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The long shot is to provide more or less untrainded clinical staff to evaluate FC NMR data. This project aims at large data sets. A server-client set-up let's you analyse huge datasets in a complicated way with say a laptop. However we only started. So: We try to hack some tool together that is useful for evaluating FC NMR data. For first tries …

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harrispilton/fieldcycling

 
 

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fieldcycling

parameters often used in stelar data files (.sdf)

  • DW, (dwell time) is the time step used in the digitilization in units of microseconds (1/sampling rate) (e.g. of FID)
  • NS, (number of scans) number of repetition of experiment
  • NBLK, (number of blocks) how many shots/blocks (e.g. FIDs) were recorded in one experiment
  • BS, (block size) number of points in one shot/block (e.g. FID)
  • BINI, time step of the first block in units of seconds
  • BEND, time step of the last block in units of seconds
    • watch out: BINI and BEND can contain a string instead of a float number. It has to be evaluated (e.g. BEND = '5 * T
  • BGRD, is the grid of the time steps (lin, log or list)
  • TIME, start time of experiment
  • BRLX, relaxation field of the experiment (variable for the dispersion)

dependencies

this program was built using python 3.6 (32bit) and some of the following libraries (I tried to sort it out in requierments.txt, needs testing) appdirs (1.4.3) bleach (2.0.0) bokeh (0.12.5) colorama (0.3.7) colorcet (0.9.1) cycler (0.10.0) dask (0.14.1) datashader (0.4.0) datashape (0.5.2) decorator (4.0.11) entrypoints (0.2.2) h5browse (0.2) h5py (2.7.0) html5lib (0.999999999) ipykernel (4.5.2) ipython (5.3.0) ipython-genutils (0.2.0) ipywidgets (6.0.0) Jinja2 (2.9.6) jsonschema (2.6.0) jupyter (1.0.0) jupyter-client (5.0.0) jupyter-console (5.1.0) jupyter-core (4.3.0) llvmlite (0.16.0) MarkupSafe (1.0) matplotlib (2.0.0) memory-profiler (0.43) mistune (0.7.4) multipledispatch (0.4.9) nbconvert (5.1.1) nbformat (4.3.0) networkx (1.11) notebook (4.4.1) numba (0.31.0) numpy (1.12.1+mkl) odo (0.5.0) olefile (0.44) packaging (16.8) pandas (0.19.2) pandocfilters (1.4.1) pickleshare (0.7.4) pillow (4.1.0) pip (9.0.1) prompt-toolkit (1.0.13) psutil (5.2.0) Pygments (2.2.0) pyparsing (2.2.0) pyqtgraph (0.10.0) python-dateutil (2.6.0) pytz (2016.10) PyYAML (3.12) pyzmq (16.0.2) qtconsole (4.2.1) requests (2.13.0) scipy (0.19.0rc2) setuptools (34.3.1) simplegeneric (0.8.1) six (1.10.0) testpath (0.3) toolz (0.8.2) tornado (4.4.2) traitlets (4.3.2) wcwidth (0.1.7) webencodings (0.5) wheel (0.29.0) widgetsnbextension (2.0.0) xarray (0.9.2)

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The long shot is to provide more or less untrainded clinical staff to evaluate FC NMR data. This project aims at large data sets. A server-client set-up let's you analyse huge datasets in a complicated way with say a laptop. However we only started. So: We try to hack some tool together that is useful for evaluating FC NMR data. For first tries …

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