diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 00000000..b120f35c --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,51 @@ +name: Check Sphinx Docs + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + docs: + name: Build Sphinx Documentation + runs-on: ubuntu-latest + defaults: + run: + shell: bash -eo pipefail {0} # Fail on error and enable pipefail + + steps: + - uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3.11 + cache: "pip" + cache-dependency-path: "**/pyproject.toml" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip wheel + pip install ".[doc]" + + - name: Build Documentation and Check for Issues + working-directory: docs + run: | + make clean + # Build the docs and capture output, treat warnings as errors (-W) + sphinx-build -b html -W . _build/html 2>&1 | tee sphinx-output.log + + - name: Upload Documentation Artifacts (HTML Pages) + if: always() + uses: actions/upload-artifact@v3 + with: + name: sphinx-docs + path: docs/_build/html + + - name: Upload Log if Build Failed + if: failure() + uses: actions/upload-artifact@v3 + with: + name: sphinx-output-log + path: docs/sphinx-output.log diff --git a/.github/workflows/ruff.yaml b/.github/workflows/ruff.yaml new file mode 100644 index 00000000..e7ec16d9 --- /dev/null +++ b/.github/workflows/ruff.yaml @@ -0,0 +1,16 @@ +name: Ruff + +on: + push: + branches: [main] + pull_request: + branches: [main] + schedule: + - cron: "0 5 1,15 * *" + +jobs: + ruff: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: chartboost/ruff-action@v1 diff --git a/README.md b/README.md index e5f7dddf..960c3022 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ pip install torch pip install crested ``` -3. If you plan on doing motif analysis using the tf-modisco (lite) and tangermeme functionality inside CREsted, you will need to run the following additional install: +3. If you plan on doing motif analysis using the tf-modisco (lite) and tangermeme's tomtom functionality inside CREsted, you will need to run the following additional install: ```bash pip install crested[tfmodisco] diff --git a/docs/_static/img/examples/hist_locus_scoring.png b/docs/_static/img/examples/hist_locus_scoring.png new file mode 100644 index 00000000..c4156370 Binary files /dev/null and b/docs/_static/img/examples/hist_locus_scoring.png differ diff --git a/docs/_static/img/examples/pattern_class_instances.png b/docs/_static/img/examples/pattern_class_instances.png new file mode 100644 index 00000000..45bd6014 Binary files /dev/null and b/docs/_static/img/examples/pattern_class_instances.png differ diff --git a/docs/_static/img/examples/pattern_clustermap.png b/docs/_static/img/examples/pattern_clustermap.png new file mode 100644 index 00000000..da0d1796 Binary files /dev/null and b/docs/_static/img/examples/pattern_clustermap.png differ diff --git a/docs/_static/img/examples/pattern_selected_instances.png b/docs/_static/img/examples/pattern_selected_instances.png new file mode 100644 index 00000000..2530a266 Binary files /dev/null and b/docs/_static/img/examples/pattern_selected_instances.png differ diff --git a/docs/_static/img/examples/pattern_similarity_heatmap.png b/docs/_static/img/examples/pattern_similarity_heatmap.png new file mode 100644 index 00000000..3e0d1cd4 Binary files /dev/null and b/docs/_static/img/examples/pattern_similarity_heatmap.png differ diff --git a/docs/_static/img/examples/pattern_tf_motif_clustermap.png b/docs/_static/img/examples/pattern_tf_motif_clustermap.png new file mode 100644 index 00000000..b32f7754 Binary files /dev/null and b/docs/_static/img/examples/pattern_tf_motif_clustermap.png differ diff --git a/docs/api/index.md b/docs/api/index.md index 5c89b079..cb4ca5c2 100644 --- a/docs/api/index.md +++ b/docs/api/index.md @@ -12,5 +12,5 @@ io preprocessing tools/index.md plotting/index.md -logging +utils ``` diff --git a/docs/api/logging.md b/docs/api/logging.md deleted file mode 100644 index 6173784f..00000000 --- a/docs/api/logging.md +++ /dev/null @@ -1,14 +0,0 @@ -# Logging - -Helper functions for logging during use of package. - -```{eval-rst} -.. currentmodule:: crested -``` - -```{eval-rst} -.. autosummary:: - :toctree: _autosummary - - setup_logging -``` \ No newline at end of file diff --git a/docs/api/plotting/hist.md b/docs/api/plotting/hist.md index eca43fff..9d823b71 100644 --- a/docs/api/plotting/hist.md +++ b/docs/api/plotting/hist.md @@ -11,4 +11,5 @@ Plots for inspecting distributions of ground truth and predictions? :toctree: _autosummary distribution + locus_scoring ``` diff --git a/docs/api/plotting/patterns.md b/docs/api/plotting/patterns.md index 51ed776d..34d6492e 100644 --- a/docs/api/plotting/patterns.md +++ b/docs/api/plotting/patterns.md @@ -12,4 +12,10 @@ Plot contribution scores and analyze them using tfmodisco. contribution_scores modisco_results + selected_instances + class_instances + clustermap + clustermap_tf_motif + tf_expression_per_cell_type + similarity_heatmap ``` diff --git a/docs/api/tools/index.md b/docs/api/tools/index.md index fedad79e..5695fd1d 100644 --- a/docs/api/tools/index.md +++ b/docs/api/tools/index.md @@ -10,11 +10,9 @@ Crested TaskConfig - tfmodisco default_configs ``` - ```{toctree} :maxdepth: 2 :hidden: @@ -23,6 +21,7 @@ data zoo losses metrics +modisco ``` ## Data @@ -59,4 +58,24 @@ metrics metrics.PearsonCorrelation metrics.PearsonCorrelationLog metrics.ZeroPenaltyMetric -``` \ No newline at end of file +``` + +## Modisco + +```{eval-rst} +.. autosummary:: + modisco.tfmodisco + modisco.match_h5_files_to_classes + modisco.process_patterns + modisco.create_pattern_matrix + modisco.generate_nucleotide_sequences + modisco.pattern_similarity + modisco.find_pattern + modisco.find_pattern_matches + modisco.calculate_similarity_matrix + modisco.calculate_mean_expression_per_cell_type + modisco.generate_html_paths + modisco.read_motif_to_tf_file + modisco.create_pattern_tf_dict + modisco.create_tf_ct_matrix +``` diff --git a/docs/api/tools/modisco.md b/docs/api/tools/modisco.md new file mode 100644 index 00000000..737f7c9d --- /dev/null +++ b/docs/api/tools/modisco.md @@ -0,0 +1,27 @@ +# Modisco `tl.modisco` + +Tfmodisco (utility) functions. Requires the `modisco-lite` package to be installed. + +```{eval-rst} +.. currentmodule:: crested.tl.modisco +``` + +```{eval-rst} +.. autosummary:: + :toctree: _autosummary + + tfmodisco + match_h5_files_to_classes + process_patterns + create_pattern_matrix + generate_nucleotide_sequences + pattern_similarity + find_pattern + find_pattern_matches + calculate_similarity_matrix + calculate_mean_expression_per_cell_type + generate_html_paths + read_motif_to_tf_file + create_pattern_tf_dict + create_tf_ct_matrix +``` diff --git a/docs/api/utils.md b/docs/api/utils.md new file mode 100644 index 00000000..080ab052 --- /dev/null +++ b/docs/api/utils.md @@ -0,0 +1,18 @@ +# Utils + +CREsted provides a few utility function to help with sequence encoding, function optimization, ... + +```{eval-rst} +.. currentmodule:: crested.utils +``` + +```{eval-rst} +.. autosummary:: + :toctree: _autosummary + + EnhancerOptimizer + extract_bigwig_values_per_bp + hot_encoding_to_sequence + one_hot_encode_sequence + setup_logging +``` diff --git a/docs/conf.py b/docs/conf.py index 430314cc..c9cfc210 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -96,6 +96,8 @@ "anndata": ("https://anndata.readthedocs.io/en/stable/", None), "numpy": ("https://numpy.org/doc/stable/", None), "matplotlib": ("https://matplotlib.org/stable/", None), + "pandas": ("http://pandas.pydata.org/pandas-docs/stable/", None), + "seaborn": ("https://seaborn.pydata.org/", None), } # List of patterns, relative to source directory, that match files and @@ -137,7 +139,9 @@ ("py:class", "keras.metrics.Metric"), ("py:class", "keras.src.losses.loss.Loss"), ("py:class", "keras.src.metrics.metric.Metric"), + ("py:class", "seaborn.matrix.ClusterGrid"), ] + suppress_warnings = [ "autosummary.import_cycle", ] diff --git a/docs/tutorials/enhancer_code_analysis.ipynb b/docs/tutorials/enhancer_code_analysis.ipynb index c343d636..f6bc93a6 100644 --- a/docs/tutorials/enhancer_code_analysis.ipynb +++ b/docs/tutorials/enhancer_code_analysis.ipynb @@ -35,26 +35,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [ - "# Optional \n", - "#import os\n", - "#os.environ['PATH'] += ':/home/VIB.LOCAL/niklas.kempynck/.conda/envs/crested/bin/'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-10-01 10:01:03.016169: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2024-10-01 10:01:03.050541: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-10-09 14:43:18.087285: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-10-09 14:43:18.121350: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-10-01 10:01:07.122491: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + "2024-10-09 14:43:20.612062: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], @@ -64,7 +53,7 @@ "\n", "adata = anndata.read_h5ad(\"mouse_biccn_data_filtered.h5ad\")\n", "\n", - "genome_file=\"/home/VIB.LOCAL/niklas.kempynck/nkemp/software/dev_DeepPeak/DeepPeak/data/raw_mm/genome.fa\"\n", + "genome_file = \"/home/VIB.LOCAL/niklas.kempynck/nkemp/software/dev_DeepPeak/DeepPeak/data/raw_mm/genome.fa\"\n", "datamodule = crested.tl.data.AnnDataModule(\n", " adata,\n", " genome_file=genome_file,\n", @@ -73,14 +62,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-10-01 10:01:19.392315: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:55:00.0, compute capability: 9.0\n" + "2024-10-09 14:43:28.368411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:68:00.0, compute capability: 9.0\n" ] } ], @@ -96,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -104,10 +93,10 @@ "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1727769681.140101 3714656 service.cc:145] XLA service 0x7fb5440046b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1727769681.140133 3714656 service.cc:153] StreamExecutor device (0): NVIDIA H100 80GB HBM3, Compute Capability 9.0\n", - "2024-10-01 10:01:21.156098: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", - "2024-10-01 10:01:21.247142: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" + "I0000 00:00:1728477809.701115 668861 service.cc:145] XLA service 0x7f834c014b10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1728477809.701148 668861 service.cc:153] StreamExecutor device (0): NVIDIA H100 80GB HBM3, Compute Capability 9.0\n", + "2024-10-09 14:43:29.717493: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-10-09 14:43:29.801547: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" ] }, { @@ -121,7 +110,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "I0000 00:00:1727769689.865180 3714656 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + "I0000 00:00:1728477818.482204 668861 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { @@ -129,7 +118,7 @@ "output_type": "stream", "text": [ "\u001b[1m348/348\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 58ms/step\n", - "2024-10-01T10:01:50.106485+0200 INFO Adding predictions to anndata.layers[biccn_model].\n" + "2024-10-09T14:43:58.718361+0200 INFO Adding predictions to anndata.layers[biccn_model].\n" ] } ], @@ -153,24 +142,26 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "adata_combined = adata.copy() # Copy the peak heights\n", - "adata_combined.X = (adata_combined.X+adata_combined.layers['biccn_model'])/2 # Take the average with the predictions" + "adata_combined = adata.copy() # Copy the peak heights\n", + "adata_combined.X = (\n", + " adata_combined.X + adata_combined.layers[\"biccn_model\"]\n", + ") / 2 # Take the average with the predictions" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-01T10:02:01.874716+0200 INFO After sorting and filtering, kept 19000 regions.\n" + "2024-10-09T14:44:09.855244+0200 INFO After sorting and filtering, kept 19000 regions.\n" ] }, { @@ -191,7 +182,7 @@ " layers: 'biccn_model'" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -199,8 +190,10 @@ "source": [ "# most informative regions per class\n", "adata_filtered = adata_combined.copy()\n", - "top_k=1000\n", - "crested.pp.sort_and_filter_regions_on_specificity(adata_filtered, top_k=top_k, method='proportion')\n", + "top_k = 1000\n", + "crested.pp.sort_and_filter_regions_on_specificity(\n", + " adata_filtered, top_k=top_k, method=\"proportion\"\n", + ")\n", "adata_filtered" ] }, @@ -235,7 +228,9 @@ ], "source": [ "# calculate contribution scores for all regions and save them to output_dir\n", - "evaluator.tfmodisco_calculate_and_save_contribution_scores(adata=adata_filtered,output_dir=\"modisco_results4\")" + "evaluator.tfmodisco_calculate_and_save_contribution_scores(\n", + " adata=adata_filtered, output_dir=\"modisco_results4\"\n", + ")" ] }, { @@ -251,7 +246,7 @@ "source": [ "When this is done, you can run TFModisco Lite on the saved contribution scores to find motifs that are important for the classification/regression task. \n", "\n", - "You could use the tfmodisco package directly to do this, or you could use the {func}`crested.tl.tfmodisco` function which is essentially a wrapper around the tfmodisco package.\n", + "You could use the tfmodisco package directly to do this, or you could use the {func}`crested.tl.modisco.tfmodisco` function which is essentially a wrapper around the tfmodisco package.\n", "\n", "**Note that from here on, you can use contribution scores from any model trained in any framework, as this analysis just requires a set of one hot encoded sequences and contribution scores per cell type stored in the same directory.**" ] @@ -263,23 +258,16 @@ "outputs": [], "source": [ "# run tfmodisco on the contribution scores\n", - "crested.tl.tfmodisco(\n", + "crested.tl.modisco.tfmodisco(\n", " window=1000,\n", - " output_dir = 'modisco_results4',\n", - " contrib_dir='modisco_results4',\n", - " report=True, # Optional, will match patterns to motif MEME database\n", - " meme_db='/home/VIB.LOCAL/niklas.kempynck/nkemp/tools/motifs.meme', # File to MEME database\n", - " max_seqlets=20000\n", + " output_dir=\"modisco_results4\",\n", + " contrib_dir=\"modisco_results4\",\n", + " report=True, # Optional, will match patterns to motif MEME database\n", + " meme_db=\"/home/VIB.LOCAL/niklas.kempynck/nkemp/tools/motifs.meme\", # File to MEME database\n", + " max_seqlets=20000,\n", ")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -296,14 +284,14 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-01T11:44:37.633495+0200 INFO Starting genomic contributions plot for classes: ['Astro', 'L5ET', 'Vip', 'Oligo']\n" + "2024-10-09T14:44:14.663438+0200 INFO Starting genomic contributions plot for classes: ['Astro', 'L5ET', 'Vip', 'Oligo']\n" ] }, { @@ -327,7 +315,14 @@ ], "source": [ "%matplotlib inline\n", - "crested.pl.patterns.modisco_results(classes=['Astro','L5ET','Vip','Oligo'], contribution='positive', contribution_dir='modisco_results4', num_seq=top_k, y_max=0.15, viz='pwm') # You can also visualize in 'pwm' format" + "crested.pl.patterns.modisco_results(\n", + " classes=[\"Astro\", \"L5ET\", \"Vip\", \"Oligo\"],\n", + " contribution=\"positive\",\n", + " contribution_dir=\"modisco_results4\",\n", + " num_seq=top_k,\n", + " y_max=0.15,\n", + " viz=\"pwm\",\n", + ") # You can also visualize in 'pwm' format" ] }, { @@ -343,22 +338,27 @@ "source": [ "Since we have calculated per cell type the patterns independently of each other, we do not know quantitavely how and if they overlap.\n", "It can be interesting to get an overview of which patterns are found across multiple cell types, how important they are, and if there are unique patterns only found in a small selection of classes.\n", - "Therefore, we have made a pattern clustering algorithm, which start from the results of tfmodisco-lite, and return a pattern matrix, which contains the importance of the clustered patterns per cell type, and a pattern dictionary, describing all clustered patterns." + "Therefore, we have made a pattern clustering algorithm, which starts from the results of tfmodisco-lite, and return a pattern matrix, which contains the importance of the clustered patterns per cell type, and a pattern dictionary, describing all clustered patterns.\n", + "\n", + "First, we'll obtain the modisco files per class by using {func}`crested.tl.modisco.match_h5_files_to_classes` using our selected classes.\n", + "Then, we'll cluster these patterns using {func}`crested.tl.modisco.process_patterns` and create a pattern matrix with {func}`crested.tl.modisco.create_pattern_matrix`" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# First we obtain the resulting modisco files per class\n", - "matched_files = crested.tl.match_h5_files_to_classes(contribution_dir='modisco_results4', classes=list(adata.obs_names))" + "matched_files = crested.tl.modisco.match_h5_files_to_classes(\n", + " contribution_dir=\"modisco_results4\", classes=list(adata.obs_names)\n", + ")" ] }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -366,260 +366,260 @@ "output_type": "stream", "text": [ "Reading file modisco_results4/Astro_modisco_results.h5\n", - "Match between Astro_pos_patterns_9 and Astro_pos_patterns_8 with similarity score 4.786168838167354\n", + "Match between Astro_pos_patterns_9 and Astro_pos_patterns_8 with similarity score 4.79\n", "Reading file modisco_results4/Endo_modisco_results.h5\n", - "Match between Endo_neg_patterns_4 and Astro_neg_patterns_1 with similarity score 5.137184669031875\n", - "Match between Endo_neg_patterns_20 and Endo_neg_patterns_15 with similarity score 4.017644243881689\n", - "Match between Endo_neg_patterns_26 and Endo_neg_patterns_3 with similarity score 4.200937019944412\n", - "Match between Endo_neg_patterns_27 and Endo_neg_patterns_20 with similarity score 4.721001880903637\n", - "Match between Endo_pos_patterns_0 and Endo_neg_patterns_28 with similarity score 4.873348370010543\n", - "Match between Endo_pos_patterns_1 and Astro_pos_patterns_0 with similarity score 5.270968075152102\n", - "Match between Endo_pos_patterns_6 and Astro_pos_patterns_8 with similarity score 4.1948062832792905\n", - "Match between Endo_pos_patterns_7 and Endo_pos_patterns_1 with similarity score 4.530609643392677\n", - "Match between Endo_pos_patterns_10 and Endo_pos_patterns_8 with similarity score 3.7413860695640873\n", - "Match between Endo_pos_patterns_13 and Endo_pos_patterns_0 with similarity score 4.776119871722337\n", - "Match between Endo_pos_patterns_15 and Endo_pos_patterns_3 with similarity score 3.85418418524511\n", - "Match between Endo_pos_patterns_17 and Endo_pos_patterns_3 with similarity score 3.6682014374496883\n", - "Match between Endo_pos_patterns_21 and Endo_pos_patterns_0 with similarity score 4.191796227153649\n", + "Match between Endo_neg_patterns_4 and Astro_neg_patterns_1 with similarity score 5.14\n", + "Match between Endo_neg_patterns_20 and Endo_neg_patterns_15 with similarity score 4.02\n", + "Match between Endo_neg_patterns_26 and Endo_neg_patterns_3 with similarity score 4.20\n", + "Match between Endo_neg_patterns_27 and Endo_neg_patterns_20 with similarity score 4.72\n", + "Match between Endo_pos_patterns_0 and Endo_neg_patterns_28 with similarity score 4.87\n", + "Match between Endo_pos_patterns_1 and Astro_pos_patterns_0 with similarity score 5.27\n", + "Match between Endo_pos_patterns_6 and Astro_pos_patterns_8 with similarity score 4.19\n", + "Match between Endo_pos_patterns_7 and Endo_pos_patterns_1 with similarity score 4.53\n", + "Match between Endo_pos_patterns_10 and Endo_pos_patterns_8 with similarity score 3.74\n", + "Match between Endo_pos_patterns_13 and Endo_pos_patterns_0 with similarity score 4.78\n", + "Match between Endo_pos_patterns_15 and Endo_pos_patterns_3 with similarity score 3.85\n", + "Match between Endo_pos_patterns_17 and Endo_pos_patterns_3 with similarity score 3.67\n", + "Match between Endo_pos_patterns_21 and Endo_pos_patterns_0 with similarity score 4.19\n", "Reading file modisco_results4/L2_3IT_modisco_results.h5\n", - "Match between L2_3IT_pos_patterns_0 and L2_3IT_neg_patterns_3 with similarity score 5.5033611073888205\n", - "Match between L2_3IT_pos_patterns_1 and Endo_pos_patterns_8 with similarity score 8.226714901473475\n", - "Match between L2_3IT_pos_patterns_2 and L2_3IT_neg_patterns_1 with similarity score 3.6694912071028427\n", - "Match between L2_3IT_pos_patterns_5 and Endo_pos_patterns_1 with similarity score 3.803013971712323\n", - "Match between L2_3IT_pos_patterns_10 and Astro_pos_patterns_7 with similarity score 6.702169488028179\n", - "Match between L2_3IT_pos_patterns_11 and Endo_neg_patterns_4 with similarity score 3.855185015905188\n", - "Match between L2_3IT_pos_patterns_13 and Endo_pos_patterns_8 with similarity score 5.100074733380917\n", + "Match between L2_3IT_pos_patterns_0 and L2_3IT_neg_patterns_3 with similarity score 5.50\n", + "Match between L2_3IT_pos_patterns_1 and Endo_pos_patterns_8 with similarity score 8.23\n", + "Match between L2_3IT_pos_patterns_2 and L2_3IT_neg_patterns_1 with similarity score 3.67\n", + "Match between L2_3IT_pos_patterns_5 and Endo_pos_patterns_1 with similarity score 3.80\n", + "Match between L2_3IT_pos_patterns_10 and Astro_pos_patterns_7 with similarity score 6.70\n", + "Match between L2_3IT_pos_patterns_11 and Endo_neg_patterns_4 with similarity score 3.86\n", + "Match between L2_3IT_pos_patterns_13 and Endo_pos_patterns_8 with similarity score 5.10\n", "Reading file modisco_results4/L5ET_modisco_results.h5\n", - "Match between L5ET_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.182841946662512\n", - "Match between L5ET_pos_patterns_1 and L2_3IT_pos_patterns_6 with similarity score 6.060890581052783\n", - "Match between L5ET_pos_patterns_2 and Endo_neg_patterns_21 with similarity score 6.607606826495387\n", - "Match between L5ET_pos_patterns_3 and L2_3IT_pos_patterns_14 with similarity score 4.536417385557874\n", - "Match between L5ET_pos_patterns_4 and L2_3IT_pos_patterns_4 with similarity score 6.1184181793403365\n", - "Match between L5ET_pos_patterns_5 and Endo_pos_patterns_8 with similarity score 4.303104366604146\n", - "Match between L5ET_pos_patterns_7 and L2_3IT_pos_patterns_3 with similarity score 3.754135248582907\n", - "Match between L5ET_pos_patterns_9 and L2_3IT_pos_patterns_0 with similarity score 8.29399177856999\n", - "Match between L5ET_pos_patterns_10 and L2_3IT_pos_patterns_9 with similarity score 7.012259648185789\n", - "Match between L5ET_pos_patterns_11 and L2_3IT_pos_patterns_2 with similarity score 3.6277212193862236\n", + "Match between L5ET_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.18\n", + "Match between L5ET_pos_patterns_1 and L2_3IT_pos_patterns_6 with similarity score 6.06\n", + "Match between L5ET_pos_patterns_2 and Endo_neg_patterns_21 with similarity score 6.61\n", + "Match between L5ET_pos_patterns_3 and L2_3IT_pos_patterns_14 with similarity score 4.54\n", + "Match between L5ET_pos_patterns_4 and L2_3IT_pos_patterns_4 with similarity score 6.12\n", + "Match between L5ET_pos_patterns_5 and Endo_pos_patterns_8 with similarity score 4.30\n", + "Match between L5ET_pos_patterns_7 and L2_3IT_pos_patterns_3 with similarity score 3.75\n", + "Match between L5ET_pos_patterns_9 and L2_3IT_pos_patterns_0 with similarity score 8.29\n", + "Match between L5ET_pos_patterns_10 and L2_3IT_pos_patterns_9 with similarity score 7.01\n", + "Match between L5ET_pos_patterns_11 and L2_3IT_pos_patterns_2 with similarity score 3.63\n", "Reading file modisco_results4/L5IT_modisco_results.h5\n", - "Match between L5IT_neg_patterns_0 and Astro_pos_patterns_1 with similarity score 4.753487131926378\n", - "Match between L5IT_neg_patterns_1 and Endo_neg_patterns_25 with similarity score 4.631111803044842\n", - "Match between L5IT_neg_patterns_5 and Endo_neg_patterns_24 with similarity score 3.822256705054802\n", - "Match between L5IT_neg_patterns_8 and L5IT_neg_patterns_3 with similarity score 4.197392821775915\n", - "Match between L5IT_pos_patterns_0 and L5ET_pos_patterns_6 with similarity score 8.147002751661207\n", - "Match between L5IT_pos_patterns_1 and L5ET_pos_patterns_2 with similarity score 4.672669032371982\n", - "Match between L5IT_pos_patterns_2 and L2_3IT_pos_patterns_0 with similarity score 10.042133884852815\n", - "Match between L5IT_pos_patterns_3 and L2_3IT_pos_patterns_2 with similarity score 8.134279520752608\n", - "Match between L5IT_pos_patterns_4 and Astro_pos_patterns_2 with similarity score 7.343489507720666\n", - "Match between L5IT_pos_patterns_5 and L5ET_pos_patterns_5 with similarity score 4.589527615804052\n", - "Match between L5IT_pos_patterns_6 and Endo_pos_patterns_1 with similarity score 4.395912736409217\n", - "Match between L5IT_pos_patterns_7 and Endo_neg_patterns_13 with similarity score 6.138038673349215\n", - "Match between L5IT_pos_patterns_8 and L2_3IT_pos_patterns_3 with similarity score 3.930056423518166\n", - "Match between L5IT_pos_patterns_9 and L5ET_pos_patterns_4 with similarity score 6.15954178760573\n", - "Match between L5IT_pos_patterns_10 and L5IT_neg_patterns_1 with similarity score 5.351894095246008\n", - "Match between L5IT_pos_patterns_11 and L5IT_pos_patterns_4 with similarity score 5.007630023389534\n", - "Match between L5IT_pos_patterns_12 and L5IT_neg_patterns_1 with similarity score 7.209981904721086\n", - "Match between L5IT_pos_patterns_13 and L2_3IT_pos_patterns_8 with similarity score 4.980019234602362\n", - "Match between L5IT_pos_patterns_15 and L2_3IT_pos_patterns_0 with similarity score 7.08144502696325\n", + "Match between L5IT_neg_patterns_0 and Astro_pos_patterns_1 with similarity score 4.75\n", + "Match between L5IT_neg_patterns_1 and Endo_neg_patterns_25 with similarity score 4.63\n", + "Match between L5IT_neg_patterns_5 and Endo_neg_patterns_24 with similarity score 3.82\n", + "Match between L5IT_neg_patterns_8 and L5IT_neg_patterns_3 with similarity score 4.20\n", + "Match between L5IT_pos_patterns_0 and L5ET_pos_patterns_6 with similarity score 8.15\n", + "Match between L5IT_pos_patterns_1 and L5ET_pos_patterns_2 with similarity score 4.67\n", + "Match between L5IT_pos_patterns_2 and L2_3IT_pos_patterns_0 with similarity score 10.04\n", + "Match between L5IT_pos_patterns_3 and L2_3IT_pos_patterns_2 with similarity score 8.13\n", + "Match between L5IT_pos_patterns_4 and Astro_pos_patterns_2 with similarity score 7.34\n", + "Match between L5IT_pos_patterns_5 and L5ET_pos_patterns_5 with similarity score 4.59\n", + "Match between L5IT_pos_patterns_6 and Endo_pos_patterns_1 with similarity score 4.40\n", + "Match between L5IT_pos_patterns_7 and Endo_neg_patterns_13 with similarity score 6.14\n", + "Match between L5IT_pos_patterns_8 and L2_3IT_pos_patterns_3 with similarity score 3.93\n", + "Match between L5IT_pos_patterns_9 and L5ET_pos_patterns_4 with similarity score 6.16\n", + "Match between L5IT_pos_patterns_10 and L5IT_neg_patterns_1 with similarity score 5.35\n", + "Match between L5IT_pos_patterns_11 and L5IT_pos_patterns_4 with similarity score 5.01\n", + "Match between L5IT_pos_patterns_12 and L5IT_neg_patterns_1 with similarity score 7.21\n", + "Match between L5IT_pos_patterns_13 and L2_3IT_pos_patterns_8 with similarity score 4.98\n", + "Match between L5IT_pos_patterns_15 and L2_3IT_pos_patterns_0 with similarity score 7.08\n", "Reading file modisco_results4/L5_6NP_modisco_results.h5\n", - "Match between L5_6NP_neg_patterns_1 and L2_3IT_neg_patterns_4 with similarity score 5.834904406996271\n", - "Match between L5_6NP_neg_patterns_3 and L2_3IT_neg_patterns_2 with similarity score 4.226205691997605\n", - "Match between L5_6NP_neg_patterns_5 and L5IT_neg_patterns_1 with similarity score 8.207760462759747\n", - "Match between L5_6NP_neg_patterns_8 and L5IT_neg_patterns_7 with similarity score 4.5885345514774265\n", - "Match between L5_6NP_pos_patterns_0 and L5IT_pos_patterns_0 with similarity score 7.107681562809481\n", - "Match between L5_6NP_pos_patterns_1 and L5_6NP_neg_patterns_5 with similarity score 7.871597767935951\n", - "Match between L5_6NP_pos_patterns_2 and Endo_pos_patterns_1 with similarity score 4.187296327036998\n", - "Match between L5_6NP_pos_patterns_3 and L2_3IT_pos_patterns_3 with similarity score 7.7826391234612275\n", - "Match between L5_6NP_pos_patterns_5 and Endo_pos_patterns_2 with similarity score 8.030899880273823\n", - "Match between L5_6NP_pos_patterns_7 and L2_3IT_pos_patterns_7 with similarity score 8.42677103738297\n", + "Match between L5_6NP_neg_patterns_1 and L2_3IT_neg_patterns_4 with similarity score 5.83\n", + "Match between L5_6NP_neg_patterns_3 and L2_3IT_neg_patterns_2 with similarity score 4.23\n", + "Match between L5_6NP_neg_patterns_5 and L5IT_neg_patterns_1 with similarity score 8.21\n", + "Match between L5_6NP_neg_patterns_8 and L5IT_neg_patterns_7 with similarity score 4.59\n", + "Match between L5_6NP_pos_patterns_0 and L5IT_pos_patterns_0 with similarity score 7.11\n", + "Match between L5_6NP_pos_patterns_1 and L5_6NP_neg_patterns_5 with similarity score 7.87\n", + "Match between L5_6NP_pos_patterns_2 and Endo_pos_patterns_1 with similarity score 4.19\n", + "Match between L5_6NP_pos_patterns_3 and L2_3IT_pos_patterns_3 with similarity score 7.78\n", + "Match between L5_6NP_pos_patterns_5 and Endo_pos_patterns_2 with similarity score 8.03\n", + "Match between L5_6NP_pos_patterns_7 and L2_3IT_pos_patterns_7 with similarity score 8.43\n", "Reading file modisco_results4/L6CT_modisco_results.h5\n", - "Match between L6CT_neg_patterns_0 and L5IT_pos_patterns_4 with similarity score 3.538455126742857\n", - "Match between L6CT_neg_patterns_1 and L5IT_neg_patterns_0 with similarity score 4.812331317200568\n", - "Match between L6CT_neg_patterns_4 and L5ET_neg_patterns_0 with similarity score 5.870608050649052\n", - "Match between L6CT_neg_patterns_5 and L5IT_neg_patterns_5 with similarity score 3.6739286654838668\n", - "Match between L6CT_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.17843273230711\n", - "Match between L6CT_pos_patterns_1 and L5IT_pos_patterns_7 with similarity score 7.095851130767715\n", - "Match between L6CT_pos_patterns_2 and L5IT_pos_patterns_0 with similarity score 7.806651350283674\n", - "Match between L6CT_pos_patterns_3 and L2_3IT_pos_patterns_0 with similarity score 8.66196767196959\n", - "Match between L6CT_pos_patterns_4 and L5_6NP_pos_patterns_1 with similarity score 8.722546036774917\n", - "Match between L6CT_pos_patterns_5 and L2_3IT_pos_patterns_3 with similarity score 3.6737695333291107\n", - "Match between L6CT_pos_patterns_8 and L5ET_pos_patterns_5 with similarity score 3.537603231453024\n", - "Match between L6CT_pos_patterns_9 and L5IT_pos_patterns_14 with similarity score 6.704232263938709\n", - "Match between L6CT_pos_patterns_10 and L6CT_pos_patterns_3 with similarity score 5.454213728217549\n", - "Match between L6CT_pos_patterns_11 and L5IT_pos_patterns_9 with similarity score 5.686606087721874\n", + "Match between L6CT_neg_patterns_0 and L5IT_pos_patterns_4 with similarity score 3.54\n", + "Match between L6CT_neg_patterns_1 and L5IT_neg_patterns_0 with similarity score 4.81\n", + "Match between L6CT_neg_patterns_4 and L5ET_neg_patterns_0 with similarity score 5.87\n", + "Match between L6CT_neg_patterns_5 and L5IT_neg_patterns_5 with similarity score 3.67\n", + "Match between L6CT_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.18\n", + "Match between L6CT_pos_patterns_1 and L5IT_pos_patterns_7 with similarity score 7.10\n", + "Match between L6CT_pos_patterns_2 and L5IT_pos_patterns_0 with similarity score 7.81\n", + "Match between L6CT_pos_patterns_3 and L2_3IT_pos_patterns_0 with similarity score 8.66\n", + "Match between L6CT_pos_patterns_4 and L5_6NP_pos_patterns_1 with similarity score 8.72\n", + "Match between L6CT_pos_patterns_5 and L2_3IT_pos_patterns_3 with similarity score 3.67\n", + "Match between L6CT_pos_patterns_8 and L5ET_pos_patterns_5 with similarity score 3.54\n", + "Match between L6CT_pos_patterns_9 and L5IT_pos_patterns_14 with similarity score 6.70\n", + "Match between L6CT_pos_patterns_10 and L6CT_pos_patterns_3 with similarity score 5.45\n", + "Match between L6CT_pos_patterns_11 and L5IT_pos_patterns_9 with similarity score 5.69\n", "Reading file modisco_results4/L6IT_modisco_results.h5\n", - "Match between L6IT_neg_patterns_0 and L5ET_neg_patterns_1 with similarity score 4.029172524319456\n", - "Match between L6IT_neg_patterns_1 and L6CT_neg_patterns_9 with similarity score 3.538594268390316\n", - "Match between L6IT_neg_patterns_2 and L6CT_neg_patterns_7 with similarity score 5.341655643265147\n", - "Match between L6IT_pos_patterns_0 and L6CT_pos_patterns_3 with similarity score 8.72986951185205\n", - "Match between L6IT_pos_patterns_1 and Endo_pos_patterns_1 with similarity score 4.182841666175201\n", - "Match between L6IT_pos_patterns_2 and L5IT_pos_patterns_0 with similarity score 8.67835744441799\n", - "Match between L6IT_pos_patterns_3 and L5IT_pos_patterns_9 with similarity score 4.676102473557681\n", - "Match between L6IT_pos_patterns_4 and Endo_neg_patterns_17 with similarity score 3.587530644384946\n", - "Match between L6IT_pos_patterns_5 and L5IT_pos_patterns_1 with similarity score 6.904811656075893\n", - "Match between L6IT_pos_patterns_6 and Endo_pos_patterns_1 with similarity score 3.938549926894804\n", - "Match between L6IT_pos_patterns_7 and L5IT_pos_patterns_7 with similarity score 10.08920978337451\n", - "Match between L6IT_pos_patterns_8 and L5ET_pos_patterns_5 with similarity score 4.825161345365527\n", - "Match between L6IT_pos_patterns_9 and L5_6NP_pos_patterns_1 with similarity score 4.510344352837777\n", - "Match between L6IT_pos_patterns_10 and L2_3IT_pos_patterns_7 with similarity score 12.0\n", - "Match between L6IT_pos_patterns_11 and L2_3IT_pos_patterns_3 with similarity score 6.780732133017951\n", - "Match between L6IT_pos_patterns_13 and L6IT_pos_patterns_4 with similarity score 3.881862318453569\n", - "Match between L6IT_pos_patterns_14 and L5IT_pos_patterns_1 with similarity score 5.075058243011537\n", + "Match between L6IT_neg_patterns_0 and L5ET_neg_patterns_1 with similarity score 4.03\n", + "Match between L6IT_neg_patterns_1 and L6CT_neg_patterns_9 with similarity score 3.54\n", + "Match between L6IT_neg_patterns_2 and L6CT_neg_patterns_7 with similarity score 5.34\n", + "Match between L6IT_pos_patterns_0 and L6CT_pos_patterns_3 with similarity score 8.73\n", + "Match between L6IT_pos_patterns_1 and Endo_pos_patterns_1 with similarity score 4.18\n", + "Match between L6IT_pos_patterns_2 and L5IT_pos_patterns_0 with similarity score 8.68\n", + "Match between L6IT_pos_patterns_3 and L5IT_pos_patterns_9 with similarity score 4.68\n", + "Match between L6IT_pos_patterns_4 and Endo_neg_patterns_17 with similarity score 3.59\n", + "Match between L6IT_pos_patterns_5 and L5IT_pos_patterns_1 with similarity score 6.90\n", + "Match between L6IT_pos_patterns_6 and Endo_pos_patterns_1 with similarity score 3.94\n", + "Match between L6IT_pos_patterns_7 and L5IT_pos_patterns_7 with similarity score 10.09\n", + "Match between L6IT_pos_patterns_8 and L5ET_pos_patterns_5 with similarity score 4.83\n", + "Match between L6IT_pos_patterns_9 and L5_6NP_pos_patterns_1 with similarity score 4.51\n", + "Match between L6IT_pos_patterns_10 and L2_3IT_pos_patterns_7 with similarity score 12.00\n", + "Match between L6IT_pos_patterns_11 and L2_3IT_pos_patterns_3 with similarity score 6.78\n", + "Match between L6IT_pos_patterns_13 and L6IT_pos_patterns_4 with similarity score 3.88\n", + "Match between L6IT_pos_patterns_14 and L5IT_pos_patterns_1 with similarity score 5.08\n", "Reading file modisco_results4/L6b_modisco_results.h5\n", - "Match between L6b_neg_patterns_1 and L5IT_neg_patterns_2 with similarity score 4.8597371241487695\n", - "Match between L6b_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.229584169788626\n", - "Match between L6b_pos_patterns_1 and L6CT_pos_patterns_9 with similarity score 7.120964462409043\n", - "Match between L6b_pos_patterns_2 and L5ET_pos_patterns_5 with similarity score 3.906209717258604\n", - "Match between L6b_pos_patterns_3 and L2_3IT_pos_patterns_2 with similarity score 7.542786817155169\n", - "Match between L6b_pos_patterns_5 and L6IT_pos_patterns_15 with similarity score 4.111479834017894\n", - "Match between L6b_pos_patterns_6 and L2_3IT_pos_patterns_7 with similarity score 5.365398533092353\n", - "Match between L6b_pos_patterns_7 and L6IT_pos_patterns_3 with similarity score 5.361397540613931\n", - "Match between L6b_pos_patterns_8 and L6b_pos_patterns_4 with similarity score 4.248159285700335\n", - "Match between L6b_pos_patterns_9 and L2_3IT_pos_patterns_3 with similarity score 6.086658106035049\n", - "Match between L6b_pos_patterns_10 and L6CT_pos_patterns_3 with similarity score 4.859980824324897\n", + "Match between L6b_neg_patterns_1 and L5IT_neg_patterns_2 with similarity score 4.86\n", + "Match between L6b_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.23\n", + "Match between L6b_pos_patterns_1 and L6CT_pos_patterns_9 with similarity score 7.12\n", + "Match between L6b_pos_patterns_2 and L5ET_pos_patterns_5 with similarity score 3.91\n", + "Match between L6b_pos_patterns_3 and L2_3IT_pos_patterns_2 with similarity score 7.54\n", + "Match between L6b_pos_patterns_5 and L6IT_pos_patterns_15 with similarity score 4.11\n", + "Match between L6b_pos_patterns_6 and L2_3IT_pos_patterns_7 with similarity score 5.37\n", + "Match between L6b_pos_patterns_7 and L6IT_pos_patterns_3 with similarity score 5.36\n", + "Match between L6b_pos_patterns_8 and L6b_pos_patterns_4 with similarity score 4.25\n", + "Match between L6b_pos_patterns_9 and L2_3IT_pos_patterns_3 with similarity score 6.09\n", + "Match between L6b_pos_patterns_10 and L6CT_pos_patterns_3 with similarity score 4.86\n", "Reading file modisco_results4/Lamp5_modisco_results.h5\n", - "Match between Lamp5_neg_patterns_2 and Endo_pos_patterns_3 with similarity score 3.5705699796199273\n", - "Match between Lamp5_neg_patterns_5 and L5_6NP_neg_patterns_4 with similarity score 3.51221248314615\n", - "Match between Lamp5_neg_patterns_7 and L5IT_neg_patterns_11 with similarity score 4.52126266241515\n", - "Match between Lamp5_neg_patterns_9 and Lamp5_neg_patterns_7 with similarity score 3.744420594713821\n", - "Match between Lamp5_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.501964655936938\n", - "Match between Lamp5_pos_patterns_1 and Lamp5_neg_patterns_1 with similarity score 3.753598863167038\n", - "Match between Lamp5_pos_patterns_2 and L5IT_pos_patterns_1 with similarity score 6.337856349476206\n", - "Match between Lamp5_pos_patterns_3 and L6IT_pos_patterns_9 with similarity score 8.569049521521386\n", - "Match between Lamp5_pos_patterns_4 and L5ET_pos_patterns_5 with similarity score 5.600020828090589\n", - "Match between Lamp5_pos_patterns_5 and L6CT_neg_patterns_1 with similarity score 4.6177575977608845\n", - "Match between Lamp5_pos_patterns_8 and L2_3IT_pos_patterns_3 with similarity score 3.852368770186881\n", - "Match between Lamp5_pos_patterns_12 and L2_3IT_pos_patterns_3 with similarity score 4.508754330527462\n", + "Match between Lamp5_neg_patterns_2 and Endo_pos_patterns_3 with similarity score 3.57\n", + "Match between Lamp5_neg_patterns_5 and L5_6NP_neg_patterns_4 with similarity score 3.51\n", + "Match between Lamp5_neg_patterns_7 and L5IT_neg_patterns_11 with similarity score 4.52\n", + "Match between Lamp5_neg_patterns_9 and Lamp5_neg_patterns_7 with similarity score 3.74\n", + "Match between Lamp5_pos_patterns_0 and Endo_pos_patterns_1 with similarity score 4.50\n", + "Match between Lamp5_pos_patterns_1 and Lamp5_neg_patterns_1 with similarity score 3.75\n", + "Match between Lamp5_pos_patterns_2 and L5IT_pos_patterns_1 with similarity score 6.34\n", + "Match between Lamp5_pos_patterns_3 and L6IT_pos_patterns_9 with similarity score 8.57\n", + "Match between Lamp5_pos_patterns_4 and L5ET_pos_patterns_5 with similarity score 5.60\n", + "Match between Lamp5_pos_patterns_5 and L6CT_neg_patterns_1 with similarity score 4.62\n", + "Match between Lamp5_pos_patterns_8 and L2_3IT_pos_patterns_3 with similarity score 3.85\n", + "Match between Lamp5_pos_patterns_12 and L2_3IT_pos_patterns_3 with similarity score 4.51\n", "Reading file modisco_results4/Micro_PVM_modisco_results.h5\n", - "Match between Micro_PVM_neg_patterns_0 and Endo_neg_patterns_1 with similarity score 5.371618927595235\n", - "Match between Micro_PVM_neg_patterns_1 and Endo_neg_patterns_4 with similarity score 5.454694905798706\n", - "Match between Micro_PVM_neg_patterns_2 and Lamp5_neg_patterns_9 with similarity score 4.575772913534419\n", - "Match between Micro_PVM_neg_patterns_3 and Endo_pos_patterns_1 with similarity score 4.205584610733869\n", - "Match between Micro_PVM_neg_patterns_6 and Endo_pos_patterns_3 with similarity score 5.395531038174372\n", - "Match between Micro_PVM_pos_patterns_0 and Endo_pos_patterns_2 with similarity score 5.2254679197201215\n", - "Match between Micro_PVM_pos_patterns_3 and L6CT_neg_patterns_0 with similarity score 4.511302994099949\n", - "Match between Micro_PVM_pos_patterns_6 and Lamp5_pos_patterns_2 with similarity score 8.936806417581902\n", - "Match between Micro_PVM_pos_patterns_8 and Lamp5_neg_patterns_6 with similarity score 3.687897918886299\n", - "Match between Micro_PVM_pos_patterns_9 and Lamp5_neg_patterns_3 with similarity score 4.859299799966342\n", - "Match between Micro_PVM_pos_patterns_10 and Lamp5_pos_patterns_3 with similarity score 6.334904480121293\n", - "Match between Micro_PVM_pos_patterns_12 and Micro_PVM_pos_patterns_2 with similarity score 3.8292122704771208\n", - "Match between Micro_PVM_pos_patterns_14 and Micro_PVM_pos_patterns_9 with similarity score 4.11086460105565\n", - "Match between Micro_PVM_pos_patterns_17 and Endo_neg_patterns_10 with similarity score 4.137612514291694\n", + "Match between Micro_PVM_neg_patterns_0 and Endo_neg_patterns_1 with similarity score 5.37\n", + "Match between Micro_PVM_neg_patterns_1 and Endo_neg_patterns_4 with similarity score 5.45\n", + "Match between Micro_PVM_neg_patterns_2 and Lamp5_neg_patterns_9 with similarity score 4.58\n", + "Match between Micro_PVM_neg_patterns_3 and Endo_pos_patterns_1 with similarity score 4.21\n", + "Match between Micro_PVM_neg_patterns_6 and Endo_pos_patterns_3 with similarity score 5.40\n", + "Match between Micro_PVM_pos_patterns_0 and Endo_pos_patterns_2 with similarity score 5.23\n", + "Match between Micro_PVM_pos_patterns_3 and L6CT_neg_patterns_0 with similarity score 4.51\n", + "Match between Micro_PVM_pos_patterns_6 and Lamp5_pos_patterns_2 with similarity score 8.94\n", + "Match between Micro_PVM_pos_patterns_8 and Lamp5_neg_patterns_6 with similarity score 3.69\n", + "Match between Micro_PVM_pos_patterns_9 and Lamp5_neg_patterns_3 with similarity score 4.86\n", + "Match between Micro_PVM_pos_patterns_10 and Lamp5_pos_patterns_3 with similarity score 6.33\n", + "Match between Micro_PVM_pos_patterns_12 and Micro_PVM_pos_patterns_2 with similarity score 3.83\n", + "Match between Micro_PVM_pos_patterns_14 and Micro_PVM_pos_patterns_9 with similarity score 4.11\n", + "Match between Micro_PVM_pos_patterns_17 and Endo_neg_patterns_10 with similarity score 4.14\n", "Reading file modisco_results4/OPC_modisco_results.h5\n", - "Match between OPC_neg_patterns_0 and L6CT_neg_patterns_4 with similarity score 3.7407117060084953\n", - "Match between OPC_neg_patterns_3 and OPC_neg_patterns_0 with similarity score 3.8238333879615425\n", - "Match between OPC_pos_patterns_0 and Astro_pos_patterns_4 with similarity score 6.018484605442814\n", - "Match between OPC_pos_patterns_2 and Astro_pos_patterns_3 with similarity score 4.487354230284243\n", - "Match between OPC_pos_patterns_4 and Endo_neg_patterns_4 with similarity score 3.9545782570205734\n", - "Match between OPC_pos_patterns_5 and L6CT_neg_patterns_1 with similarity score 4.293249415252733\n", - "Match between OPC_pos_patterns_6 and Micro_PVM_pos_patterns_17 with similarity score 3.890910273576981\n", - "Match between OPC_pos_patterns_7 and Astro_pos_patterns_5 with similarity score 4.086357637578603\n", - "Match between OPC_pos_patterns_9 and OPC_pos_patterns_3 with similarity score 4.265391733529058\n", + "Match between OPC_neg_patterns_0 and L6CT_neg_patterns_4 with similarity score 3.74\n", + "Match between OPC_neg_patterns_3 and OPC_neg_patterns_0 with similarity score 3.82\n", + "Match between OPC_pos_patterns_0 and Astro_pos_patterns_4 with similarity score 6.02\n", + "Match between OPC_pos_patterns_2 and Astro_pos_patterns_3 with similarity score 4.49\n", + "Match between OPC_pos_patterns_4 and Endo_neg_patterns_4 with similarity score 3.95\n", + "Match between OPC_pos_patterns_5 and L6CT_neg_patterns_1 with similarity score 4.29\n", + "Match between OPC_pos_patterns_6 and Micro_PVM_pos_patterns_17 with similarity score 3.89\n", + "Match between OPC_pos_patterns_7 and Astro_pos_patterns_5 with similarity score 4.09\n", + "Match between OPC_pos_patterns_9 and OPC_pos_patterns_3 with similarity score 4.27\n", "Reading file modisco_results4/Oligo_modisco_results.h5\n", - "Match between Oligo_neg_patterns_0 and OPC_neg_patterns_0 with similarity score 3.8907771959450654\n", - "Match between Oligo_neg_patterns_1 and Oligo_neg_patterns_0 with similarity score 3.795897389488977\n", - "Match between Oligo_neg_patterns_2 and Oligo_neg_patterns_0 with similarity score 3.74474703863082\n", - "Match between Oligo_neg_patterns_3 and Oligo_neg_patterns_0 with similarity score 3.6989917196465543\n", - "Match between Oligo_neg_patterns_4 and L5_6NP_neg_patterns_1 with similarity score 5.086383830167129\n", - "Match between Oligo_neg_patterns_5 and Micro_PVM_neg_patterns_0 with similarity score 4.037353647463917\n", - "Match between Oligo_neg_patterns_6 and Astro_neg_patterns_0 with similarity score 4.572000398143344\n", - "Match between Oligo_pos_patterns_0 and L6IT_neg_patterns_2 with similarity score 4.585237862250889\n", - "Match between Oligo_pos_patterns_1 and OPC_pos_patterns_3 with similarity score 12.0\n", - "Match between Oligo_pos_patterns_2 and OPC_pos_patterns_1 with similarity score 5.395906666305187\n", - "Match between Oligo_pos_patterns_4 and OPC_pos_patterns_4 with similarity score 5.09042336626844\n", - "Match between Oligo_pos_patterns_5 and Oligo_pos_patterns_3 with similarity score 4.253522659363072\n", - "Match between Oligo_pos_patterns_6 and Endo_pos_patterns_12 with similarity score 3.8375782303196027\n", - "Match between Oligo_pos_patterns_7 and OPC_pos_patterns_6 with similarity score 6.010745068202029\n", - "Match between Oligo_pos_patterns_9 and Oligo_pos_patterns_1 with similarity score 5.427733279443906\n", - "Match between Oligo_pos_patterns_10 and Astro_pos_patterns_10 with similarity score 4.360525489159412\n", - "Match between Oligo_pos_patterns_11 and Astro_pos_patterns_4 with similarity score 5.072647944722572\n", - "Match between Oligo_pos_patterns_12 and Lamp5_pos_patterns_11 with similarity score 5.252506251459947\n", - "Match between Oligo_pos_patterns_13 and Oligo_pos_patterns_5 with similarity score 4.205585680411334\n", - "Match between Oligo_pos_patterns_14 and Micro_PVM_pos_patterns_8 with similarity score 4.63061950260944\n", + "Match between Oligo_neg_patterns_0 and OPC_neg_patterns_0 with similarity score 3.89\n", + "Match between Oligo_neg_patterns_1 and Oligo_neg_patterns_0 with similarity score 3.80\n", + "Match between Oligo_neg_patterns_2 and Oligo_neg_patterns_0 with similarity score 3.74\n", + "Match between Oligo_neg_patterns_3 and Oligo_neg_patterns_0 with similarity score 3.70\n", + "Match between Oligo_neg_patterns_4 and L5_6NP_neg_patterns_1 with similarity score 5.09\n", + "Match between Oligo_neg_patterns_5 and Micro_PVM_neg_patterns_0 with similarity score 4.04\n", + "Match between Oligo_neg_patterns_6 and Astro_neg_patterns_0 with similarity score 4.57\n", + "Match between Oligo_pos_patterns_0 and L6IT_neg_patterns_2 with similarity score 4.59\n", + "Match between Oligo_pos_patterns_1 and OPC_pos_patterns_3 with similarity score 12.00\n", + "Match between Oligo_pos_patterns_2 and OPC_pos_patterns_1 with similarity score 5.40\n", + "Match between Oligo_pos_patterns_4 and OPC_pos_patterns_4 with similarity score 5.09\n", + "Match between Oligo_pos_patterns_5 and Oligo_pos_patterns_3 with similarity score 4.25\n", + "Match between Oligo_pos_patterns_6 and Endo_pos_patterns_12 with similarity score 3.84\n", + "Match between Oligo_pos_patterns_7 and OPC_pos_patterns_6 with similarity score 6.01\n", + "Match between Oligo_pos_patterns_9 and Oligo_pos_patterns_1 with similarity score 5.43\n", + "Match between Oligo_pos_patterns_10 and Astro_pos_patterns_10 with similarity score 4.36\n", + "Match between Oligo_pos_patterns_11 and Astro_pos_patterns_4 with similarity score 5.07\n", + "Match between Oligo_pos_patterns_12 and Lamp5_pos_patterns_11 with similarity score 5.25\n", + "Match between Oligo_pos_patterns_13 and Oligo_pos_patterns_5 with similarity score 4.21\n", + "Match between Oligo_pos_patterns_14 and Micro_PVM_pos_patterns_8 with similarity score 4.63\n", "Reading file modisco_results4/Pvalb_modisco_results.h5\n", - "Match between Pvalb_neg_patterns_0 and L6b_neg_patterns_3 with similarity score 4.397944351759039\n", - "Match between Pvalb_neg_patterns_1 and Micro_PVM_neg_patterns_0 with similarity score 4.742695679912346\n", - "Match between Pvalb_pos_patterns_0 and Lamp5_pos_patterns_2 with similarity score 6.124778496430855\n", - "Match between Pvalb_pos_patterns_2 and Micro_PVM_pos_patterns_2 with similarity score 4.003648891366183\n", - "Match between Pvalb_pos_patterns_3 and OPC_pos_patterns_4 with similarity score 5.0033656422492685\n", - "Match between Pvalb_pos_patterns_5 and Micro_PVM_pos_patterns_9 with similarity score 4.84968372705209\n", - "Match between Pvalb_pos_patterns_6 and Lamp5_pos_patterns_13 with similarity score 7.528957863984156\n", - "Match between Pvalb_pos_patterns_7 and Micro_PVM_pos_patterns_16 with similarity score 4.01624042345759\n", - "Match between Pvalb_pos_patterns_8 and Pvalb_pos_patterns_6 with similarity score 5.278418309512277\n", - "Match between Pvalb_pos_patterns_9 and L6IT_pos_patterns_3 with similarity score 5.134512663843641\n", - "Match between Pvalb_pos_patterns_11 and Oligo_pos_patterns_14 with similarity score 6.231918992754008\n", + "Match between Pvalb_neg_patterns_0 and L6b_neg_patterns_3 with similarity score 4.40\n", + "Match between Pvalb_neg_patterns_1 and Micro_PVM_neg_patterns_0 with similarity score 4.74\n", + "Match between Pvalb_pos_patterns_0 and Lamp5_pos_patterns_2 with similarity score 6.12\n", + "Match between Pvalb_pos_patterns_2 and Micro_PVM_pos_patterns_2 with similarity score 4.00\n", + "Match between Pvalb_pos_patterns_3 and OPC_pos_patterns_4 with similarity score 5.00\n", + "Match between Pvalb_pos_patterns_5 and Micro_PVM_pos_patterns_9 with similarity score 4.85\n", + "Match between Pvalb_pos_patterns_6 and Lamp5_pos_patterns_13 with similarity score 7.53\n", + "Match between Pvalb_pos_patterns_7 and Micro_PVM_pos_patterns_16 with similarity score 4.02\n", + "Match between Pvalb_pos_patterns_8 and Pvalb_pos_patterns_6 with similarity score 5.28\n", + "Match between Pvalb_pos_patterns_9 and L6IT_pos_patterns_3 with similarity score 5.13\n", + "Match between Pvalb_pos_patterns_11 and Oligo_pos_patterns_14 with similarity score 6.23\n", "Reading file modisco_results4/Sncg_modisco_results.h5\n", - "Match between Sncg_neg_patterns_0 and Micro_PVM_neg_patterns_0 with similarity score 4.191796719566743\n", - "Match between Sncg_neg_patterns_2 and L5IT_pos_patterns_0 with similarity score 5.767560698241968\n", - "Match between Sncg_neg_patterns_4 and L6CT_pos_patterns_3 with similarity score 5.187232032510671\n", - "Match between Sncg_neg_patterns_6 and Sncg_neg_patterns_3 with similarity score 3.7646546985356335\n", - "Match between Sncg_pos_patterns_0 and Lamp5_pos_patterns_1 with similarity score 4.44166764776981\n", - "Match between Sncg_pos_patterns_1 and Astro_pos_patterns_6 with similarity score 8.271735684453544\n", - "Match between Sncg_pos_patterns_2 and L5ET_pos_patterns_1 with similarity score 3.8999419646142215\n", - "Match between Sncg_pos_patterns_3 and Oligo_pos_patterns_0 with similarity score 4.631259353032871\n", - "Match between Sncg_pos_patterns_4 and OPC_pos_patterns_1 with similarity score 4.425874624623736\n", - "Match between Sncg_pos_patterns_5 and OPC_pos_patterns_1 with similarity score 5.270968365564769\n", - "Match between Sncg_pos_patterns_6 and Lamp5_pos_patterns_2 with similarity score 10.26223291987266\n", - "Match between Sncg_pos_patterns_7 and Pvalb_pos_patterns_2 with similarity score 3.7964248687712745\n", - "Match between Sncg_pos_patterns_9 and Pvalb_pos_patterns_5 with similarity score 5.808519215050711\n", - "Match between Sncg_pos_patterns_10 and Lamp5_pos_patterns_3 with similarity score 6.089269252192284\n", - "Match between Sncg_pos_patterns_11 and L2_3IT_pos_patterns_12 with similarity score 4.0742056034302925\n", - "Match between Sncg_pos_patterns_12 and Oligo_pos_patterns_8 with similarity score 4.582995709684456\n", - "Match between Sncg_pos_patterns_13 and OPC_pos_patterns_1 with similarity score 3.851864124044568\n", + "Match between Sncg_neg_patterns_0 and Micro_PVM_neg_patterns_0 with similarity score 4.19\n", + "Match between Sncg_neg_patterns_2 and L5IT_pos_patterns_0 with similarity score 5.77\n", + "Match between Sncg_neg_patterns_4 and L6CT_pos_patterns_3 with similarity score 5.19\n", + "Match between Sncg_neg_patterns_6 and Sncg_neg_patterns_3 with similarity score 3.76\n", + "Match between Sncg_pos_patterns_0 and Lamp5_pos_patterns_1 with similarity score 4.44\n", + "Match between Sncg_pos_patterns_1 and Astro_pos_patterns_6 with similarity score 8.27\n", + "Match between Sncg_pos_patterns_2 and L5ET_pos_patterns_1 with similarity score 3.90\n", + "Match between Sncg_pos_patterns_3 and Oligo_pos_patterns_0 with similarity score 4.63\n", + "Match between Sncg_pos_patterns_4 and OPC_pos_patterns_1 with similarity score 4.43\n", + "Match between Sncg_pos_patterns_5 and OPC_pos_patterns_1 with similarity score 5.27\n", + "Match between Sncg_pos_patterns_6 and Lamp5_pos_patterns_2 with similarity score 10.26\n", + "Match between Sncg_pos_patterns_7 and Pvalb_pos_patterns_2 with similarity score 3.80\n", + "Match between Sncg_pos_patterns_9 and Pvalb_pos_patterns_5 with similarity score 5.81\n", + "Match between Sncg_pos_patterns_10 and Lamp5_pos_patterns_3 with similarity score 6.09\n", + "Match between Sncg_pos_patterns_11 and L2_3IT_pos_patterns_12 with similarity score 4.07\n", + "Match between Sncg_pos_patterns_12 and Oligo_pos_patterns_8 with similarity score 4.58\n", + "Match between Sncg_pos_patterns_13 and OPC_pos_patterns_1 with similarity score 3.85\n", "Reading file modisco_results4/Sst_modisco_results.h5\n", - "Match between Sst_neg_patterns_1 and Endo_pos_patterns_3 with similarity score 4.338415052543058\n", - "Match between Sst_neg_patterns_4 and L5IT_neg_patterns_5 with similarity score 3.50236638507162\n", - "Match between Sst_neg_patterns_5 and L6CT_neg_patterns_6 with similarity score 5.208394443687165\n", - "Match between Sst_neg_patterns_13 and Sst_neg_patterns_10 with similarity score 4.079358041816989\n", - "Match between Sst_pos_patterns_0 and OPC_pos_patterns_4 with similarity score 4.287462647477037\n", - "Match between Sst_pos_patterns_1 and Pvalb_pos_patterns_1 with similarity score 4.943609795425469\n", - "Match between Sst_pos_patterns_2 and L2_3IT_pos_patterns_8 with similarity score 3.655153966913179\n", - "Match between Sst_pos_patterns_3 and Pvalb_pos_patterns_2 with similarity score 4.226884955315703\n", - "Match between Sst_pos_patterns_4 and L5ET_pos_patterns_3 with similarity score 4.680407419781811\n", - "Match between Sst_pos_patterns_5 and Sncg_pos_patterns_8 with similarity score 6.1064180459638395\n", - "Match between Sst_pos_patterns_6 and L5ET_pos_patterns_1 with similarity score 3.900608206109665\n", - "Match between Sst_pos_patterns_7 and Oligo_pos_patterns_6 with similarity score 3.600895992586014\n", - "Match between Sst_pos_patterns_8 and Sncg_pos_patterns_6 with similarity score 5.924828732304704\n", - "Match between Sst_pos_patterns_9 and Pvalb_pos_patterns_7 with similarity score 4.396527131718086\n", - "Match between Sst_pos_patterns_10 and L2_3IT_pos_patterns_3 with similarity score 6.582050971030263\n", - "Match between Sst_pos_patterns_11 and L6IT_pos_patterns_3 with similarity score 4.405624362136759\n", - "Match between Sst_pos_patterns_12 and Astro_pos_patterns_10 with similarity score 4.3953350955746995\n", + "Match between Sst_neg_patterns_1 and Endo_pos_patterns_3 with similarity score 4.34\n", + "Match between Sst_neg_patterns_4 and L5IT_neg_patterns_5 with similarity score 3.50\n", + "Match between Sst_neg_patterns_5 and L6CT_neg_patterns_6 with similarity score 5.21\n", + "Match between Sst_neg_patterns_13 and Sst_neg_patterns_10 with similarity score 4.08\n", + "Match between Sst_pos_patterns_0 and OPC_pos_patterns_4 with similarity score 4.29\n", + "Match between Sst_pos_patterns_1 and Pvalb_pos_patterns_1 with similarity score 4.94\n", + "Match between Sst_pos_patterns_2 and L2_3IT_pos_patterns_8 with similarity score 3.66\n", + "Match between Sst_pos_patterns_3 and Pvalb_pos_patterns_2 with similarity score 4.23\n", + "Match between Sst_pos_patterns_4 and L5ET_pos_patterns_3 with similarity score 4.68\n", + "Match between Sst_pos_patterns_5 and Sncg_pos_patterns_8 with similarity score 6.11\n", + "Match between Sst_pos_patterns_6 and L5ET_pos_patterns_1 with similarity score 3.90\n", + "Match between Sst_pos_patterns_7 and Oligo_pos_patterns_6 with similarity score 3.60\n", + "Match between Sst_pos_patterns_8 and Sncg_pos_patterns_6 with similarity score 5.92\n", + "Match between Sst_pos_patterns_9 and Pvalb_pos_patterns_7 with similarity score 4.40\n", + "Match between Sst_pos_patterns_10 and L2_3IT_pos_patterns_3 with similarity score 6.58\n", + "Match between Sst_pos_patterns_11 and L6IT_pos_patterns_3 with similarity score 4.41\n", + "Match between Sst_pos_patterns_12 and Astro_pos_patterns_10 with similarity score 4.40\n", "Reading file modisco_results4/SstChodl_modisco_results.h5\n", - "Match between SstChodl_neg_patterns_0 and L5_6NP_neg_patterns_1 with similarity score 4.778373546711901\n", - "Match between SstChodl_neg_patterns_2 and Sst_neg_patterns_2 with similarity score 4.509149845917192\n", - "Match between SstChodl_neg_patterns_3 and Lamp5_neg_patterns_5 with similarity score 3.5076029701121887\n", - "Match between SstChodl_neg_patterns_5 and Sst_neg_patterns_11 with similarity score 3.5436584328397296\n", - "Match between SstChodl_neg_patterns_6 and Endo_neg_patterns_22 with similarity score 3.7205485760343353\n", - "Match between SstChodl_neg_patterns_9 and Lamp5_pos_patterns_6 with similarity score 4.2460948804997685\n", - "Match between SstChodl_pos_patterns_0 and L5IT_neg_patterns_5 with similarity score 3.619678502661291\n", - "Match between SstChodl_pos_patterns_2 and Astro_pos_patterns_4 with similarity score 5.633133912802437\n", - "Match between SstChodl_pos_patterns_3 and L6IT_pos_patterns_3 with similarity score 5.0182957813379785\n", - "Match between SstChodl_pos_patterns_4 and Oligo_pos_patterns_5 with similarity score 4.6425810361185915\n", - "Match between SstChodl_pos_patterns_6 and Endo_pos_patterns_4 with similarity score 5.2952041139208275\n", - "Match between SstChodl_pos_patterns_7 and L5ET_pos_patterns_8 with similarity score 5.8122900188970315\n", - "Match between SstChodl_pos_patterns_8 and Astro_pos_patterns_4 with similarity score 7.06947879056414\n", - "Match between SstChodl_pos_patterns_9 and OPC_pos_patterns_10 with similarity score 5.070245192151081\n", - "Match between SstChodl_pos_patterns_10 and L2_3IT_pos_patterns_7 with similarity score 11.14910250518686\n", + "Match between SstChodl_neg_patterns_0 and L5_6NP_neg_patterns_1 with similarity score 4.78\n", + "Match between SstChodl_neg_patterns_2 and Sst_neg_patterns_2 with similarity score 4.51\n", + "Match between SstChodl_neg_patterns_3 and Lamp5_neg_patterns_5 with similarity score 3.51\n", + "Match between SstChodl_neg_patterns_5 and Sst_neg_patterns_11 with similarity score 3.54\n", + "Match between SstChodl_neg_patterns_6 and Endo_neg_patterns_22 with similarity score 3.72\n", + "Match between SstChodl_neg_patterns_9 and Lamp5_pos_patterns_6 with similarity score 4.25\n", + "Match between SstChodl_pos_patterns_0 and L5IT_neg_patterns_5 with similarity score 3.62\n", + "Match between SstChodl_pos_patterns_2 and Astro_pos_patterns_4 with similarity score 5.63\n", + "Match between SstChodl_pos_patterns_3 and L6IT_pos_patterns_3 with similarity score 5.02\n", + "Match between SstChodl_pos_patterns_4 and Oligo_pos_patterns_5 with similarity score 4.64\n", + "Match between SstChodl_pos_patterns_6 and Endo_pos_patterns_4 with similarity score 5.30\n", + "Match between SstChodl_pos_patterns_7 and L5ET_pos_patterns_8 with similarity score 5.81\n", + "Match between SstChodl_pos_patterns_8 and Astro_pos_patterns_4 with similarity score 7.07\n", + "Match between SstChodl_pos_patterns_9 and OPC_pos_patterns_10 with similarity score 5.07\n", + "Match between SstChodl_pos_patterns_10 and L2_3IT_pos_patterns_7 with similarity score 11.15\n", "Reading file modisco_results4/VLMC_modisco_results.h5\n", - "Match between VLMC_pos_patterns_1 and Endo_pos_patterns_1 with similarity score 5.5719977798097196\n", - "Match between VLMC_pos_patterns_2 and Endo_pos_patterns_3 with similarity score 4.4975961228153025\n", - "Match between VLMC_pos_patterns_3 and Sncg_pos_patterns_6 with similarity score 3.6008651299397316\n", - "Match between VLMC_pos_patterns_6 and Endo_pos_patterns_11 with similarity score 5.469387586803207\n", - "Match between VLMC_pos_patterns_7 and OPC_pos_patterns_4 with similarity score 4.919094290938067\n", - "Match between VLMC_pos_patterns_8 and Endo_pos_patterns_9 with similarity score 7.323020904841309\n", - "Match between VLMC_pos_patterns_9 and Endo_pos_patterns_6 with similarity score 6.081859470750559\n", - "Match between VLMC_pos_patterns_11 and L2_3IT_pos_patterns_3 with similarity score 4.883769204480631\n", - "Match between VLMC_pos_patterns_13 and VLMC_pos_patterns_12 with similarity score 3.9543223119688453\n", - "Match between VLMC_pos_patterns_14 and VLMC_pos_patterns_1 with similarity score 5.132665912562707\n", - "Match between VLMC_pos_patterns_15 and L6CT_neg_patterns_0 with similarity score 4.511305172523018\n", + "Match between VLMC_pos_patterns_1 and Endo_pos_patterns_1 with similarity score 5.57\n", + "Match between VLMC_pos_patterns_2 and Endo_pos_patterns_3 with similarity score 4.50\n", + "Match between VLMC_pos_patterns_3 and Sncg_pos_patterns_6 with similarity score 3.60\n", + "Match between VLMC_pos_patterns_6 and Endo_pos_patterns_11 with similarity score 5.47\n", + "Match between VLMC_pos_patterns_7 and OPC_pos_patterns_4 with similarity score 4.92\n", + "Match between VLMC_pos_patterns_8 and Endo_pos_patterns_9 with similarity score 7.32\n", + "Match between VLMC_pos_patterns_9 and Endo_pos_patterns_6 with similarity score 6.08\n", + "Match between VLMC_pos_patterns_11 and L2_3IT_pos_patterns_3 with similarity score 4.88\n", + "Match between VLMC_pos_patterns_13 and VLMC_pos_patterns_12 with similarity score 3.95\n", + "Match between VLMC_pos_patterns_14 and VLMC_pos_patterns_1 with similarity score 5.13\n", + "Match between VLMC_pos_patterns_15 and L6CT_neg_patterns_0 with similarity score 4.51\n", "Reading file modisco_results4/Vip_modisco_results.h5\n", - "Match between Vip_neg_patterns_2 and L5_6NP_neg_patterns_7 with similarity score 3.701037249777491\n", - "Match between Vip_pos_patterns_0 and L6IT_neg_patterns_0 with similarity score 3.5528723703433496\n", - "Match between Vip_pos_patterns_1 and OPC_pos_patterns_1 with similarity score 5.571997780025679\n", - "Match between Vip_pos_patterns_2 and OPC_pos_patterns_4 with similarity score 5.203352993609433\n", - "Match between Vip_pos_patterns_3 and Oligo_pos_patterns_1 with similarity score 5.440966201479892\n", - "Match between Vip_pos_patterns_5 and Sncg_pos_patterns_1 with similarity score 4.5614773190891755\n", - "Match between Vip_pos_patterns_7 and L6IT_pos_patterns_3 with similarity score 5.106957623793277\n", - "Match between Vip_pos_patterns_9 and Lamp5_pos_patterns_14 with similarity score 4.209494269460239\n", + "Match between Vip_neg_patterns_2 and L5_6NP_neg_patterns_7 with similarity score 3.70\n", + "Match between Vip_pos_patterns_0 and L6IT_neg_patterns_0 with similarity score 3.55\n", + "Match between Vip_pos_patterns_1 and OPC_pos_patterns_1 with similarity score 5.57\n", + "Match between Vip_pos_patterns_2 and OPC_pos_patterns_4 with similarity score 5.20\n", + "Match between Vip_pos_patterns_3 and Oligo_pos_patterns_1 with similarity score 5.44\n", + "Match between Vip_pos_patterns_5 and Sncg_pos_patterns_1 with similarity score 4.56\n", + "Match between Vip_pos_patterns_7 and L6IT_pos_patterns_3 with similarity score 5.11\n", + "Match between Vip_pos_patterns_9 and Lamp5_pos_patterns_14 with similarity score 4.21\n", "Merged patterns Astro_neg_patterns_0 and Oligo_neg_patterns_0 with similarity 3.5897681444438128\n", "Merged patterns Astro_neg_patterns_0 and OPC_neg_patterns_1 with similarity 3.58637465998967\n", "Merged patterns Astro_neg_patterns_0 and Sncg_neg_patterns_6 with similarity 5.2674311332894\n", @@ -675,21 +675,23 @@ "(19, 119)" ] }, - "execution_count": 209, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Then we cluster matching patterns, and define a pattern matrix [#classes, #patterns] describing their importance\n", - "all_patterns = crested.tl.process_patterns(\n", + "all_patterns = crested.tl.modisco.process_patterns(\n", " matched_files,\n", - " sim_threshold=3.5, # The similarity threshold used for matching patterns. We take the -log10(pval), pval obtained through TOMTOM matching from tangermeme\n", - " trim_ic_threshold=0.1, # Information content (IC) threshold on which to trim patterns\n", - " discard_ic_threshold=0.2, # IC threshold used for discarding single instance patterns\n", - " verbose=True # Useful for doing sanity checks on matching patterns\n", - ") \n", - "pattern_matrix = crested.tl.create_pattern_matrix(classes=list(adata.obs_names), all_patterns=all_patterns, normalize=True)\n", + " sim_threshold=3.5, # The similarity threshold used for matching patterns. We take the -log10(pval), pval obtained through TOMTOM matching from tangermeme\n", + " trim_ic_threshold=0.1, # Information content (IC) threshold on which to trim patterns\n", + " discard_ic_threshold=0.2, # IC threshold used for discarding single instance patterns\n", + " verbose=True, # Useful for doing sanity checks on matching patterns\n", + ")\n", + "pattern_matrix = crested.tl.modisco.create_pattern_matrix(\n", + " classes=list(adata.obs_names), all_patterns=all_patterns, normalize=True\n", + ")\n", "pattern_matrix.shape" ] }, @@ -697,12 +699,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can plot a clustermap of cell types/classes and patterns, where the classes are clustered purely on pattern importance." + "Now we can plot a clustermap of cell types/classes and patterns, where the classes are clustered \n", + "purely on pattern importance with {func}`crested.tl.modisco.generate_nucleotide_sequences` and {func}`crested.pl.patterns.clustermap`" ] }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -717,8 +720,10 @@ } ], "source": [ - "pat_seqs = crested.tl.generate_nucleotide_sequences(all_patterns)\n", - "crested.pl.patterns.create_clustermap(pattern_matrix, list(adata.obs_names), figsize=(25,8), pat_seqs=pat_seqs, grid=True)" + "pat_seqs = crested.tl.modisco.generate_nucleotide_sequences(all_patterns)\n", + "crested.pl.patterns.clustermap(\n", + " pattern_matrix, list(adata.obs_names), figsize=(25, 8), pat_seqs=pat_seqs, grid=True\n", + ")" ] }, { @@ -730,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -746,8 +751,16 @@ ], "source": [ "%matplotlib inline\n", - "pat_seqs = crested.tl.generate_nucleotide_sequences(all_patterns)\n", - "crested.pl.patterns.create_clustermap(pattern_matrix, classes=list(adata.obs_names), subset=['Astro','OPC', 'Oligo'], figsize=(10,3), pat_seqs=pat_seqs, grid=True, dy=0.0025)" + "pat_seqs = crested.tl.modisco.generate_nucleotide_sequences(all_patterns)\n", + "crested.pl.patterns.clustermap(\n", + " pattern_matrix,\n", + " classes=list(adata.obs_names),\n", + " subset=[\"Astro\", \"OPC\", \"Oligo\"],\n", + " figsize=(10, 3),\n", + " pat_seqs=pat_seqs,\n", + " grid=True,\n", + " dy=0.0025,\n", + ")" ] }, { @@ -761,12 +774,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is always interesting to investigate specific patterns show in the clustermap above. Here there are some example function on how to do that." + "It is always interesting to investigate specific patterns show in the clustermap above. Here there are some example function on how to do that.\n", + "\n", + "Plotting patterns based on their indices can be done with {func}`crested.pl.patterns.selected_instances`" ] }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -781,10 +796,10 @@ } ], "source": [ - "from crested.pl.patterns import plot_patterns, plot_pattern_instances\n", - "\n", - "pattern_indices = [0,1,2,3]\n", - "plot_patterns(all_patterns, pattern_indices) # The pattern that is show is the most representative pattern of the cluster with the highest average IC" + "pattern_indices = [0, 1, 2, 3]\n", + "crested.pl.patterns.selected_instances(\n", + " all_patterns, pattern_indices\n", + ") # The pattern that is shown is the most representative pattern of the cluster with the highest average IC" ] }, { @@ -796,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -818,24 +833,23 @@ } ], "source": [ - "from crested.tl import pattern_similarity\n", - "idx1=1\n", - "idx2=49\n", - "sim = pattern_similarity(all_patterns,idx1,idx2)\n", - "print('Pattern similarity is ' + str(sim))\n", - "plot_patterns(all_patterns, [idx1, idx2])" + "idx1 = 1\n", + "idx2 = 49\n", + "sim = crested.tl.modisco.pattern_similarity(all_patterns, idx1, idx2)\n", + "print(\"Pattern similarity is \" + str(sim))\n", + "crested.pl.patterns.selected_instances(all_patterns, [idx1, idx2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "And plot all the instances of patterns in the same cluster." + "We can plot all the instances of patterns in the same cluster with {func}`crested.pl.patterns.class_instances`" ] }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -850,39 +864,38 @@ } ], "source": [ - "plot_pattern_instances(all_patterns,33)" + "crested.pl.patterns.class_instances(all_patterns, 33)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you want to find out in which pattern cluster a certain pattern is from your modisco results, you can call the find_pattern function." + "If you want to find out in which pattern cluster a certain pattern is from your modisco results, you can call the {func}`crested.tl.modisco.find_pattern` function." ] }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "from crested.tl._tfmodisco import find_pattern\n", - "idx = find_pattern('L6CT_pos_patterns_4', all_patterns)\n", + "idx = crested.tl.modisco.find_pattern(\"L6CT_pos_patterns_4\", all_patterns)\n", "if idx is not None:\n", - " print('Pattern index is '+str(idx))\n", - " plot_pattern_instances(all_patterns,idx, class_representative=True)" + " print(\"Pattern index is \" + str(idx))\n", + " crested.pl.patterns.class_instances(all_patterns, idx, class_representative=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally we can also plot the similarity between all patterns." + "Finally we can also plot the similarity between all patterns with {func}`crested.tl.modisco.calculate_similarity_matrix` and {func}`crested.pl.patterns.similarity_heatmap`" ] }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -897,10 +910,8 @@ } ], "source": [ - "from crested.tl import calculate_similarity_matrix\n", - "from crested.pl.patterns import plot_similarity_heatmap\n", - "sim_matrix, indices = calculate_similarity_matrix(all_patterns)\n", - "plot_similarity_heatmap(sim_matrix, indices, fig_size=(42,17))" + "sim_matrix, indices = crested.tl.modisco.calculate_similarity_matrix(all_patterns)\n", + "crested.pl.patterns.similarity_heatmap(sim_matrix, indices, fig_size=(42, 17))" ] }, { @@ -924,32 +935,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Load scRNA-seq data" + "#### Load scRNA-seq data\n", + "\n", + "Load scRNA seq data and calculate mean expression per cell type using {func}`crested.tl.modisco.calculate_mean_expression_per_cell_type`" ] }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/data/projects/c04/cbd-saerts/nkemp/software/CREsted/src/crested/tl/_tfmodisco.py:1122: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", - " \n" + "/data/projects/c04/cbd-saerts/nkemp/software/CREsted/src/crested/tl/modisco/_tfmodisco.py:1167: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " mean_expression_per_cell_type: pd.DataFrame = gene_expression_df.groupby(\n" ] } ], "source": [ - "file_path='/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/Mouse_rna.h5ad' #Locate h5 file containing scRNAseq data\n", - "cell_type_column= 'subclass_Bakken_2022'\n", - "mean_expression_df = crested.tl.calculate_mean_expression_per_cell_type(file_path, cell_type_column)" + "file_path = \"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/Mouse_rna.h5ad\" # Locate h5 file containing scRNAseq data\n", + "cell_type_column = \"subclass_Bakken_2022\"\n", + "mean_expression_df = crested.tl.modisco.calculate_mean_expression_per_cell_type(\n", + " file_path, cell_type_column\n", + ")" ] }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1574,7 +1589,7 @@ "[19 rows x 24275 columns]" ] }, - "execution_count": 238, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1592,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1604,12 +1619,15 @@ } ], "source": [ - "import numpy as np\n", - "classes=np.array(adata.obs_names)\n", - "contribution_dir='modisco_results4'\n", - "html_paths = crested.tl.generate_html_paths(all_patterns, classes, contribution_dir)\n", + "classes = list(adata.obs_names)\n", + "contribution_dir = \"modisco_results4\"\n", + "html_paths = crested.tl.modisco.generate_html_paths(\n", + " all_patterns, classes, contribution_dir\n", + ")\n", "\n", - "pattern_match_dict = crested.tl.find_pattern_matches(all_patterns, html_paths, q_val_thr=0.1) #q_val threshold to only select significant matches\n", + "pattern_match_dict = crested.tl.modisco.find_pattern_matches(\n", + " all_patterns, html_paths, q_val_thr=0.1\n", + ") # q_val threshold to only select significant matches\n", "print(pattern_match_dict)" ] }, @@ -1622,7 +1640,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1962,13 +1980,15 @@ "[22476 rows x 15 columns]" ] }, - "execution_count": 240, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "motif_to_tf_df = crested.tl.read_motif_to_tf_file('/data/projects/c04/cbd-saerts/nkemp/tools/Motif_collection.tsv')\n", + "motif_to_tf_df = crested.tl.modisco.read_motif_to_tf_file(\n", + " \"/data/projects/c04/cbd-saerts/nkemp/tools/Motif_collection.tsv\"\n", + ")\n", "motif_to_tf_df" ] }, @@ -1983,12 +2003,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We calculate a pattern-tf by cell type matrix which contains the imporatance of each pattern linked to a TF per cell type." + "We calculate a pattern-tf by cell type matrix which contains the imporatance of each pattern linked to a TF per cell type using {func}`crested.tl.modisco.create_pattern_tf_dict` and {func}`crested.tl.modisco.create_tf_ct_matrix`" ] }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2002,21 +2022,36 @@ } ], "source": [ - "cols = ['Mouse_Direct_annot', 'Mouse_Orthology_annot', 'Cluster_Mouse_Direct_annot', 'Cluster_Mouse_Orthology_annot']\n", - "pattern_tf_dict, all_tfs = crested.tl.create_pattern_tf_dict(pattern_match_dict, motif_to_tf_df, all_patterns, cols)\n", - "tf_ct_matrix, tf_pattern_annots = crested.tl.create_tf_ct_matrix(pattern_tf_dict, all_patterns, mean_expression_df, classes, log_transform=True, normalize=True, min_tf_gex=0.3)" + "cols = [\n", + " \"Mouse_Direct_annot\",\n", + " \"Mouse_Orthology_annot\",\n", + " \"Cluster_Mouse_Direct_annot\",\n", + " \"Cluster_Mouse_Orthology_annot\",\n", + "]\n", + "pattern_tf_dict, all_tfs = crested.tl.modisco.create_pattern_tf_dict(\n", + " pattern_match_dict, motif_to_tf_df, all_patterns, cols\n", + ")\n", + "tf_ct_matrix, tf_pattern_annots = crested.tl.modisco.create_tf_ct_matrix(\n", + " pattern_tf_dict,\n", + " all_patterns,\n", + " mean_expression_df,\n", + " classes,\n", + " log_transform=True,\n", + " normalize=True,\n", + " min_tf_gex=0.3,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can plot a clustermap of potential pattern-TF matches, and their importance per cell type." + "Finally, we can plot a clustermap of potential pattern-TF matches and their importance per cell type with {func}`crested.pl.patterns.clustermap_tf_motif`" ] }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2031,7 +2066,14 @@ } ], "source": [ - "crested.pl.patterns.plot_clustermap_tf_motif(tf_ct_matrix, cluster_on_dim='gex', class_labels=classes, pattern_labels=tf_pattern_annots, color_idx='gex', size_idx='contrib')" + "crested.pl.patterns.clustermap_tf_motif(\n", + " tf_ct_matrix,\n", + " cluster_on_dim=\"gex\",\n", + " class_labels=classes,\n", + " pattern_labels=tf_pattern_annots,\n", + " color_idx=\"gex\",\n", + " size_idx=\"contrib\",\n", + ")" ] }, { diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 461d4be3..4edeeecf 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -3,7 +3,8 @@ ```{toctree} :maxdepth: 1 -introduction +model_training_and_eval topic_classification +enhancer_code_analysis multi_gpu ``` diff --git a/docs/tutorials/introduction.ipynb b/docs/tutorials/introduction.ipynb deleted file mode 100644 index 4d744af5..00000000 --- a/docs/tutorials/introduction.ipynb +++ /dev/null @@ -1,1853 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to CREsted with Peak Regression\n", - "\n", - "In this introductory notebook, we will train a peak regression model on the mouse BICCN data and inspect the results to get a feel for the capabilities of the CREsted package. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "\n", - "For this tutorial, we will use the mouse BICCN dataset which is available in the {func}`~crested.get_dataset` function. \n", - "To train a CREsted peak regression model on your data, you need: \n", - "1. A consensus regions BED file containing all the regions of interest accross cell types.\n", - "2. A folder containing the bigwig files per cell type. Each file should be named according to the cell type: {cell type name}.bw.\n", - "3. A genome fasta file and optionally a chromosome sizes file.\n", - "\n", - "You could use a tool like SnapATAC2 to generate the consensus regions and bigwig files from your own data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import crested" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the tutorial data\n", - "import os\n", - "\n", - "os.environ[\n", - " \"CRESTED_DATA_DIR\"\n", - "] = \"../../../Crested_testing/data/tmp\" # Change this to your desired directory\n", - "bigwigs_folder, regions_file = crested.get_dataset(\"mouse_cortex_bigwig\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the {func}`~crested.import_bigwigs` function to import bigwigs per cell type and a consensus regions BED file into an {class}`anndata.AnnData` object,\n", - "with the imported cell types as the `AnnData.obs` and the consensus peak regions as the `AnnData.var`. \n", - "\n", - "Optionally, provide a chromsizes file to filter out regions that are not within the chromsizes. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T15:13:51.224593+0200 WARNING Chromsizes file not provided. Will not check if regions are within chromosomes\n", - "2024-08-13T15:13:56.211715+0200 INFO Extracting values from 19 bigWig files...\n" - ] - }, - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 19 × 546993\n", - " obs: 'file_path'\n", - " var: 'chr', 'start', 'end'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata = crested.import_bigwigs(\n", - " bigwigs_folder=bigwigs_folder,\n", - " regions_file=regions_file,\n", - " target_region_width=1000, # optionally, use a different width than the consensus regions file (500bp) for the .X values calculation\n", - " target=\"mean\", # or \"max\", \"count\", \"logcount\" --> what we will be predicting\n", - ")\n", - "adata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To train a model, we always need to add a *split* column to our dataset, which we can do using {func}`crested.pp.train_val_test_split`. \n", - "This will add a column to the `AnnData.obs` with the split type for each region (train, val, or test)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "split\n", - "train 440993\n", - "val 56064\n", - "test 49936\n", - "Name: count, dtype: int64\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " chr start end split\n", - "region \n", - "chr9:65604601-65606715 chr9 65604601 65606715 test\n", - "chr19:18669675-18671789 chr19 18669675 18671789 train\n", - "chr9:65674756-65676870 chr9 65674756 65676870 test\n", - "chr19:18686717-18688831 chr19 18686717 18688831 train\n", - "chr19:18712057-18714171 chr19 18712057 18714171 train\n", - "... ... ... ... ...\n", - "chr5:76528212-76530326 chr5 76528212 76530326 train\n", - "chr9:65434711-65436825 chr9 65434711 65436825 test\n", - "chr9:65460550-65462664 chr9 65460550 65462664 test\n", - "chr9:65461589-65463703 chr9 65461589 65463703 test\n", - "chr13:30711675-30713789 chr13 30711675 30713789 train\n", - "\n", - "[20894 rows x 4 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crested.pp.normalize_peaks(adata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize the normalization factor for each cell type using the {func}`crested.pl.bar.normalization_weights` function to inspect which cell type peaks were up/down weighted." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "crested.pl.bar.normalization_weights(adata, title=\"Normalization Weights per Cell Type\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Specificity Filtering \n", - "\n", - "Optionally we can filter the regions to only include those that are specific to a cell type. \n", - "This will reduce our training dataset size (meaning faster training times) and could improve the model's performance if the filtered out regions are not specific enough to be informative. \n", - "\n", - "A common training approach we've found to be succesful is to first train on all the regions and then filter out the non-specific regions to fine-tune the model with a lower learning rate.\n", - "\n", - "Read the documentation of the {func}`crested.pp.filter_regions_on_specificity` function for more information on how the filtering is done. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T15:14:50.559572+0200 INFO After specificity filtering, kept 89009 out of 546993 regions.\n" - ] - }, - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 19 × 89009\n", - " obs: 'file_path'\n", - " var: 'chr', 'start', 'end', 'split'\n", - " obsm: 'weights'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crested.pp.filter_regions_on_specificity(adata, gini_std_threshold=1.0)\n", - "adata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is no single best way to preprocess your data, so we recommend experimenting with different preprocessing steps to see what works best for your data. \n", - "Likewise there is no single best training approach, so we recommend experimenting with different training strategies." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the final preprocessing results\n", - "adata.write_h5ad(\"../../../Crested_testing/data/tmp/preprocessed_data.h5ad\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Training\n", - "\n", - "The entire CREsted workflow is built around the {func}`crested.tl.Crested` class.\n", - "Everything that requires a model (training, evaluation, prediction) is done through this class. \n", - "This class has a couple of required arguments:\n", - "- `data`: the {class}`crested.tl.data.AnnDataModule` object containing all the data (anndata, genome) and dataloaders that specify how to load the data.\n", - "- `model`: the {class}`keras.Model` object containing the model architecture.\n", - "- `config`: the {class}`crested.tl.TaskConfig` object containing the optimizer, loss function, and metrics to use in training. \n", - "\n", - "Generally you wouldn't run these steps in a notebook, but rather in a script or a python file so you could run it on a cluster or in the background.\n", - "\n", - "### Data\n", - "\n", - "We'll start by initializing the {class}`crested.tl.data.AnnDataModule` object with our data. \n", - "This will tell our model how to load the data and what data to load during fitting/evaluation.\n", - "The main arguments to supply are the `adata` object, the `genome` file path, and the `batch_size`. \n", - "Other optional arguments are related to the training data loading (e.g. shuffling, whether to load the sequences into memory, ...).\n", - "\n", - "The genome file you need to provide yourself as this is not included in the crested package." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import crested" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# read in your preprocessed data\n", - "import anndata\n", - "\n", - "adata = anndata.read_h5ad(\"../../../Crested_testing/data/tmp/preprocessed_data.h5ad\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T15:22:51.519020+0200 WARNING Chromsizes file not provided when shifting. Will not check if shifted regions are within chromosomes\n" - ] - } - ], - "source": [ - "datamodule = crested.tl.data.AnnDataModule(\n", - " adata,\n", - " genome_file=\"../../../Crested_testing/data/tmp/mm10.fa\",\n", - " batch_size=128, # lower this if you encounter OOM errors\n", - " max_stochastic_shift=3, # optional augmentation\n", - " always_reverse_complement=True, # default True. Will double the effective size of the training dataset.\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model definition \n", - "\n", - "Next, we'll define the model architecture. This is a standard Keras model definition, so you can provide your own model definition if you like. \n", - "Alternatively, there are a couple of ready-to-use models available in the `crested.tl.zoo` module. \n", - "Each of them require the width of the input sequences and the number of output classes (your `Anndata.obs`) as arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Load chrombpnet architecture for a dataset with 2114bp regions and 19 cell types\n", - "model_architecture = crested.tl.zoo.chrombpnet(seq_len=2114, num_classes=19)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TaskConfig\n", - "\n", - "The TaskConfig object specifies the optimizer, loss function, and metrics to use in training (we call this our 'task'). \n", - "Some default configurations are available for some common tasks such as 'topic_classification' and 'peak_regression',\n", - "which you can load using the {func}`crested.tl.default_configs` function. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" - ] - } - ], - "source": [ - "# Load the default configuration for training a topic classication model\n", - "from crested.tl import default_configs, TaskConfig\n", - "\n", - "config = default_configs(\"peak_regression\")\n", - "print(config)\n", - "\n", - "# If you want to change some small parameters to an existing config, you can do it like this\n", - "# For example, the default learning rate is 0.001, but you can change it to 0.0001\n", - "# config.optimizer.learning_rate = 0.0001" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can create your own TaskConfig object and specify the optimizer, loss function, and metrics yourself if you want to do something completely custom." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TaskConfig(optimizer=, loss=, metrics=[])\n" - ] - } - ], - "source": [ - "# Create your own configuration\n", - "# for example if doing classification instead of regression you could something like this:\n", - "import keras\n", - "\n", - "optimizer = keras.optimizers.Adam(learning_rate=1e-3)\n", - "loss = keras.losses.BinaryCrossentropy(from_logits=False)\n", - "metrics = [\n", - " keras.metrics.AUC(\n", - " num_thresholds=200,\n", - " curve=\"ROC\",\n", - " )\n", - "]\n", - "alternative_config = TaskConfig(optimizer, loss, metrics)\n", - "print(alternative_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training\n", - "\n", - "Now we're ready to train our model.\n", - "We'll create a {class}`~crested.tl.Crested` object with the data, model, and config objects we just created. \n", - "Then, we can call the {meth}`~crested.tl.Crested.fit` method to train the model. \n", - "Read the documentation for more information on all available arguments to customize your training (e.g. augmentations, early stopping, checkpointing, ...). \n", - "\n", - "By default: \n", - "1. The model will continue training until the validation loss stops decreasing for 10 epochs with a maximum of 100 epochs. \n", - "2. Every best model is saved based on the validation loss.\n", - "3. The learning rate reduces by a factor of 0.25 if the validation loss stops decreasing for 5 epochs." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# setup the trainer\n", - "trainer = crested.tl.Crested(\n", - " data=datamodule,\n", - " model=model_architecture,\n", - " config=config,\n", - " project_name=\"mouse_biccn\", # change to your liking\n", - " run_name=\"chrombpnet_filtered_2114bp\", # change to your liking\n", - " logger=None, # or 'wandb', 'tensorboard'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Model: \"functional\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"functional\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
-       "│ sequence            │ (None, 2114, 4)   │          0 │ -                 │\n",
-       "│ (InputLayer)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ conv1d (Conv1D)     │ (None, 2114, 512) │     10,240 │ sequence[0][0]    │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ batch_normalization │ (None, 2114, 512) │      2,048 │ conv1d[0][0]      │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ activation          │ (None, 2114, 512) │          0 │ batch_normalizat… │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ dropout (Dropout)   │ (None, 2114, 512) │          0 │ activation[0][0]  │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_1conv         │ (None, 2110, 512) │    786,432 │ dropout[0][0]     │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_1bn           │ (None, 2110, 512) │      2,048 │ bpnet_1conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_1activation   │ (None, 2110, 512) │          0 │ bpnet_1bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_1crop         │ (None, 2110, 512) │          0 │ dropout[0][0]     │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add (Add)           │ (None, 2110, 512) │          0 │ bpnet_1activatio… │\n",
-       "│                     │                   │            │ bpnet_1crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_1dropout      │ (None, 2110, 512) │          0 │ add[0][0]         │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_2conv         │ (None, 2102, 512) │    786,432 │ bpnet_1dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_2bn           │ (None, 2102, 512) │      2,048 │ bpnet_2conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_2activation   │ (None, 2102, 512) │          0 │ bpnet_2bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_2crop         │ (None, 2102, 512) │          0 │ bpnet_1dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_1 (Add)         │ (None, 2102, 512) │          0 │ bpnet_2activatio… │\n",
-       "│                     │                   │            │ bpnet_2crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_2dropout      │ (None, 2102, 512) │          0 │ add_1[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_3conv         │ (None, 2086, 512) │    786,432 │ bpnet_2dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_3bn           │ (None, 2086, 512) │      2,048 │ bpnet_3conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_3activation   │ (None, 2086, 512) │          0 │ bpnet_3bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_3crop         │ (None, 2086, 512) │          0 │ bpnet_2dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_2 (Add)         │ (None, 2086, 512) │          0 │ bpnet_3activatio… │\n",
-       "│                     │                   │            │ bpnet_3crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_3dropout      │ (None, 2086, 512) │          0 │ add_2[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_4conv         │ (None, 2054, 512) │    786,432 │ bpnet_3dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_4bn           │ (None, 2054, 512) │      2,048 │ bpnet_4conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_4activation   │ (None, 2054, 512) │          0 │ bpnet_4bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_4crop         │ (None, 2054, 512) │          0 │ bpnet_3dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_3 (Add)         │ (None, 2054, 512) │          0 │ bpnet_4activatio… │\n",
-       "│                     │                   │            │ bpnet_4crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_4dropout      │ (None, 2054, 512) │          0 │ add_3[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_5conv         │ (None, 1990, 512) │    786,432 │ bpnet_4dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_5bn           │ (None, 1990, 512) │      2,048 │ bpnet_5conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_5activation   │ (None, 1990, 512) │          0 │ bpnet_5bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_5crop         │ (None, 1990, 512) │          0 │ bpnet_4dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_4 (Add)         │ (None, 1990, 512) │          0 │ bpnet_5activatio… │\n",
-       "│                     │                   │            │ bpnet_5crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_5dropout      │ (None, 1990, 512) │          0 │ add_4[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_6conv         │ (None, 1862, 512) │    786,432 │ bpnet_5dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_6bn           │ (None, 1862, 512) │      2,048 │ bpnet_6conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_6activation   │ (None, 1862, 512) │          0 │ bpnet_6bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_6crop         │ (None, 1862, 512) │          0 │ bpnet_5dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_5 (Add)         │ (None, 1862, 512) │          0 │ bpnet_6activatio… │\n",
-       "│                     │                   │            │ bpnet_6crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_6dropout      │ (None, 1862, 512) │          0 │ add_5[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_7conv         │ (None, 1606, 512) │    786,432 │ bpnet_6dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_7bn           │ (None, 1606, 512) │      2,048 │ bpnet_7conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_7activation   │ (None, 1606, 512) │          0 │ bpnet_7bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_7crop         │ (None, 1606, 512) │          0 │ bpnet_6dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_6 (Add)         │ (None, 1606, 512) │          0 │ bpnet_7activatio… │\n",
-       "│                     │                   │            │ bpnet_7crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_7dropout      │ (None, 1606, 512) │          0 │ add_6[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_8conv         │ (None, 1094, 512) │    786,432 │ bpnet_7dropout[0… │\n",
-       "│ (Conv1D)            │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_8bn           │ (None, 1094, 512) │      2,048 │ bpnet_8conv[0][0] │\n",
-       "│ (BatchNormalizatio… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_8activation   │ (None, 1094, 512) │          0 │ bpnet_8bn[0][0]   │\n",
-       "│ (Activation)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_8crop         │ (None, 1094, 512) │          0 │ bpnet_7dropout[0… │\n",
-       "│ (Cropping1D)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ add_7 (Add)         │ (None, 1094, 512) │          0 │ bpnet_8activatio… │\n",
-       "│                     │                   │            │ bpnet_8crop[0][0] │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bpnet_8dropout      │ (None, 1094, 512) │          0 │ add_7[0][0]       │\n",
-       "│ (Dropout)           │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ global_average_poo… │ (None, 512)       │          0 │ bpnet_8dropout[0… │\n",
-       "│ (GlobalAveragePool… │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ dense_out (Dense)   │ (None, 19)        │      9,747 │ global_average_p… │\n",
-       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
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\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", - "│ sequence │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2114\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2114\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m10,240\u001b[0m │ sequence[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2114\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv1d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2114\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2114\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_1conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2110\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_1bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2110\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_1conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_1activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2110\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_1bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_1crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2110\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2110\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_1activatio… │\n", - "│ │ │ │ bpnet_1crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_1dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2110\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_2conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2102\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_1dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_2bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2102\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_2conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_2activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2102\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_2bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_2crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2102\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_1dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_1 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2102\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_2activatio… │\n", - "│ │ │ │ bpnet_2crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_2dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2102\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_3conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2086\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_2dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_3bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2086\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_3conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_3activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2086\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_3bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_3crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2086\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_2dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_2 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2086\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_3activatio… │\n", - "│ │ │ │ bpnet_3crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_3dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2086\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_4conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2054\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_3dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_4bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2054\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_4conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_4activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2054\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_4bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_4crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2054\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_3dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_3 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2054\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_4activatio… │\n", - "│ │ │ │ bpnet_4crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_4dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2054\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_5conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1990\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_4dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_5bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1990\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_5conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_5activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1990\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_5bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_5crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1990\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_4dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_4 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1990\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_5activatio… │\n", - "│ │ │ │ bpnet_5crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_5dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1990\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_6conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1862\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_5dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_6bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1862\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_6conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_6activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1862\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_6bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_6crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1862\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_5dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_5 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1862\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_6activatio… │\n", - "│ │ │ │ bpnet_6crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_6dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1862\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_7conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1606\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_6dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_7bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1606\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_7conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_7activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1606\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_7bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_7crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1606\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_6dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_6 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1606\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_7activatio… │\n", - "│ │ │ │ bpnet_7crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_7dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1606\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_8conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1094\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ bpnet_7dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mConv1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_8bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1094\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ bpnet_8conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_8activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1094\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_8bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_8crop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1094\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_7dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mCropping1D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ add_7 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1094\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_8activatio… │\n", - "│ │ │ │ bpnet_8crop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ bpnet_8dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1094\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bpnet_8dropout[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ dense_out (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m19\u001b[0m) │ \u001b[38;5;34m9,747\u001b[0m │ global_average_p… │\n", - "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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cosine_similarity: 0.5142 - loss: 2.5419 - mean_absolute_error: 0.6500 - mean_squared_error: 2.9634 - pearson_correlation: 0.0715 - pearson_correlation_log: 0.2853 - zero_penalty_metric: 776.53042024-08-13T15:24:23.706543+0200 WARNING Training interrupted by user.\n" - ] - } - ], - "source": [ - "# train the model\n", - "trainer.fit(epochs=60)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate the model\n", - "\n", - "After training, we can evaluate the model on the test set using the {meth}`~crested.tl.Crested.test` method. \n", - "If we're still in the same session, we can simply continue using the same object. \n", - "If not, we can load the model from disk using the {func}`~crested.tl.Crested.load_model` method.\n", - "This means that we have to create a new {class}`~crested.tl.Crested` object first. \n", - "However, this time, since the taskconfig and architecture are saved in the .keras file, we only have to provide our datamodule." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import anndata\n", - "import crested\n", - "\n", - "adata = anndata.read_h5ad(\"../../../Crested_testing/data/tmp/preprocessed_data.h5ad\")\n", - "\n", - "datamodule = crested.tl.data.AnnDataModule(\n", - " adata,\n", - " genome_file=\"../../../Crested_testing/data/tmp/mm10.fa\",\n", - ")\n", - "\n", - "evaluator = crested.tl.Crested(data=datamodule)\n", - "\n", - "# load an existing model\n", - "evaluator.load_model(\n", - " \"../../../Crested_testing/mouse_biccn/chrombpnet_filtered_2114bp/15.keras\",\n", - " compile=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you experimented with many different hyperparameters for your model, chances are that you will start overfitting on your validation dataset. \n", - "It's therefore always a good idea to evaluate your model on the test set after getting good results on your validation data to see how well it generalizes to unseen data." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 135ms/step - loss: 0.0239\n", - "2024-08-13T15:53:51.932398+0200 INFO Test loss: 0.0239\n" - ] - } - ], - "source": [ - "# evaluate the model on the test set\n", - "evaluator.test()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predict\n", - "\n", - "After training, we can also use the {meth}`~crested.tl.Crested.predict` method to predict the labels for new data and add them as a layer to the `AnnData` object. \n", - "A common use case is to compare the predicted labels to the true labels for multiple trained models to see how well they compare. \n", - "\n", - "We can initiate a new Crested object (if you have different data) or use the existing one. \n", - "Here we continue with the existing one since we'll use the same data as we trained on." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m348/348\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 119ms/step\n", - "2024-08-13T15:54:40.788075+0200 INFO Adding predictions to anndata.layers[checkpoint_15].\n" - ] - } - ], - "source": [ - "# add predictions for model checkpoint to the adata\n", - "evaluator.predict(\n", - " adata, model_name=\"checkpoint_15\"\n", - ") # adds the predictions to the adata.layers[\"checkpoint_15\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you don't want to predict on the entire dataset, you can also predict on a given sequence or region using the {meth}`~crested.tl.Crested.predict_sequence` or {meth}`~crested.tl.Crested.predict_regions` methods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many of the plotting functions in the `crested.pl` module can be used to visualize these model predictions. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contribution Scores\n", - "\n", - "We can calculate the contribution scores for a **sequence** of interest using the {meth}`~crested.tl.Crested.calculate_contribution_scores_sequence` method. \n", - "You always need to ensure that the sequence or region you provide is the same length as the model input (2114bp in our case). " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T15:55:10.951464+0200 INFO Calculating contribution scores for 2 class(es) and 1 region(s).\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Region: 100%|██████████| 1/1 [00:03<00:00, 3.66s/it]\n" - ] - } - ], - "source": [ - "# random sequence of length 2114bp as an example\n", - "sequence = \"A\" * 2114\n", - "\n", - "scores, one_hot_encoded_sequences = evaluator.calculate_contribution_scores_sequence(\n", - " sequence, class_names=[\"Astro\", \"Endo\"]\n", - ") # focus on two cell types of interest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can calculate contribution scores for **regions** of interest using the {meth}`~crested.tl.Crested.calculate_contribution_scores_regions` method. \n", - "These regions don't have to be in your original dataset, as long as they exist in the genome file that you provided to the `AnnDataModule` and they are the same length as the model input. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T15:57:15.341186+0200 INFO Calculating contribution scores for 2 class(es) and 1 region(s).\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Region: 100%|██████████| 1/1 [00:02<00:00, 2.63s/it]\n" - ] - } - ], - "source": [ - "# focus on two cell types of interest\n", - "regions_of_interest = [\n", - " \"chr18:61107770-61109884\"\n", - "] # FIRE enhancer region, should only have motifs in Micro_PVM\n", - "classes_of_interest = [\"Astro\", \"Micro_PVM\"]\n", - "scores, one_hot_encoded_sequences = evaluator.calculate_contribution_scores_regions(\n", - " region_idx=regions_of_interest, class_names=classes_of_interest\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Contribution scores for regions can be plotted using the {func}`crested.pl.patterns.contribution_scores` function. \n", - "This will generate a plot per class per region." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "crested.pl.patterns.contribution_scores(\n", - " scores,\n", - " one_hot_encoded_sequences,\n", - " sequence_labels=regions_of_interest,\n", - " class_labels=classes_of_interest,\n", - " zoom_n_bases=600,\n", - " title=\"FIRE Enhancer Region\",\n", - ") # zoom in on the center 600bp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sequence evolution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create synthetic enhancers for a specified class using in silico evolution with the {meth}`~crested.tl.Crested.enhancer_design_in_silico_evolution` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "designed_sequences = evaluator.enhancer_design_in_silico_evolution(\n", - " target_class=\"Astro\", n_sequences=1, n_mutations=25\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TFModisco\n", - "\n", - "For ease of use, we've included the TFModisco Lite functionality inside the CREsted package. \n", - "\n", - "To calculate the contribution scores in the format required for TFModisco, use the {meth}`~crested.tl.Crested.tfmodisco_calculate_and_save_contribution_scores` method. \n", - "This will save the contribution scores in the correct format for TFModisco Lite. \n", - "\n", - "Since this will calculate contribution scores for all regions in your dataset, it's recommended to only run this on a subset of your data. \n", - "We've included a preprocessing function {func}`crested.pp.sort_and_filter_regions_on_specificity` to keep the top k most specific regions per cell type that you can use to filter your data before running TFModisco." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T16:43:03.596883+0200 INFO After sorting and filtering, kept 9500 regions.\n" - ] - }, - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 19 × 9500\n", - " obs: 'file_path'\n", - " var: 'chr', 'start', 'end', 'split', 'Class name', 'rank', 'gini_score'\n", - " obsm: 'weights'\n", - " layers: 'checkpoint_15'" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# most informative regions per class\n", - "adata_filtered = adata.copy()\n", - "crested.pp.sort_and_filter_regions_on_specificity(adata_filtered, top_k=500)\n", - "adata_filtered" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Remember to reinitialize your crested object with the new data\n", - "datamodule = crested.tl.data.AnnDataModule(\n", - " adata_filtered,\n", - " genome_file=\"../../../Crested_testing/data/tmp/mm10.fa\",\n", - ")\n", - "\n", - "evaluator = crested.tl.Crested(data=datamodule)\n", - "\n", - "# load an existing model\n", - "evaluator.load_model(\n", - " \"../../../Crested_testing/mouse_biccn/chrombpnet_filtered_2114bp/15.keras\",\n", - " compile=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you can calculate the contribution scores for all the regions in your filtered anndata. \n", - "By default, the contribution scores are calculated using the expected integrated gradients method, but you can change this to simple integrated gradients to speed up the calculation (this might result in less accurate scores)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate contribution scores for all regions and save them to output_dir\n", - "evaluator.tfmodisco_calculate_and_save_contribution_scores(\n", - " output_dir=\"../../../Crested_testing/mouse_biccn/chrombpnet_filtered_2114bp/contribution_scores\",\n", - " method=\"integrated_grad\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When this is done, you can run TFModisco Lite on the saved contribution scores to find motifs that are important for the classification/regression task. \n", - "\n", - "You could use the tfmodisco package directly to do this, or you could use the {func}`crested.tl.tfmodisco` function which is essentially a wrapper around the tfmodisco package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# run tfmodisco on the contribution scores\n", - "crested.tl.tfmodisco(\n", - " contrib_dir=\"../../../Crested_testing/mouse_biccn/chrombpnet_filtered_2114bp/contribution_scores\",\n", - " output_dir=\"../../../Crested_testing/mouse_biccn/chrombpnet_filtered_2114bp/tfmodisco_results\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When this is done, you can plot the results using the {func}`crested.pl.patterns.modisco_results` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "crested.pl.modisco_results(\n", - " classes=[\"Astro\", \"Endo\"],\n", - " contribution=\"positive\",\n", - " contribution_dir=\"../../../Crested_testing/mouse_biccn/chrombpnet_filtered_2114bp/tfmodisco_results\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting\n", - "\n", - "There is a bunch of plotting functions available in the `crested.pl` module to visualize the results of the model.\n", - "We will show some examples below, but refer to the documentation for more information. \n", - "\n", - "All the plotting functions using {func}`crested.pl.render_plot` to render the plots. You can refer to the documentation of this function to customize the plots to your liking." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Regions of interest" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T16:16:09.870822+0200 INFO Plotting bar plots for region: chr18:60577255-60579369, models: ['checkpoint_15']\n" - ] - }, - { - "data": { - "image/png": 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IiIiIiIiIiKgsMOjA3/bt22FiYoKhQ4eq0hQKBQYPHowpU6YgKSkJNWrUKHDd5s2bqwb9AMDDwwPt27fH999/z4E/IiIiIiIiIiIiIqIS4tTnr3DqcyovDDrV57lz51CvXj1YW1urpbdo0QIAcP78+XzXy8vLw4ULF9CsWTONZS1atEBCQgKePHkieXmJiIiIiIiIiIiIiIiIyiqDPvGXkpICJycnjXRlWnJycr7rpaWlITs7u8h169evn+/62dnZyM7OVv2dkZGh2u6LF4b7xUC59NzQBQCe47nByyHJeyUZSwASxLIMxBFgLKXEWEqjLMQRYCylwnZHOoyldBhL6TCW0mEspcNYSscY+kMAYymVshBHgLGUEmMpDbY70mEspcNYSkeSWL5llA+7CSGKzCsT2uQqJbVr10b9+vWxf/9+tfQbN26gdu3aWLRoEcaOHauxXlJSEmrWrIn58+dj0qRJasvWr1+PwYMH49y5c/D29s53vzNmzMDMmTOlqgYRERERERERERERERFRqUpKSoKLi0uheQz6xJ+5ubnak3dKWVlZquUFrQegWOsCwOTJk/Hpp5+q/s7Ly0NaWhrs7Owgk8m0rwAZ3OPHj1GjRg0kJSVpTBlLumEspcNYSoexlAbjKB3GUjqMpXQYS+kwltJhLKXDWEqHsZQOYykNxlE6jKV0GEvpMJbSYSylw1iWX0IIPHnyBM7OzkXmNejAn5OTE+7evauRnpKSAgAFVqBKlSqQy+WqfLqsCwByuRxyuVwtzdbWVttiUxlkbW3NE5VEGEvpMJbSYSylwThKh7GUDmMpHcZSOoyldBhL6TCW0mEspcNYSoNxlA5jKR3GUjqMpXQYS+kwluWTjY2NVvkqlHI5CuXt7Y2rV6/i8ePHaum//fabanl+KlSogEaNGuHMmTMay3777Te4u7ujUqVKkpeXiIiIiIiIiIiIiIiIqKwy6MBfYGAgcnNzsWbNGlVadnY2NmzYgJYtW6JGjRoAgNu3b+PKlSsa654+fVpt8O+vv/7Cjz/+iKCgIP1UgIiIiIiIiIiIiIiIiKiMMOhUny1btkRQUBAmT56Me/fuoU6dOti4cSMSExOxbt06Vb4BAwbg2LFjEEKo0kaMGIG1a9eia9eumDBhAkxNTbFw4UI4Ojpi/PjxhqgOGYBcLsf06dM1pm4l3TGW0mEspcNYSoNxlA5jKR3GUjqMpXQYS+kwltJhLKXDWEqHsZQG4ygdxlI6jKV0GEvpMJbSYSzfDjLx+miaAWRlZeHzzz/Hli1bkJ6ejsaNG2P27Nno1KmTKo+vr6/GwB8A3LlzB+PGjUNcXBzy8vLg6+uLRYsWoU6dOvquBhEREREREREREREREZFBGXzgj4iIiIiIiIiIiIiIiIhKzqDv+CMiIiIiIiIiIiIiIiIiaXDgj4iIiIiIiIiIiIiIiMgIcOCPiLQycOBAuLm5GboYRERERAbh6+sLLy8vQxeDygCZTIYZM2YYuhhEpe67776DTCZDYmKiKs3X1xe+vr4GK9Pb6KeffoJMJsNPP/2kSmObRFS+KL/H27dvL/V95Xf/jn0XUsqvbSfjxIE/MpgVK1ZAJpOhZcuWxVr/8uXLmDFjxlt/olKesAv6/Prrr4YuYpmijNeZM2fyXZ6UlISZM2eiRYsWqFy5MqpWrQpfX18cPnxY531lZmZi8ODB8PLygo2NDaysrNCkSRMsWbIEL168UMs7Y8YMyGQyPHjwQNUh1OZTVhUVZwAF1unLL79Uyzdw4MAC8yoUCgCAm5ubVvH67rvvSrPapU7quFpZWaltt6hPWRz8lzIm2kpISEDfvn3h4OAAc3Nz1K1bF1OnTlXL4+vrC5lMhoCAAI31ExMTIZPJ8PXXX6vS3vzem5qawt3dHQMGDMCNGzeKVc7Sok3MASA1NRXDhg1D9erVoVAo4ObmhsGDB+eb96effkKvXr1QrVo1mJmZwcHBAQEBAYiNjQXwTzyL+pTni1kp42os328plMbxWt5pG5OyRHnezO8THR1d7O2+/v04fvy4xnIhBGrUqAGZTIZu3bqVpAp6oU1bsnDhQshkskL7tmvXroVMJsPu3bsB/HMOrlu3br754+PjVfvUxw1NpUuXLqFfv36oXr065HI5nJ2dERYWhkuXLqnle/M8qFAoUK9ePYwaNQqpqaka201NTcWECRPg4eEBCwsLWFpaomnTppgzZw4ePXqkp9qVLm1jR5p0PZ5IOxcvXkRgYCBcXV2hUChQvXp1+Pn5YdmyZYYumlGRIs7JycmYMWMGzp8/X3oFlYi+6lvUtczbpnv37rCwsMCTJ08KzBMWFgYzMzM8fPgQMpkMo0aNKnSbJe2LJCQkYNiwYXB3d4dCoYC1tTXeffddLFmyBJmZmbpVsAzSNeb0dqho6ALQ2ysyMhJubm44deoUrl+/jjp16ui0/uXLlzFz5kz4+voa7c0qXcyaNQu1atXSSNc1rm+7Xbt2Yf78+ejRowfCw8Px8uVLbNq0CX5+fli/fj0GDRqk9bYyMzNx6dIl+Pv7w83NDRUqVMCJEycwbtw4/Pbbb4iKisp3vQYNGmDz5s1qaZMnT4aVlZXG4EJ55+fnhwEDBqilvfPOOxr55HI5vv32W410ExMTAMDixYvx9OlTVfr+/fuxdetWLFq0CFWrVlWlt27dWqqil2naxlWpbdu2GsfcRx99hBYtWmDo0KGqNOVAQnmka0wKcv78efj6+qJ69eoYP3487OzscPv2bSQlJeWbf+/evTh79iyaNm2q1fY/+eQTNG/eHC9evMDvv/+ONWvWYN++fbh48SKcnZ11Lq+hJCUl4d133wUADB8+HNWrV0dycjJOnTqlkXf69OmYNWsW6tati2HDhsHV1RUPHz7E/v370bt3b0RGRmLq1Kn46KOPVOucPn0aS5cuxZQpU9CgQQNVeuPGjUu/cgakS1yV3obvd0kVJ65kGKGhofD391dLa9WqVYm3q1AoEBUVhTZt2qilHzt2DHfu3IFcLldLz8zMRMWKZfdSurC2JCQkBBMnTkRUVBQ6dOiQ7/pRUVGws7NDly5dVGkKhQLXr1/HqVOn0KJFC7X8kZGRUCgUyMrKKtV6vS42NhahoaGoUqUKBg8ejFq1aiExMRHr1q3D9u3bER0djZ49e6qto7xeysrKwvHjx7Fy5Urs378ff/75JywsLAC8al/8/f3x9OlT9OvXT9V+nzlzBl9++SV+/vlnxMXF6a2epaE4sXtdea+/VLQ5nkg7J06cQLt27VCzZk0MGTIE1apVQ1JSEn799VcsWbIEo0ePNnQRjYJUcU5OTsbMmTPh5uYGb2/v0i10Ceirvtpcy/Tt21fi2pVtYWFh2LNnD3bu3KlxDQ4Az58/x65du9C5c2fY2dlpvd3i9kX27duHoKAgyOVyDBgwAF5eXsjJycHx48cxceJEXLp0CWvWrNG9omWILjEfO3YsRo4cqdG/JSMkiAzgxo0bAoCIjY0V9vb2YsaMGTpvY9u2bQKAOHr0aJF58/LyxPPnz4tR0rJvw4YNAoA4ffp0qe4nPDxcuLq6luo+9KGoeP3555/i/v37amlZWVnCw8NDuLi4SFKGUaNGCQAiJSVFlTZ9+nQBQGPfSp6ensLHx0eS/euDNsclADFy5MgitxUeHi4sLS112n9ERIQAIG7evKnTemWdPuNqaWkpwsPDi1NMvZIyJkXJzc0VXl5eomXLlkW2KT4+PqJmzZqicuXKIiAgQG3ZzZs3BQARERGhSjt69KgAILZt26aWd+nSpQKAmDdvXonLLxVtYt6lSxdRq1Yt8eDBg0K3pWzLAwMDRU5OjsbygwcPij179hS4njZ9gPJCyrgay/dbClLG1cfHR3h6ekpdRL3TV99RSvmdN6WgjEWvXr1E1apVxYsXL9SWDxkyRDRt2lS4urqKrl27SrpvIYR4+vSppNvTti1p3769sLGxEVlZWRrbuHPnjqhQoYIYPny4Kk157NevX1+MHTtWLX9mZqawtrYWvXv3znffpeH69evCwsJCeHh4iHv37qktu3//vvDw8BCWlpYiISFBCFHwMf/pp58KACIqKkoIIUR6erqoXr26cHR0FP/73/809vv333+L2bNnl1Kt9KO4sTO2PnVJaHs8SUn53X6932MsbZIQQvj7+wt7e3uRnp6usSw1NVX/BTJSUsX59OnTAoDYsGGDdIUrBfqory7XMgW10aUhv/t3AMT06dNLfd9CCPH8+XNRqVIl0alTp3yXR0VFCQAiOjpaVbairteL2xe5ceOGsLKyEh4eHiI5OVlju9euXROLFy/WtYpljq4xp7cDp/okg4iMjETlypXRtWtXBAYGIjIyUiNPdHQ0mjZtikqVKsHa2hqNGjXCkiVLALyaXiMoKAgA0K5dO9Xj3Mo5793c3NCtWzccOnQIzZo1g7m5OVavXg0AuHHjBoKCglClShVYWFjg3//+N/bt26efihvI69PKrVmzBrVr14ZcLkfz5s1x+vRpjfw//PADvLy8oFAo4OXlhZ07d+a73WfPnmH8+PGoUaMG5HI56tevj6+//hpCiNKuUqnx9PRUe0IMePW0mb+/P+7cuVPoY/PaUj6haixTBZVUZmamXn8h/rZgXDWVNCZxcXH4888/MX36dJibm+P58+fIzc0tMH+lSpUwbtw47NmzB7///nux9vn+++8DAG7evFms9Q3hypUrOHDgACZOnAg7OztkZWVpTG+s9Pnnn6NKlSpYv349TE1NNZZ36tSpXEyvpw+6xJW0V5y4nj17Fq1bt4a5uTlq1aqFVatW6am0+pGTk4Np06ahadOmsLGxgaWlJd577z0cPXpULd/r/ctvvvkG7u7usLCwQMeOHZGUlAQhBGbPng0XFxeYm5vjgw8+QFpamto2lH32uLg4eHt7Q6FQoGHDhoVOjfXs2TPk5ORIWufQ0FA8fPgQ8fHxanHYvn17vr/Sz29q4bt372Lw4MFwdnaGXC5HrVq18PHHH6vKqpwe8NixYxgxYgQcHBzg4uKiWn/FihXw9PRUTbs4cuRIyfqKb7Yl/fr1Q0ZGRr7XQNHR0cjLy0NYWJjGstDQUMTExCAvL0+VtmfPHjx//hx9+vSRpKzaiIiIwPPnz7FmzRrY29urLatatSpWr16NZ8+e4auvvip0O2/GZfXq1bh79y4WLlwIDw8PjfyOjo747LPPJKqFYUgRO9983vF369YtdO/eHZaWlnBwcMC4ceNw6NAhtWt0pW3btqFp06YwNzdH1apV0a9fP9y9e1eqKhqM8ni6du0aZDIZNm7cqJFHGZO9e/cCeBW3ESNGoH79+jA3N4ednR2CgoJ0ep2JMbRJCQkJ8PT0hK2trcYyBwcH1f8rpwNU3q+Qy+Xw9PTEwYMHNdYr6pwMABcuXICPjw/Mzc3h4uKCOXPmYMOGDZDJjPPdV9rGOT4+Hm3atIGtrS2srKxQv359TJkyBcCrKS2bN28OABg0aFCZfqWGPupbnGuZvLw8zJ07Fy4uLlAoFGjfvj2uX7+usa6250pt79/pk7m5OXr16oUjR47g3r17GsujoqJQqVIldO/eXedt69oX+eqrr/D06VOsW7cOTk5OGsvr1KmDMWPG6FyOskaXmOf3jr/i9Mmp7OPAHxlEZGQkevXqBTMzM4SGhuLatWtqA1Dx8fEIDQ1F5cqVMX/+fHz55Zfw9fXFf//7XwCvpq365JNPAABTpkzB5s2bsXnzZrXpvv766y+EhobCz88PS5Ysgbe3N1JTU9G6dWscOnQII0aMwNy5c5GVlYXu3buXicaxJDIyMvDgwQO1z5vzNkdFRSEiIgLDhg3DnDlzkJiYiF69eqnd4IqLi0Pv3r0hk8nwxRdfoEePHhg0aJDGe2CEEOjevTsWLVqEzp07Y+HChahfvz4mTpyITz/9VC911qe///4bFhYWxZq2JScnBw8ePEBSUhJ27tyJr7/+Gq6urpyGFa9ugllaWsLc3BwNGzYscPpTABrH94MHD/D48WM9lrb80CWubwspYqJ8H5JcLkezZs1gaWkJCwsLhISEaNzMVhozZgwqV65c7HfPJSQkAIBOU6AYmjJOjo6OaN++PczNzWFubo4uXbqoXVxcu3YNV65cQY8ePVCpUiUDlbb80DaupBtd45qeng5/f380bdoUX331FVxcXPDxxx9j/fr1ei556Xn8+DG+/fZb+Pr6Yv78+ZgxYwbu37+PTp065ft+m8jISKxYsQKjR4/G+PHjcezYMfTp0wefffYZDh48iP/85z8YOnQo9uzZgwkTJmisf+3aNQQHB6NLly744osvULFiRQQFBakNwinNnDkTVlZWUCgUaN68uWRTDrq5uaFVq1bYunWrKu3AgQPIyMhASEhIkesnJyejRYsWiI6ORnBwMJYuXYr+/fvj2LFjeP78uVreESNG4PLly5g2bRr+7//+D8Crdz2PHDkSzs7OWLBgAXr37o3Vq1ejY8eOkgzwv9mW9OrVSzW96ZuioqLg6uqqmv72dX379kVKSoraQE5UVBTat2+vdhO1tO3Zswdubm5477338l3etm1buLm5Ffnjzjfjsnv3bpibmyMwMFDaApchUsXudc+ePcP777+Pw4cP45NPPsHUqVNx4sQJ/Oc//9HI+91336FPnz4wMTHBF198gSFDhiA2NhZt2rQp9z+KVB5Pjo6OcHd3x/fff6+RJyYmBpUrV0anTp0AvJpa9sSJEwgJCcHSpUsxfPhwHDlyBL6+vhrnjvwYS5vk6uqKs2fP4s8//ywy7/HjxzFixAiEhITgq6++QlZWFnr37q1270Obc/Ldu3fRrl07XLp0CZMnT8a4ceMQGRmp+rG5MdImzpcuXUK3bt2QnZ2NWbNmYcGCBejevbvqXlyDBg0wa9YsAMDQoUNV9+Latm2rlzroorTrW9xrmS+//BI7d+7EhAkTMHnyZPz6668aP7bR9lyp7f07QwgLC8PLly81zoVpaWk4dOgQevbsCXNzc523q2tfZM+ePXB3d38rXvtS0pjr0iencsLATxzSW+jMmTMCgIiPjxdCvJqG08XFRYwZM0aVZ8yYMcLa2lq8fPmywO0UNs2Xq6urACAOHjyolj527FgBQPzyyy+qtCdPnohatWoJNzc3kZubW7LKGYByqpH8PnK5XAjxz/RIdnZ2Ii0tTbXurl27BAC1adS8vb2Fk5OTePTokSotLi5OAFCbKuCHH34QAMScOXPUyhMYGChkMpm4fv16KdW4ZIozvdW1a9eEQqEQ/fv3L9Y+t27dqvbv0qxZM3HhwgW1PG/jVJ+tW7cWixcvFrt27RIrV64UXl5eAoBYsWKFWr7w8PACj/GCpjF4m6f61CWuxjAVoJQxKUr37t1V59KwsDCxfft28fnnn4uKFSuK1q1bi7y8PFXe16dfmjlzpgAgzp49K4QofKrP9evXi/v374vk5GSxb98+4ebmJmQyWZmakq+omH/yySeqOHXu3FnExMSIiIgIYWVlJWrXri2ePXsmhPinDVq0aJHOZXgbp/rUNq5CGM/3WwpSxtXHx0cAEAsWLFClZWdnC29vb+Hg4JDvFE9lUVExefnypcjOzlZLS09PF46OjuLDDz9UpSnPZfb29mr9xsmTJwsAokmTJmpTZ4aGhgozMzO16SWVffYdO3ao0jIyMoSTk5N45513VGm3bt0SHTt2FCtXrhS7d+8WixcvFjVr1hQVKlQQe/fulSQWy5cvF5UqVVJN5RwUFCTatWunKufrU33ijemyBgwYICpUqJBvTJVtg3Jfbdq0UbvGuXfvnjAzMxMdO3ZUuxZZvny5ql3Qli5tSVBQkFAoFCIjI0OVduXKFQFATJ48WW27r7dpzZo1E4MHDxZCvDouzMzMxMaNG/U2hdmjR48EAPHBBx8Umk/ZZj9+/FgV+8OHD4v79++LpKQkER0dLezs7IS5ubm4c+eOEEKIypUriyZNmpRq+Q2pJLF7vU/t4+Ojdl2yYMECAUD88MMPqrTMzEzh4eGh1lbn5OQIBwcH4eXlJTIzM1V59+7dKwCIadOmSVHNUqfN8TR58mRhamqqdu2dnZ0tbG1t1c6j+U0df/LkSQFAbNq0SZVW0FSfxtAmCfHqfoOJiYkwMTERrVq1EpMmTRKHDh3SqAMAYWZmpnav4Y8//hAAxLJly1Rp2pyTR48eLWQymTh37pxq2cOHD0WVKlWM8jpSCO3ivGjRokLvSwhRfqb6LO366noto/weN2jQQK2ftWTJEgFAXLx4UQih27lS2/t3Quh3qk8hXvUnnZycRKtWrdTSV61aJQCIQ4cOqZVN26k+hdC+L5KRkaFVu2cstI15fm27tn1yKl/4xB/pXWRkJBwdHdGuXTsAr6ZrCA4ORnR0tGrKNFtbWzx79qxEvyqoVauW6pd0Svv370eLFi3Qpk0bVZqVlRWGDh2KxMREXL58udj7M7RvvvkG8fHxap8DBw6o5QkODkblypVVfyt/6Xnjxg0AQEpKCs6fP4/w8HDY2Nio8vn5+aFhw4Zq29q/fz9MTExUT14qjR8/HkIIjX2XV8+fP0dQUBDMzc3x5ZdfFmsb7dq1Q3x8PLZt24bhw4fD1NQUz549k7ik5c9///tfjBkzBt27d8fw4cNx9uxZeHl5YcqUKcjMzFTLq1AoNI7v+Pj4Yv+bGDNd4vq2kComT58+BQA0b94cW7ZsQe/evTFr1izMnj0bJ06cwJEjR/JdT/nU38yZM4vcx4cffgh7e3s4Ozuja9euePbsGTZu3IhmzZppXU5DU8apWrVq2LdvH/r06YMJEyZg7dq1SEhIUD1honxil0/7aUfbuJJudI1rxYoVMWzYMNXfZmZmGDZsGO7du4ezZ8/qteylxcTEBGZmZgBeTUeVlpaGly9folmzZvlOWxwUFKTWb2zZsiWAV9NJVqxYUS09JydHY5oqZ2dn9OzZU/W3tbU1BgwYgHPnzuHvv/8GANSsWROHDh3C8OHDERAQgDFjxuDcuXOwt7fH+PHjJal3nz59kJmZib179+LJkyfYu3dvvtN8vikvLw8//PADAgIC8j1Xy2Qytb+HDBkCExMT1d+HDx9GTk4Oxo4diwoVKqjls7a2LtYrCbRpS/r164esrCy1KZyUx3t+03wq9e3bF7GxsaqpUE1MTNT+/Uqbctr9otoO5fLXZ4fo0KED7O3tUaNGDYSEhMDKygo7d+5E9erVVXmNuU0qSewKc/DgQVSvXl1tyjaFQoEhQ4ao5Ttz5gzu3buHESNGQKFQqNK7du0KDw+Pcvf6jcKOp+DgYLx48ULt+xUXF4dHjx4hODhYlfb6kxcvXrzAw4cPUadOHdja2mo1TbyxtEl+fn44efIkunfvjj/++ANfffUVOnXqhOrVq2P37t1qeTt06IDatWur/m7cuDGsra1V9zS0PScfPHgQrVq1gre3t2pZlSpVCj3/lXfaxFk5LeauXbvUplIsj0q7vsW9lhk0aJCqnwVo3pfT9lypy/07QzAxMUFISAhOnjypNotGVFSUaqaN4tK2L/K2XW+WNOba9MmpfOHAH+lVbm4uoqOj0a5dO9y8eRPXr1/H9evX0bJlS6Smpqpumo4YMQL16tVDly5d4OLigg8//DDfedsLU6tWLY20W7duoX79+hrpyilCb926VYxalQ0tWrRAhw4d1D7KwVWlmjVrqv2tHARMT08H8E/969atq7H9N+N269YtODs7azSgxhBLpdzcXISEhODy5cvYvn07nJ2di7UdR0dHdOjQAYGBgVi5ciW6desGPz8/NpxvMDMzw6hRo/Do0SONC1UTExON47tDhw5qF2qUv8Li+rYqbkyUN2dCQ0PV0pU3hk+cOJHvejY2Nhg7dix2796Nc+fOFbqPadOmIT4+Hj/++CMuXLiA5ORk9O/fX+sylgXKOPXp00ftJnZQUBAqVqyoipO1tTUASPLu1LeBtnEl3egaV2dnZ1haWqql1atXDwCMasrVjRs3onHjxlAoFLCzs4O9vT327duHjIwMjbxv9i+VN59q1KiRb7qy36lUp04djcExbWJapUoVDBo0CH/99Rfu3LmjXcUKYW9vjw4dOiAqKgqxsbHIzc3VasrH+/fv4/Hjx/Dy8tJqP29eoyj7zG/2tc3MzODu7l6sPrU2bUmXLl1QpUoVtcHtrVu3okmTJvD09Cxw2yEhIcjIyMCBAwcQGRmJbt266fWGmnJfRbUd+Q1yKX8oefToUVy+fBk3btxQ+6GotbW1UbdJJYldYW7duoXatWtrfI/ffK1BQcc6AHh4eJS768fCjqcmTZrAw8MDMTExqvwxMTGoWrWq6l2AwKv3T0+bNg01atSAXC5H1apVYW9vj0ePHuV7vn2TMbVJzZs3R2xsLNLT03Hq1ClMnjwZT548QWBgoNoPtN9sc4BX9zWUbYu25+Rbt27l++oNY38dR1FxDg4OxrvvvouPPvoIjo6OCAkJwffff19uBwFLs77FvZbR9r5cUedKXe7fGYpyIF3Z17hz5w5++eUXhISEqP0ISlfa9kXexuvNksS8uH1yKrs48Ed69eOPPyIlJQXR0dGoW7eu6qN8AWtkZCSAVy/aPX/+PHbv3o3u3bvj6NGj6NKlC8LDw7XeV3HmijZ2BZ3khRB6Lkn5MGTIEOzduxffffed2gVaSQUGBuLp06fYtWuXZNs0FsqbhAW9L42Kh3HVVJyYKAf/HR0d1dKV7xJ482b268aMGQNbW9sin/pr1KiR6ocbjRo1UntaprwoKE4mJiaws7NTxcnDwwMAcPHiRf0WsJzSNq6kG8ZV05YtWzBw4EDUrl0b69atw8GDBxEfH4/3338/3xthBfUv9dHvlLp969u3Lw4cOIBVq1ahS5cuqicBpKSPaxRt2hJTU1P06dMHP/74I1JTU3H69Glcu3atyKddnJyc4OvriwULFuDnn3/W6qlIKdnY2MDJyQkXLlwoNN+FCxdQvXp11U0/4J8fSvr6+qJBgwZqg/3Aq3bp6tWryMnJKZWyG1pJYkeaijqegoODcfToUTx48ADZ2dnYvXs3evfurfZ9HD16NObOnYs+ffrg+++/R1xcHOLj42FnZ1duB1pKyszMDM2bN8e8efOwcuVKvHjxAtu2bVMt5z0NaRQUZ3Nzc/z88884fPgw+vfvjwsXLiA4OBh+fn6qGbrKo9Kob3GvZd6mY7hp06bw8PBQvUN569atEEKU+Mlabfsi1tbWcHZ21ur9ocaitGJO5RMH/kivIiMj4eDggG3btml8QkNDsXPnTtW0a2ZmZggICMCKFSuQkJCAYcOGYdOmTbh+/ToAzWlztOHq6oq//vpLI/3KlSuq5W8zZf2vXbumsezNuLm6uiI5OVnjlzPGEsuJEydiw4YNWLRokcbTPSWlPMa1+RXn20Y5vYW9vb2BS2JcGFdNxYlJ06ZNAUBjmrrk5OQit6V86m/Xrl1FPvVX3hUUp5ycHDx48EAVp3r16qF+/frYtWuXarpFKpi2cSXd6BrX5ORkjem6r169CgBwc3MrvYLq0fbt2+Hu7o7Y2Fj0798fnTp1QocOHZCVlVUq+7t+/brGzS5tYyp1+9azZ09UqFABv/76q9YDWvb29rC2ti72TSVln/nNvnZOTg5u3rxZqn3qsLAw5ObmIiYmBlFRUZDJZFr1e/v27YtffvkF1tbW8Pf3L7XyFaRbt264efMmjh8/nu/yX375BYmJiejWrZtO2w0ICEBmZiZ27NghRTHLpNKInaurKxISEjS+x8rr9tfzAZrHujKtvF8/vik4OBgvX77Ejh07cODAATx+/BghISFqebZv347w8HAsWLAAgYGB8PPzQ5s2bfDo0SOt9mHsbZJyqs6UlBSt19H2nOzq6qpxjAKax+3b4M04V6hQAe3bt8fChQtx+fJlzJ07Fz/++COOHj0KoHj34soSqepbWtcy2p4rdbl/Z0hhYWH4888/ceHCBURFRaFu3bpo3rx5iberbV+kW7duSEhIwMmTJ0u8z/KiuDEvSZ+cyiYO/JHeZGZmIjY2Ft26dUNgYKDGZ9SoUXjy5Al2796Nhw8fqq1boUIFNG7cGACQnZ0NAKopLbTtFAOAv78/Tp06pXbCf/bsGdasWQM3N7cyMQ+2ITk5OcHb2xsbN25UG5SKj4/XeP+hv78/cnNzsXz5crX0RYsWQSaToUuXLnopc2mIiIjA119/jSlTpmDMmDHF3s6DBw/y/dXWt99+CwDl6p1dUrt//75G2pMnT7B48WJUrVpVdSOWdMO4apIyJh988AHkcjk2bNig9its5Xfaz8+v0PXHjh0LW1tbzJo1S+t9lke+vr5wcHBAZGSk2kDBd999h9zcXLU4zZw5Ew8fPsRHH32Ely9famwrLi4Oe/fu1Uu5yzpd4kra0zWuL1++xOrVq1V/5+TkYPXq1bC3tzeac6zyl+iv92F+++23UrthkpycjJ07d6r+fvz4MTZt2gRvb29Uq1YNQP7n8rt372L9+vVo3LgxnJycJCmLlZUVVq5ciRkzZiAgIECrdSpUqIAePXpgz549OHPmjMbyon7B36FDB5iZmWHp0qVqedetW4eMjAx07dpVt0ro4N1334Wbmxu2bNmCmJgY+Pj4wMXFpcj1AgMDMX36dKxYsULtPUX6MnHiRJibm2PYsGEa141paWkYPnw4LCwsMHHiRJ22O3z4cDg5OWH8+PGqG12vu3fvHubMmVOishtaacSuU6dOuHv3rtq72LKysrB27Vq1fM2aNYODgwNWrVqluqYHgAMHDuB///tfqR7rhtCgQQM0atQIMTExiImJgZOTE9q2bauWx8TEROMcsWzZMq2frDKWNuno0aP5niv3798PQLdpC7U9J3fq1AknT57E+fPnVcvS0tJUs1AZI23inN8T9MrXa5TkXpwh6KO+pXEto+25Upf7d4akfNJs2rRpOH/+vGRPnmnbF5k0aRIsLS3x0UcfITU1VWN5QkIClixZIkmZyorixlybPjmVL+Vv/igqt3bv3o0nT56ovfT7df/+979hb2+PyMhIREdHIy0tDe+//z5cXFxw69YtLFu2DN7e3qp3yHl7e8PExATz589HRkYG5HI53n//fdWUa/n5v//7P2zduhVdunTBJ598gipVqmDjxo24efMmduzYoTE9R3ly4MAB1dN2r2vdurVO9friiy/QtWtXtGnTBh9++CHS0tKwbNkyeHp6qv2KKSAgAO3atcPUqVORmJiIJk2aIC4uDrt27cLYsWPVXrhdFq1fvz7f90a6urpi0qRJqFu3Lho0aIAtW7aoLffz89OYDqwgW7ZswapVq9CjRw+4u7vjyZMnOHToEOLj4xEQECDp9KFlVUFxTk9Px5EjRxAQEICaNWsiJSUF69evx+3bt7F582aNjtvLly81/i2UevbsqfFuC2MnVVyNiT5iUq1aNUydOhXTpk1D586d0aNHD/zxxx9Yu3YtQkNDi/wVnY2NDcaMGVPkdJ/lRUExHzNmDCIiIhAeHo62bduif//+uH37NpYsWYL33nsPvXr1UuUNDg7GxYsXMXfuXJw7dw6hoaFwdXXFw4cPcfDgQRw5ckTt/VNvAyniSpqkiquzszPmz5+PxMRE1KtXDzExMTh//jzWrFkDU1NTfVVHEgXFxNfXF7GxsejZsye6du2KmzdvYtWqVWjYsGGpPJ1br149DB48GKdPn4ajoyPWr1+P1NRUbNiwQZVn0qRJSEhIQPv27eHs7IzExESsXr0az549k/xmjS6vFlCaN28e4uLi4OPjg6FDh6JBgwZISUnBtm3bcPz48UKnDLW3t8fkyZMxc+ZMdO7cGd27d8dff/2FFStWoHnz5ujXr18JalM4mUyGvn37Yt68eQCg9Q9TbGxsMGPGjFIrV1Hq1q2LjRs3IiwsDI0aNcLgwYNRq1YtJCYmYt26dXjw4AG2bt2q8/VI5cqVsXPnTvj7+8Pb2xv9+vVTDZ78/vvv2Lp1K1q1alUaVdKb0ojdsGHDsHz5coSGhmLMmDFwcnJCZGQkFAoFgH+eljE1NcX8+fMxaNAg+Pj4IDQ0FKmpqViyZAnc3Nwwbty4UqmzIQUHB2PatGlQKBQYPHiwxnV5t27dsHnzZtjY2KBhw4Y4efIkDh8+DDs7O622byxt0ujRo/H8+XP07NkTHh4eyMnJwYkTJxATEwM3NzcMGjRIp+1pc06eNGkStmzZAj8/P4wePRqWlpb49ttvUbNmTaSlpZX7p9ryo02cZ8yYgZ9//hldu3aFq6sr7t27hxUrVsDFxQVt2rQBANSuXRu2trZYtWoVKlWqBEtLS7Rs2VLjHbaGpo/6lsa1jC7nSm3v3xlSrVq10Lp1a9WrbgoahDpz5ky+P67x9fVV/Vu8Ttu+SO3atREVFYXg4GA0aNAAAwYMgJeXl+p42LZtGwYOHKhTnco6bWP+Jm365FTOCCI9CQgIEAqFQjx79qzAPAMHDhSmpqZi+/btomPHjsLBwUGYmZmJmjVrimHDhomUlBS1/GvXrhXu7u7CxMREABBHjx4VQgjh6uoqunbtmu8+EhISRGBgoLC1tRUKhUK0aNFC7N27V7J66tuGDRsEgAI/GzZsEDdv3hQAREREhMb6AMT06dPV0nbs2CEaNGgg5HK5aNiwoYiNjRXh4eHC1dVVLd+TJ0/EuHHjhLOzszA1NRV169YVERERIi8vrxRrXDJFxWvw4MGFLlceY9o4ffq0CAoKEjVr1hRyuVxYWlqKf/3rX2LhwoXixYsXanmnT58uAIj79+/nuy1PT0/h4+NTgprrV1Fx3rRpk/Dz8xPVqlUTpqamwtbWVnTs2FEcOXJEY1vh4eGFbuvmzZsa60RERBS4rDyTOq6WlpYF7svS0lKEh4eXYm2kIWVMtJGXlyeWLVsm6tWrJ0xNTUWNGjXEZ599JnJyctTy+fj4CE9PT43109PThY2NjcY5+ejRowKA2LZtW7HKpU9FxTwpKUkIIcTWrVtFkyZNhFwuF46OjmLUqFHi8ePH+W7zyJEj4oMPPhAODg6iYsWKwt7eXgQEBIhdu3blm3/btm06n5PLOinjaizfbylIGVfl9/rMmTOiVatWQqFQCFdXV7F8+XJDVK3YiorJ7du3xbx584Srq6uQy+XinXfeEXv37tXoCxbUvyzofKbc7+nTp1Vpyj77oUOHROPGjYVcLhceHh4a60ZFRYm2bdsKe3t7UbFiRVG1alXRs2dPcfbsWUli8XqZ8vPmtUV+/edbt26JAQMGCHt7eyGXy4W7u7sYOXKkyM7O1mpfy5cvFx4eHsLU1FQ4OjqKjz/+WKSnp+tUn+K0JZcuXRIAhFwuL3B/BbVpJd13SV24cEGEhoYKJycnYWpqKqpVqyZCQ0PFxYsX1fJp+++slJycLMaNGyfq1asnFAqFsLCwEE2bNhVz584VGRkZpVEVvdM1dq/3qX18fDSuS27cuCG6du0qzM3Nhb29vRg/frzYsWOHACB+/fVXtbwxMTHinXfeEXK5XFSpUkWEhYWJO3fulFZVJafL8XTt2jXVufX48eMay9PT08WgQYNE1apVhZWVlejUqZO4cuWKcHV1VWunld+v1/s9xtImCSHEgQMHxIcffig8PDyElZWVMDMzE3Xq1BGjR48WqampqnwAxMiRIzXWfzNeQhR9ThZCiHPnzon33ntPyOVy4eLiIr744guxdOlSAUD8/fffpVZfQ9Emzso+ubOzszAzMxPOzs4iNDRUXL16VW1bu3btEg0bNhQVK1ZU3Xcqa/RZX22uZQpqJ5X9qTe3qe25Utv7d/n1XfTlm2++EQBEixYt8l1eWL909uzZQoiS90WuXr0qhgwZItzc3ISZmZmoVKmSePfdd8WyZctEVlZWyStZxhQW8/zadm375FS+yIQwwreHEhERERERUZnm5uYGLy8vTulLZIQWL16McePG4c6dO6hevbqhi0OklbFjx2L16tV4+vSpauprIiJjxz65cSq/8xoSEREREREREZFBZWZmqv2dlZWF1atXo27duhz0ozLrzeP24cOH2Lx5M9q0acNBPyIiKvf4jj8iIh3l5OTk+xLo19nY2MDc3FxPJSKiksjMzFR7IXp+qlSpYtTvSCQiorKL7RSVdb169ULNmjXh7e2NjIwMbNmyBVeuXEFkZKShi0ZUoFatWsHX1xcNGjRAamoq1q1bh8ePH+Pzzz83dNGIiIhKjAN/REQ6OnHiBNq1a1dong0bNhjdC4KJjFVMTAwGDRpUaJ6jR4/C19dXPwUiIiJ6DdspKus6deqEb7/9FpGRkcjNzUXDhg0RHR2N4OBgQxeNqED+/v7Yvn071qxZA5lMhn/9619Yt24d2rZta+iiERERlRjf8UdEpKP09HScPXu20Dyenp5wcnLSU4mIqCRSUlJw6dKlQvM0bdoUlStX1lOJiIiI/sF2ioiIiIiIdMGBPyIiIiIiIiIiIiIiIiIjUMHQBSAiIiIiIiIiIiIiIiKikuPAHxEREREREREREREREZER4MAfERERERERERERERERkRHgwB8RERERERERERERERGREeDAHxEREREREREREREREZER4MAfERERERERERERERERkRHgwB8RERERERERERERERGREeDAHxEREREREREREREREZER+H+ypQ0Rg8XzRgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot predictions vs ground truth for a random region in the test set\n", - "crested.pl.bar.region_predictions(\n", - " adata, \"chr18:60577255-60579369\", title=\"Predictions vs Ground Truth\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Class Distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T16:20:00.057748+0200 INFO Plotting histograms for target: groundtruth, classes: ['Astro', 'Endo']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crested.pl.hist.distribution(\n", - " adata,\n", - " target=\"groundtruth\",\n", - " class_names=[\"Astro\", \"Endo\"],\n", - " log_transform=True,\n", - " share_y=True,\n", - " title=\"Ground Truth Distribution\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T16:20:26.880627+0200 INFO Plotting histograms for target: checkpoint_15, classes: ['Astro', 'Endo']\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crested.pl.hist.distribution(\n", - " adata,\n", - " target=\"checkpoint_15\",\n", - " class_names=[\"Astro\", \"Endo\"],\n", - " log_transform=True,\n", - " share_y=True,\n", - " title=\"Predictions Distribution\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T16:26:24.294927+0200 INFO Plotting density scatter for class: Astro, models: ['checkpoint_15'], split: test\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crested.pl.scatter.class_density(\n", - " adata,\n", - " class_name=\"Astro\",\n", - " model_names=[\"checkpoint_15\"],\n", - " split=\"test\",\n", - " log_transform=True,\n", - " width=5,\n", - " height=5,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Correlation Heatmaps" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crested.pl.heatmap.correlations_self(\n", - " adata, title=\"Self Correlation Heatmap\", x_label_rotation=90, width=6, height=6\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-13T16:24:09.270310+0200 INFO Plotting heatmap correlations for split: test, models: ['checkpoint_15']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crested.pl.heatmap.correlations_predictions(\n", - " adata,\n", - " split=\"test\",\n", - " title=\"Correlations between Groundtruths and Predictions\",\n", - " x_label_rotation=90,\n", - " width=6,\n", - " height=6,\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/model_training_and_eval.ipynb b/docs/tutorials/model_training_and_eval.ipynb index 05193281..959dbb4e 100644 --- a/docs/tutorials/model_training_and_eval.ipynb +++ b/docs/tutorials/model_training_and_eval.ipynb @@ -32,11 +32,16 @@ "source": [ "# Feel free to ignore this, necessary for my own setup\n", "import os\n", - "os.environ[\"PATH\"] = \"/data/projects/c04/cbd-saerts/nkemp/tools:\" + os.environ[\"PATH\"]\n", "import sys\n", - "sys.path.insert(0, '/home/VIB.LOCAL/niklas.kempynck/.conda/envs/crested/lib/python3.11/site-packages')\n", - "sys.path.insert(0,'/data/projects/c04/cbd-saerts/nkemp/software/CREsted/src')\n", - "sys.path.insert(0, '/data/projects/c04/cbd-saerts/nkemp/tools/')" + "\n", + "os.environ[\"PATH\"] = \"/data/projects/c04/cbd-saerts/nkemp/tools:\" + os.environ[\"PATH\"]\n", + "\n", + "sys.path.insert(\n", + " 0,\n", + " \"/home/VIB.LOCAL/niklas.kempynck/.conda/envs/crested/lib/python3.11/site-packages\",\n", + ")\n", + "sys.path.insert(0, \"/data/projects/c04/cbd-saerts/nkemp/software/CREsted/src\")\n", + "sys.path.insert(0, \"/data/projects/c04/cbd-saerts/nkemp/tools/\")" ] }, { @@ -48,10 +53,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-10-01 15:38:52.543881: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2024-10-01 15:38:52.590499: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-10-09 14:34:29.606108: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-10-09 14:34:29.645116: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-10-01 15:38:55.580591: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + "2024-10-09 14:34:32.865724: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], @@ -87,9 +92,11 @@ "metadata": {}, "outputs": [], "source": [ - "bigwigs_folder='/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/bigwigs/bws/'\n", - "regions_file = \"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/consensus_peaks_inputs.bed\"\n", - "chromsizes_file = '/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/mm.chrom.sizes'" + "bigwigs_folder = \"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/bigwigs/bws/\"\n", + "regions_file = (\n", + " \"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/consensus_peaks_inputs.bed\"\n", + ")\n", + "chromsizes_file = \"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/mm.chrom.sizes\"" ] }, { @@ -111,7 +118,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:01:59.749114+0200 INFO Extracting values from 19 bigWig files...\n" + "2024-10-09T14:34:54.305843+0200 INFO Extracting values from 19 bigWig files...\n" ] }, { @@ -345,7 +352,9 @@ "outputs": [], "source": [ "crested.pp.change_regions_width(\n", - " adata, 2114,chromsizes_file='/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/mm.chrom.sizes'\n", + " adata,\n", + " 2114,\n", + " chromsizes_file=\"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/mm.chrom.sizes\",\n", ") # change the adata width of the regions to 2114bp" ] }, @@ -369,8 +378,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:02:39.721561+0200 INFO Filtering on top k Gini scores...\n", - "2024-09-30T13:02:45.443483+0200 INFO Added normalization weights to adata.obsm['weights']...\n" + "2024-10-09T14:35:30.673088+0200 INFO Filtering on top k Gini scores...\n", + "2024-10-09T14:35:34.107825+0200 INFO Added normalization weights to adata.obsm['weights']...\n" ] }, { @@ -487,7 +496,7 @@ " \n", " \n", "\n", - "

47959 rows × 4 columns

\n", + "

47958 rows × 4 columns

\n", "" ], "text/plain": [ @@ -505,7 +514,7 @@ "chr5:76587680-76589794 chr5 76587680 76589794 train\n", "chr9:65523082-65525196 chr9 65523082 65525196 test\n", "\n", - "[47959 rows x 4 columns]" + "[47958 rows x 4 columns]" ] }, "execution_count": 7, @@ -514,7 +523,9 @@ } ], "source": [ - "crested.pp.normalize_peaks(adata, top_k_percent=0.03) # The top_k_percent parameters can be tuned based on potential bias towards cell types. If some weights are overcompensating too much, consider increasing the top_k_percent. Default is 0.01" + "crested.pp.normalize_peaks(\n", + " adata, top_k_percent=0.03\n", + ") # The top_k_percent parameters can be tuned based on potential bias towards cell types. If some weights are overcompensating too much, consider increasing the top_k_percent. Default is 0.01" ] }, { @@ -526,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -611,14 +622,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-26T14:41:29.333831+0200 WARNING Chromsizes file not provided when shifting. Will not check if shifted regions are within chromosomes\n" + "2024-10-09T14:35:34.327199+0200 WARNING Chromsizes file not provided when shifting. Will not check if shifted regions are within chromosomes\n" ] } ], @@ -646,20 +657,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-09-25 13:23:44.193365: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:55:00.0, compute capability: 9.0\n" + "2024-10-09 14:35:35.492463: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:68:00.0, compute capability: 9.0\n" ] } ], "source": [ "# Load chrombpnet architecture for a dataset with 2114bp regions and 19 cell types\n", - "model_architecture = crested.tl.zoo.chrombpnet(seq_len=2114, num_classes=len(list(adata.obs_names)))" + "model_architecture = crested.tl.zoo.chrombpnet(\n", + " seq_len=2114, num_classes=len(list(adata.obs_names))\n", + ")" ] }, { @@ -675,22 +688,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" + "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" ] } ], "source": [ "# Load the default configuration for training a peak regression model\n", - "from crested.tl import default_configs, TaskConfig\n", - "\n", - "config = default_configs(\"peak_regression\") # or \"topic_classification\" for topic classification\n", + "config = crested.tl.default_configs(\n", + " \"peak_regression\"\n", + ") # or \"topic_classification\" for topic classification\n", "print(config)\n", "\n", "# If you want to change some small parameters to an existing config, you can do it like this\n", @@ -707,37 +720,35 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" + "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" ] } ], "source": [ "# Create your own configuration\n", "# I recommend trying this for peak regression with a weighted cosine mse log loss function\n", - "from crested.tl import default_configs, TaskConfig\n", - "\n", "import keras\n", "\n", "optimizer = keras.optimizers.Adam(learning_rate=1e-3)\n", "loss = crested.tl.losses.CosineMSELogLoss(max_weight=100)\n", "metrics = [\n", - " keras.metrics.MeanAbsoluteError(),\n", - " keras.metrics.MeanSquaredError(),\n", - " keras.metrics.CosineSimilarity(axis=1),\n", - " crested.tl.metrics.PearsonCorrelation(),\n", - " crested.tl.metrics.ConcordanceCorrelationCoefficient(),\n", - " crested.tl.metrics.PearsonCorrelationLog(),\n", - " crested.tl.metrics.ZeroPenaltyMetric(),\n", + " keras.metrics.MeanAbsoluteError(),\n", + " keras.metrics.MeanSquaredError(),\n", + " keras.metrics.CosineSimilarity(axis=1),\n", + " crested.tl.metrics.PearsonCorrelation(),\n", + " crested.tl.metrics.ConcordanceCorrelationCoefficient(),\n", + " crested.tl.metrics.PearsonCorrelationLog(),\n", + " crested.tl.metrics.ZeroPenaltyMetric(),\n", "]\n", - " \n", - "alternative_config = TaskConfig(optimizer, loss, metrics)\n", + "\n", + "alternative_config = crested.tl.TaskConfig(optimizer, loss, metrics)\n", "print(alternative_config)" ] }, @@ -760,7 +771,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -771,7 +782,7 @@ " config=alternative_config,\n", " project_name=\"mouse_biccn\", # change to your liking\n", " run_name=\"dyn_log_loss\", # change to your liking\n", - " logger='wandb', # or 'wandb', 'tensorboard'\n", + " logger=\"wandb\", # or 'wandb', 'tensorboard'\n", ")" ] }, @@ -817,14 +828,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:02:54.779289+0200 INFO After specificity filtering, kept 88835 out of 546993 regions.\n" + "2024-10-09T14:35:41.937474+0200 INFO After specificity filtering, kept 88835 out of 546993 regions.\n" ] }, { @@ -836,13 +847,15 @@ " obsm: 'weights'" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "crested.pp.filter_regions_on_specificity(adata, gini_std_threshold=1.0) # All regions with a Gini index 1 std above the mean across all regions will be kept\n", + "crested.pp.filter_regions_on_specificity(\n", + " adata, gini_std_threshold=1.0\n", + ") # All regions with a Gini index 1 std above the mean across all regions will be kept\n", "adata" ] }, @@ -864,14 +877,14 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-25T14:18:02.963100+0200 WARNING Chromsizes file not provided when shifting. Will not check if shifted regions are within chromosomes\n" + "2024-10-09T14:35:42.021323+0200 WARNING Chromsizes file not provided when shifting. Will not check if shifted regions are within chromosomes\n" ] } ], @@ -880,60 +893,61 @@ " adata,\n", " genome_file=genome_file,\n", " batch_size=64, # Recommended to go for a smaller batch size than in the pretrained model\n", - " max_stochastic_shift=3, \n", - " always_reverse_complement=True, \n", + " max_stochastic_shift=3,\n", + " always_reverse_complement=True,\n", ")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# First load the pretrained model on all peaks\n", "import keras\n", - "model_architecture=keras.models.load_model('deeppeak_benchmarking/dyn_log_loss/checkpoints/18.keras', compile=False)" + "\n", + "model_architecture = keras.models.load_model(\n", + " \"deeppeak_benchmarking/dyn_log_loss/checkpoints/18.keras\", compile=False\n", + ")" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" + "TaskConfig(optimizer=, loss=, metrics=[, , , , , , ])\n" ] } ], "source": [ "# Use the same config you used for the pretrained model. EXCEPT THE LEARNING RATE, make sure that is lower than it was on the epoch you select the model from.\n", - "from crested.tl import default_configs, TaskConfig\n", - "\n", "import keras\n", "\n", - "optimizer = keras.optimizers.Adam(learning_rate=1e-4) # Lower LR!\n", + "optimizer = keras.optimizers.Adam(learning_rate=1e-4) # Lower LR!\n", "loss = crested.tl.losses.CosineMSELogLoss(max_weight=100)\n", "metrics = [\n", - " keras.metrics.MeanAbsoluteError(),\n", - " keras.metrics.MeanSquaredError(),\n", - " keras.metrics.CosineSimilarity(axis=1),\n", - " crested.tl.metrics.PearsonCorrelation(),\n", - " crested.tl.metrics.ConcordanceCorrelationCoefficient(),\n", - " crested.tl.metrics.PearsonCorrelationLog(),\n", - " crested.tl.metrics.ZeroPenaltyMetric(),\n", + " keras.metrics.MeanAbsoluteError(),\n", + " keras.metrics.MeanSquaredError(),\n", + " keras.metrics.CosineSimilarity(axis=1),\n", + " crested.tl.metrics.PearsonCorrelation(),\n", + " crested.tl.metrics.ConcordanceCorrelationCoefficient(),\n", + " crested.tl.metrics.PearsonCorrelationLog(),\n", + " crested.tl.metrics.ZeroPenaltyMetric(),\n", "]\n", - " \n", - "alternative_config = TaskConfig(optimizer, loss, metrics)\n", + "\n", + "alternative_config = crested.tl.TaskConfig(optimizer, loss, metrics)\n", "print(alternative_config)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -944,7 +958,7 @@ " config=alternative_config,\n", " project_name=\"mouse_biccn\", # change to your liking\n", " run_name=\"dyn_log_loss_FT\", # change to your liking\n", - " logger='wandb', # or 'wandb', 'tensorboard'\n", + " logger=\"wandb\", # or 'wandb', 'tensorboard'\n", ")" ] }, @@ -972,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -990,23 +1004,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-10-01 15:39:09.336900: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78790 MB memory: -> device: 0, name: NVIDIA H100 80GB HBM3, pci bus id: 0000:55:00.0, compute capability: 9.0\n" - ] - } - ], + "outputs": [], "source": [ "# load an existing model\n", "evaluator = crested.tl.Crested(data=datamodule)\n", "\n", "evaluator.load_model(\n", - " \"deeppeak_benchmarking/dyn_log_loss_TL/checkpoints/01.keras\", # Load your model\n", + " \"deeppeak_benchmarking/dyn_log_loss_TL/checkpoints/01.keras\", # Load your model\n", " compile=True,\n", ")" ] @@ -1021,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1029,24 +1035,24 @@ "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1727789951.120449 3739484 service.cc:145] XLA service 0x7f4530006040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1727789951.120484 3739484 service.cc:153] StreamExecutor device (0): NVIDIA H100 80GB HBM3, Compute Capability 9.0\n", - "2024-10-01 15:39:11.147396: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", - "2024-10-01 15:39:11.272221: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" + "I0000 00:00:1728477344.361151 665810 service.cc:145] XLA service 0x7f0b64017970 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1728477344.361176 665810 service.cc:153] StreamExecutor device (0): NVIDIA H100 80GB HBM3, Compute Capability 9.0\n", + "2024-10-09 14:35:44.390616: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-10-09 14:35:44.514826: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m 3/31\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - concordance_correlation_coefficient: 0.6900 - cosine_similarity: 0.8519 - loss: 0.2898 - mean_absolute_error: 0.1821 - mean_squared_error: 0.1373 - pearson_correlation: 0.7178 - pearson_correlation_log: 0.7053 - zero_penalty_metric: 1185.4198" + "\u001b[1m 3/31\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - concordance_correlation_coefficient: 0.6900 - cosine_similarity: 0.8519 - loss: 0.2898 - mean_absolute_error: 0.1821 - mean_squared_error: 0.1373 - pearson_correlation: 0.7178 - pearson_correlation_log: 0.7053 - zero_penalty_metric: 1185.4197" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "I0000 00:00:1727789960.332699 3739484 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + "I0000 00:00:1728477353.565676 665810 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { @@ -1061,9 +1067,9 @@ "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1727789962.601327 3739589 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_574', 64 bytes spill stores, 64 bytes spill loads\n", + "I0000 00:00:1728477355.805278 665910 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_574', 64 bytes spill stores, 64 bytes spill loads\n", "\n", - "I0000 00:00:1727789962.966940 3739582 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_574', 12 bytes spill stores, 12 bytes spill loads\n", + "I0000 00:00:1728477355.943902 665901 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_574', 12 bytes spill stores, 12 bytes spill loads\n", "\n" ] }, @@ -1071,15 +1077,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 365ms/step - concordance_correlation_coefficient: 0.7083 - cosine_similarity: 0.8608 - loss: 0.2679 - mean_absolute_error: 0.1789 - mean_squared_error: 0.1375 - pearson_correlation: 0.7292 - pearson_correlation_log: 0.7288 - zero_penalty_metric: 1154.3245\n", - "2024-10-01T15:39:31.347008+0200 INFO Test concordance_correlation_coefficient: 0.7149\n", - "2024-10-01T15:39:31.347520+0200 INFO Test cosine_similarity: 0.8572\n", - "2024-10-01T15:39:31.347728+0200 INFO Test loss: 0.2746\n", - "2024-10-01T15:39:31.347912+0200 INFO Test mean_absolute_error: 0.1842\n", - "2024-10-01T15:39:31.348085+0200 INFO Test mean_squared_error: 0.1440\n", - "2024-10-01T15:39:31.348450+0200 INFO Test pearson_correlation: 0.7359\n", - "2024-10-01T15:39:31.348629+0200 INFO Test pearson_correlation_log: 0.7267\n", - "2024-10-01T15:39:31.348781+0200 INFO Test zero_penalty_metric: 1129.3738\n" + "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 363ms/step - concordance_correlation_coefficient: 0.7083 - cosine_similarity: 0.8608 - loss: 0.2679 - mean_absolute_error: 0.1789 - mean_squared_error: 0.1375 - pearson_correlation: 0.7292 - pearson_correlation_log: 0.7288 - zero_penalty_metric: 1154.3245\n", + "2024-10-09T14:36:04.505906+0200 INFO Test concordance_correlation_coefficient: 0.7149\n" ] } ], @@ -1103,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1111,7 +1110,7 @@ "output_type": "stream", "text": [ "\u001b[1m348/348\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 58ms/step\n", - "2024-10-01T15:39:52.425900+0200 INFO Adding predictions to anndata.layers[biccn_model].\n" + "2024-10-09T14:36:25.588753+0200 INFO Adding predictions to anndata.layers[biccn_model].\n" ] } ], @@ -1124,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1133,7 +1132,7 @@ "Layers with keys: biccn_model" ] }, - "execution_count": 7, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1172,7 +1171,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1310,20 +1309,20 @@ "[7932 rows x 4 columns]" ] }, - "execution_count": 8, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define a dataframe with test set regions\n", - "test_df = adata.var[adata.var['split']=='test']\n", + "test_df = adata.var[adata.var[\"split\"] == \"test\"]\n", "test_df" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1331,7 +1330,7 @@ "output_type": "stream", "text": [ "chr18:3968712-3970826\n", - "2024-10-01T15:39:52.480301+0200 INFO Plotting bar plots for region: chr18:3968712-3970826, models: ['biccn_model']\n" + "2024-10-09T14:36:25.621762+0200 INFO Plotting bar plots for region: chr18:3968712-3970826, models: ['biccn_model']\n" ] }, { @@ -1351,9 +1350,7 @@ "idx = 22\n", "region = test_df.index[idx]\n", "print(region)\n", - "crested.pl.bar.region_predictions(\n", - " adata, region, title=\"Predictions vs Ground Truth\"\n", - ")" + "crested.pl.bar.region_predictions(adata, region, title=\"Predictions vs Ground Truth\")" ] }, { @@ -1365,33 +1362,34 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 842ms/step\n" + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 837ms/step\n" ] } ], "source": [ "from pysam import FastaFile\n", + "\n", "genome = FastaFile(genome_file)\n", "\n", - "chrom = 'chr3'#'chr18'\n", - "start = 72535878-807#61107770\n", - "end = 72536378+807#61109884\n", + "chrom = \"chr3\" #'chr18'\n", + "start = 72535878 - 807 # 61107770\n", + "end = 72536378 + 807 # 61109884\n", "\n", - "sequence = genome.fetch(chrom,start, end).upper()\n", + "sequence = genome.fetch(chrom, start, end).upper()\n", "\n", "prediction = evaluator.predict_sequence(sequence)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1406,8 +1404,7 @@ } ], "source": [ - "from crested.pl.bar import prediction_bar\n", - "prediction_bar(prediction, classes=list(adata.obs_names))" + "crested.pl.bar.prediction(prediction, classes=list(adata.obs_names))" ] }, { @@ -1417,13 +1414,20 @@ "#### Prediction on gene locus" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also score a gene locus by using a sliding window over a predefined genomic range. We can compare those predictions then to the bigwig we did the predictions for, to see if the profile matches the CREsted predictions. " + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "chrom = 'chr4'\n", + "chrom = \"chr4\"\n", "start = 91209533\n", "end = 91374781\n", "\n", @@ -1431,27 +1435,27 @@ " chr_name=chrom,\n", " gene_start=start,\n", " gene_end=end,\n", - " class_name='Sst',\n", - " strand='-',\n", + " class_name=\"Sst\",\n", + " strand=\"-\",\n", " upstream=50000,\n", " downstream=10000,\n", - " step_size=100\n", + " step_size=100,\n", ")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "bigwig = '/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/bigwigs/bws/Sst.bw'\n", - "bw_values, midpoints = crested.tl.extract_bigwig_values_per_bp(bigwig, coordinates)" + "bigwig = \"/home/VIB.LOCAL/niklas.kempynck/nkemp/mouse/biccn/bigwigs/bws/Sst.bw\"\n", + "bw_values, midpoints = crested.utils.extract_bigwig_values_per_bp(bigwig, coordinates)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1466,7 +1470,14 @@ } ], "source": [ - "crested.pl.hist.locus_scoring(scores, coordinates, (min_loc,max_loc), gene_start=start,gene_end=end, title='CREsted prediction around Elavl2 gene locus for Sst', bigwig_values=bw_values, bigwig_midpoints=midpoints\n", + "crested.pl.hist.locus_scoring(\n", + " scores,\n", + " (min_loc, max_loc),\n", + " gene_start=start,\n", + " gene_end=end,\n", + " title=\"CREsted prediction around Elavl2 gene locus for Sst\",\n", + " bigwig_values=bw_values,\n", + " bigwig_midpoints=midpoints,\n", ")" ] }, @@ -1493,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1502,7 +1513,7 @@ "Layers with keys: biccn_model" ] }, - "execution_count": 25, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1513,19 +1524,19 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:05:23.842418+0200 INFO Plotting density scatter for class: L2_3IT, models: ['biccn_model'], split: test\n" + "2024-10-09T14:39:11.357010+0200 INFO Plotting density scatter for class: L2_3IT, models: ['biccn_model'], split: test\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1535,7 +1546,7 @@ } ], "source": [ - "classn = 'L2_3IT'\n", + "classn = \"L2_3IT\"\n", "crested.pl.scatter.class_density(\n", " adata,\n", " class_name=classn,\n", @@ -1556,14 +1567,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:05:25.638048+0200 INFO Plotting heatmap correlations for split: test, models: ['biccn_model']\n" + "2024-10-09T14:39:12.997222+0200 INFO Plotting heatmap correlations for split: test, models: ['biccn_model']\n" ] }, { @@ -1585,7 +1596,7 @@ " x_label_rotation=90,\n", " width=6,\n", " height=6,\n", - " log_transform=True\n", + " log_transform=True,\n", ")" ] }, @@ -1598,7 +1609,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1637,27 +1648,27 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:05:33.257814+0200 INFO Calculating contribution scores for 2 class(es) and 1 region(s).\n" + "2024-10-09T14:39:18.002301+0200 INFO Calculating contribution scores for 2 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:04<00:00, 4.32s/it]\n" + "Region: 100%|██████████| 1/1 [00:04<00:00, 4.25s/it]\n" ] } ], "source": [ "# random sequence of length 2114bp as an example\n", - "sequence = 'A'*2114\n", + "sequence = \"A\" * 2114\n", "\n", "scores, one_hot_encoded_sequences = evaluator.calculate_contribution_scores_sequence(\n", " sequence, class_names=[\"Astro\", \"Endo\"]\n", @@ -1681,21 +1692,21 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:05:37.580861+0200 INFO Calculating contribution scores for 3 class(es) and 1 region(s).\n" + "2024-10-09T14:39:22.257945+0200 INFO Calculating contribution scores for 3 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:03<00:00, 3.74s/it]\n" + "Region: 100%|██████████| 1/1 [00:03<00:00, 3.71s/it]\n" ] } ], @@ -1720,7 +1731,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1733,7 +1744,7 @@ }, { "data": { - "image/png": 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" ] @@ -1751,7 +1762,7 @@ " class_labels=classes_of_interest,\n", " zoom_n_bases=500,\n", " title=\"FIRE Enhancer Region\",\n", - ") # zoom in on the center 500bp\n" + ") # zoom in on the center 500bp" ] }, { @@ -1763,21 +1774,21 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30T13:05:49.889925+0200 INFO Calculating contribution scores for 2 class(es) and 1 region(s).\n" + "2024-10-09T14:39:33.513404+0200 INFO Calculating contribution scores for 2 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:02<00:00, 2.51s/it]\n" + "Region: 100%|██████████| 1/1 [00:02<00:00, 2.81s/it]\n" ] } ], @@ -1793,7 +1804,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1806,7 +1817,7 @@ }, { "data": { - "image/png": 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kyJFERUXlzDN37lzOOOMMlixZQkREBI0aNaJ06dIkJyezaNEisrKyaN26NZMmTcqZJyUlhcTERJKTk6lWrRp169YlKiqKrl27cvfdd+9zH3fs2JGxY8fy7bff0rFjxwLH7O++3uUf//gHDz74IAA1atSgevXqzJs3j7S0NB544AHuvfdeTj75ZL7++uuceZYuXcoRRxxBnTp1WLp0aZ46Fi5cSKdOnVi5ciUxMTEceeSRpKSk5OzTK664giFDhuQJcBs8eDA9e/bkqquuYvDgwfm2bdd9rEOHDowZM2af+0uSJEmSJEmSJEmSJEmSVLQiirsASZIkSZIkSZIkSZIkSfqrSEhIYMSIEbz99tt06dKF7du38+OPP7Jx40ZatGjBXXfdxZQpU3ICxRo0aMArr7zChRdeSKVKlZg/fz4LFiygRo0aXHfddcyePTtPeBtAvXr1+Oyzz+jQoQPJyclMmDCBsWPH5gsWK8h9993H3XffzXHHHcfWrVuZPn06O3bsoEOHDrzxxhv5wtsAGjduzIwZM3j88cc57rjjWLVqFdOnTyc9PZ0OHTrw9NNP89577+WZp3Tp0owcOZLTTz+dtLQ0Jk6cyNixY5k7d+7v28G72d99vcsDDzzA+++/z/HHH58TXteuXTsmTJjAUUcdBUCpUqUKXUf9+vX56aef6NevH7Vr12bWrFmsX7+ek046iTfffDNfeJskSZIkSZIkSZIkSZIk6Y8vFP7tV7xKkiRJkiRJkiRJkiRJkqR8nnnmGfr168ctt9zCc889V9zlSJIkSZIkSZIkSZIkSZKKSURxFyBJkiRJkiRJkiRJkiRJ0h9dVlYWb7zxBgBt27Yt5mokSZIkSZIkSZIkSZIkScXJADdJkiRJkiRJkiRJkiRJkn41aNAgxo8fn6ctKSmJHj168PPPP1O9enXOPvvsYqpOkiRJkiRJkiRJkiRJkvRHEFXcBUiSJEmSJEmSJEmSJEmS9Ecxfvx4evfuTUJCAvXq1SMcDjNnzhwyMjKIj4/nzTffJDY2trjLlCRJkiRJkiRJkiRJkiQVIwPcJEmSJEmSJEmSJEmSJEn61VVXXUVGRgaTJk1i0aJFpKenU716dU455RTuvPNOGjVqVNwlSpIkSZIkSZIkSZIkSZKKWSgcDoeLuwhJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJOhxEFHcBkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJknS4MMBNkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkgrJADdJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJKiQD3CRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpkAxwkyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqRCMsBNkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkgrJADdJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJKiQD3CRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpkAxwkyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqRCMsBNkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkgrJADdJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJKiQD3CRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpkAxwkyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqRCMsBNkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkgrJADdJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJKiQD3CRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpkAxwkyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqRCMsBNkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkgrJADdJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJKiQD3CRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpkAxwkyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqRCMsBNkiRJkiRJkiRJkiRJkg6S/v37EwqFGDNmTHGXIkmSJEmSJEmSJEmSJEmSiogBbpIkSZIkSZIkSZIkSZJ0GPnss8+4+eabadu2LSVLliQUCtG/f/+9zrN69WpuueUWmjZtSsmSJalSpQrt2rXjzTffJCsrq9DrfuWVVzj77LM54ogjKFmyJGXKlOGoo47igQceICkpqcB5kpOT6devH/Xr1ycmJoZKlSpxwQUXMGvWrL2ua8yYMXTr1o3KlSsTExNDrVq1OPfcc5kxY8bv2r5doXp7++nVq1eeecaNG0e/fv3o1KkTZcqUIRQK0aNHjz3WPmHCBG6//XaOOeYYKlSoQGxsLI0bN+auu+5i8+bNe93u3wqHw9xxxx107NiR6tWrExsbS5UqVTjxxBMZNGgQGRkZBc43d+5cLr/8cqpWrUpMTAx16tThlltuKfDvdCD7BGDt2rX07t2batWqERsbS8OGDfnHP/5Benp6gTVt3ryZBx54gBYtWlCqVCkqVqzIcccdx3/+8x927tyZZ+yqVat47rnn6Ny5M7Vr1yY6OpqqVaty/vnnM3ny5L3usyVLltCnTx/q1KlDTEwMVapUoVOnTnzwwQd7nU+SJEmSJEmSJEmSJEmSCiuquAuQJEmSJEmSJEmSJEmSJBXeM888w9ixYyldujTVq1dn4cKFex2/ePFiWrduzaZNm+jSpQtnn302KSkpDBs2jCuvvJJvvvmG119/vVDrfvPNN0lOTqZ9+/ZUq1aNtLQ0Jk2axEMPPcSQIUOYPHkyVatWzRm/adMmTjjhBBYsWMAJJ5xAt27dWLNmDR999BHDhw/nm2++oXXr1vnW88gjj3DfffdRvXp1unfvTsWKFVm3bh3fffcdM2fO5Kijjjrg7evYseMet+/VV19l1apVdOnSJU/7a6+9xpAhQ4iPj6d27dqkpKTsdT9dcMEFbNy4kXbt2nHllVcSCoUYM2YMTz75JB999BHff/89lStX3tfuBiArK4vnn3+eY489ljPPPJNKlSqRnJzMiBEj6N27Nx988AFffvklERG53+c6adIkTj31VHbs2EG3bt2oV68e06dP59///jcjRozg+++/p0KFCr9rn6xdu5bWrVuzYsUKunfvTsOGDZkwYQIPPvggEydO5IsvvshT0+bNmznmmGNYvHgx7dq149prryUtLY3hw4dz880388knnzBq1KiceZ5//nmeeOIJ6tWrx2mnnUblypVZsGABw4YNY9iwYbz77rtcdNFF+eodNWoU3bt3B+Dss8+mbt26JCcn8/PPPzN69GguvPDCQu13SZIkSZIkSZIkSZIkSdqbUDgcDhd3EZIkSZIkSZIkSZIkSZL0Z9S/f38GDBjAt99+u9eQrP0xfvx4qlatSv369Xn//fe59NJLefDBB+nfv3+B42+44QZeeukl/vWvf9G3b9+c9s2bN9OyZUuWLVvG0qVLqVOnzj7XvXPnTmJjY/O133///Tz88MP069ePp556Kqf9pptu4oUXXuC2227jmWeeyWmfOHEi7du3p1GjRsycOTNP0Nenn35Kt27d6N69O++88w5xcXF51pWZmUlUVO53lxbV9q1bt46aNWtSpkwZVq9eTXR0dE7fDz/8QFxcHI0bN2bq1KmccMIJXHXVVQwePLjAZT3xxBNceeWVVKtWLactHA5z44038tJLL3HDDTfwwgsv7LWe3RW03zMzM+ncuTPffvstn3/+OWeeeWZOX7NmzZg1axb/+9//OOecc3Lan3rqKe68806uvfZaBg4cuM/17m2fXHXVVbzxxhu8+OKLXH/99Tnb2LNnT4YMGcJrr71Gz549c8Y/+eST3HXXXfzf//0f//znP3Pa09PTadeuHVOnTmXs2LGcdNJJAHz88cdUqlSJ9u3b56lp/PjxnHLKKZQqVYrVq1cTExOT07dixQqaNWtGlSpVGD16NLVr1863z3a/70iSJEmSJEmSJEmSJEnSgYrY9xBJkiRJkiRJkiRJkiRJUkHGjx/PueeeS5UqVYiJiaFWrVqcd955TJgwId/YoUOHcvTRRxMXF0e1atXo27cvO3bsyDNmzJgxhEIh+vfvz8SJE+nSpQtly5YlFArljGnfvj0NGjTI07Y3ixcvBuCMM87I0162bFnatm0LwIYNGwq1rILC2wAuvPBCABYuXJinfdiwYURERDBgwIA87SeccAJnn302s2fPZuzYsXn67r77bkqVKsXgwYPzhbcB+QK4imr7Bg8eTGZmJldccUWeoDKAY489liOPPJLIyMh9LgfgrrvuyhPeBhAKhbj//vsB8m3zvhS036OioujevTuQd78vXLiQWbNmcdxxx+UJbwO4/fbbqVChAm+++Sbbtm3b53r3tE9SU1N5//33qVu3Ltddd12ebXzssceIiIjglVdeybOsPf2doqOjOe200wBYv359Tvt5552XL7wNgvt/p06dSEpKYubMmXn6Hn30UVJSUhg4cGC+8DbIf9+RJEmSJEmSJEmSJEmSpANlgJskSZIkSZIkSZIkSZIkHYAXXniBDh06MHLkSE477TRuv/12Tj75ZGbMmMGHH36Yb+zVV19NkyZNuP766ylXrhzPP/88vXv3LnDZ33//PR06dADgmmuu4eKLLz7gOo888kgARowYkac9JSWF7777jipVqtC0adM8fYmJiYRCIZYuXVqodXzxxRcANGvWLE/7unXrqFixIgkJCfnmOeKIIwD45ptvctp+/vln5syZw2mnnUZCQgLDhw/niSee4Pnnn2fGjBlFtn0Fee211wD2+DcpCiVKlACKJkgsOzs7Z5t33+/r1q0Dcvfv7iIiIqhduzbbt29n0qRJ+1zHnvbJxIkTSUtL47TTTssXJFitWjWaN2/O5MmT2blzZ077nv5OGRkZjB49mri4OE444YR91gQF78dwOMzQoUOpUKECJ598MtOmTeOf//wnTz/9NKNHjyY7O7tQy5YkSZIkSZIkSZIkSZKkwvDrJCVJkiRJkiRJkiRJkiRpP82cOZNbbrmFatWq8d1335GYmJjTFw6HWbNmTZ7xo0aNYtq0aTRq1AiARx55hJYtW/Luu+/y1FNPUb169XzjBw0axNVXX/27a73jjjv49NNPueWWWxg+fDjNmzcnJSWF//3vf5QoUYKPPvqI+Pj4/Vrm4MGDWbp0Kampqfz444+MGTOGVq1acdttt+UZV6lSJdatW8fWrVvzhbgtWbIEgPnz5+e0/fDDDwBUqFCBdu3a5QsZu/zyy3nttdeIjo4u0u0bP3488+fPp02bNjlBYwfDrkC0zp07H9D8/fv3B2Djxo18/fXXzJ07lx49enDKKafkjKlUqRKQu393l52dzfLly4Fgv+8+32/tbZ8sWLAAgAYNGhQ4b4MGDZgxYwaLFy/OCc/r3bs3b775Js888ww//PADxx13HGlpaYwYMYLk5GTeeecdatSosc99sHz5ckaPHk3VqlVp3rx5TvuSJUtISkriuOOO4/rrr2fgwIF55mvVqhWffvopNWvW3Oc6JEmSJEmSJEmSJEmSJGlfDHCTJEmSJEmSJEmSJEmSpP00cOBAsrKyePjhh/OEtwGEQqF8gWy33HJLTngbQFxcHJdeeikDBgxg2rRp+ca3atWqSMLbAKpWrcrEiRO5/PLL+fLLL/nyyy8BiI2N5c4776RVq1b55vn666/JyMjYY6DW4MGDGTt2bM7tzp078+abb1KuXLk8404//XRee+01BgwYwFNPPZXTPmXKFD7//HMANm/enNO+fv16IAg6O+KII/jmm2847rjjWLBgATfeeCNvv/02NWrU4Iknnvhd2/dbgwYNAoKQsYNl+vTpDBgwgMqVK3PnnXce0DIGDBiQMx0KhejXrx+PPfZYnjENGzakXr16TJ06lS+++IIzzzwzp+/ZZ59l06ZNQN79XpC97ZMtW7YAUKZMmQLnLV26dJ5xENznx4wZw7XXXstbb72Vc/+JiIjgpptuol27dnutByAjI4MrrriCtLQ0nnzySSIjI3P6dt13fvzxR+bMmcPrr79Ot27d2LJlC48++iivvPIKF1xwQb5QQEmSJEmSJEmSJEmSJEk6EBHFXYAkSZIkSZIkSZIkSZIkHW6mTJkCBMFlhXH00Ufna6tZsyZQcJDW8ccff+DF/caiRYto3749SUlJjBs3jtTUVFasWMGAAQN49NFH6dSpExkZGXnmqVevHo0bN6ZEiRIFLnPMmDGEw2E2bNjA559/zsqVKzn66KP5+eef84wbMGAA1apV4+mnn6Zdu3b069ePyy+/nPbt29O0aVOAPCFc2dnZOb+HDh1Kp06dSEhIoFWrVgwbNoxSpUrxn//8h7S0tN+1fbtLSUnhgw8+ICEhgYsvvnj/dm4hLVmyhLPOOousrCzee+89KlasmKe/f//++X4Kul+Ew2GysrJYsWIFL774Iq+++iodO3YkJSUlz7gXXniBEiVKcM4553D++edz55130qVLF/r160fz5s2BvPv9tw7GPtm4cSOnnXYakyZN4osvvmDz5s2sXbuWgQMH8vrrr9O6dWuSk5P3OH92djZXX30148aNo0+fPlxxxRX5+gGysrJ46KGH6NGjB+XKlSMxMZH//ve/tG7dmsmTJzNhwoQi2R5JkiRJkiRJkiRJkiRJf21RxV2AJEmSJEmSJEmSJEmSJB1uNm/eTCgUolq1aoUaX6ZMmXxtUVHB6VtZWVn5+qpUqfL7CtxNz549WbZsGYsXL6Zq1aoAJCQkcOedd5KUlMQTTzzBW2+9Rc+ePfd72RUrVuTMM8+kRYsWNGjQgD59+jB58uSc/po1azJ16lQefPBBhg8fzpQpU6hVqxb/+Mc/SExM5JJLLqFSpUo543ftp5o1a9KqVas866pcuTKtW7dm9OjRzJkzh5YtWxbJ9r377rts376dXr16kZCQsN/7YF+WLVtGp06d2LBhAx999BGdOnXKN2bAgAH52nr06EHZsmXztUdERFCzZk2uu+46KlSowEUXXcQjjzzCE088kTOmS5cujB8/noceeohvvvmGL774gmbNmvHJJ5/w9ddfM3PmzDz7/bf2tU92/Z22bNlS4Py7AuV2v9/fdtttfP/998yYMYMWLVrk9Pfp04esrCyuv/56nnvuuQL3RTgcpk+fPrz11lv87W9/Y+DAgXusCeCcc87J13/22WczefJkfvjhB9q1a7fHbZckSZIkSZIkSZIkSZKkwogo7gIkSZIkSZIkSZIkSZIk6XBTtmxZwuEwa9asOSjLD4VCRbKc1NRUxo8fT5MmTXLCzXZ38sknAzBt2rTftZ5atWrRpEkTpk6dyvbt2/P01ahRg1dffZVVq1aRnp7OokWLuOuuu5gzZw4Axx57bM7YRo0aARQYXLZ7+44dO4ps+wYNGgRA7969C7Gl+2fp0qV07NiR1atXM3ToUM4666wCx4XD4Xw/iYmJ+1x+586dARgzZky+vtatW/P555+TnJzMzp07+eGHH+jevTszZ84E8u7339rXPmnQoAEACxYsKLB/wYIFREREULdu3Zy2L774gvLly+eEt+1ub3+n7OxsevXqxWuvvcall17K4MGDiYjIf+pj/fr1iYyMBAq+//z2viNJkiRJkiRJkiRJkiRJv4cBbpIkSZIkSZIkSZIkSZK0n44//ngARo4cWcyV7F16ejoAGzduLLB/w4YNAMTExPzuda1Zs4ZQKJQTorU3WVlZvPfee0RFRXH++efntLdp04a4uDgWL17Mzp078823K/RtV7jZ792+mTNnMnXqVI488kjatGmzz7r3x67wtlWrVvH+++/TrVu3Il0+wOrVqwGIiooq1Phly5YxYcIEmjZtSvPmzQscU5h90qZNG2JiYhg1ahThcDhP35o1a5g5cyatW7cmNjY2pz09PZ2UlJScv9nu9vR3ys7Opnfv3rz++utcfPHFvPnmm3u8f8XExHDiiScCMHv27Hz9u9oKE4wnSZIkSZIkSZIkSZIkSftigJskSZIkSZIkSZIkSZIk7afrrruOyMhI7rvvPpYtW5anLxwOs2bNmmKqLK8KFSrQqFEjli9fzquvvpqnLyUlhSeffBKATp065elbtGgRc+fOJSMjI6dt06ZNzJo1K986wuEw/fv3Z926dXTq1ClPCFdGRgY7duzIMz47O5t+/foxb948br75ZqpXr57Tl5CQwBVXXMG2bdt4+OGH88z35ptvMmvWLNq1a0e1atV+1/btMmjQIAB69epVYP+B2j287b333uPcc8894GXNnTuX9evX52vfvn07t912GwCnn356nr6tW7fmC1bbsmULV1xxBVlZWTz22GN7XF9h9knp0qW5+OKLWbx4MQMHDsxpD4fD3HPPPWRnZ9OnT58887Rt25bMzEweeuihPO1paWk5bbv/nbKzs+nVqxevv/46F154IW+99dY+wwGvv/56APr3709aWlpO+9y5cxk8eDClSpWia9eue12GJEmSJEmSJEmSJEmSJBVGKPzbs7QkSZIkSZIkSZIkSZIkSfv0n//8h759+xIfH0/37t2pU6cOa9euZdy4cZx55pk899xz9O/fnwEDBvDtt9/SsWPHPPMPHjyYnj178vrrr9OjRw8AxowZQ6dOnXjwwQfp379/gesdNmwYw4YNA2DJkiWMGzeOo446ipYtWwLQrl07evfunTN+xIgRnH322WRmZnLyySdz9NFHs3nzZj777DPWrVvHWWedxaeffkooFMqZJzExkWXLlrFkyRISExMBmD59Oq1ateL444+nadOmVK1alY0bNzJ+/HjmzZtH1apVGTNmDI0aNcpZzsqVKznyyCPp3LkzRxxxBOnp6Xz11VfMnTuXM888k48++ihP4BsEQXEnnngi8+fPp0OHDhx77LEsWLCAzz77jLJlyzJhwgSaNm36u7YPID09nerVq5OamsqqVauoWLHiHv/WEyZMyAmI27BhA19++SX16tWjXbt2ADRu3Ji777473/5r06YNXbp0KXCZe/r7/tZzzz3HXXfdRceOHalbty5lypRh1apVDB8+nE2bNtG2bVtGjhxJfHx8zjxvvfUWf//73zn55JOpXr0669ev59NPP2XDhg089NBD3HfffQWua3/2yZo1a2jdujUrV67k3HPPpWHDhowfP57vvvuOLl268OWXXxIRkfsds9OnT+ekk04iNTWV448/nrZt27Jz506++uorFi9ezDHHHMOECROIjY3N2T8DBgwgISGBW265haioqHw1dO/ePed+D0GA3EUXXcSHH35Io0aN6NKlC1u2bOGjjz5i+/btvPHGG1x++eWF2u+SJEmSJEmSJEmSJEmStDf5z2iSJEmSJEmSJEmSJEmSJO3TTTfdRLNmzXjmmWcYPnw4W7dupXLlyrRu3ZqLLrrooK13+vTpDBkyJE/bjBkzmDFjRs7t3QPcunbtyqRJk3jyyScZP34848aNIyYmhqZNm3LPPfdw44035gs3K0idOnW45557GDNmDF9++SVJSUnExsbSoEED7rvvPm699VYqVKiQZ54yZcrQrVs3vvvuOz7//HNKlChBs2bNeOWVV7j66qvzBHztUqFCBSZOnMiAAQP45JNP+P777ylfvjx/+9vf6N+/P3Xr1s0z/kC3b9iwYWzatImLLrpor0FlAAsXLsy3zxctWsSiRYsA6NChQ54At2XLlgEwadIkJk2aVOAyCxvgduqpp9KrVy8mTJjA1KlTSU1NpUyZMjRr1oxLLrmE3r175ws3a968OUcddRQjR45k48aNlClThjZt2nDbbbfRqVOnPa5rf/ZJtWrVmDx5Mvfddx9ffPEFn3/+ObVr12bAgAHcdddd+f62LVu2ZNq0aTz22GN8/fXX/Oc//yEqKor69eszYMAA+vXrlxPeBrB06VIAtm7dyiOPPFJgDYmJiXkC3EKhEO+++y4nnngigwYN4uWXXyYmJoYTTzyRv//973To0GGv2yRJkiRJkiRJkiRJkiRJhRUKh8Ph4i5CkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkg4H+b++VJIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZJUoD9kgNvWrVu59dZbqV69OrGxsbRs2ZL33ntvn/N9/PHHXHrppdSvX5+4uDgSExO5/PLLWbBgwSGoWpIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSdKfXSgcDoeLu4jf6ty5M1OnTuXxxx+nYcOGvPPOO7z66qu8/fbbXHbZZXucr3Xr1lStWpXu3btTt25dVqxYwaOPPsqKFSuYNGkSRx555CHcCkmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEl/Nn+4ALcvv/ySM888k3feeYdLL700p71z587MmjWL5cuXExkZWeC869evp3LlynnaVq9eTWJiIldeeSWvvvrqQa1dkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ0p9bRHEX8FuffPIJCQkJXHjhhXnae/bsyerVq5k8efIe5/1teBtA9erVqVmzJitWrCjyWiVJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiT9tUQVdwG/9csvv9CkSROiovKW1qJFi5z+E088sdDLW7x4McuWLaN79+57HZeWlkZaWlrO7ezsbJKSkqhQoQKhUKjwGyBJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiTpsBIOh0lNTaV69epERETsdewfLsBt06ZN1K1bN197+fLlc/oLKzMzk169epGQkMD//d//7XXsY489xoABA/avWEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEl/GitWrKBmzZp7HfOHC3ADCIVCB9S3u3A4TK9evRg/fjwfffQRtWrV2uv4e+65h9tuuy3n9pYtW6hduzYrVqygdOnShStckiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ0mEnJSWFWrVqUapUqX2O/cMFuFWoUIFNmzbla09KSgKgfPny+1xGOBymd+/evPXWWwwZMoRu3brtc56YmBhiYmLytZcuXdoAN0mSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJOkvIBQK7XNMxCGoY780b96cOXPmkJmZmad95syZADRr1myv8+8Kb3v99dd59dVX+dvf/nbQapUkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZL01/KHC3A799xz2bp1Kx999FGe9iFDhlC9enVat269x3nD4TB9+vTh9ddf5+WXX6Znz54Hu1xJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJfyFRxV3Ab51++umcdtppXH/99aSkpFC/fn3effddRowYwVtvvUVkZCQAvXr1YsiQISxatIg6deoA0LdvXwYNGsTVV19N8+bNmTRpUs5yY2JiaNWqVbFskyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqQ/hz9cgBvAxx9/zL333ssDDzxAUlISjRs35t133+WSSy7JGZOVlUVWVhbhcDin7bPPPgPgtdde47XXXsuzzDp16rB06dJDUr8kSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZKkP6dQePcENOVISUmhTJkybNmyhdKlSxd3OZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIOkv3JHos4RDVJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ0mHPALe/sMTEREKhEIMHDy7uUg6ZUCi0159LLrmkuEuUJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEnSH1hUcRcgFYe2bdsW2N64ceNDXIkkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIOJwa46S9pwoQJxV2CJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSDkMRxV2AJEmSJEmSJEmSJOkwkbUTfroTRrWDuc9COLu4K5IkSZIkSZIkSZIkSZIkSZIk6ZAzwE2FsnbtWp5//nm6dOlCYmIisbGxlCtXjg4dOvDmm28WOM/SpUsJhUIkJiYC8Oqrr9KqVSvi4+OpUaMGffv2JTU1FYCsrCyeeeYZjjzySOLi4qhZsyZ333036enp+Zbbv39/QqEQ/fv3Z+3atfTq1Yvq1asTGxtLkyZNePrpp8nMzDxo+0KSJEmSJEmSJEn6y5p6Pcx9CjZ+Bz/dBtP6FndFkiRJkiRJkiRJkiRJkiRJkiQdcga4qVBeffVV+vbty/jx44mKiqJ58+aULl2acePGceWVV3L99dfvdf7bb7+dPn36kJqaSr169Vi/fj3PP/883bt3Jzs7mwsuuIB+/foRDoepU6cOq1ev5oknnqBPnz57XOamTZs4/vjjGTJkCFWqVKFOnTrMnTuXO+64gwsvvJDs7Ow9ztu3b186d+5M165dueGGG/jyyy8Jh8MHvH8kSZIkSZIkSZKkP73NM2HJkLxtC16A1V8WTz2SJEmSJEmSJEmSJEmSJEmSJBUTA9xUKB07duSbb74hNTWVhQsXMnXqVJYtW8aMGTNo0qQJAwcOZOzYsQXOu2rVKgYNGsTo0aNZuHAhM2fO5KeffqJChQp88803nH/++fzwww/89NNPzJ49m7lz5/LNN98QHR3NG2+8wezZswtc7sCBAylbtiwLFy7kp59+Yt68eYwdO5YyZcowbNgwXnrppT1uz/PPP8+oUaP46quveOmllzjzzDPp2LEjGzZsKJL9JUmSJEmSJEmSJP3p/Hw/UMCXIs188JCXIkmSJEmSJEmSJEmSJEmSJElScTLATYXSrl07OnXqRGRkZJ72Fi1a8PzzzwPw9ttvFzhvZmYm/fv355RTTslpa9asGddccw0Aw4YN4/nnn6dly5Y5/R07duS8884D4KuvvtrjcgcPHkxiYmJO20knncRDDz0EwNNPP004nPcCkq5duzJ06FAWLVrEzp07WblyJc8//zylS5dm3LhxnH322WRmZhZij0iSJEmSJEmSJEl/IembYfXnBfftXFdwezgbsv3sTZIkSZIkSZIkSZIkSZIkSZL05xNV3AXo8JGamsp7773HhAkTWLNmDTt27CAcDpOWlgbAjBkz9jjv1Vdfna9tV2Bb+fLl6d69e77+Vq1a8d5777F48eICl3nCCSdw9NFHF7iuO++8k6VLlzJv3jwaN26c0zd8+PA8Y2vUqMFNN91E69atadu2LZMnT+bdd9/liiuu2OO2SJIkSZIkSZIkSX85a0dBOKvw41cPhyl9IH0THHEVtHoWouIOXn2SJEmSJEmSJEmSJEmSJEmSJB1CBripUH766SfOOussVq9evccxSUlJBbZXqlSJ0qVLF9gOUK9evT3OB7B169YC+5s0aVJge8mSJalVqxYLFixg/vz5eQLc9uS4447jggsu4N133+Xjjz82wE2SJEmSJEmSJEna3dqRhR+7fRV8dxFk/vo538KXYec6aPcxhEIHpz5JkiRJkiRJkiRJkiRJkiRJkg6hiIOx0LS0NDIzMw/GolUMsrKyuOiii1i9ejVnnHEGY8eOZePGjWRmZhIOh1mwYAEAGRkZBc4fHx9fYHvo14sz9tUfDocL7K9cufIea65SpQoAqampexzzWyeccAIACxcuLPQ8kiRJkiRJkiRJ0l9C0o+FH/tTv9zwtl1WDoOlbxZpSZIkSZIkSZIkSZIkSZIkSZIkFZcDDnCbMGEC//jHP9i8eXNO26ZNmzj99NNJSEigdOnS3HvvvUVRo4rZlClTWLhwIXXq1OHjjz/mpJNOokKFCkRGRgKwYsWKYqlrw4YNe+xbv349AKVKlSr08kqUKAFg+KAkSZIkSZIkSZK0u+xM2DIrb1vZlhBZwBc1pcyH5e8VvJyV/yvy0iRJkiRJkiRJkiRJkiRJkiRJKg4HHOD2zDPPMGTIEMqWLZvTdvvtt/PVV19Rt25dypYty+OPP86HH35YFHWqGC1duhSAY445hpiYmHz9M2bMOMQVBebMmVNg+/bt21m+fDkADRs2LPTyZs0KLjqpWbPm7y9OkiRJkiRJkiRJ+rNInQ/Zabm3E6+E03+C02dAXLW8Y1d+cmhrkyRJkiRJkiRJkiRJkiRJkiSpGBxwgNv06dNp3759zu3t27czdOhQOnfuzLx585g3bx61a9fmxRdfLJJCVXzi4uIAWLduXb6+jIwMnnvuuUNcUeD7779n+vTp+dpfe+01du7cSZ06dWjUqFGhlrVu3TrefvttAE499dSiLFOSJEmSJEmSJEk6vG1dkjsdlQAtHw+mS9WHY3/zefCq/x26uiRJkiRJkiRJkiRJkiRJkiRJKiYHHOC2fv16atSokXN74sSJ7Ny5k549ewJQqlQpzjrrLObOnfv7q1SxatOmDVFRUXz33Xe88cYbOe1btmzh8ssvLzDY7VCIioqiR48eLFu2LKdtwoQJPPDAAwD069ePUCiU03fPPffw9ttvs3379jzLmTFjBqeddhrJyclUrlyZa6+99tBsgCRJkiRJkiRJknQ4SNuQO13jHIirttvtblDxhGA6Kw2Sfji0tUmSJEmSJEmSJEmSJEmSJEmSVAwOOMAtNjaW1NTUnNtjx44lFArRoUOHnLaEhASSk5N/X4U66G6++WYqVqy4x5+NGzdy6623AnDVVVdRp04djj32WKpVq8awYcN49tlni6Xua6+9lqSkJOrXr0+rVq1o3Lgx7du3Jzk5mbPPPpsbbrghz/g5c+bwt7/9jTJlytCkSRPatGlDvXr1aNmyJTNnzqRKlSp89tlnlC1btli2R5IkSZIkSZIkSfpD2rk+d7pKp7x9oRA0vCWY3jwTsjNy+0qUhupnQlTCwa9RkiRJkiRJkiRJkiRJkiRJkqRDKOpAZ6xfvz4jRowgLS2NiIgI3n//fZo2bUrVqlVzxixfvpzKlSsXSaE6eLZu3crWrVv32J+ZmcmTTz5JzZo1GThwIIsXL2b79u2ceuqp3HvvvVSpUuUQVpurYsWKTJkyhXvvvZfhw4ezadMmGjVqxNVXX81tt91GRETefMLrr7+eKlWqMGXKFFavXs3ChQuJj4/nuOOO48wzz+TGG2+kYsWKxbItkiRJkiRJkiRJ0h9W2obc6bIt8/dXPCH4nfRD3vY2b0DNbrBlNow+6aCVJ0mSJEmSJEmSJEmSJEmSJEnSoXbAAW59+vThmmuuoUGDBkRHR7NkyRKefPLJPGMmT55M06ZNf3eROjiWLl26X+NvueUWbrnllgL7wuFwvrbExMQC23fp2LHjXvt79OhBjx499lpT1apVGTRo0F7H7NKlSxe6dOlSqLGSJEmSJEmSJEmSfrV7gFvpxvn7Q6Hg97ZluW01uwfhbQBlmsKx/4HlHxy0EiVJkiRJkiRJkiRJkiRJkiRJOpQiDnTGXr16cccdd7B9+3Y2b97Mtddey6233prT/+2337J48WJOOeWUoqhTkiRJkiRJkiRJklQcdgW4lSgDJRL2PG7n2tzpOpfn7at9MZQ/puhrkyRJkiRJkiRJkiRJkiRJkiSpGEQd6IyhUIgnnniCJ554osD+tm3bkpycTMmSJQ+4OEmSJEmSJEmSJElSMdu5PvgdXX4f43YLcKvSMW9fKAQNbijSsiRJkiRJkiRJkiRJkiRJkiRJKi4HHOC2L9HR0URHRx+sxUuSJEmSJEmSJEmSDoW0DcHvmAp7H7dzXfA7rjrEVMzfH122SMuSJEmSJEmSJEmSJEmSJEmSJKm4RPzeBXzyySdcdNFFtGjRgvr16+e0z507lyeffJJVq1b93lVIkiRJkiRJkiRJkopL2sbgd3T5vY/buTb4XarBwa1HkiRJkiRJkiRJkiRJkiRJkqRiFnWgM2ZnZ3PppZfy4YcfAhAXF8eOHTty+suVK8e9995LVlYW99xzz++vVPpV//796d+/f3GXIUmSJEmSJEmSJB3eQqF9jwmHIWtnMB1TYS/jsmHn+l/HVfr9tUmSJEmSJEmSJEmSJEmSJEmS9AcWcaAzPvvss3zwwQdce+21JCcn069fvzz9VapUoX379nzxxRe/u0hJkiRJkiRJkiRJUjEIhyGcFUyXKLvncWmbcsfFVDzoZUmSJEmSJEmSJEmSJEmSJEmSVJyiDnTGwYMHc+yxx/Liiy8CECrg29nr169vgJskSZIkSZIkSZIkHa7CmbnTESVyp+c8BRu/z719RI/c6ZhKB70sSZIkSZIkSZIkSZIkSZIkSZKK0wEHuC1cuJAbb7xxr2MqVKjApk2bDnQVkiRJkiRJkiRJkqTilJ2ROx3a7ePlTVNg5bDc29XOyJ2OqXjQy5IkSZIkSZIkSZIkSZIkSZIkqTgdcIBbXFwcKSkpex2zbNkyypYte6CrkCRJkiRJkiRJkiQVp3Bm7nQosnDjSpTJnc7cDoRzb0fGQyhUZOVJkiRJkiRJkiRJkiRJkiRJklQcDjjArVWrVnz11VekpaURExOTrz8pKYkRI0Zw0kkn/a4CJUmSJEmSJEmSJEnFpZBha9m7BbhFlMid/rIpbFuWe/uiHRAZWzSlSZIkSZIkSZIkSZIkSZIkSZJUTCIOdMa+ffuyYsUKLrjgAlatWpWnb9GiRZx77rls2bKFvn37/u4iJUmSJEmSJEmSJElFLBzO+1NQe2i37wQLZ+ZfRk5fVu50xAF/j5gkSZIkSZIkSZIkSZIkSZIkSYeFAz5zvlu3btx99908/vjj1K5dm5IlSwJQuXJlNm3aRDgc5v777+fkk08usmIlSZIkSZIkSZIkSYdQRCED3MjebTp0sKqRJEmSJEmSJEmSJEmSJEmSJOkPIeL3zPzoo4/y1VdfcdZZZxEfH09kZCTZ2dl07dqV4cOHM2DAgKKqU5IkSZIkSZIkSZJ0qIV2C3DLStvLuMjc6XDWfq1i7VqYPh02bdq/0iRJkiRJkiRJkiRJkiRJkiRJKi5R+x5SsOXLlxMdHc1pp53GaaedVpQ1SZIkSZIkSZIkSZL+CEKhIJwtnAXpyXsZt9tHz9mZhVr0mjVw223w3nu5qzrrLPj3vyEx8cBLliRJkiRJkiRJkiRJkiRJkiTpYIs40BmPOOII7r333qKsRZIkSZIkSZIkSZL0RxNVMvidnpTbdtSj0O7D3Nt5Atx27nORP/wAjRvnhrcBhMPw2Wdw/vm/s15JkiRJkiRJkiRJkiRJkiRJkg6yqH0PKVj58uUpX758UdYiSZIkSZIkSZIkSfq9QqHCjQuHCzcuphJkpOQNcCvVgDzfFxYRmTudtmmvi0tPh0sugZSUPfdLkiRJkiRJkiRJkiRJkiRJkvRHFrHvIQVr3749kyZNKspaJEmSJEmSJEmSJBWjORvm8PO6n5m9YXZxl6I/kphKwe/05D2PiSqVO717gNvpP8Pxr+YZ+uabsGhR7u0TT4RXXoH+/aFGjd9friRJkiRJkiRJkiRJkiRJkiRJB9sBB7g99thj/PLLLwwYMIDMzMyirEmSJEmSJEmSJEnSIZaWmcZRA4/iqIFH0eKlFuzI2JF3QCi07x/9OcX+GuC2ezDbb0WXy51O25g7XaI0RJXMM/TDD3OnL7wQxo6F3r3hwQdh5kxo374IapYkSZIkSZIkSZIkSZIkSZIk6SCKOtAZn3jiCZo1a8Y//vEP/vvf/3LUUUdRpUoVQr+5MCMUCjFo0KDfXagkSZIkSZIkSZKkg+fndT+TkZ0BQFY4i+lrp3NCrROKuSodkHA47+1dn+H+tr2wYn4NcMtMhfTkvGFtu0TGQlQCZG6F9I35+3+VnQ2TJuWW9dRTELXbp9blysHzzx9YmZL+ALavhK2Lg8eE0k2hREJxVyRJkiRJkiRJkiRJkiRJkiQdFAcc4DZ48OCc6TVr1rBmzZoCxxngJkmSJEmSJEmFEA5DdgZERhd3JZKkv6gpq6bku22Am4DcADeAlLlQcQ/3i9iqsHUh7Cj4s2OA+fNDbN4cTDdrBnXq5B8TGXngpUo6SLYuhs0zg+lSDaB0k9xwSIDkn+GXAbDyE+DXsMiIElDzXDjmeYitfMhLliRJkiRJkiRJkiRJkiRJkg6mAw5wW7JkSVHWIUmSJEmSJEl/PeEwrPkKFjwPa0cFAW7R5aFyB2h4M1TpVNwVSpL+QqaungpAfIl4tmdsz7mdIxzOnd49tGdP7Xuz+zz649s9wC3px70EuFUJAtxS5kA4G0IR+YZM+zH3PnLUUUVdqKQilzwDfr4XVn9JTjAbQEI9aD0oeO2S9BN8fRJkbs07b3YGLB8K9a83wE2SJEmSJEmSJEmSJEmSJEl/Ogcc4FanoK9ClyRJkiRJkiQV3k/9YN4/87alJ8HKT2DnWjjt++KpS5L0lzRl1RQALj7yYl6f/nrObSlP8NL6b6HhjQWPi6sa/M7cBqnzoXTjfEOSknID3Bo2LMoiJRW5lLlBMFtGSv6+rYtg4+QgwG3qNbnhbdVOhwbXQ0Q0bBgPCwbmm3XxYnj/fViwALKy4IgjoEsXaNOm8DmgklQsstJg+/IgoDKuBkSXKWDMTlgzArYuhlBkEHhZqX3BYyVJkiRJkiRJkiRJkiRJh7UDDnCTJEmSJEmSJP0Oq0fkhrfFVoXG/aD80bBjDaz8GHasLd76JEl/KalpqczdOBeAXq168fr011mQtIDNOzdTNrZs8Ran4rd7gNvK/0HKPCjdqIBxVXKn14wsMMBt27bc6fj4IqxRUtH76fbc8LaGfaHJncH/+ZZfYOF/g2Ci1IWQ9EMwptaF0Pb93BS2al2g0f8FgUfAunXQoweMGJF/VQMGwGuvQc+eB3+zJGm/pS6A2U/Aig/yhlqWaQ4tHoKa3SCcDbMegXnPQnpy3vkjYqD9MKje9ZCWLUmSJEmSJEmSJEmSJEk6uH53gNs777zD4MGDmT59Olu2bKF06dK0atWKHj16cNlllxVFjZIkSZIkSZL057NkSPA7IgZOGQulG+b2JV4G25bn3t65AZYMhpWfQMpcCGcFoW8VjocGN0HF1oe0dEnSn8+0NdMIE6ZcbDlOrHUilUtWZv229fyw+gdOrXtq4RcUDue9vSvE57ftOryU2i2sLZwJP/4ftPsAlr+fd1xstdzpef+Eer0gqmSeITExudMZGQehVklFI30LrB0VTNc8F475V25fuZZw3IuQnQELB+a2N7w593F/l5gKweLSoWtXmD49aG7QAK66CipXhoUL4f33gzGS9Iez+RcY3Q4ytgS342tDfA3Ytgy2zAweK2t2g5kPwqyHgzExFaHmeRCVAFtmwfpvIG19nsWGwzB/PixaFBwTVakCLVoYcCtJkiRJkiRJkiRJkiRJh5MDDnDLzs7m4osv5uOPPyYcDhMXF0f16tVZv349o0eP5uuvv+ajjz7igw8+ICIioihrliRJkiRJkqTDW1Y6rP4imK56at7wtl1K1g5+b54J35yae6FvfG2Iqw47VsLStyC6ggFukqR9GjJ9CEs3LwXg2mOvpWpC1Tz9U1ZNAaBVtVaEQiFaVW3FV4u+YsqqKfsX4KY/p5J1IKoUZKYGt9cMhw8S8o8r2yx3etsy+PFWaHgLLH49pzlht9lWrz445UoqAmtHBQFtALUvLnhMRAlImpY7XeH4YDprJ6Qn546LjOOzz8rmhLeddBKMHJk30PGhh2B93mwjSfpj+Om2ILwtIgbavg81zgnCKsNhSPoBti2BjFSY+0wwvkJr6DQaSux20LNjTU4A3LZt8Mgj8PbbsHx53lWVKAH//jdcd90h2jZJkiRJkiRJkiRJkiRJ0u9ywAFuzz//PB999BEdOnTg8ccfp3Xr3AsEp0yZwt13382wYcN4/vnnueWWW4qkWEmSJEmSJEn6U9ixOjcApeKJue3LP4Sd63JvH3EFTL02CG+LKgXtP4Gqp+T2b5kN21ccmpolFdrKlTB7dnBhflwc1KkDDRtCZGT+sZmZkJ4OsbHg9+Fof2VmwsyZMGkSLFsGGRlQrhw0agSdOkHFisG4Dds2cM3n15CelQ5ARCiC+zvcn2dZU1dPBeDoqkcHv6sdzVeLvspp119cKARlW8DG7/Y+rtzReW8vejX42U3jRtk50zNmFFWBkvZLKLTvMbOfzp0u1yr4nZEC0+/ObY+rHrQBRJWGyF8T2daOhnFn546r24vBg3MfC+66K294G0B0NNSsuR/bIEm/V2EeC7etDB7TAOr1gZrd8s5f4bjgZ8mbkLUjaD/y3rzhbQBx1SCuGpmZcMopMHly0Ny+PVx+OVSvDuvWwddfB78lSZIkSZIkSZIkSZIkSYeHAw5wGzx4MI0aNWLUqFFEReVdzPHHH8/IkSNp0aIFr7/+ugFukiRJkiRJ+suavWE257x7DgClYkoxoecESkaXzB1QmItFCyscLrpl6eDK3pk7HVEid3ruM7BpUu7thLqwcWIw3fSuvOFtAGWaBj8Ef/5Jk+CLL2DaNFiyJAiFSkiAunXh/PPhiisO0vZIYts2ePhheOMNWL06aCtZErKzYccOqFcPFi4MArbeegs++wymTg3C3iAIdzviCLj6arjnnuLbDh0+Pv4Ybr0VVqwIDieaNoXKlWHLFpg1C3r3hv/8Jxj7+vTXSc9Kp265uixOXsx/f/wv97S/h6iI3M/4pqyaAkBsVCwTV0wk5tcQnl3tEhWO33eAW3wtiKkEaRv2OOTYY8NERQUBhD/+GDxGxsUVca2S9m731467vybdvX32k7vN8GvwYuYOWPhSbnOZ5lC2eTCdsRnC2RAqOJF21qzc6XbtDqhqSTog4XDwei0jA0qX3i1YuzCPhSs+AX69Xa1rbvvi14PHPICIaNjyS25fpfbB7+2rYd4/c9tLNeSd76/JCW+75BJ45528q+7dG7KyDmQrJUmSJEmSJEmSJEmSJEnF4YAD3ObNm8dNN92UL7wtZ8FRUZx11ln8Z9eVIZIkSZIkSdJfUP8x/VmUvIhS0aVYlLyIF6a+wJ1t78wdUNDFovsKYivsOBWtwobtFebvElMxd3r7qtzpyidBRBRsmBDcTpmX21f++NzpsWflXihcojThE9+jR48gOAqgc2f4v/+DatVg+3aYPRtmzixc+dKfRnYmbJ4JO9dBenLwPxxdIQhGTDhij+EiByIchu7dYfTo4PZ998HNNwdhWuEwrFoF48cHYUWnngrjxgXjrrsOLrgg93/1l18gOfnXhabMh6VvwpqvYOcaSEsKao6tDKUaQZvBwbT+vLLSIW0jpCdBOBNKlIa4GhAZw48/woUXBgGBTZrA//4HDRrkzrp9exDsBpAdzublaS8D8GyXZ7ntq9tYlLyI4QuGc3ajswFYv209y7csB+Dh8Q/z8PiHc5a1OnU1q1NXU71U9aLdvqJ8XtWhUbUzzHt272NCIah6Gix7Z49D4uKgZUv44QfYuhVeeQX69s075n//g27dfn/Jkn6H2Cq509tWQOnGEBUHDfvC+rGweUbQl1Av+B3Ogm3LguOsSm2h82SY0gc2/wxAxG6HXtnZh2gbdFgKh8OcP/R8Zq4PXsTe1fYueh/d++CuNHM7rPsWti0Ojr+yM4Jjr/jawev0+JoHd/0qchMnBsHZX30VhGanp0N8POzcGQRn//e/0KlTIRaUnZY7vXsA/9TrIDs9mC5RFurtdh8NZwa/0zYEQf27VD2Nd965JufmzTcXfEicEzAnFZWsdNgwDpKmBY9xmakQGQex1aD8sVD15OKuUJIkSZIkSZIkSZIkSTpsHfDVSdHR0Wzbtm2vY7Zt20Z0dPR+L3vr1q3ceuutVK9endjYWFq2bMl7771XqHnXr19Pjx49qFixIvHx8Zxwwgl8/fXX+12DJEmSJEk6cMk7krnso8s4f+j5nD/0fBYlLSpw3JrUNTw87mEeHvcwz058lqzsrLwDQqF9/0h/YDPWzuCD2R9QJqYM/7vkfwA8+d2TpKalFnNlOiDhcN6fPbUX5rErpiKUaR7Mv2Y47Hr8a/kEtMgNzSEyJnc6c7f7zZZZsPH7YN61o3n77dzwtiuvhBEj4Npr4Zxz4JJL4B//gCefPDi7RfrDydwBU2+ATyrD6Haw6GVImgJJP8DSN2DC+cH/UGGPMwoxbvTo3PC2c86Bhx4Kwtt2zV6zJlx6Kbz8cm542623wksvwSmnQNOmcOyx0KNHEL7I+nEwvBnMehjKNIX2/4Puq4Kfkz6HOpdByKv6/7RW/g++7QwflobRbWH6ncF9YXJv+KIRLBrE00/nht889lje8DYIwikaNQqmRy0axeLkxVQpWYUzGpxBj5Y9AHjph5dyxk9dNXWvJe2r/4AU9nlVfxyVT4KImIL7QhEQXyuYrnHOnpcRigAiOOWU3KY774R//zsIHly4EPr0CYIwJRWzqrv9o678OPhdojQc8y+oslvqUbUuudOLXw9+R5eDCsdDVKmcrqOOyh02ZkwR1ZidCUk/wopPYPFgWPgyLHkrCMDdPShbh5W3Z77NJ3M/IToymqWbl3LHqDtYv239wVlZOAw/3w8fV4AJ58GmKVCiDJRMBCJg7WhYM+LgrFsHzRtvwIknwosvBsfEkyZBRkYQHJuaGgS71apVyIWVPCJ3OnW3kP1jnoda5+feLtUwd3rzL8HvhCOC12+VTsrpStstDy4hYd+rD4fDDJ01lEE/DmLQj4PYtH1TIQuXfrXqMxhWFb49DdaPCY7ZK50EZVtA1jZY+UlxVyhJkiRJkiRJkiRJkiQd1qIOdMZWrVoxdOhQ7r33XqpXr56vf82aNQwdOpSjjz56v5d93nnnMXXqVB5//HEaNmzIO++8w6WXXkp2djaXXXbZHudLS0vjlFNOYfPmzfzrX/+icuXKvPDCC3Tt2pXRo0fToUOH/a5FkiRJkiRRuKC0X8MVssPZXDnsSj6f/znnNzmfj+Z8xKKkRUzsNZG4EnE5w9My0zhv6HlMWjmJ42scz5RVU9iwfQOPnvJovmXmqeG3IQ6FDXEz/EHFoP/Y/gDcdPxNdDqiE13qdeGrRV/x78n/5t6T7j1o6928czNX/+9qNmzfAMBVR11F76N7H7T16TcKeuz6bTtAzW6wZSakzofZj0DTv0PEb96yrdAGImMhaycsHwq1zgvaz1kCc56G6XcA8OOPubO0bbvvh8aN2zfS9a2uLN+ynOxwNuc1OY+BZw0kInTA3/kh/XHMuAcWvgQR0XDmHEioC+mbYcfq3DHRFQr/v1qIcZs/yG0uV27PpS3aLdO2adO9bMPMByE7I7iwuvXrwXo3TYXVX+SO2TwTqnTcy0L0R/XIuEe479v7KBFRgujIaL6+8mta12wddK4dDePPBcJw9HPQsC+EsyFjy25LCO/xblmQgdMGAlC3XF1e++k10rPSARixcARLkpdwRLkjmLJqCgCVS1bm+BrH58z7w+ofWLt1LVNWTaFb426/c8sPUCFfi2RnB8+H06cHIWBbtwYhdxEREBkZhCr27QulSu1zadqTqHg44kpY9Er+vsQrg5ASgOpnBI+z6QUEjNS7FiKjuekmePZZSE8PgkxuuSX42aVZs4OzCTqEwtmQnR4EjkaUKO5qCiU1Fd5+Owj6mTMHKlWCMmUgKip42k9Lg7vvhlatirvSQyS+JpQ7GpJ/DILZqnUNAhpDobzHRxVa5/7Pz30qeCxIvCLoy9qRM+zqq+HjX3PgHn44CLHd/TE5NRXWroUGDQvxuJ+dDT/fB/P/DaEoqNcHyhwZBGVn7ghC3bavgnpXF8GOUFFJz0rn/KHnM27ZOCJCEbSo0oIvL/uSktElc8Zs3rmZfiP7AfDK2a8wdNZQ/jX5X9w1+i5e7/ZrQGBh36cszLiVnwZBuQAnvAOJlwbBgOt/TV0ufyxExe15fv0h3XNP7vQ77wSP5bvExUHr1vuxsArHByFu25bAvH9B3ashqiTUvwYyt8GKj4Jxtc6DH26AcGZwn6rULgi9rHEmLHs7Z3Enn5wbYvnRR9Cixd5X/+ykZ7l95O00qdiEORvn8PbMt/nqb19RIvLweG5VMcvYCt9fBplboc6lcOI7QfvGyZC2EWJ+TZ7PSAnur5IOnnA4eN7I3BYcI4cig+eT6LK/Bp1LkiRJkiRJkiRJkqTD1QEHuN1+++2cc845HHvssdx+++106NCBKlWqsG7dOsaMGcM///lPkpKSuO222/ZruV9++SWjRo3KCW0D6NSpE8uWLeOOO+7g4osvJjIyssB5Bw0axC+//ML333/PCSeckDPvUUcdxZ133snkyZMPdHMlSZIkHW6y0oMLErLTIJwVXMgXGQdRCRBR8GsK6bAVDgNhIFT4MLPfzL5pE6xeHYQcpKVBZmZwgXJ8PNSsCTUKG3ACPD7hcT6f/zld6nVh6IVD6Tu8Ly9MfYEbvryB1855jVAoRDgc5vovrmfSyklcf+z1PN35aVq/2prHJjzGUVWO4uJmF+/n9u9mT0FvB0F2OJtnvn+Gj+d+TLnYcuzM3MljpzyWG8Chv7Rpq6cxbO4wAD6c/SEjFo5g3bZ1ADw98WluPP5GysaWLfL1btm5hS5vdWHqqqm81u01Xp/+On0+6wOQG+KWthHWjIKtC4PnydjKwQVDhILbkfFQ96oir02/0fBmWPgypG0IwpoWvQqlmwSBbrtEl4W6vWDBC7D8/SDMLfEKiKsKqQtyhl12Gfz735CVBf/9L1xwAZQvn3d1qalBQMKu+8iPa37k/QveZ8TCEbzy4yuULFGSf3b5J6EDeC7Rn0haUhAWmPxTcOF5Qv3gfheKDIJYwhnBRb/xNYu70j3buTb4HRkHsVWD6c0zYMGLsHo4ZKZC437Q6qkiW+VZZ0G9ekFA2wcfwBVXBKEku1uyBHr0gOefD461nn8+mK9atbzjNm2CCqXqw/oxkJ4EO9ZAfPXg7xBTEX6+HzI2QzOKL8AtOzMIxNuxGjK3B6+7ACJioEQClG580C7+Ts9KZ1FSkIQXFRFF/fL1D6vHrZd/eJn7vr2Pk+qcxL+7/puOQzpyxjtnML7neJpWagpbZhMc2xOEPoRCsHMjTLoSUmbD9pVQ/jj69ZvC0KFBfs3f/w5HHhncB3fZsQNWrID4qiv5dN6nAExcOZGJKyfmjAkT5r/T/stjpz7G1NVTAbjkyEv41+n/yhlzx8g7eHri0zn9xaIQr0VSUuDUU2Hq1OD574034KSTcoOBMjJg+XKIjj5ENR8CO3YEQUfJyUEIWnp6EFQXFxfsg8TEg7TiI++HJUOCYK5dostDi4dyb5coFdz+4Ya888ZVg+YDgOB15uOPw35+lKw/sjVfBce2qQuh/DFBiFdUQnD8kLUTCEOz+4u7yj0Kh6FNG5g9G0qUCAIhCwoS/Mtlw7d4GMaeETzXj+8eHBtGl4PNP+eOiYiClk/AlN7B33ry1TD1+qBv1zEC0KULtG8P48fDtGlQvz5ccglUqRIEbw4bBo89Bg0K8x7U2m9g9q9fAND6dajbI5ieekPwnjBA6jyocwksexc2fBe0lzkyuF+GIoHs4JimyslQ4dj92i3hcJjZG2azM3MnAA0rNKRUTKndBwTHcNuXB8dzWTuDY+td71HHVYdyR+3XOg93WdlZXP7x5Xw+/3Me7PAgFeMrcvPwmzn3/XP57NLPiImKAeCBbx9g3bZ11ClTh2mrp1EuNkhHHjx9ML1b9aZt7bZFGsTMio9zpyODGsjOgBVDg4CjzdOD+/3ZC1i1CmbMgJUrYft2SEgInntDoeC1eL160LHjge+jw872VcFj/9aFwX37t+8tlShNOPEKJk6E0aNh8eLg2KxOneBxNiIiOJbNyir644GWLYP3ugG++w7OOON3LCwUgib94IcbYesiGH5UEOIWWwXWjMgdF1MB6vWGhQNh3dcwoiXUviS4X23KPZa++ebgteCGDfDoo8E+uPxyqF4d1q0L9lVsLPTsCV8u+JI7Rt1BvXL1+L7X99w56k5e+fEV+g7vy4tnvnhYvQ5SMQmFcoOhwtm54ZbJ02D9eFj+XtDXdQaU20eaoKR81qyBX36BVatg27bg9fiuU5uzsoLXE8fX+Azm/jM4Nq12OpRrCVGlgXAQnpi2Hpr1z//FLpIkSZIkSZIkSZIk6bBxwJ/6n3XWWTz77LPccccd3HnnnXn6wuEwUVFRPP3005x11ln7tdxPPvmEhIQELrzwwjztPXv25LLLLmPy5MmceOKJe5y3UaNGOeFtAFFRUfztb3/j73//O6tWraJGjRr7Vc/JJwcnDfbpE5w4HBWV9yTCzp0hJma/FnlYy8wMLubfvj2YzsrK7YuKgtKl835zrSRJkn5jx1pYMji4yKNU418vGIuDlPlAODh5vmQi09Z05c03gwtaGjaEdu2CC1t2XYuRnQ0VKkCrVsHtcDjM2q1rycjOAKBMTBnKxO52YJadFQS1ZKYGF81FRAO7LuwIQyiS9Oi6DB8OCxYE4U0tW0LZssFJxhERwTn9WVlw/PHBhc8bNwbHhRkZwQXQERFBfdnZwcU3RXmhcHY2PPhgcKHNxo1w3XVQo0buesPh4Pi07bEbKTfnckiaBpU7BBfRxFSEjd9B2qbgorkSCXD0c4f9N1knJcGECcEJ4VFRwQWPCQnB/oBgn8XHBxfxJ+9I5oWpL/Dfaf+lW6NurN++nulrp3P7Cbdz5VFXEhsVW7wb8xcUDuf+hEK5P7ss27yMn9b+RGxULNsztlO/fH2aV24eXJCVvgWm3RyEqpRpBjXOCcIxNk2GjC3Bt7bHVITmAwiHSrBtW/C/mp4e3C+ys4N1RUUFF4KNGAH/93/BBWNXXw233gp16wavdTMzg7Cf9PQ9bko+oxeP5v5v76d0TGk27dhEm1fbECZMpfhKDJ4+mBNrnkifY/rwnyn/4fXpr1MhrgIz1s3g9LdPJzYqltioWHr+ryeNKjaiZdWWRb3r999eLgJdlbKKq4ZdxddLvubshmdzSbNLuHPUnbR9rS0DOg7g7nZ3E2lg5F/aA2MeAKB74+6cXv/0nPaXp73Mj2t+5NmJzzKg04AiXWdKWgpd3+7KlFVTqFuuLuOWjaNifEWiIqK45rNriAxF0vOoK+GrY2HbMqhyCnT4IriYdN23kDInuHgPoFJbKFU/d+F7+n8o5MWiK1eEmTIlCBopVSo4VoiLy3t8U7EiHHHE79sHh5XYytBpFEz8G2z5BbavCH52qdQ+CEdo9Uzw+L5kcBCYsmRI3uXE1+DYY+Htt4PH9GnTglCUTp2gatUg4GX27OBi4A//t52z3z2bH9f8SGLZRIbNHUZGdgYlS5TkucnPUTa2LA92fPCQ7oaDaU3qGl758RXGLhvL2Q3P5uslX1OnTB1uOO6GIChpP2RmZ/LxnI95ZuIzVE2oypGVjuSD2R9wWbPLuOn4m6hUslLO2LTMNKatmcaOjB2ECVMquhStqrUiOvIwSA6a9yzMehgiSsDpM6FUQ9i2FNaODi5OT0+CUAk2VLiVBQuC49Lo6OD1SXR03v/pmjXzBwkWqT2Fth79bBAikDQNvjoOjrgC4mtDje6wfmzwuqiIxcXBZ59Br14wcWIQJNW4cXBclZUF8+cH762vXw9Dh0LfvjBzZhBe0K5dEOK2bVvwv9qgAXzxyZPB4/TaUcGF/7UugJKJv25v1t5KOfiWvBWEMmWmwgnvBM8X2emw8fvgte6O1cHj1xFXFnqR67au47WfXmPssrGc0eAMvl36LXXL1uXaY6+lYYWGQBD68c7Md3hwzIOUiS3Dpc0u5blJz1G3XF0ePeVRTqpz0sHa4iLzwawPuP6L64mNiiUmMoZHxj9CYtlEfl73M53f7Mx3V39HnfrXwYZxsOIjGHcO1L82CMRrcCPM/1cQ4AYccwy8/35w/D5rVnC/OfLI4LOszZuDtl69oOIFr5Idzub4GsfzycWf5NTy5YIv6fNZHwb9NIj+HfszZdUUAI6udnSemnfdnrp6KuFw+A8bEhERkfv5VGYmbNkS7IeoqOB9jZSU4Hb16gdh5dkZQShQ5g4gO3hvB4IAk4gSEF2hSIPkZ86ECy+EefOCsKmnnw62Ky4ueDjcti3Y/oOmZK0gBHNXcFJkPJz4Tv5gz3p9YPFrkPRDcDu6ArQfBrG5z5e33hr83e64I3g+2SU+Hm655SBug/ZbOBxmxroZfLngSyrFV2JL2hZiImM4t8m51Cz9699+1iOwYTyUbQnN+wfPveu+De4Di18NxpQ/jvSKXVm3LrivpqUF703sel8xHA6OIerWDd5f3G97ODYYMwZGjQrCfFq1gkaNgv+ZXbNkZQXPyWedFfxvZWTAe+/BuefmvgeZkhIEQTZokD989U+t+unQejBMvyMInt66MLcvogTU7BZM1+sF6ZvhlwG570HvElcNanYjKgo+/xxuuinYv+vXByHUuyv0uQaVTgzeE1v1Kcx5Inivt8yRwfPmsvdgzuPB43CNs2FKHyAM7T6EmucGj9nL3oUN38PWBcE2VTiWcDjMT2t/4tN5n1IpvhLbMrYRDoc5v+n51C+f+9p4yqop3DX6Ln5e9zN3nHgHwxcOZ/aG2dx/0v1ce8y1xIQIXnNv+QWqdoZjXwj2wcbJwXH15nGQtR2OeiwIfPoLCIfDXPPZNXw4+0PKxZZj7sa5hEIhqpSswqjFo7js48t4/4L3mbluJi9MfYGYyBjOaHAGczfOBeDshmfz2fzPuOHLG5h2zTSiijLgpOa50Oj/YP6/YeIVweuess2hYlvYuiRn2MCBcP2vuYR9+wbPv799nMrIKLqyDobMzOBxcNOm4H2C+PjgPeBd701nZwf/g4U6XsrOgJHHBZ+5VD8D2n0CkdHBlwWkzAmeD4Cl2zpyyim12LkTjj4aRo4MXjvubn/eey6swYODALThw6FbN+jaNfjMKyEh2P7p04PPmc47r5ALrH89pCfDrEeD1zw/35u3v+yvwVdHPxe8Xlv8GmyZBTN3Dy4NQblWlC0L48YFx+rffw8PPxz87O7ee2H2htlc8uElREVEUS6uHBcMvYCM7AzKx5Vn4LSBNKvcjBuPv7Fw9WfugE2TgtcSkXEQX+vXzwl//WKYcDh4j6pk7ULukD+wwr5e+SMksh6KL6KJKgknvgeTegRfDJGeFDw3xVTIfY+hOK0dHXy2lZ0O5Y+H6DKQkRo8T4azgtdWNc6BqPjirlR/IpnZmUxeOZlFyYsoF1uOLWlbOL7G8TQo32C/3vN4+ungtSzAPfcE5zLsfh5xOBy83mHKoOBLGhLqQ+Pbgi9w2fAdbJqS+7q6dBPWxV3OsmXB6/mYmOA5OerXQ55dr1kaNAheP+1TdhbsWBmcH5KVFoSsR+x24BLODr70IuZgvnGrYpGdCevHwbYlQcBuwhHBc384M/cL4UqUhtJHQkYS7NwAmduCLw6JKEGec4hiKgTnHEiFlJWdRdavn11EhiI9T0KS9Osb/hnkfnb2a8h4RInD/tzdP4Ls7Nwv6c3+NbM9IiL4XDQ2Njdc+q8gMzM4r3zbNti5M/fLIyD3HN0GDQ7oe5ElSdLhLJyd+1kDYYLj0chff/58Bwb7uj5p/xaU9es5qrv2W0TwfuOfcL/pr2XlyuC8sKSk4FqWihXzfvlcOAy1axf9ee8pKcF5Ctu2BevYdZ4GBLfj44Pz7fUnlJ4cXEueuTX4gvTIXddzh4BsiCoF8UH2UVZWcC5PRkbeaz8jI4P7TPzh9JF5OLzbedQROXf4FSvghx+CcwbLlIFatYJt2/UaPisrOAe+9p/g1BX9dYXC4d93FtCSJUt46623mD59OikpKZQuXZpWrVpx2WWXUbdu3f1e3gknnEBWVhZTpkzJ0z5r1iyaNWvGyy+/zDXXXFPgvNWqVaN9+/YMHTo0T/sXX3zBWWedxVdffUXnzp0LnDctLY20tNyTiFNSUqhVqxYvv7yFlJTSlC2bG+C268TFAgPc9nQha1oSpM4PvjUvtkpwUkQ4HHz4nbk1OJiNrxl8YL4P4XBwkcm2bcGD8G9PqoSgvoQECEVkkZGdQVZ2FmHCREVEkZmdSYgQURFRlIgsQUQh3/yeNy84mTIpKXjga9Uqb4hIejqULBm8+blgQXAySZUqwcmXu05c3XUySZUqUC57anCiAEBCgyBII2MrZG0LTh4JRUGVTsHJZPuSkQpbZgdPZDHlIbp88IC+a3nZ6RBTGco0ge2rgxNGd6zJ/ZbrUCRk7wzWVaZ5MP+WX4I6ostBiTK5HxIk/xSc1FD5JGYvq8OaNcETYs2awfZHROR9s7dWrUJeYJCVHpwol53+65Pwbt/uvOsEiqi4YNy2xcG2xVcPLowJZwbf2p2RCmRDQr1gm9KTgie4EmWCky+zM4L7Wtb2YHviarJmx2YWJC1g3dZ1xJWIo0apGqxIWUFaZhrVSlWjYYWGVC5ZuVD3kS1bgm+cXrcu2OYaNfJeiB0OB/fLMmWCb59cvz7oq1o17wFnVlZwUvDmErMYu2wsFeIqkFg2kciISMLhMBu2b2BR0iKaVW5G+9rtWbplKdNWTyMrnEXN0jWpVboWi5IXsTp1NWViynBM9WOoWbJiEIyTsTk4oIkuF+yjzK3BvstOC/ZTmSaF2tbk5KD+HTuCbdr17dq77NrWwh4QpWWmsT1je07oTnRkNDszdxIZiiQmKoa4qDiiIkrkvAiAvC+kd3+4CZPF9oztbM/YTlY4i+jIaMLhMOlZ6ZSILEF8iXjiouLyniCxt28m310RjNs9+HFPbwQsWxZcbL5xY/Bip3bt3MfaXY/BFSoU/iBsV7hQdnawnF1/q11vSITDweP7ypXBhbThMJQrF1yQs2uduzYjrkwqIxaOYP229RxZ+UhKx5QGYEfGDmZvmE1MVAxd63ct9P9NUdoVXrRrH++64Om3+ziqkNc07LrQb1cw064P83bfbzExwcWgCxYE+7lpU6hcOe8L1/R0qFwlm582TWDG2hkcUe4IqpSsQigUIjuczfIty1m3dR1ta7ctdEjLlp1bGLloJBu3b6RxxcaUjwteFSfvTGbOhjlUjK9I53qd84ZX7cX6beuZs2EOq1NXUyqmFHXL1WVlyko2bd9EhfgKNKnYhJoJVQit+wZ2rgtOUivdCCJLBo+1manBcxARUK1Lod6Q2rh9I1/M/4K0rDSaVW5GiV9PlkzLSmPmupmUiS3DmQ3OLPQ2TFs9jYkrJ1ItoRq1y9QmIhRBdjibNVvXsGzzMo6pfgwnVD+WUOp8SN8EEbHBxZI5j4XpQSBEZBzpZY5k5rqZ/LzuZ8rEluGIskcQXyKe+Zvms37beuqXr0+raq3IyMpg5KKRpKan0qhCI8rFlcvZn/M2zqNWmVqccsQplIyKhs0/B0EUGVuDE69DUUA4eD6OSoBK7XJebO5VVlrwnJ+2MXjejq0ULCtzW1B/1k6ISiCzdCtSUoL7X0REcP/ddd/f9f8cEQHhqO0sTl7Mss3L2JG5g+qlqrMjYwfJO5MpE1OGuuXqUqdsnUJfDLQ1fStJO5LYlr6NMGESohNITUslMiKShOgEyseVJz4UgnXfBBeFRcQEx38RMcHfYduy4EL3uKqkVL2ZCRNgyZLgQ/a6dXPDuXY9FpYvD7UbbWLq6qnMXDeTmKgYmlduTvLOZBYlLaJkdEmOq34cLSo3J2bdCEiZG6yrcsfg2CttYxAEtX05RJQgs1JXvplYjYULg//pxo2Dx8PfBrgdcUQQTLBhQ/C83q5dcBy0+3N+VBRUq7WThUkLWZi0kPSsdGqVrkWYMCtTVhIdGU2D8g2oX74+21Ji2LAheF4tVSpYVkHPq2vWwJQpwe+4uOBYY9fjza7HwBNPCFM+4mdI/hF2roe4GsF9JSIquL1taXBCaqPbCnXxdGZ2Jpu2byJpRxI7M3eSEJ1AdjibHZk7iIuKo3xcecrHlSdy+zJIXRQcU8bXCo7RstODY4y0pODYq1xLNqZWYPbs4Dhi1/PZ7tuQlQXHHBMmMnYH29K3kZ6VTigUIiYyhh2ZO4gIRRAXFUfJ6JIkb4xmzJggwC0+Pvh77f53gGA/xVVfzKAfBxEmTNtabXOO/9Oy0hi3bByV4itxzTHXUCG+AlnZWWSHswkTznkcCREiFAoRGYokFAqxJnUN8zbNY+3WtSREJ1C7TG2Wb1nO1vStVEuoRqOKjaiaUDU49tw8M9jn6ZshnJH7vx9ZEuKqBsf7hbB8eRBkkZQUPM9UrRrst98GGq5dG/zPpKYG/zNly+a+htv1YUSVKrlv9mRnBxeNxccH97ndLxSuWjU4mWPXRXfR0bn3y933cWxs8KASZs/HRiFCzJ4dYvLkYL01agQXBcfH564zIwNq1gyzI34BoxaNYuP2jdQvX5+jqh7FoqRFzNs0j8zsTE6teyrHVD2ayC0/B69N0pOCC6KjEoL7WdoGSJkHJcqwqeKtDPssmlWrgrDtNm2Cx41d+yQ9PVh3vXrw448wZ07w/1W+fPC/vXsYYM2awf967kYVfLyXHc7mkXGPsGH7Bs5vcj4dEjvk9M3eMJuBPwwkJjKGfif244nvniAzO5Nb29xK3XK57x98ueBLRiwcQWLZRP6vzf/lvViisBc0FeWFT3vY1m3p23j6+6fZtGMT5zU5L/eCdeDrxV8za8MsOtTpwPlNzz+w2vY0LiM1eN4LZwevEUNRuR8wZqcDoeD1TmEDgtKTg+fpHWt+XWY8EPHrOjKDE/Ornx70b5kVvHkcCgXPJ6GIoJ6IEkHIV3S5YEzmdijTNLhfZm4Pjj22LWPXRYpExgSPDdmZwXshUfHBc3jWzqCeUFQwLnla8NgdXS5YfkR0cCyWviV4DouMZnulk/lo3uds3rmZE2qdQEJ0Qs6mTV87nQ3bNtAxsSNxJeL4csGXlCxRkiuPupISkcGx1/+zd99xVlT3/8ffc/v2vuwuZelNqiJFRbGAjcQSFVti16iJ0cRojA1ixJKvMVWjMVHzw4CKJbEbFVAUFAtSpMMusMD2vnvv3jK/P85WaXdhYRd5PR+Pfey9M2dmzpQ7Z86ZM58JR8KatWyWKgOVOqXvKS3BnAKl5uGZQHHLOnrTpLi+Zt2a6ux7UOGv0D3zTAC32469rc0xsmjLIs1eMVuJ3kTdd+J9HXqcz1kxR59s+UTDM4frmqOuaR5eWFOoBxY+IEn61XG/UpYvwTzEUbPebFNvqtn2kQaz3p5UKffiNgE22l0n/Faa9evNg6mFheZ83BQssnXdNi2tnQHcImFzDNesbwxi6TftNo0PIVZ6e+pfy/8tSfrhyB8q2ZfcPOmiLYu0ZNsS5STk6Lyh50ky5erq1aZMqasz12EulzkvDhxoguVEs667TLOndLZtHlgsXmCueeN6SZknSsnD2qarL5QKXpWq10uyzG8obZyUdnRz21VDg2mf+eKLlmvJhASzXSdOlFZF/qv3Nr6nfin9dNO4m5qPv+pAtWYsmKGGcIN+Nu5n6pfabw8b/ltqNplrTX9RywOWDo9pg/FlmsC6DpdZz/Klpm0rUGrSuZOkuFzzgLzb1LEVbjAPb9YXNJ4XHOaYjOtjtomz8Sm1YJVUstgsN1hplunLlBL6y44fpE8KPtXLq15WVnyWhmUOk8/lUzAS1KbyTVpZvFIn9j5R3xv0PTnk0urVpj2iuNhcV7a+hoiLk04+vVrPLvunNpZv1LG9jm1uDwhHwlqybYnqg/W6fNTlyk7I1of5H+qzgs+UHZ+t0dmjFY6E9XXh1yqsKdT4HuM1MXei3A63iuuKVVZfpoZwg5J9yaoP1isYCSrWHavMuEylxqRqzoo5Kqkr0cReEzU6e3TzJl9ftl5vrntTse5Y/XDED7Vo6yItL1yuvil9lRWfJYflUEO4QRvLN6oyUKlJvSdpcPpgs20rV5p90FBp6lGyzTZ1J0vxfaXUI02asi/N+drhMed9h9PsN1eclH6sPlx1jJYvN+1kRx1lftffvkbr0UPa5G9bX3U6nApHwtpatVUF1QUa232sju42QlbZZy11xNgeJk8N5Y1BkWrNsdJt0q5/X7v6bdm2VL1GKpwvBYoajyW3Kdvi+5qgYzHZO89rd/OLMp1tm0Dcn35q2kzq6kydtHdvcz02cqRJFwpJy5ZJS5aY9E2/1X79pOOPN+dISVJNnlT4nimDG0rNvvBlmoBe6cdInuTd57Wd67CjZodeWPmCnJZTV46+UjHumOZx7218T98Uf6MBqQN0er9TzG+vapXZrr4sUzZaTnN+skNS6hhT3y1fan6fCYPMbzwSMPvTXyxZluy0CfqkbGub32qsO1YN4QZtKN+gVcWrdFKfkzSl3xTNWjZLy4uWa1z3ccpJaIkqsaJohdaVrdPp/U/XpN6T9NHmj7S2dK36p/ZXTkKOHJZDFf4KrSlZI4/To8n9JitL9aYc9O8w29Kbbs5JDRXmOsMOSalHmT+ZoCPNm0x2cz2pWSRkpmnq6NWcOCLJPJBQHQxoxoe/UUO4QTeNu6lNIJhXVr2ieXnzNChtUEsQhroCUzbUF5jfnmT2d2wvk6/GMiIYNAG9Fi0ydaeGBlMXGjLEBPL8x5oHta16m6YOnKop/VrukflDft3+v9tly9YVo67Q00ufliT9fMLP1Tu5d3O6LZVb9LtPfidJuu/E+9q2Ve3ntcE+pdvLMtevN+Xg+vWmblhbayaJjzfnqcsvN+f1vQlFQqoKVKnSX6mGcIN8Lp8sy1J9sF5up7s5cL6rfKl5EDtcZwIX+rLM9VRDubkGtoNS2vi2ZXoU26221rSBFBWZurDDYfI/ZIi5JvnmGxN4ZPt2cz2VkdFSr21qBz/rrN1su47YD7ZtAkBUr5N6XWDaSnclVC+t+6skW+p9advzbislJdK//mXWp3dv6Qc/MPXypnNP/9T+OmPAGW2meXnVy9patVVHZh+p43od12bcpk3mJQDBoHTMMSZY16589ZX58/nM72W/gnKVfy2VLTFtQunHSPG9d0pSX29+qxs2mLylpUkjRpj9+q+v/6UKf4WOzz2+TTt5xI7osSWPKWJHdOaAM/XW+rcUsSM71UMLqgr00qqXZMnShcMu1PMrn5dt27pkxCXN7eeS9GH+h1q6Y6l6J/fW9wd93wyszTdtOIFSc19VMm03vgwpYZC2KUYvffOSCqoLNDxzuAamDVRDuEGrSlbpm+JvNLb7WJ054EwleOJNsLaK5aYt2LJM2Su78V5tukLdztILb/TUli2mHeykk1peIPHtAG6SKUu3bDGd2Gy7bbr0dOmMtofFbo/fzZvNtt+2zbTH9O5t9ntTXaQpgFufPmZZX3xhll1Zac4lkYg5d2RlST/4ga0670Yt3bFU/pBf3RO7KzcpV+vK1mlHzQ6lxaRpVNYodU9sbO+OhM12qcs35wY7YsrE2B4m4FjTA/GBUql0iamPhaolORrTdZeSjlBdOF0rV5p1qKsz69+0DpJZh759TbvOihXm91Rb23K/q3X749FHm+E7bbddbLtmoXpp8xypcpWpIycNlbJP2zkAWUO5lDfLHFfuRCn1aCnrlLYBI2TOba+8Yu4vNbU5T5liXlKxy326u7zVbJSKF5pjrqHClMeuONM3IKG/CSxVt9Vcu9SsN2W2M8bUT4JVJl+JQ7UjeaxeXPmitlRt0chuI9U/tb9CkZDWlK7R8sLlGp09WmcNOktfF36tud/MVc/EnhrbfWxzNkrrSzU/b776JPfRj8f8WDHBcvNSkdp8c53kTjHLDNU2tlEny9/9Kn28JFkFBeY6euhQ04bXdJyHQuZvwIBd75IDzo6Y+zE1G811aKRV+67DK3lT5U87Ti+vfV2ldaU6uvvRbdplVhStUHFtsU7sc6ICoYCe/fpZJXoTdc8J9zQHto7YEc38aKaKaot03tDztKl8k77Y/oXG5IzRj0a2BAMOhoP69fu/ViAc0DVHXqPh3Ya35LOj7nM3lEvb3zX7J1Bi1t+TbOqrKUfKThqmb74x1xlbtpj9lJLS9p5Nr14mkHO77SZvwaAJQLljh1nO8OFmmU39T8Jhc+0XbTvKihUmcFhZmTmuBg9uG4Tbtk37x7K6t7W2dK2GpA/R5H6Tm6ev8Fdo1rJZkqRLR1yqZJfbBMiuXmfaz5r6YEQazDHjSZV6/1A1wVR9/LGp9xQWmuuy1veSHQ7ppz+NclvVbjHtQMFKE3zKnWh+96E60xfEjpjzU7wpSIqLpffeM+09xcVmm6akmPWfPPlbweSiOZaCVSbQcuUqkz4uV8o43pzPW09fv0Pa/ELjOcBnAqNnTTZ9fFrJyzP9sDZuNPuyWzdpzBhTJ3zsiz9qQ/kGndL3lJYyW+blL48sekQuh0v3nnBvdPeSA2UmIHvdVnMPK76POf9VrTH18ECx5MvUluzz9erqV+Vz+fT9Qd9vbkON2BG9tuY11TTU6IwBZ5g2o5qNpg+Ov1BtHr4O1Zp6feoYU9+q22KuBxIHN9bzy8x2rNti2hu6nSTbm9F8b6opaHTTf0uWHJZj56BKHVkXiaKOsWmTOX6Dwbb39VwuU34lJbXty7Or2e/UhWB/72PUF5ptHAma6wmn13yOBM1+kMy+dieY8q94oamjN5Q07qc4KSbHHL9pE1Re5Wnuy1Vf33KPrem3Gom0vfbavNmcV0pLzUMHTfduR40yx3JUyr4y19ChGtNu4Elq6UtZ8qm5B9Jrmvld740dMeciO2jORQ6PJEdjn7vGeygOV0v7416sKVmjdze8qyRfUpv6hj/k1/y8+XI73Dr/iPOV7EmQKpeb33tDRct9aTtivrsTFU4/Vi9t+kg7anbo6JyjlZuc2zy/1SWrtaJohfqm9NXUgVOj3HAy61qx3NznD9U0ltOND565k0wdzRVv2viC1W2PhVBT30xLSjlSEV93ffONKd9KSswx3rqjfCgkHXtcWJ9VvKG8ijwNyxymjNiW+wYbyzcqvzJfR2UfpWN7HRv9OkSjodKc3yNB09bs9DTeDwubdk3LMtd94XqzHaTGe6ZOk84OmfZ2y6GQJ02fbvtC7254V4neRA3JGKKeiT21omiF1pSuUYovRacPOH3nQG67+a1GIqZtb8UK09/M6zW/m9YPGAwYIE08zpYqvjblV9O9Mctpzpl2WPKmK5J1hv7zft/m9vzjjmu5V9t0volEpL79wvqm/Et9vu1zZSdkKzcpV7HuWBXVFmlTxSY5LIdOyD1BPZ1haftbZj+nHm3qHnbYnHvrt5lyM763NiccqdfXvi6fy6ejc45uObwiQS3eulg+l0/nDjlXyXa9aYNvum/o8Db2hWhoPN9Y5tq7I4Mt1m839aiGCnM8e1LMOviLTNkT9rfcV6jfYdocPakmb3bYnAuazoUxOdG1odq2OZaa2viaApo1te81Bx1xmX7PwUrTBuBJbuy3VNPSR8SVICUO7LjtYdum/3ndVpOP2BxTrwnXm2M8WGXOPYlDzfVZzUZzvksaYvqH1eaZNvLafJPnPpc3HiPFJhBtQn+zbrWbG++NNvZzTxsrlSwywxIGmOD8dtise9M9nuThKvF21+fbPldBVYFyEnI0IG2AKvwVyqvIU1WgSkdmH6lhmcPkqtlg9m3TfnF6zfEY9pt6sMOtYPxoff6VTzt2mHK3d++2faWa+3P0KdPzK56XLVun9z9dse6W4+/9Te+rpK5EJ/Y+UV6XV++sf0cJ3gRNHThVVmOQuoZwg/675r8KRoI6d8i5qirI0fr1ph0gN7flurv1b7BbN3MPcds205dk0KCWfsOt0+XmtvQ77RA1eaYPVEOFKbc9KZIay71AiTnukoapwPZp4eaFqg/Vq3dyb3VP6K6qQJXyK/NVUleiMTljNLLbSG2r3qblRctVVl+mzLhM9U3pqw1lG1RcV6z02HQNzxyubrHdtXmzKeMl09bQuu4gmc8JKabP7IqiFSr3lysnIUcep0ebyjcp0ZuooRlDNbzb8DZ1xb2xbbu5/40lq81ny7JMGVD+lTkXONym3cThaXxeoN4cS06f/PHHamOeSxUVJq+ZmW37A0tm33V0kP6PPzblQn296Y+UlLTzMZKZKdVGyrS9ersqA5WyZCnZl6ySuhJ5nB6lxqQqOyFbse5YldaVqrC2UDUNNfI6vfK5fKrwV8jr8io9Nl3d4rrJ6/I2B9CL2BE5LEfzdnRYjuaAeuGIeaYkFAnJts0zJa37iLkdbrkcLllNQZ5bb6yWHdTyY/RvN+c+y23KX4fT3Ie0G69JHW4FPbmqqDC/Gcsy14ytA5Q27Yf6enPdXVFh7pVlZLT9Hdl2y0O1HSUcNr/nmsZLiOTkts+7NG0CT0KV/rP6P6puqNborNHN9SRJ+nrH1wrbYU0dOFU5LoepH4dqWvor2iFzXAYrG1dioCS78To+ZO73OTyN5UewpU0wcWB0z8/YtjmH2uFWbbGSKbds899yKmL5FAiY86rD0fZ+btP6NrVt+P3m+s/tbvv8S+v9VRYo0vLC5SqoLlB6bLoGpA7Q1qqt2l6zXXHuOA3vNlx9kvvsXI/bjbpgXXM/SafDKbfDbZ6hcDgV44pRvCc+6qCQBQWm/lZVZc5dTeevps3V1Oe2Tdvo7kRCjeebHeYao6nsClaZ8jdUbcrjjOPUJhjZftb18vLMPYSqKnO+aN0W1LSfMjPNMbvXVYiYduKampbA/d9uC5JMebZjh2lPdjpNG2XrZUpmuCeuVqtKVmlNyRrFuGPUL6WfAuGA8ivyFbEjGpoxVIPSB8lT/oXpcxWqM30PPGnmPlqoxlxvKCI75/uqDvdQIGB+j637eH+7/6uj1ebdX8FwUOX+clX6KxUIBxTviVcwHGy+F5gSk6JkX3LUz9hF7IiC4aDCdli2bTefb5v62zY9rxcIBRQIB9o8y9cQbpDDcsjj9Mjn8skZCZjjLVzfWP/xNR64jdfeikjuZC1fl6GFC80+69/f3Dds3Q+1ocGcRy3LtGXU15vvTX2LW+//tLSWl93seUWDjfeQ/I19gWIkOUy+mupeDq/Jc91Ws7/dyS11hHCgsW9KRPKkSzFRNBzYtnmmr3azJLuxX7bPHFcRv6kvOtzyx43T8y/Ha9s2c5xOmtRSN2vaJuGw1K9/RLXBalUGzL1fj9Mjl8Ol2oZauRwuJXgTlORNUnGhW0uXmvpxYqLp87Krl2BkZNersLZQpXWlCoQDSo1JVW1DrYKRoBI8CeoW302pvjQVFVmqqDDn4KSklvp26/2QnCwF7BpV+ivb9BOvaaiRy+FSnCdOyb5kNYQb9Hpje/yIbiPavEByRdEKReyIzhhwhuI8cfpkyyfaUrlFvZN7q3dyb4UiIW2q2KQtlVs0JGOIxnUfp4qSmOZ7Tikppqxt3VdKMsOjeSYuEDB15MLClrbypt9v6/lFfb8j3Fi3siMt7Y5NZZsdlqmzeaWKpaa+YznNtbLDa8rd5vZHt5QxUXmBgJbuWKqahhrlJOSoT3IfrS9brx01O5QSk6JRWaOU7umhzz8362Db5rq+6RnRpnWwLKlv/6C2Vm3V5srNqm6oVpI3SU6HU2X1ZYr3xDc/0+h17b1/adRs21z/hmrMtnAlmDpgOGB+f5EGcx0Q22MXjZA7q6w05U1NjTn/pqaa875tt5xzm+5Btn5uMi7OHCNN20Iyx8eyZaauVFFh6klpaS3tik2/wSFDzDG2N+GwmssGqeWYbNL0W4y2zlUfrNeOmh0qrS9VINT4Ww3WKhQJKd4Tr25x3ZQemx71dUs06upMH8SqKpP/pheiNrFts70zo3zksLy+XGtL1yq/Ml9uh1t9UvqouNb0OUz2JWtQ+iD1SuwpR6im5QVbTp9M22hYLb8bhyLuRG2u2qp1petUVl+mlJgUpcWkKb8yX7Ztq2dSTw1MG6hkp8vc2w7WmPYFV3zjTXV/Y9tJWPKkKejpqUDA/O6dzpZ7nK05nVJtqFJ5FXnaUbND/pBf2QnZKq0rVcSOKDUmVb2TeysrPkvFdcXaVL5JxXXFsm1b3eK7aXv1drmdbmXGZapPch+lxXbsy7ECoYAKawtVUlcif8ivFF+K/CF/83ND3eK7KS0mXWWlDlVWmnI2MXHX5/OEhCiPzWCNaS8MFJs6vrfxub5gtemvEaqXvOmqSRiszwo+09rSteqZ2FN9U/rKlq2N5Ru1tWqrBqUN0tjuY+VyuFRSV6Jyf7kCoYDphxqqVzAcVJwnTumx6UrxJpu6XhPLkloH8m9aiVYBVvYkHG6JbWBZ5phu/fxX0yyius6QOS+Vl7fESmj9/GpT1mJjJac7KH/Ir4ZwgyJ2RF6XV4FQQJZlyeP0KMYVI7cdlLa93tg+lmzKB1ds47NzFeYepitRyr3QXLNEQuac2tS3RXbj9Y1tnrFzeFThr2juPxbniVMoElIgFJDP5VOyL1lJvqTorh9t27RvNp3P3QmN91aDJh+RgPke012BSKj5WfOmOntTPcnr9CrWHaviIqfmzzdt+3FxbduqmvZTQoI0eEhEDeEGNYTNW7VcDpds21bYDjdfj7odblnFH5k6UNhvnuPwpDTes6lufOGsJfU8t/m+ntTSj7J1f0pLlqyw3/Qpbygz5xBvWksbathvrufcSQonDlVpfcvzejGuGDksh2oaauRz+ZQak6q02DS5ml6uaodNPcxyNu6rSOO1ssz5KornOyIRc56uqDD139Z9b5rKfPPcma3KQIVK60tVH6yXy+GSz+VTVaBKXpdXyb5kpcakRv1S702bzEugKypMO2N6ekv9rHWbUTis5udhW9fhWvfLTkyK6JvSZfqs4DOlxaQpNzlXid5EFdYUalPFJjktp47rdVyb+1F71FBu7sOHakxfMWdM47YNmOtvO2LqxPVbTbt9qFZKGmbuR4XrzPf6bZLlkJ11mpas7KFNm8x5oum5vm+3jw0Y8K1zRLT3fvfTggWmP1h5uWm7y8xsyZvUeC3bM6JU31ZzzrAcjfcA3GZ7RILm+LVc5rkiR6uT1W7WYVO+U2+/be6p5uSYPlvffn7G5zPD8vJMP7WEhJZrtNZlTVMZ5Pe3jZXw7UW73aZtoaTEnFu7dWu5Pm8drK5nz44N/r12remLWVpq2l1zcnaOHZKdbdaj6Rpid+vg8djaWpOvFUUrVBWoUmZcpnom9tT6svUq95crOz5bwzKHKdXbrfklhg5HyzVr0y5pusZNjO4WsTZvbtkPTW1IrduVw2Hz+42mXUahWin/edOOGpsrpYw0derazaY8ChRLDq+KEq7Rm29Zys838z3qqJZ79U3Pmzqdpo9PU5yMpgDuretbUsvwvWmKOVIfqlcgZK4hfS6fAmHz2e0wsUjcDrfK/GXaUbNDVYEqOS2nEr2JKqkrkdflVVpMmrLisxQXrm7s777V9OWJ7SHJYcqBQIm5hkwcrNXlJ2rRopZna/v02bnOnZkppWbVqKCqQMV1xfKH/MqMy1SFv0LhSFjJvmR1T+yuNF+G1qyxVFhojqfs7JZn0lufM3v2lMKWv7nObslcN9SHWs7tse5YuSIBU96FahvLlMbKcKTB/O7tiCkbd9MH+lsb2JwvK78x0ycOMu29TTEQ/IWS5ZadNUUV9Wmqrzd5jY3duQ4ktdRNmx2kc6aZd9M5zrGLcZItq7ns3FX2Wg9rM3w3bailpaY/aUWFWe/MzJb4Mq3bMjMytHdN91f9hY1lWbZpNwjXN97nrjbn8+SRjW1du1iBxoVWVVUpKSlJlZWVStzLCWW/A7h1tIEDB6pv3756++232wzfvn27cnJyNHPmTN1xxx27nNbj8ejKK6/U3/72tzbDFy1apGOOOUb//ve/ddFFF+1y2unTp2vGjBk7DT/ttNPk3qfXe6M9HJYtS3bLCdG2FLHNl/++9tpep//+974X1XLOH7pOPxy5WsGwQ3e8f4zWliYrNcavwenlGtmtRPGeoL7cnqG6oEtDMsolSV9tz1Bd0K1kn19Jvga5HBE5LFsL8nqouqEj77J3Llu2wo6WyFquSJQRnlrx+1NVXj5IgUCS3O4axcXtkMPRIMtqPAnbllyugPz+VJWWHqFAIElJSRsa04VkHmSTJIe83jLFxJQp4Apoe/J2VcZWKr06XRVxFXKH3Mouz1ZCoG3rXb27Xn6PX5ZtKn2xgVh5wx3Y6Ak0smWrPK5cO5J3yBv0yhV2qTq2WhmVGcqozpDD7sA7hYcIU3l2KRJxy7YdkhxqariyrIhcLr8sy1aNt0bbUrYp4A4opSZFpQmlSqhPUE5FjnzBnV8T27oM+Pa53patythKFSUWNU/rd/uVWZWppLqk5g5X7VoP2Qq4WgK6esKenfanxxmW2xGR0xGRw1LjeloKRyw1hB0KhNueP5vWYVdlVdgKqyixSIXJhUqsS1TEEVGtt1bZFdlKr0qXQ+07lurd9dqevF01MTVKq05TWXyZYhpilFOeo9iG9neUtGX2WcgZkmWbRrR4f7yctrNNmoq4CpUklCgmEKOQM6SwI6zMqkwl+M15+uojV+ikPltVH3Tqr0tGqLAmVhlx9eoWV69B6eWKcwf1/5YN1mPPzd9rnqIt8/eFrbYdwPblGMLuhayQIg5T1jsjzjbH0eHCti2tXXuhKiv7KRTyKTf3XXm9FbKsxptjsmTbDiUnr5fL5e/s7O6VLVtBZ7D5uzvsbv7d9Eys1kOTP1a8J6jnVwzQf9b0VU2DR8fnblW/lEoNzShTXdCle+dPaDPP3Z0z/f5k1dT0UDAYL7e7Th5PReP1Y2NebEseT7XCYa/q6jIVCsXK46mS210ry4rIsuzG+rIln69MlhVSMJigcNgr27bkcAQb6yIt160xMWWyrIiA77Ks+Fo9cPLHSosN6I21vfXSqn6qafBoZLcSdYuvU+/kKtUHXVpWmKbbjv1SbmdEf/p0pD7M7y6nFdERmWXKTapSTkKt8isT9N7GXsqKr1OMK6i6oFv+kEuR1o3BllRUG6NwlNfL8Z4GJXkb5HGG5Q85FYq0TGdZUkPYoQq/TyFHSPnp+dqUuUkxDTFKqkvS1rStyqjKUL/CfkquS5Yk5afna0XPFbJlq29RX1m2pY3dNkqShhQMUd+ivkqPrdfN47/SERml2lqVoMVbs1RW75Nl2frRyFWKdYf17NLBemlVS2+iPV2zYv/taftOHbhRFw9fI4dl6811fbSuNEn1IZd6JVXr6iO/kST95M0T9L6vUKt7rFZMIEbJdcmybEtBZ1Bl8WVy2A4du+ZYhbYfqa++ukW1tT2UkrJKAwa8qLi4bXK5/LJthxoaEhQOe5Sauuagrn9Xsbv9EOsOaubJn6hvSpUWb+2mfy8frKLaGI3vsUPDMkt1Qu5WSdIV/5msCT2268Jh65Ts82vpjgxtqkhSIOTUkdlFGpJRrsKaGF372sm66siVOqXvFlmytWRbN22rjlM44tDJfbcoK75OH2/O1mNLRugnY7/WmJxCVTd49FlBN5XWxchh2Tp3yHp5XRE9/dUQvbL629EfdtbQEKf58x9TIJCi2NgdGjduhuLitjW3Z0lSMBgnl6s2mr4pHa7WU6vP+n+mWl+tUmtSlVqTqqqYKhUnFssT8ujoDUc3n+dqPbXakbxDQVdQiXWJKosvU3JtsrpVdpM74taIbsW67dgvlOgN6h9fDtXCzTlqCDs1MbdAY7sXqn9qhTaWJ+meeRP2nKl94Hf7tT15u2q9tUqtTVVZXJniAnHKqshSTHBPPXVMnRvR6YgyaVXOKq3PXq+YQIxSa1Nl2ZbqPfXNbQzHrDlmL/usa6jz1KkosUi2Zcsb9KrWV6u06jSl1KZQ30eXVlubrZUrr1Rx8WjZtlOpqd8oNrZQlhWW35+miooBOvHEG+TxVHd2Vg+K/PR8LctdJl+DT5OXtwSQiSiid0a+o5ArpDEbxii7wnSKiERcWrfuPK1ff54ikZZ7eDk5H2nEiMfkdpuHtwOBBH3zzVXauvWk5jSWFVS/fv/RoEGz5HC0tAfs7dwa6w7qspGrdGr//Mb2YmPRliw98cVwldX7VFeXrpUrr1ZR0dGKRHa+79yv30sKnfYL5Wfkq1tFN43d0BIUqiShRIsGLpIz7NSUZVP0Ve+vtCNlh3LKcpRb3NLRbnP6ZhWkFSirPEtHbzxaW9K26Ovcr+UKu5RZmSl32K2qmCqVx5UrLhCn8evGa3BsULcf97l6JNbq/Y09tCC/u2oaPMqKr9X3Bm7SkIxyfb0jXXcfgGuDvamu7qmPPvqdwuFYpaSs0lFHPayYmNLm8bYthUJxzfu0swRcAfnd5t6kLCkmECN3q3087Yi1OmvwRrkdYS3cnKPNlQkKhJ06pud2jehWqu3VsfrVe8fql8d+oaEZZVpbmqIFed1VUudTxLZ06Yg16pNSpQ/zc3TLi7dox46xCgRS1K3bp0pMzG9s02pq93IoNrZQGzd+XyUlwxUMJqhfv5cVG1sop7NBlhWRbTsViTiUnLxOHk/nbju0aLrHZtsu2balpntspl0zLGerNlhpz/ed2ptud+c4nyukv54xTxlxfn2Yn6OnvhymCr9XR2YXqXdylcbkFEqSHvhojCqCLm1N3apN3TbJFXYpqS5J21K2KasiS32K+ijRH2UvSOxWOOxSJOJRJOKSuY9gChzLsmVZYXk8NR3ar6bB2aAl/ZaoLL5MifWJyqzMVMAd0LaUbbItW6PyRql7eRQvZdoP0R7naKsj6oSl8aVa3mu5qn3V6lPUR86IUxu6bVBsQ6yGbRmmzKpMXX3kCn1/0CZVB9y684NjlFeRqG5xteqdXKVRWSXyusL6ZEu2VhWnKDUmIK/LtO8Gw87mx4MsSRFbKq7rwGA/Hcy2pYUL/08VFQPlcAQ0YcI9SklZ0+beUTjslmVF5GjV3+pQFAgk6Ysvfqmqqt7yeGo0ZMgziokpkdNp+k6Ewx6FQrFKT1+ubduO1bp1F6iqqo/c7iqlpy+T11upSMSlurosWVZY48dPbzP/jvxNH8z2+IrYCq3NXqvixGL1Lu6tWm+tihOLlVucq/47+ssX8umO45ZoQs8d2lIZr//75Ehtq45TTkKtsuJrNTKrRLHukF5YOUB5NTFa3229NnbbqNiGWOUW52pjt40KOoMasH2A+hT32alvyu7W9aQ+W3TD0cv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" ] @@ -1843,7 +1854,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1861,7 +1872,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1869,7 +1880,7 @@ "evaluator = crested.tl.Crested(data=datamodule)\n", "\n", "evaluator.load_model(\n", - " \"deeppeak_benchmarking/dyn_log_loss_TL/checkpoints/01.keras\", # Load your model\n", + " \"deeppeak_benchmarking/dyn_log_loss_TL/checkpoints/01.keras\", # Load your model\n", " compile=True,\n", ")" ] @@ -1890,22 +1901,22 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-01T16:20:49.161191+0200 INFO Loading sequences into memory...\n" + "2024-10-09T14:39:57.113865+0200 INFO Loading sequences into memory...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 88835/88835 [00:01<00:00, 85887.34it/s]\n", - "100%|██████████| 10/10 [00:21<00:00, 2.17s/it]\n" + "100%|██████████| 88835/88835 [00:01<00:00, 86667.70it/s]\n", + "100%|██████████| 10/10 [00:24<00:00, 2.43s/it]\n" ] } ], @@ -1917,19 +1928,19 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step\n" + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 249ms/step\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1940,26 +1951,26 @@ ], "source": [ "prediction = evaluator.predict_sequence(designed_sequences[0])\n", - "prediction_bar(prediction, classes=list(adata.obs_names))" + "crested.pl.bar.prediction(prediction, classes=list(adata.obs_names))" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-01T16:21:16.238588+0200 INFO Calculating contribution scores for 1 class(es) and 1 region(s).\n" + "2024-10-09T14:40:31.856432+0200 INFO Calculating contribution scores for 1 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]\n" + "Region: 100%|██████████| 1/1 [00:01<00:00, 1.25s/it]\n" ] } ], @@ -1971,7 +1982,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1984,7 +1995,7 @@ }, { "data": { - "image/png": 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" ] @@ -2014,34 +2025,36 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "designed_sequences = evaluator.enhancer_design_motif_implementation(\n", - " patterns = {\n", - " 'SOX10':'AACAATGGCCCCATTGT',\n", - " 'CREB5':'ATGACATCA',\n", + " patterns={\n", + " \"SOX10\": \"AACAATGGCCCCATTGT\",\n", + " \"CREB5\": \"ATGACATCA\",\n", " },\n", - " target_class=\"Oligo\", n_sequences=5, target_len=500\n", + " target_class=\"Oligo\",\n", + " n_sequences=5,\n", + " target_len=500,\n", ")" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n" + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -2052,38 +2065,38 @@ ], "source": [ "prediction = evaluator.predict_sequence(designed_sequences[0])\n", - "prediction_bar(prediction, classes=list(adata.obs_names))" + "crested.pl.bar.prediction(prediction, classes=list(adata.obs_names))" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-01T15:52:20.959460+0200 INFO Calculating contribution scores for 1 class(es) and 1 region(s).\n" + "2024-10-09T14:41:11.716004+0200 INFO Calculating contribution scores for 1 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]\n" + "Region: 100%|██████████| 1/1 [00:01<00:00, 1.25s/it]\n" ] } ], "source": [ "scores, one_hot_encoded_sequences = evaluator.calculate_contribution_scores_sequence(\n", - " designed_sequences[3], class_names=[\"Oligo\"]\n", + " designed_sequences[0], class_names=[\"Oligo\"]\n", ") # focus on two cell types of interest" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2096,7 +2109,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -2114,7 +2127,7 @@ " class_labels=[\"Oligo\"],\n", " zoom_n_bases=500,\n", " title=\"synth Oligo\",\n", - ") " + ")" ] }, { @@ -2126,67 +2139,68 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 30/30 [01:00<00:00, 2.01s/it]\n" + "100%|██████████| 30/30 [01:00<00:00, 2.03s/it]\n" ] } ], "source": [ - "from crested.tl._utils import EnhancerOptimizer\n", "import numpy as np\n", "\n", "from sklearn.metrics import pairwise\n", + "\n", + "\n", "def L2_distance(\n", " mutated_predictions: np.ndarray,\n", " original_prediction: np.ndarray,\n", " target: np.ndarray,\n", - " classes_of_interest: list[int]):\n", + " classes_of_interest: list[int],\n", + "):\n", + " \"\"\"Calculate the L2 distance between the mutated predictions and the target class\"\"\"\n", " if len(original_prediction.shape) == 1:\n", " original_prediction = original_prediction[None]\n", " L2_sat_mut = pairwise.euclidean_distances(\n", - " mutated_predictions[:,classes_of_interest],\n", - " target[classes_of_interest].reshape(1, -1)\n", + " mutated_predictions[:, classes_of_interest],\n", + " target[classes_of_interest].reshape(1, -1),\n", " )\n", " L2_baseline = pairwise.euclidean_distances(\n", " original_prediction[:, classes_of_interest],\n", - " target[classes_of_interest].reshape(1, -1))\n", + " target[classes_of_interest].reshape(1, -1),\n", + " )\n", " return np.argmax((L2_baseline - L2_sat_mut).squeeze())\n", "\n", - "L2_optimizer = EnhancerOptimizer(\n", - " optimize_func = L2_distance\n", - ")\n", + "\n", + "L2_optimizer = crested.utils.EnhancerOptimizer(optimize_func=L2_distance)\n", "\n", "target_cell_type = \"L2_3IT\"\n", "\n", "classes_of_interest = [\n", - " i for i, ct in enumerate(adata.obs_names)\n", - " if ct in ['L2_3IT', 'L5ET','L5IT','L6IT']\n", + " i\n", + " for i, ct in enumerate(adata.obs_names)\n", + " if ct in [\"L2_3IT\", \"L5ET\", \"L5IT\", \"L6IT\"]\n", "]\n", - "target = np.array(\n", - " [\n", - " 3 if x == target_cell_type else 0\n", - " for x in adata.obs_names\n", - " ]\n", - ")\n", + "target = np.array([3 if x == target_cell_type else 0 for x in adata.obs_names])\n", "intermediate, designed_sequences = evaluator.enhancer_design_in_silico_evolution(\n", - " target_class=None, n_sequences=5, n_mutations=30,\n", - " enhancer_optimizer = L2_optimizer,\n", - " target = target,\n", - " return_intermediate = True,\n", - " no_mutation_flanks = (807, 807),\n", - " classes_of_interest = classes_of_interest\n", + " target_class=None,\n", + " n_sequences=5,\n", + " n_mutations=30,\n", + " enhancer_optimizer=L2_optimizer,\n", + " target=target,\n", + " return_intermediate=True,\n", + " no_mutation_flanks=(807, 807),\n", + " classes_of_interest=classes_of_interest,\n", ")" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2198,7 +2212,7 @@ }, { "data": { - 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" ] @@ -2208,40 +2222,40 @@ } ], "source": [ - "idx=1\n", + "idx = 0\n", "prediction = evaluator.predict_sequence(designed_sequences[idx])\n", - "prediction_bar(prediction, classes=list(adata.obs_names))" + "crested.pl.bar.prediction(prediction, classes=list(adata.obs_names))" ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-01T16:45:41.770130+0200 INFO Calculating contribution scores for 4 class(es) and 1 region(s).\n" + "2024-10-09T14:42:33.750345+0200 INFO Calculating contribution scores for 4 class(es) and 1 region(s).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Region: 100%|██████████| 1/1 [00:04<00:00, 4.97s/it]\n" + "Region: 100%|██████████| 1/1 [00:05<00:00, 5.02s/it]\n" ] } ], "source": [ "scores, one_hot_encoded_sequences = evaluator.calculate_contribution_scores_sequence(\n", - " designed_sequences[idx], class_names=['L2_3IT', 'L5ET','L5IT','L6IT']\n", + " designed_sequences[idx], class_names=[\"L2_3IT\", \"L5ET\", \"L5IT\", \"L6IT\"]\n", ") # focus on two cell types of interest" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2254,7 +2268,7 @@ }, { "data": { - "image/png": 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" ] @@ -2269,10 +2283,10 @@ " scores,\n", " one_hot_encoded_sequences,\n", " sequence_labels=\"\",\n", - " class_labels=['L2_3IT', 'L5ET','L5IT','L6IT'],\n", + " class_labels=[\"L2_3IT\", \"L5ET\", \"L5IT\", \"L6IT\"],\n", " zoom_n_bases=500,\n", " title=\"\",\n", - ") " + ")" ] }, { diff --git a/docs/tutorials/topic_classification.ipynb b/docs/tutorials/topic_classification.ipynb index 7e76a104..eeddcb74 100644 --- a/docs/tutorials/topic_classification.ipynb +++ b/docs/tutorials/topic_classification.ipynb @@ -13,7 +13,7 @@ "source": [ "We can use the outputs of [pycistopic](https://pycistopic.readthedocs.io/en/latest/) to train a model to predict topic probabilities for a given sequence. \n", "\n", - "Since we plan on adding detailed use cases describing topic classification later on, we will only provide a brief overview of the workflow here. Refer to the [introductory notebook](introduction.ipynb) for a more detailed explanation of the CREsted workflow." + "Since we plan on adding detailed use cases describing topic classification later on, we will only provide a brief overview of the workflow here. Refer to the [introductory notebook](model_training_and_eval.ipynb) for a more detailed explanation of the CREsted workflow." ] }, { diff --git a/pyproject.toml b/pyproject.toml index 2c9700d3..8719aa0f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,8 @@ doc = [ "sphinx-copybutton", "pandas", "tensorflow", + "ruff", + "modisco-lite", ] test = [ "pytest", diff --git a/src/crested/__init__.py b/src/crested/__init__.py index 5d828765..ac352f04 100644 --- a/src/crested/__init__.py +++ b/src/crested/__init__.py @@ -20,18 +20,17 @@ import sys from importlib.metadata import version -from . import pl, pp, tl +from . import pl, pp, tl, utils from ._datasets import get_dataset, get_motif_db from ._io import import_beds, import_bigwigs -from ._logging import setup_logging __all__ = [ "pl", "pp", "tl", + "utils", "import_beds", "import_bigwigs", - "setup_logging", "get_dataset", "get_motif_db", ] @@ -41,4 +40,4 @@ os.environ["AUTOGRAPH_VERBOSITY"] = "0" # Setup loguru logging -setup_logging(log_level="INFO", log_file=None) +utils.setup_logging(log_level="INFO", log_file=None) diff --git a/src/crested/_io.py b/src/crested/_io.py index 0ba7e7b0..44cb2f99 100644 --- a/src/crested/_io.py +++ b/src/crested/_io.py @@ -17,7 +17,7 @@ from scipy.sparse import csr_matrix -def _sort_files(filename: str): +def _sort_files(filename: PathLike): """Sorts files. Prioritizes numeric extraction from filenames of the format 'Class_X.bed' (X=int). @@ -110,7 +110,11 @@ def _read_consensus_regions( ) -> pd.DataFrame: """Read consensus regions BED file and filter out regions not within chromosomes.""" consensus_peaks = pd.read_csv( - regions_file, sep="\t", header=None, usecols=[0, 1, 2], dtype={0: str, 1: 'Int32', 2: 'Int32'} + regions_file, + sep="\t", + header=None, + usecols=[0, 1, 2], + dtype={0: str, 1: "Int32", 2: "Int32"}, ) consensus_peaks["region"] = ( consensus_peaks[0].astype(str) @@ -475,7 +479,10 @@ def import_bigwigs( df = pd.DataFrame( data_matrix, columns=consensus_peaks["region"], - index=[os.path.basename(file).rpartition('.')[0].replace('.', '_') for file in bw_files], + index=[ + os.path.basename(file).rpartition(".")[0].replace(".", "_") + for file in bw_files + ], ) # Create AnnData object diff --git a/src/crested/pl/bar/__init__.py b/src/crested/pl/bar/__init__.py index 30e1974e..ff5d52bc 100644 --- a/src/crested/pl/bar/__init__.py +++ b/src/crested/pl/bar/__init__.py @@ -1,2 +1,2 @@ from ._normalization_weights import normalization_weights -from ._region import region, region_predictions, prediction_bar +from ._region import prediction, region, region_predictions diff --git a/src/crested/pl/bar/_normalization_weights.py b/src/crested/pl/bar/_normalization_weights.py index 83cd97e3..70f98b79 100644 --- a/src/crested/pl/bar/_normalization_weights.py +++ b/src/crested/pl/bar/_normalization_weights.py @@ -5,8 +5,8 @@ import matplotlib.pyplot as plt from anndata import AnnData -from crested._logging import log_and_raise from crested.pl._utils import render_plot +from crested.utils._logging import log_and_raise def normalization_weights(adata: AnnData, **kwargs) -> plt.Figure: diff --git a/src/crested/pl/bar/_region.py b/src/crested/pl/bar/_region.py index d9aac402..1ed73948 100644 --- a/src/crested/pl/bar/_region.py +++ b/src/crested/pl/bar/_region.py @@ -7,8 +7,8 @@ from anndata import AnnData from loguru import logger -from crested._logging import log_and_raise from crested.pl._utils import render_plot +from crested.utils._logging import log_and_raise def region_predictions( @@ -179,23 +179,31 @@ def _check_input_params(): return render_plot(fig, **kwargs) -def prediction_bar(prediction: np.array, classes: list, ylabel='Prediction', xlabel='Cell types', title='Prediction plot', **kwargs) -> plt.Figure: + +def prediction( + prediction: np.array, + classes: list, + ylabel="Prediction", + xlabel="Cell types", + title="Prediction plot", + **kwargs, +) -> plt.Figure: """ Bar plot for predictions comparing different classes or cell types. Parameters ---------- - prediction : np.array + prediction An array containing the prediction values for each class or cell type. It is reshaped if necessary. - classes : list + classes A list of class or cell type labels corresponding to the predictions. - ylabel : str, optional + ylabel Label for the y-axis. Default is 'prediction'. - xlabel : str, optional + xlabel Label for the x-axis. Default is 'cell types'. - title : str, optional + title Title of the plot. Default is 'Prediction plot'. - kwargs : dict, optional + kwargs Additional keyword arguments to pass to `render_plot`. Returns @@ -208,7 +216,9 @@ def prediction_bar(prediction: np.array, classes: list, ylabel='Prediction', xla prediction = prediction.flatten() if len(prediction) != len(classes): - raise ValueError("The length of prediction array must match the number of classes.") + raise ValueError( + "The length of prediction array must match the number of classes." + ) # Create the bar plot fig, ax = plt.subplots() @@ -233,4 +243,4 @@ def prediction_bar(prediction: np.array, classes: list, ylabel='Prediction', xla kwargs["height"] = default_height # Use render_plot to finalize and return the figure - return render_plot(fig, **kwargs) \ No newline at end of file + return render_plot(fig, **kwargs) diff --git a/src/crested/pl/heatmap/_correlations.py b/src/crested/pl/heatmap/_correlations.py index c30953c6..b2994091 100644 --- a/src/crested/pl/heatmap/_correlations.py +++ b/src/crested/pl/heatmap/_correlations.py @@ -8,8 +8,8 @@ from anndata import AnnData from loguru import logger -from crested._logging import log_and_raise from crested.pl._utils import render_plot +from crested.utils._logging import log_and_raise def _generate_heatmap(correlation_matrix, classes, vmin, vmax): diff --git a/src/crested/pl/hist/_distribution.py b/src/crested/pl/hist/_distribution.py index 361c1572..484d657a 100644 --- a/src/crested/pl/hist/_distribution.py +++ b/src/crested/pl/hist/_distribution.py @@ -8,8 +8,8 @@ from anndata import AnnData from loguru import logger -from crested._logging import log_and_raise from crested.pl._utils import render_plot +from crested.utils._logging import log_and_raise def distribution( diff --git a/src/crested/pl/hist/_locus_scoring.py b/src/crested/pl/hist/_locus_scoring.py index 7551e0c8..2e5ec3a2 100644 --- a/src/crested/pl/hist/_locus_scoring.py +++ b/src/crested/pl/hist/_locus_scoring.py @@ -4,40 +4,81 @@ import matplotlib.pyplot as plt import numpy as np -import seaborn as sns -from anndata import AnnData -from loguru import logger -def locus_scoring(scores, coordinates, range, gene_start=None, gene_end=None, title='Predictions across Genomic Regions', bigwig_values=None, bigwig_midpoints=None, filename=None): + +def locus_scoring( + scores: np.ndarray, + range: tuple[int, int], + gene_start: int | None = None, + gene_end: int | None = None, + title: str = "Predictions across Genomic Regions", + bigwig_values: np.ndarray | None = None, + bigwig_midpoints: list[int] | None = None, + filename: str | None = None, +): """ Plot the predictions as a line chart over the entire genomic input and optionally indicate the gene locus. - Additionally, plot values from a bigWig file if provided. - - Parameters: - scores (np.array): An array of prediction scores for each window. - coordinates (np.array): An array of tuples, each containing the chromosome name and the start and end positions of the sequence for each window. - model_class (int): The class index to plot from the prediction scores. - gene_start (int, optional): The start position of the gene locus to highlight on the plot. - gene_end (int, optional): The end position of the gene locus to highlight on the plot. - title (str): The title of the plot. - bigwig_values (np.array, optional): A numpy array of values extracted from a bigWig file for the same coordinates. - bigwig_midpoints (list, optional): A list of base pair positions corresponding to the bigwig_values. - """ - # Extract the midpoints of the coordinates for plotting - midpoints = [(int(start) + int(end)) // 2 for _, start, end in coordinates] + Also plots values from a bigWig file if provided. + + Parameters + ---------- + scores + An array of prediction scores for each window. + range + The genomic range of the input. + model_class + The class index to plot from the prediction scores. + gene_start + The start position of the gene locus to highlight on the plot. + gene_end + The end position of the gene locus to highlight on the plot. + title + The title of the plot. + bigwig_values + A numpy array of values extracted from a bigWig file for the same coordinates. + bigwig_midpoints + A list of base pair positions corresponding to the bigwig_values. + filename + The filename to save the plot to. + See Also + -------- + crested.tl.Crested.score_gene_locus + crested.utils.extract_bigwig_values_per_bp + + Example + -------- + >>> crested.pl.hist.locus_scoring( + ... scores, + ... range=(0, 1000), + ... gene_start=100, + ... gene_end=200, + ... title="Predictions across Genomic Regions", + ... bigwig_values=bigwig_values, + ... bigwig_midpoints=bigwig_midpoints, + ... ) + + .. image:: ../../../../docs/_static/img/examples/hist_locus_scoring.png + """ # Plotting predictions plt.figure(figsize=(30, 10)) # Top plot: Model predictions plt.subplot(2, 1, 1) - plt.plot(np.arange(range[0], range[1]), scores, marker='o', linestyle='-', color='b', label='Prediction Score') + plt.plot( + np.arange(range[0], range[1]), + scores, + marker="o", + linestyle="-", + color="b", + label="Prediction Score", + ) if gene_start is not None and gene_end is not None: - plt.axvspan(gene_start, gene_end, color='red', alpha=0.3, label='Gene Locus') + plt.axvspan(gene_start, gene_end, color="red", alpha=0.3, label="Gene Locus") plt.title(title) - plt.xlabel('Genomic Position') - plt.ylabel('Prediction Score') + plt.xlabel("Genomic Position") + plt.ylabel("Prediction Score") plt.ylim(bottom=0) plt.xticks(rotation=90) plt.grid(True) @@ -46,11 +87,19 @@ def locus_scoring(scores, coordinates, range, gene_start=None, gene_end=None, ti # Bottom plot: bigWig values if bigwig_values is not None and bigwig_midpoints is not None: plt.subplot(2, 1, 2) - plt.plot(bigwig_midpoints, bigwig_values, linestyle='-', color='g', label='bigWig Values') + plt.plot( + bigwig_midpoints, + bigwig_values, + linestyle="-", + color="g", + label="bigWig Values", + ) if gene_start is not None and gene_end is not None: - plt.axvspan(gene_start, gene_end, color='red', alpha=0.3, label='Gene Locus') - plt.xlabel('Genomic Position') - plt.ylabel('bigWig Values') + plt.axvspan( + gene_start, gene_end, color="red", alpha=0.3, label="Gene Locus" + ) + plt.xlabel("Genomic Position") + plt.ylabel("bigWig Values") plt.xticks(rotation=90) plt.ylim(bottom=0) plt.grid(True) diff --git a/src/crested/pl/patterns/__init__.py b/src/crested/pl/patterns/__init__.py index 7e67fad8..cd5c30f9 100644 --- a/src/crested/pl/patterns/__init__.py +++ b/src/crested/pl/patterns/__init__.py @@ -1,14 +1,17 @@ from importlib.util import find_spec + from loguru import logger from ._contribution_scores import contribution_scores + def _optional_function_warning(*args, **kwargs): logger.error( "The requested functionality requires the 'tfmodisco' package, which is not installed. " "Please install it with `pip install crested[tfmodisco]`.", ) + if find_spec("modiscolite") is not None: MODISCOLITE_AVAILABLE = True else: @@ -17,25 +20,28 @@ def _optional_function_warning(*args, **kwargs): if MODISCOLITE_AVAILABLE: try: import modiscolite + # Import all necessary functions from _modisco_results from ._modisco_results import ( - create_clustermap, + class_instances, + clustermap, + clustermap_tf_motif, modisco_results, - plot_patterns, - plot_similarity_heatmap, - plot_pattern_instances , - plot_clustermap_tf_motif + selected_instances, + similarity_heatmap, + tf_expression_per_cell_type, ) except ImportError as e: logger.error(f"Import error: {e}") raise else: - create_clustermap = _optional_function_warning + clustermap = _optional_function_warning modisco_results = _optional_function_warning - plot_patterns = _optional_function_warning - plot_similarity_heatmap = _optional_function_warning - plot_pattern_instances = _optional_function_warning - plot_clustermap_tf_motif = _optional_function_warning + selected_instances = _optional_function_warning + similarity_heatmap = _optional_function_warning + class_instances = _optional_function_warning + clustermap_tf_motif = _optional_function_warning + tf_expression_per_cell_type = _optional_function_warning # Export these functions for public use __all__ = [ @@ -45,11 +51,12 @@ def _optional_function_warning(*args, **kwargs): if MODISCOLITE_AVAILABLE: __all__.extend( [ - "create_clustermap", + "clustermap", "modisco_results", - "plot_patterns", - "plot_similarity_heatmap", - "plot_pattern_instances", - "plot_clustermap_tf_motif" + "class_instances", + "similarity_heatmap", + "selected_instances", + "clustermap_tf_motif", + "tf_expression_per_cell_type", ] ) diff --git a/src/crested/pl/patterns/_contribution_scores.py b/src/crested/pl/patterns/_contribution_scores.py index 744c7562..b0a542ed 100644 --- a/src/crested/pl/patterns/_contribution_scores.py +++ b/src/crested/pl/patterns/_contribution_scores.py @@ -5,8 +5,8 @@ import matplotlib.pyplot as plt import numpy as np -from crested._logging import log_and_raise from crested.pl._utils import render_plot +from crested.utils._logging import log_and_raise from ._utils import ( _plot_attribution_map, diff --git a/src/crested/pl/patterns/_modisco_results.py b/src/crested/pl/patterns/_modisco_results.py index 44e03419..2bfae55e 100644 --- a/src/crested/pl/patterns/_modisco_results.py +++ b/src/crested/pl/patterns/_modisco_results.py @@ -1,21 +1,24 @@ from __future__ import annotations import h5py -import matplotlib.pyplot as plt import matplotlib.colors as mcolors -import modiscolite as modisco +import matplotlib.pyplot as plt import numpy as np import pandas as pd -from scipy.cluster.hierarchy import linkage, leaves_list import seaborn as sns from loguru import logger +from scipy.cluster.hierarchy import leaves_list, linkage -from crested._logging import log_and_raise from crested.pl._utils import render_plot -from crested.tl._modisco_utils import _trim_pattern_by_ic, _get_ic, _trim, _pattern_to_ppm, compute_ic +from crested.tl.modisco._modisco_utils import ( + _pattern_to_ppm, + _trim_pattern_by_ic, + compute_ic, +) from ._utils import _plot_attribution_map + def modisco_results( classes: list[str], contribution: str, @@ -62,6 +65,11 @@ def modisco_results( kwargs Additional keyword arguments for the plot. + See Also + -------- + crested.tl.modisco.tfmodisco + crested.pl.render_plot + Examples -------- >>> crested.pl.patterns.modisco_results( @@ -74,11 +82,6 @@ def modisco_results( ... ) .. image:: ../../../../docs/_static/img/examples/genomic_contributions.png - - See Also - -------- - crested.tl.tfmodisco - crested.pl.render_plot """ if background is None: background = [0.27, 0.23, 0.23, 0.27] @@ -131,7 +134,7 @@ def modisco_results( all_pattern_names = list(hdf5_results[metacluster_name]) for i in range(len(all_pattern_names)): - pattern_name = 'pattern_'+str(i) + pattern_name = "pattern_" + str(i) if len(classes) > 1: ax = axes[motif_counter - 1, idx] elif max_num_patterns > 1: @@ -188,56 +191,74 @@ def modisco_results( return render_plot(fig, **kwargs) -def create_clustermap( + +def clustermap( pattern_matrix: np.ndarray, classes: list[str], subset: list[str] | None = None, # Subset option - figsize: tuple[int, int] = (25,8), + figsize: tuple[int, int] = (25, 8), grid: bool = False, - color_palette: str | list[str] = "hsv", cmap: str = "coolwarm", center: float = 0, method: str = "average", dy: float = 0.002, fig_path: str | None = None, - pat_seqs: list[tuple[str, np.ndarray]] | None = None + pat_seqs: list[tuple[str, np.ndarray]] | None = None, ) -> sns.matrix.ClusterGrid: """ Create a clustermap from the given pattern matrix and class labels with customizable options. Parameters ---------- - pattern_matrix : np.ndarray + pattern_matrix 2D NumPy array containing pattern data. - classes : list[str] + classes List of class labels, matching the rows of the pattern matrix. - subset : list[str], optional + subset List of class labels to subset the matrix. - figsize : tuple[int, int], optional + figsize Size of the figure. - grid : bool, optional + grid Whether to add a grid to the heatmap. - color_palette : str or list[str], optional - Color palette for the row colors. - cmap : str, optional + cmap Colormap for the clustermap. - center : float, optional + center Value at which to center the colormap. - method : str, optional + method Clustering method to use. - dy: float, optional - Scaling parameter for vertical distance between nucleotides (if pat_seqs is not None) in xticklabels. - fig_path : str, optional + dy + Scaling parameter for vertical distance between nucleotides (if pat_seqs is not None) in xticklabels. + fig_path Path to save the figure. - pat_seqs : list[tuple[str, np.ndarray]], optional + pat_seqs List of sequences to use as xticklabels. + + See Also + -------- + crested.tl.modisco.create_pattern_matrix + crested.tl.modisco.generate_nucleotide_sequences + + Examples + -------- + >>> pat_seqs = crested.tl.modisco.generate_nucleotide_sequences(all_patterns) + >>> crested.pl.patterns.clustermap( + ... pattern_matrix, + ... classes=list(adata.obs_names) + ... subset=["Lamp5", "Pvalb", "Sst", "Sst-Chodl", "Vip"], + ... figsize=(25, 8), + ... grid=True, + ... ) + + .. image:: ../../../../docs/_static/img/examples/pattern_clustermap.png """ # Subset the pattern_matrix and classes if subset is provided if subset is not None: - subset_indices = [i for i, class_label in enumerate(classes) if class_label in subset] + subset_indices = [ + i for i, class_label in enumerate(classes) if class_label in subset + ] pattern_matrix = pattern_matrix[subset_indices, :] classes = [classes[i] for i in subset_indices] - + # Remove columns that contain only zero values non_zero_columns = np.any(pattern_matrix != 0, axis=0) pattern_matrix = pattern_matrix[:, non_zero_columns] @@ -248,25 +269,8 @@ def create_clustermap( data = pd.DataFrame(pattern_matrix) - #if isinstance(color_palette, str): - # palette = sns.color_palette(color_palette, len(set(classes))) - #else: - # palette = color_palette - - #class_lut = dict(zip(set(classes), palette)) - #row_colors = pd.Series(classes).map(class_lut) - if pat_seqs is not None: plt.rc("text", usetex=False) # Turn off LaTeX to speed up rendering - scaling_factor = 10 - - # Plot the scaled x-tick labels based on the importance scores - xtick_labels = [ - (letters, scores) for letters, scores in pat_seqs - ] - - else: - xtick_labels = True g = sns.clustermap( data, @@ -275,14 +279,18 @@ def create_clustermap( row_colors=None, yticklabels=classes, center=center, - xticklabels=True if pat_seqs is None else False, # Disable default xticklabels if pat_seqs provided. #xticklabels=xtick_labels, + xticklabels=True + if pat_seqs is None + else False, # Disable default xticklabels if pat_seqs provided. #xticklabels=xtick_labels, method=method, dendrogram_ratio=(0.1, 0.1), - cbar_pos=(1.05, 0.4, 0.01, 0.3) + cbar_pos=(1.05, 0.4, 0.01, 0.3), ) col_order = g.dendrogram_col.reordered_ind cbar = g.ax_heatmap.collections[0].colorbar - cbar.set_label('Motif importance', rotation=270, labelpad=20) # Rotate label and add padding + cbar.set_label( + "Motif importance", rotation=270, labelpad=20 + ) # Rotate label and add padding g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(), rotation=0) # Get the reordered column indices from the clustermap @@ -292,23 +300,31 @@ def create_clustermap( if pat_seqs is not None: reordered_pat_seqs = [pat_seqs[column_indices[i]] for i in col_order] ax = g.ax_heatmap - x_positions = np.arange(len(reordered_pat_seqs)) + 0.5 # Shift labels by half a tick to the right + x_positions = ( + np.arange(len(reordered_pat_seqs)) + 0.5 + ) # Shift labels by half a tick to the right - constant = (1/figsize[1])*64 + constant = (1 / figsize[1]) * 64 for i, (letters, scores) in enumerate(reordered_pat_seqs): previous_spacing = 0 - for j, (letter, score) in enumerate(zip(reversed(letters), reversed(scores))): - fontsize = score*10 - vertical_spacing = max((constant * score * dy), constant * 0.1 * dy) # Spacing proportional to figsize[1] + for _, (letter, score) in enumerate( + zip(reversed(letters), reversed(scores)) + ): + fontsize = score * 10 + vertical_spacing = max( + (constant * score * dy), constant * 0.1 * dy + ) # Spacing proportional to figsize[1] ax.text( - x_positions[i], -(constant*0.002) - previous_spacing, # Adjust y-position based on spacing - letter, + x_positions[i], + -(constant * 0.002) + - previous_spacing, # Adjust y-position based on spacing + letter, fontsize=fontsize, # Constant font size - ha='center', # Horizontal alignment - va='top', # Vertical alignment - rotation=90, # Rotate the labels vertically - transform=ax.get_xaxis_transform() # Ensure the text is placed relative to x-axis + ha="center", # Horizontal alignment + va="top", # Vertical alignment + rotation=90, # Rotate the labels vertically + transform=ax.get_xaxis_transform(), # Ensure the text is placed relative to x-axis ) previous_spacing += vertical_spacing @@ -318,7 +334,7 @@ def create_clustermap( if grid: ax = g.ax_heatmap # Define the grid positions (between cells, hence the +0.5 offset) - x_positions = np.arange(pattern_matrix.shape[1] + 1) + x_positions = np.arange(pattern_matrix.shape[1] + 1) y_positions = np.arange(len(pattern_matrix) + 1) # Add horizontal grid lines @@ -337,16 +353,28 @@ def create_clustermap( plt.show() -def plot_patterns(pattern_dict: dict, idcs: list[int]) -> None: +def selected_instances(pattern_dict: dict, idcs: list[int]) -> None: """ Plots the patterns specified by the indices in `idcs` from the `pattern_dict`. Parameters ---------- pattern_dict - A dictionary containing pattern data. + A dictionary containing pattern data. Each key corresponds to a pattern ID, and the value is a nested structure containing + contribution scores and metadata for the pattern. Refer to the output of `crested.tl.modisco.process_patterns`. idcs - A list of indices specifying which patterns to plot. + A list of indices specifying which patterns to plot. The indices correspond to keys in the `pattern_dict`. + + See Also + -------- + crested.tl.modisco.process_patterns + + Examples + -------- + >>> pattern_indices = [0, 1, 2] + >>> crested.pl.patterns.selected_instances(pattern_dict, pattern_indices) + + .. image:: ../../../../docs/_static/img/examples/pattern_selected_instances.png """ figure, axes = plt.subplots(nrows=len(idcs), ncols=1, figsize=(8, 2 * len(idcs))) if len(idcs) == 1: @@ -364,25 +392,42 @@ def plot_patterns(pattern_dict: dict, idcs: list[int]) -> None: plt.tight_layout() plt.show() -def plot_pattern_instances(pattern_dict: dict, idx: int, class_representative: bool = False) -> None: + +def class_instances( + pattern_dict: dict, idx: int, class_representative: bool = False +) -> None: """ - Plots all the pattern instances clustered together in the pattern dictionary for a given pattern index. + Plots instances of a specific pattern, either the representative pattern per class or all instances for a given pattern index. Parameters ---------- pattern_dict - A dictionary containing pattern data. - idcs - Index specifying from which pattern the instances to plot. + A dictionary containing pattern data. Each key corresponds to a pattern ID, and each value contains instances of the pattern + across different classes, along with their contribution scores. Refer to the output of `crested.tl.modisco.process_patterns`. + idx + The index specifying which pattern's instances to plot. This corresponds to a key in the `pattern_dict`. class_representative - Boolean to plot the best pattern per class, or all instances of a pattern in the same class if there would be multiple instances in one class. Default False. + If True, only the best representative instance of each class is plotted. If False (default), all instances of the pattern + within each class are plotted. + + See Also + -------- + crested.tl.modisco.process_patterns + + Examples + -------- + >>> crested.pl.patterns.class_instances(pattern_dict, 0, class_representative=False) + + .. image:: ../../../../docs/_static/img/examples/pattern_class_instances.png """ if class_representative: - key = 'classes' + key = "classes" else: - key='instances' + key = "instances" n_instances = len(pattern_dict[str(idx)][key]) - figure, axes = plt.subplots(nrows=n_instances, ncols=1, figsize=(8, 2 * n_instances)) + figure, axes = plt.subplots( + nrows=n_instances, ncols=1, figsize=(8, 2 * n_instances) + ) if n_instances == 1: axes = [axes] @@ -400,7 +445,8 @@ def plot_pattern_instances(pattern_dict: dict, idx: int, class_representative: b plt.tight_layout() plt.show() -def plot_similarity_heatmap( + +def similarity_heatmap( similarity_matrix: np.ndarray, indices: list, fig_size: tuple[int, int] = (30, 15), @@ -419,6 +465,19 @@ def plot_similarity_heatmap( Size of the figure for the heatmap. fig_path Path to save the figure. If None, the figure will be shown but not saved. + + See Also + -------- + crested.tl.modisco.calculate_similarity_matrix + + Examples + -------- + >>> sim_matrix, indices = crested.tl.modisco.calculate_similarity_matrix( + ... all_patterns + ... ) + >>> crested.pl.patterns.similarity_heatmap(sim_matrix, indices, fig_size=(42, 17)) + + .. image:: ../../../../docs/_static/img/examples/pattern_similarity_heatmap.png """ fig, ax = plt.subplots(figsize=fig_size) heatmap = sns.heatmap( @@ -445,99 +504,138 @@ def plot_similarity_heatmap( plt.savefig(fig_path) plt.show() -def plot_tf_expression_per_cell_type(df: pd.DataFrame, tf_list: list, log_transform: bool = False, title: str = "TF Expression per Cell Type") -> None: + +def tf_expression_per_cell_type( + df: pd.DataFrame, + tf_list: list, + log_transform: bool = False, + title: str = "TF Expression per Cell Type", +) -> None: """ Plots the expression levels of specified transcription factors (TFs) per cell type. - Parameters: - - df (pd.DataFrame): The DataFrame containing mean gene expression data per cell type. - - tf_list (list): A list of transcription factors (TFs) to plot. - - log_transform (bool): Whether to log-transform the TF expression values. - - title (str): The title of the plot. - - Returns: - - None + Parameters + ---------- + df + The DataFrame containing mean gene expression data per cell type. + tf_list + A list of transcription factors (TFs) to plot. + log_transform + Whether to log-transform the TF expression values. + title + The title of the plot. """ # Check if all specified TFs are in the DataFrame missing_tfs = [tf for tf in tf_list if tf not in df.columns] if missing_tfs: - raise ValueError(f"The following TFs are not found in the DataFrame: {missing_tfs}") + raise ValueError( + f"The following TFs are not found in the DataFrame: {missing_tfs}" + ) # Subset the DataFrame to include only the specified TFs tf_expression_df = df[tf_list] # Apply log transformation if specified if log_transform: - tf_expression_df = np.log(tf_expression_df+1) + tf_expression_df = np.log(tf_expression_df + 1) # Plot the TF expression per cell type - ax = tf_expression_df.plot(kind='bar', figsize=(12, 5), width=0.8) + ax = tf_expression_df.plot(kind="bar", figsize=(12, 5), width=0.8) ax.set_title(title) ax.set_xlabel("Cell Type") ax.set_ylabel("Log Mean TF Expression" if log_transform else "Mean TF Expression") ax.legend(title="Transcription Factors") - plt.xticks(rotation=45, ha='right') + plt.xticks(rotation=45, ha="right") plt.tight_layout() plt.show() -def plot_clustermap_tf_motif( - data: np.ndarray, - cluster_on_dim: str = 'gex', - class_labels: Optional[List[str]] = None, - pattern_labels: Optional[List[str]] = None, - color_idx: str = 'gex', - size_idx: str = 'contrib', + +def clustermap_tf_motif( + data: np.ndarray, + cluster_on_dim: str = "gex", + class_labels: None | list[str] = None, + pattern_labels: None | list[str] = None, + color_idx: str = "gex", + size_idx: str = "contrib", grid: bool = True, - log_transform: bool = False, - normalize: bool = False + log_transform: bool = False, + normalize: bool = False, ) -> None: """ - Plot a clustermap from a 3D matrix where one third dimension is indicated by dot size - and the other by color. - - Parameters: - - data: 3D numpy array with shape (len(classes), #patterns, 2) - - cluster_on_dim: str, either 'gex' or 'contrib', indicating which third dimension to cluster on - - class_labels: list of strings, labels for the classes - - pattern_labels: list of strings, labels for the patterns - - color_idx: str, either 'gex' or 'contrib', indicating the dimension to use for color - - size_idx: str, either 'gex' or 'contrib', indicating the dimension to use for size - - grid: bool, whether to add a grid to the figure - - log_transform: bool, whether to apply log transformation to the data - - normalize: bool, whether to normalize the data + Plot a clustermap from a 3D matrix where one third dimension is indicated by dot sizen and the other by color. + + Parameters + ---------- + data + 3D numpy array with shape (len(classes), #patterns, 2) + cluster_on_dim + either 'gex' or 'contrib', indicating which third dimension to cluster on + class_labels + labels for the classes + pattern_labels + labels for the patterns + color_idx + either 'gex' or 'contrib', indicating the dimension to use for color + size_idx + either 'gex' or 'contrib', indicating the dimension to use for size + grid + whether to add a grid to the figure + log_transform + whether to apply log transformation to the data + normalize + whether to normalize the data + + See Also + -------- + crested.tl.modisco.create_tf_ct_matrix + + Examples + -------- + >>> crested.pl.patterns.clustermap_tf_motif( + ... tf_ct_matrix, + ... cluster_on_dim="gex", + ... class_labels=classes, + ... pattern_labels=tf_pattern_annots, + ... color_idx="gex", + ... size_idx="contrib", + ... ) + + .. image:: ../../../../docs/_static/img/examples/pattern_tf_motif_clustermap.png """ # Ensure data is a numpy array data = np.array(data) assert data.shape[2] == 2, "The third dimension of the data should be 2." # Some additional data prep for more logical plotting - if color_idx=='gex': + if color_idx == "gex": for col_idx in range(data.shape[1]): for ct_idx in range(data.shape[0]): - if data[ct_idx, col_idx,1]<0: - data[ct_idx, col_idx,0] = -data[ct_idx, col_idx,0] - data[ct_idx, col_idx,1] = np.abs(data[ct_idx, col_idx,1]) + if data[ct_idx, col_idx, 1] < 0: + data[ct_idx, col_idx, 0] = -data[ct_idx, col_idx, 0] + data[ct_idx, col_idx, 1] = np.abs(data[ct_idx, col_idx, 1]) # Default labels if none provided if class_labels is None: - class_labels = [f'Class {i}' for i in range(data.shape[0])] + class_labels = [f"Class {i}" for i in range(data.shape[0])] if pattern_labels is None: - pattern_labels = [f'Pattern {i}' for i in range(data.shape[1])] + pattern_labels = [f"Pattern {i}" for i in range(data.shape[1])] # Mapping from string to index - dim_mapping = {'gex': 0, 'contrib': 1} + dim_mapping = {"gex": 0, "contrib": 1} # Choose the dimension to cluster on clustering_data = data[:, :, dim_mapping[cluster_on_dim]] - + if log_transform: clustering_data = np.log(clustering_data) - + if normalize: - clustering_data = clustering_data / np.linalg.norm(clustering_data, axis=1, keepdims=True) + clustering_data = clustering_data / np.linalg.norm( + clustering_data, axis=1, keepdims=True + ) # Perform hierarchical clustering - linkage_matrix = linkage(clustering_data, method='ward') + linkage_matrix = linkage(clustering_data, method="ward") cluster_order = leaves_list(linkage_matrix) # Reorder data according to clustering @@ -555,24 +653,28 @@ def plot_clustermap_tf_motif( # Determine color scale limits to center on zero max_val = np.max(color_data) min_val = np.min(color_data) - + # Define the normalization to center at zero norm = mcolors.TwoSlopeNorm(vmin=min_val, vcenter=0, vmax=max_val) # Use the norm parameter in scatter sc = ax.scatter( - np.tile(np.arange(data_ordered.shape[1]), data_ordered.shape[0]), - np.repeat(np.arange(data_ordered.shape[0]), data_ordered.shape[1]), - s=size_data.flatten() * 500, - c=color_data.flatten(), - cmap='coolwarm', + np.tile(np.arange(data_ordered.shape[1]), data_ordered.shape[0]), + np.repeat(np.arange(data_ordered.shape[0]), data_ordered.shape[1]), + s=size_data.flatten() * 500, + c=color_data.flatten(), + cmap="coolwarm", alpha=0.6, - norm=norm # Apply the centered colormap -) + norm=norm, # Apply the centered colormap + ) # Add color bar cbar = plt.colorbar(sc, ax=ax) - label = 'Average pattern contribution score' if dim_mapping[color_idx] == 1 else 'Average TF expression, signed by activation/repression' + label = ( + "Average pattern contribution score" + if dim_mapping[color_idx] == 1 + else "Average TF expression, signed by activation/repression" + ) cbar.set_label(label) # Set labels @@ -580,12 +682,14 @@ def plot_clustermap_tf_motif( ax.set_yticks(np.arange(data_ordered.shape[0])) # Reduce the number of x-axis labels displayed - ax.set_xticklabels([pattern_labels[i] for i in range(data_ordered.shape[1])], rotation=90) + ax.set_xticklabels( + [pattern_labels[i] for i in range(data_ordered.shape[1])], rotation=90 + ) ax.set_yticklabels([class_labels[i] for i in cluster_order]) ax.set_xlim([-0.5, len(pattern_labels) + 0.5]) - plt.xlabel('Patterns') - plt.ylabel('Classes') + plt.xlabel("Patterns") + plt.ylabel("Classes") plt.grid(grid) plt.tight_layout() plt.show() diff --git a/src/crested/pl/scatter/_class_density.py b/src/crested/pl/scatter/_class_density.py index 3386355c..c17ad539 100644 --- a/src/crested/pl/scatter/_class_density.py +++ b/src/crested/pl/scatter/_class_density.py @@ -8,8 +8,8 @@ from loguru import logger from scipy.stats import gaussian_kde, pearsonr, spearmanr -from crested._logging import log_and_raise from crested.pl._utils import render_plot +from crested.utils._logging import log_and_raise def class_density( diff --git a/src/crested/pp/_regions.py b/src/crested/pp/_regions.py index 05df2076..38d58db7 100644 --- a/src/crested/pp/_regions.py +++ b/src/crested/pp/_regions.py @@ -9,7 +9,7 @@ from anndata import AnnData from loguru import logger -from crested._logging import log_and_raise +from crested.utils._logging import log_and_raise def _read_chromsizes(chromsizes_file: PathLike) -> dict[str, int]: @@ -95,5 +95,4 @@ def _check_input_params(chromsizes_file): if len(regions_to_keep) < len(adata.var_names): adata._inplace_subset_var(regions_to_keep) - adata.var_names.name = "region" diff --git a/src/crested/pp/_utils.py b/src/crested/pp/_utils.py index 144aa04e..4d94cce9 100644 --- a/src/crested/pp/_utils.py +++ b/src/crested/pp/_utils.py @@ -15,12 +15,12 @@ def _calc_gini(targets: np.ndarray) -> np.ndarray: Parameters ---------- - targets : np.ndarray + targets A 2D numpy array where each row represents a set of target values. Returns ------- - np.ndarray + gini scores A 2D numpy array with the same shape as `targets` containing Gini scores, where each score is assigned to the position of the maximum value in each row. """ @@ -45,6 +45,7 @@ def _gini(array: np.ndarray) -> float: return gini_scores + def _calc_proportion(arr: np.ndarray, scale=False): """ Compute relative specificity scores for a given 1D or 2D array. @@ -55,9 +56,9 @@ def _calc_proportion(arr: np.ndarray, scale=False): Parameters ---------- - arr : np.ndarray + arr Input array (1D or 2D). - scale : bool, optional + scale Whether to scale the specificity scores by multiplying with orginal array. Returns @@ -78,8 +79,10 @@ def _calc_proportion(arr: np.ndarray, scale=False): elif arr.ndim == 2: total_per_row = np.sum(arr, axis=1, keepdims=True) total_per_row[total_per_row == 0] = 1e-9 - specificity_scores = (arr / total_per_row) * arr if scale else arr / total_per_row + specificity_scores = ( + (arr / total_per_row) * arr if scale else arr / total_per_row + ) else: raise ValueError("Input array must be 1D or 2D.") - + return specificity_scores diff --git a/src/crested/tl/__init__.py b/src/crested/tl/__init__.py index 72ff740a..25796bb0 100644 --- a/src/crested/tl/__init__.py +++ b/src/crested/tl/__init__.py @@ -5,15 +5,6 @@ from . import data, losses, metrics, zoo from ._configs import TaskConfig, default_configs from ._crested import Crested -from ._utils import extract_bigwig_values_per_bp - - -def _optional_function_warning(*args, **kwargs): - logger.error( - "The requested functionality requires the 'tfmodisco' package, which is not installed. " - "Please install it with `pip install crested[tfmodisco]`.", - ) - if find_spec("modiscolite") is not None: MODISCOLITE_AVAILABLE = True @@ -24,38 +15,14 @@ def _optional_function_warning(*args, **kwargs): try: import modiscolite - from crested.tl._tfmodisco import ( - calculate_similarity_matrix, - create_pattern_matrix, - generate_nucleotide_sequences, - match_h5_files_to_classes, - pattern_similarity, - process_patterns, - generate_html_paths, - calculate_mean_expression_per_cell_type, - read_motif_to_tf_file, - tfmodisco, - find_pattern_matches, - create_pattern_tf_dict, - create_tf_ct_matrix - ) + from . import modisco except ImportError as e: logger.error(f"Import error: {e}") raise else: - create_pattern_matrix = _optional_function_warning - generate_nucleotide_sequences = _optional_function_warning - match_h5_files_to_classes = _optional_function_warning - process_patterns = _optional_function_warning - tfmodisco = _optional_function_warning - calculate_similarity_matrix = _optional_function_warning - pattern_similarity = _optional_function_warning - calculate_mean_expression_per_cell_type = _optional_function_warning - generate_html_paths = _optional_function_warning - find_pattern_matches = _optional_function_warning - read_motif_to_tf_file = _optional_function_warning - create_pattern_tf_dict = _optional_function_warning - create_tf_ct_matrix = _optional_function_warning + logger.warning( + "modiscolite is not installed, 'crested.tl.modisco' module will not be available." + ) __all__ = [ @@ -66,24 +33,7 @@ def _optional_function_warning(*args, **kwargs): "TaskConfig", "default_configs", "Crested", - "extract_bigwig_values_per_bp", ] if MODISCOLITE_AVAILABLE: - __all__.extend( - [ - "calculate_similarity_matrix", - "create_pattern_matrix", - "generate_nucleotide_sequences", - "match_h5_files_to_classes", - "pattern_similarity", - "process_patterns", - "tfmodisco", - "calculate_mean_expression_per_cell_type", - "generate_html_paths", - "find_pattern_matches", - "read_motif_to_tf_file", - "create_pattern_tf_dict", - "create_tf_ct_matrix" - ] - ) + __all__.extend("modisco") diff --git a/src/crested/tl/_configs.py b/src/crested/tl/_configs.py index a6d99bce..91f944b5 100644 --- a/src/crested/tl/_configs.py +++ b/src/crested/tl/_configs.py @@ -7,7 +7,7 @@ import keras -from crested.tl.losses import CosineMSELoss, CosineMSELogLoss +from crested.tl.losses import CosineMSELogLoss from crested.tl.metrics import ( ConcordanceCorrelationCoefficient, PearsonCorrelation, @@ -159,7 +159,7 @@ def to_dict(self) -> dict: } -def default_configs(task: str, num_classes: int = None) -> TaskConfig: +def default_configs(task: str, num_classes: int | None = None) -> TaskConfig: """ Get default loss, optimizer, and metrics for an existing task. diff --git a/src/crested/tl/_crested.py b/src/crested/tl/_crested.py index b127e553..a277f576 100644 --- a/src/crested/tl/_crested.py +++ b/src/crested/tl/_crested.py @@ -4,28 +4,29 @@ import os from datetime import datetime +from typing import Any import keras import numpy as np from anndata import AnnData from loguru import logger -from tqdm import tqdm -from typing import Callable, Any from pysam import FastaFile +from tqdm import tqdm - -from crested._logging import log_and_raise from crested.tl import TaskConfig -from crested.tl._utils import ( - _weighted_difference, +from crested.tl.data import AnnDataModule +from crested.tl.data._dataset import SequenceLoader +from crested.utils import ( EnhancerOptimizer, - generate_motif_insertions, - generate_mutagenesis, hot_encoding_to_sequence, one_hot_encode_sequence, ) -from crested.tl.data import AnnDataModule -from crested.tl.data._dataset import SequenceLoader +from crested.utils._logging import log_and_raise +from crested.utils._utils import ( + _weighted_difference, + generate_motif_insertions, + generate_mutagenesis, +) if os.environ["KERAS_BACKEND"] == "tensorflow": from crested.tl._explainer_tf import Explainer @@ -522,7 +523,7 @@ def test(self, return_metrics: bool = False) -> dict | None: # Log the evaluation results for metric_name, metric_value in evaluation_metrics.items(): logger.info(f"Test {metric_name}: {metric_value:.4f}") - + return None if return_metrics: return evaluation_metrics @@ -602,6 +603,7 @@ def predict( if anndata is not None and model_name is not None: logger.info(f"Adding predictions to anndata.layers[{model_name}].") anndata.layers[model_name] = predictions.T + return None else: return predictions @@ -644,8 +646,6 @@ def predict_sequence(self, sequence: str) -> np.ndarray: Parameters ---------- - model - A trained Keras model sequence A string containing a DNA sequence (A, C, G, T). @@ -667,59 +667,60 @@ def score_gene_locus( gene_start: int, gene_end: int, class_name: str, - strand: str = '+', + strand: str = "+", upstream: int = 50000, downstream: int = 10000, window_size: int = 2114, central_size: int = 1000, step_size: int = 50, - ): + ) -> tuple[np.ndarray, np.ndarray, int, int, int]: """ Score regions upstream and downstream of a gene locus using the model's prediction. + The model predicts a value for the central 1000bp of each window. - Parameters: + Parameters ---------- - chr_name : str + chr_name The chromosome name (e.g., 'chr12'). - gene_start : int + gene_start The start position of the gene locus (TSS for + strand). - gene_end : int + gene_end The end position of the gene locus (TSS for - strand). - class_name : str + class_name Output class name for prediction. - strand : str + strand '+' for positive strand, '-' for negative strand. Default '+'. - upstream : int + upstream Distance upstream of the gene to score. Default 50 000. - downstream : int + downstream Distance downstream of the gene to score. Default 10 000. - window_size : int + window_size Size of the window to use for scoring. Default 2114. - central_size : int + central_size Size of the central region that the model predicts for. Default 1000. - step_size : int + step_size Distance between consecutive windows. Default 50. - Returns: - -------- - scores : np.array + Returns + ------- + scores An array of prediction scores across the entire genomic range. - coordinates : np.array + coordinates An array of tuples, each containing the chromosome name and the start and end positions of the sequence for each window. - min_loc : int + min_loc Start position of the entire scored region. - max_loc : int + max_loc End position of the entire scored region. - tss_position : int + tss_position The transcription start site (TSS) position. """ # Adjust upstream and downstream based on the strand - if strand == '+': + if strand == "+": start_position = gene_start - upstream end_position = gene_end + downstream tss_position = gene_start # TSS is at the gene_start for positive strand - elif strand == '-': + elif strand == "-": end_position = gene_end + upstream start_position = gene_start - downstream tss_position = gene_end # TSS is at the gene_end for negative strand @@ -734,10 +735,7 @@ def score_gene_locus( # Initialize an array to store the scores, filled with zeros scores = np.zeros(total_length) - # List to store coordinates of each window - coordinates = [] - - # Get class index + # Get class index all_class_names = list(self.anndatamodule.adata.obs_names) idx = all_class_names.index(class_name) @@ -765,25 +763,35 @@ def score_gene_locus( all_coordinates.append((chr_name, int(window_start), int(window_end))) # Stack sequences for batch processing - all_sequences = np.squeeze(np.stack(all_sequences),axis=1) + all_sequences = np.squeeze(np.stack(all_sequences), axis=1) # Perform batched predictions predictions = self.model.predict(all_sequences, verbose=0) # Map predictions to the score array - for i, (pos, prediction) in enumerate(zip(range(start_position, end_position, step_size), predictions)): + for _, (pos, prediction) in enumerate( + zip(range(start_position, end_position, step_size), predictions) + ): window_start = pos central_start = pos + (window_size - central_size) // 2 central_end = central_start + central_size - scores[central_start - start_position:central_end - start_position] += prediction[idx] - #if strand == '+': + scores[ + central_start - start_position : central_end - start_position + ] += prediction[idx] + # if strand == '+': # scores[central_start - start_position:central_end - start_position] += prediction[idx] - #else: + # else: # scores[total_length - (central_end - start_position):total_length - (central_start - start_position)] += prediction[idx] # Normalize the scores based on the number of times each position is included in the central window - return scores / ratio, np.array(all_coordinates), start_position, end_position, tss_position + return ( + scores / ratio, + np.array(all_coordinates), + start_position, + end_position, + tss_position, + ) def calculate_contribution_scores( self, @@ -1105,7 +1113,7 @@ def tfmodisco_calculate_and_save_contribution_scores_sequences( sequences=sequences, class_names=[class_name], method=method, - disable_tqdm=True + disable_tqdm=True, ) # Transform the contrib scores and one hot numpy arrays to (#regions, 4, seq_len), the expected format of modisco-lite. @@ -1124,7 +1132,6 @@ def tfmodisco_calculate_and_save_contribution_scores_sequences( f"Contribution scores and one-hot encoded sequences saved to {output_dir}" ) - def tfmodisco_calculate_and_save_contribution_scores( self, adata: AnnData, @@ -1226,7 +1233,7 @@ def enhancer_design_motif_implementation( target_len: int | None = None, preserve_inserted_motifs: bool = True, enhancer_optimizer: EnhancerOptimizer | None = None, - **kwargs: dict[str, Any] + **kwargs: dict[str, Any], ) -> tuple[list[dict], list] | list: """ Create synthetic enhancers for a specified class using motif implementation. @@ -1278,13 +1285,12 @@ def enhancer_design_motif_implementation( target = all_class_names.index(target_class) elif target is None: - raise ValueError("`target` need to be specified when `target_class` is None") - + raise ValueError( + "`target` need to be specified when `target_class` is None" + ) if enhancer_optimizer is None: - enhancer_optimizer = EnhancerOptimizer( - optimize_func = _weighted_difference - ) + enhancer_optimizer = EnhancerOptimizer(optimize_func=_weighted_difference) # get input sequence length of the model seq_len = ( @@ -1359,14 +1365,16 @@ def enhancer_design_motif_implementation( masked_locations=inserted_motif_locations, ) - mutagenesis_predictions = self.model.predict(mutagenesis, verbose=False) + mutagenesis_predictions = self.model.predict( + mutagenesis, verbose=False + ) # determine the best insertion site best_mutation = enhancer_optimizer.get_best( - mutated_predictions = mutagenesis_predictions, - original_prediction = current_prediction, - target = target, - **kwargs + mutated_predictions=mutagenesis_predictions, + original_prediction=current_prediction, + target=target, + **kwargs, ) sequence_onehot = mutagenesis[best_mutation : best_mutation + 1] @@ -1411,7 +1419,7 @@ def enhancer_design_in_silico_evolution( no_mutation_flanks: tuple | None = None, target_len: int | None = None, enhancer_optimizer: EnhancerOptimizer | None = None, - **kwargs: dict[str, Any] + **kwargs: dict[str, Any], ) -> tuple[list[dict], list] | list: """ Create synthetic enhancers for a specified class using in silico evolution (ISE). @@ -1458,12 +1466,12 @@ def enhancer_design_in_silico_evolution( target = all_class_names.index(target_class) elif target is None: - raise ValueError("`target` need to be specified when `target_class` is None") + raise ValueError( + "`target` need to be specified when `target_class` is None" + ) if enhancer_optimizer is None: - enhancer_optimizer = EnhancerOptimizer( - optimize_func = _weighted_difference - ) + enhancer_optimizer = EnhancerOptimizer(optimize_func=_weighted_difference) # get input sequence length of the model seq_len = ( @@ -1500,10 +1508,7 @@ def enhancer_design_in_silico_evolution( designed_sequences: list[str] = [] intermediate_info_list: list[dict] = [] - sequence_onehot_prev_iter = np.zeros( - (n_sequences, seq_len, 4), - dtype=np.uint8 - ) + sequence_onehot_prev_iter = np.zeros((n_sequences, seq_len, 4), dtype=np.uint8) # calculate total number of mutations per sequence _, L, A = sequence_onehot_prev_iter.shape @@ -1521,11 +1526,10 @@ def enhancer_design_in_silico_evolution( for _iter in tqdm(range(n_mutations)): baseline_prediction = self.model.predict( - sequence_onehot_prev_iter, - verbose = False + sequence_onehot_prev_iter, verbose=False ) - - if _iter == 0 : + + if _iter == 0: for i in range(n_sequences): # initialize info intermediate_info_list.append( @@ -1534,9 +1538,7 @@ def enhancer_design_in_silico_evolution( sequence_onehot_prev_iter[i] ), "changes": [(-1, "N")], - "predictions": [ - baseline_prediction[i] - ], + "predictions": [baseline_prediction[i]], "designed_sequence": "", } ) @@ -1544,36 +1546,35 @@ def enhancer_design_in_silico_evolution( # do all possible mutations for i in range(n_sequences): mutagenesis[i] = generate_mutagenesis( - sequence_onehot_prev_iter[i: i+1], - include_original=False, flanks=no_mutation_flanks + sequence_onehot_prev_iter[i : i + 1], + include_original=False, + flanks=no_mutation_flanks, ) mutagenesis_predictions = self.model.predict( mutagenesis.reshape( (n_sequences * TOTAL_NUMBER_OF_MUTATIONS_PER_SEQ, seq_len, 4) ), - verbose=False + verbose=False, ) mutagenesis_predictions = mutagenesis_predictions.reshape( ( n_sequences, TOTAL_NUMBER_OF_MUTATIONS_PER_SEQ, - mutagenesis_predictions.shape[1] + mutagenesis_predictions.shape[1], ) ) for i in range(n_sequences): best_mutation = enhancer_optimizer.get_best( - mutated_predictions = mutagenesis_predictions[i], - original_prediction = baseline_prediction[i], - target = target, - **kwargs + mutated_predictions=mutagenesis_predictions[i], + original_prediction=baseline_prediction[i], + target=target, + **kwargs, ) sequence_onehot_prev_iter[i] = mutagenesis[ - i, - best_mutation : best_mutation + 1, - : + i, best_mutation : best_mutation + 1, : ] if return_intermediate: mutation_index = best_mutation // 3 + no_mutation_flanks[0] @@ -1590,24 +1591,18 @@ def enhancer_design_in_silico_evolution( # get final sequence for i in range(n_sequences): best_mutation = enhancer_optimizer.get_best( - mutated_predictions = mutagenesis_predictions[i], - original_prediction = baseline_prediction[i], - target = target, - **kwargs + mutated_predictions=mutagenesis_predictions[i], + original_prediction=baseline_prediction[i], + target=target, + **kwargs, ) designed_sequence = hot_encoding_to_sequence( - mutagenesis[ - i, - best_mutation : best_mutation + 1, - : - ] - ) - - designed_sequences.append( - designed_sequence + mutagenesis[i, best_mutation : best_mutation + 1, :] ) + designed_sequences.append(designed_sequence) + if return_intermediate: intermediate_info_list[i]["designed_sequence"] = designed_sequence diff --git a/src/crested/tl/_explainer_tf.py b/src/crested/tl/_explainer_tf.py index 7dd36917..bcf0bacf 100644 --- a/src/crested/tl/_explainer_tf.py +++ b/src/crested/tl/_explainer_tf.py @@ -7,7 +7,7 @@ import numpy as np import tensorflow as tf -from crested.tl._utils import generate_mutagenesis +from crested.utils._utils import generate_mutagenesis class Explainer: diff --git a/src/crested/tl/data/_dataset.py b/src/crested/tl/data/_dataset.py index 173fb8dc..dc2013dd 100644 --- a/src/crested/tl/data/_dataset.py +++ b/src/crested/tl/data/_dataset.py @@ -13,7 +13,7 @@ from scipy.sparse import spmatrix from tqdm import tqdm -from crested.tl._utils import one_hot_encode_sequence +from crested.utils import one_hot_encode_sequence def _read_chromsizes(chromsizes_file: PathLike) -> dict[str, int]: diff --git a/src/crested/tl/modisco/__init__.py b/src/crested/tl/modisco/__init__.py new file mode 100644 index 00000000..1d136d60 --- /dev/null +++ b/src/crested/tl/modisco/__init__.py @@ -0,0 +1,16 @@ +from ._tfmodisco import ( + calculate_mean_expression_per_cell_type, + calculate_similarity_matrix, + create_pattern_matrix, + create_pattern_tf_dict, + create_tf_ct_matrix, + find_pattern, + find_pattern_matches, + generate_html_paths, + generate_nucleotide_sequences, + match_h5_files_to_classes, + pattern_similarity, + process_patterns, + read_motif_to_tf_file, + tfmodisco, +) diff --git a/src/crested/tl/_modisco_utils.py b/src/crested/tl/modisco/_modisco_utils.py similarity index 81% rename from src/crested/tl/_modisco_utils.py rename to src/crested/tl/modisco/_modisco_utils.py index ba4b826e..475fd2ee 100644 --- a/src/crested/tl/_modisco_utils.py +++ b/src/crested/tl/modisco/_modisco_utils.py @@ -3,14 +3,14 @@ import modiscolite as modisco import numpy as np import pandas as pd -from tangermeme.tools import tomtom as tangermeme_tomtom -import torch +from loguru import logger + def _trim_pattern_by_ic_old( pattern: dict, pos_pattern: bool, min_v: float, - background: list[float] = None, + background: list[float] | None = None, pseudocount: float = 1e-6, ) -> dict: """ @@ -55,6 +55,7 @@ def _trim_pattern_by_ic_old( return _trim(pattern, start_idx, end_idx) + def _trim_pattern_by_ic( pattern: dict, pos_pattern: bool, @@ -99,6 +100,7 @@ def _trim_pattern_by_ic( return _trim(pattern, start_idx, end_idx) + def _trim(pattern: dict, start_idx: int, end_idx: int) -> dict: """ Trims the pattern to the specified start and end indices. @@ -123,23 +125,23 @@ def _trim(pattern: dict, start_idx: int, end_idx: int) -> dict: for k in pattern["seqlets"].keys(): seqlet_dict[k] = pattern["seqlets"][k][:] # do actual trimming - seqlets_sequences = pattern['seqlets']['sequence'] + seqlets_sequences = pattern["seqlets"]["sequence"] trimmed_sequences = [seq[start_idx:end_idx] for seq in seqlets_sequences] - seqlet_dict['sequence'] = trimmed_sequences + seqlet_dict["sequence"] = trimmed_sequences return { "sequence": np.array(pattern["sequence"])[start_idx:end_idx], "contrib_scores": np.array(pattern["contrib_scores"])[start_idx:end_idx], "hypothetical_contribs": np.array(pattern["hypothetical_contribs"])[ start_idx:end_idx ], - "seqlets" : seqlet_dict + "seqlets": seqlet_dict, } def _get_ic( contrib_scores: np.ndarray, pos_pattern: bool, - background: list[float] = None, + background: list[float] | None = None, ) -> np.ndarray: """ Computes the information content (IC) for the given contribution scores. @@ -170,35 +172,42 @@ def _get_ic( ) return ppm * (np.sum(ic, axis=1)[:, None]) + def _one_hot_to_count_matrix(sequences): """ Convert a set of one-hot encoded sequences to a count matrix. - Args: - sequences (numpy.ndarray): A numpy array of shape (n_sequences, sequence_length, 4), - representing the one-hot encoded sequences. + Parameters + ---------- + sequences + A numpy array of shape (n_sequences, sequence_length, 4), representing the one-hot encoded sequences. - Returns: - count_matrix (numpy.ndarray): A count matrix of shape (sequence_length, 4) - where each entry represents the count of - A, C, G, or T at each position. + Returns + ------- + count_matrix + A count matrix of shape (sequence_length, 4) where each entry represents the count of A, C, G, or T at each position. """ # Sum the one-hot encoded sequences along the first axis (the sequence axis) count_matrix = np.sum(sequences, axis=0) return count_matrix + def _count_matrix_to_ppm(count_matrix, pseudocount=1.0): """ - Convert a count matrix to a position weight matrix (PWM) by adding pseudocounts - and normalizing by the total counts per position. + Convert a count matrix to a position weight matrix (PWM) by adding pseudocounts and normalizing by the total counts per position. - Args: - count_matrix (numpy.ndarray): A count matrix of shape (sequence_length, 4). - pseudocount (float): A pseudocount added to each nucleotide count to avoid zeros. + Parameters + ---------- + count_matrix + A count matrix of shape (sequence_length, 4). + pseudocount + A pseudocount added to each nucleotide count to avoid zeros. - Returns: - pwm (numpy.ndarray): The position weight matrix of shape (sequence_length, 4). + Returns + ------- + pwm + The position weight matrix of shape (sequence_length, 4). """ # Add pseudocount to avoid zero probabilities count_matrix += pseudocount @@ -211,17 +220,22 @@ def _count_matrix_to_ppm(count_matrix, pseudocount=1.0): return ppm + def _ppm_to_pwm(ppm, background_frequencies=None): """ Convert a Position Probability Matrix (PPM) to a Position Weight Matrix (PWM) using log-odds. - Args: - ppm (numpy.ndarray): A PPM of shape (sequence_length, 4) where each value is a probability. - background_frequencies (list or numpy.ndarray): Background frequencies for A, C, G, T. - Default is [0.27, 0.23, 0.23, 0.27]. + Parameters + ---------- + ppm + A PPM of shape (sequence_length, 4) where each value is a probability. + background_frequencies + Background frequencies for A, C, G, T. Default is [0.27, 0.23, 0.23, 0.27]. - Returns: - pwm (numpy.ndarray): The Position Weight Matrix of shape (sequence_length, 4). + Returns + ------- + pwm + The Position Weight Matrix of shape (sequence_length, 4). """ if background_frequencies is None: # Uniform background frequencies for A, C, G, T @@ -237,28 +251,37 @@ def _ppm_to_pwm(ppm, background_frequencies=None): return pwm + def _pattern_to_ppm(pattern): - seqs = np.array(pattern['seqlets']['sequence']) + seqs = np.array(pattern["seqlets"]["sequence"]) count_matrix = _one_hot_to_count_matrix(seqs) ppm = _count_matrix_to_ppm(count_matrix) return ppm -def compute_ic(ppm, background_freqs=[0.28,0.22,0.22,0.28]): +def compute_ic(ppm, background_freqs: list | None = None): """ Compute the information content (IC) of a Position Probability Matrix (PPM). - Args: - ppm: 2D numpy array where rows correspond to positions in the motif, and - columns correspond to symbols (A, T, C, G for example). - background_freqs: 1D numpy array with the background frequencies of each symbol. + Parameters + ---------- + ppm + 2D numpy array where rows correspond to positions in the motif, and columns correspond to symbols (A, T, C, G for example). + background_freqs + 1D numpy array with the background frequencies of each symbol. - Returns: - total_ic: Total information content of the PPM. - ic_per_position: Information content per position in the motif. - ic_per_element: 2D array of information content per element in the PPM. + Returns + ------- + total_ic + Total information content of the PPM. + ic_per_position + Information content per position in the motif. + ic_per_element + 2D array of information content per element in the PPM. """ # Ensure ppm is a numpy array + if background_freqs is None: + background_freqs = [0.28, 0.22, 0.22, 0.28] ppm = np.array(ppm) background_freqs = np.array(background_freqs) @@ -269,7 +292,9 @@ def compute_ic(ppm, background_freqs=[0.28,0.22,0.22,0.28]): for i in range(ppm.shape[0]): # for each position in the motif for j in range(ppm.shape[1]): # for each symbol (A, T, C, G) if ppm[i, j] > 0: # Avoid log(0) - ic_per_element[i, j] = ppm[i, j] * np.log2(ppm[i, j] / background_freqs[j]) + ic_per_element[i, j] = ppm[i, j] * np.log2( + ppm[i, j] / background_freqs[j] + ) # IC per position is the sum of IC values across symbols at each position ic_per_position = np.sum(ic_per_element, axis=1) @@ -279,6 +304,7 @@ def compute_ic(ppm, background_freqs=[0.28,0.22,0.22,0.28]): return total_ic, ic_per_position, ic_per_element + def l1(X: np.ndarray) -> np.ndarray: """ Normalizes the input array using the L1 norm. @@ -357,6 +383,7 @@ def pad_pattern(pattern: dict, pad_len: int = 2) -> dict: ) return p0 + def match_score_patterns(a: dict, b: dict) -> float: """ Computes the match score between two patterns. @@ -372,13 +399,18 @@ def match_score_patterns(a: dict, b: dict) -> float: ------- Match score between the patterns. """ - - _,_,ic_a = compute_ic(a['ppm']) - _,_,ic_b = compute_ic(b['ppm']) + _, _, ic_a = compute_ic(a["ppm"]) + _, _, ic_b = compute_ic(b["ppm"]) + try: + from tangermeme.tools import tomtom as tangermeme_tomtom + except ImportError as e: + raise ImportError("Please install tangermeme to use this function.") from e try: - score = tangermeme_tomtom.tomtom(Qs = [ic_a.T], Ts = [ic_b.T])[0,0][0] - except Exception as e: - print(f"Warning: TOMTOM error while comparing patterns {a['id']} and {b['id']}. Returning no match.") + score = tangermeme_tomtom.tomtom(Qs=[ic_a.T], Ts=[ic_b.T])[0, 0][0] + except Exception as e: # noqa: BLE001 + print( + f"Warning: TOMTOM error while comparing patterns {a['id']} and {b['id']}. Returning no match." + ) print(f"Error details: {e}") score = 1 @@ -386,6 +418,7 @@ def match_score_patterns(a: dict, b: dict) -> float: return log_score + def _match_score_patterns_old(a: dict, b: dict) -> float: """ Computes the match score between two patterns. diff --git a/src/crested/tl/_tfmodisco.py b/src/crested/tl/modisco/_tfmodisco.py similarity index 89% rename from src/crested/tl/_tfmodisco.py rename to src/crested/tl/modisco/_tfmodisco.py index 6e3e398b..5cd252d4 100644 --- a/src/crested/tl/_tfmodisco.py +++ b/src/crested/tl/modisco/_tfmodisco.py @@ -3,15 +3,22 @@ import os import re +import anndata import h5py import modiscolite as modisco import numpy as np import pandas as pd from loguru import logger -import anndata -from crested._logging import log_and_raise -from ._modisco_utils import match_score_patterns, read_html_to_dataframe, _get_ic, _trim_pattern_by_ic, _pattern_to_ppm, compute_ic +from crested.utils._logging import log_and_raise + +from ._modisco_utils import ( + _pattern_to_ppm, + _trim_pattern_by_ic, + compute_ic, + match_score_patterns, + read_html_to_dataframe, +) def _calculate_window_offsets(center: int, window_size: int) -> tuple: @@ -194,20 +201,21 @@ def add_pattern_to_dict( ------- Updated dictionary with the new pattern. """ - ppm = _pattern_to_ppm(p) ic, ic_pos, ic_mat = compute_ic(ppm) - p['ppm']=ppm + p["ppm"] = ppm p["ic"] = np.mean(ic_pos) all_patterns[str(idx)] = {} all_patterns[str(idx)]["pattern"] = p all_patterns[str(idx)]["pos_pattern"] = pos_pattern - - all_patterns[str(idx)]['ppm']=ppm - all_patterns[str(idx)]["ic"] = np.mean(ic_pos)#np.mean(_get_ic(p["contrib_scores"], pos_pattern)) + + all_patterns[str(idx)]["ppm"] = ppm + all_patterns[str(idx)]["ic"] = np.mean( + ic_pos + ) # np.mean(_get_ic(p["contrib_scores"], pos_pattern)) all_patterns[str(idx)]["instances"] = {} - all_patterns[str(idx)]["instances"][p['id']] = p + all_patterns[str(idx)]["instances"][p["id"]] = p all_patterns[str(idx)]["classes"] = {} all_patterns[str(idx)]["classes"][cell_type] = p return all_patterns @@ -264,10 +272,10 @@ def match_to_patterns( ppm = _pattern_to_ppm(p) ic, ic_pos, ic_mat = compute_ic(ppm) p_ic = np.mean(ic_pos) - p['ic'] = p_ic - p['ppm'] = ppm + p["ic"] = p_ic + p["ppm"] = ppm - p['class']=cell_type + p["class"] = cell_type for pat_idx, pattern in enumerate(all_patterns.keys()): sim = match_score_patterns(p, all_patterns[pattern]["pattern"]) @@ -288,8 +296,10 @@ def match_to_patterns( all_patterns[str(match_idx)]["instances"][pattern_id] = p - if(cell_type in all_patterns[str(match_idx)]["classes"].keys()): - ic_class_representative = all_patterns[str(match_idx)]["classes"][cell_type]['ic'] + if cell_type in all_patterns[str(match_idx)]["classes"].keys(): + ic_class_representative = all_patterns[str(match_idx)]["classes"][cell_type][ + "ic" + ] if p_ic > ic_class_representative: all_patterns[str(match_idx)]["classes"][cell_type] = p else: @@ -332,13 +342,13 @@ def post_hoc_merging( pattern_list = list(all_patterns.items()) def should_merge(p1, p2): - """ Helper to check if two patterns should merge based on the similarity threshold. """ + """Helper to check if two patterns should merge based on the similarity threshold.""" sim = max( match_score_patterns(p1["pattern"], p2["pattern"]), - match_score_patterns(p2["pattern"], p1["pattern"]) + match_score_patterns(p2["pattern"], p1["pattern"]), ) return sim > sim_threshold, sim - + iterations = 0 # Track number of iterations for debugging while True: merged_patterns = {} @@ -408,15 +418,17 @@ def should_merge(p1, p2): print(f"Total iterations: {iterations}") # Debugging step to check for any remaining patterns exceeding the similarity threshold - for i, (idx1, pattern1) in enumerate(final_patterns.items()): - for j, (idx2, pattern2) in enumerate(final_patterns.items()): + for i, (idx1, _) in enumerate(final_patterns.items()): + for j, (idx2, _) in enumerate(final_patterns.items()): if i >= j: continue sim = pattern_similarity(final_patterns, idx1, idx2) if sim > sim_threshold: - print(f"Warning: Patterns {idx1} and {idx2} exceed similarity threshold with similarity {sim}") + print( + f"Warning: Patterns {idx1} and {idx2} exceed similarity threshold with similarity {sim}" + ) return final_patterns @@ -439,20 +451,23 @@ def merge_patterns(pattern1: dict, pattern2: dict) -> dict: merged_classes = {} for cell_type in pattern1["classes"].keys(): if cell_type in pattern2["classes"].keys(): - ic_a = pattern1["classes"][cell_type]['ic'] - ic_b = pattern2["classes"][cell_type]['ic'] - merged_classes[cell_type] = pattern1["classes"][cell_type] if ic_a > ic_b else pattern2["classes"][cell_type] + ic_a = pattern1["classes"][cell_type]["ic"] + ic_b = pattern2["classes"][cell_type]["ic"] + merged_classes[cell_type] = ( + pattern1["classes"][cell_type] + if ic_a > ic_b + else pattern2["classes"][cell_type] + ) else: - merged_classes[cell_type] = pattern1["classes"][cell_type] + merged_classes[cell_type] = pattern1["classes"][cell_type] for cell_type in pattern2["classes"].keys(): if cell_type not in merged_classes.keys(): - merged_classes[cell_type] = pattern2["classes"][cell_type] + merged_classes[cell_type] = pattern2["classes"][cell_type] merged_classes = {**pattern1["classes"], **pattern2["classes"]} merged_instances = {**pattern1["instances"], **pattern2["instances"]} - if pattern2["ic"] > pattern1["ic"]: representative_pattern = pattern2["pattern"] highest_ic = pattern2["ic"] @@ -464,13 +479,11 @@ def merge_patterns(pattern1: dict, pattern2: dict) -> dict: "pattern": representative_pattern, "ic": highest_ic, "classes": merged_classes, - "instances": merged_instances + "instances": merged_instances, } -def pattern_similarity( - all_patterns: dict, idx1: int, idx2: int -) -> float: +def pattern_similarity(all_patterns: dict, idx1: int, idx2: int) -> float: """ Computes the similarity between two patterns. @@ -528,13 +541,13 @@ def normalize_rows(arr: np.ndarray) -> np.ndarray: return normalized_array -def find_pattern(id_: str, pattern_dict: dict) -> int | None: +def find_pattern(pattern_id: str, pattern_dict: dict) -> int | None: """ Finds the index of a pattern by its ID. Parameters ---------- - id_ + pattern_id The ID of the pattern to find. pattern_dict A dictionary containing pattern data. @@ -544,10 +557,10 @@ def find_pattern(id_: str, pattern_dict: dict) -> int | None: The index of the pattern if found, otherwise None. """ for idx, p in enumerate(pattern_dict): - if id_ == pattern_dict[p]["pattern"]["id"]: + if pattern_id == pattern_dict[p]["pattern"]["id"]: return idx for c in pattern_dict[p]["classes"]: - if id_ == pattern_dict[p]["classes"][c]["id"]: + if pattern_id == pattern_dict[p]["classes"][c]["id"]: return idx return None @@ -565,6 +578,10 @@ def match_h5_files_to_classes( classes list of class names to match against file names. + See Also + -------- + crested.tl.modisco.tfmodisco + Returns ------- A dictionary where keys are class names and values are paths to the corresponding .h5 files if matched, None otherwise. @@ -584,7 +601,7 @@ def match_h5_files_to_classes( def process_patterns( matched_files: dict[str, str | list[str] | None], - sim_threshold: float = 3, + sim_threshold: float = 3.0, trim_ic_threshold: float = 0.1, discard_ic_threshold: float = 0.1, verbose: bool = False, @@ -605,6 +622,10 @@ def process_patterns( verbose Flag to enable verbose output. + See Also + -------- + crested.tl.modisco.match_h5_files_to_classes + Returns ------- All processed patterns with metadata. @@ -631,22 +652,20 @@ def process_patterns( for metacluster_name in list(hdf5_results.keys()): pattern_idx = 0 for i in range(len(list(hdf5_results[metacluster_name]))): - p = 'pattern_'+str(i) + p = "pattern_" + str(i) pattern_ids.append( f"{cell_type.replace(' ', '_')}_{metacluster_name}_{pattern_idx}" ) is_pos = metacluster_name == "pos_patterns" pattern = _trim_pattern_by_ic( - hdf5_results[metacluster_name][p], - is_pos, - trim_ic_threshold - ) + hdf5_results[metacluster_name][p], + is_pos, + trim_ic_threshold, + ) # store file path so it is possible to track back # where the pattern comes from. pattern["file_path"] = h5_file - trimmed_patterns.append( - pattern - ) + trimmed_patterns.append(pattern) is_pattern_pos.append(is_pos) pattern_idx = pattern_idx + 1 @@ -694,6 +713,11 @@ def create_pattern_matrix( normalize Flag to indicate whether to normalize the rows of the matrix. + See Also + -------- + crested.tl.modisco.process_patterns + crested.pl.patterns.clustermap + Returns ------- The resulting pattern matrix, optionally normalized. @@ -729,6 +753,10 @@ def calculate_similarity_matrix(all_patterns: dict) -> np.ndarray: Returns ------- A 2D numpy array containing the similarity values. + + See Also + -------- + crested.pl.patterns.similarity_heatmap """ indices = list(all_patterns.keys()) num_patterns = len(indices) @@ -751,6 +779,10 @@ def generate_nucleotide_sequences(all_patterns: dict) -> list[tuple[str, np.ndar all_patterns dictionary containing pattern data. + See Also + -------- + crested.tl.modisco.process_patterns + Returns ------- list of tuples containing sequences and their normalized heights. @@ -885,7 +917,7 @@ def find_pattern_matches( + "_" + pattern_id_parts[-2] + "." - + 'pattern' + + "pattern" + "_" + pattern_id_parts[-1] ) @@ -1005,7 +1037,7 @@ def create_tf_ct_matrix( classes: list[str], log_transform: bool = True, normalize: bool = True, - min_tf_gex: float = 0 + min_tf_gex: float = 0, ) -> tuple[np.ndarray, list[str]]: """ Creates a tensor (matrix) of transcription factor (TF) expression and cell type contributions. @@ -1013,11 +1045,11 @@ def create_tf_ct_matrix( Parameters ---------- pattern_tf_dict - A dictionary with pattern indices and their TFs. + A dictionary with pattern indices and their TFs. See `crested.tl.modisco.create_pattern_tf_dict`. all_patterns - A list of patterns with metadata. + A list of patterns with metadata. See `crested.tl.modisco.process_patterns`. df - A DataFrame containing gene expression data. + A DataFrame containing gene expression data. See `crested.tl.modisco.calculate_mean_expression_per_cell_type` classes A list of cell type classes. log_transform @@ -1027,12 +1059,16 @@ def create_tf_ct_matrix( min_tf_gex The minimal GEX value to select potential TF candidates. Default 0. + See Also + -------- + crested.tl.modisco.create_pattern_tf_dict, crested.tl.modisco.process_patterns, crested.tl.modisco.calculate_mean_expression_per_cell_type + Returns ------- A tuple containing the TF-cell type matrix and the list of TF pattern annotations. """ total_tf_patterns = sum(len(pattern_tf_dict[p]["tfs"]) for p in pattern_tf_dict) - tf_ct_matrix = np.zeros((len(classes), total_tf_patterns,2)) + tf_ct_matrix = np.zeros((len(classes), total_tf_patterns, 2)) tf_pattern_annots = [] counter = 0 @@ -1040,9 +1076,7 @@ def create_tf_ct_matrix( ct_contribs = np.zeros(len(classes)) for ct in all_patterns[p_idx]["classes"]: idx = np.argwhere(np.array(classes) == ct)[0][0] - contribs = np.mean( - all_patterns[p_idx]["classes"][ct]["contrib_scores"] - ) + contribs = np.mean(all_patterns[p_idx]["classes"][ct]["contrib_scores"]) ct_contribs[idx] = contribs for tf in pattern_tf_dict[p_idx]["tfs"]: @@ -1065,27 +1099,31 @@ def create_tf_ct_matrix( # Logic to remove columns where tf_gex is zero for all non-zero ct_contribs initial_columns = tf_ct_matrix.shape[1] columns_to_keep = [] - + for col in range(initial_columns): tf_gex_col = tf_ct_matrix[:, col, 0] ct_contribs_col = tf_ct_matrix[:, col, 1] - + # Identify non-zero ct_contribs non_zero_contribs = ct_contribs_col != 0 - + # Check if all non-zero ct_contribs have zero tf_gex values - if np.any(non_zero_contribs) and np.any(tf_gex_col[non_zero_contribs] > min_tf_gex): + if np.any(non_zero_contribs) and np.any( + tf_gex_col[non_zero_contribs] > min_tf_gex + ): columns_to_keep.append(col) # Convert columns_to_keep to a boolean mask columns_to_keep = np.array(columns_to_keep) - + # Filter the matrix and annotations based on the columns_to_keep final_columns = len(columns_to_keep) removed_columns = initial_columns - final_columns tf_ct_matrix = tf_ct_matrix[:, columns_to_keep, :] - tf_pattern_annots = [annot for i, annot in enumerate(tf_pattern_annots) if i in columns_to_keep] + tf_pattern_annots = [ + annot for i, annot in enumerate(tf_pattern_annots) if i in columns_to_keep + ] # Print stats about the number of columns removed print(f"Total columns before filtering: {initial_columns}") @@ -1094,20 +1132,23 @@ def create_tf_ct_matrix( return tf_ct_matrix, tf_pattern_annots + def calculate_mean_expression_per_cell_type( - file_path: str, - cell_type_column: str - ) -> pd.DataFrame: + file_path: str, cell_type_column: str +) -> pd.DataFrame: """ - Reads an AnnData object from an H5AD file and calculates the mean gene expression - per cell type subclass. + Reads an AnnData object from an H5AD file and calculates the mean gene expression per cell type subclass. - Parameters: - - file_path (str): The path to the H5AD file containing the single-cell RNA-seq data. - - cell_type_column (str): The column name in the cell metadata that defines the cell type subclass. + Parameters + ---------- + file_path + The path to the H5AD file containing the single-cell RNA-seq data. + cell_type_column + The column name in the cell metadata that defines the cell type subclass. - Returns: - - pd.DataFrame: A DataFrame containing the mean gene expression per cell type subclass. + Returns + ------- + A DataFrame containing the mean gene expression per cell type subclass. """ # Read the AnnData object from the specified H5AD file adata: anndata.AnnData = anndata.read_h5ad(file_path) @@ -1123,7 +1164,8 @@ def calculate_mean_expression_per_cell_type( raise ValueError(f"Column '{cell_type_column}' not found in cell metadata") # Calculate the mean gene expression per cell type subclass - mean_expression_per_cell_type: pd.DataFrame = gene_expression_df.groupby(cell_metadata[cell_type_column]).mean() + mean_expression_per_cell_type: pd.DataFrame = gene_expression_df.groupby( + cell_metadata[cell_type_column] + ).mean() return mean_expression_per_cell_type - diff --git a/src/crested/tl/zoo/_simple_convnet.py b/src/crested/tl/zoo/_simple_convnet.py index ed360fed..149ff6e8 100644 --- a/src/crested/tl/zoo/_simple_convnet.py +++ b/src/crested/tl/zoo/_simple_convnet.py @@ -10,7 +10,7 @@ def simple_convnet( num_classes: int, num_conv_blocks: int = 3, num_dense_blocks: int = 2, - residual: int = 0, + residual: bool = False, first_activation: str = "exponential", activation: str = "swish", output_activation: str = "softplus", diff --git a/src/crested/tl/zoo/utils/__init__.py b/src/crested/tl/zoo/utils/__init__.py index f1629954..5dcdb780 100644 --- a/src/crested/tl/zoo/utils/__init__.py +++ b/src/crested/tl/zoo/utils/__init__.py @@ -1 +1 @@ -from ._layers import * +from ._layers import * # noqa: F403 diff --git a/src/crested/tl/zoo/utils/_layers.py b/src/crested/tl/zoo/utils/_layers.py index bcdcbd27..9f790d40 100644 --- a/src/crested/tl/zoo/utils/_layers.py +++ b/src/crested/tl/zoo/utils/_layers.py @@ -360,7 +360,7 @@ def dilated_residual( inputs: keras.KerasTensor, filters: int, kernel_size: int = 3, - rate_mult: int = 2, + rate_mult: float = 2.0, dropout: float = 0, conv_type: str = "standard", repeat: int = 1, diff --git a/src/crested/utils/__init__.py b/src/crested/utils/__init__.py new file mode 100644 index 00000000..48f686d6 --- /dev/null +++ b/src/crested/utils/__init__.py @@ -0,0 +1,7 @@ +from ._logging import setup_logging +from ._utils import ( + EnhancerOptimizer, + extract_bigwig_values_per_bp, + hot_encoding_to_sequence, + one_hot_encode_sequence, +) diff --git a/src/crested/_logging.py b/src/crested/utils/_logging.py similarity index 95% rename from src/crested/_logging.py rename to src/crested/utils/_logging.py index ad6e3119..bd81d4cb 100644 --- a/src/crested/_logging.py +++ b/src/crested/utils/_logging.py @@ -32,7 +32,7 @@ def setup_logging(log_level: str = "INFO", log_file: str | None = None): ) -def log_and_raise(exception_class: Exception): +def log_and_raise(exception_class: type[Exception]): """Decorator to both log and raise exceptions.""" def decorator(func): diff --git a/src/crested/tl/_utils.py b/src/crested/utils/_utils.py similarity index 72% rename from src/crested/tl/_utils.py rename to src/crested/utils/_utils.py index 52f61485..fa5f8b26 100644 --- a/src/crested/tl/_utils.py +++ b/src/crested/utils/_utils.py @@ -1,8 +1,10 @@ from __future__ import annotations +import os from typing import Any, Callable import numpy as np +import pandas as pd import pyBigWig @@ -19,7 +21,9 @@ def str_to_uint8(string) -> np.ndarray: return np.frombuffer(string.encode("ascii"), dtype=np.uint8) # 256 x 4 - hot_encoding_table = np.zeros((np.iinfo(np.uint8).max + 1, len(alphabet)), dtype=dtype) + hot_encoding_table = np.zeros( + (np.iinfo(np.uint8).max + 1, len(alphabet)), dtype=dtype + ) # For each ASCII value of the nucleotides used in the alphabet # (upper and lower case), set 1 in the correct column. @@ -42,7 +46,23 @@ def str_to_uint8(string) -> np.ndarray: def one_hot_encode_sequence(sequence: str, expand_dim: bool = True) -> np.ndarray: - """One hot encode a DNA sequence.""" + """ + One hot encode a DNA sequence. + + Will return a numpy array with shape (len(sequence), 4) if expand_dim is True, otherwise (4,). + Alphabet is ACGT. + + Parameters + ---------- + sequence + The DNA sequence to one hot encode. + expand_dim + Whether to expand the dimensions of the output array. + + Returns + ------- + The one hot encoded DNA sequence. + """ if expand_dim: return np.expand_dims( HOT_ENCODING_TABLE[np.frombuffer(sequence.encode("ascii"), dtype=np.uint8)], @@ -95,11 +115,24 @@ def generate_motif_insertions(x, motif, flanks=(0, 0), masked_locations=None): return np.concatenate(x_mut, axis=0), insertion_locations + class EnhancerOptimizer: - def __init__( - self, - optimize_func: Callable[..., np.intp] - ) -> None: + """ + Class to optimize the mutated sequence based on the original prediction. + + Can be passed as the 'enhancer_optimizer' argument to :func:`crested.tl.Crested.enhancer_design_in_silico_evolution` + + Parameters + ---------- + optimize_func + Function to optimize the mutated sequence based on the original prediction. + + See Also + -------- + crested.tl.Crested.enhancer_design_in_silico_evolution + """ + + def __init__(self, optimize_func: Callable[..., int]) -> None: self.optimize_func = optimize_func def get_best( @@ -107,20 +140,19 @@ def get_best( mutated_predictions: np.ndarray, original_prediction: np.ndarray, target: int | np.ndarray, - **kwargs: dict[str, Any] - ) -> np.intp: + **kwargs: dict[str, Any], + ) -> int: + """Get the index of the best mutated sequence based on the original prediction.""" return self.optimize_func( - mutated_predictions, - original_prediction, - target, - **kwargs + mutated_predictions, original_prediction, target, **kwargs ) + def _weighted_difference( mutated_predictions: np.ndarray, original_prediction: np.ndarray, target: int, - class_penalty_weights: np.ndarray | None = None + class_penalty_weights: np.ndarray | None = None, ): if len(original_prediction.shape) == 1: original_prediction = original_prediction[None] @@ -144,7 +176,9 @@ def _weighted_difference( def build_one_hot_decoding_table() -> np.ndarray: """Get hot decoding table to decode a one hot encoded sequence to a DNA sequence string.""" - one_hot_decoding_table = np.full(np.iinfo(np.uint8).max + 1, ord("N"), dtype=np.uint8) + one_hot_decoding_table = np.full( + np.iinfo(np.uint8).max + 1, ord("N"), dtype=np.uint8 + ) one_hot_decoding_table[1] = ord("A") one_hot_decoding_table[2] = ord("C") one_hot_decoding_table[4] = ord("G") @@ -157,7 +191,18 @@ def build_one_hot_decoding_table() -> np.ndarray: def hot_encoding_to_sequence(one_hot_encoded_sequence: np.ndarray) -> str: - """Decode a one hot encoded sequence to a DNA sequence string.""" + """ + Decode a one hot encoded sequence to a DNA sequence string. + + Parameters + ---------- + one_hot_encoded_sequence + A numpy array with shape (x, 4) with dtype=np.float32. + + Returns + ------- + The DNA sequence string of length x. + """ # Convert hot encoded seqeuence from: # (x, 4) with dtype=np.float32 # to: @@ -189,24 +234,30 @@ def hot_encoding_to_sequence(one_hot_encoded_sequence: np.ndarray) -> str: ) return sequence - + + def get_value_from_dataframe(df: pd.DataFrame, row_name: str, column_name: str): """ Retrieves a single value from a DataFrame based on the given row index and column name. - - Parameters: - - df: pd.DataFrame - The DataFrame to retrieve the value from. - - row_name: str - The name of the row. - - column_name: str - The name of the column. - - Returns: - - The value at the specified row index and column name, or an error message if the column is not found. + + Parameters + ---------- + df + The DataFrame to retrieve the value from. + row_name + The name of the row. + column_name + The name of the column. + + Returns + ------- + The value at the specified row index and column name, or an error message if the column is not found. """ try: # Check if the column exists in the DataFrame if column_name not in df.columns: raise KeyError(f"Column '{column_name}' not found in DataFrame.") - + # Retrieve the value value = df.loc[row_name, column_name] return value @@ -215,25 +266,29 @@ def get_value_from_dataframe(df: pd.DataFrame, row_name: str, column_name: str): return str(e) except IndexError: # Handle the case where the row index is out of bounds - return f"Row index '{row_index}' is out of bounds for DataFrame with {len(df)} rows." - except Exception as e: - # Handle any other unexpected exceptions - return f"An error occurred: {str(e)}" + return f"Row index is out of bounds for DataFrame with {len(df)} rows." -def extract_bigwig_values_per_bp(bigwig_file, coordinates): +def extract_bigwig_values_per_bp( + bigwig_file: os.PathLike, coordinates: list[tuple[str, int, int]] +) -> tuple[np.ndarray, list[int]]: """ Extract per-base pair values from a bigWig file for the given genomic coordinates. - Parameters: - bigwig_file (str): Path to the bigWig file. - coordinates (np.array): An array of tuples, each containing the chromosome name and the start and end positions of the sequence. - - Returns: - bw_values (np.array): A numpy array of values from the bigWig file for each base pair in the specified range. - all_midpoints (list): A list of all base pair positions covered in the specified coordinates. + Parameters + ---------- + bigwig_file + Path to the bigWig file. + coordinates + An array of tuples, each containing the chromosome name and the start and end positions of the sequence. + + Returns + ------- + bw_values + A numpy array of values from the bigWig file for each base pair in the specified range. + all_midpoints + A list of all base pair positions covered in the specified coordinates. """ - # Calculate the full range of coordinates min_coord = min([int(start) for _, start, _ in coordinates]) max_coord = max([int(end) for _, _, end in coordinates]) @@ -258,4 +313,4 @@ def extract_bigwig_values_per_bp(bigwig_file, coordinates): bw.close() - return bw_values, all_midpoints \ No newline at end of file + return bw_values, all_midpoints