diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..b07f3c114a43e6e1f312e018911a76d93b5cb738 --- /dev/null +++ b/.gitlab-ci.yml @@ -0,0 +1,41 @@ +default: + image: python:3.9-slim-buster + cache: + key: "$CI_PIPELINE_ID" + paths: + - venv/ + - $CI_PROJECT_DIR/.cache/pip + before_script: + - python -V + - python -m venv venv + - source venv/bin/activate + - pip install --upgrade pip + - pip install --force-reinstall --index-url https://pypi.anaconda.org/intel/simple --no-dependencies numpy scipy==1.7.3 + - pip install numpy scipy==1.7.3 + #- pip install torch --index-url https://download.pytorch.org/whl/cpu + - pip install joblib scikit-learn + - pip install matplotlib lmfit seaborn extra_data + +stages: + - test + +#setup-environment: +# stage: environment +# script: +# - apt-get update && apt-get install -y python3-venv +# - python3 -m venv .venv +# - source .venv/bin/activate +# - python3 -m pip install --upgrade pip +# - python3 -m pip install --force-reinstall --index-url https://pypi.anaconda.org/intel/simple --no-dependencies numpy scipy==1.7.3 +# - python3 -m pip install numpy scipy==1.7.3 +# #- python3 -m pip install torch --index-url https://download.pytorch.org/whl/cpu +# - python3 -m pip install joblib scikit-learn +# - python3 -m pip install matplotlib lmfit seaborn extra_data + +test_import: + stage: test + script: + # cannot do this, because I cannot read data ... + #- ./pes_to_spec/test/offline_analysis.py -p 900331 -r 69 -t 70 -d results_ard --model-type ard + - python -m unittest ./pes_to_spec/test/test_import.py + diff --git a/notebook/Example offline analysis.ipynb b/notebook/Example offline analysis.ipynb index 9d42a092f5045f15ed7686de4b11032d5ad5706a..d92c70f5ca8cf646963a6a6287822cbee87cc4a0 100644 --- a/notebook/Example offline analysis.ipynb +++ b/notebook/Example offline analysis.ipynb @@ -5,11 +5,35 @@ "id": "59f50187-f73f-471b-b668-9126e6f48501", "metadata": {}, "source": [ - "# Learning high-resolution data from low-resolution\n", + "# Automated virtual spectrometer example offline analysis\n", "\n", - "This is an example notebook showing how to use the `pes_to_spec` infrastructure in this package.\n", + "This is an example notebook showing how to use the `pes_to_spec` infrastructure in this package. The objective here is to calibrate the photo-electron spectrometer data automatically. This is done by using data from one \"training\" run that contains data from both the photo-electron spectrometer (PES), XGM and the grating spectrometer and then using the correlation between them to calibrate the PES data when no grating spectrometer is available.\n", "\n", - "We start by importing some modules. The key module here is called `pes_to_spec`." + "This notebook includes the main analysis to simple get a calibrated PES spectrum using this automated method, as well as further analyses on the output in a second section. The objective of the second sections are only to validate results and may probably be skipped in most cases, if one needs only the PES spectrum.\n", + "\n", + "We start by importing some modules. The key module here is called `pes_to_spec`. It can be cloned from its repository using the following command (for example) in Maxwell:\n", + "\n", + "`git clone https://git.xfel.eu/machineLearning/pes_to_spec.git`\n", + "\n", + "After that the notebook in the directory `pes_to_spec/notebook` can be opened and executed using the kernel `xfel (current)`. The specialized environment mentioned in the `README.md` file of the package may offer a slightly better performance and allow for expert features, but this is not necessary for the standard analysis." + ] + }, + { + "cell_type": "markdown", + "id": "0d0003a3-4165-447d-bdb5-758c323a3fbf", + "metadata": {}, + "source": [ + "## Core automated virtual spectromater usage" + ] + }, + { + "cell_type": "markdown", + "id": "aadd23ab-b894-467c-bb28-c1d25b8e94a2", + "metadata": {}, + "source": [ + "Here we expand on the usage of the virtual spectrometer. We assume that 2 runs of data have been collected. A first run contains both XGM, PES and grating spectrometer data.\n", + "\n", + "A test run may contain only the XGM and PES data. If the test run contains also the grating spectrometer, it can be used also for validation, but in a real use-case this may not be available." ] }, { @@ -34,7 +58,15 @@ "execution_count": 2, "id": "da002d3e-c0da-419b-922b-0ab5c6deece8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: BNN model disabled. It requires PyTorch. Check if it is installed.\n" + ] + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -45,7 +77,7 @@ "import scipy\n", "from extra_data import open_run, by_id\n", "from itertools import product\n", - "from pes_to_spec.model import Model, matching_ids\n", + "from pes_to_spec.model import get_model_with_resolution, Model, matching_ids, matching_two_ids\n", "\n", "from typing import Any, Dict" ] @@ -55,18 +87,21 @@ "id": "494a729c-dff4-4501-b828-fba2aaae5a23", "metadata": {}, "source": [ - "# Input data\n", + "### Input data\n", "\n", - "Read data from two runs. One shall be used for training the model. The second one is used for testing it.\n", "Note that the data in the training run must be large enough, compared to the number of model parameters.\n", "\n", - "Only the SPEC, PES and XGM data is used for training, while only the PES and XGM data is needed for testing.\n", - "However, more data is collected here to validate the results." + "Only the grating spectrometer, PES and XGM data is used for training, while only the PES and XGM data is needed for testing.\n", + "However, more data is collected here to validate the results.\n", + "\n", + "Please adjust the proposal number, run number and name of the devices below as needed. Specifically, note that the grating spectrometer name changes depending on where the data has been collected. If unsure, try `run.info()` to check if the it contains `SPECTROMETER_SCS_NAVITAR`, `SPECTROMETER_SQS_NAVITAR` or `SPECTROMETER_SXP_NAVITAR`, etc.\n", + "\n", + "Additionally, please check that the list of PES channels includes the channels active during the runs. It may be that data from all channels are written to disk, but not all channels are active (in which case they contain only noise). This would not hurt the procedure, but could lead to erroneous comparison between the results." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "4a301f2a-dedb-46e4-b096-fc9c6cf5b23a", "metadata": {}, "outputs": [], @@ -75,7 +110,7 @@ "run_test = open_run(proposal=900331, run=70)\n", "\n", "# useful names to avoid repeating it all over the notebook, in case they ever change\n", - "spec_name = \"SA3_XTD10_SPECT/MDL/SPECTROMETER_SCS_NAVITAR:output\"\n", + "grating_name = \"SA3_XTD10_SPECT/MDL/SPECTROMETER_SCS_NAVITAR:output\"\n", "pes_name = \"SA3_XTD10_PES/ADC/1:network\"\n", "xgm_name = \"SA3_XTD10_XGM/XGM/DOOCS:output\"\n", "\n", @@ -102,34 +137,44 @@ " gas = \"_\".join(gas_active)\n", " return gas\n", "\n", - "def get_tids(run, need_spec:bool=True) -> np.ndarray:\n", + "def get_tids(run) -> np.ndarray:\n", " \"\"\"Get which train IDs contain all necessary inputs for training.\"\"\"\n", - " spec_tid = run[spec_name, \"data.trainId\"].ndarray()\n", + " if grating_name in run.all_sources:\n", + " spec_tid = run[grating_name, \"data.trainId\"].ndarray()\n", + " else:\n", + " spec_tid = None\n", " pes_tid = run[pes_name, \"digitizers.trainId\"].ndarray()\n", " xgm_tid = run[xgm_name, \"data.trainId\"].ndarray()\n", "\n", " # match tids to be sure we have all inputs:\n", - " tids = matching_ids(spec_tid, pes_tid, xgm_tid)\n", + " if spec_tid is None:\n", + " tids = matching_two_ids(pes_tid, xgm_tid)\n", + " else:\n", + " tids = matching_ids(spec_tid, pes_tid, xgm_tid)\n", + "\n", " return tids\n", "\n", "def get_data(run, tids) -> Dict[str, Any]:\n", " \"\"\"Get all relevant data.\"\"\"\n", " data = dict()\n", - " data[\"int\"] = run[xgm_name, \"data.intensitySa3TD\"].select_trains(by_id[tids]).ndarray()[:, 0][:, np.newaxis]\n", - " data[\"pressure\"] = run[pres_name, \"value\"].select_trains(by_id[tids]).ndarray()\n", - " data[\"voltage\"] = run[volt_name, \"u212.value\"].select_trains(by_id[tids]).ndarray()\n", - " data[\"energy\"] = run[spec_name, \"data.photonEnergy\"].select_trains(by_id[tids]).ndarray()\n", - " data[\"spec\"] = run[spec_name, \"data.intensityDistribution\"].select_trains(by_id[tids]).ndarray()\n", + " data[\"intensity\"] = run[xgm_name, \"data.intensitySa3TD\"].select_trains(by_id[tids]).ndarray()[:, 0][:, np.newaxis]\n", " data[\"pes\"] = {ch: run[pes_name,\n", " f\"digitizers.{ch}.raw.samples\"].select_trains(by_id[tids]).ndarray()\n", " for ch in channels}\n", + " # this may not be available in testing\n", + " if grating_name in run.all_sources:\n", + " data[\"grating\"] = run[grating_name, \"data.intensityDistribution\"].select_trains(by_id[tids]).ndarray()\n", + " data[\"energy\"] = run[grating_name, \"data.photonEnergy\"].select_trains(by_id[tids]).ndarray()\n", + " # only for validation\n", + " data[\"pressure\"] = run[pres_name, \"value\"].select_trains(by_id[tids]).ndarray()\n", + " data[\"voltage\"] = run[volt_name, \"u213.value\"].select_trains(by_id[tids]).ndarray()\n", " data[\"gas\"] = get_gas(run)\n", " return data\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "210c0550-1abb-43a0-99a5-7c35d2766be0", "metadata": {}, "outputs": [], @@ -137,11 +182,7 @@ "\n", "# get the matched train IDs\n", "tids = get_tids(run)\n", - "\n", - "# we don't need the spec for testing in reality,\n", - "# but it is nice to plot it in the test run too,\n", - "# to check that this works during validation\n", - "test_tids = get_tids(run_test, need_spec=True)\n", + "test_tids = get_tids(run_test)\n", "\n", "# get the data\n", "data = get_data(run, tids)\n", @@ -158,12 +199,14 @@ "\n", "Note that for training, it is assumed that only one pulse is present. For testing there is no such requirement.\n", "\n", - "First output some general information about the conditions of the measurement device." + "First output some general information about the conditions of the measurement device.\n", + "\n", + "The method expects the run conditions to be the same in training and inference, so check that there is not a significant deviation here." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "956105a6-d37e-453c-bfeb-2b1c876ee3f2", "metadata": {}, "outputs": [ @@ -183,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "4654f205-edc6-45f7-97bd-0d088c38edb0", "metadata": {}, "outputs": [ @@ -191,7 +234,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Voltage in training: -116.00 +/- 0.01\n", + "Voltage in training: -116.01 +/- 0.01\n", "Voltage in testing: -116.00 +/- 0.01\n" ] } @@ -203,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "fa662544-3caa-4404-bb61-fa41add82642", "metadata": {}, "outputs": [ @@ -226,7 +269,7 @@ "id": "d9b62e3a-aff9-4436-905d-ea02c11aecf6", "metadata": {}, "source": [ - "## Using the virtual spectrometer" + "### Using the virtual spectrometer" ] }, { @@ -234,14 +277,17 @@ "id": "5962e483-60da-4c70-bb09-dce5fc9745e0", "metadata": {}, "source": [ - "Now we will actually train the model. We do that by creating a `Model` object (from `pes_to_spec`) and calling the `fit` function.\n", - "The `fit` function requires the PES intensity, the SPEC intensity, the energy axis from SPEC (stored as a reference only), as well as the energy measured in the XGM (which has better resolution than the integral of the PES)." + "Now we will actually train the model. We do that by creating a `Model` object (from `pes_to_spec`) and fitting the data. It requires the PES intensity, the grating spec. intensity, the energy axis from grating spec., as well as the energy measured in the XGM (which has better resolution than the integral of the PES).\n", + "\n", + "We actually do this twice: in the first time we do it without any preprocessing on the grating spectrometer data. After that step, we record the maximum resolution achievable with the virtual spectrometer and then redo the estimate using the discovered resolution to pre-process the grating spectrometer data, so that this information is taken into account in the uncertainty estimate.\n", + "\n", + "The work of calculating the expected resolution and adapting to it is done by the `get_model_with_resolution`" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "a0adb57b-7496-4781-9511-ac2a8d05658d", + "execution_count": 15, + "id": "70c4e386-61b2-49e7-b759-8c0175ee457a", "metadata": {}, "outputs": [ { @@ -251,13 +297,14 @@ "Checking data quality in high-resolution data.\n", "Selected 7165 of 7165 samples\n", "Fitting PCA on low-resolution data.\n", - "Using 1000 comp. for PES PCA (asked for 1000, out of 7201, in 7165 samples).\n", + "Using 556 comp. for PES PCA.\n", "Fitting PCA on high-resolution data.\n", + "Using 24 comp. for grating spec. PCA.\n", "Fitting outlier detection\n", "Fitting model.\n", "Calculate PCA unc. on high-resolution data.\n", "Calculate transfer function\n", - "Resolution: 0.9825132876894322\n", + "Resolution: 0.8169395778859361\n", "Calculate PCA on channel_1_A\n", "Calculate PCA on channel_1_B\n", "Calculate PCA on channel_1_C\n", @@ -270,67 +317,73 @@ "Calculate PCA on channel_4_B\n", "Calculate PCA on channel_4_C\n", "Calculate PCA on channel_4_D\n", - "End of fit.\n" + "End of fit.\n", + "Resolution: 0.82 eV\n" ] } ], "source": [ - "# this is the main object holding all\n", - "# information needed for training and prediction\n", - "# the default parameters should be sufficient in most times\n", - "model = Model(channels=channels,\n", - " high_res_sigma=0.0,\n", - " )\n", - "\n", - "# this trains the model\n", - "# the first parameter is expected to be a dictionary with the channel name as a key\n", - "model.fit(data['pes'],\n", - " data['spec'],\n", - " data['energy'],\n", - " pulse_energy=data['int'])\n", - "\n", - "# save it for later usage:\n", - "model.save(\"model.joblib\")\n", - "\n", - "# load a model (you can start from here if working on an existing model)\n", - "model = Model.load(\"model.joblib\")\n", - "\n", - "# and use it to map a low-resolution spectrum to a high-resolution one\n", - "# as before, the low_resolution_raw_data refers to a dictionary mapping the channel name\n", - "# in the format \"channel_[1-4]_[A-D]\" to the 2D numpy array with shape (number_of_train_IDs, features)\n", - "# all names and shapes must match the format in training, except for the number_of_train_IDs, which may vary\n", - "pred = model.predict(data['pes'], pulse_energy=data['int'])\n" + "model, resolution = get_model_with_resolution(data['pes'],\n", + " data['grating'],\n", + " data['energy'],\n", + " data['intensity'],\n", + " channels=channels,\n", + " )\n", + "print(f\"Resolution: {resolution:.2f} eV\")" ] }, { "cell_type": "markdown", - "id": "e0286ae3-1a59-468f-ae40-c3ed94b7b301", + "id": "2f6c9d0a-29af-42c9-ba3c-08aae561232b", + "metadata": {}, + "source": [ + "We can save the resulting model for later usage using the `save` method and then reload it later with the `load` function as shown below. This can be useful if one wants to reuse information at a later stage." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a0adb57b-7496-4781-9511-ac2a8d05658d", "metadata": {}, + "outputs": [], "source": [ - "Now we can try it in the test dataset:" + "# save it for later usage:\n", + "model.save(\"model.joblib\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "67616041-4239-48f0-a4f7-2caf5d896652", + "metadata": {}, + "outputs": [], + "source": [ + "# load a model -- you can skip the fit above if you just start from this line\n", + "#model = Model.load(\"model.joblib\")" ] }, { "cell_type": "markdown", - "id": "ffc06362-3479-4cb9-b102-b438a83d2950", + "id": "de6cf667-2f50-47c2-983a-a6c0b2d17714", "metadata": {}, "source": [ - "We can predict it in the training data itself, but this is a bit biased, since we used the same information to fit the model." + "Now we can use this mapping on new data. In this example, we try it in the test dataset mentioned." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "id": "917156f3-9476-48e0-9121-5f75f185045f", "metadata": {}, "outputs": [], "source": [ - "pred = model.predict(data_test['pes'], pulse_energy=data_test['int'])\n", + "pred = model.predict(data_test['pes'], pulse_energy=data_test['intensity'])\n", "\n", "# add the references in this array in the same array format, so we can plot them later\n", "pred[\"energy\"] = model.get_energy_values()\n", - "\n", - "pred['spec'] = data_test['spec'][:, np.newaxis, :]" + "pred['grating'] = data_test['grating'][:, np.newaxis, :]\n", + "# let us show also how the virtual spectrometer looks like smeared by the virtual spectrometer resolution\n", + "pred['grating_smooth'] = model.preprocess_high_res(data_test['grating'], resolution=model.resolution)[:, np.newaxis, :]" ] }, { @@ -346,13 +399,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "ed62606a-4ea7-4e0a-8b61-73e682cacf04", "metadata": {}, "outputs": [], "source": [ "# choose train ID of the test dataset by XGM intensity\n", - "test_intensity = np.argsort(data_test['int'][:,0])\n", + "test_intensity = np.argsort(data_test['intensity'][:,0])\n", "example_tid = test_intensity[-1]" ] }, @@ -366,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "id": "fd42984c-554c-4c69-bf8a-119eeb0cca62", "metadata": {}, "outputs": [], @@ -376,13 +429,14 @@ " fig = plt.figure(figsize=(12, 8))\n", " gs = GridSpec(1, 1)\n", " ax = fig.add_subplot(gs[0, 0])\n", - " ax.plot(data[\"energy\"], data[\"spec\"], c='b', lw=3, label=\"High-res. measurement\")\n", - " ax.plot(data[\"energy\"], data[\"expected\"], c='r', ls='--', lw=3, label=\"High-res. prediction\")\n", + " ax.plot(data[\"energy\"], data[\"grating\"], c='b', lw=3, label=\"Grating spec. measurement\")\n", + " ax.plot(data[\"energy\"], data[\"grating_smooth\"], c='g', lw=3, label=\"Smoothened grating spec. measurement\")\n", + " ax.plot(data[\"energy\"], data[\"expected\"], c='r', ls='--', lw=3, label=\"Prediction\")\n", " ax.fill_between(data[\"energy\"], data[\"expected\"] - data[\"total_unc\"], data[\"expected\"] + data[\"total_unc\"], facecolor='gold', alpha=0.5, label=\"68% unc.\")\n", " ax.legend(frameon=False, borderaxespad=0, loc='upper left')\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['right'].set_visible(False)\n", - " Y = np.amax(data[\"spec\"])\n", + " Y = np.amax(data[\"grating\"])\n", " ax.set(\n", " xlabel=\"Photon energy [eV]\",\n", " ylabel=\"Intensity\",\n", @@ -392,18 +446,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "id": "bbbf77b5-f914-4b47-8ab6-fd3a89d0f983", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", "text/plain": [ - "<Figure size 1200x800 with 1 Axes>" + "<Figure size 864x576 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -412,7 +468,7 @@ "# except for the energy axis, which is always the same\n", "plot({k: v[example_tid, 0, :] if k != \"energy\" else v\n", " for k, v in pred.items()\n", - " if k in [\"expected\", \"total_unc\", \"spec\", \"energy\"]})" + " if k in [\"expected\", \"total_unc\", \"grating\", \"grating_smooth\", \"energy\"]})" ] }, { @@ -432,84 +488,57 @@ "id": "eca3a06a-d613-4206-b128-6d81031de1d1", "metadata": {}, "source": [ - "### Resolution assessment using the autocorrelation\n", + "### Resolution assessment using the auto-covariance\n", "\n", - "We establish the resolution of the virtual spectrometer using the autocorrelation function, which estimates which level of detail can be observed in the test dataset.\n", + "We establish the resolution of the virtual spectrometer using the auto-covariance function, which estimates which level of detail can be observed in the test dataset.\n", "\n", - "The autocorrelation function cannot assess which effect are physically relevant and which are simply noise. Therefore this method can only provide a rough estimate of the resolution. It is not expected to be very precise, but it can be used for a quick assessment.\n" + "The auto-covariance function cannot assess which effect are physically relevant and which are simply noise. Therefore this method can only provide a rough estimate of the resolution. It is not expected to be very precise, but it can be used for a quick assessment.\n" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "1491550c-6940-425e-a557-f2cd381d287b", - "metadata": {}, - "outputs": [], - "source": [ - "def fwhm(x: np.ndarray, y: np.ndarray) -> float:\n", - " \"\"\"Return the full width at half maximum of x.\"\"\"\n", - " # half maximum\n", - " half_max = np.amax(y)*0.5\n", - " # signum(y - half_max) is zero before and after the half maximum,\n", - " # and it is 1 in the range above the half maximum\n", - " # The difference will be +/- 1 only at the transitions\n", - " d = np.diff(np.sign(y - half_max))\n", - " left_idx = np.where(d > 0)[0][0]\n", - " right_idx = np.where(d < 0)[-1][-1]\n", - " return x[right_idx] - x[left_idx]\n", - "\n", - "def autocorrelation(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", - " \"\"\"Given the energy axis in x and the intensity in y, calculate the auto-correlation function.\"\"\"\n", - " mean_y = np.mean(y, keepdims=True, axis=0)\n", - " e = x - np.mean(x)\n", - " Rxx = np.mean(np.fft.fftshift(np.fft.ifft(np.absolute(np.fft.fft(y - mean_y))**2), axes=(-1,)), axis=(0,1))\n", - " Rxx /= np.amax(Rxx)\n", - " return Rxx\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "45fc52a4-5716-42b9-adbd-a307d16c0c34", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(-0.17591992514901664, 1.05)" + "(-0.16367467274350672, 1.05)" ] }, - "execution_count": 18, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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\n", "text/plain": [ - "<Figure size 1000x800 with 1 Axes>" + "<Figure size 720x576 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 8))\n", - "R = dict()\n", - "res = dict()\n", + "R = {\"expected\": model.auto_corr_virt,\n", + " \"grating\": model.auto_corr_hr}\n", + "fwhm = {\"expected\": model.fwhm_virt,\n", + " \"grating\": model.fwhm_hr}\n", + "e = pred[\"energy\"] - np.mean(pred[\"energy\"])\n", "for instr, title in {\"expected\": \"Virtual spectrometer\",\n", - " \"spec\":\"Grating spectometer\",\n", - " #\"pes\": \"PES\",\n", + " \"grating\": \"Grating spectometer\",\n", " }.items():\n", - " e = pred[\"energy\"] - np.mean(pred[\"energy\"])\n", - " R[instr] = autocorrelation(pred[\"energy\"], pred[instr])\n", - " res[instr] = fwhm(e, R[instr])\n", - " plt.plot(e, R[instr], lw=2, label=f\"{title} (FWHM = {res[instr]:.2f} eV)\")\n", + " plt.plot(e, R[instr], lw=2, label=f\"{title} (FWHM = {fwhm[instr]:.2f} eV)\")\n", "\n", "plt.legend(frameon=False)\n", "plt.xlabel(\"Energy [eV]\")\n", - "plt.ylabel(\"Autocorrelation\")\n", + "plt.ylabel(\"Auto-covariance\")\n", "plt.xlim((-3, 3))\n", "plt.ylim((None, 1.05))" ] @@ -529,50 +558,25 @@ "\n", "where $\\epsilon$ is zero-mean Gaussian noise.\n", "\n", - "Under such an approach, one can calculate the function $g$ exactly, by performing a deconvolution between $\\hat{y}$ and $y$." + "Under such an approach, one can calculate the function $g$ exactly: depending on the research area it may be referred to as the \"Green's function\" or the \"impulse response function\". It encodes mathematically how the virtual spectrometer would show a single well-resolved (in the limit of zero-width) peak measured by the grating spectrometer, assuming that the grating spectromter has perfect resolution. This is, of course, never exactly true, but this mathematical thought experiment tells us how much worse the virtual spectrometer does, when compared to the grating spectrometer." ] }, { "cell_type": "code", - "execution_count": 19, - "id": "7ed071e5-4f60-4195-830a-73ab8e5c2577", - "metadata": {}, - "outputs": [], - "source": [ - "def deconv(y: np.ndarray, yhat: np.ndarray) -> np.ndarray:\n", - " \"\"\"Given the grating spectrometer data and the virtual spectrometer data,\n", - " calculate the deconvolution between them.\n", - " \"\"\"\n", - " # subtract the mean spectra to remove the FEL bandwidth\n", - " yhat_s = yhat - np.mean(yhat, keepdims=True, axis=(0, 1))\n", - " y_s = y - np.mean(y, keepdims=True, axis=(0, 1))\n", - " # Fourier transforms\n", - " Yhat = np.fft.fft(yhat_s)\n", - " Y = np.fft.fft(y_s)\n", - " # spectral power of the assumed \"true\" signal (the grating spectrometer data)\n", - " Syy = np.mean(np.absolute(Y)**2, axis=(0, 1))\n", - " Syh = np.mean(Y*np.conj(Yhat), axis=(0, 1))\n", - " # approximate transfer function as the ratio of power spectrum densities\n", - " H = Syh/Syy\n", - " return np.fft.fftshift(np.fft.ifft(H))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "a1c5137f-fe6b-4930-aff5-90026ad5f3c3", "metadata": {}, "outputs": [], "source": [ "# centered energy axis\n", "e = pred[\"energy\"] - np.mean(pred[\"energy\"])\n", - "# impulse response\n", - "g = deconv(pred[\"spec\"], pred[\"expected\"])" + "# impulse response function g calculated by the model fit above\n", + "g = model.impulse_response" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "id": "75d3ddc3-a4b6-4869-bc1c-f08320089845", "metadata": {}, "outputs": [], @@ -588,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "f9bbd13c-d972-4af1-a6c1-0fe12509317a", "metadata": {}, "outputs": [], @@ -598,28 +602,30 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "id": "26641ed6-47cd-418d-ab3c-e63c83962387", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x2b5378c4e700>" + "<matplotlib.legend.Legend at 0x2b0784541460>" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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\n", "text/plain": [ - "<Figure size 1000x800 with 1 Axes>" + "<Figure size 720x576 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -649,27 +655,27 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "id": "2f466226-fbad-4f64-86d3-9b5d0685678b", "metadata": {}, "outputs": [], "source": [ - "total_resolution = np.sqrt(width**2 + res[\"spec\"]**2)" + "total_resolution = np.sqrt(fwhm[\"grating\"]**2 + width**2)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "id": "051d329e-3527-4ce1-abfe-6da441ce2e4b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.0442815003593549" + "0.9812634092770942" ] }, - "execution_count": 26, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -688,23 +694,23 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "id": "e8d3b2f8-09ba-4e9f-9b54-b48867dadc89", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.9825132876894713" + "0.9825132876894322" ] }, - "execution_count": 27, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res[\"expected\"]" + "fwhm[\"expected\"]" ] }, { @@ -714,7 +720,7 @@ "source": [ "Notice, however, that the response function is not Gaussian and therefore, one could use the full function. to actually simulate the virtual spectrometer.\n", "\n", - "Furthermore, this ignores the uncertainty effect, which could be seen as an extra noise level added on top of the virtual spectrometer." + "Furthermore, this ignores the uncertainty effect, which harms the capability of the virtual spectrometer to measure the intensity.\n" ] }, { @@ -724,12 +730,12 @@ "source": [ "### Validation: compare grating spectrometer and simulated virtual spectrometer\n", "\n", - "To check that the resolution estimate is correct, we take an example grating spectrometer pulse and smear it by the impulse response function $g$ above. If it is correct, we should get a similar result as the virtual spectrometer itself.\n" + "To check that the resolution estimate is correct, we take an example grating spectrometer pulse and smear it by the impulse response function $g$ above. If it is correct, we should get a similar result as the virtual spectrometer itself. This, however, assumes this function is valid for all energy range, which is in general not true: the resolution response of the virtual spectrometer also varies with energy. The result shown here is therefore approximate.\n" ] }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 33, "id": "36e25952-b295-43d1-bffc-d62dc1cd0a95", "metadata": {}, "outputs": [], @@ -740,39 +746,41 @@ "g_simple = np.exp(-0.5 * (pred[\"energy\"] - np.mean(pred[\"energy\"]))**2/(sigma**2))\n", "g_simple /= np.sum(g_simple)\n", "# smear the grating spectrometer data\n", - "y_simul = scipy.signal.fftconvolve(pred[\"spec\"], g_simple*np.ones_like(pred[\"spec\"]), mode=\"same\", axes=-1)" + "y_simul = scipy.signal.fftconvolve(pred[\"grating\"], g_simple*np.ones_like(pred[\"grating\"]), mode=\"same\", axes=-1)" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 35, "id": "e25f2828-fb32-4747-b7ea-24752c390625", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x2b53789a2ca0>" + "<matplotlib.legend.Legend at 0x2b07846c10a0>" ] }, - "execution_count": 92, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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\n", "text/plain": [ - "<Figure size 1000x800 with 1 Axes>" + "<Figure size 720x576 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 8))\n", - "plt.plot(pred[\"energy\"], y_simul[example_tid,0], c='b', lw=3, label=\"Smeared grating spec.\")\n", + "plt.plot(pred[\"energy\"], y_simul[example_tid,0], c='b', lw=3, label=f\"Smeared grating spec. (resolution = {width:.2f})\")\n", "plt.plot(pred[\"energy\"], pred[\"expected\"][example_tid,0], c='r', ls='--', lw=3, label=\"Virtual spectrometer\")\n", "plt.fill_between(pred[\"energy\"],\n", " pred[\"expected\"][example_tid, 0] - pred[\"total_unc\"][example_tid,0],\n", @@ -788,16 +796,14 @@ "id": "db3ddff6-dad5-40f5-af6c-680ca2657a24", "metadata": {}, "source": [ - "## Improve the resolution further: Wiener deconvolution\n", - "\n", - "If we know the impulse response of the virtual spectrometer, we can undo that effect. This assumes however, that the resolution function is very accurate. This may not be true, as approximations are made previously (such as assuming the same resolution for all energies and linearity).\n", + "### Improve the resolution further: Wiener deconvolution\n", "\n", - "Given the limitation created by the uncertainty, this is often not very reliable.\n" + "If we know the impulse response of the virtual spectrometer, we can undo that effect. This assumes however, that the resolution function is very accurate. This may not be true, as approximations are made previously (such as assuming the same resolution for all energies, perfect linearity and zero uncertainty) and for this reason it is not done by default.\n" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 36, "id": "cb27ad10-5cb0-4f77-a6d6-8a1a41b6853f", "metadata": {}, "outputs": [], @@ -807,34 +813,36 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 37, "id": "a52d01fb-878f-4f3b-940f-c47013df24ca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x2b56166ab340>" + "<matplotlib.legend.Legend at 0x2b078471fdf0>" ] }, - "execution_count": 94, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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\n", "text/plain": [ - "<Figure size 1000x800 with 1 Axes>" + "<Figure size 720x576 with 1 Axes>" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 8))\n", - "plt.plot(pred[\"energy\"], pred[\"spec\"][example_tid,0], c='b', lw=3, label=\"Grating spec.\")\n", + "plt.plot(pred[\"energy\"], pred[\"grating\"][example_tid,0], c='b', lw=3, label=\"Grating spec.\")\n", "plt.plot(pred[\"energy\"], dec[example_tid,0], c='r', ls='--', lw=3, label=\"Virtual spectrometer (deconvolved)\")\n", "plt.fill_between(pred[\"energy\"],\n", " dec[example_tid, 0] - pred[\"total_unc\"][example_tid,0],\n", @@ -844,21 +852,13 @@ "plt.ylabel(\"Intensity [a.u.]\")\n", "plt.legend(frameon=False)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76eaa0ed-d4c5-426d-9b10-c58b14ffb059", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "pes_to_spec", + "display_name": "xfel (current)", "language": "python", - "name": "pes_to_spec" + "name": "xfel-current" }, "language_info": { "codemirror_mode": { diff --git a/pes_to_spec/__init__.py b/pes_to_spec/__init__.py index 5f0058f9a7a57175f4856886218332aff14efaf7..ba929ca8d21d03d460a87d0c69aa955121184d7a 100644 --- a/pes_to_spec/__init__.py +++ b/pes_to_spec/__init__.py @@ -2,4 +2,4 @@ Estimate high-resolution photon spectrometer data from low-resolution non-invasive measurements. """ -VERSION = "0.3.2" +VERSION = "0.3.3" diff --git a/pes_to_spec/bnn.py b/pes_to_spec/bnn.py index 5f8832ee8448f975f21851ef514c934b581aae62..242a507481f322d87182a70239ae5df2d8bd51fc 100644 --- a/pes_to_spec/bnn.py +++ b/pes_to_spec/bnn.py @@ -1,14 +1,22 @@ +""" +BNN implementation. +""" + from sklearn.base import BaseEstimator, RegressorMixin from typing import Any, Dict, Optional, Union, Tuple import numpy as np -import math from scipy.special import gamma -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import TensorDataset, DataLoader +from pes_to_spec.exception import MethodNotAvailableException + +try: + import torch + import torch.nn as nn + import torch.nn.functional as F + from torch.utils.data import TensorDataset, DataLoader +except ImportError: + raise MethodNotAvailableException("PyTorch not available. BNN deactivated.") class BayesLinearEmpiricalPrior(nn.Module): """ diff --git a/pes_to_spec/exception.py b/pes_to_spec/exception.py new file mode 100644 index 0000000000000000000000000000000000000000..75a169d28e3b7187023cce21b0808e9e70e13b88 --- /dev/null +++ b/pes_to_spec/exception.py @@ -0,0 +1,13 @@ +""" +Module containing package-specific exceptions. +""" + +class MethodNotAvailableException(Exception): + """ + Flags that one of the methods is not available. + """ + def __init__(self, msg: str="Method not available."): + self.msg = msg + def __str__(self) -> str: + return self.msg + diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py index 57cf0767eeafa3bcf02fcf1ef1e04d8ea13ef12a..02512dce0c401171d7d9cafd4e3d53d31b6930e2 100644 --- a/pes_to_spec/model.py +++ b/pes_to_spec/model.py @@ -1,5 +1,7 @@ from __future__ import annotations +from typing import Any, Dict, List, Optional, Union, Tuple, Literal + import joblib import numpy as np @@ -19,9 +21,15 @@ from sklearn.base import clone, MetaEstimatorMixin from joblib import Parallel, delayed from copy import deepcopy -from pes_to_spec.bnn import BNNModel +from pes_to_spec.exception import MethodNotAvailableException + +is_bnn_available = False +try: + from pes_to_spec.bnn import BNNModel + is_bnn_available = True +except MethodNotAvailableException: + print("Warning: BNN model disabled. It requires PyTorch. Check if it is installed.") -from typing import Any, Dict, List, Optional, Union, Tuple, Literal def matching_ids(a: np.ndarray, b: np.ndarray, c: np.ndarray) -> np.ndarray: """Returns list of train IDs common to sets a, b and c.""" @@ -81,7 +89,7 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator): Smoothens out the high resolution data. Args: - high_res_sigma: Energy resolution in eV. + high_res_sigma: Energy resolution in eV. If None, guess. """ def __init__(self, high_res_sigma: float=0.2 @@ -595,6 +603,41 @@ class UncorrelatedDeviation(OutlierMixin, BaseEstimator): """ return accuracy_score(y, self.predict(X), sample_weight=sample_weight) +def get_model_with_resolution(low_res_data: Dict[str, np.ndarray], + high_res_data: np.ndarray, + high_res_photon_energy: np.ndarray, + pulse_energy: np.ndarray, + **kwargs) -> Tuple[Model, float]: + """ + Create a model to obtain the resolution and then use the discovered resolution + to update the model. + + Args: + low_res_data: Low resolution data as a dictionary with the key set to `channel_{i}_{k}`, + where i is a number between 1 and 4 and k is a letter between A and D. + For each dictionary entry, a numpy array is expected with shape + (train_id, ToF channel). + high_res_data: Reference high resolution data with a one-to-one match to the + low resolution data in the train_id dimension. Shape (train_id, ToF channel). + high_res_photon_energy: Photon energy axis for the high-resolution data. + pulse_energy: XGM intensity. + **kwargs: Other arguments sent to the Model constructor. + This includes the `channels` argument with a list of channels, for example. + + Returns: Model and resolution. + """ + #kwargs_no_smear = {k: v for k, v in kwargs.items() + # if k != "high_res_fwhm"} + #model = Model(**kwargs_no_smear) + #model.fit(low_res_data, high_res_data, high_res_photon_energy, pulse_energy=pulse_energy) + #resolution = model.resolution + #model = Model(high_res_fwhm=resolution*0.5, + # **kwargs_no_smear) + model = Model(**kwargs) + model.fit(low_res_data, high_res_data, high_res_photon_energy, pulse_energy=pulse_energy) + resolution = model.resolution + return model, resolution + class Model(TransformerMixin, BaseEstimator): """ Object representing a previous fit of the model to be used to predict high-resolution @@ -602,45 +645,43 @@ class Model(TransformerMixin, BaseEstimator): Args: channels: Selected channels to use as an input for the low resolution data. - n_pca_lr: Number of low-resolution data PCA components. - n_pca_hr: Number of high-resolution data PCA components. - high_res_sigma: Resolution of the high-resolution spectrometer in electron-Volts. + pca_threshold: Variance threshold to keep. + high_res_fwhm: Resolution of the high-resolution spectrometer in electron-Volts. tof_start: Start looking at this index from the low-resolution spectrometer data. Set to None to perform no selection delta_tof: Number of components to take from the low-resolution spectrometer. Set to None to perform no selection. - validation_size: Fraction (number between 0 and 1) of the data to take for - validation and systematic uncertainty estimate. model_type: Which model to use. "bnn" for a BNN, "bnn_rvm" for a BNN with RVM, "ridge" for Ridge and "ard" for ARD. - n_peaks: Minimum numbr of peaks in the grating spectrometer. + n_peaks: Minimum number of peaks in the grating spectrometer. n_bnn_epochs: Number of BNN epochs for training. """ def __init__(self, channels:List[str]=[f"channel_{j}_{k}" for j, k in product(range(1, 5), ["A", "B", "C", "D"])], - n_pca_lr: int=1000, - n_pca_hr: int=40, - high_res_sigma: float=0, + pca_threshold: float=0.90, + high_res_fwhm: float=0, tof_start: Optional[int]=None, delta_tof: Optional[int]=300, - validation_size: float=0.05, model_type: Literal["bnn", "bnn_rvm", "ridge", "ard"]="ard", n_peaks: int=0, n_bnn_epochs: int=500, ): - self.high_res_sigma = high_res_sigma + if model_type in ["bnn", "bnn_rvm"] and not is_bnn_available: + raise MethodNotAvailableException("The BNN model requires a PyTorch installation. Please do `pip install torch` or `conda install pytorch` to be able to use the BNN model.") + self.pca_threshold = pca_threshold + self.high_res_fwhm = high_res_fwhm # models self.x_select = SelectRelevantLowResolution(channels, tof_start, delta_tof, poly=False) #(model_type not in ["bnn", "bnn_rvm"])) x_model_steps = list() x_model_steps += [ - ('pca', PCA(n_pca_lr, whiten=True)), + ('pca', PCA(None, whiten=True)), ('unc', UncertaintyHolder()), ] self.x_model = Pipeline(x_model_steps) self.y_model = Pipeline([ - ('smoothen', HighResolutionSmoother(high_res_sigma)), - ('pca', PCA(n_pca_hr, whiten=True)), + ('smoothen', HighResolutionSmoother(high_res_fwhm)), + ('pca', PCA(None, whiten=True)), ('unc', UncertaintyHolder()), ]) self.ood = {ch: UncorrelatedDeviation(sigma=5) @@ -650,9 +691,9 @@ class Model(TransformerMixin, BaseEstimator): elif model_type == "bnn_rvm": self.fit_model = BNNModel(n_epochs=n_bnn_epochs, rvm=True) elif model_type == "ridge": - self.fit_model = MultiOutputRidgeWithStd(BayesianRidge(n_iter=300, tol=1e-8, verbose=True), n_jobs=8) + self.fit_model = MultiOutputRidgeWithStd(BayesianRidge(tol=1e-8, verbose=True), n_jobs=8) elif model_type == "ard": - self.fit_model = MultiOutputGenericWithStd(ARDRegression(n_iter=300, tol=1e-8, verbose=True), n_jobs=8) + self.fit_model = MultiOutputGenericWithStd(ARDRegression(tol=1e-8, verbose=True), n_jobs=8) self.model_type = model_type self.n_obs = 0 @@ -665,21 +706,34 @@ class Model(TransformerMixin, BaseEstimator): self.channel_pca = {ch: IncrementalPCA(n_pca_lr_per_channel, whiten=True) for ch in channels} - # size of the test subset - self.validation_size = validation_size - # minimum number of peaks self.n_peaks = n_peaks # other characteristics for inspection and validation to be set in self.fit(...) + self.auto_corr_virt = None + self.auto_corr_hr = None + self.fwhm_hr = None + self.fwhm_virt = None self.resolution = None + self.wiener_filter = None self.wiener_filter_ft = None self.wiener_energy = None self.wiener_energy_ft = None self.transfer_function = None self.impulse_response = None - self.auto_corr = None + + self.extra_options = ["mu_xgm", "sigma_xgm", + "wiener_filter_ft", "wiener_filter", + "wiener_energy_ft", "wiener_energy", + "resolution", "fwhm_virt", "fwhm_hr", + "transfer_function", "impulse_response", + "auto_corr_virt", "auto_corr_hr", + "model_type", + "n_obs", + "pca_threshold", + "high_res_fwhm", + ] def n_pars(self) -> float: """Get number of parameters.""" @@ -712,7 +766,7 @@ class Model(TransformerMixin, BaseEstimator): """ self.x_select.debug_peak_finding(low_res_data, filename) - def preprocess_high_res(self, high_res_data: np.ndarray) -> np.ndarray: + def preprocess_high_res(self, high_res_data: np.ndarray, resolution: Optional[float]=None) -> np.ndarray: """ Preprocess high-resolution data to remove high requency components. @@ -721,7 +775,10 @@ class Model(TransformerMixin, BaseEstimator): Returns: Smoothened spectrum. """ - return self.y_model['smoothen'].transform(high_res_data) + if resolution is None: + return self.y_model['smoothen'].transform(high_res_data) + s = HighResolutionSmoother(resolution) + return s.fit_transform(high_res_data, energy=self.get_energy_values()) def uniformize(self, intensity: np.ndarray) -> np.ndarray: """ @@ -774,6 +831,7 @@ class Model(TransformerMixin, BaseEstimator): high_res_data: np.ndarray, high_res_photon_energy: np.ndarray, weights: Optional[np.ndarray]=None, pulse_energy: Optional[np.ndarray]=None, + ood: bool=True ) -> np.ndarray: """ Train the model. @@ -786,8 +844,11 @@ class Model(TransformerMixin, BaseEstimator): high_res_data: Reference high resolution data with a one-to-one match to the low resolution data in the train_id dimension. Shape (train_id, ToF channel). high_res_photon_energy: Photon energy axis for the high-resolution data. + weights: If set, use them to weigh the data. + pulse_energy: XGM intensity. + ood: Whether to fit out-of-sample detection to test data compatibility later. - Returns: Smoothened high resolution spectrum. + Returns: Input high resolution spectrum. """ print("Checking data quality in high-resolution data.") peaks = self.count_peaks(high_res_data, high_res_photon_energy) @@ -801,13 +862,31 @@ class Model(TransformerMixin, BaseEstimator): B, P, _ = low_res_select.shape low_res_select = low_res_select.reshape((B*P, -1)) - n_components = min(self.x_model["pca"].n_components, low_res_select.shape[0]) - print(f"Using {n_components} comp. for PES PCA (asked for {self.x_model['pca'].n_components}, out of {low_res_select.shape[1]}, in {low_res_select.shape[0]} samples).") + # estimate number of PCA components + pca_test = PCA(None, whiten=True) + pca_test.fit(low_res_select) + n_components = np.where(np.cumsum(pca_test.explained_variance_ratio_) > self.pca_threshold)[0] + if len(n_components) > 0: + n_components = n_components[0] + + print(f"Using {n_components} comp. for PES PCA.") self.x_model.set_params(pca__n_components=n_components) x_t = self.x_model.fit_transform(low_res_select) #print("PCA fraction of variance (LR): ", np.cumsum(self.x_model["pca"].explained_variance_ratio_)) + print("Fitting PCA on high-resolution data.") + # estimate number of PCA components + pca_test = PCA(None, whiten=True) + pca_test.fit(high_res_data) + n_components_hr = np.where(np.cumsum(pca_test.explained_variance_ratio_) > self.pca_threshold)[0] + if len(n_components_hr) > 0: + n_components_hr = n_components_hr[0] + + print(f"Using {n_components_hr} comp. for grating spec. PCA.") + self.y_model.set_params(pca__n_components=n_components_hr) + y_t = self.y_model.fit_transform(high_res_data, smoothen__energy=high_res_photon_energy) + #print("PCA fraction of variance (HR): ", np.cumsum(self.y_model["pca"].explained_variance_ratio_)) print("Fitting outlier detection") @@ -836,7 +915,7 @@ class Model(TransformerMixin, BaseEstimator): # n: noise (uncertainty) # e: energy # true signal (as far as we can get -- it is smoothened, but this is the model target) - y = high_res[inliers & filter_hr] + y = high_res_data[inliers & filter_hr] y_pred, n = self.fit_model.predict(x_t[inliers & filter_hr], return_std=True) y_hat = self.y_model['pca'].inverse_transform(y_pred) @@ -868,15 +947,37 @@ class Model(TransformerMixin, BaseEstimator): h = np.fft.fftshift(np.fft.ifft(H)) self.impulse_response = h + # get grating spec. resolution + mean_y = np.mean(y, keepdims=True, axis=0) + self.auto_corr_hr = np.mean(np.fft.fftshift(np.fft.ifft(np.absolute(np.fft.fft(y - mean_y))**2), axes=(-1,)), axis=0) + self.auto_corr_hr = np.real(self.auto_corr_hr) + self.auto_corr_hr /= np.amax(self.auto_corr_hr) + try: + self.fwhm_hr = fwhm(e_axis, self.auto_corr_hr) + except: + self.fwhm_hr = -1.0 + + # get virtual spectrometer resolution mean_y_hat = np.mean(y_hat, keepdims=True, axis=0) - self.auto_corr = np.mean(np.fft.fftshift(np.fft.ifft(np.absolute(np.fft.fft(y_hat - mean_y_hat))**2), axes=(-1,)), axis=0) - self.auto_corr = np.real(self.auto_corr) - self.auto_corr /= np.amax(self.auto_corr) + self.auto_corr_virt = np.mean(np.fft.fftshift(np.fft.ifft(np.absolute(np.fft.fft(y_hat - mean_y_hat))**2), axes=(-1,)), axis=0) + self.auto_corr_virt = np.real(self.auto_corr_virt) + self.auto_corr_virt /= np.amax(self.auto_corr_virt) try: - self.resolution = fwhm(e_axis, self.auto_corr) + self.fwhm_virt = fwhm(e_axis, self.auto_corr_virt) except: - self.resolution = -1.0 - print("Resolution:", self.resolution) + self.fwhm_virt = -1.0 + + self.resolution = -1.0 + if self.fwhm_hr > 0 and self.fwhm_virt > self.fwhm_hr: + self.resolution = np.sqrt(self.fwhm_virt**2 - self.fwhm_hr**2) + if self.resolution < 0: + print("Warning: Resolution calculation failed. The model can still be used, but this is probably a red flag!") + else: + print("Resolution:", self.resolution) + + # this speeds things up considerably, when we do not care about that + if not ood: + return high_res.reshape((B, P, -1)) # for consistency check per channel selection_model = self.x_select @@ -889,7 +990,7 @@ class Model(TransformerMixin, BaseEstimator): print("End of fit.") - return high_res.reshape((B, P, -1)) + return high_res_data.reshape((B, P, -1)) def check_compatibility_per_channel(self, low_res_data: Dict[str, np.ndarray], pulse_spacing: Optional[Dict[str, List[int]]]=None) -> Dict[str, np.ndarray]: """ @@ -1038,27 +1139,15 @@ class Model(TransformerMixin, BaseEstimator): Args: filename: File name where to save this. """ + extra = {k: getattr(self, k) + for k in self.extra_options} joblib.dump([self.x_select, self.x_model, self.y_model, self.fit_model.state_dict() if self.model_type in ("bnn", "bnn_rvm") else self.fit_model, self.channel_pca, #self.channel_fit_model - DataHolder(dict( - mu_xgm=self.mu_xgm, - sigma_xgm=self.sigma_xgm, - wiener_filter_ft=self.wiener_filter_ft, - wiener_filter=self.wiener_filter, - wiener_energy=self.wiener_energy, - wiener_energy_ft=self.wiener_energy_ft, - resolution=self.resolution, - transfer_function=self.transfer_function, - impulse_response=self.impulse_response, - auto_corr=self.auto_corr, - model_type=self.model_type, - n_obs=self.n_obs, - ) - ), + DataHolder(extra), self.ood, self.kde_xgm, ], filename, compress='zlib') @@ -1084,22 +1173,18 @@ class Model(TransformerMixin, BaseEstimator): obj = Model() extra = extra.get_data() - obj.mu_xgm = extra["mu_xgm"] - obj.sigma_xgm = extra["sigma_xgm"] - obj.wiener_filter_ft = extra["wiener_filter_ft"] - obj.wiener_filter = extra["wiener_filter"] - obj.wiener_energy_ft = extra["wiener_energy_ft"] - obj.wiener_energy = extra["wiener_energy"] - obj.resolution = extra["resolution"] - obj.transfer_function = extra["transfer_function"] - obj.impulse_response = extra["impulse_response"] - obj.auto_corr = extra["auto_corr"] - obj.model_type = extra["model_type"] - obj.n_obs = extra["n_obs"] + for k in obj.extra_options: + if k not in extra: + setattr(obj, k, None) + else: + setattr(obj, k, extra[k]) obj.x_select = x_select obj.x_model = x_model obj.y_model = y_model + if obj.model_type in ["bnn", "bnn_rvm"] and not is_bnn_available: + raise MethodNotAvailableException("Attempted to load a BNN model, but it requires a PyTorch installation. " + "Please do `pip install torch` or `conda install pytorch` to be able to load this model.") if obj.model_type == "bnn": obj.fit_model = BNNModel(state_dict=fit_model, rvm=False) elif obj.model_type == "bnn_rvm": diff --git a/pes_to_spec/test/__init__.py b/pes_to_spec/test/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pes_to_spec/test/offline_analysis.py b/pes_to_spec/test/offline_analysis.py index 11966b7475f3d334ace2d200a944826e2929888d..90ce3ce5eacc6733fa7343d9db891fc2513975ff 100755 --- a/pes_to_spec/test/offline_analysis.py +++ b/pes_to_spec/test/offline_analysis.py @@ -240,13 +240,15 @@ def main(): t = list() t_names = list() - model = Model(channels=channels, model_type=args.model_type) - train_idx = np.isin(tids, train_tids) & (xgm_flux[:,0] > args.xgm_cut) + # we just need this for training and we need to avoid copying it, which blows up the memoray usage for k in pes_raw.keys(): pes_raw[k] = pes_raw[k][train_idx] + # do it again with smearing, but now with the knowledge of the resolution + model = Model(channels=channels, model_type=args.model_type) + model.debug_peak_finding(pes_raw, os.path.join(args.directory, "test_peak_finding.pdf")) if len(args.model) == 0: print("Fitting") @@ -289,14 +291,14 @@ def main(): pca = PCA(None, whiten=True) pca.fit(pes_raw_select) df = pd.DataFrame(dict(variance_ratio=pca.explained_variance_ratio_, - n_comp=1000*np.ones_like(pca.explained_variance_ratio_), + n_comp=model.x_model.get_params()["pca__n_components"]*np.ones_like(pca.explained_variance_ratio_), )) df.to_csv(os.path.join(args.directory, "pca_pes.csv")) pca_spec = PCA(None, whiten=True) pca_spec.fit(spec_raw_int[train_idx]) df = pd.DataFrame(dict(variance_ratio=pca_spec.explained_variance_ratio_, - n_comp=40*np.ones_like(pca_spec.explained_variance_ratio_), + n_comp=model.y_model.get_params()["pca__n_components"]*np.ones_like(pca_spec.explained_variance_ratio_), )) df.to_csv(os.path.join(args.directory, "pca_spec.csv")) @@ -335,8 +337,8 @@ def main(): showSpec = False if len(args.model) == 0: showSpec = True - # always smooth by 0.2, even if no smoothing was requested, for visualization - smoother = HighResolutionSmoother(0.2) + # always smoothen by the resolution, even if no smoothing was requested, for visualization + smoother = HighResolutionSmoother(model.resolution) spec_smooth = smoother.fit_transform(spec_raw_int_t, energy=spec_raw_pe_t) # use whatever smoothing was used in the model for the calculations spec_data = model.preprocess_high_res(spec_raw_int_t) @@ -396,6 +398,15 @@ def main(): df = pd.DataFrame(q) df.to_csv(os.path.join(args.directory, "quality.csv")) + df = pd.DataFrame(dict(auto_corr_hr=model.auto_corr_hr, + auto_corr_virt=model.auto_corr_virt, + energy=model.wiener_energy + fwhm_hr=model.fwhm_hr*np.ones_like(model.auto_corr_hr), + fwhm_virt=model.fwhm_virt*np.ones_like(model.auto_corr_virt), + ) + ) + df.to_csv(os.path.join(args.directory, "resolution.csv")) + first, last = model.get_low_resolution_range() # plot high_int_idx = np.argsort(xgm_flux_t[:,0]) diff --git a/pes_to_spec/test/prepare_plots.py b/pes_to_spec/test/prepare_plots.py index 288b66fe66dc7e194ff2bf0293086feaf106b188..f103df5a1405045f8311682c203e48b4859bc63e 100755 --- a/pes_to_spec/test/prepare_plots.py +++ b/pes_to_spec/test/prepare_plots.py @@ -50,6 +50,25 @@ def plot_final(df: pd.DataFrame, smooth: bool, filename: str): fig.savefig(filename) plt.close(fig) +def plot_autocov(df: pd.DataFrame, filename: str): + fig = plt.figure(figsize=(12, 8)) + gs = GridSpec(1, 1) + ax = fig.add_subplot(gs[0, 0]) + fwhm_hr = df.fwhm_hr[0] + fwhm_virt = df.fwhm_virt[0] + ax.plot(df.energy, df.auto_corr_hr, c='b', lw=3, label="FEL + Grating spec. (FWHM={fwhm_hr:.2} eV)") + ax.plot(df.energy, df.auto_corr_virt, c='r', lw=3, label="FEL + Virtual spec. (FWHM={fwhm_cirt:.2} eV)") + ax.legend(frameon=False, borderaxespad=0, loc='upper left') + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.set( + xlabel="Photon energy [eV]", + ylabel="Auto-covariance [a.u.]", + ylim=(0, 1.0)) + plt.tight_layout() + fig.savefig(filename) + plt.close(fig) + def plot_chi2(df: pd.DataFrame, filename: str): fig = plt.figure(figsize=(12, 8)) gs = GridSpec(1, 1) @@ -115,7 +134,7 @@ def plot_chi2_intensity(df: pd.DataFrame, filename: str): # fill=True, # ax=ax) sns.scatterplot(x=df.chi2_prepca/df.ndof.iloc[0], y=df.xgm_flux_t*1e-3, - s=5, + s=20, #alpha=0.4, c="tab:red", #size=df.root_mean_squared_pca_unc, @@ -360,7 +379,7 @@ def plot_pes(df: pd.DataFrame, label: Optional[Dict[str, str]]=None, refs: Optional[Dict[str, Dict[int, float]]]=None, counts_to_mv: Optional[float]=None, - tof: bool=False, + tof: bool=True, ): """ Plot low-resolution spectrum. @@ -529,3 +548,5 @@ if __name__ == '__main__': pca_variance_plot(pd.read_csv(f'{indir}/pca_spec.csv'), f'pca_spec.pdf', max_comp_frac=0.99) pca_variance_plot(pd.read_csv(f'{indir}/pca_pes.csv'), f'pca_pes.pdf', max_comp_frac=0.95) + plot_autocov(pd.read_csv(f'{indir}/resolution.csv'), f'autocov.pdf') + diff --git a/pes_to_spec/test/test_import.py b/pes_to_spec/test/test_import.py new file mode 100644 index 0000000000000000000000000000000000000000..0c21b36f6271e8439934f831a8941d5dd3f6060e --- /dev/null +++ b/pes_to_spec/test/test_import.py @@ -0,0 +1,10 @@ +import unittest + +class TestImport(unittest.TestCase): + def test_model(self): + from pes_to_spec.model import Model + model = Model() + +if __name__ == '__main__': + unittest.main() +