From 365824c435d16121229a8b5b78853315de404e18 Mon Sep 17 00:00:00 2001 From: Carter Capetz Date: Mon, 14 Jul 2025 08:54:49 -0700 Subject: [PATCH 1/7] All early initial work --- .DS_Store | Bin 10244 -> 10244 bytes baler/modules/data_processing.py | 20 +- baler/modules/helper.py | 6 + baler/modules/models.py | 41 +- baler/modules/plotting.py | 18 +- baler/modules/training.py | 4 +- notebooks-collection-opendata | 1 - poetry.lock | 1023 +++++++++++++++++++++++++++++- pyproject.toml | 1 + 9 files changed, 1059 insertions(+), 55 deletions(-) delete mode 160000 notebooks-collection-opendata diff --git a/.DS_Store b/.DS_Store index 43325880044aafe39ea1ec1692f86ab42a1427d6..d87b31c7b18e451ddbd4417e700c3e150b5a7b08 100644 GIT binary patch delta 264 zcmZn(XbG4Q!Iht!l#`#tz`!7|F>xvTW_At^4t8b+JrJDSCN3`wWha#v7eE#6oFrVR zAz58*Xl|&ZU}R)btD{hDXkcQhqhM}mR$I%-A*!rz9TcCPlbe^{J^6#6ylxNBc&IsL z!9{sF`FZI;5k@4@;*y+_%7WC)_pXG;8Kn!HC+XmXcOvWU5n zxsHOdfmyANLbbVxk&c3ixxr*cVdKd{(xRKKWEhz@vn%{!*{mey#Y9n9P#%^l07#8< AVgLXD diff --git a/baler/modules/data_processing.py b/baler/modules/data_processing.py index d14d9958..0713c723 100644 --- a/baler/modules/data_processing.py +++ b/baler/modules/data_processing.py @@ -131,25 +131,21 @@ def find_minmax(data): def normalize(data, custom_norm: bool): - """This function scales the data to be in the range [0,1], based on the Min Max normalization method. It finds - the minimum and maximum values of each column and computes the values according to: x_norm = (x - x_min) / (x_max - - x_min). - - Args: data (ndarray): A dataset of type `ndarray`. custom_norm (boolean): If you want to do Min Max normalization - or any custom normalization. Custom normalization is not supported at the moment. - - Returns: ndarray: If not custom_norm: Input data where every column is scaled to be in the range [0, - 1]. Otherwise, the input data is returned + """This function scales the data to be in the range [0,1], based on the Min Max normalization method. """ - data = np.array(data) + # Convert to float32 first to avoid uint8 division issues + data = np.array(data, dtype=np.float32) + if custom_norm: pass elif not custom_norm: true_min = np.min(data) true_max = np.max(data) feature_range = true_max - true_min - data = [((i - true_min) / feature_range) for i in data] - data = np.array(data) + # Avoid division by zero + if feature_range == 0: + return data - true_min + data = (data - true_min) / feature_range return data diff --git a/baler/modules/helper.py b/baler/modules/helper.py index dcacdc21..ddf1823f 100644 --- a/baler/modules/helper.py +++ b/baler/modules/helper.py @@ -433,6 +433,9 @@ def get_device(): if torch.cuda.is_available(): dev = "cuda:0" device = torch.device(dev) + # elif torch.backends.mps.is_available(): + # dev = "mps" + # device = torch.device(dev) else: dev = "cpu" device = torch.device(dev) @@ -584,6 +587,9 @@ def compress(model_path, config): for idx, data_batch in enumerate(tqdm(data_dl)): data_batch = data_batch.to(device) + #float32 + data_batch = data_batch.to(torch.float32) + compressed_output = model.encode(data_batch) if config.save_error_bounded_deltas: diff --git a/baler/modules/models.py b/baler/modules/models.py index 62075b92..f547bd2c 100644 --- a/baler/modules/models.py +++ b/baler/modules/models.py @@ -125,15 +125,28 @@ def __init__(self, n_features, z_dim, *args, **kwargs): self.activations = {} # encoder - self.en1 = nn.Linear(n_features, 200, dtype=torch.float64) - self.en2 = nn.Linear(200, 100, dtype=torch.float64) - self.en3 = nn.Linear(100, 50, dtype=torch.float64) - self.en4 = nn.Linear(50, z_dim, dtype=torch.float64) + self.en1 = nn.Linear(n_features, 200, dtype=torch.float32) + self.en2 = nn.Linear(200, 100, dtype=torch.float32) + self.en3 = nn.Linear(100, 50, dtype=torch.float32) + self.en4 = nn.Linear(50, z_dim, dtype=torch.float32) + + # Initialize weights properly + nn.init.xavier_uniform_(self.en1.weight) + nn.init.xavier_uniform_(self.en2.weight) + nn.init.xavier_uniform_(self.en3.weight) + nn.init.xavier_uniform_(self.en4.weight) + # decoder - self.de1 = nn.Linear(z_dim, 50, dtype=torch.float64) - self.de2 = nn.Linear(50, 100, dtype=torch.float64) - self.de3 = nn.Linear(100, 200, dtype=torch.float64) - self.de4 = nn.Linear(200, n_features, dtype=torch.float64) + self.de1 = nn.Linear(z_dim, 50, dtype=torch.float32) + self.de2 = nn.Linear(50, 100, dtype=torch.float32) + self.de3 = nn.Linear(100, 200, dtype=torch.float32) + self.de4 = nn.Linear(200, n_features, dtype=torch.float32) + + # Initialize weights properly + nn.init.xavier_uniform_(self.de1.weight) + nn.init.xavier_uniform_(self.de2.weight) + nn.init.xavier_uniform_(self.de3.weight) + nn.init.xavier_uniform_(self.de4.weight) self.n_features = n_features self.z_dim = z_dim @@ -412,18 +425,18 @@ def __init__(self, n_features, z_dim, *args, **kwargs): super(FPGA_prototype_model, self).__init__(*args, **kwargs) # encoder - self.en1 = nn.Linear(n_features, 20, dtype=torch.float64) + self.en1 = nn.Linear(n_features, 20, dtype=torch.float32) self.en_act1 = nn.ReLU() - self.en2 = nn.Linear(20, 10, dtype=torch.float64) + self.en2 = nn.Linear(20, 10, dtype=torch.float32) self.en_act2 = nn.ReLU() - self.en3 = nn.Linear(10, z_dim, dtype=torch.float64) + self.en3 = nn.Linear(10, z_dim, dtype=torch.float32) # decoder - self.de1 = nn.Linear(z_dim, 10, dtype=torch.float64) + self.de1 = nn.Linear(z_dim, 10, dtype=torch.float32) self.de_act1 = nn.ReLU() - self.de2 = nn.Linear(10, 20, dtype=torch.float64) + self.de2 = nn.Linear(10, 20, dtype=torch.float32) self.de_act2 = nn.ReLU() - self.de3 = nn.Linear(20, n_features, dtype=torch.float64) + self.de3 = nn.Linear(20, n_features, dtype=torch.float32) self.n_features = n_features self.z_dim = z_dim diff --git a/baler/modules/plotting.py b/baler/modules/plotting.py index aa59656f..3b3f310c 100644 --- a/baler/modules/plotting.py +++ b/baler/modules/plotting.py @@ -19,6 +19,7 @@ from matplotlib.backends.backend_pdf import PdfPages from tqdm import tqdm from tqdm import trange +from . import mnist_plotting def loss_plot(path_to_loss_data, output_path, config): @@ -409,8 +410,14 @@ def plot_2D(project_path, config): # tile_data_decompressed = data_decompressed[ind][0] # elif config.model_type == "dense": # tile_data_decompressed = data_decompressed[ind][0] - tile_data = data[ind] - tile_data_decompressed = data_decompressed[ind] + for ind in trange(num_tiles): + # For MNIST data, we need to handle the extra dimension + if config.model_type == "convolutional": + tile_data = data[ind].squeeze() # Remove the extra dimension + tile_data_decompressed = data_decompressed[ind].squeeze() # Remove the extra dimension + else: + tile_data = data[ind] + tile_data_decompressed = data_decompressed[ind] diff = tile_data - tile_data_decompressed @@ -438,12 +445,7 @@ def plot_2D(project_path, config): def plot(output_path, config): - """Runs the appropriate plotting function based on the data dimension 1D or 2D - - Args: - output_path (path): The path to the project directory - config (dataclass): The config class containing attributes set in the config file - """ + """Runs the appropriate plotting function based on the data dimension 1D or 2D""" if config.data_dimension == 1: plot_1D(output_path, config) elif config.data_dimension == 2: diff --git a/baler/modules/training.py b/baler/modules/training.py index ca189def..b7b9f679 100644 --- a/baler/modules/training.py +++ b/baler/modules/training.py @@ -227,8 +227,8 @@ def train(model, variables, train_data, test_data, project_path, config): train_data.shape[0], 1, train_data.shape[1], train_data.shape[2] ) elif config.data_dimension == 1: - train_ds = torch.tensor(train_data, dtype=torch.float64, device=device) - valid_ds = torch.tensor(test_data, dtype=torch.float64, device=device) + train_ds = torch.tensor(train_data, dtype=torch.float32, device=device) + valid_ds = torch.tensor(test_data, dtype=torch.float32, device=device) # Pushing input data into the torch-DataLoader object and combines into one DataLoader object (a basic wrapper # around several DataLoader objects). diff --git a/notebooks-collection-opendata b/notebooks-collection-opendata deleted file mode 160000 index 65998477..00000000 --- a/notebooks-collection-opendata +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 65998477161005630d0fd388515e19bb0ac2fc82 diff --git a/poetry.lock b/poetry.lock index 7b949e57..d650f7e1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. 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{file = "wrapt-1.14.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3"}, {file = "wrapt-1.14.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef"}, @@ -2473,6 +3458,8 @@ version = "3.17.0" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] +markers = "python_version <= \"3.9\"" files = [ {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, @@ -2480,12 +3467,12 @@ files = [ [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-ruff"] [extras] hls4ml = ["hls4ml", "tensorflow"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.8, <3.11.10" -content-hash = "0b0cf4871b8e171e15c6001e9f68266ef3585ecf750d1aee374b68f94ee3cf72" +content-hash = "fe06f75f8ce053fef7270eef5b6d20777f2142f373359dbc48874d3d83d1fd4c" diff --git a/pyproject.toml b/pyproject.toml index 193d2759..2dfcc2e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,6 +21,7 @@ numpy = "1.23.5" [tool.poetry.group.dev.dependencies] pytest = "^7.2.1" black = "^23.1.0" +ipykernel = "^6.29.5" [tool.poetry.extras] hls4ml = [ "hls4ml", "tensorflow" ] From 5e10c0105cb970772bddfdca38128ba2772dd711 Mon Sep 17 00:00:00 2001 From: Carter Capetz Date: Mon, 21 Jul 2025 01:46:50 -0700 Subject: [PATCH 2/7] Save .DS_Store changes --- .DS_Store | Bin 10244 -> 10244 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index d87b31c7b18e451ddbd4417e700c3e150b5a7b08..3142dd99ce9fc05fd5f5140644ed7d591184902f 100644 GIT binary patch delta 450 zcmZn(XbG4Q!I6`07@VA+Td=Wk8vDcs!OiR(92}bs1Z~)@Wf}4q@)=4PQW=tfbUs5i zLotIcLo$$+1B9tSRtZBUke3G(&1Wb8isUh*FeCy+6DNxa$Wx@>M&Lv}1@@N(7v<&T z=cO|+Ffal!StbcVyi!(Nl2cMykeZpCSGGA*;vCy%c7xvTW_At^4t8b+JrJDSCN2+WKM+5m2^B93F3QWv w&r1htVgzECXmLqSNo7H5W^!KHW*z~1_RThur`aYJT-eO6@QY>h17T)n0CVpikN^Mx From 9358eb733cd78cb23d05b1ac1bfac55d94d138a3 Mon Sep 17 00:00:00 2001 From: Carter Capetz Date: Mon, 4 Aug 2025 10:22:14 -0700 Subject: [PATCH 3/7] initial myy loss function --- baler/modules/utils.py | 51 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/baler/modules/utils.py b/baler/modules/utils.py index 23b63687..8c047b94 100644 --- a/baler/modules/utils.py +++ b/baler/modules/utils.py @@ -172,6 +172,57 @@ def mse_loss_l1(model_children, true_data, reconstructed_data, reg_param, valida else: return mse_loss, 0, 0 +def calc_myy_torch(photon_pt,photon_eta,photon_phi,photon_E): + # first photon is [0], 2nd photon is [1] etc + px_0 = photon_pt[0]*torch.cos(photon_phi[0]) # x-component of photon[0] momentum + py_0 = photon_pt[0]*torch.sin(photon_phi[0]) # y-component of photon[0] momentum + pz_0 = photon_pt[0]*torch.sinh(photon_eta[0]) # z-component of photon[0] momentum + px_1 = photon_pt[1]*torch.cos(photon_phi[1]) # x-component of photon[1] momentum + py_1 = photon_pt[1]*torch.sin(photon_phi[1]) # y-component of photon[1] momentum + pz_1 = photon_pt[1]*torch.sinh(photon_eta[1]) # z-component of photon[1] momentum + sumpx = px_0 + px_1 # x-component of diphoton momentum + sumpy = py_0 + py_1 # y-component of diphoton momentum + sumpz = pz_0 + pz_1 # z-component of diphoton momentum + sump = torch.sqrt(sumpx**2 + sumpy**2 + sumpz**2) # magnitude of diphoton momentum + sumE = photon_E[0] + photon_E[1] # energy of diphoton system + return torch.sqrt(sumE**2 - sump**2)/1000 #/1000 to go from MeV to GeV + +def mse_loss_myy_l1(model_children, true_data, reconstructed_data, reg_param, validate): + + """ + modification of mse_loss_l1 to include diphoton invariant mass + """ + mse = nn.MSELoss() + mse_loss = mse(reconstructed_data, true_data) + # Assuming data format: [pt1, eta1, phi1, E1, pt2, eta2, phi2, E2] + true_pt = true_data[:, [0, 4]] # [batch_size, 2] + true_eta = true_data[:, [1, 5]] # [batch_size, 2] + true_phi = true_data[:, [2, 6]] # [batch_size, 2] + true_E = true_data[:, [3, 7]] # [batch_size, 2] + + recon_pt = reconstructed_data[:, [0, 4]] # [batch_size, 2] + recon_eta = reconstructed_data[:, [1, 5]] # [batch_size, 2] + recon_phi = reconstructed_data[:, [2, 6]] # [batch_size, 2] + recon_E = reconstructed_data[:, [3, 7]] # [batch_size, 2] + + # Calculate myy for true and reconstructed data + true_myy = calc_myy_torch(true_pt, true_eta, true_phi, true_E) + recon_myy = calc_myy_torch(recon_pt, recon_eta, recon_phi, recon_E) + + # Myy-based loss (MSE between invariant masses) + myy_loss = mse(recon_myy, true_myy) + + l1_loss = torch.Tensor(0) + values = true_data + if not validate: + for i in range(len(model_children)): + values = functional.relu(model_children[i](values)) + l1_loss += torch.mean(torch.abs(values)) + + loss = myy_loss + mse_loss + reg_param * l1_loss + return loss, myy_loss, l1_loss + else: + return myy_loss def mse_sum_loss_l1(model_children, true_data, reconstructed_data, reg_param, validate): """ From fb50edf13ad6f94c1a847a09d35643f6d0c9bb52 Mon Sep 17 00:00:00 2001 From: cartercapetz Date: Sun, 31 Aug 2025 22:35:11 -0700 Subject: [PATCH 4/7] added new physics informed AE model for diphoton data from the ATLAS dataset --- .DS_Store | Bin 10244 -> 10244 bytes baler/modules/mnist_plotting.py | 47 ++++++++++++++++++++++++++++++++ baler/modules/training.py | 6 ++-- baler/modules/utils.py | 4 +-- 4 files changed, 53 insertions(+), 4 deletions(-) create mode 100644 baler/modules/mnist_plotting.py diff --git a/.DS_Store b/.DS_Store index 3142dd99ce9fc05fd5f5140644ed7d591184902f..1627516c11cbf7469059bb87de2570b1d14c8344 100644 GIT binary patch delta 63 zcmZn(XbG6$&uFnRU^hRb#bzD>Z^p^5#rJL&7P4U7Y$N%L3B;THNLq9=m+W7rjRl_= SH?u4JV%a3>%{19Yat8qE%NM%< delta 597 zcmZn(XbG6$&uFtTU^hRb&1N0}Z^p@UB!n0_Ca;%>mgeM)7m%o~HnlL+Q7|?%tkqGd zwlK5MQ7|(yoBUqXc(aI*1#7)5LmopuLkUAFLlThAXUJwKX3%9w2C{O1FcruuVaNpX z@_?fG3kk Date: Sun, 31 Aug 2025 22:58:53 -0700 Subject: [PATCH 5/7] Add ATLAS workspace and update .gitignore to include it --- .gitignore | 1 + workspaces/ATLAS_Workspace/.DS_Store | Bin 0 -> 10244 bytes .../ATLAS_Workspace/ATLAS_Project/.DS_Store | Bin 0 -> 6148 bytes .../config/ATLAS_Project_config.py | 68 + ...data_preprocessing_all_periods_fixed.ipynb | 644 ++ .../Hyy_data_preprocessing_two_photons.ipynb | 540 ++ .../ATLAS_Project/config/generate_data.py | 110 + .../old_code/Hyy_data_preprocessing.ipynb | 381 + .../Hyy_data_preprocessing_all_periods.ipynb | 504 ++ .../Hyy_data_preprocessing_backup.ipynb | 435 + .../Hyy_data_preprocessing_two_photons.py | 341 + .../config/old_code/Hyy_root_to_csv_npz.ipynb | 221 + .../config/old_code/convert_photon_data.py | 70 + .../processed_diphoton_data_all_periods.npz | Bin 0 -> 1029376 bytes .../config/old_code/processed_photon_data.csv | 7033 +++++++++++++++++ .../config/old_code/processed_photon_data.npz | Bin 0 -> 438806 bytes .../processed_photon_data_all_periods.npz | Bin 0 -> 1029376 bytes .../processed_photon_data_converted.npz | Bin 0 -> 218592 bytes 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.../processed_diphoton_data_all_periods.npz | Bin 0 -> 1029376 bytes .../V5_results/create_proper_scales.py | 73 + .../V5_results/generate_V5_data.py | 174 + .../V5_results/generate_scales.py | 52 + .../V5_results/setup_atlas_data.py | 172 + workspaces/ATLAS_Workspace/data/.DS_Store | Bin 0 -> 6148 bytes workspaces/ATLAS_Workspace/data/photon_e.npz | Bin 0 -> 6788500 bytes .../data/photon_e_converted.npz | Bin 0 -> 13167544 bytes .../processed_diphoton_data_all_periods.npz | Bin 0 -> 1029376 bytes ...processed_photon_data_all_periods copy.npz | Bin 0 -> 3103520 bytes .../processed_photon_data_all_periods.npz | Bin 0 -> 16250914 bytes .../data/processed_photon_data_converted.npz | Bin 0 -> 218592 bytes 46 files changed, 11904 insertions(+) create mode 100644 workspaces/ATLAS_Workspace/.DS_Store create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/.DS_Store create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/ATLAS_Project_config.py create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/Hyy_data_preprocessing_all_periods_fixed.ipynb create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/Hyy_data_preprocessing_two_photons.ipynb create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/generate_data.py create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing.ipynb create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_all_periods.ipynb create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_backup.ipynb create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_two_photons.py create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_root_to_csv_npz.ipynb create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/convert_photon_data.py create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_diphoton_data_all_periods.npz create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_photon_data.csv create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_photon_data.npz create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_photon_data_all_periods.npz create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_photon_data_converted.npz create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/.DS_Store create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/compressed_output/compressed.npz create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/compressed_output/model.pt create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/decompressed_output/decompressed.npz create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/plotting/.DS_Store create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/plotting/Loss_plot.pdf create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/plotting/comparison.pdf create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/training/activations.npy create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/training/loss_data.npy create mode 100644 workspaces/ATLAS_Workspace/ATLAS_Project/output/training/normalization_features.npy create mode 100644 workspaces/ATLAS_Workspace/V5_results/.DS_Store create mode 100644 workspaces/ATLAS_Workspace/V5_results/README_V5.md create mode 100644 workspaces/ATLAS_Workspace/V5_results/V5_calc_masses_and_plot.py create mode 100644 workspaces/ATLAS_Workspace/V5_results/V5_precut_scales.npz create mode 100644 workspaces/ATLAS_Workspace/V5_results/V5_results/.DS_Store create mode 100644 workspaces/ATLAS_Workspace/V5_results/V5_results/V1/HyyAnalysis_comparison.png create mode 100644 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To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (2.3.1)\n", + "Requirement already satisfied: numpy>=1.22.4 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from pandas) (1.23.5)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from pandas) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from pandas) (2025.2)\n", + "Requirement already satisfied: six>=1.5 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: requests in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (2.32.4)\n", + "Requirement already satisfied: aiohttp in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (3.12.13)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in 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"Requirement already satisfied: pytz>=2020.1 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from pandas>=1.2->awkward-pandas) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from pandas>=1.2->awkward-pandas) (2025.2)\n", + "Requirement already satisfied: zipp>=0.5 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from importlib-metadata>=4.13.0->awkward>=2.0.0->awkward-pandas) (3.17.0)\n", + "Requirement already satisfied: six>=1.5 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas>=1.2->awkward-pandas) (1.16.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: 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\u001b[0m\u001b[32;49m25.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade uproot\n", + "!{sys.executable} -m pip install --upgrade pandas\n", + "!{sys.executable} -m pip install requests aiohttp\n", + "!{sys.executable} -m pip install awkward-pandas\n", + "!{sys.executable} -m pip install vector " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "ClientConnectorDNSError", + "evalue": "Cannot connect to host atlas-opendata.web.cern.ch:443 ssl:default [nodename nor servname provided, or not known]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/connector.py:1512\u001b[0m, in \u001b[0;36mTCPConnector._create_direct_connection\u001b[0;34m(self, req, traces, timeout, client_error)\u001b[0m\n\u001b[1;32m 1508\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;66;03m# Cancelling this lookup should not cancel the underlying lookup\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;66;03m# or else the cancel event will get broadcast to all the waiters\u001b[39;00m\n\u001b[1;32m 1511\u001b[0m \u001b[38;5;66;03m# across all connections.\u001b[39;00m\n\u001b[0;32m-> 1512\u001b[0m hosts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resolve_host(host, port, traces\u001b[38;5;241m=\u001b[39mtraces)\n\u001b[1;32m 1513\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/connector.py:1128\u001b[0m, in \u001b[0;36mTCPConnector._resolve_host\u001b[0;34m(self, host, port, traces)\u001b[0m\n\u001b[1;32m 1127\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1128\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mshield(resolved_host_task)\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mCancelledError:\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/connector.py:1159\u001b[0m, in \u001b[0;36mTCPConnector._resolve_host_with_throttle\u001b[0;34m(self, key, host, port, futures, traces)\u001b[0m\n\u001b[1;32m 1157\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m trace\u001b[38;5;241m.\u001b[39msend_dns_resolvehost_start(host)\n\u001b[0;32m-> 1159\u001b[0m addrs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resolver\u001b[38;5;241m.\u001b[39mresolve(host, port, family\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_family)\n\u001b[1;32m 1160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m traces:\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/resolver.py:40\u001b[0m, in \u001b[0;36mThreadedResolver.resolve\u001b[0;34m(self, host, port, family)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mresolve\u001b[39m(\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28mself\u001b[39m, host: \u001b[38;5;28mstr\u001b[39m, port: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, family: socket\u001b[38;5;241m.\u001b[39mAddressFamily \u001b[38;5;241m=\u001b[39m socket\u001b[38;5;241m.\u001b[39mAF_INET\n\u001b[1;32m 39\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[ResolveResult]:\n\u001b[0;32m---> 40\u001b[0m infos \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_loop\u001b[38;5;241m.\u001b[39mgetaddrinfo(\n\u001b[1;32m 41\u001b[0m host,\n\u001b[1;32m 42\u001b[0m port,\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39msocket\u001b[38;5;241m.\u001b[39mSOCK_STREAM,\n\u001b[1;32m 44\u001b[0m family\u001b[38;5;241m=\u001b[39mfamily,\n\u001b[1;32m 45\u001b[0m flags\u001b[38;5;241m=\u001b[39m_AI_ADDRCONFIG,\n\u001b[1;32m 46\u001b[0m )\n\u001b[1;32m 48\u001b[0m hosts: List[ResolveResult] \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.21/lib/python3.9/asyncio/base_events.py:861\u001b[0m, in \u001b[0;36mBaseEventLoop.getaddrinfo\u001b[0;34m(self, host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 859\u001b[0m getaddr_func \u001b[38;5;241m=\u001b[39m socket\u001b[38;5;241m.\u001b[39mgetaddrinfo\n\u001b[0;32m--> 861\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_in_executor(\n\u001b[1;32m 862\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m, getaddr_func, host, port, family, \u001b[38;5;28mtype\u001b[39m, proto, flags)\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.21/lib/python3.9/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.21/lib/python3.9/socket.py:966\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 965\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 966\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 967\u001b[0m af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n", + "\u001b[0;31mgaierror\u001b[0m: [Errno 8] nodename nor servname provided, or not known", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mClientConnectorDNSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m test_path \u001b[38;5;241m=\u001b[39m path \u001b[38;5;241m+\u001b[39m samples_list[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.root\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Inspect the file to find available trees\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43muproot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_path\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeys in ROOT file:\u001b[39m\u001b[38;5;124m\"\u001b[39m, file\u001b[38;5;241m.\u001b[39mkeys())\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/uproot/reading.py:142\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, object_cache, array_cache, custom_classes, decompression_executor, interpretation_executor, **options)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(file_path, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28mhasattr\u001b[39m(file_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(file_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseek\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 135\u001b[0m ):\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 137\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be a string, pathlib.Path, an object with \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mseek\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m methods, or a length-1 dict of \u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124mfile_path: object_path}, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 140\u001b[0m )\n\u001b[0;32m--> 142\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[43mReadOnlyFile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[43m \u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mobject_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobject_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43marray_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43marray_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_classes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecompression_executor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecompression_executor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpretation_executor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpretation_executor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m object_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m file\u001b[38;5;241m.\u001b[39mroot_directory\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/uproot/reading.py:573\u001b[0m, in \u001b[0;36mReadOnlyFile.__init__\u001b[0;34m(self, file_path, object_cache, array_cache, custom_classes, decompression_executor, interpretation_executor, **options)\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_options[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbegin_chunk_size\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m<\u001b[39m _file_header_fields_big\u001b[38;5;241m.\u001b[39msize:\n\u001b[1;32m 566\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 567\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbegin_chunk_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m is not enough to read the TFile header (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 568\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_options[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbegin_chunk_size\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 569\u001b[0m _file_header_fields_big\u001b[38;5;241m.\u001b[39msize,\n\u001b[1;32m 570\u001b[0m )\n\u001b[1;32m 571\u001b[0m )\n\u001b[0;32m--> 573\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_begin_chunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_source\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 574\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_options\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbegin_chunk_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 575\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdetach_memmap()\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhook_before_interpret()\n\u001b[1;32m 579\u001b[0m (\n\u001b[1;32m 580\u001b[0m magic,\n\u001b[1;32m 581\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fVersion,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_begin_chunk, _file_header_fields_small, {}\n\u001b[1;32m 596\u001b[0m )\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/uproot/source/fsspec.py:95\u001b[0m, in \u001b[0;36mFSSpecSource.chunk\u001b[0;34m(self, start, stop)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_requested_chunks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_requested_bytes \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m stop \u001b[38;5;241m-\u001b[39m start\n\u001b[0;32m---> 95\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcat_file\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_file_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m future \u001b[38;5;241m=\u001b[39m uproot\u001b[38;5;241m.\u001b[39msource\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mTrivialFuture(data)\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39msource\u001b[38;5;241m.\u001b[39mchunk\u001b[38;5;241m.\u001b[39mChunk(\u001b[38;5;28mself\u001b[39m, start, stop, future)\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/fsspec/asyn.py:118\u001b[0m, in \u001b[0;36msync_wrapper..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m obj \u001b[38;5;129;01mor\u001b[39;00m args[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/fsspec/asyn.py:103\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m FSTimeoutError \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mreturn_result\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m return_result\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/fsspec/asyn.py:56\u001b[0m, in \u001b[0;36m_runner\u001b[0;34m(event, coro, result, timeout)\u001b[0m\n\u001b[1;32m 54\u001b[0m coro \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mwait_for(coro, timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 56\u001b[0m result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[1;32m 58\u001b[0m result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m ex\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/fsspec/implementations/http.py:231\u001b[0m, in \u001b[0;36mHTTPFileSystem._cat_file\u001b[0;34m(self, url, start, end, **kwargs)\u001b[0m\n\u001b[1;32m 229\u001b[0m kw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m headers\n\u001b[1;32m 230\u001b[0m session \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_session()\n\u001b[0;32m--> 231\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m session\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode_url(url), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw) \u001b[38;5;28;01mas\u001b[39;00m r:\n\u001b[1;32m 232\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m r\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_not_found_for_status(r, url)\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/client.py:1482\u001b[0m, in \u001b[0;36m_BaseRequestContextManager.__aenter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1481\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__aenter__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m _RetType:\n\u001b[0;32m-> 1482\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resp: _RetType \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_coro\n\u001b[1;32m 1483\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resp\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__aenter__\u001b[39m()\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/client.py:770\u001b[0m, in \u001b[0;36mClientSession._request\u001b[0;34m(self, method, str_or_url, params, data, json, cookies, headers, skip_auto_headers, auth, allow_redirects, max_redirects, compress, chunked, expect100, raise_for_status, read_until_eof, proxy, proxy_auth, timeout, verify_ssl, fingerprint, ssl_context, ssl, server_hostname, proxy_headers, trace_request_ctx, read_bufsize, auto_decompress, max_line_size, max_field_size, middlewares)\u001b[0m\n\u001b[1;32m 767\u001b[0m handler \u001b[38;5;241m=\u001b[39m _connect_and_send_request\n\u001b[1;32m 769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 770\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m handler(req)\n\u001b[1;32m 771\u001b[0m \u001b[38;5;66;03m# Client connector errors should not be retried\u001b[39;00m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\n\u001b[1;32m 773\u001b[0m ConnectionTimeoutError,\n\u001b[1;32m 774\u001b[0m ClientConnectorError,\n\u001b[1;32m 775\u001b[0m ClientConnectorCertificateError,\n\u001b[1;32m 776\u001b[0m ClientConnectorSSLError,\n\u001b[1;32m 777\u001b[0m ):\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/client.py:725\u001b[0m, in \u001b[0;36mClientSession._request.._connect_and_send_request\u001b[0;34m(req)\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connector \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 724\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 725\u001b[0m conn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connector\u001b[38;5;241m.\u001b[39mconnect(\n\u001b[1;32m 726\u001b[0m req, traces\u001b[38;5;241m=\u001b[39mtraces, timeout\u001b[38;5;241m=\u001b[39mreal_timeout\n\u001b[1;32m 727\u001b[0m )\n\u001b[1;32m 728\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mTimeoutError \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 729\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConnectionTimeoutError(\n\u001b[1;32m 730\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection timeout to host \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mreq\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 731\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/connector.py:622\u001b[0m, in \u001b[0;36mBaseConnector.connect\u001b[0;34m(self, req, traces, timeout)\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m trace \u001b[38;5;129;01min\u001b[39;00m traces:\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m trace\u001b[38;5;241m.\u001b[39msend_connection_create_start()\n\u001b[0;32m--> 622\u001b[0m proto \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_connection(req, traces, timeout)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m traces:\n\u001b[1;32m 624\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m trace \u001b[38;5;129;01min\u001b[39;00m traces:\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/connector.py:1189\u001b[0m, in \u001b[0;36mTCPConnector._create_connection\u001b[0;34m(self, req, traces, timeout)\u001b[0m\n\u001b[1;32m 1187\u001b[0m _, proto \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_proxy_connection(req, traces, timeout)\n\u001b[1;32m 1188\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1189\u001b[0m _, proto \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_direct_connection(req, traces, timeout)\n\u001b[1;32m 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m proto\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/aiohttp/connector.py:1518\u001b[0m, in \u001b[0;36mTCPConnector._create_direct_connection\u001b[0;34m(self, req, traces, timeout, client_error)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# in case of proxy it is not ClientProxyConnectionError\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;66;03m# it is problem of resolving proxy ip itself\u001b[39;00m\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ClientConnectorDNSError(req\u001b[38;5;241m.\u001b[39mconnection_key, exc) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m 1520\u001b[0m last_exc: Optional[\u001b[38;5;167;01mException\u001b[39;00m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1521\u001b[0m addr_infos \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_hosts_to_addr_infos(hosts)\n", + "\u001b[0;31mClientConnectorDNSError\u001b[0m: Cannot connect to host atlas-opendata.web.cern.ch:443 ssl:default [nodename nor servname provided, or not known]" + ] + } + ], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "import vector\n", + "import awkward as ak\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "# Test with first sample to verify file structure\n", + "test_path = path + samples_list[0] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(test_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of entries in the tree are: 11371\n", + "The information stored in the tree is: ['num_events', 'sum_of_weights', 'sum_of_weights_squared', 'corrected_xsec', 'dsid', 'category', 'sig_ph', 'n_sig_ph', 'ScaleFactor_PILEUP', 'mcWeight', 'xsec', 'filteff', 'kfac', 'channelNumber', 'eventNumber', 'runNumber', 'trigP', 'trigDT', 'trigT', 'trigE', 'trigM', 'trigMET', 'ScaleFactor_BTAG', 'jet_n', 'jet_pt', 'jet_eta', 'jet_phi', 'jet_e', 'jet_btag_quantile', 'jet_jvt', 'largeRJet_n', 'largeRJet_pt', 'largeRJet_eta', 'largeRJet_phi', 'largeRJet_e', 'largeRJet_m', 'largeRJet_D2', 'ScaleFactor_ELE', 'ScaleFactor_MUON', 'scaleFactor_LepTRIGGER', 'lep_n', 'lep_type', 'lep_pt', 'lep_eta', 'lep_phi', 'lep_e', 'lep_charge', 'lep_ptvarcone30', 'lep_topoetcone20', 'lep_z0', 'lep_d0', 'lep_d0sig', 'lep_isTightID', 'lep_isMediumID', 'lep_isLooseID', 'lep_isTightIso', 'lep_isLooseIso', 'ScaleFactor_PHOTON', 'photon_n', 'photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_ptcone20', 'photon_topoetcone40', 'photon_isLooseID', 'photon_isTightID', 'photon_isLooseIso', 'photon_isTightIso', 'ScaleFactor_TAU', 'ScaleFactor_TauTRIGGER', 'ScaleFactor_DiTauTRIGGER', 'tau_n', 'tau_pt', 'tau_eta', 'tau_phi', 'tau_e', 'tau_charge', 'tau_nTracks', 'tau_isTight', 'tau_RNNJetScore', 'tau_RNNEleScore', 'met', 'met_phi', 'met_mpx', 'met_mpy']\n" + ] + } + ], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(test_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{photon_e: [90.1, 37.6, 12.6, 2.88]}, {...}, ..., {photon_e: [140, ..., 2.14]}]\n" + ] + } + ], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction quality of the two photons: [True] [False]\n", + "Apply cut based on reconstruction quality requirements?[True]\n", + "The transverse momentum of the two photons: [84] [27.7]\n", + "Apply cut based on the transverse momentum requirements?[True]\n", + "The calorimeter isolation of the two photons: [0] [2.27]\n", + "Apply cut based on the calorimeter isolation requirements?[True]\n", + "The eta of the two photons: [-0.379] [0.823]\n", + "Apply cut based on the eta requirements?[False]\n", + "The invariant mass of the 2-photon state is: [114] GeV\n", + "The invariant mass based isolation of the two photons: [0.735] [0.242]\n", + "Apply cut based on the invariant mass based isolation requirements?[True]\n" + ] + } + ], + "source": [ + "# This selects the first entry of the tree\n", + "entry = tree.arrays(library=\"ak\")[:1]\n", + "\n", + "# Cut on the photon reconstruction quality\n", + "photon_reconstruction = entry['photon_isTightID']\n", + "# isTightID==True means a photon identified as being well reconstructed, only the events which have True for both photons are kept\n", + "photon_reconstruction_cut_bool = (photon_reconstruction[:, 0] == False) | (photon_reconstruction[:, 1]==False) # [:, i] selects the i-th lepton in each event\n", + "print(\"The reconstruction quality of the two photons:\", photon_reconstruction[:,0], photon_reconstruction[:,1])\n", + "print(f\"Apply cut based on reconstruction quality requirements?{photon_reconstruction_cut_bool}\")\n", + " \n", + "# Cut on the transverse momentum\n", + "photon_pt = entry['photon_pt']\n", + "# Only the events where the leading photon has transverse momentum (pt) > 50 GeV and the sub-leading photon has pt > 30 GeV are kept\n", + "# Since the two photons for each entry are ordered, the first photon is the leading one and the second one is the sub-leading one\n", + "photon_pt_cut_bool = (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "print(\"The transverse momentum of the two photons:\", photon_pt[:,0], photon_pt[:,1])\n", + "print(f\"Apply cut based on the transverse momentum requirements?{photon_pt_cut_bool}\")\n", + "\n", + "# Cut on the calorimeter isolation\n", + "photon_ptcone20 = entry['photon_ptcone20']\n", + "# Only the events where the invidivual photon calorimeter isolation is less than 5.5% are kept\n", + "photon_caloiso_cut_bool = (((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055))\n", + "print(\"The calorimeter isolation of the two photons:\", (photon_ptcone20[:,0]/photon_pt[:,0]), (photon_ptcone20[:,1]/photon_pt[:,1]))\n", + "print(f\"Apply cut based on the calorimeter isolation requirements?{photon_caloiso_cut_bool}\")\n", + "\n", + "# Cut on the pseudorapidity in the barrel/end-cap transition region\n", + "photon_eta = entry['photon_eta']\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + "condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + "condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + "photon_eta_cut_bool = (condition_0 | condition_1)\n", + "print(\"The eta of the two photons:\", photon_eta[:,0], photon_eta[:,1])\n", + "print(f\"Apply cut based on the eta requirements?{photon_eta_cut_bool}\")\n", + "\n", + "# This calculates the invariant mass of the 2-photon state\n", + "p4 = vector.zip({\"pt\": entry['photon_pt'], \"eta\": entry['photon_eta'], \"phi\": entry['photon_phi'], \"e\": entry['photon_e']})\n", + "invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + "print(f\"The invariant mass of the 2-photon state is: {invariant_mass} GeV\")\n", + "\n", + "# Cut on the invariant mass based isolation\n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "photon_massiso_cut_bool = ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)\n", + "print(\"The invariant mass based isolation of the two photons:\", (photon_pt[:,0]/invariant_mass), (photon_pt[:,1]/invariant_mass))\n", + "print(f\"Apply cut based on the invariant mass based isolation requirements?{photon_massiso_cut_bool}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cut on the photon reconstruction quality\n", + "def cut_photon_reconstruction(photon_isTightID):\n", + " # Only the events which have True for both photons are kept\n", + " return (photon_isTightID[:,0]==False) | (photon_isTightID[:,1]==False) \n", + "\n", + "# Cut on the transverse momentum\n", + "def cut_photon_pt(photon_pt):\n", + "# Only the events where photon_pt[0] > 50 GeV and photon_pt[1] > 30 GeV are kept\n", + " return (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "\n", + "# Cut on the energy isolation\n", + "def cut_isolation_pt(photon_ptcone20, photon_pt):\n", + "# Only the events where the calorimeter isolation is less than 5.5% are kept\n", + " return ((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055)\n", + "\n", + "# Cut on the pseudorapidity in barrel/end-cap transition region\n", + "def cut_photon_eta_transition(photon_eta):\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + " condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + " condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + " return condition_0 | condition_1\n", + "\n", + "# This function calculates the invariant mass of the 2-photon state\n", + "def calc_mass(photon_pt, photon_eta, photon_phi, photon_e):\n", + " p4 = vector.zip({\"pt\": photon_pt, \"eta\": photon_eta, \"phi\": photon_phi, \"e\": photon_e})\n", + " invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + " return invariant_mass\n", + "\n", + "# Cut on null diphoton invariant mass\n", + "def cut_mass(invariant_mass):\n", + " return (invariant_mass == 0)\n", + "\n", + "# Cut on diphoton invariant mass based isolation \n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "def cut_iso_mass(photon_pt, invariant_mass):\n", + " return ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing data15_periodD...\n", + " data15_periodD: 131 events after cuts\n", + "Processing data15_periodE...\n", + " data15_periodE: 1198 events after cuts\n", + "Processing data15_periodF...\n", + " data15_periodF: 899 events after cuts\n", + "Processing data15_periodG...\n", + " data15_periodG: 2088 events after cuts\n", + "Processing data15_periodH...\n", + " data15_periodH: 743 events after cuts\n", + "Processing data15_periodJ...\n", + " data15_periodJ: 4009 events after cuts\n", + "Processing data16_periodA...\n", + " data16_periodA: 1440 events after cuts\n", + "Processing data16_periodB...\n", + " data16_periodB: 3120 events after cuts\n", + "Processing data16_periodC...\n", + " data16_periodC: 5142 events after cuts\n", + "Processing data16_periodD...\n", + " data16_periodD: 7986 events after cuts\n", + "Processing data16_periodE...\n", + " data16_periodE: 2457 events after cuts\n", + "Processing data16_periodF...\n", + " data16_periodF: 5527 events after cuts\n", + "Processing data16_periodG...\n", + " data16_periodG: 6601 events after cuts\n", + "Processing data16_periodK...\n", + " data16_periodK: 3722 events after cuts\n", + "Processing data16_periodL...\n", + " data16_periodL: 10340 events after cuts\n", + "\n", + "Total events across all samples: 55403\n" + ] + } + ], + "source": [ + "# Define empty list to hold all data for all samples\n", + "all_samples_data = []\n", + "\n", + "# Process each sample in the samples_list\n", + "for sample_name in samples_list:\n", + " print(f\"Processing {sample_name}...\")\n", + " \n", + " # Construct the file path for this sample\n", + " sample_path = path + sample_name + \".root\"\n", + " \n", + " # Define empty list to hold all data for this sample\n", + " sample_data = []\n", + " \n", + " try:\n", + " # Perform the cuts for each data entry in the tree\n", + " with uproot.open(sample_path + \":analysis\") as tree:\n", + " for data in tree.iterate(variables, library=\"ak\"):\n", + " \n", + " # data[~boolean] is used to remove entries from the data set\n", + " photon_isTightID = data['photon_isTightID']\n", + " data = data[~cut_photon_reconstruction(photon_isTightID)]\n", + "\n", + " photon_pt = data['photon_pt']\n", + " data = data[~cut_photon_pt(photon_pt)]\n", + "\n", + " data = data[~cut_isolation_pt(data['photon_ptcone20'],data['photon_pt'])]\n", + "\n", + " photon_eta = data['photon_eta']\n", + " data = data[~cut_photon_eta_transition(photon_eta)]\n", + " \n", + " data['mass'] = calc_mass(data['photon_pt'], data['photon_eta'], data['photon_phi'], data['photon_e'])\n", + " \n", + " data = data[~cut_mass(data['mass'])]\n", + " \n", + " data = data[~cut_iso_mass(data['photon_pt'], data['mass'])]\n", + " \n", + " # Append data to the sample data list\n", + " sample_data.append(data)\n", + " \n", + " # turn sample_data back into an awkward array\n", + " if sample_data:\n", + " processed_sample = ak.concatenate(sample_data)\n", + " # Add sample identifier\n", + " processed_sample['sample'] = sample_name\n", + " all_samples_data.append(processed_sample)\n", + " print(f\" {sample_name}: {len(processed_sample)} events after cuts\")\n", + " else:\n", + " print(f\" {sample_name}: No events passed cuts\")\n", + " \n", + " except Exception as e:\n", + " print(f\" Error processing {sample_name}: {e}\")\n", + " continue\n", + "\n", + "# Combine all samples into one dataset\n", + "if all_samples_data:\n", + " all_data = ak.concatenate(all_samples_data)\n", + " print(f\"\\nTotal events across all samples: {len(all_data)}\")\n", + "else:\n", + " print(\"\\nNo data was successfully processed from any sample.\")\n", + " all_data = None" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " photon_pt photon_eta photon_phi photon_e \\\n", + "entry subentry \n", + "0 0 83.005539 1.693584 -0.613051 233.361725 \n", + " 1 73.862511 1.159767 0.796867 129.360947 \n", + "1 0 129.382782 0.716575 2.482590 164.046448 \n", + " 1 104.054314 0.481084 -0.747131 116.329597 \n", + " 2 2.080437 -0.029949 -2.290774 2.081371 \n", + "\n", + " photon_isTightID photon_ptcone20 mass sample \n", + "entry subentry \n", + "0 0 True 0.0 109.939896 data15_periodD \n", + " 1 True 0.0 109.939896 data15_periodD \n", + "1 0 True 0.0 233.445709 data15_periodD \n", + " 1 True 0.0 233.445709 data15_periodD \n", + " 2 False 0.0 233.445709 data15_periodD \n", + "Saved as processed_photon_data_all_periods.csv\n", + "Saved as processed_photon_data_all_periods.npz in baler-compatible format\n", + "\n", + "Processed data summary:\n", + "Number of events after cuts: 55403\n", + "Data shape for baler: (55403, 7)\n", + "Features included: ['photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_isTightID', 'photon_ptcone20', 'mass']\n", + "\n", + "Breakdown by sample:\n", + " data15_periodD: 452 events\n", + " data15_periodE: 3893 events\n", + " data15_periodF: 2931 events\n", + " data15_periodG: 7032 events\n", + " data15_periodH: 2505 events\n", + " data15_periodJ: 13168 events\n", + " data16_periodA: 5163 events\n", + " data16_periodB: 11999 events\n", + " data16_periodC: 19312 events\n", + " data16_periodD: 31454 events\n", + " data16_periodE: 9416 events\n", + " data16_periodF: 22912 events\n", + " data16_periodG: 27496 events\n", + " data16_periodK: 15772 events\n", + " data16_periodL: 44897 events\n" + ] + } + ], + "source": [ + "# Convert processed data (with cuts applied) to dataframe\n", + "if all_data is not None:\n", + " processed_df = ak.to_dataframe(all_data)\n", + " print(processed_df.head())\n", + " \n", + " # # Save processed data to CSV\n", + " # processed_df.to_csv(\"processed_photon_data_all_periods.csv\", index=False)\n", + " # print(\"Saved as processed_photon_data_all_periods.csv\")\n", + " \n", + " # Convert to the format baler expects using the pandas dataframe\n", + " # Extract the main features (excluding sample identifier for now)\n", + " feature_columns = ['photon_pt', 'photon_eta', 'photon_phi', 'photon_e', \n", + " 'photon_isTightID', 'photon_ptcone20', 'mass']\n", + " \n", + " # Use the pandas dataframe which is already properly structured\n", + " # Group by entry (event) and take the first photon's data for each event\n", + " event_data = []\n", + " \n", + " # Get unique event indices\n", + " unique_events = processed_df.index.get_level_values('entry').unique()\n", + " \n", + " for event_idx in unique_events:\n", + " event_df = processed_df.loc[event_idx]\n", + " \n", + " # If it's a single row (Series), convert to DataFrame\n", + " if isinstance(event_df, pd.Series):\n", + " event_df = event_df.to_frame().T\n", + " \n", + " # Take the first photon's data for each feature\n", + " event_row = []\n", + " for col in feature_columns:\n", + " if col in event_df.columns:\n", + " # Get the first value for this feature in this event\n", + " first_value = event_df[col].iloc[0]\n", + " event_row.append(float(first_value))\n", + " else:\n", + " event_row.append(0.0)\n", + " \n", + " event_data.append(event_row)\n", + " \n", + " # Convert to numpy array\n", + " data_array = np.array(event_data)\n", + " \n", + " # Create column names\n", + " column_names = np.array(feature_columns)\n", + " \n", + " # Save in baler-compatible format\n", + " np.savez('original.npz', \n", + " data=data_array,\n", + " names=column_names)\n", + " print(\"Saved as original.npz in baler-compatible format\")\n", + " \n", + " # Print summary of processed data\n", + " print(f\"\\nProcessed data summary:\")\n", + " print(f\"Number of events after cuts: {len(unique_events)}\")\n", + " print(f\"Data shape for baler: {data_array.shape}\")\n", + " print(f\"Features included: {list(column_names)}\")\n", + " \n", + " # Show breakdown by sample\n", + " print(f\"\\nBreakdown by sample:\")\n", + " sample_counts = processed_df.groupby('sample').size()\n", + " for sample, count in sample_counts.items():\n", + " print(f\" {sample}: {count} events\")\n", + "else:\n", + " print(\"No data to save.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'all_data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Generate scale files for normalization (similar to generate_data.py)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mall_data\u001b[49m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata_array\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlocals\u001b[39m():\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mGenerating scale files for normalization...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Define normalization functions (same as in generate_data.py)\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'all_data' is not defined" + ] + } + ], + "source": [ + "# Generate scale files for normalization (similar to generate_data.py)\n", + "if all_data is not None and 'data_array' in locals():\n", + " print(\"\\nGenerating scale files for normalization...\")\n", + " \n", + " # Define normalization functions (same as in generate_data.py)\n", + " def minmax(arr):\n", + " return (arr-min(arr))/(max(arr)-min(arr)), (np.astype(min(arr), np.float32), np.astype(max(arr), np.float32))\n", + " \n", + " def log_minmax(arr):\n", + " return ((np.log10(arr)-np.log10(min(arr)))/(np.log10(max(arr))-np.log10(min(arr))),\n", + " (np.astype(np.log10(min(arr)), np.float32),\n", + " np.astype(np.log10(max(arr)), np.float32)))\n", + " \n", + " # Apply normalization to each feature\n", + " # For photon_pt, photon_e, and mass: use log_minmax (logarithmic scaling)\n", + " # For photon_eta, photon_phi, photon_isTightID, photon_ptcone20: use minmax (linear scaling)\n", + " \n", + " # Initialize normalized data array\n", + " normalized_data = np.empty_like(data_array, dtype=np.float32)\n", + " scales = []\n", + " \n", + " # Normalize each feature\n", + " for i, col_name in enumerate(feature_columns):\n", + " if col_name in ['photon_pt', 'photon_e', 'mass']:\n", + " # Use logarithmic scaling for energy/momentum/mass quantities\n", + " normalized_feature, scale = log_minmax(data_array[:, i])\n", + " normalized_data[:, i] = normalized_feature\n", + " scales.append(scale)\n", + " print(f\" {col_name}: log_minmax scaling applied\")\n", + " else:\n", + " # Use linear scaling for angular and categorical quantities\n", + " normalized_feature, scale = minmax(data_array[:, i])\n", + " normalized_data[:, i] = normalized_feature\n", + " scales.append(scale)\n", + " print(f\" {col_name}: minmax scaling applied\")\n", + " \n", + " # Convert scales to numpy array format\n", + " scales_array = np.array(scales, dtype=np.float32)\n", + " \n", + " # Save normalized data and scales\n", + " np.savez('normalized.npz', \n", + " data=normalized_data,\n", + " names=column_names)\n", + " print(\"Saved normalized data as normalized.npz\")\n", + " \n", + " np.savez('scales.npz', \n", + " data=scales_array,\n", + " names=column_names)\n", + " print(\"Saved scales as scales.npz\")\n", + " \n", + " print(f\"\\nNormalization complete!\")\n", + " print(f\"Original data shape: {data_array.shape}\")\n", + " print(f\"Normalized data shape: {normalized_data.shape}\")\n", + " print(f\"Scales shape: {scales_array.shape}\")\n", + " print(f\"Features and their scaling methods:\")\n", + " for i, col_name in enumerate(feature_columns):\n", + " scaling_type = \"log_minmax\" if col_name in ['photon_pt', 'photon_e', 'mass'] else \"minmax\"\n", + " print(f\" {col_name}: {scaling_type}\")\n", + "else:\n", + " print(\"No data available for normalization.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/Hyy_data_preprocessing_two_photons.ipynb b/workspaces/ATLAS_Workspace/ATLAS_Project/config/Hyy_data_preprocessing_two_photons.ipynb new file mode 100644 index 00000000..0e4ca9d2 --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/Hyy_data_preprocessing_two_photons.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uproot in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (5.6.3)\n", + "Requirement already satisfied: awkward>=2.4.6 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2.8.4)\n", + "Requirement already satisfied: cramjam>=2.5.0 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2.10.0)\n", + "Requirement already satisfied: fsspec in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2024.2.0)\n", + "Requirement already 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pip install --upgrade pandas\n", + "!{sys.executable} -m pip install requests aiohttp\n", + "!{sys.executable} -m pip install awkward-pandas\n", + "!{sys.executable} -m pip install vector " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys in ROOT file: ['analysis;1']\n" + ] + } + ], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "import vector\n", + "import awkward as ak\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "# Test with first sample to verify file structure\n", + "test_path = path + samples_list[0] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(test_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of entries in the tree are: 11371\n", + "The information stored in the tree is: ['num_events', 'sum_of_weights', 'sum_of_weights_squared', 'corrected_xsec', 'dsid', 'category', 'sig_ph', 'n_sig_ph', 'ScaleFactor_PILEUP', 'mcWeight', 'xsec', 'filteff', 'kfac', 'channelNumber', 'eventNumber', 'runNumber', 'trigP', 'trigDT', 'trigT', 'trigE', 'trigM', 'trigMET', 'ScaleFactor_BTAG', 'jet_n', 'jet_pt', 'jet_eta', 'jet_phi', 'jet_e', 'jet_btag_quantile', 'jet_jvt', 'largeRJet_n', 'largeRJet_pt', 'largeRJet_eta', 'largeRJet_phi', 'largeRJet_e', 'largeRJet_m', 'largeRJet_D2', 'ScaleFactor_ELE', 'ScaleFactor_MUON', 'scaleFactor_LepTRIGGER', 'lep_n', 'lep_type', 'lep_pt', 'lep_eta', 'lep_phi', 'lep_e', 'lep_charge', 'lep_ptvarcone30', 'lep_topoetcone20', 'lep_z0', 'lep_d0', 'lep_d0sig', 'lep_isTightID', 'lep_isMediumID', 'lep_isLooseID', 'lep_isTightIso', 'lep_isLooseIso', 'ScaleFactor_PHOTON', 'photon_n', 'photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_ptcone20', 'photon_topoetcone40', 'photon_isLooseID', 'photon_isTightID', 'photon_isLooseIso', 'photon_isTightIso', 'ScaleFactor_TAU', 'ScaleFactor_TauTRIGGER', 'ScaleFactor_DiTauTRIGGER', 'tau_n', 'tau_pt', 'tau_eta', 'tau_phi', 'tau_e', 'tau_charge', 'tau_nTracks', 'tau_isTight', 'tau_RNNJetScore', 'tau_RNNEleScore', 'met', 'met_phi', 'met_mpx', 'met_mpy']\n" + ] + } + ], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(test_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{photon_e: [90.1, 37.6, 12.6, 2.88]}, {...}, ..., {photon_e: [140, ..., 2.14]}]\n" + ] + } + ], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction quality of the two photons: [True] [False]\n", + "Apply cut based on reconstruction quality requirements?[True]\n", + "The transverse momentum of the two photons: [84] [27.7]\n", + "Apply cut based on the transverse momentum requirements?[True]\n", + "The calorimeter isolation of the two photons: [0] [2.27]\n", + "Apply cut based on the calorimeter isolation requirements?[True]\n", + "The eta of the two photons: [-0.379] [0.823]\n", + "Apply cut based on the eta requirements?[False]\n", + "The invariant mass of the 2-photon state is: [114] GeV\n", + "The invariant mass based isolation of the two photons: [0.735] [0.242]\n", + "Apply cut based on the invariant mass based isolation requirements?[True]\n" + ] + } + ], + "source": [ + "# This selects the first entry of the tree\n", + "entry = tree.arrays(library=\"ak\")[:1]\n", + "\n", + "# Cut on the photon reconstruction quality\n", + "photon_reconstruction = entry['photon_isTightID']\n", + "# isTightID==True means a photon identified as being well reconstructed, only the events which have True for both photons are kept\n", + "photon_reconstruction_cut_bool = (photon_reconstruction[:, 0] == False) | (photon_reconstruction[:, 1]==False) # [:, i] selects the i-th lepton in each event\n", + "print(\"The reconstruction quality of the two photons:\", photon_reconstruction[:,0], photon_reconstruction[:,1])\n", + "print(f\"Apply cut based on reconstruction quality requirements?{photon_reconstruction_cut_bool}\")\n", + " \n", + "# Cut on the transverse momentum\n", + "photon_pt = entry['photon_pt']\n", + "# Only the events where the leading photon has transverse momentum (pt) > 50 GeV and the sub-leading photon has pt > 30 GeV are kept\n", + "# Since the two photons for each entry are ordered, the first photon is the leading one and the second one is the sub-leading one\n", + "photon_pt_cut_bool = (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "print(\"The transverse momentum of the two photons:\", photon_pt[:,0], photon_pt[:,1])\n", + "print(f\"Apply cut based on the transverse momentum requirements?{photon_pt_cut_bool}\")\n", + "\n", + "# Cut on the calorimeter isolation\n", + "photon_ptcone20 = entry['photon_ptcone20']\n", + "# Only the events where the invidivual photon calorimeter isolation is less than 5.5% are kept\n", + "photon_caloiso_cut_bool = (((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055))\n", + "print(\"The calorimeter isolation of the two photons:\", (photon_ptcone20[:,0]/photon_pt[:,0]), (photon_ptcone20[:,1]/photon_pt[:,1]))\n", + "print(f\"Apply cut based on the calorimeter isolation requirements?{photon_caloiso_cut_bool}\")\n", + "\n", + "# Cut on the pseudorapidity in the barrel/end-cap transition region\n", + "photon_eta = entry['photon_eta']\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + "condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + "condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + "photon_eta_cut_bool = (condition_0 | condition_1)\n", + "print(\"The eta of the two photons:\", photon_eta[:,0], photon_eta[:,1])\n", + "print(f\"Apply cut based on the eta requirements?{photon_eta_cut_bool}\")\n", + "\n", + "# This calculates the invariant mass of the 2-photon state\n", + "p4 = vector.zip({\"pt\": entry['photon_pt'], \"eta\": entry['photon_eta'], \"phi\": entry['photon_phi'], \"e\": entry['photon_e']})\n", + "invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + "print(f\"The invariant mass of the 2-photon state is: {invariant_mass} GeV\")\n", + "\n", + "# Cut on the invariant mass based isolation\n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "photon_massiso_cut_bool = ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)\n", + "print(\"The invariant mass based isolation of the two photons:\", (photon_pt[:,0]/invariant_mass), (photon_pt[:,1]/invariant_mass))\n", + "print(f\"Apply cut based on the invariant mass based isolation requirements?{photon_massiso_cut_bool}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cut on the photon reconstruction quality\n", + "def cut_photon_reconstruction(photon_isTightID):\n", + " # Only the events which have True for both photons are kept\n", + " return (photon_isTightID[:,0]==False) | (photon_isTightID[:,1]==False) \n", + "\n", + "# Cut on the transverse momentum\n", + "def cut_photon_pt(photon_pt):\n", + "# Only the events where photon_pt[0] > 50 GeV and photon_pt[1] > 30 GeV are kept\n", + " return (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "\n", + "# Cut on the energy isolation\n", + "def cut_isolation_pt(photon_ptcone20, photon_pt):\n", + "# Only the events where the calorimeter isolation is less than 5.5% are kept\n", + " return ((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055)\n", + "\n", + "# Cut on the pseudorapidity in barrel/end-cap transition region\n", + "def cut_photon_eta_transition(photon_eta):\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + " condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + " condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + " return condition_0 | condition_1\n", + "\n", + "# This function calculates the invariant mass of the 2-photon state\n", + "def calc_mass(photon_pt, photon_eta, photon_phi, photon_e):\n", + " p4 = vector.zip({\"pt\": photon_pt, \"eta\": photon_eta, \"phi\": photon_phi, \"e\": photon_e})\n", + " invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + " return invariant_mass\n", + "\n", + "# Cut on null diphoton invariant mass\n", + "def cut_mass(invariant_mass):\n", + " return (invariant_mass == 0)\n", + "\n", + "# Cut on diphoton invariant mass based isolation \n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "def cut_iso_mass(photon_pt, invariant_mass):\n", + " return ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing data15_periodD...\n", + " data15_periodD: 131 events after cuts\n", + "Processing data15_periodE...\n", + " data15_periodE: 1198 events after cuts\n", + "Processing data15_periodF...\n", + " data15_periodF: 899 events after cuts\n", + "Processing data15_periodG...\n", + " data15_periodG: 2088 events after cuts\n", + "Processing data15_periodH...\n", + " data15_periodH: 743 events after cuts\n", + "Processing data15_periodJ...\n", + " data15_periodJ: 4009 events after cuts\n", + "Processing data16_periodA...\n", + " data16_periodA: 1440 events after cuts\n", + "Processing data16_periodB...\n", + " data16_periodB: 3120 events after cuts\n", + "Processing data16_periodC...\n", + " data16_periodC: 5142 events after cuts\n", + "Processing data16_periodD...\n", + " data16_periodD: 7986 events after cuts\n", + "Processing data16_periodE...\n", + " data16_periodE: 2457 events after cuts\n", + "Processing data16_periodF...\n", + " data16_periodF: 5527 events after cuts\n", + "Processing data16_periodG...\n", + " data16_periodG: 6601 events after cuts\n", + "Processing data16_periodK...\n", + " data16_periodK: 3722 events after cuts\n", + "Processing data16_periodL...\n", + " data16_periodL: 10340 events after cuts\n", + "\n", + "Total events across all samples: 55403\n" + ] + } + ], + "source": [ + "# Define empty list to hold all data for all samples\n", + "all_samples_data = []\n", + "\n", + "# Process each sample in the samples_list\n", + "for sample_name in samples_list:\n", + " print(f\"Processing {sample_name}...\")\n", + " \n", + " # Construct the file path for this sample\n", + " sample_path = path + sample_name + \".root\"\n", + " \n", + " # Define empty list to hold all data for this sample\n", + " sample_data = []\n", + " \n", + " try:\n", + " # Perform the cuts for each data entry in the tree\n", + " with uproot.open(sample_path + \":analysis\") as tree:\n", + " for data in tree.iterate(variables, library=\"ak\"):\n", + " \n", + " # data[~boolean] is used to remove entries from the data set\n", + " photon_isTightID = data['photon_isTightID']\n", + " data = data[~cut_photon_reconstruction(photon_isTightID)]\n", + "\n", + " photon_pt = data['photon_pt']\n", + " data = data[~cut_photon_pt(photon_pt)]\n", + "\n", + " data = data[~cut_isolation_pt(data['photon_ptcone20'],data['photon_pt'])]\n", + "\n", + " photon_eta = data['photon_eta']\n", + " data = data[~cut_photon_eta_transition(photon_eta)]\n", + " \n", + " data['mass'] = calc_mass(data['photon_pt'], data['photon_eta'], data['photon_phi'], data['photon_e'])\n", + " \n", + " data = data[~cut_mass(data['mass'])]\n", + " \n", + " data = data[~cut_iso_mass(data['photon_pt'], data['mass'])]\n", + " \n", + " # Append data to the sample data list\n", + " sample_data.append(data)\n", + " \n", + " # turn sample_data back into an awkward array\n", + " if sample_data:\n", + " processed_sample = ak.concatenate(sample_data)\n", + " # Add sample identifier\n", + " processed_sample['sample'] = sample_name\n", + " all_samples_data.append(processed_sample)\n", + " print(f\" {sample_name}: {len(processed_sample)} events after cuts\")\n", + " else:\n", + " print(f\" {sample_name}: No events passed cuts\")\n", + " \n", + " except Exception as e:\n", + " print(f\" Error processing {sample_name}: {e}\")\n", + " continue\n", + "\n", + "# Combine all samples into one dataset\n", + "if all_samples_data:\n", + " all_data = ak.concatenate(all_samples_data)\n", + " print(f\"\\nTotal events across all samples: {len(all_data)}\")\n", + "else:\n", + " print(\"\\nNo data was successfully processed from any sample.\")\n", + " all_data = None" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " photon_pt photon_eta photon_phi photon_e \\\n", + "entry subentry \n", + "0 0 83.005539 1.693584 -0.613051 233.361725 \n", + " 1 73.862511 1.159767 0.796867 129.360947 \n", + "1 0 129.382782 0.716575 2.482590 164.046448 \n", + " 1 104.054314 0.481084 -0.747131 116.329597 \n", + " 2 2.080437 -0.029949 -2.290774 2.081371 \n", + "\n", + " photon_isTightID photon_ptcone20 mass sample \n", + "entry subentry \n", + "0 0 True 0.0 109.939896 data15_periodD \n", + " 1 True 0.0 109.939896 data15_periodD \n", + "1 0 True 0.0 233.445709 data15_periodD \n", + " 1 True 0.0 233.445709 data15_periodD \n", + " 2 False 0.0 233.445709 data15_periodD \n", + "Total events: 55403\n", + "Events with exactly 2 photons: 9883\n", + "Saved as processed_diphoton_data_all_periods.npz in baler-compatible format\n", + "\n", + "Processed data summary:\n", + "Number of events with exactly 2 photons: 9883\n", + "Data shape for baler: (9883, 13)\n", + "Features included: ['mass', 'photon_pt_ph1', 'photon_pt_ph2', 'photon_eta_ph1', 'photon_eta_ph2', 'photon_phi_ph1', 'photon_phi_ph2', 'photon_e_ph1', 'photon_e_ph2', 'photon_isTightID_ph1', 'photon_isTightID_ph2', 'photon_ptcone20_ph1', 'photon_ptcone20_ph2']\n", + "\n", + "Breakdown by sample:\n", + " data15_periodD: 62 events\n", + " data15_periodE: 714 events\n", + " data15_periodF: 534 events\n", + " data15_periodG: 1130 events\n", + " data15_periodH: 372 events\n", + " data15_periodJ: 2422 events\n", + " data16_periodA: 666 events\n", + " data16_periodB: 1098 events\n", + " data16_periodC: 2086 events\n", + " data16_periodD: 2724 events\n", + " data16_periodE: 956 events\n", + " data16_periodF: 1572 events\n", + " data16_periodG: 1930 events\n", + " data16_periodK: 982 events\n", + " data16_periodL: 2518 events\n" + ] + } + ], + "source": [ + "# Convert processed data (with cuts applied) to dataframe\n", + "if all_data is not None:\n", + " processed_df = ak.to_dataframe(all_data)\n", + " print(processed_df.head())\n", + " \n", + " # Save processed data to CSV\n", + " #processed_df.to_csv(\"processed_photon_data_all_periods.csv\", index=False)\n", + " #print(\"Saved as processed_photon_data_all_periods.csv\")\n", + " \n", + " # Filter to only events with exactly two photons\n", + " event_photon_counts = processed_df.groupby('entry').size()\n", + " events_with_two_photons = event_photon_counts[event_photon_counts == 2].index\n", + " \n", + " print(f\"Total events: {len(event_photon_counts)}\")\n", + " print(f\"Events with exactly 2 photons: {len(events_with_two_photons)}\")\n", + " \n", + " # Filter the dataframe\n", + " filtered_df = processed_df.loc[processed_df.index.get_level_values('entry').isin(events_with_two_photons)]\n", + " \n", + " # Convert to the format baler expects\n", + " feature_columns = ['photon_pt', 'photon_eta', 'photon_phi', 'photon_e', \n", + " 'photon_isTightID', 'photon_ptcone20']\n", + " \n", + " event_data = []\n", + " \n", + " for event_idx in events_with_two_photons:\n", + " event_df = filtered_df.loc[event_idx]\n", + " \n", + " # Take both photons' data for each feature\n", + " event_row = []\n", + " \n", + " # Add mass first (it's per event, not per photon)\n", + " if 'mass' in event_df.columns:\n", + " event_row.append(float(event_df['mass'].iloc[0]))\n", + " else:\n", + " event_row.append(0.0)\n", + " \n", + " # Add photon-specific features for both photons\n", + " for col in feature_columns:\n", + " if col in event_df.columns:\n", + " photon_values = event_df[col].values\n", + " event_row.extend([float(photon_values[0]), float(photon_values[1])])\n", + " else:\n", + " event_row.extend([0.0, 0.0])\n", + " \n", + " event_data.append(event_row)\n", + " \n", + " # Convert to numpy array\n", + " data_array = np.array(event_data)\n", + " \n", + " # Create column names for both photons\n", + " column_names = ['mass'] # Event-level feature\n", + " for col in feature_columns:\n", + " column_names.extend([f'{col}_ph1', f'{col}_ph2']) # Photon-specific features\n", + " \n", + " column_names = np.array(column_names)\n", + " \n", + " # Save in baler-compatible format\n", + " np.savez('processed_diphoton_data_all_periods.npz', \n", + " data=data_array,\n", + " names=column_names)\n", + " print(\"Saved as processed_diphoton_data_all_periods.npz in baler-compatible format\")\n", + " \n", + " # Print summary of processed data\n", + " print(f\"\\nProcessed data summary:\")\n", + " print(f\"Number of events with exactly 2 photons: {len(events_with_two_photons)}\")\n", + " print(f\"Data shape for baler: {data_array.shape}\")\n", + " print(f\"Features included: {list(column_names)}\")\n", + " \n", + " # Show breakdown by sample\n", + " print(f\"\\nBreakdown by sample:\")\n", + " sample_counts = filtered_df.groupby('sample').size()\n", + " for sample, count in sample_counts.items():\n", + " print(f\" {sample}: {count} events\")\n", + "else:\n", + " print(\"No data to save.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/generate_data.py b/workspaces/ATLAS_Workspace/ATLAS_Project/config/generate_data.py new file mode 100644 index 00000000..a14b5303 --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/generate_data.py @@ -0,0 +1,110 @@ +import numpy as np +import uproot +import awkward +import pandas +import time +from tqdm import tqdm + + +def cut_photon_pt(photon_pt): + return (photon_pt[:,0] > 40000) & (photon_pt[:,1] > 30000) + +def cut_photon_eta_transition(photon_eta): + return ((abs(photon_eta[:,0])>1.52) | (abs(photon_eta[:,0])<1.37)) & ((abs(photon_eta[:,1])>1.52) | (abs(photon_eta[:,1])<1.37)) + +def cut_tightID(tightIDs): + # with np.load("/Users/oscarfuentes/masters_project/baler_sem1/workspaces/higgs/data/V4_photon_isTightID.npz") as f: + # tightIDs = f["data"].astype(bool) + return tightIDs[:,0] & tightIDs[:,1] + +def cut_isolation_et(photon_etcone20): + return (photon_etcone20[:,0]<4000) & (photon_etcone20[:,1]<4000) + +def minmax(arr): + return (arr-min(arr))/(max(arr)-min(arr)), (np.astype(min(arr), np.float32), np.astype(max(arr), np.float32)) + +def log_minmax(arr): + return ((np.log10(arr)-np.log10(min(arr)))/(np.log10(max(arr))-np.log10(min(arr))), + (np.astype(np.log10(min(arr)), np.float32), + np.astype(np.log10(max(arr)), np.float32))) + +tuple_path = "https://atlas-opendata.web.cern.ch/atlas-opendata/samples/2020/GamGam/Data/" +save_path = "/Users/cartercapetz/Desktop/baler/carter_data/" + +samples = [tuple_path + "data_" + letter + ".GamGam.root" for letter in ["A", "B", "C", "D"]] + +full_data = np.empty((0, 8)) +full_norm_data = np.empty((0,8)) +length = 0 + +for path, letter in zip(samples, ["A", "B", "C", "D"]): + print(f"Processing data {letter}") + start = time.time() + + with uproot.open(path + ":mini") as t: + tree = t + print(f"File open, took {time.time() - start:.2f}s") + + for data in tree.iterate( + [ + "photon_pt", + "photon_eta", + "photon_phi", + "photon_E", + "photon_isTightID", + "photon_etcone20" + ], + library="pd", + ): + print("Applying cuts...\n") + lens = data.photon_pt.apply(len) + len_mask = (lens == 2).to_numpy() + data = data[len_mask] + print("Len cut complete") + data = data[cut_tightID(data.photon_isTightID.to_numpy())] + print("Tight ID cut complete") + data = data[cut_isolation_et(data.photon_etcone20.to_numpy())] + print("Isolation cut complete") + data = data[cut_photon_pt(data.photon_pt.to_numpy())] + print("pt cut complete") + data = data[cut_photon_eta_transition(data.photon_eta.to_numpy())] + print("eta transition cut complete") + + print("\nStacking...") + full_data = np.vstack((full_data, np.column_stack([data.photon_pt, + data.photon_eta, + data.photon_phi, + data.photon_E]))) + + length += len(data) + + print(f"Finished processing {letter}\n\nTook {time.time() - start:.2f}s") + print(f"Current Length {length}\n") + +full_data = np.array(full_data, dtype=np.float32) + +pt1, scale0 = log_minmax(full_data[:,0]) +pt2, scale1 = log_minmax(full_data[:,1]) +eta1, scale2 = minmax(full_data[:,2]) +eta2, scale3 = minmax(full_data[:,3]) +phi1, scale4 = minmax(full_data[:,4]) +phi2, scale5 = minmax(full_data[:,5]) +E1, scale6 = log_minmax(full_data[:,6]) +E2, scale7 = log_minmax(full_data[:,7]) + +full_norm_data = np.vstack((full_norm_data, np.column_stack([pt1, pt2, + eta1, eta2, + phi1, phi2, + E1, E2]))) +scales = np.column_stack([[scale0, scale1], [scale2, scale3], [scale4, scale5], [scale6, scale7]]) + +print(type(full_data)) +print(type(full_data[0])) +print(type(full_data[0][0])) +print(f"Final data shape: {full_data.shape}") + +names = ["pt1", "pt2", "eta1", "eta2", "phi1", "phi2", "E1", "E2"] + +np.savez(save_path+"V4_precut_original.npz", data=full_data, names=names) +np.savez(save_path+"V4_precut_normalised.npz", data=full_norm_data, names=names) +np.savez(save_path+"V4_precut_scales.npz", data=scales, names=names) diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing.ipynb b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing.ipynb new file mode 100644 index 00000000..d8662d42 --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uproot in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (5.6.2)\n", + "Requirement already satisfied: awkward>=2.4.6 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2.8.4)\n", + "Requirement already satisfied: cramjam>=2.5.0 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2.10.0)\n", + "Requirement already satisfied: fsspec in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2024.2.0)\n", + "Requirement already satisfied: numpy in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (1.23.5)\n", + "Requirement already satisfied: packaging in 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already satisfied: numpy>=1.13.3 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from vector) (1.23.5)\n", + "Requirement already satisfied: packaging>=19 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from vector) (23.2)\n", + "Downloading vector-1.6.2-py3-none-any.whl (177 kB)\n", + "Installing collected packages: vector\n", + "Successfully installed vector-1.6.2\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade uproot\n", + "!{sys.executable} -m pip install --upgrade pandas\n", + "!{sys.executable} -m pip install requests aiohttp\n", + "!{sys.executable} -m pip install awkward-pandas\n", + "!{sys.executable} -m pip install vector " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys in ROOT file: ['analysis;1']\n" + ] + } + ], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "import vector\n", + "import awkward as ak\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "data_15G_path = path + samples_list[3] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(data_15G_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of entries in the tree are: 164583\n", + "The information stored in the tree is: ['num_events', 'sum_of_weights', 'sum_of_weights_squared', 'corrected_xsec', 'dsid', 'category', 'sig_ph', 'n_sig_ph', 'ScaleFactor_PILEUP', 'mcWeight', 'xsec', 'filteff', 'kfac', 'channelNumber', 'eventNumber', 'runNumber', 'trigP', 'trigDT', 'trigT', 'trigE', 'trigM', 'trigMET', 'ScaleFactor_BTAG', 'jet_n', 'jet_pt', 'jet_eta', 'jet_phi', 'jet_e', 'jet_btag_quantile', 'jet_jvt', 'largeRJet_n', 'largeRJet_pt', 'largeRJet_eta', 'largeRJet_phi', 'largeRJet_e', 'largeRJet_m', 'largeRJet_D2', 'ScaleFactor_ELE', 'ScaleFactor_MUON', 'scaleFactor_LepTRIGGER', 'lep_n', 'lep_type', 'lep_pt', 'lep_eta', 'lep_phi', 'lep_e', 'lep_charge', 'lep_ptvarcone30', 'lep_topoetcone20', 'lep_z0', 'lep_d0', 'lep_d0sig', 'lep_isTightID', 'lep_isMediumID', 'lep_isLooseID', 'lep_isTightIso', 'lep_isLooseIso', 'ScaleFactor_PHOTON', 'photon_n', 'photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_ptcone20', 'photon_topoetcone40', 'photon_isLooseID', 'photon_isTightID', 'photon_isLooseIso', 'photon_isTightIso', 'ScaleFactor_TAU', 'ScaleFactor_TauTRIGGER', 'ScaleFactor_DiTauTRIGGER', 'tau_n', 'tau_pt', 'tau_eta', 'tau_phi', 'tau_e', 'tau_charge', 'tau_nTracks', 'tau_isTight', 'tau_RNNJetScore', 'tau_RNNEleScore', 'met', 'met_phi', 'met_mpx', 'met_mpy']\n" + ] + } + ], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(data_15G_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{photon_e: [53, 41.5, 2.67, 5.41, 2.53]}, {...}, ..., {photon_e: [752, ...]}]\n" + ] + } + ], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction quality of the two photons: [True] [False]\n", + "Apply cut based on reconstruction quality requirements?[True]\n", + "The transverse momentum of the two photons: [35.9] [39.5]\n", + "Apply cut based on the transverse momentum requirements?[True]\n", + "The calorimeter isolation of the two photons: [0.265] [0.106]\n", + "Apply cut based on the calorimeter isolation requirements?[True]\n", + "The eta of the two photons: [-0.941] [-0.321]\n", + "Apply cut based on the eta requirements?[False]\n", + "The invariant mass of the 2-photon state is: [78.8] GeV\n", + "The invariant mass based isolation of the two photons: [0.455] [0.501]\n", + "Apply cut based on the invariant mass based isolation requirements?[False]\n" + ] + } + ], + "source": [ + "# This selects the first entry of the tree\n", + "entry = tree.arrays(library=\"ak\")[:1]\n", + "\n", + "# Cut on the photon reconstruction quality\n", + "photon_reconstruction = entry['photon_isTightID']\n", + "# isTightID==True means a photon identified as being well reconstructed, only the events which have True for both photons are kept\n", + "photon_reconstruction_cut_bool = (photon_reconstruction[:, 0] == False) | (photon_reconstruction[:, 1]==False) # [:, i] selects the i-th lepton in each event\n", + "print(\"The reconstruction quality of the two photons:\", photon_reconstruction[:,0], photon_reconstruction[:,1])\n", + "print(f\"Apply cut based on reconstruction quality requirements?{photon_reconstruction_cut_bool}\")\n", + " \n", + "# Cut on the transverse momentum\n", + "photon_pt = entry['photon_pt']\n", + "# Only the events where the leading photon has transverse momentum (pt) > 50 GeV and the sub-leading photon has pt > 30 GeV are kept\n", + "# Since the two photons for each entry are ordered, the first photon is the leading one and the second one is the sub-leading one\n", + "photon_pt_cut_bool = (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "print(\"The transverse momentum of the two photons:\", photon_pt[:,0], photon_pt[:,1])\n", + "print(f\"Apply cut based on the transverse momentum requirements?{photon_pt_cut_bool}\")\n", + "\n", + "# Cut on the calorimeter isolation\n", + "photon_ptcone20 = entry['photon_ptcone20']\n", + "# Only the events where the invidivual photon calorimeter isolation is less than 5.5% are kept\n", + "photon_caloiso_cut_bool = (((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055))\n", + "print(\"The calorimeter isolation of the two photons:\", (photon_ptcone20[:,0]/photon_pt[:,0]), (photon_ptcone20[:,1]/photon_pt[:,1]))\n", + "print(f\"Apply cut based on the calorimeter isolation requirements?{photon_caloiso_cut_bool}\")\n", + "\n", + "# Cut on the pseudorapidity in the barrel/end-cap transition region\n", + "photon_eta = entry['photon_eta']\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + "condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + "condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + "photon_eta_cut_bool = (condition_0 | condition_1)\n", + "print(\"The eta of the two photons:\", photon_eta[:,0], photon_eta[:,1])\n", + "print(f\"Apply cut based on the eta requirements?{photon_eta_cut_bool}\")\n", + "\n", + "# This calculates the invariant mass of the 2-photon state\n", + "p4 = vector.zip({\"pt\": entry['photon_pt'], \"eta\": entry['photon_eta'], \"phi\": entry['photon_phi'], \"e\": entry['photon_e']})\n", + "invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + "print(f\"The invariant mass of the 2-photon state is: {invariant_mass} GeV\")\n", + "\n", + "# Cut on the invariant mass based isolation\n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "photon_massiso_cut_bool = ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)\n", + "print(\"The invariant mass based isolation of the two photons:\", (photon_pt[:,0]/invariant_mass), (photon_pt[:,1]/invariant_mass))\n", + "print(f\"Apply cut based on the invariant mass based isolation requirements?{photon_massiso_cut_bool}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Cut on the photon reconstruction quality\n", + "def cut_photon_reconstruction(photon_isTightID):\n", + " # Only the events which have True for both photons are kept\n", + " return (photon_isTightID[:,0]==False) | (photon_isTightID[:,1]==False) \n", + "\n", + "# Cut on the transverse momentum\n", + "def cut_photon_pt(photon_pt):\n", + "# Only the events where photon_pt[0] > 50 GeV and photon_pt[1] > 30 GeV are kept\n", + " return (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "\n", + "# Cut on the energy isolation\n", + "def cut_isolation_pt(photon_ptcone20, photon_pt):\n", + "# Only the events where the calorimeter isolation is less than 5.5% are kept\n", + " return ((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055)\n", + "\n", + "# Cut on the pseudorapidity in barrel/end-cap transition region\n", + "def cut_photon_eta_transition(photon_eta):\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + " condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + " condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + " return condition_0 | condition_1\n", + "\n", + "# This function calculates the invariant mass of the 2-photon state\n", + "def calc_mass(photon_pt, photon_eta, photon_phi, photon_e):\n", + " p4 = vector.zip({\"pt\": photon_pt, \"eta\": photon_eta, \"phi\": photon_phi, \"e\": photon_e})\n", + " invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + " return invariant_mass\n", + "\n", + "# Cut on null diphoton invariant mass\n", + "def cut_mass(invariant_mass):\n", + " return (invariant_mass == 0)\n", + "\n", + "# Cut on diphoton invariant mass based isolation \n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "def cut_iso_mass(photon_pt, invariant_mass):\n", + " return ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Define empty list to hold all data for this sample\n", + "sample_data = []\n", + "\n", + "# Perform the cuts for each data entry in the tree\n", + "for data in tree.iterate(variables, library=\"ak\"):\n", + " \n", + " # data[~boolean] is used to remove entries from the data set\n", + " photon_isTightID = data['photon_isTightID']\n", + " data = data[~cut_photon_reconstruction(photon_isTightID)]\n", + "\n", + " photon_pt = data['photon_pt']\n", + " data = data[~cut_photon_pt(photon_pt)]\n", + "\n", + " data = data[~cut_isolation_pt(data['photon_ptcone20'],data['photon_pt'])]\n", + "\n", + " photon_eta = data['photon_eta']\n", + " data = data[~cut_photon_eta_transition(photon_eta)]\n", + " \n", + " data['mass'] = calc_mass(data['photon_pt'], data['photon_eta'], data['photon_phi'], data['photon_e'])\n", + " \n", + " data = data[~cut_mass(data['mass'])]\n", + " \n", + " data = data[~cut_iso_mass(data['photon_pt'], data['mass'])]\n", + " \n", + " # Append data to the whole sample data list\n", + " sample_data.append(data)\n", + "\n", + "# turn sample_data back into an awkward array\n", + "data15_periodG = ak.concatenate(sample_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " photon_e\n", + "0 [52.95711898803711, 41.52253723144531, 2.66612...\n", + "1 [144.42855834960938, 25.760665893554688, 6.258...\n", + "2 [47.989742279052734, 31.802453994750977]\n", + "3 [32.105987548828125, 43.45836639404297]\n", + "4 [335.6337890625, 60.06889343261719, 2.30734062...\n", + "Saved as photon_e.csv\n", + "saved as photon_e.npz\n" + ] + } + ], + "source": [ + "tree_name = \"analysis;1\"\n", + "branches = [\"photon_e\"]\n", + "\n", + "#convert branch to dataframe\n", + "with uproot.open(data_15G_path) as file:\n", + " tree = file[tree_name]\n", + " df = tree.arrays(branches, library=\"pd\")\n", + " print(df.head())\n", + "\n", + "#csv\n", + "df.to_csv(\"photon_e.csv\", index=False)\n", + "print(\"Saved as photon_e.csv\")\n", + "\n", + "#npz\n", + "np.savez('photon_e.npz', photon_e = df.values)\n", + "print(\"saved as photon_e.npz\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_all_periods.ipynb b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_all_periods.ipynb new file mode 100644 index 00000000..bc1b7ce0 --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_all_periods.ipynb @@ -0,0 +1,504 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uproot in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (5.6.3)\n", + "Requirement already satisfied: awkward>=2.4.6 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from uproot) (2.8.4)\n", + "Requirement already satisfied: cramjam>=2.5.0 in 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/Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from vector) (23.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade uproot\n", + "!{sys.executable} -m pip install --upgrade pandas\n", + "!{sys.executable} -m pip install requests aiohttp\n", + "!{sys.executable} -m pip install awkward-pandas\n", + "!{sys.executable} -m pip install vector " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys in ROOT file: ['analysis;1']\n" + ] + } + ], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "import vector\n", + "import awkward as ak\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "# Test with first sample to verify file structure\n", + "test_path = path + samples_list[0] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(test_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of entries in the tree are: 11371\n", + "The information stored in the tree is: ['num_events', 'sum_of_weights', 'sum_of_weights_squared', 'corrected_xsec', 'dsid', 'category', 'sig_ph', 'n_sig_ph', 'ScaleFactor_PILEUP', 'mcWeight', 'xsec', 'filteff', 'kfac', 'channelNumber', 'eventNumber', 'runNumber', 'trigP', 'trigDT', 'trigT', 'trigE', 'trigM', 'trigMET', 'ScaleFactor_BTAG', 'jet_n', 'jet_pt', 'jet_eta', 'jet_phi', 'jet_e', 'jet_btag_quantile', 'jet_jvt', 'largeRJet_n', 'largeRJet_pt', 'largeRJet_eta', 'largeRJet_phi', 'largeRJet_e', 'largeRJet_m', 'largeRJet_D2', 'ScaleFactor_ELE', 'ScaleFactor_MUON', 'scaleFactor_LepTRIGGER', 'lep_n', 'lep_type', 'lep_pt', 'lep_eta', 'lep_phi', 'lep_e', 'lep_charge', 'lep_ptvarcone30', 'lep_topoetcone20', 'lep_z0', 'lep_d0', 'lep_d0sig', 'lep_isTightID', 'lep_isMediumID', 'lep_isLooseID', 'lep_isTightIso', 'lep_isLooseIso', 'ScaleFactor_PHOTON', 'photon_n', 'photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_ptcone20', 'photon_topoetcone40', 'photon_isLooseID', 'photon_isTightID', 'photon_isLooseIso', 'photon_isTightIso', 'ScaleFactor_TAU', 'ScaleFactor_TauTRIGGER', 'ScaleFactor_DiTauTRIGGER', 'tau_n', 'tau_pt', 'tau_eta', 'tau_phi', 'tau_e', 'tau_charge', 'tau_nTracks', 'tau_isTight', 'tau_RNNJetScore', 'tau_RNNEleScore', 'met', 'met_phi', 'met_mpx', 'met_mpy']\n" + ] + } + ], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(test_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{photon_e: [90.1, 37.6, 12.6, 2.88]}, {...}, ..., {photon_e: [140, ..., 2.14]}]\n" + ] + } + ], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction quality of the two photons: [True] [False]\n", + "Apply cut based on reconstruction quality requirements?[True]\n", + "The transverse momentum of the two photons: [84] [27.7]\n", + "Apply cut based on the transverse momentum requirements?[True]\n", + "The calorimeter isolation of the two photons: [0] [2.27]\n", + "Apply cut based on the calorimeter isolation requirements?[True]\n", + "The eta of the two photons: [-0.379] [0.823]\n", + "Apply cut based on the eta requirements?[False]\n", + "The invariant mass of the 2-photon state is: [114] GeV\n", + "The invariant mass based isolation of the two photons: [0.735] [0.242]\n", + "Apply cut based on the invariant mass based isolation requirements?[True]\n" + ] + } + ], + "source": [ + "# This selects the first entry of the tree\n", + "entry = tree.arrays(library=\"ak\")[:1]\n", + "\n", + "# Cut on the photon reconstruction quality\n", + "photon_reconstruction = entry['photon_isTightID']\n", + "# isTightID==True means a photon identified as being well reconstructed, only the events which have True for both photons are kept\n", + "photon_reconstruction_cut_bool = (photon_reconstruction[:, 0] == False) | (photon_reconstruction[:, 1]==False) # [:, i] selects the i-th lepton in each event\n", + "print(\"The reconstruction quality of the two photons:\", photon_reconstruction[:,0], photon_reconstruction[:,1])\n", + "print(f\"Apply cut based on reconstruction quality requirements?{photon_reconstruction_cut_bool}\")\n", + " \n", + "# Cut on the transverse momentum\n", + "photon_pt = entry['photon_pt']\n", + "# Only the events where the leading photon has transverse momentum (pt) > 50 GeV and the sub-leading photon has pt > 30 GeV are kept\n", + "# Since the two photons for each entry are ordered, the first photon is the leading one and the second one is the sub-leading one\n", + "photon_pt_cut_bool = (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "print(\"The transverse momentum of the two photons:\", photon_pt[:,0], photon_pt[:,1])\n", + "print(f\"Apply cut based on the transverse momentum requirements?{photon_pt_cut_bool}\")\n", + "\n", + "# Cut on the calorimeter isolation\n", + "photon_ptcone20 = entry['photon_ptcone20']\n", + "# Only the events where the invidivual photon calorimeter isolation is less than 5.5% are kept\n", + "photon_caloiso_cut_bool = (((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055))\n", + "print(\"The calorimeter isolation of the two photons:\", (photon_ptcone20[:,0]/photon_pt[:,0]), (photon_ptcone20[:,1]/photon_pt[:,1]))\n", + "print(f\"Apply cut based on the calorimeter isolation requirements?{photon_caloiso_cut_bool}\")\n", + "\n", + "# Cut on the pseudorapidity in the barrel/end-cap transition region\n", + "photon_eta = entry['photon_eta']\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + "condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + "condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + "photon_eta_cut_bool = (condition_0 | condition_1)\n", + "print(\"The eta of the two photons:\", photon_eta[:,0], photon_eta[:,1])\n", + "print(f\"Apply cut based on the eta requirements?{photon_eta_cut_bool}\")\n", + "\n", + "# This calculates the invariant mass of the 2-photon state\n", + "p4 = vector.zip({\"pt\": entry['photon_pt'], \"eta\": entry['photon_eta'], \"phi\": entry['photon_phi'], \"e\": entry['photon_e']})\n", + "invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + "print(f\"The invariant mass of the 2-photon state is: {invariant_mass} GeV\")\n", + "\n", + "# Cut on the invariant mass based isolation\n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "photon_massiso_cut_bool = ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)\n", + "print(\"The invariant mass based isolation of the two photons:\", (photon_pt[:,0]/invariant_mass), (photon_pt[:,1]/invariant_mass))\n", + "print(f\"Apply cut based on the invariant mass based isolation requirements?{photon_massiso_cut_bool}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Cut on the photon reconstruction quality\n", + "def cut_photon_reconstruction(photon_isTightID):\n", + " # Only the events which have True for both photons are kept\n", + " return (photon_isTightID[:,0]==False) | (photon_isTightID[:,1]==False) \n", + "\n", + "# Cut on the transverse momentum\n", + "def cut_photon_pt(photon_pt):\n", + "# Only the events where photon_pt[0] > 50 GeV and photon_pt[1] > 30 GeV are kept\n", + " return (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "\n", + "# Cut on the energy isolation\n", + "def cut_isolation_pt(photon_ptcone20, photon_pt):\n", + "# Only the events where the calorimeter isolation is less than 5.5% are kept\n", + " return ((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055)\n", + "\n", + "# Cut on the pseudorapidity in barrel/end-cap transition region\n", + "def cut_photon_eta_transition(photon_eta):\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + " condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + " condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + " return condition_0 | condition_1\n", + "\n", + "# This function calculates the invariant mass of the 2-photon state\n", + "def calc_mass(photon_pt, photon_eta, photon_phi, photon_e):\n", + " p4 = vector.zip({\"pt\": photon_pt, \"eta\": photon_eta, \"phi\": photon_phi, \"e\": photon_e})\n", + " invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + " return invariant_mass\n", + "\n", + "# Cut on null diphoton invariant mass\n", + "def cut_mass(invariant_mass):\n", + " return (invariant_mass == 0)\n", + "\n", + "# Cut on diphoton invariant mass based isolation \n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "def cut_iso_mass(photon_pt, invariant_mass):\n", + " return ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing data15_periodD...\n", + " data15_periodD: 131 events after cuts\n", + "Processing data15_periodE...\n", + " data15_periodE: 1198 events after cuts\n", + "Processing data15_periodF...\n", + " data15_periodF: 899 events after cuts\n", + "Processing data15_periodG...\n", + " data15_periodG: 2088 events after cuts\n", + "Processing data15_periodH...\n", + " data15_periodH: 743 events after cuts\n", + "Processing data15_periodJ...\n", + " data15_periodJ: 4009 events after cuts\n", + "Processing data16_periodA...\n", + " data16_periodA: 1440 events after cuts\n", + "Processing data16_periodB...\n", + " data16_periodB: 3120 events after cuts\n", + "Processing data16_periodC...\n", + " data16_periodC: 5142 events after cuts\n", + "Processing data16_periodD...\n", + " data16_periodD: 7986 events after cuts\n", + "Processing data16_periodE...\n", + " data16_periodE: 2457 events after cuts\n", + "Processing data16_periodF...\n", + " data16_periodF: 5527 events after cuts\n", + "Processing data16_periodG...\n", + " data16_periodG: 6601 events after cuts\n", + "Processing data16_periodK...\n", + " data16_periodK: 3722 events after cuts\n", + "Processing data16_periodL...\n", + " data16_periodL: 10340 events after cuts\n", + "\n", + "Total events across all samples: 55403\n" + ] + } + ], + "source": [ + "# Define empty list to hold all data for all samples\n", + "all_samples_data = []\n", + "\n", + "# Process each sample in the samples_list\n", + "for sample_name in samples_list:\n", + " print(f\"Processing {sample_name}...\")\n", + " \n", + " # Construct the file path for this sample\n", + " sample_path = path + sample_name + \".root\"\n", + " \n", + " # Define empty list to hold all data for this sample\n", + " sample_data = []\n", + " \n", + " try:\n", + " # Perform the cuts for each data entry in the tree\n", + " with uproot.open(sample_path + \":analysis\") as tree:\n", + " for data in tree.iterate(variables, library=\"ak\"):\n", + " \n", + " # data[~boolean] is used to remove entries from the data set\n", + " photon_isTightID = data['photon_isTightID']\n", + " data = data[~cut_photon_reconstruction(photon_isTightID)]\n", + "\n", + " photon_pt = data['photon_pt']\n", + " data = data[~cut_photon_pt(photon_pt)]\n", + "\n", + " data = data[~cut_isolation_pt(data['photon_ptcone20'],data['photon_pt'])]\n", + "\n", + " photon_eta = data['photon_eta']\n", + " data = data[~cut_photon_eta_transition(photon_eta)]\n", + " \n", + " data['mass'] = calc_mass(data['photon_pt'], data['photon_eta'], data['photon_phi'], data['photon_e'])\n", + " \n", + " data = data[~cut_mass(data['mass'])]\n", + " \n", + " data = data[~cut_iso_mass(data['photon_pt'], data['mass'])]\n", + " \n", + " # Append data to the sample data list\n", + " sample_data.append(data)\n", + " \n", + " # turn sample_data back into an awkward array\n", + " if sample_data:\n", + " processed_sample = ak.concatenate(sample_data)\n", + " # Add sample identifier\n", + " # processed_sample['sample'] = sample_name\n", + " all_samples_data.append(processed_sample)\n", + " print(f\" {sample_name}: {len(processed_sample)} events after cuts\")\n", + " else:\n", + " print(f\" {sample_name}: No events passed cuts\")\n", + " \n", + " except Exception as e:\n", + " print(f\" Error processing {sample_name}: {e}\")\n", + " continue\n", + "\n", + "# Combine all samples into one dataset\n", + "if all_samples_data:\n", + " all_data = ak.concatenate(all_samples_data)\n", + " print(f\"\\nTotal events across all samples: {len(all_data)}\")\n", + "else:\n", + " print(\"\\nNo data was successfully processed from any sample.\")\n", + " all_data = None" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " photon_pt photon_eta photon_phi photon_e \\\n", + "entry subentry \n", + "0 0 83.005539 1.693584 -0.613051 233.361725 \n", + " 1 73.862511 1.159767 0.796867 129.360947 \n", + "1 0 129.382782 0.716575 2.482590 164.046448 \n", + " 1 104.054314 0.481084 -0.747131 116.329597 \n", + " 2 2.080437 -0.029949 -2.290774 2.081371 \n", + "\n", + " photon_isTightID photon_ptcone20 mass \n", + "entry subentry \n", + "0 0 True 0.0 109.939896 \n", + " 1 True 0.0 109.939896 \n", + "1 0 True 0.0 233.445709 \n", + " 1 True 0.0 233.445709 \n", + " 2 False 0.0 233.445709 \n", + "Saved as processed_photon_data_all_periods.csv\n" + ] + }, + { + "ename": "ValueError", + "evalue": "cannot convert to RegularArray because subarray lengths are not regular (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-46/awkward-cpp/src/cpu-kernels/awkward_ListOffsetArray_toRegularArray.cpp#L22)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 16\u001b[0m\n\u001b[1;32m 12\u001b[0m feature_columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_pt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_eta\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_phi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_e\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_isTightID\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_ptcone20\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Convert to numpy array format that baler expects\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m data_array \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcolumn_stack([\n\u001b[1;32m 17\u001b[0m ak\u001b[38;5;241m.\u001b[39mto_numpy(all_data[col])\u001b[38;5;241m.\u001b[39mflatten() \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m feature_columns\n\u001b[1;32m 18\u001b[0m ])\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Create column names\u001b[39;00m\n\u001b[1;32m 21\u001b[0m column_names \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(feature_columns)\n", + "Cell \u001b[0;32mIn[18], line 17\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 12\u001b[0m feature_columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_pt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_eta\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_phi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_e\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_isTightID\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton_ptcone20\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Convert to numpy array format that baler expects\u001b[39;00m\n\u001b[1;32m 16\u001b[0m data_array \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcolumn_stack([\n\u001b[0;32m---> 17\u001b[0m \u001b[43mak\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mflatten() \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m feature_columns\n\u001b[1;32m 18\u001b[0m ])\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Create column names\u001b[39;00m\n\u001b[1;32m 21\u001b[0m column_names \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(feature_columns)\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/_dispatch.py:75\u001b[0m, in \u001b[0;36mnamed_high_level_function..dispatch\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[1;32m 72\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhigh-level functions should only implement a single yield statement\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 73\u001b[0m )\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gen_or_result\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/_errors.py:80\u001b[0m, in \u001b[0;36mErrorContext.__exit__\u001b[0;34m(self, exception_type, exception_value, traceback)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slate\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mclear()\n\u001b[1;32m 79\u001b[0m \u001b[38;5;66;03m# Handle caught exception\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecorate_exception(exception_type, exception_value)\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 82\u001b[0m \u001b[38;5;66;03m# Step out of the way so that another ErrorContext can become primary.\u001b[39;00m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprimary() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/_dispatch.py:67\u001b[0m, in \u001b[0;36mnamed_high_level_function..dispatch\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# Failed to find a custom overload, so resume the original function\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgen_or_result\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m err\u001b[38;5;241m.\u001b[39mvalue\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/operations/ak_to_numpy.py:48\u001b[0m, in \u001b[0;36mto_numpy\u001b[0;34m(array, allow_missing)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m (array,)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;66;03m# Implementation\u001b[39;00m\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_missing\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/operations/ak_to_numpy.py:60\u001b[0m, in \u001b[0;36m_impl\u001b[0;34m(array, allow_missing)\u001b[0m\n\u001b[1;32m 57\u001b[0m backend \u001b[38;5;241m=\u001b[39m NumpyBackend\u001b[38;5;241m.\u001b[39minstance()\n\u001b[1;32m 58\u001b[0m numpy_layout \u001b[38;5;241m=\u001b[39m layout\u001b[38;5;241m.\u001b[39mto_backend(backend)\n\u001b[0;32m---> 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnumpy_layout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_backend_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mallow_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_missing\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/contents/content.py:1121\u001b[0m, in \u001b[0;36mContent.to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m 1119\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1120\u001b[0m backend \u001b[38;5;241m=\u001b[39m regularize_backend(backend)\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_backend_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mallow_missing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/contents/indexedarray.py:1071\u001b[0m, in \u001b[0;36mIndexedArray._to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m 1070\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_to_backend_array\u001b[39m(\u001b[38;5;28mself\u001b[39m, allow_missing, backend):\n\u001b[0;32m-> 1071\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproject\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_backend_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mallow_missing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/contents/listarray.py:1533\u001b[0m, in \u001b[0;36mListArray._to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_ListOffsetArray64(\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39m_to_backend_array(\n\u001b[1;32m 1530\u001b[0m allow_missing, backend\n\u001b[1;32m 1531\u001b[0m )\n\u001b[1;32m 1532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_RegularArray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_to_backend_array(allow_missing, backend)\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/contents/listarray.py:320\u001b[0m, in \u001b[0;36mListArray.to_RegularArray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mto_RegularArray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 319\u001b[0m offsets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compact_offsets64(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 320\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_broadcast_tooffsets64\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffsets\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_RegularArray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/contents/listoffsetarray.py:291\u001b[0m, in \u001b[0;36mListOffsetArray.to_RegularArray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 286\u001b[0m _size \u001b[38;5;241m=\u001b[39m Index64\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mnplike)\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 288\u001b[0m _size\u001b[38;5;241m.\u001b[39mnplike \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mnplike\n\u001b[1;32m 289\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offsets\u001b[38;5;241m.\u001b[39mnplike \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mnplike\n\u001b[1;32m 290\u001b[0m )\n\u001b[0;32m--> 291\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaybe_kernel_error\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 292\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m 293\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mawkward_ListOffsetArray_toRegularArray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[43m_size\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_offsets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 296\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[43m_size\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_offsets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_offsets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlength\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 301\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 302\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mnplike\u001b[38;5;241m.\u001b[39mindex_as_shape_item(_size[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 303\u001b[0m length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offsets\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/awkward/_backends/backend.py:62\u001b[0m, in \u001b[0;36mBackend.maybe_kernel_error\u001b[0;34m(self, error)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat_kernel_error(error))\n", + "\u001b[0;31mValueError\u001b[0m: cannot convert to RegularArray because subarray lengths are not regular (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-46/awkward-cpp/src/cpu-kernels/awkward_ListOffsetArray_toRegularArray.cpp#L22)" + ] + } + ], + "source": [ + "# Convert processed data (with cuts applied) to dataframe\n", + "if all_data is not None:\n", + " processed_df = ak.to_dataframe(all_data)\n", + " print(processed_df.head())\n", + " \n", + " # Save processed data to CSV\n", + " processed_df.to_csv(\"processed_photon_data_all_periods.csv\", index=False)\n", + " print(\"Saved as processed_photon_data_all_periods.csv\")\n", + " \n", + " # Convert to the format baler expects\n", + " # Extract the main features (excluding sample identifier for now)\n", + " feature_columns = ['photon_pt', 'photon_eta', 'photon_phi', 'photon_e', \n", + " 'photon_isTightID', 'photon_ptcone20', 'mass']\n", + " \n", + " # Convert to numpy array format that baler expects\n", + " data_array = np.column_stack([\n", + " ak.to_numpy(all_data[col]).flatten() for col in feature_columns\n", + " ])\n", + " \n", + " # Create column names\n", + " column_names = np.array(feature_columns)\n", + " \n", + " # Save in baler-compatible format\n", + " np.savez('processed_photon_data_all_periods.npz', \n", + " data=data_array,\n", + " names=column_names)\n", + " print(\"Saved as processed_photon_data_all_periods.npz in baler-compatible format\")\n", + " \n", + " # Print summary of processed data\n", + " print(f\"\\nProcessed data summary:\")\n", + " print(f\"Number of events after cuts: {len(all_data)}\")\n", + " print(f\"Data shape for baler: {data_array.shape}\")\n", + " print(f\"Features included: {list(column_names)}\")\n", + " \n", + " # Show breakdown by sample\n", + " # print(f\"\\nBreakdown by sample:\")\n", + " # sample_counts = processed_df.groupby('sample').size()\n", + " # for sample, count in sample_counts.items():\n", + " # print(f\" {sample}: {count} events\")\n", + "else:\n", + " print(\"No data to save.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_backup.ipynb b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_backup.ipynb new file mode 100644 index 00000000..ccca618b --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_backup.ipynb @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uproot in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (5.6.2)\n", + "Collecting uproot\n", + " Using cached uproot-5.6.3-py3-none-any.whl.metadata (33 kB)\n", + "Requirement already satisfied: awkward>=2.4.6 in 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"Keys in ROOT file: ['analysis;1']\n" + ] + } + ], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "import vector\n", + "import awkward as ak\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "data_15G_path = path + samples_list[3] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(data_15G_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aggregated_path = path\n", + "\n", + "for sample in samples_list:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of entries in the tree are: 164583\n", + "The information stored in the tree is: ['num_events', 'sum_of_weights', 'sum_of_weights_squared', 'corrected_xsec', 'dsid', 'category', 'sig_ph', 'n_sig_ph', 'ScaleFactor_PILEUP', 'mcWeight', 'xsec', 'filteff', 'kfac', 'channelNumber', 'eventNumber', 'runNumber', 'trigP', 'trigDT', 'trigT', 'trigE', 'trigM', 'trigMET', 'ScaleFactor_BTAG', 'jet_n', 'jet_pt', 'jet_eta', 'jet_phi', 'jet_e', 'jet_btag_quantile', 'jet_jvt', 'largeRJet_n', 'largeRJet_pt', 'largeRJet_eta', 'largeRJet_phi', 'largeRJet_e', 'largeRJet_m', 'largeRJet_D2', 'ScaleFactor_ELE', 'ScaleFactor_MUON', 'scaleFactor_LepTRIGGER', 'lep_n', 'lep_type', 'lep_pt', 'lep_eta', 'lep_phi', 'lep_e', 'lep_charge', 'lep_ptvarcone30', 'lep_topoetcone20', 'lep_z0', 'lep_d0', 'lep_d0sig', 'lep_isTightID', 'lep_isMediumID', 'lep_isLooseID', 'lep_isTightIso', 'lep_isLooseIso', 'ScaleFactor_PHOTON', 'photon_n', 'photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_ptcone20', 'photon_topoetcone40', 'photon_isLooseID', 'photon_isTightID', 'photon_isLooseIso', 'photon_isTightIso', 'ScaleFactor_TAU', 'ScaleFactor_TauTRIGGER', 'ScaleFactor_DiTauTRIGGER', 'tau_n', 'tau_pt', 'tau_eta', 'tau_phi', 'tau_e', 'tau_charge', 'tau_nTracks', 'tau_isTight', 'tau_RNNJetScore', 'tau_RNNEleScore', 'met', 'met_phi', 'met_mpx', 'met_mpy']\n" + ] + } + ], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(data_15G_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{photon_e: [53, 41.5, 2.67, 5.41, 2.53]}, {...}, ..., {photon_e: [752, ...]}]\n" + ] + } + ], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction quality of the two photons: [True] [False]\n", + "Apply cut based on reconstruction quality requirements?[True]\n", + "The transverse momentum of the two photons: [35.9] [39.5]\n", + "Apply cut based on the transverse momentum requirements?[True]\n", + "The calorimeter isolation of the two photons: [0.265] [0.106]\n", + "Apply cut based on the calorimeter isolation requirements?[True]\n", + "The eta of the two photons: [-0.941] [-0.321]\n", + "Apply cut based on the eta requirements?[False]\n", + "The invariant mass of the 2-photon state is: [78.8] GeV\n", + "The invariant mass based isolation of the two photons: [0.455] [0.501]\n", + "Apply cut based on the invariant mass based isolation requirements?[False]\n" + ] + } + ], + "source": [ + "# This selects the first entry of the tree\n", + "entry = tree.arrays(library=\"ak\")[:1]\n", + "\n", + "# Cut on the photon reconstruction quality\n", + "photon_reconstruction = entry['photon_isTightID']\n", + "# isTightID==True means a photon identified as being well reconstructed, only the events which have True for both photons are kept\n", + "photon_reconstruction_cut_bool = (photon_reconstruction[:, 0] == False) | (photon_reconstruction[:, 1]==False) # [:, i] selects the i-th lepton in each event\n", + "print(\"The reconstruction quality of the two photons:\", photon_reconstruction[:,0], photon_reconstruction[:,1])\n", + "print(f\"Apply cut based on reconstruction quality requirements?{photon_reconstruction_cut_bool}\")\n", + " \n", + "# Cut on the transverse momentum\n", + "photon_pt = entry['photon_pt']\n", + "# Only the events where the leading photon has transverse momentum (pt) > 50 GeV and the sub-leading photon has pt > 30 GeV are kept\n", + "# Since the two photons for each entry are ordered, the first photon is the leading one and the second one is the sub-leading one\n", + "photon_pt_cut_bool = (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "print(\"The transverse momentum of the two photons:\", photon_pt[:,0], photon_pt[:,1])\n", + "print(f\"Apply cut based on the transverse momentum requirements?{photon_pt_cut_bool}\")\n", + "\n", + "# Cut on the calorimeter isolation\n", + "photon_ptcone20 = entry['photon_ptcone20']\n", + "# Only the events where the invidivual photon calorimeter isolation is less than 5.5% are kept\n", + "photon_caloiso_cut_bool = (((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055))\n", + "print(\"The calorimeter isolation of the two photons:\", (photon_ptcone20[:,0]/photon_pt[:,0]), (photon_ptcone20[:,1]/photon_pt[:,1]))\n", + "print(f\"Apply cut based on the calorimeter isolation requirements?{photon_caloiso_cut_bool}\")\n", + "\n", + "# Cut on the pseudorapidity in the barrel/end-cap transition region\n", + "photon_eta = entry['photon_eta']\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + "condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + "condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + "photon_eta_cut_bool = (condition_0 | condition_1)\n", + "print(\"The eta of the two photons:\", photon_eta[:,0], photon_eta[:,1])\n", + "print(f\"Apply cut based on the eta requirements?{photon_eta_cut_bool}\")\n", + "\n", + "# This calculates the invariant mass of the 2-photon state\n", + "p4 = vector.zip({\"pt\": entry['photon_pt'], \"eta\": entry['photon_eta'], \"phi\": entry['photon_phi'], \"e\": entry['photon_e']})\n", + "invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + "print(f\"The invariant mass of the 2-photon state is: {invariant_mass} GeV\")\n", + "\n", + "# Cut on the invariant mass based isolation\n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "photon_massiso_cut_bool = ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)\n", + "print(\"The invariant mass based isolation of the two photons:\", (photon_pt[:,0]/invariant_mass), (photon_pt[:,1]/invariant_mass))\n", + "print(f\"Apply cut based on the invariant mass based isolation requirements?{photon_massiso_cut_bool}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Cut on the photon reconstruction quality\n", + "def cut_photon_reconstruction(photon_isTightID):\n", + " # Only the events which have True for both photons are kept\n", + " return (photon_isTightID[:,0]==False) | (photon_isTightID[:,1]==False) \n", + "\n", + "# Cut on the transverse momentum\n", + "def cut_photon_pt(photon_pt):\n", + "# Only the events where photon_pt[0] > 50 GeV and photon_pt[1] > 30 GeV are kept\n", + " return (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "\n", + "# Cut on the energy isolation\n", + "def cut_isolation_pt(photon_ptcone20, photon_pt):\n", + "# Only the events where the calorimeter isolation is less than 5.5% are kept\n", + " return ((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055)\n", + "\n", + "# Cut on the pseudorapidity in barrel/end-cap transition region\n", + "def cut_photon_eta_transition(photon_eta):\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + " condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + " condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + " return condition_0 | condition_1\n", + "\n", + "# This function calculates the invariant mass of the 2-photon state\n", + "def calc_mass(photon_pt, photon_eta, photon_phi, photon_e):\n", + " p4 = vector.zip({\"pt\": photon_pt, \"eta\": photon_eta, \"phi\": photon_phi, \"e\": photon_e})\n", + " invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + " return invariant_mass\n", + "\n", + "# Cut on null diphoton invariant mass\n", + "def cut_mass(invariant_mass):\n", + " return (invariant_mass == 0)\n", + "\n", + "# Cut on diphoton invariant mass based isolation \n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "def cut_iso_mass(photon_pt, invariant_mass):\n", + " return ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Define empty list to hold all data for this sample\n", + "sample_data = []\n", + "\n", + "# Perform the cuts for each data entry in the tree\n", + "for data in tree.iterate(variables, library=\"ak\"):\n", + " \n", + " # data[~boolean] is used to remove entries from the data set\n", + " photon_isTightID = data['photon_isTightID']\n", + " data = data[~cut_photon_reconstruction(photon_isTightID)]\n", + "\n", + " photon_pt = data['photon_pt']\n", + " data = data[~cut_photon_pt(photon_pt)]\n", + "\n", + " data = data[~cut_isolation_pt(data['photon_ptcone20'],data['photon_pt'])]\n", + "\n", + " photon_eta = data['photon_eta']\n", + " data = data[~cut_photon_eta_transition(photon_eta)]\n", + " \n", + " data['mass'] = calc_mass(data['photon_pt'], data['photon_eta'], data['photon_phi'], data['photon_e'])\n", + " \n", + " data = data[~cut_mass(data['mass'])]\n", + " \n", + " data = data[~cut_iso_mass(data['photon_pt'], data['mass'])]\n", + " \n", + " # Append data to the whole sample data list\n", + " sample_data.append(data)\n", + "\n", + "# turn sample_data back into an awkward array\n", + "data15_periodG = ak.concatenate(sample_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " photon_pt photon_eta photon_phi photon_e \\\n", + "entry subentry \n", + "0 0 122.060272 -1.669817 1.423065 335.633789 \n", + " 1 34.432838 -1.154988 2.904564 60.068893 \n", + " 2 1.800235 -0.733997 1.289606 2.307341 \n", + "1 0 59.504601 0.336426 -1.137864 62.903931 \n", + " 1 53.576233 1.083889 2.160784 88.251633 \n", + "\n", + " photon_isTightID photon_ptcone20 mass \n", + "entry subentry \n", + "0 0 True 0.0 93.781326 \n", + " 1 True 0.0 93.781326 \n", + " 2 False 0.0 93.781326 \n", + "1 0 True 0.0 120.579010 \n", + " 1 True 0.0 120.579010 \n", + "Saved as processed_photon_data.csv\n", + "Saved as processed_photon_data.npz with all variables\n", + "\n", + "Processed data summary:\n", + "Number of events after cuts: 2088\n", + "Variables included: ['photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_isTightID', 'photon_ptcone20', 'mass']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages/numpy/lib/npyio.py:716: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " val = np.asanyarray(val)\n" + ] + } + ], + "source": [ + "# Convert processed data (with cuts applied) to dataframe\n", + "processed_df = ak.to_dataframe(data15_periodG)\n", + "print(processed_df.head())\n", + "\n", + "# Save processed data to CSV\n", + "processed_df.to_csv(\"processed_photon_data.csv\", index=False)\n", + "print(\"Saved as processed_photon_data.csv\")\n", + "\n", + "# Save processed data to NPZ with all variables\n", + "np.savez('processed_photon_data.npz', \n", + " photon_pt=ak.to_list(data15_periodG['photon_pt']),\n", + " photon_eta=ak.to_list(data15_periodG['photon_eta']),\n", + " photon_phi=ak.to_list(data15_periodG['photon_phi']),\n", + " photon_e=ak.to_list(data15_periodG['photon_e']),\n", + " photon_isTightID=ak.to_list(data15_periodG['photon_isTightID']),\n", + " photon_ptcone20=ak.to_list(data15_periodG['photon_ptcone20']),\n", + " mass=ak.to_list(data15_periodG['mass']))\n", + "print(\"Saved as processed_photon_data.npz with all variables\")\n", + "\n", + "# Print summary of processed data\n", + "print(f\"\\nProcessed data summary:\")\n", + "print(f\"Number of events after cuts: {len(data15_periodG)}\")\n", + "print(f\"Variables included: {list(data15_periodG.fields)}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_two_photons.py b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_two_photons.py new file mode 100644 index 00000000..3f12b1bb --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_data_preprocessing_two_photons.py @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade uproot\n", + "!{sys.executable} -m pip install --upgrade pandas\n", + "!{sys.executable} -m pip install requests aiohttp\n", + "!{sys.executable} -m pip install awkward-pandas\n", + "!{sys.executable} -m pip install vector " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "import vector\n", + "import awkward as ak\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "# Test with first sample to verify file structure\n", + "test_path = path + samples_list[0] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(test_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(test_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# This selects the first entry of the tree\n", + "entry = tree.arrays(library=\"ak\")[:1]\n", + "\n", + "# Cut on the photon reconstruction quality\n", + "photon_reconstruction = entry['photon_isTightID']\n", + "# isTightID==True means a photon identified as being well reconstructed, only the events which have True for both photons are kept\n", + "photon_reconstruction_cut_bool = (photon_reconstruction[:, 0] == False) | (photon_reconstruction[:, 1]==False) # [:, i] selects the i-th lepton in each event\n", + "print(\"The reconstruction quality of the two photons:\", photon_reconstruction[:,0], photon_reconstruction[:,1])\n", + "print(f\"Apply cut based on reconstruction quality requirements?{photon_reconstruction_cut_bool}\")\n", + " \n", + "# Cut on the transverse momentum\n", + "photon_pt = entry['photon_pt']\n", + "# Only the events where the leading photon has transverse momentum (pt) > 50 GeV and the sub-leading photon has pt > 30 GeV are kept\n", + "# Since the two photons for each entry are ordered, the first photon is the leading one and the second one is the sub-leading one\n", + "photon_pt_cut_bool = (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "print(\"The transverse momentum of the two photons:\", photon_pt[:,0], photon_pt[:,1])\n", + "print(f\"Apply cut based on the transverse momentum requirements?{photon_pt_cut_bool}\")\n", + "\n", + "# Cut on the calorimeter isolation\n", + "photon_ptcone20 = entry['photon_ptcone20']\n", + "# Only the events where the invidivual photon calorimeter isolation is less than 5.5% are kept\n", + "photon_caloiso_cut_bool = (((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055))\n", + "print(\"The calorimeter isolation of the two photons:\", (photon_ptcone20[:,0]/photon_pt[:,0]), (photon_ptcone20[:,1]/photon_pt[:,1]))\n", + "print(f\"Apply cut based on the calorimeter isolation requirements?{photon_caloiso_cut_bool}\")\n", + "\n", + "# Cut on the pseudorapidity in the barrel/end-cap transition region\n", + "photon_eta = entry['photon_eta']\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + "condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + "condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + "photon_eta_cut_bool = (condition_0 | condition_1)\n", + "print(\"The eta of the two photons:\", photon_eta[:,0], photon_eta[:,1])\n", + "print(f\"Apply cut based on the eta requirements?{photon_eta_cut_bool}\")\n", + "\n", + "# This calculates the invariant mass of the 2-photon state\n", + "p4 = vector.zip({\"pt\": entry['photon_pt'], \"eta\": entry['photon_eta'], \"phi\": entry['photon_phi'], \"e\": entry['photon_e']})\n", + "invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + "print(f\"The invariant mass of the 2-photon state is: {invariant_mass} GeV\")\n", + "\n", + "# Cut on the invariant mass based isolation\n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "photon_massiso_cut_bool = ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)\n", + "print(\"The invariant mass based isolation of the two photons:\", (photon_pt[:,0]/invariant_mass), (photon_pt[:,1]/invariant_mass))\n", + "print(f\"Apply cut based on the invariant mass based isolation requirements?{photon_massiso_cut_bool}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Cut on the photon reconstruction quality\n", + "def cut_photon_reconstruction(photon_isTightID):\n", + " # Only the events which have True for both photons are kept\n", + " return (photon_isTightID[:,0]==False) | (photon_isTightID[:,1]==False) \n", + "\n", + "# Cut on the transverse momentum\n", + "def cut_photon_pt(photon_pt):\n", + "# Only the events where photon_pt[0] > 50 GeV and photon_pt[1] > 30 GeV are kept\n", + " return (photon_pt[:,0] < 50) | (photon_pt[:,1] < 30)\n", + "\n", + "# Cut on the energy isolation\n", + "def cut_isolation_pt(photon_ptcone20, photon_pt):\n", + "# Only the events where the calorimeter isolation is less than 5.5% are kept\n", + " return ((photon_ptcone20[:,0]/photon_pt[:,0]) > 0.055) | ((photon_ptcone20[:,1]/photon_pt[:,1]) > 0.055)\n", + "\n", + "# Cut on the pseudorapidity in barrel/end-cap transition region\n", + "def cut_photon_eta_transition(photon_eta):\n", + "# Only the events where modulus of photon_eta is outside the range 1.37 to 1.52 are kept\n", + " condition_0 = (np.abs(photon_eta[:, 0]) < 1.52) & (np.abs(photon_eta[:, 0]) > 1.37)\n", + " condition_1 = (np.abs(photon_eta[:, 1]) < 1.52) & (np.abs(photon_eta[:, 1]) > 1.37)\n", + " return condition_0 | condition_1\n", + "\n", + "# This function calculates the invariant mass of the 2-photon state\n", + "def calc_mass(photon_pt, photon_eta, photon_phi, photon_e):\n", + " p4 = vector.zip({\"pt\": photon_pt, \"eta\": photon_eta, \"phi\": photon_phi, \"e\": photon_e})\n", + " invariant_mass = (p4[:, 0] + p4[:, 1]).M # .M calculates the invariant mass\n", + " return invariant_mass\n", + "\n", + "# Cut on null diphoton invariant mass\n", + "def cut_mass(invariant_mass):\n", + " return (invariant_mass == 0)\n", + "\n", + "# Cut on diphoton invariant mass based isolation \n", + "# Only the events where the invididual photon invariant mass based isolation is larger than 35% are kept\n", + "def cut_iso_mass(photon_pt, invariant_mass):\n", + " return ((photon_pt[:,0]/invariant_mass) < 0.35) | ((photon_pt[:,1]/invariant_mass) < 0.35)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Define empty list to hold all data for all samples\n", + "all_samples_data = []\n", + "\n", + "# Process each sample in the samples_list\n", + "for sample_name in samples_list:\n", + " print(f\"Processing {sample_name}...\")\n", + " \n", + " # Construct the file path for this sample\n", + " sample_path = path + sample_name + \".root\"\n", + " \n", + " # Define empty list to hold all data for this sample\n", + " sample_data = []\n", + " \n", + " try:\n", + " # Perform the cuts for each data entry in the tree\n", + " with uproot.open(sample_path + \":analysis\") as tree:\n", + " for data in tree.iterate(variables, library=\"ak\"):\n", + " \n", + " # data[~boolean] is used to remove entries from the data set\n", + " photon_isTightID = data['photon_isTightID']\n", + " data = data[~cut_photon_reconstruction(photon_isTightID)]\n", + "\n", + " photon_pt = data['photon_pt']\n", + " data = data[~cut_photon_pt(photon_pt)]\n", + "\n", + " data = data[~cut_isolation_pt(data['photon_ptcone20'],data['photon_pt'])]\n", + "\n", + " photon_eta = data['photon_eta']\n", + " data = data[~cut_photon_eta_transition(photon_eta)]\n", + " \n", + " data['mass'] = calc_mass(data['photon_pt'], data['photon_eta'], data['photon_phi'], data['photon_e'])\n", + " \n", + " data = data[~cut_mass(data['mass'])]\n", + " \n", + " data = data[~cut_iso_mass(data['photon_pt'], data['mass'])]\n", + " \n", + " # Append data to the sample data list\n", + " sample_data.append(data)\n", + " \n", + " # turn sample_data back into an awkward array\n", + " if sample_data:\n", + " processed_sample = ak.concatenate(sample_data)\n", + " # Add sample identifier\n", + " processed_sample['sample'] = sample_name\n", + " all_samples_data.append(processed_sample)\n", + " print(f\" {sample_name}: {len(processed_sample)} events after cuts\")\n", + " else:\n", + " print(f\" {sample_name}: No events passed cuts\")\n", + " \n", + " except Exception as e:\n", + " print(f\" Error processing {sample_name}: {e}\")\n", + " continue\n", + "\n", + "# Combine all samples into one dataset\n", + "if all_samples_data:\n", + " all_data = ak.concatenate(all_samples_data)\n", + " print(f\"\\nTotal events across all samples: {len(all_data)}\")\n", + "else:\n", + " print(\"\\nNo data was successfully processed from any sample.\")\n", + " all_data = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert processed data (with cuts applied) to dataframe\n", + "if all_data is not None:\n", + " processed_df = ak.to_dataframe(all_data)\n", + " print(processed_df.head())\n", + " \n", + " # Save processed data to CSV\n", + " processed_df.to_csv(\"processed_photon_data_all_periods.csv\", index=False)\n", + " print(\"Saved as processed_photon_data_all_periods.csv\")\n", + " \n", + " # Filter to only events with exactly two photons\n", + " event_photon_counts = processed_df.groupby('entry').size()\n", + " events_with_two_photons = event_photon_counts[event_photon_counts == 2].index\n", + " \n", + " print(f\"Total events: {len(event_photon_counts)}\")\n", + " print(f\"Events with exactly 2 photons: {len(events_with_two_photons)}\")\n", + " \n", + " # Filter the dataframe\n", + " filtered_df = processed_df.loc[processed_df.index.get_level_values('entry').isin(events_with_two_photons)]\n", + " \n", + " # Convert to the format baler expects\n", + " feature_columns = ['photon_pt', 'photon_eta', 'photon_phi', 'photon_e', \n", + " 'photon_isTightID', 'photon_ptcone20']\n", + " \n", + " event_data = []\n", + " \n", + " for event_idx in events_with_two_photons:\n", + " event_df = filtered_df.loc[event_idx]\n", + " \n", + " # Take both photons' data for each feature\n", + " event_row = []\n", + " \n", + " # Add mass first (it's per event, not per photon)\n", + " if 'mass' in event_df.columns:\n", + " event_row.append(float(event_df['mass'].iloc[0]))\n", + " else:\n", + " event_row.append(0.0)\n", + " \n", + " # Add photon-specific features for both photons\n", + " for col in feature_columns:\n", + " if col in event_df.columns:\n", + " photon_values = event_df[col].values\n", + " event_row.extend([float(photon_values[0]), float(photon_values[1])])\n", + " else:\n", + " event_row.extend([0.0, 0.0])\n", + " \n", + " event_data.append(event_row)\n", + " \n", + " # Convert to numpy array\n", + " data_array = np.array(event_data)\n", + " \n", + " # Create column names for both photons\n", + " column_names = ['mass'] # Event-level feature\n", + " for col in feature_columns:\n", + " column_names.extend([f'{col}_ph1', f'{col}_ph2']) # Photon-specific features\n", + " \n", + " column_names = np.array(column_names)\n", + " \n", + " # Save in baler-compatible format\n", + " np.savez('processed_photon_data_all_periods.npz', \n", + " data=data_array,\n", + " names=column_names)\n", + " print(\"Saved as processed_photon_data_all_periods.npz in baler-compatible format\")\n", + " \n", + " # Print summary of processed data\n", + " print(f\"\\nProcessed data summary:\")\n", + " print(f\"Number of events with exactly 2 photons: {len(events_with_two_photons)}\")\n", + " print(f\"Data shape for baler: {data_array.shape}\")\n", + " print(f\"Features included: {list(column_names)}\")\n", + " \n", + " # Show breakdown by sample\n", + " print(f\"\\nBreakdown by sample:\")\n", + " sample_counts = filtered_df.groupby('sample').size()\n", + " for sample, count in sample_counts.items():\n", + " print(f\" {sample}: {count} events\")\n", + "else:\n", + " print(\"No data to save.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_root_to_csv_npz.ipynb b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_root_to_csv_npz.ipynb new file mode 100644 index 00000000..457c7be6 --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/Hyy_root_to_csv_npz.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uproot in 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importlib-metadata>=4.13.0->awkward>=2.0.0->awkward-pandas) (3.17.0)\n", + "Requirement already satisfied: six>=1.5 in /Users/cartercapetz/Library/Caches/pypoetry/virtualenvs/baler-NkYVSVSD-py3.9/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas>=1.2->awkward-pandas) (1.16.0)\n", + "Using cached awkward_pandas-2023.8.0-py3-none-any.whl (11 kB)\n", + "Installing collected packages: awkward-pandas\n", + "Successfully installed awkward-pandas-2023.8.0\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade uproot\n", + "!{sys.executable} -m pip install --upgrade pandas\n", + "!{sys.executable} -m pip install requests aiohttp\n", + "!{sys.executable} -m pip install awkward-pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys in ROOT file: ['analysis;1']\n" + ] + } + ], + "source": [ + "import uproot\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/13TeV/GamGam/Data/\"\n", + "\n", + "samples_list = ['data15_periodD','data15_periodE','data15_periodF','data15_periodG','data15_periodH','data15_periodJ','data16_periodA','data16_periodB','data16_periodC','data16_periodD','data16_periodE','data16_periodF','data16_periodG','data16_periodK','data16_periodL']\n", + "\n", + "data_15G_path = path + samples_list[3] + \".root\"\n", + "\n", + "# Inspect the file to find available trees\n", + "with uproot.open(data_15G_path) as file:\n", + " print(\"Keys in ROOT file:\", file.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of entries in the tree are: 164583\n", + "The information stored in the tree is: ['num_events', 'sum_of_weights', 'sum_of_weights_squared', 'corrected_xsec', 'dsid', 'category', 'sig_ph', 'n_sig_ph', 'ScaleFactor_PILEUP', 'mcWeight', 'xsec', 'filteff', 'kfac', 'channelNumber', 'eventNumber', 'runNumber', 'trigP', 'trigDT', 'trigT', 'trigE', 'trigM', 'trigMET', 'ScaleFactor_BTAG', 'jet_n', 'jet_pt', 'jet_eta', 'jet_phi', 'jet_e', 'jet_btag_quantile', 'jet_jvt', 'largeRJet_n', 'largeRJet_pt', 'largeRJet_eta', 'largeRJet_phi', 'largeRJet_e', 'largeRJet_m', 'largeRJet_D2', 'ScaleFactor_ELE', 'ScaleFactor_MUON', 'scaleFactor_LepTRIGGER', 'lep_n', 'lep_type', 'lep_pt', 'lep_eta', 'lep_phi', 'lep_e', 'lep_charge', 'lep_ptvarcone30', 'lep_topoetcone20', 'lep_z0', 'lep_d0', 'lep_d0sig', 'lep_isTightID', 'lep_isMediumID', 'lep_isLooseID', 'lep_isTightIso', 'lep_isLooseIso', 'ScaleFactor_PHOTON', 'photon_n', 'photon_pt', 'photon_eta', 'photon_phi', 'photon_e', 'photon_ptcone20', 'photon_topoetcone40', 'photon_isLooseID', 'photon_isTightID', 'photon_isLooseIso', 'photon_isTightIso', 'ScaleFactor_TAU', 'ScaleFactor_TauTRIGGER', 'ScaleFactor_DiTauTRIGGER', 'tau_n', 'tau_pt', 'tau_eta', 'tau_phi', 'tau_e', 'tau_charge', 'tau_nTracks', 'tau_isTight', 'tau_RNNJetScore', 'tau_RNNEleScore', 'met', 'met_phi', 'met_mpx', 'met_mpy']\n" + ] + } + ], + "source": [ + "# Accessing the file from the online database (\":analysis\" opens the tree in a desired manner)\n", + "with uproot.open(data_15G_path + \":analysis\") as t:\n", + " tree = t\n", + "\n", + "# The number of entries in the tree can be viewed\n", + "print(\"The number of entries in the tree are:\", tree.num_entries)\n", + "\n", + "# All the information stored in the tree can be viewed using the .keys() method.\n", + "print(\"The information stored in the tree is:\", tree.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{photon_e: [53, 41.5, 2.67, 5.41, 2.53]}, {...}, ..., {photon_e: [752, ...]}]\n" + ] + } + ], + "source": [ + "print(tree[\"photon_e\"].arrays())\n", + "\n", + "variables = [\"photon_pt\",\"photon_eta\",\"photon_phi\",\"photon_e\",\n", + " \"photon_isTightID\",\"photon_ptcone20\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " photon_e\n", + "0 [52.95711898803711, 41.52253723144531, 2.66612...\n", + "1 [144.42855834960938, 25.760665893554688, 6.258...\n", + "2 [47.989742279052734, 31.802453994750977]\n", + "3 [32.105987548828125, 43.45836639404297]\n", + "4 [335.6337890625, 60.06889343261719, 2.30734062...\n", + "Saved as photon_e.csv\n", + "saved as photon_e.npz\n" + ] + } + ], + "source": [ + "tree_name = \"analysis;1\"\n", + "branches = [\"photon_e\"]\n", + "\n", + "#convert branch to dataframe\n", + "with uproot.open(data_15G_path) as file:\n", + " tree = file[tree_name]\n", + " df = tree.arrays(branches, library=\"pd\")\n", + " print(df.head())\n", + "\n", + "#csv\n", + "df.to_csv(\"photon_e.csv\", index=False)\n", + "print(\"Saved as photon_e.csv\")\n", + "\n", + "#npz\n", + "np.savez('photon_e.npz', photon_e = df.values)\n", + "print(\"saved as photon_e.npz\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "baler-NkYVSVSD-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/convert_photon_data.py b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/convert_photon_data.py new file mode 100644 index 00000000..d0b748ec --- /dev/null +++ b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/convert_photon_data.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +""" +Script to convert photon data from variable-length list format to fixed-width array format +for use with Baler compression. +""" + +import numpy as np +import os + +def convert_photon_data(input_path, output_path, max_photons=2): + """ + Convert photon data from variable-length lists to fixed-width array. + + Args: + input_path: Path to the input .npz file with 'photon_e' key + output_path: Path to save the converted .npz file with 'data' key + max_photons: Maximum number of photons to consider per event + """ + print(f"Loading data from {input_path}...") + + # Load the original data + data = np.load(input_path, allow_pickle=True) + photon_energies = data['photon_e'] + + print(f"Original data shape: {photon_energies.shape}") + print(f"Sample data: {photon_energies[:3]}") + + # Convert to fixed-width array + num_events = len(photon_energies) + converted_data = np.zeros((num_events, max_photons)) + + for i, event_photons in enumerate(photon_energies): + # The data is stored as a 2D array where each row has one element containing a list + # Extract the list from the first (and only) element + if len(event_photons) > 0: + energies = event_photons[0] # Get the list from the first element + else: + energies = [] + + # Fill the fixed-width array + for j, energy in enumerate(energies[:max_photons]): + converted_data[i, j] = float(energy) + + print(f"Converted data shape: {converted_data.shape}") + print(f"Sample converted data:\n{converted_data[:3]}") + + # Create column names + column_names = [f"photon_e_{i}" for i in range(max_photons)] + + # Save the converted data + np.savez(output_path, data=converted_data, names=column_names) + print(f"Saved converted data to {output_path}") + + return converted_data, column_names + +if __name__ == "__main__": + input_file = "workspaces/ATLAS_Workspace/data/processed_photon_data.npz" + output_file = "workspaces/ATLAS_Workspace/data/processed_photon_data_converted.npz" + + # Create output directory if it doesn't exist + os.makedirs(os.path.dirname(output_file), exist_ok=True) + + # Convert the data + converted_data, column_names = convert_photon_data(input_file, output_file, max_photons=10) + + print(f"\nConversion complete!") + print(f"Input file: {input_file}") + print(f"Output file: {output_file}") + print(f"Data shape: {converted_data.shape}") + print(f"Column names: {column_names}") \ No newline at end of file diff --git a/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_diphoton_data_all_periods.npz b/workspaces/ATLAS_Workspace/ATLAS_Project/config/old_code/processed_diphoton_data_all_periods.npz new file mode 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