{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":70367,"databundleVersionId":9188054,"sourceType":"competition"}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Data Preparation\n\n![data.jpg](attachment:08b1c039-1e43-4b50-87fe-48444478348b.jpg)\n\nIn this competition we are tasked with large amounts of noisy data. We first need to reduce the complexity and use domain knowledge to clean the data and to help modeling it. By doing so, we need to keep track of time, as our submission can only take 9h and involves the preprocessing of ~800 planets! \n\nThe pipeline below mostly follows the steps outlined by the hosts here: https://www.kaggle.com/code/gordonyip/update-calibrating-and-binning-astronomical-data/notebook.","metadata":{},"attachments":{"08b1c039-1e43-4b50-87fe-48444478348b.jpg":{"image/jpeg":"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"}}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport itertools\nfrom tqdm import tqdm\nimport multiprocessing as mp\nfrom numpy.polynomial import Polynomial\nfrom astropy.stats import sigma_clip\nimport numba\n\n\nROOT = \"/kaggle/input/ariel-data-challenge-2024/\"\nVERSION = \"v2\"\n\nsensor_sizes_dict = {\n    \"AIRS-CH0\": [[11250, 32, 356], [32, 356]],\n    \"FGS1\": [[135000, 32, 32], [32, 32]],\n}  # input, mask","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:49:52.837964Z","iopub.execute_input":"2024-08-27T18:49:52.838336Z","iopub.status.idle":"2024-08-27T18:49:52.844595Z","shell.execute_reply.started":"2024-08-27T18:49:52.838305Z","shell.execute_reply":"2024-08-27T18:49:52.843571Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"planet_id = 14485303\nsensor = \"FGS1\"\n\nMODE = \"train\"","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:49:52.849126Z","iopub.execute_input":"2024-08-27T18:49:52.849447Z","iopub.status.idle":"2024-08-27T18:49:52.857048Z","shell.execute_reply.started":"2024-08-27T18:49:52.849403Z","shell.execute_reply":"2024-08-27T18:49:52.856212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_gain_offset(planet_id, sensor, mode):\n    \"\"\"\n    Get the gain and offset for a given planet and sensor\n    \"\"\"\n    gain_offset_csv = pd.read_csv(f\"{ROOT}/{mode}_adc_info.csv\")\n    planet_gain_offset = gain_offset_csv[gain_offset_csv[\"planet_id\"] == planet_id]\n\n    gain = planet_gain_offset[sensor + \"_adc_gain\"].values\n    offset = planet_gain_offset[sensor + \"_adc_offset\"].values\n    return gain, offset","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:49:52.858381Z","iopub.execute_input":"2024-08-27T18:49:52.858614Z","iopub.status.idle":"2024-08-27T18:49:52.870583Z","shell.execute_reply.started":"2024-08-27T18:49:52.858594Z","shell.execute_reply":"2024-08-27T18:49:52.869807Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nget_gain_offset(planet_id, sensor, MODE)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:49:52.872182Z","iopub.execute_input":"2024-08-27T18:49:52.872525Z","iopub.status.idle":"2024-08-27T18:49:55.474213Z","shell.execute_reply.started":"2024-08-27T18:49:52.872496Z","shell.execute_reply":"2024-08-27T18:49:55.473296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def read_data(planet_id, sensor, mode=\"train\"):\n    \"\"\"\n    Read the data for a given planet and sensor\n    \"\"\"\n    # get all noise correction frames and signal\n    signal = pd.read_parquet(\n        ROOT + \"/train/\" + str(planet_id) + \"/\" + sensor + \"_signal.parquet\",\n        engine=\"pyarrow\",\n    )\n    dark_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/dark.parquet\",\n        engine=\"pyarrow\",\n    )\n    dead_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/dead.parquet\",\n        engine=\"pyarrow\",\n    )\n    linear_corr_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/linear_corr.parquet\",\n        engine=\"pyarrow\",\n    )\n    flat_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/flat.parquet\",\n        engine=\"pyarrow\",\n    )\n    # read_frame = pd.read_parquet(\n    #     f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/read.parquet\",\n    #     engine=\"pyarrow\",\n    # )\n\n    # reshape to sensor shape and cast to float64\n    signal = signal.values.astype(np.float64).reshape(sensor_sizes_dict[sensor][0])\n    dark_frame = dark_frame.values.astype(np.float64).reshape(\n        sensor_sizes_dict[sensor][1]\n    )\n    dead_frame = dead_frame.values.reshape(sensor_sizes_dict[sensor][1])\n    flat_frame = flat_frame.values.astype(np.float64).reshape(\n        sensor_sizes_dict[sensor][1]\n    )\n    # read_frame = read_frame.values.reshape(sensor_sizes_dict[sensor][1])\n    linear_corr = linear_corr_frame.values.astype(np.float64).reshape(\n        [6] + sensor_sizes_dict[sensor][1]\n    )\n\n    return (\n        signal,\n        dark_frame,\n        dead_frame,\n        linear_corr,\n        flat_frame,\n        # read_frame,\n    )","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:49:55.476029Z","iopub.execute_input":"2024-08-27T18:49:55.476481Z","iopub.status.idle":"2024-08-27T18:49:55.48671Z","shell.execute_reply.started":"2024-08-27T18:49:55.476445Z","shell.execute_reply":"2024-08-27T18:49:55.485816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nread_data(planet_id, sensor, mode=MODE)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:49:55.487808Z","iopub.execute_input":"2024-08-27T18:49:55.488115Z","iopub.status.idle":"2024-08-27T18:50:00.909852Z","shell.execute_reply.started":"2024-08-27T18:49:55.488091Z","shell.execute_reply":"2024-08-27T18:50:00.90883Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### AIRS data is larger and takes longer.\n\nNote: it will already take about 800 * (1.46s + 0.47s) = ~26 minutes to just load the data into RAM for further processing! (single core estimate here and below)","metadata":{}},{"cell_type":"code","source":"def ADC_convert(signal, gain, offset):\n    \"\"\"\n    Step 1: Analog-to-Digital Conversion (ADC) correction\n\n    The Analog-to-Digital Conversion (adc) is performed by the detector to convert the\n    pixel voltage into an integer number. We revert this operation by using the gain\n    and offset for the calibration files 'train_adc_info.csv'.\n    \"\"\"\n\n    signal /= gain\n    signal += offset\n    return signal","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:00.911947Z","iopub.execute_input":"2024-08-27T18:50:00.912233Z","iopub.status.idle":"2024-08-27T18:50:00.916849Z","shell.execute_reply.started":"2024-08-27T18:50:00.912209Z","shell.execute_reply":"2024-08-27T18:50:00.915915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal, dark_frame, dead_frame, linear_corr, flat_frame = read_data(\n    planet_id, sensor, mode=MODE\n)\ngain, offset = get_gain_offset(planet_id, sensor, mode=MODE)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:00.918012Z","iopub.execute_input":"2024-08-27T18:50:00.918346Z","iopub.status.idle":"2024-08-27T18:50:01.62846Z","shell.execute_reply.started":"2024-08-27T18:50:00.918315Z","shell.execute_reply":"2024-08-27T18:50:01.627652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:01.629645Z","iopub.execute_input":"2024-08-27T18:50:01.629985Z","iopub.status.idle":"2024-08-27T18:50:01.832953Z","shell.execute_reply.started":"2024-08-27T18:50:01.629958Z","shell.execute_reply":"2024-08-27T18:50:01.832017Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nADC_convert(signal, gain, offset)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:01.834271Z","iopub.execute_input":"2024-08-27T18:50:01.835007Z","iopub.status.idle":"2024-08-27T18:50:03.596735Z","shell.execute_reply.started":"2024-08-27T18:50:01.834971Z","shell.execute_reply":"2024-08-27T18:50:03.595841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal = ADC_convert(signal, gain, offset)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:03.598127Z","iopub.execute_input":"2024-08-27T18:50:03.598508Z","iopub.status.idle":"2024-08-27T18:50:03.82255Z","shell.execute_reply.started":"2024-08-27T18:50:03.598474Z","shell.execute_reply":"2024-08-27T18:50:03.821812Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:03.823607Z","iopub.execute_input":"2024-08-27T18:50:03.823905Z","iopub.status.idle":"2024-08-27T18:50:04.026823Z","shell.execute_reply.started":"2024-08-27T18:50:03.823881Z","shell.execute_reply":"2024-08-27T18:50:04.025904Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def mask_hot_dead(signal, dead, dark):\n    \"\"\"\n    Step 2: Mask hot/dead pixel\n\n    The dead pixels map is a map of the pixels that do not respond to light and, thus,\n    can't be accounted for any calculation. In all these frames the dead pixels are\n    masked using python masked arrays. The bad pixels are thus masked but left\n    uncorrected. Some methods can be used to correct bad-pixels but this task,\n    if needed, is left to the participants.\n    \"\"\"\n\n    hot = sigma_clip(dark, sigma=5, maxiters=5).mask\n    hot = np.tile(hot, (signal.shape[0], 1, 1))\n    dead = np.tile(dead, (signal.shape[0], 1, 1))\n\n    # Set values to np.nan where dead or hot pixels are found\n    signal[dead] = np.nan\n    signal[hot] = np.nan\n    return signal","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:04.031637Z","iopub.execute_input":"2024-08-27T18:50:04.032002Z","iopub.status.idle":"2024-08-27T18:50:04.037995Z","shell.execute_reply.started":"2024-08-27T18:50:04.031976Z","shell.execute_reply":"2024-08-27T18:50:04.037128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nmask_hot_dead(signal, dead_frame, dark_frame)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:04.039153Z","iopub.execute_input":"2024-08-27T18:50:04.039481Z","iopub.status.idle":"2024-08-27T18:50:15.033851Z","shell.execute_reply.started":"2024-08-27T18:50:04.039451Z","shell.execute_reply":"2024-08-27T18:50:15.032893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal = mask_hot_dead(signal, dead_frame, dark_frame)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:15.034948Z","iopub.execute_input":"2024-08-27T18:50:15.035228Z","iopub.status.idle":"2024-08-27T18:50:15.178034Z","shell.execute_reply.started":"2024-08-27T18:50:15.035203Z","shell.execute_reply":"2024-08-27T18:50:15.177171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:15.179244Z","iopub.execute_input":"2024-08-27T18:50:15.17954Z","iopub.status.idle":"2024-08-27T18:50:15.38496Z","shell.execute_reply.started":"2024-08-27T18:50:15.179514Z","shell.execute_reply":"2024-08-27T18:50:15.384029Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def apply_linear_corr(c, signal):\n    \"\"\"\n    Step 3: linearity Correction\n\n    The non-linearity of the pixels' response can be explained as capacitive leakage\n    on the readout electronics of each pixel during the integration time. The number\n    of electrons in the well is proportional to the number of photons that hit the\n    pixel, with a quantum efficiency coefficient. However, the response of the pixel\n    is not linear with the number of electrons in the well. This effect can be\n    described by a polynomial function of the number of electrons actually in the well.\n    The data is provided with calibration files linear_corr.parquet that are the\n    coefficients of the inverse polynomial function and can be used to correct this\n    non-linearity effect.\n    Using horner's method to evaluate the polynomial\n    \"\"\"\n    assert c.shape[0] == 6  # Ensure the polynomial is of degree 5\n\n    return (\n        (((c[5] * signal + c[4]) * signal + c[3]) * signal + c[2]) * signal + c[1]\n    ) * signal + c[0]","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:15.386116Z","iopub.execute_input":"2024-08-27T18:50:15.386465Z","iopub.status.idle":"2024-08-27T18:50:15.39247Z","shell.execute_reply.started":"2024-08-27T18:50:15.386428Z","shell.execute_reply":"2024-08-27T18:50:15.391603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import cupy as cp\n\ndef apply_linear_corr_gpu(linear_corr, clean_signal):\n    \"\"\"\n    Step 3: linearity Correction on GPU\n\n    The non-linearity of the pixels' response can be explained as capacitive leakage\n    on the readout electronics of each pixel during the integration time. The number\n    of electrons in the well is proportional to the number of photons that hit the\n    pixel, with a quantum efficiency coefficient. However, the response of the pixel\n    is not linear with the number of electrons in the well. This effect can be\n    described by a polynomial function of the number of electrons actually in the well.\n    The data is provided with calibration files linear_corr.parquet that are the\n    coefficients of the inverse polynomial function and can be used to correct this\n    non-linearity effect.\n    \"\"\"\n    # Convert the input arrays to CuPy arrays\n    linear_corr_gpu = cp.asarray(linear_corr)\n    clean_signal_gpu = cp.asarray(clean_signal)\n\n    corrected_signal_gpu = (\n        (\n            (\n                (linear_corr_gpu[5] * clean_signal_gpu + linear_corr_gpu[4])\n                * clean_signal_gpu\n                + linear_corr_gpu[3]\n            )\n            * clean_signal_gpu\n            + linear_corr_gpu[2]\n        )\n        * clean_signal_gpu\n        + linear_corr_gpu[1]\n    ) * clean_signal_gpu + linear_corr_gpu[0]\n\n    # Convert the result back to a NumPy array (if needed)\n    corrected_signal = cp.asnumpy(corrected_signal_gpu)\n\n    return corrected_signal","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:15.393805Z","iopub.execute_input":"2024-08-27T18:50:15.394146Z","iopub.status.idle":"2024-08-27T18:50:15.410314Z","shell.execute_reply.started":"2024-08-27T18:50:15.394116Z","shell.execute_reply":"2024-08-27T18:50:15.409434Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.nanmax(signal), np.nanmin(signal), np.nanmean(signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:15.411313Z","iopub.execute_input":"2024-08-27T18:50:15.411567Z","iopub.status.idle":"2024-08-27T18:50:16.357268Z","shell.execute_reply.started":"2024-08-27T18:50:15.411545Z","shell.execute_reply":"2024-08-27T18:50:16.356349Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\napply_linear_corr(linear_corr, signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:16.358563Z","iopub.execute_input":"2024-08-27T18:50:16.359022Z","iopub.status.idle":"2024-08-27T18:50:40.714322Z","shell.execute_reply.started":"2024-08-27T18:50:16.358988Z","shell.execute_reply":"2024-08-27T18:50:40.713266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\napply_linear_corr_gpu(linear_corr, signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:40.715515Z","iopub.execute_input":"2024-08-27T18:50:40.715818Z","iopub.status.idle":"2024-08-27T18:50:51.252785Z","shell.execute_reply.started":"2024-08-27T18:50:40.71578Z","shell.execute_reply":"2024-08-27T18:50:51.251756Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"assert np.allclose(apply_linear_corr(linear_corr, signal), apply_linear_corr_gpu(linear_corr, signal), equal_nan=True)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:51.25384Z","iopub.execute_input":"2024-08-27T18:50:51.254093Z","iopub.status.idle":"2024-08-27T18:50:59.863606Z","shell.execute_reply.started":"2024-08-27T18:50:51.254071Z","shell.execute_reply":"2024-08-27T18:50:59.862691Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal = apply_linear_corr(linear_corr, signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:50:59.864754Z","iopub.execute_input":"2024-08-27T18:50:59.865064Z","iopub.status.idle":"2024-08-27T18:51:02.713574Z","shell.execute_reply.started":"2024-08-27T18:50:59.865038Z","shell.execute_reply":"2024-08-27T18:51:02.712617Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:02.714756Z","iopub.execute_input":"2024-08-27T18:51:02.715069Z","iopub.status.idle":"2024-08-27T18:51:02.932573Z","shell.execute_reply.started":"2024-08-27T18:51:02.715042Z","shell.execute_reply":"2024-08-27T18:51:02.931537Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def clean_dark(signal, dark, dt):\n    \"\"\"\n    Step 4: dark current subtraction\n\n    The data provided include calibration for dark current estimation, which can be\n    used to pre-process the observations. Dark current represents a constant signal\n    that accumulates in each pixel during the integration time, independent of the\n    incoming light. To obtain the corrected image, the following conventional approach\n    is applied: The data provided include calibration files such as dark frames or\n    dead pixels' maps. They can be used to pre-process the observations. The dark frame\n    is a map of the detector response to a very short exposure time, to correct for the\n    dark current of the detector.\n\n    image - (dark * dt)\n\n    The corrected image is conventionally obtained via the following: where the dark\n    current map is first corrected for the dead pixel.\n    \"\"\"\n\n    dark = np.tile(dark, (signal.shape[0], 1, 1))\n    signal -= dark * dt[:, np.newaxis, np.newaxis]\n    return signal","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:02.934287Z","iopub.execute_input":"2024-08-27T18:51:02.935121Z","iopub.status.idle":"2024-08-27T18:51:02.942534Z","shell.execute_reply.started":"2024-08-27T18:51:02.935081Z","shell.execute_reply":"2024-08-27T18:51:02.941365Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"axis_info = pd.read_parquet(ROOT + \"axis_info.parquet\")\ndt_airs = axis_info[\"AIRS-CH0-integration_time\"].dropna().values\n\ndt_fgs = np.ones(len(signal)) * 0.1","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:02.944153Z","iopub.execute_input":"2024-08-27T18:51:02.944534Z","iopub.status.idle":"2024-08-27T18:51:02.973086Z","shell.execute_reply.started":"2024-08-27T18:51:02.944503Z","shell.execute_reply":"2024-08-27T18:51:02.972097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if sensor == \"AIRS-CH0\":\n    dt = dt_airs\n    dt[1::2] += 4.5\nelif sensor == \"FGS1\":\n    dt = dt_fgs\n    dt[1::2] += 0.1","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:02.974298Z","iopub.execute_input":"2024-08-27T18:51:02.974626Z","iopub.status.idle":"2024-08-27T18:51:02.980199Z","shell.execute_reply.started":"2024-08-27T18:51:02.974598Z","shell.execute_reply":"2024-08-27T18:51:02.979054Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nclean_dark(signal, dark_frame, dt)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:02.981601Z","iopub.execute_input":"2024-08-27T18:51:02.981949Z","iopub.status.idle":"2024-08-27T18:51:13.156343Z","shell.execute_reply.started":"2024-08-27T18:51:02.98192Z","shell.execute_reply":"2024-08-27T18:51:13.154938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal = clean_dark(signal, dark_frame, dt)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:13.158574Z","iopub.execute_input":"2024-08-27T18:51:13.159217Z","iopub.status.idle":"2024-08-27T18:51:14.483066Z","shell.execute_reply.started":"2024-08-27T18:51:13.159172Z","shell.execute_reply":"2024-08-27T18:51:14.481851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:14.484162Z","iopub.execute_input":"2024-08-27T18:51:14.484466Z","iopub.status.idle":"2024-08-27T18:51:14.703605Z","shell.execute_reply.started":"2024-08-27T18:51:14.48444Z","shell.execute_reply":"2024-08-27T18:51:14.702465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_cds(signal):\n    \"\"\"\n    Step 5: Get Correlated Double Sampling (CDS)\n\n    The science frames are alternating between the start of the exposure and the end of\n    the exposure. The lecture scheme is a ramp with a double sampling, called\n    Correlated Double Sampling (CDS), the detector is read twice, once at the start\n    of the exposure and once at the end of the exposure. The final CDS is the\n    difference (End of exposure) - (Start of exposure).\n    \"\"\"\n\n    return np.subtract(signal[1::2, :, :], signal[::2, :, :])","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:14.710571Z","iopub.execute_input":"2024-08-27T18:51:14.710917Z","iopub.status.idle":"2024-08-27T18:51:14.716451Z","shell.execute_reply.started":"2024-08-27T18:51:14.710889Z","shell.execute_reply":"2024-08-27T18:51:14.715495Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nget_cds(signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:14.717556Z","iopub.execute_input":"2024-08-27T18:51:14.717837Z","iopub.status.idle":"2024-08-27T18:51:16.468589Z","shell.execute_reply.started":"2024-08-27T18:51:14.717813Z","shell.execute_reply":"2024-08-27T18:51:16.467375Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal = get_cds(signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:16.469816Z","iopub.execute_input":"2024-08-27T18:51:16.470147Z","iopub.status.idle":"2024-08-27T18:51:16.699316Z","shell.execute_reply.started":"2024-08-27T18:51:16.470119Z","shell.execute_reply":"2024-08-27T18:51:16.698143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:16.700817Z","iopub.execute_input":"2024-08-27T18:51:16.701161Z","iopub.status.idle":"2024-08-27T18:51:16.816943Z","shell.execute_reply.started":"2024-08-27T18:51:16.701131Z","shell.execute_reply":"2024-08-27T18:51:16.815906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def correct_flat_field(flat, signal):\n    \"\"\"\n    Step 6: Flat Field Correction\n\n    The flat field is a map of the detector response to uniform illumination, to\n    correct for the pixel-to-pixel variations of the detector, for example the\n    different quantum efficiencies of each pixel.\n    \"\"\"\n\n    return signal / flat","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:16.818371Z","iopub.execute_input":"2024-08-27T18:51:16.818696Z","iopub.status.idle":"2024-08-27T18:51:16.824435Z","shell.execute_reply.started":"2024-08-27T18:51:16.818668Z","shell.execute_reply":"2024-08-27T18:51:16.823408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\ncorrect_flat_field(flat_frame, signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:16.82582Z","iopub.execute_input":"2024-08-27T18:51:16.826534Z","iopub.status.idle":"2024-08-27T18:51:20.588614Z","shell.execute_reply.started":"2024-08-27T18:51:16.826498Z","shell.execute_reply":"2024-08-27T18:51:20.587634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"signal = correct_flat_field(flat_frame, signal)","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:20.589644Z","iopub.execute_input":"2024-08-27T18:51:20.589942Z","iopub.status.idle":"2024-08-27T18:51:21.058955Z","shell.execute_reply.started":"2024-08-27T18:51:20.589917Z","shell.execute_reply":"2024-08-27T18:51:21.058144Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.isnan(signal).sum()","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:21.060075Z","iopub.execute_input":"2024-08-27T18:51:21.060374Z","iopub.status.idle":"2024-08-27T18:51:21.169094Z","shell.execute_reply.started":"2024-08-27T18:51:21.060349Z","shell.execute_reply":"2024-08-27T18:51:21.168187Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%timeit\n\nmean_signal = np.nanmean(signal, axis=(1, 2))","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:21.170291Z","iopub.execute_input":"2024-08-27T18:51:21.171041Z","iopub.status.idle":"2024-08-27T18:51:24.340905Z","shell.execute_reply.started":"2024-08-27T18:51:21.171014Z","shell.execute_reply":"2024-08-27T18:51:24.339891Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mean_signal = np.nanmean(signal, axis=(1, 2))","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:24.34211Z","iopub.execute_input":"2024-08-27T18:51:24.342421Z","iopub.status.idle":"2024-08-27T18:51:24.744383Z","shell.execute_reply.started":"2024-08-27T18:51:24.342394Z","shell.execute_reply":"2024-08-27T18:51:24.743408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mean_signal","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:24.745589Z","iopub.execute_input":"2024-08-27T18:51:24.745921Z","iopub.status.idle":"2024-08-27T18:51:24.752431Z","shell.execute_reply.started":"2024-08-27T18:51:24.745895Z","shell.execute_reply":"2024-08-27T18:51:24.751573Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Total\n\n| Task | Time for AIRS [s] | Time for FGS [s] | Time for AIRS [s] UPDATE | Time for FGS [s] UPDATE |\n| --- | --- | --- | --- | --- |\n| Loading Data | 1.28 | 0.45 | 1.27 | 0.67 |\n| ADC Conversion | 0.34 | 0.36 | 0.20 | 0.22 |\n| Masking Hot/Dead Pixels | 3.13 | 3.35 | 0.125 | 0.134 |\n| Linear Correction | 19.4 | 25.8 | 3.49 (1.96 GPU) | 5.61 (1.98 GPU) |\n| Dark Cleaning | 1.44 | 1.68 | 0.898 | 0.957 |\n| Correlated Double Sampling (CDS) | 0.21 | 0.28 | 0.189 | 0.246 |\n| Transposing | 0.00 (negligible) | 0.00 | 0.00 | 0.00 |\n| Flat Field Correction | 1.39 | 1.47 | 1.38 | 1.5 |\n| Feature Engineering | 0.27 | 0.28 | 0.31 | 0.34 |\n\n![preprocessing_time.png](attachment:513b6349-2b28-4906-b768-1ef20c6891c3.png)\nUPDATE:\n![image.png](attachment:a629fb4d-0abc-432f-8152-9306145494e8.png)\n\n~~Total: 27.46s or a bit over 6h just to process the AIRS data on single core. Without Linear correction, we can bring it down to ~1h:50min. Impact on scores need to be checked! FGS takes even longer: 33.67s or ~7.5h~~\n\nWith the latest update, we can process all planets including Linear Correction!","metadata":{},"attachments":{"513b6349-2b28-4906-b768-1ef20c6891c3.png":{"image/png":"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"},"a629fb4d-0abc-432f-8152-9306145494e8.png":{"image/png":"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"}}},{"cell_type":"code","source":"import os\n\n\nos.environ[\"PREPROCESS_MODE\"] = \"train\"","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:24.753617Z","iopub.execute_input":"2024-08-27T18:51:24.753991Z","iopub.status.idle":"2024-08-27T18:51:24.763672Z","shell.execute_reply.started":"2024-08-27T18:51:24.753963Z","shell.execute_reply":"2024-08-27T18:51:24.762639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile preprocess.py\n\nimport pandas as pd\nimport numpy as np\nimport itertools\nfrom tqdm import tqdm\nimport multiprocessing as mp\nfrom astropy.stats import sigma_clip\nimport os\nfrom numpy.polynomial import Polynomial\n\n\nROOT = \"/kaggle/input/ariel-data-challenge-2024/\"\nVERSION = \"v4\"\n\nMODE = os.getenv('PREPROCESS_MODE')\n\n\nsensor_sizes_dict = {\n    \"AIRS-CH0\": [[11250, 32, 356], [32, 356]],\n    \"FGS1\": [[135000, 32, 32], [32, 32]],\n}  # input, mask\n\n\ndef get_gain_offset(planet_id, sensor, mode):\n    \"\"\"\n    Get the gain and offset for a given planet and sensor\n    \"\"\"\n    gain_offset_csv = pd.read_csv(f\"{ROOT}/{mode}_adc_info.csv\")\n    planet_gain_offset = gain_offset_csv[gain_offset_csv[\"planet_id\"] == planet_id]\n\n    gain = planet_gain_offset[sensor + \"_adc_gain\"].values\n    offset = planet_gain_offset[sensor + \"_adc_offset\"].values\n    return gain, offset\n\n\ndef read_data(planet_id, sensor, mode):\n    \"\"\"\n    Read the data for a given planet and sensor\n    \"\"\"\n    # get all noise correction frames and signal\n    signal = pd.read_parquet(\n        ROOT + \"/train/\" + str(planet_id) + \"/\" + sensor + \"_signal.parquet\",\n        engine=\"pyarrow\",\n    )\n    dark_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/dark.parquet\",\n        engine=\"pyarrow\",\n    )\n    dead_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/dead.parquet\",\n        engine=\"pyarrow\",\n    )\n    linear_corr_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/linear_corr.parquet\",\n        engine=\"pyarrow\",\n    )\n    flat_frame = pd.read_parquet(\n        f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/flat.parquet\",\n        engine=\"pyarrow\",\n    )\n    # read_frame = pd.read_parquet(\n    #     f\"{ROOT}/{mode}/{planet_id}/{sensor}_calibration/read.parquet\",\n    #     engine=\"pyarrow\",\n    # )\n\n    # reshape to sensor shape and cast to float64\n    signal = signal.values.astype(np.float64).reshape(sensor_sizes_dict[sensor][0])\n    dark_frame = dark_frame.values.astype(np.float64).reshape(\n        sensor_sizes_dict[sensor][1]\n    )\n    dead_frame = dead_frame.values.reshape(sensor_sizes_dict[sensor][1])\n    flat_frame = flat_frame.values.astype(np.float64).reshape(\n        sensor_sizes_dict[sensor][1]\n    )\n    # read_frame = read_frame.values.reshape(sensor_sizes_dict[sensor][1])\n    linear_corr = linear_corr_frame.values.astype(np.float64).reshape(\n        [6] + sensor_sizes_dict[sensor][1]\n    )\n\n    return (\n        signal,\n        dark_frame,\n        dead_frame,\n        linear_corr,\n        flat_frame,\n        # read_frame,\n    )\n\n\ndef ADC_convert(signal, gain, offset):\n    \"\"\"\n    Step 1: Analog-to-Digital Conversion (ADC) correction\n\n    The Analog-to-Digital Conversion (adc) is performed by the detector to convert the\n    pixel voltage into an integer number. We revert this operation by using the gain\n    and offset for the calibration files 'train_adc_info.csv'.\n    \"\"\"\n\n    signal /= gain\n    signal += offset\n    return signal\n\n\ndef mask_hot_dead(signal, dead, dark):\n    \"\"\"\n    Step 2: Mask hot/dead pixel\n\n    The dead pixels map is a map of the pixels that do not respond to light and, thus,\n    can't be accounted for any calculation. In all these frames the dead pixels are\n    masked using python masked arrays. The bad pixels are thus masked but left\n    uncorrected. Some methods can be used to correct bad-pixels but this task,\n    if needed, is left to the participants.\n    \"\"\"\n\n    hot = sigma_clip(dark, sigma=5, maxiters=5).mask\n    hot = np.tile(hot, (signal.shape[0], 1, 1))\n    dead = np.tile(dead, (signal.shape[0], 1, 1))\n\n    # Set values to np.nan where dead or hot pixels are found\n    signal[dead] = np.nan\n    signal[hot] = np.nan\n    return signal\n\n\ndef apply_linear_corr(c, signal):\n    \"\"\"\n    Step 3: linearity Correction\n\n    The non-linearity of the pixels' response can be explained as capacitive leakage\n    on the readout electronics of each pixel during the integration time. The number\n    of electrons in the well is proportional to the number of photons that hit the\n    pixel, with a quantum efficiency coefficient. However, the response of the pixel\n    is not linear with the number of electrons in the well. This effect can be\n    described by a polynomial function of the number of electrons actually in the well.\n    The data is provided with calibration files linear_corr.parquet that are the\n    coefficients of the inverse polynomial function and can be used to correct this\n    non-linearity effect.\n    Using horner's method to evaluate the polynomial\n    \"\"\"\n    assert c.shape[0] == 6  # Ensure the polynomial is of degree 5\n\n    return (\n        (((c[5] * signal + c[4]) * signal + c[3]) * signal + c[2]) * signal + c[1]\n    ) * signal + c[0]\n\n\ndef clean_dark(signal, dark, dt):\n    \"\"\"\n    Step 4: dark current subtraction\n\n    The data provided include calibration for dark current estimation, which can be\n    used to pre-process the observations. Dark current represents a constant signal\n    that accumulates in each pixel during the integration time, independent of the\n    incoming light. To obtain the corrected image, the following conventional approach\n    is applied: The data provided include calibration files such as dark frames or\n    dead pixels' maps. They can be used to pre-process the observations. The dark frame\n    is a map of the detector response to a very short exposure time, to correct for the\n    dark current of the detector.\n\n    image - (dark * dt)\n\n    The corrected image is conventionally obtained via the following: where the dark\n    current map is first corrected for the dead pixel.\n    \"\"\"\n\n    dark = np.tile(dark, (signal.shape[0], 1, 1))\n    signal -= dark * dt[:, np.newaxis, np.newaxis]\n    return signal\n\n\ndef get_cds(signal):\n    \"\"\"\n    Step 5: Get Correlated Double Sampling (CDS)\n\n    The science frames are alternating between the start of the exposure and the end of\n    the exposure. The lecture scheme is a ramp with a double sampling, called\n    Correlated Double Sampling (CDS), the detector is read twice, once at the start\n    of the exposure and once at the end of the exposure. The final CDS is the\n    difference (End of exposure) - (Start of exposure).\n    \"\"\"\n\n    return np.subtract(signal[1::2, :, :], signal[::2, :, :])\n\n\ndef correct_flat_field(flat, signal):\n    \"\"\"\n    Step 6: Flat Field Correction\n\n    The flat field is a map of the detector response to uniform illumination, to\n    correct for the pixel-to-pixel variations of the detector, for example the\n    different quantum efficiencies of each pixel.\n    \"\"\"\n\n    return signal / flat\n\n\ndef process_planet(planet_id):\n    \"\"\"\n    Process a single planet's data\n    \"\"\"\n    axis_info = pd.read_parquet(ROOT + \"axis_info.parquet\")\n    dt_airs = axis_info[\"AIRS-CH0-integration_time\"].dropna().values\n\n    for sensor in [\"FGS1\", \"AIRS-CH0\"]:\n        # load all data for this planet and sensor\n        signal, dark_frame, dead_frame, linear_corr, flat_frame = read_data(\n            planet_id, sensor, mode=MODE\n        )\n        gain, offset = get_gain_offset(planet_id, sensor, mode=MODE)\n\n        # Step 1: ADC correction\n        signal = ADC_convert(signal, gain, offset)\n\n        # Step 2: Mask hot/dead pixel\n        signal = mask_hot_dead(signal, dead_frame, dark_frame)\n\n        # Step 3: linearity Correction\n        signal = apply_linear_corr(linear_corr, signal)\n\n        # Step 4: dark current subtraction\n        if sensor == \"FGS1\":\n            dt = np.ones(len(signal)) * 0.1\n            dt[1::2] += 4.5\n        elif sensor == \"AIRS-CH0\":\n            dt = dt_airs\n            dt[1::2] += 0.1\n\n        signal = clean_dark(signal, dark_frame, dt)\n\n        # Step 5: Get Correlated Double Sampling (CDS)\n        signal = get_cds(signal)\n\n        # Step 6: Flat Field Correction\n        signal = correct_flat_field(flat_frame, signal)\n\n        # Feature engineering (just mean over all pixels, but masked via NaNs)\n        mean_signal = np.nanmean(signal, axis=(1, 2))\n\n        # save the processed signal\n        np.save(\n            str(planet_id) + \"_\" + sensor + f\"_signal_{VERSION}.npy\",\n            mean_signal.astype(np.float64),\n        )\n\n\nif __name__ == \"__main__\":\n    adc_info = pd.read_csv(ROOT + f\"/{MODE}_adc_info.csv\", index_col=\"planet_id\")\n    planet_ids = adc_info.index.tolist()\n\n    # Use up to 4 threads!\n    with mp.Pool(processes=4) as pool:\n        list(tqdm(pool.imap(process_planet, planet_ids), total=len(planet_ids)))\n\n    # join processed signals in a single file\n    f_raw = np.full((len(planet_ids), 67500), np.nan, dtype=np.float64)\n    a_raw = np.full((len(planet_ids), 5625), np.nan, dtype=np.float64)\n\n    for i, planet_id in tqdm(enumerate(planet_ids)):\n        f_raw[i] = np.load(\n            str(planet_id) + f\"_FGS1_signal_{VERSION}.npy\"\n        )\n        a_raw[i] = np.load(\n            str(planet_id) + f\"_AIRS-CH0_signal_{VERSION}.npy\"\n        )\n\n    np.save(f\"fgs_{VERSION}.npy\", f_raw, allow_pickle=False)\n    np.save(f\"airs_{VERSION}.npy\", a_raw, allow_pickle=False)\n\n    print(\"Processing complete!\")","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:24.765517Z","iopub.execute_input":"2024-08-27T18:51:24.765885Z","iopub.status.idle":"2024-08-27T18:51:24.78284Z","shell.execute_reply.started":"2024-08-27T18:51:24.765854Z","shell.execute_reply":"2024-08-27T18:51:24.781743Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python preprocess.py","metadata":{"execution":{"iopub.status.busy":"2024-08-27T18:51:24.784286Z","iopub.execute_input":"2024-08-27T18:51:24.784926Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Clean up intermediate data\n\nRead more about it here and how to prevent the \"Submission CSV Not Found\" Error: https://www.kaggle.com/competitions/ariel-data-challenge-2024/discussion/528657","metadata":{}},{"cell_type":"code","source":"!rm -rf *FGS1_signal*","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!rm -rf *AIRS-CH0_signal*","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}