import streamlit as st import numpy as np import pandas as pd # Mutation site headers removed 3614, mutation_site_headers_actual = [ 3244, 3297, 3350, 3399, 3455, 3509, 3562, 3665, 3720, 3773, 3824, 3879, 3933, 3985, 4039, 4089, 4145, 4190, 4245, 4298, 4349, 4402, 4455, 4510, 4561, 4615, 4668, 4720, 4773, 4828, 4882 ] # Thresholds for each mutation site removed 3614: 0.091557752, thresholds_actual = pd.Series({ 3244: 1.096910677, 3297: 0.923658795, 3350: 0.668939037, 3399: 0.914305214, 3455: 1.297392984, 3509: 1.812636208, 3562: 1.185047484, 3665: 0.298007308, 3720: 0.58857544, 3773: 0.882561082, 3824: 1.149082617, 3879: 0.816050702, 3933: 2.936517653, 3985: 1.597166791, 4039: 0.962108082, 4089: 1.479783497, 4145: 0.305853225, 4190: 1.311869541, 4245: 1.707556905, 4298: 0.875013076, 4349: 1.227704526, 4402: 0.593206446, 4455: 1.179633137, 4510: 1.272477799, 4561: 1.293841573, 4615: 1.16821885, 4668: 1.40306, 4720: 0.706530878, 4773: 1.483114072, 4828: 0.954939873, 4882: 1.47524328 }) # Mutation site headers reordered: 4402 to 3244, 4882 to 4455 mutation_site_headers = [ 4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039, 3985, 3933, 3879, 3824, 3773, 3720, 3665, 3562, 3509, 3455, 3399, 3350, 3297, 3244, # 1–23 4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455 # 24–32 ] # Thresholds reordered accordingly thresholds = pd.Series({h: thresholds_actual[h] for h in mutation_site_headers}) # === Utility functions === # Voyager ASCII 6-bit conversion table voyager_table = { i: ch for i, ch in enumerate([ ' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '.', ',', '(', ')','+', '-', '*', '/', '=', '$', '!', ':', '%', '"', '#', '@', "'", '?', '&' ]) } reverse_voyager_table = {v: k for k, v in voyager_table.items()} def string_to_binary_labels(s: str) -> list[int]: bits = [] for char in s: val = reverse_voyager_table.get(char.upper(), 0) char_bits = [(val >> bit) & 1 for bit in range(5, -1, -1)] bits.extend(char_bits) return bits def binary_labels_to_string(bits: list[int]) -> str: chars = [] for i in range(0, len(bits), 6): chunk = bits[i:i+6] if len(chunk) < 6: chunk += [0] * (6 - len(chunk)) val = sum(b << (5 - j) for j, b in enumerate(chunk)) chars.append(voyager_table.get(val, '?')) return ''.join(chars) # === Streamlit App === st.title("ASCII & Binary Label Converter") tab1, tab2, tab3, tab4, tab5 = st.tabs(["Text to Binary Labels (31)", "EF → Binary → String (31)", "Text to Binary Labels (32)", "EF → Binary (32)", "Binary → String"]) # Tab 1: Text to Binary with tab1: user_input = st.text_input("Enter text", value="DNA", key="input_text_31") if user_input: ascii_codes = [reverse_voyager_table.get(c.upper(), 0) for c in user_input] binary_labels = string_to_binary_labels(user_input) # st.subheader("Voyager ASCII Codes") # st.write(ascii_codes) st.subheader("Binary Labels per Character") grouped = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)] for i, bits in enumerate(grouped): st.write(f"'{user_input[i]}' → {bits}") st.subheader("Binary Labels (31-bit groups)") groups = [] for i in range(0, len(binary_labels), 31): group = binary_labels[i:i+31] group += [0] * (31 - len(group)) groups.append(group + [sum(group)]) df = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers] + ["Edited Sites"]) st.dataframe(df) st.download_button("Download as CSV", df.to_csv(index=False), "text_31_binary_labels.csv", key="download_csv_tab1_31csv") ascending_headers = sorted(mutation_site_headers_actual) df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]].copy() if "3614" not in df_sorted.columns: idx = df_sorted.columns.get_loc("3562") + 1 # Insert after 3562 df_sorted.insert(idx, "3614", 0) st.subheader("Binary Labels (Ascending Order 3244 → 4882)") st.dataframe(df_sorted) st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv", key="download_csv_tab1_ascend") # === Robot Preparation Script Generation === st.subheader("Robot Preparation Script") robot_template = pd.read_csv("/home/user/app/Robot.csv", skiprows=3) robot_template.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name'] # Add Sample numbers for well referencing df_sorted.insert(0, 'Sample', range(1, len(df_sorted)+1)) # Step 1: Count the number of edited sites per row df_sorted['# donors'] = df_sorted.iloc[:, 1:].sum(axis=1) # Step 2: Calculate volume per donor (32 / # donors) df_sorted['volume donors (µl)'] = 32 / df_sorted['# donors'] # Step 3: Generate the robot script robot_script = [] source_wells = robot_template['Source'].unique().tolist() if len(source_wells) < 32: source_wells += [f"Fake{i}" for i in range(32 - len(source_wells))] source_wells = source_wells[:32] st.write(f"Number of source wells: {len(source_wells)}") st.write(f"Number of binary columns: {len(df_sorted.columns[1:33])}") for i, col in enumerate(df_sorted.columns[1:33]): for row_idx, sample in df_sorted.iterrows(): if sample[col] == 1: source = source_wells[i] dest = f"A{sample['Sample']}" vol = round(sample['volume donors (µl)'], 2) robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol}) robot_script_df = pd.DataFrame(robot_script) st.dataframe(robot_script_df) st.download_button("Download Robot Script CSV", robot_script_df.to_csv(index=False), "robot_script.csv", key="download_csv_tab1_robot") # === Robot Preparation Script (Custom Order: 4402 → 3244, 4882 → 4455) === st.subheader("Robot Preparation Script (Custom Order: 4402 → 3244, 4882 → 4455)") # Include 3614 in custom header list custom_headers = [ 4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039, 3985, 3933, 3879, 3824, 3773, 3720, 3665, 3614, 3562, 3509, 3455, 3399, 3350, 3297, 3244, 4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455 ] # Create a copy of df and reorder columns based on custom headers df_sorted_custom = df[[str(h) for h in custom_headers if str(h) in df.columns]].copy() # Insert fake column "3614" if missing if "3614" not in df_sorted_custom.columns: idx = custom_headers.index(3614) insert_at = idx # 0-based index df_sorted_custom.insert(insert_at, "3614", 0) # Insert 'Sample' if missing if "Sample" not in df_sorted_custom.columns: df_sorted_custom.insert(0, 'Sample', range(1, len(df_sorted_custom) + 1)) # Calculate donor info df_sorted_custom['# donors'] = df_sorted_custom.iloc[:, 1:].sum(axis=1) df_sorted_custom['volume donors (µl)'] = 32 / df_sorted_custom['# donors'] # Generate robot script robot_script_custom = [] for i, col in enumerate(df_sorted_custom.columns[1:33]): # 32 columns after Sample for row_idx, sample in df_sorted_custom.iterrows(): if sample[col] == 1: source = source_wells[i] dest = f"A{sample['Sample']}" vol = round(sample['volume donors (µl)'], 2) robot_script_custom.append({'Source': source, 'Destination': dest, 'Volume': vol}) robot_script_custom_df = pd.DataFrame(robot_script_custom) st.dataframe(robot_script_custom_df) st.download_button("Download Custom Order Robot Script CSV", robot_script_custom_df.to_csv(index=False), "robot_script_custom_order.csv", key="download_csv_tab1_robot_custom") # Tab 2: EF → Binary with tab2: st.write("Upload an Editing Frequency CSV or enter manually:") st.write("**Note:** Please upload CSV files **without column headers**, in ascending order from 3244 to 4882.") ef_file = st.file_uploader("Upload EF CSV", type=["csv"], key="ef") if ef_file: ef_df = pd.read_csv(ef_file, header=None) ef_df.columns = [str(site) for site in sorted(mutation_site_headers_actual)] else: ef_df = pd.DataFrame(columns=[str(site) for site in sorted(mutation_site_headers_actual)]) edited_df = st.data_editor(ef_df, num_rows="dynamic") if st.button("Convert to Binary Labels", key="convert_button_tab2"): binary_part = pd.DataFrame() for col in sorted(mutation_site_headers_actual): col_str = str(col) threshold = thresholds_actual[col] binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int) binary_reordered = binary_part[[str(h) for h in mutation_site_headers if str(h) in binary_part.columns]] def color_binary(val): if val == 1: return "background-color: lightgreen" if val == 0: return "background-color: lightcoral" return "" st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)") styled = binary_reordered.style.applymap(color_binary) st.dataframe(styled) st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv", key="download_csv_tab2_csv") all_bits = binary_reordered.values.flatten().tolist() decoded_string = binary_labels_to_string(all_bits) st.subheader("Decoded String (continuous across rows)") st.write(decoded_string) st.subheader("Binary Labels (Ascending 3244→4882)") st.dataframe(binary_part.style.applymap(color_binary)) st.download_button("Download Ascending Order CSV", binary_part.to_csv(index=False), "ef_binary_labels_ascending.csv", key="download_csv_tab2_ascend") all_bits = binary_part.values.flatten().tolist() decoded_string = binary_labels_to_string(all_bits) st.subheader("Decoded String (continuous across rows)") st.write(decoded_string) # Mutation site headers did not remove 3614, mutation_site_headers_actual_3614 = [ 3244, 3297, 3350, 3399, 3455, 3509, 3562, 3614, 3665, 3720, 3773, 3824, 3879, 3933, 3985, 4039, 4089, 4145, 4190, 4245, 4298, 4349, 4402, 4455, 4510, 4561, 4615, 4668, 4720, 4773, 4828, 4882 ] # Thresholds for each mutation site removed 3614: 0.091557752, thresholds_actual_3614 = pd.Series({ 3244: 1.096910677, 3297: 0.923658795, 3350: 0.668939037, 3399: 0.914305214, 3455: 1.297392984, 3509: 1.812636208, 3562: 1.185047484, 3614: 0.157969131375, 3665: 0.298007308, 3720: 0.58857544, 3773: 0.882561082, 3824: 1.149082617, 3879: 0.816050702, 3933: 2.936517653, 3985: 1.597166791, 4039: 0.962108082, 4089: 1.479783497, 4145: 0.305853225, 4190: 1.311869541, 4245: 1.707556905, 4298: 0.875013076, 4349: 1.227704526, 4402: 0.593206446, 4455: 1.179633137, 4510: 1.272477799, 4561: 1.293841573, 4615: 1.16821885, 4668: 1.40306, 4720: 0.706530878, 4773: 1.483114072, 4828: 0.954939873, 4882: 1.47524328 }) # Mutation site headers reordered: 4402 to 3244, 4882 to 4455 mutation_site_headers_3614 = [ 4402, 4349, 4298, 4245, 4190, 4145, 4089, 4039, 3985, 3933, 3879, 3824, 3773, 3720, 3665, 3614, 3562, 3509, 3455, 3399, 3350, 3297, 3244, # 1–23 4882, 4828, 4773, 4720, 4668, 4615, 4561, 4510, 4455 # 24–32 ] # Thresholds reordered accordingly thresholds_3614 = pd.Series({h: thresholds_actual_3614[h] for h in mutation_site_headers_3614}) # === Utility functions === reverse_voyager_table = {v: k for k, v in voyager_table.items()} # Tab 3: Text to Binary (32) with tab3: user_input_32 = st.text_input("Enter text", value="DNA", key="input_text_32") if user_input_32: ascii_codes = [ord(c) for c in user_input_32] binary_labels = string_to_binary_labels(user_input_32) st.subheader("ASCII Codes") st.write(ascii_codes) st.subheader("Binary Labels per Character") grouped = [binary_labels[i:i+6] for i in range(0, len(binary_labels), 6)] for i, bits in enumerate(grouped): st.write(f"'{user_input_32[i]}' → {bits}") st.subheader("Binary Labels (32-bit groups)") groups = [] for i in range(0, len(binary_labels), 32): group = binary_labels[i:i+32] group += [0] * (32 - len(group)) groups.append(group + [sum(group)]) df = pd.DataFrame(groups, columns=[str(h) for h in mutation_site_headers_3614] + ["Edited Sites"]) st.dataframe(df) st.download_button("Download as CSV", df.to_csv(index=False), "text_32_binary_labels.csv", key="download_csv_tab3_csv") ascending_headers = sorted(mutation_site_headers_actual_3614) df_sorted = df[[str(h) for h in ascending_headers if str(h) in df.columns]] st.subheader("Binary Labels (Ascending Order 3244 → 4882)") st.dataframe(df_sorted) st.download_button("Download Ascending Order CSV", df_sorted.to_csv(index=False), "text_binary_labels_ascending.csv", key="download_csv_tab3_ascend") # === Robot Preparation Script Generation === st.subheader("Robot Preparation Script") robot_template = pd.read_csv("/home/user/app/Robot.csv", skiprows=3) robot_template.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name'] # Add Sample numbers for well referencing df_sorted.insert(0, 'Sample', range(1, len(df_sorted)+1)) # Step 1: Count the number of edited sites per row df_sorted['# donors'] = df_sorted.iloc[:, 1:].sum(axis=1) # Step 2: Calculate volume per donor (32 / # donors) df_sorted['volume donors (µl)'] = 32 / df_sorted['# donors'] # Step 3: Generate the robot script robot_script = [] source_wells = robot_template['Source'].unique().tolist()[:32] for i, col in enumerate(df_sorted.columns[1:33]): for row_idx, sample in df_sorted.iterrows(): if sample[col] == 1: source = source_wells[i] dest = f"A{sample['Sample']}" vol = round(sample['volume donors (µl)'], 2) robot_script.append({'Source': source, 'Destination': dest, 'Volume': vol}) robot_script_df = pd.DataFrame(robot_script) st.dataframe(robot_script_df) st.download_button("Download Robot Script CSV", robot_script_df.to_csv(index=False), "robot_script.csv", key="download_csv_tab3_robot") # Tab 4: EF → Binary (32) with tab4: st.write("Upload an Editing Frequency CSV or enter manually:") st.write("**Note:** Please upload CSV files **without column headers**, in ascending order from 3244 to 4882.") ef_file_2 = st.file_uploader("Upload EF CSV", type=["csv"], key="ef2") if ef_file_2: ef_df = pd.read_csv(ef_file_2, header=None) ef_df.columns = [str(site) for site in sorted(mutation_site_headers_actual_3614)] else: ef_df = pd.DataFrame(columns=[str(site) for site in sorted(mutation_site_headers_actual_3614)]) edited_df = st.data_editor(ef_df, num_rows="dynamic") if st.button("Convert to Binary Labels", key="convert_button_tab4"): binary_part = pd.DataFrame() for col in sorted(mutation_site_headers_actual_3614): col_str = str(col) threshold = thresholds_actual_3614[col] binary_part[col_str] = (edited_df[col_str].astype(float) >= threshold).astype(int) binary_reordered = binary_part[[str(h) for h in mutation_site_headers_3614 if str(h) in binary_part.columns]] def color_binary(val): if val == 1: return "background-color: lightgreen" if val == 0: return "background-color: lightcoral" return "" st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)") styled = binary_reordered.style.applymap(color_binary) st.dataframe(styled) st.download_button("Download CSV", binary_reordered.to_csv(index=False), "ef_binary_labels.csv", key="download_csv_tab4_csv") all_bits = binary_reordered.values.flatten().tolist() decoded_string = binary_labels_to_string(all_bits) st.subheader("Decoded String (continuous across rows)") st.write(decoded_string) st.subheader("Binary Labels (Ascending 3244→4882)") st.dataframe(binary_part.style.applymap(color_binary)) st.download_button("Download Ascending Order CSV", binary_part.to_csv(index=False), "ef_binary_labels_ascending.csv", key="download_csv_tab4_ascend") all_bits = binary_part.values.flatten().tolist() decoded_string = binary_labels_to_string(all_bits) st.subheader("Decoded String (continuous across rows)") st.write(decoded_string) def get_well_position(sample_index): """ Convert sample index (1-based) into well position (e.g., A1, A2, ..., B1, B2, ..., etc.) """ row_letter = chr(65 + (sample_index - 1) // 12) # 65 = 'A' col_number = ((sample_index - 1) % 12) + 1 return f"{row_letter}{col_number}" # Tab 5: Binary → String with tab5: st.header("Decode Binary Labels to String") # Utility: Track source volumes and update if exceeds limit def track_and_replace_source(source_list, robot_script, volume_limit=170): source_volumes = {} adjusted_sources = [] for entry in robot_script: src = entry['Source'] vol = entry['Volume'] if src not in source_volumes: source_volumes[src] = 0 source_volumes[src] += vol if source_volumes[src] > volume_limit: row_letter = src[0] col_number = src[1:] new_row_letter = chr(ord(row_letter) + 4) new_src = f"{new_row_letter}{col_number}" entry['Source'] = new_src if new_src not in source_volumes: source_volumes[new_src] = 0 source_volumes[new_src] += vol source_volumes[src] -= vol adjusted_sources.append(entry) return adjusted_sources, source_volumes # Utility: Generate fixed-volume D source to all sample wells def generate_fixed_d_source_instructions_to_all_samples(n_samples, fixed_volume=16, volume_limit=170): d_source_volumes = {} d_source_script = [] current_d_index = 1 for i in range(n_samples): dest = get_well_position(i + 1) current_d_well = f"D{current_d_index}" if current_d_well not in d_source_volumes: d_source_volumes[current_d_well] = 0 if d_source_volumes[current_d_well] + fixed_volume > volume_limit: current_d_index += 1 current_d_well = f"D{current_d_index}" d_source_volumes[current_d_well] = 0 d_source_volumes[current_d_well] += fixed_volume tool = 'TS_10' if fixed_volume < 10 else 'TS_50' d_source_script.append({ 'Source': current_d_well, 'Destination': dest, 'Volume': fixed_volume, 'Tool': tool }) return d_source_script, d_source_volumes def generate_source_wells(n): wells = [] rows = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' for i in range(n): row = rows[i // 12] # cycle through A, B, C... col = (i % 12) + 1 # 1 to 12 wells.append(f"{row}{col}") return wells # ========== 32-BIT DECODING ========== st.subheader("32-bit Binary per Row") st.write("Upload CSV with 32 columns (0 or 1), no headers, from EF Binary format or enter manually below.") binary32_file = st.file_uploader("Upload 32-bit Binary CSV", type=["csv"], key="binary_32") st.subheader("Optional Metadata (Optional)") barcode_id_input = st.text_input("Barcode ID (applied to all rows, optional)", value="") labware_source_input = st.text_input("Labware for Source (optional, default = 1)", value="1") labware_dest_input = st.text_input("Labware for Destination (optional, default = 1)", value="1") name_input = st.text_input("Name field (optional, default = blank)", value="") if binary32_file: df_32 = pd.read_csv(binary32_file, header=None) df_32.columns = [str(h) for h in mutation_site_headers_actual_3614] else: df_32 = st.data_editor( pd.DataFrame(columns=[str(h) for h in mutation_site_headers_actual_3614]), num_rows="dynamic", key="manual_32_input" ) if not df_32.empty: reordered_df_32 = df_32[[str(h) for h in mutation_site_headers_3614 if str(h) in df_32.columns]] st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)") st.dataframe(reordered_df_32.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral")) st.download_button("Download Reordered CSV", reordered_df_32.to_csv(index=False), "decoded_binary_32_reordered.csv", key="download_csv_tab5_32_reordered") decoded_reordered = binary_labels_to_string(reordered_df_32.values.flatten().astype(int).tolist()) st.subheader("Decoded String (Reordered 4402→3244, 4882→4455)") st.write(decoded_reordered) st.download_button("Download Concatenated Output", decoded_reordered, "decoded_32bit_string_reordered.txt", key="download_txt_tab5_32") df_32_asc = df_32[[str(h) for h in mutation_site_headers_actual_3614 if str(h) in df_32.columns]] st.subheader("Binary Labels (Ascending 3244→4882)") st.dataframe(df_32_asc.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral")) st.download_button("Download Ascending CSV", df_32_asc.to_csv(index=False), "decoded_binary_32_ascending.csv", key="download_csv_tab5_32_ascend") decoded_asc = binary_labels_to_string(df_32_asc.values.flatten().astype(int).tolist()) st.subheader("Decoded String (Flattened 32-bit Ascending)") st.write(decoded_asc) st.download_button("Download Concatenated Output", decoded_asc, "decoded_32bit_string_ascending.txt", key="download_txt_tab5_32_asc") st.subheader("Robot Preparation Script from 32-bit Binary") df_32_robot = df_32.copy() df_32_robot.insert(0, 'Sample', range(1, len(df_32_robot)+1)) df_32_robot['# donors'] = df_32_robot.iloc[:, 1:].astype(int).sum(axis=1) df_32_robot['volume donors (µl)'] = 64 / df_32_robot['# donors'] robot_script_32 = [] source_wells_32 = generate_source_wells(df_32.shape[1]) used_destinations = set() for i, col in enumerate(df_32.columns): for row_idx, sample in df_32_robot.iterrows(): if int(sample[col]) == 1: source = source_wells_32[i] dest = get_well_position(int(sample['Sample'])) used_destinations.add(dest) vol = round(sample['volume donors (µl)'], 2) tool = 'TS_10' if vol < 10 else 'TS_50' robot_script_32.append({ 'Source': source, 'Destination': dest, 'Volume': vol, 'Tool': tool }) robot_script_32, source_volumes_32 = track_and_replace_source(source_wells_32, robot_script_32) d_script, d_volumes = generate_fixed_d_source_instructions_to_all_samples(len(df_32_robot)) full_robot_script = robot_script_32 + d_script robot_script_32_df = pd.DataFrame(full_robot_script) robot_script_32_df.insert(0, 'Barcode ID', barcode_id_input) robot_script_32_df.insert(1, 'Labware_Source', labware_source_input) robot_script_32_df.insert(3, 'Labware_Destination', labware_dest_input) robot_script_32_df['Name'] = name_input robot_script_32_df = robot_script_32_df[['Barcode ID', 'Labware_Source', 'Source', 'Labware_Destination', 'Destination', 'Volume', 'Tool', 'Name']] st.dataframe(robot_script_32_df) st.download_button("Download Robot Script (32-bit)", robot_script_32_df.to_csv(index=False), "robot_script_32bit.csv", key="download_robot_32") st.subheader("Total Volume Used Per Source") combined_volumes = {**source_volumes_32, **d_volumes} source_volume_df = pd.DataFrame(list(combined_volumes.items()), columns=['Source', 'Total Volume (µl)']) st.dataframe(source_volume_df) st.download_button("Download Source Volumes", source_volume_df.to_csv(index=False), "source_total_volumes.csv", key="download_volume_32") st.markdown("---") # ========== 31-BIT DECODING ========== st.subheader("31-bit Binary Grouped per Row") st.write("Upload CSV with 31 columns (no headers), each row = one 6-bit ASCII character group or enter manually below.") binary31_file = st.file_uploader("Upload 31-bit Group CSV", type=["csv"], key="binary_31") if binary31_file: df_31 = pd.read_csv(binary31_file, header=None) df_31.columns = [str(h) for h in mutation_site_headers_actual] # assume ascending else: df_31 = st.data_editor( pd.DataFrame(columns=[str(h) for h in mutation_site_headers_actual]), num_rows="dynamic", key="manual_31_input" ) if not df_31.empty: reordered_df_31 = df_31[[str(h) for h in mutation_site_headers if str(h) in df_31.columns]] st.subheader("Binary Labels (Reordered 4402→3244, 4882→4455)") st.dataframe(reordered_df_31.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral")) st.download_button("Download Reordered CSV", reordered_df_31.to_csv(index=False), "decoded_binary_31_reordered.csv", key="download_csv_tab5_31_reordered") decoded_flat_reordered = binary_labels_to_string(reordered_df_31.values.flatten().astype(int).tolist()) st.subheader("Decoded String (Flattened 31-bit Reordered)") st.write(decoded_flat_reordered) st.download_button("Download Concatenated Output", decoded_flat_reordered, "decoded_31bit_string_reordered.txt", key="download_csv_tab5_31") df_31_asc = df_31[[str(h) for h in mutation_site_headers_actual if str(h) in df_31.columns]] st.subheader("Binary Labels (Ascending 3244→4882)") st.dataframe(df_31_asc.style.applymap(lambda v: "background-color: lightgreen" if v == 1 else "background-color: lightcoral")) st.download_button("Download Ascending CSV", df_31_asc.to_csv(index=False), "decoded_binary_31_ascending.csv", key="download_csv_tab5_31_ascend") decoded_flat_asc = binary_labels_to_string(df_31_asc.values.flatten().astype(int).tolist()) st.subheader("Decoded String (Flattened 31-bit Ascending)") st.write(decoded_flat_asc) st.download_button("Download Concatenated Output", decoded_flat_asc, "decoded_31bit_string_ascending.txt", key="download_csv_tab5_31_asc") # === Robot Preparation Script from 31-bit Binary === st.subheader("Robot Preparation Script from 31-bit Binary") robot_template_31 = pd.read_csv("/home/user/app/Robot2.csv", skiprows=3) robot_template_31.columns = ['Labware', 'Source', 'Labware_2', 'Destination', 'Volume', 'Tool', 'Name'] df_31_robot = df_31.copy() df_31_robot.insert(0, 'Sample', range(1, len(df_31_robot)+1)) df_31_robot['# donors'] = df_31_robot.iloc[:, 1:].astype(int).sum(axis=1) df_31_robot['volume donors (µl)'] = 64 / df_31_robot['# donors'] robot_script_31 = [] source_wells_31 = robot_template_31['Source'].unique().tolist() if len(source_wells_31) < df_31.shape[1]: source_wells_31 += [f"Fake{i}" for i in range(df_31.shape[1] - len(source_wells_31))] source_wells_31 = source_wells_31[:df_31.shape[1]] for i, col in enumerate(df_31.columns): for row_idx, sample in df_31_robot.iterrows(): if int(sample[col]) == 1: source = source_wells_31[i] dest = get_well_position(int(sample['Sample'])) vol = round(sample['volume donors (µl)'], 2) robot_script_31.append({'Source': source, 'Destination': dest, 'Volume': vol}) robot_script_31_df = pd.DataFrame(robot_script_31) st.dataframe(robot_script_31_df) st.download_button("Download Robot Script (31-bit)", robot_script_31_df.to_csv(index=False), "robot_script_31bit.csv", key="download_robot_31")