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
parent
2f37a7329b
commit
b04009ad27
7
.gitignore
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560
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|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"
|
||||
|
||||
[[package]]
|
||||
name = "walkdir"
|
||||
version = "2.5.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "29790946404f91d9c5d06f9874efddea1dc06c5efe94541a7d6863108e3a5e4b"
|
||||
dependencies = [
|
||||
"same-file",
|
||||
"winapi-util",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wasm-bindgen"
|
||||
version = "0.2.100"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1edc8929d7499fc4e8f0be2262a241556cfc54a0bea223790e71446f2aab1ef5"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"once_cell",
|
||||
"rustversion",
|
||||
"wasm-bindgen-macro",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wasm-bindgen-backend"
|
||||
version = "0.2.100"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2f0a0651a5c2bc21487bde11ee802ccaf4c51935d0d3d42a6101f98161700bc6"
|
||||
dependencies = [
|
||||
"bumpalo",
|
||||
"log",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn",
|
||||
"wasm-bindgen-shared",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wasm-bindgen-macro"
|
||||
version = "0.2.100"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7fe63fc6d09ed3792bd0897b314f53de8e16568c2b3f7982f468c0bf9bd0b407"
|
||||
dependencies = [
|
||||
"quote",
|
||||
"wasm-bindgen-macro-support",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wasm-bindgen-macro-support"
|
||||
version = "0.2.100"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8ae87ea40c9f689fc23f209965b6fb8a99ad69aeeb0231408be24920604395de"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn",
|
||||
"wasm-bindgen-backend",
|
||||
"wasm-bindgen-shared",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wasm-bindgen-shared"
|
||||
version = "0.2.100"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1a05d73b933a847d6cccdda8f838a22ff101ad9bf93e33684f39c1f5f0eece3d"
|
||||
dependencies = [
|
||||
"unicode-ident",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "winapi-util"
|
||||
version = "0.1.9"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "cf221c93e13a30d793f7645a0e7762c55d169dbb0a49671918a2319d289b10bb"
|
||||
dependencies = [
|
||||
"windows-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-core"
|
||||
version = "0.52.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "33ab640c8d7e35bf8ba19b884ba838ceb4fba93a4e8c65a9059d08afcfc683d9"
|
||||
dependencies = [
|
||||
"windows-targets",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-sys"
|
||||
version = "0.59.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1e38bc4d79ed67fd075bcc251a1c39b32a1776bbe92e5bef1f0bf1f8c531853b"
|
||||
dependencies = [
|
||||
"windows-targets",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-targets"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9b724f72796e036ab90c1021d4780d4d3d648aca59e491e6b98e725b84e99973"
|
||||
dependencies = [
|
||||
"windows_aarch64_gnullvm",
|
||||
"windows_aarch64_msvc",
|
||||
"windows_i686_gnu",
|
||||
"windows_i686_gnullvm",
|
||||
"windows_i686_msvc",
|
||||
"windows_x86_64_gnu",
|
||||
"windows_x86_64_gnullvm",
|
||||
"windows_x86_64_msvc",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows_aarch64_gnullvm"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "32a4622180e7a0ec044bb555404c800bc9fd9ec262ec147edd5989ccd0c02cd3"
|
||||
|
||||
[[package]]
|
||||
name = "windows_aarch64_msvc"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "09ec2a7bb152e2252b53fa7803150007879548bc709c039df7627cabbd05d469"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnu"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8e9b5ad5ab802e97eb8e295ac6720e509ee4c243f69d781394014ebfe8bbfa0b"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnullvm"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0eee52d38c090b3caa76c563b86c3a4bd71ef1a819287c19d586d7334ae8ed66"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_msvc"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "240948bc05c5e7c6dabba28bf89d89ffce3e303022809e73deaefe4f6ec56c66"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnu"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "147a5c80aabfbf0c7d901cb5895d1de30ef2907eb21fbbab29ca94c5b08b1a78"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnullvm"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "24d5b23dc417412679681396f2b49f3de8c1473deb516bd34410872eff51ed0d"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_msvc"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
|
||||
15
asotr_csv/Cargo.toml
Normal file
15
asotr_csv/Cargo.toml
Normal file
@ -0,0 +1,15 @@
|
||||
[package]
|
||||
name = "asotr_csv"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
authors = ["Danila Gamkov <danila_gamkov@cosmos.ru"]
|
||||
description = "The parser for converting data from the ASOTR MVN control channels into the CSV format (see files in asotr.tar.gz)"
|
||||
|
||||
[dependencies]
|
||||
byteorder = "1.4.3"
|
||||
chrono = "0.4"
|
||||
strum = { version = "0.26", features = ["derive"] }
|
||||
clap = { version = "4.*", features = ["derive"] }
|
||||
lazy_static = { version = "1.5.0", features = ["spin_no_std"] }
|
||||
regex = "1.7.0"
|
||||
walkdir = "2.3.2"
|
||||
122
asotr_csv/README.markdown
Normal file
122
asotr_csv/README.markdown
Normal file
@ -0,0 +1,122 @@
|
||||
# asotr_csv
|
||||
|
||||
The parser for converting data from the ASOTR MVN control channels into the CSV format
|
||||
|
||||
## Contents
|
||||
|
||||
- **Setup**
|
||||
- **Using**
|
||||
- parsing ASOTR MVN data files in specified directory
|
||||
- parsing all ASOTR MVN data files in specified directory and subdirectories
|
||||
- plot data in python
|
||||
- **Output asotr_csv data files description**
|
||||
- **Contacts**
|
||||
|
||||
**Note**: \<PATH_TO_ASOTR_CSV\> - path where is asotr_csv program is cloned from heagit
|
||||
|
||||
## Setup
|
||||
1. Install Rust compiler (if you don't have).
|
||||
Installation on Linux:
|
||||
```
|
||||
curl --proto '=https' --tlsv1.2 https://sh.rustup.rs -sSf | sh
|
||||
```
|
||||
|
||||
Installation on Windows:
|
||||
Go to address https://www.rust-lang.org/tools/install and follow instructions
|
||||
For more detailed information you can go to: https://doc.rust-lang.ru/book/ch01-01-installation.html
|
||||
|
||||
**Instruction for setup asotr_csv project**
|
||||
|
||||
2. Clone the repo to your computer:
|
||||
|
||||
```
|
||||
git clone http://heagit.cosmos.ru/gamkov/asotr_csv.git
|
||||
```
|
||||
|
||||
3. Enter the repo and compile it:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>
|
||||
cargo build --release
|
||||
```
|
||||
|
||||
After running this commands you will get an execution file (asotr_csv) in the following directory:
|
||||
\<PATH_TO_ASOTR_CSV\>/target/release/
|
||||
|
||||
## Using
|
||||
### Parsing ASOTR MVN data files in specified directory
|
||||
1. Donwload data from science data server to directory \<PATH_TO_ASOTR_DATA\>.
|
||||
If you don't have MVN data, you might download it from server with science SRG data (IP: 193.232.11.95).
|
||||
For questions about downloading science data contact Shtykovsky A. (a.shtykovsky@cosmos.ru) or Chelovekov I. (chelovekov@cosmos.ru)
|
||||
|
||||
2. Run linux bash script **asotr_unzip.sh** for directory with MVN data in order to unpack **asotr.tar.gz** archive with ASOTR MVN data, for example:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>
|
||||
./asotr_unzip.sh <PATH_TO_ASOTR_DATA>/20241231-001
|
||||
```
|
||||
|
||||
**Note**: the script **asotr_unzip.sh** will not work on windows, you will need to unpack the archive **\<PATH_TO_ASOTR_DATA\>/20241231-001/data/asotr.tar.gz** manually or write the corresponding Windows bat-file
|
||||
|
||||
3. Run program asotr_csv:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>/target/release/
|
||||
./asotr_csv -d <PATH_TO_ASOTR_DATA>/20241231-001
|
||||
```
|
||||
csv data are ready to use in directory:
|
||||
\<PATH_TO_ASOTR_CSV\>/target/release/
|
||||
|
||||
### Parsing all ASOTR MVN data files in specified directory and subdirectories
|
||||
1. Donwload data from science data server to directory \<PATH_TO_ASOTR_DATA\>.
|
||||
If you don't have MVN data, you might download it from server with science SRG data (IP: 193.232.11.95).
|
||||
For questions about downloading science data contact Shtykovsky A. (a.shtykovsky@cosmos.ru) or Chelovekov I. (chelovekov@cosmos.ru)
|
||||
|
||||
2. Run linux bash script **asotr_all_unzip.sh** for directory with MVN data in order to unpack all **asotr.tar.gz** archives with ASOTR MVN data, for example:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>
|
||||
./asotr_all_unzip.sh <PATH_TO_ASOTR_DATA>/
|
||||
```
|
||||
|
||||
**Note**: the script **asotr_all_unzip.sh** will not work on windows, you will need to unpack the each archive **\<PATH_TO_ASOTR_DATA\>/\<DIRECTORY_WITH_DATA\>/data/asotr.tar.gz** manually or write the corresponding Windows bat-file
|
||||
|
||||
If you want to parse astor data in specified directory, run program asotr_csv directly:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>/target/release/
|
||||
./asotr_csv -d <PATH_TO_ASOTR_DATA>
|
||||
```
|
||||
Or if you want to parse all raw data from ASOTR into csv files and plot csv data you might use shell script:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>
|
||||
./asotr_all_unzip_auto.sh <PATH_TO_ASOTR_DATA>/
|
||||
```
|
||||
csv data will be in directory:
|
||||
\<PATH_TO_ASOTR_CSV\>/data/
|
||||
|
||||
### Plot csv data in Python
|
||||
If you want to parse all raw data from astor into csv files and plot csv data you might use shell script:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>
|
||||
./asotr_all_unzip_auto.sh
|
||||
```
|
||||
|
||||
or if you already have csv files with ASOTR data, you might use plot script only:
|
||||
```
|
||||
cd <PATH_TO_ASOTR_CSV>/data/
|
||||
python3 plot_flight_all.py
|
||||
```
|
||||
|
||||
## Output asotr_csv data files description
|
||||
**description:**
|
||||
asotr01_data_T.csv - ASOTR1 temperature data in channels 1-6 (in Celsius)
|
||||
asotr01_data_P.csv - ASOTR1 power data in channels 1-6 (in %)
|
||||
asotr01_data_TSET.csv - ASOTR1 temperature sets in channels 1-6 (in Celsius)
|
||||
asotr02_data_T.csv - ASOTR2 temperature data in channels 1-6 (in Celsius)
|
||||
asotr02_data_P.csv - ASOTR2 power data in channels 1-6 (in %)
|
||||
asotr02_data_TSET.csv - ASOTR2 temperature sets in channels 1-6 (in Celsius)
|
||||
|
||||
**file data csv fromat:**
|
||||
column 1: Unix timestamp in seconds
|
||||
column 2: timestamp (data and time)
|
||||
columns 3-8 - data from control channels (power, temperature or temperature set)
|
||||
|
||||
|
||||
## Contatcs
|
||||
For questions about the program, please contact Danila Gamkov, email: danila_gamkov@cosmos.ru
|
||||
16
asotr_csv/asotr_all_unzip_auto.sh
Normal file
16
asotr_csv/asotr_all_unzip_auto.sh
Normal file
@ -0,0 +1,16 @@
|
||||
#! /bin/bash
|
||||
|
||||
if [ $# != 1 ]
|
||||
then
|
||||
echo "erorr use $0. Right use this script: "
|
||||
echo "$0 path"
|
||||
else
|
||||
cp ../asotr_csv/target/release/asotr_csv ../data/asotr
|
||||
path_=$1
|
||||
find ${path_} -maxdepth 1 -type d | xargs -I {} ./asotr_unzip.sh {}
|
||||
|
||||
cd ../data/asotr
|
||||
./asotr_csv -d ${path_}
|
||||
|
||||
python3 ../../bin/plot_asotr_flight_all.py
|
||||
fi
|
||||
30
asotr_csv/data/prepare_csv.sh
Executable file
30
asotr_csv/data/prepare_csv.sh
Executable file
@ -0,0 +1,30 @@
|
||||
#! /bin/bash
|
||||
|
||||
if [ $# != 2 ]
|
||||
then
|
||||
echo "error use $0. Right use this script: "
|
||||
echo "$0 path_to_file data_type (flight or KDI)"
|
||||
echo "example 1: $0 ./data/flight/30_12_2024/ASOTR_1_SOTR_T flight"
|
||||
else
|
||||
data_file=$1
|
||||
data_type=$2
|
||||
|
||||
if [ "$data_type" == "flight" ]
|
||||
then
|
||||
cat ${data_file}.csv | grep -Eo '[0-9]{2}\.[0-9]{2}\.[0-9]{4}' > file1
|
||||
cat ${data_file}.csv | grep -Eo [0-9]{2}:.* > file2
|
||||
|
||||
elif [ "$data_type" == "KDI" ]
|
||||
then
|
||||
cat ${data_file}.csv | grep -Eo [0-9]{2}.[0-9]{2}.[0-9]{4} > file1
|
||||
cat ${data_file}.csv | grep -Eo [0-9]{2}:.* > file2
|
||||
else
|
||||
echo "error argument of data_type: write \"flight\" or \"KDI\" in second argument"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
paste --delimiter=' ' file1 file2 > file.csv
|
||||
echo "timestamp;ch1;ch2;ch3;ch4;ch5;ch6" > ${data_file}_clear.csv
|
||||
cat file.csv >> ${data_file}_clear.csv
|
||||
rm file1 file2 file.csv
|
||||
fi
|
||||
355
asotr_csv/src/main.rs
Normal file
355
asotr_csv/src/main.rs
Normal file
@ -0,0 +1,355 @@
|
||||
use clap::{Parser};
|
||||
|
||||
pub mod asotr_data {
|
||||
use std::{fs::File, io::Read};
|
||||
use byteorder::{LittleEndian, ReadBytesExt};
|
||||
use chrono::{DateTime, Utc};
|
||||
use std::time::{SystemTime, UNIX_EPOCH, Duration};
|
||||
use strum::FromRepr;
|
||||
use lazy_static::lazy_static;
|
||||
use regex::Regex;
|
||||
use walkdir::WalkDir;
|
||||
|
||||
lazy_static! {
|
||||
pub static ref support_dtypes: String =
|
||||
String::from(".data01.asotr01(02), data02.asotr01(02), data06.asotr01(02)");
|
||||
|
||||
pub static ref patterns_fnames_csv_data: Vec<(String, String)> = {
|
||||
let mut patterns: Vec<(String, String)> = Vec::new();
|
||||
patterns.push((String::from(".*data01.asotr01"),
|
||||
String::from("../data/asotr/asotr01_data_T.csv")));
|
||||
patterns.push((String::from(".*data02.asotr01"),
|
||||
String::from("../data/asotr/asotr01_data_P.csv")));
|
||||
patterns.push((String::from(".*data06.asotr01"),
|
||||
String::from("../data/asotr/asotr01_data_TSET.csv")));
|
||||
patterns.push((String::from(".*data01.asotr02"),
|
||||
String::from("../data/asotr/asotr02_data_T.csv")));
|
||||
patterns.push((String::from(".*data02.asotr02"),
|
||||
String::from("../data/asotr/asotr02_data_P.csv")));
|
||||
patterns.push((String::from(".*data06.asotr02"),
|
||||
String::from("../data/asotr/asotr02_data_TSET.csv")));
|
||||
|
||||
patterns
|
||||
};
|
||||
|
||||
pub static ref patterns_disp: Vec<String> = {
|
||||
let mut patterns: Vec<String> = Vec::new();
|
||||
patterns.push(String::from("ASOTR01 temperature"));
|
||||
patterns.push(String::from("ASOTR01 power"));
|
||||
patterns.push(String::from("ASOTR01 temperature setpoint"));
|
||||
patterns.push(String::from("ASOTR02 temperature"));
|
||||
patterns.push(String::from("ASOTR02 power"));
|
||||
patterns.push(String::from("ASOTR02 temperature setpoint"));
|
||||
patterns
|
||||
};
|
||||
}
|
||||
|
||||
#[derive(Debug, FromRepr, PartialEq)]
|
||||
enum AsotrDataType {
|
||||
Temp = 1,
|
||||
Pow = 2,
|
||||
TempSet = 6,
|
||||
}
|
||||
|
||||
struct AsotrDataDesc {
|
||||
time_s: u64,
|
||||
time_mks: u32,
|
||||
date: String,
|
||||
time: String,
|
||||
data_type: AsotrDataType,
|
||||
// kit: u8,
|
||||
}
|
||||
|
||||
impl AsotrDataDesc {
|
||||
pub fn new(time_s: u64, time_mks: u32, date: String, time: String,
|
||||
data_type: AsotrDataType) -> AsotrDataDesc {
|
||||
AsotrDataDesc { time_s, time_mks, date, time, data_type }
|
||||
}
|
||||
}
|
||||
|
||||
pub fn read_data(filename_full: String) -> Result<String, String> {
|
||||
let ch_u16: [u16; 6];
|
||||
let ch_f32: [f32; 6];
|
||||
|
||||
let asotr_head = parse_filename(filename_full.clone())?;
|
||||
|
||||
let mut buf = Vec::new();
|
||||
let mut out = String::new();
|
||||
|
||||
let mut data = match File::open(filename_full.clone())
|
||||
{
|
||||
Ok(file) => file,
|
||||
Err(msg) => { return Err(format!("Error opening data file {}: {}", filename_full, msg)) }
|
||||
};
|
||||
|
||||
match data.read_to_end(&mut buf) {
|
||||
Ok(stat) => stat,
|
||||
Err(msg) => { return Err(format!("Error reading data file {}: {}", filename_full, msg)) }
|
||||
};
|
||||
|
||||
out.push_str(&format!("{};{} {}.{:02};",
|
||||
&asotr_head.time_s,
|
||||
&asotr_head.date,
|
||||
&asotr_head.time,
|
||||
asotr_head.time_mks));
|
||||
|
||||
if asotr_head.data_type == AsotrDataType::Temp ||
|
||||
asotr_head.data_type == AsotrDataType::TempSet {
|
||||
ch_f32 = parse_data_f32(buf)?;
|
||||
for elem in ch_f32 {
|
||||
out.push_str(&elem.to_string());
|
||||
out.push(';');
|
||||
}
|
||||
}
|
||||
else if asotr_head.data_type == AsotrDataType::Pow {
|
||||
ch_u16 = parse_data_u16(buf)?;
|
||||
for elem in ch_u16 {
|
||||
out.push_str(&elem.to_string());
|
||||
out.push(';');
|
||||
}
|
||||
}
|
||||
|
||||
out.remove(out.len() - 1);
|
||||
return Ok(out);
|
||||
}
|
||||
|
||||
pub fn parse_data_dir(dir: &str, disp: bool) -> Result<(), String> {
|
||||
let mut data: Vec<String> = Vec::new();
|
||||
|
||||
println!("parse data from directory: {}", dir);
|
||||
for (i, (pattern, fname)) in patterns_fnames_csv_data.iter().enumerate() {
|
||||
let files = find_files_regex(dir, pattern)?;
|
||||
|
||||
for elem in files {
|
||||
data.push(read_data(elem)?);
|
||||
}
|
||||
|
||||
data.sort();
|
||||
data.dedup();
|
||||
|
||||
if disp { disp_data(&data, &patterns_disp[i])?; }
|
||||
|
||||
println!("save csv data to file: {}", fname);
|
||||
|
||||
save_data_csv(data.clone(), fname)?;
|
||||
data.clear();
|
||||
}
|
||||
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
fn parse_data_f32(buf: Vec<u8>) -> Result<[f32; 6], String> {
|
||||
let mut data = &buf[..];
|
||||
let mut ch: [f32; 6] = [0.0; 6];
|
||||
|
||||
for i in 0..6 {
|
||||
ch[i] = match data.read_f32::<LittleEndian>() {
|
||||
Ok(val) => val,
|
||||
Err(msg) => {
|
||||
return Err(format!(
|
||||
"Error parsing file: failed parsing float32 data: {}", msg)); }
|
||||
}
|
||||
}
|
||||
|
||||
return Ok(ch);
|
||||
}
|
||||
|
||||
fn parse_data_u16(buf: Vec<u8>) -> Result<[u16; 6], String> {
|
||||
let mut data = &buf[..];
|
||||
let mut ch: [u16; 6] = [0; 6];
|
||||
|
||||
for i in 0..6 {
|
||||
ch[i] = match data.read_u16::<LittleEndian>() {
|
||||
Ok(val) => val,
|
||||
Err(msg) => {
|
||||
return Err(format!(
|
||||
"Error parsing file: failed parsing uint16 data: {}", msg)); }
|
||||
}
|
||||
}
|
||||
|
||||
return Ok(ch);
|
||||
}
|
||||
|
||||
fn parse_filename(filename_full: String) -> Result<AsotrDataDesc, String> {
|
||||
let mut fname = String::new();
|
||||
let msg_prev = format!("Error parsing filename {}:", filename_full);
|
||||
|
||||
match filename_full.rfind('/') {
|
||||
Some(val) => { fname = (filename_full[val+1..filename_full.len()]).to_string(); }
|
||||
_ => { fname = filename_full.clone(); }
|
||||
}
|
||||
|
||||
if fname.len() != 32 {
|
||||
return Err(format!("{} unsupported file", msg_prev));
|
||||
}
|
||||
|
||||
let time_unix_ = fname[0..10].parse::<u64>();
|
||||
let time_unix = match &time_unix_ {
|
||||
Ok(data) => data,
|
||||
Err(msg) => {
|
||||
return Err(format!("{} expected digits in timestamp sec part ({})",
|
||||
msg_prev, msg));
|
||||
}
|
||||
};
|
||||
|
||||
let data_type_ = fname[22..24].parse::<u8>();
|
||||
let data_type_u8 = match &data_type_ {
|
||||
Ok(data) => data,
|
||||
Err(msg) => {
|
||||
return Err(format!("{} expected digits in data type part ({})",
|
||||
msg_prev, msg));
|
||||
}
|
||||
};
|
||||
|
||||
if *data_type_u8 == 1 || *data_type_u8 == 2 || *data_type_u8 == 6 { }
|
||||
else {
|
||||
return Err(format!("{} parser supports data types: {}",
|
||||
msg_prev, support_dtypes.to_string()));
|
||||
}
|
||||
|
||||
let data_type = match AsotrDataType::from_repr(*data_type_u8 as usize) {
|
||||
Some(value) => value,
|
||||
_ => return Err(format!("{} expected digits in data type part",
|
||||
msg_prev))
|
||||
};
|
||||
|
||||
// let _kit = filename[30..32].parse::<u8>();
|
||||
// let kit = match &_kit {
|
||||
// Ok(data) => data,
|
||||
// Err(msg) => { return Err(format!("{}: expected digits in asotr kit part ({})",
|
||||
// msg_prev, msg)); }
|
||||
// };
|
||||
|
||||
let _time_str_mks = fname[11..14].parse::<u32>();
|
||||
let time_mks = match &_time_str_mks {
|
||||
Ok(data) => data,
|
||||
Err(msg) => { return Err(format!("{}: expected digits in timestamp mks part ({})",
|
||||
msg_prev, msg)); }
|
||||
};
|
||||
|
||||
let time: SystemTime = UNIX_EPOCH + Duration::from_secs(*time_unix);
|
||||
let date_time = DateTime::<Utc>::from(time);
|
||||
let date_s = date_time.format("%d.%m.%Y").to_string();
|
||||
let time_s = date_time.format("%H:%M:%S").to_string();
|
||||
|
||||
let head = AsotrDataDesc::new(*time_unix, *time_mks, date_s, time_s, data_type);
|
||||
|
||||
return Ok(head);
|
||||
}
|
||||
|
||||
fn find_files_regex(dir: &str, template: &str) -> Result<Vec<String>, String> {
|
||||
let mut path_vec: Vec<String> = Vec::new();
|
||||
|
||||
let regex = match Regex::new(template) {
|
||||
Ok(val) => val,
|
||||
Err(msg) => {
|
||||
return Err(format!("Error create regex template ({}): {}",
|
||||
template, msg));
|
||||
}
|
||||
};
|
||||
|
||||
let data = WalkDir::new(dir)
|
||||
.into_iter()
|
||||
.filter_map(|e| e.ok())
|
||||
.filter(|e| regex.is_match(e.file_name().to_str().unwrap()))
|
||||
.map(|e| e.into_path());
|
||||
|
||||
for elem in data {
|
||||
path_vec.push(elem.display().to_string());
|
||||
}
|
||||
|
||||
if path_vec.len() == 0 {
|
||||
return Err(format!(
|
||||
"Error searching files for pattern ({}): files not found in directory {}",
|
||||
template, dir));
|
||||
}
|
||||
|
||||
return Ok(path_vec);
|
||||
}
|
||||
|
||||
fn disp_data(data: &Vec<String>, about: &str) -> Result<(), String> {
|
||||
println!("{}", about);
|
||||
println!("{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}",
|
||||
"timestamp_sec", "timestamp",
|
||||
"ch1", "ch2", "ch3", "ch4", "ch5", "ch6");
|
||||
|
||||
for elem in data {
|
||||
println!("{}", elem.replace(';', "\t"));
|
||||
}
|
||||
|
||||
return Ok(())
|
||||
}
|
||||
|
||||
fn save_data_csv(data: Vec<String>, fname: &str) -> Result<(), String> {
|
||||
|
||||
let mut data_w: Vec<String> = Vec::new();
|
||||
data_w.push(format!("{};{};{};{};{};{};{};{}",
|
||||
"timestamp_sec", "timestamp",
|
||||
"ch1", "ch2", "ch3", "ch4", "ch5", "ch6"));
|
||||
|
||||
for elem in data {
|
||||
data_w.push(elem);
|
||||
}
|
||||
|
||||
match std::fs::write(fname, data_w.join("\n")) {
|
||||
Ok(f) => f,
|
||||
Err(msg) => {
|
||||
return Err(format!("Error write data to csv file {}: {}", fname, msg))
|
||||
}
|
||||
}
|
||||
|
||||
return Ok(());
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Parser, Debug)]
|
||||
#[command(version, author, about, long_about = None)]
|
||||
struct Cli {
|
||||
/// file with ASOTR MVN data (.data01.asotr01(02), data02.asotr01(02), data06.asotr01(02))
|
||||
#[arg(long, short = 'f', value_name = "FILE_DATA")]
|
||||
filename: Option<String>,
|
||||
|
||||
/// directory with ASOTR MVN data
|
||||
#[arg(long, short = 'd', value_name = "DIRECTORY_DATA")]
|
||||
directory: Option<String>,
|
||||
|
||||
/// show data in console
|
||||
#[arg(short = 's')]
|
||||
show: bool,
|
||||
}
|
||||
|
||||
|
||||
fn main() {
|
||||
use crate::asotr_data::*;
|
||||
|
||||
let cli = Cli::parse();
|
||||
let show = cli.show;
|
||||
|
||||
if let Some(fname) = &cli.filename {
|
||||
|
||||
let s = match read_data(fname.clone()) {
|
||||
Ok(elem) => elem,
|
||||
Err(msg) => { println!("{}", msg); return; }
|
||||
};
|
||||
|
||||
println!("{}", s);
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
if let Some(dir) = &cli.directory {
|
||||
match parse_data_dir(&dir.clone(), show) {
|
||||
Ok(elem) => elem,
|
||||
Err(msg) => { println!("{}", msg); return; }
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
println!("Unexpected command. Type --help for more iformation");
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
1
asotr_csv/target/.rustc_info.json
Normal file
1
asotr_csv/target/.rustc_info.json
Normal file
@ -0,0 +1 @@
|
||||
{"rustc_fingerprint":2742313010855374649,"outputs":{"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":""},"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":""}},"successes":{}}
|
||||
3
asotr_csv/target/CACHEDIR.TAG
Normal file
3
asotr_csv/target/CACHEDIR.TAG
Normal file
@ -0,0 +1,3 @@
|
||||
Signature: 8a477f597d28d172789f06886806bc55
|
||||
# This file is a cache directory tag created by cargo.
|
||||
# For information about cache directory tags see https://bford.info/cachedir/
|
||||
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'
|
||||
@ -26,6 +26,15 @@ class TimeIndexNotFound(Exception):
|
||||
|
||||
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
|
||||
@ -377,7 +386,6 @@ def plot_signal_profile(time, data, pattern_t, pattern, method, shift_flag, peak
|
||||
|
||||
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 = []
|
||||
@ -486,26 +494,26 @@ def get_step_response_diff(data, thermocycle_info, channel='ch1',
|
||||
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"]})'
|
||||
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=(8, 6), dpi=200)
|
||||
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 ступенчатое воздействие')
|
||||
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='момент подачи ступенчатого воздействия')
|
||||
label= thermocycle_info['type_ru'] + ' воздействие, начало')
|
||||
|
||||
date_formatter = dates.DateFormatter(plot_info['ox_dtime_format'])
|
||||
ax1.xaxis.set_major_formatter(date_formatter)
|
||||
@ -515,17 +523,17 @@ def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
|
||||
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')
|
||||
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 ступенчатое воздействие')
|
||||
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', fontsize=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'])
|
||||
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'])
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,25 +1,27 @@
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
path_data = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
|
||||
fname_cmd_flight = '/home/danila/Danila/work/MVN/Soft/PID/data/flight/cmd_asotr/all_flight_cmd_asotr.csv'
|
||||
fname_cmd_temp = './data/flight_cmd_temp.csv'
|
||||
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('./data/cmd_human.csv', 'w') as file:
|
||||
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
|
||||
## 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:
|
||||
@ -28,16 +30,16 @@ with open(fname_cmd_temp, 'w') as file:
|
||||
## 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)
|
||||
|
||||
## form timestamp file where minimum of temperatures 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=';',
|
||||
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}'
|
||||
|
||||
@ -57,7 +59,7 @@ for kit in range(1,3):
|
||||
|
||||
min_temp_ch.append(min_temp_period)
|
||||
|
||||
fname = f'./data/asotr{asotr_kit}_min_T.csv'
|
||||
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=';',
|
||||
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)
|
||||
|
||||
@ -2,7 +2,7 @@ import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import matplotlib.pyplot as plt
|
||||
@ -11,7 +11,7 @@ import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
asotr_kit = 1
|
||||
fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
|
||||
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)
|
||||
@ -25,7 +25,7 @@ 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'{name}.png',
|
||||
'name_fig': f'../plots/response/{name}.png',
|
||||
'font': 10}
|
||||
|
||||
tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
|
||||
@ -35,7 +35,7 @@ _, interp_imp_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin,
|
||||
accuracy=data_info['find_accuracy'])
|
||||
|
||||
|
||||
interp_imp_resp.to_csv(f'./data/asotr0{asotr_kit}_{name}.csv', index=False, sep=';',
|
||||
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]))
|
||||
|
||||
|
||||
@ -8,14 +8,6 @@ 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(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=True):
|
||||
print_width = 20
|
||||
print_height = 12
|
||||
@ -23,7 +15,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
|
||||
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')
|
||||
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"
|
||||
@ -44,8 +36,8 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
|
||||
width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
|
||||
|
||||
# get from files and prepare data
|
||||
start_date = begin + " 00:00:00"
|
||||
end_date = end + " 23:59:59"
|
||||
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:
|
||||
@ -74,6 +66,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
|
||||
|
||||
plt.tight_layout()
|
||||
fig.savefig(pict_name)
|
||||
print(f'figure saved: {pict_name}')
|
||||
if show_flag == True:
|
||||
plt.show()
|
||||
|
||||
@ -84,9 +77,8 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
|
||||
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
|
||||
|
||||
if cmd == '1':
|
||||
fname = './flight_cmd_human.txt'
|
||||
try:
|
||||
cmd_human = pd.read_csv('./data/cmd_human.csv',
|
||||
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}')
|
||||
@ -161,6 +153,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
|
||||
fig.suptitle(title, fontsize=font)
|
||||
plt.tight_layout()
|
||||
fig.savefig(pict_name)
|
||||
print(f'figure saved: {pict_name}')
|
||||
if show_flag == True:
|
||||
plt.show()
|
||||
|
||||
@ -1,23 +1,21 @@
|
||||
#! /bin/bash
|
||||
|
||||
if [ $# != 5 ]
|
||||
if [ $# != 2 ]
|
||||
then
|
||||
echo "erorr use $0. Right use this script: "
|
||||
echo "$0 path_to_csv_astor_data/ 25.02.2025 10.03.2025 14 0"
|
||||
echo "$0 25.02.2025_00:00:00 10.03.2025_23:59:59"
|
||||
else
|
||||
path_csv_data=$1
|
||||
begin=$2
|
||||
end=$3
|
||||
font=$4
|
||||
cmd_flag=$5
|
||||
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} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 001000 -a 01 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 000011 -a 01 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 111100 -a 02 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 010100 -a 02 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 010000 -a 02 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 000100 -a 02 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
python3 plot_flight_borders.py -s ${path_csv_data} -c 000011 -a 02 -b ${begin} -e ${end} -f ${font} --cmd ${cmd_flag}
|
||||
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 ${end}
|
||||
fi
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
import matplotlib.pyplot as plt
|
||||
@ -8,8 +8,8 @@ from matplotlib import dates
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
pict_name = 'periods_profile_10042025.png'
|
||||
path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
|
||||
path = '../data/asotr/'
|
||||
pict_name = '../plots/periods_profile/periods_profile_10042025.png'
|
||||
channel = 'ch1'
|
||||
asotr_kit = '01'
|
||||
start_date = '24.04.2025 22:30:00'
|
||||
@ -37,10 +37,6 @@ for idx, elem in enumerate(peaks):
|
||||
delta = time1.iloc[elem] - peaks_forecast[idx-1]
|
||||
delta_sec.append(delta.total_seconds())
|
||||
|
||||
|
||||
# asotr.plot_signal_profile(time1, data1, [], [], method='peaks', shift_flag=shift)
|
||||
# asotr.plot_signal_profile(time1, data1, periods_t[0], periods[0], method='corr', shift_flag=shift, peak_height=0.7)
|
||||
|
||||
time_, periods_ = asotr.get_signal_profile_corr(time1, data1, periods[0], shift, peak_height=0.7)
|
||||
print(f'Найдено {len(periods_)} периодов.')
|
||||
|
||||
@ -66,7 +62,6 @@ for elem in periods_:
|
||||
delta1 = elem.values - periods[0].values
|
||||
delta.append(delta1)
|
||||
|
||||
# ax3.plot(delta[1], label=f'период 1', marker='o', linewidth=2)
|
||||
for idx, elem in enumerate(delta):
|
||||
if idx == len(delta) - 1:
|
||||
ax3.plot(elem, label=f'период {idx}', marker='|', linewidth=2)
|
||||
@ -77,8 +72,6 @@ for idx, elem in enumerate(delta):
|
||||
elif idx > 0:
|
||||
ax3.plot(elem, label=f'период {idx}')
|
||||
|
||||
# ax4.plot(delta_sec)
|
||||
|
||||
ax3.set_title(r'$\Delta$$T_i$ = $T_i$ - $T_1$')
|
||||
ax1.set_ylabel('Температура, $^\circ$C')
|
||||
ax2.set_ylabel('Температура, $^\circ$C')
|
||||
@ -87,7 +80,6 @@ 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)
|
||||
@ -6,7 +6,7 @@ reload(asotr)
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
path = './data/experiments/'
|
||||
path = '../data/experiments/'
|
||||
|
||||
timestamp = '04.05.2025 00:42:00'
|
||||
cyclogram_file = 'cyclogram_step_ident_ch3.xls'
|
||||
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])
|
||||
|
||||
@ -2,7 +2,7 @@ import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
from importlib import reload
|
||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
|
||||
@ -12,8 +12,7 @@ import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
asotr_kit = 1
|
||||
fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
|
||||
|
||||
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)
|
||||
|
||||
@ -71,7 +70,7 @@ 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': 'step_response_diff_KDI_20240401.png', 'font': 10}
|
||||
'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'],
|
||||
@ -85,7 +84,7 @@ _, interp_step_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin,
|
||||
|
||||
max_ = min(len(interp_step_resp), len(step_resp_cut))
|
||||
|
||||
interp_step_resp.to_csv(f'./data/asotr0{asotr_kit}_{thermocycle_info["type"]}_{thermocycle_info["date"].replace(".","")}.csv', index=False, sep=';', encoding='utf-8-sig', decimal='.')
|
||||
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"]})'
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import sys
|
||||
import statistics
|
||||
from importlib import reload
|
||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
||||
sys.path.append('./')
|
||||
import asotr
|
||||
reload(asotr)
|
||||
from datetime import datetime, timedelta
|
||||
@ -9,7 +9,7 @@ import matplotlib.pyplot as plt
|
||||
from matplotlib import dates
|
||||
from datetime import timedelta
|
||||
|
||||
path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
|
||||
path = '../data/asotr/'
|
||||
channel = 'ch1'
|
||||
asotr_kit = '01'
|
||||
start_date = '25.04.2025 00:00:00'
|
||||
@ -34,7 +34,7 @@ _, _, 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:
|
||||
with open('../data/asotr/peaks_forecast.txt', 'w') as file:
|
||||
for elem in peaks_forecast:
|
||||
file.write(f'{str(elem)}\n')
|
||||
|
||||
@ -1 +0,0 @@
|
||||
,danila,danila-IdeaPad,21.05.2025 11:32,file:///home/danila/.config/libreoffice/4;
|
||||
Binary file not shown.
4
data/cmd_asotr/concat_data.sh
Executable file
4
data/cmd_asotr/concat_data.sh
Executable file
@ -0,0 +1,4 @@
|
||||
file_itog=../all_flight_cmd_asotr.csv
|
||||
cd ./csv_data
|
||||
ls ./ | head -1 | xargs head -1 > ${file_itog}
|
||||
find ./ | sort | xargs cat | grep -P '^[0-9].*' >> ${file_itog}
|
||||
2
data/cmd_asotr/get_flight_cmd_asotr.sh
Executable file
2
data/cmd_asotr/get_flight_cmd_asotr.sh
Executable file
@ -0,0 +1,2 @@
|
||||
find /home/danila/Danila/work/MVN/flight/data/ -type f | grep -P 'out_trans_[0-9]{2}.asotr|out_exec_asotr|in_exec_asotr_|in_trans.asotr0' | sort > flight_cmd_asotr.txt
|
||||
|
||||
@ -1,67 +0,0 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
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 pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
asotr_kit = 1
|
||||
# fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
|
||||
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': '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}
|
||||
|
||||
asotr.plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
|
||||
cut_step_resp, plot_info)
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user