This function intialises the monte carlo random walk class. Here most of the simulation task will be done. Each fly is represented by 3 values its position [mm], the environment temperature at this position [°C] and its body temperature [°C]. The gradient is a one dimensional strip of x mm length that has a linear temperature gradient with gradientExt as the gradient extreme temperatures. The lower extreme temperature is situated at 0 mm the hotter extreme temperature at the far end of the gradient. The fly is further described by its rearing temperature (default: 25°C).
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Here we present a mathematical model that estimates the behaviour of individual Drosophila in temperature gradients. The model takes the spatial dimension and temperature difference of the gradient into account, as well as the rearing temperature of the flies. Furthermore, it allows quatifying cold-trapping, reveals true temperature preference, …
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Here we present a mathematical model that estimates the behaviour of individual Drosophila in temperature gradients. The model takes the spatial dimension and temperature difference of the gradient into account, as well as the rearing temperature of the flies. Furthermore, it allows quatifying cold-trapping, reveals true temperature preference, …
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