def test_build_params(self):
self.assertEqual(build_params(one=1, two=2), {'one': 1, 'two': 2})
self.assertEqual(build_params(), {})
self.assertEqual(build_params(a='a', b=None, c='b'), {
'a': 'a',
'c': 'b'
})
def __init__(self, filter=False, *largs, **kwargs):
"""
@parm filter: bool enables filter plugin
"""
self.js_multiselect_params = build_params(kwargs, self.JS_ATTRS)
self.filter = filter
if self.filter:
self.js_filter_plugin_attrs = build_params(kwargs, self.JS_PLUGIN_FILTER_ATTRS)
super(jQueryUIMultiSelect, self).__init__(*largs, **kwargs)
def initialize_transaction(self,
amount,
email,
callback_url=None,
reference=None,
plan=None,
invoice_limit=None,
metadata=None,
subaccount=None,
transaction_charge=None,
bearer=None,
channels=['card', 'bank']):
params = build_params(callback_url=callback_url,
reference=reference,
amount=amount,
email=email,
plan=plan,
invoice_limit=invoice_limit,
metadata=metadata,
subaccount=subaccount,
transaction_charge=transaction_charge,
bearer=bearer,
channels=channels)
self.ctx.post(self.url, json=params)
def create_invoice(self,
customer,
amount,
due_date,
description=None,
line_items=None,
tax=None,
currency='NGN',
metadata=None,
send_notification=True,
draft=False,
has_invoice=False,
invoice_number=None):
params = build_params(customer=customer,
amount=amount,
due_date=due_date,
description=description,
line_items=line_items,
tax=tax,
currency=currency,
metadata=metadata,
send_notification=send_notification,
draft=draft,
has_invoice=has_invoice,
invoice_number=invoice_number)
self.ctx.post(self.url, json=params)
def list_invoices(self,
customer=None,
paid=None,
status=None,
currency=None,
include_archive=None):
params = build_params(customer=customer,
paid=paid,
status=status,
currency=currency,
include_archive=include_archive)
self.ctx.get(self.url, payload=params)
def create_customer(self,
email,
first_name=None,
last_name=None,
phone=None,
metadata=None):
params = build_params(email=email,
first_name=first_name,
last_name=last_name,
phone=phone,
metadata=metadata)
self.ctx.post(self.url, json=params)
def create_refund(self,
transaction,
amount=None,
currency=None,
customer_note=None,
merchant_note=None):
params = build_params(transaction=transaction,
amount=amount,
currency=currency,
customer_note=customer_note,
merchant_note=None)
self.ctx.post(self.url, json=params)
def create_page(self,
name,
description=None,
amount=None,
slug=None,
redirect_url=None,
custom_fields=None):
params = build_params(name=name,
description=description,
amount=amount,
slug=slug,
redirect_url=redirect_url,
custom_fields=custom_fields)
url = '{host}/page'.format(host=self.api_url)
self.ctx.post(url, json=params)
def freq():
# Params from inputs.
url = request.args.get('url')
b64 = request.args.get('image')
method = request.args.get('method') or 'xing'
avg = request.args.get('avg') or 'mean'
region = request.args.get('region')
ntraces = request.args.get('ntraces') or '10'
trace_spacing = request.args.get('trace_spacing') or 'regular'
bins = request.args.get('bins') or '11'
t_min = request.args.get('tmin') or '0'
t_max = request.args.get('tmax') or '1'
dt_param = request.args.get('dt') or 'auto'
# Booleans.
spectrum = request.args.get('spectrum') or 'false'
segy = request.args.get('segy') or 'false'
nope = {i: False for i in ('none', 'false', 'no', '0')}
spectrum = nope.get(spectrum.lower(), True)
segy = nope.get(segy.lower(), True)
# Condition or generate params.
ntraces = int(ntraces)
bins = int(bins)
t_min = float(t_min)
t_max = float(t_max)
uuid1 = str(uuid.uuid1())
if region:
region = [int(n) for n in region.split(',')]
else:
region = []
# Fetch and crop image.
if url:
try:
r = requests.get(url)
im = Image.open(BytesIO(r.content))
except Exception:
payload = {'job_uuid': uuid1}
payload['parameters'] = utils.build_params(method, avg,
t_min, t_max, dt_param,
region,
trace_spacing,
url=url)
mess = 'Unable to open image from target URI.'
raise InvalidUsage(mess, status_code=410, payload=payload)
elif b64:
try:
im = Image.open(BytesIO(base64.b64decode(b64)))
except Exception:
payload = {'job_uuid': uuid1}
payload['parameters'] = utils.build_params(method, avg,
t_min, t_max, dt_param,
region,
trace_spacing,
url=url)
mess = 'Could not decode payload image. Check base64 encoding.'
raise InvalidUsage(mess, status_code=410, payload=payload)
else:
payload = {'job_uuid': uuid1}
payload['parameters'] = utils.build_params(method, avg,
t_min, t_max, dt_param,
region,
trace_spacing,
url=url)
mess = 'You must provide an image.'
raise InvalidUsage(mess, status_code=410, payload=payload)
if region:
try:
im = im.crop(region)
except Exception:
mess = 'Improper crop parameters '
raise InvalidUsage(mess, status_code=410)
width, height = im.size[0], im.size[1]
# Calculate dt and interpolate if necessary.
if dt_param[:4].lower() == 'orig':
dt = (t_max - t_min) / (height - 1)
else:
if dt_param[:4].lower() == 'auto':
dts = [0.0005, 0.001, 0.002, 0.004, 0.008]
for dt in sorted(dts, reverse=True):
target = int(1 + (t_max - t_min) / dt)
# Accept the first one that is larger than the current height.
if target >= height:
break # dt and target are set
else:
dt = float(dt_param)
target = int((t_max - t_min) / dt)
# If dt is not orig, we need to inpterpolate.
im = im.resize((width, target), Image.ANTIALIAS)
# Set up the image.
grey = geophysics.is_greyscale(im)
i = np.asarray(im) - 128
i = i.astype(np.int8)
if (not grey) and (i.ndim == 3):
r, g, b = i[..., 0], i[..., 1], i[..., 2]
i = np.sqrt(0.299 * r**2. + 0.587 * g**2. + 0.114 * b**2.)
elif i.ndim == 3:
i = i[..., 0]
else:
i = i
# Get SEGY file link, if requested.
if segy:
try:
databytes = BytesIO()
write_segy(i, databytes, dt, t_min)
databytes.seek(0)
except:
print('Write SEGY failed')
else:
file_link = utils.get_url(databytes, uuid1)
# Do analysis.
print("Starting analysis")
m = {'auto': geophysics.freq_from_autocorr,
'fft': geophysics.freq_from_fft,
'xing': geophysics.freq_from_crossings}
traces = geophysics.get_trace_indices(i.shape[1], ntraces, trace_spacing)
specs, f_list, p_list, snr_list, mis, mas = geophysics.analyse(i,
t_min,
t_max,
traces,
m[method])
print("Finished analysis")
# Compute statistics.
print("***** f_list:", f_list)
fsd, psd = np.nanstd(f_list), np.nanstd(p_list)
fn, pn = len(f_list), len(p_list)
if avg.lower() == 'trim' and fn > 4:
f = geophysics.trim_mean(f_list, 0.2)
if np.isnan(f):
f = 0
elif avg.lower() == 'mean' or (avg == 'trim' and fn <= 4):
f = np.nanmean(f_list)
else:
mess = 'avg parameter must be trim or mean'
raise InvalidUsage(mess, status_code=410)
if avg.lower() == 'trim' and pn > 4:
p = geophysics.trim_mean(p_list, 0.2)
elif avg.lower() == 'mean' or (avg == 'trim' and pn <= 4):
p = np.nanmean(p_list)
else:
mess = 'avg parameter must be trim or mean'
raise InvalidUsage(mess, status_code=410)
snrsd = np.nanstd(snr_list)
snr = np.nanmean(snr_list)
# Spectrum.
print("Starting spectrum")
try:
spec = np.nanmean(np.dstack(specs), axis=-1)
fs = i.shape[0] / (t_max - t_min)
freq = np.fft.rfftfreq(i.shape[0], 1/fs)
f_min = np.amin(mis)
f_max = np.amax(mas)
except:
print("Failed spectrum")
# Probably the image is not greyscale.
payload = {'job_uuid': uuid1}
payload['parameters'] = utils.build_params(method, avg,
t_min, t_max, dt_param,
region,
trace_spacing,
url=url)
mess = 'Analysis error. Probably the colorbar is not greyscale.'
raise InvalidUsage(mess, status_code=410, payload=payload)
# Histogram.
if bins:
hist = np.histogram(i, bins=bins)
else:
hist = None
# Construct the result and return.
result = {'job_uuid': uuid1}
result['status'] = 'success'
result['message'] = ''
result['result'] = {}
result['result']['freq'] = {'peak': np.round(f, 2),
'sd': np.round(fsd, 2),
'n': fn,
'min': np.round(f_min, 2),
'max': np.round(f_max, 2)}
result['result']['phase'] = {'avg': np.round(p, 2),
'sd': np.round(psd, 2),
'n': pn}
result['result']['snr'] = {'avg': np.round(snr, 2),
'sd': np.round(snrsd, 2)}
result['result']['greyscale'] = grey
result['result']['dt'] = dt
result['result']['img_size'] = {'original_height': height,
'width': width,
'resampled_height': target}
if segy:
result['result']['segy'] = file_link
if spectrum:
result['result']['spectrum'] = spec.tolist()
result['result']['frequencies'] = freq.tolist()
if hist:
result['result']['histogram'] = {'counts': hist[0].tolist(),
'bins': hist[1].tolist()
}
result['parameters'] = utils.build_params(method, avg,
t_min, t_max, dt_param,
region,
trace_spacing,
url=url)
return jsonify(result)
def freq():
# Params from inputs.
url = request.args.get('url')
method = request.args.get('method') or 'xing'
avg = request.args.get('avg') or 'mean'
region = request.args.get('region')
ntraces = request.args.get('ntraces') or '10'
trace_spacing = request.args.get('trace_spacing') or 'regular'
bins = request.args.get('bins') or '9'
t_min = request.args.get('tmin') or '0'
t_max = request.args.get('tmax') or '1'
dt_param = request.args.get('dt') or 'auto'
# Booleans.
spectrum = request.args.get('spectrum') or 'false'
segy = request.args.get('segy') or 'false'
nope = {i: False for i in ('none', 'false', 'no', '0')}
spectrum = nope.get(spectrum.lower(), True)
segy = nope.get(segy.lower(), True)
# Condition or generate params.
ntraces = int(ntraces)
bins = float(bins)
t_min = float(t_min)
t_max = float(t_max)
uuid1 = str(uuid.uuid1())
if region:
region = [int(n) for n in region.split(',')]
else:
region = []
# Fetch and crop image.
try:
r = requests.get(url)
im = Image.open(BytesIO(r.content))
except Exception:
result = {'job_uuid': uuid.uuid1()}
result['status'] = 'failed'
m = 'Error. Unable to open image from target URI. '
result['message'] = m
result['parameters'] = utils.build_params(method, avg,
t_min, t_max,
region,
trace_spacing,
url=url)
return jsonify(result)
if region:
try:
im = im.crop(region)
except Exception:
m = 'Improper crop parameters '
raise InvalidUsage(m+region, status_code=410)
width, height = im.size[0], im.size[1]
# Calculate dt and interpolate if necessary.
if dt_param[:4].lower() == 'orig':
dt = (t_max - t_min) / (height - 1)
else:
if dt_param[:4].lower() == 'auto':
dts = [0.0005, 0.001, 0.002, 0.004, 0.008]
for dt in sorted(dts, reverse=True):
target = int(1 + (t_max - t_min) / dt)
# Accept the first one that is larger than the current height.
if target >= height:
break # dt and target are set
else:
dt = float(dt_param)
target = int((t_max - t_min) / dt)
# If dt is not orig, we need to inpterpolate.
im = im.resize((width, target), Image.ANTIALIAS)
# Set up the image.
grey = geophysics.is_greyscale(im)
i = np.asarray(im) - 128
i = i.astype(np.int8)
if not grey:
r, g, b = i[..., 0], i[..., 1], i[..., 2]
i = np.sqrt(0.299 * r**2. + 0.587 * g**2. + 0.114 * b**2.)
else:
i = i[..., 0]
# Get SEGY file link, if requested.
if segy:
try:
databytes = BytesIO()
write_segy(i, databytes, dt, t_min)
databytes.seek(0)
except:
print('Write SEGY failed')
else:
file_link = utils.get_url(databytes, uuid1)
# Do analysis.
m = {'auto': geophysics.freq_from_autocorr,
'fft': geophysics.freq_from_fft,
'xing': geophysics.freq_from_crossings}
traces = geophysics.get_trace_indices(i.shape[1], ntraces, trace_spacing)
specs, f_list, p_list, snr_list, mis, mas = geophysics.analyse(i,
t_min,
t_max,
traces,
m[method.lower()])
# Compute statistics.
fsd, psd = np.nanstd(f_list), np.nanstd(p_list)
fn, pn = len(f_list), len(p_list)
if avg.lower() == 'trim' and fn > 4:
f = geophysics.trim_mean(f_list, 0.2)
elif avg.lower() == 'mean' or (avg == 'trim' and fn <= 4):
f = np.nanmean(f_list)
else:
m = 'avg parameter must be trim or mean'
raise InvalidUsage(m, status_code=410)
if avg.lower() == 'trim' and pn > 4:
p = geophysics.trim_mean(p_list, 0.2)
elif avg.lower() == 'mean' or (avg == 'trim' and pn <= 4):
p = np.nanmean(p_list)
else:
m = 'avg parameter must be trim or mean'
raise InvalidUsage(m, status_code=410)
snrsd = np.nanstd(snr_list)
snr = np.nanmean(snr_list)
# Spectrum.
try:
spec = np.mean(np.dstack(specs), axis=-1)
fs = i.shape[0] / (t_max - t_min)
freq = np.fft.rfftfreq(i.shape[0], 1/fs)
f_min = np.amin(mis)
f_max = np.amax(mas)
except:
# Probably the image is not greyscale.
result = {'job_uuid': uuid.uuid1()}
result['status'] = 'failed'
m = 'Analysis error. Probably the colorbar is not greyscale.'
result['message'] = m
result['parameters'] = utils.build_params(method.lower(), avg.lower(),
t_min, t_max,
region,
trace_spacing,
url=url)
return jsonify(result)
# Histogram.
if bins:
hist = np.histogram(i, bins=bins)
else:
hist = None
# Construct the result and return.
result = {'job_uuid': uuid1}
result['status'] = 'success'
result['message'] = ''
result['result'] = {}
result['result']['freq'] = {'peak': np.round(f, 2),
'sd': np.round(fsd, 2),
'n': fn,
'min': np.round(f_min, 2),
'max': np.round(f_max, 2)}
result['result']['phase'] = {'avg': np.round(p, 2),
'sd': np.round(psd, 2),
'n': pn}
result['result']['snr'] = {'avg': np.round(snr, 2),
'sd': np.round(snrsd, 2)}
result['result']['greyscale'] = grey
result['result']['dt'] = dt
result['result']['img_size'] = {'original_height': height,
'width': width,
'resampled_height': target}
if segy:
result['result']['segy'] = file_link
if spectrum:
result['result']['spectrum'] = spec.tolist()
result['result']['frequencies'] = freq.tolist()
if hist:
result['result']['histogram'] = {'counts': hist[0].tolist(),
'bins': hist[1].tolist()
}
result['parameters'] = utils.build_params(method, avg,
t_min, t_max, dt_param,
region,
trace_spacing,
url=url)
return jsonify(result)
