359 lines
13 KiB
Python
359 lines
13 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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from canny_shock_finder import canny_shock_finder
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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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# Data Paths
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DATA_PATH = "./data"
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DATA_INFO = "_info.yaml"
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TUNNEL_INFO_FILE = "./tunnel-info.yaml"
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SAMPLES_TO_AVG = 500
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CANNY_TIME_OFFSET = 50 #us
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with open(TUNNEL_INFO_FILE, 'r') as file:
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TUNNEL_INFO = yaml.safe_load(file)
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data_to_load = [
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"x2s5823",
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"x2s5824",
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"x2s5827",
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"x2s5829",
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]
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# ==== Data Loading & Processing ====
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def load_data(data_path: str, data={}) -> dict:
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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 '{data_path}'")
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return None
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# Load Shot Data Info YAML File (Cal)
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with open(data_info_path, 'r') as file:
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dataInfo = yaml.safe_load(file)
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# Grab the shot name
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x2_shot = dataInfo["shot-info"]["name"]
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# Load Raw Data
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# TDMS File (X2 DAQ Data)
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x2_tdms_data = TdmsFile.read(data_path + dataInfo["shot-info"]['tdms'], raw_timestamps=True)
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x2_channels = x2_tdms_data.groups()[0].channels()
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x2_channel_names = tuple(c.name for c in x2_channels)
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# Scope info _if it exists_
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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"] # [TODO] Read this file
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# Generate Data Headers - This could be better
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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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# Load the Scope CSV Data
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scope_data = np.loadtxt(scope_data_path, delimiter=',', skiprows=2)
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# Build a data object (this could be cached - or partially cached if I was clever enough)
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# Raw Data is always added - processing comes after
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data[x2_shot] = {
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"info": dataInfo,
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"shot_time": np.datetime64(f"{dataInfo["date"]}T{dataInfo["time"]}"),
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"raw-data":{
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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-channels": x2_channel_names,
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"x2-tdms": x2_tdms_data
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},
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"time": {
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"x2": None,
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"trigger_index": None
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},
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"data": {
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"x2": {} # Only pop channels with a voltage scale in ./tunnel-info.yaml
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},
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"shock-speed": {}
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}
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# === Process the data ===
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# Generate X2 time arrays
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time_data = x2_channels[0]
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ns_time = time_data[:].as_datetime64('ns')
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x2_time_seconds = (ns_time - ns_time[0]) # timedelta64[ns]
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x2_time_us = x2_time_seconds.astype("float64") / 1000 # Scale to us
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#second_fractions = np.array(time_data[:].second_fractions, dtype=int) # 2^-64 ths of a second
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#x2_time_seconds = (second_fractions - second_fractions[0]) / (2**(-64)) # 0 time data and convert to seconds
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#x2_time_us = x2_time_seconds * 1000 # Scale to us
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# --- Un Scale Data ---
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for channel, vScale in TUNNEL_INFO["volt-scale"].items():
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# Get the channel index from its name
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chIndex = x2_channel_names.index(channel)
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# Calculate the average noise offset
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avg_noise = x2_channels[chIndex][0:SAMPLES_TO_AVG].mean()
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# Save the channel data
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data[x2_shot]["data"]["x2"][channel] = (x2_channels[chIndex][:] - avg_noise) * vScale
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# Process Trigger Info
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trigger_volts = data[x2_shot]["data"]["x2"]["trigbox"] # Use a mean to offset
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x2_trigger_index = np.where(trigger_volts > 1)[0][0]
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x2_trigger_time = x2_time_us[x2_trigger_index]
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# Add the time data
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data[x2_shot]["time"] = {
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"x2": x2_time_us,
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"trigger_index": x2_trigger_index
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}
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# Scope timing _if it exists_
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if dataInfo["probe-info"]["data-record"]["type"] == "scope":
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trigger_info = dataInfo["probe-info"]["data-record"]["trigger"] # Get the scope trigger info
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# Calc the scope time & apply any manual offsets
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scope_time = (scope_data[:, 0] - scope_data[0, 0]) * 1e6 # to us
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scope_time -= trigger_info["alignment-offset"] # manual offset delay
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# Trigger Alignment
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scope_trigger_volts = (scope_data[:, 3] - scope_data[0:SAMPLES_TO_AVG, 3].mean()) # Use a mean here too
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scope_trigger_index = np.where(scope_trigger_volts > 1)[0][0]
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scope_trigger_time = scope_time[scope_trigger_index]
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scope_alignment = x2_trigger_time - scope_trigger_time
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scope_time += scope_alignment
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# Offset any trigger delays
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scope_time += trigger_info["delay"] # us delay from the actual trigger signal to the scope received trigger
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data[x2_shot]["time"]["scope"] = scope_time
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data[x2_shot]["time"]["scope-offset"] = scope_alignment
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data[x2_shot]["data"]["scope"] = {}
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for i, header in enumerate(scope_header):
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if i == 0: continue # Don't record time
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# Python reference so its the same object
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ref = scope_data[:, i]
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data[x2_shot]["data"]["scope"][i] = ref
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data[x2_shot]["data"]["scope"][header] = ref
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# Find Shock Times
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# X2 - Canning Edge
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data[x2_shot]["shock-point"] = {}
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for ref in dataInfo["pcb-refs"]:
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refData = data[x2_shot]["data"]["x2"][ref]
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first_value, first_value_uncertainty, _, _ = canny_shock_finder(x2_time_us, refData, plot=False, print_func=None)
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shock_point = np.where(x2_time_us >= first_value)[0][0] # [BUG] Seems to give n+1
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data[x2_shot]["shock-point"][ref] = shock_point, first_value
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for i, probe in enumerate(dataInfo["probe-info"]["locations"]):
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probeCh1 = data[x2_shot]["data"]["scope"][1]
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probeCh2 = data[x2_shot]["data"]["scope"][2]
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# Get the canny-args
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cArgs = dataInfo["canny-args"]
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doCannyPlot = False
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if i in range(len(cArgs)):
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sigma = cArgs[i]["sigma"]
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post_pres = cArgs[i]["post_pres"]
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else:
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sigma = cArgs[-1]["sigma"]
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post_pres = cArgs[-1]["post_pres"]
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# If this _isn't_ the first probe then apply a time offset
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if i > 0:
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privPoint = dataInfo["probe-info"]["locations"][i-1]
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time_offset = data[x2_shot]["shock-point"][f"{privPoint}-g1"][1] + CANNY_TIME_OFFSET
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else:
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time_offset = None
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# Find G1 Shock Time
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first_value, first_value_uncertainty, _, _ = canny_shock_finder(scope_time, probeCh1, sigma=sigma, post_suppression_threshold=post_pres, plot=doCannyPlot, start_time=time_offset, print_func=None)
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shock_point = np.where(scope_time >= first_value)[0][0] # [BUG] Seems to give n+1
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data[x2_shot]["shock-point"][f"{probe}-g1"] = shock_point, first_value
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# Do the same for G2
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if i > 0:
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time_offset = data[x2_shot]["shock-point"][f"{privPoint}-g2"][1] + CANNY_TIME_OFFSET
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# Find G2 Shock Time
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first_value, first_value_uncertainty, _, _ = canny_shock_finder(scope_time, probeCh2, sigma=sigma, post_suppression_threshold=post_pres, plot=doCannyPlot, start_time=time_offset, print_func=None)
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shock_point = np.where(scope_time >= first_value)[0][0] # [BUG] Seems to give n+1
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data[x2_shot]["shock-point"][f"{probe}-g2"] = shock_point, first_value
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# Calculate Shock Speeds
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print("="*25, x2_shot, "="*25)
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for probe in dataInfo["probe-info"]["locations"]:
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g1_time = data[x2_shot]["shock-point"][f"{probe}-g1"][1] / 1e6 # Convert to seconds
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g2_time = data[x2_shot]["shock-point"][f"{probe}-g2"][1] / 1e6 # Convert to seconds
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c2c_dist = dataInfo["probe-info"]["c2c"] / 1000 # convert to m
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probe_velocity = c2c_dist / abs(g2_time - g1_time) # m/s
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print(f"{probe} Measured a shock speed of {probe_velocity:.2f} m/s ({probe_velocity/1000:.2f} km/s)")
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data[x2_shot]["shock-speed"][probe] = probe_velocity # m/s
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if len(dataInfo["probe-info"]["locations"]) > 1:
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for i in range(len(dataInfo["probe-info"]["locations"]) - 1):
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probe_locs = dataInfo["probe-info"]["locations"]
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p1_g1_time = data[x2_shot]["shock-point"][f"{probe_locs[i]}-g1"][1] / 1e6 # Convert to seconds
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p1_g2_time = data[x2_shot]["shock-point"][f"{probe_locs[i]}-g2"][1] / 1e6 # Convert to seconds
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p2_g1_time = data[x2_shot]["shock-point"][f"{probe_locs[i+1]}-g1"][1] / 1e6 # Convert to seconds
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p2_g2_time = data[x2_shot]["shock-point"][f"{probe_locs[i+1]}-g2"][1] / 1e6 # Convert to seconds
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p2p = (TUNNEL_INFO["distance"][probe_locs[1]] - TUNNEL_INFO["distance"][probe_locs[0]]) / 1000 # convert to m
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p2p_1 = p2p / abs(p2_g1_time - p1_g1_time) # m/s
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p2p_2 = p2p / abs(p2_g2_time - p1_g2_time) # m/s
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print(f"{probe_locs[i]}-{probe_locs[i + 1]} - G1 - Measured a shock speed of {p2p_1:.2f} m/s ({p2p_1/1000:.2f} km/s)")
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print(f"{probe_locs[i]}-{probe_locs[i + 1]} - G2 - Measured a shock speed of {p2p_2:.2f} m/s ({p2p_2/1000:.2f} km/s)")
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data[x2_shot]["shock-speed"][f"{probe_locs[i]}-{probe_locs[i + 1]}-g1"] = p2p_1
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data[x2_shot]["shock-speed"][f"{probe_locs[i]}-{probe_locs[i + 1]}-g2"] = p2p_2
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print()
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# Return the data & the successfully loaded data keys
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return data #, tuple(data.keys())
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data = {}
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for dp in data_to_load:
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pdp = f"{DATA_PATH}/{dp}/"
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load_data(pdp, data)
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loaded_data = tuple(data.keys())
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print("Loaded Data")
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#[TODO] Refactor
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def genGraph(gData: dict, showPlot: bool = True):
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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 ($\\mu$s)",
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"yLabel": "Voltage Reading (V)",
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"grid": True,
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"figSize": (8,6.5),
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"ledgLoc": 'upper left',
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"plots": []
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}
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lims = []
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for label in gData["info"]["pcb-refs"]: # + ["trigbox"]:
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graphData["plots"].append({
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"x": gData["time"]["x2"],
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"y": gData["data"]["x2"][label],
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"label": label
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})
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if label in gData["info"]["pcb-refs"]:
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graphData["plots"].append({
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"type": "axvLine",
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"x": gData["shock-point"][label][1],
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"label": f"{label} - Shock Point {gData["shock-point"][label][1]:.2f}$\\mu$s",
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"colour": "gray",
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"args":{"zorder":2, "linestyle":"--"}
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})
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lims.append(gData["shock-point"][label][1]) # [TODO this but better]
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for label, d in [("1 [V]", "G1"),("2 [V]", "G2")]: #, ("4 [V]", "Gauge Trigger")]:
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graphData["plots"].append({
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"x": gData["time"]["scope"],
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"y": gData["data"]["scope"][label],
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"label": d
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})
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for i, probe in enumerate(gData["info"]["probe-info"]["locations"]):
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graphData["plots"].append({
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"type": "axvLine",
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"x": gData["shock-point"][f"{probe}-g1"][1],#[i],
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"label": f"{probe}-G1 - Shock Point {gData["shock-point"][f"{probe}-g1"][1]:.2f}$\\mu$s",
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#"colour": "gray",
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"args":{"zorder":2, "linestyle":"--"}
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})
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graphData["plots"].append({
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"type": "axvLine",
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"x": gData["shock-point"][f"{probe}-g2"][1],#[i],
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"label": f"{probe}-G2 - Shock Point {gData["shock-point"][f"{probe}-g2"][1]:.2f}$\\mu$s",
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#"colour": "gray",
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"args":{"zorder":2, "linestyle":"--"}
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})
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lims.append(gData["shock-point"][f"{probe}-g2"][1])
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lims.append(gData["shock-point"][f"{probe}-g1"][1])
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probeText = ""
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for shock_speed_loc in gData["shock-speed"]:
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probeText += f"\n{shock_speed_loc} - {gData["shock-speed"][shock_speed_loc]/1000:.2f} km/s"
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graphData["plots"].append({
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"type": "text",
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"text": f"Measured Shock Speeds{probeText}",
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"align": ("top", "right"),
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"x": 0.94, "y": 0.94
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})
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if len(lims) > 1:
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OFFSET = 10
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graphData["xLim"] = (float(min(lims) - OFFSET), float(max(lims) + OFFSET))
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makeGraph(graphData, doProgramBlock=False, showPlot=showPlot, figSavePath="./images/{0}.png")
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print("Graphing Data")
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for shot in loaded_data:
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#if shot != loaded_data[-2]: continue
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genGraph(data[shot], showPlot=False)
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# This forces matplotlib to hang until I tell it to close all windows
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pltKeyClose()
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print("Done")
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