def zspline(typ, n1, n2, n3, n4, xin, n):
# remove z-trend with a z-spline
fn32 = n3 - n2
x2 = xin[n2 - 1]
x3 = xin[n3 - 1]
x = xin.copy()
if n1 < 1 or n2 < n1 + 1 or n3 < n2 + 1 or n4 < n3 + 1 or n4 > n:
print 'wrong parameters for zspline!'
sys.exit(1)
a, b = trend(typ, n1, n2, n3, n4, x[n1:], n2 - n1)
#for i in np.arange(n1, n, 1):
# x[i] = xa[i-n1]
a1 = a
b1 = b
# print 'n1: ', n1
# print 'n2: ', n2
# print 'n3: ', n3
# print 'n4: ', n4
# print 'a1/b1:', a1, b1
a, b = trend(typ, n1, n2, n3, n4, x[n3:], n4 - n3)
#for i in np.arange(n3, n, 1):
# x[i] = xa[i-n3]
a3 = a
b3 = b
# print 'a3/b3:', a3, b3
#for j in np.arange(n1,n2+1,1):
for j in np.arange(n1 - 1, n2, 1):
x[j] = x[j] - (a1 + b1 * (j - n1 + 2))
#for j in np.arange(n2+1,n3,1):
for j in np.arange(n2, n3 - 1, 1):
x[j] = x[j] - (x2 + (j - n2 + 1) / fn32 * (x3 - x2))
#for j in np.arange(n3,n4,1):
for j in np.arange(n3 - 1, n4 - 1, 1):
x[j] = x[j] - (a3 + b3 * (j - n3 + 2))
return x
def zspline(typ, n1, n2, n3, n4, xin, n):
# remove z-trend with a z-spline
fn32 = n3 - n2
x2 = xin[n2-1]
x3 = xin[n3-1]
x = xin.copy()
if n1 < 1 or n2 < n1+1 or n3 < n2+1 or n4 < n3+1 or n4 > n:
print 'wrong parameters for zspline!'
sys.exit(1)
a, b = trend(typ, n1, n2, n3, n4, x[n1:], n2-n1)
#for i in np.arange(n1, n, 1):
# x[i] = xa[i-n1]
a1 = a
b1 = b
# print 'n1: ', n1
# print 'n2: ', n2
# print 'n3: ', n3
# print 'n4: ', n4
# print 'a1/b1:', a1, b1
a, b = trend(typ, n1, n2, n3, n4, x[n3:], n4-n3)
#for i in np.arange(n3, n, 1):
# x[i] = xa[i-n3]
a3 = a
b3 = b
# print 'a3/b3:', a3, b3
#for j in np.arange(n1,n2+1,1):
for j in np.arange(n1-1,n2,1):
x[j] = x[j] - (a1 + b1 * (j - n1 + 2))
#for j in np.arange(n2+1,n3,1):
for j in np.arange(n2,n3-1,1):
x[j] = x[j] - (x2 + (j - n2 + 1) / fn32 * (x3 - x2))
#for j in np.arange(n3,n4,1):
for j in np.arange(n3-1,n4-1,1):
x[j] = x[j] - (a3 + b3 * (j - n3 + 2))
return x
def mspline(typ, n1, n2, n3, n4, xin, n):
# remove trend with a m-spline
fn32 = n3 - n2
x2 = xin[n2-1]
x3 = xin[n3-1]
x = xin.copy()
if n1 < 1 or n2 < n1+1 or n3 < n2+1 or n4 < n3+1 or n4 > n:
print 'wrong parameters for zspline!'
sys.exit(1)
a, b = trend(typ, n1, n2, n3, n4, x[n1:], n2-n1)
a1 = a
b1 = b
a, b = trend(typ, n1, n2, n3, n4, x[n3:], n4-n3)
a3 = a
b3 = b
for j in np.arange(n1-1,n4,1):
x[j] = x[j] - (a1 + b1 * (j - n1 + 2)) / 2.0
for j in np.arange(n1-1,n4-1,1):
x[j] = x[j] - (a3 + b3 * (j - n3 + 2)) / 2.0
return x
def test_1():
"""An example from the IPL dataset
question : annual trend of avg win_by_runs over date range 2008-05-08 to 2017-04-12
"""
table = pandas.read_csv('data/matches.csv')
query_result = trend.trend(table, 'win_by_runs', Granularities.ANNUALLY,
SummaryOperators.MAX,
date_range=('2008-05-08', '2010-04-12'),
date_column_name='date',
date_format='%Y-%m-%d')
print(query_result)
expected_result = """ date win_by_runs
0 2008-01-01 105
1 2009-01-01 92
2 2010-01-01 98"""
assert(expected_result == query_result.to_string())
def walking_bolBands(data, interval, ran, std):
count_of_up_punches = 0
count_of_down_punches = 0
transdata = data[::-1].reset_index(drop=True)
MSE_STD = [[transdata.Close[i: interval + i].mean(), transdata.Close[i: interval + i].std()] for i in range(ran)]
up = []
down = []
for i in MSE_STD:
up.append(i[0] + std * i[1])
down.append(i[0] - std * i[1])
for i in range(ran):
if transdata.High[i] > up[i]:
count_of_up_punches += 1
if transdata.Low[i] < down[i]:
count_of_down_punches += 1
trend_bolB = trend(data, ran)[0]
return trend_bolB, count_of_up_punches, count_of_down_punches
def dispcal(data, samprate, fcs, hs, gap, volts, displ, bfc=0.0, bm=0):
dt = 1.0 / samprate
npts = len(data)
npol = 3
perc1 = 50
perc2 = 95
fac = volts / displ * 0.001
# remove offset using the first 6 seconds that have to be quiet!
z = np.ones(npts)
offset = np.sum(data[0:int(6.0*samprate)])
data = data - offset
# apply butterworth low pass filter if required
if bfc > 0.0 and bm > 0:
bt0 = 1.0 / bfc
mm = int(bm / 2.0)
# if odd order apply first order low pass
if bm > 2 * mm:
f0_lp1, f1_lp1, f2_lp1, g1_lp1, g2_lp1 = lp1(bt0*samprate)
data = rekfl(data, npts, f0_lp1, f1_lp1, f2_lp1, g1_lp1, g2_lp1)
# now apply second order low pass
for i in range(mm):
h = np.sin(np.pi / 2.0 * (2.0 * i - 1.0) / bm)
f0_lp2, f1_lp2, f2_lp2, g1_lp2, g2_lp2 = lp2(bt0*samprate, h)
data = rekfl(data, npts, f0_lp2, f1_lp2, f2_lp2, g1_lp2, g2_lp2)
# report filtering
print 'Input data filtered with low pass Butterworth filter:'
print ' corner frequency: ' + str(bfc) + 'Hz'
print ' order: ' + str(bm) + '.'
raw = data.copy()
# deconvolution to velocity step
# done by appling an exact invers second order high pass filter (infinit period)
t0 = 1.0e12
h = 1.0
t0s = 1.0 / fcs
f0_he2, f1_he2, f2_he2, g1_he2, g2_he2 = he2(t0/dt, t0s/dt, h, hs)
data = rekfl(data, npts, f0_he2, f1_he2, f2_he2, g1_he2, g2_he2)
data = politrend(npol, data, npts, z)
x = data.copy()
deconv = data.copy()
# store deconvolves data
np.savetxt('dispcal_vel.txt', deconv)
# loop over fife trial values if no gap is given
if gap > 0.0:
onegap = True
ngap = 1
else:
#gap = 0.125
#onegap = False
#ngap = 5
print 'parameter "gap" not specified!'
sys.exit(1)
for k in range(ngap):
avglen = 6.0 * gap
iab = int(gap / dt)
iavg = int(avglen / dt)
data = x
z = np.ones(npts)
P0 = np.zeros(npts)
y = np.zeros(npts)
ja = np.ones(99)
# search for quiet section of minimum length 2*iab+1 samples
P = krum(data, npts, 2*iab+1, P0)
for j in range(npts):
P[j] = np.log10(max(P[j], 1.0e-6))
P1 = P.copy()
# import ipdb; ipdb.set_trace()
z = heapsort(npts, P1)
n50 = int(perc1 * npts / 100.0)
n95 = int(perc2 * npts / 100.0)
zlim = 0.5 * (z[n50] + z[n95])
P = P - zlim
z = z - zlim
# create trigger z = 0 if quiet, z = 1 if pulse
for j in range(npts):
if P[j] < 0:
z[j] = 1.0
else:
z[j] = 0.0
#zn1 = z[npts-1]
#zn = z[n]
#z[n-1] = -0.4
#z[n] = 1.4
#z[n-1] = zn1
#z[n] = zn
data = politrend(npol, data, npts, z)
data_c = data.copy()
# cut out pulses
quiet = data * z
# identifiy pulses
number = 0
i = 0
while i < npts and z[i] < 0.5:
i = i + 1
n1 = i
while i < npts and z[i] > 0.5:
i = i + 1
n2 = i - 1
if i == npts:
print 'No pulses found! Check dispcal.??? for pulses!'
sys.exit(1)
# continue if interval is too small (< gap)
while n2 < n1 + iab:
while i < npts and z[i] < 0.5:
i = i + 1
n1 = i
while i < npts and z[i] > 0.5:
i = i + 1
n2 = i - 1
# now we have the first (n1) and the last (n2) sample of the first quiet interval
if i == n:
print 'No pulses found! Check dispcal.??? for pulses!'
sys.exit(1)
# now continue with the next pulses
steps = []
marks = []
while i < npts:
while i < npts and z[i] < 0.5:
i = i + 1
if i == npts: break
n3 = i
while i < npts and z[i] > 0.5:
i = i + 1
if i == npts: break
n4 = i - 1
# continue if interval is too small (< gap)
while n4 < n3 + iab:
while i < npts and z[i] < 0.5:
i = i + 1
if i == npts: break
n3 = i
while i < npts and z[i] > 0.5:
i = i + 1
if i == npts: break
n4 = i - 1
# now we have the first (n1) and the last (n2) sample of the next quiet interval
number = number + 1
if number == 1:
data_c = zspline('zsp', 1, n2, n3, npts, data_c, npts)
n2null = n2
elif n4 >= n3+iab:
data_c = zspline('zsp', n1, n2, n3, npts, data_c, npts)
n3null = n3
marks.append((n2,n3-1))
print 'found pulse # ' + str(number) + ': from sample ' + str(n2) + ' to sample ' + str(n3 - 1)
n1a = 0
n2a = n2 - n1
n3a = n3 - n1
n4a = n4 - n1
n1a = max(n1a, n2a-iavg)
n4a = min(n4a, n3a+iavg)
for ii in range(n4a):
y[ii] = data[n1-1+ii]
y_z = zspline('zsp', n1a, n2a, n3a, n4a, y, n4a)
y_int = integrate(y_z, npts, dt)
stp = step(y_int, n1a, n2a, n3a, n4a, n4a)
n1 = n3
n2 = n4
steps.append(stp)
dtr = trend('tre', 0, n2-n1+1, 0, 0, data_c[n1:], npts-n1)
data2 = data_c.copy()
for i in np.arange(n1, npts, 1):
data2[i] = dtr[i-n1]
y = data_c * z
ymin = 0.0
ymax = 0.0
for i in np.arange(n2null+1, n3null-1,1):
ymin = min(ymin,y[i])
ymax = max(ymax,y[i])
for i in range(n2null):
y[i] = min(ymax, max(ymin, y[i]))
for i in np.arange(n3null, npts, 1):
y[i] = min(ymax, max(ymin, y[i]))
np.savetxt('dispcal_res.txt', y)
y_dis = integrate(data2, npts, dt)
# remove offset from displacement
offset = np.mean(y_dis)
y_dis = y_dis - offset
displ = y_dis.copy()
np.savetxt('dispcal_dis.txt', displ)
##############################################################
# Statistics
total = sum(np.abs(steps))
avgs = total / number
sigma = np.std(np.abs(steps))
print ''
print '-------------------------------------------------------------'
print 'Raw average step: ' + str(np.round(avgs,3)) + ' +/- ' + str(np.round(sigma, 3))
print 'Raw generator constant: ' + str(np.round((avgs * fac) * 1e6,3)) + ' +/- ' + str(np.round((sigma * fac) *1e6, 3)) + 'Vs/m'
print '-------------------------------------------------------------'
if number <= 2:
return raw, deconv, displ, marks
# import ipdb; ipdb.set_trace()
ja = np.ones(len(steps))
for nrest in np.arange(number-1,int(float(number+1.0)/2.0),-1):
siga = sigma
kmax = np.argmax(steps)
ja[kmax] = 0
total = 0
#for k in range(number):
# total = total + ja[k] * steps[k]
stepsa = ja * steps
total = sum(np.abs(stepsa))
#print total
avgs = total / nrest
#print avgs
total = 0
for k in range(number):
total = total + ja[k] * (abs(steps[k]) - avgs)**2
sigma = np.sqrt(total / nrest)
print 'Pulse ' + str(kmax) + ' eliminated; ' + str(nrest) + ' pulses remaning: ' + str(np.round((avgs * fac) * 1e6,3)) + ' +/- ' + str(np.round((sigma * fac) *1e6, 3)) + 'Vs/m'
# if sigma < 0.05*avgs and siga-sigma < siga/ nrest: break
steps[kmax] = 0.0
print '------------------------------------------------------------'
print ''
return raw, deconv, displ, marks
from market import * from market import supports most_goals = max(map(partition(goals, 'team'), len)) total_goals = len(goals) first_goal = attr(nth(goals, 0), 'team') assert supports(result, most_goals) assert supports(result, total_goals) assert supports(trend, most_goals) assert supports(trend, total_goals) assert supports(selns, most_goals) assert supports(selns, total_goals) print result(most_goals, football_state) print result(most_goals, football_nil) print selns(most_goals) print trend(most_goals, football_nil, football_state) print trend(most_goals, football_nil, football_nil) print result(total_goals, football_state) print result(total_goals, football_nil) print selns(total_goals) print trend(total_goals, football_nil, football_state) calculate = gfootball(football_state) print calculate(most_goals) print calculate(total_goals) print calculate(first_goal)
def hello_http(request):
"""HTTP Cloud Function.
Args:
request (flask.Request): The request object.
<http://flask.pocoo.org/docs/1.0/api/#flask.Request>
Returns:
The response text, or any set of values that can be turned into a
Response object using `make_response`
<http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>.
"""
request_json = request.get_json(silent=True)
request_args = request.args
# extracting the intent parameters from the json
intent = _get_value(request_json, 'intent')
table = _get_value(request_json, 'table')
metric = _get_value(request_json, 'metric')
dimensions = _get_value(request_json, 'dimensions')
summary_operator = _get_value(request_json, 'summaryOperator')
slices = _get_value(request_json, 'slices')
is_asc = _get_value(request_json, 'isAsc')
k = _get_value(request_json, 'topKLimit')
slices = _get_value(request_json, 'slices')
slice_comparision_arg = _get_value(request_json, 'comparisonValue')
time_comparision_arg = _get_value(request_json, 'compareDateRange')
date = _get_value(request_json, 'dateRange')
time_granularity = _get_value(request_json, 'timeGranularity')
correlation_metrics = _get_value(request_json, 'correlationMetrics')
rangeA1Notation = _get_value(request_json, 'rangeA1Notation')
# Converting the list of list into a pandas dataframe.
query_table = []
for row in range(1, len(table)):
if row != 0:
query_table.append(table[row])
query_table_dataframe = pandas.DataFrame(query_table, columns=table[0])
(all_dimensions,
all_metrics) = _list_all_dimensions_metrics(query_table_dataframe,
dimensions, metric)
# Remove empty columns
query_table_dataframe = remove_empty_columns(query_table_dataframe)
# Remove duplicate named columns
query_table_dataframe = remove_duplicate_named_columns(
query_table_dataframe)
# Converting the variables that contain denote the
# date range into the desired format.
date_column_name = None
date_range = None
day_first = None
if date != None:
date_columns = request_json['dateColumns']
date_column_name = date['dateCol']
date_range = (date['dateStart'], date['dateEnd'])
day_first = date_columns[date_column_name]['day_first']
# Converting the Slices passed in the json into a
# list of tuples (col, operator, val)
slices_list = None
if slices != None:
slices_list = []
for item in slices:
val = item['sliceVal']
col = item['sliceCol']
operator = _str_to_filter_enum(item['sliceOp'])
slices_list.append((col, operator, val))
if dimensions == 'null':
dimensions = None
if slice_comparision_arg is not None:
slice_compare_column = slice_comparision_arg['comparisonColumn']
slice1 = slice_comparision_arg['slice1']
slice2 = slice_comparision_arg['slice2']
if time_comparision_arg is not None:
time_compare_column = time_comparision_arg['dateCol']
date_range1 = (time_comparision_arg['dateStart1'],
time_comparision_arg['dateEnd1'])
date_range2 = (time_comparision_arg['dateStart2'],
time_comparision_arg['dateEnd2'])
day_first = request_json['dateColumns'][time_compare_column][
'day_first']
if metric == 'null':
metric = None
summary_operator = _str_to_summary_operator_enum(summary_operator)
time_granularity = _str_to_time_granularity_enum(time_granularity)
suggestions = []
wrong_points_suggestion = wrong_points.wrong_points(query_table_dataframe)
if intent == 'show':
query_table_dataframe = show(query_table_dataframe,
slices=slices_list,
metric=metric,
dimensions=dimensions,
summary_operator=summary_operator,
date_column_name=date_column_name,
day_first=day_first,
date_range=date_range)
if summary_operator == enums.SummaryOperators.MEAN:
suggestions.append(get_hardcoded_mean_vs_median_suggestion())
updated_suggestions = []
for suggestion in suggestions:
updated_suggestion = suggestion
if 'change_list' in suggestion.keys():
updated_suggestion['json'] = func(request_json,
suggestion['change_list'])
updated_suggestions.append(updated_suggestion)
suggestions = updated_suggestions
elif intent == 'topk':
query_result = topk.topk(query_table_dataframe,
metric,
dimensions,
is_asc,
k,
summary_operator=summary_operator,
slices=slices_list,
date_column_name=date_column_name,
day_first=day_first,
date_range=date_range)
query_table_dataframe = query_result[0]
suggestions = query_result[1]
updated_suggestions = []
for suggestion in suggestions:
updated_suggestion = suggestion
if 'change_list' in suggestion.keys():
updated_suggestion['json'] = func(request_json,
suggestion['change_list'])
updated_suggestion['oversight'] = updated_suggestion[
'oversight'].name
updated_suggestions.append(updated_suggestion)
suggestions = updated_suggestions
elif intent == 'slice_compare':
query_result = slice_compare.slice_compare(
query_table_dataframe,
metric,
all_dimensions,
all_metrics,
slice_compare_column,
slice1,
slice2,
summary_operator,
date_column_name=date_column_name,
date_range=date_range,
day_first=day_first,
slices=slices_list,
dimensions=dimensions)
query_table_dataframe = query_result[0]
suggestions = query_result[1]
updated_suggestions = []
for suggestion in suggestions:
updated_suggestion = suggestion
if 'change_list' in suggestion.keys():
updated_suggestion['json'] = func(request_json,
suggestion['change_list'])
updated_suggestion['oversight'] = updated_suggestion[
'oversight'].name
updated_suggestions.append(updated_suggestion)
suggestions = updated_suggestions
elif intent == 'time_compare':
query_result = time_compare.time_compare(query_table_dataframe,
metric,
all_dimensions,
time_compare_column,
date_range1,
date_range2,
day_first,
summary_operator,
slices=slices_list,
dimensions=dimensions)
query_table_dataframe = query_result[0]
suggestions = query_result[1]
updated_suggestions = []
for suggestion in suggestions:
updated_suggestion = suggestion
if 'change_list' in suggestion.keys():
updated_suggestion['json'] = func(request_json,
suggestion['change_list'])
updated_suggestion['oversight'] = updated_suggestion[
'oversight'].name
updated_suggestions.append(updated_suggestion)
suggestions = updated_suggestions
elif intent == 'correlation':
query_table_dataframe = correlation.correlation(
query_table_dataframe,
correlation_metrics['metric1'],
correlation_metrics['metric2'],
slices=slices_list,
date_column_name=date_column_name,
day_first=day_first,
date_range=date_range,
dimensions=dimensions)
elif intent == 'trend':
query_table_dataframe = trend.trend(query_table_dataframe,
metric,
time_granularity,
summary_operator,
date_column_name=date_column_name,
day_first=day_first,
date_range=date_range,
slices=slices_list)
else:
raise Exception("Intent name does not match")
if wrong_points_suggestion is not None:
wrong_points_suggestion['oversight'] = wrong_points_suggestion[
'oversight'].name
suggestions = [wrong_points_suggestion] + suggestions
final_table = []
# converting into a json object and returning
final_table = query_table_dataframe.values.tolist()
final_table.insert(0, list(query_table_dataframe.columns.values))
json_ret = {'outputTable': final_table, 'suggestions': suggestions}
if rangeA1Notation is not None:
all_row_labels = _get_all_row_labels(rangeA1Notation)
all_column_labels = _get_all_column_labels(rangeA1Notation)
cheader_to_clabel = _get_cheader_to_clabel(table, all_column_labels)
if slices_list is not None:
json_ret[
'slicing_passed_list'] = insert_as_column.insert_as_column_show(
table,
cheader_to_clabel,
all_row_labels[0],
all_row_labels[-1],
all_column_labels[0],
all_column_labels[-1],
slices=slices_list)
if intent == 'topk' and summary_operator is None:
filter_column_label_number = _get_number_of_column_label(
all_column_labels[-1]) + 1
filter_column_label = _get_label_from_number(
filter_column_label_number)
json_ret[
'list_topk_indices'] = insert_as_column.insert_as_column_topk_column(
table, cheader_to_clabel, all_row_labels[0],
all_row_labels[-1], all_column_labels[0],
all_column_labels[-1], filter_column_label, metric, is_asc,
k)
json_string = json.dumps(json_ret)
return json_string
def dispcal(data, samprate, fcs, hs, gap, volts, displ, bfc=0.0, bm=0):
dt = 1.0 / samprate
npts = len(data)
npol = 3
perc1 = 50
perc2 = 95
fac = volts / displ * 0.001
# remove offset using the first 6 seconds that have to be quiet!
z = np.ones(npts)
offset = np.sum(data[0:int(6.0 * samprate)])
data = data - offset
# apply butterworth low pass filter if required
if bfc > 0.0 and bm > 0:
bt0 = 1.0 / bfc
mm = int(bm / 2.0)
# if odd order apply first order low pass
if bm > 2 * mm:
f0_lp1, f1_lp1, f2_lp1, g1_lp1, g2_lp1 = lp1(bt0 * samprate)
data = rekfl(data, npts, f0_lp1, f1_lp1, f2_lp1, g1_lp1, g2_lp1)
# now apply second order low pass
for i in range(mm):
h = np.sin(np.pi / 2.0 * (2.0 * i - 1.0) / bm)
f0_lp2, f1_lp2, f2_lp2, g1_lp2, g2_lp2 = lp2(bt0 * samprate, h)
data = rekfl(data, npts, f0_lp2, f1_lp2, f2_lp2, g1_lp2, g2_lp2)
# report filtering
print 'Input data filtered with low pass Butterworth filter:'
print ' corner frequency: ' + str(bfc) + 'Hz'
print ' order: ' + str(bm) + '.'
raw = data.copy()
# deconvolution to velocity step
# done by appling an exact invers second order high pass filter (infinit period)
t0 = 1.0e12
h = 1.0
t0s = 1.0 / fcs
f0_he2, f1_he2, f2_he2, g1_he2, g2_he2 = he2(t0 / dt, t0s / dt, h, hs)
data = rekfl(data, npts, f0_he2, f1_he2, f2_he2, g1_he2, g2_he2)
data = politrend(npol, data, npts, z)
x = data.copy()
deconv = data.copy()
# store deconvolves data
np.savetxt('dispcal_vel.txt', deconv)
# loop over fife trial values if no gap is given
if gap > 0.0:
onegap = True
ngap = 1
else:
#gap = 0.125
#onegap = False
#ngap = 5
print 'parameter "gap" not specified!'
sys.exit(1)
for k in range(ngap):
avglen = 6.0 * gap
iab = int(gap / dt)
iavg = int(avglen / dt)
data = x
z = np.ones(npts)
P0 = np.zeros(npts)
y = np.zeros(npts)
ja = np.ones(99)
# search for quiet section of minimum length 2*iab+1 samples
P = krum(data, npts, 2 * iab + 1, P0)
for j in range(npts):
P[j] = np.log10(max(P[j], 1.0e-6))
P1 = P.copy()
# import ipdb; ipdb.set_trace()
z = heapsort(npts, P1)
n50 = int(perc1 * npts / 100.0)
n95 = int(perc2 * npts / 100.0)
zlim = 0.5 * (z[n50] + z[n95])
P = P - zlim
z = z - zlim
# create trigger z = 0 if quiet, z = 1 if pulse
for j in range(npts):
if P[j] < 0:
z[j] = 1.0
else:
z[j] = 0.0
#zn1 = z[npts-1]
#zn = z[n]
#z[n-1] = -0.4
#z[n] = 1.4
#z[n-1] = zn1
#z[n] = zn
data = politrend(npol, data, npts, z)
data_c = data.copy()
# cut out pulses
quiet = data * z
# identifiy pulses
number = 0
i = 0
while i < npts and z[i] < 0.5:
i = i + 1
n1 = i
while i < npts and z[i] > 0.5:
i = i + 1
n2 = i - 1
if i == npts:
print 'No pulses found! Check dispcal.??? for pulses!'
sys.exit(1)
# continue if interval is too small (< gap)
while n2 < n1 + iab:
while i < npts and z[i] < 0.5:
i = i + 1
n1 = i
while i < npts and z[i] > 0.5:
i = i + 1
n2 = i - 1
# now we have the first (n1) and the last (n2) sample of the first quiet interval
if i == n:
print 'No pulses found! Check dispcal.??? for pulses!'
sys.exit(1)
# now continue with the next pulses
steps = []
marks = []
while i < npts:
while i < npts and z[i] < 0.5:
i = i + 1
if i == npts: break
n3 = i
while i < npts and z[i] > 0.5:
i = i + 1
if i == npts: break
n4 = i - 1
# continue if interval is too small (< gap)
while n4 < n3 + iab:
while i < npts and z[i] < 0.5:
i = i + 1
if i == npts: break
n3 = i
while i < npts and z[i] > 0.5:
i = i + 1
if i == npts: break
n4 = i - 1
# now we have the first (n1) and the last (n2) sample of the next quiet interval
number = number + 1
if number == 1:
data_c = zspline('zsp', 1, n2, n3, npts, data_c, npts)
n2null = n2
elif n4 >= n3 + iab:
data_c = zspline('zsp', n1, n2, n3, npts, data_c, npts)
n3null = n3
marks.append((n2, n3 - 1))
print 'found pulse # ' + str(number) + ': from sample ' + str(
n2) + ' to sample ' + str(n3 - 1)
n1a = 0
n2a = n2 - n1
n3a = n3 - n1
n4a = n4 - n1
n1a = max(n1a, n2a - iavg)
n4a = min(n4a, n3a + iavg)
for ii in range(n4a):
y[ii] = data[n1 - 1 + ii]
y_z = zspline('zsp', n1a, n2a, n3a, n4a, y, n4a)
y_int = integrate(y_z, npts, dt)
stp = step(y_int, n1a, n2a, n3a, n4a, n4a)
n1 = n3
n2 = n4
steps.append(stp)
dtr = trend('tre', 0, n2 - n1 + 1, 0, 0, data_c[n1:], npts - n1)
data2 = data_c.copy()
for i in np.arange(n1, npts, 1):
data2[i] = dtr[i - n1]
y = data_c * z
ymin = 0.0
ymax = 0.0
for i in np.arange(n2null + 1, n3null - 1, 1):
ymin = min(ymin, y[i])
ymax = max(ymax, y[i])
for i in range(n2null):
y[i] = min(ymax, max(ymin, y[i]))
for i in np.arange(n3null, npts, 1):
y[i] = min(ymax, max(ymin, y[i]))
np.savetxt('dispcal_res.txt', y)
y_dis = integrate(data2, npts, dt)
# remove offset from displacement
offset = np.mean(y_dis)
y_dis = y_dis - offset
displ = y_dis.copy()
np.savetxt('dispcal_dis.txt', displ)
##############################################################
# Statistics
total = sum(np.abs(steps))
avgs = total / number
sigma = np.std(np.abs(steps))
print ''
print '-------------------------------------------------------------'
print 'Raw average step: ' + str(np.round(avgs, 3)) + ' +/- ' + str(
np.round(sigma, 3))
print 'Raw generator constant: ' + str(np.round(
(avgs * fac) * 1e6, 3)) + ' +/- ' + str(
np.round((sigma * fac) * 1e6, 3)) + 'Vs/m'
print '-------------------------------------------------------------'
if number <= 2:
return raw, deconv, displ, marks
# import ipdb; ipdb.set_trace()
ja = np.ones(len(steps))
for nrest in np.arange(number - 1, int(float(number + 1.0) / 2.0), -1):
siga = sigma
kmax = np.argmax(steps)
ja[kmax] = 0
total = 0
#for k in range(number):
# total = total + ja[k] * steps[k]
stepsa = ja * steps
total = sum(np.abs(stepsa))
#print total
avgs = total / nrest
#print avgs
total = 0
for k in range(number):
total = total + ja[k] * (abs(steps[k]) - avgs)**2
sigma = np.sqrt(total / nrest)
print 'Pulse ' + str(kmax) + ' eliminated; ' + str(
nrest) + ' pulses remaning: ' + str(
np.round((avgs * fac) * 1e6, 3)) + ' +/- ' + str(
np.round((sigma * fac) * 1e6, 3)) + 'Vs/m'
# if sigma < 0.05*avgs and siga-sigma < siga/ nrest: break
steps[kmax] = 0.0
print '------------------------------------------------------------'
print ''
return raw, deconv, displ, marks
ola Valtercio bem vindo de volta. Estou a sua desposição.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
''')
perg = str(input('Voce: ')) # entrada com o nome jarvis
print()
jarvis = ''
if perg == 'não quero fazer nada': # comando para sair do Bot
print() # para da espaços entre as linhas
print('Ok senhor, até a proxima...')
if perg == 'jarvis': # comando para iniciar o Bot
jarvis = str(
input('ola senhor o que deseja? ')) # escolha das funções do Bot.
print() # para da espaços entre as linhas
if jarvis == 'faca uma busca' or jarvis == 'faça uma busca': # Bot faz uma busca no google ou youtube
google.google()
if jarvis == 'abra um site' or jarvis == 'quero abrir um site': # Bot abrirá um site
web.site() # funcao para abrir sites
if jarvis == 'quero conversar': # para conversar com o Bot
chat.conv() # funcao do chat
if jarvis == 'faca uma busca na wikipedia' or jarvis == 'faça uma busca na wikipedia': # Bot pesquisa na wikipedia
wikipedia.wiki() # funcao da wikipedia
if jarvis == 'faca uma busca na trend':
trend.trend()
