README.ipynb

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   "source": [
    "# medsrtqc\n",
    "\n",
    "[![Check](https://github.com/ArgoCanada/medsrtqc/actions/workflows/check.yaml/badge.svg)](https://github.com/ArgoCanada/medsrtqc/actions/workflows/check.yaml)\n",
    "[![Codecov test coverage](https://codecov.io/gh/ArgoCanada/medsrtqc/branch/master/graph/badge.svg)](https://codecov.io/gh/ArgoCanada/medsrtqc?branch=master)\n",
    "\n",
    "A possible container for MEDS real-time quality control code.\n",
    "\n",
    "## Installation\n",
    "\n",
    "You can install the `medsrtqc` package using `pip`.\n",
    "\n",
    "``` bash\n",
    "pip install git+https://github.com/ArgoCanada/medsrtqc.git\n",
    "```\n",
    "\n",
    "## Examples\n",
    "\n",
    "QC code in `medsrtqc` is built on the `Trace` and `Profile` classes. A `Profile` is roughly what you would get from a single-cycle Argo NetCDF file. The `Profile` can be subclassed to abstract away the storage details such that QC code can be tested on NetCDF files but run with an arbitrary storage backend. You can get started with the NetCDF backend in a few lines:"
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      "text/plain": [
       "Trace(\n",
       "    value=[13.3847, 12.9791, 12.3505, [194 values], 7.2176, 7.2166, 7.2168],\n",
       "    qc=[b'1', b'1', b'1', [194 values], b'1', b'1', b'1'],\n",
       "    adjusted=[masked, masked, masked, [194 values], masked, masked, masked],\n",
       "    adjusted_error=[masked, masked, masked, [194 values], masked, masked, masked],\n",
       "    pres=[5.27, 5.79, 6.48, [194 values], 201.5, 202.54, 203.5],\n",
       "    mtime=[masked, masked, masked, [194 values], masked, masked, masked]\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "from medsrtqc.nc import read_nc_profile\n",
    "url = 'https://data-argo.ifremer.fr/dac/coriolis/6904117/profiles/R6904117_085.nc'\n",
    "profile = read_nc_profile(url)\n",
    "profile['TEMP']"
   ]
  },
  {
   "source": [
    "You can specify multiple files to combine e.g. a core and a BGC file that represent the same `Profile` but are split into two files for historical and/or logistical reasons."
   ],
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     "text": [
      "Trace(\n    value=[1.5622, 1.533, 1.533, [1267 values], 0.3577, 0.365, 0.3504],\n    qc=[b'2', b'2', b'2', [1267 values], b'2', b'2', b'2'],\n    adjusted=[0.7811, 0.7665, 0.7665, [1267 values], 0.17885, 0.1825, 0.1752],\n    adjusted_error=[masked, masked, masked, [1267 values], masked, masked, masked],\n    pres=[0.3, 0.3, 0.3, [1267 values], 204.1, 204.0, 204.0],\n    mtime=[0.007025253, 0.0069095124, 0.006793772, [1267 values], -0.052315023, -0.052430764, -0.052546505]\n)\nTrace(\n    value=[13.3847, 12.9791, 12.3505, [194 values], 7.2176, 7.2166, 7.2168],\n    qc=[b'1', b'1', b'1', [194 values], b'1', b'1', b'1'],\n    adjusted=[masked, masked, masked, [194 values], masked, masked, masked],\n    adjusted_error=[masked, masked, masked, [194 values], masked, masked, masked],\n    pres=[5.27, 5.79, 6.48, [194 values], 201.5, 202.54, 203.5],\n    mtime=[masked, masked, masked, [194 values], masked, masked, masked]\n)\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "url_bgc = re.sub(r'/R', '/BR', url)\n",
    "profile = read_nc_profile(url, url_bgc)\n",
    "print(repr(profile['CHLA']))\n",
    "print(repr(profile['TEMP']))"
   ]
  },
  {
   "source": [
    "You can use the interactive plotter to quickly visualize all or part of a profile:"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from medsrtqc.interactive import plot\n",
    "fig, axs = plot(profile, vars=profile.keys()[:6])"
   ]
  },
  {
   "source": [
    "You can use the `Trace` and `Profile` classes directly to generate unit test data with known statistical properties:"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Trace(\n",
       "    value=[1.0, 2.0, 4.0],\n",
       "    qc=[masked, masked, masked],\n",
       "    adjusted=[2.0, 3.0, 5.0],\n",
       "    adjusted_error=[masked, masked, masked],\n",
       "    pres=[0.0, 1.0, 2.0],\n",
       "    mtime=[masked, masked, masked]\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "from medsrtqc.core import Trace, Profile\n",
    "Trace([1, 2, 4], adjusted=[2, 3, 5], pres=[0, 1, 2])"
   ]
  },
  {
   "source": [
    "## QC A VMS .DAT file\n",
    "\n",
    "This doesn't quote work (it looks like the pressure QC flags aren't updating) but it's close!"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 4 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from medsrtqc.vms import read_vms_profiles, write_vms_profiles\n",
    "from medsrtqc.resources import resource_path\n",
    "from medsrtqc.qc.flag import Flag\n",
    "from medsrtqc.qc.named_tests import PressureIncreasingTest\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load file\n",
    "profs = read_vms_profiles(resource_path('BINARY_VMS.DAT'))\n",
    "\n",
    "# run test on a prof that will fail\n",
    "test = PressureIncreasingTest()\n",
    "prof = profs[0]\n",
    "test.run(prof)  # False\n",
    "\n",
    "# check result (should have one point marked as bad)\n",
    "fig, axs = plot(prof, vars=('PRES', 'TEMP', 'PSAL'))\n",
    "for i, var in enumerate(('PRES', 'TEMP', 'PSAL')):\n",
    "    t = prof[var]\n",
    "    bad = t.qc == Flag.BAD\n",
    "    plt.subplot(2, 2, i + 1).scatter(t.value[bad], t.pres[bad])\n",
    "\n",
    "# if you want to write changes to disk\n",
    "# profs[0] = prof\n",
    "# write_vms_profiles(profs, 'output_file.DAT')"
   ]
  },
  {
   "source": [
    "## QC a NetCDF file\n",
    "\n",
    "This should work but I haven't tested properly yet."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "prof = read_nc_profile(resource_path('R6904117_085.nc'), mode='r+')\n",
    "test = PressureIncreasingTest()\n",
    "test.run(prof)\n",
    "# prof.close() if you want to write changes to disk"
   ]
  }
 ]
}