Python: 如何使用graphviz套件繪製流程圖? from graphviz import Digraph ; dot = Digraph() ; dot.attr(rankdir=’TB’, size=’10,15′) #”TB”: Top to Bottom, 從上而下的佈局

# -*- coding: utf-8 -*-
"""
Created on Sun Oct 27 00:33:23 2024

@author: SavingKing
"""

# -*- coding: utf-8 -*-
"""
Created on Sat Oct 26 23:41:55 2024

@author: SavingKing
"""

from graphviz import Digraph

# 定義步驟列表
steps = [
    "get uut ip", "connect to ovss", "bmc ip check", "bmc comm set to ssh",
    "fis check bmc mac number", "Check BMC MAC", "check fru common header",
    "bmc led check", "create meltan ssh", "gsys cpu info", "gsys storage info",
    "USB info chain check", "gsys restart service", "gsys version",
    "gsys system thermal test", "CPU dimm virtual sensor check", "gsys hw configuration",
    "gsys dimm info", "gsys fan count check", "gsys bmc fw check", "bios info",
    "cpld version check", "CPLD OTP update", "meltan havenmap", "meltan ncsi_cable",
    "gsys check fru", "ipmitool check fru", "ipmi fan speed max check",
    "ipmi fan speed min check", "chassis info"
]

# 創建一個有向圖，設置預設方向為從上到下
dot = Digraph() #graphviz.graphs.Digraph
dot.attr(rankdir='TB', size='10,15')  # 設置畫布大小

# 定義每列的步驟數
steps_per_column = 10

# 創建節點
for step in steps:
    dot.node(step, step)
    #dot.node(ID, 標籤名)

# 添加邊，連接節點
for i in range(len(steps) - 1):
    if (i + 1) % steps_per_column == 0:  # 如果是列的最後一個節點
        # 將這一列的最後一個節點與下一列的第一個節點連接，並設置為稍微向右的線
        dot.edge(steps[i], steps[i + 1], constraint='false')
    else:
        # 連接同一列內的節點
        dot.edge(steps[i], steps[i + 1])

# 渲染圖形到PNG文件
output_path = dot.render('step_flow_chart04', format='png', cleanup=True)
print(f"圖形已渲染於 {output_path}")

for i, step in enumerate(steps):
    unique_name = f"{step}_{i}"  # 创建唯一名称
    dot.node(unique_name, step)  # 使用唯一名称作为节点名称，原名称作为标签

step_dict = {f"{step}_{i}": step for i, step in enumerate(steps)}
for unique_name, step_label in step_dict.items():
    dot.node(unique_name, step_label)

# -*- coding: utf-8 -*-
"""
Created on Mon Oct 27 20:32:43 2024

@author: SavingKing
ver02:
    ver 01 OK,多建一個export 子資料夾輸出圖形
    steps 要從xlsx中讀取,有重複的steps,
    dot.node(step, step),第一個參數必須是獨一無二的ID,不可使用有重複的step
    for loop中的dot.edge() 也要修改
ver03:
    ver02 OK, df_xlsx_test_sq 有 5 columns,
    取5個Series並轉成list,看能不能用for迴圈,
    還是想要用Series,可以用Series.name(源自於df.columns) 作為輸出檔名中的一部分
"""

from graphviz import Digraph
import os
import pandas as pd
import numpy as np

def mkdir_export(dirname,sub_dirname="export"):
    """
    在原本一堆log的資料夾之下,
    建立一個export的子資料夾,放自己輸出的檔案
    Parameters
    ----------
    dirname : TYPE str
        DESCRIPTION
        D:\\user\\240414~240504_FBT_log_PASS\\CMOIVT
    Returns
    -------
    dirname_ex : TYPE str
        DESCRIPTION.
        D:\\user\\240414~240504_FBT_log_PASS\\CMOIVT\export
    """
    #dirname_ex = dirname+"\export"
    #dirname_ex = dirname+"\\" +sub_dirname
    dirname_ex = os.path.join(dirname, sub_dirname)
    
    if not os.path.exists(dirname_ex):
        os.mkdir(dirname_ex)
        print(f"\n已經建立資料夾:\n{dirname_ex}")
    else:
        print(f"\n資料夾已經存在:\n{dirname_ex}")
    return dirname_ex

dirname_graphviz = r"D:\user\Python\test_plan\python_graphviz"
basename_png = "flow_chart.png"
main_fname_png = os.path.splitext(basename_png)[0]
#'step_flow_chart' 

dirname_ex = mkdir_export(dirname_graphviz)
#'D:\\user\\Python\\test_plan\\python_graphviz\\export'

path_ex = os.path.join(dirname_ex, main_fname_png)
#'D:\\user\\Python\\test_plan\\python_graphviz\\export\\step_flow_chart'
#注意: 沒有.png 的副檔名 
#dot.render() 就是需要這樣的參數,用format='png'定義附檔名

pd.set_option('display.max_columns', None)
basename_xlsx_test_sq= "Capala MFG BFT L6_L10 Test Coverage & Test Capacity (go_capala-mfg-tests).xlsx"
path_xlsx_test_sq = os.path.join(dirname_graphviz,basename_xlsx_test_sq)
#'D:\\user\\Python\\test_plan\\python_graphviz\\Test Capacity (go_capala-mfg-tests).xlsx'
sheet_name="Test Sequence"
df_xlsx_test_sq = pd.read_excel(path_xlsx_test_sq,sheet_name=sheet_name,header=0,index_col="Item")
#[64 rows x 5 columns]

#df_xlsx_test_sq.columns
#Out[32]: Index(['MOBO', 'HSBP', 'FAN', 'FAT', 'Run-in'], dtype='object')
ser_MOBO = df_xlsx_test_sq['MOBO']
ser_HSBP = df_xlsx_test_sq['HSBP']
ser_FAN  = df_xlsx_test_sq['FAN']
ser_FAT  = df_xlsx_test_sq['FAT']
ser_RunIn= df_xlsx_test_sq['Run-in']

def rm_NaN(lis):
    """
    TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
類型檢查：首先檢查 x 是否是整數或浮點數 (isinstance(x, (int, float)))。
NaN 檢查：如果 x 是數值類型，再檢查它是否不是 NaN (not np.isnan(x))。
組合邏輯：使用 or 將兩個條件組合。如果 x 不是數值類型，則第一個條件 not isinstance(x, (int, float)) 就為真，元素會被保留。如果 x 是數值類型且不是 NaN，第二條件 not np.isnan(x) 為真，元素同樣被保留。
這種邏輯有效防止了在 np.isnan(x) 調用時出現的類型錯誤，並確保了所有非 NaN 的數值和所有非數值類型的元素都被正確保留。
    """
    
    #lis_No_NaN = [x for x in lis if not isinstance(x, (int, float)) or not np.isnan(x)]
    #type(np.NaN)  #Out[83]: float
    
    lis_No_NaN = [x for x in lis if not isinstance(x, float) or not np.isnan(x)]
   
    return lis_No_NaN


def df2lis_lis_steps(df):
    """
    df_xlsx_test_sq #[64 rows x 5 columns]
    有5個columns ,可以拆分為5個Series,並轉為list
    一開始先一個空list,裝載這5個list
    """
    lis_lis_steps = []
    for col in df_xlsx_test_sq.columns:
        tests = df_xlsx_test_sq[col].to_list()
        tests_No_NaN = rm_NaN(tests)
        lis_lis_steps.append(tests_No_NaN)
    return lis_lis_steps

lis_lis_steps = df2lis_lis_steps(df=df_xlsx_test_sq)
#長度5 沒錯,但元素lis_steps 含有很多pd.NaN
#多定義了function: rm_NaN 


def df2lis_ser_steps(df):
    """
    跟 df2lis_lis_steps(df) 相仿
    lis_ser_steps 原為空list
    要裝5個Series,使用dropna() 去掉np.nan
    """
    lis_ser_steps=[]
    for col in df_xlsx_test_sq.columns:
        ser_tests = df_xlsx_test_sq[col]
        ser_tests.dropna(inplace=True)
        lis_ser_steps.append(ser_tests)
    
    return lis_ser_steps
lis_ser_steps = df2lis_ser_steps(df=df_xlsx_test_sq)

# 定義步驟列表
# =============================================================================
# steps = [
#     "get uut ip", "connect to ovss", "bmc ip check", "bmc comm set to ssh",
#     "fis check bmc mac number", "Check BMC MAC", "check fru common header",
#     "bmc led check", "create meltan ssh", "gsys cpu info", "gsys storage info",
#     "USB info chain check", "gsys restart service", "gsys version",
#     "gsys system thermal test", "CPU dimm virtual sensor check", "gsys hw configuration",
#     "gsys dimm info", "gsys fan count check", "gsys bmc fw check", "bios info",
#     "cpld version check", "CPLD OTP update", "meltan havenmap", "meltan ncsi_cable",
#     "gsys check fru", "ipmitool check fru", "ipmi fan speed max check",
#     "ipmi fan speed min check", "chassis info"
# ]
# =============================================================================
#使用從Test Coverage.xlsx 中的Test Sequence提取steps資料
#steps = ser_MOBO.to_list()

def create_flow_chart(steps,path_ex,steps_per_column=16,format="png"):
    #from graphviz import Digraph #未免func外部沒有import,內部import也可
    # 創建一個有向圖，設置預設方向為從上到下
    dot = Digraph() #graphviz.graphs.Digraph
    dot.attr(rankdir='TB', size='10,15')  # 設置畫布大小
    
    # 定義每列的步驟數
    steps_per_column = steps_per_column
    
    # 創建節點
    for idx,step in enumerate(steps):
        dot.node(f"{step}_{idx}", step)
        #dot.node(ID, 標籤名)
    
    # 添加邊，連接節點
    for i in range(len(steps) - 1):
        if (i + 1) % steps_per_column == 0:
            dot.edge(f"{steps[i]}_{i}", f"{steps[i + 1]}_{i + 1}", constraint='false')
        else:
            dot.edge(f"{steps[i]}_{i}", f"{steps[i + 1]}_{i + 1}")
    
    # 渲染圖形到PNG文件
    output_path = dot.render(path_ex, format=format, cleanup=True)
    print(f"圖形已渲染於 {output_path}")
    return None

""" len(ser):
63 19 18 59 16 
#長度最長是59,63 (16個step為一個column)
其他10個step一個column
"""
#for i,steps in enumerate(lis_lis_steps):
for ser in lis_ser_steps:
    if len(ser)>50:
        create_flow_chart(ser.values,path_ex=path_ex+f"_{ser.name}",steps_per_column=16,format="png")
    #steps_per_column=16對於step 較多的MOBO適合,
    #但是其他step較少的flow就不適合,可能不使用迴圈,
    #或者迴圈中還要訂step數多少以上的話,才用steps_per_column=16
    else:
        create_flow_chart(ser.values,path_ex=path_ex+f"_{ser.name}",steps_per_column=10,format="png")