TPL Parsing
1. pypontem tpl parsing code examples
tplParser: a class which holds the tpl parser functions
Importing pypontem package
from pypontem.tpl.tplparser import tplParser
Instantiating a tplParser object
tpl = tplParser('data/example_tpl.tpl')
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metadata
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Extracts metadata from the content stored in the object and returns it as a pandas DataFrame.
metadata = tpl.metadata
print(metadata)
version Input file pvt \
0 OLGA 2015.3.1.142077 Simulation.genkey ./Fluid_Tuned.tab
time project title author geometry
0 24-05-10 17:49:57 OLGA Basic Case Pontem Analytics M
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branch_names
Extracts branch names from the content stored in the tpl file.
branch_names = tpl.branch_names
print(branch_names)
["'JUMPER-1'", "'FLOWLINE-1'", "'FLOWLINE-2'", "'JUMPER-2'", "'FLOWLINE-3'", "'COILEDTUBING'"]
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branch_profiles
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Extracts and displays elevation for a specific branch or all branches in the file.
branch_profiles = tpl.branch_profiles.get("FLOWLINE-2")
print(branch_profiles)
{ 'FLOWLINE-2': Lengths_(m) Elevations_(m)
0 0.000000 -1920.240000
1 453.518520 -1912.620000
2 907.037040 -1905.000000
3 1213.865904 -1901.189991
4 1520.694768 -1897.380000
5 1962.662832 -1895.475001
6 2404.630897 -1893.570003
7 2846.598961 -1891.665004
8 3288.567026 -1889.760005}
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n_vars
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Parses the number of variables from a given file path.
n_vars = tpl.n_vars
print(n_vars)
140
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catalog
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Extracts variable information from the provided content using a regular expression pattern.
returned_catalog = tpl.catalog
print(returned_catalog.head())
varname BRANCH PIPE NR POSITION out_unit \
0 QLST JUMPER-1 PIPE-1 1 NaN (SM3/S)
1 QOST JUMPER-1 PIPE-1 1 NaN (SM3/S)
2 QWST JUMPER-1 PIPE-1 1 NaN (SM3/S)
3 QGST JUMPER-1 PIPE-1 1 NaN (SM3/S)
4 GT JUMPER-1 PIPE-1 1 NaN (KG/S)
Description
0 Liquid volume flow at standard conditions
1 Oil volume flow at standard conditions
2 Water volume flow at standard conditions
3 Gas volume flow at standard conditions
4 Total mass flow
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search_catalog:
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Searches for variables containing a keyword in their names within a DataFrame.
search_catalog = tpl.search_catalog('QLST')
print(search_catalog.head())
varname BRANCH PIPE NR out_unit \
0 QLST JUMPER-1 PIPE-1 1 (SM3/S)
13 QLST FLOWLINE-1 LPL-4 1 (SM3/S)
25 QLST FLOWLINE-2 LPL-5 1 (SM3/S)
26 QLST FLOWLINE-2 LPL-6 1 (SM3/S)
27 QLST FLOWLINE-2 LPL-7 1 (SM3/S)
Description
0 Liquid volume flow at standard conditions
13 Liquid volume flow at standard conditions
25 Liquid volume flow at standard conditions
26 Liquid volume flow at standard conditions
27 Liquid volume flow at standard conditions
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extract_trends
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Search for variables in the DataFrame based on variable names, branches, and pipe names, and display their information.
1. Extracting trends from a DataFrame created using a csv file.
import pandas as pd
input_data = pd.read_csv('data/test_inputs.csv')
trends = tpl.extract_trend(input_data)
print(trends.head())
PT_psig_JUMPER-1 TM_degK_FLOWLINE-1 Time_(hour) 0.0 1442.121 304.671 2.0 1468.391 304.671 4.0 1465.278 304.667 6.0 1463.820 304.669 8.0 1464.741 304.668
2. Extracting trends from a DataFrame created using a dictionary.
matrix = {"varname":"PT",
"BRANCH":"JUMPER-1",
"PIPE":None,
"out_unit":"psig",
"time_unit":"h"}
matrix_pd = pd.DataFrame(matrix, index=[0]) trends = tpl.extract_trend(matrix_pd)
print(trends.head(3))
PT_psig_JUMPER-1
Time_(h)
0.0 1442.121
2.0 1468.391
4.0 1465.278
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calc_average
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Calculate the average of values in the DataFrame between the specified start and end indices.
1. Calculating averages from a DataFrame created using a csv file.
average = tpl.calc_average(input_data,start_index=1,end_index=4)
print(average)
PT_psig_JUMPER-1 1459.9025 TM_degK_FLOWLINE-1 304.6695 dtype: float64
2. Calculating averages from a DataFrame created using a dictionary.
average_dict = tpl.calc_average(matrix_pd, start_index=1,end_index=4)
print(average_dict)
PT_psig_JUMPER-1 1459.9025 dtype: float64
3. Users can also calculate the average of any quantity based on the time units specified in the input matrix. The argument n_timeunits takes an integer value to represent the number of time units to be extracted. Note: a positive value, say x, denotes the first x time units and a negative value, say -x, denotes the last x time units. In the examples below,we extract the first 4 and last 4 hours from a tpl file
average_last_n_hours = tpl.calc_average(matrix_pd, n_timeunits=-4)
print("The average of the first 4 hours is:")
print(average_first_n_hours)
print("The average of the last 4 hours is:")
print(average_last_n_hours)
The average of the first 4 hours is:
PT_psig_JUMPER-1 1458.596667
dtype: float64
The average of the last 4 hours is:
PT_psig_JUMPER-1 1464.553667
dtype: float64
2. pypontem tpl batch parsing code examples
tplBatchParser: a class to handle batches of tpl files
Note that: tpl batch parsing functionality only works for files of the same batch of simulations with the same branch names and structure, otherwise this will raise an error. For illustration purposes below, we have duplicated the same file
Importing tpl batch parser class
from pypontem.tpl.tplparser import tplBatchParser
Instantiating an object with a list of tpl files
files = ['data/example_tpl.tpl','data/example_tpl.tpl']
tplbatch = tplBatchParser(files)
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extract_trends:
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Function to extract trends from a batch of tpl files
1. Extracting batch trends from a DataFrame created using a csv file.
batch_trends = tplbatch.extract_trends(input_data)
print(batch_trends.head(3))
PT_psig_JUMPER-1_example_tpl.tpl_x \
Time_(hour)
0.0 1442.121
2.0 1468.391
4.0 1465.278
TM_degK_FLOWLINE-1_example_tpl.tpl_x \
Time_(hour)
0.0 304.671
2.0 304.671
4.0 304.667
PT_psig_JUMPER-1_example_tpl.tpl_y \
Time_(hour)
0.0 1442.121
2.0 1468.391
4.0 1465.278
TM_degK_FLOWLINE-1_example_tpl.tpl_y
Time_(hour)
0.0 304.671
2.0 304.671
4.0 304.667
2. Extracting batch trends from a DataFrame created using a dictionary.
batch_trends_dict = tplbatch.extract_trends(matrix_pd)
print(batch_trends_dict.head(3))
PT_psig_JUMPER-1_example_tpl.tpl_x \
Time_(h)
0.0 1442.121
2.0 1468.391
4.0 1465.278
PT_psig_JUMPER-1_example_tpl.tpl_y
Time_(h)
0.0 1442.121
2.0 1468.391
4.0 1465.278
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calc_averages
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Calculates the average of values in the DataFrame up to the specified index or of the last n values.
1. Calculating batch averages from a DataFrame created using a csv file.
batch_average = tplbatch.calc_averages(input_data,start_index=1,end_index=4)
print(batch_average)
Average PT_psig_JUMPER-1_example_tpl.tpl_x 1459.9025 TM_degK_FLOWLINE-1_example_tpl.tpl_x 304.6695 PT_psig_JUMPER-1_example_tpl.tpl_y 1459.9025 TM_degK_FLOWLINE-1_example_tpl.tpl_y 304.6695
2. Calculating batch averages from a DataFrame created using a dictionary.
batch_average = tplbatch.calc_averages(matrix_pd,start_index=1,end_index=4)
print(batch_average)
Average PT_psig_JUMPER-1_example_tpl.tpl_x 1459.9025 PT_psig_JUMPER-1_example_tpl.tpl_y 1459.9025