How to use norm_row method in Testify

Best Python code snippet using Testify_python

motifEnrichment.py

Source:motifEnrichment.py

1""" ATAC-Seq peaks motif enrichment analysis"""2import pandas as pd3import os4import glob5import seaborn as sns6import matplotlib.pyplot as plt7# read in all results files in folder8# def motif_enrichment(known_results_folder):9known_results_folder = "C:\\Users\\libin\\UCSF\\motif_analysis\\200_HOCOMOCO_Dec.11\\set1"10size = 20011list_of_result = glob.glob(known_results_folder + "\\*.txt")12cell_types = [os.path.split(path)[-1][13:-4] for path in list_of_result]13# 1e-749 will be read in as 0 if not read in as str14results = [pd.read_table(result, sep="\t", dtype=str) for result in list_of_result]15# data cleaning; rename motif name16# for hocomoco dataset17for res in results:18    res["Motif Name"] = res["Motif Name"].str.extract(r"([A-Za-z0-9]+)_.+", expand=False)19# for combined hocomoco20results_cleaned = [result.filter(["Motif Name", "Log P-value"], axis=1).drop_duplicates(subset="Motif Name") for result in results]21# results_cleaned = [result.filter(["Motif Name", "Log P-value"], axis=1) for result in results]22# extract top motifs23sig_results_cleaned = [result.iloc[0:51,:] for result in results_cleaned]24for (c, r) in zip(cell_types, sig_results_cleaned):25    # for homer dataset26    # r["Motif Name"] = r["Motif Name"].str.extract(r"^([^\/]+)", expand=False)27    r.rename(columns={"Log P-value": c}, inplace=True)28    r.to_csv(known_results_folder + "\\" + c + str(size) + ".csv", sep=",", index=False)29# find common sig motifs30[sig_result.set_index("Motif Name", inplace=True) for sig_result in sig_results_cleaned]31common_motif = pd.concat(sig_results_cleaned, axis=1, join="inner")32common_motif.to_csv(known_results_folder + "\\common_motif.csv")33# find cell-type specific motifs34#[re.set_index("Motif Name", inplace=True) for re in results_cleaned]35#for (c1, r1) in zip(cell_types, results_cleaned):36# common_motif = common_motif.reset_index()37# drop motif name for plotting38# common_motif_1 = common_motif.drop(["Motif Name"], axis = 1)39common_motif = common_motif[common_motif.columns].astype(float)40# norm_common_motif = ((common_motif - common_motif.min())/ (common_motif.max() - common_motif.min())) * -10041# average = common_motif.mean(axis=0).mean()42# midpoint = 0 - (common_motif.values.max() - common_motif.values.min()) / 243## Below are ploting parts44# normalize column wide45norm_col = common_motif.sub(common_motif.mean(axis=0),axis=1)46norm_col = norm_col.div(common_motif.std(axis=0),axis=1)47norm_row = common_motif.sub(common_motif.mean(axis=1),axis=0)48norm_row = norm_row.div(common_motif.std(axis=1),axis=0)49average_row = norm_row.mean(axis=0).mean()50average_col = norm_col.mean(axis=0).mean()51average_all = common_motif.mean(axis=0).mean()52plt.figure(figsize=(11,15))53ax = sns.heatmap(norm_col, vmin=norm_col.values.min(), vmax=norm_col.values.max(), cmap="RdBu", center=average_col, annot=True)54plt.title("Column-Normalized Log P-value", y =1.01, x=1.1, fontsize =13)55for item in ax.get_xticklabels():56    item.set_rotation(45)57    58plt.figure(figsize=(11,15))59ax1 = sns.heatmap(common_motif, vmin=common_motif.values.min(), vmax=common_motif.values.max(), cmap = "RdBu", center = average_all, annot=True)60plt.title("Log P-value", y =1.01, x=1.1, fontsize =13)61for item in ax.get_xticklabels():62    item.set_rotation(45)63    64plt.figure(figsize=(11,15))65ax2 = sns.heatmap(norm_row, vmin=norm_row.values.min(), vmax=norm_row.values.max(), center = average_row, cmap="RdBu", annot=True)66plt.title("Row-Normalized Log P-value", y =1.01, x=1.1, fontsize =13)67for item in ax.get_xticklabels():68    item.set_rotation(45)   69# plt.figure(figsize=(5,7))70cluster_row = sns.clustermap(common_motif, cmap = "RdBu", standard_scale=0, figsize=(11,15)) 71plt.title("Row-Normalized cluster", y=1.2, x=1.1, fontsize=13)72cluster_column = sns.clustermap(common_motif, standard_scale=1, figsize=(11,15))73plt.title("Col-Normalized cluster", y=1.2, x=1.1, fontsize=13)...

items.py

Source:items.py

1"""Define Scrapy Item corresponding to a single WARN notice entry"""2import scrapy3from scrapy.item import Item4from scrapy.loader import ItemLoader5class Entry(Item):6    # Metadata7    state_name = scrapy.Field()8    timestamp = scrapy.Field()9    url = scrapy.Field()10    # WARN notice data provided for the state, as dictionaries11    fields = scrapy.Field()12    normalized_fields = scrapy.Field() # Will be populated by get_normalized_fields13class UnpackedEntry(dict, Item):14    """Flexible Item whose items can be populated using an item loader15    16    This is used to unpack fields and normalized_fields from the Entry item,17    for use in saving these to individual CSV files. 18    19    Note that for the exporting to work, the Fields Items generated by a 20    particular state must be consistent in the actual field names!"""21    pass22def unpack_entry_fields_as_items(item):23    """Unpack dictionaries defined in Entry item (fields and normalized_fields)24    and generate new UnpackedEntry items from them25    These will be processed by separate pipelines from the Entry items26    27    Parameters:28        item:   fully loaded Entry item29        30    Returns:31        rawItem:    UnpackedEntry item with each value from item.fields in its own Field32        normItem:   UnpackedEntry item with each value from item.normalized_fields in its own Field33    """34    35    assert isinstance(item, Entry)36    rawLoader = ItemLoader(UnpackedEntry())37    normLoader = ItemLoader(UnpackedEntry())38    rawLoader.add_value('state_name', item.state_name)39    normLoader.add_value('state_name', item.state_name)40    for key, value in item.fields:41        rawLoader.add_value(key, value)42    43    for key, value in item.normalized_fields:44        normLoader.add_value(key, value)45    rawItem = rawLoader.load_item()46    normItem = normLoader.load_item()47    return (rawItem, normItem)48def get_normalized_fields(name_dict, row):49    """Grab and rename columns which are to be normalized across states50    51    Parameters:52        names:  dictionary of normalized_name:original_name pairs53        row:    pd.Series containing original_name:value fields54    Returns:55        norm_row:   pd.Series containing normalized_name:value fields56    """57    columns_to_keep = [x for x in name_dict.values() if x] # Some columns may not be present58    norm_row = row[row.index.intersection(columns_to_keep)]59    name_dict_inverse = {v: k for (k, v) in name_dict.items()}60    norm_row.rename(index=name_dict_inverse, inplace=True)61    return norm_row...

level_3_doomsday_fuel.py

Source:level_3_doomsday_fuel.py

1from fractions import Fraction, gcd2def find_lcm(denominator_list):3    lcm = 14    for i in denominator_list:5        lcm = lcm * i // gcd(lcm, i)6    return lcm7def normalize_matrix(m):8    matrix = []9    for row in m:10        row_sum = sum(row)11        norm_row = []12        for col in row:13            if row_sum > 0:14                # norm_row.append(Fraction(col, row_sum))15                norm_row.append(float(col) / float(row_sum))16            else:17                norm_row.append(0)18        matrix.append(norm_row)19    return matrix20def dot(m1, m2):21    return [22        [sum(x * y for x, y in zip(m1_r, m2_c)) for m2_c in zip(*m2)] for m1_r in m123    ]24def solution(m):25    matrix = normalize_matrix(m)26    terminal_states = [i for i, row in enumerate(m) if sum(row) == 0]27    # s0 is a terminal state, what to do?28    if terminal_states[0] == 0:29        return [1, 1]30    states = [[0] for _ in range(len(m))]31    states[0][0] = 132    state_acc = [0 for _ in range(len(m))]33    for _ in range(1000):34        states = dot(states, matrix)35        state_acc = [state_acc[i] + states[0][i] for i in range(len(states))]36    fractions = []37    for terminal_state in terminal_states:38        node_prob = state_acc[terminal_state]39        numerator, denominator = float(node_prob).as_integer_ratio()40        fractions.append(Fraction(numerator, denominator).limit_denominator())41    least_common_multiple = find_lcm([i.denominator for i in fractions])42    probabilities = [int(fraction.numerator * (float(least_common_multiple) / float(fraction.denominator))) for fraction in fractions]43    probabilities.append(int(least_common_multiple))44    return probabilities45print(46    solution([47        [0, 2, 1, 0, 0],48        [0, 0, 0, 3, 4],49        [0, 0, 0, 0, 0],50        [0, 0, 0, 0, 0],51        [0, 0, 0, 0, 0]52    ])53)54print(55    solution([56        [0, 1, 0, 0, 0, 1],  # s0, the initial state, goes to s1 and s5 with equal probability57        [4, 0, 0, 3, 2, 0],  # s1 can become s0, s3, or s4, but with different probabilities58        [0, 0, 0, 0, 0, 0],  # s2 is terminal, and unreachable (never observed in practice)59        [0, 0, 0, 0, 0, 0],  # s3 is terminal60        [0, 0, 0, 0, 0, 0],  # s4 is terminal61        [0, 0, 0, 0, 0, 0],  # s5 is terminal62    ])...

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