def test_fit():
data_string = """
time velocity uncertainty
2.23 139 16
9.37 96 9
11.64 62 17
16 20 25
23.21 -55 10
"""
df_data = read_data(data_string)
time = df_data['time'].values
velocity = df_data['velocity'].values
dy = df_data['uncertainty'].values
slope_w, yint_w, var_slope, var_inter = LineFitWt(time, velocity, dy)
ntest.assert_almost_equal(
[slope_w, yint_w, var_slope, var_inter],
[-9.9656001, 179.924646, 0.5982539, 140.7892905])
get_ipython().magic('matplotlib inline')
plt.close('all')
from a212libs.utils import read_data
from a212libs.linfit import LineFitWt
data_string = """
time velocity uncertainty
2.23 139 16
9.37 96 9
11.64 62 17
16 20 25
23.21 -55 10
"""
df_data = read_data(data_string)
time = df_data['time'].values
velocity = df_data['velocity'].values
dy = df_data['uncertainty'].values
fig,ax = plt.subplots(1,1,figsize=(10,8))
out=ax.errorbar('time','velocity',yerr='uncertainty',data=df_data,
fmt='bo',ecolor='r',marker='d',mec='r',mfc='y',ms=12,zorder=10,
label='data')
limits=ax.axis()
xvals=np.array(limits[:2])
slope_w,yint_w, var_slope, var_inter = LineFitWt(time,velocity,dy)
yvals = yint_w + slope_w*xvals
ax.plot(xvals,yvals,'g-',label = 'weights')
sigma_slope = np.sqrt(var_slope)
sigma_intercept = np.sqrt(var_inter)
return chisquare
data_string = """
time velocity uncertainty
2.23 139 16
4.78 123 16
7.21 115 4
9.37 96 9
11.64 62 17
14.23 54 17
16.55 10 12
18.70 -3 15
21.05 -13 18
23.21 -55 10
"""
df_data = read_data(data_string)
time = df_data['time'].values
velocity = df_data['velocity'].values
dy = df_data['uncertainty'].values
fig,ax = plt.subplots(1,1)
out=ax.errorbar('time','velocity',yerr='uncertainty',data=df_data,
fmt='bo',ecolor='r',marker='d',mec='r',mfc='y',ms=12,zorder=10,
label='data')
limits=ax.axis()
slope_nw,yint_nw = LineFit(time,velocity)
xvals=np.array(limits[:2])
yvals = yint_nw + slope_nw*xvals
chir_nw = reduced_chi(time,velocity,slope_nw,yint_nw)
ax.plot(xvals,yvals,'b-',label = 'noweights')
ax.text(0.2,0,'unwght slope, int = {:5.2f}, {:5.2f}'.format(slope_nw,yint_nw))
