Pyerrors

Added
- New fit functions for fitting with and without x-errors added which use automatic differentiation and should be more reliable than the old ones.
- Fitting with Bayesian priors added.
- New functions for visualization of fits which can be activated via the kwargs resplot and qqplot.
- chisquare/expected_chisquared which takes into account correlations in the data and non-linearities in the fit function can now be activated with the kwarg expected_chisquare.
- Silent mode added to fit functions.
- Examples reworked.
- Changed default function to compute covariances.
- output of input.bdio.read_mesons is now a dictionary instead of a list.

Deprecated
- The function `fit_general` which is based on numerical differentiation will be removed in future versions as new fit functions based on automatic differentiation are now available.

Added
- mesons bdio functionality improved and accelerated, progress report added.
- added the possibility to manually supply a jacobian to derived_observable via the kwarg `man_grad`. This feature was not implemented for the user, but for internal optimization of most basic arithmetic operations which now do not require a call to the autograd package anymore. This results in a speed up of 2 to 3, especially relevant for the multiplication of large matrices.

Changed
- input.py and bdio.py moved into submodule input. This should not affect the user API.
- autograd.numpy was replaced by pure numpy wherever it was possible. This should result in a slight speed up.

Fixed
- fixed bias_correction which broke as a result of the vectorized derived_observable.
- linalg.eig does not give an error anymore if the eigenvalues are complex by just truncating the imaginary part.

Added
- Matrix pencil method for algebraic extraction of energy levels implemented according to [Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)](https://ieeexplore.ieee.org/document/56027) in module `mpm.py`.
- Import API simplified. After `import pyerrors as pe`, some submodules can be accessed via `pe.fits` etc.
- `derived_observable` now supports functions which have single- or multi-dimensional numpy arrays as input and/or output (Works only with automatic differentiation).
- Matrix functions accelerated by using the new version of `derived_observable`.
- New matrix functions: Moore-Penrose Pseudoinverse, Singular Value Decomposition, eigenvalue determination of a general matrix (automatic differentiation included from autograd master).
- Obs can now be compared with < or >, a list of Obs can now be sorted.
- Numerical differentiation can now be controlled via the kwargs of numdifftools.step_generators.MaxStepGenerator.
- Tuned standard parameters for numerical derivative to `base_step=0.1` and `step_ratio=2.5`.

Changed
- Matrix functions moved to new module `linalg.py`.
- Kolmogorov-Smirnov test moved to new module `misc.py`.

Added
- Numerical differentiation is now based on the package numdifftools which should be more reliable.

Changed
- kwarg `h_num_grad` changed to `num_grad` which takes boolean values (default False).
- Speed up of rfft calculation of the autocorrelation by reducing the zero padding.