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first release of ASOTR data parser (flight)

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Danila Gamkov 2025-04-23 19:05:48 +03:00
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.gitignore vendored Normal file
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*.csv
*.png
*.swp
*.swo
*.zip
*.log
*.txt
/__pycache__
/data
/reports

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.vimrc Normal file
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set tabstop=4
set softtabstop=4
set shiftwidth=4
set noexpandtab
set colorcolumn=90
highlight ColorColumnt ctermbg=darkgray
augroup project
autocmd!
autocmd BufRead,BufNewFile *.h,*.c set filetype=c.doxygen
augroup END
let &path.="src/include, src/source,"
" Включаем использование системного буфера
set clipboard=unnamedplus
" Работа с текстом
" Python использует 4 пробела для отступов
autocmd FileType python setlocal tabstop=4 shiftwidth=4
" Кодировка текста
set encoding=utf-8
set fileencoding=utf-8
set fileencodings=utf-8,cp1251,koi8-r,cp866
" Поиск по тексту
set hlsearch " подсвечивать результаты поиска
" Перемещение по тексту
" Когда достигаем границ строки, то перемещаемся на предыдующую/следующую
set whichwrap+=h,l,<,>,[,]
set number
" Настройки автодополнения
set completeopt=menu,menuone,noselect
" Разделение экрана
set splitbelow " разбивать вниз
set splitright " разбивать вправо
" сочетание клавиш
" Использование h, j, k, l для перемещения с зажатым Ctrl в режиме
" редактирования
inoremap <C-h> <Left>
inoremap <C-j> <Down>
inoremap <C-k> <Up>
inoremap <C-l> <Right>
let g:mapleader = "\<Space>"
" Переключение между вкладками
nnoremap <leader>t :tabnext<CR>
nnoremap <leader>T :tabprevious<CR>
" Список вкладок
nnoremap <leader>tl :tabs<CR>
" nnoremap <leader>tn :tabnew<CR>
nnoremap <leader>tc :tabclose<CR>
nnoremap <leader>to :tabonly<CR>
nnoremap <leader>tm :tabmove<CR>
" Редактировать файл в новой вкладке
nnoremap <leader>te :tabedit |
" Выбор вкладки
nnoremap <leader>1 1gt
nnoremap <leader>2 2gt
nnoremap <leader>3 3gt
nnoremap <leader>4 4gt
nnoremap <leader>5 5gt
nnoremap <leader>6 6gt
nnoremap <leader>7 7gt
nnoremap <leader>8 8gt
nnoremap <leader>9 9gt
nnoremap <leader>0 :tablast<CR>
" Разбиение окон
nnoremap <leader>s :split<CR>
nnoremap <leader>v :vsplit<CR>
" Выбор окна
nnoremap <C-h> <C-w>h
nnoremap <C-j> <C-w>j
nnoremap <C-k> <C-w>k
nnoremap <C-l> <C-w>l
" Размер окна
nnoremap <C-u> <C-w>+
nnoremap <C-d> <C-w>-
nnoremap <C-p> <C-w><
nnoremap <C-n> <C-w>>
" Vimspector
" nnoremap <leader><F2> <F10>
" nnoremap <leader>q <F11>
nmap <Leader><Right> <Plug>VimspectorStepOver
nmap <Leader><Down> <Plug>VimspectorStepInto
nmap <Leader><Up> <Plug>VimspectorStepOut
nmap <Leader><Tab> <Plug>VimspectorDisassemble
" Сделать окна одного размера
nnoremap <leader>= <C-w>=
" Переключения между буферами
" nnoremap <leader>b :bnext<CR>
" nnoremap <leader>B :bprevious<CR>
" nnoremap <leader>l :ls<CR>
" nnoremap <leader>d :bd<CR>
" " Скрыть/раскрыть блок кода
" nnoremap <leader>z za
" настройка плагинов
" настройки для отступов
" let g:indent_guides_enable_on_vim_startup = 1
" Настройки для разноцветной подсветки скобок
let g:rainbow_active = 1
" Настройки для vim-airline
let g:airline#extensions#tabline#enabled = 1
let g:airline#extensions#tabline#buffer_nr_show = 1
let g:airline#extensions#tabline#formatter = 'unique_tail'
let g:airline_powerline_fonts = 1
let g:airline_solarized_bg = 'luna'
let g:vimspector_enable_mappings = 'HUMAN'

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import pandas as pd
from datetime import datetime, timedelta
from scipy import signal
from scipy.signal import find_peaks
import matplotlib.pyplot as plt
import numpy as np
import json
import pytz
from matplotlib import dates
fname_json_decode = '/home/danila/Danila/work/MVN/Soft/PID/python/decode_asotr_cmd.json'
def get_utc_seconds(timestamp_str, timestamp_format):
dt_obj = datetime.strptime(timestamp_str, timestamp_format)
utc_timezone = pytz.utc
dt_utc = dt_obj.replace(tzinfo=utc_timezone)
timestamp = int(dt_utc.timestamp())
return timestamp
def load_cmd_decode(fname):
with open(fname, 'r') as file:
data = json.load(file)
return data
def bitmask_to_num(data):
num = int(data)
res = []
d = bin(num)
d1 = d[::-1]
for i in range(num.bit_length()):
if d1[i] == '1':
res.append(i + 1)
return res
def flight_temperature_decode(cmd_string):
decode = load_cmd_decode(fname_json_decode)
asotr_kit = ''
temp = []
cmd = cmd_string.split(' ')
if len(cmd) == 8:
temp = [cmd[2], cmd[3], cmd[4], cmd[5], cmd[6], cmd[7]]
asotr_kit = cmd[0][1]
return (asotr_kit, temp)
def cmd_decode(cmd_string):
decode = load_cmd_decode(fname_json_decode)
asotr_kit = 0;
msg_decode = ''
out = ''
if 'OK' in cmd_string:
return out
cmd = cmd_string.split(' ')
if len(cmd) > 5:
return out
if cmd[1] == '':
return out
if '1' in cmd[0]:
asotr_kit = 1
elif '2' in cmd[0]:
asotr_kit = 2
msg_ = f'{cmd[1]} {cmd[2]}'
msg_decode = decode[msg_]
if (len(cmd) == 4):
value = ''
if cmd[2] == '32':
value1 = bitmask_to_num(cmd[3])
if (len(value1) == 0):
value = 'запрет всех'
else:
value = ', '.join(map(str, value1))
elif (cmd[2] == '20' or cmd[2] == '21' or cmd[2] == '22'
or cmd[2] == '23' or cmd[2] == '24' or cmd[3] == '25'):
if cmd[3] == '0':
value = 'ПИД-регулирование'
elif cmd[3] == '1':
value = 'релейное регулирование'
elif cmd[3] == '2':
value = 'постоянная мощность'
else:
value = cmd[3]
out = f'АСОТР{asotr_kit}: {msg_decode} ({value})'
else:
if msg_decode != '':
out = f'АСОТР{asotr_kit}: {msg_decode}'
return out
def cmd_flight_parse(asotr_data):
decode_list = []
temperature_list = []
for elem in asotr_data.itertuples():
elem_msg = cmd_decode(elem.cmd_answer.strip())
if elem_msg != '':
str_ = f'{elem.timestamp};{elem_msg}'
decode_list.append(str_)
asotr_kit, temp = flight_temperature_decode(elem.cmd_answer.strip())
if len(temp) > 0:
timestamp = get_utc_seconds(elem.timestamp, '%d.%m.%Y %H:%M:%S.%f')
str_ = f'{timestamp};{elem.timestamp};{asotr_kit};{temp[0]};{temp[1]};{temp[2]};{temp[3]};{temp[4]};{temp[5]}'
temperature_list.append(str_)
return (decode_list, temperature_list)
# accuracy: minutes, hours
def find_best_time_idx(time_arr, user_time, accuracy='minutes'):
tstamp = datetime.strptime(user_time, "%d.%m.%Y %H:%M:%S")
if accuracy == 'minutes':
delta = timedelta(minutes=30)
elif accuracy == 'hours':
delta = timedelta(hours=24)
elif accuracy == 'seconds':
delta = timedelta(seconds=30)
low = 0
high = len(time_arr) - 1
mid = len(time_arr) // 2
if mid not in time_arr.index:
# print(f'mid not in time_arr: {mid}')
return -1
a = time_arr[mid]
while ((a < (tstamp - delta)) or (a > (tstamp + delta))) and low < high:
if tstamp > a:
low = mid + 1
else:
high = mid - 1
mid = (low + high) // 2
# print(f'mid: (low + high)/2: {mid}')
if mid not in time_arr.index:
# print(f'mid not in time_arr: {mid}')
return -1
a = time_arr[mid]
if low > high:
# print(f'low > high: {mid}')
mid = high
if mid > 30:
for j in range(mid-30, len(time_arr)):
# print(f'{time_arr[j]} < {tstamp}: {j}')
if time_arr[j] >= tstamp:
# print(f'{time_arr[j]} > {tstamp}: {j}')
return j
else:
for j in range(0, len(time_arr)):
# print(f'{time_arr[j]} < {tstamp}: {j}')
if time_arr[j] >= tstamp:
# print(f'{time_arr[j]} > {tstamp}: {j}')
return j
return mid
# raise ValueError("data not found!")
def find_time_idx(data_list, keys_list, timestamp, accuracy):
out_dict = dict.fromkeys(keys_list, -1)
for i, elem in enumerate(data_list):
out_dict[keys_list[i]] = find_best_time_idx(elem['timestamp'], timestamp, accuracy)
return out_dict
def get_cmd_data(fname):
asotr_data = pd.read_csv(fname, delimiter=';')
cmd_list, temperature_list = cmd_flight_parse(asotr_data)
return (cmd_list, temperature_list)
def get_data(path, asotr_kit, start_date, end_date, time_accuracy):
ch_signs = ["temp", "temp_set", "pow"]
fname_temp = "asotr" + asotr_kit + "_data_T.csv"
fname_tempSet = "asotr" + asotr_kit + "_data_TSET.csv"
fname_pow = "asotr" + asotr_kit + "_data_P.csv"
fname = [path + fname_temp, path + fname_tempSet, path + fname_pow]
dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
data = [ pd.read_csv(fname[0], sep=";", parse_dates=["timestamp"], date_parser=dateparse),
pd.read_csv(fname[1], sep=";", parse_dates=["timestamp"], date_parser=dateparse),
pd.read_csv(fname[2], sep=";", parse_dates=["timestamp"], date_parser=dateparse),]
ch = [[], [], [], [], [], []]
data_dict = {
"temp": ch,
"temp_set": ch,
"pow": ch,
"time_temp": [],
"time_temp_set": [],
"time_pow": [],
}
idxb = dict.fromkeys(ch_signs, -1)
idxe = dict.fromkeys(ch_signs, -1)
idxb = find_time_idx(data, ch_signs, start_date, time_accuracy)
idxe = find_time_idx(data, ch_signs, end_date, time_accuracy)
data_dict["time_temp"] = data[0]["timestamp"][idxb["temp"] : idxe["temp"]]
data_dict["time_temp_set"] = data[1]["timestamp"][idxb["temp_set"] : idxe["temp_set"]]
data_dict["time_pow"] = data[2]["timestamp"][idxb["pow"] : idxe["pow"]]
col = ["ch1", "ch2", "ch3", "ch4", "ch5", "ch6"]
for j in range(len(ch_signs)):
data_dict[ch_signs[j]] = data[j][['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6']][idxb[ch_signs[j]]:idxe[ch_signs[j]]]
raw_data = data
return (raw_data, data_dict)
# shift_flag - normalization of the offset of all samples of each period to the first period
# peaks: min, max
def find_periods(time, data, shift_flag, peaks='min'):
if peaks == 'min':
idx, _ = find_peaks(-data, distance=80)
else:
idx, _ = find_peaks(data, distance=80)
periods = []
periods_t = []
for i in range(1, len(idx)):
period_t = time.iloc[idx[i-1]:idx[i]]
period = data.iloc[idx[i-1]:idx[i]]
periods.append(period)
periods_t.append(period_t)
if shift_flag == True:
res = shift_data_(periods)
else:
res = periods
return (periods_t, res, idx)
# shift_flag - normalization of the offset of all samples of each period to the first period
def get_signal_profile_corr(time, data, pattern, shift_flag, peak_height):
period_cnts = len(pattern)
periods = []
periods_t = []
# find correlation between signal and pattern
correlation = signal.correlate(data, pattern, mode='same', method='fft')
normalized_correlation = correlation / max(abs(correlation))
# find correlation peaks
# peak_height = 0.7
peaks_indices = signal.find_peaks(normalized_correlation, height=peak_height)[0]
# separate and collect each finded period
for peak_idx in peaks_indices:
start_index = peak_idx - period_cnts // 2 # peak center
end_index = start_index + period_cnts
if 0 <= start_index < len(data) and 0 <= end_index < len(data):
period = data.iloc[start_index:end_index]
period_t = time.iloc[start_index:end_index]
periods.append(period)
periods_t.append(period_t)
if shift_flag == True:
res = shift_data_(periods)
else:
res = periods
return (periods_t, res)
def shift_data_(data):
first = [list_.iloc[0] for list_ in data]
delta = []
for i in range(1, len(first)):
delta.append(first[i] - first[0])
res = []
res.append(data[0])
for idx, elem in enumerate(data):
if idx > 0:
corr = elem - delta[idx-1]
res.append(corr)
return res
def get_peak_temp_forecast(cur_time, num_periods):
peaks_forecast = []
period = timedelta(hours=1, minutes=33, seconds=0, milliseconds=150)
time = cur_time
for i in range(num_periods):
time = time + period
peaks_forecast.append(time)
return peaks_forecast
def plot_signal_profile(time, data, pattern_t, pattern, method, shift_flag, peak_height=0.8):
if method == 'corr':
periods_t, periods = get_signal_profile_corr(time, data, pattern, shift_flag, peak_height)
print(f'Найдено {len(periods)} периодов.')
elif method == 'peaks':
periods_t, periods, peaks = find_periods(time, data, shift_flag, peaks='min')
print(f'Найдено {len(periods)} периодов.')
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 6))
for idx, period in enumerate(periods):
ax1.plot(np.arange(len(period)), period)
ax1.grid(True)
ax2.plot(time, data)
ax2.grid(True)
plt.grid(True)
plt.show()
def insert_temp_data_from_flight_cmd(fname_cmd_temp):
dir_asotr = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
fname_asotr = [f'{dir_asotr}asotr01_data_T.csv', f'{dir_asotr}asotr02_data_T.csv']
df_cmd = pd.read_csv(fname_cmd_temp, sep=';')
df_asotr = []
df_cmd_temp = []
for i, fname in enumerate(fname_asotr):
df = pd.read_csv(fname, sep=';')
df_asotr.append(df)
df = df_cmd[df_cmd['asotr_kit'] == i + 1]
df = df.drop(['asotr_kit'], axis=1)
df_cmd_temp.append(df)
df_asotr_ = [
pd.concat(
[df_asotr[0], df_cmd_temp[0]], ignore_index=True).sort_values(by='timestamp_sec'),
pd.concat(
[df_asotr[1], df_cmd_temp[1]], ignore_index=True).sort_values(by='timestamp_sec')
]
return df_asotr_
def subtract_data(data1, data2):
init_shift = data2[0] - data1[0]
out = data2 - data1 - init_shift
return pd.Series(out)
# for transmit data: cmd_list, temp, power = get_cmd_data(fname)
def cut_data(data, time_begin, duration_sec, accuracy='seconds'):
time_format = "%d.%m.%Y %H:%M:%S";
delta = timedelta(seconds=duration_sec)
tstamp_begin = datetime.strptime(time_begin, time_format)
tstamp_end = tstamp_begin + delta
time_end = tstamp_end.strftime(time_format)
idx_begin = find_best_time_idx(data['timestamp'], time_begin, accuracy)
idx_end = find_best_time_idx(data['timestamp'], time_end, accuracy)
out = data.loc[idx_begin : idx_end]
return out
def cut_norm_data(data, time_begin, duration_sec, channel='ch1',
interp={'method': 'cubic', 'order': 2}, accuracy='seconds'):
data_period = cut_data(data, time_begin, duration_sec, accuracy)
temp_norm = data_period[channel].values - data_period[channel].iloc[0]
time_l = list(data_period['timestamp'])
temp_l = list(temp_norm)
orig_data = pd.DataFrame({ 'timestamp': time_l, 'temp': temp_l })
interp_data = orig_data.set_index('timestamp')
interp_data = interp_data.resample('S').mean().interpolate(method=interp["method"],
order=interp["order"])
interp_data = interp_data.reset_index(names=['timestamp'])
return orig_data, interp_data
def get_step_response_diff(data, thermocycle_info, channel='ch1',
interp={'method': 'cubic', 'order': 2}, accuracy='seconds', cut_step_resp={}):
date = thermocycle_info['date']
time_begin_orig = date + ' ' + thermocycle_info['time_begin'][0]
time_begin_step = date + ' ' + thermocycle_info['time_begin'][1]
duration_sec = thermocycle_info['duration_sec']
_, orig_interp_cycle = cut_norm_data(data, time_begin_orig, duration_sec, channel,
interp, accuracy)
_, step_interp_cycle = cut_norm_data(data, time_begin_step, duration_sec, channel,
interp, accuracy)
max_ = min(len(orig_interp_cycle), len(step_interp_cycle))
subtract_step = subtract_data(
orig_interp_cycle['temp'].iloc[0:max_].values,
step_interp_cycle['temp'].iloc[0:max_].values)
step_time = list(step_interp_cycle['timestamp'].iloc[0:max_])
step_temp = list(subtract_step)
step_response = pd.DataFrame({'timestamp': step_time, 'temp': step_temp})
if len(cut_step_resp) > 0:
time_begin = date + ' ' + cut_step_resp['time_step_begin']
step_response = cut_data(step_response, time_begin,
cut_step_resp['step_duration'], accuracy='seconds')
first = step_response['temp'].iloc[0]
step_response['temp'] = step_response['temp'] - first
return (step_response, orig_interp_cycle, step_interp_cycle)
def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
cut_step_resp, plot_info):
title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР КДИ СПИН-X, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
step_resp, orig_interp_cycle, step_interp_cycle = get_step_response_diff(
data_info['data'], thermocycle_info, channel=data_info['channel'],
interp=interp, accuracy=data_info['find_accuracy'])
fig = plt.figure(figsize=(8, 6), dpi=200)
fig.suptitle(title, fontsize=plot_info['font'])
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
ax1.plot(step_resp['timestamp'], step_resp['temp'],
label='реакция на ступенчатое воздействие')
step_begin = thermocycle_info['date'] + ' ' + cut_step_resp['time_step_begin']
idx = find_best_time_idx(step_interp_cycle.timestamp, step_begin,
accuracy=data_info['find_accuracy'])
ax1.axvline(x = step_interp_cycle.timestamp[idx], color='r', linestyle='-.',
label='момент подачи ступенчатого воздействия')
date_formatter = dates.DateFormatter(plot_info['ox_dtime_format'])
ax1.xaxis.set_major_formatter(date_formatter)
ax1.legend(loc=plot_info["legend_pos"][0], fontsize=plot_info['font'])
ax1.grid(True)
ax1.tick_params(axis='both', width=1, labelsize=plot_info['font'])
ax1.set_ylabel(r'$\Delta$T, $^\circ$C', fontsize=plot_info['font'])
ax2.axvline(x = step_interp_cycle.timestamp[idx], color='r', linestyle='-.',
label='момент подачи ступенчатого воздействия')
ax2.plot(orig_interp_cycle['timestamp'], orig_interp_cycle['temp'], '--',
label='термоцикл')
ax2.plot(step_interp_cycle['timestamp'], step_interp_cycle['temp'],
label='термоцикл с реакцией на ступенчатое воздействие')
ax2.xaxis.set_major_formatter(date_formatter)
ax2.legend(loc=plot_info["legend_pos"][1], fontsize=plot_info['font'],
fancybox=True, framealpha=0.4)
ax2.grid(True)
ax2.tick_params(axis='both', width=1, labelsize=plot_info['font'])
ax2.set_xlabel('время', fontsize=plot_info['font'])
ax2.set_ylabel(r'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
fig.suptitle(title, fontsize=plot_info['font'])
plt.tight_layout()
fig.savefig(plot_info["name_fig"])
plt.show()
def plot_imp_response(data, data_info, plot_info, thermocycle_info):
title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР КДИ СПИН-X, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
fig = plt.figure(figsize=(11, 6), dpi=200)
fig.suptitle(title, fontsize=plot_info['font'])
ax1 = fig.add_subplot(1,1,1)
date_formatter = dates.DateFormatter(plot_info['ox_dtime_format'])
ax1.xaxis.set_major_formatter(date_formatter)
ax1.plot(data['timestamp'], data['temp'], '.', label='реакц. на импульсное воздействие')
ax1.xaxis.set_major_formatter(date_formatter)
ax1.legend(loc=plot_info["legend_pos"][0], fontsize=plot_info['font'],
fancybox=True, framealpha=0.4)
ax1.grid(True)
ax1.tick_params(axis='both', width=1, labelsize=plot_info['font'])
ax1.set_xlabel('время', fontsize=plot_info['font'])
ax1.set_ylabel(r'$t_{norm}$, $^\circ$C', fontsize=plot_info['font'])
fig.suptitle(title, fontsize=plot_info['font'])
plt.tight_layout()
fig.savefig(plot_info["name_fig"])
plt.show()
#timestamp as string format: dd:mm:YYYY HH:MM:SS
def insert_data_cyclo(base_time_str, fname):
path = '/home/danila/Danila/work/MVN/flight/experiments/'
time_format = "%d.%m.%Y %H:%M:%S"
cyclogram_file = path + fname
df = pd.read_excel(cyclogram_file)
base_time = pd.to_datetime(base_time_str)
df['timestamp'] = df.iloc[:, 0].apply(lambda x: base_time + timedelta(seconds=x))
df.iloc[:, 0] = df['timestamp'].dt.strftime(time_format)
df = df.drop(['timestamp'], axis=1)
fname = cyclogram_file.replace('.xls', '.csv')
df.to_csv(fname, index=False, sep=';', encoding='utf-8-sig')

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{
"write 0": "маркер начала сектора",
"write 10": "Разрешение/блокировка работы мотора",
"write 11": "режим управления мотором",
"write 12": "направление вращения",
"write 14": "уставка защиты по току мотора кратковременная (мА)",
"write 15": "уставка защиты по току мотора среднему за 20с (мА)",
"write 16": "Время разгона мотора, секунд",
"write 17": "Время останова мотора, секунд",
"write 18": "Номинальный ток мотора( мА)",
"write 20": "СОТР1 режим управления каналом",
"write 21": "СОТР2 режим управления каналом",
"write 22": "СОТР3 режим управления каналом",
"write 23": "СОТР4 режим управления каналом",
"write 24": "СОТР5 режим управления каналом",
"write 25": "СОТР6 режим управления каналом",
"write 26": "СОТР1 уставка мощности в канале в %",
"write 27": "СОТР2 уставка мощности в канале в %",
"write 28": "СОТР3 уставка мощности в канале в %",
"write 29": "СОТР4 уставка мощности в канале в %",
"write 30": "СОТР5 уставка мощности в канале в %",
"write 31": "СОТР6 уставка мощности в канале в %",
"write 32": "Маска-разрешения работы каналов СОТР",
"write 50": "Уставка Kp ПИД-регулятора мотора",
"write 51": "Уставка Kd ПИД-регулятора мотора",
"write 52": "Уставка Ki ПИД-регулятора мотора",
"write 53": "заданная скорость вращения, об/мин",
"write 55": "СОТР1 - уставка температуры канала",
"write 56": "СОТР2 - уставка температуры канала",
"write 57": "СОТР3 - уставка температуры канала",
"write 58": "СОТР4 - уставка температуры канала",
"write 59": "СОТР5 - уставка температуры канала",
"write 60": "СОТР6 - уставка температуры канала",
"write 61": "СОТР1 - уставка Kp ПИД-регулятора канала",
"write 62": "СОТР2 - уставка Kp ПИД-регулятора канала",
"write 63": "СОТР3 - уставка Kp ПИД-регулятора канала",
"write 64": "СОТР4 - уставка Kp ПИД-регулятора канала",
"write 65": "СОТР5 - уставка Kp ПИД-регулятора канала",
"write 66": "СОТР6 - уставка Kp ПИД-регулятора канала",
"write 67": "СОТР1 - уставка Kd ПИД-регулятора канала",
"write 68": "СОТР2 - уставка Kd ПИД-регулятора канала",
"write 69": "СОТР3 - уставка Kd ПИД-регулятора канала",
"write 70": "СОТР4 - уставка Kd ПИД-регулятора канала",
"write 71": "СОТР5 - уставка Kd ПИД-регулятора канала",
"write 72": "СОТР6 - уставка Kd ПИД-регулятора канала",
"write 73": "СОТР1 - уставка Ki ПИД-регулятора канала",
"write 74": "СОТР2 - уставка Ki ПИД-регулятора канала",
"write 75": "СОТР3 - уставка Ki ПИД-регулятора канала",
"write 76": "СОТР4 - уставка Ki ПИД-регулятора канала",
"write 77": "СОТР5 - уставка Ki ПИД-регулятора канала",
"write 78": "СОТР6 - уставка Ki ПИД-регулятора канала",
"write 79": "СОТР1 - уставка гистерезиса релейн. регулятора",
"write 80": "СОТР2 - уставка гистерезиса релейн. регулятора",
"write 81": "СОТР3 - уставка гистерезиса релейн. регулятора",
"write 82": "СОТР4 - уставка гистерезиса релейн. регулятора",
"write 83": "СОТР5 - уставка гистерезиса релейн. регулятора",
"write 84": "СОТР6 - уставка гистерезиса релейн. регулятора",
"write 85": "СОТР1 - уставка Ro термодатчика канала",
"write 86": "СОТР2 - уставка Ro термодатчика канала",
"write 87": "СОТР3 - уставка Ro термодатчика канала",
"write 88": "СОТР4 - уставка Ro термодатчика канала",
"write 89": "СОТР5 - уставка Ro термодатчика канала",
"write 90": "СОТР6 - уставка Ro термодатчика канала",
"write 91": "СОТР1 - уставка Alpha термодатчика канала",
"write 92": "СОТР2 - уставка Alpha термодатчика канала",
"write 93": "СОТР3 - уставка Alpha термодатчика канала",
"write 94": "СОТР4 - уставка Alpha термодатчика канала",
"write 95": "СОТР5 - уставка Alpha термодатчика канала",
"write 96": "СОТР6 - уставка Alpha термодатчика канала",
"func 1": "Перезапуск процессора МУП",
"func 2": "Перезапуск МУП через питание",
"func 3": "Запись текущих уставок в сектор I памяти FLASH",
"func 4": "Запись текущих уставок в сектор J памяти FLASH",
"func 5": "Чтение уставок из сектора I памяти FLASH",
"func 6": "Чтение уставок из сектора J памяти FLASH",
"func 7": "Запуск мотора (разгон и поддержание скорости вращения)",
"func 8": "Останов мотора (торможение и остановка)",
"func 9": "Применить уставки СОТР из ОЗУ в алгоритме ПИД-регуляторов",
"status 1": "",
"status 2": "",
"status 3": "",
"status 4": "",
"status 5": "",
"status 6": "",
"status 7": "",
"status 8": "",
"status 9": ""
}

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decode_cmd_data.py Normal file
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import sys
from importlib import reload
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
import asotr
reload(asotr)
import pandas as pd
from datetime import datetime, timedelta
fname = '/home/danila/Danila/work/MVN/Soft/PID/data/flight/cmd_asotr/all_flight_cmd_asotr.csv'
fname_cmd_temp = './data/flight_cmd_temp.csv'
## get flight commands file (generated by mvn_log_viewer)
## Translate to human-readeble format and take temperatures from flight commands file
cmd_list, temperature_list = asotr.get_cmd_data(fname)
with open('./data/cmd_human.csv', 'w') as file:
for elem in cmd_list:
file.write(f'{elem}\n')
## temperatures from flight commands file save to file
with open(fname_cmd_temp, 'w') as file:
file.write(f'timestamp_sec;timestamp;asotr_kit;ch1;ch2;ch3;ch4;ch5;ch6\r\n')
for elem in temperature_list:
file.write(f'{elem}\n')
## insert temperatures from flight commands file to main asotr temperatures data files
df_asotr_ = asotr.insert_temp_data_from_flight_cmd(fname_cmd_temp)
## form timestamp file where minimum of temperatures were registered
end_date = ''
for i, data in enumerate(df_asotr_):
end_date = data['timestamp'].iloc[len(data) - 1][0:18]
data.to_csv(f'./data/asotr0{i+1}_data_T.csv', index=False, sep=';',
encoding='utf-8-sig', decimal='.')
path_data = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
timeformat = '%d.%m.%Y %H:%M:%S'
prev_days = 14
delta_date = datetime.strptime(end_date, timeformat) - timedelta(days=prev_days)
start_date = delta_date.strftime(timeformat)
for kit in range(1,3):
asotr_kit = f'0{kit}'
print(asotr_kit)
_, data_dict = asotr.get_data(path_data, asotr_kit, start_date, end_date, 'minutes')
min_temp_ch = []
for channel in range(1,7):
ch = f'ch{channel}'
data1 = data_dict['temp'][ch]
time1 = data_dict['time_temp']
periods_t, periods, _ = asotr.find_periods(time1, data1, shift_flag=False, peaks='min')
min_temp_period = []
for elem in periods_t:
min_temp_period.append(elem.iloc[0].strftime('%d.%m.%Y %H:%M:%S.%f')[:-3])
min_temp_ch.append(min_temp_period)
df = pd.DataFrame(min_temp_ch).transpose()
df.to_csv(f'./data/asotr{asotr_kit}_min_T.csv', header=False, index=False, sep=';',
encoding='utf-8-sig', decimal='.')
df1 = pd.read_csv(f'./data/asotr{asotr_kit}_min_T.csv', sep=';',
names=['ch1','ch2','ch3','ch4','ch5','ch6'])
df1.to_csv(f'./data/asotr{asotr_kit}_min_T.csv', index=False, sep=';',
encoding='utf-8-sig', decimal='.')

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plot_flight_borders.py Normal file
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import matplotlib.pyplot as plt
from matplotlib import dates
import argparse
import sys
from importlib import reload
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
import asotr
reload(asotr)
import pandas as pd
def convert_to_str(lst):
index = [i for i, x in enumerate(lst) if x == 1]
res = f"ch{index[0] + 1}"
for idx in index[1:]:
res += f"_{idx + 1}"
return res
def plot_asotr_borders(ch, asotr_kit, begin, end, font=14, cmd=0):
print_width = 20
print_height = 12
width = 1
plot_windows = 2
channels = list(map(int, ch))
pict_name = (f'./reports/ASOTR{asotr_kit}_flight_T_P_{convert_to_str(channels)}_{begin[0:5].replace(".", "")}_{end[0:5].replace(".", "")}_{end[6:]}.png')
plot_task = {"temp": 1, "temp_set": 1, "pow": 1}
ox_dtime_format = "%d.%m.%Y"
legend = [
"канал 1 (БРД1)",
"канал 2 (БРД2)",
"канал 3 (БРД3)",
"канал 4 (БРД4)",
"канал 5 (плита МУП МВН)",
"канал 6 (плита МУП МВН)",
]
legend_set = list(map(lambda x: x + " уставка", legend))
width = [1, 1, 1, 1, 1, 1]
width_set = [3, 3, 3, 3, 3, 3]
marker = ["-", "--", "-.", "-", "-", "--"]
width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
# get from files and prepare data
path = "/home/danila/Danila/work/MVN/Soft/asotr_csv/data/"
start_date = begin + " 00:00:00" # Начальная граница
end_date = end + " 23:59:59" # Конечная граница
data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
if plot_windows == 1:
fig, ax = plt.subplots(figsize=(print_width, print_height), dpi=200)
if plot_task["temp"] == 1:
for i in range(len(channels)):
if channels[i] == 1:
ax.plot(data_dict["time_temp"],
data_dict['temp'].iloc[:,[i]],
marker[i],
linewidth=width[i],
label=legend[i],)
ax.tick_params(axis="both", width=1, labelsize=font)
ax.grid(visible=True, linestyle="dotted")
ax.set_ylabel("Температура, $^\circ$C", fontsize=font)
ax.set_xlabel("Время", fontsize=font)
ax.legend(fontsize=font)
date_formatter = dates.DateFormatter(ox_dtime_format)
ax.xaxis.set_major_formatter(date_formatter)
plt.tight_layout()
fig.savefig(pict_name)
plt.show()
elif plot_windows == 2:
fig = plt.figure(figsize=(print_width, print_height), dpi=200)
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
if cmd == '1':
cmd_human = pd.read_csv('./flight_cmd_human.txt',
delimiter=';', names=['timestamp', 'cmd'])
for i, row in cmd_human.iterrows():
if i > 20:
row_time = row['timestamp'][0:len(row['timestamp']) - 4]
idx = asotr.find_best_time_idx(data_dict['time_temp'],
row_time, accuracy='minutes')
if idx != -1:
ax1.axvline(x = data_dict['time_temp'][idx], color='r',
linestyle='-.')
ax1.text(data_dict['time_temp'][idx], 30, row['cmd'],
rotation=45, va='bottom', fontsize=font)
if plot_task["temp"] == 1:
for i in range(len(channels)):
if channels[i] == 1:
ax1.plot(data_dict["time_temp"],
data_dict['temp'].iloc[:,[i]],
marker[i],
linewidth=width[i],
label=legend[i],)
if plot_task["temp_set"] == 1:
for i in range(len(channels)):
if channels[i] == 1:
ax1.plot(data_dict["time_temp_set"],
data_dict['temp_set'].iloc[:,[i]],
marker[i],
linewidth=width_set[i],
label=legend_set[i],)
if plot_task["pow"] == 1:
for i in range(len(channels)):
if channels[i] == 1:
ax2.plot(data_dict["time_pow"],
data_dict['pow'].iloc[:,[i]],
marker[i],
linewidth=width[i],
label=legend[i],)
ax1.tick_params(axis="both", width=1, labelsize=font)
ax1.grid(visible=True, linestyle="dotted")
ax1.set_ylabel("Температура, $^\circ$C", fontsize=font)
ax1.set_xlabel("Время", fontsize=font)
ax1.legend(fontsize=font)
date_formatter = dates.DateFormatter(ox_dtime_format)
ax1.xaxis.set_major_formatter(date_formatter)
ax2.tick_params(axis="both", width=1, labelsize=font)
ax2.grid(visible=True, linestyle="dotted")
ax2.set_ylabel("Мощность, %", fontsize=font)
ax2.set_xlabel("Время", fontsize=font)
ax2.set_ylim(-5,105)
ax2.legend(fontsize=font)
date_formatter = dates.DateFormatter(ox_dtime_format)
ax2.xaxis.set_major_formatter(date_formatter)
title = (f'работа АСОТР{asotr_kit} в период с {start_date[0:10]} по {end_date[0:10]} г.')
fig.suptitle(title, fontsize=font)
plt.tight_layout()
fig.savefig(pict_name)
plt.show()
if __name__ == '__main__':
argparser = argparse.ArgumentParser("plot_flight_borders.py")
argparser.add_argument('-c', '--channel', required=True,
help='type channel (example: 000011)')
argparser.add_argument('-a', '--asotr', required=True,
help='type asotr kit (01 or 02)')
argparser.add_argument('-b', '--begin', required=True,
help='type begin date if dd.mm.YYYY format')
argparser.add_argument('-e', '--end', required=True,
help='type end date if dd.mm.YYYY format')
argparser.add_argument('-f', '--font', required=False,
help='type font size (from 1 to 30)')
argparser.add_argument('-d', '--cmd', required=False,
help='type display commands flag (0/1)')
args = argparser.parse_args()
plot_asotr_borders(args.channel, args.asotr, args.begin, args.end,
args.font, args.cmd)

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plot_flight_borders.sh Executable file
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#! /bin/bash
if [ $# != 2 ]
then
echo "erorr use $0. Right use this script: "
echo "$0 25.02.2025 10.03.2025"
else
begin=$1
end=$2
python3 plot_flight_borders.py -c 111100 -a 01 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 001000 -a 01 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 000011 -a 01 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 111100 -a 02 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 010100 -a 02 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 010000 -a 02 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 000100 -a 02 -b ${begin} -e ${end}
python3 plot_flight_borders.py -c 000011 -a 02 -b ${begin} -e ${end}
fi

94
plot_periods_profile.py Normal file
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import sys
from importlib import reload
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
import asotr
reload(asotr)
import matplotlib.pyplot as plt
from matplotlib import dates
import numpy as np
from datetime import timedelta
pict_name = 'periods_profile_10042025.png'
path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
channel = 'ch1'
asotr_kit = '01'
start_date = '10.04.2025 04:00:00'
end_date = '10.04.2025 12:00:00'
# start_date = '06.01.2025 22:40:00'
# end_date = '21.01.2025 01:20:00'
shift = True
raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
data1 = data_dict['temp'][channel]
time1 = data_dict['time_temp']
periods_t, periods, _ = asotr.find_periods(time1, data1, shift_flag=False, peaks='min')
_, _, peaks = asotr.find_periods(time1, data1, shift_flag=False, peaks='max')
peaks_forecast = asotr.get_peak_temp_forecast(time1.iloc[peaks[0]], 1000)
delta_sec = []
for idx, elem in enumerate(peaks):
if idx > 0:
print(f'peak____: {time1.iloc[elem]}')
print(f'forecast: {peaks_forecast[idx-1]}')
delta = time1.iloc[elem] - peaks_forecast[idx-1]
delta_sec.append(delta.total_seconds())
# asotr.plot_signal_profile(time1, data1, [], [], method='peaks', shift_flag=shift)
# asotr.plot_signal_profile(time1, data1, periods_t[0], periods[0], method='corr', shift_flag=shift, peak_height=0.7)
time_, periods_ = asotr.get_signal_profile_corr(time1, data1, periods[0], shift, peak_height=0.7)
print(f'Найдено {len(periods_)} периодов.')
ox_dtime_format = "%H:%M:%S"
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 10))
date_formatter = dates.DateFormatter(ox_dtime_format)
ax1.xaxis.set_major_formatter(date_formatter)
ax1.plot(time1, data1)
for elem in peaks:
ax1.axvline(x = time1.iloc[elem], color='r', linewidth=0.5)
ax1.set_title(f'Температура на орбите: АСОТР{asotr_kit}, канал {channel[2]}')
for idx, period in enumerate(periods_):
ax2.plot(np.arange(len(period)), period, label=f'период {idx}')
ax2.set_title('Профиль изменения температуры АСОТР по периоду')
delta = []
for elem in periods_:
delta1 = elem.values - periods[0].values
delta.append(delta1)
# ax3.plot(delta[1], label=f'период 1', marker='o', linewidth=2)
for idx, elem in enumerate(delta):
if idx == len(delta) - 1:
ax3.plot(elem, label=f'период {idx}', marker='|', linewidth=2)
elif idx == len(delta)//2:
ax3.plot(elem, label=f'период {idx}', marker='^', linewidth=2)
elif idx == 1:
ax3.plot(elem, label=f'период {idx}', marker='o', linewidth=2)
elif idx > 0:
ax3.plot(elem, label=f'период {idx}')
# ax4.plot(delta_sec)
ax3.set_title(r'$\Delta$$T_i$ = $T_i$ - $T_1$')
ax1.set_ylabel('Температура, $^\circ$C')
ax2.set_ylabel('Температура, $^\circ$C')
ax3.set_ylabel(r'$\Delta$$T_i$, $^\circ$C')
ax3.set_xlabel("Время, мин.")
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
# ax4.grid(True)
ax2.legend()
ax3.legend()
fig.savefig(pict_name)
plt.show()

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prepare_flight_cyclo.py Normal file
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import sys
from importlib import reload
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
import asotr
reload(asotr)
import pandas as pd
from datetime import datetime, timedelta
timestamp = '25.04.2025 01:18:00'
cyclogram_file = 'cyclogram_step_ident_ch1.xls'
asotr.insert_data_cyclo(timestamp, cyclogram_file)
timestamp = '25.04.2025 04:24:00'
cyclogram_file = 'cyclogram_imp_ident_ch1.xls'
asotr.insert_data_cyclo(timestamp, cyclogram_file)

86
temp_peaks_forecast.py Normal file
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import sys
import statistics
from importlib import reload
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
import asotr
reload(asotr)
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
from matplotlib import dates
from datetime import timedelta
path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
channel = 'ch1'
asotr_kit = '01'
start_date = '20.04.2025 00:00:00'
forecast_days = 20
# end_date = '26.03.2025 01:20:00'
timeformat = '%d.%m.%Y %H:%M:%S'
delta_date = datetime.strptime(start_date, timeformat) + timedelta(days=forecast_days)
end_date = delta_date.strftime(timeformat)
num_peaks_forecast = forecast_days * 20
# start_date = '06.01.2025 22:40:00'
# end_date = '21.01.2025 01:20:00'
shift = True
raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
data1 = data_dict['temp'][channel]
time1 = data_dict['time_temp']
periods_t, periods, _ = asotr.find_periods(time1, data1, shift_flag=False, peaks='min')
_, _, peaks = asotr.find_periods(time1, data1, shift_flag=False, peaks='max')
peaks_forecast = asotr.get_peak_temp_forecast(time1.iloc[peaks[0]], num_peaks_forecast)
with open('peaks_forecast.txt', 'w') as file:
for elem in peaks_forecast:
file.write(f'{str(elem)}\n')
delta_sec = []
for idx, elem in enumerate(peaks):
if idx > 0:
delta = time1.iloc[elem] - peaks_forecast[idx-1]
# print(delta)
delta_sec.append(delta.total_seconds())
delta_self_sec = []
delta_self_sec1 = []
for idx, elem in enumerate(periods_t):
delta1 = elem.iloc[len(elem)-1] - elem.iloc[0]
delta_self_sec.append(delta1.total_seconds())
for idx, elem in enumerate(delta_self_sec):
if idx > 0:
delta_self_sec1.append(delta_self_sec[idx] - delta_self_sec[idx - 1])
# print(delta_self_sec)
print(statistics.median(delta_self_sec))
ox_dtime_format = "%d.%m.%Y %H:%M"
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 6))
date_formatter = dates.DateFormatter(ox_dtime_format)
ax1.xaxis.set_major_formatter(date_formatter)
ax1.plot(time1, data1)
for elem in peaks:
ax1.axvline(x = time1.iloc[elem], color='r', linewidth=0.5)
ax1.set_title(f'температура на орбите: АСОТР{asotr_kit}, канал {channel[2]}')
ax2.set_title('Разница по времени между временем i-го пика и i-м предсказанием пика')
ax2.set_ylabel(r'$\Delta$$t_{peak}$ = $timePeak_i$ - $timeForecast_i$, сек')
ax2.plot(delta_sec)
ax3.set_title('Разница по времени между первым и последующим периодами')
ax3.set_ylabel(r'$\Delta$$t_{period}$ = $period_i$ - $period_0$, сек')
ax3.plot(delta_self_sec1)
ax1.set_ylabel('Температура, град.')
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
plt.show()