def save(self, basename):
fullname = self._get_filename(basename)
#need vdiff_vect here and in the labels
## data = column_stack([self.nvect, self.ref, self.uL, self.uR, \
## self.sensor_mat, self.error])
col_list = [self.nvect, self.ref]
if hasattr(self, 'vdiff_vect'):
col_list.append(self.vdiff_vect)
col_list.extend([self.uL, self.uR, \
self.sensor_mat, self.error])
data = column_stack(col_list)
data_str = data.astype('S30')
rows, N_sense = self.sensor_mat.shape
sen_labels = ['sensor %i' % ind for ind in range(N_sense)]
labels = ['n','R (ref)']
if hasattr(self, 'vdiff_vect'):
labels.append('vdiff_vect')
labels += ['uL','uR'] + sen_labels + ['error']
str_mat = row_stack([labels, data_str])
txt_mixin.dump_delimited(fullname, str_mat, delim=',')
self.data_file_name = fullname
return str_mat
def save(self, basename):
fullname = self._get_filename(basename)
#need vdiff_vect here and in the labels
## data = column_stack([self.nvect, self.ref, self.uL, self.uR, \
## self.sensor_mat, self.error])
col_list = [self.nvect, self.ref]
if hasattr(self, 'vdiff_vect'):
col_list.append(self.vdiff_vect)
col_list.extend([self.uL, self.uR, \
self.sensor_mat, self.error])
data = column_stack(col_list)
data_str = data.astype('S30')
rows, N_sense = self.sensor_mat.shape
sen_labels = ['sensor %i' % ind for ind in range(N_sense)]
labels = ['n', 'R (ref)']
if hasattr(self, 'vdiff_vect'):
labels.append('vdiff_vect')
labels += ['uL', 'uR'] + sen_labels + ['error']
str_mat = row_stack([labels, data_str])
txt_mixin.dump_delimited(fullname, str_mat, delim=',')
self.data_file_name = fullname
return str_mat
def save(self, filename, folder=''):
outpath = os.path.join(folder,filename)
if not os.path.exists(outpath):
txt_mixin.dump_delimited(outpath,self.data_array,labels=self.labels)
print('saved: %s' % outpath)
else:
print('already exists: %s' % outpath)
def save(self, pathout, delim='\t'):
"""I need to add an intermediate step to quote strings for csv
compliance, i.e. 'this is one really, really, long cell with
commas.'"""
## data_out = prep_data_for_save(self.data)
## misc_utils.dump_matrix(pathout, data_out, self.labels, \
## fmt='%s', delim=delim)
quoted_labels = ['"%s"' % item for item in self.labels]
txt_mixin.dump_delimited(pathout,self.data,delim=delim, \
labels=quoted_labels)
def save(self, pathout, delim='\t'):
"""I need to add an intermediate step to quote strings for csv
compliance, i.e. 'this is one really, really, long cell with
commas.'"""
## data_out = prep_data_for_save(self.data)
## misc_utils.dump_matrix(pathout, data_out, self.labels, \
## fmt='%s', delim=delim)
quoted_labels = ['"%s"' % item for item in self.labels]
txt_mixin.dump_delimited(pathout,self.data,delim=delim, \
labels=quoted_labels)
def save_csv(self, delim="\t"):
if not hasattr(self, "csv_data"):
self.build_csv_data()
fno, ext = os.path.splitext(self.filename)
outname = fno + '_clean_out.csv'
txt_mixin.dump_delimited(outname, \
self.csv_data, \
delim=delim)
def save_csv(self, delim="\t"):
if not hasattr(self, "csv_data"):
self.build_csv_data()
fno, ext = os.path.splitext(self.filename)
outname = fno + '_clean_out.csv'
txt_mixin.dump_delimited(outname, \
self.csv_data, \
delim=delim)
def save_data(self, filename=None):
if filename is None:
filename = self.find_filename()
if os.path.exists(filename):
print("exists: %s" % filename)
return
else:
print("saving: %s" % filename)
data = np.column_stack([self.nvect, self.t, self.u, \
self.v, self.enc, self.accel_shifted])
mylabels = ['n','t','u','v','enc','accel (shifted)']
txt_mixin.dump_delimited(filename, data, labels=mylabels)
def on_save_data(self, event):
#need filepath dialog here
filepath = wx_utils.my_file_dialog(parent=self, \
msg="Save data as txt file", default_file="", \
wildcard=txt_wildcard, \
kind="save", \
check_overwrite=False)
if filepath:
plot_list = self.plot_panel.get_plot_list()
data_list = self.validate_plot_list_and_get_data(plot_list)
if data_list:
label_list = self.get_labels(plot_list)
all_data = [self.t] + data_list
all_labels = ['Time (sec.)'] + label_list
c_labels = clean_labels(all_labels)
data_mat = np.column_stack(all_data)
# I could kind of use a way to clean the labels from latex to plain ascii
# i.e. $\ddot{x}_{tip}$ --> accel
txt_mixin.dump_delimited(filepath, data_mat, labels=c_labels)
def dumpcsv(outpath, csvlist, ids=False, extra_col_labels=None):
last_names, first_names = _get_names(csvlist)
N = len(last_names)
vector_list = [last_names, first_names]
labels = ["Last Name", "First Name"]
if ids:
banner_ids = get_banner_ids(csvlist)
vector_list.append(banner_ids)
labels.append("ID")
if extra_col_labels is not None:
labels.extend(extra_col_labels)
Nec = len(extra_col_labels)
for i in range(Nec):
cur_empty = [""] * N
vector_list.append(cur_empty)
data = column_stack(vector_list)
data2 = row_stack([labels, data])
txt_mixin.dump_delimited(outpath, data2, delim=",", fmt="%s")
def dumpcsv(outpath, csvlist, ids=False, extra_col_labels=None):
last_names, first_names = _get_names(csvlist)
N = len(last_names)
vector_list = [last_names, first_names]
labels = ['Last Name', 'First Name']
if ids:
banner_ids = get_banner_ids(csvlist)
vector_list.append(banner_ids)
labels.append('ID')
if extra_col_labels is not None:
labels.extend(extra_col_labels)
Nec = len(extra_col_labels)
for i in range(Nec):
cur_empty = [''] * N
vector_list.append(cur_empty)
data = column_stack(vector_list)
data2 = row_stack([labels, data])
txt_mixin.dump_delimited(outpath, data2, delim=',', fmt='%s')
def save_bodes_to_file(filepath, freq, bode_list, labels): import txt_mixin data_out = bodes_to_string_matrix(freq, bode_list, labels) txt_mixin.dump_delimited(filepath, data_out)
accel = dttmm_sys.sys.sensors[-1]
encoder = dttmm_sys.sys.sensors[0]
enc_counts = encoder.signal_vect#*JVC_model_dict['H']
u = sim_sys.blocks[0].output
sum1 = sim_sys.get_block('sum1')
v = sum1.output
figure(1)
clf()
plot(t, u)
figure(2)
clf()
plot(t, enc_counts)
plot(t, dttmm_sys.output[:,0])#*JVC_model_dict['H'])
plot(df.t, df.theta)
figure(3)
clf()
plot(t, accel.signal_vect)
plot(t, dttmm_sys.output[:,1])
plot(df.t, df.a)
data = column_stack([t, u, v, enc_counts, accel.signal_vect])
txt_mixin.dump_delimited(filename, data, labels=['t','u','v','theta','a'])
show()
