def tryFits1(fName):
distances, heights = getTrajectoryData(fName)
distances = pylab.array(distances) * 36
totHeights = pylab.array([0] * len(distances))
for h in heights:
totHeights = totHeights + pylab.array(h)
pylab.title('Trajectory of Projectile (Mean of 4 Trials)')
pylab.xlabel('Inches from Launch Point')
pylab.ylabel('Inches Above Launch Point')
meanHeights = totHeights / float(len(heights))
pylab.plot(distances, meanHeights, 'bo')
a, b = pylab.polyfit(distances, meanHeights, 1)
altitudes = a * distances + b
pylab.plot(distances,
altitudes,
'r',
label='Linear Fit' + ', R2 = ' +
str(round(rSquare(meanHeights, altitudes), 4)))
a, b, c = pylab.polyfit(distances, meanHeights, 2)
altitudes = a * (distances**2) + b * distances + c
pylab.plot(distances,
altitudes,
'g',
label='Quadratic Fit' + ', R2 = ' +
str(round(rSquare(meanHeights, altitudes), 4)))
pylab.legend()
def fitline(x, y, lo=0, hi=1, doplot=1, quiet=1, linewidth=2):
"""Fitline takes the position of low and high fit limits
and returns the slope. Integer indices in the x data set can
be specified for the low and high limits, otherwise use a
fraction between 0 and 1.
In application to fractal dimension estimation, if
x = log d (radius, a.k.a. inter-point distance) and
y = log v (index, a.k.a. estimate of volume at a given radius)
then the slope is the dimension estimate of the data set.
"""
lendat = len(x)
assert hi > lo
if lo >= 0 and hi <= 1:
loix = int(lendat*lo)
hiix = int(lendat*hi)
elif lo >=0 and hi > 0:
loix = int(lo)
hiix = int(hi)
else:
raise ValueError, "Invalid low and high fit limits"
if quiet==0:
print "lo ix %d, hi ix %d, max ix %d"%(loix, hiix, lendat-1)
pfit = polyfit(x[loix:hiix],y[loix:hiix],1)
# print pfit
if doplot==1:
plot([x[loix],x[hiix]],
[x[loix]*pfit[0]+pfit[1],x[hiix]*pfit[0]+pfit[1]],
linewidth=linewidth)
return pfit[0]
def fitData1(fileName):
xVals, yVals = getData(fileName)
xVals = pylab.array(xVals)
yVals = pylab.array(yVals)
xVals = xVals * 9.81 # convert mass to force (F = mg)
pylab.plot(xVals, yVals, 'bo', label='Measured displacements')
pylab.title('Measured Displacement of Spring')
pylab.xlabel('|Force| (Newtons)')
pylab.ylabel('Distance (meters)')
a, b = pylab.polyfit(xVals, yVals, 1)
estYVals = a * xVals + b
pylab.plot(xVals, estYVals, label='Linear fit')
a, b, c, d = pylab.polyfit(xVals, yVals, 3)
estYVals = a * (xVals**3) + b * xVals**2 + c * xVals + d
pylab.plot(xVals, estYVals, label='Cubic fit')
pylab.legend(loc='best')
def fitData(inputFile):
masses, distances = getData(inputFile)
#newMasses = masses + [x/100 for x in range(105, 155, 5)]
masses = np.array(masses[:-6])
distances = np.array(distances[:-6])
forces = masses * 9.81
#newForces = np.array(newMasses)*9.81
plt.plot(forces, distances, 'bo', label="Measured displacements")
plt.title("测得的弹簧位移")
plt.xlabel('|阻力|(牛顿)')
plt.ylabel("距离 (米)")
#最小二乘法线性拟合(1次)
a, b = plt.polyfit(forces, distances, 1)
predictDistances = a*forces + b
k = 1/a
plt.plot(forces, predictDistances, label="预测线性拟合k="+str(round(k,5))+"的位移")
'''
# 最小二乘法线性拟合(3次)
a, b, c, d = plt.polyfit(forces, distances, 3)
predictDistances = a*(forces**3) + b*(forces**2) + c*forces + d
plt.plot(forces, predictDistances, "r:", label="三次拟合")
'''
plt.legend(loc='best')
plt.savefig('chapter15_5.jpg', dpi=100)
plt.show()
'''
def fitline(x, y, lo=0, hi=1, doplot=1, quiet=1, linewidth=2):
"""Fitline takes the position of low and high fit limits
and returns the slope. Integer indices in the x data set can
be specified for the low and high limits, otherwise use a
fraction between 0 and 1.
In application to fractal dimension estimation, if
x = log d (radius, a.k.a. inter-point distance) and
y = log v (index, a.k.a. estimate of volume at a given radius)
then the slope is the dimension estimate of the data set.
"""
lendat = len(x)
assert hi > lo
if lo >= 0 and hi <= 1:
loix = int(lendat * lo)
hiix = int(lendat * hi)
elif lo >= 0 and hi > 0:
loix = int(lo)
hiix = int(hi)
else:
raise ValueError, "Invalid low and high fit limits"
if quiet == 0:
print "lo ix %d, hi ix %d, max ix %d" % (loix, hiix, lendat - 1)
pfit = polyfit(x[loix:hiix], y[loix:hiix], 1)
# print pfit
if doplot == 1:
plot([x[loix], x[hiix]],
[x[loix] * pfit[0] + pfit[1], x[hiix] * pfit[0] + pfit[1]],
linewidth=linewidth)
return pfit[0]
def _fit_leastsq(self, volume_lst, energy_lst, fittype="birchmurnaghan"):
"""
Internal helper function for the least square fit
Args:
volume_lst (list/numpy.dnarray/None): vector of volumes
energy_lst (list/numpy.dnarray/None): vector of energies
fittype (str): on of the following ['birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet']
Returns:
list: [E0, B0, BP, V0], [E0_err, B0_err, BP_err, V0_err]
"""
try:
import matplotlib.pylab as plt
except ImportError:
import matplotlib.pyplot as plt
vol_lst = np.array(volume_lst).flatten()
eng_lst = np.array(energy_lst).flatten()
a, b, c = plt.polyfit(vol_lst, eng_lst, 2)
v0 = -b / (2 * a)
ev_angs_to_gpa = (
1e21 / scipy.constants.
physical_constants["joule-electron volt relationship"][0])
pfit_leastsq, perr_leastsq = self._fit_leastsq_funct(
[a * v0**2 + b * v0 + c, 2 * a * v0 * ev_angs_to_gpa, 4, v0],
vol_lst,
eng_lst,
fitfunction,
fittype,
)
return pfit_leastsq, perr_leastsq # [e0, b0, bP, v0]
def plot_zipf_law_on_corpus(corpus):
words = getWordsFromCorpus(corpus)
words = remove_stopwords_from_corpus_words(words)
fdist = FreqDist(words)
words = fdist.most_common()
x = [math.log(i[1]) for i in words]
y = [math.log(i) for i in range(1, len(x) + 1)]
(m, b) = pylab.polyfit(x, y, 1)
yp = pylab.polyval([m, b], x)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
pylab.plot(x, y, 'r')
pylab.plot(x, yp, 'b')
pylab.ylim([min(y), max(y)])
pylab.xlim([min(x), max(x)])
pylab.text(x=1,
y=1,
s="Best Fit Line (Blue) \nslope = {slope}".format(
slope=np.round(slope, 2)))
pylab.grid(True)
pylab.ylabel('Counts of words (log)')
pylab.xlabel('Ranks of words (log)')
pylab.title(
'ZIPF LAW TEST ON CORPUS. IDEALLY SLOPE OF THE LINE MUST BE = -1 for IDEAL ZIPF CASE'
)
pylab.show()
def test_generate_models(self):
degs_msg = "generate_models should return one model for each given degree"
list_type_msg = "generate_models should return a list of models"
array_type_msg = "each model returned by generate_models should be of type pylab.array"
coefficient_mismatch = "coefficients of returned model are not as expected"
# simple y = x case.
x = pylab.array(range(50))
y = pylab.array(range(50))
degrees = [1]
models = ps5.generate_models(x, y, degrees)
self.assertEquals(len(models), len(degrees), degs_msg)
self.assertIsInstance(models, list, list_type_msg)
self.assertIsInstance(models[0], pylab.ndarray, array_type_msg)
self.assertListEqual(list(models[0]), list(pylab.polyfit(x, y, 1)),
coefficient_mismatch)
# two models for y = 2x case
y = pylab.array(range(0, 100, 2))
degrees = [1, 2]
models = ps5.generate_models(x, y, degrees)
self.assertEquals(len(models), len(degrees), degs_msg)
self.assertIsInstance(models, list, list_type_msg)
for m in models:
self.assertIsInstance(m, pylab.ndarray, array_type_msg)
for i in range(2):
self.assertListEqual(list(models[i]),
list(pylab.polyfit(x, y, degrees[i])),
coefficient_mismatch)
# three models
degrees = [1, 2, 20]
models = ps5.generate_models(x, y, degrees)
self.assertEquals(len(models), len(degrees), degs_msg)
self.assertIsInstance(models, list, list_type_msg)
for m in models:
self.assertIsInstance(m, pylab.ndarray, array_type_msg)
for i in range(3):
self.assertListEqual(list(models[i]),
list(pylab.polyfit(x, y, degrees[i])),
coefficient_mismatch)
def plot_regression(self):
# sample plot of points
x = self.NaN_filtered['tmax']
y = self.NaN_filtered['tmin']
fit = polyfit(x,y,1)
fit_fn = poly1d(fit)
# takes in x and returns an estimate for y
plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
plt.title("Temperature pattern regression plot")
plt.xlabel("tmax")
plt.ylabel("tmin")
plt.show()
def plot_regression(self):
# sample plot of points
x = self.NaN_filtered['tmax']
y = self.NaN_filtered['tmin']
fit = polyfit(x, y, 1)
fit_fn = poly1d(fit)
# takes in x and returns an estimate for y
plt.plot(x, y, 'yo', x, fit_fn(x), '--k')
plt.title("Temperature pattern regression plot")
plt.xlabel("tmax")
plt.ylabel("tmin")
plt.show()
def _fit_leastsq(self, volume_lst, energy_lst, fittype='birchmurnaghan'):
try:
import matplotlib.pylab as plt
except ImportError:
import matplotlib.pyplot as plt
vol_lst = np.array(volume_lst).flatten()
eng_lst = np.array(energy_lst).flatten()
a, b, c = plt.polyfit(vol_lst, eng_lst, 2)
v0 = -b / (2 * a)
pfit_leastsq, perr_leastsq = self.fit_leastsq(
[a * v0**2 + b * v0 + c, 2 * a * v0 * 160.21766208, 4, v0],
vol_lst, eng_lst, self.fitfunction, fittype)
return pfit_leastsq, perr_leastsq # [e0, b0, bP, v0]
def fitDataWithCubic(inputFile):
masses, distances = getData(inputFile)
distances = pylab.array(distances)
masses = pylab.array(masses)
forces = masses * 9.81
pylab.plot(forces, distances, 'bo', label='Measured displacements')
pylab.title('Measured Displacement of Spring')
pylab.xlabel('|Force| (Newtons)')
pylab.ylabel('Distance (meters)')
# find linear fit
a, b = pylab.polyfit(forces, distances, 1)
predictedDistances = a * pylab.array(forces) + b
k = 1.0 / a
pylab.plot(forces,
predictedDistances,
label='Displacements predicted by\nlinear fit, k = ' +
str(round(k, 5)))
a, b, c, d = pylab.polyfit(forces, distances, 3)
predictedDistances = a * (forces**3) + b * forces**2 + c**forces + d
pylab.plot(forces, predictedDistances, 'b:', label='cubic fit')
pylab.legend(loc='best')
pylab.show()
def processTrajectories(fileName):
distances, heights = getTrajectoryData(fileName)
numTrials = len(heights[0])
distances = np.array(distances)
meanHeights = list(map(lambda x: sum(x)/len(x), heights))
meanHeights = np.array(meanHeights)
plt.title("抛弹的弹道("+str(numTrials)+"条弹道的平均路径)")
plt.xlabel("距离抛射点的英寸数")
plt.ylabel("抛射点上方的英寸数")
plt.plot(distances, meanHeights, 'bo', label="实验点")
a, b = plt.polyfit(distances, meanHeights, 1)
altitudes = a*distances + b
print("RSquare of linear fit =", rSquared(meanHeights, altitudes))
plt.plot(distances, altitudes, 'b', label="线性拟合")
a, b, c = plt.polyfit(distances, meanHeights, 2)
altitudes = a*(distances**2) + b*distances + c
getHorizontalSpeed(a, b, c, distances[-1], distances[0])
print("RSquare of quadratic fit =", rSquared(meanHeights, altitudes))
plt.plot(distances, altitudes, 'b:', label="二次拟合")
plt.legend()
plt.savefig("chapter15_6.jpg", dpi=100)
plt.show()
def plot_msd(I_m, r_average, r_error, rx_av, rx_error, ry_av, ry_error,
folder):
x = range(len(I_m))
# Linear fit of msd data, the slot should be 1 in random walk
m, b = pl.polyfit(x, r_average, 1)
plt.rcParams["figure.figsize"] = [15, 5]
plt.subplot(121)
plt.plot(x, r_average, 'r') #Data
plt.plot(x, x, '--b') #What should be
plt.plot(x, m * x + b, '--k') #Fitting
plt.fill_between(x,
np.asarray(r_average) - np.asarray(r_error),
np.asarray(r_average) + np.asarray(r_error),
alpha=0.2,
facecolor='r',
linewidth=4,
linestyle='dashdot',
antialiased=True)
plt.xlabel('Iteration')
plt.ylabel('MSD')
plt.legend(['Data', 'm = 1', 'm = %.2f' % m])
plt.subplot(122)
plt.plot(x, rx_av, 'b', label='MSD X')
plt.plot(x, ry_av, 'r', label='MSD Y')
plt.fill_between(x,
np.asarray(rx_av) - np.asarray(rx_error),
np.asarray(rx_av) + np.asarray(rx_error),
alpha=0.2,
facecolor='b',
linewidth=4,
linestyle='dashdot',
antialiased=True)
plt.fill_between(x,
np.asarray(ry_av) - np.asarray(ry_error),
np.asarray(ry_av) + np.asarray(ry_error),
alpha=0.2,
facecolor='r',
linewidth=4,
linestyle='dashdot',
antialiased=True)
plt.legend()
plt.xlabel('Iteration')
plt.ylabel('MSD')
plt.savefig(folder + '/MSD.png')
plt.show()
def fitData(fileName):
xVals, yVals = getData(fileName)
xVals = pylab.array(xVals)
yVals = pylab.array(yVals)
xVals = xVals * 9.81 # convert mass to force (F = mg)
pylab.plot(xVals, yVals, 'bo', label='Measured points')
pylab.title('Measured Displacement of Spring')
pylab.xlabel('Force (Newtons)')
pylab.ylabel('Distance (meters)')
a, b = pylab.polyfit(xVals, yVals, 1) # fit y = ax + b
# use line equation to graph predicted values
estYVals = a * xVals + b
k = 1 / a
pylab.plot(xVals, estYVals, label='Linear fit, k = ' + str(round(k, 5)))
pylab.legend(loc='best')
def _fit_leastsq(self, volume_lst, energy_lst, fittype='birchmurnaghan'):
try:
import matplotlib.pylab as plt
except ImportError:
import matplotlib.pyplot as plt
vol_lst = np.array(volume_lst).flatten()
eng_lst = np.array(energy_lst).flatten()
a, b, c = plt.polyfit(vol_lst, eng_lst, 2)
v0 = -b / (2 * a)
ev_angs_to_gpa = 1e21 / scipy.constants.physical_constants[
'joule-electron volt relationship'][0]
pfit_leastsq, perr_leastsq = self.fit_leastsq(
[a * v0**2 + b * v0 + c, 2 * a * v0 * ev_angs_to_gpa, 4, v0],
vol_lst, eng_lst, self.fitfunction, fittype)
return pfit_leastsq, perr_leastsq # [e0, b0, bP, v0]
def plotScatter(pearsonStats,data,args,color='b'):
""""""
fig = pl.figure()
ax = fig.add_subplot(111)
if args.log:
ax.set_xscale('log')
ax.set_yscale('log')
ax.scatter(data[0],data[1], s=15, c=color, marker='o', alpha=1)
if not args.log:
ax.set_autoscale_on(False)
ax.set_xlabel(args.label1)
ax.set_ylabel(args.label2)
upperLim = max(data[0]+data[1])
m,b = pl.polyfit(data[0],data[1],1)
bfYs = pl.polyval([m,b], [1,max(data[0])])
ax.plot([1,max(data[0])],bfYs,'r-')
pl.text(0.02,0.95,'Pearson: %.4f, %s\nBest Fit: y=%.3f*x+%.3f' % (pearsonStats[0],pearsonStats[1],m,b),
bbox=dict(facecolor='#87AACD', alpha=1),
horizontalalignment='left',
verticalalignment='top',
transform = ax.transAxes)
if args.pdf:
pdf_or_png = 'pdf'
else:
pdf_or_png = 'png'
# construct outfile name
if not args.log:
outName = '%s_%s_vs_%s.%s' % (args.out,args.label1.replace(' ','_'),args.label2.replace(' ','_'),pdf_or_png)
else:
outName = '%s_%s_vs_%s.log.%s' % (args.out,args.label1.replace(' ','_'),args.label2.replace(' ','_'),pdf_or_png)
pl.savefig(outName)
if args.galaxy:
os.rename(outName,args.out)
print 'Show? %s' % (args.show)
if args.show:
pl.show()
def plot_scores():
from matplotlib import pylab
import numpy
savedsco = open("savedsco","r")
scores = pickle.load(savedsco)
scores[0] = 0
savedsco.close()
pylab.clf()
figure = pylab.figure()
pylab.plot(scores)
x = numpy.arange(len(scores))
scores = numpy.array(scores)
m,b = pylab.polyfit(x,scores,1)
print m
#pylab.plot(x, m*x+b)
pylab.savefig("averagescores.png")
def lc_linfit(x, y, breaks):
bks = np.hstack((min(x) - 1, breaks, max(x)))
yfit = []
for i in range(len(bks) - 1):
w = np.where((x > bks[i]) & (x <= bks[i + 1]))
m, b = plot.polyfit(np.log10(x[w]), np.log10(y[w]), 1)
if i == 0:
norm = 10**b
p = np.array(norm)
pnames = np.array('norm')
yfit = np.append(yfit, 10**b * x[w]**m)
p = np.hstack((p, -m, bks[i + 1]))
pnames = np.hstack((pnames, 'pow' + str(i + 1), 'break' + str(i + 1)))
p = p[0:len(p) - 1]
return p, yfit, pnames
def get_level(data, slope_threshold=1.5, window_size=15):
"""
Automatically determine the fluid level from the slope change
"""
found = False
n = data.shape[0]
for i in range(window_size, n - window_size):
window_ind = range(i - window_size, i + window_size)
window_data = data[i - window_size:i + window_size]
fit = pylab.polyfit(window_ind, window_data, 1)
if fit[0] > slope_threshold:
found = True
break
if found:
return i, data[i]
else:
return None
def fit_data(input):
masses, distances = get_data(input)
masses = pylab.array(masses)
distances = pylab.array(distances)
forces = masses * 9.81
pylab.plot(forces, distances, 'bo', label='Measured displacements')
pylab.xlabel('|Force|(Newtons)')
pylab.ylabel('Distance(meters')
a, b = pylab.polyfit(forces, distances, 1)
predict_distances = a * pylab.array(forces) + b
k = 1.0 / a
pylab.plot(forces,
predict_distances,
label='Displacement pridicted by \nlinear fit k=' +
str(round(k, 5)))
pylab.legend(loc='best')
def markup_image(self, image):
if not self.tracker.markup_image:
return
text = "#{0} ({1}, {2})".format(self.tracker.cnt, self.__width, self.__height)
text += " {0}%".format(self.tracker.middle_percent)
if self.__contours is not None:
all_x = []
all_y = []
for i in range(len(self.__contours)):
box_x, box_y, box_w, box_h = cv2.boundingRect(self.__contours[i])
center_x = self.__moments[i][2]
center_y = self.__moments[i][3]
all_x.append(center_x)
all_y.append(center_y)
if self.draw_box:
cv2.rectangle(image, (box_x, box_y), (box_x + box_w, box_y + box_h), BLUE, 2)
if self.draw_contour:
cv2.drawContours(image, [self.__contours[i]], -1, GREEN, 2)
# Draw center
cv2.circle(image, (center_x, center_y), 4, RED, -1)
# text += " Avg: ({0}, {1})".format(self.avg_x, self.avg_y)
if self.draw_line and len(all_x) >= 2:
m, b = polyfit(all_x, all_y, 1)
cv2.line(image, (0, int(b)), (self.__width, int((self.__width * m) + b)), BLUE, 2)
# Draw the alignment lines
# if self.vertical_lines:
# cv2.line(image, (mid_x - mid_inc, 0), (mid_x - mid_inc, self.height), x_color, 1)
# cv2.line(image, (mid_x + mid_inc, 0), (mid_x + mid_inc, self.height), x_color, 1)
# if self.horizontal_lines:
# cv2.line(image, (0, mid_y - mid_inc), (self.width, mid_y - mid_inc), y_color, 1)
# cv2.line(image, (0, mid_y + mid_inc), (self.width, mid_y + mid_inc), y_color, 1)
if self.display_text:
cv2.putText(image, text, defs.TEXT_LOC, defs.TEXT_FONT, defs.TEXT_SIZE, RED, 1)
def generate_models(x, y, degs):
"""
Generate regression models by fitting a polynomial for each degree in degs
to points (x, y).
Args:
x: an 1-d pylab array with length N, representing the x-coordinates of
the N sample points
y: an 1-d pylab array with length N, representing the y-coordinates of
the N sample points
degs: a list of degrees of the fitting polynomial
Returns:
a list of pylab arrays, where each array is a 1-d array of coefficients
that minimizes the squared error of the fitting polynomial
"""
result = []
for d in degs:
result.append(pylab.polyfit(x, y, d))
return result
def plot_regression(self):
# sample plot of points
x = self.filtered.tmax
y = self.filtered.tmin
fit = polyfit(x,y,1)
fit_fn = poly1d(fit)
# takes in x and returns an estimate for y
# fig 1
plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
plt.title("Temperature pattern regression plot")
plt.xlabel("tmax")
plt.ylabel("tmin")
# fig 2
plt.figure()
andrews_curves(self.filtered[[2,3]], 'tmin')
plt.title("Andrews curve plot for tmin")
plt.show()
# fig 3
plt.figure()
self.df[[2,3]].boxplot()
# fig 4
self.filtered.plot()
plt.show()
def plot_regression(self):
# sample plot of points
x = self.filtered.tmax
y = self.filtered.tmin
fit = polyfit(x, y, 1)
fit_fn = poly1d(fit)
# takes in x and returns an estimate for y
# fig 1
plt.plot(x, y, 'yo', x, fit_fn(x), '--k')
plt.title("Temperature pattern regression plot")
plt.xlabel("tmax")
plt.ylabel("tmin")
# fig 2
plt.figure()
andrews_curves(self.filtered[[2, 3]], 'tmin')
plt.title("Andrews curve plot for tmin")
plt.show()
# fig 3
plt.figure()
self.df[[2, 3]].boxplot()
# fig 4
self.filtered.plot()
plt.show()
def plotScatter(pearsonStats,normedTxCntsList,opts):
""""""
fig = pl.figure()
ax = fig.add_subplot(111)
if opts.log:
ax.set_xscale('log')
ax.set_yscale('log')
ax.scatter(normedTxCntsList[0],normedTxCntsList[1], s=15, c='b', marker='o', alpha=1)
if not opts.log:
ax.set_autoscale_on(False)
ax.set_xlabel(opts.name_a)
ax.set_ylabel(opts.name_b)
upperLim = max(normedTxCntsList[0]+normedTxCntsList[1])
m,b = pl.polyfit(normedTxCntsList[0],normedTxCntsList[1],1)
bfYs = pl.polyval([m,b], [1,max(normedTxCntsList[0])])
ax.plot([1,max(normedTxCntsList[0])],bfYs,'r-')
pl.text(0.01,0.99,'Pearson: %.4f, %s\nBest Fit: y=%.3f*x+%.3f' % (pearsonStats[0],pearsonStats[1],m,b),
bbox=dict(facecolor='#87AACD', alpha=1),
horizontalalignment='left',
verticalalignment='top',
transform = ax.transAxes)
mkdirp(opts.dir)
if not opts.log:
pl.savefig('%s%s_vs_%s.png' % (opts.dir,opts.name_a,opts.name_b))
else:
pl.savefig('%s%s_vs_%s.log.png' % (opts.dir,opts.name_a,opts.name_b))
print 'Show? %s' % (opts.show)
if opts.show:
pl.show()
ax = fig.add_subplot(111)
if opts.log:
ax.set_xscale('log')
ax.set_yscale('log')
ax.scatter(vecFile[1],vecFile[2], s=15, c='b', marker='o', alpha=1)
if not opts.log:
ax.set_autoscale_on(False)
ax.set_xlabel(vecFile[0][0])
ax.set_ylabel(vecFile[0][1])
upperLim = max(vecFile[1]+vecFile[2])
m,b = pl.polyfit(vecFile[1],vecFile[2],1)
bfYs = pl.polyval([m,b], [1,max(vecFile[1])])
ax.plot([1,max(vecFile[1])],bfYs,'r-')
pl.text(0.01,0.99,'Pearson: %.4f, %s\nBest Fit: y=%.3f*x+%.3f' % (pearson[0],pearson[1],m,b),
bbox=dict(facecolor='#87AACD', alpha=1),
horizontalalignment='left',
verticalalignment='top',
transform = ax.transAxes)
pl.savefig('%s_vs_%s.png' % (vecFile[0][0],vecFile[0][1]))
print 'Show? %s' % (opts.show)
if opts.show:
pl.show()
'''
def plot_author_success(successlist):
xvals = [value[0] for key, value in successlist.iteritems()]
yvals = [value[2] for key, value in successlist.iteritems()]
labellist = [key for key, value in successlist.iteritems()]
fig, ax = plt.subplots()
ax.scatter(xvals, yvals)
for i, txt in enumerate(labellist):
ax.annotate(txt, (xvals[i],yvals[i]))
plt.title("Active Users with their success rate")
plt.xlabel("No. distinct users")
plt.ylabel("fraction of users with multiple posts")
#remove_border()
plot_author_success(authorstats)
#regression line
m_fit,b_fit = plt2.polyfit(big_table.comments, big_table.score, 1)
plt2.plot(big_table.comments, big_table.score, 'yo', big_table.comments, m_fit*big_table.comments+b_fit, color='purple', alpha=0.3)
plt.title("Comments versus Score")
plt.xlabel("price")
plt.ylabel("title")
plt.xlim(-10, max(big_table.comments) * 1.05)
plt.ylim(-10, max(big_table.score) * 1.05 )
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as plb
from scipy.stats import linregress
labels, values = zip(*aggCount.most_common(50))
indexes = np.arange(len(labels))
width = 1
plt.bar(indexes, values, width)
plt.xticks(indexes + width * 0.5, labels)
plt.show()
X = indexes + 1
Y = values
plt.plot(X, Y, 'bo')
plt.savefig('rawplot.png')
plt.loglog(X, Y, 'bo')
plt.savefig('loglogplot.png')
m, b = plb.polyfit(plb.log10(X), plb.log10(Y), 1)
slope, intercept, r_value, p_value, std_err = linregress(
plb.log10(X), plb.log10(Y))
rsquared = r_value**2
plt.plot(plb.log10(X), plb.log10(Y), 'bo', plb.log10(X),
plb.log10(X) * slope + intercept, '--k')
plt.savefig('loglogplot_fitted.png')
def draw_regression_line(self, x_data, y_data):
m, b = plb.polyfit(x_data.ravel(), y_data.ravel(), 1)
x_lim = plt.xlim()
data_y = [x_lim[0] * m + b, x_lim[1] * m + b]
line = mpl_lines.Line2D(x_lim, data_y, color='blue', linewidth=0.4)
plt.axes().add_line(line)
'Mid_Call'
]
puts.columns = [
'UnderlyingPrice', 'Expiration', 'Strike', 'Ask_Put', 'Bid_Put', 'Mid_Put'
]
# join
result = pd.merge(calls, puts, on=['UnderlyingPrice', 'Expiration', 'Strike'])
del calls
del puts
del prices
result = result.apply(compute_expiration, axis=1)
# C-P = S0-Kexp(-rT)
# K = F -> C-P=0
result['cp'] = result['Mid_Call'] - result['Mid_Put']
T = result.groupby('T')['cp'].apply(list).index
Strike = result.groupby('T')['Strike'].apply(list).values
cp = result.groupby('T')['cp'].apply(list).values
result['F'] = 0
for t in range(len(T)):
a, b = plb.polyfit(Strike[t], cp[t], 1)
result.loc[result['T'] == T[t], 'F'] = b
result = result.drop(['cp', 'Expiration'], axis=1)
result = result.apply(f, axis=1)
result.to_csv('imp_vol_yahoo_vix 1602.csv',
sep=',',
encoding='utf-8',
index=False)
def control_dif2D(file, folder):
# Relevant parameters of the simulation
data = np.load(file)
U_matrix = data['U_m']
dt = data['dt']
save = data['save']
d = data['var'][0]
shape = U_matrix.shape
mid = int(shape[1] / 2)
# Wide at half width, total concentration, maximum peak
wide_x, wide_y, area, maximum = [], [], [], []
time_it = []
plt.rcParams["figure.figsize"] = [15, 4]
plt.subplot(121)
color1 = pl.cm.Blues(np.linspace(1, 0, len(U_matrix)))
for i, elements in enumerate(U_matrix):
# The total concetration is the sum in every voxel
area.append(np.sum(elements))
# Maximum peak for each time iter
maximum.append(np.amax(elements))
# Chemical profile through a middle line
ux = elements[mid, :]
uy = elements[:, mid]
# Calculates FWHM
arr = np.linspace(0, shape[1] - 1, shape[1]) #Array x
wide_x.append(FWHM(ux, arr))
wide_y.append(FWHM(uy, arr))
plt.plot(arr, ux, alpha=.5, color=color1[i])
# Time of the iteration
time_it.append(i * save)
plt.xlabel('Position')
plt.ylabel('Concentration')
# Deletes the initial conditions
del wide_x[0], time_it[0], wide_y[0]
# Linear fitting of parameters
wide_x = np.asarray(wide_x)
wide_y = np.asarray(wide_y)
time_it = np.asarray(time_it)
mx, bx = pl.polyfit(time_it, wide_x**2, 1)
my, by = pl.polyfit(time_it, wide_y**2, 1)
# Teoretical and fitted difussion coefficients
dif_x = mx * 10000 * dt / 2
dif_y = my * 10000 * dt / 2
dif_teo = d * 2 / (10000 * dt) # Teoretical slot
plt.subplot(122)
plt.plot(time_it, wide_x**2, 'r', time_it, mx * time_it + bx, '--r')
plt.plot(time_it, wide_y**2, 'b', time_it, my * time_it + by, '--b')
plt.plot(time_it, dif_teo * time_it, '--k')
plt.xlabel('Iteration')
plt.ylabel('FWHM**2')
plt.legend([
'x',
'Dx = %.2f' % dif_x, 'y',
'Dy = %.2f' % dif_y,
'Dteo = %.1f' % d
])
plt.savefig(folder + '/Difussion.png')
plt.show()
plt.rcParams["figure.figsize"] = [15, 4]
# Linear fittinf of maximum vs sqrt(iteration)
intime = np.array(range(1, len(maximum))) * dt
del maximum[0]
maximum = np.asarray(maximum)
ml, bl = pl.polyfit(intime, 1 / maximum, 1)
# Toretical and fitted difussion coefficients
dif = (ml * area[0]) / (4 * np.pi * save)
d_teo = (d * (4 * np.pi * save)) / area[0] # Teoretical slot
plt.subplot(121)
plt.plot(range(len(area)) * save, area)
plt.xlabel('Iteration')
plt.ylim(0, 2 * area[0])
plt.ylabel('Total Concentration')
plt.subplot(122)
plt.plot(intime, 1 / maximum, 'r', intime, ml * intime + bl, '--k')
plt.plot(intime, d_teo * intime, '--b')
plt.xlabel('Iteration^(0.5)')
plt.ylabel('Max')
plt.legend(['Data', 'D = %.2f' % dif, 'Dteo = %.1f' % d])
plt.savefig(folder + '/Difussion2.png')
plt.show()
yvals = [value[2] for key, value in successlist.iteritems()]
labellist = [key for key, value in successlist.iteritems()]
fig, ax = plt.subplots()
ax.scatter(xvals, yvals)
for i, txt in enumerate(labellist):
ax.annotate(txt, (xvals[i], yvals[i]))
plt.title("Active Users with their success rate")
plt.xlabel("No. distinct users")
plt.ylabel("fraction of users with multiple posts")
#remove_border()
plot_author_success(authorstats)
#regression line
m_fit, b_fit = plt2.polyfit(big_table.comments, big_table.score, 1)
plt2.plot(big_table.comments,
big_table.score,
'yo',
big_table.comments,
m_fit * big_table.comments + b_fit,
color='purple',
alpha=0.3)
plt.title("Comments versus Score")
plt.xlabel("price")
plt.ylabel("title")
plt.xlim(-10, max(big_table.comments) * 1.05)
plt.ylim(-10, max(big_table.score) * 1.05)
def fitExpData(xVals, yVals):
logVals = []
for y in yVals:
logVals.append(math.log(y, 2.0))
a, b = plt.polyfit(xVals, logVals, 1)
return a, b, 2.0
# continue
line = line[:-1]
elems = line.rsplit(' ')
X.append(float(elems[0]))
Y.append(float(elems[1]))
return plt.array(X), plt.array(Y)
if __name__ == '__main__':
times, speeds = getData('springData.txt')
# Analyze data
plt.scatter(times, speeds)
plt.xlabel('Time (seconds)')
plt.ylabel('Speed')
a, b = plt.polyfit(times, speeds, 1)
yVals = a * times + b
plt.plot(times, yVals, color='g', linewidth=2, label='Linear')
plt.title('Speed Versus Time')
plt.legend()
a, b, c = plt.polyfit(times, speeds, 2)
yVals = a * (times**2) + b * times + c
plt.plot(times, yVals, c='r', linewidth=4, label='Quadratic')
plt.legend()
plt.show()
print(input('Enter anything to finish the program'))
result = pd.merge(calls,
puts,
on=['UnderlyingPrice', 'Expiration', 'Strike'])
del calls
del puts
del prices
result = result.apply(compute_expiration, axis=1)
# C-P = S0-Kexp(-rT)
# K = F -> C-P=0
result['cp'] = result['Mid_Call'] - result['Mid_Put']
result['cpab'] = result['Ask_Call'] - result['Bid_Put']
result['cpba'] = result['Bid_Call'] - result['Ask_Put']
T = result.groupby('T')['cp'].apply(list).index
Strike = result.groupby('T')['Strike'].apply(list).values
cp = result.groupby('T')['cp'].apply(list).values
cpab = result.groupby('T')['cpab'].apply(list).values
cpba = result.groupby('T')['cpba'].apply(list).values
result['F'] = 0
result['Fab'] = 0
result['Fba'] = 0
for t in range(len(T)):
a, b = plb.polyfit(Strike[t], cp[t], 1)
result.loc[result['T'] == T[t], 'F'] = b
a, b = plb.polyfit(Strike[t], cpab[t], 1)
result.loc[result['T'] == T[t], 'Fab'] = b
a, b = plb.polyfit(Strike[t], cpba[t], 1)
result.loc[result['T'] == T[t], 'Fba'] = b
result = result.drop(['cp', 'Expiration'], axis=1)
result.to_csv('month\mydata\options2_{}.csv'.format(str(j)))
infile1 = DATDIR + 'g2g_ccpa_' + vday + '.dat'
infile2 = DATDIR + 'g2g_ccpax_' + vday + '.dat'
infile3 = DATDIR + 'g2g_ST4_' + vday + '.dat'
# plot diagonal line:
x = arange(0., 51., 1)
plt.plot(x, x, color='black', linestyle='dotted')
# make x/y scales equal (without the line below, python plot's y-scale tend
# to be smaller than its x-scale, to fit a 'landscape' page):
plt.gca().set_aspect('equal', adjustable='box')
#
tbl = ascii.read(infile1)
tbl.colnames
plt.scatter(tbl['col4'], tbl['col5'], s=1, color='red') #'s' is size of dots
#
# draw regression line:
fit = plt.polyfit(tbl['col4'], tbl['col5'], deg=1)
plt.plot(x, fit[0] * x + fit[1], linewidth=2, color='red')
label1 = "CCPA: %6.2f" % rmse(tbl['col4'], tbl['col5'])
print(label1)
#print RMSE of RTMA: rmse(tbl['col4'], tbl['col5'])
print "fit0, fit1 of CCPA regression line:", fit[0], fit[1]
#
# Now do the same for the second dataset:
tbl = ascii.read(infile2)
tbl.colnames
plt.scatter(tbl['col4'], tbl['col5'], s=1, color='blue',
alpha=0.2) #'s' is size of dots
fit = plt.polyfit(tbl['col4'], tbl['col5'], deg=1)
print rmse(tbl['col4'], tbl['col5'])
print "fit0, fit1 of CCPAX regression line:", fit[0], fit[1]
plt.plot(x, fit[0] * x + fit[1], linewidth=2, color='blue')
Both lists should be `numSteps` items long.
"""
ret_rabbit = [CURRENTRABBITPOP]
ret_fox = [CURRENTFOXPOP]
for i in range(numSteps):
if CURRENTFOXPOP >= 10 and CURRENTRABBITPOP >= 10:
rabbitGrowth()
foxGrowth()
ret_rabbit.append(CURRENTRABBITPOP)
ret_fox.append(CURRENTFOXPOP)
return ret_rabbit, ret_fox
rp, fp = runSimulation(200)
rl, = pyplot.plot(rp, label="Rabbit population")
fl, = pyplot.plot(fp, label="Fox population")
pyplot.legend(handles=[rl, fl])
pyplot.show()
rcoeff = pylab.polyfit(range(len(rp)), rp, 2)
rcl, = pyplot.plot(pylab.polyval(rcoeff, range(len(rp))),
label="Rabbit Coefficients")
fcoeff = pylab.polyfit(range(len(fp)), fp, 2)
fcl, = pyplot.plot(pylab.polyval(fcoeff, range(len(fp))),
label="Fox Coefficients")
pyplot.legend(handles=[rcl, fcl])
pyplot.show()
print "Drawing..."
fig = pl.figure()
ax = fig.add_subplot(111)
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.scatter(vector0,vector1, s=10, c='b', marker='o', alpha=0.6)
# -- set axis labels --
xLab = DEGseqParser._file.name.split('_')[0]
yLab = DEGseqParser._file.name.split('_')[2]
ax.set_xlabel(xLab)
ax.set_ylabel(yLab)
m,b = pl.polyfit(vector0,vector1,1)
min_xVec = min(vector0)
max_xVec = max(vector0)
bfYs = pl.polyval([m,b], [min_xVec,max_xVec])
ax.plot([min_xVec,max_xVec],bfYs,'r-')
pl.text(0.01,0.99,'Pearson: %.4f, %s\nBest Fit: y=%.3f*x+%.3f' % (pearson[0],pearson[1],m,b),
bbox=dict(facecolor='#87AACD', alpha=1),
horizontalalignment='left',
verticalalignment='top',
transform = ax.transAxes)
pl.savefig('%s_vs_%s.indvDEG.png' % (xLab,yLab))
from geobricks_raster_correlation.core.raster_correlation_core import get_correlation
from matplotlib import pyplot as plt
from matplotlib.pylab import polyfit, polyval
# input to your raster files
raster_path1 = "../../tests/data/geoserver_data_dir/data/workspace/wheat_actual_biomprod_201010_doukkala/wheat_actual_biomprod_201010_doukkala.geotiff"
raster_path2 = "../../tests/data/geoserver_data_dir/data/workspace/wheat_potential_biomprod_201010_doukkala/wheat_potential_biomprod_201010_doukkala.geotiff"
# Number of sampling bins
bins = 150
corr = get_correlation(raster_path1, raster_path2, bins)
x = []
y = []
colors = []
#print corr['series']
for serie in corr['series']:
colors.append(serie['color'])
for data in serie['data']:
x.append(data[0])
y.append(data[1])
# Adding regression line
(m, b) = polyfit(x, y, 1)
yp = polyval([m, b], x)
plt.plot(x, yp)
# plotting scatter
plt.scatter(x, y, c=colors)
plt.show()
def draw_regression_line(self, x_data, y_data):
m, b = plb.polyfit(x_data.ravel(), y_data.ravel(), 1)
x_lim = plt.xlim()
data_y = [x_lim[0] * m + b, x_lim[1] * m + b]
line = mpl_lines.Line2D(x_lim, data_y, color='blue', linewidth=0.4)
plt.axes().add_line(line)
from geobricks_raster_correlation.core.raster_correlation_core import get_correlation
from matplotlib import pyplot as plt
from matplotlib.pylab import polyfit, polyval
# input to your raster files
raster_path1 = "../../tests/data/geoserver_data_dir/data/workspace/wheat_actual_biomprod_201010_doukkala/wheat_actual_biomprod_201010_doukkala.geotiff"
raster_path2 = "../../tests/data/geoserver_data_dir/data/workspace/wheat_potential_biomprod_201010_doukkala/wheat_potential_biomprod_201010_doukkala.geotiff"
# Number of sampling bins
bins = 150
corr = get_correlation(raster_path1, raster_path2, bins)
x = []
y = []
colors = []
#print corr['series']
for serie in corr['series']:
colors.append(serie['color'])
for data in serie['data']:
x.append(data[0])
y.append(data[1])
# Adding regression line
(m, b) = polyfit(x, y, 1)
yp = polyval([m, b], x)
plt.plot(x, yp)
# plotting scatter
plt.scatter(x, y, c=colors)
plt.show()
END of each time step, and fox_populations is a record of the fox population
at the END of each time step.
Both lists should be `numSteps` items long.
"""
ret_rabbit = [CURRENTRABBITPOP]
ret_fox = [CURRENTFOXPOP]
for i in range(numSteps):
if CURRENTFOXPOP >= 10 and CURRENTRABBITPOP >= 10:
rabbitGrowth()
foxGrowth()
ret_rabbit.append(CURRENTRABBITPOP)
ret_fox.append(CURRENTFOXPOP)
return ret_rabbit, ret_fox
rp, fp = runSimulation(200)
rl, = pyplot.plot(rp, label="Rabbit population")
fl, = pyplot.plot(fp, label="Fox population")
pyplot.legend(handles=[rl, fl])
pyplot.show()
rcoeff = pylab.polyfit(range(len(rp)), rp, 2)
rcl, = pyplot.plot(pylab.polyval(rcoeff, range(len(rp))), label="Rabbit Coefficients")
fcoeff = pylab.polyfit(range(len(fp)), fp, 2)
fcl, = pyplot.plot(pylab.polyval(fcoeff, range(len(fp))), label="Fox Coefficients")
pyplot.legend(handles=[rcl, fcl])
pyplot.show()
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as plb
from scipy.stats import linregress
labels, values = zip(*aggCount.most_common(50))
indexes = np.arange(len(labels))
width = 1
plt.bar(indexes, values, width)
plt.xticks(indexes + width * 0.5, labels)
plt.show()
X=indexes +1
Y=values
plt.plot(X,Y, 'bo')
plt.savefig('rawplot.png')
plt.loglog(X,Y,'bo')
plt.savefig('loglogplot.png')
m,b=plb.polyfit(plb.log10(X),plb.log10(Y),1)
slope, intercept, r_value, p_value, std_err=linregress(plb.log10(X),plb.log10(Y))
rsquared=r_value**2
plt.plot(plb.log10(X),plb.log10(Y),'bo',plb.log10(X), plb.log10(X)*slope +intercept, '--k')
plt.savefig('loglogplot_fitted.png')