diff --git a/.gitignore b/.gitignore index ff8e08d..9225404 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,17 @@ +# stuff I added +example_scripts/demo_output/ +example_scripts/sample_data/ +example_scripts/simple_example.ipynb +data/ +results/ +science_paper_repo/ +data +results +slurm_logs +inference/results +inference/viz +tmp* + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/README.md b/README.md index 87fd3ed..f682783 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,28 @@ +# about +This repo is inference for image-based orphan protein analysis in the paper [Global organelle profiling reveals subcellular localization and remodeling at proteome scale](https://www.biorxiv.org/content/10.1101/2023.12.18.572249v1). This analysis is by [James Burgess](https://jmhb0.github.io/) and [Chad Liu](https://www.linkedin.com/in/chad-liu-14a77749/). + +It copies the [cytoself repo](https://github.com/royerlab/cytoself) for training a Cytoself representation learning model. We modify the training a little bit (see next section), and then write some inference code for comparing representations of orphan proteins to representations from [Opencell](https://opencell.czbiohub.org/). In this Readme, the content after the section called `cytoself` is from the README of the original project. + + +# changes from the original cytoself (the training part) +- changed `requirements.txt` for torch and torchvision. Previously it was (`torch>=1.11`) but I was getting some error in the torch Upsample layer. I think it was some issue with torch 2.0, so I set it to `torch==1.13.1` and `torchvision==0.12`. +- scripts to train cytoself from scratch in `scripts` +- added the 10 `.npy` file and 10 `.csv` files from `https://github.com/royerlab/cytoself/tree/main` and put them in `data/opencell_crops` (which is obviously not comitted to the repo). +- In the run scripts, I save the `train_args` and `model_args`. This is so that I can more easily reconstruct the model in the inference scripts. +- in the `cytoself/datamanager/opencell.py` I add a step to the end of the dataloading of the dataloader. This saves the test dataset - it's indices and its crops into separate files. This is useful for doing analysis afterwards. You can load the embeddings and these labeles or images. It's necessary for doing inference with a new dataset. That puts data files in 'data/test_dataset_metadata/` +- TODO: include the `data/cz_infectedcell_finalwellmapping.csv` in the eventual repo. + +# inference pipeline +The new inference code for is in `inference/`. +- `python inference/load_inf_data.py` saves stacks as max-intensity projs, and does some fov-level normalizations. Saved to `results/load_inf_data`. +- `python inference/nuclear_segmentation.py`. Saves masks to `inference/nuclear_segmentation/all_masks.pt`. Issue is that if you take only a subset of these images, then you overwrite the pt file with only these images (this is not true of other steps) +- `python inference/crop.py`. For each segmented nucleus in the fov, take a crop around it. There is a `VERSION` parameter that controls how normalization is done. If `VERSION==0` (recommended), then do `[0,1]` normalization within each crop, treating nucleus and target channel independently. If `VERSION==1` then normalize the FOV before cropping. IMO this is worse because, for example, you can get very bright (e.g. due to mitosis I guess), so doing normalization at the whole-FOV level makes the rest of the image very dim. If you normalize at the crop level, then only the abnormally bright areas are affected. Results saved in `inference/results/crop/` as `crops_v0` for `VERSION==0` or `crops_v1` for `VERSION==1`. Also the crop-metadata is saved to `crops_meta.csv`, which has the fov filename, the centroid coords (in the fov-space) of the nucleus that is centered in this crop, and some other stuff. +- `python inference/get_crop_features.py` loads the pretrained cytoself model. For model name, for example `20240129_train_all`, features saved to `inference/results/get_crop_features/results/20240129_train_all/ckpt_None/`. Need to define the pretrained models in the bottom of the script. Also an option to create features for rotated versions of the crops, which should give better robustness overall according to [this paper](https://www.nature.com/articles/s41467-024-45362-4). (you have to make sure the `VERSION` matches what was used in the cropping - sorry, this should have been handled automatically) +- `python inference/compare_opencell_targets.py` gets the 'consensus embeddings' for each protein by averaging over the crop representations for that protein. It does it for opencell and orphans, and makes a distance matrix for all proteins. + + + + # cytoself [![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-397/) diff --git a/analysis/image_processing/20231010_generate_crops.py b/analysis/image_processing/20231010_generate_crops.py new file mode 100644 index 0000000..f07d29d --- /dev/null +++ b/analysis/image_processing/20231010_generate_crops.py @@ -0,0 +1,103 @@ +from pathlib import Path +import ipdb +import torch +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from PIL import Image +import os + +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("analysis/results") / current_filename.stem + +fname_mask = Path('analysis/results/20231013_segment_nuclei/all_segmasks.pt') +masks, fnames_nuc = torch.load(fname_mask) + +width, height = 200, 200 +crops_all = [] +df_meta_all = [] + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +for i, mask in enumerate(masks): + crops = [] + df_meta = pd.DataFrame(columns=["fname_pro", "fname_nuc", "cell_centroid", "mask_idx"]) + + n_instances = len(np.unique(mask))-1 + f_nuc = fnames_nuc[i] + f_pro = str(f_nuc)[:-7] + "pro.png" + img_nuc = np.array(Image.open(f_nuc)) + img_pro = np.array(Image.open(f_pro)) + + for j in range(1,n_instances+2): + target_indices = np.argwhere(mask == j) + centroid = target_indices.mean(axis=0).astype(int) + + # do image crop, normalize each channel independently. + # and check that it's not hitting the image border + # ipdb.set_trace() + slc_0 = slice(centroid[0] - height//2, centroid[0] + height//2) + slc_1 = slice(centroid[1] - width//2, centroid[1] + width//2) + img_pro_crop = img_pro[slc_0, slc_1] + img_nuc_crop = img_nuc[slc_0, slc_1] + if img_pro_crop.shape != (width, height): + continue # skip the crop bc we hit a border + img_crop = np.stack(( + norm_0_1(img_pro_crop), + norm_0_1(img_nuc_crop), + )) + + # add the crop and metadata + crops.append(img_crop) + row_meta = pd.DataFrame(dict( + fname_pro=[f_pro], + fname_nuc=[f_nuc], + cell_centroid=[centroid], + mask_idx=[j], + )) + df_meta = pd.concat([df_meta, row_meta]) + + + # optionally visualize crops in a grid + if 1: + if len(crops) < 1: + continue + from torchvision.utils import make_grid + crops_arr = torch.from_numpy(np.stack(crops)) # (N,2,H,W) + + grid_nuc = make_grid(crops_arr[:,[1]], nrow=8, pad_value=0.5) + f, axs = plt.subplots() + axs.imshow(grid_nuc.permute(1,2,0)) + f.savefig(results_dir / (f"{f_nuc.stem}" + "nucleus_only.png")) + plt.close() + + grid_pro = make_grid(crops_arr[:,[0]], nrow=8, pad_value=0.5) + f, axs = plt.subplots() + axs.imshow(grid_pro.permute(1,2,0)) + f.savefig(results_dir / (f"{f_nuc.stem}" + "protein_only.png")) + plt.close() + + + # # for visualization, put protein in green, the nucleus in red for RGBs + # imgs_rgb_tmp = torch.from_numpy(np.stack(crops)).clone() + # imgs_rgb = torch.zeros((len(imgs_rgb_tmp), 3, *imgs_rgb_tmp.shape[2:])) + # imgs_rgb[:,1] = imgs_rgb_tmp[:,0] + # imgs_rgb[:,2] = imgs_rgb_tmp[:,1] + + # grid = make_grid(imgs_rgb, nrow=5) + # f, axs = plt.subplots() + # axs.imshow(grid.permute(1,2,0)) + # fname_save = results_dir / f"" + # f.savefig() + # # gri + crops_all.extend(crops) + df_meta_all.append(df_meta) + + + +ipdb.set_trace() +pass diff --git a/analysis/image_processing/20231010_lookat_imageset48.py b/analysis/image_processing/20231010_lookat_imageset48.py new file mode 100644 index 0000000..43762e1 --- /dev/null +++ b/analysis/image_processing/20231010_lookat_imageset48.py @@ -0,0 +1,97 @@ +# should be run in the project dir for cytoself +# for developing the inference pipeline, let's process the images + +import ipdb +from PIL import Image +from aicsimageio import AICSImage +from pathlib import Path +import matplotlib.pyplot as plt +import os +import pandas as pd +import numpy as np +from skimage.io import imread, imsave +from skimage import color +from PIL import Image + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +# make the results_dir +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("analysis/results") / current_filename.stem +results_dir.mkdir(exist_ok=True, parents=True) + +results_maxproj_set48 = Path("analysis/results/20231010_maxproj_set48") +results_maxproj_set48.mkdir(exist_ok=True, parents=True) + +DATA_DIR = Path("/hpc/instruments/leonetti.dragonfly/infected-cell-microscopy/TICM048-1/raw_data") +fnames = [f for f in DATA_DIR.glob("*.tif")] +print(f"Num files {len(fnames)}") + +# get the orphan proteins in `df_exp` and filter for the ones with good signal have col is_greed +fname_exp = Path("data/orphan_protein.csv") +df_exp = pd.read_csv(fname_exp) +df_exp_green = df_exp[df_exp['is_green']==1] + +# construct a dataframe for the image metadata +df_imgs_meta = pd.DataFrame(columns=["fname", "well_id", "fov_id", "is_green"]) +for fname in fnames: + + # metadata structure "raw_data_MMStack_592-B8-16.ome.tif". Well=B8, FOV=16 + well_id, fov_id = fname.stem.split(".")[0].split("-")[-2:] + + idxs_well_id = np.where(well_id == df_exp['well_id'].values)[0] + if len(idxs_well_id)==0: + print(f"did not find meta for well_id={well_id}") + elif len(idxs_well_id)>1: + print(f"More than one match for well_id={well_id}") + else: + assert len(idxs_well_id) == 1 + row = pd.DataFrame(dict( + fname=[fname], + well_id=[well_id], + fov_id=[fov_id], + is_green=[df_exp.iloc[idxs_well_id[0]]['is_green']], + )) + df_imgs_meta = pd.concat([df_imgs_meta, row], ignore_index=True) + +df_imgs_meta_green = df_imgs_meta[df_imgs_meta['is_green']==1] +print(f"Number of zstacks that are 'green' {len(df_imgs_meta_green)}") +print(f"Number of unique wells that are 'green' {len(df_imgs_meta_green.well_id.unique())}") + +# now get the zstacks for the green images +for idx, row in df_imgs_meta_green.iterrows(): + fname = row.fname + aics_img = AICSImage(fname) + # ipdb.set_trace() + x = aics_img.data # (1, 2, Z, H, W) + assert x.ndim == 5 and x.shape[:2] == (1,2) + + # max intensity projection for the nucleus and protein channel independently + x_pro_map = x[0,1].max(0) + x_nuc_map = x[0,0].max(0) + + x_pro_map = norm_0_1(x_pro_map) + x_nuc_map = norm_0_1(x_nuc_map) + + # save as 1-channel png + fname_nuc = results_maxproj_set48 / f"max_proj_{row.well_id}_{row.fov_id}_nuc.png" + fname_pro = results_maxproj_set48 / f"max_proj_{row.well_id}_{row.fov_id}_pro.png" + Image.fromarray((x_nuc_map*255).astype(np.uint8), mode="L").save(fname_nuc) + Image.fromarray((x_pro_map*255).astype(np.uint8), mode="L").save(fname_pro) + + # viewing + f, axs = plt.subplots(1,2) + axs[0].imshow(x_nuc_map, cmap='gray') + axs[1].imshow(x_pro_map, cmap='gray') + axs[0].set(title="nucleus") + axs[1].set(title="protein") + fname_save = results_dir / (fname.stem + "_max_projs.png") + f.savefig(fname_save, dpi=200) + plt.close() + +ipdb.set_trace() +pass \ No newline at end of file diff --git a/analysis/image_processing/20231010_validate_embeddings.py b/analysis/image_processing/20231010_validate_embeddings.py new file mode 100644 index 0000000..2587e03 --- /dev/null +++ b/analysis/image_processing/20231010_validate_embeddings.py @@ -0,0 +1,56 @@ +# run in the home directory as python analysis/20231010_validate_embeddings.py +import ipdb +import os +from pathlib import Path +import numpy as np +import torch +import pandas as pd + +# load embeddings from a results dir +RESULTS_DIR = Path("results/20231009_train_all_no_nucdist") +embed_images = torch.from_numpy(np.load(RESULTS_DIR / "embeddings/vqvec2.npy" )) +embed_images = embed_images.view((len(embed_images), -1)) # flatten the array + +# load labels for the test that was saved during datamanager +DIR_TEST_DATASET_META = Path("data/test_dataset_metadata") +labels = np.load(DIR_TEST_DATASET_META / "test_dataset_labels.npy", allow_pickle=True) +df = pd.DataFrame(labels, columns=["ensg", "name", "loc_grade"]) + +# check that embeddings and labels are at least the same dimension +assert len(embed_images) == len(labels) +prots = sorted(df.name.unique()) +assert len(prots) == 1311 + +# consensus embedding as a mean +embed_consensus = [] +img_counts = [] +localization_labels = [] + +for prot in prots: + idxs = np.where(df.name==prot)[0] + z = embed_images[idxs] + embed_consensus.append(z.mean(0)) + img_counts.append(len(z)) + + localization_label = df[df.name==prot].loc_grade.unique() + localization_labels.append(localization_label[0]) + +embed_consensus = torch.stack(embed_consensus) # (n_prot, 1024) +img_counts = torch.tensor(img_counts) +dist = torch.cdist(embed_consensus, embed_consensus) +argsortdist = torch.argsort(dist, dim=1, descending=False) +assert torch.all(argsortdist[:,0] == torch.arange(len(argsortdist))) # nearest neighbor should be itself +argsortdist = argsortdist[:,1:] + +ipdb.set_trace() +pass +# lets do the most basic possible test based on L2 distance which, remember, is not actually the proposed method by Kobayashi et al. +for i in range(20): + print(localization_labels[i], end= " ") + print(localization_labels[argsortdist[i,0].item()], "\t\t\t", localization_labels[argsortdist[i,1].item()]) + + + + +if __name__=="__main__": + pass \ No newline at end of file diff --git a/analysis/image_processing/20231013_segment_nuclei.py b/analysis/image_processing/20231013_segment_nuclei.py new file mode 100644 index 0000000..4235b3f --- /dev/null +++ b/analysis/image_processing/20231013_segment_nuclei.py @@ -0,0 +1,58 @@ +# options: choose a diameter + +diameter = 120 +diameter = None +from pathlib import Path +from PIL import Image +import numpy as np +import ipdb +import torch +import os + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("analysis/results") / current_filename.stem +results_dir.mkdir(exist_ok=True, parents=True) +results_dir_cellpose = results_dir / "cellpose_segs" +results_dir_cellpose.mkdir(exist_ok=True, parents=True) + +results_dir_tmp = Path("analysis/tmp") +results_dir_tmp.mkdir(exist_ok=True) + +# baseline code from the Colab https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/run_cellpose_GPU.ipynb + +### our data +data_maxproj_set48 = Path("analysis/results/20231010_maxproj_set48") +fnames = [l for l in data_maxproj_set48.iterdir()] +fnames_pro = sorted([l for l in fnames if "_pro.png" in str(l)]) +fnames_nuc = sorted([l for l in fnames if "_nuc.png" in str(l)]) + + +# imgs_pro = [np.array(Image.open(f)) for f in fnames_pro] +imgs_nuc = [np.array(Image.open(f)) for f in fnames_nuc] + +from cellpose import models +model = models.Cellpose(gpu=True, model_type='cyto') +print("Running cellpose") +masks, flows, styles, diams = model.eval(imgs_nuc, diameter=diameter, + flow_threshold=None, channels=None) +fname_mask = results_dir / f"all_segmasks.pt" +torch.save([masks, fnames_nuc], fname_mask) +ipdb.set_trace() + +import matplotlib.pyplot as plt +# save the mask and also visualize them +for i in range(len(masks)): + f, axs = plt.subplots(1,2) + axs[0].imshow(imgs_nuc[i],cmap='gray') + axs[1].imshow(masks[i], cmap='gray') + f.savefig(results_dir / f"seg_sample_{fnames[i].stem}.png", dpi=200) + plt.close() + +plt.close() + diff --git a/analysis/image_processing/20240127_compare_crops_opencell2_orphan.py b/analysis/image_processing/20240127_compare_crops_opencell2_orphan.py new file mode 100644 index 0000000..4ffbf23 --- /dev/null +++ b/analysis/image_processing/20240127_compare_crops_opencell2_orphan.py @@ -0,0 +1,96 @@ +""" +I re-processed the OpenCell crops to be data "data/opencell_crops_processed2" +""" +import ipdb +import os +from pathlib import Path +import sys +import numpy as np +import pandas as pd +from aicsimageio import AICSImage +from PIL import Image +import matplotlib.pyplot as plt +from torchvision.utils import make_grid +import torch + +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("analysis/results") / current_filename.stem +results_dir.mkdir(exist_ok=True, parents=True) + +nrow, ncol = 8, 8 +nimgs = nrow * ncol + +EXP = 0 +for EXP in [0,1,2]: + if EXP == 0: + # both are actually `ANKRD46` + prot_opencell = "ANKRD46" + target_well_inf = "A1" # ANKRD46 + elif EXP == 1: + # two random ER proteins + prot_opencell = "ATL3" + target_well_inf = "E2" # C4orf3 + elif EXP == 2: + # two lysosomal proteins + prot_opencell = "ARFGAP1" + target_well_inf = "F11" # "TSPAN6" + else: + raise + + # opencell crops for ANKRD46 + dir_opencell_crops = Path("data/opencell_crops_processed2") + + img_pro_opencell = np.load(dir_opencell_crops / f"{prot_opencell}_pro.npy") + img_nuc_opencell = np.load(dir_opencell_crops / f"{prot_opencell}_nuc.npy") + img_nucdist_opencell = np.load(dir_opencell_crops / + f"{prot_opencell}_nucdist.npy") + img_nucdist_opencell = np.load(dir_opencell_crops / + f"{prot_opencell}_nucdist.npy") + img_nucdist_opencell = (img_nucdist_opencell+1)/2 # for viz only + img_opencell = np.stack( + (img_pro_opencell, img_nuc_opencell, img_nucdist_opencell), axis=1) + + # opencell + idxs = np.arange(len(img_opencell)) + idxs = idxs[:nimgs] + np.random.shuffle(idxs) + grid_opencell = make_grid( + torch.from_numpy(img_opencell)[idxs], ncol) + + grid_opencell = (grid_opencell*255).numpy().astype(np.uint8).transpose(1,2,0) + Image.fromarray(grid_opencell[...,0], "L").save(results_dir / f"exp{EXP}_grid_pro_opencell.png") + Image.fromarray(grid_opencell[...,1], "L").save(results_dir / f"exp{EXP}_grid_nuc_opencell.png") + Image.fromarray((grid_opencell[...,2]/255)+127.5, "L").save(results_dir / f"exp{EXP}_grid_nucdist_opencell.png") + + + #### orphans + VERSION = 0 + # version 0 has crop-level norm, version 1 has protein level norm + if VERSION == 0: + crops = torch.load("inference/results/crop/crops.pt") + crops_meta = pd.read_csv("inference/results/crop/crops_meta.csv") + elif VERSION == 1: + crops = torch.load("inference/results/crop/crops_v1.pt") + crops_meta = pd.read_csv("inference/results/crop/crops_meta.csv") + else: + raise ValueError() + + crops_meta['well_id'] = [ + row.split("/")[-1].split("_")[2] for row in crops_meta['fname_pro'] + ] + idxs_target_inf = np.where(crops_meta['well_id'] == target_well_inf)[0] + crops_target_inf = crops[idxs_target_inf] + crops_target_inf[:,2] = (crops_target_inf[:,2]+1)/2 + + idxs = np.arange(len(crops_target_inf)) + np.random.shuffle(idxs) + idxs = idxs[:nimgs] + grid_inf = make_grid(crops_target_inf[idxs], nrow).permute(1, 2, 0) + grid_inf = (grid_inf*255).numpy().astype(np.uint8) + Image.fromarray(grid_inf[...,0], "L").save(results_dir / f"exp{EXP}_grid_pro_inf.png") + Image.fromarray(grid_inf[...,1], "L").save(results_dir / f"exp{EXP}_grid_nuc_inf.png") + Image.fromarray((grid_inf[...,2]/255)+127.5, "L").save(results_dir / f"exp{EXP}_grid_nucdist_inf.png") + +ipdb.set_trace() +pass + diff --git a/analysis/image_processing/20240127_compare_fovs_opencell_orphan.py b/analysis/image_processing/20240127_compare_fovs_opencell_orphan.py new file mode 100644 index 0000000..1da476f --- /dev/null +++ b/analysis/image_processing/20240127_compare_fovs_opencell_orphan.py @@ -0,0 +1,52 @@ +import ipdb +import os +from pathlib import Path +import sys +import numpy as np +import pandas as pd +from aicsimageio import AICSImage +from PIL import Image + +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("analysis/results") / current_filename.stem +results_dir.mkdir(exist_ok=True, parents=True) + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +## what are the `pixel sizes` ? +# Opencell `/clean` stuff +dir_data = Path("/hpc/projects/group.leonetti/opencell-microscopy/") +df_meta = pd.read_csv(dir_data / "2021-08-30_good-fovs.csv") +dir_seg = Path("inference/results/nuclear_segmentation/opencell/") +fname = dir_data / df_meta.iloc[0]['filepath'] +img_ = AICSImage(fname) +img0_proj_nuc = img_.data[0,0].max(0) +img0_cropped_proj_nuc = img0_proj_nuc[:600,:600] +print("Shape ", img_.shape) +print("Pixel sizes ", img_.physical_pixel_sizes) +print("Meta ", img_.metadata) + +# OpenCell in the `public/` repo +fname = "/hpc/projects/group.leonetti/opencell-microscopy/public/AAMP_ENSG00000127837/OC-FOV_AAMP_ENSG00000127837_CID001050_FID00013888_stack.tif" +img_ = AICSImage(fname) +img1_proj_nuc = img_.data[0,0].max(0) +print("Pixel sizes ", img_.physical_pixel_sizes) +print("Meta ", img_.metadata) +print("Shape ", img_.shape) # (1, 2, 51, 600, 600) +print("Pixel sizes ", img_.physical_pixel_sizes) # PhysicalPixelSizes(Z=0.5, Y=0.2, X=0.2) +print("Meta ", img_.metadata) + +# save +Image.fromarray((norm_0_1(img0_proj_nuc)*255).astype(np.uint8), "L").save(results_dir / "img0_proj_nuc.png") +Image.fromarray((norm_0_1(img0_cropped_proj_nuc)*255).astype(np.uint8), "L").save(results_dir / "img0_cropped_proj_nuc.png") +Image.fromarray((norm_0_1(img1_proj_nuc)*255).astype(np.uint8), "L").save(results_dir / "img1_proj_nuc.png") + +### + + +ipdb.set_trace() +pass diff --git a/analysis/image_processing/crop.py b/analysis/image_processing/crop.py new file mode 100644 index 0000000..2b6940a --- /dev/null +++ b/analysis/image_processing/crop.py @@ -0,0 +1,120 @@ +""" +Run in project home dir as + python inference/crop.py + +Get the FOVs for `inference/results/load_inf_data/` and the segmentation maps +from `inference/results/nucelear_segmentation/all_segmasks.pt`. + +For each nucleus, generate a crop centered around that nucleus with 200px width +and height. The crop has the protein in ch0 and protein in ch1. They are saved to +inference/results/crop/ + + +Optionally add a check that there is protein signal in the crop. +""" + +from pathlib import Path +import ipdb +import torch +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from PIL import Image +import os + +def crop_ + +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("analysis/results") / current_filename.stem + +fname_mask = Path('analysis/results/20231013_segment_nuclei/all_segmasks.pt') +masks, fnames_nuc = torch.load(fname_mask) + +width, height = 200, 200 +crops_all = [] +df_meta_all = [] + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +for i, mask in enumerate(masks): + crops = [] + df_meta = pd.DataFrame(columns=["fname_pro", "fname_nuc", + "cell_centroid", "mask_idx"]) + + n_instances = len(np.unique(mask))-1 + f_nuc = fnames_nuc[i] + f_pro = str(f_nuc)[:-7] + "pro.png" + img_nuc = np.array(Image.open(f_nuc)) + img_pro = np.array(Image.open(f_pro)) + + for j in range(1,n_instances+2): + target_indices = np.argwhere(mask == j) + centroid = target_indices.mean(axis=0).astype(int) + + # do image crop, normalize each channel independently. + # and check that it's not hitting the image border + # ipdb.set_trace() + slc_0 = slice(centroid[0] - height//2, centroid[0] + height//2) + slc_1 = slice(centroid[1] - width//2, centroid[1] + width//2) + img_pro_crop = img_pro[slc_0, slc_1] + img_nuc_crop = img_nuc[slc_0, slc_1] + if img_pro_crop.shape != (width, height): + continue # skip the crop bc we hit a border + img_crop = np.stack(( + norm_0_1(img_pro_crop), + norm_0_1(img_nuc_crop), + )) + + # add the crop and metadata + crops.append(img_crop) + row_meta = pd.DataFrame(dict( + fname_pro=[f_pro], + fname_nuc=[f_nuc], + cell_centroid=[centroid], + mask_idx=[j], + )) + df_meta = pd.concat([df_meta, row_meta]) + + # optionally visualize crops in a grid + if 1: + if len(crops) < 1: + continue + from torchvision.utils import make_grid + crops_arr = torch.from_numpy(np.stack(crops)) # (N,2,H,W) + + grid_nuc = make_grid(crops_arr[:,[1]], nrow=8, pad_value=0.5) + f, axs = plt.subplots() + axs.imshow(grid_nuc.permute(1,2,0)) + f.savefig(results_dir / (f"{f_nuc.stem}" + "nucleus_only.png")) + plt.close() + + grid_pro = make_grid(crops_arr[:,[0]], nrow=8, pad_value=0.5) + f, axs = plt.subplots() + axs.imshow(grid_pro.permute(1,2,0)) + f.savefig(results_dir / (f"{f_nuc.stem}" + "protein_only.png")) + plt.close() + + + # # for visualization, put protein in green, the nucleus in red for RGBs + # imgs_rgb_tmp = torch.from_numpy(np.stack(crops)).clone() + # imgs_rgb = torch.zeros((len(imgs_rgb_tmp), 3, *imgs_rgb_tmp.shape[2:])) + # imgs_rgb[:,1] = imgs_rgb_tmp[:,0] + # imgs_rgb[:,2] = imgs_rgb_tmp[:,1] + + # grid = make_grid(imgs_rgb, nrow=5) + # f, axs = plt.subplots() + # axs.imshow(grid.permute(1,2,0)) + # fname_save = results_dir / f"" + # f.savefig() + # # gri + crops_all.extend(crops) + df_meta_all.append(df_meta) + + + +ipdb.set_trace() +pass diff --git a/analysis/organelle_marker_analysis/Suppl_Fig_S6_cytoself.ipynb b/analysis/organelle_marker_analysis/Suppl_Fig_S6_cytoself.ipynb new file mode 100644 index 0000000..3492e56 --- /dev/null +++ b/analysis/organelle_marker_analysis/Suppl_Fig_S6_cytoself.ipynb @@ -0,0 +1,2698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Organelle classification analysis\n", + "##### takes similarity matrix vs organelle marker genes to calculate where an orphan gene is most likely to localize\n", + "##### matrix contains localization similarity scores of orphan proteins and the whole OpenCell collection\n", + "\n", + "##### similarity matrix from James Burgess, adapted from cytoself (Kobayashi et al. Nature Methods 2022)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/manuel.leonetti/miniconda3/envs/kibeom_env/bin/python\n" + ] + } + ], + "source": [ + "import sys\n", + "\n", + "print(sys.executable)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import matplotlib.patches as patches\n", + "plt.rcParams['pdf.fonttype'] = 42\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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prot_nn2C4orf32EMC4SLC16A2SLC7A6CD276SLC16A10PIP5K1ASLC4A2CLMNNPM3...TSPAN3TPRA1ELOVL1AGO1pML545SLC16A10MAPK8HSF1CD9ZCCHC7
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" + ], + "text/plain": [ + " ANKRD46 C4orf3 \\\n", + "protein \n", + "orgIP_cluster_annotation ER ER \n", + "plate33_eyeballed_annotation nice ER nice ER \n", + "prot_nn0 NAT14 ANKRD46 \n", + "prot_nn1 ODR4 SEC61B \n", + "prot_nn2 C4orf32 EMC4 \n", + "\n", + " CD151 \\\n", + "protein \n", + "orgIP_cluster_annotation ER, new analysis says plasma membrane \n", + "plate33_eyeballed_annotation nice lysosome \n", + "prot_nn0 PIP4P1 \n", + "prot_nn1 SLC2A1 \n", + "prot_nn2 SLC16A2 \n", + "\n", + " CD276 \\\n", + "protein \n", + "orgIP_cluster_annotation plasma membrane \n", + "plate33_eyeballed_annotation few cells, maybe some aggregates \n", + "prot_nn0 MYO1C \n", + "prot_nn1 CMTM4 \n", + "prot_nn2 SLC7A6 \n", + "\n", + " CD99 CLMN \\\n", + "protein \n", + "orgIP_cluster_annotation plasma membrane ER \n", + "plate33_eyeballed_annotation negative dim ER, very few cells \n", + "prot_nn0 SLC16A1 SVIL \n", + "prot_nn1 MYO1C SLC4A2 \n", + "prot_nn2 CD276 SLC16A10 \n", + "\n", + " CMTM4 \\\n", + "protein \n", + "orgIP_cluster_annotation recycling endosome \n", + "plate33_eyeballed_annotation plasma membrane, nice \n", + "prot_nn0 CD276 \n", + "prot_nn1 YES1 \n", + "prot_nn2 PIP5K1A \n", + "\n", + " CMTM7 \\\n", + "protein \n", + "orgIP_cluster_annotation recycling endosome \n", + "plate33_eyeballed_annotation nice, complex plasmam membrane + lysosome? \n", + "prot_nn0 pML545 \n", + "prot_nn1 SLC16A10 \n", + "prot_nn2 SLC4A2 \n", + "\n", + " COMTD1 \\\n", + "protein \n", + "orgIP_cluster_annotation mitochondrion \n", + "plate33_eyeballed_annotation dim, maybe endo-lysosome \n", + "prot_nn0 PI4K2A \n", + "prot_nn1 SLC37A3 \n", + "prot_nn2 CLMN \n", + "\n", + " GLT8D1 ... TPRA1 \\\n", + "protein ... \n", + "orgIP_cluster_annotation Golgi ... ER \n", + "plate33_eyeballed_annotation nucleolus, very few cells ... NaN \n", + "prot_nn0 GNL3 ... TMEM184C \n", + "prot_nn1 RRP1 ... CLCN7 \n", + "prot_nn2 NPM3 ... TSPAN3 \n", + "\n", + " TSPAN3 TVP23C \\\n", + "protein \n", + "orgIP_cluster_annotation lysosome trans-Golgi \n", + "plate33_eyeballed_annotation nice lysosome nice Golgi \n", + "prot_nn0 TSPAN6 MAN1A2 \n", + "prot_nn1 TMEM184C COMTD1 \n", + "prot_nn2 TPRA1 ELOVL1 \n", + "\n", + " ZZEF1 pML544 pML545 \\\n", + "protein \n", + "orgIP_cluster_annotation endocytic plasma membrane NaN NaN \n", + "plate33_eyeballed_annotation cytoplasmic NaN NaN \n", + "prot_nn0 ZNF598 REEP4 CMTM7 \n", + "prot_nn1 AKAP13 REEP3 SLC4A2 \n", + "prot_nn2 AGO1 pML545 SLC16A10 \n", + "\n", + " pML548 pML549 TSPAN6 well_H8_protein \n", + "protein \n", + "orgIP_cluster_annotation NaN NaN NaN NaN \n", + "plate33_eyeballed_annotation NaN NaN NaN NaN \n", + "prot_nn0 MAPKAPK3 TAF12 CD81 RNF149 \n", + "prot_nn1 IPO8 RBM7 SPG21 CLMN \n", + "prot_nn2 MAPK8 HSF1 CD9 ZCCHC7 \n", + "\n", + "[5 rows x 41 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load knn data to define nearest neighbors in cystoself latent space\n", + "\n", + "knn_num_df = pd.read_csv('./tspan6_knn_opencell_preds_manualfilter_True__representation_vqvec2__rotations_False__mode_correlation__pcadim_200.csv',\n", + " index_col=0, header=2)\n", + "\n", + "\n", + "knn_num_df = knn_num_df.drop(columns=[\"TSPAN6\"])\n", + "knn_num_df = knn_num_df.rename(columns={\"well_H5_protein\":\"TSPAN6\"})\n", + "\n", + "knn_num_df.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# define list of orphan proteins\n", + "\n", + "orphans = ['ANKRD46', 'C4orf3', 'SFT2D3', 'KLRG2', 'NAT14', 'ODR4',\n", + " 'CD276', 'TSPAN3', 'MFSD5', 'TP53I11', 'CD99', 'SUSD5',\n", + " 'TMEM245', 'TMEM41A', 'TMEM209', 'ZZEF1', 'KIAA1522',\n", + " 'MPZL1', 'TMEM184C', 'TVP23C', 'SFT2D2', 'CMTM4', 'SCAMP3',\n", + " 'CD151', 'SLC35F1', 'SLC35F2', 'CMTM7', 'SCAMP4', 'TSPAN6',\n", + " 'TPRA1', 'GLT8D1']\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# organelle analysis of orphan proteins\n", + " gois = ['ANKRD46', \n", + " 'C4orf3', \n", + " 'NAT14',\n", + " 'ODR4',\n", + " 'TP53I11',\n", + " 'TMEM245',\n", + " 'TMEM41A',\n", + " 'TMEM209',\n", + " 'SFT2D3', \n", + " 'MFSD5',\n", + " 'TVP23C',\n", + " 'TPRA1',\n", + " 'GLT8D1',\n", + " 'KLRG2', \n", + " 'CD276',\n", + " 'CD99',\n", + " 'SUSD5',\n", + " 'KIAA1522',\n", + " 'MPZL1',\n", + " 'SFT2D2',\n", + " 'CMTM4',\n", + " 'CD151',\n", + " 'TSPAN3',\n", + " 'TMEM184C',\n", + " 'TSPAN6',\n", + " 'SCAMP3',\n", + " 'ZZEF1']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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organellesourceNote
gene_name_canonical
ACTN1actin-binding_proteinItzhak et al 2016 PMID: 27278775confidence score: high
ACTN4actin-binding_proteinItzhak et al 2016 PMID: 27278775confidence score: high
COBLactin-binding_proteinItzhak et al 2016 PMID: 27278775confidence score: very high
CORO1Bactin-binding_proteinItzhak et al 2016 PMID: 27278775confidence score: high
CORO2Aactin-binding_proteinItzhak et al 2016 PMID: 27278775confidence score: high
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" + ], + "text/plain": [ + " organelle source \\\n", + "gene_name_canonical \n", + "ACTN1 actin-binding_protein Itzhak et al 2016 PMID: 27278775 \n", + "ACTN4 actin-binding_protein Itzhak et al 2016 PMID: 27278775 \n", + "COBL actin-binding_protein Itzhak et al 2016 PMID: 27278775 \n", + "CORO1B actin-binding_protein Itzhak et al 2016 PMID: 27278775 \n", + "CORO2A actin-binding_protein Itzhak et al 2016 PMID: 27278775 \n", + "\n", + " Note \n", + "gene_name_canonical \n", + "ACTN1 confidence score: high \n", + "ACTN4 confidence score: high \n", + "COBL confidence score: very high \n", + "CORO1B confidence score: high \n", + "CORO2A confidence score: high " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load Suppl Table 1: reference markers for individual organelles using literature-curated data\n", + "\n", + "markers = pd.read_excel('./Supplementary_Table_1_literature-curated_organelle_ground-truth.xlsx', sheet_name='organelle_markers', index_col=0) \n", + "markers.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num ER markers in OC: 66\n", + "Num ERGIC markers in OC: 20\n", + "Num Golgi markers in OC: 2\n", + "Num lysosome markers in OC: 4\n", + "Num mitochondria markers in OC: 8\n", + "Num PM markers in OC: 39\n", + "Num early endosome markers in OC: 10\n", + "Num actin markers in OC: 13\n", + "Num peroxisome markers in OC: 1\n", + "Num cytosol markers in OC: 26\n", + "Num nucleus markers in OC: 47\n", + "Num nucleolus markers in OC: 9\n", + "Num nuclear pore markers in OC: 6\n" + ] + } + ], + "source": [ + "# define list of reference organelle markers also present in OpenCell, including classical nucleolar marker genes for GLT8D1 analysis\n", + "# outputs number of markers found in OpenCell\n", + "\n", + "\n", + "er_markers = markers[markers['organelle'] == 'ER'].index\n", + "ergic_markers = markers[markers['organelle'] == 'ERGIC'].index\n", + "golgi_markers = markers[markers['organelle'] == 'Golgi'].index\n", + "lyso_markers = markers[markers['organelle'] == 'lysosome'].index\n", + "mito_markers = markers[markers['organelle'] == 'mitochondria'].index\n", + "pm_markers = markers[markers['organelle'] == 'plasma membrane'].index\n", + "ee_markers = markers[markers['organelle'] == 'early_endosome'].index\n", + "actin_markers = markers[markers['organelle'] == 'actin-binding_protein'].index\n", + "perox_markers = markers[markers['organelle'] == 'peroxisome'].index\n", + "cytosol_markers = markers[markers['organelle'] == 'cytosol'].index\n", + "nucleus_markers = markers[markers['organelle'] == 'nucleus'].index\n", + "nuc_pore_markers = markers[markers['organelle'] == 'nuclear_pore'].index\n", + "nucleolus_marker_genes = ['GNL2', 'GNL3', 'NCL', 'NOL10', 'NOL11', 'NOM1', 'NOP53', 'NPM1', 'NPM3']\n", + "\n", + "er_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(er_markers)]\n", + "print('Num ER markers in OC:', len(er_markers_in_OC))\n", + "ergic_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(ergic_markers)]\n", + "print('Num ERGIC markers in OC:', len(ergic_markers_in_OC))\n", + "golgi_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(golgi_markers)]\n", + "print('Num Golgi markers in OC:', len(golgi_markers_in_OC))\n", + "lyso_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(lyso_markers)]\n", + "print('Num lysosome markers in OC:', len(lyso_markers_in_OC))\n", + "mito_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(mito_markers)]\n", + "print('Num mitochondria markers in OC:', len(mito_markers_in_OC))\n", + "pm_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(pm_markers)]\n", + "print('Num PM markers in OC:', len(pm_markers_in_OC))\n", + "ee_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(ee_markers)]\n", + "print('Num early endosome markers in OC:', len(ee_markers_in_OC))\n", + "actin_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(actin_markers)]\n", + "print('Num actin markers in OC:', len(actin_markers_in_OC))\n", + "perox_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(perox_markers)]\n", + "print('Num peroxisome markers in OC:', len(perox_markers_in_OC))\n", + "cytosol_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(cytosol_markers)]\n", + "print('Num cytosol markers in OC:', len(cytosol_markers_in_OC))\n", + "nucleus_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(nucleus_markers)]\n", + "print('Num nucleus markers in OC:', len(nucleus_markers_in_OC))\n", + "nucleolus_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(nucleolus_marker_genes)]\n", + "print('Num nucleolus markers in OC:', len(nucleolus_markers_in_OC))\n", + "nuclear_pore_markers_in_OC = similarity_scores_df[similarity_scores_df.index.isin(nuc_pore_markers)]\n", + "print('Num nuclear pore markers in OC:', len(nuclear_pore_markers_in_OC))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ANKRD46C4orf3CD151CD276CD99CLMNCMTM4CMTM7COMTD1GLT8D1...YWHAHYWHAQYWHAZYY1ZCCHC17ZCCHC7ZCCHC9ZNF326ZNF598ZYX
ACTN1-0.0698420.0636660.1296870.1260640.1158990.0776580.1093250.1606980.2435930.082961...-0.140502-0.1215450.0550990.0600270.136528-0.0031810.1037530.199995-0.2190830.299736
ACTN4-0.0119410.0930180.1594320.3652390.3445470.0215210.2808570.2339420.1164930.097764...-0.171882-0.130835-0.0867150.1976300.113199-0.0034890.1085810.138354-0.2604250.059173
CORO1B-0.094426-0.0627620.3071760.2063100.2118550.0470910.2483100.1210060.024416-0.096400...-0.226964-0.168616-0.0611010.0198260.013837-0.0528110.0191920.105386-0.1804400.000333
CTTN-0.041342-0.0015650.1443870.1070380.120973-0.0765620.128744-0.083129-0.0296650.070220...-0.151205-0.125870-0.0039550.0110850.1267870.0125300.0284660.179940-0.1370300.166764
DBNL0.0360250.0753820.0215310.0472200.0378470.0469500.0286990.0490490.1280000.008608...-0.120903-0.103547-0.0206610.0510200.164435-0.0039280.1119340.131739-0.1670590.139881
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5 rows × 1352 columns

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" + ], + "text/plain": [ + " ANKRD46 C4orf3 CD151 CD276 CD99 CLMN CMTM4 \\\n", + "ACTN1 -0.069842 0.063666 0.129687 0.126064 0.115899 0.077658 0.109325 \n", + "ACTN4 -0.011941 0.093018 0.159432 0.365239 0.344547 0.021521 0.280857 \n", + "CORO1B -0.094426 -0.062762 0.307176 0.206310 0.211855 0.047091 0.248310 \n", + "CTTN -0.041342 -0.001565 0.144387 0.107038 0.120973 -0.076562 0.128744 \n", + "DBNL 0.036025 0.075382 0.021531 0.047220 0.037847 0.046950 0.028699 \n", + "\n", + " CMTM7 COMTD1 GLT8D1 ... YWHAH YWHAQ YWHAZ \\\n", + "ACTN1 0.160698 0.243593 0.082961 ... -0.140502 -0.121545 0.055099 \n", + "ACTN4 0.233942 0.116493 0.097764 ... -0.171882 -0.130835 -0.086715 \n", + "CORO1B 0.121006 0.024416 -0.096400 ... -0.226964 -0.168616 -0.061101 \n", + "CTTN -0.083129 -0.029665 0.070220 ... -0.151205 -0.125870 -0.003955 \n", + "DBNL 0.049049 0.128000 0.008608 ... -0.120903 -0.103547 -0.020661 \n", + "\n", + " YY1 ZCCHC17 ZCCHC7 ZCCHC9 ZNF326 ZNF598 ZYX \n", + "ACTN1 0.060027 0.136528 -0.003181 0.103753 0.199995 -0.219083 0.299736 \n", + "ACTN4 0.197630 0.113199 -0.003489 0.108581 0.138354 -0.260425 0.059173 \n", + "CORO1B 0.019826 0.013837 -0.052811 0.019192 0.105386 -0.180440 0.000333 \n", + "CTTN 0.011085 0.126787 0.012530 0.028466 0.179940 -0.137030 0.166764 \n", + "DBNL 0.051020 0.164435 -0.003928 0.111934 0.131739 -0.167059 0.139881 \n", + "\n", + "[5 rows x 1352 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sanity-check an example of marker list\n", + "\n", + "actin_markers_in_OC.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ANKRD46 nearest genes:\n", + " NAT14 | score: 0.9376207974297083 | OC annot: ORF \n", + " ODR4 | score: 0.8785008720094776 | OC annot: ORF \n", + " C4orf32 | score: 0.8222811683343549 | OC annot: er \n", + " TMEM208 | score: 0.8146864030006423 | OC annot: er \n", + " EMC8 | score: 0.7580737438466164 | OC annot: er \n", + " CERS5 | score: 0.7473037767185666 | OC annot: er \n", + " CERS6 | score: 0.7330287921362265 | OC annot: er \n", + " STT3A | score: 0.7059087705559011 | OC annot: er \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C4orf3 nearest genes:\n", + " ANKRD46 | score: 0.85528443754386 | OC annot: er \n", + " SEC61B | score: 0.8346703925797482 | OC annot: er \n", + " EMC4 | score: 0.8129425298276458 | OC annot: er \n", + " TMEM208 | score: 0.7985038902096805 | OC annot: er \n", + " C4orf32 | score: 0.7244788230547076 | OC annot: er \n", + " VMP1 | score: 0.7145194448778729 | OC annot: er \n", + " ANKRD46 | score: 0.6729894714472429 | OC annot: ORF \n", + " SGPL1 | score: 0.6655737949537373 | OC annot: er \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SFT2D3 nearest genes:\n", + " SLC18B1 | score: 0.8091091212343262 | OC annot: vesicles \n", + " SLC15A4 | score: 0.8018286378874911 | OC annot: vesicles \n", + " SLC11A2 | score: 0.7814471744655007 | OC annot: vesicles \n", + " SLC2A8 | score: 0.7727253084726915 | OC annot: vesicles \n", + " TSPAN6 | score: 0.7660215011816969 | OC annot: ORF \n", + " TMEM184C | score: 0.7496005611577852 | OC annot: ORF \n", + " MCOLN1 | score: 0.7478471509701111 | OC annot: vesicles \n", + " TSPAN3 | score: 0.7431436174959112 | OC annot: ORF \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KLRG2 nearest genes:\n", + " FARP1 | score: 0.8566747355247658 | OC annot: membrane \n", + " IGF1R | score: 0.8362659286642267 | OC annot: membrane \n", + " PIP5K1A | score: 0.8095142252377436 | OC annot: membrane \n", + " ATP2B1 | score: 0.7999738265899824 | OC annot: membrane \n", + " GOLGA7 | score: 0.7787551517239044 | OC annot: membrane;vesicles \n", + " CMTM4 | score: 0.7716309134495088 | OC annot: ORF \n", + " SLC26A6 | score: 0.7689333971026359 | OC annot: membrane;vesicles \n", + " SLC16A10 | score: 0.7318396653999681 | OC annot: vesicles;membrane \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NAT14 nearest genes:\n", + " ODR4 | score: 0.9485550177967019 | OC annot: ORF \n", + " ANKRD46 | score: 0.9376207974297083 | OC annot: ORF \n", + " STT3A | score: 0.761708777745892 | OC annot: er \n", + " CERS5 | score: 0.7562490221382894 | OC annot: er \n", + " C4orf32 | score: 0.7444794122964966 | OC annot: er \n", + " CERS6 | score: 0.7375242954187154 | OC annot: er \n", + " EMC8 | score: 0.6640271120808448 | OC annot: er \n", + " TMEM208 | score: 0.6564940413917988 | OC annot: er \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ODR4 nearest genes:\n", + " NAT14 | score: 0.9485550177967019 | OC annot: ORF \n", + " ANKRD46 | score: 0.8785008720094776 | OC annot: ORF \n", + " CERS5 | score: 0.7548556899192701 | OC annot: er \n", + " EMC9 | score: 0.7401232636415561 | OC annot: er \n", + " CERS6 | score: 0.7035750216822043 | OC annot: er \n", + " STT3A | score: 0.6856884715409837 | OC annot: er \n", + " PKMYT1 | score: 0.6817598202487791 | OC annot: er \n", + " CDS1 | score: 0.6765382279749946 | OC annot: er \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CD276 nearest genes:\n", + " MYO1C | score: 0.9307604043463606 | OC annot: membrane \n", + " CMTM4 | score: 0.8843862969148798 | OC annot: ORF \n", + " SLC7A6 | score: 0.8605840950366981 | OC annot: membrane;vesicles \n", + " SLC7A5 | score: 0.7616687950455953 | OC annot: membrane;vesicles \n", + " MYO1B | score: 0.7461077558932052 | OC annot: membrane \n", + " YES1 | score: 0.7414777828476453 | OC annot: membrane \n", + " CD99 | score: 0.7227151039726161 | OC annot: ORF \n", + " PTK7 | score: 0.7056346343969763 | OC annot: membrane;vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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jIiIiIiJSIAZzRERERERECsRgjoiIiIiISIEYzBERERERESkQgzkiIiIiIiIFYjBHRERERESkQAzmiIiIiIiIFIjBHBERERERkQIxmCMiIiIiIlIgBnNEREREREQKxGCOiIiIiIhIgRjMERERERERKRCDOSIiIiIiIgViMEdERERERKRADOaIiIiIiIgUiMEcERERERGRAjGYIyIiIiIiUiAGc0RERERERArEYI6IiIiIiEiBGMwREREREREpEIM5IiIiIiIiBWIwR0REREREpEAM5oiIiIiIiBSIwRwREREREZECMZgjIiIiIiJSIAZzRERERERECsRgjoiIiIiISIEYzBERERERESkQgzkiIiIiIiIFYjBHRHZhMABarbwkIiIiIut52LsBROT8DAagpgbw9wfUaiA/H0hLA3Q6IDAQGDcOiI21dyuJiIiIlIXBHBHZjF6vx9atvyMz0x+1te7w82vC4MG1OHjQD1qtCSZTIdzcopCX54aUlEp4e4s2z5GQkABfX187tJ6IiIjIsTGYI6ILkpubi+rq6nbv27fvCObM+RZACIBiABEAGgB4ASgCYACgBtADS5duAFDe5jnWrVuHxMTEdp8/ICAA/fr164K9ICIiIlIeBnNEdN5yc3PRv3//s2wRBmAGZCBn+OMyEkA9ZGBnDvAqANS2+wyzZ88+axtycnIY0BEREZFLYjBHROfNPCLX0eiZ0ahCamowdDoPaDQN0Go9ERjYiMGDa7Fnjxfy8ysRGxuMSy81IjJysVWvnZWVhdmzZ3c4KkhERETk7BjMEdEFS0xMRFJSUrv3RUefKXYSFXWm2MnkyUBtLeDnJ4uiEBEREZF1GMwRkU3FxgLTp7cN3NRqBnFEREREF4LBHBHZHAM3IiIioq7HRcOJiIiIiIgUiMEcERERERGRAjGYIyIiIiIiUiAGc0RERERERArEAihEdN5UjQYMj3CDT2UOUNi9fUM+lTkYHuEGVaOhW1+XiIiIyFEwmCOi86auyUfmfH8gfT6Q3r2vnQggc74/smryAYzq3hcnIiIicgAM5ojovBn8Y5G0qgbr169HYkJCt752VnY2Zs2ahdVTYrv1dYmIiIgcheKCuRUrVuDFF19EUVERBg0ahOXLl2PMmDEdbm80GvH0009j3bp1KC4uRq9evfDYY49hzpw53dhqIuckPNTYU2xCXXB/IGpYt752XbEJe4pNEB5cwI6IiIhck6KCuQ0bNmDRokVYsWIFRo8ejVWrVmHy5Mk4fPgwYmPb752fMWMGTp8+jdWrV6Nv374oKSlBY2NjN7eciIiIiIioaykqmHv55Zdx5513Yu7cuQCA5cuX4/vvv8ebb76JZcuWtdl+06ZNSEtLw7FjxxAaGgoAiIuL684mExERERER2YRigrn6+npkZGRgyZIlLW6fNGkStm/f3u5jvvrqK4wYMQL/+te/8P7778PPzw/XXHMNnnnmGfj4+LT7GKPRCKPRaLmu0+kAACaTCSaTqYv2hsg5mL8T9vh+2PO1iYiIiGzFmvMaxQRzWq0WTU1NCA8Pb3F7eHg4iouL233MsWPH8Msvv0CtVuPzzz+HVqvFwoULUV5ejjVr1rT7mGXLluGpp55q9/WbB3lEBFRUVFguS0tLXea1iYiIiGylurq609sqJpgzU6lULa4LIdrcZmYymaBSqbB+/XoEBQUBkKmaN9xwA9544412R+ceffRRLF682HJdp9MhJiYGGo0GgYGBXbgnRMoXEhJiuezRo4dVjzUYgJoawN8fUJ9HDZMLee3udqH7SkRERK7D29u709sqJpjTaDRwd3dvMwpXUlLSZrTOLDIyEtHR0ZZADgASExMhhMDJkyfRr1+/No/x9vZu9w/o5uYGN7fuXRSZyNGZvxPWfj/y84G0NECnAwIDgXHjgA5qGHX5a3e3rthXIiIich3WnNcoJpjz8vJCcnIyNm/ejL/+9a+W2zdv3oxrr7223ceMHj0aH3/8MWpqauDv7w8AyMnJgZubG3r16tUt7SailgwGGdxUVAAREUBxsbw+fbpzjFrp9XpkZ2cDAIxGFVJTg6HTeUCjaUBenidyc+sQH5+B/v1jO5y7CwAJCQnw9fXtrmYTERGRAikmmAOAxYsX45ZbbsGIESMwcuRIvPXWW8jPz8eCBQsAyBTJU6dO4b333gMA3HzzzXjmmWdwxx134KmnnoJWq8UjjzyCOXPmnPUkiohsp6ZGjlJFRAA+PvKytBSorXWOYC47OxvJycl/XAsDMANAKQADADWAHgA2ACg/6/NkZGQgKSnJhi0lIiIipVNUMDdz5kyUlZXh6aefRlFREQYPHozvvvsOvXv3BgAUFRUhPz/fsr2/vz82b96M++67DyNGjEBYWBhmzJiBZ5991l67QOTy/P1lumFx8ZmRuZAQwM/P3i3rGgkJCcjIyADQdmROq/VETU0+Vq5cg3Xr1iExMfGsz0NERER0NooK5gBg4cKFWLhwYbv3rV27ts1tCQkJ2Lx5s41bRUSdpVbLeWNpaXJELiREXneGUTkA8PX1bTGiFh19Zs5cVBQQGlqJlSuNSExM5MgbERERXRDFBXNEpHyxsXKOXG2tHJFzlkCuPa339fDhBns3iYiIiJwEgzkisgu12rmDuOZcaV+JiIio+zhuPW8iIiIiIiLqEIM5IiIiIiIiBWIwR0REREREpEAM5oiIiIiIiBSIwRwREREREZECMZgjIiIiIiJSIAZzRERERERECsRgjoiIiIiISIEYzBERERERESkQgzkiIiIiIiIFYjBHRERERESkQAzmiIiIiIiIFIjBHBERERERkQIxmCMiIiIiIlIgBnNEREREREQKxGCOiIiIiIhIgTzO50E///wzVq1ahd9//x2ffPIJoqOj8f777yM+Ph6XX355V7eRiByUXq8HAGRmZrZ7f11dHfLy8i7oNeLi4uDj49Pm9qysrAt6XiIiIiKlszqY+/TTT3HLLbdg1qxZ2LNnD4xGIwCguroazz//PL777rsubyQROabs7GwAwLx58+zWhoCAALu9NhEREZE9WR3MPfvss1i5ciVuvfVWfPjhh5bbR40ahaeffrpLG0dEjm3atGkAgISEBPj6+ra535Yjc4AM5Pr163dBz09ERESkVFYHc0eOHMHYsWPb3B4YGIjKysquaBMRKYRGo8HcuXPPus3o0aO7qTVERERErsXqAiiRkZE4evRom9t/+eUX9OnTp0saRURERERERGdndTA3f/58PPDAA9i1axdUKhUKCwuxfv16PPzww1i4cKEt2khEREREREStWJ1m+be//Q1VVVWYMGECDAYDxo4dC29vbzz88MO49957bdFGIiIiIiIiasWqYK6pqQm//PILHnroITz22GM4fPgwTCYTBg4cCH9/f1u1kYiIiIiIiFqxKphzd3fHVVddhaysLISGhmLEiBG2ahcRERERERGdhdVz5oYMGYJjx47Zoi1ERERERETUSVYHc8899xwefvhhfPPNNygqKoJOp2vxj4iIiIiIiGzP6gIoV199NQDgmmuugUqlstwuhIBKpUJTU1PXtY6IiIiIiIjaZXUwt3XrVlu0g4iIiIiIiKxgdTA3btw4W7SDiIiIiIiIrGB1MAcAlZWVWL16NbKysqBSqTBw4EDMmTMHQUFBXd0+IiIiIiIiaofVBVB2796Niy66CP/5z39QXl4OrVaLl19+GRdddBEyMzNt0UYiIiIiIiJqxeqRuQcffBDXXHMN/vvf/8LDQz68sbERc+fOxaJFi5Cent7ljSQiIiIiIqKWrA7mdu/e3SKQAwAPDw/87W9/4yLiRETnwWAAamoAf39ArbZ3a4iIiEgprA7mAgMDkZ+fj4SEhBa3FxQUICAgoMsaRkTkCvLzgbQ0QKcDAgOBceOA2Fh7t4qIiIiUwOo5czNnzsSdd96JDRs2oKCgACdPnsSHH36IuXPn4qabbrJFG4mInJLBIAO5igqgRw95mZYmbyciIiI6F6tH5l566SWoVCrceuutaGxsBAB4enri7rvvxgsvvNDlDSQiclY1NXJELiIC8PGRl6WlQG0t0y2JiIjo3KwO5ry8vPDKK69g2bJl+P333yGEQN++feHr62uL9hEROS1/f5laWVwsA7niYiAkBPDzs3fLiIiISAmsDuaqqqrQ1NSE0NBQDBkyxHJ7eXk5PDw8EBgY2KUNJCJyVmq1nCOXliZH5EJC5HWOyhEREVFnWD1n7sYbb8SHH37Y5vaPPvoIN954Y5c0iojIVcTGAtOnAzfeKC9Z/ISIiIg6y+pgbteuXZgwYUKb28ePH49du3Z1SaOIiFyJWg2EhXFEjoiIiKxjdTBnNBothU+aa2hoQF1dXZc0ioiIiIiIiM7O6mDuT3/6E9566602t69cuRLJycld0igiIiIiIiI6O6sLoDz33HNISUnBvn37MHHiRADAli1b8Ntvv+GHH37o8gYSERERERFRW1aPzI0ePRo7duxATEwMPvroI3z99dfo27cv9u/fjzFjxtiijURERERERNSK1SNzADBs2DCsX7++q9tCREREROTQDAagpkauFcrCVWRvVo/MZWZm4sCBA5brX375JaZNm4b/+7//Q319fZc2joiIiIjIUeTnAx9/DGzYIC/z8+3dInJ1Vgdz8+fPR05ODgDg2LFjmDlzJnx9ffHxxx/jb3/7W5c3kIiIiIjI3gwGIC0NqKgAevSQl2lp8nYie7E6mMvJycGwYcMAAB9//DHGjRuH//3vf1i7di0+/fTTrm5fGytWrEB8fDzUajWSk5Px888/d+px27Ztg4eHh6XtRERERESdVVMD6HRARATg4yMvdTqgttbeLSNXZnUwJ4SAyWQCAKSmpmLKlCkAgJiYGGi12q5tXSsbNmzAokWL8Nhjj2HPnj0YM2YMJk+ejPxzjHFXVVXh1ltvtVTfJHJ0BgOg1bK3j4iIyFH4+wOBgUBxMVBXJy8DAwE/P3u3jFyZ1cHciBEj8Oyzz+L9999HWloapk6dCgA4fvw4wsPDu7yBzb388su48847MXfuXCQmJmL58uWIiYnBm2++edbHzZ8/HzfffDNGjhxp0/YRdQXm4xMRETketRoYNw4ICQFKS+XluHEsgkL2ZXU1y+XLl2PWrFn44osv8Nhjj6Fv374AgE8++QSjRo3q8gaa1dfXIyMjA0uWLGlx+6RJk7B9+/YOH/fOO+/g999/x7p16/Dss8/arH1EZ6PX65Gdnd3h/XV1dcjLy0NkZDy2bYuETucBjaYBeXmeyMtrREpKJby9BRISEuDr69uNLSciIiKz2Fhg+nSZWunn55qBHKt5Oharg7mLL764RTVLsxdffBHu7u5d0qj2aLVaNDU1tRn9Cw8PR3FxcbuPyc3NxZIlS/Dzzz/Dw6Nzu2o0GmE0Gi3XdTodAMBkMlnSS7sbvzTKd/jwYfzpT3/qxJZhAGYAKAVgAKAG0ANLl24AUI7ffvsNSUlJNmwp2Zr5OGLPYwoREZ0/Ly/5DwBc7TCenw+kp8u5goGBwNixMsClrmXN+cF5rTPXHnU3RRkqlarFdSFEm9sAoKmpCTfffDOeeuop9O/fv9PPv2zZMjz11FNtbtdqtS2CvO5y8qQbdu70gk6nQmCgwGWX1aNXLxc7cjiBsLAw/PDDDx3en5ubi3vuuQfLlz+HwsJRqK72REiIERUV3ggIaMDYsdfDy8uEsLAwlJaWdmPLqatVVFRYLvleOjeDAaitVcHPT7Ajjogcml6vx9GjRzu832Aw4NixQpw48ScYDL4ICTHi8GFvHDrUgLFjS+DlZULfvn2ZPdRFqqurO71tlwVztqbRaODu7t5mFK6kpKTduXrV1dXYvXs39uzZg3vvvReAjHKFEPDw8MAPP/yAK664os3jHn30USxevNhyXafTISYmBhqNBoGBgV26T+dKvauqMuLjjwFv7whERAgcP+6JsrImTJxYAW9vAQBMu1OQ3r17d3hfSEgIAGD06GRoNIMsvV4DBph7vQZ1VzPJxszvdUhICHr06GHn1pCtsPeaiJQkMzMTkyZNOsdW7WcP/etfZ7KHznauQ53n7e3d6W0VE8x5eXkhOTkZmzdvxl//+lfL7Zs3b8a1117bZvvAwMA26aArVqzAjz/+iE8++QTx8fHtvo63t3e7f0A3Nze4uVldL+ascnJyzpF61/6X5okn5JcGADIyMph25wTMny03NzfExbkhIsK18/GdWfP3uquPKeQYDAbg55+BykpZury4WF6fPp3fZyJyTAMHDkRGRkaH92dlZWH27DuxYMEs+PvHQqNpgFbricDARqSkzLPM6+fvWtew5u+omGAOABYvXoxbbrkFI0aMwMiRI/HWW28hPz8fCxYsACBH1U6dOoX33nsPbm5uGDx4cIvH9+zZE2q1us3t9pKQkHDWL86+fUcwZ863uOWWv2HQoLA2Xxrzc5DzUat50kekVO2tRVVaKjto+L0mIkfk6+vbicEBI/7850CUl8dApwOiomQ1T2Yd2JfVwdxPP/2E8ePH26Ap5zZz5kyUlZXh6aefRlFREQYPHozvvvvOMqRbVFR0zjXnHEnnvjh/R79+z8DfP55fGiIiBWi+FpV5ZC4khGtREZHyRUY2YOJEZg85EquDuauvvhrR0dG44447cNtttyEmJsYW7erQwoULsXDhwnbvW7t27Vkf++STT+LJJ5/s+kbZVAFSUirRvz+/NERESmBeiyotjWtREZFzMBpVAMJgNKqYPeRgrE5sLSwsxAMPPIDPPvsM8fHxuOqqq/DRRx+hvr7eFu0jAN7eAmFh/OIQESmFeS2qG2+Ul8yoICKlys8HUlODAcxAamowFJQE5xKsDuZCQ0Nx//33IzMzE7t378aAAQNwzz33IDIyEvfffz/27dtni3YSEREpiloNdsQRkaIZDDLLQKfzAFAKnc4DaWnydnIMF1RyZtiwYViyZAnuuece1NbWYs2aNUhOTsaYMWNw6NChrmojORmDAdBqeSAgIiIicmTmgk4aTQMAAzSaBuh0cs4cOYbzCuYaGhrwySefYMqUKejduze+//57vP766zh9+jSOHz+OmJgYTJ8+vavbSk4gPx/4+GNgwwZ5yaF6IiIiIsdkLuik1XoCUP9RWZ0FnRyJ1QVQ7rvvPnzwwQcAgNmzZ+Nf//pXi1L/fn5+eOGFFxAXF9dljSTnYB6qr6g4U+EtLc151l7Kzc1FdXX1eT02KyurxaW1AgIC0K9fv/N6LBERETm/8z1PCQ31RE2NDkAP1NTkIzS0EocPN1j1HDxPsR2rg7nDhw/jtddew/XXXw8vL692t4mKisLWrVsvuHHkXJx57aXc3Fz079//gp9n9uzZ5/3YnJwcHiiJiIiojQs/T/EG4IeVK9dg5UrjeT0Dz1Nsw+pg7oknnsCoUaPg4dHyoY2Njdi+fTvGjh0LDw8PjBs3rssaSc7BmddeMvd0rVu3DomJiVY/vq6uDnl5eYiLi4OPj49Vj83KysLs2bPPe1SQiIiInBvPU5yX1cHchAkTUFRUhJ49e7a4vaqqChMmTEBTU1OXNY6ciyusvZSYmNiJheDbN3r06C5uDREREdEZPE9xPlYHc0IIqFSqNreXlZXBzxmGWMimzGsv1dZyEXQiIiIiJTEY5LQZf3+ewzmKTgdz1113HQBApVLh9ttvh7e3t+W+pqYm7N+/H6NGjer6FpLTUat5ACAiIiJSkvx885pzctrMuHGyk57sq9PBXFBQEAA5MhcQENAiX9bLywuXXXYZ5s2b1/UtJCIiIiIiu3H2iuRK1ulg7p133gEAxMXF4eGHH2ZKJRERERGRC3DmiuRKZ/Wi4U888QQDOSIiIiIiF9G8Inldnbzk4uGOoVMjc0lJSdiyZQtCQkIwfPjwdgugmGVmZnZZ44iIiIiIyL5coSK5UnUqmLv22mstBU+mTZtmy/YQESlCbm7uea2Zk5WV1eLSWgEBAVx0lYiIuh0rkjumTgVzTzzxBABZtXL8+PG4+OKLERISYtOGERE5qtzcXPTv3/+CnmP27Nnn/dicnBwGdERE1O1YkdzxWLXOnLu7O6666ipkZWUxmCMil2UekVu3bh0SExOtemxdXR3y8vIQFxfXoipwZ2RlZWH27NnnNSJIREREzsfqRcOHDBmCY8eOIT4+3hbtIVIkVaMBwyPc4FOZAxRaXVfogvhU5mB4hBtUjYZufV0CEhMTkZSUZPXjRo8ebYPWEBERkauxOph77rnn8PDDD+OZZ55BcnJym8qWgYGBXdY4IqVQ1+Qjc74/kD4fSO/e104EkDnfH1k1+QBGde+LExEREZHdWB3MXX311QCAa665pkVVSyEEVCoVmpqauq51RAph8I9F0qoarF+/HokJCd362lnZ2Zg1axZWT4nt1te1NYNBrmvj78/8fCIiIqL2WB3Mbd261RbtIFI04aHGnmIT6oL7A1HDuvW164pN2FNsgvBwnognP1+WP9bp5Do248bJKlpEREREdIbVwdy4ceNs0Q5qxjwiYTR2vJ4fkbMyGGQgV1EBRETIhUnT0mQ5ZI7QEREREZ1hdTBnptfrkZ+fj/r6+ha3X3zxxRfcKFfWfESivDwYQIy9m0TUrWpq5Oc/IgLw8ZGXpaVyXRsGc0RERERnWB3MlZaW4o477sDGjRvbvZ9z5s5f6xGJvDwPAGM5Qkcuxd9fplYWF58ZmQsJkQuUEhEREdEZVtdQX7RoESoqKrBz5074+Phg06ZNePfdd9GvXz989dVXtmijy2g9IqHRNAAIRF1d95a6J7IntVrOkQsJkSNyISHyOkfliIiIiFqyemTuxx9/xJdffok//elPcHNzQ+/evXHllVciMDAQy5Ytw9SpU23RTpfQekRCq/UEoIOPj8neTSPqVrGxco5cba0ckVNqIGee/+rhATQ2sjInERERdS2rg7na2lr07NkTABAaGorS0lL0798fQ4YMQWZmZpc30JWYRyTS0uSIRGBgI4B0eHsvtnfTiLqdWq3swMc8/zUvDzh1CoiOBuLiWJmTiIiIuo7V+XsDBgzAkSNHAADDhg3DqlWrcOrUKaxcuRKRkZFd3kBXYx6RmDYNGDGiGkCJvZtERFYyz38tKQFOn5adM6dPy+tpafJ+IiIiogtl9cjcokWLUFRUBAB44okncNVVV2H9+vXw8vLC2rVru7p9Lsl8wnfoUDCAG1BU5GnvJhGRFczzX4OCZHplbCxQVwcEB8vbWZmTiIiIuoLVwdysWbMs/x8+fDjy8vKQnZ2N2NhYaDSaLm2cK2pe0TIkpBFACDIz/TFxIk/+HJlerweA8041rqurQ15eHuLi4uDj42PVY7Oyss7rNcl2zPNfS0rkfLn8fCAyEqisBHr2ZGVOIiIi6hrnvc6cma+vL5KSkrqiLYSWFS0LCwWAYtTWurMn38FlZ2cDAObNm2e3NgQEBNjttaml5vNf9Xo5OhceLgM5VuYkIlImc1ErFrMiR9KpYG7x4s4X4Hj55ZfPuzHUsqKlXF8uAn5+TezJd3DTpk0DACQkJMDX19fqx2dlZWH27NlYt24dEhMTrX58QEAA+vXrZ/XjyHaaV+R0dweampRdmfNC8SSIXAE/587LXNRKp5PnaSxmRY6iU8Hcnj17OvVkKhUXt74Q5h+BSy8Fdu0CCgs9AFQgKamGPwoOTqPRYO7cuRf8PImJiRzpdiJKr8jZVXgSRK6An3PnoNfrLdk2ZkajCqmpwdDpPBAQUINdu2qQmxuEyZNr4e0t2n2e8+3ctRVVowHDI9zgU5kDFHbv+sU+lTkYHuEGVSOrf9lCp4K5rVu32rodLq/1j8CllwLR0RX45z8/QWQklyYgciT2+lFU4g9i83nAEREy6yAtTY5aMtAlR6ItKsDPn69u9z69vha//36sw8c2NbmjsHAg6ut9oFbXwGDwR/qGOkRFHYa7e5Nlu4su6gNf37apNtHRUbhk8mzAy3FO/l1VdnY2kpOTW90aBuBmADUAKgG4A+iBZ57ZAKC83efJyMhwqM5ZdU0+Muf7A+nzgfTufe1EAJnz/ZFVkw9gVPe+uAu44DlzdOHaO9nZtQvo29cEwGjv5hFRK/b6UVTiD2LzecA+PvKytJQVPcnx/Pz5avy15D8dbxB+jieI+v7cL1Lzx7/WSoDjPXoiftS0cz8H2VRCQgIyMjJa3HbihBfWreuJqioPqFSnkZr6IRYsmIRbb5131pE5R2Lwj0XSqhqsX78eid3ctqzsbMyaNQurp3Co2hY6Fcxdd911WLt2LQIDA3HdddedddvPPvusSxrmSjo62amr695hcHvjXANSCnv9KCrxB7H5PODQUJmFEB7Oip7keMb89U58/nn7953PyJyXl5UjcyMmXfA+0IVrXdjPYAAOHQKGDAG0WkBmYIZj0qQgjBw52G7ttJbwUGNPsQl1wf2BqGHd+tp1xSbsKTZBePDkzhY6FcwFBQVZ5sMFBQXZtEGuqPnJjnlkLiQE8PEx2btp3YZzDUhJ7PWjqMQfRHNlz08/BTZvlrf5+splG/gdJ0eiiYzBXxc+ed6P5++YczJ3uPftC/TuDQQG6rBlSzaCgkbbu2nUhTozoOCogw6dCubeeeeddv9PXaN5GfPSUhnIjRsHaLXtD90rXevJxc4wsZjIWo76o2ALPXvKBdQHDJAnt+XlnDdHzqd5BVtXrlzrbPz9gYYG4PvvAS8voKjIH4DK0uHuSsdyZ9WZjhhH7qzhnDkH0d6PgFZr71bZRtvJxWEAZgAoBWAAoIbSJhYTWcORfxRsoaYGqKuTPds+PvKEiPPmyBmxgq1zEi36lc9Ubne1Y7kzKi/X4913T0On84BG04C8PE/k5TUiJaUSJpMeeXl5iIyMx7Ztke1u4+0t7D7AYHUwV1ZWhqVLl2Lr1q0oKSmBydQyFbC8vP2Tbzo3V/kRaD25uPnInMGQh9df/wgLFtyoqInFRJ3litUdO0ol57w5ImVypdGomhrZAXXVVYBKBRw/rsPGjQ2oqnLH0aOudSx3Rnv25GLp0lVoPaCwdGnzAYX2Bx3M29h7gMHqYG727Nn4/fffceeddyI8PJxry9mIwQBUVLgD8G5xmzMcPFtPLgaA6Gh5EDx0SACowJ//HIiRI4fYp4F2ZDSqoNUq/z2mjrlidceOUsmddX+JnJmrjUaZO6NOn5bp4mVlHgB0EML1juXOaPjwfnj66Ucso25arScCAxuRkjIPx44dxuzZs7FmzWs4efKydrcxj8zZk9XB3C+//IJffvkFQ4cOtUV7CGcOlIcOhQC4AUVFnk5/8DSnmbr22noxSE0NRmamc77HJLnqKBXnExEpz9nmuGs0DcjJEdi+vRjTp6sQHNz+l9reKWgXSq2WKeLbtsn5vk1N3gCOIji4ySWP5c4mNNQXt90WbznHjoo6c/5lzg4bOnQAJk5sfxtHYHUwl5CQgLq6Olu0hdAyBSskpBFACH79NQDFxfIkyJmH8tVqIDi4Cc64tl7rH8TW9u07AmAscnO1GDRItMnHNlP6jyK59iiVq6SSEzmLzs5xX7nSeee4GwzA0aNAv35AcDBw8KARQF8Arnssdzad6Wx05A5Jq4O5FStWYMmSJVi6dCkGDx4MT0/PFvcHBgZ2WeNcUfMUrMJCAaAYFRUe8PUF4uM5lK9UbX8QW5M/kO+//y90nLOt/B9Fkhz5R4GIyOxsc9w1mgYcOlSG999/BWvWvI6hQwd0+BxKZj4vi4kxn4M1AAhEXZ0bj+VOpDOdjY7aIWl1MBccHIyqqipcccUVLW4XQkClUqGpqamDR9K5GAzyn4+PHH0zGlUAIhAS0giNxrmH8s3zAeU+t7zNGeaPtf5BbK2y0oCPPxbw8opAVJSqTT528+ch5+CoPwpERGZnm+Ou0wH9+qkApGPo0MVO29HYOjVeq/UEoLMsTcBjOdmb1cHcrFmz4OXlhf/9738sgNKF8vOB1FQ54ubmBnh6AhUVHgAqcMkl1RgyxHmH8pvPBywvDwYQg6IiTxw65DxzBNv7QWytb184bD42ERER0DKzICenEkuXFti7STbVOjU+MLARQDqAh1iwjByC1cHcwYMHsWfPHgwY0P5wOlnPYAA2bAB27JBBnJsbkJQEjB9faSkG4qxD+a3LtOfleQCYiF27AqDROPccwdac9T0mIiLnYh6N6mj5IGfTNoAFC5aRw3Cz9gEjRoxAQYFz98J0t717gY8+AvbvBw4dkhNtd+4033umGIhaDYSFOddJvlYLFBYCoaEyvVSjaQCgQVWVR4tyvzqdPIg6O2d8j4mIiJTO/PssjYVO54EePWRndFqa7JwmsgerR+buu+8+PPDAA3jkkUcwZMiQNgVQLr744i5rnCswGIBffgHKyuSIHCD/7+kJ1NfLFFZnXXssPx/YvBk4fBg4eBAYOdKci65FUFCjU88RJCLn4kxzfImoY3q9G4BAaDQNLEpHDsHqYG7mzJkAgDlz5lhuU6lULIBynmpq5ElAjx7y/25u8l9oKODlJeCsa4+Z0ytra4FLL5Upprt2AXFxTQC24NJLF6O83DnnCBLxxN+5OPs6oER0hq+vCYAOxcWeCAsDqqqAnj3Z4Uz2Y3Uwd/z4cVu0w2X5+wORkbI61OnT8iQvNBQYMQJwcxMwD+UPGOBcc8eaL8Hg4yNPgE6dAoYO1QEoQGRkAyZO5Pwxcj488Xceer0e+/YdaVGqvfkakSaTHnl5eYiLi4OPj0+Hz8P1I4mUQ84TPIr8fG+cOiXP2UaN4nkK2Y/VwVzv3r1t0Y5OW7FiBV588UUUFRVh0KBBWL58OcaMGdPutp999hnefPNN7N27F0ajEYMGDcKTTz6Jq666qptb3TG1GrjySnlil5Ehe3jUahnU/fBDCIA4pxzKb13qt7xcVnAMCjozsstyv+RsWhf8caYOGleUnZ2NUaMmob1FlFuvEXk2XD+SSDnkEkp9ERtrxODBQGWlrHUwfLhzH8eZUeK4OhXMffXVV5g8eTI8PT3x1VdfnXXba665pksa1p4NGzZg0aJFWLFiBUaPHo1Vq1Zh8uTJOHz4MGLb6dpOT0/HlVdeieeffx7BwcF455138Je//AW7du3C8OHDbdZOa8XGAnffLUem3n8fyMmRQdvJk34ABqC42BPx8UBBgRzFcne3d4svXOtSv+ZUSq3WNSpjkWtqPSLtTB00righIQHbt29uMTLXfI3IY8cOY/bsO/Hmm+swdOhFHVb+4/qRRMoh58yFwt+/CcHBQECA8x/HmVHi2DoVzE2bNg3FxcXo2bMnpk2b1uF2tp4z9/LLL+POO+/E3LlzAQDLly/H999/jzfffBPLli1rs/3y5ctbXH/++efx5Zdf4uuvv3aoYA4wl/gFjh+XlxoNUFIiAHjDw0PgyBEZ7EVHA19/7RxfpPZK8Wu19m4Vke20HpFmcR9l8/X1xciRw1ssotx8jcjycg8ANyAv7xL4+cU6xXGbyNXpdO4AErBjRyAqKmTNgz59nPc4zowSx9epYM5kMrX7/+5UX1+PjIwMLFmypMXtkyZNwvbt2zv1HCaTCdXV1QgNDe1wG6PRCKPxzHIAOp3O8lhb77vJBAghL+VLCQC1GDGiEmVlvaBWA+HhwIkTwA8/ADffrPwvkpeX/AeY99v0x/9t//cmOl/n+zn18gLGjAHS04GSEiA4WF738jJ/523zumRbvXoB11/fsmNKrwcyMvwAhCAoqB5lZSb89BNwww3KP24TmbnaMclgAA4c8AVQjJCQehQXm9DQANx4Y+eP4/Zyvu+VTgdLRXU3N1nsRasFqqvPnL/Z6rXtzZ7ttub1rJ4zZy9arRZNTU0IDw9vcXt4eDiKi4s79Rz//ve/UVtbixkzZnS4zbJly/DUU0+1+/rNgzxbEALo21eNAwe8cPKkgF5fD2AvmpouglZbg4YGgSNH3KHXq7Bvnwrx8XoMHuxc1UMrKiosl6WlpXZuDVH7LuRz6uMjR270ehV8fQXUapmiY+vXpe5RXS3/lZWpUFpqBFCMhoZqaDRVOHXKHSdOGBAaynRycg6udkw6873OQkJCMUJD1aiudoPJZEBpqWN/rwsLCwHIKUjm960zTp70wZYtvVFe7gYPDy2io70RG9uAX38tgZdX5wKO3NxcAMr7nNjz811dXd3pbTsdzO3atQvl5eWYPHmy5bb33nsPTzzxBGprazFt2jS89tpr8Pb2tq61VlKpVC2um5dEOJcPPvgATz75JL788kv07Nmzw+0effRRLF682HJdp9MhJiYGGo0GgYGB59/wTrr1ViA1Va41V1trxI8/fg5v7zuRn++P/ftlL0h4uDwhPH5cjZEjHbOXNzc316oPotnp06cBAAUFpVCpiuDj09ThPJOOBAQEoF+/fla/NlFnhYSEWC579Ojh9K9L1gsIAHr0qAEQAU/PANTWBiE6Gujd298hj9lE58PVjknNv9e+vv5QqQIRF6eM77V54OOhhx6y4lHeAG4A0AeABkAEAC2A/+Bf/zpqdRtiY2MV9Tmx5+fbmniq08Hck08+ifHjx1uCuQMHDuDOO+/E7bffjsTERLz44ouIiorCk08+aXWDO0Oj0cDd3b3NKFxJSUmb0brWNmzYgDvvvBMff/wxUlJSzrqtt7d3u39ANzc3uJlX9bahuDhg9mwZzO3fXwPAgN27gxAQ4AZvb5m6U10NjB0LGI1AXR3gaBWtc3NzL3BCf1/ceefPAHZCVoNLB1Bg1TPk5OQwoCObMR8Luuu4YO/XJev5+gLJybUAKlBV5YVevdwwbpzjHa+JLoSrHZOU/L2+7rrr4ObmZtVSKBUV7ti8OQQhIY0oLDyJV199B7NnL8Qdd3yC4GDrMsOU2NFuz8+3Na/X6WBu7969eOaZZyzXP/zwQ1x66aX473//CwCIiYnBE088YbNgzsvLC8nJydi8eTP++te/Wm7fvHkzrr322g4f98EHH2DOnDn44IMPMHXqVJu07UK1LvdaUiInl+7YEQbgBfzyS2CLSfN1dbIYypAhjjnh1jwit27dOiQmJlr12CNHTHj77UA0NYWgZ08TgoMbERNTj5SUyk6N0GVlZWH27NnnNSpIRNSVIiMbAHyCSZMewahRsQ7fc09E56bU77VGo7EUEOwsgwEoLJTFT6KiAMAf/ftrMGpUvGL22xV0OpirqKhoMQKWlpaGq6++2nL9T3/6EwoKrBs9sdbixYtxyy23YMSIERg5ciTeeust5OfnY8GCBQBkiuSpU6fw3nvvAZCB3K233opXXnkFl112mWVUz8fHB0FBQTZtq9m50g2LijyRmemP2lp3+Pk1YfDgWhw86Ifycg/k5tYCCEJZWR1KSz3h5ibg6SlQXu6GnBw9Ro4sweHD9R0+t717QRITE61aO8lgAHbulAtw9uoliwl4ecm0hv79gbAwGzaWiMgmjAgObuKJD5FTcY3vdfNlpAoLPQBUICmpxun3W2k6HcyFh4fj+PHjiImJQX19PTIzM1sUCqmuroanp6dNGmk2c+ZMlJWV4emnn0ZRUREGDx6M7777zrKQeVFREfLz8y3br1q1Co2Njbjnnntwzz33WG6/7bbbsHbtWpu2FZCBXP/+/c+yhTkXOQRAMWQuciMATwA1AP4EQI+srCYAUX885iCAXBw+3IAvv1yPcy1Kq6R0w5oaoL5ezgk0V4U7eRIYNMgxRyCJiIjI+TXPoOrodmcNcMzLSEVHV+Cf//wEkZGyroQr7LtSdDqYu/rqq7FkyRL885//xBdffAFfX1+MGTPGcv/+/ftx0UUX2aSRzS1cuBALFy5s977WAdpPP/1k8/aczbnSDZvnInt7CxiNKpSWesLT0wSDwQ35+W7Iz29AdLQXamq80dQEjBgRg8bGqQgNbURKyh0dph4qMd3Q31+OvlVWyrK3J0/K6xMn8kBBRERE3a/1gtn+/l4AwnDihBcOHXKNhbTVavwxR05Wdeci4o6l08Hcs88+i+uuuw7jxo2Dv78/3n33XXg1W2BizZo1mDRpkk0aqXQdpRs2z0UODZVfjoEDgcsvB3btkrf16CEXCq+rA3JzgePHw6DRACkpwMiRdtgZGzIP5wNy/a2BA+V+nnVwk4iIiMgGWi+YnZUF7N/fE8CtWLeuJy6+GEhMdI6FtPV6PbKzszu8PysrCwCwb98RnDwZAp3OAxpNA/LyPJGX14iUlEoMHTqg08VVqOt0Opjr0aMHfv75Z1RVVcHf3x/u7u4t7v/444/h33r8mc7KHLx89hmwebO8zcdHXk6YAKhUMr0wJwd46y2gsVGOVGk0wNGjwPDhyj1odMQ8nN988V0iIiKi7lZTI0efIiKApiagoAAoL/cA0ICqKg+UlgKDB8v7S0vluYtSz1uys7ORnJx8zu3mzLkPwAwApQAMANQAemDp0g3IyNhsVa0EZfBGRYU7DAbHfW+tXjS8o8IhoaGhF9wYV9SzpyzwkZAAxMQAv/8O/OtfcomCyEhgzBhg926gqkreVlsr0xDlOnSO+8G6EGq1c+4XEbk2V5lj0t5+usq+k3K0V6DOaFRBr3eDr6/JMv2lvDwY+/Z5oaTEC9nZPnB3NwBogkpVguxsICREB4PBHYGBjcjJ6VzlbXsXqGtPQkICMjIyOry/rq4OeXl5iIyMx7ZtkZaROa3WE4GBjUhJmYeEhAHd2GLbKyryBHADNm8OQWGh46aTWh3MUdeqqZE/chddBLi7A8eOyWH8mhpg3z7ZC+Tn51pFQfijT0TOxlXmmLS3n4Br7DspR/sF6mIAjAUQCECHM2vc9gXwIICeAAIANAAYhNTUrwEEYsuWQzCvibt0aeerujtagTpfX99zjqqNHj0aANC375nvdFSU43+nz1VZvj1GowrffKMHEILa2hPYv19lSSftTMBu1h2BO4M5O/P3lz9uxcWyDP8fKcmoqpI52qdOAZMmydRKVygK0tEJDwM8IlIqnc4NO3fKuc8xMcqfX9PR3BqjUYXU1GAUFwsYjafh7R2O3Fw3qFSAXu/eYn7NPfeEIzSUc2vIPloXqDN/dtuONlVCr3fDxo0h8PU1obFRhT171MjNvQRxccCgQUZcdtkYxMUZ4e29uFOvrcQCda0paUrMuSvLdyQM5nTS11//F5qnk56rknxrtg7cGczZWfM1PE6cADw8ZOBSXg4IIUv16/VAnz7OXxSk9URj8wnPpZfKgjDs1SUi5YnB99+H4PffZVp9SIjy59d0PLcmDMB8AOGQS+w0AKgGUAkgD83n14wePR9XXDG0W9pL1BFzgTqtFsjMBAYMkLUL6urkd7R/fxmsmIvVqdXA/v1ynty11wLV1bKeQWKivHSlDmelTIk5V2X5jhiNKmzc6Ie8vCr07euP6mp/SzppZ0fmuitwZzDnAMw9HGVlsrDJl1/KgwIgA7gDB+Qi2hMmyAONEr4856P5RGMfH3lZWAhs2QI0NLQM8JTao01ErkOrdQcwA1VVHujZEygqkieM8fEysFNqqnxHc2t0Oje89loU8vP12L37U4wYcT2io30xcGAdGhrcWox4DBsWboeWE7WveZaU+VwjJOTMqNO4ccDq1cD338v7evWS2VK9ewNHjgD/+5/sgGeHs+PqqLL82URHn8kWi4933PeWwZyDUKvlaNzhw4DRKCtZqlSy16euTo7O7d8vgzlnY06h9PCQB8KCAiAoSKaa+vjIv0dU1JkAT8k92kTkGnbsAF59NQrAjcjI8MPEifL2khKZYTFunHKPYR3NrdFqgWHDAHf309i92xfx8T4YMqQnrrlG/n41n1/DmmnkSJpnSZWWykCu+Xc0MFBOewkJkUXrSkpkYDd+vLzd19c5UqipJaWkkzKYcxAGA7BxowzaIiPlB6euTs6jCwiQ683pdM4XxLRdjBPYtk0GtqGhwI03yuCuvd4yIiJHVFkJvPsuUFPjDqAEFRWJ+PVXeeI3aBBw000y68LZ+PvLqsvl5fUAfkV09J8RFyc7IQcMcPwTInJtZztxP31anqckJMglCtzcZNDX2ChHb2Ji2OHsrJSQTspgzkHU1MiRuJ49ZYATECBHpDw9ZfGTysozaTmOXgxE1WjA8Ag3+FTmAIVuHW5nrAcOpAIeOqBfGHC6CDicD1waC4QMlaOStUeBsYOBgweBmkLgIn8gaRCg7mDuqU9lDoZHuEHVaLDR3hERnd3p07JDKiqqAUAOwsJGo7xcHq+nTnXOQA44M7qRl9cIwAehoY0tRjcc8feKqLmOTtzDw2UHc36+DPq8vIB+/WTHzK+/ssOZ7IvBnIPw95cHgqFDZdGT48dl2mFwsExdMZnkj2RJieOXeFbX5CNzvj+QPl9W9u2AN4Cp5iu6Py41AOoBnP7juh7ASaC3ebtKeb0jiQAy5/sjqyYfwKjzaT4R0QUJD5fH5yNHvAAA3t5NGDAAmD1bHuedWWwskJJSiaVLNyAlZZ7D/T4RnY/gYJkp9M47QHa27GwfMEAGcn37ynoH7aVnEnUHBnM21NkRKkDW90oZBIQagcFhQE6OHLJXqWQFpbpjgDFP3m4eySorBQ78AISnAN5eLZ/PniNUBv9YJK2qwfr165GYkNDhdsZ6IDVVBqZhYbI329zrFR4uC8IEBgJjx8pUBl/ftvvZWlZ2NmbNmoXVU3gGQUT2odPJ1Kvdu90BXAZ3d4Hbb3f+QM5MVnort2otJlIWc4aQ0aiyd1O6RX6+nPKRkCA73Hv3BpKS5Ejc0aPAX/4i0y+ZRkz2wGDOhjo7QmUWiWYjVX2a3WE+AfgRsKxSoWt2ubbtc9lzhEp4qLGn2IS64P5A1LAOt/MGMGSSHGnM1QGBkcAVY+SBMVcHBPYAQvsCX1ixLEFdsQl7ik0QHjyaElH3My+xEhYGjB9fiX37CuHh0Q+//y6XmOFIFSld87nu5eXBkIttO6/myyb17Ck7l2tqAHf3M3Pkmprkd57IHhjM2VBnR6iaKyqWa6r9tBXIPSqH7NVqWQHsoovk6FRjozxomEeuUtoZmVPKCFV7E46HD5fX3d2Br79uu+4cq0QRkaMyL7ESGgps3+4NmTcuK9456/HL0edxU9dpvR5sXp4HgLFOPULXfNkkd3eZOXT6tKxlUFvLOXJkfwzmbKizI1RmBgOQugUo0QN59cBvJwFTvjxQXNQExI0Ehl4ugz3zyNWQcYB3O/GakkaoWk84Nl/XatuuO8cqUUTkyMzrVe3fDxw86AsgCbm5PujRQ6bNO9vxq3VF4nHj7N0isqXW68FqNA0AAlFXd/apJErWeg26Hj1kp3p1texY5xw5sjcGc3bWvEfTfJAE5OKyvXrJH35fX3nf4MFA//5yNMsVSjyfbRFPIqLukpubi+rq6k5v7+/vhezscJw+7Q0AcHOrRkaGCTU1ehw4UIjAQFOnnicgIAD9+vU794Y20Jl9NhpVSE0Nhk7nAY2mAXl5nsjLa0SvXkcAAFlZWef12vbcbzq71r/LWq0nAB18fDr3mVai1mvQ9ekjq1iGhTn/eRgpA4M5O2rdozlsmKxi+eOP8j4h5EEjPl4uURAdLR+nhDUvusK5FvEkshe9Xg8AyMzMtPqxdXV1yMvLQ1xcHHx8fKx67PmeHNP5y83NRf/+/a18VBiAeQCSAajx++8eANTIzs7El1++BKCo08+Uk5PT7YFN5/c5DMAMAKUADJClvHoA2AAAmD179nm3wR77TefW+nc5MLARQDq8vRfbu2k2pZTFo8k1MZizk9Z554cPA1u2AAcOyOUHALlo+IkTcsLtlVe65uRaHkDJEWVnZwMA5s2bZ5fXDwgIsMvrWsNZ5lGZR6fWrVuHxMTETj3GaFRh06YQ7Nzpg5qaaqhUQaiq8kavXoPxpz9dh0svrUZkZMNZnyMrKwuzZ8+2akSwq3R2n1uPzGm1nggMbMTo0bNRVHT8vDss7LXf1DnNf5dzciqxdGmBvZvULVylI52Uh8GcnbSeUKvVytRKQKZVqtUyL7uqSgYxU6a47kGEB1ByNNOmTQMAJCQkwNfX16rHmk9WrQkOmlNCClp786iUXsUxMTERSUlJnd4+OlpmE+zeHY68PLnAcEoK0NioQXk5MHGi4x/XOrPP0dFn3uuoKPN7HQ+u8+nczL/LXH6CyP4YzNlJ87xzX19ZGalXL7kQZVmZvE1OLpYnAZwnRuQ4NBoN5s6de0HPYW1w4Oj0ej2ys7PbnUeVm1uH+PgM9O8fe9aRmvMJjh1VbCywYAGQmwt8+SUQFyeP6RUV8hjvLIVQmD1BrqR5xgEgO+IBea7Gzz7ZC4M5O2med15WJlMoIyNlQHfypPxhDA6WQd3Jk8CmTTLVUum92+fiLKlZRK4mOzsbycnJOPs8qvKzPkdGRoZTBbhqteyM690byM6WnXfl5TJ13nzcdwbMniBX0DzjoLZWzhksKJD1DUaMAGbOdP5zNHJMDObsqHmPZlmZXHJApwPmzJHrl+Tny8Inl14qt3HWNYrMnDE1i8iZnK3CYV1dHdatW4f6ejf8+mssamu9EBRkQFWVGnr9KXz66Ro888wziI+P7/D56+rqOiwqo4T0UqBth5RaDcTEAO+8I0flwsJkcLdrlzy+OevxnFyTs3bINq9zIATw+eeyo12tliPS+fnyPG3RItkRT9SdGMzZmfnHPiys5ZIDWi2wfr2cjxAUJIuhOPMaa60LwnCBcCLHYl1VxxgAYwGEAmgAkArAiH/84x8X1AZHr3Bo7pAqKwO8vOQcudhYWeAqOFiO0jU0yH/OlGpJBCirQ1bVaMDwCDf4VOYAhedeI6+uEvAqA/r6A3v3AXHegH+QTJ328pJTZHLSgI2BwPjxQGREx8/lU5mD4RFuUDUaumx/yLUxmHMgzVNVNBo5mby8XB4onG2Ntda9d1otUFgog1cuEE7keKyt6njihBd+/TUADQ0q+Po+iLCwQxgxoqfV1Q0BZVQ4NHdIHTsmj12nT8uFw++4A6ivl8fz+nrZOXfyJDBokPMcz8k1GQxARYU7AG8YjSpFdciqa/KROd8fSJ8PpJ97+xAAMwGgEpgSBmB8BxvqAHx19udKBJA53x9ZNflgoSDqCgzmHJQzr7HWuveub18gK0v2Xh88CIwcCTQ2OlfwSuQsOlO4xWAADh2S84DNJ3YhIf2RnOwcx7D21NTI0bbSUhm0mec/79wpj3Majey0OnlSZmIooZolUUfMv+P79oUCuAM5OWrU1MjvuxI6ZA3+sUhaVYP169cjMSGhU48pKpbp0ZmZ8vxFq5X/GhuB8HBgwABgRDJQXQNcdRUQHNT+82RlZ2PWrFlYPcVBhy1JcRjMOTBnrBLWOp2yoADYtk2mH116KbBjhzxYjh3rPMErkatpvvSKEk7suoK/v8yiMFcmrq2VJ3hCAMnJcv5zcDAwcKBMv7R6HXIiB9F8FDo/3wvABHzxRRhGjgSampp34Dhuh6zwUGNPsQl1wf2BqGGdekxkFHD1QCB+FJCRAeTlAU25cgkpEQxohgBH9UBIGKCOh6z91I66YhP2FJsgPJz0YEjdjsGcg3O2KmGtT/KCgmQqaXCwrPAWGCjTLa++WqZcEpHyNF96xXxi5+cn5/4aDM51TDNTq2WQtn+/HH0LD5ejcWFhssd+wADn6pg7X85aIMOVNB+Fbmx0A1CA6moPNDTIz7uzZRO1Fh0tixg1Ncn5r9nZwJEjZzKKnHW/yXGPXwzmHIyjflCsodfrAaDdqnRGowrl5cHIy5NrUBUXe6KpyRsHDxoREdGAwkKB+vpiHDmiwunT1v8BsrKyLrj9RHRhWqeJNzUB1dVyvTVHL4xwIfr3BxYvBlJTZaplQAAwZIi8z9k65qxh/l0rLz9TtdmZPwfOzjwKfeoU4OYmAAQhLKwRfn6yI9Zc4dHZPu/tTRE5elRe9/EBLrtMdto4236T5MgFfhjMORBH/qBYIzs7GwAwb968DrYwV7oLhJwtfBRA32bX07FyZcEFtSEgIOCCHk9EF8acJl5WBnz/vTyZ79HD8QsjXKj+/eW+HzkiU7G2bQMOHJDH8549ld9Z1xnNOyVLSs5U+MzOliO1Awc6/+fAGTV/XwcOBD75BDh1yhdADDw8BMLC5MicM76fZ5siEhMjP8/798tgjpyPo1dcZzDnIBz9g2KNadOmAQASEhLg6+vb7jZGowp1dW7w8THB21tYrp84cRhz5qzvdMW89ihlPSoiZ6dWA97eMr3SlebPAfLEzlwQorgY+OwzOVJnMCi7s+5cmndKqtVyRNbNTe57WRng4QG4u7vO58BZtPe+jhoFHDigR05ONQwGFS691HnfS3NqaUCA/PwGBcniJwkJ/Dy7AkefB85gzkE4+gfFGhqNBnPnzj2vx+7YYQIQhj59BiIpaXjXNoyIul178+ccuTBCV2h9PA8NBTZvlid+F13k+J111q7BZWasBw6kAh46oF+YHL3QHgeGDgUC3IDkP5bbcS8B6uuAuEDAvxqA8cxzcA2u7pebm3vWZT+MRhVSU4Oh08npEYcPe+PECW8kJ1cjPLwQwPfw8emNkyeLUFPTZNVrK6XztbxcjiyXlcn5sCqVvC0tTc6h69ED6NPHuY9rrszRf8cYzDkIR/+gdIf8fCA1NRjADKSmBiM62jl7rolciTMvs9IRDw95sldQIFOw8vPl7TExyuiss3YNLjNvAFPNV3R/XMYAKJf/rg6BXLDrdLNt1rZ8Dq7B1b1yc3PR/5ylVcMAzABQCsAAIADANHz7rQlADYDLsX79OqxfvwotIvNOysnJceiAzmCQcz0jIuR3u7AQqKyUa0UKISvYNjYCN93kmN9nunBqtay4npoq3/+wMMf6HWMw5yDaO+G59FLZw2u+35mZ00x1Og8ApdDpPBy655rI1ZzvaA0AxHoAM8bIdEsfH8DbA0Bh5x+vpNEaczpaWZksEKHXy557Hx/Zk+/l5fidddauwWWsl/vp4QGkp8tRyaBAYPsO+Z6Hhsry7UFBwIwZchTDxwfw9mr7XFyDq3uZR+TONrWh9chccbEnDh3yQUODG7y961FdXYvk5Mtx0023w9tbdPq1s7KyMHv27LOOCjoC80j7wIEypbKgAPjxRzlCFxMjA7nqanmCT84pP18G9PX1curApZc61mADgzkH0rxgQEEB8PPPzj+/wsx8sNRoGgAYoNE0QKdz3J5rIldzvqM1Zt5//DsfShmtaT73ecAAwNdX/rvuOnl8U8ropDVrcOXnA2k/N6vwNwgoPwocKAQKGoFLL5cnvZWV8oQ3dBAQfJaTXq7BZR+JiYlISkrq8P7o6DNz5sLC5Nwxf3/5+R4wQK6j2L+/cwY0rTOnfv8dyMmRlSwjImQacVKS43bOuLoL6YgEWqaPDwyT5+i56UBvr/Y7pJrrro5IBnMOpqREDuOmpQEmEzBypDwxcPZRKvPBMi/PE4AaWq0noqJ4cCRyFNaO1pyNeSTH1/fcP4aAckZrWs+Vi4k5szSDubPOmdaaa69w19GjwF/+IvfTXMW0qUleDwvjMV2pmnc2f/ON/O66ucnPQEYGMH688763zTOnTpwAdu+WtwshA7u6OmDOHOf4TjujruiIbJM+3k6KeHu6qyOSwZwDMf8wFhfLdBWTSf4wJiXJFBVnHqUyHyzz8hoB9EBgYKND91wTuRprRmvOpvVITmeyDpQyWnOuuc/OttZcR4W7mprkSE5KinJGI+nczNVpGxtlR/PRo/LcxM0NGDHCud9bczD766/Axx/Lz3pwsPxsNzTIObLkmC60I9JYLwdZzKPSZWXyOJ+Scu7OyO7qiGQw50C0WjmxMjJS/r+iQh4oCwqAXr2ct9fLLDYWSEmpxNKlG5CSMs+p00qJXJEzLcHSkSFDgMxM1whgzhW8OuNopKszv+cVFbKjOT9fvveusL6aWg1ERcl5r3V18jOt18uODB8fe7eOOnKhHZHeAIZMkr9VuTogsAcwZBzg3Ylz1O7qiGQw5yDy82Xp6sOHgYMH5YGxqEiOzoWGOvcJQXNy8nS5VZOoiUgZnGkJltaaFz5RqYDLLpPH8cZGGcQqff/a05lKpc42Gunqmr/nVVUyldhVzk8A2bE+caJMNc3OlqmWvXvL0WhyXj17AhMmyGN7WJjjfd4ZzDkAc291ba2skLNjB7B3r5xgbC6F62qMRhW0WtkL6GhfGiI6P0pcgqUzk+fNE+T1JwFDhaxa+dU+WRBCrZb7nZQEREZ0/nWVUsGTo2+ux5Xfc7UamDVLzp0rLpbBXa9estJhbKxr/S1chbmjzpqpAd3NBcMEx9O6t1qtlhPH4+PlIrMFBbIX6OabZY6284tBamowMjMd94tDRNZT4ppznZk832KCvHktNUAuyWUAUAngpHWvq5QKngBH31yRK7/noaHAsGGywz04WI7KOUuGAbWklKkBDOYcQHu91Wo10KePDPSOH5dVLgHgz3927sDGaFQBGAudzgMDBjjuF4eIzo/SevU7M3neWA98+aUsjhAcLOfRlJTI+TWXXSaP4wcPymN6jx6dG6VTSgVPIlfj7y8DudOnZUGY8nLHzzCg86OUqQEM5hxA897qwkI5uTYuDjh5EsjLk5e9eskJt84e2Oj1bgACodE0OPQXh4jOn5J69Tszed4bQM+hQOaXQNlBeaLn4wMYDUC8G/DjH6XM+8cAv5cD5YeA6QPP/jdQSgVPIldTUiLXTDxyRP4bNszxMwzo/ChlaoD1q+eRTcTGyvlylZXAzp3A/v1Aerr8f3W1nETv4wPLQtrOytfXBEAHrdYTdXXyixMY6HhfHCIiM4NBpsMnJMhjlXkpmZgYeeLn5iZLuQcFyRMCZz+Ok2sxGGQFboNjT+/sEua0Ozc34MorZZGjoCBZIIOcj3mwJSTEsacGcGTOhvR6PQAgMzPznNsajSp8+KEGP/0UBJNJBZVKQK8HTCYDNBqB06fd8c03KowYUY2cnIpzVnvMysrqkn3obnK/0hEY2OjQXxwiOjuDQaaouEIRo5oaueZWTo6sbqfRyPSr8HC5gPbWrTJ4M3dQOWLPLtH5UEJxiK7UOu2ub19mDzk7JUwNYDBnQ9nZ2QCAefPmdWLrKAAvAogDcBpAMIBIANk4fvwUAC8AHti48T945pkDnW5DQECAVW12DAVISalE//6O+8Uhoo652gmeh4dMiddq5WLZFRXytvp62Wt/5ZXKKvpC1BlKKQ7RlZSSdkddy9GnBjCYs6Fp06YBABISEuDr63vWbU+f9sCrr0ahuNgL7u6DYTCoUFwsUFZ2AHPnDoWXVzw0mgZcffWwTq/BFhAQgH79+l3obtiFt7dAWJi9W0FE1nLFE7yaGplmFRoqU+UDAmQw5+cn/4WFOX7PLpG1lFIcoispsSIvOT8Gczak0Wgwd+7cTm1rMMgFKL//Xvbw1tUBPXrUoKwsG2Fhl2LQoGhceikQGhrnEmlLRKRMrnaCl58PpKbKuXH+/jKoq6+XlykpZ/bZ0Xt2iayl5FEqa6bBtFZXV4fGxnzEx/dBSIgXtFoBrbbzj1fqNBhyXAzmHIRaDUydChw6BLi7y/LV7u46ZGcfw/jxlejTJxa7drlO2hIRKZOST/CsZR6FrKmRBax27JBz5kaNAq6+Wi4aTuSslDxKZd00GNtQ5jQYckQM5hxIaCgweLCsehYcDBw9agDgAwDYtcu10paISJmUfIJnreajkO7uMogrLweuv17OnXMFrlTohtpSQnGI9lgzDcbMaFRBr3dDfv5hzJlzM9atW4fExMTzen0lT4Mhx8NgzoE079H29ga0Wk8AOgjhWmlLROR4rE1L6ttXhbo6N/j4mFBQoMfPP+chMjIeQvjB19fU6bm/gOOmJZmP2VlZ8phcWCjny5WXu0Yw52qFbqh9SkwhtmYaDNDys15eHgggBomJiRg4MImdGWR3DOYcSEmJXJ/IvBClRgMA6QgOfshl0paMRhWAsD8uichRXHhaUgyAsQACAegApAMosOoZHC0tSa2W6ZXbtsl15urrAaMReOMNYPFi5aZZdiZwNxpVSE0Nhk7nAY2mAXl5nsjLa0RKSiVMJj3y8vIQFxcHHx8fq17bUQN3cl3NizqFhgKHD3sBmIgTJ7xw6BA7M8j+FBfMrVixAi+++CKKioowaNAgLF++HGPGjOlw+7S0NCxevBiHDh1CVFQU/va3v2HBggXd2OLOMR8s3N1lGeuCAqCurglACby9hUukLclCAsEAZiA1NRjR0TwwEjmK80lLMtu37wjmzPkWt9zyAAYNCoNW64nAQHnir/TqvKGhwEUXAY2NgMkklyI4eRLYskUev5R4nO5c4B4GYAaAUgAGAGoAPbB06QYA5RfcBkcL3Ml1mdOpPTyAzEwgP98LwGRs3BhiWTS8pITTX8h+FBXMbdiwAYsWLcKKFSswevRorFq1CpMnT8bhw4cR285Z//HjxzFlyhTMmzcP69atw7Zt27Bw4UL06NED119/vR32oGOtK8B5eQGZmW4A5PCbUvPSO8sczOp0HgBKodN58MBI5ECsTUtqrqLCHcDPGDQoDAMH9kFdneyY6t8fil+CxN9fHpOrqoBeveQxOjxcjtApNRW+M4F765G5MwH6PBw7dhizZ88+7zlFjhq4k2vy95fnZVu3ynOVkye9AfRBenog9Hp5v4cHoNcr9ztPyqaoYO7ll1/GnXfeaTmhWL58Ob7//nu8+eabWLZsWZvtV65cidjYWCxfvhwAkJiYiN27d+Oll15yuGCuvQpwfn5NAGot2ygxL72zzMGsRtMAwACNpgE6HQ+MRM7A19cEQAet1hN1dc6VKq5WyyUI9u+XI3Lh4bIacViYcvevs4F7dPSZeURRUWfSzMyjrYmJiUhKSrJ1c4lsSq0GkpKAH3+UI3Du7gLAAeh0g7F3LzBmDFBUJEfn3d3t3Vrqakoo8qSYYK6+vh4ZGRlYsmRJi9snTZqE7du3t/uYHTt2YNKkSS1uu+qqq7B69Wo0NDTA09OzzWOMRiOMRqPluk6nAwCYTCaYTKYL3Y0OeXnJA0J6ujxY+PoCYWH6bnltR+DrKwsHHD/uDkCN0lJ3REaa4OMjU5eInIX5u+wK32szT88mAOkICGhASYkJwcHyeOfl5fjf75qaGgDA7t27z/p+XXmlF377LRD19SqoVE0ICanB7t06y9wxtZVnAea5Y478OenVS1bubJ4xYjK55mdcqez5Xinpc9K/PzBihByBDw/XIyMjACqVCXq9CQcOAH36yA6NhgbHP6a5mgv5nOXny/Ny87zIsWOtm/5zIa9tzfaKCea0Wi2ampoQHh7e4vbw8HAUFxe3+5ji4uJ2t29sbIRWq0VkZGSbxyxbtgxPPfVUu6/fPMizBR8f2bN59Kg79u/3REaGO4A7cOCAATExpTZ9bUcwaJAbDh3SAegBd3cdBg0qQ3W1CdXV9m4ZUdepqKiwXJaWOv/3GjDvcwGSk4+jTx9/+PoKqNUy1dLR7d69GwAwf/78TmztDSAUgApAGYAL/81oaGhQxOekuhqWY7UrfsaVyp7vldI+J5dc4oY9e/xw9KgKQBC8vRug0bgjNLQJ1dUqBATUQ683MJhzMIWFhQCA9PR0y2euM+rr3bBlSwgKCmrQp48vqqv9cehQA8aOLYGXV+fe5NzcXADn9xmvtuLkVzHBnJlK1bLKoRCizW3n2r69280effRRLF682HJdp9MhJiYGGo0GgYGB59vsTjPPHausBMrKDAAm4Jtv+mPUKA2cfQpBjx6AwZCPf/1rA6655k4MH67wyTRE7QgJCbFc9ujRw86t6R7mfQ4PD8KAARo7t8Y6s2fPhr+/f6cKvxQXeyEz0x+1te7w82tCYOBePPjg9Xj//feRkJBg9Wsrde6YK37Glcqe75XSPifm9Ok33tBi+/YKDBxYj4iIADQ0yPTxkSO9ERPDwj2Oxjzg89BDD1n5yPaLPP3rX9YXeYqNjbX6M+7t7d3pbRUTzGk0Gri7u7cZhSspKWkz+mYWERHR7vYeHh4I62DWvbe3d7t/QDc3N7i5uZ1n6ztPr5drFJWVAUajGwAtyss9sGWLG3r3dtx83a7i46MCUA4fH1W3/L2Jupv5c91dxxRHoOR97tmzJ+66665zbmcwyPXmgoOBhAQ5L7CqSgXAGwMHDnSJuWPmuSUNDXLikBLfb1djz++mEo8LAwYA111Xjtde+xHx8SMREeGGzEyZWXXokEy1ZBVux3LdddfBzc3N6krMRqMK772nx8qVH+Lee2dArY6zFHmyZp3U8+2Us+Y7oZhgzsvLC8nJydi8eTP++te/Wm7fvHkzrr322nYfM3LkSHz99dctbvvhhx8wYsSIdufLOQJ/fzmP5NgxoKLCA8AQ6PXuKC5mMRAici5KmFjeWa0rEkdEAIWF7jBXJHZ2LRdVDoZcV5DI+QQGmgDsxcmT3vj1V1nJcvJkeY7GKtyO50IqMZeXH8DKlRXw8+uNQYNiHHYtQWV0hfxh8eLFePvtt7FmzRpkZWXhwQcfRH5+vmXduEcffRS33nqrZfsFCxbgxIkTWLx4MbKysrBmzRqsXr0aDz/8sL124ZzUalkYwGAAyss9AVTDw0P8UUHJ3q0jIuoa+fnAxx8DGzbIy/x8e7fowjSvSGyu2Nm6IrGzar6oco8e5iVmxsJo7HgKBDkHWapf/jMY7N2a7iE/130RHt6Aiy6SxU/Ky+Wak+Yq3OQcIiMbAHyCSZMqMH26YwZygIJG5gBg5syZKCsrw9NPP42ioiIMHjwY3333HXr37g0AKCoqQn6zM4L4+Hh89913ePDBB/HGG28gKioKr776qsMtS9BaZCRw+eXA/v11yMk5iV696hEdDTQ12btlREQXrvnJv3kpFqX3aKvVsoBVWpos7BISAlx0UQ26ogiKo2s9KimXmAlEXZ2i+ovJSvn5wKefAnv3yuvDhwPXXee4J7xdRa93AxCIXr2MKC+Xx7GqKqCgQFZ4VeqSJNQRI4KDmxz6t0lRwRwALFy4EAsXLmz3vrVr17a5bdy4ccjMzLRxq7qGOeXIwwPo1w+orjYA2IGoqMmIi+MBgoicQ3spiaWlyk8lj42VAam5VP/hww32blK3aL1OqlbrCUAHHx+W9XNWBgOweTOQmSnPWQAgI0N+Dm6+Wdnf43Mxr5tZVeWJvn2BHTsANzegZ0/ZoePM+06OSXHBnLNqPt8gMBDo2xfIy2sE4IPQ0EYeIIhIMc41F671yb+zLSIOyP13lTTD1qOSvr5NAA7Yu1lkQzU1gFYr5/hr/ihQq9XKf0rvlDkXWfwiHYGBjRACmDABSE6WxVGceb/JcTGYcwDtpRwdPQqMGVMFYANSUuZZ0hacqWBAx7xRUeEOg8GZ95HIObXumBo3ru027aUkOkuHlasWAjGPSh45IlPvgNFITQ1GdLTzp925In9/GcQdPAiUlMjbGhvlbc7QKQOc63yrACkplejfX+6vMxy7SLkYzDmAjlKOTCZZpt9cArW9kyRn+5EsKvIEcAM2bw5BYaFz7iORM9Lr9di37whSU4Oh03lAo2lAXp4n8vIa0avXEQBAVlZWi8f07atCXZ0bfHxM0GoFNBrrSkc7mtYdc3l5rlcIZP9+oK7OHUApdDoPxc+FpPap1cCVV8rzEfOcueRkICXFOd7rzpxveXsLdLDKFVG3YjDnADpKOWo+38AZCwa0ZjAAmZn+AEIQEtKIigrn20ciZ5WdnY1RoyahvUVWgQ0A5ALcZ5ORkaHo9dhcvRCIef/lfhug0TRYqvvxGO58YmOBu++W6+ICckFtZ3ifXeF8i5wLgzkH0DrlyM8PGDJElrc2c9aCAc3V1AC1te4AiuHtLZxyH4mcVUJCArZv39xiZE6r9URgYCNGj56NoqLjiIuLg4+Pz1mfQ8lcvRCIef/z8jwBqKHVeiIqynnS7qgttRqIjrZ3K7pW6/Ot0FDg1CkZtDrbvtLZyayKMIfPrmAw5yCazzfIyAC2bWs538KZCwY0r+Ip12WKgNGocqp9JHJ2vr6+GDlyOKKjz6QnRUWZ05PiAYyydxNtrnXHXGBgI4B0eHsvtnfTuoV5/2Xxrh4IDGTxLlKe5udbHh5nqlV+/71MIyXXkJ8PpKYGA5jh8PN/XSP3Q0H275eBTeuFV80/kiEhzlUwoPnCwV9/DfTqZQRQgYoKD6fZRyJXYu6YuvFGOPQiq7bSs6esbjdtGpCSUgmgwM4t6l6xseb93oCUlEqXe/9J+cznW35+wK5d8rZLL5XnZmlprlOl1pWZU23lefiZ+b8Gg71b1j6OzDmQc823aL2GkVKDHL1ej+zsbBiNqjbFEmpqygB8jbi4a9G3bxm0WgGttu1zJCQou1ACkTNTq5V7fLoQrYsmhIa65k+sLNp1pngXkdLExgJXXSVTK6OigKAgOfWltBQuMwfWlSlt/q9r/tI4qM7Mt3CGk6Ts7GwkJycDCEP7xRI8cPfdM876HEovlEBEzqW9ogl5ef4AArnUCpECaTRAZCRQXi7X02uvOB05J6XN/2Uw50BcZb5FQkICMjIy2ozMabWe8PGpQ3z8GPTvH+vUhRKIyLm0V6QqO9sLwE1caoVIgTpaD1Or5Yizs1Pa/F8Gcw6meSplTk4lli51vvkWvr6+llG19osl9LdzC4mIrNM6s6KgADh92hOAL5daIVKo9qa3tDf1g5yPef7v0qUbkJIyz6E74hjMOSBzKqUrzDdwlnmAROTaWvfi+/gAPXs2ACjgUitECuYM01vo/Chl/i+DOYUwl+/393e+gwoPlETkDJp3Trm7A6+9Vg8utUJERLbEYE4Bioo8cejQmQppnHdBROSYmndOJSXVwLzUijmNnB1XRETUlVhf1eF5IzPTHxUVcu0587wLR13rgoiIpMjIBgCfYNKkCpdcc49ci8Eg55Px/ISoe3FkzuH5o7bWHQMGnKmQxnkXRERKYURwcBOP1+TUWq+xyAwiou7DkTmHVwM/vyYUF8sFK4uL5YGS8y6IiIjI3pqvscgMIqLux2DOAZlTFYxGFQAjkpJqEBLScp0T9vISETkugwGoqHAH4G3vphDZVHtrLOp0MoOIiGyPaZYOpnmqQnl5MIAYREY2YOJElu8nIlIC83H80KEQADegqMjT3k0ispnWayyycitR92Iw50CapypERAB5eR4AxsJoVLF8PxGRAjQ/joeENAIIQWamPyZO5DGcHI+q0YDhEW7wqcwBCs8vWUsNIGUQkJkJ1BQCF/kDSYMAdfnZH+dTmYPhEW5QNTIfk+hCMJhzIK1TFTSaBgCBqKtjNiwRkRI0P44XFgoAxaitdWfRKnJI6pp8ZM73B9LnA+nn/zyRAKaar1QCOHnuxyQCyJzvj6yafACjzv/FiVwcgzkH0jpVQav1BKCDj4/J3k0jIqJOaH4cl/OeI+Dn18SUM3JIBv9YJK2qwfr165GYkNDhdkXFf4y81cjPeFISEBlxYa+dlZ2NWbNmYfUU5Za9NBjO/E3YWUP2wmDOgajVsrhJWposdhIY2AggHd7ei+3dNCIi6oTmx/HCQg8AFUhKquGJHjkk4aHGnmIT6oL7A1HD2t3GYABStwAVeiAiCvi9GCg/BEwfeGEBTF2xCXuKTRAeyvxyFBV54tAhLsdA9sf8PQcTGwtMnw7ceCOQklIJoMDeTSIiIiuYj+OTJlUA+OSPxcOJlInVKtvjjcxMfy7HQA6BwZwDUquBsDDA21vYuylERHQe1GogOLgJgNHeTSG6IM1Th7nerZk/amvdGeCSQ2AwR0REZANyzlzYH5dEymROHeZ6t83VwM+viQEuOQTOmSMiIupi+flAamowgBlITQ1GdLTzzqdhEQjnZ04d5nq3ZkYkJdWgvJwBLtkfgzkiIqIuZF5rTqfzAFAKnc4DaWnyZNjZTvbMC6Q3LwJBzonr3bYUGdmAiRMZ4JL9MZgjIiLqQuaCEXKtUAM0mgbLfBpnOOHT6/XIzs6G0ahCamowdDoPaDQNyMvzRF5eI3r1OgIAyMrK6vA5EhIS4Ovr211NJrIJBriuydGyERjMERERdSFzwYi8PE8Aami1noiKcp75NNnZ2UhOTgYQBmAGgFIABgBqAD0AbAAAzJ49u8PnyMjIQFJSks3bSkTUldrLRrB3Cj2DOSIioi5kLhiRl9cIoAcCAxudaj5NQkICMjIy2ozMabWeCAxsxOjRs1FUdBxxcXHw8fHp8DmIiOzNnGnQHnN2gfmyo2yEe+4JR2io/TINGMwRERF1sdhYuVbo0qUbkJIyz+49t13J19fXMqoWHX2mlzoqytxLHQ9glH0bSUTUCWcyDTp2Jsug/WyE0aPn44orhtq0nWfDYI6IqNt5o6LCHQaD84zWUFtyrdByp14zlFUOiUjJzJkG7amrq0NeXp4ly6CjbIRhw8K7udUtMZgjIupGRUWeAG7A5s0hKCx0jHx7ogvBIhCuzdGKQRBZo3mmQXtGjx7d4np72QihobZu5dkxmCMi6kJny783GlX45hs9gBDU1p7A/v0q5OU1IiWlss3oDav9EZGjc8RiEES25IjZCAzmiIi60Nnz78/k27/++r9gzrdfunQDgPIWW7LaHxE5MvN6ihUVQEQEUFwMp11PsfnoI5GjZSMwmCMi6kJny783GlXYuNEPeXlV6NvXH9XV/ggMbERKyrx2R+aIiByVeT3FiAjAx0delpY6z3qKZq1HH0NDPe3dJKIWGMwREXWhc+XfN8+3j49nWhIRKZN5PcXi4jMjcyEhyl9PsXmqfHul6GtqdAC8LeXqO8JUeeouDOaIiLqRI+bbExFZy7yeYlqaHJELCYFTrKfYMlW+/VL0gF+zcvXtY6o8dRcGc0RE3czR8u2JiM6HM3ZONU+Vb68UvY9PHeLjx6B//1j4+Pic9XmIugODOSIiIiI6L87WOdU6Vb69UvSxsf3t2EKilhjMOSiDAaiocAfgbe+mEBEREbkkZxx9JOfCYM4BmSsnHToUAuCGPxYZJiIiIqLu5myjj+RcGMzZUXuLCzfPzzYYTgAIwTff6BAauqdN6XKA1ZKIiIiIiFwVgzk7an9x4baVk1auXI+VK9suKgywWhIRERERkatiMGdH7S0u3HxkLiCgBkeP1iAuLgiTJ7ddVNj8HERERERE5HoYzNlRR4sLc1FhIiJlYxErIiLqDgzmHBArJxERKReLWBERUXdhMOegWDmJiMixdUURK4CFrIiI6PwxmCMiIjoPXVHECmAhK1IegwGoqQH8/dnxTGRvignmKioqcP/99+Orr74CAFxzzTV47bXXEBwc3O72DQ0NePzxx/Hdd9/h2LFjCAoKQkpKCl544QVERUV1Y8uJiMgZdUURK/PzECmFOY1YpwMCAzmvn8jeFBPM3XzzzTh58iQ2bdoEALjrrrtwyy234Ouvv253e71ej8zMTPzjH//A0KFDUVFRgUWLFuGaa67B7t27u7PpRETkhFjEilyNwSA/2xUVQEQEUFwsr0+fzhE6IntRRDCXlZWFTZs2YefOnbj00ksBAP/9738xcuRIHDlyBAMGDGjzmKCgIGzevLnFba+99houueQS5OfnI5a/rEREZAMsYkXOqqZGdlJERAA+PvKytFR+1vk5J7IPRQRzO3bsQFBQkCWQA4DLLrsMQUFB2L59e7vBXHuqqqqgUqk6TM0EAKPRCKPRaLmu0+kAACaTCSaT6fx2gIiIXIqXl/wHAPzpIEdlPq/p7DmOry8QEAAUFp4ZmQsNBVQqoKTEujl01r42kSux5juhiGCuuLgYPXv2bHN7z549UVxc3KnnMBgMWLJkCW6++WYEBgZ2uN2yZcvw1FNPtbldq9W2CPKIiIiIlKyiosJyWVpa2qnHDBrkhp07vXD8uAqBgQJhYY344AMP6HTy+mWX1aNXr3OfiJ7PaxO5iurq6k5va9dg7sknn2w3cGrut99+AwCoVKo29wkh2r29tYaGBtx4440wmUxYsWLFWbd99NFHsXjxYst1nU6HmJgYaDSaswaBREREREoSEhJiuezRo0enHtOjh5wLWlIiC6CkpgINDfK24mLg0CEgMfHcI3Tn89pErsLb27vT29o1mLv33ntx4403nnWbuLg47N+/H6dPn25zX2lpKcLDw8/6+IaGBsyYMQPHjx/Hjz/+eM6AzNvbu90/oJubG9zc3M76WCIiIiKlMJ/XWHOO07yapUoFlJUBAwbIOXRRUXIOXV2dTMns6tcmchXWfCfsGsxpNBpoNJpzbjdy5EhUVVXh119/xSWXXAIA2LVrF6qqqjBq1KgOH2cO5HJzc7F161aEhYV1WduJiIiIXEnrapYFBcCJE3Je6EUXAeXlQEiILPxDRN1DEV0hiYmJuPrqqzFv3jzs3LkTO3fuxLx58/DnP/+5RfGThIQEfP755wCAxsZG3HDDDdi9ezfWr1+PpqYmFBcXo7i4GPX19fbaFSIiIiJFal3N0sdHBnaHDwObN8ugbtw4VrYk6k6KKIACAOvXr8f999+PSZMmAZCLhr/++usttjly5AiqqqoAACdPnrQsMD5s2LAW223duhXjx4+3eZuJiIiInIW/v5wnZ65iuWOHTKecMAE4fVpWumynXh0R2ZBigrnQ0FCsW7furNsIISz/j4uLa3GdiIiIiM6fWi1H3tLSgFOnADc34NJLZVEUf3+uOUdkD4oJ5oiIiIjIvmJjgenTZeGTTZtk8FZXJ0frOF+OqPspYs4cERERETkGtRqIjgauvFIGcKWl8pLz5Yi6H0fmiIgciMEgiwz4+/OkiIgcm3mUrrZWjsjxmEXU/RjMERE5iObrNwUGyl7u2Fh7t4qInJVerwcAZGZmdmp7o1EFvd4Nvr4meHsL1NXVIS8vD3FxcfDx8bHqtbOysqxuLxG1xWCOiMgBtF6/qbhYXp8+nb3dRGQb2dnZAIB58+Z1YusYAGMBBALQAUgHUHDBbQgICLjg5yByZQzmiIgcQOv1myIiWBnO2TCFlhzNtGnTAMh1en19fTvczmhUITU1GDqdBzSaBmi1nggMbESvXjsxZ87NWLduHRITE61+/YCAAPTr1+98m09EYDBHROQQmq/fZB6ZY2U458EUWnJEGo0Gc+fOPed2Wi2QmQkMGCA7m+rqZGdT7946AEBiYiKSkpJs3VwiagerWRIROQDz+k2sDOd8mqfQ9ughL9PS5O1EStC8s8m8DEFgIODjY7J304hcHkfmiIgcBCvDOSem0JLSNV8svHlnk1Yr7N00IpfHYI6IyIGo1TzBdzZMoSVn0F5nk1Zr71YREdMsiYiIbIgptOQs1GogLIyfXSJHwpE5IiIiG2MKLRER2QKDOSIiom7AFFpydlx+g6j7MZgjIiIiogvC5TeI7INz5oiIiIjovBmNKi6/QWQnDOaIiIhswGCQ1f54QkvOTq93a7P8hk4n54gSkW0xzZKIiKiLMeWMXImvr4nLbxDZCUfmiIiIupDBAKackUvx9hZcfoPITjgyR0RE1IVqaoCyMlnRz91djlSUlsqUM57ckjMxGlUAwmA0qrj8BpGdMJgjIiLqQuXlQHa2DOjCwwGNBrjoIqackfMwGORnfNOmEAAzkJoajOhomUrMII6oezGYIyIi6iIGA7BrFxAZCXh4AKdPA42NwM038ySXnEN+PrB5M5CeDpSW+gMQ0Ok8kJYmR+b4OSfqXgzmiIiIukhNjSx6kpgIDB4MVFYC1dVAWJi9W0Z04czzQU+flp0VHh4CQF8EBTVYqlcymCPqXiyAQkRE1EX8/WGp6tfUJE9uw8KYYknOwdxZERsLBAUBQqgABKKoyBuBgfycE9kDR+bOQQgBANDpdHZuCRERKUFSErBtm0xHCwyU1+vr5T8iJTOZAE9PoLAQiI4GDhyoAdCAgIByJCUF8HNO1EXMcYc5DjkblejMVi7s5MmTiImJsXcziIiIiIjIhRQUFKBXr15n3YbB3DmYTCYUFhYiICAAKpWqW19bp9MhJiYGBQUFCAwM7NbXthdX3GfANffbFfcZcM39dsV9BrjfrrTfrrjPgGvutyvuM+Ca+23PfRZCoLq6GlFRUXBzO/usOKZZnoObm9s5I2JbCwwMdJkvjpkr7jPgmvvtivsMuOZ+u+I+A9xvV+KK+wy45n674j4Drrnf9trnoKCgTm3HAihEREREREQKxGCOiIiIiIhIgRjMOTBvb2888cQT8Pb2tndTuo0r7jPgmvvtivsMuOZ+u+I+A9xvV9pvV9xnwDX32xX3GXDN/VbKPrMAChERERERkQJxZI6IiIiIiEiBGMwREREREREpEIM5IiIiIiIiBWIwR0REREQ2xRINRLbBYI4UyRV+FIqKivDDDz/YuxlEZCetj3OucNxrztX331mcPHkSAKBSqfgeEtkAgzlSjIMHD2Lbtm0A5I+CM2tqasLGjRsRExODqqoqezeHiLqREAImk8lynKutrUV9fb3TH/daM+/vxx9/DL1e73L77yxuueUWJCUlAXC9gI4dEh1z5r9Fd+8bgzkHtXPnTqxYsQLff/89Kisr7d0cu9u4cSNmz56NHTt2YN++ffZujs25u7tj1qxZ0Gg0uPfee/Hee+/Zu0kOp/nB0pl/FM7FVfbdVfYTAAoKCuDmJn+eX3rpJcyePRsjR47EZ599hlOnTtm5dd2rrq4Or7/+OkpLS+3dFJtr7zPuDJ/7rVu3wsPDA1dccQUA1wnohBCWDoi9e/eisbGRHRJ/MP9ttm7dijfffBOvvPIKampq7N2sLtH8fT98+DDq6ups/poM5hzQ999/j9mzZyMjIwO333473n33XQDOcVA/H99++y0efPBBvPrqq3j44YcxdOhQezfJpszvs9FohI+PD+Lj45GWloYPP/zQzi1zDOa/T3FxMerr61FTU+MyJwetNf/RyMjIwIkTJ6DVau3cqq5lfl9ra2sBACaTCYAcvXY2TU1NKC8vR1xcHN544w0cPHgQ33zzDV566SXcdNNN+Oijj/DNN98AcJ3fAy8vL+j1ehw7dszeTbGp5t/lXbt2IS0tDdXV1Yo/+W9sbAQA/PrrrygvL8eECRMAOH9A1/z9fPXVV/Hkk0+26Ihx5n3vDJVKhW+++QaPPPIIAgICsHLlSvz973+3d7MuWPP3ffny5XjiiSdadETZ7H0X5FC+++47MXz4cJGeni6EEGLDhg2iT58+QqvV2rll3c9kMonq6moxdepU8e2331puE0KIpqamNts6A/N+ZGdni7lz54qsrCxRXV0tnn/+eXHbbbeJDRs22LmFjuHbb78V48aNEw899JCYPn26y34/zF5++WUxZswYce2114p7771XbNu2zY4t6zrmfdy0aZO44YYbxO233y6eeOIJUVVVZdmmtLRU1NTU2KuJXUqv1wsh5O+Aj4+PmDhxovjss88s9//vf/8TcXFx4tSpU/ZqYrd54403xJIlS8QzzzwjZs6cKd58801x9OhRIYQQtbW1dm6d7bz66qvi8ssvF7fccovo16+f2LNnj72bdF7M393y8nJRUFBguf2SSy4R48aNa7Ods1qxYoUYM2aMKC0tFUIIUVRUJAwGgxCi7XmMKykoKBCTJk0SZWVl4pNPPhGjR48WJ0+eFCaTSTQ2Ntq7eRfstddeE2PGjBElJSVCCCFKSkpERUWFEMI27ztH5hxIVVUVli5diujoaIwZMwYAMGPGDPTv3x9r1qzBF198gf3799u5ld1HpVLBZDKhqqoKAwcOBHCmV96cgpSWlmbZVunEHz06P/zwA5YsWYJvvvkGy5Ytw7Fjx3D//fcjMTERn376KT744AN7N9Wu0tPT8Y9//ANvv/02GhsbUVxcDA8PD3s3q9uZP/NvvPEGvv32W6SnpyMoKAg///wz1qxZg127dtm5hRfGPGcsPT0d999/P+644w6MGDECp0+fxoIFC1BbW4vTp0/jsccec4p5pd988w2SkpKQm5uLyZMnY9OmTfjll1/w7bffWra56aabcPHFF6O4uNiOLbUN0arHOjQ0FAkJCaiursb+/fvx3HPPYebMmbjiiivw17/+1TLi40y++OILfPHFF0hNTcXFF1+MuLg4XHzxxZb7W/+NHJX5t+ybb77BtddeiylTpmDOnDk4fPgwdu3aBYPBgEsvvRSAc/x2N2d+j8Qf81537NiBRYsWoaSkBM8//zyuvfZaDBs2DHV1dZbzGFdh/tuUlJQgMjISgwcPxn//+1+8+OKLePfddxEdHY0vvvgCn3/+uZ1bar3m302TyYTdu3fjySefRGFhIV544QVcf/31mDJlCkpKSmzyvrvWJ8mBffXVV3j77bfx/vvvo6CgAA8//DAAYPHixThw4IDlx2zq1KmYN2+eU/6QNWeeJxgQEACDwYCffvoJgJxL1nzfU1NTkZmZaYcWdj2VSoVffvkFixYtwrPPPotVq1YhKCgIr7/+Ok6ePIm7774bQ4YMsQS2rmr79u147rnncPLkSWzfvh3vvfcegoKCsG3bNhiNRns3z+YyMjLw6aefApA/IHq9Hu+++y5efvlllJaWYtWqVcjMzMQzzzyD9PR0O7fWesXFxZYfPHMhoDlz5mDKlCmYP38+7r33Xuj1euzfvx/h4eF46KGHEBUVZe9mX7BPP/0UR44cwbRp05CVlYWxY8di06ZNWLt2LV588UUcP34cH3/8Mfbv34+IiAh7N7dLiWapSdu2bUN2djaSkpJw22234Z///CfuuOMOXHPNNdi1axfef/99vP32207ZgaNSqfDII49g1apV+OGHH/DNN9/Azc0N69atU9R8K5VKhR07duCxxx7Dv//9b3z55Zfw8vLC8uXL0dTUhJ07d0Kv1yu+w6k95vcoNzcXbm5uiI2NxSuvvIL77rsPISEhWL9+PYYOHYrc3Fw7t7T7qVQqbN68GVOmTMGBAwdQWFiIDz74AG+99RYuuugipKen49FHH0VkZKS9m2q11u+7n58fnn32WSxevBjBwcF44YUXMHjwYNvVwOjysT6y2vfffy+GDh0qvvnmGyGEEMePHxfDhg0TQ4cOFX/+859FQ0ODEEIInU4n8vLyRG5urj2ba3PZ2dnirrvuElu2bBFCCPH3v/9d3HLLLWLnzp0ttnv//ffFiBEjRGFhoT2a2SVKS0vFgQMHLNdfeeUVMW/ePMv13377TQwfPlzMmjVLZGdn26OJDuett94SkydPFqNGjRLHjx8XQgjxww8/iBkzZoji4mL7Ns7G6uvrxRdffCGKiorE77//LoSQKRvHjx8XEydOFOXl5UIIIebPny8WLVqkuL+HXq8XDz/8sLjhhhss3+uXXnpJ3HHHHaKsrMyy3V//+lfxxRdf2KuZNnHo0CGxePFiMXfuXBEdHS0OHjwohBAiNTVV+Pn5iSFDhoh///vf4tChQ3Zuqe0sX75cjBkzRjz88MMiKSlJ7N69WwghRFpampg5c6adW9e12ku1+uijj4Sfn1+LNMS1a9eK4cOHi6Kiom5s3YXbsGGDePjhhy3XS0tLxeDBg8VLL71kx1Z1j8OHD4thw4aJr776SjQ0NIiMjAzL8euLL74QgwcPVtz72RVycnLEokWLLN/r1atXi6lTp4p58+aJZ555RiQmJlrOg5Vo7969YsqUKeLbb78VBoNBbN++3ZJm+fnnn4vBgwfb7HyVI3N2tmnTJjz66KN45ZVXMHXqVOTm5mLnzp3YuHEjvLy80KdPH3h4eKCpqQl+fn7o3bs3+vbta+9m25Rer4eHhwc+/fRTHDhwAPfffz+0Wi1ee+01rFixAllZWVi5ciVeeOEFrF27VpG9OIAscLJ06VK89dZb2Lt3LwAgPDwcVVVVqKioQFNTE0aMGIEpU6bA3d0dX3/9NQDlpNp0BfO+njhxAvX19QCAxMREZGVlYe7cuQgLC8Ovv/6KRx55BLNmzUJ4eLg9m2tTQgh4enri2muvRUNDA+69916sWLECbm5uCA0NhZ+fH77++musXbsWx48fx+LFixX391Cr1ZgxYwYiIiLw+OOPo7KyEpdffjlKSkqQmpqKvLw8HD16FMeOHUNsbKy9m3vBDAaD5TMeEBCA3NxcXHvttXjkkUdw9dVX4/Dhw5g4cSK+/PJLnD59GjfeeKPTjsx/9tlnlnThuro6xMXFYfjw4Zb7Dx482OLvpXTmVKuffvoJ3333HYxGI6ZPn445c+bA09MTW7ZswauvvorXXnsN77//vsOPxprfl4qKCkvGQPN1UjUaDebOnQt/f/8W2zuD1vsSGhqK+fPn4+WXX8Znn32GpKQk+Pr6YtWqVXj88cfx4YcfOvz72ZVMJhMqKipw55134ocffkBFRQUAYM6cObjvvvtw8cUXw9/fH6tWrcLUqVPt3NrOa/2+BwUFYfz48Xjvvffw9ddfY+TIkQgNDcXrr7+OJ554Ah9++KHtzldtEiJSp1RWVgo/Pz/x8ssvCyGEyM/PF5dddpl49tlnhRBCHDt2TAwbNkzcddddTj3h26ygoMAysnDgwAHx0EMPiXnz5okjR46IwsJC8dRTT4nk5GQxefJkcf3117cY0VKq7du3i9tvv10sWbJEZGdni7KyMjFq1CixZMkS8fPPP4v09HQxYcIE8dJLL4mUlBTLxGlX8u2334oBAwaIqVOnihdffFEYDAaxbt06MXnyZDFhwgQxfvx48eWXXwohnHcyffP9ev/998Vrr70m3nrrLXH99deLlStXCiGEeOKJJ8RNN90khgwZosjRG/Ok961bt4q77rpLXHTRRWLhwoVCr9eLDRs2iBkzZohx48aJkSNHtigKolQbN24UY8aMEc8884yll37z5s1iypQpori4WPz9738Xffr0Efv37xdCOF/Rj9bf1bVr14qPP/5Y/Oc//xFXXnmlMBqNQgghPvvsM3HixAlLD7czef3110WvXr3E1VdfLXr37i3KyspEeXm5ePrpp8Wf//xnMXfuXEV8l83v5ddffy3mzZsnTp8+LYQQYty4ceKyyy4TOTk5YuPGjSIhIUFs3brVji21rd9++83y/5KSErFmzRoxZswY8fXXXwshZKEqV86w+fXXX8UVV1whnn766RZFcZRu3759lnOzEydOiFdeeUVMnz7d8r4///zzIisry6ZtYDBnZ1u2bBGXXHKJpZrPG2+8IYQ4c2Kza9cucdlll1kOjs5q48aNIiUlRSxdutTyJd+zZ48l7Wjfvn2WbRsaGiw/9EplMpksP4A7d+4Us2bNEg899JAoKioSJ0+eFDfddJO47rrrLKlGaWlpYvLkyaK6utrOLe8e5r9NaWmpmDBhgti5c6dYu3atmDt3rnj88ceFXq8XOp3O8vdq/hhn0nqf9u7dK6655hqh1+tFbW2tWL16tbjmmmvEhx9+KISQxw0lV/bcsmWL5YTvhRdeEHPmzBF33XWX0Ov1oq6uThw7dkwcO3ZMCKHs99toNIpHHnlExMTEiOHDh4tx48aJ//znP2LNmjXiscceEzk5OUIIIe655x4xePBgYTQanarynVartVQg/fHHH0VZWZl45513RFJSkrj22mstv3+rVq0SEydObJFiq2TN38PS0lLxxBNPWAL5WbNmidjYWEvQWl9fr6jP+Ndffy2GDRsmfv75Z8ttDQ0NYs6cOeIvf/mLGDdunKUqtTP6/fffxZQpU8Q//vEPy22FhYVi+vTpok+fPuKHH36wY+u6n/mz++uvv4pVq1aJb7/9VlRWVoqDBw+KcePGiWXLlon8/Hw7t/L8ZGdnWypGZ2dni+uvv168+OKLlvPS48ePi8mTJ4tLLrmk2953BnMO4KeffhJBQUFi0aJFLW5/6623xHPPPSd0Op2dWtY9zD8C6enplnlAZkeOHBGLFy8Wd999t9i8ebOdWti1zAe5U6dOWd7b3NxcS0BXUFAgmpqaRH19vaioqBBffPGFSEpKEnv37rVns7vdr7/+KtLT08Vjjz1mue2zzz4T8+bNEw899JBifwis0Xwf3333XdGnTx/x9ttvW27TarXinXfeEePHjxdr1qyxRxO71N/+9jfx+OOPCyHk92Tr1q1ixIgR4vbbb3e6cvzHjh0TTzzxhPjHP/4hHn74YfHll1+KSy65RLi5uVmyM4QQTjki9f7774u77rpLPPvss2Lo0KHCYDCI48ePixEjRojHH39cbNy4Ubz99tti+PDhTpGB0dq7774rHnnkEXH11Ve3mDt1++23C29vb0sZe6XQ6/Vi2rRp4pdffhGVlZViw4YNYubMmZZjt8FgsJRlV1KAejat98NgMIhPP/1UTJ8+XTz99NOW259//nmxZMkSy/xuV2LOqlmwYIFISUkRs2bNEjk5OWLPnj1ixIgR4tlnn1VctlFtba24/fbbxYIFC8Tu3btFY2OjWLVqlbj99tvFK6+8Ytmfp59+WjzyyCOWzmZbYzDnIH7++Wdx8cUXW6L9//3vfyI5ObnFiJQz2rt3rxgwYIDYvn17i9tfeuklsWLFClFXVyeOHj0q5s+fLx588EHLOkxK1TwdZcKECSIlJUXccsstYvfu3SIvL0/ceuut4v777xcZGRlCCCHq6urEY4895jKBnPnvs3XrVhERESFSUlJEz549xfr16y3bbNiwQcyZM8ep01VMJpMoKysTKpVK/Pvf/xZCCFFRUSF69+4txowZ02Lb0tJSsW7dOqdIW1mxYoW49dZbW5zg3nTTTeLGG290ymNhVlaW+Mc//iHuuusu8csvv4ja2lrx7bffOuW+CtFyZGr48OHC399f7Nq1y3Lbnj17xPz588WsWbPELbfcYikCo3TNT/zXrVsn+vfvL1566SXh5+cnVqxY0SLTZMGCBZaRWaWora0VU6dOFTfffLO4+uqrxeOPPy6ee+45ceutt3bbyWx3av5+/u9//xPr168X7777rhBCdjjOmDFDzJkzR6xevVpcdtllTvk3OJfi4mIxYcIEy7ndoUOHxNNPPy2WLFkihJCDGDt27LBnE8/b4cOHxbx588R9990n9u3bJ5qamsR///tfcdttt4kHHnhAvPnmm2L06NHixIkT3dYmBnMO5McffxRJSUni0UcfFX/6058UkSt/obZs2SIWLlwohJBpJUII8fjjj4vo6Ghxww03WOYTHjhwwGlSTXft2iUGDhwo9uzZI9LT08Wrr74qxo8fL/Ly8kRmZqa46aabnDpQOZddu3aJ+fPni4MHD4qqqirx6quvimnTpokPPvjAso3SqjRay3zS+8svvwhvb2/x5ptvCiHkPNuoqCgxe/bsdrdXEvMJ0bFjx4ROpxMGg0Hs3btXTJ48WaxevVocOnRIHDlyREycONHm8w3sKScnRzz22GPi9ttvFz/99JO9m2MzzU+AP/roI7Fs2TJx5ZVXiunTp4u8vDzL/eaUSqX12HfGxo0bxT333GP5bf/+++9FfHy8eOONNxQ1dcD8Xh05ckQcPXpUaLVaUVBQIJYvX24JzjMyMsTFF19sSYt2RsuXLxeXX/7/7d15QE35/z/wZ4qxNxgzxjLG2MqaSiEhTCUpayUxobFlHUvTzNhGTLKWSGXLkqVVSPqQkmQNochSjBCJkvbb8/eHX+dbZj6fGTN03dv78Q+d+uN17jn33vM65/16vXozJCSEKioqDA4OZl5eHuPi4jh27FgOHTpUqnmtavLz82lpaclTp05J58vBgwdpaGiokPW/ZftQ9m9ycjInTJhAR0dHXrlyhTKZjIcOHeKUKVNoYmJS6TeiRDL3kTlx4gSbN29eJRI5klyzZg11dHSkn589e8YdO3aQfNPK1dbWVuGWnLwtNTWVO3fulGoJtm/fTnt7e+n3T548oa2tLXfu3EmS0nKUqujVq1ccO3YsGzduLL0OaWlp9PT05MCBA7l79275BljJrl69ytGjR1NFRYWenp4k/+8JnYWFhZyj+/fCw8PZtm1bmpubc8GCBczOzmZERARtbW3Zp08famlpMSQkRN5hfnC3b9/mokWLaGVlpTTLyf+b4OBgGhgYMDs7myRpZmbGIUOGMCcnh76+vly+fDllMpnSLMcj39xskclktLGxYatWrejn58f8/HySb5re1K9fnz4+PnKO8t0cOnSI3bt35+DBg6mvr09vb2/pd2FhYdTS0pIaQCijS5cu0cTEhCS5atUqDh48mEVFRVK9J0mFX0n0Lsrer2U3FouKimhtbc3ly5dLtf5Xr16lqampwl3Tlf8sSkxM5L179/jq1StmZGRw4sSJdHR05OXLl6W/kUeyKpK5j1BV+gC4d+8eDQ0NuX37dmlb2R3ZAwcOcOTIkXz58qWcovv3kpOTqa+vz59++onh4eEkyYiICI4YMYJJSUnS382dO5eurq4kFfMpy7/x9kXbrVu32LdvX44aNUr6XWpqKtevXy/Np6kK1q9fTz09PakZiKqqKtetW0eSzMrKoqampkLXkSUkJNDExIRnz57lwYMHOX36dE6ePJmZmZksKSnh77//LtXQKvKF/X+LvfxFH/mmkN7FxUWh52b+ldOnT3Pw4MGMioqqsH3QoEEcPHgw27RpozRLTMsf97KnbsXFxZw5cyZtbW15/vx5aYZsVFSUQs2PTUpKYseOHXn9+nVmZmYyKiqKHTp0oL+/P/Py8mhlZaV0idzly5elG2rkm8ZlM2bMoIuLC01NTaXkfO3atTx16pS8wpSryMhIjh07ls7OzgwPD+ejR4+oo6PDMWPGcNasWezUqZNC35xbt24de/Xqxb59+9LS0pJHjx7l8+fPOWnSJE6YMEFaNiqP7yuRzAmVKj4+nhs3bmRERARfvHjBvLw8LliwgLa2thUaO+zatYv6+voVEh5Fk5SUxE6dOlWo9yLf3LW0sLDg2rVruX//fl64cIGampoVuoBVFWUfemfOnGFQUJD0dLKsQ9SYMWOk5Lbsy7IqKC4upr29fYXk9dChQ6xevbqU0ClygpOSksLRo0dzypQp0rbo6GjOmDGDY8eOVagL2/+l/DHauXMnt2/fLi2ZJf+Y0JUtNVcW5e/Wl5aW0tvbm126dOGYMWP+0Jn34sWLSjNIufxx3759u9S0KSoqiqWlpZw8eTLt7OwYFxcnJXQfs7c/a+Lj49m/f/8K25YtWyY1PCk7tor8GVVecXExt23bRhMTE+n9m5GRwd69e7Nnz57SDWg/Pz9269aNaWlp8gxXLmJjY9m9e3euXbuWy5cvZ7t27bhr1y4+f/6cfn5+dHd3l65xFPG8CA8Pp66uLvPz83nz5k36+/uzf//+vHnzJm/dusXp06fLtfxDJHNCpYmIiGDr1q05YcIENmnSRKqHS0tLo7OzMw0NDdmxY0eOHz+enTp1Uui15kVFRZw0aRI3bNhQYbuHhwcHDRrEQYMGcfbs2bS2tqapqak0J60qOnz4MDt16sQFCxawdevWHDlyJIuKinj79m2amppy1KhRJBXzC+Dv+rN9GzhwYIVkJzs7m2ZmZmzQoAGzsrIU+gnutWvXOHnyZOro6FRYVnj8+HFOmzZNod/7f8bDw4O9evXi7t272axZMzo6Okq/ezuhUxblz+my+aHkm5tZdnZ2XLlypTSeQFl5e3uzW7duDA4O5pQpU2hra0t/f3+WlJRwxIgRnDRp0kd/kyovL0/qKHrp0iUePXqUd+7cobGxMQ8dOiQdZ3d3d06ZMoWlpaVKdU6X7d/Tp0/p5+dHCwsLKaFbuXIlx44dy3HjxtHV1ZVdu3ZVmqY97yI5OZnDhw+XVh+RZExMDHv27PmHDuWKysfHh9999530c0ZGBq2traXSD3nXvIpkTqgU4eHh7Natm7T8YP/+/WzVqpXU1OTVq1d88uQJt27dynPnzilFZz4bGxspSZPJZDxz5gw1NDTo5eXFyZMn09XVtcJcMGVOVv6bsiHp5btade/enRMnTiT55qI/ISFBXuFVivLHPT09Xep8dv78eVpaWkrLb/fv38/ly5crZCOgsn28ceMGU1NTmZ6eztzcXM6bN4+TJ0/miRMnpL9VlpliZQ4ePEgTExPKZDK6uLjQzMyMXbp04eTJk+UdWqXw8fGhqakpra2t6ezsTJIMDAykvb09Fy9erJDNEP6KTCZjVlYWBw8eLHWofvHiBX18fDh69GiSby7+FGGZ9JUrV/jLL79w+vTpbN++PW/evEmZTEZnZ2c6Ojryp59+YmRkJDU1NZWu3rP8Z3PZ8uft27fT0tJS6l55+vRpOjs708PDo0o2LispKeGaNWv4zTff0MHBQdqek5NDCwsLhVxd9WfXYhERERw6dGiFY+zo6Ch1m5b39ZtI5oQP7uXLl9TV1aW5uXmF7aamply5ciWDgoKUpk6CfPOmzs/Pp6GhofT0kXzz4Va2/OTXX3+t8Luq6smTJ9TW1q6wvOrGjRs0NjaW+52uylD+C8DNzY3a2trU1dWVviBCQ0PZsWNHDhw4kG3btlXomVtHjhyhjo4Of/nlF3711VdMSUlhUlISnZ2daWdnpzQXgm9/qZ89e5Z37tzh1q1b2bdvX8pkMnp5eVFFRUXq5Kustm/fzm7dujEuLo4hISE0Njbm999/T5L09fWlo6OjQg+5L+/PLubs7Ozo7+8vLaV89OgRDQwMFG4ZnpOTE6tVq0YnJydp2+PHj7lr1y7a29vT3t6ehw8flmOEH9bGjRvZr18/vn79Whpwb2Fhwa1bt8o7tI9CTk4ON23axDFjxnDjxo0k39S+a2hoKNwqi/Lv471799Lf35/bt29nSUkJraysOGfOHHp4eNDPz4+ampq8c+eOHKP9P2oQhA8oLCwMt2/fxq5du2BjY4N58+Zh9erV+OGHH3Dt2jV89tlnCAgIwJMnT2BiYoLNmzdDTU2xT0sVFRXUrFkT06dPx48//ojWrVvDwsICdevWhYqKCi5cuIDIyEi4urrKO9RKRxIqKipISEhAy5Yt8cUXX6BDhw5YuHAh3N3dUbt2bdy/fx/FxcUoLCxE9erVoaKiIu+wP5iyfTt79iwuXryIHTt24NmzZzA3NwdJzJ07F4MGDcL9+/fx6aefonHjxnKO+J+5dOkSFi1ahNDQUISGhuKzzz6Duro6Pv/8c4wfPx6bN2/GF198Ie8w/7Wy8xsAXrx4gQYNGkBfXx/FxcW4fPkyfvnlF1SrVg1qampYt24dzM3N5Rzx+1W2/6WlpZDJZDh//jwWLlyIXr16gSQ6d+4MBwcHJCUlYfz48cjNzYW6urq8w/7Xyh/3vXv3giRsbW3x5ZdfIiAgAFpaWtDU1MTly5ehqqqKevXqyTnivy8hIQF9+vRB/fr1cffuXfj6+sLS0hJNmjSBsbEx7OzsUFRUhBo1alR4HZTFtm3b4Ofnhz179qB27dqoXbs2LC0tUa1aNezYsQMymQzff/+9vMOsNG8f49LSUtSrVw+2trYoLCzEli1bEBAQgHr16sHDwwOdO3eWY7Tvrmzf3N3dERgYiLlz52LMmDFo1qwZ3Nzc4O/vj4SEBMhkMgQEBKB169Zyjvj/k1saKSi9Y8eOsWvXrtIdu9TUVGppabFr1640NzeX7lbm5OQwLS1NaZoelMnLy+PixYupoaFBb29vnjt3jhEREWzXrh2PHDki7/DkJjw8nK1atZKGiZ4/f56TJk1ily5duHHjRmpqalap1+fIkSP8/PPPGRAQIG07d+4c69WrJy1LU3TBwcH08vJiREQE9fT0pDqK0NBQ5ubmKl0H3zVr1nDw4ME0MTHh06dPSb55utGsWTO6uLiwffv2Cvd05q+Uv6Nd1sjFycmJ9vb2FcatWFhYKOyw4L/i4eFBPT29CqOFpk6dygEDBtDc3Jx6enoKtQqlsLCQdnZ23Lx5M8k3nRpHjx7NnTt3MiQkhHZ2dkrbfVUmkzE/P5/fffcdIyMjSVasi8rLy6O/v79SlIS8q9DQ0Ar1cWXv/VevXtHT05P29vZcvXr1H36vKN4eO2FmZvaHlUIfW62rSOaED+Lo0aPU1taWhuCmpKRw7969fPz4Mbt3786ZM2eSfLPeWpEbOfyV3Nxc7ty5k507d5aaeYSFhck7LLmJj4+nhoaGVAeXnp7O5ORkPnjwgO7u7vTy8pLqpxTtC+Dvenu/nj59Sl1dXerq6lbYHhcXxyZNmjAzM1PhX4uYmBj26NGDOjo60sVPdHQ0DQwMmJKSIufo/r3yx+fChQvU1dXltWvXaGlpyd69ezMlJYX5+fl0c3PjlClTlK5JQvn9d3d357Rp01hSUsLw8HBOmDCBnp6ezMjI4OHDh6mnp6c0F8Dl6zsjIyPZtWtX5uTksLi4mIcOHaKbmxtJ8u7duzx79qxUD6soZDIZly5dWqG+093dnQ4ODmzTpg1DQ0PlGN2HVXZOOzg40MvLq8I57uPjI9VCVjXXrl2jnp4eY2JiKmwve31evnzJ9evXc9iwYVJd4cfun4yd+Ni+k0UyJ7x3L1++ZJ06daSasAcPHrBHjx50cXEh+Wa2nJaWFidNmqSUxe9/Jjs7mzKZTOre9rF9EHxoZfvr5eXFmTNn8sGDB1yxYgW1tLTYu3dvpb4oKK/8cc/IyJAuarOysmhoaEgzM7MKf/+x3f37p9LS0jhkyBAuWLCAkZGRjIqKYteuXZWii+vbA2UDAgIYHBwsbRs3bhz79evH5ORkklSIVvT/lIeHB3v27Fnh6ZOPjw/HjRtHAwMD9unTR6GeTP0vd+7c4Q8//CC9R+Pj4+no6EgnJyfOnTuXVlZW1NbWVpi6yN9//126+ZqVlSV1IH348CE1NDQq3ITMzMyUElNl/C47ceIEN23axNLSUq5du5ampqZMSEhgXl4eg4KCqKOjw1u3bsk7zEqXmJjISZMmSSMoyIrHv+z/L1684KZNmxSiwY+yjJ1QIUl5L/UUlE9UVBScnZ2xYMECrFu3Dra2tpg2bRpkMhlUVVVx/vx5zJo1CwcPHsTnn38u73ArTWlpKapVqybvMCoN31pff/v2bfTp0wctW7bEpEmTMGTIEKxZswaNGjXC/Pnz5Rhp5dqwYQMCAwORmZmJnj17YsmSJahZsyasra1RVFSE2NhYeYf4rxQWFuKTTz6psO3cuXMICgpCYmIiPv30U4wZMwZDhgxR6Dqb8rGvWbMGO3fuxKNHj2BqagpXV1c0a9YMADB8+HC8fv0ahw8fRvXq1eUZ8gfz9OlTTJw4EV5eXmjevDkKCgpQs2ZNAEBJSQkePXqEevXqoUGDBnKO9P14+PAh6tati5s3byI3Nxfa2tpYu3Yt7t69izlz5kBPTw/79u1DYmIili9f/lGf48nJybCzs8O3334LPT09bN26FbVr14aVlRVMTU3h4eGBVq1awdbWVqHfr3+FJGQyGX777TckJyfD0tIS1tbWmDdvHq5fvw4VFRVkZ2fD29tb4WrB3ofo6GisXLkS+fn52LBhw5++BmXnhyKcJ2UxPnv2DEePHkVQUBAGDRqEKVOmwM3NTTrmHTp0wN69e7Fnzx507NhR3mH/OXllkYLyi46Oprq6OmfPnl1hu4+PD5cvX86cnBw5RSZUhrK7dKdOneKSJUvo4eHBpKQkFhQUSF09k5KSqKWlxaioKHmGWql2795NHR0d3rt3j6mpqbSwsJBaOmdmZtLMzIwPHjyQc5T/XFJSEpcuXUqZTPaHJdSFhYWUyWRKN1Q4JiaG5ubmLCwsZEREBI2MjOju7l7hzrQi3KX+N/Ly8mhkZFShloZ8M1dOmebJvX3OOjk50dTUlBcuXKiwfc+ePdTW1v7oO9AmJSWxc+fO3LVrl7Tt4sWL3LdvH/X09Dhnzhw2bNiQHTp0UMr6uPIz8crO09zcXK5atYrjxo3j/v37Sb7pRJqSkiLVwFYF5ZdOlv3/8uXLtLGx4aJFiyrUhioaZRs7IZI54YOKjY1lly5dpPXl/v7+1NHRUZqlNsL/duzYMbZt25aurq4cNmwYra2tpQ/Kw4cPs02bNlWuhnD9+vXcsmWL9POLFy/YokUL+vv7k6TC15Du2rWLhoaGf/o7ZUneyjtz5gy//fZb2traStvCwsJoZGREV1fXCmM3lFlubi6nT5/O1atXS0vQ9u3bxw4dOvD+/ftyju79279/P588ecLbt29zxYoVtLS0lOp9AwICaGBg8NEncsXFxZw0aRI3bNhA8v8Sm7J/Hz16xFu3bnHWrFkcMGCAtCxaWd7H8fHx3LdvH8k3SytXr14tLR/Nzc2li4sLDQwMuG3bNqVeHv1nyo7xwYMHOXz4cFpbWzM0NJQymYwXL16knZ0d58+fr/D1v8oydkIkc8IHFxUVRW1tbTo7O7N79+4KfTdH+HtkMhmzs7NpYWHBQ4cOkXwzl8jHx4cODg7MysrilStX/lBErWz+7KLH2dmZ3bp1q7Bt7ty5f3iioSj+LPk0NDSU5g0pm/LHNCsri7m5uXRycqKxsTEPHz4svR6BgYE0MzOTao+qgri4OA4fPpwDBw6kjY0NO3XqpPAXe38mKyuLJiYmUv3M7du36eLiwhEjRjAmJoZ5eXl88uSJnKP8e2xsbKQaz7Ikruwcv3XrlpTEODs7c+rUqfIJ8gPx9vampqYmQ0JCuHbtWo4aNYqenp5SLXNOTg61tLQ4c+bMCh1Zq4rDhw9TW1ubqampNDY2ZteuXenu7s7S0lKePXuWVlZWCl07uHXrVurp6VXopJ6VlUU/Pz8aGRnRx8dHjtG9G8Ue6CUoBCMjI6xatQrfffcdjh07hg4dOsg7JOEDq1atGurXr48GDRogOzsbMpkMTZo0Qb9+/eDr64vc3Fx07dpV3mFWmitXrqCgoAA9evTAihUrkJKSAnNzc3h6euLkyZOIjIzE1KlT5R3mO8nOzkZJSQkaNWqE48ePQ1VVFc2bN0fbtm1ha2uLBw8eyDvE947l6kA8PT2RmpoKV1dXODs7Y9myZQgKCgJJmJmZYcSIETAxMUHdunXlHPX7wz+pgymrgwaAXr16oUWLFnj8+DFevnwJTU1NtGjRQh6hflANGjRArVq1cPHiRbRs2RJt2rSBlZUVcnNz4e3tDT09vY9+biJJFBYW4vHjx7h37x4AQFVVFaWlpSAJVVVVBAYGQltbG6ampmjdujWCgoIq1EIqukmTJqG0tBSrV6+Gs7MzatWqhbi4OMhkMtja2iIxMREaGhr48ccf8emnn8o73EqVnZ2NAwcOYMeOHbh27RpevXoFGxsbeHt7o6ioCDNmzICvry/q168v71DfWWlpKYqKinDq1Cm4uLigTZs20qzEBg0aYNSoUahevToMDQ3lHerfJpI5oVL0798fKSkpqFWrlrxDET6Qsgu9M2fO4OnTpxg6dCg+/fRTHD9+HP3798eXX34JFRUV1KhRQ96hfnBRUVFo3bo1WrZsiVWrVmHLli0AgPbt2+PAgQPYtGkTZsyYgenTp6OgoAB79+79eIaP/g3Z2dmYN28eNDU14eDggJs3byIsLAylpaUwMTHB119/jWXLlmHkyJHQ1taWd7jvTVkis2HDBuzduxcHDhxA9erVoa6ujhUrVmD58uXYsWMH1NTUYGpqijp16sg54verbP/Pnz+P169fw8jISErkyt7/LVq0UMoEDgA2b96M8+fPo3bt2lBVVcXdu3dx584dtGnTBs2aNcOPP/6I4uJihUh2VFRUULNmTTg6OmLBggVo164dhgwZAhUVFVSrVg0XLlzA/v378e2336KwsBAZGRlwdXVViH37X06dOoVXr16hS5cuaNGiBaZMmYKaNWti2bJlWLhwIXr27ImzZ88iMDAQWVlZCAgIwJdffinvsCuduro6XF1d8erVKyxbtgxhYWFo0qQJIiIiEB8fj1GjRqFly5byDvMfKTv3q1evjrt372LgwIHSdYmvry86duyI0aNHyznKdyOSOaHSiEROuamoqCA8PBxOTk5Yv349AOC3337DmDFjMH78eDRq1Ag3btzA4sWLlfZir0xgYCC2b9+OsLAw3L9/H6dPn0bDhg1hYGAAOzs77N+/H/v370dBQQFIKtx7Q11dHVpaWjh37hz27NmDqVOnYtKkSUhOTsby5cvx6NEjZGRk4Pjx49DW1q7w9EbRZWRkICgoCFu3bkWNGjWwceNGREREwMDAAD///DNcXFykp84feze3v6t8F95169Zh06ZNqFOnDurWrYtDhw6hQYMGSrOv5b39JFJDQwN16tTBo0ePEBsbi6ioKMTExODZs2eoXbs2IiIioK6uLseI3525uTlu3LiB+fPnIz09HVpaWsjOzsacOXOwcuVKdO/eHQAwf/58he/Emp2dDSMjI5DE4MGDkZeXh/Hjx8PY2BiFhYVwd3fHvHnz0LdvXxQVFaFhw4YKm7C8q7Jz/cKFCygpKUFJSQkMDQ2Rk5ODNm3aoLS0FJcuXULjxo3h7OyssK9LVFQUbt26hSlTpqBDhw44ePAg9PX1oaGhgaNHj8Lb2xv+/v7yDvOdidEEgiC8F5cuXYKdnR1CQkKgoaGBq1evIjU1FUOHDsXp06fx7NkztGjRArq6ugrRtvifKH/RO3fuXLi7u+OHH36Am5sbAKC4uBhGRkaoUaMGwsPDFfIud1lilp6eDj8/PyQmJqJv374YPnw4vvjiCxQUFKCoqAgbNmzAnj17cPXqVYW+CHz7XH369CmmTJkiHee2bduibdu2OHLkCAIDAwEoTxJXpuy8PnfuHAICAuDk5ITGjRvDzMwMBQUFCAoKUpqRA2XKH/dz585BTU0N9erVQ7t27QAAXl5eOHXqFPbu3Yu7d++iXr16Cjtm5/Xr1wgODsbq1avRtGlT1KtXD2PHjsWQIUOUbpxOfHw8rKys4O7ujqSkJGRkZCA0NBQ2NjZYv3496tevj82bN2PUqFHyDrXSHTlyBD/99BMcHBwwa9YshIWFSasvvvrqK5w8eRK+vr4wMTGRd6jvjMo+dqLSq/QEQVAa5ZtBXLhwgY6OjvT19eXixYtpYWHBr776itOnT5djhJWn/GtRVizv7OzMWrVqVRg1UFRUxIEDB0pF9oqkbB9jY2NpamrK58+fc9u2bbS2tubGjRuZkZFR4e+NjY15+fJlOUT6fpQ/pklJSdI4lbi4OHp7e/PevXskyQMHDtDAwEDpxq2cPXtW+v/FixepoqLC77//vsLfDB48mNra2krbIGLNmjXU19fngAEDOHjwYC5atIjkm5Erw4cPl3N071dOTg5LS0ulFv3K0rXybVFRUdTR0eG5c+dIvhmGfebMGU6YMIF9+vRhcnKynCOsfMnJyezevTsfPnzIAwcOsFu3btI4lbS0NMbHx0uvl6KoSmMnRDInCMK/Eh0dTQ8PDz58+JBTpkxh7969GRgYyOfPnzMqKorz58+v8KGqjMpf9Kxbt44ODg58+fIlSXLatGn87LPPpM53iu7cuXMcP358hU6kW7Zs4ZgxY7h+/XrpCzE6OpqtWrWSWn0rmrePae/evTlhwgTOnTtXmkskk8m4bds2duvW7aNvQ/+uSkpK6OjoWGG2mIuLCz/55JM/JOgjR45UmvO7/HGPiopi9+7dKZPJ+OLFC548eZKDBw/msWPHeOnSJerr6zM7O1vhx4m8Tdn2589ERUVRU1OTsbGx0rbS0lIWFBTIMarKk56eziNHjkg/x8fHc9GiRQwLC6Oenh7v3LlD8s2omaSkJHmF+Y9VtbETIpkTBOFfiYyMZJs2bZiZmcn8/Hzm5+eTfHPRr6WlJY0mqAo8PT3Zo0cP3r17t8L2WbNmUUVFRaGHgZNkQUEBly1bRhUVlT/MB9y0aRNHjhwpzRRLTk5W6PliZRf1GzZs4MCBA5mfn8+hQ4dSR0eHtra2fPDgAR89ekRbW1ulbL9f5vz58+zYsaP086JFi1irVi1evHhRjlF9GOUTufz8fJ45c4ZmZmbStpycHE6ePJnr16+nTCZjZmamPMIU3pOTJ0+ya9eujI6OlncolaqkpIReXl7s168fQ0NDSZL379+nrq4uNTU1paTn9OnT1NLSYkJCgjzD/Ueq2tgJ0QBFEIR3wrdqiHr16gVDQ0NcvXoV/fv3R0FBAY4dO4ZffvkFS5cuhbm5udLWyJVXVFSEc+fOYceOHfjmm29QWFgINTU1qKqqYv369ahZsyby8/PlHeY7K3/sPvnkE8yePRvPnj3D4sWL0apVK3Tq1AkAMHXqVAwZMgTNmzcH8KZZhCI6e/YsCgoK0K9fP+Tl5SEpKQnbtm2Dl5cXXr9+DTc3Nzg5OWHWrFnw9vbG1q1bFbL28b/JyclBtWrVULduXSQkJOCrr75CkyZN0L9/f0RFRWHp0qVQVVVF9+7dkZCQAC0tLXmH/F6UP889PDwQGxuL6dOnQ01NDRcuXICWlpZUF/f06VNUq1YNjRo1knPUwr/Rr18/uLm5wdnZGSdOnFC4RlT/lKqqKkxMTFBYWIitW7cCACwtLTFgwABkZGRg7969aNeuHRYuXAgXFxd069ZNzhG/u6o2dkI0QBEE4Z0lJCTg1KlTmD17NgBg9erVCAkJQVxcHADg1q1bKCoqQufOnZU2kXt7v/Lz89GzZ09MmzYNkyZNkraHhISgU6dOaNu2rTzC/FfK9vHUqVO4e/cu6tSpg2HDhoEkFi9ejLi4OHh4eCjNBX1paSlWrVqFHTt2wMvLC/369UNhYSHu3LmDmTNn4sSJEwAAKysrNG7cGD///DOaNm0q56jfn4KCAkRGRuLBgwd4+fIlEhMTsX37dpSWlsLa2hq5ubk4deoUAMDV1RVDhw5V2KT9v7l8+TI8PDzg6emJOnXqYPLkySgqKkKTJk3QtGlTbNy4EWFhYVIjFEHx5eXloXbt2vIOo1KUNbQpKipCSUkJNm/ejOjoaMyaNQv6+voIDQ1FcHAwWrZsiW+//RZmZmYK8x3+9tgJANixYwc2b96MhQsX4v79+zh79izS0tKksROamppyjvo9kdMTQUEQFEhqaqpU/FxUVMQ9e/awS5cutLGxobOzM69fv05LS0teuXJFzpFWjvLLsQoKCpidnU2S9PHx4dSpUxkREUGS3L17N9u3b8/U1FR5hPleHD58mF27duWqVauor69PBwcHFhQUsLCwkHPmzGH37t356tUrhW+W8OrVKxYVFfHVq1d0d3enrq4uT548SZK8e/cuDQwMGBkZyZCQEBobG/PJkyfyDfgDiY6OZseOHdmiRYsKDVCys7M5ePBgdurUSY7RfTilpaW8cuUKO3fuzD59+khLzQoKCujt7c0ff/yRjo6OClk/JAjk/31vhYeH08zMjHl5eXzw4AHXrFlDc3NzHjt2TPrbsjp3Rflcf/nyJatVq0YVFRWam5uzf//+3LVrF9PT07l582Z+++23PHbsGK9fv86EhASlqfEtI5I5QRD+UmxsLL/88kvGxcVV2H7s2DHOnTuXXbp0Yc2aNblgwQI5RSgfGzZs4PDhw9mxY0du27aNsbGxXLFiBb/55hva2Niwc+fOCldPlZubK32Bnz17lvr6+nz8+DH3799PHR0dWlpacty4cVJCd/PmTTlH/O8dPnyY5ubm7N69O/38/Hj79m2uXr26QkK3YMECGhsbs3PnzkrX7KT8BVtmZiYnTJhAKysrLl++vMKNiMzMTI4bN06hb06U92cXqjt37qSuri4DAgL+UEujDI0ShKotMjKSnTt3lm44kuTvv/9Od3d3DhgwgAEBAXKM7t85c+YMmzdvzqCgIC5btozTp09n8+bNOW/ePKqpqbFhw4Y8cOCAvMP8IEQyJwjC3xIdHc0OHTrw9OnT0kVQ2b+HDx9mcHAwDQwMlOZC76/4+PhQT0+P169f5759+9ijRw96e3uTJG/fvs2kpCQ+fvxYzlG+mxs3btDU1FR6KpGSksL4+HieOHGCWlpavH37Nvfs2cOGDRty5MiRCnPX9n85duwYtbS0eOLECa5du5ba2tqMiIhgfn4+3dzcqKury0uXLpEknzx5UqG7ozIofwy3bt1KV1dXlpSU8NixY5w4cSJ//vlnFhUV8T//+Q937typNJ1py3dsDAwMpJeXF8PCwlhcXExfX1/279+fgYGBUldaUnGeUgjC22QyGfPy8jhu3DiGhISQrHhzIiMjg25ubrxw4YKcInw/qurYCZHMCYLwt0VFRbFjx44V2jlHR0ezVq1avHTpEi0tLXnr1i05Rlh5Zs+ezfDwcOnnkydPUl1dXSE7f5HkzZs32bNnT27cuJEkKyTsixcvpoeHB0ny6NGj/Omnn6QER5HFxcWxYcOGPHHihLRtxowZnDVrFkkyKyuLq1evZuvWrXnq1Ck5RVk5NmzYQD09PSYmJkrbQkJC6ODgQCMjI2poaDAlJUWOEX4Y69atY58+fbhy5Uq2b9+e27ZtI0lu3LiR3bp1Y2hoqEjiBKUxceJE+vr6srS0VDqv//Of//DOnTtKc55XxbET1eRdsycIguIwMjKCp6cnJk+ejDt37iA2Nhb29vbYs2cPGjVqhOPHj+OTTz6Rd5jvXVZWFmQyGQBIDSAePHiAgIAA6W/69euHYcOGQU1N8ZoE37x5E+PGjYODgwOmTZuGkpISTJw4EVlZWVBRUcGXX34JDw8PrF27FnPnzoWxsTG0tbXlHfa/1qhRI9SuXRuXL19GSUkJgDdNX8qamjRo0AD29vaYPXu21KVTGd26dQsHDhzAwYMH0blzZxQXFwMAhg4dCicnJ0ydOhVhYWEK2cTnbampqbh8+TLy8/MRExOD6OhoxMTEQE1NDW3atMGYMWNQXFyMadOmwdHREVpaWgrR/EEQ/psbN24gKCgIANC0aVPExsbi8ePHUFFRQWJiIn744Qc8fPhQac5zIyMjbNq0CdOnT0dMTAwAQEVFRSmvTcqIbpaCILyz6OhoWFtbgyS8vb0xbNgwFBUV4ffff0fr1q3lHd57JZPJEBwcjKioKGhoaCAoKAhRUVG4e/cuxo4dC1NTU/z666/YvXs3Vq5ciaNHjyrUhX9JSQkGDBiAatWq4eTJkygtLcXw4cPxxRdfwNvbGwDw+PFjBAUFITY2Ft999x3MzMzkHPX7c+3aNYwcORL29vbIysrCzZs3ERISUiEpL+sAp2zK9is9PR3Tp0+Hj48P6tevL130xMTEoFevXqhevbqcI30/wsPD4eTkBFVVVbRs2RKWlpYoKirCgwcPcP78eRw9ehTVq1fHtm3boKuriy5dusg7ZEH4x0iiqKgIS5cuRVpaGr7//nsYGhrC2toaeXl5qFOnDm7fvo1ly5bBwsJC3uG+d5GRkViyZEmVGDshkjlBEP6R+Ph4vHz5EoMGDUJxcbHSXPCVl5aWhq+//hovXryAvr4+cnJyEBMTg/bt26OgoABXrlzB+PHj0b59e6Snp2PHjh3o2LGjvMN+Z4mJibCxscGIESNw+fJlaGhoYPXq1dLvnz9/jkaNGqGkpARqamoK06r670pMTIStrS3y8vJw7do11KlTR2kTuEuXLuHp06cYNGiQtC01NRUWFhb48ccfMWbMGADA/v374ePjA39/f3zxxRfyCve9iYiIwKJFi+Dh4YEePXrAysoKERER+Pzzz/HNN98gMjISwJtW5qtXr8aRI0fQsmVLOUctCO+u7PNZJpNBVVUVv//+O3x9ffHw4UPY29ujd+/euHjxInJzc9G4cWOlHiFUVcZOiGROEIR/RVm/BNLT0+Hl5YV58+ahTp06+O6775CTk4O6devC29sb6urqAN48uSssLERBQQEaNmwo56j/uWvXrmHMmDGoWbMmzp8/L22PiYnB0qVL4efnJ83uUUa3bt2ChYUFHBwc4OjoqHQXACSRmZmJ5s2bo7i4GBMnTkTfvn1hamqKzz77DJGRkbCzs8OgQYNQu3ZtnD59Gvv27VPImxNvy87ORrNmzbBixQrMnDkTwJsbNcuWLUPLli3h4+ODKVOmIDs7G5GRkfD391eK/RaqlrLkDQCuXr2KK1euYOjQoVBXV0d6ejo2bdqEa9euwdHRESYmJnKOVniflO+2oyAIlUoZE7m4uDgcOnQITk5OSE1NxZYtW+Dv74/du3dDRUUF9vb2AIDg4GB4enqidu3aCp3IAUDnzp1x4MABZGdn49dffwXw5unrvHnzMGvWLKVO5ACgffv2CAwMxKpVq+Dh4SHvcN47FRUVNG7cGIsXL8acOXPw+eefIzw8HAMGDMCePXugr6+P69evo3///tLwYGVJaNTV1REWFgY/Pz+cO3cOAODh4QGZTIZFixbBzc0NNWvWxGeffYbAwECl2W+h6nj48CEWLlyItLQ0AG+WGIaGhuLQoUPSzYwJEyYgKSkJBw4cwOPHj+UbsPBeiSdzgiAI5Tx79gxff/01NDU1MXHiRKiqqiIyMhJ9+vTBzJkzkZaWhrlz5yIlJQVqamrYvXu3Ul38JSYmYvTo0ejatStu3rwJFxcXmJmZKe0T2LclJSXhk08+UbrazzJ+fn4ICgpCYGAgatSogfHjx+PQoUNo3LgxzMzMMHnyZLRr107eYX4QJ0+exKxZs9CpUye8fv0aBw4cUOqmCELV8ejRI5ibm6Nv375YuHAhGjZsiHXr1uHixYswNTXF2LFj8eDBA8ydOxeLFy9Gp06d5B2y8B6JZE4QBKGc/Px8TJ06FXl5eejYsSPU1NTQsmVLhIeHo0ePHpg5cyZIIjAwELq6umjVqpW8Q37vymro3NzcYG5uLu9whPesf//+GD58OLp164YJEyZg1apV+Prrr7Fo0SJ4enoqVAOfdxUXFwcLCwsEBwejb9++FZamCYIiu3//Puzt7aGpqQkXFxcpoTtz5gzy8/Nx9+5duLu7w9jYWN6hCu+ZSOYEQRDe4ufnh99++w02NjZ4/vw5mjVrhqZNm+L48eNo3749fv75Z3mH+MGVFY5XlSdyVUHZsQwJCYGXlxfu3LmDNWvWYNiwYQBQZRKb6Oho/PDDD1i1ahUGDBgg73AE4R9JSUnBrl27YGVlhfr166Nly5Z4/Pgx7O3t0aZNGyxZsgSNGzfGhQsXcO/ePTRt2hSGhobyDlv4AEQyJwhClfef//wH2dnZ6Nu3Lxo3bgwAWLJkCXr27ImUlBRcu3YNbdu2hbq6Oi5duoTffvtN4Wvk/opI4pRXeno6jIyMMHToULi5uaH8ZUBVOeZVqW25oHxKSkowcOBAnDp1CkOGDMHly5cxfvx4aGpqQkdHB+PGjYOxsTEmT54szc0UlJdI5gRBqNKys7PRoEED1KlTBw4ODigpKcGKFSvwyy+/oEmTJnB2dsbGjRsRHx8PXV1djB8/XupkKQiKav/+/dizZw82b95cZS/2qkrbckE53bp1C6NHj4axsTEMDAyQlpYGX19f9OzZE+Hh4UhPT8e4cePg7e0takOVnOhmKQhClaauro7Tp0/j008/RZcuXZCdnQ0nJydkZmZizZo1uHfvHuzs7GBgYAAbGxuRyAlKQV9fHySrdDJTlfddUHzt27eHn58fgoODcfPmTcyYMQMJCQmYP38+li1bhnHjxmHUqFEikasCxJM5QRAEvJmnNn/+fGzZsgUlJSVITk5GdHQ0fvrpJ7Rq1UosOxSUTn5+vlhiKAgKLjExEaNGjcKoUaOwdOlSqe61qKgINWrUEN9dVYBI5gRBEP6/qKgozJkzB5s2bYKBgQFyc3NRt25deYclCIIgCP/VtWvXMHr0aJibm2PJkiWoWbOmvEMSKpFI5gRBEMo5efIkZs+eDXd3d/Tr10/e4QiCIAjCX7py5QpGjBiBiIgItG3bVt7hCJVIJHOCIAhvEZ3uBEEQBEUjVpNUTSKZEwRB+BOi050gCIKgSER9XNUkkjlBEARBEARBEAQFJEYTCIIgCIIgCIIgKCCRzAmCIAiCIAiCICggkcwJgiAIgiAIgiAoIJHMCYIgCIIgCIIgKCCRzAmCIAiCIAiCICggkcwJgiAIgiAIgiAoIJHMCYIgCIIgCIIgKCCRzAmCIAiCIAiCICggkcwJgiAIgiAIgiAooP8HSqEUVDXDktQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TSPAN3 nearest genes:\n", + " TSPAN6 | score: 0.9170464622149193 | OC annot: ORF \n", + " TMEM184C | score: 0.9163788197095625 | OC annot: ORF \n", + " TPRA1 | score: 0.7522184246952881 | OC annot: ORF \n", + " SFT2D3 | score: 0.7431436174959112 | OC annot: ORF \n", + " SPNS1 | score: 0.6646362925026508 | OC annot: vesicles \n", + " TMEM192 | score: 0.6553244294108607 | OC annot: vesicles \n", + " LAMP2 | score: 0.6513651127670179 | OC annot: vesicles \n", + " SLC15A4 | score: 0.635030731882582 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MFSD5 nearest genes:\n", + " SLC37A4 | score: 0.8270945839044421 | OC annot: golgi \n", + " SLC30A5 | score: 0.813938299815762 | OC annot: golgi \n", + " SLC35E1 | score: 0.7611229129194087 | OC annot: golgi \n", + " MAN1A2 | score: 0.7202094401998735 | OC annot: vesicles \n", + " SFT2D3 | score: 0.7148642959526779 | OC annot: ORF \n", + " TVP23C | score: 0.6879611346345407 | OC annot: ORF \n", + " ELOVL1 | score: 0.6826969566786831 | OC annot: vesicles \n", + " WWOX | score: 0.6823825519870121 | OC annot: golgi \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TP53I11 nearest genes:\n", + " SMIM13 | score: 0.7872356097982389 | OC annot: ORF \n", + " SLC27A4 | score: 0.7580054993928795 | OC annot: er \n", + " PTDSS1 | score: 0.7457254612175913 | OC annot: er \n", + " EMC10 | score: 0.7396668191323938 | OC annot: er \n", + " SCD5 | score: 0.7292382944622691 | OC annot: er \n", + " GPAT3 | score: 0.7004236050796163 | OC annot: er \n", + " DHCR24 | score: 0.6984943296607511 | OC annot: vesicles \n", + " ALG2 | score: 0.6846160527002724 | OC annot: er \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CD99 nearest genes:\n", + " SLC16A1 | score: 0.916384076431277 | OC annot: membrane;vesicles \n", + " MYO1C | score: 0.794511221680026 | OC annot: membrane \n", + " CD276 | score: 0.7227151039726161 | OC annot: ORF \n", + " SLC7A5 | score: 0.7140130841430596 | OC annot: membrane;vesicles \n", + " MARK2 | score: 0.6900316500881274 | OC annot: membrane;cytoplasmic \n", + " EZR | score: 0.6567665398491145 | OC annot: membrane \n", + " TFRC | score: 0.6461195355796184 | OC annot: membrane;vesicles \n", + " SLC1A5 | score: 0.6170747165261576 | OC annot: membrane;vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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IiIiIiAUpmBMREREREbEgBXMiIiIiIiIWpGBORERkgNntUFZm3oqIiAyWAHc3QERExJsUFMCaNVBTA1FRMHMmpKS4u1UiIuKNFMyJiIj0Q0NDA3l5eZ22ORw2Vq+OoaYmgMjIOtavr2Pbtmhmz64nONjo8XnS0tIICwsbiiaLiIiXUTAnIiLSD3l5eWRmZnbZGgecC5QCdiAEGM4ddywHKnp8nuzsbDIyMgazqSIi4qUUzImIiPRDWloa2dnZnbZ1HJmz2/N5+OH/cMkl5/HrX/+WqKi2Xp9HRESkPxTMiYiI9ENYWFiPI2qjRplz5tat8wMOITT0IHbuHKW5cyIiMuBUzVJERGQApaTAqadCTEwLsI3Rox1UVpoBnqpbiojIQFIwJyIiMsBaWsAwbMAugoMNEhLM6pb19e5umYiIeBMFcyIiIgMsIgLCw1uBBBwOG8XF5jIF4eHubpmIiHgTBXMiIiIDLCQEMjLqgEoqKwOIjTXXmwsJcXfLRETEmyiYExERGQSJic3AK8yaVck556j4iYiIDDwFcyIiIoPGQUxMq0bkRERkUCiYExERERERsSAFcyIiIiIiIhakYE5ERERERMSCFMyJiIgMIofDRlmZFgwXEZGBF+DuBoiIiHgTux3q6swgDpJZvTqGnBxznbmZM1XVUkREBo6CORERkQFSUABr1kBNDezZEwv8ipqaAMaPh+Ji875zztF6cyIiMjCUZikiIjIA7HYzWKushOho2LUrGMgkOrqZ0FBISDCDvPp6d7dURES8hYI5ERGRAVBXZwZrAQGQkwOlpYHAaLZuDaOx0RyZi4qC8HB3t1RERLyFgjkREZEBEBFhpk9+/rk5OmezGUAptbV+FBZCbKw5Z04pliIiMlAUzImIiAyAkBDIzAQ/P/NfZGQr8CFjxzqYM8ecK6fiJyIiMpBUAEVERGSApKWZo2/FxdDW1gAEEB3dQnKyRuRERGTgaWRORERkgISEQFYWJCdDXV0AUElGRp0CORERGRQK5kRERAZQSoqZUjlrViXwComJze5ukoiIeCkFcyIiIgMsJARiYloBh7ubIiIiXkzBnIiIiIiIiAUpmBMREREREbEgywVzS5YsYcyYMYSEhJCZmcknn3yyz/0dDgc33XQTo0ePJjg4mLFjx/Lkk08OUWtFREREREQGh6WWJli+fDlXX301S5Ys4ZhjjmHp0qXMnj2bLVu2kNLL4j3nnnsue/bs4YknnuCQQw6hpKSElpaWIW65iIiIiIjIwLJUMHf//fdz2WWXMX/+fAAeeOAB3n//fR599FHuuuuubvu/9957rFmzhu3btzNs2DAAUlNTh7LJIiIiIiIig8IywVxTUxPZ2dnccMMNnbbPmjWLdevW9fiYN998k6lTp/KPf/yD5557jvDwcE477TTuuOMOQkNDe3yMw+HA4dhbfaympgaAtrY22traBuhoRETE2zm/M/T9ISIirnDlO8MywVxZWRmtra2MHDmy0/aRI0dSXFzc42O2b9/Op59+SkhICP/5z38oKyvjiiuuoKKiotd5c3fddRe33XZbj6/fMcgTERHZl8rKyvbb0tJSN7dGRESsora2ts/7WiaYc7LZbJ1+Ngyj2zantrY2bDYbzz//PNHR0YCZqnn22WfzyCOP9Dg6d+ONN7Jo0aL2n2tqakhOTiY+Pp6oqKgBPBIREfFmsbGx7bfDhw93c2tERMQqgoOD+7yvZYK5+Ph4/P39u43ClZSUdButc0pMTGTUqFHtgRxAeno6hmGwa9cuxo0b1+0xwcHBPf4C/fz88POzXPFPERFxE+d3hr4/RETEFa58Z1jm2yUoKIjMzExWrVrVafuqVauYPn16j4855phjKCwspK6urn3b1q1b8fPz46CDDhrU9oqIiIiIiAwmywRzAIsWLeLf//43Tz75JLm5uVxzzTUUFBSwcOFCwEyRvPjii9v3v/DCC4mLi+M3v/kNW7ZsYe3atVx//fVceumlvRZAERERERERsQLLpFkCnHfeeZSXl3P77bdTVFTEhAkTeOeddxg9ejQARUVFFBQUtO8fERHBqlWr+MMf/sDUqVOJi4vj3HPP5c4773TXIYiIiIiIiAwIm2EYhrsb4clqamqIjo6murpaBVBERKTPcnJyyMzMJDs7m4yMDHc3R0RELMKV+MNSaZYiIiIiIiJiUjAnIiIiIiJiQQrmRERERERELEjBnIiIiIiIiAUpmBMREREREbEgBXMiIiIiIiIWpGBORERERETEghTMiYiIiIiIWJCCOREREREREQtSMCciIjKE7HYoKzNvRUREDkSAuxsgIiLiKwoKYM0aqKmBqCiYORNSUtzdKhERsSqNzImIiAwBu90M5CorYfhw83bNGo3QiYhI/ymYExERGQJ1deaIXEIChIaatzU1UF/v7paJiIhVKZgTEREZAhERZmplcTE0Npq3UVEQHu7ulomIiFUpmBMRERkCISHmHLnYWCgtNW9nzjS3i4iI9IcKoIiIiAyRlBQ45xwztTI8XIGciIgcGAVzIiIiQygkREGciIgMDKVZioiIiIiIWFC/grlPPvmEefPmMW3aNHbv3g3Ac889x6effjqgjRMREREREZGeuRzMvfrqq5x00kmEhoayceNGHA4HALW1tfztb38b8AaKiIiI57DboaxM6+OJiHgCl4O5O++8k8cee4x//etfBAYGtm+fPn06OTk5A9o4ERERq3I4bEDcT7feoaAAVqyA5cvN24ICd7dIRMS3uVwA5bvvvuPYY4/ttj0qKoqqqqqBaJOIiIilFRTA6tUxwLmsXh3DqFFmJUsrs9thzRrYs6eJ1tZd5OcHkp/fQlZWFcHBRvt+jY2N5Ofnk5qaSmhoaI/PlZaWRlhY2FA1XUTEa7kczCUmJvL999+Tmpraafunn37KwQcfPFDtEhERsSRn0FNTEwCUUlMTwJo15pIEVq5iWVcHNTXQ2rqLG274IxACDGfx4uVAhUvPlZ2dTUZGxmA0U0TEp7gczF1++eX88Y9/5Mknn8Rms1FYWMjnn3/Oddddx+LFiwejjSIiIpbhDHri45sBO/HxzdTUmGvLWTmYi4iAqChobDyIu+9+kLKyQKKiWsjKWtBpZC43N5d58+axbNky0tPTe3yutLS0oWq2iIhXczmY+9Of/kR1dTXHH388drudY489luDgYK677jquvPLKwWijiIiIZTiDnvz8QCCEsrJAkpLMRcKtLCQEZs6ENWuCqKk5mKQk8+fe0kfT09M1+iYiMshcCuZaW1v59NNPufbaa7npppvYsmULbW1tHHbYYURERAxWG0VERCzDGfTk57cAw4mKamHmTGuPyjmlpJjpovX1ZnDqDcckImJlLgVz/v7+nHTSSeTm5jJs2DCmTp06WO0SERGxrBEjYOrUWuB1srIWWL74SUchIQriREQ8hctLExxxxBFs3759MNoiIiJiec7y/WvWxAC/oKLC5RkNIiIifeJyMPfXv/6V6667jrfffpuioiJqamo6/RMREfFVzkqWlZUQG9sCxJKTE6EFtkVEZFC43F148sknA3Daaadhs+1dCNUwDGw2G62trQPXOhEREQtxVrJMSIDCQgMopr7e3/KVLEVExDO5HMx99NFHg9EOERERy3NWsiwuBofDBiQQHt5q+UqWIiLimVwO5mbOnDkY7RAREbG8veX7obAwAKgkI6NOo3IiIjIo+jUru6qqiieeeILc3FxsNhuHHXYYl156KdHR0QPdPhEREUtxlu8fNaqSv//9FRITF7m7SSIi4qVcLoCyYcMGxo4dy//93/9RUVFBWVkZ999/P2PHjiUnJ2cw2igiImIpISEQE9MKONzdFBER8WIuj8xdc801nHbaafzrX/8iIMB8eEtLC/Pnz+fqq69m7dq1A95IERERERER6czlYG7Dhg2dAjmAgIAA/vSnP2kRcRER8Wl2u1nRMiLC3S0RERFf4HIwFxUVRUFBAWlpaZ2279y5k8jIyAFrmIiIiJUUFJiFT2pqzIqWw4YFurtJIiLi5VyeM3feeedx2WWXsXz5cnbu3MmuXbt46aWXmD9/PhdccMFgtFFERMSjdVwsfPhw8zYnJwIIdnfTRETEi7k8Mnfvvfdis9m4+OKLaWlpASAwMJDf/e533H333QPeQBEREU/XcbHw0FDnouH+gBaYExGRweNyMBcUFMSDDz7IXXfdxQ8//IBhGBxyyCGEhYUNRvtEREQ8XsfFwhMSzNvw8FagHofDRlmZuY/WmxMRkYHkcjBXXV1Na2srw4YN44gjjmjfXlFRQUBAAFFRUQPaQBEREU/XcbHw0lKIjYWxY+uAZN54YxgJCRAXZ+6TkuLu1oqIiLdwec7c+eefz0svvdRt+8svv8z5558/II0SERGxGudi4eefb942NdmAa9iwIZLvv4cffjCDPbvd3S0VERFv4fLI3Pr167n//vu7bT/uuOO46aabBqRRIiIinmLbtm3U1ta69BiHw8abbzYB8QQE7GH37lZKSw3q6uyMGlX504Li+xYZGcm4ceP62WoREfEFLgdzDoejvfBJR83NzTQ2Ng5Io0RERDzBtm3bOPTQQ/vxyDjgQuAQ3n+/EjBTLuG/3Hff04CjT8+ydetWBXQiItIrl4O5n/3sZzz++OM89NBDnbY/9thjZGZmDljDRERE3M05Irds2TLS09P7/DiHw8a774bzzTeN2GzDqa4OITq6hXnzDmP06N/u9/G5ubnMmzfP5RFBERHxLS4Hc3/961/Jysriq6++4oQTTgDgww8/5Msvv+SDDz4Y8AaKiIi4W3p6OhkZGS49ZtQoc45ceTkEB8MJJ8Chh44YpBaKiIgvcjmYO+aYY/j888+55557ePnllwkNDWXixIk88cQTSgURERH5ibMgSn09hIdrWQIRERl4LgdzAJMnT+b5558f6LaIiIh4lZAQBXEiIjJ4XF6aICcnh6+//rr95zfeeIMzzjiD//f//h9NTU0D2jgRERERERHpmcvB3OWXX87WrVsB2L59O+eddx5hYWGsWLGCP/3pTwPeQBEREREREenO5WBu69atTJ48GYAVK1Ywc+ZMXnjhBZ5++mleffXVgW5fN0uWLGHMmDGEhISQmZnJJ5980qfHffbZZwQEBLS3XURERERExMpcDuYMw6CtrQ2A1atXc8oppwCQnJxMWVnZwLaui+XLl3P11Vdz0003sXHjRmbMmMHs2bMpKCjY5+Oqq6u5+OKL26tvioiIiIiIWJ3LwdzUqVO58847ee6551izZg1z5swBYMeOHYwcOXLAG9jR/fffz2WXXcb8+fNJT0/ngQceIDk5mUcffXSfj7v88su58MILmTZt2qC2T0REREREZKi4HMw98MAD5OTkcOWVV3LTTTdxyCGHAPDKK68wffr0AW+gU1NTE9nZ2cyaNavT9lmzZrFu3bpeH/fUU0/xww8/cMsttwxa20RERERERIaay0sTTJw4sVM1S6d77rkHf3//AWlUT8rKymhtbe02+jdy5EiKi4t7fMy2bdu44YYb+OSTTwgI6NuhOhwOHA5H+881NTUAtLW1taeXioiIb3Ce94f6O8BdrzsQrNx2ERFP4Mq5s1/rzPUkZIgW0rHZbJ1+Ngyj2zaA1tZWLrzwQm677TYOPfTQPj//XXfdxW233dZte1lZWacgT0REvF9lZWX7bWlpqde/7kCwcttFRDxBbW1tn/cdsGBusMXHx+Pv799tFK6kpKTHuXq1tbVs2LCBjRs3cuWVVwJmlGsYBgEBAXzwwQf84he/6Pa4G2+8kUWLFrX/XFNTQ3JyMvHx8URFRQ3wUYmIiCeLjY1tvx0+fLjXv+5AsHLbRUQ8QXBwcJ/3tUwwFxQURGZmJqtWreJXv/pV+/ZVq1Zx+umnd9s/KiqqWzrokiVL+O9//8srr7zCmDFjenyd4ODgHn+Bfn5++Pm5PMVQREQszHneH+rvAHe97kCwcttFRDyBK+dOywRzAIsWLeKiiy5i6tSpTJs2jccff5yCggIWLlwImKNqu3fv5tlnn8XPz48JEyZ0evyIESMICQnptl1ERET6z26HujqIiHB3S0REfIvLwdzHH3/McccdNwhN2b/zzjuP8vJybr/9doqKipgwYQLvvPMOo0ePBqCoqGi/a86JiIjIwCkogDVroKYGoqJg2LBAdzdJRMRnuJz/cPLJJzN27FjuvPNOdu7cORht2qcrrriC/Px8HA4H2dnZHHvsse33Pf3003z88ce9PvbWW29l06ZNg99IERERH2C3m4FcZSUMH27e5uREAH2f7yEiIv3ncjBXWFjIH//4R1577TXGjBnDSSedxMsvv0xTU9NgtE9EREQ8VF2dOSKXkAChoeZtfb0/EO7upomI+ASXg7lhw4Zx1VVXkZOTw4YNGxg/fjy///3vSUxM5KqrruKrr74ajHaKiIiIh4mIMFMri4uhsdG8DQ9vBerd3TQREZ9wQGWmJk+ezA033MDvf/976uvrefLJJ8nMzGTGjBl8++23A9VGERERS7PboazMvPUmISEwcybExkJpqXmbkVEHaF1WEZGh0K9grrm5mVdeeYVTTjmF0aNH8/777/Pwww+zZ88eduzYQXJyMuecc85At1VERMRyCgpgxQpYvty89bY6XSkpcM45cP755m1iYrO7myQi4jNcrmb5hz/8gRdffBGAefPm8Y9//KNTqf/w8HDuvvtuUlNTB6yRIiIiVuIs1R8QsLdASEKCmYa4Zo0Z9ISEuLuVAyckxLuOR0TEKlwO5rZs2cJDDz3EWWedRVBQUI/7JCUl8dFHHx1w40RERKymY6l+mw3Ky2H8+L0FQkpLob7e+4Mfh8NGWZk5r87bj1VExF1cTrO85ZZbOOecc7oFci0tLaxduxaAgIAAZs6cOTAtFBERsYiupfobG2H3bti5c2+BkKgoCPf6Yo/JrF4d47WppSIinsLlYO7444+noqKi2/bq6mqOP/74AWmUiIiIFXUt1Z+cDKNGmf93FgiZOdO7R6ocDhtwLDU1Ae1rz61Z433FX0REPIHLaZaGYWCz2bptLy8vJ9z7uxpFRER61bFUv3OOXGoqnHoqtLaaI3LeHMgBNDT4AVHExzf7XGqpiMhQ63Mwd+aZZwJgs9m45JJLCA4Obr+vtbWVzZs3M3369IFvoYiIiEU4S/WvWdN9JK6uzt2tGxphYW1ADWVlge2ppbGxvpBaKiIy9PoczEVHRwPmyFxkZCShoaHt9wUFBXH00UezYMGCgW+hiIiIhThL9dfXmwFMSYk5b6ymxhy1mznT3MdbBQcbwFqiolp8JrVURMRd+hzMPfXUUwCkpqZy3XXXKaVSRERkHwyjc0EUb16aoLudZGVVceihvpFaKiLiLi7PmbvlllsGox0iIiJeQUsTmIKDDeLi3N0KERHv1qdgLiMjgw8//JDY2FimTJnSYwEUp5ycnAFrnIiIiJV0HYnbudNcmiAszKxsqfljIiIykPoUzJ1++untBU/OOOOMwWyPiIiIZfW0NEFDg28tTSAiIkOnT8GcM7WytbWV4447jokTJxIbGzuoDRMREbEaLU0gIiJDyaVFw/39/TnppJOoqqoapOaIiIhYl3NpgtjYziNxMTEQF6dATkREBpbLBVCOOOIItm/fzpgxYwajPSIiIpbmXJqgvNysaBkfv/c+u91MxYyI8J3AzhePWURkqLgczP31r3/luuuu44477iAzM7PbEgVRUVED1jgRERErKinZW9HSubYcdN/mzevNQefKnr5yzCIiQ8nlYO7kk08G4LTTTutU1dIwDGw2G62trQPXOhEREYvpaW251avNUbr6emusN7dt2zZqa2v79djc3FwAvvrqO3btiqWmJoD4+Gby8wPJz28hK6vqp4XFexYZGcm4ceP69doiIr7G5WDuo48+Gox2yD4oRUVExDq6VrRMSIAdO8z7xozx/PXmtm3bxqGHHnrAz3PppX8AzgVKATsQAgxn8eLlQMU+H7t161YFdCIifeByMDfTmSsiQ0IpKiIi1tJTRcvhw82RuY7bPHW9OeeI3LJly0hPT3f58Y2NjeTn55OYOIbPPktsH5krKwskKqqFrKwFvY7M5ebmMm/evH6PCoqI+BqXgzmnhoYGCgoKaGpq6rR94sSJB9woX9HQ0EBeXl6v91dV2VmxwiAoKIGkJFuPKSppaWmEhYUNVZNFRGQ/nBUt16zpXNESum/ztFG5jtLT08nIyOjXY4855hgADjlkb4dkUpI6JEVEBprLwVxpaSm/+c1vePfdd3u8X3Pm+i4vL4/MzMx97BHH/lJUsrOz+/1lKyIig8NZ0bK+vvPacj1t82a9/R5ERGRguBzMXX311VRWVvLFF19w/PHH85///Ic9e/Zw5513ct999w1GG71WWloa2dnZvd7/1VffcemlK7nooj9x+OFxPaaopKWlDVVzRUTEBSEh3YOXnrZ5O188ZhGRoeJyMPff//6XN954g5/97Gf4+fkxevRoTjzxRKKiorjrrruYM2fOYLTTK4WFhfVhVO3PjBt3BxERY5SiIiIiIiIi7VwO5urr6xkxYgQAw4YNo7S0lEMPPZQjjjiCnJycAW+g7CQrq4pDD1WKioiIiIiI7OXn6gPGjx/Pd999B8DkyZNZunQpu3fv5rHHHiMxMXHAGygQHGwQF6dATkRERERE9urXnLmioiIAbrnlFk466SSef/55goKCePrppwe6fSIiIpahdUFFRGQouRzMzZ07t/3/U6ZMIT8/n7y8PFJSUoiPjx/QxklnukgQEfFcWhdURESGWr/XmXPqWxEPOVBbt8Lq1dDUBHFxukgQEfEkdrsZyFVW7l0UfM0asyy/Ot9ERGSw9CmYW7RoUZ+f8P777+93Y6RnP/4YxPvvQ3k5jBwJVVXmdl0keAeNuIpYX12dOSKXkAChoeZtaam5vpo+1yIiMlj6FMxt3LixT09ms9kOqDHSk2D+979IysvhoIPMC4OyMoiJ0UWCN1Baloh3iIgwP8PFxXtH5mJjzSrEvkCdUiIi7tGnYO6jjz4a7HZIryJobvZj5EgzeAsPh1274LDDfOciwRs0NDSQl5fXaZvDYWP16hhqagKIjKxj/fo6tm2LZvbs+vZF4btKS0sjLCxsKJosIi4ICTE7Y9asMUfkYmPNn30hsFGnlIiI+xzwnDkZbHVER7cQGWleIOzaZc6Zy8ryjYsEb5GXl0dmZmaXrXHAuUApYAdCgOHcccdyoKLH58nOztYcVREPlZJipr/X14O/P7S0mCNW3nyu1lxBERH36lMwd+aZZ/L0008TFRXFmWeeuc99X3vttQFpmDg5yMioo6ICoqPh8MPhhBPg0EPd3a6hY5X0nW3btlFbW9vjfY2NjSxbtqzTtqYmP/73vxTq64NwOAp4+eWPOeusE5gzZwZBQW29Pk9OTk637ZGRkYwbN+7AD0JEDkhICJSU+M5IleYKioi4V5+Cuejo6Pb5cNHR0YPaIOkuMbGZE07Ym2bpS1+QVknf+T73a877xeR+PDIBmAJEMCWhju2f/Z6HPivuVxte/u8mDkk/ol+PFZGB4WsjVb4+V1BExN36FMw99dRTPf5fhk5IiPdcCPQ0f8ypsbGR/Px8UlNT8fMLa59TFh/fTH5+IPn5LWRlVTFp0niPmjvWXPQtOZdH9OORdcAnXbb153kgt+hbUDAn4la+NlLly3MFRUQ8gebMWZhV0g+76nn+WFfBQApwMlBExzllixcvJzt7lUfNHav0jydjaR1/+ctfSEtL6/PjysoCyMsLo6amDYejnKOOCiQpyc+l196xYwc333wzT5zigUOWIj7GF0eqOs4V9LXsERFfYtXrTm/ncjBXXl7O4sWL+eijjygpKaGtrfPcnoqKngs3yMCySvphT9LS0sjOzu7xvtzcXObNu5GFC18kPDyZHTtCiItrZuxYB2VlgURFtZCVtYC0tPFD3Op927Itn43FbZz5+9tceFQwcDYQCxQDCfBwJfAK4HC5DRGxw11+jIgMLF8dqfKm7BER6c7K153ezuVgbt68efzwww9cdtlljBw5UmvLuYHV52SEhYX1OqrmcNiAY4mISOFnP0smIsI8vuBgmDjRc08eZ5xxBtD70gFmkDqvy9YIIAozkLP/dDscCKe3YG7ZsmWkp6d3264CKCKeQyNVImI1+5oC43DYePfdcPLzqznkkAjy8yPap710XEpJyye5h8vB3Keffsqnn37KpEmTBqM90gfePCejocEPiCI+vpnQUHM9vZgYmDMHkpM99/ji4+OZP39+r/f3NBrZdZ2577+vIzU1mtmzF2idORGL00iViPfzprTDfU+B6XkppcWLOy+lpOWT3MPlYC4tLY3GxsbBaIv0kTfPyQgLawNqKCsLpLHRPLa4OM8O5Pqit9HIUaP2pi2MGeO5I48iIiI98aaAxhXelna4rykwDoeNZ59t4LHHXuLKK88lJCS1fdpL15E5GXouB3NLlizhhhtuYPHixUyYMIHAwMBO90dFRQ1Y46Rn3jwnwzwprCUqqsXrjq0nSscSERGr8raABvadbghm1e2tWwvYsSOTxsbQbtW2ncGN1TJp9jUFBqCi4msee6wOm20M6ekHkZVl/b+1t3A5mIuJiaG6uppf/OIXnbYbhoHNZqO1tXXAGie98+4gYCdZWVUceqg3Hlt3SscSEbEmXx2VAuvP3+9N3ypu7z/t0FtTDm02MHqeCSJu4nIwN3fuXIKCgnjhhRdUAMXNvDkICA42iItzdytERER65o2jUq7w1vn7+0o3BGdBs8tYuHAuEREpxMc3d6q23XFkzlvY7ZCTEwFEkJTURH29dwTu3sLlYO6bb75h48aNjB/vWaXhvVcwlZX+2O36wIiIiAyWsqKdfPKfJ3q8r6Ghnh9+2N7+c2urP4WFh9HUFEpISB12ewRrlzeSlLQFf//eM5TGjj2YsLDuE9xHjUriyNnzIMg6aXneOn9/f+mGJge//GUUFRXJ1NRAUpJ3B/N1dVBf7w8UExxseE3g7i1cDuamTp3Kzp07FcwNgaKiQOBsVq2KpbDQu08UIiIi7vTJf57gVyX/1/sOI7v8nPS+6y9S99O/rkpgx/ARjJl+huvP6SbePH+/LxITmznhBG+d7tJZRASEh7cCCTgcNq8J3L2Fy8HcH/7wB/74xz9y/fXXc8QRR3QrgDJx4sQBa5wv2zukHUtsbAuVlRrSFhGxCl+eS2VVM351Gf/5T8/39WVkLijoAEfmps464GMYat49f3/fHA6bz3zGQ0IgI6MOqKSyMqB9JNLbj9sqXA7mzjvvPAAuvfTS9m02m00FUAaYhrTFF+kCWLxB17lURx0Fw4bpfe3p4hOT+dUVt/Z5f1+fM+fkzfP3e5fM6tUx5OT4zt8+MbEZeIVZs65n+vQUH/ybey6Xg7kdO3YMRjukCw1pi6/RhZF4IluLnSkJfoRWbYVCv/3u72iCr1dDQA2Mi4MdP8CK9ZCaCjExkJEBiQn7f93Qqq1MSfDD1mI/4GOQweHLo1K+zOGwAcdSUxPA+PHmXMFVq+C44yA4GOLjvfm94CAmptWLj8+aXA7mRo8ePRjt6LMlS5Zwzz33UFRUxOGHH84DDzzAjBkzetz3tdde49FHH2XTpk04HA4OP/xwbr31Vk466aQhbrXrNKQt3mBf6/U0NjaSn59Pamoqfn5hrF4dQ01NQLc1eyZNGm+ptXrEu4TUFZBzeQSsvRzW7n//YGCO84ean27j2TtXalffXjcdyLk8gty6AmC6S22WoeObo1K+obdMkYYGPyCK+PhmQkMhIADeeccM6IKDYfJkOOssdUbK0OlTMPfmm28ye/ZsAgMDefPNN/e572mnnTYgDevJ8uXLufrqq1myZAnHHHMMS5cuZfbs2WzZsoWUHj41a9eu5cQTT+Rvf/sbMTExPPXUU5x66qmsX7+eKVOmDFo7B4JZvbINeEtD2mJZfVuvB/a1Zk929iqvXKtHrMEekULG0jqef/550vtQatzRBKtXmyPMoaGwbp2ZYvmzn0Frq7km10knQUz0vp8nNy+PuXPn8sQpuiIUGWr7yhQJC2sDaigrC6S6Gj79FIqKYOxY8POjPfVy7lzPC/S3bdtGbW1tvx6bm5vb6dZVkZGRjBs3rl+PlX3rUzB3xhlnUFxczIgRIzjjjDN63W+w58zdf//9XHbZZcyfPx+ABx54gPfff59HH32Uu+66q9v+DzzwQKef//a3v/HGG2/w1ltveXQw5zyJfPttLHAqjY1+HndCEOmLfa3XY67VM49ly5Zx8MGHdRqZ67hmT1qaKueK+xgBIWwsbqMx5lBImrzf/YOBI2aZAd0PhVARDGGxUB70U+n2OAgZg9lfsQ+NxW1sLG7DCNDJX2Qo7W8xdHMdubVERbXw/fdQVWXul5RkPr6szPznaTUOtm3bxqGHHnrAzzNv3rx+P3br1q0K6AZBn4K5tra2Hv8/lJqamsjOzuaGG27otH3WrFmsW7euT8/R1tZGbW0tw4YN63Ufh8OBw+Fo/7mmpqb9sUNx7HY7fPwxVFRAdHQTEEt2djjHH9/mUSeFweL8HQ/V71sGV0hICJMnT+7xPuffd/z48WRkTCIxEdauNXtCExPh2GMhJWV0p31Fhlp/zkltbea/wEA49FAzDaukxJwzN2MGBAWZ9w/06w4UX31tETC/g6qrYcQIM21yxAgzOKutdX5224CdxMQ4WLOmjaIi877GRnPf1laIizNH5j3pLVxdXQ3As88+S3p6usuPt9vt7VMjQly8IM3NzeXiiy+murpan+s+cuX35PKcOXcpKyujtbWVkSM7L/QycuRIiouL+/Qc9913H/X19Zx77rm97nPXXXdx22239fj6HYO8wVJebmP37hDi41upqakFiiktdfDjj2UMG2YM+uu7W2VlZfttaWmpm1sjg6nr3zo01ExlaWiwERZmEBJiVnAVcSdXz0l2O6xcGUJVlY0RI1opKfEnKMjg5z93EBvb9/e1O8+FvvraImB+hm22EH74Ye9nOCbGoKHBTlub8z16GE88MZyWlhYiItqoq/Nj61YbwcHNZGa2MGWKg9raNvqZ0TgonJ+thIQEkpOT+/Uc/R1V0+fada6kw/Y5mFu/fj0VFRXMnj27fduzzz7LLbfcQn19PWeccQYPPfQQwcHBrrXWRTabrdPPziUR9ufFF1/k1ltv5Y033mDEiBG97nfjjTeyaNGi9p9rampITk4mPj6eqKio/je8jyIjYdQoc2QuMDASSGD48GBGj473iZG52NjY9tvhw4e7uTUymPS3Fitw9X1aVgaGYc6fCQ01z+llZTBqVCRxcYP3ugPJV19bxGnOnL2ZIsnJZqZIcnIkAGFhw4DZNDYGc/DBAbS2mkVSgoLgmmv8+dnPICQkwr0H0AN9rq3FlXiqz8HcrbfeynHHHdcezH399ddcdtllXHLJJaSnp3PPPfeQlJTErbfe6nKD+yI+Ph5/f/9uo3AlJSXdRuu6Wr58OZdddhkrVqwgKytrn/sGBwf3+Av08/PDz2//ZakPVFiYWd7WnDMXBFSSmVlPWNjgv7YnMH/HwVRXB9LUpLmC3sz5eRqqz5ZIf7j6Po2KguhoM60yIcG8jY01gzpX3ubu/Hz46muLOKWmmp/fnpadsNsDgDZiYlqorvYjNhZ274YJE8zOeD8/1z7rQ0Wfa2tx5ffU5z03bdrECSec0P7zSy+9xFFHHcW//vUvFi1axD//+U9efvll11rqgqCgIDIzM1m1alWn7atWrWL69N7LNr/44otccsklvPDCC8yZM6fX/dzFbjd7be0dlhJyrl0za1Yl8MpPCzX6hqKiQOBsVq2KZcUKsxiMiIhVhISY6cKxsWY6ZWysdy8r09N3mIg3CAkx5751/eya1SxLGDvWQVgYFBaaHfEpKfD+++jaRYZcn0fmKisrO42ArVmzhpNPPrn955/97Gfs3LlzYFvXxaJFi7jooouYOnUq06ZN4/HHH6egoICFCxcCZork7t27efbZZwEzkLv44ot58MEHOfroo9tH9UJDQ4mO3k9d6CGwr9K3ISEQE9MKDP48PU9ht0NOTgQQS2xsC5WVnStIiWdytdSxw2GjocGPgoLvgP6XOQaVOhbP5CuLSe/rO0zEu33NuHGNTJkCLS1manVEBAwf3r36pchg63MwN3LkSHbs2EFycjJNTU3k5OR0KhRSW1tLYGDgoDTS6bzzzqO8vJzbb7+doqIiJkyYwDvvvNO+kHlRUREFHbpDli5dSktLC7///e/5/e9/377917/+NU8//fSgtnV/9lf61hfV1UF9vT9QTHCwwbBhZupCebmZuiCex/VSx8nAsUAU5orKyQdU5hhU6lg8k7cvJq3vMPFFBQWwenUMcAyGAbNmmaN3b7xhBnKhoebnobTU85YmEO/V52Du5JNP5oYbbuDvf/87r7/+OmFhYcyYMaP9/s2bNzN27NhBaWRHV1xxBVdccUWP93UN0D7++ONBb09/1dWZvZkJCfrwO0VEQHh4K5BAcXEA2dlm3vl778GJJ6rH1xM5R+SWLVu231LHDoet01pyhYUGTU3FnHOOjZgY19/0znXq+rsAqoj0n77DxNc4OzBqagKAUhob/dm8GU491RyZLi7e27ERG2uOyosMhT4Hc3feeSdnnnkmM2fOJCIigmeeeYagoKD2+5988klmzZo1KI30RhER+//wOxw2IO6nW+8XEgIZGXVAHd98E05sLBx1lHlxoB5fz5aenk5GRsY+9ykrg5wcGD/evPgbMwZKS8cyaRIuVfkTEffry3eYiDepqzMzhUJCWoFW4uObqakx15WbOdO8TvGFebLiefoczA0fPpxPPvmE6upqIiIi8Pf373T/ihUriIjwvFKsnso5Sb63D//eofxzWb06hlGjfGNkyiz28gFjx97Bz35mVoVrbFSPrzfQxZ/vsNvNCx/nV4Lz//r8eo/9fYeJeJuiIti4EQoKooDj+OGHYGbMAH9/swDKqaeagZ03z5MVz+TyouG9FQ4ZNmzYATfG1/Q2Sb7rUH5NTYDXj0w5L/7MUchy4uObqagw123RRb930MWfb+hYFKO52SwMEBRkBvJHHQXDhvlmYNcxwPWWY/eVQi8iW7fCI4+YGSYNDf7AwZSXB5KcDG+91bkIkDJNZKi5HMzJwOppkrxzLkJ8fDNgbx/K99aRqY4XfxUVMcAIMjLqqKjQRb+30cWf92poaOCrr75rnxcZHd3MunVRgI3MzFL++18Hr746gkMOaSE6uoWMjLpuy66kpaURFhbmngMYRN5c9dHbC72I9+prJWaHw8abbw7j++8jGTGimbq6WqCQgICdvP9+Iy0tfsTHN5OfH0h+fgtZWVUEBxv7fE5VYpaBpGDOAznT0fLzA4EQysoCSUryzpGprhXR8vMDgGMZNqyFE07QRb830sWfd8rLy2P69FnAuUApEAQcB9h49921wFQgAXgXiADMdTQ7Lr+SnZ2937mXVqOqjyKex7VKzHHAhcAhQDBQByTz8ssvAM1AEWAHQoDhLF68HKjY77OqErMMFAVzHsiZjpaf3wIMJyqqxWtHprpWRDNHI6NobPTTRb+IhaSlpbFu3aoeR+ZGjJjAM88UkJV1BLNmZdLWZqOyMoBZs67/aT3Nvc/hbVT1UcTz9KcS886dQVRVBVBS4gdUcfbZh1JSEkNjoz/x8c2UlQUSFdVCVtaCfY7MqRKzDDQFcx4qJQWysqpYvHg5WVkLvCYlp6uuRTHKygKBGkJD29zdNOkDW4udKQl+hFZthUK/IX3t0KqtTEnww9ZiH9LXlZ6FhYUxbdoURo3am1I4a5Y5Z2737noghzFjohg9ehTFxZCUBNOnp3h9QKPCPyKeqy+VmIH281p5udkRExQ0HJvNnAPsnBeclORdKdRiHQrmPJjZs1NPQ4Mfdrt39uJ2LYoRFdUCrCU4eJG7myZ9EFJXQM7lEbD2clg7tK+dDuRcHkFuXQEwfWhfXHrVdV4kwLp1ldx33/+RnDzb5+bBqvCPiPU5z2vl5fD+++aI+/DhZudMRAScdJJZ+ESfa3EHBXMerKgoEDibVatiKSz03h6fjhd/W7dWsXjxTnc3SfrIHpFCxtI6nn/+edKHOEUuNy+PuXPn8sQpXvihsLiuKdJmKuX3ZGVVceihvjcPVoV/RKwvJASCg83lkrqmTWtaiLiTgjkPZbdDTk4EEEtsbAuVld49ad55ItxfBSjxLEZACBuL22iMORSSJg/pazcWt7GxuA0jwJofCG8sVb8/wcGGz5bt1sWeiPUpbVo8kYI5D1VXB/X1/kAxwcGGJs2LeBFvLlUvIuKtfDFtureOR1/skPRUCuY8VEQEhIe3Agk4HDb1/ohYRENDA3l5eb3eX1VlZ8UKg6CgBJKSbL2uTeSta675Cl3oiHinfaVNe9vnvreOR3VIehYFcx4qJAQyMuqASiorA9qrJHnDyUGkN97wRZiXl0dmZuY+9ohj71psva9N5IlrrvV1kd2ucnNzO932h5UW2dWFjngzbzhPH4jejt/TP/euVp92NMHXqyGgBsbFQXkpfP0BxB4LX6/tvn1kFgQH9fxcqj49uBTMeZCuJ4jExGbgFWbNut4nSniLb/P0L8K+SktLIzs7u9f7v/rqOy69dCUXXfQnDj88rte1iTxtzTXXFtnt2bx58w7o8VZYZLfrIuE7d8Lbb8OFF0JMjLtbJ3JgvOU83V8FBbBqFZSVQXw8nHiiefxdP/fFxZ5X58DV6tPBwBznDzUdbl/oZfvTvT+Xqk8PLgVzHqLjCTI0FDIyzIUqwUFMTKtXD+OLWOGLsK/CwsL6MKL2Z8aNu4OIiDGWWZvIlUV2u2psbCQ/P5/U1FRCQ0Ndfm0rLbLbcZHwujrYsQNKSsBmgzlzPP/vLNJRx7Rx5+LZNTUBxMc3k58fyLZtjYwZk82hh6b0+tn2lpRxux1efRVycsx15b75xvys/+53nT/3HatcelKdA1erTzuaYPVq87ji4sxlGaKi4NhjYe3a7tuz9jEyp+rTg0vBnAfoeCEbEAAffWT+PHp0LJDcaV9f7xUT72SFL8KBtdOyZfr7ushuV8ccc8wgtMbzOKvd7dxpBnJFRZCYCA0N1u2gEN/VOW285xRx6Jwi3pUnpoz3R1kZbNpkXqfFx5udNJs2mcFMXJznV7l0tfp0MHDELPO8ta0GoobDETMhMgWOCOm+PXgf16JWrz7t6RTMeQDnheywYWaPT0AAtLXBjz8GA6dTU2PmNnvT6IVIR75Y7tmXy/R7M2e1u7ffNi/2EhPNTIuICO/toFC2iPfqmDbedWSurCyQuroCHnvsyX2O2Htayvhg8NYql70Ve9HamZ5FwZwHcF7IFhRAdbX5pVhaClVVYcAFrFgRz8EHQ1iYr41eiK/w1i9C8U0pKeYcOZvNHJGLiPDeDgpli3i3rmnjo0bt/XsnJcGwYVU89pij3yP2VhIfD1OmQHa2OUrX0gKZmbR3ynlrgNPbGplaO9NzKJjzAM4L2dWr4dtvYdcuaG2FlhY/oJnvvw9l9Wo480zfG70Q3+GtX4Tim2JizDlyzg6K8HA44gh3t6pv+lr1rrdqdx2r2jmazIA2LKz3+TQdqeqdZ+t6nt6ypdndTRoyISF7r8OKisxOmtmzO39XKcDxDlbLNlAw5yGcPbkHHwwPPwy7d0NMTAvwLaGhh1FWZgZ4Gr0Qb6YvQvEmzgvf774ze/M/+wy+/trzR6/6WvWu12p3T3feJ9iF11bVO8/ny+fplBT42c/gvfegqQnWrzd/FyNGWOviX3pnxWwDBXMeJCQEjj7arJD0wQfQ1NQKhFFX5090tNkLFhen0QsRESvZvNm80LPKXOe+Vr3rrdpdVpZ5f2/37WuETlXvxJNt3QqPPGK+n0eONK/FysshMtIczbHKxb/sta+KrVu3GqxbV8w559iIien9hO3uiq0K5jxMSQk0NjoLoIQAh9HUZKO62rwvJcW3e8VERKzEipVa+1r1rrdqd8Ep5pyifAcMPwgcoeAfCvmlUBcJwfso/KOqd+Kp7Hazg6K8HA46yPwMFxfD99/D4YfD2LHW6KyRzvpSsfWxxzy7YquCOQ/iXMNk82ZzvkVzM4A/4eEtfP01BAaa65noBCEiYg3eXqm1t7mu3n7c4nvq6szUSueIXHg4/PCD2UkzcqR5zTZsmFnIzpM7a6SzfVVs/fbbcp577kGefPJhJk0av8/ncCcFc4Ns27ZtfV7ods+eAD78MAmbDYKDW6mtDQEiaWqqoKrKxocfwtixRYSHtxEW1kZwsLHP54uMjGTcuHEDcBQiPWtoaAAgJyfH5ccOxELSIp7OFyq19pQt4gvHLb4lIsJMGa6uNt/Tu3aZc+WGDzfXBw4KMoO9jAx1WljJviq2jhtnA9YyadIij67WqmBuEG3bto1DDz3UhUckAXcCbZjDu1mAjf/+9wMgDIhk5cq1P91fgzkzfec+n3Hr1q0K6GTQOPPMFyxY4LY2REZGuu21RfrCVyu1+upxi3dydlCAWZ119Gg4/nj49FMzsHOy2dzTPhkYHc9bW7dWsXjxvq+zPYGCuUHkHJHb12KaHTkcNl55JZ7c3FD8/Q22bQvG4XAwfvw5GEYAYGPMmGM46CAH5eWBhIS0cdJJlURFtXV7rtzcXObNm9fnUUGR/jjjjDOA/k3+db5H+/r56IlGn8UKrFbmeiBpjrd4k5QUOOoo+PBDcDjgf/+DigqYPt18nwcGKs3SGzjPW/vLgPMUCuaGgCuLaY4aZU6wLSuDY481txmGuU7P11+bOdn5+eZtYyNs2TKaX/7S8yonuZJe2pEzde5AUuh0gT904uPjmT9//gE9hy8sNiu+y4plrkWkZ3a7uRxBc7O5aPrmzbBunVnnICnJXFh87FilWcrQUjDnYZzrzTnTUsCsnPTWW+ZE26YmM5iz2yEz0wzoPK1ykuvppd3NmzfvgB6v9FIRcTe73Tw/V1ZaZ1kCkX3x5VFmMI+9vByCg6Gqyux4DwkxC5/s2QMtLeY1nC/+bsR9FMx5oK5pKcHB5oLh06aZo3MtLWa+dno6JCZ6XplrV9NLOxqIohhKLxURT2DFZQlEetPTKLMpmMpKf+x2739fV1TAhg3menP+/mbW1OTJcMIJ5v9ra80iKWJNdrsZoIM5ymry/Pe3gjkP1bH3y1niubISfvYzs1coIMA8YXhyuef+ps8dc8wxg9AaEZGhpfL84i16G2WOiAgCzmbVqlgKC62TRmxrsTMlwY/Qqq1Q6NenxziaIPe/kGBASLz5c00rxNohuh6ogYQoiKgFHL0/T2jVVqYk+GFrsQ/IscjAKCiA116DjRvNnydPhpiYMKzw/lYw5wG6pi301PvlLPFcUwNTp5rplmVlZkCncs8ins/5OXc4zFJnDoeNsjLfTVfyBSrPL96ip1HmwkLYsiUSiCU2toXKSuukEYfUFZBzeQSsvdwsDN4HwcDpwOk9zeBwVrOsAZ7e9/OkAzmXR5BbVwBM79uLywHZXx0Hh8PGe+/F8sUXZnXsgACD996z4XD4ASOor/+RzZtt5Oe3kJVV5VJhlKGo46Bgzs26Bm5HHWVOrq2sNHOwCwrMgigXXmieIL/7DrKzzf2Dg839PbGXQET26vg5r6iIAY5i9eoYcnJUFMPbqTy/eIOeRpmDgqC52QYUExxsWCqN2B6RQsbSOp5//nnS97Hgc1Ex5OSYwWxwsFmpcts2MzsKzGkvkyebBeuioyE4aP+vnZuXx9y5c3niFJ30h0Lf6jjEAddgBtdNmEOrzYANqOThh/8BhADDWbx4OVDhUhsGu46Dgjk36iltYfVqc9QtKMg8gZSXw1dfweGHw6RJZuWkujqzalJxsRn4paR4/onTydcnT4v36GvFVofDxurVMdTUBBAf30xubgVwMXl55UycaJCfH+hSb5+qtVqPyvOL1fU0ynzUUbB8eSuQgMNhs1QasREQwsbiNhpjDoWkyT3uY7fD6g+hsgESkqCgGFqDIToNvvnG3GfyZDjydBjhQlzWWNzGxuI2jACdFIZCXWUpUxL8uPPOOxkzZkyP+9TX+/HCC8P58ccQQkKCaGqKxGYzGDnSTmTkiaSmhtLQEEZ4eCtHHTWTwMC+jczt2LGDm2++mbrKUkDBnFfqmrYwbJhZqTIsDD7/3DyRFBeba5n8618wf761J9P3tUS3Aj7xdK5VbI0DzgVKATswHJjFCy88wgsvlNGf3j5VaxWRodbTKHNGRh1QSWVlAElJ3pVG3FsBo3PP3btPXJz3HK+3ak+p3Xk37GP976njgfG93GkAoUAb8HnfXzsdOGUIUmoVzLlRx7SFgAAzgPPzgzFjzJNIRYVZLemII8yh/fXrITLSmpPp+1qiW2syiRW4UrG168jc9u02tmxp5bDDjuLggw3KygKJimohK2vBfkfmvKVaqzpsRKyp6yhzYmIz8AqzZl3P9OkpXvV57q2AUccAzln9UOcyz9WXlFpHk5kZV1Fh/h0rK2HECJg927y/sdEM6PuSRtvRUKXUKphzI2fawurVZgADZtpCY6MZoDU1wcEHmyNzI0eai4dnZpqpllabTN+XEt1ak0mspq8VW0eNMt/L+flgs5mf87o68zM+caJvdVqow0bE2ziIiWn1uu/p/RUwstq5rKGhAYCcnJx+Pf5Alo7Kzc3t12sOhL6k1AYDR8wy/54lNRB1MGTMhOCUvff3x1Cl1CqYc7OUFJg1yzxRjBplTqBtbDRPDj/8AD/+aM6Pi483e4PGjzf/WW0yfV9KdJeVmdWxRo2yZhqpSG9SUuDUU+HFF8006uRk2LnT/P+pp0JMjLtbODTUYSMiVtJbASMrnsvy8vIAWLBggdvaEBkZ6bbX3p8DKVbl7mwTBXMeID7eDNgqKszCJ8XF5sXd4YfDpk3mGyQ6unOPkKeeLHrTlx6u1athyxZzYvG0aWaVKKukkYrsT0sLtLWZgVxoqHlbWgqtre5u2dDxhUW03f2l7kn0uxBv0FMBIyuey8444wwA0tLSCAsLc/nxzjT/vkwv6IkVinf1p1iVJ4zQKpjzAF0DnaAgs4plSIjZa19QYAZzI0a4u6UHZn89XHV1ZvrZ55+b8wOPPdY6aaQi+6MFpL3/d+AJX+qeQr8L8WZWPJfFx8czf/78A36evk4v8AWeMkLbt2XvZdA5A52f/9ycI/fVV7Bjhzmn5pBDzNTL+np3t/LAhYR0r/7UsYcrORlOOskclTz5ZH35i/dwdtrExlpvzutA8ebfQccv9eHDoaQE3n4bqqrc3bKhV1VlHntJifm7cC4mbbe7u2UiA8Obz2XSdz2N0NbUDP31ukbmPMzmzWY61ogRUFRkrjU3Zoz5syf3+ByIrj1cFRWQmGgGfSLeRAtIe+/voOOXel2dubDwrl1mYHP22dDnlSwsrqDADOQ+/tj83oqNtUYKmoirvPVc1lVVFezZAzU1Gv+BzunjnjJCq2DOgzgvBpKTzTdDTo7Zs3n44d7d49M1zTQiwlyOQcQb+eoC0l3nT3nb78D5pb5zp9kpt3GjmWVRVGTOBV682DoBXX+r3jmX4SguNqisdLBnTzylpQZJSQ6GDWth69aqPi2/IWIV3ngu6+jzz+GZZ8xO9tbWJOAodzfJrXpKH99XPYihomDOgwQEmGXLt283U1MOOggOOwwuuMD7q905e7i++w42bIDPPoOvv9Y8CxFvYOX5U64ENsOGBbJ5czRffjmMujo/oqObqK9vJDs7nH/9q4ozzyzfbzDj5M6gpv9V7+KAc4FSIBKYDIwAPgbeYfHifazY24UnV70TAe8r8NPQ0ND+2QdzJO6hh5Koqgpg5MhmduxoBC5mw4ZtvT5Hf4urDKYDWZKhsbGRrVsLGDHiYMLDg/nkk+j2NWPz8wPJz28hK6vqp+lQfoSGtlFWZlBWZj5+qM7jCuY8hLOa47ffmv8iIsxg7te/9v5ArqPNm82UBauU+hWRfXPOn2psNLMOrPa5dj2wGQPcA4xi585CzKAmgLy8D7j33seBCpde3x1BTX+r3jlH5rZtK+O55/7BBRdMZ/To4Zx0UhpRUb/t8/NYoeqd+DYrd1D1Ji8vj8zMzA5bxgG3A7uBeiAcGMXll98E/NDjc2RnZ3tccZQDW5IhGTgWiAIMYBiwFbADIcBwFi9ezv7O64N9Hlcw5wHsdnjtNbOC4+7de4OZ1FT4/nuYMsUaFz0HyoqlfkV8ha3FzpQEP0KrtkJh3+ZOFBXDJ5/Aj9lm+klwOKRFQ2U52HdASHTfXju0aitTEvywtQx9BQ1XAxuHw8ZLL8Xx8cex1NcfzA8/bCEzM5U5cy7n5JPP7fPIHLgvqHG16l3HEYpRo8y0LBhOenocv/71aFJSRg9aW0WGQsf3OHhGBcOBlpaWRnZ2dvvPXUfmdu/2IyCgkuuvf44RI3peRjstLW2omttnB9I59eyzDTz22EtceeW5BASkUlAQTEqKg4SEZsrKAomKaiEra8E+z+tDcR5XMOcBysrM+RWGAcHB4OdnnhwCAswlCpzBjLcN6XflKRNJRaS7kLoCci6PgLWXw9q+PSYRM+nu3IN/2lDc4c7lfX/tdCDn8ghy6wqA6X1/4ADoTznvUaPMfx9+2MQPP+xk5syxXHZZMikpqYPTSDfqaYQiK6uKxYuXk5W1wPKjFSJd3+NHHGFem0VEgL+/93Q8h4WFdRtVCw7eO2cuPR1+/evhTJvmpgb2U3+XZCgrg/DwnUA9KSlJjB6dzLBhZnE+wzDXh/aUEVkFcx4kMNBcY6601AzcPvrITEtyBnTeNqTf1f4WFhcR97FHpJCxtI7nn3+e9D70vlZWwQcfmJ/jxkbIzTV7sjMzYcYMSEzo+2vn5uUxd+5cnjjFGie9lBS46iqYOHE3H3zwZ84+eyUpKcnubtaA622NpUMOAahwaRRSrMXZuexw2NzdlEHV03t81Spzfn91NYwcaU6FSUoyAztvM22aGcSVlpq1HHxp2k9FBezYEQyczJdfRtDQAAcfbK7/3NrqWRVMFcwNor6mJQ1vgVlHmBc7CbEQUAohUZA2CkbEw5YPzcIoAQ0wLg7KS+HrD2BkFgQH9fyc7kxLOhAjRsDxx5vH23U9OvFODoeNsjLvHXH2FkZACBuL22iMORSSJu93/9Bh0BQHeT9dBAWmQGoazOpHQafG4jY2FrdhBHj2G6Rr9sSIES1AkdcGNb2lxjc2qoS5NysoMAOasjKor4/FnFfknerqzA71yEgzWBs2zDz2hATzM759u/m5//nP4dVXzc6qtDTv+i6LifHuIK5r4RfYO/8XaoBi8vMbaW0tYeLEErZvb+r2HO4u/KJgbhD1NS0pGJgLe8+Hh3TZYU+H/9d0uH269+d0Z1pSf3njhGLZn2RWr44hJ0d/c28TEgJHHWUWdiosNDtqZs703ouCns5f3q631PjQ0DZ3N00Gid1uBi05OWYmUVFRJPArrx2hq6iAvDwzoBs5EpzX60ccYQZ3dru5T0SEubbi2rXOVGN9l1lF98Iv0LkybytffrmRL7+M5LXXnqenYifuLvyiYG4QuZqW5GgyezWffNK8IEhMMAsIREXB+PFmD1FiAlT/dLGQtY+ROaulJfWWrmP1CcW+rqceL6evvvoOOJZt28o4/HCjU5nfjiMZ7u7xkv4pKDCLOjU1mfMujjrKey9uejt/JSf7AXFee6HbW2p8WZl3jkSKORq3aZM5pz8+HkpKDGAy1dXel2Not5vnsIQE83j37IHoaJgwwQzgwsPNVMsRI2DXLnMfZ80DXb9YR9fCL7B3ZK6srI22tkL8/JKIj/cjK+s3PWZauLvwi4K5QeRqWlIwEBIBtiSw+8O2egiIBSMYvq+HrduAbTB5MhwzC4L3cWFklbQkJ1Wy9E4993g5mT1fzz33D/ZV5tfdPV7iuo7BTVKSeXGzfr0ZzHnj57mn89d338GWLbHAuaxeHcOoUd4ZzDrXCK2v3zuHxLnGkng/m817A3fn5/qww8xRuKoqqK01p4Js3myO1sXFmZ3r5eXmY6Kjzc9EdbWuX6yip8IvYBaxMrMtxnp85pDlgrklS5Zwzz33UFRUxOGHH84DDzzAjBkzet1/zZo1LFq0iG+//ZakpCT+9Kc/sXDhwiFssWsiIswlCcLCzHSk0lLIzzcviE480eztjo42e4K8iSpZeqeeerycqqrsrFhhEBSUQFKSrdcyv+7u8RLX+VrnTNfz186d5jIzoaF+QCk1NQFe3VMfEuKdxyXdxcebyyVlZ5vv94YGf+AboqMnu7tpA67r57q+3gzexo83/9XXm0HcJ5+Y6ZVtbWbxn4oKXb94g546qjyVpYK55cuXc/XVV7NkyRKOOeYYli5dyuzZs9myZQspPYTLO3bs4JRTTmHBggUsW7aMzz77jCuuuILhw4dz1llnueEI9q9j2opz7sWoUWZVy9BQ80ThjRdFqmTpnXrr8XI65JC973VPKvO7P/1Zc20gWKWwka91znQ9f4WGmuftoKBmwE58fDM1Nd533hbfExICZ54Jzc2wYQMEBbUBLVRUWOpysk/2d10SEmIGdykpMHWqGeA2Nur6xZtYpaPKUp+++++/n8suu6x9vYgHHniA999/n0cffZS77rqr2/6PPfYYKSkpPPDAAwCkp6ezYcMG7r33Xo8K5rpWQOvYG+DvD2+9Za2Lov5e6KYEwLkzzJNhaCgEBwCFrr22VS52xWSlnq+O+rPm2kCwSmEjX+yc6em8vXlzIBBCWVkgSUmefd4W6asRI8yqjpMnQ2trLe++20ZOTgQnnOB9n/H9fUc5r986jtZZ6btMvINlgrmmpiays7O54YYbOm2fNWsW69at6/Exn3/+ObNmzeq07aSTTuKJJ56gubmZwMDAbo9xOBw4HI72n2tqzPKRbW1ttLW5VqHLuf++HltQYA7PO0fhjj3WPHkEBZn/wFyTae1aKCkxUy9nzDDv21dz+vLagyW4Nr/fF7rBP/3rL+fF7pbafNrajj6AZ5Kh0vG9PsRv1X5rDD+IjKV1LFv2HGlp6UP2unl5ucybdxH/nn3QkH+uXT2nHHQQnHVW54ub/jbZneczV3Q9b+/Y0QwMJzKymRkz2vZ73vYWVvl7iWnbtm3U1tb2ef+qqgC2bo0lJqaZioqdQDHff1/Kp59WERPT6tJrR0ZGMm7cOBdbfOBceY/29h3V2/Vb1/0O5LXFd7ny3rBMMFdWVkZraysjR47stH3kyJEUFxf3+Jji4uIe929paaGsrIzExMRuj7nrrru47bbbenz9jkFeX1RWVrbflpaWdrvfboeVK0OoqrIxYkQrO3f6s3KlwS9/ae/UqxMaavZqNzTYCAszCAkxe7sP5LUHU1FzFPOW1rFkyZIhP0lv27aNK664gnuOjiJuiI9bfEd5TSMbi9vY3TacuICkIXvd3W1lbCxuo7ymccg/1wdyTqmtNf+547XdJTQUMjN3AMvJzDyL0NDw/Z63vYUV/16+avv27Uyf7uoofxRwARAK7AISePbZf/Lss68Arl0nAaxbt46DDz7Y5ccdiAN9j/b1+m0wXlt8gysdLJYJ5pxsts4lng3D6LZtf/v3tN3pxhtvZNGiRe0/19TUkJycTHx8PFFRUS61NTY2tv12+PDh3e4vKwPDgLFjzS/+yEhzW1hYJOHhnVMvXbW/1x5M0fEJbCxuIyA5k9jD9l2FsGuK6YEKsEexsbiN6PiEIT9u8R3u+ny583Ptq699IEaOjAYqGDky2lLtPlBW/Xv5op07dwLw7LPPkp6+/yyD4uIgcnIiKCoKoqQkiNjYBgICdnPiiUGMHn21S6+dm5vLxRdfTGBgoOXOKfu6fouLG9zXFt8QHNz3PDXLBHPx8fH4+/t3G4UrKSnpNvrmlJCQ0OP+AQEBxPXyaQsODu7xF+jn54efn2uFDpz79/bYqCizMmVJiTkfrqTEnFtSWQnvvXdgi2fv77UHU19fezAWCXfncYvvcNf7zAqfa2977QNh1XYfKF89bity/n0OP/zw/S4BY7dDbq453SMtzazaGhYGF1yQSkxM/1/biueU3q7fIiPNteYG87XFN7jy3rDMuygoKIjMzExWrVrVafuqVat6TRGYNm1at/0/+OADpk6d2uN8uaHmLBIQG7u3SMBRR5nrMZWUmAvtlpSYAY/dbv4rKzNvra7jOlTDh5u3zuMUERERz9J1yZHkZHNuWKtr0+S8Qk/Xb95e5Ek8l2VG5gAWLVrERRddxNSpU5k2bRqPP/44BQUF7evG3XjjjezevZtnn30WgIULF/Lwww+zaNEiFixYwOeff84TTzzBiy++6M7D6KRrpaS6OnNduT17oKUFAgKgocFcgDY72zxpDB8OWVnWKOHeG19bh0pExFc4HDYg7qdb8RYBAWCzmSNyycnWqK49mKxajVm8j6WCufPOO4/y8nJuv/12ioqKmDBhAu+88w6jR48GoKioiIKCgvb9x4wZwzvvvMM111zDI488QlJSEv/85z89alkC6LyOhd1uLjZbWmqeKAoKzG3vvANbtpgVlb75xgyEFi607snD19ahEhHxBQUFsHp1DHAuq1fHMGqUtTsexeScFlFebl6jNDRAaqpvj0YN9Jx/kf6yVDAHcMUVV3DFFVf0eN/TTz/dbdvMmTPJyckZ5FYNnJYWc7HZgABzvbXERHPkatMmMzc9Pt5Mvdy40Typjhrl7hb3jy+uQyUi4uSNF4LO9PmamgCgiuLiIFatgrlzvecYfVHHaRHjx5vXImFhcOqp9GuunDcYjDn/Iv1luWDO2wUEQFwcBAaa6ZRVVea2oiJ3t2zgKUVBRLyV3Q6Vlf70tHKmt14IOtPnAwLagEx27Qqirg6mToVJk9zdOumvnubKlZb65lw5Z+2Cd981O9RTUqCiwvw8n3OOrmPEPRTMeZCuaQzNzZCUBIcfDg4HfP21eRJpaYGMDPZb/tYKOqaYioh4A+e5/NtvY4GzKSraW3Cr4yiHM8XcaheCDQ0N5OXlddvucNjYsyeWtWtbgVgqKqpobrbx2mt1NDRUEhxstO+blpZGWFjYELZa+ssbp0U0NDQAuJS5VVQUSE5OBN9958+XXwYzapQfiYmtJCc7KCy0MWpUZZ8WTc/Nze13u0V6omDOQ/SUxmC3m4FbdrY5V+6II8x1TeLj4cQTrfPFLyLiKzqey2NjW4BYcnIiOOEE85ztDcWf8vLyyMzM7OXeicDVQCuffvom8D3vvmvj9tuXAxXte2VnZ++3FL54Bm+cFuHsjFiwYEEfHxEMnA2MAMYCU9m61Q7swCwM/xF///uLuLJoemRkpAstFumdgjkP0fULfuRIWLXKXK9k7FizJyw+Hk46yRyRs/JJVETEW3U8lxcWGkAx9fX+7cGaN4xypKWlkZ2d3eN9DoeNN98M54cf6hk/PhS7PZyoqBayshZ0G5kT6+g4LcLf33yf79plXpdY8XrkjDPOAPo+QlxZ6c+qVbEEBraxbl0T//3vxxx22KmkpEzA399g7typjB//hz6/fmRkJOPGjetv80U6UTDnIbp+wTuLciYnm3npbW3mffX13pFe2RfeWCBABPTe9mYdz+Vmaf4EwsNb24M1bxjlCAsL2+eo2qhR3jkn0NeFhJgF2F57zSzCBjB5Mpx1lvX+vvHx8cyfP7/P+9vtUFhoHn9c3B4glvR0fyZOjGPUKPjVr+It9RkW76JgzkN0/YJ3jtBlZ5tLERQWmimWJSWQmeldX449Xdh6a4EAEb23vVvHc3lhYQBQSUZGXacLPW8v/uTtx+er7HZYvdq8Lgn46eoxJ8c8j3l7xdKOn+u4uBagktDQVqKiYMYM7z528Xx+7m6A7OX8Ajz/fLjwQnNh8K++MhcQ9/MzR+e++cYsjrJmjXlitbqCAlixApYvN2+d6+o555wMH27eesvxim/Te9s3OM/ls2ZVAq+QmNjcbZ+QEO9Omff24/NFdXVmZ3NQEIwYYf4LCjILs9XXu7t1g8/5uT722GrgB/bsCWLDBnMd4A5LHIsMOQVzHiYkxOzJrKszR+KGDYMpU8z15saONS/6QkPNXn2rnzx7u7AtK+teIMAbjlekp+IXem97p5AQfqps1/eCCCKeLCLC/K5uajKzhEpKzP/Hx5vz6MrKfKNjKjc3DDiM4GCDwEBzpHL1at84dvFMSrP0MB1TsPz9zVSGkhIzsNu1yzyRNjaacxKsNGG+J71VdbPZrF8gQKQn3lD8QkR8U0iImTFUU7N3zlxGBhx2GLz1lm+kjtfVQVVVANDMsGEtjBhhBrHO0UmNRIs7KJjzIF3XH8rNNbdVV0NDg3nBN3myGchZbcJ8T3q7sI2Ls36BAJGeeEPxCxHxXSkpsHChuR6u3W52NK9dawY5Vl030RUBARAc3AqEU1wcSFubuT0+Xp1y4j4K5jxIx5Eqf3/zYi8wEC64wBydGzECzjzTe+Yh7OvCVhPoxVvpvS0iVhYSYlbZXrfOLM62ZQscdZR1103sK2fmVHFxMHAQO3YE09AAv/iFOWLpbccr1qFgzoM4R6q2b4fmZvPEkZICSUlmoLN7t7mfN50wUlLg1FP3BqsxMXvvCwnZe6wq5S7epON7W0TESjpmEY0aZRZm+/xz8/qlosI7U8edx2zOE/QDSjj00AamTIkgJcW8fhFxFwVzHsS5oOwHH5gnSYfDnCO3Z495ovTzg/ffN3uAvCUfvS9l2lXKXURExDN0ne8+bRqsX2+O0iUmekfqeNcOZOcxR0dDa6sNyCck5FhSU819vXEkUqxDwdwgamhoACAnJ6dP+9fU+LFsWRJtbQEcfHALBQUBrFvnID/fn+BgPyZOrCc314/du1vIyqoiONjo9blyc3MH5BgGU8fevWHDzKBt9WpzWYaOI3Id5xF6ez6+iIiIJ+s6372lBY49Fk4+2TumgfTUgTxihPn/khLw9zeAFPz82igtNbf7+7u71eLLFMwNory8PAAWLFjQx0eMA24HdgP1QDgw9qfn2cy779YCIcBwFi9eDlTs9xkjIyNdb/gB6msQW1npz7ffxgIG69cHU1/vj8PRRnHxBmbOjCE0NLR9n9jYFgoLDRwOG4WFAYwaVflT2e/OrBDEioiIWFVv891HjXJ3yw7cvjqQ9y4a3gyUEhRkkJ9vHvdbbylrSNxHwdwgOuOMMwBIS0sjLCxsv/vX1Pjx0ENJVFUFMHJkM9u2NbJhw/v85jcziYubS3x8M2VlgURFtZCVtWCfI3NgBnLjxo0biENxSd+D2GDgAuB4oBmwAf68997HwIuY6zMFA2cDsUAxkABU8ve/v8K+1m9yRxArIiLiC7y1kFNvSybV1+895lGjynj00VtJTp5DfDwkJytrSNxLwdwgio+PZ/78+fvdr2NudnAwPPOMOYk4JaWEDRue4qyz/kVFRTI1NWYxFE/v/XEliM3LC+GFF0bg728QGdlGQMCPPPPMeh599DmOPHIsDoeNHTuCycsLo6nJj/DwVjIy6khMXNTrc7oriBUREfEV3ljIaX9rgYaE8FNWUBuGYSM52fureIrnUzDnZj3lZt99t3lS2L27kNdeW09iYjMnnGCdHrC+BrFgLjba2GjmoScnw1dfNQE1TJp0CPHxU9p/N6mpkJkJ48d7/vGL9EQVWUVEPNv+1gK1280pItBMeHhrr0GfyFBSMOdG+8rNHjcOamvb2vf1xh4wMI/pxBPN466uhqioFmAtcG23383mzWYwJ2I1qsgqImINvaWQOs/j5lz/UznoIAcBAT0HfSJDScGcG+0rN3tfJwRv6+HveOLcurWKxYt30tDg16/fjYinUUVWEfFUthY7UxL8CK3aCoV+Q/raoVVbmZLgh63FPqSv2xddO9A7nsdjY1uAWHbtCuYPfzAXUPf3N6t62u06r8vQUzDnRvvLze6Jt/bwO0+czqIuYWFtLv9uRDxRfzttREQGW0hdATmXR8Day82kmCGUDuRcHkFuXQEwfWhf3EUdz+OFhQZQTH29P62t5rncG6/LxDoUzLlRx9zswkIICoKjjur9As+XeviDg4195q2LWEV/Om3EmwRTWemvHnvxSPaIFDKW1vH888+TnpY2pK+dm5fH3LlzeeIUz498Op7HHQ4bkEB4eCv+/r5zXSaeS8Gcm6WkmAHchx+CwwHr15sngJ56dXyth99bSx+Lb9nfhHrxXkVFgcDZrFoVS2GheuzF8xgBIWwsbqMx5lBImjykr91Y3MbG4jaMAM8/GXbufA8AKsnIqKOlxbeuy8QzKZhzM7vdDOCam81lBzr26nTliz383lr4RaytoaEBgJycnD4/5pBDbDQ2+gH1fPLJDlJTUwkNDXXpdXNzc13aX9zHboecnAggltjYFior1WMvYmV715kz17pNTFzkk9dl4nkUzLlZ19G2YcNg924oL+++r3r4RTxDXl4eAAsWLHDL60dGRrrldaXv6uqgvt4fKCY42FCPvYgXCAmB0NA2IAKHw6brMvEICubcrGOvTkAAfPIJtLWZvTrDhoUAwZ32V+qhiPudccYZAKSlpREWFubSY3Nzc5k3bx7Lli0jPT3d5deOjIxk3LhxLj9OhlZEBISHtwIJOBw29diLeIGCAli9OgY4l9WrYxg1Stdl4n4K5tzM2auzejWsXGkWQomMhOefh7CwBOCCn+ZddH6MThYi7hMfH8/8+fMP6DnS09PJyMgYoBaJpwkJgYyMOqCSysoAkpLUYy/ewduWR+orZxG6mpoAoJSamoBOqdO+9LsQzzK0i4pIj1JS4NhjzQIoYKZZlpTAnj0BQBI5ORHYPW8ZFhER2YfExGbgFWbNquScczoXP7HboawMndvFUgoKYMUKWL7cvC0ocHeLho5zWkx8fDNgJz6+mZoac0ROxJ0UzHmQqirzi93PD2w2aGgwKybV1/vrZCEiYkkOYmJaO/Xa+/IFsVhXx+WRhg+nvaiPr3RIOKfFlJUFAiGUlQUSFaXUaXE/BXMeIiTELIISFmbOmTMMCAtrBWIJD2/VyUJExAv4+gWxWFdPyyP50siUc1pMVFQLMJyoqBalTotHUDDnIeLj4ec/N9Nwxo2DESNg5MhmoJCMjDqdLEREPFxfUid9/YJYrKtjwbbqavj+ezOQ8ff3nZThlBTIyqoClpOVVaV1I8UjqACKhwgJgTPPdA7hQ3Q0REUV89//vkhi4h/c3TwREdmHggJncQTzPD5zZs/7aV0qsSrnyNSrr8KqVeY2hwOeegqCgva+7709wAkONoCKn25F3E8jcx4kJQUuvBAuuwx+/WsYP94OONzdLBER2YfeUicdDlu3fZ0XxLGxWpdKrGfECLOzefx4OO442L4dcnLMbUoZFnEPjcx5GGd5W7sdKiv96brOnDfqWOZYRMRqekqdLC2Fxsae+0u1LpVYVV0dNDaa7+G6OrNgW0CAWbTN+b6vr9d7WmQoKZjzQM50nW+/jQXO7rbOnDfpmpo0bJj3HquIeKfeUidDQ9t6fYzWpRIrioiApiZ4/30zkPv+exg50izappRhEfdQmqWH6ZiuExvbAsR67TpzPaUm5eRE4AujkSLiPXpLndScGvFGtp+yhwMCzLTL0FBzrr9ShkXcQyNzHqauDsrLzd4vcxHx4vZ15rztBNlTalJhoT+gbj3xPs504p7mUYn19ZQ6WVbm7laJDKyyMvM8NnWqOU8uIMDcNmcOJCd733WKiBUomPMwFRWQl2cGdH5+EUC6164z11NqUnh4K6Aa3eJdOqYTV1TEAMnubpLLGhoaAMjJyXH5sY2NjeTn55OamkpoaKjLj8/NzXX5Me6g1EnxZgUF5kL377xjzpsbP97sxEhLUyAn4k4K5jyI3Q7r10NiotnblZcXACQwYUK9V54knalJa9bsTU0aO7YOVfAUb9IxnTghAfLzA4BjLTdCl5eXB8CCBQvc1obIyEi3vbaIL7PbYfVq+Pxzc85cWRns2mUGccOHQ0mJmXLpLGbmjdcsIp5KwZwHcaYdpqfDhAkQG1vDhx/mER19jLubNmicqUnl5eYE6p07W9zdJJEB1TWdOD6+GYjqtdKhpzrjjDMASEtLIywszKXH5ubmMm/ePJYtW0Z6enq/Xj8yMpJx48b167Ei0rO+jrhXVvqzYUMc330XTX29P3a7P3a7jZqaRr74Yge7dkURE+OHw+FHeHgrGRl1JCY27/M5rTLiLuLpFMx5kK5ph3a7P1DRqSJaxzL+3tLzVVLSNQXtECor/bHbvecYxXd1/VyXlQUCNfusdOiJ4uPjmT9//gE9R3p6OhkZGQPUIs/mS8vLiHX1fcQ9GPgdcMVPP4cCftTVVfLDD08CU4DvgB+ABKASeIW+ZNpoxF3kwCiY8yBd0w6jolqAtQQHLwK6l/GfOdMc2bKyriloX30VBFzDu+/GUljoHccovm1/n2vxPr60vIxYmysj7ps3h3LPPYlUVfnT3OyHzWYQGhrIjz+O4uc/P5aTTppJRISBw2GjsjKAWbOuJyamdZ/PqRF3kQOnYM7DdKyItnVrFYsX7wS6Bz3FxebP55xjvdGrhoaG9t7Aykp/vv02ltjYFn780WDHjiYgnqqq3WzebCM/v4WsrKpuJb77k+ol4i69fa7F2jqey5wcDhurV8dQUxOA3f4jEMvbb9cwbNjGXpcq0PlM3MWVEffDDjMzadavh+pqyM8HP79WIJ60tFCiopLar0+SkmD69BTLXZ+IWJGCOQ/krIjW8Yu/pzL+paVYcsmCvLw8MjMzf/opGDgbiMWsYnkyUMwTT7wE+APDWbx4OVDR6Tmys7N9Jl1LvENPn2uxts7nMqc44FygFLADITz22PM89lj385iTzmdiBSEhcP75ZmbQRx9BXBwkJFSwbds3REWdSERE53UWrXZtImJVCuYsoqcy/rGxWHLJgrS0NLKzs9t/LioKJCcngurqAL7/PgB//xImTTqR2toIoqJayMpa0OPInIiIO3U9l0HnkbnIyDq+/76O1NRoZs/ufh7r+DwiVpCSAr/8pdm5nJQEZWVNwC4CA80ArrXVrGoZE+Pulor4DgVzHspuhz17AoAkHA5bj2X8rdrzFRYW1q0X+oQTzFHG8nJYvz6RmhoYM0Zz5sQ3eGNhI1/Q07kMYNSovfObdR4TbxMfby6hVFHBT0usJNDcbOPjj81zmbfM6RexCgVzHqigAF59FT78MAm4k1deiWfUqM7zbsLDveuiz5mCFhdnHqc3HqNIT7yxsJGv8+ZztUjHzuXCwgCgDjCor7f+nH4RK1Iw52Hsdli1CnJywGYDsPHVV+G8+y78+td7gx5v5gvHKN6rp6IYHTnXVsrNze2Ukhcf30x+fmB70Z9Jk8arKIaF6Twm3szZYTFqVCV///sHBAbe5hVz+vdHS46IJ1Iw52Hq6qCsDIKCICjIAOIoKQngo4/g6KNh0iR3t1BE9qXnohjdzZs3j56KZTiL/mRnr1JRDBHxWCEh/LT0QDnh4a1eMad/X7TkiHgqBXMeJiLCzEf/6ivYvj0YiMPfH/z8YMMGGD/e+3q6RLxJT0UxOmpsbCQ/P5/U1FT8/MI6jcyVlQW2F/1JSxs/hK0WEekvBxkZdVRUWH9OP2jJEbEeywRzlZWVXHXVVbz55psAnHbaaTz00EPE9FIyqbm5mZtvvpl33nmH7du3Ex0dTVZWFnfffTdJSUlD2HLXhITAiSdCURFs3mwDHIwebefnP4/CbvfOtAURb9JbUYyOjjnmmPb/dyyWkZSkOXMiYj2Jic3thcysPk9US46I1VgmmLvwwgvZtWsX7733HgC//e1vueiii3jrrbd63L+hoYGcnBz+8pe/MGnSJCorK7n66qs57bTT2LBhw1A23WUjRsAFF0B5eQXffvshU6em0tLinWkLIr7OG4tlqDqniO/xlnmiWnJErMYSwVxubi7vvfceX3zxBUcddRQA//rXv5g2bRrfffcd48d3T0eKjo5m1apVnbY99NBDHHnkkRQUFJDioV3fHSvb+fn5AbXU1fkzerS10xZEpHfechEEPVfnFBGxCi05IlZjiWDu888/Jzo6uj2QAzj66KOJjo5m3bp1PQZzPamursZms/WamgngcDhwOBztP9fU1ADQ1tZGW1tb/w6gj+x2+Phjc+2WhATYsaMNqOHYYys47riDCAmBQW6CiEi/dT2HFRebP48da/ZcD8V5VESGjvPz7Auf7YMOgrPO6pxF4eWHLG7kyufJEsFccXExI0aM6LZ9xIgRFBcX9+k57HY7N9xwAxdeeCFRUVG97nfXXXdx2223ddteVlbWKcgbDOXlNnbvDiE+vpXmZggIqAHiqa6upra2lNraQX15EZED0vUcFh4Ou3f7Ex5unrwqKyspLS11cytFZCDY7ZCfXwcE+9xnu7YWXZPJoKp14Q3m1mDu1ltv7TFw6ujLL78EwGYuutaJYRg9bu+qubmZ888/n7a2NpYsWbLPfW+88UYWLVrU/nNNTQ3JycnEx8fvMwgcCJGR5jB+RQU4HLB5cytwGFu2pDBt2nAN54uIR+t4DouMhPJy8+eEhEgAYmNjGT58uJtbKSIHqqAA1q6Fb7+1A2fjcIzUZ1tkAAUH930tQ7cGc1deeSXnn3/+PvdJTU1l8+bN7Nmzp9t9paWljBw5cp+Pb25u5txzz2XHjh3897//3W9AFhwc3OMv0M/P76c5bAOnp/K3cXGB/PBDJNnZEVRXVwP/48cfJ/Dcc5CVVdVtoq1K34qIpwgLg+OOM+eVlJdDXJw5r6SszOx0G4zzqIgMnv2X6S8AYlm5spa4uK96LAai6xQR17nyXWkzDKPnMjweJDc3l8MOO4z169dz5JFHArB+/XqOPvpo8vLyep0z5wzktm3bxkcffdSvXqOamhqio6Oprq4e8JG5nJycXhYXTgIuBHYDtTgXEobuJXBV+lZEPI1zGRXnvBLnuU7nKxFr6fk6pXuZ/t6uUUDXKSL94Ur8YYk5c+np6Zx88sksWLCApUuXAubSBL/85S87BXJpaWncdddd/OpXv6KlpYWzzz6bnJwc3n77bVpbW9vn1w0bNoygoCC3HEtHvS0u7Oz1Kitro62tED+/JOLj/cjK6l4CV6VvRcTTdK3O6XDYgLifbkXEKgaiTL+uU0QGlyWCOYDnn3+eq666ilmzZgHmouEPP/xwp32+++67n1ITYdeuXe0LjE+ePLnTfh999BHHHXfcoLd5f/a1uPDeErhj28t7a86ciFhNQQGsXh0DnMvq1TGMGqVzmYhVqEy/iOezTDA3bNgwli1bts99OmaMpqamYoEM0l5540LCIuJb7HbnBV8AUEpNTQBr1pjnNp3TRKxL1yginsMywZwv8qaFhEXE99TVmT338fHNgJ34+GZqaswLQJ3bRKxN1yginkFlxUREZFBEREBUFJSVBQIhlJUFEhVl9uSLiIjIgVMwJyIigyIkxJxLExXVAgwnKqqFmTPVmy8iIjJQFMyJiMigSUkx18iE5WRlValIgoiIyABSMCciIoPKLFdeT0ODH3a7u1sjIiLiPRTMiYjIoCoqCgTOZtWqWFasMJcrEBERkQOnapYiInLAGhoayMvL67bd4bDx9tsNQCz19T+yebON/PwWsrKqui0wnJaWRlhY2BC1WERExPpshpUXYxsCNTU1REdHU11dTVRUlLubIyLikXJycsjMzOzhnjjgXKAUsAMhwHBgOVDRac/s7OweFygWERHxJa7EHxqZExGRA5aWlkZ2dna37Q6HjXffDSc/v5pDDomgtjaCqKgWsrIW9DgyJyIiIn2nYE5ERA5YWFhYr6Nqo0bBmjXmAuJjxpjLFaiqpYiIyIFTMCciIoMqJQXOOQfq680Fw7XOnIiIyMBQMCciIoMuJERBnIiIyEDT0gQiIiIiIiIWpGBORERERETEghTMiYiIiIiIWJCCOREREREREQtSMCciIiIiImJBCuZEREREREQsSMGciIiIiIiIBSmYExERERERsSAFcyIiIiIiIhakYE5ERERERMSCFMyJiIiIiIhYUIC7G+DpDMMAoKamxs0tERERERERb+eMO5xxyL4omNuP2tpaAJKTk93cEhERERER8RW1tbVER0fvcx+b0ZeQz4e1tbVRWFhIZGQkNpttSF+7pqaG5ORkdu7cSVRU1JC+trv44jGDbx63Lx4z+OZx++Ixg47bl47bF48ZfPO4ffGYwTeP253HbBgGtbW1JCUl4ee371lxGpnbDz8/Pw466CC3tiEqKspnPjhOvnjM4JvH7YvHDL553L54zKDj9iW+eMzgm8fti8cMvnnc7jrm/Y3IOakAioiIiIiIiAUpmBMREREREbEgBXMeLDg4mFtuuYXg4GB3N2XI+OIxg28ety8eM/jmcfviMYOO25eO2xePGXzzuH3xmME3j9sqx6wCKCIiIiIiIhakkTkRERERERELUjAnIiIiIiJiQQrmRERERERELEjBnIiIiIgMKpVoEBkcCubEknzhS6GoqIgPPvjA3c0QETfpep7zhfNeR75+/N5i165dANhsNv0NRQaBgjmxjG+++YbPPvsMML8UvFlrayvvvvsuycnJVFdXu7s5IjKEDMOgra2t/TxXX19PU1OT15/3unIe74oVK2hoaPC54/cWF110ERkZGYDvBXTqkOidN/8uhvrYFMx5qC+++IIlS5bw/vvvU1VV5e7muN27777LvHnz+Pzzz/nqq6/c3ZxB5+/vz9y5c4mPj+fKK6/k2WefdXeTPE7Hk6U3fynsj68cu68cJ8DOnTvx8zO/nu+9917mzZvHtGnTeO2119i9e7ebWze0GhsbefjhhyktLXV3UwZdT+9xb3jff/TRRwQEBPCLX/wC8J2AzjCM9g6ITZs20dLSog6Jnzh/Nx999BGPPvooDz74IHV1de5u1oDo+HffsmULjY2Ng/6aCuY80Pvvv8+8efPIzs7mkksu4ZlnngG846TeHytXruSaa67hn//8J9dddx2TJk1yd5MGlfPv7HA4CA0NZcyYMaxZs4aXXnrJzS3zDM7fT3FxMU1NTdTV1fnMxUFXHb80srOz+fHHHykrK3NzqwaW8+9aX18PQFtbG2COXnub1tZWKioqSE1N5ZFHHuGbb77h7bff5t577+WCCy7g5Zdf5u233wZ85/sgKCiIhoYGtm/f7u6mDKqOn+X169ezZs0aamtrLX/x39LSAsD//vc/KioqOP744wHvD+g6/j3/+c9/cuutt3bqiPHmY+8Lm83G22+/zfXXX09kZCSPPfYYf/7zn93drAPW8e/+wAMPcMstt3TqiBq0v7shHuWdd94xpkyZYqxdu9YwDMNYvny5cfDBBxtlZWVubtnQa2trM2pra405c+YYK1eubN9mGIbR2trabV9v4DyOvLw8Y/78+UZubq5RW1tr/O1vfzN+/etfG8uXL3dzCz3DypUrjZkzZxrXXnutcc455/js58Pp/vvvN2bMmGGcfvrpxpVXXml89tlnbmzZwHEe43vvvWecffbZxiWXXGLccsstRnV1dfs+paWlRl1dnbuaOKAaGhoMwzC/B0JDQ40TTjjBeO2119rvf+GFF4zU1FRj9+7d7mrikHnkkUeMG264wbjjjjuM8847z3j00UeN77//3jAMw6ivr3dz6wbPP//5T+PnP/+5cdFFFxnjxo0zNm7c6O4m9Yvzs1tRUWHs3LmzffuRRx5pzJw5s9t+3mrJkiXGjBkzjNLSUsMwDKOoqMiw2+2GYXS/jvElO3fuNGbNmmWUl5cbr7zyinHMMccYu3btMtra2oyWlhZ3N++APfTQQ8aMGTOMkpISwzAMo6SkxKisrDQMY3D+7hqZ8yDV1dUsXryYUaNGMWPGDADOPfdcDj30UJ588klef/11Nm/e7OZWDh2bzUZbWxvV1dUcdthhwN5eeWcK0po1a9r3tTrjpx6dDz74gBtuuIG3336bu+66i+3bt3PVVVeRnp7Oq6++yosvvujuprrV2rVr+ctf/sK///1vWlpaKC4uJiAgwN3NGnLO9/wjjzzCypUrWbt2LdHR0XzyySc8+eSTrF+/3s0tPDDOOWNr167lqquu4je/+Q1Tp05lz549LFy4kPr6evbs2cNNN93kFfNK3377bTIyMti2bRuzZ8/mvffe49NPP2XlypXt+1xwwQVMnDiR4uJiN7Z0cBhdeqyHDRtGWloatbW1bN68mb/+9a+cd955/OIXv+BXv/pV+4iPN3n99dd5/fXXWb16NRMnTiQ1NZWJEye239/1d+SpnN9lb7/9NqeffjqnnHIKl156KVu2bGH9+vXY7XaOOuoowDu+uzty/o2Mn+a9fv7551x99dWUlJTwt7/9jdNPP53JkyfT2NjYfh3jK5y/m5KSEhITE5kwYQL/+te/uOeee3jmmWcYNWoUr7/+Ov/5z3/c3FLXdfxstrW1sWHDBm699VYKCwu5++67OeusszjllFMoKSkZlL+7b72TPNibb77Jv//9b5577jl27tzJddddB8CiRYv4+uuv27/M5syZw4IFC7zyi6wj5zzByMhI7HY7H3/8MWDOJet47KtXryYnJ8cNLRx4NpuNTz/9lKuvvpo777yTpUuXEh0dzcMPP8yuXbv43e9+xxFHHNEe2PqqdevW8de//pVdu3axbt06nn32WaKjo/nss89wOBzubt6gy87O5tVXXwXML5CGhgaeeeYZ7r//fkpLS1m6dCk5OTnccccdrF271s2tdV1xcXH7F56zENCll17KKaecwuWXX86VV15JQ0MDmzdvZuTIkVx77bUkJSW5u9kH7NVXX+W7777jjDPOIDc3l2OPPZb33nuPp59+mnvuuYcdO3awYsUKNm/eTEJCgrubO6CMDqlJn332GXl5eWRkZPDrX/+av//97/zmN7/htNNOY/369Tz33HP8+9//9soOHJvNxvXXX8/SpUv54IMPePvtt/Hz82PZsmWWmm9ls9n4/PPPuemmm7jvvvt44403CAoK4oEHHqC1tZUvvviChoYGy3c49cT5N9q2bRt+fn6kpKTw4IMP8oc//IHY2Fief/55Jk2axLZt29zc0qFns9lYtWoVp5xyCl9//TWFhYW8+OKLPP7444wdO5a1a9dy4403kpiY6O6muqzr3z08PJw777yTRYsWERMTw913382ECRMGrwbGgI/1icvef/99Y9KkScbbb79tGIZh7Nixw5g8ebIxadIk45e//KXR3NxsGIZh1NTUGPn5+ca2bdvc2dxBl5eXZ/z2t781PvzwQ8MwDOPPf/6zcdFFFxlffPFFp/2ee+45Y+rUqUZhYaE7mjkgSktLja+//rr95wcffNBYsGBB+89ffvmlMWXKFGPu3LlGXl6eO5rocR5//HFj9uzZxvTp040dO3YYhmEYH3zwgXHuuecaxcXF7m3cIGtqajJef/11o6ioyPjhhx8MwzBTNnbs2GGccMIJRkVFhWEYhnH55ZcbV199teV+Hw0NDcZ1111nnH322e2f63vvvdf4zW9+Y5SXl7fv96tf/cp4/fXX3dXMQfHtt98aixYtMubPn2+MGjXK+OabbwzDMIzVq1cb4eHhxhFHHGHcd999xrfffuvmlg6eBx54wJgxY4Zx3XXXGRkZGcaGDRsMwzCMNWvWGOedd56bWzewekq1evnll43w8PBOaYhPP/20MWXKFKOoqGgIW3fgli9fblx33XXtP5eWlhoTJkww7r33Xje2amhs2bLFmDx5svHmm28azc3NRnZ2dvv56/XXXzcmTJhgub/nQNi6datx9dVXt3+un3jiCWPOnDnGggULjDvuuMNIT09vvw62ok2bNhmnnHKKsXLlSsNutxvr1q1rT7P8z3/+Y0yYMGHQrlc1Mudm7733HjfeeCMPPvggc+bMYdu2bXzxxRe8++67BAUFcfDBBxMQEEBrayvh4eGMHj2aQw45xN3NHlQNDQ0EBATw6quv8vXXX3PVVVdRVlbGQw89xJIlS8jNzeWxxx7j7rvv5umnn7ZkLw6YBU4WL17M448/zqZNmwAYOXIk1dXVVFZW0traytSpUznllFPw9/fnrbfeAqyTajMQnMf6448/0tTUBEB6ejq5ubnMnz+fuLg4/ve//3H99dczd+5cRo4c6c7mDirDMAgMDOT000+nubmZK6+8kiVLluDn58ewYcMIDw/nrbfe4umnn2bHjh0sWrTIcr+PkJAQzj33XBISErj55pupqqri5z//OSUlJaxevZr8/Hy+//57tm/fTkpKirube8Dsdnv7ezwyMpJt27Zx+umnc/3113PyySezZcsWTjjhBN544w327NnD+eef77Uj86+99lp7unBjYyOpqalMmTKl/f5vvvmm0+/L6pypVh9//DHvvPMODoeDc845h0svvZTAwEA+/PBD/vnPf/LQQw/x3HPPefxorPPvUllZ2Z4x0HGd1Pj4eObPn09ERESn/b1B12MZNmwYl19+Offffz+vvfYaGRkZhIWFsXTpUm6++WZeeuklj/97DqS2tjYqKyu57LLL+OCDD6isrATg0ksv5Q9/+AMTJ04kIiKCpUuXMmfOHDe3tu+6/t2jo6M57rjjePbZZ3nrrbeYNm0aw4YN4+GHH+aWW27hpZdeGrzr1UEJEaVPqqqqjPDwcOP+++83DMMwCgoKjKOPPtq48847DcMwjO3btxuTJ082fvvb33r1hG+nnTt3to8sfP3118a1115rLFiwwPjuu++MwsJC47bbbjMyMzON2bNnG2eddVanES2rWrdunXHJJZcYN9xwg5GXl2eUl5cb06dPN2644Qbjk08+MdauXWscf/zxxr333mtkZWW1T5z2JStXrjTGjx9vzJkzx7jnnnsMu91uLFu2zJg9e7Zx/PHHG8cdd5zxxhtvGIbhvZPpOx7Xc889Zzz00EPG448/bpx11lnGY489ZhiGYdxyyy3GBRdcYBxxxBGWHL1xTnr/6KOPjN/+9rfG2LFjjSuuuMJoaGgwli9fbpx77rnGzJkzjWnTpnUqCmJV7777rjFjxgzjjjvuaO+lX7VqlXHKKacYxcXFxp///Gfj4IMPNjZv3mwYhvcV/ej6WX366aeNFStWGP/3f/9nnHjiiYbD4TAMwzBee+0148cff2zv4fYmDz/8sHHQQQcZJ598sjF69GijvLzcqKioMG6//Xbjl7/8pTF//nxLfJadf8u33nrLWLBggbFnzx7DMAxj5syZxtFHH21s3brVePfdd420tDTjo48+cmNLB9eXX37Z/v+SkhLjySefNGbMmGG89dZbhmGYhap8OcPmf//7n/GLX/zCuP322zsVxbG6r776qv3a7McffzQefPBB45xzzmn/u//tb38zcnNzB7UNCubc7MMPPzSOPPLI9mo+jzzyiGEYey9s1q9fbxx99NHtJ0dv9e677xpZWVnG4sWL2z/kGzdubE87+uqrr9r3bW5ubv+it6q2trb2L8AvvvjCmDt3rnHttdcaRUVFxq5du4wLLrjAOPPMM9tTjdasWWPMnj3bqK2tdXPLh4bzd1NaWmocf/zxxhdffGE8/fTTxvz5842bb77ZaGhoMGpqatp/Xx0f4026HtOmTZuM0047zWhoaDDq6+uNJ554wjjttNOMl156yTAM87xh5cqeH374YfsF3913321ceumlxm9/+1ujoaHBaGxsNLZv325s377dMAxr/70dDodx/fXXG8nJycaUKVOMmTNnGv/3f/9nPPnkk8ZNN91kbN261TAMw/j9739vTJgwwXA4HF5V+a6srKy9Aul///tfo7y83HjqqaeMjIwM4/TTT2///lu6dKlxwgkndEqxtbKOf8PS0lLjlltuaQ/k586da6SkpLQHrU1NTZZ6j7/11lvG5MmTjU8++aR9W3Nzs3HppZcap556qjFz5sz2qtTe6IcffjBOOeUU4y9/+Uv7tsLCQuOcc84xDj74YOODDz5wY+uGnvO9+7///c9YunSpsXLlSqOqqsr45ptvjJkzZxp33XWXUVBQ4OZW9k9eXl57xei8vDzjrLPOMu65557269IdO3YYs2fPNo488sgh+7srmPMAH3/8sREdHW1cffXVnbY//vjjxl//+lejpqbGTS0bGs4vgbVr17bPA3L67rvvjEWLFhm/+93vjFWrVrmphQPLeZLbvXt3+99227Zt7QHdzp07jdbWVqOpqcmorKw0Xn/9dSMjI8PYtGmTO5s95P73v/8Za9euNW666ab2ba+99pqxYMEC49prr7XsF4ErOh7jM888Yxx88MHGv//97/ZtZWVlxlNPPWUcd9xxxpNPPumOJg6oP/3pT8bNN99sGIb5Ofnoo4+MqVOnGpdcconXlePfvn27ccsttxh/+ctfjOuuu8544403jCOPPNLw8/Nrz84wDMMrR6See+4547e//a1x5513GpMmTTLsdruxY8cOY+rUqcbNN99svPvuu8a///1vY8qUKV6RgdHVM888Y1x//fXGySef3Gnu1CWXXGIEBwe3l7G3ioaGBuOMM84wPv30U6OqqspYvny5cd5557Wfu+12e3tZdisFqPvS9Tjsdrvx6quvGuecc45x++23t2//29/+Ztxwww3t87t9iTOrZuHChUZWVpYxd+5cY+vWrcbGjRuNqVOnGnfeeaflso3q6+uNSy65xFi4cKGxYcMGo6WlxVi6dKlxySWXGA8++GD78dx+++3G9ddf397ZPNgUzHmITz75xJg4cWJ7tP/CCy8YmZmZnUakvNGmTZuM8ePHG+vWreu0/d577zWWLFliNDY2Gt9//71x+eWXG9dcc037OkxW1TEd5fjjjzeysrKMiy66yNiwYYORn59vXHzxxcZVV11lZGdnG4ZhGI2NjcZNN93kM4Gc8/fz0UcfGQkJCUZWVpYxYsQI4/nnn2/fZ/ny5call17q1ekqbW1tRnl5uWGz2Yz77rvPMAzDqKysNEaPHm3MmDGj076lpaXGsmXLvCJtZcmSJcbFF1/c6QL3ggsuMM4//3yvPBfm5uYaf/nLX4zf/va3xqeffmrU19cbK1eu9MpjNYzOI1NTpkwxIiIijPXr17dv27hxo3H55Zcbc+fONS666KL2IjBW1/HCf9myZcahhx5q3HvvvUZ4eLixZMmSTpkmCxcubB+ZtYr6+npjzpw5xoUXXmicfPLJxs0332z89a9/NS6++OL/396dB9SU//8Dfyb71mDMGMsYYytrkkJClkpS1iQhNLasY0kz1hFD1hKpbFlCKyHpQ7IkWUKIspQlRKKk/fb8/eF3z7fMfD4zZui6t/fjHzr3/vE695x7z/t1zvv1epfaYLY0FT+evr6+3Lt3L318fEi+v+FoZWXFcePGcdu2bezcubNKfgZ/5fnz5zQyMpLGdrdu3eJvv/3G+fPnk3z/ECM6OlqRIf5j8fHx/Omnnzht2jRev36dMpmM3t7eHDNmDGfMmEEPDw8aGBjw4cOHpRaTSOa+IBEREdTR0aGTkxM7deqkFHPl/62TJ09yypQpJN9PKyHJBQsWsEGDBhw6dKhUT3jjxg2VmWoaExPDVq1a8erVqzxz5gzd3NzYs2dPJicnMzY2liNGjFDpROWvxMTEcOLEibx58yYzMjLo5ubGgQMHct++fdJ7lK1L48eSD3rPnTvHSpUq0cPDg+T7Otv69evT1tb2T9+vTOQDogcPHjAzM5O5ubm8du0a+/Xrx23btvHWrVtMSEhg7969P3u9gSIlJiby119/pZ2dHSMjIxUdzmdTfADs5+fH33//nX379uWwYcOYnJwsvS6fUqlsd+z/jmPHjtHBwUG6th8/fpxNmjThpk2blKp0QH6sEhISeO/ePaalpfHx48fcsGGDlJxfuXKF7dq1k6ZFq6INGzawW7duDA4OppqaGoOCgpidnc2oqCiOGjWKAwcOlGpey5qcnBxaWlryzJkz0vly6NAhGhoaKmX9r3wf5P/evn2b48aNo4ODA69du0aZTMbDhw9z0qRJNDExKfUbUSKZ+8KcPHmSDRs2LBOJHEmuXbuWHTt2lP5++fIld+7cSfJ9K1cbGxulm3LyoaSkJO7atUuqJdixYwft7Oyk158/f04bGxvu2rWLJKXpKGXR27dvOWrUKNatW1f6HJKTk+nu7s4+ffpwz549ig2wlF2/fp0jRoygmpoa3d3dSf7fEzoLCwsFR/fvhYaGsnnz5jQ3N+e8efOYkZHBsLAw2tjYsHv37tTW1mZwcLCiw/zs7t69y0WLFtHKykplppP/N0FBQTQwMGBGRgZJ0szMjAMGDGBmZia9vb25fPlyymQylZmOR76/2SKTyWhtbc0mTZrQx8eHOTk5JN83valZsya9vLwUHOXHOXz4MDt16sT+/ftTX1+fnp6e0mshISHU1taWGkCooitXrtDExIQkuXr1avbv35/5+flSvSdJpZ9J9DHk31f5jcX8/HwOHz6cy5cvl2r9r1+/TlNTU6Ub0xX/LYqLi+ODBw/49u1bpqamcvz48XRwcODVq1el9ygiWRXJ3BeoLP0APHjwgIaGhtyxY4e0TX5H1s/Pj0OHDuWbN28UFN2/d/v2berr6/OXX35haGgoSTIsLIxDhgxhfHy89L7Zs2dz5cqVJJXzKcu/8eGgLSEhgT169OCwYcOk15KSkrhhwwZpfZqyYMOGDdTT05Oagairq3P9+vUkyfT0dGppaSl1HVlsbCxNTEx44cIFHjp0iFOnTuXEiROZlpbGwsJCPn78WKqhVeaB/X+Lvfigj3xfSO/s7KzU62b+lXPnzrF///6MiIgosb1fv37s378/mzVrpjJTTIsfd/lTt4KCAk6fPp02Nja8ePGitIZsRESEUq0fGx8fz9atW/PmzZtMS0tjREQEW7VqRV9fX2ZnZ9PKykrlErmrV69KN9TI943Lpk2bRmdnZ5qamkrJ+bp163jmzBlFhalQ4eHhHDVqFJ2cnBgaGsqnT5+yY8eOHDlyJGfMmME2bdoo9c259evXs2vXruzRowctLS157Ngxvnr1ihMmTOC4ceOkaaOKuF6JZE4oVdHR0dy0aRPDwsL4+vVrZmdnc968ebSxsSnR2GH37t3U19cvkfAom/j4eLZp06ZEvRf5/q6lhYUF161bxwMHDvDSpUvU0tIq0QWsrJD/6J0/f56BgYHS00l5h6iRI0dKya38YlkWFBQU0M7OrkTyevjwYVaoUEFK6JQ5wUlMTOSIESM4adIkaVtkZCSnTZvGUaNGKdXA9n8pfox27drFHTt2SFNmyT8mdPKp5qqi+N36oqIienp6sl27dhw5cuQfOvNevnxZZRZSLn7cd+zYITVtioiIYFFRESdOnEhbW1tGRUVJCd2X7MPfmujoaPbq1avEtmXLlkkNT+THVpl/o4orKCjg9u3baWJiIn1/U1NT2a1bN3bp0kW6Ae3j48MOHTowOTlZkeEqxNmzZ9mpUyeuW7eOy5cvZ4sWLbh7926+evWKPj4+dHV1lcY4ynhehIaGUldXlzk5Obxz5w59fX3Zq1cv3rlzhwkJCZw6dapCyz9EMieUmrCwMDZt2pTjxo1jvXr1pHq45ORkOjk50dDQkK1bt+bYsWPZpk0bpZ5rnp+fzwkTJnDjxo0ltru5ubFfv37s168fZ86cyeHDh9PU1FRaJ60sOnLkCNu0acN58+axadOmHDp0KPPz83n37l2amppy2LBhJJXzAvB3/dm+9enTp0Syk5GRQTMzM9aqVYvp6elK/QT3xo0bnDhxIjt27FhiWuGJEyc4ZcoUpf7u/xk3Nzd27dqVe/bsYYMGDejg4CC99mFCpyqKn9Py9UPJ9zezbG1tuWrVKml5AlXl6enJDh06MCgoiJMmTaKNjQ19fX1ZWFjIIUOGcMKECV/8Tars7Gypo+iVK1d47Ngx3rt3j8bGxjx8+LB0nF1dXTlp0iQWFRWp1Dkt378XL17Qx8eHFhYWUkK3atUqjho1iqNHj+bKlSvZvn17lWna8zFu377NwYMHS7OPSPL06dPs0qXLHzqUKysvLy+OGTNG+js1NZXDhw+XSj8UXfMqkjmhVISGhrJDhw7S9IMDBw6wSZMmUlOTt2/f8vnz59y2bRtjYmJUojOftbW1lKTJZDKeP3+empqa9PDw4MSJE7ly5coS64KpcrLy38gXSS/e1apTp04cP348yfeD/tjYWEWFVyqKH/eUlBSp89nFixdpaWkpTb89cOAAly9frpSNgOT7eOvWLSYlJTElJYVZWVmcM2cOJ06cyJMnT0rvVZU1xeQOHTpEExMTymQyOjs708zMjO3atePEiRMVHVqp8PLyoqmpKYcPH04nJyeSZEBAAO3s7Lh48WKlbIbwV2QyGdPT09m/f3+pQ/Xr16/p5eXFESNGkHw/+FOGadLXrl3jggULOHXqVLZs2ZJ37tyhTCajk5MTHRwc+MsvvzA8PJxaWloqV+9Z/LdZPv15x44dtLS0lLpXnjt3jk5OTnRzcyuTjcsKCwu5du1a/vjjj7S3t5e2Z2Zm0sLCQilnV/3ZWCwsLIwDBw4scYwdHBykbtOKHr+JZE747N68eUNdXV2am5uX2G5qaspVq1YxMDBQZeokyPdf6pycHBoaGkpPH8n3P27y6Se//fZbidfKqufPn1NHR6fE9Kpbt27R2NhY4Xe6SkPxC4CLiwt1dHSoq6srXSAOHjzI1q1bs0+fPmzevLlSr7l19OhRduzYkQsWLOD333/PxMRExsfH08nJiba2tiozEPzwon7hwgXeu3eP27ZtY48ePSiTyejh4UE1NTWpk6+q2rFjBzt06MCoqCgGBwfT2NiYP/30E0nS29ubDg4OSr3IfXF/NpiztbWlr6+vNJXy6dOnNDAwULppeI6OjixXrhwdHR2lbc+ePePu3btpZ2dHOzs7HjlyRIERfl6bNm1iz549+e7dO2mBewsLC27btk3RoX0RMjMzuXnzZo4cOZKbNm0i+b72XVNTU+lmWRT/Hu/bt4++vr7csWMHCwsLaWVlxVmzZtHNzY0+Pj7U0tLivXv3FBjt/ykPQfiMQkJCcPfuXezevRvW1taYM2cO1qxZg59//hk3btzA119/DX9/fzx//hwmJibYsmULypdX7tNSTU0NlStXxtSpUzF//nw0bdoUFhYWqF69OtTU1HDp0iWEh4dj5cqVig611JGEmpoaYmNj0bhxY3z77bdo1aoVFi5cCFdXV1StWhUPHz5EQUEB8vLyUKFCBaipqSk67M9Gvm8XLlzA5cuXsXPnTrx8+RLm5uYgidmzZ6Nfv354+PAhvvrqK9StW1fBEf8zV65cwaJFi3Dw4EEcPHgQX3/9NTQ0NPDNN99g7Nix2LJlC7799ltFh/mvyc9vAHj9+jVq1aoFfX19FBQU4OrVq1iwYAHKlSuH8uXLY/369TA3N1dwxJ+WfP+Lioogk8lw8eJFLFy4EF27dgVJtG3bFvb29oiPj8fYsWORlZUFDQ0NRYf9rxU/7vv27QNJ2NjY4LvvvoO/vz+0tbWhpaWFq1evQl1dHTVq1FBwxH9fbGwsunfvjpo1a+L+/fvw9vaGpaUl6tWrB2NjY9ja2iI/Px8VK1Ys8Tmoiu3bt8PHxwd79+5F1apVUbVqVVhaWqJcuXLYuXMnZDIZfvrpJ0WHWWo+PMZFRUWoUaMGbGxskJeXh61bt8Lf3x81atSAm5sb2rZtq8BoP55831xdXREQEIDZs2dj5MiRaNCgAVxcXODr64vY2FjIZDL4+/ujadOmCo74/1NYGimovOPHj7N9+/bSHbukpCRqa2uzffv2NDc3l+5WZmZmMjk5WWWaHshlZ2dz8eLF1NTUpKenJ2NiYhgWFsYWLVrw6NGjig5PYUJDQ9mkSRNpMdGLFy9ywoQJbNeuHTdt2kQtLa0y9fkcPXqU33zzDf39/aVtMTExrFGjhjQtTdkFBQXRw8ODYWFh1NPTk+ooDh48yKysLJXr4Lt27Vr279+fJiYmfPHiBcn3TzcaNGhAZ2dntmzZUumezvyV4ne05Y1cHB0daWdnV2K5FQsLC6VdLPivuLm5UU9Pr8TSQpMnT2bv3r1pbm5OPT09pZqFkpeXR1tbW27ZsoXk+06NI0aM4K5duxgcHExbW1uV7b4qk8mYk5PDMWPGMDw8nGTJuqjs7Gz6+vqqREnIxzp48GCJ+jj5d//t27d0d3ennZ0d16xZ84fXlcWHy06YmZn9YabQl1brKpI54bM4duwYdXR0pEVwExMTuW/fPj579oydOnXi9OnTSb6fb63MjRz+SlZWFnft2sW2bdtKzTxCQkIUHZbCREdHU1NTU6qDS0lJ4e3bt/no0SO6urrSw8NDqp9StgvA3/Xhfr148YK6urrU1dUtsT0qKor16tVjWlqa0n8Wp0+fZufOndmxY0dp8BMZGUkDAwMmJiYqOLp/r/jxuXTpEnV1dXnjxg1aWlqyW7duTExMZE5ODl1cXDhp0iSVa5JQfP9dXV05ZcoUFhYWMjQ0lOPGjaO7uztTU1N55MgR6unpqcwAuHh9Z3h4ONu3b8/MzEwWFBTw8OHDdHFxIUnev3+fFy5ckOphlYVMJuPSpUtL1He6urrS3t6ezZo148GDBxUY3eclP6ft7e3p4eFR4hz38vKSaiHLmhs3blBPT4+nT58usV3++bx584YbNmzgoEGDpLrCL90/WXbiS7smi2RO+OTevHnDatWqSTVhjx49YufOnens7Ezy/dpy2tranDBhgkoWv/+ZjIwMymQyqXvbl/ZD8LnJ99fDw4PTp0/no0ePuGLFCmpra7Nbt24qPSgorvhxT01NlQa16enpNDQ0pJmZWYn3f2l3//6p5ORkDhgwgPPmzWN4eDgjIiLYvn17leji+uGCsv7+/gwKCpK2jR49mj179uTt27dJUila0f9Tbm5u7NKlS4mnT15eXhw9ejQNDAzYvXt3pXoy9b/cu3ePP//8s/QdjY6OpoODAx0dHTl79mxaWVlRR0dHaeoiHz9+LN18TU9PlzqQPnnyhJqamiVuQqalpUmJqSpey06ePMnNmzezqKiI69ato6mpKWNjY5mdnc3AwEB27NiRCQkJig6z1MXFxXHChAnSEhRkyeMv///r16+5efNmpWjwoyrLTqiRpKKnegqqJyIiAk5OTpg3bx7Wr18PGxsbTJkyBTKZDOrq6rh48SJmzJiBQ4cO4ZtvvlF0uKWmqKgI5cqVU3QYpYYfzK+/e/cuunfvjsaNG2PChAkYMGAA1q5dizp16mDu3LkKjLR0bdy4EQEBAUhLS0OXLl2wZMkSVK5cGcOHD0d+fj7Onj2r6BD/lby8PFSqVKnEtpiYGAQGBiIuLg5fffUVRo4ciQEDBih1nU3x2NeuXYtdu3bh6dOnMDU1xcqVK9GgQQMAwODBg/Hu3TscOXIEFSpUUGTIn82LFy8wfvx4eHh4oGHDhsjNzUXlypUBAIWFhXj69Clq1KiBWrVqKTjST+PJkyeoXr067ty5g6ysLOjo6GDdunW4f/8+Zs2aBT09Pezfvx9xcXFYvnz5F32O3759G7a2tujbty/09PSwbds2VK1aFVZWVjA1NYWbmxuaNGkCGxsbpf6+/hWSkMlk+P3333H79m1YWlpi+PDhmDNnDm7evAk1NTVkZGTA09NT6WrBPoXIyEisWrUKOTk52Lhx459+BvLzQxnOE3mML1++xLFjxxAYGIh+/fph0qRJcHFxkY55q1atsG/fPuzduxetW7dWdNh/TlFZpKD6IiMjqaGhwZkzZ5bY7uXlxeXLlzMzM1NBkQmlQX6X7syZM1yyZAnd3NwYHx/P3NxcqatnfHw8tbW1GRERochQS9WePXvYsWNHPnjwgElJSbSwsJBaOqelpdHMzIyPHj1ScJT/XHx8PJcuXUqZTPaHKdR5eXmUyWQqt6jw6dOnaW5uzry8PIaFhdHIyIiurq4l7kwrw13qfyM7O5tGRkYlamnI9+vKqdJ6ch+es46OjjQ1NeWlS5dKbN+7dy91dHS++A608fHxbNu2LXfv3i1tu3z5Mvfv3089PT3OmjWLtWvXZqtWrVSyPq74mnjy8zQrK4urV6/m6NGjeeDAAZLvO5EmJiZKNbBlQfGpk/L/X716ldbW1ly0aFGJ2lBlo2rLTohkTviszp49y3bt2knzy319fdmxY0eVmWoj/G/Hjx9n8+bNuXLlSg4aNIjDhw+XfiiPHDnCZs2albkawg0bNnDr1q3S369fv2ajRo3o6+tLkkpfQ7p7924aGhr+6WuqkrwVd/78efbt25c2NjbStpCQEBoZGXHlypUllt1QZVlZWZw6dSrXrFkjTUHbv38/W7VqxYcPHyo4uk/vwIEDfP78Oe/evcsVK1bQ0tJSqvf19/engYHBF5/IFRQUcMKECdy4cSPJ/0ts5P8+ffqUCQkJnDFjBnv37i1Ni1aV73F0dDT3799P8v3UyjVr1kjTR7Oysujs7EwDAwNu375dpadH/xn5MT506BAHDx7M4cOH8+DBg5TJZLx8+TJtbW05d+5cpa//VZVlJ0QyJ3x2ERER1NHRoZOTEzt16qTUd3OEv0cmkzEjI4MWFhY8fPgwyffrEnl5edHe3p7p6em8du3aH4qoVc2fDXqcnJzYoUOHEttmz579hycayuLPkk9DQ0NpvSFVU/yYpqenMysri46OjjQ2NuaRI0ekzyMgIIBmZmZS7VFZEBUVxcGDB7NPnz60trZmmzZtlH6w92fS09NpYmIi1c/cvXuXzs7OHDJkCE+fPs3s7Gw+f/5cwVH+PdbW1lKNpzyJk5/jCQkJUhLj5OTEyZMnKybIz8TT05NaWloMDg7munXrOGzYMLq7u0u1zJmZmdTW1ub06dNLdGQtK44cOUIdHR0mJSXR2NiY7du3p6urK4uKinjhwgVaWVkpde3gtm3bqKenV6KTenp6On18fGhkZEQvLy8FRvdxlHtBL0EpGBkZYfXq1RgzZgyOHz+OVq1aKTok4TMrV64catasiVq1aiEjIwMymQz16tVDz5494e3tjaysLLRv317RYZaaa9euITc3F507d8aKFSuQmJgIc3NzuLu749SpUwgPD8fkyZMVHeZHycjIQGFhIerUqYMTJ05AXV0dDRs2RPPmzWFjY4NHjx4pOsRPjsXqQNzd3ZGUlISVK1fCyckJy5YtQ2BgIEjCzMwMQ4YMgYmJCapXr67gqD8d/kkdjLwOGgC6du2KRo0a4dmzZ3jz5g20tLTQqFEjRYT6WdWqVQtVqlTB5cuX0bhxYzRr1gxWVlbIysqCp6cn9PT0vvh1E0kiLy8Pz549w4MHDwAA6urqKCoqAkmoq6sjICAAOjo6MDU1RdOmTREYGFiiFlLZTZgwAUVFRVizZg2cnJxQpUoVREVFQSaTwcbGBnFxcdDU1MT8+fPx1VdfKTrcUpWRkQE/Pz/s3LkTN27cwNu3b2FtbQ1PT0/k5+dj2rRp8Pb2Rs2aNRUd6kcrKipCfn4+zpw5A2dnZzRr1kxaK7FWrVoYNmwYKlSoAENDQ0WH+reJZE4oFb169UJiYiKqVKmi6FCEz0Q+0Dt//jxevHiBgQMH4quvvsKJEyfQq1cvfPfdd1BTU0PFihUVHepnFxERgaZNm6Jx48ZYvXo1tm7dCgBo2bIl/Pz8sHnzZkybNg1Tp05Fbm4u9u3b9+UsPvo3ZGRkYM6cOdDS0oK9vT3u3LmDkJAQFBUVwcTEBD/88AOWLVuGoUOHQkdHR9HhfjLyRGbjxo3Yt28f/Pz8UKFCBWhoaGDFihVYvnw5du7cifLly8PU1BTVqlVTcMSflnz/L168iHfv3sHIyEhK5OTf/0aNGqlkAgcAW7ZswcWLF1G1alWoq6vj/v37uHfvHpo1a4YGDRpg/vz5KCgoUIpkR01NDZUrV4aDgwPmzZuHFi1aYMCAAVBTU0O5cuVw6dIlHDhwAH379kVeXh5SU1OxcuVKpdi3/+XMmTN4+/Yt2rVrh0aNGmHSpEmoXLkyli1bhoULF6JLly64cOECAgICkJ6eDn9/f3z33XeKDrvUaWhoYOXKlXj79i2WLVuGkJAQ1KtXD2FhYYiOjsawYcPQuHFjRYf5j8jP/QoVKuD+/fvo06ePNC7x9vZG69atMWLECAVH+XFEMieUGpHIqTY1NTWEhobC0dERGzZsAAD8/vvvGDlyJMaOHYs6derg1q1bWLx4scoO9uQCAgKwY8cOhISE4OHDhzh37hxq164NAwMD2Nra4sCBAzhw4AByc3NBUum+GxoaGtDW1kZMTAz27t2LyZMnY8KECbh9+zaWL1+Op0+fIjU1FSdOnICOjk6JpzfKLjU1FYGBgdi2bRsqVqyITZs2ISwsDAYGBvj111/h7OwsPXX+0ru5/V3Fu/CuX78emzdvRrVq1VC9enUcPnwYtWrVUpl9Le7DJ5GampqoVq0anj59irNnzyIiIgKnT5/Gy5cvUbVqVYSFhUFDQ0OBEX88c3Nz3Lp1C3PnzkVKSgq0tbWRkZGBWbNmYdWqVejUqRMAYO7cuUrfiTUjIwNGRkYgif79+yM7Oxtjx46FsbEx8vLy4Orqijlz5qBHjx7Iz89H7dq1lTZh+Vjyc/3SpUsoLCxEYWEhDA0NkZmZiWbNmqGoqAhXrlxB3bp14eTkpLSfS0REBBISEjBp0iS0atUKhw4dgr6+PjQ1NXHs2DF4enrC19dX0WF+NLE0gSAIn8SVK1dga2uL4OBgaGpq4vr160hKSsLAgQNx7tw5vHz5Eo0aNYKurq5StC3+J4oPemfPng1XV1f8/PPPcHFxAQAUFBTAyMgIFStWRGhoqFLe5ZYnZikpKfDx8UFcXBx69OiBwYMH49tvv0Vubi7y8/OxceNG7N27F9evX1fqQeCH5+qLFy8wadIk6Tg3b94czZs3x9GjRxEQEABAdZI4Ofl5HRMTA39/fzg6OqJu3bowMzNDbm4uAgMDVWbJAbnixz0mJgbly5dHjRo10KJFCwCAh4cHzpw5g3379uH+/fuoUaOG0i6z8+7dOwQFBWHNmjWoX78+atSogVGjRmHAgAEqt5xOdHQ0rKys4Orqivj4eKSmpuLgwYOwtrbGhg0bULNmTWzZsgXDhg1TdKil7ujRo/jll19gb2+PGTNmICQkRJp98f333+PUqVPw9vaGiYmJokP9aFT1ZSdKvUpPEASVUbwZxKVLl+jg4EBvb28uXryYFhYW/P777zl16lQFRlh6in8W8mJ5JycnVqlSpcRSA/n5+ezTp49UZK9M5Pt49uxZmpqa8tWrV9y+fTuHDx/OTZs2MTU1tcT7jY2NefXqVQVE+mkUP6bx8fHScipRUVH09PTkgwcPSJJ+fn40MDBQueVWLly4IP3/8uXLVFNT408//VTiPf3796eOjo7KNohYu3Yt9fX12bt3b/bv35+LFi0i+X7JlcGDBys4uk8rMzOTRUVFUot+Vela+aGIiAh27NiRMTExJN8vhn3+/HmOGzeO3bt35+3btxUcYem7ffs2O3XqxCdPntDPz48dOnSQllNJTk5mdHS09Hkpi7K07IRI5gRB+FciIyPp5ubGJ0+ecNKkSezWrRsDAgL46tUrRkREcO7cuSV+VFVR8UHP+vXraW9vzzdv3pAkp0yZwq+//lrqfKfsYmJiOHbs2BKdSLdu3cqRI0dyw4YN0gUxMjKSTZo0kVp9K5sPj2m3bt04btw4zp49W1qXSCaTcfv27ezQocMX34b+YxUWFtLBwaHE2mLOzs6sVKnSHxL0oUOHqsz5Xfy4R0REsFOnTpTJZHz9+jVPnTrF/v378/jx47xy5Qr19fWZkZGh9MuJfEjV9ufPREREUEtLi2fPnpW2FRUVMTc3V4FRlZ6UlBQePXpU+js6OpqLFi1iSEgI9fT0eO/ePZLvl5qJj49XVJj/WFlbdkIkc4Ig/Cvh4eFs1qwZ09LSmJOTw5ycHJLvB/3a2trS0gRlgbu7Ozt37sz79++X2D5jxgyqqakp9WLgJJmbm8tly5ZRTU3tD+sDbt68mUOHDpXWFLt9+7ZSry8mH9Rv3LiRffr0YU5ODgcOHMiOHTvSxsaGjx494tOnT2ljY6OS7fflLl68yNatW0t/L1q0iFWqVOHly5cVGNXnUTyRy8nJ4fnz52lmZiZty8zM5MSJE7lhwwbKZDKmpaUpIkzhEzl16hTbt2/PyMhIRYdSqgoLC+nh4cGePXvy4MGDJMmHDx9SV1eXWlpaUtJz7tw5amtrMzY2VpHh/iNlbdkJ0QBFEISPwg9qiLp27QpDQ0Ncv34dvXr1Qm5uLo4fP44FCxZg6dKlMDc3V9kaueLy8/MRExODnTt34scff0ReXh7Kly8PdXV1bNiwAZUrV0ZOTo6iw/xoxY9dpUqVMHPmTLx8+RKLFy9GkyZN0KZNGwDA5MmTMWDAADRs2BDA+2YRyujChQvIzc1Fz549kZ2djfj4eGzfvh0eHh549+4dXFxc4OjoiBkzZsDT0xPbtm1TytrH/yYzMxPlypVD9erVERsbi++//x716tVDr169EBERgaVLl0JdXR2dOnVCbGwstLW1FR3yJ1H8PHdzc8PZs2cxdepUlC9fHpcuXYK2trZUF/fixQuUK1cOderUUXDUwr/Rs2dPuLi4wMnJCSdPnlS6RlT/lLq6OkxMTJCXl4dt27YBACwtLdG7d2+kpqZi3759aNGiBRYuXAhnZ2d06NBBwRF/vLK27IRogCIIwkeLjY3FmTNnMHPmTADAmjVrEBwcjKioKABAQkIC8vPz0bZtW5VN5D7cr5ycHHTp0gVTpkzBhAkTpO3BwcFo06YNmjdvrogw/xX5Pp45cwb3799HtWrVMGjQIJDE4sWLERUVBTc3N5UZ0BcVFWH16tXYuXMnPDw80LNnT+Tl5eHevXuYPn06Tp48CQCwsrJC3bp18euvv6J+/foKjvrTyc3NRXh4OB49eoQ3b94gLi4OO3bsQFFREYYPH46srCycOXMGALBy5UoMHDhQaZP2/+bq1atwc3ODu7s7qlWrhokTJyI/Px/16tVD/fr1sWnTJoSEhEiNUATll52djapVqyo6jFIhb2iTn5+PwsJCbNmyBZGRkZgxYwb09fVx8OBBBAUFoXHjxujbty/MzMyU5hr+4bITALBz505s2bIFCxcuxMOHD3HhwgUkJydLy05oaWkpOOpPREFPBAVBUCJJSUlS8XN+fj737t3Ldu3a0dramk5OTrx58yYtLS157do1BUdaOopPx8rNzWVGRgZJ0svLi5MnT2ZYWBhJcs+ePWzZsiWTkpIUEeYnceTIEbZv356rV6+mvr4+7e3tmZuby7y8PM6aNYudOnXi27dvlb5Zwtu3b5mfn8+3b9/S1dWVurq6PHXqFEny/v37NDAwYHh4OIODg2lsbMznz58rNuDPJDIykq1bt2ajRo1KNEDJyMhg//792aZNGwVG9/kUFRXx2rVrbNu2Lbt37y5NNcvNzaWnpyfnz59PBwcHpawfEgTy/65boaGhNDMzY3Z2Nh89esS1a9fS3Nycx48fl94rr3NXlt/1N2/esFy5clRTU6O5uTl79erF3bt3MyUlhVu2bGHfvn15/Phx3rx5k7GxsSpT4ysnkjlBEP7S2bNn+d133zEqKqrE9uPHj3P27Nls164dK1euzHnz5ikoQsXYuHEjBw8ezNatW3P79u08e/YsV6xYwR9//JHW1tZs27at0tVTZWVlSRfwCxcuUF9fn8+ePeOBAwfYsWNHWlpacvTo0VJCd+fOHQVH/O8dOXKE5ubm7NSpE318fHj37l2uWbOmREI3b948Ghsbs23btirX7KT4gC0tLY3jxo2jlZUVly9fXuJGRFpaGkePHq3UNyeK+7OB6q5du6irq0t/f/8/1NKoQqMEoWwLDw9n27ZtpRuOJPn48WO6urqyd+/e9Pf3V2B0/8758+fZsGFDBgYGctmyZZw6dSobNmzIOXPmsHz58qxduzb9/PwUHeZnIZI5QRD+lsjISLZq1Yrnzp2TBkHyf48cOcKgoCAaGBiozEDvr3h5eVFPT483b97k/v372blzZ3p6epIk7969y/j4eD579kzBUX6cW7du0dTUVHoqkZiYyOjoaJ48eZLa2tq8e/cu9+7dy9q1a3Po0KFKc9f2fzl+/Di1tbV58uRJrlu3jjo6OgwLC2NOTg5dXFyoq6vLK1eukCSfP39eorujKih+DLdt28aVK1eysLCQx48f5/jx4/nrr78yPz+f//nPf7hr1y6V6UxbvGNjQEAAPTw8GBISwoKCAnp7e7NXr14MCAiQutKSyvOUQhA+JJPJmJ2dzdGjRzM4OJhkyZsTqampdHFx4aVLlxQU4adRVpedEMmcIAh/W0REBFu3bl2inXNkZCSrVKnCK1eu0NLSkgkJCQqMsPTMnDmToaGh0t+nTp2ihoaGUnb+Isk7d+6wS5cu3LRpE0mWSNgXL15MNzc3kuSxY8f4yy+/SAmOMouKimLt2rV58uRJadu0adM4Y8YMkmR6ejrXrFnDpk2b8syZMwqKsnRs3LiRenp6jIuLk7YFBwfT3t6eRkZG1NTUZGJiogIj/DzWr1/P7t27c9WqVWzZsiW3b99Okty0aRM7dOjAgwcPiiROUBnjx4+nt7c3i4qKpPP6P//5D+/du6cy53lZXHainKJr9gRBUB5GRkZwd3fHxIkTce/ePZw9exZ2dnbYu3cv6tSpgxMnTqBSpUqKDvOTS09Ph0wmAwCpAcSjR4/g7+8vvadnz54YNGgQypdXvibBd+7cwejRo2Fvb48pU6agsLAQ48ePR3p6OtTU1PDdd9/Bzc0N69atw+zZs2FsbAwdHR1Fh/2v1alTB1WrVsXVq1dRWFgI4H3TF3lTk1q1asHOzg4zZ86UunSqooSEBPj5+eHQoUNo27YtCgoKAAADBw6Eo6MjJk+ejJCQEKVs4vOhpKQkXL16FTk5OTh9+jQiIyNx+vRplC9fHs2aNcPIkSNRUFCAKVOmwMHBAdra2krR/EEQ/ptbt24hMDAQAFC/fn2cPXsWz549g5qaGuLi4vDzzz/jyZMnKnOeGxkZYfPmzZg6dSpOnz4NAFBTU1PJsYmc6GYpCMJHi4yMxPDhw0ESnp6eGDRoEPLz8/H48WM0bdpU0eF9UjKZDEFBQYiIiICmpiYCAwMRERGB+/fvY9SoUTA1NcVvv/2GPXv2YNWqVTh27JhSDfwLCwvRu3dvlCtXDqdOnUJRUREGDx6Mb7/9Fp6engCAZ8+eITAwEGfPnsWYMWNgZmam4Kg/nRs3bmDo0KGws7NDeno67ty5g+Dg4BJJubwDnKqR71dKSgqmTp0KLy8v1KxZUxr0nD59Gl27dkWFChUUHOmnERoaCkdHR6irq6Nx48awtLREfn4+Hj16hIsXL+LYsWOoUKECtm/fDl1dXbRr107RIQvCP0YS+fn5WLp0KZKTk/HTTz/B0NAQw4cPR3Z2NqpVq4a7d+9i2bJlsLCwUHS4n1x4eDiWLFlSJpadEMmcIAj/SHR0NN68eYN+/fqhoKBAZQZ8xSUnJ+OHH37A69evoa+vj8zMTJw+fRotW7ZEbm4url27hrFjx6Jly5ZISUnBzp070bp1a0WH/dHi4uJgbW2NIUOG4OrVq9DU1MSaNWuk11+9eoU6deqgsLAQ5cuXV5pW1X9XXFwcbGxskJ2djRs3bqBatWoqm8BduXIFL168QL9+/aRtSUlJsLCwwPz58zFy5EgAwIEDB+Dl5QVfX198++23igr3kwkLC8OiRYvg5uaGzp07w8rKCmFhYfjmm2/w448/Ijw8HMD7VuZr1qzB0aNH0bhxYwVHLQgfT/77LJPJoK6ujsePH8Pb2xtPnjyBnZ0dunXrhsuXLyMrKwt169ZV6SWEysqyEyKZEwThX1HVi0BKSgo8PDwwZ84cVKtWDWPGjEFmZiaqV68OT09PaGhoAHj/5C4vLw+5ubmoXbu2gqP+527cuIGRI0eicuXKuHjxorT99OnTWLp0KXx8fKS1e1RRQkICLCwsYG9vDwcHB5UbAJBEWloaGjZsiIKCAowfPx49evSAqakpvv76a4SHh8PW1hb9+vVD1apVce7cOezfv18pb058KCMjAw0aNMCKFSswffp0AO9v1CxbtgyNGzeGl5cXJk2ahIyMDISHh8PX11cl9lsoW+TJGwBcv34d165dw8CBA6GhoYGUlBRs3rwZN27cgIODA0xMTBQcrfApqd5tR0EQSpUqJnJRUVE4fPgwHB0dkZSUhK1bt8LX1xd79uyBmpoa7OzsAABBQUFwd3dH1apVlTqRA4C2bdvCz88PGRkZ+O233wC8f/o6Z84czJgxQ6UTOQBo2bIlAgICsHr1ari5uSk6nE9OTU0NdevWxeLFizFr1ix88803CA0NRe/evbF3717o6+vj5s2b6NWrl7R4sKokNBoaGggJCYGPjw9iYmIAAG5ubpDJZFi0aBFcXFxQuXJlfP311wgICFCZ/RbKjidPnmDhwoVITk4G8H6K4cGDB3H48GHpZsa4ceMQHx8PPz8/PHv2TLEBC5+UeDInCIJQzMuXL/HDDz9AS0sL48ePh7q6OsLDw9G9e3dMnz4dycnJmD17NhITE1G+fHns2bNHpQZ/cXFxGDFiBNq3b487d+7A2dkZZmZmKvsE9kPx8fGoVKmSytV+yvn4+CAwMBABAQGoWLEixo4di8OHD6Nu3bowMzPDxIkT0aJFC0WH+VmcOnUKM2bMQJs2bfDu3Tv4+fmpdFMEoex4+vQpzM3N0aNHDyxcuBC1a9fG+vXrcfnyZZiammLUqFF49OgRZs+ejcWLF6NNmzaKDln4hEQyJwiCUExOTg4mT56M7OxstG7dGuXLl0fjxo0RGhqKzp07Y/r06SCJgIAA6OrqokmTJooO+ZOT19C5uLjA3Nxc0eEIn1ivXr0wePBgdOjQAePGjcPq1avxww8/YNGiRXB3d1eqBj4fKyoqChYWFggKCkKPHj1KTE0TBGX28OFD2NnZQUtLC87OzlJCd/78eeTk5OD+/ftwdXWFsbGxokMVPjGRzAmCIHzAx8cHv//+O6ytrfHq1Ss0aNAA9evXx4kTJ9CyZUv8+uuvig7xs5MXjpeVJ3JlgfxYBgcHw8PDA/fu3cPatWsxaNAgACgziU1kZCR+/vlnrF69Gr1791Z0OILwjyQmJmL37t2wsrJCzZo10bhxYzx79gx2dnZo1qwZlixZgrp16+LSpUt48OAB6tevD0NDQ0WHLXwGIpkTBKHM+89//oOMjAz06NEDdevWBQAsWbIEXbp0QWJiIm7cuIHmzZtDQ0MDV65cwe+//670NXJ/RSRxqislJQVGRkYYOHAgXFxcUHwYUFaOeVlqWy6onsLCQvTp0wdnzpzBgAEDcPXqVYwdOxZaWlro2LEjRo8eDWNjY0ycOFFaN1NQXSKZEwShTMvIyECtWrVQrVo12Nvbo7CwECtWrMCCBQtQr149ODk5YdOmTYiOjoauri7Gjh0rdbIUBGV14MAB7N27F1u2bCmzg72y0rZcUE0JCQkYMWIEjI2NYWBggOTkZHh7e6NLly4IDQ1FSkoKRo8eDU9PT1EbquJEN0tBEMo0DQ0NnDt3Dl999RXatWuHjIwMODo6Ii0tDWvXrsWDBw9ga2sLAwMDWFtbi0ROUAn6+vogWaaTmbK874Lya9myJXx8fBAUFIQ7d+5g2rRpiI2Nxdy5c7Fs2TKMHj0aw4YNE4lcGSCezAmCIOD9empz587F1q1bUVhYiNu3byMyMhK//PILmjRpIqYdCionJydHTDEUBCUXFxeHYcOGYdiwYVi6dKlU95qfn4+KFSuKa1cZIJI5QRCE/y8iIgKzZs3C5s2bYWBggKysLFSvXl3RYQmCIAjCf3Xjxg2MGDEC5ubmWLJkCSpXrqzokIRSJJI5QRCEYk6dOoWZM2fC1dUVPXv2VHQ4giAIgvCXrl27hiFDhiAsLAzNmzdXdDhCKRLJnCAIwgdEpztBEARB2YjZJGWTSOYEQRD+hOh0JwiCICgTUR9XNolkThAEQRAEQRAEQQmJpQkEQRAEQRAEQRCUkEjmBEEQBEEQBEEQlJBI5gRBEARBEARBEJSQSOYEQRAEQRAEQRCUkEjmBEEQBEEQBEEQlJBI5gRBEARBEARBEJSQSOYEQRAEQRAEQRCUkEjmBEEQBEEQBEEQlJBI5gRBEARBEARBEJTQ/wPJVKvv0b/LXAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUSD5 nearest genes:\n", + " UTRN | score: 0.7242469499939785 | OC annot: membrane \n", + " KIAA1522 | score: 0.704259016956014 | OC annot: ORF \n", + " SLC44A2 | score: 0.6943505750400003 | OC annot: membrane \n", + " FARP1 | score: 0.6810209582297198 | OC annot: membrane \n", + " SLC23A2 | score: 0.6472279986830514 | OC annot: membrane;vesicles \n", + " GOLGA7 | score: 0.6455619274331441 | OC annot: membrane;vesicles \n", + " CD9 | score: 0.6428722351750686 | OC annot: membrane;vesicles \n", + " SLC1A3 | score: 0.6392028450953957 | OC annot: membrane;vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TMEM245 nearest genes:\n", + " EMC1 | score: 0.782172540682746 | OC annot: er \n", + " EMC3 | score: 0.7341954736326658 | OC annot: er \n", + " MMGT1 | score: 0.7322330412215641 | OC annot: er \n", + " SRPRB | score: 0.6558774117870102 | OC annot: er \n", + " EMC10 | score: 0.6329191499248996 | OC annot: er \n", + " REEP5 | score: 0.627543809942336 | OC annot: er \n", + " STIM1 | score: 0.5672712014228136 | OC annot: er \n", + " LNPK | score: 0.5450654438101074 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TMEM41A nearest genes:\n", + " LMAN1 | score: 0.7646116364966192 | OC annot: vesicles \n", + " SCD | score: 0.7259653584129308 | OC annot: vesicles \n", + " RABL3 | score: 0.6963714148140795 | OC annot: vesicles \n", + " ELOVL5 | score: 0.6928751680979759 | OC annot: vesicles \n", + " ERG28 | score: 0.6653847568751026 | OC annot: vesicles \n", + " STX10 | score: 0.640103880840701 | OC annot: vesicles \n", + " SLC7A1 | score: 0.6153054712095866 | OC annot: vesicles \n", + " well_H5_protein | score: 0.6083163722466514 | OC annot: ORF \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TMEM209 nearest genes:\n", + " RANBP2 | score: 0.7162073609834287 | OC annot: nuclear_membrane;big_aggregates \n", + " SYNE3 | score: 0.6822384859477664 | OC annot: nuclear_membrane \n", + " NUP35 | score: 0.6746813603942349 | OC annot: nuclear_membrane \n", + " NUP107 | score: 0.6168157343512022 | OC annot: nuclear_membrane \n", + " COMTD1 | score: 0.5568857501392214 | OC annot: ORF \n", + " KIAA1522 | score: 0.5518373263257675 | OC annot: ORF \n", + " LEMD3 | score: 0.5518270876852913 | OC annot: nuclear_membrane \n", + " SEC24D | score: 0.5393038813332391 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZZEF1 nearest genes:\n", + " ZNF598 | score: 0.9044098521546364 | OC annot: cytoplasmic \n", + " AKAP13 | score: 0.9008194288146301 | OC annot: cytoplasmic \n", + " AGO1 | score: 0.8825852470776903 | OC annot: cytoplasmic \n", + " NEMF | score: 0.8807614583127528 | OC annot: cytoplasmic \n", + " GRK2 | score: 0.8643492165817898 | OC annot: cytoplasmic \n", + " PDPK1 | score: 0.8561953492352351 | OC annot: cytoplasmic \n", + " DNM1 | score: 0.8549107337671303 | OC annot: cytoplasmic \n", + " MYO9B | score: 0.8529307938328681 | OC annot: cytoplasmic \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KIAA1522 nearest genes:\n", + " SLC44A2 | score: 0.7770869424097445 | OC annot: membrane \n", + " SEPTIN5 | score: 0.7164463172278682 | OC annot: membrane \n", + " SUSD5 | score: 0.704259016956014 | OC annot: ORF \n", + " PHLDB1 | score: 0.5752695424263902 | OC annot: membrane \n", + " ITSN2 | score: 0.5734281822298031 | OC annot: membrane;cell_contact \n", + " KIAA0319L | score: 0.5620153989318182 | OC annot: vesicles \n", + " TVP23C | score: 0.554297678214488 | OC annot: ORF \n", + " SLC35F2 | score: 0.5536365422170145 | OC annot: ORF \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPZL1 nearest genes:\n", + " GJA1 | score: 0.8926439502851992 | OC annot: cell_contact \n", + " PNPLA2 | score: 0.6332948144785123 | OC annot: vesicles \n", + " DCP2 | score: 0.5789654418330679 | OC annot: vesicles \n", + " TJP1 | score: 0.5608436673655813 | OC annot: cell_contact \n", + " DCP1A | score: 0.5424217295833258 | OC annot: vesicles \n", + " FTH1 | score: 0.5184367836485115 | OC annot: vesicles \n", + " DDX6 | score: 0.4979397147270107 | OC annot: big_aggregates \n", + " PHLDB1 | score: 0.4835976671929144 | OC annot: membrane \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TMEM184C nearest genes:\n", + " TSPAN3 | score: 0.9163788197095625 | OC annot: ORF \n", + " TSPAN6 | score: 0.8939167853854877 | OC annot: ORF \n", + " TPRA1 | score: 0.7953723868025155 | OC annot: ORF \n", + " SFT2D3 | score: 0.7496005611577852 | OC annot: ORF \n", + " SPNS1 | score: 0.6849853539525421 | OC annot: vesicles \n", + " TMEM192 | score: 0.6665728679278552 | OC annot: vesicles \n", + " DNAJC11 | score: 0.6492007562393566 | OC annot: mitochondria \n", + " LAMP2 | score: 0.6343788840913568 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TVP23C nearest genes:\n", + " MAN1A2 | score: 0.7337693715506618 | OC annot: vesicles \n", + " COMTD1 | score: 0.7110155275219708 | OC annot: ORF \n", + " ELOVL1 | score: 0.7049623438481825 | OC annot: vesicles \n", + " COMMD2 | score: 0.6923458359538682 | OC annot: vesicles \n", + " COMMD6 | score: 0.6884494789528093 | OC annot: vesicles \n", + " MFSD5 | score: 0.6879611346345407 | OC annot: ORF \n", + " PI4K2A | score: 0.6735303284798994 | OC annot: vesicles \n", + " SC5D | score: 0.6719115617803798 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SFT2D2 nearest genes:\n", + " SCAMP4 | score: 0.8542980447568638 | OC annot: ORF \n", + " SLC16A2 | score: 0.755185503047515 | OC annot: membrane;vesicles \n", + " VAMP3 | score: 0.724145375572953 | OC annot: membrane;vesicles \n", + " SLC7A6 | score: 0.7008783611901465 | OC annot: membrane;vesicles \n", + " SLC46A1 | score: 0.6546661760505301 | OC annot: membrane;vesicles \n", + " SLC1A5 | score: 0.6496341177953022 | OC annot: membrane;vesicles \n", + " SCAMP4 | score: 0.6435447078294111 | OC annot: vesicles;membrane \n", + " CD276 | score: 0.6205277677147549 | OC annot: ORF \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMTM4 nearest genes:\n", + " CD276 | score: 0.8843862969148798 | OC annot: ORF \n", + " YES1 | score: 0.8184243527501599 | OC annot: membrane \n", + " PIP5K1A | score: 0.8129703008229727 | OC annot: membrane \n", + " SLC7A6 | score: 0.7792560468672773 | OC annot: membrane;vesicles \n", + " KLRG2 | score: 0.7716309134495088 | OC annot: ORF \n", + " SLC5A6 | score: 0.7697986541370735 | OC annot: membrane;vesicles \n", + " ATP2B1 | score: 0.7690910343349567 | OC annot: membrane \n", + " MYO1C | score: 0.7367052606401117 | OC annot: membrane \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SCAMP3 nearest genes:\n", + " VTI1A | score: 0.8058870791559639 | OC annot: vesicles \n", + " ATP6AP1 | score: 0.774333698038128 | OC annot: vesicles \n", + " ATP6AP2 | score: 0.764188056747871 | OC annot: vesicles \n", + " STX12 | score: 0.7006159249263502 | OC annot: vesicles \n", + " AGTRAP | score: 0.6526921229528642 | OC annot: vesicles;golgi \n", + " ATP6V0A1 | score: 0.6501872036251085 | OC annot: vesicles \n", + " VAMP2 | score: 0.6464713738467239 | OC annot: membrane;vesicles \n", + " VAMP8 | score: 0.6422138590919367 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CD151 nearest genes:\n", + " PIP4P1 | score: 0.7854568528778665 | OC annot: vesicles \n", + " SLC2A1 | score: 0.7545506212488908 | OC annot: vesicles \n", + " SLC16A2 | score: 0.7247093301663649 | OC annot: membrane;vesicles \n", + " HLA-A | score: 0.6944801871162429 | OC annot: membrane;vesicles \n", + " SLC1A5 | score: 0.6692968173553022 | OC annot: membrane;vesicles \n", + " TFRC | score: 0.6649911873852573 | OC annot: membrane;vesicles \n", + " ATP1A1 | score: 0.6528571579951367 | OC annot: vesicles \n", + " CD151 | score: 0.6517011517368207 | OC annot: membrane;vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SLC35F1 nearest genes:\n", + " SLC35F2 | score: 0.8122641976225794 | OC annot: ORF \n", + " SLC35F2 | score: 0.7020967320868265 | OC annot: vesicles \n", + " KIAA0319L | score: 0.6504153516897591 | OC annot: vesicles \n", + " LNPK | score: 0.5678767022629629 | OC annot: vesicles \n", + " ATG9A | score: 0.5287882653546235 | OC annot: vesicles \n", + " SCAMP1 | score: 0.5254696579488072 | OC annot: golgi;vesicles \n", + " SCAMP3 | score: 0.5190250874728677 | OC annot: ORF \n", + " ATP6V0D1 | score: 0.5188225946134251 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SLC35F2 nearest genes:\n", + " KIAA0319L | score: 0.8637431662566397 | OC annot: vesicles \n", + " SLC35F1 | score: 0.8122641976225794 | OC annot: ORF \n", + " TMEM87A | score: 0.6595041691336665 | OC annot: vesicles;golgi \n", + " CMTM7 | score: 0.6351929921072356 | OC annot: ORF \n", + " TVP23C | score: 0.6259600230564497 | OC annot: ORF \n", + " pML545 | score: 0.620334736382858 | OC annot: ORF \n", + " NUF2 | score: 0.5941440471960097 | OC annot: cytoskeleton \n", + " PI4K2A | score: 0.5647546702837197 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CMTM7 nearest genes:\n", + " pML545 | score: 0.8414962629751966 | OC annot: ORF \n", + " SLC16A10 | score: 0.816229044173187 | OC annot: vesicles;membrane \n", + " SLC4A2 | score: 0.7716173956040394 | OC annot: membrane \n", + " SVIL | score: 0.7283770664925101 | OC annot: membrane \n", + " IGF1R | score: 0.7181668802221329 | OC annot: membrane \n", + " SLC26A6 | score: 0.7140500697450235 | OC annot: membrane;vesicles \n", + " ORAI1 | score: 0.6969858324561907 | OC annot: membrane \n", + " NUF2 | score: 0.6820441150183214 | OC annot: cytoskeleton \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SCAMP4 nearest genes:\n", + " SFT2D2 | score: 0.8542980447568638 | OC annot: ORF \n", + " SCAMP4 | score: 0.8455050843898695 | OC annot: vesicles;membrane \n", + " VAMP3 | score: 0.7431641456285305 | OC annot: membrane;vesicles \n", + " ESYT1 | score: 0.6795592783584903 | OC annot: membrane;er \n", + " VAMP2 | score: 0.647023136246985 | OC annot: membrane;vesicles \n", + " SLC16A2 | score: 0.5781865071383153 | OC annot: membrane;vesicles \n", + " SLC1A5 | score: 0.572310421409287 | OC annot: membrane;vesicles \n", + " MYO1B | score: 0.5654959762369448 | OC annot: membrane \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TSPAN6 nearest genes:\n", + " CD81 | score: 0.7326389659751747 | OC annot: membrane;vesicles \n", + " SPG21 | score: 0.7168265934345555 | OC annot: vesicles \n", + " CD9 | score: 0.6561401240835337 | OC annot: membrane;vesicles \n", + " TMEM41A | score: 0.6083163722466514 | OC annot: ORF \n", + " LMAN1 | score: 0.6022832368476057 | OC annot: vesicles \n", + " SLC19A1 | score: 0.5814683838536313 | OC annot: membrane;vesicles \n", + " RABL3 | score: 0.5709538021836296 | OC annot: vesicles \n", + " CD151 | score: 0.5594402618124354 | OC annot: membrane;vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TPRA1 nearest genes:\n", + " TMEM184C | score: 0.7953723868025155 | OC annot: ORF \n", + " CLCN7 | score: 0.7585604936859593 | OC annot: vesicles \n", + " TSPAN3 | score: 0.7522184246952881 | OC annot: ORF \n", + " SFT2D3 | score: 0.736427743258703 | OC annot: ORF \n", + " MCOLN1 | score: 0.7238928893420639 | OC annot: vesicles \n", + " TSPAN6 | score: 0.7128011173713963 | OC annot: ORF \n", + " SPNS1 | score: 0.7056238145434247 | OC annot: vesicles \n", + " SLC11A2 | score: 0.7026920191482058 | OC annot: vesicles \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLT8D1 nearest genes:\n", + " GNL3 | score: 0.7570999762176368 | OC annot: nucleolus_gc \n", + " RRP1 | score: 0.6249678283168596 | OC annot: nucleolus_gc \n", + " NPM3 | score: 0.5765090089101162 | OC annot: nucleolus_gc \n", + " FBL | score: 0.5610833174981389 | OC annot: nucleolus_fc_dfc \n", + " NPM1 | score: 0.5083272506609682 | OC annot: nucleolus_gc \n", + " NOL10 | score: 0.4920334264575916 | OC annot: nucleolus_gc \n", + " MYBBP1A | score: 0.4550657782223265 | OC annot: nucleolus_gc \n", + " PES1 | score: 0.45462583451046545 | OC annot: nucleolus_gc \n", + "\n" + ] + } + ], + "source": [ + "# for orphan protein, find its similarity score to reference organelle marker and output plot; also output nearest genes by distance, their scores, and OpenCell annotation\n", + "# output new dict with aggregate quantification of similarity scores\n", + "\n", + "agg_similarity_score = pd.DataFrame()\n", + "\n", + "for goi in orphans:\n", + " df_goi = similarity_scores_df[goi]\n", + "\n", + " org_scores = {\n", + " 'ER': er_markers_in_OC[goi],\n", + " 'ERGIC': ergic_markers_in_OC[goi],\n", + " 'Golgi': golgi_markers_in_OC[goi],\n", + " 'Lysosome': lyso_markers_in_OC[goi],\n", + " 'Mitochondria': mito_markers_in_OC[goi],\n", + " 'Early endo.': ee_markers_in_OC[goi],\n", + " 'Plasma Mb': pm_markers_in_OC[goi],\n", + " 'Actin cortex': actin_markers_in_OC[goi],\n", + " 'Peroxisome': perox_markers_in_OC[goi],\n", + " 'Cytosol': cytosol_markers_in_OC[goi],\n", + " 'Nucleus': nucleus_markers_in_OC[goi],\n", + " 'Nuclear pore': nuclear_pore_markers_in_OC[goi],\n", + " 'Nucleolus': nucleolus_markers_in_OC[goi]\n", + " }\n", + "\n", + " fig = plt.figure(figsize=(8, 4))\n", + " ax = fig.add_axes([0, 0, 1, 1])\n", + " bp = ax.boxplot(org_scores.values(), showfliers=False)\n", + "\n", + " for count, dict_key in enumerate(org_scores):\n", + " y = org_scores[dict_key]\n", + " x = np.random.normal(count + 1, 0.05, size=len(y))\n", + " plt.plot(x, y, 'b.', alpha=0.3)\n", + "\n", + " # get whisker boundaries\n", + " box_median = np.median(y)\n", + " upper_quartile = np.percentile(y, 75)\n", + " lower_quartile = np.percentile(y, 25)\n", + "\n", + " iqr = upper_quartile - lower_quartile\n", + " upper_whisker = y[y<=upper_quartile+1.5*iqr].max()\n", + " lower_whisker = y[y>=lower_quartile-1.5*iqr].min()\n", + " # build dict\n", + " agg_similarity_score.loc[goi, dict_key] = upper_whisker\n", + "\n", + " \n", + "\n", + " ax.set_xticklabels(list(org_scores.keys()),\n", + " rotation=45, fontsize=8)\n", + "\n", + " ax.set_ylim(-0.3, 1)\n", + " ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)\n", + " plt.title(goi)\n", + " plt.ylabel('Similarity score')\n", + " plt.show()\n", + "\n", + " print(goi, 'nearest genes:\\n', \n", + " knn_num_df[goi].prot_nn0, '| score:', knn_num_df[goi].dist0, '| OC annot: ', knn_num_df[goi].loc_grade1_nn0, '\\n',\n", + " knn_num_df[goi].prot_nn1, '| score:', knn_num_df[goi].dist1, '| OC annot: ', knn_num_df[goi].loc_grade1_nn1, '\\n',\n", + " knn_num_df[goi].prot_nn2, '| score:', knn_num_df[goi].dist2, '| OC annot: ', knn_num_df[goi].loc_grade1_nn2, '\\n',\n", + " knn_num_df[goi].prot_nn3, '| score:', knn_num_df[goi].dist3, '| OC annot: ', knn_num_df[goi].loc_grade1_nn3, '\\n',\n", + " knn_num_df[goi].prot_nn4, '| score:', knn_num_df[goi].dist4, '| OC annot: ', knn_num_df[goi].loc_grade1_nn4, '\\n',\n", + " knn_num_df[goi].prot_nn5, '| score:', knn_num_df[goi].dist5, '| OC annot: ', knn_num_df[goi].loc_grade1_nn5, '\\n',\n", + " knn_num_df[goi].prot_nn6, '| score:', knn_num_df[goi].dist6, '| OC annot: ', knn_num_df[goi].loc_grade1_nn6, '\\n',\n", + " knn_num_df[goi].prot_nn7, '| score:', knn_num_df[goi].dist7, '| OC annot: ', knn_num_df[goi].loc_grade1_nn7, '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ERERGICGolgiLysosomeMitochondriaEarly endo.Plasma MbActin cortexPeroxisomeCytosolNucleusNuclear poreNucleolus
ANKRD460.7473040.1733780.0516680.1820980.0939000.2001160.3151420.1469270.0305350.1697810.3446810.1538340.154763
C4orf30.8346700.2064410.0056630.1816270.0769570.2305200.2414110.1256470.1474100.0917330.2369370.0876890.106372
SFT2D30.2698240.4014620.5478720.5922480.5183710.4499230.3077440.2476360.3929760.2679830.4080260.3281570.174303
KLRG20.2804780.1444930.1910570.1041890.1643040.0008890.8566750.2909000.1164360.4208960.3300420.1163420.205313
NAT140.7617090.2275320.1061340.1875530.1699020.1893930.2804960.0787170.0526030.1857150.3593840.1574940.213015
\n", + "
" + ], + "text/plain": [ + " ER ERGIC Golgi Lysosome Mitochondria Early endo. \\\n", + "ANKRD46 0.747304 0.173378 0.051668 0.182098 0.093900 0.200116 \n", + "C4orf3 0.834670 0.206441 0.005663 0.181627 0.076957 0.230520 \n", + "SFT2D3 0.269824 0.401462 0.547872 0.592248 0.518371 0.449923 \n", + "KLRG2 0.280478 0.144493 0.191057 0.104189 0.164304 0.000889 \n", + "NAT14 0.761709 0.227532 0.106134 0.187553 0.169902 0.189393 \n", + "\n", + " Plasma Mb Actin cortex Peroxisome Cytosol Nucleus \\\n", + "ANKRD46 0.315142 0.146927 0.030535 0.169781 0.344681 \n", + "C4orf3 0.241411 0.125647 0.147410 0.091733 0.236937 \n", + "SFT2D3 0.307744 0.247636 0.392976 0.267983 0.408026 \n", + "KLRG2 0.856675 0.290900 0.116436 0.420896 0.330042 \n", + "NAT14 0.280496 0.078717 0.052603 0.185715 0.359384 \n", + "\n", + " Nuclear pore Nucleolus \n", + "ANKRD46 0.153834 0.154763 \n", + "C4orf3 0.087689 0.106372 \n", + "SFT2D3 0.328157 0.174303 \n", + "KLRG2 0.116342 0.205313 \n", + "NAT14 0.157494 0.213015 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save the top similarity score vs each organelle for each orphan protein\n", + "\n", + "agg_similarity_score.to_csv(\"aggregate_similarity_score.csv\")\n", + "agg_similarity_score.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ERERGICGolgiLysosomeMitochondriaEarly endo.Plasma MbActin cortexPeroxisomeCytosolNucleusNuclear poreNucleolus
ANKRD460.7473040.1733780.0516680.1820980.0939000.2001160.3151420.1469270.0305350.1697810.3446810.1538340.154763
C4orf30.8346700.2064410.0056630.1816270.0769570.2305200.2414110.1256470.1474100.0917330.2369370.0876890.106372
NAT140.7617090.2275320.1061340.1875530.1699020.1893930.2804960.0787170.0526030.1857150.3593840.1574940.213015
ODR40.7548560.2003650.0560160.1482050.1418630.1619490.2996260.0703900.0151600.1893200.3673830.1479210.204652
TMEM2450.6558770.1436600.0862730.2261770.1885180.1759530.4504090.1698680.0613700.0466000.2236060.1629870.162854
\n", + "
" + ], + "text/plain": [ + " ER ERGIC Golgi Lysosome Mitochondria Early endo. \\\n", + "ANKRD46 0.747304 0.173378 0.051668 0.182098 0.093900 0.200116 \n", + "C4orf3 0.834670 0.206441 0.005663 0.181627 0.076957 0.230520 \n", + "NAT14 0.761709 0.227532 0.106134 0.187553 0.169902 0.189393 \n", + "ODR4 0.754856 0.200365 0.056016 0.148205 0.141863 0.161949 \n", + "TMEM245 0.655877 0.143660 0.086273 0.226177 0.188518 0.175953 \n", + "\n", + " Plasma Mb Actin cortex Peroxisome Cytosol Nucleus \\\n", + "ANKRD46 0.315142 0.146927 0.030535 0.169781 0.344681 \n", + "C4orf3 0.241411 0.125647 0.147410 0.091733 0.236937 \n", + "NAT14 0.280496 0.078717 0.052603 0.185715 0.359384 \n", + "ODR4 0.299626 0.070390 0.015160 0.189320 0.367383 \n", + "TMEM245 0.450409 0.169868 0.061370 0.046600 0.223606 \n", + "\n", + " Nuclear pore Nucleolus \n", + "ANKRD46 0.153834 0.154763 \n", + "C4orf3 0.087689 0.106372 \n", + "NAT14 0.157494 0.213015 \n", + "ODR4 0.147921 0.204652 \n", + "TMEM245 0.162987 0.162854 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set orphans in the same order as Figure 4\n", + "\n", + "list_order = ['ANKRD46',\n", + " 'C4orf3',\n", + " 'NAT14',\n", + " 'ODR4',\n", + " 'TMEM245',\n", + " 'TMEM41A',\n", + " 'TP53I11',\n", + " 'ZZEF1',\n", + " 'CD151',\n", + " 'CD99',\n", + " 'SFT2D2',\n", + " 'CD276',\n", + " 'KLRG2',\n", + " 'SUSD5',\n", + " 'TMEM184C',\n", + " 'TSPAN3',\n", + " 'MFSD5',\n", + " 'TVP23C',\n", + " 'SCAMP3',\n", + " 'TSPAN6',\n", + " 'TMEM209',\n", + " 'CMTM4',\n", + " 'CMTM7',\n", + " 'SCAMP4',\n", + " 'SLC35F1',\n", + " 'SLC35F2',\n", + " 'SFT2D3',\n", + " 'KIAA1522',\n", + " 'MPZL1',\n", + " 'TPRA1',\n", + " 'GLT8D1']\n", + "\n", + "# re-index scoring matrix\n", + "\n", + "agg_similarity_score_sorted = agg_similarity_score.reindex(list_order)\n", + "agg_similarity_score_sorted.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
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ERERGICGolgiLysosomeMitochondriaEarly endo.Plasma MbActin cortexPeroxisomeCytosolNucleusNuclear poreNucleolus
ANKRD460.7473040.00.00.00.00.00.00.00.00.00.00.00.0
C4orf30.8346700.00.00.00.00.00.00.00.00.00.00.00.0
NAT140.7617090.00.00.00.00.00.00.00.00.00.00.00.0
ODR40.7548560.00.00.00.00.00.00.00.00.00.00.00.0
TMEM2450.6558770.00.00.00.00.00.00.00.00.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " ER ERGIC Golgi Lysosome Mitochondria Early endo. \\\n", + "ANKRD46 0.747304 0.0 0.0 0.0 0.0 0.0 \n", + "C4orf3 0.834670 0.0 0.0 0.0 0.0 0.0 \n", + "NAT14 0.761709 0.0 0.0 0.0 0.0 0.0 \n", + "ODR4 0.754856 0.0 0.0 0.0 0.0 0.0 \n", + "TMEM245 0.655877 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Plasma Mb Actin cortex Peroxisome Cytosol Nucleus Nuclear pore \\\n", + "ANKRD46 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "C4orf3 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "NAT14 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "ODR4 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "TMEM245 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Nucleolus \n", + "ANKRD46 0.0 \n", + "C4orf3 0.0 \n", + "NAT14 0.0 \n", + "ODR4 0.0 \n", + "TMEM245 0.0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# threshold similarity score heatmap keeping only scores >= 0.6\n", + "\n", + "agg_similarity_score_sorted_threshold_0p6 = agg_similarity_score_sorted\n", + "agg_similarity_score_sorted_threshold_0p6[agg_similarity_score_sorted_threshold_0p6 < 0.6] = 0\n", + "agg_similarity_score_sorted_threshold_0p6.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot thresholded heatmap and save PDF for Suppl Figure S6C\n", + "\n", + "plt.figure(figsize=(5,8))\n", + "sns.heatmap(data=agg_similarity_score_sorted_threshold_0p6, cmap=\"YlGnBu\", vmin=0.4)\n", + "plt.savefig(\"Suppl_Figure_S6C_cytoself_scores_heatmap.pdf\")\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "kibeom_env", + "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": "3.10.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/organelle_marker_analysis/Suppl_Figure_S6C_cytoself_scores_heatmap.pdf b/analysis/organelle_marker_analysis/Suppl_Figure_S6C_cytoself_scores_heatmap.pdf new file mode 100644 index 0000000..0f345ef Binary files /dev/null and b/analysis/organelle_marker_analysis/Suppl_Figure_S6C_cytoself_scores_heatmap.pdf differ diff --git a/analysis/organelle_marker_analysis/Supplementary_Table_1_literature-curated_organelle_ground-truth.xlsx b/analysis/organelle_marker_analysis/Supplementary_Table_1_literature-curated_organelle_ground-truth.xlsx new file mode 100644 index 0000000..e01616c Binary files /dev/null and b/analysis/organelle_marker_analysis/Supplementary_Table_1_literature-curated_organelle_ground-truth.xlsx differ diff --git a/analysis/organelle_marker_analysis/aggregate_similarity_score.csv b/analysis/organelle_marker_analysis/aggregate_similarity_score.csv new file mode 100644 index 0000000..fe874ba --- /dev/null +++ b/analysis/organelle_marker_analysis/aggregate_similarity_score.csv @@ -0,0 +1,32 @@ +,ER,ERGIC,Golgi,Lysosome,Mitochondria,Early endo.,Plasma Mb,Actin cortex,Peroxisome,Cytosol,Nucleus,Nuclear pore,Nucleolus 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+,ANKRD46,C4orf3,CD151,CD276,CD99,CLMN,CMTM4,CMTM7,COMTD1,GLT8D1,GRK6,KIAA1522,KLRG2,MFSD5,MPZL1,NAT14,ODR4,RNF149,SCAMP3,SCAMP4,SFT2D2,SFT2D3,SLC35F1,SLC35F2,SMIM13,SUSD5,TMEM184C,TMEM209,TMEM245,TMEM41A,TP53I11,TPRA1,TSPAN3,TSPAN6,TVP23C,ZZEF1,pML544,pML545,pML548,pML549,well_H5_protein,well_H8_protein,AAMP,AATF,ABCE1,ABR,ACAP2,ACAP3,ACLY,ACTB,ACTG1,ACTN1,ACTN4,ACTR2,ACTR5,AGAP1,AGAP3,AGO1,AGO2,AGPAT2,AGTRAP,AHCY,AHSA1,AKAP12,AKAP13,AKAP8L,AKR1A1,AKT1,AKT1S1,AKT2,ALCAM,ALDH16A1,ALDH9A1,ALG2,AMBRA1,ANAPC11,ANAPC16,ANAPC2,ANAPC4,ANKRD10,ANKRD11,ANKRD13A,ANKRD13C,ANKRD17,ANKRD27,ANKRD28,ANKRD39,ANKRD40,ANKRD46,ANKRD52,ANKRD54,ANKZF1,ANXA11,AP2B1,AP2S1,APC,APPL1,APPL2,ARAF,ARF1,ARF3,ARF4,ARF5,ARF6,ARFGAP1,ARFGAP2,ARFGAP3,ARFGEF1,ARFGEF2,ARFIP1,ARFIP2,ARHGAP1,ARHGAP11A,ARHGAP17,ARHGAP21,ARHGAP35,ARHGAP8,ARHGEF12,ARHGEF18,ARHGEF7,ARID1A,ARID1B,ARL1,ARL13B,ARL14EP,ARL2,ARL2BP,ARL3,ARL6,ARL6IP1,ARL6IP4,ARL6IP5,ARL6IP6,ARL8A,ARL8B,ARPC2,ARPC3,ASS1,ATG101,ATG12,ATG13,ATG14,ATG2A,ATG2B,ATG3,ATG4B,ATG7,ATG9A,ATL2,ATL3,ATM,ATP1A1,ATP2B1,ATP6AP1,ATP6AP2,ATP6V0A1,ATP6V0A2,ATP6V0D1,ATP6V1A,ATP6V1B2,ATP6V1D,ATP6V1E1,ATP6V1F,ATP6V1G1,ATP6V1H,ATXN10,ATXN2L,ATXN3,AURKB,BAG4,BAG6,BAIAP2,BAIAP2L1,BANF1,BAZ1A,BAZ1B,BCAP31,BCAR1,BECN1,BET1,BET1L,BIN1,BIN3,BLM,BLVRA,BLVRB,BMS1,BNIP1,BOP1,BPNT1,BRD2,BRD3,BRD4,BTF3,BUB1,BUB1B,BUD23,BYSL,C12orf57,C14orf119,C18orf21,C19orf53,C4orf32,C7orf50,C8orf33,C9orf16,C9orf78,CAD,CALD1,CALM1,CALM2,CALM3,CAMK1,CAMK2G,CANX,CAPRIN1,CAPZB,CASP2,CASP3,CASP6,CASP7,CBX1,CCAR1,CCDC22,CCDC47,CCDC86,CCDC93,CCNB1,CCT2,CCT3,CCT4,CCT5,CCT6A,CCT7,CCT8,CCZ1B,CD151,CD2BP2,CD63,CD81,CD9,CDC16,CDC20,CDC23,CDC26,CDC27,CDC37,CDC42,CDC42BPB,CDC7,CDH2,CDIPT,CDK1,CDK11B,CDK12,CDK13,CDK2,CDK4,CDK5,CDK7,CDK9,CDKN2A,CDS1,CDS2,CENPA,CENPM,CENPW,CEP131,CEP135,CEP164,CEP170,CEP192,CEP250,CEP350,CEP72,CEP78,CEP85,CEPT1,CERS5,CERS6,CERT1,CETN2,CETN3,CFAP298,CHEK1,CHEK2,CHM,CHMP1A,CHMP2A,CHMP2B,CHMP3,CHMP4A,CHMP4B,CHMP6,CHMP7,CHN1,CHPT1,CHUK,CKAP2,CKAP2L,CKAP4,CKAP5,CKB,CLASP1,CLASP2,CLCN7,CLINT1,CLIP1,CLK2,CLK3,CLNS1A,CLPTM1,CLTA,CLTB,CLTC,CNBP,CNN2,COIL,COMMD1,COMMD2,COMMD4,COMMD6,COPA,COPB2,COPE,COPG1,COPG2,COPZ1,CORO1B,CORO1C,CPSF6,CSE1L,CSK,CSNK1A1,CSNK1D,CSNK1E,CSNK1G3,CSNK2A1,CSNK2A2,CSNK2B,CTBP1,CTBP2,CTCF,CTNNB1,CTNNBL1,CTPS1,CTTN,CUTC,CYB5R3,CYP51A1,CZIB,DAD1,DAPK3,DBNDD1,DBNL,DCP1A,DCP1B,DCP2,DCTN2,DDB1,DDOST,DDX21,DDX39B,DDX47,DDX5,DDX56,DDX6,DEGS1,DEPDC1B,DHCR24,DHX37,DHX9,DIAPH1,DKC1,DNAJA1,DNAJA2,DNAJB1,DNAJB2,DNAJB4,DNAJB6,DNAJC11,DNAJC13,DNAJC17,DNAJC18,DNAJC2,DNAJC24,DNAJC5,DNAJC7,DNAJC8,DNAJC9,DNM1,DNM1L,DNM2,DNTTIP1,DOCK11,DOCK7,DPH2,DRG1,DYNC1H1,DYNC1I2,DYNC1LI1,DYNC1LI2,DYNC2LI1,DYNLL1,DYNLL2,EBNA1BP2,EBP,ECPAS,ECT2,EDC3,EDC4,EDF1,EEA1,EEF1G,EFTUD2,EIF2AK2,EIF2S3,EIF3B,EIF3E,EIF3G,EIF3I,EIF3K,EIF3M,EIF4A1,EIF4A3,EIF5,ELOVL1,ELOVL5,ELOVL6,EMC1,EMC10,EMC2,EMC3,EMC4,EMC7,EMC8,EMC9,EMG1,ENO1,ENY2,EPN1,EPN2,EPS15,ERG28,ERI3,ESD,ESYT1,EWSR1,EZR,FAM117B,FAM120A,FAM174C,FAM207A,FAM50A,FAM50B,FANCA,FARP1,FASN,FAU,FBL,FCHSD2,FDPS,FKBP5,FKBP8,FNBP1,FNBP1L,FTH1,FXR1,FXR2,FYN,G3BP1,G3BP2,G6PD,GAK,GAPDH,GAR1,GATAD1,GBF1,GDI1,GDI2,GET4,GGPS1,GJA1,GLUL,GNB1,GNL2,GNL3,GOLGA2,GOLGA7,GOLT1B,GORASP2,GOSR1,GOSR2,GPAT3,GPAT4,GPR107,GRB2,GRK2,GSK3A,GSK3B,GSPT1,GSTP1,GTF2F1,GTPBP4,GYG1,GYG2,GYS1,H1-0,H2AZ1,H2BC21,HDAC1,HDAC2,HDAC3,HEATR3,HGS,HIP1,HLA-A,HMGA1,HMGB2,HMGCS1,HMGN2,HMGN5,HNRNPA0,HNRNPC,HNRNPD,HNRNPH1,HNRNPL,HNRNPU,HOMER1,HSF1,HSP90AA1,HSP90AB1,HSP90B1,HSPA12A,HSPA14,HSPA1B,HSPA4,HSPA4L,HSPA8,HSPB1,HSPB11,HSPBP1,HSPH1,HUS1,IDI1,IGF1R,IGF2BP1,IGF2BP3,IKBKB,ILF3,ILK,IMP3,IMPA2,INO80,INPP4A,INPP5E,INPP5K,INPPL1,INTS14,INTS5,INTS9,IPO11,IPO13,IPO4,IPO5,IPO7,IPO8,IRAK1,IST1,ITPR3,ITSN1,ITSN2,JUN,JUNB,KDSR,KEAP1,KIAA0319L,KIAA1143,KIF11,KIF1B,KIF20A,KIF22,KIF2A,KIF2C,KIF3A,KIF3B,KIF4A,KIF5B,KIF7,KIFAP3,KIFBP,KIFC1,KLC1,KLC2,KLC4,KPNA1,KPNA2,KPNA3,KPNA4,KPNA6,KPNB1,KRAS,KRT18,KRTCAP2,LAMP1,LAMP2,LAMTOR2,LARP7,LBR,LDHA,LDHB,LEMD2,LEMD3,LGALS1,LIG1,LIMK2,LMAN1,LMNA,LMNB1,LNPK,LPCAT1,LPCAT2,LSM14A,LSM14B,LSS,LTV1,LZTS2,MAN1A2,MAP1LC3B,MAP2K1,MAP2K2,MAP2K3,MAP2K7,MAP3K7,MAP4,MAP4K4,MAPK1,MAPK14,MAPK3,MAPK8,MAPK9,MAPKAPK2,MAPKAPK3,MAPRE1,MARK2,MARK3,MARK4,MBOAT2,MBOAT7,MCM10,MCOLN1,MDH1,MECP2,MED10,MED11,MED14,MED15,MED17,MED18,MED19,MED20,MED21,MED22,MED24,MED25,MED27,MED28,MED29,MED31,MED4,MED6,MED8,MED9,MELK,METAP2,MFN1,MFN2,MFSD11,MFSD12,MIF,MINK1,MIS12,MKI67,MKNK1,MKNK2,MMGT1,MPHOSPH10,MRTO4,MSH6,MSN,MTMR1,MTMR2,MTOR,MVB12A,MVD,MVK,MYBBP1A,MYH10,MYH9,MYL12A,MYL12B,MYL6,MYL6B,MYO19,MYO1B,MYO1C,MYO1E,MYO6,MYO9A,MYO9B,NAF1,NANS,NAPA,NAPG,NCAPH,NCK1,NCK2,NCKAP1,NCKAP5L,NCL,NCLN,NCOA1,NECAP1,NECAP2,NEDD1,NEK9,NEMF,NEPRO,NET1,NFKB1,NFKB2,NHP2,NIFK,NMD3,NOL10,NOL11,NOM1,NONO,NOP10,NOP2,NOP53,NOP58,NPM1,NPM3,NRBP1,NSA2,NSDHL,NUCKS1,NUF2,NUFIP2,NUMA1,NUP107,NUP153,NUP155,NUP214,NUP35,NUP98,NUTF2,NVL,NXF1,NXT2,OCRL,ORAI1,OSBP,OSBPL11,OSBPL8,OST4,OSTC,OXSR1,PABPC4,PACSIN2,PACSIN3,PAIP1,PAK1,PAK2,PAK4,PARP1,PASK,PATZ1,PBK,PCNA,PDAP1,PDCD11,PDCD6,PDCD6IP,PDCL3,PDPK1,PES1,PEX3,PFDN6,PFN1,PGK1,PGRMC1,PGRMC2,PHAX,PHF10,PHGDH,PHKG2,PHLDB1,PI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diff --git a/analysis/organelle_marker_analysis/tspan6_knn_opencell_preds_manualfilter_True__representation_vqvec2__rotations_False__mode_correlation__pcadim_200.csv b/analysis/organelle_marker_analysis/tspan6_knn_opencell_preds_manualfilter_True__representation_vqvec2__rotations_False__mode_correlation__pcadim_200.csv new file mode 100644 index 0000000..cc7dd47 --- /dev/null +++ b/analysis/organelle_marker_analysis/tspan6_knn_opencell_preds_manualfilter_True__representation_vqvec2__rotations_False__mode_correlation__pcadim_200.csv @@ -0,0 +1,29 @@ +,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41 +well_id,A1,E2,F7,G1,G2,F5,E3,B8,A4,C3,F6,C4,E5,C11,C5,A6,E7,A8,B9,B10,A9,E10,E11,A12,G6,B12,G8,F1,G9,C10,F2,C12,B3,B11,F4,C7,D1,D2,D3,D4,H5,H8 +protein,ANKRD46,C4orf3,CD151,CD276,CD99,CLMN,CMTM4,CMTM7,COMTD1,GLT8D1,GRK6,KIAA1522,KLRG2,MFSD5,MPZL1,NAT14,ODR4,RNF149,SCAMP3,SCAMP4,SFT2D2,SFT2D3,SLC35F1,SLC35F2,SMIM13,SUSD5,TMEM184C,TMEM209,TMEM245,TMEM41A,TP53I11,TPRA1,TSPAN3,TSPAN6,TVP23C,ZZEF1,pML544,pML545,pML548,pML549,well_H5_protein,well_H8_protein +orgIP_cluster_annotation,ER,ER,"ER, new analysis says plasma membrane",plasma membrane,plasma membrane,ER,recycling endosome,recycling endosome,mitochondrion,Golgi,ER,plasma membrane,plasma membrane,Golgi,plasma membrane,ER,ER,lysosome,ER,recycling endosome,plasma membrane,Golgi,recycling endosome,recycling endosome,ER,plasma membrane,lysosome,ER,ER,ER,ER,ER,lysosome,ER,trans-Golgi,endocytic plasma membrane,,,,,, +plate33_eyeballed_annotation,nice ER,nice ER,nice lysosome,"few cells, maybe some aggregates",negative,"dim ER, very few cells","plasma membrane, nice","nice, complex plasmam membrane + lysosome?","dim, maybe endo-lysosome","nucleolus, very few cells",unclear,negative,nice plasma membrane,aggregate,negative,nice ER,nice ER,"aggregates, very few cells",nice lysosome?,nice plasma membrane,recycling plasma membrane? nice,endolysosome? interesting signal inside,"nice, recycling endosome?","nice, recycling endosome?",negative,negative,weird lysosome,nuclear membrane,nice ER,aggregates,"nice, maybe ER",,nice lysosome,"complex, plasma membrane + lysosome",nice Golgi,cytoplasmic,,,,,, +prot_nn0,NAT14,ANKRD46,PIP4P1,MYO1C,SLC16A1,SVIL,CD276,pML545,PI4K2A,GNL3,IPO13,SLC44A2,FARP1,SLC37A4,GJA1,ODR4,NAT14,well_H8_protein,VTI1A,SFT2D2,SCAMP4,SLC18B1,SLC35F2,KIAA0319L,TP53I11,UTRN,TSPAN3,RANBP2,EMC1,LMAN1,SMIM13,TMEM184C,TSPAN6,TSPAN3,MAN1A2,ZNF598,REEP4,CMTM7,MAPKAPK3,TAF12,CD81,RNF149 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+loc_grade1_nn0,ORF,er,vesicles,membrane,membrane;vesicles,membrane,ORF,ORF,vesicles,nucleolus_gc,,membrane,membrane,golgi,cell_contact,ORF,ORF,ORF,vesicles,ORF,ORF,vesicles,ORF,vesicles,ORF,membrane,ORF,nuclear_membrane;big_aggregates,er,vesicles,ORF,ORF,ORF,ORF,vesicles,cytoplasmic,er,ORF,nucleoplasm;cytoplasmic,nucleoplasm,membrane;vesicles,ORF +loc_grade1_nn1,ORF,er,vesicles,ORF,membrane,membrane,membrane,vesicles;membrane,vesicles,nucleolus_gc,membrane;cytoplasmic;nucleoplasm,membrane,membrane,golgi,vesicles,ORF,ORF,ORF,vesicles,vesicles;membrane,membrane;vesicles,vesicles,vesicles,ORF,vesicles,ORF,ORF,nuclear_membrane,er,vesicles,er,vesicles,ORF,ORF,ORF,cytoplasmic,er,membrane,nucleoplasm;cytoplasmic,nucleoplasm,vesicles,ORF +loc_grade1_nn2,er,er,membrane;vesicles,membrane;vesicles,ORF,vesicles;membrane,membrane,membrane,ORF,nucleolus_gc,nucleoplasm;cytoplasmic,ORF,membrane,golgi,vesicles,er,er,ORF,vesicles,membrane;vesicles,membrane;vesicles,vesicles,vesicles,vesicles;golgi,vesicles,membrane,ORF,nuclear_membrane,er,vesicles,er,ORF,ORF,ORF,vesicles,cytoplasmic,ORF,vesicles;membrane,cytoplasmic;nucleoplasm,nucleoplasm,membrane;vesicles,nucleolus_gc +loc_grade1_nn3,er,er,membrane;vesicles,membrane;vesicles,membrane;vesicles,membrane,membrane;vesicles,membrane,ORF,nucleolus_fc_dfc,cytoplasmic;nucleoplasm,membrane,membrane,vesicles,cell_contact,er,er,ORF,vesicles,membrane;er,membrane;vesicles,vesicles,vesicles,ORF,er;vesicles,membrane,ORF,nuclear_membrane,er,vesicles,er,ORF,ORF,vesicles,vesicles,cytoplasmic,vesicles,membrane,nucleoplasm;cytoplasmic,nucleoplasm,ORF,vesicles +loc_grade1_nn4,er,er,membrane;vesicles,membrane,membrane;cytoplasmic,vesicles,ORF,membrane,membrane,nucleolus_gc,er,membrane;cell_contact,membrane;vesicles,ORF,vesicles,er,er,vesicles,vesicles;golgi,membrane;vesicles,membrane;vesicles,ORF,vesicles,ORF,er,membrane;vesicles,vesicles,ORF,er,vesicles,er,vesicles,vesicles,ORF,vesicles,cytoplasmic,cytoskeleton,cytoskeleton,cytoplasmic;nucleoplasm,nucleoplasm,vesicles,vesicles +loc_grade1_nn5,er,er,membrane;vesicles,membrane,membrane,ORF,membrane;vesicles,membrane;vesicles,mitochondria,nucleolus_gc,nucleoplasm;cytoplasmic,vesicles,ORF,ORF,vesicles,er,er,big_aggregates,vesicles,membrane;vesicles,membrane;vesicles,ORF,golgi;vesicles,ORF,er,membrane;vesicles,vesicles,ORF,er,vesicles,er,ORF,vesicles,vesicles,ORF,cytoplasmic,golgi;vesicles,er,cytoplasmic;nucleoplasm,nucleoplasm,membrane;vesicles,centrosome +loc_grade1_nn6,er,ORF,vesicles,ORF,membrane;vesicles,membrane,membrane,membrane,membrane,nucleolus_gc,nucleoplasm;cytoplasmic,ORF,membrane;vesicles,vesicles,big_aggregates,er,er,vesicles,membrane;vesicles,membrane;vesicles,vesicles;membrane,vesicles,ORF,cytoskeleton,er,membrane;vesicles,mitochondria,nuclear_membrane,er,vesicles,vesicles,vesicles,vesicles,vesicles,vesicles,cytoplasmic,golgi,ORF,nucleoplasm;cytoplasmic,nucleoplasm,vesicles,chromatin +loc_grade1_nn7,er,er,membrane;vesicles,membrane;vesicles,membrane;vesicles,membrane,membrane,cytoskeleton,ORF,nucleolus_gc,centrosome;vesicles,ORF,vesicles;membrane,golgi,membrane,er,er,centrosome;vesicles,vesicles,membrane,ORF,ORF,vesicles,vesicles,vesicles;membrane,membrane;vesicles,vesicles,vesicles,vesicles,ORF,er,vesicles,vesicles,vesicles,vesicles,cytoplasmic,golgi,membrane,nucleoplasm;cytoplasmic,nucleoplasm,membrane;vesicles,ORF diff --git a/cytoself/analysis/utils/compare_opencell_targets.py b/cytoself/analysis/utils/compare_opencell_targets.py new file mode 100644 index 0000000..7ffe0b1 --- /dev/null +++ b/cytoself/analysis/utils/compare_opencell_targets.py @@ -0,0 +1,45 @@ +""" +Run in project home dir as + python inference/compare_opencell_targets.py + +For a pretrained model in ``, read in the embeddings for +that model on the OpenCell dataset. This is the dataset the model was trained on + +Next get the new inference dataset, its crops, and the embeddings that were +computed in `inference/get_crop_features.py`. These embedding should have been +taken from model from ``. +""" + +import ipdb +from pathlib import Path +import torch + +def get_nearest_proteins(fname_crops, fname_crops_meta, dir_pretrained_model): + # results dirs + current_filename = Path(os.path.basename(__file__)) + dir_results = Path("inference/results") / current_filename.stem / dir_pretrained_model + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + dir_viz.mkdir(exist_ok=True, parents=True) + + # get embeddings + dir_embeddings = Path("inference/results/get_crop_features") / dir_pretrained_model + f_embeddings_vqvec2 = dir_embeddings / "embeddings_vqvec2.pt" + embeddings, labels = torch.load(f_embeddings_vqvec2) + df_meta = pd.read_csv(dir_embeddings / "crops_meta.csv") + + ipdb.set_trace() + pass + + + +if __name__=="__main__": + fname_crops = "inference/results/crop/crops.pt" + fname_crops_meta = "inference/results/crop/crops_meta.csv" + dir_pretrained_model = "results/20231011_train_all_no_nucdist" + + get_nearest_proteins( + fname_crops=fname_crops, + fname_crops_meta=fname_crops_meta, + dir_pretrained_model=dir_pretrained_model + ) diff --git a/cytoself/datamanager/opencell.py b/cytoself/datamanager/opencell.py index 7e064dd..2207221 100644 --- a/cytoself/datamanager/opencell.py +++ b/cytoself/datamanager/opencell.py @@ -3,6 +3,7 @@ from os.path import basename, dirname, join from typing import List, Optional, Sequence, Union +import ipdb import numpy as np import pandas as pd import torch @@ -10,7 +11,7 @@ from numpy.distutils.misc_util import is_sequence from numpy.typing import ArrayLike from pandas import DataFrame -from torch.utils.data import DataLoader +from torch.utils.data import DataLoader, WeightedRandomSampler from torchvision import transforms from tqdm import tqdm @@ -18,6 +19,16 @@ from cytoself.datamanager.preloaded_dataset import PreloadedDataset from cytoself.datamanager.utils.splitdata_on_fov import splitdata_on_fov +# these lyso annotations are based on my looking at it ... not used for any +# supervsion ... only for class balanced sampling +lyso_proteins = ['ARL8B','ATP6V1F','ATP6V1B2','LAMTOR2','LAMP1','LAMP2', + 'MFSD12','MFSD11','RAB7A','TMEM106B','TMEM192','TMEM63B' +] +lyso_proteins_maybe = ['SLC37A3', 'PTK7','RAB3D','VAMP4',] +golgi_trans_prots = ['ARF1','ARFGAP3','ARFGAP2','ARFGAP1','ARFIP1','ARFIP2','ARL1','CERT1', + 'COPB2','COPE','COPZ1','GOLGA2','GPR107','OSBP','RAB1A','RER1','SACM1L', + 'SLC30A5','SLC35E1','STX5'] + class DataManagerOpenCell(DataManagerBase): """ @@ -33,6 +44,7 @@ def __init__( fov_col: Optional[int] = -1, shuffle_seed: int = 1, intensity_adjustment: Optional[dict] = None, + sampling_strategy = None, ): """ Parameters @@ -51,6 +63,9 @@ def __init__( Rnadom seed to shuffle data intensity_adjustment : dict Intensity adjustment for each channel. + sampling_strategy : str + None, then random. 'class_weighted_X' where X is an int for the + strategy So far, only implemented class_weighted_0. """ super().__init__(basepath=basepath, data_split=data_split, shuffle_seed=shuffle_seed) self.label_col = label_col @@ -59,6 +74,8 @@ def __init__( self.train_variance = None self.val_variance = None self.test_variance = None + self.sampling_strategy = sampling_strategy + self.basepath = basepath # Make sure label is in the list as data splitting depends on label information. if 'label' not in channel_list: @@ -67,6 +84,7 @@ def __init__( self.channel_list = channel_list # Intensity adjustment is needed when using nucdist. + # raise NotImplementedError("fix the nucdist adjustment ") intensity_adjustment_default = {'pro': 1, 'nuc': 1, 'nucdist': 0.01} if isinstance(intensity_adjustment, dict): self.intensity_adjustment = intensity_adjustment @@ -216,6 +234,63 @@ def const_label_book(self, label_data: ArrayLike): """ self.unique_labels = np.unique(label_data[:, self.label_col]) + def get_data_loader_sampler(self, train_dataset, num_samples, + add_lysosome_class=True): + """ + + """ + if self.sampling_strategy is None: + return None + + elif self.sampling_strategy in (1,2,): + # some tests + df = pd.DataFrame(train_dataset.label, columns=['ensg_id','name','loc']) + locs = df['loc'].values + + # lets maually add lysosome as a tag here. it's a bit slow + if add_lysosome_class: + for i in range(len(locs)): + if df.iloc[i]['name'] in lyso_proteins + lyso_proteins_maybe: + locs[i] += ';lysosome' + if self.sampling_strategy == 2: + if df.iloc[i]['name'] in golgi_trans_prots: + locs[i] += ';golgi_trans' + + # this gets one row per protein (1311,) not per crop + df_lookup = df.groupby('name').sample(1) # one row per protein + locs_prots = df_lookup['loc'].values # at the prot + + # get unique localization labels + unique_locs = [l.split(";") for l in locs_prots] + unique_locs = np.unique([l for lst in unique_locs for l in lst]) + unique_locs = np.array([l for l in unique_locs if l!='']) + + # count appearances of each loc. `membrane;cytoskeleton` will be + # counted for both membrane and cytoskeleton + cnts = [] + for loc in unique_locs: + cnts.append(sum([loc in l for l in locs])) + + weights_loc = 1 / np.array(cnts) # (n_uniq_locs,) + weights_lookup = dict(zip(unique_locs, weights_loc)) + weights_per_sample = np.zeros(len(df)) + + # choose the max probability + for i in range(len(locs)): + loc_lst = locs[i].split(";") # all locs in this sample + if loc_lst == ['']: + continue + loc_lst_weights = [weights_lookup[l] for l in loc_lst] + weights_per_sample[i] = max(loc_lst_weights) + + sampler = WeightedRandomSampler(weights_per_sample, + num_samples=num_samples) + + return sampler + + else: + raise NotImplementedError() + def const_dataloader( self, batch_size: int = 32, @@ -297,9 +372,37 @@ def const_dataloader( train_label, train_data, transform_all, self.unique_labels, label_format, self.label_col ) _assert_dtype(train_dataset.label, train_dataset.label_format) + + sampler = self.get_data_loader_sampler(train_dataset, + num_samples=len(train_dataset)) + if sampler is not None: + shuffle = False + self.train_loader = DataLoader( - train_dataset, batch_size, shuffle=shuffle, num_workers=self.num_workers, **kwargs + train_dataset, batch_size, sampler=sampler, shuffle=shuffle, + num_workers=self.num_workers, **kwargs ) + + # manual checking that the sampler is behaving reasonable. + if 0: + df = pd.DataFrame(train_dataset.label, columns=['ensg_id','name','loc']) + df_lookup = df.groupby('name').sample(1) # one row per protein + self.num_workers = 0 + self.train_loader = DataLoader( + train_dataset, batch_size, sampler=sampler, shuffle=shuffle, + num_workers=self.num_workers, **kwargs + ) + loader = iter(self.train_loader) + + + cnt = 0 + cnt_mito = 0 + for i in range(100): + data = next(loader) + locs_this = [df_lookup.iloc[idx.item()]['loc'] for idx in data['label'].long()] + cnt += len(data) + cnt_mito += sum([1 if 'mito' in s else 0 for s in locs_this]) + print('Computing variance of training data...') self.train_variance = np.var(train_data).item() if len(val_ind) > 0: @@ -317,7 +420,9 @@ def const_dataloader( ) print('Computing variance of validation data...') self.val_variance = np.var(val_data).item() + if len(test_ind) > 0: + test_label = label_all[test_ind] if len(image_all) > 0: test_data = image_all[test_ind] @@ -333,6 +438,26 @@ def const_dataloader( print('Computing variance of test data...') self.test_variance = np.var(test_data).item() + # JB added this branch: log metadata about the saved test model + # ipdb.set_trace() + if 0: + assert shuffle_test is False + from pathlib import Path + # DIR_TEST_DATASET_META = Path(f"data/test_dataset_metadata_v1/{self.basepath}") + # DIR_TEST_DATASET_META.mkdir(exist_ok=True) + + if test_data.shape[1] == 2: + DIR_TEST_DATASET_META = Path(f"data/test_dataset_nonucdist_metadata_v1/{self.basepath}") + elif test_data.shape[1] == 3: + DIR_TEST_DATASET_META = Path(f"data/test_dataset_metadata_v1/{self.basepath}") + + DIR_TEST_DATASET_META.mkdir(exist_ok=True, parents=True) + + np.save(DIR_TEST_DATASET_META / "test_dataset_labels.npy", test_label) + np.save(DIR_TEST_DATASET_META / "test_dataset_indices.npy", test_ind) + np.save(DIR_TEST_DATASET_META / "test_dataset_crops.npy", test_data) + + @staticmethod def download_sample_data(output: Optional[str] = 'sample_data'): """ @@ -409,3 +534,4 @@ def get_file_df(basepath: str, suffix: Union[str, Sequence] = 'label', extension else: raise TypeError('Only str or list is accepted for suffix.') return df + diff --git a/cytoself/trainer/autoencoder/decoders/resnet2d.py b/cytoself/trainer/autoencoder/decoders/resnet2d.py index 91faef7..ea79793 100644 --- a/cytoself/trainer/autoencoder/decoders/resnet2d.py +++ b/cytoself/trainer/autoencoder/decoders/resnet2d.py @@ -1,4 +1,5 @@ from typing import Optional +import ipdb import numpy as np from torch import Tensor, nn diff --git a/cytoself/trainer/basetrainer.py b/cytoself/trainer/basetrainer.py index c9e1009..a9ba227 100644 --- a/cytoself/trainer/basetrainer.py +++ b/cytoself/trainer/basetrainer.py @@ -331,7 +331,13 @@ def infer_one_epoch(self, data_loader: DataLoader, _model): if len(output_label) == len(output): output_label = np.vstack(output_label) else: - _output_label = np.hstack(output_label) + # JB added on top of OG cytoself authors. The next few lines are a + # hack to deal with some difference I didn't properly fix. + from cytoself.datamanager import preloaded_dataset + if type(data_loader.dataset) == preloaded_dataset.PreloadedDataset: + _output_label = torch.cat(output_label) + else: + _output_label = np.hstack(output_label) if len(_output_label) == len(output): output_label = _output_label else: @@ -442,6 +448,7 @@ def fit( self.best_model = deepcopy(self.model) # Save the best model checkpoint self.save_checkpoint() + count_early_stop = 0 # JB added to be in line with the paper else: count_lr_no_improve += 1 count_early_stop += 1 diff --git a/example_scripts/simple_example.ipynb b/example_scripts/simple_example.ipynb index 362bfee..3bfd8e5 100644 --- a/example_scripts/simple_example.ipynb +++ b/example_scripts/simple_example.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Running cytoself in Colab\n", "\n", @@ -21,14 +22,17 @@ "Note: In case an error occurs, which is observed occasionally, please be patient and try to run the cell again. If the error persists, please try to restart the runtime.\n", "\n", "### 0. Prepare environment (required for Google Colab)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "#install python 3.9\n", @@ -42,23 +46,24 @@ "!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2\n", "\n", "!pip install --upgrade git+https://github.com/royerlab/cytoself.git" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 1. Prepare data" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "from os.path import join\n", @@ -76,23 +81,24 @@ "DataManagerOpenCell.download_sample_data(datapath) # donwload data\n", "datamanager = DataManagerOpenCell(datapath, data_ch, fov_col=None)\n", "datamanager.const_dataloader(batch_size=32, label_name_position=1)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 2. Create and train a cytoself model" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "model_args = {\n", @@ -113,43 +119,45 @@ "}\n", "trainer = CytoselfFullTrainer(train_args, homepath='demo_output', model_args=model_args)\n", "trainer.fit(datamanager, tensorboard_path='tb_logs')" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 2.1 Generate training history" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "plot_history_cytoself(trainer.history, savepath=trainer.savepath_dict['visualization'])" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 2.2 Compare the reconstructed images as a sanity check" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy()\n", @@ -174,63 +182,66 @@ "fig.tight_layout()\n", "fig.show()\n", "fig.savefig(join(trainer.savepath_dict['visualization'], 'reconstructed_images.png'), dpi=300)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 3. Analyze embeddings" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "analysis = AnalysisOpenCell(datamanager, trainer)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 3.1 Generate bi-clustering heatmap" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "analysis.plot_clustermap(num_workers=4)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 3.2 Generate feature spectrum" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1')\n", @@ -248,23 +259,24 @@ "fig.tight_layout()\n", "fig.show()\n", "fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], 'feature_spectrum.png'), dpi=300)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### 3.3 Plot UMAP" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "umap_data = analysis.plot_umap_of_embedding_vector(\n", @@ -278,19 +290,19 @@ " alpha=0.5,\n", " show_legend=True,\n", ")\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [], "metadata": { - "collapsed": false - } + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -302,16 +314,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/example_scripts/simple_example.py b/example_scripts/simple_example.py index e7e1297..75f712f 100644 --- a/example_scripts/simple_example.py +++ b/example_scripts/simple_example.py @@ -1,4 +1,5 @@ from os.path import join +import ipdb import matplotlib.pyplot as plt import numpy as np @@ -11,7 +12,8 @@ # 1. Prepare Data data_ch = ['pro', 'nuc'] -datapath = 'sample_data' # path to download sample data +# datapath = 'sample_data' # path to download sample data +datapath = "/home/james.burgess/cytoself_inference/cytoself/example_scripts/sample_data" DataManagerOpenCell.download_sample_data(datapath) # donwload data datamanager = DataManagerOpenCell(datapath, data_ch, fov_col=None) datamanager.const_dataloader(batch_size=32, label_name_position=1) @@ -34,7 +36,7 @@ 'reducelr_increment': 0.1, 'earlystop_patience': 6, } -trainer = CytoselfFullTrainer(train_args, homepath='demo_output', model_args=model_args) +trainer = CytoselfFullTrainer(train_args, homepath='example_scripts/demo_output', model_args=model_args) trainer.fit(datamanager, tensorboard_path='tb_logs') # 2.1 Generate training history diff --git a/inference/clustering_leiden.py b/inference/clustering_leiden.py new file mode 100644 index 0000000..15303f8 --- /dev/null +++ b/inference/clustering_leiden.py @@ -0,0 +1,178 @@ +""" +Based on the Science analysis in +https://nbviewer.org/github/czbiohub/2021-opencell-figures/blob/master/notebooks/localization_clustering/clustering-performance.ipynb#Plots-of-ARI-vs-Leiden-resolution + +Compute the nearest neighbour graph for Leiden clustering with the Euclidean +metric. +""" +import ipdb +import anndata as ad +import datetime +import numpy as np +import pandas as pd +import pathlib +from pathlib import Path +import scanpy as sc +import seaborn as sns +import sys +import os +from collections import OrderedDict +import torch + +import sklearn.manifold +import sklearn.metrics +import sklearn.neighbors +from sklearn.metrics import pairwise_distances + +from matplotlib import pyplot as plt +from matplotlib import rcParams + +current_filename = Path(os.path.basename(__file__)) + +# this needs to git pull from https://github.com/czbiohub-sf/2021-opencell-figures/tree/master +# then use path append to add to i t +sys.path.insert(0, '../../2021-opencell-figures') +import scripts.cytoself_analysis.clustering_workflows +import scripts.cytoself_analysis.ground_truth_labels +import scripts.cytoself_analysis.go_utils +from scripts.cytoself_analysis import (clustering_workflows, + ground_truth_labels, go_utils) + +# hyperparams from the OG notebook +n_neighbors = 10 +n_pcs = 200 +metric = 'euclidean' + +sc.settings.set_figure_params(dpi=80, facecolor='white', frameon=False) +rcParams['font.family'] = 'sans-serif' +rcParams['axes.grid'] = False + +data_dir = Path('inference/results/compare_opencell_targets/') +output_dir = pathlib.Path('inference/results/clustering_leiden/') +output_dir.mkdir(exist_ok=True, parents=True) + + +def timestamp(): + return datetime.datetime.now().strftime('%Y-%m-%d') + + +# opencell labels +dir_opencell_test = Path("data/test_dataset_metadata") +labels_opencell_crops = np.load( + dir_opencell_test / + "test_dataset_labels.npy") # (93037, 3) protid, prot, loc_grade1 +df_opencell_lookup = pd.DataFrame(np.unique(labels_opencell_crops, axis=0), + columns=['ensg_id', 'prot_id', 'loc_grade1' + ]).sort_values('prot_id') + +# generate_inference_crop_grids("results/20231218_train_all_no_nucdist_balanced_classes_2", +# fname_crops, nsamples=100, ncols=10) +dir_pretrained_models_checkpoints = [ + # ["results/20231218_train_all_balanced_classes_2", None], + # ["results/20231218_train_all_no_nucdist_balanced_classes_2", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231222_train_all_no_nucdist_balanced_classes_1", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231221_train_with_orphans", None], + # ["results/20231221_train_with_orphans_no_nucdist", None], + # ["results/20231222_train_all_no_nucdist_balanced_classes_1", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231218_train_all_no_nucdist", None], + # ["results/20231022_train_all", None], + ["results/20240129_train_all_no_nucdist_balanced_classes_1", 36], + ["results/20240129_train_all_balanced_classes_2", None], + ["results/20240129_train_all", None], + ["results/20240129_train_all_no_nucdist", None], +] +for (dir_pretrained_model, checkpoint) in dir_pretrained_models_checkpoints: + + dir_results = Path("inference/results/compare_opencell_targets/" + ) / dir_pretrained_model / f"ckpt_{checkpoint}" + data_opencell = torch.load(dir_results / "consensus_embeddings.pt").numpy() + prots_opencell = torch.load(dir_results / "consensus_labels.pt") + # ipdb.set_trace() + # assert all(labels_opencell_crops == df_opencell_lookup['prot_id'].values) + + data_inf = torch.load(dir_results / + "consensus_embeddings_inference.pt").numpy() + prots_inf = torch.load(dir_results / "consensus_labels_inference.pt") + + data = np.concatenate((data_opencell, data_inf)) + prots_all = np.concatenate((prots_opencell, prots_inf)) + labels_loc_grade1 = np.concatenate((df_opencell_lookup["loc_grade1"], + np.array(["ORF"] * len(prots_inf)))) + + adata = ad.AnnData(data) + cwv = clustering_workflows.ClusteringWorkflow(adata=adata) + cwv.calculate_neighbors(n_neighbors=n_neighbors, + n_pcs=n_pcs, + metric=metric) + + # resolution = 20 # 0.63 # 0.63 + # random_state = 0 + all_results = [] + all_cols = [] + for resolution in [1, 5, 10, 20]: + for random_state in [0,1]: + print(resolution, random_state) + cwv.run_leiden(resolution=resolution, random_state=random_state) + y_leiden = np.array(adata.obs.leiden) + + res = OrderedDict() + for prot in prots_inf: + idx = np.where(prots_all == prot)[0][-1] # choose `-1` bc it will choose the orphan if it's also in opencell + cluster_idx = y_leiden[idx] + prot_idxs_this_cluster = np.where(y_leiden==cluster_idx) + prots_this_cluster = prots_all[prot_idxs_this_cluster] + labels_loc_grade1_this_cluster = labels_loc_grade1[prot_idxs_this_cluster] + res[prot + "_prots"] = str(prots_this_cluster) + res[prot + "_locs"] = str(labels_loc_grade1_this_cluster) + + all_results.append(pd.Series(res)) + all_cols.append(f"res_{resolution}_seed_{random_state}") + + df = pd.concat(all_results, axis=1) + df.columns=all_cols + dir_save = Path(__file__).parent / "results/clustering_leiden" / Path(dir_pretrained_model) + dir_save.mkdir(exist_ok=True, parents=True) + df.to_csv(dir_save / "clustering_sets.csv") + ipdb.set_trace() + pass + + # print out some samples from each cluster to check it's coherent +# n_samples = 50 +# y='7' +# for y in np.unique(y_leiden): +# idxs = np.where(y_leiden == y)[0] +# print(f"Label: {y}, num_prots: {len(idxs)}") +# print(f"Annots: ") +# print(labels_loc_grade1[idxs[:n_samples]]) +# print(prots_all[idxs[:n_samples]]) +# print() + +# ipdb.set_trace() +# pass + +# # for a target protein, get the protein and annotations of the other prots in the cluster +# prot = "TMEM184C" # "ANKRD46" +# idx = np.where(prots_all == prot)[0] # idx = 1311 +# cluster_idx = y_leiden[idx] +# prot_idxs_this_cluster = np.where(y_leiden==cluster_idx) +# prots_this_cluster = prots_all[prot_idxs_this_cluster] +# labels_loc_grade1_this_cluster = labels_loc_grade1[prot_idxs_this_cluster] +# print(prots_this_cluster) +# print(labels_loc_grade1_this_cluster) + + + +# # now get the nearest neighbors on the raw distance +# idx = np.where(prots_all == prot)[0] # idx = 1311 +# neighbors_idx = adata.obsp['distances'][idx].indices +# neighbors_prots = prots_all[[neighbors_idx]] +# neighbors_loc_grade1 = labels_loc_grade1[[neighbors_idx] + + +# # dist = pairwise_distances(data, data, metric=metric) +# # dist = torch.from_numpy(dist) +# # dist_argsort = torch.argsort(dist, axis=1) +# # \ No newline at end of file diff --git a/inference/compare_data_sources.py b/inference/compare_data_sources.py new file mode 100644 index 0000000..37abd20 --- /dev/null +++ b/inference/compare_data_sources.py @@ -0,0 +1,184 @@ +""" +Want to check that the OpenCell data and the orphan data are close enough: + +The FOVs for the inference data are in `inference/results/load_inf_data/`. They +were constructed in `load_inf_data`. For each channel, they normalize and do max +projection and that's it. + +On the other hand, the FOVs from OpenCell are in /hpc/projects/group.leonetti/opencell-microscopy +""" +import ipdb +from aicsimageio import AICSImage +from pathlib import Path +import os +from PIL import Image +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import torch +import pandas as pd +from torchvision.utils import make_grid + +current_filename = Path(os.path.basename(__file__)) +results_dir = Path("inference/results") / os.path.join(current_filename.stem, "") +results_dir.mkdir(exist_ok=True, parents=True) + +## first let's compare the metadata in the two files + +# a similar protein to A1 is CERS6 +# fname_fov_opencell = Path("/hpc/projects/group.leonetti/opencell-microscopy/public/CERS6_ENSG00000172292/OC-FOV_CERS6_ENSG00000172292_CID001010_FID00013480_stack.tif") +fname_fov_opencell = Path("/hpc/projects/group.leonetti/opencell-microscopy/public/ANKRD46_ENSG00000186106/OC-FOV_ANKRD46_ENSG00000186106_CID001394_FID00033371_stack.tif") +aics_img_opencell = AICSImage(fname_fov_opencell) +data_stack_opencell = aics_img_opencell.data + +# get an orphan fov - 3d - well A1, which is ANKRD46, which is ER +# fname_fov_inf = Path("/hpc/instruments/leonetti.dragonfly/infected-cell-microscopy/TICM052-1/raw_data/raw_data_MMStack_9-A1-9.ome.tif") +fname_fov_inf = Path("/hpc/instruments/leonetti.dragonfly/infected-cell-microscopy/TICM052-1/raw_data/raw_data_MMStack_18-A1-18.ome.tif") +aics_img_inf = AICSImage(fname_fov_inf) +data_stack_inf = aics_img_inf.data + +print("Pyhsical pixel sizes") +print(f" OpenCell: {aics_img_opencell.physical_pixel_sizes}") +print(f" Inference: {aics_img_inf.physical_pixel_sizes}") +print("Shapes") +print(f" OpenCell: {data_stack_opencell.shape}") +print(f" Inference: {data_stack_inf.shape}") + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +def get_max_proj(data): + prot_zstack = norm_0_1(data[0,1]) + nuc_zstack = norm_0_1(data[0,0]) + prot_proj = prot_zstack.max(0) + nuc_proj = nuc_zstack.max(0) + return prot_proj, nuc_proj + +prot_proj_opencell, nuc_proj_opencell = get_max_proj(data_stack_opencell) +prot_proj_inf, nuc_proj_inf = get_max_proj(data_stack_inf) + +Image.fromarray((prot_proj_opencell*255).astype(np.uint8), "L").save(results_dir / "FOV_prot_ANKRD46_opencell.png") +Image.fromarray((nuc_proj_opencell*255).astype(np.uint8), "L").save(results_dir / "FOV_nuc_ANKRD46_opencell.png") +Image.fromarray((prot_proj_inf*255).astype(np.uint8), "L").save(results_dir / "FOV_prot_ANKRD46.png") +Image.fromarray((nuc_proj_inf*255).astype(np.uint8), "L").save(results_dir / "FOV_nuc_ANKRD46.png") + + +## crop comparisons +results_idx = 7 +results_dir = results_dir / f"compare_{results_idx}" +results_dir.mkdir(exist_ok=True) + +# variable `VERSION` is 0 if norming each crop, and 1 if norming each FOV +if results_idx == 0: + target_prot_opencell = "ANKRD46" + target_well_inf = "A1" + VERSION = 0 +elif results_idx == 1: + # the lysosome ones + target_prot_opencell = "SLC37A3" + target_well_inf = "F3" # TSPAN3 + VERSION = 0 +elif results_idx == 2: + # same as results_idx 0 except with version 1 for norming the whol fov + target_prot_opencell = "ANKRD46" + target_well_inf = "A1" + VERSION = 1 +elif results_idx == 3: + # some nucleolus proteins with the old normalization + target_prot_opencell = "AATF" + target_well_inf = "C3" + VERSION = 0 +elif results_idx == 4: + # + target_prot_opencell = "TMEM192" + target_well_inf = "F11" # TSPAN6 + VERSION = 0 +elif results_idx == 5: + target_prot_opencell = "TMEM192" + target_well_inf = "F11" # TSPAN6 + VERSION = 1 +elif results_idx == 6: + # + target_prot_opencell = "TMEM192" + target_well_inf = "F3" # TSPAN6 + VERSION = 0 +elif results_idx == 7: + target_prot_opencell = "TMEM192" # TSPAN6 + target_well_inf = "F3" + VERSION = 1 + + +# get the right inference crops based on the version. +if VERSION == 0: + crops = torch.load("inference/results/crop/crops.pt") + crops_meta = pd.read_csv("inference/results/crop/crops_meta.csv") +elif VERSION == 1: + crops = torch.load("inference/results/crop/crops_v1.pt") + crops_meta = pd.read_csv("inference/results/crop/crops_meta.csv") +else: + raise ValueError() + +crops_meta['well_id'] = [row.split("/")[-1].split("_")[2] for row in crops_meta['fname_pro']] +idxs_target_inf = np.where(crops_meta['well_id']==target_well_inf)[0] +crops_target_inf = crops[idxs_target_inf] +f,axs = plt.subplots(1,4) +_ = [axs[i].imshow(crops_target_inf[0,i], cmap='gray') for i in range(3)] +axs[3].imshow(np.zeros_like(crops_target_inf[0,0])) +f.savefig(results_dir / "sample_crop_inf_img.png") + +nrow, ncol = 8,8 +nimgs = nrow*ncol +# make a grid for inf +grid = make_grid(crops_target_inf[:nimgs], nrow=nrow).permute(1,2,0).numpy() +Image.fromarray((grid[...,0]*255).astype(np.uint8), "L").save(results_dir / "crops_prot_inf.png") +Image.fromarray((grid[...,1]*255).astype(np.uint8), "L").save(results_dir / "crops_nuc_inf.png") +Image.fromarray((norm_0_1(grid[...,2])*255).astype(np.uint8), "L").save(results_dir / "crops_nucdist_inf.png") + +## for opencell first figure out which crops file has the target protein we want +labels_all = [] +for i in range(10): + labels = pd.read_csv(f"data/opencell_crops/label_data{i:02d}.csv", low_memory=False) + labels['file'] = i + labels_all.append(labels) +labels_all = pd.concat(labels_all) +idx_crop_file = labels_all[labels_all['name']==target_prot_opencell]['file'].unique()[0] +# it's 6, so get that crop - getting this npy file is slow +crops_opencell_this = np.load(f"data/opencell_crops/image_data{idx_crop_file:02d}.npy") +labels_opencell_this = pd.read_csv(f"data/opencell_crops/label_data{idx_crop_file:02d}.csv", low_memory=False) + +# opencell +idxs_target_opencell = np.where(labels_opencell_this['name']==target_prot_opencell)[0] +crops_target_opencell = crops_opencell_this[idxs_target_opencell] +# inf +f,axs = plt.subplots(1,4) +_ = [axs[i].imshow(crops_target_opencell[0,...,i], cmap='gray') for i in range(4)] +f.savefig(results_dir / "sample_crop_opencell_img.png") + +grid = make_grid(torch.from_numpy(crops_target_opencell[:nimgs]).permute(0,3,1,2), nrow=nrow).permute(1,2,0).numpy() +Image.fromarray((grid[...,0]*255).astype(np.uint8), "L").save(results_dir / "crops_prot_opencell.png") +Image.fromarray((grid[...,1]*255).astype(np.uint8), "L").save(results_dir / "crops_nuc_opencell.png") +Image.fromarray((norm_0_1(grid[...,2])*255).astype(np.uint8), "L").save(results_dir / "crops_nucdist_opencell.png") + + +for i in range(len(crops_target_opencell)): + crops_target_opencell[i,...,0] = norm_0_1(crops_target_opencell[i,...,0]) + crops_target_opencell[i,...,1] = norm_0_1(crops_target_opencell[i,...,1]) +grid = make_grid(torch.from_numpy(crops_target_opencell[:nimgs]).permute(0,3,1,2), nrow=nrow).permute(1,2,0).numpy() +Image.fromarray((grid[...,0]*255).astype(np.uint8), "L").save(results_dir / "crops_prot_opencell_normed.png") +Image.fromarray((grid[...,1]*255).astype(np.uint8), "L").save(results_dir / "crops_nuc_opencell_normed.png") + +ipdb.set_trace() +pass + + + + + + + + + + diff --git a/inference/compare_opencell_targets.py b/inference/compare_opencell_targets.py new file mode 100644 index 0000000..a934973 --- /dev/null +++ b/inference/compare_opencell_targets.py @@ -0,0 +1,1041 @@ +""" +Run in project home dir as + python inference/compare_opencell_targets.py + +For a pretrained model in ``, read in the embeddings for +that model on the OpenCell dataset. This is the dataset the model was trained on + +Next get the new inference dataset, its crops, and the embeddings that were +computed in `inference/get_crop_features.py`. These embedding should have been +taken from model from ``. +""" + +import ipdb +import numpy as np +import pandas as pd +from pathlib import Path +import os +from sklearn.metrics import pairwise_distances +import matplotlib.pyplot as plt +import torch +import torchvision.transforms as T +from torchvision.utils import make_grid +from typing import Literal +from tqdm import tqdm +from collections import Counter +from sklearn.decomposition import PCA + +VERSION = 0 + +# protein_filter = dict( +# # GLT8D1=[1, 2, 4, 11, 13, 18, 52, 64, 96, 97], +# GLT8D1=[2, 4, 11, 18, 52, 64, 96, 97], +# # GLT8D1=[97], +# MFSD5=[ +# 0, 2, 8, 12, 13, 15, 21, 22, 24, 25, 40, 42, 44, 46, 48, 51, 57, 67, +# 68, 71, 73, 75, 77, 78, 86, 89, 92, 94, 96, 97, 98, 99 +# ], +# MPZL1=[ +# 19, 24, 26, 27, 39, 35, 36, 39, 40, 41, 42, 45, 46, 47, 48, 49, 50, 51, +# 53, 54, 55, 56, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 85, 86, 87, +# 88, 89, 91, 93, 94, 95, 97 +# ], +# TSPAN3=[ +# 0, 1, 23, 8, 9, 10, 11, 12, 13, 15, 19, 21, 22, 29, 30, 31, 32, 33, 34, +# 37, 39, 40, 42, 43, 44, 45, 49, 51, 52, 54, 63, 64, 90, 92, 98 +# ], +# ) + +current_filename = Path(os.path.basename(__file__)) +mito_proteins = np.array([ + 'ACAP3', 'DNAJC11', 'PKBP8', 'MFN1', 'MFN2', 'MYO19', 'RAB24', 'SAR1A', + 'TOMM20', 'TOMM70', 'VDAC1', 'VDAC2', 'VDAC3' +]) + + +def get_nearest_proteins( + fname_crops, + fname_crops_meta, + fname_annotations, + dir_pretrained_model, + pca_dim=200, + mode='correlation', + representation='vqvec2', # or 'hist' + combine_inference_wells=True, + do_filtering=False, + do_visualize_knns=True, + checkpoint=None, + do_include_nn_losses=False, + combine_augmented_embeddings=False, + do_compare_orphans=True, + test=None): + """ + deorphaning analysis + Args: + do_filtering (bool). + """ + dir_results = Path( + "inference/results" + ) / current_filename.stem / dir_pretrained_model / f"ckpt_{checkpoint}" + if VERSION > 0: + dir_results = dir_results / f"version{VERSION}" + dir_results.mkdir(exist_ok=True, parents=True) + + dir_viz = dir_results + + # results dirs + embed_opencell, labels_opencell, crops_opencell, df_opencell, df_opencell_lookup = get_embeds_and_crops( + dir_pretrained_model, + checkpoint=checkpoint, + representation=representation, + combine_augmented_embeddings=combine_augmented_embeddings, + dataset="opencell") + lookup_prot_to_locgrade1 = dict( + zip(df_opencell_lookup['prot_id'].values, + df_opencell_lookup['loc_grade1'].values)) + + # get inference embeddings for the new dataset + embed_inf, labels_inf, crops_inf, df_meta_inf, df_annotations, df_inf_lookup = get_embeds_and_crops( + dir_pretrained_model, + checkpoint=checkpoint, + representation=representation, + combine_augmented_embeddings=combine_augmented_embeddings, + dataset='inference') + n_inf = len(df_inf_lookup) + + ## get "consensus encodings" - this one is the elementwise mean of the vqvec2 vecotors + protein_label_opencell = labels_opencell[:, 1] + # label for doing consensus in opencell is protein id + embeds_consensus_opencell, labels_consensus_opencell = get_consensus_encoding( + embed_opencell, protein_label_opencell) + torch.save(embeds_consensus_opencell, + dir_results / "consensus_embeddings.pt") + torch.save(labels_consensus_opencell, dir_results / "consensus_labels.pt") + + lookup_loss_ce, lookup_loss_recon = get_opencell_protein_losses( + dir_pretrained_model, checkpoint) + + # label for doing consensus in inference is the well id. Multiple wells have the same protein + if combine_inference_wells: + protein_label_inf = df_meta_inf['protein'].values + sort_key = None + ipdb.set_trace() + df_inf_lookup = df_inf_lookup.sort_values( + by='protein' + )#.iloc[::2] # we'll have an assert check that this is fine + else: + protein_label_inf = df_meta_inf['well_id'].values + sort_key = custom_sort_key + + # do the conensus embeddings + embeds_consensus_inf, labels_consensus_inf = get_consensus_encoding( + embed_inf, + protein_label_inf, + sort_key=sort_key, + do_filtering=do_filtering, + crops=crops_inf) + torch.save(embeds_consensus_inf, + dir_results / "consensus_embeddings_inference.pt") + torch.save(labels_consensus_inf, + dir_results / "consensus_labels_inference.pt") + + # check that the labels for the consensus embeddings are sorted the same as their lookup dataframes + # for inference dataset, if combine_inference_wells, sort by protein, otherwise sort by well id. + assert np.all( + df_opencell_lookup['prot_id'].values == labels_consensus_opencell) + if combine_inference_wells: + assert np.all(df_inf_lookup['protein'].values == labels_consensus_inf) + else: + assert np.all(df_inf_lookup['well_id'].values == labels_consensus_inf) + + if pca_dim is not None and pca_dim > 0: + pca = PCA(n_components=pca_dim, random_state=0) + pca.fit(embeds_consensus_opencell) + embeds_consensus_opencell = pca.transform(embeds_consensus_opencell) + embeds_consensus_inf = pca.transform(embeds_consensus_inf) + + # debugging whether the inference images come from the same place + if 0: + embeds_all = np.vstack( + (embeds_consensus_opencell, embeds_consensus_inf)) + prots_all = np.concatenate((df_opencell_lookup['prot_id'].values, + df_inf_lookup['protein'].values)) + mode = 'euclidean' + dist = pairwise_distances(embeds_all, embeds_all, metric=mode) + dist = torch.from_numpy(dist) + dist_argsort = torch.argsort(dist, axis=1) + + dist_argsort[1311] + idxs_nearest = dist_argsort[1320] + print(idxs_nearest, prots_all[idxs_nearest]) + + # branch for manual checking + if 0: + # get distances + dist = pairwise_distances(embeds_consensus_inf, + embeds_consensus_opencell, + metric=mode) + dist = torch.from_numpy(dist) + # matrix (n_well_ids_inf, n_prots_opencell), which here is (80,1311) + dist_argsort = torch.argsort(dist, axis=1) + + target_protein = 'GLT8D1' + idxs = np.where(df_inf_lookup['protein'] == target_protein) + assert len(idxs) > 0 + idx = idxs[0] + + dist_target = dist[idx[0]] + dist_argsort_target = np.argsort(dist_target) + prots_nearest = labels_consensus_opencell[dist_argsort_target] + dist_nearest = dist_target[dist_argsort_target] + locgrade1_nearest = np.array( + [lookup_prot_to_locgrade1[k] for k in prots_nearest]) + + n_prots = 15 + for i in range(n_prots): + print( + f"{i:3} {prots_nearest[i]:8} {locgrade1_nearest[i]:22} {1-dist_nearest[i]:.3f} " + ) + + # this function is where the csv file gets saved + fname_stem = f"manualfilter_{do_filtering}__representation_{representation}__rotations_{combine_augmented_embeddings}__mode_{mode}__pcadim_{pca_dim}" + df_results_summary = find_nearest_analysis( + embeds_consensus_opencell, + labels_consensus_opencell, + embeds_consensus_inf, + labels_consensus_inf, + df_opencell_lookup, + df_inf_lookup, + dir_results, + dir_viz, + fname_stem, + do_include_nn_losses=do_include_nn_losses, + mode=mode, + lookup_loss_ce=lookup_loss_ce, + lookup_loss_recon=lookup_loss_recon, + do_compare_orphans=do_compare_orphans, + k_nearest_prots=8) + if do_visualize_knns: + visualize_knns( + df_results_summary, + crops_inf, + df_meta_inf, + crops_opencell, + df_opencell, + dir_viz, + fname_stem, + n_samples=10, + k_nearest_prots=4, + ) + + +def nearest_prots_within_opencell( + dir_pretrained_model, + mode='correlation', + target_protein='C4orf32', + pca_dim=200, + representation='vqvec2', + get_rank=['STT3A', 'STT3B', 'OSTC', 'DAD1', 'KRTCAP2']): + """ + Given a target_protein that is in OpenCell, get the nearest proteins that are also + in OPenCell (this does not support a separate orphan protein). + Args: + target_protein (str): target protein whose neighbors we're looking for + get_rank (List[str]): list of proteins whose position in the kNN list we want to query + """ + ## results dirs + dir_results = Path( + "inference/results") / current_filename.stem / dir_pretrained_model + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + dir_viz.mkdir(exist_ok=True, parents=True) + + embed_opencell, labels_opencell, crops_opencell, df_opencell, df_opencell_lookup = get_embeds_and_crops( + dir_pretrained_model, + representation=representation, + combine_augmented_embeddings=combine_augmented_embeddings, + dataset="opencell") + lookup_prot_to_locgrade1 = dict( + zip(df_opencell_lookup['prot_id'].values, + df_opencell_lookup['loc_grade1'].values)) + + ## get "consensus encodings" - this one is the elementwise mean of the vqvec2 vecotors + protein_label_opencell = labels_opencell[:, 1] + # label for doing consensus in opencell is protein id + embeds_consensus_opencell, labels_consensus_opencell = get_consensus_encoding( + embed_opencell, protein_label_opencell) + # distance matrix + dist = pairwise_distances(embeds_consensus_opencell, + embeds_consensus_opencell, + metric='correlation') + # + target_protein = 'LAMP1' + idx = np.where(labels_consensus_opencell == target_protein)[0] + assert len(idx) == 1 + dist_target = dist[idx[0]] + dist_argsort_target = np.argsort(dist_target) + prots_nearest = labels_consensus_opencell[dist_argsort_target] + dist_nearest = dist_target[dist_argsort_target] + locgrade1_nearest = np.array( + [lookup_prot_to_locgrade1[k] for k in prots_nearest]) + n_prots = 30 + for i in range(n_prots): + print( + f"{i:3} {prots_nearest[i]:8} {locgrade1_nearest[i]:22} {1-dist_nearest[i]:.3f} " + ) + + for prot in get_rank: + idx = np.where(prot == prots_nearest)[0] - 1 + print(prot, idx, locgrade1_nearest[idx]) + + +def get_embeds_and_crops( + dir_pretrained_model, + checkpoint, + representation="vqvec2", #("vqvec2",'hist') + do_filtering=False, + combine_augmented_embeddings=False, + dataset: Literal['opencell', 'inference'] = "opencell"): + """ + Get cytoself embeddings and image crops for either opencell or the inference dataset. + Args: + 'dataset' (str): one of 'opencell' + combine_inference_wells (bool): if True AND if dataset=='opencell', then + combine the encodings + do_filtering: for inferencendataset, filter the crops to only include the ones listed + """ + print(f"loading dataset embeddings and crops [{dataset}]") + + augmentations = ("0", "90", "180", "270", "0_f", "90_f", "180_f", "270_f") + if not combine_augmented_embeddings: + augmentations = augmentations[:1] + + if dataset == "opencell": + # get the presaved embeddings from OpenCell + # f_embed = Path(dir_pretrained_model) / "embeddings/vqvec2.npy" + dir_embeddings = Path("inference/results/get_crop_features" + ) / dir_pretrained_model / f"ckpt_{checkpoint}" + # if VERSION == 1: + # dir_embeddings = dir_embeddings / "version1" + + embeds = [] + for augmentation in augmentations: + if representation == 'vqvec2': + f_embed = dir_embeddings / f"embeddings_vqvec2_opencell_aug{augmentation}.pt" + elif representation == 'hist': + f_embed = dir_embeddings / f"embeddings_vqindhist1_opencell_aug{augmentation}.pt" + else: + raise + embed_opencell, _ = torch.load(f_embed) + embed_opencell = torch.tensor(embed_opencell).view( + len(embed_opencell), -1) + embeds.append(embed_opencell) + embeds = torch.stack(embeds) + + if "2023" in dir_pretrained_model: + dir_opencell_test = Path("data/test_dataset_metadata") + elif "2024" in dir_pretrained_model: + dir_opencell_test = Path("data/test_dataset_metadata_v1/data/opencell_crops_processed2/") + else: + raise + + crops_opencell = np.load( + dir_opencell_test / + "test_dataset_crops.npy") # (93037, 3, 100, 100) + crops_opencell = crops_opencell[:, : + 2] # if there is a nuclear distance channel, then remove it + labels_opencell = np.load( + dir_opencell_test / + "test_dataset_labels.npy") # (93037, 3) protid, prot, loc_grade1 + + df_opencell = pd.DataFrame( + labels_opencell, columns=['ensg_id', 'prot_id', 'loc_grade1']) + + df_opencell_lookup = pd.DataFrame( + np.unique(labels_opencell, axis=0), + columns=['ensg_id', 'prot_id', + 'loc_grade1']).sort_values('prot_id') + + return embeds, labels_opencell, crops_opencell, df_opencell, df_opencell_lookup + + ## get inference embeddings for the new dataset + elif dataset == "inference": + dir_embeddings = Path("inference/results/get_crop_features" + ) / dir_pretrained_model / f"ckpt_{checkpoint}" + if VERSION > 0: + dir_embeddings = dir_embeddings / f"version{VERSION}" + + embeds = [] + for augmentation in augmentations: + if representation == 'vqvec2': + f_embed = dir_embeddings / f"embeddings_vqvec2_aug{augmentation}.pt" + elif representation == 'hist': + f_embed = dir_embeddings / f"embeddings_vqindhist1_aug{augmentation}.pt" + else: + raise + + embed_inf, labels_inf = torch.load(f_embed) + embed_inf = torch.tensor(embed_inf).view(len(embed_inf), -1) + + embeds.append(embed_inf) + embeds = torch.stack(embeds) + + if VERSION == 0: + df_meta_inf = pd.read_csv(dir_embeddings / "crops_meta.csv") + elif VERSION > 0: + df_meta_inf = pd.read_csv(dir_embeddings / f"crops_meta_v{VERSION}.csv") + else: + raise ValueError() + + df_annotations = pd.read_csv(fname_annotations) + crops_inf = torch.load(fname_crops) + crops_inf = T.Resize(100)(crops_inf) + + ## join the inference metadata with the csv that has their protids and annotations + df_meta_inf['well_id'] = [ + Path(f).stem.split("_")[-3] for f in df_meta_inf['fname_pro'] + ] + df_meta_inf['fov_id'] = [ + Path(f).stem.split("_")[-2] for f in df_meta_inf['fname_pro'] + ] + df_meta_inf = df_meta_inf.merge(df_annotations, + how='left', + left_on='well_id', + right_on='well_id_new') + # manually add these things on in df_lookup + df_meta_inf.loc[df_meta_inf['well_id']=="H5", 'protein'] = 'well_H5_protein' + df_meta_inf.loc[df_meta_inf['well_id']=="H8", 'protein'] = 'well_H8_protein' + + df_inf_lookup = get_df_inf_lookup(df_meta_inf) + + assert df_meta_inf.shape[0] == embeds.shape[1] + + # ipdb.set_trace() + + return embeds, labels_inf, crops_inf, df_meta_inf, df_annotations, df_inf_lookup + + else: + raise ValueError(f"dataset argument was [{dataset}]") + + +def order_by_frequency_with_counts(strings): + # Count the frequency of each string + freq = Counter(strings) + + # Sort the items by frequency (descending) and then alphabetically + sorted_items = sorted(freq.items(), key=lambda x: (-x[1], x[0])) + + return sorted_items + + +def get_opencell_protein_losses(dir_pretrained_model, checkpoint): + """ + For the proteins in the OpenCell test set, measure the get the average + of the losses (the protein classification loss & the recon loss). + """ + fname_loss_ce = Path( + "inference/results/get_crop_features" + ) / dir_pretrained_model / f"ckpt_{checkpoint}" / "loss_ce.csv" + loss_ce_per_protein = pd.read_csv(fname_loss_ce) + lookup_loss_ce = dict( + zip(loss_ce_per_protein['protein'].values, + loss_ce_per_protein['loss_ce'].values)) + + fname_loss_recon = Path( + "inference/results/get_crop_features" + ) / dir_pretrained_model / f"ckpt_{checkpoint}" / "loss_recon.csv" + loss_recon_per_protein = pd.read_csv(fname_loss_recon) + lookup_loss_recon = dict( + zip(loss_recon_per_protein['protein'].values, + loss_recon_per_protein['loss_recon'].values)) + + return lookup_loss_ce, lookup_loss_recon + + +def find_nearest_analysis(embeds_consensus_opencell, + labels_consensus_opencell, + embeds_consensus_inf, + labels_consensus_inf, + df_opencell_lookup, + df_inf_lookup, + dir_results, + dir_viz, + fname_stem, + mode, + lookup_loss_ce, + lookup_loss_recon, + do_include_nn_losses=False, + k_nearest_prots=10, + do_compare_orphans=True, + return_dists_only=False): + """ + Take the consensus embeddings for opencell and inference data. For each inf + well_id, get the nearest `k_nearest_prots` proteins in Opencell. + The option `mode`, controls how 'nearest' is defined: + `mode==correlation` is wrt vqvec2. + + If `return_dists_only` then return early and just return the distance matrix. This is + useful for doing thresholding. + """ + + ## compute the distance matrix between the inf prots and all prots (in opencell) + if not do_compare_orphans: + embeds_compare = embeds_consensus_opencell + prots_compare = df_opencell_lookup['prot_id'].values + loc_grade1_compare = df_opencell_lookup['loc_grade1'].values + else: + embeds_compare = np.concatenate( + (embeds_consensus_opencell, embeds_consensus_inf)) + prots_compare = np.concatenate((df_opencell_lookup['prot_id'].values, + df_inf_lookup['protein'].values)) + loc_grade1_compare = np.concatenate((df_opencell_lookup['loc_grade1'].values, + np.array(["ORF"]*len(df_opencell_lookup)))) + + dist = pairwise_distances(embeds_consensus_inf, + embeds_compare, + metric=mode) + dist = torch.from_numpy(dist) + + # matrix (n_well_ids_inf, n_prots_opencell), which here is (80,1311) + dist_argsort = torch.argsort(dist, axis=1) + if do_compare_orphans: + dist_argsort = dist_argsort[:,1:] # nearest will always be itself + dist_argsort = dist_argsort[:, :k_nearest_prots] + + prots_nearest = prots_compare[dist_argsort] # (n_well_ids_inf,k_nearest_prots) + dist_nearest = np.zeros(dist_argsort.shape) + for i in range(len(dist_nearest)): + dist_nearest[i] = dist[i, dist_argsort[i]] + if mode == 'correlation': + dist_nearest = 1 - dist_nearest + loc_grade1_nearest = loc_grade1_compare[dist_argsort] # (n_well_ids_inf,k_nearest_prots) + + if return_dists_only: + return dist, dist_argsort, prots_nearest, loc_grade1_nearest + + ## stick the knn preds together. Note that the rows of `df_inf_lookup` are aligned + # with the rows of `prots_nearest` and `loc_grade1_nearest`. This was checked in + # the function caller (it should check that `df_inf_lookup` algin with `labels_consensus_inf` + df_results_summary = df_inf_lookup[[ + 'well_id', 'protein', 'orgIP_cluster_annotation', + 'plate33_eyeballed_annotation' + ]] + df_results_summary.index = range(len(df_results_summary)) + df_prot_nearest = pd.DataFrame( + prots_nearest, + columns=[f'prot_nn{i}' for i in range(prots_nearest.shape[1])]) + df_dist_nearest = pd.DataFrame( + dist_nearest, + columns=[f'dist{i}' for i in range(prots_nearest.shape[1])]) + df_loc_grade1_nearest = pd.DataFrame( + loc_grade1_nearest, + columns=[ + f'loc_grade1_nn{i}' for i in range(loc_grade1_nearest.shape[1]) + ]) + + df_results_summary = pd.concat([ + df_results_summary, + df_prot_nearest, + df_dist_nearest, + df_loc_grade1_nearest, + ], + axis=1) + # measure the loss for the nearest neighbour proteins + if do_include_nn_losses: + m, n = prots_nearest.shape + loss_ce = np.zeros((m, n)) + loss_recon = np.zeros((m, n)) + for i in range(m): + for j in range(n): + loss_ce[i, j] = lookup_loss_ce[prots_nearest[i, j]] + loss_recon[i, j] = lookup_loss_recon[prots_nearest[i, j]] + + df_loss_ce = pd.DataFrame( + loss_ce, + columns=[f'loss_ce{i}' for i in range(prots_nearest.shape[1])]) + df_loss_recon = pd.DataFrame( + loss_recon, + columns=[f'loss_recon{i}' for i in range(prots_nearest.shape[1])]) + + df_results_summary = pd.concat( + [df_results_summary, df_loss_ce, df_loss_recon], axis=1) + + # save the 'knn predictions' + f_save = dir_results / f"knn_opencell_preds_{fname_stem}.csv" + print(f"saving results csv [{f_save}]") + df_results_summary.transpose().to_csv(f_save) + + # create a distance matrix of everything to everything and save it + if 1: + embeds_all = np.concatenate( + (embeds_consensus_inf, embeds_consensus_opencell)) + prots_all = np.concatenate( + (labels_consensus_inf, labels_consensus_opencell)) + dist_all = pairwise_distances(embeds_all, embeds_all, metric=mode) + if mode == 'correlation': + dist_all = 1 - dist_all + + df_dist_all = pd.DataFrame(data=dist_all, + index=prots_all, + columns=prots_all) + f_save = dir_results / f"distmat_{fname_stem}.csv" + df_dist_all.to_csv(f_save) + + return df_results_summary + + +def norm_0_1_perimg_perch(crops): + """ + input is (n,nchanells,Y,X) + Normalize to [0,1] within each image and within each channel. + """ + if type(crops) == np.ndarray: + crops = torch.from_numpy(crops) + crops_view = crops.view((*crops.shape[:2], -1)) + l = crops_view.min(dim=-1)[0] + l = l.unsqueeze(-1).unsqueeze(-1) + u = crops_view.max(dim=-1)[0] + u = u.unsqueeze(-1).unsqueeze(-1) + crops = (crops - l) / (u - l) + + return crops + + +def visualize_knns(df_results_summary, + crops_inf, + df_inf, + crops_opencell, + df_opencell, + dir_viz, + fname_stem, + n_samples=10, + k_nearest_prots=10): + """ For each inference protein, create a lookup. """ + np.random.seed(0) + for idx, row in df_results_summary.iterrows(): + imgs = [] + well_id = row['well_id'] + protein = row['protein'] + idxs_img = np.where(df_inf['well_id'] == well_id)[0] + np.random.shuffle(idxs_img) + imgs.append(crops_inf[idxs_img[:n_samples]][:, :2]) + + # get the nearest protein set + prots_nearest = [] + dist_nearest = [] + + for j in range(k_nearest_prots): + prot = row[f'prot_nn{j}'] + prots_nearest.append(prot) + idxs_img = np.where(df_opencell['prot_id'] == prot)[0] + np.random.shuffle(idxs_img) + imgs.append(torch.from_numpy(crops_opencell[idxs_img[:n_samples]])) + + dist_ = row[f'dist{j}'] + dist_nearest.append(f"{dist_:.2f}") + + imgs = torch.cat(imgs) + imgs = norm_0_1_perimg_perch(imgs) + imgs_show = torch.zeros(len(imgs), + 3, + *imgs.shape[-2:], + dtype=imgs.dtype) + imgs_show[:, 1] = imgs[:, 0] # protein in green channel + imgs_show[:, 2] = imgs[:, 1] # nucleus in green channel + grid = make_grid(imgs_show, nrow=n_samples).permute(1, 2, 0) + dir_viz_ = dir_viz / ('viz_' + fname_stem) + dir_viz_.mkdir(exist_ok=True) + f_save = dir_viz_ / f"knn_samples_prot_{protein}_wellid_{well_id}.png" + + title = f"well_{well_id}_prot_{protein}\nnearest={prots_nearest}\ndist={dist_nearest}" + f, axs = plt.subplots(figsize=(15, 15)) + axs.imshow(grid) + axs.set(title=title) + f.savefig(f_save, dpi=200) + plt.close() + + pass + + +def get_consensus_encoding(embed_data, + labels, + sort_key=None, + do_filtering=False, + crops=None): + """ + `crops` only used for DEBUG=True + """ + labels_uniq = np.array(sorted(np.unique(labels), key=sort_key)) # (1311,) + + embeds_consensus = [] + + assert embed_data.shape[0] in ( + 1, 8 + ), "embed_data should be shape (n_augs,n_samples,ndim), where n_augs is 1 or 8 for rotation augmentation" + + for label in labels_uniq: + idxs = np.where(labels == label)[0] + + if do_filtering and label in protein_filter.keys(): + idxs = idxs[protein_filter[label]] + + DEBUG = 0 + if label == "GLT8D1": + print(label, len(idxs)) + if DEBUG: + imgs = make_crops_imgs(crops[idxs]) + grid = make_grid(imgs, ncols=10) + f, axs = plt.subplots() + axs.imshow(grid.permute(1, 2, 0)) + axs.set_axis_off() + f.savefig("tmp.png", dpi=200) + + embed = embed_data[:, idxs] + embed = embed.view(-1, embed.shape[-1]).mean(0) + embeds_consensus.append(embed) + embeds_consensus = torch.vstack(embeds_consensus) + + return embeds_consensus, labels_uniq + + +def threshold_based_filtering(self): + raise NotImplementedError( + "below is the code that did this, but it needs to be rewritten to be usable" + ) + # prots_nearest = df_opencell_lookup['prot_id'].values[dist_argsort] # (n_well_ids_inf,k_nearest_prots) + # loc_grade1_nearest = df_opencell_lookup['loc_grade1'].values[dist_argsort] # (n_well_ids_inf,k_nearest_prots) + threshold = 0.6 + is_under = dist < threshold + is_under = is_under.fill_diagonal_(0) + n_matches = is_under.sum(1) + + matches_loc_grade1_all = [] + votes_all = [] + prediction_all = [] + df_inf_predictions = df_inf_lookup.copy() + for i in range(n_inf): + row_index = df_inf_predictions.iloc[i].name + matches_loc_grade1 = df_opencell_lookup['loc_grade1'].values[ + is_under[i]] + matches_loc_grade1_all.append(matches_loc_grade1) + res = order_by_frequency_with_counts(matches_loc_grade1) + votes_all.append(res) + prediction_all.append(res[0][0]) + + # df_inf_predictions['pred_votes'].iloc[row_index] = res + # df_inf_predictions['top_pred'].iloc[row_index ] = res[0][0] + # print( df_inf_lookup[['protein','orgIP_cluster_annotation','plate33_eyeballed_annotation']].iloc[i]) + # print(res) + # print() + # print() + + df_inf_predictions.to_csv(f_save) + df_inf_predictions['predictions'] = prediction_all + df_inf_predictions['votes'] = votes_all + f_save = dir_results / f"threshold_opencell_preds_mode_{mode}_threshold_{threshold:.03f}.csv" + df_inf_predictions.to_csv(f_save) + + +def custom_sort_key(x): + """ + in `df_inf_lookup`, sort the wellids ('A1','A2','A10'...) according to + the letter and then the number, where you treat the number as an int and not a string + So, split the well_id into a prefix (e.g., 'A') and a numeric part + (e.g., '1', '3', '10', '11') + """ + prefix, numeric = x[0], int(x[1:]) + return (prefix, numeric) + + +def get_df_inf_lookup(df_meta_inf): + """ + `df_meta_inf` is a row for each crop. Each crop from the same well_id has + identical rows. This function returns a lookup function that is well_id + """ + df_inf_lookup = df_meta_inf.groupby('well_id').sample( + n=1).sort_values('well_id') + df_inf_lookup['sort_key'] = df_inf_lookup['well_id'].apply(custom_sort_key) + df_inf_lookup = df_inf_lookup.sort_values(by='sort_key').drop( + columns=['sort_key']) + df_inf_lookup.index = df_inf_lookup['well_id'] + + return df_inf_lookup + + +def analysis_choose_opencell_threshold(dir_pretrained_model, + mode="correlation", + do_umap=False): + current_filename = Path(os.path.basename(__file__)) + dir_results = Path( + "inference/results") / current_filename.stem / dir_pretrained_model + dir_results.mkdir(exist_ok=True, parents=True) + + # results dirsgg + embed_opencell, labels_opencell, crops_opencell, df_opencell, df_opencell_lookup = get_embeds_and_crops( + dir_pretrained_model, + checkpoint=checkpoint, + representation=representation, + dataset="opencell") + + ## get "consensus encodings" - this one is the elementwise mean of the vqvec2 vecotors + protein_label_opencell = labels_opencell[:, 1] + # get 'consensus encoding' which is one vector for the whole protein + embeds_consensus_opencell, labels_consensus_opencell = get_consensus_encoding( + embed_opencell, protein_label_opencell) + + # + if do_umap: + print("Doing umap embeddings") + import umap + reducer = umap.UMAP(n_components=2, verbose=True) + embedding = reducer.fit_transform(embeds_consensus_opencell) + # overwrite it + embeds_consensus_opencell = embedding + + # measure distances + if mode == "correlation": + dist = pairwise_distances(embeds_consensus_opencell, + embeds_consensus_opencell, + metric='correlation') + elif mode == "euclidean": + dist = pairwise_distances(embeds_consensus_opencell, + embeds_consensus_opencell, + metric='euclidean') + else: + raise + dist = torch.from_numpy(dist) + + ## let's plot the distribution of distances of one protein to other proteins, grouped + ## by whether or not they have the same loc_grade1 annotation. + is_same_all_classes = np.zeros_like(dist, dtype=np.int8) + is_overlapping_classes = np.zeros_like(dist, dtype=np.int8) + loc_grade_1 = np.array(df_opencell_lookup['loc_grade1']) + + # (this bit should be solvable with broadcasting, but it's too annoying) + n = len(dist) + for i in range(n): + for j in range(n): + is_same_all_classes[i, j] = (loc_grade_1[i] == loc_grade_1[j]) + overlapping_classes = set(loc_grade_1[i].split(";")).intersection( + set(loc_grade_1[j].split(";"))) + is_overlapping_classes[i, j] = (len(overlapping_classes) > 0) + is_no_overlapping_classes = -1 * (is_overlapping_classes - 1) + np.fill_diagonal(is_same_all_classes, 0) + np.fill_diagonal(is_overlapping_classes, 0) + np.fill_diagonal(is_no_overlapping_classes, 0) + + dists_same_all_classes = dist[np.where(is_same_all_classes)] + dists_overlapping_classes = dist[np.where(is_overlapping_classes)] + dists_no_overlap = dist[np.where(is_no_overlapping_classes)] + + import matplotlib.pyplot as plt + f, axs = plt.subplots(3, 1, sharex=True, figsize=(7, 20)) + bins = 100 + axs[0].hist(dists_same_all_classes, bins=bins) + axs[1].hist(dists_overlapping_classes, bins=bins) + axs[2].hist(dists_no_overlap, bins=bins) + + f_save = dir_results / f"compare_distances_1_mode_{mode}_doUmap_{do_umap}.png" + f.savefig(f_save) + plt.close() + + ### let's do the same thing but on a per-class basis (where class is defined as each specific combo, ~100 classes) + uniq_loc_grade_1 = np.unique(loc_grade_1) + f, axs = plt.subplots(3, + len(uniq_loc_grade_1), + figsize=(500, 30), + sharey=True) + if 0: # there's a bug here, no time to deal with it + for i, cls_loc_grade_1 in tqdm(enumerate(uniq_loc_grade_1)): + # index out prots from only that class. Need to take the subset of both + # the distance matrix and the masking matrices + + idxs = np.where(loc_grade_1 == cls_loc_grade_1)[0] + dist_subset = dist[idxs][:, idxs] + is_same_all_classes_subset = is_same_all_classes[idxs][:, idxs] + is_overlapping_classes_subset = is_overlapping_classes[idxs][:, + idxs] + is_no_overlapping_classes_subset = is_no_overlapping_classes[ + idxs][:, idxs] + + dists_same_all_classes_subset = dist_subset[np.where( + is_same_all_classes_subset)] + dists_overlapping_classes_subset = dist_subset[np.where( + is_overlapping_classes_subset)] + dists_no_overlap_subset = dist_subset[np.where( + is_no_overlapping_classes_subset)] + + try: + axs[0, i].set(title=f"n={len(idxs)} - {cls_loc_grade_1}") + axs[0, i].hist(dists_same_all_classes_subset, bins=bins) + axs[1, i].hist(dists_overlapping_classes_subset, bins=bins) + axs[2, i].hist(dists_no_overlap_subset, bins=bins) + except: + print( + f"Could not make histogram for class {cls_loc_grade_1}, n={len(cls_loc_grade_1)}" + ) + + f_save = dir_results / f"compare_distances_1_all_classes_mode_{mode}_doUmap_{do_umap}.png" + f.savefig(f_save) + + ## now let's suppose that we chose some threshold `t`. How many other proteins + ## would fit within that threshold? For each protein, we cound them, `k`, and + ## now let's plot a histogram of `k`. The idea: if there's lots that are 0, + ## then it's too tight. Plot it for a range of `t`. + + ts = np.linspace(0, 0.6, 10) + + f, axs = plt.subplots(len(ts), 1, figsize=(5, 10), sharex=True) + bins = 50 + for i, t in tqdm(enumerate(ts)): + is_under = np.array(dist < t).astype(int) + np.fill_diagonal(is_under, 0) + cnt_under_t = is_under.sum( + 1) # for each prot, how many other prots under threshold + axs[i].hist(cnt_under_t, bins=bins) + axs[i].set(title=f"threshod {t:.3f}") + + f_save = dir_results / f"simulate_threshold_buckets_1_mode_{mode}_doUmap_{do_umap}.png" + f.savefig(f_save) + + pass + + +def make_crops_imgs(imgs): + """ + image crops are either 2 channel (protein+nucleus) or 3-channel (protein+ + nucleus+distance transform). Make an image where the protein is in the green + channel, and the nucleus in the blue channel, and the red channel is all 0s. + """ + imgs_show = torch.zeros(len(imgs), 3, *imgs.shape[-2:], dtype=imgs.dtype) + imgs_show[:, 1] = imgs[:, 0] # protein in green channel + imgs_show[:, 2] = imgs[:, 1] # nucleus in green channel + + return imgs_show + + +def generate_inference_crop_grids(dir_pretrained_model, + fname_crops, + nsamples=100, + ncols=10, + checkpoint=None): + """ + """ + dir_results = Path( + "inference/results") / current_filename.stem / dir_pretrained_model + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = dir_results / 'viz_proteins' + dir_viz.mkdir(exist_ok=True, parents=True) + + # get the images and metadata (and also embeddings as a byproduct) + embed_inf, labels_inf, crops_inf, df_meta_inf, df_annotations, df_inf_lookup = get_embeds_and_crops( + dir_pretrained_model, + checkpoint=checkpoint, + representation='vqvec2', + dataset='inference') + + for protein in df_meta_inf['protein'].unique(): + # protein = "GLT8D1" + idxs = np.where(df_meta_inf['protein'] == protein)[0][:nsamples] + imgs = crops_inf[idxs] + imgs_show = torch.zeros(len(imgs), + 3, + *imgs.shape[-2:], + dtype=imgs.dtype) + imgs_show[:, 1] = imgs[:, 0] # protein in green channel + imgs_show[:, 2] = imgs[:, 1] # nucleus in green channel + grid = make_grid(imgs_show, nrow=ncols).permute(1, 2, 0) + + f_save = dir_viz / f"viz_{protein}.png" + f, axs = plt.subplots() + axs.imshow(grid) + axs.set_axis_off() + f.savefig(f_save, dpi=500) + + pass + + # grid = make_grid() + + +if __name__ == "__main__": + fname_crops = "inference/results/crop/crops.pt" + fname_crops_meta = "inference/results/crop/crops_meta.csv" + if VERSION>0: + fname_crops = f"inference/results/crop/crops_v{VERSION}.pt" + fname_crops_meta = "inference/results/crop/crops_meta_v{VERSION}.csv" + + # this file is protein ids and estimates of their annotations + fname_annotations = "data/cz_infectedcell_finalwellmapping.csv" + pca_dim = 200 + + # generate_inference_crop_grids("results/20231218_train_all_no_nucdist_balanced_classes_2", + # fname_crops, nsamples=100, ncols=10) + + dir_pretrained_models_checkpoints = [ + # ["results/20231218_train_all_no_nucdist_balanced_classes_2", None], + # ["results/20231218_train_all_balanced_classes_2", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231222_train_all_no_nucdist_balanced_classes_1", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231221_train_with_orphans", None], + # ["results/20231221_train_with_orphans_no_nucdist", None], + # ["results/20231222_train_all_no_nucdist_balanced_classes_1", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231218_train_all_no_nucdist", None], + # ["results/20231022_train_all", None], + + ["results/20240129_train_all_no_nucdist_balanced_classes_1", 36], + ["results/20240129_train_all_balanced_classes_2", None], + ["results/20240129_train_all", None], + ["results/20240129_train_all_no_nucdist", None], + ] + + # analyse distances + representation = 'vqvec2' #'hist' + mode = 'correlation' + combine_inference_wells = True + do_include_nn_losses = False + do_compare_orphans = True # when doing kNN's, include the orphans in the new orphans in the lookup + do_filtering = False # now set to true because we use the intesnity based filtering + + do_visualize_knns = False # create an image for each protein visualizing nearest-neighbors + # combine_augmented_embeddings = False + + # for representation in ('vqvec2', 'hist'): + for (dir_pretrained_model, + checkpoint) in dir_pretrained_models_checkpoints: + for representation in ('vqvec2', 'hist'): + for pca_dim in ( + 200, + None, + ): + for mode in ( + 'correlation', + 'euclidean', + ): + # if we have VERSION==2 then the filtering is done ahead of time + for do_filtering in ( + False, + # True, + ): + for combine_augmented_embeddings in ( + False, + # True, + ): + get_nearest_proteins( + fname_crops=fname_crops, + fname_crops_meta=fname_crops_meta, + fname_annotations=fname_annotations, + dir_pretrained_model=dir_pretrained_model, + representation=representation, + combine_inference_wells=combine_inference_wells, + pca_dim=pca_dim, + do_filtering=do_filtering, + do_visualize_knns=do_visualize_knns, + checkpoint=checkpoint, + combine_augmented_embeddings= + combine_augmented_embeddings, + do_include_nn_losses=do_include_nn_losses, + do_compare_orphans=do_compare_orphans, + mode=mode) + diff --git a/inference/crop.py b/inference/crop.py new file mode 100644 index 0000000..50dc7bf --- /dev/null +++ b/inference/crop.py @@ -0,0 +1,518 @@ +""" +Run in project home dir as + python inference/crop.py + +Get the FOVs for `inference/results/load_inf_data/` and the segmentation maps +from `inference/results/nucelear_segmentation/all_segmasks.pt`. + +For each nucleus, generate a crop centered around that nucleus with 200px width +and height. If the crop extends beyond the boundaries of the FOV image (i.e. it's +too close to the border), then we skip it. +The crop has the protein in ch0 and protein in ch1. They are saved to +`inference/results/crop/`. Each channel in each crop is normalized to range [0,1] +independently to the rest of the FOV. + +The output is `inference/results/crops/crops.pt` + +Also visualize the crops in, inference/viz/crop/. Create one image in that folder +for nucleus and one for protein. Each grid item is a crop around one nucleus. + +Optionally add a filter for whether the protein channel has enough signal. + +** I really should have put the two separate functions into one function, but what can you do +""" + +from pathlib import Path +import ipdb +import torch +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from PIL import Image +from PIL import UnidentifiedImageError +from skimage import measure +from torchvision.utils import make_grid +import os +from skimage.morphology import binary_erosion, binary_dilation +from scipy.ndimage import distance_transform_edt +import torchvision.transforms as T +from aicsimageio import AICSImage +import tqdm + +# VERSION defines the cropping strategy, and defines how the crops will be saved +# If 0 do the normalization on each crop (recommended) +# If 1, do the normalization on each FOV. +# If 2, do crop-level normalization (just like version 0) AND also do intensity-threshold based filtering. +# This version produces logs btw. +VERSION = 0 + +# these params are all relevant for VERSION 2 only +LOG_VERSION_2_RESULTS = 1 +THRESH = 10 +PCNT_PIXELS = 0.01 +num_pixels_over_thresh = -1 + + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x - l) / (u - l) + + +def generate_crops(dir_infdata, + fname_segmasks, + min_nuclear_diam=50, + width=200, + height=200, + DO_VIZ=True, + viz_dpi=150): + dir_infdata = Path(dir_infdata) + fname_segmasks = Path(fname_segmasks) + + # results dirs + current_filename = Path(os.path.basename(__file__)) + dir_results = Path("inference/results") / os.path.join( + current_filename.stem, "") + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + dir_viz.mkdir(exist_ok=True, parents=True) + + if LOG_VERSION_2_RESULTS: + dir_viz = dir_viz / "version2" + dir_viz.mkdir(exist_ok=True, parents=True) + + # get masks and the fname of the corresponding FOV nucleus channel + masks, fnames_nuc = torch.load(fname_segmasks) + + crops_all = [] + df_meta_all = [] + + # iterate over the FOVs + + for i, mask in tqdm.tqdm(enumerate(masks), total=len(masks)): + print("Mask ", i) + crops = [] + df_meta = pd.DataFrame(columns=[ + "fname_pro", "fname_nuc", "nuc_centroid", "idx_instance", + "idx_mask", "axis_major_length" + ]) + + n_instances = len(np.unique(mask)) - 1 + f_nuc = fnames_nuc[i] + f_pro = str(f_nuc)[:-7] + "pro.png" + print(f_pro) + # if "C3" not in f_pro: + # continue + try: + img_nuc = np.array(Image.open(f_nuc)) + img_pro = np.array(Image.open(f_pro)) + except UnidentifiedImageError as e: + print("caught error") + print(e) + + if VERSION == 1: + # normalize the whole FOV + img_nuc = norm_0_1(img_nuc) + img_pro = norm_0_1(img_pro) + + if VERSION == 2: + # compute an intensity threshold. The `img_pro` is already put in the + # range [0,255]. + num_pixels_over_thresh = PCNT_PIXELS * height * width + + # within a FOV, iterate over the nucleus instances + crops_keep, crops_reject = [], [] + for j in range(1, n_instances + 1): + # compiute the centroid + target_indices = np.argwhere(mask == j) + centroid = target_indices.mean(axis=0).astype(int) + + mask_this_idx = np.zeros_like(mask) + mask_this_idx[mask == j] = 1 + axis_major_length = measure.regionprops( + mask_this_idx)[0].axis_major_length + if axis_major_length < min_nuclear_diam: + continue + + # do image crop, normalize each channel independently. + # and check that it's not hitting the image border + slc_0 = slice(centroid[0] - height // 2, centroid[0] + height // 2) + slc_1 = slice(centroid[1] - width // 2, centroid[1] + width // 2) + img_pro_crop = img_pro[slc_0, slc_1] + img_nuc_crop = img_nuc[slc_0, slc_1] + mask_crop = mask[slc_0, slc_1].copy() + mask_crop[mask_crop > 0] = 1 + if img_pro_crop.shape != (width, height): + continue # skip the crop bc we hit a border + nuc_dist_map = do_nuclear_dist_transform(mask_crop) + + if VERSION == 2: + # the skip case + if (img_pro_crop>THRESH).sum() < num_pixels_over_thresh: + crops_reject.append(img_pro_crop.copy()) + continue + # the keep case + else: + crops_keep.append(img_pro_crop.copy()) + + if VERSION in (0,2): + img_pro_crop = norm_0_1(img_pro_crop) + img_nuc_crop = norm_0_1(img_nuc_crop) + img_crop = np.stack(( + img_pro_crop, + img_nuc_crop, + nuc_dist_map, + )) + + # add the crop and metadata + crops.append(img_crop) + row_meta = pd.DataFrame( + dict( + fname_pro=[f_pro], + fname_nuc=[f_nuc], + nuc_centroid=[centroid], + idx_instance=[j], + idx_mask=[i], + axis_major_length=[axis_major_length], + )) + df_meta = pd.concat([df_meta, row_meta]) + + # visualization + if 1: + f_save_original_image = dir_viz / (Path(f_pro).stem+".png") + Image.fromarray(img_pro, "L").save(f_save_original_image) + + nrow=3 + pad_value = 122 + + if len(crops_keep) > 0: + f_save_keep = dir_viz / (Path(f_pro).stem + f"_keep_{len(crops_keep)}.png") + grid_keep = make_grid(torch.from_numpy(np.stack(crops_keep)).unsqueeze(1), nrow=3, pad_value=pad_value) + grid_keep = (grid_keep[0]).numpy().astype(np.uint8) + Image.fromarray(grid_keep, "L").save(f_save_keep) + + if len(crops_reject) != 0: + f_save_reject = dir_viz / (Path(f_pro).stem + f"_reject_{len(crops_reject)}.png") + grid_reject = make_grid(torch.from_numpy(np.stack(crops_reject)).unsqueeze(1), nrow=3, pad_value=pad_value) + grid_reject = (grid_reject[0]).numpy().astype(np.uint8) + Image.fromarray(grid_reject, "L").save(f_save_reject) + + # optionally visualize crops for this FOV in a grid + if 0: + if len(crops) < 1: + continue + + crops_arr = torch.from_numpy(np.stack(crops)) # (N,2,H,W) + + grid_nuc = make_grid(crops_arr[:, [1]], nrow=8, pad_value=0.5) + f, axs = plt.subplots() + axs.imshow(grid_nuc.permute(1, 2, 0)) + f.savefig(dir_viz / (f"{f_nuc.stem}" + "nucleus_only.png"), + dpi=viz_dpi) + plt.close() + + grid_pro = make_grid(crops_arr[:, [0]], nrow=8, pad_value=0.5) + f, axs = plt.subplots() + axs.imshow(grid_pro.permute(1, 2, 0)) + f.savefig(dir_viz / (f"{f_nuc.stem}" + "protein_only.png"), + dpi=viz_dpi) + plt.close() + + crops_all.extend(crops) + df_meta_all.append(df_meta) + + # save the crops + crops_all_save = torch.from_numpy( + np.stack(crops_all)) # (n,2,width,height) + df_meta_all_save = pd.concat(df_meta_all) + if VERSION > 0: + f_save_crop = dir_results / f"crops_v{VERSION}.pt" + f_save_df_meta = dir_results / f"crops_meta_v{VERSION}.csv" + else: + f_save_crop = dir_results / f"crops.pt" + f_save_df_meta = dir_results / f"crops_meta.csv" + torch.save(crops_all_save, f_save_crop) + df_meta_all_save.to_csv(f_save_df_meta) + + +def generate_crops_opencell(min_nuclear_diam=50, + width=200, + height=200, + resize_dim=100, + DO_VIZ=True, + viz_dpi=150): + """ + The width and height params are the number pixels in the original FOV that + we'll crop from. + The `resize_dim will then resize that. + """ + + # results dirs + current_filename = Path(os.path.basename(__file__)) + # dir_results = Path("inference/results") / os.path.join(current_filename.stem, "") + # dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem / "opencell" + dir_viz.mkdir(exist_ok=True, parents=True) + + dir_output = Path("data/opencell_crops_processed2") + + dir_data_opencell = Path( + "/hpc/projects/group.leonetti/opencell-microscopy/") + dir_masks_opencell = Path("inference/results/nuclear_segmentation/opencell/") + df_meta_opencell = pd.read_csv(dir_data_opencell / "2021-08-30_good-fovs.csv") + + proteins = np.sort(df_meta_opencell.target_name.unique()) + df_meta_all = [] + # iterate over proteins + for protein in proteins: + df_this_prot = df_meta_opencell[df_meta_opencell['target_name'] == protein] + filepaths = df_this_prot["filepath"] + crops_all = [] + + for idx_mask, filepath in enumerate(filepaths): + try: + fname = dir_data_opencell / filepath + img_proj = AICSImage(fname).data[0].max(1) + img_nuc, img_pro = img_proj[0], img_proj[1] + + except: + continue + + mask = torch.load(dir_masks_opencell / (Path(filepath).stem+".pt")) + assert mask.shape == img_proj.shape[1:] + n_instances = len(np.unique(mask))-1 + + # in this version, do the normalization at the FOV level + if VERSION == 1: + img_nuc = norm_0_1(img_nuc) + img_pro = norm_0_1(img_pro) + + num_pixels_over_thresh = -1 + if VERSION == 2: + # compute an intensity threshold. The `img_pro` is already put in the + # range [0,255]. + num_pixels_over_thresh = PCNT_PIXELS * height * width + + crops, df_meta = crop_one_fov(mask, img_nuc, img_pro, f_pro=fname, f_nuc=filepath, + height=height, width=width, idx_mask=idx_mask, num_pixels_over_thresh=num_pixels_over_thresh) + + crops_all += crops + df_meta_all.append(df_meta) + + if 1: + # channels are (protein, nucleus, nuclear transform) + # load something from Orphans + # img_nuc = np.load("data/opencell_crops_proteins_plus_orphans/TSPAN3_nuc.npy") + # img_pro = np.load("data/opencell_crops_proteins_plus_orphans/TSPAN3_pro.npy") + # img_nucdist = np.load("data/opencell_crops_proteins_plus_orphans/TSPAN3_nucdist.npy") + # imgs_inf = np.stack((img_pro, img_nuc, img_nucdist), axis=1) + + import matplotlib.pyplot as plt + for k in range(5): + f, axs = plt.subplots(1,3) + for i in range(3): + axs[i].imshow(crops[k][i]) + axs[i].set_axis_off() + # axs[1,i].imshow(imgs_inf[k,i]) + f.savefig(dir_viz / f"crops_sample_{protein}_{k}.png") + plt.close() + + # combine and resize the crops + crops_all = np.stack(crops_all) + crops_all = T.Resize(resize_dim)(torch.from_numpy(crops_all)).float().numpy() + + # generate the labels by just copying it from the old one and adjusting the length + labels_old = np.load(f"data/opencell_crops_proteins/{protein}_label.npy") + labels_new = np.array(list([labels_old[0]])*len(crops_all)) + + # save + np.save(dir_output / f"{protein}_pro", crops_all[:,0]) + np.save(dir_output / f"{protein}_nuc", crops_all[:,1]) + np.save(dir_output / f"{protein}_nucdist", crops_all[:,2]) + np.save(dir_output / f"{protein}_label", labels_new) + + + df_meta_all = pd.concat(df_meta_all) + df_meta_all.to_csv(dir_viz / "df_meta_all.csv") + + + +def crop_one_fov(mask, img_nuc, img_pro, f_pro, f_nuc, height, width, + num_pixels_over_thresh, idx_mask=-1,): + assert VERSION in (0,1,2) + if VERSION == 1: + img_nuc = norm_0_1(img_nuc) + img_pro = norm_0_1(img_pro) + + n_instances = len(np.unique(mask))-1 + crops = [] + df_meta = pd.DataFrame(columns=[ + "fname_pro", "fname_nuc", "nuc_centroid", "idx_instance", + "idx_mask", "axis_major_length" + ]) + + for j in range(1, n_instances + 1): + # compiute the centroid + target_indices = np.argwhere(mask == j) + centroid = target_indices.mean(axis=0).astype(int) + + mask_this_idx = np.zeros_like(mask) + mask_this_idx[mask == j] = 1 + axis_major_length = measure.regionprops( + mask_this_idx)[0].axis_major_length + + if axis_major_length < min_nuclear_diam: + continue + + # do image crop, normalize each channel independently. + # and check that it's not hitting the image border + slc_0 = slice(centroid[0] - height // 2, centroid[0] + height // 2) + slc_1 = slice(centroid[1] - width // 2, centroid[1] + width // 2) + img_pro_crop = img_pro[slc_0, slc_1] + img_nuc_crop = img_nuc[slc_0, slc_1] + mask_crop = mask[slc_0, slc_1].copy() + mask_crop[mask_crop > 0] = 1 + if img_pro_crop.shape != (width, height): + continue # skip the crop bc we hit a border + nuc_dist_map = do_nuclear_dist_transform(mask_crop) + + if VERSION == 2: + # the skip case - for opencell, we don't actually do the filtering + if (img_pro_crop>THRESH).sum() < num_pixels_over_thresh: + # crops_reject.append(img_pro_crop.copy()) + continue + # the keep case + else: + pass + # crops_keep.append(img_pro_crop.copy()) + + if VERSION in (0,2): + img_pro_crop = norm_0_1(img_pro_crop) + img_nuc_crop = norm_0_1(img_nuc_crop) + img_crop = np.stack(( + img_pro_crop, + img_nuc_crop, + nuc_dist_map, + )) + + # some debugging stuff + if 0: + f, axs = plt.subplots(1, 3) + for i in [0, 1, 2]: + axs[i].imshow(img_crop[i]) + f.savefig('tmp.png') + + # add the crop and metadata + crops.append(img_crop) + row_meta = pd.DataFrame( + dict( + fname_pro=[f_pro], + fname_nuc=[f_nuc], + nuc_centroid=[centroid], + idx_instance=[j], + idx_mask=[idx_mask], + axis_major_length=[axis_major_length], + )) + df_meta = pd.concat([df_meta, row_meta]) + + return crops, df_meta + + +def do_nuclear_dist_transform(mask_crop): + # next two lines are from chatgpt + boundary = binary_dilation(mask_crop) ^ binary_erosion(mask_crop) + distance_array = distance_transform_edt(~boundary) + + # normalize distance + assert distance_array.shape[0] == distance_array.shape[ + 1], "if shapes not square, rethink how to normalize" + distance_array = distance_array / distance_array.shape[0] + + # make the outisde-nucleus stuff negative distance + distance_array[~(mask_crop.astype(bool))] *= -1 + + if 0: + f, axs = plt.subplots(1, 3) + axs[0].imshow(mask_crop) + axs[1].imshow(boundary) + im = axs[2].imshow(distance_array) + f.colorbar(im, ax=axs[2]) + f.savefig("tmp.png") + + return distance_array + + +def save_crops_to_dataset(): + """ + Save the crops in the style needed to be added to the training dataset. + This was for doing training jointly with the OpenCell data. + """ + fname_crops = "inference/results/crop/crops.pt" + fname_crops_meta = "inference/results/crop/crops_meta.csv" + dir_data = Path("data/opencell_crops_proteins_plus_orphans") + + crops_inf = torch.load(fname_crops) + crops_inf = T.Resize(100)(crops_inf) + + df_meta_inf = pd.read_csv(fname_crops_meta) + df_meta_inf['well_id'] = [ + s.split("_")[-3] for s in df_meta_inf['fname_pro'] + ] + fname_annotations = "data/cz_infectedcell_finalwellmapping.csv" + df_annotations = pd.read_csv(fname_annotations) + + df_meta_inf = df_meta_inf.merge(df_annotations, + how='left', + left_on='well_id', + right_on='well_id_new') + # the next file we pull is created by get_crop_features.py + from compare_opencell_targets import get_df_inf_lookup + df_inf_lookup = get_df_inf_lookup(df_meta_inf) + + proteins = df_inf_lookup.protein.unique() + proteins = [p for p in proteins if 'pML' not in p] + for prot in proteins: + print(prot) + row_lookup = df_inf_lookup[df_inf_lookup['protein'] == prot] + well_ids = row_lookup.well_id + idxs = np.where(df_meta_inf['well_id'].isin(well_ids))[0] + crops_this = crops_inf[idxs].numpy() + + protein_id = row_lookup['protein_id'].values[0] + labels = np.array([[protein_id, prot, '']] * len(crops_this)) + np.save(dir_data / f"{prot}_pro.npy", crops_this[:, 0]) + np.save(dir_data / f"{prot}_nuc.npy", crops_this[:, 1]) + np.save(dir_data / f"{prot}_nucdist.npy", crops_this[:, 2]) + np.save(dir_data / f"{prot}_label.npy", labels) + + pass + + +if __name__ == "__main__": + # crops for opencell + if 0: + min_nuclear_diam = 50 # px + width, height = 200, 200 # px + generate_crops_opencell(min_nuclear_diam=min_nuclear_diam, + width=200, + height=200, + DO_VIZ=True, + viz_dpi=150) + + # crops for orphans (the inference set) + if 1: + min_nuclear_diam = 50 # px + width, height = 200, 200 # px + dir_infdata = "inference/results/load_inf_data/" + fname_segmasks = "inference/results/nuclear_segmentation/all_segmasks.pt" + generate_crops(dir_infdata, + fname_segmasks, + min_nuclear_diam=min_nuclear_diam, + width=width, + height=height, + DO_VIZ=True) + # save_crops_to_dataset() + + + diff --git a/inference/get_crop_features.py b/inference/get_crop_features.py new file mode 100644 index 0000000..434a29b --- /dev/null +++ b/inference/get_crop_features.py @@ -0,0 +1,490 @@ +""" +Run in project home dir as + python inference/get_crop_features.py + +Get the crops from `inference/results/crops/crops.pt` and metadata from +`inference/results/crops/crops_meta.csv`. Create a `datamanager` object (an +object type that was defined for the cytoself project). At this stage + +Following the code in `example_scripts/simple_example.py`, + +Embeddings will put in `inference/results/get_crop_features/` +where `` is an arg to `get_features()`. This way, we can +do the analysis for multiple pretrained models. +""" +import ipdb +import os +import numpy as np +import pandas as pd +from pathlib import Path +import torch +import json +import torchvision.transforms as T +from torchvision.utils import make_grid +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from sklearn.metrics import pairwise_distances +from skimage import io +from skimage.morphology import binary_erosion, binary_dilation +from scipy.ndimage import distance_transform_edt +import tqdm +import pandas as pd + +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer + +# description of the versions in the +VERSION = 0 +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +def get_features(fname_crops, + fname_crops_meta, + dir_pretrained_model, + checkpoint=None, + do_reconstructions=False, + augmentation=None, + do_retrieval_test=False, + ): + """ + augmentation: one of 8 image augmentations. None,90,180,270,0_f,90_f,180_f,270_f + This will rotate/flip the image + and save the embeddgin to a different file with the augmentation as the suffix + (unless augmentation=None, then there is no suffix) + + """ + # handle image augmentations + stem_fname = augmentation + if augmentation is None: + augmentation = "0" + assert augmentation in ("0","90","180","270","0_f","90_f","180_f","270_f") + augmentation_rot = int(augmentation.split("_")[0]) + augmentation_do_flip = True if len(augmentation.split("_"))>1 else False + + # results dirs + current_filename = Path(os.path.basename(__file__)) + dir_results = Path( + "inference/results") / current_filename.stem / dir_pretrained_model / f"ckpt_{checkpoint}" + if VERSION > 0: + dir_results = dir_results / f"version{VERSION}" + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + dir_viz.mkdir(exist_ok=True, parents=True) + + # reproduce the trainer object + model_args, train_args = get_model_trainer_args(dir_pretrained_model) + trainer = CytoselfFullTrainer(train_args, + homepath=dir_results, + model_args=model_args) + + # the model was created by the trainer. + # Load the 'best' checkpoint + if checkpoint is None: + fs_models = [f for f in Path(dir_pretrained_model).glob("model*pt")] + assert len(fs_models) == 1 + fs_model = fs_models[0] + trainer.model = torch.load(fs_model) + + else: + fs_model = Path(dir_pretrained_model + ) / f'checkpoints/checkpoint_ep{checkpoint}.chkp' + chkp = torch.load(fs_model) + trainer.model.load_state_dict(chkp['model_state_dict']) + + # load crops and metadata. Put it into datamanager. + crops = torch.load(fname_crops) + assert crops.shape[-2:] == (200, 200) + crops = T.Resize(100)(crops).to(device) + if augmentation_rot != 0: + crops = T.functional.rotate(crops, augmentation_rot) + if augmentation_do_flip: + crops = T.functional.vflip(crops) + + if model_args['input_shape'][0] == 2: + crops = crops[:, :2] + + ## now deal with augmentations + df_meta = pd.read_csv(fname_crops_meta) + labels, map_int_to_wellid = get_labels_from_df_meta(df_meta) + datamanager = PreloadedDataset(label=labels, data=crops) + + # validation: image reconstructions - results in`inference/viz/get_crop_features/reconstructions` + if do_reconstructions: + test_reconstructions(trainer, crops, dir_viz, n_samples=20) + + # the standard embeddings: the output of the vq encoder at level 2 + loader_inf = DataLoader(datamanager, batch_size=64) + trainer.model.eval() + embeddings_vqvec2 = trainer.infer_embeddings(loader_inf, + output_layer='vqvec2') + f_save = dir_results / f"embeddings_vqvec2_aug{stem_fname}.pt" + torch.save(embeddings_vqvec2, f_save); + + # the feature spectrum encodings + vqindhist1 = trainer.infer_embeddings(loader_inf, 'vqindhist1') + f_save = dir_results / f"embeddings_vqindhist1_aug{stem_fname}.pt" + torch.save(vqindhist1, f_save) + + # re-save the metadata again + df_meta.to_csv(dir_results / (Path(fname_crops_meta).stem + ".csv")) + + # another validation check: knn lookup + + if do_retrieval_test: + test_knn_retrieval_embeddings(crops, + embeddings_vqvec2, + dir_viz=dir_viz, + metric='euclidean') + test_knn_retrieval_embeddings(crops, + embeddings_vqvec2, + dir_viz=dir_viz, + metric='correlation') + + +def get_labels_from_df_meta(df_meta): + """ + Take df_meta dataframe, and convert to a tensor of integer labels, where + each well_id is its own class. + + Output: + labels (np.array): shape (n,3). Col0 is an int for well_id, col1 is + fov_id, and col2 is a unique identifier for the data sample. + map_int_to_wellid (dict): dictionary mapping ints in labels[:,0] to a + well_id like "A10"" + """ + y = np.array([Path(l).stem.split("_")[2:4] for l in df_meta.fname_pro]) + well_ids = y[:, 0] + fov_ids = y[:, 1].astype(int) + well_ids_uniq, fov_ids_uniq = np.unique(well_ids), np.unique(fov_ids) + + map_int_to_wellid = dict(zip(range(len(well_ids_uniq)), well_ids_uniq)) + map_wellid_to_int = {v: k for k, v in map_int_to_wellid.items()} + well_ids_int = np.array([map_wellid_to_int[w] for w in well_ids]) + + labels = np.stack((well_ids_int, fov_ids, np.arange(len(well_ids_int))), + axis=1) + + return labels, map_int_to_wellid + + +def get_model_trainer_args(dir_pretrained_model): + dir_pretrained_model = Path(dir_pretrained_model) + + f_args = dir_pretrained_model / "model_args.json" + assert f_args.exists(), f_args + with open(f_args, 'r') as f: + model_args = json.load(f) + f_args = dir_pretrained_model / "train_args.json" + assert f_args.exists(), f_args + with open(dir_pretrained_model / "train_args.json", 'r') as f: + train_args = json.load(f) + return model_args, train_args + + +def test_reconstructions(trainer, crops, dir_viz, n_samples=20, dpi=300): + + dir_viz_recons = Path(dir_viz) / "test_reconstructions" + dir_viz_recons.mkdir(exist_ok=True, parents=True) + + # randomly sample some of the images + dtype = crops.dtype + idxs = np.arange(len(crops)) + np.random.seed(0) + np.random.shuffle(idxs) + idxs = idxs[:n_samples] + + # reconstruction with autoencoder + crops_test = crops[idxs].numpy() + crops_recon = trainer.infer_reconstruction(crops_test) + + # visualize + imgs = torch.zeros((len(crops_test) * 2, *crops_test.shape[1:]), + dtype=dtype) + imgs[::2] = torch.tensor(crops_test) + imgs[1::2] = torch.tensor(crops_recon) + grid_pro = make_grid(imgs, nrow=10)[0] + grid_nuc = make_grid(imgs, nrow=10)[1] + f, axs = plt.subplots() + axs.imshow(grid_pro) + axs.set_axis_off() + f.savefig(dir_viz_recons / "reconstructions_protein.png", dpi=dpi) + f, axs = plt.subplots() + axs.imshow(grid_nuc) + axs.set_axis_off() + f.savefig(dir_viz_recons / "reconstructions_nucleus.png", dpi=dpi) + + +def test_knn_retrieval_embeddings(crops, + embeddings_vqvec2, + dir_viz, + metric='euclidean', + n_samples=30, + k_nearest=20, + seed=0, + dpi=200): + """ + Get the nearest `k` images from the same dataset (includeing itself) for `n` + images. + The `metric` is passed to sklearn.metrics.pairwise_distances. + n_samples (int): how many images to do retrieval for + seed (int): for sampling query images in the knn retrieval. + """ + dir_viz_knn = Path(dir_viz) / f"test_knn" + dir_viz_knn.mkdir(exist_ok=True, parents=True) + + # get the distrance matrix + z = torch.tensor(embeddings_vqvec2[0]) + z = z.view(len(z), -1) + dist = pairwise_distances(z, metric=metric) + + # randomly sample some of the images + idxs = np.arange(len(z)) + np.random.seed(seed) + np.random.shuffle(idxs) + idxs = idxs[:n_samples] + + # for the image subset, to knn retrieval + dist_argsort = torch.argsort(torch.from_numpy(dist[idxs]), dim=1) + idxs_nearest = dist_argsort[:, :k_nearest] # get the k_nearest indexes + idxs_nearest = idxs_nearest.flatten() + imgs = crops[idxs_nearest] + grid = make_grid(imgs, nrow=k_nearest) + grid_pro = grid[0] + grid_nuc = grid[1] + + # visualize + f, axs = plt.subplots() + axs.imshow(grid_pro) + axs.set_axis_off() + f.savefig(dir_viz_knn / f"knn_{metric}_seed{seed}_protein.png", dpi=dpi) + f, axs = plt.subplots() + axs.imshow(grid_nuc) + axs.set_axis_off() + f.savefig(dir_viz_knn / f"knn_{metric}_seed{seed}_nucleus.png", dpi=dpi) + + +def get_features_opencell(fname_crops, + fname_crops_meta, + dir_pretrained_model, + checkpoint=None, + augmentation=None, + do_retrieval_test=False, + do_reconstructions=False): + + # handle image augmentations + stem_fname = augmentation + if augmentation is None: + augmentation = "0" + assert augmentation in ("0","90","180","270","0_f","90_f","180_f","270_f") + augmentation_rot = int(augmentation.split("_")[0]) + augmentation_do_flip = True if len(augmentation.split("_"))>1 else False + + # results dirs + current_filename = Path(os.path.basename(__file__)) + dir_results = Path( + "inference/results") / current_filename.stem / dir_pretrained_model / f"ckpt_{checkpoint}" + dir_results.mkdir(exist_ok=True, parents=True) + + dir_viz = Path("inference/viz") / current_filename.stem + dir_viz.mkdir(exist_ok=True, parents=True) + + # reproduce the trainer object + model_args, train_args = get_model_trainer_args(dir_pretrained_model) + trainer = CytoselfFullTrainer(train_args, + homepath=dir_results, + model_args=model_args) + + # the model was created by the trainer. + # Load the 'best' checkpoint + if checkpoint is None: + fs_models = [f for f in Path(dir_pretrained_model).glob("model*pt")] + assert len(fs_models) == 1 + fs_model = fs_models[0] + trainer.model = torch.load(fs_model) + + else: + fs_model = Path(dir_pretrained_model + ) / f'checkpoints/checkpoint_ep{checkpoint}.chkp' + chkp = torch.load(fs_model) + trainer.model.load_state_dict(chkp['model_state_dict']) + + # load crops and metadata. Put it into datamanager. + crops = np.load(fname_crops) + # assert crops.shape[-2:] == (200,200) + # crops = T.Resize(100)(crops) + crops = torch.from_numpy(crops) + if augmentation_rot != 0: + crops = T.functional.rotate(crops, augmentation_rot) + if augmentation_do_flip: + crops = T.functional.vflip(crops) + + if model_args['input_shape'][0] == 2: + crops = crops[:, :2] + + if model_args['input_shape'][0] == 2: + crops = crops[:, :2] + + # df_meta = pd.read_csv(fname_crops_meta) + df_meta = np.load(fname_crops_meta) + labels = np.arange(len(crops))[:, None] + datamanager = PreloadedDataset(label=labels, data=crops) + + # validation: image reconstructions - results in`inference/viz/get_crop_features/reconstructions` + if do_reconstructions: + test_reconstructions(trainer, crops, dir_viz, n_samples=20) + + # the standard embeddings: the output of the vq encoder at level 2 + loader_inf = DataLoader(datamanager, batch_size=64) + trainer.model.eval() + embeddings_vqvec2 = trainer.infer_embeddings(loader_inf, + output_layer='vqvec2') + f_save = dir_results / f"embeddings_vqvec2_opencell_aug{stem_fname}.pt" + torch.save(embeddings_vqvec2, f_save) # + + # the feature spectrum encodings + vqindhist1 = trainer.infer_embeddings(loader_inf, 'vqindhist1') + f_save = dir_results / f"embeddings_vqindhist1_opencell_aug{stem_fname}.pt" + torch.save(vqindhist1, f_save) + + loss_ce_per_protein, loss_recon_per_protein = get_protein_losses(trainer, crops, df_meta, dir_pretrained_model) + loss_ce_per_protein.to_csv(dir_results / "loss_ce.csv") + loss_recon_per_protein.to_csv(dir_results / "loss_recon.csv") + +def get_protein_losses(trainer, crops, df_meta, dir_pretrained_model): + """ """ + from torch.utils.data import DataLoader, TensorDataset + uniq_prot_locgrade = np.unique(df_meta[:,[1,2]], axis=0) + lookup_prot_to_locgrade = dict(zip(uniq_prot_locgrade[:,0], uniq_prot_locgrade[:,1])) + + # the classification indices are defined by the ensg_id + ensg_id = df_meta[:,0] + ensg_id_uniq = np.unique(ensg_id) + lookup_ensg_id_to_label = dict(zip(ensg_id_uniq, np.arange(len(ensg_id_uniq)))) + labels = np.array([lookup_ensg_id_to_label[i] for i in ensg_id]) + # also get the protein names + proteins = df_meta[:,1] + proteins_uniq = np.sort(np.unique(proteins)) + lookup_prot_to_label = dict(zip(proteins_uniq, np.arange(len(proteins_uniq)))) + lookup_label_to_prot = {v:k for k,v in lookup_prot_to_label.items()} + + dset = TensorDataset(crops, torch.from_numpy(labels)) + loader = DataLoader(dset, batch_size=512) + crops_pred, logits = [], [] + with torch.no_grad(): + for img, label in tqdm.tqdm(loader): + img = img.to(trainer.device) + crops_pred_, logits_ = trainer.model(img) + crops_pred.append(crops_pred_) + logits.append(logits_) + + crops_pred = torch.cat(crops_pred).cpu() + logits = torch.cat(logits).cpu() + + # careful: not sure if the datamanager does normalization of the images + loss_recon = torch.nn.functional.mse_loss(crops_pred, crops, reduction="none") + loss_recon = loss_recon.view(len(loss_recon), -1).mean(1) + + # loss_ce + loss_ce = torch.nn.functional.cross_entropy(logits, torch.from_numpy(labels), reduction="none") + preds = torch.argmax(logits, dim=1) + preds_protein = np.array([lookup_label_to_prot.get(i.item(),"") for i in preds]) + + # analye the reconstruction losses + df_loss_recon = pd.DataFrame(dict(loss_recon=loss_recon, protein=proteins)) + loss_recon_per_protein = df_loss_recon.groupby("protein")['loss_recon'].mean() + prots_sorted = loss_recon_per_protein.sort_values().index.values[::-1] + + # analyse the ce loss + df_loss_ce = pd.DataFrame(dict(loss_ce=loss_ce, protein=proteins)) + loss_ce_per_protein = df_loss_ce.groupby("protein")['loss_ce'].mean() + + idxs_least = np.argsort(loss_ce_per_protein.values)[::-1] + prots_sorted = loss_ce_per_protein.sort_values().index.values[::-1] + + if 0: + import matplotlib.pyplot as plt + f, axs = plt.subplots() + axs.hist(loss_ce_per_protein, bins=50) + f.savefig("hist_2.png") + + # plot the cumulative density + f, axs = plt.subplots() + data_sorted = np.sort(loss_ce_per_protein) + cumulative = np.arange(1, len(data_sorted) + 1) / len(data_sorted) + axs.plot(data_sorted, cumulative, marker='.', linestyle='none') + f.savefig("cdf_2.png") + + return loss_ce_per_protein, loss_recon_per_protein + + + +if __name__ == "__main__": + + # a tuple with the pretrained directory and the chosen checkpoint (None if best) + dir_pretrained_models_checkpoints = [ + + # ["results/20231218_train_all_balanced_classes_2", None], + # ["results/20231218_train_all_no_nucdist_balanced_classes_2", None], + # ["results/20231222_train_all_no_nucdist_balanced_classes_1", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231221_train_with_orphans", None], + # ["results/20231221_train_with_orphans_no_nucdist", None], + # ["results/20231222_train_all_no_nucdist_balanced_classes_1", None], + # ["results/20231222_train_all_balanced_classes_1", None], + # ["results/20231218_train_all_no_nucdist", None], + # ["results/20231022_train_all", None], + ["results/20240129_train_all_no_nucdist_balanced_classes_1", 36], + ["results/20240129_train_all_balanced_classes_2", None], + ["results/20240129_train_all", None], + ["results/20240129_train_all_no_nucdist", None], + ] + # inference crops + if VERSION > 0 : + fname_crops = f"inference/results/crop/crops_v{VERSION}.pt" + fname_crops_meta = f"inference/results/crop/crops_meta_v{VERSION}.csv" + else: + fname_crops = f"inference/results/crop/crops.pt" + fname_crops_meta = "inference/results/crop/crops_meta.csv" + do_reconstructions = False + + for augmentation in ("0","90","180","270","0_f","90_f","180_f","270_f"): + for (dir_pretrained_model, checkpoint) in dir_pretrained_models_checkpoints: + # for augmentation in ("0"): # (,"90","180","270","0_f","90_f","180_f","270_f"): + get_features( + fname_crops=fname_crops, + fname_crops_meta=fname_crops_meta, + dir_pretrained_model=dir_pretrained_model, + checkpoint=checkpoint, + do_reconstructions=do_reconstructions, + augmentation=augmentation, + do_retrieval_test=False, + ) + + # get the features from OpenCell TODO: consolidate into one function + + if "2023" in dir_pretrained_model: + dir_opencell_test = Path("data/test_dataset_metadata") + elif "2024" in dir_pretrained_model: + dir_opencell_test = Path("data/test_dataset_metadata_v1/data/opencell_crops_processed2/") + else: + raise + + # fname_crops_opencell = dir_opencell_test / "test_dataset_crops.npy" + # fname_crops_opencell_meta = dir_opencell_test / "test_dataset_labels.npy" + + # get_features_opencell( + # fname_crops=fname_crops_opencell, + # fname_crops_meta=fname_crops_opencell_meta, + # dir_pretrained_model=dir_pretrained_model, + # checkpoint=checkpoint, + # do_reconstructions=do_reconstructions, + # augmentation=augmentation, + # do_retrieval_test=False, + # ) + +candidate_shitty_proteins = ["MYO19","DNAJC11"] +candidate_shitty_proteins = ["MYO19","MFN2","VDAC2","DNAJC11","VDAC3"] # mito +candidate_shitty_proteins = ["SLC37A3","PI4K2A"] + +candidate_good_proteins = ["STT3A","CERS6","C4orf32","CERS5",] + + diff --git a/inference/load_inf_data.py b/inference/load_inf_data.py new file mode 100644 index 0000000..96baea0 --- /dev/null +++ b/inference/load_inf_data.py @@ -0,0 +1,122 @@ +""" +Run in project home dir as + python inference/load_inf_data.py +Or load as a module and run load_inf_dataset52() + +Function: get raw data FOVs into a standard format and save to +`inference/results/load_inf_data`. Each channel is saved as a separate grayscale +png image. + +The standard format is max-intensity projection, channel 0 protein, channel +1 nucleus, and normalize to [0,1]. If things like denoising or max-intensity +supression were needed, this would be the spot for that preprocessing. + +Also save a DataFrame with metadata about each FOV. + +Also, visualize separate channels in `inference/viz/load_inf_data` + +The following file had a problem: + /hpc/instruments/leonetti.dragonfly/infected-cell-microscopy/TICM052-1/raw_data/raw_data_MMStack_16-A1-16.ome.tif +""" +import ipdb +from PIL import Image +from aicsimageio import AICSImage +from pathlib import Path +import matplotlib.pyplot as plt +import os +import pandas as pd +import numpy as np +from skimage.io import imread, imsave +from skimage import color +from PIL import Image + + +def norm_0_1(x): + l, u = x.min(), x.max() + if u == l: + raise ValueError("constant image pixel vaues") + return (x-l) / (u-l) + +def load_inf_dataset52(dir_fovs, DO_VIZ=True): + dir_fovs = Path(dir_fovs) + + # make directories for storing results + current_filename = Path(os.path.basename(__file__)) + dir_results = Path("inference/results") / os.path.join(current_filename.stem, "") + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + dir_viz.mkdir(exist_ok=True, parents=True) + + fnames = [f for f in dir_fovs.glob("*.tif")] + + # manual filter + if 0: + fnames = [f for f in fnames if 'H5' in str(f)] + print(f"Num files {len(fnames)}") + + # construct a dataframe for the image metadata + df_imgs_meta = pd.DataFrame(columns=["fname", "well_id", "fov_id", "is_green"]) + + + # create the metadata dataframe before reading the images + for fname in fnames: + # metadata structure "raw_data_MMStack_592-B8-16.ome.tif". Well=B8, FOV=16 + well_id, fov_id = fname.stem.split(".")[0].split("-")[-2:] + + row = pd.DataFrame(dict( + fname=[fname], + well_id=[well_id], + fov_id=[fov_id], + is_green=[1], + )) + df_imgs_meta = pd.concat([df_imgs_meta, row], ignore_index=True) + + # now read in the iamges, create the FOVs for the next stage, and visualize + for idx, row in df_imgs_meta.iterrows(): + print(idx, fname) + fname = row.fname + + # there was an issue with this one file + if fname.stem == "raw_data_MMStack_1-A1-1.ome": + continue + aics_img = AICSImage(fname) + x = aics_img.data # (1, 2, Z, H, W) + assert x.ndim == 5 and x.shape[:2] == (1,2) + + # max intensity projection for the nucleus and protein channel independently + x_pro_map = x[0,1].max(0) + x_nuc_map = x[0,0].max(0) + + x_pro_map = norm_0_1(x_pro_map) + x_nuc_map = norm_0_1(x_nuc_map) + + # save as 1-channel png + fname_nuc = dir_results / f"max_proj_{row.well_id}_{row.fov_id}_nuc.png" + fname_pro = dir_results / f"max_proj_{row.well_id}_{row.fov_id}_pro.png" + Image.fromarray((x_nuc_map*255).astype(np.uint8), mode="L").save(fname_nuc) + Image.fromarray((x_pro_map*255).astype(np.uint8), mode="L").save(fname_pro) + + # viewing + if DO_VIZ: + f, axs = plt.subplots(1,2) + axs[0].imshow(x_nuc_map, cmap='gray') + axs[1].imshow(x_pro_map, cmap='gray') + axs[0].set(title="nucleus") + axs[1].set(title="protein") + fname_save = dir_viz / (fname.stem + "_max_proj.png") + f.savefig(fname_save, dpi=200) + plt.close() + + del aics_img + + ipdb.set_trace() + pass + +if __name__ == "__main__": + dir_fovs = "/hpc/instruments/leonetti.dragonfly/infected-cell-microscopy/TICM052-1/raw_data" + dir_fovs = "/hpc/instruments/leonetti.dragonfly/infected-cell-microscopy/TICM048-1/raw_data" + load_inf_dataset52(dir_fovs=dir_fovs, DO_VIZ=True) + + + + diff --git a/inference/nuclear_segmentation.py b/inference/nuclear_segmentation.py new file mode 100644 index 0000000..4533d2a --- /dev/null +++ b/inference/nuclear_segmentation.py @@ -0,0 +1,137 @@ +""" +Run in project home dir as + python inference/nuclear_segmentation.py +Or load as a module and run segment_nuclei_fovs() + +Get the data processed FOV files that were prepared by `load_inf_data.py`, which +should be sitting in `inference/results/load_inf_data/`. They are 2d max projections + +Do instance segmentation of the nucleus with CellPose and save the results to a +single file: `inference/results/nucelear_segmentation/all_segmasks.pt`. + +The segmentation maps can be inspected in `inference/results/load_inf_data/`. +""" + +from pathlib import Path +from PIL import Image +import numpy as np +import ipdb +import torch +import os +import pandas as pd +import matplotlib.pyplot as plt +from aicsimageio import AICSImage +import tqdm + +def segment_nuclei_fovs(dir_data, diameter=None, batch_size=2048, is_opencell=False): + """ + diameter is the parameter passed to Cellpose segmentation. By deafult we keep + it None. + """ + + dir_data = Path(dir_data) + fnames = [l for l in dir_data.iterdir()] + if 0: + fnames = [f for f in fnames if 'H5' in str(f)] + + # results dirs + current_filename = Path(os.path.basename(__file__)) + dir_results = Path("inference/results") / os.path.join(current_filename.stem, "") + if is_opencell: + dir_results = dir_results / "opencell" + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + if is_opencell: + dir_viz = dir_viz / "opencell" + dir_viz.mkdir(exist_ok=True, parents=True) + + # baseline CellPose code from: https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/run_cellpose_GPU.ipynb + + # fnames_pro = sorted([l for l in fnames if "_pro.png" in str(l)]) + fnames_nuc = sorted([l for l in fnames if "_nuc.png" in str(l)]) + + # imgs_pro = [np.array(Image.open(f)) for f in fnames_pro] + imgs_nuc = [np.array(Image.open(f)) for f in fnames_nuc] + print(len(imgs_nuc), " images") + + from cellpose import models + is_cuda = torch.cuda.is_available() + if not is_cuda: + raise "No GPU found. If that is intentional, then comment out this line" \ + "and change the next line to have `models.Cellpose(gpu=False, model_type='cyto')`" + model = models.Cellpose(gpu=True, model_type='cyto') + print("Running cellpose ... may take a minute ") + + masks, flows, styles, diams = model.eval(imgs_nuc, diameter=diameter, + flow_threshold=None, channels=None, batch_size=batch_size, progress=True) + + # save the masks + fname_mask = dir_results / f"all_segmasks.pt" + torch.save([masks, fnames_nuc], fname_mask) + + # visualize the masks + import matplotlib.pyplot as plt + for i in range(len(masks)): + f, axs = plt.subplots(1,2) + axs[0].imshow(imgs_nuc[i],cmap='gray') + axs[1].imshow(masks[i], cmap='gray') + f.savefig(dir_viz / f"seg_sample_{fnames[i].stem}.png", dpi=200) + plt.close() + +def segment_opencell(): + is_opencell=True + dir_data = Path("/hpc/projects/group.leonetti/opencell-microscopy/") + df_meta = pd.read_csv(dir_data / "2021-08-30_good-fovs.csv") + fnames = [dir_data / f for f in df_meta.filepath.values] + + # results dirs + current_filename = Path(os.path.basename(__file__)) + dir_results = Path("inference/results") / os.path.join(current_filename.stem, "") + if is_opencell: + dir_results = dir_results / "opencell" + dir_results.mkdir(exist_ok=True, parents=True) + dir_viz = Path("inference/viz") / current_filename.stem + if is_opencell: + dir_viz = dir_viz / "opencell" + dir_viz.mkdir(exist_ok=True, parents=True) + + from cellpose import models + is_cuda = torch.cuda.is_available() + if not is_cuda: + raise "No GPU found. If that is intentional, then comment out this line" \ + "and change the next line to have `models.Cellpose(gpu=False, model_type='cyto')`" + model = models.Cellpose(gpu=True, model_type='cyto') + + for file in tqdm.tqdm(fnames): + try: + img = AICSImage(file).data + except: + print("Didn't find file ", file, " skipping ") + continue + img_nuc_proj = img[0,0].max(0) + img_nuc_proj = (img_nuc_proj / img_nuc_proj.max() * 255).astype(np.uint8) + img_nuc = img_nuc_proj.copy() + + diameter=None + batch_size=4 + masks, flows, styles, diams = model.eval(img_nuc, diameter=diameter, + flow_threshold=None, channels=None, batch_size=batch_size) + + f_save = dir_results / f"{file.stem}.pt" + torch.save(masks, f_save) + + # f,axs = plt.subplots(1,2) + # axs[0].imshow(img_nuc_proj) + # axs[1].imshow(masks) + # f.savefig("tmp.png") + +if __name__ == "__main__": + # orphan proteins + if 1: + dir_data = Path("inference/results/load_inf_data/") + is_opencell = False + segment_nuclei_fovs(dir_data, is_opencell) + + if 0: + segment_opencell() + diff --git a/inference/tmp.py b/inference/tmp.py new file mode 100644 index 0000000..dea366a --- /dev/null +++ b/inference/tmp.py @@ -0,0 +1,313 @@ +""" +Run in project home dir as + python inference/compare_opencell_targets.py + +For a pretrained model in ``, read in the embeddings for +that model on the OpenCell dataset. This is the dataset the model was trained on + +Next get the new inference dataset, its crops, and the embeddings that were +computed in `inference/get_crop_features.py`. These embedding should have been +taken from model from ``. +""" + +import ipdb +import numpy as np +import pandas as pd +from pathlib import Path +import os +from sklearn.metrics import pairwise_distances +import matplotlib.pyplot as plt +import torch +import torchvision.transforms as T +from torchvision.utils import make_grid +from typing import Literal + + +def get_nearest_proteins(fname_crops, + fname_crops_meta, + fname_annotations, + dir_pretrained_model, + mode='correlation'): + # results dirs + embed_opencell, labels_opencell, crops_opencell, df_opencell, df_opencell_lookup = get_embeds_and_crops(dir_pretrained_model, + dataset="opencell") + + # get inference embeddings for the new dataset + embed_inf, labels_inf, crops_inf, df_meta_inf, df_annotations = get_embeds_and_crops(dir_pretrained_model, dataset='inference') + + ## get "consensus encodings" - this one is the elementwise mean of the vqvec2 vecotors + protein_label_opencell = labels_opencell[:, 1] + # label for doing consensus in opencell is protein id + embeds_consensus_opencell, labels_consensus_opencell = get_consensus_encoding( + embed_opencell, protein_label_opencell) + # label for doing consensus in inference is the well id. Multiple wells have the same protein + protein_label_inf = df_meta_inf['well_id'].values + embeds_consensus_inf, labels_consensus_inf = get_consensus_encoding( + embed_inf, protein_label_inf, sort_key=custom_sort_key) + # check that the labels for the consensus embeddings are sorted the same as their lookup dataframes + assert np.all( + df_opencell_lookup['prot_id'].values == labels_consensus_opencell) + assert np.all(df_inf_lookup['well_id'].values == labels_consensus_inf) + + ## do the whole analysis for mode 'correlation' + df_results_summary = find_nearest_analysis(embeds_consensus_opencell, + labels_consensus_opencell, + embeds_consensus_inf, + labels_consensus_inf, + df_opencell_lookup, + df_inf_lookup, + dir_results, + dir_viz, + mode='correlation', + k_nearest_prots=10) + visualize_knns(df_results_summary, + crops_inf, + df_meta_inf, + crops_opencell, + df_opencell, + dir_viz, + n_samples=10, + k_nearest_prots=10) + + ## now do the whole analysis for mode 'hist' + pass + + +def get_embeds_and_crops(dir_pretrained_model, + dataset: Literal['opencell', + 'inference'] = "opencell"): + """ + Get cytoself embeddings and image crops for either opencell or the inference dataset. + Args: + 'dataset' (str): one of 'opencell' + """ + print(f"loading dataset embeddings and crops [{dataset}]") + + if dataset == "opencell": + # get the presaved embeddings from OpenCell + f_embed = Path(dir_pretrained_model) / "embeddings/vqvec2.npy" + embed_opencell = np.load(f_embed) # (93037, 64, 4, 4) + embed_opencell = torch.tensor(embed_opencell).view(len(embed_opencell), -1) + dir_opencell_test = Path("data/test_dataset_metadata") + crops_opencell = np.load(dir_opencell_test / + "test_dataset_crops.npy") # (93037, 3, 100, 100) + crops_opencell = crops_opencell[:, : + 2] # if there is a nuclear distance channel, then remove it + labels_opencell = np.load( + dir_opencell_test / + "test_dataset_labels.npy") # (93037, 3) protid, prot, loc_grade1 + df_opencell = pd.DataFrame(labels_opencell, + columns=['ensg_id', 'prot_id', 'loc_grade1']) + df_opencell_lookup = pd.DataFrame( + np.unique(labels_opencell, axis=0), + columns=['ensg_id', 'prot_id', 'loc_grade1']).sort_values('prot_id') + + return embed_opencell, labels_opencell, crops_opencell, df_opencell, df_opencell_lookup + + + ## get inference embeddings for the new dataset + elif dataset == "inference": + dir_embeddings = Path( + "inference/results/get_crop_features") / dir_pretrained_model + f_embeddings_vqvec2 = dir_embeddings / "embeddings_vqvec2.pt" + embed_inf, labels_inf = torch.load(f_embeddings_vqvec2) + embed_inf = torch.tensor(embed_inf).view(len(embed_inf), -1) + df_meta_inf = pd.read_csv(dir_embeddings / "crops_meta.csv") + df_annotations = pd.read_csv(fname_annotations) + crops_inf = torch.load(fname_crops) + crops_inf = T.Resize(100)(crops_inf) + + return embed_inf, labels_inf, crops_inf, df_meta_inf, df_annotations + + + else: + raise ValueError(f"dataset argument was [{dataset}]") + + + +def find_nearest_analysis(embeds_consensus_opencell, + labels_consensus_opencell, + embeds_consensus_inf, + labels_consensus_inf, + df_opencell_lookup, + df_inf_lookup, + dir_results, + dir_viz, + mode='correlation', + k_nearest_prots=10): + """ + Take the consensus embeddings for opencell and inference data. For each inf + well_id, get the nearest `k_nearest_prots` proteins in Opencell. + The option `mode`, controls how 'nearest' is defined: + `mode==correlation` is wrt vqvec2. + """ + ## compute the distance matrix between the inf proteins and the 1311 OpenCell proteins + if mode == "correlation": + dist = pairwise_distances(embeds_consensus_inf, + embeds_consensus_opencell, + metric='correlation') + else: + raise + dist = torch.from_numpy(dist) + # matrix (n_well_ids_inf, n_prots_opencell), which here is (80,1311) + dist_argsort = torch.argsort(dist, axis=1)[:, :k_nearest_prots] + + prots_nearest = df_opencell_lookup['prot_id'].values[ + dist_argsort] # (n_well_ids_inf,k_nearest_prots) + loc_grade1_nearest = df_opencell_lookup['loc_grade1'].values[ + dist_argsort] # (n_well_ids_inf,k_nearest_prots) + + ## stick the knn preds together. Note that the rows of `df_inf_lookup` are aligned + # with the rows of `prots_nearest` and `loc_grade1_nearest`. This was checked in + # the function caller (it should check that `df_inf_lookup` algin with `labels_consensus_inf` + df_results_summary = df_inf_lookup[[ + 'well_id', 'protein', 'orgIP_cluster_annotation', + 'plate33_eyeballed_annotation' + ]] + df_results_summary.index = range(len(df_results_summary)) + df_loc_grade1_nearest = pd.DataFrame( + loc_grade1_nearest, + columns=[ + f'loc_grade1_nn{i}' for i in range(loc_grade1_nearest.shape[1]) + ]) + df_prot_nearest = pd.DataFrame( + prots_nearest, + columns=[f'prot_nn{i}' for i in range(prots_nearest.shape[1])]) + df_results_summary = pd.concat( + [df_results_summary, df_loc_grade1_nearest, df_prot_nearest], axis=1) + + # save the 'knn predictions' + f_save = dir_results / f"knn_opencell_preds_mode_{mode}.csv" + df_results_summary.to_csv(f_save) + + return df_results_summary + + +def norm_0_1_perimg_perch(crops): + """ + input is (n,nchanells,Y,X) + Normalize to [0,1] within each image and within each channel. + """ + if type(crops) == np.ndarray: + crops = torch.from_numpy(crops) + crops_view = crops.view((*crops.shape[:2], -1)) + l = crops_view.min(dim=-1)[0] + l = l.unsqueeze(-1).unsqueeze(-1) + u = crops_view.max(dim=-1)[0] + u = u.unsqueeze(-1).unsqueeze(-1) + crops = (crops - l) / (u - l) + + return crops + + +def visualize_knns(df_results_summary, + crops_inf, + df_inf, + crops_opencell, + df_opencell, + dir_viz, + n_samples=10, + k_nearest_prots=10): + """ For each inference protein, create a lookup. """ + + np.random.seed(0) + for idx, row in df_results_summary.iterrows(): + imgs = [] + well_id = row['well_id'] + protein = row['protein'] + idxs_img = np.where(df_inf['well_id'] == well_id)[0] + np.random.shuffle(idxs_img) + imgs.append(crops_inf[idxs_img[:n_samples]]) + + # get the nearest protein set + prots_nearest = [] + for j in range(k_nearest_prots): + prot = row[f'prot_nn{j}'] + prots_nearest.append(prot) + idxs_img = np.where(df_opencell['prot_id'] == prot)[0] + np.random.shuffle(idxs_img) + imgs.append(torch.from_numpy(crops_opencell[idxs_img[:n_samples]])) + + # if 1: + # imgs_tmp = crops_opencell[:5] + # imgs_tmp = norm_0_1_perimg_perch(torch.tensor(imgs_tmp)) + # f,axs = plt.subplots(1,3) + # [axs[i].imshow(imgs_tmp[0,i]) for i in range(3)] + # f.savefig(dir_viz / "tmp.png", dpi=200) + + imgs = torch.cat(imgs) + imgs = norm_0_1_perimg_perch(imgs) + imgs_show = torch.zeros(len(imgs), + 3, + *imgs.shape[-2:], + dtype=imgs.dtype) + imgs_show[:, 1] = imgs[:, 0] # protein in green channel + imgs_show[:, 2] = imgs[:, 1] # nucleus in green channel + grid = make_grid(imgs_show, nrow=n_samples).permute(1, 2, 0) + f_save = dir_viz / f"knn_samples_wellid_{well_id}_prot_{protein}" + title = f"well_{well_id}_prot_{protein}\nnearest={prots_nearest}" + f, axs = plt.subplots(figsize=(15, 15)) + axs.imshow(grid) + axs.set(title=title) + f.savefig(f_save, dpi=200) + plt.close() + + # ipdb.set_trace() + + pass + + +def get_consensus_encoding(embed_data, labels, sort_key=None): + """ """ + labels_uniq = np.array(sorted(np.unique(labels), key=sort_key)) # (1311,) + + embeds_consensus = [] + for label in labels_uniq: + idxs = np.where(labels == label)[0] + embed = embed_data[idxs].mean(0) + embeds_consensus.append(embed) + embeds_consensus = torch.vstack(embeds_consensus) + + return embeds_consensus, labels_uniq + + +def custom_sort_key(x): + """ + in `df_inf_lookup`, sort the wellids ('A1','A2','A10'...) according to + the letter and then the number, where you treat the number as an int and not a string + So, split the well_id into a prefix (e.g., 'A') and a numeric part + (e.g., '1', '3', '10', '11') + """ + prefix, numeric = x[0], int(x[1:]) + return (prefix, numeric) + + +def get_df_inf_lookup(df_meta_inf): + """ + `df_meta_inf` is a row for each crop. Each crop from the same well_id has + identical rows. This function returns a lookup function that is well_id + """ + df_inf_lookup = df_meta_inf.groupby('well_id').sample( + n=1).sort_values('well_id') + df_inf_lookup['sort_key'] = df_inf_lookup['well_id'].apply(custom_sort_key) + df_inf_lookup = df_inf_lookup.sort_values(by='sort_key').drop( + columns=['sort_key']) + df_inf_lookup.index = df_inf_lookup['well_id'] + + return df_inf_lookup + + +if __name__ == "__main__": + fname_crops = "inference/results/crop/crops.pt" + fname_crops_meta = "inference/results/crop/crops_meta.csv" + # this file is protein ids and estimates of their annotations + fname_annotations = "data/cz_infectedcell_finalwellmapping.csv" + dir_pretrained_model = "results/20231011_train_all_no_nucdist" + + # let's look at statistics for cytoself stuff + + # the main prediction function + get_nearest_proteins(fname_crops=fname_crops, + fname_crops_meta=fname_crops_meta, + fname_annotations=fname_annotations, + dir_pretrained_model=dir_pretrained_model) diff --git a/inference/verify_opencell_published_embeds.py b/inference/verify_opencell_published_embeds.py new file mode 100644 index 0000000..a1a16d0 --- /dev/null +++ b/inference/verify_opencell_published_embeds.py @@ -0,0 +1,110 @@ +""" +We donwload the pretrained embeddings from +""" +import ipdb +from pathlib import Path +import numpy as np +import pandas as pd +import torch +from sklearn.metrics import pairwise_distances + + +def load(): + dir_pretrained_model = Path("results/pretrained_tensorflow_model") + fname_embeds = dir_pretrained_model / "test_vqvec2_flat.npy" + fname_labels = dir_pretrained_model / "test_label_nucenter.csv" + embeds = np.load(fname_embeds) + df_labels = pd.read_csv(fname_labels) # one per crop + labels_prots = df_labels['name'].values.astype(str) + labels_loc1 = df_labels['loc_grade1'].values.astype(str) + + # get consensus + embeds_consensus, prots = get_consensus_encoding(embeds, + labels_prots, + sort_key=None) + df_lookup = df_labels.groupby('name').sample(n=1) + assert np.all(prots == df_lookup.name.values) + + return embeds_consensus, df_lookup + + +def get_consensus_encoding(embed_data, labels, sort_key=None): + """ """ + labels_uniq = np.array(sorted(np.unique(labels), key=sort_key)) # (1311,) + + embeds_consensus = [] + for label in labels_uniq: + idxs = np.where(labels == label)[0] + embed = embed_data[idxs].mean(0) + embeds_consensus.append(embed) + embeds_consensus = np.vstack(embeds_consensus) + + return embeds_consensus, labels_uniq + +def get_dist_Science_paper_workflow(embeds_consensus, + df_lookup, + mode='correlation', + pca_dim=-1): + from science_paper_repo.scripts.cytoself_analysis import clustering_workflows + pass + + +def get_nearest_prots(embeds_consensus, + df_lookup, + mode='correlation', + pca_dim=-1, + target_protein='C4orf32', + get_rank=['STT3A', 'STT3B', 'OSTC', 'DAD1', 'KRTCAP2']): + """ + Args: + pca_dim (int): if <1, then no PCA + """ + # target_protein='C4orf32' + if pca_dim > 0: + from sklearn.decomposition import PCA + pca = PCA(n_components=200) + X = pca.fit_transform(embeds_consensus) + embeds_consensus = X + raise NotImplementedError() + + labels_protein = df_lookup['name'].values + labels_loc1 = df_lookup['loc_grade1'].values + lookup_prot_to_locgrade1 = dict( + zip(df_lookup['name'].values, + df_lookup['loc_grade1'].values)) + dist = pairwise_distances(embeds_consensus, + embeds_consensus, + metric=mode) + # query the nearest stuff + idx = np.where(labels_protein == target_protein)[0] + assert len(idx) == 1 + dist_argsort_target = np.argsort(dist[idx[0]]) + dists_nearest = dist[idx[0]][dist_argsort_target] + assert dist_argsort_target[0] == idx[0] + prots_nearest = labels_protein[dist_argsort_target] + locgrade1_nearest = np.array( + [lookup_prot_to_locgrade1[k] for k in prots_nearest]) + + # print out the nearest 20 + n_print=20 + print(prots_nearest[:n_print]) + print(locgrade1_nearest[:n_print]) + print(1-dists_nearest[:n_print]) + + ipdb.set_trace() + + # compare a specific list of proteins that we expect to be close + for prot in get_rank: + idx = np.where(prot == prots_nearest)[0] + print(prot, idx, locgrade1_nearest[idx]) + + if 0: + is_er = np.array(['er' in s for s in labels_loc1.astype(str)]) + is_mito = np.array(['mito' in s for s in labels_loc1.astype(str)]) + is_vesicle = np.array(['vesicle' in s for s in labels_loc1.astype(str)]) + + +if __name__ == "__main__": + + embeds_consensus, df_lookup = load() + get_nearest_prots(embeds_consensus, df_lookup) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 79b417c..d2a26da 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy>=1.21 -torch>=1.11 -torchvision>=0.12 +torch==1.13.1 +torchvision==0.12 tqdm>=4.63 pandas>=1.4 joblib>=1.1 @@ -11,4 +11,5 @@ umap-learn==0.5.3 gdown>=4.3 natsort colorcet>=3 -seaborn>=0.11 \ No newline at end of file +seaborn>=0.11 +ipdb diff --git a/run_training2_slurm.sh b/run_training2_slurm.sh new file mode 100644 index 0000000..50c1ee5 --- /dev/null +++ b/run_training2_slurm.sh @@ -0,0 +1,25 @@ +set -e +cur_fname="$(basename $0 .sh)" +script_name=$(basename $0) +partition=gpu + +cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_cytoself_on_opencell_no_nucdist.py" +mem="256gb" + +sbatch <<< \ + "#!/bin/bash +#SBATCH --job-name=${cur_fname}-${partition} +#SBATCH --output=slurm_logs/${cur_fname}-${partition}-%j-out.txt +#SBATCH --error=slurm_logs/${cur_fname}-${partition}-%j-err.txt +#SBATCH --gpus=1 +#SBATCH --mem=$mem +#SBATCH -c 2 +#SBATCH -p $partition +#SBATCH --time=144:00:00 +#SBATCH --mail-user=jmhb@stanford.edu +#SBATCH --mail-type=FAIL + +echo \"$cmd\" +eval \"$cmd\" +" + diff --git a/run_training_slurm.sh b/run_training_slurm.sh new file mode 100644 index 0000000..44430d5 --- /dev/null +++ b/run_training_slurm.sh @@ -0,0 +1,32 @@ +set -e +cur_fname="$(basename $0 .sh)" +script_name=$(basename $0) +partition=gpu + +cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_cytoself_on_opencell_no_nucdist_balanced_classes.py" +#cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_cytoself_on_opencell_balanced_classes.py" +#cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_cytoself_on_opencell.py" +#cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_cytoself_on_opencell_no_nucdist.py" + +#cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_cytoself_on_opencell_no_nucdist.py" +#cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_with_orphans.py" +#cmd="/hpc/mydata/james.burgess/.conda/envs/cytoself/bin/python scripts/train_with_orphans_no_nucdist.py" +mem="700gb" + +sbatch <<< \ + "#!/bin/bash +#SBATCH --job-name=${cur_fname}-${partition} +#SBATCH --output=slurm_logs/${cur_fname}-${partition}-%j-out.txt +#SBATCH --error=slurm_logs/${cur_fname}-${partition}-%j-err.txt +#SBATCH --gpus=1 +#SBATCH --mem=$mem +#SBATCH -c 2 +#SBATCH -p $partition +#SBATCH --time=144:00:00 +#SBATCH --mail-user=jmhb@stanford.edu +#SBATCH --mail-type=FAIL + +echo \"$cmd\" +eval \"$cmd\" +" + diff --git a/scripts/train_cytoself_on_opencell.py b/scripts/train_cytoself_on_opencell.py new file mode 100644 index 0000000..8698cea --- /dev/null +++ b/scripts/train_cytoself_on_opencell.py @@ -0,0 +1,139 @@ +from os.path import join +import ipdb + +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import torch +import tqdm +import json +from pathlib import Path + +from cytoself.analysis.analysis_opencell import AnalysisOpenCell +from cytoself.datamanager.opencell import DataManagerOpenCell +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer +from cytoself.trainer.utils.plot_history import plot_history_cytoself + +# declare results dir +results_dir = Path("results/20240129_train_all") +results_dir.mkdir(exist_ok=True) +tensorboard_path = "logs" + +# 1. Prepare Data +data_ch = ['pro', 'nuc', 'nucdist'] # ['pro', 'nuc', 'nucdist'] +# data_ch = ['pro', 'nuc'] # + +# datapath = Path("data/opencell_crops_proteins/") +datapath = Path("data/opencell_crops_processed2/") + +# DataManagerOpenCell.download_sample_data(datapath) # donwload data +intensity_adjustment = {'pro': 1, 'nuc': 1, 'nucdist': 1} +datamanager = DataManagerOpenCell(datapath, + data_ch, + fov_col=None, + intensity_adjustment=intensity_adjustment) +datamanager.const_dataloader(batch_size=32, label_name_position=1) + +# 2. Create and train a cytoself model +model_args = { + 'input_shape': (len(data_ch), 100, 100), + 'emb_shapes': ((25, 25), (4, 4)), + 'output_shape': (len(data_ch), 100, 100), + 'fc_output_idx': [2], + 'vq_args': { + 'num_embeddings': 512, + 'embedding_dim': 64 + }, + 'num_class': len(datamanager.unique_labels), + 'fc_input_type': 'vqvec', +} +train_args = { + 'lr': 0.0004, + 'max_epoch': 100, + 'reducelr_patience': 4, + 'reducelr_increment': 0.1, + 'earlystop_patience': 12, +} + +# log the training and model args +with open(results_dir / "model_args.json", 'w') as f: + json.dump(model_args, f) +with open(results_dir / "train_args.json", 'w') as f: + json.dump(train_args, f) + +trainer = CytoselfFullTrainer(train_args, + homepath=results_dir, + model_args=model_args) +trainer.fit(datamanager, tensorboard_path=tensorboard_path) + +# 2.1 Generate training history +plot_history_cytoself(trainer.history, + savepath=trainer.savepath_dict['visualization']) + +# 2.2 Compare the reconstructed images as a sanity check +img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy() +torch.cuda.empty_cache() +reconstructed = trainer.infer_reconstruction(img) +fig, ax = plt.subplots(2, + len(data_ch), + figsize=(5 * len(data_ch), 5), + squeeze=False) +for ii, ch in enumerate(data_ch): + t0 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(img[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t0[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + t1 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(reconstructed[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t1[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + ax[0, ii].imshow(t0, cmap='gray') + ax[0, ii].axis('off') + ax[0, ii].set_title('input ' + ch) + ax[1, ii].imshow(t1, cmap='gray') + ax[1, ii].axis('off') + ax[1, ii].set_title('output ' + ch) +fig.tight_layout() +fig.show() +fig.savefig(join(trainer.savepath_dict['visualization'], + 'reconstructed_images.png'), + dpi=300) + +# 3. Analyze embeddings +analysis = AnalysisOpenCell(datamanager, trainer) + +# 3.1 Generate bi-clustering heatmap +analysis.plot_clustermap(num_workers=4) + +# 3.2 Generate feature spectrum +vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1') +ft_spectrum = analysis.compute_feature_spectrum(vqindhist1) + +x_max = ft_spectrum.shape[1] + 1 +x_ticks = np.arange(0, x_max, 50) +fig, ax = plt.subplots(figsize=(10, 3)) +ax.stairs(ft_spectrum[0], np.arange(x_max), fill=True) +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlabel('Feature index') +ax.set_ylabel('Counts') +ax.set_xlim([0, x_max]) +ax.set_xticks(x_ticks, analysis.feature_spectrum_indices[x_ticks]) +fig.tight_layout() +fig.show() +fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], + 'feature_spectrum.png'), + dpi=300) + +# 3.3 Plot UMAP +umap_data = analysis.plot_umap_of_embedding_vector( + data_loader=datamanager.test_loader, + group_col=2, + output_layer=f'{model_args["fc_input_type"]}2', + title=f'UMAP {model_args["fc_input_type"]}2', + xlabel='UMAP1', + ylabel='UMAP2', + s=0.3, + alpha=0.5, + show_legend=True, +) diff --git a/scripts/train_cytoself_on_opencell_balanced_classes.py b/scripts/train_cytoself_on_opencell_balanced_classes.py new file mode 100644 index 0000000..4656894 --- /dev/null +++ b/scripts/train_cytoself_on_opencell_balanced_classes.py @@ -0,0 +1,148 @@ +from os.path import join +import ipdb + +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import torch +import tqdm +import json +from pathlib import Path + +from cytoself.analysis.analysis_opencell import AnalysisOpenCell +from cytoself.datamanager.opencell import DataManagerOpenCell +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer +from cytoself.trainer.utils.plot_history import plot_history_cytoself + +# declare results dir +# results_dir = Path("results/20231011_train_all_no_nucdist") +# results_dir = Path("results/20231025_train_all_no_nucdist") +sampling_strategy = 2 # this does balanced sampling +sampling_strategy = 1 # this does balanced sampling + +results_dir = Path( + f"results/20240129_train_all_balanced_classes_{sampling_strategy}" +) +results_dir.mkdir(exist_ok=True) +tensorboard_path = "logs" + + +# 1. Prepare Data +# data_ch = ['pro', 'nuc', 'nucdist'] # ['pro', 'nuc', 'nucdist'] +data_ch = ['pro', 'nuc','nucdist'] # + +# datapath = Path("data/opencell_crops_proteins/") +datapath = Path("data/opencell_crops_processed2/") + +# DataManagerOpenCell.download_sample_data(datapath) # donwload data +intensity_adjustment = {'pro': 1, 'nuc': 1, 'nucdist': 1} +datamanager = DataManagerOpenCell(datapath, + data_ch, + fov_col=None, + sampling_strategy=sampling_strategy, + intensity_adjustment=intensity_adjustment) +datamanager.const_dataloader(batch_size=32, label_name_position=1) + +# 2. Create and train a cytoself model +model_args = { + 'input_shape': (len(data_ch), 100, 100), + 'emb_shapes': ((25, 25), (4, 4)), + 'output_shape': (len(data_ch), 100, 100), + 'fc_output_idx': [2], + 'vq_args': { + 'num_embeddings': 512, + 'embedding_dim': 64 + }, + 'num_class': len(datamanager.unique_labels), + 'fc_input_type': 'vqvec', +} +train_args = { + 'lr': 0.0004, + 'max_epoch': 100, # 100 + 'reducelr_patience': 4, + 'reducelr_increment': 0.1, + 'earlystop_patience': 12, +} + +# log the training and model args +with open(results_dir / "model_args.json", 'w') as f: + json.dump(model_args, f) +with open(results_dir / "train_args.json", 'w') as f: + json.dump(train_args, f) + +trainer = CytoselfFullTrainer(train_args, + homepath=results_dir, + model_args=model_args) +trainer.fit(datamanager, tensorboard_path=tensorboard_path) + +# 2.1 Generate training history +plot_history_cytoself(trainer.history, + savepath=trainer.savepath_dict['visualization']) + +# 2.2 Compare the reconstructed images as a sanity check +img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy() +torch.cuda.empty_cache() +reconstructed = trainer.infer_reconstruction(img) +fig, ax = plt.subplots(2, + len(data_ch), + figsize=(5 * len(data_ch), 5), + squeeze=False) +for ii, ch in enumerate(data_ch): + t0 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(img[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t0[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + t1 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(reconstructed[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t1[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + ax[0, ii].imshow(t0, cmap='gray') + ax[0, ii].axis('off') + ax[0, ii].set_title('input ' + ch) + ax[1, ii].imshow(t1, cmap='gray') + ax[1, ii].axis('off') + ax[1, ii].set_title('output ' + ch) +fig.tight_layout() +fig.show() +fig.savefig(join(trainer.savepath_dict['visualization'], + 'reconstructed_images.png'), + dpi=300) + +# 3. Analyze embeddings +analysis = AnalysisOpenCell(datamanager, trainer) + +# 3.1 Generate bi-clustering heatmap +analysis.plot_clustermap(num_workers=4) + +# 3.2 Generate feature spectrum +vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1') +ft_spectrum = analysis.compute_feature_spectrum(vqindhist1) + +x_max = ft_spectrum.shape[1] + 1 +x_ticks = np.arange(0, x_max, 50) +fig, ax = plt.subplots(figsize=(10, 3)) +ax.stairs(ft_spectrum[0], np.arange(x_max), fill=True) +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlabel('Feature index') +ax.set_ylabel('Counts') +ax.set_xlim([0, x_max]) +ax.set_xticks(x_ticks, analysis.feature_spectrum_indices[x_ticks]) +fig.tight_layout() +fig.show() +fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], + 'feature_spectrum.png'), + dpi=300) + +# 3.3 Plot UMAP +umap_data = analysis.plot_umap_of_embedding_vector( + data_loader=datamanager.test_loader, + group_col=2, + output_layer=f'{model_args["fc_input_type"]}2', + title=f'UMAP {model_args["fc_input_type"]}2', + xlabel='UMAP1', + ylabel='UMAP2', + s=0.3, + alpha=0.5, + show_legend=True, +) diff --git a/scripts/train_cytoself_on_opencell_no_nucdist.py b/scripts/train_cytoself_on_opencell_no_nucdist.py new file mode 100644 index 0000000..7f1e2f1 --- /dev/null +++ b/scripts/train_cytoself_on_opencell_no_nucdist.py @@ -0,0 +1,142 @@ +from os.path import join +import ipdb + +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import torch +import tqdm +import json +from pathlib import Path + +from cytoself.analysis.analysis_opencell import AnalysisOpenCell +from cytoself.datamanager.opencell import DataManagerOpenCell +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer +from cytoself.trainer.utils.plot_history import plot_history_cytoself + +# declare results dir +results_dir = Path("results/20231011_train_all_no_nucdist") +results_dir = Path("results/20231025_train_all_no_nucdist") +results_dir = Path("results/20240129_train_all_no_nucdist") +#results_dir = Path("results/tmp") +results_dir.mkdir(exist_ok=True) +tensorboard_path = "logs" + +# 1. Prepare Data +# data_ch = ['pro', 'nuc', 'nucdist'] # ['pro', 'nuc', 'nucdist'] +data_ch = ['pro', 'nuc'] # + +# datapath = Path("data/opencell_crops_proteins/") +datapath = Path("data/opencell_crops_processed2/") + +# DataManagerOpenCell.download_sample_data(datapath) # donwload data +intensity_adjustment = {'pro': 1, 'nuc': 1, 'nucdist': 1} +datamanager = DataManagerOpenCell(datapath, + data_ch, + fov_col=None, + intensity_adjustment=intensity_adjustment) +datamanager.const_dataloader(batch_size=32, label_name_position=1) + +# 2. Create and train a cytoself model +model_args = { + 'input_shape': (len(data_ch), 100, 100), + 'emb_shapes': ((25, 25), (4, 4)), + 'output_shape': (len(data_ch), 100, 100), + 'fc_output_idx': [2], + 'vq_args': { + 'num_embeddings': 512, + 'embedding_dim': 64 + }, + 'num_class': len(datamanager.unique_labels), + 'fc_input_type': 'vqvec', +} +train_args = { + 'lr': 0.0004, + 'max_epoch': 100, + 'reducelr_patience': 4, + 'reducelr_increment': 0.1, + 'earlystop_patience': 12, +} + +# log the training and model args +with open(results_dir / "model_args.json", 'w') as f: + json.dump(model_args, f) +with open(results_dir / "train_args.json", 'w') as f: + json.dump(train_args, f) + +trainer = CytoselfFullTrainer(train_args, + homepath=results_dir, + model_args=model_args) +trainer.fit(datamanager, tensorboard_path=tensorboard_path) + +# 2.1 Generate training history +plot_history_cytoself(trainer.history, + savepath=trainer.savepath_dict['visualization']) + +# 2.2 Compare the reconstructed images as a sanity check +img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy() +torch.cuda.empty_cache() +reconstructed = trainer.infer_reconstruction(img) +fig, ax = plt.subplots(2, + len(data_ch), + figsize=(5 * len(data_ch), 5), + squeeze=False) +for ii, ch in enumerate(data_ch): + t0 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(img[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t0[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + t1 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(reconstructed[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t1[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + ax[0, ii].imshow(t0, cmap='gray') + ax[0, ii].axis('off') + ax[0, ii].set_title('input ' + ch) + ax[1, ii].imshow(t1, cmap='gray') + ax[1, ii].axis('off') + ax[1, ii].set_title('output ' + ch) +fig.tight_layout() +fig.show() +fig.savefig(join(trainer.savepath_dict['visualization'], + 'reconstructed_images.png'), + dpi=300) + +# 3. Analyze embeddings +analysis = AnalysisOpenCell(datamanager, trainer) + +# 3.1 Generate bi-clustering heatmap +analysis.plot_clustermap(num_workers=4) + +# 3.2 Generate feature spectrum +vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1') +ft_spectrum = analysis.compute_feature_spectrum(vqindhist1) + +x_max = ft_spectrum.shape[1] + 1 +x_ticks = np.arange(0, x_max, 50) +fig, ax = plt.subplots(figsize=(10, 3)) +ax.stairs(ft_spectrum[0], np.arange(x_max), fill=True) +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlabel('Feature index') +ax.set_ylabel('Counts') +ax.set_xlim([0, x_max]) +ax.set_xticks(x_ticks, analysis.feature_spectrum_indices[x_ticks]) +fig.tight_layout() +fig.show() +fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], + 'feature_spectrum.png'), + dpi=300) + +# 3.3 Plot UMAP +umap_data = analysis.plot_umap_of_embedding_vector( + data_loader=datamanager.test_loader, + group_col=2, + output_layer=f'{model_args["fc_input_type"]}2', + title=f'UMAP {model_args["fc_input_type"]}2', + xlabel='UMAP1', + ylabel='UMAP2', + s=0.3, + alpha=0.5, + show_legend=True, +) diff --git a/scripts/train_cytoself_on_opencell_no_nucdist_balanced_classes.py b/scripts/train_cytoself_on_opencell_no_nucdist_balanced_classes.py new file mode 100644 index 0000000..82fab65 --- /dev/null +++ b/scripts/train_cytoself_on_opencell_no_nucdist_balanced_classes.py @@ -0,0 +1,149 @@ +from os.path import join +import ipdb + +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import torch +import tqdm +import json +from pathlib import Path + +from cytoself.analysis.analysis_opencell import AnalysisOpenCell +from cytoself.datamanager.opencell import DataManagerOpenCell +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer +from cytoself.trainer.utils.plot_history import plot_history_cytoself + +# declare results dir +# results_dir = Path("results/20231011_train_all_no_nucdist") +# results_dir = Path("results/20231025_train_all_no_nucdist") +# results_dir = Path("results/20231221_train_with_orphans_no_nucdist") +sampling_strategy = 2 # this does balanced sampling +# sampling_strategy = 1 # this does balanced sampling +results_dir = Path( + #f"results/20240129_train_all_no_nucdist_balanced_classes_{sampling_strategy}" + f"results/20240130_train_all_no_nucdist_balanced_classes_{sampling_strategy}" +) + +results_dir.mkdir(exist_ok=True) +tensorboard_path = "logs" + +# 1. Prepare Data +# data_ch = ['pro', 'nuc', 'nucdist'] # ['pro', 'nuc', 'nucdist'] +data_ch = ['pro', 'nuc'] # + +# datapath = Path("data/opencell_crops_proteins/") +datapath = Path("data/opencell_crops_processed2/") + +# DataManagerOpenCell.download_sample_data(datapath) # donwload data +intensity_adjustment = {'pro': 1, 'nuc': 1, 'nucdist': 1} +datamanager = DataManagerOpenCell(datapath, + data_ch, + fov_col=None, + intensity_adjustment=intensity_adjustment, + sampling_strategy=sampling_strategy) +datamanager.const_dataloader(batch_size=32, label_name_position=1) + +# 2. Create and train a cytoself model +model_args = { + 'input_shape': (len(data_ch), 100, 100), + 'emb_shapes': ((25, 25), (4, 4)), + 'output_shape': (len(data_ch), 100, 100), + 'fc_output_idx': [2], + 'vq_args': { + 'num_embeddings': 512, + 'embedding_dim': 64 + }, + 'num_class': len(datamanager.unique_labels), + 'fc_input_type': 'vqvec', +} +train_args = { + 'lr': 0.0004, + 'max_epoch': 100, # 100 + 'reducelr_patience': 4, + 'reducelr_increment': 0.1, + 'earlystop_patience': 12, +} + +# log the training and model args +with open(results_dir / "model_args.json", 'w') as f: + json.dump(model_args, f) +with open(results_dir / "train_args.json", 'w') as f: + json.dump(train_args, f) + +trainer = CytoselfFullTrainer(train_args, + homepath=results_dir, + model_args=model_args) +trainer.fit(datamanager, tensorboard_path=tensorboard_path) + +# 2.1 Generate training history +plot_history_cytoself(trainer.history, + savepath=trainer.savepath_dict['visualization']) + +# 2.2 Compare the reconstructed images as a sanity check +img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy() +torch.cuda.empty_cache() +reconstructed = trainer.infer_reconstruction(img) +fig, ax = plt.subplots(2, + len(data_ch), + figsize=(5 * len(data_ch), 5), + squeeze=False) +for ii, ch in enumerate(data_ch): + t0 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(img[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t0[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + t1 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(reconstructed[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t1[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + ax[0, ii].imshow(t0, cmap='gray') + ax[0, ii].axis('off') + ax[0, ii].set_title('input ' + ch) + ax[1, ii].imshow(t1, cmap='gray') + ax[1, ii].axis('off') + ax[1, ii].set_title('output ' + ch) +fig.tight_layout() +fig.show() +fig.savefig(join(trainer.savepath_dict['visualization'], + 'reconstructed_images.png'), + dpi=300) + +# 3. Analyze embeddings +analysis = AnalysisOpenCell(datamanager, trainer) + +# 3.1 Generate bi-clustering heatmap +analysis.plot_clustermap(num_workers=4) + +# 3.2 Generate feature spectrum +vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1') +ft_spectrum = analysis.compute_feature_spectrum(vqindhist1) + +x_max = ft_spectrum.shape[1] + 1 +x_ticks = np.arange(0, x_max, 50) +fig, ax = plt.subplots(figsize=(10, 3)) +ax.stairs(ft_spectrum[0], np.arange(x_max), fill=True) +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlabel('Feature index') +ax.set_ylabel('Counts') +ax.set_xlim([0, x_max]) +ax.set_xticks(x_ticks, analysis.feature_spectrum_indices[x_ticks]) +fig.tight_layout() +fig.show() +fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], + 'feature_spectrum.png'), + dpi=300) + +# 3.3 Plot UMAP +umap_data = analysis.plot_umap_of_embedding_vector( + data_loader=datamanager.test_loader, + group_col=2, + output_layer=f'{model_args["fc_input_type"]}2', + title=f'UMAP {model_args["fc_input_type"]}2', + xlabel='UMAP1', + ylabel='UMAP2', + s=0.3, + alpha=0.5, + show_legend=True, +) diff --git a/scripts/train_with_orphans.py b/scripts/train_with_orphans.py new file mode 100644 index 0000000..b2878fd --- /dev/null +++ b/scripts/train_with_orphans.py @@ -0,0 +1,141 @@ +from os.path import join +import ipdb + +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import torch +import tqdm +import json +from pathlib import Path + +from cytoself.analysis.analysis_opencell import AnalysisOpenCell +from cytoself.datamanager.opencell import DataManagerOpenCell +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer +from cytoself.trainer.utils.plot_history import plot_history_cytoself + +# declare results dir +# results_dir = Path("results/20231011_train_all_no_nucdist") +# results_dir = Path("results/20231025_train_all_no_nucdist") +# results_dir = Path("results/20231218_train_all_no_nucdist_balanced_classes_2") +results_dir = Path("results/20231221_train_with_orphans") + +results_dir.mkdir(exist_ok=True) +tensorboard_path = "logs" +sampling_strategy = None # this does balanced sampling + +# 1. Prepare Data +# data_ch = ['pro', 'nuc', 'nucdist'] # ['pro', 'nuc', 'nucdist'] +data_ch = ['pro', 'nuc', 'nucdist'] # + +datapath = Path("data/opencell_crops_proteins_plus_orphans") +# DataManagerOpenCell.download_sample_data(datapath) # donwload data +datamanager = DataManagerOpenCell(datapath, + data_ch, + fov_col=None, + sampling_strategy=sampling_strategy) +datamanager.const_dataloader(batch_size=32, label_name_position=1) + +# 2. Create and train a cytoself model +model_args = { + 'input_shape': (len(data_ch), 100, 100), + 'emb_shapes': ((25, 25), (4, 4)), + 'output_shape': (len(data_ch), 100, 100), + 'fc_output_idx': [2], + 'vq_args': { + 'num_embeddings': 512, + 'embedding_dim': 64 + }, + 'num_class': len(datamanager.unique_labels), + 'fc_input_type': 'vqvec', +} +train_args = { + 'lr': 0.0004, + 'max_epoch': 100, # 100 + 'reducelr_patience': 4, + 'reducelr_increment': 0.1, + 'earlystop_patience': 12, +} + +# log the training and model args +with open(results_dir / "model_args.json", 'w') as f: + json.dump(model_args, f) +with open(results_dir / "train_args.json", 'w') as f: + json.dump(train_args, f) + +trainer = CytoselfFullTrainer(train_args, + homepath=results_dir, + model_args=model_args) +trainer.fit(datamanager, tensorboard_path=tensorboard_path) + +# 2.1 Generate training history +plot_history_cytoself(trainer.history, + savepath=trainer.savepath_dict['visualization']) + +# 2.2 Compare the reconstructed images as a sanity check +img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy() +torch.cuda.empty_cache() +reconstructed = trainer.infer_reconstruction(img) +fig, ax = plt.subplots(2, + len(data_ch), + figsize=(5 * len(data_ch), 5), + squeeze=False) +for ii, ch in enumerate(data_ch): + t0 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(img[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t0[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + t1 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(reconstructed[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t1[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + ax[0, ii].imshow(t0, cmap='gray') + ax[0, ii].axis('off') + ax[0, ii].set_title('input ' + ch) + ax[1, ii].imshow(t1, cmap='gray') + ax[1, ii].axis('off') + ax[1, ii].set_title('output ' + ch) +fig.tight_layout() +fig.show() +fig.savefig(join(trainer.savepath_dict['visualization'], + 'reconstructed_images.png'), + dpi=300) + +# 3. Analyze embeddings +analysis = AnalysisOpenCell(datamanager, trainer) + +# 3.1 Generate bi-clustering heatmap +analysis.plot_clustermap(num_workers=4) + +# 3.2 Generate feature spectrum +vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1') +ft_spectrum = analysis.compute_feature_spectrum(vqindhist1) + +x_max = ft_spectrum.shape[1] + 1 +x_ticks = np.arange(0, x_max, 50) +fig, ax = plt.subplots(figsize=(10, 3)) +ax.stairs(ft_spectrum[0], np.arange(x_max), fill=True) +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlabel('Feature index') +ax.set_ylabel('Counts') +ax.set_xlim([0, x_max]) +ax.set_xticks(x_ticks, analysis.feature_spectrum_indices[x_ticks]) +fig.tight_layout() +fig.show() +fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], + 'feature_spectrum.png'), + dpi=300) + +# 3.3 Plot UMAP +umap_data = analysis.plot_umap_of_embedding_vector( + data_loader=datamanager.test_loader, + group_col=2, + output_layer=f'{model_args["fc_input_type"]}2', + title=f'UMAP {model_args["fc_input_type"]}2', + xlabel='UMAP1', + ylabel='UMAP2', + s=0.3, + alpha=0.5, + show_legend=True, +) diff --git a/scripts/train_with_orphans_no_nucdist.py b/scripts/train_with_orphans_no_nucdist.py new file mode 100644 index 0000000..17d2653 --- /dev/null +++ b/scripts/train_with_orphans_no_nucdist.py @@ -0,0 +1,141 @@ +from os.path import join +import ipdb + +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import torch +import tqdm +import json +from pathlib import Path + +from cytoself.analysis.analysis_opencell import AnalysisOpenCell +from cytoself.datamanager.opencell import DataManagerOpenCell +from cytoself.datamanager.preloaded_dataset import PreloadedDataset +from cytoself.trainer.cytoselflite_trainer import CytoselfFullTrainer +from cytoself.trainer.utils.plot_history import plot_history_cytoself + +# declare results dir +# results_dir = Path("results/20231011_train_all_no_nucdist") +# results_dir = Path("results/20231025_train_all_no_nucdist") +# results_dir = Path("results/20231218_train_all_no_nucdist_balanced_classes_2") +results_dir = Path("results/20231221_train_with_orphans_no_nucdist") + +results_dir.mkdir(exist_ok=True) +tensorboard_path = "logs" +sampling_strategy = None # this does balanced sampling + +# 1. Prepare Data +# data_ch = ['pro', 'nuc', 'nucdist'] # ['pro', 'nuc', 'nucdist'] +data_ch = ['pro', 'nuc', ] # + +datapath = Path("data/opencell_crops_proteins_plus_orphans") +# DataManagerOpenCell.download_sample_data(datapath) # donwload data +datamanager = DataManagerOpenCell(datapath, + data_ch, + fov_col=None, + sampling_strategy=sampling_strategy) +datamanager.const_dataloader(batch_size=32, label_name_position=1) + +# 2. Create and train a cytoself model +model_args = { + 'input_shape': (len(data_ch), 100, 100), + 'emb_shapes': ((25, 25), (4, 4)), + 'output_shape': (len(data_ch), 100, 100), + 'fc_output_idx': [2], + 'vq_args': { + 'num_embeddings': 512, + 'embedding_dim': 64 + }, + 'num_class': len(datamanager.unique_labels), + 'fc_input_type': 'vqvec', +} +train_args = { + 'lr': 0.0004, + 'max_epoch': 100, # 100 + 'reducelr_patience': 4, + 'reducelr_increment': 0.1, + 'earlystop_patience': 12, +} + +# log the training and model args +with open(results_dir / "model_args.json", 'w') as f: + json.dump(model_args, f) +with open(results_dir / "train_args.json", 'w') as f: + json.dump(train_args, f) + +trainer = CytoselfFullTrainer(train_args, + homepath=results_dir, + model_args=model_args) +trainer.fit(datamanager, tensorboard_path=tensorboard_path) + +# 2.1 Generate training history +plot_history_cytoself(trainer.history, + savepath=trainer.savepath_dict['visualization']) + +# 2.2 Compare the reconstructed images as a sanity check +img = next(iter(datamanager.test_loader))['image'].detach().cpu().numpy() +torch.cuda.empty_cache() +reconstructed = trainer.infer_reconstruction(img) +fig, ax = plt.subplots(2, + len(data_ch), + figsize=(5 * len(data_ch), 5), + squeeze=False) +for ii, ch in enumerate(data_ch): + t0 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(img[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t0[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + t1 = np.zeros((2 * 100, 5 * 100)) + for i, im in enumerate(reconstructed[:10, ii, ...]): + i0, i1 = np.unravel_index(i, (2, 5)) + t1[i0 * 100:(i0 + 1) * 100, i1 * 100:(i1 + 1) * 100] = im + ax[0, ii].imshow(t0, cmap='gray') + ax[0, ii].axis('off') + ax[0, ii].set_title('input ' + ch) + ax[1, ii].imshow(t1, cmap='gray') + ax[1, ii].axis('off') + ax[1, ii].set_title('output ' + ch) +fig.tight_layout() +fig.show() +fig.savefig(join(trainer.savepath_dict['visualization'], + 'reconstructed_images.png'), + dpi=300) + +# 3. Analyze embeddings +analysis = AnalysisOpenCell(datamanager, trainer) + +# 3.1 Generate bi-clustering heatmap +analysis.plot_clustermap(num_workers=4) + +# 3.2 Generate feature spectrum +vqindhist1 = trainer.infer_embeddings(img, 'vqindhist1') +ft_spectrum = analysis.compute_feature_spectrum(vqindhist1) + +x_max = ft_spectrum.shape[1] + 1 +x_ticks = np.arange(0, x_max, 50) +fig, ax = plt.subplots(figsize=(10, 3)) +ax.stairs(ft_spectrum[0], np.arange(x_max), fill=True) +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlabel('Feature index') +ax.set_ylabel('Counts') +ax.set_xlim([0, x_max]) +ax.set_xticks(x_ticks, analysis.feature_spectrum_indices[x_ticks]) +fig.tight_layout() +fig.show() +fig.savefig(join(analysis.savepath_dict['feature_spectra_figures'], + 'feature_spectrum.png'), + dpi=300) + +# 3.3 Plot UMAP +umap_data = analysis.plot_umap_of_embedding_vector( + data_loader=datamanager.test_loader, + group_col=2, + output_layer=f'{model_args["fc_input_type"]}2', + title=f'UMAP {model_args["fc_input_type"]}2', + xlabel='UMAP1', + ylabel='UMAP2', + s=0.3, + alpha=0.5, + show_legend=True, +)