Refactor data loading
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Cal Wing
parent
5a24ab0bc1
commit
7d6c0514f6
@ -28,6 +28,6 @@ probe-info:
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trigger:
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type: "channel"
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channel: 4
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alignment-offset: 601000 # ns
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alignment-offset: 601 # us [TODO] Make this auto-magic
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delay: 100 # us
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@ -29,7 +29,7 @@ probe-info:
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trigger: # Redundant?
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type: "channel"
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channel: 4
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alignment-offset: 499500 # ns
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alignment-offset: 601 # us [TODO] Make this auto-magic
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delay: 100 # us
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@ -30,7 +30,7 @@ probe-info:
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trigger: # Redundant?
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type: "channel"
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channel: 4
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alignment-offset: 499500 # ns
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alignment-offset: 601 # us [TODO] Make this auto-magic
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delay: 100 # us
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157
main.py
157
main.py
@ -19,13 +19,14 @@ 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 Paths
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DATA_PATH = "./data"
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DATA_INFO = "_info.yaml"
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PCB_INFO_FILE = "./pcb-info.yaml"
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TUNNEL_INFO_FILE = "./tunnel-info.yaml"
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SAMPLES_TO_AVG = 500
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with open(PCB_INFO_FILE, 'r') as file:
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PCB_INFO = yaml.safe_load(file)
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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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@ -33,8 +34,9 @@ data_to_load = [
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"x2s5827"
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]
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# ==== Data Loading & Processing ====
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def load_data(data_to_load: list[str]) -> dict:
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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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@ -43,19 +45,25 @@ for dp in data_to_load:
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print(f"[WARN] Not Loading Data '{dp}'")
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continue
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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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# Load data info (Cal)
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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"]
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scope_config_path = data_path + dataInfo["probe-info"]["data-record"]["config"] # [TODO] Read this file
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# Generate Headers
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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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@ -75,60 +83,96 @@ for dp in data_to_load:
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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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# 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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}
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loaded_data = list(data.keys())
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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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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 ms
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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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scope_time = (scope_data[:, 0] - scope_data[0, 0]) * 1000 # to us
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scope_time -= trigger_info["alignment-offset"] # manual offset delay
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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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# 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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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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data[x2_shot]["data"]["scope"][header] = scope_data[i]
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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, loaded_data = load_data(data_to_load)
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print("Loaded Data")
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def process_data(gData: dict):
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#x2_time = (gData["x2"][0][:] - gData["x2"][0][0]).astype('timedelta64[ns]') # Convert x2 to timedelta64[ns]
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time_data = data[loaded_data[0]]["x2"][0]
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second_fractions = np.array(time_data[:].second_fractions, dtype=int)
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seconds = (second_fractions - second_fractions[0]) * (2**(-64))
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ns_seconds = seconds * 1E9
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x2_time = ns_seconds
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trigger_info = gData["info"]["probe-info"]["data-record"]["trigger"] # Get the scope trigger info
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# Convert the scope times into timedelta64 & apply config offsets & delays
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#scope_time = np.array([ pd.Timedelta(t, 's').to_numpy() for t in (gData["probes"][:, 0] - gData["probes"][0, 0])])
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#scope_time -= np.timedelta64(trigger_info["alignment-offset"], 'ns')
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#scope_time += np.timedelta64(trigger_info["delay"], 'us')
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scope_time = (gData["probes"][:, 0] - gData["probes"][0, 0]) * 1E9 # to ns
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scope_time -= trigger_info["alignment-offset"]
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scope_time += trigger_info["delay"] * 1000 # us -> ns
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start_timestamp = np.datetime64(f"{gData["info"]["date"]}T{gData["info"]["time"]}")
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# start_time = 0
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# x2_timesteps = np.array([0 for _ in x2_time])
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# for i, dt in enumerate(x2_time):
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# dt = dt.astype("int")
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# if i == 0:
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# x2_timesteps[i] = start_time + dt # should be 0
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# else:
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# x2_timesteps[i] = x2_timesteps[i-1] + dt
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# test = x2_time.cumsum()
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return x2_time, scope_time
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#[TODO] Refactor
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def genGraph(gData: dict, showPlot: bool = True):
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x2_time, scope_time = process_data(gData)
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graphData = {
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"title": f"Shock response Time\nFor {gData['info']['long_name']}",
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@ -173,22 +217,19 @@ def genGraph(gData: dict, showPlot: bool = True):
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makeGraph(graphData, doProgramBlock=False, showPlot=showPlot, figSavePath="./images/{0}.png")
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#print("Graphing showPlot=showPlot, Data")
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genGraph(data[loaded_data[0]], showPlot=False)
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genGraph(data[loaded_data[1]], showPlot=False)
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#genGraph(data[loaded_data[0]], showPlot=False)
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#genGraph(data[loaded_data[1]], showPlot=False)
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# Try to process things
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gData = data[loaded_data[0]]
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x2_time, scope_time = process_data(gData)
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#time = (gData["x2"][0][:] - gData["x2"][0][0])
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#x2_out = canny_shock_finder(x2_time, (gData["raw-data"]["x2"][16][:] - gData["raw-data"]["x2"][16][0]))
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x2_out = canny_shock_finder(x2_time, (gData["x2"][4][:] - gData["x2"][4][0]) * 0.0148)
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#print(x2_out)
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print(x2_out)
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# This forces matplotlib to hang untill I tell it to close all windows
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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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@ -32,3 +32,6 @@ volt-scale:
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at4: 0.01435 #V/kPa
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at5: 0.01447 #V/kPa
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at6: 0.01442 #V/kPa
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trigbox: 0.001 #V / mV
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trigbox_delay: 0.001 #V / mV
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