Mock community Zymo D6331, standard input library: wall clock 15.7 h on 32 cpus, peak memory 121.7 GB. Mock community ATCC MSA-1003 (with -S -lowq-10 50) Strain
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Sheep fecal material dataset: wall clock 17.2 h on 48 cpus, peak memory 183.3 GB. Currently there's a blocking sequential step. Lower value means less reads kept, if read selection is triggered. lowq-10Lower 10% quantile kmer frequency threshold, runtime. (otherwise if total number of read overlaps force-preovec Force kmer frequency-based read selection. See also README_ha.md, the stable hifiasm doc. Meta needs to store extra info from overlap & error correction step. Non-release commits may have extra debug outputs for dev/debug purposes, even without -V.īin file is one-way compatible with the stable hifiasm for now: stable hifiasm can use hifiasm_meta's bin file, but not vice versa. Special Notesīased on the limited available test data, real datasets are unlikely to require read selection mock datasets, however, might need it. Unitig/Contig naming: ^s+\.tg where the s+ is a subgraph label. Hifiasm_meta -t32 -S -o asm reads.fq.gz 2>asm.log // if the dataset has high redundancy, or overlap & error correction takes way too long Output filesĬontig graph: asm.p_ctg*.gfa and asm.a_ctg*.gfa Hifiasm_meta -t32 -oasm reads.fq.gz 2>asm.log
Klib ketopt install#
This is expected to be fixed in pandas 1.1.# Install hifiasm-meta (g++ and zlib required) Temporarily not converting to integers due to an issue in pandas. convert_datatypes ( data:, category: bool = True, cat_threshold: float = 0.05, cat_exclude: Optional]] = None ) → ¶Ĭonverts columns to best possible dtypes using dtypes supporting pd.NA.
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Pandas DataFrame with cleaned column names Print out hints on column name duplication and colum name length, by default True Original Dataframe with columns to be cleaned hints : bool, optional clean_column_names ( data:, hints: bool = True ) → ¶Ĭleans the column names of the provided Pandas Dataframe and optionally provides hints on duplicate and long column names. Set to “None” to hide the spines on all plots or use any valid matplotlib color argument, by default “#EEEEEE” Sort columns based on missing values in descending order and drop columns without any missing values, by default False spine_color : str, optional Use to control the figure size, by default (20, 20) sort : bool, optional More information can be found in the matplotlib documentation, by default “PuBuGn” figsize : Tuple, optional If a Pandas DataFrame is provided, the index/column information is used to label the plots cmap : str, optionalĪny valid colormap can be used. Parameters:ĢD dataset that can be coerced into Pandas DataFrame. Two-dimensional visualization of the missing values in a dataset. missingval_plot ( data:, cmap: str = 'PuBuGn', figsize: Tuple = (20, 20), sort: bool = False, spine_color: str = '#EEEEEE' ) ¶ Returns the Axes object with the plot for further tweaking. Type of split to be performed, by default None If a Pandas DataFrame is provided, the index/column information is used to label the plots split : Optional, optional Returns a color-encoded correlation matrix. corr_mat ( data:, split: Optional = None, threshold: float = 0, target: Union = None, method: str = 'pearson', colored: bool = True ) → Union ¶ Use to control the color of the bars indicating the least common values, by default “#d8b365” Use to control the color of the bars indicating the most common values, by default “#5ab4ac” bar_color_bottom : str, optional
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Show the “bottom” most frequent values in a column, by default 3 bar_color_top : str, optional Show the “top” most frequent values in a column, by default 3 bottom : int, optional Use to control the figure size, by default (18, 18) top : int, optional If a Pandas DataFrame is provided, the index/column information is used to label the plots figsize : Tuple, optional Two-dimensional visualization of the number and frequency of categorical features.