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import os |
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import pandas as pd |
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basePath = "./data/output/data/" |
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uploadPath = "./data/latents/" |
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def getALlData(): |
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files = os.listdir(basePath) |
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merged_df = None |
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for each in files: |
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df = pd.read_csv(os.path.join(basePath, each)) |
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if merged_df is None: merged_df = df |
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else: merged_df = pd.concat([merged_df, df], ignore_index=True) |
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grouped_df = merged_df.groupby(['methods', 'datasets']).mean().reset_index() |
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grouped_df = grouped_df.fillna(0) |
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for col in grouped_df.select_dtypes(include=['float']).columns: |
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grouped_df[col] = grouped_df[col].round(4) |
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data = grouped_df.to_dict(orient='records') |
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return data |
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def getList(datatype): |
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if datatype == "Integration Accuracy": |
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file = "integration_accuracy.csv" |
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elif datatype == "Batch Correction": |
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file = "batch.csv" |
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elif datatype == "Bio Conservation": |
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file = "biomarker.csv" |
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path = os.path.join(basePath, file) |
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df = pd.read_csv(path) |
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df["object_type"] = datatype |
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data = df.to_dict(orient='records') |
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return data |
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def getListByName(file, name): |
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path = os.path.join(basePath, file) |
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df = pd.read_csv(path) |
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filtered_records = df[df['methods'] == name] |
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data = filtered_records.to_dict(orient='records') |
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return data |
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def uploadFile(uploadFiles): |
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for file in uploadFiles: |
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save_path = os.path.join(uploadPath, file.filename) |
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file.save(save_path) |
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