project reorganization: 1. executable files in bin directory now. 2. add recursive_unpack_targz.py for recursive unpacking specified in this script archives tar.gz with MVN data. 3. add asotr_unzip_plot.sh bash file for unpacking MVN data, collect asotr data into csv files and plot asotr MVN data. 4. add brd_wheel_1Hz_parser.py for demonstrate how to work with brd telemetry data
This commit is contained in:
Danila Gamkov
1
bin/.rustc_info.json
Normal file
1
bin/.rustc_info.json
Normal file
@@ -0,0 +1 @@
|
||||
{"rustc_fingerprint":2742313010855374649,"outputs":{"4614504638168534921":{"success":true,"status":"","code":0,"stdout":"rustc 1.83.0 (90b35a623 2024-11-26)\nbinary: rustc\ncommit-hash: 90b35a6239c3d8bdabc530a6a0816f7ff89a0aaf\ncommit-date: 2024-11-26\nhost: x86_64-unknown-linux-gnu\nrelease: 1.83.0\nLLVM version: 19.1.1\n","stderr":""},"15729799797837862367":{"success":true,"status":"","code":0,"stdout":"___\nlib___.rlib\nlib___.so\nlib___.so\nlib___.a\nlib___.so\n/home/danila/.rustup/toolchains/stable-x86_64-unknown-linux-gnu\noff\npacked\nunpacked\n___\ndebug_assertions\npanic=\"unwind\"\nproc_macro\ntarget_abi=\"\"\ntarget_arch=\"x86_64\"\ntarget_endian=\"little\"\ntarget_env=\"gnu\"\ntarget_family=\"unix\"\ntarget_feature=\"fxsr\"\ntarget_feature=\"sse\"\ntarget_feature=\"sse2\"\ntarget_has_atomic=\"16\"\ntarget_has_atomic=\"32\"\ntarget_has_atomic=\"64\"\ntarget_has_atomic=\"8\"\ntarget_has_atomic=\"ptr\"\ntarget_os=\"linux\"\ntarget_pointer_width=\"64\"\ntarget_vendor=\"unknown\"\nunix\n","stderr":""}},"successes":{}}
|
||||
132
bin/.vimrc
Normal file
132
bin/.vimrc
Normal file
@@ -0,0 +1,132 @@
|
||||
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'
|
||||
585
bin/asotr.py
Normal file
585
bin/asotr.py
Normal file
@@ -0,0 +1,585 @@
|
||||
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
|
||||
|
||||
"""
|
||||
module asotr parses csv data from flight device ASOTR MVN
|
||||
|
||||
Danila Gamkov []
|
||||
danila_gamkov@cosmos.ru
|
||||
# License: IKI RAN
|
||||
"""
|
||||
__author__ = 'Danila Gamkov'
|
||||
|
||||
|
||||
class IdxNotFound(Exception):
|
||||
pass
|
||||
|
||||
class TimeIndexNotFound(Exception):
|
||||
pass
|
||||
|
||||
fname_json_decode = './decode_asotr_cmd.json'
|
||||
|
||||
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 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 '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: 'seconds', 'minutes', 'hours'
|
||||
def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
|
||||
"""
|
||||
finds best time index according specified user_time in time_arr and with specified accuracy
|
||||
|
||||
Args:
|
||||
time_arr(pandas.core.series.Series[datetime]): timestamp array
|
||||
user_time(string): time value, which index required find in time_arr (string in the following format: d.m.Y HH:MM:SS)
|
||||
accuracy(string): specify accuracy which time index required find in time_arr(type 'seconds', 'minutes' or 'hours')
|
||||
|
||||
Returns:
|
||||
int: index that has been found in time_arr (or -1 if index has not been found)
|
||||
"""
|
||||
|
||||
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 = time_arr.idxmin()
|
||||
high = time_arr.idxmax()
|
||||
mid = time_arr.idxmin() + (time_arr.idxmax() - time_arr.idxmin()) // 2
|
||||
|
||||
if mid not in time_arr.index:
|
||||
# print(f'mid not in time_arr: {mid}, {time_arr.index}, {time_arr.idxmin()}')
|
||||
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 - low) // 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
|
||||
|
||||
if mid <= low + 1 or mid >= high - 1:
|
||||
return -1
|
||||
|
||||
return mid
|
||||
|
||||
def find_time_idx(data_list, keys_list, timestamp, accuracy):
|
||||
out_dict = dict.fromkeys(keys_list, -1)
|
||||
|
||||
for i, elem in enumerate(data_list):
|
||||
idx = find_best_time_idx(elem['timestamp'], timestamp, accuracy)
|
||||
if idx != -1:
|
||||
out_dict[keys_list[i]] = idx
|
||||
else:
|
||||
raise TimeIndexNotFound(f'index corresponding to time {timestamp} in times array not found!')
|
||||
|
||||
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")
|
||||
|
||||
try:
|
||||
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),]
|
||||
except FileNotFoundError:
|
||||
print(f'Error opening file: one (or all) file not found in directory: \n{fname}')
|
||||
return
|
||||
except pd.errors.EmptyDataError:
|
||||
print(f'Error opening file: one (or all) file is empty or have incorrect format. \nLook at the files: {fname}')
|
||||
return
|
||||
except pd.errors.ParserError:
|
||||
print(f'Error parsing file: file have incorrect structure. \nLook at the files: {fname}')
|
||||
return
|
||||
except Exception as e:
|
||||
print(f'Error parsing file: {e}. \nLook at the files: {fname}')
|
||||
return
|
||||
|
||||
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)
|
||||
|
||||
try:
|
||||
idxb = find_time_idx(data, ch_signs, start_date, time_accuracy)
|
||||
idxe = find_time_idx(data, ch_signs, end_date, time_accuracy)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return
|
||||
|
||||
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):
|
||||
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)
|
||||
|
||||
if idx_begin == -1 or idx_end == -1:
|
||||
raise IdxNotFound(f"error finding array index corresponding to timestamp: check time_begin ({time_begin}) or time_end ({time_end})")
|
||||
|
||||
idx_end = idx_end - 1
|
||||
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'):
|
||||
|
||||
try:
|
||||
data_period = cut_data(data, time_begin, duration_sec, accuracy)
|
||||
except IdxNotFound as e:
|
||||
print(f'{e}')
|
||||
|
||||
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={}):
|
||||
|
||||
time_begin_orig = thermocycle_info['time_begin'][0]
|
||||
time_begin_step = 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 = cut_step_resp['time_step_begin']
|
||||
try:
|
||||
step_response = cut_data(step_response, time_begin,
|
||||
cut_step_resp['step_duration'], accuracy='seconds')
|
||||
except IdxNotFound as e:
|
||||
print(f'{e}')
|
||||
|
||||
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, {data_info["device"]} СПИН-X1-МВН, 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=(9, 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 ' + thermocycle_info['type_ru'] + ' воздействие')
|
||||
|
||||
step_begin = 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= thermocycle_info['type_ru'] + ' воздействие, начало')
|
||||
|
||||
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= thermocycle_info['type_ru'] + ' воздействие, начало')
|
||||
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 ' + thermocycle_info['type_ru'] + ' 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, ЧЧ:MM:CC', 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):
|
||||
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, format='%d.%m.%Y %H:%M:%S')
|
||||
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')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
BIN
bin/asotr_csv
Executable file
BIN
bin/asotr_csv
Executable file
Binary file not shown.
14
bin/asotr_unzip_plot.sh
Executable file
14
bin/asotr_unzip_plot.sh
Executable file
@@ -0,0 +1,14 @@
|
||||
#! /bin/bash
|
||||
|
||||
if [ $# != 1 ]
|
||||
then
|
||||
echo "erorr use $0. Right use this script: "
|
||||
echo "$0 path"
|
||||
else
|
||||
cp ../asotr_csv/target/release/asotr_csv ./
|
||||
path_=$1
|
||||
|
||||
python3 recursive_unpack_targz.py ${path_}
|
||||
./asotr_csv -d ${path_}
|
||||
python3 plot_asotr_flight_all.py
|
||||
fi
|
||||
150
bin/brd_wheel_1Hz_parser.py
Normal file
150
bin/brd_wheel_1Hz_parser.py
Normal file
@@ -0,0 +1,150 @@
|
||||
import pandas as pd
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
tstamp_s = '%d.%m.%Y %H:%M:%S.%f'
|
||||
ox_dtime_format = '%d.%m.%Y %H:%M'
|
||||
|
||||
path_itog_brd_data = '../data/brd_data/'
|
||||
|
||||
class PathFileNotFound(Exception):
|
||||
pass
|
||||
|
||||
def find_required_files(root_dir, pattern):
|
||||
result = []
|
||||
for dirpath, _, filenames in os.walk(root_dir):
|
||||
for filename in filenames:
|
||||
match = re.match(pattern, filename)
|
||||
if match:
|
||||
result.append(dirpath + '/' + filename)
|
||||
|
||||
if len(result) == 0:
|
||||
raise PathFileNotFound(f'error: check that the path is correct ({root_dir}) or files pattern is correct ({pattern})')
|
||||
|
||||
return sorted(result)
|
||||
|
||||
def read_files_into_df(fname_list, column_list, dtype_columns={}):
|
||||
data_itog = pd.DataFrame()
|
||||
epoch_start = pd.Timestamp('2000-01-01')
|
||||
|
||||
for fname in fname_list:
|
||||
data = pd.read_csv(fname, sep=r'\s+', dtype=str)
|
||||
data = data.dropna()
|
||||
data = data[column_list]
|
||||
|
||||
if 'TIME' in column_list:
|
||||
# convert TIME value to human-readable timestamp (sinse epoch 01.01.2000)
|
||||
time = data['TIME'].astype(float)
|
||||
tstamp = epoch_start + pd.to_timedelta(time, unit='s')
|
||||
timestamp = tstamp.dt.strftime(tstamp_s)
|
||||
data['timestamp'] = timestamp
|
||||
|
||||
# clear dataframe rows where time value == 0
|
||||
data['time'] = time
|
||||
data_clear = data.query('time != 0.0')
|
||||
|
||||
data_itog = pd.concat([data_itog, data_clear], ignore_index=True)
|
||||
|
||||
return data_itog
|
||||
|
||||
|
||||
def collect_tm_brd_files(root_dir_tm_data, column_list, column_list_itog):
|
||||
patterns_tm = [r'mvn_tm_brd01_(.*)', r'mvn_tm_brd02_(.*)', r'mvn_tm_brd03_(.*)',
|
||||
r'mvn_tm_brd04_(.*)']
|
||||
|
||||
for pattern in patterns_tm:
|
||||
fname = path_itog_brd_data + pattern[:12] + '.csv'
|
||||
try:
|
||||
found_files = find_required_files(root_dir_tm_data, pattern)
|
||||
data = read_files_into_df(found_files, column_list, dtype_columns={11: float})
|
||||
except KeyError as e:
|
||||
print(f'error in collect_tm_brd_files: the specified column name was not found in the data file (path: {root_dir_tm_data}) ({e})')
|
||||
break
|
||||
except Exception as e:
|
||||
print(f'error in collect_tm_brd_files: {e}')
|
||||
break
|
||||
|
||||
data.to_csv(fname, index=False, sep=';', columns=column_list_itog, encoding='utf-8-sig')
|
||||
print('data saved: ' + fname)
|
||||
|
||||
|
||||
def collect_tm_brd_wheel_data(root_dir_wheel_data, column_list, column_list_itog):
|
||||
patterns_wheel = [r'mvn_wheel_brd01_(.*)', r'mvn_wheel_brd02_(.*)', r'mvn_wheel_brd03_(.*)',
|
||||
r'mvn_wheel_brd04_(.*)']
|
||||
|
||||
for pattern in patterns_wheel:
|
||||
fname = path_itog_brd_data + pattern[:15] + '.csv'
|
||||
try:
|
||||
found_files = find_required_files(root_dir_wheel_data, pattern)
|
||||
data = read_files_into_df(found_files, column_list, dtype_columns={0: float, 1: int})
|
||||
except KeyError as e:
|
||||
print(f'error in collect_tm_brd_wheel_data: the specified column name was not found in the data file (path: {root_dir_tm_data}) ({e})')
|
||||
break
|
||||
except Exception as e:
|
||||
print(f'error in collect_tm_brd_wheel_data: {e}')
|
||||
break
|
||||
|
||||
mask = data['STATE'] == '0'
|
||||
data = data[mask]
|
||||
data.to_csv(fname, index=False, sep=';', columns=column_list_itog, encoding='utf-8-sig')
|
||||
print('data saved: ' + fname)
|
||||
|
||||
|
||||
## collect raw tm brd data into one file for each brd
|
||||
|
||||
root_dir_tm_data = '/home/danila/Danila/work/MVN/flight/brd_data/arch_for_MB/archive_tm_data_txt/'
|
||||
column_list = ['TIME', 'PER_1Hz', 'ST_HV']
|
||||
column_list_itog = ['TIME', 'timestamp', 'PER_1Hz', 'ST_HV']
|
||||
|
||||
collect_tm_brd_files(root_dir_tm_data, column_list, column_list_itog)
|
||||
|
||||
|
||||
root_dir_wheel_data = '/home/danila/Danila/work/MVN/flight/brd_data/arch_for_MB/archive_wheel_data_txt/'
|
||||
column_list = ['TIME', 'STATE']
|
||||
column_list_itog = ['TIME', 'timestamp', 'STATE']
|
||||
|
||||
collect_tm_brd_wheel_data(root_dir_wheel_data, column_list, column_list_itog)
|
||||
|
||||
|
||||
|
||||
## plot 'evolution' 1 Hz from tm brd data
|
||||
|
||||
fname = path_itog_brd_data + 'mvn_tm_brd01.csv'
|
||||
dateparse = lambda x: datetime.strptime(x, tstamp_s)
|
||||
df = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
|
||||
|
||||
plt.plot(df['timestamp'], df['PER_1Hz'], '.')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
border_clr_wheel = 2
|
||||
fname = path_itog_brd_data + 'mvn_wheel_brd01.csv'
|
||||
wheel_df = pd.read_csv(fname, sep=';')
|
||||
wheel_df['TIME_diff'] = wheel_df['TIME'].diff()
|
||||
median_tdiff = wheel_df['TIME_diff'].median()
|
||||
|
||||
wheel_df_clear = wheel_df[(wheel_df['TIME_diff'] > median_tdiff - border_clr_wheel) &
|
||||
(wheel_df['TIME_diff'] < median_tdiff + border_clr_wheel)]
|
||||
|
||||
wheel_df_peaks = wheel_df[(wheel_df['TIME_diff'] <= median_tdiff - border_clr_wheel) |
|
||||
(wheel_df['TIME_diff'] >= median_tdiff + border_clr_wheel)]
|
||||
|
||||
|
||||
plt.plot(wheel_df_clear['TIME'], wheel_df_clear['TIME_diff'])
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# df1 = df[df['TIME_diff'] < 30.6]
|
||||
# print(df[df['TIME_diff'] > 30.6 or df['TIME_diff'] < 29.4] )
|
||||
|
||||
# for idx, row in df.iterrows():
|
||||
# print(row['TIME'])
|
||||
|
||||
|
||||
|
||||
|
||||
88
bin/decode_asotr_cmd.json
Normal file
88
bin/decode_asotr_cmd.json
Normal file
@@ -0,0 +1,88 @@
|
||||
{
|
||||
"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": ""
|
||||
}
|
||||
80
bin/decode_cmd_data.py
Normal file
80
bin/decode_cmd_data.py
Normal file
@@ -0,0 +1,80 @@
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
path_data = '../data/asotr/'
|
||||
fname_cmd_flight = '../data/cmd_asotr/all_flight_cmd_asotr.csv'
|
||||
fname_cmd_temp = '../data/cmd_asotr/flight_cmd_temp.csv'
|
||||
fname_cmd_human = '../data/cmd_asotr/cmd_human.csv'
|
||||
timeformat = '%d.%m.%Y %H:%M:%S'
|
||||
prev_days = 25
|
||||
|
||||
## get flight commands file (generated by mvn_log_viewer)
|
||||
## Translate to human-readeble format and take temperatures from flight commands file
|
||||
## save in cmd_human
|
||||
cmd_list, temperature_list = asotr.get_cmd_data(fname_cmd_flight)
|
||||
with open(fname_cmd_human, 'w') as file:
|
||||
for elem in cmd_list:
|
||||
file.write(f'{elem}\n')
|
||||
|
||||
## temperatures from flight commands file save to file flight_cmd_temp
|
||||
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, path_data)
|
||||
|
||||
end_date = ''
|
||||
for i, data in enumerate(df_asotr_):
|
||||
end_date = data['timestamp'].iloc[len(data) - 1][0:18]
|
||||
data.to_csv(f'{path_data}asotr0{i+1}_data_T.csv', index=False, sep=';',
|
||||
encoding='utf-8-sig', decimal='.')
|
||||
|
||||
delta_date = datetime.strptime(end_date, timeformat) - timedelta(days=prev_days)
|
||||
start_date = delta_date.strftime(timeformat)
|
||||
|
||||
## form timestamp file where minimum of temperatures registered
|
||||
for kit in range(1,3):
|
||||
asotr_kit = f'0{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)
|
||||
|
||||
fname = f'{path_data}asotr{asotr_kit}_min_T.csv'
|
||||
|
||||
df = pd.DataFrame(min_temp_ch).transpose()
|
||||
df.to_csv(fname, header=False, index=False, sep=';',
|
||||
encoding='utf-8-sig', decimal='.')
|
||||
df1 = pd.read_csv(fname, sep=';',
|
||||
names=['ch1','ch2','ch3','ch4','ch5','ch6'])
|
||||
df1.to_csv(fname, index=False, sep=';',
|
||||
encoding='utf-8-sig', decimal='.')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
164
bin/flight_temp_forecast.py
Normal file
164
bin/flight_temp_forecast.py
Normal file
@@ -0,0 +1,164 @@
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.widgets import Slider
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
from datetime import datetime, timedelta
|
||||
from matplotlib import dates
|
||||
|
||||
def get_raw_data(year, path_with_data, asotr_kit, data_borders):
|
||||
|
||||
if data_borders['flag'] == True:
|
||||
start_date = data_borders['begin'] + " 00:00:00"
|
||||
end_date = data_borders['end'] + " 23:59:59"
|
||||
accuracy = 'minutes'
|
||||
else:
|
||||
start_date = '01.01.' + year + " 00:00:00"
|
||||
end_date = '01.01.' + year + " 23:59:59"
|
||||
accuracy = 'hours'
|
||||
|
||||
try:
|
||||
data, data_dict_borders = asotr.get_data(path_with_data, asotr_kit,
|
||||
start_date, end_date, accuracy)
|
||||
|
||||
ch_signs = ["temp", "temp_set", "pow"]
|
||||
ch = [[], [], [], [], [], []]
|
||||
data_dict = {
|
||||
"temp": ch,
|
||||
"temp_set": ch,
|
||||
"pow": ch,
|
||||
"time_temp": [],
|
||||
"time_temp_set": [],
|
||||
"time_pow": [],
|
||||
}
|
||||
|
||||
data_dict["time_temp"] = data[0]["timestamp"]
|
||||
data_dict["time_temp_set"] = data[1]["timestamp"]
|
||||
data_dict["time_pow"] = data[2]["timestamp"]
|
||||
|
||||
col = ["ch1", "ch2", "ch3", "ch4", "ch5", "ch6"]
|
||||
|
||||
for j in range(len(ch_signs)):
|
||||
data_dict[ch_signs[j]] = data[j][col]
|
||||
|
||||
except Exception as e:
|
||||
print(f'exception: {e}')
|
||||
raise
|
||||
|
||||
try:
|
||||
fname_beta = path_with_data + 'beta_' + year + '.xlsx'
|
||||
dateparse_beta = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
|
||||
|
||||
data_beta = pd.read_excel(fname_beta, sheet_name=0, usecols=[0,1,2], header=4,
|
||||
names=['turn_num', 'beta_angle', 'timestamp'], parse_dates=['timestamp'],
|
||||
date_parser=dateparse_beta)
|
||||
except Exception as e:
|
||||
print(f'exception: {e}')
|
||||
raise
|
||||
|
||||
return (data_dict, data_dict_borders, data_beta)
|
||||
|
||||
|
||||
def plot_asotr_borders(year, path_with_data, ch, asotr_kit, data_borders,
|
||||
font=14, save_flag=True):
|
||||
# get from files and prepare data
|
||||
print_width = 20
|
||||
print_height = 12
|
||||
width = 1
|
||||
plot_windows = 1
|
||||
|
||||
channels = list(map(int, ch))
|
||||
|
||||
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]
|
||||
|
||||
try:
|
||||
data_dict, data_dict_borders, data_beta = get_raw_data(year, path_with_data,
|
||||
asotr_kit, data_borders)
|
||||
except Exception as e:
|
||||
print(f'{e}')
|
||||
return
|
||||
|
||||
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:
|
||||
line, = ax.plot(data_dict_borders["time_temp"],
|
||||
data_dict_borders['temp'].iloc[:,i],
|
||||
'--',
|
||||
linewidth=1,
|
||||
label=legend[i],)
|
||||
|
||||
ax.plot(data_dict["time_temp"],
|
||||
data_dict['temp'].iloc[:,i],
|
||||
marker[i],
|
||||
linewidth=width[i],
|
||||
label=legend[i],)
|
||||
ch = 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)
|
||||
|
||||
ax2 = ax.twinx()
|
||||
ax2.plot(data_beta['timestamp'], data_beta['beta_angle'], marker[4],
|
||||
color='r', linewidth=width[5], label='угол Бета')
|
||||
ax2.set_ylabel('Угол Бета', fontsize=font)
|
||||
ax2.tick_params(axis='y', width=1, labelsize=font)
|
||||
ax2.legend(fontsize=font, loc='lower right')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
def update(val):
|
||||
shift_amount = val * pd.Timedelta(days=1)
|
||||
shifted_timestamps = data_dict_borders['time_temp'] + shift_amount
|
||||
scaled_values = data_dict_borders['temp'].iloc[:,ch] + 5
|
||||
line.set_data(shifted_timestamps, scaled_values)
|
||||
fig.canvas.draw_idle()
|
||||
|
||||
slider_ax = plt.axes([0.25, 0.05, 0.65, 0.03])
|
||||
slider = Slider(slider_ax, 'Shift days', -100, 100, valinit=0)
|
||||
slider.on_changed(update)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if save_flag == True:
|
||||
pict_name = (f'../plots/reports/ASOTR{asotr_kit}_flight_T_P_{asotr.convert_to_str(channels)}_{data_borders["begin"][0:5].replace(".", "")}_{data_borders["end"][0:5].replace(".", "")}_{data_borders["end"][6:]}.png')
|
||||
fig.savefig(pict_name)
|
||||
|
||||
|
||||
ch = '100000'
|
||||
year = '2025'
|
||||
path_with_data = '../data/asotr/'
|
||||
asotr_kit = '01'
|
||||
data_borders = {'flag': True, 'begin': '15.03.2025', 'end': '01.05.2025'}
|
||||
|
||||
plot_asotr_borders(year, path_with_data, ch, asotr_kit, data_borders, font=6, save_flag=True)
|
||||
|
||||
41
bin/impulse_response.py
Normal file
41
bin/impulse_response.py
Normal file
@@ -0,0 +1,41 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
asotr_kit = 1
|
||||
fname = f'../../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
|
||||
|
||||
dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
|
||||
data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
|
||||
|
||||
interp = {'method': 'polynomial', 'order': 2}
|
||||
thermocycle_info = {'date': '14.03.2025', 'type': 'impuse'}
|
||||
cut_step_resp = {'orig_time_step_begin': '14.03.2025 13:49:32', 'orig_step_duration': 20*60}
|
||||
data_info = {'data': data, 'channel': 'ch1', 'period': '1 сек', 'find_accuracy': 'seconds'}
|
||||
|
||||
name = f'{thermocycle_info["type"]}_response_{thermocycle_info["date"].replace(".","")}'
|
||||
|
||||
plot_info = {'title': 'Реакция на импульсное воздействие',
|
||||
'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper right', 'lower left'],
|
||||
'name_fig': f'../plots/response/{name}.png',
|
||||
'font': 10}
|
||||
|
||||
tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
|
||||
|
||||
_, interp_imp_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin,
|
||||
cut_step_resp['orig_step_duration'], channel='ch1', interp=interp,
|
||||
accuracy=data_info['find_accuracy'])
|
||||
|
||||
|
||||
interp_imp_resp.to_csv(f'../data/asotr/response/asotr0{asotr_kit}_{name}.csv', index=False, sep=';',
|
||||
encoding='utf-8-sig', decimal='.')
|
||||
|
||||
asotr.plot_imp_response(interp_imp_resp, data_info, plot_info, thermocycle_info)
|
||||
184
bin/plot_asotr_flight_all.py
Normal file
184
bin/plot_asotr_flight_all.py
Normal file
@@ -0,0 +1,184 @@
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
import sys
|
||||
|
||||
font = 6
|
||||
print_width = 10
|
||||
print_height = 6
|
||||
width = 1
|
||||
plot_windows = 2
|
||||
channels = [1, 1, 1, 1, 1, 1]
|
||||
asotr_kit = '01'
|
||||
|
||||
xborders=False
|
||||
begin=0;
|
||||
end=0;
|
||||
|
||||
path = '../data/asotr/'
|
||||
fname_B = f'{path}beta_2025.xlsx'
|
||||
|
||||
fname = 'asotr' + asotr_kit + '_data_T.csv'
|
||||
fname_pow = 'asotr' + asotr_kit + '_data_P.csv'
|
||||
|
||||
pict_name = '../plots/' + 'ASOTR' + asotr_kit + '_flight_T_P_all'
|
||||
ox_dtime_format = '%Y.%m.%d %H:%M'
|
||||
|
||||
legend=['БРД1', 'БРД2', 'БРД3', 'БРД4', 'плита МУП МВН, датчик1', 'плита МУП МВН, датчик 2']
|
||||
width=[1, 1, 1, 1, 1, 1]
|
||||
|
||||
marker = ['-', '-', '-', '-', '--', '-'];
|
||||
width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
|
||||
|
||||
dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
|
||||
dparse_b = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
|
||||
|
||||
data_b = pd.read_excel(fname_B,
|
||||
sheet_name=0,
|
||||
usecols=[0,1,2],
|
||||
header=4,
|
||||
names=['turn_num', 'beta_angle', 'timestamp'],
|
||||
parse_dates=['timestamp'],
|
||||
date_parser=dparse_b)
|
||||
|
||||
|
||||
fname = [path + fname, path + fname_pow]
|
||||
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)]
|
||||
|
||||
ch= [[], [], [], [], [], []]
|
||||
ch_signs = ["temp", "pow"]
|
||||
data_dict = {"temp": ch, "pow": ch, "time": []}
|
||||
data_dict["time"] = data[0]['timestamp']
|
||||
col=['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6', 'ch7']
|
||||
|
||||
for j in range(2):
|
||||
for index, row, in data[j].iterrows():
|
||||
for i in range(6):
|
||||
ch[i].append(float(row[col[i]]))
|
||||
data_dict[ch_signs[j]] = ch
|
||||
ch= [[], [], [], [], [], []]
|
||||
|
||||
len_data = [len(data_dict['temp'][0]), len(data_dict['pow'][0])]
|
||||
len_ = min(len_data)
|
||||
|
||||
if xborders == False:
|
||||
begin = 0
|
||||
end = len_ - 1
|
||||
|
||||
|
||||
|
||||
if plot_windows == 1:
|
||||
fig, ax = plt.subplots(figsize=(print_width, print_height), dpi=200)
|
||||
|
||||
i = 0
|
||||
for elem in data_dict['temp']:
|
||||
if channels[i] == 1:
|
||||
ax.plot(data_dict['time'][begin:end], elem[begin:end], marker[i], linewidth=width[i], label=legend[i])
|
||||
i += 1
|
||||
|
||||
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)
|
||||
|
||||
i = 0
|
||||
for elem in data_dict['temp']:
|
||||
if channels[i] == 1:
|
||||
ax1.plot(data_dict['time'][begin:end], elem[begin:end], marker[i], linewidth=width[i], label=legend[i])
|
||||
i += 1
|
||||
|
||||
ax3 = ax1.twinx()
|
||||
ax3.plot(data_b['timestamp'], data_b['beta_angle'], marker[4], color='r', linewidth=width[5], label='угол Бета')
|
||||
ax3.set_ylabel('Угол Бета', fontsize=font)
|
||||
ax3.tick_params(axis="y", width=1, labelsize=font)
|
||||
ax3.legend(fontsize=font, loc='upper right')
|
||||
|
||||
i = 0
|
||||
for elem in data_dict['pow']:
|
||||
if channels[i] == 1:
|
||||
ax2.plot(data_dict['time'][begin:end], elem[begin:end], marker[i], linewidth=width[i], label=legend[i])
|
||||
i += 1
|
||||
|
||||
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, loc='lower right')
|
||||
|
||||
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.legend(fontsize=font, loc='lower right')
|
||||
|
||||
date_formatter = dates.DateFormatter(ox_dtime_format)
|
||||
ax2.xaxis.set_major_formatter(date_formatter)
|
||||
|
||||
plt.title('АСОТР ' + asotr_kit, fontsize=font)
|
||||
plt.tight_layout()
|
||||
fig.savefig(pict_name)
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# asotr_kit2 = '02'
|
||||
# fname2 = 'asotr' + asotr_kit2 + '_data_T.csv'
|
||||
# fname_pow2 = 'asotr' + asotr_kit2 + '_data_P.csv'
|
||||
# legend2=['2 БРД1', '2 БРД2', '2 БРД3', '2 БРД4', '2 плита МУП МВН, датчик1', '2 плита МУП МВН, датчик 2']
|
||||
|
||||
# fname2 = [path + fname2, path + fname_pow2]
|
||||
# data2 = [pd.read_csv(fname2[0], sep=';', parse_dates=['timestamp'], date_parser=dateparse),
|
||||
# pd.read_csv(fname2[1], sep=';', parse_dates=['timestamp'], date_parser=dateparse)]
|
||||
|
||||
# ch= [[], [], [], [], [], []]
|
||||
# ch_signs = ["temp", "pow"]
|
||||
# data_dict2 = {"temp": ch, "pow": ch, "time": []}
|
||||
# data_dict2["time"] = data2[0]['timestamp']
|
||||
# col=['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6', 'ch7']
|
||||
|
||||
# for j in range(2):
|
||||
# for index, row, in data2[j].iterrows():
|
||||
# for i in range(6):
|
||||
# ch[i].append(float(row[col[i]]))
|
||||
# data_dict2[ch_signs[j]] = ch
|
||||
# ch= [[], [], [], [], [], []]
|
||||
|
||||
# len_data2 = [len(data_dict2['temp'][0]), len(data_dict2['pow'][0])]
|
||||
# len_2 = min(len_data2)
|
||||
|
||||
# if xborders == False:
|
||||
# begin2 = 0
|
||||
# end2 = len_2 - 1
|
||||
|
||||
|
||||
# i = 0
|
||||
# for elem in data_dict2['temp']:
|
||||
# if channels[i] == 1:
|
||||
# print('legend2: ' + legend2[i])
|
||||
# ax1.plot(data_dict2['time'][begin2:end2], elem[begin2:end2], marker[i], linewidth=width[i], label=legend2[i])
|
||||
# i += 1
|
||||
|
||||
# ax2.plot(pd.Series(data_dict2['temp'][0]) - pd.Series(data_dict['temp'][0]))
|
||||
|
||||
|
||||
182
bin/plot_flight_borders.py
Normal file
182
bin/plot_flight_borders.py
Normal file
@@ -0,0 +1,182 @@
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
import argparse
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import pandas as pd
|
||||
|
||||
def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=True):
|
||||
print_width = 20
|
||||
print_height = 12
|
||||
width = 1
|
||||
plot_windows = 2
|
||||
|
||||
channels = list(map(int, ch))
|
||||
pict_name = (f'../plots/reports/ASOTR{asotr_kit}_flight_T_P_{asotr.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
|
||||
start_date = begin.replace('_', ' ')
|
||||
end_date = end.replace('_', ' ')
|
||||
try:
|
||||
data, data_dict = asotr.get_data(path_with_data, asotr_kit, start_date, end_date, 'minutes')
|
||||
except Exception as e:
|
||||
return
|
||||
|
||||
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)
|
||||
print(f'figure saved: {pict_name}')
|
||||
if show_flag == True:
|
||||
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':
|
||||
try:
|
||||
cmd_human = pd.read_csv('../data/cmd_asotr/cmd_human.csv',
|
||||
delimiter=';', names=['timestamp', 'cmd'])
|
||||
except Exception as e:
|
||||
print(f'Error parsing file: {e}')
|
||||
return
|
||||
|
||||
max_temp = max(data_dict['temp'].iloc[:,1])
|
||||
min_temp = min(data_dict['temp'].iloc[:,1])
|
||||
# print(cmd_human)
|
||||
step = 0
|
||||
for i, row in cmd_human.iterrows():
|
||||
row_time = row['timestamp'][0:len(row['timestamp']) - 4]
|
||||
# print(row_time)
|
||||
idx = asotr.find_best_time_idx(data_dict['time_temp'],
|
||||
row_time, accuracy='minutes')
|
||||
|
||||
# print(idx)
|
||||
if idx != -1:
|
||||
ax1.axvline(x = data_dict['time_temp'][idx], color='r',
|
||||
linestyle='-.')
|
||||
ax1.text(data_dict['time_temp'][idx], max_temp - step, row['cmd'],
|
||||
rotation=45, va='bottom', fontsize=font)
|
||||
step += (max_temp - min_temp)/20
|
||||
|
||||
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)
|
||||
print(f'figure saved: {pict_name}')
|
||||
if show_flag == True:
|
||||
plt.show()
|
||||
|
||||
if __name__ == '__main__':
|
||||
argparser = argparse.ArgumentParser("plot_flight_borders.py")
|
||||
|
||||
argparser.add_argument('-s', '--source', required=True,
|
||||
help='type path with asotr csv data')
|
||||
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)')
|
||||
argparser.add_argument('-p', '--plot', required=False,
|
||||
help='display data in plot flag (0/1)')
|
||||
args = argparser.parse_args()
|
||||
|
||||
plot_asotr_borders(args.source, args.channel, args.asotr, args.begin, args.end,
|
||||
args.font, args.cmd, show_flag=args.plot)
|
||||
21
bin/plot_flight_borders.sh
Executable file
21
bin/plot_flight_borders.sh
Executable file
@@ -0,0 +1,21 @@
|
||||
#! /bin/bash
|
||||
|
||||
if [ $# != 2 ]
|
||||
then
|
||||
echo "erorr use $0. Right use this script: "
|
||||
echo "$0 25.02.2025_00:00:00 10.03.2025_23:59:59"
|
||||
else
|
||||
path_csv_data=../data/asotr/
|
||||
begin=$1
|
||||
end=$2
|
||||
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 111100 -a 01 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 001000 -a 01 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 000011 -a 01 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 111100 -a 02 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 010100 -a 02 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 010000 -a 02 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 000100 -a 02 -b ${begin} -e ${end}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 000011 -a 02 -b ${begin} -e ${end}
|
||||
fi
|
||||
|
||||
86
bin/plot_periods_profile.py
Normal file
86
bin/plot_periods_profile.py
Normal file
@@ -0,0 +1,86 @@
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
path = '../data/asotr/'
|
||||
pict_name = '../plots/periods_profile/periods_profile_10042025.png'
|
||||
channel = 'ch1'
|
||||
asotr_kit = '01'
|
||||
start_date = '24.04.2025 22:30:00'
|
||||
end_date = '25.04.2025 02:00:00'
|
||||
# start_date = '06.01.2025 22:40:00'
|
||||
# end_date = '21.01.2025 01:20:00'
|
||||
shift = False
|
||||
|
||||
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())
|
||||
|
||||
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)
|
||||
|
||||
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}')
|
||||
|
||||
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)
|
||||
ax2.legend()
|
||||
ax3.legend()
|
||||
fig.savefig(pict_name)
|
||||
plt.show()
|
||||
19
bin/prepare_flight_cyclo.py
Normal file
19
bin/prepare_flight_cyclo.py
Normal file
@@ -0,0 +1,19 @@
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
path = '../data/experiments/'
|
||||
|
||||
timestamp = '04.05.2025 00:42:00'
|
||||
cyclogram_file = 'cyclogram_step_ident_ch3.xls'
|
||||
asotr.insert_data_cyclo(timestamp, cyclogram_file, path)
|
||||
|
||||
timestamp = '04.05.2025 03:48:00'
|
||||
cyclogram_file = 'cyclogram_imp_ident_ch3.xls'
|
||||
asotr.insert_data_cyclo(timestamp, cyclogram_file, path)
|
||||
|
||||
|
||||
37
bin/recursive_unpack_targz.py
Normal file
37
bin/recursive_unpack_targz.py
Normal file
@@ -0,0 +1,37 @@
|
||||
import os
|
||||
import tarfile
|
||||
|
||||
def extract_tar_gz(filepath, extract_dir):
|
||||
""" Unpack archive in specified directory """
|
||||
try:
|
||||
with tarfile.open(filepath, "r:gz") as tar:
|
||||
tar.extractall(path=extract_dir)
|
||||
print(f"[+] Extracted: {filepath}")
|
||||
except Exception as e:
|
||||
print(f"[!] Error extracting {filepath}: {e}")
|
||||
|
||||
def should_extract(archive_path):
|
||||
""" check exist directory's name without .tag.gs """
|
||||
dirname = os.path.splitext(os.path.splitext(archive_path)[0])[0]
|
||||
list_ignore = ['brd.tar.gz', 'aux_data.tar.gz', 'uvi.tar.gz', 'aux.tar.gz']
|
||||
|
||||
if all(elem not in archive_path for elem in list_ignore):
|
||||
return not os.path.isdir(dirname)
|
||||
|
||||
def walk_and_extract(start_dir):
|
||||
""" recursive directory traversal with unpacking """
|
||||
for root, _, files in os.walk(start_dir):
|
||||
for file in files:
|
||||
if file.endswith(".tar.gz"):
|
||||
archive_path = os.path.join(root, file)
|
||||
target_dir = os.path.splitext(os.path.splitext(archive_path)[0])[0]
|
||||
if should_extract(archive_path):
|
||||
extract_tar_gz(archive_path, target_dir)
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python recursive_unpack_targz.py /path/to/start/dir")
|
||||
else:
|
||||
walk_and_extract(sys.argv[1])
|
||||
|
||||
294
bin/step_response.py
Normal file
294
bin/step_response.py
Normal file
@@ -0,0 +1,294 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
asotr_kit = 1
|
||||
# fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
|
||||
fname = f'../data/asotr/asotr0{asotr_kit}_data_T.csv'
|
||||
|
||||
dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
|
||||
data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
|
||||
|
||||
# date = '20.03.2025'
|
||||
# period = '1 мин'
|
||||
# time_begin_orig = date + ' 17:10:11'
|
||||
# time_begin1 = date + ' 18:10:17'
|
||||
# time_begin2 = date + ' 19:10:23'
|
||||
# step_begin = time_begin2
|
||||
# duration = 3600
|
||||
# accuracy = 'seconds'
|
||||
# name_fig = 'step_response_KDI_20242003.png'
|
||||
|
||||
# date = '21.03.2025'
|
||||
# period = '1 мин'
|
||||
# time_begin_orig = date + ' 14:00:11'
|
||||
# time_begin1 = date + ' 15:00:16'
|
||||
# time_begin2 = date + ' 16:00:16'
|
||||
# step_begin = time_begin2
|
||||
# duration = 3600
|
||||
# accuracy = 'seconds'
|
||||
# name_fig = 'step_response_KDI_20242103.png'
|
||||
|
||||
# date = '24.03.2025'
|
||||
# period = '1 сек'
|
||||
# time_begin_orig = date + ' 19:45:11'
|
||||
# time_begin1 = date + ' 20:45:13'
|
||||
# time_begin2 = date + ' 21:45:17'
|
||||
# step_begin = time_begin2
|
||||
# duration = 3600
|
||||
# accuracy = 'seconds'
|
||||
# name_fig = 'step_response_KDI_20242403.png'
|
||||
|
||||
|
||||
|
||||
# interp = {'method': 'polynomial', 'order': 1}
|
||||
# thermocycle_info = {'date': '01.04.2025',
|
||||
# 'time_begin': ['01.04.2025 16:27:00', '01.04.2025 18:00:00'],
|
||||
# 'duration_sec': 92*60, 'type': 'step'}
|
||||
# cut_step_resp = {'time_step_begin': '01.04.2025 18:53:21', 'step_duration': 25*60}
|
||||
# data_info = {'data': data, 'device': 'KDI', 'channel': 'ch1', 'period': '1 мин',
|
||||
# 'find_accuracy': 'seconds'}
|
||||
# name = f'{thermocycle_info["type"]}_response_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
|
||||
# plot_info = {'title': 'Реакция на ступенчатое воздействие',
|
||||
# 'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
|
||||
# 'name_fig': f'{name}.png', 'font': 10}
|
||||
|
||||
|
||||
|
||||
interp = {'method': 'polynomial', 'order': 1}
|
||||
|
||||
data_info_list = []
|
||||
thermocycle_info_list = []
|
||||
cut_step_resp_list = []
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '25.04.2025',
|
||||
'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'],
|
||||
'duration_sec': 92*60, 'type': 'step', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '25.04.2025',
|
||||
'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'],
|
||||
'duration_sec': 92*60, 'type': 'step1_to2', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '25.04.2025',
|
||||
'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'],
|
||||
'duration_sec': 92*60, 'type': 'impulse', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 15*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '25.04.2025',
|
||||
'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'],
|
||||
'duration_sec': 92*60, 'type': 'impulse1_to2', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 20*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '30.04.2025',
|
||||
'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'],
|
||||
'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '30.04.2025',
|
||||
'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'],
|
||||
'duration_sec': 93*60, 'type': 'step2_to1', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '30.04.2025',
|
||||
'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'],
|
||||
'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 15*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '30.04.2025',
|
||||
'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'],
|
||||
'duration_sec': 93*60, 'type': 'impulse2_to1', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 20*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '02.05.2025',
|
||||
'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'],
|
||||
'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '02.05.2025',
|
||||
'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'],
|
||||
'duration_sec': 93*60, 'type': 'step4_to3', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '02.05.2025',
|
||||
'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'],
|
||||
'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 15*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '02.05.2025',
|
||||
'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'],
|
||||
'duration_sec': 93*60, 'type': 'impulse4_to3', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 20*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '04.05.2025',
|
||||
'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'],
|
||||
'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 26*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '04.05.2025',
|
||||
'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'],
|
||||
'duration_sec': 93*60, 'type': 'step3_to4', 'type_ru': 'ступенчатое'}
|
||||
cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 30*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '04.05.2025',
|
||||
'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'],
|
||||
'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 15*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
thermocycle_info = {'date': '04.05.2025',
|
||||
'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'],
|
||||
'duration_sec': 93*60, 'type': 'impulse3_to4', 'type_ru': 'импульсное'}
|
||||
cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 20*60}
|
||||
|
||||
data_info_list.append(data_info)
|
||||
thermocycle_info_list.append(thermocycle_info)
|
||||
cut_step_resp_list.append(cut_step_resp)
|
||||
|
||||
|
||||
def get_step_response(data_info, thermocycle_info, cut_step_resp):
|
||||
name = f'{data_info["channel"]}_{thermocycle_info["type"]}_response_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
|
||||
plot_info = {'title': 'Реакция на ' + thermocycle_info['type_ru'] + ' воздействие',
|
||||
'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
|
||||
'name_fig': f'../plots/response/{name}.png', 'font': 10}
|
||||
|
||||
asotr.plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
|
||||
cut_step_resp, plot_info)
|
||||
|
||||
step_resp_cut, _, _ = asotr.get_step_response_diff(data_info['data'], thermocycle_info,
|
||||
channel=data_info['channel'], interp=interp, accuracy=data_info['find_accuracy'],
|
||||
cut_step_resp=cut_step_resp)
|
||||
|
||||
max_ = len(step_resp_cut)
|
||||
|
||||
step_resp_cut.to_csv(f'../data/asotr/response/asotr0{asotr_kit}_{data_info["channel"]}_{thermocycle_info["type"]}_{thermocycle_info["date"].replace(".","")}.csv', index=False, sep=';', encoding='utf-8-sig', decimal='.')
|
||||
|
||||
|
||||
title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР, {data_info["device"]} СПИН-X1-МВН, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
|
||||
|
||||
fig = plt.figure(figsize=(10, 6), dpi=200)
|
||||
fig.suptitle(title, fontsize=plot_info['font'])
|
||||
ax1 = fig.add_subplot(1,1,1)
|
||||
|
||||
ax1.plot(step_resp_cut['timestamp'].iloc[0:max_], step_resp_cut['temp'].iloc[0:max_], '-',
|
||||
label='реакция на ' + thermocycle_info['type_ru'] + ' воздействие с термоциклом')
|
||||
|
||||
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'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
|
||||
ax1.set_xlabel('Время, ЧЧ:MM:CC', fontsize=plot_info['font'])
|
||||
|
||||
plt.tight_layout()
|
||||
fig.savefig(plot_info["name_fig"])
|
||||
plt.show()
|
||||
|
||||
for i, elem in enumerate(data_info_list):
|
||||
get_step_response(data_info_list[i], thermocycle_info_list[i], cut_step_resp_list[i])
|
||||
|
||||
110
bin/step_response_diff.py
Normal file
110
bin/step_response_diff.py
Normal file
@@ -0,0 +1,110 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
asotr_kit = 1
|
||||
fname = f'../../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
|
||||
dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
|
||||
data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
|
||||
|
||||
# date = '20.03.2025'
|
||||
# period = '1 мин'
|
||||
# time_begin_orig = date + ' 17:10:11'
|
||||
# time_begin1 = date + ' 18:10:17'
|
||||
# time_begin2 = date + ' 19:10:23'
|
||||
# step_begin = time_begin2
|
||||
# duration = 3600
|
||||
# accuracy = 'seconds'
|
||||
# name_fig = 'step_response_KDI_20242003.png'
|
||||
|
||||
# date = '21.03.2025'
|
||||
# period = '1 мин'
|
||||
# time_begin_orig = date + ' 14:00:11'
|
||||
# time_begin1 = date + ' 15:00:16'
|
||||
# time_begin2 = date + ' 16:00:16'
|
||||
# step_begin = time_begin2
|
||||
# duration = 3600
|
||||
# accuracy = 'seconds'
|
||||
# name_fig = 'step_response_KDI_20242103.png'
|
||||
|
||||
# date = '24.03.2025'
|
||||
# period = '1 сек'
|
||||
# time_begin_orig = date + ' 19:45:11'
|
||||
# time_begin1 = date + ' 20:45:13'
|
||||
# time_begin2 = date + ' 21:45:17'
|
||||
# step_begin = time_begin2
|
||||
# duration = 3600
|
||||
# accuracy = 'seconds'
|
||||
# name_fig = 'step_response_KDI_20242403.png'
|
||||
|
||||
|
||||
# interp = {'method': 'polynomial', 'order': 2}
|
||||
# thermocycle_info = {'date': '24.03.2025', 'time_begin': ['20:45:00', '21:45:11'],
|
||||
# 'duration_sec': 60*60, 'type': 'step'}
|
||||
# cut_step_resp = {'time_step_begin': '21:45:11', 'step_duration': 60*60,
|
||||
# 'orig_time_step_begin': '19:45:11', 'orig_step_duration': 25*60}
|
||||
# data_info = {'data': data, 'device': 'KDI', 'channel': 'ch1', 'period': '1 мин',
|
||||
# 'find_accuracy': 'seconds'}
|
||||
# name = f'{thermocycle_info["type"]}_response_diff_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
|
||||
# plot_info = {'title': 'Реакция на ступенчатое воздействие',
|
||||
# 'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
|
||||
# 'name_fig': f'{name}.png', 'font': 10}
|
||||
|
||||
|
||||
interp = {'method': 'polynomial', 'order': 2}
|
||||
thermocycle_info = {'date': '01.04.2025',
|
||||
'time_begin': ['01.04.2025 16:27:00', '01.04.2025 18:00:00'],
|
||||
'duration_sec': 92*60, 'type': 'step'}
|
||||
cut_step_resp = {'time_step_begin': '01.04.2025 18:53:21', 'step_duration': 24*60,
|
||||
'orig_time_step_begin': '01.04.2025 15:22:10', 'orig_step_duration': 24*60}
|
||||
data_info = {'data': data, 'channel': 'ch1', 'period': '1 мин',
|
||||
'find_accuracy': 'seconds'}
|
||||
plot_info = {'title': 'Реакция на ступенч. воздейств.',
|
||||
'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['lower right', 'lower left'],
|
||||
'name_fig': '../plots/response/step_response_diff_KDI_20240401.png', 'font': 10}
|
||||
|
||||
step_resp_cut, _, _ = asotr.get_step_response_diff(data_info['data'], thermocycle_info,
|
||||
channel=data_info['channel'], interp=interp, accuracy=data_info['find_accuracy'],
|
||||
cut_step_resp=cut_step_resp)
|
||||
|
||||
tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
|
||||
|
||||
_, interp_step_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin,
|
||||
cut_step_resp['orig_step_duration'], channel='ch1', interp=interp,
|
||||
accuracy=data_info['find_accuracy'])
|
||||
|
||||
max_ = min(len(interp_step_resp), len(step_resp_cut))
|
||||
|
||||
step_resp_cut.to_csv(f'../data/asotr/response/asotr0{asotr_kit}_{thermocycle_info["type"]}_{thermocycle_info["date"].replace(".","")}.csv', index=False, sep=';', encoding='utf-8-sig', decimal='.')
|
||||
|
||||
title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР КДИ СПИН-X1-МВН, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
|
||||
|
||||
fig = plt.figure(figsize=(6, 6), dpi=200)
|
||||
fig.suptitle(title, fontsize=plot_info['font'])
|
||||
ax1 = fig.add_subplot(1,1,1)
|
||||
|
||||
ax1.plot(step_resp_cut['timestamp'].iloc[0:max_], step_resp_cut['temp'].iloc[0:max_], '--',
|
||||
label='реакция на ступенчатое воздействие с термоциклом')
|
||||
ax1.plot(step_resp_cut['timestamp'].iloc[0:max_], interp_step_resp['temp'].iloc[0:max_],
|
||||
label='реакция на ступенчатое воздействие')
|
||||
|
||||
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'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
|
||||
ax1.set_xlabel('Время', fontsize=plot_info['font'])
|
||||
|
||||
plt.tight_layout()
|
||||
fig.savefig(plot_info["name_fig"])
|
||||
plt.show()
|
||||
86
bin/temp_peaks_forecast.py
Normal file
86
bin/temp_peaks_forecast.py
Normal file
@@ -0,0 +1,86 @@
|
||||
import sys
|
||||
import statistics
|
||||
from importlib import reload
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
from datetime import datetime, timedelta
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
from datetime import timedelta
|
||||
|
||||
path = '../data/asotr/'
|
||||
channel = 'ch1'
|
||||
asotr_kit = '01'
|
||||
start_date = '25.04.2025 00:00:00'
|
||||
end_date = '25.04.2025 08:00:00'
|
||||
forecast_days = 20
|
||||
|
||||
|
||||
timeformat = '%d.%m.%Y %H:%M:%S'
|
||||
num_peaks_forecast = forecast_days * 20
|
||||
shift = True
|
||||
|
||||
try:
|
||||
raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
|
||||
except Exception as e:
|
||||
sys.exit()
|
||||
|
||||
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('../data/asotr/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()
|
||||
Reference in New Issue
Block a user