<jupyter_start><jupyter_text>Pull ncsr data from file ncsr_importncsr = ncsr_data()   ncsr.ncsr holds all of the data from NCS 20240   https://www.icpsr.umich.edu/web/ICPSR/studies/20240   Alegria, Margarita, Jackson, <NAME>. (<NAME>), <NAME>., and <NAME>. Collaborative Psychiatric Epidemiology Surveys (CPES), 2001-2003 [United States]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2016-03-23. https://doi.org/10.3886/ICPSR20240.v8   Key Functions:     ncsr.search_for_description looks for a descriotion of a column name in ncs1.dxdm or ncs1.survey     ncsr.get_variable_info queries icpsr for information like values, variable descriptions, and more     ncsr.get_value_from_string takes in a string that is a var name of dataframe in the ncsr description tree. Tree can be traversed using ncsr.root   Key Variables:     ncsr.ncsr      ncsr.tree (Tree including descriptions of survey and dxdm columns)     ncsr.root (root used in traversing ncsr.tree)  below code is used to cut out variables that have do do with onset age of symptomsage_variable_subset = pd.DataFrame(columns = ['VarName', 'Description', 'Root_DF', 'Start', 'End', 'DataFrame', 'recursion_flag'])b = pd.DataFrame(columns = ['VarName', 'Description', 'Root_DF', 'Start', 'End', 'DataFrame', 'recursion_flag'])c = pd.DataFrame(columns = ['VarName', 'Description', 'Root_DF', 'Start', 'End', 'DataFrame', 'recursion_flag'])for x in range(1, len(ncsr.root)):    age_variable_subset = age_variable_subset.append(        ncsr.get_value_from_string(ncsr.root.iloc[x,0])[            np.array(ncsr.get_value_from_string(ncsr.root.iloc[x,0])['Description'].str.match(".*.*", False)) & np.array(ncsr.get_value_from_string(ncsr.root.iloc[x,0])['Description'].str.match(".*(^Age |^Age| age )+.*", False)) & np.logical_not(np.array(ncsr.get_value_from_string(ncsr.root.iloc[x,0])['Description'].str.match(".*(^Remember|^Exact|^Age$||biological|when you were born|^Freq)+.*", False)))            ]    )     this checks each regex to see the variables gut out. pd.concat below checks the difference between DFs    b = b.append(        ncsr.get_value_from_string(ncsr.root.iloc[x,0])[            np.array(ncsr.get_value_from_string(ncsr.root.iloc[x,0])[                'Description'].str.match(".*(first|1st|onset)+.*", False)) & np.array(ncsr.get_value_from_string(ncsr.root.iloc[x,0])[                    'Description'].str.match(".*(^Age |^Age| age | age|age | old|old | old |^DSM|^ICD)+.*", False))            ]    )    c = c.append(        ncsr.get_value_from_string(ncsr.root.iloc[x,0])[            np.array(ncsr.get_value_from_string(ncsr.root.iloc[x,0])['Description'].str.match(".*(first|1st|onset)+.*", False))            ]    )    pd.concat([,c]).drop_duplicates(keep=False).to_csv('out.csv')age_variable_subset = age_variable_subset.reset_index(drop=True)ncsr.ncs.to_csv('initialized_age_vars.csv') ncsr_age_subset = ncsr.ncsr[list(age_variable_subset.iloc[:, 0])] Go in and pull out the variables for a subject that are valid and in a for y in range(0, len(ncsr_age_subset)):    current_case = y    case_age_vars = []    for val, x in enumerate(age_variable_subset.iloc[:, 0]):             if ncsr_age_subset.loc[y, x] != ' ':                if ncsr_age_subset.loc[y, x] != '.' and int(ncsr_age_subset.loc[y,x]) > 1 and int(ncsr_age_subset.loc[y,x]) < 100:                    ncsr_age_subset.loc[y, x] = int(ncsr_age_subset.loc[y,x])                else:                     ncsr_age_subset.loc[y, x] = -1            else:                ncsr_age_subset.loc[y,x] = -1    ncsr_age_subset_correct_vars.append(case_age_vars.append(age_variable_subset.loc[y, "CASEID"]))ncsr_age_subset<jupyter_code>import ncsr_import 
ncsr = ncsr_import.data()
ncsr_age = pd.read_csv('age_subset.csv', index_col=0)
ncsr_age.insert(loc=0, column = "CASEID", value = ncsr_age.index + 1)
dsm_cases = []
dsm_names = []
for filename in os.listdir(r'C:\Users\galyn\Documents\GitHub\Seminar\dsm_out'):
    dsm = filename.split('.')[0]
    dsm_names.append(dsm)
    dsm_cases.append(pd.read_csv('dsm_out/' + filename))
for dsm_name in dsm_names:
    os.mkdir('individual_graphs/'+dsm_name)
dsm_names[0]
len(os.listdir(r'C:\Users\galyn\Documents\GitHub\seminar\individual_graphs\DSM_ALA'))
for idx, cases_df in tqdm(enumerate(dsm_cases)):
    dsm_subset = ncsr_age[[x in cases_df.values for x in ncsr_age['CASEID']]].reset_index(drop=True)
    for case in range(len(dsm_subset)):
        case_number = dsm_subset.loc[case, 'CASEID'] 
        ordered_vals = []
        ordered_vars = []
        vals = []
        case_age_vars = []
        for symptom in dsm_subset:
            if symptom != "CASEID":
                if dsm_subset.loc[case, symptom] > 0:
                    vals.append(dsm_subset.loc[case, symptom])
                    case_age_vars.append(symptom)
        # initialize the ordered lists
        #order ints by age symptom occured
        for x in range(0, len(vals)):
            m = vals.index(min(vals))
            ordered_vals.append(vals[m])
            ordered_vars.append(case_age_vars[m])
            del vals[m]
            del case_age_vars[m]
        levels = []
        levels.append([])
        count = 0
        for idx1, val in enumerate(ordered_vals):
            if idx1 == 0:
                levels[count].append([ordered_vars[idx1], val])
            else: 
                if val == levels[count][0][1]:
                    levels[count].append([ordered_vars[idx1], val])
                else: 
                    levels.append([])
                    count += 1
                    levels[count].append([ordered_vars[idx1], val])

        G = nx.DiGraph()
        for x in range(0, len(ordered_vals)):
            G.add_node(ordered_vars[x], age = ordered_vals[x])

        #print(len(levels), case_number)
        for idx1, level in enumerate(levels): 
            if idx1+1 == len(levels): 
                continue
            for from_node in level: 
                for idx2, level2 in levels[1:]:
                    for to_node in level2: 
                        G.add_edges_from([(from_node[0], to_node[0])])
        
        nx.write_gpickle(G, 'individual_graphs/' + dsm_names[idx] + '/graph_case_' + str(case_number)+'.pkl')
"""                 
        mult_flag = 0
        back_start = -1
        back_end = -1
        back_flag = 0
        start = 0
        for x in range(0, len(ordered_vars)-1):
            if ordered_vals[x] != ordered_vals[x+1]:
                if not mult_flag:
                    G.add_edges_from([(ordered_vars[x], ordered_vars[x+1])])
                    back_flag = 0
                else: 
                    for y in range(start, x+1):
                        G.add_edges_from([(ordered_vars[y], ordered_vars[x+1])])
                        back_flag = 1
                        back_end = x
                back_start = start
                start = x + 1
                mult_flag = 0
            elif ordered_vals[x] == ordered_vals[x+1]:
                mult_flag = 1
                if back_start<0:
                    continue
                if back_flag: 
                    for y in range(back_start, back_end + 1):
                        G.add_edges_from([(ordered_vars[y], ordered_vars[x+1])])
                else:
                    G.add_edges_from([(ordered_vars[back_start], ordered_vars[x+1])]) 
"""
      #  ncsr_age_graphs.append(G)

import dgl
import torch
for node in G.nodes:
    G.nodes[node]['name'] = list(ncsr_age.columns).index(node)
G.nodes['SO3A']
nx_g = nx.DiGraph()
# Add 3 nodes and two features for them
nx_g.add_nodes_from([0, 1, 2], feat1=np.zeros((3, 1)), feat2=np.ones((3, 1)))
# Add 2 edges (1, 2) and (2, 1) with two features, one being edge IDs
nx_g.add_edge(1, 2, weight=np.ones((1, 1)), eid=np.array([1]))
nx_g.add_edge(2, 1, weight=np.ones((1, 1)), eid=np.array([0]))
nx_g.nodes[0]
dgl.from_networkx(G, node_attrs = ['age', 'name'])
ncsr_age_edges_init = pd.DataFrame(0, columns = ncsr_age.iloc[:,0], index=ncsr_age.iloc[:,0])
edges = pd.DataFrame(0, columns = list(ncsr_age.columns)[1:], index=list(ncsr_age.columns)[1:])
average_onset_age_init = {x: 0 for x in edges}
average_onset_age_init = {}
for x in ncsr_age_edges_init:
    average_onset_age_init[x] = 0
ncsr_age_edges = ncsr_age_edges_init.copy(deep=True)
average_onset = copy.deepcopy(average_onset_age_init)
onset_count = copy.deepcopy(average_onset_age_init)
ncsr_age_graphs = []
for case in range(len(comorbid_subset)):
    case_number = ncsr_age_subset.iloc[case, 0]
    ordered_vals =[]
    ordered_vars = []
    vals = []
    case_age_vars = []
    for symptom in ncsr_age_subset:
        if symptom != "CASEID":
            if ncsr_age_subset.loc[case, symptom] > 0:
                vals.append(ncsr_age_subset.loc[case, symptom])
                case_age_vars.append(symptom)
    # initialize the ordered lists
    #order ints by age symptom occured
    for x in range(0, len(vals)):
        m = vals.index(min(vals))
        ordered_vals.append(vals[m])
        ordered_vars.append(case_age_vars[m])
        del vals[m]
        del case_age_vars[m]

    G = nx.DiGraph()
    for x in range(0, len(ordered_vals)):
        G.add_node(ordered_vars[x], age = ordered_vals[x])
    mult_flag = 0
    back_start = -1
    back_end = -1
    back_flag = 0
    start = 0
    for x in range(0, len(ordered_vars)-1):
        if ordered_vals[x] != ordered_vals[x+1]:
            if not mult_flag:
                G.add_edges_from([(ordered_vars[x], ordered_vars[x+1])])
                back_flag = 0
            else: 
                for y in range(start, x+1):
                    G.add_edges_from([(ordered_vars[y], ordered_vars[x+1])])
                    back_flag = 1
                    back_end = x
            back_start = start
            start = x + 1
            mult_flag = 0
        elif ordered_vals[x] == ordered_vals[x+1]:
            mult_flag = 1
            if back_start<0:
                continue
            if back_flag: 
                for y in range(back_start, back_end + 1):
                    G.add_edges_from([(ordered_vars[y], ordered_vars[x+1])])
            else:
                G.add_edges_from([(ordered_vars[back_start], ordered_vars[x+1])]) 

    ncsr_age_graphs.append(G)
"""
for x in add_graphs: 
    node_age_added = []
    for from_node in age_variable_subset.iloc[:,0]:
        for to_node in age_variable_subset.iloc[:,0]:
            if x.has_edge(from_node, to_node):
                if from_node not in node_age_added: 
                    onset_count[from_node] += 1
                    average_onset[from_node] += x.nodes[from_node]['age']
                    node_age_added.append(from_node)
                if to_node not in node_age_added:
                    onset_count[to_node] += 1
                    average_onset[to_node] += x.nodes[to_node]['age']
                    node_age_added.append(to_node)
                
                ncsr_age_edges.loc[from_node, to_node] += 1
"""
for var, val in onset_count.items(): 
    if val > 0: 
        average_onset[var] = average_onset[var]/val
for x in range(len(ncsr_age_edges)):
    for y in range(len(ncsr_age_edges)):
        ncsr_age_edges.iloc[x, y] = ncsr_age_edges.iloc[x, y]/len(ncsr_age_subset)

all_matrix = pd.read_csv('all_adjacency_matrix.csv', index_col=0)
num = 0
main_graph = nx.DiGraph()
added_nodes = []
count = 0
for x in add_edges:
    for y in add_edges:
        if add_edges.loc[x, y] > .1 and all_matrix.loc[x,y]<.1: 
            count+=1
            if x not in added_nodes:
                main_graph.add_node(x)
                added_nodes.append(x)
            if y not in added_nodes:
                main_graph.add_node(y)
                added_nodes.append(y)
            main_graph.add_edges_from([(x, y)], weight = add_edges.loc[x, y]*50)
print(added_nodes)
ncsr.get_value_from_string(ncsr.root.iloc[0,0])<jupyter_output><empty_output><jupyter_text>vals = list(ncsr.ncsr.loc[current_case, case_age_vars])  get all of the values for the case_age_varsvals = [int(i) for i in vals]  make sure that the vals are intsordered_vals =[]ordered_vars = []   initialize the ordered listsorder ints by age symptom occuredfor x in range(0, len(vals)):    m = vals.index(min(vals))    ordered_vals.append(vals[m])    ordered_vars.append(case_age_vars[m])    del vals[m]    del case_age_vars[m] G = nx.DiGraph()for x in range(0, len(ordered_vals)):    G.add_node(ordered_vars[x], age = ordered_vals[x])flag = 1back_start = -1back_flag = 0start = -1for x in range(1, len(ordered_vars)):    print(ordered_vars[x], flag, start, back_flag,  back_start)    print(ordered_vals[x], ordered_vals[x-1])    if ordered_vals[x-1] != ordered_vals[x] and flag == 1:        if back_flag:             for y in range(back_start, x):                print("G.add_edges_from([(ordered_vars["+str(y)+"], ordered_vars["+str(x)+"])])")                G.add_edges_from([(ordered_vars[y], ordered_vars[x])])        else:             G.add_edges_from([(ordered_vars[x-1], ordered_vars[x])])        back_start=start        start = x        back_flag = 0        flag = 1    elif x + 1 == len(ordered_vals) and flag == 0:        if back_flag:            for y in range(back_start, start):                for z in range(start, x):                    G.add_edges_from([(ordered_vars[y], ordered_vars[z]), (ordered_vars[z], ordered_vars[x])])        else:            for y in range(start, x):                G.add_edges_from([(ordered_vars[start], ordered_vars[y]), (ordered_vars[y], ordered_vars[x])])        start = x    elif x + 1 == len(ordered_vals) and flag == 1:        if back_flag:             for y in range(back_start, x):                G.add_edges_from([(ordered_vars[y], ordered_vars[x])])        else:             G.add_edges_from([(ordered_vars[x-1], ordered_vars[x])])        start =x    elif ordered_vals[x-1] != ordered_vals[x] and start == -1:         start = x        for y in range(0, x):           G.add_edges_from([(ordered_vars[y], ordered_vars[x])])        back_flag = 0        back_start = 0    elif ordered_vals[x-1] != ordered_vals[x] and flag == 0:         if back_flag:            for y in range(back_start, start):                for z in range(start, x):                    G.add_edges_from([(ordered_vars[y], ordered_vars[z]), (ordered_vars[z], ordered_vars[x])])        else:            for y in range(start, x):                G.add_edges_from([(ordered_vars[start], ordered_vars[y]), (ordered_vars[y], ordered_vars[x])])        flag = 1        back_start = start        start = x        back_flag = 1    else:        flag = 0         G = copy.deepcopy(main_graph)for x in range(0, len(ordered_vals)):    G.add_node(ordered_vars[x], age = ordered_vals[x])mult_flag = 0back_start = -1back_end = -1back_flag = 0start = 0for x in range(0, len(ordered_vars)-1):    if ordered_vals[x] != ordered_vals[x+1]:        if not mult_flag:            G.add_edges_from([(ordered_vars[x], ordered_vars[x+1])])            back_flag = 0        else:             for y in range(start, x+1):                G.add_edges_from([(ordered_vars[y], ordered_vars[x+1])])                back_flag = 1                back_end = x        back_start = start        start = x + 1        mult_flag = 0    elif ordered_vals[x] == ordered_vals[x+1]:        mult_flag = 1        if back_start<0:            continue        if back_flag:             for y in range(back_start, back_end + 1):                G.add_edges_from([(ordered_vars[y], ordered_vars[x+1])])        else:            G.add_edges_from([(ordered_vars[back_start], ordered_vars[x+1])])     add_graphs.append(G) import matplotlib.pyplot as pltimport networkx as nximport pydotfrom networkx.drawing.nx_pydot import graphviz_layoutimport pygraphvizpos = nx.drawing.nx_agraph.graphviz_layout(G)pos = nx.kamada_kawai_layout(G, pos = nx.spring_layout(G, k=3, iterations=10, dim=2, seed = 3))from plotly.offline import download_plotlyjs, init_notebook_mode, iplotimport plotly.graph_objs as goG = nx.read_gpickle('graph.pkl')pos = graphviz_layout(G, 'dot')for n, p in pos.items():    G.nodes[n]['pos'] = pedge_trace = go.Scatter(    x=[],    y=[],    line=dict(width=1,color='888'),    hoverinfo='none',    mode='lines')for edge in G.edges():    x0, y0 = G.nodes[edge[0]]['pos']    x1, y1 = G.nodes[edge[1]]['pos']    edge_trace['x'] += tuple([x0, x1, None])    edge_trace['y'] += tuple([y0, y1, None])node_trace = go.Scatter(    x=[],    y=[],    text=[],    mode='markers',    hoverinfo='text',    marker=dict(        showscale=True,        colorscale='RdBu',        reversescale=True,        color=[],        size=10,        colorbar=dict(            thickness=10,            title='Age',            xanchor='left',            titleside='right'        ),        line=dict(width=0)))for node in G.nodes():    x, y = G.nodes[node]['pos']    node_trace['x'] += tuple([x])    node_trace['y'] += tuple([y])for x in G.nodes(data = True):    node_trace['marker']['color']+=tuple([x[1]['age']])    node_info = x[0] +' Age: '+str(x[1]['age'])    node_trace['text']+=tuple([node_info])fig = go.Figure(data=[edge_trace, node_trace],             layout=go.Layout(                title='Network for CASEID 445',                titlefont=dict(size=16),                showlegend=False,                hovermode='closest',                margin=dict(b=20,l=5,r=5,t=40),                annotations=[ dict(                    text="",                    showarrow=False,                    xref="paper", yref="paper") ],                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)))iplot(fig)<jupyter_code>#G = nx.read_gpickle('graph.pkl')
#G = main_graph
#pos = nx.kamada_kawai_layout(G, pos = nx.spring_layout(G, k = .15, iterations = 100, seed = 10020) )#

pos = graphviz_layout(G, prog="dot")
"""
#pos = nx.spring_layout(G, k = 4, iterations = 100, seed = 10020)
levels = []
vals = {}
for x in list(G.nodes):
    for x in G.nodes:
        vals[x] = len(dsm_subset[dsm_subset[x]!= -1])/len(dsm_subset.iloc[:, 1])

level_vals = list(vals.values())
col = cm = plt.get_cmap('Blues')
cNorm  = colors.Normalize(vmin=min(level_vals)-.1, vmax=max(level_vals)+.1)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=col)
plt.set_cmap(col)
f = plt.figure(3, figsize=(20,15))
"""
nx.draw_networkx(G, pos)
"""
#, node_size= 4000, vmax = max(level_vals) + .1, vmin = min(level_vals) - .1, with_labels = True, edge_color = list(nx.get_edge_attributes(G,'weight').values()), arrowsize= 20, width = 3, edge_cmap =  plt.get_cmap('Greens'), edge_vmin = 0, connectionstyle="arc3,rad=.3")
ax = []
#for x in level_vals:
    #ax.append(.Line2D([], [], color=scalarMap.to_rgba(level_set.index(x) + 1), label = "Category: " + x, marker="o", markersize=20))
#plt.legend(prop={'size': 20}, handles = ax)
nx.draw_networkx_nodes(G, pos, node_color = 'white', node_size = 2500)
nodes = nx.draw_networkx_nodes(G, pos, node_color = level_vals, vmax = max(level_vals) + .1, vmin = max(level_vals) - .1, node_size = 0)
edges = nx.draw_networkx_edges(G, pos, edge_color = [x/50 for x in nx.get_edge_attributes(G,'wt').values()], edge_cmap =  plt.get_cmap('Greens'), arrows=False, edge_vmin=0)
edge_cbar = plt.colorbar(edges, orientation='horizontal', pad = 0.03)
node_cbar = plt.colorbar(nodes, orientation='horizontal', pad = 0)
edge_cbar.set_label("Edge Prevalence", size = 25)
node_cbar.set_label("Node Prevalence", size = 25)
plt.title("MDD/GAD Comorbid Network", fontsize = 40)#diag1 + " - " + diag2, fontsize = 40)
bbox_props = dict(boxstyle= "round, pad =.4", ec = 'black', fc = 'white')
g = r"$\bf{" + "Node" + "}$" + " " +  r"$\bf{" + "Descriptions:" + "}$" + "\n"
for x in list(G.nodes):
 g += x + ": " + ncsr.search_for_description(x)[1] + "\n" 
t = plt.annotate(g, xy = (1.01,0.2), xycoords='axes fraction', bbox = bbox_props, size = 20)
plt.savefig('plots/case_mad_gad.jpg', bbox_inches = 'tight')
"""<jupyter_output><empty_output><jupyter_text>G = nx.read_gpickle('graph.pkl')G = main_graphpos = nx.kamada_kawai_layout(G, pos = nx.spring_layout(G, k = .15, iterations = 100, seed = 10020) )pos = graphviz_layout(G, prog="twopi")pos = nx.spring_layout(G, k = 4, iterations = 100, seed = 10020)levels = []vals = {}for x in list(G.nodes):    for x in G.nodes:        vals[x] = len(comorbid_subset[comorbid_subset[x]!= -1])/len(comorbid_subset.iloc[:, 1])level_vals = list(vals.values())col = cm = plt.get_cmap('Blues')cNorm  = colors.Normalize(vmin=min(level_vals)-.1, vmax=max(level_vals)+.1)scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=col)plt.set_cmap(col)f = plt.figure(3, figsize=(20,15))nx.draw_networkx(G, pos, node_color=level_vals, node_size= 4000, vmax = max(level_vals) + .1, vmin = min(level_vals) - .1, with_labels = True, edge_color = list(nx.get_edge_attributes(G,'weight').values()), arrowsize= 20, width = 3, edge_cmap =  plt.get_cmap('Greens'), edge_vmin = 0, connectionstyle="arc3,rad=.3")ax = []for x in level_vals:    ax.append(.Line2D([], [], color=scalarMap.to_rgba(level_set.index(x) + 1), label = "Category: " + x, marker="o", markersize=20))plt.legend(prop={'size': 20}, handles = ax)nx.draw_networkx_nodes(G, pos, node_color = 'white', node_size = 2500)nodes = nx.draw_networkx_nodes(G, pos, node_color = level_vals, vmax = max(level_vals) + .1, vmin = max(level_vals) - .1, node_size = 0)edges = nx.draw_networkx_edges(G, pos, edge_color = [x/50 for x in nx.get_edge_attributes(G,'wt').values()], edge_cmap =  plt.get_cmap('Greens'), arrows=False, edge_vmin=0)edge_cbar = plt.colorbar(edges, orientation='horizontal', pad = 0.03)node_cbar = plt.colorbar(nodes, orientation='horizontal', pad = 0)edge_cbar.set_label("Edge Prevalence", size = 25)node_cbar.set_label("Node Prevalence", size = 25)plt.title(diag1 + " - " + diag2, fontsize = 40)bbox_props = dict(boxstyle= "round, pad =.4", ec = 'black', fc = 'white')g = r"$\bf{" + "Node" + "}$" + " " +  r"$\bf{" + "Descriptions:" + "}$" + "\n"for x in list(G.nodes): g += x + ": " + ncsr.search_for_description(x)[1] + "\n" t = plt.annotate(g, xy = (1.01,0.2), xycoords='axes fraction', bbox = bbox_props, size = 20)plt.savefig('plots/case_' + diag1 + "_" + diag2 + '.jpg', bbox_inches = 'tight')<jupyter_code>ncsr.ncsr["CH30_1A"]
levels<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14859240030434587, 'fraction_numerical': 0.01942442823255606, 'mean_word_length': 2.923441615452151, 'pattern_counts': {'":': 4, '<': 22, '<?xml version=': 0, '>': 22, 'https://': 2, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '379742', 'license': 'Unlicense', 'n_tokens_mistral': 8325, 'n_tokens_neox': 7867, 'n_words': 1567, 'path': 'scratch/turn_to_time_series.ipynb', 'repo_name': 'sjaya09/Atrial-Fibrillation-UNCW-project-2021', 'size': '22370'}