<jupyter_start><jupyter_text>TESPy Tutorial: offdesign performance prediction The offdesign mode- How to switch modes- How to test offdesign setup  Using your own characteristics- Specify characteristics curves for component parameters   - why do we need 1/1 cross?   - how to change to relative data?<jupyter_code>mass_flow_ratio = [0.1, 0.3, 0.5, 0.7, 0.9, 1.1]
efficiency = [0.6, 0.65, 0.75, 0.82, 0.85, 0.79]<jupyter_output><empty_output><jupyter_text>- Specification of generator/motor characteristics<jupyter_code>x = np.array([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55,
              0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1, 1.05, 1.1, 1.15,
              1.2, 10])
y = np.array([0.01, 0.3148, 0.5346, 0.6843, 0.7835, 0.8477, 0.8885, 0.9145,
              0.9318, 0.9443, 0.9546, 0.9638, 0.9724, 0.9806, 0.9878, 0.9938,
              0.9982, 1.0009, 1.002, 1.0015, 1, 0.9977, 0.9947, 0.9909, 0.9853,
              0.9644]) * 0.97<jupyter_output><empty_output><jupyter_text>- Generate characteristics from measurement data, e.g. isentropic efficiency of a turbine<jupyter_code>p_inl = [10, 12, 16, 20]
p_outl = [1.05, 1.2, 1.5, 1.7]
t_inl = [500, 490, 520, 520]
t_outl = [220, 205, 214, 206]

power = -1e6 * np.array([2.8, 3.1, 3.7, 4.4])<jupyter_output><empty_output><jupyter_text>- Generate characteristics from measurement data, e.g. kA of a heat district heating pipe<jupyter_code>mass_flow = np.array([0.1, 0.3, 0.5, 0.7, 1.0, 2.0, 3.0]) * 1000
outflow_temperature = np.array([118.75, 119.25, 119.4, 119.5, 119.63, 119.8, 119.85])<jupyter_output><empty_output>
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