pengxie-bioinfo/neuro_morpho_toolbox

neuro_morpho_toolbox's Issues

swc.py line 34

f.close()的缩进是不是应该在with之后？

igraph-->jgraph

今天我在给这个工具包装环境。在装到igraph的时候跳出来一个deprecationWarning，说是这个包的名称已经更改为jgraph了。我们的代码里是不是要变一下。

DeprecationWarning: To avoid name collision with the igraph project, this visualization library has been renamed to 'jgraph'. Please upgrade when convenient.

Documentation on usage

I have installed the tool but how can i use it for visualizing ccfv3 atlas as in fig2b in https://www.biorxiv.org/content/10.1101/675280v3.full.pdf ? if i download the morphology from neuromorpho in swc, can i import it somehow?

Thanks

brain_structure的初始化中变量dict_to_selected结果异常

源码：

def __init__(self, input_file):
       # Read table
       MAXSIZE = 100
       my_cols = [i for i in range(MAXSIZE)]
       df = pd.read_csv(input_file, names=my_cols, engine='python',
                        skiprows=[0],
                        index_col=[1],
                        skipinitialspace=True)
       df = df.drop([0, 3, 4, 5, 6, 7, 8], axis=1)
       for i in range(df.shape[0]):
           for j in range(df.shape[1]):
               if df.iloc[i, j] is None:
                   df.iloc[i, j] = np.nan
       df.columns = ["Abbrevation"] + [i for i in range(1, df.shape[1])]
       df_isnull = df.isnull()

       # Get levels of each row
       level = []
       description = []
       for i in range(len(df)):
           for j in range(1, df.shape[1]):
               if not df_isnull.iloc[i, j]:
                   level.append(j)
                   description.append(df.iloc[i, j])
                   break
       MAXLEVEL = np.max(level)
       level = pd.DataFrame({'level': level, 'Abbrevation': df.Abbrevation.tolist(), 'Description': description},
                            index=df.index)

       # Drop redundant columns
       df = df.iloc[:, :(MAXLEVEL + 2)]  # The last column will contain only NaN
       df_isnull = df.isnull()

       # Fill empty slots in the table
       df_fill = df.copy()
       for i in range(1, df.shape[1]):
           cur_region = None
           for j in range(df.shape[0]):
               if not df_isnull.iloc[j, i]:
                   cur_region = df.iloc[j, i]
                   cur_level = level.loc[df.index[j], 'level']
                   # print(j, cur_region, cur_level)
               elif (not cur_region is None) & (cur_level < level.loc[df.index[j], 'level']):
                   df_fill.iloc[j, i] = cur_region

       self.input_file = input_file
       self.df = df_fill
       self.level = level
       self.selected_regions = self.df.index.tolist()
       self.dict_to_selected = {}
       for cur_region in self.selected_regions:
           child_ids = self.get_all_child_id(cur_region)
           for i in child_ids:
               self.dict_to_selected[i] = cur_region
       return

其中self.dict_to_selected初始化后结果是（用Mouse.csv文件）：

这个变量是不是用来返回父亲节点的？这个现象是不是因为get_all_child_id会返回自己？