def get_fit_parameters():
global FIT_PARAMETERS
if not FIT_PARAMETERS:
df = dataframe_from_csv("../data/calibration_params.csv")
FIT_PARAMETERS = tuple(df["value"])
return FIT_PARAMETERS
def get_doublets():
df = dataframe_from_csv("../data/doublets.csv")
df["lambdas"] = calibration(df["degrees"])
df["lambdas"] = round(df["lambdas"], 1)
df["table_values"] = round(df["table_values"], 1)
df["deltas_table"] = df["table_values"] - df["table_values"].shift(-1)
df["deltas_measured"] = df["lambdas"] - df["lambdas"].shift(-1)
return df
def get_spread():
df = dataframe_from_csv("../data/spread.csv")
df["dx"] = df["dU"] * 21.2
df["q"] = 3 * df["dx"] / 2 - C / (df["dx"] / 2)
df["grad_B"] = grad_B(df["B"])
return df
def get_proud(file):
return dataframe_from_csv(file,
sep=" ",
names=["sys_time", "delta_time", "x", "y"],
header=None)
def get_calibration_points():
return dataframe_from_csv("../data/calibration.csv")
def get_heating():
return dataframe_from_csv("../data/heating.csv")