From 4434a6ba94d62bb2cd18a5ca21b51fa5f4980db5 Mon Sep 17 00:00:00 2001
From: Saku Hakamaa <sajuhak@utu.fi>
Date: Tue, 27 Nov 2018 13:13:06 +0200
Subject: [PATCH] reorder code
---
sim_controller.py | 6 +--
sim_utilities.py | 102 ++++++++++++++++++++++++++++++++++++++++-
vis.py | 112 +++-------------------------------------------
3 files changed, 111 insertions(+), 109 deletions(-)
diff --git a/sim_controller.py b/sim_controller.py
index fbbf263..f61774a 100644
--- a/sim_controller.py
+++ b/sim_controller.py
@@ -124,9 +124,9 @@ class SimController(object):
for element, item in self.figure_params.items()
if element in ase.data.chemical_symbols])
print("Drawing figures")
- z_positions, dict_concs_mean, energy_list, frame_list = vis.read_conc_data(figure_out_path,
- symbol_marker_color_dict,
- figure_range_dict)
+ z_positions, dict_concs_mean, energy_list, frame_list = sim_utilities.read_conc_data(figure_out_path,
+ symbol_marker_color_dict,
+ figure_range_dict)
vis.draw_figures(z_positions,
dict_concs_mean,
diff --git a/sim_utilities.py b/sim_utilities.py
index ef5a309..60c8375 100644
--- a/sim_utilities.py
+++ b/sim_utilities.py
@@ -26,6 +26,26 @@ import ase
import numpy as np
+def average_filter_function(data):
+ """
+ Applies a 3-wide average filter to data to make it smoother.
+
+ :param data: the list of data to filter
+ :return: filtered data
+ """
+ temp_data = []
+ for i in range(len(data)):
+ sum = data[i]
+ if i == 0:
+ sum += data[i] + data[i + 1]
+ elif i == len(data) - 1:
+ sum += data[i - 1] + data[i]
+ else:
+ sum += data[i - 1] + data[i + 1]
+ temp_data.append(sum / 3)
+ return temp_data
+
+
class ParsingError(Exception):
pass
@@ -43,6 +63,7 @@ def compress_frames(job_out_path):
os.rmdir(sim_frames_folder_path)
+# TODO: combine all three range parsing methods
def convert_bool_to_range(range_list):
if range_list is True:
return [(0, math.inf)]
@@ -205,7 +226,6 @@ def parse_conf(conf_filename):
figure_params = None
return job_params, sim_params, lattice_params, bulk_dopants, empty_space_dopants, figure_params
-# TODO: combine all three range parsing methods
def parse_figure_range_str(figure_params):
@@ -241,6 +261,7 @@ def parse_param_str_to_bool(parameter):
raise ParsingError('unknown boolean type: "{}"'.format(parameter))
+# TODO: combine all three range parsing methods
def parse_range_str(parameter, lim_min=0, lim_max=math.inf):
"""
Converts a range specified in a string into a list of upper and lower bounds.
@@ -266,6 +287,7 @@ def parse_range_str(parameter, lim_min=0, lim_max=math.inf):
return ids
+# TODO: combine all three range parsing methods
def range_list_to_indexes(ranges, indexes):
ids = set()
for index in indexes:
@@ -282,6 +304,84 @@ def range_list_to_indexes(ranges, indexes):
return sorted(list(ids))
+def read_conc_data(in_path, symbol_marker_color_dict, figure_range_dict):
+ # ------------------------------
+ # CALCULATING MEAN
+ # ------------------------------
+ # TODO: make easier to understand
+ # TODO: stop shadowing names from outer scope
+ frame_list = []
+ dict_concs_per_element = {}
+ energy_list = []
+ z_positions = []
+
+ # read concentrations
+ conc_path = os.path.join(in_path, "sim_conc")
+ file_list = [entry for entry in os.listdir(conc_path) if entry.endswith(".txt")]
+
+ with open(os.path.join(in_path, "energy.txt"), 'r') as energy_file:
+ energy_file_header = energy_file.readline()
+ while True:
+ energy_file_line = energy_file.readline()
+ if energy_file_line == "":
+ break
+
+ energy_list.append(float(energy_file_line.split()[1]))
+
+ for filename in file_list:
+ conc_file_path = os.path.join(conc_path, filename)
+ with open(conc_file_path, 'r') as conc_file:
+ symbols = conc_file.readline().split()[2:]
+ dict_concs_per_z = {}
+ for symbol1, symbol2 in zip(symbols, symbol_marker_color_dict):
+ dict_concs_per_z[symbol1] = []
+
+ if not symbol1 == symbol2:
+ raise Exception
+
+ while True:
+ conc_file_line = conc_file.readline()
+ if conc_file_line == "":
+ break
+
+ values = conc_file_line.split()
+ z_pos = float(values.pop(0))
+ if z_pos not in z_positions:
+ z_positions.append(z_pos)
+
+ for symbol, value in zip(symbol_marker_color_dict, values):
+ dict_concs_per_z[symbol].append(float(value))
+
+ frame_list.append(dict_concs_per_z)
+
+ # convert list of frame data to lists of data per element
+ for i, dict_concs_per_z in enumerate(frame_list):
+ for symbol, concs_list in dict_concs_per_z.items():
+ if symbol not in dict_concs_per_element.keys():
+ dict_concs_per_element[symbol] = []
+ for z_pos in z_positions:
+ dict_concs_per_element[symbol].append([])
+ for i, z_pos in enumerate(z_positions):
+ dict_concs_per_element[symbol][i].append(concs_list[i])
+
+ # collect the average concentrations into a list per symbol and z-level
+ dict_concs_mean = {}
+ profile_mean_range = figure_range_dict['profile_mean_range']
+ for symbol, concs_list in dict_concs_per_element.items():
+ if symbol not in dict_concs_mean.keys():
+ dict_concs_mean[symbol] = [0] * len(z_positions)
+ for z, concs in enumerate(concs_list):
+ indexes = sim_utilities.range_list_to_indexes(profile_mean_range, range(len(concs)))
+ array_mask = [True] * len(concs)
+ for i, _ in enumerate(concs):
+ if i in indexes:
+ array_mask[i] = False
+ masked_concs = np.ma.array(concs, mask=array_mask)
+ dict_concs_mean[symbol][z] = np.mean(masked_concs)
+
+ return z_positions, dict_concs_mean, energy_list, frame_list
+
+
def read_job_name_and_end_energy(report_file):
energy_report_str = "Final potential energy: "
job_name = report_file.readline()
diff --git a/vis.py b/vis.py
index 8649a6b..c4e6bb6 100644
--- a/vis.py
+++ b/vis.py
@@ -17,7 +17,6 @@
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# ------------------------------
import os
-import math
import argparse
# import matplotlib.animation as ani
@@ -30,104 +29,6 @@ import ase
import sim_utilities
-def average_filter_function(data):
- """
- Applies a 3-wide average filter to data to make it smoother.
-
- :param data: the list of data to filter
- :return: filtered data
- """
- temp_data = []
- for i in range(len(data)):
- sum = data[i]
- if i == 0:
- sum += data[i] + data[i + 1]
- elif i == len(data) - 1:
- sum += data[i - 1] + data[i]
- else:
- sum += data[i - 1] + data[i + 1]
- temp_data.append(sum / 3)
- return temp_data
-
-
-def read_conc_data(in_path, symbol_marker_color_dict, figure_range_dict):
- # ------------------------------
- # CALCULATING MEAN
- # ------------------------------
- # TODO: make easier to understand
- # TODO: stop shadowing names from outer scope
- frame_list = []
- dict_concs_per_element = {}
- energy_list = []
- z_positions = []
-
- # read concentrations
- conc_path = os.path.join(in_path, "sim_conc")
- file_list = [entry for entry in os.listdir(conc_path) if entry.endswith(".txt")]
-
- with open(os.path.join(in_path, "energy.txt"), 'r') as energy_file:
- energy_file_header = energy_file.readline()
- while True:
- energy_file_line = energy_file.readline()
- if energy_file_line == "":
- break
-
- energy_list.append(float(energy_file_line.split()[1]))
-
- for filename in file_list:
- conc_file_path = os.path.join(conc_path, filename)
- with open(conc_file_path, 'r') as conc_file:
- symbols = conc_file.readline().split()[2:]
- dict_concs_per_z = {}
- for symbol1, symbol2 in zip(symbols, symbol_marker_color_dict):
- dict_concs_per_z[symbol1] = []
-
- if not symbol1 == symbol2:
- raise Exception
-
- while True:
- conc_file_line = conc_file.readline()
- if conc_file_line == "":
- break
-
- values = conc_file_line.split()
- z_pos = float(values.pop(0))
- if z_pos not in z_positions:
- z_positions.append(z_pos)
-
- for symbol, value in zip(symbol_marker_color_dict, values):
- dict_concs_per_z[symbol].append(float(value))
-
- frame_list.append(dict_concs_per_z)
-
- # convert list of frame data to lists of data per element
- for i, dict_concs_per_z in enumerate(frame_list):
- for symbol, concs_list in dict_concs_per_z.items():
- if symbol not in dict_concs_per_element.keys():
- dict_concs_per_element[symbol] = []
- for z_pos in z_positions:
- dict_concs_per_element[symbol].append([])
- for i, z_pos in enumerate(z_positions):
- dict_concs_per_element[symbol][i].append(concs_list[i])
-
- # collect the average concentrations into a list per symbol and z-level
- dict_concs_mean = {}
- profile_mean_range = figure_range_dict['profile_mean_range']
- for symbol, concs_list in dict_concs_per_element.items():
- if symbol not in dict_concs_mean.keys():
- dict_concs_mean[symbol] = [0] * len(z_positions)
- for z, concs in enumerate(concs_list):
- indexes = sim_utilities.range_list_to_indexes(profile_mean_range, range(len(concs)))
- array_mask = [True] * len(concs)
- for i, _ in enumerate(concs):
- if i in indexes:
- array_mask[i] = False
- masked_concs = np.ma.array(concs, mask=array_mask)
- dict_concs_mean[symbol][z] = np.mean(masked_concs)
-
- return z_positions, dict_concs_mean, energy_list, frame_list
-
-
def draw_figures(z_positions, dict_concs_mean, energy_list, frame_list, fig_range_dict, symbol_marker_color_dict, no_gui, average_filter, out_path):
import matplotlib
if no_gui:
@@ -179,7 +80,7 @@ def draw_figures(z_positions, dict_concs_mean, energy_list, frame_list, fig_rang
# draw on filtered figure
if average_filter:
plt.figure(figure_mean_filtered.number)
- plt.plot(z_positions, average_filter_function(data), marker=marker, color=color, label=label,
+ plt.plot(z_positions, sim_utilities.average_filter_function(data), marker=marker, color=color, label=label,
zorder=2)
# set mean figure appearance and save it
plt.figure(figure_mean.number)
@@ -227,7 +128,7 @@ def draw_figures(z_positions, dict_concs_mean, energy_list, frame_list, fig_rang
atom_dict_concs = frame_list[0]
data = atom_dict_concs[symbol]
if average_filter:
- data = average_filter_function(data)
+ data = sim_utilities.average_filter_function(data)
line = plt.plot(z_positions, data, marker=marker, color=color, label=label,
zorder=2)
lines.append((symbol, line[0]))
@@ -251,7 +152,7 @@ def draw_figures(z_positions, dict_concs_mean, energy_list, frame_list, fig_rang
for symbol, line in lines:
data = atom_dict_concs[symbol]
if average_filter:
- data = average_filter_function(data)
+ data = sim_utilities.average_filter_function(data)
line.set_data(z_positions, data)
figure_anim.set_size_inches(7, 7)
n_frames_width = len(str(len(frame_list)))
@@ -461,9 +362,10 @@ if __name__ == '__main__':
symbol_marker_color_dict = OrderedDict([(element, (item.split(',')[0], item.split(',')[1]))
for element, item in figure_params.items()
if element in ase.data.chemical_symbols])
- z_positions, dict_concs_mean, energy_list, frame_list = read_conc_data(sim_path,
- symbol_marker_color_dict,
- figure_range_dict)
+ z_positions, dict_concs_mean, \
+ energy_list, frame_list = sim_utilities.read_conc_data(sim_path,
+ symbol_marker_color_dict,
+ figure_range_dict)
if not multi_enable:
print("Drawing figures for {}...".format(os.path.basename(sim_path)), end="")
draw_figures(z_positions,
--
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