jj-github-jj

from collections import namedtuple
import re
#str1, n = re.subn('[0-9]', 'X',str1)
def ntuples(df):
    list_of_names = df.columns.values
    print(list_of_names[0:2])
    list_of_names_dict = {x:x for x in list_of_names}

    Varnames = namedtuple('Varnames', list_of_names) 

jj-github-jj

# create an 8 row by 2 columns dataframe with column labels
df=pd.DataFrame(np.reshape(np.zeros(16),(8,2)),columns=['x','y']) # create an 8 row by 2 columns df

c=df23.columns.to_list()
c[0]

df.iloc[0:,0:2] # rows 0 through end of column 0 and 1
#----------
df.drop(df.columns[[0, 4, 2]], axis = 1, inplace = True) #drop columns using column index
#----------

jj-github-jj

# Turns a dictionary into a class
# type variable name such as t4. and wait for the class attributes to appear in editor using Kite or other language processors

class Dict2Class(object):
      
    def __init__(self, my_dict):
        
        for key in my_dict:
            key2=key.replace(" ","_")  #key2 for valid names without spaces for object attributes
            key2=key2.replace(":","_")

jj-github-jj

# for key,value in x.iteritems():
#     new_key = key.replace(" ","_")
#     del x[key] # Deleting Previous Key
#     x[new_key] = value # Adding Modified key

# for key,value in x.iteritems():
#     new_key = key.replace(" ","_")
#     del x[key] # Deleting Previous Key
#     x[new_key] = value # Adding Modified key

jj-github-jj

tdms_file=TdmsFile.read(fpath)
for name, value in tdms_file.properties.items():
    print("{0}: {1}".format(name, value))
    
print ("------ Groups --------")
all_groups = tdms_file.groups()
for group in all_groups:
    print ("Group Name:",group.name)
    

jj-github-jj

# speed up figures with lots of data
	pip install plotly-resampler
from plotly_resampler import register_plotly_resampler, unregister_plotly_resampler

register_plotly_resampler(mode="auto", default_n_shown_samples=1000)

import plotly.io as pio
pio.renderers.default = "notebook" # patch for exporting to html in vscode for plotly interactive plots

jj-github-jj

def find_nearest(array, value):
    array = np.asarray(array)
    idx = (np.abs(array - value)).argmin()
    return idx,array[idx] #return in x,y format x is index, y is value


[print (i,v/1e9) for i,v in enumerate(x)] #print index and value of elements in list


def avg_dB(dB):

jj-github-jj

# convert data frame strings to numeric where possible and drop rows with non numeric content
df23=df23.apply(pd.to_numeric, errors='ignore') # converts to numbers when possible for all  columns 
df23
c=df23.columns.to_list()
data_columns=[c[x] for x in range(1,6)]
# Eliminate invalid data from dataframe (see Example below for more context)

num_df = (df23.drop(data_columns, axis=1)
         .join(df23[data_columns].apply(pd.to_numeric, errors='coerce'))) #numeric dataframe

jj-github-jj

def ntuples_list(list_of_names):
    """
   
    """
    list_of_names_dict = {x:x for x in list_of_names}
    Varnames = namedtuple('Varnames', list_of_names) 
    return Varnames(**list_of_names_dict)

def change_to_valid_column_names(df3):
    df3.columns=[(re.sub("[/' ()-+]","_",x)) for x in df3.columns.to_list()] #replace invalid char with _

jj-github-jj

from dataclasses import dataclass, is_dataclass

def nested_dataclass(*args, **kwargs):
    def wrapper(cls):
        cls = dataclass(cls, **kwargs)
        original_init = cls.__init__
        def __init__(self, *args, **kwargs):
            for name, value in kwargs.items():
                field_type = cls.__annotations__.get(name, None)
                if is_dataclass(field_type) and isinstance(value, dict):