140 lines
4.0 KiB
Python
140 lines
4.0 KiB
Python
# Cal Wing (c.wing@uq.net.au) - Oct 2024
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# Thesis Graphing
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import os
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import numpy as np
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import pandas as pd
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import yaml
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from nptdms import TdmsFile
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from makeGraph import makeGraph, pltKeyClose, UQ_COLOURS as UQC
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# Folder correction
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# Make sure the relevant folders folder exists
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folders = ["./images"]
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for folder in folders:
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if not os.path.isdir(folder): os.mkdir(folder)
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# Load Data
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DATA_PATH = "./data"
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DATA_INFO = "_info.yaml"
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data_to_load = [
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"x2s5823",
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"x2s5824"
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]
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data = {}
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for dp in data_to_load:
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data_path = f"{DATA_PATH}/{dp}/"
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data_info_path = data_path + DATA_INFO
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if not os.path.exists(data_info_path):
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print(f"[ERR] Could not find data info file: '{data_info_path}'")
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print(f"[WARN] Not Loading Data '{dp}'")
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continue
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with open(data_info_path, 'r') as file:
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# Load data info (Cal)
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dataInfo = yaml.safe_load(file)
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x2_shot = dataInfo["shot-info"]["name"]
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x2_tdms_data = TdmsFile.read(data_path + dataInfo["shot-info"]['tdms'])
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x2_channels = x2_tdms_data.groups()[0].channels()
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if dataInfo["probe-info"]["data-record"]["type"] == "scope":
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scope_data_path = data_path + dataInfo["probe-info"]["data-record"]["data"]
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scope_config_path = data_path + dataInfo["probe-info"]["data-record"]["config"]
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# Generate Headers
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with open(scope_data_path, 'r') as dfile:
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scope_header = []
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header_lines = []
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for i, line in enumerate(dfile):
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if i > 1: break
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header_lines.append(line.strip().split(","))
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for i, name in enumerate(header_lines[0]):
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if name == "x-axis":
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name = "Time"
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if header_lines[1][i] in ["second", "Volt"]:
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outStr = f"{name} [{header_lines[1][i][0]}]"
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else:
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outStr = f"{name} [{header_lines[1][i]}]"
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scope_header.append(outStr)
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#scope_data = pd.read_csv(scope_data_path, names=scope_header, skiprows=2)
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scope_data = np.loadtxt(scope_data_path, delimiter=',', skiprows=2)
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data[x2_shot] = {
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"info": dataInfo,
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"probe_headers": scope_header,
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"probes": scope_data,
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"x2": x2_channels,
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"x2-tdms": x2_tdms_data
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}
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loaded_data = list(data.keys())
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print("Loaded Data")
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print("Graphing Data")
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gData = data[loaded_data[0]]
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x2_time = (gData["x2"][0][:] - gData["x2"][0][0]).astype('timedelta64[ns]')
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trigger_info = gData["info"]["probe-info"]["data-record"]["trigger"]
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scope_time = np.array([ pd.Timedelta(t, 's').to_numpy() for t in (gData["probes"][:, 0] - gData["probes"][0, 0])]) - np.timedelta64(trigger_info["alignment-offset"], 'ns') + np.timedelta64(trigger_info["delay"], 'us')
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graphData = {
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"title": f"Shock response Time\nFor {gData["info"]["long_name"]}",
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"xLabel": "Time (ns)",
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"yLabel": "Voltage Reading (V)",
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"grid": True,
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"plots": [
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{
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"x": x2_time,
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"y": (gData["x2"][4][:] - gData["x2"][4][0]) * 0.0148,
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"label": "ST1"
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},
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{
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"x": x2_time,
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"y": (gData["x2"][6][:] - gData["x2"][6][0]) * 0.0148,
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"label": "ST3"
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},
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{
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"x": x2_time,
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"y": (gData["x2"][16][:] - gData["x2"][16][0])/1000,
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"label": "Trigger"
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},
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{
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"x": scope_time,
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"y": (gData["probes"][:, 1] - gData["probes"][0, 1]),
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"label": "ST2-G1"
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},
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{
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"x": scope_time,
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"y": (gData["probes"][:, 2] - gData["probes"][0, 2]),
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"label": "ST2-G2"
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},
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{
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"x": scope_time,
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"y": (gData["probes"][:, 3] - gData["probes"][0, 3]),
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"label": "ST2-Trigger"
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},
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]
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}
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makeGraph(graphData)
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