in calibration picture\n",
+ "Last_Chamber_Calibration = 40 # Last chamber seen in calibration picture\n",
+ "\n",
+ "step = (23460-60)/(Total_Number_chambers - 1) # From CleWin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Chamber µcount µarea Vertical_Position Horizontal_Position \\\n",
+ "0 51_5203.tif 0.114939 0.112700 20 21 \n",
+ "1 43_5324.tif 0.156517 0.134781 21 37 \n",
+ "2 59_4000.tif 0.058854 0.057505 17 1 \n",
+ "3 48_5227.tif 0.154510 0.117399 20 27 \n",
+ "4 58_4001.tif 0.056443 0.045918 18 1 \n",
+ "5 55_5107.tif 0.136154 0.079641 20 12 \n",
+ "6 41_5334.tif 0.173374 0.144660 23 39 \n",
+ "7 54_5113.tif 0.105533 0.071008 22 14 \n",
+ "8 47_5235.tif 0.137327 0.129064 20 29 \n",
+ "9 56_5032.tif 0.057942 0.059125 21 9 \n",
+ "10 42_5330.tif 0.120376 0.115164 23 38 \n",
+ "11 52_5130.tif 0.116758 0.092196 23 18 \n",
+ "12 50_5210.tif 0.068696 0.088671 23 23 \n",
+ "13 44_5321.tif 0.153691 0.127842 22 36 \n",
+ "14 53_5120.tif 0.068368 0.079463 21 16 \n",
+ "\n",
+ " Intensity Channel \n",
+ "0 57.554204 6.0 \n",
+ "1 92.659707 6.0 \n",
+ "2 13.672325 5.0 \n",
+ "3 70.718767 6.0 \n",
+ "4 13.672325 5.0 \n",
+ "5 37.807358 6.0 \n",
+ "6 97.047895 6.0 \n",
+ "7 42.195546 6.0 \n",
+ "8 75.106955 6.0 \n",
+ "9 31.225077 6.0 \n",
+ "10 94.853801 6.0 \n",
+ "11 50.971922 6.0 \n",
+ "12 61.942392 6.0 \n",
+ "13 90.465613 6.0 \n",
+ "14 46.583734 6.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract Postion from Naming of Image Sequence\n",
+ "\n",
+ "Channels = []\n",
+ "Horizontal_Positions = []\n",
+ "Vertical_Positions = []\n",
+ "Intensities = []\n",
+ "\n",
+ "for chamber in rates_df['Chamber']: # Extract Postion from Naming of Image Sequence\n",
+ " Identifier_a = float(chamber[3]) # First number decodes channel\n",
+ " Identifier_b = float(chamber[4]) # The last three numbers decode Position\n",
+ " Identifier_c = float(chamber[5:7])\n",
+ " Channel = Identifier_a +1\n",
+ " Channels.append(Channel)\n",
+ " Horizontal_Position = int(Identifier_b*10 + round(((Identifier_c + 1)/4) + 0.49)) # Calculate Horizontal Position\n",
+ " Vertical_Position = int((((Identifier_c + 1)/4 - round(((Identifier_c + 1)/4) - 0.49))*4) + Identifier_a * 4) # Calculate Vertical Position\n",
+ " Intensity = intercept + ((Horizontal_Position - 1) - First_Chamber_Calibration)*step*slope\n",
+ " Horizontal_Positions.append(Horizontal_Position)\n",
+ " Vertical_Positions.append(Vertical_Position)\n",
+ " Intensities.append(Intensity)\n",
+ "\n",
+ "rates_df['Vertical_Position'] = Vertical_Positions # Append Vertical Postion to rates_df \n",
+ "rates_df['Horizontal_Position'] = Horizontal_Positions # Append Horizontal Postion to rates_df\n",
+ "rates_df['Intensity'] = Intensities # Append Postion to rates_df\n",
+ "rates_df['Channel'] = Channels # Append Channels to rates_df\n",
+ "\n",
+ "print(rates_df)\n",
+ "rates_df.to_csv(str('rates_df.csv'), sep=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1000x300 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from copy import copy\n",
+ "\n",
+ "Vertical_Positions_rel = []\n",
+ "\n",
+ "for n in range(0, len(rates_df)):\n",
+ " Vertical_Position_rel = (rates_df.iloc[n, 3] - min(rates_df['Vertical_Position'])) + 1\n",
+ " Vertical_Positions_rel.append(Vertical_Position_rel)\n",
+ " \n",
+ "rates_df['Vertical_Position_rel'] = Vertical_Positions_rel\n",
+ "\n",
+ "Vertical = range(0, max(rates_df['Vertical_Position_rel']))\n",
+ "Horizontal = range(1, Total_Number_chambers)\n",
+ " \n",
+ "uarea = np.zeros((max(rates_df['Vertical_Position_rel']),Total_Number_chambers))\n",
+ "ucount = np.zeros((max(rates_df['Vertical_Position_rel']),Total_Number_chambers))\n",
+ "\n",
+ "for n in range(0, len(rates_df)):\n",
+ " uarea[rates_df.iloc[n, 7] -1, rates_df.iloc[n, 4]-1] = rates_df.iloc[n,2]\n",
+ " \n",
+ "for n in range(0, len(rates_df)):\n",
+ " ucount[rates_df.iloc[n, 7] -1, rates_df.iloc[n, 4]-1] = rates_df.iloc[n,1]\n",
+ " \n",
+ "# print(uarea)\n",
+ "\n",
+ "cmap=plt.colormaps['viridis']\n",
+ "cmap.set_under(\"white\")\n",
+ "\n",
+ "fig, ax = plt.subplots(2,1, figsize=(10, 3), sharex=False)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].imshow(uarea, vmin=min(rates_df.iloc[:,2]), cmap=cmap)\n",
+ "im = ax[1].imshow(ucount, vmin=min(rates_df.iloc[:,1]), cmap=cmap)\n",
+ "\n",
+ "ax[1].set_xlabel('Horizontal Position')\n",
+ "\n",
+ "ax[0].set_ylabel('Vertical Position')\n",
+ "ax[1].set_ylabel('Vertical Position')\n",
+ "\n",
+ "ax[0].set_title('Cell area')\n",
+ "ax[1].set_title('Cell count')\n",
+ "\n",
+ "ax[0].set_xlim(-0.5, 40.5)\n",
+ "ax[1].set_xlim(-0.5, 40.5)\n",
+ "\n",
+ "# Add a colorbar\n",
+ "cbar = fig.colorbar(im, ax=ax, location='right')\n",
+ "cbar.set_label('Growth rate [1/h]')\n",
+ "\n",
+ "plt.savefig('Heatmap.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " experiment µcount µarea std_count std_area \\\n",
+ "0 2023.07.11_40uE_AmbientCO2 0.105556 0.094260 0.010693 0.008905 \n",
+ "1 2023.08.01_140uE_AmbientCO2 0.156868 0.133332 0.007998 0.009671 \n",
+ "2 2023.08.08_50uE_AmbientCO2 0.097737 0.083292 0.014908 0.011790 \n",
+ "3 2023.06.27_20uE_AmbientCO2 0.042605 0.035323 0.004299 0.003111 \n",
+ "4 2023.07.18_60uE_AmbientCO2 0.103217 0.086537 0.009645 0.005367 \n",
+ "5 2023.07.25_30uE_AmbientCO2 0.065164 0.059754 0.010197 0.008437 \n",
+ "\n",
+ " Intensity \n",
+ "0 40.0 \n",
+ "1 140.0 \n",
+ "2 50.0 \n",
+ "3 20.0 \n",
+ "4 60.0 \n",
+ "5 30.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "PI_curve = Path(\"..\") / \"..\" / \"..\"/ \"..\"/ \"A2.2_PI_Curve_µFluidic_newSegAI\" / \"PI_curve_CscB.csv\" # read previous experimentall data to compare\n",
+ "\n",
+ "df_PI_curve = pd.read_csv(PI_curve, sep = ';', encoding = 'utf8', header = 0, index_col=0)\n",
+ "print(df_PI_curve)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fit a PI curve model to data\n",
+ "\n",
+ "import numpy as np\n",
+ "from scipy.optimize import curve_fit\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def tanh_function(x, umax, a):\n",
+ " \"\"\"\n",
+ " Tanh function: a * tanh(b * (x - c)) + d\n",
+ " Parameters:\n",
+ " - umax: amplitude\n",
+ " - a: initial slope\n",
+ " \"\"\"\n",
+ " return umax * np.tanh(a*x/umax)\n",
+ "\n",
+ "def fit_tanh_to_data(x_data, y_data):\n",
+ " \"\"\"\n",
+ " Fit a tanh function to the given data.\n",
+ "\n",
+ " Parameters:\n",
+ " - x_data: Input data (independent variable)\n",
+ " - y_data: Output data (dependent variable)\n",
+ "\n",
+ " Returns:\n",
+ " - popt: Optimal values for the parameters (a, b, c, d)\n",
+ " \"\"\"\n",
+ "\n",
+ " # Initial guess for the parameters (you may need to adjust these)\n",
+ " initial_guess = (0.06, 0.0001)\n",
+ "\n",
+ " # Perform the curve fitting using scipy.optimize.curve_fit\n",
+ " popt, pcov = curve_fit(tanh_function, x_data, y_data, p0=initial_guess)\n",
+ "\n",
+ " return popt\n",
+ "\n",
+ "x_data = np.linspace(0,150,16)\n",
+ "\n",
+ "para_Homo_area = fit_tanh_to_data(df_PI_curve['Intensity'], df_PI_curve['µarea'])\n",
+ "para_Homo_count = fit_tanh_to_data(df_PI_curve['Intensity'], df_PI_curve['µcount'])\n",
+ "fit_Homo_area = tanh_function(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), * para_Homo_area)\n",
+ "fit_Homo_count = tanh_function(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), * para_Homo_count)\n",
+ "fit_Homo_area_extra = tanh_function(x_data, * para_Homo_area)\n",
+ "fit_Homo_count_extra = tanh_function(x_data, * para_Homo_count)\n",
+ "para_Grad_area = fit_tanh_to_data(rates_df['Intensity'], rates_df['µarea'])\n",
+ "para_Grad_count = fit_tanh_to_data(rates_df['Intensity'], rates_df['µcount'])\n",
+ "fit_Grad_area = tanh_function(np.linspace(min(rates_df['Intensity']), max(rates_df['Intensity']), 50), * para_Grad_area)\n",
+ "fit_Grad_count = tanh_function(np.linspace(min(rates_df['Intensity']), max(rates_df['Intensity']), 50), * para_Grad_count)\n",
+ "fit_Grad_area_extra = tanh_function(x_data, * para_Grad_area)\n",
+ "fit_Grad_count_extra = tanh_function(x_data, * para_Grad_count)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharey=True)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].errorbar(df_PI_curve['Intensity'], df_PI_curve['µcount'], yerr = df_PI_curve['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='Homogeneous', zorder = 1)\n",
+ "ax[0].scatter(rates_df['Intensity'], rates_df['µcount'], color='#023d6b', label = 'Gradient', zorder = 2)\n",
+ "ax[1].errorbar(df_PI_curve['Intensity'], df_PI_curve['µarea'], yerr = df_PI_curve['std_area'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', zorder = 1)\n",
+ "ax[1].scatter(rates_df['Intensity'], rates_df['µarea'], color='#023d6b', zorder = 2)\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, 150)\n",
+ "ax[1].set_xlim(0, 150)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharey=True)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].errorbar(df_PI_curve['Intensity'], df_PI_curve['µcount'], yerr = df_PI_curve['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='Homogeneous', zorder = 1)\n",
+ "ax[0].scatter(rates_df['Intensity'], rates_df['µcount'], color='#023d6b', label = 'Gradient', zorder = 2)\n",
+ "ax[0].plot(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), fit_Homo_count, color='#fab45a', zorder = 0)\n",
+ "ax[0].plot(x_data, fit_Homo_count_extra, color='#fab45a', zorder = 0, linestyle = 'dotted')\n",
+ "ax[0].plot(np.linspace(min(rates_df['Intensity']), max(rates_df['Intensity']), 50), fit_Grad_count, color='#023d6b', zorder = 0)\n",
+ "ax[0].plot(x_data, fit_Grad_count_extra, color='#023d6b', zorder = 0, linestyle = 'dotted')\n",
+ "ax[1].errorbar(df_PI_curve['Intensity'], df_PI_curve['µarea'], yerr = df_PI_curve['std_area'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', zorder = 1)\n",
+ "ax[1].scatter(rates_df['Intensity'], rates_df['µarea'], color='#023d6b', zorder = 2)\n",
+ "ax[1].plot(np.linspace(min(df_PI_curve['Intensity']), max(df_PI_curve['Intensity']), 50), fit_Homo_area, color='#fab45a', zorder = 0)\n",
+ "ax[1].plot(x_data, fit_Homo_area_extra, color='#fab45a', zorder = 0, linestyle = 'dotted')\n",
+ "ax[1].plot(np.linspace(min(rates_df['Intensity']), max(rates_df['Intensity']), 50), fit_Grad_area, color='#023d6b', zorder = 0)\n",
+ "ax[1].plot(x_data, fit_Grad_area_extra, color='#023d6b', zorder = 0, linestyle = 'dotted')\n",
+ "\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, 150)\n",
+ "ax[1].set_xlim(0, 150)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve_with_fit.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Extract Postion from Naming of Image Sequence\n",
+ "\n",
+ "Channels = []\n",
+ "Horizontal_Positions = []\n",
+ "Vertical_Positions = []\n",
+ "Intensities = []\n",
+ "\n",
+ "for chamber in area_df['experiment']: # Extract Postion from Naming of Image Sequence\n",
+ " Identifier_a = float(chamber[3]) # First number decodes channel\n",
+ " Identifier_b = float(chamber[4]) # The last three numbers decode Position\n",
+ " Identifier_c = float(chamber[5:7])\n",
+ " Channel = Identifier_a +1\n",
+ " Channels.append(Channel)\n",
+ " Horizontal_Position = int(Identifier_b*10 + round(((Identifier_c + 1)/4) + 0.49)) # Calculate Horizontal Position\n",
+ " Vertical_Position = int((((Identifier_c + 1)/4 - round(((Identifier_c + 1)/4) - 0.49))*4) + Identifier_a * 4) # Calculate Vertical Position\n",
+ " Intensity = intercept + ((Horizontal_Position - 1) - First_Chamber_Calibration)*step*slope\n",
+ " Horizontal_Positions.append(Horizontal_Position)\n",
+ " Vertical_Positions.append(Vertical_Position)\n",
+ " Intensities.append(Intensity)\n",
+ "\n",
+ "information_position = pd.DataFrame({'Chamber': count_df['experiment'],\n",
+ " 'Vertical_Position': Vertical_Positions,\n",
+ " 'Horizontal_Position': Horizontal_Position,\n",
+ " 'Intensity': Intensities,\n",
+ " 'Channel': Channels}).reset_index()\n",
+ "\n",
+ "area_df.to_csv(str('µarea.csv'), sep=';')\n",
+ "count_df.to_csv(str('µcount.csv'), sep=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_62680/3294531781.py:40: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+ " ax[0].set_xticklabels(Cycle_Number, rotation=45)\n",
+ "/tmp/ipykernel_62680/3294531781.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+ " ax[1].set_xticklabels(Cycle_Number, rotation=45)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plot growth rate over Cycle\n",
+ "\n",
+ "Cycle_Number = ['Day 1', 'Night 1', 'Day 2', 'Night 2', 'Day 3', 'Night 3', 'Day 4', 'Night 4'] \n",
+ "\n",
+ "# calculate mean growth rates\n",
+ "\n",
+ "mu_count_mean = []\n",
+ "mu_area_mean = []\n",
+ "mu_count_std = []\n",
+ "mu_area_std = []\n",
+ "\n",
+ "for n in range(0,len(Cycle_Number)):\n",
+ " count_mean = np.mean(count_df.iloc[:,n+1])\n",
+ " area_mean = np.mean(area_df.iloc[:,n+1])\n",
+ " count_std = np.std(count_df.iloc[:,n+1])\n",
+ " area_std = np.std(area_df.iloc[:,n+1])\n",
+ " mu_count_mean.append(count_mean)\n",
+ " mu_area_mean.append(area_mean)\n",
+ " mu_count_std.append(count_std)\n",
+ " mu_area_std.append(area_std)\n",
+ "\n",
+ "# plot; first count then area\n",
+ "fig, ax = plt.subplots(1,2, figsize=(12, 4), facecolor='white')\n",
+ "\n",
+ "for n in range(0,len(count_df['experiment'])):\n",
+ " ax[0].plot(Cycle_Number, count_df.iloc[n,range(1,len(Cycle_Number)+1)], color='#adbde3', marker = 'o', linestyle = 'dotted')\n",
+ " ax[1].plot(Cycle_Number, area_df.iloc[n,range(1,len(Cycle_Number)+1)], color='#adbde3', marker = 'o', linestyle = 'dotted')\n",
+ "ax[0].errorbar(Cycle_Number, mu_count_mean, color='r', yerr=mu_count_std, marker = 'o', linestyle = 'dotted')\n",
+ "ax[1].errorbar(Cycle_Number, mu_area_mean, color='r', yerr=mu_area_std, marker = 'o', linestyle = 'dotted')\n",
+ " \n",
+ "ax[0].set_ylabel('Grwoth rate [1/h]')\n",
+ "ax[1].set_ylabel('Grwoth rate [1/h]')\n",
+ "ax[0].set_title('Cell Count')\n",
+ "ax[1].set_title('Cell Area')\n",
+ "\n",
+ "ax[0].set_xticklabels(Cycle_Number, rotation=45)\n",
+ "ax[1].set_xticklabels(Cycle_Number, rotation=45)\n",
+ "\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[0].set_xlim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "ax[1].set_xlim(0, )\n",
+ "\n",
+ "plt.savefig('Growth_Rate_Day_Night_Cycle.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot PI Curve for each Day\n",
+ "\n",
+ "# plot; first count then area\n",
+ "fig, ax = plt.subplots(1,2, figsize=(12, 4), facecolor='white', sharey = True)\n",
+ "corperate_idendity = ['#023d6b', '#adbde3', '#faeb5a', '#eb5f73', '#b9d25f', '#af82b9', '#fab45a', '#ebebeb'] # Fz Juelich corperate identity\n",
+ "\n",
+ "for n in range(0,int(len(Cycle_Number)/2)):\n",
+ " ax[0].scatter(information_position['Intensity'], count_df.iloc[:,(2*n+1)], color=corperate_idendity[n], label = f'Day {n+1}')\n",
+ " ax[1].scatter(information_position['Intensity'], area_df.iloc[:,(2*n+1)], color=corperate_idendity[n])\n",
+ "\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, 150)\n",
+ "ax[1].set_xlim(0, 150)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
+ "\n",
+ "plt.savefig('Growth_Rate_Day_Night_Cycle_over_Intensity.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "43e720662e2b73f3f858656968524fca68eb44fc0b1d15b9eb878c7d185562f9"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assays/Microfluidic cultivation with gradient growth light/protocols/Growth_Rate.ipynb b/assays/Microfluidic cultivation with gradient growth light/protocols/Growth_Rate.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..31599b08bdd0615475cebcef9f77271729bb4db5
--- /dev/null
+++ b/assays/Microfluidic cultivation with gradient growth light/protocols/Growth_Rate.ipynb
@@ -0,0 +1,646 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Welcome to this analysis notebook\n",
+ "\n",
+ "This notebook is designed to perform analyses based on the request: https://jugit.fz-juelich.de/j.seiffarth/analysis-projects/-/issues/1 and has been jointly developed by Markus Leygeber and Johannes Seiffarth 💪\n",
+ "\n",
+ "Therfore, we concentrate on:\n",
+ "\n",
+ "1. Perform segmentation on an omero sequence\n",
+ "2. Extracting individual cell information\n",
+ "3. Filtering cells based on there individual information to reduce the number of artifacts\n",
+ "4. Plot the quantities of interest\n",
+ "\n",
+ "Please make sure that you replace `<your username>` and `<your password>` with your OMERO credentials in the following code snippets 👇. The cell segmentation is performed on an image sequence specified by the `image_id` parameter. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from pathlib import Path\n",
+ "\n",
+ "# your omero credentials\n",
+ "username = \"lwitting\"\n",
+ "password = \"lwitting\"\n",
+ "\n",
+ "# OMERO image that you want to analyze\n",
+ "image_id = 25394 # change the id if you want to apply the analysis to different image data\n",
+ "\n",
+ "image_channels = [1]\n",
+ "\n",
+ "# the address of the segmentation service\n",
+ "segmentation_service = os.environ.get(\"SEGMENTATION_SERVICE\", \"http://main/segService\")\n",
+ "\n",
+ "# use current working directory as default storage folder for outputs\n",
+ "storage_folder = os.getcwd()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create the output directory\n",
+ "output_path = Path(storage_folder) / \"tmp/\"\n",
+ "output_path.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# make path relative (advantage in video embedding)\n",
+ "output_path_rel = output_path.relative_to(Path(os.getcwd()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# do not change the lines below\n",
+ "assert username != \"<your username>\", \"Please replace '<your username>' with your OMERO username\"\n",
+ "assert password != \"<your password>\", \"Please replace '<your password>' with your OMERO username\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "\n",
+ "if not \"OMERO_SERVER\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_SERVER' defined. Fallback to default OMERO_SERVER address 'omero'! This can lead to connection faults!\")\n",
+ "if not \"OMERO_WEB\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_WEB' defined. Links to view OMERO data in web viewer might not work!\")\n",
+ "\n",
+ "credentials = dict(\n",
+ " serverUrl= os.environ.get('OMERO_SERVER', 'omero'),\n",
+ " username= username,\n",
+ " password = password,\n",
+ " port = int(os.environ.get('OMERO_PORT', '4064'))\n",
+ ")\n",
+ "\n",
+ "omero_cred = dict(\n",
+ " host = credentials['serverUrl'],\n",
+ " username = credentials['username'],\n",
+ " passwd = credentials['password'],\n",
+ " port = credentials['port'],\n",
+ " secure = True\n",
+ ")\n",
+ "\n",
+ "omero_web = os.environ.get(\"OMERO_WEB\", \"<Your OMERO_WEB address should be here>\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Information about the image stack"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from acia.segm.omero.utils import getImage\n",
+ "from omero.gateway import BlitzGateway\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "with BlitzGateway(**omero_cred) as conn:\n",
+ " image = getImage(conn, image_id)\n",
+ " dataset = image.getParent()\n",
+ " project = dataset.getParent()\n",
+ " group = image.getDetails().getGroup()\n",
+ " owner = image.getOwner()\n",
+ " \n",
+ " channels = image.getChannels()\n",
+ " \n",
+ " # display markdown\n",
+ " from IPython.display import Video, Markdown, display\n",
+ " display(Markdown(\"# Image information\"))\n",
+ "\n",
+ " dataset_name = dataset.getName()\n",
+ " \n",
+ " table = f\"\"\"\n",
+ "| Value | Content |\n",
+ "| --- | --- |\n",
+ "| Project Name | {project.getName()} |\n",
+ "| Dataset Name | {dataset_name} |\n",
+ "| Image Name | {image.getName()} |\n",
+ "| Data Owner | [{owner.getName()}]({omero_web}/webclient/active_group/?active_group={group.getId()}&url=/webclient/userdata/?experimenter={owner.getId()}) |\n",
+ "| Group | [{group.getName()}]({omero_web}/webclient/active_group/?active_group={group.getId()}&url=/webclient/userdata/?experimenter=-1) |\n",
+ "| Omero Web Link | {omero_web}/webclient/?show=image-{image.getId()} |\n",
+ "| View Image Data | {omero_web}/webclient/img_detail/{image.getId()}/?dataset={dataset.getId()} |\n",
+ "| Open in SegUI | Coming soon! |\n",
+ "| T Size | { image.getSizeT() } |\n",
+ "| Z Size | { image.getSizeZ() } |\n",
+ "| Channels | {','.join([ch.getLabel() for ch in channels])} |\n",
+ " \"\"\"\n",
+ "\n",
+ " display(Markdown(table))\n",
+ " display(Markdown(f\"## Preview of channels\"))\n",
+ "\n",
+ " image.setGreyscaleRenderingModel()\n",
+ " size_c = image.getSizeC()\n",
+ " z = image.getSizeZ() // 2\n",
+ " t = image.getSizeT() // 2\n",
+ " \n",
+ " width = image.getSizeX()\n",
+ " height = image.getSizeY()\n",
+ " \n",
+ " image_size = width * height\n",
+ " \n",
+ " print(image_size)\n",
+ " \n",
+ " fig, ax = plt.subplots(1, size_c, figsize=(15, 15))\n",
+ " for i, c in enumerate(range(1, size_c + 1)): # Channel index starts at 1\n",
+ " channels = [c] # Turn on a single channel at a time\n",
+ " image.setActiveChannels(channels)\n",
+ " rendered_image = image.renderImage(z, t)\n",
+ " \n",
+ " if size_c > 1:\n",
+ " loc_ax = ax[i]\n",
+ " else:\n",
+ " loc_ax = ax\n",
+ " loc_ax.imshow(rendered_image)\n",
+ " loc_ax.set_title(f\"Channel {i}, t: {t} , z: {z}\")\n",
+ " \n",
+ " plt.tight_layout()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Cell Segmentation\n",
+ "\n",
+ "No we specify the segmentation model: [Omnipose](https://doi.org/10.1101/2021.11.03.467199) and the channel we want to select to extract the image data. The channel data can be observed in the [Omero Web Viewer](http://ibt056.ibt.kfa-juelich.de:4080/). Please keep in mind that you have to enter the channel value+1 in `image_channels`. With the model and image sequence we kick off the segmentation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from acia.segm.omero.storer import OmeroRoIStorer, OmeroSequenceSource\n",
+ "from acia.segm.processor.online import FlexibleOnlineModel, ModelDescriptor\n",
+ "from urllib.parse import urljoin\n",
+ "\n",
+ "# the model description\n",
+ "model_desc = ModelDescriptor(\n",
+ " repo=\"https://gitlab+deploy-token-281:TZYmjRQZzLZsBfWsd2XS@jugit.fz-juelich.de/mlflow-executors/omnipose-executor.git\",\n",
+ " entry_point=\"main\",\n",
+ " version=\"main\",\n",
+ " parameters={\n",
+ " # specific model trained on cyanobacteria? http://ibt082:5000/#/experiments/711115886395583850/runs/3e50bc690ed147559dbf0254d7e701bb\n",
+ " \"model\": \"https://fz-juelich.sciebo.de/s/SJHXyT7xQfITHgw/download\"\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# connect to remote machine learning model\n",
+ "model = FlexibleOnlineModel(urljoin(segmentation_service, 'batch-image-prediction/'), model_desc, batch_size=30, timeout=600*30)\n",
+ "\n",
+ "\n",
+ "# create local image data source\n",
+ "source = OmeroSequenceSource(image_id, **credentials, channels=image_channels)\n",
+ "\n",
+ "# perform overlay prediction\n",
+ "print(\"Perform Prediction...\")\n",
+ "result = model.predict(source)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To validate the segmentation result, we create a short video:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import acia\n",
+ "from acia.segm.output import renderVideo\n",
+ "\n",
+ "framerate=2\n",
+ "\n",
+ "# Make a video with\n",
+ "video_file = str(output_path_rel / \"segmented.mp4\")\n",
+ "renderVideo(source, result.timeIterator(), filename=video_file, codec=\"vp09\", framerate=framerate, draw_frame_number=True)\n",
+ "\n",
+ "# display markdown\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "display(Markdown(\"# Your segmentation\"))\n",
+ "Video(video_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Extracting individual cell properties\n",
+ "\n",
+ "Now that we have the cell segmentation, we can move on and extract individual cell properties like Area, Time, Length, ....\n",
+ "and visualize them in a table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from acia.analysis import ExtractorExecutor, AreaEx, IdEx, FrameEx, TimeEx, LengthEx, FluorescenceEx\n",
+ "from acia import ureg\n",
+ "import pint\n",
+ "import numpy as np\n",
+ "\n",
+ "# create local image data source\n",
+ "source = OmeroSequenceSource(image_id, **credentials, channels=image_channels)\n",
+ "\n",
+ "assert source.pixelSize, \"The pixel size is not saved in omero -> we cannot extract meaningful area or length because we do not know the size of the pixels\"\n",
+ "\n",
+ "ex = ExtractorExecutor()\n",
+ "\n",
+ "df = ex.execute(result, source, [\n",
+ " # define the cell properties that you want to extract here\n",
+ " AreaEx(input_unit=(source.pixelSize[0] * ureg.micrometer) ** 2), # pass the correct area of pixels\n",
+ " LengthEx(input_unit=source.pixelSize[0] * ureg.micrometer), # pass the correct size of pixels\n",
+ " IdEx(),\n",
+ " FrameEx(),\n",
+ " TimeEx(input_unit=\"2 * hour\"), # one picture every 2 hour\n",
+ " FluorescenceEx(channels=[1], channel_names=[\"autofluorescence_sum\"], summarize_operator=np.sum, parallel=1), \n",
+ " FluorescenceEx(channels=[1], channel_names=[\"autofluorescence_mean\"], summarize_operator=np.mean, parallel=1),\n",
+ " FluorescenceEx(channels=[1], channel_names=[\"autofluorescence_std\"], summarize_operator=np.std, parallel=1)\n",
+ "])\n",
+ "\n",
+ "print(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_size_um = image_size * (source.pixelSize[0] * ureg.micrometer) ** 2\n",
+ "image_size_um"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# 3. Filtering artifacts in segmentation\n",
+ "\n",
+ "In the segmentation, we can often observe artifacts, that is objects that are mistakenly recoginzed as cells. To reduce the number of artifacts in our analysis we can utilize some simple filtering functionality for the area: We only keep all the objects that have an area between `min_area` and `max_area` as defined below in the code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "min_area = 0.7 # the minimal area in micrometer ** 2. All smaller objects are dropped\n",
+ "max_area = 10 # the maximal area in micrometer ** 2. All larger objects are dropped\n",
+ "\n",
+ "fig, ax = plt.subplots(2, 1, facecolor='white', figsize=(15,10))\n",
+ "\n",
+ "area_unit = ex.units['area']\n",
+ "\n",
+ "# plot the area distribution before filtering\n",
+ "ax[0].hist(df['area'], bins=100)\n",
+ "ax[0].set_title('Area distribution before filtering')\n",
+ "ax[0].set_ylabel('Frequency')\n",
+ "ax[0].set_xlabel(f'Cell area [${area_unit:~L}$]')\n",
+ "\n",
+ "# filter by area\n",
+ "filtered_df = df[(min_area < df['area']) & (df['area'] < max_area)]\n",
+ "\n",
+ "# plot the area distribution after filtering\n",
+ "ax[1].hist(filtered_df['area'], bins=100)\n",
+ "ax[1].set_title('Area distribution after filtering')\n",
+ "ax[1].set_ylabel('Frequency')\n",
+ "ax[1].set_xlabel(f'Cell area [${area_unit:~L}$]')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "\n",
+ "# export with german decimal: ,\n",
+ "filtered_df.to_csv(str(output_path / 'allcells.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "print(\"Done\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now let's look at the new video with filtered content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create local image data source\n",
+ "source = OmeroSequenceSource(image_id, **credentials, channels=image_channels)\n",
+ "\n",
+ "# Make a video with\n",
+ "video_file = str(output_path_rel / \"filter_segmented.mp4\")\n",
+ "renderVideo(source, result.timeIterator(), filename=video_file, codec=\"vp09\", framerate=framerate, draw_frame_number=True, filter_contours=lambda i,c: c.id in filtered_df['id'])\n",
+ "\n",
+ "# display markdown\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "display(Markdown(\"# Your segmentation\"))\n",
+ "Video(video_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# 4. Visualizing interesting properties\n",
+ "\n",
+ "We start with the count of cells per frame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count_df = filtered_df.groupby(['frame', 'time']).size().reset_index(name='counts')\n",
+ "\n",
+ "# export with german decimal: ,\n",
+ "count_df.to_csv(str(output_path / 'counts.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "print(count_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate min_time and max_time from % chamber filling\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#set min and max time for fitting\n",
+ "\n",
+ "min_thresh = 4 # minimal number of cells\n",
+ "max_thresh = 0.6 # maximal area filling of the chamber\n",
+ "\n",
+ "#try getting cell count and sum area from variables, otherwise load .csv files\n",
+ "\n",
+ "try:\n",
+ " sum_df = filtered_df.groupby(['frame', 'time']).sum().reset_index()\n",
+ "except:\n",
+ " filtered_df = pd.read_csv('tmp/allcells.csv', delimiter=';')\n",
+ " sum_df = filtered_df.groupby(['frame', 'time']).sum().reset_index()\n",
+ "\n",
+ "min_time = 0\n",
+ "max_time = 24\n",
+ " \n",
+ "try:\n",
+ " timed_df = count_df[(count_df['time'] >= min_time) & (count_df['time'] <= max_time)]\n",
+ "except:\n",
+ " count_df = pd.read_csv('tmp/counts.csv', delimiter=';')\n",
+ " timed_df = count_df[(count_df['time'] >= min_time) & (count_df['time'] <= max_time)]\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a figure\n",
+ "fig, ax1 = plt.subplots(facecolor='white')\n",
+ "\n",
+ "# plot the cell count\n",
+ "ax1.plot(count_df['time'], count_df['counts'], label='Cell count', color='#023d6b')\n",
+ "\n",
+ "# fit a model N=m*t+b\n",
+ "m_count, b_count = np.polyfit(timed_df['time'], np.log(timed_df['counts']), 1)\n",
+ "\n",
+ "# plot the fit\n",
+ "ax1.plot(timed_df['time'], np.exp(m_count * timed_df['time'] + b_count), label='fit count [h$^{-1}$]', color='#b9d25f')\n",
+ "\n",
+ "ax1.set_xlabel(f'Time [h$^{-1}$]')\n",
+ "ax1.set_ylabel('Cell count', color='#023d6b')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "# plot the sum cell area\n",
+ "ax2 = ax1.twinx()\n",
+ "ax2.plot(sum_df['time'], sum_df['area'], label='Cell area', color='#adbde3')\n",
+ "\n",
+ "timedsum_df = sum_df[(sum_df['time'] >= min_time) & (sum_df['time'] <= max_time)]\n",
+ "\n",
+ "# fit a model N=m*t+b\n",
+ "m_area, b_area = np.polyfit(timedsum_df['time'], np.log(timedsum_df['area']), 1)\n",
+ "\n",
+ "# plot the fit\n",
+ "ax2.plot(timedsum_df['time'], np.exp(m_area * timedsum_df['time'] + b_area), label='fit area [h$^{-1}$]', color='#fab45a')\n",
+ "\n",
+ "ax2.set_ylabel('Cell area', color='#adbde3')\n",
+ "ax2.set_yscale('log')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.1), ncol=4)\n",
+ "\n",
+ "#plt.yscale('log')\n",
+ "\n",
+ "plt.savefig('tmp/Growth_Rate_Count_vs_Are.png', bbox_inches='tight', transparent=1)\n",
+ "\n",
+ "#summerize growth rates for group statistics\n",
+ "\n",
+ "rates = [m_count, m_area]\n",
+ "labels = ['µ_count [1/h]', 'µ_area [1/h]']\n",
+ "\n",
+ "df_results = pd.DataFrame(rates, labels)\n",
+ "df_results.to_csv(str('tmp/results.csv'), decimal='.', sep=';')\n",
+ "print(df_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from scipy.signal import argrelextrema\n",
+ "\n",
+ "mean_df = filtered_df.groupby(['frame', 'time']).mean().reset_index()\n",
+ "\n",
+ "std_df = filtered_df.groupby(['frame', 'time']).std().reset_index()\n",
+ "\n",
+ "std_df.to_csv(str('tmp/std_df.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "# create a figure\n",
+ "plt.figure(facecolor='white')\n",
+ "\n",
+ "# Find local peaks\n",
+ "\n",
+ "n = 2 # number of points to be checked before and after\n",
+ "\n",
+ "mean_df['max'] = mean_df.iloc[argrelextrema(mean_df.area.values, np.greater_equal, order=n)[0]]['area']\n",
+ "\n",
+ "\n",
+ "mean_df.to_csv(str('tmp/ mean_df.csv'), decimal='.', sep=';')\n",
+ "\n",
+ "# calculate doubling time\n",
+ "\n",
+ "extrema_df = mean_df.dropna(subset=['max']).reset_index()\n",
+ "\n",
+ "extrema_df['doubling_time'] = extrema_df['time'].diff(1)\n",
+ "\n",
+ "# plot mean area over time with error\n",
+ "fig, ax1 = plt.subplots(facecolor='white')\n",
+ "ax1.scatter(mean_df['time'], mean_df['max'], c='#b9d25f',zorder=2)\n",
+ "ax1.errorbar(mean_df['time'], mean_df['area'], yerr=std_df['area'], label='Average cell area', color='#adbde3', ecolor='#ebebeb',zorder=1)\n",
+ "\n",
+ "ax2 = ax1.twinx()\n",
+ "ax2.plot(extrema_df['time'], extrema_df['doubling_time'], label='Doubling Time', color='#fab45a',zorder=3)\n",
+ "\n",
+ "ax1.set_xlabel(f'Time [h$^{-1}$]')\n",
+ "ax1.set_ylabel('Average cell area [µm$^2$]', color='#adbde3')\n",
+ "ax2.set_ylabel('Doubling Time [h]', color='#fab45a')\n",
+ "ax2.set_ylim(0, 20)\n",
+ "ax2.set_xlim(0, 122)\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.1), ncol=3)\n",
+ "\n",
+ "plt.savefig('tmp/Mean_Area_Over_Time.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "# calculate mean area and lenght again\n",
+ "\n",
+ "mean_df = filtered_df.groupby(['frame', 'time']).mean().reset_index()\n",
+ "\n",
+ "std_df = filtered_df.groupby(['frame', 'time']).std().reset_index()\n",
+ "\n",
+ "# plot mean area over mean lenght\n",
+ "fig, ax = plt.subplots(facecolor='white', figsize=(6, 5))\n",
+ "\n",
+ "ax.errorbar(mean_df['area'], mean_df['length'], yerr=std_df['area'], xerr=std_df['length'], fmt=\"o\",color='#adbde3', ecolor='#ebebeb', markersize = 0,zorder=1)\n",
+ "im = ax.scatter(mean_df['area'], mean_df['length'], s=10, c=mean_df['time'], cmap='rainbow',zorder=2)\n",
+ "\n",
+ "# Add a colorbar\n",
+ "cbar = fig.colorbar(im, ax=ax)\n",
+ "cbar.set_label('Time in [h]',rotation=270)\n",
+ "\n",
+ "ax.set_xlabel('Mean cell lenght [µm]')\n",
+ "ax.set_ylabel('Mean cell area [µm$^2$]')\n",
+ "ax.set_ylim(0, 8)\n",
+ "ax.set_xlim(0, 8)\n",
+ "\n",
+ "plt.savefig('tmp/Mean_Area_Over_Mean_Cell_Lenght.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Calculate Fluorescence per cell area\n",
+ "\n",
+ "mean_df['Fluorescence/Cell_Area'] = mean_df['area']/mean_df['autofluorescence_sum']\n",
+ "\n",
+ "# Plot mean autofluorescence of the cells\n",
+ "\n",
+ "fig, ax = plt.subplots(facecolor='white')\n",
+ "ax.plot(mean_df['time'], mean_df['Fluorescence/Cell_Area'], color='#023d6b')\n",
+ "\n",
+ "ax.set_ylim(0, 0.00003)\n",
+ "ax.set_xlim(0, 122)\n",
+ "\n",
+ "ax.set_xlabel('Time [h]')\n",
+ "ax.set_ylabel('Autofluorescence/Cell area [µm$^-$$^2$]')\n",
+ "\n",
+ "plt.savefig('tmp/Mean_Fluorescence_per_Cell_Area.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "43e720662e2b73f3f858656968524fca68eb44fc0b1d15b9eb878c7d185562f9"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assays/Microfluidic cultivation with gradient growth light/protocols/ScalingAnalysis_SequenceNames.ipynb b/assays/Microfluidic cultivation with gradient growth light/protocols/ScalingAnalysis_SequenceNames.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c127523af6012d3f3b6dd44e406e5695ae50deb0
--- /dev/null
+++ b/assays/Microfluidic cultivation with gradient growth light/protocols/ScalingAnalysis_SequenceNames.ipynb
@@ -0,0 +1,779 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Scaling Analysis\n",
+ "\n",
+ "You have developed your analysis notebook that works perfectly for a single cultivation chamber 💪? And now you you want to apply it for all cultivation chambers in our experiment but it is lots of work to apply the scripts one by one 🤔? That's why this example shows how you can quickly apply your single analysis script to a large amount of image sequences organized in the OMERO `project` or `dataset` structures 🚀! Therefore, your custom developed analyses can scale to large image volumes without you touching or changing the code!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Setup\n",
+ "\n",
+ "Define the `omero_id` and `omero_type` of the image data you would like to process. The `omerod_id` is the number you can find in the top right corner when selecting a OMERO `project`, `dataset` or `image` in the `OMERO Web` application. The `omero_type` must be `project` or `dataset` when the OMERO id points to a project or dataset and `image` if it is just a single image! Please note that if you define the wrong `omero_type` you will get an error lateron 🤯!\n",
+ "\n",
+ "Also provide your credentials for the OMERO server!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "# OMERO resource that you want to analyze\n",
+ "omero_type = \"dataset\" # can be \"image\", \"project\" or \"dataset\"\n",
+ "omero_id = 2860 # change the id if you want to apply the analysis to a different omero resource\n",
+ "\n",
+ "# your omero credentials\n",
+ "username = \"lwitting\"\n",
+ "password = \"lwitting\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# prepare credentials (usually you do not have to change this!)\n",
+ "\n",
+ "import logging\n",
+ "\n",
+ "if not \"OMERO_SERVER\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_SERVER' defined. Fallback to default OMERO_SERVER address 'omero'! This can lead to connection faults!\")\n",
+ "if not \"OMERO_WEB\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_WEB' defined. Links to view OMERO data in web viewer might not work!\")\n",
+ "\n",
+ "credentials = dict(\n",
+ " serverUrl= os.environ.get('OMERO_SERVER', 'omero'),\n",
+ " username= username,\n",
+ " password = password,\n",
+ " port = int(os.environ.get('OMERO_PORT', '4064'))\n",
+ ")\n",
+ "\n",
+ "omero_cred = dict(\n",
+ " host = credentials['serverUrl'],\n",
+ " username = credentials['username'],\n",
+ " passwd = credentials['password'],\n",
+ " port = credentials['port'],\n",
+ " secure = True\n",
+ ")\n",
+ "\n",
+ "omero_web = os.environ.get(\"OMERO_WEB\", \"<Your OMERO_WEB address should be here>\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.2 Specify the analysis script\n",
+ "\n",
+ "Now you have to specify the name of the analysis script you want to apply to the image data. At best copy the script to the same location as this script! Then you only have to specify the name of the script!\n",
+ "\n",
+ "**Note:** If the analysis script is not located in the same folder you need to specify the path to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "analysis_script = \"Growth_Rate.ipynb\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Information about the underlying data\n",
+ "\n",
+ "We summarize the amount of underlying data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[27288, 27293, 27295, 27308, 27312, 27289, 27290, 27292, 27294, 27300, 27291, 27296, 27298, 27306, 27310, 27311, 27304, 27313]\n",
+ "{27288: PosixPath('01_1000_cropped.tif'), 27293: PosixPath('06_1113_cropped.tif'), 27295: PosixPath('08_1119_cropped.tif'), 27308: PosixPath('15_1307_cropped.tif'), 27312: PosixPath('19_1333_cropped.tif'), 27289: PosixPath('02_1002_cropped.tif'), 27290: PosixPath('03_1020_cropped.tif'), 27292: PosixPath('05_1101_cropped.tif'), 27294: PosixPath('07_1117_cropped.tif'), 27300: PosixPath('11_1200_cropped.tif'), 27291: PosixPath('04_1035_cropped.tif'), 27296: PosixPath('09_1125_cropped.tif'), 27298: PosixPath('10_1132_cropped.tif'), 27306: PosixPath('13_1217_cropped.tif'), 27310: PosixPath('16_1312_cropped.tif'), 27311: PosixPath('18_1327_cropped.tif'), 27304: PosixPath('12_1209_cropped.tif'), 27313: PosixPath('20_1337_cropped.tif')}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from acia.segm.omero.utils import list_image_ids_in, getImage\n",
+ "from omero.gateway import BlitzGateway\n",
+ "from pathlib import Path\n",
+ "\n",
+ "image_names = {}\n",
+ "\n",
+ "with BlitzGateway(**omero_cred) as conn:\n",
+ " image_ids = list_image_ids_in(omero_id, omero_type, conn)\n",
+ " \n",
+ " # get all the image names\n",
+ " for image_id in image_ids:\n",
+ " image_names[image_id] = Path(getImage(conn, image_id).getName())\n",
+ "\n",
+ "## TODO: give an overview about the data\n",
+ "print(image_ids)\n",
+ "print(image_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. Scale the analysis script to all image sequences\n",
+ "\n",
+ "Now we apply the analysis script to every image sequence individually 🚀! You can lean back and enjoy the working computer 😎 🥂\n",
+ "\n",
+ "**Note:** For heavy analysis scripts or for larget `datasets` or `projects` this process may take a while (from minutes to hours or days). The top-level progress bar will indicate the total progress and give you an indication how long this will take. For large image data volumes we can recommend execution over night 🌔!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results are stored in: /home/jovyan/work/A3_IntensityGradient/2023.12.15_24000u/Growth_Rate/S. elongatus UTEX2973/automated_executions\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b030cb52388b4105b0563fd2b2d97fa7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/18 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9e8fe885d970476fb24c451cda5ec833",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/27 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "from pathlib import Path\n",
+ "from acia.analysis import scale\n",
+ "\n",
+ "# set the base path for all results\n",
+ "stem = Path(analysis_script).stem\n",
+ "output_path = Path(\"./automated_executions\") \n",
+ "\n",
+ "print(f\"Results are stored in: {output_path.absolute()}\")\n",
+ "\n",
+ "# scale your analysis script to many images\n",
+ "result = scale(output_path, analysis_script=analysis_script, image_ids=image_ids, additional_parameters=dict(username=username, password=password), exist_ok=True, execution_naming=lambda image_id: image_names[image_id])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Inspect your analysis results\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib.parse\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "\n",
+ "base_url = os.environ.get(\"JUPYTERHUB_SERVICE_PREFIX\", None)\n",
+ "\n",
+ "if base_url is None:\n",
+ " url = f\"file://{output_path.absolute()}\"\n",
+ "else:\n",
+ " url = f\"{base_url}lab/tree/{urllib.parse.quote(str(output_path))}\"\n",
+ "\n",
+ "output = f\"\"\"# Inspect your analyses\n",
+ "You can find all the individual analysis scripts here: <a href=\"{url}\">{url}</a>\"\"\"\n",
+ "\n",
+ "display(Markdown(output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Generate Summary Statistics\n",
+ "\n",
+ "In this section you can generate your custom summary statistics that combine the results of all experiment analyses. Just design the analysis script that you scaled above such that it outputs the results into a local files. Here, these results can be loaded, merged together and further processed or visualized!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get results.csv from each individual chamber\n",
+ "\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "data_folder = Path(\"./automated_executions\") \n",
+ "dfs = []\n",
+ "for sub_folder in data_folder.glob(\"*\"): # hole dir alle Ordner, die mit UTEX enden\n",
+ " try:\n",
+ " data_file = sub_folder / \"tmp\" / \"results.csv\"\n",
+ " sub_df = pd.read_csv(data_file, delimiter = ';')\n",
+ " sub_df[\"experiment\"] = sub_folder.name\n",
+ " dfs.append(sub_df)\n",
+ " except:\n",
+ " print('No results.csv found in {}'.format(sub_folder))\n",
+ "\n",
+ "joint_df = pd.concat(dfs, ignore_index=True)\n",
+ "\n",
+ "# Group dataframe by category (code by chat gpt) \n",
+ "grouped_df = joint_df.groupby('Unnamed: 0')\n",
+ "\n",
+ "count_df = grouped_df.get_group('µ_count [1/h]')\n",
+ "\n",
+ "area_df = grouped_df.get_group('µ_area [1/h]')\n",
+ "\n",
+ "# print(joint_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now let's plot the growth rates\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "data = [count_df['0'], area_df['0']]\n",
+ "\n",
+ "fig, ax1 = plt.subplots(facecolor='white')\n",
+ "ax1.boxplot(data,labels=['Cell count','Cell area'])\n",
+ "ax1.set_ylabel('Growth rate [h$^{-1}$]')\n",
+ "ax1.set_ylim(0, )\n",
+ "\n",
+ "plt.savefig('Boxplot_growth_rates.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Calculate Mean, Median and Standard deviation\n",
+ "\n",
+ "mean = [np.mean(count_df['0']), np.mean(area_df['0'])]\n",
+ "median = [np.median(count_df['0']), np.median(area_df['0'])]\n",
+ "std = [np.std(count_df['0']), np.std(area_df['0'])]\n",
+ "\n",
+ "statistics_df = pd.DataFrame({'Chamber': ['Mean','Median','STD'],\n",
+ " 'µcount': [mean[0], median [0], std[0]],\n",
+ " 'µarea': [mean[1], median [1], std[1]]})\n",
+ "# print(statistics_df)\n",
+ "\n",
+ "# Rearrange Growth rates for setting up results.csv\n",
+ "\n",
+ "results_df_1 = pd.DataFrame({'Chamber': count_df['experiment'],\n",
+ " 'µcount': count_df['0']}).reset_index()\n",
+ "\n",
+ "results_df_2 = pd.DataFrame({'µarea': area_df['0']}).reset_index()\n",
+ "\n",
+ "rates_df = pd.concat([results_df_1, results_df_2], axis=1)\n",
+ "\n",
+ "del rates_df['index']\n",
+ "\n",
+ "result_df = pd.concat([rates_df, statistics_df])\n",
+ "\n",
+ "# print(result_df)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Power STL Slope Intercept Distance\n",
+ "0 20 0.0007 20.1367 8315.4611\n",
+ "1 40 0.0013 36.6751 8315.4611\n",
+ "2 50 0.0017 45.8486 8315.4611\n",
+ "3 60 0.0020 53.5403 8315.4611\n",
+ "4 70 0.0023 61.8869 8315.4611\n",
+ "5 80 0.0027 70.4610 8315.4611\n",
+ "6 90 0.0030 78.5073 8315.4611\n",
+ "7 100 0.0033 86.4714 8315.4611\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from pathlib import Path\n",
+ "\n",
+ "# Grab calibration results from Calibration folder\n",
+ "\n",
+ "Calibration = Path(\"..\") / \"..\" / \"Calibration_Slider\" / \"Calibration_result.csv\"\n",
+ "\n",
+ "df_calibration = pd.read_csv(Calibration, sep = ';', encoding = 'utf8', header = 0, index_col=0, decimal=',')\n",
+ "\n",
+ "# Then specify the gradient that was used\n",
+ "\n",
+ "Power_STL_used = 60 # Manually specify the Power of gradient used\n",
+ "\n",
+ "print(df_calibration) # Iterate over df_calibration to find index with Power_STL_used\n",
+ "for n in range(len(df_calibration)):\n",
+ " if Power_STL_used == df_calibration.iloc[n,0]:\n",
+ " index = n\n",
+ " else:\n",
+ " ()\n",
+ " \n",
+ "slope = df_calibration.iloc[index,1]\n",
+ "intercept = df_calibration.iloc[index,2]\n",
+ "distance = df_calibration.iloc[index,3]\n",
+ "\n",
+ "Total_Number_chambers = 40 # Specify number of chambers present on chip\n",
+ "First_Chamber_Calibration = 27 # First chamber seen in calibration picture\n",
+ "Last_Chamber_Calibration = 40 # Last chamber seen in calibration picture\n",
+ "\n",
+ "step = distance / ((Last_Chamber_Calibration - First_Chamber_Calibration) - 1) # Calculate step change of intensity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Chamber µcount µarea Vertical_Position \\\n",
+ "0 07_1117_cropped.tif 0.077940 0.077020 6 \n",
+ "1 11_1200_cropped.tif 0.083044 0.077278 5 \n",
+ "2 08_1119_cropped.tif 0.081826 0.075322 4 \n",
+ "3 15_1307_cropped.tif 0.098470 0.081718 4 \n",
+ "4 03_1020_cropped.tif 0.063269 0.060977 5 \n",
+ "5 01_1000_cropped.tif 0.051161 0.047352 5 \n",
+ "6 02_1002_cropped.tif 0.057577 0.052852 7 \n",
+ "7 06_1113_cropped.tif 0.071611 0.071658 6 \n",
+ "8 05_1101_cropped.tif 0.059928 0.060197 6 \n",
+ "9 12_1209_cropped.tif 0.085352 0.080510 6 \n",
+ "10 13_1217_cropped.tif 0.079229 0.075380 6 \n",
+ "11 20_1337_cropped.tif 0.081001 0.076455 6 \n",
+ "12 16_1312_cropped.tif 0.090946 0.080839 5 \n",
+ "13 19_1333_cropped.tif 0.087223 0.076813 6 \n",
+ "14 10_1132_cropped.tif 0.084659 0.076469 5 \n",
+ "15 18_1327_cropped.tif 0.078509 0.075189 4 \n",
+ "16 09_1125_cropped.tif 0.077735 0.072239 6 \n",
+ "17 04_1035_cropped.tif 0.045705 0.061675 4 \n",
+ "\n",
+ " Horizontal_Position Intensity Channel \n",
+ "0 15 35.523468 2.0 \n",
+ "1 21 43.838929 2.0 \n",
+ "2 15 35.523468 2.0 \n",
+ "3 32 59.083941 2.0 \n",
+ "4 6 23.050276 2.0 \n",
+ "5 1 16.120725 2.0 \n",
+ "6 1 16.120725 2.0 \n",
+ "7 14 34.137557 2.0 \n",
+ "8 11 29.979827 2.0 \n",
+ "9 23 46.610749 2.0 \n",
+ "10 25 49.382569 2.0 \n",
+ "11 40 70.171222 2.0 \n",
+ "12 34 61.855761 2.0 \n",
+ "13 39 68.785312 2.0 \n",
+ "14 19 41.067108 2.0 \n",
+ "15 37 66.013492 2.0 \n",
+ "16 17 38.295288 2.0 \n",
+ "17 9 27.208007 2.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract Postion from Naming of Image Sequence\n",
+ "\n",
+ "Channels = []\n",
+ "Horizontal_Positions = []\n",
+ "Vertical_Positions = []\n",
+ "Intensities = []\n",
+ "\n",
+ "for chamber in rates_df['Chamber']: # Extract Postion from Naming of Image Sequence\n",
+ " Identifier_a = float(chamber[3]) # First number decodes channel\n",
+ " Identifier_b = float(chamber[4]) # The last three numbers decode Position\n",
+ " Identifier_c = float(chamber[5:7])\n",
+ " Channel = Identifier_a +1\n",
+ " Channels.append(Channel)\n",
+ " Horizontal_Position = int(Identifier_b*10 + round(((Identifier_c + 1)/4) + 0.49)) # Calculate Horizontal Position\n",
+ " Vertical_Position = int((((Identifier_c + 1)/4 - round(((Identifier_c + 1)/4) - 0.49))*4) + Identifier_a * 4) # Calculate Vertical Position\n",
+ " Intensity = intercept + ((Horizontal_Position - 1) - First_Chamber_Calibration)*step*slope\n",
+ " Horizontal_Positions.append(Horizontal_Position)\n",
+ " Vertical_Positions.append(Vertical_Position)\n",
+ " Intensities.append(Intensity)\n",
+ "\n",
+ "rates_df['Vertical_Position'] = Vertical_Positions # Append Vertical Postion to rates_df \n",
+ "rates_df['Horizontal_Position'] = Horizontal_Positions # Append Horizontal Postion to rates_df\n",
+ "rates_df['Intensity'] = Intensities # Append Postion to rates_df\n",
+ "rates_df['Channel'] = Channels # Append Channels to rates_df\n",
+ "\n",
+ "print(rates_df)\n",
+ "rates_df.to_csv(str('rates_df.csv'), sep=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1000x300 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from copy import copy\n",
+ "\n",
+ "Vertical_Positions_rel = []\n",
+ "\n",
+ "for n in range(0, len(rates_df)):\n",
+ " Vertical_Position_rel = (rates_df.iloc[n, 3] - min(rates_df['Vertical_Position'])) + 1\n",
+ " Vertical_Positions_rel.append(Vertical_Position_rel)\n",
+ " \n",
+ "rates_df['Vertical_Position_rel'] = Vertical_Positions_rel\n",
+ "\n",
+ "Vertical = range(0, max(rates_df['Vertical_Position_rel']))\n",
+ "Horizontal = range(1, Total_Number_chambers)\n",
+ " \n",
+ "uarea = np.zeros((max(rates_df['Vertical_Position_rel']),Total_Number_chambers))\n",
+ "ucount = np.zeros((max(rates_df['Vertical_Position_rel']),Total_Number_chambers))\n",
+ "\n",
+ "for n in range(0, len(rates_df)):\n",
+ " uarea[rates_df.iloc[n, 7] -1, rates_df.iloc[n, 4]-1] = rates_df.iloc[n,2]\n",
+ " \n",
+ "for n in range(0, len(rates_df)):\n",
+ " ucount[rates_df.iloc[n, 7] -1, rates_df.iloc[n, 4]-1] = rates_df.iloc[n,1]\n",
+ " \n",
+ "# print(uarea)\n",
+ "\n",
+ "cmap=plt.colormaps['viridis']\n",
+ "cmap.set_under(\"white\")\n",
+ "\n",
+ "fig, ax = plt.subplots(2,1, figsize=(10, 3), sharex=False)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].imshow(uarea, vmin=min(rates_df.iloc[:,2]), cmap=cmap)\n",
+ "im = ax[1].imshow(ucount, vmin=min(rates_df.iloc[:,1]), cmap=cmap)\n",
+ "\n",
+ "ax[1].set_xlabel('Horizontal Position')\n",
+ "\n",
+ "ax[0].set_ylabel('Vertical Position')\n",
+ "ax[1].set_ylabel('Vertical Position')\n",
+ "\n",
+ "ax[0].set_title('Cell area')\n",
+ "ax[1].set_title('Cell count')\n",
+ "\n",
+ "ax[0].set_xlim(-0.5, 40.5)\n",
+ "ax[1].set_xlim(-0.5, 40.5)\n",
+ "\n",
+ "# Add a colorbar\n",
+ "cbar = fig.colorbar(im, ax=ax, location='right')\n",
+ "cbar.set_label('Growth rate [1/h]')\n",
+ "\n",
+ "plt.savefig('Heatmap.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " experiment µcount µarea std_count std_area \\\n",
+ "0 2023.08.15_10uE_AmbientCO2 0.021800 0.019503 0.002515 0.001579 \n",
+ "1 2023.08.01_140uE_AmbientCO2 0.097658 0.086454 0.004956 0.003792 \n",
+ "2 2023.03.01_80uE_AmbientCO2 0.088108 0.083899 0.005103 0.005700 \n",
+ "3 2023.08.08_50uE_AmbientCO2 0.083962 0.074748 0.004330 0.003089 \n",
+ "4 2023.06.27_20uE_AmbientCO2 0.045981 0.037946 0.005256 0.004308 \n",
+ "5 2023.07.18_60uE_AmbientCO2 0.087836 0.074547 0.006631 0.004350 \n",
+ "6 2023.07.25_30uE_AmbientCO2 0.069880 0.064130 0.005927 0.004519 \n",
+ "\n",
+ " Intensity \n",
+ "0 10.0 \n",
+ "1 140.0 \n",
+ "2 80.0 \n",
+ "3 50.0 \n",
+ "4 20.0 \n",
+ "5 60.0 \n",
+ "6 30.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "PI_curve = Path(\"..\") / \"..\" / \"..\"/ \"..\"/ \"A2.2_PI_Curve_µFluidic_newSegAI\" / \"PI_curve_UTEX.csv\" # read previous experimentall data to compare\n",
+ "\n",
+ "df_PI_curve = pd.read_csv(PI_curve, sep = ';', encoding = 'utf8', header = 0, index_col=0)\n",
+ "print(df_PI_curve)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fit a PI curve model to data\n",
+ "\n",
+ "import numpy as np\n",
+ "from scipy.optimize import curve_fit\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def tanh_function(x, umax, a):\n",
+ " \"\"\"\n",
+ " Tanh function: a * tanh(b * (x - c)) + d\n",
+ " Parameters:\n",
+ " - umax: amplitude\n",
+ " - a: initial slope\n",
+ " \"\"\"\n",
+ " return umax * np.tanh(a*x/umax)\n",
+ "\n",
+ "def fit_tanh_to_data(x_data, y_data):\n",
+ " \"\"\"\n",
+ " Fit a tanh function to the given data.\n",
+ "\n",
+ " Parameters:\n",
+ " - x_data: Input data (independent variable)\n",
+ " - y_data: Output data (dependent variable)\n",
+ "\n",
+ " Returns:\n",
+ " - popt: Optimal values for the parameters (a, b, c, d)\n",
+ " \"\"\"\n",
+ "\n",
+ " # Initial guess for the parameters (you may need to adjust these)\n",
+ " initial_guess = (0.06, 0.0001)\n",
+ "\n",
+ " # Perform the curve fitting using scipy.optimize.curve_fit\n",
+ " popt, pcov = curve_fit(tanh_function, x_data, y_data, p0=initial_guess)\n",
+ "\n",
+ " return popt\n",
+ "\n",
+ "x_data = np.linspace(0,150,16)\n",
+ "\n",
+ "para_Homo_area = fit_tanh_to_data(df_PI_curve['Intensity'], df_PI_curve['µarea'])\n",
+ "para_Homo_count = fit_tanh_to_data(df_PI_curve['Intensity'], df_PI_curve['µcount'])\n",
+ "fit_Homo_area = tanh_function(x_data, * para_Homo_area)\n",
+ "fit_Homo_count = tanh_function(x_data, * para_Homo_count)\n",
+ "para_Grad_area = fit_tanh_to_data(rates_df['Intensity'], rates_df['µarea'])\n",
+ "para_Grad_count = fit_tanh_to_data(rates_df['Intensity'], rates_df['µcount'])\n",
+ "fit_Grad_area = tanh_function(x_data, * para_Grad_area)\n",
+ "fit_Grad_count = tanh_function(x_data, * para_Grad_count)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharey=True)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].errorbar(df_PI_curve['Intensity'], df_PI_curve['µcount'], yerr = df_PI_curve['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='Homogeneous', zorder = 1)\n",
+ "ax[0].scatter(rates_df['Intensity'], rates_df['µcount'], color='#023d6b', label = 'Gradient', zorder = 2)\n",
+ "ax[1].errorbar(df_PI_curve['Intensity'], df_PI_curve['µarea'], yerr = df_PI_curve['std_area'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', zorder = 1)\n",
+ "ax[1].scatter(rates_df['Intensity'], rates_df['µarea'], color='#023d6b', zorder = 2)\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, 150)\n",
+ "ax[1].set_xlim(0, 150)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharey=True)\n",
+ "fig.tight_layout(pad = 2)\n",
+ "\n",
+ "ax[0].errorbar(df_PI_curve['Intensity'], df_PI_curve['µcount'], yerr = df_PI_curve['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='Homogeneous', zorder = 1)\n",
+ "ax[0].scatter(rates_df['Intensity'], rates_df['µcount'], color='#023d6b', label = 'Gradient', zorder = 2)\n",
+ "ax[0].plot(x_data, fit_Homo_count, color='#fab45a', zorder = 0)\n",
+ "ax[0].plot(x_data, fit_Grad_count, color='#023d6b', zorder = 0)\n",
+ "ax[1].errorbar(df_PI_curve['Intensity'], df_PI_curve['µarea'], yerr = df_PI_curve['std_area'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', zorder = 1)\n",
+ "ax[1].scatter(rates_df['Intensity'], rates_df['µarea'], color='#023d6b', zorder = 2)\n",
+ "ax[1].plot(x_data, fit_Homo_area, color='#fab45a', zorder = 0)\n",
+ "ax[1].plot(x_data, fit_Grad_area, color='#023d6b', zorder = 0)\n",
+ "\n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, 150)\n",
+ "ax[1].set_xlim(0, 150)\n",
+ "\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [1/h]')\n",
+ "ax[1].set_ylabel('Growth rate [1/h]')\n",
+ "\n",
+ "ax[0].set_title('Cell count')\n",
+ "ax[1].set_title('Cell area')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve_with_fit.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "43e720662e2b73f3f858656968524fca68eb44fc0b1d15b9eb878c7d185562f9"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assays/Microfluidic cultivation with gradient growth light/protocols/Summarize_PI_Curves.ipynb b/assays/Microfluidic cultivation with gradient growth light/protocols/Summarize_PI_Curves.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2e5be87ad7ffab4a607aac836d7823a5f1ce8d47
--- /dev/null
+++ b/assays/Microfluidic cultivation with gradient growth light/protocols/Summarize_PI_Curves.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "dba12853-0e46-4a1c-9031-62a3ed081dbd",
+ "metadata": {},
+ "source": [
+ "# Summerize PI Curves\n",
+ "\n",
+ "This skript was written by Lennart Ole Witting. It is desinged to automatically summerize PI curves of different Organisms in one Gradient Experiment.\n",
+ "\n",
+ "The skript is suppost to be placed in a folder containing subfolders for the channels on the microfluidic chip."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c9467fee-7edb-44f1-97ec-b61fd05b9f4b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['S. elongatus PCC7942 CscB', 'Synechocystis sp. PCC6803', 'S. elongatus UTEX2973']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Create a list with all organisms\n",
+ "\n",
+ "path = Path(\"./Growth_Rate\")\n",
+ "\n",
+ "organisms = []\n",
+ "rates = []\n",
+ "\n",
+ "for sub_folder in path.glob(\"S*\"): # grad all folders \n",
+ " rates_df = pd.read_csv(sub_folder / 'rates_df.csv' , delimiter = ';')\n",
+ " organisms.append(sub_folder.name)\n",
+ " rates.append(rates_df)\n",
+ " \n",
+ "print(organisms) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c3547b2a-7a21-47ee-b950-2353b62dd230",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Area')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Now finally plot the results\n",
+ "\n",
+ "corperate_idendity = ['#023d6b', '#adbde3', '#faeb5a', '#eb5f73', '#b9d25f', '#af82b9', '#fab45a', '#ebebeb'] # Fz Juelich corperate identity\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharex = False, sharey = True)\n",
+ "\n",
+ "for n in range(0, len(organisms)):\n",
+ " rates_df = rates[n]\n",
+ " ax[0].scatter(rates_df['Intensity'], rates_df['µcount'],color=corperate_idendity[n] , label=organisms[n])\n",
+ " ax[1].scatter(rates_df['Intensity'], rates_df['µarea'],color=corperate_idendity[n])\n",
+ " \n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, )\n",
+ "ax[1].set_xlim(0, )\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [h$^{-1}$]')\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.2), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curves.png', bbox_inches='tight', transparent=1)\n",
+ "\n",
+ "ax[0].set_title('Count')\n",
+ "ax[1].set_title('Area')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "2c9cf72f-8752-4a09-a604-5c14fcecae77",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[array([0.18895347, 0.00307391]), array([0.06044401, 0.00269813]), array([0.08828825, 0.00318098])]\n",
+ "[array([0.16110462, 0.00290684]), array([0.05864564, 0.00236637]), array([0.07918347, 0.00347111])]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from scipy.optimize import curve_fit\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def tanh_function(x, umax, a):\n",
+ " \"\"\"\n",
+ " Tanh function: a * tanh(b * (x - c)) + d\n",
+ " Parameters:\n",
+ " - umax: amplitude\n",
+ " - a: initial slope\n",
+ " \"\"\"\n",
+ " return umax * np.tanh(a*x/umax)\n",
+ "\n",
+ "def fit_tanh_to_data(x_data, y_data):\n",
+ " \"\"\"\n",
+ " Fit a tanh function to the given data.\n",
+ "\n",
+ " Parameters:\n",
+ " - x_data: Input data (independent variable)\n",
+ " - y_data: Output data (dependent variable)\n",
+ "\n",
+ " Returns:\n",
+ " - popt: Optimal values for the parameters (a, b, c, d)\n",
+ " \"\"\"\n",
+ "\n",
+ " # Initial guess for the parameters (you may need to adjust these)\n",
+ " initial_guess = (0.06, 0.0001)\n",
+ "\n",
+ " # Perform the curve fitting using scipy.optimize.curve_fit\n",
+ " popt, pcov = curve_fit(tanh_function, x_data, y_data, p0=initial_guess, bounds=(0, 10))\n",
+ "\n",
+ " return popt\n",
+ "\n",
+ "x_data = np.linspace(0,150,51)\n",
+ "\n",
+ "# Fit tanh function to the data\n",
+ "\n",
+ "PI_parameters_area = []\n",
+ "PI_curves_area = []\n",
+ "PI_curves_area_extra = []\n",
+ "PI_parameters_count = []\n",
+ "PI_curves_count = []\n",
+ "PI_curves_count_extra = []\n",
+ "\n",
+ "for n in range(0, len(organisms)):\n",
+ " rates_df = rates[n]\n",
+ " x_min = min(rates_df['Intensity'])\n",
+ " x_max = max(rates_df['Intensity'])\n",
+ " optimal_params_area = fit_tanh_to_data(rates_df['Intensity'], rates_df['µarea'])\n",
+ " y_data_fit_area = tanh_function(np.linspace(x_min, x_max,51), * optimal_params_area)\n",
+ " y_data_fit_area_extra = tanh_function(x_data, * optimal_params_area)\n",
+ " optimal_params_count = fit_tanh_to_data(rates_df['Intensity'], rates_df['µcount'])\n",
+ " y_data_fit_count = tanh_function(np.linspace(x_min, x_max,51), * optimal_params_count)\n",
+ " y_data_fit_count_extra = tanh_function(x_data, * optimal_params_count)\n",
+ " PI_curves_area.append(y_data_fit_area)\n",
+ " PI_parameters_area.append(optimal_params_area)\n",
+ " PI_curves_count.append(y_data_fit_count)\n",
+ " PI_parameters_count.append(optimal_params_count)\n",
+ " PI_curves_area_extra.append(y_data_fit_area_extra)\n",
+ " PI_curves_count_extra.append(y_data_fit_count_extra)\n",
+ "\n",
+ "\n",
+ "print(PI_parameters_count)\n",
+ "print(PI_parameters_area)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "17a1395e-865d-454b-9b54-ae541e635009",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Area')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now finally plot the results\n",
+ "\n",
+ "corperate_idendity = ['#023d6b', '#adbde3', '#faeb5a', '#eb5f73', '#b9d25f', '#af82b9', '#fab45a', '#ebebeb'] # Fz Juelich corperate identity\n",
+ "\n",
+ "fig, ax = plt.subplots(1,2,facecolor='white',figsize=(9, 4), sharex = False, sharey = False)\n",
+ "\n",
+ "for n in range(0, len(organisms)):\n",
+ " rates_df = rates[n]\n",
+ " x_min = min(rates_df['Intensity'])\n",
+ " x_max = max(rates_df['Intensity'])\n",
+ " ax[0].scatter(rates_df['Intensity'], rates_df['µcount'],color=corperate_idendity[n] , label=organisms[n])\n",
+ " ax[0].plot(np.linspace(x_min, x_max,51), PI_curves_count[n], color=corperate_idendity[n])\n",
+ " ax[0].plot(x_data, PI_curves_count_extra[n], color=corperate_idendity[n], linestyle = 'dotted')\n",
+ " ax[1].scatter(rates_df['Intensity'], rates_df['µarea'],color=corperate_idendity[n])\n",
+ " ax[1].plot(np.linspace(x_min, x_max,51), PI_curves_area[n], color=corperate_idendity[n])\n",
+ " ax[1].plot(x_data, PI_curves_area_extra[n], color=corperate_idendity[n], linestyle = 'dotted')\n",
+ " \n",
+ "ax[0].set_ylim(0, )\n",
+ "ax[1].set_ylim(0, )\n",
+ "\n",
+ "ax[0].set_xlim(0, 100 )\n",
+ "ax[1].set_xlim(0, 100)\n",
+ "\n",
+ "ax[0].set_ylabel('Growth rate [h$^{-1}$]')\n",
+ "ax[0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.25), ncol=1)\n",
+ "\n",
+ "plt.savefig('PI_curves_fitted.png', bbox_inches='tight', transparent=3)\n",
+ "\n",
+ "ax[0].set_title('Count')\n",
+ "ax[1].set_title('Area')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c05524b-cc45-4256-86f0-17166f520c18",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "01b6cf95-ae4f-4ae7-be0e-e2a9715b00bb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/README.md b/assays/Microfluidic cultivation with homogeneous growth light/README.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..e100804ec56802d04d46abcf909dfb8a46efacf3 100644
--- a/assays/Microfluidic cultivation with homogeneous growth light/README.md
+++ b/assays/Microfluidic cultivation with homogeneous growth light/README.md
@@ -0,0 +1,5 @@
+All the notebooks needed to analyse the data are uploaded into the protocols folder.
+
+File_strucure.png explains the file structure.
+
+The script Total_number_segmented_cells.ipynb has been used to produce summary statistics.
\ No newline at end of file
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/isa.assay.xlsx b/assays/Microfluidic cultivation with homogeneous growth light/isa.assay.xlsx
index 7ebbac514c9ce1aee7b31e8ab621fc79e61f12d9..39af0d2a3678e9a7c00625f4559741c1d2f2b084 100644
Binary files a/assays/Microfluidic cultivation with homogeneous growth light/isa.assay.xlsx and b/assays/Microfluidic cultivation with homogeneous growth light/isa.assay.xlsx differ
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/protocols/File_structure.png b/assays/Microfluidic cultivation with homogeneous growth light/protocols/File_structure.png
new file mode 100644
index 0000000000000000000000000000000000000000..5a91550db4986b0606cecc9262bc14d4ea264fff
Binary files /dev/null and b/assays/Microfluidic cultivation with homogeneous growth light/protocols/File_structure.png differ
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/protocols/Growth_Rate.ipynb b/assays/Microfluidic cultivation with homogeneous growth light/protocols/Growth_Rate.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f8d5934df8b1cff038d4382e80fe0e706d33f85a
--- /dev/null
+++ b/assays/Microfluidic cultivation with homogeneous growth light/protocols/Growth_Rate.ipynb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2d74c32b584a967f9ac627f021abbe501f917b11c26dd3bcc9aef0a20eefcdb
+size 1073240
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/protocols/Overall_PI_curve.ipynb b/assays/Microfluidic cultivation with homogeneous growth light/protocols/Overall_PI_curve.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..338b7d41fa6a4fbe0fdb296b003cd1c2d127d824
--- /dev/null
+++ b/assays/Microfluidic cultivation with homogeneous growth light/protocols/Overall_PI_curve.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b4f611ab-62bf-4bb3-a9a3-cee1fb5f43a0",
+ "metadata": {},
+ "source": [
+ "# Overall PI Curve\n",
+ "\n",
+ "This skript was written by Lennart Ole Witting. It is desinged to automatically summerize growth experiments for photoautotrophic organisms grown at different light intensities.\n",
+ "\n",
+ "The skript is suppost to be placed in a folder containing folders for each indivuduall experiment. Subfolders for the channels on the microfluidic chip a placed in each experiment subfolder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0da56ddd-dc1f-4184-afab-bc3734cd5606",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['2023.08.15_10uE_AmbientCO2', '2023.07.11_40uE_AmbientCO2', '2023.08.01_140uE_AmbientCO2', '2023.03.01_80uE_AmbientCO2', '2023.08.08_50uE_AmbientCO2', '2023.06.27_20uE_AmbientCO2', '2023.07.18_60uE_AmbientCO2', '2023.07.25_30uE_AmbientCO2']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Create a list with all experiments\n",
+ "\n",
+ "path = Path('./')\n",
+ "\n",
+ "experiments = []\n",
+ "\n",
+ "for sub_folder in path.glob(\"*CO2\"): # grad all folders that end with 'CO2'\n",
+ " experiments.append(sub_folder.name)\n",
+ "\n",
+ "print(experiments) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2e263fa3-09dd-4130-85d6-6edb23e94f77",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "S_elongatus_UTEX2973:\n",
+ " experiment µcount µarea std_count std_area\n",
+ "0 2023.08.15_10uE_AmbientCO2 0.021800 0.019503 0.002515 0.001579\n",
+ "1 2023.08.01_140uE_AmbientCO2 0.097658 0.086454 0.004956 0.003792\n",
+ "2 2023.03.01_80uE_AmbientCO2 0.088108 0.083899 0.005103 0.005700\n",
+ "3 2023.08.08_50uE_AmbientCO2 0.083962 0.074748 0.004330 0.003089\n",
+ "4 2023.06.27_20uE_AmbientCO2 0.045981 0.037946 0.005256 0.004308\n",
+ "5 2023.07.18_60uE_AmbientCO2 0.087836 0.074547 0.006631 0.004350\n",
+ "6 2023.07.25_30uE_AmbientCO2 0.069880 0.064130 0.005927 0.004519\n",
+ "S_elongatus_PCC7942_CscB:\n",
+ " experiment µcount µarea std_count std_area\n",
+ "0 2023.07.11_40uE_AmbientCO2 0.105556 0.094260 0.010693 0.008905\n",
+ "1 2023.08.01_140uE_AmbientCO2 0.156868 0.133332 0.007998 0.009671\n",
+ "2 2023.08.08_50uE_AmbientCO2 0.097737 0.083292 0.014908 0.011790\n",
+ "3 2023.06.27_20uE_AmbientCO2 0.042605 0.035323 0.004299 0.003111\n",
+ "4 2023.07.18_60uE_AmbientCO2 0.103217 0.086537 0.009645 0.005367\n",
+ "5 2023.07.25_30uE_AmbientCO2 0.065164 0.059754 0.010197 0.008437\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Go into each experiment and grab results.csv \n",
+ "\n",
+ "dfs_UTEX = []\n",
+ "\n",
+ "dfs_CscB = []\n",
+ "\n",
+ "for experiment in experiments: #Iterate Experiments; first UTEX then CscB\n",
+ " data_folder = Path(experiment)\n",
+ " for sub_folder in data_folder.glob(\"*UTEX2973\"):\n",
+ " try:\n",
+ " result_df = pd.read_csv(sub_folder / \"result_df.csv\", delimiter = ';')\n",
+ " l = len(result_df) \n",
+ " data = [[experiment, result_df.loc[l-3 , 'µcount'], result_df.loc[l-3, 'µarea'], result_df.loc[l-1, 'µcount'], result_df.loc[l-1, 'µarea']]]\n",
+ " sub_df = pd.DataFrame(data)\n",
+ " dfs_UTEX.append(sub_df)\n",
+ " except:\n",
+ " print('No results.csv found in {}'.format(sub_folder))\n",
+ " \n",
+ "UTEX = pd.concat(dfs_UTEX, ignore_index=True)\n",
+ "UTEX.columns = ['experiment', 'µcount', 'µarea', 'std_count', 'std_area']\n",
+ "print('S_elongatus_UTEX2973:')\n",
+ "print(UTEX)\n",
+ " \n",
+ "for experiment in experiments: #Iterate Experiments; first UTEX then CscB\n",
+ " data_folder = Path(experiment)\n",
+ " for sub_folder in data_folder.glob(\"*scB\"):\n",
+ " try:\n",
+ " result_df = pd.read_csv(sub_folder / \"result_df.csv\", delimiter = ';')\n",
+ " l = len(result_df) \n",
+ " data = [[experiment, result_df.loc[l-3 , 'µcount'], result_df.loc[l-3, 'µarea'], result_df.loc[l-1, 'µcount'], result_df.loc[l-1, 'µarea']]]\n",
+ " sub_df = pd.DataFrame(data)\n",
+ " dfs_CscB.append(sub_df)\n",
+ " except:\n",
+ " print('No results.csv found in {}'.format(sub_folder))\n",
+ " \n",
+ "CscB = pd.concat(dfs_CscB, ignore_index=True)\n",
+ "CscB.columns = ['experiment', 'µcount', 'µarea', 'std_count', 'std_area']\n",
+ "print('S_elongatus_PCC7942_CscB:')\n",
+ "print(CscB)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4888c9f5-6177-4b0c-9bef-7d6f7acf2bb1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "S_elongatus_UTEX2973:\n",
+ " experiment µcount µarea std_count std_area \\\n",
+ "0 2023.08.15_10uE_AmbientCO2 0.021800 0.019503 0.002515 0.001579 \n",
+ "1 2023.08.01_140uE_AmbientCO2 0.097658 0.086454 0.004956 0.003792 \n",
+ "2 2023.03.01_80uE_AmbientCO2 0.088108 0.083899 0.005103 0.005700 \n",
+ "3 2023.08.08_50uE_AmbientCO2 0.083962 0.074748 0.004330 0.003089 \n",
+ "4 2023.06.27_20uE_AmbientCO2 0.045981 0.037946 0.005256 0.004308 \n",
+ "5 2023.07.18_60uE_AmbientCO2 0.087836 0.074547 0.006631 0.004350 \n",
+ "6 2023.07.25_30uE_AmbientCO2 0.069880 0.064130 0.005927 0.004519 \n",
+ "\n",
+ " Intensity \n",
+ "0 10.0 \n",
+ "1 140.0 \n",
+ "2 80.0 \n",
+ "3 50.0 \n",
+ "4 20.0 \n",
+ "5 60.0 \n",
+ "6 30.0 \n",
+ "S_elongatus_PCC7942_CscB:\n",
+ " experiment µcount µarea std_count std_area \\\n",
+ "0 2023.07.11_40uE_AmbientCO2 0.105556 0.094260 0.010693 0.008905 \n",
+ "1 2023.08.01_140uE_AmbientCO2 0.156868 0.133332 0.007998 0.009671 \n",
+ "2 2023.08.08_50uE_AmbientCO2 0.097737 0.083292 0.014908 0.011790 \n",
+ "3 2023.06.27_20uE_AmbientCO2 0.042605 0.035323 0.004299 0.003111 \n",
+ "4 2023.07.18_60uE_AmbientCO2 0.103217 0.086537 0.009645 0.005367 \n",
+ "5 2023.07.25_30uE_AmbientCO2 0.065164 0.059754 0.010197 0.008437 \n",
+ "\n",
+ " Intensity \n",
+ "0 40.0 \n",
+ "1 140.0 \n",
+ "2 50.0 \n",
+ "3 20.0 \n",
+ "4 60.0 \n",
+ "5 30.0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now extract light intensity from experiment name\n",
+ "\n",
+ "intensities_UTEX = []\n",
+ "\n",
+ "intensities_CscB = []\n",
+ "\n",
+ "for experiment in UTEX['experiment']:\n",
+ " try:\n",
+ " intensity = float(experiment[11:14])\n",
+ " intensities_UTEX.append(intensity) \n",
+ " except:\n",
+ " intensity = float(experiment[11:13])\n",
+ " intensities_UTEX.append(intensity) \n",
+ "\n",
+ "for experiment in CscB['experiment']:\n",
+ " try:\n",
+ " intensity = float(experiment[11:14])\n",
+ " intensities_CscB.append(intensity) \n",
+ " except:\n",
+ " intensity = float(experiment[11:13])\n",
+ " intensities_CscB.append(intensity) \n",
+ "\n",
+ " \n",
+ "UTEX['Intensity'] = intensities_UTEX\n",
+ "CscB['Intensity'] = intensities_CscB\n",
+ "\n",
+ "print('S_elongatus_UTEX2973:')\n",
+ "print(UTEX)\n",
+ "\n",
+ "print('S_elongatus_PCC7942_CscB:')\n",
+ "print(CscB)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d87b3793-e736-4658-b291-369e2490451b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np # Fit a model for PI Curve on data. Model by Jassby and Platt\n",
+ "from scipy.optimize import curve_fit\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def tanh_function(x, umax, a):\n",
+ " \"\"\"\n",
+ " Tanh function: a * tanh(b * (x - c)) + d\n",
+ " Parameters:\n",
+ " - umax: amplitude\n",
+ " - a: initial slope\n",
+ " \"\"\"\n",
+ " return umax * np.tanh(a*x/umax)\n",
+ "\n",
+ "def fit_tanh_to_data(x_data, y_data):\n",
+ " \"\"\"\n",
+ " Fit a tanh function to the given data.\n",
+ "\n",
+ " Parameters:\n",
+ " - x_data: Input data (independent variable)\n",
+ " - y_data: Output data (dependent variable)\n",
+ "\n",
+ " Returns:\n",
+ " - popt: Optimal values for the parameters (a, b, c, d)\n",
+ " \"\"\"\n",
+ "\n",
+ " # Initial guess for the parameters (you may need to adjust these)\n",
+ " initial_guess = (0.06, 0.0001)\n",
+ "\n",
+ " # Perform the curve fitting using scipy.optimize.curve_fit\n",
+ " popt, pcov = curve_fit(tanh_function, x_data, y_data, p0=initial_guess, bounds=(0, 10))\n",
+ "\n",
+ " return popt\n",
+ "\n",
+ "x_data = np.linspace(0,150,50)\n",
+ "\n",
+ "para_UTEX_area = fit_tanh_to_data(UTEX['Intensity'], UTEX['µarea'])\n",
+ "para_UTEX_count = fit_tanh_to_data(UTEX['Intensity'], UTEX['µcount'])\n",
+ "fit_UTEX_area = tanh_function(np.linspace(min(UTEX['Intensity']), max(UTEX['Intensity']), 50), * para_UTEX_area)\n",
+ "fit_UTEX_count = tanh_function(np.linspace(min(UTEX['Intensity']), max(UTEX['Intensity']), 50), * para_UTEX_count)\n",
+ "fit_UTEX_area_extra = tanh_function(x_data, * para_UTEX_area)\n",
+ "fit_UTEX_count_extra = tanh_function(x_data, * para_UTEX_count)\n",
+ "para_CscB_area = fit_tanh_to_data(CscB['Intensity'], CscB['µarea'])\n",
+ "para_CscB_count = fit_tanh_to_data(CscB['Intensity'], CscB['µcount'])\n",
+ "fit_CscB_area = tanh_function(np.linspace(min(CscB['Intensity']), max(CscB['Intensity']), 50), * para_CscB_area)\n",
+ "fit_CscB_count = tanh_function(np.linspace(min(CscB['Intensity']), max(CscB['Intensity']), 50), * para_CscB_count)\n",
+ "fit_CscB_area_extra = tanh_function(x_data, * para_CscB_area)\n",
+ "fit_CscB_count_extra = tanh_function(x_data, * para_CscB_count)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8dbfb111-7c40-4e0a-b50a-5420d57483c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x900 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Now finally plot the results\n",
+ "\n",
+ "fig, ax = plt.subplots(2,2,facecolor='white',figsize=(9, 9), sharex = False, sharey = False)\n",
+ "\n",
+ "ax[0,0].errorbar(UTEX['Intensity'], UTEX['µcount'], yerr = UTEX['std_count'], fmt='o', capsize=3,ecolor='#000000', color='#023d6b', label='S. elongatus UTEX2973 µ count')\n",
+ "ax[0,0].plot(x_data, fit_UTEX_count_extra, color='#023d6b', linestyle = 'dotted')\n",
+ "ax[0,0].plot(np.linspace(min(UTEX['Intensity']), max(UTEX['Intensity']), 50), fit_UTEX_count, color='#023d6b')\n",
+ "ax[1,0].errorbar(UTEX['Intensity'], UTEX['µarea'], yerr = UTEX['std_area'], fmt='v', ecolor='#000000', capsize=3, color='#023d6b', label='S. elongatus UTEX2973 µ area')\n",
+ "ax[1,0].plot(x_data, fit_UTEX_area_extra, color='#023d6b', linestyle = 'dotted')\n",
+ "ax[1,0].plot(np.linspace(min(UTEX['Intensity']), max(UTEX['Intensity']), 50), fit_UTEX_area, color='#023d6b')\n",
+ "\n",
+ "ax[0,1].errorbar(CscB['Intensity'], CscB['µcount'], yerr = CscB['std_count'], fmt='o', ecolor='#000000', capsize=3, color='#fab45a', label='S. elongatus PCC7941 CscB µ count')\n",
+ "ax[0,1].plot(x_data, fit_CscB_count_extra, color='#fab45a', linestyle = 'dotted')\n",
+ "ax[0,1].plot(np.linspace(min(CscB['Intensity']), max(CscB['Intensity']), 50), fit_CscB_count, color='#fab45a')\n",
+ "ax[1,1].errorbar(CscB['Intensity'], CscB['µarea'], yerr = CscB['std_area'], fmt='v', ecolor='#000000', capsize=3, color='#fab45a', label='S. elongatus PCC7941 CscB µ area')\n",
+ "ax[1,1].plot(x_data, fit_CscB_area_extra, color='#fab45a', linestyle = 'dotted')\n",
+ "ax[1,1].plot(np.linspace(min(CscB['Intensity']), max(CscB['Intensity']), 50), fit_CscB_area, color='#fab45a')\n",
+ "\n",
+ "\n",
+ "ax[0,0].set_ylim(0, 0.17)\n",
+ "ax[0,1].set_ylim(0, 0.17)\n",
+ "ax[1,0].set_ylim(0, 0.17)\n",
+ "ax[1,1].set_ylim(0, 0.17)\n",
+ "\n",
+ "ax[0,0].set_xlim(0, 150)\n",
+ "ax[0,1].set_xlim(0, 150)\n",
+ "ax[1,0].set_xlim(0, 150)\n",
+ "ax[1,1].set_xlim(0, 150)\n",
+ "\n",
+ "\n",
+ "ax[1,1].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[1,0].set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')\n",
+ "ax[0,0].set_ylabel('Growth rate [h$^{-1}$] count based')\n",
+ "ax[1,0].set_ylabel('Growth rate [h$^{-1}$] area based')\n",
+ "\n",
+ "ax[0,0].set_title('S. elongatus UTEX2973')\n",
+ "ax[0,1].set_title('S. elongatus PCC7942 CscB')\n",
+ "\n",
+ "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.05), ncol=2)\n",
+ "\n",
+ "plt.savefig('PI_curve.png', bbox_inches='tight', transparent=1)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "05158697-f0b6-4345-828d-4f45b5bb7c20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# export results to .csv for analysis in OriginPro2020\n",
+ "\n",
+ "UTEX.to_csv(str('PI_curve_UTEX.csv'), sep=';')\n",
+ "CscB.to_csv(str('PI_curve_CscB.csv'), sep=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f9b6968-47e2-4348-9272-a71d12fa63f4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/protocols/ScalingAnalysis_SequenceNames.ipynb b/assays/Microfluidic cultivation with homogeneous growth light/protocols/ScalingAnalysis_SequenceNames.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..35ab3633b3964d48bb3142fa556b1a674212b467
--- /dev/null
+++ b/assays/Microfluidic cultivation with homogeneous growth light/protocols/ScalingAnalysis_SequenceNames.ipynb
@@ -0,0 +1,442 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Scaling Analysis\n",
+ "\n",
+ "You have developed your analysis notebook that works perfectly for a single cultivation chamber 💪? And now you you want to apply it for all cultivation chambers in our experiment but it is lots of work to apply the scripts one by one 🤔? That's why this example shows how you can quickly apply your single analysis script to a large amount of image sequences organized in the OMERO `project` or `dataset` structures 🚀! Therefore, your custom developed analyses can scale to large image volumes without you touching or changing the code!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Setup\n",
+ "\n",
+ "Define the `omero_id` and `omero_type` of the image data you would like to process. The `omerod_id` is the number you can find in the top right corner when selecting a OMERO `project`, `dataset` or `image` in the `OMERO Web` application. The `omero_type` must be `project` or `dataset` when the OMERO id points to a project or dataset and `image` if it is just a single image! Please note that if you define the wrong `omero_type` you will get an error lateron 🤯!\n",
+ "\n",
+ "Also provide your credentials for the OMERO server!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "# OMERO resource that you want to analyze\n",
+ "omero_type = \"dataset\" # can be \"image\", \"project\" or \"dataset\"\n",
+ "omero_id = 2678 # change the id if you want to apply the analysis to a different omero resource\n",
+ "\n",
+ "# your omero credentials\n",
+ "username = \"lwitting\"\n",
+ "password = \"lwitting\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# prepare credentials (usually you do not have to change this!)\n",
+ "\n",
+ "import logging\n",
+ "\n",
+ "if not \"OMERO_SERVER\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_SERVER' defined. Fallback to default OMERO_SERVER address 'omero'! This can lead to connection faults!\")\n",
+ "if not \"OMERO_WEB\" in os.environ:\n",
+ " logging.warning(\"No 'OMERO_WEB' defined. Links to view OMERO data in web viewer might not work!\")\n",
+ "\n",
+ "credentials = dict(\n",
+ " serverUrl= os.environ.get('OMERO_SERVER', 'omero'),\n",
+ " username= username,\n",
+ " password = password,\n",
+ " port = int(os.environ.get('OMERO_PORT', '4064'))\n",
+ ")\n",
+ "\n",
+ "omero_cred = dict(\n",
+ " host = credentials['serverUrl'],\n",
+ " username = credentials['username'],\n",
+ " passwd = credentials['password'],\n",
+ " port = credentials['port'],\n",
+ " secure = True\n",
+ ")\n",
+ "\n",
+ "omero_web = os.environ.get(\"OMERO_WEB\", \"<Your OMERO_WEB address should be here>\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.2 Specify the analysis script\n",
+ "\n",
+ "Now you have to specify the name of the analysis script you want to apply to the image data. At best copy the script to the same location as this script! Then you only have to specify the name of the script!\n",
+ "\n",
+ "**Note:** If the analysis script is not located in the same folder you need to specify the path to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "analysis_script = \"Growth_Rate.ipynb\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Information about the underlying data\n",
+ "\n",
+ "We summarize the amount of underlying data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[25038, 25039, 25040, 25041, 25042, 25043, 25044, 25045, 25046, 25047, 25049, 25050, 25051, 25052, 25053, 25054, 25055, 25056, 25057, 25058, 25059, 25060, 25061, 25062, 25063, 25064, 25065, 25066, 25067]\n",
+ "{25038: PosixPath('31_C3_CscB_cropped.tif'), 25039: PosixPath('32_C3_CscB_cropped.tif'), 25040: PosixPath('33_C3_CscB_cropped.tif'), 25041: PosixPath('34_C3_CscB_cropped.tif'), 25042: PosixPath('35_C3_CscB_cropped.tif'), 25043: PosixPath('36_C3_CscB_cropped.tif'), 25044: PosixPath('37_C3_CscB_cropped.tif'), 25045: PosixPath('38_C3_CscB_cropped.tif'), 25046: PosixPath('39_C3_CscB_cropped.tif'), 25047: PosixPath('40_C3_CscB_cropped.tif'), 25049: PosixPath('42_C3_CscB_cropped.tif'), 25050: PosixPath('43_C3_CscB_cropped.tif'), 25051: PosixPath('44_C3_CscB_cropped.tif'), 25052: PosixPath('45_C3_CscB_cropped.tif'), 25053: PosixPath('46_C4_CscB_cropped.tif'), 25054: PosixPath('47_C4_CscB_cropped.tif'), 25055: PosixPath('48_C4_CscB_cropped.tif'), 25056: PosixPath('49_C4_CscB_cropped.tif'), 25057: PosixPath('50_C4_CscB_cropped.tif'), 25058: PosixPath('51_C4_CscB_cropped.tif'), 25059: PosixPath('52_C4_CscB_cropped.tif'), 25060: PosixPath('53_C4_CscB_cropped.tif'), 25061: PosixPath('54_C4_CscB_cropped.tif'), 25062: PosixPath('55_C4_CscB_cropped.tif'), 25063: PosixPath('56_C4_CscB_cropped.tif'), 25064: PosixPath('57_C4_CscB_cropped.tif'), 25065: PosixPath('58_C4_CscB_cropped.tif'), 25066: PosixPath('59_C4_CscB_cropped.tif'), 25067: PosixPath('60_C4_CscB_cropped.tif')}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from acia.segm.omero.utils import list_image_ids_in, getImage\n",
+ "from omero.gateway import BlitzGateway\n",
+ "from pathlib import Path\n",
+ "\n",
+ "image_names = {}\n",
+ "\n",
+ "with BlitzGateway(**omero_cred) as conn:\n",
+ " image_ids = list_image_ids_in(omero_id, omero_type, conn)\n",
+ " \n",
+ " # get all the image names\n",
+ " for image_id in image_ids:\n",
+ " image_names[image_id] = Path(getImage(conn, image_id).getName())\n",
+ "\n",
+ "## TODO: give an overview about the data\n",
+ "print(image_ids)\n",
+ "print(image_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. Scale the analysis script to all image sequences\n",
+ "\n",
+ "Now we apply the analysis script to every image sequence individually 🚀! You can lean back and enjoy the working computer 😎 🥂\n",
+ "\n",
+ "**Note:** For heavy analysis scripts or for larget `datasets` or `projects` this process may take a while (from minutes to hours or days). The top-level progress bar will indicate the total progress and give you an indication how long this will take. For large image data volumes we can recommend execution over night 🌔!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results are stored in: /home/jovyan/work/A2.2_PI_Curve_µFluidic_newSegAI/2023.07.25_30uE_AmbientCO2/S. elongatus PCC7942 CscB/automated_executions\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "238a4381081240aa9332a64407b756f1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/29 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "73c6e0c537df450c847eeec2599d8776",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Executing: 0%| | 0/27 [00:00<?, ?cell/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "from pathlib import Path\n",
+ "from acia.analysis import scale\n",
+ "\n",
+ "# set the base path for all results\n",
+ "stem = Path(analysis_script).stem\n",
+ "output_path = Path(\"./automated_executions\") \n",
+ "\n",
+ "print(f\"Results are stored in: {output_path.absolute()}\")\n",
+ "\n",
+ "# scale your analysis script to many images\n",
+ "result = scale(output_path, analysis_script=analysis_script, image_ids=image_ids, additional_parameters=dict(username=username, password=password), exist_ok=True, execution_naming=lambda image_id: image_names[image_id])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Inspect your analysis results\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib.parse\n",
+ "from IPython.display import Video, Markdown, display\n",
+ "\n",
+ "base_url = os.environ.get(\"JUPYTERHUB_SERVICE_PREFIX\", None)\n",
+ "\n",
+ "if base_url is None:\n",
+ " url = f\"file://{output_path.absolute()}\"\n",
+ "else:\n",
+ " url = f\"{base_url}lab/tree/{urllib.parse.quote(str(output_path))}\"\n",
+ "\n",
+ "output = f\"\"\"# Inspect your analyses\n",
+ "You can find all the individual analysis scripts here: <a href=\"{url}\">{url}</a>\"\"\"\n",
+ "\n",
+ "display(Markdown(output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Generate Summary Statistics\n",
+ "\n",
+ "In this section you can generate your custom summary statistics that combine the results of all experiment analyses. Just design the analysis script that you scaled above such that it outputs the results into a local files. Here, these results can be loaded, merged together and further processed or visualized!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No results.csv found in automated_executions/.ipynb_checkpoints\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get results.csv from each individual chamber\n",
+ "\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "data_folder = Path(\"./automated_executions\") \n",
+ "dfs = []\n",
+ "for sub_folder in data_folder.glob(\"*\"): # hole dir alle Ordner, die mit UTEX enden\n",
+ " try:\n",
+ " data_file = sub_folder / \"tmp\" / \"results.csv\"\n",
+ " sub_df = pd.read_csv(data_file, delimiter = ';')\n",
+ " sub_df[\"experiment\"] = sub_folder.name\n",
+ " dfs.append(sub_df)\n",
+ " except:\n",
+ " print('No results.csv found in {}'.format(sub_folder))\n",
+ "\n",
+ "joint_df = pd.concat(dfs, ignore_index=True)\n",
+ "\n",
+ "# Group dataframe by category (code by chat gpt) \n",
+ "grouped_df = joint_df.groupby('Unnamed: 0')\n",
+ "\n",
+ "count_df = grouped_df.get_group('µ_count [1/h]')\n",
+ "\n",
+ "area_df = grouped_df.get_group('µ_area [1/h]')\n",
+ "\n",
+ "# print(joint_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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x8fHWOceMGaPDhw/b7HfkyBHdfvvt1+U44BxX/5sJCwuTj4+PXXOVl5crLS1NmzZtUnh4uN299e3bV6GhoXbPAwC4PlwuFElSRkaG5syZo9GjRys2NlZr1qxRQ0OD0tPTJUmzZ8/WkCFDlJWVJUlatGiREhMTlZ2drWnTpmnz5s06cOCANmzYYJ1zyZIlSklJ0YQJEzRp0iTl5+frf/7nf7Rnzx5nHCKuE39/f82bN8+hc4aHh3PFGADcAFwyFKWkpOj06dNasWKFTCaToqOjlZ+fb11MXVVVJTe3f96jIiEhQbm5uVq+fLmWLVum0NBQ5eXlWe9RJEnf//73lZOTo6ysLD3++OO688479cYbb2j8+PFdfnwAAMD1uGQokqQFCxa0+XVZa2d3ZsyYoRkzZlxzzh/+8If64Q9/6Ij2AABAD+M6twQFAABwIkIRAACACEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSpF7ObgAAgPa4cOGCJKmkpMTuuS5evKjKykoFBwfL29vbrrnKy8vt7geuoVOh6K233urwPt/5znfs/g8PAHDjqqiokCTNnz/fyZ20rm/fvs5uAXbqVChKTk7uUL3BYNDRo0c1dOjQzrwcAADWz56wsDD5+PjYNVd5ebnS0tK0adMmhYeH291b3759FRoaavc8cK5Of31mMpk0cODAdtWSngEA9vL399e8efMcOmd4eLhGjRrl0DnRfXVqofWcOXM69FVYWlqafH19O/NSAAAAXaJTZ4pefvnlDtW/+OKLnXkZAACALuOwS/L37dvnqKkAAAC6nMNC0YwZMxw1FQAAQJfr0NdnDz30UKvjFotFZ86ccUhDAAAAztChULRr1y699tpruummm2zGLRaL3n//fYc2BgAA0JU6FIomTpyovn37asKECS22jRw50mFNAQAAdLUOhaJt27a1uW3nzp12NwMAAOAs/CAsAACA7AxFJpPJUX0AAAA4lV2haMqUKY7qAwAAwKnsCkUWi8VRfQAAADiVXaHIYDA4qg8AAACnYqE1AACACEUAAACS7AxF7u7ujuoDAADAqewKRaWlpY7qo4X169crODhYXl5eiouL0/79+69Zv3XrVoWFhcnLy0uRkZHasWOHzfYf/OAHMhgMNo+pU6det/4BAED34pJfn23ZskUZGRlauXKlSkpKFBUVJaPRqJqamlbrCwsLlZqaqrlz56q0tFTJyclKTk5WWVmZTd3UqVN16tQp6+OPf/xjVxwOAADoBjr0Mx9tKSgoUEFBgWpqamQ2m222bdy4scPzrV69WvPnz1d6erokKScnR2+//bY2btyopUuXtqhfu3atpk6dqiVLlkiSnnnmGe3cuVPr1q1TTk6Otc7T01OBgYEd7gcAAPR8dp8pWrVqlaZMmaKCggLV1tbqH//4h82jo5qamlRcXKykpKR/NunmpqSkJBUVFbW6T1FRkU29JBmNxhb1e/bs0cCBA3XnnXfq0Ucf1ZdffnnNXhobG1VfX2/zAAAAPZPdZ4pycnL0yiuvaNasWY7oR7W1tWpublZAQIDNeEBAgCoqKlrdx2QytVr/rz9DMnXqVE2fPl0hISE6fvy4li1bpu9+97sqKipqc8F4VlaWVq1aZecRAQCA7sDuUNTU1KSEhARH9HJdzZw50/rPkZGRGjlypO644w7t2bNHkydPbnWfzMxMZWRkWJ/X19crKCjouvcKAAC6nt1fn82bN0+5ubmO6EWS5O/vL3d3d1VXV9uMV1dXt7keKDAwsEP1kjR06FD5+/vr2LFjbdZ4enrK19fX5gEAAHqmTp0p+tezJ2azWRs2bNCuXbs0cuRI9e7d26Z29erVHZrbw8NDMTExKigoUHJysvU1CgoKtGDBglb3iY+PV0FBgRYvXmwd27lzp+Lj49t8nb///e/68ssvNWjQoA71BwAAeqZOhaKv358oOjpaklpcAt/Z30bLyMjQnDlzNHr0aMXGxmrNmjVqaGiwXo02e/ZsDRkyRFlZWZKkRYsWKTExUdnZ2Zo2bZo2b96sAwcOaMOGDZKk8+fPa9WqVXrggQcUGBio48eP66c//amGDRsmo9HYqR4BAEDP0qlQtHv3bkf3YSMlJUWnT5/WihUrZDKZFB0drfz8fOti6qqqKrm5/fObv4SEBOXm5mr58uVatmyZQkNDlZeXp4iICElf3Xn70KFDevXVV3X27FkNHjxYU6ZM0TPPPCNPT8/reiwAAKB7MFgsFktHdzp06JAiIiJsgsm1fPrpp7rzzjvVq5dDbovkNPX19fLz81NdXR3ri24AJSUliomJUXFxsUaNGuXsdgA4EO/vG0t7P787tdD6rrvu+sZ7/Pyr+Ph4VVVVdealAAAAukSnTt1YLBY99dRT8vHxaVd9U1NTZ14GAACgy3QqFE2YMEGHDx9ud318fLy8vb0781IAAABdolOhaM+ePQ5uAwAAwLnsvnkjAABAT0AoAgAAEKEIAABAEqEIAABAEqEIAABAkoNC0d69e5WWlqb4+HidPHlSkvTaa6/pgw8+cMT0AAAA153doeiNN96Q0WiUt7e3SktL1djYKEmqq6vTr371K7sbBAAA6Ap2h6Jf/OIXysnJ0R/+8Af17t3bOj5u3DiVlJTYOz0AAECXsDsUHT58WBMmTGgx7ufnp7Nnz9o7PQAAQJewOxQFBgbq2LFjLcY/+OADDR061N7pAQAAuoTdoWj+/PlatGiR9u3bJ4PBoC+++EKvv/66nnzyST366KOO6BEAAOC669Rvn/2rpUuXymw2a/Lkybpw4YImTJggT09PPfnkk1q4cKEjegQAALju7A5FJ06cUGZmppYsWaJjx47p/PnzGjFihPr06aOqqirddtttjugTAADgurI7FIWEhOjUqVMaOHCgRowYYR3/8ssvFRISoubmZntfAgAA4Lqze02RxWJpdfz8+fPy8vKyd3oAAIAu0ekzRRkZGZIkg8GgFStWyMfHx7qtublZ+/btU3R0tN0NAgAAdIVOh6LS0lJJX50p+uSTT+Th4WHd5uHhoaioKD355JP2dwgAQAdcuHBBFRUV16wpLy+3+fNawsLCbP7ij56r06Fo9+7dkqT09HStXbtWvr6+DmsKAIDOqqioUExMTLtq09LSvrGmuLhYo0aNsrctdAN2L7R++eWXHdEHAAAOERYWpuLi4mvWXLx4UZWVlQoODpa3t/c3zocbg92h6KrPPvtMVVVVampqshm/7777HPUSAAB8Ix8fn3ad2Rk3blwXdIPuxO5Q9Le//U3f//739cknn8hgMFivRjMYDJLEJfkAAKBbsPuS/EWLFikkJEQ1NTXy8fHRp59+qvfff1+jR4/Wnj17HNAiAADA9Wf3maKioiK9++678vf3l5ubm9zc3DR+/HhlZWXp8ccft16lBgCAK2hubtbevXt16tQpDRo0SHfffbfc3d2d3RZcgN1nipqbm9W3b19Jkr+/v7744gtJ0u23367Dhw/bOz0AAA6zbds2DRs2TJMmTdK//du/adKkSRo2bJi2bdvm7NbgAuwORREREfr4448lSXFxcfr1r3+tDz/8UD//+c81dOhQuxsEAMARtm3bpgcffFCRkZEqKirSuXPnVFRUpMjISD344IMEI8hgaet3OtrpnXfeUUNDg6ZPn65jx47pnnvu0ZEjRzRgwABt2bJF3/72tx3Vq9PV19fLz89PdXV13JfpBlBSUqKYmBjuUQL0AM3NzRo2bJgiIyOVl5cnN7d/nhMwm81KTk5WWVmZjh49yldpPVB7P7/tXlNkNBqt/zxs2DBVVFTozJkz6t+/v/UKNMDVcMdb4Mayd+9eVVZW6o9//KNNIJIkNzc3ZWZmKiEhQXv37tXEiROd0ySczq5QdPnyZU2dOlU5OTkKDQ21jt988812NwZcT9zxFrixnDp1StJXSz5ac3X8ah1uTHaFot69e+vQoUOO6gXoMtzxFrixDBo0SJJUVlamsWPHttheVlZmU4cblMVOixcvtvzsZz+zd5oW1q1bZ7n99tstnp6eltjYWMu+ffuuWf+nP/3Jcuedd1o8PT0tERERlrfffrvN2h/96EcWSZYXXnihQz3V1dVZJFnq6uo6tB8AwLmuXLliCQ4Ottx7772W5uZmm23Nzc2We++91xISEmK5cuWKkzrE9dTez2+71xRduXJFGzdu1K5duxQTE6M+ffrYbF+9enWH59yyZYsyMjKUk5OjuLg4rVmzRkajUYcPH9bAgQNb1BcWFio1NVVZWVm65557lJubq+TkZJWUlLQ4Vfrmm2/qr3/9qwYPHtzhvgAA3ZO7u7uys7P14IMPKjk5WZmZmYqIiFBZWZmysrK0fft2/fd//zeLrG9wdl99NmnSpLYnNxj07rvvdnjOuLg4jRkzRuvWrZP01ZUBQUFBWrhwoZYuXdqiPiUlRQ0NDdq+fbt1bOzYsYqOjlZOTo517OTJk4qLi9M777yjadOmafHixVq8eHG7++LqMwDo3rZt26YnnnhClZWV1rGQkBD95je/0fTp053XGK6rLrv6bPfu3fZOYaOpqUnFxcXKzMy0jrm5uSkpKUlFRUWt7lNUVKSMjAybMaPRqLy8POtzs9msWbNmacmSJfrWt77Vrl4aGxvV2NhofV5fX9+BIwEAuJrp06fr/vvv547WaJXdocjRamtr1dzcrICAAJvxgICANi+hNplMrdabTCbr8+eee069evXS448/3u5esrKytGrVqg50DwBwde7u7lx2j1bZfUfr7qC4uFhr167VK6+80qF7J2VmZqqurs76OHHixHXsEgAAOJPLhSJ/f3+5u7ururraZry6ulqBgYGt7hMYGHjN+r1796qmpka33XabevXqpV69eunzzz/XE088oeDg4DZ78fT0lK+vr80DAAD0TC4Xijw8PBQTE6OCggLrmNlsVkFBgeLj41vdJz4+3qZeknbu3GmtnzVrlg4dOqSDBw9aH4MHD9aSJUv0zjvvXL+DAQAA3YbLrSmSpIyMDM2ZM0ejR49WbGys1qxZo4aGBqWnp0uSZs+erSFDhigrK0uStGjRIiUmJio7O1vTpk3T5s2bdeDAAW3YsEGSNGDAAA0YMMDmNXr37q3AwEDdeeedXXtwAADAJTkkFBUUFKigoEA1NTUym8022zZu3Njh+VJSUnT69GmtWLFCJpNJ0dHRys/Pty6mrqqqsvntmoSEBOXm5mr58uVatmyZQkNDlZeX1+bt3AEAAL7O7vsUrVq1Sj//+c81evRoDRo0qMVC5jfffNOuBl0J9ykCAKD76bL7FOXk5OiVV17RrFmz7J0KAADAaexeaN3U1KSEhARH9AIAAOA0doeiefPmKTc31xG9AAAAOE2nvj7715/UMJvN2rBhg3bt2qWRI0eqd+/eNrWd+UFYAACArtapUFRaWmrzPDo6WpJUVlZmM96Ru0cDAAA4U6dC0b/+CGxVVZVuvfVWm0vkJclisfCzGAAAoNuwe01RSEiIamtrW4yfOXNGISEh9k4PAADQJewORW3d5uj8+fPy8vKyd3oAAIAu0en7FF1dbG0wGLRixQr5+PhYtzU3N2vfvn3WtUYAAACurtOh6Opia4vFok8++UQeHh7WbR4eHoqKitKTTz5pf4cAAABdoNOh6Opi6/T0dK1du5afvQAAAN2a3T/z8fLLLzuiDwAAAKeye6H17NmztXHjRh0/ftwR/QAAADiF3aHIw8NDzz77rEJDQxUUFKS0tDT953/+p44ePeqI/gAAALqEwdLWNfUddPLkSb3//vt677339N577+nIkSMaNGiQ/v73vztiepdQX18vPz8/1dXVsYYKAIBuor2f33afKbqqf//+GjBggPr3769+/fqpV69euuWWWxw1PQAAwHVldyhatmyZEhISNGDAAC1dulSXLl3S0qVLZTKZWvxGGgAAgKuy++szNzc33XLLLfrJT36i6dOna/jw4Y7qzeXw9RkAAN1Pez+/7b4kv7S0VO+995727Nmj7OxseXh4KDExURMnTtTEiRN7dEgCAAA9h8MWWl/18ccf64UXXtDrr78us9ms5uZmR07vVJwpAgCg++myM0UWi0WlpaXas2eP9uzZow8++ED19fUaOXKkEhMT7Z0eAACgS9gdim6++WadP39eUVFRSkxM1Pz583X33XerX79+DmgPAACga9gdijZt2qS7776br5MAAEC3ZncomjZtms6ePavs7GyVl5dLkkaMGKG5c+fKz8/P7gYBAAC6gt33KTpw4IDuuOMOvfDCCzpz5ozOnDmjF154QXfccYdKSkoc0SMAAMB1Z/fVZ3fffbeGDRumP/zhD+rV66sTT1euXNG8efP0t7/9Te+//75DGnUFXH0GAED3097Pb7tDkbe3t0pLSxUWFmYz/tlnn2n06NG6cOGCPdO7FEIRAADdT5f99pmvr6+qqqpajJ84cUJ9+/a1d3oAAIAuYXcoSklJ0dy5c7VlyxadOHFCJ06c0ObNmzVv3jylpqY6okcAAIDrzu6rz37zm9/IYDBo9uzZunLliiSpd+/eevTRR/Xss8/a3SAAAEBXcNjPfFy4cEHHjx+XJN1xxx3y8fFxxLQuhTVFAAB0P12ypujy5cuaPHmyjh49Kh8fH0VGRioyMrJHBiIAANCz2RWKevfurUOHDjmqFwAAAKexe6F1WlqaXnrpJUf0YmP9+vUKDg6Wl5eX4uLitH///mvWb926VWFhYfLy8lJkZKR27Nhhs/3pp59WWFiY+vTpo/79+yspKUn79u1zeN8AAKB7snuh9ZUrV7Rx40bt2rVLMTEx6tOnj8321atXd3jOLVu2KCMjQzk5OYqLi9OaNWtkNBp1+PBhDRw4sEV9YWGhUlNTlZWVpXvuuUe5ublKTk5WSUmJIiIiJEnDhw/XunXrNHToUF28eFEvvPCCpkyZomPHjumWW27p3MEDAIAew+6F1pMmTWp7coNB7777bofnjIuL05gxY7Ru3TpJktlsVlBQkBYuXKilS5e2qE9JSVFDQ4O2b99uHRs7dqyio6OVk5PT6mtcXXS1a9cuTZ48uV19sdAaAIDup72f33afKdq9e7e9U9hoampScXGxMjMzrWNubm5KSkpSUVFRq/sUFRUpIyPDZsxoNCovL6/N19iwYYP8/PwUFRXVZi+NjY1qbGy0Pq+vr+/AkQAAgO6k06Ho4sWLKigo0D333CNJyszMtAkQvXr10s9//nN5eXl1aN7a2lo1NzcrICDAZjwgIEAVFRWt7mMymVqtN5lMNmPbt2/XzJkzdeHCBQ0aNEg7d+6Uv79/m71kZWVp1apVHeofAAB0T51eaP3qq6/q97//vfX5unXrVFhYqNLSUpWWluq1117Tiy++6JAmHWXSpEk6ePCgCgsLNXXqVD300EOqqalpsz4zM1N1dXXWx4kTJ7qwWwAA0JU6HYpef/11PfLIIzZjubm52r17t3bv3q3nn39ef/rTnzo8r7+/v9zd3VVdXW0zXl1drcDAwFb3CQwMbFd9nz59NGzYMI0dO1YvvfSSevXqdc0r5zw9PeXr62vzAAAAPVOnQ9GxY8cUGRlpfe7l5SU3t39OFxsbq88++6zD83p4eCgmJkYFBQXWMbPZrIKCAsXHx7e6T3x8vE29JO3cubPN+n+d91+/8gMAADeuTq8pOnv2rE2gOH36tM12ewJHRkaG5syZo9GjRys2NlZr1qxRQ0OD0tPTJUmzZ8/WkCFDlJWVJUlatGiREhMTlZ2drWnTpmnz5s06cOCANmzYIElqaGjQL3/5S913330aNGiQamtrtX79ep08eVIzZszoVI8AAKBn6XQouvXWW1VWVqY777yz1e2HDh3Srbfe2qm5U1JSdPr0aa1YsUImk0nR0dHKz8+3LqauqqqyOSuVkJCg3NxcLV++XMuWLVNoaKjy8vKs9yhyd3dXRUWFXn31VdXW1mrAgAEaM2aM9u7dq29961ud6hEAAPQsnb5P0aJFi7Rr1y4VFxe3uMLs4sWLGj16tJKSkrR27VqHNOoKuE8RAADdT3s/vzsdiqqrqxUdHS0PDw8tWLBAw4cPlyQdPnxY69at05UrV1RaWtriUvnujFAEAED3c91v3hgQEKDCwkI9+uijWrp0qa5mK4PBoO985zv6j//4jx4ViAAAQM9m1x2tQ0JClJ+frzNnzujYsWOSpGHDhunmm292SHMAAABdxe6f+ZCkm2++WbGxsY6YCgAAwCk6fZ8iAACAnoRQBAAAIEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJBcORevXr1dwcLC8vLwUFxen/fv3X7N+69atCgsLk5eXlyIjI7Vjxw7rtsuXL+tnP/uZIiMj1adPHw0ePFizZ8/WF198cb0PAwAAdBMuGYq2bNmijIwMrVy5UiUlJYqKipLRaFRNTU2r9YWFhUpNTdXcuXNVWlqq5ORkJScnq6ysTJJ04cIFlZSU6KmnnlJJSYm2bdumw4cP67777uvKwwIAAC7MYLFYLM5u4uvi4uI0ZswYrVu3TpJkNpsVFBSkhQsXaunSpS3qU1JS1NDQoO3bt1vHxo4dq+joaOXk5LT6Gh999JFiY2P1+eef67bbbmtXX/X19fLz81NdXZ18fX07cWQAAKCrtffz2+XOFDU1Nam4uFhJSUnWMTc3NyUlJamoqKjVfYqKimzqJcloNLZZL0l1dXUyGAzq169fmzWNjY2qr6+3eQAAgJ7J5UJRbW2tmpubFRAQYDMeEBAgk8nU6j4mk6lD9ZcuXdLPfvYzpaamXjMxZmVlyc/Pz/oICgrq4NEAAIDuwuVC0fV2+fJlPfTQQ7JYLHrxxRevWZuZmam6ujrr48SJE13UJQAA6Gq9nN3A1/n7+8vd3V3V1dU249XV1QoMDGx1n8DAwHbVXw1En3/+ud59991vXBfk6ekpT0/PThwFAADoblzuTJGHh4diYmJUUFBgHTObzSooKFB8fHyr+8THx9vUS9LOnTtt6q8GoqNHj2rXrl0aMGDA9TkAAADQLbncmSJJysjI0Jw5czR69GjFxsZqzZo1amhoUHp6uiRp9uzZGjJkiLKysiRJixYtUmJiorKzszVt2jRt3rxZBw4c0IYNGyR9FYgefPBBlZSUaPv27WpubrauN7r55pvl4eHhnAMFAAAuwyVDUUpKik6fPq0VK1bIZDIpOjpa+fn51sXUVVVVcnP750muhIQE5ebmavny5Vq2bJlCQ0OVl5eniIgISdLJkyf11ltvSZKio6NtXmv37t2aOHFilxwXAABwXS55nyJXxX2KAADofrrtfYoAAACcgVAEAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkFw5F69evV3BwsLy8vBQXF6f9+/dfs37r1q0KCwuTl5eXIiMjtWPHDpvt27Zt05QpUzRgwAAZDAYdPHjwOnYPAAC6G5cMRVu2bFFGRoZWrlypkpISRUVFyWg0qqamptX6wsJCpaamau7cuSotLVVycrKSk5NVVlZmrWloaND48eP13HPPddVhAACAbsRgsVgszm7i6+Li4jRmzBitW7dOkmQ2mxUUFKSFCxdq6dKlLepTUlLU0NCg7du3W8fGjh2r6Oho5eTk2NRWVlYqJCREpaWlio6O7lBf9fX18vPzU11dnXx9fTt+YAAAoMu19/Pb5c4UNTU1qbi4WElJSdYxNzc3JSUlqaioqNV9ioqKbOolyWg0tlnfXo2Njaqvr7d5AACAnsnlQlFtba2am5sVEBBgMx4QECCTydTqPiaTqUP17ZWVlSU/Pz/rIygoyK75AACA63K5UORKMjMzVVdXZ32cOHHC2S0BAIDrpJezG/g6f39/ubu7q7q62ma8urpagYGBre4TGBjYofr28vT0lKenp11zAACA7sHlzhR5eHgoJiZGBQUF1jGz2ayCggLFx8e3uk98fLxNvSTt3LmzzXoAAICvc7kzRZKUkZGhOXPmaPTo0YqNjdWaNWvU0NCg9PR0SdLs2bM1ZMgQZWVlSZIWLVqkxMREZWdna9q0adq8ebMOHDigDRs2WOc8c+aMqqqq9MUXX0iSDh8+LOmrs0z2nlECAADdn0uGopSUFJ0+fVorVqyQyWRSdHS08vPzrYupq6qq5Ob2z5NcCQkJys3N1fLly7Vs2TKFhoYqLy9PERER1pq33nrLGqokaebMmZKklStX6umnn+6aAwMAAC7LJe9T5Kq4TxEAAN1Pt71PEQAAgDMQigAAAEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkOTCoWj9+vUKDg6Wl5eX4uLitH///mvWb926VWFhYfLy8lJkZKR27Nhhs91isWjFihUaNGiQvL29lZSUpKNHj17PQwAAAN2IS4aiLVu2KCMjQytXrlRJSYmioqJkNBpVU1PTan1hYaFSU1M1d+5clZaWKjk5WcnJySorK7PW/PrXv9Zvf/tb5eTkaN++ferTp4+MRqMuXbrUVYcFAABcmMFisVic3cTXxcXFacyYMVq3bp0kyWw2KygoSAsXLtTSpUtb1KekpKihoUHbt2+3jo0dO1bR0dHKycmRxWLR4MGD9cQTT+jJJ5+UJNXV1SkgIECvvPKKZs6c2a6+6uvr5efnp7q6Ovn6+jrgSAEAwPXW3s/vXl3YU7s0NTWpuLhYmZmZ1jE3NzclJSWpqKio1X2KioqUkZFhM2Y0GpWXlydJ+r//+z+ZTCYlJSVZt/v5+SkuLk5FRUVthqLGxkY1NjZan9fV1Un66l8uAADoHq5+bn/TeSCXC0W1tbVqbm5WQECAzXhAQIAqKipa3cdkMrVabzKZrNuvjrVV05qsrCytWrWqxXhQUNA3HwgAAHAp586dk5+fX5vbXS4UuZLMzEybM1Bms1lnzpzRgAEDZDAYnNgZukJ9fb2CgoJ04sQJvi4Fehje3zcWi8Wic+fOafDgwdesc7lQ5O/vL3d3d1VXV9uMV1dXKzAwsNV9AgMDr1l/9c/q6moNGjTIpiY6OrrNXjw9PeXp6Wkz1q9fv/YeCnoIX19f/qcJ9FC8v28c1zpDdJXLXX3m4eGhmJgYFRQUWMfMZrMKCgoUHx/f6j7x8fE29ZK0c+dOa31ISIgCAwNtaurr67Vv37425wQAADcWlztTJEkZGRmaM2eORo8erdjYWK1Zs0YNDQ1KT0+XJM2ePVtDhgxRVlaWJGnRokVKTExUdna2pk2bps2bN+vAgQPasGGDJMlgMGjx4sX6xS9+odDQUIWEhOipp57S4MGDlZyc7KzDBAAALsQlQ1FKSopOnz6tFStWyGQyKTo6Wvn5+daF0lVVVXJz++dJroSEBOXm5mr58uVatmyZQkNDlZeXp4iICGvNT3/6UzU0NOiRRx7R2bNnNX78eOXn58vLy6vLjw/dg6enp1auXNniK1QA3R/vb7TGJe9TBAAA0NVcbk0RAACAMxCKAAAARCgCAACQRChCD/f000/b3IvqBz/4AVccAj0U73fYi1AEl2UymbRw4UINHTpUnp6eCgoK0r333tvinlQ90cSJE7V48WJntwF0mRv5/Q7X4ZKX5AOVlZUaN26c+vXrp+eff16RkZG6fPmy3nnnHT322GNt/g4egO6nO73fm5qa5OHh4ew2cJ1wpggu6cc//rEMBoP279+vBx54QMOHD9e3vvUtZWRk6K9//au17uzZs5o3b55uueUW+fr66tvf/rY+/vhju177ww8/1MSJE+Xj46P+/fvLaDTqH//4hySpsbFRjz/+uAYOHCgvLy+NHz9eH330kXXfV155pcVPweTl5dn8Vt7VU/yvvfaagoOD5efnp5kzZ+rcuXOSvjrl/95772nt2rUyGAwyGAyqrKy065gAV+as9/uXX36p1NRUDRkyRD4+PoqMjNQf//hHm5qJEydqwYIFWrx4sfz9/WU0GiVJZWVl+u53v6ubbrpJAQEBmjVrlmpra6375efna/z48erXr58GDBige+65R8ePH+90r+gahCK4nDNnzig/P1+PPfaY+vTp02L7v4aOGTNmqKamRv/7v/+r4uJijRo1SpMnT9aZM2c69doHDx7U5MmTNWLECBUVFemDDz7Qvffeq+bmZklf3QT0jTfe0KuvvqqSkhINGzZMRqOxw693/Phx5eXlafv27dq+fbvee+89Pfvss5KktWvXKj4+XvPnz9epU6d06tQpBQUFdep4AFfnzPf7pUuXFBMTo7fffltlZWV65JFHNGvWLO3fv9+m7tVXX5WHh4c+/PBD5eTk6OzZs/r2t7+tu+66SwcOHFB+fr6qq6v10EMPWfdpaGhQRkaGDhw4oIKCArm5uen73/++zGZzp3pFF7EALmbfvn0WSZZt27Zds27v3r0WX19fy6VLl2zG77jjDsvvf/97i8VisaxcudISFRVl3TZnzhzL/fff3+acqamplnHjxrW67fz585bevXtbXn/9detYU1OTZfDgwZZf//rXFovFYnn55Zctfn5+Nvu9+eabln99q61cudLi4+Njqa+vt44tWbLEEhcXZ32emJhoWbRoUZt9Aj2FM9/vrZk2bZrliSeesD5PTEy03HXXXTY1zzzzjGXKlCk2YydOnLBIshw+fLjVeU+fPm2RZPnkk0861A+6FmuK4HIs7bzJ+scff6zz589rwIABNuMXL17s9GnqgwcPasaMGa1uO378uC5fvqxx48ZZx3r37q3Y2FiVl5d36HWCg4PVt29f6/NBgwappqamUz0D3Zkz3+/Nzc361a9+pT/96U86efKkmpqa1NjYKB8fH5u6mJiYFr3s3r1bN910U4s5jx8/ruHDh+vo0aNasWKF9u3bp9raWusZoqqqKpufoIJrIRTB5YSGhspgMHzj4srz589r0KBB2rNnT4ttX1/X017e3t6d2u8qNze3Fv+Tv3z5cou63r172zw3GAycVscNyZnv9+eff15r167VmjVrFBkZqT59+mjx4sVqamqyqfv613rnz5/Xvffeq+eee67FnIMGDZIk3Xvvvbr99tv1hz/8QYMHD5bZbFZERESLueFaWFMEl3PzzTfLaDRq/fr1amhoaLH97NmzkqRRo0bJZDKpV69eGjZsmM3D39+/U689cuTINi8BvuOOO6zrCq66fPmyPvroI40YMUKSdMstt+jcuXM2fR88eLDDfXh4eFjXMQE9mTPf7x9++KHuv/9+paWlKSoqSkOHDtWRI0e+cb9Ro0bp008/VXBwcIte+vTpoy+//FKHDx/W8uXLNXnyZIWHh1sv1oBrIxTBJa1fv17Nzc2KjY3VG2+8oaNHj6q8vFy//e1vFR8fL0lKSkpSfHy8kpOT9Ze//EWVlZUqLCzU//t//08HDhzo1OtmZmbqo48+0o9//GMdOnRIFRUVevHFF1VbW6s+ffro0Ucf1ZIlS5Sfn6/PPvtM8+fP14ULFzR37lxJUlxcnHx8fLRs2TIdP35cubm5euWVVzrcR3BwsPbt26fKykqbU+9AT+Ss93toaKh27typwsJClZeX60c/+pGqq6u/cb/HHntMZ86cUWpqqj766CMdP35c77zzjtLT09Xc3Kz+/ftrwIAB2rBhg44dO6Z3331XGRkZneoRXYtQBJc0dOhQlZSUaNKkSXriiScUERGh73znOyooKNCLL74o6auvnHbs2KEJEyYoPT1dw4cP18yZM/X5558rICCgU687fPhw/eUvf9HHH3+s2NhYxcfH689//rN69frqm+Znn31WDzzwgGbNmqVRo0bp2LFjeuedd9S/f39JX/2td9OmTdqxY4f18t6nn366w308+eSTcnd314gRI3TLLbeoqqqqU8cDdAfOer8vX75co0aNktFo1MSJExUYGNiuO2APHjxYH374oZqbmzVlyhRFRkZq8eLF6tevn9zc3OTm5qbNmzeruLhYERER+slPfqLnn3++Uz2iaxks7V3lBgAA0INxpggAAECEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEnS/wcC17SZnVDAXQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Now let's plot the growth rates\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "data = [count_df['0'], area_df['0']]\n",
+ "\n",
+ "fig, ax1 = plt.subplots(facecolor='white')\n",
+ "ax1.boxplot(data,labels=['Cell count','Cell area'])\n",
+ "ax1.set_ylabel('Growth rate [h$^{-1}$]')\n",
+ "ax1.set_ylim(0, )\n",
+ "\n",
+ "plt.savefig('Boxplot_growth_rates.png', bbox_inches='tight', transparent=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Chamber µcount µarea\n",
+ "0 59_C4_CscB_cropped.tif 0.071677 0.063002\n",
+ "1 34_C3_CscB_cropped.tif 0.046111 0.045090\n",
+ "2 50_C4_CscB_cropped.tif 0.070692 0.067599\n",
+ "3 33_C3_CscB_cropped.tif 0.067499 0.062321\n",
+ "4 57_C4_CscB_cropped.tif 0.071765 0.061475\n",
+ "5 40_C3_CscB_cropped.tif 0.060264 0.056178\n",
+ "6 49_C4_CscB_cropped.tif 0.081445 0.075017\n",
+ "7 54_C4_CscB_cropped.tif 0.074514 0.063124\n",
+ "8 37_C3_CscB_cropped.tif 0.056376 0.052910\n",
+ "9 53_C4_CscB_cropped.tif 0.078641 0.071221\n",
+ "10 47_C4_CscB_cropped.tif 0.067563 0.062317\n",
+ "11 39_C3_CscB_cropped.tif 0.057813 0.054169\n",
+ "12 44_C3_CscB_cropped.tif 0.061857 0.058490\n",
+ "13 43_C3_CscB_cropped.tif 0.061245 0.060131\n",
+ "14 32_C3_CscB_cropped.tif 0.066183 0.060252\n",
+ "15 56_C4_CscB_cropped.tif 0.073714 0.067981\n",
+ "16 35_C3_CscB_cropped.tif 0.044272 0.043716\n",
+ "17 51_C4_CscB_cropped.tif 0.081493 0.076070\n",
+ "18 58_C4_CscB_cropped.tif 0.069346 0.062385\n",
+ "19 60_C4_CscB_cropped.tif 0.072985 0.067380\n",
+ "20 42_C3_CscB_cropped.tif 0.058852 0.055642\n",
+ "21 45_C3_CscB_cropped.tif 0.049887 0.048543\n",
+ "22 38_C3_CscB_cropped.tif 0.067609 0.064063\n",
+ "23 46_C4_CscB_cropped.tif 0.068570 0.059383\n",
+ "24 36_C3_CscB_cropped.tif 0.041426 0.041506\n",
+ "25 52_C4_CscB_cropped.tif 0.071729 0.066342\n",
+ "26 31_C3_CscB_cropped.tif 0.059402 0.051018\n",
+ "27 55_C4_CscB_cropped.tif 0.067611 0.055083\n",
+ "28 48_C4_CscB_cropped.tif 0.069228 0.060447\n",
+ "0 Mean 0.065164 0.059754\n",
+ "1 Median 0.067609 0.060447\n",
+ "2 STD 0.010197 0.008437\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Calculate Mean, Median and Standard deviation\n",
+ "\n",
+ "mean = [np.mean(count_df['0']), np.mean(area_df['0'])]\n",
+ "median = [np.median(count_df['0']), np.median(area_df['0'])]\n",
+ "std = [np.std(count_df['0']), np.std(area_df['0'])]\n",
+ "\n",
+ "statistics_df = pd.DataFrame({'Chamber': ['Mean','Median','STD'],\n",
+ " 'µcount': [mean[0], median [0], std[0]],\n",
+ " 'µarea': [mean[1], median [1], std[1]]})\n",
+ "# print(statistics_df)\n",
+ "\n",
+ "# Rearrange Growth rates for setting up results.csv\n",
+ "\n",
+ "results_df_1 = pd.DataFrame({'Chamber': count_df['experiment'],\n",
+ " 'µcount': count_df['0']}).reset_index()\n",
+ "\n",
+ "results_df_2 = pd.DataFrame({'µarea': area_df['0']}).reset_index()\n",
+ "\n",
+ "rates_df = pd.concat([results_df_1, results_df_2], axis=1)\n",
+ "\n",
+ "del rates_df['index']\n",
+ "\n",
+ "result_df = pd.concat([rates_df, statistics_df])\n",
+ "\n",
+ "print(result_df)\n",
+ "\n",
+ "result_df.to_csv(str('result_df.csv'), sep=';')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "43e720662e2b73f3f858656968524fca68eb44fc0b1d15b9eb878c7d185562f9"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assays/Microfluidic cultivation with homogeneous growth light/protocols/Total_number_segmented_cells.ipynb b/assays/Microfluidic cultivation with homogeneous growth light/protocols/Total_number_segmented_cells.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..98c2929000a0bacb7ac2966bd8793ad252700832
--- /dev/null
+++ b/assays/Microfluidic cultivation with homogeneous growth light/protocols/Total_number_segmented_cells.ipynb
@@ -0,0 +1,233 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "08efcff8-5b9f-4704-9900-0aa36ee01b83",
+ "metadata": {},
+ "source": [
+ "# Calculate number of segmented cells\n",
+ "This notebook was desinged to calcute how many cells were segmented within the µFluidic_PI_curve Project"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "33e67adf-c0a9-4dc0-b79b-9b921612a1d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['2023.08.15_10uE_AmbientCO2', '2023.08.01_140uE_AmbientCO2', '2023.03.01_80uE_AmbientCO2', '2023.08.08_50uE_AmbientCO2', '2023.06.27_20uE_AmbientCO2', '2023.07.18_60uE_AmbientCO2', '2023.07.25_30uE_AmbientCO2']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# First all performed experiments are collected\n",
+ "\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Create a list with all experiments\n",
+ "\n",
+ "path = Path('./')\n",
+ "\n",
+ "experiments = []\n",
+ "\n",
+ "for sub_folder in path.glob(\"*CO2\"): # grab all folders that end with 'CO2'\n",
+ " experiments.append(sub_folder.name)\n",
+ "\n",
+ "print(experiments) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "067c91fd-b99c-4ba4-b563-05fb4ebb9349",
+ "metadata": {},
+ "source": [
+ "# S. elongatus UTEX2973"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2f2ca0b8-a907-48b8-8d8b-f80095263fce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Then for each experiment\n",
+ "\n",
+ "import pandas as pd\n",
+ "\n",
+ "number_cells_UTEX = 0\n",
+ "number_sequences_UTEX = 0\n",
+ "number_images_UTEX = 0\n",
+ "\n",
+ "for experiment in experiments: # Iterate Experiments\n",
+ " data_folder = Path(experiment)\n",
+ " # print(experiment)\n",
+ " for sub_folder in data_folder.glob(\"*UTEX2973\"): # Iterate Channel\n",
+ " sub_folder = sub_folder/'automated_executions'\n",
+ " for sub_sub_folder in sub_folder.glob(\"*\"): # Iterate Chambers\n",
+ " if Path.exists(sub_sub_folder/'tmp'/'results.csv') == True:\n",
+ " number_sequences_UTEX = number_sequences_UTEX + 1\n",
+ " try: \n",
+ " count_df = pd.read_csv(sub_sub_folder/'tmp'/'counts.csv', delimiter = ';')\n",
+ " number_cells_UTEX = number_cells_UTEX + count_df['counts'].sum()\n",
+ " number_images_UTEX = number_images_UTEX + len(count_df)\n",
+ " except:\n",
+ " print('No counts.csv in: {}'.format(sub_sub_folder)) \n",
+ " else:\n",
+ " ()\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8bafbbcd-7fdb-4286-9b59-6e8c1260e2a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "107 image sequences were analyzed\n",
+ "containing a total of 11670 images\n",
+ "1817542 S. elongatus UTEX2973 cells were segmented\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print results\n",
+ "\n",
+ "print('{} image sequences were analyzed'.format(number_sequences_UTEX))\n",
+ "print('containing a total of {} images'.format(number_images_UTEX))\n",
+ "print('{} S. elongatus UTEX2973 cells were segmented'.format(number_cells_UTEX))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48cfb893-093d-4fdd-a47e-70a722f597da",
+ "metadata": {},
+ "source": [
+ "# S. elongatus PCC7942 CscB"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "704f491e-fbf4-4806-98cb-25ac5849c999",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Then for each experiment\n",
+ "\n",
+ "import pandas as pd\n",
+ "\n",
+ "number_cells_CscB = 0\n",
+ "number_sequences_CscB = 0\n",
+ "number_images_CscB = 0\n",
+ "\n",
+ "for experiment in experiments: # Iterate Experiments\n",
+ " data_folder = Path(experiment)\n",
+ " # print(experiment)\n",
+ " for sub_folder in data_folder.glob(\"*scB\"): # Iterate Channel\n",
+ " sub_folder = sub_folder/'automated_executions'\n",
+ " for sub_sub_folder in sub_folder.glob(\"*\"): # Iterate Chambers\n",
+ " if Path.exists(sub_sub_folder/'tmp'/'results.csv') == True:\n",
+ " number_sequences_CscB = number_sequences_CscB + 1\n",
+ " try: \n",
+ " count_df = pd.read_csv(sub_sub_folder/'tmp'/'counts.csv', delimiter = ';')\n",
+ " number_cells_CscB = number_cells_CscB + count_df['counts'].sum()\n",
+ " number_images_CscB = number_images_CscB + len(count_df)\n",
+ " except:\n",
+ " print('No counts.csv in: {}'.format(sub_sub_folder)) \n",
+ " else:\n",
+ " ()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8eb5f7dd-28e8-472d-939b-5c5921edcc93",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "103 image sequences were analyzed\n",
+ "containing a total of 9819 images\n",
+ "2535406 S. elongatus PCC7942 CscB cells were segmented\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print results\n",
+ "\n",
+ "print('{} image sequences were analyzed'.format(number_sequences_CscB))\n",
+ "print('containing a total of {} images'.format(number_images_CscB))\n",
+ "print('{} S. elongatus PCC7942 CscB cells were segmented'.format(number_cells_CscB))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5519179e-5fea-4e2c-9981-4c5ce20e7bb0",
+ "metadata": {},
+ "source": [
+ "# Total"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "df5d2194-15f5-4376-876f-01074712c66f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "210 image sequences were analyzed\n",
+ "containing a total of 21489 images\n",
+ "4352948 cells were segmented\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Combine statistics\n",
+ "\n",
+ "number_cells = number_cells_UTEX + number_cells_CscB\n",
+ "number_sequences = number_sequences_UTEX + number_sequences_CscB\n",
+ "number_images = number_images_UTEX + number_images_CscB\n",
+ "\n",
+ "print('{} image sequences were analyzed'.format(number_sequences))\n",
+ "print('containing a total of {} images'.format(number_images))\n",
+ "print('{} cells were segmented'.format(number_cells))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}