def generate_curves_specific_function(self, function, path: list,
curve_content, x_values, all_ids,
requested_indices):
if function not in curve_content:
print("no '{}' available under this path. Current content: {}".
format(function, curve_content))
return None
y_values = self.read_numpy_ndarray_values_from_path([1, 2], *path,
function)
if y_values.ndim == 1:
curve_name = self.get_curve_name(path, function)
curve = Curve(curve_name, x_values, y_values)
tag = self.create_tag(path, function)
self._curves.add_curve(curve, tag)
elif y_values.ndim == 2:
if all_ids is None:
print(
"no 'ids' available under this path. Current content: {}".
format(curve_content))
return None
for ind in requested_indices:
curve_name = self.get_curve_name(path, function, all_ids[ind],
ind)
curve = Curve(curve_name, x_values, y_values[..., ind])
tag = self.create_tag(path, function, all_ids[ind])
self._curves.add_curve(curve, tag)
def post_metapost_run(self):
for slot in self._slots:
for value_extraction in self._value_extractions:
file_path = self.get_history_curve_filename(
slot, value_extraction)
try:
csv_content = read_csv(file_path,
sep=",",
skip_blank_lines=True,
decimal=".",
engine='python',
error_bad_lines=True,
skiprows=[0, 1, 2, 3, 4],
skipfooter=1)
number_columns = csv_content.shape[1]
if isnull(csv_content.iloc[:, number_columns - 1]).all():
csv_content = csv_content.drop(
csv_content.columns[[number_columns - 1]], axis=1)
curve_title = self.get_history_curve_name(
slot, value_extraction)
x_values = csv_content.iloc[:, 0]
y_values = csv_content.iloc[:, 1]
curve = Curve(curve_title, x_values, y_values)
self._curves.add_curve(curve, curve_title)
except FileNotFoundError:
print("Curve Name doesn't exist: {}".format(
value_extraction))
self.store_curves_in_register()
def post_animator_run(self):
for slot in self._slots:
for current_id in self._ids:
for value_extraction in self._value_extractions:
file_path = self.get_history_curve_filename(
slot, current_id,
self._variable_names_dict[value_extraction])
try:
csv_content = read_csv(file_path,
sep=",",
skip_blank_lines=True,
decimal=".",
error_bad_lines=True,
skiprows=[1, 2])
number_columns = csv_content.shape[1]
if isnull(csv_content.iloc[:,
number_columns - 1]).all():
csv_content = csv_content.drop(
csv_content.columns[[number_columns - 1]],
axis=1)
curve_title = self.get_history_curve_name(
slot, current_id, value_extraction)
x_values = csv_content.iloc[:, 0]
y_values = csv_content.iloc[:, 1]
curve = Curve(curve_title, x_values, y_values)
self._curves.add_curve(curve, curve_title)
except FileNotFoundError:
print("Node Id doesn't exist: {}".format(current_id))
self.store_curves_in_register()
def generate_curve_data(self, index_data_start: int, count_data_lines: int,
curve_title: str) -> Curve:
index_data_end = index_data_start + count_data_lines
x_values = []
y_values = []
for line in self._crv_content[index_data_start:index_data_end]:
match_data_line = re.match(r'^\s*([^\s]+)\s+([^\s]+)\s*$', line)
x_values.append(float(match_data_line.group(1)))
y_values.append(float(match_data_line.group(2)))
return Curve(curve_title, x_values, y_values)
def generate_curves(self):
columns = list(self._csv_content.columns)
number_columns = self._csv_content.shape[1]
for column_id in range(1, number_columns):
curve_title = columns[column_id]
if self.is_curve_requested(curve_title):
x_values = self._csv_content.iloc[:, 0]
y_values = self._csv_content.iloc[:, column_id]
curve = Curve(curve_title, x_values, y_values)
self._curves.add_curve(curve, curve_title)
def generate_curve(self):
print(self._data_table)
count_lines = len(self._data_table[0])
if self._line_number_x_values >= count_lines or self._line_number_y_values >= count_lines:
print("data table doesn't contain enough lines")
raise ValueError
x_line = []
y_line = []
for col in self._data_table:
x_line.append(col[self._line_number_x_values])
y_line.append(col[self._line_number_y_values])
self._x_label = x_line[0]
x_values = x_line[1:]
self._y_label = y_line[0]
y_values = y_line[1:]
self._curve = Curve("curve", x_values, y_values)
def apply_modification(self, curve: Curve) -> Curve:
return Curve(curve.name, curve.df.X, curve.df.Y * self._factor)
def apply_modification(self, curve: Curve) -> Curve:
data_frame = curve.df
result_df = self.apply_filter(data_frame)
result_curve = Curve(curve.name, result_df.X, result_df.Y)
return result_curve