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[
{
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	"uri": "/prepare_venue/sample/",
	"title": "Sample Shipping",
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	"tags": [],
	"description": "",
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	"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"
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},
{
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	"uri": "/prepare_venue/proposal_confirmation/",
	"title": "Proposal Confirmation",
	"tags": [],
	"description": "",
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	"content": "To be completed **by the Principal Investigator** (PI) of the experiment at least **3 weeks** before beam time.  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"
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},
{
	"uri": "/prepare_venue/safety/",
	"title": "Engineering & Equipment Safety",
	"tags": [],
	"description": "",
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	"content": "To be completed **by the Principal Investigator** (PI) of the experiment   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"
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},
{
	"uri": "/prepare_venue/access/",
	"title": "Access",
	"tags": [],
	"description": "",
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	"content": "This must be completed by **all** participants to the experiment **before the first day** of experiment.   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"
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},
{
	"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": "",
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	"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  \n Link not working? \u0026lt;/span\u0026gt; \u0026lt;/div\u0026gt; \u0026lt;div class=\u0026quot;expand-content\u0026quot; style=\u0026quot;display: none;\u0026quot;\u0026gt;  \u0026lt;/div\u0026gt;  \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"
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},
{
	"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/",
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	"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.   Data Analysis   I forgot my XCAMS password   Simply go to How to reset password web page.     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](/en/credits#jean_bilheux) to discuss your needs. By going over your experiment together, Jean will show you how to run the current tools and will develop customed python notebooks if needed.     What are those \u0026#39;jupyter notebooks\u0026#39;?   The *jupyter notebook* developed by [jupyter](http://jupyter.org/) are an easy way to run python code using only a browser. By accessing our analysis computer, you won't have anythign to install. Refer to our [How To](/en/tutorial/) page to learn how to do that.     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  Access your data](/en/tutorial/how_to_access_data/)     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. To fix this issue, just open a terminal and type `rm -rf ~/.mozilla` This should fix your issue and you should be able to start the jupyter notebooks now.     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     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  Start the python notebooks](/en/tutorial/how_to_start_notebooks)   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  Tags 65000 to 650009 have no name and are used for timestamps and a unique ID  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 \u0026 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    Work With Us   Visiting Researcher Program   Link here     Minority Serving Institutions Partnership Program   Link here   "
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},
{
	"uri": "/tutorial/how_to_start_notebooks/",
	"title": "Start the python notebooks",
	"tags": [],
	"description": "",
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	"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.  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.  "
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{
	"uri": "/links/",
	"title": "Links",
	"tags": [],
	"description": "",
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	"content": " Laboratory Utilities   ORNL User Program Guide   User Program Guide.     Submit a proposal   Go to the Proposal System.     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   Analysis 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/   References   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   Shortcuts  ONCat - https://oncat.ornl.gov/#/   "
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},
{
	"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 "
},
{
	"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"
},
{
	"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/",
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	"title": "MCP Det. Corr.",
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	"tags": [],
	"description": "",
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	"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_use_oncat/",
	"title": "Use ONCat",
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	"tags": [],
	"description": "",
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	"content": " ONCat is a web tool that allows you to browse through the archived data.\nTo access ONCat, go to https://oncat.ornl.gov/#/\nHow to reach your data  Click EXPLORE enter your UCAMS and password Select your facility Select your instrument Select your IPTS, or perform a search to quickly find the right IPTS  Preview of your data By clicking the Thumbnails button, you can get a preview of every single file archived.\nHow to access the metadata From the top page of your IPTS, click the Files button at the top.\nA predefined list of metadata is display in the table.\nBut if you want to preview the entire list of metadata in your files, click your file name in the left column of this table and navigate through the tree shown.\n"
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	"uri": "/tutorial/how_to_start_amira/",
	"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](/credits/)  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  CLCK HERE: Introduction to Python Scripting   "
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{
	"uri": "/tutorial/how_to_other/",
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	"title": "Divers ...",
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	"tags": [],
	"description": "",
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	"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.   "
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},
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{
	"uri": "/tutorial/notebooks/",
	"title": "Notebooks Tutorials",
	"tags": [],
	"description": "",
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	"content": " bin_images   bragg_edge_signal_vs_powder_peaks  calibrated_transmission  combine_folders   combine_all_images_selected   create_list_of_file_name_vs_time_stamp  cylindrical_geometry_correction  deal_images   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   frederick_ipts  from_attenuation_to_concentration  from_dsc_time_info_to_ascii_file_vs_time  gamma_filtering_tool   images_and_metadata_extrapolation_matcher   integrated_roi_counts_vs_file_name_and_time_stamp  list_element_bragg_edges  list_metadata_and_time_with_oncat   list_tiff_metadata  metadata_ascii_parser   metadata_overlapping_images   normalization   normalization_batch   profile  radial_profile   registration  rename_files  resonance_imaging_experiment_vs_theory  rotate_and_crop_images   sequential_combine_images_using_metadata   template_ui   topaz_config_generator  water_intake_profile_calculator \nTools File Selector  Select IPTS \n Recently updated\n"
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{
	"uri": "/tutorial/notebooks/bin_images/",
	"title": "Bin Images",
	"tags": [],
	"description": "",
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	"content": " Notebook name: bin_images.ipynb\nDescription This notebook rebin a image, or set of images. That means the pixels will be group according to the rebin parameter you will select.\nFor example, if you select a rebin value of 2, pixels will be group 2x2 (see images as an illustration of such a rebin) and those pixels will be averaged.\nIn the following example, we start with an image of 8 by 8 pixels. Please note the value of each pixels (number of counts).\nfig 1: original 8 by 8 image.\nThen we selected a rebin value of 2.\nfig 2: Rebin by 2 for a final image size of 4 by 4.\nIn the case where the width and/or height of the image can not be evenly divided by the rebin coefficient, the last uncompleted bin will be removed from the final image.  Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the images to rebin Using the file selection tool, select the images you want to rebin.\nNB: After selection of the images, those will be automatically loaded in memory.\nSelect Bin Parameter It\u0026rsquo;s time to select your bin parameter. The cell will also gives you, for information, the size of your image before and after rebin.\nSelect Output Folder Just one more step. Select where you want those new images to be created. Once selected using the [folder selection tool]((/tutorial/notebooks/file_selector/#activate-search), the images will be automatically rebin and exported. The program will create a folder called after the input folder from where the initial images were retrieved inside the output folder you selected following this convention\n Input folder: my_data Output folder: my_data_rebin_by_2 (or whatever the rebin coefficient you selected)  "
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},
{
	"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": "",
161
	"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](/tutorial/notebooks/file_selector/#folder_navigation) 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"
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},
{
	"uri": "/tutorial/notebooks/calibrated_transmission/",
	"title": "Calibrated Transmission",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/select_ipts/#activate-search)?  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](/tutorial/notebooks/file_selector/#activate-search)?  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"
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},
{
	"uri": "/categories/",
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	"title": "Categories",
	"tags": [],
	"description": "",
	"content": ""
},
{
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	"uri": "/tutorial/notebooks/combine_all_images_selected/",
	"title": "Combine All Images Selected",
180 181
	"tags": [],
	"description": "",
182
	"content": " Notebook name: combine_all_images_selected.ipynb\nDescription This notebook allows you to combine the images selected. 3 algorithms are currently available\n Arithmetic Mean   Geometric Mean   Add  Not seeing the algorithm you want to use? Please let me know and I will add it (Jean Bilheux)  Example:\nInput\n image001.fits image002.fits image003.fits  Output\n image001_image002_image003.fits  Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the images to merge Using the file selection tool, select the images you want to combine.\nMerging method It\u0026rsquo;s time to select your merging algorithm (check the description at the top of this web page for the explanation of the algorithms).\nSelect output location Using the folder selection tool, select the location where you want to create the combine image.\nPersonalize the output file name If you don\u0026rsquo;t like the default output file name offered, you have here the option to define your own.\nMerging Running the next cell will create the merge image and will inform you of the progress via a progress bar.\n"
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},
{
185 186
	"uri": "/tutorial/notebooks/combine_folders/",
	"title": "Combine Folders",
187 188
	"tags": [],
	"description": "",
189
	"content": " Notebook name: combine_folders.ipynb\nDescription There are situations where you want to combine similar images from different folders. This is the notebook you were looking for.\nYou will need to select the input folders first. The program will let you know if they do not have the same number of images and will stop there.\nThen you will just need to specify the kind of combination you are looking for (add, mean) and then the output folder\nFig 1: Example of input folders\nFig 2: What the output may look like\nSelect your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the Input Folders Using the folder selection tool, select the folders you want to combine.\nThe program will then automatically check that the input folders have the same number of files.\nIf they do, you will see the following green message\nBut if they don\u0026rsquo;t, you will quickly see that something is wrong when you see the red message\nThe only requirement of this notebook is that the folders must contain the same number of images.\nHow many folders do you want to combine together? According to the number of folders selected, the program will let you choose the way you want them combined.\nFor example, if you select 4 folders, you will have the option to combine them by 2, 3 or 4. If you select 3, the 4th folder won\u0026rsquo;t be used.\nHow do you want to combine the folders What algorithm do you want to use to combine them. You have the option to add or to mean those images.\nNot seeing the algorithm you want to use? Please let me know and I will add it (Jean Bilheux)  Select output folder Using the folder selection tool, select the output location.\nA double progress bar (folder and file progress) will show you the progress of the combination of the folders.\nIn our example, let\u0026rsquo;s pretend we selected to combine the folders 2 by 2, the output will look like this\n"
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},
{
	"uri": "/tutorial/notebooks/sequential_combine_images_using_metadata/",
	"title": "Combine Images Sequentially using the Metadata Information",
	"tags": [],
	"description": "",
196
	"content": " Notebook name: sequential_combine_images_using_metadata.ipynb\nDescription This notebook was implemented to automatically combine runs according to any of the metadata you select.\nIn other words, let\u0026rsquo;s pretend you take 10 images at a given sample position, move the sample then take another 10 images, until the end of the cycle. The algorithm will look at the metadata value you selected, in this case 2 motor positions, and if they match within a given range (1 by default), the images are considered to be of the same sample at the same place. They will be combined. If one of the metadata is outside the error range, or if the name of the run changed, then this is considered to be a different run or/and different location.\nThe notebook will create a dictionary of run # -\u0026gt; position# -\u0026gt; list of files. This then wll be used to combine the images using 1 of 3 different algorithms (check the following notebook to learn more about those algorithms [combine_images]())\nTutorial Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the Input Folders Using the folder selection tool, select the folders containing the images you want to work on.\nselect metadata to match Using the first file of the folder selected, the notebook will display the list of metadata (and their value) to let you select which metadata we need to check to decide if the new file needs to be combine to the previous one or not.\nBy default, 2 metadata are selected\n MotLiftTable.RBV MtLongAxis.RBV  How do you want to combine the folders What algorithm do you want to use to combine them. You have the option to add or to mean (arithmetic mean or geometric mean) those images.\nNot seeing the algorithm you want to use? Please let me know and I will add it (Jean Bilheux)  Create and Checking Merging List The program will load the data, in order to retrieve the metadata, and will create the list that will be used in the final step to merge the data. If anything seems wrong during this step, contact me (Jean Bilheux)\nSelect output folder Using the folder selection tool, select the output location.\nA double progress bar (folder and file progress) will show you the progress of the combination of the folders.\n"
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},
{
	"uri": "/credits/",
	"title": "Contacts",
	"tags": [],
	"description": "",
203
	"content": "            Hassina Bilheux - Instrument ScientistExpertise: HFIR and SNS imaging beam lines    \u0026lt;div class=\u0026quot;expand-label\u0026quot; style=\u0026quot;cursor: pointer;\u0026quot; onclick=\u0026quot;$h = $(this);$h.next('div').slideToggle(100,function () {$h.children('i').attr('class',function () {return $h.next('div').is(':visible') ? 'fa fa-chevron-down' : 'fa fa-chevron-right';});});\u0026quot;\u0026gt; \u0026lt;i style=\u0026quot;font-size:x-small;\u0026quot; class=\u0026quot;fa fa-chevron-right\u0026quot;\u0026gt;\u0026lt;/i\u0026gt; \u0026lt;span\u0026gt; Short Bio ... \u0026lt;/span\u0026gt; \u0026lt;/div\u0026gt; \u0026lt;div class=\u0026quot;expand-content\u0026quot; style=\u0026quot;display: none;\u0026quot;\u0026gt; Dr. Hassina Bilheux obtained her Ph.D. in Physics at the Univ. of Versailles, France. \u0026lt;br\u0026gt;Her work focused on plasma physics at the Oak Ridge National Laboratory’s Physics Division.\u0026lt;br\u0026gt; She 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. \u0026lt;/div\u0026gt;   bilheuxhn@ornl.gov (865) 384 - 9630 (865) 574 - 0241 researchGate Scholar Google| | | Yuxuan Zhang (Shawn) - Instrument ScientistExpertise: 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 |\nPast 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  "
204 205 206 207 208 209
},
{
	"uri": "/tutorial/notebooks/create_list_of_file_name_vs_time_stamp/",
	"title": "Create List of Files of Names vs Time Stamp",
	"tags": [],
	"description": "",
210
	"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](/tutorial/notebooks/file_selector/#folder_navigation) 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 ...  "
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},
{
	"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"
},
{
220 221
	"uri": "/tutorial/notebooks/deal_images/",
	"title": "Deal Images",
222 223
	"tags": [],
	"description": "",
224 225 226 227 228 229 230 231
	"content": " Notebook name: deal_images.ipynb\nDescription There will be cases where a folder contains several different experiment, or set up, or measure. You will need to sort those images into their own folder. Hopefully this notebook can save you a ton of time by doing it automatically. The algorithm to \u0026lsquo;group\u0026rsquo; the images is based on the name of those files.\nThe following picture illustrates the principle of the deal\nThis program assumes that the file names follow this schema ``` text_. ``` If it does not, run the notebook [rename_files.ipynb](/tutorial/notebooks/rename_files/).  Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the Input Folder Using the folder selection tool, select the folder that contains the images you need to separate.\nSelect the Output Folder Using the folder selection tool, select the folder that will contain the folders of all the deal images.\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"
232 233 234 235 236 237
},
{
	"uri": "/tutorial/notebooks/display_counts_of_region_vs_stack/",
	"title": "Display Counts of ROI vs Stack",
	"tags": [],
	"description": "",
238
	"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](/tutorial/notebooks/file_selector/#activate-search)?  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) "
239 240 241 242 243 244
},
{
	"uri": "/tutorial/notebooks/display_file_names_vs_time_stamp/",
	"title": "Display File Names vs Time Stamp",
	"tags": [],
	"description": "",
245
	"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](/tutorial/notebooks/file_selector/#folder_navigation) 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"
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},
{
	"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": "",
259
	"content": " Notebook name: display_integrated_stack_of_images.ipynb\nDescription This notebook will simply the integrated image of all the images (TIFF or FITS) from the input folder.\nSelect your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the Input Folder Using the folder selection tool, select the folder that contains the images you need to separate.\nIntegrated image Then the integrated image is displayed with a colorbar.\n"
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},
{
	"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": "",
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	"content": " Notebook name: fix_images.ipynb\ndescription This notebook allows you to fix a set of images by replacing all the pixels having the same value, by another value. For example, you can replace all the Inf values by 0 or np.NaN.\nHere is the list of things you can fix:\n Inf values NaN values Negative values  and here is the list of things you can replace with:\n np.NaN 0  Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select your images Using the files selection tool, select the images you want to fix.\nGive statistics In order to get an idea of what you need to correct, the next cell will display the following informations.\n name of the file total number of pixels in the image number and percentage of pixels with negative values number and percentage of pixels with infinite (negative or positive) values number and percentage of pixels with non defined (NaN) values  A slider allows you to check those parameters for each of the images you loaded.\nDisplay images and histograms This is where you are able to visualize the corrections you are about to apply to the data. You can select which corrections you want to apply and see the effect on the image and its histogram.\nYou can make the following corrections\n Negative values -\u0026gt; np.NaN or 0 Infinite values -\u0026gt; np.NaN or 0 NaN values -\u0026gt; np.NaN or 0  where np.NaN is the numpy NaN.\nYou can slide through the images to display\n the raw image (top left corner) the raw histogram (top right corner) the new corrected image (bottom left corner) the new histogram (bottom right corner)\n  Export images Using the folder selection tool to select the output location of all the images corrected.\n"
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},
{
	"uri": "/tutorial/notebooks/frederick_ipts/",
	"title": "Frederick IPTS",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/select_ipts/#activate-search)?  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"
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{
	"uri": "/tutorial/notebooks/from_dsc_time_info_to_ascii_file_vs_time/",
	"title": "From .dsc Time Info to ASCII File vs Time",
	"tags": [],
	"description": "",
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	"content": " Notebook name: from_dsc_time_info_to_ascii_file_vs_time.ipynb\nDescription There are cases where you will need to use the metadata from the dsc file to retrieve the time information as the files themselves do not have this information any more (because they are not the original images anymore, because they have been moved, \u0026hellip;).\nSo this notebook will use the dsc files and match the time stamp information they contain with the base file name. Then will create an ascii file of the equivalent TIFF image vs time stamp\nINPUT\nfolder containing .dsc files\nOUTPUT\nfilename_vs_timestamp.txt\nsample_0000.tif 1045454545 sample_0001.tif 1045454546 sample_0002.tfi 1045454547  Instructions Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the DSC Folder Using the folder selection tool, select the folder that contains the .dsc files.\nSelect the Image Folder Using the folder selection tool, select the folder that contains the images (TIFF or FITS) to match with the dsc files.\nSelect the Output Folder Using the folder selection tool, select the folder where the ascii file listing the image files and their time stamp will be created.\nAscii File Created Once you have selected the output folder, the ascii file will be automatically created and will look something like this\nwhere the columns information are in order\n base file name time stamp (unix format) time stamp (user format) acquisition time (s)  "
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{
	"uri": "/tutorial/notebooks/from_attenuation_to_concentration/",
	"title": "From Attenuation to Concentration",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/file_selector/#activate-search)?  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](/tutorial/notebooks/file_selector/#activate-search)?  Video showing entire process "
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},
{
	"uri": "/showcase/",
	"title": "Gallery",
	"tags": [],
	"description": "",
	"content": ""
},
{
	"uri": "/tutorial/notebooks/gamma_filtering_tool/",
	"title": "Gamma Filtering Tool",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/file_selector/#folder_navigation) 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"
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{
	"uri": "/github/",
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	"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  "
},
{
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	"uri": "/tutorial/notebooks/images_and_metadata_extrapolation_matcher/",
	"title": "Images and Metadata Extrapolation Matcher",
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	"tags": [],
	"description": "",
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	"content": " Notebook name: images_and_metadata_extrapolation_parser.ipynb\nDescription There are cases where you need to define the metadata value of a given image. This is challenging when the file recording the metadata has no correlation with the images taken. This notebook will allow you to calculate, according to the time stamp of the image file, the metadata value.\nThis notebook takes 2 text files as input:\n filename vs timestamp text file\n created by the notebook create_list_of_file_name_vs_time_stamp.ipynb  metadata vs timestamp text file\n created by metadata_ascii_parser   or\n created by list_metadata_and_time_with_oncat   Using those input files, the notebook will create a new text file of the file name and the corresponding metadata values you previously selected.\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](/tutorial/notebooks/select_ipts/#activate-search)?  Select the file name vs time stamp text file Using the file selection tool, select your metadata file.\nAs specified in the notebook, this is the file you created using the notebook create_list_of_file_name_vs_time_stamp.ipynb\nThis file looks like this\nSelect the metadata vs time stamp text file This file can be created\n using the notebook metadata_ascii_parser when the metadata have not been saved inside the image file (ex: hardware used to apply, and record, metadata was not connected to our data aquisition system (DAS))  or\n using the notebook list_metadata_and_time_with_oncat when the metadata are inside the image file.  Need help figuring out the metadata stored in your image file? Check this tutorial on [how to use ONCat](/tutorial/how_to_use_oncat/#activate-search)  Select data to merge You have now the option to select which of the metadata you want to keep and extrapolate for the given image files.\nUse CTRL + Click to select more than one metadata.\nExtrapolate and display results Running this cell will give you a preview of the interpolation of the data. Original values of the metadata are displayed in green and extrapolated values are displayed in orange. The orange data will be the one exported in the final text file as those corespond to the image file time stamps.\nSelect output folder Using the folder selection tool, select the output folder.\nOnce you have selected this folder, the output ascii file is automatically created and a message informs you of the name of the file as well as the location of this file.\n"
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},
{
	"uri": "/tutorial/notebooks/integrated_roi_counts_vs_file_name_and_time_stamp/",
	"title": "Integrated ROI Counts vs File Name and Time Stamp",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/file_selector/#folder_navigation) 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"
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Bilheux, Jean-Christophe's avatar
Bilheux, Jean-Christophe committed
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{
	"uri": "/tutorial/notebooks/list_element_bragg_edges/",
	"title": "List Bragg Edges of an Element",
	"tags": [],
	"description": "",
	"content": " Notebook name: list_element_bragg_edges.ipynb\nDescription This notebook lists the given number of hkl, d-spacing and Bragg Edges values for one of the listed element.\nIf you need help accessing this notebook, check the How To \u0026gt; Start the python notebooks tutorial.\nHow to Use It? Select the entries Select the element and the number of data you want to see.\nTable Result The next cell will display the following information\n name of the material you selected lattice value crystal structure if the program used the local metadata table or retrieved the information from the NIST web site directly d-spacing and Bragg edges values for each hkl  Export table You have the option to export the table information as a CSV (comma separated file format).\nSelect the output folder where the file will be created. The file will then be created automatically using the following file name convention\nbragg_edges_of_\u0026lt;name_of_element\u0026gt;.txt\n"
},
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{
	"uri": "/tutorial/notebooks/list_metadata_and_time_with_oncat/",
	"title": "List Metadata and Time with ONCat",
	"tags": [],
	"description": "",
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	"content": " Notebook name: list_metadata_and_time_with_oncat.ipynb\nDescription Using the ORNL Database service ONCat in the back, users can create an ASCII file with the acquisition time and metadata of interest selected. The output file will look like this\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 TIFF images Select the list of tiff images using the file selector\nNeed help using the [File Selector](/tutorial/notebooks/file_selector/#activate-search)?  Log in to ONCat In order to be able to retrieve the metadata from our database (ONCat) you will need to log in by typing your XCAMS password, then hit ENTER. A message will show you if you entered the right password or not.\nSelect Metadata to Keep The notebook will list all the metadata found in the first image you selected with an example of values, again found in the first file. Select all the metadata you want to see in the final ASCII output file. Use CTRL + CLICK to select more than one metadata.\nCreate and Export ASCII File It\u0026rsquo;s now time to select your output folder.\nNeed help using the [Folder Selector](/tutorial/notebooks/file_selector/#activate-search)?  The program will automatically name the file based on the input folder\nFor example:\n input folder: 20180814\n ASCII file: 20180814_metadata_report.txt\n  "
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{
	"uri": "/tutorial/notebooks/list_tiff_metadata/",
	"title": "List TIFF Metadata",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/file_selector/#activate-search)?  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"
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},
{
	"uri": "/tutorial/notebooks/math_images/",
	"title": "Math with Images",
	"tags": [],
	"description": "",
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	"content": " Notebook name: math_images.ipynb\nDescription This notebook allows you to perform simple math (addition or subtraction for now) on a stack of images.\nThe current requirement is that you can add or subtract a single image (that you will select) on a stack of images.\nNot seeing the algorithm you want to use? Please let me know and I will add it (Jean Bilheux)  Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select the images to operate on Using the file selection tool, select the images you want use in the math.\nselect the second image to use in the math Using the file selection tool, select the image you want to use on the other side of the math.\nMath Method You can currently only select add or subtraction.\nRecap The program gives you a recapitulation of the math you are about to perform, such as:\n the number of files affected the math algorithm the name of the second file image  Select output location Using the folder selection tool, select the location where you want to create the final images.\nOnce the files have been created, a message will let you know where you can check the result.\n"
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{
	"uri": "/tutorial/notebooks/metadata_ascii_parser/",
	"title": "Metadata Ascii Parser",
	"tags": [],
	"description": "",
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	"content": " Notebook name: metadata_ascii_parser.ipynb\nDescription Because of the large variety of sample environment used, we need to face a large variety of metadata files outputs. Those metadata files contain the sample environment data, such as temperature of a furnace, pressure of a cell, voltage of a power supply, etc. This notebook will allow you to select this metadata and isolate the set of information you want to retrieve. A new ascii file will then be created in a more common format that other notebooks will be able to use.\nIf your metadata file can not be parsed by this program, this means we need to write a new plugin for that particular file, please contact Jean Bilheux.  The metadata file currently supported by this application looks like this:\nand will create the following type of output file\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](/tutorial/notebooks/select_ipts/#activate-search)?  Select the metadata file Using the file selection tool, select your metadata file.\nSelect the infos you want to keep The program will list the name of each column of metadata, as well as a data/time columns created by the program itself. Select the metadata you want to keep (any number of columns is allowed).\nUse SHIFT or COMMAND + CLICK to select more than one metadata.\nSelect output folder and filename It is now time to export the new ascii metadata file.\nDefine the output file name. The extension .txt will be automatically added if you don\u0026rsquo;t specify it. Then select the output folder.\nThe new comma separated ascii file will have the following columns\nIndex, timestamp (s), metadata you selected \u0026hellip;\n"
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{
	"uri": "/tutorial/notebooks/metadata_overlapping_images/",
	"title": "Metatadata Overlapping Images",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/file_selector/#activate-search)?  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'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"
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{
	"uri": "/",
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	"title": "Neutron Imaging",
	"tags": [],
	"description": "",
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	"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.  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).    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 "
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{
	"uri": "/tutorial/notebooks/normalization/",
	"title": "Normalization",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/select_ipts/#activate-search) 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"
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	"uri": "/tutorial/notebooks/normalization_batch/",
	"title": "Normalization_batch",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/select_ipts/#activate-search) 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"
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{
	"uri": "/tutorial/notebooks/profile/",
	"title": "Profile",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/select_ipts/#activate-search)?  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](/tutorial/notebooks/file_selector/#activate-search)?  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"
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{
	"uri": "/tutorial/notebooks/radial_profile/",
	"title": "Radial Profile",
	"tags": [],
	"description": "",
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	"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](/tutorial/notebooks/select_ipts/#activate-search)?  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](/tutorial/notebooks/file_selector/#activate-search)?  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  "
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},
{
	"uri": "/tutorial/notebooks/registration/",
	"title": "Registration",
	"tags": [],
	"description": "",
420
	"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](/tutorial/notebooks/select_ipts/#activate-search)?  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](/tutorial/notebooks/file_selector/#activate-search)?  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.  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,)}  "
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},
{
	"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": "",
441
	"content": " Notebook name: rotate_and_crop_images.ipynb\nDescription You will use this notebook if you need to crop and/or rotate a set of images.\nThe application will ask you to select the images (FITS or TIFF) you want to modify. Then a User Interface (UI) will allow you to define the cropping region as well as the rotation angle to apply to the images. You will be able to browse through the images to check the result and then create the transformed images before exporting them to a new output folder as TIFF images.\nHow it Works Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select your Images Using the file selection tool, select the images you want to work with.\nOnce selected, your images will be automatically loaded.\nCrop and/or Rotate your Images This is where you will apply the correction to your images.\nHow to crop the images:  move the box around by clicking one of the edge and without releasing the mouse move the box to the new location resize the box by clicking the bottom right corner and dragging it to the new size.  How to rotate the images: Simply by entering the angle value in the text field. The angle is using the geometrical convention, with counter clock wise rotation direction.\nApply changes to all images: By clicking the apply on all images button, you will apply those correction to all images and quit this UI\nExport Images Using the folder selection tool, select where you want the output images to be created.\nThe program will automatically create a folder based on the rotation angle you defined\nFor example:\nA folder named rotated_0.5deg is created when the angle is 0.5 degrees.\n"
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},
{
	"uri": "/tutorial/notebooks/select_ipts/",
	"title": "Select IPTS",
	"tags": [],
	"description": "",
	"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 "
},
{
	"uri": "/tags/",
452 453 454 455
	"title": "Tags",
	"tags": [],
	"description": "",
	"content": ""
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},
{
	"uri": "/tutorial/notebooks/template_ui/",
	"title": "Template UI Builder",
	"tags": [],
	"description": "",
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	"content": " Notebook name: template_ui.ipynb\nDescription This notebook was implemented for the most aventurous python developers. This notebook offers the minimum required to start the development of a more complex user interface (UI).\nThe notebook is limited to the following features\n select your instrument/IPTS select list of images UI comes to life and there you can\n slide through the images to display them display name of image selected   Description of the notebook Select your IPTS Need help using the [IPTS selector](/tutorial/notebooks/select_ipts/#activate-search)?  Select your images Using the files selection tool, select the images you want to fix.\nDisplay images This is where the UI will comes to life. In order to improve or modify this ui, you will need to edit the file __code/template_ui.py\nfrom IPython.core.display import HTML from IPython.display import display import pyqtgraph as pg try: from PyQt4.QtGui import QFileDialog from PyQt4 import QtCore, QtGui from PyQt4.QtGui import QMainWindow except ImportError: from PyQt5.QtWidgets import QFileDialog from PyQt5 import QtCore, QtGui from PyQt5.QtWidgets import QApplication, QMainWindow from NeuNorm.normalization import Normalization from __code.ui_template import Ui_MainWindow as UiMainWindow from __code.file_folder_browser import FileFolderBrowser class InterfaceHandler(FileFolderBrowser): def __init__(self, working_dir=''): super(InterfaceHandler, self).__init__(working_dir=working_dir) def load(self): list_images = self.list_images_ui.selected o_norm = Normalization() o_norm.load(file=list_images, notebook=True) self.o_norm = o_norm class Interface(QMainWindow): live_data = [] def __init__(self, parent=None, o_norm=None): display(HTML('\u0026lt;span style=\u0026quot;font-size: 20px; color:blue\u0026quot;\u0026gt;Check UI that poped up \\ (maybe hidden behind this browser!)\u0026lt;/span\u0026gt;')) self.o_norm = o_norm self.list_files = self.o_norm.data['sample']['file_name'] self.list_data = self.o_norm.data['sample']['data'] QMainWindow.__init__(self, parent=parent) self.ui = UiMainWindow() self.ui.setupUi(self) self.init_statusbar() self.setWindowTitle(\u0026quot;Template UI\u0026quot;) self.ui.image_view = pg.ImageView() self.ui.image_view.ui.roiBtn.hide() self.ui.image_view.ui.menuBtn.hide() bottom_layout = QtGui.QHBoxLayout() # file index slider label_1 = QtGui.QLabel(\u0026quot;File Index\u0026quot;) self.ui.slider = QtGui.QSlider(QtCore.Qt.Horizontal) self.ui.slider.setMaximum(len(self.list_files) - 1) self.ui.slider.setMinimum(0) self.ui.slider.valueChanged.connect(self.file_index_changed) # spacer spacer = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) bottom_layout.addWidget(label_1) bottom_layout.addWidget(self.ui.slider) bottom_layout.addItem(spacer) bottom_widget = QtGui.QWidget() bottom_widget.setLayout(bottom_layout) vertical_layout = QtGui.QVBoxLayout() vertical_layout.addWidget(self.ui.image_view) vertical_layout.addWidget(bottom_widget) self.ui.widget.setLayout(vertical_layout) self.init_widgets() self.file_index_changed() def init_widgets(self): pass def init_statusbar(self): self.eventProgress = QtGui.QProgressBar(self.ui.statusbar) self.eventProgress.setMinimumSize(20, 14) self.eventProgress.setMaximumSize(540, 100) self.eventProgress.setVisible(False) self.ui.statusbar.addPermanentWidget(self.eventProgress) def apply_clicked(self): # do stuff self.close() def cancel_clicked(self): self.close() def file_index_changed(self): file_index = self.ui.slider.value() new_live_image = self.list_data[file_index] self.ui.image_view.setImage(new_live_image) self.ui.file_name.setText(self.list_files[file_index]) def display_image(self, image): self.ui.image_view.setImage(image) def closeEvent(self, eventhere=None): print(\u0026quot;Leaving Parameters Selection UI\u0026quot;)  "
},
{
	"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  "
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},
{
	"uri": "/tutorial/notebooks/water_intake_profile_calculator/",
	"title": "Water Intake Profile Calculator",
	"tags": [],
	"description": "",
476
	"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](/tutorial/notebooks/select_ipts/#activate-search)?  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](/tutorial/notebooks/file_selector/#activate-search)?  \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!  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']  "
477
}]