Rstoolbox IO & Silent Extraction¶

# 初始化pyrosetta
from pyrosetta import *
init()

# 初始化
import rstoolbox as rs
from rstoolbox.io import parse_rosetta_file

# 最简单的读取方式(全读取式):
rules = {'sequence': 'A'}
silent_file = './data/design_result.silent'
raw_df = parse_rosetta_file(silent_file, rules)

# 打印读取数据:
raw_df

# 定义description, description是一个字典格式;
rules = {'sequence': 'A'}
# rules = {'scores_ignore': [''], 'sequence': 'HL'}

# 根据rules进行读取:
silent_file = './data/design_result.silent'
df_ignore = parse_rosetta_file(silent_file, rules)

# 打印读取数据:
df_ignore

# 例句1:
df_selection1 = raw_df[(raw_df['score'] <= -115)]
df_selection1

# 例句2:
df_selection2 = raw_df[(raw_df['score'] <= -115) & (raw_df['percent_core'] <= 0.25)]
df_selection2

# 例句3--排序:
df_selection3 = raw_df.sort_values('score')
df_selection3

# 例句4--截断:
df_selection4 = raw_df.sort_values('score').head(5)
df_selection4

# 例句5--截断:
df_selection5 = raw_df.sort_values('score').tail(5)
df_selection5

# plot的score选定(此处我们关注4个score)
# values = ["score", "hbond_sr_bb", "fa_elec", "hbond_bb_sc", "rama_prepro", "lk_ball"]

values = list(raw_df.columns)[:12]

# 定义图表
fig  = plt.figure(figsize=(12, 6))
grid = [2, 6]  # 第一个数值代表行数量，第二个数值代表列数量，共计可放入2x3项score。

# 制图
axes = rs.plot.multiple_distributions(df=raw_df, fig=fig, grid=grid, values=values)

# 展示
plt.tight_layout()
plt.show()

# 选取需要比较的metrics;
values = list(raw_df.columns)[:6]

# 定义图表
fig  = plt.figure(figsize=(12, 6))
grid = [2, 3]  # 第一个数值代表行数量，第二个数值代表列数量，共计可放入2x6项score。

# 制图
axes = rs.plot.multiple_distributions(df=new_df, fig=fig, grid=grid, values=values, x='design_method', hue='design_method',)

# 展示
plt.tight_layout()
plt.show()

# 从选择语句:
df_selection4
df_selection4 = rs.utils.add_column(df_selection4, 'rank', 'worst')

df_selection5
df_selection5 = rs.utils.add_column(df_selection5, 'rank', 'best')

# 合并df;
new_df2 = pd.concat([df_selection4, df_selection5], ignore_index=False)

# 选取需要比较的metrics;
values = list(raw_df.columns)[:6]

# 定义图表
fig  = plt.figure(figsize=(12, 6))
grid = [2, 3]  # 第一个数值代表行数量，第二个数值代表列数量，共计可放入2x3项score。

# 制图
axes = rs.plot.multiple_distributions(df=new_df2, fig=fig, grid=grid, values=values, x='rank', hue='rank',)

# 展示
plt.tight_layout()
plt.show()

from pyrosetta.io import poses_from_silent

# 读取silent;
poses = poses_from_silent(silent_file)

# 按照全面筛选得到data.
df_selection5

# 获取description列信息:
selected_pdb_list = list(df_selection5['description'])
print(selected_pdb_list)

# 分离结构:
import os

for pose in poses:
    # 获取pose中的description信息;
    description = pose.pdb_info().name()
    if description in selected_pdb_list:
        pose.dump_pdb(os.path.join('./data', description))

from rstoolbox.io import read_fasta, write_fasta
df_selection5