def plotCov(cov, refl, point, map, grid_spacing, file_name, roi=50, normalize=(-1, 1)):
pylab.figure(figsize=(10, 8))
pt_z, pt_lat, pt_lon = point
pt_x, pt_y = map(pt_lon, pt_lat)
dx, dy = grid_spacing
nx, ny = cov.shape
xs, ys = np.meshgrid(dx * np.arange(nx), dy * np.arange(ny))
lbound, ubound = normalize
dbound = (ubound - lbound) / 20.
levels = np.arange(lbound, ubound + dbound, dbound)
map.contourf(xs, ys, cov, levels=levels)
pylab.colorbar()
pylab.contour(xs, ys, refl, levels=[20., 40.], colors='k')
pylab.plot(pt_x, pt_y, 'ko')
roi_circle = CirclePolygon((pt_x, pt_y), roi * 1000., resolution=40, ec='k', fc='none')
axes_box = Rectangle((0, 0), 1, 1, ec='w', fc='w', alpha=0.5, clip_path=roi_circle, transform=(pylab.gca().transAxes + pylab.gca().transData.inverted()))
path_codes = [ Path.MOVETO ] + (axes_box.get_verts().shape[0] - 1) * [ Path.LINETO ] + [ Path.MOVETO ] + (roi_circle.get_verts().shape[0] - 1) * [ Path.LINETO ]
path_verts = np.concatenate((axes_box.get_verts(), roi_circle.get_verts()[::-1]))
mask_path = Path(path_verts, path_codes)
mask_patch = PathPatch(mask_path, fc='w', ec='w', alpha=0.7)
pylab.gca().add_patch(mask_patch)
map.drawcoastlines(linewidth=1.5)
map.drawcountries(linewidth=1.5)
map.drawstates(linewidth=1.0)
if not hasattr(map, 'counties'):
map.readshapefile('countyp020', 'counties', linewidth=0.5)
else:
for county, data in izip(map.counties_info, map.counties):
if county['STATE'] in ['NE', 'WY', 'CO']:
pylab.gca().add_patch(Polygon(data, ec='k', fc='none', linewidth=0.5))
pylab.savefig(file_name)
pylab.close()
return
def test_rotate_rect():
loc = np.asarray([1.0, 2.0])
width = 2
height = 3
angle = 30.0
# A rotated rectangle
rect1 = Rectangle(loc, width, height, angle=angle)
# A non-rotated rectangle
rect2 = Rectangle(loc, width, height)
# Set up an explicit rotation matrix (in radians)
angle_rad = np.pi * angle / 180.0
rotation_matrix = np.array([[np.cos(angle_rad), -np.sin(angle_rad)], [np.sin(angle_rad), np.cos(angle_rad)]])
# Translate to origin, rotate each vertex, and then translate back
new_verts = np.inner(rotation_matrix, rect2.get_verts() - loc).T + loc
# They should be the same
assert_almost_equal(rect1.get_verts(), new_verts)
def test_rotate_rect():
loc = np.asarray([1.0, 2.0])
width = 2
height = 3
angle = 30.0
# A rotated rectangle
rect1 = Rectangle(loc, width, height, angle=angle)
# A non-rotated rectangle
rect2 = Rectangle(loc, width, height)
# Set up an explicit rotation matrix (in radians)
angle_rad = np.pi * angle / 180.0
rotation_matrix = np.array([[np.cos(angle_rad), -np.sin(angle_rad)],
[np.sin(angle_rad), np.cos(angle_rad)]])
# Translate to origin, rotate each vertex, and then translate back
new_verts = np.inner(rotation_matrix, rect2.get_verts() - loc).T + loc
# They should be the same
assert_almost_equal(rect1.get_verts(), new_verts)
def plotCov(cov,
refl,
point,
map,
grid_spacing,
file_name,
roi=50,
normalize=(-1, 1)):
pylab.figure(figsize=(10, 8))
pt_z, pt_lat, pt_lon = point
pt_x, pt_y = map(pt_lon, pt_lat)
dx, dy = grid_spacing
nx, ny = cov.shape
xs, ys = np.meshgrid(dx * np.arange(nx), dy * np.arange(ny))
lbound, ubound = normalize
dbound = (ubound - lbound) / 20.
levels = np.arange(lbound, ubound + dbound, dbound)
map.contourf(xs, ys, cov, levels=levels)
pylab.colorbar()
pylab.contour(xs, ys, refl, levels=[20., 40.], colors='k')
pylab.plot(pt_x, pt_y, 'ko')
roi_circle = CirclePolygon((pt_x, pt_y),
roi * 1000.,
resolution=40,
ec='k',
fc='none')
axes_box = Rectangle(
(0, 0),
1,
1,
ec='w',
fc='w',
alpha=0.5,
clip_path=roi_circle,
transform=(pylab.gca().transAxes + pylab.gca().transData.inverted()))
path_codes = [Path.MOVETO
] + (axes_box.get_verts().shape[0] - 1) * [Path.LINETO] + [
Path.MOVETO
] + (roi_circle.get_verts().shape[0] - 1) * [Path.LINETO]
path_verts = np.concatenate(
(axes_box.get_verts(), roi_circle.get_verts()[::-1]))
mask_path = Path(path_verts, path_codes)
mask_patch = PathPatch(mask_path, fc='w', ec='w', alpha=0.7)
pylab.gca().add_patch(mask_patch)
map.drawcoastlines(linewidth=1.5)
map.drawcountries(linewidth=1.5)
map.drawstates(linewidth=1.0)
if not hasattr(map, 'counties'):
map.readshapefile('countyp020', 'counties', linewidth=0.5)
else:
for county, data in izip(map.counties_info, map.counties):
if county['STATE'] in ['NE', 'WY', 'CO']:
pylab.gca().add_patch(
Polygon(data, ec='k', fc='none', linewidth=0.5))
pylab.savefig(file_name)
pylab.close()
return
sample = fk.Sample(f)
sample.apply_transform(xform)
new_name = os.path.basename(f).replace('fcs', 'csv')
sample.export_csv(source='xform', filename=new_name, directory=xform_dir)
# Extract Batch Control:
from matplotlib.patches import Rectangle
from matplotlib.path import Path
import pandas as pd
import numpy as np
fs = glob('./csv_xform/*')
for f in fs:
df = pd.read_csv(f)
x = df['Pr141Di']
y = df['Y89Di']
r1 = Rectangle((0.1, 0.5),
0.4,
0.3,
linewidth=2,
edgecolor='red',
facecolor='none')
p1 = Path(r1.get_verts())
bc = df[p1.contains_points(np.c_[x, y])]
fn = f.split('/')[-1]
bc.to_csv(os.path.join('csv_xform', fn.replace('.csv', '_bc.csv')))
