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
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# This file is automatically @generated by Cargo.
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||||||
|
[[package]]
|
||||||
|
name = "same-file"
|
||||||
|
version = "1.0.6"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "93fc1dc3aaa9bfed95e02e6eadabb4baf7e3078b0bd1b4d7b6b0b68378900502"
|
||||||
|
dependencies = [
|
||||||
|
"winapi-util",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "shlex"
|
||||||
|
version = "1.3.0"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "spin"
|
||||||
|
version = "0.9.8"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "strsim"
|
||||||
|
version = "0.11.1"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "strum"
|
||||||
|
version = "0.26.3"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "8fec0f0aef304996cf250b31b5a10dee7980c85da9d759361292b8bca5a18f06"
|
||||||
|
dependencies = [
|
||||||
|
"strum_macros",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "strum_macros"
|
||||||
|
version = "0.26.4"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "4c6bee85a5a24955dc440386795aa378cd9cf82acd5f764469152d2270e581be"
|
||||||
|
dependencies = [
|
||||||
|
"heck",
|
||||||
|
"proc-macro2",
|
||||||
|
"quote",
|
||||||
|
"rustversion",
|
||||||
|
"syn",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "syn"
|
||||||
|
version = "2.0.96"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "d5d0adab1ae378d7f53bdebc67a39f1f151407ef230f0ce2883572f5d8985c80"
|
||||||
|
dependencies = [
|
||||||
|
"proc-macro2",
|
||||||
|
"quote",
|
||||||
|
"unicode-ident",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "unicode-ident"
|
||||||
|
version = "1.0.14"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "adb9e6ca4f869e1180728b7950e35922a7fc6397f7b641499e8f3ef06e50dc83"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "utf8parse"
|
||||||
|
version = "0.2.2"
|
||||||
|
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'
|
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):
|
def get_utc_seconds(timestamp_str, timestamp_format):
|
||||||
dt_obj = datetime.strptime(timestamp_str, timestamp_format)
|
dt_obj = datetime.strptime(timestamp_str, timestamp_format)
|
||||||
utc_timezone = pytz.utc
|
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):
|
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']
|
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_cmd = pd.read_csv(fname_cmd_temp, sep=';')
|
||||||
|
|
||||||
df_asotr = []
|
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,
|
def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
|
||||||
cut_step_resp, plot_info):
|
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(
|
step_resp, orig_interp_cycle, step_interp_cycle = get_step_response_diff(
|
||||||
data_info['data'], thermocycle_info, channel=data_info['channel'],
|
data_info['data'], thermocycle_info, channel=data_info['channel'],
|
||||||
interp=interp, accuracy=data_info['find_accuracy'])
|
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'])
|
fig.suptitle(title, fontsize=plot_info['font'])
|
||||||
ax1 = fig.add_subplot(2,1,1)
|
ax1 = fig.add_subplot(2,1,1)
|
||||||
ax2 = fig.add_subplot(2,1,2)
|
ax2 = fig.add_subplot(2,1,2)
|
||||||
|
|
||||||
ax1.plot(step_resp['timestamp'], step_resp['temp'],
|
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']
|
step_begin = cut_step_resp['time_step_begin']
|
||||||
|
|
||||||
idx = find_best_time_idx(step_interp_cycle.timestamp, step_begin,
|
idx = find_best_time_idx(step_interp_cycle.timestamp, step_begin,
|
||||||
accuracy=data_info['find_accuracy'])
|
accuracy=data_info['find_accuracy'])
|
||||||
ax1.axvline(x = step_interp_cycle.timestamp[idx], color='r', linestyle='-.',
|
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'])
|
date_formatter = dates.DateFormatter(plot_info['ox_dtime_format'])
|
||||||
ax1.xaxis.set_major_formatter(date_formatter)
|
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'])
|
ax1.set_ylabel(r'$\Delta$T, $^\circ$C', fontsize=plot_info['font'])
|
||||||
|
|
||||||
ax2.axvline(x = step_interp_cycle.timestamp[idx], color='r', linestyle='-.',
|
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'], '--',
|
ax2.plot(orig_interp_cycle['timestamp'], orig_interp_cycle['temp'], '--',
|
||||||
label='термоцикл')
|
label='термоцикл')
|
||||||
ax2.plot(step_interp_cycle['timestamp'], step_interp_cycle['temp'],
|
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.xaxis.set_major_formatter(date_formatter)
|
||||||
ax2.legend(loc=plot_info["legend_pos"][1], fontsize=plot_info['font'],
|
ax2.legend(loc=plot_info["legend_pos"][1], fontsize=plot_info['font'],
|
||||||
fancybox=True, framealpha=0.4)
|
fancybox=True, framealpha=0.4)
|
||||||
ax2.grid(True)
|
ax2.grid(True)
|
||||||
ax2.tick_params(axis='both', width=1, labelsize=plot_info['font'])
|
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'])
|
ax2.set_ylabel(r'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
|
||||||
|
|
||||||
fig.suptitle(title, 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
|
import sys
|
||||||
from importlib import reload
|
from importlib import reload
|
||||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
sys.path.append('./')
|
||||||
import asotr
|
import asotr
|
||||||
reload(asotr)
|
reload(asotr)
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime, timedelta
|
||||||
|
|
||||||
path_data = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
|
path_data = '../data/asotr/'
|
||||||
fname_cmd_flight = '/home/danila/Danila/work/MVN/Soft/PID/data/flight/cmd_asotr/all_flight_cmd_asotr.csv'
|
fname_cmd_flight = '../data/cmd_asotr/all_flight_cmd_asotr.csv'
|
||||||
fname_cmd_temp = './data/flight_cmd_temp.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'
|
timeformat = '%d.%m.%Y %H:%M:%S'
|
||||||
prev_days = 25
|
prev_days = 25
|
||||||
|
|
||||||
## get flight commands file (generated by mvn_log_viewer)
|
## get flight commands file (generated by mvn_log_viewer)
|
||||||
## Translate to human-readeble format and take temperatures from flight commands file
|
## 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)
|
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:
|
for elem in cmd_list:
|
||||||
file.write(f'{elem}\n')
|
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:
|
with open(fname_cmd_temp, 'w') as file:
|
||||||
file.write(f'timestamp_sec;timestamp;asotr_kit;ch1;ch2;ch3;ch4;ch5;ch6\r\n')
|
file.write(f'timestamp_sec;timestamp;asotr_kit;ch1;ch2;ch3;ch4;ch5;ch6\r\n')
|
||||||
for elem in temperature_list:
|
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
|
## 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)
|
df_asotr_ = asotr.insert_temp_data_from_flight_cmd(fname_cmd_temp, path_data)
|
||||||
|
|
||||||
## form timestamp file where minimum of temperatures registered
|
|
||||||
end_date = ''
|
end_date = ''
|
||||||
for i, data in enumerate(df_asotr_):
|
for i, data in enumerate(df_asotr_):
|
||||||
end_date = data['timestamp'].iloc[len(data) - 1][0:18]
|
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='.')
|
encoding='utf-8-sig', decimal='.')
|
||||||
|
|
||||||
delta_date = datetime.strptime(end_date, timeformat) - timedelta(days=prev_days)
|
delta_date = datetime.strptime(end_date, timeformat) - timedelta(days=prev_days)
|
||||||
start_date = delta_date.strftime(timeformat)
|
start_date = delta_date.strftime(timeformat)
|
||||||
|
|
||||||
|
## form timestamp file where minimum of temperatures registered
|
||||||
for kit in range(1,3):
|
for kit in range(1,3):
|
||||||
asotr_kit = f'0{kit}'
|
asotr_kit = f'0{kit}'
|
||||||
|
|
||||||
@ -57,7 +59,7 @@ for kit in range(1,3):
|
|||||||
|
|
||||||
min_temp_ch.append(min_temp_period)
|
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 = pd.DataFrame(min_temp_ch).transpose()
|
||||||
df.to_csv(fname, header=False, index=False, sep=';',
|
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 matplotlib.pyplot as plt
|
||||||
import sys
|
import sys
|
||||||
from importlib import reload
|
from importlib import reload
|
||||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
sys.path.append('./')
|
||||||
import asotr
|
import asotr
|
||||||
reload(asotr)
|
reload(asotr)
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
@ -11,7 +11,7 @@ import pandas as pd
|
|||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
|
|
||||||
asotr_kit = 1
|
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")
|
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)
|
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',
|
plot_info = {'title': 'Реакция на импульсное воздействие',
|
||||||
'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper right', 'lower left'],
|
'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}
|
'font': 10}
|
||||||
|
|
||||||
tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
|
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'])
|
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='.')
|
encoding='utf-8-sig', decimal='.')
|
||||||
|
|
||||||
asotr.plot_imp_response(interp_imp_resp, data_info, plot_info, thermocycle_info)
|
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)
|
reload(asotr)
|
||||||
import pandas as pd
|
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):
|
def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=True):
|
||||||
print_width = 20
|
print_width = 20
|
||||||
print_height = 12
|
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
|
plot_windows = 2
|
||||||
|
|
||||||
channels = list(map(int, ch))
|
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}
|
plot_task = {"temp": 1, "temp_set": 1, "pow": 1}
|
||||||
ox_dtime_format = "%d.%m.%Y"
|
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]
|
width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
|
||||||
|
|
||||||
# get from files and prepare data
|
# get from files and prepare data
|
||||||
start_date = begin + " 00:00:00"
|
start_date = begin.replace('_', ' ')
|
||||||
end_date = end + " 23:59:59"
|
end_date = end.replace('_', ' ')
|
||||||
try:
|
try:
|
||||||
data, data_dict = asotr.get_data(path_with_data, asotr_kit, start_date, end_date, 'minutes')
|
data, data_dict = asotr.get_data(path_with_data, asotr_kit, start_date, end_date, 'minutes')
|
||||||
except Exception as e:
|
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()
|
plt.tight_layout()
|
||||||
fig.savefig(pict_name)
|
fig.savefig(pict_name)
|
||||||
|
print(f'figure saved: {pict_name}')
|
||||||
if show_flag == True:
|
if show_flag == True:
|
||||||
plt.show()
|
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)
|
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
|
||||||
|
|
||||||
if cmd == '1':
|
if cmd == '1':
|
||||||
fname = './flight_cmd_human.txt'
|
|
||||||
try:
|
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'])
|
delimiter=';', names=['timestamp', 'cmd'])
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
print(f'Error parsing file: {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)
|
fig.suptitle(title, fontsize=font)
|
||||||
plt.tight_layout()
|
plt.tight_layout()
|
||||||
fig.savefig(pict_name)
|
fig.savefig(pict_name)
|
||||||
|
print(f'figure saved: {pict_name}')
|
||||||
if show_flag == True:
|
if show_flag == True:
|
||||||
plt.show()
|
plt.show()
|
||||||
|
|
||||||
@ -1,23 +1,21 @@
|
|||||||
#! /bin/bash
|
#! /bin/bash
|
||||||
|
|
||||||
if [ $# != 5 ]
|
if [ $# != 2 ]
|
||||||
then
|
then
|
||||||
echo "erorr use $0. Right use this script: "
|
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
|
else
|
||||||
path_csv_data=$1
|
path_csv_data=../data/asotr/
|
||||||
begin=$2
|
begin=$1
|
||||||
end=$3
|
end=$2
|
||||||
font=$4
|
|
||||||
cmd_flag=$5
|
|
||||||
|
|
||||||
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 111100 -a 01 -b ${begin} -e ${end}
|
||||||
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 001000 -a 01 -b ${begin} -e ${end}
|
||||||
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 000011 -a 01 -b ${begin} -e ${end}
|
||||||
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 111100 -a 02 -b ${begin} -e ${end}
|
||||||
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 010100 -a 02 -b ${begin} -e ${end}
|
||||||
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 010000 -a 02 -b ${begin} -e ${end}
|
||||||
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 000100 -a 02 -b ${begin} -e ${end}
|
||||||
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 000011 -a 02 -b ${begin} -e ${end}
|
||||||
fi
|
fi
|
||||||
|
|
||||||
@ -1,6 +1,6 @@
|
|||||||
import sys
|
import sys
|
||||||
from importlib import reload
|
from importlib import reload
|
||||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
sys.path.append('./')
|
||||||
import asotr
|
import asotr
|
||||||
reload(asotr)
|
reload(asotr)
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
@ -8,8 +8,8 @@ from matplotlib import dates
|
|||||||
import numpy as np
|
import numpy as np
|
||||||
from datetime import timedelta
|
from datetime import timedelta
|
||||||
|
|
||||||
pict_name = 'periods_profile_10042025.png'
|
path = '../data/asotr/'
|
||||||
path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
|
pict_name = '../plots/periods_profile/periods_profile_10042025.png'
|
||||||
channel = 'ch1'
|
channel = 'ch1'
|
||||||
asotr_kit = '01'
|
asotr_kit = '01'
|
||||||
start_date = '24.04.2025 22:30:00'
|
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 = time1.iloc[elem] - peaks_forecast[idx-1]
|
||||||
delta_sec.append(delta.total_seconds())
|
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)
|
time_, periods_ = asotr.get_signal_profile_corr(time1, data1, periods[0], shift, peak_height=0.7)
|
||||||
print(f'Найдено {len(periods_)} периодов.')
|
print(f'Найдено {len(periods_)} периодов.')
|
||||||
|
|
||||||
@ -66,7 +62,6 @@ for elem in periods_:
|
|||||||
delta1 = elem.values - periods[0].values
|
delta1 = elem.values - periods[0].values
|
||||||
delta.append(delta1)
|
delta.append(delta1)
|
||||||
|
|
||||||
# ax3.plot(delta[1], label=f'период 1', marker='o', linewidth=2)
|
|
||||||
for idx, elem in enumerate(delta):
|
for idx, elem in enumerate(delta):
|
||||||
if idx == len(delta) - 1:
|
if idx == len(delta) - 1:
|
||||||
ax3.plot(elem, label=f'период {idx}', marker='|', linewidth=2)
|
ax3.plot(elem, label=f'период {idx}', marker='|', linewidth=2)
|
||||||
@ -77,8 +72,6 @@ for idx, elem in enumerate(delta):
|
|||||||
elif idx > 0:
|
elif idx > 0:
|
||||||
ax3.plot(elem, label=f'период {idx}')
|
ax3.plot(elem, label=f'период {idx}')
|
||||||
|
|
||||||
# ax4.plot(delta_sec)
|
|
||||||
|
|
||||||
ax3.set_title(r'$\Delta$$T_i$ = $T_i$ - $T_1$')
|
ax3.set_title(r'$\Delta$$T_i$ = $T_i$ - $T_1$')
|
||||||
ax1.set_ylabel('Температура, $^\circ$C')
|
ax1.set_ylabel('Температура, $^\circ$C')
|
||||||
ax2.set_ylabel('Температура, $^\circ$C')
|
ax2.set_ylabel('Температура, $^\circ$C')
|
||||||
@ -87,7 +80,6 @@ ax3.set_xlabel("Время, мин.")
|
|||||||
ax1.grid(True)
|
ax1.grid(True)
|
||||||
ax2.grid(True)
|
ax2.grid(True)
|
||||||
ax3.grid(True)
|
ax3.grid(True)
|
||||||
# ax4.grid(True)
|
|
||||||
ax2.legend()
|
ax2.legend()
|
||||||
ax3.legend()
|
ax3.legend()
|
||||||
fig.savefig(pict_name)
|
fig.savefig(pict_name)
|
||||||
@ -6,7 +6,7 @@ reload(asotr)
|
|||||||
import pandas as pd
|
import pandas as pd
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime, timedelta
|
||||||
|
|
||||||
path = './data/experiments/'
|
path = '../data/experiments/'
|
||||||
|
|
||||||
timestamp = '04.05.2025 00:42:00'
|
timestamp = '04.05.2025 00:42:00'
|
||||||
cyclogram_file = 'cyclogram_step_ident_ch3.xls'
|
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 matplotlib.pyplot as plt
|
||||||
import sys
|
import sys
|
||||||
from importlib import reload
|
from importlib import reload
|
||||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
sys.path.append('./')
|
||||||
import asotr
|
import asotr
|
||||||
reload(asotr)
|
reload(asotr)
|
||||||
|
|
||||||
@ -12,8 +12,7 @@ import pandas as pd
|
|||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
|
|
||||||
asotr_kit = 1
|
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")
|
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)
|
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'}
|
'find_accuracy': 'seconds'}
|
||||||
plot_info = {'title': 'Реакция на ступенч. воздейств.',
|
plot_info = {'title': 'Реакция на ступенч. воздейств.',
|
||||||
'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['lower right', 'lower left'],
|
'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,
|
step_resp_cut, _, _ = asotr.get_step_response_diff(data_info['data'], thermocycle_info,
|
||||||
channel=data_info['channel'], interp=interp, accuracy=data_info['find_accuracy'],
|
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))
|
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"]})'
|
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 sys
|
||||||
import statistics
|
import statistics
|
||||||
from importlib import reload
|
from importlib import reload
|
||||||
sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
|
sys.path.append('./')
|
||||||
import asotr
|
import asotr
|
||||||
reload(asotr)
|
reload(asotr)
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime, timedelta
|
||||||
@ -9,7 +9,7 @@ import matplotlib.pyplot as plt
|
|||||||
from matplotlib import dates
|
from matplotlib import dates
|
||||||
from datetime import timedelta
|
from datetime import timedelta
|
||||||
|
|
||||||
path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
|
path = '../data/asotr/'
|
||||||
channel = 'ch1'
|
channel = 'ch1'
|
||||||
asotr_kit = '01'
|
asotr_kit = '01'
|
||||||
start_date = '25.04.2025 00:00:00'
|
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)
|
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:
|
for elem in peaks_forecast:
|
||||||
file.write(f'{str(elem)}\n')
|
file.write(f'{str(elem)}\n')
|
||||||
|
|
||||||
@ -1 +0,0 @@
|
|||||||
,danila,danila-IdeaPad,21.05.2025 11:32,file:///home/danila/.config/libreoffice/4;
|
|
||||||
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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