{ "cells": [ { "cell_type": "markdown", "id": "c517647d-3226-4224-bfac-5ebc53a64bbe", "metadata": { "tags": [] }, "source": [ "# Statistical analyses" ] }, { "cell_type": "markdown", "id": "3d8939aa-84cf-4ed5-9de3-cf115acb5c9e", "metadata": {}, "source": [ "The general statistical checks available are **`checkConstantSlope`**, **`checkGaps`**, and **`allChanStats`**. The first checks that the data in the requested channels are either constant, or varying at a constant rate. The second checks for gaps in the data channels. The third reports the basic statistics of every channel for every flight-line.\n", "\n", "Here we demonstrate their use on the Canobie airborne gravity gradiometer survey data.\n", "\n", "Ensure you have run the `Prepare_XYZ` notebook first so that the Canobie data are prepared for review." ] }, { "cell_type": "markdown", "id": "3e3dd541-b136-48bf-8e00-346686e0f00e", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "___" ] }, { "cell_type": "markdown", "id": "eb43d643-2bb1-4d89-b920-39255b583a27", "metadata": {}, "source": [ "First, import the required modules, and set the path to the geowhizz files." ] }, { "cell_type": "code", "execution_count": 1, "id": "6541a9e4-d579-44cf-839d-a97a80ef6513", "metadata": { "tags": [] }, "outputs": [], "source": [ "from pathlib import Path\n", "import galileoQC as qc" ] }, { "cell_type": "code", "execution_count": 2, "id": "b0ec9460-4290-44bd-8840-e76d537ee3e0", "metadata": {}, "outputs": [], "source": [ "canobieHDF_file = Path(r'./CanobieData/Canobie.hdf5')" ] }, { "cell_type": "markdown", "id": "ec577b7b-e7ff-435c-bec6-614053df8667", "metadata": { "tags": [] }, "source": [ "___" ] }, { "cell_type": "markdown", "id": "02ba7573-20c3-451f-8530-b25d803e97a7", "metadata": {}, "source": [ "Some channels in the data are expected to either remain constant, or vary uniformly with sampling along the survey line. Obvious examples are channels containing the date, line number, flight number, project number and so forth which should be constant; and channels containing the time or fiducial which should vary uniformly.\n", "\n", "Errors in these channels are usually trivial and minor but nevertheless require fixing when they occur.\n", "\n", "A common reported fault is when a channel containing the local time of day in seconds past midnight fails to be of constant slope at or very near the value of 86400.0. That value corresponds to midnight and, since the time of day resets to 0.0 at midnight, the slope will dramatically change. The cause is usually that the clock is not set to local time (since the survey flights do not take place at night).\n", "\n", " **`checkConstantSlope`** uses first differences along each survey line to check that the requested channels are either of constant value, or vary at a fixed rate along each flight-line.\n", "\n", "You can choose the channels to check from those listed by `reportChannels` or `reportWhizz`.\n", "\n", "The cell below shows the channels selected from the Canobie data. In this case, only the two time channels are occasionally failing this check. The differences are less than $1\\,msec$ which is the precision of the text-formatted input data so these errors are caused simply by the limited precision of the data and are clearly acceptable." ] }, { "cell_type": "code", "execution_count": 3, "id": "c7daf534-a0bf-4d19-a91c-99bb264046fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " 100010.000; Time_Day Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100010.000; Time_1980 Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100020.000; Time_Day Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100020.000; Time_1980 Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100030.000; Time_Day Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100030.000; Time_1980 Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100040.000; Time_Day Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100040.000; Time_1980 Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100050.000; Time_Day Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100050.000; Time_1980 Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100060.000; Time_Day Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n", " 100060.000; Time_1980 Largest difference (= 0.000999) > 0.1% of mean difference (= 0.00125)\n" ] } ], "source": [ "qc.checkConstantSlope(\n", " canobieHDF_file, channels=[\n", " 'Date', 'FIDUCIAL', 'FLIGHT', 'LINE', 'Time_Day', 'JOB_ID','Time_1980'])" ] }, { "cell_type": "markdown", "id": "2e65355b-dfda-40b6-a0ca-d0c6766dbe7d", "metadata": {}, "source": [ "___" ] }, { "cell_type": "markdown", "id": "efa036c4-7b51-4b00-beab-61f5aef86250", "metadata": {}, "source": [ "Occasionally, there are gaps in a data channel. With modern data acquisition technology, gaps due to missing values in measurement are very rare although still possible. Gaps occur in magnetic data when a spike in the data has been removed (these are usually interpolated over) but this is not usually seen in gravity data. Finally, gaps can occur when processed data have been deliberately removed. This usually is in parts of the flight-line outside the survey boundary and when the data are unreliable due to filtering effects at the start or end of a flight-line.\n", "\n", "The **`checkGaps`** function checks all channels for all flight-lines and reports any gaps found." ] }, { "cell_type": "code", "execution_count": 4, "id": "4100c278-71a1-433e-b316-a93fceb904e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking for all gaps in 33 channels on 6 lines.\n", "0 lines failed.\n", "\n" ] } ], "source": [ "qc.checkGaps(canobieHDF_file)" ] }, { "cell_type": "markdown", "id": "e247fe68-388e-4fa6-88f8-24684fc72f34", "metadata": {}, "source": [ "___" ] }, { "cell_type": "markdown", "id": "b2fa21d6-b3c4-4d7f-94d4-376226c5b6db", "metadata": {}, "source": [ "The channel statistics analysis plots the mean, standard deviation and range of every channel as a variation of a \"box and whisker plot\" for every flight-line.\n", "\n", "In the plots, the horizontal axis is flight-line number, and at each flight-line, there are a pair of circles plotted for the minimum and maximum values in the channel for that flight line, the filled solid square is at the mean value, and the small horizontal lines are at one standard deviation above and below the mean.\n", "\n", "A lot of information is condensed into these plots. One can just quickly glance at each plot looking for outliers or unexpected results, and checking the vertical scales to ensure that the values are in about the right range.\n", "\n", "By default, the **`allChanStats`** function creates these statistical analysis plots for every channel in the database. Usually this means a very large number of plots, so here the channels to analyse are explicitly listed (and kept to a small number).\n", "\n", "Here the CLEARANCE has an obvious outlier maximum in line 100050. This could be followed up by further analysis, or by enquiring the reason of the acquisition company." ] }, { "cell_type": "code", "execution_count": 5, "id": "2417385d-5597-4f9e-ae83-23f9eb69f581", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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FmZiYGNOjRw+zcePG877emjVrek5DDg4ONldeeaW57bbbvP5mjHGfMj1gwAATGRlprrjiCjNw4EDz9NNPmyZNmnjW+fnnnz2/R511WvOTTz7peQ233367mTx5cr7fRadOnczYsWPPO07ACg5j/LwTHoDlfvzxR1177bX69ttv8x0AChTF5s2b1b59e+3YsaPYV2AGioNdQkApVLVqVU2ZMiXf2TFAUR0+fFgzZswgrKDEMcMCAABsjxkWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAPmsXbtWSUlJSkxMVPv27fX1118rJSVFQ4YM8VpvwIABat68uZKSkpSUlKTt27dLkjIzM1W2bFmtWrUq37oJCQnq1auXnE6nZ1l8fLxGjRrluT99+nRFR0fr5MmTkqQhQ4YoJSVFkrR161Z17txZiYmJSkhI0JIlS7R3715VqlTJM45hw4Z5jfPgwYO68cYblZSUpFatWum7777Tb7/9ptmzZ1+wDtOnT1d2dnbRCwjA5wgsALwcPXpUAwcO1MyZM7Vy5UrNnz9fAQHnf6uYNm2aUlJSlJKSonr16kmSPvnkE911112aM2dOvnVXrVqlK664QkuXLpUkHThwQDVq1PAEkjxVq1bVlClTvNqcTqfuuOMOvfrqq1q5cqWSk5MVEREhSUpMTPSMY+zYsV6Pe/XVV/WPf/xDKSkpSk1NVVxcHIEFuMQQWAB4WbRokbp3766YmBhJUmRkpK6//voi9TF//nyNGjVKW7duVW5ubr7lGRkZMsZIkubOnau+ffvqmmuu0bZt2zzrDBgwQO+++65ycnI8bevXr1eTJk10zTXXSJJCQkIUHx9/0fGULVtWK1eu1LFjx1SmTBmFh4fr7bff1sqVK5WUlKQtW7Zo8ODBSkxMVIsWLZSWlqZ169YpLS1NXbt21aRJk/TOO++oRYsWat++vebPn1+kegAovjIlPQAA9nLo0CFVq1at0OvfddddKleunCTp448/lsPhkMvlUnR0tBITE7VmzRq1bdvWs+6xY8dUq1Ytde7cWZK0dOlSLViwQFFRUZozZ47++c9/SpJCQ0N16623atasWYUaW174kKQePXroscce8yx78sknNWrUKMXHx+vKK6/UjBkzNHDgQO3evVtz586VJL3wwgsKCwvTd999p/Hjx+v9999X06ZNtXDhQoWHh6t9+/b64osvFBERUWAIA+BfBBYAXqpVq6adO3cWev1p06apYcOGnvvvv/++du/erS5duujkyZM6cuSIJ7BMmzZNV111lbp06aJjx44pOztb6enp6tatm4wxysjI8AQWSXr44Yd18803q1WrVp6xLVq0qMBxJCYmesLHucqXL6/x48dr/Pjx+ve//63JkyfrwQcf9Fpn/Pjx+uKLLyRJZcrkf2scN26cHnvsMRljNGzYMM/uLwDWYJcQAC9//etf9fHHH+vw4cOS3AfQfvvtt4V+/Ny5c7V8+XItWbJEq1at0ubNmz27fyT37plBgwZpwoQJmjt3riZPnqwlS5bo888/1/XXX+85cFeSKlSooL/85S/6/PPPJUktW7bUxo0bPbuOsrOztW7duouOaffu3Z5ZkcqVK8sYo6CgILlcLknSr7/+quTkZKWmpuqVV17xjPfsdRo1aqRp06bp/vvv10svvVToegDwDWZYAHiJiorS22+/rTvuuEPGGAUGBmr8+PGSpDlz5igtLU2SdOedd0ry3iU0cuRIHTp0SJUqVfL0V69ePa1evdrrOXr06KFRo0YpNDRUS5Ys8bS3a9dOs2fPVmxsrKftH//4h9544w1J7gAxc+ZMPfbYYzp9+rRyc3M1fPhwxcTEeO0SatCggd566y1PHytWrFCfPn0UFhamoKAgTZ8+XVWrVtWpU6fUq1cvjRkzRlFRUUpKSlLLli09j7v11lt122236W9/+5vWrl2rvXv3KisrS2PGjClumQEUkcOc/dEHAADAhtglBAAAbI/AAgAAbI/AAgAAbI/AAgAAbI/AAgAAbI/AAgAAbK9UXIclNzdXhw4dUvny5eVwOEp6OAAAoBCMMTp+/LiqVat2wS9ZlUpJYDl06JDXhaYAAMClI+9b2y+kVASW8uXLS3K/4LyvmvcVp9OppUuXqlOnTgoKCvJp3ziDOluDOluDOluDOlvHX7XOzMxUbGysZzt+IaUisOTtBoqIiPBLYAkLC1NERAT/IfyIOluDOluDOluDOlvH37UuzOEcHHQLAABsj8ACAABsj8ACAABsj8ACAABsj8ACAABsj8ACAABsj8ACAABsj8ACAABsj8ACALjkuFzSypUOrVpVXStXOuRylfSI4G8EFgDAJWXePKluXaljxzKaNKmZOnYso7p13e0ovQgsAIBLxrx5Uq9eUqNG0rp10vHj7p+NGrnbCS2lF4EFAHBJcLmkJ56Qbr5ZWrBAatlSCg93/1ywwN0+ZIjYPVRKEVgAAJeE1FRp717pmWekgHO2XgEB0rBh0p497vVQ+hBYAACXhMOH3T8bNix4eV573nooXQgsAIBLQkyM+2d6esHL89rz1kPpQmABAFwS2raVatWSXnxRys31XpabK40dK9Wu7V4PpQ+BBQBwSQgMlCZOlBYulLp39z5LqHt3d/uECe71UPqUKekBAABQWD17SnPnus8Wio8/0167tru9Z8+SGxv8i8ACALik9OwpdesmrViRo88+S1PXrk3Vrl0ZZlZKOQILAOCSExgoJSYa/f77D0pMbEJYuQxwDAsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9AgsAALA9SwLLTz/9pPj4eCUmJqp9+/Y6fPiwVq9erfj4eLVp00abNm2SJP3444/q1KmTWrdurffee8+KoQEAgEtAGSueJDo6WqtXr1ZAQICmT5+uKVOmKDk5WYsWLdLx48f14IMPavHixXrppZc0dOhQJSUlqW3bturVq5dCQ0Pz9ZeVlaWsrCzP/czMTEmS0+mU0+n06djz+vN1v/BGna1Bna1Bna1Bna3jr1oXpT9LAktgYKDn9vHjx3XVVVdp+fLlqlChgipUqKCjR49KkjZs2KCJEycqICBAzZo1U3p6upo1a5avv7Fjx2rkyJH52pcuXaqwsDC/vIbk5GS/9Atv1Nka1Nka1Nka1Nk6vq71yZMnC72uJYFFktLS0vTAAw/ot99+09KlSzVr1qwzgyhTRtnZ2XI6nQoIcO+lioyM9ASZcw0bNkyDBw/23M/MzFRsbKw6deqkiIgIn47b6XQqOTlZHTt2VFBQkE/7xhnU2RrU2RrU2RrU2Tr+qnXeHpLCsCywNG3aVF9++aVmz56tMWPGeA0yJydHwcHBCgoKUm5urgICApSRkaGoqKgC+woJCVFISEi+9qCgIL/90fqzb5xBna1Bna1Bna1Bna3j61oXpS9LDrrNzs723I6MjFR4eLhycnL022+/6cCBA55g0rx5c6WkpCgnJ0fffPONrr32WiuGBwAAbM6SGZa0tDQNGTJEgYGBCg0N1dSpU7Vz507ddNNNcjgceuuttyRJTz31lP7+979r+PDhevDBB1W2bFkrhgcAAGzOksDSokULrVq1yqstJiZGa9euzdfGwVMAAOBcXDgOAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYHoEFAADYniWBZcOGDWrVqpUSEhJ0xx13yOl0Ki4uTklJSUpKSlJycrIkadu2bUpISFB8fLyWLVtmxdAAAMAloIwVTxIbG6vly5erbNmyGjZsmD7++GNFRkYqJSXFa71nnnlGU6ZMUZUqVdS1a1d16NDBiuEBAACbsySwxMTEeG4HBwcrICBAJ06cUGJioqpXr6433nhDUVFROnTokOLi4iRJUVFROnLkiKKjo/P1l5WVpaysLM/9zMxMSZLT6ZTT6fTp2PP683W/8EadrUGdrUGdrUGdreOvWhelP4cxxvj02S9g37596t27t1atWqXMzExVrFhRM2bM0FdffaXXX39dLVq00IYNGyRJd955p5577jldffXV+fp5/vnnNXLkyHztH3zwgcLCwvz+OgAAQPGdPHlSffr0UUZGhiIiIi64riUzLJJ7FqRfv36aPn26goKCVLFiRUlSr1699J///EeSFBBw5pCajIwMRUVFFdjXsGHDNHjwYK++Y2Nj1alTp4u+4KJyOp1KTk5Wx44dFRQU5NO+cQZ1tgZ1tgZ1tgZ1to6/ap23h6QwLAksOTk56t27t0aMGKF69eopOztbxhiFhIQoNTVVdevWleTedbR7925VrlxZR48eLXB3kCSFhIQoJCQkX3tQUJDf/mj92TfOoM7WoM7WoM7WoM7W8XWti9KXJYFl5syZ+vLLLzV69GiNHj1aAwcO1Msvv6xy5copJCREU6dOlSSNGTNGAwYMkMvlKnCXDwAAuDxZElj69eunfv36ebXdfvvt+dZr0KCBUlNTrRgSAAC4hHDhOAAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHsEFgAAYHtlCrviyZMnz7ssICBAoaGhPhkQAADAuQodWKpXr67rrrtOxhhPm8PhkDFGP/30k7Zs2eKXAQIAABQ6sNx///166aWXClz21FNP+WxAAAAA5yr0MSznCysXWwYAAFBcRT7oNiUlRe3atVPjxo3lcrn0+OOP+2FYAAAAZxQ5sAwfPlyLFi1SxYoVFRgYqE2bNvljXAAAAB5FDiwBAQEKCwuTw+GQJOXk5Ph8UAAAAGcrcmC555571LVrV+3atUu33HKL7rvvPn+MCwAAwKPQZwlJkjFGVapU0fvvv6/du3erTp06qlixor/GBgAAIKmIMywOh0Nvv/22oqKi1Lx5c8IKAACwRJFmWCQpLCxM/fv3V/PmzRUQ4M47Dz30kM8HBgAAkKfIgaVLly5e9/MOvgUAAPCXIgeWrVu3aty4cZ77Y8eO9emAAAAAzlXoY1iOHDmizZs3a8WKFdqyZYu2bNmiTZs26YsvvvDn+AAAAAo/w7JmzRotWLBA+/fv1/jx4yVJQUFBGjRokN8GBwAAIBUhsHTr1k3dunXT3r17VbNmTf3yyy+qXLmyP8cGAAAg6U9cOO7LL79U27ZtdeONN8rlcql3797+GBcAAIBHkQPL66+/rlWrVnm+S+jnn3/2x7gAAAA8/tR3CeXm5srhcCgnJ0e5ubn+GBcAAIBHkU9rfvbZZ5WUlKQdO3aoQ4cOevbZZ/0xLgAAAI8iB5bOnTurc+fO+uWXXxQdHc2F4wAAgN8VObCsW7dO//rXv3Ts2DEZYyRJn3zyic8HBgAAkKfIgeXBBx/Uu+++q+rVq/tjPAAAAPkUObDExcWpfv36CgoK8sd4AAAA8ilyYOnWrZtq1aqlq6++WpL7yw+XL1/u84EBAADkKXJgmTBhglJSUlStWjV/jAcAACCfIgeW66+/XjVq1FDZsmX9MR4AAIB8ihxYvvzyS9WoUUN16tSR5N4ltGHDBp8PDAAAIE+RA8uWLVv8MQ4AAIDzKvSl+RcuXPinlgEAABRXoWdYBg8erP379+drN8Zo+vTpuvnmm306MAAAgDyFDiwX+s6gp59+2ieDAQAAKEihA0v//v39OQ4AAIDzKvQxLAAAACWFwAIAAGyv0IHl7GNYXn/9dc/tQYMG+XZEAAAA5yh0YFm3bp3n9vz58z23C3Ndlg0bNqhVq1ZKSEjQHXfcIafTqTlz5ig+Pl4dOnTQwYMHJUnbtm1TQkKC4uPjtWzZsqK8DgAAUIoV+cJxf0ZsbKyWL1+usmXLatiwYfr44481adIkrVy5Ul999ZVGjx6tf/3rX3rmmWc0ZcoUValSRV27dlWHDh0K7C8rK0tZWVme+5mZmZIkp9Mpp9Pp07Hn9efrfuGNOluDOluDOluDOlvHX7UuSn+FDizp6em67bbbZIzxur158+aLPjYmJsZzOzg4WNu3b1f9+vUVHBys1q1ba8iQIZKkQ4cOKS4uTpIUFRWlI0eOKDo6Ol9/Y8eO1ciRI/O1L126VGFhYYV9SUWSnJzsl37hjTpbgzpbgzpbgzpbx9e1PnnyZKHXLXRg+eqrr/7UYM62b98+LV26VOPGjdMvv/ziaXe5XJKk3NxcT1tkZKSOHj1aYGAZNmyYBg8e7LmfmZmp2NhYderUSREREcUe59mcTqeSk5PVsWNHBQUF+bRvnEGdrUGdrUGdrUGdreOvWuftISmMQgeW7du3KyIiQi1btvS0rV+/XsePH1fNmjULNah+/fpp+vTpcrlcXoMMDAyUJAUEnDmkJiMjQ1FRUQX2FRISopCQkHztQUFBfvuj9WffOIM6W4M6W4M6W4M6W8fXtS5KX4U+6PbFF1/UDTfc4NV2ww036IUXXrjoY3NyctS7d2+NGDFC9erVU1xcnLZu3ars7GytXbtWjRs3luTedbR7924dP378vLMrAADg8lOkg27PTUJBQUFyOBwXfdzMmTP15ZdfavTo0Ro9erQGDhyoxx9/XElJSQoNDdV///tfSdKYMWM0YMAAuVyuAo9RAQAAl6dCB5bQ0FDt3LnTc1CsJO3YsUOhoaEXfWy/fv3Ur1+/fO2333671/0GDRooNTW1sEMCAACXiUIHlsmTJ+u2225T06ZNVa1aNR08eFCbNm3SzJkz/Tk+AACAwgeW+vXr66uvvtL69et16NAhde3aVS1btlSZMpZcygUAAFzGCp02Vq1a5bldtWpV5ebmau3atZKkhIQE348MAADgD4UOLJ9++qnndt6BtkuWLNGOHTt0+vRp348MAADgD4UOLOPHj5fkvsjbrFmz9NZbb6ljx45avHix3wYHAAAgFSGwnD59WlOmTNG7776rW2+9VYsWLVJkZKQ/xwYAACCpCIElNjZWMTEx6t+/v0JDQ/X+++97lj300EN+GRwAAIBUhMAyYcKEAtsLc+E4AACA4ih0YOnfv3++th07dmjOnDk+HRAAAMC5inwRlR07dmj27NlavHixrr/+erVr184f4wIAAPAodGB54YUXlJKSomuuuUa9e/fW6tWr9cYbb/hzbAAAAJKK8G3Nn332mSpWrKjOnTurRYsWCggo9EMBAACKpdCpY82aNZo4caJ2796t7t27Kz09XfPmzdOxY8f8OT4AAIDCBxZJqlGjhh5//HEtXrxY69at04EDB9SlSxd/jQ0AAEBSEY5hcblcWrp0qYwx6ty5s0JCQnTo0CGdOnXKn+MDAAAofGC54447FBkZqYyMDL3yyityOp0aNGiQXnzxRX+ODwAAoPCB5aefftLs2bMlSXFxcUpPT1dISIjfBgYAAJCn0IHl1KlT2rp1q4wxqlChgv73v//JGCNJatCggd8GCAAAUOjAcu211+rll1/Od9vhcGjq1Kn+GR0AAICKEFimTZtWYPvq1at9NhgAAICCFPnS/JL7mixz587V5s2bVa9ePbVp08bX4wIAAPAodGBZvXq15syZo61btyohIUHfffedUlJS/Dg0AAAAt0JfOK5Tp05yuVz68MMPNXz4cIWFhflzXAAAAB5FOq35k08+0f333y+Xy6UffvhBR48eVVRUlD/HBwAAUPjAUr58efXt21d9+/bV8ePH9emnn+ree+/V4cOHtW7dOn+OEQAAXOYKHVh27typuLg4z/0+ffqoT58+WrVqlV8GBgAAkKfQx7A88MADntvdunXz3H7++ed9OiAAAIBzFTqw5F3V9kK3AQAA/KHQu4R+//13bd26Vbm5uTp58qTXbQAAAH/6U5fmb9Cggddl+gEAAPyp2JfmnzFjhs8GAwAAUJBCH8NyPu+9954vxgEAAHBexQ4sAAAA/lboXUJPPvmkHA6HV5sxRjt37vT5oAAAAM5W6MBy8803F6kdAADAV4p0af6AgAA1bdrU0/bdd9/5Y0wAAABeCn0My9ChQ3XVVVd5tdWtW1dPPvmkzwcFAABwtkIHlpycHJUvX96rrXz58srJyfH5oAAAAM5W6MDicDj0888/e7X9+OOP+Q7EBQAA8LVCH8MyZswYderUST169FC1atV08OBBffLJJ3r77bf9OT4AAIDCz7DEx8drxYoVuuqqq3Ts2DHFxcVp+fLlatmypT/HBwAAUPgZFkmqUKGC7rzzTn+NBQAAoEBc6RYAANgegQUAANgegQUAANgegQUAANgegQUAANgegQUAANgegQUAANgegQUAANgegQUAANgegQUAANgegQUAANieZYElIyNDLVq0UHh4uNLT0yVJcXFxSkpKUlJSkpKTkyVJ27ZtU0JCguLj47Vs2TKrhgcAAGysSF9+WBxhYWFatGiRnnzySU9bZGSkUlJSvNZ75plnNGXKFFWpUkVdu3ZVhw4drBoiAACwKcsCS1BQkCpVquTVduLECSUmJqp69ep64403FBUVpUOHDikuLk6SFBUVpSNHjig6OtrrcVlZWcrKyvLcz8zMlCQ5nU45nU6fjjuvP1/3C2/U2RrU2RrU2RrU2Tr+qnVR+rMssBRkzZo1qlixombMmKERI0bo9ddfV25urmd5ZGSkjh49mi+wjB07ViNHjszX39KlSxUWFuaXsebtsoJ/UWdrUGdrUGdrUGfr+LrWJ0+eLPS6JRpYKlasKEnq1auX/vOf/0iSAgLOHFaTkZGhqKiofI8bNmyYBg8e7LmfmZmp2NhYderUSRERET4do9PpVHJysjp27KigoCCf9o0zqLM1qLM1qLM1qLN1/FXrvD0khVFigSU7O1vGGIWEhCg1NVV169aVJMXExGj37t2qXLlygbMrkhQSEqKQkJB87UFBQX77o/Vn3ziDOluDOluDOluDOlvH17UuSl+WBpabbrpJaWlp2r59u7p3767Zs2erXLlyCgkJ0dSpUyVJY8aM0YABA+RyuQrc7QMAAC4/lgaWxYsXe91/6qmn8q3ToEEDpaamWjUkAABwCeDCcQAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAA4LxcLmnlSodWraqulSsdcrlKZhwEFgAAUKB586S6daWOHcto0qRm6tixjOrWdbdbjcACAD5kl0+jQHHNmyf16iU1aiStWycdP+7+2aiRu93q0EJgAQAfsdOnUaA4XC7piSekm2+WFiyQWraUwsPdPxcscLcPGSJLAzmBBQB8wG6fRoHiSE2V9u6VnnlGCjgnKQQESMOGSXv2uNezCoEFuEywq8J/7PhpFCiOw4fdPxs2LHh5XnveelYgsACXAXZV+JcdP40CxRET4/6Znl7w8rz2vPWsQGABSjl2VfifHT+NAsXRtq1Uq5b04otSbq73stxcaexYqXZt93pWsSywZGRkqEWLFgoPD1f6H9Fszpw5io+PV4cOHXTw4EFJ0rZt25SQkKD4+HgtW7bMquEBpRK7Kqxhx0+jQHEEBkoTJ0oLF0rdu3t/2One3d0+YYJ7PatYFljCwsK0aNEi9erVS5KUk5OjSZMmKSUlRaNGjdLo0aMlSc8884ymTJmiJUuW6LnnnrNqeAVinz8udeyqsIYdP40CxdWzpzR3rrRpkxQfL0VEuH+mp7vbe/a0djxlrHqioKAgVapUyXN/586dql+/voKDg9W6dWsNGTJEknTo0CHFxcVJkqKionTkyBFFR0d79ZWVlaWsrCzP/czMTEmS0+mU0+n0yXjnz3foqacCtXdvGUnNNGmSVKuW0UsvudSjh/HJc+CMvN+br35/cDtwwCGpzEV3VRw4kCOnk7/r4njpJYd69w5U9+7SsGEONWzofmMfO9Zo4ULpww9dys01+QIN/jzeN/zvllukm26SUlJcSk5OV8eODZWUFKjAQMkXZS/K786ywHKuY8eOKSIiwnPf9cf0Re5Z/5sjIyN19OjRfIFl7NixGjlyZL4+ly5dqrCwsGKPbd26GL38cnP99a/SzJnyvPGMGSP17h2ooUO/UqtW7Iz2FZdL2rKloo4dq65Nm75Vgwa/WjrNWJrt21dRUhulp7t3A50rb1fFvn3rtXjxr5aOrbQJCZGGDo3R9OmNFB9f1tNeteopDR2arpCQw1q8uAQHWIolJyeX9BAuCwkJUlbWD/r8c9/1efLkyUKv6zDGWPqxasCAARoyZIgCAgI0YcIETZ06VZLUqlUrrVu3Ti1bttT69eslSbfccoumTZtWqBmW2NhYHTlyxCsE/Rkul1S/fhk1aiQtWODwmkbPzZW6dzdKT5e2bMlho+oDZ2ayHJ42ZrJ8h79n67lcBX8ahe85nU4lJyerY8eOCgoKKunhlGr+qnVmZqaio6OVkZFx0e13ic2wxMXFaevWrcrOztbXX3+txo0bS5JiYmK0e/duVa5cucDZFUkKCQlRSEhIvvagoKBiF3LNGvc+/5kzz7fP36H4eGn9+iAlJRXrqS578+ZJvXu7D/w8eybrxRcd6t27TInsIy1tgoLcB8716qU/dlXorF0V0sKFDs2dK4WG8mbvK0FBUocO7k+iHTo0YUNqAV+896NwfF3rovRlaWC56aablJaWpu3bt+uBBx7Q448/rqSkJIWGhuq///2vJGnMmDEaMGCAXC5Xgbt9/I3TE61x7tkreeEw7+yV7t3dZ69062btUeilUd6Bc0884T5gLk/t2iVz4BwA/BmWBpbFBezAvf32273uN2jQQKkleMrC2acnXmifP6cnFk/e2Svnn8lyb1xTU8VMlg/07OkOfytW5Oizz9LUtWtTtWtXhjAI4JLBhePOwemJ1mAmy3qBgVJiolFCwg9KTDSEFQCXFALLOex4sZzSiAttAQCKgsBSALtdLKc0YiYLAFAUBJbz6NlT2rVLSk7O0eDBXys5OUc7dxJWfIWZLABAUZTYac2Xgrx9/r///oMSE5uw8fQxzl4BABQWgQUlirNXABTF4cNnDsbPyZF2747Ud99JZf7YmsXEcOxbaUVgQYljJgulARtSa/zrX9KZS3QFSUryWj5ihPT889aOCdYgsBSANx4ARcWG1BoPPCDdeqt06pTUpo27LSXFqfLl3VdM5b3Zd+y2LSSwFODll6VXXsm7l/+N5/HHpcmTLR0SAJtjQ2qNvI3k77+faWvSRLriihIbUqlltxBOYDnH/v3S229feJ2335b+8Q/pyiutGVNpZbf0DhSH01m85Sic/fulI0fcwTDP999L5cu7b0dH897sK3YL4QSWcxw5Ip31RdAFyspyr8d/iuJhJssaBEP/279fqldPOn3auz0p6cwXu4WGStu3875RHNTZWnabzSKwoEQwk2Udu03rlkZHjuTfiJ7r9Gk+6BQXdbaW3WazCCwoEcxkWeevf5Xi4tz1vOced9v//Z9TYWHuT6VXX12CgwNgS3aczSKwAKXY/v1SQkL+N53772cKHcD52XE2i0vzA6VYUd50AMDOCCwAAMD2CCwAAMD2CCzniI4+c7rn+ZQp414PAABYg4Nuz3HlldLatdKOHe77LpdTaWnfq2nTJgoMPHNWBQcoAsgTHe0+ePlCxwuFhvJBp7io8+WNwFKA5s3d/yT31SkjI3/QTTc1UVDQhR+HwsubycrJOf86zGThUnHlle4zrfKuWVHQVUG5AmvxUefLG4EFJYKZLJQ2V17p/meXq4KWVhf74MgHS9+w42wWgQUlhpks/7Pjm05plfcVCOe7KihfgeAb3ldudjv7YmZcudk37DibRWABSrErr5RWrXLPZF3oSrfMZBUfG1Jr5H0hnyTl5Di1evUatWnTWmXK8K3Yvma3WUMCywW4XNLKlQ6tWlVd5co51K6dFBhY0qMCimbRovwb0rOvdDtixJmZLvx5bEitcfZMldMpHT6coeuuY1eQP9ht1pDAch7z5klPPCHt3VtGUjNNmiTVqiVNnCj17FnSoytdCIb+xYbUGmxIUdrYbdaQwFKAefOkXr2km2+WZs6UGjaU0tOlF190t8+dS2jxFYKh/7EhBfBn2O3DDheOO4fL5d6A3nyztGCB1LKlFB7u/rlggbt9yBD3eiievGDYqJG0bp10/Lj7Z6NG7vZ580p6hKXL2TNZK1c6+BsGcEExMdL117v/XXeddNVV7g87eW0ElhKWmirt3Ss984wUcE51AgKkYcOkPXvc6+HPIxhaa948qW5dqWPHMpo0qZk6diyjunUJhbh0EcAvPwSWcxw+7P7ZsGHBy/Pa89bDn0MwtA4zWShtCOCXJwLLOfKmuNLTC16e186BisVDMLQGM1kobQjgly8CyznatnUf9Pnii1Jurvey3Fxp7Fipdm33evjzCIbWYCbLeuyq8B8C+OWNwHKOwED3GSoLF0rdu3sn+O7d3e0TJnDabXERDK3BTJa12FXhXwTwkmGXEE5gKUDPnu5TlzdtkuLjpYgI98/0dE5p9hWCoTWYybIOuyr8jwBuPVuFcFMKZGRkGEkmIyPDp/3m5BiTnOw0gwd/ZZKTnSYnx6fdwxjz0UfG1KpljHTmX+3a7nYUX06Ou7633GKMy+W9zOVyt9eubfjbLibqbI0VK9zvEevWFbx87Vr38hUrrBxV6fXRR8Y4HO6/33XrjDl+3P3zllvc7b54ny7K9tthjDElkJN8KjMzU5GRkcrIyFBERIRP+3Y6nVq8eLFuuukmBXGlLb9wuaQVK3L02Wdp6tq1qdq1K8PMig+dfSHEYcPOXAhx7Fj3TBazhsWXkiK1a+eeUWnZMv/ydevcs7QrVkhJSVaPrvRwudyf9hs1ch+zcvZuodxc9+xserq0cyezs8VlVa2Lsv1mlxBKXGCglJholJDwgxITDW80PsYuTv9jV4U12JVsHTseL0RgAS4DPXtKu3ZJyck5Gjz4ayUn52jnTsKKr3CskHUI4NawYwgnsACXCWay/Iez3qxFAPc/O4ZwAgsAFBO7KqxHAPcvO4ZwAgsA+AC7KlCa2DGEl7HuqQCgdOvZU+rWjbPeUDrkhfAnnnCH7zy1a5dMCCewAIAP5e2q+P33H5SY2ISwgkuanUI4gQUAAJyXXUI4x7AAAADbI7AAAADbI7AAAADbI7AAAADbI7AAAADbI7AAAADbI7AAAADbI7AAAADbKxUXjjPGSJIyMzN93rfT6dTJkyeVmZmpoKAgn/cPN+psDepsDepsDepsHX/VOm+7nbcdv5BSEViOHz8uSYqNjS3hkQAAgKI6fvy4IiMjL7iOwxQm1thcbm6uDh06pPLly8vhcPi078zMTMXGxurAgQOKiIjwad84gzpbgzpbgzpbgzpbx1+1Nsbo+PHjqlatmgICLnyUSqmYYQkICFCNGjX8+hwRERH8h7AAdbYGdbYGdbYGdbaOP2p9sZmVPBx0CwAAbI/AAgAAbI/AchEhISEaMWKEQkJCSnoopRp1tgZ1tgZ1tgZ1to4dal0qDroFAAClGzMsAADA9ggsAADA9ggsAADA9kp9YMnIyFCLFi0UHh6u9PR0T/ucOXMUHx+vDh066ODBg5Kkbdu2KSEhQfHx8Vq2bFm+vlavXq34+Hi1adNGmzZt8vRTr149NWvWzLOey+XS3XffrbZt2+rxxx/37wu0CX/XeeTIkWrZsqVatmyp9957TxJ19kedp06dqrZt26ply5YaNmyYZ92nnnpKbdu2Vb9+/eR0Ov38Kkuev+ucp0uXLhoyZIgk/p79Uefnn39ejRo1UlJSkp544glJ1NkfdTbGaNiwYerQoYOSkpJ0+vRpSX543zClXHZ2tvn5559N//79zaZNm4wxxjidTtOyZUuTlZVlVq9ebe6//35jjDE9evQwO3bsMBkZGSY+Pj5fXwkJCebo0aNm3759pmvXrsYYY44cOWKysrLMDTfc4FlvwYIF5tlnnzXGGHPvvfeatWvX+vtlljh/13n37t3GGGOysrJMw4YNTW5uLnX2Q52zsrI8yxMTE82BAwdMWlqa6du3rzHGmBdeeMF88MEH/n6ZJc7fdTbGmNWrV5vOnTubJ554whjD+4Y/6jxixAjz6aefeq1HnX1f5zlz5phXXnnFaz1/vG+U+hmWoKAgVapUyatt586dql+/voKDg9W6dWtt3LhRknTo0CHFxcUpIiJCUVFROnLkiOcxp06dUmBgoCpUqKArr7xSR48elSRVrFhRwcHBXv2vXbtWnTp1kuT+BLVmzRp/vkRb8Hed69Sp43mewMBASdQ5jy/rnPe3nJOTowoVKigqKoo6/8GXdZak1157TYMGDfLcp85uvq7zP//5TyUmJmr58uWSqHMeX9b5k08+0f79+5WUlKSRI0dK8k+dS31gKcixY8e8Li3scrkkub+TKE9kZKTXH/25jylTpoyys7Mv2v+5/VxO/FHnV155Rb169ZLD4aDOf/B1nceNG6e4uDhVrlxZYWFh1PkPvqzzqlWr1KRJE4WHhxe4LnX2TZ0fffRRfffdd5o9e7Yee+wxZWdnU+c/+LLOP/30k6pWraqUlBRt2bJF69ev90udL8vAcsUVV3i+0lqS5xP72V+8lJGRoaioqPM+JicnJ9/MSkHrntvP5cTXdV66dKlSU1P1zDPP5FuXOvuuzk8//bR27dqlw4cPa/369dT5D76s86uvvuo1u3LuutTZN3XOW6dKlSqqX7++Dh48SJ3/4Ms6X3HFFWrfvr0kqX379tq8ebNf6nxZBpa4uDht3bpV2dnZWrt2rRo3bixJiomJ0e7du3X8+HEdPXpU0dHRnseEhYUpJydHv/32mw4cOHDB4sfHx+uLL76QJH3++edq3bq1f1+QTfmyzps2bdLo0aM1Y8YMz38o6uzmyzpnZWVJcr95lStXTmFhYdT5D76s865du3Tbbbdp6NCh+uijj/Tpp59S5z/4ss55G8yTJ09q27ZtiomJoc5/8GWdW7durbS0NElSWlqa6tSp4586F/somEtA165dTUxMjGnZsqWZNm2aMcaYDz/80LRq1cq0a9fO7N+/3xhjzObNm02bNm1Mq1atzNKlS/P1s3LlStOqVSsTHx9v0tLSjDHGrFixwnTo0MGUL1/edOjQwfzwww/G6XSa/v37mzZt2phHHnnEstdZ0vxZ5/bt25sGDRqYxMREk5iYaH777Tfq7Ic6jxgxwiQmJprWrVub4cOHe9YdMmSIadOmjenTp4/XgbmlmT/rnGfFihWeg275e/Z9ne+//37TqlUr06JFCzNz5kxjDHX2R51PnDhhevXqZRISEsx9993nWdfX7xtcmh8AANjeZblLCAAAXFoILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAAAwPYILAD+tL1796pXr15ebePGjdOePXtKaERnFDQ2AJeuMiU9AACly9NPP13SQyiW3Nxcr+9TAWAP/K8E4FMDBgxQenq6UlJS1KVLF/Xo0UNNmjRRenq6JGnJkiVq27at4uPjNXPmzHyPr1+/vvr376+mTZvq/fff9+pTkoYMGaKUlBSlpKSoc+fOnv5nzZqlzp07q0WLFvr1118lST/88IN69uyp66+/XsuXL5ckff3112rXrp3atm2rCRMmSJKef/55DRgwQDfddJM2btzo9xoBKDoCCwC/cTqdmj9/vsaNG6epU6fKGKPRo0dr2bJlSk1N1RtvvOH5Wvs8P/74o15//XWtWrVKr7322gX7z83N1fz58/Xwww/rww8/1Oeff66+ffvq448/9vQ1c+ZMLV26VM8++6wk9wzQvHnzlJqaqpUrV+qnn36SJMXGxmrx4sVq2rSp7wsBoNjYJQTAb/I2/rGxsTp27Jh++eUX7dixQ506dZIk/fbbb/rll19UtWpVz2Pq1KmjiIgISfKEGYfD4Vl+9tef5X3DbLVq1Ty3q1evrn379kmSGjZsqJCQEIWEhCgnJ0eStHHjRvXo0UOSdOzYMR04cECS1Lx5c9++eAA+RWAB4DfnBo3o6Ghdc801Wrp0qYKDg+V0OhUUFHTex+SpUKGCDh48qIYNG2rjxo265ZZb8q1bUKjZvHmzsrOzdeLECZUp4367a9KkiebOnavIyEi5XC4FBARo4cKFHLcC2ByBBUCxpKam6sYbb5Qkz8/zCQgI0PDhw9WxY0cFBASoUqVKmj179kWfY8CAAerXr5/+/e9/KywsrNBjq1Gjhu644w7t2bNHL7/8siT3WUw9e/ZUbm6uQkJCNH/+/EL3B6DkOMzZ86sAAAA2xBwoAACwPQILAACwPQILAACwPQILAACwPQILAACwPQILAACwPQILAACwPQILAACwPQILAACwPQILAACwvf8PiyRAVNp0zroAAAAASUVORK5CYII=", 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "qc.allChanStats(\n", " canobieHDF_file, [\n", " 'CLEARANCE', 'Noise_NE', 'Noise_UV', 'gD_Fourier_2p67'\n", " ])" ] }, { "cell_type": "code", "execution_count": null, "id": "8193d8f4-1919-49cb-8669-6a5953913a5a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }