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[
{
	"uri": "/prepare_venue/sample/",
	"title": "Sample Shipping",
	"tags": [],
	"description": "",
	"content": " Sample Shipping and/or Handling at HFIR Your samples must be confirmed in IPTS before shipping.\nNeutron Sciences User Sample IPTS #XXXX Oak Ridge National Laboratory / HFIR Site Bldg 7972 DP1 Oak Ridge, TN, 37831 USA  If you prefer to bring your samples, please go first to the Sample Management Desk located in the Cold Guide Hall and work with Kristin Nevius to have all your samples labeled with an item Barcode before proceeding to the beamline.\n"
},
{
	"uri": "/prepare_venue/proposal_confirmation/",
	"title": "Proposal Confirmation",
	"tags": [],
	"description": "",
	"content": "To be completed by the Principal Investigator (PI) of the experiment at least 3 weeks before beam time.\n You can access your proposal using the IPTS page\n Confirmed samples (adding any new samples, and including composition)?\n Confirmed need for lab space?\n Confirmed experiment team that will be present at beam time (add any new members)?\n Confirmed experiment equipment and/or sample environment?\n"
},
{
	"uri": "/prepare_venue/safety/",
	"title": "Engineering & Equipment Safety",
	"tags": [],
	"description": "",
	"content": "To be completed by the Principal Investigator (PI) of the experiment\n  Provided all safety documentation for user provided equipment immediately?\n Shipped/delivered all user provided equipment at least 2 weeks prior to beam time for electrical and safety inspection?\n Answered all safety related questions through IPTS?\n Ensured that sample environment requested equipment is available and compatible with experiment/beam line?\n Written a Job Hazard Analysis (JHA) for hands on work at HFIR?\n"
},
{
	"uri": "/prepare_venue/access/",
	"title": "Access",
	"tags": [],
	"description": "",
	"content": "This must be completed by all participants to the experiment before the first day of experiment.\n  Received training e-mail from user office (neutronusers@ornl.gov)?\n Completed online training if assigned? ORNL guess portal\n Scheduled onsite training and tour for HFIR access?\n Followed attached procedure to access CG-1D data?\n Followed attached procedure to access CG-1D analysis computer?\n"
},
{
	"uri": "/prepare_venue/",
	"title": "Before your Arrival",
	"tags": [],
	"description": "",
	"content": " Prepare your experiment In order to optimize your experiment time, we recommend you to go through the check lists from this Before your Arrival menu.\nFeel free to print out the pdf version and check out the items one by one.\n"
},
{
	"uri": "/tutorial/how_to_access_data/",
	"title": "Access your data",
	"tags": [],
	"description": "",
	"content": "  This will require that you complete 2 steps:\n Step 1: request access to our computer Step 2: reach your data   Step 1. Request access to our computers 1. Create an XCAMS account\nIf you have not done so, please create an XCAMS account 2. Request access to HFIR data\nYou can request access to your data by visiting this page https://neutronsr.us/accounts/request.html   Link not working?    \nTOP \n Step 2. Reach your data If the goal is to bring your data to your computer for local visualization and analysis -not recommended due to the size of the data and the tools we offer via our analysis computer - you can either use:\n FileZilla Cyberduck  \nUsing FileZilla 1. Install FileZilla.\n2 Create and configure a new bookmark\nEnter the information as followed:\n Host: analysis.sns.gov Port: 22 Protocol: Select SFTP - SSH File Transfer Protocol Logon Type: Normal User: \u0026lt;your xcams\u0026gt; Password: \u0026lt;your password\u0026gt;  Click Connect\nYou can now browse to your data by following the structure /HFIR/CG1D/IPTS-XXXX\n3. Import Data\nIf you want to copy your data to your local computer, simply DRAG and DROP the folder of interest into your Desktop display on the left side of the window.\n\nUsing Cyberduck 1. Install Cyberduck.\n2. Create and configure a new bookmark\nEnter the information as followed:\n SFTP (SSH File Transfer Protocol) Server: analysis.sns.gov Port: 22 Username: \u0026lt;your xcams\u0026gt; Password: \u0026lt;your password\u0026gt; SSH Private Key: None  Click Connect\nYou can now browse to your data by following the structure /HFIR/CG1D/IPTS-XXXX\n3. Import Data\nIf you want to copy your data to your local computer, simply DRAG and DROP the folder of interest into your Desktop.\nFile Structure If you get lost in the file system, here is a typical map of the file structure.\n\n"
},
{
	"uri": "/capabilities/",
	"title": "Capabilities",
	"tags": [],
	"description": "",
	"content": " Imaging Facilities   CG-1D radiography/tomography with cold neutron spectrum.    VENUS time-of-flight radiography/tomography with epithermal to cold neutrons spectra.   Xray CT complementary microCT capabilities   Technical Infos     Resolution: 1 micron ultimate resolution with 1mm field-of-view  Resolution: 65 microns with 50mm field-of-view  160 kV max  50mm or less sample size      "
},
{
	"uri": "/tutorial/how_to_access_computer/",
	"title": "Connect to our computer",
	"tags": [],
	"description": "",
	"content": " For any personal tutorial, demonstration or request, please contact Jean Bilheux.\nIn order to analyze or visualize your data, please follow the following recipe.\nConnect to our analysis computer Using your favorite browser, go to https://analysis.sns.gov\nEnter your XCAMS and your password and hit Login\nThe first time you log in, you will be presented the following display\njust click OK to finish up logging in.\nAnd if it is not the first time, you will probably see a black screen. Just click anywhere within the window to activate the screensaver log in.\nEnter your password and click Unlock.\nYou are now connected to our analysis computer.\n"
},
{
	"uri": "/faq/",
	"title": "Frequently Asked Questions",
	"tags": [],
	"description": "",
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	"content": " Before Your Venue Proposal   How do I submit a proposal?    To learn more about submitting a proposal for beam time, go to neutrons.ornl.gov/users. To submit your proposal, go to the proposal system.    During your Experiment   Where can I stay?   A few options are available off-site and on-site. Check the neutron.ornl.gov users page for more infos.\n  Data Analysis   I forgot my XCAMS password   Simply go to How to reset password web page.\n    How can I browse my data?   Using ONCat, you will be able to  view your data view the metadata and get infos about such or such data set find an experiment using keyword More features coming soon      How can I get help analyzing my data?   Just contact Jean Bilheux to discuss your needs.\nBy going over your experiment together, Jean will show you how to run the current tools and will develop customed python notebooks if needed.\n    What are those \u0026#39;jupyter notebooks\u0026#39;?   The jupyter notebook developed by jupyter are an easy way to run python code using only a browser. By accessing our analysis computer, you won\u0026rsquo;t have anythign to install. Refer to our How To page to learn how to do that.\n    Where are my data and how can I access them?   The following tutorial will show you where are you data and how you can access them. Just go to How To \u0026gt; Access your data\n    I get a firefox error message when trying to start the jupyter notebooks on the analysis machine.   After double clicking the start jupyter icon, I get an Firefox error message telling me that I have another Firefox window opened.\nThe short way to fix that is by starting the Help me application.\nThen go to the Desperate Actions\nand click the Fix Firefox!\nThis should fix your issue and you should be able to start the jupyter notebooks now.\n    Where do I find ImageJ (or Fiji) on the analysis computer?   Just follow the following path to find and start ImageJ. If you need to learn how to use ImageJ, check their tutorial web site\n    How to cite our work?    Use of CG1D beam line iMars3D iBeatles      Metadata of the images and their meaning   You can retrieve the metadata of your TIFF images using:  ONCat the jupyter notebook list_tiff_metadata.ipynb. Check How To \u0026gt; Start the python notebooks \n Tag NameDescription ImageWidthThe number of columns in the image ImageHeightThe number of rows in the image BitsPerSampleThe number of bits per component (ie. 16-bits or 32-bits for each greyscale pixel in our case) SampleFormatSpecifies how to interpret each data sample in a pixel (1 = unsigned integer) SamplesPerPixelThe number of components per pixel (1 in our case, which is grey scale) Compression1 = None PhotometricInterpretation1 = Min is black MakeStrThe detector manufacture (eg. 'Andor' or 'SBIG') ModelStrThe detector model number SoftwareStrEPICS areaDetector  \nTags 65000 to 650009 have no name and are used for timestamps and a unique ID\n Tag NameDescription 65000EPICS timestamp. The timestamp is made when the image is read out from the camera. Format is seconds.nanoseconds since Jan 1st 00:00 1990. 65001Unique ID for the image. Always 1 for single image acquisition, and incrementing up for camera and CT scans. Should always match the ImageCounter value. 65002EPICS timestamp (seconds part only) 65003EPICS timestamp (nanoseconds part only)  Scan Information  Tag NameDescription FileNameStrThe original file name part of the constructed file name (see below) InstrumentStr'CG1D' or 'VENUS' IPTSIPTS Number ITEMSITEMS Number SampleDescStrSample description (user entered) NotesStrUser notes DataSetStr'2D' or '3D' DataAcqModeStr'White Beam', 'TOF-cold/thermal', 'Epithermal' or 'Monochromatic' DataTypeStr'OB', 'Raw' or 'DF'  Camera/Image Information  Tag NameDescription ExposureTimeExposure time for the image (in seconds) ExposurePeriodExposure period for the image (Exposure Time + Readout Time in seconds). Not relevant for single image exposures. NumImages1= single image exposure (our normal mode of operation) ImageCounterAlways 1 for single image acquisition, and incrementing up for camera and CT scans. MinXMin X pixel (0 for full frame images) MinYMin Y pixel (0 for full frame images) SizeXSize of image in X dimension (should be equal to the ImageWidth value) SizeYSize of image in Y dimension (should be equal to the ImageLength value) TemperatureThe setpoint temperature (in C) TemperatureActualThe actual temperature read from the detector (in C)  Motor Position \u0026amp; Scan Device  Tag NameDescription MotScanDeviceStr'Small Rot' or 'Large Rot' used for this CT scan (if we are doing a camera scan or single image acquisition, this is not relevant) RotationActualActual position of the rotation stage used in the CT scan (or the previous scan if we are doing a camera scan or single image acquisition) MotRotTable.RBVLarge rotation table actual position MotRotTableLarge rotation table setpoint MotSmallRotTable.RBVSmall rotation table actual position MotSmallRotTableSmall rotation table setpoint MotLiftTable.RBVLift Table actual position MotLiftTableLift table setpoint MotShortAxis.RBVShort axis actual posiiton ...  TIFF File Header Example  TIFF Directory at offset 0x800008 (8388616) Image Width: 2048 Image Length: 2048 Bits/Sample: 16 Sample Format: unsigned integer Compression Scheme: None Photometric Interpretation: min-is-black Samples/Pixel: 1 Rows/Strip: 2048 Planar Configuration: single image plane Make: Unknown Model: Unknown Software: EPICS areaDetector Tag 65000: 837380408.136687 Tag 65001: 1 Tag 65002: 837380408 Tag 65003: 148080423 Tag 65010: FileNameStr:TiffHeaderTests Tag 65011: InstrumentStr:CG1D Tag 65012: IPTS:17255 Tag 65013: ITEMS:-1 Tag 65014: SampleDescStr:polarization test Tag 65015: NotesStr:polarization test Tag 65016: DataSetStr:2D Tag 65017: DataAcqModeStr:White Beam Tag 65018: DataTypeStr:Raw Tag 65019: ModelStr:DW936_BV Tag 65020: ManufacturerStr:Andor Tag 65021: ExposureTime:1.000000 Tag 65022: ExposurePeriod:5.451660 Tag 65023: NumExposures:1 Tag 65024: NumImages:1 Tag 65025: ImageCounter:1 Tag 65026: MinX:0 Tag 65027: MinY:0 Tag 65028: SizeX:2048 Tag 65029: SizeY:2048 Tag 65030: Temperature:-60.000000 Tag 65031: TemperatureActual:-57.830002 Tag 65032: MotScanDeviceStr:Small Rot Tag 65033: RotationActual:183.000132 Tag 65034: MotLiftTable.RBV:247.500452 Tag 65035: MotLiftTable:247.500452 Tag 65036: MotShortAxis.RBV:76.000000 Tag 65037: MotShortAxis:76.000000 Tag 65038: MotLongAxis.RBV:193.016000 Tag 65039: MotLongAxis:193.016000 Tag 65040: MotRotTable.RBV:182.996500 Tag 65041: MotRotTable:183.000000 Tag 65042: MotSmallRotTable.RBV:183.000132 Tag 65043: MotSmallRotTable:183.000000 Tag 65044: MotDetTable.RBV:200.000000 Tag 65045: MotDetTable:200.000000 Tag 65046: MotCameraVert.RBV:-51.699796 Tag 65047: MotCameraVert:-51.699796 Tag 65048: MotHoriTrans.RBV:28.000000 Tag 65049: MotHoriTrans:28.000000 Tag 65050: MotVertTrans.RBV:60.000000 Tag 65051: MotVertTrans:60.000000 Tag 65052: MotDiffuser.RBV:86.300000 Tag 65053: MotDiffuser:86.300000 Tag 65054: MotAperture.RBV:138.700000 Tag 65055: MotAperture:138.700000 Tag 65056: MotSlitVB.RBV:39.969938 Tag 65057: MotSlitVB:39.969938 Tag 65058: MotSlitVT.RBV:39.860484 Tag 65059: MotSlitVT:39.860484 Tag 65060: MotSlitHR.RBV:40.000000 Tag 65061: MotSlitHR:40.000000 Tag 65062: MotSlitHL.RBV:39.977781 Tag 65063: MotSlitHL:39.977781 Tag 65064: AndorCCDCooler:1 Tag 65065: AndorCCDTempStatusStr:Not stabilized at set point Tag 65066: AndorCCDPreAmpGain:0 Tag 65067: AndorCCDADCSpeed:2   \nWork With Us   Visiting Researcher Program   Link here\n    Minority Serving Institutions Partnership Program   Link here\n \n"
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},
{
	"uri": "/tutorial/how_to_start_notebooks/",
	"title": "Start the python notebooks",
	"tags": [],
	"description": "",
	"content": "We provide a set of jupyter python notebooks. Those provide many advantages:\n rapid implementation for your special needs ease of use easy to modify if you need to use of python language (widely used in the scientific world) nothing to install for you as we take care of this for you when you use your analysis computer  To launch the jupyter notebooks, navigate to Applications \u0026gt; Analysis \u0026gt; Jupyter. Be patient as the python server starts a firefox browser with the right python environment and move to the right folder, for you!\nGetting the browser up and running with the notebooks page display takes around 10 to 20 seconds.\n To start a notebook, just click any of the .ipynb file (normalization.ipynb in this tutorial).\nFirst thing we recommend at this point is to make a copy of this notebook. This way, update of the notebooks will not overwrite your work.  Make sure you do not close the terminal window that you can find behind the browser as killing it will terminate your notebook sesssion.  "
},
{
	"uri": "/links/",
	"title": "Links",
	"tags": [],
	"description": "",
	"content": " Laboratory Utilities   ORNL User Program Guide   User Program Guide.     Submit a proposal   Go to the Proposal System.\n    Neutron Imaging Facility Official Web Page   Neutron Imaging Facility Official Web Page.     Neutrons (SNS/HFIR) Web Page   https://neutrons.ornl.gov/     ORNL Web Page   https://www.ornl.gov/     ORNL guess portal   https://user.ornl.gov  \nAnalysis Tools   Sample Activation Calculators   https://sac.ornl.gov/     Calculate Transmission and Scattering Power   https://webapps.frm2.tum.de/intranet/neutroncalc/     Neutron Transmission Calculator   http://apps.jcns.fz-juelich.de/toolbox/nXsection.php     Neutron Scattering Lengths and Cross Sections   https://www.ncnr.nist.gov/resources/n-lengths/     Neutron Activation and Scattering Calculator   ncnr.nist.gov/resources/activation/     Cold Neutron (HFIR) Transmission and Resonance   https://isc.sns.gov/  \nReferences   Standard Guide for Thermal Neutron Radiography of Materials   ASTM Guide for Thermal Neutron Radiography.     International Society for Neutron Radiology (ISNR)   International Society for Neutron Radiology  \nShortcuts  ONCat - https://oncat.ornl.gov/#/   "
},
{
	"uri": "/tutorial/how_to_run_notebooks/",
	"title": "Run a jupyter notebook",
	"tags": [],
	"description": "",
	"content": "  Our jupyter python notebooks are very straighforward to use but there are just a few key points you need to know.\n notebook layout run the notebook modify the notebook  \n Notebook layout \n Run the notebook Execute cells The notebooks must be run from top to bottom. Place the cursor in the first cell and either click the  icon, or by using the keyboard shortcut SHIFT + ENTER.\nThis is showing a busy notebook with the background cell green and the full circle at the top right corner of the notebook.\n\nFolder Navigation At least one time per notebook, you will need to select a folder or one or more files. This widget will look like this The UI is pretty self explanatory but you just need to know that:\n In the file/folder listing widget\n . means refresh of the current location .. means moving up one directory, or folder.  The file or folder will be really selected only once you have clicked the Select button at the bottom right corner.\n To select more than one file (when this option is available), CLICK first file then SHIFT + CLICK the last one. Or ALT + CLICK to individually select each file.\n  Output and widgets The cell is then executed and depending on the contain of this one, you may see or not an output to the cell. The output is always displayed just below the cell ran. In some of our notebooks you will see widgets that you will need to interact with. Here are a few examples of widgets you may encounter in your notebook.\nIn some notebooks, the output may even show up in the back of the notebook (the default cell output will give you a message that let you know where to look). Those outside widgets are more complex user interface such as the one shown here.\n\n Modify the notebook "
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{
	"uri": "/tutorial/how_to_run_imars3d/",
	"title": "Do a CT reconstruction",
	"tags": [],
	"description": "",
	"content": "This tutorial explains how to use SNS jupyter notebook site to perform CT reconstruction.\nYou will find this tutorial here\n"
},
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{
	"uri": "/tutorial/",
	"title": "Tutorials",
	"tags": [],
	"description": "",
	"content": "You will find here various step by step tutorial showing you:\n how to access your data how to use our analysis computer how to run the analysis software etc.  "
},
{
	"uri": "/tutorial/how_to_do_bragg_edge_modeling/",
	"title": "Bragg Edge Modeling",
	"tags": [],
	"description": "",
	"content": " The following tutorial will guide, step by step, in a full Bragg Edge Modeling\nRequirements Input file You will need a VDrive file (vdrive_filename.txt) generated by the VULCAN instrument. This file should looks like this\nPython code You need to clone the bragg edge repository (this won\u0026rsquo;t be necessary as soon as the library has been deployed).\n start a terminal session clone braggedgemodeling repo\n cd ~/\ngit clone https://github.com/ornlneutronimaging/braggedgemodeling\ncd braggedgemodeling\n switch to texture branch\n git checkout texture\n add to python path\n export PYTHONPATH=$PWD:$PYTHONPATH\n  Step 1 - Prepare VDrive file In order to run the VDrive file through the Matlab code (step 2), we need to change the format of this file. To do so simply run the following command line (make sure you provide your own file path)\nNB: Add braggedgemodeling repo to python path !\n\u0026gt; python braggedgemodeling/scripts/vdrive_handler_to_mtex.py -i \u0026lt;full path to the vdrive filename.txt\u0026gt; -io \u0026lt;specify full filename of an intermediate file\u0026gt; -o \u0026lt;full path to output file, prefered extension .rpf\u0026gt;  Example\n\u0026gt; python scripts/vdrive_handler_to_mtex.py -i ~/Desktop/vdrive_filename.txt -o ~/Desktop/VULCAN.rpf -io ~/Desktop/intermediate_file  Here is what the output file should look like\nStep 2 - Matlab code  Right now, this code only works on VULCAN2 computer, so connect to https://vulcan2.sns.gov\n From there, launch Matlab2014a as this version is the only working for the code we are going to use.\n   Within Matlab, move to folder /usr/local/mtex-3.5.0 start startup_mtex from the Matlab command   We need to import the pole figure (file created in Step 1),  click the Import pole figure data blue line click + to select input file (~/Desktop/VULCAN.rpf) click Next \u0026gt;\u0026gt; and select Crystal Coordinate System Laue Group _ click several times Next \u0026gt;\u0026gt; or just Finish    A matlab script is created, make sure the specimen symetry is the one you specified in the GUI (BUG in the Matlab program!!!). If incorect, make sure you edit the file and replace it with the orientation you choosed.   Save the file and Run it. This will produce a few variables listed in the workspace window (right side of Matlab UI).   We need to create now the ODF file by just entering the following script in Matlab command line   And now the final step where we need to export the ODF data. But we first need to specify the number of graind orientation (default 5000) by editing the export_vPSC1 file.  FIX ME!!! find a way to make this script available to user (put it inside the mtex code\n Run the export_vPSC1 script from Matlab command line  And here is the final file created, texture.txt\n"
},
{
	"uri": "/tutorial/how_to_run_mcp_detector_correction/",
	"title": "MCP Detector Correction",
	"tags": [],
	"description": "",
	"content": " The following tutorial will guide, step by step, in running the MCP correction program\nDescription Every single data acquired using the MCP detector needs to be corrected for detector efficiency.\nOld Way\nIn the past, one had to go through the following steps:\n start the windows command line tool locate the .exe script (using file browser) and drag and drop it into the command line window add a space in the command line locate the first image of the data set to correct Run the script and wait \u0026hellip; rename the folder correction created by the script grab this folder and move it to final location repeat for all other set of data  New Way\n start MCPcorrection program select root folder containing all data set select output folder click correction button  The parent folder must be the folder seating on top of the data folder! This looks confusing? Well just follow the following diagram. In such a case, you should select the first folder!  Step by Step Select the parent folder Make sure the folder you select contains all the data folders. Any other folder selected (higher or lower in the hierarchy will crash the application)!\nSelect the data folder to correct In the table, simply select all the data folder you want to correct. If a row is highlighted, this folder will be corrected\nSelect the output folder The data corrected will be moved into this folder\nNaming convention\nEach data set corrected will be copied inside a folder based on the name of all the images and inside a folder of the data folder + \u0026ldquo;_corrected\u0026rdquo;.\nFull correction animation Configuration This is where you can define the starting default folder, default output folder and also the run correction time out. The script run in the back requires user input to finish it, because we wanted to allow you to run several job one after the other without your input, we, using the time out value, stop the process manually. During testing, we found that 35s is long enough to allow all the files to be translater. In case you find that the time out shows up too early, you may need to increase the time here.\n"
},
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	"uri": "/tutorial/how_to_start_amira/",
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	"title": "How to work with Amira",
	"tags": [],
	"description": "",
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	"content": " This tutorial will show you how to start and then do some of the most basics things with Amira\nConnect to the analysis computer You need first to connect to the analysis computer. To do so, just follow the step by step tutorial in the how to connect to the analysis computer \nStart Amira Start a terminal session (click small black icon at the top of the screen) and type\namira  You should then see the Amira icon showing up\nNB: You may see some error messages in the terminal window, just ignore them !\nIf you see the following message, contact your local contact  Video Tutorials To get quickly started with Amira, here are a few YouTube videos that you may find interesting.\n Getting Started Tutorial Amira Basics Visualizing in 3D images Advanced Image Processing and Quantitative Analysis Measuring Diameter and Sphericity of Pores Getting Started with the Segmentation Editor Amira Image Segmentation Tutorial    CLICK HERE: Getting Started Tutorial     CLICK HERE: Amira Basics     CLICK HERE: Visualizing 3D Images     CLICK HERE: Advanced Image Processing and Quantitative Analysis     CLICK HERE: Measuring Diameter and Sphericity of Pores     CLICK HERE: Getting Started with the Segmentation Editor     CLICK HERE: Amira Image Segmentation Tutorial   "
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{
	"uri": "/tutorial/notebooks/",
	"title": "Notebooks Tutorials",
	"tags": [],
	"description": "",
	"content": " bin_images  bragg_edge_signal_vs_powder_peaks    calculate_water_intake_profile  calibrated_transmission  combine_folders  combine_images  create_list_of_file_name_vs_time_stamp  cylindrical_geometry_correction  deal_files  display_and_export_images_with_metadata_profile  display_and_export_images_with_timestamp  display_counts_of_region_vs_stack   display_file_names_vs_time_stamp   display_integrated_stack_of_images  file_name_and_metadata_vs_time_stamp  fix_images  fix_images_with_negative_pixels  frederick_ipts  from_attenuation_to_concentration   from_dsc_time_info_to_ascii_file_vs_time  ipts_18813  gamma_filtering_tool   integrated_roi_counts_vs_file_name_and_time_stamp   list_tiff_metadata    locate_pixels  metadata_ascii_parser  IN PROGRESS  metadata_overlapping_images    normalization   normalization_batch   profile  radial_profile  registration  rename_files    resonance_imaging_experiment_vs_theory  rotate_and_crop_images  template_ui  topaz_config_generator  water_intake_profile_calculator \nTools File Selector  Select IPTS \n Recently updated\n"
},
{
	"uri": "/tutorial/how_to_other/",
	"title": "More ...",
	"tags": [],
	"description": "",
	"content": "  How can I create a movie from a sequence of images?   A full tutorial showing you step by step how to create a movie using ImageJ is available at this post.\n  "
},
{
	"uri": "/tutorial/notebooks/bragg_edge/",
	"title": "Bragg Edge",
	"tags": [],
	"description": "",
	"content": " Notebook name: bragg_edge.ipynb\nDescription Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nloading the Normalized Images Select the working folder. All the\n"
},
{
	"uri": "/tutorial/notebooks/bragg_edge_signal_vs_powder_peaks/",
	"title": "Bragg Edge - Signal vs Powder Peaks",
	"tags": [],
	"description": "",
	"content": " Notebook name: bragg_edge_signal_vs_powder_peaks.ipynb\nDescription The main goal of this notebook is to display the ideal Bragg Edges of a list of elements and compare their signature to the signal of a set of FITS (MCP data) taken. Experimental set up can be changed by:\n distance source - detector detector time offset  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect Working Folder Select the working folder using the file selector. If the time spectra file is part of the folder, it will be automatically loaded, if not, you will have the option to select the time spectra file next.\nCheck the file selection tool tutorial to learn how to use the file selector tool.  Selection of Sample in the Image In order to calculate the signal of the loaded images, you must define the location of your sample in the image. But before doing so, the program will need to load a random subset of those images. You will need to define how many images you want to use to select that sample. A subset has been defined for you by default. Using this value (N), the program will load N images randomly choosen within the list of images in the working folder.\nRemember The more images you want to load, the longer it will take.\nOnce this subset has been defined, running the next cell will bring a user interface that will allow you to define the location(s) of your sample.\nSelect Powder Elements It\u0026rsquo;s now time to select the powder elements for which you want to see the Bragg Edges on top of your signal.\nDefine Experiment Setup This is where you can set up your experiment and play with these values to make sure the Bragg Edges of the reference powder elements show up at the right lambda in your signal data.\nDisplay Bragg Edges vs Signal Feel free to play with the iPlot widgets (above the plot) to zoom, pan, \u0026hellip; etc\n"
},
{
	"uri": "/tutorial/notebooks/calculate_water_intake_profile/",
	"title": "Calculate Water Intake",
	"tags": [],
	"description": "",
	"content": "Notebook name: calculate_water_intake.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/calibrated_transmission/",
	"title": "Calibrated Transmission",
	"tags": [],
	"description": "",
	"content": " Notebook name: calibrated_transmission.ipynb\nDescription The goal of this notebook is to display the average counts of region selected for each file. It\u0026rsquo;s possible to calibrate up to 2 regions by giving them a value. If a region with a mean counts of 100 is calibrated to 1 for example, all the other value displayed will use this new scaled defined. That means, if for a file the mean counts of a region is 200, the program will display 2. When 2 calibrated region are defined, a linear interpolation is used to dislay the mean counts of the region selected. When no calibrated region have been defined, the true (raw) mean counts are displayed.\nStart the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n Select Images to Process Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n Calibrated Transmission UI Presentation How Does it Work ? Define the Calibration Regions  Activate or deactivate the calibration region you want/do not want. Define the position and size of the region (using mouse in the image viewer, or by manual input) Select the value corresponding to this region Select the file to use corresponding to that region  Define the Measurement Regions  Go the Measurement Regions tab Add new regions by clicking the + button at the bottom right Change the position (using mouse in image viewer or by manual input in the table) Remove regions using the - button  Summary Tab The Summary tab display the list of files, time stamp, relative time (using the first file as reference or time 0), and mean counts calibrated for each measurement regions.\nExport Calibrated Transmission You can now export the calibrated transmission signal for each file by clicking the Export Calibrated Transmission button. Just select the output location. The file name created will be based on the input data folder and will look like this.\nIf input data file is light_loop1_flow2, the output file will be named light_loop1_flow2_calibrated_transmission.txt\n"
},
{
	"uri": "/categories/",
	"title": "Categories",
	"tags": [],
	"description": "",
	"content": ""
},
{
	"uri": "/tutorial/notebooks/combine_folders/",
	"title": "Combine Folders",
	"tags": [],
	"description": "",
	"content": "Notebook name: combine_folders.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/combine_images/",
	"title": "Combine Images",
	"tags": [],
	"description": "",
	"content": "Notebook name: combine_images.ipynb\n"
},
{
	"uri": "/credits/",
	"title": "Contacts",
	"tags": [],
	"description": "",
189
	"content": "            Hassina Bilheux - Instrument ScientistExpertise: HFIR and SNS imaging beam lines  Short Bio ...   Dr. Hassina Bilheux obtained her Ph.D. in Physics at the Univ. of Versailles, France. Her work focused on plasma physics at the Oak Ridge National Laboratory’s Physics Division.\nShe has developed neutron imaging capabilities at ORNL’s High Flux Isotope Reactor CG-1D beamline and is prototyping neutron imaging at the Spallation Neutron Source. Her interests comprise the development of advanced neutron imaging techniques at the Spallation Neutron Source for material and biological applications.   bilheuxhn@ornl.gov (865) 384 - 9630 (865) 574 - 0241 researchGate Scholar Google    Yuxuan Zhang (Shawn) - PostdoctorateExpertise: Resonance Imaging zhangy6@ornl.gov     Jean Bilheux - Computer ScientistExpertise: Python Notebooks and Data Analysis bilheuxjm@ornl.gov (865) 406 - 1704 (865) 574 - 4637 jbilheux.com jeanbilheux.pages.ornl.gov     Jiao Lin - Computer ScientistExpertise: 3D Reconstruction and visualization linjiao@ornl.gov (626) 200 - 5247 (626) 200 - 5247     Paris Cornwell - Scientific Associate  cornwellpa@ornl.gov (865) 257 - 1127 (865) 574 - 2122    Brianne Beers - Graduate Student    Past Team Members  Keita DeCarlo, Princeton Univ. Indu Dhiman Granger Endsley, Oak Ridge High School Vincenzo Finochiarro, Italy Sarah Hammer, Virginia State Univ. Susan Herringer, Brown Univ. Misun Kang, Univ. of TN-Knoxville Felix Kim, Univ. of TN-Knoxville Chad Lani, Penn State University Lou Santodonato, Advanced Research Systems, Inc Gian Song, Univ. of TN-Knoxville Sophie Voisin, Univ. of TN-Knoxville Lakeisha Walker, ORNL  "
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},
{
	"uri": "/tutorial/notebooks/create_list_of_file_name_vs_time_stamp/",
	"title": "Create List of Files of Names vs Time Stamp",
	"tags": [],
	"description": "",
	"content": " Notebook name: create_list_of_file_name_vs_time_stamp.ipynb\nDescription This notebook extracts the acquisition time of the selected files and output an ascii file with the following informations\n full path of File name time stamp (unix format) time stamp (user format) time offset (ms) relative to first image  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect Data Set Select Images you want to check\nCheck the file selection tool tutorial to learn how to use the file selector tool.  Once you have selected your data, you will see a progress bar as the time stamp metadata are retrieved from the files.\nSelect Output Folder Simple, just navigate to the location where you want your ascii file written.\nOutput Folder Created The Ascii file created will look like this\n#filename, timestamp(s), timestamp_user_format, timeoffset(s) /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0001.tiff, 1532610101.2023919, 2018-07-26 09:01:41, 0.0 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0002.tiff, 1532610101.2320013, 2018-07-26 09:01:41, 0.02960944175720215 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0003.tiff, 1532610101.261922, 2018-07-26 09:01:41, 0.059530019760131836 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0004.tiff, 1532610101.2918322, 2018-07-26 09:01:41, 0.08944034576416016 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0005.tiff, 1532610101.32187, 2018-07-26 09:01:41, 0.11947822570800781 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0006.tiff, 1532610101.352108, 2018-07-26 09:01:41, 0.14971613883972168 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0007.tiff, 1532610101.3818908, 2018-07-26 09:01:41, 0.17949891090393066 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0008.tiff, 1532610101.411917, 2018-07-26 09:01:41, 0.20952510833740234 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0009.tiff, 1532610101.4418778, 2018-07-26 09:01:41, 0.2394859790802002 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0010.tiff, 1532610101.4718854, 2018-07-26 09:01:41, 0.26949357986450195 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0011.tiff, 1532610101.5023768, 2018-07-26 09:01:41, 0.2999849319458008 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0012.tiff, 1532610101.531988, 2018-07-26 09:01:41, 0.32959604263305664 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0013.tiff, 1532610101.5619035, 2018-07-26 09:01:41, 0.3595116138458252 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0014.tiff, 1532610101.59197, 2018-07-26 09:01:41, 0.38957810401916504 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0015.tiff, 1532610101.6220326, 2018-07-26 09:01:41, 0.41964077949523926 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0016.tiff, 1532610101.6519294, 2018-07-26 09:01:41, 0.4495375156402588 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0017.tiff, 1532610101.6820183, 2018-07-26 09:01:41, 0.4796264171600342 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0018.tiff, 1532610101.7119684, 2018-07-26 09:01:41, 0.5095765590667725 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0019.tiff, 1532610101.7419527, 2018-07-26 09:01:41, 0.5395607948303223 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0020.tiff, 1532610101.7720513, 2018-07-26 09:01:41, 0.5696594715118408 /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-21115/Huggies_3cm_thick/20180726_Huggies_3cm_0000_0021.tiff, 1532610101.802108, 2018-07-26 09:01:41, 0.5997161865234375 ...  "
},
{
	"uri": "/tutorial/notebooks/cylindrical_geometry_correction/",
	"title": "Cylindrical Geometry Correction",
	"tags": [],
	"description": "",
	"content": " Notebook name: cylindrical_geometry_correction.ipynb\nDescription Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nloading the Images You need to provide the list of samples, OB and DF (if needed). To do so, just SHIFT + ENTER the following cell to display a selection tool wizard.\nThe dialogbox will start listing the files from the IPTS folder you selected in the first cell. Feel free to navigate to find your data set.\n"
},
{
	"uri": "/tutorial/notebooks/deal_files/",
	"title": "Deal Files",
	"tags": [],
	"description": "",
	"content": "Notebook name: deal_files.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/display_counts_of_region_vs_stack/",
	"title": "Display Counts of ROI vs Stack",
	"tags": [],
	"description": "",
	"content": " Notebook name: display_counts_of_region_vs_stack.ipynb\nDescription In this notebook, you will be able to get the average number of counts of a given region vs all the images of the selected folder.\nStart the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect the Images Select the folder containing the images you want to process using the File Selector. The images will then be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n Launch the UI SHIFT + ENTER in the next cell (Launch UI) to start the interface.\nUI Presentation Region of Interest (ROI) By default, a ROI is already defined for you in the top left corner of the preview region.\nTo move it, click inside the ROI (edges will turn yellow). To resize it, grab the bottom right corner and move it to resize it.\nThe Average counts of this entire region, for all the images from the folder, will be displayed live at the bottom of the interface.\nTime Spectra (OPTIONAL) "
},
{
	"uri": "/tutorial/notebooks/display_file_names_vs_time_stamp/",
	"title": "Display File Names vs Time Stamp",
	"tags": [],
	"description": "",
	"content": " Notebook name: display_file_names_vs_time_stamp.ipynb\nDescription This notebook extracts the acquisition time and displays\n time stamp vs file index relative time stamp offset vs file index (offset between each file) absolute time stamp offset vs file index (offset relative to first file loaded)  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect Data Set Select Images you want to check\nCheck the file selection tool tutorial to learn how to use the file selector tool.  Once you have selected your data, you will see a progress bar as the time stamp metadata are retrieved from the files.\nDisplay Time Stamp The next cell will plot on the left side the relative time offset in seconds, and on the right side, the absolute time offset in seconds.\nRelative time offset (s)\nThis is the time between each image.\nrelative_time(i) = acquisition_time(image(i)) - acquisition_time(image(i-1))  Absolute time offset (s)\nThis is the time spent since we started to acquire the images. For the first image, this absolute time is 0 of course.\nabsolute_time(i) = acquisition_time(image(i)) - acquisition_time(image(0))  Here is the plot we got when using an acquisition time of 30ms List Files Loaded The next cell display a list of all the fulle, relative and absolute time stamps.\n"
},
{
	"uri": "/tutorial/notebooks/display_and_export_images_with_metadata_profile/",
	"title": "Display Images and Metadata",
	"tags": [],
	"description": "",
	"content": "Notebook name: display_and_export_images_with_metadata.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/display_integrated_stack_of_images/",
	"title": "Display Integrated Images",
	"tags": [],
	"description": "",
	"content": "Notebook name: display_integrated_stack_of_images.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/display_and_export_images_with_timestamp/",
	"title": "Display and Export Images",
	"tags": [],
	"description": "",
	"content": "Notebook name: display_and_export_images_with_timestamp.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/file_name_and_metadata_vs_time_stamp/",
	"title": "File Name and Metadata vs Timestamp",
	"tags": [],
	"description": "",
	"content": "Notebook name: file_name_and_metadata_vs_time.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/file_selector/",
	"title": "File Selector",
	"tags": [],
	"description": "",
	"content": " Description The File Selector tool allows you to navigate through the file system to select files or folders.\nNavigation Manual Definition of Folder In order to make your way to your file(s)/folder(s), you can either chose to define your folder using the manual input field and click Jump\nOr/And you can navigate using the file browser box.\nSelection To select a folder, you have two ways:\n select the folder and click SELECT  enter the folder and click SELECT   To move up the tree (go to the parent folder), select ..\nMoving Up the Tree Filter for File Selection You can sometimes narrow down the list of files/folders displayed by using the filter option.\nAll Features Tutorial If you want to check all the features offered by the fileselector tool, go to the Fileselector hands-on tutorial\n"
},
{
	"uri": "/tutorial/notebooks/fix_images/",
	"title": "Fix Images",
	"tags": [],
	"description": "",
	"content": "Notebook name: fix_images.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/fix_images_with_negative_pixels/",
	"title": "Fix Images with Negative Pixels",
	"tags": [],
	"description": "",
	"content": "Notebook name: fix_images_with_negative_pixels.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/frederick_ipts/",
	"title": "Frederick IPTS",
	"tags": [],
	"description": "",
	"content": " Notebook name: frederick_ipts.ipynb\nDescription This notebook will re-group the data using the metadata recorded in the name of the files.\nStart the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n Select Images to Process Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nSorting Files Using the name of the files, the program will retrieve the Temperature (T) and Pressure (P) of the data set.\nHere is what those file names look like\nThe program will then re-group the files by time stamp first and then by T and P parameters.\nThis process may take some times as the program is loading all the data and calculating the groups.\nDisplay Images By running the display images cell, a UI pops up that will allow you to browse your data using the groups extracted from the previous step.\n"
},
{
	"uri": "/tutorial/notebooks/from_dsc_time_info_to_ascii_file_vs_time/",
	"title": "From .dsc Time Info to ASCII File vs Time",
	"tags": [],
	"description": "",
	"content": "Notebook name: from_dsc_time_info_to_ascii_file_vs_time.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/from_attenuation_to_concentration/",
	"title": "From Attenuation to Concentration",
	"tags": [],
	"description": "",
	"content": " Notebook name: from_attenuation_to_concentration.ipynb\nDescription This notebook takes normalized data (attenuation) as input and will apply the formula defined to the data. The data are then exported as TIFF. This formula convert attenuation into concentration values.\nStart the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect data folder Select the folder that contains the normalized images\nNeed help using the File Selector?\n Define conversion formula Define the conversion equation to use where\n A(x,Y) is the Attenuation (normalized) image H(x,y) is the concentration array that will be created  Converting data Run this cell to convert the data. A progress bar will show you the evolution of the correction, then disapear once it\u0026rsquo;s done.\nSelect output folder location Select the location where the output folder will be created based on the name of the input folder\nif input folder is data_03 output folder will be data_03_concentration\nNeed help using the File Selector?\n Video showing entire process "
},
{
	"uri": "/showcase/",
	"title": "Gallery",
	"tags": [],
	"description": "",
	"content": ""
},
{
	"uri": "/tutorial/notebooks/gamma_filtering_tool/",
	"title": "Gamma Filtering Tool",
	"tags": [],
	"description": "",
	"content": " Notebook name: gamma_filtering_tool.ipynb\nDescription This notebook will allow you to compare data loaded without any correction against data loaded with gamma filtering on. You will be able to change the gamma filtering coefficient to optimize the cleaning of the gammas without dammaging the images.\nStart the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect Images Simply select all the images you want to work on.\nCheck the file selection tool tutorial to learn how to use the file selector tool.  Display Images click the Auto scale to initialize the image just after launching the UI.  Mouse Infos / Zoom and Pan Moving the mouse over the raw or filtered image will give you its value in the status bar (bottom left) of the UI. Also any zoom or pan transformation in one of the image will be reproduced in the other image.\nChanging the filtering coefficient After changing the gamma filtering coefficient and hitting ENTER, the entire stack of data will be reloaded using the new filter coefficient. The table will show you the new percentage and number of pixels cleaned. The gamma filtered plot will be refreshed to display the new cleaned selected image.\nGamma Filtering Algorithm If you wonder how the gamma filtering algorithm works and what is the meaning behind this magic gamma filtering coefficient\nHere is the workflow:\n user determine the gamma filtering coefficient (coefficient). Value between 0 and 1. Image per image, the program calculate the average counts (image_average_counts). if the coefficient * pixel_value \u0026gt; image_average_counts then this pixel is considered to be a gamma and is replaced by the average value of its 8 neighbor pixels.  Feel free to move the plot around and resize them!  Histogram of Raw and Filtered Data Sets A newer version of the UI offers the histogram of the images before and after filtering. This helps figuring out where the gamma are located.\n"
},
{
	"uri": "/github/",
	"title": "Github Repositories",
	"tags": [],
	"description": "",
	"content": "Our gloal is to only develop open source tools. You will find here a list of the major repositories hosting our programs\n jupyter notebooks used in the analysis of the data python_notebooks python library used for normalization neunorm Imaging Resonance python library imagingReso iBeatles for Bragg Edge fitting iBeatles set of jupyter widgets ipywe Root analysis library RootPlantProcessing  "
},
{
	"uri": "/tutorial/notebooks/integrated_roi_counts_vs_file_name_and_time_stamp/",
	"title": "Integrated ROI Counts vs File Name and Time Stamp",
	"tags": [],
	"description": "",
	"content": " Notebook name: integrated_roi_counts_vs_file_name_and_time_stamp.ipynb\nDescription This notebook will create an ASCII file with the following information\n file index file name time stamp of file Mean or total number of counts of each region of interest for each image  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nloading the Images Simply select all the images you want to work on.\nCheck the file selection tool tutorial to learn how to use the file selector tool.  Presentation of the UI Step by Step ROI selection You have the option with this UI to select more than one region of interest (ROI). Click the + button on the right to add a ROI, and - if you want to remove the ROI selected. You can also enabled or disabled any of the ROIs.\nTo modify any of the ROI, you can either:\n move or resize the ROI directly from the Image window manually change x0, y0, width or/and height in the ROI table  The total conts vs file index plot will be updated live as you modified the ROI. This plot displays the mean or total counts of the ROIs for each image (1 image = 1 data point)\nIntegration Algorithm The current implementation provides 2 algorithms to integrate the counts within each ROI\n Add - sum counts of all the pixels in the ROI Mean - average counts of all the pixels in the ROI  The total counts vs file index plot will be updated live as you change the algorithm.\nExport the Counts vs file name and time stamp information Click the Export Counts vs File Name and Time Stamp \u0026hellip; button to create the ascii file version of the data found int the Summary Tab table.\nThe following 3 images shows an example where 3 ROIs have been created, how the summary tab looks like and the final output file created.\n"
},
{
	"uri": "/tutorial/notebooks/list_tiff_metadata/",
	"title": "List TIFF Metadata",
	"tags": [],
	"description": "",
	"content": " Notebook name: list_tiff_metadata.ipynb\nDescription This notebook will list the value of the metadata you selected for the full stack of images you loaded. You will then have the option to export this table in an ascii (comma separeated) file.\n select your tiff images select the metadata you want to see metadata are listed (OPTION) select folder to export the table  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect TIFF images Select the list of tiff images using the file selector\nNeed help using the File Selector?\n Select metadata to display Using the first image selected, the program will determine the list of metadata available and will display the list here. Select the one you want to output.\nMetadat displayed The selected metadata value for all the images is displayed in a dropdown list.\nExport table Select output folder location and click export in next cell.\n"
},
{
	"uri": "/tutorial/notebooks/locate_pixels/",
	"title": "Locate Pixels",
	"tags": [],
	"description": "",
	"content": "Notebook name: locate_pixels.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/metadata_ascii_parser/",
	"title": "Metadata Ascii Parser",
	"tags": [],
	"description": "",
	"content": " Notebook name: metadata_ascii_parser.ipynb\nDescription Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n "
},
{
	"uri": "/tutorial/notebooks/metadata_overlapping_images/",
	"title": "Metatadata Overlapping Images",
	"tags": [],
	"description": "",
	"content": " Notebook name: metadata_overlapping_images.ipynb\nDescription This notebook allows you to add a dimension scale to your images. You can also add a given metadata value for each of the images, or a graph showing the entire metadata plot and the current image metadata position on this graph.\n Scale added on top of image  Metadata of current image  Graph of all metadata and current metadata value   Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nloading the Images Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n Profile UI Presentation How Does it Work ? Scale Select the scale option to add a dimension marker on top of the images. You can define:\n orientation of the scale size and label color thickness of the line position of the scale/label in the image  Metadata When using metadata, you can display the current metadata as a string. But if you chose to, you can also display a graph showing the current file metadata position over the entire set of images metadata.\nText If you are working with a TIFF image, you will have the option of selecting one of the metadata available. Feel free to change the label of this one if needed. You can also define your own metadata by editing the table, or loading a file_vs_metadata file created by a notebook specially dedicated to this purpose (WORK IN PROGRESS).\nIt\u0026rsquo;s sometimes necessary to format the default metadata value (especially if you want to display the graph). To do so, right click in the right column of the table to reach the metadata string parser (string filter). Then define the first and last part of the string to remove  Export NB: Your application will look a little bit different from this animation\nYou can now export all your images. The PNG files created will be a copy of the current UI image preview.\n"
},
{
	"uri": "/",
	"title": "Neutron Imaging",
	"tags": [],
	"description": "",
	"content": " User Home Page Welcome to the ORNL Neutron Imaging Website! This site is designed to help you with the preparation of your experiment and subsequent data processing and analysis. If you are not familiar with neutron imaging and may be interested in collaborating with us, visit the publications page to review the science we do.\nFor industrial applications, please contact Hassina Bilheux\nWe recommend that you discuss your experiment with the instrument team as soon as you receive approval of your beam time.\n Main features  Prepare your arrival: Everything you will need to do before coming to our laboratory. Capabilities: list of imaging instruments available. How to: short tutorials such as how to access your data, connect to the computers, etc. Frequently Asked Questions: answers to the most frequent questions we got from our users. Links: handy links.  We would like to thank the contribution from the research community in the implementation of this web site, and always welcome your comments to improve it (contact Jean Bilheux).\n   Web site logo: Ryzewski K., Herringer S., Bilheux H.Z., Walker L., Sheldon B., Voisin S., Bilheux J., Finocchiaro V., Neutron imaging of archaeological bronzes at the Oak Ridge National Laboratory Physics Procedia, 43, 343-351 (2013).\n "
},
{
	"uri": "/tutorial/notebooks/normalization/",
	"title": "Normalization",
	"tags": [],
	"description": "",
	"content": " Notebook name: normalization.ipynb\nDescription This notebook normalized the imaging data (tiff or fits) by removing the background and fixing the fluctuations of the neutron beam. You will need:\n select your images select your open beam (OB) optional - select the dark field (DF) optional - select one or more background region in your sample images run the normalization select output folder where the normalized images will be saved  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nloading the Images You need to provide the list of samples, OB and DF (if needed). To do so, just SHIFT + ENTER the following cell to display a selection tool wizard.\nThe dialogbox will start listing the files from the IPTS folder you selected in the first cell. Feel free to navigate to find your data set.\nCheck the file selection tool tutorial to learn how to use the file selector tool.   Select your sample images click Next Step\u0026gt;\u0026gt; Select your OB images click Next Step\u0026gt;\u0026gt; OPTIONAL Select your DF images click Next Step\u0026gt;\u0026gt; to let the program load all your images.  Select Background Region SHIFT + ENTER to run the cell. A new User Interface (UI) will come to life. If you can not see the UI, check behind the notebook window.\nJust click OK if you do not want to select any region of interest (ROI).\nIf you want to provide one, or more, ROI, click + on the right of the GUI and move/resize the ROI using the mouse, or the table values.\nNormalization This cell runs the normalization and let you know the progress of the calculation via a progress bar.\nExport Select where you want to output the normalized data, then run the following cell. Once the output folder selected, a folder name after the input data folder is created and will contain all the normalized data.\n"
},
{
	"uri": "/tutorial/notebooks/normalization_batch/",
	"title": "Normalization_batch",
	"tags": [],
	"description": "",
	"content": " Notebook name: normalization_batch.ipynb\nDescription This notebook normalized the imaging data (tiff or fits) by removing the background and fixing the fluctuations of the neutron beam. You will need:\n select your images select your open beam (OB) optional - select the dark field (DF) optional - select one or more background region in your sample images select output folder where the normalized images will be saved normalization will run in the background  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nloading the Images You need to provide the list of samples, OB and DF (if needed). To do so, just SHIFT + ENTER the following cell to display a selection tool wizard.\nThe dialogbox will start listing the files from the IPTS folder you selected in the first cell. Feel free to navigate to find your data set.\nCheck the file selection tool tutorial to learn how to use the file selector tool.   Select your sample images click Next Step\u0026gt;\u0026gt; Select your OB images click Next Step\u0026gt;\u0026gt; OPTIONAL Select your DF images click Next Step\u0026gt;\u0026gt; to let the program load all your images.  Select Background Region SHIFT + ENTER to run the cell. A new User Interface (UI) will come to life. If you can not see the UI, check behind the notebook window.\nJust click OK if you do not want to select any region of interest (ROI).\nIf you want to provide one, or more, ROI, click + on the right of the GUI and move/resize the ROI using the mouse, or the table values.\nExport Folder Select where you want to output the normalized data, then run the following cell. Once the output folder selected, a folder name after the input data folder is created and will contain all the normalized data.\nNormalization (in background) This cell runs the normalization in batch mode, this means that you can keep working on the notebook (select more data to normalized) while the data are normalized behind.\n"
},
{
	"uri": "/tutorial/notebooks/profile/",
	"title": "Profile",
	"tags": [],
	"description": "",
	"content": " Notebook name: profile.ipynb\nDescription We created this notebook to get very precise profile of samples. For example, if you want to select a profile of exactly the center of your cylindrical sample, then this notebook is for you.\nStart the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n Select Images to Process Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n Profile UI Presentation How Does it Work ? Tool to Help you Select the Profile(s) The guide (red box surrounding the profile) is only there to help you define and move your profile with precision. Once the profile has been defined, disable the guide to lock the position of the profile.\nProfiles Plots The profile plots of the current image selected are displayed at the bottom of the interface.\nAll Profiles / All Images By going to the tab All Profiles / All Images, you can display on the same plot the profiles you select with the images you select.\nExport Profiles After clicking the Export Profiles\u0026hellip; button, and selecting an output folder, each profile will produce an ascii file. The top of this ascii file contains some metadata such as size of profile, list of input file name\u0026hellip;. Then the profile for each file will be saved into a comma separated table where each column is a file.\n"
},
{
	"uri": "/tutorial/notebooks/radial_profile/",
	"title": "Radial Profile",
	"tags": [],
	"description": "",
	"content": " Notebook name: radial_profile.ipynb\nDescription This notebook allows you to display and export the radial profile of a set of images. Radial profile means that, after defining the center of your profile region, the closest pixel to the center will produces the first data point. Then the next ones will produces the second data point (average counts over the set of each pixels).\nConfusing!\nWell the following drawings should help you understand how that works.\nFull circle  Step1: User defined the center of the profile (Red pixel in this example) Step2: User defined the sector to use (in this case, we used the entire sector 0 -\u0026gt; 360degrees) Program then calculate the position of each pixel relative to the new center defined Program order the pixels by their distance relative to the center. All the pixels at the same distance from the center will have their counts averaged. Profile of Counts vs pixel index position is then calculated.   Sector  Step1: User defined the center of the profile (Red pixel in this example) Step2: User defined the sector to use Program keeps only the pixel that are within the sector defined Program then calculate the position of each pixel relative to the new center defined Program order the pixels by their distance relative to the center. All the pixels at the same distance from the center will have their counts averaged. Profile of Counts vs pixel index position is then calculated.   Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n Select Images to Process Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n Profile UI Presentation After running the lauch User Interface cell, the following GUI pops up.\nSelection of Sector Center Using the mouse, click the vertical and then horizontal lines on the image window to define the center of the sector.\nSelection of Sector Range Grid Settings It\u0026rsquo;s possible to change the settings of the grid (usefull when color of image and grid are too close).\nCalculate Radial Profiles Jump to the Profile tab and click the Calculate Profiles to calculate the radial profile of each image loaded. All those profiles will be displayed in the same plot below.\nExport Profiles Once the calculation of all profiles has been performed, the Export Profiles button becomes available. Click the Export Profiles \u0026hellip; and select where you want to create the ascii files.\nFile Name Convention The ASCII files creates will have the names bases such as\n\u0026lt;name_of_image\u0026gt;_profile_c_x\u0026lt;x_center\u0026gt;_y\u0026lt;y_center\u0026gt;angle\u0026lt;from_angle_in_deg\u0026gt;to\u0026lt;to_angle_in_deg\u0026gt;.txt\nwhere:\n name_of_image: is the name of the source image without the extension (20170811_Nautical_compass_0030_356_250_1875) x_center: the x axis position of the center (1024.0) y_center: the y axis position of the center (1024.0) from_angle_in_deg: starting sector angle in degrees (24.0) to_angle_in_deg: ending sector angle in degrees (137.0)  giving a name of\n20170811_Nautical_compass_0030_356_250_1875_profile_c_x1024.0_y1024.0_angle_24.0_to_137.0.txt\nFile Format Each ASCII file produced start with the following metadata\n# source image: /Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19621-CLOCK/CT/20170811_Nautical_compass_0030_356_250_1875.tiff # center [x0, y0]: [1024.0,1024.0] # angular range from 24.0degrees to 137.0degrees #pixel_from_center, Average_counts  "
},
{
	"uri": "/tutorial/notebooks/bin_images/",
	"title": "Rebin Images",
	"tags": [],
	"description": "",
	"content": "Notebook name: bin_images.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/registration/",
	"title": "Registration",
	"tags": [],
	"description": "",
	"content": " Notebook name: registration.ipynb\nDescription This notebook will allow the registration (alignment) of a set of images using a reference image of your choice.\nHere are the steps (bold for user input/manipulation)\n Select the stack of images launch application Perform Auto alignment and/or Align images manually use profile to help in the alignment and sliders to check images overlap  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n Select Images to Process Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n Registration UI Registration Methods You will find 4 ways to register your data\n Automatic mode (let the program try to align your data for you) Manual mode (you have full control of how to move each of the image manually) Marker mode (define markers on the images and align them) Profile mode (use high contrast feature to align horizontally and vertically images)  \nAuto Registration Let the program perform auto-registration of all the images selected using the default first image as a reference by clicking Auto Registration\n\nManual Registration If you choose to manually align the images (except the reference image), click the Manual Registration button. Then move manually the images selected using the manual registration tool widgets.\n\nRegistration using Markers Click the Markers\u0026hellip; button to launch a new window.\nYou can define as manay markers as you want for each image. Then using Align images Using Markers button will align all the images according to the best overlap value of the markers.\nYou can copy/paste markers position using right click on the table.\n\nRegistration using Profiles Using a high contrast feature of the images (like a man made marker on the side of the sample), the program calculates the edge of this feature for all the images. This edge position (vertically and horizontally) is then used to register the images. If you are curious about the algorithm used to define the edge position, we are using the same algorithm as the water intake algorithm called sliding average.\n position the horizontal and vertical profiles on top of high contrast object change size (lenght and width) of profile regions if needed calculate edge (peak position) of marker in all images   select one of the bottom 3 options to export, save registered images.  Tools to Help You Grid You can add a grid on top of your images to help you in the manual alignment. Click the Grid \u0026gt; display at the top left corner of the UI. Then play with the slider to change the size of the grid.\nProfile You can display the profile of the image selected and of the reference image to help align the images.\nSimply move the edge of the profile lines. The profile of all the images selected (+ reference image) will be displayed.\nOpacity Slider The UI provides two types of opacity sliders.\n Selection vs Reference Image Images Selected  Selection vs Reference Image This slider will show up whenever at least one file, other than the reference image, is selected in the table (bottom of UI).\n When the cursor is at the top of the slider, the mean of all the images selected is displayed (100% of selection is displayed) When the cursor is at the bottom of the slider, only the reference image is displayed (0% of selection is displayed) All other position will display a x% of the selected images and then (1-x)% of the reference image.   Images Selected Whenever at least two images are selected (other than reference image), a checkbox labeled all and a slider will show up on the left of the UI.\nBy default all the images selected are display (mean of all images). But if you uncheck the all checkbox, then you can gradually display the imagess from the first one to the last one.\nThis slider will allow to go from the first image selected to the second by increasing the opacity of the first one, and decreasing the opacity of the second one. Once only the second image is display, keep moving the cursor will bring to display the third image and the second image will vanish.\nThe goal of this slider is to gradually display the images one by one.\nThe right slider to display current image vs reference image is always available.\n Export By clicking the Export \u0026hellip; button (bottom right) you will export the images registered into a folder you select.\nAdvanced users All the data registered can be access from the python notebook\n\u0026gt;\u0026gt;\u0026gt; data_registered = o_registration.data_dict \u0026gt;\u0026gt;\u0026gt; print(np.shape(data_registered['data'])) (8, 2048, 2048)  you can also reach the list of file names and their metadata\n\u0026gt;\u0026gt;\u0026gt; import pprint \u0026gt;\u0026gt;\u0026gt; pprint.pprint(data_registered['file_name']) ['/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0000.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0031.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0032.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0033.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0034.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0035.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0036.tif', '/Volumes/my_book_thunderbolt_duo/IPTS/IPTS-19921-Charles/02/im0037.tif'] \u0026gt;\u0026gt;\u0026gt; pprint.pprint(data_registered['metadata'][0]) {256: 2048, 257: 2048, 258: (16,), 259: 1, 262: 1, 270: 'slope = 2.13626E-05 \\roffset = 0.00000E+00\\r', 273: (8,), 277: 1, 278: 2048, 279: (8388608,), 282: 10.0, 283: 10.0, 296: 3, 320: (0,), 339: (1,)}  "
},
{
	"uri": "/tutorial/notebooks/rename_files/",
	"title": "Rename Files",
	"tags": [],
	"description": "",
	"content": " Notebook name: rename_files.ipynb\nDescription This notebook allows you to rename a set of files. You can define\n the prefix file name the starting index the index number of digits  Main use of this notebook In this notebook, we will correctly format the file name index of the file selected. This allows the sorting of the file name to works all the time. For example, there are many cases where the images are saved using the following convention\nWrong filename index\nimage_1.tif image_2.tif image_3.tif image_4.tif ... image_10.tif image_11.tif ... image_100.tif  Using this convention, any sorting algorithm will sort them as followed\nWrong sorting\nimage_1.tif image_10.tif image_100.tif ...  So this notebook will fix this issue by renaming the files\ncorrect filename index\nimage_001.tif image_002.tif image_003.tif image_004.tif ... image_010.tif image_011.tif ... image_100.tif  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select your IPTS Check the full tutorial here\nSelect images to rename Using the file dialog tool, select the folder for which you want to renmame the files.\nOnly the most dominant file type (ex: tiff, fits) from the folder will be renamed\nDefine new naming schema Current schema name This is where you define the way the file name is defined. You need to specify what string separates the first part of the file name with the index. The entire part of the string before this separator will be replaced by your own string (defined in New Naming Schema).\nThe Random input dropdown list displays a random selection of the input files, to help you in defining the separator string.\nNew naming schema Here, you define the new prefix file name, index separator, number of digits and offset of this index.\nExample:\n old file name: experiment_0845454_10.tiff separator: _ new file name prefix: my_image number of digits: 4 offset: 15 new file name will be: my_image_0025.tiff  Result The bottom part of the cell will show the result of the naming on the first image selected.\nIf the new name shows an Error, checks your pre index separator.\nOutput folder You simply need to select where the new renamed file will be created. Then the program will copy the selected files and renamed them in the folder you selected.\nFeel free to check the dropdown list that shows the old name versus the new name of each file.\n"
},
{
	"uri": "/tutorial/notebooks/resonance_imaging_experiment_vs_theory/",
	"title": "Resonance Imaging Experiment vs Theory",
	"tags": [],
	"description": "",
	"content": "Notebook name: resonance_imaging_experiment_vs_theory.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/rotate_and_crop_images/",
	"title": "Rotate and Crop Images",
	"tags": [],
	"description": "",
	"content": "Notebook name: rotate_and_crop_images.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/select_ipts/",
	"title": "Select IPTS",
	"tags": [],
	"description": "",
427
	"content": " The select IPTS tool that you will find at the top of most of the notebooks allows you to quickly select your IPTS. This information will then be used by the program to quickly jump to that IPTS. Any file or folder selection will starts from this IPTS project, this way you won\u0026rsquo;t have to look around to find your data.\nSelect your Instrument Due to the success of the imaging technique ( :-) ), we are not limited to HFIR anymore. That means you will need to select your instrument to allow to list the IPTS of this particular beam line.\nEnter IPTS Use the top text box to specify your IPTS number. As you enter the number, the program check if the IPTS exists or not. A message will be displayed on the right side informing you if the file exist or not.\nIf the IPTS can be located, the second widget (list of IPTS) will automatically jump to that folder.\nSelect IPTS The second widget list all the IPTS found for your instrument (Imaging by default). Just select your IPTS.\nHELP Button Brings a new window in your browser with this help page.\nLive Demo "
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},
{
	"uri": "/tutorial/notebooks/topaz_config_generator/",
	"title": "TOPAZ config file generator",
	"tags": [],
	"description": "",
	"content": " Notebook name: TOPAZ_config_generator.ipynb\nStart the Notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nFirst Time Look If you are using the notebook for the first time, it should look like this\nDefine IPTS Starting from the top SHIFT + ENTER the cells in order to run them. The 4th cell will ask you to select the IPTS of your experiment.\nInitialize Parameters Using Config File (Optional) In case you want to initialize the content of all the widgets by a config file you created in the past, you can simply select that config file.\nRun All Cells at Once Select the next cell and click Cell \u0026gt; Run All Below.\nFile or Folder Selection Tool In order to select a file or a folder you need to know the following:\n one dot (.) means current folder 2 dots (..) means above folder click select to validate your file or folder selection selection made will be display above the selection tool   Widget Interaction You will find that some of the widgets display depends on your input in other widgets. This has been implemented to help you in your choice of parameters.\nAdvanced Options Instrument scientists and super users can have access to advanced options by entering a pasword in the Advanced Options cell.\nCreate Config File And finally, re-run the last cell Export the Config File in order to produce the config file. If any information is missing, a text will show you what is missing. Just click on this link to quickly jump to the widgets in the notebook.\nIf nothing is missing, the full path to the config file created will be displayed!\nFor advanced users who entered the instrument password, they will have a preview of the config file when creating the configuration file.  Run Reduction The next cell will build up the command line you will need to copy/paste into a terminal. To do so\n Copy the command line green text using Right click + Copy \u0026hellip; click the terminal icon at the top of the desktop Paste the text hit ENTER  "
},
{
	"uri": "/tags/",
	"title": "Tags",
	"tags": [],
	"description": "",
	"content": ""
},
{
	"uri": "/tutorial/notebooks/template_ui/",
	"title": "Template UI Builder",
	"tags": [],
	"description": "",
	"content": "Notebook name: template_ui.ipynb\n"
},
{
	"uri": "/tutorial/notebooks/water_intake_profile_calculator/",
	"title": "Water Intake Profile Calculator",
	"tags": [],
	"description": "",
	"content": " Notebook name: water_intake_profile_calculator.ipynb\nDescription This notebook will calculate the water intake profile vs time of a sample.\nThis application is still under heavy development. The look of the UI will differ from the screenshot you can see in this tutorial and features are added on a regular basis. The tutorial will be fully rewrite once the development is done.  Here are the steps (bold for user input/manipulation)\n select the normalized images images sorted by time (by default) User Interface pops up! region of interest selected (optional) change the sorting algorithm  by name by date  (optional) select a folder containing the original .dsc files (which contain the right time stamp) profile of counts vs vertical-pixel calculated (select integrated algorithm: mean/median/sum) water intake profile vs file index or vs time export profiles  Start the notebook If you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nSelect your IPTS Need help using the IPTS selector?\n Select Images to Process Select the images you want to process using the File Selector. Once you click the Select button, the time stamp and the images will be automatically loaded. Wait for the progress bar to be done.\nNeed help using the File Selector?\n \nWater Intake Calculator UI Resizing widgets It\u0026rsquo;s possible to resize or move any of the plots.\nSorting Algorithm By default, all the images are sorted using their time stamps. But in some cases (old IPTS), the time stamp may be wrong. So It\u0026rsquo;s possible to:\n either sort them using their name and let you define the time interval between the runs define a folder that contains the .dsc files created by the MCP. Those files contain in all cases, the correct time stamp.   Profile Algorithm You can select the profile algorithm to use (to integrate over the x-axis of the region selected) using the profile algorithms available\n add mean median  The profile is calculated using the following method:\n retrieve the region you defined in the top left image using the profile algorithm you selected, will integrate over the x-axis of the ROI display the profile vs the y-pixels.  Using Pixel or Size Reference By default the water intake profile display the position of the \u0026ldquo;wave\u0026rdquo; as a pixel number vs the time. But it\u0026rsquo;s also possible to display this one using a real dimension (mm). To do so, just click the water intake y_axis -\u0026gt; distance check box and define the dimension of the pixel.\nIntegration Direction In the new version of the application, it is now possible to specify the direction of integration of the profiles. Select either y_axis or x_axis to change this direction.\nWater Intake Algorithms It\u0026rsquo;s possible to chose between 2 different algorithms to calculate the \u0026ldquo;wave\u0026rdquo; front position.\nSliding Average This method is fully demonstrated in this PDF document\nError Function Fitting The signal is fitted using a modified version of the error function as shown here\nChange Point You can now select a 3rd algorithm based on the following python library (changepy)\nRebin For very poor statistics data, you can rebin the data by 2, 3 or more pixels. This will decrease the resolution of the water intake peak position, but will improve its calculation by the various algorithms (sliding average, error function, \u0026hellip;)\nLive Demo Export Results You can export the following data\n profile (Export \u0026gt; Profiles \u0026hellip;) water intake (Export \u0026gt; Water Intake \u0026hellip;) Table (Export Table \u0026hellip;, on the right of the table)  For Advanced Users. keep reading!\n Want to Work on the Data in the Notebook? If you want to play yourself with the data loaded, you can easily access all the data and metadata loaded\nlist_of_data = o_gui.dict_data['list_data']  list_of_files = o_gui.dict_data['list_images']  list_of_time_stamp = o_gui.dict_data['list_time_stamp'] list_of_time_stamp_user_format = o_gui.dict_data['list_time_stamp_user_format']  "
}]