import numpy as np
from fconcrete import config as c
import matplotlib.pyplot as plt
import time
import ezdxf
import pandas as pd
_Q = c._Q
[docs]def cond(x, singular=False, order=0):
"""
If It is singular, return 1 if x>0 else 0.
If It is not singular, return x**order if x>0 else 0
"""
if singular:
return 1 if x>0 else 0
return x**order if x>0 else 0
[docs]def integrate(f, a, b, N=100):
"""
Integrate f from a to b in N steps
"""
x = np.linspace(a, b, N)
y = np.apply_along_axis(f, 0, np.array([x]))
return np.trapz(y, dx=(b-a)/(N-1))
[docs]def duplicated(array):
"""
Check if it is duplicated.
"""
s = np.sort(array, axis=None)
duplicated = s[:-1][s[1:] == s[:-1]]
return np.isin(s, duplicated)
[docs]def to_unit(input, expected_unit, return_unit=False):
"""
Convert between unities according to expected_unit and return_unit.
Call signatures:
fc.helpers.to_unit(input, expected_unit, return_unit=False)
>>> unit1 = fc.helpers.to_unit("10cm", "m")
>>> unit1
0.1
>>> unit2 = fc.helpers.to_unit(20, "m", return_unit="cm")
>>> unit2
2000.0
Parameters
----------
input : number or str
Represents the input unit of the user.
expected_unit : str
The expected unit to be given. Useful when input is a number.
return_unit : `bool`, optional
The desired unit to return
"""
try:
input = float(input)
value = _Q(input, expected_unit)
except:
pass
try:
value = _Q(input).to(expected_unit)
except: raise Exception("String does not have valid format. See documentation.")
if return_unit:
return value.to(return_unit).magnitude
return value.magnitude
[docs]def getAxis(xy0=(0,0), xy1=(0,0)):
"""
Create axis with equal aspect. xy0 and xy1 represent the visible area.
"""
x0, y0 = xy0
x1, y1 = xy1
fig, ax = plt.subplots()
ax.set_aspect("equal")
ax.plot([x0, x1], [y0, y1], color="None")
return fig, ax
[docs]def timeit(do=True, name=""):
"""
Decorator to print the time that the function has taken to execute.
"""
def inner0(function):
if not do: return function
def inner(*args, **kw):
start = time.time()
val = function(*args, **kw)
end = time.time()
print("{} executed in {}s".format(function.__name__ if name == "" else name, end-start))
return val
return inner
return inner0
# https://gist.github.com/snakers4/91fa21b9dda9d055a02ecd23f24fbc3d
[docs]def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = "\r"):
"""
Call in a loop to create terminal progress bar
"""
percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
filledLength = int(length * iteration // total)
bar = fill * filledLength + '-' * (length - filledLength)
print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)
# Print New Line on Complete
if iteration == total:
print()
[docs]def make_dxf(ax, **options):
"""
Matplotlib graph to modelspace (preparation to dxf).
Returns ax and msp.
"""
msp = options["msp"] if options.get("msp") else False
scale_y = options["scale_y"] if options.get("scale_y") else 1
scale_x = options["scale_x"] if options.get("scale_x") else 1
xy_position = options["xy_position"] if options.get("xy_position") else (0,0)
if msp == False:
doc = ezdxf.new('AC1032')
doc.header['$INSUNITS'] = 5
msp = doc.modelspace()
for element in ax.get_children():
element_type = str(type(element))
if element_type == "<class 'matplotlib.lines.Line2D'>":
xy_data = element.get_xydata()
xy_data[:, 1] = xy_data[:, 1]*scale_y
xy_data[:, 0] = xy_data[:, 0]*scale_x
points = xy_data[np.invert(np.isnan(xy_data[:, 1]))]+xy_position
msp.add_lwpolyline(points)
elif element_type == "<class 'matplotlib.patches.Rectangle'>":
#p1, p2 = element.get_bbox().get_points()
points = element.get_patch_transform().transform(element.get_path().vertices[:-1]) #np.array([p1, [p1[0], p2[1]], p2, [p2[0], p1[1]], p1])
points = np.array([*points, points[0]])+xy_position
msp.add_lwpolyline(points)
if element.get_hatch():
hatch = msp.add_hatch()
hatch.set_pattern_fill('ANSI31', scale=0.5, angle=element.angle)
hatch.paths.add_polyline_path(points, is_closed=1)
elif element_type == "<class 'matplotlib.patches.Circle'>":
msp.add_circle(np.array(element.center)+xy_position, element.radius)
return ax, msp
[docs]def to_pandas(array_table):
df_table = pd.DataFrame(array_table)
df_table.columns = df_table.iloc[0]
df_table = df_table.drop(df_table.index[0])
return df_table