def split(self):
if hasattr(self, 'elmdf'):
return self.elmdf
self.elms = self.elm_loc()
elm_cycles = {}
for elm_no, elm_time in enumerate(self.elms[:-1]):
if self.elms[elm_no + 1] - self.elms[elm_no] <= self.min_elm_window: # default min_elm_window = 50
continue
start_ielm = index_match(self.arr[0], elm_time)
stop_ielm = index_match(self.arr[0], self.elms[elm_no + 1])
for ielm_time in self.arr[0][start_ielm:stop_ielm]:
'''MAX: Uncomment the lines below to get % of ELM'''
ielm_index = np.argwhere(self.arr[0] == ielm_time)[0][0]
elm_cycles[(elm_no, ielm_index, ielm_time, self.arr[0][stop_ielm] - ielm_time)] = self.arr[1].T[ielm_index]
# elm_cycles[(elm_no, ielm_index, ielm_time, (ielm_time-self.arr[0][start_ielm])/(self.arr[0][stop_ielm]-self.arr[0][start_ielm]))] = self.arr[1].T[ielm_index]
index = pd.MultiIndex.from_tuples(elm_cycles.keys(), names=['ELM_No', 'Index', 'Time (ms)', 'T - ELM (ms)'])
# index = pd.MultiIndex.from_tuples(elm_cycles.keys(), names=['ELM_No', 'Index', 'Time (ms)', '% ELM'])
self.elmdf = pd.DataFrame(elm_cycles.values(), index=index)
return self.elmdf
def slice1d(self, time, smooth=False, ax=None):
'''
This plots the 1D slice in plot slice. Not super necessary as an extra function, but I kinda wanted to see if
I could move matplotlib objects between functions.
'''
if ax is None:
ax = plt.gca()
index = index_match(self.arr[0],
time) # finds index of time entered. Kind of slow?
if len(self.arr[0]) < index < 0:
raise ValueError(
'Selected time is out of bounds of run time for shot.')
slce = self.arr[1][:, index]
ax.plot(self.arr[2], slce)
ax.set_xlabel('Frequency (kHz)')
if smooth:
smooth_arr = gaussian_filter1d(slce, 10)
peaks, _ = find_peaks(smooth_arr,
prominence=(np.mean(abs(smooth_arr)), None),
distance=75)
ax.plot(self.arr[2], smooth_arr, 'y')
ax.plot(self.arr[2][peaks], smooth_arr[peaks], 'ro')
else:
peaks, _ = find_peaks(slce,
prominence=(np.mean(abs(slce)), None),
distance=75)
ax.plot(self.arr[2][peaks], slce[peaks], 'ro')
return peaks
def split_from_spec(self, plot=False):
'''
split returns a dataframe of the original array which excludes all arrays belonging to ELM's.
It also returns a hot array with 0 for all intra-elm indices and 1 for all elm indices (the ones that were excluded
from the dataframe.
'''
_, pm = self.get_peaks()
stop = self.stop_height # default 1500
pm_norm = (pm[:, stop:] - np.amin(pm[:, stop:])) / (np.amax(pm[:, stop:]) - np.amin(pm[:, stop:]))
sums = np.sum(pm_norm, axis=1)
# peaks, props = find_peaks(sums, distance=100, prominence=(1, None), width=(None, None), rel_height=1.0)
# l_elms = props['left_ips']
# r_elms = props['right_ips']
elms = self.elm_loc()
for i, elm in enumerate(elms):
i_elm = index_match(self.arr[0], elm)
# l_elms[i] = i_l_elm
l_elm = np.argmax(np.gradient(sums[i_elm - 50:i_elm + 50])) + i_elm - 50
r_elm = np.argmin(np.gradient(sums[i_elm - 50:i_elm + 50])) + i_elm - 50
l_elms[i] = l_elm
r_elms[i] = r_elm
#
# for i, r_elm in enumerate(r_elms):
# i_r_elm = index_match(self.arr[0], r_elm)
# r_elms[i] = i_r_elm
# hot = np.zeros_like(self.arr[0])
# for i in np.column_stack((l_elms, r_elms)):
# hot[i[0]:i[1]] = 1
r_elms = r_elms.astype(int)
l_elms = l_elms.astype(int)
if plot:
fig, ax = plt.subplots(1, 1)
self.heatmap2d(ax=ax)
# for i, num in enumerate(hot):
# if num == 1:
# ax.axvline(self.arr[0][i], c='orange')
for i in l_elms:
ax.axvline(self.arr[0][i], ymin=stop / self.arr[1].shape[0], c='red')
for i in r_elms:
ax.axvline(self.arr[0][i], ymin=stop / self.arr[1].shape[0], c='green')
# for elm in elms:
# i_elm = index_match(self.arr[0], elm)
# ax.axvline(self.arr[0][i_elm], c='blue')
plt.show()
# make dict with keys, values, times
elm_cycles = {}
for i in range(len(r_elms) - 1):
for j in self.arr[0][r_elms[i]:l_elms[i + 1]]:
k = np.argwhere(self.arr[0] == j)[0][0]
elm_cycles[(i, j, k)] = self.arr[1].T[np.argwhere(self.arr[0] == j)][0][0]
index = pd.MultiIndex.from_tuples(elm_cycles.keys(), names=['ELM_No', 'Time(ms)', 'Index'])
elmdf = pd.DataFrame(elm_cycles.values(), index=index)
return elmdf
def split(self):
self.elms = self.elm_loc()
elm_cycles = {}
for elm_no, elm_time in enumerate(self.elms[:-1]):
if self.elms[elm_no + 1] - self.elms[
elm_no] <= self.min_elm_window: #default min_elm_window = 50
continue
start_ielm = index_match(self.arr[0], elm_time)
stop_ielm = index_match(self.arr[0], self.elms[elm_no + 1])
for ielm_time in self.arr[0][start_ielm:stop_ielm]:
ielm_index = np.argwhere(self.arr[0] == ielm_time)[0][0]
elm_cycles[(elm_no, ielm_time,
ielm_index)] = self.arr[1].T[ielm_index]
index = pd.MultiIndex.from_tuples(
elm_cycles.keys(), names=['ELM_No', 'Time(ms)', 'Index'])
self.elmdf = pd.DataFrame(elm_cycles.values(), index=index)
return self.elmdf
def time_to_elm(self):
if not hasattr(self, 'elmdf'):
self.split()
ielm_time = np.array([i[1] for i in self.elmdf.index])
dict = {}
prev_elm_index = 0
for i, elm in enumerate(self.elms):
next_elm_index = index_match(ielm_time, elm)
for j, ielm in enumerate(ielm_time[prev_elm_index:next_elm_index]):
dict[(i, ielm,
j + prev_elm_index)] = ielm_time[next_elm_index] - ielm
prev_elm_index = next_elm_index
index = pd.MultiIndex.from_tuples(
dict.keys(), names=['ELM_No', 'Time (ms)', 'Index'])
t_to_elm = pd.DataFrame(dict.values(),
index=index,
columns=['Time to Next ELM (ms)'])
return t_to_elm
def split(self, plot=False):
'''
split returns a dataframe of the original array which excludes all arrays belonging to ELM's.
It also returns a hot array with 0 for all intra-elm indices and 1 for all elm indices (the ones that were excluded
from the dataframe.
'''
if hasattr(self, 'elmdf'):
return self.elmdf
pm = self.arr[1].T
stop = self.stop_height # default 1500
pm_norm = (pm[:, stop:] - np.amin(pm[:, stop:])) / (
np.amax(pm[:, stop:]) - np.amin(pm[:, stop:]))
sums = np.sum(pm_norm, axis=1)
peaks, props = find_peaks(sums,
distance=10,
prominence=(0.5, None),
width=(10, 50),
rel_height=0.95)
l_elms = props['left_ips']
r_elms = props['right_ips']
r_elms = np.asarray([*map(np.ceil, r_elms)]).astype(int)
l_elms = np.asarray([*map(np.floor, l_elms)]).astype(int)
l_elms_time = self.arr[0][l_elms]
r_elms_time = self.arr[0][r_elms]
if plot:
fig, ax = plt.subplots(1, 1)
self.heatmap2d(ax=ax)
for t in l_elms_time:
ax.axvline(t, ymin=stop / self.arr[1].shape[0], c='red')
for t in r_elms_time:
ax.axvline(t, ymin=stop / self.arr[1].shape[0], c='green')
plt.show()
# make dict with keys, values, times
elm_cycles = {}
elms = list(zip(l_elms_time, r_elms_time))
for elm_no, _ in enumerate(elms[:-1]):
if elms[elm_no + 1][0] - elms[elm_no][
1] <= self.min_elm_window: # default min_elm_window = 50
continue
start_ielm = index_match(self.arr[0], elms[elm_no][1])
stop_ielm = index_match(self.arr[0], elms[elm_no + 1][0])
for ielm_time in self.arr[0][start_ielm:stop_ielm]:
ielm_index = np.argwhere(self.arr[0] == ielm_time)[0][0]
elm_cycles[(elm_no, ielm_index, ielm_time,
self.arr[0][stop_ielm] -
ielm_time)] = self.arr[1].T[ielm_index]
# elm_cycles[(elm_no, ielm_index, ielm_time, (ielm_time - self.arr[0][start_ielm]) /
# (self.arr[0][stop_ielm] - self.arr[0][start_ielm]))] = self.arr[1].T[ielm_index]
index = pd.MultiIndex.from_tuples(
elm_cycles.keys(),
names=['ELM_No', 'Index', 'Time (ms)', 'T - ELM (ms)'])
# index = pd.MultiIndex.from_tuples(elm_cycles.keys(), names=['ELM_No', 'Index', 'Time (ms)', '% ELM'])
self.elmdf = pd.DataFrame(elm_cycles.values(), index=index)
return self.elmdf