# Path: pythics/lib.py
# class CircularArray(object):
#     def __init__(self, length, cols=1, dtype=np.float64):
#         self.__cols = cols
#         # actual array size is twice the requested
#         self.__N = int(length)
#         # the next place to put a value in the buffer
#         self.__n_next = 0
#         self.__data = np.zeros((2*self.__N, self.__cols), dtype=dtype)
#         # the number of rows that have been filled, max is self.__N
#         self.__n_filled = 0
#         # properties of arrays
#         self.ndim = 2
# 
#     def clear(self):
#         self.__n_filled = 0
# 
#     def append(self, value):
#         # convert the appended object to an array if it starts as something else
#         if type(value) is not np.ndarray:
#             value = np.array(value)
#         # add the data
#         if value.ndim == 1:
#             # adding a single row of data
#             # put data in two places so we can always find a contiguous array
#             n = self.__n_next
#             m = n + self.__N
#             self.__data[n] = value
#             self.__data[m] = value
#             self.__n_next = (self.__n_next + 1) % self.__N
#             self.__n_filled = min(self.__n_filled+1, self.__N)
#         elif value.ndim == 2:
#             # adding multiple rows of data
#             # avoid loops for appending large arrays
#             n = self.__n_next
#             m = n + self.__N
#             L = value.shape[0]
#             # grab only last N elements if value array is too long
#             if L > self.__N:
#                 L = self.__N
#                 value = value[-L:]
#             # n is smaller, so writing is contiguous starting at n
#             self.__data[n:n+L] = value
#             # writing at m must generally be broken up into two parts
#             #   the second part must be wrapped around
#             wrapped_L = m + L - 2*self.__N
#             if wrapped_L <= 0:
#                 # no need for wrapping
#                 self.__data[m:m+L] = value
#             else:
#                 unwrapped_L = L - wrapped_L
#                 self.__data[m:m+unwrapped_L] = value[0:unwrapped_L]
#                 self.__data[0:wrapped_L] = value[unwrapped_L:L]
#             self.__n_next = (self.__n_next + L) % self.__N
#             self.__n_filled = min(self.__n_filled+L, self.__N)
# 
#     def __as_array(self):
#         # choose the copy of the data which is guaranteed to be contiguous
#         n = ((self.__n_next - 1) % self.__N) + self.__N + 1
#         return self.__data[n-self.__n_filled:n]
# 
#     # some standard array methods
# 
#     @property
#     def shape(self):
#         return (self.__n_filled, self.__cols)
# 
#     def __getitem__(self, key):
#         return self.__as_array().__getitem__(key)
# 
#     def __iter__(self):
#         return self.__as_array().__iter__()
# 
#     def __len__(self):
#         return self.__n_filled
# 
#     def __repr__(self):
#         return self.__as_array().__repr__()
# 
#     def __setitem__(self, key, value):
#         # this is inefficient but simple
#         # rewrite the whole array for any change
#         a = self.__as_array()
#         a.__setitem__(key, value)
#         self.clear()
#         self.append(a)
# 
#     def __str__(self):
#         return self.__as_array().__str__()
# 
#     def sum(self, *args, **kwargs):
#         return self.__as_array().sum(*args, **kwargs)


import numpy as np
from pythics.lib import CircularArray
        x_label='spatial f',
        y_label='A(f)',
        x_scale='linear',
        y_scale='linear',
        aspect_ratio='auto')
    plot_3.set_plot_properties(
        title='Displacement of One Point',
        x_label='t',
        y_label='y',
        x_scale='linear',
        y_scale='linear',
        aspect_ratio='auto')
    plot_4.set_plot_properties(
        title='FFT of y(t) at One Point',
        x_label='f',
        y_label='A(f)',
        x_scale='linear',
        y_scale='linear',
        aspect_ratio='auto')
    plot_1.new_curve('xy', animated=True, line_style='-', line_color='green',
                     marker_style='+', marker_edge_color='green', marker_width=3)
    plot_2.new_curve('xf', animated=True, line_style='-', line_color='blue',
                     marker_style='+', marker_edge_color='blue', marker_width=3)
    plot_3.new_curve('ty', animated=True, line_style='-', line_color='red',
                     marker_style='+', marker_edge_color='red', marker_width=3)
    plot_4.new_curve('tf', animated=True, line_style='-', line_color='red',
                     marker_style='+', marker_edge_color='red', marker_width=3)


def run(w_L, w_N, w_dt, w_pluck, w_monitor, w_N_tfft, w_N_tfft_display, w_drag, 

# Path: pythics/lib.py
# class CircularArray(object):
#     def __init__(self, length, cols=1, dtype=np.float64):
#         self.__cols = cols
#         # actual array size is twice the requested
#         self.__N = int(length)
#         # the next place to put a value in the buffer
#         self.__n_next = 0
#         self.__data = np.zeros((2*self.__N, self.__cols), dtype=dtype)
#         # the number of rows that have been filled, max is self.__N
#         self.__n_filled = 0
#         # properties of arrays
#         self.ndim = 2
# 
#     def clear(self):
#         self.__n_filled = 0
# 
#     def append(self, value):
#         # convert the appended object to an array if it starts as something else
#         if type(value) is not np.ndarray:
#             value = np.array(value)
#         # add the data
#         if value.ndim == 1:
#             # adding a single row of data
#             # put data in two places so we can always find a contiguous array
#             n = self.__n_next
#             m = n + self.__N
#             self.__data[n] = value
#             self.__data[m] = value
#             self.__n_next = (self.__n_next + 1) % self.__N
#             self.__n_filled = min(self.__n_filled+1, self.__N)
#         elif value.ndim == 2:
#             # adding multiple rows of data
#             # avoid loops for appending large arrays
#             n = self.__n_next
#             m = n + self.__N
#             L = value.shape[0]
#             # grab only last N elements if value array is too long
#             if L > self.__N:
#                 L = self.__N
#                 value = value[-L:]
#             # n is smaller, so writing is contiguous starting at n
#             self.__data[n:n+L] = value
#             # writing at m must generally be broken up into two parts
#             #   the second part must be wrapped around
#             wrapped_L = m + L - 2*self.__N
#             if wrapped_L <= 0:
#                 # no need for wrapping
#                 self.__data[m:m+L] = value
#             else:
#                 unwrapped_L = L - wrapped_L
#                 self.__data[m:m+unwrapped_L] = value[0:unwrapped_L]
#                 self.__data[0:wrapped_L] = value[unwrapped_L:L]
#             self.__n_next = (self.__n_next + L) % self.__N
#             self.__n_filled = min(self.__n_filled+L, self.__N)
# 
#     def __as_array(self):
#         # choose the copy of the data which is guaranteed to be contiguous
#         n = ((self.__n_next - 1) % self.__N) + self.__N + 1
#         return self.__data[n-self.__n_filled:n]
# 
#     # some standard array methods
# 
#     @property
#     def shape(self):
#         return (self.__n_filled, self.__cols)
# 
#     def __getitem__(self, key):
#         return self.__as_array().__getitem__(key)
# 
#     def __iter__(self):
#         return self.__as_array().__iter__()
# 
#     def __len__(self):
#         return self.__n_filled
# 
#     def __repr__(self):
#         return self.__as_array().__repr__()
# 
#     def __setitem__(self, key, value):
#         # this is inefficient but simple
#         # rewrite the whole array for any change
#         a = self.__as_array()
#         a.__setitem__(key, value)
#         self.clear()
#         self.append(a)
# 
#     def __str__(self):
#         return self.__as_array().__str__()
# 
#     def sum(self, *args, **kwargs):
#         return self.__as_array().sum(*args, **kwargs)


# Path: pythics/examples/waves.py
import numpy as np
from pythics.lib import CircularArray

        x_label='spatial f',
        y_label='A(f)',
        x_scale='linear',
        y_scale='linear',
        aspect_ratio='auto')
    plot_3.set_plot_properties(
        title='Displacement of One Point',
        x_label='t',
        y_label='y',
        x_scale='linear',
        y_scale='linear',
        aspect_ratio='auto')
    plot_4.set_plot_properties(
        title='FFT of y(t) at One Point',
        x_label='f',
        y_label='A(f)',
        x_scale='linear',
        y_scale='linear',
        aspect_ratio='auto')
    plot_1.new_curve('xy', animated=True, line_style='-', line_color='green',
                     marker_style='+', marker_edge_color='green', marker_width=3)
    plot_2.new_curve('xf', animated=True, line_style='-', line_color='blue',
                     marker_style='+', marker_edge_color='blue', marker_width=3)
    plot_3.new_curve('ty', animated=True, line_style='-', line_color='red',
                     marker_style='+', marker_edge_color='red', marker_width=3)
    plot_4.new_curve('tf', animated=True, line_style='-', line_color='red',
                     marker_style='+', marker_edge_color='red', marker_width=3)


def run(w_L, w_N, w_dt, w_pluck, w_monitor, w_N_tfft, w_N_tfft_display, w_drag, 

        stop, messages, plot_1, plot_2, plot_3, plot_4, **kwargs):