Sourcery refactored master branch #1
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| fig = plt.figure() | ||
| ax = fig.gca(projection='3d') | ||
| ax.plot_trisurf(df['Chunk Size'], df['p4,p11'], df['Match Rate'], linewidth=0.2) | ||
| ax.set_xlabel("Chunk Size") | ||
| ax.set_ylabel("Parameters") | ||
| ax.set_zlabel("Match Rate") | ||
| plt.show() | ||
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| df2 = df[df["Tool"] == "mgm2"].groupby(["p4", "p11"], as_index=False).mean() | ||
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| idx = df2["Match Rate"].argmax() | ||
| p4 = df2.at[idx, "p4"] | ||
| p11 = df2.at[idx, "p11"] | ||
| df_best = df[(df["p4"] == p4) & (df["p11"] == p11)] | ||
| df_alex = df[(df["p4"] == 10) & (df["p11"] == 20)] | ||
| fig, ax = plt.subplots() | ||
| sns.lineplot("Chunk Size", "Match Rate", data=df_best, label="Optimized") | ||
| sns.lineplot("Chunk Size", "Match Rate", data=df[df["Tool"] == "mprodigal"], label="MProdigal") | ||
| sns.lineplot("Chunk Size", "Match Rate", data=df_alex, label="Original") | ||
| ax.set_ylim(0, 1) | ||
| plt.show() |
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Function main refactored with the following changes:
- Remove unreachable code (
remove-unreachable-code)
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| labels_per_seqname[lab.seqname()] = list() | ||
| labels_per_seqname[lab.seqname()] = [] | ||
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| labels_per_seqname[lab.seqname()].append(lab) | ||
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| counter = 0 | ||
| for seqname in labels_per_seqname: | ||
| for counter, (seqname, value) in enumerate(labels_per_seqname.items()): |
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Function get_features_from_prediction refactored with the following changes:
- Replace
list()with[](list-literal) - Use items() to directly unpack dictionary values (
use-dict-items) - Replace manual loop counter with call to enumerate (
convert-to-enumerate) - Replace comparison with min/max call [×2] (
min-max-identity)
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| list_entries = list() | ||
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Function build_gcode_features_for_gi_for_chunk refactored with the following changes:
- Move assignment closer to its usage within a block (
move-assign-in-block) - Replace
list()with[](list-literal) - Merge append into list declaration (
merge-list-append)
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| list_df = list() | ||
| list_df = [] |
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Function build_gcode_features_for_gi refactored with the following changes:
- Replace
list()with[][×2] (list-literal)
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| # type: (Environment, GenomeInfoList, str, List[int], Dict[str, Any]) -> pd.DataFrame | ||
| list_df = list() | ||
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| for gi in gil: | ||
| list_df.append( | ||
| build_gcode_features_for_gi(env, gi, tool, chunks, **kwargs) | ||
| ) | ||
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| list_df = [ | ||
| build_gcode_features_for_gi(env, gi, tool, chunks, **kwargs) | ||
| for gi in gil | ||
| ] |
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Function build_gcode_features refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension) - Replace
list()with[](list-literal)
This removes the following comments ( why? ):
# type: (Environment, GenomeInfoList, str, List[int], Dict[str, Any]) -> pd.DataFrame
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| list_entries = list() | ||
| list_entries = [] |
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Function add_codon_probabilities refactored with the following changes:
- Replace
list()with[](list-literal)
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| x_out = list() | ||
| y_out = list() | ||
| x_out = [] | ||
| y_out = [] |
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Function compute_bin_averages refactored with the following changes:
- Replace
list()with[][×2] (list-literal) - Simplify sequence length comparison (
simplify-len-comparison)
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| for gc_tag in sc_gc.keys(): | ||
| for gc_tag in sc_gc: |
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Function add_start_context_probabilities refactored with the following changes:
- Remove unnecessary call to keys() (
remove-dict-keys)
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| list_mgm_models = list() # type: List[List[float, float, MGMMotifModelV2]] | ||
| list_mgm_models = [] |
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Function build_mgm_motif_models_for_all_gc refactored with the following changes:
- Replace
list()with[](list-literal) - Simplify sequence length comparison (
simplify-len-comparison)
This removes the following comments ( why? ):
# type: List[List[float, float, MGMMotifModelV2]]
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| if True or "RBS" in output_tag: | ||
| # create a label for each shift | ||
| for shift, prob in motif._shift_prior.items(): | ||
| prob /= 100.0 | ||
| output_tag_ws = f"{output_tag}_{int(shift)}" | ||
| try: | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_MAT"] = motif._motif[shift] | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_POS_DISTR"] = motif._spacer[ | ||
| shift] | ||
| except KeyError: | ||
| pass | ||
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| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}"] = 1 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_ORDER"] = 0 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_WIDTH"] = width | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_MARGIN"] = 0 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_MAX_DUR"] = dur | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_SHIFT"] = prob | ||
| else: | ||
| # promoter aren't shifted (for now) | ||
| best_shift = max(motif._shift_prior.items(), key=operator.itemgetter(1))[0] | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}_MAT"] = motif._motif[best_shift] | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}_POS_DISTR"] = motif._spacer[best_shift] | ||
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| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}"] = 1 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}_ORDER"] = 0 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}_WIDTH"] = width | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}_MARGIN"] = 0 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag}_MAX_DUR"] = dur | ||
| # create a label for each shift | ||
| for shift, prob in motif._shift_prior.items(): | ||
| prob /= 100.0 | ||
| output_tag_ws = f"{output_tag}_{int(shift)}" | ||
| try: | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_MAT"] = motif._motif[shift] | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_POS_DISTR"] = motif._spacer[ | ||
| shift] | ||
| except KeyError: | ||
| pass | ||
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| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}"] = 1 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_ORDER"] = 0 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_WIDTH"] = width | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_MARGIN"] = 0 | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_MAX_DUR"] = dur | ||
| mgm.items_by_species_and_gc[genome_tag][str(gc)].items[f"{output_tag_ws}_SHIFT"] = prob |
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Function add_motif_probabilities refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if)
This removes the following comments ( why? ):
# promoter aren't shifted (for now)
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| mgm, | ||
| "RBS", f"RBS_{o}", genome_type, plot=plot | ||
| ) | ||
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| for o, l in zip(output_group, learn_from): | ||
| add_motif_probabilities( | ||
| env, | ||
| df_type[(df_type["GENOME_TYPE"].isin(l))], | ||
| mgm, | ||
| "RBS", f"RBS_{o}", genome_type, plot=plot | ||
| ) | ||
| if "Promoter" in components: | ||
| df_type = df[df["Type"] == genome_type] | ||
| if genome_type == "Archaea": | ||
| output_group = ["D"] | ||
| learn_from = [{"D"}] # always learn Promoter form group D | ||
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| df_type = df[df["Type"] == genome_type] | ||
| for o, l in zip(output_group, learn_from): | ||
| add_motif_probabilities( | ||
| env, | ||
| df_type[(df_type["GENOME_TYPE"].isin(l))], | ||
| mgm, | ||
| "PROMOTER", f"PROMOTER_{o}", genome_type, plot=plot | ||
| ) | ||
| else: | ||
| output_group = ["C"] | ||
| learn_from = [{"C"}] # always learn Promoter form group C | ||
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| df_type = df[df["Type"] == genome_type] | ||
| for o, l in zip(output_group, learn_from): | ||
| add_motif_probabilities( | ||
| env, | ||
| df_type[(df_type["GENOME_TYPE"].isin(l))], | ||
| mgm, | ||
| "PROMOTER", f"PROMOTER_{o}", genome_type, plot=plot | ||
| ) | ||
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| for o, l in zip(output_group, learn_from): | ||
| add_motif_probabilities( | ||
| env, | ||
| df_type[(df_type["GENOME_TYPE"].isin(l))], | ||
| mgm, | ||
| "PROMOTER", f"PROMOTER_{o}", genome_type, plot=plot | ||
| ) | ||
| # Start Context | ||
| if "Start Context" in components: | ||
| if genome_type == "Archaea": | ||
| output_group = ["A", "D"] | ||
| learn_from = learn_from_arc | ||
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| for o, l in zip(output_group, learn_from): | ||
| df_curr = df[(df["Type"] == genome_type) & (df["GENOME_TYPE"].isin(l))] | ||
| add_start_context_probabilities(df_curr, mgm, "SC_RBS", f"SC_RBS_{o}", genome_type=genome_type, | ||
| plot=plot) | ||
| else: | ||
| output_group = ["A", "B", "C", "X"] | ||
| learn_from = [{"A"}, {"B"}, {"C"}, {"A"}] | ||
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| for o, l in zip(output_group, learn_from): | ||
| df_curr = df[(df["Type"] == genome_type) & (df["GENOME_TYPE"].isin(l))] | ||
| add_start_context_probabilities(df_curr, mgm, "SC_RBS", f"SC_RBS_{o}", genome_type=genome_type, | ||
| plot=plot) | ||
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| for o, l in zip(output_group, learn_from): | ||
| df_curr = df[(df["Type"] == genome_type) & (df["GENOME_TYPE"].isin(l))] | ||
| add_start_context_probabilities(df_curr, mgm, "SC_RBS", f"SC_RBS_{o}", genome_type=genome_type, | ||
| plot=plot) | ||
| # promoter | ||
| if genome_type == "Archaea": | ||
| output_group = ["D"] | ||
| learn_from = [{"A", "D"}] # always learn RBS form group A | ||
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| for o, l in zip(output_group, learn_from): | ||
| df_curr = df[(df["Type"] == genome_type) & (df["GENOME_TYPE"].isin(l))] | ||
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| # NOTE: SC_PROMOTER is intentionally learned from SC_RBS. This is not a bug | ||
| # GMS2 has equal values for SC_RBS and SC_PROMOTER. Training from SC_RBS therefore allows us | ||
| # to learn from group A genomes as well. | ||
| add_start_context_probabilities(df_curr, mgm, "SC_RBS", f"SC_PROMOTER_{o}", genome_type=genome_type, | ||
| plot=plot) | ||
| else: | ||
| output_group = ["C"] | ||
| learn_from = [{"C"}] | ||
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| for o, l in zip(output_group, learn_from): | ||
| df_curr = df[(df["Type"] == genome_type) & (df["GENOME_TYPE"].isin(l))] | ||
| # NOTE: SC_PROMOTER is intentionally learned from SC_RBS. This is not a bug | ||
| # GMS2 has equal values for SC_RBS and SC_PROMOTER. Training from SC_RBS therefore allows us | ||
| # to learn from group A genomes as well. | ||
| add_start_context_probabilities(df_curr, mgm, "SC_RBS", f"SC_PROMOTER_{o}", genome_type=genome_type, | ||
| plot=plot) | ||
| for o, l in zip(output_group, learn_from): | ||
| df_curr = df[(df["Type"] == genome_type) & (df["GENOME_TYPE"].isin(l))] | ||
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| # NOTE: SC_PROMOTER is intentionally learned from SC_RBS. This is not a bug | ||
| # GMS2 has equal values for SC_RBS and SC_PROMOTER. Training from SC_RBS therefore allows us | ||
| # to learn from group A genomes as well. | ||
| add_start_context_probabilities(df_curr, mgm, "SC_RBS", f"SC_PROMOTER_{o}", genome_type=genome_type, | ||
| plot=plot) |
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Function build_mgm_models_from_gms2_models refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if) - Hoist repeated code outside conditional statement [×6] (
hoist-statement-from-if)
This removes the following comments ( why? ):
# add_stop_codon_probabilities(df, mgm, genome_type=genome_type, plot=plot)
# NOTE: SC_PROMOTER is intentionally learned from SC_RBS. This is not a bug
# to learn from group A genomes as well.
# add_stop_codon_probabilities(df, mgm, genome_type="Archaea", plot=plot)
# if "Stop Codons" in components:
# GMS2 has equal values for SC_RBS and SC_PROMOTER. Training from SC_RBS therefore allows us
| list_entries = list() | ||
| list_entries = [] |
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Function collect_start_info_from_gil refactored with the following changes:
- Replace
list()with[](list-literal)
| list_entries = list() | ||
| list_entries = [] |
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Function collect_start_info_from_gil_and_print_to_file refactored with the following changes:
- Replace
list()with[](list-literal)
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| df[f"CONSENSUS_RBS_MAT"] = df.apply(lambda r: get_consensus_sequence(r["Mod"].items["RBS_MAT"]), axis=1) | ||
| df["CONSENSUS_RBS_MAT"] = df.apply( | ||
| lambda r: get_consensus_sequence(r["Mod"].items["RBS_MAT"]), axis=1 | ||
| ) |
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Function load_gms2_models_from_pickle refactored with the following changes:
- Replace f-string with no interpolated values with string (
remove-redundant-fstring)
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| peak_to_list_pos_dist[peak] = list() | ||
| peak_to_list_pos_dist[peak] = [] | ||
| peak_to_list_pos_dist[peak].append(l) | ||
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| # average positions (per peak) | ||
| values = dict() | ||
| peak_counter = 0 | ||
| for peak in peak_to_list_pos_dist.keys(): | ||
| for peak in peak_to_list_pos_dist: |
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Function merge_spacers_by_peak refactored with the following changes:
- Replace
list()with[][×3] (list-literal) - Remove unnecessary call to keys() [×3] (
remove-dict-keys) - Replace identity comprehension with call to collection constructor (
identity-comprehension)
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| elif tool == "mprodigal" or tool == "prodigal": | ||
| elif tool in ["mprodigal", "prodigal"]: | ||
| gcode_per_contig = get_gcode_per_contig_for_mprodigal(pf_prediction) | ||
| else: | ||
| raise ValueError("Unknown tool") | ||
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| num_matches = sum([1 for v in gcode_per_contig.values() if str(v) == gcode_true]) | ||
| num_mismatches = sum([1 for v in gcode_per_contig.values() if str(v) != gcode_true]) | ||
| num_matches = sum(1 for v in gcode_per_contig.values() if str(v) == gcode_true) | ||
| num_mismatches = sum( | ||
| 1 for v in gcode_per_contig.values() if str(v) != gcode_true | ||
| ) |
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Function get_accuracy_gcode_predicted refactored with the following changes:
- Replace multiple comparisons of same variable with
inoperator (merge-comparisons) - Replace unneeded comprehension with generator [×2] (
comprehension-to-generator)
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| list_entries = list() | ||
| list_entries = [] |
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Function compute_gcode_accuracy_for_tool_on_sequence refactored with the following changes:
- Replace
list()with[](list-literal)
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| list_entries = list() | ||
| list_entries = [] |
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Function compute_gcode_accuracy_for_tools_on_chunk_deprecated refactored with the following changes:
- Replace
list()with[](list-literal)
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| list_df = list() | ||
| list_df = [] |
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Function compute_gcode_accuracy_for_tools_on_chunk refactored with the following changes:
- Replace
list()with[](list-literal)
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| list_df = list() | ||
| list_df = [] |
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Function compute_gcode_accuracy_for_gi refactored with the following changes:
- Replace
list()with[][×2] (list-literal)
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| # type: (Environment, GenomeInfoList, List[str], List[int], Dict[str, Any]) -> pd.DataFrame | ||
| list_df = list() | ||
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| for gi in gil: | ||
| list_df.append( | ||
| compute_gcode_accuracy_for_gi(env, gi, tools, chunks, **kwargs) | ||
| ) | ||
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| list_df = [ | ||
| compute_gcode_accuracy_for_gi(env, gi, tools, chunks, **kwargs) | ||
| for gi in gil | ||
| ] |
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Function compute_gcode_accuracy refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension) - Replace
list()with[](list-literal)
This removes the following comments ( why? ):
# type: (Environment, GenomeInfoList, List[str], List[int], Dict[str, Any]) -> pd.DataFrame
| if p_true == 0: | ||
| return float('inf') | ||
| return 1.0 / p_true - 1 | ||
| return float('inf') if p_true == 0 else 1.0 / p_true - 1 |
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Function ratio_false_true refactored with the following changes:
- Lift code into else after jump in control flow (
reintroduce-else) - Replace if statement with if expression (
assign-if-exp)
| logger.critical("Random-seed: {}".format(rs)) | ||
| logger.critical(f"Random-seed: {rs}") |
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Function main refactored with the following changes:
- Replace call to format with f-string [×3] (
use-fstring-for-formatting) - Swap positions of nested conditionals [×6] (
swap-nested-ifs)
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| return list() | ||
| return [] | ||
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| if len(list_tag_value_pairs) % 2 != 0: | ||
| raise ValueError("Tag/value pairs list must have a length multiple of 2.") | ||
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| list_parsed = list() | ||
| for i in range(0, len(list_tag_value_pairs), 2): | ||
| list_parsed.append((list_tag_value_pairs[i], list_tag_value_pairs[i + 1])) | ||
| return list_parsed | ||
| return [ | ||
| (list_tag_value_pairs[i], list_tag_value_pairs[i + 1]) | ||
| for i in range(0, len(list_tag_value_pairs), 2) | ||
| ] |
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Function parse_tags_from_list refactored with the following changes:
- Replace
list()with[][×2] (list-literal) - Convert for loop into list comprehension (
list-comprehension) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| elif prl_options["use-pbs"]: | ||
| # setup PBS jobs | ||
| pbs = PBS(env, prl_options, splitter=split_gil, merger=merge_identity) | ||
| pbs.run( | ||
| data={"gil": gil}, | ||
| func=helper_run_mgm_on_genome_list, | ||
| func_kwargs={"env": env, "pf_mgm_mod": pf_mgm_mod, **kwargs} | ||
| ) | ||
| else: | ||
| # PBS Parallelization | ||
| if prl_options["use-pbs"]: | ||
| # setup PBS jobs | ||
| pbs = PBS(env, prl_options, splitter=split_gil, merger=merge_identity) | ||
| pbs.run( | ||
| data={"gil": gil}, | ||
| func=helper_run_mgm_on_genome_list, | ||
| func_kwargs={"env": env, "pf_mgm_mod": pf_mgm_mod, **kwargs} | ||
| ) | ||
| # Multithreading parallelization | ||
| else: | ||
| # parallel using threads | ||
| run_n_per_thread( | ||
| list(gil), run_mgm_on_gi, "gi", | ||
| {"env": env, "pf_mgm_mod": pf_mgm_mod, **kwargs}, | ||
| simultaneous_runs=prl_options.safe_get("num-processors") | ||
| ) | ||
| # parallel using threads | ||
| run_n_per_thread( | ||
| list(gil), run_mgm_on_gi, "gi", | ||
| {"env": env, "pf_mgm_mod": pf_mgm_mod, **kwargs}, | ||
| simultaneous_runs=prl_options.safe_get("num-processors") | ||
| ) |
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Function run_mgm_on_genome_list refactored with the following changes:
- Merge else clause's nested if statement into elif (
merge-else-if-into-elif)
This removes the following comments ( why? ):
# Multithreading parallelization
# PBS Parallelization
| # type: (Environment, GenomeInfoList, List[str], List[int], Dict[str, Any]) -> None | ||
| list_df = list() | ||
| for gi in gil: | ||
| list_df.append(run_tools_on_gi(env, gi, tools, chunks, **kwargs)) | ||
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| list_df = [run_tools_on_gi(env, gi, tools, chunks, **kwargs) for gi in gil] |
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Function run_tools_on_gil refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension) - Replace
list()with[](list-literal)
This removes the following comments ( why? ):
# type: (Environment, GenomeInfoList, List[str], List[int], Dict[str, Any]) -> None
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| if len(pf_predictions) == 0: | ||
| if not pf_predictions: | ||
| return pd.DataFrame() | ||
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| name_to_labels = { | ||
| t: read_labels_from_file(pf_predictions[t], shift=-1, name=t) for t in pf_predictions.keys() | ||
| } # type: Dict[str, Labels] | ||
| t: read_labels_from_file(pf_predictions[t], shift=-1, name=t) | ||
| for t in pf_predictions | ||
| } |
There was a problem hiding this comment.
Function stats_per_gene_for_gi refactored with the following changes:
- Simplify sequence length comparison (
simplify-len-comparison) - Remove unnecessary call to keys() [×2] (
remove-dict-keys) - Replace
list()with[](list-literal) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# df[f"5p-{t}"] = df[f"5p-{t}"].astype(int)
# for t in tools.keys():
# type: Dict[str, Labels]
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| list_df = list() | ||
| for gi in gil: | ||
| list_df.append(stats_per_gene_for_gi(env, gi, tools, **kwargs)) | ||
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| if len(list_df) == 0: | ||
| if list_df := [ | ||
| stats_per_gene_for_gi(env, gi, tools, **kwargs) for gi in gil | ||
| ]: | ||
| return pd.concat(list_df, ignore_index=True, sort=False) | ||
| else: | ||
| return pd.DataFrame() | ||
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| return pd.concat(list_df, ignore_index=True, sort=False) |
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Function helper_stats_per_gene refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension) - Replace
list()with[](list-literal) - Simplify sequence length comparison (
simplify-len-comparison) - Lift code into else after jump in control flow (
reintroduce-else) - Swap if/else branches (
swap-if-else-branches) - Use named expression to simplify assignment and conditional (
use-named-expression)
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| df = pd.concat(list_df, ignore_index=True, sort=False) | ||
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| # threading |
There was a problem hiding this comment.
Function stats_per_gene refactored with the following changes:
- Hoist repeated code outside conditional statement (
hoist-statement-from-if)
This removes the following comments ( why? ):
# threading
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| tool_to_dir = {a: b for a, b in zip(tools, dn_tools)} | ||
| tool_to_dir = dict(zip(tools, dn_tools)) |
There was a problem hiding this comment.
Function main refactored with the following changes:
- Replace identity comprehension with call to collection constructor (
identity-comprehension)
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