Thesis/main.py

# Cal Wing (c.wing@uq.net.au) - Oct 2024
# Thesis Graphing

import os

import numpy as np
import pandas as pd

import yaml

from nptdms import TdmsFile
from makeGraph import makeGraph, pltKeyClose, UQ_COLOURS as UQC

from canny_shock_finder import canny_shock_finder

# Folder correction
#  Make sure the relevant folders folder exists
folders = ["./images"]
for folder in folders:
    if not os.path.isdir(folder): os.mkdir(folder)

# Data Paths
DATA_PATH = "./data"
DATA_INFO = "_info.yaml"
TUNNEL_INFO_FILE = "./tunnel-info.yaml"
SAMPLES_TO_AVG = 500
CANNY_TIME_OFFSET = 50 #us

with open(TUNNEL_INFO_FILE, 'r') as file:
    TUNNEL_INFO = yaml.safe_load(file)

data_to_load = [
    #"x2s5823",
    #"x2s5824",
    #"x2s5827",
    "x2s5829",
    #"x2s5830",
    "x2s5831",
    "x2s5832"
]

# ==== Data Loading & Processing ====
def load_data(data_path: str, data={}) -> dict:
    data_info_path = data_path + DATA_INFO
    if not os.path.exists(data_info_path):
        print(f"[ERR] Could not find data info file: '{data_info_path}'")
        print(f"[WARN] Not Loading Data '{data_path}'")
        return None

    # Load Shot Data Info YAML File (Cal)
    with open(data_info_path, 'r') as file:
        dataInfo = yaml.safe_load(file)

    # Grab the shot name
    x2_shot = dataInfo["shot-info"]["name"]

    # Update shot-info values to use the name
    dataInfo["shot-info"]["tdms"] = dataInfo["shot-info"]["tdms"].format(x2_shot)
    dataInfo["shot-info"]["config"] = dataInfo["shot-info"]["config"].format(x2_shot)
    dataInfo["shot-info"]["info"] = dataInfo["shot-info"]["info"].format(x2_shot)

    # Load Raw Data
    # TDMS File (X2 DAQ Data)
    x2_tdms_data = TdmsFile.read(data_path + dataInfo["shot-info"]['tdms'], raw_timestamps=True)
    x2_channels = x2_tdms_data.groups()[0].channels()
    x2_channel_names = tuple(c.name for c in x2_channels)

    data_locs = [dr["type"] for dr in dataInfo["probe-info"]["data-records"]]

    # Scope info _if it exists_
    if "scope" in data_locs:
        scope_data_info = dataInfo["probe-info"]["data-records"][data_locs.index("scope")]

        scope_data_path = data_path + scope_data_info["data"]
        scope_config_path = data_path + scope_data_info["config"] # [TODO] Read this file

        # Generate Data Headers - This could be better
        with open(scope_data_path, 'r') as dfile:
            scope_header = []

            header_lines = []
            for i, line in enumerate(dfile):
                if i > 1: break
                header_lines.append(line.strip().split(","))

            for i, name in enumerate(header_lines[0]):
                if name == "x-axis":
                    name = "Time"

                if header_lines[1][i] in ["second", "Volt"]:
                    outStr = f"{name} [{header_lines[1][i][0]}]"
                else:
                    outStr = f"{name} [{header_lines[1][i]}]"

                scope_header.append(outStr)

        # Load the Scope CSV Data
        scope_data = np.loadtxt(scope_data_path, delimiter=',', skiprows=2)


    # Build a data object (this could be cached - or partially cached if I was clever enough)
    # Raw Data is always added - processing comes after
    data[x2_shot] = {
        "info": dataInfo,
        "shot_time": np.datetime64(f"{dataInfo["date"]}T{dataInfo["time"]}"),
        "raw-data":{
            "probe_headers": scope_header,
            "probes": scope_data,
            "x2": x2_channels,
            "x2-channels": x2_channel_names,
            "x2-tdms": x2_tdms_data
        },
        "time": {
            "x2": None,
            "probes": None, # This may be x2 but may not - ie a scope was used
            "trigger_index": None,
        },
        "data": {
            "x2": {}, # Only pop channels with a voltage scale in ./tunnel-info.yaml
            "probes": [[None], [None]] # Save probe data in volts - [G1, G2]
        },
        "shock-speed": {} # Note all in us
    }

    # === Process the data ===
    # Generate X2 time arrays
    time_data = x2_channels[0]

    ns_time = time_data[:].as_datetime64('ns')
    x2_time_seconds = (ns_time - ns_time[0]) # timedelta64[ns]
    x2_time_us = x2_time_seconds.astype("float64") / 1000 # Scale to us

    #second_fractions = np.array(time_data[:].second_fractions, dtype=int) # 2^-64 ths of a second
    #x2_time_seconds = (second_fractions - second_fractions[0]) / (2**(-64)) # 0 time data and convert to seconds
    #x2_time_us = x2_time_seconds * 1000 # Scale to us

    # --- Un Scale Data ---
    for channel, vScale in TUNNEL_INFO["volt-scale"].items():
        # Get the channel index from its name
        chIndex = x2_channel_names.index(channel)

        # Calculate the average noise offset
        avg_noise = x2_channels[chIndex][0:SAMPLES_TO_AVG].mean()

        # Save the channel data
        data[x2_shot]["data"]["x2"][channel] = (x2_channels[chIndex][:] - avg_noise) * vScale

    # Process Trigger Info
    trigger_volts = data[x2_shot]["data"]["x2"]["trigbox"] # Use a mean to offset
    x2_trigger_index = np.where(trigger_volts > 1)[0][0]
    x2_trigger_time = x2_time_us[x2_trigger_index]

    # Add the time data
    data[x2_shot]["time"] = {
        "x2": x2_time_us,
        "trigger_index": x2_trigger_index,
        "probes": x2_time_us, # Until otherwise overridden - probe time is x2 time
    }


    # Scope timing _if it exists_
    if "scope" in data_locs:
        scope_data_info = dataInfo["probe-info"]["data-records"][data_locs.index("scope")]

        trigger_info = scope_data_info["trigger"] # Get the scope trigger info

        # Calc the scope time & apply any manual offsets
        scope_time  = (scope_data[:, 0] - scope_data[0, 0]) * 1e6 # to us
        scope_time -= trigger_info["alignment-offset"] # manual offset delay

        # Trigger Alignment
        scope_trigger_volts = (scope_data[:, 3] - scope_data[0:SAMPLES_TO_AVG, 3].mean()) # Use a mean here too
        scope_trigger_index = np.where(scope_trigger_volts > 1)[0][0]
        scope_trigger_time = scope_time[scope_trigger_index]

        scope_alignment = x2_trigger_time - scope_trigger_time

        scope_time += scope_alignment

        # Offset any trigger delays
        scope_time += trigger_info["delay"] # us delay from the actual trigger signal to the scope received trigger

        data[x2_shot]["time"]["scope"] = scope_time
        data[x2_shot]["time"]["scope-offset"] = scope_alignment

        data[x2_shot]["data"]["scope"] = {}
        for i, header in enumerate(scope_header):
            if i == 0: continue # Don't record time

            # Python reference so its the same object
            ref = scope_data[:, i]
            data[x2_shot]["data"]["scope"][i] = ref
            data[x2_shot]["data"]["scope"][header] = ref

    # Save Probe Data
    if "scope" in data_locs:
        data[x2_shot]["data"]["probes"] = [data[x2_shot]["data"]["scope"][1], data[x2_shot]["data"]["scope"][2]]
        data[x2_shot]["time"]["probes"] = data[x2_shot]["time"]["scope"]


    # Find Shock Times
    # X2 - Canning Edge
    data[x2_shot]["shock-point"] = {}
    for ref in dataInfo["pcb-refs"]:
        refData = data[x2_shot]["data"]["x2"][ref]
        first_value, first_value_uncertainty, _, _ = canny_shock_finder(x2_time_us, refData, plot=False, print_func=None)
        shock_point = np.where(x2_time_us >= first_value)[0][0] # [BUG] Seems to give n+1

        data[x2_shot]["shock-point"][ref] = shock_point, first_value

    for i, probe in enumerate(dataInfo["probe-info"]["locations"]):
        probeCh1 = data[x2_shot]["data"]["probes"][0]
        probeCh2 = data[x2_shot]["data"]["probes"][1]

        # Get the canny-args
        cArgs = dataInfo["canny-args"]
        doCannyPlot = False
        if i in range(len(cArgs)):
            sigma = cArgs[i]["sigma"]
            post_sup_thresh = cArgs[i]["post_pres"]
        else:
            sigma = cArgs[-1]["sigma"]
            post_sup_thresh = cArgs[-1]["post_pres"]

        # If this _isn't_ the first probe then apply a time offset
        if i > 0:
            privPoint = dataInfo["probe-info"]["locations"][i-1]
            time_offset = data[x2_shot]["shock-point"][f"{privPoint}-g1"][1] + CANNY_TIME_OFFSET
        else:
            time_offset = None

        # Find G1 Shock Time
        first_value, first_value_uncertainty, _, _ = canny_shock_finder(scope_time, probeCh1, sigma=sigma, post_suppression_threshold=post_sup_thresh, plot=doCannyPlot, start_time=time_offset, print_func=None)
        if first_value is None:
            print(f"[ERROR] {x2_shot} - {probe}-g1 could not be detected using: Sigma = {sigma}, post_suppression_threshold = {post_sup_thresh}")
            raise ValueError(f"{probe}-g1 not detected")

        shock_point = np.where(scope_time >= first_value)[0][0] # [BUG] Seems to give n+1

        data[x2_shot]["shock-point"][f"{probe}-g1"] = shock_point, first_value

        # Do the same for G2
        if i > 0:
            time_offset = data[x2_shot]["shock-point"][f"{privPoint}-g2"][1] + CANNY_TIME_OFFSET

        # Find G2 Shock Time
        first_value, first_value_uncertainty, _, _ = canny_shock_finder(scope_time, probeCh2, sigma=sigma, post_suppression_threshold=post_sup_thresh, plot=doCannyPlot, start_time=time_offset, print_func=None)
        if first_value is None:
            print(f"[ERROR] {x2_shot} - {probe}-g2 could not be detected using: Sigma = {sigma}, post_suppression_threshold = {post_sup_thresh}")
            raise ValueError(f"{probe}-g2 not detected")

        shock_point = np.where(scope_time >= first_value)[0][0] # [BUG] Seems to give n+1
        data[x2_shot]["shock-point"][f"{probe}-g2"] = shock_point, first_value

    # Calculate Shock Speeds
    print("="*30, x2_shot, "="*30)
    print("--", dataInfo["long_name"], "--")

    for i, refProbe in enumerate(dataInfo["pcb-refs"]):
        if i == 0: continue
        p1_time = data[x2_shot]["shock-point"][refProbe][1] / 1e6 # Convert to seconds
        p2_time = data[x2_shot]["shock-point"][dataInfo["pcb-refs"][i-1]][1] / 1e6 # Convert to seconds
        p2p_dist = (TUNNEL_INFO["distance"][refProbe] - TUNNEL_INFO["distance"][dataInfo["pcb-refs"][i-1]]) / 1000 # convert to m

        probe_velocity = p2p_dist / abs(p2_time - p1_time) # m/s
        print(f"{refProbe}-{dataInfo["pcb-refs"][i-1]} Measured a shock speed of {probe_velocity:.2f} m/s ({probe_velocity/1000:.2f} km/s)")


    print()
    for probe in dataInfo["probe-info"]["locations"]:
        g1_time = data[x2_shot]["shock-point"][f"{probe}-g1"][1] / 1e6 # Convert to seconds
        g2_time = data[x2_shot]["shock-point"][f"{probe}-g2"][1] / 1e6 # Convert to seconds
        c2c_dist = dataInfo["probe-info"]["c2c"] / 1000 # convert to m

        probe_velocity = c2c_dist / abs(g2_time - g1_time) # m/s

        print(f"{probe} Measured a shock speed of {probe_velocity:.2f} m/s ({probe_velocity/1000:.2f} km/s)")
        data[x2_shot]["shock-speed"][probe] = probe_velocity # m/s

    if len(dataInfo["probe-info"]["locations"]) > 1:
        for i in range(len(dataInfo["probe-info"]["locations"]) - 1):
            probe_locs = dataInfo["probe-info"]["locations"]
            p1_g1_time = data[x2_shot]["shock-point"][f"{probe_locs[i]}-g1"][1] / 1e6 # Convert to seconds
            p1_g2_time = data[x2_shot]["shock-point"][f"{probe_locs[i]}-g2"][1] / 1e6 # Convert to seconds

            p2_g1_time = data[x2_shot]["shock-point"][f"{probe_locs[i+1]}-g1"][1] / 1e6 # Convert to seconds
            p2_g2_time = data[x2_shot]["shock-point"][f"{probe_locs[i+1]}-g2"][1] / 1e6 # Convert to seconds

            p2p = (TUNNEL_INFO["distance"][probe_locs[1]] - TUNNEL_INFO["distance"][probe_locs[0]])  / 1000 # convert to m

            p2p_1 = p2p / abs(p2_g1_time - p1_g1_time) # m/s
            p2p_2 = p2p / abs(p2_g2_time - p1_g2_time) # m/s

            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)")
            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)")
            data[x2_shot]["shock-speed"][f"{probe_locs[i]}-{probe_locs[i + 1]}-g1"] = p2p_1
            data[x2_shot]["shock-speed"][f"{probe_locs[i]}-{probe_locs[i + 1]}-g2"] = p2p_2

    print()

    # Return the data & the successfully loaded data keys
    return data #, tuple(data.keys())

data = {}
for dp in data_to_load:
    pdp = f"{DATA_PATH}/{dp}/"
    load_data(pdp, data)

loaded_data = tuple(data.keys())

print("Loaded Data")


#[TODO] Refactor
def genGraph(gData: dict, showPlot: bool = True):
    graphData = {
        "title": f"Shock response Time\nFor {gData['info']['long_name']}",
        "xLabel": "Time ($\\mu$s)",
        "yLabel": "Voltage Reading (V)",
        "grid": True,
        "figSize": (8,6.5),
        "ledgLoc": 'upper left',
        "plots": []
    }

    lims = []

    for label in gData["info"]["pcb-refs"]: # + ["trigbox"]:
        if label in gData["info"]["no-graph"]: continue
        graphData["plots"].append({
            "x": gData["time"]["x2"],
            "y": gData["data"]["x2"][label],
            "label": label
        })

        if label in gData["info"]["pcb-refs"]:
            graphData["plots"].append({
                "type": "axvLine",
                "x": gData["shock-point"][label][1],
                "label": f"{label} - Shock Point {gData["shock-point"][label][1]:.2f}$\\mu$s",
                "colour": "gray",
                "args":{"zorder":2, "linestyle":"--"}
            })
            lims.append(gData["shock-point"][label][1]) # [TODO this but better]


    for label, d in [("1 [V]", "G1"),("2 [V]", "G2")]: #, ("4 [V]", "Gauge Trigger")]:
        graphData["plots"].append({
            "x": gData["time"]["scope"],
            "y": gData["data"]["scope"][label],
            "label": d
        })

    for i, probe in enumerate(gData["info"]["probe-info"]["locations"]):
        graphData["plots"].append({
            "type": "axvLine",
            "x": gData["shock-point"][f"{probe}-g1"][1],#[i],
            "label": f"{probe}-G1 - Shock Point {gData["shock-point"][f"{probe}-g1"][1]:.2f}$\\mu$s",
            #"colour": "gray",
            "args":{"zorder":2, "linestyle":"--"}
        })
        graphData["plots"].append({
            "type": "axvLine",
            "x": gData["shock-point"][f"{probe}-g2"][1],#[i],
            "label": f"{probe}-G2 - Shock Point {gData["shock-point"][f"{probe}-g2"][1]:.2f}$\\mu$s",
            #"colour": "gray",
            "args":{"zorder":2, "linestyle":"--"}
        })

        lims.append(gData["shock-point"][f"{probe}-g2"][1])
        lims.append(gData["shock-point"][f"{probe}-g1"][1])

    probeText = ""
    for shock_speed_loc in gData["shock-speed"]:
        probeText += f"\n{shock_speed_loc} - {gData["shock-speed"][shock_speed_loc]/1000:.2f} km/s"

    graphData["plots"].append({
        "type": "text",
        "text": f"Measured Shock Speeds{probeText}",
        "align": ("top", "right"),
        "x": 0.94, "y": 0.94
    })

    #if len(lims) > 1:
    #    OFFSET = 10
    #    graphData["xLim"] = (float(min(lims) - OFFSET), float(max(lims) + OFFSET))


    makeGraph(graphData, doProgramBlock=False, showPlot=showPlot, figSavePath="./images/{0}.png")



print("Graphing Data")
for shot in loaded_data:
    #if shot != loaded_data[-2]: continue
    genGraph(data[shot], showPlot=False)

# This forces matplotlib to hang until I tell it to close all windows
pltKeyClose()

print("Done")