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")